We propose a WiFi Channel State Information (CSI) sensing framework for multi-station deployments that addresses two fundamental challenges in practical CSI sensing: station-wise feature missingness and limited labeled data. Feature missingness is commonly handled by resampling unevenly spaced CSI measurements or by reconstructing missing samples, while label scarcity is mitigated by data augmentation or self-supervised representation learning. However, these techniques are typically developed in isolation and do not jointly address long-term, structured station unavailability together with label scarcity. To bridge this gap, we explicitly incorporate station unavailability into both representation learning and downstream model training. Specifically, we adapt cross-modal self-supervised learning (CroSSL), a representation learning framework originally designed for time-series sensory data, to multi-station CSI sensing in order to learn representations that are inherently invariant to station-wise feature missingness from unlabeled data. Furthermore, we introduce Station-wise Masking Augmentation (SMA) during downstream model training, which exposes the model to realistic station u
As the space industry matures, large space stations will be built. This paper organizes and documents constraints on the size of these space stations. Human frailty, station design, and construction impose these constraints. Human limitations include gravity, radiation, air pressure, rotational stability, population, and psychology. Station design limitations include gravity, population, material, geometry, mass, air pressure, and rotational stability. Limits on space station construction include construction approaches, very large stations, and historic station examples. This paper documents all these constraints for thoroughness and review; however, only a few constraints significantly limit the station size. This paper considers rotating stations with radii greater than 10 kilometers. Such stations may seem absurd today; however, with robotic automation and artificial intelligence, such sizes may become feasible in the future.
Urban weather station networks (WSNs) are widely used to monitor urban weather and climate patterns and aid urban planning. However, maintaining WSNs is expensive and labor-intensive. Here, we present a step-wise station removal procedure to thin an existing WSN in Freiburg, Germany, and analyze the ability of WSN subsets to reproduce air temperature and humidity patterns of the entire original WSN for a year following a simulated reduction of WSN density. We found that substantial reductions in station numbers after one year of full deployment are possible while retaining high predictive accuracy. A reduction from 42 to 4 stations, for instance, increased mean prediction RMSEs from 0.69 K to 0.83 K for air temperature and from 3.8% to 4.4% for relative humidity, corresponding to RMSE increases of only 20% and 16%, respectively. Predictive accuracy is worse for remote stations in forests than for stations in built-up or open settings, but consistently better than a state-of-the-art numerical urban land-surface model (Surface Urban Energy and Water Balance Scheme). Stations located at the edges between built-up and rural areas are most valuable when reconstructing city-wide climate
The prototype station of the Surface Array Enhancement at the IceCube Neutrino Observatory has been taking data in its final design since 2023. This station is part of the planned extension within the footprint of the existing surface array, IceTop. One station consists of 8 scintillator detectors, 3 radio antennas, and a central DAQ. The final upgrade of the scintillation detectors and their firmware at the prototype station has extended the dynamic range and increased the data-taking up-time, thereby expanding the observation window for air showers. This contribution will discuss the performance of the upgraded prototype station after commissioning and its angular resolution capabilities when observing air showers with the scintillation detectors and in coincidence with IceTop. Furthermore, the integration of additional stations during the most recent deployment will be discussed.
LOFAR is a low-frequency array distributed across several European countries. Each LOFAR station contains thousands of antennas and associated electronics, making monitoring and thorough testing of those components essential to ensuring station reliability. This paper discusses various anomalies that may arise in LOFAR antennas, tile elements, modems, and summators. We also introduce two diagnostic pipelines designed to detect these anomalies: a real-time station monitoring system and an offline stationtest system. These pipelines provide valuable insights into the operational status of each antenna, issuing alerts to minimize observational disruptions while maximizing station uptime, reliability, and sensitivity. By enhancing the efficiency and stability of LOFAR stations, they also serve as a foundation for future large-scale arrays like SKA-Low. The experience gained from their development and deployment will contribute to the construction and maintenance of SKA-Low, improving monitoring and diagnostic capabilities for large-scale antenna networks. Ultimately, these systems play a crucial role in ensuring continuous observations and maintaining data integrity.
The paper studies a closed queueing network containing two types of node. The first type (server station) is an infinite server queueing system, and the second type (client station) is a single server queueing system with autonomous service, i.e. every client station serves customers (units) only at random instants generated by strictly stationary and ergodic sequence of random variables. It is assumed that there are $r$ server stations. At the initial time moment all units are distributed in the server stations, and the $i$th server station contains $N_i$ units, $i=1,2,...,r$, where all the values $N_i$ are large numbers of the same order. The total number of client stations is equal to $k$. The expected times between departures in the client stations are small values of the order $O(N^{-1})$ ~ $(N=N_1+N_2+...+N_r)$. After service completion in the $i$th server station a unit is transmitted to the $j$th client station with probability $p_{i,j}$ ~ ($j=1,2,...,k$), and being served in the $j$th client station the unit returns to the $i$th server station. Under the assumption that only one of the client stations is a bottleneck node, i.e. the expected number of arrivals per time unit
Bike sharing has become one of the major choices of transportation for residents in metropolitan cities worldwide. A station-based bike sharing system is usually operated in the way that a user picks up a bike from one station, and drops it off at another. Bike stations are, however, not static, as the bike stations are often reconfigured to accommodate changing demands or city urbanization over time. One of the key operations is to evaluate candidate locations and install new stations to expand the bike sharing station network. Conventional practices have been studied to predict existing station usage, while evaluating new stations is highly challenging due to the lack of the historical bike usage. To fill this gap, in this work we propose a novel and efficient bike station-level prediction algorithm called AtCoR, which can predict the bike usage at both existing and new stations (candidate locations during reconfiguration). In order to address the lack of historical data issues, virtual historical usage of new stations is generated according to their correlations with the surrounding existing stations, for AtCoR model initialization. We have designed novel station-centered heatma
We introduce the STATION, an open-world multi-agent environment for autonomous scientific discovery. The Station simulates a complete scientific ecosystem, where agents can engage in long scientific journeys that include reading papers from peers, formulating hypotheses, collaborating with peers, submitting experiments, and publishing results. Importantly, there is no centralized system coordinating their activities. Utilizing their long context, agents are free to choose their own actions and develop their own narratives within the Station. Experiments demonstrate that AI agents in the Station achieve new state-of-the-art performance on a wide range of benchmarks, spanning mathematics, computational biology, and machine learning, notably surpassing AlphaEvolve in circle packing. A rich tapestry of unscripted narratives emerges, such as agents collaborating and analyzing other works rather than pursuing myopic optimization. From these emergent narratives, novel methods arise organically, such as a new density-adaptive algorithm for scRNA-seq batch integration that borrows concepts from another domain. The Station marks a first step towards autonomous scientific discovery driven by
We study multiple base station, multi-access systems in which the user-base station adjacency is induced by geographical proximity. At each slot, each user transmits (is active) with a certain probability, independently of other users, and is heard by all base stations within the distance $r$. Both the users and base stations are placed uniformly at random over the (unit) area. We first consider a non-cooperative decoding where base stations work in isolation, but a user is decoded as soon as one of its nearby base stations reads a clean signal from it. We find the decoding probability and quantify the gains introduced by multiple base stations. Specifically, the peak throughput increases linearly with the number of base stations $m$ and is roughly $m/4$ larger than the throughput of a single-base station that uses standard slotted Aloha. Next, we propose a cooperative decoding, where the mutually close base stations inform each other whenever they decode a user inside their coverage overlap. At each base station, the messages received from the nearby stations help resolve collisions by the interference cancellation mechanism. Building from our exact formulas for the non-cooperativ
Designing for rotational stability can dramatically affect the geometry of a space station. If improperly designed, the rotating station could end up catastrophically tumbling end-over-end. Active stabilization can address this problem; however, designing the station with passive rotation stability provides a lower-cost solution. This paper presents passive rotational stability guidelines for four space station geometries. Station stability is first analyzed with thin-shell and thick-shell models. Stability is also analyzed with models of the station's major constituent parts, including outer shells, spokes, floors, air, and shuttle bays.
Electric vehicle adoption is considered to be a promising pathway for addressing climate change. However, the market for charging stations suffers from a market failure: a lack of EV sales disincentives charging station production, which in turn inhibits mass EV adoption. Charging station subsidies are often discussed as policy levers that can stimulate charging station supply and correct this market failure. Nonetheless, there is limited research examining the extent such subsidies are successful in promoting charging station supply. Using annual data on electric vehicle sales, charging station counts, and subsidy amounts from 57 California counties and a staggered difference-in-differences methodology, I find that charging station subsidies are highly effective: counties that adopt subsidies experience a 36% increase in charging station supply 2 years following subsidy adoption. This finding suggests that governmental intervention can help correct the market failure in the charging station market.
This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two competing charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. We also develop a low complexity algorithm
Performance of digitally beamformed phased arrays relies on accurate calibration of the array by obtaining gains of each antenna in the array. The stations of the Square Kilometer Array-Low (SKA-Low) are such digital arrays, where the station calibration is currently performed using conventional interferometric techniques. An alternative calibration technique similar to holography of dish based telescopes has been suggested in the past. In this paper, we develop a novel mathematical framework for holography employing tensors, which are multi-way data structures. Self-holography using a reference beam formed with the station under test itself and cross-holography using a different station to obtain the reference beam are unified under the same formalism. Besides, the relation between the two apparently distinct holographic approaches in the literature for phased arrays is shown, and we show that under certain conditions the two methods yield the same results. We test the various holographic techniques on an SKA-Low prototype station Aperture Array Verification System 2 (AAVS2) with the Sun as the calibrator. We perform self-holography of AAVS2 and cross-holography with simultaneous
This paper presents a solution to the problem of optimal ground station selection for low-Earth orbiting (LEO) space missions that enables mission operators to precisely design their ground segment performance and costs. Space mission operators are increasingly turning to Ground-Station-as-a-Service (GSaaS) providers to supply the terrestrial communications segment to reduce costs and increase network size. However, this approach leads to a new challenge of selecting the optimal service providers and station locations for a given mission. We consider the problem of ground station selection as an optimization problem and present a general solution framework that allows mission designers to set their overall optimization objective and constrain key mission performance variables such as total data downlink, total mission cost, recurring operational cost, and maximum communications time-gap. We solve the problem using integer programming (IP). To address computational scaling challenges, we introduce a surrogate optimization approach where the optimal station selection is determined based on solving the problem over a reduced time domain. Two different IP formulations are evaluated usi
Bike-sharing systems are becoming important for urban transportation. In such systems, users arrive at a station, take a bike and use it for a while, then return it to another station of their choice. Each station has a finite capacity: it cannot host more bikes than its capacity. We propose a stochastic model of an homogeneous bike-sharing system and study the effect of users random choices on the number of problematic stations, i.e., stations that, at a given time, have no bikes available or no available spots for bikes to be returned to. We quantify the influence of the station capacities, and we compute the fleet size that is optimal in terms of minimizing the proportion of problematic stations. Even in a homogeneous city, the system exhibits a poor performance: the minimal proportion of problematic stations is of the order of (but not lower than) the inverse of the capacity. We show that simple incentives, such as suggesting users to return to the least loaded station among two stations, improve the situation by an exponential factor. We also compute the rate at which bikes have to be redistributed by trucks to insure a given quality of service. This rate is of the order of th
We present an efficient and cost-effective way to train operators of complex systems with limited accessibility, such as sub-detectors of big experiments at the CERN Large Hadron Collider (LHC). Our coaching station was developed to train on-call shifters for the ALICE Fast Interaction Trigger (FIT). This coaching station significantly reduces the training period and enhances the trainee's confidence in their actions. We hope this work will inspire the construction of comparable training systems for other sub-detectors.
We consider a two station cascade system in which waiting or externally arriving customers at station $1$ move to the station $2$ if the queue size of station $1$ including a customer being served is greater than a given threshold level $C_{1} \ge 1$ and if station $2$ is empty. Assuming that external arrivals are subject to independent renewal processes satisfying certain regularity conditions and service times are $i.i.d.$ at each station, we derive necessary and sufficient conditions for a Markov process describing this system to be positive recurrent in the sense of Harris. This result is extended to the cascade system with a general number $k$ of stations in series. This extension requires the actual traffic intensities of stations $2,3,\ldots, k-1$ for $k \ge 3$. We finally note that the modeling assumptions on the renewal arrivals and $i.i.d.$ service times are not essential if the notion of the stability is replaced by a certain sample path condition. This stability notion is identical with the standard stability if the whole system is described by the Markov process which is a Harris irreducible $T$-process.
The energy transition in transportation benefits from demand-based models to determine the optimal placement of refueling stations for alternative fuel vehicles such as battery electric trucks. A formulation known as the refueling station location problem with routing (RSLP-R) is concerned with minimizing the number of stations necessary to cover a set of origin-destination trips such that the transit time does not exceed a given threshold. In this paper we extend the RSLP-R by station capacities to limit the number of vehicles that can be refueled at individual stations. The solution to the capacitated RSLP-R (CRSLP-R) avoids congestion of refueling stations by satisfying capacity constraints. We devise two optimization methods to deal with the increased difficulty to solve the CRSLP-R. The first method extends a prior branch-and-cut approach and the second method is a branch-cut-and-price algorithm based on variables associated with feasible routes. We evaluate both our methods on instances from the literature as well as a newly constructed network and find that the relative performance of the algorithms depends on the strictness of the capacity constraints. Furthermore, we show
Asteroid restructuring uses robotics, self replication, and mechanical automatons to autonomously restructure an asteroid into a large rotating space station. The restructuring process makes structures from asteroid oxide materials; uses productive self-replication to make replicators, helpers, and products; and creates a multiple floor station to support a large population. In an example simulation, it takes 12 years to autonomously restructure a large asteroid into the space station. This is accomplished with a single rocket launch. The single payload contains a base station, 4 robots (spiders), and a modest set of supplies. Our simulation creates 3000 spiders and over 23,500 other pieces of equipment. Only the base station and spiders (replicators) have advanced microprocessors and algorithms. These represent 21st century technologies created and trans-ported from Earth. The equipment and tools are built using in-situ materials and represent 18th or 19th century technologies. The equipment and tools (helpers) have simple mechanical programs to perform repetitive tasks. The resulting example station would be a rotating framework almost 5 kilometers in diameter. Once completed, it
With the rapid development of high-speed railways, demands on high mobility wireless communication increase greatly. To provide stable and high data rate wireless access for users in the train, it is necessary to properly deploy base stations along the railway. In this paper, we consider this issue from the perspective of channel service which is defined as the integral of the time-varying instantaneous channel capacity. It will show that the total service quantity of each base station is a constant. In order to keep high service efficiency of the railway communication system with multiple base stations along the railway, we need to use the time division to schedule the multiple stations and allow one base station to work when the train is running close to it. In this way, we find a fact that if the ratio of the service quantity provided by each station to its total service quantity is given, the base station interval(i.e. the distance between two adjacent base stations) is a constant, regardless of the speed of the train. On the other hand, interval between two neighboring base stations will increase with the speed of the train. Furthermore, using the concept of channel service, w