esa-sceva/satcom-chunk-collection huggingface dataset:
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 4 Result and Discussion
### Small Office  
Figure 12(a) shows the features obtained by the existing method in the same office. Here, the first eigenvector and the first eigenvalue obtained in the environment without a person were used as reference data to calculate the features. The features, \(P\), which are the correlation of the eigenvector obtained in environments with and without a person, fluctuated significantly in three locations, in Area 1 due to the shielding effect of the direct wave. However, Areas 3 and 0 were relatively stationary compared to Area 1, even though the direct wave also passed these areas due to the height of the transmitting antenna, which was higher than the subject. Therefore, the direct wave might not be shielded in Area 3 and not completely shielded depending on the orientation of the person's body in Area 0.  
Figure 12(b) shows the features obtained by the proposed method in a small office. The solid and dotted lines denote Delay Taps 1 and 2, respectively. The colors blue, orange, and green represent Beams 1, 2, and 3, respectively. Relatively large phase fluctuations can be seen on the first delay tap of Beam 1, as the direct wave was shielded. Furthermore, many fluctuations can also be confirmed at the second delay tap, which was considered to be the result of shielding the single-bounce reflection wave showing that the events can be detected even when multiple waves with a small power were blocked.  
The classification results of the existing and proposed methods are shown in Figure 14. The classification was performed using the support vector machine. For the existing method, the success rate was 35.8%. The average F-score was 0.31, which was not correctly classified due to insufficient features, feature \(P\) being relatively stationary. For the proposed method, the success rate and average F-score improved to 64.5% and 0.63, respectively. However, misclassification occurred and was considered to be due to the effect of similar propagation channel conditions, which could improve if the specific area of the room, such as the area covered by the sofa, table, and corridor, were well-defined rather than considering the room to be equally divided.  
Figure 12: Fluctuation of the impulse response. (**a**) Without a human. (**b**) With a human.  
Figure 13: _Cont._
# The Hydroological Balance in Micro-Watersheds Is Affected by Climate Change and Land Use Changes
## 2 Materials and Methods  
### Study Area  
The Chapingo, Texcoco, and San Bernardino Rivers micro-watersheds in the municipality of Texcoco, near Ciudad de Mexico, are included in the study area (Figure 1). The micro-watersheds belong to the Texcoco aquifer, which is part of the Panuco River Hydroological Region and the \"Aguas del Valle de Mexico\" Administrative Hydrological Region. This is the most densely populated region in Mexico, so its aquifers are classified as overexploited since they have an extraction volume greater than the value of their recharge [41]. The hydrographic system of the aquifer consists of torrential runoff, with short duration, and dry during the dry season [41]. The micro-watersheds are located between coordinates 509 117 and 529 827 W, 2 146 699, and 2 157 465 N. They have an average altitude gradient of 3161 m asl and cover a total land area of 77.4 km\({}^{2}\). The mean annual temperature is 11 \({}^{\circ}\)C and 652.5 mm of mean annual precipitation. The predominant climate according to Koeppen classification is classified as dry temperate with main rains in summer [42].  
Geologically, micro-watersheds are covered mainly by sedimentary breccia material [43]. Hydrogeologically, micro-watersheds have medium to high permeability. According to INEGI [44], the type of soil with the greatest distribution is the epipetric Phaeozem. According to [PERSON] [27], in the study area, there are 10 classes of predominant land use and vegetation (Table 1): rainfed agriculture (29.6%), temperate forest (26.1%), reforestation (14.8%), urban (12.3%), irrigated agriculture (7%), secondary vegetation (5.7%), mine (2.7%), protected agriculture (1%), grassland (0.7%), and water (0.1%).  
Figure 1: Geographic location of the study area.
# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 2 Method  
When objects with the same appearance and texture frequently appear around an object, the predicted bounding boxes are easily transferred to similar objects. In the long-term tracking process, the model may fail to accurately locate the true object, eventually deviating from it. To address the issues of insufficient feature representation and error accumulation in TransT, this paper proposes a model that focuses explicitly on the object itself, thereby effectively distinguishing the object from similar entities in the background. Since cross-attention can enhance focus on key regions by leveraging the interrelations between different images and suppress irrelevant background information, and the mask mechanism can further filter critical regions and eliminate redundant features, this work introduces an object semantic enhancement model to ensure that the algorithm concentrates on the object, reducing background interference. Furthermore, to improve the model's adaptability to variations in object appearance and spatial position, the object template update mechanism guided by the judgment conditions [22] is adopted. This enhances tracking accuracy and stability in complex scenes with background distractions, providing a more robust solution for object tracking tasks. The overall tracking framework of the algorithm proposed in this paper is shown in Figure 1.
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 2 Materials and Methods
### Optimal Design of the Anchor Frame  
Selecting an appropriate prior frame is essential for enhancing the training efficiency of target detection networks. However, remote sensing image targets exhibit significant variations in scale, morphology, and distribution compared to natural image datasets like COCO [31]. To improve the model's prior knowledge, the K-means++ algorithm was utilized to cluster the labels in the remote sensing image dataset, resulting in 12 sets of anchor frames with different sizes. The clustering outcomes are presented in Table 1. By employing the K-means++ algorithm, more consistent and stable clustering results can be  
Figure 5: Sensitivity analysis of IoU for small-scale and normal-scale objects. Each grid represents one pixel, box A represents the real bounding box, and boxes B and C represent the predicted bounding boxes with 1- and 4-pixel diagonal deviations, respectively.  
obtained, which yields anchor frames that align closely with the actual size distribution of the dataset.
# Characterization of a COTS-Based RF Receiver for Cubesat Applications +
## 4 Test Method
### Board-Level Test Description  
The board-level tests have allowed to verify the functional performance of the whole receiver from the electrical point of view, i.e., to verify its compliance with the high-level requirements defined in [17]:  
* Sensitivity range: \(-\)115--70 dBm;
* Noise figure: \(<\)3 dB @ \(-\)115 dBm;
* Intermediate Frequency: 390 MHz;
* IF output power: 0 dBm \(\pm\)1 dB;
* DC power absorption: \(<\)3.5 W.  
The verification of these requirements needs a test setup that links the instrumentation to the main external interfaces of the receiver, i.e., the coaxial ports RF, IF, Local Oscillator (LO), and the access points for the power supply voltages. The board-level test requires that the whole board is switched on, so that all amplifiers allow the passage of the signal from the input (the RF) up to the output (the IF). Moreover, the power-on of the board allows to measure the DC power absorption.
# Intelligent Scheduling with Reinforcement Learning
## 5 Computational Study
### Efficiency Analysis
As expected, we got a \(p\)-value \(<2\times 10^{-16}\) so we reject the null hypothesis and can conclude that there significant differences between the runtime algorithms considered. In order to detect pairwise runtime algorithms that have a significant difference we conduct a _Nemenyi_ test [64] and we got the pairwise \(p\)-values shown on Table 1.  
By analyzing Figures 4 and 5 and Table 1, we can conclude that the two heuristic approaches (_SPT_ and _LPT_) and the intelligent agent discussed in this work (_CUN_) improve significantly the runtime relatively to the _Zhang_ and _Peng_ methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Zhang** & **Peng** & **CUN** & **SPT** \\ \hline Peng & 0.81939 & - & - & - \\ CUN & \(1.5\times 10^{-6}\) & \(2.9\times 10^{-9}\) & - & - \\ SPT & \(4.9\times 10^{-4}\) & \(1.4\times 10^{-12}\) & \(0.011\) & - \\ LPT & \(<\)\(2\times 10^{-16}\) & \(4.5\times 10^{-14}\) & \(5.7\times 10^{-9}\) & \(0.02464\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pairwise \(p\)-values of **Nemenyi**[64] test for CPU runtime. Figure 4: Boxplot of the runtime, in seconds (scientific notation), of algorithms: LPT, SPT, CUN, Peng and Zhang. Figure 5: Runtime, in seconds (scientific notation), for each problem (TAI1-TAI50). For problems with 20 or less jobs and 15 machines (TAI01 to TAI20), the agent (_CUN_) takes less than 1 s to calculate a solution; _Zhang_ and _Peng_ take more than 4 min. Instances with 20 machines and 20 jobs (TAI21 to TAI30) take less than 1.5 s, and the other proposals are already around 7 min. This discrepancy is consistent throughout all instances, increasing the separation in execution times exponentially as the complexity of the problems grows. In fact, in terms of speed from the moment the problem reaches the system until the solution is calculated, no similar execution time has been found in contemporary literature to those presented here, except for deterministic heuristics (such as _SPT_ and _LPT_) or random job allocation approaches. We should also point out that, between _CUN_, _SPT_ and _LPT_, _CUN_ spends takes significantly more time to conclude, with a significance level of 5%, than the two heuristics. Although _CUN_ is slower than _SPT_ and _LPT_, we expect that the quality of _CUN_'s solutions will compensate this draw back. This issue is covered in the quality analysis Section 5.2.
## 1 Introduction  
In recent years, the applications of nanotechnology and nanoscience have increased, demanding high accuracy positioning systems capable of working in large ranges at the nanometre scale. These positioning stages can be used for measuring or nanomanufacturing applications by integrating different devices [1]. The performance of these processes is directly related to the accuracy of the positioning systems and their working range [2]. Therefore, accurate positioning control in a large working range is one of the main necessities of nanotechnology applications [3].  
At the University of Zaragoza, a novel 2D nanopositioning platform (NanoPla) is in development [4]. This NanoPla has been designed to work together with different kinds of tools and probes in various applications, such as metrology or nanomanufacturing. In particular, the main application of this first prototype is surface topography characterisation at the atomic scale of samples with relatively large planar areas. The measuring device will be attached to a moving platform that performs a large displacement of 50 mm \(\times\) 50 mm. The NanoPla architecture is based on a scheme of the four integrated custom-made Halbach linear motors, which allow the implementation of planar motion [5]. The XY position of the platform is measured by a 2D plane mirror laser interferometer system. The design of the NanoPla has been optimised to achieve a nanometre resolution along its whole working range [6].  
A commercial control solution for these custom-made linear motors is not currently available. Thus, in other previous works [7, 8], control hardware and software were specifically designed and built for control issues. In contrast, in this project, the use of commercial control hardware for generic motors has been proposed as a novel solution for their control and drive systems. The purpose of this research is to facilitate the future replicability of the system. In a previous study [9], a 1D positioning control strategy was designed and implemented in the selected commercial control hardware for one linear motor performing movement on a linear guide. The right performance of this control system, according to the established design requirements, was experimentally verified. In this work, the control strategy is optimised for 2D movement and implemented in the NanoPla to control and drive the planar motion of a nanopositioning platform in a range of 50 mm \(\times\) 50 mm. Subsequently, the positioning uncertainty of the proposed control system is analysed.  
This article is an extension of the work presented in a previous conference paper [10] and is divided as follows. Firstly, an overview of the NanoPla is presented, and the Halbach linear motors, the positioning sensor, and the hardware of the control system are described. Secondly, a positioning 2D control system is proposed and experimentally validated. Then, the positioning uncertainty of the 2D positioning control system is assessed. Finally, conclusions are developed.
# Imaging Top of Volcanic Mounds Using Seismic Time- and Depth-Domain Data Processing
## 2 Structural and Geological Characteristics  
The SCSK is located in the north of the East China Sea, which represents the western arm of the Pacific Ocean. The rocks of the region are igneous, metamorphic and sedimentary types, of which aged Precambrian gneiss and schist are the oldest [19]. Tertiary Pleistocene strata are widespread, and large deltas and marine terrace deposits can be found along the coasts. Several small, NNE-trending basins have been found, with a large area in the intermediate basement [20]. A previous study noted that a sequence of Late Cretaceous consists of fluviolacustrine, which is affected by volcanic activity [21].  
Seismic reflections from the sequence between the Late Cretaceous and Late Eocene/ Early Oligocene are low to very high in amplitude, showing the various depositional processes. The volcanic intrusion is thought to date back to the Early Miocene, and the flow was covered by silt and siltstone until the Late Miocene [20; 21]. The sedimentary layer below the basalt eruption is mainly composed of unconsolidated inter-bedding sandstones, claystones and occasionally siltstones, attributed to the subsidence of the Jeju Basin during the Late Eocene to Early Oligocene [22]. A recent study focused on the potential for CO2 storage using the basalt flow's sealing ability above the sedimentary layer [16]. In 2018, the Korea Institute of Geoscience and Mineral Resources (KIGAM) carried out a two-dimensional seismic survey on the Jeju Basin, north of the East China Shelf Basin, to assess its potential for the storage of CO2 (Figure 1a). A total of nine multichannel seismic lines were surveyed, covering an area of 130 square kilometers (Figure 1b).  
The two formations of interest in this study were volcanic mounds and the lower depositional layer, which can be cap rocks and storage for CO2. The results of the well-log analysis showed that the petro-physical properties of good permeability and porosity are important for potential CO2 storage [23]. The top volcanic mounds were identified by anomaly in the well-log analysis for sonic and porosity properties (Figure 2). A vertical-depth error can occur and/or be accumulated in the seismic data that normally are recorded and displayed in the time-domain vertical axis.  
Figure 1: (**a**) Survey area map and (**b**) seismic acquisition lines (modified from [24]). The bold red line is a representative seismic processing line.  
Figure 2: (**a**) Geological summary of previous well-log of an adjacent study area (modified from [23]) and (**b**) well-log analysis of (**a**) with storage potential zone. SPHI: sonic-derived porosity. Vsh: shale volume.
## 5 Explainability of the Proposed Model
Figure 10: The relevance of the normalized standard deviation of building distance to the proposed path loss model. Figure 9: The relevance of the normalized mean value of building distance to the proposed path loss model. Figure 11: The relevance of the density of buildings to the proposed path loss model. Figure 12: The relevance of the average building width (or average street width) to the proposed path loss model. Figure 14: The relevance of the height to the nearest building from the Rx to the proposed path loss model. Figure 13: The relevance of the distance to the nearest building from the Rx to the proposed path loss model. Figure 15 shows the prediction performance of the proposed deep learning model and the explanatory model. The graphics are plotted in the sense of time series. The blue curve is the outcome of the deep learning model, whereas the red dots are the values predicted using the explanatory model. To quantify the accuracy of the explanatory model, the accuracy metric is defined as the correlation coefficient between the explanatory model and the proposed deep learning model. \(\mu_{\tilde{y}}\) and \(\mu_{\tilde{y}}\) are denoted as the mean values of the path loss predictions by using the explanatory model and the proposed deep learning model, respectively. Additionally, \(\sigma_{\tilde{y}}\) and \(\sigma_{\tilde{y}}\) are denoted as the standard deviation of the path loss predictions by using the explanatory model and the proposed deep learning model, respectively. The accuracy metric is defined as:  
\[\rho=\frac{E\Big{[}\big{(}\widetilde{y}-\mu_{\tilde{y}}\big{)}\big{(}\hat{y}- \mu_{\tilde{y}}\big{)}\Big{]}}{\sigma_{\tilde{y}}\sigma_{\tilde{y}}} \tag{15}\]  
where \(E[\cdot]\) is the expectation operation. As a result, the accuracy reaches 72%. Note that this accuracy metric can be also considered as the transparency of the proposed deep learning model, which is a black box in nature.
## 3 Results
### Analysis of Simulation Results  
The purpose of conducting the positioning measurements was to study a linear object. For this reason, it is important that the available samples are distributed evenly and as densely as possible. The following methodology appears to be appropriate. The trajectory is divided into small segments (a few or several metres in length). In each segment, a sample with a min. DOP value is selected, from which the average geometric coefficient value is calculated. The ratio of the segments containing any samples to all the segments is provided as well. The obtained average DOP value reflects the quality of the positioning  
Figure 12: Window of the software for the planning of a linear object GNSS measurement campaign for the optimum variant during the performance of the second test series.  
Figure 13: Diagram of the optimal 2D route during the second measurement campaign implementation.  
measurements being carried out. The calculated ratio illustrates the availability of these surveys along the length of the linear object.  
The software was configured to calculate the points that make up the optimal way to cover the planned route.  
During the data processing, the points that are removed at the beginning include those at which the inspection vehicle did not move and those for which the DOP value was greater than the pre-defined max DOP value. The other points are subjected to the above-described segmentation to calculate the average DOP value and availability.
## 3 Thermal Control System Scheme and Unit Design
### First-Stage TCB  
#### 3.2.1 Operating Principle  
The TCB in the space station module is mainly composed of fluid drive unit and system pipe network, and the fluid drive unit, as the power unit of the bus, is mainly responsible for the fluid circulation drive and the health state management of the fluid system. As shown in Figure 5, the bus is driven by the centrifugal pump. The flow rate and pressure head are provided by the centrifugal pump. To avoid the backflow in the parallel system, a one-way valve is set at the outlet of the circulating pump. After the fluid is pressurized by the circulating pump, it flows through the one-way valve downstream. The flow rate is measured and monitored by the flowmeter based on the venturi tube principle and then output to the fluid bus in the module. After the heat is collected, the fluid returns to the drive unit to measure the temperature of the backwater, and the heat exchange will take place between TCB and the platform thermal control loop though the liquid-liquid heat exchanger.  
A centrifugal gas-liquid separator is arranged inside the drive unit, and the separator is in the form of an inverted cone. The working fluid flows through the separator at a certain speed; under the action of the centrifugal force, the gas in the working fluid will gather in the center of the separator. In order to avoid the influence of impurities on the system, a filter is arranged in the system, and the filtering precision reaches 40 \(\upmu\)m. In addition, a metal bellows-type energy storage device is placed at the entrance of the circulating pump; on the one hand, it can provide a constant inlet pressure for the system, and, on the other hand, it can compensate for the change in working fluid volume caused by the change in temperature, and it can also compensate for the micro-leakage during the long-term use of the system.  
Figure 4: Bus-type load rack thermal management system product architecture.  
In order to control the pressure and output flow of the TCB and avoid major fluctuation of the system when changing the working mode in the module, the bypass valve of the system is set up to ensure the relative stability of the system pressure and pump output parameters.