esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Seaborne Passive Radar
## 1 Introduction  
Passive coherent location (PCL) radar, also known as passive bistatic radar, relies on the signals emitted by illuminators of opportunity, such as radio or television transmitters [1]. The radar is equipped with at least two receiving channels. One channel is connected to an antenna that is pointing towards the transmitter. In this way, the reference signal is received. The second channel is connected to an antenna that is used to receive the echo signal. By correlating the reference and echo signals, one can estimate the time delay and frequency shift. The system can localize the target in the Cartesian coordinates by combining the delay and frequency shift measurements from multiple transmitter-receiver pairs. As a result, passive radar can be effectively used for the tracking of moving targets.  
Most passive radars are stationary ground-based systems. The technology is relatively mature and well-understood. One of the challenges in passive radar technology is installing passive radar on moving platforms. This includes airplanes [2; 3; 4; 5; 6], ground vehicles [7], and ships [8; 9].  
In this paper, we focus on seaborne passive radar, where the system was installed on a ship. First, the passive radar system is presented. Next, the processing algorithms are introduced. Finally, the real-life clutter analysis and the results of the experiments on the detection of airborne targets using digital radio (DAB) and digital television (DVB-T) are shown. The measurement campaign in which we collected the data was the NATO APART-GAS 2019 (active passive radar trials ground-based, airborne, seaborne) trials, which occurred in Poland on the Baltic Sea in September 2019 [10; 11; 12; 13]. The trial involved numerous cooperative targets, such as aircrafts and ships, and various radar sensors, including passive and active systems either stationary or installed onboard different moving platforms.  
The paper's novelty is the presentation of real-life clutter characteristics during different sea states and the detection of cooperative and noncooperative airborne targets by seaborne passive radar, which confirms its usefulness.
## 2 Related Work  
### Hierarchical Routing Algorithm  
Hierarchical or cluster-based routing protocols are well-known techniques with special advantages related to scalability and efficient communication. In a hierarchical network architecture, all nodes are grouped into clusters. Every cluster has a cluster head, the election of which is based on different election algorithms. The cluster heads act as local coordinators to resolve channel scheduling, perform power measurement, maintain time division frame synchronisation and enhance the spatial reuse of time slots and codes [15].  
[PERSON] et al. [2] considered a self-organising, multi-hop, mobile radio network for their proposed clustering algorithm. In the proposed hierarchical architecture, nodes are organised into non-overlapping clusters. The clusters are independently controlled and are dynamically reconfigured as nodes move. Simulation shows that this architecture provides an efficient, stable infrastructure for the integration of different types of traffic in a dynamic radio network.  
An energy-aware clustering and resource management framework was presented by utilising the wireless powered communication (WPC) technique [4]. In the proposed framework, the network initially forms different clusters based on the Chinese restaurant process. Following this process, the cluster head is selected by considering the WPC paradigm. The performance of the proposed approach is evaluated via modelling and simulation and under various topologies and scenarios where its operational efficiency and effectiveness is demonstrated.  
[PERSON] et al. [5] presented a distributed power control framework towards determining each node's optimal transmission power in order to fulfil its QoS prerequisites. In the cluster formation process and the cluster head selection, the authors considered energy availability, interest and physical ties. The performance of the proposed approach is evaluated via modelling and simulation, and its improved performance is compared to other state-of-the-art approaches.
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 1 Introduction
[16] examined current data and utilized the stacked autoencoder method to detect failures in RPM equipment, achieving 100% accuracy. However, these studies give numerous methods for detecting or diagnosing failures that use two or more sensors in data monitoring. The smaller number of sensors utilized in the FD method could reduce installation costs [17]. So, further study on the use of only electric current data is required to give low-cost and accurate solutions in the FD method (multi-classification problems) for RPM equipment. As a result, the purpose of this research is to examine the use of time-series current data and machine learning (ML) techniques in fault diagnosis for railway point machines. When compared to frequency-domain and time-frequency-domain analysis, time-domain analysis is one of the most frequently utilized signal-processing methods since it has the lowest computational cost without neglecting useful information from motor conditions [18; 19]. Six ML algorithms will be employed in order to determine which model performs the best in the fault diagnosis of railway point machines: Logistic Regression (LR), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The remainder of this paper is structured as follows. Section 2 describes the methodology. Section 3 presents the research outcomes. Section 4 discusses the results. Section 5 presents the conclusions.
## 3 Opportunities, Challenges and Suitability
### Opportunities  
RDMA-enabled protocols provide important features to accelerate data transfer without network software stack involvement (zero-copy), context switches (kernel bypass), and any intervention (e.g., cache pollution, CPU cycles) of the remote processor (no CPU involvement). These features allow RDMA applications to saturate the network using few cores, unlike traditional distributed systems. RDMA applications use CPU in an efficient way which can be beneficial in shared CPU environment (i.e., cloud solution). Furthermore, these features enrich RDMA-enabled application to achieve the highest communication throughput, in particular for small messages. RDMA provides a distributed shared memory over a reliable loss-less infrastructure through hardware-based re-transmission of lost packets [8].  
Compatibility with legacy protocols is an important capability of RDMA-enabled protocols. For example, iWARP and RoCE are designed to be compatible with legacy Ethernet protocol, and InfiniBand supports legacy socket applications through _IP over InfiniBand_ (_IPOIB_).
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 3 Concept of the Laser Crosslink Systems
### Systems Design Specifications
The payload and bus design details are described in the following sections.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Benchmark Models
#### 3.6.5 Hybrid CNN [66]
Binary cross-entropy is the loss function, and performance is evaluated using binary accuracy metrics. Data are processed in batches of 32 samples. This Vision Transformer architecture preserves the original ViT principles, tailored for smaller images by adjusting patch sizes and hyperparameters. Dropout regularization (0.1 in transformer layers and 0.5 in final layers) and layer normalization mitigate overfitting, while multihead attention helps the model understand complex spatial relationships. 3.6.8. CNN-Transformer [68]  
The model's architecture integrates a hybrid Convolutional Neural Network (CNN) with a Transformer framework, optimized for effective visual data processing. Initially, input images are processed through a custom CCTTokenizer layer that performs convolutional operations and pooling to extract spatial features, reshaping the data into token sequences that the transformer can process. Positional embeddings can be included to represent the spatial relationships among the image patches. The model's core comprises stacked transformer layers equipped with multihead self-attention, layer normalization, stochastic depth for regularization, and a feedforward network. Following the transformer layers, a SequencePooling layer utilizes soft attention on the token sequences to generate a pooled representation, which is forwarded to a fully connected output layer to facilitate either classification or regression. Various regularization techniques, such as dropout and stochastic depth, are implemented to reduce overfitting, while the model is optimized using binary cross-entropy loss and label smoothing for classification purposes.
## 2 Material and Methods
### ESO-MPC Active Control Design
#### 2.2.3 MPC Active Control Design  
MPC is based on the state space equation. The selected state variables of the system are vertical displacement, vertical speed, pitch angle, pitch-angle speed, roll angle, and roll-angle speed \(x=\begin{bmatrix}z_{s}\,\dot{z}_{s}\,\theta\,\dot{\theta}\,\varphi\end{bmatrix}^ {T}\). Control is selected for four controls, namely \(u=\begin{bmatrix}f_{1}\,f_{2}\,f_{3}\,f_{4}\end{bmatrix}^{T}\), and the disturbance quantity for \(\mathrm{w}=\begin{bmatrix}d_{z}\,d_{\theta}\,d_{\varphi}\end{bmatrix}^{T}\). According to Equation (12), the three-DOF system state space equation can be formulated as follows:  
\[\begin{cases}\dot{x}=Ax+Bu+Fw\\ y=Cx+Du+Hw\end{cases} \tag{18}\]  
The detailed descriptions of each matrix and vector are as follows:  
\[A=\begin{bmatrix}0&1&0&0&0&0\\ -\frac{2}{m_{s}}(k_{sf}+k_{sr})&\frac{2}{m_{s}}(c_{sf}+c_{sr})&0&0&0&0\\ 0&0&0&1&0&0\\ 0&0&-\frac{2\theta^{2}}{l_{x}}k_{sf}-\frac{2\theta^{2}}{l_{x}}k_{sr}&-\frac{2 \theta^{2}}{l_{x}}c_{sf}-\frac{2\theta^{2}}{l_{x}}c_{sr}&0&0\\ 0&0&0&0&1\\ 0&0&0&0&-\frac{2l_{f}^{2}}{l_{y}}k_{sf}-\frac{2l_{f}^{2}}{l_{y}}k_{sr}&-\frac{ 2l_{f}^{2}}{l_{y}}c_{sf}-\frac{2l_{f}^{2}}{l_{y}}c_{sr}\end{bmatrix}\]  
\[B=\begin{bmatrix}0&0&0&0\\ \frac{1}{m_{s}}&\frac{1}{m_{s}}&\frac{1}{m_{s}}&\frac{1}{m_{s}}\\ 0&0&0&0\\ -\frac{d}{l_{x}}&-\frac{d}{l_{y}}&\frac{b}{l_{x}}&\frac{b}{l_{x}}\\ 0&0&0&0\\ \frac{t_{f}}{l_{y}}&-\frac{t_{f}}{l_{y}}&\frac{t_{f}}{l_{y}}&-\frac{t_{f}}{l_{y }}\end{bmatrix},\,F=\begin{bmatrix}0&0&0\\ 1&0&0\\ 0&0&0\\ 0&1&0\\ 0&0&0\\ 0&0&1\end{bmatrix},\]  
Matrix \(C\) is a \(6\times 6\) identity matrix, which outputs all state quantities, \(D\) is a \(6\times 1\) all-zero matrix, and \(H\) is a \(6\times 1\) all-zero matrix.  
Model predictive control employs a discrete model to anticipate the future state of the controlled object. It derives the optimal control input by solving an optimization problem within a finite time horizon. The continuous vehicle dynamics system model, as represented by Equation (13), is converted into a discrete form via the ZOH discretization method [20].  
\[\left\{\begin{array}{c}x(k+1|k)=A_{d}x(k|k)+B_{d}u(k|k)+F_{d}w(k|k)\\ y(k|k)=C_{d}x(k|k)+D_{d}u(k|k)+H_{d}w(k|k)\end{array}\right. \tag{19}\]  
In the MPC optimization problem of active suspension control, the system output \(y(k+i|k)\) (\(i=1,2\cdots Np\)) in the future \(Np\) step predicted at time \(k\) is expected to be as small as possible to ensure better comfort and stability. The variable damping force \(u(k+j|k)\) (\(j=1,2\cdots Nc\)) is also made as small as possible by the amount of control
# Simple Compact UWB Vivaldi Antenna Arrays for Breast Cancer Detection
## 2 The Proposed Antenna Design
### Ultra Compact 2 \(\times\) 1 UWB Linear VNSA Array (Array 2)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Sim.**} & \multicolumn{2}{c}{**Meas.**} \\ \cline{2-5}  & **NTL** & **TTL** & **NTL** & **TTL** \\ \hline \multirow{2}{*}{\(S_{11}\)} & \(<\)\(-10\) dB at 2.2 to over & \(<\)\(-11.23\) dB at 2.1 to over & \(<\)\(-13.4\) dB at 1.96 to & \(<\)\(-10.6\) dB at 2.46 to over \\  & 12 GHz & 12 GHz & 11.1 GHz & 12 GHz \\ \hline \multirow{2}{*}{\(S_{22}\)} & \(<\)\(-10\) dB at below 1 to & \(<\)\(-10.34\) dB at below 1 to & \(<\)\(-11.53\) dB at below 1 to & \(<\)\(-10\) dB at 3 to over \\  & 11.3 GHz & over 12 GHz & 12 GHz & 12 GHz \\ \hline \multirow{2}{*}{\(S_{33}\)} & \(<\)\(-10.4\) dB at 3.4 to over & \(<\)\(-11.91\) dB at 2.91 to over & \(<\)\(-10\) dB at 3 to over \\  & 10.25 GHz & 12 GHz & 12 GHz & 12 GHz \\ \hline \multirow{2}{*}{\(S_{23}\)} & \(<\)\(-11.38\) dB at 3.3 to over & \(<\)\(-14.49\) dB at 1.74 to & \(<\)\(-12.15\) dB at 2.8 to over & \(<\)\(-14.3\) dB at 1.88 to over \\  & 12 GHz & over GHz & 12 GHz & 12 GHz \\ \hline \multirow{2}{*}{\(S_{12}\)} & \(-1.24\)–\(1.15\) dB & \(-1.24\)–\(1.15\) dB & \(-1.24\)–\(1.15\) dB & \(-1.24\)–\(2\) dB \\ \hline \multirow{2}{*}{\(S_{13}\)} & \(-6\pm 0.7\) dB & \(-6\pm 0.7\) dB & \(-6\pm 0.7\) dB & \(-6\pm 0.7\) dB \\ \hline \multirow{2}{*}{\(S_{12}\) GD} & around 0.23 ns & around 0.25 ns & around 0.43 ns & around 0.5 ns \\ \hline \multirow{2}{*}{\(S_{13}\) GD} & around 0.25 ns & around 0.25 ns & around 0.45 ns & around 0.5 ns \\  & \multicolumn{2}{c}{“\(<\)”: less than.} & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The simulated (Sim.) and measured (Meas.) results for UWB 3:1 unequal split TTL and NTL WPDs. Figure 7: (a) Configuration of compact UWB 3:1 unequal split TTL and NTL WPDs and (b) prototype of 3:1 TTL WPD. Figure 8: Measured and simulated (**a**) S\({}_{11}\), (**b**) S\({}_{22}\), (**c**) S\({}_{33}\), (**d**) S\({}_{12}\), (**e**) S\({}_{13}\), (**f**) S\({}_{23}\) of compact UWB 3:1, NTL and TTL WPDs. \(dis\) = 21.8 mm, and \(s_{p}\) = 50 mm are better than that at (_radsl_ = 1.135 mm (S\({}_{11}\)\(<-\)8.49 dB), 1.505 mm (S\({}_{11}\)\(<-\)9.29 dB), and 1.69 mm (S\({}_{11}\)\(<-\)8.51 dB)), (_dis_ = 19.8 mm (S\({}_{11}\)\(<-\)9.34 dB), 23.8 mm (S\({}_{11}\)\(<-\)10.28 dB) 25.8 mm (S\({}_{11}\)\(<-\)10.14 dB)), and (\(s_{p}\) = 40 mm (S\({}_{11}\)\(<-\)8.02 dB), 60 mm (S\({}_{11}\)\(<-\)6.98 dB), and 70 mm (S\({}_{11}\)\(<-\)7.66 dB)), respectively. Also here, the antenna feedline and divider port widths at the calculated value, \(W_{a}\) = 1.82 mm (S\({}_{11}\)\(<-\)7.87 dB at 2.42-12.85 GHz and maximum gain of 8.4 dBi), \(W_{p1}\) = 1.82 mm (S\({}_{11}\)\(<-\)8.63 dB) and \(W_{p2}\) = 1.82 mm (S\({}_{11}\)\(<-\)9.74 dB) are not good as compared to the optimized ones, \(W_{a}\) = 1.34 mm, \(W_{p1}\) = 2.07
## 4 Experimental Evaluation
### Scenario 1: Antenna Direction  
This scenario evaluates the performance impact by antenna direction. Section 4.2.1 describes the experimental environment, and Sections 4.2.2 and 4.2.3 explain the communication performance of IP and wireless network layers, respectively.
## 5 Experimental Results Analysis
### Evaluation of the YoloV4-Tiny Model  
The proposed end-to-end system detects Bengali sign characters and numbers using the quantized Yolov4-Tiny model. Equation (10) is used to calculate the model's mean average precision (mAP) or accuracy, where \(AP_{m}\) represents the average precision of the class m, and the number of total classes is represented by n.  
\[mAP=\frac{1}{n}\sum_{m=1}^{m=n}AP_{m} \tag{10}\]  
Figure 9 depicts the loss and mean average precision (mAP) of the proposed model. The model converges after 9000 epochs with a maximum stable accuracy of 99.7% as well as a minimum loss of 0.0816, where the \(x\)-axis represents the number of epochs and the \(y\)-axis represents the amount of loss. The initial task of the proposed system is to detect Bengali character signs in the given input sequence. The model is trained for 30,000 epochs. Initially, the loss is extremely substantial, but after 1000 epochs it drops below 2.0 and then decreases progressively. After 9000 epochs, the model converges when the loss reaches approximately 0.2. The proposed model achieves accuracy of 99.7%. Figure 8 depicts the detection of a character sign in images captured from a variety of angles and under varied lighting conditions. As a consequence of being trained on a large dataset and having its hyperparameters fine-tuned, the proposed model can detect character signs, which is consistent with the goal of this research.  
Figure 9: The mAP and loss of the proposed detection model.
## 4 Experiment and Analysis
### Results and Analysis
#### 4.5.2 Co-Occurrence Parameter Analysis  
This subsection shows the effect of choosing different co-occurrence parameters \(\phi_{ij}\) between 0.0 and 0.9 for the three data sets on the recommendation performance. In the session interaction layer, a dynamic global session graph needs to be constructed, and the connections between nodes in the global session graph are determined according to the co-occurrence parameters. There are edges only between nodes that meet the co-occurrence parameter threshold so that the neighbor sessions of the current session can be confirmed according to the global session graph. Therefore, for the session interaction layer, how to choose the appropriate co-occurrence parameters needs to be verified and discussed through experiments. The following figures show the performance of SEGR when selecting different co-occurrence parameters for three data sets. It can be observed from Figure 2 that the trend is flat between 0.3 and 0.8 on the Tmall data set, and SEGR finally chooses 0.6 as the co-occurrence parameter for the Tmall data sets. On the Yoochosoe1/64 and Diginetica data sets, the best results are achieved at 0.5 and 0.4, respectively. It can be concluded that the optimal values on the three data sets tend to be in the middle range of the interval. This is explainable. If the selection of neighbor sessions is too low and noise may be introduced to affect the performance of the recommendation algorithm, and if the selection is too high, similar neighbor features cannot be fully learned, or even there are no neighbors.  
Figure 2: Cont.
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
## 6 Conclusions  
In this study, we constructed a nonhomogeneous pore medium model by developing a random distribution algorithm to simulate the process of methane displacing saturated water in hydrate deposits. We explored the effective flow channels of methane and the characteristics of bound water under different conditions. The main conclusions obtained are as follows:  
1. [label=()]
2. Throat width is the most important factor affecting two-phase flow in inhomogeneous media. The distribution of the throat width within the pore medium gap significantly affects the flow channel. Even a small difference of 5% in the smallest throat distribution can result in a 40% difference in the effective flow space for methane. This can lead to almost half of the channel becoming a dead volume;
3. Differences in the degree of hydrophilicity of the pore medium affect the gas's ability to enter the small throat region. The stronger the hydrophilicity, the shorter the breakthrough time and the time to final dominant channel formation, and the smaller the region where effective flow channels formed. The change in water saturation over time exhibits a negative exponential correlation. The less hydrophilic the material, the more sensitive it is to changes over time;
4. Higher replacement velocities result in more pronounced fingering. The higher rate of expulsion results in a larger pressure gradient, allowing methane to easily displace water in the larger throat area, thus forming a dominant channel. The larger pressure difference allows the gas phase to enter the smaller throat area, enabling the tail breakthrough to continue replacing more water at the end, ultimately creating a larger effective flow space.  
Figure 16: The influence of displacement speed on water saturation.  
Figure 17: Transient flow characteristics in local areas.  
**Author Contributions:** Methodology, W.L.; validation, D.G.; software, J.Z. and J.L.; investigation, C.L.; writing--original draft preparation, Y.C. and H.C.; funding acquisition, B.W. and D.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the Shandong Provincial Natural Science Foundation of China (No. ZR2023 QD026), the China Postdoctoral Science Foundation (No. 2024M751860), and the Xinjiang Institute of Engineering Research Program of China (No. 2024 xgy012605).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** We gratefully acknowledge the China National Offshore Oil Company for providing the basic data used in this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Energy Efficiency of D2D Multi-User Cooperation
## 5 Conclusions  
D2D communication has great potential in handling problems within complex heterogeneous networks because of its strong flexibility, which has seen wide attention in recent years. D2D multi-user cooperation is considered as an important way of improving spectrum efficiency, throughput and energy efficiency, and is suitable as a relaying approach since D2D users can establish connections flexibly with self-organization and direct communication. Although D2D is widely applied in many networks to help enhance spectrum efficiency, capacity or coverage, etc., the research on cooperation  
Figure 4: Relationships between the total length of data \(L\), the number of data packets \(n\) and the energy efficiency (EE), and \(K=100\), SNR = 10 dB, \(\tau\)th = 2 \(\mu\)s.  
Figure 5: Relationships between the number of cluster D2D users \(K\), the total length of data \(L\) and energy efficiency, \(n=6\), SNR = 10 dB and \(\tau\)th = 2 \(\mu\)s.  
among multiple D2D users is still in its infancy. In this paper, we have proposed a cooperation scheme for multiple D2D users. In order to ensure high QoS without expending too much energy by the D2D users, we consider both energy availability and the SNR of each cooperative user and introduce the Kuhn-Munkres algorithm to help select relaying D2D users. The Kuhn-Munkres algorithm is widely applied in solving MWM problems, and is good at handling matching problems among multiple users. It may be used in the future to solve the spectrum allocation problem, which has been ignored in this paper.  
This work was supported in part by the Program for Changjiang Scholars and Innovative Research Team in University under grant No. IRT16R72, and the National Natural Science Foundation of China under Grant No. 61440062.  
The work presented in this paper is a collaborative development by all of the authors. [PERSON] proposed the idea and involved in the theoretical performance analysis. [PERSON] and [PERSON] designed and optimized the algorithm. [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.