esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
### IC Card Data Processing and POI Description  
The target data for this study were the number of passengers boarding and alighting at each bus station of the two selected routes during the morning peak hours (7:00-9:00, with a half-hour interval containing four time periods). The two selected bus routes are line 56008 and line 685. The bus route 56008 is a bus loop that passes through the central business district (CBD) and has a very large passenger flow, while the bus route 685 has a relatively small passenger flow, and the two routes interchange at Fangzhuangqiaoxi bus station. The PFP-XPOI model training set is from 8 October 2015 to 25 October 2015, and the test set is from 26 October 2015 to 30 October 2015. The total size of the dataset is 50 GB, containing 150 million swipe records. After being clustered by station and time period, the total number of data is 3 million.  
The process of cleaning the IC card data is to remove the data with empty boarding or alighting time, and to delete the data with an interval of more than 3 h from boarding to alighting time. The cleaned data account for about 1% of the total data.  
POI is a term in GIS which refers to all geographical objects that can be abstracted as points, especially some geographical entities that are closely related to people's life, such as schools, banks, restaurants, gas stations, hospitals and supermarkets. The main use of POI is to describe the address of things or events, and the number of different types of POI in a region can characterize the attributes of the region. In this study, POI data collection around bus stops was carried out based on the Amap application programming interface (API). The API refers to a series of functions that have been predefined. Developers can implement existing functions by calling API functions without accessing the source code or understanding the details of internal working mechanisms. The Amap location based services (LBS) open platform provides professional electronic maps and location services to the public. When developers integrate the corresponding software development kit (SDK),the interface can be invoked to implement many functions, such as map display, label POI, location retrieval, data storage and analysis.  
The collection process is divided into four parts: acquiring global positioning system (GPS) latitude and longitude of each station based on bus operation data, converting GPS latitude and longitude into Amap coordinates, collecting POI information based on Amap coordinates and organizing POI data into corresponding fields. To convert GPS coordinates to Amap coordinates, we need to apply the coordinate conversion method and add the corresponding parameters to the URL of the GET request. The main parameters for applying this method are listed in Table 1.
### DTC and ATC
The rectifier voltage directly depends on the voltage from the turbogenerator, with discrepancies due to the current traveling through the rectifier and the rectifier's temperature. As the temperature of the device increases, its internal resistance and subsequent voltage drop through the device increases directly with it, shown in Equation (2). This increase in resistance supplies a more significant discrepancy between the turbogenerator voltage and the rectifier bus voltage shown in Equation (3). However, with proper current and temperature monitoring, this change in rectifier forward voltage, \(V_{F,rec}\), can be estimated and accounted for in real-time or in post-processing. \[V_{F,rec}=I_{rec}*\left(\frac{dR_{rec}}{dT_{rec}}\right)*T_{rec} \tag{2}\]  
\[V_{rec}\ =\ \left[\frac{RPM_{gen}}{K_{V,gen}}\right]-V_{F,rec} \tag{3}\]  
The battery pack allows the electric loads to charge and discharge safely based on the operator's inputs. When the electric load suddenly throttles down, the turbine's lag creates a charging potential voltage difference across the battery pack. During extended charging cycles, individual cell balancing is required for safe use. Without monitoring the instantaneous voltage of every cell within the battery system, one or multiple cells could exceed or fall below safe voltage operation ranges. In this case, the batteries could swell, causing volumetric issues. Another, more detrimental failure could cause the battery to combust onboard the aircraft or lose partial, if not all, battery-supplied power to the system. Figure 3: A block diagram of the ATC operating mode.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 5 Experiments and Results
### Results
#### 5.2.5 Client Number Comparison  
To verify the scalability and robustness of the AD-FG method in federated environments, we conducted experiments under different numbers of clients: 5, 10, 15, 20, 50, and 100. We compared the performance of AD-FG with FedAvg and FedSage under these conditions. As shown in Figure 5, AD-FG consistently outperformed the other methods across all settings. While all methods showed some performance degradation as the number of clients increased, especially beyond 20 clients, AD-FG maintained a significantly higher accuracy even at 50 and 100 clients (87.85% and 87.01%) compared with FedAvg (82.68% and 79.79%) and FedSage (83.1% and 80.2%, respectively). These results demonstrate that AD-FG not only performs well in small- and medium-scale federated scenarios but also scales effectively in large-scale environments with many clients. This robustness is primarily due to its decoupled structural encoding mechanism and communication-efficient design, which allow AD-FG to capture and aggregate meaningful structural information without being heavily impacted by data heterogeneity or communication overhead.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dual-Channel** & **Cross-Gated** & **Cross-Domain** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\
**GNN** & \(\times\) & \(\times\) & 85.02 & 82.25 & 61.94 & 70.66 \\ ✓ & \(\times\) & \(\times\) & 89.23 & 88.12 & 69.58 & 77.76 \\ ✓ & ✓ & \(\times\) & 89.70 & 88.78 & 69.25 & 77.81 \\ ✓ & ✓ & ✓ & 90.20 & 89.63 & 70.94 & 79.19 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of dual-channel GNN, cross-gated, and cross-domain mechanisms.
# Performance Analysis of Rate Splitting in Massive MIMO Systems with Low Resolution ADCs/DACs
## 1 Introduction
For low-resolution ADCs only, the SE of the RS scheme is an increasing function of the signal-to-noise ratio (SNR). This trend is opposite to the conventional non-RS scheme where the SE saturates at high SNR. * We derive a closed-form power allocation solution for the common and private messages. Differently from [12], the solution takes into account not only the multiuser interference, but also the QN. From the results, low-resolution DACs have a more dominant effect on the SE performance than low-resolution ADCs. Hence, low-resolution ADCs are preferred to low-resolution DACs for increased SE performance. * Contrary to existing works [12; 13; 14] where the optimal regularization parameter is not analyzed, we derive the optimal regularization parameter for the RZF precoder accounting for the effects of quantization. * We introduce a power consumption model for the ADCs/DACs, and we analyze the SE/EE tradeoff as a function of the quantization bits. The paper is organized as follows. In Section 2, we introduce the system model of the massive MIMO system and describe the proposed RS scheme. In Section 3, we derive the SINRs of the common and private messages in the asymptotic regime and the corresponding SE. Furthermore, we analyze the power allocation and regularization parameter. Section 4 focuses on the EE analysis. In Section 5, we present the simulation results and discussions. Finally, Section 6 concludes the paper. **Notation 1**. : _In this paper, boldface lower and uppercase symbols denote vectors and matrices, respectively. The symbols \((\cdot)^{H},\mathrm{Tr}(\cdot)\), and \(\mathbb{E}\{\cdot\}\), denote the conjugate transpose, trace and expectation operators, respectively. \(\mathcal{CN}(\mu,\sigma^{2})\) denotes a circularly symmetric complex Gaussian distribution with mean \(\mu\) and variance \(\sigma^{2}\). \(\mathrm{diag}(\mathbf{X})\) returns the diagonal elements of a matrix \(\mathbf{X}\). The matrix \(\mathbf{I}_{M}\) denotes an \(M\times M\) identity matrix._
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 2 Quasi-Floating Gate Transistors
### Application of QFG Transistors to Energy-Efficient Amplifier Design
#### 2.2.2 Adaptive Bias Current Source  
Figure 4b shows how a QFG transistor can be used to implement an adaptive bias current source. In quiescent operation (\(V_{sig}\) = 0), the circuit works as a current mirror and the quiescent output current is accurately set to \(I_{Q}\) despite PVT variations. This current may be very low to save quiescent power. However, in dynamic operation (\(V_{sig}\)\(\
eq\)0), large output currents not limited by \(I_{Q}\) can be generated, due to the AC coupling of \(V_{sig}\).
## 2 Classification of Thermal Storage Systems
It accounts for the energy losses during the storage period and the charge/discharge cycle;
* Response time is defined as the speed with which the energy is absorbed or released [h];
* Cycle life refers to how many times the storage system releases the energy after each recharge;
* Costs are indicators to define the overall cost normalized on the total amount of capacity (E/kWh) or power (E/kW). They are capital costs, and operation and maintenance costs of the storage equipment during its lifetime;
* Cost per output (useful) energy is the ratio of the cost per unit energy divided by the storage efficiency;
* Cost per cycle is defined as the cost per unit energy divided by the cycle life. Typical values of the above-cited parameters for thermal energy storage technologies are reported in Table 1. With respect to the storage period, TES methods are referred as short-term when heat input and output occur within an interval of several hours or days and, instead, as long-term if the time frame is within an interval of few months or even a whole season [118]. In contrast to STES and LTES, TCTES are particularly suitable for long term storage [119]. The reason is that during the storage phase there are no significant energy losses (no self-discharge) [23]. STES and LTES require insulation systems during storage and thus, to avoid thermal losses, heat cannot be stored for a long time [21]. Despite its seasonal storage potential, TCTES for hot/cold demand is still in early development with few prototype set-ups [120]. Storage energy density is a crucial factor to select a thermal energy storage system for a particular application [121]. Because of its potentially higher energy storage density--5 to 10 times higher than latent heat storage system and sensible heat storage system respectively [112]--TCTES is receiving an increasing attention in several domains [122]. High energy density makes thermochemical thermal energy storage systems (TCTESs) such more compact energy systems so their use, reducing the volume of the system, could be very effective in the situations whereas space constraints are significant [123]. A further simplified economic comparison shows that STES is less expensive than LTES and TCTES. High capital costs are among disadvantages that make TCTESs not widely available in the market [119]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TES System** & **Capacity (kWh/l)** & **Power (MW)** & **Efficiency (\%)** & **Storage Period** & **Cost (€/kWh)** \\ \hline Sensible & 10–50 & 0.001–10.0 & 50–90 & days/months & 0.1–10 \\ Latent & 50–100 & 0.001–1.0 & 75–90 & hours/months & 10–50 \\ Thermochemical & 120–250 & 0.01–1.0 & 75–100 & hours/days & 8–100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of thermal energy storage systems (TESs) [122; 123].
# Intelligent Transportation System Technologies, Challenges and Security
## 3 The Impact of Innovative Technologies on Intelligence Transportation Systems
### Blockchain Technologies  
Blockchain technology can create up-to-date, real-time, and consistent transactions without a central authority. It will ensure that irrefutably secure vehicle and driver records are created throughout the vehicle usage lifecycle. A healthy and secure vehicle information system will be created by solving data integrity and reconciliation-related problems. At the same time, the Blockchain-based vehicle information management system, which will work by integrating with smart tool kits, will obtain real-time and reliable data. It will provide an efficient, secure, and non-human intervention-free data management and communication platform to the stakeholders in the wide and integrated connected vehicle ecosystem who do not trust each other [37].
## 2 MR Stereoscopic Image Observation Model  
As shown in Figure 1, the observed FR left view and downsampled right view constitute the MR stereoscopic video sequence. Accordingly, the MR stereoscopic video coding model can enable a large amount of data to be compressed for storage and transmission. This is because directly encoding the FR stereoscopic videos leads to doubling the required storage and bandwidth. Actually, owing to the restricted storage space and network bandwidth, this MR stereoscopic video coding model is crucial to providing clear bitrate reduction [6]. Furthermore, for ensuring stereo vision comfort, the SR of the MR stereoscopic video is needed at the decoder. Therefore, to contribute to the MR stereoscopic video coding model, we adopt an MR stereoscopic image observation model for stereoscopic image SR.  
As shown in Figure 2, a stereoscopic imaging system obtains the original FR stereoscopic image pairs (with the size of \(N_{1}\times N_{2}\) for each view). In general, we assume that there exists the observed FR  
Figure 1: Mixed resolution (MR) stereoscopic video coding model.  
left view and observed LR right view (with the size of \(M_{1}\times M_{2}\)), which is degraded on account of the blurring and downsampling operation. The degradation model is expressed by  
\[Y=DBX \tag{1}\]  
where \(\mathbf{Y}\) and \(\mathbf{X}\) denote the observed LR right view image and original FR right view image (that is the unknown FR right view image), respectively. Moreover, \(\mathbf{Y}\) and \(\mathbf{X}\) are both in vector format with the sizes of \(M_{1}M_{2}\times 1\) and \(N_{1}N_{2}\times 1\), respectively. \(\mathbf{D}\) is the downsampling matrix with the size of \(M_{1}M_{2}\times N_{1}N_{2}\), and \(\mathbf{B}\) is the blurring matrix with the size of \(N_{1}N_{2}\times N_{1}N_{2}\). In addition, let \(\mathbf{Z}\) represent the observed FR left view image, the purpose of the proposed method is to acquire an FR right view image, \(\mathbf{X}\), by making full use of the abundant information of the observed LR right view image \(\mathbf{Y}\) and observed FR left view image \(\mathbf{Z}\).
## 2 Measurement Approach
### Laminar-Turbulent Flow Transition Localization
In order to compensate for a flow transition line that is not orthogonal to the evaluated intensity profiles, a linear fit of all located flow transitions in the image is calculated. In the example of Figure 1a, all \(x_{\text{tr},i}\) for \(i=0,1,...,150\) rows along the \(y\)-dimension are used for the linear fit. Afterwards, each \(x_{\text{tr},i}\) is subtracted with the respective value of the fitted line in the row \(i\) to calculate the distance of each located flow transition position to the line fit. The error \(\sigma_{\text{rand}}\) is then estimated by the standard deviation of these distances. The error \(\sigma_{\text{syst}}\) is calculated by the average distance between the fit and a manually located reference transition line. The introduced approaches for locating the flow transition can be applied to any image with information about the spatial distribution of the laminar and turbulent flow regimes. In raw thermographic images, as in Figure 1, the image pixels' intensities represent the surface temperature. In the PC the image pixels' intensities represent their relative ratio to each other from the desired eigenvector that inherits the respective maximal variance in the image series. The pixel intensity in each PC image can therefore directly be evaluated in the same manner as the intensity in the raw thermographic images to analyze the  
Figure 1: (**a**) Flow visualization image with a laminar and turbulent flow regime distinguishable by different intensity levels. (**b**) Intensity profile (thin black line) and intensity gradient profile (thin black dotted) along the white dashed line in (**a**). Two distinct intensity plateaus corresponding to the laminar and turbulent flow regime are visible with a steep change between them. A Gaussian error function fitted to the intensity profile is shown with a thick black line, and a Gaussian function fitted to the corresponding gradient profile is shown with a thick black dotted line. The laminar-turbulent flow transition can be localized by the saddle point of the Gaussian error function and by the maximum of the Gaussian function. distinguishability of flow regimes by the CNR and to locate the laminar-turbulent flow transition. For simplicity, this pixel intensity in the PC is only refereed to as 'intensity' in this work.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 5 Proposed Continuous Authentication Method
### Sensor Data Preprocessing  
In our design, user's smartphone is deployed as the terminal device for D2D authentication, as well as the essential entity for continuous authentication. The embedded tri-axial gyroscope and accelerometer of smartphone are capable of capturing massive linear acceleration and angular velocity data, which distinctively reveals the different activities operated by the user.  
Subsequently, de-noising on the acquired raw sensor data is enabled. The following cubical smoothing algorithm with five-point approximation is utilized:  
\[\begin{cases}\overline{y}_{i-2}=\dfrac{1}{70}\left(69y_{i-2}+4y_{i-1}-6y_{i}+4y _{i+1}-y_{i+2}\right)\\ \overline{y}_{i-1}=\dfrac{1}{35}\left(2y_{i-2}+27y_{i-1}+12y_{i}-8y_{i+1}+2y_{ i+2}\right)\\ \overline{y}_{i}=\dfrac{1}{35}\left(-3y_{i-2}+12y_{i-1}+17y_{i}+12y_{i+1}-3y_{ i+2}\right),\\ \overline{y}_{i+1}=\dfrac{1}{35}\left(2y_{i-2}-8y_{i-1}+12y_{i}+27y_{i+1}+2y_{ i+2}\right)\\ \overline{y}_{i+2}=\dfrac{1}{35}\left(-y_{i-2}+4y_{i-1}-6y_{i}+4y_{i+1}+69y_{ i+2}\right)\end{cases} \tag{19}\]  
where \((y_{i-2},y_{i-1},y_{i},y_{i+1},y_{i+2})\) denotes the five adjacent points of data series, \((\overline{y}_{i-2},\overline{y}_{i-1},\overline{y}_{i},\overline{y}_{i+1}, \overline{y}_{i+2})\) are the output of the filtering operation. Note that the computation cost towards the cubical smoothing algorithm is comparatively small, thus it is suitable for the resource-limited smartphone devices. The filtered continuous sensor data are then separated into a number of sliding windows for the further training process. Note that the generated windows are half overlapped in our design. Subsequently, the segmented time window is further divided into action frames regarding to certain motion, where cycle detection is adopted with the purpose of characterizing specific data points in the acceleration sequence of each time windows.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 2 Materials and Methods  
### Structure of AMB  
The stator and rotor form the AMB actuator's structure. Figure 1 shows the cross section of the AMB actuator made of eight poles.  
The stator, which is the stationary element of the magnetic bearing, is typically built from a number of electromagnets poles. Around the revolving shaft, these magnets are arranged in a circular or annular configuration. The revolving component of a magnetic bearing, the rotor, is fastened to the machine's shaft. In order to create magnetic levitation and regulate the position of the shaft, the rotor has a collection of electromagnets that interact with the stator magnets. Position sensors are used to gauge the rotor's position and displacement with respect to the stator. Hall effect, inductive, and optical sensors are frequently utilized as position sensors in active magnetic bearings. These sensors give the control system feedback, allowing it to adjust the magnetic forces and keep the shaft in the appropriate position. The control system is in charge of keeping track of the signals from the position sensors and generating the proper control signals to modify the magnetic fields. It calculates the necessary magnetic forces to oppose any outside forces or disturbances operating on the shaft, such as vibrations or axial loads, using feedback control algorithms. To improve the stability and performance of the bearing, the control system can additionally offer active dampening and vibration control capabilities. The control signals produced by the control system are amplified by power amplifiers, which then supply the required current to the stator electromagnets. The magnetic fields produced by the stator magnets must be powerful enough to support the weight of the rotor and maintain the proper position, which is what the power amplifiers make sure of.
###### Abstract  
Wireless Sensor Networks (WSNs) consist of distributed sensor nodes that collect and transmit environmental data, often in resource-constrained and unsecured environments. These characteristics make WSNs highly vulnerable to various security threats. To address this, the objective of this research is to design and evaluate a deep learning-based Intrusion Detection System (IDS) that is both accurate and efficient for real-time threat detection in WSNs. This study proposes a hybrid IDS model combining one-dimensional Convolutional Neural Networks (Conv1 Ds), Gated Recurrent Units (GRUs), and Self-Attention mechanisms. A Conv1D extracts spatial features from network traffic, GRU captures temporal dependencies, and Self-Attention emphasizes critical sequence components, collectively enhancing detection of subtle and complex intrusion patterns. The model was evaluated using the WSN-DS dataset and demonstrated superior performance compared to traditional machine learning and simpler deep learning models. It achieved an accuracy of 98.6%, precision of 98.63%, recall of 98.6%, F1-score of 98.6%, and an ROC-AUC of 0.9994, indicating strong predictive capability even with imbalanced data. In addition to centralized training, the model was tested under cooperative, node-based learning conditions, where each node independently detects anomalies and contributes to a collective decision-making framework. This distributed approach improves detection efficiency and robustness. The proposed IDS offers a scalable and resilient solution tailored to the unique challenges of WSN security.  
intrusion detection; Deep Neural Networks (DNNs); Convolutional Neural Networks (CNNs); Gated Recurrent Units (GRU); Self-Attention mechanisms +
Footnote †: journal:  
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