esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Formulations of Operation Principle  
Figure 2 shows a block diagram of a typical MG consisting of several DGs connected to a point of common coupling (PCC) to feed the total demand loads. Each DG is equipped with a local control and transmits the generated DC power to the upstream network or to the loads, through passing the output LC filter. This low-pass filter (LPF) used in  
this scheme is also designed to eliminate the harmful high-order harmonics. The feeder impedance is then represented by \(r_{l}+jx_{l}\) to model the cable or line voltage drop.  
The main block diagram of the control scheme is shown is Figure 3 for just one DG considering the power sharing and the voltage control loops. In this way, the sampling signals are obtained from output voltage (\(v_{o}\)) and output current (\(i_{o}\)) in order to model the load characteristics, which are transferred to P/Q calculation block to determine how much power is required. Then the droop control is applied and in the voltage control loop, a classic proportional-integrator (PI) controller is hired to mitigate the voltage distortions. The classic droop control makes a lot of drawbacks explained in the previous section and these have motivated the authors to propose a novel SMC approach to overcome the difficulties.  
The equivalent circuits of a DG for both grid-connected and islanding modes are represented in Figure 4. In an islanding operation mode, the main dynamic equations proposed for the DG behavior are mathematically formulated in (1).  
\[\begin{array}{c}\frac{d}{dt}\left(i_{Lf}\right)=\frac{K_{PWM}v_{o}-v_{o}-i_{Lf}R_{f}}{L_{f}}\\ \frac{d}{dt}(v_{o})=\frac{i_{Lf}-i_{o}}{\text{C}_{f}}\end{array} \tag{1}\]  
Figure 3: Block diagram of the DG control scheme.  
Figure 2: Block diagram of a typical MG consisting of several distributed generations (DGs).  
where \(R_{f}\), \(L_{f}\) and \(C_{f}\) are the output filter resistance, inductance and capacitance, respectively. \(v_{i}\) represents the input control signal and \(K_{PWM}\) is the corresponding model of switching pattern considering the modulation index and Finally, \(i_{L,f}\) expresses the inductive part of filter current. Using some simplifications in Equation (1) leads to:  
\[\frac{d^{2}}{dt^{2}}(v_{o})=\frac{K_{PWM}v_{i}}{L_{f}C_{f}}-\frac{v_{o}}{L_{f} C_{f}}-\frac{R_{f}i_{Lf}}{L_{f}C_{f}}+\frac{R_{f}}{L_{f}}\frac{d(v_{o})}{dt}- \frac{1}{C_{f}}\frac{d(i_{o})}{dt} \tag{2}\]  
Which is compatible with the diagram represented in Figure 4a.  
In an islanding operation mode, the time-dependent equation of dynamic performance is represented in (3), where \(v_{sd}\) represents the AC voltage distortions.  
\[\frac{d}{dt}(i_{o})=\frac{K_{PWM}v_{i}-v_{s}-v_{sd}-i_{o}R_{f}}{L_{f}} \tag{3}\]
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 3 Adaptive Energy Managements: Requirements, Components, and Mechanisms
### Problem Statement and Overview of the Energy Management Ecosystem  
Because energy production is finite and not always synchronized with consumption, suppliers frequently face periods of energy scarcity or abundance. This operation condition produces that the energy demands cannot be processed due to a lack of supply or that generated energy may be wasted if it cannot be consumed or stored. Subsequently, inefficient energy distribution and use can cause partial or total energy shortages during specific periods (peak load hours) or in certain places, and an increase in tariffs for consumers. Therefore, the proper management of available energy at all times is essential. Accordingly, we propose an IoT and NFV-enabled adaptive energy management solution that is focused on the efficient (optimal) consumption of available power, whether or not it is renewable. The adaptive consumption procedure is carried out using management mechanisms (e.g., workload scheduling using time-shifting capabilities) implemented through algorithmic solutions and deployed on an NFV-enabled cloud computing infrastructure, as shown in Figure 1. In this context, if needed, Ref. [5] presents a detailed description of an NFV-enabled architecture for efficient energy management.  
In summary, as illustrated in Figure 1, an IoT and NFV-enabled ecosystem for adaptive energy management is composed of three main components: (i) an ES, which provides power to the ecosystem; (ii) an NFV-Enabled EM, which manages services (energy demands from ECs) and energy resources (from the ES), and it is responsible for executing management algorithmic strategies that enable the efficient/optimal use of available energy; and, (iii) the ECs, who demand energy and modify their energy consumption (advance or delay demand execution) according the instructions that are received from the EM. The requirements and management mechanisms for adaptive energy consumption are presented below.
## 3 Case Study
### State Recognition Performance  
In this study, the combination feature vectors were trained by the kNN classifier (\(k\) = 7), as detailed in Equation (10). During the state recognition step of the HBM, the triaxial accelerometer signal, \(R_{c}\), is acquired in real-time, capturing one signal segment per second. Similar to the training step, each segment had a duration of \(\Delta t\) = 30 s and comprised 3750 points. However, these segments overlapped in time with their adjacent counterparts. Each of the four states yielded 16,128 signals, collectively amassing nearly 242 million data points. Figure 11 presents the scatter plots of the \(\overline{R}\), \(R_{rms}\), \(R_{pp}\), \(R_{H}\), and \(R_{cc}\) for the 538 representative signal segments in each state. It can be seen that the four states had rather obvious clustering characteristics. However, compared with Figure 10, the distributions of the vibration signal feature values measured under the actual working conditions were wider, and the values were more widely cross-distributed, which poses challenges for accurate classification.  
Table 3 presents the results of the classification predictions for the four states (A, B, C, D) using a confusion matrix, along with their corresponding True Positive Rate (TPR) and  
These results suggest that the selection of \(\overline{R}\), \(R_{rms}\), \(R_{pp}\), \(R_{H}\), and \(R_{cc}\) as the feature vectors of vibration signals can lead to a more accurate classification of the four states of HBM. This classification method improves the overall recognition rate, indicating a more effective approach toward identifying the working state of HBM under general conditions. However, there are still areas for improvement. For example, in the pumping state of the HBM, 8.6% of the signals were misclassified as originating from the climbing or operating states.  
To further enhance the overall recognition rate, adjustments were made in the result output step, taking into account the actual working conditions of the HBM (\(\Delta T=90\) s, \(N=90\), and \(b=0.7\), as shown in Figure 5). Figure 12 provides a before-and-after comparison of the output state results for A, B, C, and D. With these adjustments, the number of reported errors, as shown in Figure 12a,  
In actual construction, the transitions between the four states of the HBM are natural, continuous, and not particularly distinct. Once a transition from one state to another occurs, it remains stable for a period without sudden changes. Therefore, the smooth state prediction results displayed at the bottom of Figure 12, devoid of sudden changes, align more closely with the actual application of the HBM. It is worth noting that when applying this method in real-time monitoring, more timely state recognition results can be achieved by judiciously selecting the values of \(\Delta T\) to minimize \(T_{d}\) as much as possible.
# Influence of Drift on Robot Repeatability and Its Compensation
## 5 Conclusions  
The results of the presented measurements indicate the usefulness of the developed compensation methodology since it significantly improves repeatability. When compensated, the value of the positioning error is stabilized, and the influence of the drift, caused by the structural thermal expansion, is reduced. The hypothesis that the repeatability error caused by the effect of structural thermal expansion can be reduced and the repeatability values guaranteed for a steady temperature can be achieved even during the heating of the robot to operating temperatures, was proved.  
For all the measurements performed, the repeatability with applied drift compensation is approximately 25 \(\upmu\)m at any temperature. Without compensation, the repeatability is usually in the range of 200 \(\upmu\)m. Many effects impact the repeatability value. However, the effect of thermal expansion is the most fundamental one. By reducing this effect, the repeatability can be increased significantly. In case of measurement of the target 5--performed under working speed (see Figures 10 and 11), the Z-axis drift was reduced by 84.9%. For a target 1, working speed measurement (see Figure 9), the Z-axis drift was reduced by 89.9%. To verify the results and to obtain the accurate values of robot repeatability, all measurements were performed with a highly accurate measurement system consisting of a confocal sensor and a pair of laser profilers.  
This research presents the possible solution of the robot drift compensation that potentially can save tenths of minutes of production time. Figure 12 shows that it takes the robot almost 150 min to fully stabilize the temperature. Clearly, in typical industrial applications, robots will be loaded less than in the tests carried out in this study, but it will still take tens of minutes to warm up the robot structure. When using the presented technique, the robot warming up procedure may no longer be necessary, or its duration may be significantly reduced.  
For high-precision robotic applications, it is necessary to consider the environment and take steps to maximize temperature stabilization, as rapid changes in ambient temperature can affect the robot even with the temperature compensation presented in this article. In general, the optimal deployment is when the robot workspace is enclosed in a separate air-conditioned chamber. In industrial applications, the robot wears out over time, leading to an increased backlash in the gears and differences in the resistance of the bearings. Therefore, it is necessary to accept that the compensation of the robot has limited validity, and after a certain time, it will be necessary to perform it again; in our case, it may be necessary to update the constants of the compensation equations. For a standard industrial robot, the compensation validity may be similar to gearbox oil service life.
## 4 Cryptographic Analysis
### Correlation Analysis  
The correlation coefficient between two audio files is a mathematical relationship between groups of values [26]. To calculate the relationship between audio information between the primary and the encrypted file, it is necessary to compare the values of both files by working with the samples. The correlation coefficient represents the level of correlation and is always in the range of \([-1.0,+1.0]\). Proximity to 0.0 is considered as a lack of linear connection.  
The correlation coefficient can be calculated as follows:  
\[r_{x}y=\frac{cov(x,y)}{\sqrt{D(x)}\sqrt{D(y)}}. \tag{8}\]  
where:  
\[D(x)=\frac{1}{N}\sum_{i=1}^{N}{(x_{i}-\overline{x})^{2}}, \tag{9}\]  
Figure 4: Waveform plotting of original and encrypted audio files: (**a**) cartoon-mumble-speak, (**c**) grite-wav and (**e**) a-strange-dream are the original input audio files; and (**b,d,f**) are the waveforms of the encrypted audio files.  
\[D(y)=\frac{1}{N}\sum_{i=1}^{N}{(y_{i}-\overline{y})^{2}}, \tag{10}\]  
\[cov(x,y)=\sum_{i=1}^{N}{(x_{i}-\overline{x})(y_{i}-\overline{y})}, \tag{11}\]  
Sample values of the plain and encrypted files are \(x_{i}\) and \(y_{i}\), \(x\) and \(y\) are the mean values of samples, \(N\) is the total number of samples and finally \(cov(x,y)\) is covariance between both files.  
The correlation coefficient results are shown in Table 1.  
The values in Table 1 indicate that there is no relation between the original file and the encrypted file. Compared with similar audio encryption techniques, the proposed one has correlation coefficients values close to the ideal value of 0.0.
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
###### Abstract  
Federated learning (FL) has emerged as a powerful approach for privacy-preserving model training in autonomous vehicle networks, where real-world deployments rely on multiple roadside units (RSUs) serving heterogeneous clients with intermittent connectivity. While most research focuses on single-server or hierarchical cloud-based FL, multi-server FL can alleviate the communication bottlenecks of traditional setups. To this end, we propose an edge-based, multi-server FL (MS-FL) framework that combines performance-driven aggregation at each server--including statistical weighting of peer updates and outlier mitigation--with an application layer handover protocol that preserves model updates when vehicles move between RSU coverage areas. We evaluate MS-FL on both MNIST and GTSRB benchmarks under shard- and Dirichlet-based non-IID splits, comparing it against single-server FL and a two-layer edge-plus-cloud baseline. Over multiple communication rounds, MS-FL with the Statistical Performance-Aware Aggregation method and Dynamic Weighted Averaging Aggregation achieved up to a 20-percentage-point improvement in accuracy and consistent gains in precision, recall, and F1-score (95% confidence), while matching the low latency of edge-only schemes and avoiding the extra model transfer delays of cloud-based aggregation. These results demonstrate that coordinated cooperation among servers based on model quality and seamless handovers can accelerate convergence, mitigate data heterogeneity, and deliver robust, privacy-aware learning in connected vehicle environments.  
federated learning; multi-server; mobility; hand over; aggregation; vehicular edge computing Article  
Faculty of Business and IT, University of Ontario Institute of Technology, Oshawa, ON LIG 0C5, Canada; [EMAIL_ADDRESS] (S.S.H.); [EMAIL_ADDRESS] (K.E.-K.)  
*Correspondence: [EMAIL_ADDRESS]
## 2 Network Model and Operation  
The string network under study is shown in Figure 1. There are two kinds of nodes: the sensor nodes (numbered from 2 to \(N\)) and a single sink node (the Gateway, numbered 1) located at the network edge. The seabed is neither flat nor smooth. Therefore, nodes may not be equally spaced. Nevertheless, the analysis will begin considering that the nodes are equally spaced in terms of the distance \(d\) between each of them and eventually, in Section 2.2.3, we will consider the general case of unequally spaced nodes.  
The sensor nodes provide fixed size data packets (e.g., with environmental measurements) and the Gateway node collects all the packets to forward them to a data center normally located on the water surface or on land, for later processing. The nodes will use a unicast service, so the packets of a node will be routed to the next node toward the Gateway (path in Figure 1). This multi-hop routing technique has the advantage of saving energy, an important issue in UWSNs.  
Concerning the communication technique employed, the following considerations are assumed: (i) one-way operation; (ii) non-directive transducers (projectors and hydrophones) with a power range adjusted to a single-hop distance; and (iii) a transmission time, the so-called time slot, equal to the propagation delay to reach the neighbor node. These starting conditions are pointed out in Figure 2. Because of (i), a sensor node can be in one of three states: transmission, reception, or idle. Condition (ii) allows the acoustic wave to travel in all directions from a transmitting node, reaching every  
Figure 1: String network with a set of \(N-1\) equidistant (distance \(d\)) sensor nodes and a single Gateway node (node 1) at the extreme.  
neighbor node in its transmission range, and causing interference. Due to the power control applied to the acoustic modem, it is possible to establish a hop-by-hop relayed operation with simultaneous transmissions from nodes far enough from each other, because the acoustic power received from farther than two hops will be negligible. Power control is currently a mature technology, widely used in wireless systems. Nodes transmit the power strictly needed to reach their respective destination, which substantiates the assumption of negligible interference at the second hop. Condition (iii) ensures that the time slot available to transmit is full of data, and will enhance the throughput obtained.
## 3 Proposed Method  
We propose blind detection and frame synchronization processes to satisfy the blind-detectability requirement. We also proposed two forms of data embedding for the blind detection to satisfy the confidentiality requirement.  
We assume that the LP residues of three adjacent frames are highly correlated. This suggests that the LP residue of an even-numbered frame can be used to embed security codes in odd-numbered frames, and then the LP residue of the even-numbered frame can be used to accurately detect security codes from the watermarked signal of the odd frames. We also speculate that the opposite embedding/detection form can be used by interchanging the even-numbered/odd-numbered frames. Given that the LP residues used for embedding and detection are the same, the frame synchronization process can also be taken into account by maximizing the cross-correlation between the watermarked signal and the LP residue as a norm.
## 3 UVSQ-SAT Data Method and Map Reconstruction of the Variables (Observations and Model)
### Reconstruction Method of the ERA 5 Maps to Compare with the UVSQ-SAT Maps
The value measured by the satellite \(F_{\text{sat}}\) is expressed as follows (Equation (13)):  
\[F_{\text{sat}}(\lambda,\theta)=\sum_{l=0}^{L}\sum_{m=0}^{l}[C_{lm}\overline{Y} _{lm}^{C}(\theta,\lambda)+S_{lm}\overline{Y}_{lm}^{S}(\theta,\lambda)]+e \tag{13}\]  
with \(\theta\), \(\lambda\), the colatitude and longitude of the point considered, \(\overline{Y}_{lm}^{C}\) and \(\overline{Y}_{lm}^{S}\), the spatially integrated spherical harmonics, \(C_{lm}\), \(S_{lm}\), the coefficients that we seek to determine and \(e\), the error on the measurements. The coefficients \(C_{lm}\) and \(S_{lm}\) are then estimated by the least squares method. Figure 11: **(Top)** OLR (ERA 5) during the month of February 2021 from same observation than UVSQ-SAT (\(\sigma=1^{\circ}\)). **(Bottom)** OLR (ERA 5) from same observation than UVSQ-SAT (\(\sigma=180^{\circ}\)).
## 1 Introduction
### Cold-Drawn NiTi Wires  
The application potential of cold-drawn NiTi wires is lower than that of superelastic and shape-memory NiTi wires. Thin (<1 mm diameter) cold-drawn NiTi wires, typically consisting of a mixture of austenite (B2), martensite (B19\({}^{\prime}\)), and amorphous phase, have high tensile strength at room temperature, of the order of 1.5 GPa (depending on the amount of cold work) and maximum elongation of 3-4% [21]. These mechanical properties make cold-drawn NiTi wires suitable for applications requiring high strength and moderate flexibility, though their limited superelastic behavior restricts broader use.  
To our knowledge, there are only a few studies on the fatigue strength of cold-drawn NiTi wires. [PERSON] et al. presented high fatigue resistance of NiTi micro-pillars composed of alternating amorphous and nanocrystalline layers under compressive loading [22]. [PERSON] and [PERSON], using a rotary bend fatigue tester, studied the fatigue life of superelastic wires, thus not directly the fatigue life of as-drawn wires; however, they paid attention to the amount of prior cold work [23]. This gap in research highlights the need for comprehensive studies on the fatigue performance of cold-drawn NiTi wires, especially under tensile and cyclic loading conditions relevant to practical applications. Cold drawn NiTi wires, characterized by their high tensile strength, find direct applications in environments where mechanical robustness and corrosion resistance are critical.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 4 Experimental Results
### Setup
#### 4.1.5 Classification Methods  
To evaluate the effectiveness of the proposed augmentation strategies, we employ three widely used classifiers: Random Forest (RF), K-Nearest Neighbors (KNN), and XGBoost. For each experiment, we generate synthetic samples once and apply all three classifiers independently to assess performance. This process is repeated three times under consistent experimental conditions, and the final results are reported as the average across all runs and classifiers. This setup ensures robustness and minimizes variance caused by any single classifier or run.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 2 Background  
### 6 TiSCH Network Stack  
6 TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e TSCH) [7] is a standard for connecting IPv6 and the performance of industrial protocols of the low-power wireless personal area network IEEE 802.15.4e TSCH.  
The network stack structure of 6 TiSCH is shown in Figure 1, which uses IEEE 802.15.4 PHY [8] and IEEE 802.15.4e TSCH MAC protocols, IETF 6 top, which plays the role of scheduling and monitoring IEEE 802.15.4e TSCH, 6 LoWPAN [9], which performs header compression, link step fragmentation, and reassembly mechanism for transmitting IPv6 packets to IEEE 802.15.4 frames, and a destination-oriented directed acyclic graph (DODAG) in a wireless sensor network environment [10].  
The wireless sensor network technology, 6 TiSCH, provides the ability to detect surrounding information by spraying small dust-sized sensors into physical spaces. There is also a UWB technology that can identify the location of objects indoors through the interactions of surrounding sensors through the wireless sensor network, not through GPS calculation.
# WiFi-Based Gesture Recognition for Vehicular Infotainment System--An Integrated Approach
## 4 Methodology
### CSI Pre-Processing
#### 4.1.1 Phase Calibration  
CSI raw phase measurements behave extremely randomly because of the unsynchronized time clock of transmitter and receiver. The relation between measured phase and true phase can be written as:  
\[\triangle\widehat{h_{j}}=\triangle h_{j}+2\pi\frac{n_{j}}{N}\Delta t+\beta+z, \tag{4}\]  
where \(\triangle\widehat{h_{j}}\) indicates the measured phase of \(j\)th subcarrier and \(\triangle h_{j}\) denotes the actual phase, \(\Delta t\) represents the time lag, \(n_{j}\) denotes the subcarrier index, \(N\) stands for the size of FFT, \(\beta\) indicates the unknown phase offset and \(z\) is random noise. We cannot measure the exact value of \(\beta\) and \(\Delta t\) for each packet. However, we can get the same value of \(\beta\) and \(\Delta t\) each time by using a simple transformation. Phase error \(2\pi\frac{n_{j}}{N}\Delta t+\beta\) is linear function of subcarrier index \(n_{j}\). We can define two parameters \(a\) and \(b\) for calibration of phase error such that:  
\[a=\frac{\triangle\widehat{h_{j_{N}}}-\triangle\widehat{h_{j_{1}}}}{j_{N}-j_{1}} \tag{5}\]  
\[b=\frac{1}{N}\sum_{k=1}^{N}\triangle\widehat{h_{j_{k}}}, \tag{6}\]  
Figure 1: System architecture.  
subtracting \(a_{j_{k}}+b\) from raw phase \(\widehat{L}\widehat{h}_{j_{k}}\) to get the sanitized phase \(\widehat{L}\widehat{h}_{j_{k}}\) as:  
\[\widehat{L}\widehat{h}_{j_{k}}=\widehat{L}\widehat{h}_{j_{k}}-a_{j_{k}}-b. \tag{7}\]  
The phase sanitization is performed on all the subcarriers and re-assembled according to the corresponding amplitudes.