esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Federated-Learning-Empowered UAV Computing
Therefore, the authors of [132] introduced an FL-based strategy to allow privacy-preserving collaborative ML across a federation of separate DaaS providers to develop Internet of Vehicles (IoV) applications such as traffic prediction and parking occupancy management. **Caching:**: Content caching in edge computing appears to be a viable approach [42]. It entails delivering popular material closer to the edge, which may be used locally at BSs or APs. Furthermore, UAVs can serve as BSs to improve caching efficiency by detecting users' mobility and efficiently delivering popular material [133]. However, this use case inherits the aforementioned UAV deployment issues. The hybrid CNN with LSTM method is better in this situation for dealing with the spatio-temporal aspects of both mobility patterns and content request distribution. The aggregated learning model then assists in the deployment of UAVs. As mobile users, one of the primary issues of such a paradigm is determining which contents should be efficiently saved in each cache by estimating the popularity of UAVs. However, content discrimination necessitates direct access to private UAV information, which is not feasible. FL is a match made in heaven for content popularity prediction since it allows for local model training, respecting user data privacy. For example, an Augmented Reality (AR) application needs access to users' privacy-sensitive data to collect popular augmentation elements. Because this task is a binary classification problem, an ANN algorithm may be utilized in a federated manner to learn these popular pieces before storing them locally to save latency. **Delivery Services:**: Traditional cloud-based facial recognition algorithms for receiver location and identification in UAV delivery services have several cost, latency, and dependability issues. The authors of [134] presented Fed-UAV, i.e., the edge-based FL framework to handle the person re-identification problem in the UAV delivery service. The framework allows the UAV to detect the target receivers rapidly and effectively decrease data transfer between the UAV and the cloud server, resulting in faster reaction times and data privacy protection. Experiments on three real-world datasets are undertaken, and the results indicate that Fed-UAV achieved high accuracy and efficiency in human re-identification while maintaining data privacy. The UAV primarily employs face recognition to determine the receiver's identification [135; 136]. On the other hand, facial recognition requires high-quality facial photos, which are difficult to produce in a complicated situation such as an outdoor and busy location for UAV delivery.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 2 Background and Related Work  
### LEO Satellite Network  
LSN integrates two fundamental components: a space segment comprising numerous LEO satellites interconnected via ISLs, and a ground segment consisting of user terminals and gateway stations [30]. Satellite Internet represents a revolutionary approach to global connectivity by leveraging constellations of LEO satellites. Unlike traditional geostationary (GEO) satellites operating at 36,000 km altitude, modern LEO constellations orbit below 2000 km, significantly reducing signal latency to 10-30 ms [31], while their 53\({}^{\circ}\) inclination angles ensure concentrated coverage over economically critical mid-latitude regions. Thisarchitectural shift enables near-fiber-optic performance for real-time applications like video conferencing and online gaming.  
The technological advancement of modern satellite constellations is primarily driven by two disruptive innovations: cost-effective mass production of miniaturized satellites and pioneering reusable launch architectures. Empirical data illustrates this progression: early-generation satellites documented in [32] weighed approximately 260 kg, whereas contemporary Starlink V2 variants exhibit a threefold mass increase to 800 kg, reflecting enhanced payload capacity. Concurrently, SpaceX's Falcon 9 booster has achieved unprecedented reusability milestones, with [33] reporting its 17 th successful mission deploying 28 satellites--a testament to operational maturity in reusable rocket technology. Scaling ambitions further materialize through payload optimization; current Falcon 9 configurations accommodate up to 60 satellites per launch [34], while the developmental next-generation Starship megarocket prototype promises exponential capacity expansion, projected to deliver over 400 Starlink satellites per mission upon operational deployment.  
This paradigm shifts from traditional bent-pipe relay systems to a three-stage data delivery model: terrestrial users first uplink data to overhead satellites via phased-array antennas. The data is then routed through multiple ISL hops across orbital planes using laser communication terminals (LCTs). Finally, destination satellites downlink the processed data to ground stations [35]. The first-generation Starlink architecture, in its initial deployment configuration, leverages X/Ku-band and Ka-band for user and ground station links to enable bidirectional low-latency communications [36]. This enables 4 Gbps user link throughput alongside 20 Gbps full-duplex ISL capacities [21], forming a high-efficiency space-terrestrial integrated network.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 5 Implementation of On-Board Systems
### Passive Approach
#### 5.2.3 Data Processing  
The employed data processing is sketched in Figure 14. In each run, the recordings from receivers \(A\) and \(B\) are first amplitude-clipped to within the \(\pm 3\) standard deviations to mitigate the effects of isolated spikes in the passive reconstruction of the impulse response \(G_{AB}(t)\). Then, the resulting signals undergo _fast Fourier transform_ (FFT) processing, so that cross and auto power spectra and the transfer function \(G_{AB}(\omega)\) can be computed. Finally, the time domain Green's function \(G_{AB}(t)\) is evaluated by (a) averaging the function \(G_{AB}(\omega)\) over four sensor pairs and (b) accomplishing band-pass filtering and IFFT processing. An outlier analysis is implemented in order to compute the DI related to the strength of the reconstructed transfer function; this issue has already been discussed in Section 5.1.3, when describing the active method for rail defect detection. The computation of the DI allows for normalizing the available data, thus mitigating the normal (baseline) data variability occurring in each run [48; 49].
# Motion Target Localization Method for Step Vibration Signals Based on Deep Learning
## 4 Experimental Results and Analysis  
### Experimental Dataset and Description  
In this experiment, six sensor nodes were deployed on a \(3\times 5\) m\({}^{2}\) tiled floor for measuring vertical vibrations, as shown in Figure 4. These nodes consisted of LGT-20D10 seismic detectors and AD620 voltage amplifiers connected to an MCC118 data acquisition board on a Raspberry Pi 4B. The experimental setup, detailed in Table 1, involved individuals wearing athletic shoes repeating four types of path experiments in normal walking postures: straight line, figure eight, circular, and L-shaped paths. The sampling rate was set at 3000 Hz, and after amplification and sampling, the vibration signals were transmitted in real-time via LAN from the Raspberry Pi to a host PC for processing. A total of 8000 sets of raw data were collected to build the Footstep Vibration Signal (FVS) dataset. (The dataset used in this study is publicly available on 9 Augest 2024 at [[https://github.com/RCHEN1220/FVS-DATA](https://github.com/RCHEN1220/FVS-DATA)]([https://github.com/RCHEN1220/FVS-DATA](https://github.com/RCHEN1220/FVS-DATA))).  
To label velocity information in the signals, a visual tracking system was designed. Initially, the OpenPose system [27] was used to construct real-time human skeleton diagrams and calculate the individuals' foot positions accurately. Subsequently, the pixel coordinates of foot locations between frames were connected to obtain the image trajectory of the moving target. The parameter information of the camera was obtained by camera calibration, as shown in Table 2. Through coordinate transformation techniques [28], the image trajectory was converted into the actual ground trajectory, and velocities for each frame were calculated based on timestamps.  
Figure 4: The real experimental conditions.
## 2 Methodology and Data
### Overall Technical Approach  
Upon calculation, the GOSAT data exhibit higher accuracy; however, the spatial sampling interval of the GOSAT satellite is much sparser than that of the TROPOMI instrument, thus failing to provide daily resolution with extensive XCH4 retrieval coverage. Given the higher accuracy of GOSAT data and the broader coverage of TROPOMI data, we decided to synergize the observations from GOSAT and TROPOMI to generate improved nationwide XCH4 data by local random forest model. The TCCON ground station XCH4 data serve as the reference XCH4 data and are used as the true values to measure the errors of the satellite data and the fused model data.  
However, to adapt to regions with high emission backgrounds (such as oil field areas) and achieve more detailed and precise satellite remote sensing monitoring [45], the spatial resolution of the wide-coverage, high-accuracy XCH4 dataset obtained by the LRF model fusion still does not meet monitoring requirements. We need to improve the grid resolution in specific regions. For each 7 km grid of the oil field area produced by the fusion model, we consider the results accurate. We use the GF-5 satellite as a proxy. The GF-5 satellite has a spatial resolution of 30 m, and we use matched filtering to retrieve \(\Delta\)XCH4. However, the 30 m noise is too large, so we oversample it to 1 km to match the noise scale of the fusion model. Using a regression model, we allocate the 1 km resolution \(\Delta\)XCH4 to the 7 km grid of the fusion model, generating a high-resolution, high-accuracy XCH4 grid dataset for the oil field area. The overview of the methodology workflow is in Figure 1.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
###### Abstract  
In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.  
Keywords:disturbed machine learning; sensors; dynamic synchronous parallel strategy (DSP); parameter server (PS) +
Footnote †: journal: Sensors
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 4 Preliminary Construction: TSBT-ORAM
### Data Query Process
#### 4.2.1 Client's Launching of Query  
To query a certain data block \(D_{t}\), the client first checks its index table to find out the leaf node \(v_{L-1,f}\) to which \(D_{t}\) is mapped. Hence, a path from the root to \(v_{L-1,f}\) is identified. To facilitate the presentation, we denote the selected path as follows:  
\[\overrightarrow{v}=(v_{0},\,\ldots,\,v_{L-1}). \tag{1}\]  
For each node \(v_{l}\) (\(0\,\leq\,l\,\leq\,L-1\)) on \(\overrightarrow{v}\), the client retrieves the encrypted index block from the server and checks if \(D_{t}\) is in the node. Then, it generates two query vectors \(\overrightarrow{Q}_{0}^{l}\) and \(\overrightarrow{Q}_{1}^{l}\), where each vector is a binary stream of \(3c\) log \(N\) bits and each bit corresponds to one block in the node. The contents of the vectors are determined as follows:  
\(1.\)\(\overrightarrow{Q}_{0}^{l}\) is generated randomly.  
\(2.\)\(\overrightarrow{Q}_{1}^{l}\) is first made as a copy of \(\overrightarrow{Q}_{0}^{l}\). Further, if the query target \(D_{t}\) is in node \(v_{l}\) (supposing the offset of \(D_{t}\) in the block is \(m\)), bit \(m\) of \(\overrightarrow{Q}_{1}^{l}\) is flipped.  
Note that, if \(v_{l}\) contains \(D_{t}\), \(\overrightarrow{Q}_{0}^{l}\) and \(\overrightarrow{Q}_{1}^{l}\) differ only at one bit, which corresponds to \(D_{t}\); if \(v_{l}\) does not contain \(D_{t}\), the two query vectors are the same. Then, the client sends \(\overrightarrow{Q}_{i}^{l}\) to server \(\mathcal{S}_{i}\) for \(i=0,1\), respectively.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 3 Methodology
### Limitations of the Theoretical Analysis  
Our convergence theorems (Theorems 1 and 2) assume that each client loss \(F_{i}\) is \(\mu\)-strongly convex and \(L\)-smooth, yielding a linear rate up to an \(O(\lambda)\) bias for fixed \(\lambda\) or exact \(O(1/T)\) decay under a decayed \(\lambda_{t}\). In practice, however, the ReLU-MLP and CNN models of Section 6 are fully non-convex, so those linear-rate guarantees no longer strictly apply (see Appendix B for a non-convex stationary-point proof). As with other non-convex FL analyses, one can only guarantee convergence to a stationary point rather than a global minimum. Empirically, ERFO still converges smoothly and achieves superior final accuracy and macro-F1 under non-IID data (Section 6), but a full theoretical explanation of this empirical robustness--for example, how the entropy term more effectively mitigates client drift--remains an open question for future work.
# LIMUNet: A Lightweight Neural Network for Human Activity Recognition Using Smartwatches
## 4 Experiment and Evaluation
### Algorithm Performance Evaluation  
Experiments were conducted on the PAMAP 2 and LIMU datasets, with a sliding window size of 171 and a step size of 85. The window size is explained in Section 4.3. In the human activity recognition experiment, 80% of the dataset was used for training, while the remaining 20% was used for testing. The learning rate was set to \(\alpha=0.0005\), and the number of training epochs was 100, with 64 random samples used for training in each epoch.
## 4 Discussion
### Sex and Geographic Context Considerations  
These studies suggest notable sex differentials in the intervention responses, which need consideration in the planning of subsequent programs. Female participants demonstrated larger improvements in the percentage reduction of overweight/obesity (20% in females versus 10% in males) and manifested sustained longer-term improvements in fitness [41; 46]. Male participants, however, demonstrated larger acute responses in some variables, for example, larger reduction in body fat (\(-1.0\%\) vs. \(-0.5\%\)) and larger glucose improvements (\(-0.21\) vs. \(-0.17\) mmol/L) [41]. These sex-related patterns suggest that metabolic responses to multi-component nutrition plus physical activity interventions can differ in boys from those in girls at the developmental age of childhood, likely because of the difference in growth patterns, alterations in body composition, and hormonal responses in this crucial developmental life phase [41; 43; 46].  
Interventions' efficacy also was greatly influenced by geographical and socioeconomic variables, as reflected by nonurban schools achieving higher values for certain metrics compared to their counterparts in urban settings [44], while demographic differences between populations such as urban Bronx versus suburban Westchester revealed substantial variation in intervention effectiveness [42]. Cross-country comparisons revealed substantial variation in intervention effectiveness, with metabolic improvements being most pronounced in East Asian populations [41] compared to more modest effects in Northern European settings [49]. Southern European and targeted interventions in high-risk populations consistently demonstrated stronger cardiovascular benefits [47; 50], while studies in diverse ethnic populations highlighted the importance of cultural adaptation, as evidenced by differential responses between indigenous and European populations in New Zealand [44] and successful culturally-tailored approaches in American Indian communities [45]. Notably, some European studies showed unexpected patterns with control groups outperforming interventions in certain measures [51], suggesting that intervention success may be influenced by baseline health status, cultural dietary patterns, and existing healthcare systems.  
These findings highlight the importance of tailoring intervention tactics to local conditions suggest that future interventions should consider sex-responsive program elements and context-specific adaptations to maximize effectiveness across diverse populations. The observation that girls may maintain improvements better over time while boys show stronger acute responses could inform the timing and intensity of intervention components to optimize outcomes for both sexes.
## 7 Synchrosqueezed CWT Scalogram Fault Prediction System
### Fault Prediction  
In order to observe the fluctuation of the two dominant frequency ranges change with time, the number of pulses in the frequency ranges 0.3-0.4 Hz and 0.7-0.8 Hz is plotted over time segments, as shown in Figure 11a,b.  
Figure 11: Trend of two selected frequency ranges fluctuates with time segments in two data sets: (**a**) number of pulses in the two dominant frequency ranges along time segments in a normal data set, (**b**) number of pulses in the two dominant frequency ranges along time segments in an abnormal data set.  
In Figure 11a,b, the number on the horizontal axis represents the time segment. These time segments, combined with the points on the line graph, can be compared with the recorded fault-approaching time. By comparing the pulse change of 0.3-0.4 Hz and 0.7-0.8 Hz in both normal and abnormal data sets, a phenomenon has been noticed when the fault is approaching, that is, the number of pulses in the two selected frequency ranges simultaneously reach a turning point in the fault-occurring time segment. To be more precise, the number of pulses in both frequency ranges will drop first to a turning point, then start to increase. This trend can be observed in time segment 2 and 4 of an abnormal data set. In normal data sets such as Figure 11a, such a phenomenon did not occur.  
To further confirm the phenomenon, other sets of normal and abnormal data have been collected to generate radar charts and line graphs. Figures 12 and 13 have been chosen as examples of demonstrating the result:  
Figure 12: Pulse distribution in different time segments of another two data sets: (**a**) pulse distribution of frequency ranges in different time segments of an abnormal data set, (**b**) pulse distribution of frequency ranges in different time segments of a normal data set.  
Again, the dominant frequency ranges of 0.3-0.4 Hz and 0.7-0.8 Hz were observed. The number of pulses in these frequency ranges were plotted against time segments as shown in Figure 13a,b.  
In the abnormal data set, such as Figure 13a, the decreasing and increasing trend fluctuation happened around time segments, i.e., time segment 3 and time segment 5; however, in normal data sets, such as Figure 13b, no such trend fluctuation can be found. Hence, through the trials of numerous data sets, the dominant frequency ranges are identified, the number of pulses can be clearly extracted from synchrosqueezed wavelet transform, and the trend of fault conditions can be detected in the time segments when the fault is upcoming.  
Figure 13: Trend of two selected frequency ranges fluctuates with time segments in another two data sets: (**a**) number of pulses in the two dominant frequency ranges along time segments in an abnormal date set, (**b**) number of pulses in the two dominant frequency ranges along time segments in a normal data set.
## 4 Results
### Sensitivity Analysis  
To extract detailed information about the relationship between the input variables and outcomes, sensitivity analysis is used as a valuable way to evaluate such relationship [19; 56]. In other words, sensitivity analysis investigates the relative contribution of the uncertainty (variability) of the input variables on the uncertainty (variability) in the output levels [57]. As a feature extraction method in ANN models [58], sensitivity analysis computes the importance of each predictor (independent variable) in determining the probability of an output in the neural network [27]. The results of the sensitivity analysis in the study are depicted in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**RFB**} & \multicolumn{2}{c}{**MLP**} \\ \cline{2-5}  & **Importance** & **Normalized Importance** & **Importance** & **Normalized Importance** \\ \hline Injury Nature & 0.699 & 100.0\% & 0.464 & 100.0\% \\ Class Description & 0.080 & 11.4\% & 0.126 & 27.1\% \\ Injury Type & 0.204 & 29.2\% & 0.063 & 13.5\% \\ Experience & 0.012 & 1.7\% & 0.170 & 36.7\% \\ Age & 0.005 & 0.8\% & 0.178 & 38.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Sensitivity Analysis of Independent variables importance.  
Figure 5: Receiving operating charts (ROC) for MLP Model (Test Data).  
Figure 6: ROC for RBF Model (Test Data).
## 2 System Model  
In this section, we present a detailed description of the system model used to develop the system model, starting with conventional NOMA for small-cells, followed by WHT-NOMA in small-cells, then the IRC model for suppression of the interference, caused by MUs that are unable to create NOMA pairs failing to establish connection with the SBS. The general diagram of small-cells deployed in a macrocell with NOMA and ICI is shown in Figure 2, and the transceiver design for the proposed strategy is shown in Figure 4. General notations used in this work are given in Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notations** & **Description** \\ \hline \(S\) & Transmitted Superposition coded signal at \(b\)th SBS \\ \hline \(s_{b,k}\) & Signal of \(k\)th UE in \(b\)th SBS \\ \hline \(s_{b,k}\) & Signal information of SEUs with imperfect condition in \(b\)th SBS \\ \hline \(s_{b,m}\) & Signal of UE-\(m\) in \(b\)th SBS \\ \hline \(s_{b,k}^{H}\) & Signal of the \(k\)th UE after the WHT is applied \\ \hline \(s_{b,k}^{H}\) & Signal information of SEUs with imperfect conditions at the UE-\(m\) served by the \(b\)th SBS including WHT \\ \hline \(s_{q}^{H}\) & WHT based signal information of the \(q\)th small-cell \\ \hline \(s_{bm}^{\prime H}\) & Recovered WHT signal using IRC at UE-\(m\) in \(b\)th SBS \\ \hline \(y_{b,k}\) & Received superposition coded signal at the \(k\)th UE in \(b\)th SBS \\ \hline \(y_{b,m}\) & Received signal of UE-\(m\) in the \(b\)th SBS \\ \hline \(N\) & No of UEs share a common link of the same SBS using NOMA \\ \hline \(N_{SB}\) & Number of sub-bands in the system \\ \hline \(P\) & Transmission Power \\ \hline \(P_{b,k}\) & Total transmission power of the \(k\) paired NOMA UEs in the \(b\)th SBS \\ \hline \(P_{q}\) & Transmission power of the \(q\)th small-cell \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.  
Figure 4: Transceiver block diagram for WHT with joint NOMA and IRC.