esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture: Edge-FLGuard
### Overview of the Framework  
Edge-FLGuard is structured as a three-tier hierarchical architecture, designed to balance edge autonomy and global intelligence as follows:  
* IoT Device Layer: Composed of low-power, resource-constrained devices (e.g., cameras, sensors, smart meters, and routers) that generate telemetry and network traffic. These endpoints do not perform inference but serve as data sources.
* Edge Intelligence Layer: Intermediate nodes such as gateways, micro-servers, or embedded systems capable of running lightweight deep learning models (autoencoder and LSTM). These nodes perform local anomaly detection and periodically participate in federated learning updates.
* Federated Coordination Layer: A centralized or semi-centralized aggregator (cloud or fog server) responsible for orchestrating model synchronization, update aggregation (e.g., FedAvg, Krum), and redistribution.  
Edge nodes act as dual-role agents: they autonomously classify local behavior for immediate response while collaboratively training global models to improve overall system intelligence--all without sharing raw data.  
The Edge-FLGuard framework is structured as a multi-layered system that combines edge intelligence with federated learning. Figures 1 and 2 illustrate, respectively, the overall architectural design and the detailed operational flow, including data processing, anomaly detection, secure communication, and collaborative model updates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Focus Area** & **Approach** & **Strengths** & **Limitations** \\ \hline [PERSON] et al. & & & Robust to adversaries & High cost and not \\ (2017) [31] & Byzantine FL & Krum aggregation & in theory & adapted for \\ [PERSON] et al. & Federated & FL for IIoT with & Industry-oriented and & Basic models and lacks \\ (2022) [12] & anomaly detection & mobile robots & real-world setup & privacy techniques \\ \hline This Work & & & & Real-time, \\ (Edge-FLGuard) & 5G-IoT security & FL + edge & privacy-preserving, and & (To be discussed in \\  & & & robust to heterogeneity & Results) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 3 Results  
The primary findings of this research are outlined below. The initial dataset, after applying the preprocessing methodologies described earlier, consisted of 103 variables encompassing both qualitative and quantitative attributes. This study focuses exclusively on the 72 quantitative variables, as the analytical techniques employed--such as principal component analysis and correlation analysis--require numerical input to yield meaningful results. Thus, quantitative variables were analysed to identify significant interrelationships. To examine such relationships, a correlation matrix was computed using Kendall's coefficient. Variables demonstrating a statistically significant correlation with at least one other variable in the dataset were selected for further analysis. A significance threshold of \(\alpha=0.05\) was established, ensuring that only correlations with a \(p\)-value below this threshold were considered meaningful. To enhance the interpretability and reduce the redundancy, a filtering process was implemented to retain only variables with statistically significant correlations relevant to our research objectives. As a result, multiple sub-datasets were constructed from the initial dataset based on thematic coherence, enabling a more structured and detailed analysis. Two main sub-datasets were defined, reflecting the two primary dimensions of employee retention--non-monetary and monetary factors, respectively. The list of variables included in the different sub-datasets can be found in Appendix A, Table A1. * Quantitative Emotional Salary (QES)
* Economic Compensation (EC) Within the QES sub-dataset, dimensionality reduction was performed using PCA. The results indicate that five principal components optimally encapsulate over 70% of the total variance in the data. And this was sufficient to explain the variability that exists in the data. Table 1 presents the percentage contribution of each original variable to these components, allowing for the identification of the most influential factors within the QES construct. It shows Dimension 1 is characterised by educational support between 2018-2020, while Dimension 2 highlights continuous training. Dimension 3 is defined by time in position and training, whereas Dimension 4 is associated with seniority and training, and Dimension 5 with time in position and professional development. Figure 1 below presents a graphical representation, which was constructed using the first two principal components, where each point represents an individual who was not retained, i.e., a person who left the organisation. Although the analysis focuses on quantitative variables, one qualitative variable from the original set of 103 is incorporated: the academic category at the time of turnover. This variable is used for colour coding in the visualisations, offering additional insights into potential patterns. The variable categories are listed in Appendix A, Table A2.
# Numerical Simulation and Analysis of Fish-Like Robots Swarm
###### Abstract  
Artificial fish-like robot is an important branch of underwater robot research. At present, most of fish-like robot research focuses on single robot mechanism behavior, some research pays attention to the influence of the hydro-environment on robot crowds but does not reach a unified conclusion on the efficiency of fish-like robots swarm. In this work, the fish-like robots swarm is studied by numerical simulation. Four different formations, including the tandem, the phalanx, the diamond, and the rectangle are conducted by changing the spacing between fishes. The results show that at close spacing, the fish in the back can obtain a large wake from the front fish, but suffers large lateral power loss from the lateral fish. On the contrary, when the spacing is large, both the wake and pressure caused by the front and side fishes become small. In terms of the average swimming efficiency of fish swarms, we find that when the fish spacing is less than \(1.25L\) (\(L\) is the length of the fish body), the tandem swarm is the best choice. When the spacing is \(1.25L\), the tandem, diamond and rectangle swarms have similar efficiency. When the spacing is larger than \(1.25L\), the rectangle swarm is more efficient than other formations. The findings will provide significant guidance for the control of fish-like robots swarm.  
Keywords:fish schooling; hydrodynamics; swimming efficiency; robots swarm; robot controlling +
Footnote †: journal: Applied Sciences
## 5 Automation and Teleoperation
### Technology Description
#### 5.2.1 Wi-Fi
Thus, a reduced functionality of the network can still be maintained with 2.4 GHz. One of the standard applications is the supervision and teleoperation of LHDs underground. The Wi-Fi 2.4 GHz network is mostly standard, having 10 Mbit/s available per machine and 100 ms maximum delay (incl. roaming). It is also noted that the used Wi-Fi is a limiting factor in the number of parallel running machines. An update to later Wi-Fi standards enhances network capabilities that have a direct effect on usability and thus productivity [30; 50]. Prior noted risks remain, especially when the use of the network is extended for further applications. A specific application of the Wi-Fi Network is the implementation of a so-called \"Mesh network\", which is a part of IEEE 802.11s [51]. In the ARTUS project [52], a mesh network was implemented based on 2.4 GHz Wi-Fi routers, with the goal being to enable machine-to-machine communication and dynamic expansion of a locally required network based on the Wi-Fi IEEE802.11s standard [51]. In a mesh network, all primary units act as routers that generate their own Wi-Fi signal and have a transmission function for data between routers, similar to regular Wi-Fi repeaters. This enables the routers or \"mesh nodes\" to connect to each other and potentially to a stationary modern for internet access, allowing for a completely linked network where any two points or nodes are connected through a path that may include intermediate nodes [53]. Via path optimization algorithm, it is possible to have a self-expanding and dynamic network. This can be achieved with a combination of stationery and machine-mounted nodes. The concrete use case of this Wi-Fi Option and configuration is the machine-based dynamic network in open pit and underground mines [52]. Wi-Fi has a great potential for mining applications and is already in wide use. Nevertheless, the networks always seem limited to a single use case and are always \"best-effort\" unmanaged networks, that still can serve the purpose.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 3 From the Synthesis of a 3D Scene to Its Safe Reception: Process Description
### Visualization  
The original scene can be recovered either by simulation (using convolution algorithms such as the angular spectrum [1]) (see Figure 5a) or in the laboratory (Figure 5b).  
The experimental setup for obtaining the reconstructed scene in the laboratory is shown in Figure 6. The CGH was computed for a HeNe laser source (\(\lambda\) = 0.633 \(\upmu\)m). The SLM used is a Pluto from Holoeye [40]. The plane where the camera's CCD is located must coincide with one of the planes in which the real image of the scene was computed. In the case of Figure 5b, this corresponds to the position of the dragon's head.  
In both cases (simulation and lab), the behavior of a plane wavefront disturbed by the phase information of the CGH at each pixel is visualized in a specific plane (which coincides with the position of the dragon's head in the 3D scene). To obtain the image in Figure 5b, a CCD from a lensless camera is placed in this plane.  
Figure 5: Decrypted image from the phase information of the CGH using the right key: (**a**) results obtained by simulation (propagation using the angular spectrum convolution technique); (**b**) image obtained in the laboratory. The plane of focus coincides with the dragon’s head, so the teapot is out of focus.
## 3 Methods
### ML Classifiers and Accuracy Assessment  
Applying ML classifiers, considered accurate and efficient approaches for LC mapping, has attracted many scholars. In this work, three frequently used and well-known ML algorithms, namely RF, ELM, and SVM, were assessed for LC classification in six different landscapes (For more detailed information about the ML classifiers, read [26; 27; 28; 29; 30; 31]). In the RF model, the maximum number of trees was 1000, which was selected from a range of 500 to 3000 using cross-validation. While the number of variables for each split in RF was set as the default value of 25. To get the best performance, the cutoff fraction in this model and the resampling process repeated time were set as 0.01 and 500, respectively. For the SVM, we used a kernel width (\(\gamma\)) of 0.95 and a regularization (C) parameter of 0.8. Of note, we used a tenfold cross-validation strategy to find the most suitable values for the parameters of the RF, ELM, and SVM algorithms. Three accuracy evaluation metrics, including User's Accuracy (UA), Producer's Accuracy (PA), and Overall Accuracy (OA), were computed with validation datasets to evaluate the accuracy of produced LC maps. We did not use the Kappa static because of increased criticism in literature [15; 32; 33].
## 3 Experiments and Performance Measurements
### Performance Measurement
#### 3.2.4 Experiment 4: Response Time for Concurrent Threads  
One or more clients' threads are applied to invoke the same remote control service with RESTful API and SOAP Web service interface respectively, and the value of the client threads is automatically generated and configured. The goal of this experiment is to understand the scalability, the overhead and the flexibility our proposed RESTful APIs compared with the existing SOAP based Web service implementation. The response time for both SOAP and RESTful based Web services are shown as Figure 10.  
As Figure 10 shows, the SOAP based Web service consumes much more response time. Considering the average response times, the RESTful Web service outperforms SOAP based Web service as expected.
## 3 Case Study
### Determination of the Radius of Curvature
The geometry of the chip breaker obtained from the FE modelling work is shown in Figure 7a,b with rake angle (\(\alpha\)) of 17\({}^{\circ}\), radius of curvature of 1.4 mm, land width (a) of 0.05 mm, with overall groove width of 1.0 mm and a maximum depth of 76 \(\upmu\)m.  
Using the final chip breaker geometry, as displayed in Figure 7, with the machining conditions shown in Table 1, a final simulation was conducted. The result, presented in Figure 8, shows an acceptable chip curling, with a radius of curvature corresponding to R = 1.4 mm, without clogging. Figure 8: FE simulation result for the final chip breaker geometry. Figure 7: Chip breaker geometry result from the simulation study (**a**) cross-section in 2D (**b**) geometry in 3D [all dimensions as in mm].
## 2 Mechanical Design and Working Principle  
Figure 1 shows the schematic diagram of the proposed dual-mass MEMS gyroscope design. The design consists of two separate masses for the drive and sense axis, which are fully decoupled. The drive mass \(m_{d}\) is nested inside the sense frame \(m_{s}\) through four folded beams (\(k_{x}\)), which allow motion of the mass \(m_{d}\) in the drive axis but limit its displacement in the sense axis. Another set of eight folded spring beams (\(k_{y}\)) connects the sense frame to the anchors and allows its displacement in the sense direction only. An oscillatory motion along the drive direction (\(x\)-axis) is achieved through a set of interdigitated combs-based electrostatic actuators attached to the drive mass \(m_{d}\).  
When the gyroscope is subjected to an external angular velocity in the \(z\)-axis, a Coriolis force is developed that leads to consequent displacement of the sense mass in the sense direction (\(y\)-axis), which is orthogonal to both the drive direction and angular rotation axis. The mechanical suspension beams attached to the drive mass do not allow its oscillation in the \(y\)-axis. However, since the drive mass is nested inside the sense frame, both the drive and sense frame move in the \(y\)-axis as a result of developed Coriolis force. The displacement of the sense mass is measured by using sensing parallel plates, which act as a capacitive transducer. The parallel sensing plates are designed in a gap-antigap-based differential layout and are attached to the sense frame such that their relative motion along the drive direction is restricted, thus minimizing the cross-axis coupling in the proposed MEMS gyroscope design. Finally, to control the resonant frequency difference between the two modes, due to foundry process tolerances or thermal variations, electrostatic tuning combs based on the electrical spring softening concept are attached to the sense frame. Table 1 shows the design parameters and their corresponding values for the proposed MEMS gyroscope. One of the main design constraints attributed to the SOIMUMPs microfabrication process is that the mechanical anchors or fixed parts of the MEMS device must be designed at the periphery since this process does not allow anchor regions in the center owing to the limitations of the etching step of the microfabrication process [19]. Accordingly, all the fixed parts are moved on the periphery. In addition to respecting the limits imposed by the microfabrication process, the design process has required ensuring the matching of the drive- and sense-mode resonant frequency values. Moreover, it is necessary to consider that electrostatic tuning will be implemented to compensate for the frequency mismatch. This means that the sense frequency value must be slightly greater than the drive one.  
Figure 1: Schematic diagram of the proposed dual mass resonant micro-electro-mechanical systems (MEMS) gyroscope design with single proof mass and a decoupled sense mass: (**a**) Tuning-comb, (**b**) Comb-drive and (**c**) Sensing plate pairs.
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 6 Analysis
### Metric Evaluation
#### 6.1.3 YCB-Video Dataset  
Table 11 presents the evaluation metrics for various models on the YCB-Video dataset. The **PoseRBPF**[11] achieves the highest **ADD** value of 92.4%, and **CloudAAE**[20] leads the **ADD-S** metric with a score of 93.6%. **DCL-Net**[67] records the highest **ADD-(S)** value of 99%. For the **Average Recall (AR)** metric, **[PERSON] et al.**[69] achieves the highest score of 86%. In the **AUC ADD** metric, the **DFTr network**[42] stands out with a score of 96.7%, while **[PERSON] et al.**[81] achieves the highest **AUC ADD(-S)** value of 94.8%.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Year** & **ADD** & **ADD-S** & **ADD-(S)** & **AR** & **AUC ADD** & **AUC ADD(-S)** \\ \hline C2 FNET [75] & 2023 & & & 95.92 & & & & \\ Improved PVNet 2 [78] & 2023 & & & & & & \\ SE-UF-PVNet [49] & 2023 & & & & 11.00 & 11.00 & 11.00 \\ Improved PVNet [45] & 2023 & 20.17 & & & 51.50 & & \\ SASA-PVNet [80] & 2023 & & & 68.40 & & 68.40 & \\ [PERSON] et al. [87] & 2024 & & & 49.20 & & 49.2 & \\ CoS-PVNet [51] & 2024 & & & & 49.08 & & 49.08 & \\ Focal Segmentation [53] & 2024 & 49.08 & & & & & \\ BSAM-PVNet [50] & 2024 & & & & & & \\ RNNPose [86] & 2024 & & 60.65 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Model** & **Year** & **ADD** & **ADD-S** & **ADD-(S)** & **AR** & **AUC ADD** & **AUC ADD(-S)** \\ \hline [PERSON] et al. [4] & 2015 & & & & & & \\ PoseCNN [7] & 2017 & 64.60 & 83.70 & & & & \\ PoseRBPF [11] & 2019 & & 92.40 & & & & \\ G2L-Net [15] & 2020 & & & 92.40 & & & 92.40 \\ CosyPose [14] & 2020 & & & & 89.80 & 84.50 \\ EPOS [62] & 2020 & & & & 69.60 & & \\ Self6D [64] & 2020 & & & & 56.80 & 0.66 & & 90.90 \\ CloudAAE [20] & 2021 & & & 93.60 & & & \\ [PERSON] et al. [69] & 2021 & & & & 86.00 & & \\ RePOSE [22] & 2021 & & & & & 84.50 \\ FFB6D [26] & 2021 & & & 96.6 & & & \\ ConvPoseCNN2 [66] & 2022 & & & & 63.48 & 60.91 & 79.26 \\ [PERSON] et al. [81] & 2022 & & & 97.90 & & & 94.80 \\ GDR-NET [25] & 2022 & & & 91.60 & & & \\ DCL-Net [67] & 2022 & & & 99.00 & & & \\ FS6D-DPM [28] & 2022 & 42.10 & & 88.40 & & & \\ PoET [43] & 2022 & & & 80.80 & & 92.80 \\ [PERSON] et al. [70] & 2022 & & 77.10 & & & 77.10 & \\ PVNet [32] & 2022 & & & & 73.80 & & \\ SC6D [33] & 2022 & & & & 78.80 & & \\ ZebraPose [37] & 2022 & & 80.50 & & & & 90.10 \\ CRT-6D [41] & 2023 & & & 72.10 & & & \\
3 DNEL [73] & 2023 & & & & 84.85 & & \\ DFTr network [42] & 2023 & & & 96.70 & & 96.70 & \\ SwinDePose [83] & 2023 & & 73.10 & & & & 68.40 & \\ MSDA [79] & 2023 & & 92.10 & 80.70 & & 92.10 & 80.70 \\ RNNPose [86] & 2024 & & & & & & 85.70 \\ YOLOX-6D-Pose [56] & 2024 & & & & & 88.50 & 88.90 \\ \hline \hline \end{tabular}
\end{table}
Table 11: YCB-Video Dataset Evaluation (in %).
## 6 Conclusions  
In this paper, the advantages and disadvantages of three-port and partial power conversion technologies have been analyzed in detail to help in the selection and design of PV systems. A comparison of TPCs and distributed PV systems has been presented. Compared to PV systems with two-port converters, PV systems with TPCs have the advantages of higher efficiency and power density. Based on the need for galvanic isolation, TPCs can be classified into three types: fully isolated, partly isolated, and non-isolated. Fully isolated TPCs typically achieve high voltage gain but come with a larger volume and higher cost. Partly isolated TPCs provide partial isolation between two of the three ports, offering higher efficiency and power density. Compared to the other two types, non-isolated TPCs have the advantages of higher efficiency, greater power density, a smaller size, and lower cost.  
In addition, compared to FPCs, PPCs reduce power losses and volume. By employing partial power technology in TPCs, the efficiency and power density can be further improved. Moreover, solar energy can be used in an efficient way by employing TPCs and partial power regulation in distributed PV systems with energy storage. This setup also simplifies the implementation of the DMPPT, as each PV panel is equipped with its own energy storage.  
This article can be used as a guide for selecting TPCs and distributed PV system architectures for different applications.  
Figure 20: Circuit configuration of a distributed PV system with a BESS and PPR.  
**Author Contributions:** Conceptualization, D.Y. and S.M.; data curation, D.Y.; formal analysis, D.Y. and S.M.; funding acquisition, S.M.; investigation, D.Y.; methodology, D.Y. and S.M.; project administration, S.M.; software, D.Y.; supervision, S.M.; validation, D.Y.; visualization, D.Y.; writing--original draft, D.Y. and S.M.; writing--review and editing, D.Y. and S.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Spanish national research agency Agencia Estatal de Investigacion, grant number PID2019-108966 RB-I00/AEI/10.13039/501100011033.  
**Conflicts of Interest:** The authors declare no conflicts of interest.