esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Homogeneous Ensemble Learning  
In the development of machine learning, various methods have been proposed to improve the predictive or classification accuracy of models, with ensemble learning being one of the classic approaches. Ensemble learning improves performance by constructing and combining multiple learners. Common strategies for combining learners include averaging and voting. The traditional philosophy of ensemble learning often follows the principle that \"to achieve a good ensemble, individual learners should have a certain level of accuracy and diversity among them\" [23].  
Some researchers have introduced diversity among learners by using different base models, a technique known as heterogeneous ensemble learning [23]. Heterogeneous ensemble learning improves the accuracy of base learners to a certain extent. However, using different base learners in heterogeneous ensemble learning reduces the correlation among them [18; 24]. In other words, the different base learners can only improve the overall model accuracy through competition rather than by learning from each other's strengths. This limits the potential accuracy of heterogeneous ensembles to the accuracy limits of the base learners themselves.  
The challenge this model aims to address is how to enable base learners with a certain level of accuracy to learn from each other while maintaining diversity. To facilitate  
Figure 1: An example of an asymmetric encoder–decoder network with 6 layers for the encoder and 2 layers for the decoder. Skip connections not only pass between feature maps in the same layer of the encoder and decoder but also pass between different layers inside the encoder.  
mutual learning among base learners, our model adopts a homogeneous ensemble learning approach, using multiple identical base models. During the process of mutual learning, we propose a Misaligned Merging Mechanism (MMM), as shown in the Figure 2. This mechanism allows each decoder to receive and merge feature map outputs from two base models. By decoding the merged feature maps of the two base models, this approach simultaneously considers both the deep and shallow features learned by the two different encoders. This mechanism combines the strengths of different base learners, effectively preventing the degradation in model performance caused by feature loss in a single encoder during downsampling. Moreover, it successfully overcomes the limitation that the accuracy of ensemble learning is constrained by the accuracy of individual base learners.
## 2 Amplitude and Phase Characteristics of PSCFCs in Active Distribution Networks
### Amplitude and Phase Characteristics of PSCFCs in Active Distribution Networks with IBDGs  
#### 2.2.1 Amplitude Characteristics of PSCFCs  
In existing studies, the IBDG is usually equivalent to a voltage-controlled current source [16]. Due to the short response time of power electronic devices after faults, the superposition theorem can still be applied when analyzing the fault characteristics of IBDG. A positive-sequence fault component composite sequence network of the distribution network with IBDG is shown in Figure 3.  
In Figure 3, \(Z_{\text{dg}}\) is the unloading resistance, which is used to limit the maximum output current of the IBDG; \(Z_{\text{mL}}\) is the load positive-sequence equivalent impedance; \(\Delta I_{\text{dg}}\) is the PSCFC of the IBDG output.  
Applying the superposition theorem to the composite sequence network of positive-sequence fault components shown in Figure 3, the amplitude ratio of the PSCFCs at both ends of the feeder \(MN\) can be obtained (the derivation process is shown in Appendix A).  
\[\varepsilon=\frac{\left|\Delta\dot{\Lambda}_{\text{m}}\right|}{\left|\Delta \dot{\Lambda}_{\text{n}}\right|}=\left|\frac{\dot{U}_{\text{f}}\cdot(Z^{ \prime}_{\text{n}}+Z_{\text{mL}})-\Delta\dot{I}_{\text{dg}}\cdot Z_{\text{ mL}}\cdot\Delta Z}{\dot{U}_{\text{f}}\cdot Z^{\prime}_{\text{m}}+\Delta\dot{I}_{ \text{dg}}\cdot Z_{\text{mL}}\cdot(\Delta Z+Z^{\prime}_{\text{m}})}\right| \tag{2}\]  
From Equation (2), the amplitude ratio of PSCFCs at both sides of the feeder in the distribution network with IBDG is related to the pre-fault voltage, the GRID equivalent impedance, the fault point position, the drop degree of the grid-connected point voltage, and the fault type. Usually, the short-circuit current provided by the GRID in the active distribution network is at least three times the rated current [28]. In comparison, the maximum short-circuit current provided by the IBDG is 1.2-2 times the rated current [29]. Therefore, when a fault occurs inside the feeder in the distribution network with IBDG, the amplitude ratio of PSCFCs at both ends is much greater than 1, that is, \(\varepsilon>>1\). Considering extreme cases [30], measurement errors of the current transformer (CT), and certain margins, it can be assumed that \(\varepsilon_{\text{min}}\approx 1.6\).  
Figure 3: Positive–sequence fault component composite sequence network with IBDG.
## 2 Materials and Methods
### Training Datasets Generation and Site Selection
#### 2.1.4 Training Datasets Generation  
The training datasets were preprocessed based on 2.5 m downsampled aerial orthoimages and 10 m Sentinel-2 satellite images. The first step was to transform both image sets into the same map projection system. All image sets in this study were projected into the Korea 2000 coordinate system (EPSG: 5186), corresponding to transverse mercator (TM) projection. The second step was to determine the size of training datasets based on 60 m Sentinel-2 images. After considering the computational efficiency of training processes, the 4 \(\times\) 4 pixels image size was used, corresponding to 240 \(\times\) 240 m\({}^{2}\) on the ground. For this configuration, the image size for 2.5 m and 5.0 m aerial orthoimages were 96 \(\times\) 96 pixels and 48 \(\times\) 48 pixels, respectively. For the same reason, the training image sizes of 10 m and 20 m resolution for Sentinel-2 were 24 \(\times\) 24 pixels and 12 \(\times\) 12 pixels, respectively.  
Figure 3: Acquired Sentinel-2 band number 2 (10 m) images: (**a**) Sentinel-2A 2018/04/18, (**b**) Sentinel-2A 2018/04/28, (**c**) Sentinel-2A 2018/05/28, (**d**) Sentinel-2B 2018/05/23.  
Training samples and test samples were selected randomly within the study area but did not overlap for the Daejeon area. Through this process, 32,632 training samples (6527 for validation samples, 20% of the training samples) and 8156 test samples were produced. In addition, 39,204 test samples were generated for the Sejong area. Each set consisted of twelve Sentinel-2 images (4 for 10 m, 6 for 20 m, and 2 for 60 m) and two aerial photographs (1 for 2.5 m and 1 for 5.0 m), as depicted in Figure 4. A 5.0 m aerial orthoimage was used as the GT for 2\(\times\) magnification of 10 m Sentinel-2 images and 2.5 m for 4\(\times\) magnification of 10 m Sentinel-2 images.
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 2 Materials and Methods
### Software  
MATLAB version 2019a and TEQC [13,14] were the main software used in this study. MATLAB was used for calculation and visualization, whereas TEQC was utilized to generate quality control (QC) parameters. The R Language and Environment for Statistical Computing [15] (Version 3.5.1) and the R package gstat (Version 1.1-6) [15, 16, 17] was used for the geostatistical computations.
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 3 The Design of DAHC Protocol  
This paper aims to study how to reduce the energy consumption of the nodes, balance the energy load of the cluster, and prolong the network lifetime. In previous clustering research work, researchers usually choose distributed or centralized control to select the CHs. These two methods have advantages and disadvantages. In view of previous discussions, we put forward the DAHC protocol. In this protocol, the distributed and centralized control methods are used to select the CHs in a mixed way. Simultaneously, in order to solve the problem of uneven distribution of CHs caused by the distributed method, a novel way of adaptive cluster density is proposed. The implementation process of the DAHC protocol is described in detail as follows.
## 2 Materials and Methods
### Phase Space Diagrams  
Phase space diagrams were constructed for each pig based on the EST data, using time delays as in [26]. As explained in [19], each phase space was reconstructed in a phase space graph using a time series versus itself delayed in time. The appropriate value for the time delays (td) and system dimension (N) was obtained by heuristics. In this work, the two-dimensional (N = 2) space representations were obtained by plotting the measured temperature T(k) at each time t(k) versus the temperature T(k + \(\Delta\)) at time t(k + \(\Delta\)), with an optimum step \(\Delta\) (five in this work) established by heuristics. The data acquisition interval (i.e., the fixed time step of the time series) was \(\tau\) min (3 min in this experiment), making the corresponding time delay td = \(\tau\cdot\Delta\) = 15 min. [PERSON] et al. (1992) established that sampling intervals between 5 and 12.5 min are adequate from a biologically-based point of view in characterizing responses for tympanic temperature. In the current study, to obtain the optimum time delay of 15 min, different values of \(\Delta\) were tested systematically looking for the maximum expansion of the trajectory loops or attractors in the phase space, retaining the points that could represent specific events. The area of the trajectory loops was computed using the MATLAB functions (MATLAB R2015b, MathWorks, Inc., Natick, MA, USA) convhull and polyarea. The convhull function helps select a set of points from the trajectory loop in the phase space plot and returns the smallest convex envelope containing the set of points (vertices that define the polygon). The convhull function is based on the Quickhull (Qhull) algorithm for convex hulls. The polyarea function returns the area of vertices defined by convhull [19].  
From these phase space diagrams, two variables of interest were extracted: (1) the centroid or center of gravity (\({}^{\circ}\)C) of the cloud of points that represent the arithmetic average of all ESTs registered, weighted by the local density of points or specific weight; and (2) the areas (\({}^{\circ}\)C\({}^{2}\)) that include all the points in the phase space as a measure of the total variability in EST during the selected periods, i.e., total area for the whole journey as well as for each stage. Since [27] showed how a phase space of time series can be useful to point out intense but short events, we calculated the area described by 100% of the points, as well as 75% of the points around the centroid. That helped us to identify any unique events in 25% of points furthest away from the centroid.  
Figure 2: Full journey outline from Stages 1 to 5 with indication of the characteristics of each stage.
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 3 Isogeometric Analysis
### Important Features in Current Context  
IGA has a number of advantages over FEA, such as the ability to represent exact geometries of structures or domains, non-negative basis functions and isoparametric mapping at patch level. In the context of the current work, we focus on the features of IGA that are especially important. These features are improved continuity because of the smoothness of the basis functions and the ability to perform simulations with high continuity and high regularity meshes. We look closely into each here.
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 7 Evaluation and Analysis
### Exfiltration  
The technology and solutions of power line communication have been extensively applied in commercial and household scenarios, with their maximum bit rate and distance primarily determined by the power and frequency band of the PLC transmitter. Typically, these applications are limited to within the household range, where longer communication distances generally result in lower communication rates. Notably, [PERSON] et al.'s PowerHammer study successfully demonstrated that computers themselves can serve as hardware for signal transmission without requiring additional equipment.  
In this section, we exclusively examine the distinctive characteristics of the plug as a receiver. For more comprehensive insights into visible power line communication and IoT security, we recommend interested readers refer to the relevant literature [46]. To conduct testing and an evaluation, we employed the transmitter described in Section 6 for generating power consumption fluctuation signals and performed exfiltration tests on multiple laptop brands.  
Figure 24: The smart plug is designed for this work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transmission Mode** & **Sender Unit** & **Receiver Unit** & **Content of Communication** \\ \hline \begin{tabular}{c} Exfiltration (leaking data from \\ an air-gapped computer) \\ \end{tabular} & Power adapter for your computer & Smart plug power consumption & Small amounts of data \\ \
\begin{tabular}{c} measurement module \\ \end{tabular} & (sensitive files, passwords, etc.) \\ \hline
\begin{tabular}{c} Infiltration (sending data to the \\ air-gapped computer) \\ \end{tabular} & Smart plug switch module & ACPI interface for the computer & Control parameter \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transmission mode of PowerBridge.
## 4 Experiments and Analysis of Results
### Comparison Experiments
#### 4.5.2 Comparison of Different Loss Functions  
To evaluate the effectiveness of Shape-IoU for PCB defect detection, comparative experiments were conducted by replacing the original CIoU loss function in YOLOv11n with three alternatives: SIoU, WIoU, and Shape-IoU. The performance comparison results are presented in Table 4.  
It can be seen in the data in Table 4 that the SIoU loss function shows lower performance across all metrics compared to the baseline YOLOv11n with CIoU. In contrast, WIoU achieves measurable improvements, with precision increasing by 0.8%, recall by 1.3%, mAP50 by 0.9%, and mAP50-95 by 3.1%. Shape-IoU outperforms all comparison methods, achieving the highest scores in precision, recall, and mAP50, thereby confirming its suitability for PCB defect detection applications.
## 2 Materials and Methods
### Experimental Setup
Two objects were constructed for imaging, (1) triangle: an equilateral triangular shape with 2-cm leg length (Figure 2b(i)), and (2) four leg: pyramid shape (without base) of 1.5-cm leg length using 2-mm diameter copper wire soldered together (Figure 2b(ii)). All copper rods were covered in black tape as an absorbing coating. The inner surface of the hemisphere was covered with transparent (both optically and acoustically) saran wrap (uBoxes, Miramar, FL, USA). We used a multibeam swept-source optical coherence tomography system (SS-OCT) (VivoSight, Michelson Diagnostic(tm) Inc., Maidstone, UK) and measured the thickness of the saran wrap to be 40 \(\upmu\)m [59]. The result is shown in Figure 2a(i). Next, 8% gelatin solution in distilled water was poured into the hemisphere and cured at room temperature. The transducer-sensing surfaces were coated in ultrasound gel before placing into assigned positions in contact with the saran wrap. Finally, based on the transducer diameter and central frequency, the target object in the hemisphere is within the near-field. Imaging within the near-field results in rapid oscillation in received pressure with distance away from the transducer. We have accounted for this oscillation by recording the received pressure profile by imaging a thin lead (with the diameter of 500 microns) from a distance of 4.5 to 7.5 cm in 100-\(\upmu\)m increments and recording the PA signal. We used a digital moving stage (Applied Scientific Instrumentation, Eugene, OR, USA) which travelled at 1 mm/s during acquisition. A complementary weighting profile was then constructed from the near-field profile for compensation. This experiment was repeated 10 times to reach sufficient precision. This profile was interpolated and applied to the raw data before reconstruction. It is possible to remove this step by utilizing highly focused transducers that have very short focal length.
## 4 Experimental Results
Comparison of BER Performance between 4-ASK-FOFDM and 16-QAM-OFDM in a Simulated Air Turbulence Channel Followed by a Water Channel  
To better evaluate the performance of 4-ASK-FOFDM, the BER of 4-ASK-FOFDM was compared with that of conventional 16-QAM-OFDM under the same SE of 1.68. The measured BER for 4-ASK-FOFDM and conventional 16-QAM-OFDM at different link distances is shown in Figure 8. As seen in Figure 8, as the link distance increases from 1.6 m to 8 m, the BER of 4-ASK-FOFDM were all below 3.8 \(\times\) 10\({}^{-3}\), which was the 7% FEC limit. The BER performance of conventional 16-QAM-OFDM was a little better than 4-ASK-FOFDM. But the performance penalty of 4-ASK-FOFDM was tiny. Figure 9 shows the constellations of the 4-ASK-FOFDM signal and 16-QAM-OFDM signal when the link distance through the water tank was 1.6 m and the atmospheric turbulence simulator was enabled. Figure 5: Waveforms and spectra captured over a simulated air turbulence channel followed by a water channel (**a,b**) and over a free-space air channel followed by a water channel (**c,d**). Figure 8: Curves of BER versus link distance over a simulated air turbulence channel followed by a water channel for 4-ASK-FOFDM and 16-QAM-OFDM signals. Figure 6: Curves of BER vs. link distance over a simulated air turbulence channel followed by a water channel and a free-space air channel followed by a water channel. Figure 7: Constellations of the 4 –ASK –FOFDM signal when the link distance through water tank was 1.6 m and (**a**) the atmospheric turbulence simulator was disabled or (**b**) the simulator was enabled. The insets showed the histogram of constellation points.
## 3 Experiments and Results
### Performance Comparison with Other Methods  
We compared the performance of different models and the results are shown in Figure 6, Tables 2 and 3. Figure 6 shows the accuracy and loss of training and validation for each epoch. As shown in Figure 6a,c, it can be seen that the training results of DADRN and SESADRN are similar and they are significantly better than those of SE-Resnet50, RAN56, and RAN92. The loss and accuracy of the DADRN and SESADRN models tend to stabilize more quickly, that is, they converge faster. For validation, it can be seen more clearly from the validation loss and accuracy figures that the SESADRN model has lower loss and higher accuracy. Table 2 lists the results of model accuracy, weighted precision, weighted recall, and weighted F1-scores on the test set. As shown in Table 2, the proposed method is the best of the five models for all four evaluation metrics. The accuracy, weighted precision, weighted recall, and weighted F1-scores of the proposed method are 0.956, 0.954, 0.948, and 0.960, respectively. We can also see that the DADRN model achieved better results than those of the other three models (SE-Resnet50, RAN56, RAN92). This is consistent with the results in Figure 6.  
Figure 6: Accuracy and loss of training and validation. (**a**) Training loss of 300 epochs. (**b**) Validation loss of 300 epochs. (**c**) Training accuracy of 300 epochs. (**d**) Validation accuracy of 300 epochs. The comparison of the class-wise classification accuracy of the models is shown in Table 3. For the test samples of 30 categories, the classification accuracy of the SE-Resnet50 model is the lowest, and only 10 categories are completely correctly classified. The proposed SESADRN model has the highest classification accuracy, and 23 out of 30 categories are completely correctly classified. For the SESADRN model, the classification accuracy of only one of the 30 categories is lower than that of the DADRN model. There are 4 test samples in this category. The accuracy of the SESADRN model is 0.5, while the accuracy of the DADRN model is 0.75. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & \
\begin{tabular}{c} **Weighted** \\ **Precision** \\ \end{tabular} & **Weighted Recall** &
\begin{tabular}{c} **Weighted** \\ **F1-Score** \\ \end{tabular} \\ \hline SE-Resnet50 [30] & 0.873 & 0.874 & 0.862 & 0.886 \\ RAN56 [35] & 0.861 & 0.859 & 0.847 & 0.870 \\ RAN92 [35] & 0.892 & 0.889 & 0.873 & 0.905 \\ DADRN [32] & 0.932 & 0.932 & 0.924 & 0.940 \\ SESADRN & 0.956 & 0.954 & 0.948 & 0.960 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of 30-class classification performance.
# A Review on Internet of Things for Defense and Public Safety
## 5 Building IoT for Tactical and Emergency Environments
### IoT Standardized Protocols
#### 5.1.1 Application Layer Protocols  
The following are the most popular Application Layer protocols: Constrained Application Protocol (CoAP), Message Queue Telemetry Transport (MQTT), Extensible Messaging and Presence Protocol (XMPP), Advanced Message Queuing Protocol (AMQP) and Data Distribution Service (DDS). Performance evaluations and comparisons between them have been reported in the literature [123]. Each of these protocols may perform rather well in specific scenarios, but there is no evaluation of all these protocols together. Consequently, it is not possible to provide a single prescription for all IoT applications, just that they must be designed from the ground up to enable extensible operations [124].
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
###### Abstract  
Above-ground biomass (AGB) is a core indicator for evaluating grassland ecosystem health and carbon storage. Traditional ground-based AGB measurements are labor-intensive and ill suited for large-scale monitoring. This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). Incorporating fourteen multi-source features (seven vegetation indices, e.g., Modified Vegetation Index (MVI) and Green Chlorophyll Index (Clg); four meteorological variables; three soil properties), all data were standardized via z-score normalization before training. The MLP model, optimized via six-fold cross-validation, achieved an R\({}^{2}\) of 0.765 and RMSE of 38.066 g/m\({}^{2}\), outperforming XGBoost (R\({}^{2}\) = 0.723, RMSE = 41.354 g/m\({}^{2}\)) with a statistically significant 5.8% accuracy improvement (\(p<0.05\)). Spatial analysis revealed a north-to-south AGB gradient, strongly correlated with precipitation gradients (250-350 mm/year) and soil organic carbon (R = 0.428). These findings provide a robust framework for climate-adaptive grassland management and carbon assessment in semi-arid regions.  
Keywords:above-ground biomass; multilayer perceptron; remote sensing; grasslands; machine learning +
Footnote †: journal: Applied sciences
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 8 Proposed Framework: Multi-Layer Threat-Defense Alignment Framework
### Applications of MLTDAF in Counter-Terrorism
#### 8.4.1 Layered Defense Against Hybrid Terrorist Attacks  
Proactive security hardening is made possible by MLTDAF's alignment of physical and cyber threats to architectural layers. Terrorist actors might combine cyber incursions (such as fake data injection attacks) with physical disruption (like shutting down substations). In addition to offering countermeasures such as tamper-proof hardware at the perception layer and real-time behavioral analysis in the control layer, MLTDAF assists security teams in determining which layer is being targeted. This enables the implementation of a distributed defense plan that reduces grid-wide outages and cascade failures.