esa-sceva/satcom-chunk-collection huggingface dataset:
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 3 Results
### Experimental Results
#### 3.3.3 Inference Analysis  
As listed in Table 3, we compared the proposed method with the PraNet with respect to inference times. All execution times were measured under the model declaration and data I/O times. To provide an unbiased comparison, we used the code and hyper-parameters released by the authors [27]. For the same batch size, COMMA required fewer parameters, was \(1.39\times\) faster, and performed significantly better than PraNet. Furthermore, we changed the batch size to eight and found that the average FPS was 74.70, which is sufficient for real-time operation.
# Comparative Analysis of LiDAR and Photogrammetry for 3D Crime Scene Reconstruction
## 3 Methodology  
### Research Design  
The adopted study design is a comparative experimental design, in which a controlled experimental design is combined with a comparative analysis. The controlled experimental design involves the manipulation of two independent variables:  
* Scanning duration of five (fast) and ten (slow) minutes
* Illumination condition of day and night  
This is to observe their causal effects on the dependent variables (the accuracy of the reconstructed 3D models), using accuracy metric of the mean absolute error (MAE). Three models are compared:  
* Model 1: iPhone LiDAR with Recon-3D application
* Model 2: iPhone camera with Pix4D solutions
* Model 3: Ground truth measurements and 3D map, measurements of designated markers and a Leica scanner BLK360 Second Gen., which are used as the ground truth 3D map.  
The assumptions in the study include the consistency of the iPhone LiDAR application and the recon-3D application's performance across different scanning setups, on which a fixed resolution of 5 mm is selected. Except for the controlled variables, the environmental conditions are assumed to be similar across all scanning setups. Lastly, using a laser rulerand a measuring tape, the distance measurements between the reference markers placed on the ground are considered accurate and reliable.
## 3 Experiment and Analysis
### Evaluation Metrics  
To objectively assess the segmentation performance of the model for building damage assessment and enable effective comparisons with different approaches, four widely employed evaluation metrics for semantic segmentation, namely, \(P\) (Precision), \(R\) (Recall), the \(F1\) score, and _IOU_ (Intersection over Union), are adopted to evaluate the effectiveness of the introduced model. The evaluation is conducted using the following formulas:  
\[P=\frac{TP}{TP+FP} \tag{4}\]  
\[R=\frac{TP}{TP+FN} \tag{5}\]  
\[F1=2\times\frac{P\times R}{P+R} \tag{6}\]\[IOU=\frac{TP}{TP+FP+FN} \tag{7}\]  
where \(TP\), \(TN\), \(FP\), and \(FN\) represent the counts of true positive, true negative, false positive, and false negative samples for the respective classes, respectively.
## 4 Experimental Validation and Discussion
### Wide Band Validation  
The validation of the proposed CMUDPD technique with 30 MHz OFDM signals was also conducted. The PSD results (with and without CMUDPD) are depicted in Figure 16a,b, where CMUDPD1, CMUDPD2, and CMUDPD3 represent the pre-distortion results when the outputs of modes a1/b1, a2/b2, and a3/b3 were used as feedback paths, respectively. It can be seen from Figure 16 that the proposed CMUDPD works well, achieving an ACPR reduction of 11 dB in Class AB and 13 dB in Class A. Due to the limitation of the available LTE OFDM modulation signal templates and capability of uploading wide-band pre-distortion signals in the used signal generator, the validation was performed by simulation using the nonlinear equivalent circuit model of the same PA in the experimental validation. However, we think that the proposed technique has potential for even wider band signals, e.g., 100 MHz, which may be an interesting topic for future investigation.  
Figure 14: The experiment setup with multiple PAs.  
Figure 15: Experimental results of PSD with (W) CMUDPD.  
In this work, the used a CGH40006P PA from CREE, which features a frequency range of 2-6 GHz. Hence, the 3.5 GHz center frequency was chosen for the experimental validation, and the peripheral impedance matching circuit was specifically designed based on 3.5 GHz. Nevertheless, the proposed CMUDPD method is not frequency-dependent, as can be seen in (7), and hence is applicable to other frequency ranges. Furthermore, the simulation and experimental validations were conducted using a GaN HEMT in this study. In future works, the proposed CMUDPD principle is expected to be applied to other types of transistors/PAs.
# A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection
## 4 Experiments
### Results on WIDER FACE
#### 4.2.2 The Effectiveness of Our Multi-Scale Features and Multi-Context Design  
To evaluate the effectiveness of our design, we first test our method without using multi-scale features and context (i.e., it predicts faces only from multiple layers). The APs are 81.2%, 80.2% and 71.6% respectively. We then evaluate our method without context but with feature fusion, the APs are 86.1%, 84.5% and 76.8% respectively. Compared to the high APs of 90.6%, 89.1% and 79.8% of our full version method, it can be concluded that both feature fusion and context are important for the AP. The 'hard' subset mainly contains faces with large scale changes, and our method still performs well on this subset. It implies our method is powerful for detecting multi-scale faces. Feature fusion can produce semantically strong features and the multi-context gathered from different sizes of neighboring pixels will bring more robust to scale changes; this extra information is also important for detecting small faces. This gives confidence to the effectiveness of our design, especially our context adding mechanism. On the other hand, the inference speeds of our three method variants are 6 ms, 7 ms and 9 ms, and it can be assumed that our design only brings little extra computation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Inference Time (ms)** & **AP (Easy)** & **AP (Medium)** & **AP (Hard)** \\ \hline Two-stage CNN [20] & 10 & 68.1 & 61.8 & 32.3 \\ Multiscale Cascade CNN [25] & - & 69.1 & 66.4 & 42.4 \\ Faceness [21] & 50 & 71.3 & 78.8 & 34.5 \\ FD-CNN [30] & 30 & 77.4 & 74.0 & 51.0 \\ Multitask cascade CNN [32] & 10 & 84.8 & 82.5 & 59.8 \\ CMS-RCNN [15]-VGG-16 & - & 89.9 & 87.4 & 62.4 \\ MSCNN [31]-VGG-16 & 25 & 91.6 & 90.3 & 80.2 \\ SSH [22]-VGG-16 & 30 & 93.1 & 92.1 & 84.5 \\ Ours-without feature fusion and context & 6 & 81.2 & 80.2 & 71.6 \\ Ours-without context & 7 & 86.1 & 84.5 & 76.8 \\ Ours & 9 & 90.6 & 89.1 & 79.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between our method and its state-of-the-art peers.
## 1 Introduction
### Background and Related Work
KRUM and Multi-KRUM are Byzantine-resilient algorithms designed to detect and filter out anomalous updates by comparing the updates from all clients and selecting the most similar ones for aggregation [49; 50]. To evaluate the impact of Homomorphic Encryption (HE) on Split Learning performance, we measured the encryption time, processing time, and overall model accuracy across different datasets. The results in Table 1 demonstrate the computational overhead introduced by encryption while maintaining high model accuracy. These findings highlight the trade-off between security and computational efficiency in privacy-preserving machine learning. Although effective against certain types of poisoning attacks, these methods are not easily adaptable to Split Learning, where the server does not receive updates from all clients at the same time but in a sequential manner [51]. Additionally, the unique structure of Split Learning, where clients only handle the first few layers of the model, makes it harder for techniques like KRUM to be effective [52]. While Federated Learning has benefited from several defense mechanisms, these approaches are often insufficient for Split Learning due to its different architecture and the sequential nature of client participation [53]. To address the unique vulnerabilities in Split Learning, the SafeSplit framework was introduced as a novel defense mechanism against client-side backdoor attacks [54, 55]. SafeSplit operates by performing circular backward analysis after a client's training session, detecting malicious behavior in the trained model, and rolling back to a previously verified model state if a backdoor is detected [56]. This rollback mechanism ensures that if any client attempts to introduce harmful behavior into the model, its changes can be reverted, preventing further propagation of the attack [57]. SafeSplit's dual-analysis approach (static and dynamic) is highly effective in detecting and mitigating backdoor attacks while preserving the overall utility of the model [58]. Extensive evaluations have shown that SafeSplit can detect various types of client-side backdoor attacks, even in highly non-IID settings [59, 60], making it a robust defense specifically tailored for Split Learning [61].
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 7 Numerical Results
### Comparing with Other Approaches  
As shown in Section 2, several research works have addressed UAVs' optimal 3D deployment for communications. Unfortunately, comparing our results with these works is very difficult due to: (a) the objective function and constraints are different; (b) the radio access network (WLAN versus cellular) is also different, which affects coverage and QoS. But for the sake of completeness, we compare our results with the following approaches:  
* A search method based on K-means clustering that seeks the minimum number of UAVs that satisfies the optimization problem in this paper, Equation (9). K-means clustering has also been used for 3D optimal placement in [41].
* Our previous work [12] for optimal 3D placement in search and rescue missions. This GA-based search algorithm is the only related work that has comparable constraints (i.e., speech quality in WiFi) although the objective function is not the same and does not model UAVs endurance (see Table 1 for details).  
For the comparison, we vary the number of ground users from 10 to 100 over a 10,000 m\({}^{2}\) terrain, using the rest of parameters we listed in Table 3 (some parameters, such as coverage ratio, are not supported by our previous work [12]). Then, we analyze the evolution of the drone turnover rate (\(f_{\text{obj}}\)) and the total power expenditure (i.e., adding the power of all UAVs in the solution).  
Figure 9 illustrates how the UAV turnover rate (left axis) and the total power expenditure (right axis) change while the number of ground users increases. As expected, both variables are interrelated, and scale up with the number of ground users. Results suggest that our new proposal can reduce both, UAVs turnover rate and energy consumption. Compared to the K-means search method, our method saves 60% of the energy consumption for \(U=10\), and 80% for \(U=100\). Compared to [12], our method also provides energy savings, although this is reduced up to 22%. In general, our method also reduces the number of UAVs per hour required to complete the mission (e.g., reduced from 9 to 7 in \(U=70\)).  
Figure 8: Results for the broadcast VoIP channels experiment.
## 1 Introduction
The active network subset is a minimum segment of the network needed to meet the current traffic demand based on the network topology and traffic, whereas the passive network subset is a set of switches and links which can be needed to meet future traffic demands and anomalies. Based on these subsets, route management and power management are carried out. Power management refers to switching the active components to \"on\" state, passive components to a sleep state and shutting OFF the remaining network components. The \"Rate Adaption\" module then adapts the link data rates of \"on\" components in accordance with the load, thus maximizing energy savings. We compare our proposed mechanism with the baseline e.g., fat-tree DCN and ElasticTree which is a power conservation scheme for DCNs based on resource consolidation only. The \"active network subset\" computation in our scheme is inspired by the topology-aware heuristic employed in ElasticTree [11]. Our results confirm that Adaptive TrimTree outperforms both fat-tree DCN and ElasticTree on power savings with negligible impact on delay and packet loss percentage. The remainder of the paper is organized as follows: Section 2 presents a literature review. In Section 3 we present the design of Adaptive TrimTree mechanism. Section 4 presents the mathematical analysis of our proposed mechanism and Section 5 presents the simulation results in terms of power consumption, packet loss percentage and delay. Section 6 presents the discussion; and finally Section 7 summarizes the results and concludes the paper.
## 4 Improvement of Thermal Performance
### Thermal Conductivity Enhancement Techniques  
Thermal conductivity in a thermal energy storage system is a very important factor for the efficiency of energy storing and releasing. PCMs', especially organics', main disadvantage is their low thermal conductivity. Low thermal conductivity decreases the heat transfer rate which means that the stored energy cannot be used efficiently, putting limitations on plenty of applications. As a result, the enhancement of thermal conductivity of PCMs is tremendously important for the amplification of system's performance [45]. The research that has been held for the improvement of thermal conductivity is mainly based on the improvement that encapsulation offers [10; 46; 47], the addition of highly-thermal conductive materials, such as carbon-based nanomaterials [48; 49], metallic [50], or inorganic nanoparticles [51], and in the fabrication of form-stable PCMs with highly-thermal conductive carrier materials such as metallic foams [52], expanded graphite [53], etc. Another approach is the fabrication of nanofluid PCMs, which are dispersions of thermal conductive nanoparticles, for example, TiO\({}_{2}\), in the PCM liquid [54] (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Core Material** & **Shell Material** & **Encapsulation Method** & **Capsule Size** & \begin{tabular}{c} **Thermal Conductivity of** \\ **Pure PCM (W/im.K3)** \\ \end{tabular} & \begin{tabular}{c} **Thermal Conductivity of** \\ **Encapsulated PCM** \\ **(W/im.K3)** \\ \end{tabular} & **Year** & **Ref** \\ \hline \(n\)-Octadecane & \begin{tabular}{c} Crosslinked \\ \(n\)-Eicosane \\ \end{tabular} & \begin{tabular}{c} Minemulusion \\ Polystyrene \\ \end{tabular} & \begin{tabular}{c} 136 nm \\ polymerization \\ \end{tabular} & \begin{tabular}{c} 0.22 \\ 134 nm \\ \end{tabular} & \begin{tabular}{c} 0.12 \\ 0.18 \\ \end{tabular} & \begin{tabular}{c} 2020 \\ 0.11 \\ \end{tabular} & \begin{tabular}{c} [61] \\ \end{tabular} \\ \hline \(n\)-Octadecane & \begin{tabular}{c} SiO\({}_{2}\) \\ \end{tabular} & \begin{tabular}{c} Minemulusion \\ polymerization \\ \end{tabular} & 335 nm & 0.15 & 0.38 & 2018 & [62] \\ \hline \(n\)-Eicosane-Fe\({}_{\text{FO}}\)O\({}_{4}\) & \
\begin{tabular}{c} SiO\({}_{2}\)/Cu \\ \end{tabular} &
\begin{tabular}{c} Pickering emulsion \\ interfacial \\ polymerization \\ \end{tabular} & 428–631 nm & 0.4716 & 1.3926 & 2020 & [63] \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cust._
# Developing a Novel Design for a Tubular Solid Oxide Fuel Cell Current Collector
## 4 New Current Collector Design  
A new design for a current collector was developed to solve the two main problems of tubular SOFCs: the uniform flow distribution and current collection. The new design is a trapezoidal-shaped collector, as shown in Figure 6, with different channels on each side (cathode and anode) to control the uniformity of the flow. Additionally, each channel has two ribs connected to the outer surface of both the anode and cathode at which the current is collected. Three different designs are investigated in this study with four, eight, and twelve channels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **[PERSON] et al. [38]** & **[PERSON] et al. [39]** \\ \hline Fuel cell configuration & Tubular & Planar \\ \hline \hline
3D representation & & \\ \hline Fuel composition & H\({}_{2}\) (\(\sim\)3\% H\({}_{2}\)O) & H\({}_{2}\) (\(\sim\)3\% H\({}_{2}\)O) \\ \hline Fuel flow rate & 40 mL min\({}^{-1}\) & 10 cm\({}^{3}\)/min \\ \hline Reduction medium & & \(\rm O_{2}\) (21\%) + N\({}_{2}\) (79\%) \\ \hline Reduction of medium flow rate & & 15 cm\({}^{3}\)/min \\ \hline Operating temperature & 873.15 K & 1023 K \\ \hline Pressure & 1.01325 bar & 1.01325 bar \\ \hline \hline \end{tabular}
\end{table}
Table 5: The operating conditions for the various experimental studies used for model validation.  
Figure 6: Investigated current collector designs (front view).
## 5 Discussion
### Computational Constraints of SHAP in Real-World SOC Environments  
SHAP provides theoretically grounded and comprehensive local and global explainability for machine learning models. However, its use in real-time digital forensics and security operations center (SOC) environments presents significant computational challenges. Specifically, KernelSHAP, the model-agnostic variant employed in this study, requires multiple model evaluations per instance and feature coalition, resulting in exponential time complexity with respect to the number of features.  
In our experiments with the UNSW-NB15 dataset (after dimensionality reduction to 39 features), the average explanation time per instance using KernelSHAP exceeded 8.3 s on a workstation with 64 GB of RAM and an Intel Core i9. This latency is critical when processing high-throughput network traffic in operational SOC environments, where responses are expected in near-real time.  
Furthermore, although TreeSHAP improves efficiency for tree-based models such as XGBoost, its optimizations are not applicable to architectures like TabNet, which must rely on the slower KernelSHAP variant. These performance limitations hinder SHAP's integration into live detection pipelines unless mitigated through model-specific approximations or hardware acceleration (e.g., GPU-based parallel computation).  
While SHAP offers superior transparency and legal interpretability, its computational burden poses a barrier to real-time forensic analysis. Future research should explore hybrid explainability strategies that use faster techniques like LIME for preliminary interpretation, reserving SHAP for high-confidence post-incident forensic validation.
## 4 Steady Flow Force Compensation Optimization
### Influence of Inner Chamber Dimensions on Flow Force Characteristics  
The ratio of the inner chamber area to the throttling area is defined as \(\lambda_{1}\), expressed as \(\lambda_{1}=\pi({d_{2}}^{2}-{d_{1}}^{2})/4\pi{d_{2}}x_{v}\). Before investigating the compensation effect of matching an inner chamber with a shoulder structure on the steady flow force of the spool, we first discuss the influence of \(\lambda_{1}\) on the steady flow force independently. The analysis uses a typical valve opening of \(x_{v}=1\) mm and adjusts \(\lambda_{1}\) by varying the spool diameter \(d_{1}\).  
Figure 16 presents the velocity contour plots of the internal flow field of the slide valve under different \(\lambda_{1}\) values. After being accelerated through the throttling valve port, the fluid enters the valve chamber and flows out in a relatively uniform flow pattern. Due to the restriction of the chamber walls, an axial adjustment flow occurs, and the length of this adjustment flow increases with an increase in \(\lambda_{1}\). In the case of a small \(\lambda_{1}\), the inner chamber functions as a throttling mechanism, causing the fluid to flow at high speed within the annular chamber, resulting in a concentrated outlet jet. On the other hand, as \(\lambda_{1}\) increases, the vortex region within the flow path gradually expands, leading to a reduction in both the outlet jet angle and velocity.  
Figure 17 illustrates the relationship between the slide valve's steady flow force and flow rate as a function of \(\lambda_{1}\). When \(\lambda_{1}\) exceeds 2, the flow rate through the valve chamber changes slowly. This is due to the weakened throttling effect of the annular chamber at high \(\lambda_{1}\) values, making the flow primarily influenced by the throttling valve port. However, under the same conditions, the steady flow force increases rapidly. Since the inlet flow pattern remains largely unchanged, the increase in hydrodynamic force can be attributed to changes in the fluid momentum at the outlet. This is confirmed by the flow force curves for the throttling and non-throttling valve ports under high \(\lambda_{1}\) conditions, as shown in Figure 18. When \(\lambda_{1}\) increases from 2.0 to 3.0, the flow force at the throttling valve port changes by 2.92%, while at the non-throttling valve port, it changes by 55.82%. Therefore, the size of the inner annular chamber has a significant impact on the fluid momentum at the non-throttling valve port, theoretically making it a viable method for compensating for the steady flow force of the slide valve.  
Figure 16: Velocity contour plots of the internal flow field within the slide valve chamber under different \(\lambda_{1}\) values, from left to right: \(\lambda_{1}=0.94\), 2.34, 1.6, 1.75, 2.1, 2.44, and 3, respectively.
# The Impact Assessment of Water Supply DMA Formation on the Monitoring System Sensitivity
###### Abstract
For this purpose, simulation studies were carried out for a model water supply network with a fixed and variable number of nodes and the structure of connections between them. In a further section of the article, the results of an experiment carried out in an actually existing water supply network are presented, aimed at demonstrating the effectiveness of the leakage monitoring system functioning there.
# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
## 3 TC-CNN Model Framework
### The Proposed TC-CNN Model  
The proposed TC-CNN model combines FFT, GST, and CNN, and the flowchart of the proposed TC-CNN structure used to rolling bearing fault diagnosis is shown in Figure 4.  
Figure 3: A typical CNN structure.  
The parallel convolution structure of 1D-CNN and 2D-CNN is used for feature extraction. The model includes a 1D convolutional structure based on the frequency spectrum and a 2D convolutional structure based on the GST time-frequency image. The proposed method can fully utilize the sample fault information and make the fault information complement each other. Both CNN models have two convolutional layers, two pooling layers, and one fully connected layer. The features extracted by \(FC_{1}\) and \(FC_{2}\) are stitched by feature fusion layer \(C^{*}\). To improve the generalization ability, the Dropout operation is added between \(C^{*}\) and \(FC_{1}^{*}\).  
The fault diagnosis process can be divided into four parts: (1) Firstly, frequency spectrums and GST time-frequency images are obtained by FFT and GST on the original sampled data. (2) The frequency spectrum and GST time-frequency image are input to the feature extractor for fault feature extraction and obtain the combined fault features by a feature fusion layer. (3) The convolutional network is used for supervised learning  
Figure 4: Flowchart of the proposed TC-CNN structure used for rolling bearing fault diagnosis.  
of combined fault features, and the weights and parameters in the model are trained and updated. (4) The fault classification performance of TC-CNN is verified through the fault dataset.