esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Compared Detection Methods  
To evaluate the effectiveness of our network, we conduct a comparative analysis with some excellent methods: SPAN [21], RGB-N [18], RRU-Net [7], PSCCNet [19] and ObjectFormer [22]. To ensure fairness in the experiments, we fine-tune the parameters of the compared methods to their optimal values.  
Table 2 presents quantitative results (%) obtained by each method. From the table, it is evident that the network we propose outperforms other methods on the CASIA dataset and the NIST dataset. However, on the Coverage dataset, the F1-score and AUC are slightly lower compared to ObjectFormer. We speculate that this discrepancy may be due to the limited number of images available on the Coverage dataset. It does not allow the network optimal convergence. While our network does not require a large number of images, having fewer than 100 training images may hinder the network's convergence. We provide further evidence for this in Section 4.6. Overall, our network is superior to other related methods in terms of performance.  
Moreover, we present visual forecasts of the different approaches in Figure 4. Unfortunately, we are unable to provide predictions for ObjectFormer [22] as the code for that method was not made available to us. Each column from left to right represents a distinct aspect, where the initial column showcases the forged images, the second column illustrates the visualization outcomes of the RRU-Net method's predictions, the third column displays the prediction results of the PSCC-Net method, the fourth column represents the prediction results of our method, and the last column depicts the ground truth image. Based on the visualized results, it is apparent that the RRU-Net method exhibits commendable performance in identifying general tampered regions and providing rough localization, although it still exhibits some instances of false detections and missed areas. Moreover, the method struggles to perform well when confronted with small tampered regions. In comparison, the PSCC-Net method demonstrates superior performance with fewer instances of false and missed detections. However, even in its predicted visualization results, the method does not exhibit remarkable performance in identifying minute tampered areas. Subjectively speaking, our model's predicted visualization results demonstrate its superiority and stability amongst the various methods. Not only can it accurately locate manipulation areas, but it also generates clearer boundaries.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **CASIA** & **NIST16** & **Coverage** \\ \hline SPAN [21] & 38.2/83.8 & 58.2/96.1 & 55.8/93.7 \\ RGB-N [18] & 40.8/79.5 & 72.2/93.7 & 43.7/81.7 \\ RRU-Net [7] & 45.2/79.8 & 79.8/92.3 & 61.3/80.5 \\ PSCCNet [19] & 55.4/87.5 & 81.9/99.6 & 72.3/94.1 \\ ObjectFormer [22] & 57.9/88.2 & 82.4/99.6 & 75.8/95.7 \\ \hline Ours & 61.4/88.4 & 91.2/99.7 & 72.4/94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The F1/AUC results (%) of our method and the other methods on various datasets.
## 4 AI-Driven Concepts in MCNs
### Generative AI/ML Methods  
Generative Adversarial Networks (GANs) are a class of models capable of synthesizing realistic data, including images, signals, or time series for training purposes [73]. They are valuable when collecting real maritime data is costly or unsafe. GANs consist of two neural networks (generator and discriminator) that compete to generate realistic data samples. As shown in Figure 7a, the generator learns to generate fake data, and the discriminator learns to distinguish the generator's fake data from real ones. As training progresses, the generator creates realistic samples, and the discriminator starts to classify fake data as real, leading to the generation of synthetic datasets with realistic statistical properties. In the maritime domain, GANs can be used for:  
* **Generation of synthetic sensor data:** Useful for training AI/ML models when collecting real samples (e.g., deep-sea anomaly events) is hard to collect.
* **AIS traffic generation:** For testing routing algorithms or intrusion detection systems without deploying real ships.
* **Creation of training data for rare faults:** GANs can simulate noisy patterns associated with unusual mechanical or communication failures.  
Figure 6: Federated Learning across multiple vessels (sea area 1) and Transfer Learning between distant sea areas (from sea area 1 to sea area 2).
# Sedimentary Model of Sublacustrine Fans in the Shahejie Formation, Nanpu Sag
## 4 Results and Discussion
### Tectonic Settings and Provenance  
The formation of gravity flow deposits in lacustrine basins is primarily controlled by sediment supply, paleo geomorphology, and faulting activities [36; 37]. The petrographic study of the detrital sandstone composition is an excellent approach to determining its tectonic settings and source type [38]. The Nanpu Sag in the Es\({}_{1}\) sublacustrine fan core  
Figure 11: RMS attribute feature diagram of Es\({}_{1}\) in the study area.  
Figure 12: Planar facies feature diagram of the sedimentary facies of Es\({}_{1}\) in the study area.  
exhibits characteristic sedimentary features, including lenticular gravel clasts, rhythmic bedding, mudstone rip-up clasts, and flame structures. These depositional phenomena result from syn-sedimentary tectonic activity of the Gaoliu Fault, which generated intense compressional stresses and fluctuating hydrodynamic energy during sedimentation. The observed deformation textures arise from differential mechanical responses between sandstone and mudstone layers; under tectonic compression, the less competent mudstone units undergo brittle fracturing and plastic flow deformation, producing both torn fragments (rip-up clasts) and flame structures through fluid escape mechanisms within water-saturated sediments [39].  
According to previous studies on the regional tectonic units of Nanpu Sag and its adjacent areas, Es\({}_{1}\) in Nanpu Sag extends to the Yanshan Fold Belt, indicating sustained sediment availability with a long-term provenance background. Gaoliu Fault, functioning as a major syn-depositional growth normal fault, dominantly controls the depositional patterns and distribution of Es\({}_{1}\) and Dongying Formation strata in the study area. During the Es\({}_{1}\) deposition period, the eastern segment of the Gaoliu Fault was laterally juxtaposed against the Baigezhuang Fault, while its western segment connected with the Xinanzhuang Fault in an isolated configuration. Both flanks of the Gaoliu Fault maintained continuous activity during this interval, entering the initial activation phase with slip rates peaking at 174.7 m/Ma [40]. A transstensional transition zone developed along the Gaoliu Fault during the Es\({}_{1}\) deposition period [41]. While northern-derived sediments entered the system, fault-induced subsidence created accommodation space, enabling fan delta degradation. Subsequent gravitational collapse of fan delta fronts under combined influences of steepened topography and fault-controlled subsidence led to the accumulation of mass transport deposits within the central depression zone, forming characteristic sublacustrine fans through repeated sediment embolization processes [13].
## 5 Performance Analysis
### Evaluation and Simulation Results
#### 5.3.4 Resource Utilization  
Table 4 presents the average resource utilization per edge device type during training.  
EdgeGuard showed effective resource management across a variety of device types. Compared to standard federated learning, the integration of blockchain activities resulted in an average 15% increase in energy usage. However, a decrease in communication overhead made up for this. To summarize, EdgeGuard outperformed conventional federated learning in terms of model correctness, communication effectiveness, and security robustness while exhibiting tolerable resource use increases. The adaptive methods of the system demonstrated efficacy in preserving performance in adversarial IoMT scenarios while optimizing resource utilization among heterogeneous edge devices.
## 3 Results and Discussion
### Copper Oxide
In this case, the CuO US sample has a smaller crystal size compared to the CuO CH sample. This is due to the formation of a large number of nuclei at the beginning of the crystallization stage and can be attributed to the  
Figure 3: Thermogravimetric analysis of Cu\({}_{x}\)O\({}_{y}\)H\({}_{z}\) with different treatments. cavitation effect [32]. The material with combined treatment (Cu MW-US) has a crystal size closer to that obtained for the CuO US. On the other hand, the growth in the different crystallographic directions for CuO MW-US is similar to that obtained by ultrasound. It can be assumed that the ultrasound treatment has a greater impact than microwave when it is applied in a combined method. Figure 5 shows the nitrogen adsorption-desorption isotherms of the CuO. Type IV isotherms with an H3 hysteresis loop were obtained for all the CuO samples. Type IV of isotherm is related to mesoporous solids. On the other hand, H3 hysteresis is related to materials with plate-like particles or slit-shaped pores [33; 34]. The pore diameters are calculated by the BjH method and are presented in Table 3. In all cases the average pore diameters correspond to mesoporous solids. It was also found that the microwave treatment (CuO MW) promotes the largest specific area compared to the other three treatments. The second with larger surface area corresponds to the CuO CH. Using the ultrasound and combined treatment the same specific areas were obtained. For the pore diameters, the US CuO had a larger diameter and similar pore diameters were obtained for the other treatments. In the case of the pore volumes, they are similar for all treatments. Based on Table 3, it can be deduced that the type of treatment influences the formation of the porosity. The microwave treatment the one that promotes a greater specific area. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Lattice Parameters**} \\ \cline{2-5}
**Sample** & **a (Å)** & **b (Å)** & **c (Å)** & **Crystal Size (Å)** \\ \hline CuO MW & 4.6766 & 3.4229 & 5.1232 & 159 \\ CuO US & 4.6827 & 3.4226 & 5.1275 & 440 \\ CuO MW-US & 4.6846 & 3.4236 & 5.1295 & 376 \\ CuO CH & 4.6850 & 3.4248 & 5.1311 & 540 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Lattice parameters and crystal size of the CuO synthesized with different treatments. Figure 4: X-ray diffraction patterns of CuO synthesized with microwave, ultrasound, combined mode, and conventional treatment.
## 5 Experiments
### Intention Recognition
#### 5.1.4 Strengths and Limitations  
The model performs well when the input phrases closely resemble those in the training dataset, as it effectively associates specific keywords with the appropriate intention class. However, there are limitations. The model struggles with handling complex, multi-module requests (e.g., \"Tell me what objects are in the room and remind me to call later\"). This limitation arises from the fact that the RNN is trained to classify single-intention commands, and it lacks the ability to process multiple intentions within a single input.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 2 Signal Model  
Consider a clean source speech signal \(x(t)\), noise signal \(n(t)\), mixing matrix \(A\), and received speech signal \(y(t)\) contaminated by noise. It can be mathematically modeled as  
\[y(t)=Ax(t)+n(t). \tag{1}\]  
The clean speech source signal, noise signal and the received noisy speech signal are transformed to the FD domain and these parameters are denoted by \(X(k)\), \(N(k)\), and \(Y(k)\), respectively, while \(k\) denotes the position index of the coefficient in the transformed domain. The design criteria of the estimator for the observation are to minimize the MSE given by  
\[E\{X(k)-\hat{X}(k)\}, \tag{2}\]  
where \(E\{\cdot\}\) is the expectation operator and \(\hat{X}(k)\) is the estimated source signal. Minimum Mean Square Error (MMSE) filter can be used to minimize the mean square error (MSE) in (2).  
In a given noisy observation \(\{y(t);\quad 0\leq t\leq T\}\) with received signal \(Y(K)\). The estimated \(\hat{X}(k)\) can be obtained by [35],  
\[\hat{X}(k)=E\{X(k)/Y(k)\}. \tag{3}\]  
Equation (3) can be rewritten by [PERSON]'s theorem [35; 36; 37],  
\[\hat{X}(k)=\frac{\int_{-\infty}^{\infty}a_{k}p(Y(k)/a_{k})p(a_{k})\,da_{k}}{ \int_{-\infty}^{\infty}p(Y(k)/a_{k})p(a_{k})\,da_{k}} \tag{4}\]  
where \(p(.)\) is the probability density function (pdf) and \(a_{k}\) denotes the dummy variable representing all possible values of \(X(k)\). Assuming a Gaussian distribution model, then \(p(Y(k)/a_{k})\) and \(p(a_{k})\) can mathematically written as  
\[p(Y(k)/a_{k})=\frac{1}{\sqrt{2\pi\lambda_{n}(k)}}\exp\biggl{(}-\frac{(Y(k)-a_{ k})^{2}}{2\lambda_{n}(k)}\biggr{)} \tag{5}\]  
and  
\[p(a_{k})=\frac{1}{\sqrt{2\pi\lambda_{x}(k)}}\exp\biggl{(}-\frac{a_{k}^{2}}{2 \lambda_{x}(k)}\biggr{)} \tag{6}\]where \(\lambda_{n}(k)=E\{|N(k)|^{2}\}\) and \(\lambda_{x}(k)=E\{|X(k)|^{2}\}\) are the variances of the noisy signal and clean signal, respectively. Putting (5) and (6) in (4), then \(\hat{X}(k)\) can be rewritten as [35; 36],  
\[\hat{X}(k)=\frac{\xi(k)}{\xi(k)+1}Y(k) \tag{7}\]  
where \(\xi(k)\) is the a priori SNR and \(\xi(k)=\frac{\lambda_{x}(k)}{\lambda_{n}(k)}\). The values of \(\lambda_{x}\) and \(\lambda_{n}\) must be known. [38; 39] shows the detailed method for estimating \(\lambda_{x}\). A decision directed estimating method was developed to estimate \(\lambda_{x}\)[36]. The equation of estimating \(\hat{\lambda}_{x}\) for \(\lambda_{x}\) is given by [35]  
\[\hat{\lambda}_{x}=\alpha\hat{\lambda}_{x}(k)_{p}+(1-\alpha)\max(Y(k)^{2}- \lambda_{x}(k),0) \tag{8}\]  
where \(\max(.)\) is the maximum function. It is used to obtain non-negative values. \(\hat{\lambda}_{x}(k)_{p}\) is the estimated value of \(\lambda_{x}\) of the previous frame. \(\alpha\) is the constant tuned for the best results. The value of parameter \(\lambda\) is set to 0.98. If \(\lambda\) is set to 1, it deteriorates the speech signal and smaller values result in high musical noise.
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 4 Results and Discussion
### Comparisons of PoAIR and TITAN
TITAN identified all of the simulated rainstorms as two rainstorm processes, which led to the numbers of detected production/termination stages being almost twice that of the actual ones, and the value of FAR reaches 50.00%. For statuses of merging, splitting, and merging-splitting, PoAIR has an obvious advantage over TITAN. The former has a higher value of _POD_, a higher value of _CSI_, and a lower value of _FAR_ than the latter. Although both of the algorithms use an area-overlapping threshold to link the rainstorms at successive time snapshots, PoAIR extrapolates a spatial location at the T+1 snapshot by using the spatial information of rainstorm sequences from their generation to the T snapshot, while TITAN uses only the spatial information from the T snapshot. The extrapolating algorithm with evolution sequences improves the rainstorm detection capability of the PoAIR algorithm.
## 1 Introduction
For the 2.45 GHz feeding elements (illuminators), to comply with the highly complex metallic environment where the system operates, a 3D conformal antenna arrangement has been designed, composed of three circularly polarized patch antennas rotated by 45\({}^{\circ}\) from each other and incoherently fed by three identical disjointed transmitters. For the battery-less sensor design, the same operating frequency band has been used for both WPT and LoRa communication and two antennas have been co-located on the sensor node to accomplish these two operations. A proper selection of the electromagnetic (EM) spectrum within the band has been chosen to prevent destructive interference of the high WPT signal on the low-power communication signals. This is demonstrated in this work by a measurement campaign to control the isolation of the two antennas of the nodes and interference tests when RF power and wireless communication are simultaneously running. The node implements several strategies to minimize its energy requirements and guarantee battery-less operation. These strategies are described in the paper and include the use of ultra-low-power communication technology (LoRa), minimization of both sleep and active currents, careful selection of low leakage storage elements, and a novel and extremely energy-efficient management section allowing the full node to operate with an input power down to 30 \(\mu\)W.  
Finally, an in-depth test of the overall system is presented and discussed with measurements both in free-space and in the typical working environment where the present work is proposed to be deployed, namely the engine compartment of a hatchback.
## 4 Experiment and Results
### Indicator Evaluation  
Three evaluation metrics, inverted generational distance (IGD), hypervolume metric (HV), and spacing evaluation method (Spacing), are used to evaluate the convergence, diversity, and uniformity of the algorithm.  
The IGD metric [19] serves as an evaluation criterion for assessing the convergence and distribution performance of optimization algorithms. It does so by computing the minimum sum of distances between each point on the true Pareto front and the set of solutions produced by the algorithm. A smaller IGD value indicates that the algorithm achieves faster convergence to the true Pareto front with fewer iterations while preserving comprehensive coverage of the solution set. The formula for calculation IGD is as follows:  
\[\text{IGD}(P,Q)=\frac{\sum\limits_{x\in P}d(x,Q)}{|P|}. \tag{14}\]  
In the expression, P denotes the set of points uniformly distributed across the real Pareto front, with |P| representing the number of individuals in the set. Q refers to the Pareto optimal solution set generated by the algorithm, while d(x, Q) signifies the minimum Euclidean distance from an individual point x in P to the biological minimum of the population Q.  
The HV metric [20] quantifies the volume enclosed by the Pareto optimal solution set and serves as an indicator of both the diversity and extent of coverage provided by the algorithm's output. The growth rate of the HV metric across iterations serves as a critical indicator of convergence efficiency. A significant early-stage increase in HV values demonstrates the algorithm's ability to rapidly identify high-quality solutions during initial iterations, while higher final HV values quantitatively reflect the enhanced accuracy of the Pareto optimal set. The method for computing HV is illustrated as follows:  
\[HV(X,P)=\mathop{\cup}\limits_{x\in X}^{X}v(x,P). \tag{15}\]  
In this context, v(x, P) represents the hypervolume of the region delineated by the solution x in the optimal solution set X and the reference point P.  
The Spacing metric [21] is defined as an assessment of the standard deviation of the minimum distance between each solution and its respective neighbors within the solutions set generated by her algorithm. This metric is employed to evaluate both the distribution and uniformity of solutions in the set. A smaller Spacing metric value indicates that the algorithm effectively avoids premature convergence to local optima during the search  
Figure 3: Model prediction accuracy.  
process while achieving uniform exploration of the objective space, thereby minimizing the generation of redundant or overlapping solutions. The calculation is expressed as follows:  
\[Spacing(P)=\sqrt{\frac{1}{|P|-1}\sum_{i=1}^{|P|}\left(\overline{d}-d_{i}\right)^ {2}}, \tag{16}\]  
where P denotes the total number of solutions in the solution set, \(\mathrm{d_{i}}\) represents the distance between the ith solution and its nearest neighbor, and \(\overline{d}\) signifies the mean of all di values.
# Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots
## 2 Biologically-Inspired Kalman Filter Based RBFNN Control
### Kalman Filter Based RBFNN Control  
Figure 3 depicts the block diagram of the RBFNN-based control. In Figure 3, the error between the real output \(y(k)\) and the estimated output of the neural network \(y_{m}(k)\) are considered to develop a self-learning RBFNN. The cost function or performance index is defined by the squared estimation error:  
\[J(k)=\frac{1}{2}[y(k)-y_{m}(k)]^{2}. \tag{8}\]  
Considering the gradient descent method in Equations (5)-(7), the structure parameters: \(w_{j}\), \(C_{ji}\), and \(b_{j}\) are updated online at every sampling point, expressed by:  
\[\begin{array}{l}\Delta w_{i}=[y(k)-y_{m}(k)]h_{j}\\ w_{j}(k)=w_{j}(k-1)+\eta\Delta w_{j}+\zeta[w_{j}(k-1)-w_{j}(k-2)]\\ \Delta b_{j}=[y(k)-y_{m}(k)]w_{j}h_{j}\frac{\left\|X-C_{j}\right\|^{2}}{b_{j}^{ 3}}\\ b_{j}(k)=b_{j}(k-1)+\eta\Delta b_{j}+\zeta[b_{j}(k-1)-b_{j}(k-2)]\\ \Delta c_{ji}=[y(k)-y_{m}(k)]w_{j}\frac{x_{i}-x_{j}}{b_{j}^{2}}\\ c_{ji}(k)=c_{ji}(k-1)+\eta\Delta c_{ji}+\zeta[c_{ji}(k-1)-c_{ji}(k-2)]\end{array}, \tag{9}\]  
where \(\zeta\) denotes the momentum factor and \(\eta\) is the learning rate of the neural network.  
Based on Figure 3, the proposed KF-RBFNN control scheme depicted in Figure 4 considers the process noise \(w(k)\) and measurement noise \(v(k)\). In the proposed intelligent KF-RBFNN control scheme, the KF-RBFNN serves as an on-line learning and adapting mechanism in the intelligent controller. As shown in Figure 4, the effects of the process uncertainty \(w(k)\) and the measurement noise \(v(k)\) in the control scheme can be reduced via the implementation of the Kalman filter. To retrain the uncertainty and noise, the measured output \(z(k)\) is employed to derive an estimation \(\hat{y}(k)\) of the output \(y(k)\) in the proposed KF-RBFNN. Both the control signal \(u(k)\) and output \(y_{m}(k)\) of the RBFNN are fed into the  
Figure 3: Block diagram of the RBFNN control scheme.  
neural network for on-line learning. Moreover, the estimated output value \(y_{m}(k)\) of the RBFNN is then utilized to update the control parameters to achieve self-learning control using the Kalman filter.