esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Method-Specific Insights
#### 6.2.2 Suboptimal Performance of Methods  
As delineated in Table 8, several factors contribute to the subpar performance of these techniques:  
* Sensitivity of the Hilbert-Huang Transform (HHT): The experiments exhibited a substantial reduction in accuracy by 18.33%, attributed to:  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline \multirow{3}{*}{**STFT**} & \multirow{3}{*}{Simulation} & \multirow{3}{*}{90.00\%} & 0.91 (D) & 1.00 (D) & \multirow{3}{*}{0.895} & \multirow{3}{*}{–} \\  & & & 1.00 (S) & 0.70 (S) & & \\  & & & 0.83 (T) & 1.00 (T) & & \\ \hline \multirow{3}{*}{**WT**} & \multirow{3}{*}{Simulation} & \multirow{3}{*}{93.33\%} & 0.90 (D) & 0.90 (D) & \multirow{3}{*}{0.933} & \multirow{3}{*}{–} \\  & & & 1.00 (T) & 1.00 (T) & & \\ \cline{1-1} \cline{5-7}  & Experiment & 85.00\% & 85.35\% & 85.00\% & 84.96\% & \\ \hline \multirow{3}{*}{**WVD**} & \multirow{3}{*}{Simulation} & \multirow{3}{*}{93.33\%} & 0.95 (D) & 0.90 (D) & \multirow{3}{*}{0.933} & \multirow{3}{*}{–} \\  & & & 0.95 (S) & 0.90 (S) & 0.933 & \\ \cline{1-1}  & Experiment & 94.74\% & 95.45\% & 94.44\% & 94.68\% & 0.947 \\ \hline \multirow{3}{*}{**HHT**} & \multirow{3}{*}{Simulation} & \multirow{3}{*}{88.33\%} & 1.00 (D) & 0.90 (D) & \multirow{3}{*}{0.886} & \multirow{3}{*}{–} \\  & & & 0.89 (S) & 0.85 (S) & 0.886 & \\ \cline{1-1}  & Experiment & & 0.78 (T) & 0.90 (T) & & \\ \cline{1-1} \cline{5-7}  & Experiment & & 70.00\% & 73.81\% & 70.00\% & 68.75\% & \\ \hline \multirow{3}{*}{**FRWT**} & \multirow{3}{*}{Simulation} & \multirow{3}{*}{95.00\%} & 1.00 (D) & 0.90 (D) & \multirow{3}{*}{0.95 (S)} & \multirow{3}{*}{–} \\  & & & 0.95 (S) & 0.95 (S) & 0.950 & \\ \cline{1-1}  & & & 0.91 (T) & 1.00 (T) & & \\ \cline{1-1} \cline{5-7}  & Experiment & & \multicolumn{1}{c}{–} & \multicolumn{1}{c}{–} & \multicolumn{1}{c}{–} & \multicolumn{1}{c}{–} \\ \hline \multirow{3}{*}{**SWT**} & \multirow{3}{*}{Simulation} & \multirow{3}{*}{96.67\%} & 1.00 (D) & 0.90 (D) & \multirow{3}{*}{0.967} & \multirow{3}{*}{–} \\  & & & 0.91 (S) & 1.00 (S) & 0.967 & \\ \cline{1-1} \cline{5-7}  & & & 1.00 (T) & 1.00 (T) & & \\ \cline{1-1} \cline{5-7}  & Experiment & 100.00\% & 100.00\% & 100.00\% & 100.00\% & 1.000 \\ \hline \hline \end{tabular}  
* **Note:** (D) = DFS, (S) = SPS, (I) = TTS. Simulation results use GoogLeNet; experimental results use ResNet50. Gray rows highlight experimental data. Dashes (-) indicate unavailable data.  
\end{table}
Table 7: Performance comparison: simulation (GoogLeNet) vs. experimental (ResNet50) results.  
The instability of Empirical Mode Decomposition (EMD) when subjected to experimental noise, which undermines its robustness;
* The presence of end effects, which significantly skew the evaluation of instantaneous frequency.
* Constraints of the Short-Time Fourier Transform (STFT): The adoption of a fixed window size, \(w(t)\), leads to a trade-off characterized by either:  
\[\Delta f \tag{13}\]
## 3 Proposed Design and System-Level Development
### Moderate Shading, I-V Characteristics, and Natural Alignment of Buck Converter  
Figure 4a shows the I-V characteristics of three PV modules, where only M2 is under the influence of a moderate shading condition. Due to this, \(I_{sc}\) of M2 reduces while its \(V_{oc}\) increases, as shown in the light-black dotted line in Figure 4a. This almost shifts the MPP of M2 towards the right, i.e., an increase in \(V_{mpp}\) as compared to other modules. Since the buck converter of only this module is active, this eventually increases the output current and reduces the output voltage, while M2 operates at its local shading MPP point. This makes the likelihood that with a moderate duty cycle operation, the output voltage and output current of the buck converter becomes equal to \(V_{mpp}\) and \(I_{mpp}\) of other M1 and M3 modules, respectively, as shown in Figure 4a. In this manner, the basic buck converter is a natural topology to mitigate the difference in I-V characteristics of modules, which not only supports the common \(I_{string}\), but also adjusts the voltage of shaded modules equivalent to \(V_{mpp}\) of other modules at its output. Because of this, the centralized inverter does not need to make a sizable difference in its DC-link voltage to track the MPP of the string.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 5 Conclusions
### Theoretical Contributions  
This work introduces:  
* a formal mathematical model for characterizing a class of iterative steganographic methods;
* a novel quantitative method for evaluating the performance of iterative steganographic techniques, based on the proposed Incremental Information Function (IIF);
* the application of characteristic IIF values to quantify robustness and capacity metrics in iterative steganographic systems.  
These contributions provide an effective framework for analyzing and optimizing iterative steganographic techniques, enhancing the theoretical foundations of this field and facilitating more precise evaluation of steganographic performance.
# On Combining DeepSnake and Global Saliency for Detection of Orchard Apples
## 2 Materials and Methods
### Apple Target Detection  
Most active contour models require a relatively accurate initial contour. The DeepSnake algorithm generates four offsets based on the input initial detection box to generate poles [x\({}_{i}^{ex}\)] i = 1,2,3,4]. Based on the poles and the initial detection box, an octagon is generated as the initial contour. The DeepSnake algorithm first samples \(N\) points of the initial contour, starting with the top pole x\({}_{1}^{ex}\) as the first point and uniformly sampling along the edge. The artificially labeled (Ground truth, GT) contours are also sampled in the same way. The features on the contour are usually regarded as one-dimensional discrete signals and can be processed by standard convolution, but it will destroy the topological structure of the contour. Therefore, the researchers regard the features on the contour as a periodic signal \(f_{N}\), and generate each signal through cyclic convolution. The characteristic vector of each point, the specific formula is as follows:  
\[(f_{N}*k)_{i}=\sum_{j=-r}^{r}{(f_{N})_{i+j}k_{j}} \tag{1}\]  
where \(k\) is the kernel function, and * is the standard convolution operation. Similar to standard convolution, a network layer for feature learning can be constructed on the basis of cyclic convolution. Compared with general convolution, cyclic convolution can make better use of the ring structure of the contour. The feature vector of each point is generated by using cyclic convolution and learning through cyclic convolution. After feature learning, the DeepSnake algorithm uses three \(1\times 1\) convolutional layers to process the output features of each vertex and predict the offset for each point This offset will be used to adjust the contour shape to continuously approach the target. The size of the convolution kernel in the algorithm is 9. The loss function for predicting the poles is as follows:  
\[L_{ex}=\frac{1}{4}\sum_{i=1}^{4}{l_{1}(\widetilde{x}_{i}^{ex}-x_{i}^{ex})} \tag{2}\]  
where x\({}_{i}^{ex}\) are the leftmost, rightmost, uppermost, and lowermost poles of the ground truth (GT) edge, and \(\widetilde{x}_{i}^{ex}\) are the predicted poles. The loss function of contour iterative deformation is:  
\[L_{iter}=\frac{1}{N}{\sum_{i=1}^{N}{l_{1}(\widetilde{x}_{i}-x_{i}^{gt})}} \tag{3}\]  
where x\({}_{i}^{ex}\) is the point after contour adjustment, and is the edge point of the corresponding point GT. The detection result is obtained by fusing the improved saliency detection algorithm, and the DeepSnake algorithm iteratively deforms the contour to obtain a more accurate edge, and traverses the edge coordinates to obtain the maximum and minimum values, and further obtains the rectangular detection box coordinates to quickly and accurately label the target fruit and provide a target for the robot arm of the apple picking robot to locate the fruit.  
Figure 2: DeepSnake network architecture.
## 1 Introduction
[18] introduced a semi-analytical solution for one-dimensional nonlinear consolidation within layered soil subjected to variable loading. It is noteworthy that the aforementioned studies on the one-dimensional nonlinear behavior of layered soils have not accounted for the time-dependent variations in the drainage capacity of boundary interfaces. The drainage boundaries significantly influence the consolidation rate of the soil. For instance, if a soil sample is placed in an impervious plastic bag, it cannot be consolidated no matter how long it takes and how much load applied. [PERSON] et al. [19; 20] responded to this by introducing the continuous drainage boundary, reflecting the gradual dissipation of excess pore water pressure over time at the drainage interface, and deduced an analytical solution for one-dimensional consolidation under instantaneous loading based on the continuous drainage boundary. [PERSON] et al. [21] investigated one-dimensional consolidation attributes in layered soil, emphasizing the continuous drainage boundary. Subsequently, [PERSON] et al. [22] analyzed the nonlinear consolidation of multilayer soil considering continuous drainage boundary. [PERSON] et al. studied the one-dimensional nonlinear consolidation of single-layer and double-layer soils, respectively [23; 24]. [PERSON] et al. [25] considered boundary conditions and studied one-dimensional nonlinear consolidation of unsaturated soil. In addition, many scholars have also studied the consolidation of three-dimensional soil by considering continuous drainage boundary conditions [26; 27]. However, a comprehensive examination of the integration of continuous drainage boundaries, soil stratification, and soil nonlinearity remains notably absent in the existing literature. This study aims to explore the one-dimensional nonlinear consolidation behavior of double-layered soil with continuous drainage boundary conditions. Starting with [PERSON]'s [12] nonlinear soil assumptions, this paper derives a solution for the one-dimensional nonlinear consolidation under instantaneous loading using the differential method. A com-parative analysis with established analytical and numerical solutions is performed to validate the proposed method. The investigation further delves into the impact of varying parameters on the degree of consolidation for double-layered soil. The findings could further advance the accurate prediction of marine soft soil settlement and provide ideas for accelerating soft soil consolidation.
## 3 Materials and Methods
### Topic Feature Learning of User and Item
#### 3.2.2 Parameter Estimation
First, the assignments of topic to each word are initialized according to a uniform distribution. Then, the assignment of topics to each word will be updated by examining Equation (1). Finally, \(n_{q}^{(k)}\), \(n_{p}^{(k)}\), \(n_{k}^{q(v)}\), \(n_{k}^{p(v)}\) can be counted after a sufficient number of iterations. \(n_{q}^{(k)}\) denotes the number of times the topic \(k\) occurs in the query \(q\). \(n_{p}^{(k)}\) denotes the number of times the topic \(k\) occurs in the webpage \(p\). \(n_{k}^{q(v)}\) denotes the number of times the word \(v\) is assigned as a query word to topic \(k\). \(n_{k}^{p(v)}\) denotes the number of times the word \(v\) is assigned as a webpage word to topic \(k\). Here, we only give the derivation of the parameter \(\theta_{u}\), the derivation of other parameters is the same. \[ \begin{array}{l}P(\ \theta_{u}|n_{u},\alpha)=\frac{P(n_{u}|\theta_{u})P( \theta_{u}|\alpha)}{\int P(n_{u}|\theta_{u})P(\theta_{u}|\alpha)d\theta_{u}} \\ =\frac{Mult(n_{u}|\theta_{u})Dir(\theta_{u}|\alpha)}{\int Mult(n_{u}|\theta_{ u})Dir(\theta_{u}|\alpha)d\theta_{u}}\\ =Dir(\theta_{u}|\alpha+n_{u})\end{array} \]  
Inside, \(n_{u}=n_{q}+n_{p}\)  
The estimated value of each parameter is:  
\[E(\theta_{u})=\left(\frac{n_{q}^{(1)}+n_{p}^{(1)}+\alpha}{\sum_{k=1}^{K}n_{q} ^{(k)}+n_{p}^{(k)}+K\alpha},\cdots,\frac{n_{q}^{(k)}+n_{p}^{(k)}+\alpha}{\sum_ {k=1}^{K}n_{q}^{(k)}+n_{p}^{(k)}+K\alpha},\cdots,\frac{n_{q}^{(K)}+n_{p}^{(K) }+\alpha}{\sum_{k=1}^{K}n_{q}^{(k)}+n_{p}^{(k)}+K\alpha}\right) \tag{2}\]  
\[E(\theta_{k})=\left(\frac{n_{k}^{q(1)}+n_{k}^{p(1)}+\beta}{\sum_{v=1}^{V}n_{k} ^{q(v)}+n_{k}^{p(v)}+V\beta},\cdots,\frac{n_{k}^{q(v)}+n_{k}^{p(v)}+\beta}{ \sum_{v=1}^{V}n_{k}^{q(v)}+n_{k}^{p(v)}+V\beta},\cdots,\frac{n_{k}^{q(V)}+n_{ k}^{p(V)}+\beta}{\sum_{v=1}^{V}n_{k}^{q(v)}+n_{k}^{p(v)}+V\beta}\right) \tag{3}\]  
Finally, the topic feature vector of each user can be expressed as \(\theta_{u}\). Similarly, we can use LDA [21] to mine each product's topic feature vector from its online reviews: \(\theta_{i}\).
## 3 Diagnostic and Prognostic Framework Modelling
### End of Life (EOL) Prediction Model  
The EOL prediction methodology is shown in Figure 4 and predicts the RUL of the battery through the integration of sensor measurements, a particle filter, and other calculations. The battery degradation is mainly affected by the capacity fade. The capacity must be evaluated using parameters monitored by a Battery Management System (BMS) since there is no mechanism to measure the capacity of a cell. These parameters are the cell voltage, input or output current, and temperature. Only the output current is required for estimating the cell capacity, which is calculated using the Coulomb counting method as in Equation (14):  
\[\mathrm{C}=\int\mathrm{I}\,\mathrm{dt}. \tag{14}\]  
Therefore, the cell output current must be integrated during a full discharge cycle. The important point is that the battery is not always fully charged or discharged. Therefore, tables with available test data and other estimation algorithms are required to accurately predict the battery's State of Health (SOH). The particle filter is another important factor in the EOL prediction model. The particle filter consists of two models: state transition and the measurement model. The state transition model is expressed as:  
\[\beta_{1,k+1}=\beta_{1,k}+\varphi_{1,k} \tag{15}\]  
\[\beta_{2,k+1}=\beta_{2,k}+\varphi_{2,k} \tag{16}\]  
\[C_{k+1}=\eta_{c}\cdot C_{k}+\beta_{1,k}\cdot\exp\biggl{(}\frac{-\beta_{2,k}}{ \Delta t_{k}}\biggr{)}+\varphi_{k}, \tag{17}\]  
where \(\varphi_{1,k}\), \(\varphi_{2,k}\), and \(\varphi_{k}\) denote the zero-mean Gaussian noise terms. The measurement model is expressed by [14]:  
\[\widetilde{C}_{k}=C_{k}+\psi_{k}, \tag{18}\]  
where \(\psi_{k}\) characterizes the measurement noise, again modeled as zero-mean Gaussian noise, and \(\widetilde{C}_{k}\) denotes the measured capacity. The particle filter monitors the state of the battery using these two models by producing accurate predictions every time a new measurement is present. The algorithms propagate the state of the system until the EOL capacity threshold, when an EOL prediction is required. This propagation means estimating the evolution through the state transition model using the latest update of parameters \(\beta_{1}\), \(\beta_{2}\), and \(\Delta t_{k}\). The EOL prediction for this study was chosen to be 80% of the battery nominal capacity (i.e., 1.6 Ahr for a cell of 2 Ahr nominal capacity) since it is the value utilized by the industry to reject a faulty battery. The effects of battery aging and different loading profiles are added to enhance their model.
# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 3 Materials and Methods
### Rationale for Method Selection
The RNN branch incorporates stacked GRU and LSTM layers (64 units each) to model both short- and long-term temporal dependencies across EEG channels, followed by GlobalAveragePooling1D for dimensionality reduction. The transformer branch applies a dense projection (d_model = 32), 2D positional encoding, and four transformer encoder  
Figure 8: Architecture of spatiotemporal transformer model. blocks with multi-head attention to capture global interchannel relationships and spatial context based on electrode topology. Outputs from all three branches are concatenated into a unified 320-dimensional feature vector and passed through two dense layers (128 units each) with batch normalization and dropout (rate = 0.3). The final sigmoid-activated output neuron performs binary classification between the ADHD and control groups. This integrative architecture enhances robustness, generalization, and interpretability by leveraging the complementary strengths of CNNs, RNNs, and transformers. To ensure methodological rigor and maximize diagnostic reliability, the study compares a diverse set of neural architectures--including CNN-LSTM, CNN-GRU-LSTM, transformer encoders, a spatiotemporal transformer, and the hybrid CNN-RNN-transformer model--under consistent evaluation protocols. This architectural diversity enables the extraction of localized spatial features, dynamic temporal patterns, and global interchannel relationships, offering a robust and interpretable framework for EEG-based ADHD classification. By integrating convolutional, recurrent, and attention mechanisms within a unified comparative analysis, the study advances the State of the Art in task-based EEG modeling and provides a foundation for clinically applicable, explainable deep learning in pediatric neurodiagnostics. Figure 9: Architecture of hybrid CNN–RNN–transformer model for EEG-based ADHD classification.
## 2 Chip Breaker FE Design for a PCD Turning Insert  
Although numerous tool manufacturers still base their tool-designs on try-and-see methods based on their previous experience [19], FEM is considered a reliable tool to assess the relative performance of several designs of engineering components, and has been extensively used for the design of cutting tools [32; 33; 34]. [PERSON] et al. [35] pointed out the significance of FEM to evaluate different design geometries to determine a final design before manufacturing, which is substantially cheaper than trial and error methods.  
The first stage of tool design is to identify the relevant geometrical parameters. [PERSON] and [PERSON] [36] chose a curved groove geometry as the basic design for a chip breaker. This geometry and the nomenclature were followed by other researchers [26; 36]. It is expected that including backwall height to the chip breaker geometry would significantly restrict the chip breaker design, especially the radius of curvature. For this reason, conventional curve groove geometry was considered, dismissing the backwall height [36].  
Figure 2 shows the essential parameters in the design of the cutting edge of a chip breaker: Rake angle (\(\alpha\)); land width (a); radius of curvature (R); and tool-chip contact length (L). The groove width (W), however it is not an independent parameter, but it depends on the aforementioned ones.  
The design stage requires: (1) Selection of land width and rake angle; and (2) selection of the radius of curvature. In this study, and regarding the selection of land width and rake angle, several simulations were carried out with combinations of different land widths and rake angles, with the aim of determining the combination that produced the chip with most curl, without a chip breaker. Regarding the selection of the radius of curvature, the curved groove of the chip breaker was tested with different values of the radius of curvature to ascertain the most effective value.  
FE simulations were carried out using Third Wave Systems AdvantEdge(r), Minneapolis, MN, USA, version 7.2, a commercial software package for simulating machining processes [37]. As a Lagrangian finite-element-based simulation, it can predict the chip formation process, including chip breaking. Furthermore, it enables defining custom geometries of the workpiece and tool and offers a wide range of output parameters such as strain rate, stresses, plastic strain and temperatures.
# Hierarchical Visual Place Recognition Based on Semantic-Aggregation
## 3 Experiments and Results
### Ablation Study (Effects for Hierarchy and Candidates)
#### 3.3.1 The Number of Candidates  
To analyze the influence of the number of _Candidates(n)_ on the whole method, matching time and F1-Score were adopted as the performance indicators. Note that matching time here refers to the time of coarse matching and fine matching but not the time of semantic segmentation of query and reference datasets.  
We set the number of _Candidates_ to 5, 10, 15, 20, 25, 30, and 50, respectively. The results of matching time and F1-Score with different number of _Candidates_ are shown in Figures 10 and 11.  
Figure 10: Matching time comparison of different number of _Candidates_.  
Figure 9: Segmentations of sample images from the Norland Dataset: **(a)** Norland-Spring; **(b)** Norland-Winter.  
The results in Figure 10 show that matching time of the North Campus dataset is lower than that of the Norland dataset on the whole. This is because the size of the two datasets is significantly different. The latter is 7 times more than that of the former, so the matching time of query on the Norland dataset is higher. Moreover, the matching time of the Norland Dataset increases greatly when the number of Candidates is 30 and 50. However, the maximum is only 0.501 s, which meets the real-time requirements. To sum up, matching time under 25 can be suitable for these two datasets.  
As can be seen from Figure 11, for each dataset, there is little difference in the F1-Score of different _Candidates_. However, the F1-score of the North Campus Dataset is higher than that of the Norland Dataset on the whole. This indicates that our method is robust to severe viewpoint changes and image appearance changes.  
Taking account of matching time and F1-score, we find that the effectiveness is better when the number of Candidates \(n\) is 10 or 15. Finally, in the comparison experiment, we took \(n\) for 10.