esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 2 Preliminary
### Weighted Aggregation Method  
Among the existing surrogate ensemble modeling methods, the most commonly used approach is the weighted aggregation method given by [14]  
\[\hat{y}_{WTA}(\mathbf{x})=\sum\limits_{i=1}^{n_{M}}w_{i}\hat{y}_{i}(\mathbf{x }), \tag{13}\]  
where \(\hat{y}_{WTA}\) is the prediction of the ensemble. \(n_{M}\) is the number of surrogates. \(\hat{y}_{i}\) is the predictor of the \(i\)th surrogate, and \(w_{i}\) is the associated weight calculated by  
\[w_{i}=\frac{\sum\limits_{j=1,j\
eq i}^{n_{M}}E_{j}}{(n_{M}-1)\sum\limits_{j=1 }^{n_{M}}E_{j}}, \tag{14}\]  
where \(E_{i}\) is the error of each surrogate. In this study, the generalized mean square error (GMSE) based on leave-one-out cross-validation is chosen as the error measure. For leave-one-out cross-validation, the data is divided into \(n\) disjoint subsets of equal size. Here, \(n\) is the number of the training sample points. The surrogates are constructed \(n\) times, each time leaving out one sample point from training, and using the omitted sample point to compute the error measure of interest. The error \(E_{i}\) of the \(i\)th surrogate is defined as  
\[E_{i}=\sqrt{\text{GMSE}_{i}} \tag{15}\] \[\text{GMSE}_{i}=\frac{1}{n}\sum\limits_{l=1}^{n}\left(y^{(l)}- \hat{y}_{i}^{(-l)}\right)^{2},\]  
where \(\hat{y}_{i}^{(-l)}\) represents the prediction at \(\mathbf{x}^{(l)}\) using the \(i\)th surrogate constructed with all sample points except \(\left(\mathbf{x}^{(l)},y^{(l)}\right)\). Notice that for the weighted aggregation method, all the candidate surrogates are selected into the ultimate ensemble. Although the weights of the accurate contributing surrogates are larger than those of the inaccurate contributing surrogates, the negative influence of the surrogates with lower accuracy would still lead to the relatively poor performance of the ensemble model [23].
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 4 Results and Discussion
### Comparative Analysis of Ensemble Explainability Techniques  
_First, the Grad-CAM visualizations_ in Figure 14 provide valuable insights into the spatial attention of the stacking and hard voting ensemble techniques.  
* **Color Intensity and Diagnostic Relevance:** Grad-CAM visualizations use a color spectrum from blue (very low attention, 0.0-0.33) to red (high attention, 0.66-1.0), revealing where models focus during classification. While both ensemble methods highlight similar regions, subtle but important differences exist in attention distribution. In AD cases, stacking ensembles exhibit slightly more concentrated red regions in medial temporal areas, particularly the hippocampus, indicating more precise focus on established pathological regions. Hard voting shows slightly more diffused yellow-to-red patterns, suggesting less targeted attention. These subtle differences in attention concentration correlate with stacking's marginally superior accuracy (98.0%) compared to hard voting (97.0%) in Table 2.
* **MCI Attention Patterns:** For MCI cases, the primary distinction lies in gradient differentiation--stacking displays more nuanced attention gradients with better yellow-to-orange transitions (medium-to-high attention, 0.5-0.8) distributed across temporal-parietal regions, capturing the subtle, widespread changes characteristic of early neurodegeneration. Hard voting shows slightly less intensity differentiation, potentially missing the graduated nature of early pathological changes. These subtle gradient differences may contribute to stacking's enhanced ability to detect borderline MCI cases, as evidenced by the model performance metrics in Table 8.
* **Color Distribution in Misclassifications:** Analysis of misclassified cases reveals diagnostic patterns consistent across both ensemble methods. Correctly classified AD cases display concentrated red regions in hippocampal areas, while misclassified cases (particularly AD classified as CN) show inappropriate green-to-blue coloration (low attention, 0.0-0.4) in pathologically significant regions. This color distribution anomaly provides a visual explanation for the classification errors documented in Table 7, where even the best model (ResNet) achieved only 75% accuracy on these critical cases.
# Tracking and Monitoring System Based on LoRa Technology for Lightweight Boats
## 4 IoT-Based Communication Technologies in Coastal Scenarios
### Application of LP-WAN Technologies  
In the light of the previous discussion, LP-WAN-based solutions, e.g., LoRa, rise as a promising alternative to enable long-term, scalable, and low-cost monitoring deployments for maritime environments. As discussed previously, the main limitations of these systems are their transmission restrictions in terms of packet length and data-rate. However, a number of coastal applications may fit well within these restrictions as the information to be transmitted and its periodicity might be low.  
Monitoring and alerting operations for boats and ships in coastal areas are services that can be perfectly supported by LP-WAN technologies. Thus, boats may keep port authorities and safety services periodically informed about their state and position. Although, in some cases, energy restrictions will not be an issue, the communication range will be enlarged to distances not covered by any other kind of communication technology (except satellite ones). In addition, by using a device-to-device architecture, boats can communicate with each other in case of losing connectivity with land. In the special case of ships with energetic restrictions, which is the main focus of this work, LP-WAN radio modules may fit very well due to their limited power consumption.  
Another potential field of application for LP-WANs is the monitoring of maritime of autonomous vehicles, e.g., Unmanned Surface Vehicles (USV), Unmanned Aerial Vehicles (UAV), or unmanned ships/ferries, which have been gaining great momentum in recent years [2]. Although the communication channel for managing and remote controlling operations demands low latency and certain transmission rate that are not attainable by LP-WAN solutions, this alternative communication link may be used for data-retrieving or tracking activities. These vehicles have been used as monitoring stations themselves or as collecting points for gathering data generated by elements without connectivity to land [22]. Thus, by using a P2P-based architecture of LP-WAN links [28], the transmission distance between both ends can be notably reduced, hence reducing the traveling time and distance.  
Finally, moored platforms or buoys are also widely employed for monitoring both environmental conditions and oceanic metrics. Without a proper communication technology, these systems make marine researchers to manually collect the data sampled by the monitoring system. This strategy is also prone to suffer information loss or delay in the data collection due to adverse climatic conditions. For that reason, by installing a low-cost LP-WAN-based transmission module, this task can be automatically scheduled, hence improving the frequency and easiness of this process. Please observe that this monitoring platforms should have long life periods, which is also supported by the long battery life-times of the proposed communication system.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 1 Introduction
The rest of this paper is organized as follows. The second section presents the damaged wood wall specimens, the developed polarimetric GB-SAR system and the signal processing algorithms. The third section shows the laboratory experiment results, and is followed by the discussions and conclusions.
## 4 Results and Discussion
### Comparative Analysis
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** \\ \hline C5.0 & 0.86457 & 0.85925 & 0.99706 \\ \hline RF & 0.86803 & 0.8629 & 0.9963 \\ \hline NB & 0.79996 & 0.90069 & 0.86031 \\ \hline SVM & 0.79887 & 0.92445 & 0.84364 \\ \hline Proposed Model & 0.98 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative analysis with CICIDS2017 [34]. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Learning Techniques** & **Specificity** & **Accuracy** \\ \hline SVM & 71.41 & 69.52 \\ \hline Bi-LSTM & 79.64 & 76.37 \\ \hline CNN & 80.75 & 95.01 \\ \hline MLP & 79.57 & 77.41 \\ \hline C4.5 & 83.44 & 81 \\ \hline NB & 83.21 & 81.47 \\ \hline RF & 82.35 & 80.67 \\ \hline CART & 82.71 & 80.3 \\ \hline Ensemble & 89.41 & 87.28 \\ \hline CNN-BiLSTM & 80.83 & 80.05 \\ \hline SAE-SVM-RBF & 98.35 & 95.27 \\ \hline SVM-RBF & 91.84 & 92.55 \\ \hline CFS + ANN & 99.31 & 97.49 \\ \hline Proposed System & 99.97 & 99.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparative Analysis with the NSL-KDD dataset [35]. more during evaluation. Consequently, the proposed model's performance appears more favorable when heavily weighted instances are accurately predicted, leading to higher weighted accuracy. This weighting scheme emphasizes specific cases, potentially reflecting the model's effectiveness in handling critical scenarios, thus resulting in varying accuracy scores for different weights. Thus, the weights are chosen based on the importance of different instances, and the factors include cost sensitivity, class imbalance and domain knowledge. Thus, it is evident from the comparison that the proposed model outperforms other conventional methods. With the unique optimization capacity of SCA, the present research intends to integrate SCA and WOA so as to cancel out the shortcomings of both via hybridization, thereby procuring OWSA. Further, the proposed weight-updating process is made suitable for application to an individual tree in the RF model. These innate advantages of the proposed model allow it to yield ideal outcomes in ID.
# Discrete-Time Design of Dual Internal Model-Based Repetitive Control Systems
## 1 Introduction
Tracking repetitive trajectories can be found in many applications, such as robotics, power sources, precision gantry systems, engine valve systems, etc. [3]. Here, we also consider a disturbance rejection problem, where the disturbance period is uncertain or varying. Such disturbance appeared in many practical situations, such as rotating machines [20; 21], active suspension systems [22; 23], steel casting processes [24; 25], and many others. When the actual disturbance period is subject to variation, the general internal model will operate with a period mismatch. This condition makes the RC gains at the disturbance's frequencies drastically drop to low-level magnitudes. Consequently, the perfect disturbance attenuation is no longer applicable and the tracking accuracy is degraded. Therefore, the higher-order internal model is adopted to improve the attenuation performance when the disturbance period is varied. In this paper, a systematic approach to integrating two different internal models, i.e., general and higher-order internal models, is presented. Note that the basis periods of reference and disturbance signals are different and uncorrelated. The stability analysis, the controller structure, and controller realization are also discussed. The proposed method renders a controller called a dual internal model-based RC (DIMRC). Simulation results and comparison studies validate the efficiency of the proposed DIMRC. The main contributions of this work are summarized as follows:  
* A novel dual internal model-based RC is constructed by using the denominator parts of the general and higher-order internal models. * Stability conditions of the plug-in DIMRC system are presented. The stability conditions are then used to determine the stabilizing controller. * The structure and realization of DIMRC for the non-causal stabilizing controller are developed. The remainder of this paper is structured as follows. Section 2 describes the problem statement and several assumptions used in the design. In Section 3, the fundamental notions of the discrete-time general RC and high-order RC are presented. Section 4 discusses the proposed method covering the controller structure, the closed-loop system stability, and the controller realization. Simulation results covering minimum-phase and non-minimumphase cases, followed by discussion, are provided in Section 5. Lastly, concluding remarks are drawn in Section 6.
## 2 Material and Methods
### Machine Learning Algorithms and Statistical Methods
#### 2.5.3 k-Nearest Neighbor Algorithm  
The kNN model is an important non-parametric supervised proximity-based machine learning predictive model. Initially, the algorithm considers all of the observation values as a cluster. The clusters then progressively combine to create new clusters. In kNN regression, the dependent variable is usually obtained by averaging the dependent values of k nearest neighbors in the training models. The Euclidean distance method is commonly implemented to identify the closest neighbors [40; 41]. More details on the principles of kNN regression can be found elsewhere [42]. In this work, k was set to 10 for both pH and MC predictions.
## 3 Results and Discussion
### Sensory Analysis of Fortified Wheat Breads with SWAP  
The overall acceptability scores (Figure 7a) indicated that the ORE1% sample was the most acceptable product among the tested fortified breads followed by ROS1%. These samples also presented relatively low variability and received high mean scores of overall acceptability, higher than 6.0 and close to that of control, indicating that consumers would widely prefer these products. These findings could be explained by the fact that these herbs, oregano and rosmary, especially the former, are frequently used in dishes in Mediterranean cuisine, and therefore, assessors of the present study were familiar with their aroma and taste, finding these flavor notes quite acceptable. The predominance of carvacrol in ORE breads and caryophyllene (sweety, spicy, clove odor) in ROS breads, instead of 1,8-cineole (Table 5), which is one of the major components of rosmary essential oil having camphoraceous \"medicinal\" odor [54; 55; 61], seemed to lead to a higher preference of these products by assessors (Figure 7a). In general, substituting wheat flour with SWAP at a 2% level resulted in products with low overall acceptability (mean values scores < 4). [PERSON] et al. [9] found that the addition of dried herbs of oregano, thyme, and satureja into wheat breads at a 1% level was the maximum acceptable concentration for consumers. The SPE2% fortified product was the least pleasant, as it showed the lowest mean overall acceptability score among all tested breads (Figure 7a). Probably, carvone, which is the predominant volatile compound in spearmint, providing the distinctly fresh and pungent mentholated, minty aroma to the plant [62], was detected in SPE breads at high levels (Table 5), which did not seem to fit with an acceptable flavor of such bakery products. Similarly, [PERSON] et al. [63] noted that as the level of spearmint aqueous extract increased in wheat bread formulations, the overall acceptability of the product by the assessors decreased. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline
**Volatile** & \multicolumn{6}{c}{**Distilled Solid Wastes**} & \multicolumn{6}{c}{**Breads**} \\
# Development of a New Pasta Product by the Incorporation of Chestnut Flour and Bee Pollen
## 2 Materials and Methods
### Physical Properties  
#### 2.2.1 Color  
For the evaluation of color, measurements were made using a handheld tristimulus colorimeter (Chroma Meter--CR-400, Konica Minolta, Japan). The measurement system used was the Cartesian coordinates L*, a* and b*. The L* axis represents lightness and varies from 0 (corresponding to no lightness, i.e., absolute black), to 100, which is maximum lightness (i.e., absolute white). The other axes are represented by a* and b* and they are at right angles to each other. The a* axis varies from green at one extremity (represented by \(-\)a*) to red at the other (\(+\)a*), whereas the b* axis varies from blue at one end (\(-\)b*), to yellow (\(+\)b*) at the other. Although in theory there are no extreme values of a* and b*, in practice they can frequently be numbered from \(-\)60 to \(+\)60. To perform the measurements, care was taken to promote similar light conditions when measuring the color of all samples, including the avoidance of direct sunlight and control of the incidence of artificial light. The calibration of the meter was done using a white tile, with illuminant D65 [43]. All evaluations were made on ten replicates.  
Total color difference (\(TCD\)), also sometimes defined as \(\Delta\)E, was calculated using Equation (3), which allows quantifying the overall color difference between a sample and the reference, which is this case was the control sample:  
\[TCD=\sqrt{\left(L^{*}-L_{0}^{*}\right)^{2}+\left(a^{*}-a_{0}^{*}\right)^{2}+ \left(b^{*}-b_{0}^{*}\right)^{2}} \tag{3}\]  
where \(L_{0}\)*, \(a_{0}\)*, \(b_{0}\)* are the color coordinates for the reference sample [43; 44; 45].  
A larger total color difference corresponds to a greater color change from the reference material, and one typical scale for evaluation of the color difference is as follows [46]: TCD values belonging to the interval [0.0-2.0] correspond to unrecognizable differences, TCD values belonging to the interval [2.0-3.5] correspond to differences possible to recognize by an experienced observer, and TCD values higher than 3.5 correspond to clear differences in the color of the samples under comparison.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **FC4-C50P10** & **\% in Relation to the Mass of Solids** & **\% in Relation to the Total Mass** \\ \hline Chestnut flour (g) & 37.5 & 50.0 & 33.3 \\ Pollen powder (g) & 7.5 & 10.0 & 6.7 \\ Wheat flour T55 (g) & 30.0 & 40.0 & 26.7 \\ Egg (g) & 37.5 & — & 33.3 \\
**Total (g)** & **75 /112.5 b** & **100** & **100** \\
**Pasta obtained (g)** & **79.20** & & \\ \hline \hline \end{tabular}  
* Total mass of solid ingredients; b Total mass of all ingredients.  
\end{table}
Table 4: Formulation of egg pasta with 50% chestnut flour and 10% pollen powder.
## 6 Results and Discussion
### Evaluating Multipath Scheduling with Beamforming and Selection  
We examine MS-UCB with the beamforming and selection over 802.11_ay_ by comparing the overhead (in milliseconds) with the existing 802.11 ad. In our setup, we bypass the physical layer interface and simulate the overhead of the proposed cross-layer framework  
Figure 6: Simulation network topology: Multiple devices are moving in an elliptical orbit and are associated with several 802.11_ay_ APs simultaneously using multipath TCP protocol at the TRA layer.  
in the MAC layer with multipath scheduling at the transport layer. This approach filters the physical layer impacts completely and facilitates reasonable comparison for tractability. To reduce beamforming training costs, we deploy an experimental MAC layer implementation [33] that allows us to examine PHY layer characteristics without introducing the entire complication. The whole Beacon Interval (BI) is reserved for data transmission in this MAC implementation. With the setup, we employ up to seven EDMG-SC links and eight RF chains, with multiple RF chains and multiple links serving one (selected) device simultaneously. Observe in Figure 7 that, with the increasing number of devices, there is no noticeable increase in the overhead (in milliseconds) due to MS-UCB. Unlike the 802.11 ad (dashed red curve in Figure 7), the proposed framework first selects the devices (pull-based approach) and then considers only the selected devices in the contention of the 802.11_ay_ system.  
More importantly, the overhead with the proposed MS-UCB multipath scheduling (solid blue curve in Figure 7) decreases sharply with increases in the number of devices. For example, when there is just a small number of devices (<40), the overhead is relatively low in 802.11 ad, but the multipath (MS-UCB) overhead is pretty high. The overheads of MPTCP LIA over 802.11 ad rise gradually as the number of devices increases (dashed red curve), and in practice, given a reasonable IoT scenario, a large number of devices exist. With the proposed cross-layer approach, the overhead depends only on the number of selected (and served) devices (independent of the total number of devices). In addition, the overhead due to the proposed MS-UCB multipath scheduling for a reasonable number of devices (>60) is relatively small. It is worth noting that the observed enhancements are mostly the result of MAB coupled with multipath scheduling and beam selection for hybrid beamforming.
# Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features
## 4 Experiment Settings
### Baseline Comparison  
We compare the proposed method against several representative baseline models commonly used in sarcasm detection tasks. The core characteristics of each model are briefly summarized as follows:  
* **TextCNN**[35]: Applies convolutional neural networks to textual data for sentence-level classification, leveraging n-gram patterns to capture semantic cues.
* **TextCNN-LSTM**: Integrates CNNs with long short-term memory units. CNNs handle local feature extraction, while LSTMs model long-range dependencies to improve overall classification performance.
* **BERT**: A bidirectional-transformer-based model pre-trained on large-scale corpora, known for its effectiveness in semantic representation and downstream language understanding tasks.
* **VGGish**: An audio feature extraction model based on Convolutional Neural Networks (CNNs), which extracts high-dimensional spatiotemporal features from raw audio waveforms through hierarchical convolution operations.
* **MFCC**[36]: A classical acoustic feature extraction method with strong interpretability, widely used in tasks such as speech recognition and speaker identification.
* **OPENL3**: A self-supervised learning-based audio representation model, suitable for open-domain multimodal scenarios.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Non-Sarcasm** & **Sarcasm** & **Total** & **Sentence Length (Characters)** \\ \hline Training & 3266 & 1633 & 4899 & 25.01 \\ \hline Validation & 418 & 194 & 612 & 24.69 \\ \hline Test & 403 & 210 & 613 & 24.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the multimodal sarcasm dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameters** & **Value** \\ \hline Batch size & 32 \\ \hline Dropout rate & 0.25 \\ \hline Learning rate & 0.0001 \\ \hline Epoch & 100 \\ \hline L2 regularization & 0.01 \\ \hline K-folds & 5 \\ \hline Smote & 42 \\ \hline BERT embedding dimension & 768 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters.  
* **BERT+VGGish**: A dual-stream architecture that integrates textual and acoustic information. The textual branch uses the BERT pre-trained language model to extract word-level or sentence-level semantic features, while the acoustic branch uses VGGish to capture prosodic features.
* **TextCNN+VGGish**: A multimodal framework that combines local text patterns with global acoustic features.
* **Wav2 Vec2.0 + BERT + CMA**[37]: A multimodal fusion model that combines audio and text embeddings extracted from pre-trained Wav2 Vec2.0 and BERT encoders. The two modality-specific representations are then aligned via a cross-modal attention (CMA) mechanism to capture interaction patterns for sarcasm detection.