esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection and Processing  
For this study, two datasets from a paper on \"3D Human Pose in The Wild Using IMUs and a Moving Camera\" [30] were utilized. The images of the dataset were already filed in different folders, with each folder containing images of a specific behavior. Actors were instructed to perform various expressions for every specific behavior, and the images contained these interactions from start to finish. No further processing was required by us at this point. We relied on the OpenCV library for the image reading process and loaded them into memory using cv2.imread() function. OpenCV can handle various image formats such as JPG and PNG. If an image could not be read, OpenCV converts it to an empty list to avoid code errors. For our purposes, the folders/behaviors utilized were courtyard_arguing_00 and downtown_arguing_00 for the argumentative behaviors containing interactions between two individuals arguing with each other (Figure 1), and courtyard_warmWelcome_00 and courtyard_giveDirections_00 for the non-argumentative behaviors containing interactions between two individuals casually talking, greeting each other and giving directions (Figure 2).  
In this work, we defined a frame from the courtyard_arguing_00 or downtown_arguing_00 folder as a verbal-argumentative behavior, where the two individuals are having a verbal argument and disagree with each other using their hands, body and facial expressions to express their attitude. For non-argumentative behavior, we take frames from the courtyard_warmWelcome_00 and courtyard_giveDirections_00 folder, where the two individuals are casually discussing without expressing argumentative attitude.  
1. **Argumentative Behavior Dataset**: This dataset contains a combination of images from two different folders, courtyard_arguing_00 and downtown_arguing_00. Half of the images from each folder were used for training and the other half for testing. This includes images from two individuals in an argument that gradually escalated.
2. **Non-Argumentative Behavior Dataset**: This dataset contains a combination of images from two different folders, courtyard_warmWelcome_00 and courtyard_giveDirections_00. Half of the images were used for training, and the other half for testing. This included images of two individuals in a warm greeting and giving directions to a specific destination.
* **Number of Argumentative Images for training**: 466 images;
* **Number of Non-Argumentative Images for training**: 466 images;
* **Number of Argumentative Images for testing**: 498 images;
* **Number of Non-Argumentative Images for testing**: 498
## 2 Structure of CDPDR  
The structural design of CDPDR is the basis for dynamic modeling. Before structural design, the configuration of CDPDR should be determined first. The motion platform only needs to translate and rotate in the derusting work surface, reaching a wide range of operations without a dead zone. In order to make the overall structure simple and convenient for engineering applications, a configuration of four-cable-driven parallel robot is chosen to complete the derusting operations.  
The schematic diagram of working principle is shown in Figure 1a; it consists of a CDPDR, two trolleys, a laser range finder and four linear motion guides. The trolley is used to change the operating position of the whole device. When the operation area is fixed, the trolley is not used, but the linear motion guide is directly installed on the ship. Linear motion guides are used where the curvature span of the work surface is large; by adjusting the relative positions of the four sets of linear motion guides, it is ensured that a certain distance is maintained between the motion platform and the working surface. Moreover, for the working surface with special curvature, the water pressure can be changed to ensure that the derusting agent can be sprayed to the working surface. The use of variable direction of the water gun outlet can solve the problem of dead-zone operation, and further improve the work efficiency and working area. The laser range finder is used to measure the distance between the hull and the robot, and the curvature of the hull can be calculated by measuring the distance at different points. The linear motion guide can be remotely controlled or automatically adjust the position, as its technology is relatively mature. This article does not cover the linear motion guide section, but rather conducts an in-depth study of CDPDR.  
The 3D model of the prototype is shown in Figure 1b; the prototype model is mainly composed of a motion platform, a frame, four driving motors, four tension sensors, four encoders, four cable disorder prevention devices and a bucket. Among them, the ship motion simulation platform is used to simulate the actual motion of the ship. The tension sensor is used to measure the tension value of the cable in real-time. The encoder is used to record the number of revolutions of the motor, thereby deriving the actual position of the motion platform. The cable disorder prevention device is composed of a pulley block and a linear bearing, which can prevent the cable disorder phenomenon during the actual operations.
## 4 Experiments and Results
### Results Analysis
#### 4.4.6 Comprehensive Analysis and Discussion  
In this section, we validated the effectiveness and advancedness of our proposed rock image classification method based on EfficientNet and a triplet attention mechanism through a series of experiments.  
First, we conducted ablation experiments to verify the effectiveness of data augmentation. This pre-processing method significantly increased the sample size of the dataset through various image transformations, effectively addressing the problem of insufficient sample size and uneven distribution among types in the original dataset, allowing the model to capture enough data patterns, and thus improving and enhancing the recognition accuracy and generalization ability of the model.  
Next, we fully validated the effectiveness of the transfer learning in model training by training a classification model with transfer learning and another identical model without transfer learning. The transfer learning method loads pre-trained weights into an untrained model during the initial training phase, endowing the model with strong image feature extraction capabilities, and enabling the model to achieve a higher accuracy with less time and data samples.  
We then trained five models, including EfficientNet-B7, VGG16, GoogleNet, AlexNet, and Triplet-EfficientNet, respectively, to compare their model training performance, thus  
Figure 15: The prediction probability of the classification model for each rock-type sample.  
validating the effectiveness of the EfficientNet network structure and Triplet attention mechanism in improving model training performance.  
To further validate the robustness and generalization ability of our proposed model, as well as its superiority over other mainstream and cutting-edge models, we trained six models (AlexNet, GoogleNet, VGG16, ResNet34, EfficientNet-B7, CA-EfficientNet) and our proposed Triplet-EfficientNet. We applied the same training strategy and parameter settings to all models. After training, we conducted performance testing and computational complexity testing on these models. The results showed that the high efficiency of the EfficientNet network structure allowed EfficientNet-B7, CA-EfficientNet, and Triplet-EfficientNet to achieve higher model accuracy with moderate Parameters and FLOPs. Meanwhile, the triplet attention mechanism of Triplet-EfficientNet was more efficient in image inference compared to the SE attention mechanism of EfficientNet-B7 and the coordinate attention mechanism of CA-EfficientNet, allowing EfficientNet models to further improve accuracy while reducing Parameters counts and FLOPs.  
Finally, we assessed the actual performance of our rock image classification model on seven images containing various types of rocks. The results demonstrated that the model achieved accurate classification of all test images with a prediction accuracy of over 95%. These outcomes showcase the exceptional ability of our model in classifying rock images.
# Agreement Between Tele- and Face-to-Face Assessment of Neuromotor Development in High-Risk Children
## 4 Discussion
This approach may not only result in improved developmental outcomes but also alleviate the long-term burden on families and healthcare systems by reducing the need for interventions later in life. High interrater reliability in telerehabilitation of motor development delay ensures that children receive accurate and consistent care. In the long term, this reliability supports enhanced diagnostic precision, leading to more reliable diagnoses and interventions. Reliable assessments can be adapted in diverse settings, including low-resource areas, promoting equity in care delivery. Also, it contributes to the evidence of using this kind of telematic assessment for patients who may need it, either for the large distance between them and medical services, health matters, the COVID-19 pandemic situation, or convenience. The implementation of telerehabilitation in the care of high-risk children in underserved or remote areas offers significant cost savings by reducing travel time and increasing accessibility to pediatric subspecialty care. Moreover, it has the potential to address disparities in access and improve the quality of care in these regions [39]. However, its adoption is not without challenges. Some of the key challenges previously reported include the overutilization or misuse of services, the exacerbation of existing care disparities, and concerns about patient data security. A requirement for telerehabilitation is access to high-speed Internet, which may not be available in certain rural areas. Additionally, the use of end-to-end encryption is essential to prevent patient data from being exposed to unauthorized access [40; 41]. To address these issues, investments from governments and the private sector are necessary to expand broadband infrastructure. Furthermore, regulatory policies must be developed to ensure the security of telehealth applications and platforms. Integrating this approach into clinical practice enables effective collaboration between caregivers and clinicians, creating a supportive framework for children's development. In addition, the assessment proposed in our study may be easily applied given the absence of specialized or costly software or materials. This increased accessibility ensures that high-risk children receive timely support, reducing the risk of long-term motor development delays. Thus, the development of telerehabilitation for the pediatric population improves individual outcomes and contributes to broader public health goals of equity and inclusion in pediatric healthcare. In this sense, future studies need to evaluate the use of telerehabilitation to develop intervention programs in high-risk children and their effects on preventing motor delays and complications.
## 1 Introduction  
Light emission of quantum dots (QDs) is of considerable interest for future implementations in photonic-based telecommunication technologies, light emitters, and magneto-optical applications [1; 2; 3]. Lead sulfide (PbS) QDs are, in particular, prospective candidates for such applications because their intrinsic features permit the realization of strongly confined particles with a considerable emission range, which can be tuned from near-infrared to visible light by simply shrinking the QD diameter from \(\sim\)15 nm to \(\sim\)3 nm [4; 5]. Despite progress in the field and the application potential, PbS QDs are still a subject of basic research, in particular, the chemistry of preparation (e.g., in glass or polystyrene, or as colloid, see [4; 5; 6] and references therein) and stability of the light emission are issues [7], while the temperature dependence of the photoluminescence (PL) is a chapter of its own. [PERSON] et al. [8] reported a rather flat (0.1 meV/K) blue shift of the PL peak up to 150 K, while beyond that temperature an energy shift of 0.3 meV/K was observed. In other publications, an almost constant PL energy position with a variation of \(\leq\)110 eV/K over the temperature range \(\approx\)10 K to 300 K was reported [9], and [PERSON] et al. [2]--similar to our own work [10; 11]--reported a fairly standard semiconductor-like dependence of the PL peak's energy position on temperature, i.e., only subtle alterations below 40-50 K, followed by a fairly linear growth at elevated temperature. [PERSON]'s theory [12; 13], although originally derived for bulk materials, describes that behavior very well [11].  
In this paper, we investigate the emission of PbS QDs, emphasizing in particular the deep temperature (\(<\)50 K) PL properties. The thermal shift of PL is compared with absorption measurements.
## 3 Results and Discussion
### Influence of Recipe Composition on Selected Responses
#### 3.2.5 Porosity  
Porosity is a critical factor affecting the overall quality of bread. Figure 3 shows a cross-section of the control bread used for porosity analysis. The value of bread's porosity was expected to be as in the control sample, i.e., 21.15%. The porosity was not influenced by any of the factors (\(p>0.05\)). Other studies have also not shown an effect of \(\beta\)-glucan contribution on bread porosity [51]. Porosity is a key factor in the quality of bread. It affects the texture of the bread, and too little porosity can result in harder bread. Adequate porosity is also related to the absorption and retention of water in bread. The proper porosity of the bread also indicates that the fermentation process was carried out properly, which also affects the overall quality of the bread. Porosity is also one of the most important sensory characteristics of bread, affecting its appearance and consumer perception. Ingredients such as sour fermented betoroot juice and \(\beta\)-glucan can affect the porosity of bread. Sour fermented betoroot juice contains natural yeast and bacteria that produce carbon dioxide during fermentation. This gas creates air bubbles in the dough, which contributes to the volume and porosity of bread. In addition, sour fermented betoroot juice can cause acidification of the environment, which leads to the stabilization of the gluten network by changing the gluten proteins' properties. The stabilization of the gluten network is associated with improved gas retention, which increases the porosity of the bread [52]. Further, \(\beta\)-glucan exhibits gelling properties and affects the stabilization of the gluten network. It affects gluten proteins, increasing their flexibility and stability, resulting in improved gluten network stability. In addition, \(\beta\)-glucan exhibits a high water-binding capacity, which promotes the maintenance of bread moisture. Higher bread moisture results in increased gas retention, which leads to improved bread porosity. On the other hand, due to its high water-binding capacity, beta-glucan competes for water with other ingredients, including gluten proteins, which can cause a deterioration in the quality of the gluten network. In this case, the porosity of the bread will also deteriorate. Therefore, when using \(\beta\)-glucan in bread, it is necessary to adjust the amount of water to ensure its beneficial effect on the quality of the bread [20; 32].
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 4 Experimental Results
### Performance Discussion and Contribution of Phase-Based Features
#### 4.3.1 Effectiveness of the Compact Statistical Descriptor  
It should be noted that despite the significant dimensionality reduction achieved by using the proposed compact statistical descriptor given in Equation (15), the most relevant information for classification is preserved. This is confirmed by the consistent performance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Classifier** & _Acc (\%)_ & _Pre (\%)_ & _Rec (\%)_ & **F1 (\%)** \\ \hline EfficientNetV2S & Voting KNN & 93.41 & 93.24 & 93.41 & 90.23 \\  & Stacking & 93.43 & 93.43 & 93.43 & 90.26 \\  & Bagging & 93.30 & 87.76 & 93.30 & 90.21 \\  & Boosting & 93.43 & 90.51 & 93.43 & 90.29 \\ \hline EfficientNetV2M & Voting KNN & 93.45 & 93.27 & 93.45 & 90.29 \\  & Stacking & 93.38 & 93.38 & 93.38 & 90.18 \\  & Bagging & 93.22 & 88.10 & 93.22 & 90.21 \\  & Boosting & 93.41 & 89.63 & 93.41 & 90.30 \\ \hline EfficientNetV2L & Voting KNN & 93.43 & 93.38 & 87.33 & 90.26 \\  & Stacking & 93.40 & 87.61 & 93.19 & 90.22 \\  & Bagging & 93.28 & 87.76 & 93.28 & 90.17 \\  & Boosting & 93.34 & 89.34 & 93.34 & 90.18 \\ \hline EfficientNetV2 XL & Voting KNN & 93.31 & 93.25 & 93.31 & 90.08 \\  & Stacking & 93.47 & 87.61 & 93.19 & 90.32 \\  & Bagging & 93.29 & 87.86 & 93.29 & 90.20 \\  & Boosting & 93.34 & 89.45 & 93.33 & 90.16 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification results of EfficientNetV2S, V2M, V2L, and V2 XL on the CSAW-M dataset in terms of accuracy (_Acc_), precision (_Pre_), recall (_Rec_), and F1-score.  
Figure 5: Best results of the proposed method. (**a**) EfficientNetV2M combined with the stacking classifier, tested on the combined mini-DDSM, MIAS, and CSAW-M datasets. (**b**) EfficientNetV2 XL combined with the stacking classifier, tested on the CSAW-M dataset.  
shown in Tables 6 and 7, even in the absence of data augmentation or extensive hyperparameter tuning. Furthermore, this low-dimensional representation offers practical advantages. For instance, it reduces computational complexity during both training and prediction, which is particularly suitable for implementation in resource-constrained environments. Moreover, by reducing the number of features, the model reduces the risk of overfitting, especially given the relatively small size of the available datasets.
## 4 Analysis of Experimental Results  
### Frequency Accuracy Test  
The frequencies of the six locking points were tested and measured when the laser was locked using a 780 nm wavelength meter BRISTOL 671A (wavelength accuracy \(\pm\)0.2 ppm) [28]. After the laser frequency is stabilized, the data is continuously collected at 250 s, as shown in Figure 11. We can see that the wavelength offsets are all within \(\pm\)0.2 ppm, with good frequency accuracy.
# Quantum-Secure Coherent Optical Networking for Advanced Infrastructures in Industry 4.0
## 4 Vulnerability Analysis
### Physical Layer Exploitation  
We also addressed the physical layer by studying documented hardware taps and the replication of optical signals [33]. Coherent optical networks often rely on precise amplitude and phase modulation, which can be intercepted with advanced equipment, enabling attackers to capture or modify data surreptitiously. The validation cycle tested how such taps might be combined with quantum-based cryptanalysis to decode or even alter transmissions [34]. The results indicated that while purely classical encryption can defend against basic eavesdropping, quantum-capable adversaries present a much more formidable challenge. Simple hardware changes--like adding an optical splitter--can transform the network into a vulnerable environment unless robust, layered defenses are implemented [14].  
Coherent optical networks are vulnerable targets for attackers due to their dependence on precise signal quality metrics that are implemented in the physical layer. Using optical taps allows attackers to tap in and alter signals without sacrificing the performance of the system as a whole. Specifically, these taps permit the attacker to passively observe the light signal being transmitted on the transmission line and to perform actions such as modifying the phase or amplitude of the light signal selectively.  
Advanced tools such as coherent receivers and polarization controllers simplify the job of attackers and allow them precisely to intercept and manipulate the signal. For instance, an adversary can eavesdrop on secure communications or modify some properties of a signal somewhat for the purpose of disrupting data integrity yet keeping it under the guise of normal operation. This is especially problematic in situations where data confidentiality is at stake, including government communications and financial networks.  
A comprehensive optical tap attack architecture that provides for signal interception and manipulation in coherent optical networks is shown in Figure 7. Such selective signal manipulation, while making the network appear to operate normally, can be achieved, as the diagram shows, by the placement of optical taps, monitoring equipment and signal modification points.  
Figure 6: OTU4 frame structure and vulnerability points.  
This systematic vulnerability analysis concludes that coherent optical networks demand integrated solutions. Relying solely on MACsec or on partial hardware security mechanisms risks the exploitation of architectural inefficiencies and quantum vulnerabilities that classical encryption cannot address [13]. The subsequent sections, comparing optical encryption methods and introducing a post-quantum framework, were developed precisely to tackle these multi-layered threats. Consistent with the method for domain-specific adaptation, the research method's iterative and triangulated design allowed each vulnerability to be verified across multiple data sources and scenarios, increasing confidence in this study's findings [31].
## 6 Conclusions  
### Key Findings  
This study's most significant finding is the impact of matches played (MP) on player performance. MP, with the highest correlation value of 0.24, stands out among other workload indicators like minutes played, starts, and full games. This emphasizes the central role of MP in influencing player performance. The study also reveals that for players with exceptional offensive skills, the key to success lies not just in playing more minutes but in participating in as many games as possible. This insight suggests a strategic approach for managers and coaches: systematically rotating top players. By doing so, they can ensure these players' participation in successive games, balancing the need for rest and the goal of consistent, high-level performance while reducing injury risks. Furthermore, the research points to the importance of the squad's quality in player performance. Remarkably, half of the top 20 players belonged to the top 4 teams in the league, illustrating a clear link between player excellence and team strength. This finding highlights the necessity for aspiring athletes to join top teams and for such teams to focus on recruiting consistent, talented players.  
In terms of injury prediction, squad composition emerges as a significant factor, with a feature importance score of 0.29. The study found that the 'Most Injury Affected' clubs, among the top 5 in injury incidents, accounted for a substantial portion of all injuries. This highlights the importance of squad balance and fatigue management in preventing injuries. Age also appears as a critical factor, showing a modest yet significant correlation with overall player performance and a stronger predictive power for injuries. Particularly, players in their prime (23-31 years) contribute to goals and assists. Conversely, players over 32 are more prone to injuries, suggesting the need to manage older players carefully to minimize their injury risks. Lastly, average minutes per match is identified as a key predictor of injuries. Players who play longer periods per game (>75 min on average) are more likely to sustain injuries. This finding is crucial for team management, highlighting the need for a balanced approach to player usage, emphasizing rotation and rest, especially for older players, to reduce injury risks.  
Figure 17: Injury Rate by Age Category.