esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measuring Equipment  
This study used a large number and variety of measuring equipment. During the study, a large number of values were recorded and an energy balance was constructed to determine whether the measurements were accurate. Below is a list of the measurement equipment used (Table 2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of Measuring Equipment** & **Properties** & **Notes** \\ \hline AHLBORN (Goettingen, Germany) & Measuring inputs: 9; & Used to record differential air \\ ALMEMO 2890-9 measuring instrument & Outputs: 2; & pressure values. \\ and data logger & Memory: 100,000 measured values & \\ \hline \multirow{4}{*}{Pressure measuring connector for differential pressure FDA 602 SIK} & Measuring range: \(\pm\)1250 Pa; & \multirow{4}{*}{Measures the pressure difference across the airflow measurement diaphragm.} \\  & Operating range: \(-\)10 to +60 \({}^{\circ}\)C, 10 to 90\% RH; & \\ \cline{1-1}  & Accuracy: \(\pm\)0.5\% of the final value in the range & \\ \cline{1-1}  & of 0 to the final positive value & \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline
160 mm diameter airflow & Measuring range: nominal air flow 400 m\({}^{3}\)/h; & The length of the straight section upstream of the measurement point is 12 diameters; downstream, 6 diameters. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Used measurement equipment. Figure 5: Digital photos of the experimental bench: (**a**) the experimental bench without thermal isolation and volume expansion; (**b**) the experimental bench with thermal isolation and a volume expansion unit. The following parameters were recorded during the experimental studies:  
* refrigerant pressures, temperatures and flow rates;
* airflow (determined by using the calibrated diaphragms), temperature and relative humidity. The tests were carried out under realistic climatic conditions and measurements were taken at 10 s intervals. Figure 3 shows the locations of the installed sensors and the recorded parameters. An experiment is conducted to determine how the operating parameters of the heat pump change when it is controlled by three control measures: compressor, expansion valve, and circuit volume. Throughout the study, the heat pump operates under different combinations of these three control components: the compressor speed is varied between 1800, 1920, 2100, and 2220 rpm; the expansion valve permeability is varied between 30%, 50%, and 70%; the system volume is varied between V0 and V4. All combinations of control component parameters throughout the experiment are presented in Table 3.  
The experiment commences with the smallest system volume, compressor speed, and expansion valve permeability (V0, 1800 rpm, 30%), during which the operating parameters of the heat pump (refrigerant pressures, temperatures) are observed to remain constant for 10 min, indicating a steady-state process. Subsequently, the compressor speed is increased
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 4 Experimental Validation
### Dataset Preprocessing  
The Cornell dataset is widely used for the training of grasp detection algorithms. It consists of 885 RGB images with a resolution of 640 \(\times\) 480, their corresponding depth images, and their grasp labels. Due to the small amount of data in the Cornell dataset, data enhancement is required to increase the training data. The first step is image cropping. The cropped image not only satisfies the input requirements but also saves computational resources. In this paper, the method used is to crop the image based on the center of the grasping rectangle rather than the center of the source image, which allows the target object to be able to appear at different locations in the cropped image. The size of the cropped image is 224 \(\times\) 224. The second step is image rotation, where the cropped image is randomly rotated \(\pm\frac{\pi}{2},\pm\pi\) to allow for a wider range of object grasping angles. After a series of data enhancement operations, the training data are greatly expanded, which helps to improve the model performance.
# Gesture Classification Using a Smartwatch: Focusing on Unseen Non-Target Gestures
###### Abstract  
Hand gestures serve as a fundamental means of communication, and extensive research has been conducted to develop automated recognition systems. These systems are expected to improve human/computer interaction, particularly in environments where verbal communication is limited. A key challenge in these systems is the classification of non-target actions, as everyday movements are often not included in the training set, but resembling target gestures can lead to misclassification. Unlike previous studies that primarily focused on target action recognition, this study explicitly addresses the unseen non-target classification problem through an experiment to distinguish target and non-target activities based on movement characteristics. This study examines the ability of deep learning models to generalize classification criteria beyond predefined training sets. The proposed method was validated with arm movement data from 20 target group participants and 11 non-target group participants, achieving an average F1-score of 84.23%, with a non-target classification score of 73.23%. Furthermore, we confirmed that data augmentation and incorporating a loss factor significantly improved the recognition of unseen non-target gestures. The results suggest that improving classification performance on untrained, non-target movements will enhance the applicability of gesture recognition systems in real-world environments. This is particularly relevant for wearable devices, assistive technologies, and human/computer interaction systems.  
Pattern recognition; deep neural network; gesture recognition; human/computer interface; zero-shot approach Article
## 2 Methods  
As mentioned in the introduction, MPC and DP are model-based optimization techniques that require a control-oriented simulation model of the system to be controlled. Also, both techniques were tested with a higher-fidelity simulation model. In this section, we first describe the equations governing the simulation model. Then, we describe the control-oriented model. Finally, we describe the implementation of the MPC algorithm as well as the DP algorithm that was used as a benchmark.  
External disturbances, aside from the lead vehicle's speed, are disregarded at this stage, as the primary focus is on comparing potential energy savings.
## 3 Method Proposal to Analyze AST n-Tools Combinations
### Benchmark Selection  
An adequate test bench must be credible, portable, representative, require minimum changes and be easy to implement, and the tools execution must be under the same conditions [10]. We have investigated several security benchmarks for web applications as Wavsep used in the comparisons of [66; 67]; Securebench Micro Project used in the works of [68; 69]; Software Assurance Metrics And Tool Evaluation (SAMATE) project of National Institute of Standards and Technology (NIST) used in several works [9; 28; 70; 71; 72; 73]; OWASP benchmark project [14]; Delta-bench by [PERSON] et al. [74] and OWASP Top Ten Benchmark [55] adapted for OWASP Top Ten 2013 and 2017 vulnerability categories projects and designed for static analysis only used in the work of [PERSON] et al. [8].  
Taking into account statistics of security vulnerabilities reported by several studies [2; 3; 4], the most adequate test bench for using SAST, DAST and IAST tools is OWASP benchmark project [14]. This benchmark is an open source web application in Java language deployed in Apache Tomcat. It meets the properties required for a benchmark [10] and it covers dangerous security vulnerabilities of web applications according to OWASP Top Ten 2013 and OWASP Top Ten 2017 projects. It contains exploitable test cases for detecting true and false positives, each mapped to specific CWEs, which can be analyzed by any type of application security testing (AST) tool, including SAST, DAST (like OWASP ZAP), and IAST tools. We have randomly selected 320 test cases from OWASP benchmark project and distributed them equally according to the number and type of vulnerabilities represented in the test bed. Of these cases half are false positives and half are true positives. It is easily portable as a Java project and it does not require changes with any tool. Table 1 shows the test cases distribution for each type of security vulnerability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CWE** & **Vulnerability Types** & **Number of Test Cases** \\ \hline
78 & Command Injection & 40 \\
643 & Xpath Injection & 20 \\
79 & Cross Site Scripting (XSS) & 40 \\
90 & LDAP Injection & 20 \\
22 & Path Traversal & 40 \\
89 & SQL Injection & 40 \\
614 & Secure Cookie flag & 20 \\
501 & Trust Boundary Violation & 20 \\
330 & Weak Randomness & 40 \\
327 & Weak Cryptographic & 20 \\
328 & Weak Hashing & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: OWASP Benchmark Project Test Cases.  
Figure 1: Method proposal to analyze analysis security testing (AST) n-tools combinations.
# Progressive Teaching Improvement For Small Scale Learning: A Case Study in China
## 4 Experiments and Discussions
### Learning Performance Prediction
#### 4.2.1 Comparison with State-of-the-Art Machine Learning Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Lessons** & **Techniques** & **Accuracy \%** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{6}{*}{1} & ANN & 38.74 & 0.3000 & 0.3874 & 0.3425 \\  & Logistic Regression & 41.67 & 0.4167 & 0.4167 & 0.5167 \\  & Random Forest & 26.67 & 0.2667 & 0.2667 & 0.2930 \\  & Decision Trees & 38.33 & 0.3833 & 0.3833 & 0.3930 \\  & SVM & 31.67 & 0.3167 & 0.3167 & 0.4297 \\ \hline \multirow{6}{*}{4} & ANN & 43.26 & 0.4302 & 0.4326 & 0.4338 \\  & Logistic Regression & 31.07 & 0.3107 & 0.3107 & 0.3508 \\  & Random Forest & 35.89 & 0.3589 & 0.3589 & 0.3647 \\  & Decision Trees & 32.50 & 0.3250 & 0.3250 & 0.3386 \\  & SVM & 37.86 & 0.3786 & 0.3786 & 0.4291 \\ \hline \multirow{6}{*}{8} & ANN & 47.12 & 0.4697 & 0.4712 & 0.4941 \\  & Logistic Regression & 42.27 & 0.4227 & 0.4227 & 0.5435 \\  & Random Forest & 40.36 & 0.4036 & 0.4036 & 0.4241 \\  & Decision Trees & 37.45 & 0.3745 & 0.3745 & 0.3826 \\  & SVM & 42.09 & 0.4209 & 0.4209 & 0.4678 \\ \hline \multirow{6}{*}{12} & ANN & 56.53 & 0.5500 & 0.5653 & 0.5703 \\  & Logistic Regression & 40.09 & 0.4009 & 0.4009 & 0.5398 \\ \cline{1-1}  & Random Forest & 54.00 & 0.5400 & 0.5400 & 0.5471 \\ \cline{1-1}  & Decision Trees & 46.82 & 0.4682 & 0.4682 & 0.4682 \\ \cline{1-1}  & SVM & 49.45 & 0.4945 & 0.4945 & 0.5034 \\ \hline \multirow{6}{*}{16} & ANN & 73.69 & 0.7405 & 0.7370 & 0.7372 \\ \cline{1-1}  & Logistic Regression & 45.45 & 0.4545 & 0.4545 & 0.5451 \\ \cline{1-1}  & Random Forest & 65.27 & 0.6527 & 0.6527 & 0.6527 \\ \cline{1-1}  & Decision Trees & 41.82 & 0.4182 & 0.4182 & 0.4225 \\ \cline{1-1}  & SVM & 56.36 & 0.5636 & 0.5636 & 0.5717 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metric comparisons between the proposed ANN model and other state-of-the-art methods. domain understanding of the sentence embeddings, which are high dimensional representations of text generated by deep neural networks. Thus, they fail to extract useful information from comments without specific feature engineering. At the same time, ANN can use multiple layers and non-linear activation functions to learn, understand, and utilize the representation of sentence embedding, resulting in better performance.
# A Feasibility Study for a Nonlinear Guided Wave Mixing Technique
## 1 Introduction
Nonetheless, guided waves are more appropriate than bulk waves for scanning large areas such as plates and long pipes. In addition, previous researchers have conducted guided wave mixing studies in a single mode [19; 20; 21; 22]. Representatively, [PERSON], [PERSON] et al. [23] numerically and experimentally shows the non-collinear mixing of low-order asymmetric Rayleigh-Lamb waves in the SH0 single mode. Guided waves have various modes, and the experimental results vary greatly depending on the mode selection and the angle of incidence. Therefore, the guided wave mixing technique deserves study [24; 25; 26; 27; 28; 29]. In this study, guided wave mixing was theoretically analyzed and experimentally verified. To use bulk wave mixing theory and overcome the difficulty of interpreting guided waves with different modes and frequencies, several assumptions are applied. Several assumptions help guided wave displacement components (in-plane and out-of-plane displacement) be treated as longitudinal and transverse waves. The guided wave mixing and the conventional ultrasonic nonlinear technique are compared by using the same specimen. The significant contents in this study are as follows:  
1. Based on bulk wave mixing theory and several assumptions, guided wave mixing technique is studied theoretically. 2. Based on theoretical study, an experimental test is conducted. Both the available mixing condition and non-available mixing condition are experimented with. 3. As the distance is changed, nonlinearity is measured by the conventional technique and the guided wave mixing technique for comparison.
## 5 Results
### Power Consumption  
We evaluate Adaptive TrimTree against ElasticTree protocol in terms of power consumption in order to observe power saving when Adaptive TrimTree is used. We generate three different DCN traffic patterns and observe power consumption versus time. We consider: (1) within-rack; (2) mixed 50% within-rack and 50% out-of-rack and (3) more out-of-rack (25% within-rack and 75% out-of-rack) traffic patterns and call these traffic pattern-I, traffic pattern-II and traffic pattern-III. The traffic patterns vary for different DCN applications and mixed pattern is more realistic, we consider extreme cases in order to better understand the impact of our proposed scheme. The results are shown in Figure 7. For traffic pattern-I, the maximum data rate peak is around 550 Mbps. For Traffic pattern-II, the data rate is mostly between 500 Mbps and 1 Gbps. The average traffic data rate peak is mostly around 1 Gbps for traffic pattern III. Figure 7a shows power consumption in Adaptive TrimTree and ElasticTree for traffic pattern I, Figure 7b shows power consumption in Adaptive TrimTree and ElasticTree for traffic pattern II, Figure 7c shows power consumption in Adaptive TrimTree and ElasticTree. pattern III and Figure 7d shows the average power consumption in Adaptive TrimTree and ElasticTree.
## 3 Proposed Method
### Contextual Positional Bias  
The original self-attention in transformers is equivalent to reordering, which means the output has nothing to do with the way the input tokens are shuffled. This attribute makes the model ignore the important positional information within the 2D images. To retain spatial information, different positional encoding mechanisms have been utilized in existing vision transformers. Among these methods, the relative positional encoding (RPE) [24] has shown its superior performance in vision transformers. Relative positional bias (RPB) [23; 43; 44; 45; 46] mode is the most commonly used RPE, but can only be applied to the fixed input size. Different from the existing RPB mode, we propose a more general and efficient dynamic contextual positional encoding mode (DCPE). As illustrated in Figure 5,  
Figure 4: An illustration of the GSA module. For each window, the queries, local keys, and local values are obtained within each non-overlapping window, while the global keys and values are computed from multi-scaled dilated pooling.  
compared with RPE, the input of DCPE has three parts, i.e., relative coordinate on the height axis, relative coordinate on the width axis, and the query tokens. The output of the DCPE module has the same shape as the attention map obtained by matrix multiplication, thus the output can be directly added to the attention map without any transformation as Equation (1):  
\[Attn(Q,K,V)=softmax(\frac{QK^{T}+AvgPool(Q)R^{T}}{\sqrt{d_{z}}}) \tag{1}\]  
where the \(R^{T}\) is the relative position bias obtained from the DCPE network. The DCPE network consists of three fully connected layers with layer normalization [47] and ReLU activation [48].  
Compared with RPE, DCPE can be applied to any input feature size, and the positional encodings will be changed according to the input queries. Therefore, more effective spatial and semantic information will be preserved, leading to a promising performance.
## 3 State-of-the-Art Technologies for Emotional Management
### Methods for User Evaluation
#### 3.6.1 Subjective Measures  
For evaluating the perceived emotional state of users based on subjective measures, there are several questionnaires such as PANAS [86] and SAM [87]. PANAS is an international short questionnaire composed of 10 items to evaluate the positive and negative affections. Nevertheless, one of the most commonly used questionnaires is the Self-Assessment Manikin (SAM), Figure 6. It consists of a non-verbal pictorial evaluation technique that evaluates valence, arousal and dominance associated with reaction to a stimulus. SAM has been extensively used in VR applications [88; 89; 90; 91; 92].  
Figure 6: SAM scale that measures the dimensions of pleasure, arousal and dominance using a series of graphic abstract characters horizontally arranged according to a 9-points scale. image source [93].  
Other widely used methods for evaluating the subjective experience in VR environments are subjective questionnaires. These are usually composed of several qualitative questions to be answered using Likert scales. These questionnaires evaluate characteristics such as presence, interactivity and, more commonly, immersion [94; 95; 96].  
For the measurement of the sense of agency in IVEs, researchers have developed tools to experimentally measure its components. Explicit measures of the SoA are commonly related to verbal reports in which users are asked to evaluate their SoA during a task or simply to rate whether they felt more or less that they were the agents of the actions [97]. Other researchers use Likert scales as subjective questionnaires. Their questions are commonly related to the psychophysical aspects of agency, such as confidence and feeling in control of a target [5]. There are predefined questionnaires, such as the Sense of Agency Scale [98], which consist of 13 items to measure the positive and negative sense of agency, and the Sense of Agency Rating Scale (SOARS) [99]. Specifically for IVEs, [PERSON] and [PERSON] [100] developed a set of items to measure the sensation of property and agency related to a virtual hand.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 4 Simulation Results and Discussions
### Impact of Communication Range  
This section presents a comprehensive analysis of the introduced algorithms in order to assess their impacts and performance on four different kind of network topologies while varying the communication range: square random, H-shaped, O-shaped, and W-shaped. In this scenario, the number of nodes remained constant at 200, the percentage of anchors was set at 20%, and the range of communication R was varied from 15 m to 35 m.  
The results, as depicted in Figures 5a-5a, reveal the influence of communication range on localization accuracy for each algorithm within the four network topologies. For each topology, the network becomes more connected as the communication range increases. The HW-DV-HopCSO exhibits respective localization accuracy improvements of 46%, 42%, 46%, and 33% compared to DV-Hop and W-DV-Hop and respective improvements of 36%, 33%, 26%, and 32% compared to HW-DV-Hop, MDV-Hop, and PSODV-Hop in random square, H-shaped, O-shaped, and W-shaped networks. In contrast, DV-HopCSO, W-DV-HopCSO, and HW-DV-HopCSO consistently outperform other localization algorithms across various network topologies. In addition, these algorithms outshine competitive  
Figure 4: Node distribution vs. networks: (**a**) square random, (**b**) H-shaped, (**c**) O-shaped, (**d**) W-shaped (400 nodes, 30% of anchors, and R \(=15\) m).
## 3 Results and Discussion
### Changing of TOC Removal Efficiencies during SMFC Operation  
TOC is a measurement of the organic richness of sedimentary rocks. Therefore, TOC was applied to characterize the utilization of lignocellulosicose in sediments. TOC removal efficiencies in sediments from the sites TH1 and TH2 with and without SMFC employment were determined as shown in Figure 3. It was found that, during the whole experimental period, removal efficiencies of TOC in sediments with the employment of SMFCs were higher than those without SMFCs. For site TH1, the removal efficiency of TOC in sediments with an SMFC increased by 14.4% compared to the sediment under the control treatment. Similarly, the TOC removal efficiency for site TH2 and its SMFC sediments increased 7.8% compared to that site's control treatment, but the values were significantly lower compared to site TH1 (one-way ANOVA, \(p<0.05\)). The different removal efficiencies of TOC in different types of sediments were coupled with anaerobic microbial oxidation processes. Most likely, the sediments from site TH1 provided a critical growth factor, which was essential to the success of the anode-respiring bacteria. These results demonstrated unequivocally that the application of SMFC technology could greatly enhance lignocellulosic biodegradation in different types of sediments.