esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Model for Robotic Pick-Point Evaluation for 2-F Robotic Gripper
## 4 Results  
The simulation model for robotic pick-point evaluation for the 2-F robotic gripper was evaluated with two-fold assessments: (a) simulation time evaluation and (b) grasping performance evaluation. The objective of the first test is to determine the execution time of the simulation model in single and parallel modes. In contrast, the second test directly evaluates the performance of the simulation model compared to its physical experiment with the collaborative robot UR5e, 3D Pickit vision system, and 2-F robotic gripper.
## 3 Results and Discussion  
### Time-Averaged Results  
The velocity signals of two symmetrical points at the centerline of the jet were extracted to attain the frequency of the sweeping jet in the free external domain. Two points with a 180\({}^{\circ}\) phase lag were selected, so the quality of the signals could be enhanced. By using a low-pass filter and Fast Fourier Transport (FFT) analysis on the subtracted velocity signal, the oscillation frequency was achieved, as shown in Figure 5. The frequency of the sweeping jet in the free external domain at each Reynolds number was dependent upon the mass flow rates and the internal geometry. As illustrated in Figure 5, the frequency of the oscillating jet for the free external domain, approximately increased linearly with Reynolds number, because of the increment of the mass flow rate.
## 3 Proposed Framework
### Mechanism for Enhancing Privacy and Security
#### 3.5.4 Security and Attack Complexity Analysis  
Salsa20 has been extensively studied for its resilience against common cryptographic attacks, including brute-force, differential cryptanalysis, and side-channel attacks [45]. Its 256-bit key length ensures a security level that is computationally infeasible to compromise with current methods [44; 46]. Compared to block ciphers like AES, Salsa20's ARX structure eliminates the need for S-boxes, which are often targets for side-channel attacks. This streamlined design enhances both security and efficiency in resource-constrained settings.  
While Salsa20 ensures that data-in-transit remain confidential and tamper-resistant, this work primarily focuses on the encryption mechanism rather than the surrounding key management framework. Issues such as secure key generation, distribution, and revocation, as well as the implementation of secure key provisioning protocols, are beyond the scope of this paper. We assume the necessary keys are established through a trusted channel or initialization phase.  
In the context of FL, adversaries may attempt to eavesdrop on or tamper with model updates during transmission. By encrypting updates with Salsa20, we ensure that the data remains confidential and tamper-resistant. Additionally, the low computational and energy requirements of Salsa20 align with the scalability objectives of our framework, allowing secure communication without sacrificing performance.
## 3 Results and Discussion  
### LAB Growth Curve  
The purpose of this work was to monitor and compare the cell viabilities of _L. plantarum_, _L. acidophilus_, _L. casei_, _S. thermophilus_, and _L. bulgaricus_ inoculated in oats. As shown in Figure 1, the five strains showed remarkable differences in growth activity during the first 12 h of the fermentation. The differences were caused by the strains' utilization of carbon and nitrogen sources and adaptability to substrates [20]. _L. acidophilus_ and _L. plantarum_ had the fastest growth rates. The maximum concentrations of all strains except _L. bulgaricus_ were determined after inoculation for 24 h. At this time, _L. acidophilus_ entered the stable stage, and the number of viable bacteria reached 1.36 \(\times\) 10\({}^{10}\) colony forming units (cfu)/mL. The results showed that _L. acidophilus_ and _L. plantarum_ had stronger growth abilities in oat substrate than the other strains. The viable counts of the five strains in the lag phase were all higher than the recommended minimum of 10\({}^{6}\) cfu/mL for probiotic products [21], indicating that all strains could use the nutrients in oats, and the oat substrate was a suitable fermentation substrate. Similar results were shown by [PERSON] et al. [22]. Who found that _L. plantarum_ and _L. acidophilus_ grew well in barley and malt substrates. In fact, _L. plantarum_, _L. acidophilus_, and _L. casei_ are considered common strains for cereal fermentation [12]. LAB are heterotrophic organisms that lack some biosynthetic processes and have complex nutritional needs [23]. Different LAB can metabolize different carbon sources, and each microorganism shows a specific preference for one or more sugars [24]. This is also why _L. bulgaricus_ and _S. thermophilus_ have poorer growth abilities in oat compared with other strains, and prefer to use lactose.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 4 Experiments and Results
### Results and Analysis
#### 4.2.4 Key Observations  
Three principal findings emerge from the experimental results:  
1. **Scaling Advantage**: The performance gap between _XSQ-Learning_ and _Q-Learning_ widens with state space complexity. In Scenario 1, the advantage was 22.56%, growing to 43.68% in Scenario 2 and 58.30% in Scenario 3. This demonstrates that _XSQ-Learning_'s similarity-based updates become increasingly effective as the state space grows.
2. **Stability**: Unlike some similarity-based approaches that showed oscillations or failures, _XSQ-Learning_ maintained consistent performance post-convergence across all scenarios and random seeds. This stability stems from the algorithm's controlled update propagation through the _sim_clip_value_ parameter.
3. **Robustness**: While other similarity-based methods failed completely in certain scenarios (particularly _QSLearning_), _XSQ-Learning_ demonstrated reliable learning in all test conditions. This robustness comes from the adaptive \(\delta\) threshold that automatically adjusts the similarity criteria during learning.  
The results suggest that _XSQ-Learning_ alleviates some of the primary challenges noted in previous similarity-based approaches while maintaining the theoretical guarantees of standard Q-learning. The algorithm's performance advantage is most pronounced in complex environments where traditional methods struggle with slow convergence, making it particularly suitable for real-world applications with large state spaces.
## 1 Introduction
The simulation results show that the proposed scheme can realize the effectiveness of formation generation and the stability of formation maintenance. The remainder of this paper is arranged as follows. In Section 2, we review the relevant research studies. In Section 3, we describe the system model. In Section 4, we present the formation control scheme. In Section 5, we verify the performance of the proposed formation control scheme by simulation. The paper is concluded in Section 6.
## 4 Results
A separate test without logging was conducted on the elliptical function (\(f_{3}\)) to analyze the computational costs of these algorithms (given in Table 5). As expected, DPSO is slightly more demanding than PSO by approximately 300 bytes (4.7%) for seven dimensions, further diminishing to 3.6% and 3.2% for 14 and 21 dimensions, respectively. Compared to GA and DE, both DPSO and PSO are notably more memory demanding. In terms of execution time, DPSO requires a few milliseconds more per iteration than GA and DE. The time lapse was determined by the average execution time to complete one iteration over 200 iterations and 21 dimensions. Given that the time complexities for GA and DE are similar and smaller than for PSO and DPSO, it is likely that some areas of code are not fully optimized even though the base code was the same and care was taken to minimize deviations from the base. In exchange for a slightly larger processing time relative to the other algorithms, DPSO was able to reduce the rate at which its ability to converge worsened when the disparity between population and search-space dimensionality increased. There are several components of the DPSO algorithm that contribute to its execution time. Their individual contributions can be measured by recording the corresponding time lapse. The time required for PSO to calculate momentum followed by the time lapse for local and global attraction forces can be compared to DPSO's momentum plus noise injection and dimension-wise attraction forces. The difference in calculation time for momentum (>1 \(\upmu\)s) versus momentum plus noise injection (36 \(\upmu\)s) and the regular attraction (7 \(\upmu\)s) versus the dimension-wise attraction (116 \(\upmu\)s) calculations is larger than expected. A notable portion of this increase can likely be attributed to DPSO relying on a for-loop to perform its per-dimension calculations, while PSO is able to exploit the optimizations given by the numpy library. Regardless, the velocity update steps for DPSO are expected to take more time given that the attractors are decided on a per-dimension basis.
# A Selective Dynamic Sampling Back-Propagation Approach for Handling the Two-Class Imbalance Problem
## 1 Introduction
In order to combine the multiple classifiers, it is common to use the hard and soft ensemble. The former uses binary votes, while the latter uses real-valued votes [26]. Recently, dynamic sampling methods have become an interesting way to deal with the class imbalance problem. They are attractive, because they automatically find the proper sampling amount for each class in the training stage (different from conventional strategies as over- and/or under-sampling techniques). In addition, some dynamic sampling methods also identify the \"best samples\" for classifier training. For example, [PERSON] et al. [27] propose a dynamic sampling method with the ability to identify samples with a high probability to be misclassified. The idea is that the classifier trained with these samples may produce better classification results. Other methods that can be considered as dynamic sampling are: (i) the snowball method (proposed in [28] and used as a dynamic training method in [29; 30]); (ii) the genetic dynamic training technique [31; 32]; in it, the authors employ a genetic algorithm to find the best over-sampling ratio; (iii) the mean square error (MSE) dynamic over-sampling method [19], which is based on the MSE back-propagation for automatically identifying the over-sampling rate. [PERSON] et al. [33] present a WRAPPER paradigm (for which the search is guided by the classification goodness measure as score) to discover the amount of the under-sampling and over-sampling rate for a dataset. [PERSON] et al. [34] show a very similar work. The dynamic sampling approaches are a special case of the sampling techniques. The main difference of these methods with respect to the conventional sampling strategies is in the time when they sample the data or when they select the examples to be sampled (see [19; 27; 28; 31; 32]). In this paper, a Selective Dynamic Sampling Approach (SDSA) to deal with the two-class imbalance problem is presented. This method is useful to find automatically the appropriate sampling amount for each class through the selection of the \"best samples\" to train the multilayer perceptron with the back-propagation algorithm [35]. The proposed method was tested over thirty five real datasets and compared to some state-of-the-art class imbalance approaches.
## 3 Methodology of the Survey  
In this survey, we focus mainly on evaluation methodologies for OppNet protocols than on their details or taxonomies. We are interested in how they have been evaluated and compared to other protocols and whether there are some trends to be observed in the last five years. The selection of the papers involved in this study are based on two factors: (i) The papers should be indexed in Scopus to ensure that they are peer-reviewed research works. (ii) The papers should describe pure opportunistic forwarding approaches. To be precise, we did not consider ad-hoc networks or protocols involving a combination of ad-hoc and infrastructure networks. The papers span from 2000 to 2018. Our list of papers is presented in Table 1 and the data from the papers used in this survey are presented in Appendix B. We do not claim to be exhaustive in our survey, simply because there are many works in this area and not all of them clearly position themselves in the opportunistic networks field. The OppNets evaluation methodology includes typically the following components:  
* Simulation environment or frameworks (Section 4)
* Comparative studies--which protocol has been compared to which others (Section 5)
* Network densities (Section 6)
* Mobility models (Section 7)* Cache sizes and traffic (Section 8)  
We consider these components as the most important ones, which on the one side dictate the quality and realism of the simulation studies and on the other side compare the protocols against each other. We do not consider theoretical studies or real-world studies, as those are rather rare in the area of OppNets.
## 4 Results: Analysis of the Integration of VR, AR, and MR in the Design Process
### Results of Usability Indicator Analysis
#### 4.1.4 Interactivity  
'Gravity Sketch' is characterised by highly direct and intuitive interactivity (Figure 8). It allows users to act directly on a three-dimensional model without the need for complex menus or hierarchical commands. This immediacy supports a fast learning curve for 3D sketching tasks (Figure 8a), particularly in the conceptual stages of the design process. The use of controllers, such as the 'Touch Plus Controllers' included with 'Meta Quest 3' (Figure 8b), enables direct manipulation of geometry in space, freehand drawing, and immediate surface editing. Furthermore, the integrated hand tracking option on the device provides a more natural alternative to certain navigation tasks or basic interactions, although with more limited functionality for precision design (Figure 8d). However, the wide range of stroke selection options offers considerable versatility for modelling (Figure 8e). 'Gravity Sketch' offers specific tools for volumetric modelling, symmetry application, and thickness or proportion adjustments, without relying on overloaded interfaces. It also supports the import of human models (Figure 8c,f) and elements from the intended context of use, facilitating usability analysis in real-world scenarios.  
The main advantage is that the user is literally inside the model, eliminating the need to interpret perspective from 2D projections. Any action is performed directly in three-dimensional space at the desired scale and angle. This enhances the formal, spatial, and ergonomic understanding of the design, reduces errors, and allows for more immediate validation of decisions. The manipulation of the model is more intuitive and physical, resulting in a more natural and fluid design process. Although interactivity is high, some limitations are identified. Millimetre-level accuracy is difficult to achieve without additional peripherals, such as VR styluses, haptic gloves, or high-precision optical tracking devices. Likewise, it is common to need to refine the model later in complementary software such as 'Rhinoceros' or 'SolidWorks', especially when technical geometry, tight tolerances, or exportation for manufacturing processes are required. While command access is fluid, the software interface design does not always allow for clear organisation of advanced tools. This can slow down work during later stages of product development.
## 2 Simulation Model and Method  
### Simulation Model  
Taking 1/26 of the turbine guide vane (i.e., 360\({}^{\circ}\)/26 = 13.8\({}^{\circ}\) sector) as the research object. The impingement cavity is inclined, and the upper and lower surfaces of the impingement cavity have an angle of 32\({}^{\circ}\) with respect to the horizontal plane. The schematic of the impingement cavity model is shown in Figure 1. It includes the impingement cavity, the pin fins and the air inlet and outlet. The lower surface in the impingement cavity haspin fins, and the smooth surface with a step above the cavity is called the upper surface, and the flow and heat transfer affect each other.  
The cold air passes through the impingement holes to the impingement cavity and carries out heat transfer on the lower surface. At the same time, the air flow is reflected and diffused to the upper surface for heat transfer, finally, the air flows out of the air outlet. The impingement holes and outlet holes are on the different side surfaces. There is an angle between them, ensuring that the air flow covers the impingement cavity better and improves heat transfer.  
The numerical simulation model has 14 impingement holes with a hole diameter of 7.2 mm, and a hole spacing of 17.48 mm. There are 6 outlet holes with a diameter of 6.6 mm. The thickness of the impingement hole plate is 30 mm. The diameter of pin fin is 10.8 mm, the height of pin fin is 12 mm, and the number of pin fin is \(12\times 7\). The pin fins spacing in the horizontal and vertical is 23.4 mm. Figure 1b is the size of the impingement cavity.  
The research results [35] show that the impingement cooling of the row arrangement of the pin fins is more significant than that of the staggered arrangement, and the comprehensive heat transfer efficiency is greatly improved. The investigation chooses the row arrangement of the pin fins.
## 4 Discussion
### Comparison of Different Intelligent Algorithms in EEG Analysis
#### 4.5.2 Comparison between ML/DL and Wavelet Algorithms in EEG Analysis  
Advancements in medical and clinical analysis have integrated artificial intelligence (AI) models like Machine Learning (ML) and Deep Learning (DL) algorithms. Both, being probabilistic in nature, offer a powerful tool for pattern recognition and classification tasks. ML/DL can identify subtle patterns and complex relationships in the EEG, enabling the detection of abnormalities or specific brain signatures associated with certain neurological conditions. Nevertheless, a prevailing challenge with many AI models lies in their characterization as \"black boxes\", signifying the lack of transparency and interpretability in their decision-making processes [57; 58; 59; 60; 61; 62]. Consequently, users, particularly healthcare professionals, face difficulties in comprehending and rationalizing the outcomes generated by these models, impeding their integration into clinical practice.  
Conversely, the intelligent WT algorithm emerges as a promising alternative in the realm of EEG analysis. Unlike AI models, where outcomes primarily rely on probabilistic predictions, the WT approach uncovers intrinsic patterns within EEG. Through spectral analysis, the WT algorithm decomposes EEG into distinct frequency components, revealing specific brain activity patterns associated with various neurological phenomena. These patterns provide essential insights into brain dynamics and function, facilitating a comprehensive understanding of neural activities in both health and disease [63; 64; 65; 66; 67; 68; 69]. The inherent interpretability of WT results confers significant advantages in the clinical context, where accurate and transparent diagnoses are pivotal. Healthcare professionals can confidentlyrely on the WT outcomes, as they furnish explicit information about the brain electrical activity, enhancing the reliability and informed decision-making in patient care.  
Furthermore, the transparent nature of the WT algorithm renders it a valuable educational tool. Its interpretable results aid in effectively teaching and training medical students, neuroscientists, and researchers. By facilitating the visualization and explication of brain signals, the WT approach fosters a deeper comprehension of neurophysiology and brain function [65].
## 4 Aircraft Tracking and Activity Recognition  
### Flow of Aircraft Tracking and Activity Recognition  
Using the DCNN-based rotation detector to detect the start frame, we identified the aircraft that needed to be tracked. To use the image matching method for aircraft tracking, we first obtained the template image of the aircraft from the rotation detection results. The aircraft in the template image is enveloped by a non-rotating bounding box, as shown in Figure 6. The width and height of the rotation bounding box (purple rectangle) of the detected aircraft are \(w^{\text{R}}\) and \(h^{\text{R}}\), respectively. The width and height of its corresponding horizontal bounding box (blue rectangle) are \(w^{\text{H}}\) and \(h^{\text{H}}\), respectively. Thus, for the aircraft, the width of its template image is \(w^{\text{tpl}}=w^{\text{H}}-\sqrt{w^{\text{H}}h^{\text{H}}-w^{\text{R}}h^{ \text{R}}}/2\), and the height is \(h^{\text{tpl}}=h^{\text{H}}-\sqrt{w^{\text{H}}h^{\text{H}}-w^{\text{R}}h^{ \text{R}}}/2\).  
The next process is to locate each aircraft in subsequent images and recognize its activity type. Figure 7 shows the algorithm for aircraft tracking and activity recognition. The algorithm processes each frame image in turn. In each loop, the algorithm mainly accomplishes four tasks.  
Firstly, the algorithm searches for the best matching image for each aircraft within a certain search area. The search area is calculated based on the previous tracking result of the aircraft. The search area is centered on the aircraft and is twice its size. Before matching, the search area image with a certain step size is rotated within a certain angle range to obtain a batch of search area images. The best match for the aircraft target is then identified from this batch of images and the matching result is located. Finally, we identify the position of the matching result in the current input image and achieve tracking for the target aircraft. This matching method is not only suitable for aircraft with translation and turning motion, but can also effectively prevent mistaken tracking of nearby aircraft.  
Secondly, the moving speed of each tracked aircraft is calculated using the multi-cycle backward difference method. The recognition result of the aircraft activity is then given according to its movement speed.  
Thirdly, detection of newly entering objects at the edge of the field of view is performed based on the background subtraction method and size constraint. If the bounding box of the newly added target does not overlap with all the tracking results, it can be judged as another aircraft that needs to be tracked.  
Additionally, the template image of the aircraft is periodically updated using the latest tracking results. Thus, the tracking procedure will have high adaptability to changeable or complex backgrounds.