esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Robot Control System
### Zone-Based Segmentation of the Production Environment
The corresponding cells are set to the cost value \(\Omega^{6}_{xy}=12\)  
Figure 3: Zone-based segmentation of a production environment. (**a**) Zone-based segmentation of a 60 \(\times\) 60 m environment colored from green (minimum cost value) to red (maximum cost value); (**b**) same zone setup, but with individual zone colors of layer hierarchy; (**c**) hierarchical ordered costmap layers. The Human layer contributes the information of a multilayer and redundant workspace monitoring system (WMS) described in [29]. The WMS uses RGB and thermal images as well as Lidar data. The multilayer sensor setup is improved by the implementation of redundant algorithms for human co-worker detection based on neural networks. The fused confidence intervals between 0 and 1 are provided as a 2D heatmap in form of an occupancy grid map. The corresponding cells in the costmap \(M\) are called human cells and assigned with the cost value \(\Omega^{\gamma}_{xy}=200\). A threshold of 0.5 is used for the confidence level to neglect low confidence detections. An area with an adjustable radius around each human cell is defined as a human safety zone. The Human layer calculates the Euclidean distance between the current robot position and each human cell to determine if the robot is inside a human safety zone. Following the concept of the navigation zones, the robot will preventively change its motion behavior if it is inside a human safety zone. In addition, the human cells are inflated to create a buffer zone around the human co-workers. The radius of these buffer zones is smaller compared to the human safety zones. The cost value of the corresponding cells is set to \(\Omega^{7}_{xy}=200\). Theoretically, the robot is allowed to plan close to a human, but it is highly cost inefficient. Figure 4 shows an exemplary scenario of an occupancy grid map provided by the WMS and the resulting costmap, including the buffer zones in blue and one visualized human safety zone in red. The Obstacle layer tracks the data from the 2D Lidar sensors. The Lidar data is provided in form of an array \(S\), including the sensor readings. The sensor readings are converted into the costmap space to determine the corresponding cells. Analog to the Static layer, the obstacle layer contributes to the master layer by following Equation (2). \[\Omega^{8}_{xy}=\left\{ \begin{array}{cl}0,&M_{Lidar}(x,y)==Free\\ 254,&M_{Lidar}(x,y)==Occupied\\ 255,&M_{Lidar}(x,y)==NoInformation\end{array} \right., \tag{2}\]  
where \(M_{Lidar}\) is the sensor readings in the costmap space. The Obstacle Inflation layer adds an adjustable buffer zone around the obstacle. Therefore, the distance between the obstacle and the planned path is increased.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 3 Results and Discussion
### Lower Limit for the Permittivity
This test case not only suggests a correct implementation of the model but also provides an estimate of its accuracy. The putative limit for the regime in which the resonance frequency shift scales linearly with the electron density is \(\omega_{\rm pe}\ll\omega\)[23; 25; 26]. However, several important mechanisms are not considered in this limit. When \(\omega_{\rm pe}=\omega\) and \(\
u_{\rm m}=0\) Hz, the permittivity of the plasma is zero and for this reason, it is assumed that the electron density in the plasma is sufficiently high to prevent the penetration of the microwaves into the plasma. For collisional plasmas, the reputed limit becomes even more stringent as the minimum permittivity is then larger than 0. Hence, this limit needs to be expanded for collisional plasmas to \(\omega_{\rm pe}\ll\sqrt{\omega^{2}+v_{\rm m}^{2}}\) in order to take the electron collisions into account and keep the threshold for the permittivity at \(\epsilon=0\). This expanded limit is indicated at \(\Delta\varepsilon_{\rm r}=-1\) in the graphs in this section. In this work, a deviation of 10 percent from the linear response of \(\frac{\Delta f}{f_{1}}\) due to the perturbation of the resonant field is deemed as acceptable. Here, \(f_{1}\) is the calculated resonance frequency when both regions are considered to be a vacuum. The point where the deviation becomes larger than this threshold is indicated by a circle in the graphs in this section. The interaction of the plasma with the microwave field is studied for two different cases--the plasma region at a node of the resonant field and at an antinode--both cases are discussed below.
## 3 Results
### Atomic Concentration Analysis  
Data for the atomic concentration (at.%) for key elements are summarized in Table 3, which highlights changes from the untreated baseline. Laser processing caused an increase in carbon content by up to 42% and a reduction in oxygen by up to 33%. The greatest increase in carbon content occurred for LD (42%), while the increase in carbon content was 23% for zirconia and 24% for Vita \(Et\).  
### Atomic Concentration Analysis  
Data for the atomic concentration (at.%) for key elements are summarized in Table 3, which highlights changes from the untreated baseline. Laser processing caused an increase in carbon content by up to 42% and a reduction in oxygen by up to 33%. The greatest increase in carbon content occurred for LD (42%), while the increase in carbon content was 23% for zirconia and 24% for Vita \(Et\).  
Figure 4: The effect of _f_-laser pulse number/scanning speed on wt.% constituent fractions of carbon, oxygen, aluminum and silicon in zirconia, lithium disilicate glass ceramic, and VITA ENAMIC® hybrid ceramic dental materials following ultra-short femtosecond (30 _f_s) laser irradiation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pulse Number/** & **Carbon** & **Oxygen** & **Aluminum** & **Silicon** \\ Scanning Speed & & & & \\ \hline Zirconia & & & & \\
100/200 & 41.1 (2.2) ** & 31.9 (2.1) *** & 0.2 (0.1) ** & NA \\
220/100 & 46.9 (2.7) *** & 31.7 (0.8) *** & 0.2 (0.1) ** & NA \\
440/50 & 54.9 (3.5) *** & 31.7 (2.6) *** & 0.1 (0.1) ** & NA \\ Untreated & 31.3 (4.2) & 47.6 (4.9) & 0.6 (0.2) & NA \\ \hline Lithium disilicate & & & & \\
100/200 & 40.6 (1.8) *** & 32.3 (2.2) *** & 0.6 (0.3) ** & 8.3 (1.5) *** \\
220/100 & 42.6 (2.0) *** & 33.2 (2.1) *** & 0.6 (0.3) ** & 7.0 (1.4) *** \\
440/50 & 50.1 (4.0) *** & 35.2 (1.9) *** & 0.5 (0.3) ** & 5.9 (1.4) *** \\ Untreated & 8.0 (2.0) & 66.2 (4.2) & 2.1 (0.7) & 21.0 (2.1) \\ \hline VITA ENAMIC® & & & & \\
100/200 & 37.0 (3.3) *** & 42.7 (2.6) ** & 6.3 (1.7) * & 13.5 (1.7) NS \\
220/100 & 38.8 (2.0) *** & 41.5 (3.3) *** & 5.5 (1.4) ** & 12.1 (1.8) NS \\
440/50 & 37.6 (4.3) *** & 40.9 (2.2) ** & 5.6 (1.1) ** & 10.4 (1.4) * \\ Untreated & 14.5 (2.4) & 52.5 (5.1) & 9.1 (1.8) & 14.3 (2.6) \\ \hline \hline \end{tabular}  
* Each data entry shows the mean and standard deviation, rounded to one decimal point. Asterisks indicate differences from the relevant untreated control sample, with \({}^{*}\)\(p<0.01\), **\(p<0.001\), ***\(p<0.0001\), and ***\(p<0.0001\). NS = not significant. NA = not applicable. Data sets were derived from 5 samples per group.  
\end{table}
Table 3: EDS data showing composition in atomic concentration for three distinct sample groups, namely zirconia, lithium disilicate composite material, and VITA ENAMIC® hybrid material irradiated at 20 J/cm\({}^{2}\) fluence and varying pulse numbers/scanning speeds.
## 4 Discussion  
### The Selection of Non-Volatile Compounds and Their Correlation among Different MKH Grades  
Catechins, AAs, caffeine, and other non-volatile compounds are the key taste-active compounds that contribute astringent, bitter, and delicate characteristics to the taste of tea, respectively [38; 39]. The sensory evaluation results demonstrated that the taste characteristic underwent a transition from \"mellow\" to \"mellow and thick\" and then to \"mellow and strongly thick\" during the upgrading process, which aligned with the changing trend in catechins. During the upgrading process, the content of four AAs (Ile, Thr, Asp, and GABA) decreased. These AAs possessed the following distinct taste characteristics: Ile had a bitter taste, Thr had a sweet taste, GABA had a delicate taste, and Asp had a sour taste [40; 41]. It was interesting to note that although AAs contributed to bitter and sour tastes, which were more prevalent in higher grades of MKH, they had less influence on tea flavour compared with AAs, which impart sweet and delicate tastes. This could be attributed to the difference in their taste thresholds [42]. Furthermore, AAs with bitter tastes tended to have higher thresholds, which means they play less of a role in contributing to tea flavour compared with AAs with sweet and delicate tastes [43]. Additionally, the collaborative interactions between compounds also impacted the balance and diversity of flavour.  
The AAs that possessed aroma characteristics, such as Val, Leu, Phe, and Arg, were present at higher concentrations in higher grades of MKH. During the sensory evaluation, when the liquid of tea was being judged, these non-volatile compounds interacted with aroma receptor proteins in the oral cavity, which is the functional area for taste and smell. The resulting signal was subsequently transmitted to the brain of the quality evaluator, which is similar to the mechanism when volatility occurs [44]. This may help explain why the liquid of higher-grade tea samples exhibited a stronger fragrance not only when it was being smelled and drunk. In terms of the correlation between non-volatile compounds and MKH quality, several AAs and signal catechins were strongly associated with sensory characteristics. In particular, Asn was highly associated with grade classification, as well as smooth, mellow taste and even fruity aroma. Previous studies have also demonstrated that a high content of Asn was one of the factors that contributed to the formation of excellent-quality Congou black tea [45]. This suggested that Asn could be used as an essential metabolite contributing to the taste quality of MKH.  
Figure 6: Potential marker compounds selection of different grades of MKH. (**A**) The correlation coefficient of non-volatile and volatile compounds demonstrating a highly significant positive correlation across all grades. (**B**) The aroma wheel representing different grades of MKH. A total of 45 volatile (v1 to v45) were selected based on a VIP value greater than 1.0, as exhibited in Table 2.
###### Abstract  
With the growth of deep learning in various classification problems, many researchers have used deep learning methods in environmental sound classification tasks. This paper introduces an end-to-end method for environmental sound classification based on a one-dimensional convolution neural network with Bayesian optimization and ensemble learning, which directly learns features representation from the audio signal. Several convolutional layers were used to capture the signal and learn various filters relevant to the classification problem. Our proposed method can deal with any audio signal length, as a sliding window divides the signal into overlapped frames. Bayesian optimization accomplished hyperparameter selection and model evaluation with cross-validation. Multiple models with different settings have been developed based on Bayesian optimization to ensure network convergence in both convex and non-convex optimization. An UrbanSound8K dataset was evaluated for the performance of the proposed end-to-end model. The experimental results achieved a classification accuracy of 94.46%, which is 5% higher than existing end-to-end approaches with fewer trainable parameters. Four measurement indices, namely: sensitivity, specificity, accuracy, precision, recall, F-measure, area under ROC curve, and the area under the precision-recall curve were used to measure the model performance. The proposed approach outperformed state-of-the-art end-to-end approaches that use hand-crafted features as input in selected measurement indices and time complexity.  
Bayesian optimization; convolutional neural networks; deep learning; ensemble learning; environmental sound classification; optimization; UrbanSound8k +
Footnote †: journal: Applied Sciences
## 3 Results: Combined Hashing Scheme for MVL Linked List
### Two-Steps XOR Hashing in Agent's Subsystems  
It seems more appropriate to use in internal subsystems (where possible!) simple and well-known binary logic hash functions in order to apply commercial modules and to shorten calculations. Principally, for hashing procedures one can design some MVL functions, but this seems to give advantages only in combination with specially planned AI procedures for subsystems, which are not discussed here. As it is being widely used in classic and quantum cryptography procedures [51], the well-known binary operator \(XOR\) (\(\oplus\)) [73; 74] can be the first recommended variant for internal hashing, combining one-time quasi-random fragmented key \(r_{d}^{(n)}\) and the set of \(w_{n}\) output parameters \(p_{w_{n}}^{(n)}\) of the subsystem \(S_{n}\). Operator \(XOR\) is typically included into specifications of microcontrollers [74] and needs only 1 work cycle; it is oftenly tagged as \(XRL\) in microassembler specifications. Such two-stage hashing procedure is shown in the Table 4. The first step is to be calculated in internal subsystems \(S_{2},\dots,\ S_{N}\). of the agent \(A_{1}\). The second step of hashing is to be done in the supervisor module \(S_{1}\) in order to minimize additional calculations in \(S_{2},\dots,\ S_{N}\).  
Figure 4: **(a)** Wireless QKD line can be regarded as the most secured source of quasi-random numbers for the pair of autonomous agents 0 and 1 of the MAS. **(b)** Less protected schemes may include QRNG and AGA-based random oracle.  
As it was shown in Section 2.2, several types of attacks aimed at MVL model can be based on modification of parameters of logic functions, written in the general format exp. (2) as arrays \(A,B,C\) describing subsystems of the agent. That is why \(XOR\) hashing can be directly used for data protection of such arrays. This procedure is more visually shown in Figure 5, where quasi-random key \(s\ r_{d_{n}}^{(n)}\) may be either given as additional matrix elements in rows, or processed as additional separate column. Calculation of \(XOR\) hash values is firstly to be done for rows and then for column, it is the most simple and universal procedure to verify illegal modifications of the MVL function.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Involved Subsystem** & **Expression** \\ \hline I Step & & \(H^{(n)}(r,\mathbf{p})=r_{d_{n}}^{(n)}\oplus p_{1}^{(n)}\oplus p_{2}^{(n)}\oplus \ldots\oplus p_{w_{s}}^{(n)}\), \\  & Hash value for \(S_{2}\) & \(n=2\ldots,N\) \\  & & \(H^{(2)}(r,\mathbf{p})\), calculated in \(S_{2}\) \\  & & \(\ldots\) \\  & Hash value for \(S_{N}\) & \(H^{(N)}(r,\mathbf{p})\), calculated in \(S_{N}\) \\ \hline II step & Integral hash value for all & \(H^{(int)}(r,\mathbf{p})==\) \\  & subsystems of the agent & \(H^{(2)}(r,\mathbf{p})H^{(3)}(r,\mathbf{p})\ \ldots H^{(N)}(r,\mathbf{p})\) \\  & calculated in \(S_{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Two steps of the internal hashing procedure of the agent \(A_{1}\).
# A Game Theory-Based Model for the Dissemination of Privacy Information in Online Social Networks
## 3 A Game-Based Model for Privacy Information Dissemination
### Decision on Forwarding Privacy Information
#### 3.1.2 Popularity  
In OSNs, compared to the real social world, it is easier for someone to get the privacy of public figures and to participate in spreading it to others. The privacy exposure of public figures accounts for approximately 70% of the total privacy exposure on the Internet. On the contrary, the privacy information of ordinary people with a tiny number of fans will not receive widespread attention and people tend not to spread such privacy information. Therefore, dissemination of privacy information through examining popularity cannot be simply neglected.  
The degree of a node in the network is the number of connections or edges between the nodes. In an OSN, the greater the degree of a user node, the more neighbor nodes the user has, and the higher the probability that the node is known in the network, the higher the popularity. The popularity of a node can be measured using Equation (1) in which degree(i) represents the degree of the current node, i.e., the number of fans in the network, and max degree is the largest degree of any node in the network.  
\[P_{i\_famous}=\frac{\log_{10}\text{degree(i)}}{\log_{10}degree(maxdegree)} \tag{1}\]  
The popularity distribution of nodes in the network can be obtained through calculation. Through repeated experiments, we can get the areas of the distribution of popularity with three different intervals. Based on the intervals, the popularity of the privacy subjects can be classified into the following three kinds:  
(1) High popularity users: This type of users each has a large number of fans. They are generally highly known and followed by a lot of people on the social network, such as celebrities and high-level politicians.  
(2) Middle popularity users: This type of users each is usually well-known in a certain field or topic range of the social networks with a reasonable number of fans and followers in a specific field, such as food bloggers and game anchors.  
(3) Low popularity users. This type of users are normally ordinary people with low levels of activity and only a few fans. Because of their low popularity, generally speaking, they do not attract much attention from other users.
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 3 Results
### Limitations  
The proposed framework for EGM processing and rotor tip detection not only addresses different types of noise and mapping strategies based on multiple signal characteristics but also incorporates an assessment of information quality. This allows experts to make informed decisions, improve data reliability, and optimize signal processing. However, one key limitation of this study is that the system was validated using simulated signals and noise, which may introduce biases in performance and efficacy when applied to real EGM signals. Although the method is designed to assess predictive performance on unseen data, its reliance on simulated data means that real-world performance may differ. To enhance robustness and generalizability, future research should focus on integrating the system with databases that provide continuous real-world data updates, allowing for automatic adaptations. Another limitation is the lack of a comparative evaluation against alternative models. To address this, future work will include performance comparison,including models such as Random Forest, XGBoost, and LightGBM. Additionally, the framework incorporates FIS to assess the situational context and risk/impact. While this approach is valuable for decision support, it relies heavily on the construction of rules and probabilistic values derived from the historical success of expert-driven procedures using the system. To further enhance adaptability and accuracy, the system should be equipped with mechanisms for automatic rule refinement and continuous learning from new data. Integrating database-driven updates and adaptive modeling will contribute to strengthening the system's reliability in clinical applications.  
A notable limitation of this study is the use of a large, fixed, and uniformly distributed number of EGMs, along with simplified electrode geometries. To facilitate the translation of these findings into clinical practice, the mapping system would require a larger sensory surface. Evaluating the proposed framework under lower-resolution conditions and incorporating electrode geometries that replicate clinically used catheters would provide valuable insights. Future studies should prioritize these aspects to better align the framework with real-world clinical scenarios and improve its practical applicability. Additionally, the system performance is inherently influenced by the specific techniques chosen for each level of the framework, highlighting the need for continued evaluation and optimization of its components.
# Comparison of Tracking Techniques on 360-Degree Videos
## 4 Discussion
The KCF tracker performs well on ordinary videos but not on 360-degree videos due to its fix-sized filters. The characteristics of 360-degree videos such as scale change, viewpoint change, and deformation easily lead the KCF tracker to a track loss. Thus, it is only useable to track plain targets that do not contain these characteristics. The Boost tracker achieves a fair accuracy on 360-degree videos, though it does not sense tracking failure and continues to track a wrong target as shown in video sequence 5. The parameters of tolerance need to be adjusted accordingly to avoid false tracks for the Boost tracker. The CSRT tracker is a good choice for tracking on 360-degree videos because it detects target objects using the HoG features instead of raw pixels. It can adjust the size of target window dynamically as well. Nonetheless, it still has a hard time recovering from a temporarily disappearing target as shown in video sequence 2, or tracking a fast-moving target as shown in video sequence 6. The MIL tracker can properly handle most of the cases on 360-degree videos. Its weakness is the problem of occlusion caused by change of viewpoints. The MIL tracker tends to fail in recovering the tracking target even after the occlusion. For applications with high-speed demand or large motion, the MOSSE tracker is the best choice since its tracking speed is significantly higher than other trackers, though the fix-sized tracking window could be a problem for video sequences with huge scale change. A typical example of 360-degree videos in an equirectangular format is shown in Figure 14. An ideal tracker should be able to tackle the problems in 360-degree videos such as viewpoint change, occlusion, deformation, lighting change, scale change, and camera shakiness. For viewpoint change caused by a moving camera, a motion model is helpful to assist tracking. To handle occlusion problems, the ability to recover the temporarily missing target is essential. To alleviate the nonrigid deformation,