esa-sceva/satcom-chunk-collection huggingface dataset:
# Flattening the Curve of Flexible Space Robotics [PERSON]
## 3 Results
### Comparisons of Stability Margins: Gain Stabilization Plus Structural Filters (Bandpass and Notch)  
This section summarizes the incrementally achieved results thus far in Table 5. Noteworthy performances are highlighted in bold font, indicating compensation of the first mode alone following gain compensation is the superior method based on a combined measure of both the gain and phase margin. Meanwhile, additional compensation of the first three structural modes plus the fourth anti-resonance produce the highest gain margin but suffer from a reduction in the phase margin.  
Figure 21: Five computer simulations of control of the rigid body and fully compensated modes with frequency in hertz on the abscissa and response magnitude in degrees on the ordinate. Table 4 quantitative data corresponding to qualitative presentation in Figure 21. PID (thick, solid red line); PID + modes 1–3 + bandpass 4 (dotted blue line); PID + modes 1–4 (thin, solid black line) again illustrating substantial improvement by compensating for additional (second) anti-resonance, but negligible difference also compensating for first additional (second).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System** & **Gain Margin (dB)** & **Phase Margin (Degrees)** & **Stable/Unstable** \\ \hline Uncontrolled & 64.5 & 0.000673 & Stable \\ \hline PID controlled & 4.95 & 17.9 & Stable \\ \hline PID + mode 1 & \(-25.9\)1  & **133** & Stable \\ \hline PID + modes 1–2 & 5.52 & 18.3 & Stable \\ \hline PID + modes 1–3 & 5.89 & 18.4 & Stable \\ \hline PID + modes 1–3 + & \(-26.8\) & 17 & Stable \\ \hline PID + modes 1–4 & 6.1 & 18.5 & Stable \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compensation of rigid body plus four flexible modes (resonance and anti-resonance) with step input.  
Figure 22: Six computer simulations of control of the rigid body; first flexible mode, second flexible mode, third flexible mode, and fourth flexible mode with frequency in hertz on the abscissa: uncontrolled (narrow, dashed line); PID compensated, filtered first mode (thin, solid line); PID compensated, filtered first mode; PID compensated, filtered first and second mode (thick, dashed line); and PID compensated, filtered first, second, and third mode—anti-resonance and resonance (dotted line). (**a**) Response magnitude (first mode zoomed in the inset) on the ordinate; (**b**) phase in degrees on the ordinate.  
stabilization. Next, consider utilizing single sinusoidally shaped step inputs to improve the target-tracking performance.
# Design of a Bionic Spider Robot with a Two-Degrees-of-Freedom Leg Structure and Body Frame
## 2 Analysis of Spider Locomotion Behavior  
For the purpose of adapting to a variety of natural environments, spiders have evolved a variety of movement characteristics, including crawling, running, jumping, rolling, swim ming and flying. These modes of movement allow spiders to move across a wide variety of terrain, even in challenging environments such as rugged mountains, dangerous cliffs, and complex jungles with agility and speed. Numerous researchers, dedicated to in-depth studies to develop robots capable of displaying similar movement abilities in extreme environments, draw inspiration from the unique movement features of spiders for innovative design in biomimetic robot technology. For example, [PERSON] [29; 30] observed spider locomotion on different pavement test platforms, comprising horizontal hard pavement, horizontal soft pavement, and sloped soft pavement. They analyzed the joint kinematics and driving modes of spiders under different ground conditions. [PERSON] [31] studied and analyzed the movement behavior of huwena bird-clawed spiders on the horizontal plane by using test channels, high-speed cameras and a computer-based three-dimensional motion observation system, and believed that the movement speed of bird-clawed spider was mainly achieved by increasing step frequency. Compared to other insects, spiders exhibit better movement stability. During movement, the speed and height of the center of mass exhibit periodic fluctuations, with maximum values occurring during the phase of alternating leg support and minimum values during the stable support phase. [PERSON] [32] conducted research on the kinematics and dynamics of arachids and he proposed the integration of robotics with arachid studies. [PERSON] [33] simplified the model of individual spider legs by capturing and analyzing large spiders' biomechanics. [PERSON]. [34] studied the leg joints of spiders and found that the leg movement of spiders did not rely on the muscle system, but relied on the hydraulic system of the legs, using a variety of devices and methods to observe the behavior of spiders in real time and analyze their movement patterns. Although this research has made significant progress, the relationship between the physiological structure of spiders and their locomotion mechanisms remains unclear.  
To better optimize the design of biomimetic spider robots, it is necessary to analyze the body structure and movement mechanisms of spiders in the natural world, providing a reference for the development of biomimetic spider robots. This paper delves into the analysis of the spider's movement mechanism, including its physiological structure, leg drive mechanism, and gait, thereby enhancing the adaptability and efficiency of biomimetic spider robots in complex environments.
## 3 Context-Aware Positioning Model
### Management of Contextual Information
#### 3.2.1 Request Sensing the Physical Environment  
This task is triggered when a consumer application requests the model to perform positioning. The contextual information manager sends a call for scanning the environment towards the context sensing stage (Figure 2). The goal is to obtain the current characterization of the physical environment.  
The result is a raw XML file containing feature vectors describing the scenario, and each surrounding device, which is sent to the following task for processing. This file also contains data read from peripherals, if applicable.
## 2 Theoretical Background
### Case Studies on Fire-Extinguishing Drones  
Drones significantly alleviate the aforementioned temporal and spatial constraints, which increases their utility and necessity. Both domestically and internationally, drone usage is being researched extensively in various fields, including exploration and disaster and fire monitoring. However, there is a relative lack of direct research specifically related to firefighting and fire response [14]. This highlights the need for research on the use of drones for fire suppression and response. To increase the efficiency of wildfire response, Korea Forest Service has recently partially researched developed drones equipped with conventional, cylindrical, single-shot fire extinguishers as prototypes [15]. These drones are designed to respond swiftly to wildfires, suppress the spread of fires, and facilitate effective firefighting; moreover, they are effective even in areas that are inaccessible by ground vehicles and areas where roads are not readily available. The stability and effectiveness of these fire-extinguishing drone systems have been validated by conducting practical experiments [15]. Table 2 summarizes the research conducted in [16], wherein the potential effectiveness of fire-extinguishing drones in outdoor locations was verified. In [17], experiments were conducted using fire-extinguishing balls to prove their efficacy in initial firefighting and reloading operations. These two studies validated the efficiency of using fire extinguishers in the early stages of wildfire suppression. In [18], a holder was designed for drones to store fire extinguishers using a railing-based system and releasing fire-extinguishing balls. This railing system can store and release multiple fire-suppressant balls in a controlled manner. In [19], a design that allows for the use of both launch and drop methods to deploy fire-suppressant balls was proposed.
## 5 Conclusions  
This study proposes a method for the accurate identification of individuals in search and rescue scenarios using the MHC-PSPNet architecture, addressing issues such as image blurriness and limited resources [29].  
1. The PSPNet model serves as the core network, with MobileNetV2 as the backbone to meet the performance requirements of mobile devices. To reduce computational load while preserving image integrity, the H-Swish activation function replaces ReLU6. Additionally, the CBAM is integrated into the Pyramid Pooling Module (PPM) to address image blurriness, optimizing the PSPNet model and improving its segmentation performance in complex search and rescue environments.
2. The MHC-PSPNet model achieves 97.15% accuracy, 92.17% F1 score, 83.82% Intersection over Union (IoU), and a parameter count of 2.38 M on the test set. Compared to UNet, DeepLabv3+, HRNet, and PSPNet, it demonstrates superior segmentation accuracy, offering robust technical support for post-disaster search and rescue operations.  
Figure 13: Segmentation results of the proposed network in four environments.  
3. Feature maps from four different search and rescue scenarios were visualized to illustrate the model's learning patterns and areas of focus, further validating the rationale behind the proposed network architecture.
4. Despite achieving positive results, the study reports a 2.85% mis-segmentation rate, caused by variations in lighting, the blending of individuals with complex backgrounds, and significant occlusion. Although the improved model benefits from a reduced parameter count of 2.38 M, which substantially lowers computational complexity, further optimization of the MobileNetV2 architecture will be explored to enhance its ability to extract complex features, without relying solely on attention mechanisms.
5. Future research will focus on investigating new strategies to further streamline the model, improve accuracy in challenging backgrounds, and expand the application of the proposed network to enhance its effectiveness and adaptability across diverse rescue scenarios [30]. In conclusion, the continuous optimization and expansion of this architecture will provide more robust support for post-disaster rescue operations.  
Conceptualization, D.Z. and W.Z.; methodology, W.Z.; software, W.Z.; validation, W.Z. and Y.W.; formal analysis, W.Z.; investigation, D.Z.; resources, D.Z.; data curation, W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z.; visualization, W.Z.; supervision, Y.W.; project administration, D.Z.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on Key Technologies of Rescue Robots Under Special Complex Conditions (BS318J009).  
Not applicable.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study.  
The authors declare no conflicts of interest.
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### Proposed Protocols
#### 4.4.3 Transmission Phase  
In the data transmission scheduling stage, relays assign TDMA based allocated time slots to the in-body sensor nodes. A communication link between the selected in-body sensor node and the nearby relay is established. The selected in-body sensor node transfers the sensed data during the allocated time slot to the nearby relay node. The relay node receives the data from the in-body sensor node and forwards them to the coordinator during the allocated time slots. The process will continue with the number of rounds accumulating until the energy of all the in-body sensor nodes is depleted.  
Figure 11: The information flow for the proposed protocol.
## 3 Materials and Methods
### Data Processing
#### 3.4.1 Snow Imagery Pre-Processing
The combination of these two pre-processing techniques was thoroughly tested for fifty image subsets comprising diverse types of snow surface type, illumination, camera type and image quality. For each test, the comparison between pre-processed and non-processed was made using the same Agisoft Photoscan Pro settings which were chosen depending on the photoset characteristics. The improvement was measured by observing the number of matches between image pairs, the number of sparse and dense point clouds density and changes in the number points selected with the projection error tool of Photoscan [35]. A range of different model building qualities were also tested. This process downscales the source images by a defined number of times on each side from _low_ (to 12.5%) to _high_ (no downscaling). These values were carefully evaluated considering the noise generated by clouds in the visual observations at both small (cm) and large (m) scale. Following the positive testing outcomes, both procedures were applied to all photosets. Details of such tests plus considerations for the image pre-processing workflow, including the effect of conserving high bit depth imagery by converting the RAW data to TIFF (12 bit) relative to the JPEG format (8 bit), can be found in Appendix C.
## 6 Frequency Domain Optimization
### Detailed Optimization Example: Circular Scan Pattern
While this section presented a method for optimizing lidar scan patterns for measurement accuracy, it should be noted that in practice there may be other objectives that are as important or even more important than minimizing measurement error. For example, the load reduction offered by the lidar-assisted controller is likely a more useful metric for comparing different lidar scan patterns, although it is harder to calculate using frequency domain techniques. Additionally, the extra preview time provided by longer preview distances may be useful when attempting to detect extreme wind events and take necessary actions to protect the turbine. Finally, measuring the wind at multiple range gates with a pulsed lidar offers the advantage of being able to track wind speeds as they travel towards the turbine as well as allowing measurements at different preview distances to be combined to improve the simultaneous estimation of wind shear and direction, as described by [PERSON] et al. [55],
## 7 Results and Performance Evaluation
### Jammer Location  
We chose two locations inside and two locations outside the WSN extent randomly. We found that the jamming indices of nodes decreased when the jammer was farther and increased when the jammer was closer to the nodes. For example, the JIs in respect of Node-13 in the set-up described in Figure 6 were found to be 94.43, 88.95, and 77.62 for the node-to-jammer distances of 20, 30, and 40 m respectively, keeping the other factors unchanged.  
**Figure 7**. It shows the MATLAB simulation output for the set-up shown by Figure 6.  
**Figure 8**. The JIs in the set-up described
## 4 Learning-Based Anomaly Detection
### Clustering and Labeling  
Sensor data are often multi-dimensional and exhibit complicated correlation, making it difficult to characterize the system's state solely on the basis of this data. A principal component analysis (PCA) is utilized for appropriate dimensionality reduction of the data by resolving the major correlation structure between the variables. The underlying rationale of PCA is that most system states can be sufficiently well-represented by the behavior of a few principal components [39]; PCA has been an effective feature extraction scheme in many contexts. In this work, the six-dimensional data representing the kinematic information is represented in terms of principal component axes with a rate of cumulative dispersion of at least 90% using PCA.  
This work utilizes clustering to facilitate labeling of the data that are not labeled in their raw form. Labeling allows for implementation of a supervised learning method in anomaly classification. The clustering procedure provides a grouping of data in terms of some type of similarity and distance metric so that human experts are readily able to label the data. It should be noted that the clustering result does not directly lead to classification; the role of clustering is rather limited as an aiding tool for the processing of the data in this work. Specifically, \(K\)-means clustering on the reduced space defined by the principal component analysis method is proposed in this work. The time-series data that we used for PCA comprised six variables (\(x\), \(y\), \(z\); \(v_{x}\), \(v_{y}\), \(v_{z}\)) and attached one drone label displayed after PCA. Since the principal component axis is selected so that the cumulative dispersion ratio is 90% or more, the number of dimensions reduced as a result of PCA may be different for each flight data. Clustering is performed using dimension reduced data, and the number of clusters is optimized with the elbow method [49]. By checking the clustered results, the status of each drone in the group is labeled into normal (TN) and abnormal (TA). In other words, the related variables were extracted by dividing into two cases: (i) check the results of performing a given scenario as a mission (RTK-GPS; setpoint data) or (ii) check the sensor signals to detect any anomaly in the vehicle (INS data). These processes including PCA and clustering label the unlabeled data to acquire a labeled data set for learning. As a result, unlabeled dataset can be labeled with two categories, TN and TA. The numbers of data samples in each category are 34,612 (TN) and 31,377 (TA).
Hence, the water vapor separation membrane used in industrial processes has been mainly applied to porous membranes to enable the removal of large amounts of water vapor. Relatively low selectivity of such membranes makes it difficult to use for applications in areas of MVD application which require low vapor pressure condition. In the case of air and water vapor separation for air conditioning, the partial pressure of water vapor in the atmosphere is very small, and large vapor pressure differences are observed even for small humidity variations. Therefore, appropriate performance evaluation is challenging. For this reason, the membrane used in MVD systems should have high water vapor selectivity; hence, water-selective dense membranes with a very high level of selectivity are often used. These membranes consist of a polymeric compound as a support material, which is coated with a hydrophilic membrane. Significant improvements in the performance of such membranes have been achieved by optimizing the production methods and structures. The performance limits of existing membranes in various gas-gas separation environments have been described [9; 10]. When the performance metrics of the water vapor-selective membrane are compared using the same index, it is clear that the membrane performance has evolved with the development of the technology. However, the membrane properties are sometimes reported without specific experimental conditions, or the result of simple diffusion of the target substance, which differs from the application conditions. This is the result of the study assuming that the separation process is the only effect of the ideal vapor pressure difference. In addition, the commonly used polymer membrane has many physical deformations due to pressure differences, requiring observation of effects, which has not progressed well. Accordingly, conditions similar to the actual system should be verified to confirm the effect of the variable called absolute pressure difference. In this paper, we summarized the former studies of membranes for water vapor and air separation and discussed the performance in view of MVD application. We also introduced a simple method to evaluate the performance of dense membrane under an absolute pressure difference condition. From the test, a performance variation of a sample dense membrane is explained.
## 4 Results and Discussion
### Parametric Study on Hydrogen Purification Performance
#### 4.3.4 Effect of P/F Ratio  
This section investigates the effect of various P/F ratios (0.2, 0.3, 0.4, and 0.5) on the PSA process, with the results presented in Figure 12. The amount of hydrogen fed into the AD step remains constant. Consequently, as the P/F ratio increases, an increase in the pure hydrogen used in the PG step results in a higher total amount of hydrogen consumed throughout the PSA process, leading to a significant decrease in the recovery. Using more hydrogen for purge helps reduce the residual impurity gases in the adsorption bed, thereby increasing hydrogen purity. However, as the P/F ratio continues to rise, the rate of increase in purity gradually diminishes. This indicates that the improvement in the 'cleanliness' of the fixed bed reaches a limit as the P/F ratio increases. Notably, the P/F ratio has a significantly greater influence on recovery than on purity.
## 2 End-of-Life of CRC
### Separation of Impregnated Fibers
#### 2.2.2 Thermal Recycling: Pyrolysis  
Compared to mechanical recycling, a fundamental advantage of pyrolysis is the complete separation of the polymer matrix, allowing the separate reuse of fibers and the matrix [24; 27]. These are separated in an inert atmosphere (usually nitrogen) under atmospheric pressure at a temperature of at least 350 \({}^{\circ}\)C [31; 32; 33]. During this process, so-called pyrolysis gases (e.g., H\({}_{2}\), CH\({}_{4}\), CO, and CO\({}_{2}\)) are generated from the polymer matrix [3]. Due to their high calorific value, they can be used as fuel to directly support pyrolysis and can offset some of the energy needed in the process [3; 19; 34; 35]. Burning off the polymer matrix (EP or SBR) can cause soot adhesion to the carbon fiber surface, preventing the fibers from bonding with new resin [32; 36]. Therefore, subsequent oxidation is necessary to remove the carbon black particles, negatively impacting the elastic modulus and tensile strength of the fibers [35; 37; 38; 39; 40]. The extent of this damage largely depends on the operating conditions, such as the pyrolysis and oxidation temperatures, the residence time, and the reaction atmosphere [22]. In this regard, the best mechanical properties (93% tensile strength and 96% elastic modulus) have been obtained at a pyrolysis and oxidation temperature of 500 \({}^{\circ}\)C, a pyrolysis time of one hour, and an oxidation time of two hours [41]. The oxidation process may additionally enrich the pyrolyzed fibers with oxidized groups, serving as a crosslink between recycled fibers and new resin [42]. However, the composites made with the recycled carbon fibers (rCF) tend to have poorer mechanical properties (reduced elastic modulus and tensile strength) compared to the composites made out of primary fibers [22]. rCF can be used to increase the strength of plastics in the injection-molded components as well as further processed into nonwovens [3]. Thus, the recycled fibers find application only in the field of non-structural composites, such as cladding in the automotive and aerospace industries or in lightweight sports equipment [3; 38; 42].
# Transmission Angle Analysis of a Type of Parallel Manipulators
## 4 Comparison Analysis of Condition Number and ITA/CTA  
In the field of robotics, the condition number is defined as:  
\[1\leq\kappa=\frac{\sigma_{2}}{\sigma_{1}}\leq\infty \tag{19}\]  
where \(\sigma_{1}\) and \(\sigma_{2}\) denote the minimum and maximum singular values of the Jacobian for a given posture. In order to investigate the influence of the transmission angles on the kinematic performance of this type of parallel manipulators, the Diamond robot and Delta robot are adopted here for the analysis. The schematic diagrams are shown in Figure 4.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems
### Supervised Learning for Network Slicing
#### 4.1.2 k-Nearest Neighbor (KNN)  
KNN is a supervised learning algorithm that is widely used in regression and classification problems. It classifies the data by finding the distance between training and test data samples. In network slicing, KNN algorithm can be used to group same application services.