esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Drying-Wetting Cycle and Vibration on Strength Properties of Granite Residual Soil
## 3 Test Results and Analysis
### Analysis of Direct Shear Test Results
#### 3.1.2 Variation in Shear Strength Parameters
During the experiment, the specimens indeed underwent significant irreversible shrinkage after DW cycles, as shown in Figure 8. The left and right images in Figure 8 depict the morphology of the specimen before and after DW cycles, respectively. Initially, the specimen occupies the entire space within the ring cutter. However, after the DW cycles, noticeable gaps form between the specimen and the ring cutter, clearly indicating that the specimen has experienced a reduction in volume due to the DW cycles, and the consolidation effect of DW cycles on the soil volume is greater than the development of internal cracks within the soil, resulting in an increase in the internal friction angle. Compared with the effect of the DW cycle on the internal friction angle of GRS, the effect of vibration is not significant. The vibrations not only lead to closer contact between soil particles but also cause the breakup of larger particle aggregates. Therefore, the influence of vibration on the internal friction angle is not as significant as that of the  
Figure 8: Specimen volume shrinkage diagram. Figure 7: Angle of internal friction change graph. drying-wetting cycle. In addition, the effect of vibration on the internal friction angle gradually weakens with an increase in the number of DW cycles. When the number of DW cycles reaches five, the effect of vibration is almost negligible.
# Communication Transport Protocol Strategies for Rail Applications
## 4 Implementation of the ACS Control Plane  
The creation of the virtual circuits is based on a signaling protocol, and the paradigm adopted for the ACS is the same as the SIP, which is a well-known application layer network protocol used to create, modify and terminate sessions between two or more participants.  
The ACS GWs and ACS Host Devices implement the SIP to provide end-to-end application management functions related to the connection status, session setup, QoS parameters required and negotiated, IP address settings, user authentication and profiling. More in detail, the SIP has been used to provide User Agents with the capability to register themselves to a Registrar to have assigned a logical IP address. The registration process of the User Agent is made hop-by-hop, which means each User Agent connects directly on the adjacent gateway. The OG assumes the role of SIP Proxy in the control plane, while the NG acts as SIP Registrar. SIP messages between two SIP User Agents are propagated hop-by-hop (as shown in Figure 5) since each host of the network needs to keep a local network topology database updated and a related routing table via SIP protocol message exchange. The SIP registration mechanism allows the SIP participants to announce or request a service.  
Figure 4: ACS Model Scheme for testbed.  
A SIP session and a related virtual circuit are created by an invitation mechanism, as described in RFC 3261 [16]. In Figure 6, a sequence diagram shows an example of on-board User Agent which is registering to the network gateway via an OG (acting as SIP proxy) to be assigned with a logical IP of the ACS network.  
Once two User Agents are registered, they can create a session and consequently a virtual circuit by using the SIP INVITE method, as shown in Figure 7.  
A circuit between two agents is composed of IPVLAN logical interfaces, which identify the end points of the circuit, and the tunnel interface via the local access interface that each gateway exposes to the agents. How the packets go through the bearers is decided by the gateway based on a given configuration, but such complexity is hidden to the agents, which can only address the virtual interfaces related to the multi-path tunnels. A simplified version of the ACS topology can schematically show how a virtual circuit is established. The schema shown in Figure 8 is the one used to carry out our performance tests.  
Figure 5: SIP hop-by-hop signaling process.  
Figure 6: Example of SIP registration sequence and logical IP address assignment in ACS.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 2 Materials and Methods
### Evaluation Strategy  
To ensure the reliability of our results, we trained and evaluated the models using the \(k\)-Fold Cross-Validation (CV) strategy. In this approach, the dataset is split into \(k\) equally sized subsets (folds). That means, for each of the \(k\) iterations, one fold is reserved as the test set, while the remaining \(k-1\) folds are used for training. As a result, each sample in the dataset is used exactly once for validation and \(k-1\) times for training.  
We evaluated our models using two widely adopted cross-validation schemes:  
* \(k\)-Fold Cross-Validation with \(k=5\), which offers a good trade-off between computational efficiency and statistical robustness;
* Leave-One-Subject-Out Cross-Validation (LOSO-CV), corresponding to \(k=30\), where each subject is left out once for testing while the model is trained on the data of the remaining 29 participants.  
This evaluation strategy was also chosen to ensure the compatibility of our results with those reported in other studies, particularly those based on the UniMiB-SHAR dataset, where LOSO-CV is commonly used as a benchmark protocol.  
Each model was trained for 20 epochs using the Adam optimizer, with a learning rate of 0.0003 and a batch size of 16 observations. To assess performance, the final accuracy of a given model was calculated as the average of the accuracy scores obtained across all \(k\) folds.
# A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation
## 1 Introduction
They mainly describe the overall shape of curves, and cannot express the differences between similar curves in depth. In recent years, scholars have proposed a series of methods for improving this shortcoming by introducing weight factors [31; 32; 33; 34; 35; 36], so as to modify the local shapes of curves and reflect the personality characteristics. In [37], a shape-adjustable generalized Bezier curve with high degree (a curve of degree \(n\) with \(n\) local shape parameters) was presented. This curve can explain the whole shape of a group of curves through control points, and adjust the local shapes of each curve by altering shape parameter values, so that the differences between the curves can be displayed by the differences of shape parameters. The authors of [38] illustrated a generalized extension-Bezier curve (a curve of degree \(n\) with \(n-1\) local shape parameters, GE-Bezier curve for short), which has fewer parameters compared with the one mentioned above. Furthermore, the better correspondence between parameters and local shapes of the curves could be expressed as well. However, these curves were more concerned with mathematical theory, and a few of them (such as cubic extension-Bezier and cubic quasi -Bezier) were just applied to the design of car curves and headlights [39; 40]. Therefore, a novel approach is applying the new parametric curve in gait analysis. The overarching goal in this paper was to investigate the mathematical models of lower extremity gait curves based on the characteristics of the GE-Bezier curve, and to take shape parameters as individual gait characteristics. We hypothesized that the ranges of shape parameters of healthy people represented the whole normal gait pattern while those of patients with lower limb injury in different rehabilitation periods should be out of the ranges in various degrees, so as to evaluate the rehabilitation effects. Due to the corresponding relationship between the shape parameters and the local shapes of the curves, it can also assist in judging the walking postures related to abnormal parameters, thus providing effective references for developing rehabilitation training.
## 6 Experiments and Discussion
### Evaluation Experiment of Generated Sound
#### 6.2.3 Experimental Results  
Table 9 and Figure 18 show the correct answer rate for all combinations, for each material and the difference from the preliminary experiment. \"Sample\" in Figure 18 means the data of the dataset used in the preliminary experiment, and \"generated\" means the impact sound data generated by the learning model used in the evaluation experiment.  
Among all the combinations we tested, the one with the lowest accuracy was the glass-wood combination, when paper was used as the correct class (as shown in Figure 21); the accuracy was just 37.5%. Paper had the lowest accuracy among all classes, and even when looking at other combinations, the accuracy was low when paper was included as a selection candidate.  
On the other hand, there were some combinations in which the correct answer rate was 80% or more, which is the same accuracy as the preliminary experiment. Examples include the combination of metal-wood, when cloth was the correct class (Figure 22), and the combination of cloth-paper, when glass was the correct class (Figure 19). The accuracies for the cloth and glass material classes were 69.6% and 71.9%, respectively, which is about 70% of the accuracy for all combinations. Therefore, we concluded that these two classes were able to generate an impact sound close to the sound of the dataset.  
Figure 21: Accuracy of “paper” class in main (generated) and preliminary (sample) experiments.  
Figure 20: Accuracy of “wood” class in main (generated) and preliminary (sample) experiments.  
The average Kappa coefficient of the eight subjects was 0.45. This was obtained by taking the average of the Kappa coefficients of 28 subject pairs (\(=_{\text{g}}\text{C}_{2}\)) for the results of the subjects' selection of the correct material class from each pattern of combinations. This value indicates a relatively good agreement, in that the difference among the subjects was minor. This suggests that the same tendency would be exhibited, if the number of subjects was increased; thus, the number of subjects used in the present experiment was considered to be appropriate.  
In the questionnaire, administered to subjects after the experiment, we verbally asked \"Was there a difference in sound discrimination compared to the preliminary experiment?\" and \"What kind of impression did you get from the presented impact sound?\" Regarding the sound quality of the generated impact sound, one subject responded that the generated impact sound was louder than the sound of the dataset. However, another subject answered that there was no discomfort, in terms of the impact sound. Summarizing the experimental results and the answers to the questionnaire, we concluded that the generated impact sound satisfied the necessary condition as a sound to be fed back to the user in the virtual environment, with discrimination using only the texture image.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
2D & Two dimensional \\
3D & Three dimensional \\
5G & Fifth Generation \\ ADC & Analog-to-Digital converters \\ AF & Amplify-and-Forward \\ AI & Artificial Intelligence \\ AM & Amplitude Modulation \\ AOA & Angle of Arrival \\ AOD & Angle of Departure \\ AV & Autonomous Vehicles \\ BS & Base Station \\ CSI & Channel State Information \\ DAC & Digital-to-Analog converter \\ DC & Direct Current \\ DF & Decode and Forward \\ EE & Energy Efficiency \\ EM & Electromagnetic \\ eMBB & enhanced Mobile BroadBand \\ FET & Field-Effect Transistors \\ FPGA & Field Programmable Gate Array \\ FSS & Frequency Selective Surfaces \\ FWN & Future Wireless Network \\ HetNet & Heterogeneous Network \\ HMIMO & Holographic MIMO \\ IM & Index Modulation \\ IoT & Internet of Things \\ IRS & Intelligent Reflecting Surface \\ ITU & International Telecommunication Union \\ IW & Intelligent Walls \\ LIMS & Large Intelligent Metasurface \\ LISA & Large Intelligent Surface Antenna \\ LIS & Large Intelligent Surface \\ LoS & Line of Sight \\ LTE & Long-term Evolution \\ M2M & Machine to Machine \\ MEC & Mobile Edge Computing \\ MEMS & Micro-Electromechanical System \\ mmWave & Millimeter Wave \\ MIMO & Multiple-Input Multiple-Output \\ mMIMO & massive Multiple Input and Multiple Output \\ \end{tabular}
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Thermochemical Processes
#### 2.1.1 Steam Reforming  
Steam reforming (SR) is currently the most used process to produce hydrogen [1] and can be divided into four categories, according to the substance used as feedstock and the technology implemented, namely steam methane reforming (SMR), methanol/ethanol steam reforming, sorption enhanced-methane steam reforming, and glycerol steam reforming. For hydrogen production, SMR is the most frequently used process [28]. In  
Figure 2: Schematic representation of the main hydrogen production technologies.  
SMR, natural gas and pressurized steam are used to produce carbon monoxide (CO) and hydrogen (H\({}_{2}\)) in four steps, as represented in Figure 3.  
In the first step of the SMR process, natural gas goes through a pretreatment phase to remove some of the minor elements that compose the natural gas, especially hydrogen sulfide, since sulfur compounds can cause catalyst poisoning. Then, the natural gas reacts with steam, beginning the steam reforming phase. The syngas produced in the steam reforming phase goes through a second reaction in the water-gas shift reactor [29]. The water-gas shift reaction converts carbon monoxide and steam to carbon dioxide and hydrogen. Further processing steps, such as freezing, methanization, gas scrubbing, pressure swing adsorption, selective catalytic oxidation, storage in hydrides, and membrane diffusion, depending on the application, can be implemented to remove the carbon dioxide (CO\({}_{2}\)) and leave pure hydrogen [27; 29; 30].
# Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage
## 5 Evaluation
### Part 3--Soundscape Experience  
#### 5.3.1 Methodology  
In part 3, participants were asked to use the PlugSonic Experience Mobile app to explore a soundscape within a real environment. The soundscape and physical space were the same used for part 2. The room was set up with photos and a video projection about the Voyager exploration program (Figure 6). Participants were asked to imagine visiting a museum and as part of the visit they were given a mobile device (iPad) which they could use to explore a soundscape. They were invited to explore the soundscape freely for as long as they wished while moving around the room. No specific recommendations were provided apart from the indication to point the device's camera in the direction they were facing--as the camera and inertial sensors were used by the device to infer position and orientation, which in turn were used by the mobile app to render the audio in 3D. They were also advised to ignore the device's screen as no information was going to be displayed. After exploring the soundscape, participants were asked to fill in a questionnaire about their emotional response--selecting among 11 options (indifferent, interested, uninspired, bored, excited, captivated, engaged, disappointed, satisfied, neutral and frustrated) or by adding other ones. They were also asked to answer questions about social potential and learning while using the app--using a 7-point Likert scale (with 1 _strongly disagree_ and 7 _strongly agree_). We recruited 7 participants for part 3: 5 males, 2 females in their mid 20s to mid 30s.
## 2 Overview of Unloading Experiments
The use of micro-photographs of petrographic thin sections or of fracture surfaces (e.g., [2; 10; 22; 47; 48]), SEM micrographs (e.g., [14; 26; 28; 35; 39; 40; 41; 42; 48; 49; 50]), reflection microscopy images (e.g., [48]), and (micro-)CT images (e.g., [2; 11; 28; 35; 51; 52; 53]) are hereby useful. Apart from the unloading experiments in a true-triaxial cell as mentioned above [13; 22], we studied various stress paths, including the effect of the principal stress orientations. In [PERSON] et al. [34], cyclic Brazilian tensile tests were conducted without reaching final macro-failure by rotating the disks between successive cycles. The rotation angle varied between 0\({}^{\circ}\) and 90\({}^{\circ}\). The final aim was to investigate how sensitive the Kaiser effect is towards the principal stress orientation. The Kaiser or memory effect gradually became less pronounced as the rotation angle increased, but it remained detectable for angles less than 10\({}^{\circ}\). Larger rotations resulted in complete disappearance of the Kaiser effect [34]. The overall conclusion of this study confirmed that it is essential to consider the micro-fracturing during the entire loading or unloading process. In another study, the damage around a circular opening was studied [32; 47]. Two configurations were investigated, i.e., in one set of experiments the damage was induced mainly by shear stresses (only macro-compressive stresses were present), and in the other set, the tangential stresses were tensile stresses at the macro-scale. Some specimens were tested by a succession of first macro-compressive stresses, followed by a tangential macro-tensile stress, and other specimens were tested the other way. In the latter case, i.e., specimens first damaged by a tensile macro-stress, followed by macro-compressive stresses, more damage was observed than in the other sequence. The rock material studied was crinoidal limestone, and it was clear that the damage that was observed was affected by the structure of the crinoidal limestone, e.g., its typical cleavage planes. Hence, one should be careful in extrapolating the conclusions to other types of rocks. However, the sequence of the compressive and tensile stress states within a given experiment is an important factor of the amount of (micro-)damage. In other words, the discontinuous fracturing process always should be considered. In conclusion, this literature overview clearly highlights the importance of the occurrence of micro-fractures during loading or unloading experiments. This is visualised in lab experiments, but also in numerical simulations of some of these experiments using codes which allow for the modelling of the initiation and growth of individual (micro-)fractures. In [16; 20; 28], data of lab experiments show that the conventional loading of rock specimens result in larger strength values than stress paths containing at least partly some unloading.
## 4 Results
### Potential Landslides  
Hot and cold spots were obtained through cluster analysis and 846 and 1024 MPs were clustered as hot or cold spots in the ascending and descending data sets, respectively. The hot spot maps (Figure 9) show that MPs with large deformation velocities were well extracted, and some areas that were difficult to be determined in the velocity map were well grouped through the hot spot analysis. For example, the deformation boundary at TS2 is fused and difficult to distinguish in the descending deformation map (Figure 7) because of the existence of two adjacent deformation areas on the slope. Nevertheless, it could be grouped into two different deformation areas after the cluster analysis (Figure 9b). This implies that the hot spot analysis is highly applicable for the potential landslide identification.  
Figure 6: Ground deformation velocity along the LOS direction obtained from the ascending orbit SAR images.  
Figure 10 shows the potential landslides identified by the cluster and buffer analyses, combining the ascending and descending data sets. A total of 99 deformation patches with more than 3 MPs at each patch were obtained and the total, mean, and standard deviation of the patch areas were 15.68 km\({}^{2}\), 0.158 km\({}^{2}\), and 0.284 km\({}^{2}\), respectively. These patches were mainly distributed along the rivers, accounting for approximately 80% of the total deformation patches and the largest patch appeared at TS1 with an area of 2.24 km\({}^{2}\).
# Enhanced K-Nearest Neighbor for Intelligent Fault Diagnosis of Rotating Machinery
## 4 Fault Diagnosis Case Investigation Utilizing the Proposed E KNN
### Diagnosis Results and Comparisons
For convenience, health condition \"NO\" in the source dataset is denoted as \"1\", and the others are denoted similarly using the listing order in Section 4.1. Generally, this presents that most of the features belonging to each class can be separated from those belonging to other classes in Figure 9a,b, and Figure 9b can gather the features from the same health condition more closely, which confirms that SF can extract highly discriminative features from inputs. Figure 7: Correlation vectors between the target sample and the training samples. **(a)**: NO, **(b)**: IF02, **(c)**: IF06, **(d)**: IF12, **(e)**: OF02, **(f)**: OF06, **(g)**: OF12, **(h)**: BF02, **(i)**: BF06, **(j)**: BF12  
To show the better performance of EKNN, we also calculated the precision and recall of the result. We can first obtain True Positive (TP), False Positive (FP), False Negative (FN) and True Negative (TN). Then, precision and recall can be calculated via  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{6}\]  
Figure 4:0  
The results using KNN and EKNN are listed in Tables 1 and 2, respectively, where the number of samples for each class is 200, and they are the overall results of 5 tries. As shown in Tables 1 and 2, it is very clear that the performance using EKNN outperforms that using KNN almost in all classes. It can also be observed that EKNN can prevent misclassification more effectively than KNN, namely, FP and FP values of EKNN are much smaller than those of KNN. Figure 4:1 The confusion matrices using original k-nearest neighbor (KNN) method and the proposed method: (**a**) the results of KNN and (**b**) the result of enhanced k-nearest neighbor (EKNN). Figure 4:2 Scatter plots of Principal Components (PCs) for the features learned in the bearing dataset: (**a**) features learned from raw samples; (**b**) features learned from SF. We also compared the executing time between EKNN and traditional KNN. As they are all training-free classification methods, we can obtain the executing time using only one sample. We denoted KNN using Euclid distance as KNN1 and denoted KNN using Euclid distance and Cosine distance as KNN2. Two distance evaluation matrices in KNN2 were combined with identical weight. The executing times of EKNN, KNN2 and KNN1 when classifying 200 were around 20, 15 and 10 s, respectively. Therefore, for each sample, EKNN, KNN2 and KNN1 need 0.1, 0.075 and 0.05 s, respectively. Although the executing time of EKNN is a little longer than that of KNN, the time cost is very beneficial due to the better performance of EKNN. On the other hand, the time length of the sample is 0.156 s, which is longer than the executing time using EKNN. Therefore, EKNN can also satisfy the requirement of online fault diagnosis, which benefits future online fault diagnosis well.