esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Analysis of the Device
### Superconducting Magnetic Bearings (SMB)  
Contactless bearings based in both permanent magnets and superconducting magnetic levitation are of interest in order to avoid all the tribological problems, such as friction, wear, or energy losses [21; 22; 23; 24; 25; 26] associated with contact at very low temperatures [12]. In addition, there is no contact between the moving parts, so lubrication is not required. SMB find applications in many fields where lack of contact is a requirement or an advantage, such as in flywheel systems and transportation [27], cryogenic turbine flow meters, cryocoolers, and sensitive gyroscopes for space applications, among other applications for cryogenic and space mechanisms [28; 29; 30; 31; 32; 33; 34; 35]. Recently, different shapes, sizes, and combinations of magnet-superconductors have been explored in order to obtain more stable levitation positions [36; 37; 38; 39; 40; 41]. There are some models that are useful to describe this interaction and they can be applied in finite elements programs, [42; 43; 44]. Some other parameters, such as the force relaxation or the rotation losses, have to be considered from the point of view of the mechanical engineer for an adequate design of the SMB [45; 46; 47; 48].  
A full mechanical experimental characterization of the mechanical properties of the SMB was done [49]. SMB maximum axial and radial load, stiffness, and force relaxation were characterized. Then, the total length of the SMB for input and output was defined in order to fulfill the requirements given by the magnetic gear instability analysis. The required radial stiffness also depended on the arm-distance to the magnetic gear position, shown in Figure 3. The mechanical requirements for the input axle were 50 N/mm of  
SMB were designed considering YBaCuO as a superconducting material and NdFeB N50 as a permanent magnet. The air gap between static and rotatory parts was 2 mm for the input axle and 1 mm for the output axle.  
A safety factor of 2 was considered in the final design of the SMB. This implies larger SMB elements (60 mm for input and 120 mm for output). Although MAGDRIVE SMB were oversized for safety reasons, SMB in general have much lower stiffness and load capacity than a mechanical ball bearing one. On the other hand, they do not have any friction or wear even at low temperatures, and, as explained, that was the reason for their selection.
# Metrics for Evaluating Synthetic Time-Series Data of Battery
## 2 TimeGAN  
TimeGAN [10] is a time-series data augmentation algorithm that incorporates temporal characteristics into the GAN algorithm. The GAN algorithm comprises a generator and a discriminator. Its basic structure is shown in Figure 1. Synthetic data similar to the original data are generated by using random noise as the input to the generator, whereas the discriminator distinguishes between the original and synthetic data. This algorithm can be expressed as show in Equation (1).  
\[min\;_{G}max\;_{D}V(D,G)\;=\;E_{x\;P_{data}(x)}[logD(x)]+E_{Z\;P_{Z}(z)}[log(1-D (G(z)))] \tag{1}\]  
where \(D\) represents the discriminator that classifies the original data and synthesized data of the battery, \(G\) represents the generator that generates virtual battery data, \(E\) denotes the expected value, and \(V(D,G)\) denotes the value function of the GAN algorithm. The GAN algorithm learns along the direction in which the value function is maximized, that is, along the direction in which it is difficult to distinguish between the synthesized and original battery data. When discriminator \(D\) determines that the original battery data \(x\) is True and the synthesized battery data \(G(z)\) is False, the value function is maximized.  
Based on this working principle, TimeGAN, which adds functions to generate battery data by learning the temporal characteristics of data based on the GAN algorithm, has a framework consisting of an autoencoder and a GAN, as shown in Figure 2. The autoencoder rapidly learns the features of the input data by reducing the data to a lower dimension, whereas the GAN generates and evaluates the synthetic battery data. Therefore, data reflecting both the static and temporal (dynamic) characteristics of the battery data are generated by reconstructing the reduced dimensions and generating data.  
Figure 1: GAN architecture.
# Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges
## 3 Proposed Fatigue Crack Detection Model Based on Sematic Segmentation
### Classification Network ResNet50_crack  
(1) Initial dataset for classification network  
The original image dataset includes 120 pictures with a size of 4928 \(\times\) 3264 pixels or 5152 \(\times\) 3864 pixels. A total of 100 images are randomly selected for training, while the remaining 20 images are used for testing the network. Generally, the image size has a certain impact on the training results of the deep learning model [13; 14]. Subsequently, these large images and ground truths are cropped into samples with a size of 224 \(\times\) 224 pixels, resulting in 32,128 small image samples and corresponding ground truths. However, most of these samples are intact and less informative for crack detection. For improving the computation efficiency of the training process, these intact samples can be removed from the dataset. Therefore, pixel variances in samples are evaluated and sorted, and then the 60% of samples with the smallest variance are discarded. Subsequently, samples are classified into crack and background datasets according to their ground truth. This process is schematically shown in Figure 3a.  
(2) Image screening based on active learning  
The concept of active learning [11; 12] originally referred to the case of semi-supervised learning (that is, a part of the data would be labeled, with the remaining parts unlabeled), where the algorithm could actively select the samples that were more informative and representative. These samples were artificially labeled and then added to the dataset for training. This paper adopts this idea of active learning to screen the most important samples (namely, those with larger cross-entropy) for re-tuning the classification network ResNet50_crack. The specific process is as follows, as shown in Figure 3b.  
Figure 2: Fatigue crack detection model.  
Training classification network ResNet50_crack  
Dataset 2 in Figure 3c is randomly divided into two sets, namely, the training and validation sets. This ensures that the training set and the validation set are consistent in distribution. The fully connected layers of ResNet50 for 1000 categories are modified for 2 to accommodate this crack classification. The random gradient descent method is adopted to train the network, with an initial learning rate of 0.001, a maximum epoch number of 20, and a mini-batch-size of 32.  
The tuning process for the classification network to obtain ResNet50_crack is depicted in Figure 3.  
Figure 3: Tuning process of classification network ResNet50_crack. (**a**): dataset construction; (**b**): active learning; (**c**): tuning process for ResNet50_crack.
## 7 Conclusions  
In this paper, we identified and described the different factors to be taken into account when simulating warning message dissemination in VANETs. Since the number of possible factors can be very large, we identified the representative factors by using the \(2^{k}\) factorial analysis. The purpose is to reduce the required simulation time in future research works.  
The key factors affecting the delivery of warning messages were found to be the radio propagation model, the density of vehicles, and the roadmap used. Some other factors, such as the broadcast scheme used, the channel bandwidth, and the priority and the periodicity of messages, did not have a significant impact on the metrics considered in our study. We believe that the results of our analysis can save researchers' time by discarding unnecessary factors when performing simulations for VANET-related research.  
Results obtained from our simulations confirmed that the selected roadmap is a crucial factor. In fact, performance parameters such as warning notification time, the percentage of blind vehicles, and the number of packets received per vehicle highly depend on it. To further reduce the scope of warning message dissemination tests made in real cities, we propose and evaluate a scenario classification based on three roadmap profiles, and consider that researchers must carefully determine the scenarios to assess their proposals, ideally picking at least one scenario for each profile type.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 3 Proposed Methodologies
### COVID-19 Infection Detection Using Deep Learning
#### 3.3.1 ReLU Activation Function  
The Rectified Linear Unit (ReLU) is a popular activation function commonly used in deep learning models, including Convolutional Neural Networks (CNNs). It plays a vital role in introducing non-linearity to the network, enabling it to learn complex representations from the input data [23].  
The ReLU activation function is defined as half rectified, meaning that when the input vector \(x\) is less than zero, the output \(f(x)\) is set to zero, and, when \(x\) is greater than or equal  
Figure 3: Convolutional Neural Network architecture.  
to zero, the output \(f(x)\) is equal to the input \(x\). The mathematical representation of the ReLU activation function is shown in Equation (1).  
\[f(x)=\max(0,x) \tag{1}\]  
ReLU activation serves several critical purposes in the CNN architecture for COVID-19 detection from chest X-ray images. Firstly, it introduces non-linearity, allowing the model to learn and represent complex patterns and relationships in the data. This non-linearity is essential for capturing the intricate features that distinguish COVID-19 infections from normal cases in the X-ray scans.  
Secondly, ReLU activation helps mitigate the vanishing gradient problem that can occur in deep neural networks during back-propagation. The vanishing gradient problem hampers the learning process by causing the gradients of the lower layers to become extremely small, resulting in slow or stalled learning. By setting negative values to zero and only propagating positive gradients during forward and backward passes, ReLU activation helps maintain more informative gradients, speeding up convergence and improving training efficiency.  
Moreover, ReLU activation has been empirically found to be less computationally expensive than other activation functions, such as sigmoid and tanh. The simple and efficient nature of ReLU makes it a preferred choice for deep learning models, contributing to faster training times and lower memory consumption.  
Overall, the ReLU activation function is a critical element in the CNN architecture for COVID-19 detection, enabling the model to learn powerful representations from the chest X-ray images and improving the overall accuracy and performance of the detection system.
## 5 Conclusions  
In this study, a fiber optical sensor based in a microstructured optical fiber has been proposed and demonstrated for first time for soil moisture measurements. The optical sensing head is based on a Fabry-Perot cavity made of a microstructured optical fiber with a SnO\({}_{2}\) sputtering thin film acting as sensitive layer and it was temperature and humidity characterized in a climatic chamber before its installation in soil. A comparison between a commercial capacitive FDR and the optical sensor has been carried out in two different types of soils during two weeks. The optical interrogation of the MOF-FP sensing head was carried out through the FFT technique, which avoids noise and amplitude variations' influence and enables multiplex several sensors within a single optical interrogator's channel. Results show a good agreement between capacitive and optical measurements in the range 15-100% humidity and also match with gravimetric analysis. For humidity below 15%, the optical sensor's sensitivity decreases significantly. Soil moisture can be measured with MOF-FP sensors with the advantages that they offer in comparison with traditional capacitive sensors: immunity to temperature variations (compensated), point measurements (against field measurements), and high multiplexing capability.  
These particular characteristics lead to a new set of local applications that capacitive sensors are unable to attend, such as monitoring the soil moisture distribution (radial distance) in a local irrigation point performing measurements each centimeter. Moreover, due to the versatility of the sensing heads to be customized depending on the target application, different sensing heads with different purposes (such as dissolved pollutants, volatile organic compounds, solid or dissolved ferromagnetic elements can be detected by the correctly selection of the deposited chemical agent) can be multiplexed within the same optical interrogator, reducing the relative economic cost of the system and leading to complex real time (also high sampling rate applications) multiparameter monitoring schemes.  
[PERSON] performed the experiments and wrote the paper; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] designed and deposited the SnO\({}_{2}\) sensitive layer; [PERSON], [PERSON], and [PERSON] conceived and designed the experiments and analyzed the results; [PERSON], [PERSON] and [PERSON] designed and fabricated the MOF.  
Funding: This work was supported by the Spanish Government projects TEC 2016-76021-C2-1-R, TEC2016-78047-R, TEC2016-79367-C2-2-R, Innocampus and the Cost Action MP 1401, as well as to the AEI/FEDER Funds.  
The authors are grateful to [PERSON] and [PERSON]. We also thank the Spanish Government projects TEC 2016-76021-C2-1-R, TEC2016-78047-R, TEC2016-79367-C2-2-R, Innocampus and the Cost Action MP 1401, as well as to the AEI/FEDER Funds. Also, the authors thank to the Agricultural Engineering School for sharing the Hydraulic Laboratory.  
The authors declare no conflict of interest.
# Whole-Body Motion Planning for a Six-Legged Robot Walking on Rugged Terrain
## 3 Results  
### Simulation  
To validate the whole-body planning method, a simulation platform, consisting of a patch of rugged terrain and a six-legged robot, is built, as shown in Figure 5. The size of the rugged part is \(400\times 800\) mm. The maximum peak height is about 178 mm (height of the robot torso in initial state is roughly 170 mm). The black balls in the figure correspond to rotational joints of the robot. In this simulation, the robot is commanded to walk across the rugged terrain patch along a given walking path using the alternating-tripod gait [7, 22, 23, 24].  
To implement the walking simulation, a set of footholds are given manually, according to the given walking path as well as the current position and pose of the robot. The green balls in Figure 5 correspond to those given footholds. The maximum foothold height is 80 mm while the minimum height is roughly 10 mm. On this basis, the proposed planning method is employed to produce whole-body motion of the six-legged robot. In specific, for each walking step, the six-dimensional trajectories of the robot torso are planned according to the strategies in Section 2.2. The results are shown in Figure 6. It can be seen from the torso pitch curve that the robot first performs the hill-climbing movement and then the down-hill movement, which is also consistent with the given terrain geometry. From the torso roll curve, it can be seen that obvious oscillations occur in the middle of the walking process, and this is mainly due to the larger height differences of the given footholds in these walking steps. In addition, the relatively large change in the lateral direction of the robot torso is to maximize the stability margin of the robot. The foot trajectories of swing legs are generated using the scheme in Section 2.3. After that, the planned trajectories are converted to the joint space through inverse kinematics, as shown in Figures 5 and 7 which depict the consecutive snapshots of the robot walking. By monitoring the joint positions in simulation, it can be seen that the robot maintains a proper kinematic margin during walking, thereby ensuring a good flexibility and ample room for the adjustment of robot states.  
Figure 5: Simulation platform of the six-legged robot, and consecutive snapshots of the robot walking over rugged terrain.  
Figure 6: Motion planning of the robot torso.
## 3 Link Level Simulation Model for Interference Evaluation in 5G UDN with LAB
### SIR Dependence on the Angular Separation of Two gNB-UE Links in 5G UDN with LAB
#### 3.2.2 Mathematical Model for SIR Dependence on the Angular Separation  
The calculation of the signal-to-interference ratio in the downlink is performed for each pair of values from the set of parameters \(d\) and \(\alpha\). The SIR is calculated as the ratio of the power, received by the UE, being SOI, from gNB, transmitting in beamforming mode in its direction, to the power, received by this UE, from gNB, transmitting in beamforming mode in the neighboring UE direction. In this model, the SIR value reflects the ratio of the AA gains of two beams, formed by one gNB with antenna array in two given directions. Thus, SIR does not take into account FSPL, since according to scenario, both UEs are at the same distance \(d\) from the gNB.  
The matching of the coordinate systems of the UE and gNB AA is performed according to (1)-(5). The calculation of the azimuth and elevation angle, that define the gNB\(\rightarrow\)UE direction, is performed using (6)-(8). The angles, at which gNB sees the UE, are denoted as \([\varphi_{gNB_{s}},\theta_{gNB_{s}}]\). The calculation is carried out for one UE, let's designate it as UEs.  
Calculation of the AA gain in a given direction \([\varphi,\theta]\), taking into account the vector of AA steering coefficients \(\mathbf{w}_{a}\) in (9)-(12) and the antenna array element amplitude-phase distribution vector \(\mathbf{s}_{a}\) in (9)-(12), is performed according to (13). From (13), gNB AA beam gain \(g_{s}\) for UE\({}_{\text{S}}\) and gain \(g_{i}\) for neighboring UE in the UE\({}_{\text{S}}\) direction are calculated as:  
\[g_{s}(\varphi_{eNB_{s}},\theta_{eNB_{s}})=|\mathbf{w}_{as}\mathbf{s}_{a}(\varphi_{eNB_ {s}},\theta_{eNB_{s}})|^{2}; \tag{19}\]  
\[g_{i}(\varphi_{eNB_{s}},\theta_{eNB_{s}})=|\mathbf{w}_{as}\mathbf{s}_{a}(\varphi_{eNB_ {s}},\theta_{eNB_{s}})|^{2}; \tag{20}\]  
where \(\mathbf{w}_{as}\) is the vector of steering coefficients for the gNB AA beam pattern, directed to UEs; \(\mathbf{w}_{as}\) is a vector of steering coefficients for gNB AA beam pattern, directed to the neighboring UE. The signal-to-interference ratio for UEs, expressed in dB, is defined as:  
\[SIR=10\log_{10}(g_{s}(\varphi,\theta))-10\log_{10}(g_{i}(\varphi,\theta)). \tag{21}\]
## 3 Key Findings
### What Security Enhancement Could the Quantum Mechanism Offer the IIoT Ecosystem?
#### 3.3.1 Concept of Quantum Mechanism
Also, the potential capability of the quantum for promising uncompromising-digital-sovereignty, national security, and sustainable industrial competitiveness [100] received the attention of the United States National Quantum Initiative Act of 2018, for the approval of a sum of $1.275 billion for a 5-year initiative to expedite quantum research and development. As a result of its amazing return on investment and promising future, funding earmarked in 2019 and 2020 exceeded the budget set by Congress [101; 102]. Similarly, according to the Quantum Business Report, private investors, such as venture capital financiers, are reported to have expended a total of US$2.2 billion in research and development assistance since 2017. And only in January 2019, QC startups attracted an investment of US$147 million [7]. Table 5 further illustrates the effects of the quantum principle approach in security compared to the conventional computing approach. Table 5 shows the state of conventional cryptographic algorithms in the pre- and post-quantum deployment eras. Though cryptologists dared take an advanced approach against quantum-based attacks--post-quantum cryptography--all public-key cryptography types were broken in the wake of QC technology. Another instance is the requirement of larger key sizes by the majority of symmetric key types to cope with the capability of quantum deployment.
# A Multidimensional Data Collection and Edge Computing Analysis Method
## 3 Overall System Architecture and Improved Target Detection Model
### Data Collection  
Firstly, raw data is retrieved from image sensors and other sensors. And data of different nodes are differentiated based on their unique identity. In the process of video collection, due to the limitation of the capacity of the collection terminal, the method of alternate collection of video and pictures is adopted. The process of data acquisition differentiates image-based data from non-image-based data to facilitate consecutive multidimensional data fusion analysis.
## 3 Results  
### Statistical Analysis Results  
The correlation coefficients between each parameter are shown in Figure 1 as a heatmap, with the bottom column of the heatmap representing the correlation between the objective and explanatory variables. Postoperative electrophysiological severity was positively correlated with preoperative DML and preStage (correlation coefficient: \(p\)-value = 0.52: 3.85 \(\times\) 10\({}^{-30}\), 0.40: 9.08 \(\times\) 10\({}^{-18}\), respectively) (Figure 1a). The postoperative CTSI-JSSH-SS change correlated positively with the preoperative CTSI-JSSH-SS score (correlation coefficient: \(p\)-value = 0.83: 1.33 \(\times\) 10\({}^{-106}\)) (Figure 1b). The postoperative CTSI-JSSH-FS change correlated positively with the preoperative CTSI-JSSH-FS and CTSI-JSSH-SS score (correlation coefficient: \(p\)-value = 0.87: 2.47 \(\times\) 10\({}^{-133}\), 0.48: 2.71 \(\times\) 10\({}^{-25}\), respectively) (Figure 1c).  
Figure 1: _Cont_.  
According to the results of the x-square test using Table 2, the postoperative electrophysiological severity was significantly improved compared to the preoperative value. (\(p\) = 1.0 \(\times\) 10\({}^{-4}\)). According to the results of a paired \(t\)-test pre- and postoperative CTSI scores, the mean CTSI-JSSH-SS was 2.52 \(\pm\) 0.85 preoperatively and 1.33 \(\pm\) 0.51 at one year postoperatively, indicating a statistically significant decrease. (\(p\) = 2.54 \(\times\) 10\({}^{-76}\)). The CTSI-JSSH-FS score was 2.40 \(\pm\) 0.96 preoperatively and 1.42 \(\pm\) 0.63 at one year postoperatively, showing a statistically significant decrease (\(p\) = 2.97 \(\times\) 10\({}^{-60}\)). In a logistic regression analysis comparing the mild and moderate, and severe electrophysiologic severity groups at one year postoperatively, gender, age, and preoperative DML were statistically significant variables (\(p\) = 2.60 \(\times\) 10\({}^{-5}\), \(p\) = 2.99 \(\times\) 10\({}^{-6}\), \(p\) = 3.26 \(\times\) 10\({}^{-11}\), respectively). In a multivariate analysis of changes in CTSI-JSSH-SS scores before and after surgery, preoperative CTSI-JSSH-FS and preoperative CTSI-JSSH-SS scores were statistically significant variables (\(p\) = 1.31 \(\times\) 10\({}^{-8}\), \(p\) = 0.022, respectively). In a multivariate analysis of pre-and postoperative CTSI-JSSH-FS changes, preoperative CTSI-JSSH-FS, age, and preoperative SCV were statistically significant variables (\(p\) = 1.59 \(\times\) 10\({}^{-76}\), \(p\) = 6.65 \(\times\) 10\({}^{-7}\), \(p\) = 0.026, respectively). The t-values for each item in the multiple regression analysis are summarized in Table 3 as an indication of the contribution of each item in predicting prognosis.  
Figure 1: Heatmap of the correlation coefficients between each parameter. The bottom column of the heatmap represents the correlation between the objective and explanatory variables. Warm colors show a strong positive correlation and cold colors have a strong negative correlation.