esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method of Predicting Critical Derailment Speed and Analysis of Derailment Process
## 4 Wheel-Rail Contact Analysis at Critical Derailment Speed  
### Wheel-Rail Contact Analysis Method  
In recent years, the wheel-rail elastic contact model has been widely used. The wheel-rail contact is a non-rigid contact; that different contact areas appear on the wheel tread should be considered. When multiple positions on the wheel tread are in contact with the rail at the same time, multiple contact spots can be generated [21].  
The wheel-rail elastic contact model can be divided into equivalent elastic contact and discrete elastic contact. The equivalent elastic contact converts the actual contact spot shape into an elliptical shape. The data of Hertz normal force and creep force can be obtained. The method of equivalent elastic contact calculates quickly, whereas the calculation results in a lack of accuracy. The discrete elastic contact uses the periodicity of the swept body to evaluate the size of multiple contact spots, and the discretization method is used to predict the actual shape of the non-elliptical contact area. The simulation runs slow, but the calculation result is more accurate. So, discrete elastic contact is used in this article.
# A Method for Underwater Wireless Data Transmission in a Hydroacoustic Channel under NLOS Conditions
## 4 Description of the Laboratory Stand  
The tests were carried out in a laboratory tank shown in Figure 4. The walls of the tank were smooth, made of polypropylene (PP) with a thickness of 10 mm. The internal dimensions of the tank were: 3.78 m \(\times\) 1.75 m \(\times\) 1.8 m. The water column was 1.6 m high. The tank was divided into four compartments to achieve NLOS conditions. Acoustic panels AKU-PRO 140 made of material with a density of 140 kg/m\({}^{3}\) and a thickness of 20 mm were used as partitions. The edge of the panels adjoined one of the tank walls and the bottom. The upper edge protruded above the water level. In this way, an artificial corridor was created, reflecting, for example, the interior of the wreck. On one side of the tank, in front of the compartments, a Reson TC4013 transmitting hydrophone was placed. The signal to this hydrophone was fed from a PC via a digital-to-analog NI USB6366 card and an Etec PA1001 power amplifier. Three Reson TC4014 hydrophones were placed on the receiving path. Their spatial configuration is shown in Figure 5. The hydrophones were powered by 12 V, and the signals were fed through the NI USB6366 analog-to-digital converter card to a PC, where they were recorded and processed. The software of both the transmitter and the receiver was implemented in the Matlab environment.  
The measurements were performed by locating the receiving hydrophones at 12 selected points, which are marked in Figure 4. At each point, 10 transmissions were made. In each transmission 10,000 bits were sent.  
Figure 4: Illustrative drawing of the water tank and measuring conditions.  
Figure 5: Illustrative drawing of the set of receiving hydrophones.
## 5 Simulation Results
### Experimental Results of Phase I
Results are presented in Figure 6. The system states represented as a combination of energy level \(L_{t}\in\{0,1,2,3,4\}\), event generation \(E_{t}\in\{0,1\}\), packet transmission rate \(PR_{t}\in\{0,1\}\) and link quality \(LQ_{t}\in\{0,1\}\) (as described in Equation (6)). Thus, system state \((4,0,0,1)\) implies \(L_{t}\) is 4, \(E_{t}\) is 0, \(PR_{t}\) is 0 and \(LQ_{t}\) is 1. It is observed from the outcome that the resultant utility values corresponding to each state vary significantly for these combinations due to variation in the values of (\(p_{on},p_{off}\)) pair although other probabilities remain constant which indicates the impact of event generation probabilities in resultant discounted utility values. Furthermore, since the variation in energy level subject to the event generation probability and the action performed (illustrated in Equations (10) and (11)), the resultant discounted utility values are found to be zero when the system is at very low energy level, i.e., \(L_{t}=0\). Hence, no reward is generated as there is insufficient energy to carry out any action (irrespective of events). Accordingly, the resultant utility values get larger corresponding to the system states with high energy levels and get maximized when \(L_{t}=4\). In addition, similar pattern is observed for these representative combinations of probability values which mark two different horizon of event generation process. Hence, the obtained strategy, i.e., the optimal sequence of actions to be performed corresponding to each system state if incorporated into the nodes at the time of deployment, the system will work effectively. Measurable performance could be obtained irrespective of event generation probabilities when the other probabilities related to the PR and LQ are at favorable conditions. Figure 5: Performance of MDP with varying discount factor for different combinations of probability values related to event generation P (\(p_{on},p_{off}\)), PR (\(pr_{on},pr_{off}\)) and link quality LQ (\(lq_{on},lq_{off}\)). Next, finite horizon function is performed in R [34] with formulated P and R matrix along with the number of iterations obtained from previous value iteration process as input to acquire optimal policy which indicates the end of pre-deployment phase as well.
## 4 Discussion
This finding opens the possibility of fine-tuning the absorption spectrum shape. As with SBHs, the mean absorption coefficient increases with decreasing rib thickness. Moreover, increasing the number of ribs with a fixed thickness reduces the absorbing performance, as the active surface of the MPP is reduced. Nonetheless, the presence of ribs in the structure is crucial, as they allow for the formation of a locally compliant metamaterial wall with suitable properties. Similar to SBHs, the acoustic black hole effect plays an important role in the low-frequency absorption in MPP-SBHs. In conclusion, SBHs and MPP-SBHs are promising sound-absorbing structures for waveguides with rectangular cross-sections, offering advantages over conventional treatments involving fibrous materials. We hope that this study will contribute to their wider adoption in practical applications. Conceptualization, M.C. and M.B. ; methodology, M.C. and M.B. ; software, M.C. ; validation, M.C. ; formal analysis, M.C. ; investigation, M.C. ; writing--original draft preparation, M.C. ; writing--review and editing, M.C. and M.B. ; visualization, M.C. ; supervision, M.B. ; project administration, M.B. ; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript. The authors are grateful for the financial support they received from GACR grant GA22-3896S. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors upon request. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
ABH Acoustic black hole  
FEM Finite element method  
LNSE Linearized Navier-Stokes equations  
MPP Micro-perforated plate  
SAS Sound-absorbing structure  
SBH Sonic black hole  
VBH Vibrational black hole
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 3 SC-CNN-BiLSTM-Based Signal Detection for UWA-OTFS
### Training of the Proposed SC-CNN-BiLSTM Neural Network  
In the training stage, the number of training examples is chosen through trials. We start with a small number of examples and increase the number of examples until the SC-CNN-BiLSTM training tends to be stable. SC-CNN-BiLSTM learned the mapping  
Figure 4: LSTM structure.  
relationship between the received vector and the corresponding transmitted vector. After training, the SC-CNN-BiLSTM can be used for signal detection. In the testing stage, the transmitter generates random information bits, modulates the bits by OTFS and transmits the OTFS signal over the UWA channel to the receiver. The receiver utilizes the trained SC-CNN-BiLSTM to detect symbols in the DD domain for data recovery.  
The performance of a neural network depends greatly on the training process. First, the loss function should be reasonably designed to provide an accurate measure of the distance between the outputs and true labels. The training process aims to minimize the difference between the original transmitted data sequence \(x(b)\) and the signal detection output \(\hat{x}(b)\) through the deep learning model. In this study, we define the loss function \(L(loss)\) as  
\[L(loss)=\frac{1}{N_{B}}\sum_{i=1}^{N_{B}}{[\hat{x}_{i}(b)-x_{i}(b)]^{2}}, \tag{17}\]  
where \(N_{B}\) is the batch size and \(x_{i}(b)\) represents the bits in the \(i\)th batch.  
In addition, the hyperparameters related to the network structure and training will affect the capabilities of neural networks. The learning rate affects the convergence rate and results of the DL network. The adaptive learning rate strategy is employed, which can avoid being trapped in the local optimum. In our training, the initial learning rate was set to 0.001, and the decay factor was set to 0.1.  
For training optimizer selection, we compared the performance of three typical optimizers: the stochastic gradient descent (SGD) optimizer, adaptive momentum (Adam) optimizer and root mean square propagation (RMSprop) optimizer. The test was conducted with an OTFS dataset that went through an experimental channel. As shown in Figure 5, with the SGD optimizer, the loss of the proposed neural network did not converge well during training process. The convergence results of the Adam optimizer were better than those of the SGD optimizer with much lower loss, and the convergence of the RMSprop optimizer was the best. Therefore, our proposed SC-CNN-BiLSTM employed the RMSprop optimizer for training.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements  
The deployment of wireless motes comes with its own requirements and challenges because of the significant differences between the office environment and the industrial environment. For example, in the industrial environment, the deployed wireless network must have the ability to support the low latency with secure data communication in critical processes. For inherent safety, these networks must possess a high fault-tolerance capability and have highly reliable data transmission in order to meet the industrial requirements [92; 93]. Additionally, low-power wireless motes need to be developed, since they have to operate for more extended periods between major turnarounds considering the harsh conditions [94]. In the future, the implementation of non-conventional energy-powered mote installation is anticipated to increase in industrial and process automation because it dramatically reduces power blackouts, avoids the problem of battery replacement, and has a smaller carbon footprint [27].  
The deployed WirelessHART network should be aware of unpredictable environmental parameters such as temperature, moisture, gas level, pressure, and vibrations in the environment where the field devices are located. Wireless mote signals could be severely  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref.** & **Process** & **Field Device** & **T** & **ST** & **C** & **Mo** & **Challenges** & **Results** \\ \hline \multirow{9}{*}{[87]} & \multirow{4}{*}{Transfer function of thermal process} & Linear tech Smart Mesh & & & & & & New controller performance analysis over a variable network delay, external noise, and process dead-time \\  & & & Smart Mesh WirelessHART & & & & & Latency, Data & Comparative study of WirelessHART and wired Foundation \\  & & & & & & & Signal & Fieldbus for valve control \\  & & & & & & & & & \\ \cline{1  
delayed due to various circumstances, such as interference with multiple frequency bands, reflections from the surrounding walls, external noise, signal attenuation due to leaked gases, and vibrations produced by heavy machinery [95; 96]. Most of the problems stated above will affect their deployment because a minor interruption can make the network less reliable and may lead to catastrophic failure in the process. Other major influencing parameters in the WirelessHART network are briefly discussed in the following.
# Structure, Biology, and Therapeutic Application of Toxin-Antitoxin Systems in Pathogenic Bacteria
## 4 Structural Characteristics of TA Proteins from Pathogenic Bacteria
### \(\varepsilon\)-\(\zeta\)  
The crystal structure of the \(\varepsilon\)-\(\zeta\) complex from _S. pyogenes_ shows a heterotetramer (\(\varepsilon_{2}\)-\(\zeta_{2}\)) in which the antitoxin \(\varepsilon\) is folded into a three-helices bundle and that the toxin \(\zeta\) is characteristic of \(\alpha/\beta\) structure with the central twisted \(\beta\)-sheet flanked by \(\alpha\)-helix (Figure 7A) [68]. The toxin \(\zeta\) is structurally similar to a phosphotransferase. Consistently, mutagenesis studies showed that the toxin \(\zeta\) behaves as a phosphotransferase by using ATP/GTP. The toxicity is inhibited when the \(N\)-terminal helix of the antitoxin \(\varepsilon\) occludes the ATP/GTP-binding site of the toxin \(\zeta\). The phosphorylated product inhibits MurA [67], a protein that is essential for the initial step of peptidoglycan synthesis in bacteria. Therefore, cell membrane integrity is damaged, resulting in an autolytic phenotype. PezT and \(\zeta\) toxins share 42% sequence identity and are structurally highly similar [140]. In addition, the C-terminal region of PezA shares sequential and structural homology to the \(\varepsilon\) antitoxin, although the \(N\)-terminal helix-turn-helix (HTH) motif of PezA functions as transcription repressor similarly to the \(\omega\) protein. Similar to the \(\varepsilon\)-\(\zeta\) complex, the PezAT complex forms a hetero-tetrameric structure in a binding stoichiometry of two PezA and two PezT (Figure 7B). PezA sterically blocks the nucleotide binding site of PezT [140]. The tetramer recognizes a palindrome sequence that overlaps the promoter. Mutagenesis experiments demonstrated that the toxicity of PezT requires a phosphoryl transferase active site and an ATP/GTP-binding site.  
Figure 7: Structural comparison of \(\varepsilon\)-\(\zeta\) and PezAT proteins from _S. pyogenes_ and _S. pneumoniae_. (**A**) Structure and binding interface of the \(\varepsilon\)-\(\zeta\) complex from _S. pyogenes_. The residues involved in the \(\varepsilon\)-\(\zeta\) interaction are indicated on the right side. (**B**) Structure of the PezAT complex from _S. pneumoniae_.
## 4 Discussion
### Objective 1--Part 2: Data Processing Parameters  
After gathering quality images for each site, the next step was to engage in data processing. This involved correcting image coordinates collected by the post-processing kinematic (PPK) sensor during flight before synchronizing those coordinates with their associated image in photogrammetric processing software to produce georeferenced orthomosaics. Because the datasets were captured within the state of Indiana, or just across the border into Michigan or Ohio, the sites fell within the 16 th zone of the Universal Transverse Mercator (UTM) projected coordinate system. This allowed each site to be corrected to the WGS 84 UTM Zone 16 North spatial reference, which has an estimated error of just 2 cm ([PERSON], 2014) (Table 9).  
At the orthomosaic generation stage, the photogrammetric processing parameters diverged slightly. The prairie sites performed well with the default parameters, often achieving image calibration numbers of 99% or better (Table 11). On the other hand, the timber harvest sites required more data manipulation to produce quality orthomosaics, particularly when the images were collected in adverse conditions (Table 11). Motion blur, overexposure, dark shadows, and/or too few images collected outside the study area proved to be problematic for the orthomosaic generation of the timber harvest sites. To salvage these datasets, the keypoint image scale (KPIs) was reduced from \"1\" to \"1/2\", thus reducing the number of tie-points the software would look for at one time and, therefore,performing better mosaicking than with the default KPIIS [66]. Additionally, the calibration method was changed from \"standard\" to \"alternative\", for which the back-end procedure is not publicly available. However, this parameter likely reduced the number of images discarded if enough keypoints were not identified, thus increasing the number of images kept in the resulting orthomosaic.
## 3 Results  
### Comparative Accuracy of Smartphone and Mirrorless Camera Images-Assisted Digital Shade Guides  
Mean Color Difference (\(\Delta\)E\({}_{00}\))  
A total of 29 images were captured, each using a mirrorless camera and a smartphone camera. The comparison of mean L\({}^{\star}\), a\({}^{\star}\), and b\({}^{\star}\) values among SC-DSG, MC-DSG, and spectrophotometer is shown in Figure 4. Multiple pairwise comparisons revealed that MC-DSG had significantly higher mean L\({}^{\star}\) values than SC-DSG (\(p\) = 0.024) and higher a\({}^{\star}\) values than the spectrophotometer (\(p\) = 0.013). The comparison of \(\Delta\)E\({}_{00}\) between MC-DSG and SC-DSG relative to the spectrophotometer is shown in Figure 5. SC-DSG exhibited significantly lower mean \(\Delta\)E\({}_{00}\) values as compared to MC-DSG (\(p\) < 0.001). When compared to PT and AT, MC-DSG exceeded both AT and PT for \(\Delta\)E\({}_{00}\) values. In contrast, SC-DSG showed color differences that remained within acceptable limits, with its \(\Delta\)E\({}_{00}\) values close to the AT of 1.8.  
Figure 4: Mean values of L\({}^{\star}\), a\({}^{\star}\), and b\({}^{\star}\) of MC-DSG, SC-DSG, and VITA Easyshade V. The error bars indicate the mean \(\pm\) 1 standard deviation. \({}^{\rm m}\) significant difference in L\({}^{\star}\) between MC-DSG and SC-DSG, \(p\) < 0.05. \({}^{\rm n}\) significant difference in a\({}^{\star}\) between MC-DSG and VITA Easyshade V, \(p\) < 0.05.  
Figure 5: Mean and SD values of the CIEDE2000 color difference (\(\Delta\)E\({}_{00}\)) between both MC-DSG and SC-DSG with VITA Easyshade V. The error bars indicate the mean \(\pm\) 1 standard deviation. The dashed line indicates the perceptibility (PT) threshold (\(\Delta\)E\({}_{00}\) = 0.8), while the solid line marks the acceptability (AT) threshold (\(\Delta\)E\({}_{00}\) = 1.8).
# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
## 4 Movement Monitoring System Architecture
At the Cloud level, the Thing5 peak platform has been used in which both the user interaction and higher-level data analyses occur over the span of the rehabilitation process. Both real-time and long-term process data and event monitoring over the overall rehabilitation cycle also take place. The key functionalities occurring at the Cloud level include a gateway device communication HTTP protocol for data transmission from Raspberry Pi (acting as a gateway) to the Thing5 peak Cloud, a Cloud data repository comprised of a health and knowledge data repository. The support of the data available within the Cloud repository is computed using Matlab analytics based on FFT signal processing, machine learning, and artificial intelligence techniques to display the high-level movement information with precision. Moreover, various knowledge components related to the monitoring and personalization of the subject's movement behavior and logically incorrect movement activity classification, rehabilitation progression model event detection, and the creation of various screens for data visualization also occur at the Cloud level. In addition to this, Thing5 peak has a built-in Cloud trigger reaction for a follow-up or emergency purpose that can be sent to the required personnel in the form of either a text message, an emergency alarm, or an email. Therefore, the information available in the Cloud could be made available and tailored according to key user interaction with various parties such as the patient, caregiver, physiotherapist, clinician, and nurse--as each of these play their role in contributing towards the improvement and accomplishment of the patient's rehabilitation and recovery goals. Figure 2: IoT enabled with wearables hip fracture rehabilitation movement monitoring system architectural design highlighting operational functionalities at three different levels (wireless sensing; IoT edge; and Cloud level).
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 3 Evolution of Industrial Maintenance
### Predictive Maintenance  
Predictive maintenance (PdM) is a recent preventive maintenance approach that consists of improving the performance and efficiency of the manufacturing process by increasing the life span of equipment and ensuring sustainable operational management. This implies, on the one hand, a decrease in downtime and the number of unnecessary stops, accompanied by a reduction in repair costs, by offering the possibility of interventions through the prediction of failures [26]. Intelligent predictive maintenance strategies are now being implemented by manufacturing companies. This implementation is done by evaluating the remaining life of the elements responsible for the failure and by allowing the remote and real-time monitoring of equipment failures. The latter need detection and diagnosis in order for the equipment to function properly.  
Predictive maintenance is the latest form of maintenance, offering the longest life and highest reliability of equipment (shown in Figure 4), and the most environmentally sound and cost-effective solutions [27]. The maintenance method used to troubleshoot by going down to the source is called proactive maintenance. This maintenance method, which is very effective when applied in conjunction with predictive maintenance, is becoming increasingly popular [28].  
Figure 3: Evolution of maintenance activities and methods.