esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 5 Rockburst Prediction Methods
### Microseismic Monitoring Techniques  
The first seismic network for recording mining tremors in a coal mine was established in Upper Silesia, Poland, in the late 1920s and had four power stations [177]. Microseismic monitoring systems for mining tremors have been developed in various countries, including South Africa, Canada, the US, Australia, and China [178]. Microseismic monitoring of stress waves can determine the timing and location of a mine tremor and the energy released. Most research focuses on identifying suitable geophysics or mathematical/statistical factors to predict rockbursts based on microseismic monitoring data [178]. Estimating the stress field in a rock mass is a difficult task, but microseismic monitoring of naturally occurring  
Figure 12: (**a**) Rockburst prediction accuracy from machine learning algorithms (modified from ref. [176]); (**b**) Machine learning approaches for rockburst prediction (Modified after ref. [11]).  
mine tremors provides a more precise waveform due to the broad energy bandwidth in the tremors' power spectrum.  
[PERSON] [90] reported that emissions and microseismic events are low-energy seismic events caused by sudden inelastic deformation, such as movement of existing fractures, production of new fractures, or propagation of fractures. These events occur in a specific rock volume and emit detectable waves. The main difference between acoustic emissions and microseismic events is that acoustic emission signals have higher seismic motion frequencies, as illustrated in Figure 13. Seismic waves emitted by rock microcracks can be collected by sensors and processed to determine the source parameters of microseismic events, such as time, position, energy, magnitude, and apparent stress. By analyzing the source parameters of multiple microseismic events, the damage and stress variation of rocks can be evaluated, enabling the prediction of rockburst occurrence, location, and intensity through in-depth microseismic data analysis.
# Optimization of Image Capture Distance for Facial Thermograms in Dentistry
## 1 Introduction
Therefore, the aim of the current pilot study was to evaluate the influence of the shooting distance on the detection of temperature asymmetries on the face to identify ideal shooting distances for capturing extraoral image views for the purpose of dental thermography. The study further aimed to identify whether the temperatures in the captured images were affected by the demographic factors and/or other miscellaneous influencing factors (such as use of personal effects, medications, treatments, consumption of food and beverages). Based on the study aims, the null hypothesis (H0) set for the current pilot study is that that there will be no effect of the different shooting distances on the mean overall temperature and the mean temperature at the defined region of interest (ROI) in the extraoral thermographic images of the face when the image capturing views are kept constant. The secondary hypothesis (H1) set was that the mean overall temperature and the mean temperature at the defined region of interest (ROI) in the extraoral thermographic images of the face are not influenced by demographic and other external factors.
## 3 System Model  
A typical implementation of a software update over-the-air architecture in a vehicle is shown in Figure 2. The OTA master residing in the ECU, usually the Telematics or Gateway modules within today's vehicles, is capable of connecting wirelessly to the repositories. The OTA master receives the software image from the repository and distributes it to the target ECU in the vehicle. A simplified view is shown in Figure 3 for the SOTA process carried out in several successive steps. There are four main actors in the system:  
* OEM Repository \"OemR\": This repository is responsible for signing software images. Given the role of OemR in common SOTA processes, OemR contains all of the information about images to be installed on ECUs, generally using an OEM inventory database containing information on vehicles, ECUs, software images, etc. Each soft
## 2 Downlink Beamforming Design and Uplink Power Allocation
### Methodology: Deep Learning-Based Downlink Beamforming Design and Uplink Power Allocation
#### 2.2.3 Training Procedure  
The constructed DNN is trained in a supervised manner. Specifically, let \(\mathbf{w}_{k}^{*}[n]\) and \(p_{k}^{*}[n]\) be the solutions to (P1\({}^{\prime}\)) and (P2\({}^{\prime}\)) for the \(k\)-th UAV, respectively. Then, for the training of the constructed DNN, we take the label as \(\mathbf{w}_{k}^{*}[n]\) in the downlink and \(p_{k}^{*}[n]\) in the uplink.  
For prediction with the minimum error, we select mean squared error (MSE) between the label and the output of the constructed DNN as the loss function as follows:  
\[\mathcal{L}(\theta)=\begin{cases}\frac{1}{N_{d}}\sum_{i=0}^{N_{d}}\|\mathbf{w }_{i}^{*}[n]-\hat{\mathbf{w}}_{\theta,i}[n]\|^{2},&\text{for the downlink}\\ \frac{1}{N_{u}}\sum_{i=0}^{N_{u}}\left(p_{i}^{*}[n]-\hat{p}_{\theta,i}[n]\right) ^{2},&\text{for the uplink}\end{cases} \tag{17}\]  
where \(\theta\) denotes the parameters of the constructed network. Additionally, \(N_{d}\), and \(N_{u}\) denote the number of training samples for the downlink and the number of training samples for the uplink, respectively.  
The weight update through the gradient descent is known to be effective and widely adopted [31]. The update rule of the gradient descent algorithm is described as follows:  
\[\theta_{t+1}\leftarrow\theta_{t}-\alpha\
abla_{\theta_{t}}\mathcal{L}(\theta_ {t}) \tag{18}\]  
where \(\alpha>0\) is the learning rate, which determines the step size of the update.  
For the UAV wireless communication, it is utmost important to realize good generalization behavior and fast convergence rate. Toward these goals, we adopt Nadam [31], which is a combination of nesterov accelerated gradient (NAG) [32] and adaptive moment estimation (Adam) [33] algorithm, in which the gradient can be expressed as  
\[\theta_{t+1}\leftarrow\theta_{t}-\frac{\alpha}{\sqrt{\hat{v}_{t}}+\zeta}( \beta\hat{m}_{t}+\frac{(1-\beta)\
abla_{\theta_{t}}\mathcal{L}(\theta_{t})}{1- \beta^{t}}) \tag{19}\]  
where \(\beta\), \(\hat{m}\), and \(\hat{v}\) denote the decay rate of the first momentum vector, the bias-corrected first momentum vector, and the bias-corrected second momentum vector, respectively. Additionally, \(\zeta\) is added to prevent the denominator from becoming zero (typically, set \(\zeta=1\times 10^{-6}\)).
###### Abstract  
The determination of small highly polar metabolites at low concentrations is challenging when reverse-phase (RP) chromatography is used for multiclass analysis. A mixed cationic-RP column coupled to high-resolution tandem MS (HR-MS/MS) was tested for highly polar compounds in biological fluids, i.e., trimethylamine N-oxide (TMAO) and the isobaric molecules beta-methylamino-L-alanine (BMAA) and 2,4-diaminobutyric acid (DAB). The efficient retention and separation of the above compounds were obtained with common and MS-friendly RP conditions, reaching high selectivity and sensitivity. The method was firstly assessed in plasma and urine, showing good linearity in the range 50-1000 ug/L and 500-10,000 ug/L for TMAO and both BMAA and DAB, respectively. Excellent precision (RDS \(<\) 3%) and good accuracies (71-85%) were observed except for BMAA in plasma, whose experimental conditions should be specifically optimized. Preliminary tests performed on compounds with biological relevance and a wider range of polarities proved the effectiveness of this chromatographic solution, allowing the simultaneous analysis of a larger panel of metabolites, from very small and polar compounds, like trimethylamine, to quite lipophilic molecules, such as corticosterone. The proposed LC-HRMS protocol is an excellent alternative to hydrophilic interaction liquid chromatography and ion-pairing RP chromatography, thus providing another friendly analytical tool for metabolomics.  
polar amino acids; mixed cationic-RP column; LC-HRMS; BMAA; TMAO
## 5 Experimental Setup and Results
### System Validation Criteria  
The communication system is validated through two stages. The first stage is to select the sign language dataset, which in this case are ASL, PSL, and SSL datasets. The second stage is to select a subset of these datasets. It is important to use a known input dataset and a predicted output result. This will help in understanding the system accuracy and limitations of the proposed system. Once the system is validated using these criteria, the full dataset is applied to further determine the performance of the system.  
It is important to use a known set of images for initial validation. Outside the scope of this work is a study of how sign language datasets are created and validated.  
To validate the proposed communication system, the ASL dataset images are used [46]. These are colored images of hand gestures. The SSL dataset used for validation is available online [47]. The PSL dataset is created by recording images of letters and numbers. A reference to PSL is also available from [48].  
Letters and numbers from the three sign language datasets are used for validating the communication system. For this research work, 70% of the hand gesture images from the datasets are used for training, while the remaining 30% are used for testing. The split of 70% and 30% is a good ratio for training and testing. However, a different ratio can also be used. As an example, approximately 1800 images of letters and 700 images of numbers from ASL are used, which are then split into the ratio mentioned before. Figures 7-9 show subsets of ASL, PSL, and SSL dataset images. Figure 7 shows five numbers and letters from ASL, while in Figure 8, eight letters from PSL are shown. In Figure 9, six letters from SSL are shown. The dataset used by the proposed system includes approximately 6000 hand gesture images from the three individual datasets.  
Figure 7: A subset of the American sign language dataset.
## 4 Results and Discussion
### Evaluation Metrics
#### 4.4.7 Communication Cost  
The communication cost is determined by the division of bytes from the local server to the global server with a normalization factor.
## 2 Methodology
### Distribution Probability Adjustment Confidence  
There is a predicted confidence index in the target detection results, which is the confidence level calculated and predicted by the model. The overall precision of the target detected on an image is high when the detection system chooses to use a high confidence threshold, but recall will decline. However, if a smaller confidence threshold is selected, the overall precision of the target detected on the image will decrease, and recall will increase. In practice, we often use the confidence level of 0.5 as the threshold to judge whether it is the right detection target [18]. The function of DPAC is to improve recall and AP without losing or losing small amounts of precision.  
DPAC usage method: First, obtain a matrix A [w \(\times\) h] that is the same size as the pixels in the original image through target distribution. Second, convert the distribution value in the distribution matrix into the target probability coefficient. Third, use only the predicted target center point multiplied by the coefficient of the corresponding position in the matrix during detection. This method is not very computationally intensive compared to image processing methods and requires little effort during original system detection.
# Study on the Minimum Operation Width of Human-Powered Bicycles for Safe and Comfortable Cycling
## 3 Materials and Methods
### Field Cycling Experiment Settings
#### 3.2.1 Tested Cyclists and Tested Bicycle  
Pretest Survey: To obtain the necessary test parameters, a pretest survey was conducted on the exclusive bicycle path in Beijing, which is one of China's most prominent off-street paths designated for human-powered bicycles and was opened to the public in 2019 (see Figure 2). It is also one of the most diverse locations in Beijing in terms of human-powered bicycle types.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cycling Safety and Comfort Level** & \(\Delta HRV\) \\ \hline Safe, comfortable & \(\Delta HRV\leq 19\) \\ A little stressful & \(19\) \(<\Delta HRV\leq 79\) \\ Stressful & \(\Delta HRV>79\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cycling workload threshold for different cycling safety and comfort levels.  
The survey was conducted during two typical rush hours (8:00-9:00 and 17:00-18:00) on a typical workday in June 2022, and it identified the following three predominant categories of bicycles: dock-less shared bicycles, private sport bicycles (including racing and mountain types), and private conventional bicycles. The usage distribution revealed that dock-less shared bicycles account for approximately 60% of total usage, while the combined usage of private sport and conventional bicycles represents the remaining 40%. The gender distribution among cyclists was 74% male and 26% female, and the average cycling speed on flat sections ranged from 10 to 24 km/h.  
Tested Cyclists: All participants were experienced cyclists, each with at least one year of cycling experience, in good health, and demonstrating typical riding responses. They had normal or corrected vision and no history of waist, leg, or cardiovascular conditions. The selection process aimed to reflect the gender distribution observed in the earlier survey. Consequently, 12 cyclists meeting these criteria were randomly selected, with an average age of 29.0 years (SD = 5.22), consisting of 9 males and 3 females.  
Typical Bicycle: The most prevalent type, the shared dock-less bike, was selected. Adhering to the principle of maximal representation, the largest size of shared bicycle commonly found in China was chosen, featuring a handlebar width of 620 mm, an outer wheel diameter of 610 mm, a total length of 1550 mm, and a handlebar height of 1000 mm from the ground (as depicted in Figure 3). Besides the popularity, another reason for selecting shared dock-less bicycles is their wider handlebars compared to those of privately owned conventional bicycles, rendering them particularly pertinent to bikeway width research. While private sport bikes may also feature wider handlebars, their accessibility to the general public is limited.
## 3 Methodology and Implementation  
### A Complex Network from a Visibility Graph  
The first future work path mentioned in [26] is the transformation of the time series created by IOTA and IoTeX transactions, i.e., the value transacted at each point of time, into complex networks using the visibility graph technique proposed by [PERSON] et al. [3]. The ultimate objective is to further analyse both IoT implementations. The number of transactions taking place in both networks since their inception render this proposal unrealistic if we consider a time series indexed per second as a time unit. A look at the IOTA explorer (Mainnet feed Section) [57] reveals time slots with several transactions, with zero and non-zero value IOTA token transfers, per second. Equally, in IoTeX explorer (right hand side column in its interface) [58], we see how there is a new block, including a handful of actions with zero and non-zero IoTeX token transfers, every few seconds. Alternatively, in this study, we analyse the visibility graphs created from IOTA and IoTeX tokens' daily price volatility information since day one of trading. Daily token price information is accessible and regularly and accurately registered. It acts as a valid proxy to describe how these two IoT markets behave. We analyse the complex network stemming from the volatility visibility graphs. Our conclusions carry several implications for blockchain-based IoT implementations using 5G mobile networks.
###### Abstract  
Introduction: Most of the wrist motions occur in a diagonal plane of motion, termed the dart-throwing motion (DTM) plane; it is thought to be more stable compared with movement in the sagittal plane. However, the effect of the altered corpus motion during DTM on the stress distribution at the radiocarbon joint has yet to be explored. **Aim:** To calculate and compare the stresses between the radius and two carpal bones (the scaphoid and the lunate) in two wrist positions, extension and radial extension (position in DTM), and between an adult and an elder model. **Methods**: A healthy wrist of a 40-year-old female was scanned using Magnetic Resonance Imaging in two wrist positions (extension, radial extension). The scans were transformed into three-dimensional models and meshed. Finite element (FE) analyses in each position of the wrist were conducted for both adult and elder models, which were differentiated by the mechanical properties of the ligaments. The distal surfaces of the carpal bones articulating with the metacarpals were loaded by physically accurate tendon forces for each wrist position. **Results**: The von Mises, shear stresses and contact stresses were higher in the extension model compared with the radial-extension model and were higher for the radius-scaphoid interface in the adult model compared with the elder model. In the radius-scaphoid interface, the stress differences between the two wrist positions were smaller in the elder model (11.5% to 22.5%) compared with the adult model (33.6-41.5%). During radial extension, the contact area at the radius-lunate interface was increased, more so in the adult model (222.2%) compared with the elder model (127.9%), while the contact area at the radius-scaphoid was not affected by the position of the wrist in the adult model (100.9%) but decreased in the elder model (50.2%) during radial extension. **Conclusion**: The reduced stresses during radial extension might provide an explanation to our frequent use of this movement pattern, as the reduced stresses decrease the risk of overuse injury. Our results suggest that this conclusion is relevant to both adults and elder individuals.  
aging; radiocarbon stresses; wrist injury +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
In this paper, a finite-time path following control algorithm has been presented for the underactuated MSV with external disturbances, model parametric uncertainties, position constraint and input saturation. Different from the existing literature, position error constraints, input saturation and finite convergence time are simultaneously considered in the control design. The FT-LOS guidance law is designed to obtain the desired yaw angle and simultaneously constrain the position error of the underactuated MSV. The finite-time constraint controllers are designed to achieve tracking control in finite time. Then, the finite-time disturbance observers are proposed to estimate the compound disturbance including the external disturbances and model parametric uncertainties. Additionally, the finite-time input saturation compensators are designed to avoid the actuator saturation and satisfy the requirement of finite-time convergence. The stability analysis shows that the proposed finite-time control algorithm can strictly guarantee the constraint requirement of the position, and all error signals of the whole control system can converge into a small neighborhood around zero in finite time. Eventually, comparative simulation results illustrate the effectiveness and superiority of the proposed finite-time control algorithm. In future work, we will consider the unknown sideslip angle and time delays in the underactuated MSV.  
Methodology, L.W.; Software, L.W.; Supervision, M.F.; Writing--original draft, L.W.; Writing--review and editing, L.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Nature Science Foundation of China under Grant 51879049.  
The authors declare no conflict of interest.  
Figure 10: The yaw moment \(\tau_{r}\) under the input saturation.
## 3 The Proposed Model
### Data Collection and Pre-Processing  
In this phase, the data are collected from various WBAN sensors. The WBAN in question contains many wearable sensor nodes, which are used to collect physiological data including systolic arterial blood pressure (ABPsys), pulse rate, respiration rate (RESP), heart rate (HR), mean arterial blood pressure (ABP-mean), oxygen saturation (SPO\({}_{2}\)), temperature, and diastolic arterial blood pressure (ABPdias), which are all measurements that should be taken into consideration when assessing a patient's condition. The Multiple Intelligent Monitoring in Intensive Care (MIMIC-I and II) dataset includes precise physiological data records taken from over 90 ICU patients, who are known as subjects. The dataset has seven features that represent the patient's clinical situation, which include mean arterial blood pressure (ABP-mean), oxygen saturation (SPO\({}_{2}\)), heart rate (HR), temperature, di  
Figure 1: The Proposed Model.  
astolic arterial blood pressure (ABPdias), respiration rate (RESP), systolic arterial blood pressure (ABPsys), and pulse, with the appropriate timesteps and date; this dataset was used for the purposes of this study [22]. Table 2, below, contains a sample of the sensor readings in the dataset.  
The dataset is first preprocessed and prepared for use with the deep learning models via the normalization approach, which reorders the dataset's values appropriately. The goal of normalization is to use a consistent scale to reorder the values of the dataset's numeric columns without distorting the ranges of the values or erasing data. Additionally, certain algorithms require normalization in order to correctly simulate the data [23]. For the purposes of this paper, each attribute in the dataset is normalized, as in Equation (1), in the range [0, 1]:  
\[x(i)=\frac{x(i)-\overline{x}}{S(x)} \tag{1}\]  
where \(x(i)\) is the dataset, \(\overline{x}\) is one column in the dataset, and \(S(x)\) is the number of data samples.