esa-sceva/satcom-chunk-collection huggingface dataset:
# Raw Material Flow Rate Measurement on Belt Conveyor System Using Visual Data
## 4 Experimental Evaluations
### Results & Discussion
In addition to this error analysis on each test video sequence, we also present the actual and estimated flow rates achieved by our machine learning models on sample test video to show the relative impact of this error. Figure 23 illustrates the performance of the trained models, based on the defined parameters, on a test video 4. In this specific case, the flow rate value for the example was 1637 kg/min, and through the complete procedure, the nearest model predicted its value to be approximately 1639 kg/min. We note from the experimental evaluation that the percentage error is as low as 7% which shows that the proposed framework is reliable in predicting the flow rate of raw material on the belt conveyor system. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **MAPE** & **RMSE** \\ \hline XGBoost & 16.938 & 443.814 & 0.011 & 21.067 \\ Decision Tree & 19.750 & 535.750 & 0.012 & 23.146 \\ Random Forest & 19.753 & 535.976 & 0.012 & 23.151 \\ Bagging Regressor & 16.160 & 274.602 & 0.010 & 16.571 \\ Gradient Boosting & 26.247 & 1112.44 & 0.016 & 33.353 \\ Gamma Regressor & 14.290 & 415.210 & 0.009 & 20.377 \\ Bayesian Ridge & 12.148 & 322.544 & 0.007 & 17.960 \\ RANSAC & 16.938 & 443.814 & 0.011 & 21.067 \\ Theil-Sen & 12.131 & 322.424 & 0.007 & 17.956 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of different machine learning models. Figure 23: Flow rate prediction using different machine learning (ML) models versus the actual flow rate. Figure 22: Absolute error between the estimated and the actual flow rates (kg/min) on test dataset.
## 4 Results and Discussion
### Elemental Composition  
The concentrations of trace elements of the samples are listed in Table 2. The average concentration of Au in soils above the concealed ore body (S506, S507) was 74.8 ppm; relative to Chinese soil, the enrichment coefficient of S506 is 28.6 and S507 is 84.7; the overall enrichment coefficient of the two samples is 56.6 (Figure 3i). The average concentration of Au in soils around the concealed ore body (S508, S509) was 1.86 ppm; the enrichment coefficient of S508 and S509 is 1.4; the overall enrichment coefficient of the two samples is 1.4 (Figure 3i). The average concentration of Au in fault muds is 227.61 ppm; the enrichment coefficient of FS01 is 78 and FS02 is 267; the overall enrichment coefficient of the two samples is 172 (Figure 3i). The average concentration of Au in ores is 224.34 ppm; the enrichment coefficient of R01 is 324 and R02 is 15.7; the overall enrichment coefficient of the two samples is 170 (Figure 3i).  
Moreover, the average concentrations of Au were significantly higher than those reported for soil from China by [PERSON] et al. (2007) [PERSON] et al. (2007), and other elements, such as W, Pb, Zn, and Cu are not significantly enriched in this area. At the same time, the enrichment coefficient of the ore-forming element Au decreases from ores to fault muds to soils.
# Three-Dimensional Stopping Sight Distance Calculation Method under High Slope Restraint
## 6 Conclusions and Future Work  
S.S.D. is the key to the safety of braking when the driver encounters obstacles in the process of driving, which is closely related to the safety of national life and property. Therefore, the research on accurate calculation of sight distance is of great significance.  
* This article fully considers the actual driving position of the car, the driver's apparent height, side ditches, and slopes and other actual road driving conditions and establishes a relatively realistic calculation model. Based on the calculation method proposed by highway design parameters, the precise S.S.D. value on the plane is first calculated, and then the height difference between the driver's viewpoint and the end of the sight line is calculated, and finally the 3D stopping sight distance value is obtained through the principle of horizontal and vertical combination.
* In this paper, the S.S.D. check of actual secondary roads based on the design speed and operating speed is carried out. The results show that the sight distance evaluation result obtained by the calculation method proposed in this paper is consistent with the result of the H.S.O. method.
* The radius of the horizontal curve is the main factor that affects the sight distance of the turning section, while the radius of the vertical curve has a small influence and can be ignored in the actual calculation. That is to say, the plane sight distance can be used to replace the 3D stopping sight distance in vertical curve section.
* Since the calculation method in this paper is sight distance calculation of points, the following research extends point calculation to sight distance calculation of the entire curve through software programming.  
Conceptualization, Y.Y. and J.W.; methodology, J.W.; software, Y.X.; validation, J.W., Y.X. and L.H.; formal analysis, Y.Y.; investigation, L.H.; resources, L.H.; data curation, L.H.; writing--original draft preparation, J.W.; writing--review and editing, J.W.; visualization, Y.X.; supervision, Y.X.; project administration, Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant Number 51508204) and the State Key Laboratory of Subtropical Building Science (Grant Number 2018 ZB32) and was sponsored by 2020 Characteristic Innovation Projects of Ordinary Universities in Guangdong Province (Grant Number 2020 KTSCX009) and was sponsored by the Key Laboratory of Highway Engineering of the Ministry of Education, Changsha University of Science & Technology (Grant Number kfj190201) in China.  
The authors are grateful for the support of the project panel, project team, and all persons who helped with the research.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 2 Passive Constant Flow Regulators--General Considerations
### Modeling  
The modeling of the passive constant flow regulator can be based on analytical models [7; 22; 23], but in a general way, 3D simulations using finite element methods are required to model the fluid-structure interactions (FSI) and the nonlinear behavior of membranes or flaps under large deformations. Numerical simulations are also useful to investigate the impact of machining tolerances on flow rate accuracy [9]. Specific tools using a genetic algorithm have been made to increase the yield in production by design optimization. This method was used to design a regulator with a flexible Si membrane that deflects against pillars. In addition to standard machining tolerances, other non-idealities have been introduced into the model, like wafer misalignment or valve tilt (due to particle contamination for instance) to increase the design robustness [22; 24].  
A passive flow regulator is usually part of a complex microfluidic system. In addition to these quasi-static analyses of the fluid flow in fixed pressure conditions, further simulations shall be carried out to calculate the fluid dynamics in a system that can include microchannels, check valves, micropumps, pressure sources, etc. As a passive flow regulator induces nonlinear effects, the equations governing the fluidic behavior of such systems generally have no analytical solutions. A classical approach consists of building an electrical equivalent network based on a subdivision of the system into lumped elements and the analogy between fluidic, mechanical, and electrical parameters [25; 26]. The impedance of each element is estimated beforehand. For a passive flow regulator with an integrated check-valve, its electrical analog is a diode in series with a variable resistor. The analysis of the dynamics of complex systems can then be carried out very quickly with an electrical simulation tool. This method was chosen to simulate the dynamics of a hydrocephalus shunt comprising a passive flow regulator to divert CSF from the brain ventricles to the peritoneal cavity [27]. The ventricles are equivalent to a constant current source that produces about 20 mL of CSF every hour. The numerical model allowed the determination of the time evolution of intracranial pressure during patient postural changes, changes in the CSF formation rate, oscillations due to the vasogenic system, etc., together with a direct comparison with other shunt designs. Dynamic simulations based on electrical equivalent networks are particularly useful during the design phase to determine the behavior of the passive flow regulator. For a complex system such as the hydrocephalus flow control valve, the model can be further refined by the introduction of experimental data [28].
# Triaxial Compression Performance Research of Steel Slag Concrete on the Unified Strength Theory
## 3 Test Results and Discussion
### Failure Criterion
The results are shown in \[(3+\beta-2b\beta)\frac{rho}{\sqrt{2}}\cos\theta+\sqrt{3}(1-\beta+2b)\frac{\rho} {\sqrt{2}}\sin\theta+f_{2}(\xi)=0\ \ \ \ \ \theta_{b}\leq\theta\ \ \leq 60^{0}, \tag{1b}\]4  
For Equation (4a-f), there is \(\frac{n_{0}}{m_{0}}=\frac{3-\beta}{3+\beta}\), so:  
\[(3+\beta-2b\beta)\frac{rho}{\sqrt{2}}\cos\theta+\sqrt{3}(1-\beta+2b)\frac{\rho} {\sqrt{2}}\sin\theta+f_{2}(\xi)=0\ \ \ \ \ \theta_{b}\leq\theta\ \ \leq 60^{0}, \tag{1b}\]5  
The ultimate strength expression of the failure surface can is as follows:  
\[(3+\beta-2b\beta)\frac{rho}{\sqrt{2}}\cos\theta+\sqrt{3}(1-\beta+2b)\frac{\rho} {\sqrt{2}}\sin\theta+f_{2}(\xi)=0\ \ \ \ \ \theta_{b}\leq\theta\ \ \leq 60^{0}, \tag{1b}\]6 \[(3+\beta-2b\beta)\frac{rho}{\sqrt{2}}\cos\theta+\sqrt{3}(1-\beta+2b)\frac{\rho} {\sqrt{2}}\sin\theta+f_{2}(\xi)=0\ \ \ \ \ \theta_{b}\leq\theta\ \ \leq 60^{0}, \tag{1b}\]7 (6b) \[(3+\beta-2b\beta)\frac{rho}{\sqrt{2}}\cos\theta+\sqrt{3}(1-\beta+2b)\frac{\rho} {\sqrt{2}}\sin\theta+f_{2}(\xi)=0\ \ \ \ \ \theta_{b}\leq\theta\ \ \leq 60^{0}, \tag{1b}\]8 \begin{table}
\begin{tabular}{c c c c c c c} \hline
**No** & \(m_{0}\) & \(m_{1}\) & \(m_{2}\) & \(n_{0}\) & \(n_{1}\) & \(n_{2}\) \\ \hline SSC-30 & 0.119101 & \(-\)0.647975 & \(-\)0.0121243 & 0.19794 & \(-\)1.07837 & \(-\)0.0121243 \\ SSC-70 & 0.119322 & \(-\)0.651706 & \(-\)0.0139721 & 0.197478 & \(-\)1.08024 & \(-\)0.0139721 \\ SSC-100 & 0.119839 & \(-\)0.660695 & \(-\)0.0133628 & 0.196045 & \(-\)1.08237 & \(-\)0.0133628 \\ \hline \end{tabular}
\end{table} 
Table 4: Values of \(m\) and \(n\) for SSC under triaxial compression. Substituting \(\beta\) into Equation (6a-c) the limit expression of the failure surface can be obtained. As shown in Figure 7, the tensile strength, meridian, and strength envelopes in the deviatoric plane of SSC fit well with the experimental data. In Figure 7, \(\xi\) stands for hydrostatic stress, \(\rho\) stands for deviatoric stress. When the \(\sigma_{2}\) increases, the shear strength also increases, and the trend of the meridian curve for SSC is approximately the same. Steel slag as coarse aggregate is well combined with concrete, but the steel slag replacement rate of 100% has an attenuating effect on the meridian. It can be seen from Table 5 that the theoretical value \(\rho^{\prime}\) calculated by the unified theory is close to the experimental value \(\rho^{exp}\). The relative error \(E\) reflects the difference between the computation data and the experimental data and was computed using \((\rho^{\prime}-
## 1 Introduction
Clinical data are widely used to predict the development and progression of myopia, but digital imaging remains primarily focused on detecting and classifying high and pathologic forms of myopia [16; 17; 18]. Recent research has addressed this issue by creating AI-driven models to forecast, categorize, or identify myopia in its non-pathological form, a vision condition frequently encountered by eye care professionals in everyday practice. Some of these initiatives began by utilizing retinal fundus images obtained with an FC to estimate the dioptric value of myopic refractive error [19] and to assess the five-year risk of progression to high myopia [20]. Creating, evaluating, and validating AI-driven software for medical use requires a thorough understanding of programming and algorithm design, skills that are uncommon among most healthcare professionals. To overcome this challenge and enhance accessibility, we utilized an online application programming interface (API) that incorporates machine learning (ML) technology. This method enables individuals with basic computer skills to train and develop a high-quality model [21]. For the first time, we are focusing on developing a computer-assisted model that can effectively identify myopic eyes using retinal fundus photographs, without the need for coding skills or additional clinical data. This approach allows us to improve the accuracy and efficiency of screening programs, providing tangible solutions for better patient outcomes. The contributions of this study are as follows:  
* We created a dataset of labeled fundus images from myopic and healthy Caucasian eyes, which can be used for ML and computer vision purposes;
* We tested the new computer vision model YOLO version 8 and its online API, both of which play a crucial role in our work by enabling the development of an ML model without the need for coding expertise;
* We investigated the model's performance in classifying myopic eye based on a single retinal fundus image without additional clinical information. The study is structured as follows: Section 2 reviews related work on the use of the YOLO architecture and briefly discusses previous research using YOLOv8. Additionally, it presents in details two notable works by different authors [19; 20] that focused on myopia using fundus photographs. Section 3 outlines the proposed methodology, dataset preparation, and describes the characteristics of YOLOv8. The findings are presented in Section 4. Section 5 discusses our study and provides conclusions.
## 3 System Overview
### Proposed Hybrid Framework  
The proposed hybrid system can be implemented at home, the clinic, the ambulance, or other places. The system operates wherever a patient must be remotely monitored. The number of surveillance cameras in a large-sized room can be high. Multiple patients can be monitored inside the room, thus also confirming the convenience of using multiple cameras. Figure 2 illustrates the monitoring system topology. The camera or BLE receiver collects the data sent from the patch. These collected data are processed to retrieve the original signal, then passed to an eHealth gateway for remote transmission. Several gateway architectures have been proposed [53; 54; 55]. The data are stored in the eHealth server, which can be accessed from the eHealth database thereafter. Finally, they are transmitted to the remote monitor.  
The cameras are assumed to operate by the rolling shutter technique. These rolling-shutter-based cameras are the most popular owing to their worldwide availability and reasonable cost. When a NAR is generated in the patch circuit, the data are transmitted via OCC or BLE (the OCC network is accessed for the initial data transmission). When there is link blockage and high outage probability, the access point for a new NAR switches to BLE. The initial selection or access handover to BLE depends on the position and mobility of the patient.  
Figure 1: Data acquisition procedure from the patch. OCC: optical camera communication.
# Relationship between Subjective and Biological Responses to Comfortable and Uncomfortable Sounds
## 2 Methods
### Outline of the Experiment  
Eight healthy males with normal hearing in their twenties were selected as the subjects of the experiment. It should be noted that in this research the relationship between the subjective and biological response on sound representation was assessed. So, the subjects were university students in their twenties. However, it should also be noted that, ideally, subjects should be selected from a large population of various ages, if one wants to determine the effect of the subjects' age on their interpretations. The method of the experiment is shown in Figure 1. After a one-minute period in which the subjects familiarized themselves with the experimental environment, they had a four-minute pre-resting period, listened to a five-minute-long auditory stimulus, and then had a three-minute post-resting period, for a total of 12 min. After completing the experiment, a subjective evaluation of the subjects' impressions of the presented sounds was conducted. To verify the reproducibility of this experiment, the experiments were repeated three times under the same experimental conditions as shown in Figure 1b. To prevent the disturbance of brain waves and heart rates caused by visual stimuli as much as possible, the experiment was conducted in a dark room, shown in Figure 2a. In addition, to avoid artifacts such as muscle potential and eye movement, each subject was instructed to relax in a seated position with their eyes closed during the experiment. To prevent fatigue, all three experiments were conducted with a 30 min break between each trial.  
Figure 1: Experimental processes (**a**) for each of the trials and (**b**) for the entire experiment.  
Two types of stationary auditory stimuli were used: white noise and the murmuring sound of a river. Hereafter, the white noise condition is indicated as \"SA\", while the murmuring river condition is indicated as \"SB\". The sounds were listened to using in-ear headphones (Etymotic Research, ER-4s). The reason for using in-ear headphones was that they were not expected to interfere with the wearing of an EEG headset. In addition, ER-4s were chosen since they have a frequency response that is as flat as possible. The A-weighted equivalent sound pressure levels (\(L_{\text{Aeq}}\)) of the reproduced sounds were set to 60 dB for both sounds.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Vibration Video Data Collection
#### 2.1.1 Data Compilation of Stay Cables  
Based on the basic cable information of the entire bridge, including project name, cable number, cable length, mass per unit length, flexural stiffness, etc., a standard form of cable data was created and shown in Table 2. Based on the standardized compilation of cable information, a waterproof QR code was generated, linked and attached to the corresponding stay cable, as shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **References** & **Sensors/Devices** & **Error** \\ \hline \multirow{3}{*}{Sensor-based} & [PERSON] et al. [21] & An iPhone with built-in accelerometer & Max. 4.711\% \\ \cline{2-5}  & [PERSON] et al. [27] & MEMS-based acceleration sensors & 1\% \\ \hline \multirow{6}{*}{Image-based} & [PERSON] et al. [15] & \multirow{2}{*}{A DSLR camera} & 1.09\% (Single point) \\ \cline{3-5}  & & & 3.05\% (multi-point) \\ \cline{2-5}  & [PERSON] et al. [16] & A video camera & Max. 5.4\% \\ \cline{2-5}  & [PERSON] et al. [17] & A vision-based monitoring system & Max. \(\pm\)5\% \\ \cline{2-5}  & [PERSON] et al. [19] & A fixed industrial camera & Max. 5.6\% \\ \cline{2-5}  & [PERSON] et al. [20] & A UAV-based camera & Max. 3\% in lab \\ \cline{2-5}  & [PERSON] et al. [24] & A smartphone camera & Max. 3.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary on previous studies for cable force determination.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Project Name** & **Cable Number** & **Cable Length** & **Line Density (kg/m)** & **Elastic Modulus (Pa)** & **Inertia Momento (m\({}^{4}\))** \\ \hline Zhuankou Yangtze & S1 & 143.538 & 77.8 & \(1.95\times 10^{8}\) & 2.69 \(\times 10^{-6}\) \\ River Bridge & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Standard form of stay cable data.
## 5 Discussion and Analysis
### Security Analysis  
mobiDOM contains both a malware detector and a data repairer component. The data repairer component does not have any security concerns as it runs after the malware has been detected and removed. Therefore, we focus on the security analysis over the malware detector component in the following.  
There are three major components in the malware detector: the TApp, the MDetector and the communication messages between the TApp and the MDetector (Via the CA). The security of the TApp is ensured by ARM TrustZone secure world. Without being able to compromise the TrustZone, the adversary is not able to compromise the TApp even if it can compromise the OS. In addition, the adversary will not be able to identify the existence of the TApp in the TrustZone secure world, since the TApp simply writes/reads data to/from the external flash storage (via the CA), which is pretty regular for any trusted applications running in the TrustZone. The security of the MDetector is ensured by the FTL. Due to the isolation of the FTL at the hardware level, the adversary will not be able to compromise the FTL, even if it can compromise the OS. Therefore, the malware will not be able to notice the existence of MDetector let alone compromise it.  
The communication messages between the TApp and the MDetector can be protected as analyzed in the following. First, their confidentiality can be ensured by staying hidden among regular I/Os via steganography. Specifically, the secret command messages from the TApp to the MDetector (START, STOP, QUERY) are hidden in the regular write requests on the external storage, which will be invisible to and, hence, unnoticeable by the adversary. Inaddition, the response messages are conveyed back from the MDetector to the TApp via read delays, and since the delay time is a one-time secret value, the adversary cannot interpret anything from such delays, which can be plausibly denied as occasional system delays (considering the delays in mobiDOM only happen when starting/stopping the malware or having detected the malware). In addition, the existence of CA will not give the adversary any advantage of inferring the existence of mobiDOM since it is pretty common for the TrustZone-based applications to have CAs running in the normal world to communicate with TAs located in the TrustZone secure world. Second, the integrity of the communication messages can be ensured. If the adversary modifies or replays the messages sent from the TApp to the MDetector, the MDetector can easily detect it since the secret command messages cannot be extracted successfully; if the adversary delays the communication messages sent from the TApp to the MDetector or the read responses from the MDetector to the TApp, the TApp can notice it considering the actual delay time in mobiDOM is a one-time secret value. Note that we do not consider DoS attacks in which the adversary blocks the communication messages or I/Os.