esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Interpretation and Results
### Evaluation of the Borehole Mineral Water Quality  
#### 6.4.1 Physical and Chemical Properties  
The borehole water is colorless, tasteless, and transparent, with a color range of 0 to 5, and turbidity between 0 to 0.5. The water quality is pure and the taste is genuine. Based on the current water quality analysis and historical data, the mineral water in this area has been exploited for over 30 years since 1986. The silicic acid content ranges from 37.96 to 56.27 mg/L, all meeting the standards for drinking mineral water in China. The pH ranges from 6.8 to 7.87 and the hardness is between 8.3 to 8.69 degrees of German hardness, indicating it is moderately hard water, mainly composed of calcium, magnesium, and sodium bicarbonates. The bicarbonate content ranges from 233.89 to 534.72 mg/L, calcium from 10.4 to 124.99 mg/L, sodium from 11.69 to 36.51 mg/L, magnesium from 2.81 to 43.56 mg/L, and total dissolved solids from 127 to 570 mg/L.
## 6 Conclusions  
The presented work contributed to the formulation of a decision-making method based on STPA hazard analysis and Markov decision processes in order to provide a hazard assessment monitoring tool during the operational phase of the system. We interpreted the STPA outcomes into a partial observable Markov decision process by associating a group of system's states with the respective control actions conceptually clustered based on their context. The partial observability compensates system uncertainties stemming from noisy sensors measurements and stochastic human behavior errors. A policy graph is computed based on which, given the current observation, the system probabilistically selects the next action that brings it to a safer state level. We performed STPA in a human-robot collaborative task and associated the safe, unsafe and recover states with the respective group of actions to formulate the respective POMDP model, which has been assessed in a simulation environment and proved adequate to identify loss and success scenarios given the provided observations. Contrary to the current state of art methods that employ STPA analysis as a passive tool that can identify the potential loss scenarios in a system, we introduced an STPA-based decision-making tool that can be utilized in the operational phase in order to prompt the system to select an action that will bring it to a safer level, thus avoiding hazardous situations. An obvious limitation of our method is the need to completely define the state space of the examined system and manually associate each state with any safe or unsafe control action. Moreover, even if the method aims to turn STPA into a decision making tool, so as to monitor the studied system, the complete STPA analysis should be firstly conducted in order to define the unsafe control actions.  
Conceptualization, A.Z. and I.K.; methodology, A.Z., I.K. and I.D.; formal analysis, A.Z.; writing--original draft preparation, A.Z.; writing--review and editing, A.Z., I.K., I.D.; visualization, A.Z.; supervision, I.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 3 Handshake Performance Measurements
### Network Stack Configuration
#### 3.2.2 TSCH Schedule  
Until now, the experiments use a schedule at 100% duty cycle and a single-hop network, see Table 1. All slots are either allocated for transmission or reception. Figure 5 shows the handshake duration when the network uses a more realistic duty cycle, and it is up to two hops deep. The MSF algorithm maintains the schedule. We choose a conservative DTLS timeout to prevent spurious retransmissions when lowering the duty cycle of the nodes. For both TLS and DTLS, we maximize the handshake message size, and we activate [PERSON]'s algorithm for TCP. For the single-hop setup, the latency of both the TLS and DTLS handshake is quite similar. Below a 25% duty cycle, the delay induced by the TCP acknowledgment packets becomes more significant. When we repeat the same experiment with a two-hop network, the behavior is similar and amplified.
# Design and Experiment of Discharge Control Methods for Three-Stage Coil Gun Experiments
## 2 Coil Gun Circuit Design
Although a smaller distance generates a larger electromagnetic force, it also causes an increase in the electromagnetic force acting in the opposite direction that the projectile travels when passing the center of the solenoid coil. According to the simulation results, the maximum velocity of 33.7 m/s was observed at the distance of 5 mm where the magnitude of the velocity decrease is small. Finally, the muzzle velocity is higher when the decreases in velocity are smaller. In other words, the trigger position affects the shape of the electromagnetic force received by the projectile, and accordingly, the shape of the projectile acceleration varies. Thus, the projectile position is a key design factor that affects the overall performance of a coil gun. In particular, in a multi-stage coil gun system, the discharge system must be designed to be capable of controlling the electric circuit according to the position of the moving projectile because when the high-speed moving projectile reaches the intended position, the next stage of the coil gun must be in operation. Before the fabrication of coil gun prototypes, a coil gun system was designed using simulation. The system design involved finite element analysis using the electromagnetic analysis program ANSYS Maxwell. A multi-stage coil gun system with three stages and a final velocity of 63 m/s was used. Table 1 shows the coil winding and capacitance of the simulated coil gun. The charging voltage of the capacitor was set at 400 V. The three-stage coil gun design showed a decrease in the winding amount and capacitance when the number of  
Figure 3: (**A**) Velocity of the projectile, (**B**) force acting on the projectile. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & **Diameter** & **Number** & **Capacitance** & **Initial** & **Final** & **Velocity** \\  & **of Copper** & **of Turns** & **(\#F)** & **Velocity** & **Velocity** & **Increase** \\  & **Wire (mm)** & **of Turns** & **(\#F)** & **(\#N)** & **(\#N)** & **(\#N)** \\ \hline
1 & 1.3 & 26 \(\times\) 9 & 3000 & 0 & 33.7 & 33.7 \\
2 & 1.3 & 26 \(\times\) 7 & 2400 & 33.7 & 51.8 & 18.1 \\
3 & 1.3 & 26 \(\times\) 6 & 2400 & 51.8 & 63.2 & 11.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Solenoid coil turns, capacitance, and increases in the launcher velocity in simulation. stages increased because the initial launcher velocity of the projectile increases with the higher stages. For efficient acceleration according to the increased initial velocity, the cycle of the RLC function should be shortened so that the discharge time of the capacitor decreases. Accordingly, the coil gun was designed with fewer turns of the electromagnetic coil and lower capacitance of the solenoid coil to reduce the resistance of electric circuit and inductance.
## 3 Multi Infeed High Voltage Direct Current-Based WAMPAC Control
### MIDC Vulnerable Point According to WAMPAC Platform  
Technically, as seen in Figure 6, each level of data processing may be impacted by malicious and unintended cyber accidents. Figure 7 illustrates the idea that cyberattacks might undermine the WAMPAC platform [23]. The two most frequent threats are Denial-of-Service (DoS) and False Data Injection (FDI) attacks. So, at this point, the emphasis of this study is focusing modeling on those attacks.  
DoS attacks could be enforced against the broad area platform that links each PDC which is located at substations and control centers. Even though DoS attacks cannot alter the measurement sensor interpretation included in PMU messages, this type of attack could result in significant time delays, packet losses, or even communication breakdowns by creating a large number of connection requests that overwhelm the target device's processing power. Due to these circumstances, isolated PMUs depend on WAMPAC applications that affect HVDC controllers and cause them to operate less effectively or even negatively.  
The data may be intercepted and altered by an FDI attack to accomplish the attacker's goals. For instance, the measurements at the substation level collection may be immediately modified before being sent to PMU. Once delivered to PDC, the PMU phasor data may also be altered. Similar to this, control instructions created by WAMPAC applications and communicated across local and wide area networks may also be changed to directly impact the functioning of the control.  
Figure 7: Illustration two types of DoS attack.
## 2 Materials and Methods  
### Materials  
This analytical study was approved by the Institute Ethical Committee (IEC-09/2021-2119) of the Postgraduate Institute of Medical Education and Research, Chandigarh, India. The research team collected 1653 radiographic images of 670 dentofacial patients and 983 healthy individuals for the study. The images were categorised based on pre-defined morphological shapes of ST: circular, flat, oval, and bridging. The 450 images were considered for the circular, oval, and bridging shapes, and 303 were flattened. Further, based on the complexity of these structures, we sub-grouped the Sella shapes into regular ST, moderately altered ST, and complexly altered ST to study the morphological characteristics of this important structure.  
To ensure privacy and anonymity for participants, radiographs were randomly chosen without considering any personal information, such as age and gender. The cephalometric radiographs were captured using the Carestream Panorex and followed standard procedures.  
Pre-Processing  
This study utilised image processing techniques to improve the quality of cephalometric radiographs and acquire more comprehensive information on the Sella Turcica (ST). The process involved several steps to enhance grayscale medical images. Firstly, the image resolution was adjusted to 512 px, and a weighted moving averaging filter was applied to eliminate noise [27]. This filter used a 3 \(\times\) 3 weighted grid size combined with the original radiographs. To further enhance the image's details, we modified the contrast ratio using the transformed intensity of the image [28]. Lastly, the Sobel operator enhances the edges of the image by calculating the gradient along the 'y' and 'x' dimensions [29; 30]. We then obtained a sharper image with increased edge clarity by computing convolution using the original image [31]. Lastly, we performed a negative transformation using the enhanced image as a reference for manual labelling (annotation). These pre-processing techniques resulted in reduced training complexity and more accurate analysis of cephalometric radiographs.  
A team consisting of a radiographic specialist and two dental clinicians utilised a cloud-hosted artificial intelligence platform named Apeer to meticulously annotate images pixel-by-pixel, as shown in Figure 3[24]. This platform allows for the precise storage and annotation of images with high resolution. The process involves saving segmented portions with high accuracy following rough calculations. Then, a precise binary mask is generated by assigning sub-pixel values to the chosen boundary pixels and applying pixel-level morphological operations through border modification. Apeer's workflow involves re-annotating unmarked pixels, filling the image background with a region-filling technique, clustering pixels with specific labels using a connected component labelling approach, and ultimately saving the mask images for training.
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 2 Preliminaries
### Cerificateless Signature  
The CLS scheme usually includes six algorithms:  
* [noitemsep]
* Setup \((1^{\lambda})\): The Setup algorithm is usually operated by the KGC to initialize the scheme, which receives a security parameter \(1^{\lambda}\). The system master public and secret key pair \((mpk,msk)\) will be generated;
* KeyExt \((mpk,msk,\mathsf{ID})\): The KeyExt algorithm is usually operated by the KGC, which receives the master key pair \(mpk,msk\) and a user identity ID. Finally, a partial private key \(d_{\mathsf{ID}}\) is generated and transmitted to the user;
* SecretValue \((mpk,\mathsf{ID})\): The SecretValue algorithm is usually completed by a user, which receives the master public key \(mpk\) and a user identity ID. Finally, a secret value \(sv_{\mathsf{ID}}\) is generated and returned to the user;
* PublicKey \((mpk,\mathsf{ID},sv_{\mathsf{ID}})\): The PublicKey algorithm is usually completed by a user, which receives the master public key \(mpk\), a user identity ID, and a secret value \(sv_{\mathsf{ID}}\). A user public key \(pk_{\mathsf{ID}}\) will be output;
* Sign \((mpk,d_{\mathsf{ID}},sv_{\mathsf{ID}},m)\): The Sign algorithm is usually completed by a user signing it. They receive the master public key \(mpk\), a partial private key \(d_{\mathsf{ID}}\), a secret value \(sv_{\mathsf{ID}}\), and a message \(m\). A signature \(\sigma_{m}\) on the message \(m\) will be output;
* Verify \((mpk,\mathsf{ID},m,\sigma_{m})\): The Verify algorithm is usually completed by a user verifying it. They receive the master public key \(mpk\), a user identity ID, a public key \(pk_{\mathsf{ID}}\), a message \(m\), and a signature \(\sigma_{m}\). If the output is \"1\", it means the signature is legal; otherwise, the signature is illegal.
## 3 Results and Analysis of Radiometric Thermal Data  
### Influence of Emissivity on Radiometric Data  
A developed system for proactive detection in data analysis relies on the accuracy of radiometric information. In the thermal part of the spectrum, the choice of the correct  
Figure 17: IRT images generated from estimated radiometric data (**a–d**) and IRT images generated from estimated radiometric data using different color maps (**e–h**). **LB** represents a low-temperature boundary and **HB** represents a high-temperature boundary. (**a**) Generated image: **LB:** 18, **HB:** 36. (**b**) Generated image: **LB:** 15, **HB:** 50. (**c**) Generated image: **LB:** 22, **HB:** 34. (**d**) Generated image: **LB:** 25, **HB:** 30. (**e**) Generated image: inferno cmap, **LB:** 22, **HB:** 34. (**f**) Generated image: viridis cmap, **LB:** 22, **HB:** 34. (**g**) Generated image: winter cmap, **LB:** 22, **HB:** 34. (**h**) Generated image: hsv cmap, **LB:** 22, **HB:** 34.  
emissivity of the material plays the most important role for the determination of the real temperature value. In addition, the acquisition angle should not be neglected. Figure 18 shows the characteristic curve of the emissivity for color and good radiators in red and blue for shiny metals, which have a low emissivity. The green color indicates the angle of exposure recommended for thermographic analysis. The reason for this can be clearly seen in the behavior of the curves, which significantly change the values of the coefficients after 30\({}^{\circ}\). An analysis of the sensitivity of the measurement result to different emissivity levels at a reflected ambient temperature of 13.5 \({}^{\circ}\)C shown in Figure 18 with the red temperature values was performed on the wall sample presented in Figure 15. We concluded that within the recommended acquisition angle of 120\({}^{\circ}\) the measurement error was less than 0.1 \({}^{\circ}\)C. A further deviation of the recording angle led to an error of 5 \({}^{\circ}\)C. It should be noted that the specified values for each thermogram differed considerably. There were no metal objects in the image, so an analysis of the blue emissivity characteristic of metals could not be performed. A detailed description of the directional spectral emissivity can be found in [41].
## 2 DIC and AE Monitoring Experiments
### Experimental Scheme and Equipment  
The experiments were performed with the aid of a C64.106/1000 KN electro-hydraulic servo universal testing machine. During the experiment, the AE characteristic signals of red sandstone deformation and failure were monitored and acquired by a Micro-II digital AE system (MISTRAS, Princeton Junction, NJ, USA), and the whole process of crack expansion  
Figure 1: Specim preparation and processing.  
on the red sandstone's surface was recorded by a Nikon D7000 SLR camera in the SIC system (Figure 2).  
Four AE sensors were respectively installed on the back and side of the sample. No sensor was installed directly in front to avoid errors in image acquisition. At the beginning of the experiment, stress, AE signals and images were collected at the same time, which were used for subsequent multi-source information fusion analysis. Moreover, the kernel density estimation (KDE) method was used to identify the density regions that formed, based on the AE parameters. For a detailed introduction of the KDE method, refer to the author's previous work [16]. The rise angle (RA) and average frequency (AF) are two important parameters in the KDE method.  
The camera features a sensor size of 23.6 mm \(\times\) 15.6 mm, a display resolution of 920,000 pixels, and a maximum pixel count of 16.9 million. It utilizes a CMOS photosensitive device and supports image stabilization. Video recording is available at 30 frames per second, and the image size is 3696 \(\times\) 2448. The shutter type is an electronically controlled focal plane shutter. In addition to the Nikon D7000 SLR camera, auxiliary tools such as a tripod and a fill light were also used when shooting videos.  
Because the speckles were randomly distributed on the red sandstone's surface, the subregion centered at any point in the speckle field could be taken as an information carrier for the deformation and displacement change of the point. The displacement and deformation information at this point could be obtained by measuring and analyzing the deformation change in the subregion [17; 18]. It was assumed that A(x0, y0) was the center of a subregion D on the surface of the measured object before it was deformed and that the size of the subregion D was \(m\times n\) pixels. With these assumptions, the correlation search could be employed to find the target subregion D\({}^{\prime}\) centered at A\({}^{\prime}\)(x'0, y'0); this was most relevant to subregion D in the image after this object was deformed. The displacement change at Point a can be defined as u = x'0 - x0, v = y'0 - y0, and the actual displacement at Point a was obtained by converting pixel units (Figure 3).  
Figure 2: Experimental equipment for the rock specimens: (**a**) loading system and DIC monitoring system and (**b**) AE monitoring system.
## 4 Derivation of Performance Indices and Stability Conditions
### RMS Index  
The smaller the tracking and smoothing performance indices are, the better a tracking filter is. However, there are trade-offs between these indices. To consider these trade-offs and practical performance evaluations, a comprehensive performance index in smoothing and tracking was proposed and its effectiveness for the Kalman filter verified in [4]. This index corresponds to the root-mean-square (RMS) prediction error for a constant-acceleration target (considering sensor noise) and is calculated as:  
\[\epsilon_{\rm rms}=\sqrt{\sigma_{\rm P}^{2}+e_{\rm fin}^{2}}. \tag{28}\]  
We refer to this as the RMS index.
# Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet
## 2 Materials and Methods  
The original YOLOv5 network model already includes the detection of traffic lights, but YOLOv5 is a single-stage target detection algorithm, and this type of algorithm completes the detection and recognition of objects together, so there may be false detection and missed detection in complex scenes, as shown in Figure 1.  
In Figure 1, the area positioned by the box is the result of target detection. Analysis of Figure 1 shows that in poor lighting conditions at night, relatively large targets such as pedestrians and vehicles can be accurately detected, while relatively small targets such as traffic lights are missed, and the exact location of the traffic light cannot be detected, making it impossible to classify the traffic light. Traffic light detection requires a high level of accuracy because it affects subsequent decisions on vehicle control [19]. Therefore, this paper proposes a method based on YOLOv5s target detection and AlexNet image recognition network, which processes target detection and image recognition separately. Through this method, the shortcomings of a single-target detection algorithm in terms of low recognition rate for small-target detection can be avoided, and the recognition of multiple types of traffic lights can be achieved at the same time.  
The flow chart of traffic light detection and recognition in this paper is shown in Figure 2. The solution first uses the trained YOLOv5s network to detect the location of the traffic light in the target scene, extracts the target area, and performs image processing to obtain a single traffic light image, and then feeds the obtained image into the trained AlexNet for image recognition and combines the output results to obtain the final information. This section is followed by an introduction to the network structure of YOLOv5s and AlexNet.  
Figure 1: YOLOv5 misses on traffic light.  
Figure 2: Traffic light detection and recognition flow chart.
# Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices
## 4 Experiments
### Comparison Experiments  
To further verify the detection performance of the Edge-YOLO algorithm, this section compares Edge-YOLO with Faster R-CNN, SSD, YOLOv5m, YOLOv7 [22], and other mainstream target detection algorithms for comparison experiments, and the results are shown in Table 3 below.  
From the table, we can first see that Faster R-CNN, as a two-stage algorithm, lags far behind the single-stage algorithm in detection speed, and the current detection accuracy does not have an advantage over the single-stage algorithm; while the SSD algorithm in the single-stage algorithm speeds up the detection speed compared with Faster R-CNNN, but the detection accuracy is also reduced accordingly. Both algorithms are not comparable to the current YOLO series algorithms. Second, the detection accuracy of the algorithm in this paper is basically the same compared with the YOLOv5m algorithm, but it has obvious advantages in detection speed and consumption of computational and spatial resources. Again, compared with the latest YOLOv7 algorithm, the detection accuracy of the Edge-YOLO algorithm is slightly behind that of the YOLOv7 algorithm, but the resources consumed by the YOLOv7 algorithm and its detection speed are completely inferior to that of this paper. Finally, compared with the YOLO-FIRI target detection  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**[EMAIL_ADDRESS]/\%**} & \multirow{2}{*}{**[EMAIL_ADDRESS]/\%**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**Params/M**} & \multirow{2}{*}{**Flops/G**} & \multirow{2}{*}{**Size/MB**} \\ \cline{2-2} \cline{6-7}  & **Person** & & & & & & & \\ \hline Faster & \multirow{2}{*}{76.6} & \multirow{2}{*}{50.4} & \multirow{2}{*}{85.7} & \multirow{2}{*}{70.9} & \multirow{2}{*}{17.6} & \multirow{2}{*}{66.1} & \multirow{2}{*}{152.7} & \multirow{2}{*}{133.5} \\ R-CNN & & & & & & & & \\ SSD & 63.5 & 43.5 & 79.3 & 62.1 & 31.9 & 46.9 & 117.6 & 96.1 \\ YOLOv5m & 83.8 & 64.1 & 90.6 & 79.5 & 55.9 & 20.9 & 47.9 & 42.2 \\ YOLOv7 & 86.4 & **67.3** & **91.4** & **81.7** & 27.9 & 37.2 & 105.1 & 74.8 \\ YOLO-FIRI & 79.5 & 52.4 & 88.6 & 73.5 & 71.4 & 7.2 & 20.4 & 15.0 \\
**Edge-**YOLO** & 83.2 & 63.0 & 90.2 & 78.8 & **80.7** & **5.8** & **14.2** & **12.0** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of mainstream target detection algorithms.  
algorithm proposed by [PERSON] et al. also for IR scenes, this paper achieves higher detection accuracy with less resource consumption and better results in real-time.