esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mapping Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Primary Study**} & \multicolumn{4}{c}{**Publication Type**} \\  & **P** & **F** & **M** & **I** & **E** \\ \hline An Effective Security Requirements Engineering Framework for Cyber-Physical Systems [47] & & & ✓ & \\ \hline Mitigating the Impact on Users’ Privacy Caused by over Specifications in the Design of IoT Applications [34] & & ✓ & & \\ \hline Software Engineering for IoT-Driven Data Analytic Applications [35] & ✓ & & & \\ \hline An Application Development Framework for Internet-of-Things Service Orchestration [48] & & ✓ & \\ \hline IoT-HarPSecA: A Framework and Road-map for Secure Design and Development of Devices and Applications in the IoT Space [49] & & ✓ & \\ \hline Agent-Oriented Cooperative Smart Objects: From IoT System Design to Implementation [33] & & & ✓ \\ \hline A Model-Driven Methodology for the Design of Autonomic and Cognitive IoT-Based Systems: Application to Healthcare [36] & ✓ & & \\ \hline A Technology to Support the Building of Requirements Documents for IoT Software Systems [30] & & ✓ & \\ \hline Functional Requirements Elicitation in IoT Systems: a follow-up study [44] & & ✓ & \\ \hline A Requirements Engineering Process for IoT Systems [32] & ✓ & & \\ \hline Elicitation Techniques for Internet of Things Applications Requirements: A Systematic Review [45] & & ✓ & \\ \hline COMFIT: A Development Environment for the Internet of Things [50] & & ✓ & \\ \hline Stakeholder Identification and Use Case Representation for Internet-of-Things Applications & & ✓ & \\ in Healthcare [51] & & ✓ & \\ \hline Towards a General Software Engineering Methodology for the Internet of Things [53] & ✓ & & \\ \hline REUBI: A Requirements Engineering method for ubiquitous systems [37] & ✓ & ✓ & \\ \hline Modeling IoT Applications with SysML4 IoT [38] & ✓ & & \\ \hline Specifying Functional Requirements and QoS Parameters for IoT Systems [39] & ✓ & ✓ & \\ \hline A Review of IoT Systems Engineering: Application to the Smart traffic lights system [46] & & ✓ & \\ \hline Requirements engineering methods for an Internet of Things application: fall-detection for ambient assisted living [40] & ✓ & & \\ \hline An Improved RE Framework for IoT-Oriented Smart Applications using Integrated Approach [31] & & & \\ \hline TrUStAPIS: a trust requirements elicitation method for IoT [41] & ✓ & & \\ \hline A UML-based Proposal for IoT System Requirements Specification [42] & ✓ & & \\ \hline Methodology for the Model-Driven Development of Service Oriented IoT Applications [43] & ✓ & & \\ \hline Papyrus for IoT – A Modeling Solution for IoT [52] & & ✓ & ✓ \\ \hline Total & 13 & 1 & 3 & 7 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification scheme per primary study type. implementations (frameworks, libraries, and suites). Few meta-studies, work extensions, and formalization research articles have been published.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Energy Expenditure  
Energy expenditure is highly correlated to exercise intensity, thus is essential to the planning, prescription, and monitoring of physical exercise programs [63; 67; 69]. Although its estimation is challenging, the oxygen uptake has been considered the best direct way to measure energy expenditure [64; 66]. Indirectly, it can be performed through heart rate and body acceleration [67], which involves the development and application of non-contact technology as presented in the following sections.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 3 Methods
### Viewpoint Estimated Using CNN  
Deep learning-based image classification has achieved remarkable success since 2006. Because of the difference between viewpoint estimation and image classification, we utilize a deep learning model for image classification with viewpoint labels after developing a new soft accuracy function. The accuracy function allows for the perspective estimation bias, which is defined as follows.  
\[A_{cc}=\frac{1}{N}\sum_{m=1}^{N}\delta_{m}, \tag{8}\]  
where \(N\) is the number of images, and  
\[\delta_{m}=\begin{cases}\overrightarrow{OP_{m}^{est}}\cdot\overrightarrow{OP_{ m}^{gt}},&\text{if }\overrightarrow{OP_{m}^{est}}\cdot\overrightarrow{OP_{m}^{gt}}\geq\cos( \delta_{0})\\ 0,&\text{otherwise}\end{cases}. \tag{9}\]  
In Equation (9), \(m\) is the \(m^{th}\) image, and \(\delta_{0}\) the threshold which is the maximum tolerable viewpoint deviation angle. Since our study utilizes a unit vector to represent a viewpoint direction, the inner product \(\overrightarrow{OP_{m}^{est}}\cdot\overrightarrow{OP_{m}^{gt}}\) of the vectors \(\overrightarrow{OP_{m}^{est}}\) and \(\overrightarrow{OP_{m}^{gt}}\) directly corresponds to the cosine value of the angle between these two vectors.  
According to the algorithm in Section 3.2, we have constructed an image dataset with viewpoint position labels. These images are classified according to the viewpoint positions. Using Equation (9), the loss function can be rewritten. After completing the dataset construction and loss function design, a simple CNN framework [37] is established to achieve image-based viewpoint estimation. This CNN framework consists of seven layers, and its architecture and parameters are illustrated in Figure 7.
## 4 Shannon Entropy and Kullback-Leibler Divergence
### Discrete BNs
_We construct a junction tree from_ \(\mathcal{B}\) _and we use it to compute the distributions_ \(\operatorname{P}(X_{1})\)_,_ \(\operatorname{P}(X_{2},X_{1},X_{4})\)_,_ \(\operatorname{P}(X_{3},X_{1})\) _and_ \(\operatorname{P}(X_{4},X_{3})\)_._ \[ \begin{array}{ccccc}X_{1}&&&&\{X_{1},X_{4}\}\\ &\{a,g\}&\{a,h\}&\{b,g\}&\{b,h\}\\ \hline 0.53&0.47&&X_{2}&\begin{array}{c}c\\ d\end{array}&\begin{array}{c}c\\ 0.070\\ 0.089\\ \end{array}&\begin{array}{c}0.110\\ 0.089\\ \end{array}&\begin{array}{c}0.053\\ 0.261\\ 0.076\\ \end{array}&\begin{array}{c}0.107\\ 0.235\\ 0.23\\ \end{array}\\ X_{3}&\begin{array}{c}e\\ f\end{array}&\begin{array}{c}0.289\\ 0.241\\ 0.231\\ \end{array}&\begin{array}{c}0.312\\ 0.241\\ \end{array}&\begin{array}{c}X_{4}\\ 0.158\\ \end{array}&\begin{array}{c}X_{3}\\ e\\ h\end{array}&\begin{array}{c}0.120\\ 0.481\\ \end{array}&\begin{array}{c}0.167\\ 0.231\\ \end{array}\end{array} \]
3. _We compute the cross-entropy terms for the individual variables in_ \(\mathcal{B}\) _and_ \(\mathcal{B}^{\prime}\)_:_ \[\operatorname{H}\bigl{(}X_{1}^{\mathcal{B}},X_{1}^{B^{\prime}} \bigr{)} =0.53\log 0.31+0.47\log 0.69=-0.795;\] \[\operatorname{H}\bigl{(}X_{2}^{\mathcal{B}},X_{2}^{B^{\prime}} \bigr{)} =0.070\log 0.38+0.089\log 0.62+0.110\log 0.71+0.261\log 0.29+\] \[=0.053\log 0.51+0.076\log 0.49+0.107\log 0.14+0.235\log 0.86\] \[=-0.807;\] \[\operatorname{H}\bigl{(}X_{3}^{\mathcal{B}},X_{3}^{B^{\prime}} \bigr{)} =0.289\log 0.44+0.241\log 0.56+0.312\log 0.18+0.158\log 0.82\] \[=-0.943;\] \[\operatorname{H}\bigl{(}X_{4}^{\mathcal{B}},X_{4}^{B^{\prime}} \bigr{)} =0.120\log 0.26+0.481\log 0.74+0.167\log 0.50+0.231\log 0.50\] \[=-0.582;\] _which sum up to_ \(\operatorname{H}(\mathcal{B},\mathcal{B}^{\prime})=\sum_{i=1}^{N} \operatorname{H}\Bigl{(}X_{i}^{\mathcal{B}},X_{i}^{\mathcal{B}^{\prime}} \Bigr{)}=-3.127\)_._
4. _We compute_ \(\operatorname{KL}(\mathcal{B}\,\big{\|}\,\mathcal{B}^{\prime})=2.440-3.127=0.687\)_, which matches the value we previously computed from the global distributions._
## 3 Proposed Method
### Collision Risk Assessment  
The collision risk is assessed based on the predicted future positions. As described in Section 2.2, the dynamic potential energy is defined as the risk index. The repulsive potential energy from the vehicle \(i\) is derived by  
\[f(\Delta V_{i},\theta_{i})=\frac{1}{2\pi I_{0}(\eta(\Delta V_{i}))}\exp(\eta( \Delta V_{i})\cos\theta_{i}), \tag{10}\]  
\[h(G_{i})=\frac{1}{2\pi\sigma_{i}}\exp(-\frac{G_{i}^{2}}{2\sigma^{2}}), \tag{11}\]  
\[R_{i}=f(\Delta V_{i},\theta_{i})h(G_{i}), \tag{12}\]  
where  
\[\theta_{i}=\begin{cases}\pi&(i=PR)\\ 0&(i=FO)\end{cases}. \tag{13}\]  
Here, \(\Delta V_{i}\) represents the relative speed between the ego and vehicle \(i\), and \(G_{i}\) is the distance from the vehicle \(i\). The von Mises distribution is applied in Equation (10), and \(I_{0}(\eta)\) represents a modified Bessel function of order 0. If the parameter \(\eta\) is zero, the uniform filed is generated. When the parameter \(\eta\) is not zero, the drifted distribution toward the angle \(\theta_{i}\) is generated. The value of \(\eta\) determines the drifted amount of distribution, and it is derived from the relative speed \(\Delta V_{i}\).  
Figure 7 shows the distribution of the generated potential field according to the relative speed between the ego vehicle and the vehicle \(i\). The colors of the potential field indicate the energy level. The red color depicts the high energy level, and the blue represents the low level. When the ego and the vehicle \(i\) show the same speed, the uniform distribution is generated as shown in Figure 7a. If the vehicle \(i\) moves slower than the ego vehicle, the distribution with bias toward the ego vehicle is generated as shown in Figure 7b. Consequently, the large potential energy implies the ego vehicle, and it represents the high risk of colliding with the vehicle ahead. On the other hand, if the vehicle \(i\) is faster than the ego vehicle, the potential field is drifted to the forward direction as shown in Figure 7c. Although the ego vehicle drives close to the vehicle \(i\), the collision risk is assessed as low because of the relative speed. In this case, the small amount of the repulsive potential energy indicates the low collision risk. Equation (11) defines  
the potential energy is lower when the ego vehicle is farther away. In contrast, if the two vehicles are close, the large amount of energy affects to the ego vehicle.  
The collision risks towards both the preceding and following vehicles are assessed. When the cut-in occurs in the front space of the ego vehicle, the collision risk with respect to the cut-in vehicle is assessed instead of considering the preceding vehicle of the current lane. Figure 8 represents the way how the assessment space of the collision risk is determined by the existence of cut-in vehicles. Finally, the collision risk at position \((x,y)\) is calculated by  
\[R(x,y)=\sum_{i=PR,FO}R_{i}. \tag{14}\]
## 5 DSS Techniques and Generalized Finite State Machine
### Dss1--Collaboration Protocol
#### 5.1.1 Network Architecture of Collaboration Protocol  
The proposed CP network architecture, with multiple systems consisting of different entities and established user and control links, is depicted in Figure 3. As the focus of this work is not on feeder links, they are not presented here, however, all the approaches presented here can be extended to feeder links as well. The CP disseminates its CP messages between involved entities over a wired terrestrial backbone network via their GSs and GWs, supported by existing wireless CCs. The CP can be terminated in the GS and pass any decision to the satellite or ES via standard control messages over standard CCs: in the case of satellites, there is a direct satellite control channel (SAT-CC) between GS and satellite, whereas, in the case of ES, the earth station control channel (ES-CC) is relayed between GS and ES via the satellite. In this case, the GS will act as a CP proxy for the satellite and ES. Alternatively, by extending the standard CCs (SAT-CC and ES-CC) to pass special CP messages, the CP can also be executed on the satellite and ES, enabling them to take local decisions. The CP network is also connected to the terrestrial network, where GWs have a direct connection to the CP wired network, while other MW terminals will communicate via a terrestrial wireless CC (possible via relaying over MW mesh network).
# Blasting Fragmentation Study Using 3D Image Analysis of a Hard Rock Mine
## 2 Materials and Methods  
### PortaMetrics\({}^{\text{TM}}\) Image Capture  
The second phase of the study consisted of analyzing the sizes of rock fragments obtained in blasting tests using images. A robust technology based on machine learning and image processing was used for fragmentation analysis: PortaMetrics\({}^{\text{TM}}\), equipment for capturing images in three dimensions (3D), and the image-processing platform MetricsManager\({}^{\text{TM}}\) Pro (MMPro).  
PortaMetrics\({}^{\text{TM}}\) is a safe handheld device because it can accurately calculate rock fragmentation from a distance via stereo images. With this device, there is no need for mine technicians to be near the bench to place the object to scale. The stability of the bench face can be evaluated using the integrated inclination sensor, indicating the inclination angle and the distance to the bench face [19].  
The equipment had three integrated high-resolution cameras (Figure 2) with a touch screen. Table 1 presents the main functions of the equipment.  
Image capture was performed manually by pointing and shooting the device to the blasting pile, with the least amount of disturbance in the environment (dust, vibration, and shadow). It was recommended to capture images in the morning to avoid shadows. The three cameras could detect the distance from the user to the fragments to calculate the size. The application had a NEAR/FAR mode disparity sensor, where the NEAR mode was for distances less than or equal to 2.00 m, the FAR was for distances longer than 2.00 m and shorter than 10.00 m, and the size of the smallest detectable particle was 0.03 m. Users had to adjust the region of interest to cover, at most, 80% of the screen [19] and capture an image, and the results were instantly displayed in an intuitive graphical user interface of the portable device.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Component** & **Function** \\ \hline A: Touch screen & Edits and selects images \\ B: GPS antenna & Provides positioning information \\ C: Capture button & Captures/records the fragmentation for analysis \\ D: On/off button & For turning the equipment on and off \\ E: Cameras & Three stereoscopic cameras that create and analyze 3D fragmentations \\ F: Fan & Provides cooling to the equipment, preventing damage from overheating \\ \hline \hline \end{tabular}
\end{table}
Table 1: Each component of the PortaMetrics\({}^{\text{TM}}\) equipment and its function.  
Figure 2: PortaMetrics\({}^{\text{TM}}\) equipment is used to capture 3D images.
## 4 Results and Discussion
### Results
#### 4.2.5 Supply Shortage Effects  
Table 6 shows the respective supply shortage effects of the NG supply sector in South Korea and Japan, as analyzed through the supply-driven model. The supply shortage effects refer to the decrease in production that occurs in other sectors due to a one-unit decrease in the output of the NG supply sector. The supply shortage effects of the NG supply sector were found to be 1.62724 and 0.97915 in South Korea and Japan, respectively. The loss to the national economy from supply disruptions in the NG supply sector was found to be greater in South Korea than in Japan. By sector, in South Korea, the impact of supply disruptions in the NG supply sector was significant in the order of \"16. Electricity and steam supply\", \"9. Basic metal products\", and \"7. Chemical products\". In Japan, the impact of supply disruptions in the NG supply sector was large in the order of \"21. Food services and accommodation\", \"19. Wholesale, retail trade, and commodity brokerage services\", and \"14. Transport equipment, manufacturing services, and repair services of industrial equipment\".
# Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
## 1 Introduction
If there are situations where NTN UT is located temporarily with improper GNSS coverage or disruptions due to jamming, it may be necessary to perform initial access without the help of GNSS operations as implementation technology. The proposed scheme in this paper can contribute to improving performance in the case of the provisional non-GNSS operation when there are a large of timing and frequency offsets in initial access from UTs to GW. The remainder of this paper is organized as follows. In Section 2, we describe the transmission model and explain the DVB-RCS2 demodulator structure for linear modulation. In Section 3, we discuss the existing schemes and present the proposed approach. In Section 4, we provide the comparative results and performance analysis. Finally, Section 5 concludes our paper.
## 3 Determination of Parameters for Asphalt Concrete Material
### Damage Factor  
The strain hardening and softening pairs (\(\eta\), \(\lambda\)) in Equations (10) and (11) describe the material behaviour transmitted from the yield surface to the maximum strength surface and from the maximum strength surface to the residual strength surface, respectively. During the transmission, parameter \(\eta\) varies from zero to one, depending on the accumulated effective plastic strain parameter \(\lambda\). However, it is found that the original damage factor pairs (\(\eta\), \(\lambda\)) in the KCC model are only suitable for concrete and not for the AC material due to AC having higher plastic failure strain. Thus, the input for accumulated effective plastic strain \(\lambda\) should be modified. Based on the uniaxial compressive test for AC, it was found that at peak stress, the corresponding strain was approximately 0.018, and the final failure strain was about 0.1; while for normal concrete, the corresponding strain at peak stress was around 0.0022. Hence, the \(\lambda\) is modified to give the high failure strain for AC in the current study. Additionally, it is found that when \(\lambda\) is adjusted to 10-times the original \(\lambda\) value, the numerical results seemed to show good agreement with the experimental results from the unconfined compressive test for AC. Figure 4 shows the modified and original series of (\(\eta\), \(\lambda\)) pairs. It can be seen that the modified damage factor provided smoother descending than the original damage factor and had a higher failure strain that matched the behaviour of AC very well.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(a_{0}\) & 2.071 \\ \(a_{1}\) & 0.6 \\ \(a_{2}\) & 0.0135 \\ \(a_{0y}\) & 1.183 \\ \(a_{1y}\) & 2.00 \\ \(a_{2y}\) & 0.0473 \\ \(a_{1f}\) & 0.70 \\ \(a_{2f}\) & 0.0037 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for asphalt concrete (AC) material with \(f_{c}\) = 4.6 MPa.  
Figure 4: Damage factor used for AC material.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 2 Preliminaries
### Lqr  
The LQR technique delivers ideally regulated feedback gains, allowing for the construction of closed-loop stable and high-performance systems. This theory is used in those situations where the system dynamics are defined by a set of linear differential equations and the cost is represented by a quadratic function. The LQR method simply discovers a suitable state-feedback controller in an automated manner. The use of alternative approaches, such as the complete state feedback (sometimes called pole placement), provide more visible link between controller settings and controller behavior.
## 2 Indoor and Outdoor Thermal Environment
### Test Results and Analysis  
#### 2.2.1 Outdoor Thermal Environment  
The outdoor thermal environment mainly includes outdoor air temperature and solar radiation intensity. The average diurnal variation of solar radiation intensity and the outdoor air temperature over the testing period is shown in Figures 3 and 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Survey Content** & **Main Parameters** & **Test Duration** & \begin{tabular}{c} **Measuring Instruments** \\ **(Types/Accuracy)** \\ \end{tabular} & \begin{tabular}{c} **Recording Time** \\ **Internals** \\ \end{tabular} \\ \hline \begin{tabular}{c} Indoor thermal \\ environment \\ \end{tabular} & Air temperature & 120 h & \begin{tabular}{c} Self-recording high precision \\ temperature meter \\ (175-H1/0.1 \({}^{\circ}\)C) \\ \end{tabular} & 10 min \\ \hline \begin{tabular}{c} Outdoor thermal \\ environment \\ \end{tabular} & Air temperature & 120 h & \begin{tabular}{c} Self-recording high precision \\ temperature meter \\ (175-H1/0.1 \({}^{\circ}\)C) \\ \end{tabular} & 10 min \\ \hline \
\begin{tabular}{c} Outdoor thermal \\ environment \\ \end{tabular} & Total solar radiation & 120 h (Daytime) &
\begin{tabular}{c} Solar radiometer \\ (TIDL-4/\(\pm\)2\%) \\ \end{tabular} & 1 h \\ \cline{2-4} \cline{2-4} \cline{2-4} \cline{  
As shown in Figure 3, the average sunshine duration is approximately 9 h per day in the test period, and the average radiation intensity is 436 W/m\({}^{2}\). The highest solar radiation intensity is high up to 788 W/m\({}^{2}\) at 14:00 pm. The proportion of scattered radiation to total radiation is approximately 35%. Figure 4 presents that the average, the highest, and the lowest outdoor air temperature are \(-1.7\)\({}^{\circ}\)C, 4.9 \({}^{\circ}\)C, and \(-9.8\)\({}^{\circ}\)C, respectively. Outdoor air temperature fluctuated significantly during the testing day, with a range of 14.7 \({}^{\circ}\)C.  
The measured results indicate that, in the Lhasa area, the solar radiation intensity is high, the sunshine duration is long, and the outdoor air temperature fluctuates significantly, but the outdoor air temperature is not too low. It is an ideal climatic condition for passive solar buildings to use solar energy to meet the heat need for winter heating [31].
## 5 Spectral Performance Analysis  
The spectral efficiency of a SIM OFDM system can be computed using the total number of bits carried by a sub-block at a given instance and the number of used subcarriers. Equations (9) and (10) represent the spectral efficiency of an SIM OFDM system.  
\[S.E=\frac{Number\ of\ bits\ per\ SIM\ OFDM\ symbol}{Number\ of\ Subcarriers\ per\ subblock} \tag{9}\]  
\[S.E=\frac{C(n,k)+k\ log_{2}M}{n} \tag{10}\]  
where \(n\) is the block size and k represents the number of subcarriers that are turned on. \(M\) is the modulation level of the quadrature amplitude modulation (QAM)-modulated subcarriers. In Equation (10), \(C(n,K)\) represents the index bits (B1) being carried in a sub-block, while \(k\ log_{2}M\) represents the bits that are QAM-modulated onto the subcarriers (B2). \(n\), is the number of subcarriers in a given subgroup. In case of DS-PIM this is replaced by the average number of subcarriers (\(n_{avg}\)).  
\[S.E=\frac{C(n,k)+k\ log_{2}M}{n_{avg}} \tag{11}\]
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 3 Session Key Recovery with Ternary PUFs  
### Preparation Cycle--Session Key Encapsulation  
The objective of this protocol, shown in Figure 3, is to protect a session key **Sk** by the embedded PUF during powering off cycles of a client device. It is assumed in this protocol that the databases of the client device could be lost to the opponent. Therefore, the information stored in these databases is considered public information. Without the PUF, or its image, the secret key **Sk** should not be retrievable. During the preparation phase, the challenges (**T**, _IDAccess_) generating the responses **K** from the database are transmitted to the client device by the secure server. To enhance security, it is assumed that the client device does not know the fuzzy positions that are tracked with ternary states. The data stream _IDAccess_ allows the masking of the fuzzy positions, which has the objective of reducing BERs at the client level. This requires the thorough identification and storage in the database, by the secure server, of the erratic cells during an enrollment cycle. The session key **Sk** is then encrypted by the client device using the freshly generated responses **K**'. The information stored by the client device is (**T**, _IDAccess_) the challenges, **H**(**K**'), the hash message digest of the response **K**', and **E**(**Sk**, **K**') is the ciphertext generated by encrypting the **Sk** with **K**'.