esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Approach
### Calibration of Fisheye Camera Using EUCM  
In normal visual odometry, using a fisheye image as the original form is not common because of the complexity of multi-view geometry. Thus, usually, the fisheye camera model is converted to the pinhole model to simplify the multi-view geometry. However, transforming the fisheye model to the pinhole model causes a serious distortion in the undistorted pinhole image. In addition, only the center part of the undistorted image can be used in the rasterization step, and the remaining image areas are removed.  
Therefore, instead of the pinhole model, we use the Cubemap [1] model to utilize all image areas after undistortion. To transform the fisheye image to a cubemap image, all calibration parameters of the fisheye camera must be calibrated. As the calibration model, we use the enhanced unified camera model (EUCM) [25], which is a nonlinear camera model that is generally used in omnidirectional cameras such as catadioptric systems or fisheye cameras. The EUCM has six parameters, \([\textit{f}_{x},\textit{f}_{y},\textit{c}_{x},\textit{c}_{y},\alpha,\beta]\). The curvature of the fisheye lens is calculated using \(\alpha\) and \(\beta\). Therefore, by using these parameters, the EUCM can represent the fisheye camera distortion. Figure 4 shows the image projection model to the image plane through the fisheye lens. A 3D point \(\mathbf{X}\) is projected on a curved plane P to a 3D point \(X_{P}\), as shown in Equations (1)-(5).  
\[\mathbf{X}\ =[x\,\ y\,z\ ], \tag{1}\]  
\[\rho=\sqrt{\beta(x^{2}+y^{2})+z^{2}}, \tag{2}\]  
\[\mathbf{X}_{P}=[x_{P},y_{P},z_{P}], \tag{3}\]  
Figure 3: The algorithm steps of the proposed Ground Plane Odometry.  
\[x_{P}=\frac{x}{\alpha\rho+(1-\alpha)\mathrm{z}}\, \tag{4}\] \[y_{P}=\frac{y}{\alpha\rho+(1-\alpha)\mathrm{z}} \tag{5}\]  
Then, \(\mathbf{X}_{P}\) is orthogonally projected into the M plane, which is the normal image plane, as shown in Equation (6).  
\[\mathbf{X}_{\mathbf{M}}=[x_{P},y_{P},\ 1]=[x_{M},y_{M},\ 1]. \tag{6}\]  
Finally, as shown in Equation (7), we multiply the normal plane point \(\mathbf{X}_{M}\) with the intrinsic matrix \(K\) to obtain the projection pixel \(\mathbf{X}_{I}\) on the fisheye image.  
\[\mathbf{X}_{\mathbf{I}}=K\mathbf{X}_{\mathbf{M}}=\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix}\mathbf{X}_{\mathbf{M}}=\begin{bmatrix}u\\ v\\ 1\end{bmatrix}. \tag{7}\]  
In this study, to calibrate the fisheye camera's EUCM, we use a calibration toolbox Kalibr [26; 27; 28; 29; 30], which is extremely useful for camera calibration. Kalibr offers a variety of camera models, including EUCM, pinhole model, omnidirectional model, and double sphere models.
## 5 Results and Discussions  
The proposed DCB-NS scheme compares its performance with the Epsilon-Greedy and Thompson sampling algorithms [31]. Together with UCB (which the proposed scheme utilizes), these algorithms are popular approaches for solving multi-armed bandit problems. The proposed scheme is first evaluated based on the cumulative rewards obtained by the agent using the three methods above. Then, the effectiveness of the proposed scheme through network slice provisioning for each NSR is evaluated. In addition, the throughput of the network is examined to determine how well it serves the incoming NSRs during the simulation period. In this section, the results of the simulations are presented and discussed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range** \\ \hline
**Physical Network:** & \\ \hline Access network nodes & 20, 40, 80 \\ Transport network nodes & 30, 60, 120 \\ Core network nodes & 50, 100, 200 \\ Node computing capacity & U [20, 50] \\ Node link capacity & U [20, 50] \\ \hline
**Network Slice Requests:** & \\ \hline Number of nodes per NSR & 15 \\ Maximum number of NSRs & 20 \\ NSR node computing capacity & U [5, 25] \\ NSR link capacity & U [5, 25] \\ Transmission Delay & T [0.05, 1] \\ NSR Lifetime & T [10, 50] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Configurations.
# An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers
## 3 The Extended to Identify Management Scheme for Cloud CDM
### The Operation Scheme for Trustworthiness in CDM Cloud  
In this paper we are focused on how to guarantee the trustworthiness using DID among the entities in cloud CDM. Hence, this model has no consideration of authentication and authorization based on in-person and group verification of cloud CDM. In cloud CDM, it is necessary to design a secure cloud on a permission-type blockchain in which the access control of authorized and registered researcher is established. In order to use the CDM data, the request of access permission from the researcher and IRB approval are required in the data supervision process, and the results of the process are maintained in the blockchain.  
The following shows the process for uploading the CDM derived from the researcher's query in Figure 4:  
1. A researcher registered in a medical institution, Hospital B, sends a query to the EMR DB managed Hospital A.
2. The researcher requests the trust manager of Hospital A for CDM to hold the cloud CDM based on the result of the query.
3. The trust manager of Hospital A obtains the IRB's approval for the request for the EMR data with the credential for identifying the researcher.
4. The trust manager in Hospital A builds the approved EMR data into CDM data and its metadata associated with encryption keys and storing the CDM encrypted to distribute to a repository in cloud CDM.
5. The trust manager in Hospital A uploads the encrypted data to the cloud CDM.  
We assume the requirements of authentication and authorization as the research background. The authentication is the basic process of verifying that. the entities (researcher, IRB, CDM provider, CDM consumer) are who they claim to be before allowing access. In the context of cloud CDM, authorization determines the entitlement of an entity to perform tasks that are authorized within the system. A user's authorization and authentication are initially activated by an identity provider (IRB) and provide CDM data about the person granted by the IRB.  
Figure 4: The overall concept of authentication and authorization in cloud CDM.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design  
[PERSON]  
1 Institute of Environmental Science and Technology, The University  
The idea is to compute a more precise projection onto the set of rank-constrained structured matrices by the alternating direction method of multipliers (ADMM) [26]. By numerical examples in Section 4, we show that the proposed method significantly improves the precision of the solution compared to the nuclear norm minimization [15] and the original alternating projection method [13].  
The organization of this paper is as follows: In Section 2, we show two reduced-order control problems that are described as rank-constrained LMI problems. In Section 3, we propose the alternating projection algorithm to solve the rank-constrained LMI problem. Numerical examples are shown in Section 4 to illustrate the effectiveness of the proposed method with 95 benchmark models from the COMPL\({}_{\text{e}}\)ib library [27]. A summary is given in Section 5.  
We note that the MATLAB programs to check the numerical examples and the results of stability tests for 95 benchmark models shown in Section 4 are available at the web page of [28].  
Notation  
Let \(A\) be a matrix. The transpose of \(A\) is denoted by \(A^{\top}\), the trace by \(\operatorname{tr}(A)\), and the rank by \(\operatorname{rank}(A)\). The \(i\)-th singular value of \(A\) is denoted by \(\sigma_{i}(A)\). In this paper, we use two kinds of matrix norms: one is the Frobenius norm \(\|A\|\) of \(A\) is defined by  
\[\|A\|\triangleq\sqrt{\operatorname{tr}(A^{\top}A)}=\sqrt{\sum_{i=1}^{n}\sigma _{i}^{2}(A)}. \tag{1}\]  
Matrix inequalities \(A\succ 0\), \(A\succeq 0\), \(A\prec 0\), and \(A\preceq 0\) respectively mean \(A\) is positive definite, positive semidefinite, negative definite, and negative semidefinite. For \(A\in\mathbb{R}^{n\times m}\) with \(r=\operatorname{rank}(A)<n\), \(A^{\perp}\) is a matrix that satisfies  
\[A^{\perp}\in\mathbb{R}^{(n-r)\times n},\quad A^{\perp}A=0,\quad A^{\perp}A^{ \perp\top}\succ 0. \tag{2}\]  
By \(\mathcal{S}_{n}\), we denote the set of \(n\times n\) real symmetric matrices.  
For a closed subset \(\Omega\) of \(\mathcal{S}_{n}\), the projection operator of \(X\in\mathcal{S}_{n}\) onto \(\Omega\) is denoted by \(\Pi_{\Omega}\), that is,  
\[\Pi_{\Omega}(X)\in\operatorname*{arg\,min}_{Z\in\Omega}\|Z-X\|, \tag{3}\]  
and the distance from \(X\) to \(\Omega\) is defined by  
\[\operatorname{dist}(X,\Omega)\triangleq\min_{Z\in\Omega}\|Z-X\|. \tag{4}\]
# TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications
## 5 Evaluation
### Weights for Test
#### 5.3.2 Statistical Analysis of Test Execution Time  
The maximum time \(T_{max}\), minimum time \(T_{min}\), average time \(T_{mean}\), and standard deviation of the time for exposing the last vulnerabilities \(T_{std}\) of both the ART4 SQLi and the proposed TPSQLi for the ten test targets are presented in Table 4.  
The last column in Table 4, labeled \(|Z|\), displays the values obtained from the statistical \(Z\)-_test_, calculated using the mean (\(T_{mean}\)) and standard deviation (\(T_{std}\)), specified in Equation (4). This column is intended to determine whether there is a statistically significant difference between the results of the two approaches (ART4 SQLi and TPSQLi) across the ten test cases, with a confidence level of 95%, i.e., \(Z_{0.95}=1.645\).  
\[Z=\frac{T_{mean,\ \ \text{TPSQLi}}-T_{mean,\text{ART4 SQLi}}}{\sqrt{T_{std, \text{TPSQLi}}^{2}+T_{std,\text{ART4 SQLi}}^{2}}} \tag{4}\]  
As shown in Table 4, TPSQLi outperformed ART4 SQLi across all metrics, with the exception of \(T_{mean}\) for R4. Notably, the test prioritization did not alter the order for R4 and DVWA (SQL), resulting in the same order as observed in ART4 SQLi. The \(|Z|\) values, except for those corresponding to DVWA (SQL) and R4, exceeded the critical value \(Z_{0.95}=1.645\), as indicated in the rightmost column. This suggests that the \(Z\)-_test_ confirms a significant reduction in execution time between ART4 SQLi and TPSQLi for the 10 test cases.
## 2 Application of Thermography in Pig Breeding
### Interaction or Restraint Stress Assessment
#### 2.6.2 Assessment of Stress Related to Aggressive Interaction  
[PERSON] et al. have employed thermography in order to assess changes in skin temperatures in response to a highly stressful event, such as an aggressive interaction between two individuals [36]. The problem of aggression among pigs is a common occurrence in all farms, and it is important to acquire some information to understand their physical and emotional responses, which can be achieved with noninvasive techniques such as thermography. Forty-six pigs aged 13 weeks were examined, entire males and females, who had undergone no tail cut and no teeth trimming. The test that has been carried out consisted of placing pairs of individuals unrelated to each other into an 11 m\({}^{2}\) arena, then comparing male-male, female-female, and male-female combinations in 69 encounters. The confrontation began with the entry of the two individuals into the arena and ended when any of the following three situations occurred: when there was an obvious winner, after 20 min of no interaction, and when a fear or mounting behavior  
Figure 3: The infrared image shows the skin temperature of some piglets and the environment during a long transport.  
occurred, even in a single individual. In this study, the back, particularly the area between the shoulder blades and the rump, was selected as thermal frame to analyze the exchanges in blood-irradiated areas.  
In the case of an aggressive confrontation, at the time of termination due to the loser's withdrawal, a decrease in back surface temperature was observed, although there were no differences between the winning and losing pigs. Conversely, duration of the confrontation, body weight, and sex of participants were found to be of little influence. Since the drop in temperature at the end of the confrontation was independent from the physical effort required to win or defend, the vasoconstriction observed by thermography may be the result of psychological stress rather than that of physiological adaptation. This outcome, highlighted through the thermographic application, provides us with new evidence to evaluate aggressive behavior more thoroughly in terms of animal well-being and physiological response to such behaviors.
## 4 Experimental Results and Discussions
### Performance Comparison on Our CCTV Dataset  
In the second experiment, we compared the performance of the baseline model and PSI-CNN using our dataset generated from CCTVs. This dataset has around 1,500 images of 10 individuals, comprising both high-quality images of frontal faces and low-resolution CCTV images for each person. The number of genuine and imposter pairs are 2380 and 21,420 respectively. This CCTV dataset was only used for the testing phase to demonstrate PSI-CNN's robustness towards variations in image resolutions and qualities. For performance comparisons, we used the equal error rate (EER), which defines the error rate at which the false acceptance rate (FAR) and the false rejection rate (FRR) are equal. FAR and FRR are calculated using (4) and (5) respectively. In these equations, true positive (TP) and false negative (TN) refer to cases whereby the model identifies a true match as positive and negative respectively, and vice versa for true negative (TN) and false positive(FP).  
\[FAR=\frac{FP}{TN+FP}\times 100. \tag{4}\]  
Figure 3: The ROC curve of different models on the LFW dataset  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Extended Conv. Stages** & \(\hat{\mu}\times\)**100(\%)** & \(\mathbf{S_{E}}\) \\ \hline Human & - & 97.53 & - \\ Model 1 (baseline) & - & 98.40 & 0.0024 \\ Model 2 & Stage 2 & 98.60 & 0.0024 \\ Model 3 & Stage 2, 3 & 98.65 & 0.0020 \\ Model 4 & Stage 2, 3, 4 & 98.72 & 0.0018 \\ Model 5 (Proposed model) & Full Stage & 98.87 & 0.0017 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Means and standard deviations of matching accuracies on the LFW dataset\[FRR= \frac{FN}{TP+FN}\times 100. \tag{5}\]  
As shown in Table 4, the EER of the proposed model is 15.27%, which is 33% smaller than the EER of the baseline model.  
Additionally, we sampled and classified some CCTV images into groups (Probe 1 to probe 5) according to their facial region size (see Figure 4 for a comprehensive example). In each group of these sampled images, we compared the average matching distance achieved by the baseline and PSI-CNN models. As shown in Figure 5, the proposed model achieves small error distance between the reference frontal image and low-resolution CCTV images even when the resolution of the query image is significantly reduced. This demonstrates PSI-CNN's robustness to unfavorable changes in image resolution and quality.
## 5 Connection To Numerical Modelling
### Constitutive Model Parameters  
In this section, the constitutive model parameters for the layers identified in Section 5.1 are determined. The very thin sand and silty sand layer detected at the end of the sounding is excluded from the numerical modelling. Consequently, parameters are determined only for the first five layers.  
Stress dependency of stiffness plays a crucial role in the numerical modelling of various geotechnical problems, especially when dealing with settlement prediction [66]. This aspect is incorporated in the Hardening Soil (HS) model [67], which accounts for both shear and compression hardening by implementing a cone and a cap yield surface. Additionally, the non-linear degradation of shear stiffness with shear strain can be captured using an extension of the HS model, namely the Hardening Soil model with small-strain stiffness (HSsmall) [68].
## 3 Results
### Reliability and Sensitivity
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Kinematic Variables** & **Mean \(\pm\) SD** & **Min–Max** & **CV\%\({}_{\text{(985)}}\)** & **CV\%\({}_{\text{(985)}}\)** & **SWC\%\({}_{\text{(6.9)}}\)** & **SWC\%\({}_{\text{(6.9)}}\)** & **TE** & **Sensitivity** & **ICC (CI95\%)** \\ \hline Velocity [m/s] & 6.18 \(\pm\) 0.29 & 5.53 & 6.86 & 4.71 & 1.89 & 0.06 & 0.15 & 0.10 & GOOD & **0.87** (0.66–0.94) \\ Trial 1 & 6.13 \(\pm\) 0.31 & 5.05 & 6.85 & & & & & & & \\ Trial 2 & 6.23 \(\pm\) 0.31 & 5.53 & 6.86 & & & & & & & \\
**Glide time [ms]** & **367 \(\pm\) 27** & **302** & **418** & **7.36** & **3.24** & **5** & **14** & **11** & **GOOD** & **0.82** (0.63–0.91) \\ Trial 1 & 371 \(\pm\) 28 & 315 & 451 & & & & & & & \\ Trial 2 & 363 \(\pm\) 30 & 302 & 418 & & & & & & & \\
**Double support time [ms]** & **63 \(\pm\) 12** & **37** & **90** & **19.15** & **9.73** & **2** & **6** & **6** & **SATISFACTORY** & **0.77** (0.51–0.89) \\ Trial 1 & 65 \(\pm\) 13\({}^{*}\) & 38 & 112 & & & & & & & \\ Trial 2 & 61 \(\pm\) 13 & 37 & 90 & & & & & & & \\
**Glide length [m]** & **2.31 \(\pm\) 0.20** & **1.89** & **2.70** & **8.61** & **3.33** & **0.04** & **0.10** & **0.06** & **GOOD** & **0.88** (0.76–0.94) \\ Trial 1 & 2.31 \(\pm\) 0.19 & 1.89 & 2.72 & & & & & & & \\ Trial 2 & 2.30 \(\pm\) 0.22 & 1.88 & 2.69 & & & & & & & \\
**Double support length [m]** & **0.41 \(\pm\) 0.08** & **0.23** & **0.57** & **18.48** & **9.78** & **0.02** & **0.04** & **0.04** & **SATISFACTORY** & **0.76** (0.50–0.88) \\ Trial 1 & 0.42 \(\pm\) 0.08 & 0.24 & 0.68 & & & & & & & \\ Trial 2 & 0.40 \(\pm\) 0.08 & 0.23 & 0.57 & & & & & & & \\
**Step length [m]** & **1.89 \(\pm\) 0.15** & **1.60** & **2.26** & **8.05** & **2.81** & **0.03** & **0.08** & **0.04** & **GOOD** & **0.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 1 Introduction
* It proposes an integrated access control model that combines RBAC, ABAC and RiBAC to enhance the performance, adaptability and security issues associated with modern healthcare systems. * It develops a risk-aware framework that incorporates dynamic risk assessment. This allows the system to adapt to changing threats and vulnerabilities in the healthcare environment. * It validates and evaluates the proposed risk-aware RBAC and ABAC using 43 healthcare scenarios to assess the effectiveness of the integrated model to grant access to authorized users while denying access to unauthorized users. The structure of this paper is organized as follows: Section 2 presents a comprehensive analysis of related models discussed in the existing literature. Section 3 introduces the proposed risk-aware RBAC and ABAC model. In Section 4, the implementation of the proposed model within a healthcare context is thoroughly examined and discussed. Section 5 presents the experimental results alongside an in-depth analysis, while Section 6 provides a detailed discussion and evaluation of the findings. Finally, Section 7 concludes the paper, summarizing key insights and implications of the research.
# On-Orbit Calibration Method for Correction Microwave Radiometer of the HY-2 Satellite Constellation
## 6 Summary and Conclusions
In order to improve the accuracy of products in the HY-2 satellite constellation, the task of optimizing calibration equations and retrieval algorithms to WPD from multiple directions must continue, including on-orbit calibration, adapting retrieval algorithms to various latitudes or different wind speeds, building special retrieval algorithms for insbore products, etc. Improving CMR products is a systematic project; in the future, special retrieval algorithms will be used according to wind speed or pertinent regions. Though the constellation has operated for a short time and some effective data are missing, this study has proved the effectiveness of on-orbit calibration, and the current calibration equations can still be further optimized. With more data, this method will further improve the accuracy of WPD in the HY-2 satellite constellation. Conceptualization, M.L. ; methodology, J.Z. and Y.J. ; software and validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and X.M. ; writing--review and editing, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. the National Natural Science Foundation of China (NO.42192561, NO.42192531); the National Key Research and Development Program of China (2021 YFC3101502). Data are contained within this article. The authors would like to acknowledge NSOAS for HY-2 satellite constellation data. We would also like to acknowledge ECMWF for providing reanalysis data. The authors declare no conflict of interest.
###### Abstract  
The historical management of underground utilities such as communication lines, water and sewage pipes, and power lines is essential for their effective use. However, due to the nature of the buried facilities, detecting and tracking them are challenging, although various solutions are difficult to apply in the field, especially optical cables, which are mainly used for communication, making it more difficult to apply existing solutions. There has been limited research on the practicalities of solutions, especially on multilayer structures such as road pavements. Based on a literature review, we selected ultra-high-frequency radio frequency identification (UHF RFID), which is least affected by performance degradation or interference due to batteries. We experimented to identify the signal attenuation based on the medium by controlling the materials and moisture used in the road pavement as variables. The signal reached a depth of 68 cm and this was possible under conditions of asphalt and subgrade soil. In a comparative experiment for each variable, we recognized tags of sand and coarse aggregate up to a depth of 70 cm. When the moisture was considered, the signal attenuation difference was 1.8 dBm for each level. Based on the experimental results of this study, it was confirmed that UHF RFID can be used as a marker sensor to be attached to utilities installed under the road pavement. Depending on the structure and material of the pavement, the signal is sufficiently transmitted up to 40-50 cm, which is the target installation depth of the communication line.  
ultra-high-frequency radio frequency identification; underground utilities; signal attenuation; signal-reaching performance; pavement +
Footnote †: journal: Applied Sciences
# ML-Based JIT1 Optimization for Throughput Maximization in Cluster Tool Automation
## 4 Simulation and Results
### Cluster Tool Control in Semiconductor Manufacturing Fab  
E etch clusters are operated based on various parameters. This paper proposes two strategies: the ML modeling method that could obtain the optimal JIT1 based on process time and WAC time, and the modeling that could obtain the maximized throughput by applying the optimal JIT1 to the real-time cluster facility. In general, in the case of the cluster, it was difficult to optimize throughput according to the real-time changing process time and WAC time because JIT1, to obtain maximum throughput, was used as a fixed value instead of being variable in real time. Therefore, modeling to obtain optimal JIT1 through the ML analysis was proposed. A strategy was presented to apply JIT1 to a cluster in semiconductor factories along with lot allocation through the SECS/GEM protocol. The productivity optimization process proposed in Section 3 consisted of simulations. Figure 8 shows the simulations of the cluster facility being controlled by the host. The two on the right give various commands to the equipment through the host and receive real-time data. The one on the left is a cluster facility, and when it receives a command from the host, it executes the command. These commands included the progress of the lot and the change of parameters such as stop and recovery. In addition, real-time process parameters could be changed, ensuring versatile plant control.