esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results Analysis
### Energy Dissipation  
In the seismic analysis, the equivalent damping coefficient (\(h_{\mathrm{e}}\)) and energy dissipation was used to evaluate the energy dissipation capacity. The equivalent viscous damping coefficient could be calculated as Equation (4).  
\[h_{e}\ =\ \frac{1}{2\pi}\frac{S_{(ABC+CDA)}}{S_{(\Delta\mathrm{OBE+AODF})}} \tag{4}\]  
where \(S_{(\Delta\mathrm{ABC+CDA})}\) = the area enclosed by a complete load cycle; \(S_{(\Delta\mathrm{OBE+AODF})}\) = idealized energy dissipation assuming elastoplastic behavior, as shown in Figure 9. The equivalent viscous damping coefficients for all the specimens were listed in Table 3 and Figure 10. Energy dissipation were calculated based on hysteresis curves of the specimens and the result is illustrated in Figure 11.  
(1) From Figure 10, it is observed that the axial compression increased the bending energy dissipation significantly. At the final loading stage, the bending energy dissipation capacity of specimen RCL-4 (\(n\) = 0.28) is about 11% higher than that of specimen RCL-2 (\(n\) = 0.23), while the bending energy dissipation capacity of specimen RCL-5 (\(n\) = 0.23) is around 14.5% higher than that of specimen RCL-3 (\(n\) = 0.19). For the torsion energy dissipation capacity, with the axial compression ratio increased the torsion energy dissipation capacity declined. In the failure stage, the specimen RCL-3 (\(n\) = 0.19) resulted in a 6.1% higher energy dissipation as compared to the specimen RCL-5 (\(n\) = 0.23).  
(2) With the increase of displacement, the torsion energy-dissipated capacity keeps increasing although there is a slight fluctuation. This is attributed to the initial energy-dissipation capacity offered by outer concrete, which deactivated after the cracks appeared. The energy-dissipation capacity was offered by reinforcement skeleton and core concrete, resulting in the energy-dissipation capacity continues to grow after a slight fluctuation. The bending energy consumption increased slowly at the early stage and increased rapidly at the final stage, which is due to the increase of plastic displacement and the decreases of stiffness.  
(3) The proportion of torsion energy dissipation was larger than bending energy dissipation until 5\(\Delta\)y. When the RC L-shaped column failed, the bending equivalent damping coefficient was about 0.08-0.28 and the torsion equivalent damping coefficient was about 0.13-0.23.  
Figure 10: Equivalent damping ratio curves  
Figure 9: Diagram of energy dissipation.
## 4 Secure Authentication and Prescription Safety Protocol
(i)  Decrypt \(C_{B}\) to get \(\left[(X_{B}||\ P_{B}||\ T_{3})\right]\) (ii)  Computes \(Y^{\prime}_{B}=dX_{B}\) (iii)  Decrypt \(P_{B}\) to get \(\left[(ID_{B}||M_{B}||N_{3}||\ \text{MAC}(M_{B})||ID_{TS})\right]\) (iv)  Calculate \(M^{\prime}_{B}=H(PW_{B}||ID_{TS}||ID_{B})\) (v)  If \(M^{\prime}_{B}\ NOT\ equals\ M_{B}\) then drop message (vi) \(CP_{A}=\{\ E_{H^{\prime}_{A}}(X_{B}||ID_{A}||ID_{B}||\ Y^{\prime}_{A}||N_{1}\ )\}\) (vii) \(CP_{B}=\{\ E_{H^{\prime}_{B}}(X_{A}||ID_{A}||ID_{B}||\ Y^{\prime}_{B}||N_{2}\ )\}\) \(TS\to A:\ \{ID_{A\sim T},\ CP_{A}\}\) \(TS\to B:\ \{ID_{B\sim T},\ CP_{B}\}\)  D.  Step-IV: Participant Validation and Common Session Key Generation Patient (A) decrypts \(CP_{A}\), verified by its own nonce and MAC which provide integrity and validity of the TS and the message. The common parameters generated by the trusted server are transmitted securely on each end. Upon receiving the secret credentials, the participating parties first verify message integrity and authority by verifying \(Y^{\prime}_{A}\) and \(Y^{\prime}_{B}\), respectively. After that, MAC, nonce, TS-ID, and the time stamp are also used for double-checking the source's integrity before processing secret credentials. After successful validation of both parties' identities and that of the TS, participants start to compute the common key. The proposed protocol along with stepwise execution is elaborated in Figure 3. 1. Party (A) gets: \(\left\{X_{B},ID_{A},\ ID_{B},\ Y^{\prime}_{A}\right\}\) 2. Patient (A): \(S_{kA}=H(dX_{B},ID_{B},\ ID_{A})\) 3. Party (B) gets: \(\left\{X_{A},ID_{A},\ ID_{B},\ Y^{\prime}_{B}\right\}\) 4. Doctor/Nurse (B): \(S_{kB}=H(dX_{A},ID_{B},\ ID_{A})\) 4. The novelty of our study is relying upon the creation of secret credentials of the session key for multiparty computing using ECC and symmetric parameters which have less computation cost and are hard to compromise. Upon successful authentication of end parties and common session key generation, both ends share private data securely and efficiently. SAPS attains shared verification, better forward privacy, un-traceability, and participant anonymity. It can launch a secure data sharing connection between the end user and a trusted authority. It also ensures that various attacks, such as offline password guessing, untraceable online secret parameters guessing, confidential insider attacks, card theft attacks, and replay attacks. Intruders cannot enter the system, and the user remains protected at all times.
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 3 Problem Statement & System Model
### Sequence Diagram  
Sequence diagrams describe the system's behavior by determining how objects communicate with one another to accomplish the objectives. Once we design the system, we can define the sequence using the sequence diagram. A management system shows strong  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**References** & **Description** & **Limitations** \\ \hline
[44] & The study focused on effective fire detection and proposed some algorithms to apply them on UAVs for the reduction of false alarms. Using these techniques, color identification, Smoke Motion Recognition, Fire Classification algorithms, and the helicopters cameras in them for visualization. & Using only drones for fire detection can be expensive, there is no mechanism in place to deal with drone failure and the system is not formalized. \\ \hline
[45] & A neural network has been trained in this research to conduct forest fire predictions. To predict fire hotspots, this system uses readily available remotely-sensed information in the type of satellite pictures. & This system uses satellite images for fire prediction that can cause late detection of fire. Further, there is no formalization for this system. \\ \hline
[46] & An algorithm named FFDEA has been presented using WSAN for the detection of forest fire and extinguishment system. Temperature sensors are used to detect fire and to combat the fire; robots, as actors, are utilized. This system is formalized using VDM-SL. & In this system, just a temperature sensor is utilized, which is ineffective for precise fire detection and increases the false alert rate. In addition, there is no mechanism for confirmation of fire and no network maintenance available. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Most relevant existing studies with limitations.  
behavior throughout all situations across all times; hence, every device can provide information at any time. As this system is IoT-based, there is online communication between all the objects. In Figure 2, the sequence of the system shows how the fire will be detected and how the action will be performed.
## 3 Results
### Time Displacement Trend Prediction  
The ability to predict future phenomena would be of great value for the dam stability analysis. Unfortunately, there are a large number of variable factors that affect the value of displacement increments (velocity and acceleration) in consecutive years, which excludes the possibility of accurate prediction. However, some patterns are repetitive over time. The construction of the next layers of the dam and the pouring out of tailings take place according to a fixed, unchanging system. Therefore, it is possible to search for repeating patterns in displacement values over the years. It was noticed that for the vast majority of cases, there is an increasing exponential trend for the total displacement over time, indicating that displacements are increasing over time. Fitting the trend to the total displacement also makes it possible to indicate the trend for velocity and acceleration, which are the first and second derivatives, respectively. The method developed in this way does not provide accurate information on the value of displacement in subsequent years but is able to indicate its trend, which is equally valuable information.  
Figure 8 shows two examples of benchmark displacement measurements with determined trends. It can be seen that the exponential trend describes the behavior of the total displacement well; the value of the R-squared is above 0.9. In the case of velocity and acceleration, the values are already far from the trend, but it is still visible that they are following it. In most cases (including those presented), the velocity has a slight upward trend, and the acceleration is rather constant. However, assuming the occurrence of the trend described by the exponent function, it is known that the acceleration will also start to increase after some time. This moment may be critical for the dam's stability. The trends have also been projected three years ahead. Although the values are still unknown, the method can identify a trend, allowing for a short-term prediction of the displacement trend over time.  
Figure 7: Shear risk at individual measuring points in three years.
## 2 Method
### Kinematic Analysis and Prediction Process  
We can acquire an endpoint of the manipulator using forward kinematics. Figure 3 shows the shape of the manipulator with arbitrary bending angles and parameters of the segments. The forward kinematics were solved using the Denavit-Hartenberg (D-H) parameters. The parameters applied in this method are showed in Table 1. Where the origin of the absolute coordinate system is \(o(0,0,0)\), the position of the manipulator endpoint \({}^{0}P(x_{e},y_{e},z_{e})\) in the coordinate system is expressed as  
\[{}^{0}P\ =\ {}_{1}^{0}T\cdots{}_{8}^{7}T\cdot{}^{e}P\ =\ {}_{8}^{0}T\left[\begin{array}{c}l_{e} \\ 0\\ 0\\ 1\end{array}\right]. \tag{6}\]  
\({}_{1}^{0}T\) is a transformation matrix representing the relationship between the origin and the first joint's coordinate systems and can be expressed as \({}_{8}^{0}T\) by extending the relationship from the origin to the eighth joint. \({}^{e}P\) represents the endpoint with respect to the last relative coordinate system. It can be represented by \({}^{e}P\ (l_{e},0,0)\) because the endpoint is located on the x-axis. The value of \(os\) in Figure 3, which corresponds to the column \(r_{i-1}\) of the first joint, and the value \(l_{e}\) on the matrix \({}^{e}P\) could be changed depending on the length of the segment assembled at the proximal and distal parts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(i\) & \(\mathbf{d}_{i}\) & \(\mathbf{\theta}_{i}\) & \(\mathbf{\pi}_{i-1}\) & \(\mathbf{r}_{i-1}\) \\ \hline
1 & 0 & \(\theta_{1}\) & 0 & \(os\) \\
2 & 0 & \(\theta_{2}\) & \(90^{\circ}\) & \(l\) \\
3 & 0 & \(\theta_{3}\) & \(-90^{\circ}\) & \(l\) \\
4 & 0 & \(\theta_{4}\) & \(90^{\circ}\) & \(l\) \\
5 & 0 & \(\theta_{5}\) & \(-90^{\circ}\) & \(l\) \\
6 & 0 & \(\theta_{6}\) & \(90^{\circ}\) & \(l\) \\
7 & 0 & \(\theta_{7}\) & \(-90^{\circ}\) & \(l\) \\
8 & 0 & \(\theta_{8}\) & \(90^{\circ}\) & \(l\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Denavit–Hartenberg (D-H) parameters.  
Figure 3: Coordinate system setting for kinematic analysis and explanation of each parameter.  
The overall flow chart for predicting the bending motion of the manipulator by applying the results of the kinematic analysis is shown in Figure 4. When the cable was pulled a certain distance, the cable displacement of each joint with the minimum potential energy could be known through constraint optimization, and the shape of the manipulator could be predicted using this.
## 1 Introduction  
As is well known, sulfur hexafluoride (SF\({}_{6}\)), with a good insulation performance and thermal stability, is widely used as an insulation gas and arc quenching medium in many fields such as high-voltage engineering and electrical power applications [1; 2]. Unfortunately, as one of the six greenhouse gases, SF\({}_{6}\) has an extremely high global warming potential (GWP\({}_{100}\)) at 23,500 times that of CO\({}_{2}\)[3]. Thus, in order to slow down global warming, searching for an environment-friendly insulation gas to reduce the use of SF\({}_{6}\) is of great importance.  
During the last decades, there has been some research about the discharge characteristics of c-C\({}_{4}\)F\({}_{8}\)[4; 5; 6] and CF\({}_{3}\)I [7; 8; 9], which have been thought of as potential alternative gases to SF\({}_{6}\). Recently, as another newly potential substitutional insulation gas of SF\({}_{6}\), fluoronitriles (C\({}_{4}\)F\({}_{7}\)N, also known as 3M Novec-4710), which have a GWP value of 2100, have been designed and have attracted great attention [10]. Although C\({}_{4}\)F\({}_{7}\)N gas has a good insulation performance, because of its high boiling temperature, it has to be mixed with other buffer gases such as CO\({}_{2}\) to adapt to the operating conditions under high gas pressure [11]. Nevertheless, it is worthy to obtain the basic electron swarms of C\({}_{4}\)F\({}_{7}\)N, such as the ionization coefficient \(\alpha\) and attachment coefficient \(\eta\), which can be used to evaluate the insulation performance of gases [12].  
However, it should be noted that there has been less investigation on the ionization coefficients and attachment coefficients in C4F7N. It has been reported once that the normalized effective ionization coefficient (\(\alpha-\eta\))/N within a small range of reduced electric fields (per gas density _N_) _E/N_ was obtained with the steady-state Townsend (SST) method by [PERSON] et al. (2013). The electron rate and transport coefficients in pure C4F7N were obtained with the pulsed Townsend (PT) method by [PERSON] et al. (2014). In the present work, both normalized ionization and normalized attachment coefficients, as well as effective ionization coefficients of C4F7N, were measured by the SST method, a kind of method which has been widely used to measure these coefficients in different gases like SF6 ([PERSON] et al., 2014; [PERSON] et al., 2015; [PERSON] et al., 2016), c-C4F8 ([PERSON] et al., 2016), CF3I ([PERSON] et al., 2016), and so on. It should also be noticed that although the SST method has been well developed during the last decades, and the innovation of the method itself may not be high, the experiments using this method to measure and obtain the discharge parameters and evaluate the insulation abilities of new insulation gases are still significant.
## 2 Materials and Methods
### Satellite Position Inversion
According to steps 1 to 4, calculate the radar LOS vectors corresponding to the leftmost image point \(m_{1}(r_{1},c_{1})\) and the rightmost image point \(m_{2}(r_{2},c_{2})\) of the central row of the image. The satellite position is inverted using the radar LOS vectors of image points \(m_{0}\), \(m_{1}\), and \(m_{2}\) through the autonomous intersection method, as shown in Figure 2.
6. If the SAR electromagnetic wave path is approximated as a straight line, the electromagnetic wave paths at the imaging moments of image points \(m_{0}\), \(m_{1}\), and \(m_{2}\) are denoted as \(\omega_{0}\), \(\omega_{1}\), and \(\omega_{2}\). These paths are specifically represented as follows: \[ \begin{array}{l}\omega_{0}=P_{0}+\eta_{0}\beta_{0}\\ \omega_{1}=P_{1}+\eta_{1}\beta_{1}\\ \omega_{2}=P_{2}+\eta_{2}\beta_{2}\end{array} \] (7) where \(\eta_{0}\), \(\eta_{1}\), \(\eta_{2}\) are scaling factors; and \(P_{0}(X_{0},Y_{0},Z_{0})\), \(P_{1}(X_{1},Y_{1},Z_{1})\), \(P_{2}(X_{2},Y_{2},Z_{2})\) and \(\beta_{0}(i_{0},j_{0},k_{0})\), \(\beta_{1}(i_{1},j_{1},k_{1})\), \(\beta_{2}(i_{2},j_{2},k_{2})\) represent the corresponding space rectangular coordinates of the ground points and the radar LOS vectors of image points \(m_{0}\), \(m_{1}\) and \(m_{2}\). The linear system of equations is further obtained from Equation (7) as follows: \[A_{1}\eta=l_{1}\] (8) where \[A_{1}=\left[ \begin{array}{ccc}i_{0}&-i_{1}&0\\ j_{0}&-j_{1}&0\\ k_{0}&-k_{1}&0\\ i_{0}&0&-i_{2}\\ j_{0}&0&-j_{2}\\ k_{0}&0&-k_{2}\end{array} \right],\eta=\left[ \begin{array}{c}\eta_{0}\\ \eta_{1}\\ \eta_{2}\end{array} \right],l_{1}==\left[ \begin{array}{c}X_{1}-X_{0}\\ Y_{1}-Y_{0}\\ Z_{1}-Z_{0}\\ X_{2}-X_{0}\\ Y_{2}-Y_{0}\\ Z_{2}-Z_{0}\end{array} \right]\] The scaling factor is obtained through the least squares solution as follows: \[\eta=(A_{1}^{\rm T}A_{1})^{-1}A_{1}^{\rm T}l_{1}\] (9)  
By substituting the scaling factor into the system of equations, the satellite position coordinates can be obtained.
# Multi-Dimensional Attention ```
## 5 Result and Discussion
### Comparative Methods
#### 5.6.1 Comparative Analysis Based on TP for AV Demand Forecasting Dataset  
The MMCW-BiLSTM model performed better than the meta-heuristic CNN-LSTM model when it came to predicting inventory. It showed a significant improvement of 19.47% and achieved the lowest MAE of 1.75, as illustrated in Figure 7a.  
In Figure 7b, the MMCW-BiLSTM model shows better predictive performance for inventory than the meta-heuristic CNN-LSTM model. It surpasses the meta-heuristic CNN-LSTM model by 15.74%, reaching a minimum MAPE of 3.48 with a TP of 90.  
The MMCW-BiLSTM model, shown in Figure 7c, outperforms the meta-heuristic CNN-LSTM model by 31.34% in forecasting inventory. It attains an MSE score of 6.76 with a TP of 90, surpassing existing methods.  
In Figure 7d, the MMCW-BiLSTM model outperforms the meta-heuristic CNN-LSTM model in predicting inventory by achieving an RMSE of 2.60, with a TP of 90, which is 14.60% higher than the CNN-LSTM model.  
Figure 6: Performance analysis based on TP for Dairy Goods Sales Dataset: (**a**) MAE, (**b**) MAPE, (**c**) MSE, and (**d**) RMSE.
# Retrofit of Existing Reinforced Concrete (RC) Buildings: Steel vs. RC Exoskeletons
## 2 Case Study Building
### Analysis of Bare RC Building
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Push-Over** & \(F_{max}\) & \(\Gamma\) & \(F_{max}^{\prime}\) & \(a_{u}/a_{1}\) & \(S_{e}(T^{\prime})\) & \(q^{*}\) & \(C\) & \(D\) & \(S\) \\ \hline  & **(kN)** & & **(kN)** & & **(g)** & & **(cm)** & **(cm)** & \\ \hline Mode (\(+X\)) & 3785 & 1.49 & 2536 & 0.961 & 0.334 & 1.2 & 8.65 & 9.69 & 0.89 \\ Mode (\(-X\)) & 3771 & 1.49 & 2526 & 1.06 & 0.336 & 1.3 & 10.82 & 9.64 & 1.12 \\ Mode (\(+Y\)) & 4749 & 1.43 & 3319 & 0.968 & 0.361 & 1.2 & 10.64 & 8.97 & 1.18 \\ Mode (\(-Y\)) & 4913 & 1.43 & 3434 & 0.999 & 0.352 & 1.2 & 11.20 & 9.21 & 1.21 \\ Mass (\(+X\)) & 6307 & 1.49 & 4226 & 0.793 & 0.398 & 1.0 & 11.38 & 8.15 & 1.39 \\ Mass (\(-X\)) & 6136 & 1.49 & 4111 & 1.49 & 0.40 & 1.0 & 8.34 & 8.10 & 1.03 \\ Mass (\(+Y\)) & 7547 & 1.43 & 5275 & 1.75 & 0.437 & 1.0 & 12.64 & 7.41 & 1.70 \\ Mass (\(-Y\)) & 7548 & 1.43 & 5276 & 1.55 & 0.428 & 1.0 & 12.06 & 7.56 & 1.59 \\ \hline \hline \end{tabular} \(F_{max}\): Maximum value of horizontal force applied on the structure (base shear); \(\Gamma\): participation coefficient; \(F_{max}\): \(F_{max}\): \(F_{max}/\Gamma\); \(a_{u}/a_{1}\): ratio between the value of the seismic action for which the formation of a number of plastic hinges occurs such that the structure becomes labile, and that for which the first structural element reaches plasticity; \(S_{e}(T^{\prime})\): elastic response spectrum corresponding to period \(T^{\prime}\); \(q^{*}\): behaviour factor (\(q^{*}=m^{*}\ S_{e}(T^{\prime})/F_{y}\)*); C: displacement capacity of the structure; \(D\): displacement demand for the control point of the structure; \(S=C/D\): safety index.  
\end{table} 
Table 1: Results of PO analysis for the bare RC building in the pre-intervention stage, overlooking the combinations of the seismic components. Figure 6: Results of PO analysis for the bare RC building in the pre-intervention stage, considering the combination of seismic components acting simultaneously in X- and Y-directions. In Table 2, detailed results from the PO analyses considering the combination of seismic components, as indicated in Equations (1) and (2), are also presented. As it is easy to verify, in this case, the calculated C/D ratio is less than the unity value for ten out of the sixteen examined cases (\(\approx\)0.63%), hence denoting the critical conditions of the system in its state of configuration.
## 2 Introduction to Digital Watermarking: Trajectory, Techniques, and Applications
### Reversibility:  
This characteristic determines whether the original content can be recovered after watermark extraction. Reversible watermarks allow for perfect recovery of the original medium [40], while irreversible watermarks permanently alter the content. Partially reversible approaches enable the recovery of critical content features while accepting minor losses.  
AI/ML Approaches:  
Recent advances integrate artificial intelligence with watermarking. CNN-based methods use convolutional neural networks for embedding and extraction [17]. GAN-based techniques employ generative adversarial networks to improve imperceptibility [17; 41]. Adversarial approaches incorporate adversarial training to improve robustness. Attention-based methods leverage attention mechanisms to identify optimal embedding regions [17; 41]. Transformer architectures adapt language-model approaches to watermarking, while diffusion models use generative processes. Federated and few-shot learning enable watermarking with limited data [19].
###### Abstract  
The effects of 1-methylcyclopropene (1-MCP), storage atmosphere (controlled (CA) or regular (RA)), and juice processing (clear or cloudy) on the volatile aroma compounds from McIntosh and Honeycrisp apples following 4-month storage were studied. All the major esters, aldehydes, and total volatile content from McIntosh juice were significantly affected by the two-way interaction between harvest maturity and 1-MCP treatment (\(p\leq 0.01\)), as well as harvest maturity and storage atmosphere (\(p\leq 0.001\)). In McIntosh juices, a remarkable reduction of all types of esters, aldehydes, most alcohols, and total volatile compounds was found when juices were prepared from 1-MCP-treated apples. In Honeycrisp, significant differences in the level of esters and the total volatile aroma was caused by storage atmosphere and juice processing techniques (\(p\leq 0.001\)), but not by 1-MCP treatment. As compared to clear juices, cloudy juice samples from Honeycrisp had a considerably higher content of total volatiles, esters, and aldehydes.  
juice processing; volatiles; GC-HS/SPME-MS; postharvest technology; storage; quality Article