esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temporal Evolution of Polarimetric Observables
#### 3.1.3 Correlation Coefficient and Phase Difference  
Polarimetric complex coherences contain two parameters, the correlation coefficient and the phase difference between two polarimetric channels, which can be used to help distinguish land cover types. Figure 7 shows the temporal variations of the common four complex correlation coefficients (The values of three main crops (corn, soybean, and winter wheat) are shown in the supplementary Table S3). It can be seen that \(\rho_{\text{HHVV}}\) showed more sensitivity to crop development than \(\rho_{\text{HHVV}}\), \(\rho_{\text{HHVHV}}\), and \(\rho_{\text{HH + VV,HH-VV}}\), whereas all phases were rather noisy due to the low correlations found in most part of the growing season.  
In Figure 7a, the copolarization correlation coefficient \(\rho_{\text{HHVV}}\) of corn, soybean, watermelon and winter wheat show a gradually decreasing trend first followed by an increasing trend in the growing season. The volume scattering increased with the progression of crop growth, and the surface scattering decreased with the increase of crop coverage and biomass accumulation throughout the vegetative growth period. When the crops started to mature, corn leaves started to dry up and bend down causing reduced volume scattering, and increased background soil became visible to the SAR signal, leading to increase in surface scattering. In the case of soybeans, in May to mid-July surface scattering was reduced and volume scattering increased (\(\rho_{\text{HHVV}}\) reduced from 0.7 to 0.5), while in late July to September volume scattering increased. Opposite trends were observed in watermelon, forage and tobacco when the surface scattering of other crops (corn and winter wheat) increased, and volume scattering decreased from August to September. The copolarization correlation of corn in April and May was high (about 0.7), by the middle of May and July it was on the decline (0.7 to 0.4), and then a small amplitude appeared, increasing (about 0.4 to 0.5) until September, followed by a small decline (0.4), showing that in the early stage of corn planting surface scattering was larger and volume scattering increased, because after harvest the surface scattering increased. The surface scattering of winter wheat was greater from April to late June, and the volume scattering increased from July to late September.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 2 Related Works
EAST [37] implemented multi-bit embedding on the classification logits instead of the feature map. Deep learning has rarely been applied to audio watermarking compared to image watermarking. DeAR [9] transformed the audio into a 2D spectrogram and then used an image watermarking model. A cascade pipeline of'reverberation'-'band-pass filtering'-'Gaussian noise' was also adopted to simulate the distortion of audio propagating over the air. [PERSON] et al. [38] developed a new method for audio watermarking based on adversarial examples, which embeds and recovers the information using a private DNN-based ASR (automatic speech recognition) model. However, [PERSON] et al. did not adopt some strategies to guarantee robustness, so the embedding information was utterly unrecognizable after attacks. **Adversarial perturbations**[39] are initially used to attack a deep learning model, which cause the model to output wrong [40] or specified [41] labels. Adversarial perturbations are usually tiny or imperceptible, so data that are correctly recognized by humans may cause a deep learning model to produce the wrong output. Adversarial perturbations have attracted a lot of concern in fields such as image classification [42; 43], target recognition [44; 45], and speech recognition [46; 47; 48]. Researchers have proposed some more effective perturbation methods [47; 49] or provided many strategies to resists perturbations [48]. Adversarial perturbation also enables a feature extraction network to output specified features. While destructively hazardous for most tasks, adversarial perturbations can be skillfully implemented for watermark embedding.
# Hierarchical Classification of Botnet Using Lightweight CNN
## 3 Materials and Method
### Evaluation Parameters  
Precision, recall, F1 score, and accuracy are metrics commonly used to evaluate the performance of classification models [17]. These metrics are calculated based on the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). These terms are commonly used in binary classification scenarios, where a model is predicting between two or more classes (positive and negative) [17].  
* Precision: This is also known as a positive predictive value, which measures the accuracy of positive predictions made by the model. It is calculated as the ratio of true positives to the sum of true positives and false positives as presented in Equation (12). \[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (12)
* Recall: This is also known as sensitivity or true positive rate, which measures the ability of the model to correctly identify all relevant instances in the dataset. It is calculated as the ratio of true positives to the sum of true positives and false negatives as presented in Equation (13). \[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (13)
* F1 score: This is the harmonic mean of precision and recall, providing a balance between the two metrics as presented in Equation (14). It is particularly useful when there is an uneven class distribution. \[\text{F1 Score}=\frac{2\cdot(\text{Precision}\cdot\text{Recall})}{\text{ Precision}+\text{Recall}}\] (14)
* Accuracy: This measures the overall correctness of the model by considering both true positives and true negatives. It is calculated as the ratio of correctly predicted instances (TP + TN) to the total number of instances as presented in Equation (15). \[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}}\] (15)
* Matthews correlation coefficient (MCC) [26]: This considers both true and false positives and negatives, making it a well-balanced measure suitable for scenarios where class imbalance. Ranging from \(-1\) to \(+1\), the MCC essentially represents a correlation coefficient. A value of \(+1\) indicates a flawless prediction, \(0\) denotes an average random prediction, and \(-1\) signifies an inverse prediction. The formula to calculate MCC is given in Equation (16). \[\text{MCC}=\frac{TP*TN-FP*FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}\] (16)
* Cohen's Kappa (k) [27]: This serves as a statistical metric for measuring inter-annotator agreement. This function calculates Cohen's kappa, a score that quantifies the level of agreement between two annotators in a classification problem. The definition, as presented in Equation (17), involves \(p_{o}\), the empirical probability of agreement on the label assigned to any sample (the observed agreement ratio), and \(p_{e}\), the expected agreement when annotators assign labels randomly. Estimating \(p_{e}\) utilizes a per-annotator empirical prior over the class labels.  
\[\text{k}=\frac{p_{o}-p_{e}}{1-p_{e}} \tag{17}\]
## 4 Security Analysis of the MAPP Protocol  
### Security Requirement Analysis  
Every security protocol must satisfy some primary security functions defined by the V2X security standards. The security requirements that must be satisfied by the proposed protocol is identity authentication, message authentication, non-repudiation, privacy-preserving, unlinkability, and system update [33].  
In this section, we show how the proposed MAPP protocol accomplishes the required security functions.
# Numerical and Experimental Analysis of Pressure Pulsation Attenuator Based on Helmholtz Resonator
## 3 Theoretical Model
### Model of the Three-Way Unit  
The HTPA is connected to the main oil circuit in the form of a branch pipe, as shown in Figure 4. The inlet is connected to the oil input line, the outlet is connected to the oil output line, and the branch is connected to the HTPA.  
Without considering the effect of pressure pulsation changes and local pressure losses due to structural changes at the branch of the pipeline, there are:  
\[\left\{\begin{array}{c}P_{2}(s)=P_{3}(s)=P_{4}(s)\\ Q_{3}(s)=Q_{2}(s)+Q_{4}(s)\end{array}\right. \tag{5}\]  
Let \(Y(s)=Q_{2}(s)/P_{2}(s)\), then \(Q_{3}(s)=Q_{4}(s)+P_{4}(s)\mathrm{Y}(s)\). Substituting the above equation into Equation (5), we obtain the result:  
\[\begin{bmatrix}P_{3}(s)\\ Q_{3}(s)\end{bmatrix}=\begin{bmatrix}1&0\\ Y(s)&1\end{bmatrix}\begin{bmatrix}P_{4}(s)\\ Q_{4}(s)\end{bmatrix} \tag{6}\]  
From Equations (3) and (4), it is obtained that [32]:  
\[Y(s)=\frac{Q_{2}(s)}{P_{2}(s)}=\frac{(AL+V)s}{K_{e}+\rho L(s+R_{v1})sV/A} \tag{7}\]  
Therefore, the transfer matrix \(H\) of the HTPA is:  
\[H=\begin{bmatrix}H_{11}&H_{12}\\ H_{21}&H_{22}\end{bmatrix}=\begin{bmatrix}1&0\\ \frac{(AL+V)s}{K_{e}+\rho L(s+R_{v1})sV/A}&1\end{bmatrix} \tag{8}\]
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 4 WUR in Cellular Standards
### Fifth-Generation NR LP-WUS and LP-SS
#### 4.2.2 Lp-Ss  
The LP-SS provides low-power network synchronization required for cellular IoT devices. The signal can be used to correct for frequency drift of the crystal oscillator. Minimizing timing error enables more efficient spectral usage by 5G IoT networks. One of a set of four sequences will be transmitted on the LP-SS with OOK modulation. The four sequences will be used to differentiate neighboring cells.  
The WUR turns on for a short period where it will correlate a received OOK signal with a template sequence, which is shorter than one resource block. The four sequences will be selected to maximize synchronization accuracy and will have strong cross-correlation properties in reference to each other. Current candidates for sequences include Gold Codes, m-sequences, and computer-searched sequences [42].  
Correlation of the signal can be carried out synchronously or via post-processing, as illustrated in Figure 11. The correlation value during the forecasted frequency error window is used to resynchronize the gNB and the UE. An overlaid ODFM sequence will allow existing MR cellular receivers to use the LP-SS for timing synchronization [43].
# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 2 Background
### Related Work
Simulators do not precisely reproduce the physics and randomness of real environments, resulting in biased interactions. Furthermore, such approaches assume that robots do not have acceleration constraints and may choose any velocity at any time, which is not practical in real life, and often assume that the robot is holonomic or has perfect knowledge of the environment, which is rarely the case in reality. Several works have analyzed and explained the limitations of Deep Reinforcement Learning (DRL) in the real world [24; 25], including inability to deal with environmental constraints, the limitations involved in gathering real-world training data, and partial observability. In this work, we use a modified version of S-DOVS as the local planner to avoid the limitations of reinforcement learning. Navigating in highly dynamic environments requires a local planner with the flexibility to reactively avoid obstacles, which may cause deviations from the original path. To enable long-range navigation, a global planner must be integrated into the system. The work of [26] has demonstrated promising results by subsampling the original path generated by the global planner. In Refs. [27; 28], the authors further improved upon this approach by introducing spatial horizon subgoals and replanning capabilities. Our proposed work presents a waypoint generator that utilizes subsampling and other criteria to decrease the path's density, as well as replanning capabilities and spatial horizon subgoal generation for the areas where the intermediate plan is sparse.
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
## 3 Results and Discussion
### Investigation of Fracture Behaviour  
The validated model is applied to analyze the fracture behaviour of varying target plate thickness under high-velocity impact using a blunt-shaped projectile. The study discusses the fracture behaviour of Weldox 700E steel plates with varying values of critical fracture parameters (Dc) viz. 0.6, 0.8, and 0.99. It also examines the effect of different fracture strain criteria on plates of varying target plate thicknesses viz. 2 mm, 4 mm, and 6 mm. Further, the analysis examines the effects of different projectile velocities 275.7 m/s, 300 m/s, and 400 m/s, while the projectile mass is 6.4 g and 12.8 g.
## 3 Results
### Results of Model Fit and Convergent Validity Analysis  
Table 4 presents the results of the model fit tests. The results of the model fit tests were \(\chi^{2}=233.280\) (\(p<0.001\)), GFI = 0.940 (good fit: \(0.90\leq\) GFI), RMR = 0.045 (good fit: RMR \(<0.05\)), RMSEA = 0.059 (acceptable fit: \(0.05<\) RMSEA \(\leq 0.08\)), NFI = 0.948 (good fit: \(0.90<\) NFI), RFI = 0.934 (good fit: \(0.90<\) RFI), IFI = 0.966 (good fit: \(0.90<\) IFI), CFI = 0.966 (good fit: \(0.90<\) CFI), and TLI = 0.957 (good fit: \(0.90<\) TLI). Therefore, SEM fit was evaluated as acceptable.  
The results of the convergent validity of the model can be found in Table 5. In Table 5, the CR value is greater than the acceptable criteria (0.7). The AVE values are greater than 0.5, and the correlations between the variables are less than the AVE values. The AVE values are also greater than the squared value of the correlation coefficient. These results demonstrate that the validity of the model has been achieved.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Goodness of Fit Index** & **Good Fit** & **Acceptable Fit** & **Structural Model** \\ \hline \(\chi^{2}\) & & & 233.280 \\ df & & & 83 \\ \(\chi^{2}/\)df & \textless{}2 & 2.0–5.0 & 2.811 \\ \(p\)-value & \textless{}0.001 & 0.050 & \textless{}0.001 \\ \hline RMR & \textless{}0.05 & 0.05–0.08 & 0.045 \\ RMSEA & \textless{}0.05 & 0.05–0.08 & 0.059 \\ \hline GFI & \textgreater{}0.90 & 0.85–0.90 & 0.940 \\ NFI & \textgreater{}0.90 & 0.85–0.90 & 0.948 \\ RFI & \textgreater{}0.90 & 0.85–0.90 & 0.934 \\ IFI & \textgreater{}0.90 & 0.85–0.90 & 0.966 \\ CFI & \textgreater{}0.90 & 0.85–0.90 & 0.966 \\ TLI & \textgreater{}0.90 & 0.85–0.90 & 0.957 \\ \hline \end{tabular}
\end{table}
Table 4: Results of model fit test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Factor** & **Measurement Variable** & \multicolumn{4}{c}{**Component**} \\ \cline{3-6}  & **1** & **2** & **3** & **4** \\ \hline Musculoskeletal pains & Upper limb pain & 0.087 & 0.031 & 0.128 & 0.824 \\ Backache & 0.037 & 0.122 & 0.114 & 0.800 \\ Lower limb pain & 0.031 & 0.046 & 0.118 & 0.787 \\ \hline \% of Variance & 28.899 & 21.211 & 13.845 & 9.962 \\ Cumulative (\%) & & & 73.917 & \\ Kaiser–Meyer–Olkin test & & & 0.817 & \\ Bartlett’s test & & & \(p<0.001\) & \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Goodness of Fit Index** & **Good Fit** & **Acceptable Fit** & **Structural Model** \\ \hline \(\chi^{2}\) & & & & 233.280 \\ df & & & 83 \\ \(\chi^{2}/\)df & \textless{}2 & 2.0–5.0 & 2.811 \\ \(p\)-value & \textless{}0.001 & 0.050 & \textless{}0.001 \\ \hline RMR & \textless{}0.05 & 0.05–0.08 & 0.045 \\ RMSEA & \textless{}0.05 & 0.05–0.08 & 0.059 \\ \hline GFI & \textgreater{}0.90 & 0.85–0.90 & 0.940 \\ NFI & \textgreater{}0.90 & 0.85–0.90 & 0.948 \\ RFI & \textgreater{}0.90 & 0.85–0.90 & 0.934 \\ IFI & \textgreater{}0.90 & 0.85–0.90 & 0.966 \\ CFI & \textgreater{}0.90 & 0.85–0.90 & 0.966 \\ TLI & \textgreater{}0.90 & 0.85–0.90 & 0.957 \\ \hline \end{tabular}
\end{table}
Table 4: Results of model fit test.
# EPO Extension of Dispatching Rules to Minimize Effects of Time Uncertainty in Production Scheduling
## 2 Dispatching Rules  
Dispatching rules (DRs) are one of the most widely used scheduling methods in dynamic conditions. They are used to select the next job to be scheduled on a machine. An extensive review of the 26 dispatching rules that can be applied for scheduling in the environment of unrelated machines with release times and under dynamic conditions was discussed by [PERSON] and [PERSON] [36]. We considered the following ten scheduling objectives to evaluate the quality of the schedules produced using the tested DRs:  
* Makespan (\(C_{\max}\))
* Average Completion Time (AC)
* Maximum flowtime (\(F_{\max}\))
* Maximum tardiness (\(T_{\max}\))
* Total weighted completion time (\(Cw\))
* Total weighted tardiness (\(Twt\))
* Total flowtime (\(Ft\))
* Weighted number of tardy jobs (\(Nwt\))
* Weighted earliness andweighted tardiness (\(Etwt\))
* Machine utilization (\(Mut\))  
The selected dispatching rules are listed below:  
* Minimum completion time (MCT) [37]
* Minimum execution time (MET) [37]
* Weighted shortest processing time (WSPT) [38]
* Sufferage [37]
* Sufferage2 [39]
* Just in time (JIT) [36]
* Longest processing time (LPT) [40]
* Work queue (WQ) [41]
* Relative cost (RC) [42]* Max-min [37]
* Min-max, [41]
* Earliest realease date (ERD) [40]
* Minimum execution completion time (MECT) [43]
* \(k\)-percent best (KPB) [37]
* Ordered minimum completion time (OMCT) [44]
* Opportunistic load balancing (OLB) [45]  
Dispatching rules are able to handle dynamic conditions; however, they do not guarantee the robustness of the whole schedule when actual job processing times can vary (in many cases, scheduling is performed on the basis of averaged production standards). The approach proposed (see Section 3) in this paper allows us to include the uncertainty of the job processing times, making the schedules much more robust to dynamic conditions.
## 2 Data Sources and Research Methodology  
### Study Area  
Located in the northeast region of the Tibetan Plateau, Qinghai province has a geographical position of north latitude 31\({}^{\circ}\)39\({}^{\prime}\)-39\({}^{\circ}\)19\({}^{\prime}\), east longitude 89\({}^{\circ}\)35\({}^{\prime}\)-103\({}^{\circ}\)04\({}^{\prime}\). It covers an area of 7.18 \(\times\) 10\({}^{5}\) km\({}^{2}\), ranking fourth in China (Figure 1). It has an annual average temperature between \(-\)5.6 and 8.6 \({}^{\circ}\)C and an annual precipitation ranging from 17.6 to 764.4 mm. The average elevation is over 3000 m, with a peak of 6860 m and a low point of 1650 m. As the birthplace of China's major rivers, it has a terrain higher in the southern and western region and lower in the eastern and middle region. Its sunshine is long and the solar radiation is strong. The annual sunshine reaches 2300-3600 h and the total radiation quantity in the province is 585.20-739.86 KJ cm\({}^{-2}\), second only to the Tibet autonomous region in China [22].  
Agricultural land use types in Qinghai province mainly included cropland, orchard, grassland, and forest land, with proportions of 0.76%, 0.01%, 56.25%, and 3.69%, respectively in 2006 [22]. Agricultural output value increased over 1995-2016, especially after 2007 (Figure 2). The output value from planting and stock raising contributed about 96% of the total agricultural output value. The output value from planting was basically equivalent to that from stock raising, although the latter was slightly higher than the former after the GFGP.  
Figure 1: Division of western China and location of Qinghai province, China (The division of western China was based on Wang and Shen [17]).  
Figure 2: Agricultural output value in Qinghai province over 1995–2016.
# Cable Force Health Monitoring of Tongwamen Bridge Based on Fiber Bragg Grating
###### Abstract  
A normal cable force state of a suspension bridge is extremely vital to bridge safety. For cable force monitoring of already completed suspension bridges, it is not suitable to conduct direct parameter measurements by modifying the anchor cable structure of the bridge. Instead, using an indirect measurement would not destroy the bridge's original structure and also meet engineering requirements. In this paper, a fiber Bragg grating (FBG) vibration sensor was developed for online monitoring of the cable vibration characteristics of Tongwamen bridge. The monitored vibration frequency was converted into cable force according to the theory of string vibration, so as to achieve an indirect measurement of bridge cable force. On both north and south sides of the bridge, FBG vibration sensors were mounted symmetrically on 8 of 19 cables for distributed measurement. The forces of two cables were found to be abnormal, which was considered worthy of attention and close inspection. This result is of great significance for the structural monitoring and safe operation of Tongwamen bridge.  
Fiber Bragg grating (FBG); vibration sensor; theory of string vibration; cable force monitoring; structural health monitoring
## 1 Introduction
All markers were used during static capture; however, for dynamic capture, 14 markers were excluded, including those located on both anterior superior iliac spines. The markers on the thighs and legs were retained, as they constitute a rigid cluster of four markers [12]. The authors treated the 14 excluded markers as technical markers when defining movement, a methodology commonly adopted in gait analysis. The placement of these technical markers is dependent on the physical markers and, consequently, on any associated artefacts or phenomena that may affect them. This approach in cycling research integrates the primary advantages of the two methodologies--Least-Squares Pose Estimators and non-optimal pose estimators. Specifically, it enhances participant comfort during testing, minimises marker trajectory loss, and reduces the sensitivity of the results to marker placement. The above-discussed aspects motivate the following questions. First: Can a minimised-marker protocol estimate the 3D kinematics in cycling with enough accuracy and repeatability? The next objective was established to answer this question: the development and evaluation of a new marker protocol based on the use of nonoptimal pose estimators, specific for cycling kinematic analysis. The PP will aim to be efficient by avoiding the use of technical reference systems and subsequent transformations to anatomical systems. Furthermore, it shall be based on a minimum number of markers. This helps to minimise the preparation time of the participant by defining a marker placement at points that avoid concealment or artefacts due to the movement of soft masses. Second: What is the magnitude of non-sagittal kinematics during pedalling at position, velocity and acceleration levels? A 3D kinematic analysis of the lower body during pedalling will be performed to analyse the importance of the three-dimensional character of cycling kinematics. It is well known that the main movement during pedalling is contained in the sagittal plane. However, an analysis of whether the components occurring outside this plane can be neglected or can have an important effect at the dynamic level is intended.