esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Study Area  
The investigation was carried out in the Sierra de los Donceles (municipality of Hellin, Castilla-La Mancha region, southeast Spain) (Figure 1a,b). This mountain forest lies in the most northeastern part of the Penibetic system with a southeast-northwest aspect. The landscape is patchy with steep slopes (sometimes over 25-30%), predominantly exposed to south-southeast, and ranges between 304 and 808 m above sea level with an average altitude of 506 m.  
In this forest, one catchment (hereinafter \"catchment area\" or \"catchment scale\") and one hillslope (\"hillslope area\" or \"hillslope scale\") were selected, both subject to burning and post-fire restoration actions (Figure 1c,d). In these areas, plots for survey of vegetation were installed, as detailed in Section 2.2, and remote sensing-based analysis (using satellite or drone for the catchment and hillslope scale, respectively).  
The climate of the region is semi-arid Mediterranean. The study area is located in the Mesomediterranean bioclimatic belt [41], which is characterized by a dry climate with scarce rainfalls and a large variability of temperature. The mean annual precipitation is 321 mm and the mean temperature is 16.6 \({}^{\circ}\)C (minimum and maximum mean temperatures of \(-2\) and 40 \({}^{\circ}\)C, respectively, in February and July (Spanish Agency for Meteorology, AEMET, period of 1981-2010) [42]. According to the Spanish soil map [43], soils belong to the Aridisol order and Calcic suborder, according to the classification established by the Soil Taxonomy System [44].  
A large part of the study area is covered by a sclerophyllous vegetation dominated by _Pinus halepensis_ Mill forests and with understory mainly consisting of _Quercus coccifera_, _Rhamnus lycoides_, _Halinium hutriticifolium_, _Rosmarinus officinalis_, _Cistus clusii_, _Rhamnus alaternis_, _Phamnus alaternis_, _Genista spartioides_ subsp. _Retamoid_; south-facing areas are dominated by _Stipa spartans_ and natural therophytic grasslands. Before the wildfire, tree height and density of _Pinus halepensis_ M. were 5 to 12 m and 450 to 775 individuals per hectare, respectively [45]. The wildfire occurred on July 2012, and burned a total area of 6500 ha of forest. The fire propagation was very fast, and most of the land was affected by moderate to high fire severity. After the fire, the Forest Service of Castilla-La Mancha region carried out post-fire restoration works, stabilization treatments on hillslopes (both log erosion barriers and contour-felled log debris) in autumn 2012, and checked dams in the catchment reaches in 2013 (Figure 2b,d and Figure 3).  
Figure 1: Location (**a**) and aerial map (**b**) of the study area, catchment area (**c**) and hillslope area (**d**) in Sierra de los Donceles forest (Castilla La Mancha, Spain).
## 4 The Dynamic Collision Avoidance Algorithm for the USV
### Parameter Selection of Improved Algorithm  
Considering that the CS algorithm needs to find the optimal solutions of \(\Delta\alpha\) and \(\Delta\upsilon_{U}\), the search space dimension is set to 2. The parameters of the ICS algorithm proposed in this paper include population number \(N\), crossover probability \(P_{CR}\), scaling factor \(K\) and detection probability \(P_{a}\). The detection probability \(P_{a}\) of the CS algorithm is generally 0.25 [34]. The population number \(N\) has a direct impact on the diversity of the nest position. If \(N\) is small, the algorithm converges quickly, but the probability of the algorithm falling into local optimization will also increase. Conversely, if \(N\) is set too large, the diversity of the nest position will increase, but it will increase the amount of calculation and reduce the convergence speed. For the crossover probability \(P_{CR}\), it also has a great impact on the diversity of the population. When its value is large, it is easy to guarantee the diversity of the population, and the algorithm can easily find the optimal solution. Conversely, the change in the nest position is less, which is conducive to the stable search of the algorithm, but it is possible to fall into local optimization. The scaling factor \(K\) reflects the influence on the nest position offset. A larger \(K\) makes the nest position offset larger, which is convenient for the algorithm to find the potential solution in a large range. Conversely, the convergence speed can be improved, but this also makes the algorithm easily fall into local optimization. According to the above description, the \(N\), \(P_{CR}\) and \(K\) need to be properly selected according to the solution problem.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 5 Conclusions  
We propose a reversible data-hiding scheme in encrypted images with the extended parametric binary tree labeling. By extending the representation range of prediction error, most of the non-embeddable pixels turn to be self-recordable. Thus, the required auxiliary information is greatly reduced and the data payload is significantly improved. Experimental results show that the proposed EPBTL scheme outperforms state-of-the-art schemes in embedding rate. Experimental values under different parameter settings are investigated. The data extraction and image recovery can be executed separately, which is beneficial for the applications based on the cloud storage. Security level of the proposed scheme is also analyzed. Our future work will focus on the improvement of robustness to different security attacks.  
Each author discussed the details of the manuscript. [PERSON] designed and wrote the manuscript. [PERSON] implemented the proposed technique and provided the experimental results. [PERSON], L.L. collated the results of the experiment. [PERSON]-[PERSON], L.L. reviewed and revised the article. [PERSON], [PERSON] drafted and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61866028, 61866025, 61763033, 62162045), and Technology Innovation Guidance Program Project (Special Project of Technology Cooperation, Science and Technology Department of Jiangxi Province) (20212 BDH81003).  
Not applicable.  
The datasets used in this paper are publicly available and their links are provided in the reference section.  
The authors declare no conflict of interest.
## 4 Discussion  
Studying the loss and damage mechanisms of corn harvesting platforms is crucial for the design and optimization of corn harvesting platforms. [PERSON] et al. [25] established a mathematical model for the force on the ears of corn and found that the frictional force and acceleration on the ears during picking significantly affect grain detachment and damage. [PERSON] et al. [26], through comparative experiments, discovered that the structural parameters of the picking roller and the picking ridge are the main factors affecting grain detachment and damage. [PERSON] et al. [27] used high-speed photography and found that the \"retention\" of the ears and bouncing on the picking roller are the main causes of secondary damage to the ears. However, the above studies did not consider the impact of energy changes on ear damage. Therefore, this paper established an energy balance equation for the ears and found that the change in kinetic energy before and after the collision between the ear and the picking plate is the main cause of inducing corn grain detachment and damage.  
This research shows that lengthening the header can increase the feeding angle of the plants while maintaining the header height above the ground, reducing the energy consumption of grain detachment and damage, thereby reducing harvest losses. Additionally, extending the header can increase its capacity, allowing for higher feeding speeds, while maintaining the stem-pulling roller speed at a relatively low level, reducing harvest losses and increasing harvesting efficiency. Existing loss reduction measures, such as adding cush-ioning devices or using flexible materials to reduce the force on the ears [28; 29; 30], improving the shape of the picking plate [31; 32] and the structural parameters of the stem-pulling roller [33; 34; 35; 36; 37], and implementing stem-pulling roller gap adjustment, can reduce harvest losses to some extent. These measures are highly effective when applied to large corn harvesters, but when used in single- or double-row harvesters in narrow-row corn planting areas, they can significantly reduce harvesting efficiency. The side-mounted header proposed in this paper can effectively address this issue.
## 5 Conclusions  
An inductance-enhanced patch (IEP) technique was presented that efficiently miniaturized the EBG structure while suppressing the PPW modes in high-speed packages and PCBs. The IEP effects on the stopband of the EBG structure were thoroughly examined using a dispersion diagram obtained from Floquet theory. The effective inductance of the IEP-EBG structure significantly increased, so that the stopband including the low cut-off frequency was substantially shifted down into the low frequency range without increasing the UC size of the EBG structure. It was experimentally verified that the IEP-EBG structure achieved a 62% reduction in the center frequency of the stopband when compared to a conventional MT-EBG structure. Moreover, the miniaturized IEP-EBG structure contained no reference discontinuity, so it had good signal integrity characteristics. The IEP-EBG structure is suitable for applications such as mobile devices which require a small form factor with good noise suppression characteristics  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science, ICT & Future Planning) (NRF-2016R1C1B1007123).  
The author declares no conflict of interest.
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 2 Related Work
### Simultaneous Localization and Mapping  
Operating robots in scenarios such as large indoor fires or collapsed buildings requires the robots to be able to navigate in environments with smoke and/or where GPS is not available. This involves using simultaneous localization and mapping (SLAM) techniques. SLAM consists of the concurrent construction of a model of the environment (the map that can be used also for navigation) and the estimation of the position of the robot moving within it and is considered a fundamental problem for robots to become truly autonomous. As such, over the years, a large variety of SLAM approaches have been developed, with methods based on different sensors (camera, LiDAR, RADAR, etc.), new data representations and consequently new types of maps [69; 70]. Similarly, various estimation techniques have emerged inside the SLAM field. Nonetheless, it is still an actively researched problem in robotics, so much so that the robotics research community is only now working towards designing benchmarks and mechanisms to compare different SLAM implementations. In recent years, there has been particularly intense research into VSLAM (visual SLAM) [71]. Specifically, the focus is on using primarily visual (camera) sensors to allow robots to track and keep local maps of their relative positions also in indoor environments (where GPS-based navigation fails). Extracting key features from images, the robot can determine where it is in the local environment by comparing features to a database of images taken of the environment during prior passes by the robot. As this latter procedure quickly becomes the bottleneck for its applicability, solutions are being explored to offload the processing to the cloud [72] and to the edge [56]. Additionally, in this context, we believe the cloud-to-edge-to-IoT continuum will sustain these advanced technologies.
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 2 Literature Review
### Deep Neural Network (DNN)  
Deep neural network (DNN) is one of the most common types of DL algorithm widely used in the cybersecurity domain. DNN is well-known among DL architectures due to its success in a wide range of applications [13], its ability to express complex functionswith fewer parameters, and its capability to facilitate feature extraction and representation learning [16]. However, DNN requires a substantial amount of labeled data for training. In addition, it still suffers from insufficient parameter selection techniques [17], and the learning process is time-consuming [13]. Despite its disadvantages, several research works have been conducted to examine the effectiveness of applying DNN in detecting phishing webpages. Table A1 in Appendix A summarizes previous studies related to DNN for phishing detection, in the literature.  
As a single classifier, DNN was used in [18; 19; 20] to train the classification system for the detection of phishing websites. Instead of using DNN as a stand-alone classifier, the authors in [21; 22] combined it with other DL algorithms to build a model to differentiate between malicious and benign URLs. It was observed that among these DNN-based models, parameters settings play an essential role in determining the system's performance accuracy. Nevertheless, some studies [21; 22] did not mention any of the hyper-parameters in the design of the neural network architecture, while other papers [19; 20] only specified a few of them without performing parameter optimization. The authors in [18] made additional effort in fine-tuning the parameters, but not all were included.  
Moreover, the performance metric is another crucial factor that needs to be considered when analyzing and evaluating a phishing detection system. Previous studies have shown a limited number of metrics were used to assess the performance of DL models in detecting phishing websites. For example, only two metrics were used in [19], and three were measured in [21]. More metrics were used in the studies [18; 22], yet only the selected ones were utilized to benchmark with other machine learning classifiers.
## 3 B-Mode Ultrasound Velocimetry Based on Optical Flow  
### Constraint Equation for Optical Flow Algorithm  
Multiple stationary two-dimensional underwater particle images are captured in a period by the measurement system. We need to analyze the motion state, \((u,v)\), of the underwater particles in the observed profile at a given moment, where \(u(x,y)\) and \(v(x,y)\) are the velocity components in two dimensions at pixel point \((x,y)\) in underwater images.  
In the case of a light source with a constant intensity, the instantaneous change in the grayscale of an image pixel is caused by the motions of particles. The image sequence is expressed by \(I(x,y,t)\), \((x,y)\) is a pixel, \(t\) is a time point. As mentioned above, the instantaneous rate of the change in two dimensions at a specific point in the underwater profile is expressed by \((u,v)\), which is referred to as the optical flow vector. Every optical flow vector corresponds to the instantaneous velocity vector at a point, and velocity vectors of all pixels in 2D underwater image constitute the optical flow field.  
Figure 4: Single peak (**left**) and multiple peaks (**right**) in cross-correlation plane.  
Figure 3: UIV system.  
Our task is to determine the optical flow field by the variation and correlation of the gray intensity of the pixels in the image sequence in the time domain, so as to obtain the motion of each pixel position, then obtain the 2D optical flow field.  
The optical flow calculation usually uses the brightness constancy model (BCM), of which there are two assumptions: The brightness is constant between adjacent frames; The objects' motion between adjacent frames is relatively small.  
Assuming that \(I(x,y,t)\) is the gray level of pixel \((x,y)\) at the moment \(t\), so the BCM can be expressed by the following equation:  
\[I(x+dx,y+dy,t+dt)=I(x,y,t) \tag{1}\]  
where, \(dx,dy,dt\) are the increments of \(x,y,t\) respectively.  
Expanding \(I(x+dx,y+dy,t+dt)\) with the first-order Taylor formula, we can obtain following expansion formula:  
\[I(x+dx,y+dy,t+dt)=I(x,y,z)+\frac{\partial I}{\partial x}dx+\frac{\partial I}{ \partial y}+\frac{\partial I}{\partial t}+e \tag{2}\]  
Substituting Equation (2) into Equation (1), the constraint equation for optical flow algorithm is obtained as below:  
\[I_{x}u+I_{y}v+I_{t}=0 \tag{3}\]  
where \(u(x,y),v(x,y)\) is the components of optical flow at pixel \((x,y)\), \(I_{x}\), \(I_{y}\), \(I_{t}\) are the partial derivatives of \(I\) with respect to \(x,y,t\), respectively.
## 3 Results  
Comparative Analysis of the Remote Sensing Ecological Index RSEI and Resource-Based City Ecological Index RCEI  
This study constructed the Remote Sensing Ecological Index (RSEI) and the Resource Cities Ecological Index (RCEI) using principal component analysis (PCA) with the assistance of the Remote Sensing Data Processing Platform. To demonstrate that the RCEI was superior to the RSEI in assessing the ecological quality of resource cities, this study conducted ecological calculations and assessments for both indices in Jincheng City using the Remote Sensing Data Processing Platform. A comparison of the PCA results indicated that the RCEI provided a more comprehensive representation of ecological quality than the RSEI (Table 4).  
Upon careful comparison of the PCA results of the RSEI and RCEI models, several key differences emerged, as illustrated in Figure 3a and Table 4. Firstly, the first principal component eigenvalues of the RCEI model ranged from 0.0876 to 0.1815, significantly higher than the range of 0.0354 to 0.1127 observed in the RSEI model. This indicates that the RCEI model was more effective in capturing the primary variability in the data. Notably, the smallest first principal component eigenvalues of the RCEI model were just below those of the RSEI model for the years 2018 and 2022.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{4}{c}{**Results of PCA**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Eigenvalue**} & \multicolumn{2}{c}{**Contribution Rate (\%)**} \\ \cline{2-5}  & **RSEI** & **RCEI** & **RSEI** & **RCEI** \\ \hline
1990 & 0.0837 & 0.1317 & 70.92 & 88.62 \\
1996 & 0.0786 & 0.1459 & 70.05 & 81.07 \\
2002 & 0.  
Further analysis of the contribution rate of the first principal component revealed significant differences between the RSEI and RCEI models. The contribution rate of the RSEI model fluctuated considerably, ranging from 60.45% to 80.32%, with an extreme deviation of 19.87%, as shown in Figure 3b. In contrast, the RCEI model demonstrated greater stability, with contribution rates varying from 73.13% to 89.36% and an extreme variance of only 16.23%. This smaller variance indicates that the RCEI model performed more consistently across different years. Moreover, when comparing the first principal component contributions of the two models for corresponding years, the RCEI model consistently showed higher contributions than the RSEI model. This finding further underscores the superiority of the RCEI model in characterizing ecological quality.
# A Bio-Inspired Control Strategy for Locomotion of a Quadruped Robot
###### Abstract  
In order to effectively plan the robot gaits and foot workspace trajectory (WT) synchronously, a novel biologically inspired control strategy for the locomotion of a quadruped robot based on central pattern generator--neural network--workspace trajectory (CPG-NN-WT) is presented in this paper. Firstly, a foot WT is planned via the Denavit-Hartenberg (D-H) notation and the inverse kinematics, which has the advantages of low mechanical shock, smooth movement, and sleek trajectory. Then, an improved central pattern generator (CPG) based on Hopf oscillators is proposed in this paper for smooth gait planning. Finally, a neural network is designed and trained to convert the CPG output to the preplanned WT, which can make full use of the advantages of the CPG-based method in gait planning and the WT-based method in foot trajectory planning simultaneously. Furthermore, virtual prototype simulations and experiments with a real quadruped robot are presented to validate the effectiveness of the proposed control strategy. The results show that the gait of the quadruped robot can be controlled easily and effectively by the CPG with its internal parameters; meanwhile, the foot trajectory meets the preplanned WT well.  
bio-inspired control; quadruped robot; robot trajectory; CPG; neural network +
Footnote †: journal: Applied Sciences
## 3 The Proposed Scheme
### Task Allocating Phase
Once the \(SCC\) receives the cipher message \({C_{SG_{1}}}\) at \(T_{11}\), the \(SCC\) uses the private key \({Prk_{SCC}}\) to decrypt the message:  
\[R_{SCC_{3}}=g^{r_{5}}\text{mod}p \tag{32}\]6  
Then, the \(SCC\) checks the validity of the timestamp:  
\[R_{SCC_{3}}=g^{r_{5}}\text{mod}p \tag{32}\]7  
If the timestamp interval is valid, then the \(SCC\) needs to verify the \(SG\)'s hash parameter:  
\[R_{SCC_{3}}=g^{r_{5}}\text{mod}p \tag{32}\]8  
If Equation (49) holds, the \(SCC\) sends and chains \((R_{SG_{1}},{h_{SG_{1}}},{Sig_{SG_{1}}},{Sig_{SG_{2}}},{M_{Event_{4}}})\) to the \(SCC\). Then the \(SCC\) needs to check the reply message; if the task is not accepted, then it repeats from the first step and searches the \(SG\) again. If the \(SG\) accepts it, then it goes to the next step. Step 5. Then, the \(SCC\) selects a random number \(r_{7}\), and computes the following parameter:  
\[R_{SCC_{3}}=g^{r_{5}}\text{mod}p \tag{32}\]9  
The _event ID_, \(SG\)'s \(ID\), and \(SCC\)'s staff \(ID\) are written to the event message with a timestamp \(T_{12}\):  
\[M_{Event_{3}}=(ID_{IMC}||ID_{IoT}||ID_{Event}||ID_{SCC}||ID_{SG}||T_{8}||Seq_{i}) \tag{33}\]0  
The \(SCC\) uses the hash function to compute the parameter \(h_{SCC_{4}}\):  
\[M_{Event_{3}}=(ID_{IMC}||ID_{IoT}||ID_{Event}||ID_{SCC}||ID_{SG}||T_{8}||Seq_{i}) \tag{33}\]1The SCC uses its private key \(Prk_{SCC}\) and \(M_{Event_{5}}\) to compute the signatures as follows:  
\[M_{Event_{3}}=(ID_{IMC}||ID_{IoT}||ID_{Event}||ID_{SCC}||ID_{SG}||T_{8}||Seq_{i}) \tag{33}\]2  
\[M_{Event_{3}}=(ID_{IMC}||ID_{IoT}||ID_{Event}||ID_{SCC}||ID_{SG}||T_{8}||Seq_{i}) \tag{33}\]3  
Then, the SCC sends and chains \((R_{SCC_{4}},h_{SCC_{4}},Sig_{SCC_{7}},Sig_{SCC_{8}},M_{Event_{5}})\) to the \(BCC\). \[M_{Event_{3}}=(ID_{IMC}||ID_{IoT}||ID_{Event}||ID_{SCC}||ID_{SG}||T_{8}||Seq_{i}) \tag{33}\]4 The flowchart of the allocating task phase.