esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Model
### Space-time Network
\tag{4}\]  
The energy consumed in communication between nodes \(i,\ j\ \in V\), is as follows:  
\[E(i\mapsto j)=\left\{ \begin{array}{ll}f_{ij}&for\ i\mapsto j\in L_{1}\\ 0&for\ i\mapsto j\in L_{2},\end{array} \right. \tag{5}\]  
where \(f_{ij}\) is defined by (2). The delay per unit flow for all links in \(L\) is:  
\[\Phi(i\mapsto j)=\left\{ \begin{array}{ll}\tau&for\ i\mapsto j\in L_{1}\cup L _{2},\ i\
otin\mathcal{M}\\ (a+1)\tau&for\ i\mapsto j\in L_{1},\ i\in\mathcal{M}\end{array} \right. \tag{6}\]where for \(i\in\mathcal{M}\) and \(i\mapsto j\in L_{1}\), the user postpones transmission for \(a\) time periods. We define the total communication cost per unit flow as a weighted sum of the energy consumed in transmission and the delay incurred while traversing a link in \(L\), \(c_{ij}=E(i\mapsto j)+\lambda\Phi(i\mapsto j)\), where \(\lambda\) is a weighting parameter of the delay cost for information to traverse the link \(i\mapsto j\).
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### ML Challenges in WSN
#### 8.3.1 Challenges Related to Constrained Resources  
ML algorithm selection should include consideration of the computational complexity, memory usage, and balance between the quality of learning and the associated energy budget, as the developed models are intended for deployment on resource-scarce devices. Continuous or periodic collection of network traffic results in big data issues, leading to a prominent challenge for the ML framework [86]. Moreover, the frequency of uploading data samples is different from one network scenario to another. For instance, certain networks are configured to put sensor nodes into deep sleep mode in order to conserve network energy; however, important readings may be missed in these scenarios, and a body of knowledge may be lost. Another example involves the possibility that network resource consumption may not be relative to the frequency of global aggregation and model training accuracy [87].
# Analysis of Mining Waste Dump Site Stability Based on Multiple Remote Sensing Technologies
## 3 Methodology
### Limit Equilibrium Method
In the first simulation, a plane failure surface passing through the slope toe was assumed and the slope body above the failure surface was considered as a triangular prism, as shown in Figure 7. In consideration of cohesion, the factor of safty (FOS) was calculated according to the following equation,  
\[K_{2}=\frac{tg(\phi)}{tg(\delta)}+\frac{2\cdot c\cdot\sin(\beta)}{\gamma\cdot H \cdot\sin(\beta-\delta)\cdot\sin(\delta)} \tag{8}\]  
where \(\phi\) is the internal friction angle, \(\beta\) is the gradient of the slope, \(H\) is the height of the dump, and \(\delta\) is the slope angle of the failure surface. In this simulation, a dangerous failure was determined by iteratively finding a certain slope angle \(\delta\) between 0 and \(\beta\), which made \(K_{2}\) the smallest. The FOS of each part of the profile was calculated first, then a total FOS was calculated for each slope. Figure 6: Locations of the three profiles, where two platforms divide the MM\({}^{\prime}\) and NN\({}^{\prime}\) profiles into three parts and one platform divides the KK\({}^{\prime}\) profile into two parts. Taking the cohesive force of soil into consideration, the failure surface was assumed in the second simulation to be a circular arc. By dividing the landslide body into small soil strips, the factor of safety could be calculated with the following equation:  
\[F_{s}=\frac{\sum K_{i}}{\sum\left(W_{i}+Q_{i}\right)\sin\alpha_{i}} \tag{9}\]  
where \(W_{i}\) is the gravitational force of the \(i\)th soil strip, \(\alpha_{i}\) is the slip angle at the bottom of the \(i\)th soil strip, \(Q_{i}\) is the vertical force on the \(i\) th soil strip. \(K_{i}\) and \(m_{ai}\) can be calculated with the following equations:  
\[K_{i}=\frac{c_{ni}b_{i}+U(W_{ti}+Q_{i})\,\tan\varphi_{ti}}{m_{ai}} \tag{10}\]  
\[m_{ai}=\cos\alpha_{i}+\frac{\sin\alpha_{i}\tan\varphi_{i}}{F_{s}} \tag{11}\]  
The meanings of the symbols in Equations (9-11) refer to the specifications for design of highway subgrades (JTG D30-2015) of China. The FOS in Equation (9) was obtained by summing all the soil strips respectively. In this simulation, a comprehensive search was carried out according to different arc centers and radius in order to find the smallest FOS of each profile. The FOS of all the profiles was calculated respectively, as well as the overall stability factor.
## 4 The Fast Second-Order Blind Identification (SOBI) Method for Diagonalization of Average Matrix
### The SOBI Method for Joint Approximation Diagonalization  
#### 4.1.1 The Joint Approximation Diagonalization SOBI Method  
Methods for the blind separation of second-order statistics generally include preprocessing data, calculating second-order statistics, calculating diagonalization matrices, and obtaining source and mixed process estimates.  
(1) Data Preprocessing  
The most common preprocessing step in the second-order blind separation method is whitening. When the number of source signals and the number of sensors is the same, no whitening treatment is required. However, the whitening preprocessing can remove the spatial correlation. When the number of source signals and sensors are not the same, the whitening process can estimate the number of sources, and, at the same time, eliminate the effect of additive noise on the signal. [PERSON] and [PERSON] [45] improved the algorithm by using robust orthogonalization as a preprocessing step, under the condition that the number of observations was greater than the number of sources.  
(2) Calculate Second-Order Statistics  
For colored sources with different power spectrums, a delay covariance matrix is used:  
\[M_{i}=R(\tau_{i})=E[x(t+\tau)x^{*}(t)] \tag{12}\]  
(3) Joint Matrix Diagonalization to Obtain Unitary Matrix  
The goal of joint diagonalization is to find the orthogonal matrix U, enabling a group of matrices to be diagonalized, so  
\[M_{i}=UD_{i}U^{T}~{}~{}(i=1,2,\cdots,K) \tag{13}\]  
In this formula, \(M_{i}\) is the delay covariance, and \(D_{i}\) is the real diagonal matrix.
## 5 Simulation Setup  
The developed algorithm was simulated on an application in automated manufacturing (Figure 11). This is a multi-robot system composed of a visual system and a tool  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\tau_{delay}\) **[s]** & \(E(P_{initial}\),\(P_{final})\) **[ws]** & \(t_{s}\) **[s]** \\ \hline
0 & 10.895 & 0.07 \\ \hline
0.2 & 0.102 & 0.73 \\ \hline
0.4 & 0.030 & 1.42 \\ \hline
0.6 & 0.016 & 2.11 \\ \hline
0.8 & 0.011 & 2.80 \\ \hline
1 & 0.009 & 3.49 \\ \hline \hline \end{tabular} Note: \(E(P_{initial},P_{final})\) is measured as [watt-seconds].  
\end{table}
Table 1: Estimated energy cost and settling time with varying delay constant.  
Figure 10: Response of \(q^{\mathrm{J}}(t)\) with varying \(\tau_{delay}\).  
manipulation system. In the visual system, a camera is mounted on an elbow robot arm, while a tool is held by the end-effector of the robot manipulator arm. The goal of the visual system is to provide a precise estimation of the tool pose so that the tool manipulator can control the pose with guidance from the visual system. The first step of the manufacturing process is to move the camera in the space and search for the best location so that the number of images required for averaging is minimized to reduce the image noises within an acceptable limit. Then, the camera is fixed and provides continuous vision data to the tool manipulator. Therefore, the control and operation of the two manipulators are asynchronous. The model of both robot manipulators is ABB IRB 4600 (ABB, Zurich, Switzerland) [34], and the model of the stereo camera we chose in this project is Zed 2 (Stereolabs, San Francisco, CA, USA) [35].  
We first define the camera's operational space in this application and then provide the simulation results of a made-up scenario in the next.  
In the above discussion, a camera operational space is gridded with nodes, and each node is a potential candidate for the camera's optimal position. The camera operational space is defined by the three geometric constraints below:  
1. The camera can only be allowed to locations where fiducial markers that are attached to the tool are recognizable on the image frame. Therefore, the fiducial markers are in the angle of view of the camera, and the distance between the markers and the camera center is within a threshold;
2. The camera can only be allowed to locations within the reach of the robot arm;
3. The camera can only be allowed in locations where the visual system and the tool manipulation system do not physically interfere with each other.  
From the specifications of the stereo camera and the robot arm (Appendix A, Tables A1 and A3), the geometry and dimensions of the camera operational space are analyzed in the following part of the subsections.
# Applied Research of the UAV Illumination Measurement System in Sports Stadiums
## 1 Introduction
Thus, the mounting system for UAVs can be made more intelligent and convenient with the widespread development of IoT and sensor technology. In this paper, the sports venue illumination measurement system presented is based on the UAV with the function of optical flow. The illumination acquisition system consists of the STM32F103 RCT6 micro control module, illumination acquisition module, distance measurement module, and wireless transmission module. The local-outlier factor (LOF) algorithm is chosen as the system outlier handling method confronted with anomaly values in the collected data [16; 17]. In the experiment, we obtained the local-outlier factor (LOF) algorithm training data by modeling a standard sports stadium and determined the outlier-detection threshold. According to the experiments, the root mean square error (RMSE) of the illuminance data acquired by the system and the illuminance standard value is 2.45, and the error range for dynamic measurements is between \(-2.27\%\) and \(-0.57\%\).
## 3 Simulation Results and Interpretation
### Scenario 2: Effect on the Number of Targets
#### 3.2.1 Simulation with Clutter Model 1  
We simulated the three targets with clutter model 1 (targets drowned in a cluttered Weibull distribution), where the targets are drowned in the Weibull distribution with shape parameter a and scale parameter b chosen arbitrarily such that a = 25 and b = 0.9.  
In Figure 28, we observe that the proposed detector successfully distinguishes the three targets used for the simulation at different positions (M = 41, M = 51, and M = 61).  
Figure 28: Targets simulated with clutter model 1 (a = 25 and b = 0.9) distinguished with the proposed detector (M = 64 and N = 8).  
Figure 27: Targets simulated without clutter using (a) the conventional OS-CFAR detector and (b) the proposed detector (M = 64 and N = 8).  
Figure 26: Doppler frequencies of targets at different ranges and azimuths.  
We increased the shape parameter a to 250 (a = 250) and maintained the scale parameter b at 0.9 (b = 0.9) to simulate the three targets. We maintain the same value of the RCS for the simulations; the temporal and frequency values of the Gabor transform are M = 64 and N = 8 for the first simulation, then M = 64 and N = 32 for the second simulation.  
After the simulations, we noticed a difference in the detected energy of the three targets (Figure 29) at M = 43, M= 52, and M = 62 compared to the previous simulations (Figure 28). There was a slight shift in the position of the targets in the reference positions after increasing the shape parameter a to a value greater than 200 (a = 250). In Figure 30, when we change the value of the frequency of the Gabor transform N from 8 to 32 we observe that our detector is able to detect only one target at M = 45, while the OS-CFAR detector distinguished several false alarms with high target amplitudes.  
We decreased the shape parameter a to 25 (a = 25) and maintained the scale parameter b at 0.9 (b = 0.9) to simulate the three targets. We maintained the same value of the RCS for the simulation and decreased the temporal and frequency values of the Gabor transform to M = 32 and N = 16.  
We observe that the proposed detector fails to distinguish all targets within the same range when we decrease the temporal and frequency values of the Gabor transform to M = 32 and N = 16, respectively. Only two targets were distinguished with the proposed detector at M = 23 and M = 28, as shown in Figure 31.  
Figure 30: Targets simulated with clutter model 1 (a = 250 and b = 0.9) using (**a**) conventional OS-CFAR and (**b**) proposed detector (M = 64 and N = 32).  
Figure 29: Targets simulated with clutter model 1 (a = 250 and b = 0.9) distinguished with proposed detector (M = 64 and N = 8).
## 2 Samples and Experiment
### Etching  
Most of results presented in this paper were obtained on samples with etched gratings. The gratings were patterned with an electron beam lithography (EBL). In the case of II-VI materials, low-temperature EBL procedures are preferable [32]; therefore we used CSAR 62 positive-tone resist, which requires lower backing temperatures as compared to PMMA. In our case, samples were briefly baked (during 5 min) at 155 \({}^{\circ}\)C on a hotplate. The developed resist mask (about 100 nm thick) exhibited a sufficient adhesion to the substrate, required for wet etching patterning. Etching was done with a solution of Bromium in glycol ethylene (typically, 30 mL of Br and 50 mL of glycol ethylene) and stopped in methanol. The metallization patterns (gold gratings) were also obtained with CSAR 62 resist masks by thermal evaporation of Au/Cr and a lift-off technique (the thickness of Cr layer was equal to about 10 nm).  
Profiles of gratings were analyzed with an atomic force microscope (AFM). A typical profile of an etched sample is shown in Figure 2. Let us note that the horizontal axis in the figure is compressed by a factor of about 500 with respect to the vertical one, which means that in the real perspective fluctuations of the etched profile are rather smooth and the roughness of the etched surface is equal to about 10 nm.  
A detailed study of the process of etching showed that at a given time of etching, the depth of etched grooves is correlated to the their width, as it is shown in Figure 3. However, by changing the time of etching and composition of the etching mixture, we were able to produce gratings with given period and different depths. The wafer A (single QW) was used to prepare samples with the period of grating equal to 2 \(\upmu\)m, 4 \(\upmu\)m, 6 \(\upmu\)m and 8 \(\upmu\)m. The wafer B (two QWs) was used to prepare samples with etched grating period equal to 8 \(\upmu\)m and the width of grooves changing between 2 \(\upmu\)m and 6 \(\upmu\)m. For comparison, we used also samples prepared on the wafer B with a Au/Cr grating evaporated on their surface with the period equal to 2 \(\upmu\)m, 4 \(\upmu\)m, 6 \(\upmu\)m and 8 \(\upmu\)m. The thickness of metalization was equal to about 50 nm. As it was already shown in previous studies [33], a metalization of this thickness makes the metallic layer unpenetrable for THz radiation.  
Figure 2: An electron microscope photography of etched sample (**left**) with a corresponding AFM profile (**right**); \(x\) and \(y\) coordinates describe, respectively, position of the tip of AFM in the plane of the sample and in the direction normal to this plane.
# Steering Pull Model and Its Sensitivity Analysis
## 3 Steering System Model
### Universal Joint Model  
A universal joint is connected to a rack and pinion gear. It links the pinion gear to the steering column while allowing the transmission of steering torque. Its friction is relatively very small, compared to other steering system components, and therefore, the friction force generated in the joint is neglected. A universal joint can be modeled by the following Equation (14) [18].  
\[T_{U}=\frac{\sin^{2}\theta_{B}+\cos^{2}\phi_{BU}\cos^{2}\theta_{B}}{\cos\phi_{ BU}}\cdot\frac{\sin^{2}\theta_{C}+\cos^{2}\phi_{CA}\cos^{2}\theta_{C}}{ \cos\phi_{CA}}T_{C},\]  
\[\theta_{B}=\tan^{-1}\!\left(\frac{\tan\theta_{C}}{\cos\phi_{CA}}\right)+\phi_{ AB},\;\theta_{U}=\tan^{-1}\!\left(\frac{\tan\theta_{B}}{\cos\phi_{BU}}\right) \tag{14}\]  
Figure 5: Relation between tire turning force and rack force.  
where \(T_{C}\) = steering column torque, \(\theta_{B}\) = angle of intermediate bar of universal joint, \(\theta_{C}\) = steering column angle, \(\phi_{BU}\) = tilt angle between universal joint and rack & pinion gear, \(\phi_{CA}\) = tilt angle between steering column and universal joint, \(\phi_{AB}\) = phase difference angle of intermediate bar of universal joint.
## 3 Materials and Methods
### Different Pore Pressure Prediction Methods
#### 3.2.2 Bowers Method  
The Bowers method employs a similar concept to the Eaton method, which calculates the effective stress reading. Nonetheless, the Bowers equation is superior to [PERSON]'s due to its greater emphasis on the unloading parameter. The Bowers method is ideal for predicting formations with high pore pressure readings and uses both unloading and loading mechanisms. This approach helps estimate pore pressure in shallow formations that are either poorly consolidated or unconsolidated due to the slow velocity in these formations:  
\[V=5000+A\sigma B \tag{11}\]  
\[V\ =\ 5000\ +\ A[\ \sigma\max\ \left(\ \sigma/\sigma\max\ \right)\ 1/U]B \tag{12}\]  
\[\sigma\max\ =\ \left(V\max-5000/A\right)^{1/B} \tag{13}\]  
where  
\(V\) = Velocity (Ft/s);  
\(\sigma\) = Effective pressure (Psi);  
\(\sigma max\) = Maximum effective pressure (Psi);  
\(A\) and \(B\) = Bowers empirical coefficients.
## 4 Discussion
### Limitations  
* Location constraint:  
Our study was conducted at a single WWTP, which limits the ability to generalize our findings to other plants with different configurations, capacities, and operational conditions. Future studies that include a broader range of WWTPs could provide more diverse insights into how environmental factors affect the treatment performance across different regions.  
* Time constraint:  
Although our analysis was sufficient for identifying seasonal trends, a long-term study would be necessary to observe exceptional events or trends over multiple years. Such data could provide a deeper understanding of long-term patterns and how rare weather events might impact WWTP efficiency.  
* Data depth:  
While we collected extensive data on the temperature, the rainfall, and wastewater treatment parameters, incorporating more data on influent characteristics, plant operations, and microbiological factors could further enhance understanding of the system's response. Future research should consider including these additional variables to provide a more comprehensive analysis of WWTP performance.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 2 Proposed Method  
In this section, an RDH method in encrypted image is proposed, which includes image encryption, data hiding in encrypted image, data extraction, and image restoration. The proposed method is suitable for many classic scenes. For example, as shown in Figure 1, the content owner is a user of the cloud. In order to prevent the information of the image from being leaked, he encrypted the image before uploading it to the cloud. As the third party, the cloud can provide data embedding services to generate a marked encrypted image without knowing the original image content, that is, the cloud is the data hider of our method. The recipient is also a cloud user. He can get a marked encrypted image from the cloud and then extract data and decrypt the image. We introduce our method through the content owner, data hider, and recipient. The content owner conducts a series of operations including selecting embeddable pixels, generating a location map, rearranging the image, and encrypting the image. Data hider embeds secret bits into the encrypted image by data-hiding key without knowing the contents of the original images. At the receiving end, if the legal recipient only has a data-hiding key, then he can extract secret bits without error. If he only has the encryption key, then he can obtain a reconstructed image. If he has both data-hiding key and encryption key, then he can extract data without error and restore the image losslessly.  
The outline of this section is as follows: selection of embeddable pixels is introduced in Section 2.1. Image encryption and data embedding are presented in Sections 2.2 and 2.3, respectively. Finally, data extraction and image recovery are introduced in Section 2.4.
# Design and Performance of a XBee 900 MHz Acquisition System Aimed at Industrial Applications
## 3 Design of a Wireless Monitoring System
### Implementation of the Monitoring System
XBee modules are very flexible and must be properly configured in order to achieve an optimal performance of the applications. A star topology was built for better bounding of the transmission time of the applications. This configuration was implemented by setting the Coordinator Node in the Indirect Message Coordinator mode and the Smart Measuring Nodes as the Indirect Message Poller. The maximum number of hops across the network was set to 1 in order to configure the start topology. Unicast retries were disabled to allow low sampling at the nodes and achieving higher determinism. The broadcast multitransmits and unicast retries were also disabled. The transmission power of the XBee modules was set to the highest value (+24 dB). For the serial communication between the processing nodes and XBee modules, a baud rate of 115,200 bauds without parity was selected; this is the maximum baud-rate allowed by the Arduino MKR Zero. Thus, it is possible to achieve lower sampling periods. Table 1 summarizes the major parameters used.
## 5 Experiments
### Setting  
#### 5.1.1 Datasets  
AVI Dataset: The dataset used is the AVI Dataset 1. Due to time complexity issues, only 95 AVI collection points north of the Jialing River are used for the spatiotemporal reconstruction of trajectories in the experiments.  
GPS Dataset: The GPS dataset contains GPS data for the month of October 2021 (1 October-31 October 2021) in Chongqing, including about 12 million passenger trips. Each trip point is collected by an onboard GPS device at intervals of approximately 5-30 s. All parts of the trips that pass through selected collection points are used to train and test the model, with a total of about 3 million trips.