esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Demonstration of Single-Channel EEG Signal Using 32 x 32 Pixel OLED Screen and Camera
## 6 Conclusions  
The proposed scheme results of BER obtained show that, in comparison to available literature, the link distance of 5.5 m was achieved using the DLSR camera and a \(32\times 32\)-pixel OLED screen for wireless EEG signal monitoring through the VL-OCC system. Furthermore, in comparison to the performance of existing systems in literature, the bit rate achieved in our experimental work is 2.8 kbps without any harmful radiation. The achievable bit rate could detect EEG signals in applications where instant  
Figure 8: Distance vs. BER.  
Figure 9: (**a**). Comparison of EEG signal transmitted and received; (**b**) comparison of transmitted and received normalized EEG signal.  
recording is required from a couple of seconds to a few minutes to detect brain activity. The proposed system has a potential crucial future impact and is investigated in order to be deployed in future healthcare environments due to the drawbacks of RF, especially in RF sensitive areas. Additionally, the traditional VLC systems using photodiodes as the receiver require hardware modifications and extra set up; however, deploying a camera as the receiver does not require extra modifications to the infrastructure, due to the inbuilt filter and focusing properties of cameras, hence the proposed system would be beneficial in healthcare.  
[PERSON] wrote the paper, performed experiments, methodology, PCB hardware design and experimental set up. The supervisory team, namely [PERSON], [PERSON], and [PERSON], supervised the work and provided the necessary guidance and support. [PERSON] helped in editing the paper.  
This paper is part of PhD research work of [PERSON] and she is a self-funded student.  
The authors declare no conflicts of interest.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 6 Conclusions  
In this paper, we investigated the CJ-based group secret and private key generation scheme. The legitimate nodes within the group agree on an SK that is only kept secret from [PERSON], or a PK that is kept secret from both the relay and Eve. We constrained the SK and PK capacities by establishing their lower and upper bounds. The lower bounds were determined based on the ZF method, while the upper bounds were attained by analyzing the formulated enhanced DMS models. Moreover, we confirmed that these bounds closely align with each other, indicating their correlation. Particularly, in high SNR scenarios, a consistent difference of \(\log_{2}\left(\frac{3}{2}\right)\) BPCM is evident between the SK/PK upper and lower bounds. Moreover, we also demonstrated that the proposed scheme significantly improves the security and efficiency of group key generation.  
**Author Contributions:** Conceptualization, S.F., T.L. and J.Y.; Methodology, S.F., T.L. and J.Y.; Software, S.F., T.L. and J.Y.; Validation, S.F., T.L. and J.Y.; Formal analysis, T.L. and J.Y.; Investigation, J.Y.; Writing--original draft, S.F., T.L. and J.Y.; Writing--review & editing, S.F., T.L., J.Y. and Y.L.; Visualization, Y.L.; Supervision, Y.L.; Project administration, Y.L.; Funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Chongqing Electronics Engineering Technology Research Center for Interactive Learning, in part by the Chongqing key discipline of electronic information, and in part by Chongqing Municipal Education Commission Science and Technology Research Project under Grants KJQN202201630 and KJZD-K202401604.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 8: Performance comparison under three different key generation schemes.
#### 1.1.1 Factors of High Energy Consumption  
Attempts to identify key factors of high energy consumption and barriers to energy efficiency management have been made. Key factors have been identified by various approaches and methods, including a multidimensional hybrid approach that combines energy monitoring and modeling to determine the factors of residential home energy consumption [1], urban heat on building energy use [2], and a comparison of the environmental performance of building properties using the Eco-Effect tool to determine the quality vs. energy efficiency impact [3]. Scholars have also proposed the drivers of observed changes over time of energy impacts such as the decomposition analysis of residential energy use in Hong Kong from 1990 to 2007 [4] and, later, in China from 2002 to 2010 [5].
## 4 Experiment and Result
### Multi-GNSS Kinematic PPP Solution
As shown in Table 3, there is no significant difference in the RMS of horizontal errors between GPS/BDS(EK) and GPS/BDS(CR), but the  
Figure 8: Comparison of the kinematic PPP errors between the conventional and the proposed models with respect to three combinations of GPS, BDS, and GLONASS. horizontal maximum error decreases from 9.032 m for GPS/BDS(EK) to 4.973 m for GPS/BDS(CR), and the RMS of vertical errors decreases by 43.05% to 2.062 m. Compared with GPS/BDS/GLONASS(EK), the RMS of GPS/BDS/GLONASS(CR) horizontal and vertical error are reduced by 24.29% and 20.41% respectively. Moreover, the stability and reliability of multi-GNSS positioning using data from smartphones is still restricted by the poor multipath suppression capability and phase center deviation of the passive linearly polarized embedded GNSS antenna. Compared with the geodetic receiver, the multipath effects, noise level, and the number of observations gaps are much larger, which will affect the smartphone positioning discontinuities in kinematic PPP mode.
## 3 Results
### Local Residency  
Sixty-four tiger sharks, 18 males and 46 females, were implanted with acoustic transmitters (Supplemental Table S1). At the time of tagging, the size of the tiger sharks ranged from 67 to 333 cm FL (mean = 182.9 \(\pm\) 92.9 cm); 32 tiger sharks were classified as small juveniles, 11 as large juveniles, and 21 as sexually mature. A total of 11,285 acoustic detections were recorded across 51 stations in the Bimini array.  
Overall, individual monthly residency indices within the Bimini array ranged from 0.00 to 0.14 (mean 0.01 \(\pm\) 0.03). Small juvenile tiger sharks had a lower mean residency index (mean = 0.00 \(\pm\) 0.001) when compared to large juvenile (mean = 0.02 \(\pm\) 0.03) and mature (mean = 0.02 \(\pm\) 0.04) tiger sharks (Figure 4). Nine of the twenty-one mature tiger  
Figure 3: Recapture locations of tiger sharks, _Galocerdo cvier_, originally captured and tagged in Bimini, The Bahamas (marked with a red star) with dart tags and reported through the National Marine Fisheries Service Cooperative Shark Tagging Program.  
sharks were detected in Bimini across two years, and three individuals were detected across four years (Figure 5). The results of the GLMM indicated that water temperature (\(p\)-value < 0.001) and fork length (\(p\)-value < 0.001) were significant predictors of the monthly residency index; however, sex was not (\(p\)-value = 0.71) (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Estimate** & **Std. Error** & \(z\)**Value** & **PR(\textgreater{}|z|)** \\ \hline Intercept & 2.81 & 0.88 & 3.19 & 0.001 * \\ Temperature & \(-\)0.42 & 0.02 & \(-\)17.41 & \textless{}0.001* \\ Sex (male) & \(-\)0.20 & 0.54 & \(-\)0.38 & 0.71 \\ Fork Length & 0.01 & 0.00 & 4.84 & \textless{}0.001* \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of zero-inflated generalized linear mixed model results for monthly residency of tiger sharks, _Galocerdo cvier_, in Bimini, The Bahamas. Fork length, water temperature, and sex were predictors. The * indicates statistical significance.  
Figure 4: Mean monthly residency index of small juvenile (red bars), large juvenile (green bars), and mature (blue bars) tiger sharks, _Galocerdo cvier_, in Bimini, The Bahamas. The black line represents the mean monthly water temperature.  
Figure 5: Abacus plots for acoustic detections of tiger sharks, _Galocerdo cvier_, tagged in Bimini, The Bahamas. Panels correspond to age classes: (**A**) small juveniles, (**B**) large juveniles, and (**C**) adults.
# Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection
## 4 Underwater Vision  
While visual simultaneous localisation and mapping (SLAM) has seen impressive development for autonomous ground vehicles (AGVs) [31], unmanned aerial vehicles (UAVs) [32] and unmanned underwater vehicles [33], the technical challenges presented by underwater environments have hindered progress for AUVs, particularly in real-time applications. Many unique visual phenomena affect underwater images such as wavelength-dependent attenuation, floating particles and bubbles, underwater caustics in shallow water, varying lights and shadows, moving flora and fauna and refractions through thick glass housing needed for waterproofing camera systems [34; 35], some examples of which are shown in Figure 12.  
In the RoboFish project, the research aimed to test current state-of-the-art SLAM algorithms on underwater visual datasets and to quantify performance and suitability of those algorithms for use with low-cost Raspberry Pi cameras. To achieve this, a graphical user interface (GUI) was developed in Python and OpenCV [36] to enable the real-time modification of popular feature-matching algorithm parameters whilst providing visual feedback on performance and an estimation of the camera's 3D trajectory using visual odometry (VO). The most suitable parameters and image-processing algorithms were then determined and implemented in a modified version of ORB SLAM 2 [31].  
Figure 11: RoboFish Segment Board: a board designed to accommodate an STM32 F417 Microcontroller; it serves as a networked extension to communicate with the head board and contains all of the necessary IOs for any servos or sensors and contains an LSM9 DS1 IMU.  
The GUI was built in Python using the Matplotlib library. It was decided that only ORB [37] and BRISK [38] feature-matching algorithms would be tested; however, the design enables the addition of SIFT [39] and SURF [40] feature detectors with only minor modifications. Figure 13 shows the GUI. It enables the adjustment of either ORB or BRISK parameters in real-time via sliders and buttons, with the effects of these changes visible both qualitatively in the overlaid video feeds and quantitatively in the graphs. Parameters can also be set prior to a test, and it enables a previous test's data to be displayed simultaneously on the graphs, allowing comparisons of performance for each test. The camera's position is estimated using VO, the implementation of which was based closely on PySLAM [41].  
Figure 12: RoboFish Computer Vision Challenges: (**a**) almost completely green image showing limited visibility, (**b**) floating particles in the foreground, (**c**) water caustics on a lake bed, created by the surface of the water, and (**d**) total internal reflection underwater causing a mirror image of a lake bed in the water surface.
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
## 2 The Dynamic Zoning Method  
### Overview of Our Method  
During the underground mining of coal seams, the overlying disturbed rock mass undergoes all or part of the four stages of elastic-plastic deformation, cracking, caving, and caved material deformation. Correspondingly, we simplify the complete stress-strain curve of the disturbed rock mass into four stages: the elastic deformation stage (Stage I), post-peak softening stage (Stage II), residual strength stage (Stage III), and caved material deformation stage (Stage IV).  
According to the deformation and failure characteristics of the rock mass in different stages, the zoning criteria of the rock mass are established: the rock mass in the elastic deformation stage and post-peak softening stage belongs to the continuous deformation zone; the rock mass in the residual strength stage belongs to the fractured zone or the surface cracking zone; and the rock mass in the caved material deformation stage belongs to the caved zone.  
According to the simplified complete stress-strain curve, the mechanical parameters of the disturbed rock mass are adaptively adjusted: the parameters are maintained in Stage I, reduced in Stage II, adjusted to satisfy the residual state in Stage III, and adjusted to satisfy the condition of loose deposits in Stage IV.  
According to the damage evolution process of rock mass deformation in actual underground mining, by considering the mining process simulation, rock mass zoning criteria, and adaptive adjustment of rock mass mechanical parameters, the simulation of a longwall mining process is conducted based on the FDM.  
By exploiting the abovementioned four stages, the dynamic zoning of the disturbed rock masses can be realized (Figure 2).
###### Abstract  
Background: Orthopantomography (OPG) is usually used as a primary diagnostic radiological exam in the planning of third molar surgery because it is deeply available in dental clinics and has lower radiation doses compared to Cone-beam computed tomography (CBCT). The OPG provides a bi-dimensional image, but several radiological signs have been proposed to study the position of the lower third molar and to predict surgical risks. Methods: Patients were divided into two groups, the OPG with a radiolucent area (D-group) and the OPG without any sign (C-group) in correspondence of inferior wisdom tooth roots. Results: The mean distance between the inferior third molar root and the lingual cortical mandibular bone was \(-1.09\)\(\pm\)\(1.5\) mm. The nearness of the root that is less than \(1\) mm was more frequent in the D-group (\(84.85\%\)) compared to the C-group (\(14.58\%\)) with statistical significance (Odd ratio: \(32.8\)) using the Chi-square test. Conclusions: When the root of the impacted inferior third molar is impacted into the lingual cortical plate, a periapical band-like radiolucent sign may appear in the OPG image. It could be useful for the prediction of root position and surgical risks.  
radiolucent sign; OPG; CBCT; third molar; oral surgery; periapical sign +
Footnote †: journal: Medical Medical University, 00131 Rome, Italy
## 2 Methods
### Use of Machine Learning Methods
AdaBoost  
Boosting increases the weight of wrong data in a classification model, and the wrong information is trained to strengthen the identification. The derived new classifier will reduce the chance of early error [36]. The iterative process of the AdaBoost calculation is extremely sensitive to noise and abnormal data; therefore, these should be reduced so that the process can focus on difficult-to-classify feature factors. AdaBoost analysis initially assumes that the sample weights are equal. After the k-th iteration, samples are selected on the basis of the weight \(\text{W}_{\text{k}}\) to train the classifier CK, as expressed by Equation (20). \[\left\{ \begin{array}{l}\text{D}=\{\text{x}^{1},\text{y}_{1},\cdots,\text{x} ^{n},\text{y}_{n}\}\\ \text{W}_{\text{k}}(\text{i})=\frac{\text{I}}{\text{n}},\text{i}=1\cdots,\text {n}\end{array} \right. \tag{20}\]  
where D is the sample category, (x\({}^{i}\), y\({}_{\text{i}}\)) is the sample information, n is the number of samples, and \(\text{W}_{\text{k}}\) is the weight distribution of all samples in the k-th iteration. The classification error \(\text{E}_{\text{k}}\) confirms the correctness of the classification and updates the weight \(\text{W}_{\text{k}+1}\), as shown in Equation (21). The iterative calculation of classification is completed when the error \(\text{E}_{\text{k}}\) is less than the preset threshold. \[\text{W}_{\text{k}+1}(\text{i})\leftarrow\frac{\text{W}_{\text{k}}(\text{i}) }{\text{Z}_{\text{k}}}\times\left\{ \begin{array}{l}\text{e}^{-\frac{1}{2} \ln\frac{1-\text{E}_{\text{k}}}{\text{k}}},\text{ if }\text{y}_{\text{k}}(\text{x}^{i})= \text{y}_{\text{i}}\\ \text{e}^{\frac{1}{2}\ln\frac{1-\text{E}_{\text{k}}}{\text{k}}},\text{ if }\text{y}_{\text{k}}(\text{x}^{i})\
eq \text{y}_{\text{i}}\end{array} \right. \tag{21}\]  
where \(\text{W}_{\text{k}+1}\) is the updated weight, \(\text{Z}_{\text{k}}\) is the normalization coefficient, \(\text{E}_{\text{k}}\) is the error, and \(\text{y}_{\text{k}}\) is the prediction category. * XGBoost  
The XGBoost function is composed of two components: the prediction error of boosting and the complexity of DT. The feature factors are combined and branched into a DT, and a new boost function is learned from the previous calculation residuals [37]. In Equation (22), the first component calculates the error between the prediction and actual observation, and the other component indicates the complexity of the regularized DT, which covers the number of nodes and the node probability value. \[\text{f}=\sum_{\text{i}=1}^{\text{n}}\text{E}(\text{y}_{\text{i}},\text{y}_{ \text{k}\text{i}})+\sum_{\text{k}=1}^{\text{K}}\Omega(\text{f}_{\text{k}}) \tag{22}\]  
where E is the error between the prediction and actual observation and \(\Omega(\text{f}_{\text{k}})\) is the complexity of the DT.
## 2 Numerical Modeling
### Numerical Simulation Method of Fluid-Structure Interaction  
In fluid-structure interaction technology, there are two kinds of solutions: direct solution method and separation solution method. Direct solution method refers to the fluid governing equation and solid governing equation put together in the same matrix to solve directly, this method does not exist the lag problem between the fluid and the structure, but the calculation is huge and not easy to converge, generally not considered. The other method refers to the separation solution method, in which the fluid governing equation and solid governing equation are solved separately, and the two are connected through the coupling surface to realize the coupling calculation. This method has relatively low computational requirements, and different software can be selected to solve the fluid domain and solid domain, which is more friendly and extensive in engineering applications. The separation solution method is adopted in this paper, so the conservation of displacement and pressure must be guaranteed on the coupling interface. Temperature change is not considered in this paper, so the governing equation at the interface is:  
\[\left\{\begin{array}{rl}\tau_{f}&\cdot n_{f}=\tau_{s}\cdot n_{s}\\ &d_{f}=d_{s}\\ &q_{f}=q_{s}\end{array}\right. \tag{9}\]  
where: \(\tau\) is the stress, \(d\) is the displacement, \(q\) is the heat, \(n\) is the direction cosine, subscript \(f\) is the fluid domain, and subscript \(s\) is the solid domain.  
In this paper, STAR CCM+ is used to solve the governing equation in the fluid domain, ABAQUS is used to solve the governing equation in the solid domain, and the dynamic link library of Co-simulation is used to realize the numerical simulation, that is, the blade surface pressure is obtained by solving the N-S equation through STAR CCM+. Through the compilation of the INP file of ABAQUS, the blade surface pressure of STAR CCM+ is transferred to ABAQUS to calculate the displacement and deformation, and then the deformation value is transferred to STAR CCM+. The bidirectional fluid-structure coupling calculation method used in this paper is shown in Figure 1.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 3 Indicative Use Cases
### Smart Cities  
In a smart city environment, various types of data are collected and processed from different types of diverse sources: environmental monitoring, data associated with public transportation, etc. The goal is to facilitate everyday tasks and improve the quality of life of citizens. For example, end user applications can inform registered users on public transportation issues (arrivals, delays, etc.), smart traffic systems can reduce daily peaks in traffic congestion, and lighting, heating, or cooling systems can adapt their operating hours according to human presence density and thus minimize the energy footprint. All the above can be made feasible by integrating IoT technology with the edge and cloud computing [70]. Data collected directly from sensing IoT devices can be offloaded at edge servers for latency-critical applications (e.g., everyday traffic management to avoid congestion), while big data analytics can be used in the cloud infrastructure to analyze long-period time data and reconfigure infrastructure deployment if necessary.
## 3 Results
### Objective Quality Comparison
However, there was uneven brightness during the implementation, and the input image correction was required to improve the quality of the output image. In contrast, the method proposed in this paper only needs a single image, and it has low requirements on the image content, so it has better generalization ability. Ref. [PERSON] et al. [58] adopted a deep learning-based method similar to that in this paper for virtual view image estimation, but it can be seen from the evaluation indexes that the proposed method has better expressiveness. To make a more objective evaluation, the evaluation indexes of the other methods used in the experiment were averaged first, and then the evaluation indexes of the method proposed in this paper were compared with those of the other methods. For the image sequences of the ballet and breakdancers, it can be seen that the RMSE of the proposed method was 35.5% and 24.3% lower than the average value, the SSIM of the proposed method was 5.28% and 6.76% higher than the average value, and the PSNR of the proposed method was 10.1% and 12% higher than the average value. These data show that the proposed method can significantly improve the quality of the virtual view image.