esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory of Modelling
### Cross Coupling of the Thermal-Hydraulic-Mechanical Fields  
Cross coupling among CO\({}_{2}\)-ECBM is expressed as:  
\[\left\{\begin{aligned} &\frac{\partial\frac{\varphi}{RT}P_{i}M_{i}+ \rho_{c}\rho_{i}\frac{a_{i}b_{i}P_{i}}{1+b_{i}P_{i}+b_{i}P_{i}}}{\partial t}- \
abla\cdot\left(\frac{k}{\mu_{i}}\
abla P_{i}\cdot\rho_{i}\right)-\frac{ \varphi}{RT}\
abla\cdot\left(D_{i}\
abla P_{i}\right)=Q_{i}\\ & Gu_{i,j}+\frac{G}{1-2v}u_{j,ji}-\alpha P_{1,i}-\alpha P_{2,i}-K \varepsilon_{s,i}+F_{i}=0\\ &\frac{\partial}{\partial t}\Big{(}\big{(}\rho C_{p}\big{)}_{eff }\Big{)}+\eta_{eff}\
abla T-\
abla\cdot\left(\lambda_{eff}\
abla T\right)+K \alpha_{T}T\frac{\partial\varepsilon_{s}}{\partial t}+\sum_{i=1}^{2}q_{sti} \frac{\rho_{s}\rho_{si}}{M_{i}}\frac{\partial\varepsilon_{v}}{\partial t}=0\\ & k=k_{0}\bigg{(}\frac{1}{\varphi_{0}}-\frac{1-\varphi_{0}}{ \varphi_{0}(1+\varepsilon_{v})}\bigg{(}1--\frac{\alpha}{K_{s}}(\Delta P_{1}+ \Delta P_{2})+\Delta\varepsilon_{s}\bigg{)}\bigg{)}^{3}\end{aligned}\right. \tag{26}\]  
Cross coupling among CBM is expressed as:  
\[\left\{\begin{aligned} &\frac{\partial\frac{\varphi}{RT}P_{1}M_{1}+ \rho_{c}\rho_{1}\frac{a_{i}b_{i}P_{i}}{1+b_{i}P_{i}}}{\partial t}-\
abla\cdot \left(\frac{k}{\mu_{1}}\
abla P_{1}\cdot\rho_{1}\right)-\frac{\varphi}{RT} \
abla\cdot\left(D_{i}\
abla P_{1}\right)=Q_{1}\\ & Gu_{i,ij}+\frac{G}{1-2v}u_{j,ji}-\alpha P_{1,i}-K\varepsilon_{s, i}+F_{i}=0\\ &\frac{\partial}{\partial t}\Big{(}\big{(}\rho C_{p}\big{)}_{eff }\Big{)}+\eta_{eff}\
abla T-\
abla\cdot\left(\lambda_{eff}\
abla T\right)+K \alpha_{T}T\frac{\partial\varepsilon_{s}}{\partial t}+\sum_{i=1}^{2}q_{sti} \frac{\rho_{s}\rho_{s1}}{M_{1}}\frac{\partial\varepsilon_{v}}{\partial t}=0\\ & k=k_{0}\bigg{(}\frac{1}{\varphi_{0}}-\frac{1-\varphi_{0}}{ \varphi_{0}(1+\varepsilon_{v})}\bigg{(}1--\frac{\alpha}{K_{s}}(\Delta P_{1}+ \Delta P_{2})+\Delta\varepsilon_{s}\bigg{)}\bigg{)}^{3}\end{aligned}\right. \tag{27}\]  
In summary, a fully coupled temperature-seepage-diffusion-deformation model for CO\({}_{2}\)-ECBM and CBM considering the variation of physical parameters is established in Figure 3. The thermal stress caused by the change of coal seam temperature affects the stress field of the coal seam skeleton. The heat generated by the energy dissipation in the coal seam skeleton has an impact on the coal seam temperature; The temperature change causes the gas pressure change to affect the gas; the thermal convection between the gas and coal seam skeleton affects the temperature of the coal seam. The porosity change caused by coal seam deformation affects gas flow. Gas pressure change causes coal seam deformation.  
In this study, based on COMSOL Multiphysics software, the established mathematical model is solved by full physical field coupling of the finite element method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CO\({}_{2}\)** & \(\rho_{2}\) & \(C_{g2}\) & \(\lambda_{g2}\) & \(\mu_{2}\) \\ \hline \(p00\) & \(-1026\) & \(781.6\) & \(-0.00621\) & \(5.872\times 10^{-07}\) \\ \(p10\) & \(0.0002926\) & \(-0.0009226\) & \(-4.58\times 10^{-09}\) & \(-6.52\times 10^{-13}\) \\ \(p01\) & \(8.73\) & \(0.1969\) & \(7.
# A Study of Forest Phenology Prediction Based on GRU Models
## 2 Method
### Experimental Monitoring Equipment  
The high-definition network digital camera DS-2 DC4223 IW-D/GLT(C) manufactured by China Hangzhou Hikvision Digital Technology was utilized as the remote sensing equipment for capturing the growth status of Populus tomentosa B301 in this experiment. It is equipped with a 1/2.8\" progressive scan CMOS sensor, with a maximum resolution of 1920 \(\times\) 1080 pixels, and a lens with a horizontal viewing angle of 57.6-2.7\({}^{\circ}\) (wide angle-telephoto), 360\({}^{\circ}\) horizontal rotation, and \(-\)15\({}^{\circ}\) to 90\({}^{\circ}\) vertical direction (automatic flipping). The camera can operate normally in a temperature range of \(-\)30 \({}^{\circ}\)C to +65 \({}^{\circ}\)C and supports various functions, including 23\(\times\) optical zoom, 16\(\times\) digital zoom, 3D digital noise reduction, and strong light suppression.  
Figure 1: Map of the study area.  
The camera was installed at a height of 20 m above ground level on the upper end of a state-owned forest meteorological station, facing due north (as shown in Figure 2). Due to its height, the camera is susceptible to external environmental factors such as wind and rain, which may cause inevitable movements and changes in its field of view, necessitating azimuthal and image registration calibration. Additionally, the camera was set to \"automatic mode\" for exposure and white balance adjustments. It was programmed to automatically scan three fixed preset points in the forest (Preset point 1, 3, and 5), corresponding to three different irrigation areas: CK (rain-fed), fully drip-irrigated, and fully furrow-irrigated. The images were transmitted via the China Mobile 4G signal network, using the FTP network protocol (IP address 39.102.35.120) to transfer them to an Alibaba Cloud server. To minimize the effects of different solar angles, images were taken every hour between 7:03 AM and 7:00 PM local time, resulting in a time interval of 1 h. The images were named based on the place and time of shooting and were automatically saved in JPEG format without compression, forming a time series. A total of 10,296 images were selected for extracting vegetation indices, covering the period from 24 March 2021 to 3 January 2022 [29].
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 5 Discussion  
Point Cloud is a set of disordered 3D points. Searching for _k nearest neighbors_ is a general method to extract the geometric information hidden behind the points. Some features can be computed by integrating query point with its neighbors, such as normal estimation. Searching for _k nearest neighbors_ for each point is a task with a huge amount of computation that is dependent on the data management structure. Various tree structures are used to manage Point Cloud. Kd-tree is insensitive to the data distribution. However, when faced with large-scale data, it will be too deep to search efficiently. Octree reduced the depth of the tree by dividing into eight sub-nodes. However, it is sensitive to data distribution. R*-tree is considered the most efficient in searching. How  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **Octree** & **3D R*-Tree** & **Hybrid Method** \\ \hline \multirow{3}{*}{\(2 NN\)} & _depth_ & 7 & 10 & 8 \\  & \(t_{cons}(ms)\) & 281 & 1185 & 263 \\  & \(t_{srch}(ms)\) & 68 & 31 & 13 \\  & _depth_ & 5 & 6 & 6 \\  & \(t_{cons}(ms)\) & 150 & 910 & 176 \\  & \(t_{srch}(ms)\) & 30 & 20 & 6 \\  & _depth_ & 5 & 5 & 5 \\  & \(t_{cons}(ms)\) & 109 & 778 & 121 \\  & \(t_{srch}(ms)\) & 39 & 12 & 5 \\  & \(t_{depth}\) & 4 & 4 & 4 \\
16\(NN\) & \(t_{cons}(ms)\) & 115 & 709 & 112 \\  & \(t_{srch}(ms)\) & 20 & 8 & 6 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental data. \(t_{cons}\) represents the time used by constructing structure, \(t_{srch}\) represents the time used by \(kNN\) searching.  
Figure 9: Real–world Point Cloud data.  
ever, it cannot be very deep because of overlaps between nodes. Single index structures cannot meet the real-time requirement, and hybrid index structures are widely studied.  
It is a fact that most time taken by constructing tree structure, especially R*-tree. It is acceptable because the tree is constructed once, but \(kNN\) search repeated are as many times as the number of points to be calculated. The hybrid method proposed in this paper takes about the same time as octree while faster than octree. So, comprehensively considering the time used by tree constructing and \(kNN\) searching, the hybrid method performed better than single octree and 3D R*-tree. Some thresholds need to be set and affect the performance of the index structure. This paper sets \(Children_{min}=8\cdot k\), \(Children_{max}=2\cdot Children_{min}\) empirically. The \(depth\) of octree varies with different data.
## 2 Proposed Framework and Experimental Methodology
### Problem Formulation  
Despite the recognized security and efficiency of AES encryption, traditional CPU-based implementations encounter significant performance bottlenecks in large-scale data encryption tasks due to their inherently sequential execution characteristics [9, 10]. GPU-based encryption, leveraging parallel processing capabilities, presents a promising alternative. However, purely GPU-based approaches often suffer from issues such as inefficient workload distribution, high memory transfer overhead, and the suboptimal utilization of CPU resources [19, 20]. Addressing these limitations necessitates a carefully designed hybrid CPU-GPU approach to optimally balance computational tasks, efficiently distributing workloads according to data characteristics and resource availability.
## 4 Experimental Setup  
To verify the performance of these methods of closed path determination for the GCSM, we built an experimental setup, as shown in Figure 3[24]. As shown in Figure 3, a collimated laser beam of wavelength 632.8 nm passed an aperture with a diameter of 4 mm, and was incident on a liquid crystal on silicon-spatial light modulator (LCOS-SLM). The LCOS-SLM was used to transform the incident beams into the optical vortex beams as well as bring aberrations into the beam. The beam reflected back from the LCOS-SLM was converged by a lens with a focal length of 2 m, and was split by a beam splitter into two beams. Finally, we used an SH-WFS to record the Hartmanngram and a complementary metal-oxide semiconductor (CMOS) camera to check and record the intensity profile of the OV beam. The LCOS-SLM was set at the front focal plane of the lens, while the SH-WFS as well as the CMOS camera were both located at the back focal plane of the lens.  
The LCOS-SLM (Hamamatsu, X10468-01) was a pure phase modulator consisting of \(792\times 600\) pixels with a pixel size of \(20\times 20\)\(\upmu\)m [31]. The SH-WFS consisted of two elements: a square grid lenslet array with a pitch size of 200 \(\upmu\)m and focal length of 11 mm, and a high-speed intelligent vision sensor with \(512\times 512\) pixels and a pixel size of \(20\times 20\)\(\upmu\)m [32]. The SH-WFS was mounted on a mechanical platform that could be moved along the horizontal direction. The movement was precisely controlled by a stepping motor system. The CMOS camera was \(2592\times 2048\) pixels and had a pixel size of \(4.8\times 4.8\)\(\upmu\)m.
# A Low-Overhead Message Authentication and Secure Message Dissemination Scheme for VANETs
## 3 Proposed Approach  
In this section, we present our system model and describe the proposed method for authenticated message dissemination in detail. The acronyms used in this paper are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(ID_{A}\) & Identity of Entity \(A\) \\ \hline \(PID_{A}\) & Pseudo Identity of Entity \(A\) \\ \hline \(M\) & A Message \\ \hline \(v\) & Vehicle \(v\) \\ \hline \(ts\) & Timestamp \\ \hline \(PR_{A}\) & Private Key of Entity \(A\) \\ \hline \(PU_{A}\) & Public Key of Entity \(A\) \\ \hline \(K\) & Symmetric Key established between two communicating parties \\ \hline \(SIG_{A}(M)\) & Signature of \(M\) Signed using \(A\)’s Private Key \\ \hline \(H\)0 & Hash Function \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.
## 5 Conclusions  
New technologies and communication standards have changed traditional network processes. IoT is one of the emerging technologies where the devices are connected to facilitate the users. When the networks are more congested due to a large number of users in IoT networks then the existing routing protocol and communication channel suffers from congestion, disconnection, overhead, and packet drop issues. UAVs have been introduced to facilitate these networks by collecting the data and forwarding toward the main centers. Traditional static network routing protocols have been adopted for data communication. However, the adopted protocols have suffered from various routing issues. In this paper, the DR-UAVIoT protocol was designed based on decision criteria for IoT networks. The proposed protocols are categorized into two main phases: UAV-to-IoT and UAV-to-UAV data communication. Several experiments were conducted to test the proposed routing protocol's performance in terms of data delivery, delay, and network overhead. The proposed protocol was also tested with existing protocols AODV and GyTAR to check its performance. The results indicated the better performance of the proposed protocol  
Figure 11: Overhead Analysis in the Presence of Different Numbers of UAVs Nodes.  
compared with the existing routing protocols. The proposed solution achieved around 45% (Cumulative Percentage) better performance compared with the existing solution. This percentage showed that the proposed solution is a better option for congested IoT networks. In the future, we will deploy the proposed solution with other networks such as smart grids, smart home systems, and smart cities as well.  
Conceptualization, K.N.Q. and T.N.; Methodology, K.N.Q., T.N., A.O.I. and W.N. Writing--original draft, M.A. ([PERSON]) and K.A.; Writing--review & editing, K.N.Q., T.N. and A.O.I.; Visualization, M.A. ([PERSON]) and W.N., Supervision, K.N.Q. and T.N. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the Research Management Centre (RMC), Universiti Malaysia Sabah, through the UMS/PPI-DPJ1 Journal Article Fund. This work was supported, in part, by Science Foundation Ireland grant number 16/RC/3918 to the CONFIRM Science Foundation Ireland Research Centre for Smart Manufacturing and co-funded under the European Regional Development Fund. This work additionally received support from the Higher Education Authority (HEA) under the Human Capital Initiative-Pillar 3 project, Cyberskills.  
Not applicable.  
The authors declare that they have no conflict of interest.
# A Study on Sensitive Bands of EEG Data under Different Mental Workloads
## 3 Data Analysis Method
### Feature Extraction Method  
Feature extraction is performed by calculating the power spectral density and energy of the EEG data. The corresponding power spectral density (PSD) can be calculated according to Equation (7).  
\[p(n)=\frac{F(n)F^{*}(n)}{N} \tag{7}\]  
where \(F^{*}(n)\) is the conjugate of \(F(n)\) and \(N\) is the signal length.  
Therefore, according to the frequency band distribution of the EEG signal, four energy characteristics corresponding to the four bands are calculated by Equation (8).  
The data is normalized using Equation (9).  
\[\begin{split} E_{\alpha}=\sum\limits_{freq=8}^{13}p_{freq}\\ E_{\beta}=\sum\limits_{freq=14}^{30}p_{freq}\\ E_{\theta}=\sum\limits_{freq=4}^{7}p_{freq}\\ E_{\delta}=\sum\limits_{freq=0.5}^{3}p_{freq}\end{split} \tag{8}\]  
where \(P_{freq}\) refers to the power spectral density value at a certain frequency value.  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{9}\]
###### Abstract
Nowadays, studies of Deep Learning algorithms show high accuracy rates in arrhythmia detection compared to the level of cardiologists [5]. The success of arrhythmia classification tasks with Machine Learning (ML) algorithms is based on the handcrafted extraction of features from Electrocardiography (ECG) signals [2]. The feature engineering process requires prior knowledge of the ECG processing techniques and an understanding of ECG signal interpretation. On the other hand, Deep Neural Networks (DNN) bypass the process of handcrafting feature extraction, as their hidden layers perform the task of extracting and learning specific features from raw ECG signals. It has both advantages and disadvantages; the advantage is that it eliminates the step of handcrafted features [6]. The disadvantage is that these algorithms are like a black box, and it is complex and abstract, especially in time series, to understand what the learned representations are [7]. The main purpose of this exploratory study is to evaluate and compare the 1D Convolutional Neural Network (1D-CNN) algorithm and the Support Vector Machine (SVM) classifier for the task of automatic classification of cardiac rhythms; by using a redesigned dataset from an existing public database. Two approaches for the task of arrhythmia classification will be addressed: (1) human-assisted feature extraction approaches based on traditional signal processing techniques; (2) data representation learning and classification based on the 1D-CNN algorithm. As a secondary objective, we will evaluate using two public databases combined under the same characteristics to balance and increase the volume of data. In order to have reliable results, the algorithms will be trained and evaluated with the same dataset. The paper is organized as follows: In Section 2, the materials and methodology are explained, covering the characterization of databases, the feature extraction process, classifiers, and the validation setup. Section 3 contains the obtained results. Section 4 contains the discussion, and finally, Section 5 is the conclusion.
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 3 Proposed VLC-Based IoT Scheme
### Protocol Stacks Based on Uni-Directional VLC  
Figure 3 shows the protocol stack used by each network entity in the proposed VLC-based IoT scheme, which is based on IPv6 networks.  
In the figure, PS and AA are connected through Ethernet. Connections between AA and VT can be established through Ethernet, WLAN, or WPAN. 6 LoWPAN may be used between AA and VT, depending on the underlying link, such as BLE or ZigBee. Each VT is connected to an AA. PS can send location-specific VLC data to each VT via AA.  
In the meantime, VR receives the VLC data frames from VT. This VLC data frame can contain location-specific data given by PS. Based on this location-specific VLC data, VR establishes the VR service channel with AA by using WPAN or WLAN. To further discuss the use of location-specific VLC data and the VR service channel, let us take an example of the _museum_ service, which consists of a single PS (acting as a museum administrator) and many AAs (an AA may be established at each floor in the museum). It is assumed that each AA is responsible for many VTs, and each VT supports a particular item in the museum (e.g., a famous picture). A visitor (user) is equipped with a VR device and moves across VTs to see different items. In this example, PS can provide each VT with location-specific data (an item identifier), and when VR visits the VT, that location-specific information (item identifier) is delivered from VT to VR via the VLC transmission. Based on the item identifier, VR will establish the service channel with AA and download the item (picture) file from the AA. In this way, the location-specific VLC data (possibly initiated by PS and/or AA) is delivered from VT to VR via the uni-directional VLC transmission, and the VR service channel is used between VR and AA for uplink or backward data delivery. This is an example for VLC-based IoT service, and its extension could be possible, depending on the status of VLC deployment and the features of target VLC services.
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 4 Results
### Indoor Measurements
#### 4.2.1 Attic  
The attic is located under a tiled roof which is inclined at 50\({}^{\circ}\). This is the 3 rd level of the house. Its insulation consists of 25 cm of glass wool. No windows through which the signal could potentially propagate were present in this room. Satellite signals in this room were received rather strongly. In a total of 20 satellite passes, the highest received \(C/N_{0}\) was 56.9 dB-Hz. Furthermore, all of the 20 passes were received. Notably, multiple satellite passes showed similar patterns as seen in Figure 4. When the satellite reaches its maximum elevation angle, the signal strength decreases significantly. This pattern was present in multiple satellite passes recorded in the attic. Lastly, when analyzing Figure 4 it can be observed that the signals are still well received in this room even lower elevation angle passes reached a \(C/N_{0}\) of up to 50 dB-Hz.
# Self-Supervised Shear Wave Noise Adaptive Subtraction in Ocean Bottom Node Data
## 4 Discussions
### Hyperparameters  
The settings of the hyperparameters affect the performance of the network, with inappropriate settings potentially leading to the over-fitting or under-fitting of the model. Network parameter regularization also has the effect of avoiding over-fitting. It is important to note that, in our context, over-fitting refers to the presence of some signals in the noise predicted by the network, resulting in signal damage in the output. Conversely, under-fitting means that the true noise was not adequately predicted, leaving some noise in the result. In this section, we conduct experiments when removing the noise related to the X-component (step one) for synthetic data to examine the impact of the learning rate and network parameter regularization. The initial learning rate is set to 0.001, and the coefficient for network parameter regularization is set to 0. We then vary the parameter values for testing.  
Figure 12 demonstrates the changes in the loss function values and corresponding SNRs for network training at a learning rate of 0.001. Figure 13 depicts the variations in the loss function values and SNRs for network training at a learning rate of 0.01 and 0.0001, respectively. With a learning rate of 0.01, the model exhibits over-fitting after approximately 50 iterations, causing the loss function to oscillate widely and rendering the model unstable. When the learning rate is 0.0001, the SNR increases very little as the values of loss function decrease, indicating under-fitting. Therefore, setting the learning rate to 0.001 gives more reasonable results. Furthermore, using network parameter regularization brings no significant improvement to the denoising effect in our tests (seen in Figure 14). Consequently, we choose a learning rate of 0.001 without network parameter regularization as the optimal configuration during network training.  
Figure 11: Comparison of the three components of field OBN data. (**a**) From left to right, the Y, Z, X- and Z-component. (**b**) From left to right, the Z-, X-, Z- and Y-component.  
Figure 12: Loss function and SNR curves in network training without network parameter regularization (learning rate = 0.001).
## 2 Materials and Methods
### Image Enhancement
#### 2.3.2 Gamma Correction  
This method was utilized as a nonlinear method for brightness decoding and encoding to adopt with human visual insight. The transformation method is mathematically represented as follows:  
\[T(l)=l_{max}\left(\frac{l}{l_{max}}\right)^{\gamma} \tag{2}\]  
Here, \(l\) denotes the intensity level, and \(l_{max}\) denotes the maximum intensity level of the image [18]. Usually, most of the image-taking devices do not take exact luminance and familiarize nonlinearity, thus gamma correction is required to improve the luminance. The power function in Equation (1) compensates for the nonlinearity introduced to properly reproduce the actual luminance. The gamma transformation curves for different values of gamma are plotted in Figure 1a. The value of \(\gamma<1\) raises the brightness, where a value of \(\gamma>1\) increases the darkness of the image. The value of \(\gamma=1\) shows that the gamma correction curve reduces towards the identity, and, hence, does not alter the identity. The luminance nonlinear dynamics for lower gray-levels greatly increase, whereas the dynamics for moderate gray-levels slightly increase, and the dynamic ranges for higher gray-levels are compressed. In this study, we applied the gamma values as \(\gamma=[0.03,\ 0.4,\ 0.5,\ 0.1,\ 1,\ 2,\ 4]\) according to the scientific requirements [19; 20; 21].