esa-sceva/satcom-chunk-collection huggingface dataset:
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
## 1 Introduction
On the other hand, microwave data links use different frequencies, and their influence on humans is far smaller. An example of this can be an insufficient bandwidth, affecting the transmission of large packets of data such as high-resolution videos from teleconferences or the monitoring of closed-circuit television cameras. The deep need for faster and more efficient data transfer gave us newer technologies without the above-mentioned disadvantages. Optical Wireless Links, also called Free-Space Optics (FSO), are devoid of many of the limitations possessed by microwave links [12; 13]. FSO is a wireless optical communication technology that uses the emission of optical radiation in an open space to transmit data between two points without obstructing the line of sight between them [14]. Its basic ingredients are transmitters, such as lasers or light-emitting diodes, and signal receivers, like photodetectors. Figure 1 presents the schematic image of such communication between two points placed on two skyscrapers, while Figure 2 presents a connection between other places. FSO is a relatively new technology, but it shows a lot of promise, as modern-day systems enable short-range data transmission up to 8 km with a high capacity reaching up to 100 Gb/s [15]. Such systems are bidirectional due to the optical transceivers at both ends of the link. Both transceivers are connected to a data source, such as a computer with a modem. Communication systems in open space like air or the vacuum of outer space require the use of appropriate sources of optical radiation. When selecting them, the following parameters should be considered: wavelength range, power of the emitted radiation, and compatibility of cooperation with electronic modulating systems [16]. The wavelength range of the radiation in the FSO communication system is primarily determined by the transmission properties of the medium in which the signal is transferred, like the atmosphere. This technology is very sensitive to the type of climate and the weather, including physical phenomena [17]. Another related issue is ensuring safety for both users of the FSO network and outsiders [18; 19], as this limits both the power and frequency of light emitters in the FSO. Figure 1: Schematic image of a Free-space Optical Communication link between two nearby skyscrapers, serving e.g., to create a LAN-to-LAN connection between A and B points. Figure 2: Schematic image of Optical Communication links between different objects. Data transmitted on a narrow beam of laser light is marked in red.
## 4 Numerical Simulations
### Performance Metrics  
The detection probability, as the accuracy evaluation factor of focusing directions, is built to evaluate the performance of the three different algorithms. When the absolute value of the difference between the estimated focusing direction \(\hat{s}(\theta_{i},\varphi_{j})\) and the actual focusing direction \(s(\theta_{i},\varphi_{j})\) is less than or equal to \(2^{\circ}\), the detection is defined as correctly detecting. The greater the detection probability, the higher the accuracy.  
The detection probability is defined as the relative root-mean-square error at the focusing direction \(g\).  
\[\text{P}_{g}=\sqrt{\frac{\frac{1}{q}\sum\limits_{g=1}^{q}\big{|}s_{g}(\theta_ {i},\varphi_{j})-\hat{s}_{g}(\theta_{i},\varphi_{j})\big{|}^{2}}{\frac{1}{q} \sum\limits_{g=1}^{q}\big{|}s_{g}(\theta_{i},\varphi_{j})\big{|}^{2}}} \tag{22}\]where \(q\) denotes the number of Monte Carlo experiments, \(s_{g}(\theta_{i},\varphi_{j})\) is the actual focusing direction, and \(\hat{s}_{g}(\theta_{i},\varphi_{j})\) is the corresponding focusing direction estimation.
## 5 Results and Discussion  
### Geochemical Anomalies Identified by Statistical Methods  
#### 5.1.1 Single Geochemical Element Anomaly Analysis by Statistical Methods  
There were numerous Cu deposits in Luliangshan, NQ. The spatial distributions of Cu, Zn, Co, and Cr are shown in Figure 5. Notably, high concentrations of Cu and Co were similarly located in Luliangshan and Luofengpo, as depicted in Figures 5a and 5c, respectively. The anomalies in Luliangshan were large in size, and had high elemental concentrations with a conspicuous coincidence of element assemblages. These anomalieswere oriented along the NW-SE direction and demonstrated a pronounced correlation of Cu and Co anomalies, indicating their role as ore-forming elements. We employed the Mean + 2 SD method (Table 2) to compute threshold values for identifying anomalies in Luliangshan and Luofengpo. The mean value, influenced by specific outlier values, was often excessively large, leading to a discernible deviation between the anomalies identified by the traditional method and the actual ore points. The distribution of Cu anomalies was primarily concentrated in the Luliangshan area. Consequently, due to the conspicuous Cu anomalies in this region, all three known ore points were within the range of the anomalies. In contrast, the anomalies in the Luofengpo area were relatively weak and the overlay was quite thick, resulting in the absence of known ore sites within the anomalous range. This indicated that statistical methods were not sufficiently precise in areas with thick overlay or weak anomalies.  
The results for single geochemical elements Cu (Figure 5a), Zn (Figure 5b), Co (Figure 5c), and Cr (Figure 5d), as well as statistical anomaly analysis, were as follows:
## 4 Experimental Setup and Results Analysis
### Experimental Results
#### 4.5.1 The Effect of Collaborative Attention  
To investigate the interaction between substructure interaction and cooperative attention, the impact of removing the cooperative attention layer was examined. Figure 2(a) shows the AUC, ACC, and F1-score results obtained with and without the cooperative attention layer on the DrugBank validation set. It can be seen that the combination of cooperative attention does improve performance: under the same number of attention heads and markers, using cooperative attention leads to an improvement, and its removal causes a decline. This is because when matching the substructures of two different molecules, the structural and property differences between the molecules themselves can cause many irrelevant pairings, resulting in much unmergeable information. The addition of cooperative attention allows such noise to be effectively avoided.
# TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
###### Abstract  
Efficient and accurate fault diagnosis of rotating machinery is extremely important. Fault diagnosis methods using vibration signals based on convolutional neural networks (CNNs) have become increasingly mature. They often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application of Transformer-based Vision Transformer (ViT) methods to fault diagnosis is gaining attraction. Nonetheless, these methods typically require extensive preprocessing, which increases computational complexity, potentially reducing the efficiency of the diagnosis process. Addressing this gap, this paper presents the Time Series Vision Transformer (TSViT), tailored for effective fault diagnosis. The TSViT incorporates a convolutional layer to extract local features from vibration signals alongside a transformer encoder to discern long-term temporal patterns. A thorough experimental comparison of three diverse datasets demonstrates the TSViT's effectiveness and adaptability. Moreover, the paper delves into the influence of hyperparameter tuning on the model's performance, computational demand, and parameter count. Remarkably, the TSViT achieves an unprecedented 100% average accuracy on two of the test sets and 99.99% on the other, showcasing its exceptional fault diagnosis capabilities for rotating machinery. The implementation of this model will bring significant economic benefits.  
vision transformer; fault diagnosis; rotating machinery; deep learning +
Footnote †: journal: applied sciences
## 2 Model
### Desorption Rate Ratio
#### 2.2.1 Relationship between Effective Diffusion Coefficient and Particle Size
Once desorption of gas is possible under favorable conditions, gas molecules near the surface of coal particles start escaping into the surroundings first. Therefore, the cumulative amount of gas desorbed from coal during the initial short period is mainly determined by the surface area of coal particles, which is approximately equal to the gas desorption rate if the period is sufficiently short. Since the volume of coal is constant after being broken, the change in total surface areas would be characterized by differences in the specific surface area of coal particles corresponding to intact coal and broken coal, respectively. The specific surface area of a smaller particle is greater than that of a larger particle. Therefore, we assumed that the initial gas diffusion rate was proportional to the apparent surface area of coal particles. Therefore, we assume the relationship between the size ratio and the initial diffusion rate ratio is  
\[\frac{D_{\varepsilon}(R_{2})}{D_{\varepsilon}(R_{1})}=\left(\frac{d_{1}}{d_{2 }}\right)^{\gamma}=\left(\frac{R_{1}}{R_{2}}\right)^{\gamma} \tag{19}\]  
where \(\gamma\) is the index of desorption; \(d_{1},d_{2}\) are the intact and broken coal particle sizes, respectively; The parameter \(\gamma\) represents the change in the effective diffusion coefficient with particle size. The larger \(\gamma\) is, the greater the change in the effective diffusion coefficient after coal crushing. When the coal particle is not broken, the gas desorption rate ratio and specific surface area ratio are equal at the initial time. Then, the relationship between the effective diffusion coefficients of coal with different particle sizes is:  
\[D_{\varepsilon 0}(R)=D_{\varepsilon 0}(R_{0})\left(\frac{R_{0}}{R}\right)^{\gamma} \tag{20}\]
## 4 Results and Discussion
### Comparative Analysis of Different Models
#### 4.2.1 Stability of Prediction Performance  
Based on the analysis in Section 4.1, the model exhibited relatively large errors during the 0-21-day period. To further analyze the prediction accuracy of the model, this study compares the performance of the BP, LSTM, ISVR, and SSA-ISVR models in modeling the concrete temperature of several pouring blocks. Table 5 and Figure 12 present the mean and standard deviation of prediction errors for each model.  
Figure 11: Monitoring point in 17: (**a**) temperature of 17#-078; (**b**) the regression results of the SSA-ISVR model in 17#-078; (**c**) temperature of 17#-079; (**d**) the regression results of the SSA-ISVR model in 17#-079.  
Figure 12 presents the residual statistics and symmetrical normal distribution curves for various concrete temperature prediction models across the entire test set and specific blocks, including segments 7, 11, and 17.  
As shown in Table 5 and Figure 12, SSA-ISVR consistently yields the lowest mean absolute error, approximately 0.003, with a standard deviation around 0.014, outperforming  
Figure 12: Error distribution of different algorithms: (**a**) error distribution of the entire test set; (**b**) error distribution for the monitoring point in 7; (**c**) error distribution for the monitoring point in 11; (**d**) error distribution for the monitoring point in 17.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Statistical** & **Models** & \begin{tabular}{c} **Total Test** \\ **Set** \\ \end{tabular} & \begin{tabular}{c} **Monitoring** \\ **Point in 7** \\ \end{tabular} & \
\begin{tabular}{c} **Monitoring** \\ **Point in 11** \\ \end{tabular} &
\begin{tabular}{c} **Monitoring** \\ **Point in 17** \\ \end{tabular} \\ \hline \multirow{3}{*}{Mean} & BP & 0.016 & 0.013 & 0.026 & 0.008 \\  & LSTM & 0.019 & 0.031 & 0.011 & 0.018 \\  & ISVR & 0.005 & 0.002 & 0.003 & 0.004 \\  & SSA-ISVR & 0.003 & 0.002 & 0.005 & 0.004 \\ \hline \multirow{3}{*}{Standard deviation} & BP & 0.026 & 0.014 & 0.036 & 0.016 \\  & LSTM & 0.036 & 0.036 & 0.037 & 0.031 \\ \cline{1-1}  & ISVR & 0.021 & 0.014 & 0.025 & 0.021 \\ \cline{1-1}  & SSA-ISVR & 0.015 & 0.012 & 0.016 & 0.016 \\ \hline \end{tabular}
\end{table}
Table 5: Statistical values of absolute prediction errors.  
the other models. This indicates that SSA-ISVR exhibits lower prediction errors and more concentrated error distribution during the 0-21-day period.
# A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things +
## Appendix A Beta Distribution  
The Beta (\(\alpha,\beta\)) distribution is a probability distribution that models events which are constrained to take place within an interval defined by a minimum and maximum value. It is generally a continuous probability distribution that is defined on the interval of (0,1) \(0<p<1\). Its probability density function can be presented using the following formula: \(\frac{p^{(\alpha-1)}(1-p)^{(\beta-1)}}{B(\alpha,\beta)}\), where \(\alpha\) and \(\beta\) are two positive shape parameters which control the shape of the distribution, p is a random variable and B(\(\alpha,\beta\)) can be calculated using the following equation:  
\[B(\alpha,\beta)=\frac{\Gamma(\alpha)+\Gamma(\beta)}{\Gamma(\alpha+\beta)} \tag{1}\]  
where \(\Gamma(k)!=1\times 2\times 3\times\ldots\times(k-1)\).  
The expected value of a Beta-distributed random variable \(p\) can be computed using the following equation:  
\[E(p)=\frac{\alpha}{(\alpha+\beta)} \tag{2}\]  
The variance of a Beta-distributed random variable \(p\) can be calculated using the following equation:  
\[V(p)=\frac{\alpha\beta}{(\alpha+\beta+1)(\alpha+\beta)^{2}} \tag{3}\]  
The \(\beta\) distribution is very simple and flexible to use. This is because the distribution has only two parameters (\(\alpha\), \(\beta\)) that for the random variable (\(p\)) take values between 0 and 1. Consequently, it can often be used to model percentage or fractional quantities. In general, the \(\beta\) distribution can be used a lot in Bayesian statistics in any case where we may have an unknown probabilities of events taking place. Examples of network related events that may be modeled by \(\beta\) distribution include:  
* Trust value of a node.
* The number of packets successfully delivered from source-to-destination.
* Node mobility in a wireless network.
* The time it takes for delivering of packets from source-to-destination.
# DM-SLAM: Monocular SLAM in Dynamic Environments
###### Abstract  
Many classic visual monocular SLAM (simultaneous localization and mapping) systems have been developed over the past decades, yet most of them fail when dynamic scenarios dominate. DM-SLAM is proposed for handling dynamic objects in environments based on ORB-SLAM2. This article mainly concentrates on two aspects. Firstly, we proposed a distribution and local-based RANSAC (Random Sample Consensus) algorithm (DLRSAC) to extract static features from the dynamic scene based on awareness of the nature difference between motion and static, which is integrated into initialization of DM-SLAM. Secondly, we designed a candidate map points selection mechanism based on neighborhood mutual exclusion to balance the accuracy of tracking camera pose and system robustness in motion scenes. Finally, we conducted experiments in the public dataset and compared DM-SLAM with ORB-SLAM2. The experiments corroborated the superiority of the DM-SLAM.  
static features extraction; dynamic environments; 3D reconstruction; monocular SLAM +
Footnote †: journal: Applied Sciences
# A Secure Key Aggregate Searchable Encryption with Multi Delegation in Cloud Data Sharing Service
## 4 Our Proposed Scheme
### Data Request Phase  
If \(DU_{j}\) wants to data set \(S_{i}\), \(DU_{j}\) calculates \(HID_{j}\) and \(PK_{j}\) and requests data from \(DU\). \(DO\) computes the aggregate key \(k_{s}\) corresponding data set. After that, \(DO\) creates \(GID_{l}\) by defining groups that can delegate or receive authority. \(DO\) can manage the list of \(DU_{l}\) belonging to \(GID_{l}\) when a new \(DU_{j}\) is added or an existing \(DU\) wants to leave the group. After that, \(DO\) creates an authentication credential that allows \(DU_{j}\) and \(CS\) to authenticate each other. \(DO\) generates \(TID_{j}\) and transmits it with \(k_{s}\), \(HGID_{l}\) securely to \(DU_{j}\), and generates and transmits \(HID_{j}\) and \(A_{cs}\) securely to \(CS\). \(CS\) uses this value to calculate \(ACS_{j}\), which is for the authentication credential, and stores it in its own database. Figure 4 summarizes this phase. The detailed steps involved in this phase are given below.  
**Step 1:**\(DU_{j}\) generates a secret key \(b_{j}\in Z_{q}^{*}\) and chooses an unique identity \(ID_{j}\). Then, \(DU_{j}\) computes pseudo identity \(HID_{j}=h_{1}(ID_{j}||b_{j})\) and public key \(PK_{j}=g^{b_{j}}\). \(DU_{j}\) sends \(\langle HID_{j},PK_{j},S_{i}\rangle\) securely to \(DO\), where \(S_{i}\) is a document set.  
**Step 2:** After receiving data request from \(DU_{j}\), \(DO\) generates an aggregate key \(k_{s}=\Pi_{j\in S}g_{n+1-j}^{r_{da}}\) which is corresponding document set \(S_{i}\). \(DO\) then creates \(GID_{l}\) by defining groups to determine which users can delegate or receive privileges from each other. \(DO\) computes \(TID_{j}=(DPK_{do})^{HID_{j}\rho_{da}}\) for authentication credential. Furthermore, \(DO\) computes \(HGID_{j}=h_{2}(GID_{l}||r_{do}||\rho_{do})\) and \(A_{cs}=h_{2}(r_{do}||\rho_{do})\). After that, \(DO\) sends \(\langle k_{s},TID_{j},HGID_{l}\rangle\) to securely \(DU_{j}\) and sends \(\langle HID_{j},A_{cs}\rangle\) to securely \(CS\).  
**Step 3:**\(CS\) computes \(ACS_{i}=h_{2}(HID_{j}||A_{cs})\) and public key \(PK_{cs}=g^{A_{cs}}\) after receiving messages from \(DO\). Then, \(CS\) stores \(ACS_{i}\) in \(CS\)'s database.  
Figure 3: Data upload phase.
## 3 Results and Discussion
### Continental System
(a) RT64 HC; (b) PureTemp 63; (c) THP5860. The mass loss of RT64 HC and PureTemp 63 at 120 \({}^{\circ}\)C are 0.13% and 0.09%, respectively, while THP5860 does not lose weight until the temperature reaches around 135 \({}^{\circ}\)C. The temperatures at which the mass loss reaches 1.5 wt % are 173 \({}^{\circ}\)C, 174 \({}^{\circ}\)C, and 188 \({}^{\circ}\)C for RT64 HC, PureTemp 63, and THP5860, respectively. If a lower heating rate of e.g., 1 \({}^{\circ}\)C/min was used instead of 10 \({}^{\circ}\)C/min, the temperature for the stability limit would be lower than the values shown here, but still, the mass loss in the temperature range of the application would be negligible. In summary, according to the decision matrix, the most promising PCM for the CON system, with a phase change temperature between 60 \({}^{\circ}\)C and 65 \({}^{\circ}\)C, are RT64 HC, PureTemp 63, and THP5860. Furthermore, the results obtained by DSC, FT-IR, and TGA confirm that RT64 HC and THP5860 are adequate from this point of view, while PureTemp 63 is not indicated because it failed the cycling tests.
## 3 Analysis of Shield Tunneling Parameters
### Slurry Pressure  
The correlation between the slurry pressure and the shield tunnel ring number is il-lustrated in Figure 5. Table 2 presents the statistical characteristics of the slurry pressure across three distinct stratum conditions.  
Analysis of Figure 5 and Table 2 reveals that the slurry pressure shows minimal correlation with the stratum and is predominantly linked to the tunnel burial depth. The slurry pressure during shield tunneling undergoes three distinct stages. (1) Rapid Growth Stage (56-251 rings): Slurry pressure increased swiftly from 0.139 MPa to 0.238 MPa, with a growth rate of 0.0005 MPa/ring. This surge is attributed to the shallow launching of the shield machine, leading to an initially low slurry pressure. As the machine's burial depth increases, the slurry pressure rises rapidly. (2) Fluctuation Growth Stage (252-318 rings): Slurry pressure decreased from 0.238 MPa at 251 rings to 0.195 MPa and then experienced a rapid increase. This fluctuation is tied to a decrease in the slurry pressure when the shield tunneling approaches residential areas. (3) Rapid Decline Stage (319-730 rings): Slurry pres  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stratum Serial** & **Mean** & **Standard** & **Median** & **Minimum** & **Maximum** & **Skewness** \\
**Number** & **Value** & **Deviation** & **Value** & **Value** & **Value** & **Skewness** \\ \hline I & 0.165 & 0.006 & 0.166 & 0.154 & 0.179 & \(-\)0.251 \\ II & 0.215 & 0.023 & 0.212 & 0.170 & 0.282 & 0.332 \\ III & 0.135 & 0.025 & 0.140 & 0.076 & 0.175 & \(-\)0.202 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical characteristics of different strata with the slurry pressure.  
Figure 5: The curve of the slurry pressure changing with the ring number.  
sure declined sharply from 0.280 MPa to 0.076 MPa, with a decline rate of 0.0005 MPa/ring. As the shield tunnels, the burial depth of the intermediate shield decreases, bringing the shield closer to the receiving mileage.
## 3 Dataset Description and Processing
### Dataset Preparation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & \(c\) **(KPa)** & \(\varphi\) (\({}^{\circ}\)) & \(\Phi\) (\({}^{\circ}\)) & \(H\) **(m)** & \(\gamma\) **(KN/m\({}^{3}\))** & \(r_{u}\)** & \(FoS\) \\ \hline Number & 307 & 307 & 307 & 307 & 307 & 307 & 307 \\ Max & 150.05 & 45 & 66 & 511 & 31.3 & 0.503 & 5.799 \\ Min & 0 & 0 & 8 & 3.66 & 12 & 0 & 0.46 \\ Mean & 25.3633 & 29.0593 & 35.5752 & 110.7239 & 22.1733 & 0.2694 & 1.4612 \\ Standard deviation & 25.3140 & 10.0418 & 10.7847 & 147.2382 & 4.5259 & 0.1245 & 0.7118 \\ Kurtosis & 7.3598 & 1.1794 & -0.4032 & 0.52461 & \(-0.2327\) & 0.7679 & 10.4443 \\ Skewness & 2.2021 & \(-1.1805\) & \(-0.5592\) & 1.38309 & \(-0.1840\) & \(-0.5038\) & 2.7874 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical description of the dataset employed in this study. to say, it is not easy to find an obvious relationship between the six input variables and FoS, which indicates that there is a strong nonlinear relationship between FoS and input variables. Figure 3: Correlation matrix of the dataset. Figure 2: Violin plots of input variables. In the subsequent ML modeling, the whole dataset is randomly divided into two independent subsets: a training set and test set. The training set is utilized to build and train the ML model, and then the test set is utilized to validate the predictive performance and generalization capability of the proposed model [69]. Through optimization analysis, the optimal percentages of the training set and the test set in the whole dataset are determined to obtain two sufficiently representative subsets. In this paper, 80% of the whole dataset is included in the training set (i.e., 245 cases) and the remaining 20% in the test set (i.e., 62 cases).