esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
(1) The gas reservoir space in the first member of the Lower Stone Box Formation of the D-well Zone in the Xinzhao East belt is mainly composed of intergranular pores, intragranular pores, and grain-edge micro-cracks. The gas reservoir space in the study area is characterized by complex three-dimensional spatial distribution, varying morphology and size, and strong non-homogeneity. Microcracks within the gas layer communicate with isolated pores to form a reservoir space network, which increases the size of the pore-throat and enhances the connectivity of the pore-throat. The microscopic characteristic within the gas layer is good. The first member of the Lower Stone Box Formation in the studied section can be a high-quality reservoir.  
Figure 12: The relationship between quartz particle size and pore throat radius. (**a**) Relationship between quartz particle size and pore radius; (**b**) relationship between quartz particle size and throat radius.  
(2) The contact relationship of gas layer particles in the first member of the Lower Stone Box Formation of the D-well Zone in the Xinzhao East belt is dominated by line contact and concave-convex contact. The primary porosity of the gas layer is not developed. The development of secondary lysimeters is due to dissolution. At the same time, unstable minerals such as kaolin (stone) and illite clog the pores. Reservoir properties within the gas layer are affected. The difficult-to-dissolve mud cement within the gas layer further deteriorates pore-throat connectivity. Further, the gravelly coarsegrained clastic quartz sandstones, which have a higher content of muddy cement, have poorer microscopic characteristics and reservoir material properties compared to the conglomerates. The high content of quartz brittle minerals and the development of natural microfractures within the gas formation are favorable conditions for fracture development.  
(3) Quartz grain size and pore throat size are positively correlated within the gas layer in the first member of the Lower Stone Box Formation of the D-well Zone in the Xinzhao East belt. The microscopic characterization of gas reservoir space is somewhat influenced by quartz grain size. Compared with gravelly coarse-grained clastic quartz sandstones within gas formations, larger-grained conglomerates have superior reservoir space connectivity and pore-throat size dimensions.  
Conceptualization, N.G. and W.H.; methodology, N.G. and W.H.; software, N.G., L.G. and G.L.; investigation, N.G. and L.G.; writing, N.G., L.G. and G.L.; data curation and visualization, N.G. and L.G.; supervision, G.L.; funding acquisition, W.H. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (Grant No. 51704033).  
Not applicable.  
All data and materials are available on request from the corresponding author. The data are not publicly available due to ongoing research using a part of the data.  
The authors declare no conflicts of interest.
###### Abstract  
The underwater acoustic communication technique for high-speed and highly reliable information transmission in the ocean has been one of the popular research focuses facing the fast-growing information technology sector and the accelerating development of ocean resources. In order to achieve a high information transmission rate with limited underwater acoustic channel bandwidth, researchers have paid much attention to the underwater acoustic communication technique based on orthogonal frequency division multiplexing (OFDM). A traditional OFDM-based frequency domain equalization algorithm relies on cyclic prefixes for the effective resistance to the multipath effect of an underwater acoustic channel. However, a redundant cyclic prefix may lead to a severe waste of energy and bandwidth in the underwater acoustic system if it is too long. The high utilization rate of OFDM signal channel will not be practically achieved in this case. Based on the limitations of the existing frequency domain equalization algorithm, this paper studied the influence of the multipath effect on the OFDM signal transmission. Subsequently, the principles of the OFDM-based frequency domain equalization were further explored for an improved structural model design of the communication system. On this basis, a novel frequency domain adaptive equalization algorithm was put forward. In addition, the proposed algorithm was optimized to address the problem of increased computation. The simulation results proved that the novel frequency domain equalization algorithm delivers a better symbol error ratio than the existing algorithm, and the compensation for the multipath effect through frequency selective fading. The proposed algorithm can realize the information transmission at a low symbol error ratio when fewer cyclic prefixes are used, so that it takes up a lower number of channels with cyclic prefixes in the OFDM communication system.  
underwater acoustic communication; frequency-domain equalization algorithm; orthogonal frequency division multiplexing (OFDM); circular prefix; multipath effect 22 December 2022  
1
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
###### Abstract  
The occurrence of wake can pose a threat to the flight safety of aircraft and affect the runway capacity and airport operation efficiency. To effectively identify aircraft wake, this paper proposes a novel convolutional neural network (CNN) method of aircraft wake recognition based on the improved parallel network (ParNet). Depthwise separable convolution (DSC) was introduced into the ParNet to make the wake recognition model lightweight. In addition, the convolutional block attention module (CBAM) was introduced into the wake recognition model to improve the capacity of the model to extract the spatial features of the wind field. The proposed aircraft wake recognition method was used to identify the aircraft wake based on the lidar wind field scanning image of Hong Kong International Airport. The best wake recognition effect was obtained with a recognition accuracy of 98.91% and an F1 value of 98.90%. As the number of parameters of the model was only 0.46 M, the aircraft wake could be identified on an ordinary computer. Thus, the proposed method can effectively identify aircraft wake.  
aircraft wake; lidar; ParNet; neural network Article
###### Abstract  
The widespread use of WiFi Internet-of-Things (IoT) devices has rendered them valuable tools for detecting information about the physical environment. Recent studies have demonstrated that WiFi Channel State Information (CSI) can detect physical events like movement, occupancy increases, and gestures. This paper empirically investigates the conditions under which WiFi sensing technology remains effective for keystroke detection. To achieve this timely goal of assessing whether it can raise any privacy concerns, experiments are conducted using commodity hardware to predict the accuracy of WiFi CSI in detecting keys pressed on a keyboard. Our novel results show that, in an ideal setting with a robotic arm, the position of a specific key can be predicted with 99% accuracy using a simple machine learning classifier. Furthermore, human finger localisation over a key and actual key-press recognition is also successfully achieved, with 94% and 89% reduced accuracy values, respectively. Moreover, our detailed investigation reveals that to ensure high accuracy, the gap distance between each test object must be substantial, while the size of the test group should be limited. Finally, we show WiFi sensing technology has limitations in small-scale gesture recognition for generic settings where proper device positioning is crucial. Specifically, detecting keyed words achieves an overall accuracy of 94% for the forefinger and 87% for multiple fingers when only the right hand is used. Accuracy drops to 56% when using both hands. We conclude WiFi sensing is effective in controlled indoor environments, but it has limitations due to the device location and the limited granularity of sensing objects.  
W +
Footnote †: This paper is an extended version of our paper published in the IEEE Global Communication Conference (GLOBECOM) Workshops, Kuala Lumpur, Malaysia, 8-12 December 2023.  
Article
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 3 Implementation
### System Initialization  
#### 3.1.1 System Parameter Setting  
Security parameter \(V\) is input, a group \(G\) is chosen on an elliptic curve, of prime order \(q\), with generator \(P\).  
The TA initializes: a random number \(t\in Z_{q}^{*}\) is chosen as the system master key, the public key \(T_{pub}=tP\) is computed, and a hash function \(H_{0}:G\times\{0,1\}^{*}\to Z_{q}^{*}\) is chosen.  
The KGC initializes: a random number \(s\in Z_{q}^{*}\) is chosen as the system master key, the system public key \(P_{pub}=sP\) is computed, and hash functions \(H_{1}:\{0,1\}^{*}\times G\times G\to Z_{q}^{*}\), \(H_{2}:\{0,1\}^{*}\times G\times G\times\{0,1\}^{*}\to Z_{q}^{*}\), \(H_{3}:\{0,1\}^{*}\times\{0,1\}^{*}\times G\times G\times G\times\{0,1\}^{l}\) are chosen, where \(l\) represents the length of plaintext or ciphertext messages.  
The KGC and TA are independent of each other, each keeping their master keys \(s\) and \(t\). They publish the system parameters \(params=\{G,P,q,\ P_{pub},T_{pub},H_{0},H_{1},H_{2},H_{3}\}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Meaning** \\ \hline \(V\) & System security parameter \\ \(q\) & Sufficient large prime number \\ \(s,t\) & System master key, \(s\in Z_{q}^{*}\), \(t\in Z_{q}^{*}\) \\ \(P_{pub}T_{pub}\) & System public key, \(P_{pub}=sP\), \(T_{pub}=tP\) \\ H\({}_{l}\) & Secure hash function, \(i=0\), \(1\), \(2\), \(3\), \(4\) \\ RID\({}_{i}\) & Real identity of EV\({}_{i}\) \\ PID\({}_{i}\) & Pseudonym of EV\({}_{i}\) \\ ID\({}_{\text{CP}_{i}}\) & Real identity of CP\({}_{i}\) \\ ID\({}_{\text{FN}w}\) & Real identity of FN\({}_{w}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbolic meanings of the proposed scheme.  
Figure 2: The process of the proposed scheme.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 4 Simulations
### Simulation Setup  
Consider a point target placed at the reference range distance \(R_{0}=2\,\mathrm{m}\) from a quadcopter-based THz SAR imaging system, similarly as depicted in Figure 1. Assume for simplicity that the SAR system transmits a frequency-modulated continuous signal of the form  
\[s_{\mathrm{t}}(\tau)=\mathrm{e}^{\mathrm{j}2\pi f_{\mathrm{x}}\tau+\mathrm{j} \pi\frac{f_{\mathrm{max}}-f_{\mathrm{min}}}{\tau_{\mathrm{p}}}\tau^{2}},\qquad- \frac{T_{\mathrm{p}}}{2}\leq\tau\leq\frac{T_{\mathrm{p}}}{2}, \tag{27}\]  
with a duration \(T_{\mathrm{p}}=0.1\mu\mathrm{s}\), and receives backscattered echoes of a similar waveform  
\[s_{\mathrm{r}}(\xi,\tau)=A_{\mathrm{t}1}\;\mathrm{rect}\bigg{\{}\frac{\tau- \tau_{\mathrm{t}1}}{T_{\mathrm{p}}}\bigg{\}}\mathrm{e}^{\mathrm{j}2\pi f_{ \mathrm{r}}(\tau-\tau_{\mathrm{t}1})+\mathrm{j}\pi\frac{f_{\mathrm{max}}-f_{ \mathrm{min}}}{\tau_{\mathrm{p}}}(\tau-\tau_{\mathrm{t}1})^{2}}, \tag{28}\]  
which is a reduced version of (3) for \(j=1\). The range compressed received signal is given by  
\[g(\xi,\tau)=A_{\mathrm{t}1}\;\mathrm{sinc}[\pi(f_{\mathrm{max}}-f_{\mathrm{ min}})(\tau-\tau_{\mathrm{t}1})]\mathrm{e}^{\mathrm{j}2\pi f_{\mathrm{c}}(\tau- \tau_{\mathrm{t}1})} \tag{29}\]  
for \(T_{\mathrm{p}}\gg\tau-\tau_{\mathrm{t}1}\). Note that the range-compressed version of backscattered echoes in (29) agrees with the time-domain representation of raw data obtained in (5). Here, the center frequency \(f_{\mathrm{c}}=0.275\,\mathrm{THz}\), \(f_{\mathrm{min}}=0.22\,\mathrm{THz}\), \(f_{\mathrm{max}}=0.33\,\mathrm{THz}\), and the two range time \(\tau_{\mathrm{t}1}=2R/c_{0}\), where \(R\) can be determined by (1). The system parameters are summarized in Table 1.
## 3 Results  
### Computational Efficiency  
The computational efficiency was compared between different rolling models, and the total time saving for each model with reference to the control model was calculated, as shown in Table 1.  
Figure 4: 2D short transient displacement-controlled model: (**a**) with analytic rigid roller; (**b**) with elastic roller.  
The wall clock time, i.e., the actual time required to obtain a solution, was used for the evaluation of the computational efficiency. All the models were solved using 4 CPUs, except the Eulerian model, which was solved using a single CPU since multiple CPUs are not supported for analysis with steady-state detection in Abaqus. It was found that the 2D short implicit transient model with analytic rigid roller was most efficient, which saved 99.52% time relative to the control model, while the Eulerian steady-state model was least efficient, despite 95.91% time saved.
# Study on Magnetic Resonance Characteristics of Coal Sample under Progressive Loads
###### Abstract  
With the characteristics of gradual instability in the supporting pressure area of roadway as the engineering background, this paper aims to explore the evolution law of pore and fracture in the coal sample under progressive loads. The low-field nuclear magnetic resonance (NMR) test was designed and conducted with the coal sample under different axial loads (0, 3, 5, 7, 9, and 11 MPa). The characteristic parameters such as the porosity, the pore size distribution, the transverse relaxation time (\(T_{2}\)) distribution curve, and the magnetic resonance image (MRI) were obtained. As the test results show, significant difference in the NMR characteristics of the coal samples can be observed throughout the compaction stage and the elastic stage. In the compaction stage, the porosity of the coal samples decreases slightly; the \(T_{2}\) distribution curve moves to the smaller value as a whole, and the percolation pore (PP) displays a tendency to transform to the adsorption pore (AP). In the elastic stage, the porosity of the coal samples rises gradually as the load increases; the \(T_{2}\) distribution curve moves to the larger value as a whole, and the AP tends to transform to the PP. The MRI shows that some pores and fissures in the coal sample close up and disappear as the load increases gradually, while the main pores and fissures expand and perforate till the macro failure occurs. Compared with one-time loading, the progressive multiple loads can ensure the fracture of the coal sample to develop more fully and the damage degree higher. It indirectly reflects that the instability and failure of the coal under the progressive load has the stage characteristics, verifying that the coal in the supporting pressure area needs to be controlled in advance.  
coal sample; NMR; progressive load; porosity; fissure propagation; MRI +
Footnote †: journal: Applied Sciences
# A Deep Learning Framework for Signal Detection and Modulation Classification
## 3 Deep Learning Framework for Signal Detection and Modulation Recognition
### Multi-Inputs CNNs for Modulation Recognition  
For signal modulation recognition task, the modulation set is [BPSK, QPSK, QQPSK, 8 PSK, 16 QAM, 16 APSK, 32 APSK, 64 QAM], because they are all belonging to amplitude-phase modulation and we cannot distinguish each other from time-frequency characteristic in SSD network. Hence, we use the eye diagram and vector diagram as the model inputs. The multi-inputs CNNs model is shown in Figure 7. The initial size of the samples is \(128\times 128\), and we use softmax as the output layer's active function and relu as all other network layers' active function.  
The signal features extraction can be divided into three stages. On the first stage, we use \(7\times 7\) convolution kernels to convolute IQ eye diagram and vector diagram, respectively. To ensure the dynamic range unification of the feature maps, we apply the batch normalization (BN) [49] on first layer network outputs. We perform the max pooling operation on the BN feature maps. Then we connect the feature maps fromOn the second signal feature extraction stage, we adopt the residual network structure to avoid the degradation caused by the network over-depth. The basic structure of ResNet [50] is shown in Figure 8. After the second feature extraction stage, each input feature maps are connected. After the batch normalization in the third stage, we directly process the feature maps by global maximum sampling to reduce the network parameters.  
In the processing of network optimization, we adopt Adam algorithm [51] to solve the network parameters optimal solution. The categorical cross-entropy error is chosen as the loss function, which is represented as:  
\[J_{1}(\mathbf{w},\mathbf{b}\mathbf{x}_{1},\mathbf{x}_{2},\mathbf{x}_{3},\mathbf{y})=-\sum_{i}^{N}{(\bm {y}_{i})}^{\mathrm{T}}\log(f_{1}(\mathbf{x}_{1,i},\mathbf{x}_{2,i},\mathbf{x}_{3,i};\mathbf{w}, \mathbf{b}))+\lambda_{1}\sum{\left\|\mathbf{w}\right\|}^{2} \tag{27}\]
# An Observation Scheduling System for Radio Telescope Array
## 4 Main Function Testing
### Intelligent Scheduling Module  
In the intelligent scheduling module, target sources are prioritized according to the inverse of their observable duration in the visible sky, which serves as their weight. On 1 April 2024, at 11:00:00 local time, the observation station, located at longitude 106.67\({}^{\circ}\) and latitude 26.44\({}^{\circ}\), sorted six target sources: PSR B0329+54, SGR B1935+21, PSR B0531+21, FRB190523, PSR J1846-0258, and FRB20220912A. The details are presented in Table 4. Columns 4 and 5 present the difference between the maximum and minimum elevation of the target within 24 h and the observation priority of the target. Column 6 lists the visible time range within 24-h observations for each target. Based on the information provided in the table, these sources are visualized in the system interface sequentially and then sorted accordingly. Figure 8 shows the trajectory prediction of the six targets after scheduling. As shown in Figure 7, FRB190523 has the smallest elevation difference and thus ranks first in priority. Its visible time is from 15.53 h to 20.49 h. Next is PSR J1846-0258. Since its elevation difference is the second smallest, it ranks second in priority, and its observation time is from 20.52 to 24 h and 0 to 2.47 h. By the same reasoning, the priorities and corresponding observation times of all six sources are obtained.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Target** & \(RA\) (**hm:s)** & \(DEC\) (**d:m:s)** & **Size Time** (**h:m)** & **Set Time** (**h:m)** & **Visible Time Range** (**h:m-h:m)** \\ \hline PSR B0329+54 & 03:32:59.4096 & +54:34:43.329 & 00:50 & 14:30 & 00:50–14:30 \\ PSR B0531+21 & 05:34:31.973 & +22:00:52.06 & 04:10 & 15:20 & 04:10–15:20 \\ PSR J1846-0258 & 18:46:24.94 & \(-\)02:58:30.1 & 18:10 & 03:50 & 00:00–03:50, 18:10–24:00 \\ FRB190523 & 13:48:15.60 & +72:28:11 & 09:20 & 02:40 & 00:00–02:40, 09:20–24:00 \\ SCRB1935 & 19:34:55.68 & +21:53:48.2 & 18:50 & 05:10 & 00:00–05:10, 18:50–24:00 \\ FRB20220912A & 23:09:04.90 & +48:42:25.4 & 20:50 & 09:50 & 00:00–09:20, 20:50–24:00 \\ \hline \end{tabular}
\end{table}
Table 3: Targets’ trajectory prediction results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Target** & \(RA\) (**hm:s)** & \(DEC\) (**d:m:s)** & **Size Time** (**h:m)** & **Set Time** (**h:m)** & **Visible Time Range** (**h:m-h:m)** \\ \hline PSR B0329+54 & 03:32:59.4096 & +54:34:43.329 & 00:50 & 14:30 & 00:50–14:30 \\ PSR B0531+21 & 05:34:31.973 & +22:00:52.06 & 04:10 & 15:20 & 04:10–15:20 \\ PSR J1846-0258 & 18:46:24.94 & \(-\)02:58:30.1 &
## 5 Conclusions, Policy Implications, and Future Research
### Policy Implications
Therefore, when specific needs are identified, community managers should actively seek cooperation with other enterprises or entrust specialized agencies to carry out centralized procurement, in order to reduce procurement costs. Thereafter, training on privacy security should be designed and undertaken. The training cost of personnel is one of the additional costs that the community needs to pay for implementing an active privacy protection strategy. Traditional training courses are expensive and cannot be reused. Therefore, community managers can seek professional privacy security training companies to develop relevant online courses and question banks for health technicians and staff in the community. Only after meeting the required learning time and passing the test can a person become a registered health technician or staff member in the community, so as to improve users' understanding of privacy security knowledge and reduce personnel training costs. (4) The penalties for privacy leakage should be increased. In actual situations, if there is a user privacy leakage incident in the online health community, the community's reputation not only suffers due to the exposure, but there are also administrative penalties from government regulatory authorities. In order to avoid punishment as much as possible in situations in which the punishment is severe, the community will tend to implement active privacy protection strategies. Therefore, local governments can consider increasing the punishment in the following ways. Specifically, local laws and regulations should be improved. Since November 2021, China has officially implemented the _Personal Information Protection Law of the People's Republic of China_[54]. Although the law clearly stipulates the government's punishment for enterprises, it does not involve the compensation from enterprises to users. As compared to the serious consequences of harassing marketing techniques and telecommunication fraud after user privacy leaks, the compensation from enterprises to users remains relatively limited at present. Therefore, local governments can improve the local laws and regulations to increase the compensation from enterprises. Secondly, supervision should be strengthened. Government supervision departments can implement \"integrated online and offline supervision\". On the one hand, they can hold online activities such as cyber-security offensive and defensive drills to test the level of network security of the community. On the other, they can use on-site inspections to ascertain the actual operation of the community, and urge the community to pay attention to user privacy protection through the improvement of supervision.