esa-sceva/satcom-chunk-collection huggingface dataset:
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 5 Model Application
### Effect of Anchorage Segment Diameter  
Figure 16 presents the ultimate pullout capacity, lateral resistance, and end resistance variation curves according to changes in the anchorage segment diameter. From this figure, it can be seen that the lateral resistance, end resistance, and ultimate pullout capacity all increased linearly with the increase in the anchorage segment diameter. When the diameter increased from 0.4 m to 0.8 m, the lateral resistance increased from 407.9 kN to 811.2 kN by about 99%, the end resistance increased from 184.9 kN to 251.4 kN by about 40%, and the ultimate pullout capacity increased from 592.8 kN to 1062.6 kN by about 79%. This indicated that the change in the anchorage segment diameter had a greater influence on the lateral resistance than on the end resistance. This was because the change in the anchorage segment diameter had a greater influence on the side surface area than on the three-dimensional velocity discontinuity surfaces in the logarithmic spiral region.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 6 Our Approach: Proposed Model
### Layer 2: Proposed Routing Protocol
#### 6.2.2 Protocol Phases
If the free buffer size is less than the message size, the receiver keeps deleting the oldest message in the buffer until the free buffer size is greater than or equal to the message size; then, it accepts the message. The message is accepted by replying to the sender with a RCV_OK packet containing the ID of the message.
## 2 Preliminaries
### BiLSTM  
The bidirectional long short-term memory (BiLSTM) incorporates a refined gate mechanism and memory cells, optimizing the feature extraction process by selectively retaining significant features and omitting the less relevant ones. Specifically, \(i\) represents the input gate, which is responsible for handling the input at the current sequence position; \(f\) is the forgetting gate, indicating the degree of forgetting the hidden cell state of the previous layer, which is used to forget unimportant information; \(o\) indicates the output gate, which is used to determine the output value of the information; memory cells are used to record additional information. Figure 5 depicts the architectural framework of BiLSTM.  
Figure 4: The Biaffine attention architecture.  
Figure 5: The BiLSTM architecture.  
At time \(t\), given the current input \(x_{t}\), the calculation procedure of the bidirectional LSTM is as follows:  
\[i_{t}=\sigma(W_{xi}x_{t}+W_{hi}h_{t-1}+W_{ci}c_{t-1}+b_{i}) \tag{13}\]  
\[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+W_{cf}c_{t-1}+b_{f}) \tag{14}\]  
\[c_{t}=i_{t}g_{t}+f_{t}c_{t-1} \tag{15}\]  
\[g_{t}=\tanh(W_{xc}x_{t}+W_{hc}h_{t-1}+W_{cc}c_{t-1}+b_{c}) \tag{16}\]  
\[o_{t}=\sigma(W_{xo}x_{t}+W_{ho}h_{t-1}+W_{co}c_{t}+b_{o}) \tag{17}\]  
\[h_{t}=o_{t}\tanh(c_{t}) \tag{18}\]  
where \(t\) is the current moment; \(\sigma\) represents the sigmoid function; \(h_{T-1}\) and \(c_{t-1}\) represent the hidden vector of the previous moment, respectively; \(i_{t}\) representative enter door; \(W_{l}x\), \(W_{l}h\), \(W_{l}c\), \(b_{l}\) are, respectively, corresponding to the weighting matrix and deviation; the same, \(f_{t}\) forgotten door, \(W_{xf}\), \(W_{hf}\), \(W_{fc}\), \(bf\), respectively, correspond to the weighting matrix and deviation; \(c_{t}\) is the cell state at time \(t\); \(o_{t}\) represents the output gate, \(W_{xo}\), \(W_{ho}\), \(W_{o}c\), \(b_{o}\), respectively, correspond to the weighting matrix and deviation. Finally, the results obtained from the forward LSTM and the backward LSTM are spliced together through (13) to (19) to obtain \(h_{i}\):  
\[h_{i}=\overrightarrow{h_{i}}\oplus\overleftarrow{h_{i}} \tag{19}\]
###### Abstract  
In this study, we introduced the chaos game optimization-artificial neural network (CGO-ANN) model as a novel approach for predicting peak particle velocity (PPV) induced by mine blasting. The CGO-ANN model is compared with other established methods, including the particle swarm optimization-artificial neural network (PSO-ANN), the genetic algorithm-artificial neural network (GA-ANN), single ANN, and the USBM empirical model. The aim is to demonstrate the superiority of the CGO-ANN model for PPV prediction. Utilizing a dataset comprising 180 blasting events from the Tonglushan Copper Mine in China, we investigated the performance of each model. The results showed that the CGO-ANN model outperforms other models in terms of prediction accuracy and robustness. This study highlights the effectiveness of the CGO-ANN model as a promising tool for PPV prediction in mining operations, contributing to safer and more efficient blasting practices.  
p (PPV); chaos game optimization (CGO); artificial neural network (ANN); mine blasting; prediction model; Tonglushan Copper Mine +
Footnote †: journal: Applied sciences
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### Max-Min Threshold Scheduler  
The MTS approach can be split into two different views: max-min scheduling and Threshold restrictions. The former is responsible for optimizing the resource allocation, the latter limits the allocation of RBs according to the environmental conditions with dual connectivity.
# A Review of Experimental Techniques for Measuring Micro- to Nano-Particle-Laden Gas Flows
## 2 Classification of Experimental Methods
### Classification of Characterization Methods
#### 2.2.1 Measurement Methods for the Dispersed Phase  
Measuring methods can be divided into two categories: one category deals with microscale or larger particles (within the continuum regime); the other, with particles from the nano- to sub-micro-scale (within the free molecular or transition regimes). The former methods were developed earlier, and include traditional sieving, settlement or advanced optical analysis and electrical measuring techniques. Measuring methods for microscale or larger particles are outlined in [PERSON] et al. [PERSON] et al. (2010) and summarized here in Figure 1. In contrast, sub-micron- and nanometer-sized particles can besuspended in the gas carrier phase for a very long time, even up to several months. Thus, their gravity effects are negligible compared with their aerodynamic ones. Consequently, sieving methods that depend on gravity or sedimentation are unable to detect particles in this size range. According to optics theory, nano- and micro-particles undergo Rayleigh scattering and Mie scattering, with scattering intensities approximately proportional to their particle size. However, the scattering intensity of nano-particles is often too weak to be detected consistently by current measurement technologies used for microparticles. One of the more widely-applied methods used to classify nano-particles uses electrostatic technology (e.g., the differential mobility analyzer (DMA)). In this device, particles are separated according to their stationary charge [2], and measured using electrical or optical methods. Therefore, these methods require off-line sample analysis. They are widely used in aerosol science and technology. Several important measurement methods used for submicron- and nanometer-sized particles, such as the scanning mobility particle size spectrometer (SMPS) and the fast mobility particle size spectrometer (FMPS), also are outlined in [PERSON] et al. [1].  
Figure 1: Classification of methods for particle characterization in particle-laden gas flows. Abbreviations: PIV, particle image velocimetry; PTV, particle tracking velocimetry; LDA, laser Doppler anemometry; PDA, particle Doppler analyzer; PDPA, phase Doppler particle analyzer; TEM, transmission electron microscope; SEM, scanning electron microscope; SMPS, scanning mobility particle size spectrometer; FMPS, fast mobility particle size spectrometer; EEPS, engine exhaust particle size spectrometer; EPLI, electrical low pressure impactor; APS, aerosol particle size spectrometer; TOF-AMS, time-of-flight aerosol mass spectrometer; APM, aerosol particle mass analyzer.
## 3 Results
### Modelling of the Learning Process
#### 3.1.1 Security Modelling  
During the initial analysis phase of the THREAT-ARREST lifecycle (Section 2.3.1), we analyze the piloting environment based on the STRIDE methodology [21]. STRIDE is a widely-known security model for defining threats, which was designed by Microsoft. The name is the abbreviation of the six threat categories that it analyzes: (i) Spoofing, (ii) Tampering, (iii) Repudiation, (iv) Information disclosure, (v) Denial of Service (DoS), and (vi) Elevation of privilege. Each of one of them reflects a potential violation of a desired security property in the system, i.e., authentication, integrity, non-repudiation, confidentiality, availability, and authorization, respectively. Figure 2 depicts this mapping between threats and security goals.  
The threat model assesses the detailed system design. Data-flow diagrams (DFDs) identify the involved entities, events, and the boundaries of the system. The model has been successfully applied to cyber-only and cyber-physical environments. While Microsoft no longer maintains STRIDE, the model is part of the Microsoft Security Development Lifecycle (SDL) and implemented within the Threat Modeling Tool, which is still available.  
Today, there are several threat modelling techniques [51], including Attack Tress, Security Cards, the MITRE ATT&CK framework, etc. STRIDE is a mnemonic method that focuses on assets. We choose this primitive as it can be easily understood and applied by a trainer during the design of the training programme and the underlying training procedures, correlating also threats with respective defensive countermeasures.
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
## 1 Introduction
The surface-to-plane method is a commonly used data processing technique in gravity and magnetic exploration for handling the complexity of subsurface media. Its fundamental idea is to transform the actual observed data into the responses of an equivalent layered model and then adjust the parameters of the equivalent layers to achieve the best possible fit between the mode response and the observed data. The method includes techniques such as the variable separation method [26; 27; 28], the boundary integral method [29; 30], the equivalent source method [31; 32; 33], the Taylor series method [34; 35; 36], the iterative method [37; 38], etc. However, the existing surface-to-plane methods have certain limitations. (1) The effectiveness of these algorithms depends heavily on the specific application condition, such as the location of the source and the terrain, the continuity and smoothness of the terrain, the intensity of undulations, and the number of calculated data points, which is relative to the cost of time. These limited conditions can reduce the versatility and automaticity of the methods. (2) In the geophysics inversion, the more complex the processing, the more noise and errors may be introduced into the calculation; therefore, simpler processes tend to contribute more to the original signal and provide more effective information. In conclusion, considering the desire for flexibility and reliable imaging results, we believe that two aspects of the DEXP method need to be improved. Firstly, automatic calculation should be enhanced, eliminating the need to manually set the structural index (SI) or threshold values. Secondly, the method should be able to handle undulating terrain data directly, without requiring additional processing steps. Therefore, this paper proposes an improved DEXP method. The main objectives of this method are to eliminate the need for manual intervention in setting the SI or threshold values, meanwhile ensuring stability in the imaging results by avoiding singularity or trapezoid lines and enablingdirect application to the undulating terrain data. To validate the effectiveness and reliability of the proposed method, we test the method using both synthetic data and real data from a potash salt deposit.
# Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection
## 2 Materials and Methods  
### Frequency Domain  
#### 2.1.1 Bubble Wavelet Transformation (BWT)  
With the rapid development of ultrasound molecular imaging, about a dozen of microbubble models [17] have been proposed. The dynamic behavior of a single microbubble under different ultrasound fields can be predicted with the help of these models. BWT applied the microbubble model to USCAI, where a novel mother wavelet named as a bubble wavelet was constructed based on the simulation results of the microbubble model.  
The expression of BWT in the time domain can be described as:\[bvt[x(t)]=\frac{1}{\sqrt{a}}\int\limits_{-\infty}^{+\infty}x(t)\phi^{*}(\frac{t-b}{a })dt \tag{1}\]  
where \(x(t)\) is the signal to be processed; \(\phi(t)\) is the bubble wavelet; \(\phi(\frac{t-b}{a})\) is the function of the bubble wavelet after translation and scaling; \(a\) is the scale factor; \(b\) is the time-shifting factor; and superscript \({}^{*}\) denotes the conjugation operation.  
In fact, BWT is the convolution operation of the bubble wavelet under different scale factors with the signal to be processed. The result of BWT is a series of wavelet coefficients. These coefficients, namely the function of the mother wavelet and the scale factor, illustrate the correlation between the bubble wavelet under a certain scale and the received signal. Due to the similarity of the frequency spectrum of the bubble wavelet and the UCA echoes, as well as the dissimilarity between this and the tissue echoes, the tissue signal can be weakened and the UCA signal can be strengthened after BWT.  
In the application of BWT, the choice of the bubble wavelet and the scale factor are two key elements [18; 19] that determine the image quality to a large extent. A more significant effect of CTR improvement can be achieved if the spectrum of the bubble wavelet is highly matched with that of UCA. The scale factor is another key issue. The scale resulting in the highest CTR is selected as the optimal factor.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 2 System Model
### Proposed Semantic Image-Adaptive Transmission Framework
#### 2.3.1 Swin Transformer Encoder  
The encoder \(f_{e}\) we used consists of a swin transformer encoder. The swin transformer model combines a standard multi-head self-attention (MSA) module and a feedforward network for processing [34]. The window-based self-attention mechanism allows the model to capture long-term dependencies in the image. It divides the image into window grids and applies self-attention in each window. Four cascaded swin transformer block modules are used to process high-resolution source images and learn from the changing characteristics  
Figure 2: The overall architecture of CCB for satellite–ground image transmission.  
of the transmission channel. After this step, we obtain the feature map \(\mathbf{x}\in R^{\frac{H}{6}\times\frac{W}{16}\times C}\) to represent the semantic latent representation of the input image \(\mathbf{s}\in R^{H\times W\times 3}\), where \(H\), \(W\), and \(C\) represent height, width, and channel number. The process is modeled as  
\[\mathbf{x}=f_{e}(\mathbf{s},\phi), \tag{2}\]  
where \(\phi\) contains the model parameters corresponding to the encoder.
## 5 Results and Discussion
### Comparison of Impacts of Adsorption/Desorption and Stress-Sensitivity  
As the results above have shown, stress-sensitivity mainly affects bulk phase gas flow, while adsorption/desorption affects both bulk phase gas flow and adsorbed gas flow. For bulk phase gas, the impact of stress-sensitivity was far greater than that of adsorption/desorption, while the reverse was the case for adsorbed gas. The total mass flux would have been overestimated  
Figure 16: Effect of stress-sensitivity on total mass flux. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.  
Figure 14: Effect of stress-sensitivity on slip flow regime. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.  
Figure 15: Effect of stress-sensitivity on Knudsen diffusion. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.  
without considering the impact of adsorption/desorption, with a maximum deviation of 46.59% (Figure 17). The impacts of adsorption/desorption on gas flow in shale reservoirs included two aspects: (1) shrinkage/swelling of the effective pore diameter as a result of gas desorption/adsorption impacted bulk gas flow; and (2) surface diffusion that occurred with gas desorption increased the transport capacity of adsorbed gas, especially at low pressures. At reservoir pressures of \(>\)5 MPa, the impact of stress-sensitivity on total mass flux was far greater than that of adsorption/desorption, while the reverse was the case at reservoir pressures of \(<\)5 MPa because adsorption/desorption had a significant influence on surface diffusion, which dominated the total mass flux at low pressures.
## 3 Results and Discussion
### Sleep Disturbance
Distraction also decreased slightly from 2012 to 2014 for the most irritated residents, while remaining relatively low and/or unchanged in the other groups. However, only a few participants showed evidence of noise from low-frequency wind turbines: in 2012, 8.5% reportedfeelings of pressure related to wind farms and 6.1% reported having felt vibrations in the body; in 2014, these feelings decreased to 6.8% and 3.8%, respectively. The annoyance experienced was very low, and symptoms of dizziness were not observed in this study. Regarding the effects of wind noise stress compared to road traffic noise, there were more reports of symptoms due to traffic (15.8%) than to noise from wind turbines. In 2014, it was observed that about a third (34.9%) of the participants were slightly irritated by traffic noise and 21.2%, by noise from the wind farm [44]. [PERSON] et al. [45] determined the numbers of prescriptions for anxiolytes and antidepressants for residents due to prolonged exposure to noise from wind turbines. During the survey carried out between 1996 and 2013, 68,696 adults had recourse to sleeping pills and 82,373 used antidepressants, out of a population of 583,968 and 584,891, respectively. In this study, it was observed that people over the age of 65 years were more affected by the noise of wind turbines, with an HR (hazard ratio) of 1.68 for measuring sleep and 1.23 for antidepressants being found for the group with the greatest exposure. Regarding low-frequency noise due to wind turbines in indoor environments, the risk rate among people aged 65 and over when exposed to noise equal to or higher than 15 dB was 1.37 for anxiolytes and 1.34 for antidepressants. Thus, [PERSON] et al. [45] concluded that the combination of high noise levels from wind turbines and the use of anxiolytes and antidepressants can induce sleep disturbance and, in turn, affect the mental health of the elderly [45]. Finally, [PERSON] et al. [47] demonstrated that sleep was significantly affected, both in terms of physiological measures and by self-report, during nights with exposure to 45 dB noise, although the number and size of the effects were modest. Most self-reported sleep measures were adversely affected by terrestrial railway noise. In this study, no significant differences were found in the general sleep structure or disorders and in the subjective quality of sleep between the reference tests and the 35 dB night tests. The results obtained support the value of the Swedish guidelines proposed for the maximum noise level of 35 dB for indoor environments and may be suitable for protection against adverse sleep problems due to terrestrial railway noise [47].
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 2 Cold Gas Propulsion (CGP) Systems
### Design Considerations and Technologies  
A cold gas propulsion system can use either liquid or gaseous propellants, however, using a liquid propellant will lead to a reduction in the storage volume. The propellant selected should have high-density-I\({}_{sp}\) (Specific impulse/unit volume) in order to increase the longevity of the on-board propellant. In addition, lower storage pressure of the propellant facilitates the design of storage tanks with higher safety margin. The toxicity and the ease of availability of the propellant also impacts the design cost of a propulsion system during the on-ground manufacturing and assembly operations of the spacecraft. Therefore, using environment friendly propellants will lead to a decrease in costs incurred for safety measures, storage and transportation [12]. Liquid propellants provide the advantage of reduction in storage volume, however they can result in a de-stabilizing effect due to sloshing of propellant inside the tank [13]. While no specific sloshing prevention technology is available for CubeSats, special anti-sloshing baffles technology has been used in a micro-propulsion system of a larger satellite; these baffles are used to restrain the flow of propellant and have been used in the SNAP-1 propulsion system developed by Surrey Satellite Technology Ltd. (SSTL), Guildford, UK for the Giove-A mission [12].  
A recently studied concept called solar thermal propulsion system has the potential of utilizing solar energy in improving the performance of CGP systems. In this system, concentrated solar energy is used to directly heat the propellant. As the propellant enters the nozzle at an elevated temperature, a significantly enhanced thrust and an increased specific impulse relative to a regular cold gas flow are observed. The solar thermal propulsion system concept was verified for a larger spacecraft, for instance, the orbital station-keeping scenario for a 200 kg spacecraft in a circular orbit was considered. The orbital parameters for this spacecraft were 600 km altitude, 28.5 degrees inclination and 1 km decay/day, and, it required a one minute burn of 1.9 N thrust to counter the orbital decay [14]. The 1.9 N burn was achieved with a specific impulse of 300 s, 0.64 g/s mass flow rate of propellant and an exit flow temperature of 1500 K. A major drawback of this technology is its dependence on direct solar illumination at the time of propulsive maneuvers [14]. Table 1 provides a summary of the performance parameters of the surveyed cold gas systems. As it is evident, CGP systems have been widely used on CubeSat missions; only one of the systems does not have CubeSat heritage but has flown on a larger satellite.