esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability and Control of Surrounding Rock of a Trapezoidal Roadway Retained with Hard Roof Cutting
## 6 Conclusions  
1 According to the stability mechanics model for the roof in a trapezoidal top-cutting roadway with inclined coal seam, the condition for determining that the hard roof can collapse completely is that the tensile stress of the uncut part of the roof exceeds its tensile strength. At the same time, the tensile stress of the uncut roof was inversely proportional to the coal seam dip angle, roof thickness and top-cutting height, while proportional to the top-cutting angle. According to actual engineering conditions, the top-cutting angle and height of the 21,100 panel roof were determined to be 10\({}^{\circ}\) and 5.0 m, respectively.
2 The special structure of the trapezoidal roadway will cause asymmetrical stress distribution in the surrounding rock. Stress varied significantly, especially at the roof andrib, which can easily form stress concentration areas. Using top-cutting, the pressure relief significantly reduced vertical stress in the roof and solid coal side, as shown by the simulations. Using top-cutting, the pressure relief reduced the roof stress from 6.73 MPa to 2.04 MPa, and the high stress zone moved to the inside of the solid coal. The roof showed sliding deformation along the top-cutting line, showing the characteristics of \"large deformation on the top-cutting side\". The subsidence of the roof was reduced to 32 mm, and it increased to 119 mm on the goaf side.
3. Based on the deformation characteristics of the surrounding rock on the trapezoidal top-cutting roadway, we proposed the use of high-strength prestressed long anchor cables for reinforcement support of the roof on the top-cutting side, while simultaneously employing retractable U-shaped steel and windproof cloth for rib coal protection and air leakage prevention support in the trapezoidal top-cutting roadway surrounding rock control technology. Industrial tests showed that under this supporting scheme, the maximum subsidence of the roof was 120 mm, and the maximum separation of the roof was 29 mm. The roof and sides were flat and intact, and the effect of top-cutting was efficient, fully meeting the needs of on-site production.  
Conceptualization, S.Z. and C.R.; methodology, X.G.; software, C.R. and Y.G.; validation, S.Z., X.G. and L.N.; data curation, C.R. and L.N.; writing--original draft preparation, S.Z. and Y.G.; writing--review and editing, S.L. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52204161) and the Graduate Research and Innovation Projects in Jiangsu Province (Grant No. KYCX24_2859).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company No.2 Coal Mine, Pingdingshan Tian'an Coal Mining Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 3 Methods  
Our model is a recurrent neural network (RNN) that aggregates a frame-based convolutional feature extractor across the video to detect LUS B-line artefacts. As shown in Figure 2, the model architecture has four components: (1) convolutional frame feature extraction network (CNN); (2) spatial attention mechanism; (3) bidirectional long short-term memory (LSTM) network; and (4) temporal attention mechanism. The convolutional features are attended over both spatially, in each frame, and subsequently temporally for the entire video sequence. Both employed attentions are soft attentions which means the effective final representation at time \(t\) is a spatiotemporally weighted aggregation of convolutional features across the video.
## 4 Topology Abstraction Implementation  
### Topology Abstraction-Based Routing Scheme  
The satellite moves in the orbit, resulting in the dynamic change of the visibility and distance between satellites and satellites/ground stations. The satellite is controlled by the SDN controller in the control layer to establish the quantum link with other sites (including satellites and ground stations) based on specific algorithms, for example, establishing the quantum link with the closest site. The generated secret keys will be provided for the corresponding QKP in the QKP layer. Due the periodicity of the relationship between satellites and ground stations, the arrangement of the establishment for the quantum link between two sites is also periodic based on the specific algorithm. Once a pair of sites has established a quantum link in a period, there exists a corresponding QKP in the QKP layer. Meanwhile, each site is abstracted as a fixed node in the QKP layer. If there is a QKP between a pair of abstracted nodes, then the two nodes are connected on the abstracted topology, since the corresponding nodes can share secret keys with each other relying on a QKP. The global keys can be relayed by the shared keys even if there is no quantum link of the corresponding site pair at that time, which will be detailed in Section 4.2. As exemplified in Figure 2a, the dynamic physical topology in the physical layer is translated into a fixed abstracted topology in the QKP layer. For example, although LEO-2 and LEO-3 are unconnected in the physical layer now, they will be connected in other time slots in a period. Hence, there is a link between Node-2 and Node-3 on the abstracted topology. Other links, e.g., the link between Node-1 and Node-4 as well as the link between Node-1  
Figure 1: A layered architecture for quantum satellite networks.  
and Node-3, can also be attributed to this reason. As depicted in Figure 2a, a QKD request from Ground station-a to Ground station-c is generated, and we route for this request on the abstracted topology to obtain the QKD relaying path (i.e., the red lines in Figure 2a). The QKD relaying path refers to the path of relaying the global secret keys hop by hop with the exclusive OR operation.
# Development of a High-Precision Touch-Trigger Probe Using a Single Sensor +
## 2 Structure and Principle  
This touch trigger probe, shown in Figure 1, mainly consists of three components: a QPD-based sensor, a floating plate, and a tungsten stylus with a ruby ball tip. A plane mirror is adhered to the upper surface of the floating plate. The stylus is mounted on the center position on the lower surface of the floating plate. Four V-shaped leaf springs, fixed to the probe housing, are designed to connect the floating plate. When a contact force is applied to the ball tip of the stylus, the floating plate and the stylus perform a rigid body motion. Simultaneously, the four leaf springs yield corresponding elastic deformations by the floating plate, and the plane mirror on the floating plate tilts along horizontal axes or displaced in the vertical axis. The probe housing is made of an aluminum cylinder. The QPD, the focus lens, and the circuit board are imbedded in the probe. A laser diode is placed on an adjusting mechanism whose elastic component is formed by folding a beryllium-copper alloy sheet. The adjusting mechanism is applied to assure the reflected laser beam being focused onto the centre of the QPD in the initial state.  
Figure 2 shows the optical path of the sensor system, which is composed of a laser diode, a plane mirror, a focusing lens, and a QPD. The laser beam from the laser diode with an incident angle is reflected by the plane mirror and then projected onto the QPD via the focusing lens. A tilt angle or a vertical displacement of the plane mirror causes a lateral shift of the focused light spot on the QPD.  
Figure 1: The sketch map of the probe.  
The QPD outputs the light energy of each quadrant photo detector into an electrical current signal. By applying an appropriate resistance to these current signals, four output voltage signals (\(V_{A}\), \(V_{B}\), \(V_{C}\), and \(V_{D}\)) can be obtained. Two-dimensional shifts of the focused light spot caused by the motion of the plane mirror can be expressed by Equations (1) and (2), in which \(k_{1}\) and \(k_{2}\) are constants [26]. When the ball tip is contacted in any direction by the workpiece, the probe generates a trigger signal at the same time.  
\[x =k_{1}\left[(V_{A}+V_{D})-(V_{B}+V_{C})\right] \tag{1}\] \[y =k_{2}\left[(V_{A}+V_{B})-(V_{C}+V_{D})\right] \tag{2}\]
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 3 Results and Discussion
### Mechanical Cell Disruption
#### 3.2.3 High-Pressure Homogenization
At the same time, both agreed that ultrasonication was inefficient compared to these methods, as confirmed in this study, as well as others [18]. [PERSON] et al. [44] stated that HPH was the most effective disruption technique for extracting proteins from _Microchloropsis_ species, but it also required the highest power consumption. Generally, a high solids content of the biomass to be processed helps reduce the specific energy consumption of both HPH and bead mills. However, contrary to the common view in the literature [18; 45], the solids concentration of the microalgal biomass did influence the effectiveness of all mechanical cell disruption methods examined in this study. Hence, special caution is recommended when choosing a high biomass concentration above 15% dry weight for mechanical cell disruption. Microalgal cell disruption using HPH has been examined mostly as a pretreatment method to improve the extraction yield of lipids or other intracellular products [36; 44; 46]. However, there are some quantitative data on the cell disruption of the _Microchloropsis_ species using HPH. For the _Microchloropsis_ species, a 67-100% cell disintegration degree has been reported using biomass with a low solids content below 1% and pressures up to 3000 bar [16; 18]. Similar disintegration efficiencies of 56-94% were achieved in this study using high-density _M. salina_ biomass with 10-25% solids. Additionally, 86% disruption efficiency was reproducibly achieved in this study using an industrial HPH on a pilot scale and with a high biomass density of 15% dry weight. This is considered to be a high disruption efficiency, considering that _Microchloropsis_ species are known to have a very resistant and recalcitrant cell wall containing a layer of algaean, which is a non-hydrolysable biopolymer [17], making them easier to cultivate under physically stressful conditions, while at the same time harder to disrupt compared to other species [16; 35].
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 3 Classification of Phishing Detection Strategies
### Rule-Based Methods  
Rule-based methods [50] rely on predefined if-then conditions or static rules to identify phishing emails or websites. These rules typically include the following:  
* Keyword Matching: Identifying suspicious terms (e.g., \"urgent\" and \"verify account\");
* Blacklist Checking: Comparing URLs against known malicious databases [51; 52];  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
Pattern Recognition: Validating domain signatures, mismatched sender information, and invalid SSL certificates.  
An example of a mathematical Representation for a simple rule-based detection function can be represented as shown below.  
\[D(x)=\left\{\begin{array}{ll}1&\text{if }\sum_{i=1}^{n}R_{i}(x)\geq T\\ 0&\text{Otherwise}\end{array}\right.\]  
\(D(x)\) is the detection outcome (1 = phishing; 0 = benign).  
\(R_{i}(x)\) represents the rule score for feature i.  
\(T\) is the threshold score to trigger detection.  
\(n\) is the total number of rules applied.  
Limitations include the following: While effective for known attack patterns, rule-based systems struggle with zero-day attacks and obfuscated phishing attempts. They lack adaptability and require constant manual updates to remain effective.
## 3 MTR Algorithm Based on Multi-Layer Sparse Structure
### Alternating Optimization
#### 3.4.1 Fix \(\mathbf{S}\) Update \(\mathbf{F}\) and \(\mathbf{A}\)  
With the fixed \(\mathbf{S}\), Equation (7) is changed to  
\[\min_{\mathbf{S},\mathbf{F}}\frac{1}{N}tr\Big{(}(\mathbf{Y}-\mathbf{S}( \mathbf{A}\mathbf{K}+\mathbf{F}))^{\top}(\mathbf{Y}-\mathbf{S}(\mathbf{A} \mathbf{K}+\mathbf{F}))\Big{)}+\lambda_{2}tr\Big{(}\mathbf{S}^{\top}\mathbf{D }\mathbf{S}\Big{)}+\lambda_{3}tr\Big{(}\mathbf{F}^{\top}\mathbf{F}\Big{)} \tag{11}\]  
By fixing \(\mathbf{A}\), we calculate the gradients of the objective function (11) with respect to \(\mathbf{F}\) as follows:  
\[\frac{\partial J}{\partial\mathbf{F}}=-\frac{1}{N}\mathbf{S}^{\top}(\mathbf{Y }-\mathbf{S}(\mathbf{A}\mathbf{K}+\mathbf{F}))+\lambda_{3}\mathbf{A}=0 \tag{12}\]  
Hence, \(\mathbf{F}\) can be solved by  
\[\mathbf{F}=\Big{(}\mathbf{S}^{\top}\mathbf{S}+\lambda_{3}N\mathbf{I}_{Q} \Big{)}^{-1}\mathbf{S}^{\top}(\mathbf{Y}-\mathbf{S}\mathbf{A}\mathbf{K}) \tag{13}\]  
where \(\mathbf{I}_{Q}\in\mathbb{R}^{Q\times Q}\) is the identity matrix. Similarly, by fixing \(\mathbf{F}\), we set the derivative of Equation (11) _w.r.t_\(\mathbf{A}\) to zero and we have  
\[\frac{\partial J}{\partial\mathbf{A}}=-\frac{1}{N}\mathbf{S}^{\top}(\mathbf{Y }-\mathbf{S}(\mathbf{A}\mathbf{K}+\mathbf{F}))\mathbf{K}+\lambda_{1}\mathbf{A }\mathbf{K}=0 \tag{14}\]  
Multiple \(\mathbf{K}^{-1}\) to both side on the right, we have  
\[\mathbf{S}^{\top}\mathbf{S}\mathbf{A}+\lambda_{1}N\mathbf{A}\mathbf{K}^{-1}= \mathbf{S}^{\top}(\mathbf{Y}-\mathbf{S}\mathbf{F})\mathbf{K}^{-1} \tag{15}\]  
which is a standard Sylvester equation \(\mathcal{A}\Theta+\Theta\mathcal{B}=\mathcal{C}\) and has a closed-form solution, which can be solved efficiently by a Sylvester function. For the unknown parameter matrix, \(\mathbf{A}\), we have \(\mathcal{A}=\mathbf{S}^{\top}\mathbf{S}\), \(\mathcal{B}=\lambda_{1}N\mathbf{K}^{-1}\) and \(\mathcal{C}=\mathbf{S}^{\top}(\mathbf{Y}-\mathbf{S}\mathbf{F})\mathbf{K}^{-1}\). Then the matrix \(\mathbf{A}\) can be obtained by solving the Sylvester equation.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 5 Key Cooperative and Cognitive Technologies
### Physical-Layer Security  
As a complex communication infrastructure comprising satellites in different orbits, UAVs, and other low-and high-altitude aerial platforms and terrestrial networks, an HSTN may have security risks and eavesdropping threats. Thus, methods of confidentiality shall be employed to prevent unauthorized data disclosure, as all the interconnected devices could suffer from this vulnerability [34].  
The secure transmission of sensitive data via HSTNs continues to be a severe problem due to the broadcast nature of wireless communications. Secure communication has historically been ensured by cryptographic algorithms at higher tiers of the protocol stack based on the now-debunked premise that eavesdroppers have limited computational capabilities. Communications can be safeguarded at the physical layer by making use of wireless channel characteristics and cutting-edge signal processing techniques, which has generated a lot of interest in the field recently [190].
## 4 Experimental Results
### Findings  
The experiments in Section 4.2 demonstrate the superior performance of the Cycle GAN network compared to the original GAN network across three different working conditions for the three motors. The correlation analysis between the current samples generated by our proposed method and those collected in the experiment yields a high correlation coefficient of 0.9922, which indicates that the generated samples are very close to the experimental samples in terms of similarity.  
The results presented in Section 4.3 demonstrate that the fault diagnosis model for the three motors initially exhibits an accuracy below 90%. However, upon employing Cycle GAN to expand the sample set, a significant improvement is observed, with the accuracy of the fault diagnosis model surpassing 98%. Additionally, a commendable performance is achieved by utilizing the DAE classification algorithm. These findings effectively validate both the feasibility and effectiveness of our proposed method.
## 2 Materials and Methods
### Speech Feature Extraction  
The broadband speech envelope and speech spectrogram were used as stimulus features to compute their correlation with the EEG data. The speech envelope extracted using the Hilbert transform was downsampled at a sampling rate of EEG data, at 256 Hz. The spectrogram was computed using a filter bank with a series of 64 band-pass filters with logarithmically spaced bandwidths between 250 and 8000 Hz [25].
# Green Hydrogen Production--Fidelity in Simulation Models for Technical-Economic Analysis
## 2 System Component Modeling
### Application of Hydrogen as a Fuel Cell  
#### 2.4.1 Simplified Fuel Cell Model  
The following model is generic and can emulate the behavior of any fuel cell type fed with hydrogen and air (see Figure 3). The approach involves extracting data from the data sheet to perform simulations without the need for experimental tests on an actual stack [31].  
Fuel cell models can be classified into three categories: chemical, experimental, and electrical. Chemical models include complex chemical and thermodynamic phenomena but require excessive parameters and cannot be easily added to electrical simulation programs [32; 33; 34]. Experimental models are derived from experiments and represent the fuel cell by look-up tables or empirical expressions [35; 36]. Electrical models represent the fuel cell through electrical circuit elements [31]. There, the controlled voltage source \(E\) is given by Equation (13). It is important to note that \(E_{\infty}\) represents open-circuit voltage, \(N\) the number of cells, \(A\) the Tafel slope, \(i_{o}\) the exchange current, \(T_{d}\) the response time at 95% of the final value in seconds, \(R_{ohm}\) the internal resistance, \(i_{fc}\) the FC current, and \(V_{fc}\) the FC voltage. Equation (14) refers to stack total voltage considering the losses due to electrodes and electrolytes [31].  
\[E=E_{\infty}-NAln\left[(\frac{i_{fc}}{i_{o}})\times\frac{1}{\frac{5T_{d}}{3}+ 1}\right] \tag{13}\]  
\[V_{fc}=E-R_{ohm}\times i_{fc} \tag{14}\]
# The Comparison of Electron Density between CSES In Situ and Ground-Based Observations in China
## 4 Comparing with Ionosonde Data
### Time Series Analysis  
As the first three months after the CSES launch is within the commissioning phase, the data provided by the satellite mission are only taken from 8 May 2018. Since the date of the available data is different, the time range of the comparative dataset is different for each ionosonde--Leshan station from 8 May 2018 to 13 October 2020, Tengchong station from 1 January 2019 to 31 July 2021, and Puer station from 8 May 2018 to 31 July 2021. Figure 5 shows the time series of the CSES Ne and Ne at the satellite altitude inferred by ionosonde observations and the IRI model, which is displayed with the log scale. The patterns of two curves are almost same, and the annual and semiannual variations can both be found for the two datasets, with high values in the summer and low values in the winter. Furthermore, the correlation coefficient between the two datasets was calculated according to Equation (3), where \(X\) and \(Y\) represent the Ne of the CSES and ionosonde. The scatters of the two datasets and linear fitting curves are plotted in Figure 6. The calculated values for the Leshan, Tengchong, and Puer stations are 0.4858, 0.5973 and 0.4944, respectively, which means the Ne of the CSES and ionosonde represents a medium correlation and can be well linearly fitted. In Figure 5, it can be seen that there is a difference in the Ne absolute value between the CSES and the ionosonde. The relative change between the CSES and ionosonde Ne was calculated according to Equation (1), where _Ne_QIISR_ was changed to _Ne_ionosonde_. Figure 7 shows a histogram of the relative change between the two datasets for the Leshan Figure 7a), Tengchong (Figure 7b) and Puer (Figure 7c) stations. All of the ratios are negative, which means the electron density of the CSES is lower than the Ne at the satellite altitude inferred by ionosonde observations and the IRI model. Based on the median values of the ratios in each station, the value of CSES Ne is lower by 80-90% in the daytime, corresponding to the ionosonde Ne.  
Figure 5: Time series of Ne for CSES and ionosonde. Panels (**a–c**) are for Leshan, Tengchong and Puer stations, respectively. In each panel, the red dotted line represents CSES Ne, and the blue dotted line denotes Ne at the satellite altitude inferred by ionosonde observations and IRI model. Red and blue lines are the 15-day running mean.
## 3 Results and Discussion
### Bacterial Inactivation
#### 3.3.2 Inactivation of Bacteria in a Flow Reactor
The logarithmic expression 9833-In(N${}_{0}\)) takes into account the fact that increasing the concentration of bacteria has an inhibitory effect on the inactivation of bacteria. In other words, an increase in the initial concentration of bacteria produces a leveling effect. The R2 value for this model is 0.66, which means that it is a sufficient approximation of the experimental data. The relatively low R2 value may be due to the sensitivity of the model to the initial bacterial count, which varies widely from 1200 to 54,000 CFU/L.  
To limit the influence of absolute values and ensure comparability of data, all experimental values were normalized to percentages relative to the initial bacterial count, defined as 100%. To fit the experimental values of the relative survival rate, the same second-order exponential decay model with logarithmic adjustment was used:  
\[\text{R(t, N${}_{0}$)}=\text{v}\cdot\text{e}^{-\text{x}t}+\text{y}+\text{z}\cdot \text{ln(N${}_{0}$)} \tag{7}\]  
where R(t, N${}_{0}\)) is the predicted relative survival rate (%), t is the contact time (min), NFigure 10:0{}_{0}Figure 10:1{}_{0}$)} \tag{8}\]  
Figure 10:2 3D surface plots illustrating predicted bacterial survival as a function of contact time and initial bacterial concentration, based on (**a**) Equation (6) and (**b**) Equation (8). The model surface (Figure 11b) shows characteristic features: an exponential decline in bacterial survival over time and a nonlinear, logarithmic decrease in efficiency with increasing initial bacterial numbers. At high initial bacterial numbers (\(\geq\)25,000-54,000 CFU/L), a clear flattening of the model surface is visible, indicating that a plateau of bactericidal effectiveness has been reached. The model fits the experimental data well (R\({}^{2}\) = 0.82) and allows for reliable predictions within the tested values of contact time and initial bacterial count. The high values of Akaike (AIC = 403.27) and Bayesian (BIC = 412.38) information criteria also confirm the high quality of Model Equation (8). Other models tested (Weibull, logistic, polynomial) have significantly worse performance. The model combining a second-order exponential decay with a logarithmic adjustment describes well the bactericidal effect of H\({}_{2}\)O\({}_{2}\) in the flow catalytic reactor. This model can be used to estimate the time required for effective water disinfection depending on the initial bacterial count. However, the effectiveness of the catalytic reactor also depends on the temperature, concentration of H\({}_{2}\)O\({}_{2}\) used, pH, and ions present in the aqueous matrix. Therefore, this model is rather a first step. It is necessary to develop more detailed models that would take into account the influence of the above-mentioned parameters.