esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 3 Energy Efficiency Evaluation Method for Booster Regulator with Energy Recovery (VBA-R)
### Energy Efficiency of Booster Regulator  
In practical applications, a tank is always used with a booster regulator to decrease pressure fluctuations. Therefore, the real output is the pressure in the tank, which is lower than the pressure in the chamber of the booster regulator (this will be proved later). To assess the energy efficiency of a booster regulator, considering the application and the definition of efficiency, the energy efficiency of a booster regulator is defined as the ratio of the energy output (air flow out of the tank) to the energy input (air flow in the boost and drive chambers). It is defined as:  
\[\eta=\frac{E_{t}}{E_{bc}+E_{dc}} \tag{3}\]  
Combining the air power proposed in Equation (2), the energy flow in the booster regulator in each cycle is:  
\[E_{bc}+E_{dc}=\int_{0}^{T}p_{a}(Q_{bc}+Q_{dc})\ln\frac{p_{s}}{p_{a}}dt=p_{a}( V_{bc}+V_{dc})\ln\frac{p_{s}}{p_{a}} \tag{4}\]  
It is obvious that the energy input depends only on the supply pressure, because the volume of each chamber is constant. When the supply pressure increases, more energy is consumed by the pneumatic booster regulator system.  
Energy output, like the energy input, is the integral of air power flowing out the tank in each cycle, and it can be written as:\[E_{t}=\int_{0}^{T}p_{a}Q_{a}\ln(p_{t}/p_{a})dt \tag{5}\]  
Pressure fluctuations in the tank lead to simultaneous fluctuations of the flow rate, so it is hard to calculate the energy output directly. However, if the tank is large enough, we could suppose that the fluctuation is small and can be neglected. When the system is running steadily, the flows in and out of the chambers are equal to the volume of the boost chamber. Therefore, we substitute Equations (4) and (5) into Equation (3), and the energy efficiency of the booster regulator system can be obtained as:  
\[\eta=\frac{E_{t}}{E_{bc}+E_{dc}}=\frac{\ln(p_{t}/p_{a})}{(1+V_{dc}/V_{bc})\ln( p_{s}/p_{a})} \tag{6}\]  
where \(p_{t}\) is the pressure in the tank and the ratio of \(p_{t}\) to \(p_{s}\) is defined as the boost ratio, which reflects the boost capability of the booster regulator. Therefore, Equation (6) intuitively indicates that the energy efficiency depends only on the supply pressure and boost ratio because the volume of each chamber is constant. The greater the boost ratio or the lower the supply pressure is, the higher the energy efficiency is. To understand the energy efficiency, it is necessary to identify the boost ratio of the system. Therefore, we will discuss the pressure response characteristics of the VBA-R.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 2 Materials and Methods  
### Study Area  
Xinjiang is located in northwest China, covering an area of 1.66 million km\({}^{2}\) (34\({}^{\circ}\)25\({}^{\prime}\) N-48\({}^{\circ}\)10\({}^{\prime}\) N, 73\({}^{\circ}\)40\({}^{\prime}\)E-96\({}^{\circ}\)18\({}^{\prime}\) E) (Figure 2). The region features unique topography, with three major mountain ranges (Altai Mountains, Tianshan Mountains, and Kunlun Mountains), two major basins (Junggar Basin and Tarim Basin), and numerous oases arranged from north to south, forming an alternating pattern of mountains, basins, and oases.  
Figure 1: Flowchart of research for vegetation dynamics and its climate response.  
Xinjiang has a temperate continental climate, characterized by arid conditions and large temperature differences between day and night. From 1991 to 2018, the average annual temperature was 11.6 \({}^{\circ}\)C, and the average annual precipitation was 147.0 mm. The unique natural geographic environment results in significant spatiotemporal differences in water and heat distribution. The north of the Tianshan mountains has higher precipitation and lower temperatures, while the south experiences less precipitation and higher temperatures. The vegetation in Xinjiang is diverse, with significant regional differences, and is characterized by low coverage and fragile habitats under the influence of these climatic and topographic conditions. Vegetation in Xinjiang is highly susceptible to climate change and drought stress.
# Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
## 3 Modeling Process
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Numerical Value** & **Physical Significance** \\ \hline \(C_{\mathrm{s}}\)\(\left(\mathrm{kJ}\cdot\mathrm{kg}^{-1}\cdot\mathrm{K}^{-1}\right)\) & 0.89 & specific heat capacity of soil particle \\ \(C_{w}\)\(\left(\mathrm{kJ}\cdot\mathrm{kg}^{-1}\cdot\mathrm{K}^{-1}\right)\) & 4.1 & specific heat capacity of water \\ \(C_{i}\)\(\left(\mathrm{kJ}\cdot\mathrm{kg}^{-1}\cdot\mathrm{K}^{-1}\right)\) & 2.1 & specific heat capacity of ice \\ \(\lambda_{s}\)\(\left(\mathrm{J}\cdot\mathrm{m}^{-1}\cdot\mathrm{s}^{-1}\cdot\mathrm{K}^{-1}\right)\) & 1.39 & thermal conductivity of soil \\ \(\lambda_{w}\)\(\left(\mathrm{J}\cdot\mathrm{m}^{-1}\cdot\mathrm{s}^{-1}\cdot\mathrm{K}^{-1}\right)\) & 0.56 & thermal conductivity of water \\ \(\lambda_{i}\)\(\left(\mathrm{J}\cdot\mathrm{m}^{-1}\cdot\mathrm{s}^{-1}\cdot\mathrm{K}^{-1}\right)\) & 2.24 & thermal conductivity of ice \\ \(L_{f}\)\(\left(\mathrm{kJ}\right)\)\(\left(\mathrm{kg}\right)\) & 334.56 & latent heat of fusion \\ \(\rho_{i}\)\(\left(\mathrm{kg}/\mathrm{m}^{3}\right)\) & 917 & density of ice \\ \(\rho_{w}\)\(\left(\mathrm{kg}/\mathrm{m}^{3}\right)\) & 1000 & density of water \\ \(\rho_{s}\)\(\left(\mathrm{kg}/\mathrm{m}^{3}\right)\) & 1740 & density of soil \\ \(k_{s}\)\(\left(\mathrm{m}/\mathrm{s}\right)\) & 3.63\(\cdot\)10\({}^{-8}\) & permeability coefficient of saturated soils \\ \(\mathrm{T}_{0}\)\(\left({}^{\circ}\mathrm{C}\right)\) & \(-\)0.16 & initial freezing temperature \\ \(\theta\)s & 0.58 & saturated moisture volume content \\ \(\theta\)r & 0.04 & residual moisture volume content \\ \(a\) & 2.59 & VG model parameters \\ \(m\) & 0.22 & VG model parameters \\ \(l\) & 0.5 & VG model parameters \\ B & 0.56 & constant \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters and values for numerical simulation.
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
## 2 Plant Description  
The ISCC power plant in Kureimat, Egypt is located at a northern latitude of \(29^{\circ}16^{\prime}\) and eastern longitude of \(31^{\circ}15^{\prime}\). It has 135 MW total power capacity, comprising a solar field with an electrical output of 20 MW and a combined cycle field with a power of 115 MW [19]. The integration of a combined cycle with the solar field ensures the delivery of the required electricity contribution to the grid regardless of solar radiation conditions.
## 3 Application of Functionality Loss Evaluation Model for Railway Network Components
### Construction and Visualization of the Railway Network Model
#### 3.2.1 Earthquake Scenario: 0.3 g  
To take a more conservative approach, this study assumed an earthquake scenario with a seismic acceleration of 0.3 g, which exceeds the maximum seismic acceleration (0.286 g) specified in the seismic design standard KDS 17 10 00 of Korea [21]. The earthquake was assumed to occur at a location 16 km from the railway lines, with all railway network components located within 32 km of the epicenter. The earthquake depth was assumed to be a shallow 10 km, similar to the Pohang earthquake that occurred in Korea. Based on this scenario, the distances from the epicenter to each railway facility were used to calculate the rock seismic acceleration affecting the railway structures. The maximum rock acceleration at the epicenter was assumed to be 0.3 g, and the maximum rock acceleration values at the location of each structure were calculated to decrease proportionally with distance. When an earthquake occurs at the hypocenter, seismic waves propagate along the bedrock, causing the rock acceleration to decrease with distance from the epicenter due to damping. Subsequently, as the seismic waves propagate near the surface, they are amplified due to the site response. The characteristics of the site response are determined by the shear velocity of the soil within 30 m below the surface and the depth of the bedrock. To incorporate the amplification of seismic acceleration due to local site response, the ground conditions at the locations of each component were assumed based on the ground classification in KDS 17 10 00 [21], as shown in Table 6. For embankments, the subsoil stiffness was assumed as specified in Table 7. The ground amplification factors (F\({}_{\text{a}}\)) from Table 7 of KDS 17 10 00 [21] were then applied to each railway network component to calculate the final peak ground acceleration (PGA).
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 3 Test Results and Analysis
### Load-Separation Behavior  
Figure 6 presents the load--separation curves, illustrating the progression of separation between the steel beam and the concrete slab in response to the applied pull-out force. The curves delineate three distinct stages. Initially, upon specimen loading, the load increased linearly with separation, indicating a period of elastic behavior. As the specimens approached the damage stage, a noticeable reduction in stiffness was observed, reflecting the initiation of inelastic deformation. Post-ultimate load, separation persisted, augmenting while the pull-out force diminished, signifying the onset of the post-peak softening phase.  
In this experiment, the average pull-out bearing capacity for the T-perfobond (PFR) and notched T-perfobond (PMFR) connectors was 132.0 kN and 100.5 kN, respectively, while the average peak separation was 8.83 mm and 7.73 mm, respectively. The introduction of notches decreased the pull-out bearing capacity by 23.86% and the peak separation by 12.46%. Nevertheless, due to the limited number of model specimens, additional parametric analysis is necessary to affirm the influence of notches on pull-out bearing performance.
## 2 Materials and Methods
### Leaf Biochemical Properties-Based Model  
In this study, we employed the Borel Ecosystem Productivity Simulator (BEPS) [48] for half-hourly GPP estimations. The BEPS model is based on leaf biochemical properties \begin{table}
\begin{tabular}{c c} \hline \hline
**2022** & **2023** \\ \hline June 6 th, 2022 & June 1 st, 2023 \\ July 20 th, 2022 & June 28 th, 2023 \\ July 27 th, 2022 & July 11 th, 2023 \\ August 3 rd, 2022 & July 19 th, 2023 \\ August 7 th, 2022 & July 24 th, 2023 \\ August 9 th, 2022 & July 27 th, 2023 \\ August 15 th, 2022 & August 11 th, 2023 \\ August 26 th, 2022 & August 16 th, 2023 \\ September 1 st, 2022 & August 24 th, 2023 \\ September 7 th, 2022 & August 31 st, 2023 \\ September 16 th, 2022 & September 6 th, 2023 \\ September 24 th, 2022 & September 9 th, 2023 \\ October 11 th, 2022 & September 17 th, 2023 \\ October 20 th, 2022 & September 26 th, 2023 \\ November 1 st, 2022 & October 1 st, 2023 \\  & October 6 th, 2023 \\  & October 11 th, 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The date of the UAV flight. and follows a two-leaf scheme, which separated the canopy into sunlit leaves and shaded leaves using the following methods:  
\[LAI_{sunlit}=2\cos\theta\Big{(}1-e^{-0.5\Omega LAI/\cos\theta}\Big{)} \tag{1}\]  
\[LAI_{shaded}=LAI-LAI_{sunlit} \tag{2}\]  
where \(\theta\) is the solar zenith angle; \(\Omega\) is the clumping index; \(LAI\), \(LAI_{sunlit}\), and \(LAI_{shaded}\) are the total leaf area index of the canopy, leaf area index of sunlit leaves, and leaf area index of shaded leaves, respectively. The BEPS model calculates the instantaneous GPP as the sum of photosynthesis from the sunlit leaves and shaded leaves. \[GPP=A_{sunlit}LAI_{sunlit}+A_{shaded}LAI_{shaded} \tag{3}\]  
where \(A_{sunlit}\) and \(A_{shaded}\) are the instantaneous photosynthesis rates of the representative sunlit leaf and representative shaded leaf (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)), respectively. The instantaneous photosynthesis rate is calculated using the Farquhar-von Caemmerer-Berry (FvCB) biochemical model [7]. \[A=\min\big{(}W_{c},W_{j}\big{)} \tag{4}\]  
where \(W_{c}\) is the RuBP carboxylation rate limited by Rubisco activity (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(W_{j}\) is the RuBP carboxylation rate limited by electron transport (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)); \(R_{d}\) is the leaf dark respiration during the daytime (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)).
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 4 Methodology
### Deep Learning Network Structure  
In this paper, we use the LSTM combined with the attention mechanism, as shown in Figure 3; the functions of each layer are as follows,  
1. INPUT: Input layer, the domain name is converted to a matrix of dimension 54 \(\times\) 128 after the length is padded and embedding, so the input dimension is 54 \(\times\) 128.
2. LSTM: LSTM layer, sequence output, and output 54 \(\times\) 128 feature vector.
3. ATTENTION: Attention mechanism, according to Section 3.3, and output 54 \(\times\) 128 feature vector.
4. FC: Fully connected layer, which stretches the feature vector output by ATTENTION. Each pixel represents a unit. The output feature is 6912 units using the fully connected layer operation, and the probability of DROPOUT is set to 0.5.
5. OUTPUT: Output layer, this layer is fully connected with the FC layer; the output length is the required number of classifications, which represents which classification the extracted features belong to; and the classification function is Softmax.  
Figure 2: DGA domain name length distribution.  
The parameters in the network structure are as follows.
* Classifier: Based on the characteristics of DGA domains, we use Softmax classifier to judge which type the domain belongs to. The essence of Softmax function is to map a K-dimensional arbitrary real vector to another \(K\)-dimensional real vector, where the value of each element in the vector is in the \([0,1]\) interval, as shown by Formula (11), where \(v_{j}\) is the \(j\) element of the vector, and the Softmax value of the element is \(softmax(v_{j})\), \[softmax(v_{j})=\frac{v_{j}}{\sum_{k=1}^{K}v_{k}}\] (11)
* Loss function: When the model is trained, the loss is calculated according to the loss function, and then back-propagation (BP) is used to adjust the parameter adjustment. In this paper, the Categorical Cross-Entropy Loss function is used as the loss function of the model. \[L(Y,\hat{Y})=-\sum_{i}y_{i}\times\log\hat{y_{i}}\] (12)
* Activation function: The formula of the ReLU activation function is as follows. This function can satisfy the sparsity in bionics. It activates the units when the input value is higher than a certain number, and can quickly converge in the stochastic gradient descent algorithm. The gradient of the function is 0 or constant, which can alleviate the problem of gradient disappearance, thereby improving the learning precision and speed of the neural network. Therefore, this paper uses ReLU as the activation function in two convolutional layers and two fully connected layers. \[relu(x)=\max(0,x)\] (13)  
At the same time, to prevent overfitting, a dropout layer is added to the network structure. The dropout layer prevents overfitting by preventing the synergy of certain features. At each training,  
Figure 3: Design of Classification Network Structure Based on Attention Mechanism and LSTM.  
the units are randomly removed, allowing one unit to appear independent of the other, preventing features from interdepending and reducing the transmission of erroneous information.
## 3 Results and Discussion
### Electrolyte Current  
The feasibility of the wastewater as the electrolyte was first examined. The cathodic current at a voltage of 5.0 V in the wastewater sampled from municipal drain and tapwater was compared in Figure 4. A stable current with an increasing tendency could be obtained on the HABDD when the wastewater was used as the electrolyte, both in the anodic and the cathodic cells, demonstrating the feasibility of wastewater as the electrolyte. In comparison, there was almost no response when tapwater was used as the electrolyte on both sides. It showed that the wastewater could be directly used as an electrolyte without additives.  
Diamond electrodes can continuously work in harsh environments for a long period due to their high stability and anti-corrosion properties. The HABDD, with a hierarchical hole-arrayed structure like some microchannel arrays [20], may facilitate the deionization of organics in the electrolyte [21]. Microfluidic diffusion can be formed through these arrayed holes, promoting the EO reaction rate [22]. The reaction species generated in the degradation process are quickly exchanged under a high-convection flow condition because the arranged holes create three-dimensional diffusion channels and accelerate the mass transfer of the electrolyte [17; 23]. The FNi electrode with a mesh structure can improve the reaction rate.  
The pollutant species can be oxidized on the HABDD surface as the following equations [8]:  
\[\mathrm{H_{2}O+M\to M(OH^{*})+H^{+}+e^{-}} \tag{3}\]  
\[\mathrm{R+M_{x}(OH^{*})_{2}\to CO_{2}+M_{x}+2H^{+}+2e^{-}} \tag{4}\]  
M represents the anode active sites producing adsorbed hydroxyl radicals (OH*). CO2 and other smaller molecules are generated when the organics are deeply decomposed in the presence of a large amount of OH* species. The EO reaction on HABDD is an indirect oxidation process, and the reaction rate is determined by the mass transfer rate [11].
# Quantum Computing for Healthcare: A Review
## 4 Requirements of Quantum Computing for Healthcare
### Computational Power  
Low computational time is one of the major requirements of any healthcare application. The classical computers having CPUs and GPUs are not capable of solving certain complex healthcare problems, e.g., simulating molecular structures. This motivates the need for using quantum computing that can exploit vast amounts of multidimensional spaces to represent large problems. A prominent example illustrating the power of quantum computing can be seen in [PERSON]'s search algorithm [72], which is used to search from a list of items. For instance, if we want to search a specific item in \(N\) number of items, we have to search \(\frac{N}{2}\) items on average or, in the worst case, check all \(N\) items. [PERSON]'s search algorithm searches all these items by checking \(\sqrt{n}\) items. This demonstrates remarkable efficiency in computational power. Let us assume we want to search through 1 trillion items, and every item takes 1 microsecond to check; it will take only 1 second for a quantum computer.
## 2 Materials and Methods  
### TMS Study Protocol and EEG Recording  
For this TMS-EEG study, we employed an H7 coil (Brainsway Ltd., Jerusalem, Israel) [32] connected to a Magstim Rapid2 stimulator (Magstim, Spring Gardens, United Kingdom) and placed over the mid-line central area (i.e., 8 cm above the nasion along the main reference curve (nasion-inion) and conformal to the patient's head). The H7 coil has a subdural depth and volume of stimulation of 3 cm and 40.3 cm\({}^{3}\), respectively, without significantly increasing the E-fields induced at the superficial cortical structures [33] and is safe and effective for therapeutic applications [34; 35; 36]. During the TMS sessions, 1 electrooculographic (EOG) and 60 EEG signals were recorded continuously with Ag/AgCl pellet electrodes placed according to the international 10-10 system and connected to a TMS compatible EEG amplifier (eXimia, Nexstim Ltd., Helsinki, Finland). During acquisition, the reference channel was placed on the right mastoid and the ground electrode on the right zygomatic bone. The EEG signals were analog band-pass filtered from 0.1 to 500 Hz and sampled with a 1450 Hz sampling frequency and 16-bit precision. In order to reduce the TMS-induced artifact, the EEG amplifier was temporarily blocked from 100 us before to 2 ms after the TMS pulse by a sample-and-hold circuitry [37]. Suppression of the TMS-related auditory evoked potentials was achieved with a psychophysically driven continuous white-based noise (WBN) mask combined with the use of earphones with highly effective insertion loss.  
The study's TMS-EEG protocol involved two stages. In the first stage, a wide range of SIs were used to determine visually the LT, i.e., the maximal SI that failed to produce a cortical response (TEP). This maximal SI was observed in more than one electrode in each subject, with most common electrode being C1. At this stage, we applied \(n=30\) single stimuli per SI value, with an interstimulus interval (ISI) of 1000 ms. These records constitute Dataset A.  
The LT was used as the basis for stage 2, where for each subject, a single record was produced at an SI of 120%LT with \(n=100\) single stimuli and an ISI of 2000 ms. This set of records constitute Dataset B. Demographics of the participants (3 healthy subjects and 2 patients with genetic generalized epilepsy) and information regarding the TMS-EEG records are given in Table 1. The first column provides the subject ID and condition (H for healthy and P for epileptic). Columns two to four have age, sex, and dominant hand information. The fifth column has the clinically determined LT as a percent of MSO. The final sixth column has the SIs tested for each subject in the determination of the subject-specific LT (Dataset A) and the SI for the 120% of LT (Dataset B).
## 5 Discussion
### Error Analysis  
In order to further analyze the recognition results of the structural components, the errors resulted from the proposed YOLO-based method are divided into five categories based on their types: localization error, background error, similarity error, classification error, and missing error [55]. Localization error indicates that the classification result is correct but the location of the bounding box is biased and not rightly enclosing the structural component. On the contrary, classification error happens when the bounding box locates at the right place while the target object is detected in other categories. Background error means that a bounding box appears in background areas without any component. Similarity error occurs when two or more bounding box exist for one target object, and missing error refers to the situation where the structural components in the image of 2D drawings are not detected. The causes of different types of errors vary from each other, but most of them are closely linked to the selected neural network. For instance, in YOLO used in this paper, each grid predicts two bounding boxes to detect the structural components whose coordinates of central position fall within the grid area. This may lead to missing error if the centers of multiple components fall in the same grid.  
Similar to the method mentioned in Section 4.3, the weighted average method was adopted to calculate the average proportion of all kinds of errors across five classes. As shown in Table 6, when using YOLO for structural component detection, 69.54% of the images are correctly recognized, while 30.46% of the detections have errors. It is also obviously noticed that localization error is the largest of all errors and is about five times the total amount of classification error, which indicates that YOLO has problems with locating the identified components correctly. In addition, 8.03% of detections have background error, and 5.65% and 4.43% of detections have the problems of multi-detection and omission of bounding boxes, respectively.