esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## Materials and Methods
### PWV Inversion Model Design  
Atmospheric conditions in summer and winter differ greatly, and the study area is located at 36\({}^{\circ}\)02\({}^{\prime}\)41\({}^{\prime\prime}\)-41\({}^{\circ}\)34\({}^{\prime}\)03\({}^{\prime\prime}\)N. Therefore, two atmospheric models of mid-latitude summer and mid-latitude winter, respectively, were selected to construct PWV retrieval models. The retrieval model consists of 7-D LUTs composed of sensor zenith angle, solar zenith angle, relative azimuth angle, land-cover type, water vapor column, visibility, and elevation. For carbon monoxide, carbon dioxide, ozone, methane, and other gases, the default standard atmospheric profiles of MODTRAN 5 were used as the input parameters. The important input parameters of MODTRAN 5 are shown in Table 1. An example of the relationship between transmittance and PWV is shown in Figure 2, and can be expressed by Equation (5).  
\[W=((a-ln\tau)/b)^{c} \tag{5}\]  
where \(W\) is the PWV value, \(\tau\) is the transmittance, and \(a\), \(b\) and \(c\) are the regression coefficients between \(W\) and \(\tau\).
# A Comparative Study of the CMT+P Process on 316L Stainless Steel Additive Manufacturing
## 4 Results and Analysis
### Tensile Strength
In addition to the mentioned above, deposition efficiency of SLM process are significantly lower than GMAW. For the above reasons, compared with SLM, the process used in this study has very obvious advantages and attraction for the manufacturing of parts with non-high dimensional accuracy requirements, and possesses excellent prospect in metal part AM. The calculated anisotropy rate of the average value of yield and tensile strength of the four as-deposited samples are represented by AP\({}_{\text{Y}}\) and AP\({}_{\text{T}}\) respectively [14], and the statistical diagram of the two types of data of each sample is shown in Figure 13. The results show that the anisotropy rate values of yield strength in horizontal and vertical specimens of samples #1-#4 are 8.09%, 5.0%, 2.85% and 6.80% respectively, and the corresponding values of ultimate tensile strength of samples #1-#4 are 11.10%, 9.05%, 10.69% and 8.62%, respectively. So, it can be seen that the anisotropy rate of ultimate tensile strength of CMT+P as-deposited samples is greater than that of yield strength. This may be due to the same mechanism of plastic deformation of polycrystalline metals in horizontal and vertical directions, while the tensile strength of MIG deposition samples in different directions has greater difference in bearing mechanism, which leads to the above experimental results [32]. The formation of anisotropy is mainly determined by the deposition direction, which makes coarse columnar crystals grow upwards, resulting in differences in mechanical properties in different tensile directions [33]. The SEM micrograph of the middle area of sample #4 shows that there are obvious intergranular cracks between the coarse columnar grains as shown in Figure 14, which are the weak link of the material under horizontal loading. The macro cracks are easy to generate and expand here, resulting in the strength of the horizontal tensile specimen is significantly lower than that of the vertical specimen. In horizontal direction, the deformation resistance is mainly provided by grain boundary, while in vertical direction, it mainly depends on columnar crystal growing in the same direction [34]. However, in the deposited samples, the grain boundary extended along the direction of construction with obvious uniformity, so the deformation resistance of the grain boundary decreased in the horizontal direction, which made the tensile strength of the samples represent obvious anisotropy in the horizontal and vertical directions. Figure 13: Anisotropy of yield and tensile of different specimens in horizontal and vertical directions.
## 4 Simulation and Analysis of the MRAI-LDPC Decoding Algorithm  
### Decoding Performance  
In this section, the performance of the presented MRAI-LDPC algorithm is simulated based on two quasi-cyclic LDPC codes (QC-LDPC) with different construction methods. The total frame number \(T_{total}\) is \(10^{8}\), and the maximum iteration number \(I_{\max}\) is 100. The algorithm stops decoding when \(T_{total}\) is more than \(10^{8}\) or \(I_{\max}\) is more than 100.
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 2 Background
### Transformers Model  
The Transformer model is a neural network architecture that has revolutionized NLP tasks due to its ability to capture long-range dependencies and its parallel processing capability. Introduced by [PERSON] [30], the transformer architecture relies entirely on self-attention mechanisms to process input sequences, making it highly efficient and effective for various sequence-to-sequence tasks. They have revolutionized the field of natural language processing (NLP) due to their ability to handle large contexts and capture complex dependencies in text.  
In our proposed system, the Transformer model is used to generate the final Cypher query from the extracted key information and the schema relationships. The key features of the Transformer model in our context are:  
* Encoder-Decoder Architecture: The Transformer consists of an encoder that processes the input question and additional information such as key value and relational context, while the decoder generates the corresponding Cypher query. The encoder captures the context and semantics of the input, while the decoder uses this information to generate the output query.
* Output Generation: The Transformer predicts the Cypher query by learning to map the input representations to the correct sequence of Cypher query.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 5 Experiments and Discussion
### Deployment  
The workflow of the experiment approach is presented in Figure 11. When setting up an experiment, researchers could follow the steps outlined below.  
Figure 11: Experiment working flow.
## 2 Applications of HREM
### HREM for Other Organic Materials  
The materials primarily subjected to HREM imaging are sourced from animals. However, very recently, HREM expanded its application into the realm of plant sciences. More concretely, the morphology of wild-type and genetically altered tomato plants were visualised in order to study abnormal leaf axil patterning [105]. In pilot studies, the capacity of HREM for testing paper quality [28; 106] and the fibre arrangement and the formation of keratinocytes seeded on skin replacement material [107] were evaluated. These experiments demonstrated that HREM is not restricted to biomedical research, but has yet-unknown capacities to aid research in many other fields of modern science.
## 3 Results
### Calculations of RBE  
Figure 2 shows the RBE values for all the examined models as a function of proton energy for the five types of cells (having different \((\alpha/\beta)_{R}\) ratios) listed in Table 2. These results assume a 2 Gy dose fraction which is commonly delivered at the conventional protocols. In addition, the mean empirical RBE (\(\rm RBE_{emp}\)) is also presented for comparison. Note that the models of [PERSON] and [PERSON] appearing in Table 1 are only utilized for cell line V79 (with \((\alpha/\beta)_{R}=3.76\) Gy) as their RBE values at other cell types deviate significantly from all the other models. This is probably due to the fact that those models are optimized solely upon data from this specific cell line, so they fail to predict the RBE for other cell types.  
In Figure 3, we present the relative percentage difference (at each proton energy \(T_{i}\)) between each RBE model depicted in Figure 2 and the conventional value of RBE = 1.1 calculated from:  
\[\Delta(T_{i})= \frac{\rm RBE_{model}(T_{i})-\rm RBE_{1.1}}{\rm RBE_{1.1}}\times 100\%, \tag{37}\]  
where \(T_{i}\) is the \(i\)-th discrete energy value in the 1-250 MeV interval and \(\rm RBE_{1.1}\) is the conventional fixed value of RBE = 1.1.  
Figure 1: Calculated \(y_{\rm D}\) and \(\rm LET_{\rm D}\) as a function of proton energy for a liquid water sphere of 1 \(\upmu\)m diameter.  
Using the data of Figure 3, in Figure 4 we show the mean percentage difference (MPD) of each RBE model against the value RBE = 1.1 calculated from:  
\[\text{MPD}=\frac{1}{\text{N}}\sum_{\text{i}}^{\text{N}}\lvert\Delta(T_{i})\rvert \tag{38}\]  
where \(N\) is the total number of discrete energies (\(T_{i}\)) considered in this study. In Figure 4, in addition to the 2 Gy dose fraction, we also show the MPD at 5 Gy and 7 Gy dose fractions which are used in some hypof fractionation protocols, especially on prostate, lung and liver carcinomas [74].  
Figure 3: \(Cont.\)  
Figure 2: Proton RBE values calculated by the different empirical (Table 1) and biophysical (TDRA, MKM) models examined as a function of proton energy for five cell types at a 2 Gy dose fraction. The conventional value of RBE = 1.1 is also shown for comparison.  
Figure 4: Mean percentage difference (Equation (38)) between the RBE models examined in the present work and the conventional fixed value RBE =1.1 for five cell types at 2 Gy, 5 Gy and 7 Gy dose fractions.  
Figure 3: Relative percentage difference (Equation (37)) between the RBE models of Figure 2 and the conventional value of RBE = 1.1 for five cell types at a 2 Gy dose fraction.
## 4 Results and Discussion
### Geophysical Logging Response Characteristics of Coal Quality
#### 4.3.4 Logging Response Characteristics of Fixed Carbon  
Fixed carbon content in coal initially increases before decreasing with rising apparent resistivity (Figure 9a), a trend governed by coal metamorphism. Advancing coalification in mid- to low-rank coals reduces inorganic minerals, moisture, and humic acid, sharply lowering ion conductivity and increasing fixed carbon content alongside resistivity [51; 77]. However, further coalification enhances electronic conductivity in higher-rank coals, reducing resistivity [75].  
Fixed carbon and density similarly exhibit an initial rise followed by decline (Figure 9c), influenced by coal rank, mineral content, lithotype composition, and reservoir properties [47; 53]. Increasing metamorphism in mid- to low-rank coals reduces moisture, elevating seam density, while continued coalification decreases inorganic minerals, lowering density [55; 81].  
Figure 8: Relation between volatile matter yield in coal and logging parameters. (**a**) The relationship between volatile matter yield and apparent resistivity; (**b**) The relationship between volatile matter yield and lateral resistivity; (**c**) The relationship between volatile matter yield and bulk density; (**d**) The relationship between volatile matter yield and natural gamma; (**e**) The relationship between volatile matter yield and short-spaced gamma-gamma; (**f**) The relationship between volatile matter yield and acoustic transit time.  
Fixed carbon content negatively correlates with lateral resistivity, natural gamma (GR), short-spaced gamma-gamma (CGS), and acoustic transit time (AC) (Figure 9b,d-f). Progressive coalification diminishes inorganic minerals and adsorbed radioactive elements, reducing GR [25]. Medium- to low-rank bituminous coals, with loose structures and developed pore-fracture systems, exhibit elevated lateral resistivity and AC [14; 68; 82]. As metamorphism advances, denser coal structures and enhanced electronic conductivity reduce lateral resistivity and AC [81; 83].
# Design and Study of a New Wave Actuator for a Boat
## 2 Mathematical Modeling of Wave Actuator
### Modeling of the Wave Actuator
#### 2.4.2 Wave Energy Conversion Efficiency  
Figure 7 illustrates how the efficiency of converting wave power into usable thrust depends on the design and size of the wave actuators. This efficiency relates to factors like the thrust produced for boat propulsion, as well as the wave characteristics such as wavelength, force, and height, which are expressed in Equation (32). Efficiency reflects how much wave power is transformed into useful work with minimal waste, while power measures how effectively the wave force is harnessed [26; 27].  
\[P=\frac{W}{t}=\frac{1}{\Delta t}\int_{0}^{T}F_{w}\zeta dt \tag{30}\]  
where \(F_{w}\) is the wave force and \(\zeta\) is the wave motion over time \(T\). The power absorbed and converted into thrust through the nozzle is given by the following:  
\[P_{c}=\frac{1}{\Delta t}\int_{0}^{T}F_{r}z_{t}dt \tag{31}\]  
where \(F_{r}\) is the propulsion force and \(z_{t}\) is the motion of the water tank's bottom plate. The wave power conversion efficiency is defined as the ratio of the absorbed power (\(P_{c}\)) to the  
Figure 5: Force analysis for half a wave cycle.  
Figure 6: Mean \(V_{drug}\) during half a wave cycle.  
total wave power (\(P\)). This efficiency shows how effectively the wave actuator captures power from ocean waves. It can be calculated using the following:  
\[\eta=\frac{P_{c}}{P.L} \tag{32}\]  
These efficiency values are presented in a table, alongside details about the actuator's size (the boat's length \(L\), dimensionless), wave conditions, and operating principles. The results indicate that efficiency depends on both the system's design and its dimensions, with statistical methods used to analyze these relationships across different simulation conditions of wave actuator designs.
## 3 Synthetic Cases  
We present two synthetic inversions to demonstrate a probabilistic joint inversion using the KEG. For both cases, we compare the results for the joint inversions with the results for the separate inversions of DC resistivity and FDEM data.  
The first test case is a simple toy model chosen primarily for a simplified visualization of the parameter estimates. The simplification consists in limiting the inversion to four parameters, in particular, the electrical conductivity of four subsurface layers with fixed layer boundaries.  
The second synthetic test case shows the results of a multi-layer inversion, which means that, for this case, the number of discrete layers, i.e., inversion parameters, is chosen much larger than the expected number of subsurface layers. Thus, the exact number of subsurface layers does not need to be defined a priori.  
For the inversion of VES and FDEM data, we define the model parameters of logarithmic electrical conductivity. The logarithmic barrier prevents unphysical updates, i.e., negative estimates of electrical conductivity.  
For both cases, we simulate observations for the true synthetic model using the same forward model that is used in the inversion process. Thus, errors that arise from imperfect forward models are neglected in this study. The ensemble of the prior model and forward responses are identical, thus errors that arise from sampling bias are identical for all of the separate and joint inversion results.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 3 The Design of Simulation Environments and Simulation Tasks
### Simulation Environment  
(1) Simulation platform  
This paper uses Software-In-The-Loop (SITL) in ArduPilot to simulate the flight of the UAV. ArduPilot is a versatile open-source autopilot system that can provide autopilot services for multi-rotor UAVs, fixed-wing UAVs, rovers, ships, etc.  
(2) Ground Station  
In this paper, the QGroundControl ground station is used to visualize the real-time position of the UAV. This paper uses the Python interface of Dronekit to realize the drone's control, including take-off, going to the next waypoint, outputting the current flight status of the drone on the console, returning, landing, and other functions.  
(3) Communication Control  
Dronekit-Python is an open-source Python library used to control the flight of drones. It uses the MAVLink protocol to communicate with drones through serial ports. The software-in-the-loop simulation of the drone runs on an Ubuntu 18.04 system based on VMware Workstation 16.2.3, and the ground station runs on a Windows 11 laptop with 16 GB RAM, 11 th Gen Intel(R) Core (TM) i7-11800H @2.30 GHz.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Visualization Experiments of Feature Filtering and Extraction  
The visualization experiments for feature filtering are designed to assess the attention convolutional sparse encoder and the feature filtering network. Figure 6 presents the visual results of ICSF feature filtering. Specifically, Figure 6a shows the visible and infrared images, while Figure 6b,c display the corresponding images' homogenous and isolated feature visualizations, respectively. The consistency correlation coefficient between feature maps derived from the images reveals that the homogenous feature pairs exhibit more consistent contours and texture information, indicating a positive correlation. Conversely, the isolated feature pairs tend to contain more unrelated detail information.
## 5 Engineering Case Analysis
### PNN Classification and Identification
It can be seen that, no matter the precision or the AUC value, the excellence of the classification effect is ranked as PNN, GRNN and BPNN. It can be seen that the PNN in this paper has the best recognition effect for mine microseismic signals. Figure 13 shows the comparison results of the computation time of different classifiers for different models. It can be seen from Figure 11 that the BPNN model has the longest running time, with an average running time of 0.24 s; the GRNN model has a better running time than the BPNN, with an average running time of 0.23 s; the PNN model has the shortest running time, with an average running time of 0.14 s. The method in this paper shows great advantages in terms of time. Compared with Figure 10, it is found that among the 10 eigenvector models, all three methods show the fourth eigenvector structure (FM1-FM4 as the input vector) with the highest cost performance, that is, the highest accuracy and the shortest running time. Therefore, the energy entropy of PF1-PF4  
Figure 11: Comparison of ROC curves of different classifiers. Figure 12: Comparison of ROC and precision of different classifiers. obtained in this paper with a correlation coefficient threshold of 0.3 is used as a feature vector for automatic identification of PNN microseismic signals, which has the highest accuracy and time efficiency, and has high field-application value. For the engineering site, the initial processing of the data and the later identification and classification are very important. It can be seen from the above analysis that in the identification of microseismic signals, the blasting-vibration signal and the coal-rock-mass-rupture signal are difficult to distinguish due to the similarity of the spectral characteristics. The method in this paper can greatly reduce the workload of manual identification, effectively identify effective microseismic events, and help improve the accuracy of microseismic location and microseismic-magnitude calculation.
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion  
This section is dedicated to the discussion of the findings of the review of each CubeSat application field and some attempts to draw possible conclusions for the role of CubeSats in space exploration and utilization during the present as well as the immediate future. Thus, the present Section is divided into distinct subsections dedicated to each individual CubeSat application field that has been analyzed in the present work.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 5 Numerical Experiment  
### Testing Delivery Network  
We adopted a delivery network with 24 nodes, i.e., 1 warehouse and 23 customer points as an example to determine the optimal-delivery truck-drone route using the proposed model and algorithm, assuming that at the initial time \(u_{0}\)= 5 min, three trucks delivered parcels to 23 customer points from a warehouse. Each truck carried a drone, and they jointly carried out the delivery process. Each customer point only received one parcel. The spatial layout of warehouse and customer points is shown in Figure 3. The average speed of trucks was \(V^{\mathrm{T}}\) = 40 km/h, and the average speed of drones was \(V^{\mathrm{D}}\) = 60 km/h. The distance between any two nodes is shown in Table 1, where 0 in the first row and the first column represents the warehouse ID, 1-23 represent the customer point ID, and the unit of distance is the kilometer.  
In the experiment, we used rotary-wing drones. The axle base was 450 mm, the dead weight was 3200 g, the maximum load was 3000 g, the maximum endurance was 60 min, the maximum cruising distance was 60 km, the maximum flight altitude was 2000 m, and the size was 55 cm \(\times\) 65 cm \(\times\) 32 cm.  
The trucks used in this experiment were van trucks. The wheelbase was 1360 mm, the dead weight was 1.275 ton, the load capacity was 0.8 ton, the maximum horsepower 116 kw, the torque was 150 N\(\cdot\)m, and the size was 4.915 m \(\times\) 1.63 m \(\times\) 2.475 m.
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 3 Optimization of Real-Time Performance for RISC-V Processors  
### RISC-V Processor Platform  
The RISC-V instruction set is an open-source and free instruction set architecture (ISA) that offers several advantages, including simplicity, adaptability, customizability, and the absence of conditional code and branch delay slots [21]. These advantages allow users to flexibly customize and optimize the ISA according to the specific requirements of their applications.  
The RISC-V processor prototype used in this paper is derived from the open-source project Tinyriscv and utilizes the RV32 IM instruction set. After expanding the modules of Tinyriscv, it is named Rev-riscv. Rev-riscv employs a three-stage pipeline structure, and the detailed architecture of the processor is shown in Figure 1.  
The three-stage pipeline operates as follows: fetch, decode, and execute. In the first stage (PC_IF module), the next instruction address to be fetched is calculated, and the instruction is then retrieved from the instruction memory (Rom module) via the bus (Custom_Bus module). In the second stage (ID module), the instruction is decoded, and the corresponding data are fetched from the register file (Regs module). In the third stage (EX module), the operations specified by the instruction, such as arithmetic and logic calculations, write-back, jumps, and halts, are performed. The remaining modules are  
Figure 1: Rev-riscv processor architecture diagram.  
used for data processing, pipeline control, and other functions within the processor. The descriptions of the various modules are shown in Table 1.