esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Experimental Setup  
The experimental setup for generating and measuring the PCLG vortex beam is shown in Figure 6. A coherent laser beam was emitted from a pumped solid-state laser (\(\lambda=532\) nm) and extended using a beam expander. After passing through lens L1 (\(f=100\) mm), the expanded beam was focused onto a rotating ground-glass disk. Based on the van Cittert-Zernike theorem, a partially coherent beam was generated after the collimated lens L2 (\(f=100\) mm). The coherence width is related to the size of the light spot on the rotating ground-glass disk. The larger the spot size, the lower the coherence. After the Gaussian amplitude filter and a transmitted space light modulator (SLM, 1024 \(\times\) 768 pixels with pixel size = 18 \(\upmu\)m) loaded with a computer-designed hologram, a linearly polarized PCLG vortex beam was obtained [34]. Subsequently, a circular aperture was used to filter out the useless diffraction orders.  
Subsequently, the PCLG vortex beam propagated through two cylindrical lenses (CL1 and CL2), and the beam splitter split the beam into two paths. One path was directed toward the first camera (CCD1), which was used to monitor the intensity distribution, and the other path illuminated a phase-only SLM (SLM2, 3840 \(\times\) 2160 pixels with pixel size = 3.74 \(\upmu\)m), which was used to load the window function and perturbations. The distance from CL2 to SLM2 was the same as that from CL2 to CCD1, that is, \(z=300\) mm. The perturbation point was significantly smaller than the field-of-view (e.g., \(1/10\)). The perturbation values were set as \(P_{+}=\exp(2\mathrm{i}\pi/3)\), \(P_{-}=\exp(-2\mathrm{i}\pi/3)\), and 0 (i.e., no perturbation), and the corresponding intensities, i.e., \(I_{+}\), \(I_{-}\), and \(I_{0}\), respectively, were captured by CCD2 placed at the back focal plane of L3 (\(f=300\) mm).
## 2 Methods
### Intelligent Framework of SMA Prediction  
The problem of dynamic real-time prediction of SMA parameters can be transformed into a short-time sequence prediction task. The tunneling parameters of several periods in the past (also called the time window) are retrospectively used to predict the SMA parameters of the next period. With the advancement of EPB, the data sequence is continuous with roll, achieving dynamic real-time prediction. Based on this, we proposed the prediction framework of the SMA parameters by using four ML algorithms and four DL algorithms. As shown in Figure 3, the framework included three main steps: data collection and preprocessing, model establishment, and model comparison and evaluation.  
* Step 1: Dataset collection and preprocessing  
The raw data contains many anomalies, noise, and errors, and it is inefficient and unscientific to directly put hundreds of millions of unprocessed data into the AI algorithms.  
Figure 3: Overall framework of shield machine attitude (SMA) prediction using artificial intelligence (AI) algorithms.  
Therefore, in view of the characteristics of EPB data, we develop a data preprocessing algorithm based on the Python language, which mainly includes three stages: segmentation, noise reduction, and standardization. Then, the data is divided into a training set and testing set. The training set is further subdivided into a training subset and validation subset to serve the modeling step.  
* Step 2: Model establishment  
We use eight AI algorithms (KNN, SVR, RF, AdaBoost, BPNN, CNN, LSTM, and GRU) to establish the SMA model of EPB. The input and output of the model adopt the \"five predict one\" mode; that is, the input is the parameters of the past five drilling cycles, and the target is the SMA value of the next drilling cycle. Then, the grid search and K-fold cross-validation are used to find the hyperparameter that optimizes the generalization performance of each model. Finally, we complete the modeling process.  
* Step 3: Model comparison and evaluation  
At this step, the performance of eight AI algorithms is compared through the EVS and RMSE indicators. Based on the model prediction results, we propose a warning predictor of SMA. When the predictor perceives that the SMA is about to deviate from the DTA significantly, it can provide an early warning for the machine operators to adjust the tunneling parameters.
## 3 Case Study
### The Application  
When the application launches, it searches for the fibula object in the visual field of the user. Once the Model Target of the fibula is detected, Vuforia starts tracking it and will continue as long as the bone is at least partially visible by the camera. When the fibula is detected, the application superimposes the virtual model containing the cutting guide on the physical 3D printed model (Figure 5).  
After the fibula is recognized by the application, the physical world surgical guide detection starts. This guide, hands-held by the professional using our application, is tracked by Vuforia during the procedure to overlap it with its virtual counterpart, which is visible as a hologram in the correct position Figure 5.  
To assess the alignment of the physical world surgical guide over the virtual one, we use Euclidean distance, such as in Equation (1). The subscript \"\(r\)\" indicates the coordinates of the real object and the subscript \"\(v\)\" the coordinates of the virtual model.  
\[D=\sqrt{(x_{v}-x_{r})^{2}+(y_{v}-y_{r})^{2}+(z_{v}-z_{r})^{2}} \tag{1}\]  
Rotation between the two models is computed using quaternions (2). Each rotation of a rigid body is equivalent to a single rotation of a given angle \(\phi\) around a fixed axis (called the Euler axis \(\vec{u}\)).  
Figure 5: Example of fibula tracking. (**a**) When the fibula is detected, its virtual model (white colored) appears superimposed on it along with the surgical guide in the correct position, as determined by the medical team before the procedure. (**b**) The real surgical guide is tracked during its positioning by our system, providing feedback about its current distance both in position, both in rotation, from its correct position.  
\[q=\exp\frac{\phi}{2}(u_{x}\vec{i}+u_{y}\vec{j}+u_{z}\vec{z}) \tag{2}\]  
So, for each guide movement, the system calculates the distance and the rotation between the two models.  
Using the above metrics, it is possible to follow the professional during the alignment procedure giving him feedback about how the positioning is closing the expected one.
# Strength Characteristics and Slope Stability Analysis of Expansive Soil with Filled Fissures
## 3 Experimental Study on Strength Characteristics of Expansive Soil with Filled Fissures  
### Sample Preparation Device  
At present, in order to study the mechanical characteristics of the filled fissures of the expansive soil, the triaxial test was usually carried out using the original sample. In actual operation, the following deficiencies still existed.  
1. The success rate of cutting the sample by using the original sample was low, the adhesion between the expansive soil and the filled fissure was weak, and it was easy to break along the fissure during the cutting process.
2. The sample preparation process had a large disturbance to the filled fissures of the expansive soil, the filling material was easy to break and fissure, and the bonding conditions and closed state of the in situ filled fissures were changed.
3. It was difficult to control the spatial distribution of the filled fissures. Since a group of triaxial tests often required 3 to 4 parallel samples, the filled fissure distribution of each sample was difficult to keep consistent, resulting in test errors.  
In view of the above deficiencies, based on the conventional triaxial test sample preparation method, the mold was improved, the strong expansion fissure filler was manually placed in the sample, and the thickness and the inclination angle of the filled fissure were controlled. The layered filling--bevel cutting--refilling was the basic idea of the sample preparation device and method.  
A sample preparation device for triaxial test sample containing a filled fissure was designed (Figure 3). The sample preparation device included a base and a mold fixing component, a sample pushing and positioning component, and a sample mold. The base of the device was a gate-shaped structure welded by steel plates. The center of the top plate was provided with a hole as a channel for pushing the sample. The threaded pushing screw was concentric with the hole, and there were two screws on each side of the top surface of the base to fix the mold. A positioning ruler was provided on the inner side of the base, together with a locator fixed at the bottom of the pushing screw to form the positioning component, the driving wrench was fixed at the bottom of the pushing screw, and the pushing screw was moved up and down by twisting the driving wrench to push the sample. The sample mold was composed of upper general mold, lower general mold, upper inclined mold and lower inclined mold with an inner diameter of 39.1 mm. The device adopted the modular design, and it could control the inclination of filled fissures by changing the mold, which was simple and easy to operate.  
Figure 3: Sample preparation device for triaxial sample with filled fissure.
# Application of Artificial Intelligence in Wind Power Systems
## 3 Results
### Wind Turbine Maintenance
In the paper, models using extreme gradient boosting (XGBoost) and long short-term memory (LSTM) are proposed to predict the behavior of the characteristics of critical components of a wind turbine, and statistical process control (SPC) is used to evaluate their abnormal behavior. The proposed method is tested in practice. The approach is promising but requires an investigation of the sensitivity level of the deviation to perform a fault diagnosis by deducing which specific parts (subcomponents) of the main components will fail. In [80], the generator temperature and gearbox oil temperature in SCADA data were used to model the normal temperature of wind turbine components. The residual between the predicted and actual values was calculated and the trend was monitored using an exponentially weighted moving average (EWMA) control chart. [PERSON] et al. [81] presented the development of predictive maintenance solutions in a big data environment. The proposed approach aims to develop a predictive model generator for each monitored wind turbine that provides a dashboard with failure predictions every 10 min. The task of the maintenance manager is to understand the development of anomalies and make appropriate decisions. Using machine learning techniques, artificial intelligence can analyze data from turbine sensors to identify patterns that indicate potential failures before they occur. However, unattended components or subsystems can occasionally lead to failures. Therefore, in their research, [PERSON] and [PERSON] [82] analyzed and demonstrated the possibility of the practical application of artificial intelligence to predict failures in unattended components. A proactive maintenance approach helps to reduce unplanned downtime and extend the lifetime of turbines by enabling timely interventions. Predictive maintenance not only increases operational safety, but also contributes to cost savings by minimizing repair costs and optimizing maintenance schedules [83].
# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
## 3 Data Compression and Decompression  
### Adaptive Arithmetic Coding  
Arithmetic coding, like Huffman coding, is a probability-based lossless data compression technology. Huffman coding requires counting the frequencies of all the symbols in advance and building a Huffman tree on the basis of these frequencies. This means that the entire message must be scanned before encoding, which may be impractical for large files or real-time data streams. In addition, Huffman coding also has disadvantages such as limited compression efficiency and complex decoding. Arithmetic coding can approach the entropy limit of data, that is, the theoretical minimum compression size; at the same time, it is applicable to various symbol probability distributions, especially when the symbol probability is uneven. Therefore, arithmetic coding has the advantages of a high compression rate and strong adaptability. This article uses arithmetic coding to achieve profile data compression. Arithmetic coding achieves compression by representing the entire message as a real number interval. The range of this interval is between 0 and 1, and each symbol in the message is represented by gradually reducing the interval.  
On the basis of the different symbol probability estimation methods, arithmetic coding can be divided into static arithmetic coding and adaptive arithmetic coding [31]. Static arithmetic coding must perform statistics on the entire message before encoding to determine the probability distribution of each symbol. This requires additional time and computing resources, and this preprocessing step may be impractical, especially for large files or real-time data streams. In addition, static arithmetic coding is not applicable to large-scale data or real-time data streams. Therefore, this paper adopts adaptive arithmetic coding to realize source coding. Adaptive arithmetic coding (AAC) is a lossless compression algorithm that dynamically adjusts the probability distributions of symbols. During the encoding process, it does not require the precalculation of the probability distribution of symbols, but dynamically updates the probability distribution on the basis of the actual occurrence of the data stream [24].
## 3 Evaluation of the Energy Conversion Subsystem and Energy Storage Subsystem
### Conversion Process  
The starting step of all electric Power to X (P2X) conversion processes investigated is the generation of hydrogen, which is obtained by means of electrolysis of water (Section 3.1.1). Then storage takes place either as hydrogen (liquid or gaseous) (Section 3.1.2), or further conversion to ammonia (Section 3.1.3) or methanol (Section 3.1.4) using the Haber-Bosch process and methanol synthesis, respectively.--Hydrogen storage via physisorption, metal hydrides, and organic liquids containing molecules capable of reversibly releasing it are not investigated [18].--A point to focus on would be that the processes of conversion to ammonia and methanol are assumed to be seaworthy and remotely operable on a small scale. This scale of production is not usually seen in plants that produce these fuels commercially.
## 4 Materials and Methods
### Construction and Sequencing of Transcriptome, sRNA, and Degradome Libraries  
Transcriptome cDNA, sRNA, and degradome cDNA libraries were constructed and sequenced for flowers, young fruits, and ripe fruits. In which three biological replicates were used, and a total of three transcriptome libraries, three small RNA libraries, and three degradome libraries were produced and used in our analysis. When constructing the transcriptome cDNA library, poly-(A) mRNAs were isolated using oligo-(dT) beads, after which the cDNA library was prepared using an Illumina kit according to manufacturer's recommendations. The library was sequenced using the HiSeq(tm) 4000 platform (The Beijing Genomics Institute, ShenZhen, China). Regarding the sRNA library, 18-30 ntsRNA fragments were isolated using 15% denaturing polyacrylamide gel electrophoresis and then ligated to 3\({}^{\prime}\) and 5\({}^{\prime}\) adaptors for a subsequent conversion to cDNA by a reverse transcription polymerase chain reaction. The library was sequenced using the Genome Analyzer (The Beijing Genomics Institute, ShenZhen, China). To construct the degradome library, mRNA was used to anneal with biotinylated random primers, and then, strapavidin capture of RNA fragments was performed using biotinylated random primers. Finally, a 5\({}^{\prime}\) adaptor ligation was only performed to those RNAs containing 5\({}^{\prime}\)-monophosphates, followed with reverse transcription and PCR. The resulting library underwent single-end sequencing using the HiSeq(tm 4000 platform. The sequence data have been submitted to China National GenBank([[https://db.cngb.org/cnsa/](https://db.cngb.org/cnsa/)]([https://db.cngb.org/cnsa/](https://db.cngb.org/cnsa/))) under accession no. CNP0000007 for the transcriptome sequences, CNP0000008 for the sRNA sequences, and CNP0000009 for the degradome sequences.
## Appendix A The Doppler Shift
The Poynting vector would then be orthogonal to them everywhere in the beam. The higher the OAM order \(k\), the larger is the angle the \(k\) parallel phase threads make with the beam axis (and with its transverse footprint, where its phase gradient becomes steeper in proportion to \(k\)). That is why the transverse Doppler frequency is directly proportional to OAM order \(k\). It is this motion (also expressed in (5) of the main text) of the receiving antenna through this OAM phase gradient at the footprint, which the pseudo-Doppler technique attempts to emulate. It is necessary to emulate a very high transverse Doppler frequency \(F_{T}\), so that each OAM mode can be recovered without its spectrum overlapping those of the neighboring OAM modes when the modulated carrier wave has a high bandwidth, \(B\).
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
## 4 Inlet/Isolator Simulations
### Mesh and Boundary Conditions  
This paper aims to achieve three different comparisons: 2D RANS computations with different reconstruction schemes, 2D/3D RANS computations, and RANS/DES/LES computations. Therefore, three types of meshes are involved: a 2D structural mesh, a 3D structural mesh and a quasi-3D mesh (for DES and LES computations, the effects of the side wall of the inlet/isolator are not considered here, and the two sides are set as symmetry boundary conditions).  
The mesh of the 2D computational domain and the setting of boundary conditions are shown in Figure 4. Here, three sets of multi-block structural meshes with different sparsities are used, as shown in Table 1, which are all refined near the wall. The \(Y^{+}\) of the grid's first layer near the wall is set to 1, and the aspect ratio of the grid in the mainstream area is almost 1. To accelerate the computation, the fluid domain can be divided into many more blocks.  
The 3D computations must consider the influence of the inlet/isolator's side wall on the internal flow, which introduces a strong 3D effect. For simplifying the computations, only half of the inlet/isolator is selected by using symmetry boundary condition, as shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sparse Mach** & **Medium Mesh** & **Fine Mesh** \\ \hline \(75\times 576\) & \(150\times 1252\) & \(300\times 1916\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three sets of meshes with different sparsities.  
Figure 4: Mesh of the 2D computational domain.  
Figure 3: Configuration of the inlet/isolator: h = 15 mm, 1 = 79.3 mm.  
The quasi-3D computation mesh is the stretching result of the 2D mesh in the thickness direction. To reduce the computation costs as much as possible while ensuring the accuracy of the solution, the stretched thickness \(d\) is taken as \(h/3\). From experience, if \(d\) were set too small, the computation would diverge, as shown in Figure 6.  
The free stream flow parameters for the farfield conditions are shown in Table 2, and the corresponding turbulent values: turbulent kinetic energy \(k\) and specific dissipation rate \(\omega\) are determined by the specified turbulent intensity \(I\):  
\[k=1.5(I\cdot V)^{2},\quad\omega=k/(0.001\mu), \tag{22}\]  
where \(V\) is the velocity of the free stream, and \(\mu\) is the dynamic viscosity.
# Influence of Selected Model Parameters on the Electromagnetic Levitation Melting Efficiency
## 2 Materials and Methods
### Mathematical Model of the Electromagnetic Field  
Referring to previous points, the calculations were limited to the analysis of the electromagnetic field modeled in three-dimensional space. The analysis was carried out for the harmonic field, and the main results were represented as the volumetric density of the electrodynamic forces and the electrodynamic forces acting on the batch.  
The analysis of the electromagnetic field was based on Equations (2) and (3). The value of the vector potential acquired from (2) and (3), and the magnetic induction B (4) and current density J (5) were calculated. Finally, the volumetric density of the electrodynamic forces (6) and the forces acting on the batch F (7) were obtained. Active power inducted in the electromagnetic field was based on (8).  
\[\bigtriangledown^{2}\ A\ -\ \mathrm{j}\omega\sigma\mu\ \mathbf{A}\ =\sigma\mu\bigtriangledown V _{e}, \tag{2}\]  
\[\bigtriangledown\cdot\bigtriangledown V_{e}=0, \tag{3}\]  
\[\mathbf{B}\ =\bigtriangledown\times\ \mathbf{A}\, \tag{4}\]  
\[\mathbf{J}\ =-\ \mathrm{j}\omega\sigma\mu\ \mathbf{A}\, \tag{5}\]  
\[f_{EM}=\frac{1}{2}\mathrm{Re}(\ \mathbf{J}\ \times\mathbf{B}^{*}), \tag{6}\]  
\[F_{\mathrm{EM}}=\int\limits_{\mathrm{V}}f_{\mathrm{EM}}\ \mathrm{dV}, \tag{7}\]  
\[P_{\mathrm{EM}}=\int\limits_{\mathrm{V}}\frac{1}{2}\frac{|J|^{2}}{\sigma} \mathrm{dV}, \tag{8}\]  
where:  
* \(\mathbf{A}\)--magnetic vector potential,
* j--imaginary unit,
* \(\omega\)--angular frequency,
* \(\sigma\)--conductivity,
* \(\mu\)--magnetic permeability,  
Figure 4: Geometry model of inductor.  
* \(V_{e}\)--electric potential,
* \(B\)--magnetic flux density,
* \(J\)--current density,
* \(f_{EM}\)--electromagnetic force density,
* \(F_{EM}\)--electromagnetic force,
* \(P_{EM}\)--electromagnetic power.
# Dealing with Low Quality Images in Railway Obstacle Detection System
## 3 State-of-the-Art Techniques for Blur Detection
### Edge-Based Methods
It was shown that in the presence of noise, these detectors represent a slightly improved Laplacian operator, which is reduced using a maximum a posteriori estimate of edges. Moreover, it was shown in [36] that the Laplacian operator can even be successfully used for noise variance estimation. A comprehensive overview of several different image edge detection techniques, including Sobel, Robert's cross, Prewitt, and Laplacian of Gaussian operator, as well as the Canny edge-detection algorithm, applied in various conditions, is presented in [37]. The obtained experimental results favor the Canny edge detection algorithm among other operators in almost every scenario. A similar analysis of different edge detection techniques based on the gradient and Laplacian operators was performed in [38]. The [PERSON], Sobel and Prewitt edge-detection operators, Laplacian-based edge detector and Canny edge detector are applied in a shark fish classification problem in the MATLAB framework. A blur detection problem using the Laplacian operator and Open-CV library was considered in [39]. It was demonstrated that the proposed algorithm performs well with blur detection in the case of two types of images: receipts and products. The authors of [40] proposed the pre-processing techniques for the detection of blurred images (PET-DEBI) for the classification of blurred and non-blurred images. The Laplacian operator is used to calculate the image's variance, and experiments have shown that this method has a very high precision in BD problems. A comparative study of using the classical Laplacian operator and the modern convolutional neural network in the case of BD is in the main focus of [41]. It was demonstrated that the Laplacian method provides a satisfactory accuracy rate, but it should be noted that this method is limited in its capabilities. Taking the preceding statement into consideration, the domain for the application of this method should be carefully chosen.