esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### DC Power
Within the context of the results obtained from the site studied here, we conclude that the accuracy of bifacial PV simulations is not significantly different than monofacial PV simulations. Figure 9: Cumulative distribution functions of the modeling error observed in DC power. Results from four system types and six software are shown. These plots are generated using the residuals from Figure 8.  
Figure 10a shows the MAE calculated using two approaches: (1) without error weighting and (2) by weighting the error with the inverse uncertainty (1/U\({}_{\text{C}}\)) of the solar radiation measurements during each hour of energy production. In approach (2), the errors are weighted by either the _GHI_ uncertainty or DNI uncertainty, depending on the data used in the transposition step, and the sum of all error weights equals one. The rationale behind the 1/U\({}_{\text{C}}\) weighting in approach (2) is that uncertainty in solar radiation measurements directly impacts simulated PV power, which ought to be accounted for in error analyses. As expected, weighting the error by a factor of 1/U\({}_{\text{C}}\) reduces the MAE, but the reduction is small at about 20 W, or 0.2%.
# A Through-Transmission Ultrasonic Method for the Detection of Ferrite Tile Defects
## 4 Design and Experimentation of the Ultrasonic Transmission System
### Experimental Results
#### 4.3.2 Transmission Experiment of Ferrite Tiles  
The second set of experiments involved testing ferrite tile samples provided by the factory. Examples of ferrite tiles with internal defects and external defects are shown in Figure 10a,b respectively. The test group comprised a batch of ferrite tiles with both external and internal defects, alongside a batch of defect-free ferrite tiles. The defect types observed in the ferrite tiles were external cracks, external fractures, and internal cracks. Initially, transmission experiments were conducted on 54 defect-free ferrite tiles, and their ultrasonic transmission signals were collected. The threshold for defect-free ferrite tiles was calculated using the 3\(\sigma\) significance principle. The transmission energy of the tiles under inspection was measured and compared to the transmission energy of the defect-free ferrite tiles. Any ferrite tile with transmission energy below three times the standard deviation of the defect-free tiles was classified as defective.  
The transmission energy of 54 defect-free ferrite tiles was measured for calibration. During the experiment, the probe was aligned to the same position on the ferrite tiles to measure the ultrasonic transmission energy. The mean transmission energy of the defect-free ferrite tiles was determined as 148.7866, with a variance of 6.1653. Based on the 3\(\sigma\) significance principle in mathematics, the lower limit of the 99.73% confidence interval for the transmission energy of defect-free tiles was calculated as 130.2907. Any measurement result below this threshold was considered significantly deviated from the batch of samples, thus allowing for the calculation of whether the transmission energy of the tested samples was significantly lower than that of the defect-free ferrite tiles. Samples with energy below the threshold were classified as defective.  
The inspection group measured the transmission energy of 92 internal defective samples and 41 external defective samples using the ultrasonic transmission method. By comparing these measurements with the threshold determined from OK ferrite tiles, as  
Figure 10: Ferrite tile defect example: (**a**) External crack; (**b**) Internal defects captured by X-ray imaging.  
shown in Figure 11. The threshold values well separate the OK sample from the NG sample. The detection rate for internal defects reached 96.74%, while for external defects, it reached 95.12%.  
Both experiments conducted on aluminum alloy and ferrite tiles verified the feasibility of using ultrasonic transmission for full defect detection of both internal and external defects. The method exhibited high sensitivity in detecting defects in artificially controlled samples and showed good fitting with simulation results. Moreover, in real samples, it achieved high recall rates for samples containing both internal and external defects.
## 2 Materials and Methods
### Available Data  
The digital elevation model (DEM) derived from the Shuttle Topography Radar Mission (STRM3) [19], used to determine the elevation of the Earth's surface relative to sea level of the marshes in southern Iraq and create a hydrological analysis using Arc GIS 10.4.1. In this study, imagery from three satellites, Landsat 2, 8 and Sentinel 2, was used. Table 1[20; 21] shows the characteristics of each satellite. Figure 2 shows images used in the current study with the date of each.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline \multirow{3}{*}{**Satellite**} & & \multicolumn{2}{c|}{**Swath Area**} & \multirow{3}{*}{**L Launch and work period**} & \multirow{3}{*}{**Sensor**} \\  & & **Spectral Bands (\(\upmu\)m) and** & **(Km) and Temporal** & & \\  & & **Spatial Resolution (m)** & **Temporal Resolution** & & \\  & & **Resolution** & & \\ \hline Band 4 & 80 m Blue (0.5 to 0.6 \(\upmu\)m) & 170 km x & & MSS \\  & Band 5 & 80 m Green (0.6 to 0.7 \(\upmu\)m) & 185 km & 1975–1983 & (Multispectral \\  & Band 6 & 80 m Red (0.7 to 0.8 \(\upmu\)m) & 16 days & & Scanner \\  & Band 7 & 80 m NIR (0.8 to 1.1 \(\upmu\)m) & & & System) \\ Landsat-2 & Band 1 & 30 m Visible (0.43 to 0.45 \(\upmu\)m) & 170 km x & & \\  & Band 2 & 30 m Visible (0.53 to 0.59 \(\upmu\)m) & 185 km & & OLI \\  & Band 3 & 30 m Red (0.64 to 0.67 \(\upmu\)m) & 16 days & & (Operation \\  & Band 4 & 30 m Blue (0.5 to 0.6 \(\upmu\)m) & & & Land Imager) \\  & Band 5 & 30 m Blue (0.5 to 0.6 \(\upmu\)m) & & & \\  & Band 6 & 30 m SWIR (1.57–1.65 \(\upmu\)m) & & & \\  & Band 7 & 30 m SWIR (2.11–2.29 \(\upmu\)m) & & & \\  & Band 8 & 30 m Panchromatic (0.5–0.68 \(\upmu\)m) & & & \\  & Band 9 & 30 m Cirrus (1.36–1.38 \(\upmu\)m) & & & \\  & Band 10 & 100 m Thermal (10.6–11.19 \(\upmu\)m) & & & \\  & Band 11 & 100 m Thermal (11.5–12.51 \(\upmu\)m) & & & \\ Lansat-8 & Band 1 & 60 m Visible (0.433 to 0.453 \(\upmu\)m) & 2013–2015 & \\  & Band 2 & 10 m Blue (0.458 to 0.523 \(\upmu\)m) & 290 km x & & MS1 \\  & Band 3 & 10 m Green (0.543 to 0.573 \(\upmu\)m) & 290 km & & (Multispectral \\  & Band 4 & 10 m Red (0.650 to 0.680 \(\upmu\)m) & 10 days & Scanner \\  & Band 5 & 20 m Red Edge (0.698 to 0.713 \(\upmu\)m) & & System) \\  & Band 6 & 20 m Red Edge (0.733 to 0.748 \(\upmu\)m) & & \\  & Band 7 & 20 m Red Edge (0.765 to 0.785 \(\upmu\)m) & & \\  & Band 8 & 10 m NIR (0.930 to 0.950 \(\upmu\)m) & & \\  & Band 8A & 20 m Red Edge (0.855 to 0.875 \(\upmu\)m) & & \\ Sentinel-2 & Band 9 & 60 m NIR (0.930 to 0.950 \(\upmu\)m) & & \\  & Band 10 & 60 m Cirrus (1.365–1.385 \(\upmu\)m) & & \\  & Band 11 & 20 m SWIR (1.565–1.655 \(\upmu\)m) & & \\  & Band 12 & 20 m SWIR (2100–2.280 \(\upmu\)m) & & \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of Landsat 2, 8 and Sentinel 2 satellites [20; 21].  
Figure 2: _Cont._
## 2 Theoretical Background and Mathematical Models
### The Relationship between Fields and Lines According to Rusck Model  
[PERSON] proposed a cable tying model based on the currents and voltages distributed along the line. The model's equations relate the proposed transmission lines to the total electric field on the conductor surface, using scalar and vector potentials. The transmission line conditions determined by [PERSON] are expressed in Equation (24a,b), considering the geometry displayed in Figure 9 [29]:  
\[\frac{\mathrm{d}\mathrm{v}^{\varphi}}{\mathrm{d}\mathrm{x}}(\mathrm{x},\mathrm{ t})+l^{\prime}\frac{\mathrm{d}\mathrm{i}}{\mathrm{d}\mathrm{t}}(\mathrm{x}, \mathrm{t})=0 \tag{24a}\] \[\mathrm{c}^{\prime}\frac{\mathrm{d}\mathrm{v}^{\varphi}}{\mathrm{d}\mathrm{x}}( \mathrm{x},\mathrm{t})+\frac{\mathrm{d}\mathrm{i}}{\mathrm{d}\mathrm{t}}( \mathrm{x},\mathrm{t})=\mathrm{c}^{\prime}\frac{\mathrm{d}\varphi^{\prime}( \mathrm{x},\mathrm{h},\mathrm{t})}{\mathrm{d}\mathrm{t}} \tag{24b}\]  
The variables are as follows:  
\(\varphi^{\prime}\): the scalar potential.  
\(l^{\prime}\): the line inductance, (H).  
\(\mathrm{c}^{\prime}\): the line capacitance, (F).  
Figure 9: (**a**). A straight and vertical LEMP current is moving upwards (no load model). (**b**). The negative charge is equitably disseminated along the LEMP; sometime recently, the return strike starts to account for the strike–strike of the driving shoot (loaded model). (i(t) time dependent variation of current, q(t) time dependent variation of the load).  
The total voltage across the line is expressed as v (x, t) in Equation (25):  
\[\mathrm{v(x,t)}=\mathrm{v}^{\mathrm{\varphi}}(\mathrm{x,t})+\int_{0}^{\mathrm{h}} \frac{\partial\mathrm{A}_{\mathrm{z}}^{\mathrm{i}}(\mathrm{x,z,t})}{\partial \mathrm{t}}\mathrm{d}z \tag{25}\]  
Figure 7 illustrates [PERSON]'s transmission line demonstration. The [PERSON] model assumes that the scalar and vector potentials between the electric field, ground and line height are constant and equal to the potentials on the ground surface. [PERSON] obtained equations based on these assumptions, which are expressed in Equations (26) and (27):  
\[\mathrm{v(x,t)}=\frac{Z_{0}I_{0}\mathrm{h}\beta}{4\pi}\left[\begin{array}{c} \frac{\mathrm{ct-x}}{\mathrm{d}^{2}+\beta^{2}(\mathrm{ct-x})^{2}}\left(1+ \frac{\mathrm{x}+\beta^{2}(\mathrm{ct-x})}{\sqrt{(\beta ct)^{2}+(\mathrm{x}^{2} +\mathrm{d}^{2})/\Gamma^{2}}}\right)\\ \frac{\mathrm{ct+x}}{\mathrm{d}^{2}+\beta^{2}(\mathrm{ct+x})^{2}}\left(1+ \frac{\mathrm{x}+\beta^{2}(\mathrm{ct+x})}{\sqrt{(\beta ct)^{2}+(\mathrm{x}^{2} +\mathrm{d}^{2})/\Gamma^{2}}}\right)\end{array}\right] \tag{26}\]  
If (x = 0) is written to determine the voltage closest to the LEMP, Equation (27) is obtained:  
\[\mathrm{v(0,t)}=\frac{Z_{0}I_{0}\mathrm{h}}{4\pi\mathrm{d}^{2}}\frac{2\beta ct }{1+\left(\frac{\beta ct}{\mathrm{d}^{2}}\right)}\left(1+\beta^{2}\frac{ \frac{ct}{\mathrm{d}}}{\sqrt{1+\beta^{2}\left[\left(\frac{ct}{\mathrm{d}} \right)^{2}-1\right]}}\right) \tag{27}\]
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 2 Methodology  
### Framework Overview  
To address the insufficient consideration of cutterhead and target section constraints in current section formation planning methods, which hampers roadway formation quality, this study proposes a cutting trajectory planning method for roadway section formation in a boom-type roadheader based on an improved NSGA-II. The detailed framework is illustrated in Figure 1.  
Based on the roadway coordinate system, a kinematic model of the roadheader was established to associate the roadheader, target section, and roadway. A target section model and its constraints were developed, along with a simplified cutterhead model, to calculate the spatial coordinates of the cutterhead envelope, providing a theoretical basis for section formation cutting. On this basis, the cutting space satisfying kinematic constraints was designed by integrating the kinematic model and motion range under posture-driven conditions, and the feasible cutting point set for the current posture was generated.  
By analyzing and summarizing the section formation cutting process of the roadheader, the optimization of the cutting space point set was transformed into a multi-objective optimization problem, solved using an improved NSGA-II. First, optimization objective functions for cutting space points were constructed, adjacency constraints were designed, and an adjacency matrix was established. The adjacency matrix was then incorporated into the initialization and genetic operations of NSGA-II for adaptive improvements,enhancing optimization efficiency and enabling the rapid acquisition of Pareto-optimal solutions. The resulting high-quality cutting trajectories provide a data foundation for further development of section formation cutting control.
## 2 The Interacting Multiple Model Unscented Kalman Filter
### Model Interaction/Mixing  
For given states \(\mathbf{x}_{k-1}^{j}=\mathbf{x}_{k-1|k-1}^{j}\) with corresponding covariances \(\mathbf{P}_{k-1}^{j}=\mathbf{P}_{k-1|k-1}^{j}\) and mixing probabilities \(\mathbf{\mu}_{k-1|k-1}^{i|j}\) for every model, the initial condition for the model \(j\) is:  
\[\mathbf{\hat{x}}_{k-|ik-1}^{0}=\sum\limits_{i=1}^{r}\mathbf{\hat{x}}_{k-|k-1} ^{i}\mathbf{\mu}_{k-|ik-1}^{i,j}\;,\;j=1,2,...,r \tag{6}\]The covariance corresponding to the above is:  
\[\mathbf{P}_{k-1k-1}^{0j}=\sum\limits_{i=1}^{r}\mathbf{\mu}_{k-2k-1}^{i,j}\{ \mathbf{P}_{k-2k-1}^{i}+\{\hat{\mathbf{x}}_{k-2k-1}^{i}-\hat{\mathbf{x}}_{k-2k-1 }^{0j}|\hat{\mathbf{x}}_{k-2k-1}^{i}-\hat{\mathbf{x}}_{k-2k-1}^{0j}|}\hat{ \mathbf{x}}_{k-2k-1}^{i}-\hat{\mathbf{x}}_{k-2k-1}^{0j}\} \tag{7}\]  
The model transition probabilities, which are related to Markov chain, are defined as:  
\[\pi_{ij}=p\{M_{k}^{j}\mid M_{k-1}^{i}\}=\begin{bmatrix}\pi_{11}&\pi_{12}&...& \pi_{1j}\\ \pi_{21}&\pi_{22}&...&\pi_{2j}\\ \vdots&\vdots&\ddots&\vdots\\ \pi_{i1}&\pi_{i2}&...&\pi_{ij}\end{bmatrix} \tag{8}\]  
where \(i,j=1,2,...,r\), and \(r\) is the number of sub-models. Calculating the mixing probabilities with mode switching probability matrix \(\pi_{ij}\) and the Gaussian mixing probabilities are computed via the equations:  
\[\mathbf{\mu}_{k-1k-1}^{i,j}=\frac{1}{\mathbf{\bar{c}}_{j}}\pi_{ij}\mathbf{\mu }_{k-1}^{i} \tag{9}\]  
where \(\mathbf{\bar{c}}_{j}\) is a normalization factor:  
\[\mathbf{\bar{c}}_{j}=\sum\limits_{i=1}^{r}\pi_{ij}\mathbf{\mu}_{k-1}^{i} \tag{10}\]
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 5 Multi-Identity Full Homomorphic Encryption Scheme  
In Section 4, we proposed a modified IBE scheme suited solely for single-identity scenarios, where ciphertexts encrypted under different identities are unable to directly engage in homomorphic operations with one another. To overcome this limitation, in this section,we build upon the improved IBE scheme from Section 4 and apply the decomposition method [28] for ciphertext extension, resulting in an MIBFHE scheme that does not require fresh ciphertexts.We refer to this scheme as WZ-MIBFHE.
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 1 Introduction
It can be known that appropriately estimating the stiffness of the flat slab is important for evaluating the stiffness of the entire wall-slab joints for practical design. Additionally, considering an expectation to model the wall-slab joints as a two-dimensional frame model for a simpler calculation of the structure, several attempts to simplify the flat slab as a common beam component have been widely considered. The most common concept of these approaches is the \"effective beam width method (EBWM) \", in which the slab is modeled as a beam, so it is conveniently used for two-dimensional frame analysis [8; 9; 10; 11; 12; 13; 14; 15]. This paper presents a simplified prediction method for the initial stiffness of wall-slab joints in TWTS structures based on the EBWM concept. In this method, configurations of the joint and an estimated value of input lateral load are used. Data from an experimental test from the literature is employed to estimate the input lateral load at an assumed initial deformation angle based on the current code definition. Thus, the proposed effective slab width can be applied for initial stiffness estimation in lateral load analysis of the TWTS structures. This study aims to develop an effective and simplified model to evaluate the initial stiffness by estimating the effective proportion of the slab of the entire thick wall-thick slab joints under lateral force at the initial stage (namely the \"effective beam width\" consequently described in detail in the following sections) and provide necessary information  
Figure 3: Development of thick wall-thick slab structures. for a further understanding of the seismic performance of the critical components \"thick wall-thick slab joints\" in the TWTS structures.
## 2 Materials and Methods
### RH Vacuum Degassing Process  
The RH vacuum degasser consists of a vacuum chamber with two snorkels at the bottom, vacuum facilities consisting of Water Ring Pumps (WRPs) and steam ejectors and alloy hoppers for injecting alloy ores (Figure 1). Once the ladle containing the molten steel is located under the vacuum chamber, the chamber descends to submerge the snorkels into the melt. The circulation gas (typically argon) is introduced into one of the two snorkels through gas lines installed in a circumferential direction of the snorkels and at the same time, the pressure inside the vacuum chamber is finally lowered to 1 kPa or less by using vacuum facilities. Due to the buoyancy of the circulation gas and the low pressure inside the vacuum chamber, the molten steel in the ladle rises to the vacuum chamber through the snorkels into which the circulation gas is injected.  
In the vacuum chamber, impurities such as nitrogen, hydrogen and carbon present in the molten steel are removed in a gaseous state due to the difference of gas partial pressure between the atmosphere and the melt. The molten steel inside the vacuum chamber is returned to the ladle through the opposite snorkel and this circulation process is repeated until the impurities in the molten steel are lowered to the desired level. The carbon component combines with oxygen present in the molten steel and is emitted in the form of carbon monoxide (CO) or carbon dioxide (CO\({}_{2}\)). In some cases, the TOB installed on the top or side of the vacuum chamber can accelerate the decarbarurization reaction by supplying additional oxygen to the surface of the molten steel at high speed [15,36].  
The main decarbarurization reactions are as follows:  
\[\begin{split}[\text{C}]+\frac{1}{2}\{\text{O}_{2}\}& \longrightarrow\{\text{CO}\}\\ [\text{C}]+[\text{O}]&\longrightarrow\{\text{CO}\}\\ \{\text{CO}\}+\frac{1}{2}\{\text{O}_{2}\}& \longrightarrow\{\text{CO}_{2}\}\end{split} \tag{1}\]  
where the brackets and braces represent the dissolved and gaseous states of their corresponding components, respectively. In actual operations, operators use the CO and CO\({}_{2}\) concentrations contained in the off-gas as an auxiliary means to predict the decarbarurization rate in the molten steel.
# Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks
## 3 Methodology
### Enhanced Word Embedding Based on Word2 Vec  
#### 3.1.1 Word2 Vec  
Word2 Vec is a widely used static embedding method that encodes words as dense vectors in a continuous semantic space, such that semantically related terms are positioned nearby. It employs two distinct training mechanisms: Skip-gram and CBOW. The Skip-gram approach aims to infer context words from a given center word, whereas CBOW predicts the central term by aggregating information from its surrounding words. In this work, we utilize the CBOW variant to construct word embeddings.  
As shown in Figure 2, CBOW computes the probability of a center word \(w_{n}\) given its surrounding context words \(w_{c}\) based on the embeddings of multiple surrounding words \(w_{c}\). The objective function is formulated as in [7]:  
\[p(w_{n}|w_{c})=\frac{\exp(w_{n}h_{n})}{\sum_{w^{\prime}corpus}\exp(w^{\prime}h _{n})} \tag{1}\]  
where \(w_{c}\) denotes the context words, and \(h_{n}\) represents the average embedding vector of the surrounding context window \(w_{n}\). The model is trained to predict the center word based on its surrounding context.  
Figure 1: Overall architecture of the proposed model.  
Figure 2: The structure of CBOW.
## 2 Application of Neural Networks for Water Level Prediction
### Feed Forward Neural Network (FFNN)  
In FFNN, the input information passes through neurons and synaptic weights from input layer neurons to output layer neurons. FFNN uses labeled training data to understand the relationship between the input and output of the given application and accordingly adjust the hyperparameters of the NN. It follows an optimization algorithm named Back Propagation during the training process to converge the model. The prediction error (i.e., the difference between the NN output and the desired output) must be estimated at each epoch. The calculated error propagates backward through the NN, and weights and biases update according to the gradient of error. It uses an error function named Loss Function. The Loss Function can be used to estimate the loss of the NN model so that the weights and biases can be updated to reduce the loss in the next iteration (i.e., epoch). The loss function for the \(j\)th neuron at the output layer for \(n\)th iteration can be defined as Equation (1).  
\[L(n)_{j_{\theta}}=d_{j}-y(n)_{j} \tag{1}\]where \(\mathbf{d}_{j}\) is the desired output, \(\mathbf{y}(\mathbf{n})_{j}\) is the NN predicted output, and \(\mathbf{\theta}\) denotes the trainable parameters (i.e., hyperparameters) of the NN. A standard multi-layer FFNN is presented in Figure 1.
## 4 Discussion
Fourth, although we assessed other joints' pain via sumVAS, a radiographic evaluation was not performed. Moreover, the X-ray method used for assessment of tibiofemoral joints was not a standardized method for kOA. Despite these limitations, our study has several strengths. First, this is the first uC2C study in patients undergoing TKR surgery. Second, the cohort consisted of middle-aged people (mean age < 60 years), who could therefore generally be classified as rapidly progressive cases of kOA. Third, the study group included both men and women of the same age, allowing us to explain that the correlations we discovered were predominantly seen in female patients. While the majority of authors have studied patients of both sexes together, or only females, significant gender-related differences in OA have been overlooked. Fourth, we evaluated different aspects of quality of life after TKR: subjective assessment by different questionnaires (i.e., KOOS, SF-36), and objective status of knees by functional performance tests. Summarizing these few studies on changes in biomarkers after TKR surgery, both our study and others show that when patients are viewed as a group, the biomarker levels (e.g., COMP, Coll2-INO2, uC2C) do not decrease. Thus, in general, TKR does not stop the breakdown of collagen II and possible synovitis (as reflected by COMP levels). However, individual analysis of our results revealed that those patients with high pre-TRK secretion of uC2C achieved a significant reduction. It seems that such a phenomenon--as also described by [PERSON] et al. (2008) [22] for Coll2-INO2--is worthy of further investigation. If this phenomenon is confirmed, there may be an opportunity for a close grouping of patients undergoing TRK surgery. The same trend was observed for changes in the KOOSymp scale after surgery in women (OR > 9). The possibility of a more precise categorization of patients became apparent here. Here, it should be emphasized that, KOOS, SF-36, and lower extremity performance tests demonstrated only partial improvement of patients 12 months after TKR. This was particularly evident in terms of the restrictions on the use of the lower limbs for more demanding functions. Thus, a large proportion of these patients require continued rehabilitation--especially exercise therapy--12 months after surgery. Furthermore, it should be added that uC2C levels are also affected by other factors--in the present context, by radiological changes in the joints and their progression.