esa-sceva/satcom-chunk-collection huggingface dataset:
# Phase Risk Analysis of Overhead Lines Under Complex Icing Conditions
## 3 Finite Element-Based Analytical Model of Phase-to-Ground Line Spacing for Ice-Covered Lines
### Analytical Modeling of the Dynamic Response of Conductor De-Icing Bouncing  
This article uses the aforementioned calculation method for conductor sag to establish a three-dimensional finite element model of a five-section ground wire. The conductor is modeled using the Link 10 element, which is a 3D bar element that can only endure axial forces, either in tension or compression, and can be used to simulate cables or gaps. The insulator string is simplified as a bar and modeled with the Link 8 element, a bar element with properties suitable for large deformations and stress stiffening.  
Conductor de-icing involves complex energy conversion and equilibrium processes. When ice forms on a conductor, it stores additional gravitational potential energy and elastic potential energy [11]. As environmental conditions change such as temperature rise or other external forces, the ice may suddenly detach, releasing the stored elastic energy, which is transformed into kinetic and new potential energy of the conductor, causing the conductor to oscillate. The vibration gradually diminishes due to air resistance, wire self-damping, and insulator string inertia, until the conductor stabilizes, simplifying the process into a basic mechanical model [12]:  
\[\mathrm{M\ddot{v}}+\mathrm{C\dot{v}}+\mathrm{Kv}=\mathrm{F(a)} \tag{5}\]  
where M represents mass matrix; C denotes damping matrix, K signifies stiffness matrix; \(\mathrm{\ddot{v}}\) refers to acceleration; \(\mathrm{\dot{v}}\) represents velocity; \(\mathrm{v}\) is displacement; and F(a) denotes the external force vector.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Diameter/m** & \multicolumn{2}{c}{**Cross-Sectional**} & \multicolumn{1}{c}{**Weight/(kg/km)**} & \multicolumn{1}{c}{**Elastic Modulus/GPa**} \\ \hline LGJ-630/45 & \(33.6\times 10^{-3}\) & \(666.55\times 10^{-6}\) & 2060 & 63 \\ OPGW-185 & \(18.2\times 10^{-3}\) & \(184.38\times 10^{-6}\) & 1071 & 132 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground wire models.  
Figure 4: Calibration diagram of sag value.
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 5 Conclusions  
This paper analyzes the generation structure of GNSS spread spectrum codes and proposes a universal code generator structure that supports the IZ4 ranging code in the L1-SPS signal newly released by IRNSS in India. It enables the generation of all LSFR-based spread spectrum codes listed in Table 4. By segmenting the structure of the IZ4 code generator, reconstructing the tap selection, and comparing it with the previous universal spread spectrum code generator, this paper improves the utilization and compatibility of the IZ4 code generator. Due to the additional support for IZ4 signals, this paper proposes a more complex universal spread spectrum code generator compared to the previous version. However, it maintains compatibility with various GNSS signal spread spectrum code generation techniques. The proposed code generator can simultaneously generate multiple sets of GNSS signal spread spectrum codes, such as L5 signal data and pilot channel spread spectrum codes, or five groups of L1C/A signal spread spectrum codes, four groups of B3I signal spread spectrum codes, and other mode options. This significantly enhances hardware utilization efficiency.  
**Author Contributions:** Methodology, X.B.; Software, T.L.; Validation, T.L.; Formal analysis, T.L.; Investigation, T.L.; Data curation, T.L.; Writing--original draft, T.L.; Writing--review & editing, X.B.; Visualization, T.L.; Supervision, X.B.; Project administration, X.B., W.J., J.W., B.C., L.C. and K.L.; Funding acquisition, X.B., W.J., J.W., B.C., L.C. and K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is supported by the National Key R&D Program of China (2022 YFB4300500) National Natural Science Foundation of China (62027809, T222015, U2268206), the Talent Fund of Beijing Jiaotong University (No. 2022 XKRC003).  
**Data Availability Statement:** The data and the code will be available upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Methodology
### Feature Extraction Module
#### 3.3.2 CBAM Attention-Based CNN Feature Extraction
We have used the L2 regularization technique with hyperparameter \(\lambda\) = 0.0001 for each dense layer to minimize the network complexity and loss while training the model. Then, a dropout layer is applied to the output with a 0.5 dropout value to prevent overfitting during the model's training. Finally, the model's output layer has 36 units and a Softmax activation function. It produces probabilities for each of the 36 possible classes. Algorithm 1 summarizes the overall working of the proposed network. ```
Input: Image Dataset with size (128 \(\times\) 128 \(\times\) 3) Output: Sign language Gestures //HOG feature extraction \(F_{HOG}\) Step 1: Convert each image from the training dataset into grayscale images. Step 2: For each pixel of the image gradient is calculated (\(f_{x}\), \(f_{y}\)). Step 3: Divide input images into 8 \(\times\) 8 cells, and the corresponding histogram of each cell is computed. Step 4: Merge all histograms to obtain the HOG feature vector. Output: HOG feature vector \(F_{HOG}\) of dimension 1 \(\times\) 8100. //Attention-based CNN feature extraction \(F_{CNN}\). Step 1: CNN layers are defined including convolution, normalization, CBAM, and max pooling. Step 2: Flatten the last max pooling layer CNN output to 1-dimensional feature vector \(F_{CNN}\). Step 3: Concatenate the flattened CNN feature with HOG feature \(F_{Concat}=[F_{CNN},\ F_{HOG}]\). Step 4: The fully connected layer takes the merged feature as input. Step 5: The final output layer classifies 36 sign gestures with a Softmax activation function. ```  
**Algorithm 1**Proposed Algorithm for recognition of sign language gestures
## 2 The Building, Temporary Instrumentation and Methodology
### Nonparametric Method
#### 2.4.2 Correlations  
Correlation functions can acquire parameters to quantify how similar these signals are; these functions include autocorrelation, which analyzes how much a signal varies over time, and cross-correlation, which measures the similarity of two signals. The theoretical processes are as follows:  
\[R_{xx}(\tau)=\lim_{T\to\infty}\frac{1}{T}\int_{0}^{T}x(t)\;x(t+\tau)dt \tag{4}\]  
\[R_{xy}(\tau)=\lim_{T\to\infty}\frac{
# Spatial Sound Rendering Using Intensity Impulse Response and Cardioid Masking Function
## 2 Idea of the Proposed Method and General Algorithm
Generating a set of impulse responses for a specific channel, considering measurements realized within a particular source and receiver arrangement, involves the amplitude  
Figure 1: The concept of measuring a single component of sound intensity: the classical approach with two microphones simultaneously measuring a single stimulus (**a**) and the mechatronic approach with a single microphone and sequential measurement of identical stimuli in two consecutive positions (**b**). Figure 2: Sound pressure room impulse response. modulation of successive values in the pressure impulse response of a room using a temporal weighting function, which is generated using the directional information contained in the intensity response and a spatial masking function related to the position of the loudspeaker to emit a given channel signal in a given listening system. The shape of the masking function is arbitrary. In further studies, a cardioid function was used, which enabled comparative studies of the effects of the proposed method with those of the traditional method using cardioid microphones to record channel impulse responses. The implementation of the proposed method comes down to a universal algorithm, allowing it to be applied to any M-channel audio reproduction system. Following the individual steps of the proposed algorithm:  
1. Let us divide the spherical space into a finite number of \(M\) zones (by defining solid angles set for azimuth and elevation), to which a chosen spatially oriented masking function (\(MF\)) should be assigned. In proposed method the cardioid masking functions are used (Section 4). 2. Create \(M\) vectors of the resulting impulse responses and copy the omnidirectional pressure responses (P) into them. 3. Create \(M\) vectors of the temporal weighting function (\(TWF\)) based on the directional data (Sound Intensity response, \(SI\)) and subsequent masking functions (\(MF\)) from step 1 (Equation (1)): \[TWF_{i,m}=MF_{m}[\theta(SI_{i}),\varphi(SI_{i})],\] (1) where \(i=1\ldots N\) is a sample number (\(N\) is the vector length of the recorded impulse response), \(m=1\ldots M\) and is a spatially oriented masking function number, \(SI_{i}\)--3D sound intensity vector for the \(i\)th time sample, \(\theta\) and \(\varphi\)--azimuth and elevation angle of the \(SI_{i}\) vector. 4. Multiply sample-by-sample the vectors obtained in steps 2 and 3. 5. Perform the M-channel convolution of the input signal and calculated \(M\) impulse responses to render the spatial sound. Figure 3: The 2D intensity impulse response (time coded in color scale, from blue denoting the earliest samples to dark red denoting the latest samples).
## 2 Mechanism of Microbial-Induced Calcite Precipitation (MICP)
### Influencing Factors of MICP Reaction
#### 2.3.1 Temperature and PH Value  
Temperature is the key factor in the success of MICP technology on soil treatment [50]. A change in temperature will affect the growth of bacteria, the activity of microbial enzymes, the biodegradation of bacteria, and the process of binding precipitation, thus affecting the final curing effect. Figure 2a shows the changes of urease and growth activities of BP at different temperatures. An absorbance index at the 600 nm wavelength (OD600) was wildly applied to evaluate the density and growth activity of the BP solution. It found that the growth curve of BP was different when the temperature varied from low to high [51]. It is generally believed that the growth of BP is inhibited at low temperatures, while the urease activity of BP decreases at high temperatures [52; 53]. Therefore, the BP should be cultivated under a suitable growth environment (temperature).  
In addition to the temperature, urease activity of BP is an important factor. The decrease in enzyme activity will lead to an insufficient precipitation of CaCO\({}_{3}\). [PERSON] et al. [54] found that the suitable temperature for the growth of BP Sarcina was 30 \({}^{\circ}\)C, at which the strain propagated rapidly and produced high urease activity. When the temperature was less than 10 \({}^{\circ}\)C, the urease activity was almost lost. Furthermore, since the urease activity is affected by temperature, [PERSON] et al. [55] demonstrated that CaCO\({}_{3}\) crystals generated in soil samples had a good homogeneity by grouting at low temperature.  
Figure 1: Sketch map of microbial-induced carbonate deposition process on particle surface.  
PH value is one of the important influencing factors on microbial life activities. Its influence is mainly reflected in three aspects: First, it affects the biological activity of macromolecular substances (such as proteins and nucleic acids) by changing their charge. Second, it reduces the absorption and utilization of nutrients by microorganisms by changing the charge of cell membrane. Third, it also reduces the utilization effectiveness of nutrients in the living environment of microorganisms and enhances the toxicity of harmful substances [56]. Figure 2b shows the changes of urease activity with the increasing PH value.
# Squill: Testing DBMS with Correctness Feedback and Accurate Instantiation
## 3 Design of Squill
### Semantics-Aware Instantiation  
We design an instantiation algorithm to address the challenge of correct semantics generation for SQL statements with nested structures. In instantiation, while traversing AST nodes in the order of SQL statements, we parse nodes according to the node's type and context information (such as the type of parent and adjacent nodes). In this way, we can distinguish nodes of the same type but at different levels, as their context information is different. For example, the type of the parent node of a main SELECT statement and a subquery is distinct. With this information, we can construct a series of detailed constraints on nodes based on prior knowledge (the relationship between semantics in SQL statements) and then fill them with semantics correctly according to these constraints. For example, for a table name node in a CREATE statement, we can know whether it comes from a CREATE TABLE statement or a CREATE TRIGGER statement according to the context information when parsing it. For the former, we will fill it with a newly generated unique table name. For the latter, we will randomly assign a table name to it from the currently existing table name (created in the previous SQL statement).  
We divide semantics into simple and complicated semantics, depending on the complexity of constraints. When traversing to a node, if we can instantly assign semantics to it without error, we call the semantics that the node has as simple semantics. The dependency constraints of nodes with such semantics are relatively simple, usually across statements. For example, the table name dropped in the DROP statement is from tables created in the previous CREATE statements. When filling a DROP statement with semantics, the previous CREATE statement has been traversed and instantiated. At this point, the existing table names are determined, which can be instantly assigned to the table name node in the DROP statement. When traversing to a node, if we cannot instantly assign semantics to it but need to wait until the entire SQL statement is parsed and fill it with consideration of the semantics of other nodes, we call the semantics that the node has as complicated semantics. For example, the column name in a SELECT clause depends on one of the tables in the FROM clause, which means that the former should be a column of the latter, and we need to instantiate the latter before the former.
## 6 Quality Analysis of the Reconstructed Image
### Review and Analysis of Objective Measurement Results
#### 6.2.4 Objective Quality of the Test Image Pills  
Figure 17 represents all the reconstructed images of image _pills_. Due to the high level of detail, i.e., the high frequencies in the image, it is very difficult to detect errors in the images at low compression ratios (0.8 and 0.4 bpp). The quality of JPEG images deteriorates rapidly as the compression rate increases. JPEG 2000 and EEDC-BSSP go head-to-head at high compression rates, and, at the highest compression rate (0.05 bpp), JPEG 2000 loses its integrity, while EEDC-BSSP managed to preserve the shapes. The results are consistent with the quality of the reconstructed images quality Tables 18-20.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PSNR**} & \multicolumn{3}{c}{**SSIM**} & \multicolumn{3}{c}{**MS-SSIM**} \\
**bpp** & **JPEG** & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** \\  & & **2000** & **BSSP** & & **2000** & **BSSP** & & **2000** & **BSSP** & **2000** & **BSSP** \\ \hline
0.8 & 36.22 & 38.98 & 35.79 & 0.95 & 0.97 & 0.94 & 0.99 & 0.99 & 0.99 \\
0.4 & 31.98 & 34.55 & 32.83 & 0.89 & 0.93 & 0.91 & 0.98 & 0.99 & 0.98 \\
0.2 & 26.96 & 31.01 & 30.48 & 0.76 & 0.88 & 0.87 & 0.91 & 0.97 & 0.97 \\
0.1 & 23.81 & 26.66 & 27.05 & 0.68 & 0.79 & 0.80 & 0.85 & 0.92 & 0.93 \\
0.05 & / & 22.28 & 23.35 & / & 0.66 & 0.73 & / & 0.78 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Results of the PSNR, SSIM, and MS-SSIM metrics for image _mask_.  
Figure 17: Comparative overview of reconstructed images for image _pills_ ranging from 0.8 bpp (**top row**) to 0.05 bpp (**bottom row**). Compression methods: (**a**) JPEG, (**b**) JPEG 2000, and (**c**) EEDC-BSSP.  
each image, the observers provide a rating on the image distortion compared to the original image. The ratings can vary from 1 to 5, with 1 representing complete distortion of the image and 5 representing imperceptible distortion. The ratings are provided in Table 21.  
After image grading, the average grade for all images is calculated and that grade is the _Mean Opinion Score--MOS_:  
\[MOS=\sum_{i=1}^{5}i\cdot p(i) \tag{32}\]  
where \(p(i)\) is a share of grade \(i\) in the total number of ratings. The double stimulus impairment scale provides stable results for the small amount of deterioration caused by changes in the level of distortion.  
In double stimulus, the continuous quality scale method original image is first shown in the original image, then in the same image or a deteriorated image. The observer rates the quality of the second image. The evaluation of the reference image and the test image are performed using descriptive scores via Table 22.  
For optimal conditions in the determining subjective quality, it is necessary to provide the same screen, distance, and viewing angle for all observers.
# Applications of Natural Language Processing Tools in Orthopaedic Surgery: A Scoping Review
## 3 Results
### Use in the Detection of Adverse Events  
Six studies explored the use of NLP for the detection of adverse events related to orthopaedic surgery. [PERSON] et al. compared easy-to-access traditional machine learning NLP (ML-NLP) to two newly developed deep learning NLP (DL-NLP) models for retrospective detection of hip dislocations post total hip arthroplasty [4]. The models were used on radiology reports and telephone consult notes. DL-NLP models were highly accurate and performed better than ML-NLP. In particular, DL-NLP models were able to better interpret longer phrases, i.e., \"no fracture or dislocation\" as opposed to \"no dislocation\" and they were able to better delineate a dislocated hip from other displaced or dislocated joints or bones. Their convolutional neural network proposed DL-NLP model was the best performing with a kappa of 0.97 (for radiology reports) and 1.00 (for telephone consult notes). Finally, [PERSON] et al. also showed that 25% of dislocated hip patients identified with NLP did not have valid ICD codes, which infers the potential missed data that occur with traditional coders reviewing documentation [4].  
[PERSON] et al. applied NLP models to extract data from operative reports, consultation notes, microbiology and pathology reports related to periprosthetic joint infections (PIJ) [5]. The NLP algorithm identified PJI based on the Musculoskeletal Infection Society criteria with an F1 score of 0.911 [5]. [PERSON] et al. also identified that NLP models were able to correctly perform surgical site infection surveillance, based on their ability to extract information from administrative and medical records [6]. Compared to the gold standard, manual data abstraction, the NLP models precisely identified 97% of surgical site infections. Similarly, [PERSON] et al. identified that NLP was more accurate in identifying postoperative wound infection requiring reoperation post lumbar disectomy compared to ICD codes [7]. They showed that NLP detected 15 out of 16 cases (sensitivity of 0.94) compared to ICD codes, which detected 12 out of 16 (sensitivity 0.75).  
Further, NLP was able to successfully extract data regarding intraoperative vascular injury in anterior lumbar spine surgery, identifying 18 out of 21 cases compared to ICD codes, which identified 6 of 21, with a sensitivity of 0.86 compared to 0.29, respectively [8]. Finally, NLP algorithms were found to be reproducible in geographically diverse populations for the identification of incidental durotomy post-spine surgery [9]. The same algorithm achieved an area under the curve receiver operating characteristic (AUC-ROC)  
Figure 4: Country of publication of included studies.  
ranging between 0.95 and 0.99 in three separate geographically diverse cohorts (Australia, Massachusetts, Maryland).
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 2 Theoretical Conceptualization
### Convolutional AutoEncoder  
Convolutional Neural Networks (CNNs) are specially tailored for grid-structured data, including time series and images, by replacing at least one layer's matrix multiplication with convolution [35; 38]. A Convolutional AutoEncoder applies this principle to encode and decode data, achieving success not only in image-based tasks but also in time series analysis [27]. Through 1D convolutional layers and max-pooling (Conv + Max), the encoder extracts and compresses relevant features; flattened and dense layers (Flat + Dense) then prepare the data for reconstruction. The decoder reverses this procedure using upsampling and convolutions (Up + Conv) to restore the original data. Feature extraction in CAEs is performed through 1D convolutions applied to each input channel, and max-pooling preserves the most informative responses while reducing dimensionality [27; 39]. Figure 3 provides a schematic representation of a CAE network, illustrating its encoding and decoding processes.  
Like other AutoEncoders, CAE training iteratively minimizes the discrepancy between the reconstructed and original data. The Mean Absolute Error (MAE) is commonly used as the loss function to quantify this difference. As expressed in Equation (5), the MAE calculates the mean of the absolute differences across all samples in the dataset. As highlighted by [PERSON] et al. (2024) [27] and [PERSON] et al. (2020) [40], the MAE has been widely employed in CAE applications, demonstrating its effectiveness in capturing complex patterns within the data.  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|y_{i}-x_{i}| \tag{5}\]  
Figure 2: Example of a VAE network structure.  
To conclude the AE background, Table 1 provides an overview of the characteristics that distinguish the three AutoEncoder models, allowing for a direct comparison.