esa-sceva/satcom-chunk-collection huggingface dataset:
# Gravitational Particle Production and the Hubble Tension
## 4 Gravitational Particle Production after Decoupling
### Gravitational Particle Production  
Now we use the matching conditions for the mode functions and their derivatives at boundaries at the time \(\eta\) with \(\eta_{f}-\Delta\eta\;<\;\eta\;<\;\eta_{f}\), namely,  
\[v_{k}^{(in)}(\eta) = \alpha_{k}\,v_{k}^{(out)}(\eta)\,+\,\beta_{k}\,v_{k}^{(out)*}(\eta) \tag{39}\] \[v_{k}^{(in)\prime}(\eta) = \alpha_{k}\,v_{k}^{(out)\prime}(\eta)\,+\,\beta_{k}\,v_{k}^{(out)* \prime}(\eta) \tag{40}\]  
to determine \(\beta_{k}\) (since \(|\beta_{k}|^{2}\) is the number density of the gravitationally produced particles with momentum \(\vec{k}\)). Note that \(v_{k}^{(in)}(\eta)\) in Equations (39) and (40) is its value in the \(out\) region. In this region, the form of \(v_{k}^{(in)}(\eta)\) is given by Equation (30), while that of \(v_{k}^{(out)}(\eta)\) is given by Equation (37).  
By Equation (37),  
\[\alpha_{k}\,v_{k}^{(out)}(\eta)\,+\,\beta_{k}\,v_{k}^{(out)*}(\eta) = \frac{1}{\sqrt{\omega_{k}\big{(}\eta_{f}\big{)}}}\,\Big{[}\alpha _{k}\,e^{i\big{[}\omega_{k}\big{(}\eta_{f}\big{)}\,\eta\big{]}}\,+\,\beta_{k}\, e^{-i\big{[}\omega_{k}\big{(}\eta_{f}\big{)}\,\eta\big{]}}\,\Big{]} \tag{41}\] \[\alpha_{k}\,v_{k}^{(out)\prime}(\eta)\,+\,\beta_{k}\,v_{k}^{(out)* \prime}(\eta) = i\sqrt{\omega_{k}\big{(}\eta_{f}\big{)}}\,\Big{[}\alpha_{k}\,e^{ i\big{[}\omega_{k}\big{(}\eta_{f}\big{)}\,\eta\big{]}}\,-\,\beta_{k}\,e^{-i\big{[} \omega_{k}\big{(}\eta_{f}\big{)}\,\eta\big{]}}\,\Big{]} \tag{42}\]  
In the following, we will let \(\eta_{f}=\eta\) since we consider generic \(\eta_{f}\), i.e., we may replace \(\eta_{f}\) in Equations (41) and (42) by \(\eta\) provided that \(\eta\) is in a sufficiently small interval \(\Delta\eta\).  
Hence, after using Equation (30) for \(v_{k}^{(in)}(\eta)\) and making use of Equations (39)-(42), we find  
\[|\beta_{k}|^{2}\,=\,\frac{\Big{(}\frac{m_{\phi}\,c}{\hbar}\Big{)}^{4}\,a^{2} \,a^{\prime\,2}}{16c^{2}\Big{[}\vec{k}^{2}\,+\,\Big{(}\frac{m_{\phi}\,c}{ \hbar}\Big{)}^{2}\,a^{2}\Big{]}^{3}} \tag{43}\]  
Thus,  
\[\bar{n}\,=\,\frac{1}{(2\pi)^{3}}\beta\int\,d^{3}k\,|\beta_{k}|^{2}\,=\,\frac{ 1}{512\,\pi}\Big{(}\frac{m_{\phi}\,c}{\hbar}\Big{)}\,a^{3}\,\bigg{(}\frac{H}{ c}\bigg{)}^{2} \tag{44}\]  
where \(\bar{n}\) is the number density of gravitationally produced particles in the comoving coordinates. Note that \(\vec{k}\) is the wave number vector in comoving coordinates (rather than the physical wave number vector \(\frac{1}{a}\vec{k}\)). Hence, the physical number density is  
\[n\,=\,\frac{\bar{n}}{a^{3}}\,=\,\frac{1}{512\,\pi}\Big{(}\frac{m_{\phi}\,c}{ \hbar}\Big{)}\,\bigg{(}\frac{H}{c}\bigg{)}^{2} \tag{45}\]
## 1 Introduction
The authors have already presented initial results on single scatterers in the conference papers [26; 27]. Here, we present the formulation in details, including the analysis of the structure of the matrices associated with the numerical formulation. In addition, the approach is here applied to analyze multiple scatterers, in case-studies of practical interest in the field of electromagnetic compatibility (EMC). Specifically, we efficiently evaluate the high-frequency near-field distribution, i.e., the solution in close proximity to the wire boundaries. When arbitrary geometries are chosen for the boundary, the DtN operator can only be computed numerically, whereas it can be given in analytical form on canonical geometries such as the circle used in this paper. The numerical evaluation of the DtN may require a computational cost that severely limits its advantage. It is worth noting that the DtN only depends on the shape of the boundary but is not related to the shape of the scatterers: Therefore, scatterers of arbitrary shapes may be modeled by using the same DtN operator. The flexibility of the FEM is here combined with the efficiency of the DtN boundary conditions. In addition, thanks to the analytical form of the DtN for a circular boundary, the accuracy of the solution may be easily controlled by truncating the series at an appropriate term. In addition, compared to existing approaches such as the boundary element method (BEM), where the DtN operator is implicitly imposed through an integral equation, the novelty of the approach proposed here resides in the use of an analytic form in terms of a series expansion. The paper is organized as follows. Section 2 briefly recalls the formulation of the scattering problem in terms of Helmholtz equations. Then, the exact DtN operator is derived for a circular boundary and the numerical formulation of the problem is provided. In Section 3, first a benchmark problem is analyzed, to assess the method and evaluate the accuracy. Then the proposed technique is adopted to derive the high-frequency near-field distribution of the electrical field in a three-wire bundle configuration, under the action of an external field. The cases of wires in air or embedded in a dielectric are analyzed. A quantitative comparison between the DtN approach and the classical PML approach is provided.
## 3 Research Methodology
### Measurements  
The generated SQL queries were reviewed to assess their correctness based on predefined criteria, termed _execution accuracy_ (\(EA\)). \(EA\) measures the proportion of queries that successfully achieve the expected output when executed against the database. Execution accuracy (EA) in our study was defined as the comparison between the result set produced by the LLM-generated SQL query and the ground-truth query's result set. This measure is objective in that it strictly checks whether the generated query returns the same output as the expected query when executed against the database. A query is considered correct under \(EA\) if it satisfies all the following conditions:  
* Syntactic validity: The generated SQL query must be syntactically correct and executable in an SQLite environment without errors.
* Semantic correctness: The query must return results that match the expected output for the given natural language query.
* Alignment with constraints: The query must adhere to the specified constraints, such as avoiding placeholders or using SQLite-compatible functions.
* Efficiency: While not the primary criterion, the query should avoid unnecessary complexity and redundancy where possible.  
While query efficiency in terms of query performance (e.g., execution time and resource usage) could be a qualitative measure, it was not included as a criterion in our EA metric. Our evaluation strictly compared result sets for correctness, avoiding subjectivity.  
The accuracy was then calculated as the proportion of queries meeting all these \(EA\) criteria out of the total number of queries. The variation in accuracy between the five repetitions of each query was also measured, providing information on the consistency of the model performance.  
For cost evaluation, the following factors were considered for each model:  
1. Input tokens: The number of tokens in the input prompt, including the query, schema information, instructions, and other contextual details.
2. Output tokens: The number of tokens generated by the model as part of the SQL output.
3. Pricing structure: The pricing structure specific to each model is typically defined as a cost per input and output token.  
The total cost per query was calculated as the sum of the expenses associated with input and output tokens. This approach enabled a detailed comparison of each model's cost-efficiency, complementing the accuracy and consistency evaluations.
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 2 Proposed Method
### Feature Extraction of Pictorial Regions
#### 2.3.1 Texture Features of Pictorial Regions in the Spatial Domain
Since the first-order LDP, namely the LBP, does not consider directional information, the first-order LDP has only one histogram. The second- and third-order LDPs are calculated from four directions so they generate four histograms, respectively. Thus, 90 quality-aware texture features in the spatial domain are generated in the proposed method. Figure 7 shows the examples of the second-order LDP histograms along the direction \(0^{\circ}\). From Figure 7, we can observe that pictorial regions of degraded SCIs with different distortion types can generate different LDP histograms. Consequently, the LDP histograms are discriminative in identifying the distortion types. In this work, the entire LDP features F\({}_{\text{P}}^{\text{LDP}}\) of pictorial regions in the spatial domain are obtained as follows:  
\[\text{F}_{\text{P}}^{\text{LDP}}=\Big{[}\text{H}_{n,\theta,k}^{\text{LDP}}; \ n=1,2,3;\ \theta=0^{\circ},45^{\circ},90^{\circ},135^{\circ};\ k=1,2,\ldots,10\ \Big{]} \tag{12}\]
# Current Status and Future Directions for Combined Tillage and Sowing Technology and Equipment
## 2 Status of Development of Combined Tillage and Sowing Machines
### Ridging Technology and Equipment  
Ridging refers to using relevant machinery through traction flows in field travel to draw many parallel furrows; adjacent furrows form furrows to achieve soil loosening and enhance permeability, photosynthetic efficiency, and drought and flood prevention. After this operation, the soil is less likely to be compacted, which effectively improves its quality and thus promotes increased production and income [55; 56; 57; 58].  
Existing ridging equipment is divided by ridging type and furrowing type according to the principle of ridging. CUCCHI has developed the AS2 furrowing and ridging machine, which is capable of forming ridges on the surface of the ground at a height of 30-70 cm, and by means of a flexible and easy-to-adjust mechanical structure, different shapes and sizes of ridges can be formed, which is suitable for planting asparagus, strawberries, and other operations. COSMECO presents the B10 furrow starter (Figure 8), designed as a compact structure for furrowing operations inside greenhouses, which is capable of forming ridges on harder soils and can be paired with a wide range of implements for different operations [59; 60]. CELLI has introduced the ARES Stone Burier (Figure 9) to enhance the quality of the ridge through the secondary fine crushing of the soil through the soil-crushing knife roller behind the rotary knife roller. The machine is suitable for clay soil work and is highly adaptable. [PERSON] et al. designed a narrow-row-dense-planting precision planter for American Ginseng with the function of ridging, realizing ridging, furrowing, sowing, and mulching in a single operation, which promotes the development of ridging technology in the direction of combined operation [61; 62].  
At the current stage, ridging technology can only be applied in the production of specific crops or specific environments. Many combined seeders with ridging functions have poor operation effects or are unable to operate in the production of a wider range of crops. Subsequent research needs to further enhance the adaptability of relevant machinery or conduct structural innovations to improve the multifunctionality of ridging machinery,  
Figure 8: B10 furrowing and ridging machine.  
Figure 9: ARES Stone Burier.  
aiming for high-efficiency compounding, energy reduction, resistance reduction, and intelligent precision, and explore the operation laws of different forms and sequences to promote the transformation from single ridging equipment to multifunctional composite ridging equipment.
# Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby-Mori-Tanaka Theory
## 4 Study on Modified Soil Mix Proportions with Black Shale
### Analysis of Results  
#### 4.2.1 Compaction Characteristics of Black Shale Modified Soil  
Figure 8 shows the relationship between the dry density, water absorption rate, and ratio of black shale content in the black shale modified soil. The dry density of specimens with black shale content of 40%, 55%, and 70% fluctuates around 1.90 g/cm\({}^{3}\), 1.98 g/cm\({}^{3}\), and 2.07 g/cm\({}^{3}\), respectively. The greater the content of black shale, the larger the fluctuation range. This is consistent with samples with a high content of gravel, where the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name of** & **Crushed** & & **Cement-Soil** & **Dry** & **Water** & **Absorption** & **Strength** \\
**Composite** & **Stone** & **Cement-Soil** & **Content** & **Density** & **Abo** & **\(\omega\)n/\%** & **[\(\sigma\)]/MPa** \\
**Material** & **Content R/\%** & & **n/\%** & **p/g-cm\({}^{3}\)** & **\(\omega\)n/\%** & **[\(\sigma\)]/MPa** \\ \hline Y4S10 & & & S10 & 6.00 & 1.880 & 2.68 & 0.85 \\ Y4S15 & & & S15 & 9.00 & 1.882 & 2.63 & 1.63 \\ Y4S20 & 40 & S20 & 12.0 & 1.905 & 1.75 & 1.83 \\ Y4S25 & & & S25 & 15.0 & 1.892 & 2.33 & 2.07 \\ Y4S30 & & & S30 & 18.0 & 1.926 & 1.72 & 2.70 \\ \hline Y5S10 & & & S10 & 4.50 & 1.977 & 2.72 & 0.61 \\ Y5S15 & & & S15 & 6.75 & 1.961 & 3.10 & 1.00 \\ Y5S20 & 55 & S20 & 9.00 & 2.004 & 2.09 & 1.27 \\ Y5S25 & & & S25 & 11.25 & 1.975 & 3.06 & 1.83 \\ Y5S30 & & & S30 & 13.50 & 1.988 & 2.87 & 2.02 \\ \hline Y7S10 & & & S10 & 3.00 & 2.044 & 3.23 & 0.27 \\ Y7S15 & & S15 & 4.50 & 2.102 & 1.68 & 0.97 \\ Y7S20 & 70 & S20 & 6.00 & 2.031 & 2.99 & 1.01 \\ Y7S25 & & S25 & 7.50 & 2.093 & 2.40 & 1.18 \\ Y7S30 & & S30 & 9.00 & 2.070 & 2.75 & 1.28 \\ \hline \hline \end{tabular} Note.: (1) \(R_{\rm 40}-\)The blending amount of black shale is 40%; (2) \(\eta\) = M\({}_{\rm cement}\);M\({}_{\rm total}\); (3) \(\omega\)n = (M\({}_{\rm before}\) specimen curing, - M\({}_{\rm after}\) specimen curing)/M\({}_{\rm before}\) specimen curing; (4) Y5S10\(-\)a composite material composed of 55% black shale and S10 cement-soil.  
\end{table}
Table 2: Test results of the experiments on black shale modified soil.  
Figure 7: Test procedure the experiments on black shale modified soil.  
proportion of coarse particles is relatively high, leading to a more complex internal contact and greater discreteness.  
The dry density of modified soil shows a positive correlation with the amount of black shale added, as the density of black shale is higher than that of cement-soil. The water absorption characteristics of specimens with different black shale content are relatively similar, with an overall average water absorption rate of approximately 2.53%. Under the same black shale content, the trend of the water absorption rate curve is exactly opposite to that of the dry density curve, indicating that samples with higher density have a more compact internal structure, making it more resistant to water infiltration.
## 4 Experiments
### Evaluation Metrics  
1. Mean-Squared Error (MSE): MSE measures the average squared difference between predicted and actual values, emphasizing larger errors and assessing model accuracy in regression tasks.
2. Mean Absolute Error (MAE): MAE is another metric for assessing regression models. Unlike MSE, MAE is less sensitive to outliers, providing a more robust measure of model accuracy.
3. Accuracy (ACC): It represents the proportion of correctly classified instances out of the total number of instances.
## 3 Experimental Tasks, Methods and Materials
### Task III: Comparison of Tests Results with Qatar's Construction Specifications (QCS 2014)
#### 3.3.2 TBM Muck as Fill Material (Earthworks) under Buildings  
Section 12, Part 03 of Qatar Construction Specifications (QCS 2014) [18] describes the acceptable materials for use in earthworks and as a fill material under buildings. To be accepted as a fill material under buildings, TBM muck should meet the requirements of the following tests: rock particles exceeding 75 mm (ASTM D6913) [29], material passing 0.075 sieve (ASTM D1140) [30], liquid limit (BSEN 1377-2) [31], plasticity index (BSEN 1377-2) [31], organic matter content (BSEN 1377-3) [32], water-soluble chloride (BSEN 1377-3) [32], water-soluble sulphate (BSEN 1377-3) [32], acid-soluble chloride (BSEN 1377-3) [32] and acid-soluble sulphate (BSEN 1377-3) [32]. Three random muck samples were subjected to the aforementioned tests, and the average values were compared to the specifications limits.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Types of Accelerometers
#### 1.3.2. Capacitive Spring-Mass System Base  
A capacitive accelerometer detects acceleration by measuring the displacement of a proof mass relative to its housing. Capacitive accelerometers are ideal for monitoring large structures due to their ability to measure a wide frequency range, including static acceleration, while providing greater stability, sensitivity, and resolution than piezoresistive accelerometers. However, they are highly sensitive to fluctuations in temperature and humidity, making them considerably more fragile than piezoelectric ones [5; 6].
# Topological Navigation and Localization in
## 3 Topological Navigation and Localization for LHD
### Self-Localization
#### 3.3.2 Localization Inside Intersection Nodes  
Inside intersections, the LHD's pose is defined as its 2D position and orientation. Considering that intersections are small, of just a few meters, the wheel's odometry, and the results of the ICP matching between the LIDAR point cloud and the LIDAR-landmark that describes the intersection node are used directly to update the LHD pose.
## 1 Introduction
### BNCT-Spect  
A method that applies Single Photon Emission Computed Tomography (SPECT) technology has been proposed. SPECT involves injecting a gamma ray source into the body, detecting the gamma rays emitted from it, and then reconstructing a three-dimensional image by calculating the gamma ray emission intensity distribution within the body based on measured data. In BNCT, the excited states of \({}^{7}\)Li generated during treatment immediately emit 478-keV gamma rays. The detection of these gamma rays enables the SPECT-based image reconstruction. The device used for this purpose is referred to as BNCT-SPECT [7].  
Because of the need to measure gamma rays which are emitted instantaneously, BNCT-SPECT measurements are conducted simultaneously with the treatment. This introduces two considerable challenges to BNCT-SPECT. The first is that the projection time is approximately equal to the irradiation time, which is approximately one hour, resulting in insufficient count data. According to a preliminary study from the authors' laboratory, the net count rate per hour was reported as 3200 counts, with a background count of approximately 13,100 counts. The design of the detector and collimator has been determined based on these findings. The second challenge is the limitation of the projection angle [8]. Existing Fourier-based image reconstruction methods in SPECT are applicable when the projection angle is 180 degrees or more [9]. However, owing to the design constraints of the equipment used in BNCT-SPECT, which requires the treatment device to be in close contact with the neutron-exiting walls, there are stringent limitations on the projection angle. Currently, it is predicted to be limited to approximately 90 degrees. For these reasons, BNCT-SPECT requires its image reconstruction to be carried out under more stringent constraints compared to conventional SPECT, a challenge that many researchers are actively seeking to address [10].  
This aim of this study was to investigate image reconstruction methods using limited view-angle projection data. Specifically, multiple detectors were arranged in a linear configuration, and this detector array was rotated within a range of 0 to 90 degrees for measurement. Bayesian estimation was applied to estimate the gamma ray source distribution from the measurements and to reconstruct the images. This study aimed at developing a simulation model based on these methods and sought to evaluate the proposed image reconstruction method using Bayesian estimation by comparing it with the existing Maximum Likelihood-Expectation Maximization (ML-EM) method [11].
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Acquisition** & **Response** & **Unsets** & **Matrix Size** &
\begin{tabular}{c} **UNets** \(\times\) \\ **Matrix Size** \\ \end{tabular} \\ \hline \multirow{4}{*}{With-Prior} & \(\log\)(MSE) & 2516 & 476 & 124 \\  & \(\log\)(MIE) & 1191 & 430 & 70 \\  & SSIM & 8239 & 5607 & 970 \\  & PSNR & 1585 & 254 & 88 \\ \hline \multirow{4}{*}{WithOut-Prior} & \(\log\)(MSE) & 495 & 12,133 & 59 \\  & log(MIE) & 295 & 7806 & 66 \\ \cline{1-1}  & SSIM & 931 & 114,955 & 139 \\ \cline{1-1}  & PSNR & 242 & 4674 & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: F-ratios showing the significance of each of the terms that were used in the mixed effects model. Each term is highly statistically significant as all the F-ratios are large, resulting for all cases in \(p\)-values << 0.01. Figure 9: Interaction plots for the mixed-effects model, indicating how the two factors—UNets and Matrix Size—internet with each other under (**a–d**) the With-Prior acquisition scenario (**a**) log(MSE), (**b**) log(MIE), (**c**) SSIM, and (**d**) PSNR; and under (**e–h**) the WithOut-Prior acquisition scenario (**e**) log(MSE), (**f**) log(MIE), (**g**) SSIM, and (**h**) PSNR. reconstructed images for the larger downsampled matrices for all three UNets in the two acquisition scenarios. These cases correspond to lesions that were eight (8) or fewer pixels in size in any dimension, and whose signal intensity was high with respect to neighboring pixels (greater than 50 on a scale of 0-255). When an image is downsampled to 1/8, then a lesion 8 pixels in length would correspond to a single pixel of the downsampled image. In the Without-Prior case, the low-signal-intensity lesion cannot be recovered; however, with HQCP, this is feasible. Figure 10c depicts another case in which the lesion is not observable without HQCP (columns 2-4); however, the lesion becomes visible when HQCP are available (columns 5-7). This case is representative of all lesion information lost when (i) the contrast difference between the lesion and neighboring pixel is low (less than 30 on a 0-255 scale), and (ii) the lesion is small in size (fewer than 8 pixels in a single dimension). Figure 10: Characteristic examples of upscaling a 1/8 downsampled (22 \(\times\) 26), a 1/4 downsampled (44 \(\times\) 52) and a 1/2 downsampled (88 \(\times\) 104) synLQ image, of the same set) toward the synHQ ground truth. The figure shows a comparison of two acquisition scenarios: With-Prior and WithOut-Prior. **(a)** Scenario where the tumor is visible in all reconstruction scenarios; **(b)** where the tumor is not visible during reconstruction of 1/8 downsampled synLQ cases in the WithOut-Prior acquisition scenario; (c) where the tumor is barely visible during reconstruction of almost all the synLQ cases in the WithOut-Prior acquisition scenario. This figure shows that it is impossible to reconstruct a tumor if the tumor is not visible in the synLQ images in the WithOut-Prior acquisition scenario.