esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study was carried out in two municipal slaughterhouses and six butcher shops in Al-Ahsa. The findings evidenced a considerable prevalence of well-known pathogens, including _E. coli_, _Salmonella_ spp., _Shigella_ spp., and _Klebsiella_ spp. across different types of livestock including camels, cattle, and sheep in both slaughterhouses and butcher shops. The data indicate that animal carcass exhibited high contamination levels of _E. coli_ and _Salmonella_ spp., with a rate of 70% and 40%, respectively. _E. coli_ was predominant among all isolates with 70% presence in samples of carcasses and meat cuts. _E. coli_ was found in 100% of camel carcasses, 58% of cattle carcasses, and 30% of sheep carcasses. While in butcher shops, _E. coli_ was detected in 70%, 60%, and 75% in camel, cattle, and sheep meat samples, respectively. On the other hand, _Salmonella_ was positive in 40% of camel 47.5% cattle and 32.5% sheep samples, collected from both slaughterhouses and butcher shops. Twenty-five Enterobacteriaceae species belonging to 19 bacterial genera were isolated and confirmed using PCR-test. The samples of sheep had the highest occurrence of Enterobacteriaceae with 15 different genera followed by camels and cattle samples with 14 different genera for both. In conclusion, the profound prevalence of Enterobacteriaceae among camel, cattle, and sheep carcasses collected from slaughterhouses and meat cuts obtained from butcher shops raises significant concerns regarding food safety. These findings underscore the need to enhance hygiene practices and implement stringent microbial monitoring procedures in both slaughterhouses and butcher shops. It highlights the potential risks to public health since these pathogens have historical of human foodborne illnesses outbreaks. More research is needed to identify the main reasons of the high occurrence of contamination and to help to design and implement an action plan to minimize or prevent foodborne illnesses. Designing slaughtering lines to facilitate hygienic operations is evidently critical. Moreover, the effective enforcement of sanitary practices, including the regular disinfection of working tools, plays a crucial role in mitigating the risk of microbiological contamination of carcasses.  
F.A.-A.: writing--review and editing, supervision, project administration, funding acquisition; S.H.H.: data curation, writing--original draft, preparation; S.A.A.H.: data curation, writing--original draft, preparation. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work (Project number INST114).  
Not applicable.  
Data availability Statement: Data are available upon request.  
The authors declare no conflict of interest.
## 4 Modeling Errors of KANs and Discussion
### Modeling Errors of KAN-A  
Table 5 presents the experimental results for KAN-A. KAN-A's test set errors (MSE, MAE, and sMAPE) were marginally lower than the training set values, validating the regularization strategy's effectiveness and demonstrating generalization. The MaxAE decreased from 0.7555 (training) to 0.3350 (test), which suggests improved prediction of extreme cases.  
Both R\({}^{2}\) and Adjusted R\({}^{2}\) approached 1, confirming that KAN-A explained most variance between the inputs and outputs. The model not only learned data patterns but also generalized them effectively.  
The total runtime for KAN-A was 614.5063 s. This duration is substantial but justified by its symbolic output. Once validated with low errors, the derived mathematical relationships will directly guide research and engineering efforts. There is no need for retraining, which makes it cost-efficient in the long term.
## 3 Test Results and Analysis
### Results of the CT
#### 3.3.3 Changes in Non-Connected and Connected Pores at Different Soaking Times  
As illustrated in Figure 9, the non-connected pore porosity of sandstone in its unsoaked state is 3.76%. This porosity exhibited significant changes with increasing soaking time. After one month of soaking, the porosity of non-connected pores increased to 6.16%, indicating a substantial rise. However, after two months of soaking, while the porosity further increased to 6.40%, the rate of increase began to slow. As the soaking time continued to extend, the porosity of non-connected pores experienced further alterations. By three months, the porosity reached a notable inflection point, dropping significantly to 0.841%. Following four months of soaking, another inflection point was observed, with the porosity of non-connected pores rising slightly to 1.06%. In the context of ordinary porosity testing, porosity is estimated based on the flow rate of the injected gas, meaning that the actual measured porosity reflects the connected pore porosity. In the unsoaked condition, the connected pore porosity of sandstone was 11.29%. As the soaking time increased, the connected pore porosity also changes. After one month, the connected pore porosity reached 12.13%, demonstrating an upward trend. After two months of immersion, the connected pore porosity was recorded at 13.11%, continuing to increase but at a slower rate. By three months, the connected pore porosity fell to 11.06%, marking the beginning of an inflection point with a decrease of 15.64%. After four months of soaking, the connected pore porosity experienced a slight increase to 11.12%.
## 3 Feature Extraction Method of an Impulse Fault
### Feature Extraction Method for Vibration Signals Based on Sparse Non-Negative Tensor Factorization  
Based on the above elaboration, a novel feature extraction method for vibration signals is proposed. The method combines the merits of the PSR technique and the SNTF model, to effectively extract the fault feature of periodic impulses in the vibration signals. The overall procedure is concluded in Algorithm 1 given as follows:  
```
Step 1. The original one-dimensional signal \(x(n)\) is converted into a two-dimensional phase space \(\mathbf{X}\) by the PSR technique. Step 2. Perform STFT on the phase point vectors to acquire multiple time-frequency distributions. Step 3. Permutate the multiple TFDs to generate a third-order tensor \(\underline{\mathbf{Y}}\). Step 4. Select a reduced-dimensionality index \(J\), and employ the SNTF-HALS algorithm to decompose the above tensor \(\underline{\mathbf{Y}}\). This step returns the frequency matrix \(\mathbf{A}\), the time matrix \(\mathbf{B}\), and the phase matrix \(\mathbf{C}\). Step 5. The reconstructed TFDs are obtained by \(\mathbf{a}_{\mathbf{s}}\mathbf{b}_{\mathbf{s}}{}^{T},(s=1,2,\ldots,J)\), and the principal components (waveforms) are restored by ISTFT. Step 6. Feature extraction. For a selected waveform, envelope demodulation is used to capture the characteristic frequency of certain damages.
```  
**Algorithm 1**Feature Extraction Method for Vibration Signals Based on SNTF  
The method is also visualized as a flow chart as presented in Figure 3.  
Figure 3: The overall procedure of the sparse non-negative tensor factorization (SNTF)-based feature extraction for machinery faults.
## 3 Methods
### Segmentation of the Lung Anatomical Structures
#### 3.1.1 Lung Anatomical Structures  
As depicted in the schematic diagram of lung structures in Figure 2a, there are two major organs: the left and the right lung. Each is enclosed by a pleural sac with two membranes called visceral pleura (inner membrane highlighted by the black border) and parietal pleura (outer membrane highlighted by the blue border). These membranes protect and attach the lungs to the thoracic cavity. A double-fold of visceral pleura called the lung fissure divides the lungs into separate lobes. The left lung is approximately 10% smaller than the right due to heart space in the thorax [16]. Thus, the left lung has only two lobes (upper and lower) which are separated by an oblique fissure, while the right lung has three lobes (upper, middle and lower) which are separated by oblique and horizontal fissures. As the primary function of the lungs is to carry out gas exchange, 80-90% of their area is filled with low density air having \(-\)1000 Hounsfield Unit (HU) [17]. The windpipe, also known as the trachea (denoted by green color), carries the air from the throat to the lungs and enters each lung by branching into two main bronchi (left indicated in brown and right indicated in dark blue) at the anatomical point called the carina. The right main bronchus further splits into three lobar bronchi which enter three lobes of the right lung. Likewise, the left bronchus splits into two lobar bronchi for each left lung lobe. These bronchi undergo multiple divisions, forming the bronchial airway tree. Similar to the bronchial airway system, pulmonary vessels also branch throughout the lungs in a tree structure. The pulmonary vascular tree carries out the transportation of deoxygenated blood from the heart to the lungs to perform oxygenation, whereas the bronchial airway tree supports the flow of oxygen to the lung structures, including the pulmonary vessels [18]. Both of these trees enter and exit the lungs through a place called the hilum (denoted by the green dotted oval).  
Figure 2b illustrates the lung anatomical structures on the CT scan. As we can see on the scan, there are two distinct components: the air (the black regions) and the human body (the gray regions) [17]. These two components have different radiography densities in which the air (\(-\)1000 HU) has a much lower density than the human body (\(-\)400 HU to 1000 HU). The human body consists of structures such as parenchyma or lung regions, fluid, fat, soft tissues, bones, and vessels. The radiological densities of these structures are different from each other showing different gray levels on the CT scan. Figure 3 demonstrates the range of radiological densities (HU values) of each lung structure.  
Figure 2: Anatomical structure of the human lungs: (**a**) schematic diagram of lung structures; (**b**) lung structures on the CT scan.
## 2 Classification of Routing Protocols
### Power-Efficient Gathering in Sensor Information Systems (PEGASIS)
#### 2.2.2 Data Gathering  
After selecting the leader node in the chain, which is node 1 in this case, the data aggregation is done in which the leader node sends the aggregated data to the next node 9 that is near the leader node, which becomes the leader node and then sends the aggregated data to the next nearest node continuing this process until we reach the last node. The last node, which is closest to the base station (BS), sends the data to the base station. The nodes in the figure are numbered randomly. This data gathering process is done as shown in Figure 5.
## 3 RACH Success Probability and Power Consumption Model for LEO Satellite-Based NB-IoT Networks
### The Successful Probability of Preamble Detection
The probability of successful preamble detection \(p_{cd}\) can be expressed as follows:  
\[ \begin{split} p_{cd}&=1-(1-p_{c})p_{\varepsilon}\\ &=1-\left(\int\limits_{r\min}^{r\max}\frac{r_{0}\cdot e^{-\gamma_ {th}r_{0}^{\alpha}|I_{0}|^{2}p_{s}^{-1}\sigma^{2}}}{2R_{e}(R_{e}+r_{\min})} \exp\left(-2\pi\lambda\int_{r0}^{r_{\max}}\frac{\gamma_{th}}{\gamma_{th}+\left( x/r_{0}\right)^{\alpha}}xdx\right)dr_{0}\right)e^{-\left(N_{rep}-1\right)}\end{split}  \tag{14}\]  
where \(p_{\varepsilon}\) represents the reduction factor which is influenced by the LEO satellite capture effect [23] and \(N_{rep}\) denotes the number of repetitions of the preamble.
## 4 Materials and Methods
### UPLC-QTOF/MS Conditions  
For metabolite profiling of the processed ginseng products, we used a previously constructed method based on UPLC-QTOF/MS [31]. UPLC analysis was performed by using a Waters ACQUITY H-Class UPLC (Waters Corp., Milford, MA, USA), and the used column was an ACQUITY BEH C18 column (2.1 mm \(\times\) 100 mm, 1.7 \(\upmu\)m). The temperatures of the column oven and the sample tray were maintained at 40 and 4 \({}^{\circ}\)C, respectively. The mobile phase consisted of solvent A (water:formic acid = 100:0.1, \(v/v\)) and solvent B (acetonitrile:formic acid = 100:0.1, \(v/v\)). The gradient elution program was as follows: 0-0.5 min, A 85%; 0.5-1 min, A 85-80%; 1-6 min, A 80%; 6-13 min, A 80-70%; 13-23 min, A 70-65%; 23-24 min, A 65-62%; 24-27 min, A 62-40%; 27-31 min, A 40-10%; 31-32 min, A 10-85%; 32-35 min, A 85%. The flow rate was set at 0.4 mL/min, and the sample injection volume was 2 \(\upmu\)L for each run. Next, the TOF MS experiments were performed by using a Waters Xevo G2-S QTOF MS (Waters Corp., Milford, MA, USA). The mass spectrometers performed alternative high- and low-energy scans known as the MS\({}^{\mathrm{E}}\) acquisition mode. The high-accuracy MS data were collected from \(m/z\) 100-2500 Da in negative ion mode. The cone voltage and capillary voltage were 40V and 3.0 kV, respectively. The source and desolvation temperatures were 120 \({}^{\circ}\)C and 550 \({}^{\circ}\)C, respectively. The cone gas flow and desolvation gas flows were 30 L/h and 800 L/h, respectively. To ensure the mass accuracy and reproducibility of MS condition, a lock spray with leucine-enkephalin (200 pg/mL, 10 \(\upmu\)L/min) was used as the reference (\(m/z\) 554.262 (ESI\(-\)). For the quality control sample analysis, a standard of Ginsenoside F1 was injected after every analysis of five samples. The precision of LC/MS analysis was determined by a coefficient of variation (CV) for RT (0.1%) and peak intensity (7.9%).
# Study on Asymmetric Support of Anchor Cable with C-Shaped Tube in Inclined Coal Seam Roadway
## 3 Roadway Deformation Law in Inclined Coal Seam  
### Initial Support Scheme  
In this paper, the research object is at Pangpangta Mine's 9-8032 roadway in Shanxi Province, and Figure 6 shows the specific location of the Pangpangta Coal Mine. The roadway segment is a rectangle portion, measuring 4.5 m by 3.6 m in sectional area. The estimated excavation depth of the roadway is 2050 m. The No. 9 coal seam is stable, and the lithology is mostly carbonaceous mudstone with a small amount of local mudstone. The coal seam on the working face has a monoclinic structure and is 12 m thick. The average seam inclination is 25\({}^{\circ}\), ranging from 17 to 30 degrees, making it an inclined coal seam.  
The initial support scheme for the 9-8032 roadway adopts the combined support of rock bolts and anchor cables. During the support process, both anchor cables and rock bolts are employed in an end anchoring configuration. The anchor bolts and anchor cables consist of free sections and anchoring sections, with the anchoring sections being secured using resin anchoring agents. The maximum bearing capacity of the rock bolt is 216 kN, and the maximum bearing capacity of the anchor cable is 550 kN. Six 22 mm by 2400 mm rock bolts are installed in each row of the roadway roof, with a spacing of 800 mm and a row spacing of 800 mm. Each row contains three anchor cables, each measuring 10,300 mm in length and 21.8 mm in diameter, with a spacing of 1500 mm and a row spacing of 2400 mm.  
Each row on the side has five rock bolts, each measuring 2000 mm in length and 22 mm in diameter, with a spacing of 800 mm and a row spacing of 800 mm. The prestress of the rock bolts in the roof is 30 kN, and the prestress of the anchor cables ranges from 210 kN to 290 kN. The anchor cables' prestress in the numerical simulation is 200 kN, taking prestress loss into account. The initial support scheme is displayed in Figure 7.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Equations of Flight Mechanics
Here, \(C_{M_{q}}\) is the pitch damping moment coefficient, \(C_{M_{a}}\) is the overturning moment linear coefficient, \(C_{M_{a}3}\) is the overturning moment cubic coefficient, \(C_{mm}\) is the Magnus moment coefficient and \(C_{spin}\) is the spin damping moment coefficient. Control forces (\(\overrightarrow{C}\)) and moments (\(\overrightarrow{CM}\)) are obtained from the maneuvering system, which is composed of four canard surfaces. Therefore, the contribution of each of them is summed to obtain the total control forces and moments. \[\left[ \begin{array}{c}\overrightarrow{C}\\ \overrightarrow{CM}\end{array} \right]=\sum_{i=1}^{i=4}\left[ \begin{array} []{c}\frac{1}{8}d^{2}\rho\pi\|\overrightarrow{v_{b}}\|^{2}(C_{Na_{w}}(M) \delta_{i})\overrightarrow{n_{ci}}\\ \frac{1}{8}d^{3}\rho\pi\|\overrightarrow{v_{b}}\|^{2}(C_{Na_{w}}(M)\delta_{i })(\overrightarrow{x_{b}}\times\overrightarrow{n_{ci}})\end{array} \right] \tag{4}\]  
The expressions in (4) show the mathematical functions for control forces and moments, where \(C_{Na_{w}}\) and \(C_{Ma_{w}}\) are the force and moments coefficients of the canard surface respectively, \(\overrightarrow{n_{ci}}\) is the normal vector of each canard, and \(\delta_{i}\) is the deflection angle of the canard surface. Here, \(\overrightarrow{v_{b}}\) is vehicle velocity in body axes. \[\left[ \begin{array}{c}\overrightarrow{C}+\overrightarrow{F_{ext}}\\ \overrightarrow{CM}+\overrightarrow{M_{ext}}\end{array} \right]=\left[ \begin{array}{c}\frac{dm\overrightarrow{v_{b}}}{dt_{v}}+\overrightarrow{ \omega_{b}}\times m\overrightarrow{v_{b}}\\ \frac{dL_{b}}{dt}+\overrightarrow{\omega_{b}}\times\overrightarrow{L_{b}} \end{array} \right] \tag{5}\]  
As stated before, a Newton-Euler approach is used to formulate the equations of motion of the aerial vehicle. These equations are in (5). Note that the body-fixed coordinate system (denoted by frame \(b\)) and the flat-Earth coordinate system (denoted by frame \(e\)) are related by Euler yaw (\(\psi\)), pitch (\(\theta\)), and roll (\(\phi\)) angles. In Equations (5), \(\overrightarrow{v_{b}}\) stands for vehicle speed expressed in body axes, \(\overrightarrow{\omega_{b}}\) for angular speed of the vehicle in body axes, and \(\overrightarrow{L_{b}}\) for the angular momentum also in body axes. Recall that control and external forces and control and external moments must be expressed in body axes also to be employed in Equations (5).
# Testing the Effectiveness of Voxels for Structural Analysis
## 1 Introduction
### Retopology and NURBS
The retopology process is particularly beneficial when converting the mesh into NURBS, a transition that can be automated using software such as Rhinoceros 8. This conversion improves the coherence between quad elements and the quadrangular patches that make up NURBS, thereby reducing the number of patches required for accurate representation. This is especially relevant when working with reality-based 3D models of cultural heritage, which often exhibit complex geometries while demanding precision. The resulting NURBS can then be transformed into volumetric models, representing a critical advancement since NURBS surfaces allow for the generation of curves, facilitating the accurate depiction of conical shapes. They provide local control and enable the creation of intricate forms without necessitating a substantial increase in the polynomial degree. To highlight the distinction, consider that a mesh depicts 3D surfaces using discrete polygonal faces, where smoothness depends on the polygon count--analogous to how pixels constitute an image with colored points. Conversely, NURBS surfaces are mathematical entities defined by parametric cubic curves; their edges connect to create a continuous surface grid, proficient in modeling complex geometries. Typically, a NURBS-based 3D model comprises a defined number of NURBS patches (or surfaces) that are interconnected to maintain various types of continuity--positional (G0), tangential (G1), or curvature (G2). This continuity relates to how curves or surfaces merge seamlessly. The execution of this process involves several stages, which may introduce potential inaccuracies and approximations. Naturally, the degree of approximation is influenced by the extent of modifications made to the mesh and the complexity of the object under analysis. From point cloud to mesh:  
1. Post-processing of the mesh. 2. Retopology (smoothing). 3. Closing holes and checking the topology. 4. NURBS.
## 3 Results and Discussion
### Method Validation
#### 3.2.5 Accuracy  
For the determination of accuracy, six standard solutions of the three analytes were prepared and analyzed at six concentration levels. The equations from the calibration curves were used to calculate the actual values of their concentrations and their % recovery which ranged from 99.9-100.8% for DIP, 95.4-101.1% for ACA and 99.5-101.0% for SAL (using DAD as a detector) and 98.2-101.0% for DIP by the FLD detector (Table S3).
## 3 Methodology
### SCRM Process for IFMSC Risk Mitigation
#### 3.2.4 Risk Evaluation  
A hierarchical decision tree was constructed to provide an overall view of the mitigation alternatives model for selecting the finalized ones. Figure 8 illustrates its decomposition into three levels. The first level identifies the objective of the decision, which is to maximize the safety and quality of IFM. The second level of the pyramid indicates the risk criteria that determine the goal decision behavior, while the third level contains the mitigation alternatives of the candidate set. Using the AHP model hierarchy index system, weights can be assigned to each criterion based on the pairwise comparison measurement conducted to evaluate the alternative performance rating through the questionnaire survey. Pairwise comparisons were made using a questionnaire designed to simplify the AHP rating process.  
Figure 8: AHP Hierarchy model for IFMSC.  
[PERSON] developed AHP in the 1970s as a multi-criteria decision-making method (MCDM) [68]. AHP is used to calculate the subjective weights based on the decision maker's preference. It defines the weights of the criteria and alternatives through qualitative comparisons. When there is no clear best choice, the AHP is particularly useful. The method is based on developing a model to prioritize and select the best criteria to achieve the study objective. Although AHP is the most commonly used MCDM method, it has a clear and mathematically sound structure compared to other methods. Using AHP, researchers can quantify the overall objective and numerically prioritize the analysis by breaking down complex decision problems into subproblems. Hierarchical levels assign appropriate metrics to the appropriate management levels [69; 70]. In addition, [PERSON] developed a relative measurement scale for pairwise comparisons, as shown in Table 2, to determine which scale to use. Table 2 shows the measurement scale for the pairwise comparison [51; 71].