esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost
## 2 Materials and Methods
### Machine Learning  
#### 2.5.1 Bpnn  
The BP (Back Propagation) neural network is a type of feed-forward network consisting of multiple layers, with each layer connected sequentially and information passed forward through these layers. It is one of the most widely applied neural network algorithms. The BP neural network consists of an input layer, one or more hidden layers, and an output layer. The training process involves two main phases: forward propagation of signals and backward propagation of errors. During forward propagation, input signals are processed through the hidden layers and transmitted to the output layer. If the output layer does not produce the desired output, the process shifts to the backward propagation of errors. In this phase, the network weights and thresholds are continuously updated based on the prediction error, aiming to minimize the error function [21].  
The TOC content prediction model based on the BP neural network can be expressed as Equation (9).  
\[y_{pre}=f(w_{2}\cdot f(w_{1}x+b_{1})+b_{2}) \tag{9}\]  
where, \(y_{pre}\) is the predicted value of TOC content; \(x\) is the principal component of the log after processing; \(f\) is the activation function; \(w_{1}\) is the weight between the input layer and the hidden layer; \(b_{1}\) is the threshold between the input layer and the hidden layer; \(w_{2}\) is the weight between the hidden layer and the output layer; \(b_{2}\) is the threshold between the hidden layer and the output layer.
# Optical Chirality Determined from Mueller Matrices
## 4 Results
### Structural Optical Activity in Cellulose Nanocrystals
#### 4.5.1 Method I  
Figure 13 shows the transmission Mueller matrix of a CNC film; however, no quantification using method I is meaningful due to the complexity of the sample including the mixing of information from linear and circular anisotropy as well as depolarization in the Mueller-matrix elements. Similar to beetle cuticle, right-handed circularly polarized light is transmitted in a band around 555 nm as seen in the elements \(m_{41}\) and \(m_{14}\).  
However, the central four elements indicate more complexity than a single circular Bragg resonance. Lower peaks and wider bands in the resonances in \(m_{41}\) and \(m_{14}\) compared to those in the beetle cuticle indicate qualitatively that the chiral domains in the CNC film exhibit a larger distribution in pitch than in the cuticle. The sample also exhibits linear anisotropy as seen in elements \(m_{43}\), \(m_{34}\), \(m_{42}\), and \(m_{24}\) and is classified as bianisotropic.
# Globally Guided Confidence Enhancement Network for Image-Text Matching
## 4 Experiments
### Qualitative Analysis  
In order to evaluate the effectiveness of GGCEN, we have presented a graphical representation of the outcomes of confidence enhancement under global guidance in Figure 3, where the red boxes represent not only the key scenes in the image-text matching but also the image regions that are highly correlated with the text from a global perspective, and the blue boxes represent the weakly correlated image regions.  
Qualitative Results of the Image-to-Text Retrieval. The qualitative results of text retrieval according to image queries on Flickr30K are depicted in Figure 4. For each image query, the top-5 retrieved captions are presented. The ranking is determined by GGCEN's predicted similarity scores. According to these results, our GGCEN is able to derive the correct results from the top-ranked text. The model outputs some understandable inconsistencies, such as those in Figure 4(a) (5). The visualization indicates that GGCEN can deeply understand the content of images and text, focusing on meaningful region-word matching pairs.  
Qualitative Results of the Text-to-Image Retrieval. The qualitative results of image retrieval according to text queries on Flickr30K are depicted in Figure 5. Specifically, for each text description, the top-3 retrieved images are presented. Accurate matches are highlighted in green, while inaccurate matches are denoted in red. We observe that our model successfully retrieves the ground-truth image within the top-3 results. It is worth noting that other results are also understandable, which included entities of similar objects or semantic concepts as described in the captions. Moreover, even for images with highly similar local features, our model is capable of distinguishing between them and accurately retrieving the correct image at the top-1 rank.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Text Retrieval**} & \multicolumn{3}{c}{**Image Retrieval**} & \multirow{2}{*}{**RSUM**} \\ \cline{2-2} \cline{4-7}  & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline without global feature (plus SA) & 76.4 & 93.1 & 96.6 & 56.0 & 81.6 & 86.7 & 490.3 \\ without confidence enhancement & 76.1 & 93.8 & 97.1 & 56.9 & 82.4 & 88.8 & 495.1 \\ \hline GGCEN & 80.4 & 96.0 & 97.9 & 60.9 & 84.6 & 90.1 & 509.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation on Flickr30K. Inference is accomplished by averaging the similarity scores of two trained models.  
Figure 3: Visualization the result of confidence enhancement under global guidance. The red boxes represent not only represent the key scenes in the image-text matching but also denote the image regions that are highly correlated with the text from a global perspective, and the blue boxes represent the weakly correlated image regions.
## 6 Security and Feature Analysis  
Security and feature analysis refers to evaluating and examining the security aspects and functional features of a particular system, technology, or piece of software. This section presents the security and feature analysis of the proposed 3-Tier AKA protocol presented in Section 4.  
Figure 21: Storage cost comparison (handover) of 3-Tier AKA, FogHA [44], [PERSON] et al. [62], and [PERSON] et al. [55].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol** & **Signaling Cost** \\ \hline
3-Tier AKA & \(2L_{LAS}=256\) \\ \hline FogHA [44] & \(5L_{ID}+7L_{H}+L_{8}+4L_{TS}=2688\) \\ \hline Quantum-resistant handover authentication protocol [62] & \(5L_{NTBU}+9L_{H}+2L_{TS}=3168\) \\ \hline [PERSON] et al.’s scheme [55] & \(3L_{ID^{\star}}+4L_{H^{\star}}+4L_{ICC\varphi}+2L_{SR}+4L_{TS^{\star}}=2624\) \\ \hline \hline \end{tabular}
\end{table}
Table 18: Total communication cost (bits) (handover).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Protocol** & **Storage Cost (Bits)** & **5G/4G Network/Server (Bits)** \\ \hline
3-Tier AKA & \(2L_{ID}+2L_{K}+L_{AES}=640\) & \(L_{K}=128\) \\ \hline FogHA [44] & \(4L_{ID}+6L_{H}+3L_{SP}=3200\) & \(2L_{ID}+2L_{SP}+2L_{H}=1536\) \\ \hline [PERSON] et al.’s Quantum-resistant handover authentication protocol [62] & \(3L_{ID}+4L_{H}+2L_{R}+3L_{NTBU}=1952\) & \(L_{ID}+4L_{NTBU}+4L_{H}=1792\) \\ \hline [PERSON] et al.’s scheme [55] & \(L_{H^{\star}}+L_{k}+2L_{ID^{\star}}+L_{ICC\varphi}=736\) & \(L_{TS^{\star}}+2L_{IP}+3L_{K}+3L_{ID^{\star}}+L_{ICC\varphi}=1280\) \\ \hline \hline \end{tabular}
\end{table}
Table 19: Storage cost (bits).
## 3 Results and Discussion
### Example for Grating-Based PCI  
In this paragraph, we show the functioning of our in-house cone-beam tomographic reconstruction package for laboratory phase-contrast imaging using the grating based technique as described in Section 2.2. The sample was a very-low-attenuation, porous cellulose foam sample. The scan was performed using our in-house grating-based PCI micro-CT setup. Technical details of the setup are given in [33]. The scan was performed at 40 kVp tube voltage and a tube current of 20 mA. We performed the scan with seven  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Attenuation CT** & **Paganin Method** & **BAC Method** \\ \hline horse fly & 7.5 & 17.7 & 10.8 \\ coffee bean & 2.8 & 7.0 & 3.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNR values between sample material and air for the different CT images and reconstruction methods. For the ROIs used to determine the CNR see Figures 3 and 5.  
Figure 4: (**a**) Propagation based projection image of the horse fly. (**b**) Illustration of the result of the optimization of the \(\alpha\) and \(\gamma\) parameters of the BAC method showing a comparison of the original and BAC corrected image gray scale profiles over the white horizontal line shown in (**a**).  
phase steps per projection each with 2.5 s exposure time. We took overall 360 projections over 360 degree sample rotation. The geometrical magnification of the setup is very low 1.17. Figure 6 shows some illustrative results proving the power of PCI and DFI for low-attenuation, porous samples. The attenuation image is so noisy due to the very low sample attenuation that the internal porous structure of the sample cannot be discerned. Only the phase-contrast and the dark-field CT images show these structures. They nicely illustrate inverse gray scale values with respect to each other as expected: pores are shown with low intensity in the PCI, whereas they have high intensities in the DFI and vice versa for the cellulose material.  
Figure 5: Comparison of the attenuation-based micro-CT images of the horse fly (**a**,**d**), with the propagation-based images reconstructed by using [PERSON]’s phase retrieval method, (**b**,**e**), based on Equation (6), and with the BAC method based on Equations (3) and (4), (**c**,**f**). The upper images show a horizontal slice at the region of the flight muscles (see orange line in Figure 4a), whereas the lower images are taken at the level of the compound eye showing clearly the individual ommatidia (see the blue line in Figure 4a). The areas circumvented in red indicate the ROIs used for determining the CNR values between sample material and air (see Table 1).  
Figure 6: Grating based phase contrast tomogram slice of a cellulose foam. (**a**) shows the attenuation, (**b**) the phase contrast and (**c**) the dark field, small angle scattering, image.
## 2 Methods
### Design of the mPHR Service for Emergency Situations
#### 2.1.1 Functions and Input Data That Were Considered Necessary
\begin{table}
\begin{tabular}{l l} \hline \hline
**Targeted User Groups** & **Functionalities Required** \\ \hline Patient & Registration \\  & Log-in \\  & Personal information management \\  & Customer service center \\  & Health records (Home) \\  & Medication alarm \\  & Download of medical record \\  & “Disclosure” of medical record \\  & Record of health condition \\  & Record of self-checked blood pressure \\  & Emergency ID \\  & Inquiry of ED records \\  & Inquiry of laboratory test results \\  & Inquiry of urinalysis test result \\  & Inquiry of list of ED visits \\  & Set-up of data-shareable institution \\  & Set-up of shareable details \\  & List of data utilization and withdrawal of sharing \\  & SOS to care-taker \\ \hline ED staff & Receiving temporary URL of “ED record summary” via e-mail or SNS \\  & Authentication \\  & Inquiry of “ED record summary” \\  & Inquiry of details of ED records \\ \hline Manager & Management of service usage \\  & Management of notice \\  & Dashboard \\ \hline Others & Easy-to-read terms \\  & Selective sharing method \\  & Safe data collection and utilization process \\  & Explicit consent before downloading \\  & Receipt of utilized data \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Functionalities required for targeted user groups. have different EMR systems and data recorded, which are coded in different ways for each hospital. For interoperability and standardization of data, an NEDIS conversion method was used. The coding method used to send data to the NEDIS is universal in all EDs across the country. This method enabled various information that existed in different forms at each hospital to be converted into one form. Similarly, data on medications prescribed in the ED are sent to the National Health Insurance, which also uses the electronic data interchange (EDI) code of the country's single insurer. Further, information for each medication was mapped using data from the Korea Pharmaceutical Information Center. There are technical limitations when trying to include all the laboratory tests; thus, the most frequent test items were extracted from the three hospitals' data. Two ED doctors from each hospital (Y.C., I.H.K., J.K., J.-H.L., T.K., and W.C.) reviewed the whole list of extracted laboratory tests and discussed the clinical importance of the laboratory tests for making treatment decisions in the ED. The items of the PGHD were determined by considering those items that are valuable for suspecting and diagnosing a disease in the ED.
## 3 Results
### Master Curves
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Sample** & **UV/d** & \(\mathbf{G_{g}/Pa}\) & \(\mathbf{G_{g}/Pa}\) & \(k\) & \(\mathbf{h}\) & \(\mathbf{\alpha}\) & \(\mathbf{\tau_{0}}\) & \(\mathbf{\beta}\) & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{R_{4}}^{2}\) \\ \hline WB & 3 & 2150 & \(6.4\times 10^{8}\) & 0.28 & 0.64 & 7.73 & \(3.0\times 10^{-4}\) & \(3.3\times 10^{4}\) & 39 & 343.8 & 0.920 & 0.502 \\ WB + 1\% & 3 & 4378 & \(7.6\times 10^{8}\) & 0.33 & 0.67 & 8.27 & \(3.3\times 10^{-4}\) & \(3.6\times 10^{4}\) & 140 & 1326.5 & 0.994 & 0.883 \\ WB + 3\% & 3 & 3830 & \(7.5\times 10^{8}\) & 0.31 & 0.65 & 8.07 & \(2.7\times 10^{-4}\) & \(3.8\times 10^{4}\) & 114 & 1028.4 & 0.990 & 0.838 \\ NANO-TiO\({}_{2}\) & 6 & 10,740 & \(6.6\times 10^{8}\) & 0.29 & 0.66 & 7.62 & \(3.4\times 10^{-4}\) & \(4.2\times 10^{4}\) & 11 & 90.1 & 0.995 & 0.858 \\ WB + 1\% & 6 & 3088 & \(6.4\times 10^{8}\) & 0.31 & 0.69 & 7.24 & \(3.2\times 10^{-4}\) & \(4.2\times 10^{4}\) & 11 & 93.7 & 0.997 & 0.937 \\ WB + 3\% & 6 & 3281 & \(9.8\times 10^{8}\) & 0.32 & 0.67 & 12.42 & \(4.5\times 10^{-4}\) & \(1.9\times 10^{4}\) & 238 & 1930.5 & 0.988 & 0.763 \\ WB & 9 & 6146 & \(8.7\times 10^{8}\) & 0.29 & 0.63 & 7.25 & \(2.0\times 10^{-4}\) & \(5.0\times 10^{4}\) & 267 & 2440.2 & 0.996 & 0.874 \\ WB + 1\% & 9 & 4378 & \(7.6\times 10^{8}\) & 0.33 & 0.67 & 8.27 & \(3.3\times 10^{-4}\) & \(3.6\times 10^{4}\) & 140 & 1326.5 & 0.997 & 0.903 \\ WB + 3\% & 9 & 2150 & \(6.4\times 10^{8}\) & 0.28 & 0.64 & 7.73 & \(3.0\times 10^{-4}\) & \(3.3\times 10^{4}\) & 39 & 343.8 & 0.991 & 0.886 \\ Nano-TiO\({}_{2}\) & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model coefficients for various UV-aged binders. Figure 7: Master curves of viscoelastic parameters for UV-aged WB binder modified with various contents of Nano-TiO\({}_{2}\) at a reference temperature of 22 \({}^{\circ}\)C (**a**) complex modulus; (**b**) phase angle.
###### Abstract  
Purpose: To assess the applicability of computational fluid dynamics (CFDs) in determining the flow parameters of inter-chamber nozzle openings in the differential section of a trailer air brake valve. Methodology: Numerical calculations were performed using SolidWorks Flow Simulation (SW-FS) and Ansys Fluent (A-F) with defined boundaries and initial conditions. The results were validated experimentally using the reservoir method and the lumped method for throughput identification. Results: CFD calculations determined the functional dependence of the mass flow rate on the nozzle diameter for a range of control nozzle bore diameters. The SW-FS 2024 and A-F 2023 software showed a mean difference of 4.66% in the total characteristics. The experimental validation resulted in differences of 6.31% (SW-FS) and 5.79% (A-F) compared to the CFD results. Theoretical contribution: This study fills a research gap in applying CFDs to brake valve performance analyses, providing a foundation for developing more complex numerical models to evaluate individual valve sections. Practical implications: The findings suggest that CFDs can be used to accurately determine the flow parameters of control nozzle orifices, with an average of a 6.05% difference from experimental tests. This approach can potentially streamline the design and optimization process for pneumatic brake valves.  
m achenical engineering; transportation; braking system; trailer; calculation; research Article
# Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines
## 3 Results and Discussions  
### ROI Detection  
We tested the ROI detection algorithm using the whole image and user-defined ROI as the minimum user input. The first test was performed with only vertebral body correlation maps, and the second test was performed with both correlation maps of the vertebral body and intervertebral disc. For all 85 vertebral bodies in 19 MR spine images, the number of detection failure cases in the vertebral body test was three, whereas the vertebral body and intervertebral disc test was zero. Detection error of the vertebral body and the intervertebral disc test was 10.41 pixels, which was 19.36% lower than that of the vertebral body test (12.91 pixels). Therefore, it is clear that the use of both ROIs yields better results and that the intervertebral disc exhibits more precise information of location as compared with the vertebral body. Figure 5 also shows that the use of the intervertebral discs adjusts the ROI detection more closely. Detection of ROIs using the vertebral body ROI offered by the user poses a difficulty only in finding other vertebral bodies of the MR spine images; however, more precise detection can be performed if the intervertebral disc ROI is additionally used for ROI detection. In addition, when the user ROI was set to the central vertebral body, there was no detection failure case. On the other hand, when the ROI was set to the uppermost vertebral body, the detection failure case occurred 0.93 times per image, and when the ROI was set to the undermost vertebral body, the detection failure case occurred 0.77 times per image.  
In most cases, the vertebral bodies and intervertebral discs are at a similar position in the horizontal direction. In other words, knowing the position of the intervertebral discs helps to understand the horizontal position of the vertebral bodies. As the intervertebral discs have more compact and unique shapes than the vertebral body, their correlation with other tissues is quite small. Therefore, more accurate locations of vertebral bodies can be found using correlation maps of the intervertebral discs.  
Aligning the ROIs with a fixed size cannot completely cover all the vertebral bodies because the vertebral bodies have various sizes and shapes. The ROI fine tuning process adjusts the ROIs to be fitted for each of these various vertebral bodies and aligns them to the center of the ROIs. When the ROI fine tuning process was not applied, there were several cases in which a part of the vertebral body was not included within the ROI, on average, of 1.31 cases per image.
## 3 Sampling and Methodology
### NMR Methodologies  
#### 3.3.1 NMR Measurement  
The transverse relaxation time \(T_{2}\) can be expressed as the following [47]:  
\[\frac{1}{T_{2}}=\frac{1}{T_{2B}}+\frac{1}{T_{2D}}+\frac{1}{T_{2S}}\approx\frac {1}{T_{2S}}=\rho\frac{S}{V} \tag{2}\]  
where \(T_{2\text{B}}\), \(T_{2\text{D}}\), and \(T_{2\text{S}}\) are the bulk, diffusion, and surface transversal relaxation time in ms, respectively; \(S/V\) is the surface-to-volume ratio; and \(\rho\) is the transversal surface  
Figure 2: Workflow used for pore structure evaluation.  
relaxivity (\(\mu\)m/s) [48]. Since rapid diffusion is dominated by surface relaxation in most geological media, the \(T_{2}\) time of pore space is directly related to \(S\)/\(V\) (Equation (3)).  
\[\frac{1}{T_{2}}\approx\rho\frac{S}{V}=\rho\frac{N}{r} \tag{3}\]  
where \(N\) is the pore shape factor (\(N\) = 2 for cylindrical model and \(N\) = 3 for spherical model) [49]. Generally speaking, short \(T_{2}\) relaxation times are associated with small pores, whereas long \(T_{2}\) relaxation times correspond to large pores and fractures. Thus, the pore sizes can be characterized by \(T_{2}\) relaxation times.
## 3 Controller Design
### Attitude Controller Design Based on Quaternion  
#### 3.2.1. Transformation of the Euler Angle to the Quaternion Control Target  
The relationship between \(\eta_{2}\) and \(Q\) can be derived from Equations (30) and (31), and the relationship between \(\varpi\) and \(\dot{q}_{v}\) can be derived from Equation (29). Therefore, the control problem for the Euler angle and angular velocity can be transformed into the control problem for \(\widetilde{Q}\) and \(\widetilde{\varpi}\).  
The desired attitude angle is expressed as \(\eta_{2d}=\begin{bmatrix}\phi_{d}&\theta_{d}&\psi_{d}\end{bmatrix}^{T}\); then, we can obtain  
\[Q_{d}=\begin{bmatrix}q_{0d}&q_{\tau d}^{\text{T}}\end{bmatrix}^{\text{T}}\]  
and \(\widetilde{Q}=\begin{bmatrix}\tilde{q}_{0}&\tilde{q}_{\tau}^{\text{T}}\end{bmatrix}^ {\text{T}}\), \(\widetilde{q}_{v}=\begin{bmatrix}\tilde{q}_{1}&\tilde{q}_{2}&\tilde{q}_{3} \end{bmatrix}^{\text{T}}\).  
According to the rules of quaternions, we satisfy the following relationships:  
\[\left\{\begin{array}{l}\widetilde{q}_{0}=q_{0}q_{0d}+q_{\tau d }^{\text{T}}q_{v}\\ \widetilde{q}_{v}=q_{0d}q_{v}-q_{0}q_{0d}-q_{v}^{\times}q_{\tau d}\\ \widetilde{q}_{0}^{2}+\widetilde{q}_{\tau}^{\text{T}}\widetilde{q}_{v}=1,\\ \tilde{\widetilde{q}}_{0}=-1\,\tilde{q}_{\tau}^{\text{T}}\widetilde{\omega}, \tilde{q}_{v}=G\widetilde{\varpi},\\ G=\frac{1}{2}(\widetilde{q}_{0}I_{3}+\widetilde{q}_{\tau}^{\times}), \widetilde{\omega}=\omega-R(\widetilde{Q})\omega_{d}\\ R(\widetilde{Q})=\left(\widetilde{q}_{0}^{2}-\widetilde{q}_{v}^{\text{T}} \widetilde{q}_{v}\right)I_{3}+2\widetilde{q}_{v}^{\text{T}}\widetilde{q}_{v} ^{\text{T}}-2\widetilde{q}_{0}\widetilde{q}_{v}^{\times}\end{array}\right. \tag{47}\]  
Let us define a new quaternion error equation: \(Q_{e}=\begin{bmatrix}1-\widetilde{q}_{0}&\widetilde{q}_{v}^{\text{T}}\end{bmatrix}^ {\text{T}}\); the control objective can be modified as follows:  
\[\lim_{t\to\infty}Q_{e}=0,\lim_{t\to\infty}\widetilde{\omega}=0 \tag{48}\]  
That is, \(\widetilde{q}_{v}\to 0\), \(\widetilde{q}_{0}\to 1\), \(\widetilde{\omega}\to 0\).
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 3 Topic Overview  
### Sixth-Generation Drone Networks  
Drones, also known as Unmanned Aerial Vehicles (UAVs), are defined as _all aircraft designed to fly without a pilot on board_[16]. This technology has experienced rapid growth in recent years and is expected to continue growing in both the consumer sector as well as the commercial and military sectors [17].  
In technical report 22.886 [18], 3 GPP identifies some of the envisioned use cases for 5G V2X (Vehicle-to-Everything) communication services. Among these use cases, advanced and remote driving, vehicle platooning and extended situational awareness are identified as some of the main benefits of V2V communication. All these capabilities can be leveraged by a 6G drone network to achieve fast and reliable drone-to-drone communication that, with the integration of artificial intelligence, would allow the drones to act autonomously in a coordinated manner.  
This would prove useful in a variety of fields: from autonomous irrigation as well as soil and crop health assessment in agriculture, delivery of life-saving supplies and identification of survivors in disaster response, in the delivery sector as a more eco-friendly alternative to traditional delivery options, and potentially in the mobility sector as a complement to traditional taxis and public transportation [19].  
In the military sector, the effectiveness of drones is widely recognized, with both high and low end models being used in a variety of roles, from more passive roles like surveillance and intelligence gathering, to more active roles like delivery of explosives and targeted strikes. Securing the communication channel against jamming attacks is crucial in all these applications, especially in a safety-critical environment.