esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
## 5 Discussion
### Bounding Water  
Utilizing the method illustrated in Figure 7, the changes in bounding water fluid signals during the second dissipation stage are analyzed, and the results are presented in Table 2. In Sample A, the bounding water content decreases from 63.3% to 18.5%, while in Sample B, it decreases from 77.0% to 25.4%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Permeability (mD)** & **4 h (\%)** & **8 h (\%)** & **16 h (\%)** & **32 h (\%)** & **64 h (\%)** & **96 h (\%)** \\ \hline A & 18.724 & 63.3 & 52.4 & 41.9 & 34.5 & 21.7 & 18.5 \\ B & 34.972 & 77.0 & 67.3 & 53.9 & 43.3 & 30.4 & 25.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The percentage change of bounding water fluid in the core with the dissipation time.  
Figure 6: The results of twodimensional NMR experiments conducted at various dissipation stages of sample A.  
The bounding water content at different dissipation times is statistically analyzed and regression-fitted, as shown in Figure 8. The samples exhibit a strong logarithmic relationship, with the specific fitting formulas as follows:  
\[\text{Sample A}:\ pw=-0.142 ln(t)+0.8236\quad R^{2}=0.9953, \tag{6}\]  
\[\text{Sample B}:\ pw=-0.166 ln(t)+1.0067\quad R^{2}=0.9981, \tag{7}\]  
where \(pw\) represents the remaining bounding water content, t denotes the dissipation time, and R signifies the fitting correlation coefficient, which exceeds 0.99, indicating high fitting accuracy. Furthermore, the initial bounding water content is influenced by the characteristics of the injected crude oil, particularly its viscosity. Low-viscosity crude oil, owing to its superior fluidity and smaller molecular size, displaces water more efficiently from small pores, resulting in reduced bounding water saturation.  
Figure 8: The relationship between the remaining mass percentage of irreducible water and exposure time.  
Figure 7: Schematic diagram of calculating the percentages of different fluid components using the twodimensional nuclear magnetic area integration method.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 4 Binary Graph Operations
### Union  
The _union_ of two graphs \(G_{1}\) and \(G_{2}\) with \(V(G_{1})=V(G_{2})\), denoted by \(G_{1}\cup G_{2}\), is defined as the graph with vertices \(V(G_{1})\) and edge set \(E(G_{1})\cup E(G_{2})\). Thus, two vertices are adjacent in \(G_{1}\cup G_{2}\) if and only if they are adjacent in \(G_{1}\) or in \(G_{2}\).  
In general, it is not possible to bound the tree-width (path-width) of the union of two graphs in terms of the tree-widths (path-widths) of the individual graphs. To see why this is the case, consider the following example.  
Example 4: _For \(m,n\in\mathbb{N}\), define the set of vertices \(V=\{v_{i,j}\mid 1\leq i\leq m,1\leq j\leq n\}\). Next, denote by \(G_{1}\) the disjoint union of \(m\) paths with \(n\) vertices from \(V\), \(P_{n}^{i}=(\{v_{i,j}\mid 1\leq j\leq n\},\)\(\{\{v_{i,j},v_{i,j+1}\}\mid 1\leq j<n\})\), \(1\leq i\leq m\), and by \(G_{2}\) the disjoint union of \(n\) paths on \(m\) vertices from \(V\), \(P_{m}^{j}=(\{v_{i,j}\mid 1\leq i\leq m\},\{\{v_{i,j},v_{i+1,j}\}\mid 1\leq i<m\})\), \(1\leq j\leq n\). Since paths have a tree-width (path-width) of \(1\), it follows from Theorem 4.2 that \(tw(G_{1})=tw(G_{2})=1\) (\(pw(G_{1})=pw(G_{2})=1\)) holds._  
_The union, \(G_{1}\cup G_{2}\), of \(G_{1}\) and \(G_{2}\) is an \(n\times m\)-grid graph, and [PERSON] [7] proved that the tree-width (path-width) of an \(n\times m\)-grid graph equals \(\min(n,m)\). Consequently, it is not possible to bound the tree-width (path-width) of \(G_{1}\cup G_{2}\) in the tree-widths (path-widths) of \(G_{1}\) and \(G_{2}\)._  
_See Figure 3 for an explicit example of \(G_{1},G_{2}\) and the resulting union \(G_{1}\cup G_{2}\)._
## 5 Case Studies
### Controller Structure and Parameter Identification  
The identification methodology leverages wind farm inertia response data from frequency disturbances, where simulation datasets vary with control strategies and are integrated into an iterative optimization framework (partially shown in Figure 5). After obtaining the model data, this paper sets the hyperparameters based on the reference [44], \(\lambda\) = 0.005 (sparsity regularization coefficient, promoting sparse model structures), \(\
u\) = 5 (optimize hyperparameters to control the distance between auxiliary variables and the sparse matrix), \(\varepsilon\) = 1 \(\times\) 10\({}^{-4}\) (stopping criterion), and limit the maximum number of iterations to 2000. A nonlinear feature library was developed containing frequency deviation, its derivative, active power deviation, the power change rate and frequency-power product term, ensuring the physically consistent identification of virtual/synthetic inertia dynamics.  
Figure 4: IEEE three-machine nine-bus standard system.
## 3 Results
#### MRI Assessment  
A total of one hundred twelve (112) TMJs were examined and each TMJ was treated as a single statistical unit.  
The study of MRI images revealed the following percentages of disc position in the total sample: normal disc position 41%, DDwR 51%, DDwoR 20%. Therefore, a higher prevalence of DDwR was observed. There was no statistically significant association between disc position and sex (\(p>0.05\)) or age groups (\(p>0.05\)). However, DDwoR did not occur in age group I, but was more frequent in GR-III (Table 1).  
A statistically significant association emerged between GJH and disc position, in the study group, 78% of patients showed a DD, compared to 46% of the control group (\(p=0.00\); \(\chi 2=12.431\); OR = 4.218). DDwR and DDwoR showed a higher presence in the study group (55% and 23%, respectively) compared to the control group (34% and 12%, respectively) (\(p=0.002\); \(\chi 2=12.592\)) (Figure 2).  
The analysis of the association between the GJH and DD severity showed statistically significant results (\(p<0.010\); \(\chi 2=13.196\)); the most severe DD showed a frequency of 70% in the study group compared to 30% in the control group. The specific percentages of each stage are illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Disc Position**} \\ \cline{2-7}
**Age Groups** & \multicolumn{2}{c}{**Normal**} & \multicolumn{2}{c}{**DDwR**} & \multicolumn{2}{c}{**DDwoR**} \\ \cline{2-7}  & **N** & **\%** & **N** & **\%** & **N** & **\%** \\ \hline GR-I & \multirow{2}{*}{7} & \multirow{2}{*}{17\%} & \multirow{2}{*}{7} & \multirow{2}{*}{14\%} & \multirow{2}{*}{0} & \multirow{2}{*}{0} \\ (8 to 9 years) & & & & & & \\ \hline Gr-II & \multirow{2}{*}{10 to 12 years} & \multirow{2}{*}{41\%} & \multirow{2}{*}{21} & \multirow{2}{*}{41\%} & \multirow{2}{*}{8} & \multirow{2}{*}{40\%} \\ 10 to 12 years & & & & & & \\ \hline Gr-III & \multirow{2}{*}{17} & \multirow{2}{*}{41\%} & \multirow{2}{*}{23} & \multirow{2}{*}{45\%} & \multirow{2}{*}{12} & \multirow{2}{*}{60\%} \\ 13 to 16 years & & & & & & \\ \hline Total & 41 & 100\% & 51 & 100\% & 20 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of disc position by age groups. (\(p=0.369\); \(\chi 2=4.285\)).  
Figure 2: Distribution of the disc position in control group and study group.  
Regarding the severity of DD and sex, there was no statistical correlation (\(p>0.05\)), but stage 3 and 4 of severity DD was more frequent in the female participants.  
A statistically significant association was observed between DD severity and age groups (\(p=0.017\); \(\chi 2=18.707\)), with a higher frequency of stages 2, 3, and 4 severity in age groups II and III, showing that with increasing age, the greater the severity of DD. The specific percentages of each stage are represented in Table 2.
# Strengthening Polymer Concrete with Carbon and Basalt Fibres
## 2 Methods and Materials
### Experimental Design and Research Programme  
All compression and bending test samples were measured and weighed prior to the work. The samples were left in the laboratory building for 10-20 h before testing. The temperature in the testing building was 20 \(\pm\) 2 \({}^{\circ}\)C. To test the bending and compressive strengths of the samples, a Liangong WAW-4106-E high-accuracy class hydraulic universal testing machine (Liangong Group, Jinan, China) with a maximum load of 1000 kN was used.  
Ten samples were mechanically tested for each selected composition. The statistical processing of the results of the experimental studies was performed using the software product Statistica 12.0. The samples with two types of reinforcing additives (basalt fibre and carbon fibre) were taken for testing. The control group of the samples without reinforcing additives was also tested. When choosing the percentage of reinforcement with carbon and basalt fibres, we based the choice on the data obtained from literature sources. Our analysis, provided in the introduction, showed that the optimum reinforcement percentage could vary from 0.3 to 2.5%. In a small number of works, the addition of 3-4% of fibres had a positive effect. The amount of the added fibre and its effect depended largely on the composition of the polymer concrete. Based on this, the following percentages of reinforcement were used in this work: 0.1, 0.2, 0.3, 0.4, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5, 6%. The analysis of previous works also showed that the greatest influence on the properties is the percentage of reinforcement. The length of reinforcing fibres can be chosen as the next factor in terms of the strength of influence on mechanical properties. There are few works devoted to the study of the influence of the length of reinforcing fibres. The available works most often show that for basalt and carbon fibres the optimum length is 10-24 mm. For carbon fibres, a value of 10-15 mm is more common, for basalt fibres 15-24 mm. Based on this we chose a fibre length of 10 mm for carbon fibres and 20 mm for basalt fibres. For these fibre lengths we tested specimens with different percentage of reinforcement. Then, for the percentage of reinforcement where maximum strengthening was achieved, we investigated the effect of the length of the reinforcing fibres. Fibre lengths of 5, 10, 15, 20, 25 mm were used.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 5 Experiment Results and Analysis
### Experiment on Non-Destructive Testing of Anchor Bolts
#### 5.1.2 IPE Piezoelectric Acceleration Sensor  
In this study, the Tektronix AFG3052C signal generator (From China Tektronix Technology Co., Ltd., Shanghai, China) was employed to generate a five-period sine wave with an excitation frequency of 30 kHz as the excitation signal. The excitation signal is emitted from point A, the transmitting end of the sensor, and received at point B, the receiving end of the sensor. Subsequently, it is transmitted to the DHDAS dynamic signal acquisition system. The signal acquisition process is finalized after passing through a modulated Butterworth filter for effective filtering. The non-destructive testing process of the anchor bolt is shown in Figure 10. After 12, 24, 36, 48, 60, and 72 h of corrosion, the specimens were subsequently positioned within the anchoring system and securely affixed using an anchoring agent. Then, ultrasonic guided wave detection was conducted, wherein the resulting waveforms were meticulously documented. The actual signal acquisition experiment is illustrated in Figure 11.  
Figure 10: Schematic of non-destructive testing process.  
Figure 9: MS-CNN framework.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Length/m** & **Diameter/mm** & **Poisson Ratio** & **Elasticity Modulus/GPa** & **Density kg/m\({}^{3}\)** \\ \hline Anchor Bolt & 1.5 & 20 & 0.3 & 210 & 7850 \\ Anchor agent & 1.0 & 32 & 0.2 & 25 & 2300 \\ Surrounding rock & 1.0 & 200 & 0.25 & 60 & 2600 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Corrosion results.
# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
## 2 Related Works
The available edge computing systems can offer scalability, but the major challenge is developing a system that is accurate and fast enough to enable real-time or at least near-real-time processing. This challenge is discussed in detail by [PERSON] and [PERSON] [15], utilizing 20 single camera views and a lightweight deep learning architecture coupled with edge computing devices. The authors utilized a fixed camera setup coupled with different edge devices including Nvidia Jetson TX2, Nvidia Xavier NX, and Nvidia AGX Xavier. The proposed method is effective despite some limitations, e.g., detection accuracy, optical occlusions caused by nearby reflective surfaces, and inherentreal-world implementation challenges. Similarly, the modularity of UAVs has enabled scientists to mount edge computing systems on drone systems. One of these experiments includes a DJI Phantom 3 retrofitted with an NVIDIA Xavier NX system under the moniker of a \"MultEYE\" system designed especially for real-time vehicle detection, tracking, and speed estimation. The system consists of a YOLOv4 detector coupled with a minimum output sum of squared error (MOSSE) tracker. To estimate the vehicle speed, the onboard system calculated ground sampling distances by taking the camera's UAV altitude, pixel width, and focal length. The mean average error observed is remarkably low, with a figure of 1.13 km/h. UAVs have the potential to not only reduce optical occlusions encountered but also provide an aerial perspective, greater mobility, and freedom for enhanced spatial coverage. Previous research works exhibit great potential for fixed cameras and hovering UAVs but lack the practical implementation for moving camera platforms. Our research aims to devise a solution that works with both stationary and nonstationary aerial footage with notable accuracy. The developed solution can also log the vehicle trajectories geospatially, enabling superior analytical capabilities and offering value for intelligent RTM systems.
## 5 Results and Discussion
### Impact of the Proposed Enhancement Method on the Segmentation Process  
Figure 10 depicts the validation of the McCann Retinex algorithm in the context of retinal blood vessel segmentation and brain tumor region segmentation. We used the DRIVE [46] and STARE [34] databases for retinal blood vessel segmentation and the Onsis brain MRI database for brain tumor segmentation.  
We obtained segmented vessels and a segmented tumor region using our proposed postprocessing steps [16]. The measurement parameter section defined the sensitivity, specificity, accuracy, and AUC used to evaluate the performance. We obtained a sensitivity of 0.801 and 0.782, a specificity of 0.953 and 0.951, an accuracy of 0.953 and 0.0949, and an AUC of 0.841 and 0.836 on the DRIVE and STARE databases' segmented images of retinal vessels, as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **HE** & \multicolumn{2}{c}{**CLAHE**} & \multicolumn{2}{c}{**BBHE**} & \multicolumn{2}{c}{**Proposed Method**} \\ \hline Database: & & & & & & & & \\ Images & PSNR & Contrast & PSNR & Contrast & PSNR & Contrast & PSNR & Contrast \\ Types & & & & & & & & \\ \hline HRF & & & & & & & & \\ \begin{tabular}{c} Database: \\ Low \\ Quality \\ Images \\ \end{tabular} & 30.12 & 18.17 & 32.76 & 21.34 & 29.89 & 17.83 & **39.58** & **26.46** \\ \hline HRF & & & & & & & \\ \begin{tabular}{c} Database: \\ Quality \\ Images \\ \end{tabular} & 32.83 & 21.56 & 34.12 & 24.56 & 31.07 & 20.98 & **41.69** & **44.61** \\ \hline \begin{tabular}{c} Onsis \\ Database: \\ Dataset \\ Dataset \\ Images \\ \end{tabular} & 19.12 & 39.30 & 21.22 & 40.98 & 20.01 & 38.12 & **23.73** & **63.39** \\ \hline \
\begin{tabular}{c} Onsis \\ Database: \\ Sagittal \\ Plane \\ Images \\ \end{tabular} & 20.34 & 27.21 & 20.97 & 29.01 & 19.94 & 28.98 & **24.55** & **41.02** \\ \hline
\begin{tabular}{c} Onsis \\ Database: \\ Axial Plane \\ Images \\ \end{tabular} & 18.95 & 41.01 & 22.01 & 42.97 & 21.05 & 42.02 & **23.46** & **69.79** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the Proposed Method on the Databases Based on Category.  
Figure 10: Impact of the Proposed Enhancement Method on the Segmentation Process: Application of Retinal Blood Vessels and Brain Tumor Segmentation.  
We achieved a sensitivity of 0.83, a specificity of 0.95, and an accuracy of 0.97 on the Oasis database of the brain MRI and the segmented brain tumor region; as shown in Figure 12, the tumor was detected. This performance demonstrates the capability of the enhancement techniques. They can be used in many medical imaging applications to diagnose eye diseases and allow early image details.  
The computation time was based on the size of the database, because some databases contain large images, particularly in medical imaging. Some images are large in size and require significant processing time. In our proposed algorithm, the image enhancement technique on the HRF image database took 6.15 s, and on MR brain images, it took 4.23 s. With the postprocessing module, it took 7.91 s on the HRF database and 5.65 s on the MRI brain images.
# Deep Learning Methods for Space Situational Awareness
## 3 Case Study
### Neural Network Frameworks
AlexNet demonstrated significantly higher performance by achieving high accuracy on very challenging datasets, and it can be credited with bringing deep learning to other fields, such as natural language processing and medical image analysis [118]. * GoogLeNet, developed by Google, was responsible for creating a new state of the art for classification and detection tasks in the ILSVRC. It also has been used for other computer vision activities, such as face detection and recognition, or in adversarial training. GoogLeNet's architecture is 22 levels deep, with 27 pool levels included. There are nine starter modules stacked linearly. The ends of the startup modules are connected to the global average pooling level. Since neural networks are time-consuming and expensive to train, the number of input channels is limited. The first level of convolutions uses a filter size of 7 \(\times\) 7, which is relatively large compared to other kernels within the network. The main purpose of this layer is to immediately reduce the input image without losing spatial information. To address the overfitting problem, the GoogLeNet architecture was created with the idea of having multi-dimensional filters that can operate on the same level. With this idea, the network becomes wider rather than deeper. For ILSVRC 2014, GoogLeNet ranked first with an error rate of 6.67% [119].
# Wavelength Selection for Satellite Quantum Key Distribution
## 5 Results and Discussion
### Wavelength Selection  
Figure 3 shows QKD performance across the electromagnetic spectrum, highlighting suitable windows in Tucson (top) and Waterford (bottom). The \(\lambda,D_{r}\) pair that produces the highest secret key rate in each region are selected for further analysis. To account for variations in the initial beam waist, we present secret key rates for \(w_{0}=10\) cm (shown on the left), which replicates the size limitations imposed by a CubeSat, and \(w_{0}=35\) cm (shown on the right), which replicates larger satellites. It can immediately be seen from Figure 3 how the optimum wavelength varies with the initial beam waist. It is interesting to note the following: (i) in both scenarios (Tucson and Waterford), the optimum wavelength increases with the initial beam waist, (ii) the secret key rate is high across a range of wavelength windows from 500 nm-5 \(\upmu\)m for both initial beam waists, (iii) the secret key rate is higher in Tucson than Waterford, and (iv) within each wavelength window, the secret key rate is higher for an initial beam waist of 35 cm than 10 cm across a wider range of \(D_{r}\) values. These observations assist in the selection of the optimum \(\lambda,D_{r},w_{0}\) combination in the next section.
## 2 Preliminaries
### System Model  
Figure 1 illustrates the blockchain-based handover authentication architecture within VANETs. This architecture includes four key components: the TA, RSUs, vehicles, and, finally, blockchain. Each component has a specific, well-defined role:  
1. TA: The TA disseminates vital public system parameters. During the registration phase, it selects identities and private keys for the RSUs and issues smart cards to vehicles.
2. RSUs: RSUs are crucial for vehicle authentication before establishing communication links. They have the computational capacity for authenticating in-zone vehicles and act as ledger guardians and transaction nodes on the blockchain. In handover scenarios, RSUs verify vehicle authenticity using blockchain data.
3. Vehicles: Vehicles, equipped with computationally limited OBUs, register with the TA and authenticate with RSUs to facilitate real-time traffic and entertainment data transmission.
4. Blockchain: RSUs function as active nodes in the blockchain. After successful vehicle-to-RSU authentication, RSUs upload key elements--temporary identity, randomized data, and vehicle hash--to the blockchain, enabling quick vehicle identity authentication using the stored information.
## 2 Materials and Methods
### Evaluation Metrics  
In semantic segmentation, different positions and targets may appear in each RS image, necessitating a comprehensive evaluation of the model's performance at each location and for each target. Specifically, we used the Mean Intersection over Union (_MloU_), F1-Score, and loss function as evaluation metrics to assess, in a comprehensive and objective manner, the performance of our improved lightweight structure's DeepLabv3+ model in the task of RS image semantic segmentation.  
The Mean Intersection over Union (_MloU_) [17] is a key performance metric for segmentation. For multi-class tasks, the calculation of the intersection over union (_IoU_) for each category is treated as a binary classification task: belonging to that category or not. Subsequently, the average across multiple categories is taken, and the calculation method based on the confusion matrix is shown as in the equation below. The formula for the _MIoU_ is as follows:  
\[MIoU=\frac{1}{n}\sum_{i=1}^{n}\frac{a_{ii}}{\sum_{j=1}^{n}a_{ij}+\sum_{j=1}^{n }a_{ji}-a_{ii}}. \tag{1}\]  
By considering the segmentation accuracy for each category, a detailed assessment of individual targets can be provided, emphasizing the model's performance at the pixel level. In RS images, different positions and targets may appear in each image, and introducing the _MIoU_ helps to evaluate the overall accuracy of the model, not just focusing on the performance for a single target.  
_F1-Score_[18]: By comprehensively considering _precision_ and _Recall_, it is suitable for situations with class imbalance and takes into account both misclassification and omission. The _F1-Score_ is calculated as the harmonic mean of _precision_ and _Recall_. _Precision_ and _Recall_ are defined as follows:  
\[Precision=TP+FPTP \tag{2}\]  
\[Recall=TP+FNTP \tag{3}\]  
\[F1=Precision+Recall2\cdot Precision\cdot Recall. \tag{4}\]  
In the formula: True Positive \(TP\) refers to the number of samples predicted as positive, i.e., the number of positives correctly detected by the model; False Positive \(FP\) indicates the number of samples predicted as positive, which are actually negatives mistakenly predicted as positives by the model; False Negative \(FN\)) represents the number of samples predicted as negative, which are actually positives incorrectly predicted as negatives by the model. In the context of the segmentation of RS images, due to the fact that RS images typically contain multiple categories of targets with an uneven distribution, relying solely on precision or recall may lead to performance bias. The _F1-Score_, by balancing these two metrics, effectively addresses the issue of class imbalance, ensuring the model's comprehensive and accurate recognition of all categories, thereby enhancing the accuracy and reliability of the overall segmentation effect.