esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 2 The Proposed Method
### The Transmission Tower Detection Based on the Improved YOLO
#### 2.2.2 YOLOv8  
YOLOv8 [17], an improved iteration based on the network structure of YOLOv5, is the latest model introduced by Ultralytics in 2023. Firstly, when processing image data, the initial convolution kernel size is adjusted to 3 \(\times\) 3. Secondly, YOLOv8 improves network performance by incorporating the C2f module, which encompasses the C2f_n_F and C2f_n configurations. This replaces the C3 module used in YOLOv5, consisting of the C3_n_F and C3_n. Thirdly, YOLOv8 adopts the decoupled head (anchor-free head). The decoupled head approach may accelerate training speed and enhance model accuracy. YOLOv8 network architecture and its modules are illustrated in Figure 4. However, on the flip side, it may encounter issues with regression task misalignment. Hence, the DistributedFocal Loss (DFL), an enhanced version of the focal loss, is employed as a loss function for regressing the distance from the points to the bounding boxes. Consequently, the loss function of YOLOv8 comprises classification loss, regression loss, and DFL loss. Thus, the total loss for YOLOv8 is delineated as depicted in Equation (2):  
\[Loss=\lambda_{1}L_{cls}+\lambda_{2}L_{box}+\lambda_{3}L_{dfl} \tag{2}\]  
where \(L_{cls}\) represents classification loss, \(L_{box}\) represents bounding box regression loss, \(L_{dfl}\) represents DFL loss, \(\lambda_{1}\) is the weight of the classification loss, \(\lambda_{2}\) is the weight of the bounding box regression loss, \(\lambda_{3}\) is the weight of the DFL loss.  
Figure 4: YOLOv5n-conv head and YOLOv8 architecture.
## 2 Principle of the Proposed Algorithm and Analysis of Its Residual Errors
### Principle and Process of the Proposed Algorithm
\begin{array}{ccccc}A_{1}&A_{2}&&&\\ &A_{1}&A_{2}&&\\ &&\cdots&\cdots&\\ &&A_{1}&A_{2}\\ B_{1}&B_{2}&&\\ &&B_{1}&B_{2}&\\ &&\cdots&\cdots&\\ &&&B_{1}&B_{2}\end{array} \right]_{2(N-1)\times N}\]  
\[ \begin{array}{ccccc}A_{2}=-B_{1}=\left[\begin{array}{cc}O&I_{N-1}\end{array} \right],\\ &&B_{2}=-A_{1}=\left[\begin{array}{cc}I_{N-1}&O\end{array}\right],\\ W=\left[\begin{array}{cc}W_{1,1}&W_{2,1}&\cdots&W_{N,1}&W_{1,2}&\cdots&W_{N,2}& \cdots&W_{1,N}&\cdots&W_{N,N}\end{array}\right]^{T}\end{array} \]  
\[f(x)=f(x_{0})+f^{(1)}(x_{0})(x-x_{0})+\frac{f^{(2)}(x_{0})}{2!}(x-x_{0})^{2}+ \ldots+\frac{f^{(n)}(x_{0})}{n!}(x-x_{0})^{n}+R_{n}(x), \tag{11}\]  
where \(R_{n}\) is the expansion residual error, which is obtained by Lagrange residual term expansion as follows:  
\[R_{n}(x)=\frac{f^{(n+1)}(\xi)}{(n+1)!}(x-x_{0})^{n+1}, \tag{12}\]  
Here, \(\xi\) is a value between \(x\) and \(x_{0}\). Figure 4 shows the Taylor expansions on both sides of the equation for the reconstructed difference operator using the Lagrange residue term form for the methods in this paper and those produced by [PERSON] et al., respectively, with [PERSON] et al. 's \(x_{0}\) being chosen to expand at the midpoint of the diagonal position. The value of \(x-x_{0}\) is therefore \(\frac{\sqrt{2}h}{2}\). The error brought into Equation (2) can be expressed as  
\[\text{Re1}=h^{5}\big{(}\frac{W^{(5)}(\xi)\times\sqrt{2}^{5}}{5!\times 2^{4}}- \frac{W^{(4)}(\xi)\times\sqrt{2}^{5}}{4!\times 2^{4}}+\frac{W^{(3)}(\xi) \times\sqrt{2}^{5}}{3\times 3!\times 2^{4}}\big{)}, \tag{13}\]  
where \(W^{n}\) is the \(n\)th-order derivative of the wavefront. Similarly, the method \(x_{0}\) proposed in this paper is chosen to expand at the midpoint of the horizontal and vertical positions so that the value of \(x-x_{0}\) is \(\frac{h}{2}\) with the following error of the form:  
\[\text{Re2}=2h^{5}\big{(}\frac{W^{(5)}(\xi)}{5!\times 2^{4}}-\frac{W^{(4)}(\xi)}{ 4!\times 2^{4}}+\frac{W^{(3)}(\xi)}{3\times 3!\times 2^{4}}\big{)}. \tag{14}\]  
By comparing the two equations, it can be seen that the method proposed in this paper effectively shortens the reconstruction interval by splitting the subregion to introduce the diagonal information of the reconstruction method, which reduces the reconstruction error of the differential operator and improves the reconstruction accuracy compared with previous studies.
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 3 Data Analysis for Interferometry and Real-Time Tomography
### Tomographic Reconstruction
#### 3.2.1 Maximum Likelihood-Expectation Maximization  
The basic principle of this algorithm is to advance an initial guess of the distribution iteratively by comparing the forward projected data of the current guess with the measured data from all angles. The \(n\)-th estimate of the \(i\)-th voxel is named \(x_{i}^{n}\). Often, a homogeneous distribution is chosen as the initial guess \(x_{i}^{0}\). The ML-EM step that advances the guess is computed by the following equation [19]:  
\[x_{i}^{n+1}=x_{i}^{n}\frac{1}{\sum_{j}A_{ij}}\sum_{j}A_{ij}R_{j}^{n},\]  
where \(R_{j}^{n}\) is the ratio of the value of measurement pixel \(y_{j}\) and the forward projected data of the \(n\)-th estimate:  
\[R_{j}^{n}=\frac{y_{j}}{\sum_{k}A_{kj}x_{k}^{n}}.\]The matrix \(A\) is the system matrix, which accounts for how much each voxel contributes to each measurement. By this procedure, the advanced guess will approach a distribution that is compatible with all measurements. More precisely, the computed guess converges to a distribution that has a high probability (maximum likelihood) to be the original distribution, given the measured data. First, the ML-EM algorithm is applied to a single phase projection from the undisturbed gas jet. The reconstructed radial density distribution agrees well with the result from Abel inversion (Figure 8). The Abel inversion shows non-smooth behavior around \(r=0\) due to the singularity. This problem does not arise with the ML-EM algorithm.  
To validate the performance of the algorithm on non-rotational symmetric distributions, tests with known distributions are carried out. A 2D Gaussian multiplied by a step function is chosen, as the shock wave by the razor blade is expected to have a similar shape. Figure 9 shows the generic distribution, as well as the reconstructed image from \(N_{a}=7\) projections after 15 iterations. Random noise distributed according to a Gaussian distribution with a standard deviation of \(\sigma=0.01\) is added to the projection data before running the ML-EM reconstruction, and qualitative reconstruction of the original distribution without rotational symmetry is achieved.  
Figure 8: Test of ML-EM on the undisturbed (rotationally symmetric) gas distribution. The distance from the nozzle is given in mm; the corresponding pixel row is placed in brackets. In general, good agreement is found between the Abel inversion (**A**) and ML-EM after 15 iterations (**B**). The problem due to the singularity of the Abel inversion at \(r=0\) does not arise with ML-EM.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 3 The Proposed Design Method
### Specific Design Steps
#### 3.2.1 Sender Encryption Steps  
First, the sender part is displayed and analyzed, and the encryption block diagram of the sender is shown in Figure 3.  
A.  
The sender generates a chaotic sequence \(z(n)\) by \(m=a_{1}b_{1}a_{2}b_{2}a_{3}b_{3}\cdots\), where \(a\) represents the starting point and \(b\) represents the initial value and the length of the sequence. For example, \(m=A0.55C0.84\cdots\), where \(a_{1}=A\), \(b_{1}=0.55\), \(a_{2}=C\), \(b_{2}=0.84\), the meaning represented is: \(A\)'s ASCII code is 65, \(C\)'s ASCII code is 67. First, take an initial value of 0.55 and start iterating to generate the sequence, counting from the 65 th bit of the sequence until the sequence length reaches 5500, then obtaining \(x_{1}(n)\). Then, take the initial value of 0.84 to start generating the sequence, counting from the 67 th bit of the sequence, until the sequence length reaches 8400, and get \(x_{2}(n)\), for the rest, generate the encryption sequence according to this rule. The specific process is shown in Figure 4.  
The entire iterative process uses the Logistic mapping function, as shown in the following formula:  
\[x_{n+1}=\mu x_{n}(1-x_{n})\quad x_{0}\in(0,1),\quad\mu=4 \tag{2}\]  
B.  
The encrypted sequence generated above is XORed with the information sequence to obtain an encrypted sequence, which is then modulated and transmitted.  
C.  
The sender also needs to encrypt \((n,e)\) to \(m\) with the public key published by the receiver and needs to send the encrypted ciphertext \(c\) to the receiver.  
Figure 4: Sequence generation diagram.  
Figure 3: Transmitter encryption block diagram.
# Predicting User's Measurements without Manual Measuring: A Case on Sports Garment Applications
### Research Objective  
The use of shape models has proven its use in past research projects, but has mostly been applied for designs on rigid products [14; 19; 20], e.g., helmets [21], glasses, EEG-headsets [22], headphones, and masks, rather than for textile fabrics. By contrast, this research aims to make the first attempts to provide a comparable assessment and methodology for stretchable, deforming garments that have a different take on fit [23; 24]. This article specifically focuses on the case of cycling garments.  
Based on the previously mentioned concerns, there are still great opportunities for the optimization of the process to determine a customer's optimal size without having to take measurements or fit the set of clothing. In order to fill this gap, this research aims to predict a user's garment sizes, by the means of enquiring commonly known parameters (such as age, gender, weight, and stature) to predict chest and hip circumferences, the relevant parameters for cycling garments in this case study. This is predicted through either mathematical shape models or a simplified statistical regression model. To compare additional input options, a 3D scanning method is added to provide additional input measurements to the shape model prediction, thus hoping to increase accuracy with the downside of needing physical measurements. To make statistical comparison possible, all experiments will be compared to their manually measured circumferences that are considered as ground truth throughout the experiment to make statistical analysis possible. Later, the effect on garment sizes is also considered, but with the added complexity of personal preference and personal bias, limiting the statistical insight.  
In addition, an extra study has been performed to improve size predictions for female subjects, using the bra size--another parameter that is mostly known by heart--to provide the model with information on the body proportion.
## 5 Attributes and Characteristics of FL
### Data Privacy
#### 5.1.2 Motivations for Enhanced Data Privacy  
Empowering original data is a significant aspect of FL [13]. Data remain at their origin, whether generated or stored, which ensures that data owners maintain control over their sensitive information, reducing risks and legal liabilities. Mechanisms such as secure aggregation, which uses cryptographic protocols to mix model updates from different nodes [65], help to maintain privacy during the aggregation process. Differential privacy, another key technique, ensures that the output remains statistically indistinguishable regardless of the individual data's inclusion, providing strong mathematical privacy guarantees. Homomorphic encryption and SMC further emphasize FL's commitment to privacy [66]. Enhanced data privacy in FL is driven by technological, ethical, and legal considerations, particularly for sectors like healthcare and finance.
## 3 Results and Discussion
### Catalysts Characterization
In order to analyze the impact of TiO\({}_{2}\) loads onto the Leca surface during the impregnation, the thickness layer for the immobilized TiO\({}_{2}\) onto the support using loads of 3.6% \(w/w\) and 5% \(w/w\) was also measured (Figure 9). The layer thickness for both loads is very similar despite the difference in the TiO\({}_{2}\) impregnated amount. This means that for the considered TiO\({}_{2}\) loads, there are no improvements in the TiO\({}_{2}\) layer thickness. Moreover, the 3.6% \(w/w\) reveals that the layer is not uniform since the thickness ranges between 7.6 and 10.9 \(\upmu\)m (Figure 9a). On the other hand, the different points selected for 5% \(w/w\) show a more uniform layer thickness between 8.03 and 8.25 \(\upmu\)m (Figure 9b). This result indicates a better dispersion was obtained for 5% \(w/w\) than 3.6% \(w/w\). When considering the photocatalytic oxidation using different TiO\({}_{2}\) loads, the 5% \(w/w\) shows a higher degradation rate of SMX compared with the 3.6% \(w/w\) (Figure 4). Figure 10 shows a SEM image with elemental chemical mapping highlighting the TiO\({}_{2}\) coating of a surface pore. In particular, the pink color represents the distribution of Ti in  
Figure 8: Elemental composition of Leca particles at the core and surface of the impregnated catalyst. Figure 9: TiO\({}_{2}\) layer thickness measurement through SEM for the different TiO\({}_{2}\) loads, (**a**) 3.6% \(w/w\) and (**b**) 5% \(w/w\). the Leca particle, while the blue color marks the distribution of Si. The mapping reinforces the evidence that in the chemical composition of the 5% TiO\({}_{2}\)/Leca structure, the most abundant elements are Ti and Si. The surface of the cut section highlighted in the circle of Figure 10a reveals that TiO\({}_{2}\) does not remain on the surface only. This is due to the connectivity of pores of the Leca from the surface to deeper zones of the particle. Moreover, Figure 10b proves that the TiO\({}_{2}\) impregnated enters the pore (marked section in Figure 10a) and occupies it in great deep, meaning that TiO\({}_{2}\) can continue in the Leca during different cycles of use.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 4 Seismic Capability Index System of Highway Tunnels
### Analysis of the Evaluation Index  
In the past, the project was evaluated in terms of three aspects: environmental factors, material factors and human factors. In the evaluation of the earthquake disaster resistance capability of highway tunnels during the operation period, environmental factors can be defined as engineering geological factors, material factors can be defined as tunnel structural factors and human factors can be defined as operation management factors.
## 4 Conclusions  
On the bases of the simulation and experimental investigations of the HTB of the star wheel shaft in a WSSC, remarkable achievements were made, and the following main conclusions can be drawn.  
1. A new structure of a water-lubricated HTB of star wheels was developed, in which an annular groove with numerous crossing short grooves was proposed.
2. The lubrication characteristics were compared between HTBs with two types of grooves. The results show that the bearing capacity of an HTB with an annular groove is larger than that of an HTB with a crossing groove. However, water supply is also increased. The HTB with a crossing groove is more suitable for WSSCs because of the lower water consumption on the premise of equivalent bearing capacity.
3. A distinctive HTB test rig with an electromagnet loading system was designed and constructed to test and verify the new design and calculation results of a star wheel HTB's lubricating state in WSSCs. There was good agreement between the experimental results and simulation results.
4. A proper range of hydrostatic lubrication film should be limited in the range between 0.015 mm and 0.06 mm for the star wheels of WSSCs. A new structure of a star wheel HTB proposed in this research can offer reasonable bearing capacity in this limited water film thickness, even if the gas load acting on HTB varies substantially.  
In the experiment, only axial force was applied to the HTB, and the radial bearing just bore the weight of the shaft itself. Our analysis neglects the radial displacement. We readily admit that a short test may not fully reflect the performance of the HTB in a WSSC. The results of this research may be useful for design of an HTB for WSSCs with large displacement and a high compression ratio. An important direction for further work might be to study the performance of the HTB with crossing groove in WSSCs during long-term operation. Additionally, a further experiment should be conducted with a more sophisticated measuring system with axial and radial displacement.  
The individual contributions are as follows: methodology, W.Z.; software, J.X.; validation, W.Z., C.P. and J.X.; investigation, J.X.; data curation, C.P.; writing--original draft preparation, J.X.; writing--review and editing, Q.F.; supervision, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Collection and Identification of Factors  
The Sendai Framework was followed to identify flood indicators in assessing flood disaster risk, hence identifying the parameters and the data that need to be collected. There were three identified categories of flood risks parameters, such as (a) hazard, (b) vulnerability, and (c) exposure. The hazard parameters considered were: Average annual rainfall, slope, elevation, soil type, and flood depth. The parameters for vulnerability that were considered were: Gender ratio, age, average income, physical health of the individual, educational attainment, water usage, emergency preparedness, types structures, and proximity to the evacuation center. The parameters for flood exposure were: Population density, number of households, and land use/details are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Parameter** & **Data Type** & **Duration/Year** & **Source** \\ \hline \multicolumn{5}{c}{**Flood Hazard Parameters**} \\ \hline
[8,17,35,39,40,41] & Average Annual Rainfall & Interpolated Climatological Normal using & \multirow{2}{*}{2020} & PAGASA and web search \\  & & Isobreal Method & & \\  & & & coordinates \\
[8,17,26,32,35,41] & Slope & Derived from IfSAR Data using Slope Tool & \multirow{2}{*}{2013} & \multirow{2}{*}{(NAMIA-DENR)} \\
[6,8,26,32,35,41] & Elevation & Derived from IfSAR Data using Field & \multirow{2}{*}{2013} & \multirow{2}{*}{(NAMIA-DENR)} \\  & & Contour Tool in ArcMap & & \\
[8,9,17,42] & Soil Type & Shapefile from the archive of CLUP & & \\  & & & Romblom—(CLUP) \\
[37,43,44, 45] & Flood Depth & HEC-HMS and HEC-RAS and MGB Flood & \multirow{2}{*}{2018} & MGB \\  & & Susceptibility Map & & \\ \hline \multicolumn{5}{c}{**Flood Vulnerability Parameters**} \\ \hline
[39,43] & Gender Ratio & Men to women gender ratio & 2020 & Barangay Profile \\
[43] & Average Age & Mean age of the individual & 2020 & Barangay Profile \\
[31,43] & Average Income & Annual average income per household & 2020 & Barangay Profile \\
[39,46] & Number of Persons & Number of PWD in barangay & 2022 & Barangay Management \\  & with Disabilities & & System (BMS) \\
[43,46] & Highest Educational & Average educational attainment of & 2020 & Barangay Profile \\
[43,46] & Attainment & individuals in barangay & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters with the type of data used, duration/year, and source used for hazard, vulnerability, and exposure assessment.  
Figure 1: Imagery map with barangay and municipal boundaries of the study area.
# Recent Developments in the Field of the Metal-Insulator Transition in Two Dimensions
## 5 Band Flattening: Possible Condensation of Fermions
At relatively high electron densities (\(n_{\rm s}>0.7\times 10^{15}\) m\({}^{-2}\)), this effect is not important because the single-particle spectrum does not change noticeably in the interval \(\Delta p\), and the behaviors of the energy-averaged effective mass and that at the Fermi level are similar. However, decreasing the electron density in the range \(n_{\rm s}<0.7\times 10^{15}\) m\({}^{-2}\) stimulates the flattening of the spectrum so that the effective mass at the Fermi level, \(m_{\rm F}=p_{\rm F}/V_{\rm F}\), continues to increase (here, \(V_{\rm F}\) is the Fermi velocity). In contrast, the energy-averaged effective mass does not, being not particularly sensitive to this flattening.
## 9 Challenges and Limitations
For instance, a limitation is represented by comprehensive protein coverage: mass-spectrometry-based proteomics may not detect the entire protein spectrum, potentially missing proteins present in smaller quantities. Additionally, proteomics has to face data analysis challenges; in fact, analyzing proteomic samples requires advanced computational methods, and variability in methodologies can make cross-study comparisons difficult [138; 139]. Facing these challenges is crucial for the effective application of untargeted metabolomics and proteomics in understanding and diagnosing periodontitis. Moreover, the implementation of salivary biomarker testing in clinical practice also presents considerable ethical and practical challenges. Ethically, patient privacy and data security must be ensured, as the collection and analysis of salivary biomarkers involve sensitive health information. Informed consent is another fundamental aspect, patients must fully understand the purpose, the risks, and the implications of testing to make informed decisions [140; 141]. Additionally, issues of equity and accessibility must be considered to prevent healthcare disparities and to ensure that these diagnostic tools are available to diverse populations. Another challenge is represented by the integration of salivary biomarker testing into routine clinical workflows, requiring proper training for dental professionals and updates to electronic health record systems. Regulatory approval is another significant obstacle, as agencies such as the FDA or EMA must evaluate and approve these tests before they can be widely adopted. The classification of salivary biomarker tests also has an impact on their regulatory pathways and reimbursement policies [142]. Furthermore, the cost and economic feasibility of these diagnostics must be carefully assessed, as developing and maintaining reliable biomarker tests can be expensive. Evaluating their cost-effectiveness compared to traditional diagnostic methods is essential for sustainable implementation and for ensuring equal access to new diagnostic methods for people worldwide, without leaving aside the populations that cannot afford their costs [143; 10; 144; 145; 146; 147]. Solving these ethical and practical challenges is important to successfully integrating salivary biomarker testing into clinical practice, ensuring both patient benefit and scientific reliability.