esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Coating Technologies and Manufacturing Strategies  
PLD [3] is a thin film deposition technique in which the material to be deposited is evaporated from the source target through an intense laser pulse. A dense vapour of \"ablated\" material is deposited on the substrate placed in front of the target and, then, it is adsorbed until it forms the thin film. PLD characterization demonstrated the feasibility of the scaling-up of the PLD technique to coat WCLL water pipes and BB internals before the assembly, but it is not possible to coat complex geometry or welds. Therefore, it is planned to combine PLD technique with ALD, a Chemical Vapor Deposition (CVD) process that allows to coat all internals [6]. The ALD is a technology for the growth of thin and ultra-thin films with a very accurate thickness control (of the order of 0.5 A) and with high uniformity and conformity. These characteristics are obtained thanks to a deposition of individual atomic layers in succession. Moreover, one of the main advantage of PLD and ALD coating process is the deposition temperature lower than 200 \({}^{\circ}\)C that doesn't affect the mechanical or microstructural properties of EUROFER welds.  
Corrosion compatibility tests carried out with PLD and ALD techniques have not shown corrosion attack of EUROFER or coating degradation in static LiPb at 550 \({}^{\circ}\)C after 2000 h, 4000 h and 8000 h and in flowing LiPb at 0.5 m s\({}^{-1}\), 500 \({}^{\circ}\)C at 1000 h, 2000 h and 4000 h [4]. In the frame of EUROfusion FP9 project, the coatings will be characterized up to 16,000 h in LiPb. Moreover, PLD coating has demonstrated to be able to reduce the tritium permeation flux on the EUROFER martensitic/ferritic steel substrate, the BB structural material, at least of a factor 10,000 under electron irradiation combined with thermal cycling and of more than 200 under neutron irradiation [7,8]. PLD coating, characterized in the temperature range between 20 \({}^{\circ}\)C and 450 \({}^{\circ}\)C, works also as electrical insulator, reducing the MHD effect. The main limitation of the PLD process is intrinsic to the technique, since it can only coat external surfaces. ALD technology is very promising in terms of capability to reduce tritium permeation flux and the possibility to be applied on complex geometries even at low temperature. Therefore, it is planned first to coat the pipes of the mock-upwith PLD process, then to bend the tubes and to weld them with the support structure and finally to perform ALD coating inside the mock-up to cover all the welds.  
In order to validate the quality of the coating manufactured in the TBM/BB a dedicated procedure was developed by IIT, the so called Electrochemical Quality Test (EQT): the EQT exploits the conductivity of the EUROFER and the high resistivity of the oxide coatings. The part is submerged in an electrolyte and by Electrical Impedance Spectroscopy (EIS) is possible to measure the resistance and capacitance of the system. By modelling the data, it is possible to extract an average defect density and coating resistivity on the whole coated component, even of complex shape, like a breeding blanket module.
## 3 Data and Methods
### Remote Sensing Images Processing
The partitioning into training and testing areas were based on a random selection of the pixels within region of interest. The training areas were selected based on spectral responses from various combinations of spectral bands (3,2,1; 4,3,2; and 5,4,3 for Landsat 5, and 4,3,2; 5,4,3; and 6,5,4 for Landsat 8). The training sets were created by digitizing polygons on the image. Then, the image processing software system performed a signature analysis that involved a statistical characterization of the training areas. Once a statistical characterization was achieved for each information class, the image was classified byexamining the reflectance for each pixel and by making a decision about which of the signatures it resembled most [82]. The post-processing operations included exporting the classified data as a vector format (polygon) and further analysis was performed in ArcGIS for Desktop10.5, developed by Environmental Systems Research Institute (ESRI), Redlands, CA, United States. In order to test the accuracy of the classifications, which is a crucial step in the aerial imagery classification process [83], two confusion matrices, one for each year using ground truth testing areas, were constructed. Based on the confusion matrices, the kappa index was also determined to estimate the accuracy. It should be noted that we decided to use a \"standard\" post classification comparison method (using a confusion matrix), and the changes obtained were mostly visible for areas converted from forests to pastures. The resulting land uses/land covers were introduced using two analyses: calculation of the land-use/land-cover change indicator (TRD\({}_{\text{SDLUI}}\)) and the FFPI for 1989 and 2019. Figure 3: Landsat scenes: (**a**) 1989 and (**b**) 2019.
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 2 Preliminary Laboratory Study: Sonication on a Sample
### Potential and Polydispersity Index (PDI)  
An important parameter for the characterization of sludge is represented by the \(\zeta\) potential, which measures the electric charge on suspended particles. When it has a high absolute value (positive or negative), the particles repel each other, leading to a stable suspension. On the contrary, a low absolute value for it suggests that the particles can aggregate [58]. Furthermore, for the optimization of treatment processes, operators optimize the dosage of coagulants and flocculants by varying the \(\zeta\) potential. This process ensures efficient particle aggregation and improves the overall efficiency of sludge dewatering and sedimentation processes [18]. To provide a detailed view of the causes of dispersion, aggregation, or flocculation, we measured the particle size of the sonified sludge (and thus the stability of the sludge). We measured the values of the \(\zeta\) potential [mV], defined as \(\frac{\eta\mu\eta}{\epsilon_{0}}\), the viscosity of the fluid; \(u\) the mobility; \(\epsilon\), relative dielectric constant; \(\epsilon_{0}\), permittivity of vacuum) representing the electrical potential at the level of the slipping plane (slipping plane) measuring the magnitude of electrostatic repulsion/attraction or charge between the particles during all the tests performed, showing good stability of the samples, since, for each of them, we obtained \(\zeta\gg 40\) mV. PDI values, defined as the ratio of particle size \(\sigma^{2}\) to the square of their diameter \(\bar{d}^{2}\), were also evaluated, quantifying size dispersion (a value close to zero indicates a uniform distribution of particles, while a high value reflects a broader and less homogeneous distribution). Specifically, from the initial high values of PDI obtained (ranging between 15 and 20), following treatment, they were reduced between \(0.921\pm 0.150\) and \(0.922\pm 0.150\). Both \(\zeta\) and PDI values were obtained through the Malvern Zetasizer. This instrument measures, with high accuracy, the size, charge, and concentration of particles (by dynamic light scattering (DLS) with a measurement angle of 90 degrees) and their molecular weight, as well as the amount of suspended molecules. The analysis was conducted at different temperatures and durations using appropriate cuvettes.
## 2 Materials and Methods
### Hydraulic Performance Test
\[E=\frac{Q_{\rm s(i+1)}-Q_{\rm s(i)}}{Q_{\rm s(i)}}\times 100\% \tag{1}\]  
In the equation, \(Q_{\rm s(i+1)}\) and \(Q_{\rm s(i)}\) represent the AFT outlet flow simulation results when the number of meshes is \(N_{\rm i+1}\) and \(N_{\rm i}\), respectively, with units of kg/s, where \(N_{\rm i+1}>N_{\rm i}\). (3) _Simulation settings_  
For hydraulic performance numerical simulations of the AFT and other rotating machinery, the SST \(k\)-\(\omega\) turbulence model is generally used [41]. The wall boundary establishes a no-slip condition, and the interface between the stationary domain and the rotating domain is treated as a coupled wall. The solver uses the SIMPLE algorithm, and momentum is solved using a second-order upwind scheme. The calculation time step is determined on the basis of the Courant number criterion, which is 0.0008 s, with a maximum of 20 iterations and 2000 time steps. During the numerical simulation, \(H\) was 0.14-0.18 MPa, with \(\Delta H\) = 0.01 MPa and \(H_{\rm out}\) = 0.1 MPa. For five different \(H\) conditions, \(n\) was in the range of 500-3000 rpm, with a step size (\(\Delta n\)) of 500 rpm, resulting in six values for \(n\). Each AFT included 30 operating condition combinations, and with 12 AFTs, a total of 360 simulation scenarios were generated. (4) _Monitoring and calculation indicators_  
The AFT output torque (\(T\)), AFT outlet flow rate (\(Q_{\rm s}\)), internal flow field of the AFT, and blade surface pressure (\(BP\)) curves can be obtained using the CFD postprocessing module in ANSYS Workbench (Figure 6). \(P\) can be calculated using Equation (2). In the equation, the angle \(\theta\) is the angle between the flow direction and the blade pressure surface. Along the flow direction, \(\theta\) gradually decreases. \[P=\frac{2\pi nT}{60} \tag{2}\]  
Figure 4: Fluid domain division and mesh generation of axial-flow turbine (**a**), boundary layer expansion (**b**), and impeller edge mesh refinement (**c**). Figure 5: Grid independence verification of the change rate of outlet flow (E) with the number of grids (N). In the equation, \(P\) represents the AFT output power (W), \(n\) represents the rotational speed (rpm), and \(T\) represents the AFT torque (N-m).
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 3 Proposed Method  
This paper proposes a transmission removal method based on a GAN architecture with a PatchGAN discriminator and several modules for restoring the reflection layer. Figure 2 shows the flow diagram of the proposed method. In terms of network architecture design, the known transmission information about **B** and the mixture image **I** are taken as input. Although the actual transmission layer is not available in practice, we can substitute it by some approximate methods, such as the additional flash/ambient light photo approach used in [13], or even directly replace it with the estimated edge information of **B** through the existing reflection removal network. Here, we divide the information we may obtain about **B** into two cases as shown in Figure 2. Our experiment aims to remove **B** from **I** with the loss functions that can constrain the generated results. First, we extract features from the inputs using Resnet, then analyze the features to reconstruct the predicted reflection layers via the SE module. The corresponding losses are used to constrain the content and style of the images, and finally the generated images are discriminated by a discriminator network. The remainder of this section presents the proposed overall network structure, loss function design, and application to photographer identification.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
###### Abstract  
Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high accuracy and low complexity should be proposed. In this paper, the principle and procedure of machine-learning-based path loss prediction are presented. Measured data are used to evaluate the performance of different models such as artificial neural network, support vector regression, and random forest. It is shown that these machine-learning-based models outperform the log-distance model. In view of the fact that the volume of measured data sometimes cannot meet the requirements of machine learning algorithms, we propose two mechanisms to expand the training dataset. On one hand, old measured data can be reused in new scenarios or at different frequencies. On the other hand, the classical model can also be utilized to generate a number of training samples based on the prior information obtained from measured results. Measured data are employed to verify the feasibility of these data expansion mechanisms. Finally, some issues for future research are discussed.  
5G communication systems; data expansion; machine learning; path loss prediction; wireless channel +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Genomic Characterization  
High Molecular Weight (HMW) DNA extraction from _Penicillium mexicamum_ MUM 23.42 was conducted utilizing the Nucleospin(r) Soil Kit (Macherey Nagel, Duren, Germany) in conjunction with Buffer SL2 and Enhancer SK, adhering to the manufacturer's protocols. The quantification of HMW DNA was executed using a Quantus fluorometer in conjunction with the QuantiFluor(r) dsDNA Dye kit (Promega, Madison, WI, USA) and subsequently evaluated through a 1% Tris-borate-EDTA (TBE) agarose gel electrophoresis, stained with GreenSafe Premium (NZYTech(tm), Lisboa, Portugal) and ran at 100 V for a duration of 45 min. Whole genome library preparation for Oxford Nanopore(r) MinION(tm) sequencing was performed utilizing the Rapid Barcoding Kit 24 V14 (SQK-RBK114.24), following the manufacturer's protocol. The library was subsequently loaded into a R10.4.1 flow cell (FLO-MIN114) and sequenced using a MinION(tm) Mk1B connected to a portable ASUS TUF Gaming A16 (FA607 PV-R97B46 CS1) laptop over a period of 36 h. Data processing analysis was performed with the MinKnow software v.24.06.5. Read basecalling was carried out employing the Super accurate (SUP) model with Guppy v.6.5.7, whereby barcodes and reads exhibiting a mean quality lower than 10 and with lengths less than 1000 bp were excluded from the dataset. The web-based Galaxy platform [38] was utilized for bioinformatic analysis. To evaluate the quality of initial reads and overall metrics, the NanoPlot v.1.43 [39] software was employed. Nanopore raw reads underwent assembly via the Flye assembler v.2.9.5 [40], utilizing the options--nano-raw--scaffold and conducting three internal rounds of self-polishing. The final assembly underwent evaluation with Quast v.5.2.0 [41] and gfastats v.1.3.6. Genome completeness was assessed using the Benchmarking Universal Single-Copy Orthologs (BUSCO) v.5.7.1 [42], employing the ortholog dataset designated for _Eurotiles_ (orthoDB v.10) [43]. Genomic ribosomal RNA genes were identified utilizing Barmap v.1.2.2 [44], while tRNA genes were detected with ARAGORN v.1.2.36 [45]. Repetitive elements were detected and soft-masked employing RepeatModeler v.2.0.5 [46] and RepeatMasker v.4.1.25 [47], with the genome annotation being performed with the Funannotate pipeline v.1.8.15. Coding genes underwent further functional annotation with the UniPro-tKB Swiss-Prot database (UniProt Consortium, 2017), the EggNOG Mapper v.2.1.8 [48] and InterProScan v.5.59-91.0 [49;50]. The OmicsBox software v.3.2.2 was employed to execute
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 4 Results and Discussion
### Training Time  
* Pattern Matching, SIFT, and ORB: These algorithms do not require training, making them advantageous in scenarios where rapid deployment is needed.
* Haar Classifier: This requires a substantial training time of 3.55 h, indicating an initial setup cost. However, this investment pays off with excellent detection performance.  
**Detection Latency**
* Haar Classifier: The fastest detection time (0.09 s) highlights its efficiency post-training.
* Pattern Matching: The quick detection time (0.13 s) without the need for training makes it a strong candidate for real-time applications.
* SIFT: The moderate detection time (0.39 s) reflects its computer complexity due to the detailed feature extraction process.
* ORB: Surprisingly, ORB takes the longest detection time (12.06 s), which is unexpected for a binary feature descriptor. This may be attributed to implementation details or the specific test conditions.  
**Total Matches**
* Pattern Matching and Haar Classifier: Both achieve the highest number of matches (7), indicating high effectiveness in object detection.
* SIFT: Slightly lower total matches (6), reflecting its robustness but also its selective nature.
* ORB: The lowest total matches (4), highlighting potential limitations in detecting all relevant objects, especially in more complex scenarios.  
**Precision, Recall, and F1 Score**  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Training Time (h)** & **Latency (s)** & **Total Matches** & **Precision** & **Recall** & **F1 Score** \\ \hline Pattern Matching & – & 0.13 & 7 & 1.00 & 1.00 & 1.00 \\ \hline Haar Classifier & 3.55 & 0.09 & 7 & 1.00 & 1.00 & 1.00 \\ \hline SIFT & – & 0.39 & 6 & 1.00 & 0.86 & 0.92 \\ \hline ORB & – & 12.06 & 4 & 1.00 & 0.57 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance indices of the implemented algorithmsPrecision: All four algorithms exhibit perfect precision (1.00), indicating that when they do make detections, they are consistently accurate.
* Recall: pattern matching and the Haar classifier achieve perfect recall (1.00), showing their ability to detect all relevant objects. SIFT has a slightly lower recall (0.86), while ORB has the lowest (0.57), indicating it misses more objects.
* F1 Score: The F1 Score combines precision and recall into a single index. Pattern matching and the Haar classifier both achieve the highest possible F1 score (1.00). SIFT has a respectable F1 score (0.92), while ORB lags behind at 0.73.
## 2 Taxonomy for the Threat Model
### Threats against Authentication  
In this category, we covered seven types of threats, which include brute force, replay, reuse, forgery-based, partial collision, password and recovery-based threats. The primary focus of threats against authentication is to disturb the client--server authentication mechanism. The password-based authentication schemes are primarily attacked by entities which are portrayed to be legitimate in a communication environment. In the partial collision, an attacker can employ various cryptographic methods to extract the secret keys, hash, etc. Similarly, 6G-enabled Radio Access Network (RAN) is vulnerable to collision attacks on control signals from intelligent spectrum controllers [12]. In [37], the authors presented a lightweight authentication scheme for a 6G-enabled maritime transport system to avoid forgery attacks. The scheme is a lightweight message exchange protocol to counter trust issues in public channels of communication. Anonymous mutual authentication and key establishment protocols can improve security against message recovery-based attacks in 6G-enabled IoT networks. Traditional communication protocols such as the Voice over Long-Term Evolution (VolLTE) protocol contain vulnerabilities related to keystream reuse. Therefore, transition to 6G requires critical analysis of previous protocols to avoid security incidents related to reuse attacks [13]. AI-based joint QoS and security schemes are designed with proven strength against brute force attacks [38]. In combination with the electronic Subscriber Identity Module (eSIM), Public Key Cryptography can provide resilience against replay attacks [39].
# Horizontal Bending Angle Optimization Method for Scraper Conveyor Based on Improved Bat Algorithm
## 6 Conclusions and Future Work  
To make the working mode of the coal mining face better, a new method based on the running resistance characteristics and the improved BA is proposed in this paper. Under consideration of the direction of the shearer's running direction, the mathematical model of the running resistance of the scraper conveyor was established. The improved strategy based on adjusting the step size of the search by inertia weight and expanding fly distance range obeying the t-distribution was applied in the optimization process. To verify the feasibility and superiority of MBA, a simulation comparison was made. Finally, the simulation results were applied in the 21220 fully mechanized mining face of Changcun Mine. The conclusions are as follows:  
1. The current consumption and running resistance of three different \(\alpha^{\prime}\) cases were compared. The results show that the average absolute error rate of theoretical calculation and measured current value was 3%. The correctness of the mathematical model was verified.
2. The improved bat algorithm (MBA) had faster convergence speed and higher accuracy than AMBA, BCBA, and BAO algorithms. MBA was superior in searching for optimal horizontal bending angles.
3. When \(\alpha^{\prime}\) = 0.66\({}^{\circ}\) was compared with the usual bending angle, not only the current consumption and the cost per ton of coal were reduced, but the MTBF was reduced by 50%. The correctness and feasibility of the simulation results were verified by industrial application.  
In future studies, the authors will focus on improving the proposed approach. This may be done by including unknown factors that affect the operational resistance to be explored and by developing an improved algorithm with higher execution efficiency.  
[PERSON] and [PERSON] contributed the new processing method; [PERSON] and [PERSON] designed the simulations and experiments; [PERSON] and [PERSON] performed the experiments; and [PERSON] wrote the paper.  
**Acknowledgments:** The support of the National Natural Science Foundation of China (No. U1510117), the National Key Basic Research Program of China (No. 2014 CB046301) and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions in carrying out this research are gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(m\)} & \multirow{2}{*}{\(\alpha^{\prime}\)} & \multirow{2}{*}{\(0.5^{\circ}\)} & \multicolumn{2}{c}{**0.66\({}^{\circ}\)**} & \multicolumn{2}{c}{**1.5\({}^{\circ}\)**} \\ \cline{2-7}  & & **Head** & **Tail** & **Head** & **Tail** & **Head** & **Tail** \\ \hline
1 & 2.49\% & 2.40\% & 2.45\% & 2.11\% & 3.38\% & 2.96\% \\
2 & 3.59\% & 2.56\% & 2.66\% & 2.34\% & 2.67\% & 2.63\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Actual vs. theoretical current average absolute error list at different \(\alpha^{\prime}\).
# Time Management in Wireless Sensor Networks for Industrial Process Control
## 4 Discussion
These protocols aim to ensure that synchronization sources are trustworthy, a consideration that could be integrated into our beacon validation process for enhanced reliability in hostile environments. Adaptive schemes such as MBATS [27] use mobile beacons to synchronize nodes in environments where fixed infrastructure is impractical or cost-prohibitive. Though well-suited for dynamic or mobile networks, such methods introduce complexities not aligned with our static, process-oriented deployment. However, the principle of dynamic sync intervals is relevant for optimizing future enhancements to our fixed-schedule design. While our approach focuses on static industrial deployments, existing research demonstrates value in scenario-specific designs. For example, MBATS adapts to changing sync requirements via mobile beacon mobility, while STSS [28] minimizes beacon collision in overlapping clusters. STOC [28] and related approaches manage the sync load but reduce the active slot time, affecting the throughput. Our use of grouped timeslots and enhanced beacons preserves bandwidth while ensuring tight synchronization, especially useful for control applications with feedback constraints. [PERSON] et al. [29] point to a critical need for scalable protocols that maintain synchronization under variable topologies and high node counts. Our current design is optimized for small-to-medium industrial cells, but lays the groundwork for hierarchical coordination mechanisms needed in future large-scale systems.