esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Experimental Setup and Evaluation Metrics  
#### 4.1.1 Hardware and Software Platform  
In the present study, the choice of hardware platform plays a critical role in the training and execution of DRL models. To ensure efficient training on large-scale datasets, high-performance computing resources were employed. Specifically, multiple servers equipped with NVIDIA Tesla V100 GPUs (manufactured by NVIDIA Corporation, headquartered in Santa Clara, CA, USA) were utilized, offering substantial parallel computing capabilities that significantly accelerate the training of deep learning algorithms. These servers are further supported by the latest Intel Xeon processors (manufactured by Intel Corporation,headquartered in Santa Clara, CA, USA), ensuring stable and efficient CPU-GPU cooperation during data preprocessing, model training, and results analysis. Each server is outfitted with 128 GB of DDR4 RAM (manufactured by Intel Corporation, headquartered in Santa Clara, CA, USA) to accommodate the loading and processing of large volumes of data. In addition, NVMe SSDs (manufactured by Intel Corporation, headquartered in Santa Clara, CA, USA) were used for storage to facilitate fast data reading and writing. This hardware configuration allows the DRL algorithms to iterate rapidly, greatly reducing training time and improving experimental efficiency. On the software side, Python was selected as the primary programming language, combined with the TensorFlow framework, for implementing DRL models. TensorFlow, one of the most widely used deep learning frameworks, supports GPU acceleration and enables efficient training of large-scale neural networks. Its flexibility and scalability allow for the implementation of various models and algorithms. For reinforcement learning environment interaction, the OpenAI Gym platform was employed as the training and testing environment. OpenAI Gym provides a collection of standardized, modular environments suitable for various reinforcement learning algorithms, facilitating seamless integration between agents and environments. Additionally, Pandas and NumPy were used for data cleaning, manipulation, and analysis. All experiments were conducted on the Ubuntu 20.04 Linux operating system, ensuring platform stability and compatibility. This comprehensive hardware and software setup ensures efficient execution and guarantees the reliability and accuracy of the experimental results.
# Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
## 2 Multi-View Conveyor Belt Surface Fault Online Detection System  
The multi-view conveyor belt surface fault online detection system based on machine vision comprises line-scan charge-coupled device (CCD) network cameras, linear light sources, Ethernet switch, and computer. The schematic diagram of the system components is shown in Figure 1.  
The device produces diffuse reflection light when the light is emitted by a high brightness linear light source and irradiates on the surface of the conveyor belt. The light intensity of the diffuse reflection light is related to the surface characteristics of the conveyor belt [4; 5; 6]. The multi-view line-scan CCD camera senses the diffuse reflection light through line scanning. Each scan takes a line of images perpendicular to running direction of the conveyor belt and transmits it to the computer through Ethernet. The computer processesrunning image of the conveyor belt, analyzes and recognizes the conveyor belt fault, and gives a fault alarm or shutdown control signal when a fault is found.  
The structural characteristics of the conveyor limit the installation space of the camera. Generally, the camera can only be installed close to the conveyor belt. If the image of the lower surface of the conveyor belt is to be collected for fault detection, the camera can only be installed in the narrow space between the upper and lower belts, close to the lower surface of the conveyor belt. The distance is less than 40 cm, and the object distance is small, as shown in Figure 2. The width of the conveyor belt is generally 0.8-2.4 m. Because the cross-section is arc and the field of view is large, it is difficult to detect the width and accuracy of the conveyor belt with a single camera. It is necessary to use multiple cameras to acquire images in a multi-viewpoint manner by selecting appropriate installation position and installation postures to realize the conveyor belt without blind area coverage.  
However, two adjacent images have overlapping regions that image the same conveyor belt content. Determining the two images' overlapping regions can effectively reduce the processing range of image, reduce the amount of calculation, and improve the stitching speed. The geometric shape of the conveyor belt and the influence of factors such as the parameters, installation position, and posture of the multi-view camera make it difficult to determine the exact overlapping region.
## 2 Materials and Methods
### Principal Component Background Suppression with Peak Threshold Detection  
There are three main steps of this principal component background suppression with peak threshold detection method: individual pixel frequency analysis, background suppression, and statistical filtering. Frequency analysis was chosen as the primary data processing technique due to the sensor's high temporal resolution and event-driven output. The purpose of the background suppression was to reduce the effects of the overwhelming source on the frequency spectra. Filtering was performed after the suppression to select pixels with statistically significant frequency content for target detection, regardless of true target or overwhelming source frequencies.
# Impact of Device Topology on the Performance of High-Speed 1550 nm Wafer-Fused VCSELs
## 2 VCSEL Structure and Fabrication
The sizes for \(L\)-type topology were similar to those used for our previous MBE-grown 1550 nm WF VCSEL design [35; 39]: the diameter of the 1 st mesa was chosen to be 20 \(\upmu\)m, while the sizes of the 2 nd mesa were chosen to be \(\sim\)35 \(\upmu\)m \(\times\) 25 \(\upmu\)m. In the case of the \(S\)-type topology, the mesa sizes were significantly smaller to provide less parasitic capacitance of the VCSELs, and the capacitance \(C_{\text{p}}\) increases with the mesa size. The diameter of the 1 st mesa was 12 \(\upmu\)m. The sizes of the 2 nd mesa were lowered to \(\sim\)22 \(\upmu\)m \(\times\) 17 \(\upmu\)m. As a result, the parasitic capacitance \(C_{\text{p}}\) was decreased as compared to \(L\)-type topology. It should be noted that it is very difficult to separate the reverse-biased junction capacitance \(C_{\text{p}}\) from the forward-biased junction capacitance \(C_{\text{j}}\), because they are connected in parallel. Thus, only the capacitance \(C_{\text{m}}\), which includes both components (\(C_{\text{j}}\) and \(C_{\text{p}}\)), was estimated. Evaluating the capacitance \(C_{\text{m}}\), we reconstructed the equivalent circuit (solution of the inverse problem from \(S_{11}\) data) in the frequency range up to 20 GHz. As a result, the substitution of \(L\)-type to \(S\)-type device topology revealed a drop in the \(C_{\text{m}}\) value from 1 pF to about 0.5 pF. To summarize, the device topology as well as the structure design were modified compared to previously MBE-grown 1550 nm WF VCSELs [35] to improve the modulation performance and to keep the large output power.
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 5 Discussions
This has not been attempted in the field of social infrastructure planning. Most previous studies used distance as access data [17; 22; 25]; however, the travel time may be different even at the same distance, especially in cities. 2. The AI is easy to understand because it represents an intuitive index for the overall accessibility in minutes of a region. Accessibility index in a score is difficult to understand and to use to set a goal, even if the relative comparison is possible [23; 24; 25; 31]. Therefore, urban planners can understand and compare the overall accessibility to social infrastructure and identify regional gaps. 3. This study suggested a method to identify the residential areas with poor access to social infrastructure and compute the population residing in those areas. The method was applied to an actual case to validate the applicability. The results showed that the method effectively identified residential areas with poor access to social infrastructure. This result can help authorities establish social infrastructure planning when determining which facilities to supply to which area. 4. The two-step evaluation method presented in this study provides a comprehensive understanding of the social infrastructure accessibility of a region. Previous studies have often been limited to global evaluation by computing accessibility indices of cities or neighborhoods and showing them on a map [17; 22; 23; 25; 31]. However, this study includes not only global evaluation, but also individual residential block-level analysis based on the same database. The limitations of this study are as follows. First, we only dealt with accessibility to social infrastructure on the assumption that the capacity of the facilities is sufficiently satisfied. However, the capacity that represents the facility's size, such as a park area and the number of library seats, should also be considered when social infrastructure planning. Second, we assumed that residents use only facilities located in the study area, Namdong-gu. However, residents living near the administrative boundary can use facilities outside the study area. Third, the shortest linear distance was used to match an RB and the nearest facility at a ratio of 1:1. In this case, the distance may be the closest, but the travel time may not be the shortest.
## 3 Advancements and Alternatives to Modal-Based Damage Identification
### Advancements to Machine Learning Methodologies for Damage Indentification
This is particularly useful in damage detection scenarios as the inclusion of outliers in training data can mask the occurrence of damage events. [PERSON] et al. [98] successfully employed the MSD in conjunction with the MCD to separate environmental- and operational-induced variations from real damage on the Z-24 Bridge. Another robust geometry-based outlier detection technique is the Minimum Volume Enclosing Ellipsoid (MVEE) [135]. The MVEE algorithm's aim is to determine the ellipsoid of minimum volume that will enclose the data. It is based on convex optimisation via conditional gradient estimation that results in the data being surrounded by an elliptical boundary whose centre vector is determined by that which will produce the minimum volume. It is an iterative process that involves varying the centre location \(\{c\}\) and symmetric matrix \([A]\) to attain a volume \(E\), as per Equation (9). The volume is subsequently minimised via Equation (10), where \(u_{0}\) is the volume of a unit hypersphere. \[E=\left(\{X\}\in R\left(\{x\}-\{c\}\right)^{T}[A]\left(\{x\}-\{c\}\right)\leq 1\right) \tag{9}\]  
\[vol(E)=\frac{u_{0}}{\sqrt{\det([A])}} \tag{10}\]  
In terms of the performance assessment of the above algorithms, there have been a few assessment reviews conducted. [PERSON] et al. [136] assessed the performance of the MSD, MCD, and MVEE algorithms on the Z-24 Bridge that included environmental variations. Their study showed that the MCD outperformed the MSD in terms of damage sensitivity and resolution; however, the MVEE produced significantly better results than both. [PERSON] et al. [137] used AR model parameters to review the performance of a number of popular machine learning algorithms such as, MSD, Factor Analysis (FA), Singular Value Decomposition (SVD), and Auto-Associative Neural Network (AANN). From this, it was deemed that MSD gave the best results in terms of performance, speed, and simplicity.
## 1 Introduction
[PERSON] et al. [26] deposited a thin Ni layer on the surface of a PZT pyroelectric sensor as both the top electrode and a selective absorption layer. The maximum absorption coefficient of the resulting sensor was found to be 0.8 at a wavelength of 0.664 \(\upmu\)m and 0.7 at a wavelength of 4 \(\upmu\)m. [PERSON] et al. [27] presented a pyroelectric detector consisting of a polycrystalline AlN thin film and a SiO\({}_{2}\) layer with a thickness of 1.0 \(\upmu\)m which served as both a thermal isolation layer and a diaphragm to support the sensor. It was shown that the absorptivity of the device could be enhanced by sputtering an additional Au film with a thickness of 160 nm on top of the AlN layer. Overall, the characterization results showed that the device achieved a response time of 8.0 ms and a pyroelectric current responsivity of 2.5 \(\times\) 10\({}^{-6}\) A/W. Recently, [PERSON] et al. [28] proposed a method to leverage the synergy between location and motion information to realize human simultaneous tracking and activity recognition by constructing a wireless sensor network and the sensor nodes of pyroelectric infrared sensor arrays. [PERSON] et al. [29] examined various factors influencing human identification based on pyroelectric infrared sensor. They found that distance, human target, sensor design, body type, human moving velocity, and signal modulation mask are all important factors in the mechanism. [PERSON] et al. [30] presented pyroelectric detection and sensing signal processing algorithms to create a non-wearable cooperative indoor human localization system based on pyroelectric sensor networks. While the studies above provide many useful insights into the design, fabrication and characterization of pyroelectric sensors fabricated with various metallic layers, the effects of the substrate thickness on the performance of pyroelectric sensors have thus far attracted only little attention in the literature. Accordingly, the present study fabricates pyroelectric sensors consisting of suspended zinc oxide (ZnO) pyroelectric films with substrate thicknesses ranging from 1 to 500 \(\upmu\)m supported on thermally insulated back-etched silicon substrates. The temperature variation and voltage response of the various sensors are evaluated numerically and experimentally for an infrared laser source with a power of 10 mW and a wavelength of 765 nm. In general, the results show that the performance of the proposed sensor improves as the thickness of the substrate reduces.
## 3 Neural Network Fuzzy EMS Based on DCR
### Driving Cycle Block and Its Definition  
Driving cycle blocks can be distinguished according to different operating parameters, such as maximum velocity, maximum acceleration, maximum deceleration, average driving velocity, and driving distance, thereby effectively reducing the complexity of driving cycle analysis. In addition, the driving cycle blocks are independent of each other, and any complicated actual driving conditions can be divided into a plurality of different driving cycle blocks, which improves the reuse rate of the blocks [15; 17]. The driving cycle blocks are used for the recognition of driving conditions, and the block recognition library of different scales and different levels of complexity can be established according to the control requirements, thereby designing an EMS that is highly targeted [31; 32]. For example, a driving cycle library for different cities and different road sections can be established separately.  
Taking NEDC as an example, driving cycle blocks can be divided into five basic categories [17].  
(1) Block 1 is defined for the driving condition in the city center, where the vehicle runs and stops frequently with heavy traffic;  
(2) Block 2 is defined for driving conditions in urban areas, where the vehicle runs at low velocity;  
(3) Block 3 is defined for the driving condition in the suburbs, where the vehicle runs at medium and low velocity;  
(4) Block 4 is defined for the driving condition in the suburbs, where the vehicle runs at medium and high velocity;  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Cycle Name**} & **Cycle** & **Total** & **Maximum** & **Average** & **Ambient** & **Ambient Relative** \\  & **Time** & **Distance** & **Velocity** & **Velocity** & **Temperature** & **Humidity** \\ \cline{2-7}  & **(s)** & **(km)** & **(km/h)** & **(km/h)** & **(\({}^{\circ}\)C)** & **(RH)** \\ \hline NEDC & 1180 & 11.00 & 120 & 33.6 & \(25\pm 5\) & 50\% \\ FTP75 & 2474 & 17.86 & 91.2 & 34.1 & \(25\pm 5\) & 50\% \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the New European Driving Cycle (NEDC) and Federal Test Procedure (FIT75) driving cycles.  
Figure 7: Driving cycle of Federal Test Procedure (FIT75).  
(5) Block 5 is defined for the driving condition on the highway between cities and suburbs, where the vehicle runs at high velocity.  
In fact, the NEDC basically consists of the above five basic driving conditions. The \"driving cycle block\" for NEDC defined in this paper is shown in Figure 8. Of course, these blocks are divided by different characteristic parameters of the driving conditions, which are closely related to complicated driving conditions. The block division of FTP75 is much more complicated than that of NEDC.
## 3 Results and Discussions
### Shale Inhibition Properties of Water-Based Drilling Fluid with SDCap
#### 3.3.2 Shale Swelling Tests  
The encapsulator can adsorb on the clay particles on the wellbore surface, and the water molecules entering inside the wellbore rocks can be slowed down. Therefore, the encapsulator also has a certain capability of inhibiting the swelling of shale. Due to this reason, the shale swelling tests were also conducted to evaluate the inhibition property of the encapsulators, and the results were shown in Figure 6. It was found that the swelling rate of clay sample in pure water within 8 h was up to 40.8%, indicating that the clay sample was highly prone to hydration swelling. In comparison, the swelling rate were 8.8%, 7.3%, 8.3%, 8.0% and 7.7% for WBDF-1, WBDF-2, WBDF-3, WBDF-4, and WBDF-5, respectively, indicating all the WBDFs could effectively inhibit the swelling of clay, and their inhibition effects were very similar.  
Based on the results of shale inhibition tests and rheological property tests, the WBDF with SDCap had strong shale inhibition property, and had much better low-temperature rheological property than WBDFs with FA-367 and PHPA, even the concentration of SDCap was 1.5 times of that of FA-367 and PHPA. Therefore, using SDCap as the encapsulator in deepwater drilling fluid can both improve the  
Figure 5: Shale recovery rates for different WBDFs.  
low-temperature rheological property and shale inhibition property. In addition, SDCap has good salt resistance performance indicated by its stable properties in the presence of 10-20% NaCl.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### EuroCity Persons (ECP) Dataset
#### 4.4.2 Comparison with State-of-the-Art ECP Dataset  
We compared the detection results of our method on the ECP dataset with the existing reported results, as shown in Table 5. For the training and evaluation of the ECP dataset, we did not use other datasets for pre-training, and only evaluated the DAGN method. It took 0.22 s per image in testing. The proposed method outperformed other approaches for reasonable, occluded, and overall performance. In particular, for occluded cases, the DAGN achieved the miss rate of 26.3%, which clearly exceeds the second-best result of Cascade R-CNN [31] by 5%. The visualization of detection results is shown in Figure 10.
## 2 Materials and Methods
### Target Preparation and Binding Site Identification  
The three-dimensional structures of all targets were retrieved from the Protein Data Bank (RCSB database). The structures with a high resolution and domain completeness were selected in this study. All native ligands, water molecules, and metal ions were removed. Hydrogen atoms and Gasteiger partial charge were assigned by AutoDockTools 1.5.6. The binding site of the native ligand was defined as the ligand-binding site for the molecular docking study.  
In this study, a PDB structure of human Cathepsin K was \(4\times 6\) H with a resolution of 1.0 A [34]. Prior to performing the molecular docking study, the protonation state of protein was adjusted by PROPKA to provide a structure of amino acids at pH 5.5 and then checked by Biovia Discovery Studio Visualizer [35]. Subsequently, AutoGrid 4.0 was used to define the binding site by creating a grid box with a size of 60 \(\times\) 60 \(\times\) 60 A\({}^{3}\). A grid center was set at 17.552, 8.018, \(-\)19.098 at XYZ-dimension with a grid spacing of 0.375.  
The protein structure of V-ATPase used in this study was 5 KNB, which cocrystallized with ADP ligands [36]. This structure is a V1 domain which contains subunit A and B. The active site of ADP is located between the two subunits, A and B. A grid box size of 60 \(\times\) 60 \(\times\) 60 A\({}^{3}\) centered at \(-\)11.539, 23.975, \(-\)18.976 with a grid spacing of 0.375 A was set as the binding site of this docking set.  
The \(\alpha_{\mathrm{V}}\beta_{3}\) integrin receptor used in this experiment was a PDB code of 4G1M [37]. A center of the native ligand was set as a grid box center at dimensions \(-\)19.145, 44.449, and 43.944. The grid box size of 80 \(\times\) 80 \(\times\) 80 A\({}^{3}\) with a grid spacing of 0.375 A was set as the parameters for this study.
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Invalid or Fraudulent Data Management  
The proposed VANET framework is predicated on maintaining the utmost data integrity and network efficiency. Consequently, our protocol stipulates that data deemedinvalid or unaccepted by network peers are to be discarded immediately. This decision is informed by several considerations that prioritize the real-time operational demands of vehicular networks.  
In the design of our VANET security framework, stringent measures were taken to maintain operational efficiency and data veracity. One such measure is the exclusion of invalid or unaccepted data from storage, which is a protocol that has been meticulously devised considering the unique requirements of vehicular networks. The following are the substantiated reasons for this approach:  
* Immediacy in Decision Making: The high-stakes nature of VANETs demands a system architecture that supports split-second decision making. The storage of invalid data could introduce latency that is antithetical to the need for prompt response times, thereby potentially affecting the safety-critical functions of the network.
* Strategic Data Storage Management: The sheer scale of data generated by vehicles and infrastructure in VANETs necessitates a selective approach to data retention. Our strategy prioritizes the storage of authentic and operationally pertinent data to ensure the optimal use of finite storage capabilities.
* Enhancement of Network Throughput: The exclusion of invalid data from storage is also a strategic decision to maximize network throughput. This ensures that network bandwidth is conserved for the transmission and processing of legitimate and relevant data, thereby enhancing network performance.
* Mitigation of Security Threats: The potential exploitation of stored invalid data by nefarious actors cannot be overlooked. Our proactive approach to discard such data immediately serves as a deterrent to the execution of security exploits that could compromise network integrity.  
Notwithstanding the nonretention of invalid data, our framework is architected to be congruent with intrusion detection systems (IDSs) that scrutinize vehicular data in real time. These systems are adept at identifying potential security threats as they manifest, thereby obviating the need for the retention of invalid data, which could otherwise be leveraged for postevent analysis.