esa-sceva/satcom-chunk-collection huggingface dataset:
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 3 Applications and Real-World Scenarios
### Domain Adaptation in Computer Vision: Real-World Applications
#### 3.1.2 Medical Imaging and Diagnosis  
The medical industry also reaps substantial benefits from domain adaptation techniques, particularly in the realm of computer vision-based diagnostic tools. Domain adaptation plays a pivotal role in ensuring the accuracy and reliability of medical image analysis and diagnosis by adjusting models to account for variations in imaging modalities, technology, and patient demographics. For instance, domain adaptation allows for knowledge transfer from well-annotated datasets at one medical institution to datasets with fewer labeled examples at another. This approach significantly enhances classification accuracy in medical image analysis, facilitating early disease diagnosis and the development of personalized treatment plans through the harmonization of feature distributions across multiple datasets [314].
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 2 Image DNA Techniques  
Image DNA techniques can be broadly categorized into three approaches: bitmap-based, image processing-based, and deep learning-based methods. Bitmap-based methods extract DNA directly from image bitmaps, offering advantages such as fast DNA extraction and compact DNA size, but they may struggle with identifying modified images. Image processing-based methods employ advanced computer vision techniques to extract features and generate DNA through computationally intensive processes. Recently, deep learning-based methods have been explored, leveraging pre-trained inference models or deep learning architectures to generate image DNA. Despite their promise, deep learning approaches require significant computational resources and prior training on image features, making them distinct from the other two approaches [12]. Table 1 presents a comparative analysis of the DNA features utilized in each of the three approaches.
## Materials and Methods
### Raster Data and Interpolation
Inverse distance weighting as used to reflect data fuzziness due to the discrete nature of roads and districts. The digital elevation model, which had already been interpolated from the original Shuttle Radar Topography Mission (SRTM) source [47], was resampled to fit the 500 m resolution and used to produce the slope and aspect maps. A map was created showing the incidence of wind over a surface (WIOS); it combined the wind direction and the aspect maps since the values of the former were in degrees and continuous data were needed to achieve better results with the canonical analysis. A raster file was created to detect the incidence of the wind (wind direction) over a particular surface (aspect). The windwards and leewards were identified and assigned a value depending on the angle of incidence of the wind in contraposition to the aspect:  
1. A value of 0 meant that the wind ran in the same direction of the surface (leeward). Concerning the contaminants, no deposits were assumed. 2. A value of 1 meant that the angle of incidence of the wind according to the surface was parallel and there could be either erosion or deposition. 3. A value of 2 meant that wind impacted on the surface at angles between 30 and 60 degrees; that is, oblique. High deposits and less erosion were expected. 4. A value of 3 meant that the wind impacted the surface perpendicularly, that is, in angles, close or equal to 90 degrees (windward). It was considered a surface where the possibility of high deposition of contaminants exists. (Detailed information on the geographic, demographic, and meteorological variables used is provided in Supplementary Materials Table S1).
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 2 Literature Review
First, modular vehicles are introduced into the tourism feeder scenario, where a vehicle scheduling optimization model is developed to account for dynamic passenger fluctuations, leveraging the adjustable capacity of modular vehicles. This approach addresses the issue of fixed vehicle capacity in current tourism shuttle services while overcoming the limitation of static travel demand in existing modular vehicle optimization models. Second, recognizing the uncertainty of future passenger demand in real-world conditions, data-driven algorithms are applied, and a sensitivity analysis is conducted on variables such as single-vehicle load and waiting costs. Finally, the algorithm proposed in this study is compared with the traditional instant dispatch model and the theoretical global optimal model that assumes known future passenger demand, verifying its optimization efficiency. This approach improves vehicle utilization and reduces operating costs, offering effective scheduling solutions for scenic area managers.
## 3 Test Results and Discussion
### Failure Modes  
To analyze the effect of static pre-stress and dynamic loading rate on failure patterns under superimposed static and dynamic loads, some specimens were specially selected for detailed analysis. Table 4 lists the DIC results of a principal strain field (tensile strain field) corresponding to the peak stress point under superimposed static and dynamic loads, and the failure image corresponds to post-peak failure stage--that is, the time corresponding to the picture of DIC analysis is earlier than the specimen failure image. The figure of DIC analysis and figure of failure image are put together to explore the relationship between strain field evolution and specimen failure. Based on the distribution law of the strain field, combined with the final failure pattern and deformation contour, the strain field is divided into the area within a strain localization zone (plastic zone) and a non-localization zone (elastic zone) [53], with the white dashed line is the dividing line. The strain gradient in the plastic zone is large, and it is uniform in the elastic zone.  
At the same static pre-stress but different dynamic loading rate, taking specimens D6 and D10 as examples, the strain localization zone and the maximum strain increase with increasing dynamic loading rate, and the specimen failure modes change from tensile-shear failure to tensile failure. When the dynamic loading rate is the same, taking specimens D6 and D8 as examples, as the static pre-stress increases, the strain localization zone and the maximum strain increase, but the failure mode does not change under the same dynamic loading rate.  
The dynamic loading rate plays an important role in controlling the failure mode, while the static pre-stress applied in this study does not change the failure mode and only affects the crack width. A strong correlation exists between the failure mode and the strain rates [39]. The failure characteristics of hard roof rocks may be different from limestone [54], salt rock [55] and other \"soft rocks\", the failure modes of which do not change with different loading rates.  
Moreover, strain localization is also an important feature that reflects the failure pattern. When the static pre-stress and dynamic loading rate are not high (specimen D6, 50% of the UCS for static pre-stress and 2 \(\times\) 10\({}^{-3}\) s\({}^{-1}\) for dynamic strain rate), the principal strain contour corresponding to the peak stress remains the same, at which point no cracks have been generated, and after the peak stress, cracks start to form. With increasing static pre-stress and dynamic loading rate, cracks begin to appear at the peak stress point, the number of cracks increases and the range of the strain localization zone increases.  
Figure 10: Effect of dynamic loading rate on (**a**) the released elastic energy density and (**b**) the dissipated energy density.
## 2 Materials and Methods
### Data Acquisition  
#### 2.2.1 Layout and Acquisition of Ground Checkpoints  
Ground checkpoints were deployed in the study area with reference to the optimal ground checkpoint deployment scheme, characterized by a small number of evenly spaced intermediate points around the perimeter and parallel to the flight path [28; 29]. A total of 10 ground checkpoints were deployed at the top, middle, and bottom of the Benggang, respectively. Unfortunately, the ground checkpoint situated in the middle of the bottom part of the Benggang was lost during the second test due to rainfall, resulting in 9 ground checkpoints remaining for subsequent analysis. To ensure consistency with UAV image position and orientation system (POS) data and the coordinate system of the ground checkpoints, the coordinates of the study area were kept in alignment with the WGS-1984-UTM-50N projected coordinate system.  
The Trimble R2 GPS (Westminster, CO, USA), operating in RTK network CORS mode, was employed to measure the coordinates of each ground checkpoint with an accuracy of \(\pm\)10 mm + 1 ppmRMS horizontally and \(\pm\)20 mm + 1 ppmRMS vertically. Ground checkpoints were established in the study area prior to UAV imagery collection, and the Trimble R2 GPS was used to measure the coordinates of each ground checkpoint in RTK network CORS mode. Each ground checkpoint was measured twice, with the difference between the coordinates of the two measured planes not exceeding 2 cm. Simultaneous measurements of the elevation and plane were taken for the ground checkpoints, with elevation points being measured twice, ensuring that the difference between geodesic heights in each measurement was no more than 2 cm. The acquisition parameters included  
Figure 1: Benggang study area.  
observation time of 1; sampling interval of 1 s; observation calendar of 10\"; and RTK network CORS mode acquisition operation, targeting ground checkpoint data. The average of the two measurement results served as a reference to verify the accuracy of final control-free nap-of-the-object photogrammetry images. The layout of ground checkpoints, GCPs, and ground checkpoint data acquisition in the study area are illustrated in Figure 2.
## 3 Enhanced Mathematical Model with Atmospheric and Environmental Effects
### Performance Analysis of Atmospheric Attenuation in 5G-Satellite Integrated Networks
For instance, at 3000 km, the attenuation for a 32 GHz signal at 300 km altitude approaches \(8\times 10^{4}\) dB, whereas a 24 GHz signal at 1000 km altitude remains significantly lower. This demonstrates that optimal frequency and altitude selection are crucial for long-distance 5G-satellite links, especially for IoT applications that require reliable connectivity for smart cities. The observed relationships among frequency, altitude, and distance have several implications for network design:  
* **Frequency selection**: Lower frequencies (e.g., 24 GHz) are preferable for long-distance communications, especially at lower altitudes, to mitigate atmospheric attenuation. * **Altitude optimization**: Higher satellite altitudes reduce atmospheric losses, making them more suitable for high-frequency or long-range communications. * **Distance constraints**: For applications that demand very long range communication, managing attenuation through optimal selection of altitude and frequency becomes essential to ensure sufficient signal strength. In summary, the plot highlights the complex trade-offs between frequency, distance, and altitude in the design of 5G-satellite networks. A balanced approach to selecting these parameters can enhance network performance, a necessity for the scalability and reliability of IoT applications in a smart city.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 3 An Enhanced Generative Adversarial Network for SR
### Discriminative Network Structure  
The discriminator constantly improves the learning ability to distinguish between generated images and real images during the training procedure. As shown in Figure 1, our discriminative network is optimized in structure compared with the traditional discriminator.  
Specifically, we employ 10 convolutional layers sized at \(3\times 3\) kernels. To obtain more context, the size of the receptive field is increased by a factor of 2, from 64 to 512.  
Figure 3: The convolutional skip-connections for our parallel architecture, with 5 blocks in the network. The 1 st block can extract some coarse features, and 3 convolutional skip-connections (from the 2 nd to the 4 th blocks) are used to carry semantic information for the generated images. The last convolutional layer generates the synthesized images in the 5 th block.  
Considering that the sizes are proportional to the layers, we follow the available framework of the VGG19 network. Similar to the generator, there are three CBL modules in our discriminative network. Each CBL module contains the convolutional layers BN and LReLU. We also employ two convolutional skip-connections after each CBL module, which can obtain more high-frequency details to distinguish the images. The convolutional layers are used to extract abundant features. The 512 results are followed by a dense layer and a flattened layer. These operations map each multidimensional input onto another one-dimensional vector. Then, the vectors are fed into fully connected layers to combine the features from the previous layers. Finally, a sigmoid function with one node produces a probability for the classification.
## 3 Hybrid Topology Architecture
### Implementation  
The technical details of the implementation of our proposed adaptable hybrid network topology for LoRaWAN, as depicted in the schema shown in Figure 4, are outlined in this subsection. All of the elements are explained at length below.
## 1 Introduction
At the same time, the pressure drop variation in the anode and cathode channels, the oxygen concentration distribution in the cathode channel, and the water concentration distribution in the cathode channel were predicted at 160 \({}^{\circ}\)C. [PERSON] et al. [8] studied the lateral diffusion of substances in the back of a flow field by adding valves at some outlets of the parallel flow field. In addition, the experimental platform was improved to study water migration characteristics and comparisons between the performances of parallel flow fields and interdigitated flow fields during cold start-up were made [23; 24] [PERSON] et al. simulated the different effects between a sub-channel, a bypass serpentine flow field, and a conventional design on the PEM fuel cell performance by using a combination of different flow field forms at the anode and cathode sides, with results indicating that the sub-channel and the bypass serpentine flow field showed better performances than the conventional design [25]. Compared with a traditional PEM fuel cell (operating temperature less than 100 \({}^{\circ}\)C), the HT-PEM fuel cell (operating temperature is around 150-200 \({}^{\circ}\)C) is considered to be the next-generation fuel cell, as it has the advantages of fast reaction kinetics, a high tolerance to CO poisoning, and simple water and heat management [26; 27; 28; 29; 30] Based on the above research, this work proposes a new serpentine flow field design with rib grooves (namely grooving on ribs) in the PEM fuel cell at 160 \({}^{\circ}\)C, and the magnitude order of the grooves' size is between the magnitude order of the flow field and the gas diffusion layer. The distribution of rib grooves can be adjusted according to the design. This kind of flow field structure has not yet been described in simulation or experimental studies.
# Application of Simultaneous Symmetric and Cambered Airfoils in Novel Vertical Axis Wind Turbines
## 4 Results and Discussion
### Self-Starting Ability  
The static torque coefficients of all turbines for one cycle are plotted in Figure 10a. Based on Figure 10a, the most conventional turbines, including HT-NACA0018 and HT-S815, have the lowest and highest self-starting abilities, respectively, in almost all azimuthal angles. The novel NSA-2 turbine has the highest self-starting ability at almost all angles when \(-90^{\circ}<\theta<+90^{\circ}\). Severe fluctuations can be seen in the static torque coefficient curve of NSA-2 in such a way that, in half of the cycle, it is close to the curve of HT-NACA0018; in the other half, NSA-2 has the highest self-starting ability. NSA-1 in almost all angles has self-starting ability between HT-NACA0018 and HT-S815. The overall pattern of static torque coefficients in the whole cycle is almost the same for all rotors.  
The extracted information from Figure 10b agrees well with the other studies that a high static torque coefficient is one of the attributes of non-symmetric blades [13]. That is why the HT-S815 turbine has the highest C\({}_{\rm T}\) (Figure 10b). NSA-1 and NSA-2 turbines have average static torque coefficients of about 85% of the HT-S815 VAWT; their coefficients are 18% more than HT-NACA0018.  
In Figure 10c, notably, the NSA-2 turbine with two S815 and two NACA0018 blades has the best C\({}_{\rm T}\) within 66.7% of one cycle (360\({}^{\circ}\) except for \(140^{\circ}<\theta<260^{\circ}\)) and behaves similar to the rotor with non-symmetric airfoils (HT-S815). This is an important advantage for the NSA-2 turbine due to the importance of the self-starting behavior of VAWTs at  
Figure 10: Static torque coefficient: (**a**) one cycle, for zero rotational angle (\(\Delta\theta=0^{\circ}\)), see Figure 1, (**b**) the averaged value for one cycle, (**c**) the averaged value for one cycle except for \(140^{\circ}\) to \(260^{\circ}\).  
different quadrants [5]. The two S815 blades are in front of the wind flow within the mentioned range to generate a high positive torque. However, in another half of the turbine, it has NACA0018 blades with a lower negative static torque coefficient compared to S815 at the same positions. Hence, these NACA0018 blades have a lower negative impact on the VAWT self-starting in comparison with S815. Based on the explained reason, the average C\({}_{\text{T}}\) of NSA-2 is more than that of HT-S815 within the mentioned range. NSA-2 has better self-starting than HT-NACA0018 by about 13.3% in one cycle. Additionally, according to its power coefficient in TSR of 2, it can be a good choice for this TSR with proper self-starting ability and power coefficient.
# Asymmetric Measurement-Device-Independent Quantum Key Distribution through Advantage Distillation
## 2 Methods
### Asymmetric MDI-QKD with AD
\begin{split}\overline{\lambda}_{0}&=\frac{( \lambda_{0}+\lambda_{1})^{b}+(\lambda_{0}-\lambda_{1})^{b}}{2P_{succ}},\\ \overline{\lambda}_{1}&=\frac{(\lambda_{0}+\lambda_ {1})^{b}-(\lambda_{0}-\lambda_{1})^{b}}{2P_{succ}},\\ \overline{\lambda}_{2}&=\frac{(\lambda_{2}+\lambda_ {3})^{b}+(\lambda_{2}-\lambda_{3})^{b}}{2P_{succ}},\\ \overline{\lambda}_{3}&=\frac{(\lambda_{2}+\lambda_ {3})^{b}-(\lambda_{2}-\lambda_{3})^{b}}{2P_{succ}}.\end{split}  \tag{12}\]  
The QKD protocol enhanced by the AD method can achieve the secret key at rate [20]:  
\[ \begin{split} R&\geq max_{b}\,\frac{1}{b}P_{succ} min_{\overline{\lambda}_{0},\overline{\lambda}_{1},\overline{\lambda}_{2}, \overline{\lambda}_{3}}[1-(\overline{\lambda}_{0}+\overline{\lambda}_{1})h( \frac{\overline{\lambda}_{0}}{\overline{\lambda}_{0}+\overline{\lambda}_{1}})-( \overline{\lambda}_{2}+\overline{\lambda}_{3})h(\frac{\overline{\lambda}_{2}} {\overline{\lambda}_{2}+\overline{\lambda}_{3}})\\ -h(\overline{\lambda}_{0}+\overline{\lambda}_{1})].\end{split}  \tag{13}\]  
Based on the previous analysis, the AD method can be combined with the QKD protocol. It has been widely used in other protocols in previous works. Similarly, the AD method can be applied to further optimize the properties of quantum channels in the asymmetric MDI-QKD. When the AD method is combined with the asymmetric seven-intensity MDI-QKD protocol, the secret key rate can be estimated by the following formula:  
\[R\geq P_{s_{A}}P_{s_{B}}\frac{1}{b}q_{succ}Q_{s_{A}s_{B}}^{Z}\{( \frac{P_{11}Y_{11}^{Z,L}}{Q_{s_{A}s_{B}}^{Z}})^{b}[1-(\overline{\lambda}_{0}+ \overline{\lambda}_{1})h(\frac{\overline{\lambda}_{0}}{\overline{\lambda}_{0} +\overline{\lambda}_{1}})-(\overline{\lambda}_{2}+\overline{\lambda}_{3})h( \frac{\overline{\lambda}_{2}}{\overline{\lambda}_{2}+\overline{\lambda}_{3}})] \tag{14}\] \[-f_{e}h(\overline{E}_{s_{A}s_{B}}^{Z})\},\]  
\[P_{11}=s_{A}e^{-s_{A}}s_{B}e^{-s_{B}}, \tag{15}\]  
\[q_{succ}=(E_{s_{A}s_{B}}^{Z})^{b}+(1-E_{s_{A}s_{B}}^{Z})^{b}, \tag{16}\]  
\[\overline{E}_{s_{A}s_{B}}^{Z}=\frac{(E_{s_{A}s_{B}}^{Z})^{b}}{(E_{s_{A}s_{B}}^ {Z})^{b}+(1-(E_{s_{A}s_{B}}^{Z}))^{b}}, \tag{17}\]  
where \(P_{11}\) is the probability of both [PERSON] and [PERSON]'s signal states emitting single-photon events, \(q_{succ}\) is the successful probability of the AD method, \(\overline{E}_{s_{A}s_{B}}^{Z}\) is the error rate value after the AD method, and \(e_{11}^{x}\) and \(e_{11}^{z}\) are the single-photon error rate in the \(X\) and \(Z\) bases, respectively. Note that \(Y_{11}^{X,L}\), \(e_{11}^{x}\), and \(e_{11}^{z}\) can be estimated with the decoy-state method.
## 4 Numerical Analysis  
The architecture specified in Figure 3 is used to determine the overall cost of sending a packet, \(TC_{PMIPv6}^{VANET}\), while varying various parameters for analysis purposes. Table 1[19] provides a collection of the formal notation, abbreviations, and symbols used in analyzing the intra-domain and inter-domain handover costs for vehicles in PMIPv6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline \(T_{\text{vehl}-\text{veh2}}/\text{H}_{\text{vehl}-\text{veh2}}\) & Transmission cost or hop count of transmitting a packet from one vehicle to another vehicle \\ \(T_{\text{Ttip1}-\text{Ttip2}}/\text{H}_{\text{tip1}-\text{Ttip2}}\) & Cost of sending a packet from TTP1 to TTP2 and calculated as \(\sqrt{1+N_{\text{Veh}/\text{LMA}}}\) \\ TP & Binding update/lookup cost at LMA/MAG \\ \(T_{\text{step}}\) & Setup cost of a vehicle and MAG to obtain initial connection in PMIPv6 domain \\ \(N_{\text{Veh}/\text{MAG}}\) & Number of vehicles attached per MAG \\ \(N_{\text{MAG/LMA}}\) & Number of MAGs connected to LMA \\ \(S_{control}\) & Size of control packet transmitted (bytes) \\ \(S_{Data}\) & Data packet’s size (bytes) during transmission \\ \(T_{\text{LMA}-\text{LMA}}\) & Cost of sending a packet from LMA1 to LMA2 \\ \(\beta\) & At LMA/MAG, the unit cost for a vehicle lookup \\ \(\tau\) & Cost per hop/unit transmission cost for a packet to travel across a wired link \\ \(\mu\) & Cost per hop/unit transmission cost for a packet to travel on a wireless link \\ \(\mathsf{S_{TTP}}\) & Size (bytes) of the authentication-related control packet \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of parameters used to analyze the handover cost of a vehicle.  
Figure 5: Sequence diagram for inter-domain handover for a vehicle in the PMIPv6 domain.