esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Air Mobility: Opportunities and Challenges Deploying eVTOLs for Air Ambulance Service
## 1 Introduction  
Advanced air mobility (AAM) is a broad concept enabling consumers access to on-demand air mobility, cargo and package delivery, healthcare applications, and emergency services through an integrated and connected multimodal transportation network [1, 2]. While AAM may be enabled by the convergence of several factors, a number of challenges such as community acceptance, safety, social equity, planning, airspace management, and others could create barriers to mainstreaming [2, 3]. While a number of technical and social concerns have been raised about AAM, early use cases for emergency response and aeromedical transport may be key to demonstrating the concept and building public acceptance [4]. Examples of emergency response use cases include air ambulance, emergency supply delivery, organ transport, search and rescue operations, firefighting, and other disaster response and humanitarian applications.  
Broadly, the medical transport sector provides a combination of patient ground and air transportation. The aircraft and vehicles that can be used in a medical emergency mission and provide medical transport include: (1) ground vehicles, (2) rotorcraft (also known as rotary-wing and helicopters), and fixed-wing aircraft. As of 2016, there were an estimated 50,000 ground vehicles for short-distance patient transport to medical facilities across the United States [5]. In comparison, there were 1411 aircraft and rotorcraft providing aeromedical transport in the U.S as of 2017 [5]. This paper focuses on the air ambulance use case which includes medical transport to and from a hospital (or other medical facility) for emergency and non-emergency care.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
###### Abstract  
This paper proposes a fabric defect detection algorithm based on the SA-Pix2 pix network and transfer learning to address the issue of insufficient accuracy in detecting complex pattern fabric defects in scenarios with limited sample data. Its primary contribution lies in treating defects as disruptions to the fabric's texture. It leverages a generative adversarial network to reconstruct defective images, restoring them to images of normal fabric texture. Subsequently, the reconstituted images are subjected to dissimilarity calculations against defective images, leading to image segmentation for the purpose of defect detection. This approach addresses the issues of poor defect image reconstruction accuracy due to the limited ability of remote dependency modeling within the generator's convolutional neural network. It also tackles deficiencies in the generative adversarial network's loss function in handling image details. To enhance the structure and loss function of the generative adversarial network, it introduces self-attention mechanisms, L1 loss, and an improved structural loss, thus mitigating the problems of low defect image reconstruction accuracy and insufficient image detail handling by the network. To counteract the issue of declining model training accuracy in the face of sparse complex fabric defect samples, a channel-wise domain transfer learning approach is introduced. This approach constrains the training of the target network through feature distribution, thereby overcoming the problem of target network overfitting caused by limited sample data. The study employs three methods to experimentally compare and investigate five distinct complex pattern fabric defects. The results demonstrate that, when compared to two other defect detection methods, the approach advocated in this paper exhibits superior detection accuracy in scenarios with limited sample data.  
defect detection; generative adversarial network; defect reconstruction; loss functions; self-attention mechanism; transfer learning Article
## 5 Conclusions  
The results of the present study show that the use of e-nose technology to measure the VOCs in strawberries can be used to detect the fruits damaged by impact force. For example, it is possible to directly detect impact-damaged strawberry fruits. Even when the models were calibrated based on all strawberries at 4, 8, and 24 h after the impact, the correct rate of the prediction set could still be up to 97.5%, while 100% correct rates were achieved when the modeling was based on strawberries at different times after the impact. Additionally, e-nose technology can further predict the extent of impact damage. The highest RPD values of 3.548, 3.764, and 3.614 were achieved for samples stored for 4, 8, and 24 h after impact, respectively, while the highest RPD of 2.730 was obtained for all samples stored after the impact, thus demonstrating the powerful ability of e-nose technology to detect mechanical damage to strawberries. In addition, the present study evaluated whether the e-nose technology could be used to predict the time of impact on strawberries. The results show that if strawberries with different impact extents were considered separately, then the models could predict the time after impact. However, if strawberries with different impact extents were used together in the modeling, then the time prediction was difficult. Moreover, the results show that the LS-SVM algorithm was more suitable for modeling than the PLSR algorithm in detecting strawberries that have suffered impact damage. In addition to e-nose technology, this study further analyzed the main characteristic VOCs that have changed due to impact force through GC-MS technology, which is the basis for the detection of damaged fruits by e-nose technology. This study provides a new method for the rapid detection of mechanical damage to fruits during the supply chain through the use of technologies that can non-destructively detect VOCs in fruits, such as e-noses.  
Conceptualization, Y.C., Y.Z. and D.W.; methodology, M.L. and D.W.; software, D.W.; validation, Y.C. and D.W.; formal analysis, Y.C., Y.Z., M.L. and D.W.; investigation, Y.Z. and D.W.; data curation, Y.Z., M.L. and D.W.; writing--original draft preparation, Y.C., Y.Z. and M.L.; writing--review and editing, Y.C. and D.W.; visualization, D.W.; supervision, D.W. and K.C.; project administration, D.W. and K.C.; funding acquisition, D.W. and K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (2017 YFD0401302), the Zhejiang Provincial Key R&D Program of China (2019C02074), and Fundamental Research Funds for the Central Universities (K20210202).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### EF-Bone Modeling  
A hybrid EF, combining elements of both linear and radial fixators, was designed in CATIA V8R21 (Dassault Systemes, Fr) to provide enhanced adjustability and load distribution, particularly for tibial and pilot fractures (Figure 2). The EF was based on TL-EVO design (Orthofix Orthopedics [33]), and integrates a partial 5/8 ring (arch) bridge element at the lower end, connected by to two longitudinal rods linked with clamps. Three tension wires (Kirschner wires or K-wires) are fixed at the ring level and pass through the tibia lower side for stabilization. Two cortical pins or screws are used as additional anchoring points at the upper part of the fixator, secured with modular clamps. The diameter of the ring located at distal tibia end is 160 mm (140 mm in inner and 180 mm in outer side) with 10 mm of thickness, and include 28 open hole of 6 mm diameter, evenly distributed at 10\({}^{\circ}\). The K-wires (denoted wire 1-3) that connect the ring to the bone have a diameter of 1.8 mm and are installed respecting the safe passage corridors of the distal tibia end (areas with minimal tissue coverage and no nerves), while the longitudinal rods linking the ring to the upper clamp and the pins anchoring the EF to the middle tibia shaft measure 9 mm and 6 mm in diameter, respectively. The two parallel pins are located at 30 mm distance to tibia surface and are spaced with 40 mm between them as presented in Figure 2.  
The bone geometry was obtained from computed tomography (CT) scans of a healthy male patient for ensuring real shape and dimensions. Mimics 10 (Materialise, Be) was used for CT medical reconstruction, and CATIA V5 (Dassult Systemes, Fr) for obtaining the 3D CAD solid model of the bone, with the raw geometry cleaned and prepared for FEA. This step was time-consuming and labor-intensive but essential in order to ensure a high-quality mesh and the precise application of boundary conditions.  
Figure 2: 3D model of the baseline hybrid EF–bone configuration and its components.  
A distal extra-articular fracture classified as 43-A2 under the AO system was selected for analysis, incorporating a 3 mm fracture gap to simulate callus formation during healing. The model was developed in ANSYS 19.0 (ANSYS, Inc., Canonsburg, PA, USA), with the fracture fragment highlighted in magenta and the fracture line in green (Figure 3).  
To ensure proper alignment and stabilization of the bone fragments during healing, three pre-tensioned K-wires are used. Wire 1 is positioned diagonally on vertical posts, with a Z-offset from the ring axis of 30 mm on one side and 17.5 mm on the other, allowing precise alignment and fixation of the bone fragment, as shown in Figure 3. Once the desired tension is applied, wire tensioning clamps secure the wires in place, maintaining stability throughout the healing process.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 4 Experiment and Results  
### Model Training  
Our experiments were performed on PyTorch. The training of the model was conducted on NVIDIA A100 GPU. The batch size was 4 and ADAMW was the optimizer for model training, where the range of betas was set to be (0.5, 0.999). The initial learning rate of the model was 0.0001. At the same time, CosineAnnealingLR with \(T_{max}=60\) and \(eta_{min}=1\times 10^{-7}\) were used to adjust the learning rate. During training, 80% of the dataset was used as the training set and 20% as the test set. In addition, we took the two sets of haze and haze-free Sentinel II images outside the data set and used them to produce 150 data pairs as a validation set using the approach in Section 3.1.  
Training was performed for a total of 200 Epochs, and the model preservation rule was to preserve only the best model of the validation set results. The loss was recorded during training, as shown in Figure 7. It can be seen that after 200 epochs of training, the loss curve tends to a lower and flatter value, and the training curve of the training set is  
Figure 6: The architecture of the attention module.  
clearly split from the validation set and the test set, indicating that the model has been adequately trained.
## 2 Materials and Methods
### Analysis of NP Characteristics  
To characterize the resultant NP powder, Au, Ag, and Au-Ag alloy NPs synthesized at irradiation durations of 50, 60, and 60 s were subjected to X-ray diffraction (XRD) analysis, thermogravimetric (TG) analysis, field emission scanning electron microscopy (FE-SEM), and energy-dispersive X-ray spectroscopy (EDS). XRD analysis was performed through Cu K\(\alpha\) radiation at a scanning rate of 2\({}^{\circ}\) min\({}^{-1}\) and a 2\(\theta\) value of 10-80\({}^{\circ}\) by using a Bruker X-ray diffractometer (Billerica, MA, USA). TG analysis was performed in a nitrogen atmosphere at a heating rate of 10 \({}^{\circ}\)C/min by using a PerkinElmer TG analyzer (Waltham, WA, USA). Morphological characteristics, including NP size and shape, were analyzed using FE-SEM (JEOL, Tokyo, Japan). Elemental composition was analyzed using an EDS detector (Oxford Ultim Max 100, Oxford Instruments, Abingdon, UK).
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 4 Experimental Test and Analysis
### Test and Analysis of the Factors Affecting the Quality of Download
#### 4.2.3 Test at Different Temperatures  
Keeping other parameters unchanged, the constant temperature box is used to change the ambient temperature, and representative temperature points are selected for the experiment, and the relationship between BER and ambient temperature is obtained, as shown in Figure 14.  
It can be seen from Figure 14 that the BER is 0.25% at room temperature of 25 \({}^{\circ}\)C, and 1.17% when the temperature rises to 80 \({}^{\circ}\)C. With the increase of the ambient temperature, the BER of the detection system is monotonically increasing, and the change rate is gradually increasing. The result has demonstrated that the temperature increases, which leads to the decrease of sensor sensitivity and the decrease of output voltage, which makes the BER of communication system increase. In practical application, the temperature adaptive compensation method is usually used to effectively reduce or eliminate the impact of environmental temperature changes.  
Figure 13: BER comparison under different electrode distances.
## 3 Results
### Anti-Inflammatory Activity and the Experimental Validation
#### 3.1.2 Binding Free Energy and Binding Constant  
The K and \(\Delta\)G values were determined based on changes in absorbance as the BSA concentration increased. The relationship between As/(Ac \(-\) As) and 1/[C] was plotted, as shown in Figure 4. These plots were used to calculate the values of K and \(\Delta\)G. The results are summarized in Table 2. The data provide insight into the interaction between the synthesized compound and BSA. In addition, the K values were determined to be 5.95 \(\times\) 10\({}^{3}\) (M\({}^{-1}\)) for the **3c** molecule, 16.30 \(\times\) 10\({}^{3}\) (M\({}^{-1}\)) for the **4d** molecule and 12.40 \(\times\) 10\({}^{3}\) (M\({}^{-1}\)) for Aspirin, with corresponding binding energies of \(-\)5.16, \(-\)5.75 and \(-\)5.59 Kcal.mol\({}^{-1}\), respectively. The results indicate that the synthesized compound **4c** effectively inhibits the biological function of the BSA protein. This provides strong evidence that thiazoline thione derivatives have significant potential as novel anti-inflammatory agents, making them promising candidates for the development of future therapeutic drugs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compounds** & **Equations** & **R2** & **IC50 (\#g/mL)** \\ \hline
**4d** & \(y=92.479-77.254\text{exp}(-27.241x)\) & 0.993 & 21.9 \\
**3c** & \(y=83.129-63.395 exp(-20.472x)\) & 0.990 & 31.7 \\ Aspirin & \(y=92.541-75.542 exp(-26.0095x)\) & 0.996 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: IC50 values of the best synthesized molecules and Aspirin against the BSA protein.  
Figure 4: The relationship between As/(Ac \(-\) As) and 1/[C]. (**A**) **4d**, (**B**) **3c** and (**C**) Aspirin.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 4 Numerical Evaluation of the Optimal Performance  
An evaluation of the system model previously presented in Section 3 is given for the network topology shown in Figure 2, whose links offer contact opportunities at an average rate of 1/200 contact/s. The links were defined as having identical channel rates of 800 Kbps and the same packet error rate values. The user workload consisted of 6 flows of 100 kB bundles sent at a designed rate (all at the same rate) whose value was an experimental factor. Table 1 shows details of the source and sink node numbers for each flow. While the test topology and the parameter values were arbitrarily chosen, the scenario included three main essential features found in complex space networks that were relevant to this study: the possibility of using multiple paths for data flows, each of limited availability, the presence of lossy channels, and network congestion occurring at different points due to shared use of links by the multi-hop paths. It is to be expected that other network topologies sharing these characteristics will show similar performance trends.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow ID** & **Source** & **Destination** \\ \hline
1 & 1 & 12 \\
2 & 3 & 10 \\
3 & 7 & 2 \\
4 & 11 & 9 \\
5 & 4 & 10 \\
6 & 10 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average network performance is given by the aggregated performance of six bundle flows in the evaluation study. The source-sink pairs were randomly selected and are indicated in this table.  
Figure 2: The evaluation network topology consisted of 12 nodes and 17 links. All of the links were subject to temporary but long-term disruptions, offering limited transmission opportunities between any two adjacent nodes.  
With the selected values, the nominal (error-free) average transmission time of a bundle over any link was \(S_{0}=1\). The one-way propagation delay of the links was defined as a second experimental factor. Two cases were studied.
## 5 LiDAR 3D Point-Cloud Acquisition Techniques
### Laser Scanning
#### 5.1.1 Terrestrial Laser Scanner  
The TLS is a mechanical scanner that collects spatial data from the FoV with the orientation of a single laser beam. The laser beam is directed by a galvanometer or rotating mirror in TLS. 3D coordinates (x, y, z) of the scan points are calculated from the instrument-centered polar coordinates measured. In addition, the reflection intensity (I) of the returning beam is measured and recorded for each scanning point. The color record for the scan points is applied from the in-built camera images. Pulse or phase-shift methods are used for distance measurement. Scanners using the pulse method can measure up to 6 km and are more suitable for surveying land topography. In scanners using the phase difference method, the measuring distance is about 800 m, but the accuracy is higher than in the pulse method. The range measurement accuracy of the pulse method is 5-8 cm at 1 km, and around 1 cm in the phase difference method. The measuring speed of instruments using the phase difference method is higher than the pulse time (Table 1).  
The beam deflection unit may consist of either two scanning mirrors or one rotating mirror which has a polygonal or flat shape. The scanning principle changes according to the beam deflection unit. The frame scanner, which has a fixed scanning head, uses two scanning mirrors, and measures points as rows or columns. They have limited FoV e.g., 40\({}^{\circ}\)\(\times\) 40\({}^{\circ}\). Hybrid scanners use an oscillating or rotating polygonal mirror for beam deflection. They have horizontal FoV of 360\({}^{\circ}\) and limited vertical FoV such as 60\({}^{\circ}\) (e.g., RIEGL VZ-4000). The panoramic scanner uses a flat rotating mirror. They have 360\({}^{\circ}\) horizontal FoV and around 320\({}^{\circ}\) vertical FoV (e.g., Z + F IMAGER 5006). They are proper at scanning indoors since the whole space around the scanner can be captured from a single station. They are typically used in phase shift to range measurements [40].  
Laser scanners contain many mechanical parts that must be compiled in accordance with the mathematical model, but it is very difficult to overcome this. Therefore, these scanners contain measurement errors caused by the structure of the system and must be protected against vibrations. It is, therefore, only suitable for static measurements. Solid-state LiDAR and MBL scanners have been developed to overcome their disadvantages in scanning. The traditional aerial LMLiDAR is similar to mechanical scanners, and scanning is carried out by directing a single laser beam. However, since the measuring platform is mobile, positioning is carried out using GPS and IMU components.
# Current Limitations for Predicting Liquid Dispersion in Continuous Flow Bubble Columns Using CFD
## 3 Results
### Liquid-Phase Dispersion in Air-Water Two-Phase System
As more air bubbles were introduced into the water, they created turbulence in the surrounding fluid due to the drag forces and vortices generated by their motion. This causes turbulence that ultimately affects the dynamics of the system by increasing radial and axial mixing. In summary, in the present work, the SAS model was appropriate for simulating tracer dispersion in single-phase water flows. For two-phase flows, the VOF model, which was the only two-phase model available in ANSYS Fluent(r) along with the SAS model, proved unable to simulate the effects of bubbles on tracer mixing. The grid size of the meshes that were optimum for resolving velocity and turbulence fields was not fine enough to accurately resolve the gas-liquid interphase. Similar conclusions were reported by [PERSON] et al. for their study on single bubble rising behaviors [PERSON] et al. (2018). As the concentration of bubbles increases, the interfacial area increases, and the mesh requirement increases exponentially [PERSON] et al. (2018). If the mesh is too coarse, the VOF model predictions are imprecise and, therefore, seem to limit the prediction of the gas-liquid interactions and the macroscopic flow patterns [20; 26; 40]. Two-phase flows continue to be inadequately modeled by the commonly used fluid dynamic models because limitations in computational power necessitate the use of submodels that rely on a large number of empirical parameters [20; 41]. An accurate accounting of the local interphase properties in bubble columns is necessary, especially for cases with a high gas holdup. A promising E-L approach combining VOF and a level-set (LS) method has accurately predicted the interphase topology of a bubble rising in a quiescent liquid [28], but its utility for modeling complex flow in a significantly sized bubble column with a high gas holdup remains to be proven.
# Mental Workload Classification Method Based on EEG Independent Component Features
## 3 Results of Classification Method Comparison
### Effect of The Number of ICs on Mental Workload Classification Based on EEG ICs Features  
For the presented Method 1, the effect of the number of ICs on mental workload classification will be discussed in this section. The number of ICs is separately chosen as 5, 10, 15, 20, 25, and 30 in order to analyze its effect on the classification performance of mental workloads. The mental workload classification accuracies of six cases are shown in Table 3.  
From Table 3, we can observe that the classification performance of Method 1 has improved gradually with the increase of the number of ICs. Among the 10 participants, nine participants have the highest classification accuracy when the number of ICs is 30, and one participant has the highest classification accuracy when the number of ICs is 20. However, the average accuracy of the 10 participants indicates that the classification accuracy is highest when the number of ICs is 30. Therefore, it is reasonable that the number of ICs in Method 1 is determined as 30 in this paper.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Subject** & \(\tau_{1}\) & \(\tau_{2}\) & \(\tau_{3}\) & \(\tau_{4}\) & \(\tau_{5}\) & \(\tau_{6}\) \\ \hline sub 1 & 9.678 & 2.125 & 9.276 & 3.493 & 1.567 & 4.130 \\ \hline sub 2 & 8.195 & 1.867 & 14.969 & 4.232 & 1.850 & 5.019 \\ \hline sub 3 & 9.351 & 0.763 & 10.676 & 3.282 & 2.160 & 4.378 \\ \hline sub 4 & 6.841 & 1.459 & 7.234 & 3.131 & 1.686 & 3.424 \\ \hline sub 5 & 4.605 & 2.057 & 9.510 & 4.992 & 1.898 & 4.092 \\ \hline sub 6 & 5.541 & 2.218 & 11.768 & 5.947 & 1.415 & 5.656 \\ \hline sub 7 & 5.392 & 1.856 & 14.515 & 3.208 & 1.640 & 3.96