esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Physical Measurements  
The UV-vis spectra were obtained using a Shimadzu UV/vis spectrometer. The DPP-PhCN and P3 HT films used in the UV-vis measurements were prepared by spin coating from a chloroform (CF) solution or by drop-casing from an \(o\)-dichlorobenzene (\(o\)-DCB) solution. The electrochemical properties were studied by cyclic voltammetry (CV) with a BAS 100B electrochemical analyzer. A non-aqueous reference electrode (0.1 M Ag/Ag\({}^{+}\) acetonitrile solution), a platinum working electrode, and a platinum wire as a counter electrode were used as a three-electrode system. The redox potential of the materials was measured in acetonitrile with 0.1 M (\(n\)-C\({}_{4}\)H\({}_{9}\))\(\mathrm{N}\)-\(\mathrm{P}\)\({}_{6}\) at a scan rate of 100 mV s\({}^{-1}\). The films were prepared by dip coating the solution onto the platinum working electrode, and the measurements were calibrated using the ferrocesium/ferrocene (Fc) redox value of \(-4.8\) eV as an external reference. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels were estimated according to the empirical relationship \(E_{\text{HOMO}}=-(E_{\text{onset,ox}}-E_{1/2,\text{Fc}}+4.8)\) eV and \(E_{\text{LUMO}}=-(E_{\text{onset,red}}-E_{1/2,\text{Fc}}+4.8)\) eV, where \(E_{\text{onset,ox}}\) and \(E_{\text{onset,red}}\) are the onset potentials of oxidation and reduction, respectively, assuming that the energy level of Fc is 4.8 eV below the vacuum level [22; 23; 24].
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 3 System Model  
### Digital Twin Networks  
The proposed digital twin network (DTN) architecture consists of physical, digital twin, and edge layers, as shown in Figure 1. IIoT devices in the physical layer are mapped to the digital twin layer. Then, the connectivity construction framework is designed for time-sensitive applications to maximize the data rate communication in the edge layer. In a smart factory, mobile devices need to connect with each other frequently for real-time manufacturing systems. Time-constrained communication for handling devices in the manufacturing cells and logistics sectors is one of the important demands to avoid faults and improve overall manufacturing automation efficiency for a smart factory. Thus, reliable and connective communication among devices should be constructed to support mobility.  
Figure 1: Digital twin network architecture.
## 2 Materials and Methods
### Vehicle Consumption and Carbon Emissions  
As described in the Section 1, the comparison between the two system configurations (e-HPS and ICE-HPS) was aimed to establish the potential cost savings and emissions reduction in an urban context. Cost analysis considers the purchase of the electric power system and its maintenance, in addition to the diesel fuel consumption and the electricity demand for battery recharging. The considered time horizon was taken according to the vehicle lifespan and assumed to be 12 years [34]. Furthermore, it was assumed that the vehicle was used 5 days per week, for a total of 260 collection scenarios per year.  
For the ICE-HPS, costs consider only the diesel fuel consumption required for garbage collection operations within the assumed vehicle lifespan and, for this time horizon, fuel was forecasted using a regression analysis based on a 5-year moving average of the diesel fuel price in Italy. The historical time series for the average diesel fuel price was obtained from the official website of the Italian Ministry of Economic Development [35]. For the idling phase, the fuel consumption analysis was based on the brake-specific fuel consumption (BSFC) of the 3.0-litre TDI Euro 5 engine developed by Volkswagen group [36; 37]. Thisengine, which has the same displacement and comparable power, was considered instead of the original Mitsubishi Canter 4P10T6 Euro 5 engine that is equipped in the considered compactor truck, for which no official engine maps appear to be available in the literature or technical reports.  
For the e-HPS, the price of electricity in Italy was estimated based on historical series available in the ARERA database [38] and the future price was forecasted using a regression analysis of a 2-year moving average.  
CO\({}_{2}\) emissions are directly related to the amount of fuel consumed in the combustion engine, for which it is possible to assume an emission factor of 2.668 kg CO\({}_{2}\) per liter [39].  
To allow comparison, CO\({}_{2}\) emissions were computed for both ICE-HPS and e-HPS considering the entire path traveled by the compactor truck in a typical workday. In this study, we assumed an average daily traveled distance of 10 km in an urban context and fuel consumption, for the traveling phase, of 0.335 L/km [40,41]. It should be noted that, for the e-HPS, only the traveling phase produces CO\({}_{2}\) emissions, being the garbage collection activity in the idling phase, then, full electric.
## Materials and Methods
### Volatiles Analysis  
Headspace volatile production of banana fruit was determined using the SPME technique, as described previously [48] with minor modifications. Five grams of pulp from three fruit per replicate were homogenized and immediately introduced into a sealed vial. A 50/30 \(\upmu\)m thick DVB/CAR/PDMS coated fiber (Supelco, Sigma-Aldrich, Bellefonte, PA, USA) was inserted into the vial containing the banana homogenate at a distance of 1.5 cm from the sample surface. After extraction for 25 min at room temperature, the fiber was inserted into the GC-MS (Finnigan Trace GC-MS, Santa Rosa, CA, USA). The injection port temperature was 250 \({}^{\circ}\)C, and the fiber was desorbed for 5 min (splitless). Separation was achieved on a DB-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.1 \(\upmu\)m) capillary column. The temperature program was as follows: 40 \({}^{\circ}\)C for 1 min, then increase to 60 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), hold at 60 \({}^{\circ}\)C for 2 min, increase to 180 \({}^{\circ}\)C at 20 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\), and hold at 180 \({}^{\circ}\)C for 1 min. Heliumwas used as the carrier gas, and the flow rate was 1.0 mL-min\({}^{-1}\). The FID temperature was 220 \({}^{\circ}\)C. The mass spectra (electronic impact (EI) 70 eV, quadrupole filter) were collected from \(m/z\) 35 to 300, generating 5.27 scans/s.  
The compounds were identified by comparing the results obtained with reference mass spectra from the NIST02 library using the criterion of at least 75% similarity for the mass spectra, provided with the GC-MS software. The results were confirmed by using several pure components (butanoic acid, 3-methyl-, 3-methylbutyl ester, 1-butanol, 3-methyl-, acetate, acetic acid, hexyl ester, hexanal, 1-hexanol). The total volatile production was estimated by the sum of all peak areas identified in the chromatogram. The relative content percentage of each compound was calculated via the integrated peak area.
# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation
## 5 Evaluation Results and Discussion
### Ablation Experiments
#### 5.2.3 Changing the Reduction Ratio  
We studied the model performance for different values of the reduction ratio used in CAM module. As discussed in Section 3.4, this reduction ratio is used to reduce the network parameters. In Table 4, we notice that the model performance is somehow robust for the reduction ratios of 4, 8, and 16. However, a significant drop is seen when using a reduction ratio of 32. This drop is due to the increased reduction in the number of channels, which might have suppressed useful image features. So, based on the results illustrated, we chose a reduction ratio of 8, since it achieved a fair balance between the segmentation accuracy and the model capacity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reduction Ratio (r)** & **DSC** & **Parameters** \\ \hline r = 4 & 97.8 \(\pm\) 2.3 & 7,874,433 \\ r = 8 & **98.3 \(\pm\) 2.4** & 7,830,793 \\ r = 16 & 97.6 \(\pm\) 3.1 & 7,808,973 \\ r = 32 & 95.2 \(\pm\) 1.2 & 7,798,063 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Segmentation results and trainable parameters for different values of the reduction ratio.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **DSC** & **Parameters** \\ \hline Basic U-Net & 93.9 \(\pm\) 3.8 & 7,760069 \\ CAM only & 95.3 \(\pm\) 6.2 & 7,804,669 \\ MSAM only & 96.8 \(\pm\) 3.1 & 7,779,473 \\ CAM \(+\) MSAM & **98.3 \(\pm\) 2.4** & 7,830,793 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Segmentation results after adding each attention module separately and then combining them.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **DSC** & **Parameters** \\ \hline CAM only & 95.3 \(\pm\) 6.2 & 7,804,669 \\ CAM + CBAM & 96.2 \(\pm\) 2.5 & 7,805,065 \\ CAM + BAM & 97.4 \(\pm\) 2.5 & 7,804,777 \\ CAM + MSAM & **98.3 \(\pm\) 2.4** & 7,830,793 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparing segmentation performance and trainable parameters using different spatial attention modules.
## 2 Materials and Methods
### Convolutional Neural Networks  
The design of the CNN algorithm contains feature extraction and classification, which make it possible for the CNN to learn how to improve features based on the fundamental data it receives while being trained. The neurons in the CNN are linked with the layer before it, the filter weights are shared, and they can react more efficiently with massive datasets. The output of the convolutional layer may be computed as shown in Equation (1) [33]:  
\[\mathrm{y^{k}\cdot i_{k}=f\cdot\left((w^{k}\cdot{}^{*}h)\ \mathrm{i_{j}+b_{k}} \right)} \tag{1}\]  
where f refers to an activation function, * represents a convolutional process operator, and \(w^{k}\) means the weight of the kernel.  
In general, the CNN model has two steps: The initial step of the architecture involves the convolutional layer and the pooling layer. In contrast, the final step comprises the fully connected layers. Although the CNN algorithm is frequently employed for image distinction, one-dimensional CNN models for forecasting and classification tasks using time series have just recently been proposed. Another noteworthy aspect of the 1D CNN is that it can be implemented effectively and inexpensively, owing to its straightforward and compact architecture, which performs one-dimensional convolutions [33]. A typical CNN consists of two merged layers: the fully connected layer and the feature extraction layer [34]. The convolutional layer (feature extraction layer) follows the input layer in the structure and comprises two types of layers: convolution layers and pooling layers [35]. The activation maps for the filters are generated to help the convolutional layer, which employs a set of filters to convolute over the data. In every filter, neurons are directly linked to the data points that are being input, which results in multiplying the weights by the data points. Sharing the weights of the neurons included inside one filter helps reduce the amount of time and complexity required for the CNN's optimization.  
The pooling layer is intended to reduce the overall magnitude of the matrix. The two types of pooling layers are called maximum pooling and average pooling, described below. Max pooling is a method of reducing the magnitude of a matrix by selecting the most significant value included in it. Average pooling is a method of reducing the volume of an array by specifying the median value included in it [36]. Both are utilized in the methods described in Equation (2).  
\[f\left(x\right)=max\left(0,x\right) \tag{2}\]
# Research on Snow Load Characteristics on a Complex Long-Span Root Based on Snow-Wind Tunnel Tests
## 2 Validation of Experimental Approach  
### Experimental Facility  
Experimental works were carried out based on an air-snow-wind combined experimental system, which allows for the appropriate creation of natural wind velocity and turbulence profiles, as well as the precipitation environment. The snow-wind tunnel facility used belongs to the Key Laboratoryof Structures Dynamic Behavior and Control of China Ministry of Education, Harbin Institute of Technology, Harbin, China, as shown in Figure 1. The facility was improved on the basis of previous research [14]. The snow-wind tunnel test chamber was closed, and the dimensions were \(10\times 4.5\times 3\) m. The temperature inside the chamber was the same as the outdoor air temperature, which ranged from \(-30\)\({}^{\circ}\)C to \(-10\)\({}^{\circ}\)C during winter. The wind velocity was measured with a hot-wire anemometer.  
Artificial snow particles were used to simulate the snowdrift environment to deal with the combined wind-snow engineering problems (Figure 2a). The artificial snow particles were generated by spraying water droplets into freezing air with a snowmaker. A particle feeder was fixed at the top of the chamber to simulate precipitation. The snow particle feeder (Figure 2b) consisted of a stable steel frame and vibrating sieve with a perforated bottom (bottom size: 4.5 m long, 0.4 m wide; hole diameter: 5 mm). The sieve was moved by a motor with continuous speed regulation. The snowdrift flux profile was measured with a snow particle counter [15].
## 6 Results
### Scenarios Without Load Balancing vs. Scenarios with Load Balancing
#### 6.1.1 Latency Results  
Table 2 summarizes the latency metrics for both scenarios, providing a clear comparison of system responsiveness and consistency.  
**Average latency**: The proposed model achieves a 67.5% reduction in average latency, significantly enhancing real-time responsiveness essential for MMO gaming environments.  
**Highest latency**: The highest latency value, reduced by 60.3%, highlights the model's ability to handle peak traffic more efficiently, minimizing delays during congestion.  
**Lowest latency**: With a 69.5% reduction in the minimum latency, the system demonstrates its capability to deliver near-instantaneous responses, boosting user satisfaction.  
**Standard deviation**: A 65.8% reduction in latency variability reflects consistent system performance, ensuring a predictable and smooth gaming experience.
## 3 Chang'E-5T Orbit Determination
### Kinematic and Dynamic Orbit Comparison  
There are typically two methods for GNSS-based spacecraft OD, i.e., dynamic and kinematic method. The dynamic method can generally obtain better orbit accuracy due to the constraint of dynamic models. While the kinematic method estimates the satellite's position coordinates epoch-by-epoch and requires no a priori knowledge of the spacecraft motion [37]. The kinematic approach can be applied to a wide range of situations and is of particular interest for maneuvering spacecraft and reentry vehicles due to its purely geometrical nature. Moreover, the computational complexity is significantly reduced compared to the dynamical filtering technique.  
The kinematic orbit of Chang'E-5T is also calculated in this study using the C1 code ranges by SPP approach. The differences between the kinematic orbits and the dynamic orbits can also reveal  
Figure 11: Orbit overlap comparison in radial, cross-track and along-track directions (unit: meter).  
Figure 10: Orbit overlap comparison strategy.  
the quality of the orbit solutions. Ideally, the dynamic orbits are considered of higher accuracy as the carrier-phase observations are used in addition to the code ranges. Their differences are depicted in Figure 12. As shown, the differences show significant correlations with respect to the PDOP values, which is dominated by the spacecraft altitude. The kinematic orbit precision is roughly at 90 m level on average and can reach 50 m when the altitude reaches 20,000 km. This precision is consistent with the results from [28], which were obtained by OD using C1 code ranges combined with ground-tracking measurements.
## 4 Spatiotemporal Feature Extraction Techniques
### Feature Extraction Methods Future Research Directions
#### 4.5.2 Self-Supervised and Semi-Supervised Learning  
Considering incorporating self-supervised or semi-supervised learning methods into hybrid models to leverage unlabeled data better improves the model's adaptability and generalization across different application scenarios [75; 76; 77; 78].
###### Abstract  
Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp-Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts.
## 5 Results and Evaluations
### Performance Analysis  
The performance of the blockchain network can be measured by analyzing the running time with the variation in the number of orderers and peers. The running time is short for a small network with few orderers and peers. Table 3 shows the running time with a different number of orderers and peers in Hyperledger Fabric. The results are based on the average of thirty different running times, using the same number of orderers and peers in a network. The results were gathered by ensuring that the overhead of other applications does not alter the performance of the block in the network; this was achieved by using Hyperledger Fabric to create the block in the permissioned blockchain network. Figures 10 and 11, respectively, show the time taken to upload and download the medical documents using the proposed system.  
The variation in the graph shows a gradual increase in the time required for uploading and downloading documents (i.e., a receipt, body scan, prescription, X-ray, etc.). In the proposed system, the system takes around three seconds to upload 250 Mb of data, which can contain up to sixteen documents, over a stable network connection with reasonable bandwidth.  
The uploading time increases with the increase in the file size, and it takes 7.8 s to upload a file of 500 Mb. All these results were gathered using a virtual machine with the Ubuntu 16.4 operating system, with no other application software installed except the recommended software for the proposed system. The downloading time also shows a gradual increase. As shown in Figure 11, it takes almost three and a half seconds to download a file of 100 Mb, and 26.34 s to download a file of 1000 Mb, over a stable internet connection with reasonable bandwidth.  
Figure 10: Uploading performance in seconds.  
\begin{table}
\begin{tabular}{c c c} \hline
**Peers** & **Orderers** & **Running Time** \\ \hline
3 & 1 & 3.8 s \\
3 & 2 & 3.9 s \\
5 & 1 & 4.7 s \\
5 & 2 & 4.7 s \\
7 & 1 & 5.2 s \\
7 & 2 & 5.3 s \\ \hline \end{tabular}
\end{table}
Table 3: Performance analysis of block creation.  
The time required to execute the different policies (read, write, update, delete, and revoke) can be used to analyze system performance. Figure 12 shows comparative analysis of the respective policies introduced in [51,61,62] with the system under study; each comparison is for the execution time of each policy. The experimental results clearly show that the proposed system is superior for almost every type of policy used.
## 3 Results
### Adsorption of CO, NO2, and SO2 on Ag-WS2 Monolayer
Consequently, both NO\({}_{2}\) and SO\({}_{2}\) are negatively charged after adsorption. \begin{table}
\begin{tabular}{c c c c} \hline
**adsorption Configurations** & \(\mathbf{E_{a}}\) **(eV)** & \(\mathbf{Q_{t}}\) **(e)** & **adsorption Distance (Å)** \\ \hline Ag-WS\({}_{2}\)-CO & \(-\)0.73 & +0.08 & 2.13 (Ag-C) \\ Ag-WS\({}_{2}\)-NO\({}_{2}\) (N downward) & \(-\)1.11 & \(-\)0.35 & 2.21 (Ag-N) \\ Ag-WS\({}_{2}\)-NO\({}_{2}\) (O downward) & \(-\)1.40 & \(-\)0.40 & 2.34 (Ag-O) \\ Ag-WS\({}_{2}\)-SO\({}_{2}\) & \(-\)0.69 & \(-\)0.20 & 2.30 (Ag-O) \\ \hline \end{tabular}
\end{table} 
Table 1: Adsorption energy, electron transfer, and adsorption distance of three hazardous gas molecules on Ag-WS\({}_{2}\) monolayer.