esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectroscopic and Imaging Analyses on Easel Paintings by [PERSON]
## 3 Results
### Underdrawing  
IR reflectography, with the use of two complementary IR bands, allowed the preservation state of the painting film to be clarified and both underdrawing and changes (also called pentimenti) to be visualized. As it is known, broadband IR (1-1.7 \(\upmu\)m range) allowed better transparency of areas painted with thicker layers or with some pigments such as copper-based ones or vermilion [13; 14].  
As IR reflectography images showed, [PERSON] typically outlined figures, architectural elements or other objects with grey-black lines that appear to be executed with a thin brush soaked with a liquid carbon-based ink (Figure 2a). The incisions that are sometimes read in IR in some rectilinear and curved elements indicate the use of rulers and compasses. The brush contour drawing in various works, especially the older ones such as the _Apostles_, shows enlargements--due to unequal pressure of the brush--and undulations in the presence of some folds, according to a characteristic method of the painter. In many paintings, especially belonging to the painter's maturity, a hatched underdrawing can also be seen by IR imaging, particularly in the dress figures, under the shadows (Figure 2b) [10]. The inclination of the hatching is mainly from top right to bottom left, as typical of right-handers, but not fixed even within the same work: its direction varies to follow the volume and the shape of the different parts of the object, suggesting that the author was a versatile  
Figure 1: (**a**) _Anunciation_; (**b**) _[PERSON]_ altarpiece; (**c**) [PERSON], _Corsini Muses_; (**d**) _[PERSON]_.  
draftsman. The hatching is sometimes elaborate and accurate; in other cases, it is quicker and less pronounced. The hatching was generally used to indicate the shades and shadows to be followed by paint, to have a preliminary idea of the effect of the lights and shadows and the volumes of the figures before applying the colour.  
A few changes were detected, and not particularly significant, as in the architecture of the building of the Annunciation or the shift back of [PERSON] leg in _[PERSON] and the Archangel [PERSON]_. In the _Visitation_ of Fano, the handmaid's foot in the background was drawn naked, before being covered by the pinkish shoe based on a red lake and white lead.
# Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
## 4 Experiments and Results  
### Experimental Details  
The experimental platform we use is NVIDIA GeForceMX150, Intel (R) Core (TM) i7-8550U [EMAIL_ADDRESS] GHz, 8 GB RAM, the compilation software we use are pycharm2017 and MATLAB 2018a, and the pytorch deep learning toolbox is used to build and train the network. This paper uses DIV2K dataset, which consists of 800 training images, 100 validation images and 100 testing images. We augment the training data with random horizontal flips and 90 rotations. We perform experiments on three widely used benchmark datasets Set5 [37], Set14 [38] and BSD100 [39]. All experiments are performed with a scale factor of 4\(\times\) between low- and high-resolution images. The mini-batch size is set to 16. The spatial size of cropped HR patch is \(128\times 128\).  
The training process is divided into two stages. First, we train a generative model with \(L_{1}\) loss as the objective function. Then, we use the initially trained model as the initialization of G. The generator is trained using the loss function in Equation (3). The initial learning rate is set to \(1\times 10^{-4}\). For optimization, we use Adam with \(\beta\)1 = 0.9, \(\beta\)2 = 0.999. We alternately update the generator and discriminator network until the model converges. In addition, we introduce a residual scaling [40] strategy which scales down the residuals by multiplying a constant \(\beta\) between 0 and 1 before adding them to the main path to prevent instability. \(\beta\) is set to 0.2 in this paper.  
For accurately evaluating image quality and proving the effectiveness of algorithm, Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are adopted as image quality evaluation indicators. \(\mu_{X}\) and \(\mu_{Y}\) represent the mean values of images X and Y, \(\sigma_{X}\) and \(\sigma_{Y}\) represent the standard deviations of images X and Y and \(\sigma_{XY}\) represents the covariance of images X and Y. PSNR is responsible for measuring the distortion of images from the difference in pixels, and SSIM is responsible for measuring the similarity of the images from the brightness, contrast and structure. The larger the two values, the closer the reconstruction result is to the ground-truth image.  
\[PSNR=10\times\log_{10}\frac{255^{2}\times W\times H\times C}{\sum_{i=1}^{W} \sum_{j=1}^{H}\sum_{z=1}^{C}\big{[}\vec{x}(i,j)-x(i,j)\big{]}^{2}+1\times 10^{ -9}} \tag{8}\]  
\[SSIM(X,Y)=\frac{(2\mu_{X}\mu_{Y}+C_{1})(2\sigma_{XY}+C_{2})}{(\mu_{X}{}^{2}+ \mu_{Y}{}^{2}+C_{1})(\sigma_{X}{}^{2}+\sigma_{Y}{}^{2}+C_{2})} \tag{9}\]
## 3 Results
### Performance Comparisons  
Different candidate frame selection mechanisms and bias calculations have a greater impact on the accuracy of the final target frame generation. Table 2 shows the results of the average accuracy of the target region on the same test set for the target region regression method in Mask R-CNN: This is the method used in this paper. Figure 7 shows the iterative process of the two methods in the sameenvironment. The results show that the regression method proposed by this method performs better on TRUS images.  
Table 3 is a detailed comparison of different methods. We can see that some algorithms, based on convolutional neural networks, perform worse than traditional algorithms. The reason is that these methods have not been modified to improve TRUS image segmentation. However, the low segmentation speed of traditional methods is also the reason why those methods cannot be sued for medical applications. Mask R-CNN and the method in this paper use the idea of precise positioning of the target position and segmentation of the TRUS image, within a narrow range, to make the segmentation performance more prominent. The average segmentation speed of Mask R-CNN is 0.330 s, which is 0.012 s faster than our method. The reason for this result is that the addition of the SE module increases the number of parameters and increases the amount of calculations, which adds time. Nevertheless, the time is acceptable in clinical medical applications.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 4 Simulation Results
### Case III: Joint Classification of M-QAM and M-PSK Signals  
In this simulation scenario, the eight-class problem is considered to determine the classification accuracy, where \(\Xi=[\mathbb{C}_{1},\mathbb{C}_{2},\mathbb{C}_{3},...,\mathbb{C}_{8}]\), where \(i=1\), \(2\), \(3\), \(...\), \(8\), and \(\mathbb{C}_{i}\) is the same as presented in the previous sections. The confusion matrix for the classification is shown in Table 4, indicating that the LOS model's ACA has better classification accuracy than the model without the IRS, as discussed in the previous section. Furthermore, in the confusion matrix of the eight-class problem in Table 4, the channel model is also characterized by low SNRs for the data received from the satellite with the help of the IRS. For all components involved, the QAM accuracy is 97%, while PSK has 99%, shown in the confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SNR = 5 dB** & **GA** & **GA-PS** & **GA-FM** & **GA-FS** & **GA-FU** \\ \hline
**QAM** & 99.48 & 94.68 & 98.93 & 97.23 & 96.75 \\ \hline
**16-QAM** & 99.52 & 91.23 & 98.31 & 98.11 & 98.00 \\ \hline
**64-QAM** & 99.95 & 97.23 & 98.45 & 97.54 & 98.75 \\ \hline
**256-QAM** & 99.02 & 91.45 & 97.23 & 96.55 & 97.34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ACA for M-QAM signals with IRS.  
Figure 5: MSE for M-QAM signals. (**a**) QAM; (**b**) 16-QAM; (**c**) 64-QAM; (**d**) 256-QAM.  
The classification accuracy for the eight-class problem is lower than in the cases discussed earlier because the proposed classifier now has eight different modulations to classify. The classification accuracy of BPSK is much better than the other modulation schemes. As seen in Table 4, the classification accuracy of higher-order QAMs is not better compared to lower-order QAM, but it is comparable.
## 3 Case Study
### SKAB Dataset
#### 3.2.3 Efficacy of Task Sequencing in ATASML  
This study evaluates the efficacy of task sequencing within the ATASML framework using the SKAB dataset. Unlike traditional setups, ATASML integrates advanced data augmentation and adversarial training directly into its learning process. This approach is designed to enhance the model's adaptability and robustness by utilizing sophisticated simulations of faults within the original dataset, thereby providing a rigorous testing ground for assessing the framework's generalization capabilities against other models such as MAML and GOPML.  
The classification performance of ATASML, MAML and GOPML on the SKAB dataset is summarized in Table 8. These results highlight the models' capabilities across various levels of task complexity, from 3-way 5-shot to 8-way 6-shot settings, emphasizing the benefits of ATASML's methodological innovations.  
ATASML outperforms both MAML and GOPML significantly, demonstrating its superior adaptability and efficiency in learning from limited samples. For instance, ATASML achieves an accuracy of 94.25% in the 3-way 5-shot setting, considerably higher than GOPML's 84.18% and MAML's 71.44%. This superior performance is attributed  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Configuration** & **Output Shape** \\ \hline Input & - & (None, T, F) \\ Conv1D (TimeConv) & 32 filters, kernel size 5 & (None, T-4, 32) \\ BatchNorm & - & (None, T-4, 32) \\ MaxPooling1D & Pool size 2 & (None, (T-4)/2, 32) \\ LSTM & 50 units & (None, 50) \\ Dense (Output) & Softmax activation & (None, num\_classes) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Configuration of the Convolutional and LSTM Layers for the SKAB Dataset (TimeSeriesConvLSTM_Module_SKAB).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module Number** & **Configuration** \\ \hline
1 & TimeSeriesConvLSTM\_Module\_SKAB \\
2 & Repeats Module 1 \\
3 & Repeats Module 1 \\
4 & Repeats Module 1 \\
5 & Dense Layer (Output) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Structure of the Base Learner for the SKAB Dataset Using TimeSeriesConvLSTM_Module_SKAB.  
to ATASML's use of task sequencing and its ability to leverage data augmentation and adversarial samples effectively.
## 2 Numerical Methodology
### Mesh Independence  
After the handling of the contact point between pebbles, the fluid domains in the pebble bed are meshed. In our previous investigation [16], the flow characteristics of purge gas helium in the order and randomly packed pebble beds were analyzed by the ANSYS CFX. Similar to that in reference [16], the mesh independence was conducted in the analysis of the pressure drop of helium in a pebble bed with BCC order packing.  
A constant physical isothermal flow of helium (He) at 300 \({}^{\circ}\)C was used as the fluid medium. Helium density was 0.2518 kg/m\({}^{3}\), and kinetic viscosity was 3.0705 \(\times\) 10\({}^{-5}\) kg/m\(\cdot\)s. Owing to the fact that the inlet velocity of helium in a tritium breeder pebble bed is in the range of 0.1\(\sim\)0.2 m/s, the laminar flow model was selected for steady state calculations [16; 18]. Inlet velocity and outlet pressure conditions were adopted in this study. The inlet flow velocity, \(v_{i}\), was 0.1 m/s. The outlet relative pressure, \(P_{o}\), was 0 Pa. The ambient base pressure, \(P_{a}\), was set to be 0.3 MPa. Finally, six sets of meshes with different mesh scales of 9.8 \(\times\) 10\({}^{5}\)\(\sim\)1.59 \(\times\) 10\({}^{7}\) were meshed and analyzed. The local mesh distributions are displayed in Figure 5. The pressure drop gradient in the order packed pebble bed with different mesh scales are presented in Figure 6. The deviation between the marked mesh (\(\sim\)7.46 million) and the finer one (\(\sim\)10.1 million) was smaller than 0.17%. That is to say, when the number of meshes was greater than 7.46 million, the simulation results tended to stabilize. Thus, the settings of the marked mesh, as marked by the red circle in Figure 6, were selected and applied to the other simulation in this work.  
Figure 6: Pressure drop of helium in order packed pebble beds with BCC packing.
## 5 Implementation Environment
### Optimized Recommendation Module
#### 5.4.1 Project's Credibility Estimation
Therefore, an increase in delay\({}_{\text{post}}\), will harm a project's authenticity. The values from the above equation are normalized between 0 to 1, where 0 means highly unauthentic, and 1 means highly authentic. It depicts how much trust one can put into a project. Therefore, the higher the authenticity is, the more credible a project becomes as described in the Equation (5). \[\textit{Authenticity}\ _{project}\propto\ \textit{Credibility}_{project} \tag{5}\]  
Consequently, we have five different levels of credibility as extremely low, low, normal, high, and extremely high, falling into varying degrees of authenticity range, as shown in the Table 6.  
The projects in shallow and low credibility class will have higher risks of being forged. In other words, that project has maximum chances of non-payments, non-delivery, lack of communications, and lack of responses in terms of comments or updates from the project creator. Hence, investing in such a project is not recommended. On the other hand, a project with high or extremely high credibility is an eminently favorable project to invest in, as it is more likely to get delivered on time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project’s Credibility** & \multicolumn{2}{c}{**Example Scenarios**} & **Representative Topic Classes** & **Authenticity Range** \\ \hline \multirow{4}{*}{**Extremely Low**} & • Investors are heading towards taking & & \\  & legal action against the project creator. & & \\  & & If no one has received the product or & \\  & reward for more than a year. & Topic\_3, Topic\_4, Topic\_5 & [\(>0\leq 0.3\)] \\  & & Investors are furious towards & \\  & project creators. & & \\ \hline \multirow{4}{*}{**Low**} & • Promised rewards are still pending & & \\  & & Investors did not get refunds & \\  & & Lack of communication as no updates or & Topic\_0, Topic\_1, Topic\_2, & [\(>0.3\leq 0.6\)] \\  & & comments left by the creator after & \\  & & successful funding & \\ \hline \multirow{4}{*}{**Normal**} & • No harsh sentiments exist by the investors & & \\  & • Positively waiting for the product & & \\  & • Some have received updates or rewards & & \\ \hline \multirow{4}{*}{**High**} & • Investors are happy and excited about & & \\  & & the product & \\  & & Hopes are very high & \\  & • Receiving updates & & \\ \hline \multirow{4}{*}{**Extremely High**} & • Investors have received the product & & \\  & • Talking about the working conditions of & Topic\_9, Topic\_10 & [\(>0.9\leq 1.0\)] \\ \cline{1-1}  & & the product & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Credibility (in terms of authenticity levels) of projects with example scenarios.
## 4 Improved Adaptive Backoff Algorithm (I-ABA)
### Adaptive Backoff Algorithm (ABA)  
ABA recognizes that the standard BEB, in response to collisions over the wireless medium, keeps updating the size of the contention window in a deterministic manner without regard for network size or how active the sensor nodes are in sending data. To resolve this shortcoming of BEB, ABA uses the probability of collision (\(P_{c}\)) as a parameter that controls how the contention window size grows or shrinks. The main idea behind this approach is to adapt the size of the contention window dynamically based on the conditions over the communication medium: with a high level of collisions (typical in large-sized networks), the contention window should grow in size; while with a low level of collisions (typical in small-sized networks), the contention window should shrink in size. The mathematical expression by which to update the size of the contention window is as follows:  
\[W\ =\ P_{c}W_{max}, \tag{1}\]  
where \(W\) is the contention window size, \(P_{c}\) is the probability of collision, and \(W_{max}\) is the 802.15.4's maximum contention window (set to \(2^{macMaxBE}\)). \(P_{c}\) is computed locally at each node by knowing the proportion of packets that suffered from collisions. According to Equation (1), the size of the contention window (\(W\)) cannot exceed \(W_{max}\) (see [11; 12] for full details on how the contention window its updated over time).  
Our extensive simulations showed that \(P_{c}\) is a good indicator of the size of the network. Indeed, Equation (1) manages to relate the size of the contention window to the size of the network indirectly. However, even though the achieved performance was superior to the standard BEB, the maximum contention window size we tested was limited to 256, and degradation in the performance was noticed as the size of the network exceeded 50 nodes.
# State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios
## 3 Mobile Platforms
### Air-Based Platforms
#### 3.3.1 Manned Aircrafts  
Manned aircrafts (helicopters and fixed-wing aircraft) are normally used when a large area survey and a fast deployment/survey is necessary (e.g., a large-scale release of radioactive material into the environment after a nuclear accident) [66; 67]. They also present a greater payload capacity compared to their unmanned counterpart--allowing them to transport large-volume radiation detection systems [67]. However, manned aircrafts are limited to minimum safety altitudes normally 152 m above ground level (AGL) in non-congestioned areas [68]. Moreover, the aircraft's corresponding ground speed is a limitation factor for ground contamination measurements given the low spatial resolutions at reach. Despite the possibility of using helicopters to achieve lower altitudes, they also pose the problem of radiation exposure limits for the crew in high doses environments.
## 4 Methodology
### Data Collection and Analysis  
This study electronically distributed 750 questionnaires to students enrolled in selected universities in Guangdong province and Macao on 4 October 2022; the deadline for the return of the questionnaires was 17 October 2022. We purposively sampled these universities by randomly selecting one class per major as the sampling frame and distributing electronic questionnaires to them through their class web chat groups. The total number of selected classes was 750, instead of the total number of all selected universities, so it could be considered that a total of 750 questionnaires were distributed. In total, 643 questionnaires were returned, with a return rate of 85.7%. After the invalid questionnaires had been processed, 616 valid questionnaires were obtained, with an effective rate of 95.8%.  
In this study, model testing was performed using SmartPLS4 software based on PLS-SEM. This was chosen for the following reasons. First, PLS-SEM is a structural equation modeling method that can be used to estimate complex causal relationships in path models with latent variables. Additionally, it has better parameter estimation, model fitting, and statistical power compared to CB-SEM. Second, it can better analyze complex models containing too many latent variables. Additionally, PLS-SEM is suitable for analyzing non-normally distributed data, small sample data, and formative measurement models [45]. Based on these advantages, PLS-SEM is widely used in social science research. SmartPLS4 was selected as the data analysis tool in this research due to the existence of exploratory studies on some latent variables in the reflective measurement models constructed in this study.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 1 Introduction
Additionally, three-dimensional numerical simulations were used to assess wall deformation and bending moments in deep excavations [21; 22], revealing a high level of consistency with results from two-dimensional simulations. Moreover, PLAXIS 3D 2017 finite element software was utilized to analyze buttress walls, cross walls, and sheet-pile walls for stream bank protection. The numerical results from this analysis were compared with in situ measurement data, achieving satisfactory outcomes [23; 24]. Several engineering projects have employed finite element analysis using various models, including the MCM, SSM, HSM, and hardening soil small strain (HSS) models, to evaluate the impact of wall deformation and ground settlement [25; 26; 27; 28; 29; 30; 31]. This advanced approach offers a more comprehensive understanding of how wall deformation and ground settlement affect deep excavation projects. This study used structural modeling to simulate the effects of neighboring buildings on diaphragm walls and ground settlement, and compared it with surface loading models. Recommendations for modeling underground structures are provided based on real-case observational data and software analysis, with comparisons of data and graphical results to verify the accuracy of the models. Using actual case parameters, several models were developed to explore the impact of neighboring buildings on deep-foundation excavations. The aim was to provide designers with more accurate data through precise numerical simulations, thereby ensuring that deep-foundation excavation projects are completed on time, within budget, and to the required quality, while safeguarding life and property.
## 3 Results  
### Climate Characterization of the Selected Sites  
Temperature and precipitation for each site and over the baseline period of 1991-2017 (period in which maturation data were available for study, however for the regions and vine blocks studied in this work, we only have data available since 1999 for Douro, 2004 for Alentejo and 2008 for Dao) were used (Table 2). The average growing season temperature (GST, April-October) [3], calculated over the baseline period, ranged from 18.1 \({}^{\circ}\)C in Dao, or 18.2 \({}^{\circ}\)C in Douro-C, to 20.5 \({}^{\circ}\)C in Douro-D, or 21.1 \({}^{\circ}\)C in Alentejo. The average (1991-2017) TG, TX and TN from June to August were also greater in Alentejo and Douro-D than in Douro-C and Dao. Furthermore, the average annual precipitation (1991-2017) showed that the Dao and Douro-C sites were the rainiest when compared to Alentejo and Douro-D (Table 2). Overall, warm Mediterranean-type conditions were found at the four sites, with Douro-D and Alentejo clearly warmer and drier than Dao and Douro-C. The Alentejo site is indeed close to the widely accepted upper thermal limit for viticultural suitability (22 \({}^{\circ}\)C), as previously mentioned. These conditions are in general agreement with several previous studies [39] and highlight that grapevines in Portugal are typically growing under moderate-to-severe water and heat stress conditions during the maturation period, though deficit irrigation systems are applied to avoid severe water stress [40]. Deficit irrigation is applied in order to enhance water management, owing to the low water resources available, as well as to promote berry quality [40]. Furthermore, the strong irregularity of the precipitation regime in Portugal frequently leads to severe or extreme drought conditions, driven by anomalies in the large-scale atmospheric flow [41; 42], with major implications in viticulture [43]. Precipitation extremes are also an important threat [44], including hailstorms [45]. Temperature extremes, such as heatwaves, are also an important abiotic stress to take into account in the Portuguese conditions [46]. In addition, Portugal is expected to undergo significant changes in the grapevine growing conditions over the next decades, with warmer and drier climates, thus exacerbating these current limitations [39; 47].