esa-sceva/satcom-chunk-collection huggingface dataset:
# Pulsed Thermography Applied to the Study of Cultural Heritage
## 3 Results
### Characterization of Painted Decoration on Archival and Library Supports  
#### 3.3.1. Gildings  
Concerning the study of the gildings, the adhesion and the integrity of the gold leaves represent two of the main aspects to be studied for the characterization of gilded manuscripts or paintings. In 2016, [PERSON] et al. mainly used terahertz technology to locate the gilding leaves under the paint layer and also to investigate their preservation state [75]. Recently, IRT has also been applied to detect detachments and inhomogeneities in illuminations [51]. In fact, the gold leaf areas are often affected by a fine pattern of cracks (_craquelure_). The _craquelure_, visible at the surface like in the case of the illuminated letter of Figure 9A, belonging to a XV century antiphony (_Biblioteca Angelica_ of Rome), usually consists of cracks and flakes of the gold leaf sometimes associated with detachments from the substrate. The detachments can be detected as lighter areas in the thermogram of Figure 9B, obtained by front face pulsed heating induced by two 1 kW flash lamps, where their extension is shown to be larger than that observed at the surface. However, detachment of the gold foils can occur even in areas not affected by the _craquelure_. For instance, the illumination in Figure 10 appears quite  
Figure 8: XIII century manuscript “ms 509/DS13”, _Biblioteca Angelica_ of Rome. (**A**) Photograph of _folio_ 124 _verso_; (**B**,**C**) thermograms of the areas indicated by the dashed rectangles in Figure 8A. For ease of viewing and to better compare the thermographic results with the photographic image, the thermograms are presented with an inverted grey palette.  
well preserved to a simple optical inspection (Figure 10A). However, the corresponding thermogram (Figure 10B) reveals brighter areas, indicated by the arrows, which correspond to detachment of the gold leaf. Moreover, Figure 10C shows the thermogram obtained after the restoration process, where the gold leaf had been consolidated by sturgeon glue. The absence of the brighter areas in the thermogram confirms the improved adhesion between the gold leaf and the substrate following the restoration treatments. The results point out the role that IRT can play in detecting adhesion defects in the illuminations, in being able to direct the necessary restoration steps, and finally in assessing their effectiveness [51].
## 4 Methods
### Analysis Methods
#### 4.3.2 Application Characterization  
On HPC systems, the programmer must deal with the complexity of the hardware, the different parallelization paradigms, network topology, and software builds. Hence, it is important to quantify the application's performance, using performance profiling tools, to see whether it is possible or not to obtain a better performance on the same system. These tools generate different performance metrics, identify hotspots for code, and help in the determination of adequate optimization techniques. We present the tools used to profile the code on the considered architectures:  
* Intel Vtune and Intel Software Development Emulator Intel Vtune-19.0.4 [35] and Intel SDE-9.14 [36] are used to profile the code on Intel architecture. In particular, the SDE tool can be employed to collect the number of FLOPs and the L1 data movement. Vtune is used to capture data traffic in DRAM and cache levels [37]; the total bytes are read and written in each memory level. Then, the achieved performance on the architecture \(i\) is \(P_{i}\) = SDE FLOPs/\(T\), where \(T\) is the application's elapsed time. The operational intensity of the code regarding a specific memory level \(m_{l}\) is \(I_{m_{l}}\) = SDE FLOPs/Vtune_Bytes_\(m_{l}\), where Vtune_Bytes_\(m_{l}\) is the number of Vtune memory accesses (read and write) for the memory level \(m_{l}\).
* AMD\(\mu\)Prof
* AMD\(\mu\)Prof-4.1 [38] is used to characterize the performance bottlenecks in the source code and to identify ways to optimize it on AMD architecture. The tool integrates a roofline support in order to generate the roofline data and the roofline plot. The achieved performance on the architecture \(P_{i}\) and the operational intensity of the DRAM are reported in the roofline support, while for a memory level \(m_{l}\) we have \(I_{m_{l}}=\mu\)Prof FLOPs/\(\mu\)Prof_Bytes_\(m_{l}\), where \(\mu\)Prof_Bytes_\(m_{l}\) is the number of \(\mu\)Prof memory accesses (read and write) for the memory level \(m_{l}\).
* NVIDIA Nsight Compute [39] is employed to collect performance data on Nvidia GPUs. It provides a set of metrics to evaluate in order to compute the number of FLOPs and data movement. We summarize the metrics for roofline data collection in Table 4.  
The total number of FLOPs = \(M_{64}+M_{32}+M_{16}+512\times M_{TC}\), the achieved performance \(P_{i}\) on GPU architecture \(i\) is \(P_{i}\) = FLOPs/\(T\), where \(T\) is the kernel runtime, and the operational intensity \(I_{m_{l}}\) of memory level \(m_{l}\in\{L1,L2,HBM\}\) is \(I_{m_{l}}\)= FLOPs/\(M_{m}\).
## 3 PUB-SalNet  
In this section, we describe our proposed PUB-SalNet. The goal of our work is to build a high-performance automatic salient segmentation model appropriate for biomedical tasks without ground-truth labels to improve computer-aided diagnosis. To reach this target, we design a processing pipeline with three major steps, as shown in Figure 2. In the first step, we aggregate a new biomedical data set called SalSeg-CECT. We then pre-train a deep feature extraction model specially designed for biomedical images, which can be used as a backbone module to initialize model parameters and to boost other tasks without data annotations. In the second step, we present the U-SalNet model on the basis of the U-Net architecture to learn to selectively attend to salient segmentation objects. The network includes global attention and local attention to facilitate learning saliency through global contrast and local similarity. In the last step, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by unsupervised attentional backpropagation. Details of each step are explained in the following sections.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 1 Introduction
However, the input parameters of this model are complex and have not been compared or verified with other models, such as LSTM and GRUs. The current approaches still have limitations when attempting to predict the trajectories of directional drilling wells in coal mines. First, mechanical analysis methods require the establishment of differential equations and their solutions, which are cumbersome and difficult to apply in real time onsite. Second, although the existing data-driven methods have made progress, they have not fully considered the dynamic influence of the interaction between the drill bit and the target formation under complex geological conditions. The Pi-GRU model proposed in this study differs from the traditional single-input GRU or LSTM models. It adopts a unique parallel architecture design, which can not only effectively handle the time series data provided by the downhole measurement system (such as tilt angle and azimuth angle), but also utilize BP neural networks to extract deep-level correlations in non-time series features (such as formation lithology). This design not only overcomes the problem of high computational complexity of traditional mechanical analysis methods but also makes up for the deficiency of existing data-driven models that fail to fully utilize non-temporal features. The hyperparameters were optimized by introducing orthogonal experiments, and the necessity of this parallel architecture was verified through ablation experiments, demonstrating the significant advantage of the Pi-GRU model in prediction accuracy. The overall process of this study is shown in Figure 2 below. Figure 2: Overall process diagram of this paper. The rest of this paper is structured as follows. Section 2 includes a detailed introduction to data preprocessing, the procedure for constructing the Pi-GRU model, and a hyperparameter optimization scheme implemented through orthogonal experiments. Section 3 presents experiments for comparing the proposed approach with Transform, CNN-BiLSTM, and GRU models, and the necessity of stratum information is verified through ablation studies. Moreover, the generalization ability of the Pi-GRU model in different regions was validated using data from Dehong Coal Mine. Section 4 contains a summary of the research results and an overview of our future work on lightweight model variants.
## 4 Results
### Main Experimental Results
#### 4.4.2 Thinning  
After optimizing the DCNN architecture on the dataset, the main research objectives of this study were addressed. First, the possibility of detecting the need for thinning with the optimized DCNN was evaluated, exclusively with remote sensing data (Section 4.4.3). Then, the feasibility of detecting the urgency for thinning was assessed (Section 4.4.4).
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## Materials and Methods
### SAR and AIS Data Comparison
The ship's speed is considered constant and the heading to be applied is in the opposite direction of the one from the AIS report. The position is then re-projected for a time interval equal to the difference in seconds between both \(t_{SAR}\) and the latest AIS position timestamp. The interpolation procedure is summarized pseudocode described in Algorithm 2. ```
1 functionInterpolate\((AIS,X)\); Input : AIS data flows within \(t\in[t_{SAR}-X,t_{SAR}+X]\) Output :\(AIS_{position}(t_{SAR},MMSI_{unique})\)
2 ifnumber of \(AIS_{position}(t,MMSI_{unique})\geq 2\)then
3 interpolate each two consecutive AIS positions;
4 retain positions with similar heading and speed;
5 ifall\(AIS_{position}\) acquired only after OR before \(t_{SAR}\)then
6 predict and return position for t = difference (\(t_{SAR}\), latest AIS position timestamp);
7 else
8 extract and return the interpolated position closest to \(t_{SAR}\);
9
10 end if
11
12 else
13 return single \(AIS_{position}\);
14
15 end if
16
17 end for
```  
**Algorithm 2**Interpolation of automatic identification system (AIS) data flows.
## 4 Target Performance
### Marker Direction Detection Accuracy  
The antenna mounted on the wireless relay system has an antenna diameter of 30 [cm], a frequency of 5.8 GHz, and a beamwidth of 11.854 [deg.]. The antenna diameter was tentatively determined to be a realistic size for a small UAV of 2 to 3 m in size. The antenna frequency was selected to be one of the frequency bands for image transmission using UAVs, as issued by the Ministry of Internal Affairs and Communications of Japan. Based on the above, the antenna pointing accuracy was set to \(\pm\)0.29 [deg.], which is 1/20 of the beamwidth [13].  
To achieve the determined antenna pointing accuracy, the required marker direction detector accuracy for the ground station detection system is set to be 0.14 [deg.] with a margin of 1/2 of the antenna pointing accuracy.
# Fault Coupling Analysis and Reliability Assessment of Actuation System Based on Bond Graph Model
## 2 Bond Graph Model of Actuation System  
The bond graph was proposed by Professor [PERSON] from the Massachusetts Institute of Technology in 1961 [20]. This is a very effective method for dynamic modeling and analysis that can be applied uniformly to engineering systems in various energy categories. Variables in various energy fields are classified into four categories: flow, effort, displacement, and momentum. For instance, the relationship between _flow_, _effort_, and _power_ in the bond graph mode is expressed as:  
\[effort\times flow=power \tag{1}\]  
The bond graph model can be used to efficiently construct models of complex electromechanical and hydraulic systems.  
In the widely used actuation system of the Gulfstream 650 general-purpose aircraft, the ailerons, spoilers, rudder, and elevators all use the HA/EHA dissimilar redundancy actuation system. Therefore, it is necessary to construct corresponding bond graph models for the seven control surfaces to form a complete actuation system for the aircraft. This paper focuses on the construction of a two-part dissimilar redundancy actuation system made up of HA and EHA. The actuator of physical energy transmission is modeled in one  
Figure 1: Flowchart of the proposed method.  
part, and the flight control computer representing signal transmission is modeled in the other. Table 1 displays the bond graph model's parameter definitions.
## 3 Experimental Setup
### Data-to-Image Conversion
#### 3.4.3 Three-Phase STFT Image Construction  
STFT is a representative time-frequency analysis technique that divides the time-series signal into fixed-length windows and performs Fourier transforms on each segment, allowing both time and frequency information to be captured.  
STFT parameters were configured based on the properties of the raw data used in this study. With a sampling frequency of 10 kHz, the dataset included high-frequency characteristics from localized, rapid fault changes such as 2-turn and 4-turn short faults. Accordingly, a window length of 128 samples (approx. 12.8 ms) was selected, and a time step of 27 samples was used. The frequency resolution was set to 65 frequency bins, enabling an analysis of the 0-5 kHz range at approximately 77 Hz intervals. A Hamming window was applied to reduce the spectral leakage caused by signal discontinuities at boundaries.  
The resulting spectrograms for each phase (A, B, C) were grayscale images, which were then assigned to the R, G, and B channels, respectively, to construct STFT-based RGB images.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 4 Methodology
### AI-Based Procedures to Replace Human Processing
#### 4.3.3 SVM Classifier  
A support vector machine divides the elements of a set into different subsets known as classes with the goal of finding the widest possible hyperplane that best separates these classes. The margin can be seen in Figure 5. It is defined as the maximum width of the region parallel to the hyperplane that has no interior data points. Equation (29) shows how a linear SVM predicts the class of a new \(x\) instance by calculating the decision function \(w^{T}x+b=w_{1}x_{1}++w_{n}x_{n}+b\): if the result is positive, the predicted class \(f(x)\) is the positive class (1); otherwise, it is the negative class (0) [75]. \(b\) is the bias and \(w\) is the feature weight.  
\[f(x)=\left\{\begin{array}{ccc}0&\text{si}&w^{T}x+b<0\\ 1&\text{si}&w^{T}x+b\geq 0\end{array}\right. \tag{29}\]  
To make it easier to separate the classes after this transformation, kernel functions move the data to a different, usually higher, dimensional space, potentially simplifying nonlinear complex decision boundaries in the assigned higher dimensional feature space to make them linear. The data do not have to be explicitly transformed in this process, which is known as a kernel trick [80]. A second-degree polynomial kernel is the function \(K(a,b)=\left(a^{T}b\right)^{2}\). Based on some mapping \(\phi\), the kernel \(K\) corresponds to an inner product in a feature space [81]. A kernel is a function in ML that computes the dot product \(\phi(a)^{T}-\phi(b)\) by using only the original vectors \(\mathbf{a}\) and \(\mathbf{b}\) without computing the \(\phi\) transformation. The polynomial kernel for polynomials of degree \(d\) is shown in Equation (30) [75].  
\[K(a,b)=\left(\gamma a^{T}b+r\right)^{d} \tag{30}\]  
where \(a\) and \(b\) are vectors in the input space, \(r\geq 0\) is a free parameter that compensates for the impact of higher-order terms in the polynomial versus lower-order terms in the polynomial, and \(\gamma\) is a scaling parameter.  
When \(r=0\), it is said that the kernel is homogeneous. When \(d=1\) and \(r=0\) are implemented, the result is identical to that of a linear kernel. If \(d\) is greater than one, nonlinear decision limits are produced, with the degree of nonlinearity increasing as \(d\) increases. Due to overfitting, \(d\) values greater than \(5\) are typically not recommended. Figure 5 depicts the optimal hyperplane with a polynomial kernel separating the data, where the light blue and brown dots represent data belonging to two distinct classes. The segmented red lines represent the various hyperplanes that can be constructed to partition data representing two classes between two point clouds. In a similar fashion, the red line represents the hyperplane that maximises class separability.  
Figure 5: Graph of a polynomial SVM showing the hyperplane separating the samples from the classes.
###### Abstract  
Building segmentation from high-resolution remote sensing images plays a crucial role in cadastral measurement, ecological monitoring, urban planning, and other applications. To address the current challenges in building segmentation from high-resolution remote sensing images, this paper proposes an improved deep learning-based network--REU-Net(2 EEAM). The network replaces traditional convolutional blocks in U-Net with Residual Structures, deepening the network and alleviating the issue of vanishing gradients. Additionally, it substitutes the direct skip connections with two Edge Enhancement Attention Modules (EEAMs), enhancing the network's ability to extract building edge information. Furthermore, a hybrid loss function combining edge consistency loss and binary cross-entropy loss is used to train the network, aiming to improve segmentation accuracy. Experimental results show that REU-Net(2 EEAM) achieves optimal performance across multiple evaluation metrics (such as P, MPA, MIoU, and FWIoU), particularly excelling in the accurate recognition of building edges, significantly outperforming other network models. This method provides a reliable foundation for the further optimization of building segmentation algorithms for remote sensing images.  
Keywords:remote sensing images; building segmentation; deep learning; residual structure; edge enhancement attention module +
Footnote †: journal: Applied Sciences
## 4 Discussions  
### Advantages of the Modified SWAT Model  
The SWAT model has been widely used for evaluating hydrologic and other watershed parameters worldwide [21; 29; 44; 46; 47; 48; 49; 50]. However, the SWAT has not been tested to model CO\({}_{2}\) emission in cold wetlands, which are impacted by soil respiration, soil temperature, and the groundwater table [20]. In this study, the wetland subroutine in the SWAT was developed to add the new functions of the SWAT model in predicting CO\({}_{2}\) emission, soil temperature, NEE, RECO, and the groundwater table. The modified model showed a very good agreement between the observed and simulated values at the Athabasca River Basin. The modified SWAT model captured well the dynamics and the trends of soil temperature, NEE, RECO, and CO\({}_{2}\) emission from the cold wetlands in Canada. The spatiotemporal distribution of CO\({}_{2}\) emission showed significant variation in CO\({}_{2}\) emission both in space and time.  
Our results quantified distinct seasonal variation and trends of the groundwater table and CO\({}_{2}\) emissions during the study period. The analysis showed an increased trend in CO\({}_{2}\) emissions in the summer season when the soil temperature is higher. During the mid-summer season, higher respiration was observed in the model results, which in turn was significant in affecting CO\({}_{2}\) emission. Based on our results, a high CO\({}_{2}\) emission was observed when the groundwater level remained at a depth below 0.5 m. In contrast, a lower emission was observed when the groundwater table was shallow. This can be explained by the lower groundwater table resulting in higher CO\({}_{2}\) emissions. The groundwater tables can significantly affect greenhouse gas emissions. This is in agreement with those using other approaches [51]. Their results showed that maximum CO\({}_{2}\) emission was observed from June to August, whereas minimum emission was observed from January to March. Similar studies also confirmed that the groundwater table has a significant impact on the release of CO\({}_{2}\) into the atmosphere [52; 53]. [PERSON] et al. [26] also found that lower water tables corresponded with higher CO\({}_{2}\) emissions. Similarly, it is found that ecosystem respiration increased with increasing soil temperature and with decreasing groundwater level.  
Ecosystem respiration had a good correlation with soil temperature. Similar findings reported impacts of soil temperature change on CO\({}_{2}\) emission in wetlands [19; 54; 55]. The minimum CO\({}_{2}\) emission was recorded from December to March due to lower soil temperature. The soil respiration and emission started increasing from June to September as the soil temperature increased. Increases in soil temperature increased CO\({}_{2}\) emission [56]. This indicates that CO\({}_{2}\) is affected by soil temperature which raises soil respiration during summer seasons in the wetlands [19; 57; 58; 59; 60; 61; 62]. The modified SWAT model captured well the daily NEE, RECO, water table, soil temperature, and CO\({}_{2}\) with a very good model efficiency.