esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology of Processing
### Predicting the Cumulative Ground Deformation Using Back-Propagation  
Predicting the cumulative ground deformation was required in many fields associated with urbanization, environment protection, agricultural, and ecological applications [48; 49]. However, the current prediction methods were either using linear regression or multiple linear regression. In other words, the current analysis or predicting of cumulative ground deformation was mainly dependent on the previous results of ground deformation [50; 51]. The predicting was based on the analysis of the current condition; therefore, the relationships built from linear or multiple linear regression were so simple that their uncertainties of predicted results cannot be easily controlled. Then, the predicting results were not convincing or reliable. To face this challenge, a more reliable model with precise data should be built and adopted. BP is a supervised learning method of Artificial Neural Networks (ANN), which can be used to perform a given task [52; 53; 54]. It requires a teacher who knows, or can calculate, the desired output for any input in the training set. BP requires that the activation function used by the artificial neurons (or \"nodes\") be differentiable [55; 56; 57]. Therefore, the ANN was adopted to predict the cumulative ground deformation using related independent variables such as temperature, precipitation, water level, or building area. For a given region, the related influencing factors are used as model input parameters, and the cumulative ground deformation is set as a model output parameter. The whole process needs to consider the input, output, network, node, study rate, et al.  
\[1=\sqrt{m+n}+\varepsilon,\;\varepsilon=0,\;1,\;2,\ldots,\;10 \tag{5}\]  
In the equation, \(l\) represents the number of hidden nodes; \(m\) represents the number of input nodes; \(n\) represents the number of output nodes, and \(\varepsilon\) represents the constant.  
Indicators such as the root mean square error (RMSE) are selected to evaluate the precision of the simulated versus the observed data. The RMSE reflects the distance of the training sample, testing sample, and validating sample from the real data or true data. The RMSE is defined as follows [58]:  
\[\text{RMSE}=\left(\frac{\sum_{i=1}^{n}(S_{i}-O_{i})^{2}}{n}\right) \tag{6}\]  
In the equations, \(S_{i}\) represents the simulated results; \(O_{i}\) represents the observed results; \(\overline{O}_{i}\) is the mean value of the observed data, and \(n\) is the total number of comparisons. The basic process of BP was shown in Figure 4, and the detailed workflow is defined as follows:
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Algorithm
#### 4.2.2 Assumptions  
The platform performs multi-hop communication from the source node to the destination node. There are a few assumptions in this setup as follows:  
* The delay incurred in multi-hop communication is not considered in order to focus on routes with less traffic (i.e., with low PU activities).
* The backbone and D2D routes are readily available, and the source node re-prioritizes them.
* A D2D route is up to three hops, and the source and destination nodes do not have direct communication.
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 3 Method
### Residual Spatial Attention Adaptive Aggregation Module
#### 3.3.2 Large-Receptive-Field-Based Spatial Attention Module  
To promote the feature learning capability of CRFS, introducing a spatial attention mechanism is a reasonable solution. However, some spatial attention modules [32; 33] are unable to effectively capture spatial location information that is helpful for SR task, due to the lack of large receptive fields. Non-local operations [13; 14; 15; 30; 33] serves as a promising solution to implement a spatial attention mechanism that explores correlations between any positions in a global way by capturing long-range dependencies. However, the large computational overhead is an insurmountable drawback. Considering that large receptive fields are able to fully exploit the interdependencies of local positional information, we propose an efficient Large-Receptive-field-based Spatial Attention Module (LRSAM) to enhance the sensitivity of the SR model to critical spatial content. Compared to non-local  
Figure 3: Architecture of the Contextual Residual Fusion Structure (CRFS).  
operations, LRSAM has a lower computational complexity and can be easily embedded in different network positions.  
LRSAM consists of two sub-modules, including the squeeze module and the excitation module, as shown in Figure 4. The squeeze module starts with a Conv to reduce the channel number of the input feature \(Y_{0}\), then yielding a large receptive field by a stride Conv (with stride 3) and the max-pooling operation with a large window (e.g., \(7\times 7\)) for enhancing the sensitivity of critical spatial content. To generate the spatial attention map, the squeeze module firstly applies a set of Convs followed by ReLU on the output \(Y_{1}\) of the squeeze module. In the excitation module, \(Y_{1}\) is passed through an upsampling layer of pixel-shuffle [36] and sigmoid activation to produce a spatial attention map \(Y_{mask}\), and then \(Y_{mask}\) is used to rescale \(Y_{0}\) for highlighting beneficial feature information and to obtain \(Y_{0}^{*}\).
## 3 Dynamic Cutting Model of the Cutterhead
### Normal and Rolling Forces of Disc Cutters on the Interface between Soft and Hard Strata  
In previous studies, the models of cutterhead torque and thrust only, respectively, calculate the force on the disc cutter in soft and hard strata, and there is no research analyzing the force of the cutter at the interface. However, when the cutter is at the interface of soft and hard strata, the calculation method of the cutter force should be different and is difficult to calculate theoretically. Using linear cutting test to study the law of the force may prove a wonderful method. The normal and rolling force data in the cutting process from hard rock to soft rock were recorded in previous linear cutting test [25], with penetration rates of 1 mm, 2 mm, 2.5 mm, 3 mm, and 3.5 mm. The test results are used to analyze the evolution of rolling force and normal force when the cutter ratio in soft strata increases at the interface. When penetration is constant, normal force and rolling force both resemble quadratic functions with the ratio of cutter in soft rock, as shown in Figures 12 and 13, and the fitted equations are provided in Equations (38) and (39), which are highly correlated.  
\[\left\{\begin{array}{l}y=117.554-2.147x+0.018x^{2},R^{2}=0.981,\,p=1\\ y=59.142-1.279x+0.014x^{2},R^{2}=0.974,\,p=2\\ y=137.368-3.724x+0.030x^{2},R^{2}=0.985,\,p=2.5\\ y=196.065-4.341x+0.033x^{2},R^{2}=0.915,\,p=3\\ y=265.820-3.401x+0.032x^{2},R^{2}=0.836,\,p=3.5\end{array}\right. \tag{38}\]  
\[\left\{\begin{array}{l}y=6.495+0.059x-6.467x^{2},R^{2}=0.935,\,p=1\\ y=3.115+0.069x-7.191x^{2},R^{2}=0.929,\,p=2\\ y=2.697+0.041x-5.261x^{2},R^{2}=0.909,\,p=2.5\\ y=-0.032+0.080x-6.525x^{2},R^{2}=0.938,\,p=3\\ y=10.141+0.088x-8.598x^{2},R^{2}=0.885,\,p=3.5\end{array}\right. \tag{39}\]  
where \(x\) represents the ratio of disc cutter in soft strata; \(y\) represents the force.  
It should be noted that the aforementioned equations are only analyzed based on the test of a composite rock made of granite and sandstone. For specific projects, it is necessary to conduct tests according to different geological conditions to determine the relationship between the proportion of disc cutter in soft rock and the force.  
Figure 11: Variation of torque, thrust and power under different USC during cutterhead rotation.
# A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks
## 3 Experiments and Results
### Detection of Pathologies in Concrete  
The detection experiment was done using the dataset [17], applying the YOLO architecture in version 4. It was designed to enable the ability to identify concrete pathologies in wider images, where we do not know if there is a pathology or where the pathologies are present.  
The dataset [17] was split into two subsets for training and for testing. A total of 840 images were used for training and 212 for testing, using a total of 1052 images from the dataset. Defect numbers for the following classes: crack--2507, spallation--1898, efflorescence--833, exposed bars--1507, and corrosion stain--1559 [24].  
The training was performed for 1500 epochs, and starting at epoch 300, validation tests were performed every 200 epochs with pre-selected images. The metric used was mAP, mean Average Precision; this metric considers precision and recall, and the average of the two values, precision being how good the predictions are and recall being how good all the positives are.  
Figure 6 displays the result graphically, and, in this graph, it is better to visualize the training evolution for the five trained classes. All classes have an evolution until epoch 900, but at epoch 1000, efflorescence and cracking become worse, while fragmentation, exposed bar, and corrosion still evolve. At epoch 1100, all classes worsen, resuming their evolution until epoch 1300 and worsening again at epoch 1500. The best performance, therefore, occurred at epoch 1000 for the classes fragmentation, corrosion, and exposed bar. As for cracking and efflorescence, despite the drop in performance after epoch 900, there is a recovery, and the best performance is reached at epoch 1300. Therefore, the results for detection with YOLO were measured with mAP, mean Average Precision, for five classes of concrete pathology, reaching 11.80% for fissure, 19.22% for fragmentation, 5.62% for efflorescence, 27.24% for exposed bar, and 24.44% for corrosion.  
Figure 7 shows cases of concrete pathology. It is possible to see exposed steel bars; at this same point, there is concrete fragmentation, and the steel bars show a case of corrosion. This figure was used in the trained detection network to test which pathologies could be identified.  
Figure 5: CNN Architecture and configuration.  
Figure 8 shows the detection result of the trained network. A yellow rectangle, the bounding box, indicates the location of the identified pathologies. The network detected reinforcement exposure; in this case, a confidence of 70% detection is shown, fragmentation, and reinforcement corrosion.  
Figure 8: Image of concrete pathologies after detection with YOLOv4 [17].  
Figure 6: Training with YOLOv4 on the CODEBRIM dataset [17].  
Figure 7: Image of concrete pathologies before detection with YOLOv4 [17].
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 3 SIMMA System Architecture
### Buoy Design  
The buoys are based on a design of the [PERSON] drifter buoy, which has an \"X\" style sail and four floatation devices. This design is commonly used in oceanographic surface monitoring, given its ease of implementation and the ability to use the surface layer water currents to move (Figure 9).  
The central structure of these particular buoys was made of hydraulic PVC, the sails from sheets of plastic canvas and the flotation devices from rigid styrofoam. These buoys were built and developed by the Centro de Investigacion Cientifica y Educacion Superior de Ensenada (CICESE) from Mexico, which is part of this project.  
In the upper central part of the buoy, an additional PVC tube was added to the central structure, where the slave node's electronics were housed. Furthermore, the anemometer was fixed at the top, as  
Figure 8: Control node’s task diagrams.  
Figure 9: [PERSON] drifter buoy design. Modified from [[http://www.ims.uaf.edu/NPRBdrifters](http://www.ims.uaf.edu/NPRBdrifters)]([http://www.ims.uaf.edu/NPRBdrifters](http://www.ims.uaf.edu/NPRBdrifters)).  
well as the BME280 sensor which was contained inside of a 3D printed housing which allowed the flow of air while avoiding any water intakes to get through to the sensor. A proper air exchange is required for an accurate reading of the BME280, otherwise the sensor will measure different values than that of the actual ambient. This upper structure of the buoy will be floating above the surface of the water, allowing GPS and LoRa communications (Figure 10).  
Given that this upper section of the buoy contained electronics, it was extremely important to avoid any water intakes. To try and circumvent this, the electronics were also placed in an IP67 case, and all of the connection cables for the exterior sensors that had to pass through the PVC structure were sealed with a waterproof silicon-based resin.
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 3 Results
#### 3.3.2 Group with Abdomen and Pelvis Imaging  
The 'Abdomen + Pelvis' group includes 175 patient observations. Cases were analyzed with the same set of parameters as above. Correlations with kV were also calculated (Weight \(\leftrightarrow\) kV: Pearson r = 0.07, \(p\) = 0.36; BMI \(\leftrightarrow\) kV: Pearson r = 0.012, \(p\) = 0.88) but are not displayed in Figure 11 because none reached statistical significance.  
Figure 10: Correlation coefficients between various X-ray tube output parameters and the maximum width of the patient for cases involving only the abdomen region.  
* Tube current (mA), Phase I: Pearson r = 0.23, \(p\)-value = 0.002, suggesting a weak positive relationship with Max width (cm).
* Tube current (mA), Phase II: Pearson r = 0.16, \(p\)-value = 0.034, indicating a weak positive relationship with Max width (cm).
* Tube current (mA), Phase III: Pearson r = 0.19, \(p\)-value = 0.012, showing a weak positive relationship with Max width (cm).  
Is There a Statistically Significant Difference in DLP Values Based on the Location of the Isocenter?  
The analysis was conducted separately for two groups: cases involving only the abdomen and cases involving both the abdomen and pelvis. The Mann-Whitney U test was used to determine if there is a statistically significant difference in DLP values based on the isocenter location within each group.
# Performance Estimate of a Spin-Stabilized Drag Sail for Spacecraft Deorbiting
## 2 Mathematical Model
### Earth's Oblateness  
A spacecraft in an LEO is subject to a significant perturbation induced by the Earth's oblateness. In this work, only the effect of \(J_{2}\) is taken into account, and the resultant perturbative acceleration \(\mathbf{a}_{J_{2}}\) is written in \(\mathcal{T}_{\rm RTN}\) as  
\[[\mathbf{a}_{P_{J_{2}}}]_{\rm RTN}=-\frac{3\,\mu_{\oplus}\,J_{2}\,R_{\oplus}^{2}} {r^{4}}\left[\begin{array}{c}\frac{1}{2}-\frac{6(l\sin L-k\cos L)^{2}}{(1+l^ {2}+k^{2})^{2}}\\ \frac{4(l\sin L-k\cos L)(l)\cos L+k\sin L)}{(1+l^{2}+k^{2})^{2}}\\ \frac{2(1-h^{2}-k^{2})(l\sin L-k\cos L)}{(1+h^{2}+k^{2})^{2}}\end{array}\right] \tag{13}\]  
where \(J_{2}=1.0826\times 10^{-3}\) is the Earth's second zonal harmonics, \(R_{\oplus}=6378\,\mathrm{km}\) is the Earth's equatorial radius, and \(r\) is the distance from the center of the Earth to the spacecraft, which in terms of MEOEs is \(r=p/(1+f\cos L+g\sin L)\).
## 3 Proposed Method  
### Overall Architecture  
As shown in Figure 3, MDAU-Net maintains the U-shaped architecture and retains U-Net's decoder path. In contrast to U-Net, MDAU-Net has four key improvements.  
Firstly, we redesigned the encoder structure. In the original U-Net, the basic block utilizes the ConvBlock structure depicted in Figure 3. However, in MDAU-Net, we incorporated residual connections into the basic block, resulting in the residual encoder. This  
Figure 3: The structure of MDAU-Net.  
modification bolstered the preservation of original features and supplied additional initial information for the segmentation task.  
Subsequently, to amplify information flow within the network and promote feature reuse, we substituted U-Net's bottleneck layer with a cascaded adaptive feature extraction unit (CAE).  
Additionally, we introduced a double-flow linear pooling enhancement unit (DLE) in the jump connection segment to narrow the semantic gap between deep and shallow features through a \"progressive\" feature fusion approach. This refinement aided the network in achieving more precise target area localization.  
Finally, we designed a cross-level information interaction mechanism (CII) utilizing bidirectional residual connections to address the issue of forgetting a priori knowledge during the training process.  
In Algorithm 1, we provide a pseudocode as an initial description of MDAU-Net, with a comprehensive exposition of the network's structure to follow in subsequent sections.  
```
Data: Dataset \(X\), mask \(L\), module parameters Result: Segmentation result \(Y\)
1 for\(i=1\) to \(N\)do
2 Preprocessing and enhancement of image \(X_{i}\).
3 for\(j=1\) to \(4\)do
4 Encode \(X_{i}\) as \(E_{ij}\) using ResBlock and MaxPooling.
5 Obtain the feature map \(E_{ij}\) for each encoder layer.
6 end for
7 Adaptive feature extraction by CAE module, obtain \(C_{i}\).
8 for\(k=1\) to \(4\)do
9 Calculate the DLE by \(E_{ij}\) and \(D_{i(k-1)}\), obtain the feature map \(T_{ik}\).
10 Decode \(C_{i}\) as \(D_{ik}\) using bilinear interpolation and ConvBlock.
11 Obtain the feature map \(D_{ik}\) for each decoder layer.
12 Obtain the segmentation result \(Y_{i}\) of image \(X_{i}\) as \(Y_{i}=D_{ik}\).
13 end for
14 end for
15 Output the segmentation result \(Y=[Y_{1},Y_{2},\dots,Y_{N}]\).
```  
**Algorithm 1**MDAU-Net
## 4 Materials and Methods
### Small Angle X-ray Scattering (SAXS)  
The SAXS patterns for hNudt16 in solution were recorded using the XEUSS 2.0 SAXS/WAXS laboratory beamline (XENOCS, Grenoble, France) equipped with a MetalJet microfocus X-ray source (\(\uplambda\) = 0.134 nm) with a liquid metal-jet anode (gallium alloy) (Excillum AB, Kista, Sweden) and a Pilatus 3R 1M hybrid photon counting detector (Dectris, Baden-Daettwil, Switzerland). Briefly, 50 \(\upmu\)L of a hNudt16 solution at a concentration of 2.618 mg/mL in buffer (20 mM HEPES pH 7.9, 300 mM NaCl, 10% glycerol, 0.5 mM TCEP, and 20 mM MgCl2) was injected manually into the low-noise liquid flow cell. The experimental scattering vector range was 0.0125 A\({}^{-1}\)\(<\)\(s\)\(<\) 0.58 A\({}^{-1}\) (\(s\) = (4\(\uppi\)/\(\uplambda\)) sin\(\theta\), where \(\theta\) is the scattering angle). The measurements were conducted at room temperature, and the scattering data were collected over 120 min as a series of 12 frames (600 s per frame). SAXS data reduction and processing were performed using Foxtrot 3.2.7 [44] and the PRIMUS program from the ATSAS package [45]. The radius of gyration (R\({}_{g}\)) was calculated usingPRIMUS software. The molecular weight of the hNudt16 samples was obtained from the extrapolated I(0) value compared to that of bovine serum albumin as a standard [46]. The pair distance distribution function (\(p\)(R)) and maximum particle dimension (D\({}_{\text{max}}\)) were calculated using GNOM [47; 48]. A comparison of the obtained experimental scattering data for hNudt16 with the theoretical scattering curves calculated for hNudt16 crystal structures available in PDB database was performed using CRYSOL and data truncated to 0.3 A\({}^{-1}\)[49]. The low-resolution dummy atom 3D model of hNudt16 in solution was obtained with the ab initio modeling method using DAMMIN and data truncated to 0.3 A\({}^{-1}\)[36].
# Radiation-Assisted Synthesis of Polymer-Based Nanomaterials
## 3 Metal-Based Nanomaterials
### Polymer-Nanometal Composites  
A limited number of methods have been developed for the preparation of metal-polymer nanocomposites. Usually, such techniques can be classified as in situ and ex situ methods. In the ex situ processes, metal nanoparticles are chemically synthesized and their surface is organically passivated. The derivatized nanoparticles are dispersed into a polymer solution or liquid monomer that is then polymerized.  
In the in situ methods, two steps are needed. First, the monomer is polymerized in solution with metal ions introduced before or after polymerization. Then, metal ions in the polymer matrix are reduced chemically, thermally or by radiation. The synthesis of metal nanoparticles from a diluted precursor involves several steps: reduction of metal ions by electron transfer, inducing nucleation and growth of the seeds into nanoparticles controlled by the stabilizers [13]. When aqueous solutions containing metal ions are exposed to ionizing radiation, the strongest reducing agent, namely in situ generated hydrated electron, \(\{\rm E^{\circ}\left[H_{2}O/e^{-}(aq)\right]\}=-2.87\) V, reduces any metal ions to their metallic state. The size and size distribution of metal nanoparticles can be controlled by the type (X-rays, gamma rays, accelerated electrons) of radiation, dose rate and metal ion/polymer ratio [14]. Polymer-nanometal composites with very unstable metal nanoparticles can be easily and safely prepared by this method by proper selection of polymers. One of these unstable metal nanoparticles, copper, when embedded in polymer films, was found to be stable for several months [15]. The stabilization of metal nanoparticles in polyelectrolyte matrices makes it possible to prevent their oxidation and aggregation. A wide variety of nano structures were synthesized because of the possibility of controlling the interaction between functional groups of polyanions and polycations with metal ions. The stability of metal nanoparticles can be achieved by using appropriate polymers or interpolymer complexes [16]. Bimetallic nanoparticles were synthesized in the films of interpolymer complexes. It was shown that radiation-induced reduction of (Cu/Ag) and Cu/Au) nanoparticles irradiated in the aqueous matrix of poly(ethylene imine)-poly(acrylic acid) complexes lead to the formation of bimetallic nanoparticles with narrow size distribution [17].  
A recent review summarizes the radiation-induced synthesis of various nano-sized compounds including metals, metal alloys, core-shell systems, metal oxides, etc. [18].
## 2 Network Architecture of Mobile Sink Based Wireless Sensor Network  
The mWSN network architecture differs from that of a static WSN in the sense that in the former case, the sink keeps on moving around/inside the sensor field for efficient data collection. A reference mWSN network architecture is shown in Figure 2. The main components of a mWSN are given as follows:  
_Regular Nodes_--These are the ordinary sensor nodes that are deployed in the sensor field for sensing some phenomenon of interest. Upon sensing the events, these nodes disseminate their data in a cooperative manner towards a mobile sink. Depending on their placement in the sensor field, nodes might work as relays thereby forwarding others data towards a mobile sink.  
_Mobile Sink(s)_--Depending on the application scenario, there might be single/multiple mobile sink(s) that move inside/around the sensor field for data collection. Such devices are considered unconstrained devices in terms of their resources. Mobile sink can be a sensor node attached to a human, car, animal or a robot.  
_(Optional) Sink Assistants_--In some applications, special nodes are deployed at strategic positions that provide assistance to the sink in data collection. These devices are also considered as energy rich. In static deployment, such nodes become intermediate/local data collectors from the sensor nodes and later on deliver collected data to a mobile sink upon its arrival. In the mobility case, they are meant to ensure coverage of almost the entire sensor field for real-time communication services in certain applications.