esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Nonlinear Elastic Model for Rock Material
## 5 Experimental Validation of the New Model
### Experimental and Fitting Results  
Based on the stress-strain curves under uniaxial compression, the uniaxial compressive strength was obtained, and the stress-strain curves of all the rock specimens in the initial compression stage were calculated using the method shown in Figure 1. The unified nonlinear elastic model was used to fit the curves using the least square algorithm method. The \(E_{ni}\) and \(m\) values that were obtained by fitting the curves and the coefficients of correlation R\({}^{2}\) values are listed in Table 1. This new model shows a better goodness of fit and a higher accuracy of fit since all the R\({}^{2}\) values of the specimens exceed 0.97. The _UCS_ values, the \(E_{ni}\) values and the \(m\) values of each rock specimen were different. The \(E_{ni}\) values increased as the _UCS_ value increased for each rock type, whereas the \(m\) values changed slightly.  
The nonlinear elastic deformation of the rocks in the initial compression stage exists objectively, and their regularity can be followed as well. The unified nonlinear elastic model is suitable for describing the nonlinear elastic deformation behavior of intact rocks at the initial compression stage.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Rock** & _UCS_/MPa** & \(E_{ni}\)/MPa & \(m\) &
\begin{tabular}{c} **Coefficients of** \\ **Determination R\({}^{2}\)** \\ \end{tabular} \\ \hline D01 & 62.15 & 5.82 & 0.9117 & 0.9951 \\ D02 & 115.06 & 11.01 & 0.8911 & 0.9977 \\ D03 & 124.10 & 12.79 & 0.903
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 5 Material and Methods
### UE Movement Model  
The RDM (Random Direction Mobility) model [16] is used to simulate rescue team movements during the disaster situation, as illustrated in Figure 14. Compared with other models, RDM avoids the high probability of any node selecting a new destination located around the center of the simulation area, or a destination that requires a path of the node through the center area. In our simulation, a node starts its motion by selecting a direction 0, 2 \(\times\)\(\pi\) with speed Vmax generated randomly from 0 to 4 m/s. According to [17], 1 m/s represents slow human walking, while a fast walking speed ranges between 1.50 and 2.14 m/s. Higher speed values were used to simulate cases where rescue members must run or move using light equipment.  
As soon as the node reaches the limit of the simulation zone, it waits for a random pause time from 0 to 60 s. Subsequently, the node chooses a new direction from 0 to \(\pi\) while keeping its initial speed. This process continues until the simulation ends.
## 3 Evaluation of Building Attenuation Model
### Total Blockage of Communications due to Obstacles  
In this approach, considering the effects of obstacles in VANET simulations is done by assuming that communication between two nodes is completely attenuated. Some papers, such as [11] and [12], use this focusing when evaluating their proposals.  
To detect the presence of buildings in the LOS of a communication path, we used the same idea of [3], without having to compute all of the intersection points. However, sometimes, information about buildings is not available, and their influence is modeled in a conservative way, as is done in [13]. Figure 4b shows how the packet error probability (PEP) varies when the obstructed distance between two nodes increases, as shown in the scenario of Figure 4a.  
Figure 4: Packet error probability (PEP) behavior in an obstructed communication scenario between two vehicles near a corner. Empirical IEEE 802.11p channel model. Channel Capacity= 6 Mbps. Antenna sensitivity = \(-82\) dbm. (**a**) Near-line-of-sight (NLoS) scenario at a corner; (**b**) PEP _vs._ distance.  
Figure 4b shows that the value of the PEP reaches one at around an obstructed distance of 30 m, even when a robust modulation scheme is used (as happens in a low capacity channel). High power transmission and no interference are assumed. Thus, it would not be surprising that most of these types of communication fail. Assuming a total blockage of the signal due to obstacles, the overall performance would be close to that obtained using a more realistic channel model, as we will see in Section 4.
# S seaweed Polysaccharides in Agriculture: A Next Step towards Sustainability
## 3 Seaweed Poly- and Oligosaccharides Bioactivities on Plants
### Alginates and Oligo-Alginates  
Alginates are biodegradable and non-toxic compounds traditionally used as natural fertilizers due to their superabsorbent or water-retaining properties. The carboxylic acid groups present on the alginic acid chain, combined with the metallic ions in the soil, form high-molecular-weight polymers that can absorb moisture and retain large amounts of  
Figure 4: Schematic representation of seaweed polysaccharides action in plants.  
water. Generally, water retention is a problem in sandy soils. These soils, when watered, dry up easily and drain away valuable nutrients beyond the plant roots. The use of alignates can improve this problem, stimulating plant root system development and increasing soil microbial activity [1,20].  
Seaweed alginates and oligo-alignates, produced by the enzymatic degradation of alginic acid, were reported to activate defense responses against pathogens (Table 1) in wheat plants [45], date palm roots [46], tomato plants [47], olive trees [48], and against TMV [12] by regulating defense-responsive signaling pathways. To induce resistance against viral infections, including TMV, alignates activate different defense enzymes such as phenylalanine ammonia-lyase (PAL), peroxidase (POD), and ascorbate peroxidase (AP), which elicit their metabolic pathways and the synthesis of secondary metabolites, such as phenolic compounds with antiviral activity [12,45,46]. In tomato plants (Table 1), the alginate confers resistance against a fungal infection by inducing antioxidant defense and antifungal pathogenesis-related (PR) protein expression by signaling pathways mediated by salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) [47]. In an experiment with olive trees (Table 1), the alginate induced resistance against a _Verticillium dahliae_ fungal infection by restricting the pathogen's growth and strengthening the host defense metabolism [48].  
In addition, seaweed alignates and oligo-alignates can stimulate growth, seed germination, and shoot elongation in different plant species [12,14] by enhancing nitrogen assimilation and basal metabolism [49] (Table 1).  
Therefore, alignates constitute an important source of potential elicitors in plants and a particular interest in agriculture. The chemical characterization of alignates or oligo-alignates and their mechanism to boost plant growth remains unclear.
## 4 Experimental Results  
### Dataset and Implementation Details  
As illustrated in Section 3.1, the training dataset was constructed by synthesizing five different fonts of the common Chinese character Big5 code and digits using the Pillow [37] package in Python. A total of 52,290 character images were generated. Furthermore, to accommodate the diversity of input image variations, each of these 52,290 characters was synthesized again with different noisy backgrounds or augmented with the angle rotation and resizing processes described in Section 3.2. Finally, we expanded the dataset to generate a sufficient amount of training data, ultimately including up to more than 19.6 million images. Table 3 lists the detailed content of the dataset mentioned in this study. To the best of our knowledge, no dataset with a sufficient number of Chinese character images to be used for a large-scale classifier has been developed in prior work. The proposed synthesis method can automatically generate character images through programs and can thus create a sufficient amount of training data and include augmentation to increase diversity.  
The synthetic training dataset was divided into three parts; 70%, 10%, and 20% were randomly selected for network training, validation, and testing, respectively. The validation set was used to avoid overfitting during training, while the testing set was used to verify the effects of the trained model. We set the initial learning rate as 0.01 with a 0.1 validation accuracy factor, minimum learning rate as \(10^{-5}\), dropout rate as 0.4, and mini-batch as 64. We initialized the weights using glorot uniform distribution and employed ReLU activation and a categorical cross-entropy loss function. The proposed model was trained for a maximum of 30 epochs using the Adam optimizer. Our models were implemented in Python 3.7 on the TensorFlow platform under a Windows operating system. The experiments were conducted on workstation computer with an Intel Core i9-9900X CPU at a clock rate of 3.50 GHz with 64 GB of RAM and an NVIDIA GTX 2080 Ti GPU. All of the character identification results in this work were assessed using the same testing dataset, which consisted of 4944 samples of 294 Chinese characters and was derived from 246 genuine ID card photos. Each sample has an image size of roughly 45 \(\times\) 45 pixels, clipped from ID card photos. Figure 1 shows the apparatus that was used to collect the ID card photos. Aged lamination cover, stain, and scratches are common damages in the testing dataset images, with an 8.5%, 1.6%, and 5.7% distribution, respectively. The remaining samples are undamaged.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### Overall Methodology  
The approach applied was completed in five steps (Figure 2). First, rock-fall incident data, related traffic data, and weather information were collected and prepared for analysis and variables selection phase. Then, the rock-fall risk was assessed based on the given data. Next, the rock-fall prediction model was developed based on the impact of independent variables (rock-fall influencing factors) on the dependent variable (occurrence of rock-fall). Overall performance measures validated the model. The model was developed and evaluated using SPSS V.22 software. After, the rock-fall detection model was developedand validated. Afterwards, the hybrid risk reduction module was developed. Finally, the hybrid hazard warning system platform was implemented and validated.
## 3 Numerical Method and Validation
### Modeling
#### 3.2.3 Mesh Independence Study  
The collection efficiencies of the hydraulic collector under four different mesh densities of coarse (0.51 \(\times\) 10\({}^{6}\) elements), middle (0.98 \(\times\) 10\({}^{6}\) elements), fine (1.77 \(\times\) 10\({}^{6}\) elements), and finer (2.52 \(\times\) 10\({}^{6}\) elements) were compared to ensure the mesh convergence. The collection efficiency of the hydraulic collector can be expressed as follows [17]:  
\[\eta=\frac{M_{1}}{M_{2}}. \tag{14}\]  
where \(\eta\) denotes the collection efficiency, and \(M_{1}\) and \(M_{2}\) denote the mass of nodules lifted to the exit of the transport channel and the total nodule mass, respectively.  
The collection efficiencies under the four mesh densities are listed in Table 2. As the number of elements increased, the simulation errors relative to the Very Fine case decreased gradually. The collection efficiencies for the Fine case and the Very Fine case were almost the same, i.e., less than 1%. Therefore, to balance the calculation accuracy and efficiency, the computational domain of the Fine case with 1.77 \(\times\) 10\({}^{6}\) elements was chosen to conduct the subsequent study, which is shown in Figure 9.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Type** & **Number of Elements** & **Collection Efficiency** & **Error against Very Fine** \\ \hline Coarse & 0.51 \(\times\) 10\({}^{6}\) & 95.8\% & \(-\)2.8\% \\ Medium & 0.98 \(\times\) 10\({}^{6}\) & 97.1\% & \(-\)1.5\% \\ Fine & 1.77 \(\times\) 10\({}^{6}\) & 98.2\% & \(-\)0.4\% \\ Very Fine & 2.52 \(\times\) 10\({}^{6}\) & 98.6\% & - \\ \hline \end{tabular}
\end{table}
Table 2: Collection efficiencies under different mesh densities.  
Figure 9: Mesh of the hydraulic collector in simulation.
# A Neural Network Approach for the Analysis of Reproducible Ribo-Seq Profiles
## 3 Materials and Methods
### Ribosome Profiling Data Extraction
#### 3.1.4 Identification of Significantly Reproducible Ribo-seq Profiles
\begin{table}
\begin{tabular}{c l} \hline \hline
**Genes ID** & **Annotation** \\ \hline rodZ & Cytoskeleton protein RodZ \\ arcB & Aerobic respiration control sensor protein ArcB \\ dld & Quinone-dependent D-lactate dehydrogenase \\ dnaX & DNA polymerase III subunit tau \\ fhuA & Ferrichrome outer membrane transporter/phage receptor \\ glnA & Glutamine synthetase \\ gltB & Glutamate synthase NADPH large chain \\ hisS & Histidine-tRNA ligase \\ infB & Translation initiation factor IF-2 \\ katG & Catalase-peroxidase \\ malF & Maltose transport system permease protein MalF \\ metG & Methionine-tRNA ligase \\ mukB & Chromosome partition protein MukB \\ ompC & Outer membrane protein C \\ parC & DNA topoisomerase 4 subunit A \\ secY & Protein translocase subunit SecY \\ purL & Phosphoribosylformylglycianidine synthase \\ me & Ribonuclease E \\ sucA & 2\textasciombitarate dehydrogenase E1 component \\ tufA & Elongation factor Tu 1 \\ tufB & Elongation factor Tu 2 \\ leuA & 2\textasciombropylmalate synthase \\ hokB & Toxin HokB; Toxic component of a type I toxin-antitoxin (TA) system. \\ acnA & Aconitate hydratase A \\ ubj & Ubiquitine biosynthesis protein UbiJ \\ lptD & LPS-assembly protein LptD \\ rpmC & Recombination-promoting nuclease RpnC \\ rpnA & Recombination-promoting nuclease RpnA \\ fdoG & Formate dehydrogenase-O major subunit \\ wbbH & O-antigen polymerase \\ wbbI & Beta-1,6-galactofuranosyltransferase WbbI \\ wbbK & Putative glycosyltransferase WbbK \\ rpnE & Inactive recombination-promoting nuclease-like protein RpnE \\ lpoA & Penicillin-binding protein activator LpoA \\ gspD & Putative type II secretion system protein D \\ yfI & Uncharacterized protein YfjI; Phage or Prophage Related \\ rlmL & Ribosomal RNA large subunit methyltransferase K/L \\ rssxC & Electron transport complex subunit RssxC \\ yfcI & Recombination-promoting nuclease RpnB \\ gtF & Uncharacterized protein Yfdl; Putative ligase \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Genes with significantly reproducible Ribo-seq profiles across the eight datasets. Column 1: Gene ID. Column 2: Annotation.
# Adapted B-Spline Quasi-Interpolation for Approximating Piecewise Smooth Functions
## 4 Adapted Quasi Interpolation Operators for Non-Smooth Functions in \(\mathbf{R^{2}}\)  
Consider a discontinuous piecewise smooth function \(f\) defined in \(\Omega=[0,1]^{2}\). Let \(\Gamma\) be a smooth curve that separates \(\Omega\) into two domains \(\Omega_{1}\) and \(\Omega_{2}\), and suppose that  
\[f(x,y)=\left\{\begin{array}{ll}f_{1}(x,y),&(x,y)\in\Omega_{1},\\ f_{2}(x,y),&(x,y)\in\Omega_{2},\end{array}\right. \tag{26}\]  
with \(f_{1}\in\mathcal{C}^{4}(\overline{\Omega_{1}})\) and \(f_{2}\in\mathcal{C}^{4}(\overline{\Omega_{2}})\). Assume that \(f(x,y)\) is given on the grid points \(\{(ih,jh)\}\) in \(\Omega\), \(0\leq i,j\leq N\), \(h=1/N\), and define \(f_{i,j}\equiv f(ih,jh)\).  
We wish to approximate the function using a quasi-interpolation operator of the form  
\[Qf(x,y)=\sum_{i,j}w_{i,j}B_{3}(x/h-i)B_{3}(y/h-j). \tag{27}\]  
Similarly to the one-dimensional case, the weights \(w_{i,j}\) are defined as linear combinations of the given function values, ensuring that the operator \(Q\) reproduces cubic polynomials. There are several options for defining the weights \(w_{i,j}\). For a smooth function \(f\), the optimal choice is to use the cross product of the one-dimensional symmetric quasi-interpolation operator (9). However, in the case of non-smooth function approximation, we must ensure that \(w_{i,j}\) does not depend on function values from both sides of the singularity curve \(\Gamma\).  
In the following, we describe the method for computing the weights \(w_{i,j}\) in (27). This is achieved by taking the tensor product of the one-dimensional quasi-interpolation operators \(Q^{F}\), \(Q^{B}\), and \(Q^{Sym}\) as defined in (15)-(17), respectively. This construction yields a total of nine two-dimensional quasi-interpolation operators:  
\[\begin{split} Q^{1}=Q^{S_{x}S_{y}},&\ Q^{2}=Q^{S_{ x}F_{y}},\ Q^{3}=Q^{S_{x}B_{y}},\ Q^{4}=Q^{S_{y}F_{x}},\ Q^{5}=Q^{F_{x}F_{y}},\\ &\ Q^{6}=Q^{B_{x}F_{y}},\ Q^{7}=Q^{S_{y}B_{x}},\ Q^{8}=Q^{F_{x}B_{y }},\ Q^{9}=Q^{B_{x}B_{y}},\end{split} \tag{28}\]  
where \(S_{x}\) and \(S_{y}\) denote the symmetric one-dimensional quasi-interpolation operators in the \(x-\) and \(y-\) directions, respectively; \(F_{x}\) and \(F_{y}\) represent the forward operators; and \(B_{x}\) and \(B_{y}\), the backward ones. If we denote the identity operator by \(1\), by \(\triangle_{x+1}\), the forward operator in the x direction is \(\triangle_{x+1}f_{i,j}=f_{i+1,j}\), and by \(\triangle_{y+1}\), the forward operator in the y direction is \(\triangle_{y+1}f_{i,j}=f_{i,j+1}\). We obtain nine cross products of the three quasi-interpolation operators in one dimension. For example,  
\[Q^{F_{x},F_{y}}_{i,j}f=[\frac{2}{3}I+\frac{5}{6}\triangle_{x+1}-\frac{2}{3} \triangle_{x+2}+\frac{1}{6}\triangle_{x+3}][\frac{2}{3}I+\frac{5}{6}\triangle _{y+1}-\frac{2}{3}\triangle_{y+2}+\frac{1}{6}\triangle_{y+3}]f_{i,j}, \tag{29}\]  
which gives  
\[Q^{F_{x},F_{y}}_{i,j}f=\frac{4}{3}f_{i,j}+\frac{5}{9}f_{i,j+1}-\frac{4}{3}f_{i, j+2}+\frac{1}{9}f_{i,j+3}+\frac{5}{9}f_{i+1,j}+\frac{25}{36}f_{i+1,j+1}- \frac{5}{
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
## 6 Simulation Results
Figure 5: The system utility function values under different parameters \(\phi\) for different offloading algorithms. Figure 8 shows the average system time cost and average DNNs time cost for each experiment under multiple experiments. Here, we define the average system time cost as the average time required per iteration of the system, and we define the average DNNs time cost as the average time required per output offloading decision of each DNN. From Figure 8, we can see that our proposed system only needs about 0.12 s to complete an iteration on average. For each input computing task, our DNNs only need about 0.5 ms to obtain the corresponding offloading decisions on average, and the time cost is very considerable. Figure 6: The system utility function values under different processing rates \(f^{sat}\) of the MEC server on the LEO satellite for different offloading algorithms. Figure 7: The system utility function values under different processing capabilities \(t_{local}\) of the local devices for different offloading algorithms.
## 2 Main Concepts
### Artifacts
#### 2.4.1 Motion-Related Artifacts: Blur, Ghosting, and Misalignment  
Movements of objects (or the camera itself) during the acquisition of LDR images with different exposures cause a series of artifacts in the reconstructed HDR. Blur occurs when the displacement is slight, causing the object to appear partially \"blurred\" in all captures  
Figure 1: Result of applying logarithmic tone mapping to an HDR image.  
(Figure 2a); ghosting is characterized by more pronounced displacements, resulting in multiple translucent replicas of the same object, as illustrated in Figure 2b. When the spatial discrepancy between scene elements and the background is even greater, so-called misalignment artifacts arise, manifesting as duplications or misaligned contours after fusing the exposures [12; 13; 63].  
Mitigating these phenomena is one of the main challenges of multi-frame HDR reconstruction [9]. Recent strategies include implicit alignment via multi-scale attention modules, fusion guided by long-range dependencies, and adaptive kernels capable of compensating for complex movements, showing promising results in reducing blur, ghosting, and alignment errors [64; 65; 26].
###### Abstract  
Graphene quantum dots (GQDs) play a critical role in many applications in the electrical and optical fields. We develop a simple three-step hydrothermal etching method to prepare GQDs by adopting graphene oxide (GO) as a precursor and nitric acid as an oxidant. We discuss the formation mechanism of GQDs by the characterization of products and intermediates with Scanning electronic microscopy (SEM), Transmission electron microscopic (TEM), Raman, Fourier transform infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS). Two kinds of GQDs have been obtained after the treatment of GO with different concentrations of nitric acid. The sizes of GQDs are small, with diameters of 3.38 nm and 2.03 nm on average, respectively. When excited with 365 nm UV light, the two kinds of GQDs exhibit green and yellow luminescence; the different optical properties can be attributed to the differences in degree of oxidation and nitrogen doping. The result is important for GQDs in synthesizing and optical field.  
graphene quantum dots (GQDs); fluorescence; hydrothermal method; nitrogen doping Article
# Rapid Restoration Techniques for Software-Defined Networks
## 3 Proposed Path Recovery Methods
### Path Anatomy-Based Approach  
In Section 2, we discussed techniques that pre-computed backup disjoint paths to deal with failure events. The controller first removed the entire set of old flow entries on the affected path and then installed the required rules for the alternative path. This process is time-consuming when the length of the affected path is long. A network Path Anatomy (PA) is a potential solution to this problem. A network PA partitions a path instead of partitioning the graph. An illustration of how this works for a path from point \(r_{1}\) to point \(r_{d}\) is depicted in Figure 3.  
This sequence of adjacent routers has some middle router(s), which are denoted \(r_{m}\). The path \(P\) can be divided into two segments. Finding a new path from \(r_{1}\) to \(r_{d}\) in order to overcome the failure may not be an effective solution. This is because the cost of rule replacement and updates can be high. A PA overcomes this problem. We outline two schemes to implement a PA-based approach. The first one uses the middle routers of \(P\) to consider two parts (i.e., sub-paths), one of them is running and the other is not (due to a failure for example). This means that one segment of the affected path can still be utilised as is and the operation cost to update that segment is zero. In the second scheme, instead of replacing the faulty segment of the path with a new sub-path, the search space for an alternative path is reduced to be only between the two nodes surrounding the failed link. The second approach finds an alternative path between the two nodes of the failed link. Searching for an alternative sub-path  
Figure 3: In a path anatomy the sequence of routers that form the path can be partitioned into two sub-paths which have equal length. Recovery can be achieved by either reconfiguring the flow tables associated with the half of the path which contains the failure, or the link which has failed.  
Figure 2: Running community detection on the European Reference network topology (ERnet) yields five communities. Colours followed by pairs of integers denote the names of the communities and the number of inter and intra community links. For example the red community has 4 inter community links and 7 intra-community links, Red (4,7). The remaining communities are summarized: Blue (5,9), Green (4,8), Orange (8,10) and Yellow (4,6).  
The above definition expresses failures in two cases, both with respect to a node, \(r_{i}\). The first failure is in a link with a node following \(r_{i}\), whereas the second is in a link with a node preceding \(r_{i}\).