esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
DC is a back-projection EPID in vivo dosimetry software composed of two independent algorithms. The deconvolution kernel reconstructs the entrance fluence from the EPID image, then the dose computation algorithm uses the reconstructed fluence to compute the dose inside the patient's anatomy. The new 5.8 version of DC contains, in addition to the pencil beam algorithm (PB), a collapsed cone convolution algorithm (CCC) for dose calculation. Moreover, an optimized deconvolution kernel for dealing with tissue inhomogeneities has been implemented. The previous DC version was equipped with only the PB algorithm, the results of which have been reported in previous studies [4; 6]. Compared to PB, the dosimetric advantage of the CCC algorithm, in the case of tissue inhomogeneity, is well documented [15]. However, as the deconvolution kernel is responsible for the fluence generation, the overall accuracy improvement of the new DC version in the presence of tissue inhomogeneity is not obvious. The ability of the DC to reconstruct the fluence of simple square fields was first tested in simple geometry. The accuracy of the entrance fluence reconstructed by the deconvolution kernel was assessed in both homogeneous and heterogeneous slab phantoms. Finally, the overall DC accuracy was evaluated on anthropomorphic phantom with VMAT plans.  
The DC version 5.8 evaluated in this work is a standalone software version that is not commercially available anymore. The DC is now included in the 7.2 version of RadCalc software (Lifeline Software Inc., Tyler, TX, USA).
## 2 XFEM Modelling
The spot-welded structure's three-dimensional XFEM model is depicted in Figure 1. XFEM was defined only in the overlap regions of the adherends as the potential sites for the onset and propagation of the cracks. Elsewhere, the standard FE calculationswere performed. To confirm its accuracy, the model's performance was compared to the experimental findings presented in [19]. The dimensions of the XFEM model correspond to those of the samples utilized in the referenced experimental study. The spot welding was modelled using the tie constraint module available in Abaqus where the interfaces of both adherends on the welding side were defined as master and slave surfaces. Regarding the boundary conditions, the model underwent loading from one end while being fixed at the other end. Table 1 provides details of the minimum (\(F_{\text{min}}\)) and maximum (\(F_{\text{max}}\)) forces applied, along with the load ratio (\(R\)), during the fatigue loading as well as the nugget diameter. Ten cases were studied with \(R\) ranging from 0.0 to 0.75, \(d\) ranging from 3.5 mm to 5.5 mm, \(F_{\text{min}}\) ranging from 0 N to 3000 N, and \(F_{\text{max}}\) ranging from 3200 N to 4000 N. A direct cyclic analysis with 25 Fourier terms was conducted. Prior to the cyclic loading, a static step was performed where \(F_{\text{max}}\) was applied to induce crack formation at the stress concentration site. The mesh discretization employed eight-noded plane stress quadrilateral elements (C3D8R) with varying element sizes ranging from 0.00025 mm to 0.001 mm. A finer mesh was utilized in the welding region to ensure accuracy. Convergence of the mesh was achieved by assessing the maximum von Mises stress in the element adjacent to the welding region under maximum static loading, with a change of less than 5.0% observed when compared to a mesh with element sizes twice as small [39; 40]. The material constants required for the numerical model can be found in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Nugget Diameter, \(d\) (mm)** & \(F_{\text{min}}\) (kN) & \(F_{\text{max}}\) (kN) & \(R\) \\ \hline Case 1 & 4.5 & 2000 & 4000 & 0.5 \\ \hline Case 2 & 4.5 & 1800 & 3600 & 0.5 \\ \hline Case 3 & 4.5 & 1600 & 3200 & 0.5 \\ \hline Case 4 & 4.5 & 0 & 4000 & 0.0 \\ \hline Case 5 & 4.5 & 1000 & 4000 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the cases (\(F_{\text{min}}\), \(F_{\text{max}}\), \(R\), and \(d\)) studied in the numerical analysis. Figure 1: XFEM model of spot-welded structure subjected to fatigue loading.
## 3 Proposed Method
### Haar Cascade Classification
#### 3.2.1 Haar-like Features and Integral Image  
Haar-like features consist of multiple sets of black and white rectangular masks that slide over the image and calculate the pixel sum between different pixels to extract the grained visual feature. The mask of Haar-like features consists of the white rectangular and black rectangular. The white rectangular represents areas with bright or highly reflective areas of an object. Conversely, the black rectangular represents areas with dark or shadowed areas of an object. An example of Haar-like features is illustrated in Figures 6 and 7. A feature vector can combine with multiple Haar-like features  
However, Haar-like spends too much time calculating the pixel value sum. Therefore, an integral image was designed to accelerate the calculation of pixel value sums within rectangular regions. Equation (1) defines the feature value. An integral image is defined as a matrix of the same size as the image, where each pixel value represents the cumulative sum of pixel values within a rectangular region from the top-left corner to that pixel. Thus, Equations (2) and (3) can be employed to compute the sum of pixel values within a rectangular area, eliminating redundant computations and achieving time savings.  
\[featureValue(I)=w_{white}\times\sum_{Pixel\in white}Pixel-w_{black}\times \sum_{Pixel\in black}Pixel \tag{1}\]  
\[Integral(I_{x},I_{y})=\sum_{x=0}^{x}\sum_{y=0}^{y}Pixel(x,y) \tag{2}\]  
\[rect=Integral(x+w,y+h)+Integral(x,y)-Integral(x+w,y)-Integral(x,y+h) \tag{3}\]  
where _Pixel_ represents the pixel value in position (_x,y_) of the image. While Haar-like features are a simple feature extraction method, they are widely recognized for their performance and computational efficiency in applications such as facial detection.
# Using Financial Instruments to Transfer the Information Security Risks
## 7 Demonstration: An Example Application
### Information Security Options
#### 7.1.4 Risk Hedging  
In the given scenario, there are two possible states of the underlying information security event for the given ISO. Thus, the event set of cyber-attack at the unit-1 consists of following two states:  
* \(S_{1}\) = Unit-1 suffers a (pre-specified type of) cyber-attack and production at the unit is adversely affected. * \(S_{2}\) = Unit-1 suffers no cyber-attack or the production at the unit is not affected. (1) Risk Analysis and Impact Estimation In order to devise a risk response strategy and to use the ISO contracts for impact hedging, we assume that the oil production company has done its risk analysis and impact estimation using a standard risk assessment model, such as CVaR Model [83]. The output of the CVaR model for the unit-1 of the company is as: _\"Given a successful cyber-attack, the unit-1 of the organization \(O\) would lose not more than $40,000 per day, for total of one day, with 50% probability during the three months period of 1 October 2015 to 31 December 2015\"_. Thus, if \(S_{1}\) occurs the company may lose upto $40,000. If \(S_{2}\) occurs the company continues its usual business and earns a revenue of $20,000 per day. (2) Risk Response Let us say, the ISO contract for the occurrence of cyber-attack at unit-1 of the company _\"O\"_ is currently trading at $20 (buy) and $80 (sell). The buy and sell price of the contract indicates the market participant's probabilistic estimate of the occurrence and non-occurrence of the underlying event [98]. Therefore, the buy price of $20 indicates that the market participants have estimated the probability of occurrence of the cyber-attack at unit-1 within the trading period as 20%. On the other hand, the market estimates 80% probability for the non-occurrence of the cyber-attack at the unit-1 during the contract trading period. This estimate is based on all the information known to the market participants (as per the efficient market hypothesis [99]). This market estimate is different from the output of CVaR model, _i.e._, 50% probability of cyber-attack and 50% probability of no cyber-attack at the unit-1 during the contract trading period. In such a scenario, the first question that needs to be answered is if it is worth to hedge the risk exposure or not? This decision to hedge or not to hedge the risk can be based on the
## 4 Estimating Shear Stress Versus \(m\)
### Comparison with Computed Values
: _We note the existence of an interesting and very useful feature here: if the formula is adjusted/determined using two \"sample points\" far from the vertex to yield a new \(\beta\) and \(\gamma\), then its accuracy can be highly improved in that neighbourhood. For example, it is found out that \(f^{\prime\prime}(0)=0.726\) for \(m=0\), but the estimated value from the formula is \(f^{\prime\prime}(0)=0.715\). However, when using the points where \(f^{\prime\prime}(0)=0.4\) and \(f^{\prime\prime}(0)=0.5\) as sample points, then the adjusted formula yields an estimate of \(f^{\prime\prime}(0)=0.7249\). This shows a highly significant improvement in accuracy by ten-fold._  
As for Table 3 where \(n=1.5\), note that we have \(m_{0}=-0.0718478\). The estimated values for \(f^{\prime\prime}(0)\) using (19) are shown in the table, where \(\beta\) and \(\gamma\) are determined using the two points where \(f^{\prime\prime}(0)=0.1\) and \(f^{\prime\prime}(0)=0.15\) as sample points. Once again, a relatively good accuracy is illustrated using the root formula (19). Additionally, note that, as before, if we choose different sample points, then higher accuracy is obtained within their corresponding neighbourhood. In particular, when using the points where \(m=0\) and \(m=0.02\) to obtain the root formula, we find that the estimate for the shear stress \(f^{\prime\prime}(0)\) at the point where \(f^{\prime\prime}(0)=0.4\) (to the left) is \(0.4001\), while the estimate for \(f^{\prime\prime}(0)\) at \(m=0.04\) (on the right) is \(0.51772\). Taking a \"farther\" point, such as the one where \(m=0.2\) (not shown in Table 3), we have \(f^{\prime\prime}(0)=0.75028\) while the estimated value is \(0.75496\). However, taking the points where \(m=0.16\) and \(m=0.18\) (not shown in the table), as sample points to determine the corresponding root formula, one obtains an estimated value of \(f^{\prime\prime}(0)=0.75032\) at \(m=0.2\). This shows a highly significant improvement (for a relatively large spacing of \(0.02\) in \(m\)). Table 4 shows the comparison between the computed and estimated values of shear stress where \(n=0.8\), for points close to the vertex of the root at \(m_{0}=-0.09956\). Table 5, however, shows the comparison for \(n=0.8\) for points that are farther away from the vertex. Observe that the sample points are changed. Note also that even though the spacing is larger, the approximation is (naturally) better since the points are farther away from the vertex. **Remark 6**. : _Observe that the computed values for \(\gamma\) up to four decimal places are \(\gamma=0.1669\approx 1/6\) for \(n=5\), \(\gamma=0.4094\approx 1/2.5\) for \(n=1.5\), and \(\gamma=0.5686\approx 1/1.8\) for \(n=0.8\). This is consistent with the above approximation (19) where \(\gamma\approx\frac{1}{n+1}\). The deviation is explained by the fact that the sample points for the measurement are not within infinitesimal distances from \(m_{0}\). Note also that these approximations are better for larger values of \(n\)._
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 4 Test Procedure  
This section describes a simulation and three experiments which are designed to model time-dependent varying errors, determine the baseline accuracy from a low-cost commercial GPS receiver, to assess the improvement in accuracy using the proposed FAGPS system using both a static system and a mobile robot platform.  
Figure 3: FAGPS Compensation Flow Diagram.
## 4 Core Algorithm and Implementation
### Structure Design of the Pooling Layer  
The pooling layer is sandwiched between continuous convolution layers, which reduces the parameters and computation while preserving the main features, prevents overfitting, and improves model generalization ability. Common pooling operations include maximum pooling and average pooling. Maximum pooling helps to retain the edge features of the image, while average pooling helps to retain the background features of the image. This design is mainly for handwritten digit recognition, hence the use of a maximum pool.  
When the data are propagated forward, the pooled kernel scans the data to select the maximum value in the area for output and records the position of the maximum value in the original data for backpropagation. The forward propagation algorithm of the pooling layer is implemented as shown in Algorithm 9.  
```
w: The width of the data before pooling h: The height of the data before pooling k: The dimensions of the pooled kernel input_matrix: Data before pooling output_matrix: Pooled data locate_matrix: The position matrix in pooling 1. for(int i = 0; 1 \(<\) w/k; i++) 2. for(int i = 0; \(>\) 1 \(<\) h/k; j++) 3. int max_num = -999; 4. for(int col = i \(\times\) k; col \(<\) (i + 1) \(\times\) k; col++) 5. for(int row = j \(\times\) k; row \(<\) (j + 1) \(\times\) k; row++) 6. if(input_matrix[ col \(\times\) h + row] \(>\) max_num){ 7. max_num = input_matrix[col \(\times\) h + row]; 8. locate_matrix[i \(\times\) (h/k) + j] = col \(\times\) h + row; 9. }
10. output_matrix[i \(\times\) (h/k) + j] = max_num; }
```  
**Algorithm 9** MaxPool2d.  
Figure 9: Realization of forward propagation and backpropagation of the full connection layer.  
The value of the output depends on the largest value in this range, so the gradient of the output is equal to the gradient of the largest element, and 0 for additional elements. When the loss gradient is backpropagated, its corresponding position is found for assignment, and the other gradients are assigned 0 s. The pooling layer gradient backpropagation algorithm is implemented as shown in Algorithm 10.  
```
w: The width of the data before pooling h: The height of the data before pooling k: The dimensions of the pooled kernel input_matrix: The gradient after pooling output_matrix: The gradient before pooling locate_matrix: The position matrix in pooling
1. for(int col = 0; col < w; col++)
2. for(int low = 0; low + h; low++)
3. output_matrix[col \(\times\) h+low] = 0;
4. int current_locate;
5. for(int i = 0; i < w/k; i++)
6. for(int j = 0; j < h/k; j++)
7. current_locate = locate_matrix[i \(\times\) (h/k) + j];
8. output_matrix[current_locate] = input_matrix[i \(\times\) (h/k) + j]; \(\;\) ]
```  
**Algorithm 10** MaxPooBackPropagation.  
The convolution, full connected, and activation operations in LeNet-2.22 are consistent with those described above in LeNet-3.3. Table 2 shows the hardware IP resource usage of the Le-Net-2.22 network. Compared to Table 1, adding convolutional layers in LeNet-2.22 significantly reduces the internal resource consumption of HA SoC devices.
## 3 Results
### Microbial Community Structure Analysis
#### 3.2.2 Relative Abundance  
Figure 4a and Table 4 show bacterial composition at the phylum level. It gives a reflection of bacteria distribution in the Burgos (SB.R1 and SB.R2) and Tabasco (ST.R1 and ST.R2) soil samples. We identified a total of 17 phyla from all samples. The identification of the phyla revealed, Proteobacteria as the dominant phyla, followed by Actinobacteria, Firmicutes and Cyanobacteria. Figure 4: Bacterial composition with major abundance in contaminated soil samples (replicates of soil from Burgos = SB.R1 and SB.R2, replicates of soil from Tabasco = ST.R1 and ST.R2). (**a**) Phyla (**b**) families. The percentage abundance for each phylum in the individual composite soils were: Proteobacteria (SB.R1 = 93%, SB.R2 = 85.3%, ST.R1 = 93.9%, ST.R2 = 89.5%) followed by Actinobacteria (SB.R1 = 3.2%, SB.R2 = 5.8%, ST.R1 = 3.5%, ST.R2 = 2.9%), Firmicutes (SB.R1 = 0.8%, SB.R2 = 2.7%, ST.R1 = 1.6%, ST.R2 = 3.6%) and Cyanobacteria (SB.R1 = 1.9%, SB.R2 = 3.0%, ST.R1 = 0.5%, ST.R2 = 0.6%), see Table 4.  
Figure 4b shows the 172 families found in all soil samples. The family Rhodospirillaceae was the most abundant in soil samples from Burgos, Tamaulipas (SB.R1 = 64.2%, SB.R2 = 43.8%, ST.R1 = 5.3%, ST.R2 = 6.6%) and is represented in red. In the soil samples from Tabasco, the most prominent families included Sphingomonadaceae (SB.R1 = 6.0%, SB.R2 = 6.8%, ST.R1 = 32.3%, ST.R2 = 24.3%), Kilonilecae (SB.R1 = 0.015%, SB.R2 = 0.03%, ST.R1 = 28.3%, ST.R2 = 22.3%) and unclassified bacteria from the class Alphaproteobacteria (SB.R1 = 0.5%, SB.R2 = 0.7%, ST.R1 = 9.6%, ST.R2 = 11.0%). Figure 4b shows that eight families had relative abundance values that ranged from 2.0 to 7.0% in all soil samples. The families included Acetobacteraceae, Bradyrhizobiaceae, unclassified derived from Sphingomonadales, unclassified derived from the order Rhizobiales, Microbacteriaceae, Phylobacteriaceae and Oxalobacteraceae shared the same percentage abundance. The percentages for each family were: Acetobacteraceae (SB.R1 = 5.9%, SB.R2 = 7.0%, ST.R1 = 0.1%, ST.R2 = 0.4%), Rhodobacteraceae (SB.R1 = 5.3%, SB.R2 = 5.8%, ST.R1 = 3.3%, ST.R2 = 4.6%), Bradyrhizobiaceae (SB.R1 = 5.2%, SB.R2 = 6.5%, ST.R1 = 0.1%, ST.R2 = 0.04%), Unclassified derived from Sphingomonadales order (SB.R1 = 0.04%, SB.R2 = 0.1%, ST.R1 = 5.2%, ST.R2 = 3.9%), Unclassified derived from the order Rhizobiales (SB.R1 = 2.5%, SB.R2 = 3.1%, ST.R1 = 2.8%, ST.R2 = 4.7%), Microbacteriaceae (SB.R1 = 0.1%, SB.R2 = 0.2%, ST.R1 = 2.0%, ST.R2 = 1.6%), Phylobacteriaceae (SB.R1 = 0.1%, SB.R2 = 0.1%, ST.R1 = 1.8%, ST.R2 = 2.6%) and Oxalobacteraceae (SB.R1 = 0.4%, SB.R2 = 3.6%, ST.R1 = 0%, ST.R2 = 0.04%).
## 3 Results and Discussion
### Visual Performance
In terms of the operation speed, Swin-UNet obtains the second best prediction speed, with an FPS of 110.95, due to the lower computational complexity and fewer parameters, but the model suffers from a loss of detail information due to the pure transformer operations, the insufficient edge information for crack extraction, and poor immunity to interference. In contrast, SegFormer uses a hierarchically structured transformer feature extraction network to output multi-scale feature maps, and it uses a \(3\times 3\) sized depthwise convolution instead of positional encoding for the position information. This avoids the problem of the degradation of the dense prediction performance due to the interpolation of the positional encoding when the test resolution is different from the training resolution. In addition, SegFormer uses the simplest MLP decoder for the feature information at different scales, and although it can effectively combine local attention and global attention, the model prediction is not sufficiently fine-grained compared to the method proposed in this article.
## 4 Suggested Research Directions  
Although many papers have been published in the reviewed topic, there are still many unanswered questions about the use of nanofibers and nanoparticles as tougheners in composite laminates. Here it is a list of future research works that are recommended in this area:  
Figure 16: FEG-SEM images of the fracture surfaces of a composite with a carbon aerogel content of 0.5 wt %. The surfaces show (**a**) crack pinning and (**b**) crack deflection [82].  
Figure 15: Photographs of fracture surfaces after an interlaminar shear test for CFRPs composites with different clay contents: (**a**) 0 wt %; (**b**) 3 wt % and (**c**) 5 wt % [132].  
Studying the influence of nanofiber interleaving in high-velocity impact response of laminated composites.
* Some papers have shown that thermoplastic polymers like Phenoxy and Polysulfone (PSF) [141; 142] are suitable choices for toughening epoxy-based laminates, but more work needs to be done on these polymers.
* The effect of geometrical features of nanofibers and nanofibrous mats, such as nanofiber orientation, on the impact response of nano-modified laminates should be investigated.
* To achieve practical applications of nanoparticle reinforced composites, a number of technical issues need to be solved, including the uniformity of the dispersion and the alignment of the nanoparticles, to avoid morphological changes like re-agglomeration [143], the optimal interface between nanoparticles and matrix, and the viscosity of nanoparticle-modified matrix resins for ease of fabrication of high fiber volume fraction (>60 vol %) composites.  
More studies are finally needed regarding the reinforcement of composite laminates with hybrid particles (mix of micro- and nano-scales) to obtain synergetic effects in toughening, strengthening or even multi-functionality such as sensing and shielding.
## 4 Task/Transmission Scheduling and Communication Protocols  
The real-time system design methodology proposed in this paper assumes the appropriate scheduling of calculations and transmissions. In classic system solutions, scheduling can be implemented through FIFO queuing or other static or dynamic scheduling methods [88]. Static scheduling methods are mainly concerned with the design of embedded systems, which are most often used to control various devices when there are time constraints. These systems feature a dedicated architecture and perform predetermined functions. Therefore, it is possible to estimate the execution time of each function and to perform a ranking, ensuring that all time constraints are met. IoT systems can be viewed as a generalization of embedded systems with a distributed architecture. The main difference is the communication between system components. In the case of embedded systems, the components are directly connected and the transmission time is predetermined. In the case of IoT systems, transmission time also depends on other factors, e.g., on the traffic and throughput of links and on the route determined by routing processes. Thus, it is not possible to directly apply known static task scheduling methods to IoT systems, though these methods can be considered from the point of view of ensuring optimal cooperation with relevant communication protocols. Therefore, this section examines the scheduling methods and selected communication protocols used in data transmission tasks that assume the need to meet real-time assumptions.
## 1 Introduction  
MicroRNAs (miRNAs) are part of cellular processes involved in bone metabolism regulation and ageing-related mechanisms [1]. Among bone-related microRNAs, miR-320a was found to be elevated in osteoporortic bone [2] and osteoarthritic cartilage tissue [3, 4]. This miRNA was also detected in serums as a circulating microRNA, and it was proposed as a potential biomarker for osteonecrosis of femoral head [5]. Moreover, this miRNA is mainly involved in cancer malignancies including osteosarcoma, in which it acts as a tumor suppressor [6, 7, 8, 9, 10, 11].  
In bone tissue, miR-320a is expressed both in primary human osteoblasts (hOBs) and in differentiated osteoclasts (hOCs) [12], and it is associated with hOB function [13] by targeting genes involved in bone metabolism [14, 15]. In a previous study by [PERSON] et al. (2016) [16], a reduced miR-320a expression was observed in several osteosarcoma cell lines compared to in the human normal osteoblastic cell line hFOB1.19, but the miRNA-320a effect on cell proliferation was only tested in U2 OS. Moreover, no other osteoblast functional assessments were evaluated.  
Hence, this miRNA has been extensively studied in osteosarcoma and primary osteoblasts in a separated manner, but it has never been tested in both cell types in the same experimental context.  
In this study, in vitro cell assays were performed in order to evaluate the miR-320a effect on human osteosarcoma cells compared to that on hOBs. For this purpose, miR-320a was either overexpressed or inhibited in MG-63 and U2 OS cell lines, and their cell viability, proliferation, and oxidative stress were evaluated in comparison to those of hOBs. Moreover, cell functionality by measuring matrix mineralization and alkaline phosphatase (ALP) activity was also tested.