esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Machine Learning Approach  
A CNN regression model was trained to estimate the effective mechanical properties of the BG-COL composite from microstructural images. The TensorFlow and Keras library were used to create the CNN model architecture [32]. CNN is a class of DNN that applies a series of computationally nonlinear layers to analyze visual imagery. CNN can gradually extract representations of images with higher-level generalizations by operating on two functions that produce a third function, which expresses how the shape of one is modified by the other. The current CNN model was selected after trying many architectures and tuning hyperparameters. The tuned graph of the CNN regression used is depicted in Figure 2. It contains convolutional layers (first five layers) merged by fully connected layers (last four layers). The convolution features (or kernels) are memorized hierarchically and comprised of low-level features to build more complex patterns. The input to the CNN is the 2D BG-COL composite gray images of 200 \(\times\) 200 pixels. A learnable kernel was applied to the input images to extract the convolved feature. This convolved feature is computed through a rectified linear unit (ReLU) [33] function and passed on to the following layers. The respective mechanical properties (Young's modulus and Poisson's ratio) are emanated at the outputs layer. The first five layers of architecture involve 3 \(\times\) 3 convolutional layers using a stride of 2 in both the \(x\) and \(y\) directions; 2 \(\times\) 2 maximum pooling layers; and 512, 256, 128, 64, and 32 feature maps, followed by four fully connected layers with 1000, 100, 50, and 10 units. To enhance the precision of the CNN's prediction, 20% dropout [34] was used in four fully connected layers to prevent overfitting. The Adam optimizer was used to speed up the convergence of the network [35]. Linear activation function and sigmoid in the output layer of the CNN were chosen for [PERSON]'s modulus and [PERSON]'s ratio, respectively, by using functional API. A loss function was used to estimate the differences between CNN's prediction results and the real mechanical properties of the images. The loss function is minimized via backpropagation by optimizing the CNN parameters, such as biases and kernels, in the convolutional layers and the weights in the fully connected layers, as depicted in Figure 2. The loss function is defined using the mean square error (MSE) since the prediction is a regression problem. MSE is denoted as:  
\[MSE=L[Y,\ f(X)]=\frac{1}{n}\ \sum_{i=1}^{n}[Y-f(X)]^{2} \tag{1}\]  
where \(X\) denotes the input image describing the microstructure of the BG-COL, \(n\) represents the batch size, and \(f(X)\) indicates the prediction of the CNN. \(Y\) denotes the mechanical properties of BG-COL, which includes both [PERSON]'s modulus and [PERSON]'s ratio. Performing verification to prevent overfitting during every training epoch was also done to select the best model. The CNN model was run on a workstation with the following specifications: Ryzen 9 5950X processor, 128 GB DDR4/2666 MHz memory, and Nvidia GeForce RTX 3090 GPU.
# Quantifying Privacy Risks for Continuous Trait Data
## 4 Model and Methodology
### Matching Attack  
After acquiring correlations \(\Pr\Big{(}T_{i}|S_{\Omega(i)}\Big{)}\) for all pairs \(\mathbb{T}(i,\cdot)\) and \(\mathbb{S}(i,\cdot)\), we need to infer the individual links of the testing set between \(m^{\prime}_{t}\times n^{\prime}_{t}\) continuous trait array \(\mathbb{T}^{\prime}\) containing \(n^{\prime}_{t}\geqslant 1\) members and \(m^{\prime}_{s}\times n^{\prime}_{s}\) SNP array \(\mathbb{S}^{\prime}\) containing \(n^{\prime}_{s}\geqslant 1\) members. The members of \(\mathbb{Q}^{\prime}\) and \(\mathbb{S}^{\prime}\) may be different, where \(n^{\prime}_{t}\) and \(n^{\prime}_{s}\) are not necessarily equal; similarly with the training set, \(\mathbb{T}(i,\cdot)\) and \(\mathbb{T}^{\prime}(i,\cdot)\) are corresponding to the same continuous trait, and \(\mathbb{S}(i,\cdot)\) and \(\mathbb{S}^{\prime}(i,\cdot)\) are corresponding to the same SNP, then we have \(m_{s}=m_{t}=m^{\prime}_{s}=m^{\prime}_{t}=m\).  
The adversary needs to traverse the \(n^{\prime}_{t}\) continuous trait samples and \(n^{\prime}_{s}\) SNP samples to get the best match pairs \(\{\mathbb{S}^{\prime}(\cdot,l^{*}),\mathbb{T}^{\prime}(\cdot,k^{*})\}\). For any pair of \(\{\mathbb{S}^{\prime}(\cdot,l^{\prime}),\mathbb{T}^{\prime}(\cdot,k^{\prime})\}\), The adversary first calculates the _trait propensity score_\(e(l^{\prime},k^{\prime})_{i}\) for each pair of \(\{\mathbb{S}^{\prime}(i,l^{\prime}),\mathbb{T}^{\prime}(i,k^{\prime})\}\) through correlations \(\Pr\Big{(}T_{i}|S_{\Omega(i)}\Big{)}\), where \(e(l^{\prime},k^{\prime})_{i}\) is defined as  
\[e(l^{\prime},k^{\prime})_{i}=\Pr\Big{(}S_{\Omega(i)}=\mathbb{S}^{\prime}(i,l^ {\prime})|T_{i}=\mathbb{T}^{\prime}(i,k^{\prime})\Big{)} \tag{2}\]  
Next, the adversary calculates the _individual propensity score_\(e(l^{\prime},k^{\prime})\) as  
\[e(l^{\prime},k^{\prime})=\frac{1}{m}\sum_{i=1}^{m}e(l^{\prime},k^{\prime})_{i} \tag{3}\]  
Finally, after traversing all the scores of SNP samples \(l^{\prime}\) and continuous trait samples \(k^{*}\) paired, the adversary can know that the sample \(l^{*}\) that best matches sample \(k^{*}\) is  
\[l^{*}=\arg\max_{l^{\prime}}e(l^{\prime},k^{*}) \tag{4}\]
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 1 Introduction
We denote our model as a hub-and-spoke location/transportation problem (HSLTP). The model is originally developed from the point of view of a new airline/transportation company entering the market. It is obvious that by small modifications, this model can be used for existing companies for expansion purposes or for adapting their facilities with changes in the demand/cost (any exogenous) factors. It can also be used for evaluating the status of a company, by running the optimization model and comparing the optimum network with their current network. The rest of this paper is organized as follows. In Section 2, we review the related literature to this work. Section 3 introduces the problem at hand. In this section, we propose the mathematical formulation of the problem, including linearization techniques (Appendix A) that are applied to the developed MINLP model, and transform it into the MILP model. Section 4 contains a description of an exact solution approach based on a modified Benders decomposition, followed by a heuristic algorithm developed for larger instances of the problem in Section 5. Numerical experiments are presented in Section 6. The Section 7 provides the analysis of the results and conclusion of the work, as well as addresses some future research lines.
# Simulation and Optimization of Available Local Feed Resources for Dairy Cattle in Burkina Faso
## 2 Materials and Methods
### Diet Formulation and Rationing Methodology Based on Survey Data
#### 2.6.2 Pre-Setting  
Before determining a ration for dairy cows, a few requirements need to be fulfilled. These include the live weight of the animals, which was estimated using a barometric tape as used by [14] on the basis of its correlation with thoracic girth. Eight (08) cows were included in this study: three (03) early-lactation cows, three (03) mid-lactation cows, and two dry pregnant cows. Three diets were developed, taking into account the different physiological stages of early, mid, and late lactation (dry and pregnant cows). Finally, based on surveys and the bromatological analysis of feed ingredients, four main ingredients (i.e., the most dominant, available in the study area) were selected for the preparation of diets based on local feeds, i.e., two ingredients for forage and two ingredients for concentrates. These were _Sorghum bicolor_ (_S. bicolor_) straw, _Penissetum pedicellatum_ straw (natural forage), cottonseed cake, and maize bran.
## 2 Materials and Methods
### Robust Design Methodology  
Figure 5 shows the RDANN methodology based on the Taguchi philosophy that consists of four stages. The designer must know the problem and choose an appropriate network model, as well as the parameters involved in the design of the network for its optimization (planning stage). By implementing an OA and systematically training a small number of ANN models (experimentation stage), the response to be analyzed is determined using the S/N relationship of the Taguchi method (analysis stage). Finally, through a confirmation process, the best performance values of the model are obtained (confirmation stage).  
Figure 4: Scatter matrix of the position dataset: (**a**) before applying the reduction filter; (**b**) after applying the data reduction filter.  
Figure 5: Robust design methodology for the optimization of structural parameters.  
The graphic representation of the BPNN used in this work is shown in Figure 6, with 12 input variables and 6 output variables that correspond to the position vector \([p]=\left\{p_{x},p_{y},p_{z}\right\}\) and orientation vector \([n\;o\;a]=\left\{n_{x},n_{y},n_{z},o_{x},o_{y},o_{z},a_{x},a_{y},a_{z}\right\}\) as input and the vector of joint angles \([\theta]=\left\{\theta_{1},\theta_{2},\theta_{3},\theta_{4},\theta_{5},\theta _{6}\right\}\) as output.
## 1 Introduction
In this work, based on the matrix approach for a symmetric MCR structure with dielectric BMs, expressions for the transmission and reflection coefficients are obtained and their frequency dependencies are presented for the various values of the static magnetic field and the Fermi energy of graphene monolayers. For comparison, the features of the spectra are studied for an MCR with an unfilled and a filled cavity. The working cavity is filled with a structure consisting of several \"dielectric-graphene\" or \"semiconductor-graphene\" periods with controlled material parameters. \(n-InSb\) is considered the material of semiconductor layers, the choice of which is associated with the highest mobility and mean free path of electrons (up to 78,000 cm\({}^{2}\)/V\(\cdot\) s and 0.7 \(\upmu\)m at 300 K) among all known semiconductor materials. We consider a structure with wide possibilities of controlling the transmission and reflection spectra, provided that amplification is achieved in the terahertz range, in contrast to the above works.
## 3 Results and Discussion
### Optimized Parameters  
#### 3.3.1 Generation and Recombination with AM1.5, and Atacama Spectrum  
The optical model used in this work is from Lambert-Beer, where the generation of carriers \(G(\lambda)\) is calculated by integrating the radiation flux \(\Phi_{0}(\lambda)\), by \(R(\lambda)\), \(A(\lambda)\), and \(\alpha(\lambda)\) in the wavelength [46], and Figure 6 shows that the generation of carriers with the AM1.5 standard spectrum is lower compared to the generation rate modeled with the Atacama spectrum. In Figure 6, it can also be seen that the carrier recombination rate does not depend on \(\Phi_{0}(\lambda)\). The direct dependence of the generation of carriers with the radiation flux \(G(\lambda)\propto\Phi_{0}(\lambda)\) makes the HIT heterojunction solar cell have a better energy conversion performance in places such as the Atacama Desert.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 2 System Model and Preliminaries
For CC-HARQ, the bound in [10] can be used with accumulated SNR after MRC, as follows:  
\[\epsilon_{\mathrm{cc}}\left(\Gamma^{(m)}\right)\approx Q\left(\frac{n\log_{2} (1+\sum_{j=1}^{m}\gamma_{j})-k_{i}+\log_{2}(n)}{\sqrt{nV(\sum_{i=j}^{m}\gamma_ {j})}}\right), \tag{3}\]  
and the bound in [40] for parallel AWGN channels can be used to calculate the PER for IR-HARQ [40], as follows:  
\[\epsilon_{\mathrm{ir}}\left(\Gamma^{(m)}\right)\approx Q\left(\frac{n\sum_{j=1 }^{m}\log_{2}(1+\gamma_{j})-k_{i}+\log_{2}(mn)}{\sqrt{n\sum_{i=j}^{m}V(\gamma _{j})}}\right), \tag{4}\]  
where \(\Gamma^{(m)}=[\gamma_{1},\cdots,\gamma_{m}]\) is the vector of signal to interference plus noise ratios (SINRs) for \(m\) copies of a packet, \(V(\gamma_{j})=\left(1-(1+\gamma_{j})^{-2}\right)\log_{2}^{2}(e)\) is the channel dispersion and \(Q(. )\) is the standard \(Q\)-function. \(k_{i}\) is the length of user \(i\)'s message and \(n\) is the length of the codeword in each transmission. Accordingly, the rate of user \(i\) in the first transmission is \(R_{i}=k_{i}/n\)
# High-Speed Vehicles in Low-Pressure Tunnels--Influence of Choked Flows
## 2 Base Case
### Numerical Accuracy
#### 2.6.1 Analytical Equations  
The principal limitations of the governing equations are (i) the representation of flows using cross-sectional mean values and (ii) the use of empirical expressions to represent phenomena causing non-inertial changes in stagnation pressure, stagnation temperature and hence specific entropy. The first of these precludes _detailed_ modelling of flows in locations where 3-D behaviour is significant--e.g., close to the noses of vehicles--and where the flow is greatly disturbed--e.g., in long-wake flows behind vehicles. Herein, as in most 1-D simulations, local changes in geometry are represented as abrupt discontinuities across which the flow satisfies quasi-steady expressions. Similarly, the chosen empirical expressions used to model skin friction and local pressure losses are strictly applicable only to quasi-steady flow conditions even though the author has devoted a significant part of his career to studying the limitations of such methods.  
The equations are formulated using temperature, specific entropy and Mach number as the primary variables. Analytically, it would be equally suitable to use other combinations--e.g., pressure, temperature and velocity. However, for reasons presented by [PERSON] (2023) [13], the chosen combination is more suitable for use with the desired numerical methodology. In a nutshell, it enables a partial numerical de-coupling of phenomena propagating at particle speeds from phenomena propagating at wave speeds.  
When the flow at the vehicle tail is choked, the change in stagnation pressure cannot be modelled by the empirical method described above. Instead, the conditions in the annulus just upstream of the tail are determined using (i) an MoC wave equation, (ii) an MoC path equation and (iii) the stipulation Ma = 1. This enables the determination of the mass flow rate and the stagnation temperature, both of which are assumed to be unchanged by the discontinuity. Conditions downstream of the tail region are obtained using these two parameters together with an MoC wave equation. This is considered adequate for the relatively long, uniform area vehicles considered herein, even though it might not be justified for short, longitudinally streamlined pods when supersonic flows exist.
###### Abstract
* Layer 7: Application Layer (AL): This layer allows applications that have been built using the Application Framework Layer to be installed on the system and made available to users. Through this layer, users get  
A key part of the IEE is the development of a Secure Service Ecosystem (SSE) in which services can be securely moved between different commercial Cloud Systems. As shown in Figure 2, the SSE, therefore, encompasses the three core layers of the IEE which are the HPECS, SMF, and MSL layers. This paper explores how to build an SSE and the contributions of the work are detailed below:  
* A new simple Resource Allocation Algorithm (RAA) is developed to enable mobile services. This algorithm has to be simple and fast. * A new secure transfer protocol called the Resource Allocation Secure Protocol (RASP) is detailed. This will involve the use of cryptographic protocol verifiers such as ProVerif to show that the transfer protocol is safe. * A secure access control system using capabilities to provide the Authentication, Authorization, Accounting (AAA). * The development of a new Service Management Framework (SMF) which can be used to manage applications and services in the IEE. * These mechanisms are combined to develop a capability-based Secure Service Protocol (SSP) to provide a complete SSE for the IEE. The SSP is shown to be safe using Proverif. * The implementation of a prototype SSE system using a Network Memory Server (NMS) to provide backing storage for a FUSE file system in a vehicular network is detailed. The rest of this paper is organized as follows: Section 2 reviews the Related Work. Section 3 details the Simple Resource Allocation Algorithm, while Section 4 explores the RASP Protocol using ProVerif. In Section 5, a capability system for IEE is investigated, and in Section 6, the SSP Protocol is proposed and tested using ProVerif. Section 7 covers the prototype implementation. Finally, Section 8 concludes the paper.
## 4 Discussion
### Results Interpretation
The values are lower than those obtained with all 15 factors, which indicates the need to use non-DEM dependent factors that introduce variables such as lithology, which are essential for the mechanical strength of materials [46], NDVI as an expression of the vegetation that plays an important role in the stability of the surface layer; or the distance to roads, as an anthropic factor that induces instability [53]. When considering the conditioning factors not dependent on the DEM, interesting and, above all, acceptable results were obtained, which corroborates what was previously said about the importance of these factors. In the case of the first sampling, the AUC is 0.79, and with the second sampling, an AUC of 0.77 was obtained. In both cases, the performance is higher than that obtained with the set of DEM-dependent factors but lower than that obtained with the 15 factors and even with 6 factors, which suggests the need to have a heterogeneous mix of conditioning factors, as this will improve the predictive level of the model. The F-Score values also reflect an acceptable predictive capacity of the model with values of 0.71 and 0.70, respectively, exceeding with some amplitude the results obtained with the DEM-dependent data set and being minimally lower than those obtained with the set of the six best factors. On the other hand, the levels of landslide coincidence in the high and very high susceptibility levels show differences. With the first sampling, the coincidence level of landslides falling into these susceptibility levels is 86%. Although this value is high, it is lower than those obtained with the data sets described above. With the second sampling, the level of coincidence could be cataloged as acceptable since approximately 68% of landslides fall into these susceptibility levels. What is observed is a reduction in the percentage of movements in models obtained with the second sampling (random) to the first (centroids) in all the combinations of factors considered. This can be explained by the difference in the size of the movements, which is greater in the southern part of the study area than in the northern part, where the size is smaller. Thus, the models obtained with sampling 1 show a greater susceptibility in the northwest zone, where smaller movements are concentrated and therefore more sensitive to the centroid, and also in the south and southeast where movements are greater; in general, the fit of the models with the centroids is good in both zones, hence the high proportions of movements in zones of high and very high susceptibility. On the contrary, the models obtained with sampling 2 show greater susceptibility in the south and southeast, where the largest movements are concentrated and, therefore, a greater number of random points, and not so much in the northwest, where the movements have a smaller size. Hence, when crossed with the centroids, the fit of the centroids with the high and very high susceptibility zones is lower in these models; a crossover with the full landslide area would provide a better fit.
## 4 Conclusions  
In this study, spectroscopic methods like FTIR, spectrofluorimetric, and emission spectral measurements were used as alternatives to chemical and more time-consuming methods for assessing the oxidation process of BCSO. These techniques also helped evaluate the TQ content in thermal oxidation tests. A highly statistically significant correlation was found between the techniques used, indicating the possibility of using spectroscopic methods as an alternative tool for both oxidative tests and TQ content calculation.  
Moreover, by examining the effect of temperature on the TQ content in oil under different heating conditions, we have shown a significant effect of phenolic compounds and an unnoticeable effect of TQ on the observed free radical scavenging capacity of BCSO. In addition, we discovered a new way to increase the TQ content in bulk oil by thermal treatment. This is significant, as the observed rise occurs during the initial heating period, when higher temperatures do not yet significantly affect the oil's oxidative stability. It should be noted that a detailed analysis of this initial thermal storage period should provide better insight into the kinetics of this process and contribute to improving the oil's health-promoting properties. However, it should be considered that the composition of the oils is variable and depends on many factors. Therefore, the final composition of BCSO may affect the possibility of synthesizing TQ during thermal treatment.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15010377/sl](https://www.mdpi.com/article/10.3390/app15010377/sl)]([https://www.mdpi.com/article/10.3390/app15010377/sl](https://www.mdpi.com/article/10.3390/app15010377/sl)), Figure S1: HPLC absorbance elution profile of methanolic extract of BCSO and absorption spectrum of TQ in BCSO corresponding to peak at 10.5 min (inset). Figure S2: Change of thymoquinone (TQ) content in BCSO: (a) samples open after 3 days and (b) samples open after 6 days of heating, expressed as FTIR intensity band at 1659 cm\({}^{-1}\) (black line--right scale) and quantity amount of TQ calculated spectrophotometrically and from HPLC assay (bars--left scale). Error bars show the variations of three determinations in terms of standard deviation.  
Conceptualization, G.N.; methodology, G.N., W.K. and J.N.-K.; software, G.N., W.K. and J.N.-K.; validation, G.N., W.K. and J.N.-K.; formal analysis, G.N., W.K. and J.N.-K.; investigation, G.N., W.K. and J.N.-K.; data curation, G.N. and J.N.-K.; writing--original draft preparation, G.N.; writing--review and editing, G.N., W.K. and J.N.-K.; visualization, G.N. and J.N.-K.; supervision, G.N.; project administration, G.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.