esa-sceva/satcom-chunk-collection huggingface dataset:
# A Harmonized Information Security Taxonomy for Cyber Physical Systems
## 3 Core Taxonomy Elements
### Elements
#### 3.1.4 Technology  
The IEC62443-3-1 technical report provides guidance on implementing risk mitigation technologies for the following domains:  
* Authentication and authorization;
* Filtering, blocking and access control (including firewalls);
* Encryption and data validation;
* Management, audit, measurement, monitoring and detection (including digital forensics);
* Industrial automation and control systems computer software (including embedded and operating systems);
* Physical security.  
The presented technologies are intentionally broad to ensure applicability across a variety of industries. This does, however, present problems when attempting to associate the technology's implementation with the system lifecycle.  
[PERSON] et al. [21] provided an alternative taxonomy that allows for better lifecycle phase association. The technologies are grouped into proactive and reactive technologies that allow for system lifecycle association. It should be noted that although the categories remain broad, they are much better defined than those of the IEC62443 standard.
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.1 Ablation Study
These models included ConvLSTM (based on CNNs), 3D-CNN (utilizing three-dimensional convolutional networks), U-Net (with a fully symmetric architecture), Pix2 Pix (based on conditional GAN), and the Farneback optical flow method combined with SimpleUNet. To adapt these models for satellite target prediction and tracking, we made necessary modifications. All models were trained using the labeled satellite target tracking image dataset constructed in this study, with the number of training epochs set to 50. We evaluated all models using the six metrics defined in Section 4.2. Table 2 presents the quantitative comparison between our method and the five baseline approaches. The results show that the proposed F-GCN model performed excellently across all six metrics. In particular, the RMSE dropped to 7.92--the lowest among all models--while the R\({}^{2}\) reached 0.8649, indicating the strongest explanatory power of the predicted output with respect to the variations in the ground-truth images.
## 3 Results and Analysis
### Prediction of Metabolic Pathways at Three Levels for Corn Straw Silage  
The 165 rRNA gene functional prediction analysis for fresh corn straw samples and corn straw samples after 60 days of silage fermentation is shown in Figure 5. At the primary metabolic pathway level (Figure 5A), the overall trend across all treatments remained consistent, with significant enrichment in metabolic processes. Significant differences in metabolism were observed among the treatments. The LP, CE, and XE treatments significantly promoted metabolic pathways, while the CK treatment inhibited these pathways. After silage, all treatments, except for the CK group, showed enhanced metabolic activity. At the secondary metabolic pathway level (Figure 5B), the main metabolic pathways included global metabolism, carbohydrate metabolism, amino acid metabolism, membrane transport, energy metabolism, and metabolism of cofactors and vitamins. After ensiling, the secondary metabolic pathways for the different additive treatments were consistent with those observed at the primary metabolic pathway level.  
Figure 4: Correlation analysis (RDA) of bacterial communities with nutritional and fermentation quality of corn straw silage. A redundancy analysis (RDA) ordination plot of bacteria and the explanatory variables (such as pH, NH\({}_{3}\)-N, LA, AA, DM, NDF, ADF, ADL, CP, WSC) is shown. The variables are represented as arrows. pH: pH value; LA: lactic acid; AA: acetic acid; NH\({}_{3}\)-N: ammonia nitrogen; DM: dry matter; NDF: neutral detergent fiber; ADF: acid detergent fiber; CP: crude protein; WSC: water-soluble carbohydrate.  
Figure 5: Forecast of 165 rRNA quality work of broom sorghum straw crude materials and silage tests after 60 days of aging. (**A**) Essential pathway level, (**B**) auxiliary pathway level, (**C**) tertiary pathway level; FM: fresh material; CK: control group; LP: _Lactobacillus plantarum_ treatment; CE: cellulase treatment; XE: xylanase treatment.  
The top 10 tertiary metabolic pathways are shown in Figure 5C. The relative abundance of these pathways was significantly higher than that of other metabolic pathways. The CE-, LP-, and XE-treatment groups promoted most of the metabolic pathways, including metabolic pathways, biosynthesis of secondary metabolites, and microbial metabolism in diverse environments. In contrast, the CK group inhibited metabolic pathways and microbial metabolism in diverse environments, but promoted the ribosome pathway.
# A Practical Approach to Deploying a Drone-Based Message Ferry in a Disaster Situation +
## 2 Overview of Locally Accessible Cloud System (LACS)
### Information Delivery and Collection System Using LACS
With this proposal, essential and urgent information required for an adequate disaster response can be efficiently collected and delivered. However, there may be situations where deploying a mobile LACS (i.e., a vehicle mounted with a LACS) can be challenging. Therefore, a drone-based message ferry is considered as an alternative approach.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 3 Development Work
### eNGene  
eNGene has been obtained porting the original code of NGene to an ARM-based embedded platform. During the porting operation, special care has been devoted to the translation of the functionalities coded in assembly language from the Intel processor's instruction set to the ARM one. In addition to this translation, one of the main noteworthy differences of eNGene with respect to NGene is the multiple-threads architecture. Indeed, eNGene explicitly splits functions into different threads, thereby fully exploiting all the cores available in the ARM processor, optimizing the load to enhance the real-time capability. The eNGene architecture is compatible with almost every ARM-based embedded platform, without the need for any adaptations, since it does not exploit any specific characteristic of the embedded board (e.g., FPGA based hardware accelerators).
## 7 Outcomes
### RQ2: What Is the Performance of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?
#### 7.2.1 Analysis of Accuracy Range  
Table 4 shows the accuracy range for each deep learning technique used. The CNN has a wide range that starts from 76.76% and reaches 99.88%; this technique was tested 10 times individually and integrated with another technique. In addition, AE covers a wide range starting from 80% and reaches 99.81%, and it is similar to CNN in the detection accuracy range and the one used. LSTM was used three times and achieved an accuracy between 79.58% and 98%. DBN was used two times and gained an accuracy range from 97% to 97.21 with little enhancement. RNN and DFFNN were used once. Figure 7 shows the techniques used in the studies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **No. of Study** & **Techniques Used** & **Accuracy Range** \\ \hline
[17; 34; 35; 36; 49] & 5 & CNN & (76.76–99.88\%) \\
[37; 45] & 2 & CNN + AE & (99.62–100\%) \\
[18; 41; 42; 43; 44; 48] & 6 & AE & (80–99.81\%) \\
[31; 32] & 3 & LSTM & (79.58–98\%) \\
[33] & 1 & CNN + LSTM & (94.30\%) \\
[46] & 1 & CCN + VAE + LSTM & 99\% \\
[14; 40] & 2 & DBN & (97–97.21\%) \\
[15] & 1 & VAE + DNN & 89.08\% \\
[16] & 1 & LSTM + RNN & 99.36\% \\
[39] & 1 & GNN/RNN & 99\% \\
[38] & 1 & DFFNN & 98.4\% \\
[13] & 1 & Multi-Layer deep & 99.02 \\
[49] & 1 & DML & 99.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy range for the techniques.  
Figure 7: The frequency of the techniques used in the studies.
## 1 Introduction
The following meteorological variables are included in the study: horizontal global irradiance, I\({}_{\text{GH}}\), clearness index, k\({}_{\text{t}}\), solar altitude angle, \(\alpha\), horizontal beam irradiance, I\({}_{\text{BH}}\), diffuse fraction, D, temperature, T, sky clearness, \(\varepsilon\), cloud cover, Cc, horizontal diffuse irradiance, I\({}_{\text{DH}}\), and sky brightness, \(\Delta\). First, a feature selection procedure was applied to identify related features and to remove the irrelevant or less important ones. After the feature selection procedure, two different strategies were used for modelling I\({}_{\text{UV}}\): multilinear regression and ANN modelling. Analysis and comparisons of both models were conducted to study the influence of sky conditions on the accuracy of the model. The experimental data for this study were collected during an experimental campaign that ran from March 2020 to May 2022, in Burgos, Spain. The paper is structured as follows: after the Introduction section, the characteristics of the measurement equipment and the experimental data in use are described in Section 2. In the same section, a methodology is also presented; this will be followed to obtain the independent variables (meteorological and radiation variables) that were used to develop the models. Then, in Section 3, the results of using the ANN models are shown. In Section 4, the results obtained with the regression models are analyzed. Finally, the main conclusions obtained in this work are presented in Section 5.
## 4 Simulation and Analysis
### System Performance of CPCE with Different Numbers of UEs  
A simulation was also conducted to evaluate the performance of the proposed CPCE scheme under different network scales with \(\gamma=1\). As expected, the proposed CPCE algorithm outperforms the MPC and random caching scheme in terms of the backhaul load under different scales of the network, as shown in Figure 4. When UEs are sparsely distributed in the network, the backhaul load of the CPCE scheme is almost ignorable. Even if the number of UEs increases up to 200, the backhaul load of CPCE is still a great deal lower than that of the random scheme and MPC. Figure 5 shows the normalized time delay of each content placement scheme under different network scales. It is observed that CPCE can achieve the lowest time delay compared to the MPC and random caching scheme.  
Figure 3: Time delay under different \(\gamma\) (\(|M^{\Omega}|=200\)).  
Figure 2: Backhaul load with different \(\gamma\) (\(|M^{\Omega}|=200\)).
# Progress of Process Monitoring for the Multi-Mode Process: A Review
## 3 Multi-Mode Process Monitoring Methods  
The identification and classification of multi-mode processes are based on information changes occurring in the production process relating to different modes in the process. Classification is a necessary step for multi-mode process monitoring, evaluation, modeling, quality prediction and process optimization [13]. Whether in continuous or batch processes, a multi-mode process will include both steady-state and transition modes [14]. A complete and comprehensive classification framework for multi-mode process monitoring methods requires to include both steady-state mode and transition mode process monitoring methods. A classification framework for multi-mode process monitoring methods is shown in Figure 1.  
Figure 1: A classification framework for multi-mode process monitoring methods.  
The main purpose of multi-mode process monitoring methods is to detect and avoid abnormal states during system operation to minimize possible accidents and losses and to ensure the normal and stable operation of systems. The main task of process monitoring is to identify, diagnose and isolate faults. The steps involved in multi-mode process monitoring include fault detection, fault identification, fault diagnosis and process recovery. In the monitoring and identification stage, it is necessary to determine whether the system working state is normal. In the event of abnormalities, the system data needs to be analyzed to identify variables that may lead to failure of the system. The analysis of variable data contributes to locating the fault, assessing the severity of the fault, and then enabling determination of causes. System faults are diagnosed by isolation and troubleshooting. Finally, process recovery is achieved through the action of relevant operators with respect to the fault [15].
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Universal Background Model  
SV typically uses a set of background or _cohort_ speakers, to enhance the robustness of the recognizer. Such an average _universal background model_ (UBM) represents a typical speaker, against which spectral measures are tested [35]. A common UBM may be a full-covariance or diagonal-covariance GMM with thousands of components and hundreds of _eigenvoices_ (diagonal covariance is commonly used to simplify computation, although that assumes uncorrelated vector elements). Posterior scaling can be applied to discourage frequent speaker transitions [36]. A UBM represents data from all speakers and conditions and is used to normalize SV scores.
_Applied_  
_sciences_  
_Article_  
Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning  
[PERSON] 1,2,*(), [PERSON] 3, [PERSON] 3(), [PERSON] 1(), [PERSON] 1,  
[PERSON] 4, [PERSON] 5, [PERSON] 1,6 and [PERSON] 2,7,8()  
1 TECNA1A, Basque Research and Technology Alliance (BRTA), Parque Tecnologico de Bizkaia, C./Geldo. Edificio 700, 48160 Derio, Spain; [EMAIL_ADDRESS] (A.P.); [PERSON]E.T.); [EMAIL_ADDRESS] (E.G.)  
2 Photonics Engineering Group, University of Cantabria, 39005 Santander, Spain; [EMAIL_ADDRESS]  
3 Jesusio Non Minimally Invasive Surgery Centre, Ctra. N-521, km 41.8, 10071 Caceres, Spain; [EMAIL_ADDRESS] (D.B.); [EMAIL_ADDRESS] (J.F.O.-M.)  
4 Antonario Pathology Service, Basurino University Hospital, 48013 Bilbao, Spain; [EMAIL_ADDRESS]  
5 Basque Foundation for Health Innovation and Research, BEC Tower, Aakue Kalea 1, 48902 Barakaldo, Spain; [PERSON]epartment of Cell Biology and Histology, Faculty of Medicine and Dentistry, University of the Basque Country, 48940 Leioa, Spain  
7 Valdecilla Biomedical Research Institute (IDIVAL), 39011 Santander, Spain  
8 CIBER-BBN, Biomedical Research Networking Center- Bioengineering, Biomaterials, and Nanomedicine, Aroda. Monteffe de Lemos, 3-5, Pabelleon 11, Planta 0, 28029 Madrid, Spain  
9 Correspondence: [PERSON]]; Tel: +34-946-430-850  
**Featured Application: Automatic diagnosis of colon polyps on optical coherence tomography (OCT) images for the development of computer-aided diagnosis (CADx) applications.**  
(1) Background: Clinicians demand new tools for early diagnosis and improved detection of colon lesions that are vital for patient prognosis. Optical coherence tomography (OCT) allows microscopical inspection of tissue and might serve as an optical biopsy method that could lead to in-situ diagnosis and treatment decisions; (2) Methods: A database of murine (rat) healthy, hyperplastic and neoplastic colonic samples with more than 94,000 images was acquired. A methodology that includes a data augmentation processing strategy and a deep learning model for automatic classification (benign vs. malignant) of OCT images is presented and validated over this dataset. Comparative evaluation is performed both over individual B-scan images and C-scan volumes; (3) Results: A model was trained and evaluated with the proposed methodology using six different data splits to present statistically significant results. Considering this, 0.9695 (\(\pm\)0.0141) sensitivity and 0.8094 (\(\pm\)0.1524) specificity were obtained when diagnosis was performed over B-scan images. On the other hand, 0.9821 (\(\pm\)0.0197) sensitivity and 0.7865 (\(\pm\)0.205) specificity were achieved when diagnosis was made considering all the images in the whole C-scan volume; (4) Conclusions: The proposed methodology based on deep learning showed great potential for the automatic characterization of colon polyps and future development of the optical biopsy paradigm.  
colon cancer; colon polyps; OCT; deep learning; optical biopsy; animal rat models; CADx 4.0/.**
## 2 Materials and Methods
### Experiment
#### 2.3.1 Experiment 1: 1D Nonlinear Large Strain Consolidation  
The assumptions for \(m_{\mathrm{v}}\) and \(k_{\mathrm{v}}\) made by [PERSON] et al. [3] are adopted:  
(1) The volume compression coefficient \(m_{\mathrm{v}}\) remains constant during consolidation, i.e.,  
\[m_{\mathrm{v}}=-\frac{1}{1+e}\frac{de}{d\sigma^{\prime}}=C \tag{27}\]  
(2) The permeability coefficient \(k_{\mathrm{v}}\)  
\[\frac{k_{\mathrm{v}}}{k_{\mathrm{v}0}}=\left(\frac{1+e}{1+e_{0}}\right)^{2} \tag{28}\]  
where \(\sigma^{\prime}\) is effective stress, \(e\) is void ratio, \(C\) is constant, \(k_{\mathrm{v}0}\) is initial permeability coefficient, and \(e_{0}\) is initial void ratio. Since the large strain consolidation leads to large soil deformation, the Lagrangian coordinate is used for the equation governing the excess pore water pressure \(u\):  
\[C_{\mathrm{v}0}\left[\frac{\partial^{2}u}{\partial a^{2}}+m_{\mathrm{v}}\left( \frac{\partial u}{\partial a}\right)^{2}\right]=\frac{\partial u}{\partial t} \tag{29}\]  
where \(C_{\mathrm{v}0}\) is initial coefficient of consolidation, and \(a\) is depth in the Lagrangian coordinate. The normalized parameters of Equation (6) are still used to rewrite the dimensionless governing equation:  
\[\left[\frac{\partial^{2}\overline{u}}{\partial Z^{2}}+\left(\lambda_{p}\frac {\partial\overline{u}}{\partial Z}\right)^{2}\right]=\frac{\partial\overline {u}}{\partial T_{\mathrm{v}}} \tag{30}\]  
where \(\lambda_{p}=m_{\mathrm{v}}q\) is normalized load parameter. In the computation, the following parameters are adopted: \(H=10\) m, \(m_{\mathrm{v}}=4\times 10^{-3}\) kPa\({}^{-1}\), \(q=100\) kPa, \(k_{\mathrm{v}0}=10^{-9}\) m/s, \(e_{0}=3.0\), \(G_{\mathrm{s}}=2.75\), \(\alpha=2\), \(\beta=3\), \(\gamma_{\mathrm{w}}=9.8\) kN/m\({}^{3}\).  
As for the PINN solver, we fixed the network architecture as [2; 50; 50; 50; 50; 50; 1]. The training epochs were set to 20,000 epochs. The training dataset contains 101 initial condition points, 1001 upper boundary condition points, 1001 lower edge condition points, and 10,000 residual points; the validation dataset is a uniformly distributed set of 10,201 spatio-temporal points. The model performance is assessed by measuring the relative \(L_{2}\) error of the validation dataset, which is defined as follows:  
\[relative\ L_{2}\ error=\sqrt{\frac{\sum_{x\in\mathcal{D}_{\mathrm{val}}}\left|u (x)-\hat{u}(x;\lambda)\right|^{2}}{\sum_{x\in\mathcal{D}_{\mathrm{val}}} \left|u(x)\right|^{2}}} \tag{31}\]  
Figure 3: Hyperbolic tangent function with different activation slope ‘a’ values.
# Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model
## 5 Performance Indexes for Evaluation of the Models  
For model evaluation, this study used five statistical indexes, including MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-squared error), VAF (variance accounted for), and R\({}^{2}\) (correlation coefficient). They are computed as follow:  
\[\text{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}}\Big{|}y_{flymock,i}-\hat{y}_{flymock,i}\Big{|} \tag{1}\]  
\[\text{MAPE}=\frac{100\%}{n}{\sum\limits_{i=1}^{n}}\Bigg{|}\frac{y_{flymock,i}- \hat{y}_{flymock,i}}{y_{flymock,i}}\Bigg{|} \tag{2}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}}\left(y_{flymock,i}- \hat{y}_{flymock,i}\right)^{2}} \tag{3}\]  
\[\text{VAF}=\left(1-\frac{\text{var}\left(y_{flymock,i}-\hat{y}_{flymock,i} \right)}{\text{var}\left(y_{flymock,i}\right)}\right)\times 100 \tag{4}\]  
\[\text{R}^{2}=1-\frac{\sum\limits_{i}\left(y_{flymock,i}-\hat{y}_{flymock,i} \right)^{2}}{\sum\limits_{i}\left(y_{flymock,i}-\overline{y}_{flymock} \right)^{2}} \tag{5}\]  
where \(y_{flymock,i}\) is the \(i^{th}\) of measured fly-rock; \(\hat{y}_{flymock,i}\) is the \(i^{th}\) of predicted fly-rock; \(\overline{y}_{flymock}\) is mean of measured values of fly-rock distance; \(n\) indicates number of observations in the training or testing datasets.
## 3 Results and Discussion
### Volatile Compounds of Symbiotic Yogurt  
Flavor is often the first indicator when consumers choose food. The consumers do not take interest in functional food consumption if biologically active ingredients lead to an unpleasant flavor [30]. Volatile analysis is widely applied in the objective assessment of dairy foods to determine the acceptance of new functional products by consumers [42]. Formation of volatile compounds in the symbiotic yogurt made with probiotic _S. boulardii_ and inulin, as compared to the  
Figure 3: Survival of _S. boulardii_ in the yogurt samples during storage at 4 \({}^{\circ}\)C for 28 days: Probiotic yogurt with 0.5% _S. boulardii_ (S2), symbiotic yogurt with 0.5% _S. boulardii_ + 1% inulin (S3), 0.5% _S. boulardii_ + 1.5% inulin (S4), 0.5% _S. boulardii_ + 2% inulin (S5). control plain yogurt and the probiotic yogurt with the yeast without inulin, is shown in Table 2. Analysis by HS-SPME-GC-MS showed that a total of 18 volatile compounds were identified in the symbiotic yogurt, while only five and six compounds were identified in the plain and probiotic yogurts, respectively. These volatile compounds were from different chemical families including two aldehydes (3-furaldehyde and 5-hydroxymethylfurfurl), three acids (acetic acid, hexane acid, and octanoic acid), six esters (formic acid, isopentyl ester, butanoic acid-3-methyl-3-methylbutyl ester, octanoic acidmethyl ester, octanoic acidethyl ester, decanoic acidethyl ester, and hexadecanoic acidethyl ester), two ketones (2,4-dihydroxy-2,5-dimethyl-3(2H)-furan-3-one and 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one), four alcohols (3-furanmethanol, phenylethyl alcohol, maltol, and 2-(2-butoxyethoxy)-ethanol), one pyrazole (3-methoxycarbonylpyrazole), and other two miscellaneous compounds (methoxyphenyl oxime and naphthalene). The concentration of these compounds ranged from 18.01 ug/L (2-(2-butoxyethoxy)-ethanol)) to 305.09 ug/L (hexanoic acid). The total concentration of the volatile compounds formed in the yogurt samples was in the order of S4 (1434.15 ug/L), S3 (1361.58 ug/L), S5 (948.58 ug/L), S2 (229.69 ug/L), and S1 (134.77 ug/L). Therefore, addition of inulin and the yeast significantly increased the amounts and types of volatile compounds in yogurt. This suggests the possible mechanism of prebiotic inulin to modify the metabolism of the probiotic yeast and LAB in the formation of volatiles, which needs to be further studied.