esa-sceva/satcom-chunk-collection huggingface dataset:
#### 4.2.2 Optimal Configuration of the NA2C2M Networks  
Section 4.2.1 shows that the optimally configured NA2C2S networks are more efficient than the NA2C3S ones. In this section, the results of the procedure for the investigation of the optimally configured NA2C2M networks are presented.  
In Figure 11, the OA index values extracted by the optimally configured NA2C2M networks for the three versions of the selected RC buildings are summarized. From the study of Figure 11, it can be concluded that--as in the cases examined above--the networks with two hidden layers extract higher OA values than the networks with one hidden layer. In addition, these OA values are higher than 90% for all of the three versions of RC buildings. As regards the most efficient training algorithms, no general conclusions can be extracted, since for the BB and RIB the SCG algorithm is more efficient than the RP algorithm, whereas for IIB the opposite is true. However, these differences between the OA values extracted using the two algorithms are not significant--especially for the testing subsets (less than 3.3%).  
In order to more robustly confirm the advantage of networks with two hidden layers, the CMs corresponding to the optimally configured networks with one and two hidden layers (Figure 11), on the basis of the testing subsets, are presented in Figure 12.  
The study of Figure 12 confirms (on the basis of the R-index and P-index) the conclusion that the networks with two hidden layers are more efficient than the networks with one hidden layer. Indeed, the vast majority of the R-index and P-index values of CMs that correspond to networks with two hidden layers are greater than 90%. Conversely, the vast majority of the corresponding values of the CMs of networks with one hidden layer are lower than 90%.  
Closing the presentation of the results of the investigation for the optimal configuration of MLP networks used for the implementation of A2, the parameters of the optimally configured NA2C3S, NA2C2S, and NA2C2M networks are summarized in Table 8.
# A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
###### Abstract  
The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method.  
polarimetric interferometric synthetic aperture radar (PolInSAR); vegetation height; truncated singular value decomposition (TSVD); least squares +
Footnote †: journal: Applied Sciences
## 3 Results
### Expansion Predictability  
Table 4 shows the differences between T2 (predicted outcome) and T3 (observed outcome) in the unilateral, bilateral, and single-tooth crossbite groups. Although the predicted expansion appeared greater than the observed expansion for all measurements in all groups, the difference was only significant for the intercanine width in the bilateral crossbite group (\(p=0.011\)). The highest % expansion predictability was for second interpremolar width in the unilateral and bilateral crossbite groups (79.19% and 86.02%, respectively) and for first interpremolar width in the single-tooth crossbite group (90.15%).
# Innovative Approach to Conceptual Design of Enterprise Risk Management Software
## 2 Methodology  
In this paper, a methodology for the design of ERM software was generated such that the software solution relies on the following principles:  
* Connecting ISO 31000 [27] and COSO frameworks [28];
* Application of expert knowledge in hazard identification;
* Connecting hazard and risk;
* Determination of risk appetite and adjustment of the risk matrix accordingly (5 \(\times\) 5 matrices, 3 \(\times\) 3 matrix, etc.) for as many risk types as needed (asset, safety, environment, reputation, etc.);
* Different risk assessment methodologies (quantitative, qualitative, semi-qualitative, Kinney, API 581, FMEA, etc.).  
The proposed methodology for the design of ERM software is presented in the form of algorithms in Figure 1. The algorithm contains the following steps:1. External experts input knowledge into a database: This step involves external experts providing their insights and knowledge into a centralized database;
2. Internal experts go through questionnaires: Internal experts review and respond to questionnaires created by external experts or surveys designed to assess various hazards. This step is important in order to allow internal experts to see their risks from a different angle, that is, from external expertise and the generated heuristic knowledge base;
3. Hazard database is generated according to the internal experts' answers: Based on the responses provided by internal experts, a hazard database is compiled or updated;
4. From this point, the joint teamwork of internal and external experts is needed to make a suitable decision: Is a hazard a risk? At this juncture, there is a decision point where the experts evaluate whether a hazard identified in the database poses a risk or not. 4.1. If Yes, then the flowchart branches out further: Additional steps might include risk assessment, risk mitigation strategies, etc.; 4.2. If No, then the flowchart may lead to actions such as: Further assessment to understand why it's not considered a risk.
5. Updating the hazard database accordingly.
6. Closing the assessment loop.
7. Regardless of the process of generating risks arising from hazards, the risk base is supplemented with the entry of acknowledged risks (existing risks in the observed company) which gives additional flexibility to the approach.
8. Risk Management process, updating classification, updating mitigation measures, updating risk assessments, etc.
9. Finally, based on the implemented processes of connecting risks with elements of organizational structures & units (COSO framework), it is possible to generate different risk cards, perform Risk Score analysis and Ie-factor (Investment effectiveness) analysis. Failure to achieve the goals of a part of the organizational structure can be considered a risk, so it is necessary to link risks with the organizational structure, which is a core of COSO framework approach.  
This flowchart captures the process from external knowledge input to internal evaluation, database generation, and the decision-making process regarding whether identified hazards constitute risks.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 3 Methods
### Detection of Attachment Proteins to Complex Core  
Generally, attachment proteins exist in two forms, namely overlapping and peripheral protein attachments [65]. Therefore, to identify protein attachment to the complex core, consider the identified preliminary protein complex denoted by \(C_{p}(v)\), the preliminary complex core as a sub-network represented by \(Core(\omega,C_{p}(v))=(V_{c},E_{c})\) and the set \(CAP(C_{p}(v))\) of candidate attachment proteins as a subset of the neighbors of \(Core(\omega,C_{p}(v))\). Here, our two main objectives are: first, to find a subset \(CAP(C_{p}(v))\subseteq V\) in PPI network in which each protein \(p\in CAP(C_{p}(v))\) is a candidate attachment protein with identified preliminary protein complex \(C_{p}(v)\), and secondly, to predict the category of each protein in \(CAP(C_{p}(v))\).  
To achieve the two objectives we set two basic conditions, namely: (1) The attached proteins must interact with the complex cores directly; (2) The attached proteins must be connected to at least two core proteins via complex cores since protein complexes are made of two or more complexes [66]. Therefore, if protein \(p\) fulfils the conditions as belonging to the neighborhood of \(Core(\omega,C_{p}(v))\) with \(|N(p)\bigcap V_{c}|\geq 2\), then it is selected for \(CAP(C_{p}(v))\). Below we provide a detailed description of the calculation of the overlapping and peripheral protein attachment to the complex core.
###### Abstract  
(1) Background: Metallic materials are predominantly used for spinal implants, and they can damage adjacent bones and intervertebral discs (IVDs) owing to their high elastic moduli. Consequently, there is a possibility that serious complications, such as kyphosis, may occur as the sequelae progresses. In this study, the behavior of the lumbar spine and implant system was evaluated using the finite element (FE) method, by applying the porous structure to the spinal implants to resolve the problem of metal spinal implants. (2) Methods: An FE model was developed for lumbar 3-5, and it was assumed that, owing to disease occurrence, spinal implants were placed in lumbar 3-4. Currently, Ti-6 Al-4V is the most commonly used material for spinal implants. The shape of the porous structure was set in the form of a diamond, and porosity was varied over nine values ranging from 0 to 81%. Finally, equivalent material properties of the porous structure were derived using the Ramberg-Osgood formula, with reference to experimental study. (3) Results: The range of motion was increased, and the equivalent stress of adjacent IVD, and adjacent bone stress of the pedicle screw and spinal cage, decreased with increasing porosity of the spinal implants. As the porosity decreased, the safety factor exhibited a tendency to decrease rapidly. (4) Conclusion: Motor capacity of the spine was improved, and the equivalent stress of the spinal tissues decreased with the increasing porosity of the spinal implants. Therefore, in the future, porous structures can significantly contribute to the improvement of implants through continuous complementary research.  
lumbar implant system; porous spinal implants; porosity; diamond lattice structure; Ti-6 Al-4V; spine stability; finite element method (FEM) +
Footnote †: journal: Applied Sciences
# A Method for Dynamic Insertion Order Scheduling in Flexible Job Shops Based on Digital Twins
## 4 Genetic Algorithm for Model Solving  
### Genetic Algorithm  
A flexible job shop scheduling problem is a complex nonlinear problem. Metaheuristic algorithms, such as genetic algorithms, ant colony algorithms, and particle swarm optimization algorithms can solve this problem well. Because of its simple operation, strong universality, and good robustness, the genetic algorithm is one of the most popular algorithms for solving flexible job shop scheduling problems [14].  
The evolutionary algorithm is a kind of random search technology based on the idea of biological evolution, which simulates the evolutionary process of populations. It includes methods such as genetic algorithms (GAs), evolutionary strategies (ESs), and evolutionaryprogramming (EP), among which GAs are the most widely used. They are particularly suitable for dealing with complex and nonlinear problems that are difficult to solve by traditional search methods and can be widely used in combination optimization, machine learning, planning and design, and other fields. FJS is a typical NP problem [15] and one of the most difficult combinatorial optimization problems [16]. In recent years, the development of probabilistic local search methods such as simulated annealing algorithms, tabu search algorithms, and GAs has aroused people's interest in using local search methods to solve scheduling problems. In this paper, the GA is directly applied to the FJS problem [17], eliminating the limitations of the current genetic algorithm which is mainly applied to the group technology FJS problem [18]. A GA is a kind of bionic optimization method borrowed from biological evolution, especially inspired by the principles of genetics. As a robust global optimization method, the genetic algorithm shows good searchability in solving NP problems in the field of combinatorial optimization and has been well applied in job shop scheduling problems. Solving FJS based on a genetic algorithm has been proven to be an effective solution method. Many scholars at home and abroad have studied it, mainly focusing on population initialization, genetic operation, decoding, and integration with other algorithms. When using the genetic algorithm to solve the FJSP, the quality of the initial solution has a great influence on the speed and quality of the genetic algorithm. If the quality of the initial solution is higher, the starting point of the genetic algorithm is higher, which improves the efficiency and quality of the genetic algorithm. Compared with traditional job shop scheduling, flexible scheduling is a more complex NP-hard problem that includes machine selection and operation scheduling. The rationality of machine selection has an important impact on the final result of process scheduling. Excellent machine selection will greatly reduce the search space of the FJSP algorithm.
## 4 Applications of Intelligent Acoustic Monitoring
### Public Safety and Security
#### 4.1.3 Weapon Detection in Images  
In [101], [PERSON] et al. developed the WeaponVision AI system, which automatically identified weapons, such as pistols or rifles, in images, videos, and surveillance camera transmissions. The primary objective was to develop a system that could rapidly detect threats and operate independently, which is crucial in situations such as armed robberies in public areas. The system was constructed using a modified version of the YOLOv7 model, well-known for its high object detection efficiency. Over 79,000 images, including those with challenging conditions such as blurry shots, nighttime photos, dark backgrounds, or intricate, noisy scenes, were used to train the model. Thanks to this, WeaponVision AI also coped well in unfavourable conditions, as confirmed by the achieved results--detectionprecision was almost 92%. Also, the system was equipped with a night vision function. It was designed to work on ordinary processors and graphics cards, making it more universal and easier to implement. The whole system was complemented by a straightforward graphical interface, which enabled specialists and non-technical users to operate the system without prior training. In practice, this means that WeaponVision AI can support security services, allowing for a faster and more accurate response to threats related to the use of weapons.
# Bacteriotherapy in Breast Cancer
## 4 Bacteria as a Carrier for Cancer Therapeutic Agents
### Bifidobacterium Longum  
_Bifidobacterium longum_ belongs to the genus _Bifidobacterium_ species that are part of the normal human microflora and exert probiotic effects in humans [109]. This bacterium can selectively germinate and grow in the hypoxic regions of solid tumors after intravenous injection [110]. The genetically engineered _B. longum_, used for enzyme-prodrug therapy, involves a fusion of cytosine deaminase and 5-fluorocytosine (5 FC). The cellular toxicities of 5 FC are due to the deamination via cytosine deaminase to give 5 fluorouracil (5 FU) [111]. Furthermore, a plasmid, i.e., pBLES100-S-eCD, was constructed to adapt enzyme-prodrug therapy, which used _B. longum_ as a tumor-specific gene delivery system. Thisconstruction includes the HU gene promoter and the cytosine deaminase gene of _E. coli_. The HU gene is able to encode a histon-like DNA binding protein highly expressed in _B. longum_[112]. The result of these studies shows that the transfected _B. longum_, which produces cytosine deaminase in the hypoxic tumor therapy can be an effective enzyme-prodrug therapy for the systemic administration.
## 3 Results and Discussion
### Comparison of SFE and Solvent Extraction  
The phenolic content in chokeberry pomace was 3204 mg GA per 100 g. The recovery of solvent extraction was only 13.5%, while supercritical CO\({}_{2}\) modified with ethanol (with the same ethanol-pomace ratio of 0.5) was able to elute 11.1%-44.6% of phenolics. This part of the experiments, with the use of high densities of CO\({}_{2}\), gave significantly higher yields than that from solvent extraction.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **DPPH**+** & **ABTS**+** & **NO**+** & **O\({}_{2}\)-**+** & **OH**+** \\ \hline reference compound & Trolox & Trolox & curcumin & quercetin & sorbitol \\ \hline equivalent of 100 g of pomace & 459.7–2907.0 \(\mu\)mol & 411.0–3384.1 \(\mu\)mol & 13.3–29.0 \(\mu\)mol & 249.4–1138.4 \(\mu\)mol & 16.44–72.24 \(\mu\)mol \\ \hline equivalent of 1 g of GAE & 1.87 \(\pm\) 0.33 mmol & 1.93 \(\pm\) 0.23 mmol & 36.3 \(\pm\) 21.4 \(\mu\)mol & 0.92 \(\pm\) 0.22 mmol & 58.7 \(\pm\) 19.5 mmol \\ \hline IC\({}_{50}\) of reference compound & 23.3 \(\mu\)mol/L & 10.4 \(\mu\)mol/L & 33.0 \(\mu\)mol/L & 3.89 \(\mu\)mol/L & 27.1 mmol/L \\ \hline GAE for 50\% of inhibition & 12.46 mg/L & 5.39 mg/L & 909.09 mg/L & 1.63 mg/L & 461.67 mg/L \\ \hline correlation with TPC & 0.9715 & 0.9490 & 0.6249 & 0.9803 & 0.9248 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Potential of the extracts from chokeberry pomace to scavenge selected free radicals. Abbr.: TPC = total phenolic content, GAE = gallic acid equivalents, IC\({}_{50}\) = half maximal inhibitory concentration.  
The extracts obtained with both methods exhibited a similar quantity of anthocyanins and radical scavenging activity when converted to 1 g of gallic acid equivalent. The output of solvent extraction could be undoubtedly increased by utilizing greater amounts of solvent; although one of the goals of the project was to reduce the use of these substances.
## 3 Results
### Coefficient of Determination \(R^{2}\)  
The use of polynomial degree resulted in significant increase \(R^{2}\) values in the case of GP, SVR, and Lasso (Figure 4 horizontal within-subplots asterisks and Table 3). We also observed that (Figure 4 vertical between-subplots asterisks and Table 4) the \(R^{2}\) values associated with both Lasso and SVR were significantly higher than those of GP and the fixed pre-touch distance (i.e., fixed 20.0 cm facial pre-touch distance).  
Figure 4a subplot shows that without the use of polynomial degree (i.e., degree 0) all these models performed poorly (i.e., lower \(R^{2}<0.0\)) compared to the fixed pre-touch distance (i.e., fixed 20.0 cm facial pre-touch distance). On the other hand (Table 4), the use of polynomial degree 4 (Figure 4b) resulted in Lasso and SVR significantly increased \(R^{2}\) values. However, we did not observe this increase in \(R^{2}\) in the case of GP.  
Finally, we observed that (Table 5) whereas GP performed significantly worse than fixed pre-touch distance in the case of both polynomial degrees 0 (\(p<0.001\), W = 14.11, r = 1.00, M\({}_{Fixed}=-0.94\), SD\({}_{Fixed}=0.21\)) and 4 (\(p<0.001\), W = 14.11, r = 1.0), fixed distance was significantly outperformed by SVR (0: \(p<0.001\), W = 14.11, r = 1.00 and 4: \(p<0.001\), W = 14.11, r = 1.0) and Lasso (0: \(p<0.001\), W = 14.11, r = 1.00 and 4: \(p<0.001\), W = 14.11, r = 1.00).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & \(p_{0}\)**\(<\) & \(W_{0}\)**(26)** & \(r_{0}\) & \(p_{4}\)**\(<\) & \(W_{4}\)**(26)** & \(r_{4}\) \\ \hline Lasso vs. GP & 0.001 & 12.22 & 0.86 & 0.001 & 12.21 & 0.86 \\ Lasso vs. SVR & =0.31 & 1.03 & 0.07 & 0.001 & 8.00 & 0.57 \\ Lasso vs. Fixed & 0.001 & 14.11 & 1.00 & 0.001 & 14.11 & 0.86 \\ SVR vs. GP & 0.001 & 12.22 & 0.86 & 0.001 & 12.20 & 0.86 \\ SVR vs. Fixed & 0.001 & 14.11 & 1.00 & 0.001 & 14.11 & 1.00 \\ GP vs. Fixed & 0.001 & 14.11 & 1.00 & 0.001 & 14.11 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pairwise models’ RMSE comparison. Subscripts 0 and 4 correspond to the case of polynomial degree 0 (i.e., no polynomial degree) and 4.  
Figure 4: Coefficient of Determination (\(R^{2}\)) when the models trained using (**a**) polynomial degree 0 (**b**) polynomial degree 4. The horizontal bars correspond to the difference between the polynomial degrees for each model and the vertical bars are associated with the difference between models in each polynomial degree settings. The asterisks indicate the significance in their differences (***: \(p<0.001\)).  
We also identified that (Table 5) the use of Lasso resulted in significantly higher \(R^{2}\) values than GP (0: \(p<0.001\), W = 12.22, r = 0.86 and 4: \(p<0.001\), W = 12.21, r = 0.86) and SVR (0: \(p<0.001\), W = 4.95, r = 0.35 and 4: \(p<0.001\), W = 6.84, r = 0.48). SVR also performed significantly better than GP in the case of both polynomial degrees 0 (\(p<0.001\), W = 12.22, r = 0.86) and 4 (\(p<0.001\), W = 12.20, r = 0.86).
# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
## 2 Materials and Methods
### Gas Chromatography-Mass Selective Detector Analysis  
A gas-chromatography with mass-selective detector 5977B (GC-MSD; Agilent Technologies, Inc., Santa Clara, CA, USA) equipped with an autosampler system PAL3 (CTC Analytics AG, Zwingen, Switzerland) was used to analyze the volatile aromatic compounds of the rice in duplicate samples. Helium was used as the gas carrier at a 1 mL min\({}^{-1}\) flow rate. The raw (4 g) and cooked (3 g) rice samples were weighed and put into 20 mL screw-top vials with magnetic caps. The headspace method was used for extraction, and the samples were heated with agitation at 80 \({}^{\circ}\)C for 30 min to allow the rice to release the volatile aromatic compounds in the headspace of the vials, followed by the extraction using divinylbenzene-carboxsen-polydimethylsiloxane (DVB-CAR-PDMS) 1.1 mm grey solid-phase microextraction (SPME) fiber [62; 63] to penetrate the headspace of the vial to absorb the volatiles for 60 min under 80 \({}^{\circ}\)C with agitation.  
An HP-5 MS (Agilent Technologies, Inc.; 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column was used in the analysis; this column was selected considering other studies of rice and rice bran [64; 65; 66]. The SPME fiber was desorbed in the injector at 250 \({}^{\circ}\)C for 5 min using the splitless mode. The inlet temperature was set at 250 \({}^{\circ}\)C. The GC oven temperature was programmed as follows: initial temperature of 40 \({}^{\circ}\)C for 3 min; then increased to 100 \({}^{\circ}\)C at 5 \({}^{\circ}\)C min\({}^{-1}\) and held for 3 min; and ramping to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\) and held for 4 min. A blank sample was placed at the start and between raw and cooked rice samples to prevent the carryover effect. The volatile aromatic compounds were matched with the National Institute of Standards and Technology (NIST; National Institute of Standards and Technology, Gaithersburg, MD, USA) library. Only identified compounds greater than 80% certainty are reported in this study. Additionally, compounds with a low relative abundance identified in less than three rice samples were omitted.