esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The aim of this research was to screen the stems of four plant species that are commonly used for fibers (_Linum usitatissimum L._, _Abuillon theophrasti_ Medik., _Camabis sativa_ L. and _Corchorus olivorius_ L.) using the FT-IR method in order to provide fingerprint characterization and potential quality marker regions of fiber stems. To reach this goal, three objectives were established:  
* Identification and assignment of functional groups to main bands and peaks common among the analyzed species;
* Identification of regions with highest variability as well as any particularities based on comparative assessment of spectra;
* Defining common spectral quality marker regions of cellulosic fibers among the four species. From the species selected for this study, two have been traditionally cultivated in temperate conditions of Romania (flax and hemp), one is a common fiber crop from tropical-subtropical regions (jute) and the other has been less explored so far but might have good potential (velvet leaf).
## Results  
### Screening of Medicinal Plants for AFB1 Detoxification  
Aqueous extracts derived from 57 Omani medicinal plants were assessed for their potential to detoxify AFB1. Among them, the leaf extracts of _H. bacciferum_, _O. dhofarense_, and _Z. multiiflora_ degraded 95%, 93% and 92% of AFB1, respectively, after incubation for 72 h at 37 \({}^{\circ}\)C (Table 1). The leaf extracts of _Lavandula dhofarensis_ subsp. _ayunensis_ [PERSON]. [PERSON]. (Lamiaceae), _Lavandula subunda_ Benth. (Lamiaceae), _Salvia hillcoatiae_ Hedge (Lamiaceae), _Tecomella undulata_ (Sm.) Seem. (Bignoniaceae), _Epheda foliata_ Fisch. & C.A.Mey. (Ephedracee), and _Kleinia odora_ (Forssk.) DC. (Asteraceae) degraded more than 80% of AFB1. Fourteen plant species' aqueous extracts failed to detoxify AFB1, while the remaining extracts demonstrated intermediate levels of potential detoxification. The leaf extracts of _H. bacciferum_ (HE), _O. dhofarense_ (OE), and _Z. multiiflora_ (ZE) were selected to be used in further studies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of the Plant** & **Family** & **Accession Number** & **AFB1 Detoxification (\%)** \\ \hline _Aerva javanica_ (Burm.f.) [PERSON]. ex [PERSON]. & Amaranthaceae & 201800506 & 33 \\ \hline _Searsia aucheri_ (Boiss.) Moffett & Anacardiaceae & 201800777 & 0 \\ \hline _Desmidorchis adrenensis_ (Deflers) [PERSON] \& Liede & Apocynaceae & 200604436 & 6 \\ \hline _Desmidorchis arabica_ (N.E.Br.) Mve \& Liede & Apocynaceae & 201800303 & 0 \\ \hline _Desmidorchis flava_ (N.E.Br.) Mve \& Liede & Apocynaceae & 201300308 & 32 \\ \hline _Desmidorchis penicillin_ (Deflers) Plowes & Apocynaceae & 201800596 & 0 \\ \hline _Monollum quadrangular_ (Forssk.) Plowes & Apocynaceae & 201800011 & 55 \\ \hline _Aloe dhfaternsis_ Lavranos & Asphodelaceae & 201000386 & 32 \\ \hline _Aloe praatermissa_ T.A.McCoy \& Lavranos & Asphodelaceae & 201000250 & 0 \\ \hline _Aloe vera_ (L.) Burm.f. & Asphodelaceae & 201800270 & 10 \\ \hline _Kleinia odora_ (Forssk.) DC. & Asteraceae & 201000077 & 85 \\ \hline _Pluchea arabica_ (Boiss.) [PERSON] \& Lack & Asteraceae & 201000388 & 58 \\ \hline _Pulicaria glutinos_ (Boiss.) [PERSON]. \& Spach & Asteraceae & 201200418 & 40 \\ \hline _Tecomella undulata_ (Sm.) [PERSON]. & Bignoniaceae & 200817492 & 85 \\ \hline _Heliotropin baciferum_ Forssk. & Boraginaceae & 201600290 & 95 \\ \hline _Bosvelilia sacra_ [PERSON]. & Burseraceae & 201300064 & 52 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detoxification of aflatoxin B1 by Omani medicinal plants.
## 4 Simulation Results and Discussion
### Axial Force of the Steel Bracings  
The axial forces of the steel bracings when the excavation of the foundation pit reached 31 m beneath the ground are shown in Figures 10 and 11. According to Figure 10, the axial forces at both ends of the steel bracings were high, while those at the middle of the steel bracings were low. According to Figure 11, when transverse supporting scheme A was adopted, the axial forces of the steel bracings at 4 m, 8 m, 12 m, and 16 m were respectively 258 kN, 344 kN, 320 kN, and 177 kN. When transverse supporting scheme B was adopted, the axial forces of the steel bracings at 8 m and 16 m were 454 kN and 253 kN, respectively. When transverse supporting schemes C and D were adopted, the axial forces of the corresponding steel bracings at 9 m and 16 m were 536 kN and 160 kN, respectively. Therefore, the axial forces of the steel bracings were closely related to the arrangements of the supporting elements.  
Figure 10: _Cont._  
Meanwhile, it can also be obtained from Figure 10 that the axial forces of the steel bracings near the prestressed anchorages were obviously lower compared to those located at other layers, which further indicated that prestressed anchorages were effective in terms of the axial force/NN.  
Figure 11: Maximum axial forces of the steel bracings.  
Figure 10: Axial force contour of the steel bracing.  
of improving the integrity of the surrounding rocks and thus avoid excessive displacements of the surrounding rocks.
## 4 Determination of Initial Crack Size and Critical Crack Size
From the fracture analysis model for the bearing structure at the end of cable 28 of the steel anchor box, the fitting curve of the geometric correction coefficient at the deepest point of the crack is obtained after regression analysis, as shown in Figure 8, and the geometric correction factor \(Y\) is regressed as:  
\[Y=0.9262-0.23461\left(\frac{a}{t}\right)+1.40921\left(\frac{a}{t}\right)^{2}- 1.91653\left(\frac{a}{t}\right)^{3}+0.57421\left(\frac{a}{t}\right)^{4}+0.2724 6\left(\frac{a}{t}\right)^{5} \tag{7}\]  
where \(t\) is the steel plate thickness. Substituting Equations (6) and (7) into Equation (5), the effective solution for the plate thickness range is 22.9 mm, so \(a_{f}\) is taken as 22.9 mm. Since \(a_{f}<a_{\max}\), the critical crack length is safely taken as 22.9 mm. The initial minor to major axis ratio \(a_{0}/c_{0}\) of the crack is assumed to be \(1/1\). As mentioned above, this paper safely ignores the fatigue-stress cutoff limit; therefore, in the fatigue-crack growth analysis, the fatigue-crack growth threshold value can be regarded as 0.
## 3 Critical Analysis of Selected Literature
### Infrared LED Exploitation Technique  
Although infrared light is invisible to humans, various types of cameras can effectively capture it through optical means. [PERSON] and [PERSON]'s [13] analysis revealed that an attacker could communicate over the air gap from tens to hundreds of meters using IR and surveillance cameras (Impact). The following study by [PERSON] et al. [60] showed that IR can be used to conduct a so called Invisible Infrared Shadow Attack (IRSA) and reconstruct a picture of the environment where the camera is placed from deformed IR shadows. The IR lighting, typically used for legitimate purposes, can unwittingly serve as a vulnerability that attackers exploit to infiltrate and exfiltrate data to and from indoor air-gapped security cameras. In an exfiltration scenario, malware gains access to a surveillance camera connected to the local network, utilizing it to control the IR LEDs. These LEDs generate covert IR signals, as illustrated in Figure 11, which are subsequently modulated, encoded, and transmitted as binary data. An intruder with a line of sight to the security camera at a distance can intercept these IR signals and decode the binary data. Conversely, in an infiltration scenario, a remote attacker deploys IR LEDs to produce covert IR signals. These signals are then picked up by the surveillance camera and intercepted by malware operating within the network.  
**Security Measures:** The organization may place surveillance cameras in restricted zones that are optically inaccessible to attackers (window shielding). In case of ISRA, a straightforward method to prevent this type of attack is ensuring that all windows are covered with thick curtains, so that no IR shadow can be projected towards the curtain surface.  
Additional strategies to enhance security against ISRA and similar attacks include:  
1. Disabling IR Control--Deactivating the IR control on surveillance cameras can mitigate the risk of attackers using IR signals for malicious purposes;  
Figure 11: Diagram of the IR LED exploitation technique in a physically air-gapped network.  
Signal Jamming with Unique Patterns--Introducing random and unique light patterns that are known only to the legitimate camera can disrupt unauthorized IR signal interception attempts, making it challenging for attackers to decode or misuse the signals.  
Incorporating IR Filters--Installing IR filters on surveillance cameras can help filter out unwanted IR light and shadow, reducing the potential for exploitation.  
By implementing these preventive measures, organizations can significantly enhance their security posture and reduce the vulnerability of surveillance systems to IR-based attacks.
# Optimizing Investment Portfolios with Bacterial Foraging and Robust Risk Management
## 2 Bacterial Foraging Optimization (BFO)
### Mathematical Foundations
#### 2.2.6 Application to Portfolio Optimization  
The mathematical components of BFO described above are adapted to the domain of portfolio optimization. The nutrient concentration \(C(x)\) corresponds to a portfolio performance metric (e.g., risk-adjusted return), while the bacteria's positions \(\mathrm{x_{i}}\) represent candidate portfolio allocations. Interactions among bacteria encourage diversification, reproduction propagates high-performing portfolios, and elimination-dispersal ensures robustness against local optima. These principles lay the foundation for employing BFO in optimizing portfolios under real-world constraints and dynamic market conditions.
## 2 HT Distribution and Simulation Results
### HT Feed Network Design of a Linear Array  
We designed a step-by-step power division network for the 3.5 GHz array, as shown in Figure 10, wherein the powerdivider1 contributes seven signals with powers of 0.02, 0.053, 0.144, 0.293, 0.492, 0.707, and 0.892 W into a sum power of 2.6 W. The division ratio is 2.6:1, and the power ratios of the rest of the stages are 1.42:1, 1.04:1, 0.74:1, 0.5:1, and 0.37:1. These power dividers are cascaded to form an HT power division network, and the phase can be adjusted by modifying the branch lengths. The phase of the leftmost unit, for example, \(100^{\circ}\), is used as the reference. To produce a \(100^{\circ}\) phase unit with the phase of \(50^{\circ}\), its branch length needs to be changed by about (100-50) * wavelength/360. We used this network to perform a full-wave simulation of the eight-element linear array. These results are displayed in Figure 11, where the pattern is an ONF beam with a maximum gain of 14 dBm and \(\mid\)\(S_{11}\)\(\mid\) is approximately \(-24\) dB at 3.5 GHz. Therefore, the HT method can be used in an array stimulated by a microstrip network.
## 4 Experiment and Validation
### Cross-Domain Fault Diagnosis Case 1: CWRUi
#### 4.2.2 Analysis of Results
To further evaluate the capability of the contrasting methods, as depicted in Figure 10, we employ t-SNE to display the output of the feature extractor and visualize the extracted features in a 2D plane. It can be seen that the proposed cross-domain few-shot method outperforms VGGNet, Conv5, and ProtoNet in terms of efficiency. The suggested approach enables the embedded features to be as near to independent outside the class and as independent inside each class. The distinguishability of features extracted by the extractor facilitates fault identification by the classifier and aids in the interpretation of classification results to some extent. Analogously, besides classification performance, the time consumption should also be considered. From Table 4, we compared the model time consumption for VGGNet, Conv5, ProtoNet-10 shot, and our method-10 shot. Each model was trained under the same configuration. Comparison results showed that Conv5, ProtoNet-10 shot, and our method-10 shot can complete the training quickly due to the small training set and simple model. In contrast, VGGNet takes more time due to the large amount of training operations. Compared with VGGNet, the proposed method greatly reduces the time cost. Of these, ProtoNet has the shortest training time because it is simple and efficient in optimization design and easy to optimize. Due to the attrition design, the proposed method has a slight increase in  
Figure 9: Confusion matrix using different methods. (**a**–**d**) correspond to the VGGNet, Conv5, ProtoNet-10 shot, and our method-10 shot. training time compared to ProtoNet, but it performs better on the dataset compared to the accuracy improvement, and the slight increase in training time is acceptable. Consequently, the proposed method is further demonstrated to be of higher application value.
# Research on Cloud Task Scheduling Algorithm with Conflict Constraints Based on Branch-and-Price
## 5 Cloud Task Scheduling Algorithm Based on Branch-and-Price
### Generation of Initial Solution
#### 5.1.2 Example  
Consider a cloud computing system with five VMs, and each VM has five cores of CPU and 5 GB of memory. There are five tasks in the cloud computing system that need to be scheduled, and Table 1 provides the resource requirement information of the tasks. Figure 1 shows the relationship graph between tasks, where Figure 1a is the conflict graph of the tasks. To illustrate the algorithm more intuitively, Figure 1b is provided as the complementary graph of Figure 1a, which is known as the compatibility graph. There is no conflict relationship between any two connected tasks in Figure 1b, and they can be scheduled to the same VM.  
The process of the initial solution of the root node is as follows: First, the improved Bron-Kerbosch algorithm is used to find all the task conflict maximum cliques \(\{1,2,3\},\ \{2,4\},\ \{3,5\}\) and the complete partition of the task maximal independent set  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task Number** & **CPU** & **Memory** \\ \hline
2 & 1 & 3 \\
4 & 3 & 2 \\
5 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Task resource requirements.  
Figure 1: Relationship graph between tasks. (**a**) Conflict graph. (**b**) Compatibility graph.  
\(\{1,4,5\},\ \{2\},\ \{3\}\). Obtain the first independent set \(\{1,4,5\}\). Since \(dr_{1}=2,\ dr_{4}=3\) and \(dr_{5}=4\), it is \(\{5,4,1\}\) after sorting. Enable VM 1 and sequentially traverse task list \(\{5,4,1\}\). The available resources of VM 1 at this time are \((5,5)\), which can meet the resource requirements of task 5. Therefore, task 5 is scheduled to be executed on VM 1. The available resources of VM 1 become \((1,3)\), which cannot meet the resource requirements of task 4. Therefore, VM 2 is enabled and task 4 is scheduled to be executed on VM 2. Since VM 1 cannot meet the resource requirements of task 1, and VM 2 can meet the resource requirements of task 1, task 1 is scheduled to VM 2 for execution. At this time, all tasks in the first independent set are scheduled. Similarly, VM 3 is enabled for the second independent set \(\{2\}\), and task 2 is scheduled to VM 3 for execution. Enable VM 4 for the third independent set \(\{3\}\), and schedule task 3 to VM 4 for execution. Therefore, the target value of the generated initial feasible solution is \(4\), and the generated initial feasible column is \((0,0,0,0,1,1)^{\mathrm{T}}\), \((1,0,0,1,0)^{\mathrm{T}}\), \((0,1,0,0,0,1)^{\mathrm{T}}\), \((0,0,1,0,0,1)^{\mathrm{T}}\).
## 2 Materials and Methods
### Feature Selection
#### 2.5.1 Principal Component Analysis  
PCA is a widely-used method of analyzing multidimensional data. PCA is an orthogonal projection (or transformation) of the data to a (possibly lower dimensional) subspace [46; 47]. PCA reduces the dimensionality of large datasets by creating new un  
Figure 4: Pairwise aggregation of mutual features (the shaded area).  
correlated latent variables, known as principal components, such that the variance of the projected data is maximized [48; 49]. The PC vectors can be found as the eigenvectors of the non-singular portion of the covariance matrix. In this work, we used PCA to reduce the conventional feature space by retaining the features with the highest contributions to a few most dominant PCs. PCA is an unsupervised technique that does not consider the output class labels when selecting the feature variables.
# Meander Structure Analysis Techniques Using Artificial Neural Networks
###### Abstract  
Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to perform a preliminary study of the designed structures. This study aims to explore the application of neural networks in the design and analysis of meander structures. Three different feedforward neural network (FFNN), time delay neural network (TDNN), and convolutional neural network (CNN) techniques were investigated for the analysis and design of the meander structures in this article. The geometric dimensions or top-view images of 369 different meander structures were used for training an FFNN, TDNN, and CNN. The investigated networks were designed to predict such electrodynamic parameters as the delay time (\(t_{\text{d}}\)), reflection coefficient (\(S_{11}\)), and transmission coefficient (\(S_{21}\)) in the 0-10 GHz frequency band. A sufficiently low mean absolute error (MAE) was achieved with all three methods for the analysis of MSs. Using an FFNN, the characteristic \(t_{\text{d}}\) was predicted with a 3.3 ps average MAE. The characteristic \(S_{21}\) was predicted with a 0.64 dB average MAE, and \(S_{11}\) was predicted with a 2.47 dB average MAE. The TDNN allowed the average MAEs to be reduced to 0.9 ps, 0.11 dB, and 1.63 dB, respectively. Using a CNN, the average MAEs were 27.5 ps, 0.44 dB, and 1.36 dB, respectively. The use of neural networks has allowed accelerating the analysis procedure from approximately 120 min on average to less than 5 min.  
meander structures; microwave devices; neural networks; nonlinear device modeling +
Footnote †: journal: Applied sciences
# Mining of Potential Gene Resources for Breeding Nutritionally Improved Maize
## 2 Identification of Maize Potential Gene Resources for Starch Content Improvement  
Starch accounts for most of the dry weight of corn kernels and provides calories for humans and animals. Starch comprises two types of polysaccharide molecules: amylose (Am) and amylopectin (Ap). Am is a polysaccharide made of D-glucose units, almost all of which are linked by \(\alpha\)-1,4-glycosidic bonds, while Ap molecules are linked by \(\alpha\)-1,4-glycosidic bonds and \(\alpha\)-1,6-glycosidic bonds [5]. Four major enzymes are involved in starch synthesis. ADP-glucose, a glucosyl donor for starch synthesis, is synthesized by the catalyzation of adenosine diphosphate glucose pyrophosphorylase (AGPase), using glucose-1-phosphate (G-1-P) and ATP as substrates. Starch synthases (SSs) and starch branching enzymes (SBEs)  
Figure 1: Structure and nutrient distribution of maize kernels.  
are responsible for elongating the glucose polymer and branching, respectively. Debranching enzymes (DBEs) catalyze the hydrolysis of \(\alpha\)-1,6-branch linkages of starch and other branched polyglucans, and an isoamylase-type (ISA) debranching enzyme facilitates the crystallization of amylopectin by hydrolyzing some of the branches and thus is involved in amylopectin synthesis [6].  
A type of starch, known as resistant starch, cannot be digested by the stomach and small intestine where it can be fermented by certain specialized microorganisms [7]. Resistant starch plays an essential role in human health, including lowering blood glucose and cholesterol levels [8]. The proportion of Am in starch was found to positively correlate with resistant starch content in sorghum [9]. Thus, improvement of Am content also indirectly increases the content of resistant starch.  
Am content in maize kernels could be adjusted by altering the direction of starch synthesis. According to the starch synthesis process described above, SBE is the most critical factor in converting Am and Ap. Studies on rice [10; 11], wheat [12], barley [13], potatoes [14; 15] and cassava [16] showed the content of Am was increased when the activity of SBEs was suppressed, supporting the notion that manipulation of SBE is an effective way to increase the Am content. The effect of SSs on starch synthesis has been investigated and confirmed in rice [17; 18] and sweet potatoes [19]. Granule bound starch synthase (GBSS) binds specially to starch and maintains the unbranching state of Am while the Protein Targeting to Starch 1 (PTST1) participates in the localization of GBSS in Am. Studies showed that boosting the expression of _GBSS_ and _PTST1_ resulted in the enhancement of Am production [20; 21; 22]. Thus, we assume that harnessing these key enzymes involved in starch synthesis could also effectively improve the starch content in maize kernels. Sixteen homologous genes encoding these key enzymes were identified from the maize genome (Table 1).
# Multi-Objective Routing and Categorization of Urban Network Segments for Cyclists
## 2 Materials and Methods
### Formulation of the Problem Constraints  
According to [35], the objective functions developed in the previous section take the lowest possible value of zero for the solution \(y_{ijk}=0\ \forall\ (i,j,k)\). Obviously, this solution is not acceptable because it does not represent a path from point A to point B. For this reason, we must add constraints that require us to find a solution that represents a feasible path. Starting from point A (\(i=A\)), the path should use an outbound link from that point to move to the next node. This can be enforced by using the following constraint:  
\[\sum_{ij\in R_{m}:i=A}y_{ijk}-\sum_{ij\in R_{m}:j=A}y_{ijk}=1 \tag{10}\]  
This constraint ensures that outgoing links from point A belonging to the feasible path are one more than the incoming links. In fact, we would like our feasible path to have at most one outbound link from each node. This can be achieved by setting  
\[\sum_{ij\in R_{m}:i=v}y_{ijk}\leq 1\quad\forall v\in R_{m} \tag{11}\]  
which does not allow our feasible path to have more than one outbound link from node, including the origin node A. This forces us to have only one outbound link from node A in the feasible path. In addition, by adding the following constraint:  
\[\sum_{ij\in R_{m}:i=B}y_{ijk}-\sum_{ij\in R_{m}:j=B}y_{ijk}=-1 \tag{12}\]  
we enforce our shortest path to include a link that has the destination node (point B) as an outbound node.  
Finally, with the following constrains:  
\[\sum_{\bar{y}\in R_{m}=i=v}y_{ijk}-\sum_{ij\in R_{m}:j=v}y_{ijk}=0\quad\forall v \in R_{m}-\{\text{A},\text{B}\} \tag{13}\]  
we impose that for any other node \(i\in R_{m}\) that is neither the node of point A nor the node of point B, there are only two possibilities:  
* The node is not part of the feasible route from point A to point B and there is no inbound or outbound link to or from it that is part of the feasible route.
* The node is part of the feasible path from point A to point B and there is a single inbound and a single outbound link to or from it belonging to the feasible path.