esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Species identification is the first step in the examination of newly isolated microorganism strains, including the cases when they are intended for application in the development of probiotic preparations or starters for different food products. The thorough identification process of newly isolated strains combines the application of different physiological, biochemical, and molecular genetic methods. The aim of the present study was to identify the species-level lactic acid bacteria (LAB) strains isolated from spontaneously fermented sourdoughs prepared from different flour types: Khorosan wheat, wheat, barley, buckwheat, spelled, spelt, and corn. Both classical phenotypic (cellular morphology characterization, and API 50 CHL) and molecular genetic methods (RAPD, ARDRA-analysis, 165 rDNA sequencing, and species-specific PCR) were applied. It was found that cultures with a short-rod morphology predominated among the 30 sourdough isolates. According to the RAPD profiles obtained, the isolates were divided into nine genotypes corresponding to nine genetically distinct strains. It was determined that individual sourdoughs made with different flour types shared cultures with a common genotype. The analysis of the physiological and biochemical profiles of the LAB isolates performed with the API 50 CHL system divided them into two groups according to their identification: _Lacitplantiobacillus plantarum_ (_L.p. plantarum_) 1 and _Levilactobacillus brevis_ (_Lv. brevis_) 3. According to the 165 rDNA restriction profile, the LAB isolates showed two profiles corresponding to the _Lp. plantarum_ and _Lv. brevis_ groups. 165 rDNA sequencing and a comparison of the partially read 165 rDNA sequences of the studied isolates confirmed that some of them belonged to the _Lv. Brevis_ species, but did not provide sufficient evidence that the rest of the cultures belonged to the _Lp. Plantarum_ species. The species-specific PCR clearly separated the isolates from the _Lp. plantarum_ group into two groups: isolates of the _Lp. plantarum_ species and isolates of the _Lp. plantarum_ species. The summary of the results of the conducted polyphasic taxonomic study determined the investigated LAB strains isolated from spontaneously fermented sourdoughs as representatives of the _Lv. brevis_, _Lp. plantarum_ ssp. _papplantarum_, and _Lp. parapplantarum_ species.  
Levilactobacillus brevis; Lacitplantiobacillus plantarum; RAPD; sourdough; 165 rRNA gene sequencing +
Footnote †: journal: _Sciences_
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 4 Protocol Design
### Contact Movement Path Checking
#### 4.3.3 Final Node Condition Evaluation  
To consider a neighbor node \(u\) as FN, the current node \(c\) evaluates \(u\)'s future path \([w_{k},...,w_{n}]\). For each segment \([w_{i},w_{i+1}]\), \(i=k,...,n-1\), two conditions are evaluated (see Figure 1):  
* **C1**: the waypoint \(w_{i+1}\) is located within the transmission range of the recipient \(d_{m}\).
* **C2**: the waypoint \(w_{i+1}\) is located within the truncated cone projected by the transmission range circle of \(d\), and whose top is \(w_{i}\).  
If one of these two conditions is satisfied, it is enough to make \(u\) an FN, as this means that this node will certainly connect to the recipient. The forwarding process is schematized in Figure 2. In particular it shows the forwarding strategy using TTA evaluation Variant 2 and Message deletion Variant 1. The chart starts with the two cases (a) and (b), which result both in checking the current \(u\) with the current \(m\). The planned path waypoints are taken and checked with condition C1 and C2.  
Figure 1: The two condition areas in which the next waypoint shall be to turn \(u\) into an FN.
# Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids
## 3 AE-GRU Anomaly Detection Framework
### The Proposed AE-GRUI-Driven Anomaly Detection Framework
The resulting feature space contains normal traffic communications without any attacks, which is utilized to train the anomaly detection methods considered in this study. The anomaly detection methods are trained on the feature space obtained from encoding the normal data. When a data sequence corresponds to a specific attack category, its numerical signature is encoded using the model parameters learned from the normal data. Consequently, any deviation from the encoding of the normal data can be detected by the anomaly detectors. The performance of the designed framework was assessed using four widely used statistical metrics: Accuracy, Precision, Recall, and F1-score. In a binary detection scenario,  
Figure 4: Flowchart of the AE-GRU based detection framework. these metrics are calculated based on the number of true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}, \tag{8}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{9}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{10}\]  
\[F1-score=2\frac{Precision-Recall}{Precision+Recall}=\frac{2 TP}{2 TP+FP+ FN}, \tag{11}\]  
Before presenting the results, the next section provides an overview of the IEC 608705-104 protocol and its significance in SCADA systems, particularly in smart grid networks.
## 5 Simulation Results and Analysis  
In this section, several simulations are carried out to validate the performance and efficiency of the proposed method. All the simulations were performed using Matlab R2018a on the Mac OS Monterey platform with an Intel i7 9750H [EMAIL_ADDRESS] GHz and 16 GB of RAM. We consider that there are three far-field narrowband signals impinging on a UCA with \(r/\lambda=1/4\) from \((41.91^{\circ},\,-68.92^{\circ})\), \((53.68^{\circ},\,71.97^{\circ})\) and \((47.56^{\circ},\,135.28^{\circ})\). The signals and noise are both zero-mean complex white Gaussian sequences. Without loss of generality, powers of the source signals are identical and powers of known waveforms are normalized to unit. The unknown amplitude \(\gamma\) is assumed to be a random complex: real parts are determined by signal-to-noise ratio (SNR), and the imaginary parts lie randomly in \([0,2\pi)\).  
Four simulations were designed to investigate influence of different parameters on 2D DOA estimation performance, i.e., SNR, number of sensors \(M\), number of snapshots \(N\) and correlation coefficient of signals. The source signals are assumed to be uncorrelated with each other in the first three simulations except the last one. In each simulation, the proposed interferometer-based estimator with known waveforms, the ML estimator with known waveforms and the multi-direction virtual array transformation algorithm (MVATA) without known waveforms [41] are presented for performance comparison along with the unconditional CRB and the CRB with known waveforms. For brevity, these terms are abbreviated as \"Interferometer with KW\", \"ML with KW\", \"MVATA without KW\", \"CRBu\" and \"CRBd\" in the following figures and table. To evaluate the performance of the estimators, root mean squared error (RMSE) of azimuth and elevation angle estimation at each point in the following figures are measured from 2000 independent Monte Carlo runs, which is calculated as:  
\[RMSE(\hat{\theta})=\sqrt{\text{E}\{(\hat{\theta}_{1}-\theta_{1})^{2}+(\hat{ \theta}_{2}-\theta_{2})^{2}+(\hat{\theta}_{3}-\theta_{3})^{2}\}} \tag{34}\]  
and \(RMSE(\hat{\varphi})\) is defined similarly. In addition, scatter plots of 2D DOA estimates of the proposed method are also depicted for each simulation.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### CS Generation
#### 2.5.3 CS Generation via Implicit Modeling and Mesh Data Structures
For digital storage and handling of the resulting geometry, the de facto standard in AM is the STL file. With the advent of multi-material printers and the increasing complexity of additive manufactured parts, the STL format is starting to become limiting, mainly in its inability to store other types of information (such as materials, colors, copyrights), in addition to geometry data. New file formats (3 MF and AMF) were created to address such shortcomings. Both 3 MF and AMF are XML (extensible markup language)-based, which enables them to store other information in addition to geometry. In the 3 MF and AMF file, geometry is still stored as a mesh. In AMF, a porous structure can be specified as a material. On the other hand, 3 MF now supports the representation of CSs as a set of beam elements. Since file formats store geometry information as a surface, most of the algorithms described in the previous sections aim at obtaining a mesh-based representation of CSs. Here lies the challenge in generating cellular structures; as the complexity of CSs increases, the complexity of the mesh needed to describe it increases exponentially [27]. This drives the computation time and file size upward when processing and generating CSs. The complexity of CS also pushes CAD technology to the limit. Representing large numbers of features using BREP and CSG becomes impractical [104]. Hence, there is a continued interest in representing and generating CSs either as beam elements, implicit models or voxels. Figure 23: Cellular structure generation via implicit modeling—intralattice lattice generation framework [82]. From right to left: design space (unit cell in green), lattice wireframe, lattice mesh.
# Decoupling Control of Multi-DOF Supporting System of MLDSB
## 4 Decoupling Control of the Multi-DOF System
### Design of the Controller  
The fundamental idea of the system controller is to make the closed-loop pole of the system at the desired point by adjusting the open-loop zero pole of the system to achieve the performance indexes related to the system [16].  
The main principle of applying the root locus method to the system correction is that the closed-loop leading poles of the system are first set so that the closed-loop dynamic performance of the system can be approximated by the second-order system with closed-loop leading poles [17; 18].  
Therefore, before the introduction of a lead corrector, the time domain performance index of the system must be transformed into an equation with leading poles. After the introduction of a lead corrector, the calibrated system is made to work at the desired dominant pole, while other poles of the system exist away from the imaginary axis of the s-plane or close to a certain zero of the system [19; 20; 21].  
It is assumed that the expected time-domain index for the MLDSB is as follows.  
\[\left\{\begin{array}{l}\sigma_{p}=e^{-\pi(\zeta/\sqrt{1-\zeta^{2}})}\leq 10 \%\\ t_{s}=\frac{3}{\zeta\omega_{n}}\leq 0.001s\end{array}\right. \tag{29}\]  
According to Equation (29), the expected dominant pole \(s_{d}\) after correction can be obtained as follows:  
\[s_{d}=-\zeta\omega_{n}\pm j\omega_{n}\sqrt{1-\zeta^{2}}=-3000\pm 4000j \tag{30}\]  
Establish the mathematical model of the lead correction link.  
\[\mathbf{G}_{c}(s)=\frac{s-Z_{c}}{s-P_{c}} \tag{31}\]  
According to the root trajectory diagram method, the zero poles of the lead correction link respectively are \(Z_{c}\) = 2273, \(P_{c}\) = 1.1 \(\times\) 10\({}^{4}\). According to the system gain calculation method, the system gain is \(K\) = 5.5 \(\times\) 10\({}^{7}\). The corrected root locus is shown in Figure 9.  
The root locus passes through the closed-loop leading pole \(s_{d}\), and is able to meet system requirements.
# Bioactive Glasses for Soft and Hard Tissue Healing Applications--A Short Review
## 3 BGs in Tissue Healing
### BGs in Bone Regeneration  
BGs are nowadays largely used in various fields of regenerative medicine, especially for the healing of hard tissues such as bones and teeth, as they can bind directly with bone [68]. This is possible due to their biocompatibility, bioactivity, degradability, cell viability, reduced cytotoxicity, antimicrobial activity, osteogenesis, and angiogenesis properties (Figure 3). The amount/concentration of silicate in BGs affects how biocompatible they are [69]. In order to find the most promising BGs to be used as potential bone replacements and for hard tissue regeneration and, more recently, in the treatment of infections, various aspects regarding their cytotoxicity, osteogenesis, and angiogenesis have been investigated. In particular, it was found that the ions present in BGs compositions mediate how the major proteins implicated in the process of forming new bone react [1]. The surface bioreactivity of BGs is responsible for the strong bonding with the surrounding tissue, which provides osteoconductive properties.  
A schematic figure of the surface reaction stages experienced by BGs in hard tissue healing is given in Figure 4.  
Basically, after the implantation, the BG is exposed to body fluids, and different surface reactions occur which ensure the creation of a deposit of a calcium phosphate layer [70]. A discharge from the BG surface of sodium, silica, calcium, and phosphate ions elevates the local pH and osmotic pressure; subsequently, a layer of silica gel covers the glass surface; thereafter, amorphous calcium phosphates precipitate on it [71]. These amorphous aggregates appear to trigger osteoblasts necessary for the formation of new bone upon crystallization into a hydroxyapatite-like form [1]. The glass can be absorbed as a result of the ongoing processes that take place on its surface and layer. Additionally, these surface interactions give the glass the ability to promote angiogenesis. The mechanism outlined above is based on two distinct steps: (i) the degradation of the BGs; and (ii) the release of ions from the BG. These techniques can be translated into graft-bone bonding. Depending on their composition, the BGs can disintegrate at a rate that might take anywhere from a few hours to several months [1].
# Contradiction Detection with Contradiction-Specific Word Embedding
## 1 Introduction
The model for learning CWE is optimized by minimizing the semantic gap between paraphrase pairs and maximizes the gap between contradiction pairs. Figure 1: Traditional context-based word embedding (TWE) maps contrasting words or sentences into close vectors. In (**b**), the three sentences are, “Some people and vehicles are on a **crowded** street”, “Some people and vehicles are on a **overfull** street” and “Some people and vehicles are on an **empty** street”. Figure 2: Contradiction-specific word embedding (CWE) separates contrasting words or sentences in the new semantic space. In (**b**), the three sentences are same as in Figure 1.  
To detect contradiction relation from sentence pairs, we develop a semantic relation representation learning model, and incorporate CWE to detect contradiction. We run experiments on benchmark datasets from SemEval 2014 [13]. The experiment results show that the proposed method with CWE performs comparably with top-performing systems in terms of overall classification accuracy. Specifically, it outperforms in terms of accuracy in the contradiction category. The following statements present the major contributions of this work:  
* We present a data construction method to generate a large-scale corpus for training contradiction-specific word embedding. The corpus consists of millions of contrasting pairs, which bring more guidance than prior resources with tens of thousands of antonym pairs. * Based on the large-scale corpus with contrasting pairs, we develop a neural network tailored for learning contradiction-specific word embedding. * We apply contradiction-specific word embedding in a semantic relation representation learning model to detect contradiction, and the accuracy of contradiction category outperforms the state-of-the-art method on benchmark data set by 6.11%.
## 3 Results
### 10 km Tests
#### 3.2.2 Biomechanical Variables  
Figure 3 shows the results for biomechanical parameters of the 10 km tests. For PAV, the ANOVA revealed significant differences (\(p\) = 0.004) and a small effect (d = 0.45) between S1 (757.8 \(\pm\) 105.2 deg/s) and S3 (814.1 \(\pm\) 142.7 deg/s) (Figure 3a).  
When comparing evVel, highly significant differences (\(p\) < 0.001) and large effects (d \(\geq\) 0.8) were found between S1 (552.1 \(\pm\) 162.2 deg/s) and S2 (407.3 \(\pm\) 124.9 deg/s) and between S1 and S3 (431.5 \(\pm\) 111.4 deg/s); (\(p\) < 0.001; d \(\geq\) 0.8) (Figure 3b).  
The analysis of HSA revealed the lowest value for S1 (25.2 \(\pm\) 4.0 deg) and the highest value for S3 (31.1 \(\pm\) 5.1 deg) (Figure 3c). The ANOVA showed highly significant differences between S1 and S2 (\(p\) < 0.001) and between S1 and S3 (\(p\) < 0.001). Cohen's d showed large effects (d \(\geq\) 0.8) for both significant pairwise comparisons. No significant differences were found for PTA when comparing shoe conditions (Figure 3d). The values were 7.5 \(\pm\) 2.3 g for S1, 7.2 \(\pm\) 2.3 g for S2 and 7.3 \(\pm\) 2.2 g for S3.
# Cause Analysis of Pier Inclination of An Expressway Interchange Bridge: Case Study
## 5 Numerical Analysis
### Pier Deformation Considering Asymmetric Foundation Settlement  
The Abaqus FEM model, considering soil elastoplastic and non-linear soil-structure interactions, was developed to analyze the deformation of Pier P30 under asymmetric foundation settlement caused by temporary load during construction and asymmetric filling soil load during operation (see Figure 18).  
According to the field geological prospecting condition, the soil was divided into four representative layers for modeling, and the parameters of each soil layer are given in Table 1. The Mohr-Coulomb elastoplastic constitutive model was used to evaluate soil. The pile foundation, pile cap, bridge pier and support all adopt elastic materials with a weight of 25 kN/m\({}^{3}\) and Poisson's ratio of 0.17.  
The contact surface was set between pile and soil, adopting the Columbus friction model, the parameters of contact surface mainly include normal stiffness, tangential stiff  
Figure 17: Deformation of Pier P30.  
Figure 18: Abaqus model considering soil elastoplastic.  
ness, cohesive force, and the frictional angle, which need to be selected according to the unit size and model calculation parameters. The tangential stiffness and normal stiffness should be 10 times the equivalent stiffness of the \"hardest\" adjacent areas, as follows:  
\[k_{n}=k_{s}=10 max\left[\frac{\left(K+\frac{4}{3}G\right)}{z_{min}}\right] \tag{5}\]  
where \(K\) represents the volume modulus, \(G\) represents the shear modulus, and \(z_{min}\) represents the minimum size of the connection area in the direction of the contact surface.  
In the simulation of pile-soil interaction, the frictional parameters of the contact surface, such as the frictional angle and cohesion, are more important than the others. The frictional angle should be 0.6-0.7 times that of the soil around pile, and cohesion should be about 0.5 times that in the soil around pile [36; 37].  
In this paper, the frictional parameters of the contact surface between pile and soil are as follows:  
\[\varphi^{\prime}=10.9^{\circ},c^{\prime}=8.9\text{kpa} \tag{6}\]  
Due to weak physical and hydraulic properties of the surrounding soil and the exposed pile cap, the contact surface was only established at the bottom of the pile cap, and the physical and mechanical parameters are as follows:  
\[\varphi_{pc}{}^{\prime}=2.4^{\circ},c_{pc}{}^{\prime}=6.0\text{kpa} \tag{7}\]
# Volatile Composition of Industrially Fermented Table Olives from Greece
## 3 Results and Discussion
### Volatile Composition of Table Olives
It is known that volatile phenols are likely formed during the fermentation process as a result of microorganisms' activity [30] via decarboxylation of phenolic acids [39]. Our results indicate that the formation of phenols can be affected primarily by cultivar and/or fermentation style and secondarily by the growing location of olives, which influences the phenolic content. Probably, the lye treatment used for the preparation of HLK samples (Spanish style) disintegrates the fruit matrix making thus the phenolic substrates more vulnerable to attack from microorganisms. This hypothesis can be strengthened by the fact that although HLK samples contained the lowest total polar phenolic content (as measured by Folin-Ciocalteu, data not shown), their content of volatile phenols was significantly higher than in Greek-style processed olives (CNS, KLM). Among the various terpenoids identified (Table 2), trans-\(\beta\)-ocimene was the most abundant, characterizing solely the KLM samples from both growing locations; in the other cultivars, it was found in very low levels (<0.5%). Furthermore, the CNS samples were characterized by the presence of a-farnesene, a-muurolene and copaene. The first two compounds were not detected in the other two cultivars. Generally, terpenoids can be considered as olive-derived compounds that are released during fermentation from non-volatile precursors by the action of microbial glycosidases and by the acidic environment [25]. In fact, these sesquiterpenes have already been suggested as potential biomarkers for authenticity in table olives [30] and olive oil [40]. Within the class of carbonyls, most of the compounds were present at very low levels (<0.1%). The major ketone found in all samples was 2-butanone (\(\sim\)0.1-0.7%). Although its content was relatively higher in KAV (0.7%) and MAG (0.5%) samples, it was not statistically different (\(p\) > 0.05) between the cultivars studied. 3-Methylbutanal was also found in higher levels in KLM table olives. [PERSON] and [PERSON] [13] reported that content of 2-butanone was influenced by the processing method used in table olives of the _Nocellara del Belice_ cultivar as well as by the use of inoculum in Greek-style processed Kalamata and Moresca cultivars [12]. It is noteworthy that 2-butanone has not been identified in more recent studies conducted with Greek-style processed Conservolea and Kalamata cultivars [31], Spanish-style or black-ripe processed Manzanilla, Gordal and Hoijblanca cultivars [16; 25; 30] and _alcaparras_ table olives [14]. Octane was the predominant hydrocarbon in all samples, followed by 1-dodecene. Their content was significantly higher in CNS samples. Generally, alkanes and alkenes are considered secondary oxidation products of fatty acids [27; 41]. It is worth noting that (E)-4,8-dimethyllnona-1,3,7-triene detected here has been reported in only one study [18] as a major volatile constituent of black-ripe table olives.