esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results and Discussions  
### Properties of Ash Specimens  
Figure 1 displays the particle size distribution of the ash specimens obtained using different grinding techniques. Among all ash specimens, those ground only with disintegrator presented coarser particles in comparison with those ground using ball mill and combination of both techniques. The percentage of the finer particle was increased when ground with a combination of ball mill and disintegrator. Specific surface area of 14528 cm\({}^{2}\)/g, 7251 cm\({}^{2}\)/g, and 16124 cm\({}^{2}\)/g was measured for W1, W2, and W3 ash specimens, respectively (Table 1).  
Figure 1: Distribution of particle size of WSA specimens.  
XRD results are shown in Figure 2. It can be seen that the peaks for crystalline silica (at 20\({}^{\circ}\)) reduced for W1 and W3 ash specimens as compared to that of W2 specimen. The decrease in height and broadening of the peak for silica is an indication of amplified amorphousness. It may be due to the availability of the inner amorphous silica due to extensive grinding [24]. The oxide composition of the ash specimens was determined via XRF analysis (Table 1). The combined percentage of SiO\({}_{2}\), Fe\({}_{2}\)O\({}_{3}\), and Al\({}_{2}\)O\({}_{3}\) (72%) meet the least requirement of ASTM C618 [31] for a pozzolanic material. Table 2 shows the oxide composition of WSA used in the present study and other ARAs used in previous studies. It can be seen that RHA demonstrated highest content of silica while POFA presented the least content.
## 3 Microwave NDT Techniques
### Near-Field Method
The distinguishing difference between RWGs and CWGs is that the latter have a circular cross-section, whereas RWGs have a rectangular cross-section. Given their similarities, there exist various studies that compare CWGs and RWGs, such as [75]. In this study, the authors designed a custom-made Teflon-loaded CWG to inspect defects in a honeycomb structure and compared the results with those obtained using an RWG probe. The study's findings indicated that a circular probe offers superior image resolution compared to a traditional RWG [75]. Aside from [75], various researchers have also conducted comprehensive evaluations of CWGs and compared their results with those of RWGs, such as those in [76; 77; 78]. Across these studies, a consistent finding emerged: CWGs consistently outperformed RWGs in terms of both resolution and sensitivity. Thus, CWG is a superior probe with the potential for broader use in areas where high resolution and sensitivity are crucial. A fourth probe considered in near-field MNDT is the flanged parallel-plate waveguide. It consists of parallel metal plates with a flanged edge. [PERSON] et al. utilized a flanged parallel-plate waveguide to identify two-dimensional (2D) surface cracks on a metallic plate [79]. By analyzing the measured signal characteristic for each crack, the authors could estimate its corresponding properties, including its width, depth, position, and the material within the crack. Further, in a different investigation, [PERSON] et al. [80] proposed a model in which a parallel-plate waveguide with a finite flange was designed to scan a lengthy crack in a perfect conductor. Despite the advantages of flanged parallel-plate waveguides, they suffer from multiple constraints. Notably, a flanged parallel-plate waveguide is inadequate for assessing the dielectric properties of highly conductive materials due to the material absorbing the microwave energy, thus preventing it from traveling through the waveguide [81]. Given the variety of near-field MNDT probes and the ease of conducting near-field measurements, some industry-level solutions are available. For example, an advanced microwave mapping probe (AMMP) is utilized in the aerospace industry to detect defects in radomes and to measure the thickness of non-conductive coatings [4]. This product can be either handheld or mounted on a robot. In general, near-field techniques are popular methods; however, they suffer from the disadvantage that they can be applied to small regions of the specimen. Therefore, to scan large sections of a material, the transducer needs to scan or move along the specimen, which might add complexities to the system.
## 2 Materials and Methods
### Bacterial Community Analysis  
Following the method of [PERSON] et al. (2014), DNA was extracted using an EZNA(r) Stool DNA Kit. The primers 341F (5\({}^{\prime}\)-CCTACGGGNGGGCWGCAG-3\({}^{\prime}\)) and 805R (5\({}^{\prime}\)-GACTACHVG GGTATCTAATCC-3\({}^{\prime}\)) were used for polymerase chain reaction (PCR) to amplify the V3-V4 region of the bacterial 16S rDNA gene. PCR products were purified using AMPure XT beads (Beckman Coulter Genomics, Danvers, MA, USA), quantified using Qubit (Invitrogen, Waltham, MA, USA) ([PERSON], 2016), and sequenced on the Illumina NovaSeq PE250 platform. The raw data from the sequencer were assembled using FLASH (v1.2.8), low-quality sequences were removed using fqtrim (v0.94), and chimeras were filtered using Vsearch (v2.3.4). Bacterial community diversity was calculated using QIIME2 (2019.7). Additionally, sequence annotation was performed using BLAST (v2.16.0); the comparison databases were SILVA and NT-16S. Related charts were drawn using R-3.4.4.
## 3 Results and Discussion
### Antimicrobial Activity of R. typhina Fruit Extract  
The fruits of staghorn sumac have been described as antimicrobial agents used in folk medicine [19].  
The results of antimicrobial activity of a concentrated _R. typhina_ fruit extract (BBE, 61.51% ethanol, 20/1 (\(v/w\)) solvent/solid ratio, 5 min, 2.8 mm ceramic beads) against eighteen pathogenic bacterial strains (10 Gram-positive and eight Gram-negative bacteria) and two fungal strains are presented in Table 12.  
Several microbial strains were found to be sensitive to the tested fruit extract, showing inhibition zones of different diameters, as follows: _Streptococcus pyogenes_ ATCC19615 (20 mm), _Salmonella enterica_ ATCC 13076 (12 mm), _Bacillus subtilis_ ATCC 6633 (8 mm), _Enterococcus faecalis_ ATCC 29212 (8 mm), _Klebsiella pneumoniae_ ATCC 13883 (8 mm), and _Salmonella_ group D (clinical isolate) (8 mm). Among these strains, high antimicrobial activity was noticed against _Streptococcus pyogenes_, with an inhibition zone close to standard antibiotics such as ampicillin, vancomycin, levofloxacin, and clindamycin [95], followed by the strain _Salmonella enterica_. The good inhibition of _R. typhina_ fruit extract against a _Streptococcus pyogenes_ biofilm was previously mentioned in the study by [PERSON] et al. (2022) [96]. In the study by [PERSON] et al. (2015) [97], different strains of _S. aureus, E. coli_, and _P. aeruginosa_ were inhibited by _R. typhina_ fruit extracts. Other authors have also reported no activity of _R. typhina_ fruit extract against _C. albicans_[97].  
Figure 9: HPLC chromatogram of phenolic compounds of _R. typhina_ fruit extract.
## 2 Delay Tolerant Networking Background  
This section contains background information on DTN and related work. Section 2.1 provides an overview of the history, architecture, and implementations of DTN used for experiments in this paper. In Section 2.1, the two versions of Bundle Protocol are explained and their differences highlighted. Lastly, Section 2.1 discusses the current state of DTN routing from the perspective of scalability.
# Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments
## 5 Experimental Evaluation
### Effectiveness and Robustness of the AEs in Novelty Detection  
Three experiments are performed in this section to address RQ1. The first experiment (Section 5.1.1) evaluates the effectiveness of AEs in the novelty detection task. Then, two experiments evaluate the robustness of the approach in discriminating between anomalies and novel conditions (Section 5.1.2), and in working with varying window sizes (Section 5.1.3).
# Characterization and Selection of _Lactobacillus_ Strains with Potential Robotic Applications
## 3 Results and Discussion
### Antimicrobial Activity of Lactobacillus Strains
Many scientific reports have been focused on the antimicrobial activity of strains of the genus _Lactobacillus_ against clinical isolates of various pathogens. The effectiveness of _L. casei_ ATCC 393 and _L. acidophilus_ ATCC 4356 against 100 clinical isolates of _E. coli_ was shown in a study conducted by [PERSON] et al. [46], who determined that the diameter of inhibition zones of _Lactobacillus_ extractions varied between 15 and 17 mm, and between 16 and 19 mm, respectively. The screening for probiotic properties of _Lactobacillus_ clinical isolates performed by [PERSON] et al. [47] revealed that the strain _L. rhamnosus_ AD3 possessed high antimicrobial activity against _E. faecalis_ ATCC 29212, _P. aeruginosa_ ATCC 27853 and _Candida glabna_ 14, and moderate inhibitory activity against _K. pneumoniae_ ATCC 70603, _C. albicans_ 10 and _Streptococcus agalaciae_ (a clinical isolate). A significant inhibitory effect of _L. acidophilus_ and _L. rhamnosus_ against _K. pneumoniae_ clinical isolates was confirmed by [PERSON] et al. [48].
## 6 Experimental Verification of the Mirror Alignment Method
### Experimental Alignment of the Plane Unit
#### 6.2.1 Experimental Device and Process
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Metal Plane**} & \multicolumn{2}{c}{**Coordinate of Ball**} & \multirow{2}{*}{**Coordinate of Feature Points**} & \multirow{2}{*}{**Facet Parameters**} \\ \cline{3-4}  & & **Hinge Centers** & & \\ \hline \multirow{3}{*}{Plate 1} & **A** = [\(-\)460.0, 290.0, \(-\)65.0] & **T\({}_{1}\)** = [\(-\)460.0, 290.0, \(-\)33.5] & **N\({}_{0}\)** = [0, 0, 1.0] \\ \cline{2-4}  & **B** = [\(-\)180.0, 290.0, \(-\)65.0] & **T\({}_{2}\)** = [\(-\)180.0, 290.0, \(-\)33.5] & **Q** = [\(-\)320.0, 150.0, \(-\)40.0] \\ \cline{2-4}  & **C** = [\(-\)180.0, 10.0, \(-\)65.0] & **T\({}_{3}\)** = [\(-\)180.0, 10.0, \(-\)33.5] & \\ \hline \multirow{3}{*}{Plate 2} & **A** = [\(-\)180.0, 710.0, \(-\)65.0] & **T\({}_{1}\)** = [\(-\)180.0, 710.0, \(-\)33.5] & **N\({}_{0}\)** = [0, 0, 1.0] \\ \cline{2-4}  & **B** = [\(-\)180.0, 710.0, \(-\)65.0] & **T\({}_{2}\)** = [\(-\)180.0, 710.0, \(-\)33.5] & **Q** = [\(-\)320.0, 470.0, \(-\)40.0] \\ \cline{1-1} \cline{2-4}  & **C** = [\(-\)180.0, 430.0, \(-\)65.0] & **T\({}_{3}\)** = [\(-\)180.0, 430.0, \(-\)33.5] & \\ \hline \end{tabular}
\end{table} 
Table 2: Coordinates of the ball-hinge centers, feature points and facet parameters of Plate 1 and 2. Figure 9: Experimental device for the facet alignment of a metal plane. (**a**) Experimental system, (**b**) support bolt structure and (**c**) circular markers. Using the Camera alignment Toolbox in MatlabR2012a, the inside and outside parameters of the camera were calibrated [35]. 4. The pixel coordinates of the center point of the circular marks in each photo were extracted, and then the global coordinates of each center point (in the O-_xyz_ system) were calculated according to photogrammetry theory [33]. 5. The facet parameters of the metal plane were calculated by one the translational two-rotation motion method (Section 3.2). 6. The three-rotation alignment method (Section 5.1) or two-rotation alignment method (Section 5.2) were used to calculate the adjusted values \(d_{A}\)-\(d_{C}\) of bolts 1-3, and metal plane was aligned according to the adjusted values. 7. Finally, the camera was used to take 16 photographs from different views to calculate the plane equation of the virtual mirror surface after alignment in order to verify the effects of the facet alignment. The results of the theoretical calculations along with the experimental measurements are presented in Tables 3 and 4.
# Evaluating and Managing the Microbial Contamination of Eye Drops: A Two-Phase Hospital-Based Study
## 3 Results
### Phase 1: Assessment of Microbiological Contamination In-Use Eye Drops
#### 3.1.2 Collection and Culture of In-Use Specimens
* Three different operators were involved in evaluating (1) the organoleptic characteristics, (2) labeling, and (3) packaging with Activated Rispharm(tm) packaging. One operator raised concerns about the space between the cap ring and the bottle body, which may affect the proper closure of the bottles. All operators faced labeling issues because of the width of the Activated Rispharm(tm) bottles and the parameters of the labeling machine. There were no difficulties with the secondary packaging. * Three pharmacists were assigned to simulate the administration of compounded eye drops. They all agreed on the flexibility of the Activated Rispharm(tm) bottles and the dispensing of drops. The Activated Rispharm(tm) bottles were described as flexible, providing a good grip and allowing the formation of drops with a reproducible volume and controlled administration frequency. The bottle shape during use demonstrated no changes, such as observed deformities, after emptying the bottle. 3.2.2. Validation of the Antimicrobial Properties of Pylote SAS Antimicrobial Technology  
The results of the experiment comparing Activated Rispharm(tm) packaging to standard packaging are presented in Table 1.  
The control results at C0 h and after C24 h indicated that the microbial loads of _S. aureus_ and _P. aeruginosa_ decreased by 2 log and 3 log, respectively. There was no difference in _C. albicans_ between C0 and C24h (2.3 \(\times\) 10\({}^{3}\) CFU average microbial load). Assays performed after A24 h with the standard packaging showed the following:  
* For _S. aureus_, the standard packaging showed CFU counts higher than the detection limit (3.3 \(\times\) 10\({}^{3}\) CFU) 24 h after the cap and insert inoculation. In the same conditions, the agar plates from Pylote packaging showed a countable average of 2.1 \(\times\) 10\({}^{2}\) CFU. A difference in the reduction of above 1.2 log in favor of Pylote packaging was observed compared to standard packaging. - For _P. aeruginosa_, standard packaging also showed CFU counts higher than the detection limit (3.3 \(\times\) 10\({}^{3}\) CFU) 24 h after the cap and insert inoculation. Agar plate counts from PyloteTM showed 3.2 \(\times\) 10\({}^{2}\) CFU on average, leading to a reduction higher than 1 log. - For _C. albicans_, both packaging types showed similar microbial loads: 3.0 \(\times\) 10\({}^{2}\) CFU for standard packaging and 2.4 \(\times\) 10\({}^{2}\) CFU for PyloteTM packaging.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 1 Introduction  
Underwater gliders constitute autonomous robots designed to perform multi-sensor oceanographic sampling, for long periods in large regions. They represent a valid alternative to expensive research vessels campaigns for a wide range of applications, including water quality monitoring, detection of algae bloom hazards, ocean structure characterization, pollution source identification or sea-floor mapping, to name just a few [1,2]. However, their operation nowadays still require human supervision, both for short- and long-term missions. This scheme can be suitable for simple applications, but when dealing with more complex multi-objective missions, automatic path planning tools are required; this is the main motivation of our work, contributing to thedevelopment of underwater gliders with higher autonomy and better operational capabilities for multi-objective applications.  
In this work, the usage of the non-dominated sorting genetic algorithm II (NSGA-II) algorithm [3] is analysed to tackle a multi-objective (MO) glider path planning application on a complex and changing environment. The problem includes the simulation of 3D vehicle trajectories inside a dynamic depth and time varying ocean currents field.  
The paper first introduces, in Sections 1.1 and 1.2, the concepts of the main areas our work aims to contribute to, which are underwater glider path planning and multi-objective optimization; then Section 1.3 presents the literature review regarding multi-objective path planning. The proposal is presented in Section 2, including the problem definition, in Section 2.1, and methodology, in Section 2.2, applied to address the problem of glider path planning using multi-objective optimization. Section 3 describes the simulator, test scenarios, and algorithm configurations. Section 4 presents the experiments carried out and the results obtained, which are discussed in Section 5. Finally, the conclusions and some future work are outlined in Section 6.
## 4 Results and Discussions

Table 3: Median and standard deviation (StD) values of differences among parameters obtained by numerical and experimental means. with a given discrete frequency step of 5 kHz. The frequency response signals were measured and processed to give higher information content in terms of better resolution among individually measured signals. The presented results showed that the geometry of the flaw strongly affects differential response through its magnitude and phase, and there exists a correlation between the defect geometry and signal frequency response. It was also discovered that even for the frequency range in the order of hundreds of kHz, deeper flaws are still clearly distinguished from each other based on the individual signals. The presented method of inspection of conductive structures using the SFECT method can be applied in two different approaches in real conditions: in the first approach, the SFECT method can be used after the previous investigation using the conventional ECT method; in the second approach, it can be used without previous investigation. In this case, the measuring probe must be placed directly where defects are expected to occur. This important information opens a new view into the possible utilization of the SFECT with fixed probes in the electromagnetic non-destructive evaluation of defects. Conceptualization, M.S., D.G. and F.V. ; methodology, M.S., F.V. and D.G. ; software, F.V. ; validation, M.S. and D.G. ; formal analysis, M.S. and F.V. ; investigation, F.V. ; resources F.V. and L.J. ; data curation, M.S. ; Writing--original draft preparation, M.S. and D.G. ; writing--review and editing, M.S. and D.G. ; visualization, F.V. All authors have read and agreed to the published version of the manuscript. This research received funding by the Slovak Research and Development Agency (contract number APVV-19-0214). Not applicable. The authors declare no conflict of interest.
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 1 Introduction
The main contributions of this work are as follows:  
* We develop a hybrid methodology that combines the advantages of the theoretical rigor of physical models with the advanced capabilities of deep learning to enhance photovoltaic power forecasting. * A Transformer Bi-LSTM architecture is developed, together with the transfer learning methods, to then implement a deep chain of physical models, allowing to outperform the existing results in the state-of-the-art research, specifically in metrics like MAE, MSE, RMSE, and \(R^{2}\). * Taking into account the common problems of using private datasets, our study makes effective use of public data. This ensures the reproducibility of this research, facilitates the validation, and ensures a more accurate comparison with future research, supporting the standardization of methodologies in the field. * A comprehensive comparison between the existing results in the state-of-the-art and the two most commonly used data-driven methodologies is conducted: the direct and indirect methods. Our findings reveal that the deep model chain methodology slightly outperforms the direct method, providing valuable insights that can contribute to future research in the field. * This study implements feature engineering techniques, including GHI forecast calibration, GHI decomposition, and a transposition model. These additions slightly improve the precision of the forecasting, in comparison with the direct method implemented, which only uses numerical weather predictions. * Our research uniquely tests our top model on new photovoltaic stations, not used in training, to avoid overfitting. This contrasts with other studies that often only use data from training stations for testing. This article is organized as follows: It begins with the introduction, in Section 1. Then, the methodology and resources used for this research are detailed below in Section 2. The results obtained are presented in Section 3. Finally, the discussion of these results is carried out in Section 4. A general overview of our investigation is offered below in Figure 1.  
The diagram illustrates the different stages of the proposed methodology. The green block represents the Data Selection phase, where relevant meteorological and irradiance data are collected. The red blocks depict the Feature Engineering process, including Data Calibration, Irradiance Decomposition, and Irradiance Transposition, where the data is transformed into actionable features. The yellow block denotes the implementation of advanced models, such as the Transformer BI-LSTM and Deep Chain Model, which leverage transfer learning for improved predictive accuracy. Finally, the blue block represents the Intra-hour Power Forecasting stage, where the processed data is used to generate short-term power forecasts. Each block of the chain is replaced by the proposed model, taking advantage of transfer learning.
## 3 Results and Discussion
### Correlation Analysis of Metabolites and Environmental Factors  
Rose oxide and citronellol/geraniol showed a high positive correlation (r = 0.76) (Table 8). Biochanin A and chalcone in the flavonoid class also revealed a high positive correlation (r = 0.84). Among flavonoids, diosmin showed negative correlation with average temperature (r = \(-\)0.78). It suggests that diosmin increases mainly at lower temperature. Previous reports also highlighted that high temperature can decrease the amount of some flavonoids by degrading their structure [30]. The stability and biological properties of flavonoids can be highly affected by temperature [31]. Most of the flavonoids are sensitive to high temperatures; however, glycosylated flavonoids are more resistant to high temperatures than aglycon ones. The degradation of flavonoids depends on their structural solidity, and degradation can lead to a decrease in their antioxidant capacity. So, for selecting high-diosmin rose petals, the low temperature condition might be beneficial [31]. Elevation had a positive correlation with citronellol (r = 0.49), while a negative correlation with geraniol (r = \(-\)0.74). [PERSON] et al. [32] obtained similar results in damask rose for citronellol amount as they suggested that higher altitudes can lead to increased biosynthesis of some components such as citronellol, due to higher exposure to UV radiation and reduced temperature [22]. However, the average annual rainfall revealed a different trend compared to elevation in terms of these compounds. However, a previous report on _Citrus hystrix_ revealed that citronellol has a negative correlation with rainfall intensity [33]. Although a high variation was reported within individual flavonoid groups in response to elevational  
Figure 3: Dendrogram generated from cluster analysis of nine rose damask cultivars based on flavonoid compounds using WARD based on the squared Euclidean dissimilarity calculated.  
changes, it can be suggested that long days with cool night temperatures are the most crucial one for the biosynthesis of flavonoids [34].