esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Previous studies showed that the growth-promoting effects on host glycopytes under salt stress are attributable to AMF and PGPR, which could enhance salt tolerance by altering root morphology, modifying root-to-shoot communication, increasing nutrients uptake, maintaining ion homeostasis, decreasing oxidative damage, and elevating photosynthetic capacity [10; 24; 26; 27]. As bio-inoculants, AMF and PGPR also play vital roles in soil biological activity, benefiting the remediation of saline soil, and consequently increasing the suitability of saline soil to plant growth [28; 29]. However, little is known about the inoculation effects of different beneficial microorganisms on the biomass accumulation, morphological characteristics, photosynthetic capacities, and rhizospheric soil enzymes of halophytes, especially _Elaagmus angustifolia_ L. in saline soil. Based on this existing knowledge, the objective of our study is to test the effects of AMF inoculation, both alone and in combination with PGPR, on _Elaagmus angustifolia_ L. in saline soil. We address the following research questions: (1) what are the impacts of AMF and PGPR inoculations on biomass accumulation, morphological characteristics, photosynthesis and soil enzymes of _Elaagmus angustifolia_L. in saline soils? (2) Is species diversity or a particular key species most important in controlling the growth characteristics and salt tolerance of _Elaengmus angusifolia_ L.?
## 2 Materials and Methods
### Data and Preprocessing  
#### 2.2.1 Satellite Data  
Sentinel-2 images, including L1C and L2A from both Sentinel-2A and Sentinel-2B, were used in this study [60]. The number of L2A products is very limited; Therefore, if most of the mangroves regions of interest (ROI) can be seen in one scene, we processed L1C to L2A using sen2 cor in the Sentinel Application Platform (SNAP) [61]. All bands were resampled to 10 m resolution. Sentinel-2 images were adopted for computing the fraction of absorbed photosynthetically active radiation (fAPAR) to estimate the GPP of mangroves, as well as canopy water content (CW), canopy chlorophyll content (CAB), leaf area index (LAI), and a fraction of vegetation cover (fCover) [62] to represent the biochemical and biophysical responses of mangrove ecosystems to tropical cyclones, respectively. From the performance of results in this study, a total of 68 images from November 2015 to December 2021 were used (for more details, see the Supplementary Material, Table S1). The multi-year monthly fAPAR was derived from the median composition of the corresponding months in non-tropical cyclone years. For any mangrove pixel, if there is no available value for one certain month, then a sliding weighted average of 11-month steps is performed based on the difference between the corresponding month and the multi-year monthly fAPAR.  
The Landsat 8 OLI images were obtained from the GEE platform. They were adopted for calculating vegetation indices (VIs) to confirm that the mangroves are in a period of dynamic equilibrium (mature forest). The VIs used in this study include NDVI, EVI, the near-infrared reflectance of vegetation (NIRv) [63], and kernel NDVI (kNDVI) [64]. Each index was calculated for the three years before the selected TC hit (pre-TC) and the three years after the mangrove had completely recovered (post-TC) and compared to determine that the mangroves disturbed by the tropical cyclone are in dynamic balance status, in order to remove the effects of forest growth on GPP. The maximum value composition was utilized by this study to obtain the monthly representation of pre/post-TC. If there were fewer than 3 available images in one ROI of mangrove distribution on a certain month, the calculation was not performed. The available Landsat 8 data used in the present results of this study are Path 27 and Row 07 from 3 May 2013 to 3 December 2021, a total of 60 images; see the Supporting Materials for details (Table S1).
## 3 Proposed Architecture: Multi-Model IDS Based on Traffic Load
### Proposed Architecture Components  
According to Figure 1, the proposed architecture consists of different components:  
1. Firewalls: Firewalls can be configured to block traffic from malicious IP addresses by manually adding these addresses to the firewalls' blocklist; this can be accomplished by configuring the firewalls to block traffic from malicious IP addresses via an IPS (intrusion prevention system).
2. Routers and switches: Configured to collect and report statistics on network traffic. Identifying the amount and type of traffic passing through these devices may help determine the volume and type of traffic on the network. In addition, the routers are configured with a threshold value for network traffic.
3. Multi-model ML-based NIDS (first layer): This ensures a centralized global view and inspection of the incoming traffic at the different organization servers and contains two types of ML-based IDSs: the first model focuses on the fast inspection of traffic, and the second model focuses on high accuracy.
4. ML-based NIDS (second layer): This model is highly accurate. It inspects all traffic before it is distributed to the server that requests it.
## 2 Results and Discussion
### Identification of Absorbed Constituents in Rabbit Plasma and CSF by HS-SPME-GC-MS
#### 2.2.2. Characterization of Eight Absorbed Lignans of AR by HPLC-APCI-IT-TOF-MS\({}^{n}\)
The precursor ion _m/z_ 211.0955 (C\({}_{11}\)H\({}_{15}\)O\({}_{4}\), [M+H]\({}^{+}\)) gave fragment ions at _m/z_ 193.0846 (C\({}_{11}\)H\({}_{13}\)O\({}_{3}\), [M+H-H\({}_{2}\)O]\({}^{+}\)), _m/z_ 178.0611 (C\({}_{10}\)H\({}_{10}\)O\({}_{3}\), [M+H-H\({}_{2}\)O-CH\({}_{3}\)]\({}^{+}\)), _m/z_ 161.0598 (C\({}_{10}\)H\({}_{9}\)O\({}_{2}\)[M+H-H2O-CH3 OH]^), _m/z_ 137.0541 (C8H9O2, [M+H-H2O-C3H4O]^) and _m/z_ 133.0630 (C9H9O, [M+H-H2O-CH3 OH-CO]^). According to these characteristic ions, **L4** was tentatively identified as 3,4-dimethoxybenzenpropionic acid. The fragmentation pathways of **L4** are proposed in Figure 7.  
_Identification of **L12**_. It showed [M+H]^ at _m/z_ 197.0804, indicating the molecular formula of C10H12O4. The precursor ion _m/z_ 197.0804 (C10H13O4, [M+H]^) gave fragment ions at _m/z_ 179.0720 (C10H11O3, [M+H-H2O]^), _m/z_ 155.0732 (C8H11O3, [M+H-C2H2O]^), _m/z_ 151.0756 (C9H11O2, [M+H-H2O-CO]^), _m/z_ 137.0575 (C8H9O2, [M+H-H2O-C2H2O]^), _m/z_ 123.0894 (C8H11O, [M+H-H2O-CO-CO]^), and _m/z_ 91.0513 (C7H7, [M+H-H2O-CO-CO-CH3 OH]^). According to these characteristic ions, **L12** was tentatively identified as hydrofervlic acid. The fragmentation pathways of **L12** are proposed in Figure 8.  
**Figure 8. Proposed fragmentation pathways of hydrofervlic acid (**L12**) in positive ion APCI-MS.  
**Figure 7. Proposed fragmentation pathways of 3,4-dimethoxybenzenpropionic acid (**L4**) in positive ion APCI-MS.  
2.5 Characterization of Two Absorbed Benzene Derivatives of AR by HPLC-APCI-IT-TOF-MS\({}^{\mathrm{n}}\)  
_Identification of **L6** and **L7**. Both of them showed [M+H]\({}^{+}\) at _m/z_ 183.10, indicating that their molecular formulae were C\({}_{10}\)H\({}_{14}\)O\({}_{3}\). The fragment ions at _m/z_ 168.07 (C\({}_{9}\)H\({}_{12}\)O\({}_{3}\), [M+H-CH\({}_{3}\)]\({}^{+}\)), _m/z_ 152.08 (C\({}_{9}\)H\({}_{12}\)O\({}_{2}\), [M+H-CH\({}_{3}\)O]\({}^{+}\)), _m/z_ 151.07 (C\({}_{9}\)H\({}_{11}\)O\({}_{2}\), [M+H-CH\({}_{3}\)OH]\({}^{+}\)) were observed in their MS\({}^{2}\) spectra. By comparison with reference compounds, **L6** and **L7** were unequivocally identified as 3,4,5-trimethoxytoluene and 2,3,5-trimethoxytoluene, respectively. These two compounds were also identified by HS-SPME-GC-MS technique in this study. **L6** was identical compound with **G20**, and **L7** was the same compound as **G22**.
# Explainable AI in Education: Techniques and Qualitative Assessment
## 2 Theoretical Background
### Decision Tree  
For applications involving regression and classification, a common ML method is the decision tree. By dividing data into subsets according to feature values, it creates a structure resembling a tree, with each internal node denoting a decision based on a feature and each leaf node representing an outcome or prediction. The aim is to learn decision rules from the input variables and build a model that predicts the target variable. DTs handle both category and numerical data, need minimum data preparation, and are simple to analyze.  
Entropy, information gain, and and Gini index are a few instances of statistical measurements that are utilized to determine the best splits at each node [17]. The term \"Entropy\" refers to a statistical measure of the degree of randomness or impurity in a dataset. Nodes that belong to a single class, or pure classes, have low entropy, whereas nodes that belong to combined classes have high entropy. \"Information Gain\" is a measure of the reduction in entropy that happens when a dataset is divided based on a particular feature. It is derived from entropy. Since the greatest information gain attribute maximizes the purity of the resultant nodes, it is chosen as the best split. Comparably, the \"Gini index\" is another way to quantify impurity; it is easier to calculate and shows the likelihood of incorrectly classifying a randomly selected element based on the distribution of classes inside a node.
## 3 Results and Discussion
### Going from NIPS to VIPS: A Parametric Study
#### 3.2.1 Effect of PVDF Concentration  
Polymer concentration plays a significant role in determining the demixing process. Lower polymer concentrations are more likely to experience spinodal-decomposition (SD), producing a net-like surface structure in favor of the superhydrophobic property because their initial compositions are closer to the critical point in which binodal and spinodal lines are in a very close proximity [5]. Indeed, it was found that PVDF concentration affected membrane surface morphology, as shown in Figure 4. However, a net-like structure associated with superhydrophobicity originating from SD was not observed for all membrane samples prepared in this study. It was only observed in the 8% case as can be seen in the inset of the 8% membrane in Figure 4. This is mainly because of relatively high applied concentrations of PVDF compared to the one applied by [5]. Net-like structure is expected only during SD at very low PVDF concentrations and under high casting temperature (60\({}^{\circ}\)C) and high RH (100%). Nevertheless, a low degree of spherulitic morphology coupled with a large number of nano-scale nodules (Figure 4) was observed for 8% PVDF solution, leading to a high CA of 123\({}^{\circ}\) (Table 3).  
As PVDF concentration increased, a decrease in both surface porosity as well as mean pore size was observed (Table 3). The change in PVDF concentrations clearly affected the hydrophobicity (CA) of the membranes. As the concentration increased, the CA decreased. This can be attributed to the flat morphology at a higher concentration which led to almost hydrophilic membranes (CA of 15% PVDF was below 90\({}^{\circ}\)).  
The advantage of membranes prepared from lower PVDF concentrations is diminished by their large BP and MPS (Table 3). Therefore, the obtained LEP data suggests that a desirable PVDF concentration is around 12%. Membranes prepared using lower PVDF concentrations exhibit hydrophobic properties thanks to rough surface structure, but feature a rather large BP. On the other hand, the membrane made from a higher PVDF concentration (15%) had a very flat surface that reduced its CA, but offered a substantially low BP. As a result, by merely tailoring PVDF concentration, an optimum concentration can be ball-parked with respect to contact angle and pore sizes. In this case, the value is 12%.  
Figure 3: Pore size distributions (PSD) of membranes made via the NIPS (**left**) and VIPS (**right**) processes. Casting thickness: 500 \(\upmu\)m, polymer solution: 12% \(w/w\) PVDF.
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 5 Overview of the Provided Results
### PRIME Analytics  
The developed web application provides a deep analysis of the files collected by the PRIME concentrator (S11, events file, and topology file). Each file pack is organized into the tool with a specific identifier, storing the data and allowing queries. After the files are processed, the tool shows different graphs with processed information that allows analyzing the behavior and status of the PLC network at specific time instants.  
In general, PRIME Analytics tool is designed and developed to carry out an in-depth analysis of the messages exchanged in the PRIME networks, detecting errors and locating problematic nodes both logically and physically. This is of special importance for DSOs since it allows them to better plan and prepare the in-field operations.  
The analysis of the graphical and tabulated information provided by PRIME Analytics may be used for different purposes. To illustrate the potential of PRIME Analytics for DSO, in the next sections, some use cases are analyzed.  
1. Distinguishing between problematic and non-problematic scenarios.  
Figure 11: Results display detail.  
In this case, we present an example of how PRIME Analytics can be used to distinguish between problematic and non-problematic scenarios. Graphs of packet types provide useful information for this purpose. Figure 13 shows two graphs of packet types for two different scenarios. It can be observed that the ratio of ALV B versus ALV S (keepalive messages sent by the concentrator vs. keepalive messages sent by the service nodes) is higher in Figure 13a (12.1% vs. 4.6%) than in Figure 13b (43% vs. 40.6%). The higher this parameter, the more problems there will be in the communications, since it implies that more ALV messages sent by the concentrator remain unanswered (in an ideal situation, this ratio should be 1). Figure 13 also shows that the percentage of promotion requests (PRO REQ S) is much higher in Figure 13a (53.5%) than in Figure 13b (1.2%). A low percentage like the one in Figure 13b is associated with a simple and stable topology, while a high percentage like the one in Figure 13a is associated with a complex and dynamic topology, which may be due to frequent impedance variations or the appearance of impulsive noise.  
Figure 12: Example of list of cells created by the user.  
The average SNR graph is also useful for this purpose. Figure 14 shows such a graph for the same scenarios analyzed before. In Figure 14a, there are two large groups: one with SNR above 18 dB, and another with SNR below 12 (which is the biggest one indeed). In Figure 14b, however, all the nodes have an average SNR above 18 dB. Based on the analysis of these graphs, it can be concluded that in the scenario shown in Figure 14a there is a group of nodes that present low-quality communications with the concentrator.  
Figure 13: Graphs of types of packets for: (**a**) a problematic scenario and (**b**) a non-problematic scenario.
## 4 Tests and Results
### Start-Up Times of Generation Units  
Regarding the start-up times of the generation units, Figure 11 shows that scenarios 1 and 2 present the same time. This is because in the proposed approach NCRES are considered as NBS resources whose main function is to restore demand and guarantee the load-generation balance.  
Figure 10: Demand recovery times.  
Figure 9: Unserved energy for different scenarios.  
Nonetheless, an evident improvement in the start-up process occurs when BESS units are added. Figure 12 shows the discharge power of the batteries considered in the test system. Note that there are two slopes in the discharge of these resources. According to Table 5, the first slope, until 01:45 [Hrs], represents the discharge of battery \(b=2\); while the second slope, until 04:19 [Hrs], represents the discharge of battery \(b=1\).  
Note that two relatively small storage resources of 50 [MW] each with a discharge rate of 1 and 0.5 [MW/h], respectively, accelerate the restoration times for both non-conventional and NCRES resources. This acceleration in the start-up of generation resources translates into a reduction in the time to reestablish the non-attended demand.
# Improved Durability of Wood Treated with Nano Metal Fluorides against Brown-Rot and White-Rot Fungi
## 3 Results
### In-Vitro Agar Test  
The biocidal efficacy of nano metal fluorides was determined with an _in-vitro_ agar test. The results of the test are presented in Figure 3. The \(Y\) axis is the diameter of the fungal  
Figure 1: MgF\({}_{2}\) and CaF\({}_{2}\) nanoparticles synthesized in a sol: (**a**) HAADF-STEM image and the corresponding EDX maps; (**b**) Fluorine—red; (**c**) Magnesium—green; (**d**) Calcium—blue.  
Figure 2: Cell viability of Caco-2 cells (**left**) and HepG2 cells (**right**) in MTT assays.  
colony. The dotted horizontal line is the size of the original fungal inoculum. The control which did not contain NMFs had the maximum fungal colony diameter covering the whole _Petri_ dish. Two concentration of NMFs in stand-alone and combination formulation were tested. At the lower concentration of 0.05 M, the fungal colony diameter of brown-rot fungus, _R. placenta_ was the largest for CaF2-added agar medium and lowest in the medium containing the combination of MgF2 and CaF2. In contrast, the white-rot fungus _T. versicolor_ was not inhibited by MgF2, CaF2, or their combination at the lower concentration of 0.05 M. At the higher NMF concentration of 0.4 M, there was complete inhibition of fungal growth, _T. versicolor_ and _R. placenta_. Based on _in-vitro_ agar results presented here, it was found that the combination of MgF2 and CaF2 would have more efficacy than the stand-alone NMF at the same concentration. Also, a higher concentration of NMF would increase the biocidal efficacy against brown-rot and white-rot fungi. Thus, the NMF formulations were optimized for the subsequent wood treatment and the EN 113 (1996) test.
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 3 The Framework of the Present Study
### Energy Consumption Model
Then, wireless charging device moves towards the next sensor node to charge it. 3. After completion of the one energy cycle, the wireless charging device will return to the service station to recharge itself (e.g., recharging and replacing its battery). This period is called vacation time, denoted as \(\tau_{vac}\). After servicing itself, the wireless charging device will start its next energy cycle. The \(\tau\) represents the overall time spent during one energy cycle by the wireless charging device. Furthermore, the primary goal of the study aims to recharge the battery of each sensor node in the wireless rechargeable sensor network (RWSNs) that energy of each sensor node never falls below given threshold E\({}_{min}\). In the current study, the major objective to enhance the vacation time over a cycle (i.e., \(\frac{\tau_{vac}}{\tau}\)), and minimize the total energy consumption of the network. However, [PERSON] et al. [19] considered a static multi-hop routing scheme to cope with above-mentioned issues, and in the current study, it has proposed a firework algorithm-based hard clustering technique for dynamic selection of cluster head using multi-hop routing.
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 4 Experimental Results and Analysis  
This section presents the experimental setup and results used to evaluate the proposed scheduling algorithm. The experiments were conducted in a simulated high-performance computing (HPC) environment, utilizing historical job traces from multiple real and synthetic datasets. To analyze the effectiveness of the proposed approach, training experiments were performed on different historical datasets, including random scheduling, expert-guided optimal scheduling, replay data from RLScheduler, and a hybrid dataset combining expert guidance and trial trajectories.  
The performance of the algorithm is assessed using two key optimization metrics: average waiting time (AVGwt) and average bounded slowdown (AVGbsld). The results highlight the convergence behavior under various datasets, demonstrating the advantages of combining expert-guided and trial data for more effective learning. The proposed method is further compared with existing scheduling algorithms, showing improved scheduling efficiency and stability across diverse workloads. Additionally, the computational costs of training and inference are analyzed to provide insights into the feasibility of deploying the model in practical HPC systems.
# Complete Genome Report of a Hydrocarbon-Degrading _Sphingobium yanoikuyae_ S72
## 2 Materials and Methods
### Genome Sequencing and Analysis of S. yanoikuyae S72  
We sequenced the genome of S72 to fully understand the genetic basis underlying its ability to use the tested hydrocarbons as substrate, and the mechanism associated with the observed degradation. As previously stated, genomic DNA was extracted with Promega(r) Wizard Genomic DNA purification kit (Promega Corp., Madison, WI, USA) as per the manufacturer's guideline. The whole genome was sequenced with the Illumina MiSeq(tm) platform (Illumina Inc., San Diego, CA, USA). The reads obtained from the genome assembly were trimmed with trim-gallore, which also checks the quality of the read with its embedded fastqc. The genome assembly was carried out in 2 stages: (i) de novo assembly with the Spades and Velvet Genome Assembler [24; 25] and (ii) reference-based assembly. The assembled genome obtained from the two softwares (SPAdes and Velvet) were compared using the Metassembler software which suggested the best assembly method among the software used and produced a consensus assembly. The consensus assembly was then used for the reference-based assembly [26]. The reference-based assembly was used to reduce the genome to 1 contig. This approach employed the alignment of the consensus assembly with Sphingomonas genome strain ATCC512030 retrieved from NCBI databaseusing the Nucmer software and the Consed suite [27; 28]. The assembled genome was annotated with Prokka (version 1.12) and NCBI prokaryotic genome annotation pipeline (PGAAP) that carried out automatic gene prediction and annotation [29; 30]. RNAmmer [31] and tRNAscan [32] were used for the identification of rRNA and tRNA, respectively. The KEGG and orthologous gene cluster analysis was carried out on the web based PATRIC server [33]. Alternatively, COGs for unique genes were assigned with BlastP against the COGs database downloaded from NCBI. The bacterial pan genome analysis (BPGA) tool was used for the analysis of the isolate's pan genome [34]. The Average Nucleotide Identity based on Mummer (ANIm) was determined with PYANI [35]. The genomic island in S72 was identified with an online web-based Island viewer4 [36] and pairwise analysis on Nucmer [28]. The deep gene sequence analysis for the identification of the genes that were associated with the degradation of hydrocarbon and comparative genomics such as COG abundance was carried out on the JGI-IMG database [37].