esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Isotopic Investigations  
Groundwater samples for stable isotope (\(\xi^{18/16}\)O and \(\xi^{2/1}\)H) and tritium (\({}^{3}\)H) analyses were collected four times (January, June, September and December 2019), together with the hydraulic head measurements. During the same observation period, rainwater samples for \(\xi^{(18/16}\)O), \(\xi^{(2/1}\)H) and tritium analyses were collected monthly (depending on the amount of precipitation) from one rain sampler installed at the test site (Figure 4). The rainfall was collected using ten-liter polyethylene bottles containing about 300 mL of Vaseline oil to prevent evaporation. Oil contamination was carefully avoided by syringing the water samples out of the bottles. All the samples were transported to the laboratory in a refrigerated box.  
Stable isotope analyses were carried out at the Isotope Geochemistry Laboratory of the University of Parma, Italy, using a water equilibrator at 18 \({}^{\circ}\)C equipped with a Finnigan Delta XP spectrometer. For oxygen isotope determination, 5 cm\({}^{3}\) of water was equilibrated with pure CO\({}_{2}\), whereas for hydrogen isotopes, 5 cm\({}^{3}\) of water was equilibrated with pure H\({}_{2}\) (platinum wire was used as a catalyser of gas-liquid water equilibration). The isotope ratio is expressed as:  
\[{}^{\text{A/B}}g_{i/\text{RF}}\ =\ \frac{{}^{\text{A/B}}R_{i}}{{}^{\text{A/B}}R_{ \text{RF}}}-1\ =\ \left[\left(\frac{{}^{\text{A/B}}R_{i}}{{}^{\text{A/B}}R_{ \text{RF}}}-1\right)10^{3}\right]\%_{\infty} \tag{1}\]  
where A is \({}^{18}\)O or \({}^{2}\)H, B is \({}^{16}\)O or \({}^{1}\)H, \(R\) is the ratio of the isotopic abundances, \(i\) the sample of interest, \(\%_{\infty}=10^{-3}\), and RF is the primary international standard of reference (in our case VSMOW-SLAP). The analyses of \({}^{3}\)H were carried out at the Isotope Geochemistry Laboratory of the University of Trieste, Italy. Following other authors (e.g., [46]), the samples followed the procedure of the preventive electrolytic enrichment of tritium was applied to the samples to decrease measurement errors. In this process, the 250 g of the water sample was expected to be reduced to 20 g by electrolysis.  
The analyses for the determination of the tritium activity were carried out according to the procedures provided by the International Atomic Energy Agency [47].  
Figure 6: Cluster C5: stratigraphy, screened intervals, and water levels in December 2019. DPS-anthropogenic fillings, alluvial deposits, and talus (Quaternary); RNZ-siliciclastic sediments with high organic matter content, Arenazzolo Fm. (Messinian); GPQ1 and GPQ2-gypsum-arenites and limestones and carbonate arenites, Pasquasia Fm. (Messinian).  
The analytical prediction uncertainty was \(\pm\)0.1%o for \(\delta^{18}\)O, \(\pm\)1%o for \(\delta^{2}\)H, and \(\pm\)0.5 T.U. for \({}^{3}\)H.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 4 LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES
### Lshade  
#### 4.1.1 Initialization  
To initialize LSHADE, a population of candidate solutions (decision vectors) is generated randomly within the specified upper and lower bounds. The \(m\)th component of the decision vector was formulated by the following Equation [26]:  
\[D_{m,k}^{o}=D_{m,L}+rand(0,1)(D_{m,u}-D_{m,L}) \tag{15}\]  
where rand \((0,1)\) returns a uniformly distributed random number in \([0,1]\) and superscript '\(o\)' represents initialization. If '\(d\)' is the dimension of decision vector, then \(k=1,2,\ldots,Np\) and \(m=1,2,\ldots,d\).
## 3 Results  
### Effect of Si Foliar Biostimulant Application on Phenolic Content in Olive Leaf  
Effects of Si foliar biostimulant application on thirteen different polyphenols are shown in Table 1.  
There was a general trend of an increase in the content of phenolics in the leaf with the increase in silicon levels applied. Significant differences between Si3 treatment control and/or Si1 treatment were recorded for rutin, luteolin-7, apigenin-7, luteolin-4, tyrosol, and oleupropein. Ole-aglycone was the only polyphenol that showed a decrease in concentration with the increase in Si-levels, with significant differences between control and Si2 treatment (luteolin concentrations also decreased but were not significant). Two cultivars used in this study showed contrasting concentrations of phenolic compounds--the Leccino cultivar had significantly higher concentrations of luteolin-7, apigenin-7, luteolin-4, luteolin, tyrosol, versososide, and ole-aglycone, while the Istarska Bjelica cultivar had higher concentrations of rutin and oleanolic acid. Luteolin, verbascoside, and oleanolic acid were the only polyphenols affected solely by the cultivar; Leccino had higher values for both (Table 1). Oleupropein was one of four polyphenols unaffected by cultivar and, at the same time, the only one significantly affected by Si treatment. There were no significant interactions of the main effects, except for luteolin-7. Here, the large difference between cultivars stands out, as an increasing level of foliar applied Si caused an increase in luteolin-7 content in the Leccino cultivar but not in Istarska Bjelica, where it was unchanged (Figure 1). Figure 1: The interaction effects of Si foliar treatment and cultivar on the content of luteolin-7-O-glucoside in olive leaves. Different lowercase letters represent statistically significant differences between mean values at \(p\leq 0.05\) obtained by a two-way ANOVA and Tukey’s test.
## 2 Materials and Methods
### Polymerization Kinetics and Degree of Conversion (DC)  
The polymerization kinetics and degree of conversion (DC) were evaluated by Fourier Transform Infrared (FTIR) spectroscopy (Bruker Optics, Ettinger, Germany) with a spectrometer equipped with an attenuated total reflectance (ATR) device consisting of a horizontal diamond crystal. Three samples per adhesive group (\(n=3\)) were dispensed onto the ATR using an auxiliary polyvinylsiloxane matrix with a 4 nm diameter and 1 mm thickness to standardize the samples' thickness. The LED unit tip was standardized and positioned as close as possible to the top of each sample. The absorbance spectra were obtained during 40 s of adhesive photoactivation. Data were evaluated using the Opus 6.5 software (Bruker Optics), in the range of 4000-800 cm\({}^{-1}\) with 2 scans per second, at a speed of 160 kHz and resolution of 4 cm\({}^{-1}\). The DC was calculated based on the height of the following absorption peaks: 1640 cm\({}^{-1}\) (aliphatic C=C bond) and 1610 cm\({}^{-1}\) (aromatic C=C bond), which was used as an internal standard (Equation (1)). The DC result was  
Figure 1: The scheme evidences the structure of the ionic liquid added into the experimental adhesive resin for dental restoration.  
expressed as percentages. The DC calculated the polymerization rate (Rp) as a function of the light-activation time [12].  
\[\text{DC}(\%)=1\times\frac{1640/1610\ (polymer)}{1640/1610\ (monomer)}\times 100 \tag{1}\]
# Resistance of \(L\). _monocytogenes_ and \(S\). Typhimurium
## 3 Results and Discussion
### Influence of the Biofilm Age on the CAP Efficacy Following 10 min of Treatment
This higher amount of EPS following seven days of incubation probably limits the diffusion of the plasma species into the biofilm, and, consequently, protects the biofilm-associated cells [15]. In addition, it has been reported by [PERSON] [39] that the increased production of EPS results in nutrient and oxygen gradients within the biofilm, affecting the cell metabolism and the growth rates. As mentioned previously [2], antimicrobial agents are in general more effective towards actively growing cells. Therefore, the biofilm-associated cells of the seven days old _L. monocytogenes_ biofilms might be more resistant towards those plasma species that are still able to (slowly) penetrate the biofilm. For the _S._ Typhimurium biofilms, however, the higher resistance towards CAP treatment of the seven days old biofilms cannot be the result of a difference in amount of EPS, since the OD following crystal violet staining was similar for all tested biofilm ages (Section 3.1). Nevertheless, the EPS matrix could still be involved in the increased biofilm resistance. In the research done by [PERSON] et al. [40], the influence of the biofilm age on the structure and density of oral plaque biofilms was investigated by means of Confocal Laser Scanning Microscopy (CLSM). It could be concluded that the oral plaque biofilms became denser with an increased biofilm age. A similar phenomenon could be at the basis of the increased resistance of the seven days old _S._ Typhimurium biofilms. Although there is no increase in biomass, the biofilm might have collapsed, limiting the diffusion of the plasma species into the (lower layers of the) biofilm. In addition, in the research of [PERSON] et al. [40], it was mentioned that the higher density of the biofilm can result in nutrient and oxygen limitations. Similar to the results that were obtained for the _L. monocytogenes_ biofilms, this could result in an altered growth rate and a lower susceptibility towards the created CAP species. The results for the CAP treatment of the 10 days old _L. monocytogenes_ and _S._ Typhimurium biofilms are rather surprising, i.e., for both species, it was expected that the 10 days old biofilms would (at least) be equally resistant towards the CAP treatment as the seven days old biofilms, since for both species, the seven and 10 days old biofilms had a similar OD (Section 3.1). To the best of the authors' knowledge, no explanation has been found in literature for the contradictory results obtained within the presented research. Further research, e.g., by means of gene expression analysis and/or CLSM, will be required to further investigate this phenomenon.
# Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)
## 3 Results
### Bias-Based Metrics  
The hate speech detection models could make biased predictions for particular groups who are already the target of such abuse ([PERSON] et al. 2019; [PERSON], [PERSON], and [PERSON] 2019). To measure these unintended model biases, the AUC-based metrics by [PERSON] et al. (2019) were used. We computed the subgroup AUC (area under the ROC curve), BPSN (background positive, subgroup negative) AUC, and BSNP (background negative, subgroup positive) AUC. Subgroup AUC metrics for this use case are a measure of the ability of the model to segregate the toxic and normal comments. A higher value of subgroup AUC suggests that the model is better at differentiating between toxic and normal posts. The BPSN (background positive, subgroup negative) AUC metric is a measure of false-positive rates of the model, while the BNSP (background negative, subgroup positive) AUC is a measure of false-negative rates of the model. A higher value of BPSN indicates a lower likelihood of the model giving false positives, while a higher value of BSNP indicates  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Technique** & \multicolumn{2}{c}{**Plausibility**} & \multicolumn{2}{c}{**Faithfulness**} \\ \hline  & **IOU F1** & **Token F1** & **AUPRC** & **Comprehensiveness** & **Sufficiency** \\ \hline BERT + ANN & **0.1888** & **0.5074** & **0.8384** & **0.4199** & **0.0055** \\ BERT + MLP & **0.298** & **0.5298** & **0.8589** & **0.3574** & **0.003** \\ DT-LIME & 0.1676 & 0.3887 & 0.7487 & 0.2993 & 0.0442 \\ RF-LIME & 0.2387 & 0.5118 & 0.8469 & 0.4132 & 0.0014 \\ LR-LIME & 0.1008 & 0.2271 & 0.5284 & 0.2132 & 0.0482 \\ NB-LIME & 0.1287 & 0.1818 & 0.5938 & 0.1999 & 0.0514 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Explainability metrics.  
a lower likelihood of the model giving false negatives. For this dataset, these metrics were calculated with respect to a community.  
Table 10 provides a summarized view of the bias-based metrics calculated on all the models implemented. We can see that the bias-based metrics of BERT variants were significantly more accurate than the other linear models. BERT + MLP had the highest values of subgroup AUC, BPSN AUC, and BSNP AUC with 0.8229, 0.7752, and 0.8077, respectively, followed by BERT + ANN with values of 0.7977, 0.7188, and 0.7391, respectively.
# Rearrangeable Nonblocking Conditions for Four Elastic Optical Data Center Networks
###### Abstract  
Four variants of elastic optical data center network (DCN) architectures based on optical circuit switching were proposed in an earlier study. The necessary and sufficient values of frequency slot units (FSUs) per fiber required for these four DCNs in the sense of there being strictly nonblocking (SNB) were derived, but no results in the sense of being rearrangeable nonblocking (RNB) were presented. In reality, only limited bandwidths are available, and reducing the value of FSUs per fiber has become a critical task to realize nonblocking optical DCN architectures in practice. In this paper, we derive the sufficient value of FSUs per fiber required for the four DCNs to be RNB by two multigraph approaches. Our results show that the proposed RNB conditions in terms of FSUs per fiber for a certain two of the four DCNs reduce their SNB results down to at least half for most cases, and even down to one-third.  
data center networks; elastic optical networks; rearrangeable nonblocking (RNB); edge-coloring +
Footnote †: journal: Applied Soft
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 5 Conclusions  
The SAW current sensors with different patterns of FeGa thin films were discussed. The layered media model (FeGa/SiO2/LiNbO3) was established to reveal the interaction between FeGa thin film and the applied external current, and the sensitivity of the sensor was simulated. The SAW devices with various FeGa thin-film patterns (dot, grating, and membranous) were designed and fabricated to improve the detection sensitivity and lower the hysteresis error. The experimental results show that the dotted FeGa thin film coated sensing devices can achieve large current sensitivity (37.9 kHz/A), low hysteresis error (0.81%), good repeatability, and weak fatigue, which is the result of enlarged magnetostrictive properties and reduced coercivity in the dot-film structure. In addition, compared with the existing sensor prototypes with similar structures, our proposed sensors perform far better. Those results indicate that the FeGa thin film with grating and dotted patterns can be  
Figure 10: (**a**) The fatigue measurement of developed SAW sensing devices, and the surface morphologies of the FeGa thin film in different cyclic testing runs of (**b**) 0, (**c**) 50, and (**d**) 100.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Devices**} & \multicolumn{3}{c}{**Sensitivity Decrease Rate after Long-Term Runs**} \\ \cline{2-4}  & **0** & **50** & **100** \\ \hline membranous-film & 0 & 2.26\% & 3.6\% \\ grate-film & 0 & 2.23\% & 3.2\% \\ dot-film & 0 & 2.93\% & 3.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The sensitivity decrease rate after long-term runs.  
used to enhance the performance of the SAW currents sensors. Hence, the dotted FeGa thin film coated SAW current sensors have broad application prospects in the fields of current detection. The obtained results provide very encouraging results for the development of wireless passive current sensors. Meanwhile, the structure of FeGa thin film with a dotted pattern can provide a new idea for the thin-film pattern design of wireless passive current sensors.  
Y.S.: Conceptualization, Writing--original draft, Methodology, Experiments. Y.J.: Methodology. Y.Z.: Software. L.C.: Investigation. Y.L.: Methodology. W.W.: Conceptualization, Writing--review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.11774381, U1837209 and 52072184) and the National Key Research and Development Program (2020 YFB1506203).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 2 Sound Sampling and Data Transmission
### Node Implementations
#### 2.2.1 Sensor Nodes  
The SLTB004A-based sensor nodes, as shown in Figure 2, efficiently capture and transmit environmental sound data by sampling audio signals at 8 kHz. According to [PERSON]'s theorem, this sampling rate accurately captures sounds with frequencies up to 4 kHz, which is essential for monitoring noises within the human hearing range and common urban sounds [25]. The sampled sound data are transmitted to the corresponding relay node through the built-on BLE module. To power these nodes, a 2000 mAh lithium-ion battery with a nominal voltage of 3.7 V is used, regulated by the BQ25570 module, stepping the voltage down to 2.6 V. Based on previous studies [26], each sensor node consumes 17.5 mW during operation, allowing for approximately 372 h (or 15 days) of continuous use without external power. Although a solar panel could extend this duration, it was unnecessary for the testing phase in this study.  
Figure 1: Structure of the HWASN implementation.  
Figure 2: Implementation of sensor nodes.
## 3 Security and Privacy-Preserving Solutions for IoE
### Blockchain-Based Security Solutions
#### 3.1.5 Blockchain-Based Use Cases in IoE Security  
Due to its distributed and heterogeneous structure, the IoE ecosystem is exposed to multi-layered security threats. In this context, blockchain technology offers innovative solutions to strengthen IoE security through its decentralized, immutable, and programmable features. In this subsection, the contributions of blockchain to IoE security are addressed under four main categories:  
1. _Decentralized Identity (DID)_: Enables users and devices to perform identity authentication without relying on central servers.
2. _Smart Contracts_: Allow access policies to be autonomously enforced, reducing the risk of human error.
3. _Data Integrity and Provenance_: Through the chained record structure, IoE data becomes tamper-proof, increasing reliability.
4. _Consensus Mechanisms (PoS, PBFT, Directed Acyclic Graph (DAG))_: Improve the scalability and transaction efficiency of blockchain, enabling real-time secure transactions in IoE systems.  
The corresponding use cases for these four contribution areas are summarized in Table 6.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 4 Simulation Results and Discussions
### Energy Cost per Packet Comparison (ECP)  
The results concerning the effect of altering the speed of devices with the ECP are presented in Figure 8. It can be observed that the ECP of both MP-OLSRv2 and MEQSA-OLSRv2 increased as the node speed increased such that it, at a device speed of up to 40 m/s, it is approximately 1.8 and 1.3 times more in MP-OLSRv2 and MEQSA-OLSRv2, respectively. As defined previously, this is attributed to the fact that MBMQA selects the suitable path for source-to-destination pairs while considering devices' energy and queue length based on the MARS metric. This increases the packet delivery ratio and creates fewer packet drops. Since there are minimal packet drops and retransmission, extra energy is saved by transmitting most packets only once. Therefore, energy consumption during packet transmission is reduced. Furthermore, the exploiting energy and QoS awareness
# Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review
## 4 Smart Grid and 5G
### Advantages of 5G Networks in Smart Grid  
There are many advantages of using 5G networks in smart grids that bring high quality service to the interconnections with increased scalability. The state estimation provides a complete estimate of the system containing the state variables at all nodes of the network from a completely remote set-up environment. The limitations of conventional state estimation exhibit high computational complexity and communication delays, which are caused by the dynamic and the distributed power grids. This can be easily overlooked by implementing the 5G network, as it enables advanced and distributed state estimation schemes [91]. Figure 5 represents the major advantages of using 5G in smart grids.  
Figure 4: Network domains establishment for smart grid communication.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Access Communication** & **Backhaul Communication** & **Backbone Communication** \\  & **Network Domain** & **Network Domain** & **Network Domain** \\ \hline \hline Diameter of region to be & \textless{}10 km & \textless{}100 km & \textless{}1000 km \\ covered & 1 kbps & Several Mbps & Mbps to Gbps \\ End-to-end Latency & \textless{}1s & \textless{}50 ms & \textless{}5 ms \\ Packet loss & No specific requirements & \textless{}10\textless{}6 & \textless{}10\textless{}9 \\ Availability & 9 h downtime p.a & 50 min downtime p.a & 5 min downtime p.a \\ Failure convergence time & \textless{}1 s & \textless{}1 s & \textless{}several ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of the requirements for network domains in smart grid infrastructure.  
Similarly, the circuit breakers used in the protective measures in the system find system faults that function with high time criticality. The speed of intervention of the circuit breakers in fault detection is proven to be better in the distributed network management in 5G [92] compared with the centralized network management in LTE. Demand response allows users to reduce or shift their electricity usage during peak periods based on a time dependent pricing [93]. The communication impairments can adversely affect the demand response control. This requires advanced communication technology for enabling a control feedback loop between the user and the energy providers. This can be achieved by efficient 5G small cell planning [94]. There is a need for interoperable structures with 5G and smart grids, which includes various features and is due to the resemblance of ubiquity of the 5G cellular network [95]. There are many advantages in these frameworks, including two way energy trading, dynamic energy pricing, and active wireless transmission spectra between the grid and the networks. This provides a foundation of effective energy management in these structures.
## 2 Outdoor Positioning in the IoT Ecosystem
### Common LoRaWAN-Based Localization Architectures  
A LoRaWAN-based localization system is composed of hardware and software in both the user and network control sides. User side components include, one/several sensor elements, a processing unit, a radio frequency module, an antenna etc., and the network control side typically includes a processing unit, networking unit, power source system, etc. The user agent on behalf of the end node starts sending data to the network control sides. The data transmission frequency of the end node varies in depending on the IoT application domain. Network control system determines the end-node location based on received data. On the other hand, in a large area, several service providers can deploy different methods for end-node localization and different hardware and processing techniques may be considered. Network control system must enable the cooperation between different methods and their functional modules [17,35].  
In a large area, by increasing the number of end nodes, the variety of localization systems and the number of transmitted packets to the network control system may increase, leading to a latency increment and, as a result, a performance decrease on the localization accuracy.  
Currently, two distinct localization architectures have been proposed for the IoT ecosystem: Cloud-based and Edge-AI-based. In this subsection, these architectures are explained.