esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
In this paper, we considered the energy-efficient resource management issue in D2D cooperative relay communications and proposed a pricing-based matching approach to jointly optimize relay selection, spectrum allocation and power control. First of all, we formulated the joint optimization problem as an NP-hard four-dimensional matching problem. Secondly, to provide a tractable solution, we proposed a low-complexity two-stage optimization approach, which decomposes the original joint problem into a two-dimensional matching between D2D RXs and RBs for the second hop and a three-dimensional matching among D2D TRs, RSs and RBs for the first hop. Thirdly, for the matching problem in each stage, we proposed a pricing-based iterative matching algorithm to maximize the EE while guaranteeing the QoS requirements of both D2D TRs and CUEs. Finally, the proposed algorithm was compared with some heuristic algorithms through simulations. It was demonstrated that the proposed algorithm cannot only dramatically improve the energy efficiency performance, but also significantly enhance the network coverage with low computational complexity.  
This work was partially supported by the National Science Foundation of China (NSFC) under Grant Numbers 61601180, 61601181; the Fundamental Research Funds for the Central Universities under Grant Numbers 2016 MS17, 2017 MS13; supported by Beijing Natural Science Foundation (4174104); Beijing Outstanding Young Talent under Grant Number 2016000020124G081; SGRIXTMMXS[2016]586 2016 State Grid Corporation Science and Technology Program: \"Research on communication access technology for the integration, protection, and acquisition of multiple new energy resources\".  
Figure 11: Energy efficiency of D2D pairs vs. the number of D2D CUEs.  
Figure 10: Energy efficiency of D2D pairs vs. the number of RSs.  
**Author Contributions:** [PERSON] designed the architecture and system model. [PERSON] provided the idea and wrote the paper: [PERSON] wrote some sections and performed simulations. [PERSON] analyzed the experimental results. [PERSON] and [PERSON] developed the simulation system and wrote some sections.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
## 3 Results
### Results of Protrusion on the Conductor
#### 3.1.2 Detection Results for the UHF Method  
The PRPD pattern for a 3.5 mm metal protrusion detected using the UHF method is shown in Figure 12. As is shown in Figure 12, the PDIV for a 3.5 mm protrusion detected using UHF is 225.05 kV, and the pattern shown in Figure 12a is under this voltage. At this point, PD occurs at both the negative and positive half-cycles of the AC voltage, but the amplitude for the positive is bigger than that of the negative half-cycle. As the voltage increases, PD amplitude increases and the PD phase is widened; however, the maximum amplitude of the positive is always higher than that of the negative half-cycle, as is shown in Figure 12b-e.  
Figure 13 shows the pattern for a 6 mm protrusion. It can be seen from Figure 13a,b that PD firstly occurs at the positive half-cycle of the voltage at the inception, which is different from the results for the ultrasonic method. As is shown in Figure 13c-i, when the voltage increases, PD begins to occur at the negative half-cycle and then PD amplitude increases and the PD phase is widened; however, the maximum amplitude of the positive is always higher than that of the negative half-cycle.  
Figure 11: Maximum discharge amplitude vs. voltage.  
Figure 12: PRPD pattern for 3.5 mm protrusion on the conductor detected by the UHF method (**a**) at 225 kV; (**b**) at 236 kV; (**c**) at 246 kV; (**d**) at 256 kV; (**e**) at 266 kV.  
As is shown in Figure 14a-c, for a 12 mm protrusion, PD firstly occurs at the negative half-cycle of the voltage, which is the same as the results for the ultrasonic method. Figure 14d shows that PD occurs at the positive half-cycle as voltage increases. As the voltage further increases, PD becomes strong both at the negative and positive half-cycles of voltage.  
The PDIV detected by UHF is shown in Figure 15. It can be seen that PDIV decreases as the defect length increased.  
Figure 14: PRPD pattern for 12 mm protrusion on the conductor detected by the UHF method (**a**) at 77 kV; (**b**) at 80 kV; (**c**) at 91 kV; (**d**) at 116 kV; (**e**) at 216 kV; (**f**) at 256 kV.  
Figure 13: PRPD pattern for 6 mm protrusion on the HV conductor detected by the UHF method (**a**) at 160 kV; (**b**) at 170 kV; (**c**) at 180 kV; (**d**) at 190 kV; (**e**) at 200 kV; (**f**) at 222 kV; (**g**) at 231 kV; (**h**) at 241 kV; (**i**) at 250 kV.  
Figure 16 shows the maximum discharge amplitude under different applied voltages.  
In Figure 16, it can be seen that the maximum amplitude increases as the voltage increases. At the beginning, the amplitude of a 12 mm protrusion increases slowly. Then, it increases greatly under a voltage of 105-120 kV. After that, the amplitude increases slightly, and it increases quickly under high voltage. For a 6 mm protrusion, the amplitude first greatly increases, and then slightly increases as the voltage increases. The results for a 3.5 mm protrusion are similar to those for 6 mm; that is, they first greatly increase and then slightly increase.
## 6 Discussion
### Benefits
#### 6.1.6 Boosted Automation  
One of the objectives of transferring from the SDN paradigm to the KDN paradigm is to enhance the automation of the network. KDN systems need to have a high level of automation where network decisions are taken with the aid of application guidelines and real-time network knowledge. Typically, machine learning data pre-processing and labeling for machine learning model training are performed manually by humans. If this manual approach is used in a KDN, it can hinder the efficiency of the KDN system. Data pre-processing, cleaning, and labeling tasks can be decentralized and automated by utilizing smart contracts on blockchains. Smart contracts provide a means for task automation by defining contractual terms upon meeting certain criteria or events.
# A Mathematical Model for Scheduling and Assignment of Customers in Hospital Waste Collection Routes
## 4 Results
### Computational Experiments and Findings
#### 4.1.3 Workload Balance  
The results obtained from the model can generate dispatch routes since we know the day and the truck assigned to each of the customers; this can be observed in Figure 3. The work balance can be analyzed through the average number of customers and daily containers delivered, which can be seen in Table 7. We can see that the model obtains a balanced work program.  
Table 8 shows the number of unattended visits to customers in the sub-optimal solution (company solution before executing the proposed model). As can be seen, the current solution is infeasible since the demand is not satisfied by stopping making 422 visits to customers. Note that the total number of visits obtained by the model is 1349 regardless of the used software. The obtained solution by the model is feasible, minimizing the maximum demand collected in one day. For the Maule instance, we have 5257 variables and 9545 constraints. For the Bio-Bio instance, we have 20,331 variables and 38,990 constraints. Indeed, a feasible solution for the company case study allows compliance with the contracts and regulations of the health area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Best integer** & **Best Bound** & **Gap** & **CPU Time** \\ \hline
30.0100 & 29.3835 & 2.09\% & 6360.00 \\
29.5400 & 29.3835 & 0.53\% & 6560.00 \\
29.5200 & 29.3835 & 0.46\% & 9638.70 \\
29.4100 & 29.3835 & 0.09\% & 9838.70 \\
29.4000 & 29.3835 & 0.06\% & 29,040.00 \\
29.4000 & 29.3835 & 0.06\% & 30,000.00 \\ \hline \hline \end{tabular} Source: By the authors.  
\end{table}
Table 6: Performance CPLEX solver—Biobio Instance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Incumbent** & **Best Bound** & **Gap** & **CPU Time** \\ \hline
29.96000 & 29.3900 & 1.90\% & 291.00 \\
29.49000 & 29.3900 & 0.34\% & 312.00 \\
29.44000 & 29.3900 & 0.17\% & 338.00 \\
29.43000 & 29.3900 & 0.14\% & 338.00 \\
29.42000 & 29.3900 & 0.10\% & 682.00 \\
29.40000 & 29.3900 & 0.03\% & 747.00 \\
29.39000 & 29.3900 & 0.00\% & 19,380.00 \\ \hline \hline \end{tabular} Source: By the authors.  
\end{table}
Table 5: Performance GUROBI solver—Biobio Instance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Day** & **Customer** & **Container** \\ \hline  
\hline \hline \end{tabular} Source: By the authors.  
\end{table}
Table 7: Average results of customers and containers delivered each day—Instance Biobio.  
Figure 3: Example of routes for the first Thursday of the month—Instance Biobio. Source: By the authors.
# The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis
## 4 The Interface of Privacy and Data Security in ACSs
### Privacy-Preserving Techniques Overview
#### 4.1.2 Anonymization
The main limitation of the generalization theories is that it cannot be applied to all types of data such as names or data entry identifiers, yet they remain efficient for location data. Meanwhile, even with the most refined generalization technique, the risk of inference depends on the adversary's knowledge and the selected anonymization parameters [58]. The key generalization techniques, pointed out by the WP29 [35] and ENISA [33], and which can be applied to the ACS personal data, are:k-anonymity/aggregation: hides a data subject in the crowd (called also equivalent class) by grouping their attributes with k-1 other individuals. The scheme provides a convenient protection from being singled out, though the inference risk remains important [28]. * l-diversity: handles the k-anonymity limitation by ensuring that in every crowd there are l-different values. Such difference reduces the inference risk but does not completely eliminate it [59]. * t-closeness: is a refinement of the l-diversity theory that aims to set a t-threshold by computing the resemblance of a sensitive value distribution within the equivalent class in comparison to the attribute distribution in the whole dataset [49]. More specific to location data, _obfuscation_ techniques are meant to purposely return incorrect location information to make the unauthorized tracking difficult [60]. Although the obfuscation approach is considered as a multifaceted scheme that does not have a straightforward definition in the literature, multiple researchers interpret obfuscation as a type of data masking or noise addition [31; 49; 61], while others [34; 53] define it as a segregated principle from anonymization and pseudonymization. As depicted in Figure 4, the obfuscation has been classified as an anonymization scheme for its theoretical irreversibly. Despite the definition, the obfuscation techniques come with the promise to definitively break the linkability between the individuals' data and their location in combination with time, though it is true that obfuscation techniques offer geo-indistinguishability and protect location data. However, such mechanisms remain as vulnerable to re-identification attacks as other anonymization theories, as per [PERSON] and [PERSON]'s attack simulation [60; 62]. To that end, there is no \"one-size-fits-all\" [31], and privacy-preserving is a process of combining accurate schemes depending on the nature of personal data, the acceptable level of re-identification with regard to data protection obligations, and the intended data usability.
## 3 6G State-of-the-Art
### 6G Requirements and Trends
#### 3.1.2 Superior Energy Efficiency  
6G devices consume much more energy than prior generations of devices due to their capacity to operate in higher frequency bands than earlier generations. Because of this, energy consumption and efficiency are major issues that must be addressed immediately. Nevertheless, when new techniques are developed, we will witness a rise in energy efficiency as well as the possibility of battery-free internet of things devices, such as energy harvesting in building automation and smart homes.  
Interconnectivity in three dimensions: Future networks will extend beyond the confines of two dimensions to include oceans, the atmosphere, and space, allowing for the integration of terrestrial and aerial devices. Various applications, including underwater acoustic ad hoc and sensor networks, weather forecasting, and climate monitoring, will be able to take advantage of such networks.
## 3 Materials and Methods
### Laboratory Analysis  
The samples collected were subjected to the standard laboratory process at the Isotopic Bioscience Laboratory of the University of Ghent, Belgium, to obtain the carbon chain isotopic values present in the fatty acids [17]. This included soil extraction, the methylation process, and final analysis of the compounds via isotope-ratio mass spectrometry (IRMS) of the methylated fatty acids (FAMEs).  
Determination of the bulk organic carbon content and stable carbon isotopic composition (\(\delta^{13}\)C) of source soils and sediment samples was performed employing an elemental analyzer (ANCA-SL, SerCon, Crew, UK) coupled to an isotope-ratio mass spectrometer (20-20, SerCon, Crew, UK). A 10-15 mg mass of the fine fraction (<0.1 mm) of each sample was weighed into tin capsules and loaded into an autosampler. Stable carbon isotope values were expressed as \(\delta^{13}\)C values (in % units) relative to the Vienna Pee Dee Belemnite (VPDB) international reference standard using the following equation:  
\[\delta^{13}C_{FA}=1000\times\left(\frac{Rsample}{Rstandard}-1\right) \tag{1}\]  
where \(R\) is the molar ratio of the heavy-to-light isotopes (\({}^{13}\)C/\({}^{12}\)C) and the standard is the VPDB.  
The extraction of lipids from the source material and sediment samples was carried out through accelerated solvent extraction (ASE 350, Dionex, Sydney, Australia) using dichloromethane (DCM). The volume of the total lipid extracts was reduced by evaporation at reduced pressure. Neutral and acidic compounds were separated using solid phase extraction on aminopropyl-bonded silica gel columns. The acid fraction was methylated with methanolic HCl of known carbon isotopic composition (\(\delta^{13}\)C = \(-\)40.78 \(\pm\) 0.33%). An internal standard (MeC\({}_{17}\) FA, heptadecanoic acid methyl ester) was added to each sample prior to analysis. FAMEs were subsequently quantified using gas chromatography (GC, Trace GC, Thermo Scientific, Bremen, Germany) equipped with a flame ionisation detector. After this quantification, the solvent volumes of the samples were adapted for the most abundant FAMEs to match the ideal concentration range for capillary gas chromatography-combustion-isotope-ratio mass spectrometry analysis (GC-C-IRMS; Trace GC Ultra interfaced via a GC/C III to DeltaPLUS XP, Thermo Scientific, Bremen, Germany). FAMEs were identified based on their retention time, whereas peak purity and confirmation of identity was performed on selected samples using a parallel GC-MS measurement.  
The obtained CSSI values of the FAMEs were corrected for carbon methylation during the process using the following mass balance equation:  
\[\delta^{13}C_{FA}=\frac{\delta^{13}C_{FAME}-(1-X)\delta^{13}C_{Methanol}}{X} \tag{2}\]  
where \(X\) is the fractional contribution of free fatty acids to methylester, \(\delta^{13}C_{FA}\) is the isotopic value of fatty acids, \(\delta^{13}C_{FAME}\) is the fatty acid methylester isotopic value and \(\delta^{13}C_{Methanol}\) is the isotopic value of methanol.
###### Abstract  
A marine cable-conformal dual-band omnidirectional circularly polarized waveguide slot antenna is proposed for L/S-band (1.59-1.84 GHz/2.48-2.55 GHz) maritime satellite systems. Axially symmetric X-shaped slots enable dual-band operation with 14.6% impedance bandwidth (L-band) and axial ratio \(<\) 3 dB. A three-stage tapered coaxial feeding network achieves efficient matching (1S11 \(<-\)10 dB) across a BeiDou-1 uplink (1.61-1.6265 GHz) and downlink (2.4835-2.5 GHz), delivering 4.1 dBi peak omnidirectional gain at 1.6 GHz. The compact design (radial dimension \(\leq\) 0.25x) offers robust performance in harsh marine environments with integrated wideband, high-gain, and conformal capabilities.  
s 2025 14 1326 1 1 1  
A Article]Dual-Frequency Common-Cable Waveguide Slot Satellite Communication Antenna
###### Abstract  
We investigated the repellency and toxicity of eight plant extracts containing eclangulin, cnidium lactone, matrine, nicotine, pyrethrins, rotenone, stemonine and verartine against the western flower thrips (_Frankliniella occidentalis_ Pergande; Thysanoptera: Thripidae). Flowers treated with 0.05% nicotine, pyrethrins, stemonine and rotenone harbored fewer western flower thrips in two- and multiple-choice repellency bioassays. When evaluated at concentrations varying from 0.005% to 0.1% in two-choice repellency bioassays, pyrethrins and rotenone were most repellent at 0.01% to 0.1%, and nicotine was most repellent at 0.025%. Mortality was 76% with 0.1% nicotine at 48 h, 78% with 0.5% stemonine at 72 h, and 100% with 0.1% pyrethrins and 0.5% rotenone at 48 h after contact with fresh (<1-d-old) residue. Effective residue age was 1 d for nicotine, pyrethrins and rotenone, and 5 d for stemonine in aged residual toxicity bioassays. Celangulin, cnidium lactone, matrine and verartine did not provide sufficient repellency and toxicity. Stemonine had inconsistent results. Therefore, additional evaluation of its potential as a botanical insecticide will be needed. When sprayed onto whole plants, plant extracts containing nicotine, pyrethrins, rotenone and stemonine caused unacceptable damage to flowers, suggesting that the final formulation will need to be modified to improve crop safety.  
boatical insecticides; nicotine; pest management; pyrethrins; rotenone; stemonine Article
## 4 Simulations
### Evaluation of EDETA-e Protocol Underwater  
We test the EDETA-e for UWSNs by means of simulation using the ns-3 simulator. For modeling the underwater communication, we use the Underwater Acoustic Networks (UAN) with Thorp as the acoustic propagation model. A set of EDETA-e simulations was performed to tune parameters and to study if the protocol is suitable for this new transmission medium.
# Modeling a Dual-Mode Controller Design
## 2 QRF Converter with Dual-Mode Control
### Single-Switch Flyback Converter
Thus, the switch cycle (\(T_{sw}\)) of the fly-back converter can be determined using the following equation:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]0  
and  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]1  
The switching frequency formula at this mode can be estimated as follows:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]2  
Then, the switching cycle \(T_{sw}\) of the QRF converter can be obtained as follows:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]3  
and  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]4  
The final formula of the switching frequency at this mode can be estimated as follows:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]5  
In terms of the power stage design, the inductor is the primary component for input power determination. The input power of the fly-back converter (\(P_{in}\)) can be estimated using the following equation:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]6  
The switching frequency formula can be rearranged as follows:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]7  
By substituting \(T_{sw}\) into (16), the following representation of \(I_{lp(pk)}\) is obtained:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]8  
The minimum switching frequency (\(f_{sw(\rm{min})}\)) is obtained at \(P_{o(\rm{max})}\) and \(V_{in(\rm{min})}\) based on the above formula. To confirm the relation between \(L_{p}\) and \(f_{sw(\rm{min})}\), the minimum inductance condition of \(L_{p}\) can be designed for the inequality condition as follows:  
\[I_{Lp}(t)\ =\ \frac{V_{g}}{L_{p}}t \tag{1}\]9  
The QRF converter operates in discontinuous conduction mode. Moreover, the switching frequency changes due to variations in the input voltage (Vg), output voltage (Vo), and output power (Po). Thus, the minimum limit condition of the switching frequency must be considered in the proposed circuit. Figure 3 illustrates the curve of the relationship between \(f_{sw}\) and \(V_{g}\).
## 3 Solving QTL by Multi-Criteria Optimization
### Hybrid Genetic Algorithm for QTL  
In this study an extension of the EEA method is developed for multi-objective optimization (based on the approach presented in [25]). Our MOGA is then applied to solve the QTL problem with the two objectives being to maximize \(R^{2}\) (defined in Equation (6)) and minimize the complexity \(|B_{s}|\).  
In addition to providing the objectives, in order to apply a genetic algorithm it is necessary to define a mapping from solution instances to a string representation and both recombination (or crossover) and mutation operators. Genetic algorithms typically use fixed size representations rather than variable size representations for simplicity (for some examples of variable size see [33; 34]). Hence, for the QTL problem, each solution instance is represented as a fixed size binary vector of length \((k^{2}+k)/2\). Note that each bit in the solution represents the inclusion (value \(1\)) or exclusion (value \(0\)) of the correspondingsingle or epistatic loci interaction: \(k\) bits are for single loci and the remaining \((k^{2}-k)/2\) are for epistatic interactions. Standard crossover and mutation operators are employed utilizing the parameters defined in Table 1.  
The MOGA algorithm works as follows:  
1. Generate the initial population as uniformly random binary vectors.
2. Evaluate each solution for both objectives (accuracy and complexity).
3. Rank each solution based on the number of dominating solution in the population.
4. Compute (\(Fit^{\prime}\)) according to Equation (7) for each solution.
5. Eliminate individuals from the population when \(Fit^{\prime}\) less than the stress factor.
6. For each elimination apply tournament selection to identify two extant solutions. Crossover these solutions with _crossover probability_. Replace the elimination with the offspring if created, or one of the extant solutions. Mutate the new individual with probability _mutation probability_. Evaluate the new individual.
7. If the termination condition is not satisfied go to the step 3.
###### Abstract  
The characteristics of fresh and hardened self-compacting concrete (SCC) are an essential requirement for construction projects. Moreover, the sensitivity of admixture contents of SCC in these properties is highly impacted by that cost. The current study investigates to estimate the slump-flow (S) and compressive strength (CS), as fresh and hardened properties of SCC, respectively. Four developed soft-computing approaches were proposed and compared, including the group method of data handling (GMDH), Minimax Probability Machine Regression (MPMR), emotional neural network (ENN), and hybrid artificial neural network-particle swarm optimization (ANN-PSO), to estimate the S and 28-day CS of SCC, which comprises fly ash (FA), silica fume (SF), and limestone powder (LP) as part of cement by mass in total powder content. In addition, the impact of eight admixture components is investigated and evaluated to assess the sensitivity of admixture contents for the modelling of S and CS of SCC. The results demonstrate that the performance prediction of ENN model is more significant than other models in estimating S and CS characteristics of SCC. The overall of Pearson correlation coefficient, r, and root mean square error (RMSE) of ENN model are 97.80% and 20.16 mm, respectively, for the S. These are 96.07% and 2.59 MPa, respectively, for the CS. Furthermore, the sensitivity of the powder content of fly ash is shown to have a high impact on the estimated S and CS values of SCC.  
self-compacting concrete; slump flow; compressive strength; emotional neural network +
Footnote †: journal: Applied Sciences