esa-sceva/satcom-chunk-collection huggingface dataset:
# A Replica-Selection Algorithm Based on Transmission Completion Time Estimation in ICN
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.2 Edge Node Throughput  
We measured throughput at the edge node. Figure 7(a) shows the variation in throughput over time in Experimental Scenario 1. TCTE has much higher throughput than the other two algorithms, followed by the IDRS algorithm, and the NR algorithm is the worst. This is because it is difficult for the IDRS and NR algorithms to reasonably distribute traffic to several replica nodes that do not share bottleneck links. The NR algorithm cannot select a farther replica node, even if the farther replica node can obtain a higher transmission rate. IDRS randomly selects replica nodes, which can somehow achieve dynamic distribution of traffic on different paths, so IDRS performs better than NR. However, it is difficult to achieve the best effect by random selection.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Link** & **Bandwidth (Mbps)** \\ \hline Between L1 and L2 & Random values (20 to 100) \\ Between L2 and L3 & Random values (100 to 200) \\ Between L3 and L4 & Random values (200 to 300) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bandwidth settings for Experiment Scenario 2 topology.  
Figure 5: Experimental Scenario 2 topology.  
Figure 8(b) shows the variation in throughput over time in Experimental Scenario 2. The highest throughput is still the TCTE algorithm, while IDRS and NR perform poorly. The performance of NR becomes better because the nearer replica node in this scenario has  
Figure 8: Throughput of edge nodes.  
Figure 6: User download rate over time.  
Figure 7: Mean and confidence intervals for download rates.  
a higher probability of being an intermediate node between the farther replica node and the edge node. The bottleneck bandwidth between the edge nodes and the intermediate nodes is not lower than that between the edge nodes and the farther nodes. In addition, in both scenarios, TCTE has a very stable throughput, while the throughput of the other two algorithms has large fluctuations. Therefore, TCTE not only effectively improves the throughput of edge nodes, but also keeps the throughput stable.  
In addition, in order to illustrate the reliability of the experimental results, we repeated the experiment 10 times, and took the average edge node throughput of each experiment for analysis. Figure 9 shows the mean and confidence interval (95% confidence level) for edge node throughput.
## Nomenclature  
Variables  
\(AFP\)  
aerosol formation potential (t PM 2.5 -eq)  
\(AP\)  
acidification potential (t SO\({}_{2}\)-eq)  
\(BC\)  
battery capacity (kWh)  
\(CA\)  
carbon allowance (EUR/t CO\({}_{2}\))  
emission (t)  
\begin{tabular}{l l} EF & emission factor (g emission/kg) \\ EH & energy for heating of fuel cell system (kWh) \\ FC & fuel consumption (kg) \\ GWP & global warming potential (t CO\({}_{2}\)-eq) \\ LHV & lower heating value (MJ/kg) \\ \(m\) & weight of an engine/t) \\ \(p\) & power (kW) \\ \(SFC\) & specific fuel consumption (kg/kWh) \\ \(t\) & time (h) \\ \(x\) & share (\%) \\ Subscripts & \\ A & annual \\ AE & auxiliary engine \\ ave & average \\ f & fuel \\ ME & main engine \\ p-f & pilot fuel \\ Greek letters & \\ \(\eta\) & efficiency (-) \\ Abbreviations & \\ AES & All-Electric Ship \\ A-FC & Ammonia in a Fuel Cell \\ A-ICE & Ammonia in an Internal Combustion Engine \\ APS & Announced Pledges Scenario \\ CII & Carbon Intensity Indicator \\ CNG & Compressed Natural Gas \\ D & Diesel \\ DME & Dimethyl-ether \\ ECA & Emission Control Area \\ EEDI & Energy Efficiency Design Index \\ EXI & Energy Efficiency Existing Ship Index \\ ETS & Emission Trading System \\ FAO & Food and Agriculture Organisation \\ FU & Functional Unit \\ GHG & Greenhouse Gas \\ GT & Gross tonnage \\ H-FC & Hydrogen in fuel cell \\ HFO & Heavy Fuel Oil \\ ICE & Internal Combustion Engine \\ IES & Isolated Energy System \\ IMO & International Maritime Organisation \\ LBG & Liquefied Biogas \\ LCA & Life-Cycle Assessment \\ LCCA & Life-Cycle Cost Assessment \\ LNG & Liquefied Natural Gas \\ LPG & Liquefied Petroleum Gas \\ LSFO & Low-Sulphur Fuel Oil \\ M & Manufacturing \\ MDO & Marine Diesel Oil \\ MeOH & Methanol \\ NZES & Net Zero Emissions Scenario \\ PEMFC & Proton Exchange Membrane Fuel Cell \\ PM & Particulate Matter \\ RES & Renewable Energy Source \\ SE EMP & Ship Energy Efficiency Management Plan \\ SOFC & Solid Oxide Fuel Cell \\ SPS & Stated Policies Scenario \\ TTW & Tank-to-Wake \\ WTT & Well-to-Tank \\ \end{tabular}
# Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
## 5 Solving CVRP with the Hyper-Heuristic Approach
### Evolved Heuristics in Dynamic CVRP  
A dynamic VRP environment is simulated in the following manner: a given percentage of customer orders is not revealed at the beginning, and only becomes known at a certain point in time. Although there are many ways in which the dynamic environment could be emulated, such as the breakdown of vehicles and changes in travel time, we chose the commonly used variant as outlined in [40; 42]. It should be noted that the proposed approach can cope with any type of dynamic disruption, by using the parallel meta-algorithm with the current number of vehicles (which can also be subject to change). For a problem instance with 200 customers, a varying percentage of orders (5, 25, and 50 percent) is only available at a later point in time, which corresponds to different levels of dynamism. The routes are generated on the fly using the evolved priority functions. The GP evolved solutions are compared with the \"nearest neighbour\" (NN) heuristic, which chooses the closest customer for every vehicle at every decision point. The number of vehicles is fixed to 15 according to [40]; because of this, the results in dynamic conditions show only the total travelled distance, since the vehicle count is the same for both heuristics. The results of this experiment are presented in Figure 2 and Table 5.  
Since the dynamic differences include the appearance of new orders, we expect an increase in the total duration relative to the static solution. Although most of the GP-provided priority functions react better than the NN heuristic, there are some outliers which perform worse in dynamic conditions. This indicates that the training instances for GP should also be designed with a dynamic component, since all the training instances in the previous section were taken from a static benchmark.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline
**Test Instance** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **Total** \\ \hline best known & 932.7 & 1137.2 & 881.0 & 1103.7 & 1364.2 & 1656.7 & 666.8 & 973.6 & 1338.7 & 1831.6 & 11,886.3 \\ min & 1178.2 & 1383.9 & 1060.4 & 1321.6 & 1674.7 & 2031.0 & 863.1 & 1251.3 & 1669.3 & 2230.2 & 14,843.1 \\ median & 1249.3 & 1470.8 & 1089.2 & 1422.4 & 1703.8 & 2098.5 & 938.8 & 1316.3 & 1837.5 & 2386.1 & 15,442.5 \\ max & 1318.8 & 1542.1 & 1135.1 & 1477.4 & 1778.8 & 2148.9 & 1003.6 & 1344.2 & 1875.1 & 2563.0 & 15,841.0 \\ \hline \end{tabular}
\end{table}
Table 4: Total travelled distance—CVRP test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Heuristic** & **Static** & **Dynamic 5\%** & **Dynamic 25\%** & **Dynamic 50\%** \\ \hline nearest neighbour (NN) & 3066.1 & 3805.1 & 4257.6 & 4280.2 \\ GP—min & 2980.6 & 3735.7 & 4158.6 & 4099.2 \\ GP—median & 2980.6 & 3768.2 & 4214.0 & 4274.1 \\ GP—max & 3065.9 & 3896.3 & 4269.4 & 4444.1 \\ \hline \end{tabular}
\end{table}
Table 5: Total travelled distance—dynamic CVRP.
# Characterisation of Dust Particles Deposited on Photovoltaic Panels in the United Arab Emirates
###### Abstract  
The United Arab Emirates (UAE) experiences up to 50% power losses in photovoltaic (PV) panels caused by frequent dust accumulation over the panels trailed by extreme temperature. Compositional and morphological insights into dust particle can potentially help design PV cleaning mechanisms inclusive of self-cleaning explored in the current article. Five different locations were studied to discover potential differences in dust samples. The collected samples were characterised employing Optical Microscopy, Scanning Electron Microscopy (SEM), X-ray Powder Diffraction (XRD), and Elemental Composition Analysis (Energy Dispersive Spectrometry, EDS). The micrographs revealed that the majority of particles were irregularly shaped, providing interlocking for the dust to stay over the surface. The particle size ranged from 0.01 to 300 \(\upmu\)m, and some of the collected dust exhibited cavities. XRD analyses revealed variations in the chemical composition among the samples studied. Elemental Composition Analysis via EDS revealed both consistent patterns and variations in element presence among the dust samples, highlighting specific detections of chlorine (Cl) at some sites.  
dust accumulation; photovoltaic; X-ray powder diffraction (XRD); scanning electron microscopy (SEM); dust chemical composition; performance degradation
## 3 Results
### Characterization of TSs Provides an Overview of the Terpenoid Biosynthetic Potential in Trichoderma
(**c**) Interaction of _Tgam_ with wheat roots. Total RNA was extracted from mycelium of _Tgam_ grown on PDA for 3 days (basal condition, \(2^{-\Delta\Delta\Gamma}=1\)) and from wheat roots colonized by _Tgam_ for 3 days. The \(\beta\)-_tubulin_ gene was used as control for data normalization. Values are means of three independent biological replicates with the corresponding standard deviation. Fold change in sample relative to control is expressed as \(2^{-\Delta\Delta\Gamma}\). Statistically significant values are indicated with asterisks (\(p\geq 0.05\) no significative; \(0.05>p\geq 0.01=\)°; \(0.01>p\geq 0.001=\)°; \(p<0.001=\)°). prominent changes in terpene biosynthesis when interacting with _Fgra_ on wheat spikes, under the conditions tested. Finally, we observed that root colonization affected TSs expression in _Tgam_, which differentially regulated most of its TS genes, with the exception of _ts7_, whose transcripts were not detected in any condition, and _ts1_, in which expression did not change (Figure 3c). This clearly indicates a reprogramming in terpene biosynthesis in _Tgam_ when colonizing wheat roots. In particular, a modulation in sesquiterpene biosynthesis occurred in _Tgam_ during root colonization. Whereas _ts5_ was slightly up-regulated (1.58-fold), the contact with the roots strongly repressed the expression of _ts4_ (0.03-fold) and to a lesser extent that of _ts3_ (0.59-fold). Nevertheless, the most remarkable difference was found in the expression of _tri5_, which was 134-fold up-regulated when the fungus was on the roots, suggesting that signals from the roots are responsible for triggering the expression of this gene in _Tgam_. In addition, the interaction with the roots up-regulated _ts6_ (1.52-fold) and _ts11_ (2.58-fold). Since _ts6_ was predicted to encode an SQS, this suggests that triterpene biosynthesis was slightly enhanced in _Tgam_ when colonizing the wheat roots. Interestingly, _ts9_ was found highly down-regulated (0.28-fold), thus suggesting a repression of indoleiterpene biosynthesis in the fungus during the interaction with wheat roots.
## 4 Results and Discussion
### Location-Allocation Results of Central Emergency Shelters  
The location of the central emergency shelter is based on the siting model of the established central emergency shelter. On the basis of the facility points of the fixed emergency shelters, the maximum number of demand points can be covered by maximizing the coverage model and corresponding constraint conditions. The number and coverage rate of central emergency shelters are listed in Table 3. In order to comprehensively take into account factors affecting emergency shelter configuration (i.e., fairness and efficiency principle), three central emergency shelters were eventually selected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Facility Point** & **Number of Demand Point Covered** & **Coverage Rate (\%)** \\ \hline
1 & 331 & 74.72 \\
2 & 343 & 77.42 \\
3 & 357 & 80.59 \\
4 & 366 & 82.62 \\
5 & 372 & 83.97 \\
6 & 379 & 85.55 \\
7 & 384 & 86.68 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number and coverage rate of the central emergency shelters in the Jianchuan county seat.  
Figure 3: The results of the optimal location of the fixed emergency shelter in the Jianchuan county seat.  
The central emergency shelter is the largest and the perfect emergency shelter in an urban emergency system. Regarding its location selection and optimization process, the main principle is similar to fixed emergency shelters. In addition, it is noteworthy that the location of a central emergency shelter should be selected based on the location of fixed emergency shelters, providing appropriate transportation links as far as possible between individual locations. Even in some individual locations, the central and fixed emergency shelters can be merged together considering economic efficiency. According to the analysis of the model of three places, we can conclude that (see in Figure 4): (1) from the perspective of urban design and master planning for the Jianchuan county seat, the location of the Jianchuan Hall Square is defined as the central emergency shelter, and the radiation of its service area is mainly towards the northern region of the whole town, thus it can be selected as one of the central emergency shelters; (2) Jianchuan high school and Jinhua middle school, as fixed emergency shelters, are mainly serving the population of the ancient town and the transportation condition is better, which is more reasonable to be considered as the center of the emergency shelter; and (3) the farmer's market has been located in the southern part of the county seat. The main service targets are the residents of the south part of the county seat. Therefore, it is reasonable to nominate it as an emergency shelter. The results of the analysis show that the optimal layout of the three center emergency shelters is appropriate.
## 1 Introduction
In spite of the fact that reliability is a matured research field and much information can be found in the classical literature [55; 56], the interrelation between operational reliability and PQ has usually been unnoticed and skipped--the proposed concept is given in Figure 2. Possible reasons could be figured out from [5]: insufficient scientific and technical progress (because PQ is a relatively new research field), deficiency of massive PQ monitoring systems, absence of integration with other Smart Grid applications, mainly manual data processing, gaps in PQ law, a lack of unified assessment methods, and data confidentiality issues. The primary reasons for grid faults can be treated as another overlapping field, because they can cause both (i.e., either voltage sag or interruption), and the consequences depend on many factors--number of faulted phases, grid topology, relay protection and automation, the presence of mitigation devices, user criticality, etc. Currently, the probabilistic analysis (also forecasting) of PQ events (in particular, voltage sags/interruptions) along with their primary causes remains an open research field [5]. In spite of limited scientific literature, the latest paper [57] (2021) can be considered as the best and most relevant example. It investigates blackout history of the United States, and the Minnesota electrical grid's resilience in 2009-2016 (in particular, the impact on SAIFI, CAIFI, and SAIDI) through a prism of primary causes (in particular, extreme weather conditions). The research became possible only after outage management system integration with an advanced metering infrastructure (about 24 thousand meters). In this paper, inspired by [57], the Lithuanian distribution grid will be investigated through a prism of both the primary causes and their correlation with reliability indexes (with available data for 2015-2018), which is important for a PQ monitor allocation strategy. Additionally, some information regarding grid equipment failure rates in Lithuania from 1995-2000, can be found in [56]. Considering the above, task No. 1 is to establish (investigate) the fundamental principles of voltage sags propagation and determine the universal rules of the optimization of their monitoring as much as is feasible in the created Lithuanian test grid. In addition, by implementing this task, a useful PQ database will be created as a secondary result for both manual voltage sag data analysis and further research through the application of AI. Task No. 2 is to establish (investigate) a supplementary criterion through a prism of primary reasons for voltage sag and (their correlation with) grid reliability indexes from data collected on the Lithuanian distribution grid. By combining both tasks, the main aim of this paper is to investigate the fundamental principles (rules) of voltage sags propagation and their interconnection with power supply reliability, which will have a high practical significance for PQ monitor allocation strategies.
## 3 Experimental Setups  
### Impedance Tube: 1D System  
The experimental setup used for the characterization of the reflection and absorption properties of the 1D system of Helmholtz resonators is designed following the guidelines of the Standard UNE-EN-ISO 10534-2 [45]. A picture of the experimental setup is shown in Figure 3. The impedance tube has a square shape section with side length \(w_{t}=10\) cm and a total length of 1.5 m, along the propagation direction. A loudspeaker Fonestar UT-4104, controlled by a M-AUDIO FastTrack Pro sound card, was placed at one end of the tube. The system of 5 Helmholtz resonators was manufactured using in polylactic acid (PLA) using 3D printing. The printer (Anycubic i3 Mega) operates with a layer resolution of 0.05 mm, and a nozzle diameter of 0.4 mm. It was placed at the other end of the tube backed by a totally reflecting metallic block acting as a rigid end. Two B&K 4189 microphones with their respective preamplifiers (B&K 2671) are mounted on the tube walls, separated by a distance of 9 cm. The closest microphone to the sample is placed at 10 cm from the sample. The low and high cut-off frequencies are \(f_{l}=200\) Hz, \(f_{h}=1700\) Hz, respectively.
## 2 Materials and Methods
### Cloud and Software Architecture  
The cloud and software architecture proposed in this research (Figure 2) integrates three core components: the client interface, cloud server platform, and underwater ROV system. This architectural design facilitates the smooth integration of various remote control and mobile communication technologies, ensuring seamless communication between different control platforms and the underwater ROV system. The RTSP service integrates H.264 encoding technology to provide high-quality 1080p video transmission at 2-4 Mbps transmission rates, with adaptive capabilities that automatically adjust to different network conditions. The system operates normally under different bandwidth environments, from 1 Mbps 480p resolution 15 fps basic requirements to 4 Mbps 1080p 30 fps. The smart aquaculture monitoring system incorporates cloud security measures aimed at preventing cyber threats and ensuring data integrity across all communication channels. The system protects network traffic through TLS1.3 encryption protocols, maintaining secure data transmission between system components.
# Interpretation of Frequency Response Analysis for Fault Detection in Power Transformers
## 6 Conclusions  
FRA is a reliable tool for the assessment of power transformer mechanical integrity. However, FRA signature interpretation is still challenging due to the lack of easy and straightforward guidelines. This paper proposes an easy-to-use flowchart and characteristic table as a guideline to facilitate the identification and quantification of FRA signatures. The proposed guidelines are achieved according to the following points:  
* The proposed guidelines are established based on the review of previous extensive research presented in the literature, along with various FRA international standards.
* The flowchart is developed based on three frequency regions and three levels of variations based on CC calculation.
* The characteristic table is presented graphically with arrows to provide a simple interpretation scheme for an inexperienced user.
* An experimental case study conducted on an 11/0.433 kV 500 kVA transformer with normal and short circuit faults is presented to verify the feasibility of the proposed guidelines.  
Experimental results show that the variations within the three frequency regions of the measured response are consistent with the proposed guidelines. The proposed FRA guidelines can be used to identify approximately twenty-two transformer faults and conditions. Further feasibility studies should be conducted to investigate the accuracy of using the proposed method in practical case studies. Additionally, proposed guidelines should be analyzed and tested on different power transformers of various ratings and winding configurations.  
Conceptualization, S.M.A.-A. and M.F.M.Y; methodology, S.M.A.-A., M.F.M.Y. and M.S.K.; software, S.M.A.-A. and M.F.M.Y.; validation, S.M.A.-A. and M.F.M.Y.; investigation, S.M.A.-A., M.F.M.Y. and M.S.K.; resources, S.M.A.-A. and M.F.M.Y.; writing--original draft preparation, S.M.A.-A. and M.F.M.Y.; writing--review and editing, M.F.M.Y., M.S.K., A.S. ([PERSON]),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Range** & **CC Value** \\ \hline
20 Hz–2 kHz & 0.9842 \\
2 kHz–20 kHz & 0.6483 \\
20 kHz–2 MHz & 0.9965 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Correlation coefficient results.  
Figure 12: Measured FRA for normal and short circuit faults.  
A.A.S. ([PERSON]) and M.I.M.; project administration, M.F.M.Y. and M.S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme Vot No. FRGS/1/2018/TK10/UTHM/02/10 and partially sponsored by Universitit Tun Hussein Onn Malaysia.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available in this article.  
The authors would like to thank [PERSON]. [PERSON] from TNBC Malaysia for his assistance during the experiment. We would also like to thank all the subjects who volunteered to participate in this study.  
The authors declare no conflict of interest.