esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Simulation Scenario  
Simulations were performed with Network Simulator 2 (NS2) version 2.31. As NS2 does not include ZigBee routing by default, the routing was implemented and the supplied IEEE 802.15.4 implementation was modified to support beacon scheduling as described in the ZigBee standard. The transceiver was configured to 2.4 GHz frequency band and 250 kb/s data rate. In ZigBee, IEEE 802.15.4 LR-WPAN used slotted and beacon enabled mode, because it is most suitable for low energy WSNs. Only one cluster channel was utilized, which results to a network scan time of one access cycle. Battery life extension was not used.  
For comparable results, ZigBee and TUTWSN were configured with similar throughput and access cycle length. All simulations used 18 B application payload. In ZigBee, the beacon interval was 3.94 s and superframe length was 0.24 s. TUTWSN MAC used 4 s access cycle, 4 contention-based slots, and 8 reserved slots per access cycle. Time schedules ensuring that the active periods (superframes) of nodes within interference range do not overlap were used for both MACs.  
The routing table driven approach in the ZigBee requires end-to-end messaging and can therefore be inefficient in a multihop network. However, as using the routing table is optional in the standard, the cluster tree routing was used instead. As a result, operation in both protocols is similar. A node finds a route locally by first performing a network scan and then selecting and associating to the neighbor that is nearest to the sink. Both TUTWSN and ZigBee routing used equal cost on each link, thus resulting into minimum hop forwarding.  
Simulation scenario consisted of 50 randomly deployed nodes as shown in Figure 7. Two-ray ground propagation model was used with a maximum communication range of 20 m.  
Nodes that acted as traffic sources generated packets to a sink node according to poisson distribution as it causes slight variation in traffic and thus gives more realistic results than sending traffic with constant intervals. The rate parameter \(\lambda\) for the poisson traffic was derived from the desired offered load \(o\) and the application payload as \(\lambda=\frac{18\,B}{o}\).  
The duration of each simulation was 3 hours. The results were averaged over 10 repetitions with randomly selected source nodes. To avoid path length from causing different results between repetitions, the source nodes had the same average hop count (4 hops) to the sink. In all results, 95% confidence interval was less than 10% of the measured values. Both end-to-end reliability and goodput were measured with a goodput metric that is the ratio of packets generated at the source node against the number of unique packets received at the destination node.  
Packet errors were uniformly generated. Thus, each packet has the same probability of being discarded regardless of the packet length. The default seed in ns-2 was used for the random number generator. Unless otherwise stated, the buffer size is set to 500, which is high enough to guarantee that buffer drops do not occur due to momentary network errors.
# Multimodal Natural Human-Computer Interfaces for Computer-Aided Design: A Review Paper
## 6 Devices for Natural HCI
### Devices for EEG Acquisition  
The EEG acquisition device is a piece of equipment that can record the electrical activity of the brain through some specific electrodes placed on a user's scalp [158]. In 1924, the first EEG detection device in the world was developed by [PERSON] in Jena, Germany [159]. Following the development of signal processing and microelectronics technology, the structure and function of EEG-acquisition devices have gradually matured. There are numerous EEG devices from different companies that are able to cater to the specific needs of EEG users, such as Neuroscan, Emotiv, and Neurosky MindWave.  
Generally, EEG-acquisition devices can be divided into invasive, semi-invasive, and non-invasive categories according to the connection with the brain [160]. Among them, a non-invasive EEG device is mainly used in the area of natural HCIs due to the damage of the scalp caused by the invasive device and the semi-invasive device. For non-invasive EEG devices, the types of electrodes used can be categorized into saline electrode, wet electrode, and dry electrode [160]. Saline electrodes use salt water as a conductive medium, which is easy to carry and low in cost [161]. Wet electrodes usually need to use gel as a conductive medium and can collect high-quality EEG signals. Dry electrodes can directly contact the user's scalp to achieve conduction without any conductive medium which is more natural and convenient to the user, but the signal acquisition accuracy is limited [162].  
In current studies in the literature, Emotiv Epoc+ is commonly used as an EEG-acquisition device in the application of CAD [3; 57; 58; 59], as shown in Figure 6. Emotiv Epoc+ is a small and low-cost device used to record and recognize EEG and facial muscles' movements (EMG) with 14 saline electrodes. This device can communicate wirelessly with computer via a USB dongle and provide the Emotiv API, a C++ API, which can be used to easily obtain the EEG/EMG data and transform the user-specific action command into an easy-to-user structure [3].  
Additionally, some other lightweight and low-cost EEG devices, such as Neurosky MindWave and InteraXon Muse, may also be used to build future BCI for CAD.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 3 Control Strategy of the Converter  
The LC filter can effectively decrease the surge current during LVRT, but it will lead to stability problems in the system. An appropriate control strategy should be proposed to solve the problem.
## 2 Related Work
### Raw CSI Signals  
Few studies build a model-based recognition system that achieves better classification without signal pre-processing. For example, fine-grained real-time passive human detection, FRID [47], system computes the coefficient of temporal phase variation from the phase sensitivity due to human motion and detect the passive human motion with 90% precision. [PERSON] et al. [48] proposed a threshold prediction model using Rician fading and the computation of cross-correlation factors to detect a human presence with a false positive and negative less than 4% and 5%, respectively. [PERSON] et al. [49] proposed a deep learning model for implementing a device free wireless localization and activity recognition (DFLR) system using a sparse auto-encoder neural network. DFLR achieves recognition accuracy of 90% for eight different activities by transforming the CSI information as an image feature to the deep learning network. PriLA [50], the privacy preserving location authentication system, proposed a model exploiting the channel frequency offset and multipath information as the detrimental features and achieves an accuracy of 93.2% for authenticating user's location information.
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 4 Results and Discussions
### PV Systems Analysis
#### 4.3.3. Tariff Sensitivity Analysis  
After, it became clear that the tariff of the electricity produced by the system and exported to the grid is a significant obstacle to integrating solar energy with school buildings. In this paragraph, the change in the tariff of the electricity exported to the grid was studied in relation to the feasibility of the systems. The price was changed from the current approved price of $0.0133 (\(\approx\)SAR 0.05) per kWh to the cost of retrieving electricity from the public grid for the government sector, $0.0931 (\(\approx\)SAR 0.36) per kWh to obtain a zero bill along the system's life. The increase in the tariff led to a decrease in the number of PV modules required, the system capacity, and the SPBP for all systems. The SPBP became acceptable at $0.0532 (\(\approx\)SAR 0.20 SAR) per kWh, which ranged between 8.52 and 15.98 years for undiscounted and discounted conditions, see Figure 7. The right of the dotted line represents the economically feasible region in the figures for the studied PV systems. The LCOE decreased for school 2 from $0.0124 to 0.0184, and for school 3 decreased from $0.0153 to 0.0194 per kWh. The price of electricity in the United States ranges between $0.11 and 0.30 per kWh in 2023, although it is usually less than $0.15 per kWh. The price of a unit of electricity produced from PV systems is between $0.06 and 0.08 per kWh [48]. Thus, it is often about half the price of a unit of energy imported from the grid. Therefore, it is proposed that the price of a unit of energy produced from the PV system for the government sector be raised to 50% of the price of imported electricity for these systems to be economically feasible in the KSA. [PERSON] et al. [49] used metaheuristic optimization techniques for a radio transmitter station in India to optimally design a PV system with energy storage. [PERSON] et al. [50] summarized the most essential optimization works related to the design of hybrid PV-wind systems.  
Figure 6: Energy and costs along the project life cycle for school 3.
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 4 Topology of the Modeled Network  
Figure 1 presents the topology of the modeled network.  
R1 is a router and is used only as a DHCP server that distributes IP addresses to the devices on the network--VM1 to VM4, Kali_Linux and Asterisk_Free_PBX. No other settings were changed on the router. ESW1 and ESW2 are emulated disk images of real managed switches. The settings of these switches are the factory defaults, and no additional settings were changed. VMware Workstation Pro 17 is used for the virtualization of the VMs, Kali Linux and the Asterisk FreePBX. VM1 to VM4 are virtual machines with installed Windows 10 Home on them. The softphone Linphone is installed on these virtual machines through which the Asterisk subscribers (VM1 to VM4) talk to each other. Kali_Linux is a virtual machine on which Kali Linux is installed, through which Asterisk FreePBX is attacked. Asterisk_Free_PBX is also a virtual machine on which Asterisk FreePBX is installed. Using Wireshark, the link between Asterisk_Free_PBX and ESW2 is monitored. Thus, all packets that are exchanged between the Asterisk FreePBX and the individual VMs will be captured and then analyzed, using the capabilities of the Wireshark. The modeled network is isolated, and there is no connection to the Internet. For the present study, the topology of the modeled network is slightly modified. In the previous study, Kali Linux was deployed in another network, external to the Asterisk FreePBX, behind the R1 router. In the current study, Kali Linux and the Asterisk FreePBX are on the same network to negate possible router influences.
## 2 Materials and Methods
### Determination of GSLs Composition and Content  
Total GSLs were extracted and quantified using described methods with minor modifications [45; 46]. Overall, 0.5 g broccoli samples were mixed with 0.1 mL 5 mM standards; Sinigrin (PREN, Sigma Aldrich, \(\geq\)99% purity, Merck, Shanghai, China). The mixtures were extracted with 7 mL 70% methanol at 75 \({}^{\circ}\)C for 20 min. After cooling, 2 mL barium acetate was added and centrifuged at 8000 r/min to collect the supernatants. The precipitations were reextracted with 4 mL of 70% methanol and 0.5 mL of barium acetate. The supernatants collected twice were fixed to 10 mL with 70% methanol and isolated by anion-exchange chromatography using a DEAE Sephadex A-25 column (Solarbio, Beijing, China). The column was incubated at 37 \({}^{\circ}\)C for 16 h to allow desulfation, then eluted with 2 mL of deionized water, and collected into 2 mL sample bottles through a 0.22 \(\upmu\)m filter membrane.  
DesulfoGSLs were separated using a UHPLC-MS system (Waters Acquity TQD, Waters, Shanghai, China), which were analyzed by both UV-detection at 229 nm (for quantification) and MS-detection (for identity confirmation). The detailed procedures were as described by previous studies [47; 48]. The chemical name, precursors, and GSLs abbreviations are listed in Table 1. The molecular weight, retention time, and correction coefficient of each DesulfoGSL are shown in Table S2. The sinigrin was used as the reference standard to calculate the GSLs contents per ISO 9167-12. The recovery rate of the internal standard is shown in Table S3. The calculation formula of GSLs quantification is shown in Table S4, and the unit is expressed as \(\upmu\)mol/g DW.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Trivial Name** & **Precursor** & **Abbreviation** & **Chemical Name** & **Correction** \\ \hline
1 & Glucoiberin & Met & 3 MSOP & 3-(Methylsulfinyl)propyl I GSL & 1 \\ \hline
2 & Sinigrin & Met & PREN & 2-Propenyl GSL & 1 \\ \hline
3 & Glucoraphanin & Met & 4 MSOB & 4-(Methylsulfinyl)butyl GSL & 1 \\ \hline
4 & 4-Hydroxylucobrassicin & Trp & 4 HOI3M & 4-Hydroxylind-3-ylmethyl GSL & 0.28 \\ \hline
5 & Glucobrassicin & Trp & I3M & Indol-3-ylmethyl I GSL & 0.29 \\ \hline
6 & 4-Methoxyglucobrassicin & Trp & 4 MOI3M & 4-Methoxyindol-3-ylmethyl I GSL & 0.25 \\ \hline
7 & Neoglucobrassicin & Trp & 1 MOI3M & 1-Methoxyindol-3-ylmethyl GSL & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature of the GSLs identified in broccoli leaves and roots.
## 4 Experiments and Results
To further elaborate on this \(F1\_score\), we present the macro average precision and recall values for the same MST method across different document types and annotation types. The precision and recall values are 90 \(\pm\) 6% and 78 \(\pm\) 4%, respectively. Also, the results show that both the SST and MST methods perform better in detecting tokens with the trigger label compared to detecting the tokens with the cause and effect labels. Additionally, both methods perform better on texts extracted from FMEA documents compared to texts extracted from presentation slides. Furthermore, both models show an increase in performance when changing the initial language model from BERT to MatBERT without in-domain pre-training. The MST method outperforms the SST method in detecting the different labels when using models without in-domain pre-training. The MST method experiences a positive performance impact when using BERT as a language model, texts from FMEA as the testing set, and using UM as a masking objective. This performance increases further when using PMI as a masking objective. At the same time, the MST method experiences a positive performance impact when using MatBERT as a language model, texts from presentation slides as the testing set, and UM as a masking objective. This performance also increases further when using PMI as a masking objective. Similarly, the SST method experiences a positive performance impact when using BERT as a language model, texts from presentation slides as the testing set, and UM as a masking objective. This performance increases further when using PMI as a masking objective. Surprisingly, the SST method also experiences a positive performance impact when using MatBERT as a language model, texts from FMEA documents as the testing set, and using UM as a masking objective. This performance also increases further when using PMI as a masking objective.
# Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
## 2 System Overview
### Energy Harvesting  
The in-depth evaluation of power consumption was performed by monitoring the current consumed by the device, as shown in Figure 6. With a typical LiPo battery (3.6 V, 1000 mAh), the total energy budget is \(E_{total}=3.6\) V \(\times\) 1000 mAh = 3600 mWh. The average power consumed during a single measure-and-transmit cycle is approximately \(E_{TX}=650\) uWh, and the average time required to complete the cycle is \(T_{TX}=8\) s. On the other hand, when the device is in low-power mode between two measurements, the average power consumption is \(P_{sleep}=26\) uA \(\times\) 3.6 V = 93.6 uW. When \(n\) cycles of data collection are performed per day, a total daily energy consumption is calculated, as shown in Equation (3):  
\[E_{daily}=n\times E_{TX}+(24h-n\times T_{TX})\times P_{sleep} \tag{3}\]  
Accordingly, the total lifetime of the system (in days) can be calculated as \(\frac{E_{total}}{E_{daily}}\). Table 2 shows the estimation of battery life for various numbers of daily measurement cycles, with an assumption that no energy-harvesting techniques are applied whatsoever.  
The solar panel used in the model for the battery recharge has the output power \(P_{solar}\) = 400 mW with the reference value of solar radiation of \(\frac{1\,\mathrm{kW}}{\mathrm{m^{2}}}\). Under the most unfavorable operating conditions (1 transmission per minute), it takes \(\frac{E_{daily}}{P_{solar}}=2.34\) h to fully compensate for daily consumption. Statistical data available online [33] indicate that in all grapevine growing regions, the daily amount of solar radiation is large enough to provide the uninterrupted operation of the edge node with no need to recharge, even under the most demanding operating conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sampling Period** & **n (nb. of Daily Cycles)** & \(E_{daily}\) (mWh)** & **Battery Lifetime (Days)** \\ \hline
24 h & 1 & 2.90 & 146.06 \\
12 h & 2 & 3.55 & 142.32 \\
8 h & 3 & 4.20 & 138.76 \\
6 h & 4 & 4.85 & 135.38 \\
4 h & 6 & 6.15 & 129.09 \\
3 h & 8 & 7.44 & 123.
### Research Contributions  
The following are the research contributions of the article:  
* The article presents the deployment of 6G-enabled services in a multi-UAV ecosystem to address the challenges of the COVID-19 pandemic as 3 FFs (social distancing, sanitization, and inspection and monitoring of affected zones).
* To fulfil the 3 FFs vision, we present a proposed reference architecture and components that integrate 6G-envisioned wireless coverage channels to support the multi-UAVs swarm ecosystem at extremely low delay. As a result, the accurate precision of geolocation coordinates of infected patients in densely populated areas can be achieved with high bandwidth. Thus, the proposed scheme allows real-time responsive management to tackle the viral spread of global COVID-19 and future pandemics.
* We delve into the data-centric and computational aspects of UAVs and present an effective task offloading mechanism, where the offloaded tasks to edge servers are trained by local learning models in a federated manner. These local models update the gradients of global server models (normally analytics are performed on the cloud server).
* We present a non-stationary For estimation for moving persons for thermal inspection and social distancing norms. For sanitization, we present the nature of drones specific to the task. Finally, we present the specifics of blockchain-based vaccine registration, which forms an umbrella approach (end-to-end) solution to implement 3 FFs.
* We highlight the open issues and research challenges in the integration of 6G-based UAV communication and present a use-case scenario that compares the presented 6G service set with 5G-based UAV service sets in terms of parameters such as communication latency, density, spectral efficiency, UAV mobility, radio loss, and user-experienced data rates. The presented results indicate the viability of 6G-assisted UAVs to tackle 3 FFs at a massive scale.
# A Novel Plug-in Board for Remote Insect Monitoring
## 5 Conclusions  
In this work, a novel plug-in board is presented for remote insect monitoring purposes. The main components of the board are presented in detail, supported by schematics. Thanks to the plug-in board, the sensing device is easily portable and provides long-range wireless communication. It is also worth mentioning that the device can be used for the monitoring of different insect species, and it requires only minimal human intervention (e.g., changing sticky paper).  
Based on the measurement results, we can conclude that the proposed sensing unit is able to operate for a long period with or without solar charging, to perform a deep insect-counting algorithm, and its operating temperature remains below the safe limit.  
We tried to design the plug-in board cost-efficiently to make it widely available for fruit growers. At present, only a few sensing devices are available, but we are planning to start the production of the device in cooperation with the Hungarian Ekozig company.  
**Funding:** This work has been supported by the eKOZIG Regionalis Informatikai Szolgaltato Kozpont Zrt thanks to project No. 2020-1.1.2-PIACI-KFI-2021-00249. Project No. 2020-1.1.2-PIACI-KFI-2021-00249 has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the 2020-1.1.2-PIACI KFI funding scheme.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Number** & **Cost (USD)** \\ \hline MIC29302 WU & 2 & 4 \\ MCP79410 & 1 & 1.1 \\ EMB-LR1276S & 1 & 9.3 \\ LT3652 EMSE & 1 & 6 \\ SN74 HC125 & 1 & 0.3 \\ SIM7600E-H & 1 & 30.4 \\ NCP1117-3.3 & 1 & 0.4 \\ SN74 HC595 & 1 & 0.4 \\ CD74 HC14M & 1 & 0.3 \\ Resistors & 26 & 1.1 \\ Capacitors & 26 & 6.5 \\ Antennas & 2 & 5.3 \\ Connectors & 6 & 6.3 \\ Diodes & 5 & 2.3 \\ Other & 9 & 1.6 \\ Total & 84 & 75.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cost of the used electronics components.
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 2 Numerical Analysis
### Cavitation Inception Mechanism
#### 2.1.2 Critical Pressure of Cavitation Inception  
The cavitation number \(\sigma\) can describe the cavitation phenomenon quantitatively. Set \(\sigma_{crit}\) as the critical cavitation number when cavitation has just arisen or just disappeared, no cavitation when \(\sigma>\sigma_{crit}\); when \(\sigma=\sigma_{crit}\) cavitation has just occurred or just disappeared; When \(\sigma<<\sigma_{crit}\) cavitation is fully developed, even super cavitation may appear. In Equation (1) \(p_{\infty}\) and \(u_{\infty}\) are the pressure and velocity in the far-field undisturbed reference section, and the pressure coefficient \(C_{p}(x)\) is obtained by introducing the position-of-concern pressure \(p_{x}\) and the position-of-concern velocity \(u_{x}\)[36].  
\[C_{p}(x)=\frac{p_{x}-p_{\infty}}{\frac{1}{2}\rho u_{x}^{2}} \tag{2}\]  
where \(p_{x}\) is the position-of-concern pressure, Pa; \(u_{x}\) is the position-of-concern velocity, m/s; Let \(p_{x}-p_{\infty}=\Delta p\), i.e., \(\Delta p\) is pressure difference between the position-of-concern and reference position, Pa.  
Comparing the expressions of cavitation number \(\sigma\) and pressure coefficient \(C_{p}(x)\), we can see that \(\sigma=C_{p}(x)\), when the pressure at position \(x\) is the smallest, i.e., \(p_{x}=p_{min}\), cavitation will occur first at position \(x\).