esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact Force Mechanism of a Trimaran Model and Its Experimental Validation
## 5 Water-Slamming Experiment and Force Analysis
### Summary and Analysis of Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Working** & & & & & & & & & & & & \\
**Condition-** & & & & & & & & & & & & \\
**Point** & **2 mm** & **4 mm** & **No** & **1 mm** & **2 mm** & **4 mm** & **no** & **1 mm** & **2 mm** & **4 mm** & **No** \\
**Point** & **Thick** & **Thick** & **Thick** & **Clad** & **Thick** & **Thick** & **Clad** & **Thick** & **Thick** & **Thick** & **Clad** \\ \hline P1 & 1.42 & 3.13 & 0.32 & 23.36 & 1.16 & 3.35 & 0.37 & 27.77 & 1.00 & 3.56 & 0.41 & 24.91 \\ \hline P5 & 2.16 & 4.76 & 1.47 & 14.34 & 1.73 & 3.91 & 1.98 & 13.93 & 1.99 & 3.79 & 1.72 & 14.71 \\ \hline P6 & 1.67 & 4.84 & 1.48 & 12.35 & 2.17 & 5.15 & 1.76 & 15.59 & 2.27 & 4.11 & 1.37 & 13.33 \\ \hline P10 & 1.38 & 3.91 & 1.35 & 29.25 & 0.85 & 3.56 & 1.53 & 25.01 & 0.93 & 4.50 & 1.34 & 28.46 \\ \hline \multicolumn{10}{c}{**H14**} & & & & **H15** & & & & & & & **H16** & \\ \hline P1 & 1.02 & 2.68 & 0.42 & 31.29 & 1.10 & 3.26 & 0.39 & 20.34 & 0.88 & 3.05 & 1.07 & 36.85 \\ \hline P5 & 1.39 & 3.69 & 1.62 & 12.20 & 1.49 & 3.17 & 1.41 & 15.95 & 1.20 & 2.62 & 1.24 & 12.97 \\ \hline P6 & 1.91 & 4.29 & 1.77 & 14.00 & 1.38 & 3.24 & 1.12 & 16.40 & 1.55 & 3.51 & 1.54 & 11.22 \\ \hline P10 & 0.82 & 2.84 & 1.58 & 25.57 & 2.23 & 3.11 & 0.86 & 24.72 & 1.01 & 3.11 & 1.23 & 25.03 \\ \hline \end{tabular}
\end{table} 
Table 7: Experiment data. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Working** & & & & & & & & & & \\
**Condition-** & & & & & & & & & & & \\
**Point** & **2 mm** & **4 mm** & **No** & **1 mm** & **2 mm** & **4 mm** & **no** & **1 mm** & **2 mm** & **4 mm** & **No** \\
**Point** & **Thick** & **Thick** & **Thick** & **Clad** & **Thick** & **Thick** & **Clad** & **Thick** & **Thick** & **Clad** \\ \hline P1 & 1.42 & 3.13 & 0.32 & 23.36 & 1.16 & 3.35 & 0.37 & 27.77 & 1.00 & 3.56 & 0.41 & 24.91 \\ \hline P5 & 2.16 & 4.76 & 1.47 & 14.34  
For the same working conditions and the same measuring point, the measured and calculated slamming-pressure coefficients of the original model are almost always larger than the measured and calculated bang-pressure coefficients of the original model covered with a 1 mm open-cell silica gel sheet. Thus, we can draw the conclusion that surface flexible cladding (silica gel plate) can reduce the slamming pressure.
## 3 Results and Discussion
### Survival Rate of Patients by Emergency Case
#### 3.2.2 Survival Rates by Region Depending on Emergency Cases  
We examined the survival rate by region (urban and rural areas) with different emergency situations (Case 2, Case 3, and Case 4) compared to Case 1 (Figure 7). In Case 2 when patients immediately receive CPR, the survival rate increased in 12,439 villages compared to Case 1 (Figure 7a). Case 2 seems to be an ideal situation because the survival rate increased in 70% of the villages throughout the country. This result supports the other studies [54; 55; 56] that first aid provided by a person near the patient before an EMT arrives is important in improving the survival rate. In Case 3 where first aid is provided after the EMT arrives onsite, the survival rate increased in 3440 villages, but decreased in 2627 villages in mainly Busan, Gwangju, and Gyeonggi compared to Case 1 (Figure 7b). However, the survival rate in Seoul with Case 3 increased in some areas and decreased in other areas.  
In Case 4 where first aid is provided by a healthcare provider or an EMT-P nearest the patient, the survival rate increased in 5309 villages, but decreased in 2217 villages in urban areas such as Seoul,  
Figure 7: Survival rates in emergency response cases compared to Case 1. The blue areas represent the areas where the survival rate increases through expanded emergency responses by local individuals compared to Case 1. Red areas represent areas where the survival rate decreases. In both urban and rural areas, the emergency response in Case 2 was found to be the most effective in improving the survival rate.  
Busan, Gwangju, Daegu, and Gyeonggi (Figure 7c). Case 4 does not seem to be applicable to urban areas as a policy because the time to receive help from a healthcare provider or an EMT-P increases and the time required to transfer the patient to an emergency medical facility increases. However, the survival rate in rural areas increased for 4060 villages in Case 4. This shows that establishing a system for medical staff in community health centers to provide first aid before an EMT-P arrives is an appropriate method to improve the survival rate. However, Case 4 is not a suitable method in Gangwon province, a representative mountainous region in Korea, because of the low road density and because the major medical facilities are concentrated in the central areas that are highly populated. Therefore, another alternative EMS is needed in this region to more effectively increase the survival rate of local residents.
## 3 Results and Discussion
### Experimental Measurements
#### 3.1.3 Partially Compensated Beam Number Densities  
The radial profiles for the electron density at a distance of \(\Delta x=50\) mm taken inside the beam with and without partial compensation are shown in Figure 12. No significant difference can be observed in the diagram. The points inside the beam show remarkably good repeatability, taking into account that the curves were taken on different days. The absence of the plasma density dependence on the compensation degree shows that the quasineutrality of the plume plasma is established automatically. Thermionic compensation can only change the ratio of the beam neutralization by SEE. This manifests in the reduced value of the potential jump during beam disturbance--the SEE has to be increased by a smaller degree due to the presence of a thermionic electron source.
## 3 The Multiple Workflows Allocation Model
### Problem Formulation and Parameter Estimation
The aim is to assign workflows to resources in a way that optimizes important QoS metrics, including makespan, system utilization, and flowtime. Since users should only pay for the resources they use, optimum utilization is also very important. However, because cloud environments are dynamic, it can be difficult to maintain this equilibrium when the workloads or resource availability changes. As a result, a flexible allocation model that can adjust to changing needs while maintaining the best possible performance and affordability for a range of user requirements is required. Therefore, the resulting mapping is always preferred to provide an allocation schedule for processing workflows from cloud users that delivers the best values for the developed allocation model's QoS parameters.
# Design Studies of Re-Entrant Square Cavities
## 2 Design and Operation of RRSC and RF Section  
### Design of RRSC  
A multi-cavity Klystron amplifier employs two types of cavities--the input/output cavity and the intermediate cavity. The first type facilitates the input of a low-power RF signal and its extraction after amplification. The second type facilitates the production of the required gain. Figure 1 shows a CST model of these two types of RRSCs. Figure 1a shows the input/output cavities. The rectangular i/o coupler allows the RF signal to be fed into the input cavity. It initiates velocity modulation and thereby the bunching of the electron beam. Figure 1b shows one of the intermediate cavities, and Figure 1c shows the cross-section of (b). In a re-entrant cavity, a strong electric field results in the interaction gap 'g' (Figure 1c).  
The enhanced electric field in the beam passage helps to amplify the RF by way of a more robust velocity modulation. Since the cavities are frequency-selective resonant structures, the operational bandwidth of any Klystron is inherently narrow. However, the bandwidth can be widened by slightly detuning the cavities. This can be done by changing the cavity dimensions, such as the interaction gap 'g' or re-entrant height 'b' (Figure 1c). However, changing the dimension changes the R/Q value as well. The beam field interaction degrades for a cavity with a low R/Q value. In such cases, it might not establish the required level of beam modulation to achieve the desired gain and output power specifications.  
The cavity dimensions and simulation results are listed in Table 1. The Q-factor is defined as 2\(\pi\) times the ratio of the total energy stored to the total energy dissipated per cycle. The ratio R/Q is a figure of merit, dependent only on cavity shape or geometry. The Q-factor and R/Q ratio are calculated in post-processing steps. Here, we have used copper (lossy metal type) (\(\sigma=5.7\times 10^{7}\) S/m) as the cavity material and the cavity was considered to be filled with vacuum. The R/Q is calculated as  
\[\frac{R}{Q}=\frac{V_{g}^{2}}{2\omega W}, \tag{1}\]  
where \(V_{g}\) is the interaction gap voltage, \(\omega\) is the resonant frequency, and \(W\) is stored energy. The cavity dimensions are optimized to achieve maximum R/Q ratio without deviating from the design frequency.
## 2 Mobile M2M Literature Review
### Open Challenges and Future Work  
Table 1 summarizes the main contributions of the literature presented in this document. Mobile M2M communications have the potential to introduce decisive advantages to different fields; however, from literature it is clear they will face several challenges and requirements, many introduced by M2M devices, which are usually constrained in, among others, energy, bandwidth, or processing and memory capabilities.  
The support of multitude and diversity of devices, and the traffic volume and traffic pattern generated from them will continue to be important challenges in mobile M2M communications. It will be necessary to support both legacy and new services and devices. Since existing wireless and cellular networks provide higher bandwidth for downlink communications than for uplink, mobile M2M traffic pattern can be a problem for communications if there is not a careful plan of the networks. Traffic volume envisionedfor M2M demands for capacity planning. The superposition of Human-based traffic with M2M traffic can expose network limitations in terms of the maximum capacity of network.  
In our vision, further research should focus in exploring the mobile M2M gateway concept by seeking ways to improve performance, or reduce the energy and bandwidth consumptions in transmissions, as resource usage efficiency is a common denominator in the literature due to the mass scale envisioned for mobile M2M communications. To the best of our knowledge, techniques to efficiently aggregate and process data at M2M gateways have not been sufficiently studied, and they shall depend on many characteristics of M2M devices, traffic, _etc_. Nevertheless, the concept is being discussed by current standardization efforts, like the aggregating gateway envisioned in MQTT-S or the intermediary node in CoAP, and by literature.  
Overheads from handover inefficiency, introduced by mobility, are still a problem in mobile communications and they need to be further mitigated; however, whenever a device can connect to multiple different networks, e.g., WiFi and 3G, the device can exploit the scenario for its advantage, either by using multiple paths and/or the most economic network in terms of resource usage. Future work should also seek to combine techniques, such as transmission scheduling schemes and data aggregation, and explore techniques that effectively reduce the amount of data necessary to be transmitted, such as data compression or data concatenation, to optimize the overall performance.
# Green Extraction of Valuable Compounds from Rubber Seed Trees: A Path to Sustainability
## 7 Green Extraction of Rubber Seed Oil
### Microwave Assisted Extraction (MAE)  
The use of MAE has garnered considerable interest due to its unique and effective approach in extracting bioactive compounds from rubber seed plants, as highlighted by [PERSON] et al. [89]. The method described in this study employs microwave radiation to expedite the heating process of both the solvent and the matrix, hence promoting the liberation of desired compounds into the extraction solution. The utilization of MAE presents a range of benefits, such as the reduction in extraction duration, enhancement of compound output, and safeguarding of thermally bioactive compounds. Recent research has been conducted on the use of MAE for the extraction of rubber seed trees, with a focus on investigating its efficacy in improving the extraction process. The impacts of several factors, including microwave power, extraction duration, solvent type, and solid-to-solvent ratio, have been examined by researchers in order to assess their impact on the yield and quality of extracted chemicals. Through the methodical manipulation of these factors, researchers endeavor to identify the optimal circumstances that result in the greatest extraction efficiency while preserving the bioactivity of the substances.  
In addition to the process of optimization, scholarly investigations have been directed towards the characterization of bioactive chemicals acquired using MAE from rubber seed trees. Analytical techniques, such as chromatography and spectroscopy, are used for the purpose of identifying and quantifying distinct chemical components. The present research offers valuable information into the chemical makeup and possible bioactivity of the isolated chemicals, hence providing guidance for their prospective use across diverse sectors. The broad range of applications of MAE has motivated researchers to investigate its possible use in many industries. The extracts acquired by this methodology have shown promising properties as antioxidants, antimicrobials, anti-inflammatory, and have also shown promise in other applications such as biodiesel production. The significance of MAE as an environmentally friendly extraction technique for harnessing the wide range of bioactive properties inherent in rubber seed trees is highlighted by its versatile use. In general, contemporary research on the MAE technique for rubber seed trees underscores its effectiveness in efficiently extracting bioactive chemicals that hold promise for many industrial uses. As scholars persist in enhancing and broadening their understanding of this extraction methodology, it presents a hopeful pathway for the sustainable and effective extraction of compounds from rubber seed tree sources.
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 3 Methodology
### Subnetting
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Address** & **First Octet** & **First Octet Bit** & **Address Pattern** & **Default Subnet** & **No. of** & **No. of Hosts** \\  & & **Pattern** & **Mask** & **Networks** & **per Network** \\ \hline A & 1–127 \({}^{\star}\) & 0 xxxxxxx & N.H.H.H & 255.0.0.0 & \(2^{7}\) & \((2^{24})-2\) \\ \hline B & 128–191 & 10 xxxxx & N.N.H.H & 255.255.0.0 & \(2^{14}\) & \((2^{16})-2\) \\ \hline C & 192–223 & 110 xxxxx & N.N.N.H & 255.255.255.0 & \(2^{21}\) & \((2^{8})-2\) \\ \hline D & 224–239 & 1110 xxxx & NA (multicast) & & \\ \hline E & 240–255 & 111 xxxx & NA (experimental) & & \\ \hline \hline \end{tabular} \({}^{\star}\) All zero bits and all one bit are invalid host addresses. We subtract two to account for network and broadcast addresses. N = Network; H = Host; NA = Not Applicable.  
\end{table} 
Table 2: IP address Classes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Host Bits** & **CIDR** & **Subnet Mask** & **Total IPs** & **Usable IPs** \\ \hline
7 & /25 & 255.255.255.128 & 128 & 126 \\ \hline
6 & /26 & 255.255.255.192 & 64 & 62 \\ \hline
5 & /27 & 255.255.255.224 & 32 & 30 \\ \hline
4 & /28 & 255.255.255.240 & 16 & 14 \\ \hline
3 & /29 & 255.255.255.248 & 8 & 6 \\ \hline
2 Determine the addresses: To determine the network address, broadcast address, and IP address range, begin with the base IP address, then, increase the value calculated in Step 5. In this paper, the network was assigned a base IP address of 192.168.5.0, where the fourth octet increases and changes. The network address for Subnet A is 192.168.5.0. The broadcast address will be one less than the subsequent IP subnet, which is 192.168.5.128-1, resulting in 192.168.5.127. The network address for Subnet B is 192.168.5.128 and the broadcast address is one less than the subsequent IP subnet, which is 192.168.5.192-1, leading to 192.168.5.191. The network address for Subnet A is 192.168.5.192. The broadcast address became one less than the subsequent IP subnet, which is 192.168.5.224-1, resulting to 192.168.5.223. Upon completing Subnet (LAN) A which serves as the main office, we will implement VLSM to finish up the subnetting procedure on Subnets C and B as well as LINKR1R0, LINKR1R2, and LINKR0R2 in the order starting with the Subnet with the largest number of hosts. The network address for LINKR1R0 is 192.168.5.228 and the broadcast address is one less than the subsequent IP subnet, which is 192.168.5.228-1, leading to 192.168.5.227. The network address for LINKR1R2 is 192.168.5.232 and the broadcast address is 1 less than the subsequent IP subnet, which is 192.168.5.232-1, leading to 192.168.5.231. The network address for LINKR0R2 is 192.168.5.236 and the broadcast address is 1 less than the subsequent IP subnet, which is 192.168.5.236-1, leading to 192.168.5.235. The configuration settings for the routers Router0, Router1, and Router2 are set to the values in Table 5, where NA signifies entry is Not Applicable.
### Heuristic Algorithms and Neural Networks  
Heuristic algorithms play a significant role in refining neural networks for optimizing flight paths of solar-powered UAVs. Traditional methods may struggle with the dynamic nature of these paths, but heuristic approaches, such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), or Simulated Annealing (SA), allow neural networks to adjust flight paths in real time. These heuristic methods are inspired by natural processes and are effective at finding solutions to complex problems. For example, Genetic Algorithms mimic the process of natural selection, while Particle Swarm Optimization is inspired by the social behavior of birds flocking together. This adaptability enhances UAVs' responsiveness to environmental changes affecting the solar energy availability. Heuristic algorithms are particularly advantageous because they efficiently navigate complex, multidimensional search spaces, finding optimal or near-optimal solutions with lower computational cost and time. This is crucial in scenarios with unpredictable environmental fluctuations, ensuring that solar-powered UAVs maintain optimal energy efficiency and operational reliability [17; 18; 19; 41; 42].
# Performance Evaluation of a Mesh-Topology LoRa Network
## 6 The ns-3 Simulation Model  
### Overview  
The ns-3 [32] simulation model in this study is based on a modified version of the LoRaWAN ns-3 simulation model proposed in [33], for which the source code can be found in [34]. The simulation model has been improved to support multi-hop packet routing, TDMA scheduling, RTC synchronisation, and multiple supporting performance monitors.  
The simulation model currently only supports a single gateway, unconfirmed up-link messages for data traffic, and unconfirmed down-link messages for RTC synchronisation. Furthermore, the model is currently limited to a single bandwidth and spreading factor. Support for dynamically adjusting these transmission parameters is beyond the scope of the current research.  
A static mobility model is used by all nodes and gateways. The gateway is placed at position (0,0), with the nodes distributed randomly in an area around the gateway with a defined max radius of \(R_{max}\); see Figure 4a for an example.  
In this simulation model, we base our performance measurements on the SX1272 IC [7], developed by Semtech, as this is the industry standard single-channel LoRa transceiver for the 800-1000 MHz frequency band.  
The model is a representation of an ideal real-world scenario, where node positions are static and the attenuation between nodes is constant. These assumptions are required to reduce the complexity of the simulation model and allow a comparison between a single-hop network and a multi-hop network for a static LPWAN network. The performance of the multi-hop model will be significantly different in a dynamic environment.  
\begin{table}
\begin{tabular}{c c c} \hline
**Mode** & **Current Consumption** & **Notes** \\ \hline Transmit (Tx) & 38 mA @ +14 dBm & Reduced currents @ lower Tx power \\ \hline Receive (Rx) & 11 mA & Continuous receive mode \\ \hline Sleep & 1.5 uA & Register retention mode \\ \hline \end{tabular}
\end{table}
Table 1: Power consumption of the SX1272 LoRa transceiver.
## 5 AEP Uncertainty  
According to IEC 61400-12-2, a Rayleigh cumulative distribution with annual mean wind speeds of 4 to 11 m/s is assumed to estimate the AEPs from \(\mathrm{PC_{NTF,\ NL}}\) (\(\mathrm{AEP_{NTF,\ NL}}\)), \(\mathrm{PC_{NTF,\ Cup}}\) (\(\mathrm{AEP_{NTF,\ Cup}}\)) and \(\mathrm{PC_{Cup}}\) (\(\mathrm{AEP_{Cup}}\)). The AEPs can be calculated using Equation (12):  
\[\mathrm{AEP}=\mathrm{N_{h}}\sum_{i=1}^{N}[\mathrm{F(V_{i})}-\mathrm{F(V_{i-1} )}]\bigg{(}\frac{\mathrm{P_{i-1}}+\mathrm{P_{i}}}{2}\bigg{)} \tag{12}\]  
where \(\mathrm{N_{h}}\) is the number of hours in a year and \(N\) is the number of bins. \(\mathrm{F(V_{i})}\) and \(\mathrm{F(V_{i-1})}\) are the Rayleigh cumulative probability distribution functions for wind speeds \(V_{i}\) and \(V_{i-1}\) in bins i and i \(-1\), respectively. \(\mathrm{P_{i}}\) and \(\mathrm{P_{i-1}}\) represent the average power output in bins i and i \(-1\), respectively.  
\(\mathrm{u_{AEP}}\) can be derived from the following equation:  
\[\mathrm{u_{AEP}}=\mathrm{N_{h}}\sqrt{\sum_{i=1}^{N}\mathrm{f_{i}^{2}\ S_{i}^{2}+ \left(\sum_{i=1}^{N}\mathrm{f_{i}\ u_{i}}\right)^{2}}} \tag{13}\]  
where \(\mathrm{f_{i}}\) is the relative occurrence of wind speed between \(V_{i-1}\) and \(V_{i}\) within bin i.  
Figure 10 shows the AEP ratio and the uncertainties of \(\mathrm{AEP_{NTF,\ NL}}\) (\(\mathrm{u_{AEP,\ NTF,\ NL}}\)), \(\mathrm{AEP_{NTF,\ Cup}}\) (\(\mathrm{u_{AEP,\ NTF,\ Cup}}\)) and \(\mathrm{AEP_{Cup}}\) (\(\mathrm{u_{AEP,\ Cup}}\)). A difference of 3.4% to 7.0% was found between \(\mathrm{AEP_{NTF,\ NL}}\) and \(\mathrm{AEP_{Cup}}\) by means of IEC 61400-12-1. Additionally, \(\mathrm{AEP_{NTF,\ NL}}\) was 3.5 \(\sim\) 8.3% higher than \(\mathrm{AEP_{NTF,\ Cup}}\) based on IEC 61400-12-2 for all annual mean wind speeds. \(\mathrm{u_{AEP,\ NTF,\ NL}}\) and \(\mathrm{u_{AEP,\ NTF,\ Cup}}\) were higher than \(\mathrm{u_{AEP,\ Cup}}\). On the other hand, \(\mathrm{u_{AEP,\ NTF,\ NL}}\) was almost the same as \(\mathrm{u_{AEP,\ NTF,\ Cup}}\) for every annual wind speed.  
Figure 9: Uncertainties of each component comprising the uncertainty in wind speed of wind turbine no. 15.