esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
## 4 Evaluation and Analysis
### Evaluation of the System Model  
In this subsection, the proposed downlink VLC-NOMA system is evaluated in terms of imperfect SIC by numerical results and simulation results for BER performance. Table 3 describes parameters for the evaluation, where Python was used as a simulation tool. As described in Figure 1, the system is made up of two users as receivers and an LED as a transmitter. To evaluate the system, the average BER for both users is calculated using (30) with regard to the signal-to-noise ratio (SNR).  
Figure 6(a) shows the evaluation results of FU and NU in the perfect and imperfect SICs, varying the transmit SNR. The trends show no difference between the numerical results and the simulation results. When the transmit SNR is approximately 35 dB, the far and NUs in the perfect SIC show a similar performance. Despite assuming the perfect SIC, the BER performance of NU is not lower than that of FU. From 35 dB, the BER result of NU finally starts showing lower than that of FU. The BER performance of an NU with imperfect SIC is worse until 34 dB than the others because decoding FU's signal on the side of NU for SIC can fail. If NU fails to decode the signal of FU, NU cannot SIC FU's signal and decode its own signal. However, increasing transmit SNR causes lower decoding failures for SIC and enhances the BER performance for NU with imperfect SIC. The BER performances for NU  
between imperfect SIC and perfect SIC start overlying at 4 dB. The success rate of decoding FU's signal on the side of NU affects the BER performance of NU with imperfect SIC.  
Figure 7b shows the evaluation results of FU and NU varying the power allocation ratio at 40 dB transmit SNR. The BER performances of NU with perfect SIC and FU meet at the 0.26 power ratio for NU. The average BER performance between NU with imperfect SIC and FU is the lowest at this point, and the results worsen beyond this point because the failure rate for cancellation of FU's signal increases.  
Figure 7c describes that the average BER performance is the lowest at the 0.24 power ratio in the transmit SNR 45.  
Figure 7d describes that the BER trends are similar to Figure 7b,c.  
According to Figure 7, the power ratio with the lowest point of average BER with imperfect SIC can be the target dataset to train DBPA.
# Selection of a Photovoltaic Panel Cooling Technique Using Multi-Criteria Decision Analysis
## 4 Results and Discussion
Hence, the objective is to maximize the efficiency of the operating plant, and the ranking is observed to be AFC \(>\) WSC \(>\) ESC \(>\) TPC \(>\) TCC \(>\) PFC \(>\) HPC \(>\) PPC, in which case AFC is the best option. If the objective is to minimize environmental impact by minimizing CO\({}_{2}\) emissions, then the approach suggests the preference order as TCC \(>\) PCC \(>\) AFC \(>\) WSC \(>\) ESC \(>\) TPC \(>\) HPC \(>\) FFC, which shows that TCC is the best option. A comparison of the different scenarios indicates TCC ranks well in most of the cases. Thus, when it comes to total energy economics and environmental impact, choosing the best PV panel cooling technology for a given application is one of the trickiest issues. This has grown in complexity and should make it easier to dynamically incorporate shifting cooling needs and environmental variables. It has been noted that experts' preference weights for the evaluation criterion \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Criterion**} & \multicolumn{2}{c}{**PV Panel Cooling Method**} & \multirow{2}{*}{\(\mathbf{V^{*}}\)} & \multirow{2}{*}{**Cooling Strategy**} & \multirow{2}{*}{\(\mathbf{V^{-}}\)} & \multirow{2}{*}{**Cooling Strategy**} \\ \cline{2-2} \cline{5-5}  & \(\mathbf{V^{*}}\) & & & \\ \hline C1 \({}^{\$}\) & 0.02064 & WSC\({}^{\$}\) & 0.00543 & AFC \\ \hline C2 & 0.094412 & HPC to EVC & 0 & AFC \\ \hline C3 & 0.030841 & WSC, EVC & 0.00257 & FCC \\ \hline C4 & 0.24079 & AFC & 0 & HPC to EVC \\ \hline C5 & 0.0000 & AFC & \(-\)0.04499 & TPC, WSC, EVC \\ \hline C6 & 0.004719 & TCC & 0.212367 & PCC \\ \hline C7 & 0.011261 & PCC, TCC & 0.018769 & AFC \\ \hline C8 & 0.003264 & AFC & 0.026116 & EVC \\ \hline \hline \end{tabular} Note: \({}^{\$}\) Refer to Table 5.  
\end{table} 
Table 10: The best and worst solutions for cooling PV panels for respective evaluation criterion. for various locales vary. Therefore, in order to obtain the desired global energy production with minimal cost and specialized application capability, the decision maker must choose the most appropriate cooling technique. The primary focus of this research is on choosing the most effective PV panel cooling solution using MCDM to meet cooling needs. The MCDM method and entropy weight, which both have high resolution and straightforward calculation processes, might objectively assess the PV panel cooling methods for large PV solar panels. This approach is different as compared to the other known approaches.
## 4 Experiments and Analysis
### Experiment Setting  
#### 4.2.1 Hyperparameters Setting for VGGNet/ResNet  
In order to train the initial VGGNet and ResNet for pruning, the random gradient descent optimizer with a batch size of 512, a momentum of 0.9, and a weight attenuation of \(5\times 10^{-4}\) is used. The learning rate is initialized to 0.1 and will be reduced by half every 25 epochs. The performance of VGGNet and ResNet on different datasets are shown in Table 1, including the number of parameters (Params), FLOPs, and accuracy (Acc.).
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 3 Tensile Test of Spring Protection Devices  
Through tensile tests of spring protection devices of varying diameters, the energy absorption capacity was obtained through integrated test results. Leveraging the energy conservation principle and considering the kinetic energy and speed associated with anchor cable pull-out and ejections, an evaluation was undertaken to determine the device's adequacy in meeting engineering specifications.
## 4 SLA Specification for the Fog Storage Services
### SLA Parameters
#### 4.4.1 Availability  
Service availability \(a\) is commonly defined as \(a=\frac{u}{u+d}\), where uptime \(u\) and downtime \(d\) respectively refer to the percentage of time a service is being up and functional or being down, i.e., not functional [50]. For example, many public Cloud service providers expose storage service over an HTTP API and the storage service is considered non-functional if an HTTP request towards the storage returns \"HTTP 50x\" errors. To the Cloud provider this means that a particular HTTP request cannot be fulfilled with respect to the expected result, where the causes of the error could be many, such as hardware equipment failures (e.g., disks, network switches, cables), software bugs, software updates, misconfiguration, and high requests rate. However, the availability SLA does not account for API service or network failures that might happen before a request reaches the API service.  
In our setting, we attempt to take into account any Cloud provider, being private or public, as well as any Fog and Edge provider. Another consideration is the service availability from client's perspective rather than service provider's. This means that any failures in delivering a storage service to a client that could be attributed to the respective service provider, count towards the unavailability. Therefore, SLA for the storage availability also includes uptimes of the corresponding API service, DNS service and the network paths between the client and the service provider. Under a reasonable assumption that the public Internet offers sufficient redundancy in network connectivity, we claim that long-term observations of storage service through periodic monitoring should lead to good estimations of the actual service availability figures.
## 2 Methods
### Aircraft Instruments
#### 2.2.1. _HyPlant_  
_HyPlant_ is a unique, custom high-performance imaging spectrometer system for airborne and ground-based vegetation monitoring [3] developed by the Forschungszentrum Julich (Julich, Germany) in cooperation with Specim Spectral Imaging Ltd. (Oulu, Finland). The sensor package operates in a push-broom mode and consists of two optical imaging modules: (A) the DUAL-imager, a line-imaging VSWIR spectrometer consisting of two sensors integrated in a single housing using a common fore optics to provide contiguous spectral information from 370 to 2500 nm, with 3 nm spectral resolution in the VNIR spectral range and 10 nm spectral resolution in the SWIR spectral range; and (B) the fluorescence imager (FLUO), a special module that acquires data at very high spectral resolution (0.25 nm) in the spectral regions of the two atmospheric oxygen absorption bands (670-780 nm) and is dedicated to measuring the vegetation fluorescence signal. _HyPlant_ is supported with a position and attitude GPS/INS sensor, an adjustable mount, and a data acquisition/power unit which contains two rack modules. The first module supports the DUAL imager with a data acquisition computer having system control and data acquisition software, and with power supply and control electronics. The second module includes the same equipment for the fluorescence imager. The GPS/INS sensor provides aircraft position and attitude data to enable image rectification and geo-referencing. Both _HyPlant_ imagers are mounted in a single platform with the mechanical capability to align the field of view (FOV). Descriptions and specifications of the _HyPlant_ package and examples of typical spectra were described by [PERSON] et al. [3]. The _HyPlant_ system was intensively tested during the ESA HYPLEX 2012 campaign [10], after which technical adjustments were made by Specim Spectral Imaging LTD (Oulu, Finland). Test flights were conducted in Germany during April 2013 to verify the updated performance, in advance of the FLEX-USA campaign.  
Figure 2: The flight campaigns were initiated at the Langley Research Center in Hampton Roads, VA, using a King Air aircraft, the aft section of which is shown with some of the team members, (**Left**) left to right: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; (**Right**) A view of the two instrument packages inside the King Air, G-LiHT (forward) and _HyPlant_ (aft).
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 3 Engineered Water Flooding (EWF)
### Recovery Mechanisms  
In carbonates, the main parameter contributing to whether the carbonate is slightly water-wet or highly oil-wet is the acid number (AN; mg KOH/g of oil, -COOH group) of the crude oil [2; 86; 87]. The rock tends to be more oil-wet if it contains crude oil with a higher AN due to the high attraction force between the negatively charged carboxyl group (COO-) of oil and the positively charged carbonate surface [88; 89]. Consequently, oil recovery is reduced with increasing AN [90]. The main mechanism governing EOR using chemically tuned water or LSW is the change of the carbonate rock's wettability to more water-wet conditions, subsequently improving the relative permeability and fractional flow of the oil [12; 88; 91; 92; 93]. However, the extent of this wettability change depends on many factors, including the presence of potential active ions in the low-salinity water, the reservoir temperature, the type of rock, and the composition of the crude oil.
## 4 Experimental Results
### Datasets  
To validate the proposed approach, four different datasets were used. The datasets include color datasets of different sizes of images, such as CIFAR10 [47], CIFAR100 [47], and STL10 [48]. Then, a grayscale dataset, such as fashionMNIST [49] is used for the experiments.
## 2 Developments
### Instance for Experimental Purposes  
We have considered a determined instance of the problem in order to validate the algorithm and perform the experiments. This instance is a scenario formed by two networks and up to four QoS parameters: throughput or bandwidth, delay, response time or latency, and cost (other QoS parameters can be easily added [17]). SEFI generates many weight vectors (solutions) {w\({}_{0}\),..., w\({}_{\text{NQoS-1}}\)} where NQoS is the number of QoS parameters considered in the experiments (2, 3 or 4) and the solutions  
Figure 5: Integration and behavior of SEFI in the architecture of a MS device.  
satisfy the constraint given in Equation (2), exactly, without any tolerance margin. The solutions are generated by regular intervals covering all the space of solutions, with a step precision given by the variable h.  
The experiments consider four possible profiles: profile #0 (all QoS parameters can have any weight from 0 to 1); profile #1 (all QoS parameters can have any weight from MINWEIGHT to MAXWEIGHT, where these variables have predefined values satisfying MINWEIGHT \(>0\) and MAXWEIGHT \(<1\)); profile #2 (applications where the most important QoS parameters are delay and cost); and profile #3 (applications where the most important QoS parameter is the bandwidth). On the other hand, if NQoS \(=2\), we consider throughput (bandwidth) and cost; if NQoS \(=3\), we add delay; and if NQoS \(=4\), we add response time (latency). Finally, we have considered three possible precision degrees for h: 0.05, 0.01 and 0.005.  
SEFI produces as output, for each profile and network, the following data: computing time, number of generated combinations, number of these combinations satisfying the restriction given in Equation (2), best fitness found and the corresponding network (that will be the solution chosen for the VH decision).
# Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks
## 1 Introduction
### Motivation  
While much work has been carried out to address the problem of dynamic community discovery, there are still some issues that need to be addressed.  
Firstly, most existing dynamic network models assume that it is a hidden Markov structure, in this structure, when the current network state is given, a network snapshot at any given time is conditionally independent for all previous snapshots. This approach may not be flexible enough to replicate some of the observations in real network data.  
Secondly, the dynamic system is used by filters, even for Gaussian distributions. However, after a nonlinear transformation, Gaussian terms are lost. Mean and covariance are the only measures computed by the filter. This is the result of a nonlinear transformation approximated by Gauss, and therefore this approximation may be poor.  
Finally, how to combine the information of the community structure available at the previous moment with the information available at the current moment is an important question. In the traditional hidden Markov dynamic Bayesian network model, the probability of an edge appearing in a dynamic network is realized by the estimated state.  
Therefore, this paper proposes a phylogenetic planted partition method, which uses the graph optimization strategy to continuously discover the evolving communities.
# Site-Specific Earthquake Ground Motions for Seismic Design of Port Facilities in Indonesia
###### Abstract  
In Indonesia, infrastructure, such as port facilities, has been damaged by earthquakes. Therefore, evaluating rational earthquake ground motions (EGMs) for seismic design is necessary to mitigate earthquake disasters in the future. The EGMs in the Indonesian Seismic Code are stipulated based on the ASCE standards and not on site-specific ones. This study aims to propose site-specific EGMs for the seismic design of port facilities in Indonesia. The EGM records and ground data in Indonesia were used for analysis. The EGM incidents in the bedrock were evaluated with deconvolution analysis. The obtained EGMs were amplitude-adjusted to peak ground acceleration similar to that of the EGMs in the bedrock in the Indonesian Seismic Code. A seismic response analysis considering nonlinear soil characteristics was conducted, and 144 EGMs at port sites were obtained. Considering the variation in the obtained EGMs, we propose site-specific EGMs for the seismic design of port facilities. A comparison of the proposed EGMs with those in the design code reveals that the difference between them is significant.  
seismic design; seismic response analysis; earthquake ground motion; spectral acceleration Article
# The Design of a System for the Induction Hardening of Steels Using Simulation Parameters
## 2 Materials and Methods
### Energy Efficiency  
The degree of utilization of the induction heating power is defined as the ratio of the power that raises the temperature of the batch, according to the total electrical power supplied to the converter:  
\[\rm\eta=\frac{P_{heat}}{P_{net}} \tag{2}\]  
where \(\rm P_{heat}\) is calculated as  
\[\rm P_{heat}=R_{ind,ch}\times I_{ind\ eff}^{2} \tag{3}\]  
where \(\rm I_{ind\ eff}\) is the effective current through the inductor.  
In the optimal case (described inductor completely filled with steel charge, good thermal insulation, heating below the Curie temperature, frequency slightly lower than the resonant frequency, inverter in the optimal range, etc.), the following parameters were measured and calculated:  
- Network power consumption: \(\rm P_{net}=12.87\) kW;  
- DC link voltage: \(\rm V_{DC}=250\) V;  
- DC link current: \(\rm I_{DC}=50.92\) A;  
- Inductor effective current: \(\rm I_{ind\ eff}=120\) A;  
- Equivalent serial resistance: \(\rm R_{ind,ch}=0.85\)\(\Omega\);  
- Power of induction heating: \(\rm P_{heat}=0.85\times 120^{2}=12.24\) kW.  
According to the previous measurements and calculations, the energy efficiency can be calculated:  
\[\rm\eta=\frac{12.24}{12.87}=0.951=95.1\% \tag{4}\]
## 2 Related Works
### MAC Layer  
Energy efficiency may be accomplished at a number of different layers, from application layer to routing layer and finally to the MAC layer. In WSNs, the MAC layer, which serves as the wireless channel acquisition technique, has the highest impact on the amount of energy consumed [47; 48]. The MAC protocol is in charge of controlling the radio module functionalities, i.e., transmit, receive, and listen, to coordinate sensor node access to the shared wireless medium [49]. Due to the limited resources, capacities of sensor nodes, and the features of wireless communication, achieving a good trade-off between all of these is very difficult. Since the radio module is the most energy-intensive component of a sensor node, the MAC protocol specifies a sleep schedule that allows the radio modules of sensor nodes to be placed in sleep mode as much as possible throughout the day [50; 51]. As a result, an energy-efficient MAC protocol must be developed that satisfies the real-time needs of WSNs.
# Survey of Blockchain-Based Applications for IoT
## 7 Smart Contracts and Automation
### Blockchain Oracles  
In a blockchain, oracles are external services or mechanisms that provide smart contracts with real-world data or external information that cannot be accessed independently. Oracles act as bridges between the blockchain and off-chain systems, enabling smart contracts to interact with data outside the blockchain ecosystem.  
For example, oracles in smart home applications can read off-peak hours from local utility companies and feed them to smart contracts in a blockchain to control the appliance operation time. Figure 11 below shows the logic of this example. The on-chain part includes a smart contract on the blockchain that controls the washing machine's status (on/off) depending on the electricity rate. If the rate from the utility company is off-peak, the washing machine turns on; otherwise, it stays off. The off-chain part involves the utility company providing electricity rate data via an API, which is fetched by an oracle node and sent to the blockchain. The oracle acts as a bridge, ensuring that real-world data are securely fed into the blockchain for automated decision-making.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 4 Results and Discussion
### Discussion  
#### 4.6.1 The Roles of EWT and PSO on SVM Performance Improvement  
In this study, the hybrid model EWT-PSO-SVM with the \"decomposition-prediction-reconstruction\" step achieves outstanding fitting results. In the decomposition step, the training efficiency of the model is enhanced by reducing the data dimensionality and decomposing the complex and random original runoff series into relatively stable subseries. The decomposition method used in this study is EWT, which effectively captures the time-frequency information of the original series, allowing the forecasting model to better capture the hidden periodicity and regularity in the original series. In [PERSON] et al.'s study, an EWT-PSO-SVM model was constructed to classify the fault diagnosis time series of high voltage circuit breakers [22]. The conclusion was shown that EWT can effectively extract data features of nonstationary time series, which is similar to the conclusion obtained in this study. In the forecasting step, the PSO algorithm was adopted to choose the optimal parameters \(c\) and \(g\) for the SVM model. The parameter \(c\) determines the tolerance for errors allowed by the SVM model, while \(g\) determines the number of support vectors. Both too large and too small values of \(c\) and \(g\) can have a negative impact on the performance of the SVM model. Choosing the optimal parameters can enhance the model's generalization ability, meaning that the model performs better when dealing with new data. In [PERSON] et al.'s study, the PSO method was utilized to optimize the SVM model, which achieved good prediction results [39]. In the reconstruction step, the final results are derived by combining the prediction results of each subseries, which leads to a synergistic effect that improves the forecasting performance of the developed model. In the research on series forecasting using EWT and PSO, both this study and previous research have reached similar conclusions, indicating that they can effectively enhance the capability of model runoff forecasting.