esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PSO Parallelization Based on Apache Spark and RDD Applied to Training Neural Networks
### Distributed Synchronous PSO  
The distributed synchronous PSO adopts the master/worker paradigm, wherein the master maintains the state of the entire swarm and sends particles to each worker node for evaluation, as illustrated in the workflows depicted in Figure 2. Furthermore, the master is responsible for managing the synchronization required to control specific iterations of DSPSO. Addtionally, it updates global data relevant to the algorithm through variables of the type _Broadcast_, which are a type of read-only shared variables, e.g., from the Scala programming language, that are cached and available on all nodes in a Spark cluster to be accessed or used by tasks.  
Each Worker node should perform the following steps:  
1. Wait for a particle to be received from the master node;
2. Compute the fitness function \(f\) and update the personal best position \(P_{id}\);
3. Return the evaluated particle to the master node;
4. Return to step 1 if the master node has not finished yet.  
The Master node process includes the following steps:  
1. Initialize particle parameters, positions, and velocities;
2. Assign the current iteration and initialize the state of the swarm and the received particles;
3. Start the current iteration by distributing all particles to the free executors;
4. Wait for the results of all evaluated particles (with the calculated fitness function) and the personal best position \(P_{id}\) for this iteration, which is what means the _for...sync_ pseudo-sentence;
5. Compute the global best position \(P_{bg}\) for each incoming particle until there are no more particles left;
6. Updating the velocity vector \(V_{id}\) and the position vector \(X_{id}\) of each particle based on the personal best position \(P_{id}\) and the global best position \(P_{bg}\) found by all particles;
7. Go back to step 3 if the last iteration has not been reached;
8. Return the global best position \(P_{bg}\).  
The tasks performed by the master and worker nodes in this variant are given by Algorithm 1 expressed in pseudocode.  
Figure 2: (a) Master node flowchart in DSPSO. (b) Flowchart of a worker node.  
The function _fitnessEval (f)_ is executed on the worker nodes and is responsible for receiving a particle, calculating the fitness of the current particle position using the variable broadVar of the aforementioned Scala _Broadcast_ type, and then updating the personal best position of the particle. It finally returns this value. The pseudocode employs the _closure_ anonymous function type, which encompasses the execution environment.
## 3 Results
### Specific Findings  
#### 3.2.1 Core Discovery: Cognitive Enhancement via Virtual-Physical Interaction  
Dynamic modeling superiority: The digital twin system reduced virtual simulation error rates from the literature-reported value of 42% to 5% through Multi-Agent Reinforcement Learning (MARL) and neural radiance fields (NeRFs). Virtual-physical interactions enhanced spatial perception (r = 0.68, \(p<0.01\)) by strengthening action-space mapping (23% increased parietal cortex activation). Real-time feedback value: Experimental groups received 7.3 virtual prompts per session, achieving 3\(\times\) higher error correction efficiency than traditional video reviews (average teacher feedback delay: 2.1 min).
## 2 Materials and Methods
### Application of a Hybrid Pump for Hydrogen Doping into Biogas during Methane Fermentation  
Figure 4 shows a schematic of the hydrogen production and utilization technology in combination with a biogas plant equipped with an innovative hybrid pump mixing system. This plant enables the mixing of fermenter contents using biogas as the working medium. In this technology, hydrogen is to be produced by electrolysis for which surplus electricity will be used. The surplus electricity is due to the reduced demand for energy generated during off-peak energy hours at night. These surpluses can also result from other reasons, where energy cannot be fed directly into the grid, e.g., due to technical constraints. This can be the case for renewable energy sources such as photovoltaics or wind turbines, among others, where the production of electricity is not constant over time and depends on weather factors or solar irradiation. The hydrogen produced by the process can be blended with biogas to improve combustion performance in conventional systems such as internal combustion engines or gas boilers. It is also assumed that hydrogen is dosed into the digester to improve methane yield by combining hydrogen with carbon during methane fermentation. The mixture of hydrogen and biogas can be compressed for distribution or use in transportation.  
Figure 3: View of the biogas plant with a fermenter equipped with a hybrid pump.  
For the analyzed biogas plant equipped with the hybrid pump mixing technology, research was carried out on the possibility of increasing the methane content using the innovative mixing method. For this purpose, the plant's own measuring and control apparatuses were employed to determine the content of methane and carbon dioxide in biogas.  
For the technology discussed above, changes in energy density were simulated for the mixture of biogas and hydrogen, depending on their proportion in the mixture.  
The energy balance was calculated for selected technology stages concerning hydrogen production, its doping into hydrogen, and compression for storage and distribution purposes. The calculations were based on literature data on the energy intensity of hydrogen and biogas compression systems. The calculations analyzed various variants of hydrogen content in biogas, depending on the intended use of the gas mixture. As noted  
Figure 4: Gas and energy cycle scheme.  
above, the research conducted by [PERSON] et al. [16] proved that a hydrogen addition of 10% was the most favorable for powering the engine using the mixture of biogas and hydrogen. On the other hand, according to DOE [21], the use of hydrogen at a pressure of 700 bar is the most favorable for transport. However, methane tanks presently used in CNG systems usually have a pressure of 200 bar. Therefore, it was assumed that the compression up to 200 bar would be analyzed for the gas mixture. In contrast, the 10% hydrogen content was adopted for the use of the gas mixture to power internal combustion engines as this content is the most favorable [16]. On the other hand, 8% was used for the use of gas in boilers due to the safety of gas fittings [18].
## 4 Results and Discussion
### Defect Detectability Enhancement  
Since the ATC compensation technique showed better performance than the TRC technique in the previous section, binarization for the quantitative evaluation of defect detection was evaluated depending on whether the ATC compensation technique was applied. Figures 12 and 13 show binarized images of the amplitude and phase depending on whether or not the ATC compensation technique is applied. Binarization utilized the well-known Otsu algorithm, which determines the optimal threshold values for the defective and sound area and converts them into binarized images [29].  
Figure 12: Amplitude binarization image by Otsu algorithm: (**a**) not ATC applied, (**b**) ATC applied.  
It can be seen that as contrast is improved by ATC compensation, defect detection through binarization is also improved. In the amplitude image with ATC applied, in row A (defect depth 9 mm), all defects except the smallest defect of 2 mm in diameter were detected. In the case of defect detection in row B, it can be seen that the defect size is detected relatively similar to the actual defect size in the amplitude image to which ATC is applied.  
In the case of the phase binarized image in Figure 13, more defects were detected than in the amplitude image. In the phase, defects in row C (defect depth 7 mm) were also detected, and some defects in row D (defect depth 6 mm) were also incompletely detected. The reason is that the phase is generally less sensitive to emissivity and environmental factors than the amplitude. Also, it can be seen that defect detection performance is better in phase images with ATC applied.
## 3 Results
### Question 5: How to Calculate the Individual IEQ Indexes
#### 3.5.3 Acoustic Quality Model  
In terms of acoustic quality models, almost all of them were developed based on SPL; one of the most popular models is Equation (10), which was used in several studies [15,48,52,55]. Besides this, some studies developed their acoustic models based on their own collected data, for which the linear regression model was the most common one [10,13,60,75,76], such as in Equation (11) [76]. Additionally, several logistic regression models were developed for evaluating acoustic quality [14,49,85], such as Equation (12) [14]. Another attractive acoustic-quality model that is worth mentioning is the one developed by [PERSON] et al. [56]. To achieve a holistic evaluation of acoustic quality in an indoor environment, the authors included five acoustic indicators in their model (see Equation (13)), namely, the index of background noise (IBN), the effect of noise (IEN), sound quality (ISQ), voice perception (IVP), and listening quality (ILQ), respectively. Unlike the thermal comfort or IAQ models, no quadratic regression model was developed to evaluate acoustic quality, indicating a monotonic relationship between SPL and acoustic quality--the lesser the SPL, the better the acoustic quality in the tested range:  
\[\theta_{Ac}=100-2(Actual_{SPL}-Design_{SPL}) \tag{10}\]  
\[\theta_{Ac}=-3.33 SPL+200 \tag{11}\]  
\[\theta_{Ac}=1-\frac{1}{1+e^{(23.75-0.2986 SPL)}} \tag{12}\]  
\[\theta_{Ac}=\frac{0.3 IBN+0.3 IEN+0.2 ISQ+0.1 IVP+0.11 LQ}{10}. \tag{13}\]
## 3 Results and Discussion
### Mechanical Properties  
The results of the Rockwell hardness test of the AMCs are shown in Figure 7. The standard deviation of the hardness value tended to decrease gradually, and the absolute hardness value increased as the volume fraction of SiC increased in both processes. Generally, for the same particle size, the hardness increases as the volume fraction of the reinforcement increases [33; 34]. In addition, the standard deviation of the hardness decreases as the degree of dispersion improves (i.e., smaller DI value). The deviation of the hardness value in the product of the SC process was larger than in the DC process; this is because of the presence of particle agglomeration and pores. This means that the deviation of the same sample depends on where the indenter is put to make measurements, such as in the agglomeration particle region or in an area with a lack of particles. However, the hardness results show that deviation of the hardness values rarely occurs in samples subject to the DC process, which means that the dispersion of particles was improved. The maximum hardness value was 57.6 HRB at 9 vol.%; this is about 30% higher than the hardness value of the matrix and 32% higher than that of AMCs fabricated by the SC process with the same volume fraction of particles.  
Figure 6: Degree of particle dispersion by the DC process: (**a**) 3 vol.%, (**b**) 6 vol.%, and (**c**) 9 vol.%.  
Tensile stress-strain curves of the AMCs are shown in Figure 8. The blue line shows the results of the SC AMCs, while the red line shows the results of AMCs fabricated by the DC process. In the stir-cast specimens, tensile strength and elongation decreased slightly as the volume fraction of SiC increased because particle agglomeration and pores caused stress concentration and so the mechanical properties of the AMCs decrease [35; 36]. On the other hand, there tended to be increases in tensile strength and elongation in the die-cast specimens. The ultimate tensile strength value was 313.7 MPa, which is two times higher than the SC results, and the elongation was four times greater than was seen in the SC results. As confirmed in previous dispersion results, aggregated particles and pores were removed via the DC process, and the dispersion became uniform. Changes in the microstructure led to improvement of interfaces between particles and matrix. Thus, tensile strength and elongation improved because load transfer from the matrix to the reinforcement was efficiently preformed [37]. In addition, we believe that there was slight strengthening as a result of grain refinement, as in the previous results of [PERSON] et al. [38].  
Figure 7: Results of Rockwell hardness (B scale) measurement.
# A Method to Reduce Non-Nominal Troposphere Error
## 4 VPL under Non-Nominal troposphere
### Simulation Results
#### 4.3.2 Results Analysis
From Inequality (14), we can conclude that although the satellite geometry can be improved by increasing the number of satellites in the selected subset, more visible satellites would induce larger non-nominal troposphere error and, thus, increases \(VPL_{bias}\). The results show that the decrement of \(VPL_{non}\) decreases whereas the \(VPL_{bias}\) increases when the number of visible satellites increases beyond a certain value. This behavior may result from the following reasons:  
* As the number of visible satellites increases, the satellite geometry is optimized. However, including more satellites does not further improve the satellite geometry, which can explain why the decrease of \(VPL_{non}\) becomes smaller. * Once the satellite geometry is optimized, adding additional satellites increase the non-nominal troposphere error in the constellation, which in turn increases \(VPL_{bias}\).
# Model-Driven Approach to Cloud-Portability Issue
## 4 Entities of Existing IaaS Platforms
### Microsoft Azure
#### 4.3.2 Virtual Machine  
One of the basic entities is the virtual machine (VM). The virtual machine is located under \"Microsoft.Compute\" and the entire VM definition name is \"Microsoft.Compute/ virtualMachines\". Similarly as in AWS and OpenStack, in the Azure system both mandatory and optional parameters are available as well. The only mandatory parameter is the name of the VM. All other parameters are optional. Unlike Amazon AWS and OpenStack, the parameters are more structured. The image identifier from which the VM will be cloned has the name \"storageProfile/imgeReference/id\", where the slashes separate each level. The template for VM, which sets the number of CPU cores, RAM size and HDD, has the name \"vmSize\" and is located under the \"hardwareProfile\" section. To create a VM in a specific zone, you need to define the \"zone\" directive. Under \"networkProfile/networkInterfaces/id\" it is possible to define VM connections for virtual networks. Azure allows one to run a script at the first boot of the VM. An administrator can define it in the \"OSProfile/customData\" section. For remote sign in, the public key value can be entered on the VM, specifically in \"OSProfile/linuxConfiguration/ssh/publicKeys/keyData\". The last parameter is \"tags\", which represents tags that the administrator can assign to a specific VM.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 3 Proposed Statistical Dimensioning Model
### Expressions of \(m_{N}\) and \(v_{N}\)
#### 3.3.1 Single-User Case  
**Proposition 2** (Single-user case).: _The expressions of \(m_{N}\) and \(v_{N}\) are,_  
\[m_{N} = N_{s}\ \times\sum_{k=1}^{N_{\text{max}}}k\times\mathbb{E}_{r}\ \mathbb{E}_{I,P_{n}}\ \mathbb{E}_{A_{f}}[\mathds{1}_{\{A_{f,k}\leq A_{f}<A_{f,k-1}\}}] \tag{22}\] \[v_{N} = N_{s}\ \times\sum_{k=1}^{N_{\text{max}}}k^{2}\times\mathbb{E}_{r}\ \mathbb{E}_{I,P_{n}}\ \mathbb{E}_{A_{f}}[\mathds{1}_{\{A_{f,k}\leq A_{f}<A_{f,k-1}\}}] \tag{23}\]  
_with \(N_{s}=\lambda_{a}/\lambda_{b}\) the average number of sensors as shown in (6), and \(\mathbb{E}_{r}[(.)]=\int_{0}^{\infty}(.)f(r)dr\) with \(f(r)\) defined in (7)._  
Proof.: Please refer to Appendix B.  
Note that (22) can be interpreted as following: \(N_{s}\) is the average number of sensor in the typical cell; each sensor requires each 1 to \(N_{\text{max}}\) RRs depending on their distance to the collector, the channel conditions and the additive interference and noise.
## 3 Example Analysis and Experiment
### Experimental Validation  
All experiments in this paper are based on the following environment configuration: 13 th Gen Intel (R) Core (TM) i9-13900 HX processor, 16 GB memory (Intel, Santa Clara, CA, USA), NVIDIA GeForce RTX 3060 GPU (Nvidia, Santa Clara, CA, USA), CUDA version 12.6.65, Python 3.9.1, Pytorch 2.0.0+cu118, MATLAB R2019a, and Windows 10 Professional Edition operating system. The specific experimental process is shown in Figure 8.  
All samples were divided into five equal parts, and 5-fold cross-validation was used for experimental verification. The total number of samples is 8382, with 80% (6706) used as the training set and the remaining 20% (1676) as the test set in each fold. The confusion matrix on the test set is shown in Figure 9.  
As shown in Figure 9, the test set consists of 1676 samples, including 1034 samples in the \"normal\" state, 201 samples in the \"mild degradation\" state, 95 samples in the \"moderate degradation\" state, and 346 samples in the \"fault\" state. Due to the similarity between late-stage moderate degradation samples and fault samples, the model tends to misclassify these two states. Ultimately, the model achieves an accuracy of 99.64% and an error rate of 0.36%.  
Figure 8: Experimental process.  
Figure 7: Construction of multimodal sample set for different states.  
The choice of different optimizers affects the model's recognition efficiency. To compare their performance, a contrast experiment was conducted using various optimizers. The standard deviation (SD) was used as the evaluation criterion to measure the standard error in each training iteration. A smaller SD value indicates higher model stability. The recognition loss and accuracy curves during model training are shown in Figures 10 and 11, where the shaded areas represent the standard error bands.  
As shown in Figures 10 and 11, the Adam optimizer outperforms SGD and Adadelta in terms of model convergence speed and accuracy, as evidenced by the smallest shaded area, indicating the most stable model training process.
## 2 Materials and Methods
### Measurement Methods
#### 2.3.2 Electrical Parameters  
Electrical parameters (AC electrical resistance, AC impedance spectrum and Tafel curves) were measured with two electrode methods. For determining the AC electrical resistivity, an LCR digital bridge with 10\({}^{4}\) Hz and 1 V was used. PARSTAT 3000 A, provided by AMETEK Trading (Shanghai) Co., Ltd., Shanghai, China, was used for determining the AC impedance spectrum and Tafel curves. Figure 2 illustrates the testing process. Following the test, the following equation could be used to calculate the AC electrical resistivity.  
\[\rho=\frac{RA}{L} \tag{1}\]  
The resistance \(R\) represents the average electrical resistance of the specimen, and \(A\) and \(L\) are the area and length of the electrical current passing through the testing specimen's cross section, respectively.
# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 3 Overview of Observational Methods and Platforms  
Measurements from different observing platforms, sensors, and data processing systems have different capabilities and characteristics, including differences in accuracy, number and type of parameters provided, temporal frequency, spatial coverage and resolution, and cost efficiency (Figure 3). The physical, chemical, or biogeochemical parameters of interest are manifold, but preference is increasingly given to those advanced as Essential Biodiversity, Climate or Water Variables [14; 15; 16]. In situ measurements often have high vertical (water column) resolution but low horizontal resolution, here defined as the capabilityto distinguish horizontal gradients, whereas, ocean color satellite data provide the converse. For example, in monitoring a river outflow or wastewater discharge, field sampling and sensors can provide data on many parameters at high frequency, potentially at high spatial resolution (in centimeters) and in both horizontal and vertical dimension, but low spatial coverage, whereas a satellite sensor (10 to 30 m) or drone (10 to 100 cm) may provide relevant information at sufficient spatial resolution and high spatial coverage, but miss variability in the vertical dimension. Another example may be a lake with a public drinking water intake. Here, an ocean color satellite may be useful for monitoring the entire lake, in situ sensor may benefit for high frequency sampling at the depth of the intake, discrete sampling enables event-driven responses, and models may be useful to understanding how upstream anthropogenic stressors may impact this resource and test scenarios to protect and restore water quality. If these sources are properly integrated, as proposed, many of the trade-offs and strengths are mitigated, enabling a more comprehensive toolbox for monitoring and interpreting water quality.
# A Correlation-Based Sensing Scheme for Outlier Detection in Cognitive Radio Networks
## 2 System Model  
We considered a cooperative spectrum sensing scenario in a cognitive radio network. We assumed that the total number of outliers/MUs in the network was less than the total number of normal SUs. The system model for the proposed scheme is shown in Figure 1. The SUs performed spectrum sensing and sent the report to the FC, for the presence of the PU in the network. The SUs forwarded a hard-binary decision 1 if the spectrum was occupied by the PU, and -1 if the spectrum was not occupied by the PU. The FC received the local sensing reports from all SUs. The FC then employed the proposed scheme on these reports to identify the SU as an outlier on the basis of the history of each SU energy report. Once the outliers were identified and removed, the FC then employed a simple rule to declare a global decision about the presence of the PU in the network.  
Figure 1: System model.  
The information received at the receiver SU in a particular band for the presence or absence of the PU was represented as a binary hypothesis given as  
\[y_{j}(l)=\left\{\begin{array}{ccc}n_{j}(l)&;&H_{0}\\ \\ h_{j}s(l)+n_{j}(l)&;&H_{1}\end{array}\right., \tag{1}\]  
where \(H_{0}\) is the absence hypothesis, \(H_{1}\) represents the presence hypothesis of the PU in the network, \(y_{j}(l)\) shows the received signal from the \(j\)th SU, \(n_{j}(l)\) is the additive white Gaussian noise (AWGN) in the \(l\)th time slot for the \(j\)th SU, \(s(l)\) is the signal transmitted by the PU, and \(h_{j}\) is the channel gain value between the PU and the SU in the \(l\)thtime slot.  
According to the hypotheses \(H_{0}\) and \(H_{1}\), the received signal energy of the channel by the \(j\)th SU at the \(i\)th sensing interval is  
\[E_{j}(i)=\left\{\begin{array}{ccc}\sum\limits_{l=i_{l}}^{l+K-1}\left|n_{j}(l )\right|^{2}&;&H_{0}\\ \\ \sum\limits_{l=i_{l}}^{l_{i}+K-1}\left|n_{j}s(l)+n_{j}(l)\right|^{2}&;&H_{1} \end{array}\right., \tag{2}\]  
where \(K\) denotes the number of samples in the \(i\)th sensing interval. According to central limit theorem (CLT), when the value of \(K\) is large enough, then the energy reported by each SU converges to a Gaussian random variable under \(H_{0}\) and \(H_{1}\), which can be formulated as [28]:  
\[E_{j}\sim\left\{\begin{array}{ccc}N(\mu_{0}=K,\sigma^{2}{}_{0}=2K)&;&H_{0}\\ \\ N\big{(}\mu_{1}=K\big{(}\eta_{j}+1\big{)},\sigma^{2}{}_{1}=2K\big{(}\eta_{j}+1 \big{)}\big{)}&;&H_{1}\end{array}\right., \tag{3}\]  
where \(\eta_{j}\)is the signal-to-noise ratio (SNR) between the \(j\)th SU and the PU, \((\mu_{0},\sigma^{2}{}_{0})\) is the mean and variance under \(H_{0}\), and \((\mu_{1},\sigma^{2}{}_{1})\) are the mean and variance under \(H_{1}\).