esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 4 Numerical Results
### Multihop MI-WUSEN Performance for IID of Hops  
We evaluated the performance of multihop communication under the assumption of the IID of channel states between equidistant hops (Section 3.2) and by using the evaluation parameters in Table 2. Figure 7a shows the variation of the SNR with the number of hops under IID for different values of the noise power. Evidently, for a fixed transmission distance of 25 m, the SNR improves with an increase in the number of intermediate relays. For constant conditions under the IID assumptions, all the results show similar trends. Figure 7b shows the variation of the channel capacity for different noise powers. For a given value of noise, if the internode distance is large (the case of two relays), the channel capacity remains low. For the improvement of the network channel capacity, the number of hops can be increased. For a higher noise value (\(-83\) dBm), the capacity gain remains relatively low even for a large number of relays (10 relays). The BER performance in Figure 7c shows a similar trend. For a fixed transmission distance, the BER performance improves with an increase in the number of hops. This performance improvement is greater for low noise values compared with high noise values. From Figure 7b,c, we can infer that there is a trade-off between the achievable BER and the system channel capacity when defining the optimal number of hops for a given transmission distance. The curves in Figure 7 show a constant variation trend owing to the initial assumption of the IID of channel states between hops. This analysis can therefore provide asymptotic bounds for end-to-end system performance. The next section presents a practical analysis in which the variation of the channel state between hops was considered.  
Figure 6: Variation of the SNR and BER with the internode distance at a fixed high noise power (\(P_{n}=-83\) dBm), the average noise power (\(P_{n}=-93\) dBm) and a low noise power (\(P_{n}=-103\) dBm).
## 3 Results
### Granularity of Road Material  
The classification of ballast materials used in the case of Ciobanus forest roads is made according to the size of the granules, provenance, processing technology, and granularity, as follows: sand (0-4 mm), gravel (4-31 mm), ballast (0-63 mm) and boulders (63-350 mm). SR EN 13242 + A1 [24] establishes the quality conditions for the ballast used for the maintenance of forest roads. In order to interpret the data, the results of the laboratory analyses will be compared with the permissible values for the ballast categories (ballast, optimal ballast, and ballast for completions), in order to remove the effect of the freeze-thaw phenomenon) provided in the SR 662 regulations [25]. According to the same standard, the size of the predominant fraction (over 50%) is dependent on the soil type, with values of 0.05-0.25 mm for fine sand, 0.25-0.50 mm for medium sand, 0.50-2.0 mm for large sand, 2-20 mm for small gravel, 20-70 mm for large gravel, 70-200 mm for small blocks and over 200 mm for stone blocks. Based on STAS 1243-88 [26], the soils are classified according to the proportion of the majority particle size fraction. The results of the analyzes in the 15 samples classified the soils differently, depending on the size, as follows: 12 in the category \"small gravel\" 3 in the category \"large sand\", the predominant particle size fraction being in the range of diameters 2-20 mm, specific to small gravels.  
Regarding the granulometric uniformity of the samples, their classification was also based on STAS 1243-88 [26], determining the non-uniformity coefficient, which led to acceptable values of over 15. The graph in Figure 4 shows the granulometry of the material used to make the wear surface of the forest road, by using the 11 sorting sieves, in a cumulative curve. It is clearly observed the framing of the real values between the upper and lower limits. Also, the polynomial regression curves of the cumulative values approximate with high precision the trend of the values, through coefficients of determination R\({}^{2}\) of over 0.97 for real granulometry and over 0.99 for lower/upper granulometry. High values of the coefficient demonstrate the uniformity of the values.  
Taking into account the granulometry of the ballast material that was used, the Ciobanus road corresponds to the norms and the predominant granules fall into the gravel category.  
Figure 4: Cumulative granulometry of the material used on the Ciobanus forest road.
## 4 Simulation Results
### Performance Analysis for Sunny Days  
In this subsection, the performance of WT-ANFIS-HFPSO model is compared with four solar power forecasting strategies which are known as the WT-ANFIS-FF, WT-ANFIS-PSO, WT-ANFIS and the ANFIS model. Solar power and meteorological signals are often contaminated by noise that is represented in the form of fluctuations, variations or spikes. On sunny days, the solar power signal is less fluctuating and it is not greatly impacted by noise. Hence, a forecasting model rarely requires any technique to remove noisy data on sunny days. However, this work has applied the WT technique to remove noise in solar power signals and meteorological signals on sunny days. The result of this work is shown in Table 4, where the applicability of the WT to remove the noisy signals on sunny days is deemed to be reasonable. This is proven by referring to the values of the MAE, RMSE, LCE and WIA of WT-ANFIS that are better than the ANFIS model.  
The efficiency of the WT-ANFIS depends on appropriately choosing the premise parameters or consequent parameters. In this work, the HFPSO has been proposed to determine the optimal values of premise parameters and the result obtained is compared with the application of a single PSO and FF algorithms. As can be seen in Table 4, the performance of the proposed WT-ANFIS-HFPSO model on sunny days gives supreme performance evaluation (MAE, RMSE, LCE and WIA) results compared to the WT-ANFIS-FF and WT-ANFIS-PSO. These results have verified the effectiveness of combining the WT-ANFIS with the HFPSO model.  
Figure 7 depicts the actual and forecasted average solar power of WT-ANFIS-HFPSO on sunny days. It is known from Figure 7 that the forecasted solar power data has less variation with the actual solar power data and this result verifies the higher precision and accuracy of the WT-ANFIS-HFPSO to forecast solar power data on sunny days.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Selection of Cluster Head (CH) using Fuzzy Logic  
Fuzzy logic is a strategy that utilizes more than one parameter to determine and select one node/vehicle to be the Cluster Head (CH). We maintain the membership functions in our proposed scheme, and fuzzy rules are applied during the selection process. During the fuzzification process, the fuzzy system converts the input values into fuzzy values. Input parameters are explained in the following subsections. The inference engine generates a fuzzy output according to the rules by entering the input parameters. During defuzzification, the calculated output value is converted into the actual value [30]. As a result, the CH is then selected based on the output value. The fuzzy logic procedure for selecting a CH is explained in Figure 4.  
The following input parameters are used to choose the CH in a given cluster.  
Figure 4: The fuzzy logic method used to select the CH.  
Figure 3: The proposed K-means clustering method for vehicle clustering in push-based data forwarding in VNDN.
## 2 Related Works  
### Classical Segmentation Methods  
Classical segmentation includes edge-based, region-based, and threshold-based methods. Edge-based segmentation relies on detecting edges within an image by identifying local changes in image intensity. However, this method is not suitable for images with smooth edges or many edges. In contrast, region-based segmentation depends on a seed point to initiate the segmentation process, where the region grows by examining neighboring pixels' intensity to determine whether to include them, thus separating the regions. This technique is computationally expensive, and different seed points may result in varying segmentation outcomes, which undermines the segmentation accuracy. Combining edge-based and region-based techniques addresses their individual shortcomings and leads to a more robust segmentation technique [13; 14]. Threshold-based segmentation is one of the simplest and most commonly used techniques [15; 16]. It involves calculating an optimal threshold that distinguishes between two classes while minimizing intra-class variance and maximizing inter-class variance. This method performs well when the image histogram has a bimodal distribution but cannot process images with unimodal intensity distribution [17; 18; 19].
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 4 Non-Thermal Atmospheric Plasma--NTAP
### Effects of NTAP on the Quality of Fish and seafood  
Regarding the overall quality of fresh fish and seafood products, the available information in the literature is variable. Color is one of the most important parameters, because it has been associated with the freshness of seafood products and their overall quality. When some authors investigated the color values of dried squid shreds [135] and fresh mackerel fillets [152], they found no significant changes in redness (a*) and yellowness (b*) after DBD and CDPJ plasma treatment, respectively. On the other hand, [PERSON] et al. (2020) [154] observed that the lightness (L*) and yellowness (b*) of Asian sea bass slices (ASBS) packaged under different gas combinations during storage at 4 \({}^{\circ}\)C increased after treatment with in-bag dielectric barrier discharge CP (IB-DBD-CP). Different L* and b* values also observed for the ASBS samples treated in the bag with gas A (argon and oxygen, ratio 10:90) and gas B (carbon dioxide, argon, and oxygen, ratio 60:30:10) composition. Regardless of the gas composition used, all the plasma-treated samples had lower redness (a*) value. The increase in L* and b* values for the samples in the presence of gas A attributed mainly to the existence of RNOS and ozone (O\({}_{3}\)), which can cause myoglobin denaturation [164], while lipid and pigment oxidation occurs under CO\({}_{2}\) enriched environment (gas B). Furthermore, [PERSON] and [PERSON] (2020) [146] described that high voltage cold atmospheric plasma (HV-CAP) in combination with a gas mixture of argon and oxygen (90:10) for 5 min and chitooligosaccharide (COS) from squid (_Loligo formosana_) pen increased the sensory acceptability of ASBS at 4 \({}^{\circ}\)C. It was found that the appearance, color, and texture of the fish slices were higher than the acceptable limit of likeness (>5.0 score), in agreement with [PERSON] et al. 2019b. However, the lipid oxidation and total volatile base (TVB) content resulted in unpleasant odor and off-flavor.  
Fish and seafood processing with NTAP treatment can be more effective in microbial inactivation yet can be used without negatively affecting their quality parameters. To achieve this, all the aspects of the treatment and the type of food have to be considered carefully.
# FTIR Characterization of Asphalt SARA Fractions in Response to Rubber Modification
## 2 Materials and Methods
### Methods
#### 2.2.6 TGA Analysis  
The original and extracted rubber were characterized thermally using a TA instruments Q-500 TGA (TA Instruments, New Castle, DE, USA), following ASTM E1131-20 [30], to assess the compositional changes of rubber during the interaction with asphalt. The test was conducted with two duplicates, and the mean results were evaluated. Following previous studies [40; 41], the test was conducted using a ramp heating method with a 20 \({}^{\circ}\)C heating rate from room temperature up to 600 \({}^{\circ}\)C. The rubber composition was identified based on the decomposition temperature range of each component [42]: OL decomposed at 300 \({}^{\circ}\)C, NR and SR decomposed from 300 to 500 \({}^{\circ}\)C, and FL was the remaining component at 500 \({}^{\circ}\)C. NR and SR components can be identified through the derivative of thermograph (DTG): NR is thermally degraded from 300 \({}^{\circ}\)C to the lowest point between DTG peaks, and SR decomposed from this point till 500 \({}^{\circ}\)C.
## 2 Materials and Methods
### Analytical Methods
#### 2.3.6. X-Ray Micro-Computed Tomography (XRCT) and Scanning Electron Microscopy (SEM)  
Skyscan 1272 (Bruker, Kontich, Belgium) X-ray computed microtomography (XRCT) was used for the visualization of the internal structure [34]. A piece of dried apple was excised using a scalpel and mounted with its shorter edge onto a metal table with a 25 mm diameter. Imaging was performed at a source voltage of 40 kV, a current of 193 \(\upmu\)A, a resolution of 15 \(\upmu\)m, and a rotation step of 0.3\({}^{\circ}\). The raw scan data were reconstructed with Recon software version 1.6.10.5 (Bruker, Kontich, Belgium) and subsequently binarized within the 25-925 range using CTAn software version 1.23.0.2 (Bruker, Kontich, Belgium).  
The material was placed in the EM-Tec PS4 pin vise clamp and covered with a 5 mm layer of gold using a Leica EM ACE200 (Leica Microsystems GmbH, Wetzlar, Germany). An SEM XL (Phenom) was used for the observations of the cross-section at a magnification of 220\(\times\). A vacuum of 60 Pa and a voltage of 10 kV were maintained. For each sample, ten photographs were captured.
###### Abstract  
The accuracy of noise estimation is important for the performance of a speech denoising system. Most noise estimators suffer from either overestimation or underestimation on the noise level. An overestimate on noise magnitude will cause serious speech distortion for speech denoising. Conversely, a great quantity of residual noise will occur when the noise magnitude is underestimated. Accurately estimating noise magnitude is important for speech denoising. This study proposes employing variable segment length for noise tracking and variable thresholds for the determination of speech presence probability, resulting in the performance improvement for a minima-controlled-recursive-averaging (MCRA) algorithm in noise estimation. Initially, the fundamental frequency was estimated to determine whether a frame is a vowel. In the case of a vowel frame, the increment of segment lengths and the decrement of threshold for speech presence were performed which resulted in underestimating the level of noise magnitude. Accordingly, the speech distortion is reduced in denoised speech. On the contrary, the segment length decreases rapidly in noise-dominant regions. This enables the noise estimate to update quickly and the noise variation to track well, yielding interference noise being removed effectively through the process of speech denoising. Experimental results show that the proposed approach has been effective in improving the performance of the MCRA algorithm by preserving the weak vowels and consonants. The denoising performance is therefore improved.  
noise estimation; variable segment length; speech denoising; harmonic adaptation; minimum-controlled-recursive-controlled averaging +
Footnote †: journal: Applied Sciences
## 3 Experimental Results
### Discussion  
By observing the experimental results, it can be noted that the predicted MOS in this study is generally lower than the subjective MOS. This phenomenon is partly attributed to the influence of using PESQ-MOS as labels for the air traffic control speech database. Since the PESQ method has certain limitations in assessing audio quality, its predictions may deviate from subjective evaluations to some extent. Therefore, training the model with such MOS labels in this study might lead to an underestimation of the subjective MOS. Further exploration and improvement are needed regarding the feasibility of using PESQ-MOS as database labels to enhance the accuracy and reliability of MOS prediction. A better solution would involve annotating the air traffic control speech database with the MOS based on the subjective evaluation methods proposed by ITU.  
From Figure 9, we can observe that compared to the NISQA model, the P.563 model, and the Quality-Net model, our proposed approach performs better when predicting real MOS values in the range of 2-3.5 for air traffic control speech. This improvement can be attributed to our optimization of the shallow feature extractor, considering the impact of speaking rate on the mel-frequency spectra of air traffic control speech. Thus, the predictions made by our proposed approach are closer to the real MOS values.  
As shown in Figure 12, we found that the performance of the proposed method significantly declines when the speech rate is fast. The underlying reason can be attributed to the fact that, for the same air traffic control speech under identical conditions, as the speech rate increases, the spectrogram of the speech shows dimension reduction. This results in the feature extraction module of the proposed method capturing more redundant information from the spectrogram, leading to deviations in the evaluation of air traffic control speech quality.
## 4 Reinforcement Learning-Based Joint Optimization Strategy for Flight Path and Resource Allocation
### Problem Description and Decomposition  
The objective of this paper is to maximize the overall energy efficiency of the system within a given operational cycle \(T\) (referred to as an episode). We optimize the position of the APs in each time slot, as well as the device association and power control strategies for GPFs. The optimization problem can be decomposed into subproblem \(\mathbf{P2}\) for the optimization of the AP flight paths and the joint optimization subproblem \(\mathbf{P3}\) for GPF device association and power control as follows:  
\[(\mathbf{P2})\max_{\mathcal{Q}}\eta \tag{13}\] \[\text{s.t.\ C9: }||q_{m}(t+1)-q_{m}(t)||^{2}\leq(v_{max}\cdot\Delta t )^{2},\ \ \forall m\in\mathcal{M},t\in\mathcal{T}.\]\[\begin{split}\text{(P3)}\;\;\max_{a_{s},\epsilon=m,\mathcal{P}}\;\eta\\ \text{s.t.\ C1}\sim\text{C8}.\end{split} \tag{14}\]  
C1: \(a_{n,s}(t)\in\{0,1\},\forall n\in\mathcal{N},t\in\mathcal{T}\);  
C2: \(a_{m}(t)\in\{0,1\},\forall n\in\mathcal{N},m\in\mathcal{M},t\in\mathcal{T}\);  
C3: \(\sum_{m=1}^{M}a_{n,m}(t)+a_{n,s}(t)\leq 1,\forall n\in\mathcal{N},t\in\mathcal{T}\);  
C4: \(\sum_{m=1}^{M}a_{n,m}(t)\leq L_{M},\forall n\in\mathcal{M},t\in\mathcal{T}\);  
C5: \(\sum_{m=1}^{M}\sum_{n=1}a_{n,m}(t)+\sum_{n=1}^{M}a_{n,s}(t)\leq L_{S},\forall t \in\mathcal{T}\);  
C6: \(0\leq p_{n}(t)\leq p_{max},\forall n\in\mathcal{N},t\in\mathcal{T}\);  
C7: \(\sum_{t=1}^{T}(R_{n,\text{immediate}}(t)\cdot\Delta t)\geq\mu_{n,\text{immediate} },\forall n\in\mathcal{N}\);  
C8: \(\sum_{t=1}^{T}(R_{n,\text{nom-immediate}}(t)\cdot\Delta t)\geq\mu_{n,\text{ non-immediate}}\forall n\in\mathcal{N}\).  
Subproblem **P2** assumes that the connection of devices and their transmit power are given in each time slot. The APs need to consider the communication task requirements of GPFs and the total channel gain that they can provide to GPFs to determine their movement. We propose an online path optimization algorithm based on RL to address this problem.  
Subproblem **P3** assumes that the positions of the APs are given in each time slot. GPFs need to choose an appropriate device association and provide transmit power for data transmission within each time slot. From the constraints of the subproblem, it can be observed that each GPF can only choose one device for association in each time slot, and each device has a maximum connection limit, leading to the issue of connection competition. After determining the associated device, GPFs further optimize their transmit power to achieve maximum throughput. We propose a joint optimization sub-algorithm for device association and power control based on RL to address this problem.
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 2 Quaternion Orthogonal Designs
### Symmetric-Paired Design 3: (Non-Square QODs)
: _All of the above construction techniques generate QODs for only configurations when the number of dual-polarized transmit antennas are in powers of 2, i.e., \(2,4,8,16,\dots\) which puts a heavy toll on the code rate as \(1/2^{l}\) sharply declines as the number of antennas increase._  
Following above remark, it is essential to find quaternion designs for general configuration of dual-polarized antennas like for a \((n_{T}\times 1)-\)system such that \(n_{T}\in N\) and these codes have maximal coding rates. We developed such QODs in the subsequent section.
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 2 3 GPP 5G V2X QoS Scenarios and Proposed Dynamic System Model
On the contrary, we use a geometry\(-\)based stochastic channel model, which also considers environmental issues such as buildings and vehicles (they are denoted as scatters using the GEMV model) [30]. The geometric description of the environment is used to derive the channel parameters used by the real model and not by distributions. GEMV model calculates deterministically the large\(-\)scale signal variations with additional stochastic signal variations due to scattering. A carrier frequency \(f_{c}=5.9\) GHz is also considered, similarly to [9]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scenario** & **Short Frame** & **Data Rate per** & **Vehicle** & **Doppler** & **Normalized Fade** & **Vehicle** \\
**Number** & **Length k\({}_{\text{f}}\)(bits)** & **Vehicle R\({}_{\text{b}}\) (Mbps)** & **Speed (km/h)** & **Frequency f\({}_{\text{d}}\) (Hz)** & **Rate f\({}_{\text{D}}\times\)T\({}_{\text{S}}\)** & **Density V\({}_{\text{D}}\)** \\ \hline \hline
1 & 256 & 6 & 60 & 327.77 & \(1.82\times 10^{-5}\) & 45 \\ \hline
2 & 256 & 12 & 60 & 327.77 & \(9.104\times 10^{-6}\) & 45 \\ \hline
3 & 256 & 6 & 100 & 546.29 & \(3.03\times 10^{-5}\) & 45 \\ \hline
4 & 256 & 12 & 100 & 546.29 & \(1.517\times 10^{-5}\) & 45 \\ \hline
5 & 512 & 6 & 60 & 327.77 & \(1.82\times 10^{-5}\) & 45 \\ \hline
6 & 512 & 12 & 60 & 327.77 & \(9.104\times 10^{-6}\) & 45 \\ \hline
7 & 512 & 6 & 100 & 546.29 & \(3.03\times 10^{-5}\) & 45 \\ \hline
8 & 512 & 12 & 100 & 546.29 & \(1.517\times 10^{-5}\) & 45 \\ \hline
9 & 512 & 18 & 60 & 327.77 & \(6.06\times 10^{-6}\) & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation scenarios with different parameters and constant vehicle density. At Table 29 simulation scenarios can be observed. The goal is to examine 5G V2X QoS issues and we consider four different parameters: short frame length, data rate per vehicle, vehicle speed (with the corresponding Doppler frequency and normalized fade rate) and vehicle density. The choice of most of these parameters is based on the 6 scenarios of Table 1. The two Table 2 frame lengths have been chosen similarly to [21, 22], while the data rates are similar to those in [20]. The difference with [20] is that in this work the focus is on larger short frame lengths of 256 and 512 bits, while in [20] mainly a short frame length of 128 bits is considered. Additionally, more vehicle density values are considered to examine their effect on QoS issues and in this work throughput QoS parameter is examined in more detail compared to [20]. Finally, it must be mentioned that scenario number 9 of Table 2 is considered only at Section 3.1 QoS analysis, which follows.