esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 6 Machine Learning for Enhancing PHY Layer Security  
Advances in machine-learning methods, both classical [110] and especially deep learning, have opened up opportunities for use in enhancing the security of 5G networks. To secure the wireless transmitters at the physical layer, exploitation of machine-learning techniques mainly leverages either the unique RF impairments of transmitting devices (RF fingerprinting) or channel state information inconsistencies via anomaly detection or model-based approaches. In the following, we provide an overview of the most-used machine-learning and deep-learning models and outline their use for physical layer-based authentication.
## 5 Simulation
### Signal-to-Interference Plus Noise Ratio  
Another significant parameter characterizing the performance of any linear STAP processor is the output signal-to-interference plus noise ratio (SINR) of the processor. Figure 8 shows a plot of the SINR at the output of the STAP processor for all four methods. Considering the SMI-LU algorithm, the processor achieves an SINR value of about 35 dB over almost the entire Doppler frequency range. If the Doppler frequency of the object is close to 0 Hz or 30 kHz, then the SINR value is very small, because at these frequencies, the algorithm very strongly eliminates interference; hence, the signal-to-interference plus noise ratio is also very low.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 2 System Model  
### Scenario  
Consider the downlink transmission scenario in an ISTN as shown in Figure 1. The satellite system and multiple terrestrial systems are controlled by the same NCC and share the same frequency band to improve the spectrum efficiency.  
The satellite system consists of a satellite with the orbit height of \(h_{s}\) and \(N_{sat}\) satellite terminals indexed by the set \(\mathcal{N}_{sat}=\{1,\ldots,N_{sat}\}\). The satellite is equipped with a phased array antenna [29], which can form \(N_{b}\) spot beams to serve the satellite terminals via a BH manner. A BH time window consists of \(N_{slot}\) time slots indexed by the set \(\Gamma=\{1,\ldots,N_{slot}\}\), where the duration of a time slot is denoted by \(T_{slot}\). Each spot beam can serve one satellite terminal in each time slot. Therefore, at most \(N_{b}\) satellite terminals can be served by the satellite system in each time slot. All the spot beams reuse the full transmission bandwidth.  
There are \(N_{ter}\) terrestrial systems within the footprint of the satellite, which can be indexed by the set \(\mathcal{N}_{ter}=\{1,\ldots,N_{ter}\}\). A terrestrial BS is located in the center of each  
Figure 1: The downlink transmission scenario in an ISTN.  
terrestrial system and provides services to the terrestrial terminals in this system. The locations of the terrestrial BSs are modeled by the Matern hardcore process (MHP) [30], which ensures a minimum separation distance \(\delta\) between different terrestrial BSs. It is assumed that \(\delta\) is quite larger than the cell radius of the terrestrial systems, so all the terrestrial systems can reuse the same frequency band without the inter-BS CCI.
## 4 Signal Processing Algorithms  
Signal processing is increasingly playing a key role in the analysis of optical measurements due to the enhanced power of microprocessors. There are many approaches to improving signal quality of LCI systems presented in literature; however, these again remain dispersed across the literature. This section will provide a concise overview of the key analysis procedures along with highlighting practical application signal issues which the user will encounter during real-life measurement scenarios. A signal processing framework for the steps required to go from raw detected signal to spatial distance value is illustrated in Figure 7. Here, the solid bounding boxed elements represent essential parts of post processing and the dashed bounding boxed elements represent procedures that will enhance the signal quality.  
Figure 7: Framework for LCI signal data processing and distance offset retrieval, outlining required procedures in solid bounding boxes along with processing steps which can improve the signal quality in dashed bounding boxes.
## 1 Introduction  
The use of bio-asphalt in asphalt mixtures is currently considered as one of the main alternatives for reducing bitumen consumption, which has environmental, economic, and health benefits [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Some of these bio-binders that studied and showed promising results include sugar cane molasses [11; 12; 13; 14], beet molasses [15], swine manure [1; 16; 17], waste cooking oil [6; 9; 18; 19; 20], and wood waste [21; 22]. However, the studies indicated that bio-asphalts are moisture susceptible because moisture can have serious effects on the durability of hot-mix asphalt HMA pavements. The use of sugar cane molasses as a replacement for bitumen in pavement construction has less environmental impact and construction cost than that of bitumen. It reduces the greenhouse gas emission by up to 30% and improves the thermal durability of asphalt pavements [23]. In research studies conducted to determine the optimum percentage replacement of bitumen with molasses in the production of HMA, some of the values claimed include 4.7% [13] and 13-15% [24] for 30 viscosity grade bitumen, 0-3% [25], and 9% [12] for 60/70 penetration grade bitumen and 0-10% [15] and 5-20% [11] for 50/70 penetration grade bitumen. The agreement between the research outcomes was that partial replacement of bitumen with molasses can improve the performance of the pavement. Although extensive and valuable studies have been conducted in this field, detailed information on the effects of the water content of molasses on the properties of molasses modified binders, such as rheological properties and chemical compositions, could not be found.  
This paper presents the results of an experimental study on the effect of high and low water content sugarcane molasses on the rheological and chemical properties of molasses modified binders used in HMA. Bitumen binders (BB) containing 5%, 10%, 15%, and 20% molasses were investigated as molasses modified binders (MMB). Molasses with 24.9% and 5.02% by weight of water were used to investigate the effect of moisture on the MMBs.
## Materials and Methods
### Scots Pine Canopy Sunlit Fraction Retrieval
Hence, we could convert the sunlit fraction obtained with the reference albedo in Equation (2), \(\alpha_{S,\text{ref}}=\alpha_{S}(\rho_{\text{ref}})\) to the fraction of sunlit Scots pine needles \(\alpha_{S,\text{Scots pine}}=\alpha_{S}(\rho_{\text{Scots pine}})\), hereafter denoted as \(\alpha_{S}\), by dividing \(\alpha_{S,\text{ref}}\) by 0.648. We used the IDL programming language and ENVI (Harris Geospatial Solutions, USA) software to fit Equation (1) to AHS data and to create \(\alpha_{S}\) maps for all three flight lines. In the place of BRDF, we used the AHS-measured HDRF values. In the red edge spectral region, and especially on a clear day, the difference between HDRF and BRDF is very small. Under the assumption of the sunlit fraction \(\alpha_{S}\) being the main driver of the spectral shape of BRDF, we extracted Scots pine canopy BRDF in seven \(\alpha_{S}\) ranges and evaluated the similarity of the BRDFs using the SID algorithm. We inspected sunlit fraction histograms to find the smallest sunlit fraction value ranges with more than 80 pixels for each view angle. We selected the following intervals: \(0.06<\alpha_{S}<0.08\), \(0.26<\alpha_{S}<0.29\), \(0.35<\alpha_{S}<0.36\), \(0.43<\alpha_{S}<0.45\), \(0.56<\alpha_{S}<0.60\), \(0.71<\alpha_{S}<0.73\), \(0.82<\alpha_{S}<0.86\). (Table 4). We averaged the spectra in each interval to obtain 16 Scots pine canopy reflectance spectra, specific to each view angle and \(\alpha_{S}\) interval. Note that the data for each \(\alpha_{S}\) interval were not always available due to the natural range of \(\alpha_{S}\) (Table 4).
## Appendix A The Procedure to Calculate GNSS-IR Index NMRI  
Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) provides us with a new mode to monitor the vegetation information in a long time series. It acts as a relatively new L-band remote sensing technique with relevance for measuring vegetation states using reflected GNSS signals by recording the interference between a direct GNSS signal and a reflected GNSS signal [21].  
L-band signals transmitted by GNSS satellites are reflected by the land surface and received by geodetic-quality GPS antennas a few meters above the ground. It causes the multipath effect and pseudorange multipath error (M) on the observations. It is found that the existence of vegetation has a certain effect on the amplitude of the interference between a direct GNSS signal and a reflected GNSS signal, as it decreases with the increase of vegetation water content. According to the definition and formula derivation based on M [59], M increases with the increase of the amplitude of the interference. This provides a possibility for the study of vegetation water content based on M.  
A database of daily mean MP1 rms statistics for each site is routinely compiled by the operators of the NSF EarthScope Plate Boundary Observatory (PBO), based on which pseudorange multipath error (M) can be obtained. This original objective of this GPS network is to measure deformation across active fault zones in the western USA, and the network can also be used to monitor vegetation water content information according to the above theory. To eliminate the influence on topography and get a positive-correlation index with the vegetation water content, the index NMRI was obtained by normalization of MP1 rms:  
\[NMRI=\frac{-(MP_{1}rms-\max(MP_{1}rms))}{\max(MP_{1}rms)} \tag{1}\]  
The maximum MP1 rms (shown by the dashed line) is based on the average of the largest 5% daily MP1 rms values. Finally, the index NMRI is defined, which increases as vegetation water content increases. The lowest NMRI values (bottom 5% of the observed values) are set to zero; the peak values rarely exceed 0.35.
## 5 Discussion  
Each 24 h dietary recall nutrition survey in this study took approximately 40 min. The volume and complexity of the survey data and the variation in the ability to self-assess and recall portions can lead to individual subjective differences [13]. Similarly, the researchers or the methods used to collect dietary data may be biased [18].  
Therefore, this study balanced the accuracy of nutrient intake analysis by compensating for errors through fuzzy analysis and artificial intelligence. Conventional FFQs are primarily designed to assess total nutrient intake or changes in intake over time [27; 28; 29]; however, the FFQ limits the range of foods that can be investigated as it combines food and beverages thus determining the exact amount of nutrients is less precise than other more detailed methods. It is also not possible to accurately measure absolute intakes of different food components. Moreover, FFQs require literacy and the physical ability to complete the questionnaire, and the FFQ survey can be burdensome for subjects and difficult or confusing to complete due to poor descriptions or difficult-to-understand questions. The most commonly used methods in nutrition research are the Diet Record, 24 HR, and FFQ.  
The Food Record is also used as the gold standard in validation studies [30]. Given the contingent nature of the respondents' food choices, a variety of food and beverage combinations [31] and nutrient supplementation [32] are the best methods to investigate. In order to reduce the burden on surveyors, the artificial intelligence model in this study has proven to be a feasible strategy for large-scale nutritional surveys after data discrepancy comparisons.  
When comparing the difference between our model and the data analyzed in the actual nutrition survey, it was found that the results of the \"24-h dietary recall food weight and nutrient ingredient\" method were highly accurate, with less than 2% discrepancy in analysis for almost all nutrients. This result shows that the nutrients of the ingredient data in our model are correct. In the 24 h dietary recall nutrient intake sum analysis, the model was used to conduct an artificial intelligence analysis of the dishes, meaning it conducted an automated analysis of the components and servings to estimate nutrient intake. The results show a margin of error of less than 10% thus confirming the high accuracy of the model in this study.
## 4 Simulation Results and Discussion
### Simulation Results of the Control Perormance in Far Field for Each Method
It illustrates that the proposed CLASSO-RLS algorithm exhibits negligible sound amplification after active noise control in the two directions. This is because the CLASSO-RLS algorithm places constraint points in the non-error areas (Figure 1b). Moreover, the noise reduction of the constraint points is considered in the process of selecting secondary loudspeakers to reduce the noise at the constraint points as much as possible under a stipulated condition, thereby effectively suppressing the sound amplification in the direction where the constraint points are located.
# Nighttime Image Dehazing Based on Point Light Sources
## 4 Experiment
### Main Results
#### 4.2.2 Objective Evaluation  
To quantitatively evaluate the performance of different methods, we use PSNR [36], SSIM [37], UQI [38] three reference indicators to evaluate the algorithm. PSNR only measures the reconstruction quality of the pure image by comparing the pure image and the noisy image. The higher the PSNR proves the better the quality of the processed image, as shown in the formula:  
\[PSNR=10*log_{10}\left(\frac{255^{2}}{MSE}\right) \tag{22}\]  
where MSE is mean square error:  
\[MSE=\frac{1}{mn}\sum\limits_{i=0}^{m-1}\sum\limits_{j=0}^{n-1}\left[I(i,j)-K(i, j)\right]^{2} \tag{23}\]  
Figure 3: Image detail comparison. The defogging effect can be clearly seen at the red box.  
Figure 2: The subjective experimental results. The defogging effect can be clearly seen at the red box. (1) Nighttime hazy image. (2) DCP [14]. (3) NDIM [25]. (4) OSFD [35]. (5) OURS.  
Structural similarity is used to evaluate the retention of image structure information, which can solve the problem that PSNR and MSE cannot measure the similarity of image structure, as shown in the formula:  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{x}\sigma_{y} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+\sigma _{y}^{2}+C_{2}\right)} \tag{24}\]  
where \(\mu_{x}\) and \(\mu_{y}\) represent the mean brightness of images X and Y, \(\sigma_{x}\) and \(\sigma_{y}\) represents the standard deviation.The larger the SSIM, the better the dehazing effect and the higher the retention of the important information of the original image.  
UQI is designed by modeling image distortion as a combination of three factors: correlation loss, luminance distortion, and contrast distortion. The larger the UQI, the better the image quality.  
Combining the above three indicators, as is shown in Figure 4, it can be considered that the proposed algorithm is better than other methods in terms of information retention, reconstructed image quality and structural information retention in night scenes. As is shown in Table 3, our method improves the baseline DCP by 34%, 13%, 8% in PSNR, SSIM and UQI, respectively, and it increases the existed excellent algorithm OSPF by 9.4%, 11.2%, 3.3%. In the process of dehazing, it still retains more details. Compared with the two algorithms, the algorithm in this paper has more vivid colors on the premise of no image distortion, which is more suitable for human vision. The experimental image is shown in Figure 5.
## 3 User Study  
### Study Design  
To test the educational effects of the proposed system, a randomized controlled trial was designed, with the independent variable being the training effects of face-to-face training and remote training. The dependent variables were the five quantitative metrics collected from feedback devices in the evaluation stage and survey data. Quantitative measurements were obtained for mean compression depth, mean compression rate, correct compression rate, correct compression depth, correct recoil through feedback devices, and artificial respiration was excluded due to COVID-19. According to the general CPR guidelines of KACPR, the ideal values of the individually obtained metrics are at least 50 mm for compression depth, 100-120 CPM (Compression Per Minute) for compression rate per minute, and 10 mm for complete recoil after compression. Relevant data is automatically collected during the evaluation of face-to-face and remote training. The survey was conducted over two sessions. The pre-education survey was used to determine the subjects' age, gender, experience in BLS training or education, e-learning experience, IT familiarity, and perception of remote training (for the remote group). The post-education survey was a USE survey on a 7-point Likert scale [39], and covered perception of remote training after the program and other opinions. The survey was administered via Google Forms [40].  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Theme** & **Frequency** & **Instructors, \%** & **Instructors, \(n=4\)** & **Students, \%** & **Students, \(n=24\)** \\ \hline
1. The real-time feedback feature & 13 & 75\% & 3 & 63\% & 10 \\ was useful. & 10 & 25\% & 1 & 38\% & 9 \\
2. The initial settings such as setting & & & & & \\ the app and connecting the devices & 10 & 25\% & 1 & 29\% & 5 \\
3. More detailed guidance would & & & & & \\ be needed. & & & & & \\
4. Technological challenges & & & & & \\ (network environment, & 6 & 75\% & 3 & 13\% & 3 \\ disconnection, connection & & & & & \\ being unstable). & & & & & \\
5. It is easy to use and useful in & & & & & \\ remote environment. & & & & & \\
6. Test features and detailed scoring & & & & & \\ were useful. & & & & & \\
7. Errors occurred while using & & & & & \\ the app. & & & & & \\
8. The app is easy to use after the & & & & & \\ initial settings. & 1 & 0\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Theme of Comment Fields.
## 3 Results and Discussion
### TPH Degradation in the Soil Column  
Monitored TPH concentrations during the experimental period are compiled in Figure 3. Residual TPH concentrations were averaged from the composite samples (soil mixed from all depths). After the 2 day application of Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\) in soil containing holes, almost half (49.3%) of the TPH was oxidized, while only 33.5% TPH was oxidized in the soil without vertical holes. A greater TPH degradation in the soil column with vertical holes is attributed to higher soil water content during oxidation (Figure 2a, Supplementary Figure S2), resulting in a higher infiltration of the reagent (oxidant) into the soil. In addition, the ORP of soil (initially 310 mV) increased after oxidation. When AOS was added to Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\), there was a slight decrease in ORP from 442 to 424 mV, showing that a retained capacity for oxidation after the addition of AOS could oxidize TPH in soil.  
Figure 3 shows that an enhanced TPH removal when employing vertical holes was observed after bioaugmentation of persulfate treated soil. On day 30 after bioaugmentation, the residual soil-TPH concentration with holes decreased to 776 mg kg\({}^{-1}\) showing 88.4% removal efficiency, suggesting that 39.1% of TPH was additionally removed through the biodegradation process. The overall TPH degradation efficiency was clearly higher than that without the holes by a 61% removal efficiency (final TPH, 2494 mg kg\({}^{-1}\), \"(PS + Bio) w/o holes\" in Figure 3). Our previous study found that the introduction of remedial agents using a foam spray resulted in higher infiltration and thereby enhanced TPH removal efficiency compared to the use of an aqueous solution of the same remedial agents [23]. Furthermore, in the control soil column (\"Control (water)\" in Figure 3), the  
average TPH concentration somewhat decreased and did not show a distinct change in TPH concentration, remaining above 6000 mg kg\({}^{-1}\). The TPH fractional analysis by [PERSON] et al. [24], which used the same oxidant and oil-degrading microbes, showed less biodegradation in the F\({}_{2}\) section (C\({}_{18}\)-C\({}_{22}\)) than in the F\({}_{1}\) section (C\({}_{8}\)-C\({}_{16}\)). Pretreating persulfate significantly increased the biodegradation rate of 19% for the F\({}_{2}\) section of TPH.
###### Abstract  
The aim of this study was to evaluate the impact of sumac on the oxidation status, sensory properties, and physicochemical and microbiological parameters of common carp (_Cyprinus carpio_) fish during chilled storage. Fillets of carp were treated with ground sumac at percentages of 0% (T0), 0.5% (T1), 1.5% (T2) and 2.5% (T3) and analysed after 24 and 72 h of chilled storage. The fat content, oxidation parameters (thiobarbituric acid reactive substances, TBARS), antioxidant capacity, total volatile basic nitrogen (TVB-N), free fatty acids (FFA), sensory properties, colour indicators (lightness L*, redness a*, yellowness b*), water activity, pH value, aerobic plate count (APC), psychrotrophic microorganism count (PMC), and coliform bacteria count were determined. The TBARS values of T1, T2, and T3 decreased significantly (\(p<0.05\)) after 24 h of chilled storage. The antioxidant capacity increased in a dose-dependent and time-dependent manner. As regards organoleptic properties, sumac was able to reduce the natural fishy odour, increase the sour odour and flavour, and mask the fish odour and flavour significantly. A significant effect of sumac on the redness (a*) values of muscles was observed, which increased with elevation of the sumac ratio. A decrease in fillet pH and lower APC and PMC were observed. No effect was found on water activity or the growth of coliform bacteria. The study found that sumac plays a role in the oxidation status, pH value and organoleptic properties of fish fillets, which could be beneficial for the fish and fishery products industry.  
fish fillets; ground sumac; TBARS; DPPH; organoleptic properties; pH value +
Footnote †: journal: Applied Sciences