esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 6 Conclusions  
In this paper, we conducted an experimental study to verify the feasibility of PBR based on PAR signals for aircraft target detection and localization. First, the experimental PBR system was described in general; after the analysis of the complex modulation form of PAR signals, two problems to be solved in signal processing were proposed, including the time and frequency synchronization of the PBR system and the coherent integration of radar signal with random PRI and staggered PW. Then, aiming at these two problems, the paper first proposed a direct wave parameter estimation method based on template matching to achieve a time and frequency synchronization of the PBR system and, then, proposes a coherent integration method based on Radon-NUFFT to deal with the irregular RCM and nonuniform phase fluctuations among different pulses caused by random PRI and the irregular range-Doppler coupling caused by staggered PW. Finally, the results of field experiments were presented. The performance of the matched filtering method based on reconstructed reference signal was verified by comparing the matched filtering results using different reference signals. The effectiveness of the coherent integration method based on Radon-NUFFT was verified by the coherent integration results of three targets. Finally, by comparing the target positioning results with the ADS-B tracks, the performance of target detection and localization of the PBR system based on PAR signals was verified.  
Figure 19: Result of target positioning and Automatic Dependent Surveillance-Broadcast (ADS-B) tracks.  
**Author Contributions:** [PERSON] proposed and implemented the methods, [PERSON] designed the experiment, [PERSON] and [PERSON] processed the data, and [PERSON] and [PERSON] revised the manuscript. All authors of the article provided substantive comments.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Performance Comparisons  
The comparison of security assumptions for related works are given in Table 1. These schemes are provably secure on the basis of different hardness assumptions (such as CDH, DL, RSA, quadratic residues, and cubic residues). The aim of these schemes is to find new constructions under simpler hardness assumptions.  
We denote \(M_{p}\), \(H_{m}\), \(O_{p}\) and \(E_{n}\) as the operation of scalar multiplication, map-to-point hash function, bilinear pairing, and modular exponentiation, respectively. We ran each of the above operations in a personal computer and used their times from [33] to calculate the total computational cost in the running time (milliseconds), as shown in the columns of Table 2.  
We have also compared related works on the basis of the cubic residues for the computational performance evaluation in Table 3. For consistency, we used the modular exponentiation times to evaluate the \(\mathbf{Sign}\) and \(\mathbf{Verify}\) algorithms.
## 1 Introduction
Therefore, it remains relevant to evaluate the possibilities of the complex use of pomace and probiotics for the development of new products with a higher nutritional value. Products such as various candies, gummies, or chewing gum are convenient food matrices that can be supplemented with dietary fibre and probiotics [24]. The global confectionery industry encompasses a diverse array of products and was valued at approximately USD 298.23 billion in 2021. This market is anticipated to grow at a compound annual growth rate (CAGR) of 4.3% during the period from 2022 to 2028 [25]. From a nutritional perspective, jelly candies are classified as unhealthy sugary treats with a low antioxidant content. As a result, creating healthier alternatives poses significant challenges. Current approaches focus on replacing sugar with sweeteners and/or incorporating dietary fibre into their formulations [26; 27]. However, limited attention has been given to the use of enzymatically hydrolys pomace as a functional ingredient in confectionery products. Its application in food product development remains underexplored, particularly in the context of functional confectionery, presenting opportunities to create innovative products that combine health benefits with sustainability. While several studies have focused on valorising AP through enzymatic hydrolysis, primarily targeting bioactive compoundsand fermentatable sugars [16,17], the combination of enzymatically hydrolys AP with probiotics, particularly in functional confectionery, has been insufficiently investigated, emphasizing the novelty of this approach. Therefore, the objective of this study was to analyse the dried pomace powder from apple juice extraction (AP) as sources of DF and its modification possibilities using commercial enzymes (Viscozyme(r) L, Pectinex(r) Ultra Tropical, and Celluclast(r) 1.5 L). This study was designed to investigate the composition of carbohydrates and technological properties (swelling capacity, water-retention capacity, oil-retention capacity, bulk density, and static and thermal emulsion stability) of the enzymatically modified AP. Moreover, enzymatically modified AP was applied in the jelly candy recipe as a value-added ingredient supplemented with the probiotic _Bifidobacterium animalis_ DSM 20105 strain. The integration of enzymatically hydrolys pomace into confectionery formulations not only addresses the demand for foods with a higher nutritional value but also represents an innovative approach to valorising fruit by-products in a sustainable manner. This study provides useful insights into the similarities and differences between the composition of carbohydrates and the technological properties of AP modified with different commercial enzymatic preparations and will aid in the selection of suitable dietary fibre-rich AP products as functional ingredients for further applications in foods. Moreover, this study provides information on _B. animalis_ survival in jelly candy during in vitro digestion, the viability of probiotics during storage, and the quality characteristics of candy made with modified AP products.
# Evaluating the Physicochemical Properties of Some Kosovo's and Imported Honey Samples
## 3 Results and Discussion
### Soluble Solids  
The total soluble solids expressed as \({}^{\circ}\)Brix of the honey samples from Kosovo ranged from 78.60 to 83.50 (mean value \(\pm\) SD = 81.09 \(\pm\) 1.37). The analyzed imported honey samples showed soluble solids content values between 79.50 and 82.20 (mean value \(\pm\) SD = 81.18 \(\pm\) 0.70). The comparison between the average content of soluble solids in local and imported honey samples did not show significant differences. Usually, the content of soluble solids in honey is 80 \({}^{\circ}\)Brix or above [58]. The grading system of the United States Department of Agriculture stipulates that honey is qualitative and is more stable during storage for a longer period when results exceed 80 \({}^{\circ}\)Brix (<20% water). In this study, 7 out of 33 honey samples from Kosovo had a soluble solids content below 80 \({}^{\circ}\)Brix. On the other hand, only one out of 12 imported honey samples had a soluble solids content below 80 \({}^{\circ}\)Brix. Values lower than 80 \({}^{\circ}\)Brix of soluble solids were observed in Kosovo's samples KH3 (Acacia honey, Peja region), KH6 (flower honey, Prishtina region), KH7 (forest honey, Prishtina region), KH10 (flower honey, Prishtina region), KH11 (flower honey, Prishtina region), KH15 (forest honey, Mitrovica region), KH29 (chestnut honey, Peja region), and IH9 (flower honey, N. Macedonia) of imported honey. These values are similar to those reported in other studies [15]. The soluble solids content of honey samplesfrom Portugal ranged from 79.0 to 82.2 \({}^{\circ}\)Brix, while the content of soluble solids of honey from Mexico ranged from 79.1 to 81.7 \({}^{\circ}\)Brix [59].  
Figure 2: Summary of the values shown for free acidity, HMF, and soluble solids.
## 2 Materials and Methods
### Soil Selection and Planting
#### 2.3.6 Method for Calculating Cd Concentration  
The calculation process included sample collection and processing (collecting contaminated soil or plant samples, drying, grinding, and digestion), sample analysis (using atomic absorption spectroscopy or ICP-MS technology to determine the Cd content and Cd standard solution preparation to construct a standard curve), creating a standard curve  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Instrument Name** & **Model** & **Factory** \\ \hline Electric blast drying & HG101-2A & Nanjing Experimental Instrument \\ oven & & Factory, Nanjing, China \\ Microwave digestion & & Shanghai Yiyao Instrument \\ instrument & & Technology Development Co., \\  & & Ltd. Shanghai, China \\ Visible & 723 PC & Shanghai Precision Instruments \\ spectrophotometer & & Co., Ltd. Shanghai, China \\ Electronic analytical & & Shanghai Huachao Industrial Co., \\ balances & & Ltd. Shanghai, China \\ High-speed & & Yongkang Zhanfan Industry and \\ multi-function grinder & ZF-2500A & Trade Co., Ltd. Jinhua, China \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental instruments used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Drugs** & **Purity** & **Factory** \\ \hline Nitric acid & Analytically pure & Xilong Science Co., Ltd. Shantou, China \\ Hydrogen peroxide & Analytically pure & Xilong Science Co., Ltd. Shantou, China \\ Hydrofluoric acid & Analytically pure & Xilong Science Co., Ltd. Shantou, China \\ Hydrogen chloride & Analytically pure & Xilong Science Co., Ltd. Shantou, China \\ Sodium hydroxide & Analytically pure & Xilong Science Co., Ltd. Shantou, China \\ Dithihydrazone & Analytically pure & Chengdu Kelong Chemical Co., Ltd. Chengdu, China \\ Hydroxylamine & Analytically pure & Tianjin Guangft Science and Technology Development Co., Ltd. Tianjin, China \\ OP emulsifier & Analytically pure & Tianjin Kemiou Chemical Reagent Co., Ltd. Tianjin, China \\ Potassium sodium & Analytically pure & Xilong Science Co., Ltd. Shantou, China \\ tartrate & & National Center for Analysis and Testing of Nonferrous Metals and Electronic Materials. Beijing, China \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the chemicals used in the experiment.  
(analyzing the absorption value or signal intensity of the standard solution and drawing the curve with Cd concentration as the horizontal axis), and finally, calculating the sample concentration (substituting the absorption value or signal intensity of the test sample into the standard curve equation to obtain the Cd concentration in the sample).  
The transfer factor (TF) is the ratio of the content of heavy metal elements in the aboveground part to the content of the heavy metal element in the underground part [18], that is, the Cd concentration can be calculated from Formula (1):  
\[\mathrm{C=}\frac{\mathrm{M\times\frac{V}{V}}}{\mathrm{V}}\times 1000 \tag{1}\]  
C--concentration of Cd, mg/kg;  
M--the amount of Cd ions based on the standard curve, mg;  
V--pipetting volume, mL; and  
v--dilution volume, mL.
# Enhancing the SRAM PUF with an XOR Gate
## 4 Results
### SRAM Device Characteristics  
After enrolling ten SRAM devices, we examined their properties, particularly their flakiness and likelihood to represent either a '0' or a '1'. To illustrate the characteristics of SRAM cells, we categorized all cells, regardless of their flakiness or stability, into five groups: strong/weak '1's, strong/weak '0's, and super-flaky cells. Strong '1's are SRAM cells that exhibit a higher likelihood of being a '1' when queried, only appearing as '1' during enrollment. Strong '0's, on the other hand, are more inclined to represent a '0' when queried and during enrollment, never appearing as a '1'. Super-flaky cells do not prefer either state and have an equal chance of being a '0' or a '1', indicated by showing up in the 4% range centered around the median count. Lastly, a cell is a weak '0' or '1' if it is not categorized into the other three categories and falls into its respective biasing. This categorization scheme helps visualize and differentiate the behavior and tendencies of SRAM cells.  
In this study, we analyzed ten SRAM devices at each temperature and recorded their device characteristics in Table 2. Based on the information provided in the table, it can be inferred that the devices display a slight preference for one state over the other, reducing their overall entropy. According to the classifications used in this study, approximately one-third of the SRAM cells are categorized as 'flaky'. Moreover, it is observed that the number of flaky cells tends to increase as the temperature rises.  
Figure 8: BER vs. read count at 23 \({}^{\circ}\)C. As the enrollment count increases, the error rates within responses go down as well. This pattern follows a logarithmic decrease, and due to this, only 1000 enrollments are performed.
###### Abstract  
Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability of flow predictions in the Upper Xijiang River Basin. This study evaluated the potential impacts of climate change on the streamflow and water yield of the Upper Xijiang River Basin using Arc-SWAT. The model was calibrated (1991-1997) and validated (1998-2001) using the Sequential Uncertainty Fitting Algorithm (SUFI-2). Model calibration and validation suggest a good match between the measured and simulated monthly streamflow, indicating the applicability of the model for future daily streamflow predictions. Large negative changes of low flows are projected under future climate scenarios, exhibiting a 10% and 30% decrease in water yield over the watershed on a monthly scale. Overall, findings generally indicated that winter flows are expected to be affected the most, with a maximum impact during the January-April period, followed by the wet monsoon season in the May-September period. Water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, will seriously affect the water resources and streamflow patterns in the future. Thus, critical problems, such as ground water shortages, drops in agricultural crop yield, and increases in domestic water demand are expected at the Xijiang River Basin.  
SWAT model; SUFI-2; land use change; climate change; streamflow; water yield +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Experimental Results  
Figure 5 shows the magnetic field distribution in the surroundings of the nano fab where the electromagnetic field values were measured for several cable distances ranging from 0.1 m to 3 m. At 2.3 m height, rectangular movable plate trays made of iron rods were developed to check the electromagnetic fields in the nano fabs. The magnetic fields were measured by the nano fab by using the permutations with A/m as the units. Maintaining a close proximity to transmission lines and exposure to their electromagnetic interference is harmful to human life, and this EMI may also lead to inaccurate readings in industrial measurement equipment [14; 15; 16]. Therefore, this study explains how to reduce electromagnetic interference at a specified distance from cables using the reported permutation systems.  
The maximum magnetic field distributions were observed at a radius of 1.8 m with a value of 240 mG. In the closer range of a 0.7 m radius, the magnetic fields were under 18.7 mG. At a 3 m distance from the nano fab, the magnetic field was measured to be 13.8 mG.
# An Algebraic Approach to Identifiability
## 3 Observability and Identifiability
### Global Identifiability and Observability
If the quantifier-free equivalent to Equation (9) is FALSE for some \(\mathfrak{I}_{i}\), then the corresponding real variety \(\mathbf{var}^{\mathbb{R}}(\mathfrak{I}_{i})\) is disjoint from the admissible extended state and parameter space \(\mathcal{Z}\), and we set \(\mathfrak{I}_{i}=\langle 1\rangle\). Otherwise, i.e., the quantifier-free equivalent is TRUE, there are indistinguishable tuples in \(\mathcal{Z}\), and we set \(\mathfrak{I}_{i}=\mathfrak{I}_{i}\). We collect all these components and write  
\[\mathfrak{I}=\bigcap_{i=1}^{k}\mathfrak{I}_{i}.\]  
The real variety \(\mathbf{var}^{\mathbb{R}}\big{(}\mathfrak{I}\big{)}\) is then the Zariski closure of the indistinguishable states \(\mathcal{I}\subseteq\mathcal{Z}\). Using this notation, we are able to express the global identifiability as follows: The system in Equation (1) is globally identifiable and globally observable if and only if \(\mathfrak{I}=\mathfrak{I}\). Note that if the system is globally identifiable and observable, this holds for any initial condition of the system state \(x\). With small variations on the ring and the ideals, other properties can be tested as well:
## 2 Numerical Simulation
### Extra-Thick Coal Seam Model  
The dimensions (x*y*z) of the 3D model are 300 \(\times\) 500 \(\times\) 75 m, where the corresponding grid spacing (dx, dy, and dz) are all 1 m. To balance high-resolution imaging and computational accuracy in finite-difference simulations, the main frequency of the source (which excites a P-wave) is 150 Hz, and the sampling rate is dt = 0.01 ms. A schematic of the three-layer coal seam model is shown in Figure 2a. In the z-direction, the roof layer is at depths of 0-25 m, the middle layer is the coal seam at 25-50 m, and the floor layer is at 50-75 m. Running along with the bottom interface of the middle layer and separated by a distance of 250 m, the dimensions of each roadway are 4 \(\times\) 500 \(\times\) 4 m. The receiver line is on roadway 1 (x-direction is 276-280 m; y-direction is 0-500 m; z-direction is 46-50 m). For a comparison of the homogeneous coal seam with a heterogeneous feature, a collapsed column with a design radius R of 25 m is placed in the middle of another (otherwise identical) model, as shown in Figure 2b. The source coordinates are 26, 250, and 49 m, and the sampling time is 350 ms. The parameters of the isotropic media (and collapse column) within the model are shown in Table 1. Considering that typical coal-bearing strata are characterized by a layered structure, the coal seam and its roof and floor can be approximately regarded as horizontally stratified, isotropic media. The values in Table 1 fall within the reasonable range of the physical properties for coal and rock and have been adjusted based on the velocity measurements detected at the actual working face.  
Figure 1: Diagram of the three-dimensional staggered grid.
# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 3 Materials and Methods
### Calculating Required Voice Characteristics
Subjective methods use human listeners to rate the quality of the voice based on perceptual criteria, such as clarity, naturalness, and overall acceptability. These ratings are then combined using statistical methods to calculate the AVQI score. Examples of subjective methods include the consensus auditory-perceptual evaluation of voice (CAPE-V), which uses a standardized rating scale to evaluate various aspects of voice quality. Step 2: Plot the candidate solutions on a Pareto front to visualize the trade-off between the objectives. To plot the candidate solutions on a Pareto front, we first need to define the objective functions that we want to optimize. For example, we may want to maximize the clarity of the voice while minimizing the jitter and shimmer. We can then calculate the objective values for each candidate solution and plot them on a 2D or 3D graph, where each axis represents an objective function. The Pareto front is the set of candidate solutions that cannot be improved in one objective without sacrificing another objective. Step 3: Identify the Pareto optimal solutions on the Pareto front that cannot be improved in one objective without sacrificing another objective. To identify the Pareto optimal solutions, we can use a variety of techniques, such as visual inspection, clustering, or optimization algorithms. Visual inspection involves manually examining the Pareto front and selecting the solutions that best meet the specific needs and preferences of the AVQI assessment. Clustering involves grouping similar solutions together and selecting the representative solutions from each cluster. Optimization algorithms, such as the NSGA-II (non-dominated sorting genetic Algorithm 2) can be used to automatically identify the Pareto optimal solutions. Step 4: Select the Pareto optimal solution that best meets the specific needs and preferences of the AVQI assessment. To select the Pareto optimal solution that best meets the specific needs and preferences of the AVQI assessment, we need to consider factors, such as clinical relevance, patient preference, and ease of implementation. We may also want to consult with clinicians and patients to get their feedback and ensure that the selected solution is acceptable and feasible. Step 5: Fine-tune the AVQI algorithm and the Pareto optimization parameters based on the validation results and feedback from clinicians and patients  
To fine-tune the AVQI algorithm and the Pareto optimization parameters, we need to evaluate the performance of the algorithm using validation data and feedback from clinicians and patients. We can use metrics such as accuracy, sensitivity, specificity, and AUC (area under the roc curve) to assess the performance of the AVQI algorithm. We can also ask clinicians and patients to rate the quality of the voice for a subset of the validation data and compare the ratings with the AVQI scores. Based on the validation results and feedback.