esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Effects of Roasting on Polyphenol Content
### [PERSON]  
[PERSON] et al. (2016) investigated the effects of three different roasting conditions (150 \({}^{\circ}\)C, 180 \({}^{\circ}\)C, and 200 \({}^{\circ}\)C for 5, 10, and 20 min, respectively) on almonds. They compared the phenolic compounds of these roasted almonds to raw almonds, which contained 750 mg GAE/100 g. Contrary to expectations, an increase in phenolics was observed in all three temperature groups after 20 min of roasting. Specifically, the concentration increased to 660 mg GAE/100 g at 150 \({}^{\circ}\)C, 1390 mg GAE/100 g at 180 \({}^{\circ}\)C, and 1920 mg GAE/100 g at 200 \({}^{\circ}\)C [79]. As mentioned previously, [PERSON] et al. (2020) reported an increase in polyphenol levels in almonds after the roasting process compared to raw almonds. This observation is attributed to the increased extractability of polyphenols. Moreover, the variation in polyphenols after roasting can also be influenced by the cultivar. For instance, the _Molar_ cultivar experienced a 58% decrease in antioxidant compounds after roasting, while the _Refgo_ cultivar showed a 72% decrease. These findings contradict the general trend of increased polyphenols being observed in roasted almonds [84].  
Similarly to hazelnuts, the majority of polyphenols in almonds are concentrated in the skin, in general, between 70 and 100% of the total phenolics [84]. The phenolic concentration of raw almond skin is approximately 2760 mg GAE/100 g, while that of roasted almond skin decreases to 1850 mg GAE/100 g. In general, unblanched almonds after the roasting process contain approximately 50 mg/100 g of polyphenols [85].  
[PERSON] et al. (2017) reported that the total phenolic content in raw almond samples was 580 mg GAE/100 g. After roasting at 110 \({}^{\circ}\)C for 16 min, the polyphenol content increased to 840 mg GAE/100 g [80].
###### Abstract  
Over the span of the last decade, certain pesticides have been banned in apple tree and citrus tree cultivations. Hence, it is important to conduct research focused on estimating the occurrence of residues of pesticides from the perspective of compliance with the relevant legislative regulations. Equally important is to estimate the reduction in pesticide residues through simple procedures such as washing and peeling. This research was conducted in the years 2012 and 2020. An assessment was made of the effect of in-house processing, such as conventional washing with tap water and peeling, on the level of pesticide residues in apples and citrus fruits (oranges, grapefruits and lemons). The level of pesticide residue was determined with the use of the QuEChenERS method of extraction in conjunction with LC-MS/MS analysis. One can clearly observe a smaller number of pesticides identified in the edible parts of fruits in 2020 (seven pesticides in apples and three in citrus fruits) compared to 2012 (26 pesticides in apples and 4 in citrus fruits). In apples from 2012, only in the case of disulfoton was the maximum residue limit (MRL) exceeded, while in samples of apples from 2020 no instance of exceeded MRL was noted. This study did not reveal exceeded MRL values in the edible parts of citrus fruits in the analysed years. The absence of detected instances of pesticides not approved for use in the analysed years indicates that the producers complied with the relevant legislative regulations. The results obtained indicate that conventional washing with water (about 1.5 L/one fruit) did not have any effect on the level of pesticide residues in the analysed fruits. Apple peeling allowed for a reduction in pesticide levels in the range of 24% (carbendazim) to 100% (trifluoromethyluron, thioidcarb, tebuconazole).  
pesticide residues; fruits; fruit peel; culinary processing; washing Article Assessment of Pesticide Content in Apples and Selected Citrus Fruits Subjected to Simple Culinary Processing  
1
## 2 Materials and Methods
### Methods
#### 2.3.5 Boosted Regression Tree Model  
Boosted Regression Tree (BRT) model is a machine learning method combining regression tree model and boosted model [56; 57]. This method generates data sets by randomly selecting a certain number of samples many times, and the remaining samples constitute test sets to improve the stability and accuracy of regression results [58]. The advantage of the model is that the interaction between independent variables can be ignored. Contributions of independent variables and the marginal effect within the value range are clearly shown in the model results [59; 60]. So far, BRT model has been successfully applied in urban heat island factor research [61; 62], ecological risk assessment research [57; 63] and many other fields, with high value.  
BRT model was used in this research to conduct six models. Whether the PLES transferring happened was defined as the dependent variable (transfer types include PS to LS, PS to ES, LS to PS, LS to PS, ES to PS and ES to LS). The indexes of nature, location and facilities were defined as independent variables, and the statistical average value of patch was used as sample to participate in the calculation. The cross coefficients of independent raster data were all less than 0.7. Calculation was carried out through R software and \"GBM\" package [64]. Existing studies used the consistency measurement of the training data correlation and the cross-validation (CV) correlation to measure the reliability of the BRT model parameters [57]. This study followed the rules to carry out multiple sets of verifications. After several tests, Ir = 0.01, TC = 1, BAG Fraction = 0.5 and family = \"Bernoulli\" were set in consideration of calculation rate and accuracy of results.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Attack Detection in Smart Grid  
Detection of malicious attacks is another main security concern in smart grids [24]. Different from the traditional IDS in networks, detecting malicious attacks in cyber-physical Systems (CPS) like the smart grid is far more complex. Cyber attacks in the smart grid normally are reflected in the form of changing voltage, current, or phase in the system [78]. As a result, the detection strategy mainly depends on deleting or correcting the polluted data under cyber attacks [79].  
The bad data detection methods are usually conducted after the state estimation process, which utilises the \(\chi^{2}\) detector and largest normalised residual (LNR) the most. The BDD process is used to validate the topology and the measurement data. However, a clever attacker can bypass the LNR by customising the attack vectors [56; 58].
# Incentive Price-Based Demand Response in Active Distribution Grids
## 3 Mathematical Modeling and Simulation Setup
### Linear Model of the BESS  
In this paper, a simplified discrete-time linear model of the BESS based on [31] is considered. The state-of-charge of the battery denoted by \(x_{f}\) is related to the battery discharging power \(p_{f}\) as follows.  
\[x_{f}^{\kappa+1}=x_{f}^{\kappa}-\frac{\eta_{f}\tau_{s}}{E_{f}}p_{f}^{\kappa} \tag{9}\]  
where \(\kappa\) is the time instant, \(\eta_{f}\) is the battery efficiency, \(E_{f}\) is the battery capacity and \(\tau_{s}\) is the sampling time.
# A Mask-Based Adversarial Defense Scheme
## 1 Introduction  
Deep neural networks (DNNs) have achieved great success in the past decade in research areas such as image classification, natural language processing, and data analytics, with a variety of application domains like banking, financial services and insurance, IT and telecommunications, manufacturing, and healthcare etc. [1]. However, researchers have discovered that it is possible to introduce human imperceptible perturbations to inputs of a DNN in order to induce incorrect or misleading outputs from the DNN at the choice of an adversary [2; 3; 4].  
As of today, the existing approaches to counter adversarial attacks can be roughly divided into two categories. The reactive approach focuses on the detection of adversarial inputs (e.g., [5; 6; 7]) and tries to correct the adversarial inputs. The proactive approach, sometimes known as adversarial defense, takes steps to strengthen DNNs (e.g., [8; 9; 10]), making them more robust to withstand perturbations on inputs. In this paper, we follow the latter path by enhancing robustness in the decision procedure of DNNs. Inspired by a recent paper [11] that restores missing pixels of provided images with random patching, we devise a new adversarial defense scheme called mask-based adversarial defense (MAD) for the training and testing of DNNs that perform image-classification tasks. In addition, we believe that such a mechanism may also be applicable to improve DNN robustness in other scenarios.  
In this approach, we perform a series of experiments with regard to MAD. We split an image into grids of predefined size (e.g., \(4\times 4\)), and randomly fill each grid by using a default value (e.g., the black value in RGB for those masked pixels) with a given probability (e.g., 75%) to generate training samples. After the training, we also apply masking to images at the test phase (for the classification task), as illustrated in Figure 1. Given an (unmasked original) image, we need to repeat the test process a number of times with different patternsof masking, which increases the chance of filtering out malicious perturbations, and the final decision is then defined as the most-voted class taking into account the outputs for all randomly masked inputs. In this paper, we have made the following contributions.  
* We introduce a new adversarial defense method, MAD. Our method applies randomized masking at both training phase and test phase, which makes it resistant to adversarial attacks.
* Our method seems easily applicable to many existing DNNs. Compared with other adversarial defense methods, we do not need special treatment for the structures of DNNs, redesign of loss functions, or any (autoencoder-based) input filtering.
* The experiment on a variety of models (LeNet, VGG, and ResNet) has shown the effectiveness of our method with a significant improvement in defense (up to 93% precision gain) when facing various adversarial samples, compared to models not trained with MAD.
# Development of a Digital System for Monitoring Emergency Conditions in 20 kV Distribution Networks
###### Abstract  
This article presents research on the possibilities of using information and communication technologies in monitoring systems for electrical networks with isolated neutral, aimed at improving and automating production functions in the energy sector. This aligns with the digitalization policy of Kazakhstan's economy and is part of similar programs in the field of the electric power industry. This article explores an approach to organizing a digital monitoring system for emergency conditions, specifically single-phase ground faults in medium-voltage lines within the range of 6-35 kV, including the new voltage class of 20 kV. A version of such a system is proposed, based on a combination of a server, a wireless information network, and remote digital voltage measurement nodes. This wireless information and communication network is designed to detect the locations of single-phase ground faults (SPGF) using specialized zero-sequence voltage sensors installed at various points along the power transmission lines, along with wireless signal transmission channels to the dispatcher's server. To ensure protection against industrial interference, based on the results of practical environment modeling, a transmission technology most resistant to external noise is selected. This article proposes the selection of equipment necessary for implementing wireless transmission technology and develops two versions of a digital voltmeter design based on low-power programmable microcontrollers. The proposed technical solutions require further experimental validation, and therefore, the authors plan to conduct additional research and practical experiments in the future.  
zero-sequence voltage; single-phase ground fault; digital voltmeter; modulation type selection; wireless channel; noise immunity; transmission channel modeling +
Footnote †: journal: _Article_
## 5 Applications
### Sensors  
For various SC devices, electromechanical coupling is a desirable property that can enhance the sensing properties of SCs [153]. For a specific application, mechanical strain can easily modify the material and be easily applied to the material. Because of its high electric strain property, the material can be reshaped after applying the force without permanent deformation. The material can strain again, enabling electromechanical coupling to achieve desirable properties that are good for temperature sensing.  
Figure 20: Schematic overview of diverse application areas of graphyne, including transistors, sensors, desalination, energy storage, and catalysis.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 1 Introduction
There was a possibility that some fault reasons were lost or misjudged. Fault diagnosis methods based on multiple information sources [13; 14] can provide a complete fault diagnosis report, including various fault details. However, former related studies have some shortcomings. The fault diagnosis method proposed in reference [13] fails to consider the time efficiency. Reference [14] evaluated the action behavior of protections and circuit breakers based on fault recording data, which may lead to low maintenance efficiency of relay protection devices and circuit breakers in practical engineering applications. Considering the shortcomings of existing fault diagnosis and tracking methods and applications, this paper proposes a fault diagnosis system based on multi-data sources in order to achieve rapid, accurate and comprehensive effects of fault diagnosis and tracking. The main work is as follows:  
1. Show the framework and the operation process of the fault diagnosis system;
2. Divide the whole system into three subsystems to realize, respectively, and design the cooperation scheme between them to complete the whole fault diagnosis and tracking work;
3. Take a fault scenario on the IEEE30 bus system as an example to verify the system. A transverse comparison between this system and related technologies is made to prove the advantages of this system in terms of computational efficiency, reliability, and processing the problem of partial information loss.
###### Abstract
Furthermore, our proposed matching and energy allocation algorithm between UAVs and charging towers presents the desired performance improvement. The rest of this paper is organized as follows. Section 2 summarizes the related work for UAV communications and networks research. Section 3 introduces our considered reference system model. Section 4 explains the details of the proposed algorithm, (i.e., (i) MWIS-based UAV scheduling via message-passing and (ii) joint optimization for charging matching and wireless energy transfer). Section 5 verifies the novelty of the proposed framework via data-intensive performance evaluations. Lastly, Section 6 concludes this paper and presents future research directions.
# Properties of a Lightweight Fly Ash-Slag Alkali-Activated Concrete with Three Strength Grades
## 2 Experimental Program
### Evaluation of Fracture Parameters  
Fracture energy (\(G_{\text{F}}\)) was the energy absorption per unit area in the crack propagation process determined according to the work calculation method, introduced by RILEM50-FMC [33]. Thus, \(G_{\text{F}}\) was calculated as follows:  
\[G_{\text{F}}=\frac{W_{0}+mg\delta_{0}}{B(h-a)} \tag{4}\]  
Figure 2: The three-point bending setup: (**a**) photo of three-point bending test; (**b**) schematic diagram of three-point bending test.  
where \(W_{0}=\) area under the load-mid span displacement curve (N-m); \(m=\) mass of the beam sample within the clear span (kg); \(g=\) gravity acceleration (9.81 m/s\({}^{2}\)); \(\delta_{0}=\) mid span displacement when the beam was completely ruptured (m); \(B=\) beam width (m); \(h=\) beam depth (m); and \(a=\) notch depth (m).  
The stress intensity factor was used as an indicator of the crack-tip stress concentration at the at the beginning moment of crack propagation, and the critical stress intensity factor was used to describe the crack resistance of the concrete. According to the ASTM standard E399-90 [35], the mode I fracture toughness \(K_{\text{IC}}\) of the different concrete mixtures was calculated by using Equation (5).  
\[K_{IC}=\frac{PL[2.9(\frac{a}{h})^{1/2}-4.6(\frac{a}{h})^{3/2}+21.8(\frac{a}{h })^{5/2}-37.6(\frac{a}{h})^{7/2}+38.7(\frac{a}{h})^{9/2}]}{Bh^{3/2}} \tag{5}\]  
where \(P=\) peak load (N); and \(L=\) span length (m).  
In order to describe the brittleness of the concrete, [PERSON] (1983) [33] introduced the characteristic length, \(l_{\text{ch}}\), which was calculated according to Equation (6).  
\[l_{\text{ch}}=\frac{G_{\text{F}}E_{\text{c}}}{f_{\text{I}}^{2}} \tag{6}\]  
where \(E_{\text{c}}=\) elastic modulus of the specimen (GPa); and \(f_{\text{I}}=\) tensile strength (MPa), which is replaced by the splitting tensile strength\(f_{\text{ts}}\).
# Tunable Beam Splitter Based on Acoustic Binary Metagrating
## 2 Materials and Methods
\begin{array}{c}-k_{0}\mathrm{cos}\,\theta_{i}\int_{0}^{a}e^{-j\omega_{0}x \sin\theta_{i}}e^{j\eta\mathrm{cr}}dx+\sum\limits_{n}k_{z,n}r_{n}\int_{0}^{a} e^{-j(k_{0}\sin\theta_{i}~{}+~{}nG~{}-~{}nG)x}dx\\ =\sum\limits_{m}~{}-\beta_{m}H_{m}[1~{}-~{}e^{-2j\beta_{m}d}]\int_{0}^{t} \mathrm{cos}\,\alpha_{m}x\,\mathrm{e}^{j\eta\mathrm{cr}}dx.\end{array}  \tag{18}\]  
For the ABM in the case of \(a~{}=~{}1.4\lambda_{0}\), the reflections with the normal incidence (\(\theta_{i}~{}=~{}0^{\circ}\)) can be derived from Equations (17) and (18) that \(R_{0}~{}=~{}0.00438\) and \(R_{-1}~{}=~{}R_{1}~{}=~{}0.49781\). For the ABM in the case of \(a~{}=~{}1.8\lambda_{0}\), the reflections with the normal incidence (\(\theta_{i}~{}=~{}0^{\circ}\)) can be derived from Equations (17) and (18) that \(R_{0}~{}=~{}0.00183\) and \(R_{-1}~{}=~{}R_{1}~{}=~{}0.49909\). Here, \(R_{0}\), \(R_{-1}~{}\)and \(R_{1}~{}\)are the reflections of the diffracted waves of the orders \(n=0\), \(n=-1\) and \(n=1\), respectively. It is noted that the reflection of the 0-th order diffracted waves is extremely small, and thus, the dominant propagating modes are the diffracted waves of \(n=-1\) and \(n=1\). In other words, the specular reflection is totally suppressed and the beam splitting occurs.
## 2 Materials and Methods
### Total Antioxidant Capacity (TAC) Determination  
The TAC was evaluated through two different assays, the ferric reducing antioxidant power (FRAP), and 2,2\({}^{\prime}\)-azino-bis (3-ethyl-ben-zothiazoline-6-sulfonic acid) (ABTS\({}^{\bullet+}\)) radical scavenging activity assay as follows.  
FRAP assay resulted from the method of [PERSON] [24], and it was adapted for 96-well plates and an automatic reader (Infinite 2000, Tecan, Salzburg, Austria). The method is based on the reduction of the Fe\({}^{3+}\)-2,4,\(\ell\)-tripyr-idyl-s-triazine (TPTZ) complex to its ferrous form at a low pH. Specifically, 160 \(\upmu\)L of FRAP assay solution (made from 20 mM ferric chloride solution, 10 mM TPTZ solution, and 0.3 M acetate buffer at pH 3.6) was prepared daily, mixed with 10 \(\upmu\)L of the sample, standard, or blank, and dispensed into each well of a 96-well plate. After 30 min of incubation at 37 \({}^{\circ}\)C, the absorbance was measured at 595 nm. The results were expressed as mmol Fe\({}^{2+}\) equivalents (E)/g.  
The ABTS\({}^{\bullet+}\) radical scavenging activity was analyzed through the OxiSelectTM Trolox Equivalent Antioxidant Capacity (TEAC) Assay Kit (ABTS) (Cell Biolabs Inc., San Diego, CA, USA) following the manufacturer's instruction. Specifically, samples and standards were mixed with the primed ABTS\({}^{\bullet+}\) probe, and antioxidants neutralized the radical ion in a concentration-dependent manner, which correlates with a proportional decrease in color intensity. The absorbance was recorded at 405 nm in an automatic reader (Infinite 2000, Tecan, Salzburg, Austria). A standard curve for Trolox was used to express the antioxidant capacity as mmol of Trolox equivalents (TE)/g of DW. Both the extracts (i.e., in MetOH 80% and PBS) were analyzed with both the above-described methods.
# RepNet: A Lightweight Human Pose Regression Network Based on Re-Parameterization
## 4 Experiment
### Experiment Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** & **AR** \\ \hline \multicolumn{8}{c}{Heatmap-based} \\ \hline OpenPose & PAF & — & 65.3 & 85.2 & 71.3 & 62.2 & 70.7 & — \\ \hline CPM & CPM & \(384\times 288\) & 65.1 & 86.4 & 72.9 & 61.9 & 71.6 & 71.0 \\ \hline Lite-HRNet & Lite-HRNet & \(256\times 192\) & 67.5 & 88.0 & 75.3 & 64.6 & 73.3 & 73.5 \\ \hline SimpleBaseline & ResNet-50 & \(384\times 288\) & 72.2 & 89.3 & 78.9 & 68.1 & 79.7 & 77.6 \\ \hline HRNet & HRNet & \(384\times 288\) & **75.8** & **90.6** & **82.7** & **71.9** & **82.8** & **81.0** \\ \hline \multicolumn{8}{c}{**Regression-based**} \\ \hline DeepPose & ResNet-50 & \(256\times 192\) & 54.0 & 82.3 & 65.9 & 55.2 & 65.0 & 68.5 \\ \hline Integral & ResNet-50 & \(256\times 256\) & 63.3 & 85.9 & 70.3 & 59.3 & 71.5 & **72.9** \\ \hline PointSetNet & ResNeXt & — & 65.7 & 85.4 & 71.8 & — & — & — \\ \hline CenterNet & Hourglass & — & 64.0 & — & — & — & — \\ \hline RepNet-A & RepVGG & \(256\times 192\) & 63.4 & 85.1 & 69.9 & 60.2 & 69.6 & 68.7 \\ \hline RepNet-B & RepVGG & \(256\times 192\) & **66.1** & **86.5** & **73.5** & **62.7** & **72.4** & 71.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: COCO validation experimental results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Parameter** & **Acquisition** & **GFLOPs** & **AP** & **AR** & **GPU Speed** & **CPU Speed** & **Model Size** \\ \hline OpenPose & 49.8 M & 136.0 & 65.3 & — & 656 ms & 881 ms & 199.5 M \\ \hline Integral & 34.0 M & 7.28 & 63.3 & 72.9 & 28 ms & 288 ms & 136.7 M \\ \hline DeepPose & 23.58 M & 4.04 & 54.0 & 68.5 & 24 ms & 189 ms & 97.0 M \\ \hline Lite-HRNet & 1.76 M & 0.42 & **67.5** & **73.5** & 182 ms & 347 ms & 72. M \\ \hline MobileNet & 2.36 M & 0.31 & 59.3 & 64.4 & 22 ms & 359 ms & 16.1 M \\ \hline ShuffleNet & **1.02 M** & **0.14** & 51.1 & 56.6 & 26 ms & 128 ms & **4.1 M** \\ \hline YOLOv7- & \multirow{2}{*}{80.2 M} & \multirow{2}{*}{101.6} & \multirow{2}{*}{71.4} & \multirow{2}{*}{77.6} & \multirow{2}{*}{30 ms} & \multirow{2}{*}{561 ms} & \multirow{2}{*}{153 M} \\ pose & & & & & & & \\ \hline RepNet-A & 7.17 M & 1.49 & 63.4 & 68.7 & **14 ms** & **32 ms** & 27.4 M \\ \hline RepNet-B & 11.65 M & 2.32 & **66.1** & **71.1** & 15 ms & 40 ms & 44.5 M \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reasoning speed test results.