esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Antioxidant Activities  
#### 2.5.1. ABTS Free Radical Inhibition Activity  
The antioxidant activity was determined using the chromogenic compound 2,2\({}^{\prime}\)-azin-bis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS) according to [PERSON] et al. [22]. A dilution of propolisis extract (1:1000 \(w/v\)) was made using 85% ethanol, it was homogenized until complete solubility using a vortex, then the extracts were centrifuged at 18,510\(\times\) g for 15 min at 4 \({}^{\circ}\)C (Hermle, Z 36 HK, Gosheim, Germany). Of 7 mM ABTS stock solution (36 mg of ABTS reagent), 20 mL was prepared by adjusting to 10 mL with distilled water and 10 mL of 2.45 mM potassium persulfate (5.83 mg of potassium persulfate in 10 mL of distilled water) was added. To generate the ABTS\({}^{+}\) free radicals, the mixture was kept under magnetic stirring for 24 h in total darkness. Before starting the reading of the samples, the ABTS\({}^{+}\) reagent was standardized at an absorbance of 0.7 \(\pm\) 0.01 using 20%ethanol. An amount of 0.2 mL of the supernatant with 2 mL of standardized ABTS\({}^{+}\) was placed in a test tube, homogenized and left to stand at room temperature for 6 min in the absence of light. The samples were read at 734 nm in a spectrophotometer (Jenway 6715). Gallic acid was used to prepare the standard curve (0 to 100 mg/L) and 20% ethanol was used as a blank. Antioxidant activity was expressed as mg equivalents of gallic acid in 100 g of extract, according to the following equation. All samples were analyzed in triplicate.  
Antioxidant activity (mg GA/100 g) = (GA \(\times\) V \(\times\) DF \(\times\) 100)/(Sample weight (g))  
where:  
GA = Gallic acid concentration from the standard curve (mg/mL);  
V = Sample volume (mL);  
DF = Sample dilution factor.
# Molecular Regulatory Mechanisms Affecting Fruit Aroma
## 7 Research on Flavor Quality Improvement  
Quantitative trait loci (QTLs) play an important role in the biosynthesis of fruit aroma substances, and those related to aroma quality can be accurately located by constructing molecular linkage maps. This approach can significantly enhance the characterization and assessment of the genetic capacity for improving the aroma quality [72; 73]. [PERSON] et al. [74] have used octopolid cultivated strawberry to demonstrate how genomic heterozygosity, transcriptomic intricacy, and fruit metabolomic diversity can be treated as strengths and leveraged to uncover fruit flavor genes and their regulatory elements. [PERSON] et al. [72]used the QTL method to further explore the genetic basis of apple aroma traits, and successfully identified 87 QTLs associated with 15 volatile compounds in 14 linkage groups. Additionally, they found a specific group of QTLs in the sixth linkage group that was closely related to the yield of ester substances. Further studies have shown that the MdAAT6 gene within the QTL localization interval plays a key role in the biosynthesis of ester substances [72]. Combined with aroma data and the constructed genetic linkage map, a candidate gene _AAT6_ was detected in the QTL location region. This gene led to a significant increase in the accumulation of esters in fruits after overexpression. Multi-omics datasets and comprehensive genome-wide association studies involving over 300 individuals in strawberry provide effective reference for the subsequent expression of aroma-related specific genes. Therefore, the identification of QTLs for strawberry and apple provides new insights for the genetic improvement of aroma traits.  
The flavor of fresh fruit is influenced by both the outside and the inside of the fruit. Fruit appearance, such as its size, shape, color, and nutritional composition, gives the fruit a unique visual appearance. The internal sweetness, acidity, hardness, and volatile aromatic components determine the taste and depth of flavor [5; 7]. Studies have shown that the content and diversity of lactone compounds in soft peaches are greater than those in firm peaches. In contrast, crisp and firm peaches may not have some of the important aroma qualities. Therefore, determining the correlation between aroma and other fruit traits is essential for promoting selective breeding and obtaining high-quality fruit varieties. By understanding the correlation between the aroma and characteristic traits of each fruit, targeted breeding work is expected to develop high-quality fruit varieties that excel in terms of aroma and other desirable traits in the future.
# Simplified Welch Algorithm for Spectrum Monitoring
## 5 Laboratory Tests and Real-Time Validation
### Discussion  
The purpose of this paper was to present an easily implementable spectrum monitoring design based on a modified version of the Welch algorithm. The proposed design can accurately represent a low-power signal with a less noisy spectrum reliably when standard spectrum analyzers are unable to track the input signal. Future spectrum monitoring devices such as interference mitigation and detection systems could easily implement this rugged, low-cost and effective design. The unique flexibility allows a lot of different usages such as high-precision spectrum monitoring or high responsiveness for frequency hopping interference tracking. The \(\alpha\) parameter helps to have a balance between the required accuracy and the desired convergence in different situations. For instance, the design cannot accurately display a noise-free spectrum while remaining very responsive to frequency hopping. Therefore, there is a trade-off between speed and accuracy when choosing the \(\alpha\) value. For future research, the study of this design paired with an interference detection and mitigation device could yield some interesting results. The ability to clearly display low-power interference on top of a noisy signal is very promising for interference mitigation.  
Another interesting research would be to adjust the \(\alpha\) parameter based on the input signal to noise ratio. The receiver is able to have an approximation of the channel situation and input SNR. Once the receiver is in a good position, selecting a lower \(\alpha\) value allows the system to track variable frequency components. While the system is suffering from noisy channel and low SNR, a clearer spectrum curve is available by selecting a larger \(\alpha\) parameter.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 2 Exponential Monitoring Schemes for TBE Data  
### Exponential Distribution  
Suppose the \(t\)-th sample of TBE variable is \(X_{t}(t=1,2,\cdots,t)\). Each \(X_{t}\) is independent and exponentially distributed, characterized by a mean of \(\theta\), denoted as \(\exp(\theta)\). The probability density function (p.d.f.) and cumulative distribution function (c.d.f.) of the random variable \(X_{t}\) are, respectively,  
\[f(x)= \frac{1}{\theta}e^{-\frac{x}{\theta}},\qquad x\geq 0, \tag{1}\] \[F(x)= 1-e^{-\frac{x}{\theta}},\quad x\geq 0. \tag{2}\]  
When the process is in-control, \(\theta=\theta_{0}\). For convenience of study, the random variable \(X_{t}\) is standardized using \(\theta_{0}\) to obtain \(Y_{t}\),  
\[Y_{t}=\frac{X_{t}}{\theta_{0}}=\frac{\theta}{\theta_{0}}\cdot\frac{X_{t}}{ \theta}=c\cdot S_{t}, \tag{3}\]  
where \(c=\theta/\theta_{0}\), \(S_{t}=X_{t}/\theta\). According to [27], \(S_{t}\sim\exp(1)\), and its c.d.f. is given by:  
\[F_{S}(s)=1-e^{-s},\qquad s\geq 0. \tag{4}\]  
In this study, our primary objective is to detect any deviation in the parameter \(\theta\) from its in-control value, denoted as \(\theta_{0}\). When the process shifts out-of-control, an increase in \(\theta_{0}\) is signaled by a coefficient \(c>1\), whereas a decrease is indicated by \(c<1\). It is assumed that \(\theta_{0}\) is known a priori. However, if this parameter is unknown, it must be estimated using in-control samples gathered during Phase II of the process. Future studies should delve into the influence of parameter estimation on the efficacy and reliability of the monitoring schemes.
# Contributing towards Representative PM Data Coverage by Utilizing Artificial Neural Networks
## 2 Materials and Methods
### Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **Code** & **Lon** & **Lat** &
\begin{tabular}{c} **Altitude** \\ **(m-a.s.l.)** \\ \end{tabular} & **Type** \\ \hline Ag. Paraskevi & AGP & 23\({}^{\circ}\)49\({}^{\prime}\)09\({}^{\prime\prime}\) & 37\({}^{\circ}\)59\({}^{\prime}\)42\({}^{\prime\prime}\) & 290 & Suburban/Background \\ Aristotelous & ARI & 23\({}^{\circ}\)43\({}^{\prime}\)39\({}^{\prime\prime}\) & 37\({}^{\circ}\)59\({}^{\prime}\)16\({}^{\prime\prime}\) & 95 & Urban/Traffic \\ Elfesina & ELE & 23\({}^{\circ}\)32\({}^{\prime}\)18\({}^{\prime}\) & 38\({}^{\circ}\)03\({}^{\prime}\)40\({}^{\prime\prime}\) & 20 & Suburban/Industrial \\ Thrakomakedones & THR & 23\({}^{\circ}\)45\({}^{\prime}\)29\({}^{\prime\prime}\) & 38\({}^{\circ}\)08\({}^{\prime}\)36\({}^{\prime\prime}\) & 550 & Suburban/Background \\ Konopi & KOR & 23\({}^{\circ}\)52\({}^{\prime}\)44\({}^{\prime\prime}\) & 37\({}^{\circ}\)54\({}^{\prime\prime}\) & 140 & Suburban/Background \\ Lykovrisi & LYK & 23\({}^{\circ}\)47\({}^{\prime}\)19\({}^{\prime\prime}\) & 38\({}^{\circ}\)04\({}^{\prime\prime}\) & 234 & Suburban/Background \\ Marousi & MAR & 23\({}^{\circ}\)47\({}^{\prime}\)14\({}^{\prime\prime}\) & 38\({}^{\circ}\)01\({}^{\prime}\)51\({}^{\prime\prime}\) & 170 & Urban/Background \\ Piraeus & PIR & 23\({}^{\circ}\)38\({}^{\prime}\)42\({}^{\prime\prime}\) & 37\({}^{\circ}\)56\({}^{\prime}\)40\({}^{\prime\prime}\) & 4 & Urban/Traffic \\ Peristeri & PER & 23\({}^{\circ}\)41\({}^{\prime}\)18\({}^{\prime\prime}\) & 38\({}^{\circ}\)01\({}^{\prime}\)14\({}^{\prime\prime}\) & 80 & Urban/Background \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Air quality monitoring stations, their code names, and important characteristics. Figure 1: The Attic peninsula and the locations of the air quality monitoring stations. monthly averaged concentrations for each pollutant are presented in the same diagram for comparative purposes.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 6 LoRa Performance: Using Error Correction and Filtering
### LoRa with Forward Error Correction and Filtering  
To enhance the performance of LoRa technology, the transmitter and receiver perform additional processing in addition to modulation and demodulation, as shown in Figure 15. The transmitter effects whitening, Hamming encoding, interleaving, and gray mapping before chirp modulation. The receiver effects Gray demapping, deinterleaving, Hamming decoding, and dewhitening.  
Prior to modulation, both the header and payload pass through a series of steps to be encoded. Firstly, the payload is whitened by adding a known sequence depending on the payload's coding rate parameter [36, 45]. Secondly, the resulting payload and header are encoded using the Hamming Error-correcting code. The Hamming codes used by LoRa are of the form (k, n), with k equal to 4 and n taking the values [5, 6, 7, 8], where k denotes the length of the data words and n the length of the code words [36, 46]. The resulting payload can be encoded using any of the Hamming code combinations, whereas the header, since it contains crucial payload information, can only be encoded using the two Hamming combinations (4,7) and (4,8). This is because Hamming codes (4,5) and (4,6) can only detect  
Figure 14: Pocket Error Rate over a fading channel with industrial noise for the MtO mode.  
errors, whereas Hamming codes (4,7) and (4,8) allow the detection and correction of one bit error per code word [36]. Thirdly, the payload and header are interleaved. This is done by transposing k-code words and shifting their digits to the left by r mod SF, where r is the line number. The header and payload are interleaved by (SF \(-\) 2 \(\times\) 8) and (SF \(\times\) 4 + CR), respectively. Finally, to reduce adjacent-bit errors, gray coding is applied to the interleaved symbols [36].
###### Abstract  
The converter steel slag cannot be widely used in building materials for its poor grfindability. In this paper, the grinding characteristics of untreated and pretreated (i.e., magnetic separation) steel slag were compared. Additionally, the grinding property of pretreated steel slag was also studied after adding grinding aids. The results show that the residues (i.e., oversize substance) that passed a 0.9 mm square-hole screen can be considered as the hardly grinding phases (HGP) and its proportion is about 1.5%. After the initial 20 min grinding, the RO phase (RO phase is a continuous solid solution which is composed of some divalent metal oxides, such as FeO, MgO, MnO, CaO, etc.), calcium ferrite, and metallic iron phase made up most of the proportion of the HGP, while the metallic iron made up the most component after 70 min grinding. The D50 of untreated steel slag could only reach 32.89 \(\upmu\)m after 50 min grinding, but that of pretreated steel slag could reach 18.16 \(\upmu\)m after the same grinding time. The grinding efficiency of steel slag was obviously increased and the particle characteristics were improved after using grinding aids (GA), especially the particle proportions of 3-32 \(\upmu\)m were obviously increased by 7.24%, 7.22%, and 10.63% after 40 min, 50 min, and 60 min grinding, respectively. This is mainly because of the reduction of agglomeration and this effect of GA was evidenced by SEM (scanning electron microscope) images.  
steel slag; grinding property; particle characteristics; mineral phase; hardly grinding phases; magnetic separation; grinding aids; agglomeration
###### Abstract  
Drought is considered one of the devastating natural disasters worldwide. In the context of global climate change, the frequency and intensity of drought have increased, thereby affecting terrestrial ecosystems. To date, the interactions between ecosystem change and drought, especially their mutual lag and cumulative effects is unclear. The Songnen Plain in northeastern China is one of the three major black soil areas in the world and is highly sensitive to global change. Herein, to quantify the interaction between drought and ecosystem function changes in the Songnen Plain, integrating with time-series moderate resolution imaging spectroradiometer (MODIS), leaf area Index (LAI), evapotranspiration (ET), and gross primary productivity (GPP) data, we calculated the standardized precipitation and evapotranspiration index (SPEI) based on the meteorological data, diagnosed the causal relationship between SFEI and the ecosystem function indicators i.e., LAI, ET, and GPP, and analyzed the time-lag and cumulative effects between the degree of drought and three ecosystem function indicators using impulse response analysis. The results showed that the trend of SPEI (2000-2020) was positive in the Songnen Plain, indicating that the drought extent had eased towards wetness. LAI showed insignificant changes (taking up 88.34% of the total area), except for the decrease in LAI found in some forestland and grassland, accounting for 9.43%. The pixels showing a positive trend of ET and GPP occupied 24.86% and 54.94%, respectively. The numbers of pixels with Granger causality between LAI and SPEI (32.31%), SPEI and GPP (52.8%) were greater at the significance 0.05 level. Impulse responses between each variable pair were stronger mainly between the 6 th and 8 th months, but differed significantly between vegetation types. Grassland and cropland were more susceptible to drought than forest. The cumulative impulse response coefficients values indicated that the mutual impacts between all variables were mainly positive. The increased wetness positively contributed to ecosystem function, and in turn enhanced ecosystem function improved regional drought conditions to some extent. However, in the northeastern forest areas, the SPEI showed a significant negative response to increased ET and GPP, suggesting that the improved physiological functions of forest might lead to regional drought. There were regional differences in the interaction between drought conditions and ecosystem function in the Songnen Plain over the past 21 years.  
ecosystem function; SPEI; cumulative effect; impulse-response; granger causality; vector autoregressive model +
Footnote †: journal: remote sensing
## 4 Statistical Analysis on Database of Experimental Results  
A number of computational equations have been proposed for the horizontal shear strength of the interface based on various research results [5; 6; 9; 13; 30; 32]. The current design codes specify the horizontal shear strength equation differently because they selectively reflect design parameters such as adhesion, shear friction, and dowel action of shear connectors across the interface. ACI 318 [27] classifies design cases into two categories depending on the size of the clamping stress. Case 1 corresponds to the adhesion at the interface, and Case 2 corresponds to the shear friction by shear reinforcements. However, AASHTO LRFD [29] presents the design expression in Equation (4), simultaneously considering adhesion and shear friction. The fib Model Code (MC 2010 [31]) provides the interface shear strength in Equation (5) which considering the aggregate interlock, shear friction, and dowel action together, although defining the friction factor as 0.7 for a roughened surface. AASHTO LRFD [29] estimates the interface shear strength for a clean, rough surface as follows:  
\[v_{n}=c+\mu\left(\rho_{v}f_{y}+\sigma_{c}\right)\ (\mathrm{MPa}), \tag{4}\]  
where \(c\) = cohesion factor, \(\mu\) = friction factor, \(A_{v}\) = area of shear reinforcement crossing the interface area \(A_{cv}\), and \(f_{y}\) = yield stress of reinforcement. MC 2010 [30] estimates the interface shear strength to calculate the capacity of interface reinforcement on a clean, rough surface as follows:  
\[\tau_{u}=\tau_{c}\sqrt[3]{f_{ck}}+\mu\left(\sigma_{n}+k_{1}\rho f_{y}\right)+ k_{2}\rho\sqrt{f_{cd}f_{y}}\leq\beta vf_{c}^{\prime}, \tag{5}\]  
where \(\tau_{c}\) = the coefficient for aggregate interlock effects at rough interfaces, \(\mu\) = coefficient of friction, \(k_{1}\) = coefficient of clamping effect by interface reinforcement, \(k_{2}\) = coefficient for dowel effect of reinforcement, and \(\beta_{c}\) = coefficient of diagonal concrete strut angle. Figure 6: Comparison of observed and calculated interface shear strengths. Table 2 shows the key parameters and limitations for the horizontal shear strength equation of ACI, PCI, AASHTO LRFD, and MC 2010. The purpose of this study was to assess whether current design standards adequately reflect the effect of the interface conditions of PC composite beams on the horizontal shear strength. A database was constructed from the results of 84 push-off experiments and 95 beam experiments. Tables 3 and 4 show the configuration of the database of push-off tests and beam flexural tests, respectively. The maximum horizontal shear strength of the tests is represented as the ratio with respect to the predicted values from ACI, PCI, AASHTO LRFD, and MC 2010 design equations.
## 4 Experiments and Results
### Patch Size Effect  
The proposed model requires a minimum patch size of 8 x 8; nevertheless, increasing the patch size has the potential to improve classification accuracy with a higher computational cost. To determine the optimal balance between accuracy and computational  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **ResNet50** & **EfficientNet** & **ViT** & **Swin** & **CvT** & **CoAtNet** & **WMF** & **Proposed** \\ \hline Params (millions) & 23.57 & 20.19 & 87.72 & 27.61 & 19.66 & 17.37 & 0.7 & 9.44 \\ Memory size (MB) & 90.2 & 77.9 & 335.1 & 105.8 & 75.3 & 66.4 & 8.3 & 36.3 \\ Training time (s) & 63 & 122 & 122 & 276 & 116 & 75 & 37 & 77 \\ Inference time (s) & 5.99 & 13.64 & 13.08 & 32.99 & 19.73 & 15.55 & 3.07 & 9.16 \\ OA (\%) & 90.39 & 90.18 & 87.05 & 88.75 & 90.82 & 93.34 & 90.91 & 95.36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Efficiency analysis of the DL models.  
complexity, the model's performance was evaluated with different patch sizes ranging from 8 to 20 in Figure 8. This analysis revealed that patch sizes exceeding 16 \(\times\) 16 yield a negligible improvement in OA (less than 0.03%); however, they also exhibit a drastic rise in processing time (about 24 s increase per training epoch). Accordingly, a patch size of 16 was identified as the optimum patch size, maintaining an effective balance between accuracy and computational cost.
## 3 The Algorithm of the Initial Design
### Computational Parameters
#### 3.3.1 Computing the Thickness of the Permanent Magnet  
To calculate the thickness of the permanent magnet, an equivalent magnetic circuit is used at the instance when the permanent magnets are placed exactly across the E-shaped core. The curve for changes of flux density over magnet according to the intensity of the permanent magnet field is shown in Figure 5.  
Magnetic permeability coefficient of the permanent magnet (\(\mu_{pm}\)) can be calculated from Figure 5. In fact, it can be said that \(\mu_{pm}\) is the line slope of Figure 5. Accordingly, magnetic permeability coefficient of the permanent magnet (\(\mu_{pm}\)) and its relative magnetic permeability coefficient (\(\mu_{pm}\)) are defined as Equations (1) and (2), respectively.  
\[\mu_{pm}=\frac{B_{r}}{H_{c}}=\mu_{rpm}\mu_{0} \tag{1}\]  
\[\mu_{rpm}=\frac{\mu_{pm}}{\mu_{0}}=\frac{B_{r}}{\mu_{0}H_{c}} \tag{2}\]  
According to the above equations, the permanent magnet can be modeled as the magnetic Thevenin equivalent circuit shown in Figure 6.  
In Figure 6, \(R_{pm}\) parameter represents interior reluctance of permanent magnet, and its value is calculated by Equation (3).  
\[R_{pm}=\frac{H_{c}l_{pm}}{B_{r}A_{pm}}=\frac{l_{pm}}{\mu_{pm}A_{pm}}=\frac{l_ {pm}}{\mu_{0}\mu_{rpm}A_{pm}} \tag{3}\]  
According to the equivalent permanent magnet circuit and equivalent magnetic circuit of the machine, Equation (4) can be extracted for computation of permanent magnet thickness:  
\[l_{pm}=\frac{2\times B_{g}\times B_{r}\times g}{H_{c}\times(B_{r}-B_{g})\times \mu_{0}} \tag{4}\]  
Figure 5: The curve for changes of flux according to the intensity of the permanent magnet field (reproduced with permission from [28]).  
Figure 6: The magnetic Thévenin equivalent circuit of the permanent magnet in terms of its parameters.
## 5 Major Equipment and Technologies
### Agricultural Communication
#### 5.2.3 Zigbee  
Zigbee is mainly created for a comprehensive range of applications, particularly to replace non-standard equipment. According to application needs, devices based on this etiquette can be one of the three categories: router, coordinator, and end-devices. Besides, Zigbee networks support three different topologies, namely Cluster Tree, Start, and Mesh [106]. Based on these traits and contemplating the needs of agricultural applications, ZigBee can perform a significant role, particularly for the environments of greenhouses that often require short-range transmissions. During different parameter observations, actual-time information from the sensor nodes is transmitted to the terminal server via Zigbee [107; 108]. For applications such as fertilizers and irrigation, the ZigBee module can be used for transmission, such as in drip irrigation, to check the soil moisture content.