esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
It is recommended to select at least 10 points per peak to ensure its correct definition, with 20 points being optimal. This increase resulted in the selection of 0.05 s of integration time per peak in order not to lengthen the total measurement time. Measurements were done at a medium-resolution power (R = 3000) to physically separate the ions of interest from possible polyatomic interferences. For the digestion of the samples, a portion of each was treated with a mixture of HNO3/HCl, and microwave digestion was carried out (ETHOS One, Millestone SRI, Italia). The result of digestion was made up to the volume with ultrapure water. For the measurement of rare earths in the ICP-MS, the quantification was carried out with acid calibration (HNO3 1% _v/v_) using indium (In) as the internal standard to compensate possible fluctuations in the plasma during the measurement and possible matrix effects. Rare earth element solutions were prepared in increasing concentrations, and In was added as an internal standard to achieve a final concentration of 10 ppb. This element was chosen because it is relatively close in mass to the elements of interest. Elemental patterns of 1000 \(\upmu\)g/mL according to CPAchem [77] were used. Table 6 exhibits the codes and the characteristics of each of the samples relating to the type of material, the generation process, and the type of iron ore deposit. In this way, it was possible to make three comparisons: firstly, studying the efficiency differences between the ore and the tailing; secondly, between the two types of tailing; and finally, considering two types of deposits or origins of the waste. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Code** & **Material** & **Process** & **Type of Deposit** \\ \hline A.0 & Ore & - & 1 \\ A.1 & Tailing & Flotation & 1 \\ A.2 & Tailing & Roasting and magnetic separation & 1 \\ B & Tailing & Flotation & 2 \\ C & Tailing & Flotation & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics of the samples. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Measurement Value** \\ \hline Integration time & 0.05 s \\ Width of the window & 100\% \\ Number of points per peak & 20 \\ Number of replicas & 3 \\ Acquisition mode & Electric scanning, magnetic leap \\ Power of resolution & Medium (R = 3000) \\ Detector mode & Pulse counting \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Measurement parameters used in the double-focus ICP-MS.
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 2 General Conditions of the Experiment
### Measuring Point Arrangement  
Given that the relative bond slip between CGM and the concrete interface needed to be obtained in the test, two rebound-type self-resetting linear variable differential transformer (LVDT) displacement sensors with an accuracy of 0.01 mm and a range of 10 mm were symmetrically arranged on both sides of the adhesive interface to eliminate the possible eccentricity effect in the test, as shown in Figure 3. However, the displacement observed in this way includes the deformation of the concrete and CGM itself. According to the conservation law of work, the relative slip of the interface should not include the influence of the deformation of the concrete and CGM itself. Four strain gauges numbered C1, C2, C1, and G2 were symmetrically pasted to the concrete and CGM of the specimen to measure the deformation of the two materials, as shown in Figure 3. Hence, the relative slip, \(S\), of the interface should be  
\[S=S_{LVDT}-S_{C}-S_{G}, \tag{1}\]  
where \(S_{LVDT}\) is the average value of the displacement measured by the displacement meter, \(S_{C}\) is the average value of concrete displacement, and \(S_{G}\) is the average value of CGM displacement.  
Figure 3 shows that the LVDT displacement meter measured the relative slip within the range of 250 mm in the middle of the specimen, and the strain gauge measured the material strain in the corresponding area, that is, the CGM and concrete should have a strain in the range of 200 \(-\) 25 = 175 mm. The concrete strain and grou  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group No.** & **CGM Strength /MPa** & **Concrete Strength /MPa** & **Group No.** & **CGM Strength /MPa** & **Concrete Strength /MPa** \\ \hline Z-C35 & 73.13 & 36.37 & X-C35 & 89.03 & 35.03 \\ Z-C45 & 69.35 & 47.63 & X-C45 & 68.35 & 47.63 \\ Z-C55 & 69.35 & 56.22 & X-C55 & 68.35 & 56.22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Compressive strengths of the specimen material.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Concrete Strength** & \begin{tabular}{c} **Mix Proportions (kg/m3)** \\ \end{tabular} & \begin{tabular}{c} **Water** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Water-Cement** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Sand Ratio** \\ \end{tabular} \\ \hline C35 & 466.02 (PO 32.5) & 1158.06 & 570.87 & 205.05 & 0.44 & 33\% \\ C45 & 525.62 (PO 42.5) & 1163.10 & 550.85 & 204.99 & 0.39 & 32\% \\ C55 & 545.00 (PO 52.5) & 1174.02 & 553.12 & 201.43 & 0.37 & 32\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mixture proportions of normal concrete substrate.  
were assumed to be the same, so \(S_{C}=\varepsilon_{C}l=175\varepsilon_{C}\) and \(S_{G}=\varepsilon_{G}l=175\varepsilon_{G}\), where \(\varepsilon_{C}\) and \(\varepsilon_{G}\) are the measured average strains of the concrete and CGM, respectively.
## 2 Results and Discussion
### LC/ESI/QTOF-MS Identification of Phenolic Compounds in Moringa Seedlings  
Information related to the profile of phenolic compounds in moringa seedlings is summarized in Table 3. By comparing the MS and MS/MS spectra from QTOF-MS to data from the accessible mass spectral databases, all phenolic compounds were first identified. These compounds fall under diverse chemical groups: primarily acids, glucosides, saccharides, sterols, esters, phenols, flavonoids, and their derivatives. Some of the chemicals identified in this study have previously been mentioned in earlier studies. For instance, different portions of moringa contained glucosides. [PERSON] and [PERSON] [45] employed LC/MS to identify benzoyl-\(\beta\)-D-glucose, 4-O-(6-vanilly1-1)-\(\beta\)-D-glucopyranosyl vanilly1 alcohol, and 7-rhamoglucoside in moringa seed extract. These three compounds possess biological activities such as antioxidant and anti-inflammatory activities. [PERSON] et al. [46] identified glucosinolates in moringa leaves. In this study, multiple glucosides were preliminarily identified in ethanol extracts of moringa seeds, e.g., 5,7-dimethoxy-4\({}^{\prime}\)-hydroxyflavone-4\({}^{\prime}\)-O-alpha-L-rhamnose(1-\(>\)2)-beta-D-glucose, nevadensin-7-O-[\(\alpha\)-L-rhamnosyl(1-\(>\)6)]-beta-D-glucose, taxifolia-3-O-glucose, and glucosinalbin. The UPLC-ESI-QTOF-MS analysis of hydroalcoholic extract allowed for the identification of 24 compounds, with flavonoid derivatives being the most abundant group. Furthermore, the alkaloid trigonelline and sesquiterpenoid abscisic acid were identified for the first time in _M. oleifera_ leaves [47]. Leaves of moringa collected from sub-Sahara Africa were analyzed for phenolic components by HPLC-UV-MS. Twelve flavonoids were identified, including quercetin, kaempfer glucosides, and glucoside malonates as major constituents [48]. LC-MS analysis under the optimum conditions led to the identification of 12 bioactive compounds. The major compounds were identified as 1\(H\)-pyrazol-4-yl methanol, 1-(2-methyl-5-nitro-1\(H\)-imidazol-1-yl) propan-2-ol, 1,4-naphthquinone, luteolin-6-C-glucose, and hesperidin, which possess different biological activities [45]. As expected, phenolics, flavonoids, and their derivatives are abundant in _M. oleifera_ seeds, and 4\({}^{\prime}\),5,6,7-tetramethyl-flavone, chelidimerine, maltose, 5,7-dimethoxy-4\({}^{\prime}\)-hydroxyflavone-4\({}^{\prime}\)-O-\(\alpha\)-L-rhamnose(1-\(>\)2)-beta-D-glucose, and (2\(R\),3\(R\))-3,5,7,2\({}^{\prime}\),6\({}^{\prime}\)-pentahydroxyflavone were the main compounds in the seed extract. Moreover, several
###### Abstract  
This paper discusses the development of a coupled Q-learning/fuzzy control algorithm to be applied to the control of solar domestic hot water systems. The controller brings the benefit of showing performance in line with the best reference controllers without the need for devoting time to modelling and simulations to tune its parameters before deployment. The performance of the proposed control algorithm was analysed in detail concerning the input membership function defining the fuzzy controller. The algorithm was compared to four standard reference control cases using three performance figures: the seasonal performance factor of the solar collectors, the seasonal performance factor of the system and the number of on/off cycles of the primary circulator. The work shows that the reinforced learning controller can find the best performing fuzzy controller within a family of controllers. It also shows how to increase the speed of the learning process by loading the controller with partial pre-existing information. The new controller performed significantly better than the best reference case with regard to the collectors' performance factor (between 15% and 115%), and at the same time, to the number of on/off cycles of the primary circulator (1.2 per day down from 30 per day). Regarding the domestic hot water performance factor, the new controller performed about 11% worse than the best reference controller but greatly improved its on/off cycle figure (425 from 11,046). The decrease in performance was due to the choice of reward function, which was not selected for that purpose and it was blind to some of the factors influencing the system performance factor.  
domestic hot water systems; fuzzy control; reinforced learning; simulations; solar Article
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
## 4 Deep-Learning Techniques
### Convolutional Neural Network (CNN) [43]  
The CNN solves the problem of the requirement of an excessive number of parameters, as in fully connected networks. Moreover, it preserves the spatial context, so the output of the convolutional layer takes the shape of a picture. Most importantly, it possesses translation equivariance; that is, the passage of an image through the convolutional layer is almost the same as the translation of the output of convolution; therefore, class prediction across the different layers is almost the same. It consists of four types of stacked layer, followed by an FCN to perform the classification; these layers are the convolutional layer, padding, stride, and pooling. The convolutional layer is the core of the CNN, as it reduces model capacity compared to the FCN. In addition, it is efficient at detecting spatial patterns. It mainly applies the cross-correlation with a kernel that has learnable parameters. Padding adds zeros around rows and/or columns to inflate the size of the output. The stride jumps rows and/or columns to reduce dimensionality and computation. The pooling works as a voting or averaging mechanism to make the representation manageable and smaller. It is important to note that padding, stride, and multiple channels are non-learnable parameters. An example of a CNN is shown in Figure 7.  
Figure 6: Richness and variation in Arabic fonts.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 1 Introduction
[17] developed a theoretical model for crack imagination by the nonlinear frequency-mixing technique. Based on the model, the cracks in a glass plate were successfully detected and imaged [18]. They demonstrated that the sensitivity of this technique is 20 times higher than the linear photo-acoustic techniques. In the research mentioned above, the nonlinear ultrasonic technique was demonstrated to be a good candidate for the detection of fatigue damage. However, this technique also has some shortcomings in that it is challenging to get the two most suitable independent original excitation frequencies in practice [19]. This is because the optimal choice of the excitation frequencies is also affected by the structure to be detected and the environmental condition [20]. Here we introduced the laser-generated ultrasonics technique [21] to solve this problem. Compared with traditional nonlinear techniques, only one laser pulse excitation is used to generate a broadband input signal rather than two different ultrasonic waves. So the optimal choice of the excitation frequencies is no longer a problem. But a new problem has reared up that is affected by the broadband nature of the input signal generated by the laser pulse. The new frequencies caused by the damage are submerged and cannot be detected directly. In this paper, we introduced the state space predictive model [22] to tackle this problem. The dynamic characteristics of the structure can be described by geometric figures in the state space and the damage is shown to change the properties of the geometric figure. By identifying the change in the geometric figure, the damage can be detected. Usually, the state space predictive model was applied to the vibrating structures for structural damage identification. In this paper, the model was applied to laser-generated ultrasonic signals for fatigue damage detection and quantification. This paper is organized as follows: in Section 2, the fundamental issues about the generation and modulation of laser-generated ultrasonics and attractor-based analyses are provided. How damage can be detected and quantified by normalized nonlinear prediction error is also described in Section 2; in Section 3, a laser-generated ultrasonics detecting system is built to detect the damage; in Sections 4 and 5, artificial damage to aluminum alloy plates and actual fatigue cracks in a twin-screw compressor body are detected and quantified by the proposed technique to validate the effectiveness of the technique. Finally, the results of this work are presented in Section 6.
## 1 Introduction
However, nonlocal models pose both mathematical and computational challenges, as classical theories may not be applicable, and numerical methods can be computationally demanding [25; 30; 31]. Overcoming these challenges is crucial to investigating the behavior of two-dimensional solutions involving multiple interacting species and effectively connecting the model with empirical data. In this study, we present an efficient spectral numerical scheme designed to solve a nonlocal advection-diffusion equation, specifically aimed at handling a large number of interacting species. Spectral methods have been shown to be efficient in a variety of contexts, including fluid mechanics [32], higher order reaction-diffusion equations [33], and integro-differential equations [34]. We dive into investigating the impact of various factors, such as the interaction potential, environmental conditions, diffusion strength, and the number of groups, on the behavior of equilibrium solutions. Our findings reveal that a change in the variance of an interaction potential has a pronounced effect on a population's territory. Moreover, we observe that the delicate balance between diffusion and aggregation strengths significantly influences the computational efficiency of the numerical solutions. Furthermore, our approach incorporates environmental data, which plays a crucial role in influencing species movement through diverse mechanisms. To test the validity of our method, we employ synthetic data and implement a data fitting technique that employs maximum likelihood estimation and stochastic gradient descent. Through this process, we aim to discern the primary driving forces behind the movement of social groups, shedding light on the intricate dynamics governing species' behaviors. _Outline:_ In Section 2, we provide the motivation for our study and introduce the mechanistic model under investigation. Section 3 is dedicated to describing the numerical scheme designed for a general nonlocal advection-diffusion equation in two dimensions. Here, we explore the influence of model parameters on the behavior of solutions and discuss the computational implications of adding more groups to the system. Moving on to Section 4, we integrate synthetic data into the model and employ maximum likelihood estimation to analyze the primary driving forces behind species movement. Finally, our conclusions are presented in Section 6, summarizing the key findings and implications of our study.
## 5 Results and Discussion
### RQ3: What Are the Parameters That Are Optimized to Enhance the Performance of LoRaWAN Networks? Research in the optimization of LoRaWAN networks focused mainly on enhancing network performance by adjusting various parameters like SF, TP, and Duty Cycle (DC) among others. All the studies in review have used various methodologies to achieve significant improvements in network efficiency, energy consumption, and overall reliability. The key findings from the literature can be summarized as follows in Table 9. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (s)** & **Parameters Optimized** & **Optimization Focus** & **Method of Optimization** \\ \hline \multirow{4}{*}{[PERSON] et al. [46]} & Transmission Priority and & \multirow{4}{*}{Reducing packet collision rates, transmission delays, and energy consumption by managing transmission priorities, SFs, and power levels.} & Dynamic PST, \\  & Intervals, SF, TP and Time-on-Air, & & \multirow{4}{*}{Dynamics, and energy consumption by managing transmission priorities, SFs, and power levels.} & \\  & Packet Size, Energy Consumption & & \\ \cline{1-1}  & SF, TP, Packet Success Ratio, & & \\ \cline{1-1}  & Energy Consumption & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Optimization parameters to enhance performance. Figure 7: Effectiveness of ML/AI Techniques. Findings suggested that TP, SF, CR, ADR, and BW were addressed and optimized by most of the studies, as shown in Figure 8. As TP and SF are crucial for enhancing network efficiency and coverage, the DL techniques, along with RL, have achieved optimal results and ensured minimum energy usage, as discussed in [32; 36; 37]. ADR has been optimized to reduce packet loss along with CR to enhance data transmission, as in [33; 35; 38; 40] where authors have used DL, RL, and ML to predict the adapting rates to improve error resilience and network reliability. Effective channel management is also essential for minimizing collisions and maximizing throughput. Authors in [41; 47] have used multi-agent approaches that allow dynamic adaptation to network conditions. It has significantly improved PDR and overall network performance. The optimization of these parameters has significantly improved overall network performance.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
## 2 System Model
### DF and AF Relay Network  
In this subsection, we assume that the first relay \(r_{1}\) considers the DF protocol and the second relay \(r_{2}\) considers the AF protocol. In the first time slot, the source transmits the signal \(x_{da,s}\) with transmit power \(P_{s}\) to the first relay. The received signal \(y_{da,1}\) at the first relay can be expressed as  
\[y_{da,1}=h_{1}x_{da,s}+n_{1}, \tag{8}\]  
where \(h_{1}\) is the channel coefficient from source to the first relay and \(n_{1}\) is the zero-mean AWGN with unit variance at the first relay.  
The first relay decodes the received signal. In the second time slot, the first relay transmits the signal \(x_{da,1}\) with transmit power \(P_{da,1}\) to the second relay. The received signal \(y_{da,2}\) at the second relay can be expressed as  
\[y_{da,2}=h_{2}x_{da,1}+n_{2}, \tag{9}\]  
where \(h_{2}\) is the channel coefficient from the first relay to the second relay and \(n_{2}\) is the zero-mean AWGN with unit variance at the second relay.  
In the third time slot, the second relay transmits the signal \(x_{da,2}\) with transmit power \(P_{da,2}\) to the destination. The transmitted signal \(x_{da,2}\) at the second relay is  
\[x_{da,2}=\beta_{da}y_{da,2}, \tag{10}\]  
where \(\beta_{da}\) is the amplification factor for AF relay and is given by  
\[\beta_{da}=\sqrt{\frac{P_{da,2}}{P_{da,1}|h_{2}|^{2}+1}}. \tag{11}\]  
The received signal \(y_{da,d}\) at the destination can be expressed as  
\[\begin{split} y_{da,d}&=h_{3}x_{da,2}+n_{d},\\ &=\beta_{da}h_{3}(h_{2}x_{da,1}+n_{2})+n_{d},\end{split} \tag{12}\]  
where \(h_{3}\) is the channel coefficient from the second relay to destination and \(n_{d}\) is the zero-mean AWGN with unit variance at the destination.  
By using [PERSON]'s formula in [30], the achievable rate can be obtained as  
\[R_{da}(P_{da,1},P_{da,2})=\log_{2}(1+\gamma_{da}), \tag{13}\]  
where \(\gamma_{da}\) is the SNR for the DF-AF relay network and is given by  
\[\gamma_{da}=\frac{\min(P_{s}|h_{1}|^{2},P_{da,1}|h_{2}|^{2})P_{da,2}|h_{3}|^{ 2}}{\min(P_{s}|h_{1}|^{2},P_{da,1}|h_{2}|^{2})+P_{da,2}|h_{3}|^{2}+1}. \tag{14}\]  
In other words, the SNR for the DF-AF relay network is determined by the harmonic mean between the minimum value of the SNR from the source to the DF relay, and from the DF relay to the AF relay, and the SNR from the AF relay to the destination.
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.2 A Power Control Game: Nonlinear and Adaptive Power Pricing Function  
In [18], the power control game (PCG) is an algorithm that, like PAPU, applies game theory to WBASNs. The main objective is to maximize the total system throughput, minimize power consumption, and penalize high power consumption.  
In PCG, game theory can be applied as follows: A player is the transmission link within a WBASN at a certain time slot. An action of each player is as a set of transmission power levels that must satisfy the given power constraint set. The payoff function for each player is defined as the tradeoff between network utility and price function. The price function in [18] is more effective than the cost function in [17] because the price function in PCG can adapt to dynamic changes in channel gain or power budget. More specifically, the adaptive price function is clearly defined as a function of the channel gain, SINR, and power budget of each player. Therefore, the player has a higher chance to transmit if its power budget is high or its channel condition is good. Otherwise, its transmission is penalized to preserve energy and avoid interference from others.  
Status updates within WBASNs in this algorithm are the same as in PAPU [17]. In each time slot, the player updates the transmit power to reach its best response in the next time slot. Consequently, this algorithm allows WBASNs to control their power level and maintain acceptable capacity for each user.