esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Daily EC-Derived LUE vs. Narrow-Waveband-Derived PRI  
Figures 8 and 9 show the relationships between eddy-covariance estimates of stand-level LUE against PRI observations (Figure 8), and calibrated radiance data from the 568 nm band (Figure 9). Data points represent daily averages for the period March to July for selected times between 8 a.m. and 4 p.m. PST. In general, high PRI and LUE values were found for cloudy days with low irradiance, while sunny days related to low PRI values and low LUE, in agreement with earlier findings; however, a much smaller correlation was found than previously has been reported for a similar forest environment [12]. The moderate correlation can partly be explained from marked differences in scale between the EC-derived LUE and the PRI measurements and restricted availability of data covering the growing season (_i.e._, a larger down-regulation of photosynthesis is expected to occur in the dryer and hotter months of July and August). Changes in radiance data from the 568 nm band correlated closely with LUE, as expected from the definition of LUE being the ratio of GPP and incident PAR, and a second-order polynomial fit resulted in an explained variation of \(R^{2}=0.85\). Weak correlations were found between PRI and EC-derived LUE estimates (Figure 8) and between PRI and upwelling radiance in the 568 nm band (Figure 10); however, the latter correlation was the weakest. Further research is needed to validate these results against fluorescence measurements and gas-exchange measurements performed at scales that are consistent with the PRI observations.  
In addition to correlations between LUE and PRI, trends in PRI over time were examined and exhibited a strong linear decrease in PRI over the period March to July (\(R^{2}=0.6\)), shown in Figure 11. The two largest errors in the model fit were caused by days with a prolonged period of low-light conditions and for these days some visible noise was observed in the PRI data. Restricting the time-of-day selection between 10 a.m. and 2 p.m. improved the model fit to \(R^{2}=0.8\) (data not shown). The decrease in PRI is likely linked to phenological changes, including chlorophyll:carotenoid ratio changes [13] and growth, as well as changes in irradiation quality and sun angle effects that could not be further examined using the collected data sets.  
Figure 7: PRI signal shown for 8 May 2013. Times shown on the \(x\)-axis are in the Pacific Standard time zone.  
Figure 8: Scatter plot showing the relationship between the daily averaged photochemical reflectance index (PRI) and daily-averaged light use efficiency (LUE) that was derived with the eddy-covariance technique, and selected for times between 8 a.m. and 4 p.m. Pacific standard time.  
Figure 10: Relationship between the photochemical reflectance index (PRI) and upwelling radiance measured in the reference band (568 nm).  
Figure 9: Relationship between upwelling radiance, as measured by the 568 nm band, and light-use efficiency (LUE), as derived using the eddy-covariance technique.
# Study of an Atmospheric Refractivity Estimation from a Clutter Using Genetic Algorithm
## 2 Calculation of Clutter Power Spectrums
The clutter power can be calculated from the propagation factor using the following equations [29]:  
\[A=\frac{r\theta c\tau}{2} \tag{3}\]  
\[P_{c}=\frac{P_{t}G^{2}\lambda^{2}A\sigma_{0}}{\left(4\pi\right)^{3}\prime^{4}} \tag{4}\]  
where \(P_{t}\) is the transmitted power, and \(G\) is the antenna gain of the radar, \(\lambda\) is the wavelength, and \(r\) is the range of the radar for the observation location. In addition, \(A\) is the illumination surface area by the radar signal, which can be obtained from range \(r\), antenna beamwidth \(\theta\), light speed \(c\), and radar pulse width \(\tau\). \(\sigma_{0}\) refers to the radar cross section (RCS) of the land [24] and sea surface [20]. Generally, the RCS for the land surface is higher than for the sea surface. When the atmospheric condition is normal, the strong clutter power spectrum is not observed over a range of 20 km, as shown in Figure 5a, because the high terrain blocks the wave propagation. On the other hand, when the atmospheric condition is duct, a strong clutter power is observed in a wide range, as shown in Figure 5b. Figure 4: Propagation factor according to different atmospheric conditions in the duct and normal: (**a**) using the measured data of Case 1; (**b**) using the measured data of Case 2. Figure 3: Comparison of the measured atmospheric refractivity for the different atmospheric conditions.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 1 Introduction
### Motivation
LDPC codes are particularly efficient because they provide a feasible implementation that approaches the Shannon channel capacity for reliable transmission. LDPC codes are critical for many current communication systems due to their capacity-approaching performance and low-complexity iterative decoding over noisy information channels [13]. LDPC codes outperform other error-correcting codes in many respects, including how well they manage errors and how cheap it is to encode and decode them. QC-LDPC (Quasi-Cyclic LDPC) codes, also known as architecture-aware codes, are a particularly important type of algebraically constructed LDPC code that was introduced in 2005 [14; 15]. They are included in a number of communication system standards, including IEEE 802.16 e, DVB-S2, and 802.11. QC-LDPC codes are built using finite geometries or circulant permutation matrices. This is because their parity check matrices have a unique shape that makes it easier to build a hardware encoder and decoder [14].
## 3 Results and Discussion
### Photocatalytic Tests
The H\({}_{2}\)O\({}_{2}\) generated could give rise to \(\cdot OH\) radicals through a reaction with \(O_{2}^{-}\) - or under the incidence of light (Equations (12) and (13)). Finally, all the generated radicals degraded the dye, giving rise to the degradation products (Equations (14) and (15)). \[\text{ZnO}gC_{3}+hv\ \rightarrow\ h^{+}+e^{-} \tag{6}\]  
\[h^{+}+H_{2}O\rightarrow\ \cdot OH+H^{+} \tag{7}\]  
\[h^{+}+OH^{-}\rightarrow\ \cdot OH \tag{8}\]  
\[e^{-}+O_{2}\rightarrow\ \cdot O_{2}^{-} \tag{9}\]  
\[2e^{-}+O_{2}+2H^{+}\to H_{2}O_{2} \tag{10}\]  
\[2e^{-}+O_{2}+2H^{+}\to H_{2}O_{2} \tag{11}\]  
\[H_{2}O_{2}+\ \cdot O_{2}^{-}\cdot OH+OH^{-}+O_{2} \tag{12}\]  
\[H_{2}O_{2}+hv\to 2\cdot OH \tag{13}\]  
Figure 6:0  
Figure 6:1  
The photocatalytic process was quite slow, reaching the total degradation of the dye after 3 h of treatment. Therefore, once it was confirmed that the wavelength of 395 nm effectively activates the ZnO/g-C\({}_{3}\)N\({}_{4}\) composites and that the generation of H\({}_{2}\)O\({}_{2}\) enhances the reaction, the performance of the photo-Fenton-like process was evaluated. To this end, an experiment was carried out under the same conditions as the previous tests. Before starting, H\({}_{2}\)O\({}_{2}\) was added to achieve a final concentration of 73.57 mM in the solution, aiming to accelerate the Rhodamine B removal process. As can be seen in Figure 6b, clear differences in photocatalytic efficiency are evident. In the case of photo-Fenton like, the degradation of Rhodamine B was considerably higher compared to that obtained for photocatalysis at 395 nm. Total degradation was achieved in 90 min. The results obtained with the photo-Fenton-like treatment were compared with the performed controls, ZnO/g-C\({}_{3}\)N\({}_{4}\)-H\({}_{2}\)O\({}_{2}\), and photo-H\({}_{2}\)O\({}_{2}\). In Figure 6b, it can be seen how H\({}_{2}\)O\({}_{2}\) over the catalyst without light had practically no effect on the degradation of Rhodamine B (\(<\)6%). As for photo-H\({}_{2}\)O\({}_{2}\), as explained above (Equation (13)), H\({}_{2}\)O\({}_{2}\) is activated by the incidence of light generating \(\cdot OH\) radicals, so the degradation of the dye in this case was relevant. If the values obtained after 1 h of treatment are compared, the degradations were around 51% and 94% for photo-H\({}_{2}\)O\({}_{2}\) and photo-Fenton like, respectively. At the end of the treatment, the improvement was 32.36%. The concentration of Rhodamine B decreased exponentially in both cases, according to a pseudo-first-order kinetic model (Equation (2)). The values of the kinetic constants calculated for each of the treatments were 0.0115 and 0.0514 min\({}^{-1}\), respectively. This fact evidences the synergistic effect obtained in the photo-Fenton-like treatment, where the H\({}_{2}\)O\({}_{2}\) activation under the selected light is added to the photocatalytic effect of the composites, obtaining a higher and faster degradation.
## 6 Experimental Tests
### Numerical Results  
In this section, some test problems were generated randomly with different sizes to examine the performance of the developed HCLP model. For this purpose, the test problems were designed in two main directions. In the first experiment, it is supposed that there are three regions affected by disasters with two kinds of high and low demand scenarios in three strategic periods. In the second experiment, there are four regions affected by the disasters with high and low demand scenarios in four strategic periods. A schematic illustration of three regions and four regions test problems together with the located facilities, and the module movement flow is depicted in Figure 5 for the case with 250 demand points. In each kind of experiment, the dimensions of the test problems are augmented gradually. Tables 3 and 4 contain the results of the test problems. The problems are solved using Generic Algebraic Modelling System (GAMS) (CPLEX solver) software (24.1.2) on a PC with a 3.4-GHz Core i7-6700 CPU and 8 GB of RAM running Windows 10 (64 bit). It must be noted that all parameters of the test problems are designed in a way that there would be no redundant constraints. The results for all test problems approve that problems of low scenarios result in less objective values, but a higher percentage of demands can be covered in the low scenario problems compared to the high demand scenario problems. By increasing the size of the problems, the elapsed time also increases for both kinds of problems in Tables 3 and 4. For the problem of the size 250 points of three regions, we conducted some sensitivity analyses. As mentioned, the first two problems of low and high demands are the problems that all the constraints are active. By increasing the capacity of facilities, in second and third
# Using Data Mining in Educational Administration: A Case Study on Improving School Attendance
## 3 Problem Statement and the Analytical Model
### Analytical Model
#### 3.2.3 Adapting the mt Model for School Attendance  
The mt model, adapted for school attendance, is developed below and in essence evaluates options, for example: Option \((J_{i},O)\) and \((J_{k},O)\), based on both the support and confidence of that option in the database. It is clear that \(\text{P}(J_{i})\geq P(J_{i},O)\) for all \(i\in m\), hence the underlying principle of the mt model is that it evaluates the \"effort\" required to make \(\text{P}(J_{i},O)=\text{minsup}\), which is considered to be the \"desired\" state of \(\text{P}(J_{i},O)\). Note that in this instance, the \"desired\" state is equivalent to the maximum session and overall absenteeism. Also note that minsup is user-defined, and is governed bythe Apriori principle, i.e., \(P(J_{i},O)\leq\text{minsup}\leq P(J_{i})\) for all \(i\in m\). The number of absences required for a \(P(J_{i},O)\) combination to reach the \"desired\" state is given by Equation (1), where \(|T_{i}|\) is the number of transactions in database \(T_{t}\).  
\[\text{Number of absences required for \"desired\" state}=(\text{minsup}-P(J_{i},O))\cdot|T_{t}|. \tag{1}\]  
Given that not all children absent in \(J_{i}\) will also be absent in \(O\), the \"market target\" referred to [16], or in this instance, the pupil target, may thus be defined as the number of required absences in \(J_{i}\) such that the number of absences required for the \"desired\" state of \((J_{i},O)\) in \(T_{t}\) to be reached. This is stated mathematically in Equation (2), where \(P(J_{i},O)/P(J_{i})=\text{conf}(J_{i}\to O)\).  
\[\text{pupil target}\cdot\frac{P(J_{i},O)}{P(J_{i})}=\text{Number of absences required for \"desired\" state.} \tag{2}\]  
Thus the mt equation, given in Equation (3) is obtained by combining Equations (1) and (2), and dividing both sides the minimum support, i.e., the physical number of absences equivalent to minsup in database \(T_{t}\). Note that mt is a normalised parameter, and is given by pupil target/minimum support  
\[\text{mt}=\frac{P(J_{i})}{P(J_{i},O)}-\frac{P(J_{i})}{\text{minsup}}. \tag{3}\]  
From Equation (3), it is evident that options that have the lowest mt value require the lowest \"effort\" to reach the \"desired\" state, and are thus considered the best choices for a given minsup. From a practical perspective, this implies that the school targets the school session that has the greatest propensity to lead to overall below-average school attendance. There are also practical constraints of managing a school that must be considered. In this regard, initiatives must target all, or the majority of school pupils to ensure fairness, and given that most initiatives are largely fixed costs (e.g., the effort in planning activities is similar whether the audience is 100 or 250), it makes sense to target the session which impacts overall absenteeism the most [20]. The most impactful session is the session which has the lowest mt value as it requires the least \"effort\" to reach the \"desired\" state.
## 3 Experimental Results and Analysis
### Experimental Results Analysis
Of all the projected results, the maximum distance forecast error is 0.56 m. Of the vertical distance predictions, 95% had a prediction error of fewer than 0.025 m, and the maximum distance prediction error is 0.63 m.
# MCCM: An Approach for Connectivity and Coverage Maximization
## 4 Distributed Maintaining Connectivity and Coverage Maximization (MCCM)
### Coverage Maximization  
This process starts once the discovery process finishes. It occurs in each island \(\mathcal{N}_{i}\), if it has more than one device (i.e., \(1\mathcal{N}_{i}1>\)1). Figure 3 shows how the maximization process works. The main idea is to find for each device \(D_{i}\) its location, to maximize the coverage.  
The steps are as follows:  
* A device \(D_{i}\in\mathcal{N}_{i}\) calculates the mean coordinates \(\mathcal{M}(x_{m},y_{m})\). It is the mean location of the devices in \(\mathcal{N}_{i}\), where \(x_{m}=\frac{1}{k}\sum_{l=1}^{k}(x_{l})\), and \(y_{m}=\frac{1}{k}\sum_{l=1}^{k}(y_{l})\), such that \(D_{l}(x_{l},y_{l})\in\mathcal{N}_{i}\).
* A device \(D_{i}\) moves to the location \(\mathcal{M}(x_{m},y_{m})\) if it is the closest device to it, thus: \[dist(D_{i},\mathcal{M})=min\{dist(D,\mathcal{M}),\forall D\in\mathcal{N}_{i}\}\] The device then updates its status to ready for the monitoring mission.
* Otherwise, \(D_{i}\) finds its next location by guessing the behavior of its adjacent devices located closer to \(\mathcal{M}\). \(D_{i}\) orders its neighbors by their initial position with respect to the mean location \(\mathcal{M}\). Then it chooses the neighbors that are closer to \(\mathcal{M}\). It estimates their positions and moves according to the estimated position. Assume the sequence \(D_{i+1}\), \(D_{i}\), \(D_{i-1}\) and \(D_{i-2}\) is ordered from the farthest to the closest devices to \(\mathcal{M}\). Device \(D_{i+1}\) is the farthest one. \(D_{i}\) calculates its final position in two ways: (1) either it relies on \(D_{i-1}\) estimated position, if the circle-to-circle method is to be applied, (2) or it relies on \(D_{i-1}\) and \(D_{i-2}\) positions if the circle-to-two circles method is to be used. \(D_{i+1}\) does the same. It relies on \(D_{i}\) and/or \(D_{i-1}\) estimated positions, and so. For instance, let \(\zeta\) be the set of devices located between \(D_{i}\) and \(\mathcal{M}(x_{m},y_{m})\).  
Figure 2: Neighborhood discovery process of device \(D_{i}\).  
\[\zeta=\{D\in\mathcal{N}_{i}|min(y_{i},y_{m})\leq y_{D}\leq max(y_{i},y_{m})\wedge min (x_{i},x_{m})\leq x_{D}\leq max(x_{i},x_{m})\}\]  
\(D_{i}\) evaluates the possible location of each device \(D\in\zeta\) by applying the empty-arc rules. Then it moves towards the closest device \(D_{k}\) and connects to it, where \(D_{k}=f(D_{i},\zeta)\):  
\[f(D_{i},\zeta)=\{D_{k}\in\zeta|(D_{k}\diamond\mathcal{M}\lor D_{k}\bullet \mathcal{M})\wedge dist(D_{i},D_{k})=min\{dist(D_{i},D)\}\}\]
# Assessment of Frost Depth and Frost Heave in Romania
## 3 Frost Depth Prediction
### ISO and European/UE Standards/Regulations  
The methodology EN ISO 13793:2001 (thermal performance of buildings--thermal design of foundations to avoid frost heave) [10] for frost depth prediction is based on the variation in air temperature during winter, which can be represented by the freezing index and [PERSON]'s method ([PERSON]'s equation is one of the first known theoretical formulas to calculate frost penetration), as shown in Equation (5). The recommended values of the parameters from Equation (5) are for the worst-case scenario (bare ground surface over granular soils).  
\[H_{0}=\sqrt{\frac{7200\times F_{d}\times\lambda_{f}}{L+C\times\overline{\theta_{ e}}}} \tag{5}\]  
where:  
\(F_{d}\) is the design freezing index, in K \(\times\) h;  
\(\lambda_{\text{f}}\) is the thermal conductivity of frozen soil, in W/(m \(\times\) K)--2.5 W/(m \(\times\) K);  
\(L\) is the latent heat of freezing of water in the soil per volume of soil, in J/m\({}^{3}\)--150 \(\times\) 10\({}^{6}\) J/m\({}^{3}\);  
\(C\) is the heat capacity of unfrozen soil per volume, in J/(m\({}^{3}\)\(\times\) K)--3 \(\times\) 10\({}^{6}\) J/(m\({}^{3}\)\(\times\) K) and  
\(\overline{\theta_{e}}\) is the annual average external air temperature, in \({}^{\circ}\)C;  
The design freezing index \(F_{d}\) is 24 times the FI, which is cumulative freezing degree day (\({}^{\circ}\)C \(\times\) day).  
For permanent structures, F\({}_{100}\) or F\({}_{50}\) must be used.  
For the design of buildings/roads that can tolerate some movement, as well as for non-permanent buildings/structures, a lower freezing index (e.g., F\({}_{20}\), F\({}_{10}\), F\({}_{5}\)) may be used.  
Table 5 presents the F\({}_{\text{d}}\) (\({}^{\circ}\)C \(\times\) h) values and the calculated frost depth, H\({}_{0}\) (m), for the Arad station. The values of \(H_{0}\)\({}^{X}\) are calculated with the same recommended values as the parameters of Equation (5), except of \(\lambda_{\text{f}}\), which is set to the specific value of 1.5 W/(m \(\times\) K) attributed to clayed soils.
## 5 Conclusions  
In this study, the \"double-sided rotor and stator FP-AFSRM\" structure, which combines the advantages of both the axial structure and the full pitch winding structure, and minimizes the torque ripple and axial forces, is proposed. The proposed model has twin stators and a rotor. However, it has the \"single-stator and single-rotor FP-AFSRM\" operating logic. Each stator contributes to the output torque at the points where it produces the maximum torque. Only one stator--in other words, one layer--remains in transmission at a time. Due to its operating logic, the twin stator does not have disadvantages, such as the formation of unbalanced axial forces, when angular shifting is performed in models with two air gaps. In addition, although it has the \"single-stator and single-rotor FP-AFSRM\" operating logic, it does not have a high torque ripple. Magnetostatic analyses of the proposed FP-AFSRM model were performed, and the average torque, torque ripple, and average axial force were investigated. Since the operating logic is \"single-stator and single-rotor FP-AFSRM\", magnetostatic analyses were performed by energizing a single layer of the motor and the average torque, torque ripple, and average axial force were examined. Finally, the results from both analyses were compared. The analyses of the proposed FP-AFSRM model showed a reduction in torque ripple percentages by up to \"\(-41.14\) (\(\Delta\%\))\" compared to the single stator/single rotor configuration, and an improvement in the average output torque by up to \"\(+\%\) 37.32\" was obtained. Additionally, the axial force was reduced by up to \"\(-25.4\)\". As a result, the proposed \"double-sided rotor and stator FP-AFSRM\" model demonstrated a smoother torque curve with a lower average axial force. The torque ripple ratio, which is a negative aspect of SRMs, is always desired to be minimized. For example, in a layered study conducted by [PERSON] et al. on a radial SRM, it was observed that the torque ripple decreased from 155.34% to 43.1% [36]. In the double-sided axial flux SRM, which is similar to the motor proposed by [PERSON] and [PERSON] in terms of geometric structure, but has a short-pitched winding structure, the rotor poles are given slip angles at certain angles in different directions. In the analysis of the axial SRM with shift angles ranging from 0 to 2.5 degrees, it was observed that the torque ripple varied between 61% and 62% [21]. In an earlier study conducted by [PERSON] et al., the geometric effect of pole heads was investigated. In short-pitched SRMs, improvements of up to 24.1% were observed, while fully pitched SRMs also showed improvements of up to 24.1% [44].  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 11: The flux distributions of the proposed double-sided rotor and stator FP-AFSRM at 10 amperes.  
Data Availability Statement: The original contributions presented in this study are included in this manuscript; further inquiries can be directed to the author.  
The author declares no conflicts of interest.
## 3 Experiment
### Electronic Nose System  
The E-nose system used in this paper is illustrated in Figure 4. The sample container is used to collect sufficient volatile gas of the experimental sample (its material is polyethylene with the capacity of 1 L). The gas detection chamber is employed to place the gas sensor array of the E-nose system (polyethylene, 0.9 L). The diaphragm pumps (Pump 1 and 2) are used to transfer the as-detected odor and the reference gas with a fixed flow rate 1.3 L/min. The whole experiment was carried out under the temperature of 25 \(\pm\) 1 \({}^{\circ}\)C and humidity 58 \(\pm\) 3%, and the laboratory ambient air was used as the reference gas.  
The gas sensor array of the E-nose consists of 10 metal oxide semiconductor gas sensors. More details of the material preparation process and the characterization of these gas sensors have been described in the literature [24; 36]. When the sensitive material of the sensor is exposed in the detected gas, the change in its resistance value will be converted into the corresponding electrical signal, which is collected by the personal computer with a sampling frequency of 1 Hz. The characteristics of the gas sensors are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & **Chili Powder** & **Adulterant and Its Content** \\  & **(wt\%)** & **(wt\%)** \\ \hline C-pure & 100 & – \\ C-A15 & 85 & Almond Shell Powder, 15 \\ C-A30 & 70 & Almond Shell Powder, 30 \\ C-A45 & 55 & Almond Shell Powder, 45 \\ C-B15 & 85 & Red Beetroot Powder, 15 \\ C-B30 & 70 & Red Beetroot Powder, 30 \\ C-B45 & 55 & Red Beetroot Powder, 45 \\ C-T15 & 85 & Tomato Peel Powder, 15 \\ C-T30 & 70 & Tomato Peel Powder, 30 \\ C-T45 & 55 & Tomato Peel Powder, 45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Category and content of adulterant in chili powder.