esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Selection of Evaluation Factors  
Landslide formation is a complex process influenced by various evaluation factors. Research has demonstrated that retaining highly important but low-correlation factors can effectively minimize redundant information. Consequently, this study selects and weights the evaluation factors using differentiation, the MIC method, and collinearity diagnosis. This study then calculates the membership function of these factors to assess the credibility of the negative samples.  
The 14 evaluation factors were analyzed based on differentiation. The results are presented in Figure 4. Factors with higher differentiation have a more significant influence on the landslide. To improve computational efficiency, this study established a differentiation threshold of 1.1, ultimately retaining nine evaluation factors: elevation, EGG, SEI, STK, SMC, PD, RD, DD, and FD.  
This study used the MIC method, with a threshold of 0.1, to calculate the nonlinear correlation coefficients between the evaluation factors and slope conditions (Figure 5). In addition, collinearity diagnostics were performed to address multicollinearity between the evaluation factors, with thresholds set at a significance value < 0.05 and VIF > 3. The results of these calculations are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Significance** & **VIF** \\ \hline Elevation & 0 & 1.287 \\ PD & 0.078 & 1.05 \\ SEI & 0 & 1.02 \\ RD & 0.183 & 1 \\ SMC & 0 & 1.229 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of collinearity diagnosis for evaluation factors.  
Figure 4: Preliminary selection results of evaluation factors based on the discrimination threshold.  
In summary, after conducting MIC nonlinear correlation analysis and collinearity diagnostics, seven evaluation factors were eliminated: RD, EGG, FD, DD, PD, and STK. Ultimately, three evaluation factors were retained: SEI with a weight of 0.27, elevation with a weight of 0.54, and SMC with a weight of 0.19 (Figure 6). The proportion of correlation of each factor to the total correlation sum was used as the weight for the subsequent plotting of the spatial distribution map of negative sample credibility.  
Figure 5: Nonlinear correlation analysis between evaluation factors and the slope state.  
Figure 6: Selected evaluation factors: (**a**) SEI; (**b**) elevation; and (**c**) SMC.
## 3 Theoretical Analysis
### Heat Generation Process  
As shown in formula (3), the heat generation of Ni-MH batteries includes electrochemical reaction heat (\(\Phi_{r}\)), polarization heat (\(\Phi_{p}\)), side reaction heat (\(\Phi_{s}\)), and Joule heat (\(\Phi_{j}\)) [38]. The operation process is divided into two phases: the normal charge-discharge phase and the overcharge phase. The calorific value is related to the magnitude and direction of the current (I). Because the SOC working range is controlled within 30%-70% to avoid overcharging in HEVs, the sub reaction heat is approximately zero. According to the exothermic charge and endothermic discharge, the total reaction heat can be ignored by using alternating current.  
\[\begin{array}{ll}\Phi&=\Phi_{r}+\Phi_{p}+\Phi_{j}+\Phi_{s}\\ &=(\pm)0.547I+3.6I^{2}R_{p}+3.6I^{2}R_{e}+5.334I\ (kJ/h)\\ &\approx 3.6I^{2}R_{p}+3.6I^{2}R_{e}=3.6I^{2}R_{t}\ (kJ/h)=I^{2}R_{t}\ (W)\end{array} \tag{3}\]  
Therefore, the calorific value mainly comes from the polarization heat and Joule heat. Since the polarization resistance (\(R_{p}\)) and electronic resistance (\(R_{e}\)) cannot be calculated directly, the total internal DC resistance (\(R_{t}\)) is used for the equivalent calculation, which is the sum of the polarization resistance and electronic resistance. The internal resistance of the cells is treated as a constant, which means that it does not vary with changes in the state of charge (SOC), temperature, or other parameters.
## 3 Results and Discussion
### Electrical Properties  
Figure 4 shows the resistivity variation of Mo bilayer films prepared at different bottom layer sputtering pressures (Figure 4a) and annealing temperatures (Figure 4b). It can be seen that the resistivity of the Mo bilayer films decreased with decreasing bottom layer sputtering pressures and increasing annealing temperatures. The average micro-strain (\(\varepsilon\)) and the dislocation density (\(\rho\)) (Table 1) also decreased with decreasing bottom sputtering pressures and increasing annealing temperatures, which can directly decrease the resistivity of Mo bilayer films. The resistivity is mainly related to electron scattering caused by various structural defects, including dislocations, impurities, micro-strains, grain boundaries and point defects [29]. Grain boundaries are often considered to be the main source of dislocation density, and the dislocation density is proportional to the micro-strain [30]. Therefore, with decreasing sputtering pressures and increasing annealing temperatures, the kinetic energy of Mo particles increased and the grain sizes increased. Accordingly, the number of grain boundaries decreased, the dislocation density and the micro-strain decreased, and carrier lifetime and Hall mobility increased, so the corresponding resistivity decreased.
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
## 2 Proposed UHF RFID System  
### Link Budget  
For passive RFID tags, the sensitivity and read range are mainly decided by the input RF power which is delivered to the antenna. The input power could be calculated using the Friis equation and the relationship of tag input power and distance between tag and reader is shown in Figure 1.  
The maximum read range of passive-receiving mode is determined by the power consumption of the tag and the sensitivity is typically between \(-\)15 dBm and \(-\)20 dBm. For backscatter operating  
Figure 1: Read range and operating modes of ultra-high-frequency (UHF) radio frequency identification (RFID) system.  
mode, the RF signal is modulated and reflected back to reader. Typically, the output power of the reader is 36 dBm and the maximum sensitivity of the reader is \(-80\) dBm. Hence, the tag could operate in backscatter-mode when input power is larger than \(-22\) dBm by calculation.  
For active-operating mode, the read range is increased by the active transmitter. The maximum output power of the transmitter can be selected as \(-10\) dBm by the tradeoff between read range and power consumption. Therefore, the sensitivity of active-receiving mode should be \(-40\) dBm and the read range can achieve more than 150 m.
## 3 Results
### Effects of Temperature and pH on EstDR4  
The optimum temperature of EstDR4 was tested from 0-70 \({}^{\circ}\)C using \(p\)NPC8 as the substrate. EstDR4 displayed maximum catalytic activity at 30 \({}^{\circ}\)C and retained 26.77% and 44.98% relative activity at 0 \({}^{\circ}\)C and 5 \({}^{\circ}\)C, respectively. The activity increased consistently from 0-30 \({}^{\circ}\)C and decreased rapidly at higher temperatures; the enzyme was almost inactivated at 70 \({}^{\circ}\)C (Figure 4a). EstDR4 exhibited high stability from 10-40 \({}^{\circ}\)C, with over  
Figure 3: Substrate specificity of EstDR4. (**a**) Substrate specificity of EstDR4 toward \(p\)NPC2, \(p\)NPC4, \(p\)NPC8, \(p\)NPC12, and pNPC16. The maximum activity measured towards \(p\)NPC8 was taken as 100%. (**b**) Substrate specificity of EstDR4 toward \(\alpha\)-NA and \(\beta\)-NA. The different letters between each condition in (**a,b**) indicate significant differences (\(p<0.05\)). (**c**) Substrate specificity of EstDR4 toward (R)-MHM, (S)-MHM, PA, 2-MA, TA, LA, GTB, GTO, OO.  
80% residual activity detected after 1 h of incubation, and the enzyme retained 66.32% residual activity after 6 h incubation at 40 \({}^{\circ}\)C. However, EstDR4 residual activity was sharply reduced to approximately 56.31% after incubation at 50 \({}^{\circ}\)C for 1 h (Figure 4b).  
The optimum pH of EstDR4 was determined by testing its activity from pH 5-11 at 30 \({}^{\circ}\)C using \(p\)NPC8 as the substrate. The suitable hydrolytic environment for EstDR4 was neutral and slightly alkaline between pH 7-9, with an optimum pH of 8 based on enzyme activity (Figure 4c). Furthermore, EstDR4 showed high pH stability from pH 6-8, with approximately 80% residual activity retained after incubation for 1 h at this pH range. A considerable loss in EstDR4 activity was observed after 1 h of incubation at pH 9 (Figure 4d).
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 2 Background
### Full-Duplex Jamming Practical Issues  
Although SI cancellation techniques may be employed by [PERSON] in order to improve his SINR, there are several questions that may arise regarding the practical deployment of the FD jamming scheme that are addressed here.
## 3 Simulation Results
### Joint Design System for Downlink Communication
#### 3.2.2 Realistic Channels  
Applying realistic scenarios 1 and 2 to the joint design communication and sensing system, we consider a base station equipped with four transmit antennas (\(N_{t}=4\)), while the number of receive antennas \(N_{r}\) varies from one to two and four (\(N_{r}=1,2,4\)). Similar to the previous case, these antennas are uniformly located linear arrays with half-wavelength spacing. Both the communication signal \(s(n)\) and the radar signal \(x(n)\) are modulated using the QPSK modulation scheme.  
Figure 8 displays the communication performance in terms of BER and the sensing performance in terms of MSE with fixed sensing power and varying communication power for the realistic channels.  
Figure 7: Communication and sensing performance using different numbers of receive antennas: (**a**) Communication performance, (**b**) Sensing performance.  
The figure illustrates the dynamic relationship between communication and sensing performance, where fixed sensing power is juxtaposed with varying communication power under realistic channel conditions. Generally, as the number of receive antennas increases, both communication and sensing performance witness enhancements. However, a notable departure from the Rayleigh channel scenario emerges in the behavior of the BER curves, particularly evident in scenarios with a limited number of receive antennas. Despite the increase in receive antennas, the BER curves remain largely unaffected, suggesting unique challenges introduced by the characteristics of realistic channels.  
An intriguing observation is the absence of a decrease in BER at higher communication power levels, unlike the typical decline witnessed in Rayleigh fading scenarios. This deviation could stem from specific attributes or conditions intrinsic to the measured channel. Potential factors contributing to this deviation include non-idealities, interference, or inherent limitations within the channel model. Further exploration and detailed analysis of channel characteristics and potential sources of distortion would be imperative to elucidate a more precise understanding.
# An Approach to Designing Critical Railway Voice Communication
## 4 FRMCS Common Functions as RESTful Service Blocks
### Policy API
trationPolicyEvalRequests resource contains all policy evaluation requests for arbitration and the talkerControlPolicyEvalRequests resource contains all policy evaluation requests for multiuser talker control. These resources support the HTTP GET method, which retrieves the list of all policy evaluation requests for the respective group, and the HTTP POST method, which creates a resource representing the policy evaluation requests of the respective group. The [appAuthPolicyEvalRequestID] resource, {commAuthPolicyEvalRequestID} resource, {arbitrationPolicyEvalRequestID} resource, and {talkerControlPolicyEvalRequestID} resource represent the individual policy evaluation requests of the respective group. These resources support the GET, PUT, and DELETE methods, which retrieve/update/delete information about a specific request. The subscription resources represent subscriptions for notifications about policy evaluation requests. The data model defines the data structures used in the resource representations. Each rule is represented by \"If Condition then Action\" semantics associated with a policy. The rule condition may be represented either by sub-conditions in the Disjunctive Normal Form (DNF) or the Conjunctive Normal Form (CNF). In the DNF, the rule condition is an ORed set of ANDed conditions, whereas in the CNF, it is an ANDed set of ORed conditions. Each condition can be negated or unnegated. The actions specified in the policy rule are to be executed only if the rule condition is evaluated as true.
## 3 Confounding Adjustment Methods
### Propensity Score Methods  
Propensity score (PS) methods are increasingly popular in observational studies as an alternative to traditional covariates adjustment via a regression model. PS is one of the most frequently used causal inference methods [9]. The PS methods are a set of statistical tools that seek to balance non-equivalent groups with non-randomized designs. Simply speaking, an individual's PS is their probability to have received a treatment conditional on a set of covariates, i.e., \(e(X)=\Pr(A=1|X=x)\). PS is commonly estimated by the standard logistic regression model. Other methods, such as nonparametric regression, generalized additive models, and machine learning methods, can be used to improve PS estimation [41].  
In a randomized trial, the true PS is known by design, whereas, in an observational study, the PS must be estimated. The PS is typically estimated through a logistic regression that incorporates variables that are associated with treatment assignment. Because the PS summarizes all covariates as a single PS variable, it is able to mitigate problems of overfitting for the outcome model [35; 42]. The PS approach would not help much if the outcome model is linear; however, but the PS estimation may reduce overfitting for binary outcome models (especially with rare diseases), as the PS summarizes all other covariates into a single variable for the outcome model. Since the treatment assignment between the active and the control is often well balanced (which is relatively common in practice), one can flexibly model many covariates in the PS model. After the PS scores have been obtained, we use them to estimate the treatment effect. PS-based approaches separate the design and analysis in the sense that the PS model can be developed using only data regarding the covariates and treatment variables. Excluding the outcome from development of the PS estimation can avoid the \"fishing expedition\" of fitting models until favorable results are obtained [38].  
There are various methods of using PS for estimating treatment effects: (1) matching; (2) stratification; (3) weighting; (4) using PS as a covariate in an outcome regression.
_Applied_  
_sciences_  
_Article_  
**Phytoremediation of TSS, NH\({}_{3}\)-N and COD**  
**from Sewage Wastewater by _Lemma minor_ L.,  
_Salvinia minima, Ipomea aquatica_ and**  
_Centella asiatic_  
**[PERSON] 1, [PERSON] 1,2,*, [PERSON] 1 and [PERSON] 1**  
1 Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; [EMAIL_ADDRESS] (N.I.H.A.A.); [EMAIL_ADDRESS] (N.H.H.); [EMAIL_ADDRESS] (P.A.S.F)  
2 Centre for Tropical Climate Change System, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia  
* Correspondence: [EMAIL_ADDRESS]  
Received: 7 July 2020; Accepted: 21 July 2020; Published: 5 August 2020  
**Abstract:** The rapid growth of industries has resulted in wastewater generation containing different organic and chemical substances channeled into the water body. This causes the arising of water pollution issues in many regions. The phytoremediation method was introduced in the process of treating water pollution as it is low cost and environmentally friendly. _Lemma minor, Salvinia minima, Ipomea aquatica_ and _Centella asiatica_ were chosen in this study because they have tolerance to various pollution conditions and are able to remove organic pollutants and heavy metals. The objectives of this study were to determine the water quality before and after treatment, to determine the rate of reduction in total suspended solids (TSS), ammoniacal nitrogen (NH\({}_{3}\)-N), and chemical oxygen demand (COD) in sewage water through the phytoremediation method and to assess the effectiveness of the plants in the phytoremediation of sewage wastewater. It was found that, _Lemma minor, Salvinia minima, Ipomea aquatica_ and _Centella asiatica_ were able to reduce TSS by 50.8%, 77.6%, 85.6% and 67.6%, respectively; NH\({}_{3}\)-N by 80.4%, 89.9, 97.3% and 79.1%, respectively; and COD by 75%, 82%, 44.8% and 36.46%, respectively. In this study, it was found that sewage wastewater treatment using Ipomea aquatica was more efficient in reducing NH\({}_{3}\)-N and _Salvinia minima_ was more efficient in reducing TSS and COD values.  
**Keywords:** phytoremediation; _Lemma minor_ L.; _Salvinia minima_; _Ipomea aquatics_; _Centella asiatica_; sewage wastewater treatment
## 3 FCM-ABC Method for Rock Mass Structural Plane Identification
### Clustering Validity Verification  
The clustering results obtained needed to be verified. At present, a variety of validity test indicators have been proposed. From the effectiveness of clustering, a good clustering result should be as clear of a division as possible [17; 25]. Here, two test indicators, i.e., the classification entropy coefficient \(H_{m}\) and the fuzzy classification coefficient \(F_{m}\), are employed to test the effectiveness of clustering, and the formulas for both are as follows [24]:  
\[H_{m}=-\frac{1}{n}\sum\limits_{k=1}^{n}\sum\limits_{i=1}^{c}u_{ik}\log_{a}(u_{ ik}) \tag{14}\]  
\[F_{m}=\frac{1}{n}\sum\limits_{k=1}^{n}\sum\limits_{i=1}^{c}u_{ik}^{2} \tag{15}\]  
where the base number of logarithm a\(\in\)(1, \(\infty\)), and it is agreed that when \(u_{ik}=0\), \(u_{ik}\log_{a}(u_{ik})=0\) (the natural logarithm is taken in this study). For the two test indicators \(H_{m}\) and \(F_{m}\), when \(H_{m}\to 0\) and \(F_{m}\to 1\), it indicates that the smaller the fuzziness value of the classification, the better the clustering effect.  
In addition, to make an objective judgment on the optimal clustering results, it is necessary to use multiple indicators to test the clustering effect because fewer evaluation indicators may lead to one-sided evaluation results. Thus, another evaluation indicator, i.e., the fuzzy hypervolume \(F_{h}\), is also used to evaluate the clustering effect. A minimum \(F_{h}\) value indicates the optimal fuzzy partition. The calculation formula for \(F_{h}\) is as follows [33]:  
\[F_{h}=\sum_{i=1}^{c}(\det(T_{i}))^{0.5} \tag{16}\]  
Figure 2: FCM clustering flowchart based on the ABC algorithm.  
In which, \(T_{i}\) is a fuzzy covariance matrix and the following can be written:  
\[T_{i}=\frac{\sum\limits_{j=1}^{n}\left(u_{ij}\right)^{m}\left(X_{j}-V_{i}\right) \left(X_{j}-V_{i}\right)^{\text{T}}}{\sum\limits_{j=1}^{n}\left(u_{ij}\right)^ {m}} \tag{17}\]  
According to the proposed FCM-ABC method, after the clustering effect test, the appropriate clustering number \(c\) obtained is the structural plane grouping number. Each group represents the dominant structural plane at the measurement point, and the clustering center stands for the mean normal vector of this group of the dominant structural planes in that group, which can be converted into the average occurrence according to Equation (18). Meanwhile, the sample size in each group can also reflect the different levels of importance of the dominant structural plane.  
\[X_{l}=\left(\cos\alpha_{l}\sin\beta_{l},\ \sin\alpha_{l}\sin\beta_{l},\ \sin \alpha_{l}\cos\beta_{l}\right) \tag{18}\]  
where \(\alpha_{l}\) and \(\beta_{l}\) represent the dip direction and dip angle of the \(l\)-th structural plane, respectively, and \(l\)\(\in\)[1, \(n\)].
## 4 Conclusions  
We proposed a real-time PMP method based on quaternary grating projection with a quaternary grating which is cyclically encoded with three one, two, three grayscale fringes and let the 0 grayscale correspond to the shadow. The quaternary grating is projected by DLP4500 and only one deformed pattern needs to be captured. After the specific segmentation, clustering and quaternization, three-frame interval 1/3 period equal width binarized deformed patterns can be decomposed from the captured deformed pattern. Therefore, the 3D shape of the measured object can be reconstructed by using these three binarized deformed patterns. The physical model is systematically derived. The experimental results show the effectiveness and practicability of the proposed real-time PMP method. By using DLP4500 with 2-bit depth coding mode, the quaternary grating achieves 1428 Hz high-speed projection A monochrome CCD camera is used for image capturing in a corresponding high refresh rate mode. The single-shot feature is fully capable of real-time online measurement. Compared with the traditional unequal duty cycle binary grating PMP method, it has the advantages of higher detection speed and good real-time performance. Compared with the real-time PMP method of color-coded binary grating, there is no color crosstalk and the efficiency of CCD is higher and the cost may be lower. Therefore, it has broad application prospects in the field of industrial fast real-time online 3D inspection.  
Conceptualization, C.Y. and Y.C.; methodology, C.Y.; validation, Y.C., X.H.; investigation, Y.C. and X.H.; resources, C.Y.; writing--original draft preparation, Y.C.; writing--review and editing, C.Y. and Y.C.; visualization, Y.C.; project administration, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Special Grand National Project of China, 2009 ZX02204-008.  
Not applicable.  
Data Availability StatementData is contained within the article or supplementary material. The data presented in this study are available in Single-Shot Phase Measuring Profilometry Based on Quaternary Grating Projection.  
The authors declare no conflict of interest.
# SEM Studies Assessing the Efficacy of Laser Treatment for Primary Teetis: A Systematic Review
## 2 Materials and Methods  
### Focused Question  
The systematic review adhered to the PICO framework, outlined as follows:  
PICO question: In the case of primary teeth (population), will the observation of Scanning Electron Microscopy (SEM) imaging after laser treatment (investigated condition) demonstrate alterations in the treatment's efficacy (outcome) compared among various employed laser systems (comparison condition)?