esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Boundaries for LED-Based Indoor Positioning System
## 3 Optical Boundary Definition
### Threshold Angle for Optical Boundary  
The boundary of LED footprints varies for different optical transmitter and receiver orientations as illustrated in Figure 4. In order to establish a common ground for designs, a threshold angle is defined as the angle where a minimum number of transmitted packets are received. Therefore, the threshold angle occurs when the packet delivery ratio (PDR), which is the ratio of the number of packets received to the number of packets transmitted, is greater than or equal to a specified value \(\mathcal{P}\). Given there are \(N_{p}\) independent bits in a packet and that for successful packet reception all of these bits must be received without error, the PDR is defined in terms of BER as  
\[\text{PDR}=(1-\text{BER})^{N_{p}} \tag{11}\]  
therefore the required BER to yield \(\mathcal{P}\) is given by  
\[\text{BER}=1-\mathcal{P}^{\frac{1}{N_{p}}}. \tag{12}\]  
On the basis of the relationship between the BER, SNR, and \(P_{r}\) defined in (4) and (1), the threshold angle \(\phi_{th}\) is given as  
\[\phi_{th}=\cos^{-1}\left\{\frac{2\pi h^{2}\sqrt{\sigma_{t}^{2}}\mathcal{Q}^{- 1}(1-\mathcal{P}^{\frac{1}{N_{p}}})}{\mathcal{R}P_{t}A(m+1)g(\varphi)\cos( \varphi)}\right\}^{\frac{1}{m+2}}. \tag{13}\]  
Therefore, given \(N_{p}\) number of bits in a designed positioning protocol and the minimum required PDR \(\mathcal{P}\), the threshold angle can be evaluated.
# Distributed-Satellite-Clusters-Based Spectrum Sensing with Two-Stage Phase Alignment
## 4 Sensing Performance Analysis
SR fading random variables, namely, \(Z\), are plotted for different combinations of \((L,m_{s},b_{s},\Omega)\). It can be seen that the simulated PDF matches closely with the proposed analytical PDF we derived in (15). Therefore, using (15) into (13), the closed-form expressions of \(P_{d}^{L}\) of the i.i.d. SR faded \(L\geqslant 2\) branch EGC can be written as  
\[\begin{split} P_{d}^{L}=&\Xi\frac{1}{2}\bigg{(} \frac{\Gamma\big{(}\sum_{i=1}^{L}k_{i}+\sum_{i=1}^{L-1}m_{i}+L\big{)}}{\beta^{ L-1}_{i+1}+\sum_{i=1}^{L-1}m_{i}+L}-exp\bigg{(}-\frac{\lambda}{2}\bigg{)}\bigg{(} \frac{L}{\overline{\gamma}\eta}\bigg{)}^{\sum_{i=1}^{L}k_{i}+\sum_{i=1}^{L-1}m_ {i}+LHowever, Equation (17) requires too much iterative time and calculative time, but when considering arbitrary integer-valued fading severity parameter [26; 27], we can have  
\[{}_{1}F_{1}(m_{s},1,\delta x)=e^{\delta x}\sum_{k=0}^{m_{t}-1}\frac{(1-m_{s})_{k }(-\delta x)^{k}}{\Gamma(1+k)^{2}} \tag{18}\]  
thus, the \(P_{d}\) can be rewritten as:  
\[ \begin{split} P_{d}^{L}=&\Xi^{*}\frac{1}{2}\bigg{(} \frac{\Gamma\big{(}\sum_{i=1}^{L}k_{i}+\sum_{i=1}^{L-1}m_{i}+L\big{)}}{(\beta- \delta)^{\sum_{i=1}^{L}k_{i}+\sum_{i=1}^{L-1}m_{i}+L}}-exp\bigg{(}-\frac{ \lambda}{2}\bigg{)}\bigg{(}\frac{L}{\widetilde{\gamma}\eta^{*}}\bigg{)}^{\sum _{i=1}^{L}k_{i}+\sum_{i=1}^{L-1}m_{i}+L}\\ &\sum_{j=u}^{\infty}\bigg{(}\frac{\lambda}{2}\bigg{)}^{j}\frac{ \Gamma\big{(}\sum_{i=1}^{L}k_{i}+\sum_{i=1}^{L-1}m_{i}+L\big{)}}{\Gamma(j+1) }\ _{1}F_{1}\bigg{(}\sum_{i=1}^{L}k_{i}+\sum_{i=1}^{L-1}m_{i}+L;j+1;\frac{ \lambda}{2\eta^{*}}\bigg{)}\bigg{)}\end{split}  \tag{19}\]  
where \(\eta=u+\frac{L(\beta-\delta)}{\gamma}\),  
\[ \begin{split}\Xi^{*}=&(2a)^{L}\sum_{k_{1}=0}^{m_{t }-1}\frac{(1-m_{s})_{k_{1}}(-\delta)^{k_{1}}}{\Gamma(1+k_{1})^{2}}\prod_{\rho= 2}^{L}\bigg{(}\sum_{k_{\rho}=0}^{m_{t}-1}\frac{(1-m_{s})_{k_{\rho}}(-\delta) ^{k_{\rho}}}{\Gamma(1+k_{\rho})^{2}}\\ &\sum_{l_{2\rho-3}=0}^{2\big{(}\sum_{i=1}^{L-1}k_{i}+\sum_{i=1}^ {L-2}m_{i}\big{)}+2\rho-3}\bigg{(}2\big{(}\sum_{i=1}^{L-1}k_{i}+\sum_{i=1}^{L- 2}m_{i}\big{)}+2\rho-3\bigg{)}\sum_{l_{2\rho-2}=0}^{2k_{\rho}+1}\bigg{(}\frac{ 2k_{\rho}+1}{t_{2\rho-2}}\bigg{)}\\ &\frac{(-1)^{l_{2\rho-3}}+(-1)^{l_{2\rho-2}}}{2}\bigg{(}\frac{1} {2}\bigg{)}^{2\big{(}\sum_{i=1}^{L}k_{i}+\sum_{i=1}^{L-2}m_{i}\big{)}+2\rho-1} \\ &\sum_{m_{\rho-1}=0}^{\infty}\frac{\Gamma\big{(}\frac{l_{2\rho- 3}+l_{2\rho-2}+1}{2}\big{)}}{\Gamma\big{(}\frac{l_{2\rho-3}+l_{2\rho-2}+3}{2} +m_{\rho-1}\bigg{)}}\bigg{(}\frac{\beta-\delta}{2}\bigg{)}^{m_{\rho-1}}\bigg{)} \end{split}  \tag{20}\]  
To the authors' knowledge, the latter closed-form result is new and has not been reported elsewhere in the literature. Consequentially, the closed-form expression of \(P_{d}\) for the proposed SS_DSC system is obtained. However, the \(P_{f}\) is independent of SNR, which leads to the same \(P_{f}^{L}\) for \(i=1,2,\ldots,L\) as given by (11). Figure 3: Analytical and simulated PDFs of the EGC scheme in SR fading links.
## Results and Discussion
### Selection of SPME Arrow Fibers
setidens_ Nakai. The relative standard deviation (RSD, %) and standard deviation of the RSD were evaluated for the identified volatile compounds using each of the four SPME Arrows following detection with GC-MS. The results are shown in Figure 3. All four SPME Arrows presented with a low average RSD ranging from 2.01% to 3.76%. These experiments indicated that the CWR/PDMS SPME Arrow is the most suitable to analyze volatile compounds from _C. setidens_ Nakai because it covered a wider range of polarity and showed good reproducibility.
## 3 Inversion of Mechanical Parameters of Rock Mass by Orthogonal Test
### Analysis of Inversion Results  
As shown in Figure 2 and Table 4, respectively, the test results were analyzed by range analysis and variance analysis. From the range analysis curve, it can be seen that the curve fluctuations of factors C1 and \(\Phi\)1 were the largest, indicating that these two factors had the greatest influence on the surface subsidence, while the curve fluctuation of factor G3 was the smallest, indicating that this factor had the least influence on the surface subsidence. According to the degree of curve fluctuation and the average maximum surface subsidence value corresponding to each level, the major and minor order of each factor's influence on surface subsidence and the preferred level can be obtained. However, test errors inevitably exist in the test, and range analysis cannot estimate the magnitude of the test error, so further analysis of the test results is needed, and variance analysis was performed. According to the variance analysis table, when the significance level is 0.05, the factors G1, C1, and \(\Phi\)1 are significant factors, that is, the influence on the surface subsidence is significant, and other factors are non-significant. The optimal level should be selected for the significant factors, while the non-significant factors can be selected according to the actual situation. Combining range analysis and variance analysis, the optimal level combination of various factors is finally obtained, that is, the best mechanical parameters of each rock mass, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Sum of Square**} & \multicolumn{2}{c}{**Degree of**} & \multicolumn{2}{c}{**Mean Square**} & \multirow{2}{*}{**F Value**} & \multirow{2}{*}{**Critical Value**} & \multicolumn{2}{c}{**Significance of**} \\  & & & **Freedom** & & & & **Factors** \\ \hline K1 & 0.1546 & 2 & 0.07729 & 15.457 & 19 & NO \\ G1 & 0.2239 & 2 & 0.11194 & 22.386 & 19 & YES \\ C1 & 0.7058 & 2 & 0.35288 & 70.571 & 19 & YES \\ \(\Phi\)1 &
## 1 Introduction
Previous study has shown the concentrations and the distribution of selected elements (including PTEs) in the soil and river sediments of the Baoshan area, based on the data from the 1: 250,000 scale National Geochemistry Survey of Land Quality project (NGSLQ) and archived river sediment samples collected and analyzed in the Regional Geochemistry-National Reconnaissance (RGNR) program based in this area (6220 km\({}^{2}\)) [54]. The distribution of PTEs in surface and deep soil are mainly influenced by natural factors (basic igneous rocks, carbonate rocks, and mineralization), while the surface soil near mining areas is aggravated by mining related activities in the Baoshan area [54; 55]. The environmental ecological risks of the soil and the human health risks connected with PTEs, as well as these risks' spatial distribution, have not been designated in the Baoshan area. It is critical to analyze environmental ecological and health risks and outline the risk distribution, for local and national authorities and environmental protection agencies to make environmental management decisions. The evaluated results may provide a useful reference for areas with a similar geological background or mineral resource conditions. The aims of this research were to: (1) evaluate PTE (As, Cd, Cr, Hg, Pb, Cu, Ni, and Zn) pollution levels in the soil; (2) assess the ecological risk of PTEs in soil and their health risks to humans; (3) map the risk level distribution to allow direct appraisal; and (4) make recommendations to authorities and environmental protection agencies.
## 3 Performance Analysis  
The PPM system has two demodulation methods: the suboptimal demodulation method and optimal demodulation method. The bit error performance of the two demodulation methods is analyzed below. In order to facilitate the analysis and without losing generality, we make the following assumptions in the subsequent analysis: the system has been synchronized well and the pulse signal is uniformly distributed in the possible time slot. According to the analysis in Section 2, after receiving the signal, the suboptimal PPM receiver and the optimal PPM receiver first recover the pulse signal through envelope detection. The noise envelope sampling without a signal follows the Rayleigh distribution,  
\[p(u_{N})=\frac{u_{N}}{\sigma^{2}}\exp\left(-\frac{u_{N}^{2}}{\sigma^{2}} \right)\,, \tag{2}\]  
and the envelope sampling with a signal follows the Rician distribution,  
\[p(u_{s})=\frac{u_{S}}{\sigma^{2}}\exp\left(-\frac{u_{S}^{2}+A^{2}}{2\sigma^{2} }\right)I_{0}\left(\frac{Au_{S}}{\sigma^{2}}\right)\,. \tag{3}\]  
where \(u_{N}\) is the noise slot envelope sampling value, \(u_{S}\) is the signal pulse slot envelope sampling value, \(\sigma^{2}\) is the covariance of white Gaussian noise, \(A\) is the amplitude of the pulse signal and \(I_{0}(\cdot)\) is the zero-order Bessel function.
## 5 The Estimation of the Balance Point  
Before the impact the motion variables have value \(\gamma^{-},\omega^{-},0,0,0\) and after \(\gamma^{+}\), \(\omega^{+},u_{1}^{+},u_{2}^{+},u_{3}^{+}\). The total energy and the projection on the vertical axis of the angular momentum, \(K^{\gamma}\) (the notation of [4] is maintained, even if it is shown in Appendix A that in general both speeds are present in this projection, and here an explicit dependency on \(\gamma\) is no more needed), are constant before and after the impact, however, they have a reduction during the impact. To simplify the computations and to avoid spurious solutions, the next procedure is started after the pendulum reaches the vertical position and \(\theta>0\). Let us say that at time \(t_{0}\) the state variables assume the values \(\theta_{20},\gamma_{0},\theta_{0},\omega_{0}\), the total energy \(T_{0}\), and the moment on the vertical axis \(K^{\gamma}{}_{0}\) (these last two values are the same, also, at the unknown instant of the impact \(t^{-}\)). This gives the first equation, linking all state variables at the pre-impact. \[T_{0}=T(\theta_{z}{}^{-},\gamma^{-},\theta^{-},\omega^{-}) \tag{21}\]  
At the impact the swing foot, indicated with the point B, touches the ground. The vertical coordinate of \(B\) offers the second equation, linking the pre-impact angle \(\theta^{-}\) to \(\alpha\) and \(\alpha_{z}\)  
\[B_{z}(\theta^{-},\alpha,\alpha_{z})=0 \tag{22}\]  
The constant momentum \(k^{\gamma}\) offers the third equation, linking \(\gamma^{-}\) to the other pre-impact motion variable  
\[k^{\gamma}{}_{0}=k^{\gamma}(\gamma^{-},\theta^{-},\omega^{-}) \tag{23}\]  
Switching the pivot foot after the impact, the relationship between the angles \(\theta_{z}{}^{+},\theta^{+}\) of the new pivot leg from \(\theta_{z}{}^{-},\theta^{-},\alpha,\alpha_{z}\) is obtained equating the three projections, with respect to the inertial axes, of swing and support legs (i.e., the swing leg becomes the new support leg)  
\[SU(\theta_{z}{}^{+},\theta^{+})=SW(\theta_{z}{}^{-},\theta^{-},\alpha,\alpha _{z}) \tag{24}\]  
The solution of the impact equation (performed symbolically) (20) gives the motion variables after the impact, hence the total energy and the angular momentum \(k^{\gamma}\). The total energy after the impact can be evaluated before the switching of the pivot foot so it does not require the value of \(\theta^{+}\) and \(\theta_{z}^{+}\) after switching. The angular momentum is computed on the new pivot point, so it requires the new value of \(\theta^{+}\) and \(\theta_{z}^{+}\) after the switching. This gives the third and fourth equations. \[ \begin{array}{l}TE^{+}=TE(\theta_{z}{}^{-},\theta^{-},\gamma^{+},\omega^{+},u_{1}+,u_{2}+,u_{3}+)\\ k^{\gamma}{}^{+}=k^{\gamma}(\theta_{z}{}^{+},\gamma^{+},\theta^{+},\omega^{+} )\end{array}  \tag{25}\]  
Figure 2: The kinematics of the joints—(**a**) forward/bacward motion of the flying leg, (**b**) side shuffle.
## 3 Results
### Participant Characteristics  
Recruitment ceased after thirty-six participants were enrolled (Figure 1). During the study, eleven participants discontinued for various reasons: one participant withdrew due to dislike of the test meal; three participants reported being unable to tolerate repeated blood sampling; two participants were unable to continue due to academic obligations; four participants discontinued participation due to a seasonal viral infection; and one participant was unable to attend the clinic on the scheduled study days. As a result, twenty-five participants completed all study procedures and were included in the final analysis. However, one participant was excluded from the statistical analysis due to hyperinsulinemia observed in their postprandial insulin values.  
Table 3 presents the baseline general characteristics of the 24 study participants, including demographic, anthropometric, and lifestyle variables. The sample consisted of 11 men and 13 women, with a mean age of 31.23 \(\pm\) 9.8 years. Anthropometric data showed an average weight of 73.48 \(\pm\) 19.24 kg, height of 171.24 \(\pm\) 9.45 cm, and body mass index (BMI) of 24.71 \(\pm\) 4.76 kg/m\({}^{2}\). Additional body composition parameters included fat percentage (25.59 \(\pm\) 6.08%), muscle mass (51.43 \(\pm\) 12.28 kg), and total body water percentage (51.86 \(\pm\) 3.69%). Basal metabolic rate averaged 1633.29 \(\pm\) 368.37 kcal/day, while the waist-to-hip ratio (WHR) was 0.80 \(\pm\) 0.09. Regarding lifestyle habits, 9 participants reported smokers and 15 non-smokers; physical activity levels varied, with 14 classified as low, 6 as medium, and 4 as high.  
Given the acute nature of the intervention and the relatively short study duration (-14 days), which included three visits separated by one-week washout periods, post-intervention anthropometric reassessments were not performed. Significant changes in parameters such as body weight or composition are unlikely to occur within this time-frame, especially in response to a single administration of the test product. Therefore, our analysis focused on acute metabolic and biochemical markers to assess the effects of the interventions.  
\begin{table}
\begin{tabular}{c c c} \hline
**Attribute** & **0.5\% Concentration** & **1\% Concentration** \\ \hline Taste & 7.11 \(\pm\) 1.33 a & 6.13 \(\pm\) 1.61 b \\ Aroma & 6.87 \(\pm\) 1.59 a & 6.37 \(\pm\) 1.56 a \\ Aftertaste & 7.32 \(\pm\) 1.45 a & 5.97 \(\pm\) 1.63 b \\ Color & 5.55 \(\pm\) 1.75 a & 6.66 \(\pm\) 1.16 b \\ Overall Liking & 7.53 \(\pm\) 1.48 a & 6.24 \(\pm\) 1.86 b \\ \hline \end{tabular} Values are presented as mean scores \(\pm\) standard deviation. Different superscript letters (°, b) indicate significant differences between the 0.5% and 1% olive leaf tea samples (\(p\) < 0.05) based on sensory evaluation.  
\end{table}
Table 2: Sensory evaluation results for olive leaf tea samples (0.5% and 1%) based on color, aroma, taste, aftertaste, and overall liking scores.
## 6 Conclusions  
This paper presented a GNSS-R model, which was validated with CYGNSS DDMs over two areas. The peak reflectivity of four tracks over the validation sites was studied. Additionally, three full DDMs from the studied tracks were presented with WAF projection maps for some of the DDM cells. The IGOT model results matched CYGNSS with good accuracy under most conditions, in both peak reflectivity and the DDM shape. For the studied tracks, the average error in peak reflectivity was between 1 dB and 4 dB. The model of this paper increases the fidelity of GNSS-R signal modeling for land applications.  
Conceptualization and methodology, A.M., J.D.C., E.H. and M.M.; software, A.M., J.D.C. and E.H.; validation, A.M.; resources, M.M.; writing, A.M., J.D.C., E.H. and M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
This work was performed at the University of Southern California, funded in part by NASA grant number 80 NSSC18K0704 and in part by the University of Southern California and under Contract NNL13 AQ00C with the University of Michigan.  
In situ soil moisture data used in this paper are available at SoilSCAPE website (www.soilscape.usc.edu) (accessed on 15 January 2023).  
The authors thank the SoilSCAPE installation/maintenance teams in both the Jornada and Walnut Gulch sites for their efforts in installing and maintaining the in situ soil moisture sensors and collecting surface roughness measurements. Furthermore, we would like to thank the PO.DAAC team for making the CYGNSS data publicly available.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# The Impact of Orthodontic Treatment on Pre-Existing Gingival Recessions: A Retrospective Study
## 3 Results  
### Baseline Data  
Fifty-one (5.1%) patients of the 993 assessed treated by two orthodontists during 12 years had at least one vestibular GR before OT. Of those, 37 (72.5%) patients, eight males (21.6%) and 29 females (78.4%) were included in the final analysis according to the inclusion criteria. The mean age of the included patients was 28.7 (95% CI: 26.01, 31.40, range 13-51) years, and most of them were adults (n = 33). Orthodontic treatment lasted for a mean of 21.4 (95% CI: 18.99, 23.82) months. The characteristics of the included patients are presented in Table 1.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Variable**} \\ \hline Age at T0 (years), & \\ Mean (SD): & 28.7 (8.1) \\ \textless{18 n (\%)} & 4 (10.8) \\ \textgreater{18 n (\%)} & 33 (89.2) \\ \hline Gender, n (\%) & \\ male & 8 (21.6) \\ female & 29 (78.4) \\ \hline Duration of orthodontic treatment, (months) & \\ Mean (SD) & 21.41 (7.23) \\ \textless{18 months, n (\%)} & 14 (37.8) \\ \textgreater{18 months, n (\%)} & 23 (62.2) \\ \hline Gingival biotype, n (\%) & \\ normal/thick & 22 (59.5) \\ thin & 15 (40.5) \\ \hline Overjet, n (\%) & \\ \textless{4 mm} & 30 (81.1) \\ \textgreater{4 mm} & 7 (18.9) \\ \hline Overbite, n (\%) & \\ \textless{1 mm} & 6 (16.2) \\ \textless{1-3 mm} & 25 (67.6) \\ \textgreater{3 mm} & 6 (16.2) \\ \hline Angle class of canines at T0, n (\%) & \\ I & 14 (37.8) \\ II & 14 (37.8) \\ III & 9 (24.4) \\ \hline Pre-treatment crowding, n (\%) & 28 (75.7) \\ Crowding maxillary arch, n (\%) & 4 (10.8) \\ Crowding mandibular arch, n (\%) & 9 (24.3) \\ Crowding both arches, n (\%) & 15 (40.5) \\ No pre-treatment crowding, n (\%) & 9 (24.3) \\ \hline Extraction treatment, n (\%) & \\ Maximillary dental arch & 2 (5.4) \\ Mandibular dental arch & 2 (5.4) \\ Both arches & 15 (40.5) \\ Non-extraction & 18 (48.7) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of included patients at baseline.
## 3 Evaluation Method
### Datasets  
We evaluate the three decision-map techniques (DBM, SDBM, and DV) using both a synthetic dataset and real-world datasets. The real-world datasets are chosen following the same criteria as in [22]. They are chosen because they are openly accessible, representative of different types of data (e.g., time series, image, text), and have different numbers of classes and dimensions. The datasets used are listed below and are summarized in Table 1.  
**Synthetic Blobs**: This is a synthetic dataset with five classes, 100 dimensions, and 1500 samples. All data points in a blob (following a Gaussian distribution) have the same label. Therefore, it is an easily classifiable dataset, for which we expect all three decision-map techniques to produce good-quality metric values.  
**Human Activity Recognition (HAR)**[42]: This is a dataset with time-series data from smartphone sensors. The goal is to classify the type of physical activity (e.g., walking, climbing stairs) performed by the user. This dataset has 10,299 samples, 561 dimensions, and six classes.  
**MNIST**[43]: This dataset is a collection of handwritten digits that is commonly used for training various image classification systems. The dataset contains 60,000 training images and 10,000 testing images. Each image is a 28 \(\times\) 28 grayscale image associated with a label from 0 to 9. The dataset is downsized to 10 k samples for all our experiments.  
**FashionMNIST**[44]: This is a dataset containing images of Zalando items, spanning 10 fashion categories. It is the same size as MNIST and is downsized to 10 k samples for our experiments.  
**Reuters Newswire Dataset**[45]: This dataset contains 8432 samples of news report documents, from which 5000 features were extracted using the standard TF-IDF [46] text processing method. From the full dataset, we only use the six most frequent classes.