esa-sceva/satcom-chunk-collection huggingface dataset:
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 3 Data Collection
### Ground Truth  
There is no existing mis-configuration label of the scholars' author profiles. Due to the strict requirements of accuracy and veracity, we do not resort to any automatic tool, but leverage a manual labeling system to obtain the ground truth. Although our dataset is limited to the author profile pages on Google Scholar, we resort to some extra information during the manual labeling, such as the papers' abstracts and the homepages of the authors. We randomly select 3000 scholars in the dataset and recruit three students majoring in computer science to label each scholar's author profile. In order to guarantee the reliability of the labeling result, each scholar is labeled by at least two students. If two students give conflicting labels to an ambiguous instance, the third student will check the scholar's author profile and decide the result finally. To make the evaluation criteria of labeling as consistent as possible, we establish a workflow for the manual annotation as follows and train the participated students to follow it:  
* Check whether the papers' titles and the publication venues are related to the scholar's claimed research labels. If not, and there is no other papers in the list have the same co-authors as this paper, the paper may be wrongly claimed.
* Pay attention to the scholar's full name and the name abbreviation of each paper in the paper list. Since Google Scholar provides paper recommendations according to the scholar's last name and the initial of her first name, there is a high possibility that a scholar with a common name has a mis-configured author profile.
* Click into the detailed page of each paper in the paper list, which contains the authors, abstract, publication information and the citations. Check whether the scholar is included in the author list. However, scholars may change their full name, especially for female scholars who may change their surname after marriage. Therefore, if the scholar's full name does not exist in the author list, the author profile might still be reliable.
* Check the publication year of each paper. If the time interval between two papers is too long (e.g., the time span more than a century), the possibility of the author profile being reliable is very small. In our labeled dataset, there are 33 author profiles with two papers that are published over 100 years. However, considering there may be some intrinsic mistakes in the database of Google Scholar, some of them might still be labeled as reliable.
* Check the homepage of the scholar if she puts it on the author profile and compare the publications on her homepage with the claimed paper list.  
According to the results of the labeling, among the selected 3000 scholars' profiles, 90.5% (2715) of them are reliable and 9.5% (285) of them are mis-configured. Limited by the heavy workload of labeling, we get the ground truth of a representative subset of the dataset. The complete dataset provides more comprehensive features, which can further improve the performance of our detection.
###### Abstract  
The liquid organic hydrogen carriers (LOHC) are aromatic molecules, which can be considered as an attractive option for the storage and transport of hydrogen. A considerable amount of hydrogen up to 7-8% wt. can be loaded and unloaded with a reversible chemical reaction. Substituted quinolines and pyridines are available from petroleum, coal processing, and wood preservation, or they can be synthesized from aniline. Quinolines and pyridines can be considered as potential LOHC systems, provided they have favorable thermodynamic properties, which were the focus of this current study. The absolute vapor pressures of methyl-quinolines were measured using the transpiration method. The standard molar enthalpies of vaporization of alkyl-substituted quinolines and pyridines were derived from the vapor pressure temperature dependencies. Thermodynamic data on vaporization and formation enthalpies available in the literature were collected, evaluated, and combined with our own experimental results. The theoretical standard molar gas-phase enthalpies of formation of quinolines and pyridines, calculated using the quantum-chemical G4 methods, agreed well with the evaluated experimental data. Reliable standard molar enthalpies of formation in the liquid phase were derived by combining high-level quantum chemistry values of gas-phase enthalpies of formation with experimentally determined enthalpies of vaporization. The liquid-phase hydrogenation/dehydrogen reaction enthalpies of alkyl-substituted pyridines and quinolines were calculated and compared with the data for other potential liquid organic hydrogen carriers. The comparatively low enthalpies of reaction make these heteroaromatics a seminal LOHC system.  
quinoline derivatives; LOHC; vapor pressure measurements; enthalpy of vaporization; enthalpy of formation; quantum-chemical calculations
## 3 Results
### Evaluation and Application of the EMRe Models under all sky Conditions during 2013-2016
#### 3.2.3 Daytime and Nighttime Re Simulations Using Re (LDR) Method  
As an alternative, daytime and nighttime Re values from 22 May 2013 to 31 December 2016 were computed using the 3-factor and 2-factor Re (LR) and Re (DR) methods (Equations (2) and (3)), respectively. This Re (LDR) method was applied to compute hourly Re in a whole day. The statistical metrics for the averages of the hourly sum of Re are shown in Tables 11 and 12 and Figure 10. The ratios of the calculated to the observed Re values for the daily, monthly, and annual sums of Re were similar to that of the hourly sum (not given for saving space). Generally, the calculated Re values underestimated the observations by about 13% for the mean hourly and annual sums in 4 years.
## 2 Materials and Methods
### Statistical Analysis  
All data are expressed as the mean \(\pm\) standard deviation (SD) following a test for normality of distribution (Kolmogorov-Smirnov). For data that violated the assumptions of parametric analysis (i.e., equality of variance and normality of distribution) non-parametric examination was carried out, and these data were expressed as the median (interquartile range: IQR). For comparing the differences in the measured variables before and after the intervention, the non-parametric Wilcoxon test and parametric paired-samples t-test were performed. The effect sizes (ESs) for the Wilcoxon test were estimated according to [PERSON]'s (1991) equation (r = Z/\(\surd\)N) [35]. The ESs for the paired t-test were estimated according to [PERSON] (1991) and [PERSON]'s (2005) equation (r = \(\surd\)(t2/(t2 + df)) [35]. Nominal and ordinal data were analyzed by the Chi-square test (\(\chi^{2}\)). The ESs were estimated and reported with the [PERSON]'s V value for nominal and the Gamma value for ordinal data. The ESs were interpreted according to [PERSON]'s criteria. A value of r = 0.1 was considered a small effect size, 0.3 represented a medium effect size, and 0.5 represented a large effect size [36]. A value of [PERSON]'s V = 0.1 was considered a small effect size, 0.3 represented a medium effect size and 0.5 represented a large effect size [36]. The Gamma value (\(\hat{\gamma}\)) is used to test whether there is an association between ordinal variables [37]. The Gamma value ranges from \(-\)1.00 to 1.00, where a Gamma value of 0.00 represents no association; a Gamma value of 1.00 represents a perfect positive  
The ankle prone passive dorsiflexion with flexed knees demonstrated a statistically significant improvement, \(\chi^{2}(2)=13.019\), \(p=0.005\), V = 0.285, following the implementation of the intervention programs (Figure 2). However, there was no statistically significant improvement in the ankle prone passive dorsiflexion with extended knee, although the number of the participants/players who turned to normal cases increased from 57 to 67 following the implementation of the intervention programs, \(\chi^{2}(3)=5.789\), \(p=0.122\), V = 0.190 (Figure 2). The Deep Squat variable did not demonstrate a statistically significant improvement (\(\chi^{2}(2)=5.381\), \(p=0.068\), \(\hat{\gamma}=0.420\)) following the application of the intervention programs, despite the fact that the number of participants who reached normal cases increased from 57 to 69 after the intervention (Figure 3). Following the implementation of the intervention programs, the Hurdle Test results demonstrated no statistically significant improvement, for the right side, \(\chi^{2}(2)=0.432\), \(p=0.806\), \(\hat{\gamma}=-0.092\), or for the left side, \(\chi^{2}(2)=1.927\), \(p=0.382\), \(\hat{\gamma}=0.113\) (Figure 4).
## 3 Results and Discussion
### Effect of nWTRs on As Speciation in Biosolids-Amended Soil

Table 3: Arsenic speciation (%) in soil solution of the calcareous soil that was amended with biosolids and nanoscale WTRs, using the MINEQL+ modeling calculation. and sediment types and responds to hydrologic conditions to identify areas susceptible to arsenic contamination, revealed that most arsenic-loaded sediments accumulated in the vicinity of a reservoir inlet, and arsenic in reservoir beds has a higher leaching potential than that from agricultural land and stream beds. Arsenic deposited at the bottom of reservoirs had higher mobility than that in the other soils and sediments, and arsenic leaching was closely associated with the acidity of water. Arsenic speciation analyses in the soils were also directly measured using XANES, a non-destructive analytical method (Table 4 and Figure 4) that determine the total chemical forms of As in soil sample. The XANES of control soil sample show that the dominant As chemical form was sodium arsenate of 74.8%, followed by arsenic pentoxide of 25.2%. Moreover, The XANES of original nWTRs show that the dominant As chemical form was sodium arsenate of 28.8%, followed by cacodylic acid of 14.5%, and arsenic pentoxide (56.7%). On the contrary, in the biosolids-amended soil, arsenic pentoxide was 61.5%, sodium arsenate was 22.4%, cacodylic acid was 14.9%, and arsenic sulfide was 1.2% (Table 4). Similarly, the application of 1% nWTRs to the biosolids-amended soil changed the percentage of As species such as arsenic pentoxide was 68.0%, sodium arsenate was 10.0%, and cacodylic acid was 22.0% (Table 4). [PERSON] et al. [43] indicated that the XANES is efficient in determining arsenic oxidation states of the sediments with low arsenic contents and multiple arsenic species. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**As Compounds** & \begin{tabular}{c} **Control** \\ **Soil** \\ \end{tabular} & \begin{tabular}{c} **Original** \\ **nWTRs** \\ \end{tabular} & \begin{tabular}{c} **Biosolids-Treated** \\ **Soil** \\ \end{tabular} & \begin{tabular}{c} **Biosolids- and** \\ **nWTR-Treated Soil** \\ \end{tabular} \\ \hline Sodium arsenate & 74.77 & 28.80 & 22.42 & 10.00 \\ (HAsNa2O4) & \begin{tabular}{c} \\ \end{tabular} & 14.50 & 14.88 & 22.00 \\ \
\begin{tabular}{c} [CHy2 As2O4H] \\ \end{tabular} & 25.23 & 56.70 & 61.48 & 68.00 \\ Arsenic sulfide (As2 S2) &
\begin{tabular}{c} \\ \end{tabular} & 1.22 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Speciation fraction (%) of arsenic (As) in the soils that were treated with biosolids (3%) and nanoscale WTRs (1%) using X-ray absorption near-edge structure spectroscopy (XANES) analysis. Figure 4: Spectra of arsenic (As) chemical components in the soil that was treated with biosolids (3%) and nanoscale drinking water treatment residuals (1% nWTRs), along with the As standards and the fit line. A: Control soil; B: Original nWTRs; C: Biosolids-treated soil; D: Biosolids- and nWTRs-treated soil.
## 3 Results and Discussion
### Regeneration Properties  
The regeneration process is considered an important step for the feasibility of an adsorbent and it is considered as important issue that needs to be addressed, otherwise, the used materials would create a new round of environmental problems with the disposal issues. A number of regeneration methods were summarized and discussed in recent reviews [23; 75]. In this study, a regeneration method was adopted with an efficient, eco-friendly, and easy to operate process, to reuse the spent waste bricks [19; 76]. The treatment of fresh spent waste bricks was done after the first removal of basic blue 41 by a solution containing cobalt cations and an oxidant oxone. The Co\({}^{2+}\) cations served as the homogeneous catalyst. The oxidant oxone was used to degrade the removed dye [77]. In a previous study, the concentrations initially used affected the removal efficiency during the regeneration process.  
In this case, two initial concentrations (50 ppm and 200 ppm) were used to study the regeneration process. The obtained data are presented in Figure 9A,A', and indicate that the removal efficiency of BB-Med-WB was maintained after four cycles of regeneration, with a small reduction of 10%, compared to the fresh used Med-WB, using a BB-41 C\({}_{\text{i}}\) of 50 ppm. In the case of BB-Jed-WB, it behaved differently and the removal percentage dropped significantly, with a decrease of 25% in the fourth cycle. After 6 cycles of regeneration, the BB-Med-WB exhibited a removal percentage of 80 % and 30% for Jed-WB. Using a high C\({}_{\text{i}}\) value of 200 mg/L (Figure 9A'), the used waste bricks lost their efficiency after the third recycle, with a reduction of 15% and 36% for Med-WB and Jed-WB, respectively. Further regeneration led a continuous decrease of the removal efficiency, up to 50% and 80% of their initial values. This reduction could indicate that the adsorption sites lost their activities due to the non-decomposition of the removed BB-41 cations on the surface of the WB materials during the regeneration process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples** & **q\({}_{\text{max}}\) (mg/g)** & **Reference** \\ \hline Med-WB & 30.03 & [33] \\ Jed-WB & 16.80 & This study \\ B-Med-WB & 43.50 & [33] \\ B-Jed-WB & 34.36 & This study \\ Kaolinite & 6.20 & [73] \\ HydroxyModalite & 39.37 & [73] \\ Zeolite –X & 17.69 & [73] \\ Zeolite Y & 26.80 & [73] \\ Local Clay & 73.00 & [19] \\ Acid-activated local clays & 50.34 & [19] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the maximum removed amount (q\({}_{\text{max}}\)) of BB-41 by different materials.
## 2 Dynamic Model of FPSE
From the isothermal model assumption, the hot and cold heat exchanger temperatures of \(T_{H}\) and \(T_{K}\) are equal to the expansion and compression space temperatures of \(T_{E}\) and \(T_{C}\), respectively. The regenerator space temperature is assumed the logarithmic mean of \(T_{H}\) and \(T_{K}\), given as \(T_{R}=T_{H}\)-\(T_{K}\)/ln(\(T_{H}\)/\(T_{K}\)). On the other hand, the bounce space pressure is defined considering the gas spring effect:  
\[P_{B}=P_{c}\bigg{(}\frac{V_{B0}}{V_{B}}\bigg{)}^{\gamma} \tag{3}\]  
where \(P_{c}\) is the charge pressure of the engine, and \(\gamma\) is the specific heat ratio of the working gas. The expansion, compression, and bounce space volumes are geometrically related with the PP and DP displacements. The expansion space is only a function of DP, while the compression space is related to both PP and DP. The bounce space volume is assumed to be only affected by the PP motion because the DP rod area is very small. The relations are given as follows:  
\[ \begin{array}{c}V_{E}=V_{E0}-A_{d}x_{d}\\ V_{C}=V_{C0}-\big{(}A_{p}-A_{r}\big{)}x_{p}+(A_{d}-A_{r})x_{d}\\ V_{B}=V_{B0}+\big{(}A_{p}-A_{r}\big{)}x_{p}\end{array}  \tag{4}\]  
where \(A\) is the cross-sectional area, and the subscript \(0\) denotes the static equilibrium state. \(P\) and \(P_{B}\) are the nonlinear functions of piston displacements when Equation (4) is inserted into Equations (2) and (3). The pressures are linearized using the binomial expansion, as follows:  
\[P\approx P_{c}\Bigg{\{}1+\frac{A_{d}x_{d}}{T_{E}S}+\frac{(A_{p}-A_{r})x_{p}-( A_{d}-A_{r})x_{d}}{T_{C}S}\Bigg{\}} \tag{5}\]  
\[P_{B  
where  
\[K_{pp}=\frac{k_{p}+P_{c}(A_{p}-A_{r})^{2}(1/T_{C}S+\gamma/V_{\text{PQ}})}{m_{p}}\]  
\[K_{pd}=\frac{P_{c}\big{(}A_{p}-A_{r}\big{)}(A_{d}T_{C}-(A_{d}-A_{r})/T_{C}\big{)}} {m_{p}S}\]  
\[K_{dp}=\frac{P_{c}A_{r}\big{(}A_{p}-A_{r}\big{)}(1/T_{C}S+\gamma/V_{\text{PQ}})} {m_{d}}\]  
\[K_{dd}=\frac{k_{d}+(P_{c}A_{d}A_{r})/T_{C}S+\gamma/V_{\text{PQ}})}{m_{d}}\]  
\[\text{and }C_{pp}=\frac{c_{p}}{m_{p}},C_{pd}=0,C_{dp}=0,C_{dd}=\frac{c_{d}}{m_{d}}.\]  
Consequently, the EOMs change from a forced vibration system to a free vibration system owing to the elimination of the nonlinearity in the pressure terms, implying that the working frequency is the same as the system natural frequency. In addition, the system stiffness matrix includes the effects of pressure variations, known as gas spring effect, as well as the metal spring element. The pressure-related stiffness changes depending on the values of charge pressure and operating temperatures. This implies that the system natural frequency, and thus the operating frequency as well, change under varying charge pressures and temperatures. On the other hand, the system damping matrix is shown to be constant under varying pressure and temperature conditions. Note that this model does not consider changes in the damping coefficient due to thermodynamic properties of working gas itself.
## 2 System Model
### NOMA in Small-cells
#### 2.1.1 NOMA Receiver with Ideal SIC  
In the ideal SIC case, it is assumed that the SCUs have perfect knowledge of the signal information of SEUs, and the channel effects are not considered. This means that the signal information of the SEUs is perfectly cancelled at the SCU by performing SIC. If the SCU user, served by the \(b\)th SBS, is UE-\(m\) in the \(k\) paired UEs, then its received signal, by performing perfect SIC, is given by using (5):  
\[s_{b,m}=\left\lfloor\frac{y_{b,m}-\left(\sum_{k=m+1}^{N}s_{b,k}\sqrt{P_{b,k}} \right)}{\sqrt{P_{m}}}\right\rfloor, \tag{9}\]  
where, \(\lfloor.\rfloor\) denotes the demodulation and detection of the received signal. In the case of two paired UEs, the SCU is UE-1. Thus, the received signal at UE-1 with the ideal SIC in the \(b\)th SBS is given by using (8) and (9):  
\[s_{b,1}=\left\lfloor\frac{y_{b,1}-s_{b,2}\sqrt{P_{b,2}}}{\sqrt{P_{b,1}}}\right\rfloor. \tag{10}\]
# Electromagnetic Interference Shielding Anisotropy of Unidirectional CFRP Composites
## 2 Materials and Methods
### EMI Shielding Measurement  
The EMI shielding performance of the CFRP composites was test by the free-space measurement method. The specially designed free-space measurement system (KEYCOM RTS03, KEYCOM Corporation, Tokyo, Japan) consists of two antennas, two lens and a sample stage, as shown in Figure 1. The electric field of the incident EM waves was linearly polarized. Figure 2 shows the distribution of the electric field inside the coaxial tube and the specimen dimension used for coaxial transmission line measurement (KEYCOM S-GPC7, KEYCOM Corporation, Tokyo, Japan). The value of the scattering parameters (\(S_{21}\) or \(S_{12}\)) of the CFRP materials were tested by the vector network analyzer (Anritsu 37247D, Anritsu Corporation, Kanagawa, Japan). The attenuation of the EM wave was defined as the \(SE\), which can be calculated by the following equations:  
\[SE=10\log\biggl{(}\frac{P_{T}}{P_{I}}\biggr{)} \tag{1}\]  
\[SE=-10\log\biggl{(}\left|S_{21}\right|^{2}\biggr{)}=-10\log\biggl{(}\left|S_{ 12}\right|^{2}\biggr{)} \tag{2}\]  
where \(P_{T}\) is the transmitted power, \(P_{I}\) is the incident power, and the \(S_{12}\) (\(S_{21}\)) parameters refer to the transmission coefficients.
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 2 Capabilities
### SMAP-R Characteristics
#### 2.1.1. SMAP-R vs. CYGNSS  
First, we will compare the characteristics of SMAP-R to CYGNSS. It is useful to compare the spatial and temporal sampling from each platform. Figure 3 shows spatial distribution of the peak SNR of the GPS reflections captured by SMAP-R and CYGNSS over one day.  
Figure 3 clearly shows that the sampling from SMAP over one day is limited when compared to CYGNSS, the sampling from SMAP over one day is limited. Tracks measured by SMAP-R are shorter in length in part because its beamwidth limits the field of view and incidence angles to a small range of 37.3\({}^{\circ}\) to 42.7\({}^{\circ}\). Additionally, since the antenna rotates at 14.6 rpm, there are gaps of  
Figure 2: SMAP-R coverage observed for (**a**) 1 day (285 observations), (**b**) 15 days (7168 observations), (c) 1 month (14,449 observations), and (**d**) 2 months (30,500 observations).  
-25 km between SMAP-R observations along each track, one observation period every 4 s. There are some benefits to the high gain antenna. Notably, Figure 3 shows the SNR range obtained for SMAP-R (30 dB for 25 ms integration time) and the SNR range obtained from CYGNSS (25 dB for 1000 ms integration time). The two platforms observe similar ranges of SNR, but SMAP-R data are processed at shorter integrations times. Integration time impacts the spatial resolution of each measurement. For surfaces with dominant coherence reflections (e.g., smooth surfaces), such as lakes and/or sea ice, the collected scattered energy comes from an area of about 800 m \(\times\) 1 km. SMAP-R integrates the received signal over 25 ms, smearing the observation 180 m in the along-track direction. On the other hand, CYGNSS integrates the received signal over 1000 ms, elongating the observation 6 km in the along-track direction. Consequently, the spatial resolution possible from SMAP-R observations is better than that of CYGNSS for similar SNR.  
SMAP and CYGNSS are in different orbits, and therefore cover different latitudinal ranges. CYGNSS covers only the tropics, while SMAP-R provides global coverage, including the poles. SMAP-R therefore allows for studies of such sea ice characterization in the poles (both Antarctic and Arctic) or freeze/thaw analysis.  
Finally, the frequency and polarization are different between instruments. CYGNSS receives at GPS L1 band (frequency = 1575.42 MHz, wavelength = 19 cm) while SMAP-R receives at GPS L2C band (frequency = 1227.45 MHz, wavelength = 24.42 cm). CYGNSS measures in left-hand circularly polarization (LHCP) while SMAP measures in V-polarization and H-polarization.