esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Figure 2: Methods for photodetectors integrated with optical waveguides for interconnection applications **(a)** butt coupling **(b)** grating assisted directional coupler **(c)** evanescent-wave/travelling-wave coupling **(d)** reflective coupling. The directional coupler based waveguide tap and wavelength division multiplexing (WDM) photodetector array module, combining the optical waveguide, spectrometer and photodiode in one chip, are demonstrated in Figure 3(b) [15] and Figure 3(c) [16]. The monitoring function requires high-speed response to perform signal transition sensitive measurements such as eye statistics, Q-factor, electronic signal-to-noise ratio and distortions that occur within the eye due to dispersion and nonlinear effects [17]. A total-internal-reflection (TIR) mirror vertical coupling [12], applicable to single and double hetero structure waveguides, was demonstrated to have a 5-GHz bandwidth on GaAs based optical waveguides integrated with metal-semiconductor-metal (MSM) photodetectors within the 0.8-\(\upmu\)m wavelength range. Therefore, it was highly desirable to develop higher bandwidth for OC-192 applications using the monolithic integration of In\({}_{0.53}\)Ga\({}_{0.47}\)As photodetector devices operating in the 1.3 to 1.6 \(\upmu\)m wavelength range with AlGaAs/GaAs waveguides for optical signal routing and the potential for high-speed ASICs on GaAs substrates. The GaAs based optical waveguide was then proposed to couple light to the p-i-n In\({}_{0.53}\)Ga\({}_{0.47}\)As photodetector using an etched, vertically deflecting TIR mirror. A novel metal coplanar waveguide on top of the fill material in the TIR region could be further connected to the photodiode to form the high-speed interconnect level for optical and electrical interconnections, as shown in Figure 4.
# Electric Vehicle Penetration in Distribution Network: A Swedish Case Study
## 3 Results
### Scenario 1 Parameters: Number of Electric Vehicles--Charging Rate--Charging Profile  
According to the first scenario, the impact of the integration of EVs is investigated with the following parameters analyzed: the number of grid-connected vehicles, the level of charging rate, and the charging profile. The maximum power differences seen in Tables 3 and 4 are the maximum power consumption after available power have been subtracted via the transformers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**T261** & **Max Difference** & **T262** & **Max Difference** \\ \hline Balance excluding EVs & 0 kW & Balance excluding EVs & 0 kW \\ Balance 60–3.7 & \(-\)30 kW & Balance 60—3.7 & 0 kW \\ Balance 60—6.9 & \(-\)222 kW & Balance 60—6.9 & 0 kW \\ Balance 60—11 & \(-\)468 kW & Balance 60—11 & \(-\)104 kW \\ Balance 80—3.7 & \(-\)104 kW & Balance 80—3.7 & 0 kW \\ Balance 80—6.9 & \(-\)360 kW & Balance 80—6.9 & 0 kW \\ Balance 80—11 & \(-\)688 kW & Balance 80—11 & \(-\)324 kW \\ Balance 100—3.7 & \(-\)178 kW & Balance 100—3.7 & 0 kW \\ Balance 100—6.9 & \(-\)498 kW & Balance 100—6.9 & \(-\)134 kW \\ Balance 100—11 & \(-\)908 kW & Balance 100—11 & \(-\)544 kW \\ \hline
**T273** & **Max Difference** & **T422** & **Max Difference** \\ \hline Balance excluding EVs & 0 kW & Balance excluding EVs & 0 kW \\ Balance 40—3.7 & \(-\)35 kW & Balance 20—3.7 & \(-\)22 kW \\ Balance 40—6.9 & \(-\)163 kW & Balance 20—6.9 & \(-\)86 kW \\ Balance 40—11 & \(-\)327 kW & Balance 20—11 & \(-\)168 kW \\ Balance 60—3.7 & \(-\)109 kW & Balance 40—3.7 & \(-\)96 kW \\ Balance 60—6.9 & \(-\)301 kW & Balance 40—6.9 & \(-\)224 kW \\ Balance 60—11 & \(-\)547 kW & Balance 40—11 & \(-\)388 kW \\ Balance 80—3.7 & \(-\)183 kW & Balance 60—3.7 & \(-\)170 kW \\ Balance 80—6.9 & \(-\)439 kW & Balance 60—6.9 & \(-\)362 kW \\ Balance 80—11 & \(-\)767 kW & Balance 60—11 & \(-\)608 kW \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum power balance 3.7, 6.9, or 11 kW power—constant charging.  
Table 3 presents the maximum power balance with respect to the number of EVs under power--constant charging of 3.7, 6.9, and 11 kW. Table 4 shows how the power demands change when charging occurs only at night and presents the maximum power balance with respect to the number of EVs under night-time charging of 3.7, 6.9, and 11 kW. Most of the transformers can handle a smaller number of one-phase charged vehicles on a 16 A fuse. However, when customers want to have faster charging, the situation becomes complex because the maximum power difference increases. In particular, in cases where maximum penetration of EVs is inserted in combination with 6.9 and 11 kW charging rates, it seems unrealistic to expect a stable RES because the power balance is comparable to the transformer ratings. As such, either the transformers need to be replaced or solutions that are more flexible need to be examined.
## 2 Establishment of EPS Performance Evaluation System  
### Establishment Principle of Evaluation System  
When establishing an evaluation index system for an EPS based on its inherent characteristics and usage environment, the following principles should be adhered to:  
(1) Objectivity  
The establishment of an evaluation system needs to ensure the scientific validity of the selected indicators, and the authenticity and rigor of the indicator acquisition process. It should scientifically and comprehensively reflect the various factors that influence the design and usage process of the EPS, while also avoiding the interference of unnecessary indicators. Ultimately, it should construct an orderly, efficient, and systematic hierarchical structure that strives for the objectivity of research results.  
(2) Complementarity  
Evaluating the working characteristics and usage efficiency of an EPS can be gradually layered, from the general to the specific, by selecting the overall direction first and then gradually delving into the details, thus forming an organized and hierarchical structure. The levels should be divided based on the characteristics of the matter and their relationships, and each level should have specific factors, both interdependent and interrelated. At the same time, each level's system should complement one another and cooperate to form a whole.  
(3) Completeness  
When determining the various level factors in the EPS evaluation system, no factors that affect the performance of the EPS should be missed. At the same time, the division of the level factors should not be too fine, so as to prevent a situation in which there is a heavy workload and excessive and ineffective evaluation.  
(4) Stability  
When the system is established, the number and structure of levels should be changed as little as possible. If adjustments or improvements to the level system are necessary, they should be carefully considered, evaluated, and adjusted; this is to prevent the instability caused by frequent changes from leading to biased or erroneous results.
## 5 Conclusions  
Accurate and continuous ET and GPP products with high spatiotemporal resolution are essential for monitoring farmland water consumption and productivity in irrigation oasis agricultural areas in arid regions, which can be derived from multi-source satellite remote sensing data using a data fusion algorithm and remote sensing-based models. The site-level evaluation results with ground observations showed that daily estimated ET, GPP, and WUE had better performance. The daily variations and seasonal and interannual spatial patterns of ET, GPP, and WUE were shown to be reasonable and to reveal fine-grained changes at the 100 m scale among roadways, building areas, riverways, and farmlands with different planting types throughout the oases from 2010 to 2016. This supports our understanding of cropland WUE and improving practices of refined water-saving agricultural management in irrigation oasis agricultural areas. Moreover, the results suggest that the monitoring scheme of the coupled carbon and water cycles using remote sensing-based models (e.g., SEBS and GPP models) enables a water consumption analysis and irrigation management for croplands in other regions. Future work should use satellite data with higher spatial resolution (e.g., Sentry-2) and quantification of WUE at fine scale (e.g., 10 m \(\times\) 10 m) to optimize the suitability of planting crops under restricted freshwater resources in arid and semi-arid regions.  
Conceptualization, Y.M.; methodology, Y.M., J.Y. and Z.L.; writing--original draft preparation, J.Y., Y.M., D.Z., and W.Z.; writing--review and editing, J.Y., Y.M., and D.Z.; supervision, Z.W., H.W., and L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Program of the National Natural Science Foundation of China, grant number 41701426.  
Not applicable.  
We thank all the scientists, engineers, and students who participated in field campaigns in the Heihe River Basin. The ground-measured dataset was provided by the National Tibetan Plateau Data Center ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)) accessed on 6 April 2021). We also express our sincere appreciation to four anonymous reviewers, and the guest editors ([PERSON] and [PERSON]) for their constructive comments and helpful.  
The authors declare no conflict of interest.
# Bioelectrical Impedance Vector Analysis (BIVA) and Somatotype in Female Rugby Players
###### Abstract  
Body composition (BC) determines strength, power, and agility in sports such as rugby. Not only is the study of morphology and BC essential to establish player positions, but it also allows the optimisation of competitive performance. The aim of this study was to analyse different BC models (anthropometry and bioelectrical impedance analysis (BIA)) in a female rugby team. Their somatotype was also compared with a bioelectrical impedance vector analysis (BIVA) graph. A cross-sectional study was carried out on 19 women (8 forwards and 11 backs). Anthropometric and BIA (50 kHz, tetrapolar electrode configuration) measurements were taken using standard protocols and BC was estimated. Somatotype was calculated using the Heath-Carter method. Classic and specific BIVA were applied. Student's t, Mann-Whitney, or Chi-squared tests were used for variable comparisons. Agreement was determined via intraclass coefficient correlations. Forwards showed a higher BMI and fat mass, but there were no differences in skeletal muscle masses. Most of the players were of an endomorphic mesomorph somatotype. Specific BIVA, but not classic BIVA, detected BC differences according to player position. Moreover, an individual's interpretation of BIVA was consistent with their somatotype. In a female rugby team, BC and morphology are different according to position. This was detected in both the somatotype and specific BIVA. The application of BIVA (classic and specific) may yet prove to be a very useful strategy for the study of BC in sports.  
anthropometry; bioimpedance analysis (BIA); bioelectrical impedance vector analysis (BIVA); female rugby players; somatotype
# Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors
## 3 Related Work  
CDCL SAT solvers follow primarily the Davis-Putnam-Logemann-Loveland (DPLL) algorithm [28], and they incorporate a number of effective techniques, including clause learning [29], lazy data structures [6], deletion policies [30], and restarts [31]. Typically, a CDCL SAT solver is organized into three main modules [6; 29; 32]: the decision module, used for branching; the deduction module, used for unit propagation and the identification of unsatisfied clauses (or conflicts); and the diagnosis module, which is used for conflict analysis and clause learning (see Figure 1). Accordingly, the main runtime breakdown of a CDCL SAT solver is: 10% decisions, 80% propagation, and 10% account for conflict analysis [6].
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 3 Super-Resolution Imaging for Passive Radar
### Further Discussions
#### 3.3.3 Why CS Performs Better Than ESPRIT and RELAX  
While both the ESPRIT and RELAX algorithms have been shown to be able to perform super-resolution imaging through spectra estimation; however, their reconstruction performance are restricted by the length of the measured data. For limited observation data, the passive radar imaging results obtained by the ESPRIT and RELAX algorithms will be very poor, which have been validated in Section 4.5. On the contrary, the proposed CS method can reconstruct a high-dimensional signal from low-dimensional observation data. This reason is that an unknown sparse signal may be accurately recovered from a limited number of measurements by solving a sparsity-driven optimization problem. In other words, the CS-based method is not sensitive to the data limitation. Therefore, CS can achieve better reconstruction performance than the ESPRIT and RELAX algorithms in super-resolution passive radar imaging.
## 3 Test System
### Test System Architecture  
Our test system consists of a TTCN-3 framework [25] and a hardware adaptor (Figure 7). The TTCN-3 framework controls the testing logic. The test manager provides a user interface to choose a test scenario defined by the use case elements as in ISO/IEC 15118 Part 1. The test manager also allows the user to choose the capabilities of the SUT so that unnecessary software/hardware components are disabled accordingly. Once the test configuration is set, the test manager runs pre-determined test cases in order.  
During a test, each module (yellow rectangles in Figure 7) plays independent roles simultaneously: Modules 15118-2 and 15118-3 perform the EVCC's high-level communication and low-level communication, respectively; Module 61851 performs the basic signaling; and Module 61850 takes the operator's role. Each module can be turned off depending on the testing scenario. The modules, which are unaware of the underlying communication protocols, can communicate with SUT because the SUT Adaptors (SAs) translate the high-level data from the modules to the actual communication protocol to SUT. For maximum flexibility in testing scenarios, the TTCN-3 framework provides only basic interfaces (Ethernet and serial ports), and the hardware adaptor supplements the full interface to the target EVSE.  
The hardware adaptor consists of a PLC modem, AVR ([PERSON] and Vegard's RISC) microcontroller and a set of resistance. The PLC modem performs the SLAC protocol and converts the Ethernet packets from the framework to PLC packets and _vice versa_. The AVR component monitors the control pilot signal from the SUT and reports abnormal events to the testing framework through the serial connection. The resistance consumes the electricity during the charge to emulate a battery.  
We separated the testing logic from the hardware interface for the flexibility of the test system. As the EVSE development is in its early stages in the industry, it is rare to find an EV charger with all of the features of PLC communication, control pilot, 15118 and 61850. Often, EVSE manufacturers are interested in testing only their implementation of high-level communication, setting aside low-level capabilities, such as PLC and actual energy transfer. In this case, the test system directly connects to the SUT over Ethernet, bypassing the hardware adaptor and disabling the 15118-3 and 61851 modules. Without the support of IEC 61850, we can also disable the 61850 module.
# Development of Driftwood Capture Trellis for Capturing Driftwood in Agricultural Drainage Ditches
## 2 Materials and Methods  
### Drainage Design Criteria  
Drainage refers to ditches created to drain water. Drainage is applicable to various facilities and is installed according to the recommended frequency (not based on size). Drainage systems are used as complementary facilities to culverts, roads, streets, and slopes. Different drainage design criteria exist depending on location and expected frequency, from a 2-year frequency to a maximum 100-year frequency (Table 2). The United States divides drainage systems into natural, major, and minor drainage methods, and plans around the occurrence of 2- to 100-year events [38; 39]. Japan plans around 20- to 50-year events, based on its geographical characteristics and climate conditions [40]. In Canada, drainage design plans for a 2- to 100-year event depending on the geographical characteristics of the terrain in which drainage systems are installed [41]. In Australia, the frequency varies from 3 to 40 years depending on the purpose of the specific drainage system [42]. Korea classifies drainage systems into mountainous areas, plains, and agricultural lands and sets the frequency as 20 to 50 years for mountainous areas, 10 to 30 years for plains, and 20 years for agricultural lands. Additionally, it has been suggested that at least 20% of these systems should be adjusted based on estimated flood likelihood and in consideration of the sedimentation of driftwood and soil in drainage [7; 8; 9].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Debris Classification** & **Type** \\ \hline Light Floating Debris (LFD) & small twigs, wood chips, cloth \\ Medium Floating Debris (MFD) & twigs, big wood chips \\ Heavy Floating Debris (HFD) & log, timber \\ Floating Debris (FDI) & fluent substances, including clay, silt, sand, pebble, wood chips \\ Fine Detritus (FD2) & homogeneous silt, sand, and gravel without suspended solids deposited where \\ Coarse Detritus (CD) & flow is slow \\ Boulders (B) & coarse gravel, rack fragments \\ \hline \hline \end{tabular}
\end{table}
Table 1: Type of debris.  
Drainage systems can be installed in a wide range of locations, including mountainous areas, plains, and farmlands. The recommended frequency is higher for drainage systems in agricultural and mountainous areas compared to plains and roads. This is because the probability of driftwood and soil inflow into the drainage is higher in agricultural and mountainous areas than in plains and roads. However, no adequate guidelines have been provided for installing and managing driftwood capture trellises to alleviate the damage from insufficient discharge capacity due to this increased driftwood flow. Against this backdrop, it is necessary to create guidelines for development and management of driftwood capture trellises in agricultural drainage ditch systems.
# A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
## 3 Multi-Class Classification
### Computing \(\mathbf{\beta}\) When \(\mathbf{\alpha}\) Is Fixed  
According to Section 2.2.2, we need to minimize \(g(\mathbf{\beta})\), which is of the form  
\[g(\mathbf{\beta}) =-\frac{1}{|pf|}\sum_{m\in pf}\frac{1}{n_{m}}\sum_{l=1}^{n_{m}} \sum_{k=1}^{K}\chi_{\{(y_{m})=k\}}\log\left(\frac{\exp\left(\sum_{i=1}^{S} \alpha_{m}^{i}(\mathbf{X}_{m}^{i})_{l}\beta_{k}^{i}\right)}{\sum_{k^{\prime}=1}^{ K}\exp\left(\sum_{i=1}^{S}\alpha_{m}^{i}(\mathbf{X}_{m}^{i})_{l}\beta_{k^{\prime}}^{i} \right)}\right)\] \[=\frac{1}{|pf|}\sum_{m\in pf}\frac{1}{n_{m}}\sum_{l=1}^{n_{m}} \left[\log\left(\sum_{k=1}^{K}\exp\left[\sum_{i=1}^{S}\alpha_{m}^{i}\left(\bm {X}_{m}^{i}\right)_{l}\beta_{k}^{i}\right]\right)-\sum_{i=1}^{S}\alpha_{m}^{i} \left(\mathbf{X}_{m}^{i}\right)_{l}\beta_{(y_{m})_{l}}^{i}\right].\]  
To minimize \(g\), we apply the proximal gradient iteration method, which requires computing its gradient, given by  
\[\frac{\partial g}{\partial\beta_{j}^{j}}(\mathbf{\beta})=\frac{1}{|pf|}\sum_{m\in pf }\chi_{\{m\,\&\,2^{S-j}\
eq 0\}}\frac{1}{n_{m}}\sum_{l=1}^{n_{m}}\left[\frac{\exp \left(\alpha_{m}^{i}(\mathbf{X}_{m}^{i})_{l}\beta_{l}^{i}\right)}{\sum_{k=1}^{K} \exp\left[\sum_{i=1}^{S}\alpha_{m}^{i}\left(\mathbf{X}_{m}^{i}\right)_{l}\beta_{k}^ {i}\right]}-\chi\{(y_{m})_{l}=r\}\right]\alpha_{m}^{j}\left(\mathbf{X}_{m}^{j} \right)_{l},\]  
for \(r\in\{1,2,\ldots,K\}\), \(j\in\{1,\ldots,S\}\), and \(\{m\,\&\,2^{S-j}\
eq 0\}\) indicates whether \(j\) is included in the profile \(m\).
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 3 CAMEL Uncertainty Estimates
### Results
The map of temporal uncertainty reveals that desert regions such as the Sahara, and more permanently snow-covered regions like Antarctica and the Arctic Circle are characterized by low temporal uncertainty. The northeastern United States is marked by a higher, \(\sim\)0.01 temporal uncertainty likely due to changes in snow cover over the winter months, and areas around the Indus River Valley and northern India are marked by higher uncertainties likely due to changing precipitation and meltwater levels. Such patterns in the uncertainty values reveal physical characteristics and changes of the Earth's surface. The algorithm uncertainty, which is proportional to the UW BF 8.3 \(\upmu\)m minus ASTER 8.6 \(\upmu\)m emissivity, shows the largest values of over 0.02, mostly over semi-arid and arid regions that exhibit the largest variability in emissivity for this band. This is due to larger differences between retrieved emissivity in this band from the ASTER and MODIS input products. However, because of ASTER's greater number of bands in this region (3), the derived CAMEL emissivities should be of higher accuracy than the original UW BF value, which only has one band in this region. Uncertainty values over northern Russia and Canada show larger values of \(\sim\)0.01 where snow cover signatures vary between the ASTER and the UW BF products. Figure 2: _Cont._
# A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control
## 2 Traffic Signal Timing Optimization Algorithm
### Dynamic Programming Algorithm
#### 2.1.2 Backward Recursion  
1. Set \(j=J,s_{j}^{*}=T\).
2. For \(j=J,J-1,\cdots,1\{\) Read \(x_{j}^{*}\left(s_{j}^{*}\right)\) from the table computed in forward recursion. If \((j>1)\), \(s_{j-1}^{*}=s_{j}^{*}-x_{j}^{*}\left(s_{j}^{*}\right)\).
3. The optimal plan is retrieved from barrier group \(J\) since this barrier group denotes the minimum performance measure \(v_{J}^{*}(T)\), such as the minimum delays or stops.