esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 On-Site Measurements  
### Test Bridge  
Long-span bridge designs are increasingly utilized in the construction of railway bridges, but they are inevitably disadvantageous in terms of dynamic stability and track-bridge interaction, which ultimately limits their application. Alternatively, fixed-end arch bridge designs have advantages in terms of stiffness and serviceability (specifically required for railway use), and its application is gradually increasing. Two of the most typical examples are the Contreras Bridge [19] of 261-m central span length, and the Sobre El Tajo Bridge [20] of 324-m central span length, both in Spain. These bridges, however, support ballasted track, which, compared with slab track, is easier to adjust geometrically for deformation due to thermal expansion or contraction.  
The target bridge in the present study is a three-span continuous arch bridge built for Gyungbu High Speed Railways in Korea. This comprises two side spans of 55 m and one center span of 70 m (see Figure 1). The main arch rib, of the encased composite girder type with concrete-filled steel box, is rigidly supported on the piers and foundations. This is a double-arch structure in which reinforced concrete sub-arch ribs are placed on the upper part of the main arch rib at intervals of 11 m. The wall and bridge deck are integrated with the sub arch ribs. The main arch ribs are fixed to the bridge piers and foundations in order to minimize longitudinal deformation and, thus also, axial stresses of the rail due to the track-bridge interaction. However, with this fixed-end type design, vertical deformations of the main arch rib according to temperature change are inevitable. The concrete slab tracks were laid, and the turnouts were installed on the bridge.  
Figure 1: Dimensions of test bridge: (**a**) side view; (**b**) typical cross section.
## 3 Results
### Mediation Analysis  
The mediation models were performed to illustrate to the extent to which patients' postoperative hospital LOS (dependent outcome variable) related to their BDI-II depressive symptom scores (independent, predictor variable) and whether the relationship was mediated by early and persistent postoperative CRP changes (mediator variables) or if patients' postoperative early (1-3 days postoperatively) and persistent (4-6 days post-operatively) CRP changes affected the relationship between their BDI-II depression symptom score and the postoperative hospital LOS. In Figures 2 and 3, pathway \(c^{\prime}\) represents the direct effect of the BDI-II score on postoperative hospital LOS after controlling for mediators, while pathway a1b1 represents the total indirect pathway of the mediators. Both the direct and indirect pathways in the presented models were non-significant. This indicates the lack of association of the BDI-II depression score with the length of postoperative hospital LOS and the non-existence of the mediation role of postoperative CRP levels. Nevertheless, a higher persistent CRP change (Figure 3) was proven as a significant predictor of postoperative hospital LOS in both mediation models as presented. As shown in Figures 2 and 3, significant contributors to early and persistent CRP response were smoking status and postoperative atrial fibrillation and, in the case of persistent CRP change, concomitant (post)operative infection.
# Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval
## 6 Conclusions  
Water vapor is a key detection parameter of the DPC onboard the GF-5-02 satellite. Recently, researchers have developed a PWV retrieval algorithm for the near-infrared bands (865, 910 nm). However, due to the lack of timely radiometric calibration for the DPC, there are large errors in the existing DPC PWV data. To solve this problem, a radiometric calibration method is developed for the two NIR bands. The NIR08 band of the DPC is calibrated through a cross-calibration method, and the NIR09 band of the DPC is calibrated based on the calibration result of the NIR08 band and AERONET data. The above method effectively solves the problem of the lack of a calibration method for the NIR WVA band at 910 nm of the DPC. The calibration results show that both channels of the DPC used for PWV retrieval have systematic calibration errors. The validation results based on ground-based PWV data show that calibrating these two bands can greatly reduce the error of DPC PWV data. Compared to DPC PWV data derived from the uncalibrated DPC, the RE and MAE for PWV data derived from the DPC after calibration is reduced by at least 14%. This means that the in-orbit calibration method developed in this study is effective in enhancing the quality of DPC data.  
Conceptualization, Y.X., Q.Z., S.Z., W.H., L.Z., X.L., M.Z., Y.L., Z.L. ([PERSON]), Y.W. and Z.L. ([PERSON]); methodology, Y.X. and S.Z.; validation, Y.X.; writing, Y.X., S.Z. and W.H.; funding acquisition, Y.X., S.Z. and Z.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Outstanding Youth Foundation of China (No. 41925019), the National Natural Science Foundation of China (No. 42301463, 42305155), and the Dreams Foundation of Jianghuai Advance Technology Center (No. 2023-ZM01K007).  
AERONET data, SuomiNET data, and DPC data are provided by AERONET, SuomiNET, and China Centre for Resources Satellite Data and Application, respectively. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The author thanks AERONET and SuomiNET for providing the ground-based PWV data.  
The authors declare no conflicts of interest.
## 3 Results and Analyses
### DesignBuilder Simulation Analysis  
The modeling of a photovoltaic greenhouse was conducted in Design Builder based on the size of the experimental photovoltaic greenhouse. The photovoltaic modules on the roof surface were composed of a 3 mm toughened glass and a light proof backplane, which were set as a light proof with a heat conductivity coefficient of 0.76 W/(m\(\cdot\)K); the light transmittance of the roof film was set as 85% with a heat conductivity coefficient of 0.17 W/(m\(\cdot\)K); the vent was covered with an insect-proof screen with a light transmittance of 76.5% [15] and a void ratio of 53.76% [16]; the light transmittance and the compound void ratio of the surrounding shading net were 10% and 30%, respectively.  
Figure 7: Comparison of illumininance and temperature and humidity between the photovoltaic greenhouse, the zigzag-film greenhouse, and the sunshelter: (**a**) illumininance comparison; (**b**) temperature comparison; (**c**) relative humidity comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**9 May 2015** & **Outdoor** & \
\begin{tabular}{c} **Photovoltaic** \\ **Greenhouse** \\ \end{tabular} &
\begin{tabular}{c} **Zigzag-Film** \\ **Greenhouse** \\ \end{tabular} & **Sunshelter** \\ \hline Average daylight & 49.56 & 21.51 & 18.17 & 10.66 \\ illumination (kLux) & 43.41\% & 36.67\% & 21.52\% \\ Light transmittance rate & 33.5 & 35.2 & 36.4 & 35.4 \\ Highest temperature (\({}^{\circ}\)C) & 32.5 & 33.3 & 34.4 & 32.8 \\ Lowest temperature (\({}^{\circ}\)C) & 30.7 & 31.1 & 30.2 & 30.1 \\ Highest relative humidity & 76\% & 75\% & 73\% & 78\% \\ Average relative humidity & 71\% & 69\% & 63\% & 70\% \\ Lowest relative humidity & 66\% & 61\% & 58\% & 59\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the averaged values between the photovoltaic greenhouse and other shelter types.
## 2 Background
### ANN Input Parameters Selection  
The selection of an appropriate set of input parameters (or variables) has been a longstanding concern for ANN development. When the number of available input variables is relatively large, it is sometimes recommended not to use inputs with high multi-collinearity, in order to avoid redundancy. On the other hand, it is also commonly believed that an ANN is adequately capable of identifying redundant and noise input variables during the training process, and that the trained network will use only the significant input variables to make predictions. However, the proper selection of the ANN input variables can simplify models structure and reduce computational effort, and accordingly, obtain high-quality models [46]. Different methods have been applied for this purpose including input variable selection (IVS) algorithms, dimensionality reduction techniques, and other various ad hoc and heuristic methods. [PERSON] et al. [47] described an IVS method that combines genetic algorithm optimization with a generalized regression ANN. In practice, the method demonstrated fast model's training times. [PERSON] et al. [48] proposed non-linear IVS algorithm for the development of ANN models for water quality forecasting. The ANN models constructed using this algorithm, which based on partial mutual information concept, resulted in optimal prediction with significantly low computation effort. [PERSON] et al. [49] integrated principal component analysis (PCA) with ANN for hourly prediction of building electricity consumption. As a result of PCA application, two out of four input variables were selected as significant and only those were used in the ANN proposed model. [PERSON] et al. [50] have also combined PCA and ANN to predict the mechanical behavior of binary composites. The proposed model was motivated, and found potentially capable, by the fast inference of the PCA/ANN method comparing to other empirical models used for generating stress-strain curves. [PERSON] and [PERSON] [51] proposed a combined scheme of independent component analysis (ICA) and ANN for electrocardiogram beat classification. The study results proved the effectiveness of ICA-ANN integration. [PERSON] et al. [52] has implemented the decision tree method for variable selection in developing ANN model for solar radiation prediction, herein, initial list of relevant variables were given by Waikato Environment for Knowledge Analysis software. [PERSON] et al. [53] employed a hybrid analytical-ANN approach for improving the forecasting accuracy of PV power plants output. In their approach, they used an analytical model (called the clear sky model) to obtain predictions of PV power output based on historical weather data, and they used the analytical model's output along with the historical weather data and measured PV power output to train the ANN. Then, for forecasting the PVpower output, both the weather forecast and the analytical model's output are used as ANN inputs to predict the PV power output.
## 4 Results and Discussion
The maximum gain, directivity, radiation efficiency, and overall efficiency of the antenna at various frequencies are shown in Table 2. The antenna's highest gain, 6.25 dB precisely at 9.7 GHz, is noteworthy and indicates improved directionality at this frequency. Additionally, it is clear that as the frequency within the UWB increases, so does the radiation efficiency and overall efficiency of the antenna. The performance of the proposed antenna is compared with other existing work in the literature, as shown in Table 3. It is evident from Table 3 that the proposed CMFA has significantly improved gain and miniaturization with wideband characteristics. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **Gain (dB)** & **Directivity (dB)** & \
\begin{tabular}{c} **Radiation** \\ **Efficiency (dB)** \\ \end{tabular} &
\begin{tabular}{c} **Total Efficiency** \\ **(dB)** \\ \end{tabular} \\ \hline
3.87 & 2.03 & 2.79 & \(-\)0.74 & \(-\)0.75 \\
4.02 & 2.10 & 2.83 & \(-\)0.73 & \(-\)0.73 \\
4.04 & 2.11 & 2.83 & \(-\)0.72 & \(-\)0.73 \\
7 & 2.52 & 3.39 & \(-\)0.61 & \(-\)0.87 \\
7.23 & 2.62 & 3.59 & \(-\)0.66 & \(-\)0.98 \\
9.6 & 6.23 & 7.17 & \(-\)0.94 & \(-\)0.94 \\
9.7 & 6.25 & 7.20 & \(-\)0.95 & \(-\)0.95 \\
11.46 & 5.89 & 7.25 & \(-\)1.27 & \(-\)1.35 \\
11.48 & 5.88 & 7.24 & \(-\)1.27 & \(-\)1.36 \\
12 & 5.28 & 6.80 & \(-\)1.33 & \(-\)1.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gain, directivity, radiation efficiency, and total efficiency of CMFA. Figure 11: Simulated and measured normalized radiation patterns of the circular Minkowski fractal antenna: (**a**) E-Plane (4.02 GHz), (**b**) E-Plane (9.7 GHz), (**c**) E-Plane (11.48 GHz), (**d**) H-Plane (4.02 GHz), (**e**) H-Plane (9.7 GHz), (**f**) H-Plane (11.48 GHz). Figure 12: Surface current distribution on the CMFA at **(a)** 4.02 GHz, **(b)** 9.7 GHz, and **(c)** 11.48 GHz.
# Bidirectional Atmospheric Channel Reciprocity-Based Adaptive Power Transmission
## 3 Simulation and Investigation
### The BER Performance of Conventional FTD and ATD
As for Figure 4e,f, when the signal passed through the turbulence channel, the amplitude fluctuated greatly, and when its MSE \(>0.2\), the signal distortion was serious. Figure 3: (**a**) Performance of FTD in different turbulent channels. (**b**) Performance of ATD in different turbulent channels.
## 1 Introduction
The [PERSON] group performed quantitative analyses on the effects of binder content on 3D sand mold printing and found that current sand mold printing processes often use excessively high binder concentrations, which is detrimental to printing quality [16]. [PERSON] et al. [17] studied inkjet processes from a fluid flow perspective and listed the factors that can affect the jetting performance of a printhead. They also noted that microdroplets are often accompanied by a large number of satellite droplets, which is a significant factor contributing to microdroplet landing errors. The Liu ZP group [18] designed and fabricated a piezoelectric printhead and used a laser displacement measurement system to measure its microdroplet characteristics (such as microdroplet volume). They analyzed the factors affecting these characteristics based on actuation structure and found strategies to minimize the occurrence of satellite droplets. The findings of a previous study also indicate that the appearance of uncontrolled satellite droplets around the main droplets affects the quality and performance of 3D printed products [19, 20, 21]. [PERSON] et al. [22] succeeded in eliminating all satellite droplets during 3D sand mold printing by manually destabilizing the feed pressure. However, this significantly increased the amount of fluid required to manufacture the mold and substantially reduced the final dimensions of the sand mold. This study also indirectly showed that excess microdroplet jetting affects dimensional accuracy adversely. Although these results show that excess microdroplet jetting volumes and satellite droplets affect dimensional accuracy significantly, the relationship between microdroplet volume and printing precision was not quantified. The aforementioned studies also show that the use of simulation and experimental approaches to improve inkjet control strategies based on microdroplet jetting characteristics and behaviors is crucial for improving inkjet printing quality. However, in the context of sand mold printing (particularly for high-precision sand molds), studies on microdroplet jetting behaviors and their effects on sand mold printing quality remain scarce. Therefore, this work aims to study microdroplet formation and deposition in the presence of electromechanical coupling by simulating the jetting processes of a piezoelectric printhead with varying control inputs, internal structures, and inkjet fluid properties. The relationship between structural and process parameters and microdroplet jetting behaviors was studied, and tuning strategies were formulated to improve jetting quality. The findings of this work can assist future efforts to enhance the printing quality of high-precision printed sand molds.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 2 The Dual-Platform GMTI Method against Nonuniform Environment
### High-Precision Clutter Covariance Matrix Estimation Based on Dual-Platform SAR System
#### 2.3.3 Setting the Detection Threshold  
Setting the detection threshold is a traditional problem in target detection, whose key problem is the distribution characteristics of the clutter. If the scene is nonuniform, the detection threshold will be seriously affected because of the inaccurate estimation of the covariance matrix. For example, the covariance outside the protected area is much lower than the clutter covariance of the area in which the target is located (this often happens in roads between cities), and the threshold will consequently be set much lower, which leads to a high false-alarm rate in target detection. In our proposed method, the clutter characteristics of the clutter area corresponding to the target's position in the SAR image are calculated, and then the threshold is set more reasonably, which can effectively achieve an accurate balance between false-negative, false-positive, and detection probabilities.  
The following are the two assumptions when the radar uses the STAP method to detect moving targets after STAP weighting processing :  
\[\left\{\begin{array}{l}H_{0}:\mathbf{Y}=\mathbf{w}^{H}\mathbf{n}\\ H_{1}:\mathbf{Y}=\mathbf{w}^{H}\mathbf{s}+\mathbf{w}^{H}\mathbf{n}=\mathbf{\alpha}\mathbf{w}^{H}\mathbf{v}+\bm {w}^{H}\mathbf{n}\end{array}\right.. \tag{17}\]  
During detection, when the power exceeds the detection threshold, the target exists; otherwise, it is considered there is no moving target. The target power is \(p_{t}=\left|\mathbf{w}^{H}\mathbf{v}_{t}\right|^{2}\), the noise and clutter power is \(p_{u}=\mathbf{w}^{H}R_{u}\mathbf{w}\), and the optimal output is \(SCNR=\sigma^{2}\xi_{t}\mathbf{v}_{t}^{H}\mathbf{R}_{u}^{-1}\mathbf{v}_{t}\). If the detector input is the STAP weighted output normalized by the output noise power [37,38], the detection formula is  
\[r_{mn}=\frac{\left|\mathbf{w}^{H}\mathbf{v}_{t}\right|^{2}}{\mathbf{v}_{t}^{H}\mathbf{R}_{u}^{ -1}\mathbf{v}_{t}}=\frac{\left|\mathbf{v}_{t}^{H}\mathbf{R}_{u}^{-1}\mathbf{v}_{t}\right|^{2} }{\mathbf{v}_{t}^{H}\mathbf{R}_{u}^{-1}\mathbf{v}_{t}}\overset{H_{1}}{<}T_{mm}. \tag{18}\]  
The detection threshold is several times the average power of the input clutter. When the steering vector and covariance matrix are known in advance, the false-alarm probability and detection probability can be obtained as follows [39,40]:  
\[\begin{array}{l}P_{FA}=P_{r}\{r_{mn}>T_{mn}|H_{0}\}=\int_{T_{mn}}^{\infty}p( r_{mn}\big{|}H_{0})dr_{mn}\\ =\int_{T_{mn}}^{\infty}e^{-r_{mn}}dr_{mn}=e^{-T_{mn}},\end{array} \tag{19}\]  
\[\begin{array}{l}P_{D}=P_{r}\{r_{mn}>T_{mn}|H_{1}\}=\int_{T_{mn}}^{\infty}p( r_{mn}\big{|}H_{1})dr_{mn}\\ =\int_{T_{mn}}^{\infty}e^{-r_{mn}-\rho_{mn}}I_{0}(2\sqrt{r_{mn}\rho_{mn}})dr_{ mn},\end{array} \tag{20}\]  
where \(\rho_{mn}\) is the generalized output signal-to-clutter-plus-noise ratio of the detector, and \(\rho_{mn}=v^{H}R_{u}^{-1}\mathbf{v}.I_{0}(*)\) is the first kind of zero-order modified Bessel function.
## 5 Discussions
### Findings and Contributions
#### 5.1.2 Affordable IoT Solution Challenges  
Understanding the current challenges in implementing IoT solutions in the natural disaster monitoring domain is crucial for further discussing the significance of the proposed system. Typically, such systems require expensive and complicated hardware that is difficult to maintain, especially in harsh environmental conditions. In contrast, the proposed system uses affordable and readily available hardware that is robust enough to withstand harsh environmental conditions. Furthermore, the system's task scheduling technique, coupled with the deep sleep delay function, significantly reduces power consumption, thus making it an energy-efficient solution.
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
###### Abstract  
Even though deicing or airframe coating technologies continue to develop, aircraft icing is still one of the critical threats to aviation. While the detection of potential icing clouds has been conducted using geostationary satellite data in the US and Europe, there is not yet a robust model that detects potential icing areas in East Asia. In this study, we proposed machine-learning-based icing detection models using data from two geostationary satellites--the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) and the Himawari-8 Advanced Himawari Imager (AHI)--over Northeast Asia. Two machine learning techniques--random forest (RF) and multinomial log-linear (MLL) models--were evaluated with quality-controlled pilot reports (PIREPs) as the reference data. The machine-learning-based models were compared to the existing models through five-fold cross-validation. The RF model for COMS MI produced the best performance, resulting in a mean probability of detection (POD) of 81.8%, a mean overall accuracy (OA) of 82.1%, and mean true skill statistics (TSS) of 64.0%. One of the existing models, flight icing threat (FIT), produced relatively poor performance, providing a mean POD of 36.4%, a mean OA of 61.0, and a mean TSS of 9.7%. The Himawari-8 based models also produced performance comparable to the COMS models. However, it should be noted that very limited PIREP reference data were available especially for the Himawari-8 models, which requires further evaluation in the future with more reference data. The spatio-temporal patterns of the icing areas detected using the developed models were also visually examined using time-series satellite data.  
Keywords:icing detection; machine learning; geostationary satellite data; COMS; Himawari-8 +
Footnote †: journal: Remote Sensing, 10, 631;