esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Structure and Key Technologies
### Deadweight Balance Structure  
The \(Z\)-stage is moved together with \(X\)- and \(Y\)-stages. The deadweight of the moving table will cause structure deflection in the \(XY\)-plane and uneven driving forces in the \(Z\)-axis due to gravity effect. In order to reduce this mechanical deformation and inertial force, two sets of counterweight mechanisms are proposed in the developed micro CMM. One is used to balance the deadweight of the \(Z\)-stage, which is called the \(Z\)-stage deadweight balance system. The other is to balance of the deadweight of \(X\)-, \(Y\)- and \(Z\)-stages during lateral movement, which is called the lateral deadweight balance system. Details are given as follows.
## 5 Experiments and Results
### Auditive Transparency  
The proposed data-hiding scheme was evaluated in terms of PSNR and an audio transparency metric known as the Objective Difference Grade (ODG) [28]. ODG has values in \([0,-4]\), with an ODG value between 0 and \(-1\) indicating a very good perceptual transparency [28]. Note that the free basic implementation of the PEAQ algorithm [29] was used to obtain the ODG results; therefore, they were not exactly in the range \([0,-4]\). In this section, the distortion results of data-hiding are given for both unsecured and secured data-hiding. In addition, concerning the distortion (PSNR and ODG) results, a statistical significance analysis was conducted by means of the Kruskal-Wallis test (significance level = 0.05), to evaluate whether the medians of the compared results were different [30]. Additionally, correction for multiple testing on the basis of the same data was made using the Bonferroni correction [31].
## 2 Materials and Methods
### Experimental Design
#### 2.3.1 Conventional Data Augmentation  
As mentioned before, in each channel of the RGB image, the texture image, the curvature image, and the range image are stored. We apply a conventional data augmentation approach to obtain a fifteen-fold augmentation of the training dataset obtaining a total of 2445 images. Conventional data augmentation consists of randomly applying a series of geometrical and photometrical transformations to original images. The chosen transformations are:  
* Horizontal and Vertical Shift transformation
* Horizontal and Vertical Flip transformation
* Random image rotation transformation (in a range between 0 to 180 degrees)
* Random scale transformation (maximum value 2x)
* Addition of Gaussian noise
* Brightness modification
## 2 DC Fault Current Analysis  
### Test System Introduction  
The scheme of the MMC-HVDC system can be categorized into monopolar and bipolar symmetrical wiring systems (Figure 1). The monopolar symmetrical HVDC system is also known as the pseudo bipolar HVDC system, which typically adopts a cable as a DC line. However, a real bipolar HVDC system is commonly applied in the HVDC transmission system with a high-capacity and overhead DC line. The main difference between the two schemes lies in operation mode. Bipolar systems can work under both monopolar mode and bipolar mode. Namely, the power transmission of the positive pole overhead line and negative pole overhead line is independent. Therefore, bipolar systems can work during fault by transferring power through healthy line (monopolar mode). In contrast, monopolar systems cannot maintain power transmission under fault, since they only operate under monopolar mode. Hence, the bipolar system demonstrates advantages in terms of power supply reliability and FRT capability.  
The MMC-HVDC system mainly consists of modular multilevel converter stations and DC line parts. The topology structure of MMC, which includes three converter units, is displayed in Figure 2. Each unit involves upper and lower bridge arms that are connected with the bridge arm reactor \(L_{0}\). The SMs are connected in series in every bridge arm, which consists of two switches, namely, \(T_{1}\) and \(T_{2}\) (IGBT); anti-parallel connected diodes, namely, \(D_{1}\) and \(D_{2}\); and energy storage capacitor \(C_{0}\). The DC voltage is maintained by switching \(T_{1}\) and \(T_{2}\) on and off in every SM tripped by a control signal. If the amount of SMs in each converter unit is 2N, then it has N switched-on and N switched-off SMs during normal operation. The DC side voltage \(U_{DC}\) is equal to the sum of the switched-on SM voltage \(U_{0}\). The main modulation modes are divided into pulse width modulation and nearest level modulation. The converter stations are blocked by turning off IGBTs in all SMs given the local overcurrent protection when a current reaches a threshold value (normally twice the value of the nominal current).  
Figure 1: Diagram of the modular multilevel converter-based high-voltage direct current system in (**a**) monopolar symmetrical wiring and (**b**) bipolar symmetrical wiring.
# Exploring Vitamin D Synthesis and Function in Cardiovascular Health: A Narrative Review
## 4 Vitamin D Deficiency and Cardiovascular Risk
### Vitamin D Supplementation and CVD
In summary, although Vitamin D supplementation has been widely promoted for its potential health benefits, evidence from RCTs suggests that it does not significantly reduce the incidence of major cardiovascular events or cancer or improve certain cardiovascular risk factors in the general population.
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 3 Simulation Results
### Simulation Results for Power Coverage  
Subsequently, we conducted a power coverage experiment employing the principles outlined in Section 2.2. This experiment was designed to compare our method against the MF method detailed in Reference [15]. The results of this comparison are illustrated in Figure 5c,d. These results demonstrate the effectiveness of our approach in providing targeted power coverage, as opposed to the benchmarks established by the MF method. The graphical representation in Figure 5c,d highlights the differences in power distribution and coverage area between the two methods, providing a clear visualization of the comparative advantages and potential limitations inherent in each approach. It is evident that users employing the MF method cannot achieve precise power coverage, and the coverage ratio diminishes as the transmission distance and target power coverage increase. In contrast, SMH enables users to attain a high-power coverage ratio, approaching 100% at short distances. Although this value declines at greater distances due to diffraction effects, it remains significantly higher than that achieved with the MF method.  
Figure 5c displays the power coverage areas for five users utilizing the MF method. It is evident that there is minimal variation in the power coverage, which places considerable demands on the precision of receiver alignment. A slight misalignment can lead to communication failures due to inadequate power reception. Conversely, Figure 5d illustrates that the SMH method enables precise power coverage within a three-dimensional transmission space. This ensures that signals are receivable over a larger area, significantly reducing the likelihood of communication failures. The contrast between the two methods underscores the superior adaptability and reliability of the SMH approach in maintaining effective power distribution in variable spatial configurations.
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 4 Experimental Results
### Sleep-Stage Detection Results  
Three sleep stages (wake, _REM_, and non-_REM_) were detected using the breathing and movement data of the subjects extracted using the FMCW radar. Figure 7 presents the results of the sleep-stage detection performed on the two subjects as examples. The similarity between the radar-based sleep-stage detection results and the PSG results for the entire test duration was confirmed.  
In addition, a confusion matrix was employed to assess the accuracy of FMCW radar-based three-stage sleep detection. Figure 8 shows the results of integrating and labeling PSG readings and three radar-detected sleep stages at 30 s (1-epoch) intervals for stage-specific accuracy evaluation. The wake stage was matched to 3, _REM_ stage to 2, and non-_REM_ stage to 1.
# Hailstorm Detection by Satellite Microwave Radiometers
## 3 The Hail Detection Model
### AMSU-B Matchup Data  
The training data for the hail model stems from a large database of co-located ground hail observations and AMSU-B overpasses. The matchup dataset covers the training period March-September 2000-2009 [29]. Each satellite overpass within 5 min and 25 km distance from the hailstorm location, and restricted to AMSU-B local zenith angle \(<25^{\circ}\) to avoid low resolution FOVs and reduce the impact of surface type and atmospheric moisture content [42], is retained. The training dataset was investigated by using the \(TB_{184}^{our}\) thresholds to isolate the TBs correlated with hail signatures. AMSU-B average TBs co-located with surface hail observations are displayed in Table 4.  
Only data corresponding to \(TB_{184}^{our}\) > 25% should be considered, where the likelihood of hail signatures is higher. By compacting Tables 3 and 4, the training dataset of the hail detection model is distributed as in Table 5 by assuming as limits for the TBs the average values of the two tables in the corresponding classes.  
63.60% (1834) of data populate the class \(TB_{184}^{our}\) < 35% and 36.40% (1048 data) belong to the class \(TB_{184}^{our}\) > 35%. Note that the majority of hail sizes shows d \(<10\) cm with a mode value around 5 cm. Only 324 occurrences (see Table 3) show d \(>10\) cm with maximum d = 14 cm. Thus, the model is constrained by a statistical population of hail diameters from a few cm or lower to 14 cm within the average TB range in Table 6. Finally, the absolute minimum of the two datasets defines the maximum sensitivity to hail scattering of the 150 and 190 GHz channels involved in the detection algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**TB90 (K)** & **TB150 (K)** & **TB184 (K)** & **TB186 (K)** & **TB190 (K)** & \(TB_{184}^{our}\) (\%)** & **No. of Data** \\ \hline
240.32 & 215.10 & 214.39 & 209.40 & 204.66 & 15–25 & 1814 \\
209.88 & 180.01 & 203.00 & 188.08 & 176.53 & 25–35 & 1306 \\
169.81 & 148.01 & 178.89 & 159.51 & 148.78 & \(>35\) & 903 \\ \hline \end{tabular}
\end{table}
Table 4: AMSU-B average TBs by threshold \(TB_{184}^{our}\) for the training period 2000–2009.  
Figure 6: Brightness temperature differences (BTD) fitting the three \(TB_{184}^{our}\) classes for hail diameters \(2<\) d \(<10\) cm (**a**) and d \(>10\) cm (**b**). Note that the difference (BT150–BT190) shows high sensitivity in capturing hail features with changing diameters.  
\begin{table}
\begin{tabular}{c c c} \hline
**Training Datasets** & 25**\textless{} \(TB_{184}^{our}\) (\%) \textless{} 35 & \(TB_{184}^{our}\) (\%) \textgreater{} 35 \\ \hline
1 st dataset & 528 & 145 \\
2 nd dataset & 1306 & 903 \\ \hline \end{tabular}
\end{table}
Table 5: Number of training data for the hail detection model.
## 5 Conclusions  
In this study, we tested six AI models for corn yield under extreme weather conditions and analyzed their performances for a 10-year period that included severe drought and heatwave. Given its tall canopy and broad leaves, corn is relatively sensitive to heat stress [52; 53]. In 2012, the Midwestern US experienced severe drought, leading to a 22% reduction in average corn yield. Our DNN model predicted the reduced corn yields in 2012 under drought conditions with a correlation coefficient of 0.954 in the blind tests, which indicates the suitability of our approach for crop yield prediction under extreme weather conditions. Corn yield was remarkably decreased according to the increase of the heatwave duration. Despite the unexpected heatwave, our DNN model produced stable estimates as a result of the effective optimization of hyperparameters, including hidden units, epoch number, activation function, optimizer, L1/L2 regularization, and dropout ratio. Future work using a larger database for about 20 years will be necessary to ensure more reliability of the DNN model for crop yield prediction. Also, more appropriate use of drought index will be required for the prediction of crop yields because various drought indices can represent different aspects of drought conditions.  
Losses in crop yield are dependent on the types of extreme weather and the types of crops. For example, the soybean yield is significantly reduced by low temperatures during the growing season [54; 55]. Hence, it is also necessary to examine how the crops such as soybean, wheat, and rice are affected by extreme weather conditions, including drought, heat and cold waves, and heavy rainfall. Future studies should use AI modeling to predict the yields of various crops under extreme weather conditions.  
Research design, Y.-W.L.; data analysis, N.K.; manuscript writing, N.K. and Y.-W.L.; literature review and discussion, S.-I.N., C.-W.P., M.H., J.O., K.-J.H., and J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NRF (National Research Foundation) of the Korean government (2018R1A6A3A01013215 and 2018R1D1A1B07050194). Also, this work was carried out with the support of the \"Cooperative Research Program for Agriculture Science & Technology Development (PJ014787042020)\", Rural Development Administration, Republic of Korea.  
The authors declare no conflict of interest.
## 2 Methods
### Smartphone GNSS Observations Generation  
Android-based smartphones can provide GNSS pseudo-range, carrier phase, and Doppler by using GNSS-related messages (shown in Table 1) in Android N version and later versions [18; 29]. Regarding these messages, the variables shown in Table 2 represent important information.  
To obtain the correct time information, the following two functions can be used to generate the GPS time and the UTC time, respectively:  
\[GPST=TimeNanos-(FullBiasNanos-BiasNanos) \tag{1}\]  
\[UTCT=TimsNanos-(FullBoiasNanos+BiasNanos)-LeapSecond\ *\ 10^{9} \tag{2}\]  
and the pseudo-range (\(P\)) can be generated by  
\[P=\frac{t_{Rx}-t_{Tx}}{10^{9}}\cdot c\quad[\text{m}] \tag{3}\]  
with  
\[t_{Rx_{\text{GNSS}}}=TimesNanos-TimeOffsetNanos-(FullBiasNanos+BiasNanos)\quad[ \text{ns}] \tag{4}\]  
where \(t_{Rx_{\text{GNSS}}}\) represents the signal reception time relative to a reference GNSS (such as GPS), and \(t_{Tx}\) gives the signal transmission time for each system.  
For GPS, BDS, GLONASS, and GALILEO, \(t_{Rx_{\text{GNSS}}}\) can be, respectively, expressed by  
\[t_{Rx_{\text{GPS}}}=t_{Rx_{\text{GNSS}}}-weekNumberNanos\quad[\text{ns}] \tag{5}\]  
\begin{table}
\begin{tabular}{l l} \hline \hline  
\[t_{Rx_{BDS}}=t_{Rx_{GNSS}}-weekNumberNanos-14\text{s}\quad[\text{ns}] \tag{6}\]  
\[t_{Rx_{GLO}}=t_{Rx_{GNSS}}-DayNumberNanos+3\text{h}-leapsecond\quad[\text{ns}] \tag{7}\]  
\[t_{Rx_{GAL}}=t_{Rx_{GNSS}}-weekNumberNanos\quad[\text{ns}] \tag{8}\]  
\[weekNumberNanos=floor(-\frac{FullBiasNanos}{604800\cdot 10^{9}})\cdot(604800\cdot 10^{9}) \quad[\text{ns}] \tag{9}\]  
\[DayNumberNanos=floor(-\frac{FullBiasNanos}{86400\cdot 10^{9}})\cdot(86400\cdot 10^{9}) \quad[\text{ns}] \tag{10}\]  
\[t_{Tx}=ReceiverSvTimesNanos\quad[\text{ns}] \tag{11}\]  
where \(weekNumberNanos\) is the total number of nanoseconds from the start of a GNSS time in the current week. After obtaining \(t_{Rx}\) and \(t_{Tx}\), the pseudo-range value can be obtained. \(DayNumberNanos\) is the total number of nanoseconds from the start of GNSS time in the current day, and \(leapsecond\) is the number of jump seconds between the UTC time and the GNSS time at present.  
The carrier-phase (\(\varphi\)) and Doppler observations of GNSS can be generated by  
\[\varphi=\frac{1}{\lambda}\cdot AccumulatedDeltaRangeMeters\quad[\text{cycle}] \tag{12}\]  
\[PseudorangeRateMetersPerSecond=-\lambda\cdot DopplerShift\quad[\text{m/s}] \tag{13}\]  
where \(c\) and \(\lambda\) are the constant speed of light and the wavelength, respectively.
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 3 Mission Scenario
Moon centered and Earth centered visuals in two different days; (**a**) 1 January 2025 and (**b**) 17 January 2025. In the first case, two ground stations (ESRIN and New Norcia) are visible from the Moon; in the second case, the visibility is extended to three ground stations (ESRIN, New Norcia and Guiana Space Center). The simulations have been carried out with STK. where \(B\) is the signal bandwidth and \(f_{b}\) is the data rate). In all the cases STK assumes that the receiver antenna is pointed to each of the visible ground stations. As a consequence the best achievable link characteristics can be studied in the different situations. Note that the antenna noise temperature changes as a function of the pointing directions; this because the fraction of the main antenna beam covered by the Earth also changes. Another interesting fact is that, in the second case, the Guiana Space Center station, although visible from the Moon, is affected by very high atmospheric losses. This is because the signal path is nearly tangent to the Earth surface, experimenting a longer atmospheric propagation and terrain scattering. In conclusion, the detailed simulation of the mission scenario shows the possibility of a stable communication using a minimum of three transmitters on Earth. For a more continuous communication, however, it is recommended to increase the ground stations number to contrast the effects due to the motion of celestial bodies and atmosphere. The communication links between Earth assets and lunar assets, such as the LOP-G and the LRS, could be analyzed with the same methodology.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 3 Network Architecture Model
### WBAN Traffic Categories  
In WBANs, sensory data have their own characteristics due to different requirements in terms of delay, priority, packet loss, and other factors [27]. Thus, from the viewpoint of remote healthcare monitoring and important levels, we classify the WBAN traffic into emergency and normal traffic, which are defined as follows:  
* _Emergency traffic_ includes not only the critical data packets (e.g., ECG, EEG) but also the data that are initiated by nodes when they exceed a normal threshold (e.g., blood pressure (BP) \(\geq\) 140 mmHg or body temperature \(\geq\) 40 \({}^{\circ}\)C). They are strictly required in terms of reliability and immediate response. Hence, QoS attributes, such as bandwidth, RSSI, security, data rate, latency, and reliability, are placed at a higher priority level than user preference aspects, such as the monetary cost, in the network selection.
* _Normal traffic_ is the data traffic that is used to monitor normal patient conditions without any criticality (e.g., EMD, SpO2, Non-invasive cuff). In contrast to emergency traffic, the monetary cost in normal traffic is an important attribute for network selection. In other words, normal traffic can be easily satisfied by all the candidate networks, and anetwork with a low cost could be considered the best network onthe available list.  
Figure 1: A conceptual view of wireless body area networks (WBAN).  
To gain a deeper understanding of the purpose of the above classifications, the next section proposes an efficient handover algorithm according to the QoS requirements of the application types.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 4 Comprehensive Comparison  
To get the comprehensive comparison of different methods, two combined aspects are considered as the criterions: CN0 margin and pseudorange precision. CN0 margin is the difference between the CN0 of the navigation signal in the integrated signal system and the normal GNSS signal.  
The lowest accuracy threshold is set as 33 ns (10 m) when the CN0 is 40 dBHz and \(T_{coh}\) is 10 ms. The performance of different methods is analyzed when the lowest accuracy threshold is satisfied.
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## Glossary  
AGC: Automatic Gain Control; BER: Bit Error Rate; BRAM: Block RAM; CORDIC: COordinate Rotation DIgital Computer; DSP: Digital Signal Processing; EO: Earth Observation; FFT: Fast Fourier Transform; FPGA: Field Programmable Gate Array; HDL: Hardware Design Language; LUT: Look-Up Table; MSE: Mean Square Error; PMS: Power Measurement System; PSD: Power Spectral Density; RFI: Radio Frequency Interference; RMSE: Root Mean Square Error; SMAP: Soil Moisture Active Passive; SMOS: Soil Moisture and Ocean Salinity; STFT: Short-Time Fourier Transform
## 1 Introduction  
Sonar target strength (TS) [1, 2] is an important parameter to describe the echo characteristics of underwater targets. At present, some methods are available to predict the echo intensity of the targets, such as the physical acoustic method [3, 4], T matrix method [5] and graphical acoustics computing method [6]. Particularly, the planar element method (PEM) [7, 8, 9], which uses a set of planar elements to approximate the target surface and superimposes the scattered sound field of each planar element to obtain an approximate value of the total scattered sound field. [PERSON] et al. [10] proposed to apply the Gordon integral algorithm to the PEM in order to improve the stability and rapidity of integral computation.  
The shooting and bouncing ray (SBR) [11, 12] is a mature and versatile algorithm of the high-frequency method, which is always used to predict the radar cross section (RCS) [13, 14] of electrically large and complex targets. The SBR algorithm considers multiple reflections on the basis of geometrics optics (GO) between target geometries; ray tracing is used to obtain the intersection positions between the incident ray and the triangular patch and then calculate the scattered field according to the physical optics (PO) principle. [PERSON], [PERSON] et al. [15] presented a KD-tree to reduce the times that are required to trace each ray tube. [PERSON] et al. [16] adopted the multiresolution grid algorithm to reduce the total number of ray tubes that participated in calculating the field integral. [PERSON] et al. [17] used the space-division algorithm and the multiresolution grid algorithm to speed up ray tracing in RCS prediction.  
The graphics processing unit (GPU) has developed ultra-high computing power in recent years. It is precisely by virtue of the GPU's super floating-point computing power that the Tianhe 1A supercomputer won the TOP500 championship in 2010, and the Titan supercomputer won the TOP500 championship in 2012. NVIDIA released Maxwell, Pascal, Volta, and Turing architectures in 2015, 2016, 2017, and 2018, respectively. The latest Turing architecture enables ray tracing through hybrid rendering, and the new RT Core processes rays 25 times faster than Pascal. What's more, the compute unified device architecture (CUDA) is a hardware system introduced by NVIDIA. [PERSON] et al. [18] presented a GPU-based multiresolution SBR method for the fast RCS prediction and improved the performance of the GPU-based SBR at least 4.8 times.  
We adopt a more efficient multiresolution SBR algorithm based on GPU to predict the TS value of complex underwater targets. The algorithm uses a KD-tree to improve the ray-tracing efficiency in SBR and uses the multi-resolution grid to \"grade\" the sound ray bundle which can effectively improve the target intensity-prediction efficiency.