esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monosonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95 th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.  
precipitation; GPM; IMERG; GSMaP; Nepal 20202121836; doi:10.3390/rs12111836
## 3 Development of Prediction Model of Ground Subsidence
### Additive Feature Attribution Methods  
#### 3.6.1 SHAP Values  
Recently, a novel unified approach for interpreting predictions from machine-learning models, SHAP, was introduced, applying explanation models as a method for understanding these predictions [38]. This study employed SHAP values to uniformly quantify feature importance, aligning features more appropriately with human intuition and effectively distinguishing between different model outputs. This approach, based on game theory [39], enabled the visualization of the influence of the features included in each explanatory variable on the prediction results.  
In additive feature attribution methods, the explanation model defines a linear function of binary variables as follows [38; 71]:  
\[g(z^{\prime})=\phi_{0}+\sum_{i=1}^{M}\phi_{i}z^{\prime}_{i}, \tag{2}\]  
where \(M\) is the number of input features; the \(z^{\prime}_{i}\) variables typically represent a feature being observed \(\left(z^{\prime}_{i}=1\right)\) or unknown \(\left(z^{\prime}_{i}=0\right)\), and the \(\phi_{i}\) are the feature attribution values, \(\phi_{i}\in\mathbb{R}\), and \(z^{\prime}\in\ \left\{0,\ 1\right\}^{M}\). Equation (2) suggests that the Shapley value has three properties: local accuracy, missingness, and consistency. By contrast, SHAP values are derived by combining these conditional expectations with the classic Shapley values from game theory [71].  
The SHAP values used to fairly distribute the values and assess the contribution of each explanatory variable were determined by the following equation [38; 71]:  
\[\phi_{i}=\sum_{S\subseteq N\smallsetminus\{i\}}\frac{|S|!(M-|S|-1)!}{M!}[f_{x}( S\cup\{i\})-f_{x}(S)], \tag{3}\]  
where \(N\) is the set of all input features, \(S\) is any subset of \(N\) excluding feature \(i\), \(|S|\) is the number of features in subset \(S\), and \(f_{x}(S)\) is the predicted value of the model using the set of features \(S\). This formula represents the average additional value provided by a feature.
## 6 Conclusions  
Analysis of the simulation and test results show that most of the foreign substance flow occurs during the first half of the cycle, which corresponds to the electrode displacement. If the heat input is insufficient, a solid weld or incomplete joint may result, leaving the foreign substance inside. If sufficient melting occurs within the weld, the outer weld reinforcement is significantly formed, and the foreign substance is moved out of the weld by the fluid flow. Therefore, it can be considered that the occurrence of melting that exceeds the tube thickness is an important variable in tube-cap welding, and the first half cycle is an important quality factor during welding. The main findings of this study can be summarized as follows:  
* It was observed that the tendency of foreign substances to move in the X and Y directions occurred mainly during the first half cycle of the welding time in a one-cycle process, and the amount of displacement increased as the current value increased. In addition, the study showed that an increase in the amount of molten metal resulted in a greater flow of foreign material;
* Regarding the presence of foreign substances in the base material, it was interpreted that the movement occurs mainly during the first half cycle, and if the heat input is insufficient, the welding occurs in a solid state, leaving foreign substances in the tube. Conversely, if sufficient heat is generated to cause sufficient melting in the weld area, a large amount of weld reinforcement will occur on the outside, causing the foreign substance to move out of the tube due to flow. Therefore, it can be said that the first half cycle of the welding time, such as the flow rate during the reflection cycle, is an important factor in ensuring weld quality and a sound weld.  
After actual welding, foreign substances are checked through a macrograph test, but it only checks one side of the weld in the circumferential direction, so there is a limit to reproducibility. However, the most important and vulnerable part of the fuel rod is the weld between the end plug and the cladding tube. Substances, as well as carbide-based ceramic materials, may exist in the weld, and it is significant that simulations have derived welding conditions that can remove foreign substances from the thickness of the tube under conditions that do not cause over-welding.  
Figure 9: Macrograph (cross-section) of weld joint (14 kA).  
**Author Contributions:** Writing original draft and analysis, T.N.; Writing--review and editing, T.K. and Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (2021171020001B).  
**Data Availability Statement:** Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Raman Spectroscopy  
Raman spectroscopy was employed to assess the consumption and/or depletion of sulfate ions in the EXP2 and EXP3 solutions. The vibrational mode of SO\({}_{4}\)\({}^{2-}\) ions was identified around the spectral region of 1000 cm\({}^{-1}\)[54]. Moreover, the vibration of water molecules was identified at around 3400 cm\({}^{-1}\), due to the vibration of O-H band, as shown  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Mixes** & **EXP** & **Day 1** & **Day 2** & **Day 5** & **Day 11** & **Day 18** & **Day 25** & **D25–D1** \\ \hline Mix1 & 0 & 3.92 & 5.08 & 5.19 & 4.82 & 5.54 & 5.68 & 1.76 \\ \hline Mix1 & 1 & 3.90 & 4.69 & 3.82 & 3.96 & 4.40 & 4.89 & 0.99 \\ \hline Mix1 & 2 & 3.53 & 3.93 & 3.70 & 3.20 & 2.85 & 3.32 & \(-\)0.21 \\ \hline Mix1 & 3 & 3.50 & 3.79 & 3.35 & 3.62 & 3.04 & 3.44 & \(-\)0.06 \\ \hline Mix2 & 0 & 2.28 & 2.05 & 1.78 & 1.91 & 1.75 & 1.96 & \(-\)0.32 \\ \hline Mix2 & 1 & 2.01 & 1.79 & 1.72 & 1.64 & 1.56 & 1.87 & \(-\)0.14 \\ \hline Mix2 & 2 & 1.67 & 1.43 & 1.1 & 1.06 & 1.09 & 0.91 & \(-\)0.76 \\ \hline Mix2 & 3 & 1.60 & 1.41 & 1.12 & 1.12 & 1.21 & 1.02 & \(-\)0.58 \\ \hline Mix3 & 0, 1, 2, and 3 & & & Negligible & & & NA \({}^{1}\) \\ \hline Mix4 & 0, 1, 2, and 3 & & & Negligible & & & NA \({}^{1}\) \\ \hline \end{tabular} \({}^{1}\) Not available.  
\end{table}
Table 3: Portlandite consumption (%) in the four mixes over time and in different exposure conditions.  
in Figure 5[55]. The results demonstrate that the sulfate ions present in the solutions were not fully consumed, even after a 25-day exposure, except for in the EXP2 solution containing Mix1 (100% CEMI). In this case, the sulfate ions were completely consumed within just 11 days (Figure 5). When simultaneously exposed to both sulfate and chloride ions, the peak at 1000 cm\({}^{-1}\) was still present. This suggests that the presence of chloride ions mitigates the effect of sulfate ions, and that mixtures containing SCM exhibit an improved resistance to the studied attacks.
## 5 Materials and Methods
### Experimental Environment  
The test environment was set up on two hosts, both equipped with an Intel i9-12900 processor, 32 GB of RAM, 1 TB of SSD storage, and a network interface card with 1 Gbit/s capacity. We used the NPCT75x TPM 2.0 as our hardware TPM model. All hosts ran Ubuntu 20.04 as the operating system, using QEMU version 4.2.1. For the experiments, we configured the management VM to run Ubuntu 22.04, with 4 vCPUs and 4 GB of main memory. The user VMs ran OpenSUSE 5.14, with 1 vCPU and 1.5 GB of main memory. Each VM was equipped with a vTPM instance emulated by SWTPM version 0.8.1, using the libtpms library version 0.9.6.  
To evaluate the proposed solution, we aim to analyze the overhead introduced by remote anchoring in comparison to local anchoring. For this purpose, we consider that each of the two hosts (Host 1 and Host 2) hosts ten user VMs and one management VM (m-VM1 and m-VM2). In the local anchoring scenario, we assume that each management VM is anchored locally; that is, m-VM1 is anchored to the TPM of Host 1 and m-VM2 is anchored to the TPM of Host 2. In the remote anchoring scenario, we assume that m-VM1 is remotely anchored to the TPM of Host 2.  
To modify the volatile state, 10, 100, 1000, 10,000, and 100,000 extends were performed for each scenario on each of the m-VMs. Each extend triggers an anchoring operation; therefore, the time of each anchoring on m-VM1 was measured. The goal is to evaluate the anchoring time when m-VM1 is anchored locally and remotely. Additionally, the remote anchoring scenario was executed both with and without mTLS in order to assess the overhead introduced by the protocol on anchoring time.  
Additionally, during the execution of the scenarios, we monitored CPU and memory usage to evaluate the load imposed by our solution on these resources. Furthermore, to establish a baseline for the system's standard resource consumption, we measured the resource usage of the environment without the proposed solution.  
During the experimental process, in order to mitigate threats to conclusion validity, preliminary experiments were conducted to estimate the required sample size. Furthermore, the bootstrap resampling method [60] was employed, with estimates reported along with 95% confidence intervals.
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)
#### 3.1.2 Gnss-R  
As discussed, GNSS-R is a passive RS satellite utilizing the GNSS signals to estimate the OSW speed using the L-band (frequency and wavelength around 1.5 GHz and 20 cm) [34]. GNNS-R measures multiple points over the ocean to estimate the OSW speed, and its geometry is not like a swath, individual point, or limited to the nadir direction [91]. In contrast to traditional OSW speed retrieval methods, GNSS-R has better spatiotemporal sampling due to the global accessibility of the GNNS signal, which offers greater coverage over the ocean [35]. Another advantage of GNSS-R is its lower sensitivity to small atmospheric particles and small-scale capillary waves since it utilizes a longer wavelength compared to scatterometers (i.e., wavelengths = 2-5 cm) [35]. Additionally, lower cost and lower power needed for the GNSS-R receiver are the other benefits of these systems for OSW estimation [92].  
The first GNNS-R data were collected by the UK-DMC satellite between 2004 and 2005 [37]. Later, in July 2014, the second spaceborne polar-orbiting satellite (TDS-1) was equipped with the Space GNSS Receiver Remote Sensing Instrument (SGR-ReSI) to collect the reflected GNNS signals [92]. The first OSW speed estimation from the TDS-1 mission was discussed in [92]. This study suggested an OSW speed retrieval method based on the Signal-to-Noise Ratio (SNR) and GNSS-R Biostatic Radar Equation. The Meteorological Operational (MetOp) Advanced SCATterometer (ASCAT) satellite measurements were used to validate the results. It was observed that when SNR was greater than 3 dB, OSW speed could be estimated with an accuracy of approximately 2.2 m/s for wind speed values between 3 and 18 m/s [92]. In late 2016, an advanced GNSS-R receiver was installed on the CYGNSS to provide higher-quality OSW speed products [93].  
Fusing measurements from multiple GNSS-R systems can improve the spatiotemporal resolution of the estimated OSW speed [94]. Retrieval of OSW speed from GNNS-R data is usually based on quantities known as observables, which are extracted from the GNSS delay-Doppler maps peak [7, 13]. A series of algorithms, including the delay-Doppler map variance, delay-Doppler map average, trailing edge slope, leading edge slope, and Allan delay-Doppler map variance, have been so far introduced in [8] for OSW speed estimation from GNSS-R data. The authors also suggested a new OSW speed estimation algorithm, known as minimum variance, based on the integration of OSW data from each single observation [35]. Moreover, a new parametric Geophysical Model Function (GMF), extracted from the CYGNSS measurements, was proposed by [95] to relate the OSW speed to the two observables of the slope of the leading edge of the radar return pulse scattered by the ocean surface and the normalized bistatic radar cross-section of the ocean surface. In this study, an overall RMSE of 1.4 m/s was obtained when the results were compared with in situ data [95].
## Appendix A Proof of Equation (15)  
From Equation (11), we can rewrite Equation (15) as  
\[y_{l} =\mathbf{w}_{l}^{H}[\sum_{k=1}^{K}\left(\beta_{1}\sqrt{P_{s}} \mathbf{h}(\theta_{dk})s_{k}+\beta_{2}\sqrt{P_{s}}\mathbf{T}_{k}\mathbf{z}_{ k}\right)+\mathbf{n}^{{}^{\prime}}]\] \[=\underbrace{\mathbf{w}_{l}^{H}\beta_{1}\sqrt{P_{s}}\mathbf{h}( \theta_{dl})}_{p1}s_{l}+\underbrace{\mathbf{w}_{l}^{H}\sum_{k=1,k\
eq l}^{K} \beta_{1}\sqrt{P_{s}}\mathbf{h}(\theta_{dk})s_{k}}_{p2}+\underbrace{\mathbf{ w}_{l}^{H}\sum_{k=1}^{K}\beta_{2}\sqrt{P_{s}}\mathbf{T}_{k}\mathbf{z}_{ k}}_{p3}+\underbrace{\mathbf{w}_{l}^{H}\mathbf{n}^{{}^{\prime}}}_{p4}. \tag{16}\]  
Here, for \(p1\), using Equations (13) and (15), we have  
\[p1 =(\mathbf{P}_{A}^{\perp}\mathbf{h}(\theta_{dl}))^{H}\beta_{1} \sqrt{P_{s}}\mathbf{h}(\theta_{dl})s_{l}\] \[=\mathbf{h}^{H}(\theta_{dl})(\mathbf{P}_{A}^{\perp})^{H}\beta_{1} \sqrt{P_{s}}\mathbf{h}(\theta_{dl})s_{l}\] \[=\beta_{1}\sqrt{P_{s}}s_{l}[\mathbf{h}^{H}(\theta_{dl})\mathbf{ h}(\theta_{dl})-\mathbf{h}^{H}(\theta_{dl})\mathbf{A}(\mathbf{A}^{H}\mathbf{A})^{-1} \mathbf{A}^{H}\mathbf{h}(\theta_{dl})]\] \[=\beta_{1}\sqrt{P_{s}}s_{l}(1-\xi). \tag{17}\]  
For \(p2\), using Equation (16), we have  
\[p2 =\beta_{1}\sqrt{P_{s}}s_{k}\sum_{k=1,k\
eq l}^{K}\mathbf{w}_{l}^{H} \mathbf{h}(\theta_{dk}) =0. \tag{18}\]  
Substituting Equations (14) and (15) into \(p3\) in Equation (A1) and using Equation (7), we have\[p3 =\beta_{2}\sqrt{P_{s}}\sum_{k=1}^{K}\mathbf{w}_{l}^{H}\mathbf{T}_{k} \mathbf{z}_{k}\] \[=\beta_{2}\sqrt{P_{s}}\sum_{k=1}^{K}\left[\mathbf{h}^{H}(\theta_{dl} )\mathbf{T}_{k}\mathbf{z}_{k}-\mathbf{h}^{H}(\theta_{dl})\mathbf{A}(\mathbf{A}^ {H}\mathbf{A})^{-1}\mathbf{A}^{H}\right)\mathbf{T}_{k}\mathbf{z}_{k}\right]\] \[=\beta_{2}\sqrt{P_{s}}\sum_{k=1}^{K}\left[\mathbf{0}\mathbf{z}_{k }-\mathbf{h}^{H}(\theta_{dl})\mathbf{A}(\mathbf{A}^{H}\mathbf{A})^{-1} \mathbf{0}\mathbf{z}_{k}\right]\] \[=0. \tag{10}\]  
Therefore, by Equations (A2)-(A4), Equation (A1) can be transformed into  
\[y_{l}=\beta_{1}\sqrt{P_{s}}s_{l}(1-\tilde{\xi})+n_{l}. \tag{11}\]  
The proof is completed.
# Cyclostationary Crosstalk Cancelation in High-Speed Transmission Lines
## 5 Conclusions  
The current study focuses on the realization of the parallel high-speed data transferring busses can encounter and induce unintentional emission of crosstalk signals. The assurance of signal integrity in such data transmission links can be achieved by the crosstalk cancelation using a transceiver utilizes the cyclostationary properties of crosstalk. The model of the victim signal consists of the valuable data signal corrupted by the additive of the cyclostationary aggressor signal and stationary noise. The independence of the crosstalk and informative cyclostationary processes is justified by the independence of the information bit sequences in communication lines of the aggressor and the victim. In this  
Figure 16: Eye-opening width versus bit rate from 0.4 Gbit/s to 2.5 Gbit/s.  
Figure 15: Bit error rate versus voltage for time 1.25 ns: (**a**) bit rate is 0.4 Gbit/s; (**b**) bit rate is 2.5 Gbit/s.  
case, the filtering of the observed measured random process can be implemented by the cyclic Wiener filter, estimating the message in the measured signal.  
Filtering of the random process with periodical correlation properties needs to be implemented by the linear system with a periodically changing impulse response. It is shown that the output of an LPTV structure may be expressed as a sum of the filtered versions of frequency shifted copies of the input cyclostationary random process. The equivalent time-domain sliding window transformation of the cyclostationary random process assumes the serial to parallel transformation of the input signal, multi-input to multi-output linear filtering of this vector process, and the parallel to serial transformation providing the filtered cyclostationary output signal.  
The experimental results obtained by cyclic averaging of the measured random processes before and after the cyclic Wiener filtering showed significant reduction in the crosstalk interference components in the estimated message signal. The evaluated eye diagrams provide the estimation of the bit error rate which quantitatively characterizes the transmission of data after the implemented filtering of the aggressor's interference. The investigated dependence of the eye-opening width versus bit rate of the data transferring showed the reduction in the eye width caused by a decrease in the signal to interference ratio of the measured signal.  
Conceptualization, Y.V.K., A.B.B. and M.A.K.; writing--original draft preparation, M.A.K.; writing--review and editing, Y.V.K., A.B.B. and A.A.G.; supervision, Y.V.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank [PERSON] and [PERSON] for the organization and fruitful support of the experimental investigations in the laboratory of the Munich Technical University and [PERSON] for substantial discussions of the theoretical results.  
The authors declare no conflict of interest.
## 3 Case-Study Methodology and Development
### Weibull Cumulative-Distribution Function and Reliability Function  
In this paper, two distribution functions, namely, the Weibull cumulative-distribution function (CDF) and the corresponding reliability function, have been used for the digitalization of transformer failures [22; 25; 26]. DT-failure data have been recorded in databases from 1 September 2016 to 31 October 2022 from the Western Province South II branch of Ceylon Electricity Board, and those data have been used for the analysis. The Western Province South II branch is having similar geographical and weather conditions in all over the region. Therefore, this region has been considered as acceptable for the reliability analysis of the DT-failure data.  
The following categories of transformers were recorded as showing failures for the past six years. The apparent power categories of DTs with failure data for the DT voltages \(11\) kV/0.4 kV are provided in Table 1.  
The argument power categories of \(11\) kV/0.4 kV DTs with failure data.  
The failure data of the above categories of DTs were analyzed using Python program-based Weibull analysis. Weibull CDF analysis was selected due to its versatility and modeling capability for a variety of life cycle behaviors of crucial components [13].  
The alpha and beta parameters for Weibull distributions can be calculated using maximum likelihood estimation (MLE) or the method of moments, in which sample moments (e.g., mean, standard deviation, etc.) are matched with the theoretical moments of the distribution [19]. The \"fitters\" library in the Python programming language provides easy-to-use interfaces for fitting Weibull distributions, as well as estimates for the alpha and beta parameters, including other useful features such as goodness-of-fit tests, plots, and confidence intervals with optimization algorithms [19]. Hence, the \"fitter\" library in the Python programming language -has been used to plot the Weibull CDFs and survival functions in this manuscript. The resulting Weibull CDF plots for DT-failure analysis are provided in Figures 4-9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Apparent power (kVA) category & 100 & 160 & 250 & 400 & 630 \\ \hline \end{tabular}
\end{table}
Table 1: The apparent power categories of \(11\) kV/0.4 kV DTs with failure data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Apparent power (kVA) category & 100 & 160 & 250 & 400 & 630 & 800 & 1000 \\ \hline \end{tabular}
\end{table}
Table 2: The apparent power categories of \(33\) kV/0.4 kV DTs with failure data.  
Figure 4: Weibull CDF for \(11\) kV/0.4 kV-100 kVA and \(160\) kVA DT failures.  
Figure 5: Weibull CDF for \(11\) kV/0.4 kV-250 kVA and \(400\) kVA DT failures.  
Figure 6: Weibull CDF for \(11\) kV/0.4 kV-630 kVA and \(33\) kV/0.4 kV-100 kVA DT failures.  
Figure 8: Weibull CDF for 33 kV/0.4 kV–400 kVA and 630 kVA DT failures.  
Figure 7: Weibull CDF for 33 kV/0.4 kV–160 kVA and 250 kVA DT failures.  
Figure 9: Weibull CDF for 33 kV/0.4 kV–800 kVA and 1000 kVA DT failures.