esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Spatial Distribution of Indicators Based on the 3D Geological Model  
#### 3.2.1 3D Geological Structure Model  
Based on the findings of the engineering geological survey and geotechnical investigation from the collected reports, 10 sequences of the study area are established. The collected drilling data are partitioned into stratigraphic sequences. Furthermore, the virtual drilling data are created based on the geological map to control the horizontal interface between rock and soil. The 3D geological structure model is built based on the 3D Kriging interpolation method within the EVS software, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**No.** & **Reports** \\ \hline
1 & Report of Hydrogeological Survey in Nanjing (1:200,000) \\
2 & Comprehensive Survey Report on Hydrogeology, Engineering Geology, and Environmental Geology of Nanjing (1:50,000) \\
3 & Evaluation Report on Groundwater Resources in Nanjing, Jiangsu Province \\
4 & Environmental Geological Survey Report of Urban Planning Area along the Yangtze River from Nanjing to Zhenjiang (1:50,000) \\
5 & Geotechnical Investigation Report of Nanjing Metro Line 10 \\
6 & Geotechnical Investigation Report of Nanjing Jiangbei New District Integrated Pipe Gallery Phase II Project \\
7 & Geotechnical Investigation Report of Pubin Road in Nanjing \\
8 & Geotechnical Investigation Report of other 30 construction sites \\ \hline \hline \end{tabular}
\end{table}
Table 3: Collected geological and geotechnical reports.  
Figure 3: The location of the study area based on a Google Earth image.
# Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges
## 1 Introduction
However, there is an exception if the weak scattering target of interest is subjected to some variations, which may be caused by the following:  
* temporal fluctuations inherently connected with the test scenario (e.g., the vital motion of inner organs of humans and animals, the breathing motion of buried survivors after an earthquake, and the motion of wood-destroying insects, as well as slowly running events, such as the putrefaction of biological substances, the healing process after a medical surgery [6], post-event monitoring of stroke [7], and many more);
* a targeted influence of the hidden object of interest via modification of its position in space, its volume, and its permittivity or permeability (e.g., the targeting of malignant tissue by nanoparticles, permittivity variation by local heating or cooling [8; 9], water accumulation in hygroscopic substances, etc. ); and
* small deviations between two largely identical SUTs (cancer in one of the two female breasts [10], foreign objects in chocolate, other identical food pieces, etc.). In all of these examples, we are only interested in the imaging and localization, respectively, of small differences--hence, we call it differential imaging--in the scattering scenario in its different states. Basically, the imaging may be based on a tomographic approach (see e.g., [9]) or on radar image processing. Tomographic methods usually work with narrowband signals and require a dense antenna array, while radar-based techniques need wideband sounding signals but do not require a dense antenna array [5]. The aim of the paper is to introduce the reader to the concept of differential imaging and to familiarize him or her with the experimental challenges of a successful data capturing and target extraction. For the sake of introduction, we assume here that the objects of interest are relatively small compared to the size of the observation space. This allows us to deal with the simpler radar imaging approach instead of the inverse scattering applied in microwave tomography. The experimental challenges will, however, not be touched by that simplification. In what follows, a simplified signal model to describe the scattering scenario will be introduced with emphasis on the most important aspects of differential imaging. Based on this consideration the most important parameters of UWB devices for differential imaging will be considered. Several examples and measurements are shown for illustration.
## 4 Implementation of RUL Prediction Based on Proposed Method
### Experimental Verification and Analysis
#### 4.4.4 Generalization Verification  
To verify the generalizability of the proposed method, the XJTU-SY full-life rolling bearing dataset was used for method validation. This dataset includes full-life cycle data for 15 bearings under three different operating conditions. The sampling frequency is 25.6 kHz, the sampling duration is 1.28 s, and the sampling interval is 1 min. The specific experimental procedure and details can be found in reference [46]. Bearings B2-2 and B2-3 were selected for training and testing using the same experimental setup and process as the PHM 2012 dataset. Bearing B2-2 was used as the training set, while B2-3 was used as the test set. The prediction results are shown in Figure 19 and Table 10.  
As shown in Figure 19, during the early prediction phase, all models failed to make accurate predictions. In the middle phase, all four models exhibited a certain degree of lag in their predictions, but GRU and the proposed model showed a lower level of lag. In the later prediction phase, both the TCN and Transformer models experienced lag, while GRU's predicted values were too low, leading to potential waste in predictive maintenance. In contrast, the proposed model's predictions were closest to the true values and did not exhibit any lag, making it more practical. From Table 10, it can be seen that the proposed model outperformed GRU with a 23.8% reduction in MAE and a 16.8% reduction in RMSE. Compared to TCN, MAE was reduced by 15.0% and RMSE by 10.8%. When compared to Transformer, MAE was reduced by 21.5% and RMSE by 18.6%. Through comparative analysis, it is further demonstrated that the proposed model offers higher prediction accuracy.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 6 The UAV as a Complex Maintenance System
### The Failure Rate Paradox
In light of these considerations, we review, in Figure 8 (Figure 8a refers to \"drone a\" and Figure 8b refers to \"drone b\"), the previous confidence interval area considered for uncertainty in Figure 6:  
Due to the good knowledge of the systems and subsystems of the military drone (hereafter referred to as \"drone b\"), we can have the basis for a much wider Gaussian, and conversely, the value of \(\alpha\). Reviewing the interval in Figure 7, for the two different drones we have the following uncertainties (see Figure 9):  
Considering \"Drone a\", although its reliability is considerably higher, the knowledge of the components is lower. All this is reflected, from the analytical point of view, in the \"shrinking\" of the green band (see Figure 9), or the limits of preventive and corrective maintenance which are very close to each other. From the real point of view, this means that if we do not want to overcome the new limit \(H_{i}/(1+\alpha)\), it is necessary to slightly reduce interval t, with a consequent increase of maintenance costs and a decrease in the general figure of availability. Figure 8: Confidence interval area considered for uncertainty: commercial drone (**blue**) and military (**yellow**). Figure 9: Uncertainty evaluation of corrective maintenance: original interval area (**red**) and the evaluation of the confidence interval (**green**). The \"Drone b\", (military) has, despite a bit of \"shearing\" by the \(\alpha\) factor dropping a good margin, an increase in the frequency of the maintenance cycle, which will always remain modest.
# The CYGNSS Mission: On-Going Science Team Investigations
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Member** & **[PERSON]** \\ \hline Home Institution & University of Michigan \\ CYGNSS Role & Principal Investigator \\ Areas & Earth environment remote sensing methods, instrumentation, atmosphere \\ \hline Member & [PERSON] \\ \hline Home Institution & University of Southern California \\ CYGNSS Role & Terrestrial Science Lead, Co-I \\ Areas & Inverse scattering, subsurface characterization, water resources \\ \hline Member & [PERSON] \\ \hline Home Institution & Jet Propulsion Laboratory, California Institute of Technology \\ CYGNSS Role & Atmospheric Science Lead, Co-I \\ Areas & Clouds and precipitation, data assimilation, uncertainty quantification \\ \hline Member & [PERSON] \\ \hline Home Institution & Massachusetts Institute of Technology \\ CYGNSS Role & Soil moisture sensor networks, calibration, and validation \\ Areas & Microwave remote sensing of Earth, hydrology, wireless sensor networks \\ \hline Member & [PERSON] \\ \hline Home Institution & The Ohio State University \\ CYGNSS Role & CYGNSS Science Team member \\ Areas & Microwave remote sensing of the Earth, ocean and land applications \\ \hline Member & [PERSON] \\ \hline Home Institution & Jet Propulsion Laboratory, California Institute of Technology \\ CYGNSS Role & Competed Science Team Member, CYGNSS ocean surface heat flux product \\ Areas & Extratropical cyclones \& air-sea fluxes \\ \hline Member & [PERSON] \\ \hline Home Institution & Jet Propulsion Laboratory, California Institute of Technology \\ CYGNSS Role & CYGNSS Science Team member \\ Areas & Metereological and hydrological applications, Earth sciences \\ \hline Member & [PERSON] \\ \hline Home Institution & SRI International \\ CYGNSS Role & CYGNSS Science Team member \\ Areas & Hydrology/storm surge modeling \\ \hline Member & [PERSON] \\ \hline Home Institution & University of Michigan \\ CYGNSS Role & CYGNSS Science Team member \\ Areas & Surface scattering, Earth sciences \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some key-members of the CYGNSS Science Team. The full list is available in [14].
## 3 Results and Discussion
### Chemical Analysis of Capia Pepper
Figure 2 shows the effect of drip irrigation wastewater treatments on heavy metals such as Cd, Pb, Ni, Mn, Co, and Mn in capia pepper. In both the Amfora and Yalova Yaglik varieties, the WW1 treatments achieved the highest values for all heavy metals. This was followed by WW2 and MW, respectively. In another study [37] which investigated the impact of wastewater produced by the Kalecik wastewater treatment plant on heavy metal content in capia pepper applied with the flood irrigation method, Cd and Pb contents were found to be higher than those observed in our study using the drip irrigation method. This indicates that the heavy metal content also depends on the irrigation method. Furthermore, given that the plant stem, leaves, and fruit were not subjected to wetting in the drip irrigation method, it can be concluded that the heavy metal levels in the fruit were lower. The analyses revealed that the heavy metal contents of capia pepper samples irrigated with wastewater were below the threshold levels permitted for human nutrition. The heavy metal content of the peppers irrigated with wastewater was examined, and it was found to be below the permissible limit values for vegetables according to the FAO/WHO stated in Table 6. This leads to the conclusion that there is no drawback to using the wastewater used in the study as irrigation water for capia peppers. Figure 2: Heavy metal content of capia-type pepper samples. Data are means \(\pm\) standard error, n = 3. Different letters in each column indicate that they are statistically different (\(p\leq 0.05\); LSD test). The non-letters are not statistically significant (\(p>0.05\); LSD test). MW, Municipal water; WW1, Physical treatments; WW2, Physical + biological treatment; N.C, not calculated.
## 2 The Model for Propagation
### Scattering from the Stiffener  
Complex phenomena, such as scattering, interference, and mode conversion, occur when the fault AE signal encounters deficiencies or discontinuities, generating reflection and transmission responses with a complex superposition of multimode waves converted from the initial signal. The AE signal propagating to the stiffener produces a reflection signal, s transmitted signal, and signals traveling alongside the stiffener. A diagram of the AE signal propagating in a cylindrical shell with a stiffener is depicted in Figure 2. The AE signal propagating in the discontinuity using the scattered response can be described using reflection and transmission coefficients.  
Figure 1: The dispersion curves of a cylindrical shell with an outer diameter of 1000 mm and a wall thickness of 2 mm: (**a**) phase velocities; (**b**) group velocities.  
The equation of displacement for the incident wave is as follows:  
\[u_{inc}=u\cdot e^{i(kx-\omega t)} \tag{1}\]  
where \(u,k\), and \(\omega\) are the amplitude, the wave number, and the angular frequency of the incident signal, respectively. The reflected signal can be described as a superposition of the incident signal mode and all reflected signal modes.  
\[u^{l}=u(z)\cdot e^{i(kx-\omega t)}+\sum_{n=1}^{N}u_{n}(z)\cdot e^{i(k_{n}x- \omega_{n}t)} \tag{2}\]  
where \(u(z)\) and \(u_{n}(z)\) represent the amplitude of the direct signal and that of the \(n\)-th reflected signal, respectively.  
The reflection coefficient can be defined as the amplitude ratio of the reflection signal and the incident signal. Then, the reflection coefficients \(R_{n}\) of the \(n\)-th wave packet can be written as follows:  
\[R_{n}=\left|u_{n}(z)\right/u(z)\right| \tag{3}\]  
Similarly, the transmitted field can be described as a superposition of all transmitted wave modes.  
\[u^{\prime}=\sum_{m=1}^{M}u^{\prime}_{m}(z)\cdot e^{i(k_{m}x-\omega_{m}t)} \tag{4}\]  
where \(u^{\prime}_{m}(z)\) indicates the amplitude for the \(m\)-th transmitted wave. Additionally, the transmission coefficient can be written in terms of the amplitude ratio between the transmission signal and the incident signal collected by the same position in the unstiffened cylindrical shell. The transmission coefficient \(T_{m}\) of the \(m\)-th wave packet can be calculated as follows:  
\[T_{m}=\left|u^{\prime}_{m}(z)\right/u^{\prime}_{0}(z)\right| \tag{5}\]  
where \(u^{\prime}_{0}(z)\) denotes the amplitude of the incident wave propagating to the same position in the unstiffened cylindrical shell.
# Sentinel-1 GRD Preprocessing Workflow +
## 2 Workflow
### Speckle Filtering  
Speckle, appearing in SAR images as granular noise, is due to the interference of waves reflected from many elementary scatterers [5]. Speckle filtering is a procedure to increase image quality by reducing speckle. When such a procedure is done at an early processing stage of SAR data, speckle is not propagated in ongoing processes (i.e., terrain correction or conversion to dB). Speckle filtering is not advisable when there is an interest in the identification of small spatial structures or image texture, since it might remove such information. The refined Lee filter has been found to be superior, with respect to other single product speckle filters, for visual interpretation, because of its ability to preserve edges, linear features, and point target and texture information [5]. More recently, multitemporal speckle filters have been developed to reduce speckle, taking advantages from multiple SAR observations in time. The proposed preprocessing workflow includes a speckle filtering step, which could be skipped by selecting 'None' as the filter type. Currently, one of the following filters is available in the SNAP single product speckle filter operator: 'Boxcar', 'Median', 'Frost', 'Gamma Map', 'Lee', 'Refined Lee', 'Lee Sigma', 'IDAN'.
## 5 Conclusions  
In this paper, a novel GC with an adaptive scaling scheme is proposed, verified and applied to GNSS signal acquisition through simulation and experiment. It combines an adaptive scaling scheme that guides and controls the signal acquisition parameter boundaries and population size. Indeed, this proposed method can shorten the acquisition time and yield more accurate parameters using larger numbers of initial populations. Besides, the proposed scheme is simple in operation process and easy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Signal type & \multicolumn{3}{c}{**GPS C/A PRN-8**} \\ \hline \multirow{2}{*}{Method} & \multirow{2}{*}{\begin{tabular}{c} **Traditional** \\ **(Serial search)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **GC with** \\ **adaptive** \\ **scaling scheme** \\ \end{tabular} } & \multicolumn{2}{c}{**GC w/o adaptive scaling**} \\  & & & **adaptive** \\ \hline \multirow{2}{*}{Code delay \(\hat{\tau}\) (chip)} & \multirow{2}{*}{\begin{tabular}{c} 34.43 chip \\ (550 samples) \\ \end{tabular} } & \multirow{2}{*}{553 samples} & \multirow{2}{*}{548 samples} \\  & & & & \\ \hline Frequency shift \(\hat{f}_{d}\) (Hz) & 2250 Hz & 2275 Hz & 2268 Hz \\ \hline \multirow{3}{*}{Repetitions} & \multirow{3}{*}{\begin{tabular}{c} 6563568 \\ (401 \(\times\) 16368) \\ (50 Hz step size) \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} 16048 \\ (29 iterations) \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} 84000 \\ (25 iterations) \(\times\) 1500 \\ \end{tabular} } \\  & & & & \\ \hline Post-correlation SNR (dB) & 18.7897 & 18.8675 & 18.8602 \\ \hline Execution Time (sec) & 1817.24 & 806 & 705 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of experiment results.  
Figure 6: _Cont._  
in digital circuit implementation owing to its choice of 0 and 1. In the future, the goal is oriented towards the reduction of hardware implementation complexity to meet the demand for low cost.
# Unconstrained Estimation of Multitype Car Rental Demand
## 3 Hybrid Forecasting
### Holt-Winters Model  
The Holt-Winters forecasting model is observed to outperform other techniques for the time series, having changing seasonality, mean, and growth rate [39]. It is an adaptive model that automatically recognizes changes in data patterns. For example, if the deviation is caused by internal interference, it can be considered that the new observation has the same influence as the original data, and thus gives the same weight to the data of different periods. If the deviation is caused by external interference, then new observations and the original data have different influences on the prediction results, and the new observations have a higher impact on the prediction events. In order to show that the value of the data in different periods has a different influence on the forecasting results, different weights can be given to different periods.  
The demand for car rental is a nonstationary time series with seasonal and cyclical trends. The Holt-Winters model can perform very accurate forecasting of this regular time series data, especially for trends and seasonal changes. It can decompose linear time series, seasonal variations, and random variation time series and properly filter the impact of random fluctuations. The Holt-Winters model is an improvement and development of the moving average method. It does not need to store much historical data but also considers the importance of each period of data and uses all historical data.  
First, smoothing equations can be obtained by iteration:  
\[\begin{array}{l}T_{i,t}=\alpha\frac{F_{i,t}}{U_{i,t-1}}+(1-\alpha)(T_{i,t-1}+S_ {i,t-1})\\ S_{i,t}=\beta(T_{i,t}-T_{i,t-1})+(1-\beta)S_{i,t-1}\\ U_{i,t}=\gamma\frac{F_{i,t}}{T_{i,t}}+(1-\gamma)U_{i,t-L}\end{array} \tag{30}\]  
where \(T_{i,t}\) is the smoothed value, \(S_{i,t}\) is the trend value, and \(F_{i,t}\) is the seasonal index of car type \(i\) at \(t\) after iterations. \(L\) is the length of the seasonal period, and \(\alpha\),\(\beta\),\(\gamma\) are smoothing coefficients.  
Then, the predicted value at the future time point \(k\) is  
\[\hat{F}_{i,t+k}=(T_{i,t-1}+kS_{i,t-1})U_{i,t-L+k} \tag{31}\]  
where \(U_{i,t-L+k}\) is the seasonal index of car type \(i\), which is one cycle ahead of \(t\), \(k\in\{1,2,\cdots,kL\}\).  
The way to determine the three smoothing coefficients \(\alpha\), \(\beta\), and \(\gamma\) is to minimize the error between the forecasted and actual values. In order to obtain more accurate and objective parameters, the traditional method is the residual square sum minimum method. Smoothing coefficients are all located in the interval (0, 1) and increase with 0.1 step length. The squares of the prediction residuals are calculated separately and summed until the smoothing coefficients corresponding to the sum of squares of the smallest residuals are found.
### Novelty and Purpose of the Study  
This study proposes a novel extensible data clustering-based feature matching model to address spatial variability and corrosion interaction complexities present in ILI data, two phenomena exclusively point matching-based models fail to comprehensively describe. The model's framework aims to capture these complexities by integrating data clustering and classification with point matching. First, data clustering is utilized to segment the data into spatially localized clusters. These clusters are then classified based on their density. Subsequently, the feature matching problem is addressed based on the characteristics of each cluster. As illustrated in Figure 7, this approach should enable the following:  
1. Isolated correspondence matching: Clustering identifies isolated correspondent defects along the pipeline, which are easily matched due to their strong correspondence and minimal interference from surrounding defects.
2. Merging defect matching: Clustering facilitates the development of tailored matching strategies based on cluster characteristics, such as defect merging, a critical aspect often overlooked or oversimplified by traditional feature matching models.
3. Localized transformations: Clustering segments the data into localized regions with minimized distortion and outliers, enabling more precise affine transformations that capture feature displacements.  
In addition to that, a novel clustering technique--directional epsilon neighborhood clustering (DENC)--is developed. DENC employs spatial graph structures and directional proximity thresholds:  
1. Aligning with the directional variability in ILI data: Directional thresholds effectively capture axial and circumferential variability, providing a more realistic and accurate representation of spatial relationships.  
The remainder of this paper is organized as follows. Section 2 provides a detailed description of the model's structure, accompanied by a walk-through example of the matching problem presented in Figure 4 through Figure 6. Section 3 highlights the key findings of a detailed performance analysis of the proposed model for a case study consisting of six pipeline segments with varying ILI data complexities. Section 4 discusses the results and outlines potential future work, while Section 5 presents the final conclusions.  
Figure 7: Illustration of the matching problems the proposed framework aims to solve, demonstrating how clustering should facilitate isolated correspondence matching, merging defect matching, and localized transformation problems across ILIs.