esa-sceva/satcom-chunk-collection huggingface dataset:
# Train Speed Estimation from Track Structure Vibration Measurements
## 5 Conclusions  
This paper proposed a train speed estimation method by extracting the characteristics of the vibration measurements of rail acceleration, slab acceleration, and slab displacement. The train speed can be automatically and reliably calculated by the combination of the extracted characteristics and the geometric dimension of the train. The results showed that the average differences of the train speed calculated by phase differences and the proposed method were 0.61% for slab displacement measurements, 1.39% for rail acceleration measurements, and 2.97% for slab acceleration measurements. In addition, the method is stable regardless of the sensor position and the track structure situation.  
The proposed method can be applied extensively under two conditions: the signal waveform of vertical acceleration and displacement produce pronounced peaks when the rail wheels pass through the test site, and all the passing trains have the same geometric dimensions. Therefore, this method offers an efficient way for track structural health monitoring to obtain the train speed from track structure vibration measurements without additional speed measurement module. The method meets the requirement of speed estimation in the short-term, multi-location, and tough environment of structural health monitoring. Noteworthily, it is also a successful attempt to introduce image processing technology into high speed rail. This idea can be further applied to railway analysis to make up the insufficiency of tradition signal processing methods.  
Conceptualization, X.W. and X.S.; Methodology, X.W. and J.W.; Validation, X.W.; Funding acquisition, X.S. and B.H.; Investigation, X.W., J.W., and X.Y.; Project administration, X.S. and B.H.; Supervision, X.S., X.Y., and B.H.; Writing--original draft, X.W. and J.W.; Writing--review & editing, X.S., X.Y. and B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hebei province (E2019210264) and the Fundamental Research Funds for the Central Universities in China (DUT18 GJ203). And the APC was funded by the Natural Science Foundation of Hebei province (E2019210264). The authors thank the funding supported from the Natural Science Foundation of Hebei province (E2019210264) and the Fundamental Research Funds for the Central Universities in China (DUT18 GJ203). The authors declare no conflict of interest.
###### Abstract  
Landslides are among the most serious and frequent environmental disasters, involving the fall of large masses of rock and soil that can significantly impact human structures and inhabited areas. Anticipating these events is crucial to reduce risks through real-time monitoring of areas at risk during extreme weather events, such as heavy rains, allowing for early warnings. This study aims to develop a methodology to enhance the prediction of landslide susceptibility, creating a more reliable system for early identification of risk areas. Our project involves creating a model capable of quickly predicting the susceptibility index of specific areas in response to extreme weather events. We represent the terrain using cellular automata and implement a random forest model to analyze and learn from weather patterns. Providing data with high spatial accuracy is vital to identify vulnerable areas and implement preventive measures. The proposed method offers an early warning mechanism by comparing the predicted susceptibility index with the current one, allowing for the issuance of alarms for the entire observed area. This early warning mechanism can be integrated into existing emergency protocols to improve the response to natural disasters. We applied this method to the area of Prunella, a small village in the municipality of Melito di Porto Salvo, known for numerous historical landslides. This approach provides an early warning mechanism, allowing for alarms to be issued for the entire observed area, and it can be integrated into existing emergency protocols to enhance disaster response.  
landslides; cellular automata; random forest; landslides susceptibility map; machine learning; remote sensing Article
## 3 Validation and Results
### Three Intermediate Systems  
To evaluate possible degeneracy in larger systems we generated 15 independent three-intermediate data sets using random combinations of **P** and **K**. We wanted to produce \(\vec{C}_{E}(t)\) that had characteristics similar to what could be obtained by current \(\cdot\)OH footprinting technology. We therefore constrained our choice of values in the **K** matrix in the following way: 1.) We generated a random **K** matrix with values between 0 and 1, and the reverse rates (\(r\)) were multiplied by \(10^{-3}\) while the forward rates (\(k\)) were multiplied by a factor of 10 for 8 of the theoretical data sets. We solved Equation 5 numerically to obtain \(\vec{x}(t)\) for each **K** matrix. 2.) For the other 7 theoretical data sets we chose rates such that the path U\(\rightarrow\)I\(\rightarrow\)I2\(\rightarrow\)I3\(\rightarrow\)F was favored by a factor 10 while the other rates were generated as in 1. 3.) We visually inspected the resulting \(\vec{x}(t)\) curves to confirm that this approach generated a data set that is consistent current \(\cdot\)OH footprinting technology. 4.) A random **P** matrix was then used to generate the \(\vec{C_{E}}(t)\) curves using Equation 6. 5.) We applied our algorithm to recover the **K** and **P** matrix for the time progress curves generated \(\vec{C_{E}}(t)\). Table 1 summarizes our results from this analysis and reveals that in a majority of the cases, we are indeed able to identify a single model that fits the data with a RMSE at least one order of magnitude lower than the second best fitting model (difference between first and second RMSE column in Table 1.) In the three cases where this is not the case (Test Number 1, 3, and 6), the degeneracy is a result of different **P** matrices leading to curves with identicial shape (as illustrated in Figure 3), such that the two top models are in fact equivalent, but the association of state curves to intermediates is different. (This equivalence arises from an exchange of columns in the **P** matrix however it is still unclear why in some cases a row exchange can result in an equivalent model while in others it does not.) Furthermore, our non-linear least squares optimization approach accurately finds the correct rate constants (\(k\)) from the curves, with errors in general between 5 and 15%. These results illustrate that for a wide variety of systems with three intermediates, it is generally possible to identify a single kinetic model both accurately and reproducibly. These results also suggest the method will be robust with respect to experimental noise, as the RMSE differences between the models that need to be tested are large (between one and four orders of magnitude). Three intermediate systems are the largest that are currently experimentally tractable.
## 2 Materials and Methods
### Atmospheric Correction Processors  
Atmospheric correction is a crucial step to accurately retrieve Chl-\(a\) concentrations from satellite data because at-sensor radiance is affected by atmospheric gaseous molecules and aerosols, especially at shorter wavelengths [22; 50], as well as by other factors such as air-water interface, wind, and sunlight [51]. While some atmospheric correction processors considered here can correct sunlight-related effects to a degree, an investigation of these effects is beyond the scope of our study.  
The availability of atmospheric correction processors varies across the four satellites. For Landsat 5 and 7, both Level-1 and Level-2 (LEDAPS-corrected) products are publicly available, and the Level-1 imagery is also compatible with the ACOLITE, ATCOR, DOS1, FLAASH, and QUAC processors. In the case of Landsat 8, Level-1 and Level-2 (LaSRC-corrected) are publicly available through the EarthExplorer data hub, and the satellite is compatible with all the above-mentioned processors plus C2 RCC and iCOR. Finally, Sentinel-2, through the Copernicus data hub, freely offers Level-1 and Level-2 (Sen2 Corrected) products, with compatibility with the ACOLITE, C2 RCC, DOS1, iCOR, and Polymer processors. Although atmospherically corrected Sentinel-2 products (MSILZA) are available for download, their global coverage only commenced in late 2018. Hence, we applied the Sen2 Cor processors independently on Sentinel-2 Level-1 images using the ESA SNAP software (version 9.0.0). Also of note, ACOLITE includes two correction methods: Dark Spectrum Fitting (DSF, the processor's default) and Exponential Extrapolation (EXP). Because DSF has been reported as the better choice for aquatic studies [18; 23], it was used in this study. Table S2 in Supplementary Materials summarizes the atmospheric correction methods utilized in this study.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 6 Mimblewimble Implementations
### Discussion
#### 6.3.2 Range Proofs  
[PERSON] and Beam implement range proofs using Bulletproofs [35]. Bulletproofs are a non-interactive zero-knowledge proof protocol. They are short proofs (logarithmic in the witness size) with the aim of proving that the committed value is in a certain (positive) range without reveling it. Proof generation and verification times are linear in the length of the range. Regarding our model, it is the first property a transaction should satisfy to be valid (Property 1). Furthermore, for every transaction in a bock, the range proofs of all the outputs should be valid too (Property 2).
## 2 System Description and Modelling
### Mathematical Modelling  
This study employs a numerical tool developed with the use of Matlab Simscape [24], which includes block-components that are built by specified thermodynamic equations, mass and energy equilibria to describe each process of the system. Each block of the developed model represents one of the following components of the system: the heat pump's components (compressor, condenser, expansion valve, evaporator, receiver) and the flash tank with the appropriate recirculation and vacuum pumps. The properties of the brine, such as density, boiling point elevation, specific heat capacity or enthalpy, are  
Figure 2: Phase diagrams for flash evaporation.  
calculated according to the MIT model [25,26] and are based on the salinity, while the thermodynamic properties of R134a are calculated from the CoolProp library [27]. It must be noted that the MIT model has been elaborated for salinity up to 12%.  
The performance of the heat pump under varying conditions is calculated by a physics-based model. Each component is calculated separately and then linked with the next component through the input parameters, while conservation equations of heat and mass transfer are applied. The following assumptions were considered to develop the mathematical model:  
* The components are considered to be well insulated, so heat loss to the surroundings is neglected (adiabatic).
* The model is considered quasi-steady.
* Expansion and compression processes are assumed to be adiabatic.  
As was described above, the brine that flows out of the flash tank has an increased salinity compared with the feed brine. The part of the brine that is not rejected recirculates towards the brine heat, while the rejected brine is balanced with the amount of brine coming from RO and the whole flow re-enters the flash tank. Therefore, there is only a fraction of the feed brine that recirculates at a temperature equal to the flash tank temperature, while the brine coming from RO is still cold, since it has not passed from the brine heater, which is the condenser of the heat pump. Depending on the fraction of the initial brine that recirculates, the required thermal load to heat the whole brine at the desired temperature (setpoint of the heat pump) varies. Thus, the recirculation ratio \((r)\) is considered to define the part of the brine that recirculates \(\dot{m}_{b,\;rec}\) (kg/s) over the initial total brine, \(\dot{m}_{b,\;ini}\), (kg/s), which is as follows:  
\[r=\frac{\dot{m}_{b,\;rec}}{m_{b,\;ini}} \tag{1}\]  
The recirculation ratio has values between 0 and 1 or can be presented as a percentage. Figure 3 presents the flowchart of the numerical process for the developed model, which is discussed in the following section.
## 4 Results and Analysis
### Results of PS and SBAS-InSAR Interferometry  
#### 4.2.1 Interferometric Phase Change over Time  
The interferometric phase is useful for analyzing the ground deformation trends over time in the LOS direction [49]. The time series interferograms generated from each data pair from (a) to (k) are shown in Figure 6. Generally, we could roughly identify the deformation areas in the large scale by analyzing the fringe density in differential interferograms. In addition, the sequence of color fringes in the interferogram can be a good indicator of whether uplift or subsidence has taken place. Figure 6a shows a typical interferometric phase image, in which the fringes clearly show the areas with a high deformation rate. The ENVISAT-ASAR data were in the descending orbit and observations were made from the east. As shown in Figure 6b, the sequence of color fringes shows a decrease in range (yellow-purple-blue), which denotes that the ground objective moves closer to the satellite. In this study, we did not consider the horizontal movement, and thus, it corresponded to an uplift. To identify and highlight the time series of ground deformation in the period of 30 September 2003 to 15 June 2010, we selected a main deformation area, where the location is highlighted in red dashed lines in Figure 6a. As shown in the figure, a slight deformation can be observed from the earliest image in September 2003 to September 2006 (Figure 6c-f), the rate of deformation increases significantly from  
Figure 5: Geocoded displacement map in the line of sight (LOS) direction obtained by the differential SAR interferometry (D-InSAR) analysis from the ALOS-PALSAR data pair. Positive (negative) values indicates the ground close to (away from) the satellite.  
November 2007 (Figure 6g-k), and a pattern of rebound, expanding in area and increasing in quantity, can be observed during this period.
# Hardware Design of a High Dynamic Range Radio Frequency (RF) Harmonic Measurement System
The measurement system uses the second harmonic to characterize the nonlinearities of passive RF circuit elements. The measurement system achieves the high dynamic range, of the order of 175 dBc, necessary to measure weakly nonlinear devices while covering a 20% bandwidth, something the PNA-X and other commercially-available systems cannot accomplish. The measurement system is also low complexity, not requiring complicated feedforward cancellation circuits. Section 1 introduces the mathematics required to characterize nonlinearities from circuit elements. Section 2 describes basic harmonic measurement techniques to linearize standard RF instrumentation. Section 3 considers issues in creating a high dynamic range harmonic measurement system. Section 4 discusses the high dynamic range harmonic measurement system constructed that achieves 175 dBc of dynamic range. Section 5 presents the results obtained from the measurement system of various RF system components. Section 6 concludes the paper.
## 4 Experimental Design and Result Analysis
### Experimental Results and Analysis
#### 4.3.4 Visualization of Detection Results  
Based on the analysis of ablation experimental results, a comparison was made between the original YOLOv8n model and the algorithm studied in this paper for detecting electric bicycle riders on complex roads. To more intuitively showcase the detection performance of the two algorithms, selected images from different scenarios in the dataset, including bright light, dim light, and night, were tested, as shown in Figure 9. In scenarios (a), (b), and (c), the original YOLOv8n model suffered from issues such as missed detections, false detections, and difficulties in detecting overlapping targets for electric bicycle riders. In contrast, the refined model presented in this paper excelled in detection tasks under varying lighting and scene conditions.  
In this paper, data augmentation techniques are employed on a self-built dataset to enhance data diversity. Specifically, Noise is superimposed on the images to augment the data. However, it is also acknowledged that the addition of noise may lead to information  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Model** & **_P (\%)_ & **R (\%)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]** & **Params** \\  & & & **(\%)** & **(\%)** & **(\%)** & **(10\({}^{6}\))** \\ \hline \multirow{2}{*}{TWHD} & YOLOv8n & 80.5 & 74.9 & 79.9 & 49.1 & 3.0 \\  & PRE-YOLO & **82.2** & **78.8** & **83.7** & **51.6** & **2.0** \\ \hline \multirow{2}{*}{EBHD} & YOLOv8n & 61.2 & 71.7 & 67.4 & 39.2 & 3.0 \\  & PRE-YOLO & **64.2** & **75.9** & **69.6** & **41.7** & **2.0** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of Generalization Experiments.  
Figure 9: Detection Comparison under Different Lighting and Scene Conditions.  
loss and obscure important features in the images. To validate the robustness of the proposed PRE-YOLO model, two images were selected and noise was added to them for testing, as shown in Figure 10. The model did not exhibit any issues such as missed detections or false detections due to the added noise, which fully demonstrates the ability of the proposed model to maintain its normal function, performance, or stability when confronted with unfavorable factors.
## 2 Materials and Methods
### Fuzzy Logic Overview  
Fuzzy Logic (FL) systems and theory, a subset of AI, is employed as the basis for quantifying parameters that describe the performance of a task. An accurate characterization of a system's performance requires complete knowledge of the mission and the mission requirements, which is often not known a priori [27]. Due to the infinite number of possible scenarios in a given mission and the impracticality of constraining the system and the prevalence of proprietary software, the approach for this research remains independent of internal autonomy architectures [6,8] and defines boundaries on the scope of this work without loss of generality. Additionally, this FL strategy allows flexibility for various definitions of \"success\" between users (e.g., a defense employee as opposed to a scientist) by weighting performance qualities the user deems important.  
Unlike traditional binary logic (i.e., 0, 1), FL utilizes the concept of partial truth (i.e., any value ranging from 0 and 1). This logic more closely resembles how the human brain processes information [24]. In summary, the general FL procedure takes _fuzzified_ input data (via linguistic variables and user-determined membership functions) and processes them through an \"if-then\" framed rule base, producing a set of fuzzy outputs. These outputs are then combined/weighted into a single output, which is then _defuzzified_ (via another set of membership functions) to produce the final crisp result. First presented by [28], this strategy is often employed in other sectors such as medicine, manufacturing, and business  
Figure 3: Example of a task and event flow for mission failure (**top**) vs. mission success (**bottom**).  
Figure 2: Graphical overview of the example mission for a given scenario.  
with applications to control systems, decision-making, evaluation, and management. Here, the authors review basic terminology to justify IT2-FL over other forms of FL. FL is the chosen approach for this work due to its ability to provide a strict mathematical framework in which vague and uncertain phenomena can be precisely and rigorously studied [24,27].
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 4 Results
### Spatial Distribution of Seasonal LST and Trend Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Season** & **Region** & **Z-Score** & **Slope (\({}^{\circ}\)C)** & _p-Value_ \\ \hline \multirow{5}{*}{Spring} & I & \(0.93\) & \(0.03\) & \(0.40\) \\ \cline{2-5}  & II & \(-0.14\) & \(-0.01\) & \(0.52\) \\ \cline{2-5}  & III & \(-0.71\) & \(-0.03\) & \(0.44\) \\ \cline{2-5}  & IV & \(-0.19\) & \(-0.01\) & \(0.70\) \\ \cline{2-5}  & V & \(-0.95\) & \(-0.06\) & \(0.30\) \\ \hline \multirow{5}{*}{Summer} & I & \(1.22\) & \(0.04\) & \(0.32\) \\ \cline{2-5}  & II & \(2.46\)\({}^{**}\) & \(0.07\) & \(0.05\) \\ \cline{2-5}  & III & \(2.31\)\({}^{***}\) & \(0.07\) & \(0.1\) \\ \cline{2-5}  & IV & \(2.22\)\({}^{**}\) & \(0.08\) & \(0.05\) \\ \cline{2-5}  & V & \(3.00\)\({}^{*}\) & \(0.20\) & \(0.01\) \\ \hline \multirow{5}{*}{Autumn}{Num} & I & \(-1.06\)\({}^{**}\) & \(-0.08\) & \(0.05\) \\ \cline{2-5}  & II & \(0.42\) & \(0.02\) & \(0.61\) \\ \cline{2-5}  & III & \(0.84\) & \(0.04\) & \(0.43\) \\ \cline{2-5}  & IV & \(0.84\) & \(0.03\) & \(0.44\) \\ \cline{2-5}  & V & \(1.93\)\({}^{*}\) & \(0.15\) & \(0.10\) \\ \hline \multirow{5}{*}{Winter} & I & \(1.54\) & \(0.11\) & \(0.18\) \\ \cline{2-5}  & II & \(1.42\) & \(0.12\) & \(0.20\) \\ \cline{2-5}  & III & \(2.03\)\({}^{**}\) & \(0.17\) & \(0.05\) \\ \cline{2-5}  & IV & \(1.95\)\({}^{***}\) & \(0.13\) & \(0.10\) \\ \cline{2-5}  & V & \(1.88\)\({}^{***}\) & \(0.20\) & \(0.10\) \\ \hline \hline \end{tabular} Note: Where \({}^{***}\), \({}^{***}\) and \({}^{***}\) indicate significance of the test at 1%, 5%, and 10% levels of significance.  
\end{table} 
Table 3: The mean LST difference along with standard deviation at seasonal scales.