esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Global Spatial and Temporal Variability of Soil Salinity
### The Middle East  
Over the past 40 years, soil salinization has become an increasingly significant issue in the Middle East. For instance, the Yazd-Ardakan plain in Iran exhibited a rising trend in soil salinity values from 1986 to 2030 [135]. Changes in soil salinity in the Lake Urmia area of northwestern Iran over the last 45 years indicated that non-saline lands have vanished, while the extent of highly saline and moderately saline soils has expanded [136]. Furthermore, the area of saline soils in Lake Urmia increased dramatically between 2005 and 2019 [85], primarily due to the accelerated shrinkage of the lake resulting from human activities. Since 1998, the construction of over 72 dams on rivers feeding into Lake Urmia has led to a reduction in water volume from 30 billion cubic meters to 1 billion cubic meters.  
Additionally, the push for increased agricultural production and the excessive drilling of wells have had catastrophic effects on the region. Despite research findings clearly linking climate change to soil salinization, quantifying the impact of climate change on soil salinity levels in arid regions remains a complex challenge. Climatic data from Kuwait, which represents the northwestern part of the Arabian Peninsula, reveal an increase of 1.1 \({}^{\circ}\)C in mean temperature over the last 30 years (1987-2017). Rainfall shows a strong correlation with saline areas; in years marked by low precipitation and high temperatures, soil salinity classes have transitioned dramatically from moderate to very high and extreme salinity classes (Kur-Araz lowland of Azerbaijan indicate that between 1986 and 2021, climate change and the synergistic effects of water management practices have accelerated the transport of salt from surface soil to subsurface soil, groundwater, and the Caspian Sea (Kur-Araz, 2020). As climate change continues to progress, these synergistic effects may be entirely negated by reduced rainfall, resulting in rapid re-salinization of surface soils and exacerbating other climate change impacts, such as increased evaporation, salt weathering, irrigation shortages, and salt accumulation.
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 2 Bonding of Embedded CFRP Rod in Concrete G40 and UHPFRC  
The initial step in developing an analytical model for the adhesion between CFRP rods and concrete involves assessing the CFRP rod's behaviour when it is embedded in a concrete matrix under applied stress. Given the crucial impact of the bond, especially in situations where the structure undergoes cyclical loading and subsequent unloading, which leads to crack propagation on the contact surface, it is essential to examine the bond integrity of CFRP within two distinct concrete compositions under repeated loading conditions.
## 2 Research Data and Research Methods
### Research Data  
#### 2.2.1 EVI Data  
The Enhanced Vegetation Index (EVI) was initially developed to address the potential saturation of vegetation signals in high-biomass areas, thereby enhancing sensitivity to vegetation conditions. As a measure of the \"greenness\" of vegetation canopies, EVI has become a key indicator for studying the spatial and temporal dynamics of terrestrial veg  
Figure 1: Overview map of the study area (**notes:** AR: arid region; SAR: semi-arid region; SHR: semi-humid region; HR: humid region) (GS(2023)2763).  
etation photosynthetic activity [14]. The EVI data used in this study were sourced from Zenodo ([[https://doi.org/10.5281/zenodo.7979989](https://doi.org/10.5281/zenodo.7979989)]([https://doi.org/10.5281/zenodo.7979989](https://doi.org/10.5281/zenodo.7979989)), accessed on 6 December 2024), which employs advanced spatial and temporal reconstruction techniques based on the Moderate-Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 product. This dataset covers China from 2000 to 2022, with a spatial resolution of 250 m and a temporal resolution of 16 days. The dataset reconstruction method used, known as spatial-inter-annual reconstruction (SIR), incorporates both spatial and inter-annual information to fill cloud-contaminated pixels, effectively addressing the issue of cloud masking and ensuring high accuracy of the data. The SIR method utilizes MODIS Quality Control (QC) flags to identify and retain pixels with good quality or those that meet specific criteria, such as having a Vegetation Index higher than 80% of the multi-year average on the same date [15]. Using Geographic Information Systems (GISs), the original dataset was integrated and converted into a monthly scale and then aggregated to an annual scale for long-term trend analysis. For this research, EVI data from 2001 to 2020 were selected and resampled to a 1 km \(\times\) 1 km spatial resolution using the nearest neighbor method to match the scale requirements of the study area. The nearest neighbor method was chosen to preserve the original values of the EVI data, ensuring that the Vegetation Index values remain consistent with the source dataset without introducing any interpolated values that could alter the interpretation of vegetation conditions.
## 2 UWB Signal Processing Steps
### Overlapping Windows Processing  
Our particular motivation for this work is to exploit the robustness of RPCA methods that have always been criticized for being batch-oriented and inefficient for online processing systems or processing incremental data sets. We call these methods for clutter removal in the task of detecting and localizing a moving target in real-time using an IR-UWB radar.  
The proposed method essentially follows the normal IALM to process a small batch of data which is continuously updated as new frames are captured. This framework is shown in Figure 5. The method considers a small batch of \(n\) frames as one \"window.\" The first window, called the \"base window,\" will be recorded first. This window will go through the processing steps, including data cleansing, background subtraction, target detection and localization. The next window is generated by appending the \(p\) newly recorded frames to the previous window and eliminating the \(p\) first frames of the window to keep the same window size. The new window will be processed in the same way as the base window, but for the detection task only the p new frames will be concerned. The flowchart in Figure 6 shows the working mechanism of this method.  
The choice of \(n\) (window's width) and \(p\) (shift step) are crucial and must be done counting on their effects on the overall performance of the system. In instance, a very large value of \(n\) will result in a very big dataset (window) and hence slow down the processing, and also we will have to wait longer to get the base window. In contrast, a very small \(n\) will not allow RPCA to converge to its optimal solution. On the other hand, a great value of \(p\) will devastate our \"in real-time\" concept.  
The radar module of this work has an average frame rate of 40 frames per second. Therefore, we chose \(n=20\) and \(p=4\). The value of n was decided basing on experimental observations, thus the minimum number of frames that allows us to reach the optimal solution. Each window is processed in approximately 0.1 s, so we chose \(p=4\) for time compensation since 0.1 s is the time required to capture 4 frames.  
Figure 4: 2D positioning with two radars.  
Figure 5: Overlapping windows processing framework.
# Line Segment Matching Fusing Local Gradient Order and Non-Local Structure Information
## 1 Introduction
[PERSON] and [PERSON] [20] determine the corresponding line pairs by using geometric attributes. The line-junction-line (LJL) gradient descriptor of the local region with the junction point as the center can be constructed by using the intersecting point and local texture information. [PERSON] et al. [21] implement line matching under the multi-layer Gaussian image pyramid based on the LJL gradient descriptor. For those unmatched line segments, [PERSON] et al. utilize the local homology estimated by its neighboring matched LJLs to match them. Compared with matching methods based on individual line segments, the group-based method can obtain a better correspondence relationship. Based on the studies of IJL, [PERSON] et al. propose a method to match hierarchical line segments in huge viewpoint change cases [22]. However, the calculation process is still complex and requires a large number of computing resources. Inspired by MSLD, LBD and previous studies [10; 11; 12], we design an alternative method, matching the individual lines by using the descriptor. Most of the previous approaches are focused on the regular line support region (LSR) and local appearance. Those methods are unable to describe the order of the line neighborhood pixel and the interactions between long-range pixel and a local neighborhood. Therefore, the line segment matching method is proposed. The approach is fusing the local and non-local structure information by exploring local gradient order, local intensity sequence information, global intensity sequence information, and non-local structure information. There are our main contributions:  
1. We use the line support region intensity histogram to perform adaptive intensity partitioning. The sub-regions are determined by intensity order, which increases the distance between descriptors of different line segments and will not affect the real-time performance;2. We use the local gradient order to describe the line segment. The local gradient order changes very little when the illumination changes and rotates. This means that the local gradient order of the same line segment has a high similarity in the images of different scenes;
3. We fuse local gradient order information and non-local structural information of the line segment. Non-local structural information is not easily affected by image transformation. In addition, the sampling center information neglected in the local sampling process is supplemented. We fuse that information in an attempt to improve the matching performance in various scenes. These improvements ensure certain real-time performance and excellent matching performance of the line segments matching method.
## 5 Discussion
### Classifying Challenges
#### 5.3.3 Localized Influence Challenges  
Challenges with limited roles in bridging or connecting different departments, processes, or clusters within the larger organizational network consist of relationships, interactions, and dependencies among various departments, teams, stakeholders, and processes within a government entity. They are not central to the overall flow of communication, collaboration, or innovation across the government entity. They include \"lack of qualified employees\" (C2), \"lack of training and knowledge about service innovation\" (C3), \"lack of time dedicated to service innovation\" (C5), \"rigid rules and processes within the organization\" (C7), \"managers' tendencies to avoid risk\" (C10), \"an absence of a customer-centric mindset to gauge and meet current and future needs\" (C13), \"absence of innovation strategy\" (C16), and \"a gap in marketing internally for new or improved services\" (C19).
# Multiphase Conjugate Heat Transfer Analyses on the Assembly Situation of Rotary Shaft Seals
## 1 Introduction
Depending on the complexity of the simulation model, i.e., how  
Figure 2: Factors affecting the contact temperature, according to [7]. many influencing factors are considered, the development of the simulation model can be extensive and, thus, require a great amount of time. As the shaft rotates, the sealing edge lifts up, resulting in the formation of a thin lubricating film in the sealing gap. Thus, there is no fixed boundary between the fluid to be sealed, the oil, and the surrounding fluid, the air. For the prediction of, for example, cavitation zones in the sealing gap, this multiphase flow has already been successfully simulated in the past [14; 15]. Finite element analysis (FEA) of a rotary shaft seal has already been used to analyze the radial deformation and contact pressure distribution [16; 17; 18]. In addition, elastohydrodynamic lubrication (EHL) simulations have also been performed [19]. In general, the focus of the EHL simulations was mainly the investigation of the flow in the sealing gap [15]. By focusing on the lubricant film in the sealing gap, many of the influencing factors, shown in Figure 2, could not be considered. A simulation of the whole test bench, as it was performed in this work, has been performed in the past [20]. However, the focus was not on the design of the surroundings of the sealing ring either. Therefore, it has not yet been possible to predict what impact the surroundings of the sealing ring have on the contact temperature. The work described in this paper is intended to enable an understanding of the influence of the sealing surroundings on the contact temperature. In this study, the temperature in the contact area between the sealing edge and shaft was determined for different geometric variants by measurements on the test bench and additionally by simulations. An only-slightly simplified geometry of the test bench was used for the simulations. Since the development of different simulation models is time-consuming and, therefore, it is usually not possible to develop many different models within the given time, parameterized simulation models were used. The interacting factors, highlighted with a yellow background in Figure 2, were all considered in the simulations. The factors indicated by a yellow frame were included only partially, e.g., as a boundary condition. As far as possible, we tried to reproduce reality in the simulations. However, due to limited computing power, some simplifications had to be made. The temperatures in the contact area resulting from the simulations were compared with the temperatures measured in the experiments on the test bench. In this way, it was possible to evaluate which of the simplifications made still led to accurate simulation results.
###### Abstract  
The complexity of chaotic systems, if used in information encryption, can determine the status of security. The paper proposes a novel image encryption scheme that uses chaotic maps and fuzzy numbers for the secure transmission of information. The encryption method combines logistic and sine maps to form the logistic sine map, as well as the fuzzy concept and the Henon map to form the fuzzy Henon map, in which these maps are used to generate secure secret keys, respectively. Additionally, a fuzzy triangular membership function is used to modify the initial conditions of the maps during the diffusion process. The encryption process involves scrambling the image pixels, summing adjacent row values, and XORing the result with randomly generated numbers from the chaotic maps. The proposed method is tested against various attacks, including statistical attack analysis, local entropy analysis, differential attack analysis, signal-to-noise ratio, signal-to-noise distortion ratio, mean error square, brute force attack analysis, and information entropy analysis, while the randomness number has been evaluated using the NIST test. This scheme also has a high key sensitivity, which means that a small change in the secret keys can result in a significant change in the encrypted image The results demonstrate the effectiveness of the proposed scheme in ensuring the secure transmission of information.  
image encryption; fuzzy logic; chaotic system; logistic map; sine map; Henon map +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Research Model and Methodology
### Methodology  
In this study, a non-participant observational approach was employed to examine driver behavior at roundabouts. The researchers conducted passive observation, meaning they did not interact with drivers but instead recorded their natural driving behaviors. This method ensured that the collected data remained unbiased and reflective of real-world conditions. Additionally, the observations were unobtrusive, as drivers were unaware that they were being monitored, preventing any alteration in their driving patterns due to awareness of the study. Data collection took place in an actual driving environment rather than a controlled or simulated setting, providing insights into real traffic conditions and driver responses.  
A field study was undertaken by a team of three researchers to gather data on driver behavior within roundabouts. Two roundabouts were carefully selected, one with traffic signals (signalized) and the other without (non-signalized). The primary objective of the study was to collect information regarding three key aspects: lane change behavior, correct lane usage, and turn signal usage exhibited by drivers navigating the roundabouts. The traffic volume was recorded over two days, with approximately three hours of observation per day at each roundabout. The data collectors visited the chosen roundabouts and embarked on the task of identifying and recording the aforementioned variables of interest. A checklist was provided to the data collectors to ensure accurate data collection. Positioned at a safe location near the roundabout, the data collectors attentively observed the behavior of drivers and documented their actions related to lane changes, correct lane usage, and turn signal activation. A total of 100 vehicles at each roundabout were observed, and their behavior was meticulously recorded by the data collectors. This sample size of 200 vehicles aimed to provide a representative snapshot of driver behavior within the selected roundabouts.  
Once the data collection phase was complete, the gathered information underwent analysis using the statistical software Minitab 18. This analysis was conducted to discern the frequency and patterns associated with the variables of interest. The statistical analysis facilitated an examination of the gathered data, allowing for an understanding of the prevalence and tendencies of lane changes, correct lane usage, and turn signal activation among drivers in both signalized and non-signalized roundabouts. By employing the Minitab software, the study aimed to identify any noteworthy trends or differences in driver behavior between the two types of roundabouts. The utilization of a team of data collectors, meticulous observation protocols, and robust statistical analysis ensures the validity and reliability of the findings.
## 2 Materials and Methods
### Numerical Model  
The numerical model was set up in ANSYS Fluent 15.0 (ANSYS, Inc., Canonsburg, PA, US). The simulation domain consists of three different areas--the HTF in the inner pipe, the wall of the inner pipe, and the PCM in the annular gap. Within the HTF and the PCM, a laminar flow is assumed and the density is set constant and the Boussinesq approximation is used for the PCM. An enthalpy porosity method is applied [8] for the PCM, which implies two source terms. One in the energy equation to account for the latent enthalpy and another one, a Darcy-type source term, in the momentum equation to switch off the velocity in the solid region. The continuity equation with constant density is:  
\[\
abla\times\mathbf{u}=0 \tag{5}\]  
and the momentum equation of the PCM becomes  
\[\rho\left[\frac{\partial\mathbf{u}}{\partial\mathbf{t}}+\
abla\times(\mathbf{ u}\mathbf{u})\right]=\
abla\times\boldsymbol{\tau}-\
abla\mathbf{p}+D\mathbf{u}+ \rho\mathbf{g} \tag{6}\]  
where the Boussinesq approximation is implemented by  
\[\rho\mathbf{g}=\rho_{0}\mathbf{g}[1-\beta(T-T_{0})] \tag{7}\]  
and the Darcy term is written as  
\[D=-C_{1}\frac{(1-\alpha_{F})^{2}}{\alpha_{F}^{3}+C_{\Pi}}. \tag{8}\]  
The energy equation of the PCM is  
\[\rho c\left[\frac{\partial T}{\partial t}+\
abla\times(\boldsymbol{u}T)\right] =\
abla\times(\lambda\
abla T)-\rho L\left(\frac{\partial\alpha_{F}}{\partial t }+\boldsymbol{u}\
abla\alpha_{F}\right). \tag{9}\]  
In Equations (8) and (9) \(\alpha_{F}\) describes the liquid fraction, which is defined as  
\[\alpha_{F}=\frac{h-h_{\mathrm{s}}}{h_{l}-h_{\mathrm{s}}} \tag{10}\]  
and limited to values from 0 to 1. The update technique is described in detail in literature [34].
## 2 Wenner's Method and Description of Horizontally Layered Soil Model  
### Measurements Using Wenner Method  
The Standards IEEE 81-1983 and IEEE81-2012 [33; 34] are the basis for implementing the Wenner method [13; 14; 15; 16; 17]. They are used to estimate the electrical resistivity of soil gradually from the surface to the deeper layers, based on soil resistivity measurements. Other methods are also used, but the Wenner four-electrode method is the most common.  
The four electrodes are placed in a line. All the distances between them are the same and are marked with \(d\), as presented in Figure 1.  
Figure 1: Placement of electrodes for the Wenner method.  
The voltage difference between the inner electrodes is measured, which occurs as a result of the forced current between the outer electrodes. The distance \(d\) between the electrodes is changing, and the ratio of the measured voltage \(U\) to the forced current \(I\) is recorded and gives the resistance \(R=U/I\) in ohms. Four probes are placed into the soil, all at depth \(b\) (Figure 1). The apparent resistivity \(\rho\) is defined with (1) [33].  
\[\rho=\frac{4\cdot\pi\cdot d\cdot R}{1+\frac{2.d}{\sqrt{d^{2}+4.b^{2}}}-\frac{d} {\sqrt{d^{2}+b^{2}}}} \tag{1}\]  
In practice, the depth of the electrodes \(b\) is less than \(0.1d\), and it can be assumed that \(b=0\) and (1) can be written as (2) [16; 33].  
\[\rho=2\cdot\pi\cdot d\cdot R=\frac{2\cdot\pi\cdot d\cdot U}{I} \tag{2}\]  
To determine the soil parameters, it is necessary to determine the resistances, depending on the distance between the electrodes. The distance changes from smaller to larger during the measurement. At larger distances, due to the deeper penetration of the current, a deeper layer of the soil is also covered by the measurement.