esa-sceva/satcom-chunk-collection huggingface dataset:
# Vehicle Route Planning for Relief Item Distribution under Flood Uncertainty
## 3 The Proposed Model
### Enhanced Population  
In general, initializing the population for a GA using randomization may not be efficient for solving specific problems. Therefore, a portion of the initial population is enhanced to be more effective in addressing uncertainties. This enhanced population is referred to as the enhanced population.  
Creating solutions for the enhanced population involves sequencing cities while considering the impact of selecting one city over another. This impact is the expected outcome value from selecting a city sequence under the uncertainty of road conditions. The total waiting time for assistance from unreached disaster victims and the uncertainty of road conditions must be considered. The reflection of this concept can be observed in Equations (9), (10), and (11), respectively.  
\[w_{i,j}=\sum_{i=0}^{n}\sum_{j=0}^{n-1}\left(t_{i,j}^{n}+\Delta t\right)\quad, \tag{9}\]  
\[\Delta t=\begin{cases}t_{i,j}^{f}-t_{i,j}^{n}&\text{, if the road is flooded}\\ 0&\text{, otherwise}\end{cases}\quad, \tag{10}\]  
where \(w_{i,j}\) represents the waiting time for nodes that have not yet received assistance when selecting the route from node \(i\) to \(j\), \(t_{i,j}^{n}\) denotes the travel time from node \(i\) to \(j\) under normal road conditions, \(t_{i,j}^{f}\) denotes the travel time from node \(i\) to \(j\) when the road is flooded, and \(\Delta t\) represents the additional time incurred when the road is flooded.  
\[E_{i,j}=w_{i,j}\cdot\left(1-p_{i,j}\right)+w_{i,j}^{\prime}\cdot p_{i,j}\quad, \tag{11}\]  
where \(E_{i,j}\) represents the expected outcome value in traveling from node \(i\) to node \(j\), \(w_{i,j}^{\prime}\) represents the waiting time for nodes if the road between \(i\) and \(j\) is flooded, and \(p_{i,j}\) represents the probability of road flooding between nodes \(i\) and \(j\).
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
###### Abstract  
An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed.  
floods; permanent water bodies; Sentinel-1; operational applications; near real-time Article  
A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data. _Romote Sens._**2021**, 13, 1342. [[https://doi.org/10.3390/s10371342](https://doi.org/10.3390/s10371342)]([https://doi.org/10.3390/s10371342](https://doi.org/10.3390/s10371342))  
1
# CONE: A Connected Dominating Set-Based Flooding Protocol for Wireless Sensor Networks
## 3 Design and Implementation of CONE  
In this section, we describe the design of CONE. CONE exploits the Trickle algorithm [13] for network flooding. Trickle is a popular method for data dissemination in sensor networks [22]. It has been standardized as the mechanism that regulates the transmission of the control messages used to create the network graph in RPL [23]. Constrained Low-power and Lossy networks (LLNs) which includes WSNs represent the foundation for IoT that deploys RPL.  
Optimizing Trickle has become a popular research topic [1; 22; 23; 24]. Furthermore, simulations have shown that Trickle suffers from the broadcast storm problem, especially in dense network. For these reasons, our goal is to enhance Trickle by using a CDS.  
We implement CONE in Contiki OS based on Tmote Sky sensor nodes. Contiki OS is an operating system for nodes which are used for the IoT [14]. We use the prime communication stack for network  
Figure 8: Enhanced tree growing algorithm. The dominators are colored in green. The non-dominators that are adjacent to at least one dominator are colored in grey. (**a,b**) The algorithm arbitrarily chooses a node with the largest number of white neighbors. The chosen node is colored in green and its neighbors are colored in grey. (**c,d**) The algorithm arbitrarily chooses a grey node with the largest number of green neighbors. The chosen node is colored in green. The algorithm stops in (**d**) because each node is colored in either green or grey and the set of green nodes is connected.  
communication. Rime is a lightweight communication stack which has been designed for WSNs [25]. Furthermore, the implementation of Trickle in Contiki OS employs crime for communication as well.  
To use a CDS, CONE has to construct a CDS in the first place. We describe the CDS construction in CONE in the following sections. In Section 3.1, we explain how CONE uses a CDS for the flooding process with Trickle. In Section 3.2, we detail the neighbor discover and information exchange in CONE. Furthermore, we have to consider node failures [26], since they can easily disconnect a CDS. In Section 3.3, we describe how CONE maintains a constructed CDS in case of node failures.
# Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots
## 1 Introduction
To this end, the advantage of adopting such a policy, compared to the classical CS and \"strict\" BR policies, is that it provides a flexible way to alter CBP of both new and handover calls by changing the BR policy probabilities. Besides, it is significant, from an engineering point of view, to have a simple and yet efficient algorithm for the computation of CBP under the probabilistic BR policy. Note that such an algorithm has not been presented in [7] for the determination of CBP. Based on the above, the contribution of our paper is summarized as follows: (i) we propose the probabilistic BR loss model where the probabilistic BR policy is applied only to new calls during the stop phase; (ii) we propose an iterative algorithm for the determination of the steady-state probabilities that are essential for the computation of the various performance measures (CBP and system's utilization); (iii) we extend the previous model by proposing the generalized probabilistic BR loss model, where the probabilistic BR policy is applied to new calls in both stop and moving phases; and (iv) we propose an iterative algorithm for the computation of the steady-state probabilities and the various performance measures. This paper is organized as follows: In Section 2, we propose the probabilistic BR loss model, describe the analytical model (Section 2.1), propose an iterative algorithm for the determination of the steady-state probabilities (Section 2.2) and propose formulas for the determination of the various performance measures (Section 2.3). In Section 3, we propose the generalized probabilistic BR loss model. In Section 4, we present simulation and analytical results so as to validate the accuracy of the proposed formulas. We conclude in Section 5. In Appendix A, a tutorial example is presented with the intermediate calculations of the iterative algorithm in the probabilistic BR loss model. Finally, the abbreviations and notation used in this paper are listed at the end, respectively.
# Maintenance Method for Rail Corrugation Considering Wheel-Rail Interaction Force
## 1 Introduction
It was found that suppressing both the vertical and lateral sleeper resonances has a positive effect on the mitigation of both types of rail corrugation. [PERSON] and [PERSON] et al. [12; 14] presented analytical and numerical models to investigate rail corrugations. The comparison of numerical and experimental observations, along with the results related to sleeper spacing, confirmed that a more in-depth analysis of the parameters contributing to growth via vehicle-track vibration dynamics is necessary when complex modal interactions, such as those due to sleeper passage effects, are evident. [PERSON] et al. [15] proposed a non-Hertz dynamic wheel-rail adhesion model for trains passing over wet curved tracks, taking into account wheel-rail surface roughness. Furthermore, numerical analysis showed that the influence of the yaw angle on general contact solutions, such as contact patch shape, size, and stress distribution during rolling contact simulation, was insignificant. [PERSON] et al. [16] designed an optimized rail profile to improve the dynamic performance of subway vehicle-rail systems, considering worn wheel profiles and curved tracks. They confirmed that the use of the optimal profile reduced the rail wear depth on the lower rail of the curved track. [PERSON] et al. [17] numerically analyzed the dynamic resonance and the influence of multiple flexible wheelset-rail interactions on rail corrugation in high-speed railways. As a result, it was confirmed that wheelset flexibility had a limited effect on vertical force resonance. While much of the prior research on rail corrugation has focused on its causes and characteristics, there has been limited research on the maintenance and management of rail corrugation, particularly in terms of wheel-rail interaction. Therefore, this study examines the current status and management of rail corrugation through field investigations and measurements of rail roughness, dynamic wheel load, acceleration, and displacement before and after rail grinding to analyze the impact of wheel-rail interaction on the track. In addition, based on the analyzed data, the rail grinding priority and a rail corrugation maintenance method are proposed.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 5 Security Evaluation
### Burrows-Abadi-Needham (BAN) Logic
#### 5.1.1 Formalized 5 GSBA Protocol  
In our idealized protocol, U refers to UE and C refers to one of the gNBs in the 5G cellular network (CN). All cleartext and identities in the protocol are omitted as they can be easily forged. For the notations, SUCI can be regarded as a message encrypted by the public key of 5 GC (i.e., the \(PK_{core}\)). The timestamp token is represented by \(TS\), and theone-time hash token is represented by \(Y\). In addition, all message content protected by the HMAC can be viewed as a message encrypted by the HMAC key (i.e., \(K_{hmac}\)). Therefore, the idealized 5 GSBA protocol is shown below:  
Message 1: \(U\to C:\ C\ltimes\left\{U\stackrel{{ K_{hmac}}}{{\leftrightarrow}}C,\ Y_{Y}\right\}_{PK_{cor}},\left\{\ TS,\ a\text{-}G\ \right\}_{K_{hmac}}\)  
Message 2: \(C\to U:\ U\ltimes\left\{\ TS,\ b\text{-}P\ \right\}_{K_{hmac}}\)
## 5 Proposed Quadrature Bandpass Continuous-Time Delta-Sigma ADC
### Quadrature Bandpass Continuous-Time Delta-Sigma ADC Circuit Design
#### 5.2.2 Active-RC Integrator with Proposed OTA
\[\frac{\text{K}\cdot(0.02\cdot\text{f}_{\text{REF}})}{\text{f}_{\text{GSC}}}>1 \tag{4}\]  
The capacitor bank is designed with both extensive range and precise resolution capabilities. It spans from 1.1 pF to 4.4 pF, utilizing 1.1 pF as the basic unit and incorporating a 25 fF LSB. This configuration allows for capacitance adjustments between 732 fF and 1.766 pF. The system implements 5-bit controls to accommodate all PVT conditions, including resistance variations, with a fixed resistor value of 32 k\(\Omega\). The RC product is carefully configured to achieve approximately 2% resolution, allowing for an acceptable error margin of \(\pm\)1%. The calibration process utilizes a Successive Approximation Register (SAR) method, targeting an oscillator frequency (f\({}_{\text{GSC}}\)) of 2 MHz. During calibration, the system counts reference clock cycles over four cycles of the active-RC oscillator to achieve \(\pm\)1% accuracy under ideal conditions. This count is then compared against a predefined reference value of 128. To ensure precise counting, the counter reset is synchronized with each clock cycle. Upon completion and verification of the final bit, the system generates an end-of-conversionsignal, and the calibration values are stored in eFuse as serial peripheral interface values for future calibrations. The system demonstrates performance across all PVT corners, achieving frequency convergence to 2 MHz with an accuracy between 98.9% and 104% of the target frequency. The capacitor codes demonstrate appropriate variation across different process corners and temperatures, reflecting the system's adaptability. The codes range from 31 or 30 at the lowest corner (FF process, highest voltage, and coldest temperature) to 1 or 0 at the highest corner (SS process, lowest voltage, and hottest temperatures). This range utilizes the full span of the 5-bit control for the capacitor bank, which allows for 32 possible settings (0 to 31). Throughout this range, the system consistently maintains the target frequency within the specified accuracy range. This comprehensive calibration approach effectively addresses the challenge of achieving linear coverage across the entire range of PVT variations while maintaining the desired resolution in the active-RC integrator.
# Fragility Assessment of Traffic Embarkments Exposed to Earthquake-Induced Liquefaction
## 3 Model Description  
### Embankment and Soil Profile Geometry  
The soil-structure interaction was studied on various embankment geometries, where the crest width and construction height were varied, while the slope inclination remained unchanged for a complete calculation set. The embankments were built on three- and, in the absence of a clayey crust layer, two-layered soil profiles with a horizontal ground surface. At the base, the stiff clayey layer is placed covered by a sandy layer that was susceptible to liquefaction. Four different ground profiles (Table 1) were constructed, including S1 with a 7 m thick liquefiable layer and a crust layer on top, S2 with a 7 m thick liquefiable layer without a crust layer, and S3 and S4 with a clayey crust and thickness of the sandy layer of 2 m and 4 m, respectively. A 24 m thick base layer was applied to all four soil profiles. A groundwater level (zw in Figure 2) was assigned 1 m below the free field surface in all four soil profiles.  
Initially, three different crest widths were analyzed in relation to the 4 m high embankment underlain by soil profile S1; these were 6 m, 12 m and 24 m, representing a local road, a regional long-distance road and a motorway, respectively. In all further analyses, a 24 m wide embankment crest was adopted. The widest model embankment was selected in order to reduce the influence of the crest edges (slope instability) on the magnitude of settling at the midpoint of the embankment crest when studying the effect of embankment height. The effect of four different embankment heights (2 m, 4 m, 6 m and 8 m) was examined for all four soil profiles.  
In total, 17 different model variations, regarding embankment shape and soil profile stratigraphy, were analyzed and are discriminated in Table 2. In addition, Figure 2 provides a graphical overview of the geometric variables of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Soil ID** & \begin{tabular}{c} **H\({}_{\text{C}}\)—Thickness of** \\ **Crust Layer [m]** \\ \end{tabular} & \
\begin{tabular}{c} **H\({}_{\text{L}}\)—Thickness of** \\ **Liquefiable Layer [m]** \\ \end{tabular} &
\begin{tabular}{c} **Hg—Thickness of Base** \\ **Layer [m]** \\ \end{tabular} \\ \hline S1 & 1 & 7 & 24 \\ \hline S2 & 0 & 7 & 24 \\ \hline S3 & 1 & 2 & 24 \\ \hline S4 & 1 & 4 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground profile variations.  
Figure 2: Variables.
## 3 Results and Discussion  
### Microscopic Study  
After the applied thermal treatments and the subsequent cooling of products, their microstructure was examined respectively and is presented in Figures S4-S11, Supplementary material; selected representative micrographs are shown in Figure 1 regarding the application of different thermal treatments (temperatures and heating times).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **MgO** & **SiO\({}_{2}\)** & **FeO** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **Cr\({}_{2}\)O\({}_{3}\)** & **LOI** \\ \hline \hline \multicolumn{8}{c}{**wt.\%**} \\ \hline W5-5\% Fe\({}_{2}\)O\({}_{3}\) & 39.72 & 37.29 & 11.30 & 0.43 & 0.44 & 0.03 & 8.91 \\ W6-5\% Fe\({}_{2}\)O\({}_{3}\) & 37.15 & 35.67 & 10.54 & 0.48 & 0.29 & 0.03 & 13.57 \\ W7-5\% Fe\({}_{2}\)O\({}_{3}\) & 37.72 & 36.81 & 10.63 & 0.67 & 0.39 & 0.06 & 11.86 \\ W8-5\% Fe\({}_{2}\)O\({}_{3}\) & 37.91 & 41.37 & 11.49 & 0.86 & 0.77 & 0.10 & 5.40 \\ W5-10\% Fe\({}_{2}\)O\({}_{3}\) & 37.64 & 35.28 & 15.29 & 0.41 & 0.41 & 0.03 & 8.52 \\ W6-10\% Fe\({}_{2}\)O\({}_{3}\) & 35.21 & 33.75 & 14.57 & 0.45 & 0.27 & 0.03 & 12.93 \\ W7-10\% Fe\({}_{2}\)O\({}_{3}\) & 35.75 & 34.83 & 14.66 & 0.63 & 0.36 & 0.05 & 11.31 \\ W8-10\% Fe\({}_{2}\)O\({}_{3}\) & 35.93 & 39.15 & 15.47 & 0.81 & 0.72 & 0.10 & 5.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the examined blended samples.  
Figure 1: \(Cont.\)  
As it can be observed from Figure 1, the addition of hematite alters the surface of the obtained products/specimens. As a result of applied firing procedures, the granular content of samples presents differences regarding their surface compositions [14]. The white and black particles marked with red and blue circles in Figure 1 can be attributed to the presence of different crystalline phases. The major findings from the microscopic study of the products of the different thermal treatments of sample W5 (compared with the pre-treated product as well), are summarized below:  
* Similarities are observed on the surface of products corresponding to the W5 sample with 5 wt.% of hematite addition and a thermal treatment of 30 min (Figure 1b), or of 120 min (Figure 1c)to the W5T sample (again) with 5 wt.% hematite addition and thermal treatment of 120 min (Figure 1d).
* Moreover, the increase of hematite addition from 5 (Figure 1c) to 10 wt.% (Figure 1e) shows an increase of diverse mineral phases on the surface.
* Finally, comparing the different thermal treatments at the temperatures of 1300\({}^{\circ}\) and 1600 \({}^{\circ}\)C (Figure 1d,f, respectively) it becomes obvious that at 1600 \({}^{\circ}\)C the granules obtained are larger, as compared with the thermal treatments at 1300 \({}^{\circ}\)C, while sintering is not sufficiently achieved even at the firing temperature of 1600 \({}^{\circ}\)C (green circle), due to the increase of glassy phases.  
The results are similar for all the samples examined, however with slightly different surface compositions showing an increase of the glassy phase when the thermal treatment is applied at 1600 \({}^{\circ}\)C (Figures S4-S11, Supplementary material).
## 3 Methodology
### Verification Experiment Using the Flow Visualization Method
#### 3.3.2 Laser Source and Wind Device  
The light source is an essential factor in cross-sectional photography. A laser is an amplifier of light by stimulated emission and has high energy density and excellent straightness. This experiment uses monochromatic light. Therefore, the difference in refractive index of the laser is not considered. And for tomography experiments, a low-power laser of 500 mW is used, and the green wavelength is 0.154. A cylindrical lens is included in the laser to create a two-dimensional plane. Light passing through a cylindrical lens creates flat light with a thickness of 1 mm. Additionally, a video recording device is used to extract real-time information on the flow field. Temporal resolution is dependent on the frame rate. Therefore, video recording devices use image memory and H/W dedicated to video data processing. The FPS of the video device is 29.97. The images were separated by frame, and the image and video were output. The resolution of the image is 2 k, and the size of the image is 2048 \(\times\) 1080. Laser tomography is performed in a dark laboratory. In the experimental setup, a flow stabilization device is installed in front of the CSS to stabilize the wind. Through flow stabilization, the turbulent variability of the wind flowing into the CSS window is minimized. Inject seed particles into the fluid. Then, flow information from the cross-section is extracted. The seed particles selected olive oil. The formation of airflow varies depending on the arrangement and complexity of the indoor shape. Therefore, the internal space is simplified, and the Reynolds number is the output. The scale model has a scale of 1/180, and the inflow speed is 6 m/s. The Reynolds number of the experimental model is 99,265. The CAARD model is one of the box-type large building models. The CAARD model is a wind tunnel experiment research model and has been widely used in research. According to the model, the Reynolds number for large buildings is approximately 300,000 or higher. According to [PERSON]'s study, flows at high Reynolds numbers have statistically similar trends [21]. Since the Reynolds number is over 20,000 in this experiment and CFD simulation, trend analysis is possible.