esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
## 3 Methodology
### Preprocessing
#### 3.1.1 Bounding Space of Target Structural Object  
When evaluating the integrity of a single structural object in the Scan-vs-BIM framework, the most effective way is to compare only the shape of the target object with the point cloud near the target structural object. The As-Built scan cannot be a standard for work because it consists of point cloud data with scattered points without a fixed index. Therefore, it should be defined based on the target structural object of As-Planned BIM consisting of parameters. The target structural object of the BIM model contains various data, and all of these data are defined as parameters. The structural object's parameters include the \"start point\" and \"end point\" of the object, the centerline connecting the two, and the shape of the section. In this study, the work standards were clearly defined by utilizing the characteristics of these BIM parameters. Therefore, in the preprocessing stage, the bounding and work reference bounding spaces are defined based on the parameters of the target structural object on the BIM model. The process of defining the bounding space is shown in Figure 2.  
First, the structural parameters are collected from the BIM model, which serves as the basis for the analysis. These parameters include the centerline and cross-section shapes used to construct the bounding space around the object. Second, the radius (R) of the bounding space is set based on the cross-sectional shape. The R value is set in consideration of the error range of the 3D scanner's device and the general range recognized as a structural error. The error range of the 3D scanner's device was defined to be \(\pm\)51 mm, which is the level of accuracy of the LOD Level 1 specified by GSA (Table 1). The structural error range was defined to be \(\pm\)13 mm according to the Code of Standard Practice of the American Institute of Steel Construction (AASC) [36]. Finally, in order for Distance-DNN to clearly understand structural error types (Deflection, Tilt, Rotation, Error), an additional margin was needed to understand the surrounding environment of the object. Therefore, set the R value of the bounding space to the farthest position from the center reference section (i.e., +199 mm). Third, based on the centerline, all ranges within the R value are defined as bounding space. The bounding space created is a virtual 3D range made up of equation values and becomes the reference for all preprocessing operations.
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
## 3 Proposed LDPC Codes for Information Reconciliation
### Construction Method of SC-LDPC Codes for Information Reconciliation
\begin{bmatrix}1&0&0&0&0&0&0&0&0&0\\ 1&0&0&1&0&0&0&0&0&0&0\\ 0&1&0&1&0&1&0&0&0&0&0\\ 0&0&1&0&0&1&1&0&0&0&0\\ 0&0&0&0&1&0&1&0&1&0&0\\ 0&0&0&0&0&0&0&1&0&1\\ 0&0&0&0&0&0&0&0&1&0&1\\ 0&0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&0&0\end{bmatrix} . \tag{10}\]  
As can be seen from Equations (5) and (10), when the number of repetitions is two, the code length increases from 6 to 12 while the code rate reduces from \(1/2\) to \(1/4\), which results in inescapable rate loss of \(1/4\). However, we can increase the number of repetitions to lower the rate loss \(M_{b}/N_{b}n_{b}\). For instance, the rate loss of new SC-LDPC code will be decreased to \(3/(6\times 100)=1/200\) if \(n_{b}\) increases to 100, which is much smaller than the rate loss of \(1/4\) when \(n_{b}=2\).
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 2 Materials and Methods--Acoustic Spectrum Characterization
### Materials  
Two soil types were selected to investigate spectral acoustic reflection characteristics: a fine-grained, non-cohesive sand and a coarser-grained, granular gravel. Both were analyzed using standard sieve tests to determine their particle-size distribution and gradation characteristics.  
Poorly graded sand (SP):  
The SP soil consisted of quartz-based, sub-angular particles with minimal fine content. The particle size distribution included D10 = 0.30 mm, D30 = 0.35 mm, D50 = 0.40 mm, and D60 = 0.45 mm. The calculated uniformity coefficient (Cu) was 1.50, and the coefficient of curvature (Cc) was 0.91, confirming its classification as poorly graded under the Unified Soil Classification System (USCS). The sand was oven-dried prior to testing and exhibited high permeability and low angularity.  
Poorly graded gravel (GP):  
The GP soil sample comprised sub-rounded limestone fragments with no fine material and high void content. The grading analysis yielded D10 = 2.8 mm, D30 = 4.0 mm, D50 = 5.4 mm, and D60 = 6.0 mm, resulting in a uniformity coefficient (Cu) of 2.14 and a coefficient of curvature (Cc) of 0.95. These values are consistent with a poorly graded coarse gravel, confirming its USCS classification. Like the sand, the gravel samples were oven-dried before testing to ensure consistency in the acoustic measurements.  
Full grading curves for both soils are provided in Figures 1 and 2.  
* Poorly graded sand (SP)--a fine-grained, non-cohesive sediment with uniform particle size. The grading curve for receiving SP soil appears in Figure 1.
* Poorly graded gravel (GP)--a coarser-grained material with higher permeability and varied particle distribution. The grading curve for receiving GP soil appears in Figure 2.  
The aim is to analyze the frequency-dependent acoustic characteristics of the two soil types, by measuring how much of the transmitted energy is reflected back at each frequency. In this study, the term \"spectral differences\" refers to differences in the pattern and intensity of acoustic reflection across the tested frequency range (100-400 kHz). These differences reflect the unique way each soil interacts with sound energy at different frequencies and form the basis for distinguishing between the materials.
## 2 Methods
### Intelligibility  
The test signal was an intensity-modulated noise carrier signal of a ready-made system (Bruel & Kjaer, Naerum, Denmark, Transmitter Type 4225), reproduced from its loudspeaker. The index of intelligibility RASTI was obtained by an analyzer (Bruel & Kjaer, Naerum, Denmark, Receiver Type 4419) distributed in the room. The measurement system block diagram and measuring instrument are shown in Figure 5c. The speech level was 60 dB (A) [8] at 1 m from the loudspeaker, which both meets the International Electrotechnical Commission, IEC standards and lies within universally acceptable ranges [9]. The intelligibility was measured by two sound source positions, with sound source S1 assuming the propagation of speech from the lobby to the entrance hall, and sound source S2 assuming the propagation of speech from the entrance hall to the lobby as targets. The measurement points depended on the sound source position, which, in the case of sound source S1, included 41 points, while sound source S2 included 21 points. The details of the measurement points are shown in Table 4. The positions of the sound sources and measurement points correspond to those in Figure 3. The height of the sound source loudspeaker was set at 1.5 m from the floor, assuming the height of people's mouths in the standing position, which was assumed to be the most disadvantageous situation. The height of the measuring point was similar to the measurement of sound during hotel operations.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Hybrid Protocols  
Hybrid protocols are partially synchronized and partially random. In he general approach, control signaling follows over the corresponding time slots where the data communication scheme might be based on random channel access. In another approach, we might have a superframe with predefined durations that is shared with every user in the network, while the channel access may be completely random in each control or data duration.
###### Abstract
Since the buoy location (37.54\({}^{\circ}\) N, 130.0\({}^{\circ}\) S) is out of the range of domain 3 (d03), the buoy site (in red) is marked with a box. The wind profiler radar is located at BukGangNeung (BGN). See Table 1 for the detailed information on the stations in blue. effect of the PBL schemes and LSMs on the thermal internal boundary layer formed under sea-breeze conditions [5; 18] and showed that better performance was evident for different sets of PBL and LSM schemes for different coastal environments. Various WRF physical parameterizations, including the cumulus, microphysics, and PBL schemes, had been also assessed in simulating sea breeze and their associated convective events over Florida [19], which showed diversified results in the intensity and duration of precipitation as well as the location and duration of the sea breeze events. Most of these sensitivity studies, however, were limited to experiments with only a few physical parameterization schemes due to the huge computational resources required to find the physical parameterization schemes for improving the sea breeze prediction. Although numerical sensitivity studies provide useful information on model responses to different parameterization schemes, they do not guarantee optimal solutions for accurate numerical forecast of the sea breeze circulation and its associated local weather phenomena. The use of numerical models for scientific understanding of sea breezes, including the onset and intensification of sea breeze front and the mechanisms of initiation and development of the sea breeze-induced convection and precipitation systems, should be preceded by optimization of model performance in simulating such phenomena, potentially through optimal parameter estimation [20; 21] and/or optimal selection of parameterization schemes [22; 23; 24]. Recently, an artificial intelligence-based optimization tool, called the genetic algorithm (GA) [25; 26], has been widely used in optimization problems in meteorology and hydrology [20; 21; 22; 23; 24; 27; 28]. In this study, we couple the WRF model with micro-GA to obtain an optimal combination of physical parameterization schemes for more accurate simulation of the sea breeze system in Yeongdong, Korea, focusing on near-surface weather elements and PBL winds in the mountainous coastal region. We employed the micro-GA [29], an efficient GA method, and developed the WRF-\(\upmu\)GA system for the purpose of optimization experiments. We overcame the limited experiments of previous sensitivity studies by finding the optimal combination of the physical parameterization scheme without limiting the number of physical parameterization schemes using the WRF-\(\upmu\)GA system. Section 2 describes the observational data and the sea breeze cases used in this study, and Section 3 provides a detailed description of methodology. Section 4 presents the results of simulations and optimizations. Conclusions are given in Section 5.
# Not All Seeds Are Important: Fuzzing Guided by Untouched Edges
## 6 Evaluation
### RQ2: Unique Crashes  
To further validate the effectiveness of the proposed method in vulnerability discovery, we conducted a statistical analysis of unique crashes. Table 3 presents a comparison of UntouchFuzz with four baseline fuzzers on the number of unique crashes, where the data in the table represent the total number of unique crashes discovered over ten rounds of testing. It is worth noting that, as in our experiments, we used the latest version of the djpeg program and did not find any valid crashes, so its results are not listed in Table 3.  
As shown in the experimental results in Table 3, UntouchFuzz outperforms the other four baseline seed scheduling strategies in total unique crash discoveries, discovering the highest number of unique crashes on six of the programs under test. However, on the remaining five target programs, UntouchFuzz does not achieve the best results, but the difference between it and the best-performing approach is not significant. We attribute this to the randomness of mutation and differences in fuzzing evolution.  
It is essential to note that the experimental results in Table 3 are directly taken from the unique crash metric of AFL-based fuzzers. However, the number of unique crashes may not accurately reflect the actual number of unique vulnerabilities because there is often a many-to-one relationship between them, which means that multiple crashes may correspond to a single vulnerability. For example, suppose crash one's triggering path is A \(\rightarrow\)B\(\rightarrow\)D, and crash two's triggering path is A\(\rightarrow\)C\(\rightarrow\)D, and both crashes occur at the same location in basic block D. From AFL's perspective, since the triggering paths of these two crashes are different, they are both considered unique crashes. However, from a root cause analysis perspective, both crashes are due to codes in basic block D, and thus, these two crashes should be categorized as the same vulnerability. Moreover, the differences in these two crash paths are likely related to changes in the input, where bytes in the input can affect the execution of subsequent basic blocks following basic block A. To obtain a more accurate count of unique vulnerabilities, we conducted deduplication of unique crashes based on the function call stack information provided by ASAN, selecting the top three functions and removing duplicate crashes. Table 4 presents the results after crash deduplication.  
Figure 6: Edge coverage over time in five fuzzers.  
The data in Table 4 reveal that various seed scheduling strategies exhibit distinct performances across different programs, with UntouchFuzz achieving the highest number of unique vulnerabilities in six programs. Overall, UntouchFuzz outperforms the other four baseline seed scheduling strategies based on the total number of discovered vulnerabilities. Furthermore, compared to EcoFuzz and Alphuzz, UntouchFuzz demonstrates a more stable performance across the programs.
# Space Weather Effects on Satellites
## 1 Introduction
Although intense SW events rarely occur [39], they can have substantial influence on our technology-dependent society [40; 41]. The largest GS (or/and SW) event on record is the Carrington-Hodgson event (1859-09-01) [42; 43], estimated to cause devastating effects in the current technological age [44; 45]. As SW is in essence a multi-disciplinary research topic, reviewing the representative literature is a challenging task that goes beyond the scope of our concise report. Instead, many reviews have been preferred here which contain many relevant references. The aim here is to outline the main SW effects with a special focus on the spacecraft anomalies.
# A Solar-Thermal-Assisted Adiabatic Compressed Air Energy Storage System and Its Efficiency Analysis
## 2 System Description  
The implementation of an ST-CAES with four-stage compression and three-stage expansion is illustrated in Figure 1. The system comprises three main subsystems, including a compression subsystem (COM), a solar thermal collection and storage (STC), and a turbine subsystem (TUR). The COM contains a four-stage compressor train (AC1-AC4), four-stage heat exchangers (HEX1-HEX4), a cold-water tank (CWT), and a hot water tank (HWT). It is worth noting that the number of compression stages is determined by the maximum pressure of the steel pipeline tank (SPT) and the type of compressor in use. The designed maximum pressure of (SPT) is 10 MPa and the conventional single-stage pressure ratio of the reciprocating piston compressor is approximately 3. Therefore, the four-stage compressor can meet the requirement of maximum air pressure in SPT.  
The STC consists of a parabolic trough collector (PTC), a low-temperature oil tank (LOT), and a high-temperature oil tank (HOT). The TUR includes a throttle valve (TV), an air turbine chain (AT1-AT3), a heat regenerator (HR), and three heat exchangers (HEX5-HEX7) as well. Moreover, three types of fluids are introduced into the system, namely air (AR), water (WA), and Therminol oil VP-1 (O). To clearly describe the system, all the components and streams are listed and numbered as in Figure 1.  
Although the COM and TUR subsystems are similar to the A-CAES reported in numerous studies [12; 13; 14; 15], ST-CAES features several distinct characteristics. Firstly, the compression heat is absorbed and stored in HWT and utilized to provide thermal energy to residents. Secondly, low-temperature thermal oil from LOT is heated by the PTC to attain high temperatures and then stored in an HOT that can supply stable heat resources. Thirdly, the compressed air is preheated by exhaust air before entering the air turbine, and further heated by the high-temperature oil from the HOT through heat exchangers (HEX5-HEX7) to improve the system efficiency.  
Figure 1: The design scheme of ST-CAES.
## 3 Modeling
### Vpsc90  
VPSC90 is a newer implementation of the VPSC model [10]. In VPSC90, elasticity is introduced adding an elastic strain term that is determined by the elastic stiffness \(\mathbf{\textit{C}}\) given by the ELSC (Elastic Self-Consistent) method [1]. The addition of the elastic and plastic strain must be equal to the strain defined in the boundary conditions of the problem, \(\Delta\varepsilon\):  
\[\Delta\varepsilon=\Delta\varepsilon_{\textit{el}}+\Delta\varepsilon_{\textit{ pl}}=\mathbf{\textit{C}}^{-1}\Delta\sigma+\dot{\varepsilon}\Delta t \tag{11}\]  
The input boundary conditions actually include every component of the velocity gradient tensor \(L\), allowing to also impose a rigid solid rotation. Additionally, it is possible to impose that a stress component is zero. The corresponding strain components are then calculated solving the system defined by (11).  
The main novelty of VPSC90 is the usage of a different algorithm, capable of finding a direct solution to grain stresses based on the Newton method. A first approximation of \(\sigma_{g}\) is obtained extrapolating from previous time steps or assuming elastic behavior. In successive steps, macroscopic magnitudes are calculated with (6), and an improved estimation of the solution for grain stresses is obtained applying a correction stress \(\Delta\sigma_{g}\):  
\[\Delta\sigma_{g}=\big{(}M_{g}+\widetilde{M}_{g}\big{)}^{-1}\bigg{(}\mathbf{ \textit{X}}_{g}+\big{(}M+\widetilde{M}_{g}\big{)}\left(\mathbf{\textit{C}}^{- 1}+\Delta t\mathbf{\textit{M}}\right)^{-1}\mathbf{\textit{X}}\bigg{)} \tag{12}\]  
where \(\mathbf{\textit{X}}_{g}\) is the error in the interaction equation of grain \(g\) (5) and \(\mathbf{\textit{X}}\) is the error in Equation (11):  
\[\begin{split}\mathbf{\textit{X}}_{g}&=\dot{ \varepsilon}-\dot{\varepsilon}_{g}+\widetilde{\mathbf{\textit{M}}}_{g}(\sigma- \sigma_{g})\\ \mathbf{\textit{X}}&=\Delta\varepsilon-\mathbf{ \textit{C}}^{-1}\Delta\sigma-\dot{\varepsilon}\Delta t\end{split} \tag{13}\]  
and the compliance tensors \(M_{g}\) and \(\mathbf{\textit{M}}\) are obtained by linearizing (7) and the corresponding macroscopic equation, respectively. The algorithm finishes when the desired levels of tolerance for \(\mathbf{\textit{X}}\) and \(\mathbf{\textit{X}}_{g}\) are reached.  
In this work, VPSC90 is modified with the addition of an alternative method for the calculation of strain rate sensitivity and a new hardening law, as explained in the following sections.
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Built-Up RS-Based Proxies  
The build-up environment in urban areas tends to be significantly impacted during a disaster and these adverse impacts are relatively easy to detect. It makes this category the most studied in the RS literature (Figure 1), with the majority of RS-based proxies being developed here as compared to other environments within urban DRM (Tables 1-3). To discuss and explain the proxies in detail the built-up component is separated into three main categories: Buildings, Transport, Others.
# Towards a Digital Twin Warehouse through the Optimization of Internal Transport
## 3 Results
### Analysis of Storage Configurations  
#### 3.3.1 Analysis  
In Figure 10a, the layouts with a greater number of \"transversal zones\" can be observed, layout 6 \(\times\) 3 with a 76.3% occupancy rate and layout 3 \(\times\) 3 with a 50% occupancy rate, while in Figure 10b, the layouts with a greater number of \"longitudinal zones\", layout 3 \(\times\) 6 with 65.0% occupancy rate and layout 3 \(\times\) 3 with a 50% occupancy rate can be seen. The analysis of the configurations (Table 2) under different occupancy conditions shows diverse results, although with an evident tendency towards a better performance of the layouts with a greater number of \"transversal zones\" (Figure 10a), compared to those layouts with a greater number of \"longitudinal zones\" (Figure 10b).  
For low storage requirements (less than 822 bags) and low occupancy rates (less than 65%), the 6 \(\times\) 3 configuration performs best. As more storage capacity is needed, the number of viable layouts decreases. Of those that are available, the layouts with the highest number of \"transverse zones\" and the lowest number of \"longitudinal zones\" are the most suitable.  
Figure 10: Layouts with a greater number of transversal or longitudinal zones: (**a**) layout (6 \(\times\) 3) with 76.3% occupancy rate = 50% occupancy rate layout (3 \(\times\) 3); (**b**) layout (3 \(\times\) 6) with 65.0% occupancy rate = 50% occupancy rate layout (3 \(\times\) 3).  
For high storage requirements (more than 80%), the number of available alternatives is so small that a comparison between layouts is meaningless. In these cases, the simulation only provides information on occupancy times and the number of reallocations made.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis
### Analysis of Imperceptibility
#### 5.2.3 Model Cropping  
Watermark data was independently and repeatedly embedded in the data, so after cropping the data, as long as some of the watermark embedding positions remain intact, the watermark information can be detected. The experiment cropped the watermarked data to different degrees, and the experimental results are shown in Tables 6 and 7. As shown in Table 7, when the cropping ratio was 70%, the vertex watermark algorithm was affected to varying degrees, but the copyright information can still be properly extracted; the texture watermark algorithm was basically unaffected. The experimental results showed that the algorithm in this paper was robust to cropping attacks.
# Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks
## 5 Results and Discussion
The simulation layout involved seven 5G BSs and covered a smaller area than the LTE BS (as shown in Figure 3). The 5G BSs are active for only 13 h (10 a.m. to 11 p.m.) during peak time (as shown in Figure 2) and are in sleep mode at other times. The LTE BSs are active all the time; during high-traffic periods, they ensure the coverage and support of the 5G BSs, whereas during low-traffic periods, LTE BSs have two functions, data delivery and ensuring maximum coverage and radio service. (i) Without the switch-off, all BSs are active for 24 h:  
Figure 7:3 Figure 7:4 Figure 7:5 (ii) With the proposed switch-off:  
Figure 7:6  
The energy savings that can be achieved is 3.52 kWh per day in this case study, which meets the needs of mobile subscribers for high-speed data at peak time. Figure 16 summarizes the data rate versus network power consumption over time based on the network layout shown in Figure 7:7 However, the energy savings in this approach depends on the number of BSs that will be turned off. If a large number of BSs are turned off, then the energy savings will be substantial.