esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Deficiencies  
The prediction accuracy of the machine learning models for the soil properties was affected by two major factors. First, the data of some environmental covariates were not highly refined, with raster data at a resolution of 1 km \(\times\) 1 km. Due to the small scale of the study area, local spatial autocorrelation might be ignored at a low resolution, affecting the variable selection and reducing the prediction accuracy of the soil properties. Raster resampling for the purpose of unifying the scale of the environmental covariates also led to a reduction in data accuracy and information loss.  
Second, insufficient environmental covariates were considered in this study. Bioclimatic covariates were not included as climate covariates. Temperature, which affects soil microbial decomposition processes, is more representative of the impacts of climate on the soil nutrients [64]. Additionally, there are various agricultural production activities in the study area, and the remote sensing covariates only represent agricultural activity to a certain extent. The inclusion of more agricultural activity factors (e.g., cropping system and fertilizer application patterns) as environmental covariates could improve the prediction accuracy and performance of the models.
## 3 Path Feature Recognition
### Threshold Segmentation  
Since the colour of guide paths is blue, the blue chrominance component Cb has an obvious contrast between the guide paths and the background in the YCbCr colour space, which is helpful for obtaining a better segmentation effect. Hence, the Cb component of path images was used as the raw data of path features. After the conversion of path images from the PAL standard to the  
Figure 4: The framework of path recognition based on KPCA (kernel principal component analysis)–BPNN (back-propagation neural network) and IPSO (improved particle swarm optimization)–BTGWP (binary tree guidance window partition).  
YCbCr standard with a sampling ratio of 4:2:2, the resolution of the Cb component in path images is 480 \(\times\)360 pixels.  
Path pixels can be identified by checking whether the Cb component of each pixel is larger than a threshold. If the path image is displayed in an invariant, uniform illumination circumstance, it is ready to determine the threshold that can separate the path from the background distinctly. Unfortunately, sometimes, the perceived colour of an object differs from its original colour, especially under non-uniform and dynamic illumination conditions.  
Neither the fixed threshold methods nor the iterative optimal threshold methods can obtain a suitable threshold for all conditions. Even if some new colour models [14; 16] or image enhancement methods [15] are reported to be able to separate chromaticity from brightness, a number of under- or over-segmentation error points remain around the actual path pixels. The possible path pixels are extracted with noise points from the original images in Figure 3 by means of the Otsu threshold segmentation method, as shown in Figure 6. Because of some objects covering the path, the binary image of path pixels is incomplete, with several parts missing. The non-uniform illumination interferes with the extraction of path pixels as well, e.g., the actual path becoming slender in the highlight regions, and some similarly narrow fake paths occurring beside it, as shown in Figure 6b.  
Figure 5: The recognition process of path features.
## 3 Experimental Results and Analysis
### Security Analysis
#### 3.2.1 Visual Perception  
Figure 6 exhibits the tone-mapped images generated from our scheme in different stages. We adopted the tone-mapping algorithm introduced in [2]. From Figure 6a,b, image distortion is not perceived after the format conversion and the message embedding. Figure 6c shows the encryption images which successfully shelter the outlines and detail features. The encrypted images are completely submerged by noise without revealing any meaningful information. Consequently, our encryption scheme is visually secure.
## 3 Materials and Methods
### Speed Dating
#### 3.2.2 Procedure  
Figure 2 illustrates the comprehensive process of our experiment. We conducted semi-structured interviews with participants through a video conferencing platform (i.e., Zoom). We briefly introduced the purpose of this study and started speed dating activity using storyboards. We showed participants each storyboard in random order. The storyboard describes possible interaction where an AI-based CRS supports a student's course-selection decision. This setup allowed participants to indirectly experience how five distinct types of AI-based CRS facilitate decision-making. After participants read each storyboard aloud, we asked the following questions to investigate their thoughts on specific AI-based CRS represented in individual storyboards: _\"How do you think this AI-based CRS will affect your course-selection decision-making?\"_ and _\"Do you have any expectations or concerns about this AI-based CRS?\"_ After participants reviewed all five storyboards, wrap-up questions were asked to find out general expectations for AI-based CRSs: _\"Which AI-based CRS do you think will help students' course-selection decision-making?\"_ and _\"How do you think AI-based CRS will change students' course-selection decision-making?\"_ All the questions were open-ended. Furthermore, we asked follow-up questions such as _\"Why did you think this scenario would help your course-selection decision-making?\"_ after initial responses were given. These probing questions aimed to deepen participant engagement and elicit more detailed insights. The entire interview lasted around 30.1 min (SD = 6.9 min), with 4-6 min spent sharing each storyboard and probing participants on its specific implications.
## 5 Conclusions  
This paper presents an experimental study for masonry shear walls which were built from a new type of self-insulating concrete masonry units, and subjected to in-plane cyclic loading. Based on the results of the experimental work and analysis, the following conclusions can be made:  
1. The use of the moment curvature analysis to predict the flexural capacity of SCMSW specimens is a valid assumption, with an average accuracy of 94% for all SCMSW specimens.  
2. Using small size of bars with closely-spaced shear reinforcement enabled the distribution of the stresses throughout the wall diagonals and improves the post-cracking behavior, thus leading to higher energy dissipation and more ductile behavior.  
3. The plastic hinge lengths of SCMSW specimens ranged from 12.65% to 28.85% of the plan length of the wall.  
4. The displacement ductility decreased when the flexural reinforcement ratio increased. In addition, the amount of the vertical load and aspect ratio had no significant effect on the displacement ductility of the SCMSW specimens.  
5. The existence of the boundary elements at the SCMSW specimen ends increased the ductility and the lateral load capacity.  
6. Specimens with an identical amount of axial load and aspect ratio with different amounts of the flexural reinforcement showed an increase of the lateral capacity load by 30% when the flexural reinforcement ratio increased from 0.46% to 0.72%. On the other hand, approximately the same percentage was obtained when specimens had identical amounts of flexural reinforcement and aspect ratio with varying amounts of the axial applied load from 0.5 to 1.0 N/mm\({}^{2}\).  
Acknowledgements. This research was conducted with the financial support of the National \"Twelfth Five-Year\" Research Project in the National Science & Technology Pillar Program (Grant No. 2015 BAL03B02). [PERSON] wrote the manuscript; [PERSON] and [PERSON] designed the experiments. [PERSON] and [PERSON] modified the final paper. Conflicts of Interest: The authors declare no conflict of interest.  
Figure 15: Load-displacement envelopes for different axial load ratios for SCMSW specimens.
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 1 Introduction
This kind of measurement system needs a complicated calculation process to eliminate the influence of the misalignment between the optical center and the rotation center. For any rotating optical measurement system, the motion trajectory of the optical center will be a circle centered on the rotation center. If the radius of the circle is equal to 0, it means that the optical center coincides with the rotation center. Otherwise, the optical center is not matched with the rotation center. However, since the imaging system is composed of multiple lenses, it is hard to accurately calculate and determine the real position of the optical center. Therefore, it is a challenge to guarantee a good alignment between the optical center and the rotation center, and any misalignment could affect the subsequent information stitching. In order to calculate the coordinates of the optical center and rotation center to solve the alignment problem, the idea of camera calibration ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018) is introduced. In this paper, we propose a method based on [PERSON]'s camera calibration principle ([PERSON] et al., 2019) to calculate the positions of the optical center in a unified world coordinate system from the external parameters (including translation vector and rotation matrix) of the camera in a rotating optical measurement system composed of a non-metric camera and a general rotating platform. With the rotation of the turntable, a set of external parameter matrices are obtained, from which a series of optical centers can be calculated to find out the rotation center by the least square circle (LSC) fitting method. The optical center and the rotation center provide the guidance for installation of the camera in a rotating measurement system. In order to reduce the fitting complexity and improve the fitting accuracy, we introduce an auxiliary plane coordinate system to map the optical centers on a unified virtual plane before circle fitting. In addition, for reducing the errors, we collect the images when the camera rotates from different positions, and multiple sets of optical centers are calculated simultaneously to obtain multiple fitted circle centers. The weighted algorithm is used to determine the final rotation center. Computer simulations and some experiments verify that the method can guide the installation and adjustment of the rotating optical measurement system. The rest of the paper is organized as follows: Section 2 illustrates the principle of the proposed method. Section 3 shows some simulations to validate the proposed method. Section 4 displays some experimental results to validate the proposed method and discusses the strengths and contributions of the proposed method. Section 5 summarizes this paper.
## 3 Results
### Catalytic Investigations
#### 3.2.2 Influence of Platinum Content/Redox Properties  
Furthermore, the platinum content of the catalysts was varied with a constant polymer content of 13 wt.% (Figure 5). No conversion was observed for the PSC catalyst without metal content ((13)PSC) which was expected due to the lack of hydrogenation active metal sites, and further confirms the proposed reaction scheme for the HDO of FAc given in Figure 1, in which ring opening can only occur after hydrogenation. When adding metal to (13)PSC, hydrogenated and ring-opened products are observed. A strong increase of catalyst activity in terms of yield of half-hydrogenated (HH) and ring-opened (C\({}_{8}\)-OH and \(n\)-octane) products was observed when the Pt content was increased from 0.5 to 1 wt.% ((0.5)Pt(13)PSC) and (1)Pt(13)PSC, respectively). No significant change in product yield was obtained when the Pt content was further increased to 2 wt.%. This is in agreement with the acid-catalyzed ring-opening step being rate limiting for the HDO of FAc to \(n\)-octane under the conditions applied. This has also been suggested in the literature [17] and is also consistent with the findings in Section 3.2.1, where increasing \(n\)-octane yields were observed with increasing polymer content (Figure 3).  
Figure 4: Product fractions obtained for the HDO of FAc carried out using polymer–silica composite (PSC) catalysts doped with 1 wt.% Pt and different polymer contents (T = 250 \({}^{\circ}\)C, P\({}_{\rm{H2}}\) = 100 bar, m catalyst = 0.25 g, 0.5 g FAc (24.5 mmol I\({}^{-1}\)) dissolved in 150 ml cyclohexane, stirring speed = 1000 min\({}^{-1}\), X(FAc) \(>99\%\) except for no catalyst).  
When further comparing the novel metal/polymer-silica composite catalyst (1)Pt(13)PSC with a commercially available Pt catalyst (Pt supported on \(\gamma\)-alumina, (5)Pt-Escat) it can be seen that higher product fractions of ring-opened products (0.90 vs. 0.21) were yielded with a lower noble metal content (1 wt.% vs. 5 wt.%) for (1)Pt(13)PSC as compared to (5)Pt-Escat. It is noteworthy that using (5)Pt-Escat as catalyst, a small product fraction of ring-opened products (0.21) can be observed. This is probably due to its higher support acidity as compared to (1)Pt(0)PSC. The absence of \(n\)-octane in the product fraction in the reaction using (5)Pt-Escat furthermore illustrates that acidic sites of different strength may be necessary for the ring opening on the one hand, and the final dehydration reaction of the alcohol to the alkane on the other hand. This is in agreement with results published by [PERSON] et al. [16] where Ru-nanoparticle-SILP (supported ionic liquid phase) catalysts were used for the HDO of 4-(2-tetrahydrofuryl)-2-butanol, an intermediate in the HDO of FAc. And although ring-opened products were obtained in yields \(>\)90% no, or only very low yields (\(<\)3%) of \(n\)-octane were observed.
## 1 Introduction
Aleatory uncertainty is due to randomness in the process while epistemic uncertainty is a result of lack of knowledge in the system. Reliability data and failure rates of safety components are typically uncertain due to lack of information. In [PERSON]'s [31] uncertainty framework, Level 5 uses the same kind of framework as Level 4 uncertainty but risk that is typically expressed in point estimates are replaced with probability distributions instead and confidence intervals are added to the results--this is investigated in this study. It is important to determine the degree of uncertainty in the T-H-O-Risk methodology to assess the efficacy and reliability of the model for effective fire safety measures in high-rise residential buildings. It is to be noted that sensitivity and uncertainty analysis in this article will be confined to HOEs only. Due to the highly publicized high-rise fires in recent years such as the Grenfell Tower fire in London and the Lacrosse Dockland Fire in Melbourne, fire risk is vitally important to occupants and regulators. Much of the recent research in building fire risk is heavily focused on reducing risk and developing risk-informed oversight by improving technical systems in fire risk assessments. Risks in building fires include the systems, organizations and humans and by excluding HOEs, risk is likely to be underestimated. Equipment maintenance and operation, and procedural factors have a human component, as well as building occupant behaviour during a fire event. Current methods do not include the possible impact of explicit human and organizational errors on safety performance of equipment and personnel. Stakeholders in Australia are progressively shifting towards quantifying performance in the building codes by evaluating risk levels and their tolerability levels [21]. To address the knowledge gaps in current state-of-the-art PRA for high-rise buildings identified in the literature review above, the main goals of this paper are as follows:  
* Develop an improved PRA methodology to address concerns that deterministic, fire engineering approaches significantly underestimate safety levels that lead to inaccurate fire safety levels. * Enhance existing verification methods by incorporating probabilistic risk approach and HOEs for (i) a more inclusive view of risk, and (ii) to overcome the deterministic nature of Australian verification method. * Perform comprehensive sensitivity and uncertainty analyses to address uncertainties in numerical estimates used in fault tree/event trees (FT/ET), BN and SD and their propagation in T-H-O-Risk model. Quantification of human and organizational risks for high-rise residential buildings which contributes towards Australia's agenda that is moving in the direction of a sustainable, risk-based regulatory approach. The paper proceeds as follows: Section 2 presents the methodology and materials used; case studies are explained in detail in Section 3; analysis, sensitivity, uncertainty studies are presented in Section 4 and finally, conclusions and implications are discussed in Section 5.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 5 Experiment
### Experimental Results in Real-World Application  
In this section, we evaluate the model's adaptability to depth maps with varying sparse distributions in real-world scenarios. To accomplish this, we utilized OPPO's self-developed prototype equipped with Spot-iToF316 to capture both color images and sparse depth maps across various scenarios for testing purposes.
## 1 Introduction
### Integrating FSSs into Building Design  
Beyond signal management, FSS-enabled smart skins can contribute to energy harvesting, transforming part of the blocked RF energy into usable DC electrical power. When integrated into building envelopes, these smart skins can capture and convert ambient electromagnetic energy from sources like WiFi, cellular networks, and broadcast signals, supporting low-power IoT devices or other building systems. The potential to harvest energy from the environment without relying on conventional electrical grids aligns with sustainable design goals, allowing smart skins to be part of self-sustaining energy systems for smart buildings. As urban environments evolve toward more interconnected and energy-efficient models, the role of FSS-based smart skins in building envelopes will be pivotal in optimizing wireless communication, privacy, and energy management in future cityscapes.  
The integration of FSSs into building design would necessitate incorporating electromagnetic data into Building Information Modeling (BIM). BIM is a transformative approach in architecture, engineering, and construction industries, centered around the creation and management of digital representations of the physical and functional characteristics of buildings. BIM integrates geometric, spatial, and material data into a multi-dimensional model, enabling stakeholders to collaborate more effectively throughout a building's lifecycle--from design and construction to operation and maintenance. Unlike traditional 2D drawings, BIM provides a shared platform where architects, engineers, contractors, and owners can work together in a unified digital environment, ensuring that any changes made by one party are instantly reflected throughout the model. This approach improves design accuracy, reduces errors, and enhances project coordination. Furthermore, BIM allows for simulations, performance analysis, cost estimation, and clash detection, all of which significantly improve the efficiency and sustainability of building projects. As a result, BIM is recognized as a key technology for the future of smart construction, promoting a more integrated and data-driven approach to building design and management.
# Comparative Evaluation of Different following Mechanisms in VR Guided Tour: A Preliminary Study
## 4 Results
### Cognitive Awareness and Usability of Following Mechanisms  
We can determine a participant's cognitive engagement and knowledge acquisition in the virtual tour through environmental questionnaires. The following mechanism questionnaire assesses the helpfulness of mechanisms in the tour. Figure 6 demonstrates the results for the cognitive awareness and following mechanism questionnaires. Considering the environmental questionnaires, ISL has the best performance overall. The grades of the ISL mechanism in Q15: \"I am interested in the environment\" and Q16: \"I felt immersion\" are a little higher than the limited mechanism, which infers that the immersive experience of ISL is similar to the limited mechanism and did not degrade like the original SL. For question Q17: \"Given a map with a start and an endpoint, I can draw the guiding path.\", we found ISL scores higher (M = 3.43, SD = 1.05) than L (M = 2.17, SD = 1.21) and NL (M = 2.29, SD = 0.88). Likewise, question Q18: \"Given a map, I can point out the landmarks mentioned in the guiding tour.\" shows ISL (M = 4.43, SD = 0.73) significantly ahead of L (M = 3.83, SD = 0.69) and NL (M = 3.00, SD = 1.20). In other words, ISL provides participants with a better understanding of the virtual tour layout and helps them remember the surroundings. The improved semi-limited mechanism also presents a better result than the original semi-limited in the usability questionnaires. Comparing ISL to other mechanisms, we found that Q22: \"I miss the narration.\" indicates ISL helps participants concentrate on narration more than other mechanisms. The score of ISL is (M = 2.00, SD = 0.93), which is better than SL (M = 2.89, SD = 1.12), L (M = 2.17, SD = 0.90) and NL (M = 3.29, SD = 0.45). For question Q20: \"The mechanism is annoying.\", the grade of ISL (M = 3.00, SD = 1.20) shows no significant difference from SL (M = 3.11, SD = 1.10) but is larger than L (M = 1.83, SD = 0.69) and NL (M = 2.29, SD = 0.88). This implies that transposition is an annoyance and the visual cues did not help much.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 2 Background
### Coverage Feedback in libFuzzer  
libFuzzer is one of the most successful coverage-guided fuzzers that is incorporated into compiler frameworks [18]. While libFuzzer is a powerful fuzzing tool, it requires a complete source code that is compliant with specific versions of Clang/GCC compilers, thus making it only available for open-source software that provides specific build configuration based on generic compilers. Algorithm 1 is the simplified libFuzzer's internal logic.  
```
initialize; whileNumberOfRuns < Options.MaxRunsdo  choose unit to mutate from corpus; for\(i\gets 1\)to\(Options.MutationDepth\)do ifNumberOfRuns >= Options.MaxRunsthen  break;  end if mutate;  run with mutated input; ifNewCoveragethen  add mutated input to corpus;  break;  end if  end for
```  
**Algorithm 1**libFuzzer Core Fuzzing Algorithm  
In libFuzzer's core algorithm, an input unit is selected from the corpus (a set of input data) for random mutation. The mutated test input is then tested against the target code until new coverage has been found or the maximum mutation depth is reached (libFuzzer's fuzzing also halts upon bug detection). If the mutated input reports new coverage, it is considered interesting and is added to the corpus so that it can be potentially mutated again afterwards. In our paper, we use libFuzzer as the baseline evaluation tool for our investigation. We mainly modify libFuzzer in two ways (ignoring coverage feedback and increasing the granularity of the coverage guidance feature).