esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluation of Antifungal Packaging on Postharvest Quality of Papaya Fruit  
Papaya fruits (_Caria papaya_) were obtained at the local market and then washed with a 200 ppm sodium hypochlorite solution. After this, the fruits were separated according to their maturity stage and subdivided into four groups: papaya without packaging (control), papaya packaged without neem extracts, papaya packaged with HNE-PLE, and papaya packaged with HNE-MAE. Papaya fruit was packed by immersion in each filimogenic solution previously described in Table 1 using the solvent casting method. After this, the packed papaya was kept at 18 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C under a relative humidity of 90 \(\pm\) 5% and evaluated for 0, 2, 4, 7, 9, 11 and 15 days using the following parameters:  
Fresh weight loss was calculated from the difference between the initial weighing of fruits and the final weighing at each evaluation period, and the values were expressed as a percentage.  
External appearance regarding fungal incidence was evaluated through visual inspection and quantified as the percentage of the fruit's deteriorated area, assessed using a scale from 0 to 5, where 1--no deterioration; 2--up to 5% of the deteriorated area; 3--up to 10% of the deteriorated area; 4--up to 15% of the deteriorated area; and 5--more than 15% of the deteriorated area. Fruits rated three or higher were considered unfit for consumption. External appearance included observation of damage caused by temperature, depressions, spots, wilting, and manifestations of diseases. Fruits with fungal infections on the surface were classified as deteriorated.  
A visual evaluation of fruit skin color was performed according to the subjective rating scale, where 0--fruit grown and developed (100% green); 1--up to 15% of the yellow surface; 2--up to 25% of the yellow surface; 3--up to 50% of the yellow surface; 4--50 to 75% of the yellow surface; and 5--75 to 100% of the yellow surface.  
Instrumental evaluation of fruit skin color: The fruit skin color was determined using a Minolta CR 400 colorimeter (Konica Minolta, Osaka, Japan). The L*, a*, and b* values were used to determine a three-dimensional color space, where L* indicates color lightness, with values ranging from 0 (completely black) to 100 (completely white). The a* dimension correlates variation from green (\(-60\)) to red (\(+60\)) and b* from blue (\(-60\)) to yellow (\(+60\)).  
Determination of fungus growth on papaya fruit: The mold and yeast count was performed on the last day of storage. For this, the microorganisms were seeded on PDA plates and then homogenized by using a digalski spatula. The plates were kept at 25 \(\pm\) 1 \({}^{\circ}\)C in a BOD incubator (7 Lab, Rio de Janeiro, Brazil) for 3 to 5 days, and the results were expressed as colony-forming units per gram (g\({}^{-1}\)).
## 3 Control Methods
### Fuzzy Inference System
#### 3.2.1 Green Light  
The location \(X_{r}\) of the CEV is represented as three linguistic variables: _entering_, _approaching_, and _departing_ (Figure 4a). _Entering_ is a Z-shaped MF that covers from the beginning of the road up to a fifth of the road before the intersection. _Approaching_ is represented as a two-sided Gaussian MF that runs from 0.4 to a value slightly greater than zero. Finally, _Departing_ uses a S-shaped MF that starts a road length before the intersection and ends with the road. Four variables model the velocity \(\hat{x}_{norm}\) of the CEV: _reverse_, _slow_, _normal_, and _fast_ (Figure 4b). _Reverse_ is a Z-shaped MF that goes from the negative speed limit up to zero in an open interval. _Slow_ uses a Gaussian MF representation centered at a tenth of \(\hat{x}_{norm}\). A _normal_ generalized bell-shaped MF describes speeds that are greater than _slow_ and less than the speed limit. Any velocity that is near or greater than the speed limit is part of the S-shaped MF _fast_. The motor torque \(\tau_{norm}\) is described as three variables: _reverse_, _neutral_, and _forward_ (Figure 4c). _Reverse_ covers all negative torques up to zero. A steep linear Z-shaped MF is employed to express this variable. An unbalanced triangular MF centered on zero depicts the _neutral_ torque. The negative part of the MF has a pronounced slope, while the positive ends in a fifth. This behavior was chosen to promote null or slow positive torque. The _forward_ variable is a S-shaped MF with slow growth starting from zero.  
A set of five rules models the desired behavior during the navigation with \(L_{s}=\) green (Algorithm 2). The main goal is to take advantage of the current status of the light and navigate toward the intersection in the smallest amount of time. Special conditions are considered to avoid navigating backwards or surpassing the speed limit of the road.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 5 Unstructured Background Models
### Reed-Xiaoli (RX) Algorithm
The background statistics are then determined based on these minimum bounding rectangles, which are further used to calculate the detection statistic. The authors showed that SPDW RX could provide a small increase in detection performance but a large increase in processing speed compared to the local RX detector.
# Assessment of Spatial Variability in Ground Models Using Mini-Cone Penetration Testing
## 1 Introduction
As the evaluation of spatial variability through a ground model in the laboratory has rarely been conducted, this study not only provides useful data on spatial variability in ground models but also serves as a measure for determining the appropriateness of evaluating spatial variability through laboratory experiments.
# Simulation of Nuclear Quantum Effects in Condensed Matter Systems via Quantum Baths
## 5 Applications of Quantum Baths to Realistic Systems
### Structural Properties: Hydrogen Bonds
#### 5.2.2 Alooh  
Under increasing pressure, the \(\delta\) phase of AloOH crystal undergoes a phase transition from a \(P2_{1}nm\) structure with asymmetric and disordered O\(-\)H\(\cdots\)O bonds to a stiffer \(Pnm\) phase with symmetric hydrogen bonds, which should be stable within the pressure and temperature ranges typical for the Earth's mantle. In QTB simulations at pressures as low as 10 GPa, the mean proton position equals the midpoint of the O-O distance [58], while only at much larger pressures (around 30 GPa) does the \(Pnmm\) phase with symmetric hydrogen bonds become stable according to the classical viewpoint [59]. However, the proton centering occurs when the effective potential that is seen by the proton is still asymmetric. The lack of the full inversion symmetry impacts the proton distribution, which is skewed as provided by the QTB even beyond the critical pressure [58]. At the transition the O-H stretching modes soften considerably and fade out around 10 GPa in the \(P2_{1}nm\) structure, when thermal and nuclear quantum effects are taken into account in the simulations. Later X-ray diffraction experiments [60] also confirm the transition from a hydrogen-off-centered to a hydrogen-centered phase above 10 GPa.  
Overall, the QTB provides a satisfying description of hydrogen bonds under increasing pressure, either where a center of inversion exists (as in ice X) or does not (as in \(\delta\)-AlOOH).
_Applied_  
_sciences_  
_Article_  
**Feasibility Study for Spatial Distribution of Diesel**  
**Oil in Contaminated Soils by Laser**  
**Induced Fluorescence**  
**[PERSON] 1\({}^{\copyright}\), [PERSON] 2, [PERSON] 1,* and [PERSON] 1**  
Footnote 1: footnotemark:  
Received: 15 January 2020; Accepted: 3 February 2020; Published: 7 February 2020  
**Featured Application: Fast Sensing Technologies for Disaster Monitoring.**  
**Abstract:** Laser induced fluorescence (LIF) technique has been demonstrated as a powerful technology for analyzing the contamination of petroleum due to its excellent attributes of rapid analysis speed and slight sample preparation. This study focuses on the monitoring application of LIF in petroleum hydrocarbon-contaminated soils by establishing the three-dimensional diffusion models. In this paper, to improve the analysis accuracy, the effects of soil matrix difference for fluorescence intensities were considered. In order to validate the practicability of LIF, the longitudinal penetration laws and the lateral diffusion laws of diesel oil in different humidity soils were analyzed. These laws indicate that the longitudinal penetration depth decreases and the lateral diffusion range increases with the increase of soil moisture. Then, the three-dimensional diffusion models were established, the relative standard deviation (RSD) of the predictions for diesel oil in different soil moisture are 5.09%, 9.62%, 7.92%, and the contaminated volumes of soils by diesel oil are 233.90 cm\({}^{3}\), 332.70 cm\({}^{3}\), and 660.05 cm\({}^{3}\), respectively. These results express that the soil moisture extends the extent of diesel-contaminated soils. The present work shows the feasibility of LIF technique for the field monitoring of petroleum.  
**Keywords:** laser induced fluorescence; soil; diesel oil; three-dimensional diffusion model
# Designing Social Robots with LLMs for Engaging Human Interaction
## 6 Discussion
### Turn-Taking and Adaptation Across Populations  
Effective turn-taking remains one of the most persistent challenges in spoken human-robot interaction [36]. In our evaluation, fixed timing strategies--such as a static silence threshold of 1.2 --proved inadequate across both age groups. Among younger adults, the system frequently interrupted users who paused mid-thought, mistaking reflective silence for turn completion. This often disrupted the flow of conversation and led participants to repeat themselves or disengage.  
The issue was more pronounced with older adults, whose speech rhythms were slower and more variable [46; 47]. To address this, we implemented a user-calibrated silence threshold (typically approximately 1.8 s) tailored to individual speaking patterns. This adjustment significantly reduced interruptions, with several sessions achieving zero interruptions and nearly all showing lower rates in second interactions. These results suggest not only system-level improvements, but also mutual adaptation between the user and robot over time, where users learned the robot's pacing and adjusted accordingly.  
Our approach contrasts with many prior LLM-based systems relying on static or minimally adaptive timing. For example, [PERSON] et al. [14] reported frequent interruptions in a GPT-powered companion robot for elderly users, attributing breakdowns to rigid timing logic and insufficient signalling. Similarly, [PERSON] et al. [31] observed user confusion due to awkward silences and unclear listening cues.  
In our system, multimodal indicators--such as LED eye colour to signal listening versus speaking--provided essential feedback and were frequently mentioned by participants as helpful. Nonetheless, some users still hesitated or repeated themselves when faced with longer LLM delays or subtle turn transitions. To reduce these effects, we implemented incremental streaming of LLM output, allowing the robot to begin speaking before the silence threshold was fully reached. This approach reduced latency perception and helped to smooth out the interaction rhythm.  
Taken together, these findings reinforce the need for turn-taking models that go beyond simple pause detection. Combining user-calibrated timing, real-time LLM streaming, and transparent multimodal feedback significantly improved dialogue fluidity and user confidence. Future systems should build on these principles by incorporating prosodic cues, discourse markers, and gaze or intent modelling to further enhance naturalness--especially in interactions with older or slower-speaking users.
## Data and Methods
### Wetland Probability Mapping Algorithm
#### 2.3.1 NIR and NDVI  
The near-infrared (NIR) band is represented by wavelengths between 841 nm and 876 nm on the MODIS instrument. This waveband is centered on a non-absorptive region in the water vapor spectra that minimizes interference (i.e., absorption) by atmospheric water vapor and allows for remote sensing of land surface reflectance [54; 55]. The reflectance of NIR increases during vegetation growing seasons [56], tracing the plant life cycle [57]. This occurs because chlorophyll contained in leaves reflects near-infrared light, while absorbing visible red light. These properties are exploited in the NDVI, a measurement of greenness that represents a combination of vegetation properties, including leaf chlorophyll content, leaf area, structure, and canopy cover. NDVI is a ratio that returns index values between \(-1\) and \(1\). Reflectance data from the MODIS instrument (specifications listed in Table 1) were used to calculate NDVI with the following equation [58]:  
\[\text{NDVI}=\frac{\text{NIR}_{1}-\text{red}}{\text{NIR}_{1}+\text{red}}. \tag{1}\]  
Generally, values of NDVI ranging from 0.30 to 1.00 indicate vegetation, with higher values corresponding to greener areas. Negative values and low, positive values represent water and different soils, respectively [59]. NDVI is more sensitive to vegetation cover than it is to bare soil characteristics, but its calculation does not completely remove the effects of background soil reflectance [60].
# Characterization of Natural Gypsum Materials and Their Composites for Building Applications
## 3 Results and Discussion
### Ultrasonic Waves and Mechanical Properties  
The ultrasonic technique was applied to the natural rock samples in order to evaluate their mechanical properties. The mechanical coefficients are useful in finite element simulations, to investigate the structural behavior of gypsum, in varying the temperature and the aging time, particularly to predict the formation of cracks.  
Figure 9 shows the ultrasonic signals backscattered by SG and AG samples. It is clear that the reflected signal is composed of several echoes.  
The speed of ultrasound, the mechanical properties of Moroccan gypsum rocks, and Vickers hardness are reported in Table 6.  
The results showed a small mechanical change between the studied gypsum samples when considering the difference in mineralogy, density, water content, and porosity of the crystals. The strength characteristics are mainly related to physical properties of hardened gypsum, as total open porosity and arrangement of gypsum crystals [33]. The observed change is not directly related to the thermodynamic parameters, but connected to the existence of some impurities in the AG samples that improve the Young Modulus, Shear Module, and Vickers hardness coefficients.
## 1 Introduction
Thus, for the analysis of VGI data in the case of the scarcity of authoritative dataset, researchers tend to identify intrinsic quality indicators, such as the development of nodes, contributors and their behavior [6; 27; 28; 29; 30] by considering the 'Linus law' as a benchmark [6]. In the spirit to fill in the research gap, the current study has extended the capabilities of the processing toolbox of QGIS in alignment with previous researchers [19; 26; 31]. The intrinsic indicators used in this study are network length completeness, attribute completeness, semantic accuracy and heuristic indicator to assess route navigability. The model developed utilizes the scripts developed in Python binding for QGIS (PYQGIS) and are simply plug-and-play workflows. For the demonstration of the developed models, a case study of the Punjab, India, OSM dataset has been assessed using intrinsic quality indicators, and the results are presented. The paper is divided into six sections. The next section discusses the data quality parameters and related research work. Data preparation, the methodology to develop and extend QGIS processing toolbox by developing PYQGIS scripts, is described in the third section. The fourth section elaborates the results obtained from different models, and the limitations of the current study are discussed in the fifth section. The conclusions drawn from findings of the study are provided in the last section.
# Influence of Multiple Openings on Reinforced Concrete Outrigger Walls in a Tall Building
## 3 Evaluation of the Influence of Multiple Openings  
In order to study the influence of the size of openings on the outrigger wall and whole structure, structural performances associated with the stiffness and strength are investigated by using both linear and nonlinear analyses. Since the strength of the reinforced concrete outrigger wall cannot be evaluated by linear analysis, the strength of the outrigger wall is obtained by the nonlinear finite element analysis and compared with the results from the strut-and-tie models presented in ACI 318-14. On the other hand, the initial stiffness of the outrigger wall can be estimated by linear analysis. Therefore, the stiffness of the wall was evaluated by linear and nonlinear finite element analyses, and the results from both analyses were compared.  
A single-outrigger-braced tall building with a 3-story-high outrigger system located in 39 th, 40 th, and 41 st floor of the tall building structure shown in Figure 1 was studied as a numerical example. The overall depth, \(h\), of the outrigger wall was 10.5 m. As a symmetrical structure, only the half-side outrigger wall subjected to downward forces was analyzed. Through the proposed design procedure, the cross-sectional areas of the diagonal reinforcements (\(A_{s2}\) and \(A_{s3}\)) and horizontal reinforcements (\(A_{s1}\) and \(A_{s4}\)) were assumed to be 0.057 m\({}^{2}\) and 0.099 m\({}^{2}\), respectively. The outrigger wall had a shear span, \(a\), of 14 m and an effective depth, \(d\), of 10 m. The compressive strength of concrete \(f_{c}^{\prime}\) was 60 MPa.  
The size of the openings was changed through 12 analyses models. The models were named by assigning a capital M followed by a number \(i\) (\(i=0,1,2,\ldots,11\)). Among them, model M0 refers to the outrigger wall without opening a reference model. All the openings have the specified height (\(h_{op}=2\) m). Since the 250 mm \(\times\) 250 mm size of the rectangular element was used in the finite element mesh, the length of the opening starting from 0 increased by 0.5 m steps and reached their limitations in model M11. The opening ratios (\(A_{op}/A_{out}\)) are defined as the area ratios of the four openings and outrigger walls. This study aimed to find out whether and how the stiffness and the shear strength of outrigger walls change by increasing the opening ratio. In order to illustrate the influence upon the stiffness and shear strength, parameters expressing the variation are introduced. The parameter \(\beta_{K}\) is the ratio of the stiffness \(K_{i}\) of each outrigger wall to the stiffness \(K_{0}\) of the outrigger wall without opening.  
openings. The parameter \(\beta_{V}\) is the ratio of the shear strength \(V_{i}\) to the shear strength \(V_{0}\) of the outrigger wall without openings.
## 3 Proposed Framework
### Localization with Combined Dictionary
#### 3.4.5 Update Phase (Measurements + Combined Interaction)  
Upon receiving LiDAR measurements \(\mathbf{a}_{t}^{\text{track},\ m}\) for each observed track \(m\), we update track \(m\) via a Kalman Filter correction:  
\[\mathbf{z}_{t|t,n}^{\text{track},\ m}\ =\ \mathbf{z}_{t|t-1,n}^{\text{track},\ m}\ +\ \mathbf{K}_{t}^{\text{track},\ m}\Big{(}\,\mathbf{a}_{t}^{\text{track},\ m}\ -\ \mathbf{C}^{\text{track}}\big{(}S_{t,n}^{(m)}\big{)}\,\mathbf{z}_{t|t-1,n}^{\text{ track},\ m}\Big{)}, \tag{8}\]  
where \(\mathbf{K}_{t}^{\text{track},\ m}\) denotes the Kalman gain for track \(m\), and \(\mathbf{C}^{\text{track}}\big{(}S_{t,n}^{(m)}\big{)}\) represents the observation model matrix associated with the matched cluster.  
For the ego vehicle, we combine the influence of all observed tracks using the interaction dictionaries:  
\[\mathbf{z}_{t|t,n}^{\text{ego}}\ =\ \mathbf{z}_{t|t-1,n}^{\text{ego}}\ +\ \sum_{m=1}^{M}\Big{[}\, \mathbf{D}^{(m)}\big{(}S_{t,n}^{(m)}\big{)}\,\mathbf{z}_{t|t,n}^{\text{track},\ m}\ +\ \mathbf{E}^{(m)}\big{(}S_{t,n}^{(m)}\big{)}\Big{]}, \tag{9}\]  
where \(\mathbf{D}^{(m)}\big{(}S_{t,n}^{(m)}\big{)}\) and \(\mathbf{E}^{(m)}\big{(}S_{t,n}^{(m)}\big{)}\) are the track-specific adjustment matrices and biases obtained from the combined dictionary for track \(m\).
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 2 Methodology  
This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to ensure a systematic and comprehensive review [10]. This helps to make the review process more reproducible and transparent.  
A structured search was conducted across the following databases to identify relevant scientific articles in transportation engineering, computer science, and urban planning: Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. We developed a Boolean search query to improve the retrieval of relevant articles while minimizing irrelevant results as much as possible. The final search query that was used in all databases was as follows:  
* (\"Smart intersections\" OR \"intelligent intersections\" OR \"AI-based traffic control\" OR \"adaptive intersection control\");
* AND (\"connected vehicles\" OR \"autonomous vehicles\" OR \"V2X communication\" OR \"CAVs\").  
Also, references in the top-cited review papers were manually searched to identify additional studies that were not found in the initial search due to manual searches.  
The quality and relevance of the selected studies were ensured using the following inclusion and exclusion criteria:  
* Journal articles (peer-reviewed), book chapters, and conference proceedings from the period 2015-2024;
* Smart intersection, intelligent traffic control, and smart road implementation reports;
* Articles with quantitative or technical results;
* Position papers, non-peer-reviewed and without empirical evidence;
* Only studies published two years prior (2015 or previously) unless they are foundational works related to concepts' building blocks, etc.;
* Duplicated studies across multiple databases.  
Following these criteria, 350 articles were first screened according to their titles and abstracts in total, and then, we screened 213 high-quality studies for a final in-depth analysis after screening included/exclusion criteria and removing duplicates. Two independent reviewers assessed each study and discussed discrepancies to guarantee inter-reviewer reliability. This helps guarantee the robustness of the literature screening and the reliability of the results.  
Though this study is systematic in nature, it has some limitations. Most importantly, the studies were largely from the academic literature, and important industry reports or government policy documents were possibly missed. However, the proposed methodology guarantees a systematic and transparent process of review that is the basis for analytically evaluating trends, problems, and tendencies in smart intersection development.