esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Satellite Data
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Year** & \multicolumn{2}{c}{**In situ Dataset Fields**} & **Satellite Dataset Fields** \\ \hline \multirow{3}{*}{2022} & Longitude and Latitude & Longitude and Latitude \\ \cline{2-3}  & SSS and SST data from 2021 Sea glider missions & 13 Spectral Bands from Sentinel-2, 2021 image \\ \cline{2-3}  & 13 Spectral Bands from Sentinel-2, 2021 image & Sentinel-2, 2022 image \\ \hline \multirow{3}{*}{2023} & Longitude and Latitude & Longitude and Latitude \\ \cline{2-3}  & SSS and SST data from 2021 Sea glider missions & 13 Spectral Bands from Sentinel-2, 2023 image \\ \hline \multirow{3}{*}{2024} & Longitude and Latitude & Longitude and Latitude \\ \cline{2-3}  & SSS and SST data from 2021 ARGO & 13 Spectral Bands from Sentinel-2, 2024 image \\ \cline{1-1} \cline{2-3}  & 13 Spectral Bands from Sentinel-2, 2024 image \\ \hline \end{tabular}
\end{table} 
Table 2: Fields within the SSS and SST in situ and satellite datasets. Resolution Sea Surface Temperature Analysis directory [32]. The dataset possessed a spatial resolution of 1 km and was acquired at processing Level 4 (L4), employing interpolation techniques to address potential issues such as cloud coverage or other factors resulting in missing data. Subsequently, the SST data was resized to match the dimensions of the satellite spectral band data. Using SAGA GIS, the SST data was converted into a vector file, facilitating the extraction of SST data for each pixel within the spectral band raster image (Figure 5).
# A Novel High-Precision Imaging Radar for Quality Inspection of Building Insulation Layers
## 2 Radar Inspection Principle and Main Technical Parameters
### Main Technical Parameters  
Taking the thin-plastered insulation system, which is mature and commonly used in the external insulation system of building exterior walls, as a representative, the main technical parameters of the radar system are determined, which includes detection depth, range resolution, and lateral resolution.  
According to the difference in the relative dielectric constant of the wall structure, the thin-plastered insulation system can be divided into three parts, the bonding mortar layer (\(\epsilon_{r}\) = 5\(\sim\)10), the insulation panel (\(\epsilon_{r}\) = 1-3), and the anti-cracking mortar layer (\(\epsilon_{r}\) = 5-10) [28; 29; 30]. \(\epsilon_{r}\) denotes the relative dielectric constant. The thickness of the bonding mortar layer is about 10\(\sim\)15 mm. The main materials of the insulated panels include expanded polystyrene slabs, extruded polystyrene slabs, etc. It has very low loss and the thickness usually does not exceed 100 mm. The thickness of the anti-cracking mortar layer is usually no more than 10 mm. The radar must at least detect the sum of the thin-plastered insulation system, which means the detection depth is greater than or equal to 120 mm [8]. In general, the building insulation layer peeling is caused by the detachment of the bonding mortar layer, which encourages us to pay attention to the structure of the bonding mortar layer. Therefore, the range resolution here is the minimal thickness of the insulated panel (50 mm) to ensure that we can see the reflection wave of the bonding mortar layer.  
The attached structure of the thin-plastered insulation system is the bonding mortar layer. Most of the construction methods are spot bonding methods, as shown in Figure 2. The closest edge spacing between two pieces of bonding mortar is usually 50 mm-180 mm. Here, we defined the lateral resolution as less than or equal to 50 mm, which means that the radar can distinguish two pieces of bonding mortar with an edge spacing of 50 mm.  
Figure 1: Radar inspection principle of external building insulation system.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 2 An Intelligent Transportation Credit System Based on Blockchain
### Typical Application Scenarios  
The system distributes traffic tokens. A vehicle enters the system with an initial traffic token of 0\(t\) and allows for a certain amount of overdraft. The total number of traffic tokens for the entire road transport credit system is fixed, and they circulate according to the regulations to maintain a safe and efficient transport environment, thus creating a healthy and autonomous ITS. Traffic tokens circulate in the following scenarios:  
1. Speed suggestion: It provides a reference speed suggestion. If multiple vehicles issue speed warnings and simultaneously verify that the vehicle ahead is significantly below a current reference speed, a speed warning is issued to the vehicle. If the vehicle does not accelerate in a period, a certain number of traffic tokens are deducted from the vehicle to the warning vehicles behind it for a reward.
2. Convoy driving: To improve efficiency and safety, convoy driving is a good idea. A vehicle is chosen for the convoy lead, which is responsible for speed determination and negotiating with other convoys for the passing order. Other vehicles in the convoy pay a certain amount of traffic tokens to the lead vehicle.
3. Road accident responsibility confirmation: The distributed ledger is used to determine the responsibility and traceability of accidents. Traffic accidents caused by violating traffic rules or V2X-negotiated overtaking agreements will result in a larger amount of traffic tokens being deducted from the offending vehicle.
4. Congestion evacuation: It helps to clear the congestion caused by an accident as quickly as possible and avoid secondary accidents by sending a congestion message to surrounding vehicles. Vehicles receiving the message will either avoid entering the congested lane or leave the congested lane as quickly as possible. Vehicles that help maintain traffic safety are rewarded with a certain number of traffic tokens from neighboring receiving vehicles.  
Right-of-way trade: In dense urban scenarios, fairness and efficiency dictate that the first arriving convoy passes the crossroad first. If a convoy is more time-sensitive, it initiates a trade, negotiates and pays a certain amount of traffic tokens for right-of-way. The convoy that gives up the right-of-way receives a certain number of traffic tokens.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 2 Related Works
### AI-Enabled Wireless Communication  
The burgeoning field of artificial intelligence (AI) has catalyzed a profound transformation in wireless communications. Unlike traditional end-to-end communication algorithms that rely on mathematical theories, AI leverages neural networks trained to model the input-output relationships of communication systems. By utilizing large datasets, these neural networks can learn the inherent patterns within communication systems. This approach emphasizes end-to-end optimization and data-driven methodologies, which  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Dimension** & **Paradigm Characteristics** & **Related Works** & \begin{tabular}{c} **Limitations and** \\ **Research Gaps** \\ \end{tabular} \\ \hline AI-enabled wireless communication & Neural network-based, end-to-end, data-based & [15, 16, 17, 18, 19, 20, 21, 22] & \begin{tabular}{c} Limited to low-level signal \\ processing; no semantic \\ integration; no physical \\ layer integration \\ \end{tabular} \\ \hline Semantic representation & \begin{tabular}{c} Embedding spaces, \\ contextual encoding, \\ knowledge structuring \\ \end{tabular} & [23, 24, 25, 26, 27, 28, 29, 30] & \begin{tabular}{c} Not designed for \\ communication; isolated from \\ PHY layer; static \\ representations; no \\ channel adaptation \\ \end{tabular} \\ \hline LLM-enabled frameworks & \
\begin{tabular}{c} Generative semantics, \\ knowledge grounding, \\ cross-modal alignment, \\ \end{tabular} & [13, 31, 32, 33, 34, 35] &
\begin{tabular}{c} High complexity; shallow \\ PHY integration; \\ computational overhead \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related works.  
significantly reduce design overhead. Historical efforts to integrate AI into wireless communications date back to 1992, when the authors of [15] attempted to use neural networks for modulation recognition. Contemporary AI-based methods have now surpassed classical mathematical algorithms in modulation recognition [16]. For text data transmission, the authors of [17] introduced a unique deep and lightweight Transformer variant, DeLighT, which achieves end-to-end joint source-channel coding (JSCC). Subsequently, neural network methods have been successfully applied to various domains, including neural error correction [18], channel modeling [19], encoding and decoding [20], channel estimation and equalization [21], and bandwidth compression mappings [22]. These methods have made impressive performance improvements in wireless communications. A comprehensive review of neural network-based wireless communication technologies is provided in [30]. By leveraging the capabilities of AI, wireless communication systems can achieve higher efficiency, adaptability, and performance, marking a significant advancement over traditional methods.
# Applications of Probabilistic Forecasting in Demand Response
###### Abstract  
Studies on probabilistic demand forecasting remain relatively limited compared with point forecasting, despite it being especially valuable for operational and planning purposes. This paper demonstrates different applications of probabilistic forecasting in demand response, together with Physical-Based Load Models. The first application shows how to determine the percentage of uncertainty potentially covered by demand response throughout a day, whereas the second application deals with obtaining the number of consumers that would be needed in demand response actions for a desired percentage of coverage. Finally, in the third application, a specific hourly strategy is proposed for demand response policies. These applications facilitate the aggregator's energy purchasing in the market and the planning of subsequent consumption adjustments through demand response to minimize deviations from the purchased energy. The approach is illustrated using hourly demand data from a small Spanish city, and two machine learning methods were used to produce a set of probabilistic forecasts and compare results: Linear Quantile Regression and a Quantile Regression Forest. Main results show a significant reduction in deviations from the purchased energy after applying the proposed strategy, especially in the case of the forecasting method being less accurate (40.95% and 33.62% of reduction, respectively).  
short-term load forecasting; probabilistic forecasting; linear quantile regression; quantile regression forest; demand response +
Footnote †: journal: _sciences_  
_Article_  
Applications of Probabilistic Forecasting in Demand Response
# Hydrodynamic Investigation on a Land-Fixed OWC Wave Energy Device under Irregular Waves
## 2 Numerical Model
### Pneumatic Model and OWC Hydrodynamic Efficiency  
The air pressure \(p\) at the water free surface is set to zero (i.e., atmospheric pressure) outside of the chamber. At the chamber interior, the pneumatic pressure is given by  
\[p(t)=C_{dm}U_{d}(t) \tag{3}\]  
where \(C_{dm}\) is the pneumatic damping coefficient and \(U_{d}(t)\) is the air flow velocity through the ceiling orifice and can be calculated from the variation of the water column volume under the assumption that the air volume is incompressible [24].  
The energy captured by the OWC device can be calculated as,  
\[P_{air}=\frac{1}{t_{e}-t_{s}}\int_{t_{s}}^{t_{e}}Q(t)p(t)dt=\frac{1}{t_{e}-t_{ s}}\int_{t_{s}}^{t_{e}}AU_{d}(t)C_{dm}U_{d}(t)dt \tag{4}\]  
where \(Q(t)=AU_{d}(t)\) is the air flow rate through the air duct. \(t_{s}\) and \(t_{e}\) are the start and end time of the calculation. Additional details regarding the pneumatic model can be found in [25].  
The average energy flux per unit width of incident wave is given by [26],  
\[P_{in}=\rho g\int_{\omega_{L}}^{\omega_{H}}C_{g}(\omega)S(\omega)d\omega \tag{5}\]  
\[C_{g}(\omega)=\frac{\omega}{2k}\bigg{(}1+\frac{2 kh}{\sinh(2 kh)}\bigg{)} \tag{6}\]  
\[\omega^{2}=gk\text{tanh}kh \tag{7}\]  
where \(C_{g}(\omega)\) is the wave group celerity with \(\omega\) the wave angular frequency and \(k\) the wave number.  
Thus, the hydrodynamic efficiency of an OWC device is calculated from:  
\[\xi=P_{air}/P_{in} \tag{8}\]
## 3 Circularly Polarized 5G Antenna Design and Optimization
### Geometry 2  
The geometry 2 of RDRA is shown in Figure 2b. The feeding strip was modified by adding one more length. Using this configuration, the RDRA showed behavior towards CP  
Figure 4: Axial ratios comparison of different geometries toward the development of the CP 5G RDRA.  
Figure 3: Return losses comparison of different geometries toward the development of the CP 5G RDRA.  
response. The degenerate mode was excited but still, S\({}_{11}\) bandwidth below \(-10\) dB was nil, as shown in Figure 3. As presented in Figure 3, the degenerate mode pair of the first higher-order, i.e., TE\({}^{\rm x}_{\rm 813}\) and TE\({}^{\rm y}_{\rm 183}\), was excited at 4.12 GHz and 4.66 GHz, respectively. The CP response of the antenna is depicted in Figure 4, and, as shown, the 3-dB axial ratio bandwidth was nil as well. The results of this configuration were encouraging, but needed further modification and optimization to achieve the desired bandwidths for the targeted application. Geometry 2 was further modified, and the impact of the modification is discussed in the next section.
## 4 Discussion  
Previous studies have demonstrated that \(\gamma\)-C2S undergoes low-temperature carbonation to produce calcite and silica, with the silica encapsulating uncarbonated \(\gamma\)-C2S particles and the calcite filling the pores [40; 41]. Similar structural characteristics are observed in steel slag carbonation as well [42]. Figure 14 illustrates the cementation mechanism of LFS, primarily composed of \(\gamma\)-C2S, under a carbonation environment based on the findings presented in this paper. Distinguishing itself from previous studies, this mechanism provides a comprehensive understanding of the carbonation reaction behavior by focusing on pore and particle cementation, particularly in relation to both open and closed pores. For clarity purposes, \(\gamma\)-C2S particles are used instead of LFS particles.  
This study describes the differentiation between \(<\)7% and \(>\)7% water content during carbonation behavior. At \(<\)7% water content, \(\gamma\)-C2S particles exhibit reduced formation of bridging water, facilitating easy diffusion of CO2 between and within the particles through interconnected pores (see Figure 14a). The enhanced contact area between CO2 and the particles enables a greater carbonation extent for \(\gamma\)-C2S particles. This leads to easier interior carbonation of specimens with lower water content, as demonstrated in Figure 8. However, complete carbonation of steel slag due to inherent particle size constraints leads to the presence of a porous carbonate layer in its products [43].  
At \(>\)7% water content, there is sufficient bridging water to cement the particles; however, the presence of this bridging water hinders CO2 diffusion into the interior. Calcium ions are released into the bridging water and react with CO2, resulting in precipitation of calcium carbonate. This process leads to the solidification of the bridging water and closure of gaps between particles, transforming original open pores into closed ones after carbonation (see Figure 14b). Consequently, this impedes subsequent particle gaps and internal carbonation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Samples** & **Water Content** & **Total Porosity** & **Open Porosity** & **Closed Porosity** \\ \hline a & uncarbonated & 20.44\% & 20.18\% & 0.26\% \\ b & 6\% & 17.69\% & 16.90\% & 0.79\% \\ c & 7\% & 10.60\% & 7.14\% & 3.46\% \\ d & 8\% & 13.61\% & 8.52\% & 5.09\% \\ \hline \end{tabular}
\end{table}
Table 2: Porosity of carbonated LFS with different water contents (%).  
In summary, at <7% water content, the specimens were susceptible to interior carbonation due to the prolonged presence of open pores. At >7% water content, the specimens formed closed pores as a result of bridging water hardening, thereby hindering the carbonation process. At the optimum water content, although closed pores were also formed alongside open holes in the carbonated specimen, they did not impede carbonation from reaching the interior. Furthermore, Figure 14b illustrates a significantly more cemented structure with an appropriate amount of enhancing strength.
# Correlation of Pavement Distress and Roughness Measurement
## 3 Methodology and Data Collection
### Data Collection  
The city of Riyadh is divided into smaller components for management and maintenance purposes. The city is divided into branch municipalities, districts, and regions. The region is a subdivision of the district and it's surrounded by four main streets and contains no main street within its entire area. The two main categories of the Riyadh street network are main streets and secondary streets. The main street is defined as the street that connects two main streets and is more than twenty-five meters wide or has an island. Main streets account for about 27 percent of the overall network area in Riyadh City. Secondary streets are all the categories of streets outside the definition of main streets. Secondary streets represent about 73 percent of the overall network area. Main and secondary streets are divided into sections. The segment of the main street is defined as distance between two intersections in the main street. The segment for the secondary street is defined as a region within a district. The segments for main and secondary streets are divided into several sample units; a representative unit of sample for the main streets was 100 m in length per lane. The sample unit of the secondary street is a street in a region.  
The mean source of data was the General Directorate for Maintenance and Operation, Riyadh Municipality. A representative sample of pavement sections from both main and secondary streets was randomly selected. A section may have two to three lanes. In secondary streets, each data point represents the section. A total of 1619 main street sections were included in the study, with a total distance of more than 898.258 km. For secondary street sections a total of 140 were sections covered in the study. These represent an area of about 1814 square km.  
The data for both main and secondary streets include distress data, pavement, condition, and International Roughness Index for each observation period. Time between the pavement condition survey and roughness measurements were less than 6 and 3 months for main and secondary streets, respectively. Table 1 presents the types and codes for distress that appear in both main and secondary streets according to the Riyadh Pavement Maintenance Management System (PMMS).
## 1 Introduction
However, the state-of-the-art does not include any guidelines on addressing parallelization modality selection, but only provides single implementations specifically designed for a single architecture. Consequently, the optimal parallelization modality still needs to be empirically determined, involving the potential need for multiple re-designs. Therefore, in this paper, we propose a context-adaptable design of FastSLAM 2.0 for GPU based on the CUDA programming paradigm. The proposed design enables the implementation of different parallelization modalities depending on the context. By _context_, we mean the values assumed by the set of parameters determining the algorithm's complexity, which, in turn, depends on the use case of interest. Combined with the flexible design for FastSLAM 2.0, we provide an evaluation methodology that enables the assessment of the optimal parallelization modality depending on the context and the hardware architecture in use. The design proposed in this contribution parallelizes all the functional blocks of FastSLAM 2.0, including the data association step, through the Joint Compatibility Branch and Bound (JCBB) methodology [47]. JCBB enables simultaneous handling of multiple non-uniquely identifiable observations, leveraging a branch-and-bound approach in combination with a joint compatibility test. To the best of our knowledge, this is the first parallelization of FastSLAM 2.0 tackling unknown data association, and no other parallelized version of JCBB is available. In addition, parallelization of multiple resampling approaches is available with the goal of accommodating the needs of the broadest possible variety of navigation scenarios. Our methodology was validated by deploying FastSLAM 2.0 onto a General Purpose GPU (GPGPU) interfaced with a simulation environment that included a model of an IMU and a generic range sensor. The rest of this paper is organized as follows. Section 2 provides theoretical background and discusses the state-of-the-art regarding the data association and the resampling problems. In more detail, Section 2.1 discusses the elements of the FastSLAM 2.0 method, Section 2.2 tackles the data association problem, and the resampling problem is discussed in Section 2.3. Section 3 describes the hardware and software setup: Section 3.1 provides the specification of the hardware, Sections 3.2 and 3.3 summarize the main characteristics of the CUDA programming paradigm and the PyCUDA library, and Section 3.4 addresses the memory access management. Section 4 describes our evaluation methodology, including the simulation environment (Section 4.1), the partitioning of the pipeline into functional blocks (Section 4.2), the analysis of the parameter dependencies (Section 4.3), and the time gain evaluation (Section 4.4). Section 5 tackles the main characteristics of the functional blocks' parallelization. Section 6 is dedicated to the experimental results and discussions thereabout and Section 7 draws the conclusions of this contribution and discusses future developments.
## 3 Design Process
### Data Analysis Methods and Statistical Considerations
#### 3.6.1 Primary Outcome Measures  
The primary outcome, improvement in Functional Movement Screen (FMS) scores, will be analyzed using paired \(t\)-tests or Wilcoxon signed-rank tests, depending on the normality of the data, to assess changes from baseline to the 12-week intervention within each group. Between-group comparisons will be performed using independent \(t\)-tests or Mann-Whitney U tests for non-normally distributed data.  
Real-time biofeedback from the DAid(r) smart socks system will provide continuous monitoring of foot pressure distribution and the center of pressure (COP). Key parameters such as COP1x (medial-lateral movement), COP1y (anterior-posterior movement), and other relevant metrics will be used to assess the participants' movement patterns, particularly during exercises like the single-leg stance, squats, and lateral jumps. These parameters will be compared between both groups to assess improvements in biomechanics, alignment, and movement quality.