esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fitting Results of VC to L, Grayscale, and Log-Grayscale  
The above experimental results show that the L, grayscale, and log-grayscale values have a high correlation to the VC concentration, and after fitting L, grayscale, and log-grayscale to the VC concentration, the fitting curves are shown in Figure 7. The adjusted R-Squared (Adj R-square), relation expressions, limit of detection (LODs), limit of quantification (LOQs), and ranges are shown in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Figures of Merit** & **L** & **Grayscale** & **Log-Grayscale** \\ \hline Relation expression & \(7.62x+9.29\) & \(e^{0.46x+1.66}\) & \(0.44x+1.75\) \\ Adj-R\({}^{2}\) & 0.98 & 0.99 & 0.99 \\ LOD (\(\mu\)g/mL) & 0.26 & 0.47 & 0.47 \\ LOQ (\(\mu\)g/mL) & 0.87 & 1.55 & 1.55 \\ Range (\(\mu\)g/mL) & 0.26–5.00 & 0.47–5.00 & 0.47–5.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The relation expressions, adjusted R-Squared (Adj R-square), relation expressions, limit of detection (LODs), limit of quantification (LOQs), and ranges.  
Figure 6: (**a**) The relationship between grayscale and VC concentrations. (**b**) The relationship between the ln(grayscale) and VC concentrations.  
Adj R-square is a statistical indicator to reflect the degree of correlation between the variables. Adj R-square is calculated as a product-moment method, based on the same respective means of two variables based on the deviation, multiplied by two dispersions to reflect the degree of correlation between two variables. R-Square is the square of the correlation coefficient between the expression value (measured data) and the estimated value (calculated by the fitting model). Adj R-square is obtained by adjusting R-square according to the degree of freedom error. The closer to 1, the better the fit results. According to the fitting results, the L, grayscale, and log-grayscale values all had a high goodness of fit, with the log-grayscale value having the highest.  
LOD, expressed as a concentration or quantity, is derived from the smallest measure that can be detected with reasonable certainty for a given analytical procedure. LOQ refers to the smallest concentration or the mass that can be quantitatively analyzed with reasonable reliability by a given procedure. According to International Union of Pure and Applied Chemistry (IUPAC) regulation, the LOD and LOQ were calculated as \(\text{LOD}=3 SD/k\) and \(\text{LOQ}=10 SD/k\) and were confirmed experimentally, where \(SD\) is the standard deviation of the measured signal values of the blank samples (n = 20) and \(k\) is the standard curve slope. In Table 1, the L, grayscale, and log-grayscale values all had a LOD lower than 0.5 \(\text{\mu g}\)/mL, and the L value had the lowest.  
Figure 7: **(a)** The fitting curves of the VC concentration with the L value. **(b)** The fitting curves of the VC concentration with the grayscale value. **(c)** The fitting curves of the VC concentration with the log-grayscale value.
## 3 System Model
According to [26], \(\delta_{i}\) can be measured precisely by assuming a constant value of \(\varepsilon_{i}\) and \(S_{i}\) along with changing the delay threshold value \(\tau_{i}\). Therefore, this paper considers this for success state \(\varepsilon_{i}=0.01\), \(S_{i}=0.1\) and \(\tau_{i}\) based on the different user applications. The \(\varepsilon_{i}\) is comparatively larger than \(S_{i}\) in the failure state due to the high error probability and \(\delta_{i}\) can be defined as \(\delta_{i}\approx 1\approx\varepsilon_{i}\), which can make the system unusable. Considering the error probability and the probability from failure state to success state in Equation (8) and after doing simplification, the Equation (4) can be written as follows:  
\[m_{i,j}{}^{EMLWDF}=\frac{-log\Big{(}\frac{\varepsilon_{i}(1-S_{i})^{\tau_{i}} }{\Psi_{i}}\Big{)}}{\tau_{i}}.D_{HOL,i}.\psi_{i,j}, \tag{9}\]  
\[m_{i,j}{}^{EMLWDF}=\bigg{\{}\frac{-log\varepsilon_{i}-\tau_{i}log(1-S_{i})+ logW_{i}}{\tau_{i}}\bigg{\}}.D_{HOL,i}.\psi_{i,j}, \tag{10}\]  
\[m_{i,j}{}^{EMLWDF}=\bigg{\{}\frac{1}{\tau_{i}}log\bigg{(}\frac{W_{i}}{ \varepsilon_{i}}\bigg{)}-log(1-S_{i})\bigg{\}}.D_{HOL,i}.\psi_{i,j} \tag{11}\]  
The following Figure 2 shows the step-by-step procedure of the proposed Extended MLWDF (ELMLWDF) algorithm.
# Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data +
## 7 ASP Encoding  
### ASP Syntax  
In this section, we briefly recapitulate the basic elements of ASP [36], a declarative language that proved efficient to address highly computational problems. An answer set program is a finite set of rules of the form:  
\[a_{0}\ \leftarrow\ a_{1},\ \ldots,\ a_{m},\ not\ a_{m+1},\ \ldots,\ not\ a_{n}. \tag{1}\]  
where \(n\geq m\geq 0\), \(a_{0}\) is a propositional atom or \(\bot\); all \(a_{1},\ldots,a_{n}\) are propositional atoms, and \(d\) the symbol \"_not_\" denotes negation as failure. The intuitive reading of such a rule is that whenever \(a_{1},\ldots,a_{m}\) are known to be true and there is no evidence for any of the negated atoms \(a_{m+1},\ldots,a_{n}\) to be true, then \(a_{0}\) has to be true, as well. If \(a_{0}=\bot\), then the rule becomes a constraint (in which case the symbol \(\bot\) is usually omitted). As \(\bot\) can never become true, if the right-hand side of a constraint is validated, it invalidates the whole solution.  
Figure 4: (Left) Influence graph modeling of the case study example (Figure 3); (right) one of the T-AN generated by Algorithm 1. The labels of each local transition stand for the local states of the automata, which make the transition playable, and its delay (time needed for the transition to be performed).  
In the ASP paradigm, the search of solutions consists of computing the answer sets of a given program. An answer set for a program is defined by [PERSON] and [PERSON] [37] as follows. An interpretation \(I\) is a finite set of propositional atoms. A rule \(r\) as given in (1) is true under \(I\) if and only if:  
\[\{a_{1},\ \ldots,\ a_{m}\}\subseteq I\land\{a_{m+1},\ \ldots,\ a_{n}\}\cap I= \emptyset\Rightarrow a_{0}\in I\enspace.\]  
An interpretation \(I\) is a model of a program \(P\) if each rule \(r\in P\) is true under \(I\). Finally, \(I\) is an answer set of \(P\) if \(I\) is a minimal (in terms of inclusion) model of \(P^{I}\), where \(P^{I}\) is defined as the program that results from \(P\) by deleting all rules that contain a negated atom that appears in \(I\) and deleting all negated atoms from the remaining rules. Programs can yield no answer set, one answer set or several answer sets. To compute the answer sets of a given program, one needs a grounder (to remove free variables from the rules) and a solver. For the present work, we used Clingo(we used Clingo Version 5.0: [[http://potassco.sourceforge.net/](http://potassco.sourceforge.net/)]([http://potassco.sourceforge.net/](http://potassco.sourceforge.net/))) [38], which is a combination of both.  
In the rest of this section, we use ASP to tackle the problems' learning models from the time series data (i.e., implementing Algorithm 1 and all of the refinements in Section 5).
# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
###### Abstract  
Most unmanned aerial vehicles (UAVs) nowadays engage in coverage missions using coverage paths with simple patterns that are limited in two-dimensional space, ignoring the elevation features of mountainous terrain intentionally, resulting in losing details and increasing the coverage path density and the UAV energy consumption. To address these problems, a coverage path planning method based on the DH (Diameter-Height) model for mountainous terrain is proposed. Firstly, a DH model is proposed for simplifying the 3D map construction of mountainous terrain. Secondly, in virtue of the DH model, the 3D map is partitioned into sub-regions. The equal-interval spiral ascent path and parallel circular coverage path are generated for covering the mountainous terrain with conformal coverage effects. A coverage path connection method with trajectory minimization and obstacle avoidance is proposed in the final path connection stage. The simulation experiments have demonstrated the feasibility and effectiveness of the proposed method and verified that the generated coverage path has the advantages of conformal coverage effects, trajectory minimization, and obstacle avoidance. The comparative experiment has also demonstrated the advantages of the proposed method in terms of UAV energy savings.  
Keywords:path planning; coverage path planning; conformal coverage; obstacle avoidance; energy saving; UAV
## 3 Results
### Coupling Effects of RSM and PRE on GPP  
PRE and RSM functioned as the principal water sources sustaining vegetation, with PRE serving as the direct determinant of RSM. As a result, their effects on GPP were closely interlinked. An increase in PRE induced a rise in RSM, which subsequently enhanced land-atmosphere water exchange, further promoting additional PRE increments. Owing to this coupling between RSM and PRE, areas exhibiting simultaneous upward trends in both variables showed strong positive associations with GPP (Figure 3a,b). Across the study region, correlations between RSM and GPP were largely positive (Figure 4d), except in the southwestern sector where a negative correlation was identified (Figure 4a). Similarly, PRE was positively correlated with GPP in eastern grasslands but demonstrated negative correlations in western grasslands (Figure 4b).  
In areas characterized by bare soil and sparse vegetation, both PRE and RSM exhibited negative correlations with GPP. Conversely, in regions where GPP showed significant increases, a strong correlation between RSM and PRE was observed (Figure 4c), suggesting a robust coupling relationship that jointly influenced variations in GPP. In zones with pronounced RSM-PRE coupling, the correlation between PRE and GPP in areas with elevated RSM and PRE values may have been confounded by the RSM-GPP relationship and vice versa. Consequently, it is essential to account for the interrelationship between RSM and PRE when assessing their respective impacts on GPP.  
Figure 3: (**a**), (**b**), and (**c**), respectively, show the spatial trends of GPP, RSM, and PRE in the study area from 2000 to 2022, while (**d**) presents statistics on pixel-level changes in GPP, RSM, and PRE. The hatched areas indicate regions where the results passed statistical significance tests (\(p<0.05\)).  
The ridge regression analysis revealed that RSM accounted for the largest positive influence on GPP, contributing 66%, while also exhibiting the smallest negative influence, at 34% within the study area. PRE demonstrated a higher positive contribution to GPP (58%) compared to RUM, which contributed 54%. Notably, RUM showed minimal disparity between its positive and negative effects, indicating an overall negligible impact on GPP in the region. More specifically, RSM exerted the strongest positive effect on GPP in forested ecosystems, followed by agricultural lands, whereas its influence was weakest in wetlands and in areas characterized by bare or sparse vegetation. PRE's positive contribution was most pronounced in wetlands and agricultural lands, but least evident in grasslands and sparsely vegetated or bare areas (Figure 5). Additionally, ridge regression analyses conducted using MODIS-derived GPP datasets yielded comparable findings (Figure S2).
# Seamless Indoor-Outdoor Localization Through Transition Detection
## 4 Positioning Engines
### Transition Positioning Engine  
The transition positioning engine is separately designated to handle areas where both WiFi and GNSS signals are unreliable. Basically, this algorithm is a particle filter with only the AI-IMU particles described earlier, with increasing error covariance for each propagation. The prior function in Equation (18) is not applied for the transition positioning engine (the prior function \(p(x_{k}|x_{k-1})\) is always 1) since it can block estimation moving from outdoor to indoor or vice versa when the estimated position is not accurate, worsening the position error. Note that resampling process does not occur since the prior function is always 1.  
The pseudo-code of the transition positioning algorithm is shown below in Appendix B.
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 5 Experiments
### Energy Load Profiles  
We use real energy production traces from renewable sources, obtained by analyzing the wind/solar generation data in Belgium [31]. For the smart buildings, we also use real world power consumption traces and explicitly distinguish the flexible and non-flexible appliances. In particular, for the power consumption traces, we used NEEA's RBSA dataset [34], a comprehensive metering study covering most energy end uses in over 100 residential homes. The target loads included in the RBSA metering are:  
* Heating and cooling
* Hot water
* White goods/appliance: refrigerator, freezer, clothes washer, clothes dryer
* Consumer electronics: TV, TV accessories and Computer, computer accessories
* Lighting
* Other large loads: hot tubs, well pumps, sump pumps, electric cars, etc.  
We analyzed these consumption traces to distinguish the flexible power from non-flexible power. For example, Figure 7 shows the total, flexible, and non-flexible power consumption reported in the RBSA dataset for a sample week.  
The detailed list of loads considered as flexible is detailed in Table 1 and considers all the three housing types of the RBSA dataset: single-family homes, manufactured homes, and multifamily buildings. The table captures data mostly from Heating, Ventilation and Air Conditioning (HVAC) systems but also appliances such as washing machines, dryers, dishwashers, and DHW (Domestic Hot Water) systems. The use of such data allows us to validate the feasibility of PP-Overgrid in realistic  
Figure 7: Total, flexible, and inflexible power consumption during a sample week in the NEEA’s RBSA dataset.  
scenarios, monitoring the performance of the P2P network, and analyzing the nodes consumption with and without applying the DR program.
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 4 Experiment and Result Analysis
### Evaluation Metrics  
The evaluation of dehazing algorithms is commonly divided into subjective and objective assessments. Subjective assessment is primarily based on human visual perception to judge factors such as image clarity, color fidelity, and contrast. This method of evaluationis intuitive and easily understood, yet it possesses a certain degree of subjectivity due to the influence of personal preferences and observation conditions. Objective assessment, on the other hand, is conducted through quantifiable metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), which quantify the dehazing effects and provide a replicable, standardized evaluation outcome.  
Currently, PSNR and SSIM are widely accepted and employed as benchmarks for evaluation, allowing for the convenient comparison of the effectiveness between different dehazing algorithms. Consequently, we select PSNR and SSIM as the evaluation metrics for dehazing effectiveness. Specifically, SSIM measures the structural similarity between images, taking into account variations in brightness, contrast, and structure. By focusing on visual perception quality, SSIM is able to reflect the subjective experience of image quality more accurately, in Equation (15). PSNR, being a pixel-level metric based on error, is used to measure the similarity between the original and the processed images, proving highly effective in assessing image distortion, in Equation (16).  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_{ y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{15}\]  
where \(x\) and \(y\) are the two images being compared, \(\mu_{x}\) and \(\mu_{y}\) are the mean grayscale values of the images, and \(c_{1}\) and \(c_{2}\) are constants used to prevent division by zero.  
\[PSNR=10*log_{10}(\frac{MAX^{2}}{MSE}) \tag{16}\]  
\(MAX\) is the maximum color value of the image and \(MSE\) is mean squared error, which is defined as follows:  
\[MSE=\frac{1}{mn}{\sum_{i=1}^{n}{\sum_{j=1}^{m}{||X(i,j)-Y(i,j)||^{2}}}} \tag{17}\]  
where \(m*n\) is the pixel values of the image.  
Concurrently, in order to comprehensively assess the performance of the LGCA algorithm, not only was the effectiveness of the algorithm itself considered, but also two critical indicators; namely, the number of parameters (Params) and computational cost (FLOPs) were specifically introduced for an in-depth analysis.
# Towards a Digital Twin for Open-Frame Underwater Vehicles Using Evolutionary Algorithms
## 3 Methodology Description
### Optimization Algorithm  
The previous estimation phase serves as a starting point, initializing the digital twin model for further refinement and validation through the proposed methodology. By combining the model with real data gathered from the vehicle, the hydrodynamic parameters of the model are optimized so the results of the simulations match the real data. This is a multi-objective problem, with a multitude of parameters to optimize, and a field where the use of a genetic algorithm (GA) has shown its flexibility and robustness.  
GA is a stochastic, population-based optimization technique inspired by the process of natural selection and genetic evolution [40]. It operates by iteratively evolving a population of candidate solutions, with each solution represented as a set of parameter values. The optimization process aims to maximize a predefined fitness function, so that the simulated behavior closely matches empirical data from real-world trials.
## 2 Data  
### Data Samples and Simulation Procedure  
The inherent probabilistic nature of Cherenkov radiation necessitates a statistical approach to reconstructing primary gamma-ray characteristics. Modern analysis relies on sophisticated Monte Carlo (MC) simulations that model both the atmospheric particle cascade and the subsequent detector response.  
Our simulation framework generates extensive particle event catalogs by sampling from probability distributions that define the fundamental properties of incoming particles--their energies, trajectories, and particle types. Each simulated event initiates an atmospheric cascade, producing a detailed model of secondary particle production and the resulting Cherenkov light distribution.  
For this analysis, we utilized the comprehensive _ASTRI MA Prod2-Teide_ simulation dataset [9], which encompasses the following:  
* \(4\times 10^{7}\) on-axis gamma-ray events;
* \(2\times 10^{8}\) diffuse electron events;
* \(2\times 10^{9}\) diffuse proton events.  
These events were simulated over an area encompassing all nine ASTRI Mini-Array telescopes at the Teide site. The simulation incorporates site-specific parameters, including geographical coordinates, elevation, atmospheric conditions, and precise telescope positions. Observations were modeled at 20-degree zenith angles, with dedicated simulations for both northward and southward pointing directions.  
The energy coverage spans 0.1-330 TeV for gamma-rays and electrons, extending to 600 TeV for protons. While we employed a spectral index of \(-1.5\)--flatter than observed spectra [9]--to ensure adequate statistics at high energies, our differential analysis methodology incorporates appropriate event weights to accurately reflect the physical energy distributions of each particle species.  
The simulation pipeline consists of two primary stages:  
1. **Atmospheric Shower Development**: Using the Corsika software package 7.7550 [21], we model the complete evolution of particle cascades through the atmosphere, generating detailed maps of the Cherenkov light distribution at ground level.
2. **Telescope Response Simulation:** The sim_telarray package [22] models the interaction between the Cherenkov photons and telescope systems, including optical ray-tracing and detector response characteristics, producing realistic shower images as captured by the telescope cameras.
## 2 Data and Methodologies
### Radiosonde  
The radiosonde data used in this study were provided by IGRA version 2 and consisted of quality-controlled observations of temperature, humidity, and pressure at stations across all continents. The ZTD and PWV are calculated with Equations (1)-(4) using the atmospheric profiles observed by radiosonde data. The specific humidity \(q\) is not included in the radiosonde observations and, thus, needs to be calculated with the total pressure \(P\) and the vapour pressure \(P_{v}\) provided by the IGRA using:  
\[q=\frac{0.622\cdot P_{v}}{P-0.378\cdot P_{v}} \tag{5}\]  
As suggested by [PERSON] et al. [45], a quality control procedure was carried out to exclude radiosonde observations that had a large pressure gap (greater than 200 hPa) between two adjacent observation levels.  
The maximum altitude reached by a weather balloon varies due to weather conditions and other factors, directly affecting the highest pressure level recorded by the radiosonde [51]. As a result, the ZTD derived from radiosonde data only accounts for the atmospheric contribution up to the recorded level (e.g., 50 hPa), inherently underestimating the total ZTD. To correct for this, Equation (6) [45] was utilized to calculate the contribution of the ZHD from atmospheric layers above the radiosonde's maximum altitude. This value was then integrated with the ZTD obtained from Equation (1) to provide a more accurate estimation.  
\[\Delta ZHD\approx\frac{k_{1}R_{d}P_{1}}{g_{1}}\left\{1+2\frac{R_{d}T_{1}}{(R_ {s}+z_{1})g_{1}}+2\left[\frac{R_{d}T_{1}}{(R_{s}+z_{1})g_{1}}\right]^{2}\right\} \tag{6}\]  
where \(P_{1},g_{1},T_{1},\) and \(z_{1}\) are the pressure, local gravitational acceleration, temperature, and geometric height at the top of the radiosonde observation profile, respectively. Because the water vapour density above the top of the radiosonde profiles is negligible, no contribution is added to the ZWD.  
Similar to the determination of ZTD/PWV with ERA5, interpolation or extrapolation was performed to calculate the atmospheric variables at the GNSS station altitude depending on whether the station was above or below the lowest level of the radiosonde profiles. Figure 2 shows the distribution of GNSS stations (blue point) and their co-located radiosonde sites.