esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
###### Abstract  
As one of the most important greenhouse gases, water vapor plays a vital role in various weather and climate processes. In recent years, a near-infrared ratio technique based on satellite images has become a research hotspot in the field of precipitable water vapor (PWV) monitoring. This study proposes a Level 2A PWV data retrieval method based on Sentinel-2 images (S2-L2A), which considers land-cover types and is more suitable for local areas. The radiative transfer model MODTRAN 5 is used to simulate the atmospheric radiative transfer process and obtain lookup tables (LUTs) for PWV retrieval. The spatial distribution of S2-L2A PWV is validated using Global Positioning System (GPS), Terra-MODIS PWV product (MOD05), and Level 2A product provided by ESA (ESA-L2A), while the time series results are evaluated using MOD05. Results show that the PWV retrieved by S2-L2A is both highly correlated and has low bias with the three PWV products, and is closer to the reference data than the MOD05 and ESA-L2A PWV. The relative PWV value in the morning is: bare soil > vegetation-covered area > construction land; as the elevation increases, the PWV value decreases. This study also analyzes the error distribution of the PWV data retrieved by S2-L2A, and finds that inversion error increases with AOT value, but decreases with elevation and normalized difference vegetation index (NDVI). Compared with the three water vapor products, the PWV data retrieved by the proposed method has high accuracy and can provide large-scale and high-spatial-resolution PWV data for many research fields, such as agriculture and meteorology.  
precipitable water vapor (PWV); Sentinel-2; MODTRAN 5; error analysis Article  
An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data. _Remote Sens._**2023**, _15_, 1201. [[https://doi.org/10.3390/rs15051201](https://doi.org/10.3390/rs15051201)]([https://doi.org/10.3390/rs15051201](https://doi.org/10.3390/rs15051201))
## 3 Materials and Methods
### Symbol Meanings  
To facilitate the statement of the content of this paper, we give the notation and descriptions of the symbols used in the proposed method, as shown in Table 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Forecasting Target** & **Model** & **Conclusion** \\ \hline [PERSON] et al. [20] & Carbon price & VMD-GARCH/LSTM-LSTM & The prediction error of the single model is greater than all decomposition-ensemble models. \\ [PERSON] et al. [41] & Volatility of bitcoin & Neural networks and GARCH & Consequently, compared to the best GARCH model, the best GT-VIX-GARCH-H model improves by 11\%, 2.2\% and 30\% for MAE, RMSE and MAPE, respectively. \\ [PERSON] et al. [21] & Bitcoin volatility & Hybrid GARCH and LASSO & The proposed combined LASSO and GARCH stacking model produces better forecasts than other benchmark models. \\ [PERSON] et al. [42] & Stock market volatility & GARCH Models With a Distribution Manipulation Strategy Based on LSTM & They proposed a new hybrid model with GARCH-type models based on a novel non-linear filtering method to mitigate concentration property of volatility. \\ [PERSON] et al. [43] & Commodity market returns volatility & GARCH and LSTM & The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. \\ [PERSON] et al. [44] & Stock index & AWT, LSTM, ARIMAX-GARCH-family models & The results indicate an overall improvement in forecasting of stock index using the AWT-LSTM-ARMAX-FIEGARCH model with student’s t distribution as compared to the benchmark models. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Studies on combined models based on artificial intelligence and econometric models in general price prediction.
## 7 Challenges and Limitations
### Ethical and Regulatory Issues
#### 7.4.1 Strategies for GDPR Compliance and Trustworthiness Standards  
To develop GDPR-compliant AI models, the following strategies are recommended:  
* Data Minimization and Anonymization: AI models must process only the minimum amount of data necessary for their function. Techniques such as differential privacy, where noise is added to sensitive data, can ensure compliance without sacrificing model accuracy [142].
* Transparency and Explainability: AI models should be designed with built-in explainability to ensure that users and regulators can understand decision-making processes. Techniques from explainable AI (XAI), such as feature importance analysis, can provide actionable insights into the model's behavior [143].
* Data Sovereignty and Localization: In line with GDPR's data sovereignty requirements, data storage and processing should occur within designated jurisdictions. Federated learning is an effective technique to ensure that raw data remains local while sharing model updates across regions [144].
* Regular Audits and Impact Assessments: AI systems must undergo periodic audits and Data Protection Impact Assessments (DPIAs) to ensure ongoing compliance with trustworthiness standards. These assessments can identify vulnerabilities and ensure that models remain aligned with ethical guidelines [145].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Explainability Method** & **Model Accuracy** & **Interpretability** & **Trade-Off in Performance** \\ \hline LIME (Local Surrogate) & 85\% & High & Medium \\ SHAP (Shapley Values) & 88\% & High & Low \\ Integrated Gradients & 86\% & Medium & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 12: Explainability methods for AI models in communication networks.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 3 EALC Methodology
### Computation Complexity of EALC  
It's a well-known fact that implementation of ACO- and GWO-based methods is always more complex as compared to EALC. They look for an optimal solution from the entire search space. However, EALC is much more suitable for the subject problem. It has very low computational complexity and easy implementation. The computational complexity of EALC for clustering the entire network can be computed as follows:  
_Complexity for fitness function_: This function calculates and returns fitness for every \"n\" nodes. Constant \"c\" time is required to calculate fitness of \"n\" nodes:  
Time for computing fitness for \"n\" nodes = n \(\times\) c \(\in\) O (n)  
_Complexity for k-meansSortedFitness_: This function takes fitness value of \"n\" nodes as input and returns CHs as output. Time complexity for the best case is when all nodes located much close to each and have direct connectivity with all nodes. At then nodes will form only a single cluster. On average, it divides the network into \"k\" number of clusters. In the worst case, when no node lays in the transmission range of any other node, it produces \"n\" number of clusters.  
Body of the _While_ loop in k-meansSortedFitness function can run: (4n) \(\in\) O (n)  
Time complexity for _While_ loop:  
Best case O (n) \(\times\) 1 \(\in\) O (n)  
Average case O (n) \(\times\) k \(\in\) O (kn)  
Worst Case O (n) \(\times\) n \(\in\) O (n\({}^{2}\))  
Total time complexity of EALC  
T(EALC) = T(_fitness function_) + T(_k-meansSortedFitness_)  
Best case T(EALC) = O (n) + O (n) \(\in\) O (n)  
Average case = O (n) + O (kn) \(\in\) O (kn)  
Worst Case = O (n) + O (n\({}^{2}\)) \(\in\) O (n\({}^{2}\))
## 4 Experimental Setup
### Hardware Setup  
In the testbed setup, the mentioned sensors are employed, and the data produced will be modulated and mapped to the OCC LED 8 \(\times\) 8 matrix as the transmitter. Arduino UNO is used as an intermediary device to connect the LED matrix transmitter and the sensors. Two webcams resembling the security camera (CCTV) in the building are used as OCC receivers. The LED utilized has 8 \(\times\) 8 LEDs and will accommodate the localization anchor, SN, and health monitoring data. The overall process on the receiver side with the sample image is shown in Figure 7. Two webcams were used in this study, using the Brio UHD Pro Camera, manufactured by Logitech International S.A. in Lausanne, Switzerland, on the receiver side. Both LEDs are programmed using C++ for Arduino, while the receiver camera is controlled using Python version 3.11.  
Figure 6: Experiment setups for the different scenarios.  
Figure 7: The overall process on the receiver side.
# Predictable Trajectory Planning of Industrial Robots with Constraints
## 2 Predictable Trajectory Planning with Constraints
### Joint Velocity/Acceleration Limits Avoidance  
The joint velocity and acceleration limits must be taken into account. Assume that the trajectory planned by the proposed method is,  
\[\left\{t_{w},q_{i},{}^{\bullet}_{i},{}^{\bullet\bullet}_{i}(i=1,\cdots,n)\right\} \tag{12}\]  
Introducing a set of joint velocity/acceleration limits as follows,  
\[\left\{\begin{array}{c}\left|{}^{\bullet}_{i}\right|\leq{}^{\bullet}_{i, \lim}\\ \left|{}^{\bullet\bullet}_{i}\right|\leq{}^{\bullet\bullet}_{i,\lim}\end{array}\right. \tag{13}\]  
where \({}^{\bullet}_{i,\lim}\) and \({}^{\bullet\bullet}_{i,\lim}\) are the velocity and acceleration limits of the \(i\)th joint. Define a scaling factor  
\[\alpha=\max\left\{\begin{array}{c}\left|{}^{\bullet}_{i}\right|_{i}\\ \frac{\alpha}{q_{i,\lim}},\end{array}\right.\left|{}^{\bullet\bullet}_{i, \lim},i=1,\cdots,n\right\} \tag{14}\]  
If \(\alpha\geq 1\), there is at least one of the joint velocities or accelerations out of its limit. Update the motion time by the scaling factor,  
\[t_{new}=\alpha t_{w} \tag{15}\]  
The end-effector motions are predictable. The end-effector moves along the desired paths. The end-effector velocity is renewed as,  
\[v_{new}=\frac{1}{\alpha}v \tag{16}\]  
Along the geodesic path, the tangential acceleration is still zero. The centripetal acceleration is  
\[a_{new}=\frac{v_{new}}{r}^{2}=\frac{1}{\alpha^{2}}a \tag{17}\]  
The new joint trajectories are obtained by solving the geodesic equations, Equation (9) under the updated motion time with the boundary conditions,\[\left\{\begin{array}{c}u_{i}(t_{0})={}^{0}u_{i}\\ v_{l}(t_{0})={}^{0}v_{l}\\ u_{i}(t_{0}+t_{new})={}^{f}u_{i}\\ v_{l}(t_{0}+t_{new})={}^{f}v_{l}\end{array}\right. \tag{18}\]  
Actually, the joint velocity and acceleration limits avoidance is achieved by scaling the motion time. Our method can track the assigned paths, and the velocities while the accelerations can be predicted in advance.
## 2 Results
### Impact of SLC10A1 Genetics on SLC10A1/NTCP Expression  
We next performed a correlation analysis between the identified SNPs and _SLC10A1_ mRNA expression as well as NTCP protein levels. Since 12 variants were only found heterozygosity in one or two individuals (MAF < 1%), their influence on expression levels was not statistically tested. However, we observed increased or decreased expression levels compared to the median consistently across both expression phenotypes for the INDEL rs770421447 and the SNP rs186343960 (Supplementary Figure S3). The analysis for the variants with MAF > 1% (\(n\) = 22, Supplementary Figure S4) showed significantly decreased mRNA levels for rs111500198 (\(P_{\text{dominant}}\) = 0.04). The latter association was still significant in the multivariate analysis correcting for all seven non-genetic factors. Additionally, we observed two further significant associations for rs11622925 and rs4646285 with mRNA (\(P_{\text{Focessive}}\) = 0.03) and rs76385306 with protein (\(P_{\text{additive}}\) = 0.02) expression in the multivariate analysis. However, all of these associations were no longer significant after adjustment for multiple testing. In addition, there were no further significant associations between genetic variants and NTCP protein levels in the univariate as well as a multivariate analysis correcting for all non-genetic factors. Moreover, no significant association was found between the estimated _SLC10A1_ haplotypes and altered _SLC10A1_ and NTCP expression (Supplementary Table S3).
## 3 Results and Discussion  
### Microstructure of crept HPDC AA365 Alloy  
The microstructure of crept AA365 alloys at various temperatures was shown in Figure 1. In terms of voids, as temperature increased, the presence of porosities raised relatively. This is because, with the increase in temperature, micro-voids were diffused, growing the size of the voids up [2]. When it comes to the density of the intermetallic compounds, crept AA365 alloy at 473 K shown in Figure 1b has a large amount of the intermetallic phases [15]. Before the creep test, rapid solidification caused by the HPDC process led to the non-equilibrium solute elements in the Al matrix, meaning that the supersaturated \(\alpha\)-Al was thermodynamically unstable. Therefore, upon the temperature applied during the creep test, the excessive content of the elements expelled out of the Al matrix to form the intermetallic compounds. This behavior explains why the density of the intermetallic precipitates in crept AA365 alloy at 473 K was relatively higher than other alloys. On the other hand, crept AA365 alloy at 573 K contained the lowest density of the secondary phases, as seen in Figure 1c. This was attributed to the fact that the intermetallic compounds were re-dissolved into the Al matrix at 573 K, generating the decrement in the secondary phases. The EDS analysis results showed that the brittle intermetallic compounds on the Al matrix of the crept AA365 alloys were identified as \(\pi\)-Al\({}_{12}\)FeMg\({}_{1.5}\)Si\({}_{5}\) and \(\alpha\)-Al\({}_{16}\)(Mn,Fe)Si\({}_{3}\), as shown in Figure 1d [16].  
EPMA analysis, shown in Figure 2, revealed that the brittle intermetallic compounds on the Al matrix were identified as \(\pi\)-AlFeMgSi and \(\alpha\)-AlMnFeSi phases, resulting in less detrimental effects on mechanical properties. These intermetallic precipitates were observed at the boundaries of \(\alpha\)-Al grain because grain boundaries having higher energy than intergranular grain facilitated the production of the intermetallic compounds at the boundaries.
## 2 Materials and Methods
### Workflow in Application  
Regarding our application scenario, this section provides a brief workflow introduction to help readers better understand the practical significance of the IndoorPathNet model in low-cost AMR warehouse navigation. The equipment utilized comprises a BEV camera, an AMR constructed on the Robot Operating System (ROS), and a computer equipped with a 3060 graphics card. Illustrated in the Figure 3, the workflow roughly encompasses four steps.  
Figure 2: The spatial self-attention mechanism.  
1. After calibrating the camera to correct distortions [33], the signal is transmitted from the BEV camera to the PC via WIFI, followed by key frame extraction from real-time video based on a time interval strategy.
2. Images containing indoor paths are input into the pre-trained IndoorPathNet model for path segmentation.
3. Upon obtaining results, contour consistency is employed as an unsupervised evaluation metric to gauge the alignment of segmentation results' boundaries with clearly defined edges in the images. Additionally, consistency checks are conducted on multiple results within a short time frame.
4. Results passing the consistency check and achieving satisfactory evaluation scores are transmitted to the AMR end to provide global guidance. If results do not meet the criteria, the process is reverted to the first step to supplement key frame extraction for reevaluation.  
After numerous experimental trials, the model proposed in this paper achieves an average processing speed of 0.126 s per 1024 \(\times\) 1024 image. This filtering mechanism design guarantees that the AMR receives a global directional guidance every 0.5 s, ensuring consistent and timely navigation updates. These steps contribute to mitigating instances of low-quality path segmentation, or erroneous path recognition to some extent. The key frame extraction strategy, communication methods between devices, and evaluation methods for path recognition results can all be tailored to specific requirements. For instance, considering image quality in key frame selection can reduce recognition errors stemming from low image quality. Connecting AMRs and cameras to the Internet of Things network in smart factories via V2X protocols ensures rapid response and secure communication in emergencies. Additionally, histogram analysis can be incorporated to evaluate path segmentation results as necessary.
## 3 Results and Discussion
### Determination of Charge Transfer Parameters Using EIS
This result, at first glance, contradicts the  
Figure 11: The examples of the approximation of EIS data of the Mg-electrode at 10 min immersion time and used equivalent circuit: (**a**) in 0.5 mol/L MgSO\({}_{4}\) solution and (**b**) in 0.5 mol/L Na\({}_{2}\)SO\({}_{4}\) solution. Figure 10: The equivalent electrical circuits of the Mg-electrode: (**a**) in MgSO\({}_{4}\) solutions and (**b**) in Na\({}_{2}\)SO\({}_{4}\) solution. generally accepted idea of the determining role of the oxide film in the corrosion process as a barrier layer. However, the protective properties of the film are determined not by its general conductivity, but by the absence of electronic conductivity. The polarization resistance of the film-coated electrode \(R_{pSEI}\) under direct current (DC) polarization conditions will be combined from the resistance of the electrolyte \(R_{el}\), the resistance of the magnesium oxide film \(R_{SEI(II)}\), and the resistance of the hydroxide layer \(R_{SEI(I)}\), provided that the charge transfer is limited by the corrosion film. The resistance of the electrolyte due to the high conductivity of the solutions (Table 1) was very small and had no effect on the total value of the resistance. The total resistance of the film parts, together with the polarization resistance \(R_{p}\) (_LV_) determined experimentally, are demonstrated at Figure 12. A fairly good match of the polarization resistance values of the magnesium electrode calculated from the EIS data and polarization curves can be also observed. The calculations for sodium sulfate solutions, where the absence of coincidence was obvious, acted as exceptions. The reason for the latter is most likely the inadequacy of the equivalent circuits for sodium sulfate solutions with a satisfactory approximation of the Nyquist plots. Based on the analysis of impedance measurements, it can be concluded that the hydroxide layer made the main contribution to polarization, whereas the oxide layer was very thin and its resistance were significant only at the initial corrosion time. Figure 12: Dependence on corrosion time of the resistances of oxide \(R_{SEI(II)}\) and hydroxide \(R_{SEI(I)}\) films calculated from the EIS data; their sum \(R\) and experimental polarization resistance \(R_{p(LV)}\) in different solutions: (**a**) 0.5 mol/L MgSO\({}_{4}\), (**b**) 0.5 mol/L Na\({}_{2}\)SO\({}_{4}\), (**c**) 1 mol/L MgSO\({}_{4}\), and (**d**) 2 mol/L MgSO\({}_{4}\).
## 4 Why and How to Measure Volatile Organic Compound Emissions from a Material?
### How to Sample and Analyse VOCs
#### 4.1.2 VOC Analysis  
There are two families of VOC analysis techniques: off-line and on-line. For off-line techniques, a sample is taken from the matrix, the compounds are stored in/on their collection media, and then they are usually analyzed by gas or liquid chromatography technics coupled with mass spectrometry or flame ionisation and UV detection, respectively. On-line analysis techniques can also be used, in particular PTR-MS and SIFT-MS [102; 103; 104].  
When analyzing VOCs by gas chromatography, the notion of total VOCs (TVOCs) is often introduced, being defined in the European standard ISO is ISO 16000-6 [105] as \"the sum of volatile organic compounds, sampled on a Tenax TA(r) cartridge which elutes between \(n\)-hexane and \(n\)-hexadecane included on a non-polar capillary column, detected by flame ionization or mass spectrometry and quantified by converting the total surface area of the chromatogram in this range into toluene equivalent\". Consequently, the TVOC parameter differs from the sum of all the VOCs detected.
###### Abstract  
Terrestrial evapotranspiration migration (_ET_) is a central process in the climate system, is a major component in the terrestrial water budget, and is responsible for the distribution of water and energy on land surfaces especially in arid and semiarid areas. In order to inform water management decisions especially in scarce water environments, it is important to assess _ET_ vegetation use by differentiating irrigated socio-economic areas and natural ecosystems. The global remote sensing _ET_ product MOD16 has proven to underestimate _ET_ in semiarid regions where _ET_ is very sensitive to soil moisture. The objective of this research was to test whether a modified version of the remote sensing _ET_ model PT-JPL, proven to perform well in drylands at Eddy Covariance flux sites using the land surface temperature as a proxy to the surface moisture status (PT-JPL-thermal), could be up-scaled at regional levels introducing also a new formulation for net radiation from various MODIS products. We applied three methods to track the spatial and temporal characteristics of _ET_ in the World Heritage UNESCO Donana region: (i) a locally calibrated hydrological model (WATEN), (ii) the PT-JPL-thermal, and (iii) the global remote sensing _ET_ product MOD16. The PT-JPL-thermal showed strong agreement with the WATEN _ET_ in-situ calibrated estimates (\(\rho=0.78\), \(\rho_{\text{1 month-lag}}=0.94\)) even though the MOD16 product did not (\(\rho=0.48\)). The PT-JPL-thermal approach has proven to be a robust remote sensing model for detecting _ET_ at a regional level in Mediterranean environments and it requires only air temperature and incoming solar radiation from climatic databases apart from freely available satellite products.  
Keywords:evapotranspiration; remote sensing; PT-JPL; WATEN; MOD16; hydrological model; thermal; Donana; irrigation; wetland +
Footnote †: journal: Remote Sensing, 10, 1105; doi:10.3390/rs10071105
## 4 Experiment and Discussion
### Quantitative Analysis
#### 4.4.3 Hardware Overhead  
To quantitatively evaluate the hardware overhead of DSPUIM, we observed the hardware memory occupied by the target DSP processor through the CCS software when executing the instruction to be tested. Through the test, it was found that the maximum hardware overhead occupied by the DSPUIM tool on the DSP processor was within 50 KB, and the occupied hardware size had nothing to do with the number of instructions to be tested. Taking the TMS320C6678 DSP processor as an example, the DSPUIM tool occupied 1.07% of SRAM memory in DSP. The space complexity of the DSPUIM tool was O(1). In this method, an unused memory space is opened, and each time the instruction to be tested is executed, it will jump to the initial address of the memory, so the memory space will be used repeatedly. Therefore, the DSPUIM tool greatly saves hardware overhead.