esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Parametrization and Look-Up Table (LUT) Inversion
To find the solution to the inverse problem for given canopy reflectance spectra obtained from RapidEye and Sentinel-2 images, for each modelled reflectance spectra of the LUTs, the root means square error (RMSE) between measured (reflectance value from the Rapid-Eye or Sentinel-2) and modelled spectra (in LUT) is considered as the cost function according to:  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{k=1}^{n}\bigl{(}\rho_{image\lambda}-\rho_{ LUT\lambda}\bigr{)}^{2}}{n}} \tag{1}\]  
where \(\rho_{\mathrm{image}}\) is the extracted reflectance from the Sentinel-2 or RapidEye image for the sample plots, \(\rho_{\mathrm{LUT}}\) is the simulated reflectance from the corresponding LUT, n is the number of spectral bands, and \(\lambda\) is the corresponding spectral band of Sentinel-2 and RapidEye, respectively. The set of model input parameters which corresponded to the simulated reflectance from each LUT that best matched the measured reflectance from Sentinel-2 or RapidEye with the minimum cost function (smallest RMSE) is regarded as the solution. In other words, the LAI of each plot was retrieved from the large LUTs when the measured reflectance obtained from the images had the smallest difference (RMSE) with the RTM simulated reflectance. Consequently, for each plot, the LUTs were sorted according to the cost function (RMSE) and the set of variables providing the minimum RMSE were considered as solutions (estimated LAI). However, because of the ill-posed nature of the inversion, this solution may not always be unique [106]. To address this and to improve the LAI retrieval, instead of using a single solution, the mean parameters from the best 10, 50 and 100 solutions were considered [30; 103]. To increase the robustness of inversions, the proper selection of features is recommended [3]. Selection of relevant and important bands which are well modelled by RTM is known to enhance the inversion [95; 107; 108]. Since RapidEye data had only five spectral bands, spectral subsetting was explored only for the Sentinel-2 data. We examined whether LAI estimates from Sentinel-2 data through inversion of PROSAIL would be enhanced by selecting optimal spectral subsets. Four spectral subsets were selected considering i) the spectral bands with central wavelengths comparable to those from RapidEye, ii) NIR and SWIR spectral bands, iii) red edge spectral bands, and iv) excluding spectral bands with high average absolute errors (AAE) (specified threshold (\(\mathrm{RMSE}\leq 0.02\))) between simulated and measured reflectance of Sentinel-2. More details on the calculation of AAE can be found in [27; 30].
## 4 Dataset  
In this work, two datasets have been used to perform predictions and experiments. The first one, called \"UK Dale\" [20], has been used to model domestic energy consumption behavior; this dataset is a collection of energy consumption by household appliances from five UK homes. In each house, the electricity demand of the whole house and the energy demand of individual household appliances were recorded at a six-second rate. In order to model hourly consumptions for this work, the data were aggregated in hourly format. The second one, the European Dataset PVGIS-SARAH 2 [21; 22], has been used to model solar energy production. This dataset contains the solar radiation calculated from images of the two METEOSAT geostationary satellites that cover Europe, Africa, most of Asia, and parts of South America. In addition to these datasets, weather information from the web platform \"Tomorrow.io\" [23] has been used to predict solar energy production.
###### Abstract
The key point of the OIM focuses on the construction of the covariance function, involving the estimation of the parameters like the variance of the MDT and the correlation radius. The accurate information of the MSS and especially the GGM has a direct influence on the MDT reconstruction. In fact, the GOCE mission now can detect more accurate gravity signals thanks to the equipment of the gradient gravimeter in the GOCE gravity satellite. Specifically, the European Space Agency (ESA) has released six generations of GGM based on GOCE gravity data and direct approach; the fourth-generation model (GO_CONS_GCF_2_DIR_R4, DIRR4) assimilated data from 2009 to 2012, and the fifth-generation model (GO_CONS_GCF_2_DIR_R5, DIRR5) and the sixth-generation model (GO_CONS_GCF_2_DIR_R6, DIRR6) used all the data from 2009 to 2013. In addition, the DIRR6 recalibrated the satellite's orbits compared to the DIRR5 [17]. The influence of these differences between GGMs on the constructed MDTs can be further investigated. In addition, the recently released DTU18 MSS incorporates the latest satellite altimetry data, especially Jason, CyroSat-2, and SARAL/Altika, which is expected to further improve the accuracy of the MSS. The aim of this study includes three aspects. First, we quantify the effects on MDT recovery based on the recently released GOCE-derived GGMs, which are expected to further improve the MDT modeling in terms of spatial resolution and accuracy. In addition, the choice of MSS is studied, in which the recently published MSS models are compared to see if the choice of MSS has nonignorable effects on MDT modeling. Then, we compare the performances of the traditionally used Gaussian filtering method with the optimal interpolation approach, providing an insight with respect to the choice of approach in MDT recovery. The structure of this study is as follows: In Section 2, the main principle of the OIM in MDT modeling is briefly introduced. Then, the estimation of the key parameters in the OIM is introduced. Numerical experiments are shown in Section 3, in which the Kuroshio is selected as the research area, and the oceanographic and geodetic MDTs and the buoy-measured ocean geostrophic current-velocity profiles are used for comparison and validation. The summary of this study and a brief conclusion are given in Section 4.
## 3 Results and Discussions
### Results of Predicting Age Information for Unlabeled Datasets and Exploration
#### 3.4.3. On the Quantification of Age Difference with Fuzzy Sets
However, referring to the large degree of membership in the fuzzy set \(D_{\text{High}}\), it was found that 10s had clear differences from the other age groups, i.e., 40s and \(\geq\)50s. In addition, the results \(\mu_{D_{\text{Low}}}(20s,30s)=1.0\) and \(\mu_{D_{\text{High}}}(20s,\geq 50s)=1.0\) show that there was little difference between the 20s and 30s, while there was a clear difference between the 20s and \(\geq\)50s. The difference between the 30s and \(\geq\)50s was evident, like that of the 20s and \(\geq\)50s, but the difference between the 40s and \(\geq\)50s was not found, according to \(\mu_{D_{\text{Low}}}(40s,\geq 50s)=1.0\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Age Group Pairs (\(g_{i}\),\(g_{j}\))**} & \multicolumn{2}{c}{**The Number of Statol Suspections in a Fuzzy Set**} & \multicolumn{2}{c}{**The Degree of Membership in a Fuzzy Set**} & \multicolumn{1}{c}{\(\mu_{D_{\text{High}}}(g_{i}g_{j})\)} \\ \cline{2-7}  & \(D_{\text{High}}\) & \(D_{\text{Middle}}\) & \(D_{\text{Low}}\) & \(D_{\text{High}}\) & \(D_{\text{Middle}}\) & \(D_{\text{Low}}\) \\ \hline (10s, 20s) & 0/48 & 0/48 & 48/48 & 0.0000 & 0.0000 & 1.0000 \\ \hline (10s, 30s) & 2/48 & 40/48 & 6/48 & 0.0417 & 0.8333 & 0.1250 \\ \hline (10s, 40s) & 45/48 & 3/48 & 0/48 & 0.9375 & 0.0625 & 0.0000 \\ \hline (10s, \(\geq\)50s) & 46/48 & 2/48 & 0/48 & 0.9583 & 0.0417 & 0.0000 \\ \hline (20s, 30s) & 0/48 & 0/48 & 48/48 & 0.0000 & 0.0000 & 1.0000 \\ \hline (20s, 40s) & 32/48 & 16/48 & 0/48 & 0.6667 & 0.3333 & 0.0000 \\ \hline (20s, \(\geq\)50s) & 48/48 & 0/48 & 0/48 & 1.0000 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The obtained degree of membership for an age group pair \(\left(g_{i}\),\(g_{j}\right)\) in a fuzzy set of age difference. Figure 13: The obtained correlation–similarity matrix. * indicates that it is an average value.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 3 Intersection-Based Link-Adaptive Beaconless Forwarding for City Scenarios
### Routing Protocol Design
#### 3.2.2 Forwarding Zone  
With the intention to further minimize the flow of redundant data packets in ILBFC, a forwarding zone is set such that all nodes located in the zone are in wireless range of each other. To achieve this, a forwarding zone of sector shape with an angle of \(60^{\circ}\) towards destination is set. A fixed forwarding angle of \(60^{\circ}\) limits the maximum distance within the forwarding zone to the wireless range of the node. This will enable the nodes in the forwarding zone to hear each other's packet transmission, thereby allowing only one node in the forwarding zone to relay the data packet. A node is located in the forwarding zone when it satisfies the following condition:  
\[2\times arccos\left|\frac{\overline{N}\overline{S}\cdot\overline{D}\overline{ S}}{\left|\overline{N}\overline{S}\right|\times\left|\overline{D}\overline{S} \right|}\right|\leq 60^{\circ}, \tag{1}\]where \(\vec{N}S\) is the position vector from the contending node to the previous relay node, and \(\vec{D}S\) is the position vector from the previous relay node to the destination. Nodes that do not satisfy this condition do not proceed with the contention process and discard the data packet.
## Materials and Methods
### Module Description
#### 2.3.1 Atmospheric Correction  
Satellite sensors record the extra-terrestrial radiation flowing into the sensor, a measurement that is not directly associated with a physical quantity of the study material as it depends on the atmospheric conditions at the time of image acquisition and the sensor-target illumination geometry. While, for certain applications (e.g., land cover classification), this is less important, when comparing pixel spectra between images acquired on different dates/sensors or with ground measurements, it is crucial to work with consistent, calibrated products such as surface reflectance [31].  
Several algorithms have been developed in the past to compensate for atmospheric effects such as the 6S model-based algorithm developed by NASA [32; 33], the Sen2 Cor algorithm [34] developed by the German Aerospace Center (DLR), and the MACCS (Multisensor Atmospheric Correction and Cloud Screening) algorithm developed by the French Space Agency (CNES) [35]. In our workflow implementation, we embedded the 6S model and revised and adapted it to accommodate the needs of the available image collection. The aerosol inversion relies on the assumed relationship between the surface reflectance in the blue, red, and near-infrared bands, using an algorithm similar to the one used for the MODIS aerosol product. It is described in more detail in [33]. The code is run natively by a FORTRAN program, and, for workflow embedding purposes, a Python wrapper was built around it. This module automatically downloads the auxiliary datasets required for its execution (water vapor, ozone, coarse resolution DEM obtained from the MODIS processing archive). It integrates the datasets with the selected types of satellite image to produce a common set of outputs. Our atmospheric correction module produces two outputs: the top-of-atmosphere and the surface reflectance. The latter is used in further processing. Occasionally, artifacts over water pixels are present in landscapes with irrigated agricultural areas, as the aerosol retrieval method is only valid over land pixels.
# A Review on Potential Antimutagenic Plants of Saudi Arabia
## 7 Which Are the Medicinal Plants of Saudi Arabia That Might Demonstrate Anti-Mutagenesis?
### Chenopodium murale  
_Chenopodium murale_ ([PERSON], 1778) is an annual herb that is conventionally used in the treatment of liver diseases. The plant is a member of the Chenopodiaceae and can be found in the Taif region of Saudi Arabia [90]. Phytochemical investigation of the extract has shown the presence of phenols, flavonoids, tannins, saponnins, terpenoids, caumarins, steroids, and anthraquinones. These components have been linked to the medicinal properties of the plant and the most important among them are anti-bacterial, digestive problems, sterility, anxiolytic, and anti-hypertensive. The antioxidant potential of the plant has been linked to the presence of tannins such as gallic acid and the tests were conducted by DPPH and 2,2'-azo-bis (3-ethyl benzothiazoline-6-sulphonic acid (ABTS) free radical scavenging methods [88,91].
# Advancing Phantom Fabrication: Exploring 3D-Printed Solutions for Abdominal Imaging Research
###### Abstract  
Background: The development of novel medical imaging technologies and treatment procedures hinges on the availability of accurate and versatile phantoms. This paper presents a cost-effective approach for creating anthropomorphic abdominal phantoms. Methods: This study proposes a cost-effective method using 3D printing and readily available materials (beeswax, plaster, and epoxy resin) to create high-fidelity anthropomorphic abdominal phantoms. The three-dimensionally printed phantoms exhibited X-ray attenuation properties closely matching those of human tissues, with measured Hounsfield unit (HU) values of \(-\)115.41 \(\pm\) 20.29 HU for fat, 65.61 \(\pm\) 18.06 HU for muscle, and 510 \(\pm\) 131.2 HU for bone. These values were compared against patient images and a commercially available phantom, and no statistically significant difference was observed in fat tissue simulation (\(p\) = 0.428). Differences were observed for muscle and bone tissues, in which the 3D-printed phantom demonstrated higher HU values compared with patient images (\(p\) < 0.001). The 3D-printed phantom's bone simulation was statistically like that of the commercially available phantom (\(p\) = 0.063). Conclusion: This method offers a cost-effective, accessible, and customizable alternative for abdominal phantoms. This innovation has the potential to accelerate advancements in abdominal imaging research, leading to improved diagnostic tools and treatment options for patients. These phantoms could be used to develop and test new imaging techniques with high accuracy.  
additive manufacturing; computed tomography; 3D printing; phantom manufacturing; material testing +
Footnote †: journal: Medical Image Processing
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 6 Experimental Simulation
### The Lifetime of the Network  
In Figure 14, the impacts of the number of sensor nodes and MTPs on the lifetime of the network are studied. The number of sensor nodes ranges from 200 to 600, while that of MTPs is set as 16, 32, and 64, respectively. The result shows that, when the number of MTPs is fixed, with the increase of the number of sensor nodes, the network lifetime will be increased. The reason is that the number of the redundant sensor nodes will also become larger. When there exists some dead node, more sensor nodes can be activated in order to keep the monitoring work continuous. At the same, it also can be found that the larger the number of MTPs is, the shorter the network lifetime is. This is because with the increase of the number of MTPs, the energy consumed on sensing will become higher, which will reduce the network lifetime.
## 2 Related Work
### Boundary Detection  
Shot and scene detection is one of the long-studied problems in video structure mining. There have been a lot of different approaches based on the different features used and the different clustering methods available. In this subsection we discuss the latest approaches for shot and LSU detection.  
In the existing works for shot boundary detection, there a prevailing and striking pattern of similarities. We have come to the conclusion that boundary detection is performed by calculating or learning the deviation of features over adjacent frames. Widely used features include RGB, HSV, or LUV colour histograms [17], background similarity [4], motion features [18], edge ratio change and SIFT [19], and spectral features. Ref. [17] uses a spectral clustering algorithm to cluster shots, while [18] proposes a new adaptive scene-segmentation algorithm that uses the adaptive weighting of colour and motion similarity to distinguish between two shots. They also propose an improved overlapping-links scheme to reduce shot grouping time. Recently, deep features, extracted using CNN, were employed to obtain significant state-of-the-art results [20]. This team used an end-to-end trainable CNN model that was trained using a cross entropy loss to detect shot transitions. In this work, we employ frame-level object-, person- and location-type semantic descriptions as features to estimate shot boundaries.  
For scene detection, [PERSON] et al. [14] proposed a pipeline that utilises scene descriptions for keyframes of shots, while [15] proposed a pipeline that generates sentences or captions based on objects in a keyframe. The former utilises a scene transition graph to cluster similar shots to scenes, while the latter proposes to use Jaccard-similarity for obtaining similarity between shots. As per survey [21], the LSU-detection task is understood as a three-stage problem. In the first step, frames are grouped into shots. In the second step, location, person and object descriptions are consolidated to obtain shot-level descriptions. In the third stage, shot-level descriptions are used to cluster the shots into story units, using a similarity metric and assumptions about the film structure. For shot boundary detection, we have proposed and utilised the shot-detection algorithm defined in our methodology.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 2 Machine-Learning-Based Path Loss Prediction  
The basic principle of path loss predictors based on machine learning is shown in Figure 1. After knowing the output (path loss observation) and the corresponding input features such as antenna-separation distance and frequency, we can employ machine learning methods to find a good estimation function for the path loss prediction. This function is to map input features to output path loss value, and it can be either a white box (within decision-tree-based models) or a black box (within SVR-based or ANN-based models). The procedure of machine-learning-based path loss predictors is shown in Figure 2 and is introduced step by step as follows.  
Figure 1: Principle of machine-learning-based path loss prediction.  
Figure 2: Procedure of machine-learning-based path loss prediction.
## 3 Results and Discussion
### Magnetic Field
#### 3.3.5 Training Ratio  
The sensitivity of the model to the training data ratio must be assessed by considering different ratios for the number of training data to the total data in the dataset of the model. It is common practice among AI researchers to consider a data training ratio between 50% to 90% for the training of the model and 5% to 25% for each validation and testing process equally. This ratio depends on the number of observations in a dataset, the number of input parameters, etc.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Function** & **FFNN** & **CFNN** \\ \hline [PERSON]–[PERSON] & 0.995210 & 0.995201 \\ [PERSON]–[PERSON] & 0.999231 & 0.999198 \\ [PERSON]–[PERSON] & 0.897353 & 0.998647 \\ [PERSON]–[PERSON] & 0.621461 & 0.885737 \\ [PERSON]–[PERSON] & 0.867373 & 0.867271 \\ [PERSON]–[PERSON] & 0.999280 & 0.999177 \\ [PERSON]–[PERSON] & 0.632067 & 0.879440 \\ [PERSON]–[PERSON] & 0.533737 & 0.886112 \\ \hline \hline \end{tabular}
\end{table}
Table 6: R-squared values of the pairs of activation functions for both FFNN and CFNN for the magnetic field.  
Figure 8: RMSE values of the pairs of Activation functions for both FFNN and CFNN for the magnetic field.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Activation Function** & **FFNN** & **CFNN** \\ \hline [PERSON]–[PERSON] & 0.999957 & 0.999995 \\ [PERSON]–[PERSON] & 0.999997 & 0.999979 \\ [PERSON]–[PERSON] & 0.981759 & 0.99983 \\ [PERSON]–[PERSON] & 0.998797 & 0.999928 \\ [PERSON]–[PERSON] & 0.859171 & 0.999979 \\ [PERSON]–[PERSON] & 0.998393 & 0.999986 \\ [PERSON]–[PERSON] & 0.901135 & 0.999884 \\ [PERSON]–[PERSON] & 0.999866 & 0.999949 \\ \hline \hline \end{tabular}
\end{table}
Table 5: R-squared values of the pairs of activation functions for both FFNN and CFNN for the electric field.  
In this paper, the data training ratios were considered for 50%, 60%, and 70% to find the best ratio in terms of the accuracy of the model. The reason that 80% and 90% were not studied is that there was a risk of overfitting the model. The results of Tables 7 and 8 demonstrate that 70% training ratio gave the highest accuracy for both electric and magnetic fields.
## 3 Methods
### Training and Validation  
We conducted 10 rounds of leave-one-year-out blind tests by dividing the data set into a nine-year training group and a one-year validation group from the entire match-up database (2006-2015). For example, the blind test for round #1 consisted of the validation data from 2006 and the training data from the remaining nine years; the blind test for round #2 consisted of the validation data from 2007 and the training data from the remaining nine years, and so on.
###### Abstract  
The exceptional physical properties of carbon nanotubes (CNTs) have the potential to transform materials science and various industrial applications. However, to exploit their unique properties in carbon-based electronics, CNTs regularly need to be chemically interfaced with metals. Although CNTs can be directly synthesized on metal substrates, this process typically requires temperatures above 350 \({}^{\circ}\)C, which is not compatible for many applications. Additionally, the CNTs employed here were highly densified, making them suitable as interconnecting materials for electronic applications. This paper reports a method for the chemical bonding of vertically aligned CNTs onto metal substrates that avoids the need for high temperatures and can be performed at temperatures as low as 80 \({}^{\circ}\)C. Open-ended CNTs were directly bonded onto Cu and Pt substrates that had been functionalized using diazonium radical reactive species, thus allowing bond formation with the open-ended CNTs. Careful control during grafting of the organic species onto the metal substrates resulted in functional group uniformity, as demonstrated by FT-IR analysis. Scanning electron microscopy images confirmed the formation of direct connections between the vertically aligned CNTs and the metal substrates. Furthermore, electrochemical characterization and application as a sensor revealed the nature of the bonding between the CNTs and the metal substrates.  
carbon nanotubes; metal-carbon interface; bond formation Article