esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 5 Conclusions  
This study quantitatively analyses the disturbing effect caused by the shape and length of shadows on the results of image cross-correlation (ICC). First, the impact of shadows on the development of an automatized procedure for the selection of images was determined, and it was demonstrated that adopting images acquired in conditions of diffuse illumination enables the shadow-related uncertainty to be minimized. Therefore, a possible solution was developed to ensure that the images selected were taken in diffuse illumination.  
A method for automatically classifying the images according to the illumination appearance was then proposed. Considering photos taken of various natural environments (e.g., landslides, glaciers, rock faces), SVMs were trained using different features that were representative of the photo's appearance. Of these, the number of RGB triplets was found to be the most significant feature used to discriminate between the classes. Training the SVMs with two or three features was sufficient for achieving an average misclassification error of 5.5%.  
The processing chain used to automatically select images suitable for ICC application, which includes the abovementioned classification method, is also described herein. The application of the method was applied during a survey campaign in 2018 to monitor the activity of the Planpicieux glacier for civil protection purposes, and the results showed that a high correspondence was obtained with results obtained using images selected manually. The same experiment was then conducted using time-lapse photos acquired for the Mont de La Saxe landslide.  
These results show the possibility of developing a monitoring system based on ICC that automatically selects and processes the pictures acquired. The use of image sequence analysis enables financial and human resources to be reduced in monitoring active geo-hydrological instabilities. The development of an automatic solution is a valuable tool for use in developing an autonomous monitoring system that supplies daily updates.  
Conceptualization, N.D. and D.G.; methodology and analysis, N.D.; data curation, P.A.; writing--original draft preparation, N.D.; writing--review and editing, D.G.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Experiments and Results
### Evaluation of Exploitation Capability (Functions F1-F7)  
Given that one global optimum exists for each of the unimodal functions (F1-F7), they investigate the compared algorithms' exploitation capability. Table S1 demonstrates that the proposed LO shows high performance in optimizing the functions and reducing their values, and achieving the best exploitation capability. Particularly, the proposed LO obtains the best results in optimizing F1 and F2 in terms of best, worst, and mean and gets the second-best results in most of the other functions (i.e., F3-F7). Accordingly, it is notable that the proposed LO achieves the best exploitation capability.
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.8 Volatile Phenols  
TM7-Y wine had the highest concentration of 4-ethylphenol and total volatile phenols, while the lowest levels of the former compound were observed in TM21-Y and TPHT-Y wines (Table 1). 4-Vinylguaiacol was the most abundant in TM21-Y and the least abundant in TM10-Y wine. Volatile phenols can originate from grapes or be formed during fermentation from phenolic acids and later be transformed by _Brettanomyces/Dekera_ contamination [49]. _Saccharomyces cerevisiae_ yeasts produce minimal amounts of volatile phenols due to a relatively low degree of decarboxylation of hydroxcinamic acids [59]. This aligns with the findings of this study and low volatile phenol concentrations in young wines. Previous research found volatile phenols only in wines macerated for 5 days in contrast to 10 and 15 days [41], similar to this study showing the lowest concentrations of 4-ethylphenol in wine of the maceration treatment with the longest duration. It was noted that heating at 70 \({}^{\circ}\)C promotes guaiacol development through thermal decarboxylation of hydroxcinamic acids [38], but in this study, guaiacol was not detected in young wines obtained by pre-fermentation heating. Increased volatile phenol concentrations with heating have also been observed [29]. Lower 4-ethylphenol concentration in TPHT-Y wine was likely due to thermal degradation, as reported previously [60]. Aging increased volatile phenol levels across all treatments, with the exception of 4-vinylguaiacol in TM7-A and TPHT-A. Aging in wood resulted in the occurrence of guaiacol, eugenol, and 4-ethylguaiacol. The most abundant phenols after aging were 4-ethylguaiacol and 4-ethylphenol. Volatile phenols can also be extracted from oak barrel wood, but increased concentrations in maturel wines are likely due to _Brettanomyces/Dekera_ yeasts, which thrive in aerobic conditions during oak aging [12].
## 5 Simulation and Analysis
### Comparisons of DCT and Wavelets for the Sparsity-Based Interpolation  
Now suppose we choose a 2-D wavelet as the sparsifying transform for the real data, and by calculating the normalized energy loss as Equation (16), the largest _S = 408_ wavelet coefficients are kept and the rest wavelet coefficient are set to be zeros. Figure 19 compares the estimation quality of the real data by KNN, the proposed method with wavelet or DCT dictionaries.  
Although the data is composed of sparse wavelet coefficients, obvious recovery errors are observed for the wavelet-based recovery samples as shown in Figures 19(c,g). As we explained in the Section 2,  
Figure 19: Comparisons on the wavelet-based, DCT-based sparsity-interpolation and KNN interpolation for the data represented by sparse wavelet coefficients. **(a)** A 64 \(\times\) 64 2-D smoothed real dataset in wavelet domain, which keeps more than 99.99% energy of raw data, **(b)** recovered data by KNN, **(c)** recovered data by proposed method with wavelet, **(d)** recovered data by proposed method with DCT, **(e)** available data when missing rate is 0.4 and block size is 2 \(\times\)2, **(f)**, **(g)** and **(h)** are the recovered error of (b), (c) and (d), respectively, and the RMSE of three methods are 0.31, 0.99 and 0.18, respectively.  
when the relatively large missing blocks overlaps with the compact support of wavelet basis, most of the weights in the underdetermined equations in Equation (8) will be 0. Thus, not enough information is taken use of by using the wavelet basis to recover the data. On the contrary, DCT produces the lowest RMSE in the three methods. Since DCT do not have the localized support, less entries in the underdetermined equations in Equation (8) will be nonzero, this can provide more information than wavelet to help recover the missing samples. Therefore, a non compact support basis is preferable for the spatial interpolation.
## Materials and Methods
### Characterizations  
The morphology and elemental distribution of the as-prepared samples were examined by scanning electron microscopy (SEM) and energy dispersive spectrometer (EDS) simultaneously operated at 15 kV (SEM, JSM-7001F, JEOL). The specific surface areas of the materials were calculated by specific surface area and microporous analyzer (BET, SSA-7300, BIAODE, Beijing, China). The X-ray diffractometer (XRD, Panalytical X' per PRO MPD, PANalytical B.V.) equipped with Cu K\(\alpha\) radiation was employed to detect the chemical compositions of samples. The Fourier transform infrared (FTIR, T27, Bruker) spectroscopy measurements were carried out in KBr pellet at room temperature. The chemical compositions of the material surface were determined by X-ray photoelectron spectra analysis (XPS, ESCALAB 250 Xi, Thermo Fisher Scientific, Waltham, MA, USA) using monochromatic Al K\(\alpha\) radiation. The surface zeta potentials of samples were measured using Zetasizer Nano ZS instrument (Delsa Nano C, Beckman Coulter Inc, Brea, CA, USA). The prepared emulsion and the Ti-Mt-APTES demulsification processes were observed and photographed using a microscope (BX51M, Olympus, Tokyo, Japan).
## 3 Results
### Introducing the Measured Parameters  
This section introduces the essential measured parameters in the drive test that form our feature vector. This information is used to design the network; for example, to achieve the correct redistribution and to control the radio signal's power.  
**1. Signal quality:**  
In some systems, such as UMTS, the signal quality parameter is directly dependent on the signal-to-noise ratio [45]. In other methods, such as GSM/GPRS, the signal quality is a Bit or symbol fault rate function.  
**2. Signal level:**  
The received signal level is a measure to control the power of the received signal and the transmission at the place of receiving the signal. The accepted signal range by the mobile user is between \(-110\) dbm and \(-48\) dbm. The low level of the received signal can have several reasons. For example, consider a rural area where the distance to the base transmitter and receiver station is not suitable. This area receives a weak signal, due to the long distance to the BTS, and installing a new station is impossible due to high cost. Another reason for the decrease in the signal level can be the presence of harmful interference with other received signals in the area. In addition, some tall buildings cause a rapid attenuation of the signal strength and, therefore, the signal inside the building is not strong enough. An unacceptable signal level can lead to call termination [46].  
**3. Speech quality:**  
The literature on cellular networks measures speech quality with the SQI-MOS parameter. In its calculation, an algorithm is used to consider the network mode, such as frame fault rate (FER), and bit fault rate. The algorithm can also calculate the coding type used in speech transmission, which affects the speech quality level and the highest possible quality, by calculating the difference between the analyzed sound quality on the side of the transmitter and the receiver. The result is shown as a number between 1 and 5.  
**4. Carrier-to-Interference (C/I) Ratio:**  
One of the most effective and accurate methods for analyzing interference in communication channels is measuring the Carrier-to-Interference (C/I) Ratio. Route design and equipment design are two critical factors affecting the interference level. The neighborhood of nearby systems that use the same frequency band is one of the apparent reasons for interference, due to the wrong design of routes.  
**5. Advanced Timing:**  
In the literature in this field, advanced timing refers to the time it takes for the mobile phone signal to reach the base station. Since GSM uses multiple access technology, based on time division, to share a frequency band between different users and considering that the users are located at an extra distance from the base station, this criterion can be used to estimate the distance of the user to the base station used.
## 4 Experiments and Results
### Transmission Network Expansion Planning (TNEP) Problem  
The TNEP problem entails finding the lowest cost transmission assets that can be installed in a power system to meet predicted demand over a specified time horizon [82]. Because TNEP has a long-term effect on system operation, it is a key strategic decision in power systems. Additionally, TNEP is a non-linear, non-convex, and multi-modal optimization problem that is classified as NP-hard in terms of computing complexity. Different models and strategies for solving the TNEP problem have been developed in the existing literature. To address the TNEP problem in its various manifestations, heuristic and meta-heuristic have been developed. While heuristic techniques are simple to use, they typically become stuck in locally optimal solutions. Metaheuristic techniques are more efficient search algorithms that are capable of finding better solutions than conventional heuristic techniques at the cost of increased processing time. Table 8 compares the performance of the proposed LO algorithm with eleven different comparison algorithms. In this table, the experimental results of each comparative algorithm are summarized in terms of the best, mean, worst, median, and standard derivations across 30 runs. From Table 8, it is clear that the proposed LO algorithm performance is similar to all other algorithms by obtaining the same results (i.e., it reached the optimal solution).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Problem** & **Value Range** & **Dimension** \\ \hline PEFMSW & \(x_{i}\in[-6.4,5.35]\),where \(1\leq i\leq 6\) & 6 \\ TNEP & \(x_{1},x_{2}\in[0,4]\), \(x_{3}\in[0,\pi]\), \(x_{i}\in[-4-\frac{1}{4}\lfloor\frac{i-4}{3}\rfloor,4+\frac{1}{4}\lfloor\frac {i-4}{3}\rfloor]\), where \(4\leq i\leq 30\) & 30 \\ BCBOC & \(x_{1}\in[0.6,0.9]\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The characteristics of three real-world problems.
# Incorporating Clinical Imaging into the Delivery of Microbeam Radiation Therapy
## 3 Results and Discussion
### End to End Plantom Test
The three lower images are the corresponding angles of a plastic rat skull phantom with a hole (red) and implanted 3.2 mm diameter ball bearing (grey) location super-imposed over each image. GafChromicTM HDV2 film placed on the exit side of the rat phantom, seen in Figure 8, was used to assess the alignment accuracy. Microbeams attenuated by the metal ball bearing target reduce the beams intensity, appearing lighter on the film. To find the centre of the ball bearing, a Gaussian fit was applied to the 1D vertical and horizontal intensity profile of the film. Dashed lines in the figure show the centre of the field (blue) and the location of the ball bearing (red) as determined from the centre of the Gaussian fit. This film verified that the ball bearing had been centered to the treatment field, horizontally within \(\pm 0.25\) mm and vertically to within \(\pm 0.17\) mm for a total positioning accuracy of \(\pm 0.30\) mm, within the previously estimated positional uncertainty of \(\pm 0.34\) mm. This work presents the alignment of only a single anatomical phantom. Future studies should be conducted with larger sample size to gain statistical information of the alignment reproducibility. Figure 8: Film placed on the exit side of the phantom shows the circular ball bearing attenuating the square microbeam treatment field. Adjoining plots show the 1D intensity of the beam (blue) and a Gaussian fit of the beam intensity (red) with dashed lines indicating the centre of each plot. Figure 7: Images of rat skull phantom with embedded \(3.2\) mm diameter ball bearing used for alignment. Planar X-ray radiograph from treatment room (**left**) and composite CT DRR (**right**). Blue markers indicate four of the corresponding alignment makers chosen in each image. For visual clarity of the figure, two markers on the RHS have been omitted.
## 3 Results and Discussion
### Phytochemical Characterization and Biological Activities
#### 3.3.1 Identification and Quantification of Phenolic Compounds
The freeze-drying process is responsible for the higher porosity (80% to 90%) of the freeze-dried plant material compared to the material obtained by the convective-drying method [108], increasing solvent diffusion through dried tissue, and consequently resulting in the transfer of phenolic compounds to the solvent [109]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Peak** & **Quantified Compounds 1 ** & **Pressh** & **Oven-Dried** & **Freeze-Dried** \\
**4** & **e**  
Despite the freeze-drying process generating a significant increase in the amount of dicaffeoylquinic acids and quercetin glucosides compared to the fresh plant (\(\mu\)g/g dw), for protocatechuic-arabinoside acid, ferulic acid, dihydroquercetin, \(p\)-commaroylquinic acid (isomer 1), a quercetin-methyl-ether derivative, and a kaempfer derivative, a decrease was observed, as shown in Table 4. This effect may be related to oxidation, the most important biochemical process that occurs during the processing of the plants, which starts as soon as the integrity of the cell is broken [110], namely during the grinding process for sample preparation. The total flavonoids and total hydroxycinnamic acids in the freeze-dried plant were 5151.65 \(\mu\)g/g dw and 1512.32 \(\mu\)g/g dw, while in the oven-dried plant they were lower (2393.97 \(\mu\)g/g dw and 1211.73 \(\mu\)g/g dw, respectively), as can be seen in Table 4. These results show that the oven-drying process generated a reduction of 54% in the total flavonoids and 20% in the total hydroxycinnamic acids compared to freeze-dried plant. Similar to our results, studies [80; 111; 112] have showed that the freeze-drying process preserves the bioactive compounds and retains a higher amount of polyphenols in herbs, while the oven-drying process causes major loss of these compounds, especially with drying temperatures higher than 60 \({}^{\circ}\)C. The freeze-drying process is able to preserve the polyphenol content as it prevents its thermal and oxidative degradation, and limits enzymatic reactions from polyphenol oxidase, lipoxygenase, and peroxidase [113; 114; 115]. Oven-drying methods have demonstrated to have a significant impact on the flavonoid content. Flavonoids are negatively affected by hot air-drying, which are degraded proportionally to an increase in the temperature [113; 116]. In general, the results show that the oven-drying process affects the phenolic composition, reducing the quercetin glycosides, but the chlorogenic acids and their derivatives are minimally affected. In terms of phenolic composition, the freeze-drying method is less aggressive than the oven-drying method.
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 4 Modelling
### Multinomial Logistic Regression
The probability of crash severity category \(j\) given predictor variables \(X\) is represented by \(P(Y=j\mid X)\). The estimated coefficients for each predictor variable in the model are denoted by \(\beta_{j0},\ \beta_{j1},\ \beta_{j2},\ \ldots,\ \beta_{jk}\). The predictor variables (behavioural aspects, road conditions, etc.) influencing crash severity are represented by \(X_{1},X_{2},\ \ldots,\ X_{k}\). This model, grounded in statistical principles, was designed to predict the degrees of crash severity and is helpful when multiple categories are present in the targeted variable [37]. The development phase categorizes the dataset into Multiple Binary Logistic Regression sub-models, comparing each severity category against the reference category. This step ensures that the model captures the shades of difference that define each severity level. Implementing this approach involves training Multiple Binary Logistic Regression sub-models, each offering insights into the intricate relationships between predictor variables and crash severity. The data was navigated precisely by dissecting the dataset into these sub-models, enabling a clear grasp of the factors that played a pivotal role in determining collision outcomes [38]. This phase was a technical interlude and the cornerstone of the research study's empirical foundation. The methodology aligned with the complexities of real-world collision scenarios, offering a lens to perceive the spectrum of severity levels, much like dissecting light through a prism in a study [33].
###### Abstract  
Over the last few years, a very active field of research has aimed at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. In this paper, we investigate how they might help to solve for the oversmoothing of the state-of-the-art optimal interpolation (OI) techniques in the reconstruction of sea surface height (SSH) spatio-temporal fields. We focus on two small \(10^{\circ}\times 10^{\circ}\) GULFSTREAM and \(8^{\circ}\times 10^{\circ}\) OSMOSIS regions, part of the North Atlantic basin: the GULFSTREAM area is mainly driven by energetic mesoscale dynamics, while OSMOSIS is less energetic but with more noticeable small spatial patterns. Based on observation system simulation experiments (OSSE), we used a NATL60 high resolution deterministic ocean simulation of the North Atlantic to generate two types of pseudo-altimetric observational dataset: along-track nadir data for the current capabilities of the observation system and wide-swath SWOT data in the context of the upcoming SWOT (Surface Water Ocean Topography) mission. We briefly introduce the analog data assimilation (AnDA), an up-to-date version of the DINEOF algorithm, and a new neural networks-based end-to-end learning framework for the representation of spatio-temporal irregularly-sampled data. The main objective of this paper consists of providing a thorough intercomparison exercise with appropriate benchmarking metrics to assess whether these approaches help to improve the SSH altimetric interpolation problem and to identify which one performs best in this context. We demonstrate how the newly introduced NN method is a significant improvement with a plug-and-play implementation and its ability to catch up the small scales ranging up to 40 km, inaccessible by the conventional methods so far. A clear gain is also demonstrated when assimilating jointly wide-swath SWOT and (aggregated) along-track nadir observations.  
Keywords:data-driven and learning-based approaches; interpolation; benchmarking; Nadir and SWOT altimetric satellite data; sea surface height (SSH) +
Footnote †: 28 September 2020; Accepted: 15 November 2020; Published: 20 November 2020
###### Abstract  
This investigation is centered around the application of warm mix asphalt (WMA) technologies to address workability concerns linked to rubberized asphalt binders. The primary aim of incorporating crumb rubber (CR) and WMA additives is to establish a robust paving method that fosters energy conservation, efficient waste management, noise reduction, and improved overall performance. The current study aims to comprehensively characterize and differentiate the physical attributes of rubberized asphalt binders by employing three distinct WMA additives: Sasobit, Cecabase RT and Rediset WMX. These additives are introduced into eight unique asphalt binders. Laboratory assessments are carried out to evaluate the workability and physical properties of these binders. The evaluation encompasses penetration, softening point, penetration index, penetration viscosity number, storage stability, ductility, viscosity, and stiffness modulus analyses. The findings indicate that the rubberized asphalt binder enhanced with Sasobit demonstrates the highest levels of both hardness and softening point in comparison to asphalt binders supplemented with alternative WMA additives. The evaluation of storage stability underscores the satisfactory stability across all modified asphalt binders. Both the unmodified and modified binders meet the requirements stipulated by the ductility test; the rubberized asphalt binder modified with Rediset falls short. The rubberized asphalt binder improved with Sasobit displays the most notable enhancement in workability. Furthermore, the blend of crumb rubber and Sasobit binder reveals the highest stiffness modulus values under conditions of intermediate and high temperatures with 1.88 and 0.46 MPa, respectively. In summation, the rubberized asphalt binder incorporating crumb rubber with Sasobit showcases superior improvements in both stiffness and workability compared to counterparts modified with Cecabase RT and Rediset WMX.  
warm mix asphalt (WMA); crumb rubber (CR); WMA additives; physical properties; workability; stiffness modulus +
Footnote †: journal: Applied Sciences