esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study  
This study focuses on a vineyard located in the southern region of Chile, which manages two filtration lines and two bottling lines. The characteristics of the bottling lines are detailed in Table 1, while the characteristics of the filtration lines are presented in Table 2. These lines are assigned and combined based on production needs and quality and process standards. The production period considered spans three days, detailed in Table 3. Daily production-lot assignments are established in weekly coordination meetings, while specific operational decisions regarding the order and assignment of lots are made by the production area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Line**} & **Max. Flow Rate** & **Accepted Formats** & \multirow{2}{*}{**Top Closure Type**} \\  & **(m\({}^{3}\)/h)** & **(mL)** & \\ \hline Line 1 & 3.2 & 187, 330, 700, 750, & \\  & & 1000, 1500 & \\ Line 2 & 2.8 & 187, 330, 700, 750, & \\  & & 1000, 1500 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of bottling lines 1 and 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Line**} & **Filtration Area** & **Max. Flow Rate** & **Accepted Formats** \\  & **(m\({}^{2}\))** & **(m\({}^{3}\)/h)** & **(\(\mu\)m)** \\ \hline Line 1 & 12 & 4.8 & 0.45–0.65 \\ Line 2 & 18 & 7.2 & 0.45–0.65 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of filtration lines 1 and 2.
# Infrared Thermographic Signal Analysis of Bioactive Edible Oils Using CNNs for Quality Assessment
## 2 Nutritional and Health Challenges of Edible Oils
### Pumpkin Seed Oil: Nutritional and Functional Profile  
The last sample analysed is pumpkin seed oil (PSO), characterised by a rich content of unsaturated fatty acids, mainly oleic acid (omega-9) and linoleic acid (omega-6) [68].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Per 100 g** & **\% RDA/AI** \\ \hline Energy & 900 kcal & 45\% (2000 kcal) \\ Total Fat & 100 g & 154\% (based on 65 g/day) \\ Saturated Fat & 12 g & 60\% \\ Monounsaturated Fats & 13 g & - \\ Polyunsaturated Fats & 70 g & - \\ Omega-3 Fatty Acids & 6 g & 375\% \\ Omega-6 Fatty Acids & 67 g & 558\% \\ Phytosterols (total) & 180 mg & 60\% \\ Lycopene & 1.2 mg & - \\ B-carotene & 0.8 mg & 10\% (as vitamin A equivalent) \\ Vitamin E (\(\alpha\)-tocopherol) & 24 mg & 160\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nutritional composition of tomato seed oil.  
The complex biochemical profile confers a variety of health benefits, making tomato seed oil a widely used ingredient in both functional foods and the pharmaceutical industry. In the latter case, it is used to treat intestinal parasites, support prostate and bladder health, promote cardiovascular function, and combat hair loss [69]. The fatty acid profile of pumpkin seed oil can vary based on several factors, including the genetic variety of the pumpkin, extraction and processing techniques, and geographical origin. Pumpkin seed oil has a distinctive sensory profile due to its physical properties. The high content of oleic and linoleic acids contributes to cardiovascular health by improving the lipid profile and reducing inflammation.
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Construction of 3D Model Based on SfM-MVS  
The 3D model was constructed using the ContextCapture Center software integrated with SfM-MVS technology. The SfM technology is capable of solving the spatial and geometric relationship of the target via the movement of the camera from multiple images for model construction [40].  
To begin with, in the SfM algorithm, the scale invariant feature transform (SIFT) algorithm [41] was employed to extract the feature points and obtain feature description parameters corresponding to the respective image. Next, image features were matched based on critical points on the overlapped images in combination with image pose and spatial location information, and model parameters (e.g., camera focal length, radial aberration, and tangential aberration). Lastly, a bundle block adjustment [42] was performed to optimize the key point features and camera parameter positions by minimizing the reprojection error between the locations of vital points on the image and the predicted locations [43]. A sparse point-cloud with coordinate and color information was obtained.  
The MVS technology iteratively diffused and filtered the sparse point-cloud to obtain a dense point-cloud [44]. Subsequently, the dense point-cloud was segmented into blocks, and the block point-cloud was constructed into an irregular 3D grid. Next, the white film data were acquired from the grid data, and the optimal texture was automatically found  
Figure 3: LiDAR point-cloud processing and 3D model construction (the black box represents the LiDAR point-cloud data processing process, and the blue box represents the 3D model construction process).  
by a triangular grid to achieve automatic optimal texture mapping. Lastly, a 3D model consistent with the coordinate system was obtained. The construction of sparse point-cloud and dense point-cloud data was dependent on the position and attitude information of the image, thus requiring a certain number of GCPs to participate in the calculation, and the model had accurate absolute coordinates [45]. Furthermore, a DOM with 0.05 m resolution was obtained. The 3D model construction process flow is presented in the blue box in Figure 3.  
Specifically, the absolute accuracy of the 3D model was analyzed. The root mean square error of the horizontal accuracy and elevation accuracy served as the accuracy assessment index. The accuracy results showed that the horizontal accuracy was 4.4 cm, and the elevation accuracy was 7.9 cm.
# AR Displays: Next-Generation Technologies to Solve the Vergence-Accommodation Conflict
###### Abstract  
Augmenting reality (AR) holds many benefits in how people perceive information and use it in their workflow or leisure activities. A cohesive AR experience has many components; nevertheless, the key is display technologies. The current industry standard for the core solution is still conventional stereoscopy, which has proven to be inadequate for near-work due to the caused vergence-accommodation conflict and the inability to precisely overlay the 3D content on the real world. To overcome this, next-generation technologies have been proposed. While the holographic method holds the highest potential of being the ultimate solution, its current level of maturity is not sufficient to yield a practical product. Consequently, the next solution for near-work-capable AR displays will be of another type. LightSpace Technologies have developed a static multifocal display architecture based on stacked liquid crystal-based optical diffuser elements and a synchronized high-refresh rate image projector. A stream of 2D image depth planes comprising a 3D scene is projected onto respective physically-separated diffuser elements, causing the viewer to perceive a scene as continuous and having all relevant physical as well as psychological depth cues. A system with six image depth planes yielding 6 cpd resolution and 72\({}^{\circ}\) horizontal field-of-view has been demonstrated to provide perceptually continuous accommodation over 3.2 Diopter range. A further optimization by using a conventional image combiner resulted in the compact and practical design of the AR display.  
augmented reality; virtual reality; multifocal display; vergence-accommodation conflict; optical diffuser; 3D +
Footnote †: journal: Applied  
1 July 2019; 31 July 2019; Published: 2 August 2019
## 2 Materials and Methods
### Data Collection
#### 2.2.3. Land Use Data  
Land use, shown in Figure 2, is based on Rapid Eye Science Achieve images and an image classification was performed in ArcGIS to obtain the land use maps. Land use was classified as arable land, wetlands, grassland, industrial areas, forests, small gardens, settlements, concrete surfaces, roads, water facilities, and miscellaneous, based on the image classification results. Table 1 shows the summary of the data used in this study and the related sources from where the data was obtained. Leaf area index (LAI) based on project \"Communal waters collective development in urban areas\" (KOGGE) [44] in monthly temporal resolution is shown in Figure 5 and average root depth (RD) on seasonal temporal resolution [45] is shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Data** & **Sources** \\ \hline Digital elevation model (DEM) & LUNG-MV \\ Land use & Rapid Eye Achieve images \\ Precipitation & German weather services (DWD) \\ Potential evapotranspiration (ETp) & Penman–Monteith method [41] \\ Leaf area index (LAI) & Project KOGGE [44] \\ Root depth (RD) & ATV-DVWK-M 504 [45] \\ Topography & LUNG-MV \\ Soil properties & LUNG-MV \\ Soil profiles & LUNG-MV \\ Manning roughness coefficient & Literature \\ Geological data & LUNG-MV \\ Piezometer levels & LUNG-MV \\ River network & LUNG-MV \\ River cross-sections & ADCP measurements and DEM \\ River flow & Research- Project “BOOTmonitoring” \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the data used in this study and the sources from where the data is obtained.  
Figure 4: **(Up)** Catchment area under each climate station by using Thiessen polygon interpolation method; and **(Down)** average monthly precipitation and evapotranspiration at Demmin from 2010 to 2017.
## 3 Proposed Methods
### Highway Crack Classification Methods
#### 3.2.1 Environmental Noise Removal Based on Roadbed Identification  
As shown in Figure 6, in the UAV remote sensing highway image, the highway only occupies a part of the remotely sensed image, and the same is true in the highway crack detection result map. Due to the presence of errors in the manually labeled dataset, the model may be trained with the environmental noise as cracks during the training process.  
Therefore, in addition to the road surface, some noise from the surrounding environment will also be detected as cracks in the detection result map. To avoid misclassifying the detected environmental noise as cracks in the classification process, it is necessary to first identify the roadbed and obtain the pixel coordinates of the roadbed. Only the pixel points within the pixel coordinates of the roadbed can be used for crack classification in the crack classification process. In this way, the impact of ambient noise can be reduced.  
In this paper, the DenxiDeepCrack [35] network model is used for roadbed recognition. Due to the lack of a roadbed dataset, this experiment manually labeled the roadbeds of UAV remote sensing road images with LabelMe. A series of data processing operations was then performed, resulting in a UAV remote sensing roadbed dataset containing 7025 images in the training set, 2356 images in the validation set and 2356 images in the test set. The DenxiDeepCrack network model was then trained using this dataset and used for roadbed detection.  
The yellow line in the center of the road may be identified as the roadbed in the detection result, so it must be removed to obtain the coordinates of the roadbed. Given that the central yellow line identification effect is better, the roadbed identification map matrix is subtracted from the central yellow line identification map matrix to achieve the effect of removing the yellow line.  
Then, the roadbed detection result map is binarized to find the roadbed pixel coordinates according to the pixel value, and the pixel coordinates of the roadbed are recorded.  
Based on the roadbed pixel coordinates, the environmental noise in the full-size drone aerial image will be removed such that only the highway road surface is retained. Additionally, in this section, the crack detection algorithm proposed in Section 3.1 will be applied to detect cracks on the drone aerial image of the road surface for reducing the GPU workload. The resulting crack detection image will be used for subsequent crack classification.
this article will focus on RF-based methods. The best known RF technique is the Received Signal Strength Indicator (RSSI) [17; 18; 19; 20]. Knowing the power emitted and the propagation attenuation model makes it possible to calculate the propagation distance. With the use of specific antennas, it is possible to obtain the Angle of Arrival (AOA) of the transmitted wave and to obtain the position by triangulation. Using the propagation speed, travel time measurement techniques such as Time of Arrival (ToA) or Time Difference of Arrival (TDoA) [21; 22; 23; 24] allows one to obtain the position with a trilateration calculation. In particular, TDoA-based ultra-wideband (UWB) sensors are very popular [25; 26] as they allow very accurate distance measurements at a relatively low cost.  
RF-based positioning can be solved analytically [27; 28; 29; 30] when exact models are known or using numerical optimization methods. Several optimization algorithms can be found in the literature [31] to solve multilateration. It should be mentioned that the computation may be difficult due to the non-linearity and/or non-convexity of the estimation problem, potentially leading to local minima or divergence. An interesting approach [32], based on a variant of the original Space Alternating Generalized Expectation Maximization (SAGE) algorithm [33], leads to the joint estimation of the Direction of Arrival (DoA), time-delay, and range estimation, aiming to improve the estimation accuracy of communicating vehicles.  
This work deals with the multilateration localization problem in non-GPS environments, using UWB relative distance measurements whose anchoring positions are unknown or are known but with uncertainties. We address the problem of localization in different scenarios, including (_i_) the positioning of a fleet of drones based on RF measurements to anchors with poorly known positions; (_ii_) estimating the position of UWB anchors based on the distances measured to one or more drones located by on-board sensors; and (_iii_) simultaneously combining the two estimates of the positions of the anchors and the drones to improve the localization accuracy of the system as a whole. The estimation problem is solved using gradient-descent-based optimization while considering different formulations for the cost functions and proposing a new adaptation.  
The rest of the paper is organized as follows. Section 2 presents a unified formulation of the estimation problem we address. To solve this problem, different formulations of the gradient descent-based optimization are presented in Section 3. Then, in Section 4, simulation results are described and analyzed before concluding this work in Section 5.
# Morphological Foot Model for Temperature Pattern Analysis Proposed for Diabetic Foot Disorders
## 3 Results and Discussion
### Statistical Analysis
In the present case, using the same processor, an execution time of less than 2 s was estimated for the registration of the four images associated with a single subject. Thus, the methodology presented is executed fast and without substantial technical requirements. Furthermore, it is feasible to include it in the standard clinical settings as well as in a mobile application to be used by the patient at home. The acquisition protocol associated with thermography, which is fast, simple and easily repeatable, is the major advantage as compared to a conventional thermometer. The temperature of the entire plantar aspects of both feet can be acquired in a single image by thermography. However, for practical reasons, when employing a thermometer, only a few specific locations are measured symmetrically in both feet. This is an extremely time-consuming process. In cases in which the patient is required to measure the temperature  
Figure 8: Individual mean temperature difference, left minus right foot, at each time point: (**a**) female and (**b**) male groups. several times a day [47], the assessment may be tedious for the patient as well as for the clinical practitioners. Furthermore, when considering the self-assessment performed by the patient at home, the procedure may be discouraging. A major limitation of the present study is the low number of subjects included in addition to the absence of patients with diabetes. Since, across people and over time, there is an inherent heterogeneity in foot temperature, influenced by internal and external factors, only healthy subjects were considered in the present work. In this manner, confounding factors causing thermal asymmetry, such as inflammation, ulceration and/or infection, were reduced. Nevertheless, the age range of the healthy volunteers considered may not be representative of the profiles of patients affected with diabetic foot disorders as they are usually older populations. Furthermore, although within the volunteers only one presented deformations on the feet, none presented partial amputations. Therefore, these common features of the intended pathology were not considered for the extraction of the model. Despite these limitations, the methodology presented provides promising results regarding the detection of thermal asymmetry within the entire plantar aspects of both feet. Future work will be aimed at applying the presented methodology to patients with diabetes at risk of developing foot disorders as well as exploring alternative methods to assess the variation in the temperature patterns.
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 1 Introduction
### Motivation and Contribution  
The aforementioned importance of accurate earthquake identification can hardly be overstated, given the effect of earthquakes on so many aspects of life. Moreover, the emergence of low-cost sensory equipment for such identification, and its widespread adoption nowadays, calls for research on the effectiveness and efficiency of this use. This is further exacerbated by the high density of such sensors that frequently are adjacent to publicly accessible road infrastructure which in turn contaminates the signals received by the sensors with vehicular noise. It is, thus, the lack of comprehensive studies addressing the impact of vehicular noise on earthquake signals captured by low-cost sensors that this work aims to address.  
In order to address these challenges, this work proposes the use of an amalgamated deep neural network (DNN) composed of (i) a DNN trained on earthquake signals from professional sensory equipment, as well as (ii) a DNN trained on vehicular signals from low-cost sensors, for the purpose of earthquake identification in signals from low-cost sensors contaminated with vehicular noise. The key contributions of this work can be summarised as follows:  
* Creation and dissemination of a dataset of vehicular noise measured with low-cost seismic sensor;
* Collection and dissemination of ground truth data from professional seismic measurement equipment;
* Creation of DNNs for the aforementioned dataset approaches and experimentation on their performance;
* Creation and dissemination of a two-fold synchronized dataset: seismic data from a low-cost seismic sensor, and seismic data from a professional seismic sensor. Both sensors are in very close proximity; and
* Amalgamation of the aforementioned DNNs for the identification of earthquakes in signals from low-cost sensors contaminated with vehicular noise and experimentation on the DNN.  
The rest of this paper is organized as follows: Section 2 discusses the key recent relevant studies about seismology, seismic waves, sensors for measuring and removing noise from seismic waves, and deep learning for earthquake and vehicle classification. Section 3 presents the proposed methodology and the deep learning classification algorithm utilized in this work. Section 4 details the pre-processing techniques applied to the datasets and the experiments conducted, wherein their respective results are presented and discussed. Finally, this paper is concluded in Section 5.
# Plasmonics in the Ultraviolet with Aluminum, Gallium, Magnesium and Rhodium
## 5 Cooperative Effects: Dimer Induced Hot-Spots  
In this section we will compare cooperative effects in Rh tripod star and nanocube dimers since these are two of the synthesizable geometries, and also, because the absence of an oxide-shell makes this material very appealing for UV applications. In particular, we will explore the strong interaction region between metallic NP dimers, where single molecules have been detected with other metals [81,82]. The studied interparticle distance (gaps) ranges between 1 and 10 nm. The shortest gap has been chosen to be 1 nm in order to avoid quantum tunneling effects between particles [83] (the electron tunneling transmission vanishes when the gap is larger than \(\approx\) 0.8 nm [84]).  
Figure 5 plots (a) the near-field intensity \(|E|^{2}\) maps at E = 3.82 eV (\(\lambda\) = 325 nm, HeCd laser), (b) the maximum value of the near-field intensity at the center of the gap, and (c) the absorption cross-section \(C_{abs}\) spectra as a function of the gap spacing for tripod stars (left) and nanocubes (right) dimers under normal incidence illumination and polarization parallel to the dimer axis. It can be pointed out that as the gap becomes smaller, the value of \(C_{abs}\) increases and so does the near-field intensity at the center of the gap for both geometries. Also, the peak of \(C_{abs}\) is blue-shifted. This behavior is in perfect agreement with results reported by [PERSON] et al. [85].  
Nevertheless, since the plasmonic response is very dependent on the nanoparticle's shape, tripod star and nanocube dimers behave very differently. The LSPR peak corresponding to the nanocube dimer is \(\approx\)2.5 eV blue-shifted with respect to that of the tripod star. Moreover, the latter present larger values of \(C_{abs}\) than the former. By looking at the maximum near-field enhancement at the center of the gap, the tripod star dimer presents in hot-spot a value of \(|E|^{2}\) around one order of magnitude higher than that of the nanocube for shorter gaps. This can be understood in terms of the lightning rod effect [86] since the tripod star dimer gap is between two sharp tips instead of between two flat surfaces (nanocube dimer). However, for the larger gaps, the near-field enhancement takes very similar values. Therefore, the near-field enhancement at the center of the gap decays faster with increasing interparticle distance for the tripod star dimer.