esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods
### Satellite Data  
#### 2.3.1 Sentinel-1 Data  
The Sentinel-1A (S-1A) and Sentinel-1B (S-1B) satellites were launched in April 2014 and April 2016, as part of the \"Copernicus\" program, for the observation and monitoring of Earth's surface, and the development of operational applications for environmental monitoring. The Synthetic Aperture Radars (SARs) carried by these satellites are indifferent to weather conditions and allow data acquisition day and night, in the form of single and dual polarization images (VV, VH, HH, and HV). C-band data (wavelength \(\sim\) 6 cm) in the form of Interferometric Wide (IW) swath images, acquired by the Sentinel-1 (S-1) constellation between September and early December 2017, was used for this study in the VV polarization. Fourteen S-1A and S-1B images were thus downloaded from the Copernicus web site ([[https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home)]([https://scihub.copernicus.eu/dhus/#home](https://scihub.copernicus.eu/dhus/#home))) in the form of Level-1 Ground Range Detected (GRD) products, with a spatial resolution of 10 m and a six-day periodicity (Table 1). In the case of the S-1 images, the study site was imaged at an incidence angle (\(\theta\)) of approximately 39\({}^{\circ}\). To extract the backscattering coefficient, several processing steps were performed on each image comprising thermal noise removal, radiometric calibration, geometric correction and speckle filtering [32].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Satellites** & **Dates** & **Polarization** & **Incidence Angle (\(\theta\))** \\ \hline S-1B & 08/09/2017 & VV & 39–40\({}^{\circ}\) \\ S-1A & 14/09/2017 & VV & 39–40\({}^{\circ}\) \\ S-1B & 20/09/2017 & VV & 39–40\({}^{\circ}\) \\ S-1A & 26/09/2017 & VV & 39–40\({}^{\circ}\) \\ S-1B & 02/10/2017 & VV & 39–40\({}^{\circ}\) \\ S-1A & 08/10/2017 & VV & 39–40\({}^{\circ}\) \\ S-1B & 14/10/2017 & VV & 39–40\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Sentinel-1 (S-1) data acquired over the Kairouan Plain, from September 2017 until the beginning of December 2017.  
Figure 1: Soil texture samples over the Kairouan Plain, plotted on the soil texture triangle. A single point, corresponding to the average value of several soil samples, is used to represent each of the 33 reference fields.
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 2 Materials and Methods
### Identification of Principal Variables
#### 3.2.2 Random Forest Variables  
As it was commented on before, a random forest curve fitting with 200 decision trees was completed to show the relevance of each variable. In this sense, Figure 2 shows the levels and importance of each of the 11 variables to predict the type of accident. As a result, it was determined that the most relevant variable was whether the ship complied with the minimum number of crew members. It was 12 times more relevant than the other variables and was associated with operational accidents and not with technical problems or bad weather conditions. What is more, once again, this decreased number of crew members was associated with the human factor, which is a variable of special interest in preventing accidents on board. Other variables were associated with the type of accident but with a decreased intensity than the crew members. This was the case for the ship dimensions (length and beam) and, in a decreased manner, the year of construction, wind force and nighttime period. This result is in agreement with Bayesian networks developed in previous works and is more restrictive than the previous ANOVA study.  
As it was something common at the time to define the relation between the variables in machine learning studies, it is of interest to present a confusion matrix, as shown in Figure 3.  
From Figure 3, the level of correlation between them in accordance with a colour scale is defined, as shown in Figure 3. This figure shows in blue and green squares that there is a clear relation between ship length and beam as a consequence of common ship design criteria, validating the understanding of this confusion matrix. In a decreased manner, sea conditions and wind force are related in yellow and blue squares. Despite the fact that the previous random forest and ANOVA study showed a slight relation between ship length and year of construction, it cannot be observed clearly in this matrix as a consequence of the difficulty of transforming numbers in clearly differentiated colours.
## 4 Classification of Routing Protocols
### Temperature-Aware Routing Protocols
#### 4.2.7 Thermal-Aware Shortest Hop Routing (TSHR)  
[PERSON] _et al._ proposed in [58] a thermal-aware shortest hop routing protocol for _in vivo_ bio-medical sensor networks that intends to reduce the temperature rise without affecting the packet delivery delay and power consumption of bio-medical sensor nodes. TSHR is suitable for applications having high priority for packets delivery to the destination. It also considers the retransmission of the dropped packets. The two phases of TSHR protocol are: setup phase and routing phase. In setup phase each node builds its routing table while in routing phase each node route the packets to the destination based on shortest path. TSHR defines two thresholds to control the nodes' temperature: T\({}_{\text{s}}\) and T\({}_{\text{Dn}}\). 'T\({}_{\text{s}}\)' is fixed and the nodes are not allowed to go beyond it, while 'T\({}_{\text{Dn}}\)' is dynamic and is based on the node's own temperature as well as on its neighbor nodes' temperature and can be calculated as:  
\[T_{{}_{DN}}=temp_{n}+0.25\sqrt{temp_{n}}+0.25\sqrt{avg_{n}} \tag{2}\]  
where 'temp\({}_{\text{n}}\)' is the node's temperature and 'avg\({}_{\text{n}}\)' is the average temperature of its neighbors. A node is declared as hotspot node with greater temperature as compared to 'T\({}_{\text{DN}}\)'.
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 2 Model and Preliminary Definitions
### Comparisons and Errors  
We consider the scenario in which the distances \(d(x,y)\) between points, as well as those relative to subsets (1), are not directly measurable. Distances can only be _compared_ either using a _cheap_ but _erroneous_ operation or an _expensive_ but _always correct_ operation. Specifically, we let \(O_{H}(\cdot)\) denote the expensive and reliable operation (oracle) defined as  
\[O_{H}(E_{1},E_{2})=\begin{cases}+1&\text{if }d(E_{1})>d(E_{2})\\ -1&\text{otherwise}\end{cases} \tag{2}\]  
for any two subsets \(E_{1},E_{2}\subseteq E\). The cheap but erroneous operations (oracle) is denoted by \(O_{L}(\cdot)\), and its answers are wrong with a probability of at most \(p\)_independently_ across all pairs \(E_{1}\) and \(E_{2}\). That is, for any two subsets \(E_{1},E_{2}\subseteq E\) as above,  
\[\Pr[O_{L}(E_{1},E_{2})\
eq O_{H}(E_{1},E_{2})]\leq p\;,\]  
and these errors are _persistent_, that is, repeating the same comparison operation \(O_{L}(E_{1},E_{2})\) multiple times always gives the same result (either always wrong or always correct). We shall sometimes assume that \(p<1/16\) as in prior works [21] in order to apply these results(though our approach/results are generic, in the sense that they can be parameterized by the dislocation of sorting with a generic \(p\) or even a generic error model).  
Remark 2: _A weaker model would be to allow only comparisons between the weights/distances of two edges, analogously to pairwise comparisons in noisy sorting [11]. Unfortunately, this model seems too weak for the \(k\)-medoids clustering problem, since it is not possible to directly compute the optimal centroid of a cluster of more than four nodes. For this reason, we adopt the model that allows for comparing two sets of edges._  
Observation 1: _One might be tempted to simulate repeated comparisons between two distances, \(d(x,y)\) and \(d(x^{\prime},y^{\prime})\), via various comparisons of subsets, e.g., \(d(\{(x,y),(a,b)\})\) and \(d(\{(x^{\prime},y^{\prime}),(a,b)\})\). Though this is in principle possible, since
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
###### Abstract  
Surface defect detection is a critical task in the manufacturing industry to ensure product quality and machining efficiency. Image-based precise defect detection faces significant challenges due to defects lacking fixed shapes and the detection being heavily influenced by lighting conditions. Addressing the efficiency demands of defect detection algorithms, often deployed on embedded devices, and the highly imbalanced pixel ratio between foreground and background images, this paper introduces a multi-scale fusion staged U-shaped convolutional neural network (DEU-Net). The network provides segmentation results for defect anomalies while indicating the probability of defect presence. It enables the model to train with fewer parameters, a crucial requirement for practical applications. The proposed model achieves an MIoU of 66.94 and an F1 score of 74.89 with lower Params (36.675) and Flops (19.714). Comparative analysis with FCN, U-Net, Deeplab v3+, U-Net++, Attention U-Net, and Trans U-Net demonstrates the superiority of the proposed approach in surface defect detection.  
surface defect detection; convolutional neural network; computer vision; segmentation network; stage-wise network Article
# Emerging Biomedical Applications of Carbon Dot and Polymer Composite Materials
## 2 General Aspects of Carbon Dot/Polymer Composites
### Characteristics of Carbon Dots  
CDs are semi-spherical, nanoscale substances in their physical form. In general, the size of CDs is less than 10 nm. Several strategies can be used to adjust the size, including the filter size, temperature, and reducing agent [28; 29; 30]. The physiochemical characteristics of CDs slightly differ based on their sources, preparation processes and analytical techniques [28]. For instance, the X-ray crystallography (XRD) profile of CDs obtained from  
Figure 2: Synthesis of carbon dots: top-down (TD) and bottom-up (BU) approaches.  
dextrose and nano-biomass dots, prepared by ultrasonic-assisted extraction, are reported to be centered at \(\sim\)2\(\theta\) = 26\({}^{\circ}\) and 21.5\({}^{\circ}\), respectively [31; 32]. CDs can carry a negative or positive charge, with a wide range of zeta potential based on the source of the CDs. For example, CDs prepared from _Averrho carambola_ fruit using hydrothermal treatment had zeta potential of \(-\)15.21 mV [33]. The organic components were found to vary in content as follows: nitrogen, carbon, and hydrogen contents were approximately 0-20%, 7-49%, and 2-9%, respectively, in a study using various starting materials (polyethyleneimine, N,N-dimethylethylene diamine, diethylenetriamine, ethanolamine, N'-ethylcarbodiimide hydrochloride) and synthesis methods (pyrolysis, microwave, and solvothermal) as reported by [PERSON] et al. [34]. The superficial presence of functional groups (-OH,-COOH, and -NH\({}_{2}\)), which can be attained by surface passivation techniques, improves the solubility and chemical reactivity of CDs [35].  
The absorption spectrum of CDs is very large, covering the entire UV range [2; 11; 36]. Once excited, CDs display fluorescence, electroluminescence, phosphor-luminescence, and other characteristics [37]. Several explanations of CD photoluminescence have been reported, such as conjugation of the \(\pi\)-domain with a quantum confinement effect originating from the defect/surface state and subdomain state within the graphitic carbon core; the emission may be improved by the surface state, subdomain state, and molecular state crosslink [38]. The understanding of the mechanism behind these luminescent properties remains limited; however, CDs have been utilized in biolabeling and bioimaging owing to their chemiluminescence feature [38].
## 4 Case Study
### Rank Reversal Analysis  
Even with the methodology providing the identification of the most favorable alternatives, a rank reversal procedure was carried out. The procedure is characterized by the change in the alternative ranking as a consequence of the addition or deletion of alternatives [85]. Furthermore, the process in search to identify the presence of dependencies between the alternatives is realized, by the moment that the removal of one alternative can generate a change of ranking.  
In this context, the removal of alternatives H215M and H160 will occur, as long as both of them presented low performance and interest to the objectives. Considering that the APACHE alternative is a favorable solution, it was also removed to identify a second or third favorable alternative to implementation in the operations of BN. After the procedure, a new Global Index of preference is obtained, as presented in Table 16, as well as this graphical performance, based on total outranking, is exposed in Figure 7.  
Based on the new net outranking flows and considering a total preference analysis, it is observed that the outranking relation between alternatives sustained their positions. Regarding this new evaluation, in Figure 7, it is noticeable an approximation between the alternatives T129 ATAK to Tiger HAD and a distance increase relative to alternatives MI-35M and Sikorsky S-70.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **T129** & **MI-35M** & **VIPER** & **Katran** & \
\begin{tabular}{c} **Tiger** \\ **HAD** \\ \end{tabular} &
\begin{tabular}{c} **Sikorsky** \\ **S-70** \\ \end{tabular} & \(\boldsymbol{\phi^{*}}\) & \(\boldsymbol{\phi}\) \\ \hline T129 ATAK & 0 & 0.027 & 0.008 & 0.007 & 0.017 & 0.031 & 0.090 & 0.017 \\ MI-35M & 0.013 & 0 & 0.003 & 0.003 & 0.023 & 0.017 & 0.059 & \(-\)0.065 \\ VIPER & 0.013 & 0.024 & 0 & 0.006 & 0.027 & 0.037 & 0.107 & 0.054 \\ Katran & 0.022 & 0.032 & 0.014 & 0 & 0.027 & 0.040 & 0.136 & 0.101 \\ Tiger HAD & 0.012 & 0.029 & 0.017 & 0.009 & 0 & 0.033 & 0.100 & \(-\)0.010 \\ Sikorsky & 0.013 & 0.012 & 0.011 & 0.010 & 0.015 & 0 & 0.061 & \(-\)0.097 \\ S-70 & & & & & & & & \\ \hline \(\phi^{-}\) & 0.073 & 0.124 & 0.053 & 0.035 & 0.110 & 0.158 & & \\ \hline \hline \end{tabular}
\end{table}
Table 16: Global Index of preference along with the outranking flows after rank reversal procedure.  
Figure 7: Total outranking after the rank reversal procedure.  
Even though the process did not present any change in the final outranking, the process presents great value for the analysis by the moment that this consideration proves the relation of independence into the set of alternatives; in other words, the presence of an alternative does not present any influence or dependence concerning other variables in the context. The final ranking obtained is Katran, VIPER, T129 ATAK, Tiger, MI-35M, and Sikorsky S-70, in this order, respectively.
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Olr  
The amount of raw-material added to an anaerobic digester per day per unit volume is called the organic loading rate, and is typically expressed as Equation (14):  
\[\text{OLR}=\frac{\text{C}}{\text{HRT}} \tag{14}\]  
where C is the feed concentration in g-VS/L, and HRT is the hydraulic retention time.  
OLR is an essential controlling parameter in AD, and its deliberate variation can determine the degree of digestion for a broad level of influent input. In a typical anaerobic digester application, a high operating OLR is usually preferred, as this allows enriched bacterial species, reduced reactor sizes, lowered heating requirement and a reduced investment cost [253]. Several reactor configurations have been investigated to achieve a high OLR, wherein the primary aim was to reduce HRT either by adding water to the substrate or to recirculate digestate back to the main reactor [254]. By lowering HRT of a lab-scale spiral automatic circulation (SPAC) bioreactor, a high OLR equaling to 300 kg/m\({}^{3}\)/d [144] was successfully accomplished. Another reactor type, up-flow anaerobic fixed bed (UAFB), was also reported to provide high operational OLR, but the maximum OLR level for these configurations was reported to be much lower than the SPAC type. However, a lowered HRT may lead to microbial washout and eventually digester failure. Also, there are possibilities of VFA accumulation [255] as the anaerobic digester operates either with higher OLR or forced to run with a lower HRT. Generally, methanogenic reactors were found to have higher stability at a somewhat higher HRT, typically between 10 and 25 days for CSTR type configurations. Nevertheless, [PERSON] et al. [254] recently used a novel feeding strategy where OLR was kept constant while HRT was simultaneously reduced. Thisresults in a reduction in ammonia nitrogen inhibition with concomitant improvement in methane yield. Various other approaches of OLR optimization leading to enhanced biogas production are continually being researched. Among others, microbial management [256] and use of additives [90] are gaining constant popularity. Table 5 lists a range of OLR applied in recent AD publications (during the year 2018) on a number of feedstocks including agricultural residue [257], algae [258], energy crops [259], industrial wastes [260; 261], fish waste [262], food waste [263; 264; 265; 266], horticulture waste [267], manure [268], MSW [269], and sludge [270; 271].
## 3 Sabot-Stopping System Conceptual Design
### System Design  
The design of this system is based on results from the parametric dynamic model investigation of the system developed in SimulinkTM, the structure FEA of the LGG supporting structure, and the structural FEA of the rubber cylinder structure (partial) from the sabot-stopping system.  
The first part of the design developed a mass-spring-damper SimulinkTM model to assess the reaction force of the system to the impact.  
The highest load condition case has been considered in order to have a conservative evaluation. The Simulink model has the following set:  
* Gas: Helium at 150 bar,
* Launch package mass: m = 40 g,
* All the launch package is supposed to impact the sabot stopper (worst-case scenario).  
Knowing the velocity of the launch package allows for the calculation of the transferred momentum, and following this, the reaction forces of the structure due to the impact. The first evaluations, using different time interval dt, assessed a range of this force between F = 1 \(\times\) 10\({}^{6}\) N and F = 1 \(\times\) 10\({}^{7}\) N.  
The results of this first phase allow for the definition of a preliminary design of the sacrificial tube, made of Fe-310 steel, to minimise the production costs (Figure 7).  
Figure 5: Overview of the sabot-stopping system.  
Figure 6: Sabot-stopping system exploded view.  
The preliminary structural FEA to assess the behaviour of the LGG structure has been performed with the following conservative assumptions:  
* All the impact forces are transmitted to the supporting structure,
* Fixed constrains are used, even though in the real situation, the other components will help in the damping effect after the shot.  
The results show, Figure 8, that in this worst case, the structure's deformation is within reasonable limits (approximately 7 mm).  
Figure 8: Sabot stopper supporting structure’s total deformation.  
Figure 7: Impact tube (**top**) and sabot-stopping system (**bottom**).  
This first set of results have been used as a baseline for the design procedure described in the following subsection. This details the third phase, which is the structural FEA of the rubber cylinder damping tube.
# Full Information Numerical Simulation of Two-Dimensional Steady Ground Temperature Field
## 8 Model Example
### Rolling Terrain Model  
We set the undulating terrain model. The undulating terrain is shown in Figure 10. The function expression satisfied by the terrain is \(z=\frac{1}{2}(5000-|x|)\sin(\frac{x}{4007})\). The background of the model is a uniform half-space medium with a background thermal conductivity of \(2\;\mathrm{W}/(\mathrm{m}\cdot\mathrm{\SIUnitSymbolCelsius})\) and no radiogenic heat production. The anomalies body is air with a thermal conductivity of \(0.05\;\mathrm{W}/(\mathrm{m}\cdot\mathrm{\SIUnitSymbolCelsius})\). The calculation area of the model is \(-5\)-\(5\) km in the x direction and \(0\)-\(10\) km in the z direction. The first type of boundary condition is used for the upper boundary; the temperature is \(10\;\mathrm{\SIUnitSymbolCelsius}\). The second type of boundary condition is adopted for the lower boundary, and the heat flux value is \(41.86\;\mathrm{m}\mathrm{W}/\mathrm{m}^{2}\). The grid nodes are \(101\times 101\).  
It can be seen from the simulation results that, compared with the low-lying region, the temperature in the high-lying region is more affected by terrain and thus the temperature is lower. Moreover, the influence of terrain on temperature field gradually decreases with the increase in depth.  
Figure 10: Contour map of geothermal field with terrain (**a**) Terrain model (**b**) Distribution of terrain geothermal field in the zone.  
Figure 9: Contour map of ground temperature field of complex model with high and low thermal conductivity combination.
# A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector
## 3 Materials and Methods
### Deep Learning-Based Quality Inspector
#### 3.2.1 Text Preprocessing Tasks  
Before applying the sentiment predictor to a dataset, which is composed of ratings and reviews, it is indispensable to preprocess every textual review. A total of seven sequential text preprocessing tasks were implemented to each textual opinion: (1) basic actions, (2) tokenization, (3) stop words removing, (4) stemming, (5) dictionary construction, (6) words transformation to unique integer indexes, and (7) the same length vector construction. The first task, basic actions, refers to three simple tasks: delete numbers and integers, delete the underscore symbol, and convert all the capital letters to lowercase. The second step is tokenization. For this specific research, the tokens are represented by words as the smallest indivisible units, and the tokenizer is conditioned to delete commas, apostrophes, and dots. The third step is to stop word removal. Since they are the most common words in a language and have a low contribution to the overall meaning of a sentence, they must be filtered out [48]. The fourth step is stemming, which helps to reduce words into their root forms by removing their prefixes and suffixes. As a result, a group of similar words is mapped to the same stem, even if it is not a valid word in the language.  
Afterward, the whole set of preprocessed reviews can be used to construct a particular dictionary that allows preserving only the most important domain-specific words, and to convert them into unique integer indexes (IDs). The dictionary construction is based on the frequency count C of each unique word inside the entire set of preprocessed reviews. Then, a term count threshold T, established by the users, regulates which words to keep and to remove. If C of a word is higher than T, the token will be included in the dictionary and preserved in all the reviews, if not, the token will be excluded from the dictionary and removed from all the reviews. Next, a unique ID is assigned to every word of the dictionary. The sixth step is to transfer the remaining words of each vector to unique IDs through the use of the dictionary. The last task is to build all reviews to vectors of the same length of tokens. This is achieved with a length threshold L, which is specified by the user. If the real length R of a review is longer than L, the last words of the vector will be cut up. On the other hand, if R is shorter than L, then the initial part of the review will be padded with 0 s.