esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 7 Results and Discussion
### Image Recognition
#### 7.3.2 Blue Crack Detection  
This test aims to detect a certain crack with a pre-defined width and compute the crack length. The crack is represented by a blue line which is detected and measured using Python and the open-source computer vision library OpenCV. The crack detection algorithm masks the image from the CCTV camera and allows the user to display the crack on the laptop. Here, we used a reference line with an actual length of 14.318 cm and a width ranging between 1.8 and 1.9 cm. The pixels are identified for the blue line and the crack length \(\mathrm{L}_{cr}\) is evaluated based on Equation (17). Figure 17 illustrates the detected blue line in the swimming pool. In this test, evaluated \(\mathrm{L}_{cr}\) was 13.38 cm, with an accuracy of 93%:  
\[\mathrm{L}_{cr}=\mathrm{W}_{ref}\left(\frac{\mathrm{L}_{p}}{\mathrm{W}_{p}}\right) \tag{17}\]  
where \(\mathrm{W}_{ref}\) is the reference width in cm, \(\mathrm{L}_{p}\) is the length of the blue line in pixels, and \(\mathrm{W}_{p}\) is the width of the blue line in pixels.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Datasets
#### 2.2.6 Vegetation Index  
_EVI_ and _GPP_ estimates were used in this study to analyze the vegetation behavior during drought events. The global MOD13 product from MODIS [68] was used to obtain _EVI_ data. These 16-day composites are acquired with a spatial resolution of 1 km (MOD13A2). The _GPP_ dataset was available as an 8-day composite time-series derived from MODIS [69], at a spatial resolution of 500 m (MOD17A2H version 6).
## 5 Influence of Date Synchronization Errors on Protection  
GPS and BeiDou systems are usually used for data synchronization in the transmission network with high timing accuracy. However, the economic cost is high and unsuitable for large-scale installation in distribution networks. To address this problem, experts and scholars have proposed a fault data self-synchronization (FDSS) algorithm [17], which achieves fault data synchronization with minimal economic cost.
## 4 Hybrid Models
### Utilities  
In relation to the applications of SVMs in time series forecasting, the work of [PERSON] et al. [5] deals with short-term load forecasting in energy transmission systems. Short-term load is a variable that is affected by many factors, and for this reason, it is difficult to make an accurate prediction with a single model. Utilizing the ARIMA algorithm to forecast the basic linear part of the load and the SVM algorithm to forecast the sensitive, non-linear part of the load, the paper presents a forecasting method based on a hybrid ARIMA and SVM model. ARIMA is used to forecast the daily load, and then the SVM model is used, aiming to correct the deviations from the previous forecasts. Due to their generalization ability and fast computation, SVMs show excellent performance in extracting the nonlinear part of the load and can be used to achieve the correction of the data deviation. The ARIMA-SVM hybrid model effectively combines the advantages of ARIMA and SVMs and through the simulation of a large sample of data, the results show that this hybrid model is much better than the two forecasting models applied separately.
# A Review of Plant Disease Detection Systems for Farming Applications
###### Abstract  
The globe and more particularly the economically developed regions of the world are currently in the era of the Fourth Industrial Revolution (4 IR). Conversely, the economically developing regions in the world (and more particularly the African continent) have not yet even fully passed through the Third Industrial Revolution (3 IR) wave, and Africa's economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth in the global human population, which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet the steep food demand and stabilize the economies of the African continent and countries such as India that are dependent on the agricultural sector to some extent. Technological advances in precision agriculture are already improving farm yields, although several opportunities for further improvement still exist. This study evaluated plant disease detection models (in particular, those over the past two decades) while aiming to gauge the status of the research in this area and identify the opportunities for further research. This study realized that little literature has discussed the real-time monitoring of the onset signs of diseases before they spread throughout the whole plant. There was also substantially less focus on real-time mitigation measures such as actuation operations, spraying pesticides, spraying fertilizers, etc., once a disease was identified. Very little research has focused on the combination of monitoring and phenotyping functions into one model capable of multiple tasks. Hence, this study highlighted a few opportunities for further focus.  
Fourth Industrial Revolution (4 IR); machine learning (ML); precision agriculture; support vector machine (SVM); artificial neural network (ANN); k-nearest neighbors (k-NN); fuzzy classification; global navigation satellite system (GNSS) +
Footnote †: journal: _Sci._  
_Applied_  
_sciences_
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 5 Results
### Dependences of the Efficiency on the Delay Quantile
The results for \(T_{period}=$50\,\mathrm{ms}$\) with the same values of \(\sigma=$10\,\mathrm{\SIUnitSymbolMicro s}$\) and \(M=3\) are shown in Figure 7b. In contrast to the scenario with \(M=5\), the maximum efficiency gain of the Smart PCA increases to 150% compared with the Tuned EDCA and to 40% compared with the PCA. This result shows once again that with fewer RTA STAs, the Smart PCA method developed in the paper significantly improves channel usage efficiency. Let us consider more intensive RTA traffic. Figure 7c shows results for \(T_{period}=$10\,\mathrm{ms}$\) and \(M=3\). With such intensive traffic, RTA STAs consume more channel time, which significantly reduces efficiency for PCA and Smart PCA methods because of reservation time. At the same time, a small number of RTA STAs leads to a low probability of transmission in a reservation of another STA, so the Smart PCA gain in comparison with the PCA is negligible. In case of low intensity of RTA traffic (\(T_{period}=$100\,\mathrm{ms}$\)) but a large number of RTA STAs (\(M=15\)), we obtain an opposite result (Figure 7d). The Smart PCA has a significant gain in comparison with PCA because more RTA STAs can transmit in an alien reservation. However, high \(M\) also increases the contention between RTA STAs during channel reservations, which makes it almost unprofitable to use the Smart PCA in comparison with Tuned EDCA. Finally, we consider non-strict requirements for delay (5-10\(\,\mathrm{ms}\)). In all the above scenarios, the Tuned EDCA is the most effective in this area of delay limits. For example, the Tuned EDCA can meet the delay limit of 10\(\,\mathrm{ms}\) with high efficiency (up to 80-90%). Thus,the Smart PCA is more efficient than the Tuned EDCA only in case of strict requirements for the transmission delay of RTA frames.
# Food Classification and Meal Intake Amount Estimation through Deep Learning
###### Abstract  
This paper proposes a method to classify food types and to estimate meal intake amounts in pre- and post-meal images through a deep learning object detection network. The food types and the food regions are detected through Mask R-CNN. In order to make both pre- and post-meal images to a same capturing environment, the post-meal image is corrected through a homography transformation based on the meal plate regions in both images. The 3D shape of the food is determined as one of a spherical cap, a cone, and a cuboid depending on the food type. The meal intake amount is estimated as food volume differences between the pre-meal and post-meal images. As results of the simulation, the food classification accuracy and the food region detection accuracy are up to 97.57% and 93.6%, respectively.  
object detection; food classification; food volume estimation; meal intake amount estimation +
Footnote †: journal: Medical  
two meal plate regions in the images. For each food, the 3D shape type is determined as one of a spherical cap, a cone, and a cuboid based on the food type. The food volume is calculated through the 3D shape type and the food area. The meal intake amount is estimated as the difference in the food volumes in the pre- and post-meal images.  
The contributions of this paper are that only pre- and post-meal images are needed to measure meal intake. That is, if a dedicated system attached with a camera takes a picture of a meal plate before and after a meal or takes a picture of a meal plate before and after a meal with a smartphone, then the food type can be automatically classified. In addition, the meal volume can be measured based on the detected food region and its predefined 3D shape without the need for a complex device to measure the volume.
## 3 Study Area and Data
### Sentinel Dataset
In the study, reference data were manually labeled for all Sentinel-2 images with the visual interpretation method. In visual interpretation, too small regions belonging to different classes were included in the neighboring larger area class. For example, the ships in the sea were included in the water bodies class, not the artificial surfaces class, and ground truth was prepared. Another example is the small wooded areas among the settlements, which were included in the settlement class, i.e., artificial surfaces, unless they met the condition of sufficient generalization. The class distribution of training data for the Sentinel dataset is presented in Table 1.
## 2 Related Work and Comparisons between the Methods
### Comparisons and Highlight of Our Approach  
According to above discussions, we list the general comparisons of RDH methods in Table 1. As for the histogram modification-based framework which has attracted much attention and is strongly related to our work and covers the PEE paradigm we mainly follow, we highlight in Table 2 our proposed approach by comparisons with other approaches of this type, such as [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [21], and [PERSON] et al. [5], using average experimental results for six gray-scale images. As Table 2 shows, the embedding capacity of our method is much more than the four other methods, while the image quality is a bit sacrificed due to slightly larger image distortion, though it is tolerable since the PSNR is still close to 50 dB. Our results reveal that, due to much better prediction accuracy of pixel values, our method is capable of achieving high embedding capacity while suffering only slight image distortion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RDH Method Types** & **Image Quality** & **Embedding Capacity** \\ \hline Lossless-compression & \(\times\) & \(\times\) \\ Integer-transform & \(\Delta\) & \(\Delta\) \\ Two-phased embedding+location maps & \(\circ\) & \(\circ\) \\ Histogram modification & \(\circ\) & \(\copy\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: General comparison of RDH methods. \(\times\): poor; \(\Delta\): unable to control effectively/limited; \(\circ\): good; \(\copy\): even better.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Characteristics** & **Image Quality (PSNR (dB))** & **Embedding Capacity (bits)** \\ \hline [PERSON] et al. [46] & first histogram modification/baseline & 53.04 & 4923.67 \\ [PERSON] et al. [47] & image-difference histogram & 51.75 & 9729.00 \\ [PERSON] et al. [21] & 2D-PEH modification & 51.07 & 24,612.50 \\ [PERSON] et al. [5] & 3D-PEH modification (1 st octant) & 63.72 & 9444.17 \\ \hline
**Our method** & **3D-PEH + MLP Prediction (8 octants)** & **48.55** & **48,344.17** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of histogram modification-based RDH methods. The image quality is measured by average PSNR (dB) when maximum embedding capacity is attained. Average embedding capacity are measured in bits.
# Bioassays with _Allium cepa_ for the Monitoring of Toxicity in the Groundwater of Yucatan, Mexico
## 3 Results and Discussion
### Nutrients  
The summary of the nutrient analysis is presented in Table 2. PCO analysis explained 83.76% of the total variability in the first two axes (Figure 4). PCO1 correlated with the concentration of nitrate, ammonia, phosphate, and silicate, and PCO2 correlated with the concentration of nitrites. PERMANOVA indicated significant differences between sites (\(p\) = 0.0007, Pseudo-F = 14.57) but not between transects (\(p\) = 0.2716, Pseudo-F = 1.298). Cluster analysis formed three groups: P3, P7, and a third group with the rest of the sites. P3 and P7 are the closest to the coast and have the highest values of phosphates and ammonia (Figure 5). Nitrogen compounds are regulated by NOM-127-SSA1-2021, which includes the permitted values of pollutants in water for human use or consumption. The ammonium limit is 0.5 mg N-NH\({}_{4}\)/L; site P7 is above this limit. For nitrates and nitrites, the permitted limits are 11 mg N-NO\({}_{2}\)/L and 0.9 N-NO\({}_{3}\)/L, respectively; all the samples were below these limits. The increase of nutrients in the samples closer to the coast may be related to the transport of these compounds during the rainy season. Previous works have linked the input of nutrients to the coast to the intense groundwater discharges that occur in the rainy season [34; 35].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Nutrient** & **Phosphates mg/L** & \begin{tabular}{c} **Silicates** \\ **mg/L** \\ \end{tabular} & \begin{tabular}{c} **Ammonium** \\ **mg N-NH\({}_{4}\)/L** \\ \end{tabular} & \
\begin{tabular}{c} **Nitrites** \\ **mg N-NO\({}_{2}\)/L** \\ \end{tabular} &
\begin{tabular}{c} **Nitrates** \\ **Mg N-NO\({}_{3}\)/L** \\ \end{tabular} \\ \hline \hline
**Month** & **September** & **October** & **September** & **October** & **September** & **October** & **September** & **October** & **September** & **October** \\ \hline P1 & ND & ND & 5.21 & 3.01 & ND & 0.13 & ND & ND & 1.21 & 1.64 \\ \hline P2 & ND & ND & 4.69 & 3.71 & 0.13 & 0.16 & 0.13 & 0.08 & 1.29 & 1.63 \\ \hline P3 & 0.61 & 0.12 & 1.57 & 1.78 & ND & 0.07 & 0.03 & ND & 0.05 & 0.01 \\ \hline P4 & ND & ND & 4.12 & 2.67 & 0.10 & 0.15 & ND & ND & 1.21 & 1.57 \\ \hline P5 & ND & ND & 3.34 & 3.64 & 0.01 & 0.12 & ND & ND & 1.22 & 1.60 \\ \hline P6 & ND & ND & 4.83 & 3.69 & 0.06 & 0.26 & ND & ND & 1.24 & 1.56 \\ \hline P7 & 1.86 & 1.14 & 1.62 & 1.36 & 1.11 & 1.03 & ND & ND & 0.07 & ND \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nutrient quantification in wells of Yucatan, México in September and October 2021. ND = Not detected.  
Figure 4: Principal Coordinate Analysis (PCO) of nutrients measured in September and October 2021 in wells of Yucatan, México. Inverted triangles are used in samples measured in transect 1 (Sierra Papacal to Chuburná) and circles are used in transect 2 (Nohuayán to Sisal). Colors indicate its nearness to the coast; inland samples are presented from red to green, and the sites nearest to the coast are blue.
## 3 Results and Discussion
### Sky Characteristics and Photopic Descriptions
The minimum photopic illuminance was found in the month of January in the west orientation with an angle of 45\({}^{\circ}\) (5084 lux). The maximum value was found in the month of August in the east orientation with an angle of 45\({}^{\circ}\) (99,822 lux). In general, the lowest photopic illuminances were obtained for measurements taken at 15\({}^{\circ}\), and the highest values were obtained for measurements taken at 45\({}^{\circ}\). Illuminance measurements reveal the changing position of the sun with seasons. Regarding the measurement locations, Figures 1 and 5 show that the illuminance level reached higher values while it was positioned 45\({}^{\circ}\) in the southeast during autumn-winter and northeast in spring-summer.
## 4 Experiments
### Implementation Details  
The original datasets contained well-paired texts. We used only a fraction of the paired data during the warm-up stage. The unsupervised part used text samples of the corresponding dataset without pair information.  
Since the original T5 model does not support the Chinese language, we chose Mengzi [36], a high-performing lightweight (103M parameters) pre-trained language model for Chinese in our experiments (Mengzi includes a family of pre-trained models, among which we used the T5-based one).  
We used the AdamW optimizer to train the model with the learning rate, \(\beta\)1, \(\beta\)2, \(\epsilon\), and weight decay as \(5\times 10^{-5}\), 0.9, 0.999, \(1\times 10^{-6}\), and 0.01, respectively. Moreover, we set the learning rate with a cosine decay schedule. We restricted the length of sentences in each batch to a maximum of 512 tokens, and we set the batch size to 8. The two consistency losses were weighted with factors of 0.1 for the identity mapping loss and 0.2 for the cycle consistency loss. The higher weight for the cycle consistency loss was due to its direct contribution to the model's ability to transfer texts, which was the primary objective of the task. In contrast, the identity mapping loss helped preserve the characteristics of the input texts, but it did not directly contribute to the summarization process. All of the experiments were conducted by using Python 3.7.12 with PaddlePaddle 2.3 and PyTorch 1.11 while running on an NVIDIA Tesla 32 GB V100 GPU. For clarity, the hyperparameter settings used in our experiments are presented in Table 1.
## 2 Numerical Simulation Methodology
### LES Method  
Turbulent flows are characterized by disordered, eddying fluid motions over a variety of length scales. The size of largest eddies is almost comparable to the characteristic length of the mean flow, and the dissipation of turbulence kinetic energy can be ascribed to the smallest scales. In LES, the basic idea is to establish a mathematical filter function to separate and filter large-scale eddies and small-scale eddies in turbulent flows, where the turbulent motion of large-scale eddies is resolved directly by the Navier-Stokes equation (N-S equation), and small-scale eddies are modeled by a closed model to show the dissipation relation for large-scale quantities [24].  
The Filter function equation is as follows:  
\[\overline{\phi}=\frac{1}{V}\int_{D}\phi dx \tag{1}\]  
where \(\phi\) is the transient turbulent fluctuation volume and \(V\) denotes the occupied geometric space of the governing volume.  
The N-S equation for a compressible fluid filtered by a function takes the form of:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\rho\overline{u}_{i}}{\partial x _{i}}=0 \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho\overline{u}_{i})+\frac{\partial}{\partial x _{j}}\big{(}\rho\overline{u}_{i}\overline{u}_{j}\big{)}=\frac{\partial}{ \partial x_{j}}\Bigg{(}\mu\frac{\partial\overline{u}_{i}}{\partial x_{j}} \Bigg{)}-\frac{\partial\overline{p}}{\partial x_{i}}-\frac{\partial\tau_{ij} }{\partial x_{j}} \tag{3}\]  
where \(\tau_{ij}\) is the subgrid-scale stresses term and \(\tau_{ij}\) is defined as \(\tau_{ij}=\rho\overline{u_{i}u_{j}}-\rho\overline{u_{i}}\overline{u_{j}}\). Moreover, \(u\) is the flow velocity, \(\rho\) is the density, and \(\overline{p}\) is the filtering pressure.  
Using the filtering operation, the subgrid-scale stresses are introduced and require modeling. To close the filtered N-S Equations (2) and (3), the subgrid-scale (SGS) model based on Boussinesq hypothesis is employed widely. The corresponding SGS model is:  
\[\tau_{ij}-\frac{1}{3}\tau_{kk}\delta_{ij}=-2\mu_{t}\overline{S}_{ij} \tag{4}\]  
where \(\mu_{t}\) is the subgrid-scale turbulent viscosity, \(\tau_{kk}\) is the isotropic part of the subgrid-scale stresses, and \(\overline{S_{ij}}\) is the rate-of-strain tensor for the resolved scale defined by Equation (5):  
\[\overline{S}_{ij}=\frac{1}{2}\bigg{(}\frac{\partial\overline{u}_{i}}{\partial x _{j}}+\frac{\partial\overline{u}_{j}}{\partial x_{i}}\bigg{)} \tag{5}\]  
The Smagorinsky-Lilly model is chosen here and \(\mu_{t}\) is defined as \(\mu_{t}=\rho L_{s}^{2}\Big{|}\sqrt{2\overline{S}_{ij}\overline{S}_{ij}}\Big{|}\), where \(L_{s}\) represents the mixing length for subgrid scales.
###### Abstract  
Fracturing hard roofs by ground hydraulic action is an important control technology for the strong mine pressure in the slope. In this paper, a new simulation method, \"separate + interface,\" is proposed, and two physical simulation experiments are conducted; the phenomenon of increased goal pressure and decreased front abutment pressure is discovered after fracturing in the key strata, and then the influence of different fractured crack shapes on the front abutment pressure and the goal stress is revealed. The results are as follows: Firstly, the separation under the high-level hard strata blocks the transmission of overburden load to the goal, leading to the high-stress concentration of the coal seam, which is the main reason for the large deformation of roadways and the breakage of a single hydraulic prop in the roadway. Secondly, the weakening effect of mine pressure differs when hard rock strata are fractured artificially with different types of cracks. The peak value of abutment pressure is reduced from 24.91 to 20.60 MPa, 17.80 MPa, and 16.13 MPa with the vertical crack spacing of 20 m, 15 m, and 10 m, respectively, and the related goal pressure is increased from 2.61 to 3.54 MPa, 3.91 MPa, and 4.34 MPa, respectively. The peak value of abutment pressure decreased from 24.79 to 22.08 MPa, 19.88 MPa, and 17.73 MPa. The related goal pressure increased from 2.61 to 3.39 MPa, 3.81 MPa, and 4.43 MPa, respectively, with the key strata also fractured into two horizontal layers, three horizontal layers, and four horizontal layers with horizontal fractures. Thirdly, after the hard roof is fractured above the No. 8202 working face, the first breaking step distance of the main roof decreased from 112.6 to 90.32 cm, while the first breaking step distances of KS2 and KS3 decreased from 106.3 and 135.8 cm to 93.5 cm and 104.8 cm, respectively, and the goaf pressure also increased. Compared to the adjacent unfractured No. 8203 working face, the mine pressure intensity of the No. 8202 working face is significantly reduced. The research results can provide useful guidance for the treatment of strong mine pressure.  
hard roof; ground fracturing; mining-induced stress; load transfer; mine pressure control Article