esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 2 Background and Algorithm Overview
### Applicability of Presented Open Loop Tracking Algorithm  
The GNSS-R tracking algorithm presented here are valid for any remote sensing configuration involving a forward scattered transmission signal, such as all GNSS signals (GPS, Galileo, GLONASS, SBAS, etc.) as well as any other forward scattered signal of opportunity (SoOP) transmitter (such as communication satellites) or actively transmitting radar satellite instruments. These algorithms are applicable to aircraft observations but optimized for specific implementation on-board a GNSS-R satellite instrument.  
Notably, the algorithm presented here is generally applicable to all GNSS transmitters, but has been validated only using signals from the GPS constellation received by the CYGNSS satellites. However, the general specular point derivation presented here is generic and will be valid for any transmitter-receiver combination, regardless of the specifics of the transmitted signal.
# Research on an Improved Method for Galloping Stability Analysis Considering Large Angles of Attack
###### Abstract  
In view of the deficiency of the quasi-steady galloping critical wind speed calculation method based on the classical [PERSON] criterion, this paper proposes a quasi-steady galloping stability analysis method that considers the wind attack angle criterion (\(\mathrm{d}\mathrm{C}_{\mathrm{V}}/\mathrm{d}\alpha\)) through theoretical analysis. Firstly, the tri-component force coefficients of a square-section model were measured through wind tunnel tests, and the galloping force coefficients calculated with three different galloping criteria (the \(\mathrm{d}\mathrm{C}_{\mathrm{V}}/\mathrm{d}\alpha\) criterion, [PERSON] criterion, and Xie criterion) were compared and analyzed. Secondly, to further verify the reliability and applicability of the criteria proposed in this study, wind tunnel tests and numerical simulations were conducted on an H-shaped section. The verification results of the H-shaped section showed that under the action of the incoming flow at a large angle of attack of \(70^{\circ}\), the maximum error of the classic [PERSON] criterion could reach about 44%. This study used the \(\mathrm{d}\mathrm{C}\mathrm{V}/\mathrm{d}\alpha\) criterion while considering the large angle of attack of the incoming flow, and its calculation error could be controlled within 10%. At the same time, the numerical simulation showed that there was serious aerodynamic instability in this section under the critical wind speed. A pair of periodic vortex structures were formed in the wake region of the H-shaped section, resulting in constant generation and separation phenomena, which induced structural instability.  
quasi-steady galloping; bridge engineering; galloping force coefficient; wind tunnel test; large angles of attack; numerical simulation +
Footnote †: journal: Applied Sciences
## 3 Methods
### Dense Compression Attention Block  
The architecture of DCAB is shown in Figure 3. According to Figure 3, the proposed DCAB consists of a dense compression block (termed as DCB) and an OAM. In the DCAB, the proposed OAM is used to improve the feature expression by fine-tuning the importance of each element in the input feature in the channel-spatial dimension. Moreover, the DCB is composed of a dense block [11] and a convolutional layer. In the DCB, the dense block is used to improve the training efficiency. It can be seen that the number of channels in the output feature of the dense block is drastically increased compared to the number of channels in its input features. Therefore, in order to reduce the computational complexity, a convolutional layer is subsequently used to compress the dimension in the channel axis.
## 2 Previous Studies
### Waste Sorting  
Studies on waste sorting can be further categorized into \"Automated sorting technologies\", \"Image recognition and machine vision\", \"Robotic sorting\", and \"Sensor-based sorting techniques\". Studies of automated sorting technologies deal with identifying and sorting waste items using AI based on their material composition [15]. Studies on image recognition and machine vision focus on the usage of AI to analyze images of waste items and identify their material composition [16], using X-ray images to recognize the target objects [17] and conveyor belts to detect and sort waste items at high speeds [18]. Studies on robotic sorting integrate robotics and AI to identify and pick up waste items to automate waste sorting [15; 18]. Sensor-based sorting techniques employ various sensors for recognizing and sorting the waste [15], sensing the waste input [19], and material-based sorting [20].
## 4 Experimental Setup
### Performance Evaluation Standards  
In this work, the performance of methods is evaluation by classification accuracy and F1-score. Accuracy is the most commonly used classification evaluation index. The formula of it is defined by Equation (11):  
\[Accuracy=\frac{Correctedly\ predicted\ samples}{All\ samples}\times 100 \tag{11}\]  
The F1-score is selected as another evaluation index due to the presence of a large number of similar actions in the data, it and accuracy forms the evaluation index system of the method. The formula of it is defined by Equation (12):  
\[F1\text{ score }=\frac{Precision\times Recall}{Precision+Recall}\times 2 \tag{12}\]  
in which precision and recall are determined with the following Equation:  
\[Precision=\frac{TP}{TP+FP}\ \ \ Recall=\frac{TP}{TP+FN} \tag{13}\]  
where the operators \(TP\), \(FP\), and \(FN\) are true positives, false positives, and false negatives, respectively. The results for each label are weighted by the number of samples in each class to calculate the F1-score.
## 3 Results
### Load Balance Factor (LBF)  
LBF is often used as a performance metric in research. Therefore, LBF was used in this study to assess the efficacy of the created method in comparison to other established methods. With LBF, the workload on the network is distributed evenly across all cluster heads (CHs). To maximize the longevity of both the cluster head and the network as a whole, the ideal situation is for CH to manage an equal number of nodes. When a node in a cluster moves in or out, the LBF makes sure CH is updated accordingly. Figure 10 shows that when the number of nearby vehicles was near its maximum value, p-WOA outperformed GWO and ALO in terms of tuning the network load. The proposed approach, p-WOA, was tested against existing methods, and the results are compared here to determine its efficacy.  
The new method was put through its paces in a series of additional experiments. By raising the number of network nodes to 50 while keeping the grid size at 1 km * 1 km, Figure 11 compares p-WOA to other possible solutions. The new scheme, p-WOA, was better than the older ones in distributing the workload evenly among a cluster's nodes.  
F. G. Evaluation-related statistical tests and analyses  
Fully Modified Least Squares (FMOLS) statistical tests, including the p-test, regression analysis, R-squared, and analysis of variance, were applied to the results to gauge the performance of the created p-WOA (ANOVA).  
Figure 9: Average Throughput for 30–100 Nodes by taking 1 km \(\times\) 1 km Grid Size.  
Figure 11: Load balance factor for 50 Nodes at the Grid size of 1 km \(\times\) 1 km.  
Figure 10: Load balance factor for 40 Nodes at the Grid size of 1 km \(\times\) 1 km.  
The transmission range (TR) is the independent (predictor) variable.  
No. of clusters is the dependent (outcome) variable.  
The findings of a regression study comparing the number of clusters with the communication range under ALO, p-WOA, and GWO are shown in Table 2. Here, it is claimed that the relationship between transmission range and the number of clusters obtained was inverse, with a larger transmission range value resulting in fewer cluster heads. According to the table, an increase in communication range of 1% resulted in a \(-\)0.04 reduction in the cluster head under ALO and GWO, with p values of less than 1% and 5%, respectively. On the other hand, under p-WOA, a 1% increase in transmission range caused a significant drop of 0.039. According to the adjusted R2 value for the specified transmission range, the independent variables (No. of clusters ALO, No. of clusters p-WOA, and No. of clusters GWO) adequately explain the variation. Their variations were 67.97%, 75.22%, and 66.94%, respectively, with ANOVA values of F (1 9) = 20.73 ***, 25.55 ***, and 22.81 ***, respectively.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 6 Experimental Results and Discussion
### Setup  
Ship image data along the Yangtze River are collected to make the supporting dataset. Each picture is labelled with corresponding ship categories. The training set is divided into seven categories: cargo ship, roll-roll ship, container ship, passenger ship, buoy, oil tanker, and canoeing. As the cargo ships are the majority on the Yangtze River, they occupy a relatively large proportion of the dataset, accounting for about 40% of the dataset. The remaining categories (buoy, container ship, passenger ship, and oil tanker) all account for about 50% of the dataset. The amount of roll-roll ship and canoeing in the dataset is small, accounting for only about 10 percent of the dataset. The seven categories are shown in Figure 6 below. In the following Sensitivity analysis experiment and other comparative experiments, we conducted five repeated experiments for each network that participated in the experiment and selected the best result among them.  
The Intersect Over Union (IoU) threshold parameter needs to be set for the model prediction. The Precision is the True positives (the correct ratio of all the recognized images). The recall is the ratio of correctly recognized objects to the total number of objects in the test set. The formulas are shown as follows:  
\[\text{Precision}=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}} \tag{10}\]  
\[\text{Recall}\;=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{11}\]  
where \(\mathit{TP}\) is the number of positive classes that are correctly predicted. \(\mathit{FP}\) is the number of negative classes that are predicted as positive classes, and \(\mathit{FN}\) is the number of positive classes that are predicted as negative classes.  
AP means to average the precision values on the \(\mathit{PR}\) (precision and recall) curve. For the PR curve, we use the integral to compute it. The \(\mathit{AP}\) formula is shown below:  
\[\mathit{AP}=\int_{0}^{1}P(R)dR \tag{12}\]  
In the experiment, when evaluating the detection accuracy of the algorithm, we used the \(\mathit{mAP}\) (Mean Average Precision) index commonly used in object detection to evaluate the model. [EMAIL_ADDRESS] is a \(\mathit{mAP}\) score obtained for detections corresponding to IoU > 0.5. The formula for \(\mathit{mAP}\) is as follows:  
\[\mathit{mAP}=\frac{1}{n}\sum_{i=1}^{n}\int_{0}^{1}P(R)dR \tag{13}\]  
where \(n\) represents the number of categories detected.  
Cosine annealing is used to reduce the rate of learning by using cosine functions. In the cosine function, the cosine goes down slowly as x goes up, and then it goes down faster and goes down slowly again. This decline mode can cooperate with the learning rate and produce a good effect. The more the cosine annealing learning rate algorithm is trained,  
Figure 6: Different types of ships in training datasets.  
the smaller the learning rate will be. In this experiment, our initial learning rate (lr0) is set to 0.01, and our cosine annealing learning rate (lrf) is also set to 0.01.
## 2 Selenium from Soil to Food Bowl
### Selenium in Food and Dietary Intake  
Food is the principal source of Se. In many populations, plant foods are the main dietary source of Se (mostly as Se-Met), followed by meat and seafood [44; 45]. The concentration of Se in food depends on the type of soil, the geographical area and the capacity of plants to accumulate it [23]. In addition, other factors can influence the Se content in the diet, such as consumption of local food or imported food products [19] for example, USA Se-rich cereals [23; 46].  
The bioavailability of Se in the diet depends on factors such as proteins, fats and heavy metals. Thus, food rich in protein and in the presence of vitamins A, C, E contain higher levels of Se but heavy metals and sulfur reduce Se [47; 48].  
Cereals provide 50% of daily Se intake. While meat, poultry and fish group provide about 35%. Fresh water and beverages contribute to the daily intake with only 5-25% [23]. Fresh and non-heat-treated fruits and vegetables contain small amounts of Se about 11% in a balanced diet; this is due to their low protein content and high water content [23].  
Another selenium-rich source is found in sea salt, offal, yeast (selenium-containing yeast), garlic, asparagus, kohlrabi (enriched with Se) [6; 47; 49]. High concentrations of Se have, exceptionally, also been found in Brassica genus (broccoli, cabbage, cauliflower) and onions [23]. Brazil nuts and mushrooms have extremely high concentrations of this element [6; 50].  
The reference values for dietary Se intake were estimated on the basis of the intake necessary for saturation of the selenoproteins; GPx in plasma or erythrocyte or selenoprotein P (SePP) in plasma [51; 52]. The Recommended Dietary Allowances (RDA) of Se in the United States, based on the activity maximization of GPx are around 55 \(\upmu\)g/day in adults [53]. Based on the dietary Se intake required for saturation of plasma SePP, the nutrition societies of Germany, Austria and Switzerland (D-A-CH) have recommended a Se intake reference value of 70 \(\upmu\)g/day and 60 \(\upmu\)g/day for men and women, respectively, for normal body weight, at a rate of approximately 1 \(\upmu\)g of Se per kg of body weight [54]. The World Health Organization recommends that the intake of Se should not exceed 70 \(\upmu\)g/day [55]. A dietary intake above 700 \(\upmu\)g/day can be toxic to adults [6]. Se intake in the European population varies from 30 \(\upmu\)g/day and 50 \(\upmu\)g/day [55].  
Furthermore, the biological activities of the several selenium species have not been fully characterized in order to assess their specific toxicity in nutrition, hence defining dietary windows for each of them [14; 56; 57; 58].
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 3 Calibration of SMAP-R Signals
### SMAP Antenna High Gain Effect
Since the scattering area is small, the shape change in coherently dominated surfaces is only slightly noticeable. Incoherently dominated surfaces:  
For scenes of arid, semi-arid, or vegetated land, and for ocean areas, SMAP's narrow beam antenna plays a critical role on the scattered signal received (both strength and shape). Figure 11 shows DDM measurements for the same three incidence angles used in Figure 10, for observations over the Atlantic Ocean. Similar to Figure 10, a comparison of specular point locations (center of blue ellipses) versus the SMAP antenna's boresight (center of red circle, marked with a red dot) is provided below the DDMs. Figure 11 illustrates how different shapes of DDMs are observed for measurements within the same track (same GPS transmitter). The shape of the DDM changes as a result of the observing geometry of the SMAP antenna in relation to the location of the reflection, i.e., the position of the specular point with respect to the antenna's boresight. Figures 11a and 11c show that for the more extreme cases, there is a loss of power on DDM peak power of 9 dB and a strong shape effect. Since the scattering area is only partially observed (see Figure 11d-f), there is a strong asymmetry of delay and Doppler bin cells measured, exhibited by DDM shape differences with respect to incidence angle. DDM shape differences are more evident in ocean scenes, as compared to sea ice, because the scattering surface expands to sizes much larger than the SMAP antenna footprint (see Figure 11d-f). The filtering effect will become more evident as the scattered power comes from an increasingly rough surface and the surface scattering area expands from the specular point. In order to account for the filtering effect of SMAP's antenna we will compensate for the size of the scattering area that contributed to each delay-Doppler bin in the delay-Doppler maps. Research  
Figure 10: Top: Delay–Doppler Maps of the scattered power measured by SMAP-R for the sea ice surface for (**a**) 42.5\({}^{\circ}\), (**b**) 40\({}^{\circ}\), and (**c**) 37.5\({}^{\circ}\) incidence angles. Bottom: iso-delay (blue ellipses) and iso-Doppler lines (blue hyperbolae) computed from SMAP-R and GPS geometries and velocities with the antenna beam pattern limited at –3 dB beamwidth (red) over-imposed to those isolines for the same (**d**) 42.5\({}^{\circ}\), (**e**) 40\({}^{\circ}\), and (**f**) 37.5\({}^{\circ}\) incidence angles. Note that they are not to scale: The scattering area represents 1 km major axis ellipse, while the antenna beam patter diameter is 40 km. The antenna boresight is shown as a red dot. conducted with SMAP-R need to consider that SMAP-R DDM shape is related mostly to the SMAP antenna beam footprint filter effect, rather than geophysical parameters alone.
## 4 Experiments
### Experimental Results and Analysis  
The following key event detection methods were selected for comparison:  
* [PERSON] et al. [26] can classify emerging documents into existing document clusters by training an SVM classifier.
* newsLens [27] clusters documents by processing several overlapped time windows.
* [PERSON] et al. [28], which is a modification of newsLens.
* S-BERT [29] uses Sentence Transformers to obtain a vector representation of documents, which is then clustered by processing a time window.
* EvMine [7] clusters documents using graphs constructed from detected bursts of temporal peak phrases within a certain time range.  
To avoid contingency, the results of each method were obtained by averaging the results of five experiments, which are shown in Table 1.  
As can be seen from Table 1, the proposed method outperforms the other benchmark methods in terms of precision and F-measure on the Ebola dataset. The proposed method outperforms the other benchmark methods by 6 to 13 percentage points in these two metrics. The high independence of the events detected by the model in each cluster can be seen in the precision. The score of F-measure demonstrates the good ability of our method to handle noisy data. Some of the baseline methods produce high recall values, which, after analysis, is due to the fact that these methods produce small but many key events and, therefore, coverage of key events can be high. However, since they also generate many of the same key events, their precision values are lower.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Prec** & **Rec** & **Fmeas** \\ \hline [PERSON] et al. [26] & 0.444 & 0.706 & 0.615 \\ newsLens [27] & 0.426 & 0.824 & 0.561 \\ [PERSON] et al. [28] & 0.414 & 0.706 & 0.522 \\ S-BERT [29] & 0.508 & **0.836** & 0.631 \\ EvMine [7] & 0.829 & 0.682 & 0.748 \\ GCS & **0.986** & 0.706 & **0.824** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key event detection results.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
A PCA was conducted to illustrate the similarities and differences among the samples, as depicted in Figure 2. The first principal component (PC1) accounted for approximately 69% of the overall variance, while the second principal component (PC2) explained around 18% of the variance.  
The total baking loss variable was strongly negatively correlated with TPC, ABTS, DPPH, fat, ash, and SDF (Figure 2b), which means that the loss of bread mass was related to the loss of bioactive substances, fat, and soluble fiber. On the other hand, the total baking loss was strongly positively correlated with the total carbohydrate content of the bread, which means that baking losses were, to a small extent, caused by the loss of carbohydrates.  
PCA showed a noticeable effect of the percentage of pomace addition on the physicochemical properties of bread. Analysis of the factor coordinates chart of the cases (Figure 2a) allowed us to distinguish three groups of points: point 0, denoting the control sample, point 10 FD, denoting bread with 10% of freeze-dried pomace, and points 5 CD, 5 FD, and 10 CD, denoting bread with 5% convection- and freeze-dried pomace content and with 10% convection-dried pomace content. The point 0 (the control sample) is located in the upper part of the coordinate system, which means a high carbohydrate content in the bread and a high baking loss coefficient. The group of three points (5 CD, 5 FD, and 10 CD) is located in the lower part of the coordinate system, which means that these breads are characterized by a relatively high calorific value and fat content. Point 10 FD (bread with 10% freeze-dried pomace content) is located in the upper left quadrant of the coordinate system, which means that it is characterized by a high content of bioactive ingredients, ash, soluble dietary fiber, and total dietary fiber. The principal component analysis confirmed our initial observations of high-quality bread with 10% freeze-dried pomace content.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 1 Introduction
### Organization of this Paper  
The remainder of this article is organized as follows: Section 1 introduces related works. Section 2 analyzes the research and communication scenario of VANETs, explains symbols in this paper and communication procedure. In Section 3, V2V & V2I broadcast model and IA model are constructed in detail. Combined with the characteristics of safety-related information, VANETs data-driven assisted transmission model is constructed and the optimization problem is proposed. Section 4 designs a clustering algorithm to pre-cut feasible solution space for optimization problem in Section 3. In Section 5, results of experiments are presented to evaluate performance of our approach. Section 6 concludes this paper.
## 4 Discussion
### Improvement of Meteorological Data  
Limited by the lack of meteorological data in the tropical island region, parameters such as hourly temperature, relative humidity, wind speed, and rainfall were all concentrated in the period from June 2015 to May 2016 in this study. We have realized that this is a shortcoming for our study. Therefore, we are bound to pay attention to collecting and organizing climatological data in the tropical island region uninterruptedly in order to update and improve the basic meteorological database, and then increase the reliability of correlational studies in the future works.