esa-sceva/satcom-chunk-collection huggingface dataset:
# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 3 Model Design and Implementation
### Overall Structure of CF-RCNN  
Combined with the above improvement measures, the detailed structure of the CF-RCNN algorithm is shown in Figure 8.  
Firstly, CF-RCNN takes the image stream as input, extracts image features based on the inverted residual ResNet-50 model, and obtains intermediate feature vector graphics. Secondly, the CBAM attention mechanism is used to calculate the attention weight in the two dimensions of space and channel, and adjusts the parameters of the intermediate feature map. Third, based on the FPN feature pyramid structure, the feature map is multi-scaled. Fourth, on each feature map, the RPN network uses a single scale to select regions that may contain objects and balances the positive and negative sample ratios through Soft-NMS. Finally, the classification and position regression operations are performed on the objects in the selected area.
## 4 Conclusions  
In summary, a reliable MHS-SPME-arrow-GC-MS method combined with ISTD for the quantitation of 13 pyrazines in flavor oils was developed. The SPME-arrow fiber was found to be more suitable for the MHS-SPME method, and the PDMS/DVB/CAR fiber was selected. The highest efficiency was performed under selected HS-SPME conditions. The novel method was verified with the stable isotope dilution analysis method and showed high sensitivity and accuracy. Additionally, the pyrazines of three market product samples were analyzed and quantified using the new method, which proved that the MHS-SPME-arrow-GC-MS was suitable to quantify pyrazines in oils. In the future development, this method has great opportunities in the quantitation of other aroma active compounds such as alcohols and pyrroles. At the same time, as an absolute quantitative method to eliminate the matrix effect, this method also has an obvious limitation. That is that it requires multiple extractions for a single analysis, which means it takes a much longer time than the conventional HS-SPME-GC-MS. Therefore, there are some aspects for further improvement for this newly proposed method.  
Conceptualization, C.Z.; methodology, C.Z. and Z.X.; validation, C.Z. and Z.X.; investigation, Z.X. and C.Z.; project initiation and resources, X.X.; experimental work, Z.X.; writing--original draft preparation, Z.X.; writing--review and editing, C.Z., H.S., H.Z., Y.B., and X.X. All authors contributed to the project scope and content as well as solution discussion. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Wilmar (Shanghai) Biotechnology Research & Development Center Co., Ltd.; College of Food Science and Technology, Henan University of Technology.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 3 Star Background Simulation  
Given that the simulation scenario spans less than one day, the change in the position of stars relative to Earth can be considered negligible. Thus, the initial position of each star can be assumed to be static throughout the simulation. The process for simulating the star background is illustrated in Figure 4. Firstly, we read the data from the MSX catalog to obtain the initial position and the proper motion of the star. Next, we calculated both the right ascension and declination of the star at the beginning of the simulation, and the infrared magnitude of stars at different wavelengths. This information is stored in a database that adopts the format of latitude/longitude grid-latitude band, which enhances the efficiency of the background simulation module as it facilitates the search for stars in the FOV based on the optical axis point.
## 2 Methods
### Gaussian Process with a Tapering Covariance Function for Local Variability
#### 2.3.2 Covariance Tapering  
Traditional Gaussian process models, which require the computational complexity of \(O\!\left(n^{3}\right)\) to estimate parameters with a dataset of size \(n\)[32]. In order to reduce the computational burden of Gaussian process models and satisfy the necessity in practice, we adopt a covariance tapering method to characterize the covariance function. Considered that distant pairs of temperature values are uncorrelated, the covariance tapering method aims to model a compactly supported covariance function in which the correlation of distant pairs is defined as zero. The covariance matrix of Gaussian process models obtained by the covariance tapering method is positive definite and can retain most information of the temperature field.  
We adopt a tapering covariance function in the Gaussian process model by taking the product of an original covariance function \(C(\mathbf{h};\boldsymbol{\theta})\) and a tapering function \(c_{taper}(\delta s;\gamma)\) as follows:  
\[C_{taper}(\mathbf{h};\boldsymbol{\theta},\gamma)=C(\mathbf{h};\boldsymbol{ \theta})c_{taper}(\delta s;\gamma), \tag{7}\]  
Figure 3: Time-varying grain temperature field.  
where \(c_{taper}(\delta s;\gamma)\) is defined as an isotropic correlation function in the spatial domain that is zero whenever  
\[\delta s=\left((\delta x)^{2}+(\delta y)^{2}+(\delta z)^{2}\right)^{\frac{1}{2}} \geq\gamma,\]  
where \(\gamma\) is a range parameter. We use the spherical tapering function as the tapering function in our model, which is defined by  
\[c_{taper}(\delta s;\gamma)=\left(1-\frac{\delta s}{\gamma}\right)_{+}^{2}\left( 1+\frac{\delta s}{2\gamma}\right), \tag{8}\]  
where \(\left(1-\frac{\delta s}{\gamma}\right)_{+}\) means that  
\[\left(1-\frac{\delta s}{\gamma}\right)_{+}=\left\{\begin{array}{ll}1-\frac{ \delta s}{\gamma},&1-\frac{\delta s}{\gamma}>0,\\ 0,&\text{otherwise}.\end{array}\right.\]
# Microbiota \"Fingerprint\" of Greek Feta Cheese through Ripening
## 2 Materials and Methods  
### Sample Collection  
A total of 23 Feta cheese samples were collected from various production sites (dairy establishments) in different geographical regions in Greece. All Feta cheese samples were prepared in accordance with the requirements of a Protected Destination of Origin (PDO) cheese [15]. All the samples were placed in sterile sample containers and transported to the laboratory for microbiological analysis. The cheeses were analyzed in two different ripening periods, three and six months post-production. Subsamples of each sample were kept separately in brine. Half of the samples were examined after three months (90 days) of ripening and the rest after six months (180 days) under refrigerated storage conditions (2 \({}^{\circ}\)C).
## 4 Development of the Methodology
### Study Case 1: Development of Load Profiles Model Proposed in Simulink
#### 4.1.1 Inputs of Model  
The model of the consumption of electrical appliances was adjusted according to the needs of the community. Moreover, power benchmarks were taken from the supplier pages of the appliances, considering three different capacities for each appliance from small to medium and big, to be used in the different profiles of the houses [4,5]. Table 4 shows an example for one device.  
Figure 5: Solar energy potential by Project Sunroof from Google.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Bedrooms** & **Estimate** & **Percent** \\ \hline Total housing units & 1188 & \\ \hline
0 bedroom & 41 & 3.50\% \\ \hline
1 bedroom & 108 & 9.10\% \\ \hline
2 bedrooms & 341 & 28.70\% \\ \hline
3 bedrooms & 537 & 45.20\% \\ \hline
4 bedrooms & 134 & 11.30\% \\ \hline
5 or more bedrooms & 27 & 2.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of bedrooms in the test community [7].  
From the supplier pages of the appliances, we can obtain the power data consumption and use the values in the implemented model in Simulink, as shown in Figure 6.  
The energy consumption demand is based on human behavior, which is simulated through a programmed schedule and random behavior blocks that are responsible for turning on/off basic appliances at scheduled times. The random behavior is desired, because the energy demand does not always have the same behavior, as is shown in Figures 7 and 8.  
Figure 8 shows an example of the implementation of using two ranges for a device. The output signal is triggered through the random variable, which allows the emulation of different behaviors (on/off).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size** & **Device** & **Power Ranges (W)** & **Description** \\ \hline Small & Air conditioner window & 400–1000 & 8000 BTU \\ \hline Medium & Air conditioner mini-split A/C & 1001–2500 & 12,000 BTU \\ \hline Big & Air conditioner mini-split A/C & 2501–5000 & 24,000 BTU \\ \hline \hline \end{tabular}
\end{table}
Table 4: Example of power values for an Air conditioner.  
Figure 6: Typical appliance power values running on the Simulink Model.  
Figure 7: Model structure, random behavior.
# Vision-Guided Six-Legged Walking of Little Crabster Using a Kinect Sensor
## 3 Camera-Robot Coordinate Transformation
### Derivation of Transformation Matrix Using Tsai Algorithm  
#### 3.2.1 Image Acquisition  
In order to obtain 3D coordinate information about the ground using a Kinect sensor, the 3D coordinates (\(p_{x},\ p_{y},\ p_{z}\)) of the ground points can be calculated from 2D coordinates (\(I_{x},\ I_{y}\)) and depth values (\(d\)) of the merged image. Since the units of the 2D coordinates (\(I_{x},\ I_{y}\)) are pixel; the pixel unit has to be converted to the metric unit (mm) by using a scale factor. If the scale factor is constant, the coordinate error increases proportionally to the distance from the image center. This error is from that the distance between pixels increases according to the longitudinal and lateral distances between the object and the camera (see Figure 4a). In order to accurately obtain coordinates (\(p_{x},\ p_{y},\ p_{z}\)), it is necessary to calculate the coordinates by considering the field of view (FOV) of the camera. Since the horizontal and vertical FOVs of the depth camera are 58.5 degrees and 46.6 degrees, respectively, and their horizontal and vertical pixel numbers are 320 and 240, respectively, the 3D coordinates can be obtained from the depth value \(d\) and the trigonometrical functions with rotation angles \(\theta_{1}\) and \(\theta_{2}\) of the merged image using Equation (1) (see Figure 4b).  
\[\left\{\begin{array}{c}p_{x}=d\cdot\cos\theta_{1}\cos\theta_{2}\\ p_{y}=d\cdot\cos\theta_{1}\sin\theta_{2}\\ p_{z}=d\cdot\sin\theta_{1}\end{array}\right. \tag{1}\]  
where  
\[\left\{\begin{array}{c}\theta_{1}=\frac{46.6^{\circ}}{240 pixel}I_{y}\\ \theta_{2}=\frac{58.5^{\circ}}{320 pixel}I_{x}\end{array}\right.\]  
Figure 3: (**a**) Color image from Kinect sensor; (**b**) searched target balls in color image; (**c**) searched target balls in depth image; (**d**) merged image of color and depth images.
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
###### Abstract  
The UltraViolet and infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) mission aims to demonstrate pioneering technologies for broadband measurement of the Earth's radiation budget (ERB) and solar spectral irradiance (SSI) in the Herzberg continuum (200-242 nm) using high quantum efficiency ultraviolet and infrared sensors. This research and innovation mission has been initiated by the University of Versailles Saint-Quentin-en-Yvelines (UVSQ) with the support of the International Satellite Program in Research and Education (INSPIRE). The motivation of the UVSQ-SAT mission is to experiment miniaturized remote sensing sensors that could be used in the multi-point observation of Essential Climate Variables (ECV) by a small satelliteconstellation. UVSQ-SAT represents the first step in this ambitious satellite constellation project which is currently under development under the responsibility of the Laboratory Atmospheres, Environments, Space Observations (LATMOS), with the UVSQ-SAT CubeSat launch planned for 2020/2021. The UVSQ-SAT scientific payload consists of twelve miniaturized thermopile-based radiation sensors for monitoring incoming solar radiation and outgoing terrestrial radiation, four photodiodes that benefit from the intrinsic advantages of Ga\({}_{2}\)O\({}_{3}\) alloy-based sensors made by pulsed laser deposition for measuring solar UV spectral irradiance, and a new three-axis accelerometer/gyroscope/compass for satellite attitude estimation. We present here the scientific objectives of the UVSQ-SAT mission along the concepts and properties of the CubeSat platform and its payload. We also present the results of a numerical simulation study on the spatial reconstruction of the Earth's radiation budget, on a geographical grid of \(1^{\circ}\times 1^{\circ}\) degree latitude-longitude, that could be achieved with UVSQ-SAT for different observation periods.
# Hybrid Fibres as Shear Reinforcement in High-Performance Concrete Beams with and without Openings
## 3 Experimental Results and Discussion
### Inelastic Parameters
#### 3.2.2 Toughness  
Toughness reveals the energy absorption capacity of beams with/without openings. It is assessed by measuring the area under the load-deflection curve. The toughness values are calculated and shown in Figure 9 for comparing the impact of fibre volume content on increasing the toughness of beams with or without openings. As can be seen, the lowest value of toughness belongs to beam B2, which is about 11 times lower than conventional beam B1. Beams B3 and BO3 with the highest fibre content show an increase of 109.2% and 82.2% in toughness, both higher than that of traditionally RC beams. Beam BO2 exhibits a 38.7% decrease, which can be explained as a result of the presence of a square opening in the shear span.  
Figure 8: Comparison of beam (**a**) maximum load and (**b**) over-strength factor.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Beam** & **Fibre Volume** & **Over-Strength** & **Toughness** & **Increase/Decrease** & **Ducility** & **Increase/Decrease** \\
**Notation** & **Content (\%)** & **Factor (\(\rightarrow\))** & **(kn\(\times\)mm)** & **in Toughness (\%)** & **Factor (\(\rightarrow\))** & **in Ductility (\%)** \\ \hline B1 & – & 0.91 & 15,513.26 & – & 4.37 & – \\ \hline B2 & 0.5 ST + 0.025 PP & – & 1455.24 & \(-\)966.03 & – & – \\ B3 & 1 ST + 0.05 PP & 0.86 & 32,445.63 & 109.15 & 9.77 & 123.57 \\ BO1 & – & 0.98 & 14,845.62 & – & 4.28 & – \\ BO2 & 1 ST + 0.05 PP & 0.87 & 9102.68 & \(-\)38.68 & 2.41 & \(-\)77.59 \\ BO3 & 1.5 ST +0.1 PP & 0.89 & 27,054.38 & 82.24 & 5.48 & 28.04 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Beam parameters.
# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 2 Materials and Methods
### Vegetation Index Selection  
Since the SuperDove sensor does not have a SWIR band, it was necessary to determine the best vegetation indices to allow the detection of the burned areas. For this, nine vegetation indices were evaluated. For each one, the average difference between the pre- and post-fire images in the selected area was obtained. Prior to calculating this average value, images were filtered with the provided burned-area delimitation mask. The names, equations, and references of these nine vegetation indices appear in Table 2.
## 6 Experimental Results
### Experiment B  
The second experiment dealt with the incorporation of Dataset B, i.e., the mixed dataset, using the translation technique. Table 4 tabulates the performance metrics per each algorithm. These figures were found to be the greatest when utilizing PCA transformation, retaining 30% of the variance of the mixed set.  
An examination of the fourth table reveals that the methodology of including additional features improves the classification outcome for every algorithm. However, the improvement is clearly bigger when one uses methods for performing some sort of feature selection, such as GBM and deep learning. For the former, we also observe that it has reached almost 88% in accuracy, and simultaneously, it retains high precision and recall values for both classes, while in other methods, usually the recall of the positive or negative class often varies from the other metrics. For example, in deep learning, the positive precision is 67.9%, while the recall reaches 81.3%. In other words, the algorithm attempted to fit almost all positive examples, but in this process, it was not able to generalize, thus labeling some negative examples as positives. Results were slightly worse when utilizing the FS process, with the case of keeping the top-750 features being approximately 2% to 3% lower than PCA.  
Figure 3 contains a graph depicting the improvement of the inclusion of the proposed technique with the translation of Greek tokens. As explained before, GBM and deep learning present the best improvement, reaching almost 7% better outcomes than when considering the original dataset. An interesting observation is that the mixed dataset aids all other methodologies. This might sound peculiar since doubling the initial feature set means additional load for any classifier; however, note that the translation is not a simple doubling of the size of vectors, since it encodes semantic relations between each token and the corresponding class distribution, making the decision boundaries easier to recognize.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset B: Mixed** & **Accuracy** & **Precision (+)** & **Recall (+)** & **Precision (-)** & **Recall (-)** \\ \hline Decision Trees & 70.50\% & 63.23\% & 66.60\% & 77.15\% & 86.50\% \\ SVM & 81.35\% & 83.50\% & 68.50\% & 83.00\% & 91.80\% \\ Naive Bayes & 71.80\% & 62.35\% & 67.27\% & 79.34\% & 75.06\% \\ Deep Learning & 75.70\% & 67.90\% & 81.30\% & 86.60\% & 78.50\% \\ GBM & **87.85\%** & **84.66\%** & **83.30\%** & **91.30\%** & **91.75\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of GBM against other benchmarking methods for Dataset B: Mixed.  
Figure 3: Improvement when considering the mixed dataset, in terms of accuracy, precision and recall per each class label, over the original dataset.  
In analogy with the previous experimental scenario, we tabulate the optimal hyper-parameters for the involved algorithms in the following Table 5.
## 3 Computational Examples and Analysis
### Case Study I: Bi-Objective Optimization
#### 3.1.2 MOO Problem Formulation for Basalt Fibre-Modified Asphalt Mixture  
In this work, a typical proportion optimization for asphalt mixture is discussed. A metaheuristic optimization model for asphalt mixture proportion based on an ML model is presented to maximize physical performances and satisfy the requirements of volumetric properties. Hereto, the mixture proportion problem is usually taken to be a complex optimization problem.  
(1) Objective Function  
Following the Chinese specification JTG F40-2004, the targets of asphalt mixture proportion optimization should not only meet the volumetric properties but also maximize the strength performance and minimize cost. According to the modelled GPR of basalt fibre-reinforced asphalt mixture, the objective function is calculated as follows:  
\[\left\{\begin{array}{c}\max\mathbf{F_{1}(X)=\max Y_{1}}\\ \min\mathbf{F_{2}(X)=\min(12X_{1}+2.2X_{3})}\end{array}\right. \tag{19}\]  
where \(\mathbf{Y_{1}}\) represents the Marshall stability (\(MS\)); \(\mathrm{F_{2}(X)}\) is the cost, the prices per kilogram of basalt fibre and asphalt are 12 and 2.2, respectively.  
(2) Constraints  
Following the Chinese specification JTG F40-2004, the range constraints and volumetric properties constraints for SMA-13 specimens reinforced with basalt fibre are summarized in Tables 2 and 3, respectively. The corresponding constraint expressions are written as:  
\[\left\{\begin{array}{c}0.25\leq\mathbf{X_{1}}\leq 0.55\\ 3\leq\mathbf{X_{2}}\leq 9\\ 6.4\leq\mathbf{X_{3}}\leq 6.8\\ 2\leq\mathbf{Y_{2}}\leq 5\\ 3\leq\mathbf{Y_{3}}\leq 4\\ \mathbf{Y_{4}}\geq 17\\ 75\leq\mathbf{Y_{5}}\leq 85\end{array}\right. \tag{20}\]
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 5 Experimental Evaluation
### Evaluation of the Inference Performance
#### 5.2.2 Root Mean Squared Error  
The RMSE \(E_{i}\) of an algorithm I is calculated by the equation:  
\[E_{i}=\sqrt{\frac{1}{n}}\sum_{j=1}^{n}\left(P_{(ij)}-T_{j}\right)^{2}\]  
where \(\mathbf{P}_{(ij)}\) is the value that algorithm \(\mathbf{I}\) forecasted for the sample \(\mathbf{j}\) (from a set of examples) and \(\mathbf{Tj}\) is the value of the 'target value' for the \(\mathbf{j-th}\) example. For an ideal classification, \(\mathbf{P}_{(ij)}\)= \(\mathbf{T_{j}}\) and \(\mathbf{E_{i}}\) = 0.  
Figure 14 shows the forecasting performance of the three algorithms, namely the hybrid Bayesian network, Linear Regression and Neural Networks, depicted in pairs of two (our method against each other approach suggested by literature).  
As seen above, the adopted solution outperforms Linear Regression in both error rates by more than 60%, supporting our claim that Bayesian analysis is well suited for the task at hand, providing also semantic interpretation of the variable relations. The same applies at the comparison with NN, where again, the Bayesian network performs better, portraying an error rate approximately 35% lower than that of NN.  
Despite the fact that the obvious approach when comparing two forecasting models is to select the one that has the smaller error measurement based on one of the error measurements described above, we have to decide if this difference is significant or basically due to the specific choice of data values in the sample. Hence, each of the three forecasting models, namely Linear Regression (LR), Bayesian Networks (BN) and Neural Networks (NN), was compared to the others in terms of the Diebold-Mariano (DM) test [26]. Taking the null hypothesis: \"both forecasting models have the same accuracy\" into consideration, the DM test returns two measurements, that is a _p-value_, denoting that the hypothesis holds when close to 1 or does not hold when close to 0 and _DM-statistics_, measuring the  
Figure 14: Comparing the performance of the proposed Bayesian method against Linear Regression analysis (**a**) and Neural Networks (**b**).  
squared errors of the two models. Negative values show that the squared errors of the model listed first are lower than those of the model listed last.  
Table 3 tabulates the DM-test outcome between all forecasting models. By examining the \(p\)-values of pairs LR-BN and BN-NN we can observe that they are very close to zero (i.e., 0.0073 and 0.0088 respectively) denoting that the null hypothesis that both models have the same accuracy is not statistically valid. Considering the last pair between LR and NN we can clearly observe that their \(p\)-value is significantly larger than 0. The same conclusion could be drawn by examining the DM-statistic metric. Again, it is observed that BN have significantly lower error rates than the other two models.
## 2 Materials and Methods
### Data
#### 2.2.2 GLASS NPP Products  
The Global Land Surface Satellite (GLASS) NPP dataset includes 8-day interval data and annual data with a spatial resolution of 500 m. The GLASS NPP dataset was generated based on GLASS GPP data with a relative error of <25%; these data simulate the spatial and temporal variation in global vegetation productivity well and have large interannual variations in simulations [41]. In this study, the GLASS NPP dataset with an 8-day time interval from 2010 to 2020 was used. The spatial resolution of the dataset was downscaled to 0.05\({}^{\circ}\) to ensure that the GLASS NPP data maintained the same spatial and temporal resolution as the SIF dataset.