esa-sceva/satcom-chunk-collection huggingface dataset:
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Data Preprocessing and Correction
#### 2.1.3 Comparison of Calibration Results  
This article calibrates and discusses only the acceleration data along the satellite orbit direction. The acceleration data in other directions can be performed in the same way.  
The comparison between the results from the improved correction method and the ESA correction is shown in Figure 4. Among them, Figure 4a is the result of the correction by the magnetic storm day accelerometer data. Figure 4b is the correction result of a nonmagnetic storm day.  
Figure 3: Comparison of Swarm-C satellite accelerometer data before and after correction.  
Figure 4a shows the accelerometer data during a strong magnetic storm with a level of G4, and Figure 4b shows the accelerometer data during a normal period. It can be seen that the fluctuation frequency of the accelerometer increases significantly during the strong magnetic storm occurrence period. The reason for the above phenomenon is that the magnetic pressure will cause a huge change in the particle density of the atmosphere.  
As seen in Figure 4, the accelerometer data correction results obtained using the improved correction method in this paper are very close to the accelerometer correction results published by ESA. Therefore, the coincidence between the two is very high, which shows the accuracy of the correction method in this paper.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Vehicle Onboard Sensing
#### 3.3.2 Road User Behavior Sensing  
Human drivers can recognize and predict other road users' behaviors, e.g., pedestrians crossing the street, vehicle changing lanes. For intelligent or autonomous vehicles, automat ing this kind of behavior recognition process is expected to be part of the onboard sensing functions [153; 154; 155]. Stanford University [157] published an article on pedestrian intent recognition using onboard videos. They built a graph CNN to exploit spatio-temporal relationships in the videos, which was able to show the relationships between different objects. While, for now, the intent prediction just focused on crossing the street or not, the research direction is clearly promising. They also published over 900 h of onboard videos online. Another study proposed by [PERSON] et al. [154] on pedestrian action recognition used an infrared camera, which compensates for regular cameras in the nighttime, on foggy days, and on rainy days. They built a framework composed of a pedestrian detector, an original tracking method, road segmentation, and LSTM-based action recognition. They also introduced a new dataset named CROSSIR. Likewise, vehicle behavior recognition is of the same importance for intelligent or autonomous vehicles [158; 159; 160; 161; 162]. [PERSON] et al. [159] lately developed a method using fuzzy inference and LSTM for vehicles' lane changing behavior recognition. The recognition results were used for a new intelligent path planning method to ensure the safety of autonomous driving. The method was trained and tested by NGSIM data. Another study on vehicle trajectory prediction using onboard sensors in a connected-vehicle environment was conducted. It improved the effectiveness of the Advanced Driver Assistant System (ADAS) in cut-in scenarios by establishing a new collision warning model based on lane-changing intent recognition, LSTM for driving trajectory prediction, and oriented bounding box detection [158]. Another type of road user-related sensing is passenger sensing, while for different purposes, e.g., transit ridership sensing using wireless technologies [163] and car passenger occupancy detection using thermal images for carpool enforcement [164].
## 3 The Proposed Method  
This study proposes an RDH-EI algorithm with a substantial embedding capacity, leveraging the correlation between image pixels and the encryption features of MSS. The algorithm's framework is illustrated in Figure 3. Initially, the image owner preprocesses the original image \(\mathbf{I}\) using the AMED pixel prediction technique, resulting in the transitional image \(\mathbf{IR}\). Subsequently, the image is shared using an \((r,n)\)-threshold MSS, with the concurrent embedding of identifying information. Subsequent to this stage, the resulting \(n\) encrypted images \(\mathbf{IE}_{k}\) are distributed to individual data hiders for autonomous embedding, yielding \(n\) marked encrypted images \(\mathbf{IM}_{k}\). The recipient can leverage the key to extract the embedded secret data from each marked encrypted image individually or fully reconstruct the image to accurately extract both the secret data and identifying information by collecting any \(r\) marked encrypted images. Additionally, the recipient can detect tampering attempts in the transmission environment by comparing the identifying information pre- and post-embedding. The algorithm further utilizes the threshold characteristic of secret sharing to enhance data disaster resistance, accommodating up to \((n-r)\) failure points.
## 3 Results and Discussion
### Diversity Indices  
The microbial diversity is affected due to the presence of high amount of pollutants in the soil [31]. It has been observed that the azo dye contaminated soils have changes in microbial community structure based on the phospholipid fatty acid profiles [5]. Similarly, the total number of bacterial OTUs were observed to be 8302 and 12,162 for DS and ALS respectively (Table 2), after clustering at 3% cutoff level. Significant variation observed in OUT of DS and ALS, illustrated the negative impact of azo dye on soil microbial population and diversity. Similarly, the negative impact of individual azo dyes (Direct Red 81, Reactive Black 5 and Acid Yellow) on soil bacterial and fungal diversity was reported by [PERSON] et al. [5]. In addition, the authors reported that azo dyes affect plant beneficial bacteria habitats in soil. The rarefaction curve confirmed the adequacy of sampling effort in the current microbial diversity analysis (Figure S1). The diversity indices measured in terms of Shanon and Inv-simpson indices, and the richness measured in terms of Chao1 index were higher in ALS compared to DS (Table 2). The diversity indices give the overview of richness and evenness of a community. Hence, the result of the diversity indices infers that the textile wastewater decreased the \(\alpha\)-diversity of the microbial community and also decreased the richness and evenness of the microbial community in DS [32]. The major drawback of the study is that a single replicate was used for studying the bacterial diversity. However, it can be used as a preliminary study for understanding the effect of azo dyes on bacterial community profiles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Soil Samples**} & **Dehydrogenase** & **Urease** & **Cellulose** & **Acid Phosphomoesterase** \\  & **(ng TPF** & **(mg NH\({}_{4}\)*-N** & **(\(\mu\)g Glucose** & **(\(\mu\)g Glucose** & **(\(\mu\)g Glucose g\({}^{-1}\) \\  & **g\({}^{-1}\) Soil h\({}^{-1}\))** & **g\({}^{-1}\) Soil h\({}^{-1}\))** & **(\(\mu\)g Glucose g\({}^{-1}\) \\ \hline DS & 15.37 \(\pm\) 1.96 & 3.36 \(\pm\) 0.47 \({}^{\text{a}}\) & 27.28 \(\pm\) 1.41 & 54.92 \(\pm\) 9.16 \({}^{\text{a}}\) \\ ALS & 22.35 \(\pm\) 2.68 & 5.11 \(\pm\) 0.63 \({}^{\text{a}}\) & 40.75 \(\pm\) 4.55 & 55.71 \(\pm\) 11.35 \({}^{\text{a}}\) \\ LSD & 9.17 & 1.99 & 10.56 & 37.53 \\ (\(p\leq\) 0.05) & \multicolumn{1}{c}{} & & & \\ \hline \hline \end{tabular} \({}^{\text{a}}\) The enzyme activities are not significantly different from each other at 5% threshold (LSD). Values are mean \(\pm\) SE (standard error) of three independent determinations.  
\end{table}
Table 1: Soil enzyme activities of DS and ALS.
## 4 Control of Metal Melting Process
### Neural Network Control of Metal Melting Process  
The above-mentioned feedforward backpropagation neural network was already created in order to control the metal melting process whose training algorithm is the Levenberg-Marquardt algorithm. The total set of collected data, that consist of 300 melting points, is divided into two groups. The first group consists of 280 data that are used for the training and development of neural network model and the second set consist of 20 data, specially selected for testing the performance of a developed neural network. The inputs into the neural network are: chemical composition of 2.5 tonnes of molten metal in the furnace: 1. C (%), 2. Cr (%), 3. Mn (%), 4. Si (%), chemical composition of 5 tonnes of steel waste: 5. C (%), 6. Cr (%), 7. Mn (%), 8. Si (%) and final chemical composition of molten metal: 9. C (%), 10. Cr (%), 11. Mn (%), 12. Si (%). The output from the neural network is the amount of alloying additives: carburizing agent (kg), FeCr (kg), FeMn (kg) and FeSi (kg). In the training phase, a set of 280 data was divided into the training dataset, the validation dataset and the test dataset in this percentage relation--80%:10%:10%. Several different neural network architectures were designed with a variety of hidden layers and neurons in them. Table 1 provides different neural network architectures and corresponding mean square errors in the training phase  
In Figure 9 is shown a neural network architecture, which showed the best performances.  
This neural network (NN12-16-4) has one hidden layer with 16 neurons in it, the input layer is defined by the number of inputs (12), and output layer is defined by the number of outputs (4). The neurons in input and hidden layers of neural networks had the sigmoid transfer function, while the neurons of the output layer have the linear transfer function. The mean squared error in the training phase, as a very significant performance of neural network (NN12-16-4), is 0.16. As a validity measure of a neural network, maximum and the mean errors in the test phase are used. The testing has been performed with a set of 20 data that did not participate in the development of the neural network model. The maximum and mean errors for the prediction of four alloying additives are given in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**NN Model (Input-Hidden-Output)** & **Mean Squared Error** \\ \hline
12-14-4 & 0.23 \\
12-15-4 & 0.31 \\
12-18-4 & 0.28 \\
12-16-4 & 0.16 \\
12-12-4 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean squared errors in the training phase— neural network (NN) models.  
Figure 8: Control of white cast iron production.
## 4 Discussion
Besides, remote sensing data and eddy flux data have different observation frequencies, so there are unavoidable errors in estimating daily GPP and ET from both transient remote sensing data and half-hourly eddy flux data. For example, random measurement errors in CO\({}_{2}\) and water fluxes at the eight ChinaFLUX sites were 0.29-5.0 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) and 9.2-49.8 W\(\cdot\)m\({}^{-2}\), respectively. The average daily missing rates of half-hourly GPP and ET data after quality control were 53%-68% and 39%-59%. The energy balance ratios ranged from 0.58-1.00 with an average of 0.87 over the eight ChinaFLUX sites [53], which caused the true ET to be underestimated by the eddy covariance measurements to some degree. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.
## 4 Fairness-Aware Optimal Transmission Scheme
### Model Training Process  
Prior to the beginning of training, we need to set up a memory buffer with the size of \(K\) to save the experience tuples. In particular, we randomly generate a statistical channel state vector \(\vec{s}_{t}=[\alpha_{SR},\ \beta_{SR},\ \alpha_{RD_{1}},\ \beta_{RD_{1}},\cdots,\ \alpha_{RD_{M}},\ \beta_{RD_{M}}]^{T}\), and then feed it to the actor networks to arrive at an action vector \(\vec{a}_{t}=[P_{1},\cdots,\ P_{M}]^{T}\). By substituting the obtained state vector and action vector into Equation (13), the resulting reward value \(R_{t}\) can be  
Figure 2: The configuration of the employed deep neural networks.  
calculated accordingly. The obtained new experience tuple \(\vec{a}_{t}\)-\(\vec{s}_{t}\)-\(R_{t}\) is put into the memory buffer. Note that such a process needs to be repeated several times, until the memory buffer is full.  
Once \(K\) experience tuples are available, we can start the training process. During each iteration, following a similar process described above, we first generate a new state-action-reward tuple and put it into the memory buffer to randomly replace an existing tuple. After that, we extract \(N\) experience tuples at random from the memory buffer and use them to construct a critic network for approximating the reward function. The parameter set of the critic network can be updated by minimizing the loss function, shown as follows  
\[\theta^{Q^{\prime}}=\min_{\theta^{Q}}\sum_{i=1}^{N}(Q(\vec{s}_{i},\ \vec{a}_{i}| \theta^{Q})-R_{i})^{2}. \tag{14}\]  
After obtaining an updated critic network, we can further renew the actor networks by performing the operation of joint gradient ascent with the help of chain rule. The parameter set of actor network \(j\), \(j=1,\ 2,\ \cdots,\ M\), is updated as follows  
\[\theta^{H^{j}_{j}}\leftarrow\theta^{H_{j}}+\frac{\epsilon}{N}\sum_{i=1}^{N} \bigtriangledown_{\mu_{j}(\vec{s}_{i}|\theta^{H_{j}})}Q(\vec{s},\vec{a}| \theta^{Q^{\prime}})|_{\vec{s}=\vec{s}_{i},\vec{a}=\vec{a}_{i}}\bigtriangledown _{\theta^{H_{j}}}\mu_{j}(\vec{s}_{i}|\theta^{H_{j}}), \tag{15}\]  
where  
\[\vec{a}_{i}=[\mu_{1}(\vec{s}_{i}|\theta^{H_{1}}),\cdots,\ \mu_{M}(\vec{s}_{i}| \theta^{H_{M}})]^{T}. \tag{16}\]  
Here, \(\epsilon\) denotes the learning rate for all actor networks.
## 2 Results and Discussion
### Key Aroma Compounds Identified of Three Black Teas
#### 2.1.3 Odor Activity Values (OAVs)  
[PERSON] and [PERSON] have stated that AEDA cannot fully explain the aroma contribution of a compound, and the odor activity value still needs to be introduced [21; 22]. The OAVs were calculated based on the concentration obtained by the standard curve and the threshold value of the compound in water; an OAV >1 was considered to be an important aroma compound. Threshold determinations were performed on compounds involved in aroma synergy studies, and other threshold data were obtained from the literature.  
Finally, the OAVs of 52 compounds were calculated. The compound with the highest OAV in JJM is linalool (242.34), the compound with the highest OAV in KM is hexanal (537.30), and the compound with the highest OAV in DH is furfuryl alcohol (239.89). In addition, the OAVs of the following compounds showed a high degree of aroma contribution: geraniol, phenethyl alcohol, (Z)-3-hexenol, phenylacetaldehyde, furfural, 2-methylbutanal, 3-methylbutanal, 3-methylnonane-2,4-dione, \(\beta\)-damascenone, methyl salicate, 2-heptanone, \(\beta\)-myrccene, dimethyl sulfide, and 2,6-dimethylpyrazine. The OAVs of these compounds were greater than 10. There were 39 compounds with OAV greater than 1.
## 2 Proposing a New Failure Threshold
### Copula Statistical Models  
In the next step, [PERSON]'s correlation coefficient (PCC) was applied to analyze the correlation between PC\({}_{1}\) and the time domain features. The feature with the highest correlation was selected for the next step of the analysis. Then, copula models were employed to indicate the statistical relationship between PC\({}_{1}\) and the selected feature. Hence, using the copula model to create a joint probability distribution, it is possible to obtain a conditional probability distribution for the selected feature and PC\({}_{1}\). In other words, the probability distribution of the time feature can be determined for different defect sizes.  
The copula models are mathematical functions used to create a joint probability distribution of two random variables, such as X and Y, in the form H (X, Y) = P (X \(\leq\) x, Y \(\leq\) y). [PERSON]'s theory [24] claims that if H is a cumulative joint distribution function and F and G are univariate marginal distributions, there is a C copula model where H (X, Y) = C (F(X), G(Y)). This copula is unique if F and G are continuous. Similarly, if H is joint distribution, u = F(X) and v = G(Y), the copula model is obtained by equation C (u, v) = H (F\({}^{-1}\)(u), G\({}^{-1}\)(v)). One of the most common copula families in the literature is the Gaussian copula, which is defined as follows:  
\[\mathbb{C}_{\Theta}\;=\;\Phi_{\Sigma}\left[\phi^{-1}(\text{u}),\;\phi^{-1}( \text{v})\right] \tag{5}\]  
where \(\Phi_{\Sigma}\) is the cumulative joint normal distribution function with a mean of zero, \(\Sigma\) is the covariance matrix and \(\phi^{-1}\) is the inverse indicator of the standard Gaussian distribution. Moreover, the conditional probability distribution is determined using the copula model as follows:  
\[\text{H}\left(\text{Y}\;|\;\text{X}\;=\;\text{x}\right)\;=\;\frac{\text{C} \left(\text{F}(\text{X}\;=\;\text{x})\;,\;\text{G}(\text{Y})\right)}{\text{F} (\text{X}\;=\;\text{x})} \tag{6}\]  
In this study, eight parametric models of copula families, with details listed in Table 2, were employed [25]. Moreover, the model parameters were updated using the Bayesian method. The root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) criteria were also used to assess the goodness of model fit.  
\[\text{RMSE}\;=\;\sqrt{\frac{\sum_{i=1}^{\text{n}}\left(\widetilde{\text{y}} _{\text{i}}\;-\;\text{y}_{\text{i}}(\theta)\right)^{2}}{\text{n}}} \tag{7}\]  
\[\text{NSE}\;=\;1-\frac{\sum_{i=1}^{\text{n}}\left(\widetilde{\text{y}}_{\text {i}}\;-\;\text{y}_{\text{i}}(\theta)\right)^{2}}{\sum_{i=1}^{\text{n}}\left( \widetilde{\text{y}}_{\text{i}}\;-\;\widetilde{\text{y}}_{\text{i}}\right)^{ 2}} \tag{8}\]  
In an ideal model, we would have RMSE = 0 and NSE = 1. In these equations, y\({}_{\text{i}}\) and \(\widetilde{\text{y}}_{\text{i}}\) represent the model values and the observation values, respectively.
## 4 Met-Ocean Strategy for Assessing the Feasibility of Marine Renewable Energy near Ports
### Model Runs and General Results  
The MSP model is run on the Linux system, allowing simulations to be conducted at the IHCantabria Supercomputing and Information Technologies Laboratory. This significantly alleviates the computational burden of the project. Specifically, the NEPTUNO node within the ALTAMIRA node of the University of Cantabria is employed, part of the Spanish Supercomputing Network. This node features 1296 cores, 100 TB of storage, and 5 TB of RAM. Thanks to this supercomputing infrastructure, a monochromatic wave strategy can be expedited significantly, as it can be divided among different processors, thereby parallelizing the executions. For example, for a large port such as the Port of Valencia, with 528 monochromatic waves to be executed (with 3 repetitions based on tidal levels, reaching 1584 monochromatic waves), the overall execution time can be reduced to a maximum of 3 days. This capability provided by the IHCantabria infrastructure is advantageous for any repetition of catalogues (geometry changes, validation and calibration work, etc.) typically carried out in the pre-numerical assembly phase of each port (see Figure 5). Iterations (repetitions) are crucial during this phase to provide ultimate reliability to the operational agitation system. The results of each propagated case for each tidal level are stored in a general hypermatrix for each port, containing significant wave height (Hm0), peak wave period (Tp), and wave energy potential (Pw) at every location within the entire numerical domain, for all of the monochromatic runs and water levels. These catalogues of monochromatic waves will be used to reconstruct the spectra predicted by SAPO and obtain the spectral map of Hs across the entire domain. These pre-run agitation catalogues were provided to the client in Python 3.5 format for direct implementation into their operations and Environmental Management Dashboard (CMA). Additionally, analogous matrices are generated for the free surface and phase, which will be used for the directional wave decomposition of each variable related to waves and energy potential.  
Figure 4: Example (zoom) of the triangular and depth-adaptive mesh used for the MSP model runs at Valencia Port.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 4 Resolution Analysis
### AA-BiSAR with Moving Transmitter
#### 4.1.1 Azimuth Resolution
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Definition** & **Value** \\ \hline \(f_{c}\) & Carrier Frequency & 50.5 GHz \\ \(B_{r}\) & Signal Bandwidth & 650 MHz \\ \(T_{r}\) & Sweep Time & 0.15 ms \\ \(H_{r}\) & The Height of Receiver & 200 m \\ \(R_{\text{arc}}\) & The Radius of Arc Array & 0.6 m \\ \(\varpi_{a}\) & Velocity of Azimuth Angular & 30 rad/s \\ \(\theta_{a}\) & Array Beam Width (\(-3\) dB) & 60\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. with the target azimuth angle, and for those targets with the same radius, the azimuth resolution remains unchanged. Figure 5b-d is the azimuth resolution of the target, which varies with the transmitter speed. Since, when the transmitter is moving, the azimuth frequency of the point target consists of two parts: the synthetic aperture formed by the curved array antenna, and the synthetic aperture formed by the moving transmitter. As a result, the azimuth angular resolution of targets is not only related to the radius of the arc array antenna and synthetic aperture angle, but also to the instantaneous position of the transmitter. Furthermore, the greater the velocity of the transmitter, the more obvious the change in azimuth angular resolution, and as the squint angle between the target point and the transmitter becomes larger, the azimuth angular resolutions of targets become worse. In the simulation experiment, since the azimuth time variable is set within the time of an arc synthetic aperture, when the transmitter speed is small, the targets azimuth resolution changes little, as the transmitter speed increases, the azimuth resolution is getting better within a certain range.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 8 Optimization Using the Modified GA Approach
### Mutation Parameter  
Mutating the chromosomes is necessary in genetic algorithms because it may result in a revolutionary solution that can solve our complex problems more efficiently. It consists of three parameters: child chromosomes, the mutation rate (m), and the step size \((\Delta)\). The mutation rate is used to define the number of genes in a child chromosome that will undergo mutation. A random value is generated, which is compared to that of the mutation rate, and if the value is less than the mutation rate, new genes are generated by multiplying the original gene value by the step size. In Figure 7, the genes in blue are the chromosomes that are to be mutated and are multiplied by the step size, which results in a mutated chromosome. Algorithm 3 takes a number of chromosomes (\(n\)), mutation rate (m), step size (\(\Delta\)) as input and loops over each chromosome. If a random number generated is less than a mutation factor a gene gets multiplied by a step and this process is repeated for each chromosome.  
```
Input:\(C_{i}\), m, \(\Delta\), n (number of chromosomes) i = 0 while(i<n) do  generate a random number \(V\) if(V < m) \(C_{i}k=C_{i}k*\Delta\) endif  i = i + 1 endwhile
```  
**Algorithm 3** Mutation operation.