esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation
Thus, the AP must meet the following flow conservation constraint:  
Figure 1:1  
Let \(T_{\textit{off}}\) denote the data offloading time from the UNs to the AP, resulting in the following:  
Figure 1:2  
The minimum delay problem can be modeled as the minimizing delay (MD), as follows:  
Figure 1:3 Figure 1:4 Figure 1:5  
The objective function of the MD model is to minimize the sum of the total time for energy harvesting and data offloading, subject to various constraints, including those related to energy, flow conservation, and link capacity. The MD model is a mixed-integer nonlinear programming (MINLP) model. The problem is NP-hard and cannot be directly solved using existing methods [24]. Analyzing the MD model reveals that the main challenges in solving the problem are:  
1. the product relationship between the nonlinear log function and the linear variable, as seen in the right-hand side of Constraint (6). 2. the presence of bilinear variable multiplication terms, such as the \(P_{i}t_{ij}\) term in Equation (3). The next section shows how a piecewise linearization (PWL) method is employed to convert the model into a mixed-integer linear programming (MILP) model, thereby mitigating the effects of these two challenges on the solution process.
###### Abstract  
Global information on the spatio-temporal variation of parameters driving the Earth's terrestrial water and energy cycles, such as evapotranspiration (ET) rates and surface soil moisture (SSM), is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO) technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen.  
soil surface moisture; evapotranspiration; operational products; emerging applications; agriculture 2018 10.3390/su10010181; doi:10.3390/su10010181
## 3 Overview of Hyperspectral Imaging Spectroscopy (HSI)
### Multivariate Data Exploration  
Following data preprocessing, the next step is multivariate data exploration, which can extract all information from all wavelengths of the data. Principal component analysis (PCA) and k-means clustering are popular multivariate data processing techniques. These remove meaningful information from the hyperspectral dataset and identify patterns. The next step is to develop a prediction model, which could be quantitative or classified. It establishes a quantitative relationship between the desired biochemical or physical properties and the sample's spectra. The regression model can unfold hyperspectral images and generate predictive values for each pixel [22].
## 1 Introduction
### Applications  
As applications of high-speed sensor network system, the following can be considered:  
* Robot control, which can achieve switch control using the recognition about the transition of contact/noncontact state, and an intelligent control using sensor fusion with multiple sensors' data.
* Intelligent transport system (ITS), which can perform collision avoidance, lane change, and fully automated driving control.
* Security and surveillance, which can perform the tracking of a number of humans in a huge facility, and robust target tracking under occlusion.  
As other applications, human interface, motion capture, monitoring, three-dimensional shape measurement, inspection, and factory automation are considered. A high-speed sensor network system with the proposed method has various potential applications.
# Research on the Lossless Data Compression System of the Argo Buoy Based on BiLSTM-MHSA-MLP
## 2 Probabilistic Prediction Models
### Preliminary Probability Prediction Model  
The structure of the preliminary probability prediction model is shown in Figure 2. The output of the MHSA layer is \(M\). The Linear1 linear layer is introduced to reduce the dimension of the output of the MHSA layer and output it as \(M_{1}\). The calculation formula of \(M_{1}\) is shown in Equation (6). The GELU nonlinear transformation is introduced, and its output is \(M_{2}\). The calculation formula of \(M_{2}\) is shown in Equation (7). The GELU function is selected as the activation function of the preliminary probability predictor. The GELU function is smooth over the entire real number domain, which helps reduce the gradient vanishing problem, especially in deep neural networks. The Linear2 linear layer maps the learned features to the vocabulary space, generating a tensor in which each element represents the score of a token in the vocabulary. The output of this layer is \(M_{3}\). The calculation formula of \(M_{3}\) is expressed by Equation (8).  
\[M_{1}=W_{1}M+b_{1} \tag{6}\]  
\[M_{2}=GELU(M_{1}) \tag{7}\]  
\[M_{3}=W_{2}M_{2}+b_{2} \tag{8}\]  
where \(W_{1}\) and \(W_{2}\) and \(b_{1}\) and \(b_{2}\) are the weight parameters and bias, respectively, and the dimension of \(M_{3}\) is (batch_size, vocab_size).  
To facilitate the calculation of losses, the Log_Softmax function is used to convert the original output of the model into logarithmic probability. The Log_Softmax function can avoid the numerical instability problem caused by the exponential function during the calculation process, especially when the logit value is very large or very small.  
The introduction of the BiLSTM layer can provide richer contextual information and improve the model's ability to model sequence data. The combination of the forward and backward information of the BiLSTM makes the representation input to the MHSA layer more complete, which helps generate more accurate attention weights, thereby improving the model's focus on key parts. The MHSA layer can consider the information of all other time steps in the sequence when calculating the output of each time step. In this way, the model can dynamically adjust the representation of each time step to include more contextual information. In addition, the MHSA layer allows for multiple attention heads to be calculated in parallel, which can improve the computational efficiency of the preliminary probability model. Therefore, through the combination of the BiLSTM layer and the MHSA layer, not only are the position information and modeling capabilities enriched, but also the understanding of the context is enhanced, and the performance of the model in compression tasks is improved.
## 3 Results and Discussion  
### Verification of Stacking Simulation  
The calibrated parameters were substituted for the original seed-model parameters, and the stacking simulation was conducted a second time for verification. After model calibration and vectorization, it was concluded that the simulated stacking angle was 34.90\({}^{\circ}\) (Figure 8), with a percentage error of 1.16% relative to the physical stacking angle of 34.5\({}^{\circ}\). These results indicate a minimal discrepancy between the simulation and the actual experimental data. Consequently, subsequent simulation analyses adopted the three calibrated values as the contact parameters for Sesbania seeds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source of** & **Mean** & **Freedom** &
\begin{tabular}{c} **Sum of** \\ **Squares** \\ \end{tabular} & \(p\) & **Significance** \\ \hline Model & 1771.78 & 9 & 196.86 & \textless{}0.0001 & ** \\ A & 2.74 & 1 & 2.74 & 0.2480 & \\ B & 932.26 & 1 & 932.26 & \textless{}0.0001 & ** \\ C & 509.76 & 1 & 509.76 & \textless{}0.0001 & ** \\ AB & 0.24 & 1 & 0.24 & 0.7227 & \\ AC & 1.18 & 1 & 1.18 & 0.4358 & \\ BC & 53.36 & 1 & 53.36 & 0.0008 & ** \\ A2 & 1.19 & 1 & 1.19 & 0.4334 & \\ B2 & 229.12 & 1 & 229.12 & \textless{}0.0001 & ** \\ C2 & 28.83 & 1 & 28.83 & 0.0046 & ** \\ Residual & 12.07 & 7 & 1.72 & & \\ Lack of fit & 6.68 & 3 & 2.23 & 0.3120 & \\ Pure error & 5.38 & 4 & 1.35 & & \\ Sum & 1783.85 & 16 & & & \\ \hline \end{tabular} Note: *** denotes that the item is highly significant (\(p<0.01\)), *** denotes that the item is significant (\(p<0.05\)).  
\end{table}
Table 4: Variance analysis of the regression model.
## 3 Results
### Image Quality Assessment  
Figure 14 illustrates a qualitative comparison between the original image slice and the reconstructed slices generated from our proposed approach on the ACDC dataset at the original 5 mm slice thickness. The comparison is augmented by the computed correlation coefficients (CC) and peak signal-to-noise ratio (PSNR) shown below each figure. As illustrated in Figure 14, our approach preserves the fine structural details and realistic textures while remaining visually comparable to the ground truth image. Aside from qualitative improvements, the proposed method's CC and PSNR values also prove that the synthesized image slices preserve the fine structural details.  
Table 5 shows the quantitative results of the objective quality metrics of reconstruction, indicating that the use of feature-wise linear modulation to remove domain-invariant information from the disentangled latent code guides the synthesis of more texture information. Starting with the spatial factor, we change the content of the spatial channels in Figure 15 to see how the decoder has learned a correlation between the position of each channel and different signal intensities of the skeleton parts. The senticency factor remains constant in all of these experiments. The first two columns show the original input and the reconstruction. The third row is created by the RV spatial channels and disregarding (zeroing) the MYO and LV channel. In the fourth image, we swap the RV channels with those of LV. Finally, the fifth column is produced by considering all LV, MYO and RV channels.  
Figure 14: Qualitative comparison of the original and the reconstructed slices showing that the original images are well reconstructed by combining skeleton and senticency information.The comparison is augmented by the computed correlation coefficients (CC) and peak signal-to-noise ratio (PSNR). The middle row illustrates the error images.
# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 2 Related Work
### Style Transfer Based on Multiscale Learning  
Recently, some style transfer methods have been used to transfer style patterns on the basis of multiscale learning. Multiscale holistic style transfer is achieved in Avatar-Net [23] on the basis of the use of an hourglass with multiple skip connections and a style decorator. STROTSS [2] is an image-optimization method that adopts multiscale learning to update the content image and generate high-quality stylized images. [PERSON] et al. [24] proposed a novel video style transfer framework that can render high-quality artistic portraits on the basis of the multiscale content features and preserve the frame details. A Laplacian pyramid style network (LapStyle) [25] also exhibits high visual quality and is based on a drafting network and a revision network. First, the former transfers the global style patterns, and then, the latter enhances local style details. However, too many content structure details are preserved in these methods. Key local style structures are not fused into stylized images in any of these methods. In contrast, our method transfers global style patterns at low resolution using a coarse network, which needs to be trained only once to reconstruct coarse stylized features. Our fine network enhances local style details with multiscale features from the coarse network and the high-resolution content image. As a result, our method can discard trivial local content structures and synthesize high-quality structure-aware stylized images by using a coarse-to-fine process. The differences between our method and the methods in previous studies are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Image-Optimization** & **Model-Optimization** & **Single Style** & **Multiple Style** & **Arbitrary Style** \\ \hline Ours & \(\surd\) & \(\surd\) & \(\surd\) & \\ \hline
[1,2] & \(\surd\) & & \(\surd\) & \\ \hline
[3,4,8–15,24,25] & \(\surd\) & \(\surd\) & \(\surd\) & \\ \hline
[16–19] & \(\surd\) & & \(\surd\) & \\ \hline
[5,6,20–23] & \(\surd\) & & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The differences between our method and those in previous studies.
## 3 Results of the Experimental Validation
### Quality of the Data Transfer in a Real-Case Scenario
As described above, the same laptop PC was in the backpack, the same acquisition software and data post-processing were used. The observation of the percentage packet loss in Table 2 can begin in the final raw state with the laboratory environment. The packet loss is in the same range for all five IMU units, i.e., 0.01%. Next, treadmill running is no worse for all five IMU units. The environment of a public hall brings slightly higher percentages, but remains in the same range as in the laboratory and on the treadmill. The urban street environment still provides low packet loss with four IMU units. The fifth, the pelvis IMU unit, is at 0.5% packet loss. In fact, the pelvis IMU unit is the only slightly elevated packet-loss figure.
## 2 Mission Concept
### Concept of Operations  
MeznSat Concept of Operations (ConOps) is highlighted in Figure 4. During the satellite lifetime, it will operate in five main modes. In the launch and early checksum phase, the standard operations will be performed such antenna deployment, de-tumbling, and beacon transmission. Besides, all subsystems are checked for correct functionality during the initial checksum mode.  
Figure 4: Overall modes of MeznSat operations.  
Figure 3: Illustration of the different CubeSat platforms.  
The nominal operation phase performs the main mission operations of the satellite, such as the payload operations. Normal mode is the mode in which the satellite will be in for most of its lifetime during which it will monitor all subsystem components and collect HK data while also sending a periodic beacon. In case of defects or abnormalities, the satellite enters the safe mode. Mission specific tasks like image capturing and storing image to memory are executed during payload mode. Ground Contact mode is entered when the satellite is in the range of the Ground Station during which it will be sending real-time data and receiving/executing commands.
## 4 Experiments and Results
### Performance Evaluation
#### 4.2.7 Comparison of Finger Segmentation  
We compared our finger segmentation method with the previous two, as shown in Figure 11. Since the quantitative analysis of the performance of finger segmentation was not made in those two studies, the comparison is conducted visually by showing the processing result in the form of pictures. Overall, our segmentation method shown in Figure 11b outperforms the other two in c,d.  
Figure 11b displays the result of our proposed two-phase distance transformation. The blue line is the palm kernel when \(d_{in}=0.5R_{0}\). The red line is expanded from the blue line to achieve segmentation.  
Figure 10: (**a**) The camera posture with respect to \(\theta\) and \(\varphi\). (**b–f**) Different pictures of the hand taken by the wearable camera under different parameters of \(\theta\) and \(\varphi\).  
Figure 11c presents the drawing circle method employed in [30]. The white circle is the maximum inner circle with a radius \(R_{0}\). Then, another three circles with a radius of \(1.1R_{0}\), \(1.2R_{0}\), and \(1.3R_{0}\) are drawn. However, none of them can provide a proper segmentation.  
Figure 11d tests the method in [29]. Red circle has a radius of \(1.2R_{0}\). Each yellow line connects a point on the circle and its nearest neighbor on the hand contour, and then fingers are recognized as the regions outside these yellow lines. This method works better than simply drawing a circle, but it still depends largely on an appropriate radius of the circle which deteriorates its ability to generalize.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 3 Results  
### Spatial Resolution Computation  
Based on the above results the spatial resolution of a GNSS-R system can now be defined as the width of the transition from the 10% above \(\left|\rho(\varepsilon_{r,2},\ \theta_{i})\right|^{2}\triangleq\left|\rho_{max} \right|^{2}\), and 90% of \(\left|\rho(\varepsilon_{r,1},\ \theta_{i})\right|^{2}\triangleq\left|\rho_{min} \right|^{2}\)  
\[\left|F(v_{90\%})\cdot\rho_{max}+F(-v_{90\%})\cdot\rho_{min}\right|^{2}=0.9 \cdot\left|\rho_{max}\right|^{2}, \tag{8a}\] \[\left|F(v_{10\%})\cdot\rho_{max}+F(-v_{10\%})\cdot\rho_{min}\right|^{2}=1.1 \cdot\left|\rho_{min}\right|^{2} \tag{8b}\]  
Figure 8: Graphical representation of the principle of superposition and its application to the computation of the total electric field incident. Opaque screen on the (**a**) right and (**b**) left sides block radiation.  
The solution of the above equations has to be performed numerically, and it actually depends on the values of \(\rho_{min}\) and \(\rho_{max}\). The difference between \(v_{10\%}\) and \(v_{90\%}\) leads to the width of \(v\), \(\Delta v=v_{10\%}-v_{90\%}\), which is linked to the spatial resolution by means of Equation (9). For the sake of simplicity, assuming an airborne GNSS-R instrument, so that \(R_{T}\gg R_{R}\), and approximating \(R_{R}\approx h_{R}/cos(\theta_{i})\), the following is an approximated formula for the spatial resolution:  
\[\Delta v=\Delta x\sqrt{\frac{2\cdot cos(\theta_{i})}{\lambda\cdot h_{R}}} \Rightarrow\Delta x=\Delta v\sqrt{\frac{\lambda\cdot h_{R}}{2\cdot cos(\theta _{i})}}. \tag{9}\]  
Figure 10 shows the evolution of \(\Delta v=v_{10\%}-v_{90\%}\) as a function of \(\Delta\rho=\rho_{min}/\rho_{max}\) in decibels. It varies from 0.74 for \(\rho_{min}/\rho_{max}=\ -3\) dB to 1.7 for \(\rho_{min}/\rho_{max}=\ -20\) dB. This means that for a plane flying at \(h_{R}=1000\) m height, at GPS L1/Galileo E1 (\(\lambda=19\) cm), the spatial resolution ranges from 7.2 m/cos(\(\theta_{i}\)) to 16.6 m/cos(\(\theta_{i}\)), depending on the contrast of the reflection coefficients in the transition.
## 3 Results
### Virtual Height Comparison
These virtual height profiles correspond to the electron density profiles displayed in Figures 3 and 4. The difference between the virtual heights was calculated by subtracting the ionogram heights from the GNSS-RO heights (Table 8). Difference histograms and CDFs are displayed in Figure 19, which show all of the sites except Gakona have an average difference around zero. Gakona, however, shows a positive bias of around 7 km. Figure 19 does not split the data into altitude bins as performed in Figure 16 because the difference trends over altitude were not as obvious as the electron density trends. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Site** & **Hermanus** & **I-Cheon** & **Gakona** & **Fortaleza** \\ \hline R\({}^{2}\) & 0.36 & 0.18 & 0.19 & 0.23 \\ MAE (km) & 7.6 & 8.1 & 11.0 & 8.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: MAE and R\({}^{2}\) values for the GNSS-RO and ionosonde virtual heights. Figure 18: A comparison of GNSS-RO and ionosonde virtual heights with the RO virtual heights calculated using a numerical ray tracer. The midlatitude sites show a clustering near the 1:1 line near \(fmin\) altitudes and all sites show a positive bias for the lower altitudes.
# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
## 6 Discussions
\begin{tabular}{l l} CHAMP & Challenging Minisatellite Payload \\ CODE & Center for Orbit Determination in Europe \\ DOY & Day of Year \\ DSHL & Disabling of Supplemental Heater Lines \\ ESA & European Space Agency \\ GFZ & GeoForschungsZentrum \\ GNSS & Global Navigation Satellite System \\ GOCE & Gravity field and steady-state Ocean Circulation Explorer \\ GPS & Global Positioning System \\ GRACE & Gravity Recovery and Climate Experiment \\ GRACE-FO & Gravity Recovery and Climate Experiment Follow-On \\ IGS & International GNSS Service \\ JPL & Jet Propulsion Laboratory \\ LEO & Low Earth Orbit \\ LeGNSS & Leo enhanced Global Navigation Satellite System \\ NRT & Near Real Time \\ NTC & Non Time Critical \\ POD & Precise Orbit Determination \\ PPP & Precise Point Positioning \\ RMS & Root Mean Square \\ RTPP & Real Time Pilot Project \\ \end{tabular}
# A Human Health Toxicity Assessment of Biogas Engines Regulated and Unregulated Emissions
## 2 Literature Review
### Engines Emissions Measurements  
One of the point of novelty of the proposed work is the on-site measurements of commercially available and in-operation biogas units. A need arising from the fact that the vast majority of published works analyse ICEs' emissions like NO\({}_{\rm x}\), CO\({}_{\rm 2}\), PM and unburned hydrocarbons (UHC) adopting lab-scale engines and simulated biogas as fuel (see, e.g., [37; 38; 39; 40]).  
Lab-scale engines are usually single-cylinder or car ICEs, while simulated biogas is a mixture of CO\({}_{\rm 2}\) and methane or natural gas. So, they do not include the entire set of compounds detectable in biogas produced in commercially available AD units. Simulated biogas is characterised by a constant composition of CH\({}_{\rm 4}\) and CO\({}_{\rm 2}\), while in commercial plants the biogas composition can vary throughout the year, since the AD process is influenced by several factors like the feedstock type, the biological reaction time, the temperature, etc.  
Only [PERSON] et al. [41] and [PERSON] et al. [42] examined both regulated and unregulated emissions. In particular, in 2000, [PERSON] et al. monitored several Danish facilities fed by both biogas and natural gas with the aim of computing the emission factors. The analysed biogas facilities included plants fed by manure and agricultural wastes, but also ones which treated waste-water. So, compared to the authors' plants, which adopt manure and energy crops, there is no uniformity in diets, plants nameplate power, ICEs technology and emissions legal limits. In regard to emission factors computations, [PERSON] et al. considered PAHs and aldehydes, while in the present work, authors consider PAHs, aldehydes, and dioxins and furans, substances that are difficult to track and rarely measured especially in commercially available units.  
Similarly, [PERSON] et al. [42] evaluated the emission inventories of Danish stationary combustion plants and, among them, also those of biogas facilities. The investigation referred to plants in operation before the year 2011. The considered pollutants are: CO, CO\({}_{\rm 2}\), NO\({}_{\rm x}\), NMVOCs, methane, nitrogen dioxide, ammonia, sulphur dioxide, PM, heavy metals, polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), hexachlorobenzene and PAHs. Biogas was derived from manure and organic waste (75% of the total amount of produced biogas) or from landfill and sludge.  
So, also in this case, compared to the authors' plants there is no uniformity in diets, ICEs technical features or emissions legal limits.  
Based on the literature review, it is clear that the present experimental campaign can be considered a starting point for increasing knowledges on regulated and unregulated emissions of MW-size ICEs fed by biogas derived from agricultural feedstock. In addition, this set of measurements can allow the computation of the damage to human health as well as contributing to an emissions standard update.