esa-sceva/satcom-chunk-collection huggingface dataset:
# 4D Building Reconstruction with Machine Learning and Historical Maps
## 4 Methodology
### Heights Prediction Metrics
#### 4.4.1 Evaluation of the RMSE, MAE, and R\({}^{2}\) on Randomly Split Training and Test Data  
In machine learning applications, the prediction quality considers how well a model performs on data not used when fitting the model. Therefore, data are commonly split into training and test datasets (typically 70% and 30%, respectively), where the training is used to estimate parameters of the predictive method and the test dataset for evaluating its accuracy. When data augmentation (Section 4.3) is used, those data falling in the test dataset are moved to the training dataset. Most used metrics for evaluating the quality of the models are the root mean square error (\(RMSE\)) (3), the mean absolute error (\(MAE\)) (4), and \(R^{2}\) (coefficient of determination) (5). They are respectively defined as:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}\left(y_{j}-\hat{y}_{j}\right)^{2}} \tag{3}\]  
\[MAE=\frac{1}{n}\sum_{j=1}^{n}\left|y_{j}-\hat{y}_{j}\right| \tag{4}\]  
\[R^{2}(y,\;\hat{y})=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{ n}(y_{i}-\overline{y})^{2}} \tag{5}\]  
Median of the height differences (ground truth vs. predicted) and standard deviation are also used and reported as evaluation metrics.  
A more in-depth control of the machine learning prediction quality can be performed adopting--as a test set--a small subset of still existing buildings having the same shape and being present on different historical maps.
## Materials and Methods
### Data
#### 2.2.3 Carbon Dioxide Flux (CO\({}_{2}\)Flux)  
To estimate annual forests' carbon dioxide fluxes (umol m\({}^{-2}\) s\({}^{-1}\)), we employed the carbon dioxide fluxes (CO\({}_{2}\)Flux). This is a remote sensing-based empirical model based on the Normalized Difference Vegetation Index (NDVI) (Equation (1)) and the scaled Photochemical Reflectance Index (sPRI) (Equation (2)) vegetation indices. Negative values represent a CO\({}_{2}\) sink, while positive values represent a CO\({}_{2}\) source [18; 34; 42; 61]. While NDVI reveals the vigor of photosynthetically active vegetation, in which it may be able to absorb carbon [34; 42], the sPRI estimates the carotenoid pigments of the leaves, indicating their rate of carbon dioxide storage [41]. For the calculation of NDVI, spectral bands 1 (620-670 nm) for red and 2 (841-876 nm) for NIR were used, based on the product MOD09A1 (Equation (1)). The bands used from the MOD09A1 product have a spatial and temporal resolution of 500 m, 1 to 2 days, respectively [62]. To estimate the sPRI, spectral bands 3 (459-479 nm), for blue (B), and 4 (545-565 nm), for green (G), of the MOD09A1 product were used:  
\[\mathrm{NDVI}=\ \frac{NIR-R}{NIR+R}, \tag{1}\]  
\[sPRI=\bigg{[}\bigg{(}\frac{B-G}{B+G}\bigg{)}+1\bigg{]}/2, \tag{2}\]  
The CO\({}_{2}\)Flux values (umol m\({}^{-2}\) s\({}^{-1}\)) were estimated annually by taking the annual median of the time series (Equation (3)). The factors used in the equation were calibrated based on the Eddy Covariance method in micrometeorological towers [34; 42; 61].  
\[\mathrm{CO_{2}Flux}=13.63-66.207\ (NDVI\times sPRI), \tag{3}\]  
The annual median values were calculated between 2009 and 2019 using JavaScript language as input in the Google Earth Engine platform [58].
## 2 Materials and Methods
### Cell Viability Assay  
MTT assays were used to determine the viability of 3T3-L1 preadipocyte cells treated with each compound. Cells were cultured in 96-well plates at a concentration of 1 \(\times\) 10\({}^{5}\) cells/mL for 24 h and treated with various concentrations of tilianin and ARE for 48 and 72 h, respectively. The supernatant in each well was removed, and the cells were treated with 5 mg/mL MTT for 4 h. The MTT solution was discarded, and the resulting formazan was dissolved in dimethyl sulfoxide (DMSO). After 30 min, OD\({}_{720}\) was measured using a microplate reader (fluostar omega, BMGlabtech, Offenburg, Germany).
## 3 Description of the Maritime Data Transfer Protocol
### Modelling Considerations
Even though it cannot be cabled by any means, any network relying on already established standards for wireless communications will work in an easier way than at the acoustic level. 3. Periodicity of the message. There will be messages that will be sent periodically as in any other distributed system or CPS. Commonly, these ones will be related to periodic heartbeats or keep alive notifications used to communicate both the autonomous maritime vehicles with each other, along with the CCS. 4. Purpose of the message. Depending on the nature of the information contained in the message (data requests, GPS coordinates, etc.) the messages that are used will be interchanged depending on what has been requested and the availability of information. Consequently, a taxonomy that classifies the messages that have been created according to the entity that sends them (that is, either the autonomous maritime vehicle or the CCS) has been created as a way to provide a better grasp of the messages that are used.
# Visual Static Hand Gesture Recognition Using Convolutional Neural Network
## 3 Results
### Results of Adding Dropout to the CNN Model  
The accuracy after adding dropout to the _CNN_ model is 90.5%. As we can see in Figure 6, by using a dropout rate of 0.5, the difference between the training loss and the validation loss has decreased significantly, which means that overfitting has decreased as the  
Figure 5: The figure shows the training and validation loss of the _CNN_ model without using dropout on NUS II dataset. On the x-axis is the epoch and on the y-axis is the output of the loss function.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multicolumn{10}{c}{**Confusion Matrix on NUS II Dataset with an Accuracy of 82.5\%**} \\ \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** \\ \hline A & 17 & 0 & 0 & 2 & 0 & 0 & 0 & 1 & 0 & 0 \\ \hline B & 1 & 18 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\ \hline C & 0 & 0 & 16 & 2 & 0 & 0 & 0 & 1 & 1 & 0 \\ \hline D & 2 & 0 & 0 & 17 & 0 & 0 & 1 & 0 & 0 & 0 \\ \hline E & 0 & 0 & 0 & 0 & 18 & 0 & 1 & 0 & 1 & 0 \\ \hline F & 0 & 0 & 0 & 0 & 1 & 18 & 0 & 1 & 0 & 0 \\ \hline G & 0 & 0 & 1 & 1 & 0 & 0 & 18 & 0 & 0 & 0 \\ \hline H & 2 & 1 & 1 & 3 & 1 & 1 & 0 & 10 & 1 & 0 \\ \hline I & 0 & 0 & 0 & 1 & 1 & 3 & 0 & 0 & 14 & 1 \\ \hline J & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 19 \\ \hline \end{tabular}
\end{table}
Table 3: This table shows confusion matrix of not using dropout or skin segmentation or data augmentation on NUS II dataset with an accuracy of 82.5%.  
model now generalizes better on the new data used for validation or testing. Furthermore, we can see that the overall accuracy of the model increased from 82.5% to 90.5%. As we can see in Table 4, the model improves so much with most of the classes, especially with class (H), which seems to be overfitting more than other classes as it has increased from 10 to 14, which means it has classified 4 images more correctly. The classes (B, E, G) have been classified correctly for all of the images after doing the dropout, as they have an accuracy of 100%. Hence, we can see that using dropout has improved the model so much.
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 3 Local Outlier Factor
### Implementation
#### 3.2.3 Matrix Formation  
Flight data consist of various flight parameters recorded frequently. The analysis of flight data is challenging because of the high dimensions in the dataset. In order to compare various flight parameters across the number of aircrafts, a matrix M was designed. The two-dimensional matrix consists of all the flight parameters of aircrafts under study stacked together. The matrix M is defined as [m \(\times\) n], where m is the total number of flights under study and n is the total number of features. This feature space is given by flight parameters \(p\) recorded at \(d\) time-steps for each flight. Therefore, there were \(p*d\) points in the feature space for each of the flights under study. For each flight, the feature space was defined as:  
\[[p_{1}d_{1},p_{2}d_{2}\cdots p_{i}d_{j}] \tag{1}\]  
where \(p_{i}d_{j}\) is the value of the ith parameter from \(p\) total parameters recorded at jth time-step, and as there are \(m\) total number of flights, the matrix M is given by  
\[\text{M}=\text{m}\times[p_{1}d_{1},p_{2}d_{2}\ldots\ p_{i}d_{j}] \tag{2}\]
# Modulation of Host Lipid Pathways by Pathogenic Intracellular Bacteria
## 2 Intracellular Bacteria That Replicate Inside a Vacuole during Infection
### Fatty Acid \(\beta\)-Oxidation for Energy Production
lack a large array of metabolic and lipid biosynthesis pathway thereby relying on the host for pathway intermediates and scavenged lipid nutrients [131]. As described previously, _C. pneumoniae_ replication requires the host lipid transport protein, FABP4, which regulates the release of free fatty acids from the host LDs, typically for host FAO and subsequent ATP production. These data suggest that _C. pneumoniae_ may utilize host lipids as an intracellular energy source [132] (Figure 1). In addition, a facultative intracellular bacterium, _Legionella pneumophila_, infects alveolar macrophages and replicates within an ER-derived PCV and requires host FAO during infection [133; 134; 135]. The host FAO regulation has not been shown to be used as a method of energy production for the bacteria but instead is a mechanism for modulating bacterial growth states during the transition from a replicative stage upon host cell entry. This shift in bacterial stages is regulated by nutrient acquisition in the form of ER-derived vacuole fusion with the PCV [136]. Later in the replication stage, modulation of sphingolipid metabolism and autophagy initiates auto-phagolysosome fusion with the PCV providing lysosomal characteristics such as encoded lipolytic enzymes giving potential for breakdown and utilization of other nutrients such as lipids [133; 137; 138]. Once the PCV is depleted of the nutrients required for active replication, _L. pneumoniae_ shifts to a \"transmission phenotype,\" characterized by bacterial flagella activation and a downregulation in bacterial metabolism. During the life stages of intracellular infection, _L. pneumoniae_ monitors depletion of fatty acid levels, including host malonyl-CoA and intermediates of FAO-derived acyl-chain breakdown, to initiate the stringent response triggered by ppGpp synthetases and SPoT, which regulates the phenotypic shifts required during the _L. pneumoniae_ lifecycle [134; 135; 139; 140; 141; 142] (Figure 1).
# Airborne Waveform Lidar Simulator Using the Radiative Transfer of a Laser Pulse
## Theories and Modeling
### Airborne Lidar Simulator
Point 3 interacts in the upper half circle area, resulting in 54% of relative energy, and the computed altitude is about 1 cm below the surface. The point 4 beam propagates in a westerly downward direction, making the first hit at the bottom edge of the small arc of the circle. The whole interaction area is a small arc area resulting in a relative intensity of only 14%. Considering the interaction position is the left arc of the circle, the center of the beam is already lower than the top surface. That is why the altitude (z-component) of the computed lidar point position is 95.7 cm, which is lower than the cube top altitude of 1 m. This means the computed lidar point is suspended in air at 4.3 cm lower than the top surface. In fact, the laser pulse reflection occurs exactly on the surface, which is at a 1 m altitude. However, the waveform modeling simulates the pulse-target interaction accurately and simulates the situation that a real lidar system experiences. A lidar system detects a waveform peak, but it has no idea on exactly which part of the cross-sectional area of a beam the light reflection occurs. Thus, it assumes that the interaction occurs at the beam center. Obviously, this error occurs because the radiative transfer and waveform-based rigorous computation approach is used. However, this error is exactly what must be simulated. As a result, the target will have slightly larger apparent dimension; in other words, a target enlargement effect. If the simple georeferencing was used, the cube hit position will be on the side wall of the cube, which does not happen in a real lidar system. Of course, if the beam divergence is very small and the sensor AGL is low, the target enlargement effect will be minimized accordingly. Finally, the simulator computes TPU according to the equations described above.
## 2 Bioactive Compounds of Jackfruit
### Seed  
Jackfruit seeds have many bioactive compounds such as polyphenols, and within these, mainly phenolic acids, flavonoids, and stilbenes [29]. Depending on the extraction technique used, those compounds might present variations in their chemical composition or bioactivity [20]. [PERSON] et al. [1] reported values for polyphenols (243 \(\pm\) 27.0 mg of gallic acid equivalent (GAE)/100 g dry seeds), flavonoids (2.03 \(\pm\) 0.06 mg EC/100 g dry seeds), tannins (0.06-0.229 mg/100 g of seed), ferulic acid (0.216 mg/100 g of seed), and gallic acid (1.105 \(\pm\) 0.1 2 mg/100 g of seed).  
Some studies reported the oils present in jackfruit seeds. The main fatty acids present are linoleic and linoleic acid [1]. [PERSON] et al. [30] evaluated the oils' fatty acids composition and antioxidant potential in five different jackfruit species. Their study found jackfruit to be a good source of essential fatty acids (EFAs) with a notable antioxidant activity; higher percentages were observed in the DPPH test for _Artocarpus integer_ (98.4 \(\pm\) 0.2% of inhibition/50 \(\upmu\)L), followed by _Artocarpus integrifolia_ (98.2 \(\pm\) 0.3% of inhibition/50 \(\upmu\)L) and _Artocarpus heterophyllus_ (87.4 \(\pm\) 0.2% of inhibition/50 \(\upmu\)L). Additionally, the seeds are good sources of minerals, such as potassium, which has the highest concentration (2470 ppm), followed by calcium, magnesium, and sodium; the seed's kernel contains 148.50 ppm of iron [1]. Jackfruit seeds also contain 400 mg/g extract of non-reducing sugar, a potential prebiotic ingredient [10].  
There are reports that seed powder nanoparticles showed antimicrobial activity against _Escherichia coli_ and _Bacillus megaterium_[3]. The jackfruit seed crude extracts contain jaclain protein [31]. Regarding the antioxidant activity of peptides from jackfruit seeds, [PERSON] et al. [32] found, by subjecting proteins to trypsin digestion, that the peptide JFS-2 has antioxidant activity, which can be affected by pH, temperature, and gastrointestinal digestion.
# Application of Tucker Decomposition in Temperature Distribution Reconstruction
## 3 Numerical Simulation and Analysis  
On the basis of the previous research, in order to reduce the computational cost, numerical simulation is used to quantitatively demonstrate the feasibility of the proposed algorithm based on the Tucker decomposition and determines the effectiveness of the method.  
A space of 4 m \(\times\) 4 m \(\times\) 4 m is constructed as a 3D temperature field reconstruction area. Set three different 3D temperature distribution numerical models as the research objects of the temperature reconstruction in this section, which are used to calculate the prior dataset and the test dataset. Apply the proposed 3D temperature distribution reconstruction algorithm to reconstruct the temperature profile under different test conditions. The effectiveness and feasibility of the reconstruction algorithm are analyzed. Restricted by the existing calculation conditions, the three-dimensional temperature distribution data is characterized by selecting 2D temperature field sections at different heights (0.5 m, 1.5 m, 2.5 m and 3.5 m).
## 3 Simulation Results  
In order to be able to evaluate the system independently of the problem of finding the sunrise and sunset time accurately, the algorithm proposed in Section 2.2 is tested with a theoretical celestial model. Thanks to this model, the sunrise and sunset times can be used as inputs to the system with a low error. Stationary objects are assumed since the objective of this section is to validate the celestial model and extract some conclusions prior to applying it to a real measurements.  
The theoretical celestial model used in the simulations is based on [23], and to validate that these simulations are robust, they were benchmarked against a spreadsheet developed by the National Oceanic and Atmospheric Administration of the United States Department of Commerce (NOAA) [25].  
Note that our system operates according to civil time (without taking into account the established time zones) while the NOAA calculations are based on solar times. The following equation can be used to convert between the two time-formats:  
\[t_{\text{civil}}=t_{\text{solar}}+EqT \tag{9}\]  
where the \(EqT\) value is the Equation of Time for a given day. There are different approaches for determining \(EqT\). NOAA employs a very accurate expression given by [PERSON] [23]. However, this is a complex equation which uses parameters not available for this application. Thus, the expression proposed by [PERSON] [24] was employed since it only uses information about the day \(d\):  
\[EqT= 229.18\times(0.000075+0.001868\cos(\beta)\] \[-0.032077\sin(\beta)-0.014615\cos(2\beta)\] \[-0.040849\sin(2\beta)) \tag{10}\]  
where \(\beta\) is described in Section 2.2. A comparison of the two equations of time is shown in Figure 5.  
The NOAA sunrise and sunset time predictions have been verified to be accurate within a minute for locations between \(\pm 72^{\circ}\) latitude, and within 10 min outside those latitudes. Also, the celestial  
Figure 5: Comparison between the Meeus and Spencer Equations of Time.  
model employed is valid for dates between 1901 and 2099, due to approximations used in the Julian Day calculation.  
The simulation involves finding the sunrise and sunset times, in UTC, using the NOAA approach for 2011. For each value the algorithm described in Section 2.2 is applied.  
Figure 6 shows the results of this procedure using the Oslo (Norway) coordinates (59.95\({}^{\circ}\)N, 10.75\({}^{\circ}\)E). This figure shows that the system gives values closer to the coordinates used. The longitude is theoretically perfect with zero error. For the latitude, its results are accurate, but there are error peaks close to the equinoxes, where the value of the declination of the sun \(\delta\) tends to 0. The equinoxes occur at 22, 23 March (\(d=81,82\)) and 20, 21 September (\(d=265,266\)).  
One conclusion drawn from the simulation results is that the system works according to theory. The fact that the computational complexity is not critical allows to modify the algorithm, creating complex error compensation algorithms and even to use numerical solving in order to improve the accuracy.
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 2 Design Aspects  
The fully precast lightweight steel-UHPC composite bridge (LWCB), as shown in Figure 1a, consists of several \(\pi\)-shaped steel-UHPC composite girder components (Figure 1b). Each prefabricated component is composed of one UHPC panel and two I-shaped steel girders, which are connected by headed studs. In the transverse direction of the LWCB, the prefabricated components are connected by panel-to-panel joints, while in the longitudinal direction, they are assembled together by girder-to-girder joints.  
Different from the traditional steel-concrete composite bridge, the UHPC panel is fabricated to connect the double I-shaped steel girders in the prefabricated factory. As a result, the fully prefabricated component can be transported and lifted integrally. The main advantages of the component are as  
Figure 1: View of the lightweight steel-ultra-high-performance concrete (UHPC) composite bridge (LWCB): (**a**) the whole bridge; (**b**) prefabricated component.  
follows: (1) the fully precast scheme can significantly decrease on-site construction time; (2) the shear pockets, as well as the pocket-induced problems, such as construction complexity, longer construction time, and higher requirement for fine construction, can be eliminated; (3) the manufacturing quality of the prefabricated components can be guaranteed.  
Figure 2 shows the typical bending moment envelops of a continuous beam bridge. It is seen that the negative bending moment of the main beam reaches the maximum value at the top of the pier, and gradually decreases from the peak region to the two sides. Owing to the high tensile strength of UHPC, it may be considered to cast UHPC on-site in the negative bending moment area near to the support. Note that the interfacial strength between the poured UHPC and the prefabricated part is weak due to the discontinuity of the steel fiber, the weak interfaces are thus set in the areas with relatively lower negative bending moment. As shown in the right side of Figure 2, the T-shaped UHPC joint in the negative bending moment area is proposed to significantly decrease the interfacial cracking risk during the service life. It has a lengthened top that looks like the letter \"T\" from the side view; thus, it was named a \"T-shaped joint\". Besides, the prestressed system commonly used in the negative bending moment area can be avoided, which greatly speeds up the on-site construction time.  
To validate the effectiveness of the proposed design, a comparative study on the model tests between the T-shaped joint and the traditional girder-to-girder joint (herein referred to as \"I-shaped joint\") was conducted as follows.