esa-sceva/satcom-chunk-collection huggingface dataset:
# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 2 Materials and Methods: Tsunami Modelling and Satellite-Based Emergency Mapping
### Early Tasking Approaches Based on the Tsunami Model Output
#### 3.3.2 Maximum Wave Height and Population Estimates  
This approach exploits a population estimate dataset as exposure information. Specifically, the following empirical formula is proposed:  
\[Relative\ impact\ index=Log\ (k\ Pop)\ H_{max} \tag{1}\]  
where:  
* _Pop_ is the population in an arbitrary radius of 6 km from the affected location coordinates.
* \(k\) is a factor that changes the relative weight of the population versus the tsunami impact.
* _Hmax_ is the maximum wave height estimated in the affected location.  
In the proposed scenario, \(k\) has been set equal to 0.0001 to avoid overweighting the population with respect to the wave height. Such value is a first attempt to identify the best balance between tsunami wave height and population exposure; a more systematic analysis based on future events is necessary to confirm that this value can be properly generalized. As a population data source, the Landscan ([[https://landscan.ornl.gov](https://landscan.ornl.gov)]([https://landscan.ornl.gov](https://landscan.ornl.gov)), 2016 edition) dataset has been used. According to this approach, 3 high priority and 1 medium priority areas for satellite tasking are identified (Figure 12b). It can be noted that only one area was identified as high priority in both methods covered so far, clearly highlighting the crucial role of the type of exposure data exploited in the estimation of the relative impact.
## 4 Model Optimization Strategies and Result Analysis
### Result Analysis
#### 4.2.2 Impact of Gradient Learning Rate on the Model  
To investigate the impact of the change in learning rate on the model training outcomes, under the conditions where the transfer learning method and attention mechanism remain consistent, several control experiments were designed, namely Experiments 1, 2, 5, and 6 and Experiments 3, 4, 7, and 8. The comparison results of Experiments 1 and 3 and Experiments 6 and 8 are shown in Figure 9. From the figure, it can be seen that the accuracy of Experiment 1 and Experiment 3 are 75.87% and 82.33%, respectively, with F1 scores of 0.7581 and 0.8110, respectively. The accuracy of Experiment 6 and Experiment 8 are 91.20% and 94.67%, respectively, with F1 scores of 0.9087 and 0.9438, respectively. The accuracy and F1 values of the model have been improved after using the gradient learning rate. The following can be concluded: 1 The model using the gradient learning rate has slightly higher recognition accuracy than the one with a constant learning rate. In Experiment 1 and Experiment 3, the average recognition accuracy increased significantly. This indicates that the gradient learning rate method can effectively manage and adjust the learning speed of the model during the training process, allowing the model to use a larger  
Figure 8: Control chart of experimental results with and without transfer learning.  
learning rate in the initial stage for faster convergence and gradually reduce the learning rate in the subsequent stages. This enables the model to fine-tune its parameters more delicately, thereby enhancing its generalization performance. In contrast, the model with a fixed learning rate may experience overfitting or underfitting in the early or later stages, resulting in subpar overall performance. 2 The gradient learning rate method can better balance the model's learning speed at different training stages. In deep learning models, the learning rates of different layers may have different impacts on model performance. The gradient learning rate method can adjust the learning rates of different layers according to the specific task and network structure, allowing the model to learn and update the parameters of each layer more effectively, thereby enhancing the model's expressiveness. In contrast, the model with a fixed learning rate may not fully utilize the hierarchical features of the network structure, thus affecting the recognition accuracy. In this section, the initial learning rate is set to 0.001, and the learning rate is halved every 10 epochs.  
Figure 9: Control chart of experimental results with and without asymptotic learning rate.
# An Introduction to Nonlinear Integrated Photonics: Structures and Devices
## 2 Integrated Photonic Structures
### Microresonators and Photonic Crystals
in semiconductor heterostructures grown by molecular-beam epitaxy [106] in 1992, are special wave solutions embedded in a radiative continuum, which, however, remain localized without coupling to the extended waves or radiation. BICs have been observed in photonics by [PERSON] et al. [107] by using simple structures, such as two arrays of identical dielectric gratings or two arrays of parallel dielectric cylinders [107]. BICs have then been studied in a wide range of material systems, including dielectric photonic crystals, optical fibers, and waveguides; an interesting review was published in 2016 by [PERSON] et al. [108]. Figure 4 shows an example of BIC structure based on two coupled polymer strip waveguides (WG1 and WG2) onto a lithium niobate (LN) thin film. Etching of LN is not necessary as the strips can be directly patterned through a single e-beam lithography process. When the parallel strip waveguides are placed closely (distance of the order of micrometer), the TE continuum modes exist, as shown in Figure 4b. Recently, there have been several works on highly efficient second harmonic generation using BIC structures in lithium niobate, which seem to have great potential for development of compact coherent light sources in a broad wavelength range [110; 111; 112]. Figure 5 presents a schematic illustration of some metastructures [102]. Figure 4: BIC waveguide structure based on polymer strip waveguides (WG1 and WG2) onto a lithium niobate film. Here, WG1 and WG2 are 500 nm thick and LN film is 300 nm thick. (**a**) Cross-section of the structure; (**b**) normalized electric field distribution of the TE continuous mode; (**c**) and (**d**) TM leaky modes. Under proper conditions, the coupling between the TE continuum modes and the TM bound modes can lead to well-confined BIC modes (see (**e,f**)). Reproduced from [109] under Creative Commons License.
## 6 Program Content
### Training Content Pillar #2: Trunk Control  
To appreciate fully how simulation research can be used to answer mechanistic research questions associated with injury/re-injury, one must first understand how the Newton-Euler equations of motion are used to define human movement. Briefly, a simulation of human motion is defined using the Newton-Euler equations of motion (1): Where \(\ddot{\mathrm{q}}\) is the generalised coordinate accelerations due to joint torques, \(\tau\), Coriolis, and centrifugal forces, \(C\big{(}\mathrm{q},\ \dot{\mathrm{q}}\big{)}\), as a function of generalised coordinates, \(\mathrm{q}\), and their velocities, \(\dot{\mathrm{q}}\), gravity, \(G(\mathrm{q})\), and external forces applied to the model, \(\stackrel{{\rightarrow}}{{\mathrm{F}}}\), and its inverse of the mass matrix, \(\big{[}M(\mathrm{q})\big{]}^{-1}\), which contains segment mass' and inertial properties. As observed within the Newton-Euler equations of motion, a relationship exists between the joint angles (\(\int\int\ddot{q}\)) and joint torques (\(\tau\)) to create a forward dynamic simulation. Through the manipulation of this computational relationship, which is founded in the laws of physics, researchers/clinicians are capable of changing the way an individual moves _in-silico_ to produce a new subject-specific movement pattern associated with reduced injury risk and re-injury risk, and/or mitigate disease initiation/progression.  
\[\ddot{\mathrm{q}}=[M(\mathrm{q})]^{-1}\big{\{}\tau+C\big{(}\mathrm{q},\ \dot{ \mathrm{q}}\big{)}+G(\mathrm{q})+\stackrel{{\rightarrow}}{{ \mathrm{F}}}\big{\}} \tag{1}\]  
Within the musculoskeletal modelling and simulation framework OpenSim (simTk.org) [31], [PERSON] et al. [27] developed computational methods capable of optimizing an athletes' movement patterns during change of direction sporting tasks in attempts to reduce their risk of ACL injury during unplanned sidestepping. The optimised movement strategy was to control the position and acceleration of their whole body centre of mass during change of direction sporting tasks. By redirecting an athlete's whole body centre of mass by just 4 cm in their intended direction of travel (i.e., medially), an athlete can expect to reduce their peak abduction knee moments and ACL injury risk by approximately 40%, which is a fantastic return on investment (Figure 3) [27]. As the trunk segment weighs over one-half of an individual's total mass, the dynamic control of this segment, above all others in the kinematic chain, is of paramount importance for reducing an athlete's risk of lower limb injury in sport. The applied  
Figure 2: The top pane shows the relationship between knee flexion and three-dimensional knee moments (Mx,y,z). This relationship was primarily explained by combined frontal (valgus) and transverse plane (internal rotation) knee moments (middle), not the individual knee moment components (bottom).  
clinical message from this research is that the dynamic control of the upper body during sporting tasks is important for reducing an athlete's lower limb injury risk in sport.
## 5 TSCH Link Scheduling Visualization and Data Processing
### Using the TLS-VaD  
To make this tool easy to use and more interesting, TLS-VaD uses a GUI in its application. Figure 32 shows the display of TLS-VaD. This tool provides two menus (simulation and experiment), and two input system (network topology and adjacency matrix). As mentioned in Section 5.1, the simulation menu is used to test whether the algorithm being designed is running as planned, while the experiment menu is used to obtain data so that we can measure the performance of a scheduling algorithm. Figure 32 shows the network topology that was entered into the network topology section, and the adjacency matrix section shows the relevant adjacency matrix.  
Figure 31: The flowchart of TLS-VaD.  
If the user selects the simulation menu, then after entering the input, he/she can press the generate simulation button to see the animation or visualization of the transmit and receive processes in the network (see Figure 33). Figure 33 shows red and blue nodes in the network, with red indicating a node with an empty queue and blue indicating a node with a queue filled with packets waiting to be sent. Figure 33 shows no nodes that transmit and receive activities simultaneously and no node that receives data from child nodes simultaneously. Sending data from child nodes to their parents is done alternately. To ensure that the designed algorithm runs as planned, we could observe the transmit and receive processes at each cycle, which could be played back using the Play All, Play, Rewind, and Forward buttons.  
Meanwhile, if the user is in the experiment menu, then the user will be guided to enter the desired experiment scenario. Figure 34 shows the display when the experiment scenario is entered. After the scenario is determined, the data collection process can be commenced by pressing the Start Experiment button. The results of the experiment will bring up the data summarized in the result  
Figure 33: Transmit and receive visualization on the simulation menu.  
Figure 32: Menus and input systems in TLS-VaD.  
table. Figure 35 shows the result table containing data about the number of cycles (C), channel offset (ChOff), active timeslots (\(\lambda\)), and duty cycle (DC) for network dimensions ranging from 10 to 50 nodes. These data allow researchers to analyze the performance of the developed scheduling algorithm.
# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 3 Materials and Methods
### Feature Extraction
#### 3.4.1 PSD Features  
In spectral estimation, power is distributed over frequency bands contained in a finite set of data signals. PSD and the correlation sequence are mathematically related through the discrete-time Fourier transform. The signal will be more concentrated in its power spectrum if it is more correlated. Various methods of power estimation are present that are used for feature extraction [45; 46]. However, non-parametric estimation methods are used in the EEG signal as they have higher classification performance. Non-parametric methods usually directly estimate the PSD from the EEG signal itself. The easiest way is the periodogram. The periodogram for the N-sample recorded signal is defined as  
\[P_{xx}=\frac{1}{N}\left|\sum_{m=0}^{N-1}\ x(m)^{-j_{2}\Pi I/m}\right|^{2}=\frac {1}{N}\left|x(f)\right|^{2} \tag{1}\]  
\(P_{xx}\) is the power spectral density, a function of frequency \(f\), \(N\) is the total number of samples in the signal \(x(m)\). \(x(m)\) indicates the discrete-time signal, where \(m\) is the sample index, and \(j\) is the imaginary unit (\(\sqrt{-1}\)). \(\Pi\) is the mathematical constant \(\Pi\) (approximately \(3.14159\)), \(f\) is the frequency variable in Hertz, \(\left.\left.\right|x(f)\right|^{2}\) represents the magnitude squared of the Fourier transform of \(x(m)\), evaluated at frequency \(f\). \(x(m)\) is the signal having \(N\)-samples where \(x(f)\) shows the Fourier transform of the signal, such as \(x(m)\).
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 1 Introduction
### Related Work  
A large body of knowledge exists on GNSS performance for aviation. A comprehensive performance analysis of GNSS performance for manned and unmanned aircraft including augmentation techniques was performed in [10]. Fault modes of GNSS and GNSS-INS integrated systems were identified and analysed in [11]. NLOS and multipath are accepted as being the dominant error sources in urban environments. As such, a number of methods have been developed to model signal reflections and their impact on positioning error. These methods can be either purely deterministic, which includes asymptotic methods such as ray-tracing [12; 13], statistical [14], or hybrid methods [15]. A hybrid model based on ground vehicle measurement campaign data can be found in [16; 17]. Regarding the simplification of the propagation environment, it has been found in [18] that simple canonical shapes representing building facades can be used to predict positioning error when wideband channel models are employed. GNSS coverage prediction models to adapt route-guidance are typically used in predictive-Receiver Autonomous Integrity Monitoring (p-RAIM). Planned routes can be assessed to determine whether GNSS failures can be detected based on satellite geometry projection [19]. A similar GNSS multipath prediction concept was used to drive path-planning in [20]. The risk associated with obstacles at low altitude flight was included as a cost-factor in an A* planning algorithm for a fixed wing aircraft in [21].
# Performance of Tilting Pad Journal Bearings with the Same Sommerfeld Number
## 2 Numerical Modeling of Tilting Pad Journal Bearing
### Flexible Pad Model and Nonlinear Pivot Model  
References [11; 12; 13] show that accounting for flexible pad/pivot in the bearing model provides more closely matching results compared to the experiments. For example, the flexible pivot tends to reduce the rotor critical speed while the flexible pad tends to reduce damping. Hence, the current study includes the flexible pad/pivot for the bearing predictions and compare them to TPJB with rigid pad assumption. References [19; 20; 21] describe the flexible pad model, but here is the summary of the model description.  
**Flexible Pad Model:** To solve the flexible pad model, it is important to reduce the number of degree-of-freedom of a pad to lower the computational expense. In the current study, the modal reduction technique is adopted to produce the pad elastic deformation. The pad's translation and tilting motions are expressed in terms of the rigid body mode of the pad [19].  
**Nonlinear pivot model:** The Hertzian contact theory is used to estimate the nonlinear pivot forces for transient analysis. Reference [22] presents the pivot contact area versus the pivot stiffness based on the fact that the pivot has an oval shape. The current pivot model disregards the resistance to tilting motion and the pad translation motion along the circumferential direction. The nonlinear pivot model is well described in reference [19].
## 4 Results  
Three months (June, July, and August 2020) of collocated AGRI and MODIS cloud detection and CTH retrievals over the TP area were compared. The three months of collocated AGRI and MODIS cloud retrievals resulted in approximately 3.2 and 1.5 million cases for cloud detection and cloud top height comparison, respectively. The results were separated by month and included statistics of the agreement with MODIS. For the comparison of the cloud mask results, only AGRI pixels where all the collocated MODIS pixels were identical (i.e., either all clear, all probably clear, all probably cloudy, or all cloudy) were included in the statistics. As a result of this requirement, approximately 37.5% of the collocated scenes were not included in the statistics, and approximately 2.0 million cases were included in the statistics in Table 2.
## 2 Observing Equation of Interferometric Arrays
All the elementary antennas of an interferometric array, here reduced to two antennas \(\mathcal{A}_{p}\) and \(\mathcal{A}_{q}\) separated by a baseline \(\mathbf{b}_{pq}\), are pointing in the same direction, depicted here by the main beam of the elementary patterns \(\mathcal{F}_{p}\) and \(\mathcal{F}_{q}\) (in blue). The radio signal kept by each elementary antenna is coming from any direction \(\xi\) of the surrounding space. Referring to the integral in (1), the contribution from direction \(\xi\) (in red) depends on the delay \(t_{pq}=(\mathbf{b}_{pq}\cdot\xi)/c\) between the time a wave train (dashed red) arrives on antenna \(\mathcal{A}_{p}\) and its arrival on antenna \(\mathcal{A}_{q}\). For the contribution from the nadir looking direction \(\xi=0\) (in blue), this delay is equal to zero. The two radio signals are transmitted to a complex correlation unit to be combined in-phase/in-quadrature and integrated in order to provide the real and imaginary parts of the complex visibility sample \(V\) for the baseline vector \(\mathbf{b}_{pq}\). To make the illustration simpler without losing anything of the interpretation of Equation (1), amplifiers, local oscillators, and band-pass filters are intentionally omitted (more details can be found in [6]). \[\widetilde{T}=\operatorname*{argmin}_{T}\|T\|^{2}\text{ s.t. }\mathbf{G}T=V. \tag{3}\]  
This map is obtained through the computation of the Moore-Penrose [23; 24] pseudo-inverse \(\mathbf{G}^{+}\) of \(\mathbf{G}\):  
\[\widetilde{T}=\mathbf{G}^{+}V. \tag{4}\]  
In order to filter out the Gibbs effects [25] due to the sharp frequency cut-off associated with the limited extent of the experimental frequency coverage of the instrument, \(\widetilde{T}\) has to be damped by an appropriate windowing function: \(\widetilde{\mathcal{T}}=W\star\widetilde{T}\)[26]. This map has to be compared to \(\mathcal{T}=W\star T\) (which is the \"ideal\" temperature map to be reconstructed and apodized with the same window \(W\)) and not to \(T\) (which is not at the same spatial resolution).
# Wheat Germ Agglutinin--From Toxicity to Biomedical Applications
## 1 Introduction  
Wheat (_Tritcium aestivum_ L.) is one of the most essential agricultural staple foods used for human consumption and animal feed. Approximately 21% of the world's food supplies depend on annual wheat crop harvest [1], which causes the production of byproducts that are not always used efficiently or, in some cases, entirely discarded as waste [2]. Increasing trends of consumption-based economy restructuring to more ecologically-minded circular economy models encourage the scientific development of new technologies for byproduct valorization to create products with high nutritional value. One of the wheat processing byproducts which takes 2-3% of whole wheat grain [3] is wheat germ. They are a source of oils [4], tocopherols [5], various polyphenols [6], and specific proteins, such as agglutins (lectins) [7]. Even though agglutins are considered to be antiturinets [8], they might have various prospective applications in biomedicine, biotechnology, and agriculture itself. Considering the high quantities of wheat germ available as low-value byproducts throughout the world, any valorization attempts by extracting specific proteins may lead to high impact worldwide. This review is focused on wheat germ agglutinin, its structure and specificity, extraction and purification methods, biological activity, and possible applications.
## 5 Numerical Studies
### Analysis of Results in Scenario 4
When \(\Delta P^{H}/P^{H}_{load}\) increases from 0 to 50%, the total cost is reduced by 18.82% in summer and 27.22% in winter. The reason is that the relaxation of the heat balance constraint (31) caused by the enlargement of the occupants' comfort zone, which results in a larger feasible region for the optimization problem, will lead to better outcomes. Additionally, the total cost changes more smoothly in summer than in winter, as the former has lower heat consumption, and the impact of relaxing the heat balance constraint is less significant. To assess the impact of carbon price on total cost, we have also plotted the relationship between total cost and carbon price under different seasons, as shown in Figure 12. \begin{table}
\begin{tabular}{c c c c} \hline  & **Carbon.CHP (t)** & **Carbon.GT (t)** & **Carbon.Emission (t)** \\ \hline Summer & 0.0261 & 0.298 & 0.0324 \\ \hline Winter & 0.0613 & 0.538 & 0.060 \\ \hline \end{tabular}
\end{table} 
Table 4: Total carbon on a typical day in summer and winter. Figure 10: Carbon flow in summer and winter. It can be obtained from Figure 12 that, with changes in carbon price, the total cost exhibits opposite trends in summer and winter. In summer, the carbon emissions are lower than the carbon quota, so an increase in carbon price allows for greater revenue from participating in the carbon market, thereby reducing total cost. In contrast, the situation is reversed in winter. When carbon emissions are high, there is a need to purchase quotas from the carbon market, which increases the total cost. When the energy demand of the occupants is fixed, the total cost shows an approximately linear relationship with the carbon price.