esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Hazard WebGIS Platform for Coastal Regions
## 3 Showcasing the Results: Application to the Central West Portuguese Coast
### Regional Products  
#### 3.1.1 Historical Analysis of Past Flooding and Overtopping Events  
The Historical Data of Coastal Flooding Occurrences (_historico de eventos_) was created to showcase the history of flooding and the wave overtopping events in the continental Portuguese coastal zone. Data are organized along the counties located within the Portuguese shoreline. A county in Portugal is organized as a set of parishes. Parishes data for this type of occurrence are also available for analysis on this page (Figure 7).  
Occurrences data include two major components. The first is a set of polygons that represents the area of a specific county or parish, while the other component is a numerical value that represents each variable that comes with the data. These variables are related to flood events:  
* Number of occurrences (_numero de coroncnicas_);
* Impact on humans (_imp. humanos_);
* Anthropic and infrastructures impact (_imp. antropicos e em infraestruturas_);
* Impact on natural and environmental system (_imp. sistemas naturais e ambientais_).  
After the user selects a variable, a request is sent, via AJAX that activates a view on the MOSAIC backend to load the data into the interface. These data are stored in the project database and initially loaded through a Python script that handles two different shapefiles (shp)--one corresponding to the counties and another to the parishes. The shapefiles contain polygons and attributes which are loaded and stored in the MOSAIC project database.  
When the user selects a variable and a county, filtering occurs through a Django view. A view is a Python function that processes a Web request and returns a Web response. After the necessary filtering, a JSON (json) is created and sent to the interface with the data that will be displayed on the map. Data are then loaded into the interface through a combination of pure JavaScript and the Leaflet JavaScript library ([[https://leafletjs.com/](https://leafletjs.com/)]([https://leafletjs.com/](https://leafletjs.com/)) (accessed on 10 May 2021)). The visualization that is created from this operation is a choropleth map, a type of thematic map where each area presented is colored proportionally to the statistic variable chosen by the user (Figure 8). The available variables to be chosen are the same that are loaded into the database initially: number of occurrences (_numero de coroncnicas_), impact on humans (_imp. humanos_), impact on natural and environmental systems (_imp. sistemas naturais e ambientais_) and anthropic and infrastructures impact (_imp. antropicos e em infraestruturas_). The choropleth map is interactive through the use of both hover and on-click mechanisms.
## 4 Discussion
### Far-Red vs. Red Fluorescence Radiances  
In this coniferous forest during early fall senescence, the far-red fluorescence could not be shown to be correlated with any examined reflectance or structure variable across age groups, except for a weak association to one of the LiDAR-derived 3-D structure parameters, the vertical distribution ratio (VDR) for the open canopies of younger canopies (<12 year). Most surprising, the F740 was not well related to canopy GEP in this study, which is not consistent with results from several field investigations which have found significant positive correlations between F740 and canopy GEP [36; 50; 55; 56]. One explanation may be that most of those studies were conducted during highly productive mid-season growing periods, when canopy TChl was high, and other foliar biochemical constituents and processes were in near-optimal conditions. One of the consequences of the variable fluorescence responses at different times of day and stand age classes is that an assumption that F685 (radiances or yields) can be predicted if F740 radiances (or yields) is known, as is assumed in some modeling approaches, is not supported by these campaign results (Figures 8-10). Another explanation could be that stray light was only partially corrected for F740, increasing noise in the far red spectrum. Nevertheless, the F740 was extremely useful in this study because it provided a means to normalize the F685 so that the red/far-red Fratio (F685/F740) successfully described LUE in the mature LP stand.  
The importance and success of the red fluorescence variables, as compared to the far-red fluorescence, is supported by several published radiative transfer modeling efforts and current state-of-the-art fluorescence models [57; 58; 59; 60; 61; 62]. These papers demonstrate that the red fluorescence is more sensitive to vegetation physiological variables than the far-red fluorescence and is potentially retrievable from satellite orbit [63]. Retrieval of red fluorescence from satellite was recently reported [64], however, all other satellite fluorescence retrievals have been for the far-red fluorescence. These have all been acquired from coarse spatial resolution satellite observations obtained with atmospheric chemistry missions [14; 65; 66; 67; 68; 69; 70].
## Appendix B  
Using the results in Table A2, the researchers attempt to explain why industrial density has a negative sign. To investigate this, a dummy variable for the highest manufacturing regions (_High_manufact_regions_) was introduced, representing the areas with the highest concentrations of industrial density, specifically the BKK&VIC and CE regions. The estimation results indicate a positive relationship between regions with high manufacturing density and PM\({}_{2.5}\) pollution. This finding confirms that PM\({}_{2.5}\) pollution is not confined to a single area but exhibits a clustered pattern in certain regions.  
\begin{table}
\begin{tabular}{l c c c} \hline \multicolumn{2}{c}{**Dependent Variable:**} & & \\ _LnPM\({}_{2.5}\)_ & **Random Effects** & **SLM** & **SEM** \\ \hline \multicolumn{2}{c}{**Variables**} & & \\ \hline _LnPop\_dens_ & \(\begin{array}{c}0.055\ *\\ (1.84)\end{array}\) & \(\begin{array}{c}0.083\ ***\\ (3.09)\end{array}\) & \(\begin{array}{c}0.033\ *\\ (1.95)\end{array}\) \\ \hline _LnGPPpc_ & \(\begin{array}{c}-2.031\ ***\\ (-2.92)\end{array}\) & \(\begin{array}{c}-2.052\ ***\\ (-3.27)\end{array}\) & \(\begin{array}{c}-0.776\ *\\ (-1.83)\end{array}\) \\ \hline _LnGPPpc\({}^{2}\)_ & \(\begin{array}{c}0.076\ ***\\ (2.67)\end{array}\) & \(\begin{array}{c}0.079\ ***\\ (3.11)\end{array}\) & \(\begin{array}{c}0.029\ *\\ (1.68)\end{array}\) \\ \hline _LnIndus\_dens_ & \(\begin{array}{c}-0.037\ **\\ (-2.17)\end{array}\) & \(\begin{array}{c}-0.052\ ***\\ (-3.25)\end{array}\) & \(\begin{array}{c}-0.012\\ (-1.30)\end{array}\) \\ \hline _LnEnergy\_cons\_pc_ & \(\begin{array}{c}0.070\\ (1.08)\end{array}\) & \(\begin{array}{c}0.071\\ (1.22)\end{array}\) & \(\begin{array}{c}0.017\\ (0.47)\end{array}\) \\ \hline _High\_manufact\_regions_ & \(\begin{array}{c}0.328\ ***\\ (5.21)\end{array}\) & \(\begin{array}{c}0.254\ ***\\ (4.43)\end{array}\) & \(\begin{array}{c}0.197\ ***\\ (4.63)\end{array}\) \\ \hline _Constant_ & \(\begin{array}{c}15.893\ ***\\ (3.87)\end{array}\) & \(\begin{array}{c}15.523\ ***\\ (4.19)\end{array}\) & \(\begin{array}{c}7.687\ ***\\ (3.04)\end{array}\) \\ \hline \multicolumn{2}{c}{Rho (\(\rho\))} & \multicolumn{2}{c}{\(\begin{array}{c}0.095\ ***\\ (5.17)\end{array}\)} & & \\ \hline Lambda (\(\lambda\)) & & & \(\begin{array}{c}0.966\ ***\\ (58.92)\end{array}\) \\ \hline Observations & 231 & 231 & 231 \\ \hline Within R-squared & 0.369 & & \\ \hline Between R-squared & 0.252 & & \\ \hline Overall R-squared & 0.251 & & \\ \hline Log-likelihood & \(\begin{array}{c}131.450\\ \end{array}\) & \(\begin{array}{c}242.755\\ -242.899\end{array}\) & \(\begin{array}{c}-465.510\\ -431.086\end{array}\) \\ \hline BIC & \(\begin{array}{c}-208.475\\ \end{array}\) & \(\begin{array}{c}-431.086\end{array}\) \\ \hline \multicolumn{2}{l}{Note: z-statistics are reported in parentheses. * \(p<0.10\), ** \(p<0.05\), *** \(p<0.01\).} \\ \hline \end{tabular}
\end{table}
Table A1: Pesaran’s CD Test and Breusch and Pagan LM Test.
# Exact Relationship between Black Phosphorus Thickness and Behaviors of Field-Effect Transistors
###### Abstract  
As a two-dimensional (2D) semiconductor material with excellent optoelectronic properties, black phosphorus (BP) has attracted widespread attention. It was found that the energy band structure of BP crystal changes with its thickness if BP is thin. To explore the accurate effects of the BP thicknesses on devices, BP-FETs with different BP thickness (50 nm, 40 nm, 30 nm, 20 nm, and 6 nm) as the channel material were fabricated by mechanical exfoliation technique. The output characteristics and transfer characteristics of the BP-FETs were analyzed in detail. The source-drain current (\(l_{ds}\)) of devices is directly related to the BP thickness. The larger the BP thickness, the larger the \(l_{ds}\) obtained under the same gate voltage modulation, but the electric field modulation effect decreases. Especially, the correlation between \(l_{ds}\) and BP thickness can be described by a semi-empirical formula, which predicts that only when the BP thickness is less than 21.7 nm, the band structure of BP will be significantly affected by the thickness. The mobility of the carrier increases with the increasing of the BP thickness; for BP thickness of 6 nm, 20 nm, 30 nm, 40 nm, and 50 nm, the mobility is about 52.5 cm\({}^{2}\)/Vs, 187.5 cm\({}^{2}\)/Vs, 214.4 cm\({}^{2}\)/Vs, 252.5 cm\({}^{2}\)/Vs, and 336.4 cm\({}^{2}\)/Vs. Finally, the 50 nm BP in FET was etched to 30 nm using plasma etching technology to further verify the above experimental results. It also confirmed that plasma etching methods tend to introduce structural damage and impurity elements, which in turn has an impact on the output characteristics of the device.  
black phosphorus; field-effect transistor; two-dimensional material +
Footnote †: journal: Applied Physics Letters B  
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Footnote 0: Copyright** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Summary and Discussion
It is hardly done independently without further processing. For two-dimensional data, the task is dominated by a methodology based on a two-step approach. First, the images are analyzed and used to create graphs of the processed environment. Later, the graphs are compared and their similarity is evaluated. For three-dimensional data in the form of point clouds, alignment is part of the structure acquisition process itself, known as 'point cloud registration'. **General discussion. ** One of the most important results of our work is the following: researchers should be aware of the characteristics of their input data representation and not be afraid to change it. Replacement of the acquisition device, and therefore the source data structure, as well as conversion of the already obtained data to another format, can significantly simplify the processing needed to achieve satisfactory results. Furthermore, we should generally be open to flexible and dynamic data representation changes. The dimensionality of acquired measurements is not fixed. We can reproject it and adjust it to our needs. We observed that, in some cases, it is worth trying to obtain a more complicated input structure and cast it onto a simpler one. We could see this in the processing of floor plans generated from point clouds or graphs based on the occupancy maps. What is interesting is also the visible interdisciplinarity of solutions. Many of the algorithms successfully processing N-dimensional structures could process the N + 1-dimensional samples with just minor adjustments. Segmentation and classification processes are evolving from a problem themselves to a step in more complex systems.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 4 Results and Discussion
### Theoretical Impact of Buoy Movements on the FLS Horizontal Velocity Estimates
#### 4.2.3 Yaw Movement  
As yaw only has one degree of freedom, we relate to the rays here by their more common label: \(D_{\text{LOS}}=(N,E,S,W,Z)\). The effect of yaw \(Y\) on the angle \(\theta\) of the different WindCube rays v2 is studied in Figure 7. The horizontal velocity is decomposed into two components: the \(V_{1}\) component in the N-S beams plane and the \(V_{2}\) component in the E-W beams plane. \(V_{1}\) and \(V_{2}\) correspond to the \(u\) and \(v\) axes in the WindCube v2\({}^{\text{\textregistered}}\) reference frame (Figure 1).  
For the four non-vertical beams, yaw is in the same plane as the angle \(\theta\), so it is linearly affected by the rotation: \(\Delta\theta=Y\). For the beam at the zenith, an isolated yaw motion has no effect on its geometry. However, it is important to bear in mind that yaw will affect the vertical measurement in the case of a tilted LIDAR.  
As the angles \(\theta\) of the non-vertical rays are modified by yaw, the measurements of the four rays are impacted by the two wind components \(V_{1}\) and \(V_{2}\). Here we present the effect of motion on the measurement of the N beam:  
\[V_{r_{N}}=V_{r_{N;V_{1}}}+V_{r_{N;V_{2}}} \tag{28}\]  
Figure 7: Diagram representation showing the influence of a yaw variation on the angles \(\theta\) of the five measurement directions of the WindCube v2\({}^{\text{\textregistered}}\) (top view). The suggested times are added to recall the temporality of the WindCube measurement. The horizontal velocity is decomposed into two components: \(V_{1}\) and \(V_{2}\).  
The ray N is originally aligned with the \(V_{1}\) component of the incident wind (Figure 7). Thus, the radial wind speed measurement \(V_{r_{IN,V_{1}}}\) is decreased by the LIDAR motion regardless of the yaw direction (Figure 8a). The \(V_{r_{IN,V_{2}}}\) component depends on the direction of rotation because it depends on the orientation of the beam with respect to the \(V_{2}\) component of the incident wind (Figure 8b).  
We note that, in the hypothetical case of a simultaneous measurement of all the rays, the effect of the yaw \(Y\) on the horizontal speeds measured by the four non-vertical rays would be cancelled and only the direction of the wind measured would be modified. However, in the real case, the influence of the yaw depends on the movement-measurement temporality and yaw motion affects the LIDAR's horizontal speed measurements.
## 2 Leakage Model of Shield Joints Based on the Persson Model
### Critical State of Leakage
#### 2.3.2 Critical Leakage Pressure and Self-Sealing Effect  
In addition to the formation of a leakage channel, energy that overcomes the viscous resistance of fluid and drives its flow is also needed to activate a leakage, though any sealing interface is permeable as long as the magnification is large enough. There is no leakage occurring in some sealing projects; the reason is that the interfacial fluid in the percolation channel will form a plug, which prevents further penetration. We call this phenomenon the self-sealing effect. All complete sealing projects are achieved through their self-sealing effects. The optimal sealing performance of joints depends on the self-sealing stress of the critical percolation channel. This may explain why the greater the squeezing pressure, the better the sealing performance, because, in this case, the size of the critical percolation channel is narrower and, further, provides stronger self-sealing stress. According to the Bernoulli equation [34], the energy consumption is:  
\[\frac{p_{1}}{\rho g}+\frac{v_{1}^{2}}{2g}+z_{1}=\frac{p_{2}}{\rho g}+\frac{v_{ 2}^{2}}{2g}+z_{2}+h_{w} \tag{8}\]  
where \(p_{1}\) and \(p_{2}\) are the fluid pressure on both sides of the sealing interface, respectively, \(v_{1}\) and \(v_{2}\) are the velocities of both sides of the sealing interface, \(z_{1}\) and \(z_{2}\) are the heights of both sides of the sealing interface, and \(h_{w}\) is the energy loss. When a medium (non-contact region) is filled with fluid but no leakage occurs (i.e., the fluid is finally stationary), as shown in Figure 6, the upstream pressure at the right cross section is \(P_{w}\) and the pressure potential energy is all consumed along the leak path, so the velocity and pressure become 0 at the left-side cross-section. This moment is defined as the critical leakage state. The upstream pressure \(P_{w}\) is equal to the critical leakage pressure (\(P_{w}^{c}\)), and it is also equal to the self-sealing stress \(P_{ss}\):  
\[P_{w}^{c}=P_{SS}=\rho g\left(h_{w}+z_{2}-z_{1}-\frac{\left(1/2v_{\max}\right)^ {2}}{2g}\right) \tag{9}\]  
Figure 6: Critical leakage state. Under critical leakage pressure \(p_{w}^{c}\), the fluid slowly seeps into the percolation channel, and the fluid kinetic energy is completely dissipated after the channel is just filled. The velocity of the water in the right side is the maximum (\(v_{max}\)), and it decreases gradually to 0 in the channel, permeating the left side due to the shear stress \(\tau\).
# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## 3 Machine Learning Algorithms Implementation on Low-Cost FPGAs  
Normally, the post-process of IoT data mainly focuses on two tasks: Classifications and regressions [20]. By exploiting the parallelism and low power consumption of FPGAs, MLoF offers a superior solution for these workloads. Drawn from past designs, MLoF is designed with lower computation resources, and it includes a variety of common machine learning algorithms, namely ANN, DT, k-NN, and SVM. Details will be further presented in this section.  
As shown in Figure 1, the system has consisted of the training and the MLoF modules as most machine learning models are used for inferencing and evaluating within IoT devices without requiring extensive training [21], all of the model training process is completed through PC. First, the IoT dataset is gained from the endpoints and will be sent to the training module. Then, an ML library (e.g., Scikit-Learn and TensorFlow Lite) [22] and an ML algorithm should be chosen as the first level of parameters. Thereafter, a set range of hyperparameters (the same set as in the MLoF module) are used to constrain the PC training process, as FPGA has limited local resources. Since hyperparameters are key features in ML algorithms [23; 24; 25], users could set them to different values to find the best sets for training according to Table 1. Next, with all the labeled data and hyperparameters, the best model and the best parameters (including the updated hyperparameters) are generated and sent to the MLoF module. After receiving and storing the parameters into ROM or external Flash, the algorithm is deployed on an FPGA. The language of FPGA is mainly Verilog HDL, thus the algorithms can literally be deployed on any known FPGA platform.  
In this paper, we selected six datasets, the four most representative ML algorithms, and deployed them on 10 low-cost FPGAs, with a total of 240 combinations. The experiments and evaluations are described in detail in Section 5.
## 3 Method  
### Preliminary  
Given a number of tropical cyclone records \(X=\{tc_{1},tc_{2},\ldots,tc_{n}\}\), each tropical cyclone record \(tc_{i}\in X\) has a track \(L_{i}=\left\{l_{i,t,l},l_{i,l2},\ldots,l_{i,tm}\right\}\) that is represented by a sequence of spatial coordinates denoting the locations the tropical cyclone \(tc_{i}\) has passed over, where each \(l_{i,tj}\) can be determined by a pair of spatial coordinates \(\left(\text{lat}_{i,ti},\text{lon}_{i,tj}\right)\), \(tj\in T=\{t1,t2,\ldots,tm\}\), in which \(t1\) is the first timestamp of the tropical cyclone and \(tm\) is the last timestamp. Therefore, \(l_{i,tj}\) can represent the landing location \(\left(\text{lat}_{i,ti},\text{lon}_{i,tj}\right)\) of the tropical cyclone \(tc_{i}\) at timestamp \(tj\). In addition, each tropical cyclone \(tc_{i}\) has some meteorological variables related to the tropical cyclone trajectory at timestamp \(tj\), represented by \(F_{i,tj}=\{f_{i,tj,1},f_{i,tj,2},\ldots,f_{i,tj,c}\}\), where \(c\) signifies the number of meteorological variables, and \(f_{i,tj,k}\in F_{i,tj}\) is a one-dimensional point. Therefore, a two-dimensional matrix \(tc_{i}\) can represent each tropical cyclone record.  
\[tc_{i}=\left(\begin{array}{c}lat_{i,t1},\text{lon}_{i,t1},F_{i,t1}\\ lat_{i,t2},\text{lon}_{i,t2},F_{i,t2}\\ \vdots\\ lat_{i,tm},\text{lon}_{i,tm},F_{i,tm}\end{array}\right) \tag{1}\]  
where the row in \(tc_{i}\) denotes the landing locations and the corresponding meteorological variables of this particular tropical cyclone and the column denotes an ordered set of timestamps \(T=\{t1,t2,\ldots,tm\}\).  
Given a number of tropical cyclones \(T=\{tc_{1},tc_{2},...,tc_{n}\}\), our first objective is to preprocess each \(tc_{i}\in X\) by normalizing the numerical data and recoding the non-numerical data. The next objective is to reduce the number of data dimensions and extract implicit features through an auto-encoder layer, followed by a customized batch process to equalize the sequence of features to the same length. Finally, the customized batch data are used for the training of the prediction layer based on the GRU model.
## 5 The Proposed Scheme
### Construction of ABE-RS
#### 5.1.1 Setup  
\(\mathsf{Setup}(1^{\kappa},S_{A})\rightarrow(GP,MSK)\). The process begins by generating a tuple of bilinear groups through \(\mathcal{G}(1^{\kappa})\rightarrow\{\mathbb{G},\mathbb{G}_{\mathbb{T}},p,g,e\}\), which is referred to as \(\mathbb{D}\), where \(\mathbb{G}\) and \(\mathbb{G}_{\mathbb{T}}\) are two cyclic groups of prime order \(p\) under multiplication, a generator \(g\in\mathbb{G}\), and a bilinear map \(e:\mathbb{G}\times\mathbb{G}\rightarrow\mathbb{G}_{\mathbb{T}}\). Subsequently, the AA selects the parameters \((\sigma,\alpha,\beta,\gamma)\) from \(\mathbb{Z}_{p}^{*}\) using LSSS, which computes \(g^{\sigma}\) as the master secret key \(MSK\). For each \(x\in S_{A}\), a random value \(n_{x}\in\mathbb{Z}_{p}^{*}\) is chosen. The global parameter \(GP\) is then defined as follows:  
\[GP=\{\mathbb{D},H_{1},H_{2},H_{3},Q_{x}=g^{n_{x}}_{x\in S_{A^{\prime}}}g^{\alpha },g^{\beta},g^{\gamma}\}, \tag{4}\]  
where \(H_{1}:\{0,1\}^{*}\rightarrow\mathbb{Z}_{p}^{*}\), \(H_{2}:\{0,1\}^{*}\rightarrow\mathbb{G}\) and \(H_{3}:\mathbb{G}_{\mathbb{T}}\rightarrow\{0,1\}^{\log_{p}}\) are the chosen hash functions.