esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data
#### 2.2.3 EC Flux Data  
The EC flux data of eight AmeriFlux sites were downloaded from the AmeriFlux data portal ([[https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)]([https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)) (accessed on 13 January 2021)). These eight AmeriFlux sites are GLEES (US-GLE), Willow Creek (US-WCr), SE5 A(US-PFs), RCEW Low Sagebrush (US-RIs), Reynolds Creek Wyoming big sagebrush (US-Rws), Tonzi Ranch (US-Ton), Vaira Ranch-Ione (US-Var), and Bouldin Island corn (US-Bi2), represented by ENF, DBF, MF, CSH, OSH, WSA, GRA, and CRO, respectively (Table 1). The EC flux data have a half-hourly temporal resolution, and the daily GPP values were calculated as a sum of 30-min GPP fluxes. The REddyProc online tool ([[https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html](https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html)]([https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html](https://www.bgc-jena.mpg.de/REddyProc/brew/REddyProc.html)) (accessed on 13 January 2021)) [63; 64] implements a standardized approach for processing EC data and was used to calculate GPP for the EC flux tower when GPP was not provided. Furthermore, the online tool filters out data collection during stable atmospheric conditions using u* (friction velocity) filtering, and gap-fills missing data using a marginal distribution sampling approach. Flux measurements (net ecosystem exchange, latent heat flux and sensible heat flux) and meteorology measurements (incoming shortwave radiation, vapor pressure deficit, relative humidity, air temperature, soil temperature and friction velocity) were obtained to calculate GPP.
# Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition
## 4 Experimental Setup
### Evaluation Metrics  
Generally, an NER system is evaluated by identifying boundaries and types of named entities. Hence, an NE is considered correct if recognized with both boundaries and its type match the ground truth [35]. We compute precision (P%), recall (R%), and F1-score (F1%) on the number of True-Positives (TP), False-Positives (FP), and False-Negatives (FN), respectively. The true-positive NEs are recognized by the system that match the ground truth, false-positive NEs are recognized by the system but do not match the ground truth, and false-negative NEs annotated in the ground truth but not recognized by the NER system. Precision measures the ability of the NER system to present only correct entities. Recall measures the ability of the NER system to recognize all NEs in the given input dataset. Moreover, F1-score is the harmonic mean of precision and recall. We report a micro-average score, which is an effective measure [35,55] on the large classes in a test collection. It aggregates the contributions of NEs from all categories to compute the average by equally treating all of them.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{12}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{13}\]  
\[\text{F1-score}=2\times\frac{\text{Precision}\;\times\;\text{Recall}}{ \text{Precision}\;+\;\text{Recall}} \tag{14}\]  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**NE Type** & **Label** & **Train Set** & **Valid Set** & **Test Set** \\ \hline Person names & PERSON & 21,515 & 10,540 & 3478 \\ Title of a person & TITLE & 5122 & 4703 & 2009 \\ Organizations & ORG & 1943 & 1934 & 741 \\ Geopolitical Entities & GPE & 9652 & 4814 & 3041 \\ Locations & LOC & 4628 & 1085 & 360 \\ Nationalities & NORP & 4678 & 4210 & 2950 \\ Names of Buildings & FAC & 568 & 586 & 130 \\ Events, incidents & EVENT & 885 & 399 & 575 \\ Languages & LANGUAGE & 972 & 174 & 52 \\ Art work, title of books and songs & ART & 1172 & 276 & 67 \\ Miscellaneous & OTHERS & 16,297 & 6681 & 4601 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of named entities in the SiNER dataset for the training, development, and test sets. The highest proportion named entities is Person names, followed by miscellaneous and geopolitical entities.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 3 Edge Computing and Deep Learning-Based Malware Detection Method
### Main Functions of the Edge Computing Deep Learning-Based Malware Detection System
#### 3.4.1 Model Training and Testing  
The data to be analyzed (malware classification) are transmitted from the IIoT sensor to the edge device (G/W), which extracts the characteristics of the collected data to be analyzed (Src/Dst IP/Port, Protocol, Flow metadata, etc.). Subsequently, the extracted features data are transmitted (offloaded) to the edge server, with the model created by performing local (partial) deep learning model learning. The generated model should be sufficiently light to be operated in the resource-constrained environment of the edge device. To improve the accuracy and detection performance of the global deep learning model, local model training information is sent to the cloud server to create an optimized global deep learning model.
# A Co-Rotational Meshfree Method for the Geometrically Nonlinear Analysis of Structures
## 2 Theoretical Formulation
### Geometry and Kinematics of Three-Dimensional Beam  
#### 2.2.1 Coordinate System  
In order to describe the geometry and kinematics of the beam in the undeformed and deformed configurations, we defined two sets of right-handed rectangular Cartesian coordinate systems:  
1. Fixed global coordinates:  
As shown in Figure 1, the global coordinate, which remains fixed during the entire analysis, describes the displacements, rotation, and curvature of the structure. The governing equations of the system are also constructed with respect to the global coordinates.  
2. Co-rotational local rectangular Cartesian coordinates:  
The DRK collocation method adopts a scattered set of nodal points to fulfill the discretization instead of the element adopted in FEM. Hence, a set of co-rotational local coordinates has to be defined, associated with the nodal points collocated on the centroid axis of each cross section. The origin of the co-rotational local coordinate system is tied to the centroid of the cross section. The \(x_{o}\) and \(y_{o}\) axes of the co-rotational local coordinates are taken as the principal directions of the cross section. In addition, the \(z_{o}\) axis is selected to coincide with the normal direction of the cross section. In this way, this co-rotational local coordinate will be co-rotated with the beam undergoing large deformation.
## 3 Materials and Methods
### Improved Region Growing Algorithm Based on Vehicle Trajectory Information  
#### 3.3.1 Trajectory Data Calculation  
The point cloud data collected using the mobile laser scanning system, with coordinates in the laser scanner coordinate system, are referred to as the laser scanning data. The  
Figure 3: Illustrates the point cloud after supervoxelization: (**a**) supervoxelization results of the flat road dataset; (**b**) supervoxelization results of the flat road dataset. The different colors in the image represent different supervoxel data.  
data obtained from the GNSS/IMU (Global Navigation Satellite System/Inertial Measurement Unit) integrated navigation system is referred to as POS (Position and Orientation System) data. This study aimed to fuse laser scanning and POS data through a data processing process to obtain coordinates in the Earth coordinate system. The specific process was as follows. First, the differential positioning GNSS data were processed using a Kalman filter to obtain the position and velocity information of the vehicle platform. Subsequently, by combining the pose information from the IMU data, the navigation positioning error was solved, and the error was input into the inertial navigation system to obtain the final position and attitude information. Finally, after completing the POS data computation, the obtained position and attitude information, along with the calibration information of the laser scanner placement, were used to transform the point cloud coordinates into the Earth coordinate system, and then the high-precision MLS trajectory data could be obtained. The specific process is illustrated in Figure 4.
## 3 Topological Map Representation
### Online Representation of the Map  
The metric map divides the space into a finite number of grid cells \(M\)\(=\{\)m\({}_{i}\)\(|\)i \(=\) 0, 1, 2,... n\(\}\), each m\({}_{i}\) corresponding to an occupied variable. If the grid cell is completely occupied by \"1\" and is not occupied as \"0\", then \(p(m_{i}=1)\) or \(p(m_{i})\) indicates the possibility that the grid cell is occupied [28]. Therefore, the smallest unit of the metric is the grid cell, and a grid cell is represented by only one variable.  
This only represents the information-occupied obstacles; although it can achieve accurate navigation and obstacle avoidance, it cannot achieve a higher level of control. For example, the user tells the robot \"I want to go to the kitchen!\", and the robot does not know how to perform unless the robot knows the specific location of the kitchen [29]. In order to solve this problem, a scheme combining the metric map and the topological map has been proposed.  
A topological map is an environment representation method based on an adjacent graph, which can be represented by an adjacency list structure. Similar to the concept of graphs, topological maps have two basic elements: nodes (which are called vertices in this paper) and edges. Nodes represent different locations that can be distinguished in the environment or various states of distinguishable robots; edges represent relationships between nodes, such as distance or motion control commands. Such representations have low storage requirements, and also support efficient path planning, especially for large-scale unknown environments or outdoor environments.  
In this paper, the vertices mainly include the position of the vertices, the vertex number, the adjacent vertices, and so on. The edges mainly include starting and ending vertices, weights, etc. In addition, metric maps and topological maps exist in the form of a \"hierarchical map\" in the framework proposed in this paper. The two maps interact with each other, learn from each other, and complement each other, providing different map services for the system.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 2 Related Work
### Standardization Activities
#### 2.1.6 Tosca  
In recent years, the Topology and Orchestration Specification for Cloud Applications (TOSCA) [14] has become the standard language for modeling service orchestration in cloud environments in a highly extensible and flexible manner. TOSCA focuses mainly on enhancing the portability and operational management of cloud applications and services across their entire life-cycle, by defining building blocks, requirements and capabilities, which should be taken into consideration in the service orchestration.
# Moving Deep Learning to the Edge
## 5 Deep Learning Training on Edge Devices
Gradient reduction methods discard gradients whose value is lower than a specified threshold [187]. Data privacy is an important aspect of any training solution. Federated learning keeps data local, but any problem with a device participating in the training process can ruin the model accuracy. To improve the tolerance of the training process, robust federated learning [188] filters unusual parameter updates. Still, distributed training with many end devices raises a privacy issue since there is a share of information associated with private data. One solution is to add noise to the transmitted gradients [189] and noise to the training data [190]. A different approach to protect data is to transmit only partial data. This is achieved by splitting the global model between the end device and the edge node. The process increases the workload on the end device since one partition of the model must be trained on the device. Different model splitting solutions have been proposed. Some reduce the model partition to run on the device [191]. Others consider cloud computing to speed up the training process knowing that the model split reduces the risk of data privacy violation [192]. A different solution was proposed in [193] based on knowledge distillation [194]. In this approach, a device trains and updates the network with its data subset and with the training predictions from the other devices using knowledge distillation. The method permits reducing the update frequency. Given the computational complexity to train a large deep learning model, all previous approaches considered distributed solutions among edge devices and an aggregator edge server. In this approach, many end devices participate in the training process. However, cases exist where data are local to a single end device and cannot be simply transferred to other devices and edge servers for distributed training. Training locally in an end device would be preferable. The high computing requirements of training prevent a model from being trained in a single device. This is true for batch training where the whole model must be trained with new data. Incremental learning [195; 196; 197; 198] plays a critical role in alleviating these issues. It ensures continuity in the learning process through regular model updates based only on the newly available data. Incremental learning also reduces the computational complexity of training to a level feasible for an edge or end device. This new approach for DNN deployment on the edge opens new perspectives in many areas, including healthcare, smart homes, and personal care. In these fields, a DNN model with pre-acquired knowledge about common tasks could be supplied to the users, according to their needs. Then, it could be further trained to perform new tasks. Using existing knowledge from previous and new tasks, the model should quickly grasp the new task with only limited supervision. Learning a new task is possible locally and without access to training data for the earlier tasks, which may no longer be available.
## 3 Related Work and Shortcomings
### Shortcomings
#### 3.2.1 Time Consuming  
The efficiency of sampling is directly related to the size and dimension of the sampling space. For the RRT algorithm, the processing time complexity of processing is \(O(n\,logn)\), the time complexity of the query is \(O(n)\), and the space complexity is \(O(n)\)[36]. In a large sampling space with high dimensions, the algorithm would sample many more times and spend much more time searching for the available path. For mobile manipulators performing pick and place tasks in confined spaces, the main problem is that the probability of obtaining collision-free sampled configurations by random sampling is extremely low. Consequently, the number of samples required increases significantly, resulting in a significant reduction in efficiency (as shown in Figure 3a).
## 2 Materials and Methods
### Modeling of the Composite Structure
#### 2.1.2 Transfer Matrix for the Microperforated Panel  
Transfer matrix \(M\) for the microperforated panel can be calculated using Equation (7). Here \(Z_{s}\) is acoustic impedance rate of the microperforated panel, which consists of the real part \(R\) and the imaginary part \(X\), as shown in Equation (8). \(R\) and \(X\) are calculated by the Equations (9) and (10) respectively.  
\[[M]=\left[\begin{array}{cc}1&Z_{s}\\ 0&1\end{array}\right], \tag{7}\]  
\[Z_{s}=R+jX, \tag{8}\]  
\[R=\frac{32(\mu+\upsilon)\rho}{\varepsilon}\frac{t}{d^{2}}k_{r}, \tag{9}\]  
\[X=\frac{t\omega\rho}{\varepsilon}k_{m}. \tag{10}\]  
In the Equations (9) and (10), \(\mu\) is viscosity coefficient of the air, \(1.506\cdot 10^{-5}\)m\({}^{2}\cdot\) s\({}^{-1}\); \(\upsilon\) is the temperature conduction coefficient of the metal panel, \(2.0\cdot 10^{-5}\)m\({}^{2}\cdot\) s\({}^{-1}\); \(\rho\) is density of the air, 1.21 kg \(\cdot\) m\({}^{-3}\); \(\varepsilon\) is the perforating rate, which can be calculated using Equation (11); \(k_{r}\) is the acoustic resistance constant, which can be obtained using Equation (12); \(\omega\) is still the angular frequency; \(k_{m}\) is the acoustic mass constant, which can be derived using Equation (13) [26; 38]. In the Equations (12) and (13), \(k\) is the perforated panel constant, which can be calculated using Equation (14). \(d\), \(b\), and \(t\) represent the diameter of the hole, distance between the neighboring holes, and thickness of the panel, respectively, which are consistent with definitions of structural parameters of the microperforated panel in the Figure 1.  
\[\varepsilon=\frac{\pi}{4}\Bigg{(}\frac{d}{b}\Bigg{)}^{2}, \tag{11}\]  
\[k_{r}=\sqrt{1+\frac{k^{2}}{32}}+\frac{\sqrt{2}}{8}k\frac{d}{t}, \tag{12}\]\[k_{m}=1+\left(9+\frac{k^{2}}{2}\right)^{-0.5}+0.85\frac{d}{t}, \tag{13}\]  
\[k=\sqrt{\frac{\omega}{\mu+v}}\frac{d}{2}. \tag{14}\]
## 5 Realistic Modeling of Resource Constraints
### Device Classification and Capabilities  
We categorize participating clients into three classes based on their hardware profiles and operational environments, as shown in Table 2.  
These characteristics influence the parameters of each client's utility function, guiding their strategic decisions to participate or not in each FL round.
# Wave Polarization Control in Anisotropic Locally Resonant Materials
## 3 Results
### Phase Velocity Diagrams  
Let us consider the same metamaterial of Figure 2b with horizontally eccentric fibers (\(\vartheta=0\)). For small values of the frequency, the principal values of the effective mass are positive (pass band) and quite similar, i.e., the mass tensor is almost isotropic. However, due to the anisotropy of the acoustic tensor, the wave phase velocities \(c_{\alpha}\) and \(c_{\beta}\) depend on the direction \(\mathbf{n}\) of propagation. This can be visualized through the polar diagram of Figure 5a, in which the two-wave phase velocities are shown in black as a function of \(\mathbf{n}\), for \(f^{*}=2\ \mathrm{m}/\mathrm{s}\). In the same figure, green lines represent the direction of propagation of the elastic waves, which are given by the corresponding polarization vectors \(\mathbf{p}_{\alpha}\) and \(\mathbf{p}_{\beta}\). One can recognize a behavior similar to that of isotropic materials with longitudinal and shear waves.  
If we select a higher frequency \(f^{*}=8\) m/s, always belonging to a pass band, the anisotropy of the mass tensor increases, and the wave phase velocities are modified; see Figure 5b.  
When considering a frequency belonging to a red polarization band where \(\rho_{\Pi}^{0}<0\), e.g., \(f^{*}=8.8\) m/s, only one real phase velocity exists. In this case, the ratio \(\rho_{\Pi}^{0}/|\rho_{\Pi}^{0}|=0.025\) is quite small and, hence, the polarization directions of the propagating elastic waves, as shown in Figure 5c, are aligned with the direction of positive mass.  
For a higher frequency within the same polarization band, e.g., \(f^{*}=11.6\) m/s, see Figure 5d, the ratio \(\rho_{1}^{0}/|\rho_{\Pi}^{0}|=1.12\) increases, and the polarization directions are no longer horizontal.
## 3 Methodology  
### Data Pre-Processing  
The parameters used in [PERSON]'s NDWI and [PERSON]'s NDWI require physical units, such as at-sensor radiance or top-of-atmosphere (TOA) reflectance, rather than the raw quantized calibrated pixel value (DN). There are three advantages to using TOA reflectance instead of at-sensor spectral radiance when comparing images from different sensors [42,43]. First, TOA reflectance removes the cosine effect at different solar zenith angles due to the time difference between data acquisitions. Second, TOA reflectance compensates for different values of the exo-atmospheric solar irradiance arising from spectral band differences. Third, TOA reflectance corrects for the variation in the earth-sun distance between different data acquisition dates. Therefore, we used the TOA reflectance of the ALI, TM, and ETM+ data to be used in this study. TOA reflectance can be obtained from the quantized calibrated pixel value, as given by [43]:  
\[\rho_{\lambda}=\frac{\pi\cdot L_{\lambda}\cdot\mathrm{d}^{2}}{ESUN_{\lambda} \cdot\mathrm{cos}\theta_{\mathrm{s}}} \tag{1}\]  
where \(\rho_{\lambda}\) is the TOA reflectance of wavelength \(\lambda\) [unitless], \(d\) is the earth-sun distance [astronomical units], \(ESUN_{\lambda}\) is mean exo-atmospheric solar irradiance [W/(m\({}^{2}\cdot\upmu\)m)], \(\theta_{\mathrm{S}}\) is the solar zenith angle [degrees], and \(L_{\lambda}\) is the spectral radiance at wavelength \(\lambda\) at the sensor's aperture [W/(m\({}^{2}\cdot\)sr\(\cdot\upmu\)m)]. \(L_{\lambda}\) can be obtained from the quantized calibrated pixel value also as given by [43]:  
\[L_{\lambda}=(\frac{LMAX_{\lambda}-LMIN_{\lambda}}{Q_{cal\mathrm{{}_{max}}}-Q_{ cal\mathrm{min}}})(Q_{cal}-Q_{cal\mathrm{{}_{min}}})+LMIN_{\lambda} \tag{2}\]  
where \(LMAX_{\lambda}\) is the spectral at-sensor radiance that is scaled to \(Q_{cal\mathrm{{}_{max}}}\) [W/(m\({}^{2}\) sr \(\upmu\)m)], \(LMIN_{\lambda}\) is spectral at-sensor radiance that is scaled to \(Q_{cal\mathrm{{}_{min}}}\) [W/(m\({}^{2}\cdot\)sr\(\cdot\upmu\)m)], \(Q_{cal\mathrm{{}_{max}}}\) is the maximum quantized calibrated pixel value corresponding to \(LMAX_{\lambda}\) [DN], \(Q_{cal\mathrm{{}_{min}}}\) is the minimum quantized calibrated pixel value corresponding to \(LMIN_{\lambda}\) [DN], and \(Q_{cal\mathrm{{}_{i}}}\) is the quantized calibrated pixel value [DN].  
The parameters in Equations (1) and (2) can be read from the header files of the ALI, TM, and ETM+ datasets or be retrieved from the USGS website ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))).