esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Machine Learning Models
### Healthcare Data  
In the health sector, the number of studies dealing with predictive processes related to the COVID-19 disease has increased in recent years for obvious reasons. The superiority of LSTMs over classical ARIMA is indicated once again in the work of [PERSON] et al. (2018), in which the number of total confirmed cases of the disease in different European countries is modeled.  
The work of [PERSON] et al. (2018) compares deep learning (Recurrent Neural Networks, GRU, and LSTM) and statistical (ARIMA and SARIMA) techniques to predict the total number of confirmed active and recovered cases as well as the number of deaths from the COVID-19 disease. GRU, LSTM, ARIMA and SARIMA models were trained, tested, and optimized for predicting COVID-19 trends. The best model was selected based on the lowest values of the MSE and RMSE metrics. For most time series data for different countries, the models based on deep learning (LSTM and GRU) outperform the statistical ARIMA and SARIMA models, with RMSE values 40 times smaller than those of the ARIMA models. However, for some countries, statistical models outperformed deep learning models. Due to the highly dynamic nature of disease-specific data, the information they contain depends on the country of origin, as well as the time at which they were generated. The shape of the data coming from some countries is non-linear while ARIMA models are shown to perform better in modeling data that follows linear relationships. On the other hand, RNN models performed better in countries whose data were non-linear. For the number of confirmed cases, SARIMA statistical models behaved best for India, Russia, Peru, Chile, and the United Kingdom, while ARIMA performed best for Brazil. For Mexico and Iran, the LSTM model performed best, while the GRU model performed best for the USA and South Africa. For the recovered patients, the ARIMA model performed best for the USA, Russia, and Chile, while the SARIMA model performed best for Brazil, India, and South Africa. For Mexico, Peru and the United Kingdom, the GRU model performed better than the rest of the models. Similarly for Iran, the LSTM model had the best performance compared with the rest of the models. For forecasting the death data, the LSTM model outperformed the rest for Brazil, Russia, South Africa, Peru and the United Kingdom. For the countries India and Iran, the GRU models performed best. On the other hand, the SARIMA-based models performed well for the USA, Mexico, and Chile, whereas, on the contrary, the ARIMA models of all countries have the highest values of the RMSE and MSE metrics, which suggests that the classical approach is not suitable for modeling deaths from COVID-19. Recorded disease casualties had a non-linear relationship with time, which cannot be modeled with simple ARIMA models. Therefore, SARIMA models that can capture seasonality, as well as RNNs, have better forecasting performance in countries with non-linear data relationships.
## 3 Results
### Experimental Results  
#### Evaluation Indicators  
This study used common evaluation metrics in classification tasks, such as training set accuracy, test set accuracy, precision, recall, and F1 score, to judge the model. Because the problem addressed in this study is a binary classification problem, a confusion matrix consisting of a four-unit window can be made. Figure 5 shows the confusion matrix for this study.  
The accuracy is the proportion of correctly classified samples to the total number of samples combined with the confusion matrix, and the equation is calculated as in Equation (10).  
\[\text{Accuracy}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{10}\]  
Accuracy is the simplest and most intuitive metric to evaluate in classification problems, but it has obvious drawbacks. When experimenting, it is important to note that when the proportion of samples from different categories is very uneven, the category with the largest proportion tends to be the most significant factor affecting accuracy.  
Precision refers to the proportion of the samples predicted to be positive by the model that is also actually positive as a proportion of those predicted to be positive. The calculation formula is Equation (11).  
\[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{11}\]  
Precision provides a visual indication of the classifier's ability to not mark negative samples as positive.  
Figure 5: Confusion Matrix. TP (True Positive): correct prediction for lead glass; TN (True Negative): correct prediction for potassium glass; FP (False Positive): incorrect prediction for lead glass; and FN (False Negative): incorrect prediction for potassium glass.  
Recall refers to the proportion of samples that are actually positive that are predicted to be positive out of those that are actually positive. The calculation formula is Equation (12).  
\[\text{Recall}= \frac{\text{TP}}{\text{TP}+\text{FN}} \tag{12}\]  
Recall visually illustrates the ability of the classifier to find all positive samples. The F1 score is the summed average of Precision and Recall [48], calculated as  
\[\text{F1}= \frac{2*\text{Precision}*\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{13}\]  
The F1 score can be interpreted as the weighted average of Precision and Recall. The F1 score is a combination of Precision and Recall. The higher the F1 score, the more robust the model is.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 2 Materials And Methods
### Workflow of Parallel Experiments
#### 2.5.2 Random Number Generation  
The processors of the GPU have a limited function set. This situation presents certain difficulties, such as the lack of native support for random number generation, required for several vital activities of HSA. The solution we implemented was to pre-generate random number arrays of different distributions with the CPU and sent them along with the frame image data to the GPU. Four arrays are generated and sent from the CPU to the GPU:  
1. uniformly distributed integers (using OpenCV)
2. uniformly distributed real numbers (using OpenCV)
3. beta distribution for SBX [38; 39]
4. and delta distribution for Polynomial Mutation [39].
# Analysis and Optimization of Output Low-Pass Filter
## 3 Frequency and Filter Component Normalization
### Filter Configurations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FC** & **Circuit** & **Transfer** & **Quality** & **Roll-Off Rate** \\  & & **Function \(H(s)\)** & **Factor \(Q\)** & **Beyond \(f_{R}\)** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{\(R_{D}\)} & \(s\)\(CR_{D}+1\) & \(\sqrt{\left(\frac{Z_{0}}{R_{D}}\right)^{2}+1}\) & \(-20\) dB/decade \\  & & \(C\) & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{\(C\)} & \(R_{D}\) & \(\frac{R_{D}}{Z_{0}}\) & \(-40\) dB/decade \\  & & & & \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{\(C\)} & \(sL+R_{D}\) & \(\sqrt{\left(\frac{R_{D}}{Z_{0}}\right)^{2}+1}\) & \(-20\) dB/decade \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Resistive (\(R_{D}\)) damping in second-order CL filter configurations [1]. The design region (B) in the figure is used to calculate the filter's inductance based on the cut-off frequency and filter configuration. The damping resistance can be determined using the quality factor. The inverter's performance increase if the filter's quality factor is increased. As a result, both THD and damping resistor losses would be reduced. Figure 15 shows the power loss and THD contours as 2%, 3%, 4%, and 5% for each FC. It should be noted that \(C_{n}\) and \(f_{sw}\) were kept constant at 0.12 pu and 4 kHz, respectively. Figure 14: THD and power loss contours of 5%, for filter configurations (**a**) 1, (**b**) 2, (**c**) 3, and (**d**) 4, at a 4 kHz switching frequency. Figure 15: THD and power loss contours of 2, 3, 4, and 5%, for filter configurations (**a**) 1, (**b**) 2, (**c**) 3 and (**d**) 4, considering a capacitance of 0.12 pu and a switching frequency of 4 kHz. \(P_{d}\) and THD for each filter can be decreased by increasing the quality factor, with FC 3 providing the smallest variance in both \(Q\) and \(P_{d}\).
# Artificial Intelligence-Based Autonomous UAV Networks: A Survey
## 5 Challenges and Open Research Areas
### Privacy and Security  
As discussed in Section 4 Scenario (b), many researchers have privacy concerns about UAVs and drones, especially when there are cameras involved. A research gap can be bridged here by introducing some other technologies to UAVs and drones, such as LiDAR and other sensors to move around. A recent study [108] has emerged targeting this but exploring from a privacy perspective may provide an additional edge. Moreover, to ensure secure communication through UAVs, blockchain technology can also be used in tandem with other security features.  
The main challenge is to implement data security in flying cellular base stations. To tackle the concerns about data security, the blockchain-based framework can be used to provide a security layer in the communication between (1) drone to drone and (2) user to the drone. Certainly, this is an important open research area that can be investigated for secure UAV systems.
# Development of Building CFD Model Design Process Based on BIM
## 3 Design Method of Optimized CFD Model Using BIM
### Export CFD Model for OpenFOAM Analysis  
The results of BIM data extraction, geometry simplification, grid optimization, and attribute data matching were integrated to output the OpenFOAM CFD model. OpenFOAM stores data about CFD models in case folders. A case folder consists, as follow: (1) 0 folder that contain data on the boundary conditions of the CFD model, (2) constant folder storing data about the grid and geometry, gravity, turbulence characteristics, and thermal characteristics, (3) system folder that contains data about the discretization method and operation conditions. As shown in Table 1, an OpenFOAM case was created by outputting the simplified geometry model, optimized grid model, boundary type, material name, and material thermal transmittance to the corresponding locations. The simplified geometry file was output as an OBJ file in the case folder and referred to in meshDict, the grid creation setting. The bndry file, which defines the type of surfaces that compose the subject of analysis, was output in the case folder, and referred to when setting the boundary conditions. The name and thermal diffusivity of each material layer was output in the T-file inside the case folder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CFD Model Data** & **Output Location** & **Output Format** \\ \hline Simplified geometry model & Case & OBJ \\ Optimized gird model & Constant & polyMesh files \\ Boundary type & Case & bndry \\ Name of each material layer & 0 & T \\ Thermal diffusivity & 0 & T \\ \hline \hline \end{tabular}
\end{table}
Table 1: The output of the extracted data, then converted from IFC.  
Meanwhile, OpenFOAM does not perform operations if there is not sufficient setting for simulations in the case folder. Therefore, among the information required for simulation, there is some information not included in IFC. The boundary condition information not included in IFC is defined, as shown in Table 2. The constant and system folder settings were also defined and output to the case folder. The buoyantSimpleFoam was designated as the default condition by considering thermal analysis and turbulence analysis. Gravitational acceleration was set in the -z axis direction, and the K-epsilon model was applied to configure the turbulenceProperties for the turbulence model. The air state was set to an ideal gas.  
OpenFOAM simulation was enabled by using the output case, and if necessary, the user could directly modify the boundary conditions. Conditions other than the data interlocked in IFC could be changed according to the purpose of analysis, enabling simulations in various environments for the optimized CFD geometry and grid models.
## 7 Conclusions  
We address the three-dimensional rotational flow of Casson fluids containing carbon nanotubes (SWCNTs and MWCNTs) as nanoparticles in the presence of thermal radiation along with heat generation over a stretching sheet. The effects of embedded parameters are observed and studied graphically. Moreover, the variation of the skin friction, Nusselt number, and their influences on the velocity and heat distributions are examined. The main points of the present study are shown below:  
* For CNTs, large values of \(K\) (the rotation parameter) produce smaller velocities \(f^{\prime}(\eta)\) and \(g(\eta)\), but the opposite tendency is detected for temperature profile \(\Theta(\eta)\).
* Increasing the nanoparticle volume fraction \(\varphi\) into the working liquid boosted \(f^{\prime}(\eta)\), \(g(\eta)\) and \(\Theta(\eta)\) values for SWCNTs and MWCNTs.
* A strong magnetic parameter \(M\) reduces \(f^{\prime}(\eta)\) and \(g(\eta)\), while increases the temperature \(\Theta(\eta)\).
* The thermal layer rises through heat generation for SWCNTs and MWCNTs.
* The large values of Prandlt number \(Pr\), reduces nanoparticle temperature \(\Theta(\eta)\).
* Large values of Biot number \(\lambda\) boost temperature and thicken the boundary film concentration.
* The degree of coefficient of skin friction increases for larger \(\varphi\) values. The results show that skin friction in case of SWCNTs is higher than in MWCNTs.
* Heat transfer rate is controlled by large values of \(\lambda\) and \(Pr\).
* Nusselt number is greater with SWCNTs as opposed to MWCNTs.  
The authors are thankful to the anonymous reviewers and the Editor-in-Chief for the productive comments that led to positive enhancement in the paper.  
[PERSON] and [PERSON] modelled and solved the problem. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] thoroughly checked the mathematical modeling and English language corrections. [PERSON] and [PERSON] contributed in the results and discussions. All the corresponding authors finalized the manuscript after its internal evaluation.  
The author declares that they have no competing interests.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 5 In-pond WaterMon Sensor Design and Feasibility
### Calibration Procedure  
The calibration process is performed using a one- or two-point calibration for each sensor probe. Figure 9 shows an illustration of the two-point calibration procedure. Following this calibration process for each sensor, the first step is to put the probe into the corresponding calibrating liquid and press the corresponding button on the keypad, which has been programmed to make the probe selection (for DO, EC, ORP, pH, and Temp) and write the calibration into the corresponding device. In the case of the sensors connected to 16 bits A/D converters ADS1115 (for \(NH_{4}^{+}\), \(NH_{3}\), \(NO_{2}\), and \(TDS\)), the calibration is stored on the NVRAM in the Fipy.  
Compensation  
Once every sensing probe is calibrated, the node has been programmed to consider measurement compensation for each parameter. Table 6 shows the parameter dependencies for some sensors (the other ones not appearing in the table does not need any compensation). This process needs a specific measurement collection order, which has been considered by programming the previously mentioned 74 HC237 decoder multiplexer to make the sensor selection.  
When compensation is required by a particular sensor, it has the purpose to provide the highest possible accuracy in the measurement of the values measured by such sensor. Calibration, previously described, is the first approach towards the achievement of such accuracy by dealing with the aging of the sensor, the state of the chemical membranes and liquids used to carry out the physical measurement, and other aspect about the status of the sensor. After that, the compensation will allow one to tweak the values read from an already calibrated sensor in order to adapt it with the external factors that may affect the reading of such values. Thus, for example, EC in Table 6 is dependent on water temperature. It means that the value read about the electrical conductivity in water is directly affected by the temperature of the water, and thus, by knowing that value in advance of the reading of  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Sensor** & **Parameter Dependency** \\ \hline \(EC\) & Temperature (RTC) \\ \(pH\) & Temperature (RTC) \\ \(DO\) & Temperature (RTC), Salinity (EC), Pressure (Fipy) \\ \(NH_{4}^{+}\) & Temperature (RTC), Salinity (EC), \\ \(NO_{3}^{+}\) & Temperature (RTC), pH \\ \(NO_{2}^{-}\) & Temperature (RTC), pH \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sensor’s Parameter dependencies for compensation.  
Figure 9: Sensor Calibration.  
EC values, it can be used to compensate the read value in order to provide a much more accurate result.
## 5 Performance Evaluation
### Result Analysis and Discussion
#### 5.2.3 Control Overhead  
The control overhead is another important parameter to analyze the effectiveness of our proposed work, as discussed in Figure 13 a, b. The amount of control overhead remained relatively constant, even as the number of nodes increased, as seen by the network density fluctuation in Figure 13a. However, the amount of overhead in the modified ring is less than the others, as the source and destination path is clear for the data delivery, in the first few rounds. Further, due to the movement of the sink node, the sink advertisement is easily conducted through the ring node only, rather than flooding it over the entire network. Under the static sink, the control overhead of the entire network is decreased, which increases as the mobility is introduced, as shown in Figure 13b. The modified ring routing outperforms the other routing method of the ring routing, LBDD or MSRP, as they have a smaller number of control overhead packets.
## 2 Related Work
### Basic Principles of Cryptography  
#### Public Key Infrastructure  
Asymmetric crypto-systems use a public and a private key to provide secure communication between network entities. The public key is visible to anyone whereas the private key must not be disclosed to any other party than the key owner itself. A problem of asymmetric crypto-systems is the publishing of the public key.  
Today, the exchange of public keys is done via a public key infrastructure (PKI), e.g., defined by the ITU-T standard X.509 [6, 7]. All approaches have in common that a particular user or node publishes its public key on a key server of some sort from which it can be downloaded by other peers. After that, encrypted and signed messages can be exchanged. This, of course, requires that each participating node has to trust the key server. If a key pair ever gets lost, it can be revoked by including it in the so-called Certificate Revocation List (CRL), where all invalid keys and certificates are kept [7].  
In [8] [PERSON] _et al._ argue that today's PKI based on _certificate authorities_ (CA) imposes ten major risks. They describe, for example, the problematic trust background of the self-proclaimed authorities and discuss the weakest link issue of the CA structure. Furthermore they raise the question how the certificate holder identifies himself against the CA. They state that several procedures do exist and that there are no consistencies over all CAs.
# Evaluation of VoIP QoS Performance in Wireless Mesh Networks
## 4 Results and Analysis
### Isolated Village  
In this scenario, an isolated village in a remote area is considered. The terrain size for this simulation is 1500 \(\times\) 1500 m. All the mesh nodes are stationary, as shown in Figure 11. The dotted lines connected with a cloud show the nodes' association with an IP subnet and the solid lines show VoIP peers.  
This main scenario is experimented under six sub-scenarios. The analysis of sub-scenarios shows that IV4 is the best performing sub-scenario, in which IEEE 802.11g, IEEE 802.11, G.729 codec, and AODV have been integrated. The worst performing sub-scenario is IV5, in which IEEE 802.11n,  
Figure 11: Isolated village scenario.  
Figure 10: Flooded village MOS values for VoIP initiators and receivers: (**a**) VoIP initiators; (**b**) VoIP receivers.  
IEEE 802.11e, G.711 codec, and AODV have been integrated, as shown in Figure 11a for VoIP initiators and Figure 12b for the receivers.  
The overall analysis of the sub-scenarios shows that IEEE 802.11g with AODV routing protocol without IEEE 802.11s integration results in better VoIP performance and IEEE 802.11n with AODV shows the worst VoIP performance.
## 3 Materials and Methods
### Topmodel
Therefore, substituting Equation (3) into Equation (2) yields the following equation:  
\[q_{i}=\frac{K_{0}}{f}tan\beta_{i}\exp{(-fz_{i})}=T_{0}tan\beta_{i}\exp{(-fz_{ i})} \tag{4}\]  
where \(T_{0}=K_{0}/f\) is the coefficient of permeability of the fully saturated soil. \(K_{0}\) and \(f\) were assumed to be constant in the entire watershed. In addition, the behavior of the flow in the saturation layer can be expressed as a continuous steady state as follows:  
\[q_{i}=a_{i}R=T_{0}tan\beta_{i}\exp{(-fz_{i})} \tag{5}\]  
where \(R\) (m/h) is the spatially constant recharge of the groundwater at any given time, and \(a_{i}\) (m) is the area of the slope per unit contour length drained through point \(i\). Equation (5) can be summarized based on \(z_{i}\), and the average value (\(\overline{z}\)) of the entire watershed can be obtained as follows:  
\[z_{i}=-\frac{1}{f}\bigg{(}ln\frac{Ra_{i}}{T_{0}tan\beta_{i}}\bigg{)} \tag{6}\]  
where \(R\) is spatially constant, and \(ln(\frac{a}{T_{0}tan\beta_{i}})\) is the soil topographic index. Introducing average topographic indexes to Equation (6) yields the following equation:  
\[z_{i}=\overline{z}-\frac{1}{f}\bigg{[}ln\frac{a_{i}}{T_{0}tan\beta_{i}}-E \bigg{(}ln\frac{a_{i}}{T_{0}tan\beta_{i}}\bigg{)}\bigg{]} \tag{7}\]  
where \(E(\ )\) denotes the average topographic indexes of the entire watershed [31]. Equation (7) demonstrates that depth \(z_{i}\) of the groundwater can be determined by factor f and topographic index (\(ln(\frac{a}{tang})\)). Thus, given the value of \(\overline{z}\), Equation (7) can be used to estimate the spatial distribution of the topographic indexes and to predict the behavior of the surface water and groundwater levels in the entire watershed, assuming that the topographic index is spatially constant [11]. The soil topographical factors and topographic constants represent the behavior of all the flows in the watershed. All points with the same soil topographic factors and topographic constants exhibit the same flow performance. Therefore, the soil topographical factors and topographic constants could indicate the hydrological similarities between the watersheds. The input data of TOPMODEL were divided into hydrological data, topographic data, and input parameters. The hydrological data comprised the rainfall, evapotranspiration, and observed flow data. The topographic data were obtained by extracting the DEM data from a digital map. This study applied the FORTRAN source code of TOPMODEL [1]. Figure 3 shows the calculation procedure of TOPMODEL. TOPMODEL calculated the input data, compared the calculated value with the actual value, and reduced the error generated by parameter optimization.