esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation Metrics and Results  
After collecting the raw data and extracting features (as elaborated in the above sections). The preprocessed data are trained on several machine learning techniques and achieve a promising accuracy score of up to 90%.  
The data are trained using very famous algorithms such as KNN, SVC, Random Forest, Logistic Regression, Extreme Gradian boost XGBoost, and lightGBM algorithm.  
To properly test the model, the information about the prediction of the proposed model is gathered by using a confusion matrix. The confusion matrix is used to describe the performance of different classification models using the following measures: The first two measures are the True Positives (TP) and False Positive (FP), which are used to represent the number of positive values that are correctly predicted and that are wrongly predicted, respectively. The second two measures are the True Negative (TN) and False Negative (FN), which are used to represent the number of negative values that are correctly and wrongly predicted, respectively.  
The following metrics are used [27] to compare the performance of different machine learning algorithms: The first metric is called _Accuracy_, which is used to measure the ratio between the correctly predicted positive and negative observation and the overall observation as shown in Equation (1).  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
The second used metric is _Precision_, which equals the ratio between the correctly predicted positive observation and the overall observation, as shown in Equation (2).  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
The third metric is the _TPR_ stands for the True Positive Rate, which equals the ratio between the correctly predicted positive observation and the overall observation of the actual system, as shown in Equation (3).  
\[TPR=\frac{TP}{TP+FN} \tag{3}\]The fourth metric is the _FPR_ stands for the False Positive Rate, which equals the ratio between the falsely predicted positive observation and the overall observation of the actual system, as shown in Equation (4).  
\[FPR=\frac{FP}{FP+TN} \tag{4}\]  
The last metric is the _F1-score_, which equals the ratio between the _TPR_ and _Precision_ values, as shown in Equation (5).  
\[F1\_score=\frac{2*(TPR*Precision)}{TPR+Precision} \tag{5}\]  
And lastly, the receiver operating characteristic (ROC) is calculated, which plots the relationship between the true positive rate versus the false positive rate. Afterward, the Area Under the ROC curve (AUROC) for each AI algorithm is calculated. When the value of the AUROC curve increases, it is better because it measures the degree of separation.  
As explained before, experiments are performed on two test scenarios to obtain accurate results which cover different types of questions. As shown in the subsection, results are collected for each test scenario using the explained metrics.
# Shading by Overhang PV Collectors
## 1 Introduction  
Photovoltaic modules are often integrated into the building envelope and may provide shading, and at the same time perform as an ancillary source of electrical power. Building-integrated photovoltaics (BIPV) are photovoltaic materials used for many years as replacements for conventional building material in some parts of the building such as roofs, canopies, facades, solar carports and others. Overhangs on south-facing walls in northern latitudes are protruding structures and are important in shading windows and doors from undesired solar heat by blocking the sunlight during the summer months. References that illustrate the concept of BIPV are in mentioned in [1; 2; 3; 4; 5; 6; 7; 8; 9] to name only a few. There are many tools available for shading analysis and designing overhang shading structures (Solar Pathfinder, SunEye, Pilkington, Autodesk ECOTECT, METEONORM, Shadow Analyser, Sombrero, EnergyPlus, ESP-r, and many more). EnergyPlus is a building energy simulation program to model energy consumption and water use in buildings. ESP-r is a program that simulates the energy and environmental performance of buildings. Commercial ray tracing software programs are available and may be used for shading analysis (3 Delight, Anim8 or, ASAP). Not many publications deal with analytical expressions of the shadow variation on windows, walls and on grounds cast by overhang structures [10; 11; 12; 13; 14; 15]. The methodology of the present study is different to in the above-mentioned articles including software programs. The study develops basic mathematical expressions, step by step, for calculating the shadow variation cast by overhangs on windows, carports, and for calculating the annual incident energy on overhangs made up of conventional and bifacial PV modules. The calculation of the diffuse radiation involves the calculation of view factors to sky, to ground and between overhangs. The study starts with the shadow on a wall cast by a horizontal pole and follows by shadows on a wall cast by a horizontal plate, inclined pole, inclined plate, shaded areas and overhangs placed oneabove the other (see Figure 1). The present study provides an insight and methodology for calculating shadows and incident radiation on PV modules deployed on overhangs in BIPV.
## 3 Results and Discussion
### Analytical Performance
The matrix effect values were in the range of \(-56.4\) to \(40.5\%\) for these three vegetable extracts without any treatment, and the effect was serious and could not be ignored for most of the insecticides. After the extraction with 3D-rGOPFH, the matrix effect values were reduced to neglectable levels for all of the insecticides, and the values were from \(-9.1\%\) to \(16.4\%\) (Table S2). Furthermore, very clean spectra with a low baseline were obtained in the total ion chromatograms of spiked or un-spiked blank vegetables (Figure 4). These results demonstrated that 3D-rGOPFH could be an efficient MSPE extraction sorbent for reducing the matrix interferences.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Total Error Effect  
Here, all of the errors are simultaneously activated with the values given in Table 6.  
The results are shown in Figure 16. The typical performance that we could expect is \(\sim\)2.5 km for the position (3D), \(\sim\)226 ms for the receiver clock bias, \(\sim\)9 ppb for the receiver clock drift, \(\sim\)1.6\({}^{\circ}\) for the roll angle, \(\sim\)2.2\({}^{\circ}\) for the pitch angle, and \(\sim\)4.8\({}^{\circ}\) for the yaw angle. The \"worst\" performance is \(\sim\)5 km for the position (3D), \(\sim\)505 ms for the receiver clock bias \(\sim\)18 ppb for the receiver clock drift, \(\sim\)2\({}^{\circ}\) for the roll angle, \(\sim\)2.8\({}^{\circ}\) for the pitch angle, and \(\sim\)6.1\({}^{\circ}\) for the yaw angle. We noticed that, despite the huge error sources inherent to the Iridium system used as a source of signals of opportunity for navigation, the attitude performance is relatively interesting, especially for the roll and pitch components. The convergence times of the filter states for the intermediate and worst cases are similar to that of the case using perfect satellite orbit and clock drift, mainly because we used the same initial state variance in these cases. These results assume that eight satellites are available on average during the scenario, with a measurement rate of 1 Hz, a vehicle having an initial speed of \(vo=820\) m/s, an initial spin rate of 200 rad/s and a total scenario duration of 80 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Source** & **Intermediate** & **Worst** \\ \hline Satellite clock drift & 1 ppb & 10 ppb \\ \hline Satellite tangent position error & 1700 m & 3000 m \\ \hline Satellite tangent velocity error & 1 m/s & 3 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Error sources definition (1 \(-\) \(\sigma\)) for an intermediate case and a worst case.  
Figure 16: The total navigation performance. (**a**) The position RMSE; (**b**) the attitude RMSE; (**c**) the receiver clock bias RMSE; (**d**) the receiver clock drift RMSE.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 8 Performance Evaluation and Simulation
### Network Lifetime  
The total time span of any network is referred to as the network lifetime. This is considered one of the main judging criteria of any kind of sensor network. As a WBAN is one of the prominent types of WSN the same criterion is also applied here. The proposed scheme is submitted to a network lifetime test to check its efficacy. The comparisons of the proposed scheme with other contemporary techniques in terms of network lifetime can be seen in the Table 4. It is also shown in Figure 6. These results show the supremacy of the proposed scheme. According to these simulations results, the proposed scheme went beyond round 17000 th, where in the other schemes the network lasted till 17000 th rounds. Only, E-HARP was the closet contester of EHCRP in this department while EERP is far off in this race. EB-MADM also showed good performance in this regard, as it lasted till 10000 th round. The better performance of proposed and E-HARP schemes in term of network lifespan is due to implantation of smart harvesting technique and energy efficient routing in both routing protocols.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multirow{2}{*}{**1 st Node Dies at**} & \multicolumn{8}{c}{**No. of Dead Nodes at Different No. of Rounds**} \\ \cline{3-11}  & & & **3000** & **5000** & **7000** & **9000** & **11,000** & **13,000** & **15,000** & **17,000** & **19,000** \\ \hline EHCRP & & & & & & & & & & & \\ (Proposed) & 8500 & 0 & 0 & 0 & 1 & 2 & 4 & 7 & 9 & 10 \\ E-HARP & 7500 & 0 & 0 & 0 & 1 & 3 & 5 & 10 & 14 & - \\ PCRP & 5500 & 0 & 0 & 1 & 1 & 2 & 4 & - & - & - \\ EERP & 4800 & 0 & 1 & 6 & 8 & - & - & - & - & - \\ EB-MADM & 6000 & 0 & 0 & 2 & 6 & 8 & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Lifeless Nodes vs. Rounds.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulation area & 2 × 2 m\({}^{2}\) \\ Total SNs & 10 \\ Number of SN nodes & 02 \\ Sensor’s Position and CNCs & Refer to Table 2 \\ E\({}_{\text{initial}}\) & 0.5 J \\ E\({}_{\text{T}}\) & 16.8 nJ/bit \\ E\({}_{\text{R}}\) & 36.2 nJ/bit \\ E\({}_{\text{A}}\) & 2 nJ/bit \\ Wavelength (\(\lambda\)) & 0.138m \\ Frequency (f) & 2.4 GHz \\ Payload & 3000 bits \\ \hline \hline \end{tabular}
\end{table}
Table 3: EHCRP simulation constraints.
## 3 Proposed HT Mitigation Method
### Evolutionary Algorithm  
The overall implementation of the evolutionary algorithm is shown in Figure 5. We use external evolution to search for the optimal circuit structure and deploy the optimal circuit structure that satisfies the conditions to CGRA for implementation. We use one-dimensional genetic coding to characterize individuals, where the coding length is related to the specific circuit structure, reflecting the mapping of each function in the circuit to the PE. At first, we generate an initial population, where each individual in the population represents a circuit structure, and then set up an evaluation function to evaluate the individuals to further select the best ones for subsequent evolution. For traversing the optimization space, we employ crossover and mutation to generate new (better) individuals.  
The evolutionary process is defined as a search for the optimal circuit structure _Copt_ that minimizes the value of the evaluation function, and the evaluation function is set as:  
\[Evalution\_value_{i}=\sum_{i=1}^{n}Wire\_length_{i}+\text{Cost}\ t_{i}. \tag{1}\]In Equation (1), _Wire_length_ represents the span between selected adjacent PEs and its sum is used to measure the overall circuit wiring length, representing the resource consumption and overall timing of the circuit. _Cost_ represents the feasibility limit, w.r.t., a PE in CGRA cannot perform two functions at the same time and the PE inserted by HTs can no longer be used. It is expressed as:  
\[\text{Cost}\,t_{i}=\begin{cases}10000,&if\ pos_{m}=pos_{n}(\ pos_{m},\ pos_{n}\in C)\\ 10000,&if\ pos_{i}=pos_{HT-I}(\ pos_{i}\in C)\\ 10000,&if\ pos_{i}\text{ is used by other circuits}(\ pos_{i}\in C)\\ 0,&otherwise\end{cases} \tag{2}\]  
where \(C\) represents the generated circuit structure and _pos_ represents the location of the PE. EA-based evolution involves traversing the design space by generating new (and better) individuals for each offspring. We use the following operations to generate new individuals for the next generation population.  
Selection: We used a tournament selection method to select individuals for the next generation. This selection method involves selecting the most suitable individuals, 20 (in this paper) individuals from the current population for the next generation.  
Crossover: We use the following operation to achieve crossover between two individuals of a generation--selecting a fragment of the first half of the first individual to cross with a fragment of the second half of the second individual to generate a new individual in exchange.  
Mutation: The mutation is essential to prevent the search from getting stuck in some local optimum. We use single-point mutation to randomly select available PEs, setting a 20% probability of randomly selecting a point of the current individual for single-point mutation.
## Materials and Methods  
### Metasurfaces Design  
To compare the sensitivity of selected metasurface structures, they were each designed to work under the same operating conditions, which, in this case, means the following: to have a resonant behavior (in terms of \(s_{11}\) reflection coefficient) for a frequency of \(f_{\mathrm{r}}\) = 5 GHz. The first initial geometries were determined based on a numerical modeling-based optimization procedure. During the design process, the fixed value of the slot width, \(w\) = 0.9 mm, was assumed. This resulted in various sizes of the considered unit cells, as presented in Figure 1. The dimensions of the designed unit cells are shown in Table 1. All of the metasurfaces were manufactured using FR-4 laminates of thickness \(t\) = 1.5 mm, for which the dielectric properties were as follows: \(\varepsilon_{\mathrm{r}}\) = 4.4 and tan\(\delta\) = 0.02.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### Power Measurements and Path Loss Modeling
#### 5.1.2 LoRa at 868 MHz  
Due to the fact that RSSI is a power parameter that does not represent the real received power but a relative measure with a range of values mostly defined by each chip vendor [60], the RSSI measurements taken are calibrated to refer to the standardized anchor point of a 1 m close-in free space reference distance [41; 59], where \(d_{0}=1\) m is the standard for an indoor system [43].  
Following a procedure similar to the previous one but applied to the LoRa RSSI measurements, slopes of \(n=1.96\) for the CI model and \(\beta=1.72\) for the FI model in the NLOS-1 zone are obtained. For the NLOS-2 zone, slopes of \(n=3.01\) for the CI model and \(\beta=4.59\) for the FI model are obtained. Figure 8 shows the measurements and the models, together with their dispersion and CDF; and the parameters of the path loss models are summarized in Table 3. It is observed that the FI slope in the NLOS-1 zone (\(\beta=1.72\)) is very similar to the FI slope in the NLOS-1 zone for narrowband transmission (\(\beta=1.94\)). In the NLOS-2 zone, the FI slope (\(\beta=4.59\)) grows less than the FI slope of the narrowband transmission (\(\beta=7.26\)), mainly due to resistance to multipath fading of LoRa CSS modulation [61]. The results of the CI model are very similar to those of the other works [26; 27].
## 1 Introduction
In the case of UAV rescue **(c)**, the UAV needs to understand the action of person who needs to be rescued). the drone.\"_ Ref. [10] Owing to the rapid progress in computer vision and machine learning (mainly deep learning) algorithms in the past decade, vision-based gesture and action recognition [13; 14] has undergone significant improvements. By inputting massive training samples into a three-dimensional (3D) convolutional neural network (CNN), in [15], the authors achieved impressive experimental results in a gesture recognition task under a real scenario. Further improvements in action recognition using the long short-term memory (LSTM) spatio-temporal network [16], two-stream network [17], and recurrent neural network (RNN) [18] have also been reported. Recent research demonstrated that the graph convolutional network (GCN) method [19] is superior to other action recognition methods by applying the space-time graph to human skeleton nodes to determine the accurate action category. Benefitting from the recent developments in computer vision technology, the DJI spark drone has been reported as the first commercial UAV that can understand human gestures with four functions: snapshot, landing/departing, tracking the human hand, and going away. As the maximum working range of tracking the human hand (and going away) is usually less than 3 m, this system is limited to mini drones, and is not suitable for larger industrial UAVs. Furthermore, its recognition rate for snapshot gestures is approximately 60\(\sim\)90% (according to our test results with a beginner), which is far from being suitable for applications in controlling UAV movement. Other researchers have attempted to apply recent deep learning-based gesture/action algorithms (such as LeapMotion [2; 20], OpenPose+LSTM [21; 22], YOLO [23] and P-CNN [24]) to human-UAV interaction. The general recognition accuracy of these works varied from 90% to 92%, which is not sufficient for controlling a UAV. Furthermore, it has always been assumed that only one person will be captured by the camera. Using a gesture recognition or action detection method can hardly satisfy the crucial UAV-controlling requirements (the recognition accuracy should be over 99%) owing to the limited discriminant ability [25]. Therefore, in this study, we developed a deep learning-based gesture-action joint detection algorithm for human-UAV interaction. The deep learning-based action detection and multi-feature based cascade gesture recognition algorithms were combined by a cross-validation process to obtain the corresponding operation command. As the flight strategy of a UAV will be changed according to different working conditions, we used the learning-based scene classification algorithm and recognized operation command to produce a unique flight command. Such a process can enable the UAV understand the command from the UAV pilot and automatically adjust its flight strategy according to the flight conditions, simultaneously.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Integrated Silicon Waveguides  
Silicon waveguides move light between two points of interest, in a manner similar to how metal conductors move electrical signals. This function makes waveguides ubiquitous in silicon photonic systems, and they come in various shapes and sizes depending on the optical wavelength and other parameters, such as loss and confinement [75]. An example of a waveguide with nominal dimensions and basic operation is shown in Figure 9[4; 76]. It confines light to its silicon core through the index of refraction difference between the oxide and the silicon. Low loss and low dispersion are typically desired. Radiation that alters this basic functionality must be considered when designing silicon photonic systems.
## 2 Materials and Methods
Thus, in Formula (1) \(M_{k}\ =\ L\cdot 2^{-k}\), and only \"real\" coefficients \(c_{j}^{(k)}\) are used to calculate the  
Figure 1: (**a**)—position of GPS measurement points in Japan (purple dots) and positions of 16 reference points (grey circles), red stars indicate epicenters of Tohoku (11 March 2011, \(M\) = 9.1) and Kumamoto (16 April 2016, \(M\) = 7) earthquakes; (**b**)—density of GPS stations; (**c**)—daily numbers of operational GPS stations in Japan, 2009–2021. entropy. The number \(N_{r}\) in Formula (1) is equal to the number of \"real\" coefficients, that is \(N_{r}\ =\ \sum_{k\ =\ 1}^{m}M_{k}\). By construction \(0\leq En_{W}\leq 1\). After determining the optimal orthogonal wavelet, we can calculate the average values of the squares of the wavelet coefficients at each detail level:  
\[S_{k}\ =\ \sum_{j\ =\ 1}^{M_{k}}\left|c_{j}^{(k)}\right|^{2}/M_{k}. \tag{2}\]  
The average value (2) of the squares of the wavelet coefficients is the part of the oscillation energy corresponding to the detail level \(k\). In other words, this value can be considered as an estimate of the signal \(x(t)\) power spectrum in the frequency band corresponding to the detail level \(k\)[27]:  
\[[f_{\min}^{(k)}f_{\max}^{(k)}]\ =\ [1/(2^{(k+1)}\Delta s),1/(2^{k}\Delta s)] \tag{3}\]  
where \(\Delta\)s is the length of the discretization time interval (in our case \(\Delta\)s = 5 min). Consider the values of the periods corresponding to the centers of the frequency bands (3):  
\[T_{k}\ =\ 2/(f_{\min}^{(k)}+f_{\max}^{(k)})\ =\ 2\Delta s/(2^{-k}+2^{-(k+1)}) \tag{4}\]  
Then the quantities \(S_{k}\ =\ S(T_{k})\), \(k\ =\ 1,\ldots,m\) are similar to the usual Fourier power spectra. The difference lies in the fact that the values (4) are much smoother, which is convenient when calculating the spectral index-the slope of the graph of the logarithm of the power spectrum depending on the logarithm of the period. To calculate the wavelet-based spectral index \(\beta_{W}\), consider the following model:  
\[\ln(S(T_{k}))\ =\ \beta_{W}\cdot\ln(T_{k})+c+\varepsilon_{k},\ k\ =\ 1,\ldots,m \tag{5}\]  
where \(\varepsilon_{k}\) is a sequence of independent random variables with zero mean. The parameter \(\beta_{W}\) in Formula (5) can be called the wavelet-based spectral index, the value of which can be found by the least squares method: \(\sum_{k\ =\ 1}^{m}\varepsilon_{k}^{2}\rightarrow\min\limits_{c,\beta_{W}}\).