esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network and Adversary Models
### Attacker Model  
The attacker model consists of a local and passive opponent that goes backtracking step by step to identify the true source of the information [25; 28; 38]. Due to the known position of the BS in the network, and to the fact that all data from the sensor nodes are transferred there, the adversary begins its backtracking operation from the BS. The attacker is assumed to be equipped with tools like computers and spectrum analyzers. The passive attacker performs only eavesdropping and backtracking attacks. Further, it is assumed that the attacker's device has the same radio range as that of the sensors' radio range. Before determining the location of the sending node, using its gadgets, the attacker only assesses the message's signal strength and arrival angle. An attacker goes in the direction of the sender after receiving a packet to determine its location and continues to do it until it finds the packet's origin.
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
## 4 Simulation Results  
### RF Link Simulation and Budgeting  
This scheme uses ADS 2020 software to design the gain of the RF circuit and estimate the noise figure, InP1 dB, and IIP3 values. Considering that if the received signal is required to have an anti-interference signal-to-interference ratio of >45 dB, the power range of the satellite signal reaching the ground is \(-127\)-\(-\)\(-\)\(-\)\(115\) dBm, that is, the receiver can resist interference of \(-70\) dBm (\(-115\) dBm + 45 dB). In this case, the receiver is required not to be saturated due to the interference signal. The saturated input power of a typical high-speed ADC is about 2 dBm; the receiver's in-band (200 MHz) thermal noise is about \(-90\) dBm, and the amplified thermal noise is required to be greater than the quantization noise of the ADC. Based on the above two points, it is reasonable to set the receiver's amplification factor to 55 dB. According to the above analysis, the simulation of the RF link is shown in Figure 16.  
The link budget results are shown in Figure 17.  
The budget result of the RF link shows that the link gain is approximately 56.7 dB. The noise coefficient of all LNAs in this RF link is usually between 1 dB and 2 dB. Assuming that all noise in the RF link is introduced by LNA, here we assume that the noise of the RF link is 1.5 dB. The RF circuit output thermal noise is approximately \(-37.2\) dBm, the input 1 dB gain compression point is approximately \(-36.5\) dBm, and the input third-order intermodulation cutoff point is about \(-20\) dBm.  
Figure 15: Principal block diagram of baseband processing board.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### Network Model and Assumptions  
Consider a wireless sensor network with \(N\) nodes, which move randomly in the region with an area of \(S\). In this paper, \(v_{\max}\) denotes the maximum speed of a node, and \(R\) denotes the transmission radius of a node. Each node is equipped with a GPS receiver, which can obtain its location information. Each node periodically broadcasts a \"Hello\" message, which contains the location information of themselves and their neighbor nodes. Each node knows its own neighbor nodes through an exchanging beacon.  
In this study, the following assumptions are made for the network model: (1) A two-dimensional space is considered, and \(l_{i}\) denotes the location information of node \(n_{i}\), while the coordinates of node \(i\) can be denoted by \((x_{i},y_{i})\). (2) We assume that the geographical locations of any two nodes do not overlap, that is, if \(\exists i\
eq j\), then \(l_{i}\
eq l_{j}\). (3) When a network is established, each node stores the initial location information of all the network nodes locally. (4) There is no error in data transmission within the range of transmission. A single data packet can be transmitted completely, without considering the transmission link interruption. (5) Each node in the network maintains time synchronization.  
For ease of description, a detailed list of acronyms and notations adopted in this section is presented in Table 1.
# An Explainable Scheme for Memorization of Noisy Instances by Downstream Evaluation
## 3 Methods  
This study focuses on presenting the explainability of an upstream model and how it memorizes and classifies noisy data. To conduct this, we first input noisy instances into a normally pre-trained upstream model to extract the features from the output layer prior to the softmax layer. Then, we design a downstream model consisting only of one or two layers to take these noisy features as inputs for training and testing. By mathematical analysis on the downstream model, we can evaluate the memorization score and memorization matrix for each feature which corresponds to the original noisy data. Through the memorization scores and attribution matrix, for instance, we can see the explainable visualization and how the model misclassifies the images, as well as other meaningful numerical results of the important priorities of memorization. The analysis can be divided into three parts. First, we introduce the overall workflow of the method. Second, the approach used for mixing noise to images is presented. Third, we explain the Integrated Gradients (IG) method, which is commonly used for analysis of model memorization, and discuss its purpose in this study. Finally, the fourth part focuses on the mathematical technique used to generate a memorization matrix that allows for observing the impact of noisy images on the model.
## 3 Results
### Smart G2V Charging  
In the G2V approach of charging PEVs, the aggregator can control the rate of power and time of charging. In this way, the amount of money that PEV owners should pay is much lower than the uncoordinated charging modes since the cars can be charged at the off-peak time. Electricity price is negative in several hours a day in Ontario, so it is beneficial for PEVs owners and operators to charge the PEVs at this time. PEV owners will be paid by the network operator due to generation constraints. As Table 4 shows, purchased energy cost in the smart G2V mode is lower than the uncoordinated charging mode, likewise the degradation cost. The reduction in degradation cost is caused by spreading the charging time interval.  
### Smart G2V Charging  
In smart V2G charging, the PEVs can operate in both vehicles to the grid (V2G) and grid to vehicle (G2V) modes. The charging rate in every hour is varied to minimize the operation cost and maximize the SOCs of the PEVs. The results are illustrated in Table 5. At this point, the same as G2V charging mode, the purchased energy cost is reduced in comparison with the uncoordinated and smart G2V charging mode. Degradation cost in V2G mode is higher than the G2V charging approach.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Smart G2V Charging** & **Without Wind** & **With Wind** \\ \hline Purchased energy cost (\$/day) & 4547.97 & 4423.74 \\ Power loss (kW/day) & 1956.08 & 1024.63 \\ Cost of power loss (\$/day) & 21.60 & 15.86 \\ Battery degradation cost (\$/day) & 86.55 & 86.42 \\ PEVs charging cost (\$/day) & \(-\)20.55 & \(-\)20.52 \\ PEVs charging infrastructure cost (\$/day) & 17.40 & 17.40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Power Loss and Costs of Smart G2V Charging Approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Capacity of HV/MV substation (\(\sqrt{Ra_{s}(t)+Rr_{s}(t)}\)) & 10 (MVA) \\ Voltage deviation (\(V_{\min}\), \(V_{\max}\)) & 5\% \\ \(\cos(\theta)\) & 0.9872 (lag) \\ Feeders’current limitation (\(\sqrt{Ploss_{s}(t)+Ploss_{s}(t)}/V_{s}(i,j,t)\)) & 314 (A) \\ Feeders’ resistance (\(1/G(i,j)\)) & 0.2006 (\(\Omega\)/km) \\ Feeders’ reactance (\(1/B(i,j)\)) & 0.4026 (\(\Omega\)/km) \\ Peak load in each node & 310 (kW) \\ Battery charger effective factor (\(\eta_{\mathit{dr}}\)) & 90\% \\ Reactive power cost (\(Cr_{s}(t)\)) & 5.23 (\$/MVar) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the Sample Network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Uncoordinated Charging** & **Without Wind** & **With Wind** \\ \hline Purchased energy cost (\$/day) & 5139.21 & 5006.08 \\ Power loss (kW/day) & 1888.9 0 & 1020.38 \\ Cost of power loss (\$/day) & 17.93 & 17.91 \\ Battery degradation cost (\$/day) & 340.35 & 297.45 \\ PEVs charging cost (\$/day) & 71.66 & 71.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Power loss and costs of uncoordinated charging approach.
# Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
###### Abstract  
We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster's sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.  
Keywords:seismic signal compression; nonparametric bayesian dictionary learning; clustering; sparse representation +
Footnote †: journal:  
_Article_  
Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
## 2 Materials and Methods
### Verification of the Model Results  
In this study, we evaluated the accuracy of the WRF model by comparing its results with observations from multiple sites and the high-resolution Global Wind Atlas (GWA). To ensure that the model results were consistent with the real wind conditions across Iran, we gathered wind observations from three sources: (i) synoptic weather stations, (ii) upper air stations, and (iii) meteorological masts installed by the SATBA at five locations across the country (as listed in Table 3). The wind masts recorded wind data every ten minutes at three different heights, while the weather stations recorded wind data every three hours at a height of 10 m a.g.l. Common statistical parameters were used to evaluate the model's performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Synoptic Station** & **Longitude** & **Latitude** & **Source** & **Measurement Height (m)** \\ \hline Koohin & 36.34 & 49.71 & SATBA & 40, 60 and 80 \\ \hline Khalkhal & 37.54 & 48.57 & SATBA & 10, 30 and 40 \\ \hline Sheikh-Tapeh & 37.52 & 45.08 & SATBA & 10, 30 and 40 \\ \hline Songhor & 34.83 & 47.47 & SATBA & 40, 60 and 80 \\ \hline Hajia-bad-Kermanshah & 34.34 & 47.34 & SATBA & 10, 30 and 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the wind masts in Iran used for evaluation of the WRF model performance.  
1. Information regarding temporal co-variability is provided here through the root mean square error (\(RMSE\)), which estimates systematic biases in the model skill, as \[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-o_{i})^{2}}},\] (1) where \"\(y\)\" is the simulated wind speed, \"\(o\)\" is the wind speed observation at the same place and time, and \"\(n\)\" is the total number of the values' pairs.
2. \"\(Bias\)\" is a common statistical error for comparing the wind speed distribution between observations and model simulations. \[Bias=\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-o_{i})},\] (2) If the mean \(Bias\) is positive, the simulated values tend to overestimate the real values and vice versa.
3. The Standard Deviation Error (\(STDE\)), \[STDE=\Big{(}RMSE^{2}-Bias^{2}\Big{)}^{1/2},\] (3)  
The \(STDE\) is very useful to evaluate the error dispersion. The low \(STDE\) value indicates that the error can be related to a source other than the simulation physics [17]. Therefore, priority is given to this quantity.
## 3 Pre-Processing of Power Consumption Data  
It is necessary to identify and clean abnormal data before separating the power consumption data of lighting sockets and air-conditioning. The abnormal conditions mainly include the data missing and data mutation storage. Among them, the values of missing data are displayed as \"0,\" which means that they are easy to identify and clean. As  
Figure 5: Time-series characteristics of the electricity consumption of lighting sockets.  
for data mutation, the partial power consumption data are too big or small, exceeding the actual threshold of energy consumption, as shown in Figure 5 and its description above. In this paper, the k-means clustering algorithm [30; 31; 32; 33; 34] is adopted to identify and clean the mutated data. The steps are as follows.  
1. The K sets of points are randomly selected as the initial clustering center in the sample data set. According to the three kinds of air-conditioning state (closed/slightly open/fully open) and three kinds of data state (located/above/below), K is evaluated at 3 for KNN throughout the paper.
2. The distance between other points and the initial clustering center point is calculated, and these other points are allocated to the nearest neighbor cluster.
3. After the preliminary clustering is completed, the averages of all sample points in different clusters are selected as the new clustering center, and then steps 1 and 2 are repeated.
4. The clustering center and clusters of the sample points are updated iteratively, until the clustering center no longer changes, which means the end of this clustering algorithm. Next, we can output the clustering center and K pieces of the clusters of the sample points.  
What needs illustration is that the distance between the points in step (2) can be calculated by the Minkowski distance [30; 31; 32; 33; 34], as shown in Equation (1), where \(p\) is 2, when we compute the two-dimensional point, and \(x_{1},\ x_{2}\) are the value of the two points.  
\[Dist(x_{1},x_{2})=\left[\sum_{k=1}^{n}\left|x_{1,k}-x_{2,k}\right|^{p}\right] ^{\frac{1}{p}} \tag{1}\]  
Taking Building A as an example, workdays, weekends, and holidays are selected as different types, and abnormal data are identified and cleaned day by day, as shown in Table 3 and Figure 6. Holidays are different from weekends. Holidays mean rest days, which are usually for some festivals and for summer or winter vacations, such as Christmas vacation. If the number of samples in the largest and smallest cluster centers accounts for less than 5% of the total sample number, or if the difference between the cluster centers is too large, it will be judged as a data mutation.  
From Figure 6, it is seen that the k-means method of cleaning daily data screens abnormal data effectively. The results of cleaning the abnormal hourly data in Building A are shown in Figure 7.  
Figure 6: Scatter diagram of daily electricity consumption of lighting sockets in Building A.
# SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots
## 4 The SWARMs Ontology
### Robotic Vehicles Ontology  
The Robotic Vehicles ontology models the robots and vehicles that are used in the different SWARMs missions (in the water domain only). Some important elements that capture the model are:  
* Mobile robots are vehicles and robots (polymorphism);
* Robots are either autonomous robots, automated robots or remotely piloted robots (disjunction);
* Vehicles are either underwater vehicles or surface vehicles (disjunction); and
* Vehicles are either motorized or propelled by the environment/unmotorized (disjunction).  
Figure 4 shows the taxonomy of vehicles and robots used in SWARMs. A vehicle models any platform in or by which someone travels or something is carried or conveyed. Vehicles can travel underwater or operate on the surface of the water (e.g., Vessel). Motorized vehicles (e.g., ROV, USV, and Vessel) have propulsion systems on-board, unlike unmotorized vehicles (e.g., UnderwaterGlider, SurfaceGlider, and Buoy). A robot is a mechanical device that is capable of performing a variety of complex tasks on command or by being programmed in advance. It operates by remote control, automatically or autonomously. In a general view, this also includes humanoid and service robotics. A robotic vehicle is a physical object designed by a human agent to provide a service by acting here on the water domain. Remotely piloted robots (e.g., ROV) are robots that are piloted by one or several human operators that use sensory feedback. Automated robots are robots not remotely piloted (in their main role) that are able to act as an automation but not to adapt to changes in the environment and/or to follow scripted plans. Autonomous robots (e.g., AUV and ASV) are robots neither remotely piloted  
Figure 3: A representation of the overall structure of the core ontology.  
nor automated (in their main role) that are able to perform high level tasks and adapt to changes in the environment and operations with limited human intervention. Underwater robots (e.g., AUV and ROV) are underwater and robotic vehicles. Surface robots (e.g., ASV and USV) are surface and robotic vehicles. In this robotic vehicle domain-specific ontology, the UnderwaterGlider and SurfaceGlider can glide using density-volume changes without a propulsion system and they are subsumed into the AUV and ASV class, respectively.
## 3 Results and Discussion
### Effects of Temperature, pH and Storage Time on the Lacase Activity of the bacCTS-MB1  
The effects of different temperatures on the whole-complex increase activity of the bacCTS-MB1s and the whole-cell increase activity of _P. putida_ MB285 cells were comparatively examined at a range of 15 \({}^{\circ}\)C to 85 \({}^{\circ}\)C. As shown in Figure 4A, both the bacCTS-MB1s and MB285 cells exhibited parallel variation profiles along with increasing temperature. From 15 \({}^{\circ}\)C to 25 \({}^{\circ}\)C, the activity of the bacCTS-MB1s and MB285 cells increased rapidly, with maximum activity observed at 25 \({}^{\circ}\)C; however, their activity declined sharply at temperatures above 30 \({}^{\circ}\)C. Nevertheless, for the bacCTS-MB1 complex, a greater degree of thermostability was evident, as this enzyme lost only 46.4% of its activity at 55 \({}^{\circ}\)C, 52.3% at 65 \({}^{\circ}\)C and 83.2% at 75 \({}^{\circ}\)C, whereas the MB285 cells lost 57.8% of their enzyme activity at 55 \({}^{\circ}\)C, 72.6% at 65 \({}^{\circ}\)C and 94.8% at 75 \({}^{\circ}\)C. Therefore, the bacCTS-MB1 complex exhibited improved thermostability towards higher temperatures.  
Figure 4B shows that although the pH range of the bacCTS-MB1 complex and _P. putida_ cells extended from pH 1 to 7, the optimal pH value was 2.5, indicating that the cross-linking immobilization did not alter the optimal pH value of the _P. putida_ MB285 increase. Even at in a pH value range of 3 to 7, the bacCTS-MB1 complex retained more stable lacase activity compared to that of the free _P. putida_ MB285 cells, suggesting a sheltering effect of the CTS-MB1 matrix on the immobilized cells, which led to the improved performance of the bacCTS-MB1s at higher pH values.  
Figure 3: FT-IR spectra for _P. putida_ MB285 cells and the as-prepared CTS-MB1s, aCTS-MB1s and bacCTS-MB1s.  
Figure 4: Effect of temperature (**A**), pH value (**B**) and storage time (**C**) on the whole-complex laccase enzyme activity of the bacCTS-MB1 complex. Whole-cell laccase enzyme activity of _P. putida_ MB285 cells alone was assayed in parallel.  
The bacCTS-MB1 complex and a _P. putida_ MB285 cell suspension were stored at 4 \({}^{\circ}\)C for 30 days to monitor lactose activity attenuation. As shown in Figure 4C, the bacCTS-MB1 complex was more stable, retaining its enzyme activity over the 30 days of incubation, with the loss of only 15% of its activity at day 15, 20% at day 20 and 29% at day 30; conversely, the free _P. putida_ MB285 cells lost 38% of their enzyme activity at day 15, 40% at day 20 and 46% at day 30. These results indicate that a CTS matrix platform is apparently conducive to retain the activity of _P. putida_ MB285 cells with surface-displayed lactose enzymes.
## 3 Results
### ML and DL Prediction Errors  
Due to Single Independent Variables (SIV) levels and interactions with Multiple Independent Variables (MIV) in deep learning and machine learning, the effects of prediction analysis may differ slightly from an actual dataset. It is deduced that both machine learning and deep learning have produced predictions with exceptional accuracy based on the above statistical normalized RMSE and comparison between machine and deep learning with CFD results at different inlet conditions. Based on supervised learning, Table 6 shows the normalized RMSE calculations of ML and DL performance prediction for mass flow rate, pressure ratio, temperature ratio, and efficiency at Single Parameter Variable (SPV) and Multiple Parameter Variable (MPV) inlet conditions, respectively. In most cases, the results show an accurate prediction with an error of less than 1%, which is considered significantly reliable. The swirl flow in the compressor has intricate patterns, and its dataset is slightly above 1% error but in an acceptable range of deep learning prediction analysis. The result shows the comprehensive agreement of deep learning Artificial Neural Network multiple inlet parameters predictive results with CFD results analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sr. No**} & \multirow{2}{*}{**Conditions**} & **Norm. RMSE** & **Norm. RMSE** & **Norm. RMSE** & **Norm. RMSE** \\  & & **Mass Flow Rate** & **Pressure Ratio** & **Temperature Ratio** & **Efficiency** \\ \hline
1 & Analysis of deep learning T\({}_{\text{amb}}\) & 0.0033 & 0.0039 & 0.00071 & 0.00273 \\  & Analysis of multiple & & & & \\
2 & independent variables using & 0.0089 & 0.0036 & 0.0041 & 0.0043 \\  & machine learning & & & & \\  & Analysis of multiple & & & & \\
3 & independent variables using & 0.0259 & 0.0553 & 0.0091 & 0.018 \\  & Deep Learning & & & & \\  & Analysis of multiple & & & & \\
4 & independent variables, & & & & \\  & excluding swirl flow using & 0.0218 & 0.0393 & 0.0081 & 0.0166 \\  & Deep Learning & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Machine learning and deep learning RMSE comparison.  
The Root Mean Square Error (RMSE) is frequently used to measure the difference between the predicted values of ML, deep learning analysis, and predicted CFD values. The individual differences between predicted and observed values are termed as residuals, whereas the RMSE aggregates these residuals into a single measure of predictive power. Figure 13a,b shows the RMSE comparison of the regression learner.  
As discussed earlier, based on predicted versus actual results, which are dissipated close to the regression line, the response plot of these models is selected. Figure 13 shows that GPR Exponential has better RMSE results for isentropic efficiency and mass flow rate for the selection of the model. Similarly, for selecting pressure ratio and temperature ratio, GPR Matern has a better overall RMSE value, whereas GPR Exponential and GPR Quadratic lie close to the GPR Matern.  
Figure 13: _Cont._