esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.1 Two-Class Machine Learning Model  
For the KNN algorithm, the value of \(K\) was set in the range of [3; 15] with a step size of 2. The average accuracy of the classifier with different parameters was tested. The experimental results show that the KNN algorithm produces better overall experimental results for the classifier, with the highest accuracy rates at \(K=3\) and \(K=5\). Hereafter, we use \(K=3\).  
For the DT algorithm, the maximum depth of the tree and the minimum number of samples are needed for the leaf nodes. The range of values for depth is set to [10; 30] and the range of values for the minimum number of samples required by a leaf node is [20; 2]. First, the accuracy of classification of the DT algorithm at different depths was tested. Experimental results show that by the DT algorithm has the highest accuracy when the depth is 26 and the minimum number of leaf nodes is 2.  
The confusion matrix is also known as the error matrix. It uses a matrix to visualize the performance of a machine learning algorithm. The column data of the confusion matrix represents the predicted values, while the row data represents the actual values. The confusion matrix is introduced to indicate whether there is confusion between different categories, i.e. whether there is a misclassification. The results of the confusion matrix produced by different algorithms are shown in Figure 4. The X-axis represents the predicted values and the Y-axis represents the true values. The values in the first quadrant represent data where the predicted value is an attack and the true value is normal. The values in the second quadrant represent data where the predicted value is normal and the true value is normal. Values in the third quadrant represent data where the predicted value is normal and the true value is an attack. A value in the fourth quadrant represents data for which the predicted value is an attack and the true value is an attack. From Figure 4, we can see that KNN outperforms other three classification techinques, followed by DT and BPNN. The performance of Naive Bayes is the worst in terms of two-class classification.  
The experimental results are shown in Figure 5. Results show that with two classifications, all packets were divided into two categories-abnormal data and normal data. The abnormal data was not further classified and the sample imbalance was relatively less of a problem. However, in practical applications, the categories of abnormal data need to be further divided, so the effect of sample imbalance on the training results should be considered in the subsequent multi-classification cases.  
Figure 4: The confusion matrix results of two-class classification using the existing four classification methods: KNN, Naive Bayes, BPNN and DT.  
Figure 5: Performance comparison of two-class classifications using the existing four classification methods: KNN, Naive Bayes, BPNN and DT.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Challenges of Achieving Energy Efficiency in the IoT
#### 2.2.1 Resource Constraints  
Resource constraints, such as processing power, memory capacity, and communication bandwidth are major challenges to achieving energy efficiency in the IoT domain. IoT devices are often equipped with processors with limited computing power (industrial sensors or sensor nodes, RFID tags, etc.) [15], making it difficult to perform complex tasks. For example, the limited memory capacity of these devices restricts the amount of data they can store and process, which has implications for energy efficiency. This challenge is particularly critical in application scenarios that require IoT devices to process large amounts of data in real-time [16]. Similarly, IoT applications, like smart cities, environmental monitoring, and health monitoring, require quick data processing to provide real-time feedback and decision support. However, due to the limitations of processing power, these devices often need to offload complex data analysis tasks to cloud- or edge-computing platforms. This not only increases the data transmission latency and energy consumption but also raises concerns about data privacy and security.
## 4 Proposed Work  
In this research work, we concentrate on driver behavior analysis, generating alert messages to the surrounding vehicles, and also assisting the driver. For that, a novel three layered architecture for driver behavior analysis and a personalized assistance system are designed as shown in Figure 1. The proposed work is organized in three layers as follows.  
* Layer 1 (connected vehicles): This layer consists of intelligent connected vehicles that have onboard sensors to collect information such as speed, longitudinal acceleration, yaw angle rate, gyroscope information, and magnetometer information. The drivers inside the vehicles are deployed with smart wearable sensors to gather information such as pulse rate, EEG rate, respiration rate, and heart rate. The collected data are sent to the upper layers through communication technologies. Layer 1 also includes edge-assisted road side units (E-RSUs) to speed up the process.
* Layer 2 (fog computing): This layer consists of distributed fog nodes, which are responsible for monitoring and handling separate regions in Layer 1. The nodes are connected to an E-RSU and have a computation ability greater than it.
* Layer 3 (cloud server): This is the uppermost layer in the architecture and consists of a cloud server that continuously collects and maintains the information provided by Layer-1 and Layer-2 devices.  
All of these layers are combined and work to analyze driver behavior and provide personalized assistance to the driver. For that, this work presents multiple contributions that are explained as follows.
###### Abstract  
The UK's TechDemoSat-1 (TDS-1), launched 2014, has demonstrated the use of global positioning system (GPS) signals for monitoring ocean winds and sea ice. Here it is shown, for the first time, that Galileo and BeiDou signals detected by TDS-1 show similar promise. TDS-1 made seven raw data collections, recovering returns from Galileo and BeiDou, between November 2015 and March 2019. The retrieved open ocean delay Doppler maps (DDMs) are similar to those from GPS. Over sea ice, the Galileo DDMs show a distinctive triple peak. Analysis, adapted from that for GPS DDMs, gives Galileo's signal-to-noise ratio (SNR), which is found to be inversely sensitive to wind speed, as for GPS. A Galileo track transiting from open ocean to sea ice shows a strong instantaneous SNR response. These results demonstrate the potential of future spaceborne constellations of GNSS-R (global navigation satellite system-reflectometry) instruments for exploiting signals from multiple systems: GPS, Galileo, and BeiDou.  
Keywords:global navigation satellite system; GNSS reflectometry; GNSS-R; TechDemoSat-1; TDS-1; ocean wind speed; sea ice; Galileo; BeiDou +
Footnote †: journal: Remote Sensing, 2020, 12, 2927; doi:10.3390/rs12182927
## 4 Evaluation  
### Evaluation Metrics  
Accuracy, recall (detection rate) and precision are common metrics normally used when evaluating a classifier. Accuracy, which indicates the percentage of correctly classified data items, is a poor metric for a task where the dataset is largely skewed in favor of normal samples. Similarly, a high detection rate implies fewer chances of a model missing alarming anomalies, while a high precision highlights the ability of a model to classify normal data. We consider detection rate (DR) to be more significant in our case than precision; for example, if a model predicts a normal sample as an attack, it is easier to correct the prediction of the model through domain knowledge, since there are few attack samples. However, if the model fails to detect an attack, it is difficult to find the attack in such a huge dataset. Nonetheless, we consider all three metrics; when comparing our approach with baseline models, we only considered DR since it is more significant in our situation.
## 3 Approach
### Supervised Feature Selection for Incomplete Data
#### 3.3.4 Convergence Analysis  
The convergence of the Algorithm 2 is summarized in the following theorem.  
Theorem 2: _The objective function value in problem (24) monotonically decreases until convergence by updating matrix **W** in Algorithm 2._  
Proof: For convenience, we define  
\[\kappa(\textbf{W})=Tr((\textbf{Y}-\textbf{B}\textbf{W})^{T}(\textbf{Y}-\textbf {B}\textbf{W})). \tag{32}\]  
In addition, the updated **W** is defined by \(\widetilde{\textbf{W}}\); according to Algorithm 2, we have  
\[\widetilde{\textbf{W}}=\min_{\textbf{W}}\kappa(\textbf{W})+\lambda Tr(\textbf{ W}^{T}\textbf{Q}\textbf{W})+\gamma\sum_{i=1}^{c}\textbf{w}_{i}^{T}\textbf{Z}_{i} \textbf{w}_{i}. \tag{33}\]  
Since **W** becomes smaller in each iteration, we have  
\[\kappa(\widetilde{\textbf{W}})+\lambda Tr(\widetilde{\textbf{W}}^{T} \widetilde{\textbf{Q}}\widetilde{\textbf{W}})+\gamma\sum_{i=1}^{c}\widetilde{ \textbf{w}}_{i}^{T}\widetilde{\textbf{Z}}_{i}\widetilde{\textbf{w}}_{i}\leq \kappa(\textbf{W})+\lambda Tr(\textbf{W}^{T}\textbf{Q}\textbf{W})+\gamma\sum_{i= 1}^{c}\textbf{w}_{i}^{T}\textbf{Z}_{i}\textbf{w}_{i}, \tag{34}\]  
where \(\widetilde{\textbf{Q}}\) and \(\widetilde{\textbf{Z}}_{i}\) is the function of \(\widetilde{\textbf{w}}_{i}\). According to this analysis, we obtain \[\kappa(\widetilde{\textbf{W}})+\lambda\sum\limits_{i=1}^{d}{\left(\frac{ \|\widetilde{\textbf{w}}_{i}\|_{2}^{2}}{2\|\textbf{w}_{i}\|_{2}}-\|\widetilde{ \textbf{w}}_{i}\|_{2}+\|\widetilde{\textbf{w}}_{i}\|_{2}\right)}+\gamma\sum \limits_{i=1}^{d}{\sum\limits_{j=1}^{c}{\left(\frac{\left(\widetilde{w}_{ij} \right)^{2}}{2\|w_{ij}\|}-\|\widetilde{w}_{ij}\|+\|\widetilde{w}_{ij}\|\right)}} \tag{35}\] \[\leqslant\kappa(\textbf{W})+\lambda\sum\limits_{i=1}^{d}{\left(\| \textbf{w}_{i}\|_{2}+\frac{\|\textbf{w}_{i}\|_{2}^{2}}{2\|\textbf{w}_{i}\|_{2 }}-\|\textbf{w}_{i}\|_{2}\right)}+\gamma\sum\limits_{i=1}^{d}{\sum\limits_{j =1}^{c}{\left(\|w_{ij}\|+\frac{\left(w_{ij}\right)^{2}}{2\|\widetilde{w}_{ij} \|}-\|w_{ij}\|\right)}}.\]  
According to Lemma 1, for any vector \(w\) and \(\widetilde{w}\), we obtain  
\[\|w\|_{2}-\frac{\|w\|_{2}^{2}}{2\|\widetilde{w}\|_{2}}\leqslant\|\widetilde{ w}\|_{2}-\frac{\|\widetilde{w}\|_{2}^{2}}{2\|\widetilde{w}\|_{2}}. \tag{36}\]  
Furthermore, by combining the two inequalities above, we have  
\[\kappa(\widetilde{\textbf{W}})+\lambda\sum\limits_{i=1}^{d}{\|\widetilde{ \textbf{w}}_{i}\|_{2}}+\gamma\sum\limits_{i=1}^{d}{\sum\limits_{j=1}^{c}{\left\| \widetilde{w}_{ij}\right\|}}\leqslant\kappa(\textbf{W})+\lambda\sum\limits_{i =1}^{d}{\|\textbf{w}_{i}\|_{2}}+\gamma\sum\limits_{i=1}^{d}{\sum\limits_{j=1 }^{c}{\left\|w_{ij}\right\|}}. \tag{37}\]  
which shows that the objective function decreases monotonically by updating **W** in each iteration.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results
### Lossy Compression  
The lossy algorithm we propose is often called _near lossless_, as the quantization error is bounded. In this section, we evaluate the quality of lossy compressed files, both visually and by means of the PSNR, and we show the ratio increase between levels.  
When evaluating lossy compression algorithms, it is recommended to calculate the Peak Signal to Noise Ratio (PSNR), a metric to quantify the reconstruction quality for files subject to lossy compression. It is usually defined via the Mean Squared Error (MSE):  
\[PSNR=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right), \tag{15}\]  
where \(MAX_{I}^{2}\) is the maximum value of the sample.  
The PSNR is usually evaluated in decibels (dB), with typical values between 30 and 50 dB (where higher is better) for 8-bit samples, as in our case. Figure 14 shows the relationship between different PSNR results at a given lossy level. It is worth mentioning that the near lossless approach followed implies that the PSNR values obtained with FAPEC for any file are grouped around some specific values. Note that this statement is only true if the files contain the same data types. That is, files with phase samples and files without phase samples are not comparable (see Figure 14) between them, as both the applied algorithm and the data structures differ.  
In addition to the quantitative results, Figure 15 provides a qualitative evaluation of the lossy compression. It corresponds to an EM304 survey in the Gulf of Mexico. The lossy levels of FAPEC illustrate the effect of the quantization, leading to images with lower contrast. It is worth mentioning that, even with a very high level of losses (level 10, eleven shades), we can still clearly identify the gas flare. For moderate levels of losses, such as level 4 (52 shades), the degradation is barely noticeable, but the compression ratio increases from 2.27 (lossless) to 3.43 (level 4). Note that level 16 would completely suppress the water column data, allowing us to only store the bathymetry information in the combined files.  
Figure 14: Quality (PSNR) of the reconstructed water column files at different FAPEC lossy levels.  
Figure 15e shows the compressed file sizes obtained with FAPEC, using its lossless option as well as the two lossy levels evaluated here. For comparison, we also include the raw.kmal1 size, as well as the compressed size when using _bzip2_. For further completeness, in this case, we also show the size achieved by the 7z compressor in its \"ultra\" configuration, which is even slower than _bzip2_. As can be seen, not even this option can approach FAPEC. The red line and dots indicate the compression throughput (in MB/s) for the different options, which confirms that FAPEC is much faster.  
Figure 15: Kongsberg EM304 survey in the Gulf of Mexico. (**a**) Bathymetry. (**b–d**) Mid-water backscatter showing gas flares at different levels of lossy compression. (**e**) Original and compressed file sizes. The red line and dots indicate the compression rate. Data courtesy of Fugro USA Marine.
## 3 Theoretical Analysis
### Range Characteristic between MBS and EGT
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Apogee** & **Perigee** & **Ascending Node** & **Descending Node** \\ \hline Fitting speed & 2.4462 \(\times\) 10\({}^{3}\) m/s & 2.0101 \(\times\) 10\({}^{3}\) m/s & 2.3599 \(\times\) 10\({}^{3}\) m/s & 2.4091 \(\times\) 10\({}^{3}\) m/s \\  & (2.4461 \(\times\) 10\({}^{3}\) m/s) & (2.0101 \(\times\) 10\({}^{3}\) m/s) & (2.3599 \(\times\) 10\({}^{3}\) m/s) & (2.4091 \(\times\) 10\({}^{3}\) m/s) \\ Fitting smallest & 3.9881 \(\times\) 10\({}^{8}\) m & 3.4930 \(\times\) 10\({}^{8}\) m & 3.6900 \(\times\) 10\({}^{8}\) m & 3.8459 \(\times\) 10\({}^{8}\) m \\  & (3.9881 \(\times\) 10\({}^{8}\) m) & (3.4930 \(\times\) 10\({}^{8}\) m) & (3.6900 \(\times\) 10\({}^{8}\) m) & (3.8459 \(\times\) 10\({}^{8}\) m) \\  & (3.9881 \(\times\) 10\({}^{8}\) m) & (3.4930 \(\times\) 10\({}^{8}\) m) & (3.6900 \(\times\) 10\({}^{8}\) m) & (3.8459 \(\times\) 10\({}^{8}\) m) \\ MESR & 2.9206 \(\times\) 10\({}^{-5}\) m & 4.6849 \(\times\) 10\({}^{-5}\) m & 0.0015 & 0.0013 \\ (second order) & (1.8942 \(\times\) 10\({}^{-4}\) m) & (2.2930 \(\times\) 10\({}^{-4}\) m) & (0.0058 m) & (0.0053) \\ MESR & 2.2769 \(\times\) 10\({}^{-5}\) m & 1.8835 \(\times\) 10\({}^{-5}\) m & 7.4297 \(\times\) 10\({}^{-4}\) m & 6.7282 \(\times\) 10\({}^{-4}\) m \\ (third order) & (5.4061 \(\times\) 10\({}^{-5}\)) & (4.2498 \(\times\) 10\({}^{-5}\) m) & (7.7474 \(\times\) 10\({}^{-4}\) m) & (7.0661 \(\times\) 10\({}^{-4}\) m) \\ MESR & 2.0802 \(\times\) 10\({}^{-5}\) m & 1.7405 \(\times\) 10\({}^{-5}\) m & 7.3999 \(\times\) 10\({}^{-4}\) m & 6.7180 \(\times\) 10\({}^{-4}\) m \\ (fourth order) & (3.2067 \(\times\) 10\({}^{-5}\) m) & (2.5272 \(\times\) 10\({}^{-5}\) m) & (7.5090 \(\times\) 10\({}^{-4}\) m) & (6.8456 \(\times\) 10\({}^{-4}\) m) \\ \hline \end{tabular}
\end{table} 
Table 5: The fitting parameters for MBS (0\({}^{\circ}\), 0\({}^{\circ}\)) with a look angle (left looking) about 0.3\({}^{\circ}\), with the synthetic aperture time being about 80 s (contents outside brackets) and 150 s (contents inside brackets). The time of apogee, perigee, ascending, and descending nodes are consistent with the time in Tables 2 and 3. For the process of imaging simulation in time domain, the smaller the MESR is, the better the imaging representing the actual scenario. Equation (8) indicates that the higher the carrier frequency \(f\) is, the smaller \(\left|\Delta R\right|_{max}\) will be. However, Tables 4 and 5 indicates that \(\left|\Delta R\right|_{max}\) cannot be too small, because MSER should not exceed \(\left|\Delta R\right|_{max}\) in different synthetic aperture time periods and look angles. Therefore, to integrate the experience operation of the space-borne platform and ground-based imaging radar technology, the proposed FBP algorithm will be adopted on MBS in the Earth-Moon motion scenario, as shown in the next section.
## 3 Formation of Heterogeneous Groups
### Clustering Techniques for the Formation of Homogeneous Clusters
#### 3.1.4 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)  
Density-based spatial clustering of applications with noise (DBSCAN) is a well-known unsupervised learning method that is commonly used in machine learning and data mining. The DBSCAN algorithm [39] is a famous algorithm used in detecting arbitrary-shaped clusters and removing noises. Based on the dataset provided, DBSCAN will group together points that are close to each other using the distance measurement (usually Euclidean distance), and also a minimum number of points. It will also mark, as outliers, the points of the dataset that are in low-density regions. The DBSCAN algorithm is based on the fact that clusters are dense regions in data space, separated by regions of the lower density of points, so in simple words, it is based on \"clusters\" and \"noise\". The DBSCAN algorithm basically requires 2 parameters in order to work:  
* eps: this parameter specifies how close points should be to each other in order to be considered a part of a cluster; if the distance between two points of the dataset is lower or equal to this value (eps), these points are considered neighbors by the algorithm.
* minPoints: It specifies the minimum number of points within eps radius. For instance, if we set this parameter to 10, then we will need at least 10 points of the dataset in order to form a dense region. The steps required to achieve clustering using the DBSCAN algorithm are:  
1. The algorithm finds all neighboring points within the eps to identify all central or visited points that have multiple neighboring MinPts.
2. During this phase, for each central point if it is not already assigned to a cluster, a new cluster will be created.  
Then recursively all its connected density points will be found and assigned to the same cluster as the central point. At this point, a point \"x\" and \"y\" are connected in density if there is another point \"z\" that has a sufficient number of neighboring points and both points (\"x\" and \"y\") are within the distance \"eps\". This is called the \"chaining process\". So, in other words, if \"a\" is the neighbor of \"b\" and \"b\" is the neighbor of \"c\", and \"c\" is the neighbor of \"d\", which is also the neighbor of \"e\", it implies that \"a\" is the neighbor of \"e\".
4. During this last step, all remaining unvisited points in the dataset are iterated. Those points that do not belong to any cluster are called \"noise\".
5. End.
## 5 Outlier Detection
### One-Class SVM  
Support Vector Machines (SVMs) are a class of supervised machine learning methods that are used in a variety of applications for classification. The basic idea behind SVM is to find an optimal hyperplane to separate the data. Linear SVM is used to find an optimal'maximum-margin' hyperplane to separate data. To deal with nonlinear classification tasks, kernel tricks are used. Kernels are used to map the data space into a higher-dimensional space, the feature space, where classification can be achieved. The functions that are used more as kernels are polynomial, radial basis function (rbf), and sigmoid.  
One Class Support Vector Machines (One Class SVMs) have been proposed for novelty and outlier detection ([PERSON] et al., 2016). The goal is to find a function that separates the data from the origin through separating normal data in a specific region. Data points outside that region can be considered anomalies. Consider a dataset \(X\) with \(n\) samples and \(m\) features, \(\Phi\), a feature mapping from data space \(X\) to feature space \(F\), and a kernel:  
\[k(x,y)=(\Phi(x)\cdot\Phi(y)) \tag{11}\]  
Figure 2: Isolation Forest illustration.  
The quadratic problem to be solved in the case of One Class SVM is the following:  
\[min\frac{1}{2}||w||^{2}+\frac{1}{v\cdot m}\sum\xi_{i}-\rho \tag{12}\]  
subject to:  
\[w\cdot\Phi(x_{i})\geq\rho-\xi_{i},\ \xi_{i}\geq 0 \tag{13}\]  
where \(w,\rho\) are the weight and bias terms of the hyperplane, \(\xi_{i}\) are the slack variables, and \(v\) is an adjustable parameter.  
The dual-Lagrangian problem is derived as:  
\[min_{a}\frac{1}{2}\sum_{ij}a_{i}a_{j}k(x_{i};y_{i}) \tag{14}\]  
Solving the above problem, the coefficients of the following decision function are derived:  
\[f(x)=sign\left(\sum_{i}a_{i}k(x_{i},\ x)-\rho\right) \tag{15}\]  
In Equation (12), the parameter \(v\in(0,1)\) is used in the second term to adjust the slack variables' effect on the quadratic problem. The parameter controls the trade-off between the number of outliers and the margin from the origin. If \(v\to 0\), then the boundaries of the hyperplane are loose, and then it separates all data from the origin. If \(v\to 1\), then the boundaries of the hyperplane become tighter, and it separates less data from the origin. An illustration of One Class SVM is shown in Figure 3.
# Si Photonics for Practical LiDAR Solutions
## 4 Si Photonics Optical Phased Array
### Key Components
#### 4.2.1 Antenna
(**b**) The effective index change rate with respect to wavelength as a function of waveguide width for a 220 nm thick waveguide antenna. inverse taper SSC case as the effective index at the waveguide end, i.e., the tip, is very close to the refractive index of SiO\({}_{2}\) and the reflection from either SiO\({}_{2}\) or air is minimal. A possible solution is, instead of deep etch, to polish the edge of a chip to expose waveguide facets. However, an OPA usually includes many antennae, therefore the yield of sidewall polishing must be very high to make sure all antennae facets are of good quality. Edge polishing is generally a challenging and expensive process and does not scale well. This issue may be among the reasons that discourage people from using edge emitting antennae in a Si OPA even if a 1D array design is used.