esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Method
### Step 1  
The first step of our approach is to discretize the trajectories. Each trajectory, that was first considered as a sequence of GPS points, now becomes a list of consecutive and distinct geographical cells.  
```
Input:\(D^{\prime}:\) transaction database; min_sup: the minimum support threshold for every itemset Procedure:\(\mathit{Apriori}(D^{\prime})\) \(L_{1}\)=find_frequent_1-itemsets(\(D^{\prime}\)) for(\(k=2\); \(L_{k-1}\
eq\emptyset\); \(k++\)) do \(C_{k}\)=Apriori_gen(\(L_{k-1}\)) foreachtransaction\(t\in D^{\prime}\)do \(C_{t}=\mathrm{subset}(C_{k},t)\) // get the subsets of \(t\) that are candidates foreachcandidate\(c\in C_{t}\)do \(c.count++\)  end if  end for \(L_{k}=\{c\in C_{k}|\) c.count\(\geq min\_sup\}\)  end fore return\(E\)=\(\bigcup_{k}L_{k}\)
```  
**Procedure:**_Apriori_gen(\(L_{k-1}\): frequent\((k-1)\)-itemsets) foreachitemset\(\mathcal{L}\in L_{k-1}\)do foreachitemset\(\mathcal{N}\in L_{k-1}\)do if(\(\mathcal{L}[1]=\mathcal{N}[1])\cap(\mathcal{L}[2]=\mathcal{N}[2])\cdots\cap( \mathcal{L}[k-2]=\mathcal{N}[k-2])\) \(\cap(\mathcal{L}[k-1]<\mathcal{N}[k-1])\)then \(c=\mathcal{L}\times\mathcal{N}\) // join step: generate candidates ifhas_infrequent_subset(\(c\),\(l_{k-1}\))then delete \(c\) // prune step: remove unfruitful candidate else  add \(c\) to \(C_{k}\)  end if  end for  end fore return\(C_{k}\) ```  
**Algorithm 2**Apriori  
Then, we apply the Apriori algorithm by considering that discretized trajectories correspond to \"bags\" and geographical cells correspond to \"items\":  
* First the 1-frequent items/cells \(L_{1}\) set is found, scanning the dataset of bags/trajectories to count number of occurrences of each item/cell. All the individual items/cells that satisfy the minimum support are counted. That is, they are included in at least \(S\) trajectories (\(S\) corresponds to the \(min\_sup\) parameter of the Apriori algorithm).  
* From the elements of \(L_{1}\), the set \(C_{2}\) is formed by considering all possible pairs of cells (candidate frequent pairs of cells).
* To find the \(L_{2}\) set, the algorithm applies a pruning step, considering among the pairs of \(C_{2}\) only those that have at least \(S\) trajectories (2-frequent cells).
* By increasing the size of the cells at each \(K\) iteration of Apriori the sets \(C_{K}\) and \(L_{K}\) are formed and \(C_{k}\) is generated from \(L_{k-1}\).
* The algorithm stops at job \(K\) when it is no longer possible to find a set of frequent cells of size \(K+1\).  
As a result of this step, we have identified the most frequent cell sets in trajectories. It is important to stress the fact that those cell sets may not be necessarily close in geographical terms, and for this reason they could represent disconnected paths.  
In the GeoLife case, we used a grid consisting of 1443 square cells (39 horizontally per 37 vertically) which result in cells whose side is 1300 m (see grid in Figure 3) and about half of these are crossed by at least one trajectory.
## 4 Methods Used for Fault Diagnosis
### Non-Unit Protection Scheme  
This method works based on the natural characteristics of the DC current and its first and second derivatives under the fault transients to assess the short-circuit impedance for specification and types of faults. This protection strategy is fast enough to interrupt the fault current and maintain the safety for the rest of the system. Too much communication delay can also be reduced by regional measurement of the system parameters. On the other hand, the implementation of many local components causes system complexity and needs high accuracy and initial costs [24].
# Adapted B-Spline Quasi-Interpolation for Approximating Piecewise Smooth Functions
## 4 Adapted Quasi Interpolation Operators for Non-Smooth Functions in \(\mathbf{R^{2}}\)
### Smoothness Indicator and the New Adapted Approximation in 2D  
In the two-dimensional case, the smoothness indicator is based upon the absolute value of the discrete four-direction Laplacian:  
\[\begin{split} L_{i_{1},j_{1}}=&\big{|}f_{i_{1}+1,j_{ 1}}-4f_{i_{1},j_{1}}+f_{i_{1}-1,j_{1}}+f_{i_{1},j_{1}+1}+f_{i_{1},j_{1}-1} \big{|}\\ &+\big{|}f_{i_{1}+1,j_{1}+1}-4f_{i_{1},j_{1}}+f_{i_{1}-1,j_{1}-1} +f_{i_{1}-1,j_{1}+1}+f_{i_{1}+j_{1}-1}\big{|}.\end{split} \tag{31}\]  
We note that if all the values involved in (31) lie within a smooth region of \(f\), then \(L_{i_{1},j_{1}}=O(h^{2})\) as \(h\to 0\). However, if \(L_{i_{1},j_{1}}\) incorporates values of \(f\) from both sides of the singularity curve \(\Gamma\), then \(L_{i_{1},j_{1}}=O(1)\). Figure 7 presents the values of the smoothness indicator \(\{L_{i,j}\}\) corresponding to the non-smooth test function defined in Equation (39).  
For each of the nine quasi-interpolation operators in (28), we attach a smoothness indicator by summing Laplacian values as follows:  
Each of the nine quasi-interpolation operators is computed using the values of \(f\) on a rectangular stencil of indices:  
\[Q_{i,j}=\sum_{k=k_{1}}^{k_{1}+m}\sum_{\ell=\ell_{1}}^{\ell_{1}+n}c_{k,\ell}f_ {i+k,j+\ell}. \tag{32}\]  
The corresponding smoothness indicator is defined as  
\[V_{i,j}=\sum_{k=k_{1}+1}^{k_{1}+m-1}\sum_{\ell=\ell_{1}-1}^{\ell_{1}+n-1}L_{i+ k,j_{+}}. \tag{33}\]  
Figure 7: The values of the smoothness indicators defined in Equation (31) for the function given in Equation (39).  
Thus, for each grid point \((ih,jh)\), we compute nine values, \(V^{1}_{i,j^{\prime}},\ldots,V^{9}_{i,j^{\prime}}\), corresponding to the above nine quasi-interpolation operators \(Q^{1},\ldots,Q^{9}\), applied using their respective stencils. From these, we select the minimum value, denoted as  
\[V^{kmin}_{i,j}=min_{k}\{V^{k}_{i,j}\},\]  
and assign the associated weight \(w_{i,j}=Q^{kmin}_{i,j}\) to the point \((x_{i},y_{j})\).  
Once the weights \(w_{i,j}\) are computed, they are inserted into the quasi-interpolation scheme defined in (27). The resulting approximation operator is referred to as the **adapted quasi-interpolation operator**, and is denoted as \(Q^{Adapt}\).
###### Abstract  
The synchronization between chaotic systems implemented in similar ways--e.g., computer models or circuits--is a well-investigated topic. Nevertheless, in many practical applications, such as communication, identification, machine sensing, etc., synchronization between chaotic systems of different implementation types--e.g., between an analog circuit and computer model--might produce fruitful results. In this research, we study the synchronization between a circuit modeling the Rossler chaotic system and a computer model using the same system. The theoretical possibility of this kind of synchronization is proved, and experimental evidence of this phenomenon is given with special attention paid to the numerical methods for computer model simulation. We show that synchronization between a circuit with uncertain parameters and a computer model is possible, and the parameters obtained from the synchronized computer model are in high correspondence with the circuit element specification. The obtained results establish the possibility of using adaptive generalized synchronization for the parameter identification of real systems. It was also found that [PERSON]'s method yielded the most accurate results in synchronization among second-order numerical integration methods. The best among the first-order methods appears to be the Euler-Cromer method, which can be of interest in embedded applications.  
adaptive synchronization; generalized synchronization; Rossler system; chaotic circuit; chaotic system; nonlinear dynamics; identification +
Footnote †: journal: Applied Sciences  
Adaptive Generalized Synchronization between Circuit and Computer Implementations of the Rossler System
# UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset
## 3 Experiments
### Features  
For our experiments, we decided to utilize five distinct audio features: MFCC (Mel-Frequency Cepstrum Coefficient), Mel Spectrogram, STFT (Short-Time Fourier Transform), LFCC (Linear-Frequency Cepstrum Coefficient), and Bark Spectrogram. Each feature was selected based on its unique characteristics and ability to capture specific aspects of audio signals. The model was trained on each feature and configuration for 50 epochs to evaluate its effectiveness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sound Source** & **Number of Recordings** & **Total Size [GB]** & **Length [s]** \\ \hline Ambient Noise & 4 & 1.19 & 416 \\ \hline UAV & 128 & 14.61 & 5120 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset summary.  
MFCCs provide a compact representation of the spectral envelope of a signal by mapping it onto the Mel-frequency scale, which approximates the human auditory system's response. The process involves applying a Fourier Transform, converting to the Mel scale, taking the logarithm, and applying a Discrete Cosine Transform (DCT) to extract a set of coefficients.  
A Mel Spectrogram is a time-frequency representation of an audio signal where the frequency axis is transformed to the Mel scale. It is obtained by applying a Short-Time Fourier Transform (STFT) followed by Mel filterbank processing, which smooths the spectrum into perceptually relevant frequency bands.  
STFT is a fundamental method for time-frequency analysis, dividing the signal into small overlapping windows and applying the Fourier Transform to each window separately. The result is a spectrogram representing how the signal's frequency content evolves.  
LFCCs are similar to MFCCs but use a linear frequency scale instead of the Mel scale. This allows for a more uniform representation of the spectral content across all frequencies rather than emphasizing lower frequencies, as in MFCCs.  
A Bark Spectrogram represents the power of an audio signal in critical bands based on the Bark scale, which approximates how humans perceive differences in frequency. It is computed similarly to a Mel Spectrogram but applies Bark filterbanks instead of Mel filters.  
Figures 11-15 below illustrate example visualizations of the same audio signal.  
Figure 11: Short-Time Fourier Transform (STFT).  
Figure 12: Mel Spectrogram.
# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 4 Experiments and Analysis
### Experiments  
In the first experiment, all classifiers are assessed using a set of performance metrics on data before cleaning and augmentation. The Random Forest classifier in this experiment was able to reach a maximum accuracy of 95% with a balanced accuracy of 50%. SVC has the second-best accuracy of 94% (see Figure 6); however, its balanced accuracy is only 43% (see Figure 7). The lowest BSL recorded against SVC is merely 5% (see Figure 8).  
Figure 5: Driver rating class distribution as per the total observations. N total represents the count of accidents claims for each class, fraudulent or not.  
Figure 6: Accuracy results before and after cleaning and augmentation.  
Data cleaning and augmentation is taken into account in the second experiment to make the dataset more realistic. Firstly, the dataset was modified by deleting rows with empty or zero values, as was explained in Section 3.5. Secondly, the Training dataset has an imbalance in favor of the No FraudFound class, with only a small proportion of data points belonging to the FraudFound class (Figure 4). This can cause issues when training our models, as the model may be biased towards the majority class, and have difficulty accurately identifying the minority class. To address this, we use a simple random resampling technique to balance the dataset. This is conducted by generating new data points for the minority class for training sets. By over-sampling the minority class, we can ensure that the model is exposed to a more diverse set of data during training, and is less likely to overfit to the majority class.  
Most of the classifiers' accuracy increased during this exercise (see Figure 6). Additionally, the balancing accuracy was enhanced (see Figure 7), and BSL was greatly decreased (see Figure 8). In this trial, XGBClassifier achieved the greatest accuracy of 95% with a balanced accuracy of 70% and 4% BSL. SVC, with a balanced accuracy of 65% and a BSL of 4%, achieves the second-best accuracy of 94%.  
The last experiment makes use of the proposed 1D CNN model. Before and after the augmentation process, the 1D CNN model is tested; the fully_connected_1 layer's feature has the highest accuracy. All of the classifiers that were considered for this experiment are evaluated, but only the SVC classifier's findings are shown in Figure 9 because they were superior to those of the other classifiers. When the proposed CNN model is fed a dataset without any augmentation, it achieves 97% accuracy, 78% balanced accuracy, and 8% BSL. When comparing the results after the augmentation phase to the results before augmentation, the overall accuracy is dropped to 2%, but the balanced accuracy is obtained at 84%, and BSL is reduced to 3%.  
Figure 8: BSL results before and after data augmentation.  
Figure 7: Balanced Accuracy results before and after cleaning and augmentation.
## 4 Proposed Secure MP-FrFT-OFDM Cryptosystem
### Secure MP-FrFT-OFDM Based on MCC-MF Sine Map and DCBS Generator  
The concept of OFDM is used in the physical layer communication based on Fast Fourier Transform (FFT). Also, the Fractional Fourier Transform (FrFT) is used in the OFDM system. The FrFT used only one parameter for the phase shift in the FFT which converts the FFT to FrFT. In the multi-parameters, FrFT used a vector of secret fractional values (0 to 1) with a length equal to the length of the FrFT for the OFDM system. In addition, the encryption process is applied in the frequency domain based on the OFDM system, which is the standard modulation used in the physical layer (IEEE 802.11 a/g/n). In this section, we suggested using the MPFrFT and the FrFT instead of the standard FFT in OFDM. The FFT-OFDM is only a multi-carrier transmission system. By using the FrFT instead FFT in OFDM, only one fraction order is used to change the phase in the output of the FrFT, which cannot satisfy any encryption properties. In this paper, the MPFrFT is used with the OFDM, where the MPFrFT has multi-parameters (ordered) equal to the length of the FrFT used, the length of MPFrFT can be 256, which means that the 256 parameters can be used as a secret key (secure multi-phase changing parameters). So, the encryption can be applied in the frequency domain without any additional equipment.  
The framework of the proposed secure MP-FrFT-OFDM based on MCC-MF sine map and DCBS generator is given in Figure 5. The first step in the proposed MPFrFT OFDM image encryption based on the MCC-MF sine map and DCBS generator key is changing the input image into a binary format of \((d=256\times 256\times 8)\) bits, \(d\in\{0,1\}\). The second step involves applying the convolutional coding with a code rate \((R=1/2)\) to the image data bits as an error-correcting code. The coded data sequence is mapped onto the QPSK-modulated symbols. Based on the proposed DCBS generator, the secret multi-parameters are generated for the MPFrFT OFDM image encryption process; finally, the cyclic prefixes are added to the output of the MPFrFT OFDM encrypted data. In the receiver, the inverse processes are used. The three steps of the proposed cryptosystem will be discussed. The proposed secure MP-FrFT-OFDM based on MCC-MF sine map and DCBS generator is shown in Figure 5.
## 5 Implementation and Evaluation
### Data Collection
Moreover, when a person is standing, there is a distinctive signal variation pattern that can be visualized in the waveform, as shown in the bottom right corner. After analyzing the unique patterns of signal variation for each activity, deep learning algorithms can be trained to recognize and classify different activities performed in the same location using Wi-Fi signals. Additionally, Figure 7 shows that the variation in a signal for the same activity in different locations can be described by analyzing the features of the signal. Different factors, such as distance, obstructions, and interference, can affect the signal and cause variations in the features. Figure 6: CSI phase sample activities in the same location. Figure 7: CSI amplitude and phase sample of the same activity in different locations. These variations can be observed through changes in the amplitude, phase, and frequency components of the signal. By analyzing and comparing these features across different locations, we can identify the differences and similarities in the signal patterns and use them to identify the activity and location. Additionally, our proposed system can be trained on these features to improve the accuracy of the recognition and localization process.
## 7 Evaluation
### Effectiveness in Exploit Generation  
To answer RQ2, we compared ExpGen with REX [13], an open-sourced automatic exploit-generation engine developed by the Shellphish team. We choose from benchmarks  
Figure 8: Time Cost in Partial Exploit Generation of each Engines. \begin{table}
\begin{tabular}{c c} \hline
**Engine** & **Configuration Setup** \\ \hline ExpGen & 1 AFL++ instance, 2 S2E instances \\ \hline AFL & 1 AFL master instance, 2 AFL slave instances \\ \hline AFL++ & 1 AFL++ master instance, 2 AFL++ slave instances \\ \hline S2E & 3 S2E instances working in parallel \\ \hline QSYM & 1 PIN-based concolic executor, 1 AFL master instance, 1 AFL slave instance \\ \hline \end{tabular}
\end{table} 
Table 3: Engine Configuration. listed in Table 2 that exhibits both 'explicit' and 'forge-needed' in leakage type, feed them with partial exploits that can already cause an address-leakage event as the initial seeds, test if they can generate exploit input at analysis-time and further try to perform complete exploitation on the target programs. Table 4 demonstrates the results. We can see that although REX can generate analysis-time exploit successfully as ExpGen given the same partial exploit already embodying the desired address leakage effect, it fails in actual exploitation on all the programs listed in the benchmark, while ExpGen achieves complete success. We believe the main reason for this huge difference lies in the novel runtime exploit relocation technique we outline in this paper. Working as ExpGen's back end, the runtime-exploit relocation engine dynamically fixes the gap between the analysis-time address space layout and runtime address space layout, therefore providing a unique advantage for ExpGen in complete automation of the exploitability evaluation process.
# Influence of Cavity Geometry on the Fracture Strength of Dental Restorations: Finite Element Study
###### Abstract  
The main purpose of this work was to analyze the stress distribution in premolars restored with indirect IPS Empres(r) CAD onilays. The three-dimensional geometry of a human first premolar was created using modeling software. The tooth fixation system was simulated through box geometry, comprising a cortical bone layer with 2 mm of thickness over a layer of trabecular bone with 15 mm of thickness. The tooth had the following approximated crown dimensions: 10.35 mm bucolingual length; 7.1 mm mesiodistal width; and 7.0 mm cervico-occlusal height. The meso-occluso-distal (MOD) cavity preparations followed the suggestions available in the literature. The cement geometry was modified to include cohesive zone models (CZM) to perform the adhesive joint's strength prediction. The loading body was created assuming contact between the food bolus and the tooth surface. Numerical solutions were obtained by performing static analysis and damage analysis using the finite element method. Von Mises stress values generated in the ceramic inlay restoration ranged from 1.39-181.47 MPa, which were on average 4.4% higher than those of the onlay ceramic restoration. The fracture strength of the onlay restoration was about 18% higher than that of the inlay restoration. The onlay design seems to contribute to higher homogenization of the adhesive resin cement strain and higher tooth structure protection.  
Keywords:inlay; onlay; finite element analysis; ceramic materials +
Footnote †: journal: Applied Sciences
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
###### Abstract  
Fire detection based on multi-temporal remote sensing data is an active research field. However, multi-temporal detection processes are usually complicated because of the spatial and temporal variability of remote sensing imagery. This paper presents a spatio-temporal model (STM) based forest fire detection method that uses multiple images of the inspected scene. In STM, the strong correlation between an inspected pixel and its neighboring pixels is considered, which can mitigate adverse impacts of spatial heterogeneity on background intensity predictions. The integration of spatial contextual information and temporal information makes it a more robust model for anomaly detection. The proposed algorithm was applied to a forest fire in 2009 in the Yinanhe forest, Heilongjiang province, China, using two-month HJ-1B infrared camera sensor (IRS) images. A comparison of detection results demonstrate that the proposed algorithm described in this paper are useful to represent the spatio-temporal information contained in multi-temporal remotely sensed data, and the STM detection method can be used to obtain a higher detection accuracy than the optimized contextual algorithm.  
forest fire detection; spatio-temporal model (STM); thermal infrared; HJ-1B +
Footnote †: journal: Remote Sensing, 8, 403
# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 7 Conclusions  
Probabilistic Flooding is revisited in this work in order to adapt to the new energy harvesting technological advantages. In such an environment, a certain amount of messages cannot be delivered and, therefore, conventional information dissemination mechanisms such as PF, fail to cover the network. In order to cope with this problem, a new probabilistic information mechanism is proposed called RPF, capable of dealing with nodes' operational states.  
A Markov chain model capable of capturing the nodes' behavior in energy harvesting environments is also proposed. This model along with a solar radiation dataset obtained from the NASA Langley Research Center (LaRC) POWER Project is used for the simulation purposes. It is shown that the proposed RPF mechanism outperforms PF regarding coverage at the expense of increased termination time. However, RPF introduces an extra memory overhead that is not expected to be significant since it is bounded by the number of neighbors.  
The work presented here was carried out in collaboration among all authors. Conceptualization, G.K.; Formal analysis G.K.; Software, G.K. and G.T.; Supervision, K.O., Validation, G.K.; Writing--Original Draft Preparation, K.O.; Writing--Review & Editing, K.O.  
This research received no external funding.  
This work was supported in part by project \"A Pilot Wireless Sensor Networks System for Synchronized Monitoring of Climate and Soil Parameters in Olive Groves,\" (MIS 5007309) which is partially funded by European and National Greek Funds (ESPA) under the Regional Operational Programme \"Ionian Islands 2014-2020.\"  
The authors declare no conflict of interest.