esa-sceva/satcom-chunk-collection huggingface dataset:
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Periodic Collection (PeCo)  
_PeCo_[39] is one of the first techniques that introduced the concept of fake packets against global adversary. This technique works as follows: each node in the network must obtain a shared individual key between itself and its neighboring nodes for encryption purposes. When the sensor node receives a packet, it decrypts and adds the packet to its buffer using the First-In-First-Out (_FIFO_) queuing mechanism. Every node has a timer that counts down; once the timer reaches zero, the first real packet in the buffer is encrypted and sent to the destination. However, if there is no real packet, a fake packet is generated and sent instead. On the receiving side, if a node received a fake packet, it discards the packet instantly. The main issue in _PeCo_ is the buffer size. The network nodes should have a sufficient buffer size to manage all incoming packets. The Overhead of power consumption and latency are considered serious issues in _PeCo_ because the nodes generate a fake packet whenever there is no real packet to transmit.
# Improved Anomaly Detection by Using the Attention-Based Isolation Forest
## 4 Attention-Based Isolation Forest
### Huber's Contamination Model  
We propose the use of a simple representation of attention weights presented in [23], which is based on applying [PERSON]'s \(\epsilon\)-contamination model [27]. The model is represented as a set of discrete probability distributions \(F\) of the form:  
\[F=(1-\epsilon)\cdot P+\epsilon\cdot R, \tag{15}\]  
where \(P=(p_{1},\ldots,p_{T})\) is a discrete probability distribution contaminated by another probability distribution, which is denoted as \(R=(r_{1},\ldots,r_{T})\), under condition that the probability distribution \(R\) can be arbitrary; the contamination parameter \(\epsilon\in[0,1]\) controls the degree of the contamination.  
The contaminating distribution \(R\) is a point in the unit simplex with \(T\) vertices, which are denoted as \(S(1,T)\). The distribution \(F\) is a point in a small simplex that belongs to the unit simplex. The size of the small simplex depends on the hyperparameter \(\epsilon\). If \(\epsilon=1\), then the small simplex coincides with the unit simplex. If \(\epsilon=0\), then the small simplex is reduced to a single distribution \(P\).  
We propose the consideration of every element of \(P\) as a result of the softmax operation  
\[p_{k}=\sigma\Bigg{(}-\frac{\|\mathbf{x}-\mathbf{A}_{k}(\mathbf{x})\|^{2}}{ \omega}\Bigg{)}, \tag{16}\]  
that is,  
\[P=\Bigg{(}\sigma\Bigg{(}-\frac{\|\mathbf{x}-\mathbf{A}_{1}(\mathbf{x})\|^{2}}{ \omega}\Bigg{)},\ldots,\sigma\Bigg{(}-\frac{\|\mathbf{x}-\mathbf{A}_{T}( \mathbf{x})\|^{2}}{\omega}\Bigg{)}\Bigg{)}.\]  
Moreover, we propose the consideration of the distribution \(R\) as the vector of trainable parameters \(\mathbf{w}\), that is,  
\[R=\mathbf{w}=(w_{1},\ldots,w_{T}).\]Hence, the attention weight \(\alpha(\mathbf{x},\mathbf{A}_{k}(\mathbf{x}),\mathbf{w})\) can be represented for every \(k=1,\ldots,T\) as follows:  
\[\alpha(\mathbf{x},\mathbf{A}_{k}(\mathbf{x}),\mathbf{w})=(1-\epsilon)\cdot \sigma\Bigg{(}-\frac{\left\|\mathbf{x}-\mathbf{A}_{k}(\mathbf{x})\right\|^{2} }{\omega}\Bigg{)}+\epsilon\cdot w_{k}. \tag{17}\]  
An important property of the above representation is that the attention weight linearly depends on the trainable parameters, and the softmax operation depends only on the hyperparameter \(\omega\). The trainable parameters \(\mathbf{w}=(w_{1},\ldots,w_{T})\) are restricted by the unit simplex \(S(1,T)\) and, therefore, \(\mathcal{W}=S(1,T)\). This implies that the constraints for \(\mathbf{w}\) are linear (\(w_{i}\geq 0\) and \(w_{1}+\cdots+w_{T}=1\)).
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 2 Materials and Methods
### Software  
PPPH is a freely available MATLAB-based software developed by [PERSON] and [PERSON] [28]. It has also been validated by [PERSON] and [PERSON] [28] that the behavior of PPPH is comparable to that of another PPP software namely GPS Analysis and Positioning Software (GAPS-[[http://gaps.gge.unb.ca/](http://gaps.gge.unb.ca/)]([http://gaps.gge.unb.ca/](http://gaps.gge.unb.ca/)) (accessed on 20 October 2021)). In PPPH, Hatch-Melbourne-Wubbena and geometry-free combinations are used to detect and fix cycle slips. The ionosphere-free (IF) combination is applied to mitigate first-order ionospheric error on GNSS. Furthermore, the dry part of the tropospheric error is mitigated by the Saastamoinen model and the wet part is evaluated using random walk process. In addition, antenna phase center offsets (PCOs) of GPS and GLONASS satellites are corrected using the values from the IGS absolute antenna model while those of Galileo and BeiDou satellites are corrected using the conventional PCO values. For the ambiguity, it is estimated as floating numbers in the unknown parameters in KF. Thus, the ambiguity mainly affects the accuracy of measurements during the convergence period. Considering that the code of PPPH is freely available and open to user's preferences at all processing steps, we used PPPH to modify the positioning algorithm and obtain the PPP results. We used MATLAB version 2018a (Mathworks\({}^{\text{\textregistered}}\) China, [[https://www2.mathworks.cn/en/products/matlab.html](https://www2.mathworks.cn/en/products/matlab.html)]([https://www2.mathworks.cn/en/products/matlab.html](https://www2.mathworks.cn/en/products/matlab.html)), accessed 28 November 2022) to run PPPH and to edit the code.
# Experimental Investigation on Self-Excited Thermoacoustic Instability in a Rijke Tube
## 4 Results and Discussions  
### Etalon Effects  
Etalon effects originate from optical length differences when light travels through surfaces in an optical system, which can distort the true signals. In this experiment, cautions have been taken for laser alignment to avoid etalon effects as much as possible. Nevertheless, there are still some fringes induced by etalon effects remaining. Figure 5 shows partial measurement signals and the blue line represents the raw signal. It is obvious that there exist high-frequency interferences distorting the true signal. For the high-frequency etalon effects, it is suggested to mitigate them by averaging the raw signals [32]. This method is effective but has to sacrifice the measurement rate. In this work, the lowpass filter was conducted on the raw signals to relieve the effects and the cut-off frequency was set as 120 kHz weighing up the measurement accuracy and the removal of etalon effects. The pink line represents the filtered data, and it is clearly observed the filtered data is much smoother than the raw one. Figure 5b shows the increment in every equal interval sampling in the green dot square region in Figure 5a. Theoretically, the increment should be the same value, coinciding with the slope of sawtooth waveforms. However, the increment varies due to etalon effects and other noises. As the blue line in Figure 5b shows, the increment in  
Figure 4: Schematic of the experimental setup used to capture the dynamics of thermoacoustic instability in the Rijke tube.  
raw data ranges from 0.032 to 0.058 V and its standard deviation, \(\sigma\), is 0.008 V. In contrast, that of filtered data is in range of 0.045 to 0.050 V and \(1-\sigma\) is only 0.0018 V, which is less than a quarter of the former. It is proved that the etalon effects on the signals are relieved efficiently and the measurement signals suffer less distortions.
## 3 The ML Strategy within Network Slicing  
### The Role of Machine Learning  
As previously noted, ML is one of the most promising security-enhancing techniques. ML systems are trained with a set of data and learn to make decisions, after that, they can make predictions or decisions with unknown data without explicit instructions or human intervention [79]. Conducting this with the capacity to process vast volumes of data, ML systems give valuable insights into the security situation of the network and predict/prevent potential attacks [80].  
As networks evolve in complexity, traditional security measures find it challenging to cope. However, ML brings advanced capabilities, transcending rule-based systems, enabling intelligent processing of vast network data, and identifying potential security threats across various domains, including intrusion detection, privacy preservation, secure routing, and threat intelligence [69; 81]. ML's ability to leverage past data and experiences to detect malicious activities enables real-time threat detection. Moreover, its predictive analytics provide lead time for mitigation against potential attacks. Additionally, its adaptability facilitates continuous learning and model updates to address the evolving landscape of cyber threats effectively.  
There are many ML techniques that can be used to address the security challenges in NS. Supervised learning and unsupervised learning are two widespread ML techniques. The following will discuss these techniques and their applications in the security domain.
## 4 Results and Discussions
Comparing both spaces, one can say that the data space is more stable in comparison with the measurement space; the latter actually demonstrates the temporal evolution (each measurement together with \"idle\" time occupies 13 s) of the whole process of the interaction of the amino acid with the given electrode. The DGI approach allows obtaining a set of calibration curves and establishing a natural limit associated with parameter \(I_{4}\)(c) (see Figure 8 for details). This parameter has a small value and loses its monotone behavior for the both spaces. Figure 8: Behavior of the parameter \(I_{4}\)(c) from Equation (A17). Having small values located in the interval \(10^{-6}\) to \(5\times 10^{-5}\), it has a maximal sensitivity to the presence of an uncontrollable factor that can influence the interaction between the chosen electrode with d-tryptophan additive. The two curves below \(I_{4}\)(c)-C (in data space) and \(I_{4}\)(c)-Pt (in measurement space) do not have monotone characteristics. They are located in the interval \(10^{-9}\) to \(10^{-6}\). Therefore, they appear as a couple of horizontal lines with values close to zero. **Author Contributions**: Conceptualization, R.N. and H.B. ; methodology, R.N. and A.S.; software, E.M., A.V. ; validation, R.N., H.B. and A.S.; data curation, A.S., E.M.; writing--original draft preparation, R.N. ; writing--H.B., A.S. and R.N. ; editing, R.N. ; visualization, R.N. ; supervision, H.B. ; project administration, A.S.; funding acquisition, A.S.  
**Funding**: This work was partly supported by the Russian Foundation for Basic Research, project No. 17-43-020232 r-Povolzhy-e.  
**Conflicts of Interest**: The authors declare no conflict of interest. **The Basic Abbreviations**  
DGI Discrete geometrical invariant  
SVD Single-valued decomposition  
VAG(s) Voltammogram(s)
## 4 Conclusions  
The obtained non-linear regression model allows for the estimation of the efficiency of copper and lead removal from the soil using chitosan as a washing agent and depending on the main variables affecting the washing process (S/L ratio and stirring time), and it can successfully be used to predict and estimate the effectiveness of the decontamination process at certain stirring times and various S/L ratios and concentrations of chitosan in the washing solution.  
The mathematical model developed describes with high accuracy the real process of soil depollution by washing with chitosan-containing solution. The results indicated the existence of a good concordance between the experimental data and data calculated using the mathematical model.  
However, heavy metal removal through soil washing using chitosan is site-specific and depends upon the physicochemical properties of the soil. Among other variables like initial heavy metal concentration, organic matter content, particle size distribution, etc., the suitability of the current method may be limited by the pH of the studied soil, which influences the charge of the mimic group from the chitosan structure involved in the metal complexation by chitosan. Therefore, the applied decontamination method needs to be tested with different soil types in order to draw conclusions regarding the effectiveness of the current method on various soil types.  
Conceptualization, V.M., G.C.R. and G.E.D.; investigation, G.C.R., G.E.D. and I.M.S.; methodology, G.C.R. and G.E.D.; resources, V.M. and I.M.S.; visualization, V.M., G.C.R., G.E.D. and I.M.S.; writing--original draft preparation, G.C.R. and G.E.D.; writing--review and editing, I.M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Ensemble of Networks for Multilabel Classification
## 4 Proposed Approaches
### Gated Recurrent Units (GRU)  
GRU [43], like LTSM, is also a recurrent neural network with a gating mechanism. GRUs can handle the gradient vanishing problem and increase the length of term dependencies from the input. GRU has a forget gate that enables the network learn which old information is relevant for understanding the new information [62]. Unlike LTSM, GRU has fewer parameters because there is no output gate, yet the performance of GRU is similar to LSTM at many tasks: speech signal modeling, polyphonic music modeling, and natural language processing [7; 61]. They also perform better on small datasets [63] and work well on denoising tasks [64].  
The basic components of GRU are an update gate and a reset gate. The Reset gate measures how much old information to forget. The reset gate decides which information to forget and which should be passed on to the output.  
Letting \(x_{t}\) be the input sequence and \(h_{0}=0\), the update gate vector \(z_{t}\) and the reset gate vector \(r_{t}\) can be defined as  
\[z_{t}=\sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}) \tag{15}\]  
\[r_{t}=\sigma(W_{r}x_{t}+U_{r}h_{t-1}+b_{r}), \tag{16}\]  
where \(W_{z},\ U_{z},b_{z},\ W_{r},U_{r}\) and \(b_{r}\) are matrices and vectors and \(\sigma\) is the sigmoid function.  
We define  
\[\hat{h}_{t}=\phi(W_{h}x_{t}+U_{h}(r_{t}\ \odot\ h_{t-1})+b_{h}) \tag{17}\]  
as the candidate activation vector, where \(\phi\) is the tanh activation, and \(\bigcirc\) is the Hadamard (component-wise) product. The term \(r_{t}\) is the amount of past information for the candidate activation vector.  
The output vector is  
\[\hat{h}_{t}=\phi(W_{h}x_{t}+U_{h}(r_{t}\ \odot\ h_{t-1})+b_{h}) \tag{18}\]  
As can be observed, the update gate vector \(z_{t}\) measures how much new vs. old information is combined and kept [43].
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Dummy Wake-Up Scheme (DWUS)  
The key concept of _DWUS_[50] is to create multiple dummy traffic streams to direct the adversary away from the actual location of the source node. These streams have to be toward the sink. _DWUS_ coordinates the dummy traffic streams to act similar to real traffic streams. This technique consists of three phases: First, the _WSN_ is divided into different groups of dummy populations. Each group has a group leader that is changed periodically. This group leader is responsible for selecting the fake sources. The second phase is called the Wake-up, each group leader hires a nearby fake source and sends a wake-up message to the selected fake source. In the third phase, once the fake source receives the wake-up message, it sends a fake message towards the sink using one of the selected intermediate nodes. The second and third phases are repeated using a fixed transmission rate to simulate the existence of the real asset at different locations, which subsequently confuses the adversary about the original location of the real asset.
# Heatwave Damage Prediction Using Random Forest Model in Korea
###### Abstract  
Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most models focus only on heat-associated deaths. In this study, a random forest model was developed for the weekly prediction of heat-related damages on the basis of four years (2015-2018) of statistical, meteorological, and floating population data from South Korea. The model was evaluated through comparisons with other traditional regression models in terms of mean absolute error, root mean squared error, root mean squared logarithmic error, and coefficient of determination (\(R^{2}\)). In a comparative analysis with observed values, the proposed model showed an \(R^{2}\) value of 0.804. The results show that the proposed model outperforms existing models. They also show that the floating population variable collected from mobile global positioning systems contributes more to predictions than the aggregate population variable.  
heatwaves; big data; random forest regression model; machine learning; prediction 2020
## 4 Experiment and Analysis  
This section provides a comprehensive description of the experimental data and their analysis, offering intricate details. To validate the feasibility of the AO-PSO-PF algorithm, we used Honor 50 and Nexus 5X to conduct training and positioning in different scenarios (Honor 50 is used in the offline stage, while Nexus 5X is used in the online stage). According to ISO/IEC 18305 [40], we selected two scenarios to test the AO-PSO-PF algorithm.
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### Corresponding Studies to Hyperledger Fabric
#### 4.7.3 Nexledger Accelerator  
Nexledger Accelerator [53] attempts to improve throughput with the assistance of an accelerator, which is an intermediate standalone server between applications and blockchain network/Hyperledger Fabric. Thus, no modifications occur in Hyperledger Fabric. The primary concept of this approach is the collection of those transactions that do not read or write the same key into smaller batches. These collected transactions, which are uncorrelated, are compressed into a new transaction, and this is forwarded to the blockchain network. As a result, multiple transactions can be processed in a single consensus phase.  
The Accelerator has three essential components that happen sequentially. First, the type of received transaction is determined through a combination of its channel name, chaincode name, and function name in the chaincode, resulting in classification. Following, the classified transactions are grouped on the basis of their type into a new transaction. The new transaction is then routed to the blockchain network for consensus.
# Investigation of the Performance of Basalt Fiber Reinforced Asphalt Mixture
###### Abstract  
This study is focused on the effect of basalt fiber on the road performance of the asphalt mixture. The road performance of asphalt mixture with different dosages of basalt fiber was comprehensively evaluated using Marshall Stability test, the wheel tracking test, the three-point bending beam test and the freezing-thaw splitting test. The road performance of lignin fiber reinforced asphalt mixture and polyester fiber reinforced asphalt mixture also were tested to compare with the road performance of basalt fiber reinforced asphalt mixture. The results showed that basalt fiber can enhance mechanical properties, the low-and high-temperature performance and water sensitivity of the asphalt mixture significantly. Considering the road performance and economic benefits, the appropriate dosage of basalt fiber is about 0.3%. Marshall Stability (MS), dynamic stability (DS), the maximum bending strain and the tensile strength ratio (TSR) of asphalt mixture with 0.3% basalt fiber were increased by 19.6%, 25.5%, 22.2% and 6.0%, respectively. Basalt fiber has certain advantages in improving the low-temperature performance of asphalt mixture by comparison with lignin fiber and polyester fiber.  
Keywords: asphalt mixture; basalt fiber; fiber-reinforced; road performance +
Footnote †: journal: Applied Sciences
## 1 Introduction
The existence of the false spectral energies may very likely cause mistakes in interpreting the results of the spectral decomposition [12]. Moreover, the \"false bandwidth\" of the frequency axis has a great influence on the correlation analysis. One possible way to minimize such errors is to improve the TF resolution. The generalized S transform [13] and the modified S transform have been proposed to deal with the TF resolution, but they have not solved the problem perfectly. Synchrosqueezed transform (SST) [14; 15] has recently been identified by [PERSON] et al. to serve as an empirical mode decomposition-like tool. The SST, a special case of reassignment methods, aims to'sharpen' TF representation but has the advantage of allowing the reconstruction of the signals. The SST not only has a similar TF resolution to the EMD, but also has a rigorous mathematical foundation, which provides us with the possibility of making modifications on the formula of the SST. An ST-based SST, which we refer to as the synchrosqueezed S transform (SSST) [16; 17], has been proposed in recent years to improve the TF resolution. The SSST retains the characteristics of the ST and the SST, so it is considered to be useful. The advantages of the SSST can be summarized as follows: (a) the results are directly in the TF domain and not the time-scale domain; (b) the frequency axis presents a linear and uniform distribution, which meets the basic requirements of the inverse Fourier transform; (c) the algorithm is reversible and reconfigurable; (d) the TF resolution is improved; and (e) the SSST can be used to compute complex valued time signals. The frequency axis of the SSST is directly in the frequency domain. The frequency domain signals cannot be computed by correlation analysis directly, so they need to be converted into the time domain. By using the inverse Fourier transform, the SSST can be transformed from the TF domain into the time-time domain, and a time series decomposition (TD) [18; 19] has been derived to achieve this. In this paper, a time series decomposition algorithm derived from the SSST is proposed, and TF correlation is analyzed in the TF plane, thus obtaining the correlations varying with time and frequency. In Section 2, we derive formulas for the SSST and the TD, respectively. Then, the main steps of the time-frequency correlation analysis are presented. In Sections 3 and 4, we apply traditional and the SSST-TD correlation analysis to both synthetic and experimental data and compare the corresponding results. We present our conclusions in Section 5.