esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 4 Attack Methodology
### Step 1--Training Dataset Preparation
#### 4.1.3 Features Dataset Formation  
As all the raw samples have been labeled, the next step is to calculate the features. For our analysis, we have used time-domain and frequency-domain signal properties as features. The reason for selecting these particular signal properties is based on our previous analysis on AES leaked data. We selected and analyzed more than six signal properties and concluded that a combination of time-domain and frequency-domain signal properties leads to better classification [26,29]. An explanation of each signal property (used in this work) is given below:  
* Mean of Absolute Value (MAV)--Mean of the signal is calculated.
* Kurtosis (Kur)--For Kurtosis, Frequency-distribution curve peak's sharpness is noted.
* Median PSD (FMD)--For Median PST, median is calculated in frequency domain.
* Frequency Ratio (FR)--For Frequency ratio, frequency ratio of the frequencies is recorded.
* Median Amplitude Spectrum (MFMD)--For MFMD, the median amplitude spectrum of signals is calculated.  
For all the captured \(S_{N}\) samples, the above-mentioned features are calculated, returning one sample value for each instead of \(\mathit{Len}_{\mathit{Trace}}\), hence reducing the data sample size, which is the advantage of using the above-proposed features.  
The overall training dataset preparation process is shown in Figure 5.
## 4 Conclusions
This versatility extends the framework's utility, enabling researchers to explore disaster simulations and visualize diverse natural disasters, including tornadoes, fires, and hurricanes, fostering applications in data visualization, environmental planning, education, and disaster training.
## 4 Theoretical Analysis of Double-Column Self-Centering Piers with Shear Links
### Piers with Shear Links
#### 4.2.1 Pre-Rocking State
\begin{array}{ccccc} \frac{E_{2}A_{2}}{x}&0&0&-\frac{E_{2}A_{2}}{x}&0&0\\ 0&\frac{12E_{2}I_{2}}{x^{3}}&\frac{6E_{2}I_{2}}{x^{2}}&0&-\frac{12E_{2}I_{2}} {x^{3}}&\frac{6E_{2}I_{2}}{x^{2}}\\ 0&\frac{6E_{2}I_{2}}{x^{2}}&\frac{4E_{2}I_{2}}{x}&0&-\frac{6E_{2}I_{2}}{x^{2}} &\frac{2E_{2}I_{2}}{x}\\ -\frac{E_{2}A_{2}}{x}&0&0&\frac{E_{2}A_{2}}{x}&0&0\\ 0&-\frac{12E_{2}I_{2}}{x^{3}}&-\frac{6E_{2}I_{2}}{x^{2}}&0&\frac{12E_{2}I_{2}} {x^{3}}&-\frac{6E_{2}I_{2}}{x^{2}}\\ 0&\frac{6E_{2}I_{2}}{x^{2}}&\frac{2E_{2}I_{2}}{x}&0&-\frac{6E_{2}I_{2}}{x^{2}} &\frac{4E_{2}I_{2}}{x}\end{array} \right] \tag{10}\]  
By merging the entries of the element stiffness matrices into the structural stiffness matrix based on the sequence number of the displacements, and superimposing the entries with the same displacement sequence, the structural stiffness equation can be obtained as  
\[\mathbf{F}^{c}=\mathbf{K}^{c}\mathbf{\Delta}^{c} \tag{11}\]where  
\[\mathbf{K}^{e}=\left[ \begin{array}{cccccc}\frac{24E_{1}I_{1}}{B}+\frac{E_{2}A_{2} }{x}&0&0&-12\frac{E_{1}I_{1}}{B}-\frac{E_{2}A_{2}}{x}&0\\ 0&\frac{2E_{1}A_{1}}{l}+\frac{12E_{2}I_{2}}{x^{3}}&\frac{6E_{2}I_{2}}{x^{2}}&0 &0&\frac{6E_{2}I_{2}}{x^{2}}\\ 0&\frac{6E_{2}I_{2}}{x^{2}}&\frac{8E_{1}I_{1}}{l}+\frac{4E_{2}I_{2}}{x}&-\frac {6E_{1}I_{1}}{l}&0&\frac{2E_{2}I_{2}}{x}\\ -\frac{12E_{1}I_{1}}{l^{3}}&0&-\frac{6E_{1}I_{1}}{l^{2}}&\frac{12E_{1}I_{1}}{l ^{3}}&0&0\\ -\frac{E_{2}A_{2}}{x}&0&0&0&\frac{E_{2}A_{2}}{x}&0\\ 0&\frac{6E_{2}I_{2}}{x^{2}}&\frac{2E_{2}I_{2}}{x}&0&0&\frac{4E_{2}I_{2}}{x} \end{array} \right] \tag{12}\]  
is referred to as the structural stiffness matrix. In addition, the forces and displacements in the global coordinate system can be expressed as:  
\[\mathbf{F}^{e}=\left\{ \begin{array}{cccccc}0&0&0&\frac{F_{\mathrm{x}}}{2}&0& 0\end{array} \right\}^{T} \tag{13}\]  
\[\mathbf{\Delta}^{e} =\left\{u_{1}\ u_{2}\ u_{3}\ u_{4}\ u_{5}\ u_{6}\right\}^{T} \tag{14}\] \[=\left\{u_{1}\ u_{2}\ u_{3}\ u_{x}\ u_{5}\ u_{6}\right\}^{T}\]  
These equations can be solved to determine the relationship between the lateral load and lateral displacement at the top of the pier:  
\[F_{\mathrm{x}}=\frac{12(2A_{1}E_{1}{}^{2}I_{1}{}^{2}x^{3}+3E_{2}I_{2}|E_{1}I_ {1}{}^{2}+3A_{1}E_{2}I_{2}|E_{1}I_{1}x^{2})}{P(3A_{1}E_{2}I_{2}|^{2}x^{2}+12E _{2}I_{1}I_{2}l^{2}+8A_{1}E_{1}I_{1}l^{3})}u_{x} \tag{15}\]  
Substituting Equations (1)-(7) into Equation (15) yields:  
\[F_{\mathrm{x}}=\frac{32E_{\mathrm{c}}^{2}b^{5}(b+d)^{3}+2E_{\mathrm{c}}k_{ \mathrm{SL}}hb^{6}+24E_{\mathrm{c}}k_{\mathrm{SL}}hb^{4}(b+d)^{2}}{3k_{ \mathrm{SL}}h^{4}(b+d)^{2}+k_{\mathrm{SL}}h^{4}b^{2}+16E_{\mathrm{c}}h^{3}b( b+d)^{3}}u_{\mathrm{x}} \tag{16}\]
## 2 Data and Method
### Methods
#### 2.3.4 Rain Area Delineation Method Based on PNN
(4) Based on the ground observation, a rain flag was generated for each pixel to determine whether this pixel was under raining conditions. The pixels with ground observations above 0 mm/h were deemed under raining conditions, and the rain flag was set to 2, while the pixels with ground observations equal to 0 mm/h were deemed as no raining, and the rain flag of these pixel was set to 1. In order to independently validate the new rain area delineation method, 30% of the pixels were randomly selected as the training datasets, while the rest of the pixels were used to assess the performance of the established rain area delineation method. (5) The PNN was trained with the three parameters (PCT, TD and TS) of the training datasets as inputs and the corresponding rain flag as the output. In this study, the PNN model was designed and trained by the \"newpnn\" toolkit provided by MATLAB 2009a. This toolkit could establish a PNN classification model by taking a matrix as input vectors and a matrix with classification information as target class vectors. In this toolkit, a parameter was included to control the spread of radial basis functions in PNN. If this parameter is near zero, the network will be treated as a nearest neighbor classifier. As this parameter becomes larger, the designed network will take into account several nearby input vectors and, thus, takes more time to train the PNN. In this study, the default value of this parameter provided by the MATLAB 2009a software (0.1) was used to establish the PNN model. This step will establish a PNN model that could determine whether a sample was under raining conditions. (6) The PNN-based rain area delineation method was validated using the test datasets generated in Step (4). In this step, the PNN generates a rain flag for each pixel (1 for the no rain pixels and 2 for the rain pixels). Subsequently, the rain flags determined by PNN were compared with the rain flags determined by the ground observations to assess the performance of the PNN method.
###### Abstract  
Global changes (e.g., warming and population growth) affect nutrient loadings and temperatures, but global warming also results in more frequent extreme events, such as heat waves. Using data from the world's longest-running shallow lake experimental mesocosm facility, we studied the effects of different levels of nutrient loadings combined with varying temperatures, which also included a simulated 1-month summer heat wave (HW), on nutrient and oxygen concentrations, gross ecosystem primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP) and bacterioplankton production (BACPR). The mesocosms had two nutrient levels (high (HN) and low (LN)) combined with three different temperatures according to the IPCC 2007 warming scenarios (unheated, A2 and A2 + 50%) that were applied for 11 years prior to the present experiment. The simulated HW consisted of 5\({}^{\circ}\)C extra temperature increases only in the A2 and A2 + 50% treatments applied from 1 July to 1 August 2014. Linear mixed effect modeling revealed a strong effect of nutrient treatment on the concentration of chlorophyll a (Chl-a), on various forms of phosphorus and nitrogen as well as on oxygen concentration and oxygen percentage (24 h means). Applying the full dataset, we also found a significant positive effect of nutrient loading on GPP, ER, NEP and BACPR, and of temperature on ER and BACPR. The HW had a significant positive effect on GPP and ER. When dividing the data into LN and HN, temperature also had a significant positive effect on Chl-a in LN and on orthophosphate in HN. Linear mixed models revealed differential effects of nutrients, Chl-a and macrophyte abundance (PVI) on the metabolism variables, with PVI being particularly important in the LN mesocoms. All metabolism variables also responded strongly to a cooling-low irradiance event in the middle of the HW, resulting in a severe drop in oxygen concentrations, not least in the HN heated mesocosms. Our results demonstrate strong effects of nutrients as well as an overall rapid response in oxygen metabolism and BACPR to changes in temperature, including HWs, making them sensitive ecosystem indicators of climate warming.  
climate change; extreme events; ecosystem processes; lake restoration +
Footnote †: journal: Article
# Language Inference Using Elman Networks with Evolutionary Training
## 2 Method Description
### Training Method  
Typically, the Elman networks require an extremely long training time when the network weights are estimated from positive \(\Omega^{+}\) and negative \(\Omega^{-}\) examples using the traditional error-backpropagation through time class of algorithms. Moreover, in the gradient vanishing problem, the convergence to local-minima of the error function is inevitable in these approaches. Therefore, a genetic algorithm is adopted due to the stochastic nature of this evolutionary search approach, the ability to perform the computations in parallel and the excellent behavior in the presence of large number of local optima of the optimized function.  
The weights of the Elman network are derived by a modified genetic algorithm with local search for enhanced convergence. Local search is a procedure in which, at every \(T_{l}\) iterations of the genetic algorithm, \(N_{t}\) chromosomes are randomly selected and optimized through a non-evolutionary method. In the experiments conducted in this paper, the ADAM method was selected as local search optimizer.  
The weights of the Elman network can be encoded in different depths of accuracy, i.e. double, single precision floating or fixed point numbers. In typical recurrent structures, the accuracy of an objective function is more sensitive with respect to the recurrent part of the structure, in our case the \(W_{hh}\) weights.  
The proposed training method is presented in the pseudo-code of Algorithm 1. The fitness of each chromosome is estimated from the log-probabilities of the RNN output for all positive and negative available examples. The overall fitness criterion decreases the log-probability for the negative examples, also increasing the log-probability for the positive examples:  
\[F(\Omega^{+}\cup\Omega^{-})=\sum_{\mathbf{x}\in\Omega^{+}}LogProb(\mathbf{x} )-\sum_{\mathbf{x}\in\Omega^{-}}LogProb(\mathbf{x}) \tag{5}\]  
The goal of the training method is to maximize the fitness metric of Equation (5).  
The optimal threshold is estimated by scanning the log-probability space and detecting the value where the classification error of the trained Elman RNN classifier is minimum for the training set of both positive and negative examples \(\Omega^{+},\Omega^{-}\).  
Randomly selected chromosomes are optimized for a small number of iterations through the selected local optimization method with respect to the fitness metric.  
For the local optimization method, the ADAM [23] optimizer was selected as a lightweight but fast converging algorithm. The improvements in the convergence of the model in all experiments are presented in Table 1.
# Eye State Identification Based on Discrete Wavelet Transforms
## 7 Conclusions  
We have presented a system for EEG eye state identification based on an open EEG device that captures the brain activity from only two input channels. We apply the DWT for decomposing the gathered signals and extracting the most relevant features for its subsequent classification. The performance of two different feature sets are compared in terms of accuracy and response time. We also compare the performance achieved when using one or two input channels. The results show that, for most users, using two channels does not improve the system performance significantly. On the other hand, the feature set composed by the two features (standard deviation and ratio between coefficients of alpha and beta bands) offers the best accuracy for the shortest response times, achieving an average classification accuracy with two sensors of 90.60% and 97.25% for closed and open eyes, respectively, with a response time of 1 s. Future work includes increasing the number of participants in the experiments and considering subjects with mobility disorders.  
[PERSON] and [PERSON] implemented the software; [PERSON] developed the hardware prototype; [PERSON] and [PERSON] designed the experiments; [PERSON] and [PERSON] performed the experiments and the data analysis; [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] revised the manuscript; [PERSON] and [PERSON] led the research. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Xunta de Galicia (by grant ED43 IC2020/15 and grant ED43 IC2019/01 to support the Centro de Investigacion de Galicia \"CITIC\"), the Agencia Estatal de Investigacion of Spain (by grants RED2018-102668-T and PID2019-104958 RB-C42) and ERDF funds of the EU (FEDER Galicia & AEI/FEDER, UE); and the predoctoral Grant No. ED481A-2018/156 ([PERSON]).  
Not applicable for studies involving researchers themselves.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in the study are available on request from the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
## 3 Case Study
### Comparative Analysis of Different Interval Prediction Models
#### 3.3.1 Interval Prediction Performance of SCQP
On the other hand, the Bi-SCQLSTM and Bi-QLSTM, which are based on quantile loss, have the highest computational complexities, requiring 32.261 s and 32.094 s, respectively. From the perspective of computational efficiency, the Bi-SCQLSTM does not demonstrate a significant advantage (GPR improves computational efficiency by 35.117% compared to the Bi-SCQLSTM). However, the _CWC_ of the Bi-SCQLSTM is 68.451% higher than that of GRP. In dam operation and maintenance, the accuracy of a prediction model often determines whether potential safety risks in the target structure can be accurately and promptly identified. Therefore, the performance improvement brought by the Bi-SCQLSTM, at the cost of some computational efficiency, is worthwhile.
## 2 Mathematical Model and Tracking Error Dynamics
### Dynamic Model of a Swarm of WMRs  
A typical model of the WMR, which contains two driving wheels and a passive self-adjusted supporting wheel, with the same radius express by r and separated by 2R, is shown in Figure 1. The vector \(r\) exhibits the global coordinate frame {O, X, Y} of real-time location for WMR, which is the coordinate of the point \(C\) in the universal coordinate frame, and \(\theta\) is the orientation of the local frame {C, X\({}_{c}\), Y\({}_{c}\)}. The distance between points \(r\) and \(C\) are denoted by d. Base on the above definitions, the generalized coordinate of the WMR can be represented as the equation below.  
\[r=\left[\begin{array}{cc}x_{c}&y_{c}&\theta\end{array}\right]^{T} \tag{1}\]A general WMR system, the robot can move in the direction of the axis of the driving wheels with pure rolling and non-slipping condition status, and the kinematic equation of the WMR under the constraint can be depicted as below [27]:  
\[\dot{r}=\left[\begin{array}{c}\dot{x}_{c}\\ \dot{y}_{c}\\ \dot{\theta}\end{array}\right]=\left[\begin{array}{cc}\cos\theta&-d\sin \theta\\ \sin\theta&d\cos\theta\\ 0&1\end{array}\right]\left[\begin{array}{c}v_{l}\\ \omega\end{array}\right] \tag{2}\]  
where \(v_{l}\) and \(\omega\) are the velocity of linear and angular along the robot axis, respectively.  
The above kinematic equation for WMR is used to infer the dynamics of WMR. In this research, the dynamic equation of a swarm of WMRs can be given as:  
\[M_{i}(r_{i})\ddot{r}_{i}+G_{i}(r_{i},\dot{r}_{i})\dot{r}_{i}+K_{gi}(r_{i})=O_{ i}(r_{i})\tau_{i} \tag{3}\]  
where \(M_{i}(r_{i})\in\Re^{3\times 3}\) is a symmetric positive definite inertia matrix and \(G_{i}(r_{i},\dot{r}_{i})\in\Re^{3\times 3}\) is the centripetal and Coriolis matrix. Both \(M_{i}(r_{i})\) and \(G_{i}(r_{i},\dot{r}_{i})\) satisfy the skew-symmetric property and \(K_{gi}(r_{i})\in\Re^{3\times 3}\) is the gravitational vector. Since the WMR moves in the horizontal plane, the gravitational vector \(K_{gi}(r_{i})\in\Re^{3\times 3}\) is zero, \(O_{i}(r_{i})\in\Re^{3\times 2}\) is the input transformation matrix, \(\tau_{i}\in\Re^{2\times 1}\) is then applied to the torque vector, \(\dot{r}_{i}\) and \(\ddot{r}_{i}\), and denotes velocity and acceleration vectors, and \(i\) is the number of WMRs. where:  
\[M_{i}(r_{i})=\left[\begin{array}{ccc}m_{i}&0&m_{i}d_{i}\sin\theta_{i}\\ 0&m_{i}&-m_{i}d_{i}\cos\theta_{i}\\ m_{i}d_{i}\sin\theta_{i}&-m_{i}d_{i}\cos\theta_{i}&I\end{array}\right]\]  
\[G_{i}(r_{i},\dot{r}_{i})=\left[\begin{array}{ccc}0&0&m_{i}d_{i}\dot{\theta} _{i}\cos\theta_{i}\\ 0&0&m_{i}d_{i}\dot{\theta}_{i}\sin\theta_{i}\\ 0&0&0\end{array}\right]O_{i}(r_{i})=\frac{1}{r_{i}}\left[\begin{array}{ccc} \cos\theta_{i}&\sin\theta_{i}\\ \sin\theta_{i}&\sin\theta_{i}\\ R_{i}&-R_{i}\end{array}\right]\]  
\[\tau_{i}=\left[\begin{array}{c}\tau_{ri}\\ \tau_{li}\end{array}\right]\]  
where \(\tau_{ri}\) and \(\tau_{li}\) represent right and left wheel torques, respectively.  
Figure 1: The wheeled mobile robot model.
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
#### 3.2.6 HF Radar  
Over the last few decades, HF radar systems have been successfully employed to remotely measure coastal and offshore currents using the concept of Bragg-scattering [112; 143; 122]. The local currents can be typically measured by a land-based radar antenna pair over a wide area, up to 200 km or more, and with coarse spatial resolutions (e.g., 0.5-6 km), according to the selected HF radio band, which could be in the range of 3 to 45 MHz [143; 122; 136]. Consequently, HF radar systems, as a cost-efficient tool, cover a wider range than marine radars (i.e., X-/S-band radars) [112]. Moreover, HF radar systems are typically preferred over marine radars to support the worldwide monitoring of marine and coastal ecosystems, although they cannot produce high-quality data such as X-band image sequences [79]. For example, there is a global HF radar network operating throughout the world to produce continuous maps of local OSCs (see Figure 5) [90]. This network provides NRT measurements of OSC fields which are applicable for operational marine services, coastal marine studies, OOS detection, water quality assessment, pollution tracking, search and rescue, and numerical ocean forecasting models, especially near the coast [79; 144]. Various studies have so far investigated HF radar data for OSC estimation. For example, [PERSON] et al. [145] investigated the capability of the HF Hybrid Sky-Surface Wave Radar (HFHSSWR) for OSC estimation. The results were compared with in situ measurements from a current meter, and it was observed that there was a good agreement between HFHSSWR results and in situ data, with a correlation coefficient of 0.81 and an RMSE of 0.138 m/s.  
The high sensitivity of HF radar signals to sea surface topography and electromagnetic interference from different sources, as well as data acquisition failures, causes frequent gaps in HF radar data, which is the main limitation in utilizing these datasets for continuous OSC retrieval [146]. To resolve this, [PERSON] et al. [146] presented a gap-filling algorithm for HF radar data using the complex empirical orthogonal functions and validated it using two datasets with different gap rates. The results showed that the method had high efficacy in reconstructing data. The direction pattern and magnitude of the reconstructed data were well-matched with the existing data.
# A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping
## 6 Conclusions and Future Work  
This research presents a CPP method for bathymetric mapping with AUVs. Collected data, sensor performance, exploration preference and coverage efficiency are taken into consideration in the algorithm. Test results in simulated environment with 6 DOF REMUS AUV model show that the weight assignment in the objective function is critical as they affect the survey performance. With proper weight settings, the AUV yields better survey performance, coverage rate and coverage efficiency, compared to traditional lawnmower and spiral approaches. Moreover, the proposed method can be easily adjusted or modified to achieve different coverage goals.  
From our parameter studies in the simulated environment, we found the weights setting of \(W_{e}=0\), \(W_{s}=0.5\), and \(W_{c}=0.5\) produces the most efficient survey. Further experiment has done on different terrains with the same weight setting to confirm the result. Although the exploration reward is set to 0 in the best weight setting, we still suggest to include a small weight for E to assure the quality of the survey products obtained from the missions with unexpected terminations due to AUV endurance or systematic problems.  
We plan to further improve the proposed CPP method from the following aspects. (1) The performance of the proposed method in convex and non-convex irregular workspace will be studied. (2) Practical factors such as the adversal effects posed by the ocean current and positioning error of sensors will be taken into consideration. (3) Multi-vehicle CPP will be studied based on the proposed method. (4) The proposed algorithm will be integrated into our on-board ROS (Robot Operating System) and field trials will be conducted.  
[PERSON] performed the computations and wrote the manuscript. [PERSON] developed the theory, revised the manuscript and supervised the project. All authors have read and agreed to the published version of the manuscript  
This project is supported by the Department of the Navy, Naval Undersea Warfare Center, Division Keyport, under the Grant Number N00253-19-1-0005.  
The authors declare no conflict of interest.