esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Real-Time Experimental Results  
In this section, the performance of the IT2-FPID controller is compared with the PID and the T1-FPID controllers by the data gathered from real-time experiments. All experiments are in two parts: without/with disturbance and payload on the set-point tracking. The tuned parameters without payload using the BB-BC algorithm are employed in the UAV. After that, the set-point tracking shows all the results of the compared controllers under the flexible cable-connected payload and disturbance effects. Comparison results  
Figure 11: Coverage path planning for the UAV.  
for these controllers are shown as root mean square error (RMSE) values. The sampling time (\(T_{s}\)) is chosen as 055 ms. The selected optimal value of \(T_{s}\) and the UAV IP address are communicated via Wi-Fi from the computer to the UAV. The latencies are taken into consideration, and these have a minimal impact on the real-time algorithm performance. The performance of the proposed approach can be observed via the video file provided as Supplementary Material.  
The implemented controllers are tested for three cases. First, the UAV tracks the set point without any disturbance and the payload. Secondly, the controllers try to stabilise the system under disturbance and the payload. Additionally, the square reference points are given to the UAV. The last case is giving the reference path for the UAV by the CPP algorithm. In the CPP scenario, any external disturbance is not applied because when UAV follows the reference trajectory, the flexible cable-connected payload may generate disturbances on both axes due to the fact that the payload is not connected to the CoM of the UAV.  
Disturbing the payload aims to investigate the controller responses under disturbance. In the last test, CPP aims to compare the controller performances for minimum time and maximum accuracy. Disturbing the payload and the CPP results show the robustness of the controller in real-time.
###### Abstract  
Traffic analysis attacks are common in monitoring wireless sensor networks (WSNs). In the attacks, adversaries analyze the traffic pattern to obtain critical information such as the location information of a source node. Fake source packet routing protocols are often used to ensure source location privacy (SLP) protection. The protocols rely on broadcasting fake packets from fake sources concurrently with the transmission of real packets from the real source nodes to obfuscate the adversaries. However, fake source packet routing protocols have demonstrated some performance limitations including high energy consumption, low packet delivery ratio (PDR), and long end-to-end delay (EED). In this study, two existing fake source packet routing protocols are considered. Then two new phantom-based SLP routing protocols are proposed to address the limitations. Each proposed protocol introduces a two-level phantom routing strategy to ensure two adversary confusion phases. When the adversaries perform traffic analysis attacks on the packet routes, they encounter two levels of obfuscation. Simulation results establish that the proposed protocols have superior performance features. The protocols guarantee strong SLP protection throughout the WSN domain with controlled energy consumption, PDR, and EED. Furthermore, the proposed protocols achieve more practical results under varied network configurations.  
source location privacy; wireless sensor network; fake packet routing; phantom node routing; energy consumption; end-to-end delay; packet delivery ratio +
Footnote †: journal:  
0000-0002-4500-3300][PERSON]
# A Knowledge Graph-Based Consistency Detection Method for Network Security Policies
## 7 Experiment and Analysis
### Experiment on the Importance of Policy Attributes  
As can be seen from the previous experiment, although the larger the size of policy attributes, the higher the detection rate and coverage rate will increase. However, when the size of the policy is large, their growth rate will significantly decrease (even partially stop). In real network security scenarios, policy attributes that are too large in scale will also bring huge workload in design and operation. The process of this experiment is as follows: First, the automatic generation method of the dataset is used to obtain the optimal policy set with the largest attribute size [24], and a corresponding detection logic layer graph is established. Then, the centrality algorithm is used in the detection logic layer graph to rank the importance of the policy attributes. Finally, the least important policy attributes are removed one by one, and the policy detection rate \(S_{significance}\) is calculated. At the same time, it is compared with the detection rate \(S_{best}\) of the corresponding size of the best policy set generated by the log. The smaller the difference between \(S_{significance}\) and \(S_{best}\), the more reasonable the importance ranking.  
The centrality algorithm used in this experiment is the load centrality algorithm, and the design logic of the load centrality algorithm is very close to the idea of \"sequentially removing the least important policy attributes\" in this experiment [25,26].  
The experimental results are shown in Figure 6, and can be summarized as follows:  
1. As the number of attributes being reduced increases, the difference between \(S_{significance}\) and \(S_{best}\) first increases and then decreases. In this experiment, when 28 attributes are reduced, that is, when the attribute size is 15, the difference between \(S_{significance}\) and \(S_{best}\) reaches the maximum, which is 0.035625, currently, \(S_{significance}\) is 0.964375, the difference between \(S_{significance}\) and \(S_{best}\) divided by \(S_{significance}\) is 3.6941%.
2. When there are very few or very many attributes to be reduced, that is, when the scale of attributes is extremely large or very small, \(S_{significance}\) is almost the same as \(S_{best}\). In this case, using the load centrality algorithm can efficiently find the least important policy attribute or the most important policy attribute.  
The following conclusion can be drawn: for the access control policy generated by logs, the use of load centrality algorithm in this method can assist in sorting the importance of policy attributes. In particular, when the attribute size is extremely large or very small, the load centrality algorithm can accurately find the least important or most important policy attributes.  
Figure 5: Variation Images of Policy Detection Rate and Policy Coverage for Different Attribute Scale.
## 3 Results
### Normalized Frequency Shift Calculations  
The values of the normalized frequency shift [23] for sample 1 in each group, with respect to the rest of the samples of the same group, are presented in Figure 14. Due to the geometrical variations and high amplitude of vibrations, there is up to 33% frequency change in Group 1 for mode 1, while the other modes show a small frequency shift between the first and the rest of the samples. A similar behavior of frequency change is seen in Group 2, although the maximum frequency shift is less than 30%. The frequency change between the 2-mm center hole sample and the rest of the samples in groups  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Mode**} & \multicolumn{5}{c}{**Natural Frequency (Hz)**} \\ \cline{2-6}  & **W = 48 mm, No Hole** & **W = 48 mm, D = 8 mm** & \multirow{3}{*}{**Model (Plate) 3**} \\ \cline{2-6}  & **Exp. 1** & **FEM 2** & **Exp. 1** & **FEM 2** & \\ \hline
1 & 35.0 & 34.8 & 34.9 & 34.6 & 38.0 \\
2 & 246.8 & 241.7 & 254.8 & 240.9 & 238.2 \\
3 & 697.8 & 678.6 & 699.3 & 677.5 & 666.4 \\
4 & 1295.0 & 1300.4 & 1345.3 & 1297.6 & 1302.9 \\ \hline \hline \end{tabular} 1
[FOOTNOTE:1]Footnote 1: Exp.: experimental. 2 FEM: calculated by Ansys. 3  
\end{table}
Table 6: Comparison of the natural frequency of a uniform composite rectangular cantilever plate (W = 48 mm) with experimental and FEM results for samples of the same width.  
Figure 13: Correlated mode pairs for sample 3 of all groups. The black diagonal line represents a slope equal to 1.  
3 and 4 is very little as compared to the change due to aspect ratio, as in groups 1 and 2. This further indicates that in groups 3 and 4, sample mass is the dominating factor in the frequency changes.
# The Extended Galerkin Method for Approximate Solutions of Nonlinear Vibration Equations
## 2 The Galerkin Formulation  
### State of the Art  
The utilization of the Galerkin method in this study starts from a general nonlinear differential equation which is frequently encountered in vibrations and solid mechanics in the form of [8; 9; 10]  
\[N\Big{(}u,\dot{u},\ddot{u},u^{k},\varepsilon,\ t\Big{)}=0, \tag{1}\]  
where \(u,\ \dot{u}\big{(}\ddot{u}\big{)},u^{k},\varepsilon,\) and \(t\) are the displacement, derivatives with respect to time \(t\), nonlinear term with power of \(k\), small parameter, and time, respectively. It should be emphasized that the displacement function \(u\) is defined in the physical domain \(V\) which is not shown but implied. Then the standard Galerkin method requires  
\[\int_{V}N\Big{(}u,\dot{u},\ddot{u},u^{k},\varepsilon,\ t\Big{)}\delta udV=0, \tag{2}\]  
with \(\delta u\) as arbitrary variation of displacement. Equation (2) is usually referred to as the weak form of Equation (1). It should be pointed out that in the standard Galerkin method, there is no integration over time because the temporal variable is not considered in the formulation and solution process. The effectiveness and validity of the standard Galerkin method is based on the principle of the least square method, which implies an accurate solution is achieved with the displacement solution in Equation (2).  
Without losing generality, the approximate solution is assumed as  
\[u=\sum A_{n}\cos n\omega t, \tag{3}\]  
where \(A_{n},n,\) and \(\omega\) are amplitudes, integers, and the angular frequency, respectively.  
With the solution in Equation (3), it is obvious that  
\[\begin{array}{c}u=\sum\ A_{n}\cos n\omega t,\ \ \ \ \ u^{k}=\left(\sum\ A_{n}\cos n \omega t\right)^{k},\\ \dot{u}=-\omega\sum\ nA_{n}\sin n\omega t,\ \ \ \ddot{u}=-\omega^{2}\sum\ n^{2}A_{n}\cos n \omega t.\end{array} \tag{4}\]  
Now by substituting Equation (4) into Equation (1), it yields  
\[N\Big{(}A_{n},A_{n}^{k},\omega^{m},\cos^{m}n\omega t\,\sin^{n}n\omega t, \varepsilon\Big{)}=0, \tag{5}\]  
where \(m\) is a combination of integers \(n\) and \(k\).  
As it is known, generally, there is no analytical solution to such a nonlinear equation, and approximate methods such as the harmonic balance method (HBM) [12], the Krylov-Bogoliubov-Mitropolsky method (KBM) [13], the Lindstedt-Poincare method (L-P) [14], the homotopy analysis method (HAM) [15; 16], and variations have been utilized to find solutions with different degrees of accuracy at conditions [8; 9; 10].
# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 3 Framework of the Proposed Hybrid Wind Speed Prediction Model  
The decomposition effect of signals has been effectively improved by means of CEEMD, as it eliminates the problem of modal mixing in the process of signal decomposition. In addition, The ARIMA is very good at predicting linear data, and SVM has a good predictive effect on nonlinear data. Combining the advantages of the two single models, [PERSON] and [PERSON] et al. [32] propose an ARIMA-SVM hybrid prediction model, which is successfully applied to forecast the stock price. Therefore, a hybrid short-term wind speed forecasting model, combining the advantages of CEEMD, ARIMA, and SVM, is proposed. The framework of the proposed model is shown in Figure 1. First of all, The EMD or CEEMD are employed to decompose the original wind speed series into a variety of subsequences of IMFs and a residual component. Secondly, each of the IMF and the residual are predicted by the ARIMA method to obtain the forecasting values. However, prediction errors are produced during the prediction process using the ARIMA model. To relieve the adverse influences of the errors on the prediction performance, a new subseries is reconstructed by superimposing all of the error subsequences. Thirdly, the SVM method is adopted to forecast the error subseries. Finally, all of the prediction subsequences are superimposed to obtain the predicted wind speed.
# Re-Keying Scheme Revisited: Security Model and Instantiations
## 1 Introduction
### Background on Re-Keying Schemes  
Against DPA, many countermeasures have been reported. At the device level, _masking_ and _hiding_ are studied well [2]. Masking is a countermeasure that randomizes the internal variables inside the module to disallow adversaries from analyzing the variables correctly. Hiding unlinks the internal values from the measured leakage to make the statistics meaningless. These countermeasures can be implementedwithin the device itself and do not change the interface of the device. Therefore, they can be add-ons to existing systems.  
On the other hand, as a system-level countermeasure, the re-keying scheme [3] has been proposed. It updates an encryption key frequently to make it infeasible for adversaries to collect leakage information. [PERSON] et al. [4] introduced the concept of \"separation of duties\" and proposed a concrete scheme for tiny devices. Their scheme consists of a re-keying function \(F\) and a block cipher BC. Here, \(F\) is a function that takes, as inputs, a master secret key \(mk\) and a randomness \(r\) to compute a temporal session key \(tk\). They assumed that \(F\) is easily protected from the SCAs. To encrypt a message \(m\) with the length of the block size of BC, the scheme first chooses a randomness \(r\) and computes the session key \(tk\) by \(F(mk,r)\). The scheme then encrypts \(m\) with BC using the session key \(tk\), without a countermeasure against the SCAs. [PERSON] et al. [5] provided an attack to recover the master secret key from [PERSON] et al.'s scheme [4]. In the next year, [PERSON] et al. [6] proposed two improvements. As for the first one, they reconsidered the property of \(F\). In their attack against [PERSON] et al.'s scheme, they used the property that \(F\) is invertible. To make the attack infeasible, they gave another example of \(F\), which was non-invertible and pseudo-random in the ideal cipher model [7]. As for the second improvement, they gave a generic scheme replacing the block cipher of [4] with an ideal (secure against the related key attack in addition to the chosen cipher attack) tweakable block cipher to achieve the beyond birthday security. They showed its security by proving that the second scheme is a secure tweakable block cipher.
## 4 Applications to Real Data
### Finance  
Our second study field of interest is finance, where we examine the time series S&P 500 index (S&P), federal funds rate (FFR), Deutscher Aktienindex (DAX), Dollar/Euro exchange rate (DEE), and bitcoin price (BIT). The series cover the time period from 17 July 2010 to 26 July 2019 at daily frequency, which means 2207 entries per time series because there is no data for weekends and holidays. The reason why our examined period of time starts in 2010 is that BIT was introduced in 2010 only. The left plots of Figure 11 show the development of the normalized data over the examined time period. Note that because BIT developed to extraordinarily high values, we display this series in a separate graph. Note also that FFR increased by the factor 13 approximately, from 0.19 percent in July 2010 to 2.4 percent at the end of July 2019. Furthermore, BIT develops to values larger than 200,000 although the original time series only reaches values slightly above 20,000 because it started with a value smaller than one. The right part of Figure 11 gives an overview of the distances between all time series after having applied the generalized causality algorithm. The pair with minimal distance is S&P and DAX. All pair combination with BIT show an enormous value implying that this time series has no similarity to the others.  
Figure 12 shows the causal path and local costs for the Deutscher Aktienindex (DAX) and the S&P 500 index (S&P). We see that the time series move very well together, with the fact that the representative of the US stock exchange is leading the German counterpart--there are no changes in the lead-lag structure until the end of our observed period of time. Merely the size of the lag increases visibly from the fourth quarter of 2017 onward and shrinks again from mid-2019 onward. Important economic events that might have had an influence on these structural breaks could be the European debt crisis which was still at a peak in 2014 (see [76]). With this in mind, the increase of the lag between S&P and DAX could be caused by the slowly starting recovery of the European economy.  
Figure 10: Local costs and optimal causal path of civilian unemployment rate (CUR) and economic policy uncertainty (EPU). The large bar on the x-axis represent an structural break with corresponding 95 percent confidence interval (small bars).  
To summarize Section 4.3, the generalized causality algorithm again finds an interesting causality between two of the most important stock indices of the world. The causal path shows less structural breaks than those for the macroeconomic data, yet they can still be attributed to economic events taking place at that time.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 3 Results
### Assessment on a Patient-Level Basis  
The model has been evaluated on a patient-level basis following 10-fold stratified cross-validation. In this section, the model's metrics are presented. It is highlighted that the present evaluation takes place by opposing the predicted labels to the actual labels. It does not refer to cross-examination using the resulting suggested areas, as illustrated by Grad-CAM.  
The results demonstrate significant agreement between the model and the human experts. More specifically, the DL model obtains 94.8% accuracy and an F1 score of 0.96. The model achieves high sensitivity and specificity rates (93.8% and 97.2%, respectively). PPV and NPV values are 98.8% and 86.2%, respectively. [PERSON]'s Kappa statistic score is found to be 0.91. Those results are presented in Table 3. The significantly low false positives (5) indicate the model's efficiency regarding the parathyroid-positive prediction. The lower sensitivity rate suggests that a parathyroid-negative prediction is not that reliable. The complete confusion matrix is visualised in Figure 4.  
In Table 3, the metric results of ParaNet are accompanied by results from alternative experimental Triple-CNNs. It is verified that VGG19 is superior to other state-of-the-art pre-trained CNNs when serving as the main component of the three-path network. The  
Figure 4: Confusion Matrix for patient-level evaluation.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Type** & **CNN Component** & **ACC** & **SEN** & **SPE** & **PPV** & **NPV** & **F1** \\ \hline Three-path & VGG19 & 94.8 & 93.8 & 97.2 & 98.8 & 86.2 & 96.3 \\ Three-path & VGG16 & 93.8 & 93.1 & 95.6 & 98.1 & 84.7 & 95.6 \\ Three-path & InceptionV3 & 92.7 & 92.5 & 93.3 & 97.2 & 83.2 & 94.8 \\ Three-path & Xception & 88.9 & 90.5 & 85.0 & 93.8 & 78.1 & 92.1 \\ Three-path & DenseNet & 86.9 & 85.8 & 89.4 & 95.3 & 71.6 & 90.3 \\ Three-path & ResNet v151 & 87.5 & 85.4 & 92.8 & 96.7 & 71.7 & 90.7 \\ Three-path & EfficientNet & 87.2 & 88.3 & 84.4 & 93.4 & 74.2 & 90.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Metrics for the three-path DL framework utilising various CNN components. The text in bold highlights the best observed metric scores. ACC: Accuracy, SEN: Sensitivity, SPE: Specificity, PPV: Positive Predictive Value, NPV: Negative Predictive Value, F1: F1 score.  
results are summarised in Figure 5. The reader should notice that the deployment of the rest of the networks shares the same parametrisation with VGG19. The last convolutional layer is trainable, and a global average pooling layer is added. The densely connected layers at the top of each network are the same.
# Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem
## 5 FIPA Protocols and BDI Reasoning  
According to the BDI paradigm [46], the behavior of agents is determined by the achievement of desires through the execution of plans corresponding to the intended desires caused by the perception of certain conditions (beliefs expressed as predicates). In this section, we explore the adopted desires, the firing beliefs and the corresponding plans used by agents in our system.  
The agents of our system perform their moving tasks following a repeated iteration cycle. Before such a cycle takes place, agents are initiated with:  
* A random fixed personality chosen among three possible personalities: neurotic, psychotic or extroverted;
* An initial mood (current feeling) derived from the neutral (0) PAD values of pleasure, arousal and dominance;An initial location (randomly chosen anywhere in the existing grid);
* An initial desire to be _idle_.  
Agents continuously perceive the existence of the boxes assigned to them, jointly with the destination that they have to reach and whether they have privacy issues or not. In the same way, agents continuously perceive other agents that are busy, jointly with their location. These perceptions cause the adoption of _pendingpackage_ and _busycarrier_ beliefs, respectively, and they occur in parallel with any desires of the agent.  
Once initialized, the repeating iteration cycle starts with the execution of the desire to be _idle_. The corresponding plan is accomplished according to the following steps if any pending package is perceived:  
* Transformation of the closest package among all pending packages into a _current package_ belief;
* Transformation of all other pending packages into _movingpackage_ beliefs;
* Dropping of the _idle_ desire and adoption of a _moving_ desire.  
In cases in which no pending packages are perceived, the agent moves towards the location of the closest _busycarrier_ (where busy means currently moving at least one package).  
The plan corresponding to the desire to _move_ a package also transforms all pending packages into _movingpackage_ beliefs. Then, in cases in which another agent and any other package, in addition to the current package, are present in the same cell, the following steps are carried out:  
* The farthest package is chosen as a candidate to be delegated;
* The level of trust in the encountered in the same cell is determined;
* Trust modifiers are computed based on emotions and personality;
* A decision is made to delegate the candidate package in cases of sufficient trust in the encountered agent.  
The decided delegating process is implemented as a call for proposal (CFP) interaction protocol (FIPA-compliant), as graphically outlined in Figure 1.  
Delegation can take place several times for the same package, forming sequential instances of this CFP protocol. Figure 2 graphically represents such a linked sequence of CFP protocol instances.  
Figure 1: FIPA CFP interaction protocol for delegating a task.  
Once a package has been moved to its destination, it disappears from the simulation.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 5 Feature Extraction
### Time-Frequency  
* Short-time Fourier transform. The short-time Fourier transform is based on how a signal can be sliced into several short data segments. In the end, all the segments can be concatenated from a spectral power in the time-frequency domain [41].
* Wavelet transform. According to [44], a discrete wavelet transform is \"normally, defined as a non-redundant sampled CWT. The DWT aims to represent a discrete-time series, \(x(n)\), as a set of (wavelet) coefficients\". This transformation can be represented as a filter bank of one half-band high pass and a half-band low pass component.
* Discrete wavelet transform (DWT). The DWT aims to represent a discrete-time series, \(x(n)\), as a set of (wavelet) coefficients.