esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Adaptive Attitude Control Strategy of the MAV with Actuator Dynamics
### The Adaptive Gain Scheduling Algorithm  
To realize the adaptation of the control gains of the plant and reduce the chatter in the input signal of the actuator, we employ the following control law:  
\[\delta_{p_{min}}=\dfrac{1}{b_{b_{p_{min}}}}\left[b_{b_{p_{min}}}^{*}\delta_{p_ {max}}+\left(a_{q}-a_{q}^{*}\right)\dot{q}_{m}+\left(a_{q}-a_{q}^{*}\right)q_{ m}-b_{u}u-b_{v}w-b_{p}p-b_{b_{val}}\delta_{val}-b_{v_{val}}\delta_{rot}-b_{e}\right\} \tag{24}\]  
where \(b_{b_{p_{min}}}^{*},a_{q}^{*}\) and \(a_{q}^{*}\) should be determined according to the requirements for the flying qualities and the anti-interference and anti-noise performance; \(\delta_{p_{max}}\) denotes the attitude control signal of the pitch axis. Note that we employ \(\dot{q}_{m},u,w,p,\delta_{val},\delta_{v_{val}}\) rather than \(\dot{q},u_{d},w_{d},p_{d},\delta_{p_{old}},\delta_{val}\) to design the rate loop, mainly because the latter with differential calculations may increase the chatter in the input signal of the actuator in the presence of measurement noises.  
As the identification parameters are considered to have converged sufficiently close to the corresponding parameters of the plant, the plant can be approximately expressed as:  
\[T_{a}\ddot{q}+a_{\dot{q}}^{*}\dot{q}+a_{q}^{*}q=b_{b_{p_{min}}}^{*}\delta_{p_{ mic}}+\delta_{e} \tag{25}\]  
where:  
\[\delta_{e}=T_{a}\left(b_{u}\dot{u}+b_{v}\dot{w}+b_{p}\dot{p}+b_{b_{val}}\dot{ \delta}_{val}+b_{b_{val}}\dot{\delta}_{rot}\right) \tag{26}\]As a matter of fact, changes in \(u,w,p,\delta_{rol}\) and \(\delta_{rot}\) occur slowly and are constrained within relatively tight bounds because the MAV is used mainly for low-speed flight missions, which means \(\dot{u},\dot{w},\dot{p},\dot{\delta}_{rol}\) and \(\dot{\delta}_{rot}\) remain relatively small. Furthermore, \(T_{a}\) is also very small. Thus, we can conclude that \(\delta_{e}\) can be negligible. Equation (25) can be further simplified to obtain:  
\[T_{a}\ddot{q}+a_{q}^{*}\dot{q}+a_{q}^{*}q=b_{\delta_{\mu\mu}}^{*}\delta_{\mu\upsilon} \tag{27}\]  
The combination of online parameter estimation and the adaptive gain scheduling algorithms can accommodate parametric uncertainties and eliminate the cross coupling among all axes, thus improving the control quality of the MAV. The conventional PD control law can then guarantee the attitude control performance:  
\[\delta_{price}=k_{p}e_{0}+k_{d}\dot{e}_{0} \tag{28}\]  
where:  
\[e_{0}=\theta_{e}-\theta \tag{29}\]
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 4 Undetectability Analysis
### Undetectability under an Exact Noise Level
For a binary modulation scheme of which the constellation is distributed uniformly at the points \(\pm\sigma_{c}\), the distribution of \(d\) when \(H^{\prime}{}_{1}\) is true becomes  
\[f_{b}(x)=\frac{1}{2\sqrt{2\pi\sigma_{n}^{2}}}e^{-\frac{(x-\varphi_{c})^{2}}{2 \sigma_{n}^{2}}}+\frac{1}{2\sqrt{2\pi\sigma_{n}^{2}}}e^{-\frac{(x+\sigma_{c}) ^{2}}{2\sigma_{n}^{2}}}. \tag{32}\]  
The probability of detection for a uniformly distributed binary signal is  
\[P_{D,Binary}=Q\bigg{(}\frac{\alpha\sigma_{n}-\sigma_{c}}{\sigma_{n}}\bigg{)} +Q\bigg{(}\frac{\alpha\sigma_{n}+\sigma_{c}}{\sigma_{n}}\bigg{)}. \tag{33}\]  
In contrast, consider the proposed signal of which the sample value obeys a Gaussian random distribution with zero mean and variance \(\sigma_{c}^{2}\). Then, the PDF of \((d|H^{\prime}{}_{1})\) is also a Gaussian PDF with zero mean and \(\sigma_{c}^{2}+\sigma_{n}^{2}\) variance. The \(\alpha\)-tail probability is calculated directly as  
\[P_{D,Gaussian}=2Q\bigg{(}\frac{\alpha\sigma_{n}}{\sqrt{\sigma_{n}^{2}+\sigma_ {c}^{2}}}\bigg{)}. \tag{34}\]Letting \(\beta=\sigma_{c}^{2}/\sigma_{n}^{2}\), the two probabilities obtained using Equations (33) and (34) are equivalent to Equation (27). \(\square\)  
Lemma 2 shows that if the normalized SNR of [PERSON] is \(\beta<<1\), the two probabilities are nearly identical. In contrast, if \(\beta\rightarrow\infty\), the detection probability of a Gaussian signal is lower than that of a binary signal. Thus, if the SNR of the channel between [PERSON] and [PERSON] is higher, a Gaussian signal is superior to a binary signal in terms of undetectability. At a moderate SNR, the false alarm probability of [PERSON] (parameter \(\alpha\)) determines the detection probability. As the false alarm probability increases (i.e., [PERSON] is more sensitive), a Gaussian symbol becomes more undetectable than a binary symbol.
## 2 Models and Data
### Radiative Transfer Model: AccuRT
#### 2.2.3 Snow Properties  
We assume a spherical particle shape for snow grains, which allows us to obtain their IOPs from a parameterized Mie scattering model. We use the ISIOP tool [34] to generate ice/snow IOPs for any desired wavelength from ice/snow physical parameters: real and imaginary parts of the ice/snow refractive index, asymmetry factors for scattering by snow grains, and the snow layer thickness. The parameterized Mie scattering model relies on the assumptions that the snow particles (i) can be characterized by an effective radius, (ii) are weakly absorbing, and (iii) are large compared to the wavelength of light. These assumptions imply that the calculations of the absorption and scattering coefficients and the scattering asymmetry factor can be greatly simplified. Also, the scattering phase function is approximated by the Henyey-Greenstein function, which depends only on the scattering asymmetry factor, and snow impurities are included to account for absorbing material deposited in the snowpack. This approach leads to the computed snow albedo values in agreement with available observations [35].
# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 3 Methodology
### Classifier
#### 3.5.1 Support Vector Machine  
Support vector machine (SVM) is a supervised machine learning technique based on statistical learning theory, introduced by [PERSON] and [PERSON] in 1995 [54]. The primary mechanism of SVM is to look for a hyperplane in an N-dimensional space to classify data points distinctly. A hyperplane is a decision boundary to optimize a plane with the maximum margin between data points from classes called support vectors. The previous research work on scream detection using SVM report high detection rate in noisy environments. SVM can typically be extended to multi-class classification [55]; thus, it is a suitable classifier for our multi-class problem. This classifier also efficiently handles non-linear data by applying different types of kernels as linear, polynomial, radial basis function (RBF), and sigmoid kernel.  
To feed the SVM model, all the extracted features per frame except for ZCR was further averaged over the entire audio file. The average value of highest 10% ZCR over each audio was calculated. This resulted in an input shape of (15,147, 16) for SVM classification. This step is required to reduce the number of features to avoid overfitting. For this work, we have used the built-in SVM model from'scikit-learn' library [56] of Python. We have conducted several experiments to determine optimized hyperparameters values for the SVM classifier. The hyperparameters are:  
* Kernel type: 'linear', 'rbf', 'polynomial', and'sigmoid.'
* Regularization parameter (C): For large values of C, the optimization will choose a smaller-margin hyperplane. Conversely, a particularly small value of C will cause the optimizer to look for a larger margin separating the hyperplane, even if that hyperplane misclassifies more points [57].
* Degree: Degrees of the polynomial kernel.
* Gamma (\(\gamma\)): This is the kernel coefficient. For a higher gamma value, SVM tries to exactly fit the training data set [57].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Samples** & **Training (60\%)** & **Validation** & **Test (30\%)** \\ \hline Scream & 3078 & 1938 & 216 & 924 \\ Glass breaking & 3051 & 1922 & 214 & 915 \\ Background & 3060 & 1928 & 214 & 918 \\ Alarms & 3060 & 1928 & 214 & 918 \\ Conversation & 2898 & 1825 & 203 & 870 \\ Total & 15,147 & 9542 & 1061 & 4545 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dataset split into training, validation, and test set.  
The result and analysis section documents the best result and configuration of the SVM classifier for stream classification in burning structures.
# Machine Learning-Based Security Pattern Recognition Techniques for Code Developers
## 3 Experimental Results
### C/C++ and Java Datasets  
NIST suite has defined 118 CWEs for C/C++ source code snippets. We have grouped the CWEs based on the number of vulnerable samples which we could isolate per each CWE, according to the \"separation per code snippet\" technique mentioned above. The total number of samples per each CWE is 106203, which contains both vulnerable and not vulnerable samples to a specific CWE.  
The distribution of C/C++ CWE and Java vulnerable samples in the dataset is displayed in Table 2.  
We observe that, for C/C++, there are 6 CWEs well supported with more than 5000 vulnerable samples in the NIST dataset, 14 CWEs with more than 2000 vulnerable sample (including the 6 CWE from the previous category) and so on.  
Similarly, for Java source code snippets, we have prepared vulnerable and non-vulnerable code snippets for 112 CWEs, using the separation per function mentioned above. This dataset preparation technique leads to a lower dimension of the non-vulnerable portion of the dataset, as it includes only the totally clean functions of any of the 112 weaknesses supported by NIST. The Java dataset is better balanced with vulnerable samples.
# A Study of Cellular Traffic Data Prediction by Kernel ELM with Parameter Optimization
## 3 Experimental Preparation  
### Data Acquisition and Processing  
The experimental setting in this study was a commercial street in Huainan that has a large flow of people and is largely influenced by festivals. In other words, the experimental data was the cellular flow data of the base station covering the setting. All experimental data in the study were provided by the Huainan Branch of Anhui Mobile Co., Ltd (Huainan, China). The data acquisition time period was from 15 April 2019 to 24 September 2019. Our data was evenly collected 24 times a day, i.e., the acquisition interval was 1 h. The obtained flow data is shown in Figure 2.  
In Figure 2, it can be seen that the overall cellular traffic during July and August were higher than those for other months, and the peak of traffic was also relatively high in the days around 1 May and 1 June. This is because July and August are summer holidays, 1 May is International Labor Day, and 1 June is Children's Day. During this period, most people generally choose to go out for dinner and shopping, which also causes the traffic flow of commercial streets to be significantly higher than during the working period. From Figure 2, it can be seen that the maximum daily peak value of cellular traffic data was more than twice the minimum peak value. A sudden increase in traffic demand will  
Figure 1: Algorithm steps to optimize the kernel extreme learning machine (kELM) prediction model.  
Figure 2: Cellular flow data.  
bring great pressure to the existing network architecture. Predicting traffic is of great significance for network security and resource scheduling as it allows us to do the scheduling work in advance to deal with the upcoming congestion.  
The raw data obtained was a sequence with a length of 3912 (163 \(\times\) 24), which needed to be processed into several equal-length sequences as the input to the prediction model and the corresponding output before the experiment. The processing of the data is shown in Figure 3.  
In Figure 3, \(s\) is the time step. In this study, we set the time step to 24. \(n\) is the total length of collected sequence (\(n\) = 3912 in this study), and \(X\) = {\(X_{1}\), \(X_{2}\), \(X_{3}\)}, and \(Y\) = {\(Y_{1}\), \(Y_{2}\), \(Y_{3}\)} are the inputs and outputs of the prediction model, respectively. In the experiment, we used data from 4.15 to 8.24 as the training set and the remaining data as the test set for a well-trained model test.
## 4 Results and Analysis
### Electric Vehicle (EV) Penetration in Urban Network
#### 4.1.4 Case 4: 80% EV Penetration in Urban Network  
The last set of simulations for the urban network, presented in Figure 11, examine the worst-case scenario in which, in 2030, 80% of the entire car fleet is electric. In this case, the transformer overcomes the 150% peak capacity threshold, and since it will work above 100% of its capacity for almost half of the day, even with smart techniques applied, it should be considered to substitute it with one of a bigger size. The voltage profile is critical since it almost reach the lower value accepted by the norm of 0.9, so an unexpected increase in the residential demand independent from the EVs consumption, can cause a further drop below the minimum threshold. Smart techniques, instead, allow keeping the voltage profile widely within limits.  
In Table 1 are summed up the loading data related to the 400 kVA urban transformer for the different scenarios analysed. With respect to the base case peak, which is around 80% of the nominal power of the transformer, the peak values showed in Table 1 are significantly higher. As can be seen, up to 60% EV penetration, the transformer can still withstand the additional load for some hours, even if, in order to prevent it from a reduction of useful life, the application of smart techniques, can improve a lot its working condition. In case of an 80% penetration of EVs, instead the installation of a 630 kVA can be a suggested choice. However, as can be seen from Figures 8-10, in fact, the implementation of PV coupled with _ESS_ is capable of reducing the power flowing through the transformer, even in case  
Figure 11: Comparison of urban (**a**) transformer loadings and (**b**) most loaded feeder voltage profiles with 80% EV penetration.  
Figure 10: Comparison of urban (**a**) transformer loadings and (**b**) most loaded feeder voltage profiles with 60% EV penetration.  
of maximum EV penetration, and in particular, during the central hours of the day, when the energy production from photovoltaic systems is at its maximum, the minimum loading of the transformer is even below the base case scenario.
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells
### Fabrication of Organic Solar Cells  
Organic photovoltaics fabricated as one active layer are termed as single-layer OPVs, while multiple active layers based OPV are termed as multiple layers or hetero-junctions [88]. Single-layer OPVs suffer from low PCE. The introduction of multiple layers or heterojunctions in OPVs results in an improvement in the OSC. The process of a heterojunction OPV consists of four distinct steps, namely, (i) the generation of an electron-hole pair (exciton) by absorption of the photon, (ii) diffusion of the exciton to the interface, (iii) the generation of free carriers by the dissociation of excitons, and (iv) carrier transport and collection at the electrode. The first generation of OSC was comprised of a single layer possessing very low PCE due to inefficient dissociation exciton (Figure 2A).  
Later developments in OSC technology led to heterojunction cells that could be subdivided into bilayer heterojunctions, bulk heterojunctions, and tandem heterojunctions. In bilayer or planer heterojunctions, layers of donor and acceptor materials are sandwiched between two electrodes (Figure 2B). Planer heterojunctions also suffer from low PCE due to their short diffusion length and the low mobility of the generated excitons. In order to improve the PCE, it is required to match the diffusion length to the dimensions of the bilayer heterojunction, ultimately resulting in ultrathin films with low optical absorption.  
On the other hand, dispersed or bulk heterojunctions are composed of blends of donor and acceptor materials (Figure 2C). Bulk heterojunctions facilitate the diffusion of excitons to the interface and achieve high PCE compared with planar heterojunctions. The most widely used combination of donor and acceptor materials for bulk heterojunctions is P3 HT: PCBM [89; 90]. The PCE has improved by up to 18% for bulk heterojunctions [91; 92; 93]. The absorption of light in single-layer, bilayer, and bulk heterojunction is limited to a small window of the visible light spectrum: either the short- or long-wavelength region. Tandemheterojunction provides an opportunity to exploit a wide wavelength region based on two sub-cells with complementary absorption spectra [94; 95]. The sub-cells are separated by an interlayer that is responsible for the alignment of the quasi-Fermi level of both cells (Figure 2D). Owing to the elaborated advantages, tandem solar cells are gaining more attention recently. Tandem inorganic solar cells are restricted by the high cost for the required crystal growth and lattice matching. However, tandem OSC is gaining increasing interest and application owing to the simple and economical manufacturing. For bulk heterojunctions, the input materials should possess matched absorption spectra, appropriate energy level alignment, nanoscale phase separation, and high charge mobility [96].
## 3 Kinematics Analysis  
### Mechanism Description and Coordinate System  
As shown in Figure 2, the improved Delta parallel mechanism without offsets consists of a moving platform, a base platform, and three identical branches connecting the two platforms. Each branch is connected by three revolute joints which are noncoplanar and a parallelogram mechanism. The improved Delta mechanism has three translational degrees of freedom.  
The kinematic sketch of any branch of the mechanism is shown in Figure 3. The rotation angle \(\theta_{i1}\) (\(i=1\), \(2\), \(3\)) of the three driving arms are the input motion. \(A_{i}B_{i}C_{i}\) (\(i=1\), \(2\), \(3\)) is any branch, and \(P\) is the centroid of the moving platform. The global coordinate system \((XYZ)_{O}\) is located at the centroid \(O\) of the base platform, the \(x\)-axis points to \(OA_{1}\), the \(y\)-axis is perpendicular to the surface of the platform, and the negative direction of the \(y\)-axis points to the moving platform. A local coordinate system is established for any equivalent linkage at each joint, as shown in Figure 3. The link parameters and the transformation matrices between adjacent coordinate systems are shown in Table 1.  
The locations of the revolute joints on the base platform and the moving platform of the mechanism are arranged in an equilateral triangle, and the radii of the circumcircles are \(r_{1}\) and \(r_{2}\), respectively. The length of each driving arm \(A_{i}B_{i}\) is \(a\), the length of each parallelogram mechanism \(B_{i}C_{i}\) is \(c\), and \(\alpha_{i}\) is the included angle between OAO\({}_{i}\) and \(x\)-axis of the global coordinate system. The input angle \(\theta_{i1}\), the rotation angle \(\theta_{i2}\) of each follower \(B_{i}C_{i}\), and the swing angle \(\theta_{i3}\) of each follower are shown in Figure 3.  
Figure 2: Kinematic sketch of the improved Delta mechanism.
## 4 Discussion
These visualizations are aimed at activation of the ankle muscles with LGAS lifting the body upward and TA with FIB stiffening the feet. In our study, the activity of the ankle muscles increased with the wheelbarrow imageries during demi pointe releve; however, in most cases, it was a partial and unilateral increase. On the other hand, the knee extensor's activity increased bilaterally with the wheelbarrow imageries, which may be co-activation in response to increased knee flexor (LGAS) activity. In the kneecap float and space behind the kneecap visualizations, the dancer imagines the lengthening of the knee extension moment arm [7] (p. 119), therefore, an increase in knee extensor activity was not expected. As the main burst of the EMG activity of the studied muscles in demi plie occurred when the dancer was straightening their knees from demi plie into first position, only the second phase of the movement was analyzed. As expected, the imagery did not influence the VMO activity. However, an increase in the bilateral muscle activity occurred only in the space behind the kneecap visualization for ES and TA. Lumbar extensor and foot dorsiflexor may additionally stabilize the trunk and lower limb during the second phase of the demi plie. Biomechanical tools such as EMG, dynamometry, or motion capture systems are widely used for training effectiveness or performance correctness verification [11; 12; 13; 14; 15]. EMG was used by [PERSON] and [PERSON] (2012), who analyzed the influence of the visualizations based on the Franklin Method(r) on jump height [14]. By using cameras and a motion capture system, they confirmed the effectiveness of the rocket and spring imageries in their study. According to [PERSON] and [PERSON] [14], [PERSON] created the most diverse mental imagery system and provided guidance as to how to use mental imagery in dance [7; 10; 17; 18]. A biomechanical objective tool such as EMG may be used to evaluate the impact of specific training (i.e., using mental imagery on dance performance). In classical ballet, dancers struggle with static loads that impact their bodies [19]. Dance imagery may serve to decrease these loads. No study in the field of using dance imagery in reducing loads in the everyday training of artists has been found. Most studies have highlighted the importance of feedback for movement improvement and to assess the efficacy of the mental technique. Therefore, EMG is a suitable objective method that can provide feedback for dancers, sportsmen, teachers, or coaches. Furthermore, this methodology would also be valuable in primary ballet schools in the education of young dance students.