esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model of the Proposed Scheme
### Image Encryption  
The method of image encryption in this paper is similar to that in [PERSON]'s system [4]. It is assumed that the original image consists of pixels with their gray values [0; 255], which are represented by eight bits. Let the size of the original image and the gray value be \(L\times M\) pixels and \(p_{i,j}\), respectively, where \((i,j)\) for \(0\leq i<L\) and \(0\leq j<M\) denotes the position of the pixel. In the encryption phase, the pixels are encrypted through the bitwise exclusive-or (XOR) operations of the original pixel \(p_{i,j}\). It is assumed that pseudo-random values \(r_{i,j}\) are generated by the encryption key. Then, the encrypted image \(E_{i,j}\) for pixel \((i,j)\) is determined as:  
\[E_{i,j}=p_{i,j}\oplus r_{i,j}.\]
## 2 Data and Methodology
### Statistical Data Analyses
The following three measures of accuracy of the fitted seasonal models: (1) mean absolute percentage error (MAPE); (2) mean absolute deviation (MAD); and (3) mean squared deviation (MSD), were estimated as follows:  
\[\text{MAPE}=\frac{\sum\left|\left(y_{t}-\hat{y}_{t}\right)/\ y_{t}\right|}{n }\ x100\ \ \ \left(y_{t}\
eq 0\right), \tag{7}\]  
\[\text{MAD}=\frac{\sum_{t=1}^{n}\left|\left(y_{t}-\hat{y}_{t}\right)\right|}{ n}\,, \tag{8}\]\[\text{MSD}=\frac{\sum_{t=1}^{n}\left|\left(y_{t}-\hat{y}_{t}\right)\right|^{2}}{n}, \tag{9}\]  
where \(y_{t}\) and \(\hat{y}_{t}\) refer to the actual and fitted values, respectively; and \(n\) is the number of observations. MAPE and MAD express the accuracy of fitted time series values as a percentage and in the same units as the data, respectively. MSD is a more sensitive measure of an unusually large forecast error than MAD. For all three measures, the smaller the value is, the better the fit of the model is. MSD values are computed using the same denominator (\(n\)) regardless of the model, and thus, can be compared across the models.
## 2 Materials and Methods
### Determination of Starch, Moisture, Fat, Fiber, Asl, Protein, and Sodium  
The first analytical procedures were applied to peeled bananas in their unprocessed state. This matrix was crushed in a coffee grinder in order to obtain an ideal particle size for the respective analyses, which included determining the protein, starch, moisture, fiber, ash, and fat content. Likewise, the finished product was also analyzed, for which it was necessary to reduce the particle size, for which a mortar was used. In addition, the chip preparation process was structured into eight stages, including selecting the raw materials, cleaning and removing the crust, weighing, slicing, frying or baking, cooling, and packaging. Figure 1 schematizes the chip manufacturing process.  
For the chemical and nutritional characterization of green plantains and their processed chip products, standardized methodologies were applied following the specifications of the Ecuadorian Technical Standard (NTE) and the Association of Official Analytical Chemists (AOAC), executing each procedure in triplicate. Starch content was evaluated using the procedure established in NTE INEN 524 [26]. Moisture measurements were conducted by applying the AOAC 925.10 [27] method to 2 g of pulverized sample subjected to drying in a Binder brand oven (model FD 260 L) calibrated to a temperature of 105 \(\pm\) 2 \(\lx@math@degree\)C.  
Figure 1: Diagram of the banana chip manufacturing process.  
Lipid quantification was performed using the NTE INEN 523 [28] method utilizing a Soxhlet extractor (model SH-6, RAYPA). Fiber determination was carried out following the NTE INEN 522 [29] method using a RAYPA Fibertest analyzer and an EDG Equipment-brand muffle furnace (model BERM Rev-C100) adjusted to 550 \({}^{\circ}\)C. The NTE INEN 520 [30] method was applied to the ash fraction using an OMRON-brand muffle furnace (model ESCC de SONL) also at 550 \({}^{\circ}\)C.  
Protein content estimation was conducted following NTE INEN 519: 1980 [31]. This analysis included a digestion phase using a Velp Scientific digester (model DK 6) with Fisher Kjeldahl tablets (reference Tab TT-35), followed by distillation in a Velp Scientific instrument (model UDK 129). These procedures allowed for a rigorous evaluation of the essential nutritional components of the studied samples.  
Peroxide index analysis was conducted exclusively on fried chips, as these are products subjected to oil cooking and, therefore, fall under the specific regulation NTE INEN 2 561: 2010 [32], which is applicable to vegetable snacks. The iodometric method determined the peroxide index according to the methodology stipulated by AOAC 965.33 standard 1995 [33]. The quantification of the peroxide index is expressed in terms of milliequivalents of oxygen per kilogram of fat (mEqO/kg of fat).
## 3 The Proposed Hierarchical Two-Stage Robust DES Planning Model
### The Two-Stage Robust Planning Model at the Second Level
#### 3.1.2 Constraints  
The constraints of the robust model are shown below:  
\[0\leq E_{N,i}^{ES}\leq\overline{E}_{N}^{ES} \tag{8}\]  
\[\underline{\text{P}}_{i}^{G}\leq P_{i,t}^{G}\leq\bar{\text{P}}_{i}^{G}\ i\in \bar{\text{C}}_{\Omega^{G}}\Omega^{W} \tag{9}\]  
\[0\leq P_{i,t}^{G}\leq\bar{P}_{i,t}^{W}\ i\in\Omega^{W} \tag{10}\]  
\[P_{ij,t}=\frac{\theta_{ij,t}}{x_{ij}} \tag{11}\]  
\[\underline{P}_{ij}\leq P_{ij,t}\leq\overline{\text{P}}_{ij} \tag{12}\]  
\[P_{i,t}^{G}=\sum_{j\in\Omega_{i}}P_{ij,t}+\bar{P}_{i,t}^{L}+d_{i,t}^{DR} \tag{13}\]  
\[\underline{\theta}_{ij}\leq\theta_{ij,t}\leq\overline{\theta}_{ij} \tag{14}\]  
\[\theta_{ref,t}=0 \tag{15}\]  
\[E_{i,t+1}=(1-\gamma_{loss})E_{i,t}+\left(\eta_{cla}P_{i,t}^{cha}-P_{i,t}^{dis}/ \eta_{dis}\right)\cdot\Delta t \tag{16}\]\[E_{i,0}=E_{i,T} \tag{17}\]  
\[\alpha_{min}E_{N,i}^{ES}\leq E_{i,t}\leq\alpha_{max}E_{N,i}^{ES} \tag{18}\]  
\[0\leq P_{i,t}^{cha}\leq b_{i,t}^{cha}\,P_{N,i}^{ES}\,\,b_{i,t}^{cha}\in\{0,1\} \tag{19}\]  
\[0\leq P_{i,t}^{dis}\leq b_{i,t}^{dis}\,P_{N,i}^{ES},\,\,b_{i,t}^{dis}\in\{0,1\} \tag{20}\]  
\[b_{i,t}^{cha}+b_{i,t}^{dis}=1 \tag{21}\]  
\[E_{N,i}^{ES}=\Gamma_{N}P_{N,i}^{ES} \tag{22}\]  
\[d_{i,t}^{DR}=P_{i,t}^{dis}-P_{i,t}^{cha} \tag{23}\]  
\[\alpha_{min}^{DR}\widetilde{P}_{i,t}^{L}\leq d_{i,t}^{DR}\leq\alpha_{max}^{DR} \widetilde{P}_{i,t}^{L} \tag{24}\]  
\[\text{\it Formulas (\ref{eq:P_i,t})}\,\sim\,\eqref{eq:P_i,t} \tag{25}\]  
where parameters with top or bottom lines indicate the upper or lower bounds of the corresponding variables. Specifically, formula (8) denotes the constraint of rated energy storage capacities. Formulas (9)-(15) are operational limits of the power system; \(\theta_{ij,t}\), \(x_{ij}\), and \(P_{ij,t}\) represent the angle phase difference, the reactance and the power flow of the branch \(i-j\). Formulas (16)-(22) are constraints on DES operation; \(E_{i,t}\), \(P_{N,i}^{ES}\), \(\alpha_{max}\), and \(\alpha_{min}\) denote the state-of-charge (SOC), rated power, and upper and lower SOC limit rates of the energy storage, respectively. Formulas (23)-(24) represent the demand response constraints. Formula (25) denotes the constraints of the wind power and load uncertainty sets.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 5 Performance Analysis
### Parameters Setting  
In this section, we define the parameters used in the experimental tests. For this purpose, we describe in the following the transmit power and the frame format.
## 5 Discussion
For a new position of \(P_{3}\), examine the vertical second harmonic acceleration components at the joints in the mechanism with the same dimensions except for the position of \(P_{3}(r_{0x},r_{0y})\). If the acceleration is not within an allowable range, the above procedure can be reapplied to find dimensions that compromise between the compression ratios. The four-bar linkage in the Stephenson III mechanism may take the form of a crank-rocker or double-crank type. In this paper, the Stephenson III mechanism with a crank-rocker four bar is considered for the VCR engine, which is known to have lower levels of vertical and horizontal excitation forces and can achieve the same level of booming noise performance as conventional engines without a balance shaft [26]. The Stephenson III VCR engine mechanism with a double-crank four bar shown in Figure 14 is proposed by [PERSON] and [PERSON] [12]: the domain of motion of this type is small compared to that of the Stephenson III crank-rocker version, and the second order of piston acceleration at the engine speed of 300 rpm in the single cylinder engine is lower than that of the slider-crank engine mechanism at compression ratios 8 and 9.3, but higher at compression ratio 16.5. For comparison, the vertical second harmonic acceleration components in the final design of the Stephenson III mechanism with a crank-rocker four bar shown Figure 10b were computed at low and high compression ratios by altering the position of the ground pivot, \(P_{3}\). As shown in Table 9, the vertical second harmonic acceleration components in unit of m/sec\({}^{2}\) at 300 rpm in a single cylinder engine were lower than those of the slider-crank engine mechanism at both low and high compression ratios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mechanism** & **Unit** & **Stephenson III (Crank-Rocker)** & **Slider Crank** \\ \hline Compression ratio & - & 8 & 16.5 & - \\ \(r_{0x}\) & mm & 72.100 & 78.000 & - \\ \(r_{0y}\) & mm & \(-\)46.900 & \(-\)53.900 & - \\ \hline Stroke & mm & 85.502 & 86.018 & 86.000 \\
2 nd harmonic acceleration at piston pin & m/s\({}^{2}\) & 3.701 & 0.588 & 12.691 \\ Max. 2 nd harmonic acceleration sum & m/s\({}^{2}\) & 5.542 & 3.692 & 12.691 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Vertical second harmonic acceleration components in the finally designed single-cylinder Stephenson III with crank-rocker four bar at low and high compression ratios. Figure 14: Stephenson III VCR engine mechanisms with double-crank type four-bar linkage.
# Chinese Event Detection without Triggers Based on Dual Attention
## 3 Event Detection without Triggers Based on Dual Attention
### Model Training
#### 3.3.2 Focal Loss Function  
The traditional loss function cannot solve the above problems, and the method of increasing the weight of positive samples is adopted to solve the imbalance problem between positive and negative samples. However, it is still unable to process difficult-to-classify samples. For example, when the average loss function is classified incorrectly, it does not update the weight and tends to average. When the classification of the cross-entropy loss function is correct, the weight is not updated and tends to 1. As a result, a new loss function needs to be introduced in order to solve the above two sample imbalance problems concurrently. EDWTDA uses focal loss [35] as the loss function to increase the influence of positive and difficult-to-classify samples on the model. The equation for all training examples is shown in Equation (4).  
\[J(\theta)=-\frac{1}{n}\sum_{i=1}^{n}\beta y^{(i)}(1-o(x^{(i)}))^{\gamma}\log o (x^{(i)})+(1-\beta)(1-y^{(i)})o(x^{(i)})^{\gamma}\log(1-o(x^{(i)}))+\delta\| \theta\|^{2} \tag{4}\]  
\(x\) is composed of sentences and target event types, \(y\in\{0,1\}\), \(o(x^{(i)})\) are model predictions, \(||\theta||^{2}\) is the sum of squares of each element in the model, \(\delta>0\) is the weight of the L2 normalization term, \(\beta\) is a parameter to balance the proportion of positive and negative weights of the sample, and \(\gamma\) is a parameter to balance the proportion of hard-to-classify and easy-to-classify weights of samples.  
When \(\beta=0.25\), \(\gamma=2\), focal loss works best. The model concentrates on positive and hard-to-classify samples to improve model prediction. In addition, we use L2 regularization to prevent model overfitting.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
### Contributions and Outline
#### 1.2.2 Service Awareness  
We consider a model where the sensor motes/nodes are classified into service classes depending on their service profile and fabric and are then assigned corresponding roles. The least service-demanding nodes have a higher probability of becoming cluster heads in the core of the network tasked for collecting, aggregating and forwarding data to the base station, while the most service-intensive ones are left as member nodes at the edge of the network tasked for collecting data and forwarding to the cluster head.
## 3 Simulation Tests and Analysis
### Reconstruction Error Analysis
#### 3.3.1 Effect of the Number of POD Basis Vectors  
In the reconstruction problem, the first few POD basis vectors contain most of the \"energy\", and these basis vectors have a positive influence on the accuracy of the reconstruction result. The rest of the vectors contain less energy and are sensitive to noise. The noise may come from the measurement or calculation. In this situation, a large number of POD basis vectors means a high noise sensitivity. Therefore, in the reconstruction process, a proper number of POD basis vectors is needed.  
A test was set up to investigate the changes in the reconstruction error with different numbers of POD basis vectors. In this test, 25 sensors were placed in random locations, the number of POD basis vectors varied from 1 to 15, the SNR of the noise varied from 40 to 50 dB, and the original measurement data without noise were used for comparison. As the sensor location and the noise were randomized for different conditions, the averages of reconstruction results of 1000 runs were used to draw a general conclusion. Figure 3 shows the results for two test cases.  
As shown in Figure 3, the reconstruction error decreased at first and then increased with a large number of vectors. This phenomenon is more pronounced when the noise level increases. In both test cases, a reconstruction error with eight POD basis vectors was acceptable.  
Reconstruction errors with eight POD basis vectors and 25 sensors in different test cases are shown in Table 3.  
As shown in Table 3, in all of the conditions, using the first eight basis vectors could obtain a balance between noise sensitivity and reconstruction accuracy. Therefore, in this simulation scenario, the first eight POD basis vectors resulted in better performance.  
Figure 3: Comparison of reconstruction errors between different POD basis vectors with different noise levels (original measurement data, signal-to-noise ratio (SNR) = 40 dB, SNR = 45 dB, SNR = 50 dB). (**a**) Comparison in test case 2. (**b**) Comparison in test case 4.
## 8 Challenges and Directions for Future Research
### Quantity and Quality of Datasets  
One of the challenges is the lack of dedicated datasets specifically designed for detecting malware that abuses CPLS platforms as C&C channels. Future research should focus on developing comprehensive and representative datasets that cover a wide range of CPLS platforms and abuse techniques, thereby enabling researchers to effectively evaluate and enhance detection mechanisms.
# Speaker-Independent Spectral Enhancement for Bone-Conducted Speech
## 2 Bone-Conducted Speech Blind Enhancement Technology
### Spectral Envelope Feature Transformation  
At present, most BC speech enhancement methods use the spectral envelope conversion method. This paper also uses this method to perform blind enhancement of BC speech for speaker-independent people. Its core idea is to convert the spectral envelope characteristics of BC and AC speech [10]. The spectral envelope feature represents the source-filter model derived from speech, which considers that speech is modulated by the excitation signal (source) through the sound channel (filter). BC speech enhancement can be achieved by establishing the spectral envelope feature mapping relationship between BC and AC speech. The BC speech signals \(B(n)\) and AC speech signals \(A(n)\) can be defined as:  
\[B(n)=E_{bc}(n)*S_{bc}(n) \tag{3}\]  
\[A(n)=E_{ac}(n)*S_{ac}(n) \tag{4}\]  
In Formulas (3) and (4), \(bc\) and \(ac\) represent BC and AC speech, respectively. The excitation of speech \(S_{bc}(n)\) and \(S_{ac}(n)\), respectively, represent the spectral envelope of BC and AC speech. \(*\) represents the convolution operation.  
The blind enhancement method of BC speech based on spectral envelope conversion includes two stages. Firstly, the spectral envelope and excitation features of AC and BC speech are extracted, respectively. Then the training model constructs the complex mapping relationship \(f(x)\) between source speech and target speech features. After training the model coefficients, extract the spectral envelope and excitation features of the BC speech to be enhanced according to the data processing in the previous step, and set as \(E_{bc}(t)\) and \(S_{bc}(t)\), respectively, as the input to the training-completed enhancement model to estimate the feature \(\hat{S}_{bc}(t)\).  
\[\hat{S}_{bc}(t)=f(S_{bc}(t)) \tag{5}\]  
Then synthesize the enhanced speech \(\hat{B}(t)\) according to the estimated spectral envelope features and the original excitation features of the BC speech.  
\[\hat{B}(t)=E_{bc}(t)*\hat{S}_{bc}(t) \tag{6}\]
# Natural Rubber/Styrene-Butadiene Rubber Blend Composites Potentially Applied in Damping Bearings
## 3 Result and Discussion
### Strain Sweep Analysis of Uncured NR/SBR-x Compounds  
The storage modulus-strain curves of uncured NR/SBR-x compounds are shown in Figure 1a. It can be shown that the storage modulus (\(G^{\prime}\)) of the NR/SBR-x compounds gradually becomes lower as the SBR content increases. This is due to the average molecular weight of NR being larger than that of SBR, which makes the \(G^{\prime}\) of NR higher [33; 34]. The average molecular weight of NR is 9.8 \(\times\) 10\({}^{5}\), while the average molecular weight of SBR is approximately 1.1 \(\times\) 10\({}^{5}\). In addition, with the gradual increase in strain, the \(G^{\prime}\) of the compounds shows a nonlinear decreasing trend which can reflect the strength of the Payne effect and thus the structural strength of the filler network [35; 36; 37]. It was observed that the Payne effect of NR/SBR-x compounds decreases with the increase in SBR content.  
The tan \(\delta\)-strain curves of NR/SBR-x compounds are shown in Figure 1b. It can be observed that the tan \(\delta\) of NR/SBR-x compounds shows an increasing trend with the increase in strain, which is due to the disruption and reorganization of the filler network. The magnitude of the tan \(\delta\) can reflect the activity of the rubber macromolecule chains, and the tan \(\delta\) of the compounds gradually decreases with the increase in the SBR content at the same strain. This is because the SBR macromolecule chains are not as flexible as NR due to the benzene rings contained in the SBR, which restrict the movement of the SBR chains [38].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Composites** & \(\begin{array}{c}\mathbf{M_{L}}\\ \textbf{(dN\cdot m)}\end{array}\) & \(\begin{array}{c}\mathbf{M_{H}}\\ \textbf{(dN\cdot m)}\end{array}\) & \(\begin{array}{c}\Delta\mathbf{M}\\ \textbf{(dN\cdot m)}\end{array}\) & \(\begin{array}{c}\mathbf{t_{90}}\textbf{(min.s)}\\ \textbf{(dN\cdot m)}\end{array}\) & \(\begin{array}{c}\mathbf{t_{s2}}\textbf{(min.s)}\end{array}\) & \(\begin{array}{c}\mathbf{CRI}\\ \textbf{(CRI)}\end{array}\) \\ \hline NR/SBR-0 & 1.13 & 12.34 & 11.21 & 23.30 & 9.09 & 7.04 \\ NR/SBR-10 & 1.05 & 11.96 & 10.91 & 18.52 & 7.54 & 9.11 \\ NR/SBR-20 & 1.08 & 12.51 & 11.43 & 16.11 & 6.48 & 10.38 \\ NR/SBR-30 & 0.98 & 12.12 & 11.14 & 16.11 & 7.11 & 11.11 \\ NR/SBR-40 & 0.86 & 11.30 & 10.44 & 14.29 & 7.02 & 13.76 \\ NR/SBR-50 & 0.86 & 11.03 & 10.17 & 12.04 & 6.33 & 17.51 \\ \hline \end{tabular}
\end{table}
Table 2: Curing characteristics of NR/SBR-x composites at 150 \({}^{\circ}\)C.
## 3 Results
### Benchtop Instrumentation: Confocal Raman Microspectroscopy (CRM)
In the following discussion, we report only the results obtained with the most intense band, since they showed a lower standard deviation. The CRM results agree with the OCT findings, showing thickness variability in the different points studied, due to the samples' inhomogeneity, and correlation between thickness and polymer concentration (Figure 5). Some differences in thickness measurement were detected due to optical aberrations produced by the refraction of the laser inside the polymeric film. Figure 5: Relative Raman intensities registered as a function of the stage displacement of (**a**) PB-72 at 25% (\(\delta_{\rm CH2}=1451\) cm\({}^{-1}\)), (**b**) PB-44 at 10% (\(\delta_{\rm CH2}=1451\) cm\({}^{-1}\)), and (**c**) Zapon® at 70% (\(\
u_{\rm NO2}=1284\) cm\({}^{-1}\)). The nominal depth (\(\Delta\))—full width at the half maximum—measured after Gaussian fit (grey line) is indicated for each sample. The values reported correspond to a single measurement. To account for the influence of the refraction phenomenon in the thickness measurements, we calculated the corrected thickness (\(\overline{z}\)) using Equation (1) [24]:  
\[\overline{z}=\frac{\int_{0}^{1}m\cdot e^{-2m^{2}}\cdot(NA_{eff})^{2}\cdot z(m) \cdot dm}{\int_{0}^{1}m\cdot e^{-2m^{2}}\cdot(NA_{eff})^{2}\cdot dm}; \tag{1}\]  
the equation considers the true point of focus of the laser (\(z(m)\)) and the weighting factor (\((NA_{eff})^{2}\)) that accounts for the volume from which emitted photons can be captured by the objective [38]. As proposed by [PERSON] [39], \(z(m)\) can be calculated as:  
\[z(m)=\Delta\left[m^{2}\cdot\frac{NA^{2}\big{(}n^{2}-1\big{)}}{(1-NA^{2})}+n^{2} \right]^{0.5}, \tag{2}\]  
where \(\Delta\) is the nominal depth (thickness) as obtained from the FWHM measurement from the profile, \(NA\) the objective numerical aperture, \(n\) the polymer refractive index, and \(m\) the normalized radius. On the other hand, \((NA_{eff})^{2}\) is obtained with Equation (3):  
\[\left(NA_{eff}\right)^{2}=\frac{1}{n^{2}}\left[\frac{m^{2}\cdot NA^{2}}{1-NA^{ 2}+m^{2}\cdot NA^{2}}\right], \tag{3}\]  
Table 3 summarizes the nominal depth (\(\Delta\)) measured for each sample and the corrected thickness (\(\overline{z}\)). As expected, laser refraction has a higher influence when studying thicker layers producing a higher error value (\(h_{z}\)), as calculated with Equation (4) [24]. \[h_{z}=1.34\left[\frac{\int_{0}^{1}m\cdot e^{-2m^{2}}\cdot\left(NA_{eff}\right) ^{2}\cdot z^{2}(m)\cdot dm}{\int_{0}^{1}m\cdot e^{-2m^{2}}\cdot\left(NA_{eff} \right)^{2}\cdot dm}-\overline{z}^{2}\right]^{0.5}, \tag{4}\]  
Yet, CRM provides a good estimate of the thickness of the protective layers, being sensible even to slight variations. Additionally, CRM is capable of obtaining chemical information relevant for the assessment of degradation processes, possible mixtures (e.g., pigmented coatings), or inclusions in the layer [20].