esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Chaos-Based Stream Ciphers Based on Secure PRNGs of Chaotic Sequences
### Security Analysis and Hardware Metrics of the Proposed PRNGs-CS
#### 3.3.2 Hardware Metrics of the Proposed PRNGs-CS  
We quantify the performance of the proposed PRNGs-CS implementation in regard to the resources utilized, the speed, the efficiency, and the power consumption.  
Table 7 lists the results of our three proposed PRNGs-CS designs.  
We can observe that the LST-PRNG consumes the minimum slice at 1024 slices, and the LSPT-PRNG consumes the maximum slice at 3337 slices. All proposed PRNGs-CS offer high speed. 1013.36 Mbps, 1224 Mbps, and 995.14 Mbps for LSP-PRNG, LST-PRNG, and LSPT-PRNG respectively. The LST-PRNG offers the best throughput. Additionally, the LST-PRNG architecture offers lower power consumption (83 mW) compared to LSP-PRNG and LSPT-PRNG. The LST-PRNG design is ideal for IoT applications, and its efficiency is 1.20 Mbps/Slices.  
We perform below a comparison of hardware metrics with several chaotic and non-chaotic systems for various stream ciphers based on the PRNGs-CS proposed above.  
\begin{table}
\begin{tabular}{l c} \hline
**PRNG-CS** & **HD \%** \\ \hline LSP-PRNG & 50.0022 \\ LST-PRNG & 50.0004 \\ LSPT-PRNG & 50.0012 \\ \hline \end{tabular}
\end{table}
Table 6: Values of HD for the proposed PRNGs-CS.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & & \multicolumn{3}{c}{**PRNGs-CS**} \\ \hline \multicolumn{3}{c}{} & **LSP-PRNG** & **LST-PRNG** & **LSPT-PRNG** \\ \hline \multirow{4}{*}{Resources used} & \multirow{4}{*}{Area} & LUTs & 10,400/19.55\% & 3525/6.63\% & 11,391/21.41\% \\  & & FFs & 349/0.33\% & 434/0.41\% & 480/0.45\% \\  & & Slices & 3096/23.28\% & 1024/7.70\% & 3337/25.09\% \\ \cline{2-5}  & DSPs & 13/5.91\% & 25/11.36\% & 28/12.73\% \\ \hline \multirow{4}{*}{Speed} & WNSi (ns) & 0.022 & 0.056 & 0.072 \\  & Ti (ns) & 31.60 & 26.20 & 32.30 \\ \cline{1-1}  & Max. Freq. (MHz) & 31.66 & 38.24 & 31.09 \\ \cline{1-1}  & Throughput (Mbps) & 1013.36 & 1224 & 995.14 \\ \hline Efficiency (Mbps/Slices) & 0.32 & 1.20 & 0.29 \\ \hline Power (W) & 0.146 & 0.083 & 0.162 \\ \hline \end{tabular}
\end{table}
Table 7: Hardware metrics comparison of the proposed PRNGs-CS using ZYNQ PYNQ-Z2 FPGA.
# Exploring the Potential of Event Camera Imaging for Advancing Remote Pupil-Tracking Techniques
## 3 Experimental Results
To assess the algorithm's performance, a \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Details** \\ \hline \multirow{3}{*}{Tracked Shape Points} & 11 Eye-nose Points \\  & (3 left eye, 3 right eye, 4 nose) \\ Distance between camera and users (cm) & 50 to 100 \\ Computing System & 2.0 GHz CPU \\ Event Camera Model & DAVIS 346 (Inivation) \\ Event Camera Resolution & 346 \(\times\) 260 (resized to 640 \(\times\) 480) \\ Event Camera Latency & 20 \(\upmu\)s \\ Event Frame Speed (event aggregation time) & 30 fps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pupil tracking specification. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Light Condition**} & \multirow{2}{*}{\
\begin{tabular}{c} **Tracking** \\ **Detection** \\ **Accuracy** \\ **\end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy** \\ **(Pupil Precision** \\ **< 10 mm)** \\ \end{tabular} } & \multirow{2}{*}{**Speed**} \\  & & & **\textless{} 10 mm)** \\ \hline Content-aware [11] & Normal Light & \multirow{2}{*}{99.4\%} & \multirow{2}{*}{99.4\%} & \multirow{2}{*}{200 fps (CPU)} \\ (CIS RGB Camera) & (100–400 lux) & & \\ \hline \multirow{2}{*}{Proposed Method} & Normal Light & \multirow{2}{*}{98.1\%} & \multirow{2}{*}{80.9\%} & \multirow{2}{*}{200 fps (CPU)} \\  & (100–400 lux) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the proposed event-camera-based pupil-tracking method. For a comparison, previous RGB-frame-camera-based method [11] performance is also shown.
# Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids
## 3 ESS Installation Site and Capacity Optimization  
The overall objective of the ESS sitting and sizing optimization is to minimize the average annual cost of the ESSs subject to the constraints of a power-flow balance, branch-flow limits, charging and discharging characteristics, and efficiency of ESS. The optimization model can be expressed as Equation (1).  
\[\left\{\begin{array}{ll}f=\min(Cost)\\ s.t.&P_{ESS,sum}=\sum\limits_{i}^{N}P_{ESS,i}\\ &P_{DG}+P_{S}+P_{ESS}+P_{L}+P_{loss}=0\\ &\sum\limits_{t=1}^{24}\left[P_{ESS,i}(t)\right]=0\\ &\eta_{D}=\eta_{C}=\sqrt{\eta}\\ &P_{ij\min}\leq P_{ij}\leq P_{ij\max}\\ &0\leq N\leq N_{set}\\ &V_{\min}\leq V_{j}\leq V_{\max}\\ &SOC_{\min}\leq SOC_{i}\leq SOC_{\max}\\ &-P_{ESS,i,rate}\leq P_{ESS,i}\leq P_{ESS,i,rate}\end{array}\right. \tag{1}\]  
where, cost is the average annual cost of the ESS within a project planning period; \(P_{ESS,sum}\) represents the power of ESSs in the distribution network; \(P_{ESS,i}\) is the power output of a node with ESS; \(P_{DG}\), \(P_{S}\), \(P_{L}\), \(P_{loss}\) are the active power of DG, the main grid, load, and loss, respectively; \(\eta_{D}\), \(\eta_{C}\), \(\eta\) are the efficiencies of discharge, charge, and the overall round-trip efficiency, respectively. \(P_{ijmin}\) and \(P_{ijmax}\) is the upper and lower active power limits of line \(ij\), and \(P_{ij}\) is the actual active power on line \(ij\). N is the number of nodes which must be configured with the ESS and \(N_{set}\) is the number of nodes which are nodes for the ESS pre-selection. \(V_{max}\) and \(V_{min}\) are the upper and lower voltage limits of every nodes, respectively and \(V_{j}\) is the actual voltage magnitude at node \(j\). \(SOC_{max}\) and \(SOC_{min}\) are the State-of-Charge (SOC) operating bounds to avoid the impact of over-charge and discharge on battery lifetime. SOCi is the SOC value of node \(i\). \(P_{ESS,i,rate}\) and \(P_{ESS,i}\) are the rated power and output of ESS at node \(i\), respectively. When the \(P_{ESS,i}\) is positive, the ESS is discharging and vice versa.  
A two-stage planning strategy is proposed in this paper, and the schematic diagram of overall calculation process is illustrated in Figure 2. The first stage chooses the installation nodes and confirms the scheduling of ESS with the minimum installation capacity by utilizing GA combined with SA. The second stage optimizes the installation capacity of every installation nodes aiming at minimizing the LCC based on the output of the first stage.
## 3 Quality Control of Silicon Carbide Printed Part Formation
### The Impact and Control of Key Printing Parameters on Green Part Formation Quality
#### 3.1.2 Uniform and Dense Powder Bed
iterative advancements in powder binder 3D printing technology, when the experimental range for the lateral speed of the powder spreader is expanded to 0-300 mm/s, the impact of the lateral speed of the rotating roller on the spreading quality needs to be redefined. Current research generally indicates that increasing the lateral speed of the rotating roller will decrease the density of the powder bed, and, as the translational speed of the rotating roller increases, the surface roughness monotonically increases, while the surface quality of the powder layer deteriorates with the increase in spreading speed [101, 110, 112, 113, 114, 115]. Understanding the underlying principles is crucial for precise control of the printing process. The theories of \"backflow\" and \"powder splashing\" can be used to explain this variation. When the scanning speed is high, the particles deposited on the powder bed can continue to flow for a distance due to their significant momentum. This backflow phenomenon results in uneven or even discontinuous layers on the surface [116].
# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 3 Machine Learning
### Model of Machine Learning  
This study used four ML methods to build predictive models, namely the support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), and artificial neural network (ANN). The construction process is shown in Figure 11. The data segmentation ratio was training and validation set 80% and test set 20%. Furthermore, we adopted cross-validation (K-fold cross-validation, K = 10) to reduce overfitting.  
* SVM was developed based on statistical learning frameworks [28], which could be used for both classification (SVC) and regression (SVR). The risk of overfitting is lower in SVM models. SVM models have good generalization ability in practice but are not suitable for large datasets because of the relatively long training time [29].
* RF was proposed by [PERSON] in 2001 [30] as an algorithm that belongs to ensemble learning. The concept of ensemble learning is that it combines multiple learners to produce more accurate results than a single learner [31]. RF uses the decision tree as the basic learner and adds randomly allocated training data to improve model performance.
* GBDT is an ensemble learning algorithm that combines gradient descending and boosting and uses the decision tree as the basic learner. The concept of gradient boosting was derived from the observations of [PERSON] [32] and was further developed by [PERSON] [33].
* ANN is inspired by the biological neural network [34]. It is a dense network of many neurons (operation units) connected to each other that can be simply divided into an input layer, hidden layer, and output layer. The purpose of the neural network is to find the appropriate weights and biases to minimize the value of the loss function. It is robust against irrelevant noise, but its performance is sensitive to the chosen hyperparameter values [29].  
Figure 11: Process of model construction.
## 3 Experimental Set-Up and Procedure
### Modal Parameter Estimation  
The peak picking modal estimation technique was utilized in natural frequency analysis. It has been commonly used due to its simplicity. The dynamic response of a system to an external excitation load is described as  
\[M\ddot{x}(t)+C\dot{x}(t)+Kx(t)=F(t) \tag{8}\]  
where \(M\), \(C\), and \(K\) are matrices of mass, damping, and stiffness of the system, and \(x\), \(\dot{x}\), and \(\ddot{x}\) represent the displacement, velocity, and acceleration response of the system, respectively.  
The peak picking method is considered for estimating the modal features of a dynamic system based on response data. This approach could characterize ambient vibration excitation as standard white-noise Gaussian distribution based on the fact that the frequency response function (FRF) of a system would experience peak values in the frequency domain. In the case of a white noise excitation, the _FRF_ of a system at sensor location k, \(H_{k}=(jw)\), is considered equivalent to the Fourier spectrum of the response data gathered by the sensor. This range is derived by converting the measured response to the frequency domain using _FFT_. Therefore, the natural frequency of the system is the dominant frequency of the _FRF_ of the system [19].  
The damping loss factor is analysed using the frequency response function expressed as follows,  
\[\left|\frac{x}{F}\right|=\frac{1/k}{\sqrt{1-\left(\frac{\omega^{2}}{\omega^{ 2}_{n}}\right)^{2}+\frac{\Phi^{2}\omega^{2}}{\omega^{2}_{n}}}} \tag{9}\]  
where \(\xi\) is the damping loss factor, the natural frequency of the system \(\omega_{n}=\sqrt{k/m}\), and the spring constant \(k\). \(\omega\) is excitation frequency.  
The measurements of modes at resonances are difficult for a lightly damped system. Simultaneously, the response of a heavily damped system is significantly influenced by more than one mode. The mode shapes are determined by the peak picking method using the system's FRFs.
## 3 Results
### Sequencing Summary  
16S rRNA amplicon sequencing yielded 1,222,505 raw sequences, of which 915,465 quality-filtered sequences remained from the thirty samples and four negative controls. Negative controls were removed during normalization, as their sequence counts were less than the normalization cutoff. After normalization, board communities yielded an average of 10,751 \(\pm\) 2.4 SD quality-filtered sequences per sample, resulting in 101 unique OTUs. Sequencing depths for all samples were satisfactory before and after normalization (Good's coverage: 99.8 \(\pm\) 0.0002 SD %; range: 99.88-99.97%) [26], indicating sufficient capture of the species diversity in all samples.  
Figure 2: Inhibition of _L. monocytogenes_ 2203 by broth cultures of the microbial communities from five different wooden boards (**A**–**E**). Board communities were incubated in TSB at 11 \({}^{\circ}\)C for 48 h until they reached stationary growth phase (10\({}^{8}\) CFU/mL), at which point 10\({}^{5}\) CFU/mL _L. monocytogenes_ 2203 (LM) was added. The cultures were then incubated at 11 \({}^{\circ}\)C for 8 days. Samples were removed at 2-day intervals and plated on MOX agar to quantify LM 2203 (mean \(\pm\) SEM CFU/mL). Dashed lines correspond to LM 2203 alone (positive control) and solid lines to LM 2203 in the presence of each microbial community (n = 6).  
Figure 1: Growth curves for broth cultures of wooden cheese-ripening board microbial communities and _L. monocytogenes_ 2203. (**A**) Microbial communities from five wooden cheese-ripening boards (A–E) were inoculated into TSB (initial inocula 7.2–7.8 Log10 CFU/mL) and incubated at 11\({}^{\circ}\)C. (**B**) Growth curve for _L. monocytogenes_ 2203 inoculated into TSB (initial inocula 4.1–4.6 Log10 CFU/mL) and incubated at 11 \({}^{\circ}\)C. Error bars depict standard error of the mean (n = 4).
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 1 Introduction
Constructing the node mobility model based on the historical mobility characteristics of the node. By changing the value of the node's mobile coordination factor, the possible running speed of the node at the next moment is obtained. 2. Using the Gaussian process to model the speed probability of different mobile coordination factors, and using the maximum likelihood estimation (EM) algorithm to obtain the model parameters, so that the probability of speed based on historical mobility characteristics is best. On this basis, the node movement distance and node location information are calculated. 3. Calculating the metric value of the candidate node based on the predicted location information. During the information transmission, the node does not need to store too much routing information, and the network resource consumption is significantly reduced. The use of the location information can effectively avoid loop generation during data delivery and also have strong adaptability to the dynamic network topology change. 4. The simulation platform ONE simulates a large amount of data and evaluates the performance of the RATP algorithm, with improved transmission success rate, reduced data delay and routing overhead. The rest of this paper mainly includes the following parts: the second part analyzes some existing research works; the third part analyzes and describes the algorithm; simulation results are presented in Section 4. The last section concludes this paper.
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 3 Proposed Autoencoder-Based Image Transmission System
### Semantic Decoder Architecture  
To develop a comprehensive autoencoder model, the accompanying decoder layers must also be defined in line with the encoder model described in the previous section. Figure 3 illustrates the proposed decoder architecture to use in the autoencoder. As shown in Figure 3, with the \(32\times 1\) latent vector, a dense layer in the shape of \(3136\times 1\) is defined with the ReLU activation function. The dense representation is then reshaped into a 3D tensor with a shape of \(7\times 7\) with 64 channels, which is then fed into two deconvolutional layers with two up-sampling layers to reconstruct the original input image with one channel. The final layer of the decoder uses the sigmoid activation function. Finally, the autoencoder model is then defined as a sequential model by combining the encoder and decoder layers. It is then compiled with the Adam optimizer with a learning rate of 0.001 and a binary cross-entropy loss function before training the model. The training data are used for both the input and the output.  
The hyperparameters chosen for the autoencoder model--such as the number of filters, kernel size, activation functions, learning rate, and loss function--are determined based on extensive experimental testing and optimization. These experiments are conducted to ensure that the model achieves the best possible performance while remaining computationally efficient.  
Figure 2: Proposed encoder in the autoencoder.
# Siberian Pine and Larch Response to Warming-Drying Climate in the Southern Boundary of Their Range
## 4 Discussion
### Comparative Dynamic of Land Cover Classes in the Tannu-Ola Ridge  
During the study period (1988-2023), forest loss (Siberian pine mostly) occurred at low elevations (c. \(<\)1300 m), whereas forested area gain occurred at high (\(>\)2000 m) elevations. Meanwhile, the area of shrublands increased at both high and low elevations (Figure 11). The upper boundary of the closed forests shifted uphill from 2124 to 2153 m a.s.l., or by c. +30 m. For the shrublands, those shifts were from 2273 to 2283 m by c. +10 m. Thus, the shrubs' boundary was located about 130 m ahead of the forest's. The estimated rates of the uphill migrations are 0.9 m/yr for forests and 0.3 m/yr. for shrubs.  
In the 21 st century, the area of the Siberian pine stands decreased (by \(-\)12%), whereas larch stands strongly increased (by c. +140%). Siberian pine loss was caused mostly by the transformation of pine-dominant stands into mixed forests, while the area of larch forest gained from \"mixed forests\". Siberian pine loss and gain occurred mainly at low and high elevations, respectively, whereas larch area increased at both high and low elevations. Notable, there was a strong increase in birch stands area (+100%). A similar effect, i.e., a species composition change in favor of a drought-tolerant species as the climate water deficit increases, is described in the US Rocky Mountains [52]. It agrees with the reported evidence of climate-driven risks to forest health at the global scale [53]. The increased proportions of drought-resistant larch and comparatively resistant (versus Siberian pine) birch support the hypothesis that in the warming-drying climate, Siberian pine will retreat from the southern edge of its range.  
Figure 14: Larch needles flashed while snow is still not melted.  
Meanwhile, the total area of closed forests significantly (+20%) increased, and that increase occurred mostly at high (>2000 m) elevations. The upper boundary of the closed forests was migrating uphill at a rate of about 0.8 m/yr. Those values are within the range of trees' upslope climbing rates reported in Europe, Northern America, the Himalayas, northern Chinese mountains [45; 46; 47; 48], and Siberia [44].  
Remarkably, the main source of forested area gain is shrubs and grass communities (Table 5). Together with the higher rate of forests' uphill migration, this indicates a further decrease in the shrubland area due to its transformation into a forested area. However, at the moment, the shrubs' upper boundary is located >100 m higher in comparison with forests, and shrubification is observed at high elevations.
# Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT
## 5 Result and Analysis
### Impact of the Number of Attributes on Schedule Length  
The schedule length (SL) refers to the time needed for sending data from sensor nodes to CHs. When the aggregation factor is high, the SL starts decreasing. This is because proficient data collection results in the minimum time required for sending it to the head node. EE-FDAS achieves a lower SL primarily because of the smaller data size, which effectively increases the level of aggregation and consequently reduces the probability of significant data loss. Both DICA and DICA_EXTENSION demand a longer SL as of lower aggregation, and this requirement remains to increase with the rising complexity of the environment. On the other hand, FDAS and EHDA achieve better SLs because of sustainable aggregation. In Figure 3(a), the X-axis shows the number of attributes, whereas the Y-axis signifies the average SL. Due to higher aggregation in EE-FDAS, the SL diminishes to 95 bytes when there are 10 attributes. This SL requirement is 130, 155, 157, and 115 in FDAS, DICA, DICA_EXTENSION, and EHDA, respectively.
## 3 Results
### Electrospinning Procedure and Morphological Analysis of SA/PEO Fiber Meshes  
Out of the three SA/PEO solutions evaluated for electrospinningning (Table 1), the formulation based on 7 \(w\)/\(w\)% SA51 MG03 (i.e., 7 \(w\)/\(w\)% SA51 MG03 + 3 \(w\)/\(w\)% PEO + 1 \(w\)/\(w\)% Triton X-100) proved optimal for fiber fabrication. This solution successfully produced solid, cylindrical ultrafine fibers at both 60 rpm and 1500 rpm collector speeds, as confirmed by SEM micrographs (Figure 5(A1,B1)).  
Notably, the 1500 rpm collector speed resulted in superior fiber alignment and slightly larger diameters (d = 281 \(\pm\) 79 nm) compared to 60 rpm speed (d = 264 \(\pm\) 95 nm). Upon subjecting the SA/PEO solution to crosslinking, significant changes in fiber morphology were observed, as illustrated in Figure 5. Methods (_i_) (2 \(w\)/\(v\)% CaCl\({}_{2}\) dH\({}_{2}\)O solution) and (_ii_) (2 \(w\)/\(v\)% CaCl\({}_{2}\) 30/70 \(v\)/\(v\)% ethanol/dH\({}_{2}\)O solution) allowed fibers to form welds at intersection points, leading to an interlocked network, and increasing fiber diameter. Method (_i_) induced a more pronounced diameter increase (60 rpm: d = 443 \(\pm\) 15 nm; 1500 rpm: d = 450 \(\pm\) 66 nm) compared to method (_ii_) (60 rpm: d = 404 \(\pm\) 10 nm; 1500 rpm: d = 434 \(\pm\) 92 nm), regardless the collector speed, while crosslinking strategy (_ii_) led to an increased density of welds at fiber intersection points. In contrast, method (_iii_) 30/70 \(v\)/\(v\)% ethanol/dH\({}_{2}\)O solution) had a dramatic impact, resulting in the complete fusion of fiber structures and the formation of films (not shown). The structural stability of the ionically crosslinked SA/PEO fibers via method (_ii_) was examined by incubation in simulated biological conditions (Figure 6). After 24 h, the fibers collected at 60 rpm exhibited a diameter of 853 \(\pm\) 22 nm, while those at 1500 rpm had a diameter of 666 \(\pm\) 14 nm. These results demonstrated a statistically significant increase in fiber diameter over the 24 h period, indicating ongoing swelling (Figure 7).  
Figure 5: SEM micrographs of (**A1**) uncrosslinked and (**A2,A3**) crosslinked SA/PEO fibers electrospun at 60 rpm, acquired at 5000\(\times\) magnification (**A2**: crosslinking in 2 \(w\)/\(v\)% CaCl\({}_{2}\) in H\({}_{2}\)O; A3: crosslinking in 2 \(w\)/\(v\)% CaCl\({}_{2}\) in 30 \(v\)/\(v\)% EtOH in H\({}_{2}\)O) and 15 kV. SEM micrographs of (**B1**) uncrosslinked and (**B2,B3**) crosslinked SA/PEO fibers electrospun at 1500 rpm, acquired at 5000\(\times\) magnification (**B2**: crosslinking in 2 \(w\)/\(v\)% CaCl\({}_{2}\) in H\({}_{2}\)O; B3: crosslinking in 2 \(w\)/\(v\)% CaCl\({}_{2}\) in 30 \(v\)/\(v\)% EtOH in H\({}_{2}\)O) and 15 kV.
## 3 System Model  
As shown in Figure 1, it is expected that the weather varies from location to location. The satellite over Beijing can choose a coding scheme that can transmit more data because of the clear weather and higher SNR, whereas the satellite over Shanghai can only choose a relatively conservative coding scheme because of the interference from clouds and low SNR. The SNR of the communication link is mainly determined by free-space loss (FSL) and rain loss. However, the current margin value accounting for rain loss is tremendous, which results in the waste of spectrum, so we can dynamically adjust the coding scheme according to the SNR to make full use of the spectrum source.