esa-sceva/satcom-chunk-collection huggingface dataset:
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 3 Methodology and the System Design Procedure
### Security Analysis of Chaotic Carrier Data Transmission System with Parameters Nonlinear Modulation
#### 3.3.1 Autocorrelation Analysis  
Autocorrelation is the correlation of a signal with itself, but with some time delay [45]. This estimation is also called a sequential correlation. The MATLAB system has a special function for obtaining correlograms, namely the \(xcorr\) function.  
Figure 7a,b shows that the shape of the correlograms did not indicate the presence of a useful signal in a chaotic carrier. Moreover, the shape of the correlogram of a completely random signal (Figure 8) also did not differ from the correlograms of deterministic chaos. Thus, analysis using correlograms confirmed the possibility of using this method to protect data transmission. Moreover, this ensured the steganography of the system, i.e., concealment from an external listener of the very fact that we were transmitting a useful signal.  
Figure 5: Encoded state variables at the transmitter: (**a**) state variable \(X(t)\); (**b**) state variable \(Y(t)\).  
Figure 6: Encoded signal with rectangular pulses in the communication channel (state variable \(Z(t)\)).
## Monitoring and Analysis of the Roadway Forming Effect
### Monitoring Results and Analysis of Road Forming Effect
#### 5.2.2 Monitoring Results and Analysis of Anchor Cable Force  
(1) Monitoring results of pressure distribution on the anchor cable  
As shown in Figure 18, at the first test point of the fifth cable tape, the value measured by the cable dynamometer as a function of the advance distance of the working face is as follows:  
(2) Analysis of monitoring results  
As shown in Figure 18, the pressure on the right and middle anchor cables of the roadway increases slowly with the advancement of the working face; the pressure on the anchor cable on the left side of the roadway (gob side) increases sharply during the first cycle of pressure, and then decreases sharply, suggesting that the roof rock is segregated and broken. After the second cycle of pressure, the pressure on the anchor cables smooths away. The impact of mining on the roof is gradually reduced. The mining disturbance of the roof is gradually stabilized. The surrounding rock of the roof is well controlled.  
Figure 17: Variation law of roadway surface displacement.
## 4 Discussion  
SSAE performed significantly better than LDA using both sEMG and iEMG data from able-bodied and amputee subjects (Figures 5 and 7, respectively). Moreover, SSAE was more robust to between-day variability in signal features.  
The number of hidden units in both layers was optimized and further increments did not significantly reduce the error. Performance at both layers was better when using non-linear and linear activation functions for encoders and decoders, respectively. Beside the architecture, during the training phase, we noted that some parameters of the individual layers played an important role in the error reduction for that layer. Layers were trained in a greedy layer-wise training fashion, in which each layer was trained independently of the others. At layer one, the MSE depended on SR, but was almost independent of L2R. Conversely, at layer two, MSE decreased with lower values of L2R.  
The within-day analysis revealed that SSAE performed similarly when applied to iEMG and sEMG. Although both iEMG and sEMG achieved classification errors below 1% for able-bodied subjects, iEMG (\(\sigma^{2}=0.05\)) had less variance than sEMG (\(\sigma^{2}=0.26\)) for between-subject data. For LDA, the classification error was greater than with SSAE and differed between sEMG and iEMG, with lower error rates for sEMG. These results are consistent with previous studies that showed similar performance for classifying iEMG and sEMG or slight poorer performance for iEMG [52; 53; 54; 55].  
When analysing between pairs of days, the performance of SSAE was similar to that achieved for the within-day analysis. Conversely, LDA performance substantially worsened when analysing different days. Hence, unlike LDA, SSAEs generalized well when adding data from other days.  
For between-days analysis with two-fold and seven-fold validation, SSAE outperformed (\(p<0.001\)) the LDA. In this case, sEMG led to better performance than iEMG using both SSAE and LDA and revealed that sEMG patterns are comparatively more repetitive over days than iEMG patterns. This finding could be due to the fact that sEMG (non-invasive recording) represents global information  
Figure 8: (**a**) Two-fold and (**b**) seven-fold validation. Mean (and SD) classification error obtained with two different classifiers for both kinds data of healthy and amputee subjects. The diamond symbol indicates the best EMG data type, with statistical significance (\(p<0.01\)).  
from surrounding muscles, which increases its usability and robustness, unlike iEMG that includes local information from a specific muscle.  
Moreover, when increasing the training set, the performance of SSAE relatively improved more than that of LDA (Figure 8).  
Previous studies on myocontrol that used deep networks only focused on sEMG [28; 43; 49]. For sEMG, the results of this study are comparable with those from previous studies where either deep networks performed similar to or better than classical machine learning algorithms. The present study also included iEMG data and showed that deep networks outperform LDA classification when using both sEMG and iEMG with an increased robustness across days.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 3 Hardware Structures for Nonlinear Operation
### Comparison of Two Nonlinear Types  
From the above analysis, we can see that the two types of nonlinear structures have their own advantages and disadvantages.  
The nonlinear structure based on the special materials is easier to be integrated into the chip. However, it is difficult to obtain, and it also has higher requirements for the integrated environment.  
The nonlinear structure based on the optical modulator is easier to be adjusted and obtain nonlinearity. However, the structure is larger and it is harder to integrate.
## 3 Results
### \(N_{2}\)O and \(\text{CO}_{2}\) Emissions  
Emissions of N2O-N and CO2-C are reported in Table 4. The CNTR and NZ treatments showed a similar magnitude of N2O-N emissions both with and without the addition of  
Figure 3: Gross NO3 –N consumption (on the **left**) and gross NO3 –N production (on the **right**) rates in treatments with and without DMPP addition. CNTR, NZ, CZ, error bars and letters as in Figure 1. *** Due to high variability within 15N measurements in CZ treatment, these rates resulted in slightly negative values and are hence assumed as zero. Different letters indicate significant differences between treatments as a result of ANOVA (for gross production), Kruskal-Wallis (for gross consumption) and Tukey (HSD) statistical tests (\(p\) \(<\) 0.05).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sample** & _amoA (AOA)_ & _amoA (AOB)_ & _nitS_ & _nosZ_ \\  & & **CN g–1 × 1012** & **CN g–1 × 109** & **CN g–1 × 108** & **CN g–1 × 108** \\ \hline \multirow{3}{*}{No DMPP} & CNTR & 10.3 ± 3.19 a & 2.43 ± 0.47 a & 5.62 ± 0.41 a & 8.20 ± 0.87 a \\  & NZ & 8.00 ± 1.61 a & 2.56 ± 0.43 a & 4.74 ± 1.00 abc & 6.97 ± 2.85 a \\  & CZ & 9.30 ± 2.06 a & 2.61 ± 0.29 a & 3.58 ± 0.55 bc & 3.85 ± 0.90 b \\ \hline \multirow{3}{*}{With DMPP} & CNTR & 10.1 ± 1.32 a & 1.93 ± 0.55 a & 5.28 ± 0.76 ab & 6.86 ± 0.58 a \\  & NZ & 10.4 ± 1.84 a & 1.60 ± 0.92 a & 4.14 ± 0.35 abc & 5.11 ± 2.03 ab \\ \cline{1-1}  & CZ & 7.13 ± 3.18 a & 2.71 ± 0.19 a & 2.87 ± 1.04 c & 3.71 ± 1.19 b \\ \hline \hline \end{tabular}
\end{table}
Table 3: Copy numbers (CN) of the functional genes for archaeal ammonia oxidation (_amoA AOA_), and bacterial ammonia oxidation (_amoA AOB_), as well as nitrite reductase (_nitS_) and nitrous oxide reductase (_nosZ_). Values are expressed as mean \(\pm\) standard deviation (3 replicates). Different superscript letters express significant differences as a result of ANOVA and Tukey-HSD statistical tests (\(p\) \(<\) 0.05).  
DMPP (\(p>0.05\)), while CZ always showed higher emissions (up to 10-fold higher without DMPP) (\(p<0.05\)). The addition of DMPP significantly reduced the N2O-N emissions in all the treatments (\(p<0.05\)).  
The highest CO2-C emissions were recorded in CZ treatment without DMPP addition (\(p<0.05\)), while the CNTR and NZ showed very similar emission rates (\(p>0.05\)). The addition of DMPP had no significant effects on the CNTR, while it led to a significant reduction of CO2-C emissions in both NZ and CZ treatments (\(p<0.05\)).
# Directly Measuring the Pulse Front Distortion of High-Peak-Power Femtosecond Lasers
## 4 Far Field Pulse Duration  
In order to better guide and further promote the strong laser-matter interaction investigations, the actual available focused intensity of laser should be accurately judged. Thereby, the far field pulse duration of laser should be deduced, which can be much longer than the regarded value due to the presence of PFD. To deduce the effective pulse duration of this 200 TW laser at the far field under the PFD of \(-62.8\) fs, the temporal intensity distribution of the pulse at focus was investigated. Assume the spatial beam profile of a compressed pulse is homogeneous, and the temporal profile is Gaussian type. The 85 mm in diameter compressed pulse is divided into \(N\) concentric rings by equal spacing, and then the pulse intensity ratio is equal to the area ratio of rings. In the presence of PFD, the pulse front at a different ring will arrive at the focus at a different time. The central part is delayed with respect to the other parts, and the relevant time delay is the PFD at the corresponding position. The temporal intensity distribution of the 85 mm-diameter compressed pulse at focus is simply illustrated as Figure 5 (\(N=20\)), and all the pulses are assumed as Gaussian-type pulses with 27 fs duration.  
As a result, the temporal profile of the pulse at focus is an envelope of superposed pulses from different radial positions, and the effective pulse duration at the far field is simulated to be \(\approx\)49 fs (\(N\)\(\rightarrow\)\(\infty\)), which is almost two times the generally regarded 27 fs, as shown in Figure 6. Consequently,  
Figure 4: Measured data and theoretical curve of the pulse front distortion.  
Figure 5: Temporal intensity distribution at focus of the 200 TW laser.  
the actually achieved focused intensity of this laser was only about 55% of the ideal value (without PFD). In general, the influence of PFD is small for ns-class long lasers, and therefore can be ignored. However, for high-peak-power femtosecond lasers, PFD can result in significant temporal broadening of pulse duration at focus, and then dramatically reduce the focused intensity. For example, the PFD in PEAT laser [18] induced a decrease of the focused intensity on the target by a factor of eight compared with the ideal situation. Therefore, in order to achieve higher focused intensity and further promote strong field laser physics investigations, the PFD in high-peak-power femtosecond laser systems should be compensated based on the experimentally measured results [15; 16; 18; 29].
## 4 Computational Aspects and Applications
### Comparative Analysis  
Here, we use Theorem 3 to compare the convergence domains and the error estimates of some particular methods of the family (10).  
Define the function \(\phi_{\alpha}\) by (28) in the case \(p=\infty\); that is, with \(\gamma\), \(\eta\), \(\
u\) and \(\mu\) defined by  
\[\gamma(t)=\left(1+\frac{t}{1-2t}\right)^{n-1},\quad\eta(t)=\frac{\left(n-1 \right)t^{2}}{(1-t)(1-2t)}\,,\quad\
u(t)=\frac{\left(n-1\right)\gamma(t)\,t}{ 1-2\,t}\]  
and  
\[\mu(t)=(\gamma(t)-1)^{2}+\gamma(t)^{2}\,\eta(t).\]  
Evidently, the initial condition (35) can be presented in the form \(E(x_{0})<R\), where \(R\) is the unique solution of the equation \(\phi_{\alpha}(t)=1\) in the interval \((0,1/2)\). Thus, the bigger the number \(R\), the larger the convergence domain and the better the error estimates of the respective method.  
Further on, we compare [PERSON]'s method (4) (\(\alpha=0\)) and [PERSON]'s method (8) (\(\alpha=1\)) with each other and with three other members of the family (10), obtained for \(\alpha=1/2\) and for the complex numbers \(\alpha=\alpha 1=0.722+0.126i\) and \(\alpha=\alpha 2=0.238-0.004i\), which were randomly chosen from the rectangles  
\[\{z\in\mathbb{C}\colon 0.5<Re(z)\leq 1\,\text{ and }\,|Im(z)|\leq 1\}\quad\text{and} \quad\{z\in\mathbb{C}\colon-1\leq Re(z)<0.5\,\text{ and }\,|Im(z)|\leq 1\}.\]  
Note that the last three values of \(\alpha\) were chosen to cover the three different cases in (28).  
The graphs of the functions \(\phi_{0}\), \(\phi_{1/2}\), \(\phi_{1}\), \(\phi_{\alpha 1}\) and \(\phi_{\alpha 2}\), obtained for \(n=5\), are depicted in Figure 1. It can be clearly seen from the figure that [PERSON]'s method (8) has the largest convergence domain and the best error estimates among the considered methods. However, despite the randomness of the choice, the method obtained for \(\alpha=\alpha 1\) has a very large convergence domain and relatively good error estimates.
## 4 Statistical Characterization of Boundary Kinematics on Triaxial Sand Specimens  
### Experimental Design  
The authors formulated an experimental design to specify the method for conducting a statistical analysis toward boundary kinematics. This consists of five strain levels, comprised of 0.0% to 1.0%, 1.0% to 3.0%, 3.0% to 5.0%, 5.0% to 7.0%, and 7.0% to 9.0%, selected as incremental steps as shown in Figure 1. The first two axial strain levels are associated with the elastic and hardening phases of the material's response. The peak stress was reached at approximately 3.0% of axial strain for most of the tests, subsequently the stress-strain responses showed softening until 9.0% of axial strain that presented with the onset of the critical state.  
Figure 6 presents the experimental design of the statistical characterization. Four kinematic properties, including the gradient along the \(\hat{\rho}\) axis (\(F_{11}\)), gradient along the \(\hat{\jmath}\) axis (\(F_{33}\)), divergence (\(div\,U\)), and curl along the \(\hat{\rho}\) axis (\((curl\,U)_{\hat{\rho}}\)), were calculated on all the 3D-DIC-sampled displacement fields, and the results were used for subsequent statistical characterization. These chosen kinematic properties are tightly associated with different localization effects of the triaxial sand specimen. For instance, \(F_{11}\) indicates the radial deformation gradient that can reflect the development of the expansion band, \(F_{33}\) indicates the vertical deformation gradient that can relate to the vertical compression inside the shear band, \(div\,U\) presents the volumetric change of the specimen, and \((curl\,U)_{\hat{\rho}}\) indicates the rotational deformation pertaining to the widely reported phenomenon of intense inter-particle rotations inside the shear band [8; 9; 15; 17]. The calculated mean and standard deviation fields were plotted overlaying the averaged boundary shape of specimens, to overcome the challenge that each specimen has its own distinctive boundary shape. The Lagrangian description of a displacement field was adopted, meaning that the kinematic quantities were computed from the reference to the initial image of each strain window.
## 4 Waveform Retracking Results
The reprocessing results are not affected by this distortion of the waveform and still provide steady RSL data until approximately 10 km offshore. Figure 13 shows the RSL comparisons of the reprocessing results and the SGDR data in the pass 196. Since the trajectory of the pass 196 is almost perpendicular to the shoreline, the satellite altimetry is slightly affected by the coastal land in theory. Hence, the RSL values of the reprocessing results and SGDR data are almost the same beyond 10 km offshore. Within 10 km offshore, a small amount of the RSL of the SGDR data deviates and has been corrected in the reprocessing results, especially in the range from 5 km to the island. In summary, the waveform retracking proposed in this paper can process the nearshore waveform more accurately than the standard process applied in the SGDR data. In the range within 15 km offshore, the altimetry data are improved qualitatively and have retrieved more valid observations that are always thought to be outliers to the SGDR data.
## 3 Methodology
### Time-Series Classification
#### 3.5.2 LSTM-Based Classifier  
In recent years, the recurrent neural network (RNN) and its variants (such as LSTM) have been utilized extensively in time-series analysis, such as time-series prediction [12; 13] and time-series classification [3; 8]. This study employed stacked LSTM models for crop classification [3]. In LSTM-based classification models, four LSTM layers with \(h\) (where \(h\) equals the dimension of input features) hidden neurons were first stacked to transform input time-series features into high-level features. Then, a dense layer fully connected high-level features to crop categories. A SoftMax activation function then outputs crop-type probabilities to generate crop classification maps. Furthermore, a cross-entropy loss function and an Adam (Adaptive Moment Estimation) optimizer with default parameters were employed to train LSTM-based classifiers.
## 2 The Field-Deployed 4-Core MCF Cable
The results show that the splicing losses of the 260 single-core fusion splicing connections are no more than 0.4 dB, and the average splicing loss of the entire MCF link is lower than 0.15 dB. We found that the splicing loss of MCFs was larger than that of SMFs, which was mainly caused by the slight alignment errors between the fiber cores of the two cross-sections during the fusion splicing process. The span loss and the total inter-core crosstalk of the 106 km 4-core CDM link were also measured before the field trial, and the results are shown in Table 2. The 4-core CDM link consists of a 106 km 4-core fiber and a pair of FIFO devices. The total inter-core crosstalk of each fiber core is defined as the sum of the inter-core crosstalk from the other three fiber cores to the tested core. It was found that the span losses of the four fiber cores were similar and were all no more than 33.6 dB. The total inter-core crosstalk was lower than \(-\)40 dB for each fiber core of the 106 km CDM link.
# Fault Location and Restoration of Microgrids via Particle Swarm Optimization
## 2 Problem Description of Fault Location and Service Restoration in MGs  
Fault location and restoration problems in MGs can be divided into two layers. The flow chart of the proposed approach is shown in Figure 1. The first priority is to establish the network topology by using bus connection matrices based on graph theory; the bus impedance matrix (\(Z_{Bus}\)) can be easily obtained by multiplying the bus-injection to the branch-current (_BIBC_) and the branch-current to bus-voltage (_BCBV_) matrices [25; 26] according to the system topology, which changes during pre-fault and fault conditions.  
The methods used for fault detection and isolation are summarized as follows:  
1. Impedance method;
2. High-frequency components and wavelet transform;
3. Artificial neural network;
4. Comparison measurement and simulation value;
5. Hybrid method.  
In general, the fault current contributed by the upstream main grid and DGs causes bus voltage variation, and it is used to determine the fault location in MGs. [PERSON] [15] used synchronized voltage and current measurements to detect the fault section in distribution systems with high DG penetration; however, how to effectively rebuild the \(Z_{Bus}\) after the fault has occurred is the key factor. Unfortunately, much of the available research is weak on this point, and therefore, the aim of this work is to effectively rebuild the \(Z_{Bus}\) corresponding to the network topology under post-fault conduction. The concept used in the proposed approach in this paper is based on the impedance method, PSO, and the synchronized voltage and current measurement technique proposed in [15]. A distinct, systematic and effective graph theory-based algorithm is proposed for fault location in MGs. Therefore, only a minor modification of the _BIBC_ matrix built in the pre-fault stage is needed in the fault stage; and a new, fast, and effective \(Z_{Bus}\) is derived. This approach is applicable to unbalanced distribution networks and is different from the traditional \(Z_{Bus}\) modification method, which is used in three-phase balanced systems. Service restoration implemented by PSO algorithm follows fault detection and isolation and is aimed at a minimum number of switches and loss of electricity service.  
Figure 1: Proposed fault detection, isolation, and restoration of MGs.
## Discussion
### Threats to Validity  
In software engineering, validity assessment incorporates four types, namely internal, external, construct, and conclusion validity [113]. In this survey, we followed the recommended protocols to reduce the threats to validity and improve the quality of our conclusions. As such, we have:  
* Defined the methodology, including search key terms and phrases, publication venues... etc., to enable the replicability of the survey.
* Used the manual search to incorporate any missing articles in the synthesis.
* Applied the appropriate inclusion and exclusion criteria to focus on student performance modeling using learning outcomes. These constituted the selection criteria of the survey.
* Selected all studies that meet the inclusion criteria irrespective of the researchers' background or nationality to eliminate any culture bias.
* Ensured that the primary studies are not repeated in the synthesis by removing the duplicates.
* Defined the quality assessment criteria based on previous surveys and recommendations [3].  
However, the validity of our findings was largely influenced by the quality of the models we synthesized. We noticed that most studies focused on highlighting the models and factors that succeeded in forecasting student performance, thus, introducing a publication bias. Negative results were seldom published in the selected articles, which might have affected the results of our review. Indeed, this limits the practicality of the implications and recommendations.  
Among the critical threats that hinder conducting valid surveys is missing any primary studies during the search process. To minimize this risk, we followed the best practices for conducting survey literature reviews in software engineering [10; 11]. We also varied the critical search phrases for each electronic bibliographic database to retrieve as many relevant papers as possible. To reduce any subjective interpretation, we reviewed the extracted data and classification.  
Concerning external validity, it is dangerous to assume the same observations for different disciplines (e.g., economics, history,... etc.), since most surveyed studies modeled student performance in a single discipline. Furthermore, the results ought to be treated with caution, especially with respect to generalization to other educational systems worldwide. Around 70% of studies were conducted in the USA and Europe alone, restricting the applicability of the results to the developing countries.