esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Response of Ultrafast Grating Monochromators
## 2 Ultrafast Response of Grating Monochromators
### Temporal Response of TDC Monochromators with Low Spectral Resolution  
Low-resolution monochromators are typically used with HHs when the monochromator is required to select a single harmonic within a broad plateau and behaves as a tunable broad-band filter. The bandwidth transmitted by the slit has to be narrow enough to resolve two adjacent harmonics, that are separated by \(2\lambda_{0}/[n(n+2)]\), where \(\lambda_{0}\) is the laser wavelength that is used to generate HHs and \(n\) is an odd integer (A single laser wavelength is assumed to be used to generate HHs, therefore only odd harmonics are generated.). Indeed, the output bandwidth is limited by the source itself and not by the monochromator. Comparing Equations (1) and (2), the GD results negligible with respect to the Fourier limit for:  
\[\frac{(q-p)\sigma^{2}\Delta\lambda^{2}}{0.22\lambda}<1 \tag{4}\]  
where \(\Delta\lambda\) is the bandwidth of the pulse.  
The calculations for typical parameters of low-resolution TDC monochromators are resumed in Figure 3. Two sets of gratings are used to span the 12-75 nm interval (i.e., harmonics H65-H11 of 800-nm Ti:Sa laser). XUV Fourier-limited 10 fs Gaussian pulses are assumed at the generation, of which the wavelength bandwidth is plotted in Figure 3a. The corresponding energy bandwidth is \(\Delta\)E \(\approx\) 180 meV. The bandwidth transmitted by the slit is also shown in Figure 3a: it is lower than the separation between consecutive harmonics, as requested for the selection of a single harmonic. The pulse-front tilt given by the first grating stage is plotted in Figure 3b for 4-mrad XUV divergence, that is a typical divergence for HHs generated by laser pulses in the millijoule range. The tilt is in the range 15-50 fs, therefore the original 10-fs pulse is stretched in time by the first grating. If monochromatic Fourier-limited pulses are requested at the output of the monochromator, a second grating stage is needed. The residual pulse-front distortion after the pulse-front tilt correction made by the second grating has been simulated through a ray-tracing procedure. For the parameters here assumed, it is lower than 0.5 fs within the whole interval of operation, therefore totally negligible. The GD and the GDD are plotted in Figure 3c,d for 10-fs Fourier-limited pulses. The GDD is below 10 fs2 and the GD is definitely lower than the Fourier limit, confirming that the TDC monochromator, when used as a broad-band filter, does not sensibly alter the duration of the pulse.
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 5 Study on the Deformation and Instability Modes of the Slope Under Fluid-Structure Coupling
### Shear Strain Increment and Plastic Zone  
Figures 19 and 20 show the shear strain and plastic zone contour plots of the slope under different rainfall durations. As shown in Figure 19a, after 1 day of rainfall, the maximum shear strain increment is primarily concentrated at the exposed surface of the strongly weathered zone at the slope crest, with a smaller distribution in the fourth-layer rock mass. The maximum shear strain increment is \(5.72\times 10^{-2}\), compared to a maximum shear strain of \(1.67\times 10^{-2}\) under the gravitational loading alone. After 3 days of rainfall, a significant increase in shear strain increment is observed at the exposed surfaces of the third and fourth layers, with the potential sliding surface extending deeper and forming a new dominant pathway, though it has not yet fully developed.  
Under rainfall conditions, the plastic zone increases significantly compared to the damaged area under gravity alone. The slope primarily experiences shear failure within  
Figure 19: Shear strain increment at different times: (**a**) 1 day of rainfall; (**b**) 3 days of rainfall.  
Figure 20: Plastic zone at different times: (**a**) 1 day of rainfall; (**b**) 3 days of rainfall.  
the strongly weathered zone, whereas tensile failure occurs along the contact surfaces in the sandy-gravelly interlayered zones, exhibiting a progressive failure pattern that intensifies with prolonged rainfall duration [41].  
Overall, under the influence of rainfall, the potential sliding surface of the slope gradually develops from the weak, highly weathered argillaceous siltstone zone at the top towards the deeper layers. At the interfaces between thinner interlayers, a continuous sliding surface is more prone to develop, resulting in slope failure along these planes [42].
# Tropical Cyclone Winds from WindSat, AMSR2, and SMAP: Comparison with the HWRF Model
## 4 Results
### Rain Impact  
Finally, we wanted to assess the impact of rain on the performance of the TC-winds. As a way of better understanding how rain affects the TC-winds, the satellite-HWRF biases  
Figure 14: The results shown in Figure 13, except separated into the Atlantic (**a**) and Pacific (**b**) basins. Data from 11 storms were used to make the curves for the Atlantic, while data from 7 storms were used to make curves for the Pacific.  
and standard deviations shown in Figure 14a (Atlantic Ocean) were stratified by rain rate. Figure 15 shows the satellite-HWRF binned biases and standard deviations for the Atlantic Ocean for each of the three sensors separated into three different rain regimes: light rain (0-4 mm/h), moderate rain (4-8 mm/h), and heavy rain (>8 mm/h). Note that WindSat and AMSR2 retrieved rain rates along with TC-winds. These were used to stratify the wind data from these sensors. Since SMAP itself does not measure rain rates, we used collocated rain rates from WindSat that were observed at the same location within +/\(-\)1 h of SMAP wind measurements for the rain stratification. This is made possible by fact that the SMAP and WindSat satellites have approximately the same ascending node times, which allows for an easy collocation between the two sensors.  
In this figure, there are several things worth noting. First, there does not appear to be any systematic difference in how the sensors perform in rain. The small biases tended toward negative in light and moderate rain regimes, while being slightly positive for heavy rains. Second, AMSR2 and WindSat had standard deviations that are slightly higher than SMAP (by about 2-3 m/s) in the highest rain regimes. This is not unexpected, as even though the new TC-wind retrievals were designed to mitigate rain contamination, they were based on higher-frequency channels (C-, X-band) than SMAP (L-band). Of these three channels, the L-band was affected by precipitation the least. Lastly, it is possible that the accuracy of the HWRF field itself could degrade at high precipitation rates. Overall, the nature of these results suggest that any potential rain impacts on the TC-wind retrievals were roughly equivalent between the three sensors, despite differences in the channels used for the retrievals of WindSat and AMSR2 (C- and X-band) and SMAP (L-band).
## 5 MADPSO Method Design
### Algorithm Design
#### 5.2.1 Chromosome Coding  
In response to the need to arrange the picking station and the AGV for each order simultaneously in the SACOP problem, we designed a double-layer integer encoded chromosome, and a specific example of the chromosome structure is shown in Figure 8.  
The first layer of chromosomes represents the allocation of pallet-picking tasks for each picking station, which is named the Picking Station Task-allocation Layer. The gene value \(w_{i}\) of the \(p_{j}\) gene represents the allocation of pallet \(i\) to picking station \(j\).  
The second layer of chromosomes represents the order-task sequence of each used AGV, which is named the AGV Task-allocation Layer. Referring to the example in Figure 8, \(A_{i}\), \(A_{j}\), etc., represent the AGV numbers called in this solving process. We refer to these genes as AGV Genes, while the remaining genes are referred to as Order Genes. The Order Gene string is cleaved into several Order Gene sub-strings by the AGV Genes, and each Order Gene sub-string represents a task sequence fragment belonging to its head AGV Gene. The final task sequences of each AGV can be obtained by concatenating the Order Gene sub-strings belonging to the same AGV successively.  
In addition, to facilitate the random generation of the initial population, we adopt the following method to generate the AGV Task-allocation Layer chromosome: first, all orders are randomly sorted to an initial sequence, then several breakpoints are randomly generated in the initial sequence (there will always be a breakpoint on the first gene locus), and, finally, each breakpoint is randomly assigned an available AGV number.
## 6 Results and Discussion
### Convergence Study  
The convergence study of the suggested SU-CMO scheme in contrast with traditional schemes such as ALO, AO, BOA, CMBO, and SSA for ranging iterations is illustrated in Figure 7. In this scenario, estimation is computing by varying iterations. While examining the outcomes, the suggested SU-CMO has increased the slightest cost values which is ranging from the 13 th-the 25 th iteration in the comparison with others. In the same manner, the recommended SU-CMO has grown slightly higher cost from the 0 th-12 th iterations; although, values are lower than those of those being compared such as ALO, AO, BOA, CMBO, and SSA. While, the existing CMBO has demonstrated the worse outcome i.e., high-cost value in almost all iterations when contrasted with the several alternative schemes such as ALO, AO, BOA, CMBO, and SSA. Therefore, improved convergence is achieved by SU-CMO over alternate schemes.  
Figure 7: Convergence Analysis.
# A Novel Malware Detection Model in the Software Supply Chain Based on LSTM and SVMs
## 5 Experimental Results and Analysis
### Evaluation Metrics  
The following metrics were employed to assess the model's performance.  
1. Training time [23]: The training time was measured by timing the time from when the model initiated training until the training process was completely finished, and was determined by calculating the difference between these two time points.
2. Detection time [23]: The detection time was measured by timing from the moment the model initiated detection until the detection process was completely finished, and was determined by calculating the difference between these two time points.
3. Computation time [23]: The computational time is the sum of the model training time and the detection time.
4. Accuracy [14]: Accuracy represents the proportion of accurately predicted samples to all samples. It is a simple and intuitive metric to evaluate the ability of a model to correctly classify examples. As shown in Equation (8): \[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}\] (8)  
In the formula, \(N\) (Negatives) represents the malware label, while \(P\) (Positives) stands for benign software. \(TN\) represents the software with true \(N\) and predicted \(N\); \(FN\) represents the software with true \(P\) and predicted \(N\); \(TP\) represents the software with true \(P\) and predicted \(P\); and \(FP\) represents the software with true \(N\) and predicted \(P\)Precision [14]: Precision is the percentage of samples that, when all samples are predicted by the model to be positive categories, are actually positive categories. This is shown in Equation (9).  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
Recall rate [14]: Recall rate is used to measure how many of the true positive class samples the model correctly predicted as positive. This is shown in Equation (10).  
\[Recall\ rate=\frac{TP}{TP+FN} \tag{10}\]  
F1-Score [14]: F1-Score is used to assess how well a classification model is performing and overall consideration for both precision and recall rate. Equation (11) illustrates how it is calculated.  
\[F1-Score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{11}\]
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 2 Centralized MPPT Architecture Based on LoRa
### Power Optimization of PV Systems
#### 2.1.4 Voltage Reference Estimator  
In order to calculate the voltage reference for the SMC controller, an estimator based on Gaussian Process Regression (GPR) was designed. Gaussian processes (GPs) are a Bayesian approach that are used to solve regression problems [38]. GPR is particularly well suited for applications where uncertainty quantification and flexibility in modeling non-linear patterns are of paramount importance. For example, the authors in [39] trained a GPR in order to forecast the power generation with the ambient conditions. In this work, the MPP voltage was used as the reference for the SMC control. As \(V_{\text{MPP}}\) depends on \(G\) and \(T\) (irradiance and cell temperature, respectively), they are thus the input variables for the GPR.  
GPs, the underlying mathematical foundation of GPR, provide a framework for dealing with uncertainty in a principled manner. Unlike traditional regression models that estimate point predictions, GPR delivers not only provides predictions, but also quantifies the uncertainty associated with each prediction. This inherent probabilistic nature makes GPR an ideal candidate for various domains. On the other hand, GPR is not efficient for high-dimensional feature problems. However, the voltage reference estimator only needs temperature and irradiance as features.  
At its core, GPR relies on the concept of kernels, or covariance functions, to measure the similarity between input data points. This allows the model to adapt to the intricacies of the data, capturing both local and global patterns. The choice of kernel function and associated hyper-parameters provides a flexible means to fit the model to the dataset at hand.  
The Gaussian Process is defined as follows:  
\[f(t)\sim\text{GP}\big{[}\mu(x_{i}),\,k(x_{i},\,x_{j})\big{]}, \tag{18}\]  
where \(\mu(x_{i})\) and \(k(x_{i},x_{j})\) are the mean and covariance function, respectively. An initial distribution was assigned to each function that is capable of interpolating two observations within a given dataset. Higher probabilities were then assigned to functions with more favorable properties for interpolation. The final step involves combining the data with the preceding distribution to derive a posterior distribution, ultimately determining the regression result.  
Gaussian Process Regression (GPR) hinges on a robust understanding of the prior distribution over functions, i.e., a foundation built upon a mean function and a covariance function, which is also known as the kernel. The kernel is pivotal in shaping the smoothness of the functions produced by the preceding distribution. The kernel's role extends to determining the similarity between function values based on input variables, essentially quantifying the degree of correlation. The kernel choice is intricately tied to the underlying data assumptions.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation
### Results from the Grid Topology  
To avoid the impact of topology on performance, we analyzed the effective coding benefit, the constrained coding conditions, and the EXQL metric in the grid topology. We used a \(5\times 5\) grid topology as shown in Figure 8, in which two adjacent nodes are separated 20 m vertically and horizontally. Eight sources were randomly generated, and each generated flow had a randomly-picked source, destination, and flow rate. There were 2-5 hops between each source and destination node.
## 1 Introduction
Additionally, the Proportional-Integral-Resonant Adaptive Controller (PIRAC) introduced in [21] effectively addresses speed fluctuations caused by current measurement errors through segmented phase compensation, enhancing system stability and harmonic suppression performance across different speeds. In summary, this study employs a quasi-proportional-resonant controller (QPRC). Typically, a control structure comprising several resonant controllers in parallel with a PI controller is used to regulate current and compensate for current harmonics [22]. However, traditional PI controllers are sensitive to parameter mismatches, which can reduce the tracking accuracy of the current loop. Therefore, robustness must be considered in the design of current controllers. This paper proposes a control strategy called Dynamic Harmonic Compensation Sliding Mode Control (DHC-SMC), which offers enhanced robustness and better harmonic disturbance suppression. When resonant controllers are combined with sliding mode controllers, the structure can provide extremely high gain at specific frequencies, effectively suppressing periodic disturbances or harmonics at precise frequencies. Additionally, the introduction of sliding mode controllers, which do not rely on an accurate system model and possess strong disturbance rejection capabilities, effectively complements the resonant controller in suppressing non-periodic harmonics. The remainder of this paper is organized as follows: Section 1 describes the harmonic model of the PMSM and analyzes the torque ripple caused by current harmonics. Section 2 outlines the design process and performance analysis of the proposed control method. Section 3 validates the effectiveness of the proposed control strategy through experimental verification on a PMSM drive system. Finally, Section 4 provides a summary of this paper.
## 3 Results and Discussion
### Polymorphic Discrimination at 20 \({}^{\circ}\)C
#### 3.1.2 CB-AMF Mixtures without CBE  
In the case of CB + 0.5 AMF mixture, the following diffraction peaks are identified on the diffractogram (Figure 1): 63.5 A (q = 0.099 A\({}^{-1}\)), 43.3 A (q = 0.145 A\({}^{-1}\)) and 32.4 A (q = 0.194 A\({}^{-1}\)), with high orders at 16.1 A (q = 0.391 A\({}^{-1}\)), 14.6 A (q = 0.431 A\({}^{-1}\)) and 12.9 A (q = 0.486 A\({}^{-1}\)) for SAXS and 4.00 A (q = 1.57 A\({}^{-1}\)), 3.89 A (q = 1.61 A\({}^{-1}\)), 3.79 A (q = 1.66 A\({}^{-1}\)), 3.70 A (q = 1.70 A\({}^{-1}\)) and 3.59 A (q = 1.75 A\({}^{-1}\)) for WAXS.  
Regarding the SAXS, it is interesting to note that the 2L structure seems to be imposed by the CB as the position of its third order, which is at 0.431 A\({}^{-1}\) instead of 0.453 A\({}^{-1}\) for AMF. This is less discriminant for the first orders, because the peak positions are very close but the peak at 0.145 A\({}^{-1}\) is similar to that of CB alone (0.146 A\({}^{-1}\)) and slightly different to AMF alone (0.154 A\({}^{-1}\)).  
At T = 20 \({}^{\circ}\)C, the parameter \(\rho\) becomes worthy for this mixture: \(\rho_{CB+0.5 AMF}=0.938\). Compared to CB and CBE, this parameter is more important: it increases by 87% between CB and CB + 0.5 AMF, whereas by mass TGs in \(\beta^{\prime}\) form have increased by 50%, and by 22% between CBE and CB + 0.5 AMF.  
After increasing the amount of AMF in the CB + AMF mixture it can be seen that the peak positions are practically the same as for CB + 0.5 AMF mixture. This is also true for 2L structure which keep the \"positions of CB\" even if the proportion of AMF is more important. In addition, the intensity of 2L\({}_{001}\) peak compared to 3L\({}_{001}\) and 3L\({}_{002}\) peaks is much greater for \"CB + AMF\" mixture than for \"CB + 0.5 AMF\" mixture.  
At T = 20 \({}^{\circ}\)C, we measure a value for the parameter \(\rho\) for CB + AMF of 1.531 while for \(\rho_{\text{CB + 0.5 AMF}}\)(T = 20 \({}^{\circ}\)C) = 0.936, i.e., a 38% increase of this ratio. We have to compare this parameter with the proportion by mass of TGs in IV or \(\beta^{\prime}\) form: between CB + 0.5 AMF and CB + AMF, these TGs have increased by 33%. It would seem once again that the parameter \(\rho\) linked to the results obtained in X-ray for the SAXS follows the same evolution as the parameter \(\mu\) linked to the mass distribution of TGs according to their polymorphic forms.
_J. Sens. Actuator Netw._**2012**, \(1\), 59-85; doi:10.3390/jsan1010059  
_Journal of Sensor and Actuator Networks_  
ISSN 2224-2708  
www.mdpi.com/journal/jsan/  
_Review_  
Techniques and Challenges of Data Centric Storage Scheme in Wireless Sensor Network  
**[PERSON] and [PERSON] *  
School of Electrical and Computer Engineering, RMIT University, Melbourne VIC 3001, Australia;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +61-3-9925-2000; Fax: +61-3-9925-2007.  
_Received: 19 April 2012; in revised form: 28 May 2012 / Accepted: 4 June 2012 / Published: 12 June 2012_  
**Abstract:** Storing, collecting and querying data across miniaturized battery powered Wireless Sensor Networks (WSN) is a key research focus today. Distributed Data-Centric Storage (DCS), an alternate to External Storage (ES) and Local Storage (LS), is thought to be a promising and efficient storage and search mechanism. There has been a growing interest in understanding and optimizing WSN DCS schemes in recent years, where the range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven this line of research. In this paper, an extensive state-of-the-art study is provided including the prime WSN DCS schemes, challenges that inspired these schemes, as well as drawbacks and shortcomings of existing solutions. In contrast to previous surveys that briefly discuss the contribution of a few WSN DCS mechanisms, we provide a thematic taxonomy in which schemes are classified according to the problems dealt with including range query, similarity search, data aggregation, sensor network field non-uniformity, multi-replication, load balancing and routing algorithm.  
**Keywords:** Greedy Perimeter Stateless Routing; Distributed Hash Table; Data Centric Storage; spatial temporal similarity search; range queries; multi-replication; non-uniformity of sensor network field; Load Balanced Data Centric Storage; Geographic Hash Table