esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 1 Introduction
* Considering the actual electric power system, we present a universal VPP network architecture, which combined a local network and backhaul network, including many possible link and backhaul technologies. In addition, aiming at the network architecture, a network latency processing method is proposed based on time series analysis. * We set up a real VPP system to verify the effectiveness of the proposed network latency processing method, and give exciting results. The remainder of this paper is organized as follows: Section 2 introduces the VPP practices and studies in industry and academia and summarize four typical service communication requirements. The studies on network state prediction are reviewed in this section too. The universal VPP network architecture that combined a local network and backhaul network is proposed in Section 3. In Section 4, a network latency processing method and an application matching method are proposed. Subsequently, some representative results are given to illustrate the effectiveness of the proposed network latency processing method in Section 5. Finally, the authors give discussions and conclusions in Section 6.
## 3 Results
### Prevailing Visibility--Extended Validation
It turned out that there were situations where the observer, for reasons that are not entirely clear to us, decided to send the sensor data to the METAR message, even if these data did not correspond one hundred per cent with the real atmospheric situation A possible explanation of such behavior is that the data from the visibility sensor is the only reference point against which the observer can be retrospectively checked in the current practice. Category #4 of Table 10 relates to meteorological situations with different visibility conditions in different directions. These represent difficulties for the local observer. ICAO defines the prevailing visibility \"... within at least half of the circle of the horizon... \" [1]. Sometimes it is only a question of a few degrees whether visibility for the half of the circle is or is not achieved. In other words, minor differences in the (subjective) perception may result in significant differences in the overall evaluation of the given situation. In cases like that, it really depends only on the observer which value to decide on. \begin{table}
\begin{tabular}{c c} \hline
**Category \#** & **Category Description** & **Number of Situations** \\ \hline
1 & In the range of the strict ICAO accuracy condition & 17 \\
2 & Different time of assessing the visibility & 4 \\
3 & Reporting the visibility sensor data & 2 \\
4 & Different visibility in different directions & 3 \\
5 & Different estimation by the local observer & 8 \\  & (Total) & (34) \\ \hline \end{tabular}
\end{table} 
Table 10: Classification of the situations with the lowest visibility ranges (i.e., 600 m or lower) from the extended validation experiment. \begin{table}
\begin{tabular}{c c} \hline
**Visibility Range [m]** & **Number of Situations** \\ \hline \(<\)600 & 34 \\
600–1500 & 14 \\
1500–5000 & 231 \\ \hline \end{tabular}
\end{table} 
Table 9: Results of the extended validation experiment for assessing the accuracy of the visibility by the proposed system at LZTT in the period of September 2018–April 2019. Category #5 of Table 10 counts eight situations that were impossible to classify into categories #1-4. As the extended validation indicated, there are different known (Categories #2-4) or unknown (Category #5) reasons that might explain the differences in the evaluation of the prevailing visibility during weather situations with decreased visibility. A common feature of the known or at least supposedly known reasons is the attitude of the local professional observers, which will be analyzed in detail in the Discussion section.
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 2 Materials and Methods
### Image Quality Evaluation  
Evaluating the quality of super-resolution images is crucial to any super-resolution task. The techniques mentioned are commonly used for this purpose, and each has its unique advantages:  
Peak Signal-to-Noise Ratio (PSNR): This quantitative metric is widely recognized and utilized. It is simple to calculate and offers a score for straightforward comparisons across diverse methods or datasets. PSNR's basis is the mean squared error (MSE) between the pixel values of the original and the reconstructed visuals. A higher PSNR score signifies a closer resemblance to the original image, thus implying superior image quality. Nevertheless, it is noteworthy that PSNR presumes that the utmost important factor of image quality is the faithfulness to the precise pixel values of the original image. This assumption may not invariably correspond with human perception.  
Structural Similarity Index (SSIM): SSIM is another commonly used measure that compares the similarity between two images. Unlike PSNR, SSIM considers changes in structural information, contrast, and brightness, critical factors in human visual perception. SSIM scores range from \(-1\) to \(1\), where one means the two images are identical. This makes SSIM a more reliable measure of perceived image quality.  
It is also worth noting that measures like Multi-Scale Structural Similarity (MS-SSIM), Visual Information Fidelity (VIF), and others can also be used depending on the specific requirements of the task. The selection of the appropriate measurement often hinges on the characteristics of the images under consideration and the particular facets of image quality deemed crucial in a specific scenario.
# Variance-Triggered Two-Step GPS Acquisition
###### Abstract  
The acquisition is the most time-consuming step performed by a Global Navigation Satellite System (GNSS) receiver. The objective is to detect which satellites are transmitting and what are the phase and Doppler frequency shift of the signal. It is the step with the highest computational complexity, especially for signals subjected to large Doppler shifts. Improving acquisition performance has a large impact on the overall performance of the GNSS reception. In this paper, we present a two-step Global Positioning System (GPS) acquisition algorithm whose first step performs an incremental correlation to find a coarse pair of phase and frequency and the second step, triggered by the variance of the largest correlation values, refines the first step. The proposed strategy, based on the conventional time-domain serial algorithm, reduces the average execution time of the acquisition process to about \(1/5\) of the conventional acquisition while keeping the same modest logic hardware requirements and slightly better success and false-positive rates. Additionally, the new method reduces memory usage by a factor that is proportional to the signal's sampling frequency. All these advantages over conventional acquisition contribute together to significantly improve the overall performance and cost of GPS receivers.  
GPS acquisition; variance-based detection threshold; two-step acquisition 2019
## 3 Results
### Heat Transport at Interface
The \(\Lambda\) of the metal transducer did not affect thermal modeling, after a few tens of ps, because of its thin thickness. The \(G\) at the interface and \(\Lambda\) of the substrate dominated the temperature dynamics. Because the \(\Lambda\) of the sapphire substrate, 30 W m\({}^{-1}\) K\({}^{-1}\), was the same for the Co and Co\({}_{50}\)B\({}_{50}\) samples, only \(G\) was responsible for the difference in the thermal response. When \(G\) is as high as 300 MW m\({}^{-2}\) K\({}^{-1}\), a typical value for a strongly coupled interface between a metallic transducer and insulating substrate [23], \(\Delta T\) of the transducer should decrease quickly at approximately 100 ps. By contrast, when \(G\) is as low as 35 MW m\({}^{-2}\) K\({}^{-1}\), which is close to that of the metal/graphene/substrate interface [24; 26], a significant decrease in \(\Delta T\) of the transducer occurs at a much longer  
Figure 6: Analysis of heat transport. (**a**) Schematics for the detection of \(\Delta\theta\), which is caused by changes of magnetization (\(M\)). A magnetic field (\(B\)-field) was applied to align \(M\) to the normal for the sample plane. The blue/orange/gray layers represent the transducer/interface/substrate, respectively. (**b**) The ratio of \(|\Delta\theta_{\text{in}}/\Delta\theta_{\text{out}}|\) represents the temperature response of the magnetic transducer. The black squares and red circles are experimental data of the sap/Co (10 nm)/Pt (2 nm) and sap/Co\({}_{50}\)B\({}_{50}\) (10 nm)/Pt (4 nm) samples, respectively. The black and red lines are simulation results from thermal modeling with an interfacial thermal conductance of 300 and 35 MW m\({}^{-2}\) K\({}^{-1}\), respectively. (**c**) The sensitivity plot for the thermal modeling to a thermodynamic parameter (\(\gamma\)). The black, red, and blue colors are calculation results when \(\gamma\) is the thermal conductivity of the transducer (\(\Lambda_{\text{tran}}\)), thermal conductance at the interface (\(G\)), and thermal conductivity of the substrate (\(\Lambda_{\text{sub}}\)), respectively. The solid and dashed lines are calculation results with the Co and CoB transducer, respectively. timescale of 1000 ps. Such a low \(G\) value at the Co\({}_{50}\)B\({}_{50}\)/substrate interface supports the presence of a thin, soft layer. The thermodynamic parameters for thermal modeling are summarized in Table 1.
# Waste Cork in Metakaolin-Geopolymer Matrix: Physico-Mechanical Characterization
## 4 Discussion  
The addition of lightweight waste materials in geopolymers is not studied enough [7; 14], even though this type of waste could beneficially affect both the cost of disposal and the future application of geopolymers as insulating materials. Additionally, in the literature, there are only a few cases of the valorization of waste cork used as a lightweight filler or aggregate [10].  
In the preliminary step of our research on the use of as-received waste cork, we focused our efforts on obtaining a workable formulation of geopolymers, as reported in the present manuscript. After verifying the successful cross-linking of the geopolymeric network, we moved on to the physical, mechanical, chemical and microstructural characterization of the geopolymers.  
In comparison to other studies on geopolymer composites with lightweight materials, especially those that waste cork, and after the analysis of the results reported in the previous sections, the following remarks can be drawn:  
* When compared to cement composite, the very same waste cork produced stronger materials with the use of MK-based geopolymers. The compressive strength following the addition of about 2.5% of cork in this study is around 10 MPa; on the contrary, in [16], the values do not exceed 2 MPa. A similar trend occurs with the addition of about 5% of CW.
* In the MK-based geopolymer matrix, the lightweight compositions reported in the study [10] do not exceed 5 MPa. Specifically, these authors reported a pyrolyzed cork addition of about 2.5-3.75 wt% with 3.5-5.0 MPa.
* Thermal insulation materials play an important role in construction. The production of new insulating materials makes it possible to reduce the energy consumption of buildings. External walls are one of the most important structural elements of buildings. In [PERSON]'s chart (Figure 10), which demonstrates common applications of lightweight building materials/ceramics provided in the ANSYS GRANTA EduPack R2021, it can be observed that our geopolymer lightweight composites are within the range of insulating and building materials commonly used [33]. Concerning the values of thermal conductivity reported in the literature in the study [14], we can confirm that  
Figure 9: Thermal conductivity values for samples GP0 and GP-9.1 CW after 28 days compared to the literature data of aerated Portland cement (OPC) and porous MK-based geopolymers (see text for explanation).  
our value of about 0.11 W/mK is lower than those recorded for 18 vol% pyrolyzed cork, i.e., 0.56 W/mK.  
The absence of cork waste pre-treatments and thermal curing of the final composites allowed us to indicate the mixes optimized in this work as sustainable fire-resistant insulation materials with a reduced environmental footprint, when compared to expanded polystyrene systems [34].
## 2 Study Area and Data  
### Description of the Study Area  
The study area, shown in Figure 1, is located in the vicinity of Lorca city in the Murcia Region (SE Spain). This region is located in the eastern part of the Betic Cordillera, which is a part of the Mediterranean alpine chains and extends approximately 600 km long and 200 km wide in the south and southeast of the Iberian Peninsula. The cordillera is divided into several main domains based on structural and stratigraphic differences, which allow them to evolve independently until their current structure.  
The landscape of Lorca formed about 12 million years ago (Ma) during the Upper Miocene when the Betic Cordillera was in the process of uplift and structuring. The Lorca basin is a marine basin filled with sediments from the erosion of the adjacent mountains. About 6 Ma, the closure of the communication between the Mediterranean Sea and the Atlantic Ocean transformed the Lorca basin into an evaporitic lake. Among the tectonic structures, the Alhama de Murcia Fault (AMF) began to move about 5 Ma, initiating the uplifting of the Tercia range and folding of the marine strata deposited in the Lorca basin. Subsequently, erosion has shaped the current landscape, including the incision of the fluvial network of the Guadalentin River and its streams.  
Related to the AMF, on 11 May 2011, a moderate earthquake occurred at 18:47 (local time) in Lorca city. It had a magnitude of M\({}_{\rm w}\) 5.1 [28] with a focal depth of 4 km. It was located less than 5 km NE of the city, causing nine deaths and an economic loss of more than 1200 million euros [29]. The maximum seismic acceleration value recorded was 0.36 g at the Lorca station, and less than 0.05 g at stations located 20 km from the epicenter. The horizontal Arias intensity was 0.24 m/s at the closest station, and it was less than 0.02 m/s at stations more than 20 km from the epicenter [27].
## 5 Evaluation Results and Discussion
### Objective Evaluation  
#### 5.1.1 Noise Robustness of F0 Estimation  
We used white Gaussian noise and pink noise as the background noise to test the quality of the adContF0 and also to clarify the effects of refinement. The amount of noise is specified by the signal-to-noise ratio (SNR) ranged from 0 to 40 dB. We calculated the normalized root mean square error (NRMSE) over selected sentences for each speaker.  
Figure 7a,b shows the overall NRMSE values obtained from various methods as a function of the SNR between speech signals and noise. We present the average NRMSE over all three speakers. The smaller the value of NRMSE, the better the F0 estimation's performance. The results of white and pink noise suggest that the NRMSE for all proposed methods are smaller than the baseline, and the time warping method becomes the best. This means that our proposed one is: (a) robust against white and pink noise; and (b) superior to the one based on YANGsaf. Consequently, this positive result is beneficial in TTS synthesis.  
Furthermore, Figure 8 shows the power spectral density (PSD) calculated with the periodogram method for all F0 estimators compared with ground truth. In this figure, the adContF0-based StoneMask method gives a similar performance to that of the ground truth (F0_egg) and better than the baseline [9]. It can be concluded that all refined approaches were robust against the noise and outperformed the conventional one as expected.  
Figure 8: The periodogram estimate of the PSD for the extracted F0 trajectories.  
Figure 7: Influence of the SNR on the average normalized RMSE with proposed refined contF0_AKF (adaptive Kalman filter), contF0_TWRP (time-warping), and contF0_STMSK (StoneMask) methods.
## 2 CCD-Based Fine Tracking Optical System
### The DOB Method Built in the Acceleration Loop  
Nowadays, the DOB method has got more and more applications in the industry control for it can estimate and compensate the external disturbance, which could apparently enhance the system's robustness [19; 20]. It's very suitbale to the FTOS because we can precisely build the model by physical analysis and spectrum measurement [18; 21]. The structure of the ADLC with DOB is presented as Figure 3.  
Similarly, we can deduce the DSA in Figure 3 as follows:  
\[E_{ADLC-DOB}=\frac{\theta}{\theta_{d}}=\frac{1-C_{a}\widetilde{G}_{a}C_{f}}{1 +C_{a}G_{a}+C_{p}C_{a}G_{a}\frac{1}{g^{2}}+C_{a}(G_{a}-\widetilde{G}_{a})C_{f }}\approx\frac{1}{1+C_{a}G_{a}}\cdot\frac{1-C_{a}\widetilde{G}_{a}C_{f}}{1+C_{ p}\frac{1}{g^{2}}} \tag{4}\]  
Because \(\widetilde{G}_{a}\) is highly similar to the actual transfer function \(G_{a}\), especially in low and medium frequency, the denominators of \(E_{ADLC}\) and \(E_{ADLC-DOB}\) are almost equal, which means the introduction of DOB has little effect on the stability of the system. If we set the \(C_{f}\) to be the reverse of \(C_{a}\widetilde{G}_{a}\), the numerator of \(E_{ADLC-DOB}\) will be 0 in theory, which signifies the influence of outer disturbance is almost eliminated by the DOB method. However, in fact, the improvement of the DSA by the DOB is restricted. At first, since in high-frequency domain the approximate acceleration mode \(\widetilde{G}_{a}\) is not very accurate, generally, we choose to compensate outer disturbance in low and medium frequency, abandoning the part of high frequency. Maybe this factor doesn't have a lot influence on the DSA because the high-frequency DSA mainly relies on the passive DSA. But secondly, in the low frequency, the noise of accelerometers will dirty the observed disturbance signal, leading to not apparent improvement of DSA in low frequency. Therefore, the DOB method is mainly used to reform the medium-frequency performance and we need to look for a new way to further enhance the DSA in low frequency in order to make the MEMS accelerometers substitute the FOG.
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
###### Abstract  
We present a robust video watermarking scheme and report the detailed robustness of the video watermarking assessed based on standard criteria obtained from Information Hiding and its Criteria (IHC) Committee. Using discrete cosine transform domain spread-spectrum watermarking, our system achieves robustness under various non-hostile video processing techniques, including MPEG compression and digital/analog-analog/digital (DA-AD) conversion. The proposed system ensures that a 16-bit embedded sequence can be extracted through adaptive frame selection in any 15-s interval, even with a long video clip. To evaluate the performance of the proposed watermarking scheme, we conducted robustness tests under a DA-AD conversion environment, based on the MPEG-4 part 10 (H.264) codec. The experiment results obtained indicate that, in addition to being robust against non-hostile video processes, the proposed method achieves invisibility. The assessment of the developed watermarking scheme also satisfies the third edition of the IHC video watermarking evaluation criteria.  
video watermark; signal processing; digital watermarking; video compression +
Footnote †: journal: Medical Image Analysis  
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Footnote †: journal: Medical Image Analysis  
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Footnote 1: [[https://doi.org/10.3390/electronics10202467](https://doi.org/10.3390/electronics10202467) [https://doi.org/10.3390/electronics10202467](https://doi.org/10.3390/electronics10202467)]([https://doi.org/10.3390/electronics10202467](https://doi.org/10.3390/electronics10202467) [https://doi.org/10.3390/electronics10202467](https://doi.org/10.3390/electronics10202467))
## 3 Experimental Results and Discussion
### Effect of the Loading Rate on the Conversion Coefficient
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Loading Rate** & **Strain Rate** & \multicolumn{3}{c}{**Stress Dissipation Coefficient\%**} & \multicolumn{3}{c}{**Elastic Energy Ratio\%**} \\ \hline
**mm/min** & **s-1** & & **Test Value** & & **Mean Value** & & **Test Value** & & **Mean Value** \\ \hline
0.3 & \(2\times 10^{-5}\) & 33.55 & 31.53 & 37.52 & 34.2 & 66.45 & 68.47 & 62.48 & 65.8 \\
0.6 & \(4\times 10^{-5}\) & 28.03 & 29.53 & 31.96 & 29.84 & 71.97 & 70.47 & 68.04 & 70.16 \\
1.5 & \(2\times 10^{-4}\) & 19.08 & 19.20 & 22.49 & 20.26 & 80.92 & 80.80 & 77.51 & 79.74 \\
3 & \(4\times 10^{-4}\) & 17.46 & 17.94 & 19.59 & 18.33 & 82.54 & 82.06 & 80.41 & 81.67 \\
10 & \(6\times 10^{-4}\) & 14.37 & 13.82 & 17.46 & 15.22 & 85.63 & 86.18 & 82.54 & 84.78 \\
15 & \(10^{-3}\) & 9.79 & 10.34 & 11.18 & 10.44 & 90.21 & 89.66 & 88.82 & 89.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Energy proportion of each sample at different strain rates. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strain Rate** & **Loading Rate** & **Total Energy** & **Stress Conversion Coefficient \(\times\) 10\({}^{-2}\)** & **Failure Mode** \\ \hline
**s-1** & **mm/min** & **J** & **Test Value** & **Mean Value** & \\ \hline  & & 14.42 & 6.98 & & Tensile failure \\
2 \(\times 10^{-5}\) & 0.3 & 16.11 & 7.46 & 7.56 & Tensile failure \\  & & 16.71 & 8.24 & & Tensile failure \\  & & 14.85 & 7.41 & & Tensile failure \\
4 \(\times 10^{-5}\) & 0.6 & 14.74 & 7.16 & 7.72 & Tensile failure \\  & & 16.95 & 8.59 & & Tensile failure \\  & & 16.35 & 5.21 & & Shear failure \\
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