esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications of PCMs in Buildings
### PCM Passive Application Systems in Building  
Passive application PCM systems exploit the naturally-available energy sources along with the architectural design of building components to minimize the energy requirements of building operations. The main characteristic of these systems is that there is no requirement for mechanical equipment or additional energy, due to the fact that the heat is charged or discharged only because of temperature fluctuations when the environment's temperature rises or falls beyond the PCM's phase change temperature (Figure 4). Passive application PCM systems in the building sector can be classified as systems that are integrated into the building materials, such as materials for the walls, the floor, the ceiling, etc., and as systems that are implemented as components to the building envelope, such as blinds, suspended ceilings, etc. [16].
# A Multi-Resolution Approach to GAN-Based Speech Enhancement
## 3 Multi-Resolution Approach for Speech Enhancement  
In this section, we propose a novel GAN-based speech enhancement model which consists of a progressive generator and a multi-scale discriminator. The overall architecture of the proposed model is shown in Figure 2, and the details of the progressive generator and the multi-scale discriminator are given in Figure 3.  
Figure 2: Overall architecture of the proposed GAN-based speech enhancement. The up-sampling block and the multiple discriminators \(D_{n}\) are newly added, and the rest of the architecture is the same as that of [26]. The components within the dashed line will be addressed in Figure 3.
## 4 Discussion and Outlook
### Brief Discussion on Standards  
The work is based on the widely accepted assumption in standards, as well as in the scientific and technical literature, that standards prevent obsolescence and create interoperability. By no means, everything that has become a standard actually contributes to interoperability and helps against obsolescence. One example is the IEEE's 802 committee. It has produced standards, such as 802.3 (Ethernet), 802.11 (WiFi) and 802.15 (Bluetooth and Zigbee). Yet 11 of the 21 working groups are no longer in existence [27] (p. 73). They have not contributed to the fight against obsolescence.  
There are standards that fail to gain acceptance because they are inadequate, inconsistent, underspecified, or overspecified [44]. Other standards disappear even though they had already become leading standards, because a simpler or better applicable standard has emerged. An example of this is IEEE-1394 FireWire. It has become the standard for camcorders, yet has been superseded by USB 3.0, as the standard in the domain [45]. Even though FireWire is no longer the leading standard for camcorders, plug-in cards are available to keep camcorders with FireWire ports from becoming obsolete [46].  
Ref. [46] showed five features of standards which describe their advantage. They address: (i) diffusion, (ii) extensions for purpose, (iii) supplier support, (iv) stability of standardization authority, and the (v) support of key industry players.  
Idea development: K.K. and K.-D.T.; conceptualization: K.K. and K.-D.T.; software, K.K.; validation, K.K.; writing--original draft preparation, K.K.; writing--review and editing, K.K. and K.-D.T.; visualization, K.K.; supervision, K.K. and K.-D.T.; project administration, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) in the project STEVE (project number 20Y1301G) and AGILE-VT (project number 20X1730D).  
Not applicable.  
Many thanks to [PERSON], [PERSON] and [PERSON] for the support.  
The authors declare no conflict of interest.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 4 Validation of the Experimental Results With ECMWF and Sensitivity Analysis
### Sensitivity Analysis of Significant Wave Height Estimation
#### 4.2.3 Sensitivity Analysis of Significant Wave Height to Propagation Direction of Ocean Waves  
According to Equation (19), it is nonlinear between the significant wave height and the propagation direction \(\phi\) of ocean waves, and the estimation error of the significant wave height is affected by the real propagation direction \(\phi\) of ocean waves and the error values. For a given propagation direction \(\phi\) of ocean waves, the estimation error of significant wave height changes nonlinearly with the increase of propagation direction error of ocean waves. In order to analyze the sensitivity of the significant wave height to the propagation direction \(\phi\) of ocean waves, it is assumed that the azimuth cutoff \(\lambda_{c}\) and the wavelength \(\lambda_{p}\) of ocean waves remain unchangeable. The sensitivity curve of the significant wave height to the propagation direction \(\phi\) of ocean waves within the range of \(\phi\in[0^{\circ},360^{\circ}]\) is shown in Figure 14.  
As shown in Figure 14, the sensitivity of the significant wave height to the propagation direction of ocean waves is nonlinear. When the propagation direction of ocean waves is \(\phi=64^{\circ},115^{\circ},244^{\circ},295^{\circ}\), the propagation direction error of ocean waves has the greatest impact on the result of significant wave height estimation. When the propagation direction of ocean waves is \(\phi=0^{\circ},90^{\circ},180^{\circ},270^{\circ},360^{\circ}\), the propagation direction error of ocean waves has the least impact on the result of significant wave height estimation.
## 4 Path Model Estimation  
In a realistic industrial environment, both the non-uniform illumination and various wastes on the floor can cause the unavoidable under- or over-segmentation errors in the image segmentation process. For instance, when the possible path pixels are extracted from the original images in Figure 3 by means of the Otsu threshold segmentation method, a large number of noises and fakes appear beside the actual paths, or the paths recognized are incomplete and broken, as shown in Figure 6. The pixel fakes and path incompletion will influence the extraction of the center pixels of the path negatively, thereby degrading the estimation accuracy of the path model, as shown in Figure 7.  
Table 1 shows the statistical data of pixel classification for path images in Figure 7. The image pixels are classified into three types: black background pixels, white path pixels, and blue center pixels. The blue center pixels with noise points are recorded as the following set:  
\[\mathrm{S_{C}}=\left\{\left(x_{i},y_{i}\right)|x_{i}=\frac{hl_{j}+hr_{j}}{2},y_{ i}=i;j=1,2,\ldots,N;i=1,2,\ldots,M\right\} \tag{12}\]  
In order to improve the recognition accuracy of the guide path, it is necessary to eliminate the effects of noise points and path incompletion on model estimation. Hence, IPSO is proposed to distinguish the actual path pixels from their fakes, obtaining an accurate straight path model, no matter what noise points and path incompletion occur.
## 3 Results and Discussion  
### Physical Properties  
#### 3.1.1 Moisture Content  
Moisture content of snacks prepared with fruit powders differed due to the expansion method. Application of microwaves resulted in products with higher moisture content than that obtained after frying (Table 1). It can be explained by the fact that during frying the heat is transferred from the hot oil to the surface of the product by convection and by conduction from the surface of the snacks to their centre [19] within a relatively short period of time. The water present in fried product is moved from the centre to the surface and leave the products from this point as vapour. The moisture in the surface is trapped in the confined space and when it became superheated it altered the pore walls influencing the hardness of the samples [20]. On the other hand, during the microwave process the energy of microwaves is absorbed by water which creates a relatively high pressure in the centre of material subjected to drying leading to the rapid transfer of moisture [21], however, it was assumed that the temperature of the process was lower than frying. Previously, it was indicated that diffusion of water slows down at a lower temperatures, thus application of microwaves during snacks preparation may prevent from such intense moisture loss [22] leading to higher moisture content in the final products. Addition of fruit powders into the pellets' recipe also increased water content of obtained snacks. During storage moisture content increased in all analysed samples that was connected to water absorption. In general, snacks are very hygroscopic products and one of crucial element determining their storage stability is water content. According to different norms for fried snacks it should not be higher than 5% while for snacks with low fat content (as expanded by microwaves)--below 10% [23].
## 4 Dataset Description
### Feature Fusion and Classification  
A set of 512 CNN-based features are extracted per image from our Norm-VGG16 network architecture. The features are extracted from the global average pooling layer. These features are fused with the 8100-feature array of hand-crafted HOG descriptors and 400-feature array of hand-crafted DAISY, leading to a concatenated array size of 9012 features. The classification of these feature sets is done in batches. A Neural Network consisting of three dense layers is used to train these features with first layer of input size of 9012 inputs and 2048 neurons followed by a hidden layer of 512 neurons and the final layer contains four neurons with a SoftMax activation to normalize the distribution probability as shown in Figure 11. The model is trained using Adam optimizer with batch size 32 for 100 epochs.  
Figure 10: Histogram of Oriented Gradients (HOG) descriptor pipeline.
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 3 ISAR Imaging and Method for Estimating the Stripmap SAR Observation Area  
In this section, first, the coordinate systems to describe the ISAR imaging model are defined; then the ISAR imaging principles are derived and the ISAR imaging model for spaceborne SAR is established. Finally, an objective function is constructed to estimate the look angle of the spaceborne SAR antenna.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 5 Results
### Impact of Historical Data
As shown in Table 11, when analyzing the GPU instance family, the results demonstrate consistent and robust metrics across various subsets of years. This indicates that the method \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Compute-Optimized**} \\ \hline  & _6 Years Back_ & _5 Years Back_ & _4 Years Back_ & _3 Years Back_ & _2 Years Back_ & _1 Year Back_ \\ \hline \hline _MAE_ & 0.70 & 0.72 & 0.76 & 0.76 & 0.72 & **0.57** \\ MAPE & 0.35 & 0.36 & 0.41 & 0.40 & 0.39 & **0.30** \\ RMSE & 1.19 & 1.21 & 1.24 & 1.25 & 1.20 & **1.05** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Metrics results for Compute-optimized instance family. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**GPU Instance**} \\ \hline  & _6 Years Back_ & _5 Years Back_ & _4 Years Back_ & _3 Years Back_ & _2 Years Back_ & _1 Year Back_ \\ \hline \hline _MAE_ & 0.19 & 0.18 & 0.16 & 0.15 & 0.15 & **0.13** \\ MAPE & 0.11 & 0.10 & 0.09 & 0.09 & 0.09 & **0.08** \\ RMSE & 0.38 & 0.30 & 0.25 & 0.26 & 0.25 & **0.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Metrics results for GPU instance family. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**General Purpose**} \\ \hline  & _6 Years Back_ & _5 Years Back_ & _4 Years Back_ & _3 Years Back_ & _2 Years Back_ & _1 Year Back_ \\ \hline \hline _MAE_ & 0.73 & 0.75 & 0.79 & 0.78 & 0.72 & **0.41** \\ MAPE & 0.34 & 0.34 & 0.39 & 0.36 & 0.36 & **0.22** \\ RMSE & 1.21 & 1.24 & 1.24 & 1.23 & 1.18 & **0.81** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Metrics results for general purpose instance family. utilized in this study is effective at capturing the underlying patterns and dynamics of the data, particularly in terms of feature selection, model training, and outlier detection. Despite the observed price fluctuations, the model maintains accurate and consistent GPU instance family predictions.A similar pattern is observed in the memory-optimized family, where the median price reached its apex in 2017. Comparing relative metrics between years (Table 7) reveals, however, that there has been no significant change. This finding strengthens the method's robustness and its capacity to make consistent predictions even in the presence of price anomalies or fluctuations. In the cloud market, technological advances frequently result in a shift in pricing strategies. As newer and more effective technologies become available, providers may revise their pricing structures to reflect their value and capabilities [41]. The introduction of faster CPUs, larger memory capacities, or more scalable storage solutions, for instance, can enable providers to offer cloud instances with superior performance at competitive prices. On the other hand, as outmoded technologies become less prevalent, they may be priced higher due to diminished availability or increased maintenance costs.
## 3 Results and Discussion
### Simulation of ADRC of Hot-Compression Molding Temperature
#### 3.3.3 Simulation Results under Random Signal Noise Interference  
In engineering applications, measurement noise is an inevitable challenge that often results in unnecessary production waste. Therefore, the mitigation of noise interference is a matter of significant concern within the engineering community. To emulate the impact of measurement noise within a narrow range of fluctuations around the desired value amidst random noise signals, we opted to compare the previously designed ADRC and  
Figure 8: Simulation test results of ADRC under sinusoidal signal noise interference. (**a**) Comparison results of response under sinusoidal signal noise interference; (**b**) comparison results of error output under sinusoidal signal noise interference.  
Figure 9: State estimation of ESOs in the presence of sinusoidal signal noise interference.  
PID controllers in terms of their output performance and error metrics. The simulation results, presented in Figure 10, reveal that under the influence of noise interference, the ADRC controller was capable of maintaining a stable output temperature at the desired value, exhibiting no overshoot and a smooth rise. Conversely, the PID controller failed to effectively mitigate the effects of measurement noise, resulting in noticeable errors. Although the ADRC exhibited some oscillations at steady-state temperatures, these were significantly reduced compared to the traditional PID controller.  
Figure 11 depicts the state estimation performance of the ESO. It is evident that the expanded state observer, formulated with the fal function, provided a smoother and more accurate state estimation compared to the PID approach. Coupled with the compensatory role of the control law, the ESO-based approach achieved superior interference mitigation compared to the PID controller.  
Figure 11: State estimation of ESOs in the presence of random signal noise interference.  
Figure 10: Simulation test results of ADRC under random signal noise interference. (**a**) Comparison results of response under random signal noise interference; (**b**) comparison results of error output under random signal noise interference.  
In summary, the differential tracker facilitated a smooth transition to the desired temperature, while the combined effect of the expanded state observer and the nonlinear state error feedback control law ensured robust resistance to external load perturbations. This combination resulted in an enhanced control effect for the hot-pressing temperature rise. When compared to the traditional PID control system, the ADRC control system demonstrated superior anti-interference performance for the temperature control of the hot-pressing molding of bamboo-based fiber composites.
# Efficient Parallel FDTD Method Based on Non-Uniform Conformal Mesh
## 3 Results
### Performance Analysis on Single Node  
Firstly, we designed two test cases, an unmanned helicopter and a helical antenna, to evaluate the parallel performance on a single node. The models are shown in Figure 5a,b, respectively. The frequencies are set to 1.6-1.8 GHz and 0.2-1.0 GHz, and the excitations are plane wave excitation and waveguide port excitation, respectively. Gaussian signals are used for both cases. The computational domains are discretized into non-uniform conformal grids with dimensions of 7,468,047 (183 \(\times\) 223 \(\times\) 183) and 10,400,760 (173 \(\times\) 334 \(\times\) 180), respectively. Both cases adopt 15 layers of CPML absorbing-boundary conditions to truncate the computational domain's six boundaries.  
Figure 4: Schematic of overlapping between transmission of magnetic fields across adjacent subdomains and updating of internal electric fields.  
As shown in Figure 6a-d, the far-field patterns of the unmanned helicopter and the helical antenna, respectively, show complete consistency between serial and parallel results, and are basically consistent with the CST results, indicating the correctness of the parallel algorithm results.  
Tests were conducted on the supercomputing platform, with processors counts set to 1, 2, 4, 8, 12, and 16. Specific test data are provided in Table 1, and the scalability and efficiency are illustrated in Figure 7a,b. It can be observed that the parallel efficiency of both cases is above 90%, and the scalability approaches linearity. These test results demonstrate that the non-uniform conformal parallel FDTD algorithm performs well with a small number of  
Figure 5: (a) Unmanned helicopter; (b) helical antenna.  
Figure 6: (a) Unmanned helicopter far-field pattern at phi = 90\({}^{\circ}\); (b) helical antenna far-field pattern at phi = 90\({}^{\circ}\); (c) unmanned helicopter far-field pattern at theta = 90\({}^{\circ}\); (d) helical antenna far-field pattern at theta = 90\({}^{\circ}\).  
processors on a single node, and it also presents excellent acceleration effects for CPML absorbing-boundary layers.
# Engineering a Combinatorial Laplacian Solver: Lessons Learned +
## Preliminaries
### Kosz (Simple) Solver  
The idea of the algorithm is to start with any valid flow and successively adjust the flow, such that every cycle has potential zero. We need to transform the flow back to potentials at the end, but this can be done consistently, as all potential drops along cycles are zero.  
Regarding the crucial question of what flow to start with and how to choose the cycle to be repaired in each iteration, [PERSON] et al. (2012) suggest using the cycle basis induced by a spanning tree \(T\) of \(G\) and prove that the convergence of the resulting solver depends on the stretch of \(T\). More specifically, they suggest starting with a flow that is nonzero only on \(T\) and weighting the basis cycles proportionately to their stretch when sampling them. The resulting algorithm is shown as Algorithm 1; note that we may stop before all potential drops along cycles are zero, and we can consistently compute the potentials induced by \(f\) at the end by only looking at \(T\).  
The solver described in Algorithm 1 is actually just the SimpleSolver in the [PERSON] et al. (2012) paper. They also show how to improve this solver by adapting preconditioning to the setting of electrical flows. In informal experiments, we could not determine a strategy that is consistently better than the SimpleSolver, so we do not pursue this scheme any further here. Eventually, [PERSON] et al. derive the following running time for the KOSZ (simple) solver:  
Figure 1: Transformation into an electrical network.  
**Theorem 1**: _(Theorem 3.2 in [12]) SimpleSolver can be implemented to run in time \(O(m\log^{2}n\log\log n\log(\epsilon^{-1}n))\) for computing an \(\epsilon\)-approximation of \(x\)._  
The improved running time of their FullSolver to compute an \(\epsilon\)-approximation of \(x\) is \(O(m\log^{2}n\log\log n\log(\epsilon^{-1}))\) (Theorem 7.4 in [12]).  
```
Input: Laplacian \(L=L(G)\) and vector \(b\in\text{im}(L)\). Output: Solution \(x\) to \(Lx=b\).
1\(T\leftarrow\) a spanning tree of \(G\)
2\(f\leftarrow\) unique flow with demand \(b\) that is only nonzero on \(T\)
3 whilethere is a cycle with potential drop \(\
eq 0\) in \(f\)do
4\(c\leftarrow\) cycle in \(\mathcal{C}(T)\) chosen randomly weighted by stretch
5\(f\gets f-\frac{c^{T}Rf}{c^{T}Rc}c\)
6 returnvector of potentials in \(f\) with respect to the root of \(T\)
```  
**Algorithm 1**inv-laplacian-current solver KOSZ.
# Design of Time-Synchronized Switch Based on Zynq
## 5 Test Results
### Synchronization Performance Test  
Connect the switch to the WR Node and also route the PPS signals from both to the time-to-digital converter, which measures the precision of the second pulse. The test results are shown in Figure 24. From the figure, it can be seen that the test results follow a normal distribution pattern, with a mean synchronization error of 33.76 ps. The time synchronization precision reaches the sub-nanosecond level, meeting the current environmental demands for time synchronization. Ref. [24] introduces a dual-port device with WR functionality, whose PPS deviation has a mean value of approximately 129 ps, while reference [25] describes a TSN switch that does not have WR functionality, with its PPS deviation in the 20 ns range.  
Figure 23: Time synchronization interface.