esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Fusion
### First Type of Exclusion  
In the first type of exclusion, the image plane is firstly divided into a grid, whose cell dimension is defined by the parameter \(\xi\) (its measurement unit is pixel, and each cell is composed of \(\xi\times\xi\) pixels).  
Figure 4: Graph of the function \(f_{l}\) (considering 500 internal and external layers) with the actual constant values utilized (\(c_{1}=0.01\), \(c_{2}=0.001\), \(c_{3}=0.011\)).  
All the 3D points are projected onto the image plane, and then clustered depending on which cell of the grid they occupy. For each cluster, the points \(P_{i}\), such that Equation (2) holds true, are excluded:  
\[d_{i}>d_{min}+\chi \tag{2}\]  
where \(d_{i}\) is the distance between the point \(P_{i}\) and the optical center \(O\), \(d_{min}\) the minimum distance among the distances \(d_{i}\) and \(\chi\) a fixed parameter (in millimeter, as for \(d_{i}\) and \(d_{min}\)). For higher values of \(\xi\), higher values of \(\chi\) are needed to take into account the fact that the surface region corresponding to the points not to be excluded is larger and so the difference in distances can be higher.  
Theoretically, an optimal choice of the parameters \(\xi\) and \(\chi\) would involve a non-trivial geometrical analysis, considering both the density in space of the 3D points (in general variable from region to region) and the curvature of the object surface with respect to the viewing angle. In practice, they can simply be adjusted by graphically evaluating the results. In our case, in order to obtain proper experimental results, they were computed according to the experimental relations Equations (3) and (4), suggesting a possible choice of the two parameters:  
\[\xi=\text{ceil}(Res/N)+1[\text{pixel}] \tag{3}\]  
\[\chi=5/(1-\xi/20)\ [\text{mm}] \tag{4}\]  
where \(N\) is the number of points projected on the image plane and \(Res\) the resolution of the image. In Equation (3), \"ceil\" is a function that rounds a number \(x\) to the nearest integer \(\geq x\). Since \(\chi\) is positive, according to Equation (4) \(\xi\) must be less than 20, a condition always verified for common resolutions and points cloud densities. It has to be pointed out that, for the vast majority of object geometries, the results are stable with respect to a variation of \(\xi\) and \(\chi\) of some units, and so in any case their resulting values are acceptable as long as they fall into a proper range.
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 2 Data Pre-Processing Methodology
### Feature Extraction and Dimensionality Reduction
#### 2.2.2 Application Example  
Similarly, the operational data of the 2.0 MW wind turbine mentioned in Section 2.1 are used as a case study. Data pre-processing is conducted according to the method previously described. The 28 basic variables and \(28\times 7\) derived variables are initially selected. Then, the KPCA algorithm is applied for data dimensionality reduction, using the mean method to optimize the parameter \(\sigma\) through cross-validation.  
In the practical application, the KPCA parameter \(\sigma\) is set to 600. Following the data dimensionality reduction, the original variables are efficiently combined or integrated. The relationship among the first three principal components is depicted in Figure 2. The CPV for the first nine principal elements reaches 96.6%, as shown in Figure 3. This indicates a high level of information retention, allowing for a reasonable explanation of the data characteristics.
## 1 Introduction
### Thermal Energy Storage  
The most suitable thermal storage systems for integration into Brayton batteries are based on sensible storage materials, with many concepts proposing the use of solid media. Solids can be used over a wide temperature range, have relatively high volumetric heat capacities, and are cost-effective and abundant. However, the working fluid of the cycle flows directly through these storage systems, which may require them to be pressurized, significantly increasing investment costs. Thermal fluctuations also lead to additional stresses in the containers, known as thermal ratcheting [20], which must be considered in the design.  
Solid media storage systems are characterized by a thermal gradient along the length of the storage. This poses several challenges for the design and operation of Carnot batteries. Firstly, the thermal gradient implies that it is not possible to fully charge the storage system without a significant drop in the temperature of the exiting fluid [21; 22]. Therefore, the storage must be oversized to achieve the required storage capacity. Secondly, the fluctuations in the outlet temperature can have effects on the rest of the cycle, potentially requiring regulation [23]. These issues can be mitigated through various design changes, such as incorporating electric heaters and heat dissipation devices [24] or providing a bypass to the storage, allowing for a constant mixing temperature during discharge [25].  
Using a liquid phase as a storage medium requires indirect heat transfer from the primary circuit to a storage circuit via a heat exchanger. Depending on its vapor pressure, the fluid may not need to be pressurized, reducing the costs of the storage tanks. Typical liquids, such as molten salt or thermal oil, have a limited operating temperature range and may degrade over time. Some relevant liquids are widely used in CSP (Concentrated Solar Power) plants. Therefore, Brayton batteries can benefit from these operational experiences and cost reductions. According to current technology, nitrate salt melts are limited to maximum temperatures below 565 \({}^{\circ}\)C due to salt degradation. However, recent developments in tank technology may allow the salt to be stable at temperatures up to 600 \({}^{\circ}\)C [26].
# All About Audio Equalization: Solutions and Frontiers
## 5 Other Equalization Filter Designs
### Matched EQ and Optimal Design Techniques
Another related approach is the Yule-Walker algorithm [100], which uses separate algorithms, applied in a recursive manner, to estimate the numerator and denominator coefficients. Further iterative approaches include the Steiglitz-McBride method, and its frequency domain implementations [101]. [PERSON] and [PERSON] provide an excellent overview of the field [102]. Frequency warping can be used to stretch the frequency axis at low frequencies and compress it at high frequencies, which is useful in audio [103; 104]. The frequency warping method can be applied to the target response before the filter design. This is done in the time domain by interpreting the target impulse response as an FIR filter, and by replacing each unit delay in its delay line by a first order allpass filter. The allpass filter coefficient \(\lambda\) determines the amount of warping. Finally, the designed filter can be dewarped by applying warping with the complementary coefficient \(-\lambda\). Warping has been combined successively with linear prediction, leading to a perceptually meaningful estimation of the spectral envelope, which is more accurate at low than at high frequencies [104; 105; 106]. A related early filter design method proposed by [PERSON] [107] first sets the poles of an IIR filter and then optimizes their weights. The Kautz method has been shown to suit well to audio filter design, since the distribution of poles can be used to improve the approximation at low frequencies in the same way as frequency warping [108]. Recently, [PERSON] developed an IIR filter design method in which the poles of the IIR filters are first chosen and the weights are then optimized using the least squares method. This method leads to a similar accuracy as the warped and Kautz filters and is easier to apply. The IIR filter designed this way can be implemented directly using the parallel structure. In Section 4.3 we showed how this method can be applied to parallel graphic equalizer design [56]. The parallel IIR filter design with fixed poles can be recommended for audio filter design.
## 1 Introduction
### Climate Conditions on King George Island and Topo-Climate of ASPA-128  
On King George Island, a temperature increase of 0.19 \({}^{\circ}\)C/10 years (\(r<0.05\)) was observed during the period 1948-2011 [1]. This is one of the fastest temperature increases observed in the Southern Hemisphere. The change rate of the increase varied from year to year, with the largest warming observed for the winter months (0.36 \({}^{\circ}\)C/10 years). No temperature increase was noted during the spring months. The pace of the temperature and precipitation change from Bellingshausen Station varies over different periods (Figure 2). The precipitation on King George Island is characterized by substantial inter-annual variability (Figure 2). In the years 1968-2007, the annual sum of precipitation at Bellingshausen ranged from 434.2 mm to 893.7 mm. The mean values of precipitation decreased from 669 mm in 1968-1978 to an average of 545 mm for 2000-2006 (Figure 2). From 1968-2007, a decreasing trend in the total precipitation of 2-4 mm/year was observed. The northern border of ASPA-128 is located 500 m south of Arctowski Station. The climatic conditions in ASPA-128 are influenced by large areas covered by glaciers and snow, as well as altitude and local atmospheric circulation, particularly katabatic and Foehn winds. The highest point of the Warszawa Icefield, which is a zone of accumulation for glaciers in ASPA-128, is nearly 450 m a.s.l. The climate characteristics from Arctowski Station (1977-1998, all data from [PERSON] and [PERSON] [29]), which is located at sea level, is described below. The mean annual air temperature for 1977-1998 was \(-\)1.6 \({}^{\circ}\)C and was characterized by a small seasonal amplitude. The average is similar to the Jubany and Ferraz stations and higher than at Bellingshausen Station due to the Foehn effect (Figure 2). Low-pressure centres moving around the Antarctic Peninsula result in highly variable weather with wind blowing predominately from the northern and western sectors. Due to such a minor decreasing trend, a significant portion of the air masses that reach Admiralty Bay cross over the island and are advectively warmed while falling from a 600-700 m a.s.l. elevation from the Arctowski Icefield summit to sea level. Precipitation varies significantly between the Bellingshausen and Arctowski stations due to the local topography and the Foehn effect. However, the annual sums of precipitation follow vaguely a similar trend between different stations on King George Island. The directly measured annual precipitation average at Arctowski Station is nearly 450 mm for the 1977-1998 period. The strong winds (4.5 m/s average for 1977-1998) also cause irregular accumulation and re-deposition of snow either on the island or off into the ocean, which results in the inaccurate measurement of snowfall totals. The goal of this study is to examine the temporal variability and scale of glacier retreat in the eastern part of the Warszawa Icefield in the Antarctic Special Protected Area No. 128 (ASPA-128, Figure 1) on King George Island on the background of the observed changes in weather elements.
## 6 Conclusions  
To evaluate the seismic response of CSBs subjected to NF ground motions, a scaled CSB was designed and fabricated. Two NNF and PNF motions were selected as the input excitations. The shake table test of scaled CSB subjected to uniform and non-uniform excitations were carried out. The following conclusions can be made based on the test results:  
1. The first six modes and the corresponding frequencies of the scaled CSB were identified using the SSI method. The fundamental mode shows as girder and tower longitudinal vibration with a frequency of 0.79 Hz. The 2 nd mode shows as girder vertical antisymmetric vibration combing tower longitudinal bending with a frequency of 6.61 Hz. In the first six in-plane modes, the 2 nd, 4 th and 6 th modes are antisymmetric, while the 1 st, 3 rd and 5 th modes are symmetric.
2. The maximum displacement of the tower occurs on the tower top node, the maximum acceleration response of the tower occurs on the middle cross beam, and the maximum bending moment of the tower occurs on the bottom section
3. The deformation of the tower and girder subjected to uniform excitation is not always larger than that subjected to non-uniform excitation, and therefore the non-uniform case should be considered in the seismic design of CSBs.  
Figure 28: The girder vertical deformation time history subjected to ScTCU with (**a**) uniform; (**b**) \(dt=0.1\) s; (**c**) \(dt=0.2\) s; (**d**) \(dt=0.4\) s.  
**Author Contributions:** [PERSON] and [PERSON] performed the experiment, [PERSON] and [PERSON] interpreted and discussed the experiment results; [PERSON], [PERSON] and [PERSON] prepared original draft preparation the manuscript; [PERSON] and [PERSON] review and editing the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research for this paper was supported by the Postdoctoral Science Foundation of China (Grant No: 2018M631292), Postdoctoral Science Foundation of Beijing (Grant No: 2018-ZZ-032), the Natural Science Foundation of Fujian Province (Grant No: 2019J01233), National Science Foundation of China (Grant No: E51508102).  
**Acknowledgments:** This test is finished in structure lab of Fuzhou University. Thanks for the supports provided by [PERSON]. This manuscript was prepared during the author's stay at the Centre for Infrastructural Monitoring and Protection (CIMP) at Curtin University. Thanks for the help from [PERSON] and [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 ABER for SIMO TOW Systems with Receivers' Diversity  
In this Section, we investigate spatial, time or wavelength diversity implementations, in terms of a SIMO optical communication system. In this context, it is assumed that the transmitter emits \(M\) copies of the same part of the information signal towards \(M\) receivers for the case of spatial diversity configuration or in \(M\) different time moments at the same receiver, for the time diversity case [4]. However, in time diversity system, since the transmitter is emitting \(M\) copies of the same signal in \(M\) different moments and the channel characteristics randomly change over time, each propagating copy addresses different channel characteristics. Consequently, \(M\) different virtual propagation paths (or receivers) can be considered and thus, this time diversity system can be also emulated as a SIMO one. In reality, each copy arrives at the receiver and remains at the buffer of the system until all the \(M\) copies been received. Thus, each copy of the information signal propagates through the same path but with different channel's characteristics, while the total information signal at the receiver buffer consists of \(M\) different versions of the same initial signal [4; 52]. Finally, in wavelength diversity a composite transmitter simultaneously emits \(M\) copies of the same part of the information signal at \(M\) different optical wavelengths towards \(M\) corresponding receivers [51].
# Predictive Scheduling with Markov Chains and ARIMA Models
## 4 Experimental Verification of the Proposed Solution
### Experimental Results
The tests follow the procedure presented in the preceding section and their results are given below, in Tables 9 and 10. The schedules generated with the LPT and SPT dispatching rules are shown to outperform the nominal schedule. Their accuracy of predictions is closer to the production data established in simulations. At a closer investigation, the LPT schedules (Table 9) exhibit good compliance of robust and simulated makespans. The schedule is robust for an average of 1.56 h longer than the simulated process; however, considering the nominal schedule, the completion time of all jobs is on average 4.65 h shorter. The comparable makespan length of the robust schedule and the executed production schedule is further confirmed by the mean value of indicator \(E_{C_{max}}\), which amounts to 1.03 for the robust schedule, and 0.91 for the nominal schedule. A similarity of a comparable magnitude is also shown to occur in the production process simulations conducted according to the SPT schedules (Table 10). The mean makespan of the nominal schedule is \(-\)5.75 h, while of the robust schedule 2.67 h. In the same case, the mean relative increase is 0.89 for the nominal schedule and 1.05 for the robust schedule. Nevertheless, it should be noted that in several simulations (for both the LPT and SPT rules), the nominal schedules display a closer resemblance to the executed production process; still, the robust and the executed schedules also show a good fit (e.g., simulation 2 for the LPT rule, or simulation 18 for the SPT rule). To summarize, the data obtained in the study clearly indicate that the schedule with service time buffers achieves a closer resemblance to the simulated makespan. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Increase of Makespan and Relative Increase of Makespan**} \\ \cline{2-7}
**Sim. No. ** & \begin{tabular}{c} **Executed Schedule** \\ **(Simulation)** \(C^{\prime}_{\text{max}}\) **[h]** \\ \end{tabular}  & \multicolumn{3}{c}{**Nominal Schedule**} & \multicolumn{3}{c}{**Robust Schedule**} \\ \cline{3-7}  & \(C_{\text{max}}\) **[h]** & \(\Delta C_{\text{max}}\) **[h]** & \(E_{C\text{max}}\) **[-]** & \(C_{\text{max}}\) **[h]** & \(\Delta C_{\text{max}}\) **[h]** & \(E_{C\text{max}}\) **[-]** \\ \hline
1  
Robust production schedules generated with the application of our solutions determine makespans closer to the simulated completion times of all jobs in the simulated production conditions under technological machinery failure uncertainty. This is evidenced by several indications, e.g., the fact that for the robust schedules, the values of \(\Delta C_{\text{max}}\) tend to be close to 0 (Figure 3), whereas in the case of \(E_{C_{max}}\) around 1 (Figure 4). Values 0 and 1 of the considered indicators denote igh compliance of the robust schedule with the production execution (simulation).
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 4 Results
### Comparisons
#### 4.1.3 Comparison with Method in [12]  
We compare our design with Example 2 in [12], where the compensator for Chebyshev sharpening polynomial \(p(x)=-1+27(2^{3}x^{2}-2^{8}x^{4}+2^{11}x^{6})\), \(x=H(z,\,1,\,32)\), is designed. The design's results are given in Table II in [12]. Our design, meticulously presented in Table 13, provides a clear comparison. For \(N_{2}=11\), the passband deviation equals \(0.0307\)**dB**, compared to \(0.0587\)**dB**, which needed the same number of adders as shown in the third row of Table 2 in [12]. Further increase in the number of adders, a key design parameter, results in a decrease of the passband deviation, as shown in Table 13. This relationship demonstrates the design's ability to improve performance with a slightly increased number of adders.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 3 Experiment Design and Analysis Method
### Analysis Method of Error Distribution Patterns in MLS Point Clouds  
This study proposes an analysis method for understanding the error distribution pattern in MLS point clouds. The proposed method was developed based on the following two key characteristics: (1) the use of the ICP registration, which minimizes the RMSE during the alignment of point clouds, and (2) the geometric properties of the mock-ups, which enable structured comparison and pattern identification. Reference planes were generated from TLS point clouds using the RANSAC algorithm. Figure 4 illustrates the overall procedure of the proposed analysis method.
## 3 Quantitative Analyses of Disruptive Effects of Large Frequency Errors  
According to Equation (1), when the tracking channel exhibits a large frequency tracking error, the \(\text{sinc}(\cdot)\) term will cause a large attenuation of the signal power. The signal power attenuation will cause the \(C/N_{0}\) estimation errors and code tracking estimation errors to increase. In this section, we explore the disruptive effects of large frequency tracking errors via theoretical analyses and Monte Carlo simulations.
## 3 Performance Analysis
### Channel Fading
#### 3.4.5 Weibull Fading Channel  
The Weibull fading channel system's SNR PDF can be expressed as [41]  
\[f(\gamma)=\beta\Gamma\bigg{(}1+\frac{1}{\beta}\bigg{)}\frac{\gamma^{\beta}}{ \overline{\gamma}\gamma}e^{(-(\frac{\Gamma(1+\frac{1}{2})\gamma}{\overline{ \gamma}})}\,^{\beta}), \tag{25}\]  
where \(\beta\) is the shape factor of distribution. Outage probability can be written as  
\[\begin{array}{ll}P_{out}&=P(\gamma<\gamma_{th})=\int_{0}^{\gamma_{th}}f_{ \gamma}(\gamma)d\gamma\\ &=\int_{0}^{\gamma_{th}}\beta\Gamma\Big{(}1+\frac{1}{\beta}\Big{)}\frac{\gamma ^{\beta}}{\overline{\gamma}\gamma}e^{(-(\frac{\Gamma(1+\frac{1}{2})\gamma}{ \overline{\gamma}})}\,^{\beta})d\gamma\cong 1-e^{(-(\frac{\Gamma(1+\frac{1}{2})\gamma_{th}N_{0}(4 \pi)^{2}d}{G_{\mathrm{R}}C\gamma^{\mathrm{A}}P_{\mathrm{T}}})}\,^{\beta}). \end{array} \tag{26}\]
## 2 Model Description
### Atmospheric Stability Classification
#### 2.2.3 Wind Speed Ratio (WSR) Method  
The wind speed ratio \(U_{R}\) is defined as the ratio of the wind speeds at two different heights:  
\[U_{R}=\frac{u_{2}}{u_{1}} \tag{5}\]  
According to the rule of Pasquill stability classification, [PERSON] [29] divided it into six categories according to the ratio of the wind speeds at two different heights (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stability Conditions** & **Mountain** & **Plain** \\ \hline A & \(Ri<-100\) & \(Ri<-2.51\) \\ B & \(-100\leq Ri<-1\) & \(-2.51\leq Ri<-1.07\) \\ C & \(-1\leq Ri<-0.01\) & \(-1.07\leq Ri<-0.275\) \\ D & \(-0.01\leq Ri<0.01\) & \(-0.275\leq Ri<0.089\) \\ E & \(0.01\leq Ri<10\) & \(0.089\leq Ri<0.128\) \\ F & \(10\leq Ri\) & \(0.128\leq Ri\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of stability based on \(Ri\) in different terrain conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{6}{c}{**Atmospheric Stability**} \\ \cline{2-7}  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline \(\sigma_{\theta}/^{\circ}\) & \(\sigma_{\theta}\geq 22.5\) & \(22.5>\sigma_{\theta}\geq 17.5\) & \(17.5>\sigma_{\theta}\geq 12.5\) & \(12.5>\sigma_{\theta}\geq 9.5\) & \(9.5>\sigma_{\theta}\geq 3.8\) & \(3.8>\sigma_{\theta}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relationship between \(\sigma_{\theta}\) and the atmospheric stability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Atmospheric Stability**} & **A** & **B** & **C** \\ \hline Range & \(U_{R}<1.0032\) & \(1.0032\leq U_{R}<\)1.0052 & \(1.0052\leq U_{R}<1.0101\) \\ \hline
**Atmospheric Stability** & **D** & **E** & **F** \\ \hline Range & \(1.0101\leq U_{R}<1.5717\) & \(1.5717\leq U_{R}<2.1963\) & \(U_{R}\geq 2.1963\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relationship between \(U_{R}\) and the atmospheric stability.