esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Pre-Processing Techniques
### Non-Uniform Frequency Resolution
#### 5.2.4 Multirate Approaches  
Another possibility for achieving a nonuniform frequency resolution is given by multirate approaches. In these approaches the spectrum is divided into different bands, that are down-sampled and separately processed with filters of different length. In most of the proposed approaches, one of the filters covers the low frequencies [50; 72; 116; 117; 118; 119; 120] which is used for modal equalization and low resonances control (see Section 6.5) or for bass management. Generally, the low-frequency filters must compensate very long reverberation times, and thus the filters benefit from the high down-sampling at low frequencies. The filters used for mid and high frequencies generally use a lower resolution compared to the low frequencies, with strong computational savings.  
For example, in [119] the authors propose a dual band equalization procedure. The low frequency channel is restricted approximately to the Schroeder frequency through down-sampling. An FFT-based technique with regularization is used to design a minimum-phase equalizer with homomorphic filtering. The upper band is also equalized with a minimum-phase equalizer designed with LPC analysis and warping techniques. In [120], the same authors have instead divided the spectrum into three bands: the low-, mid-, and high-frequency bands. The low band is again restricted approximately to the Schroeder frequency--specifically 150 Hz--through down-sampling, but the equalizer is now designed with the LPC technique. In the mid-frequency band from 150 Hz to 900 Hz, the equalizer is designed with a warped LPC technique to focus attention to the lower part of the band. Above 900 Hz, the high-frequency spectrum is smoothed to reduce sensitivity to position, and then the equalizer is found by inverting the smoothed spectrum, imposing a slightly decreasing target function. The authors have also combined this basic equalizer with an excess phase equalizer in the low-frequency band, and a pre-processing based on a deconvolution technique in the first 10 ms after the direct sound.  
In [72], the authors have combined the multi-point fuzzy c-means clustering technique of [121] (see Section 6.2) with a dual-band multirate approach, separating the low-frequency band below 80 Hz from the high-frequency band beyond 80 Hz. The low-frequency band is decimated by a factor of 256  
Figure 7: Bank’s parallel filter design example: (**a**) RTF; (**b**) the resulting filter frequency response. The dotted lines represent the individual transfer functions of the 16 second-order sections, while the circles display the pole frequencies.  
to work with small length room responses, prior to applying the fuzzy c-means clustering technique for designing the equalizer.
## 3 Influence of Physical Properties of NS on Communication, Navigation, and TT&C Integration
### Atmospheric Loss in NS
#### 3.3.3 NS Atmospheric Flicker
Aiming at the characteristics of the communication, navigation, and TT&C environment requirements of NS vehicles, taking the temperature value of 15 \({}^{\circ}\)C, the water vapor pressure of 10 mb, and the antenna aperture of 3 m as examples, when it is operating at 18 GHz and 40 GHz, the simulation results are shown in Figure 7.  
As can be seen from Figure 7, with the increase in the communication frequency, when the elevation angle is less than \(4^{\circ}\), the attenuation of atmospheric scintillation at different latitudes gradually decreases, approximately from the range of 96.4-99.5 dB to the range of 89.0-92.1 dB, and the average value in the worst month is between 92.8 and 95.9 dB. When the elevation angle is greater than \(4^{\circ}\), with the increase in the antenna elevation angle, the atmospheric attenuation gradually decreases, approximately from 6.1 to 11.6 dB  
Figure 7: Curves of the attenuation of atmospheric scintillation varying with frequency under different elevation angles. (**a**) The attenuation of atmospheric scintillation at different latitudes when the elevation angle is less than \(4^{\circ}\) (taking \(0.5^{\circ}\) as an example). (**b**) The attenuation of atmospheric scintillation under different elevation angles when the elevation angle is greater than \(4^{\circ}\). when the elevation angle is \(5^{\circ}\) to 0.3 to 0.6 dB when the elevation angle is \(90^{\circ}\). It can be seen that atmospheric scintillation has a relatively large impact on the signal, and the lower the elevation angle, the stronger the atmospheric scintillation. In addition, according to Equation (10), the standard deviation of atmospheric scintillation is directly proportional to the water vapor pressure, the antenna aperture, and the antenna efficiency, and inversely proportional to the temperature, but its value is generally not very large and can be ignored.
## 6 Conclusions  
Aiming at the dual carbon goal, this paper proposes a low-carbon economic optimization dispatch model of the electric-gas-heat integrated energy system, which considers P2G heat recovery and carbon capture. The influence of six scenarios combining the three factors of P2G, heat recovery and carbon capture on the system operation results is analyzed with examples. Sensitivity analysis of some parameters is carried out, and the following conclusions are obtained:  
1. Neither CCS nor P2G heat recovery alone can achieve the low-carbon and economic optimization of the system. Combining P2G heat recovery with CCS can effectively improve the system's wind power consumption and reduce CO2 emissions.
2. Considering the heat storage device, the operating flexibility of the CHP unit can be improved, and the total operating cost of the system can be reduced.
3. The larger the capacity of P2G and carbon sequestration capacity is not better. The wind power that P2G can absorb is limited. When its capacity is higher than 500 MW, the total cost of the system will not change. When carbon sequestration capacity  
Figure 10: Total cost of different carbon storage capacity and P2G capacity.  
is increased by 1900 t, the excess CO\({}_{2}\) that is not used to produce methane can be sequestered by means of carbon sequestration. Although increasing carbon sequestration capacity can effectively reduce carbon trading costs, its high carbon sequestration costs cannot be ignored.  
In summary, the conclusions obtained in this paper can provide a basis for a low-carbon economic scheduling scheme in electric-gas-heat network. The model proposed in this paper only analyzes the day-ahead economic scheduling of the coordinated operation of P2G heat recovery and carbon capture under specific conditions, without taking the investment and operation and maintenance costs of each module into consideration. The practical application needs further calculation and research. The subsequent work will be related to the investment cost and policy of carbon capture and P2G.  
Conceptualization, W.C. and J.Z.; methodology, J.Z.; software, F.L. and G.L.; validation, S.Q. and C.W.; formal analysis, W.C.; investigation, W.C.; resources, W.C.; data curation, J.Z.; writing--original draft preparation, J.Z. and C.W.; writing--review and editing, R.Z. and C.W.; visualization, W.C.; supervision, C.W.; project administration, W.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data is unavailable.  
The authors declare no conflict of interest.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Theoretical Foundations of IECPS
#### 5.1.3 Multi-Timescale Simulation of IECPS  
IECPS integrates different energy sources and couples cyber networks with physical networks. It involves not only fast information transmission and electricity transmission but also slow processes such as gas flow and thermal processes. At the system level, it involves not only real-time operation scheduling but also long-term system planning. Therefore, IECPS defense against deliberate attacks exhibits typical multi-time scale characteristics. With the help of multi-time scale simulation technology, these characteristics can be modeled and solved with differentiation, enabling comprehensive analysis of deliberate attacks and system defense [170]. Figure 16 illustrates the application of multi-time scale simulation technology in the research of IECPS deliberate attack and defense.  
As shown in Figure 16, multi-time scale simulation technology enables the understanding of the propagation process of deliberate attacks. The complex dynamic behaviors inherent in IECPS lead to unclear mechanisms of deliberate attacks. Particularly in the case of cyber-physical coordinated attacks, cyberattacks manifest within a very short time, while the cascading effects of physical failures operate on time scales of seconds or even minutes. By introducing multi-time scale simulation, the propagation paths of attack impacts across both cyber and physical domains can be dynamically traced, thereby optimizing attack models and providing more advanced experimental subjects for system defense [199]. Additionally, multi-time scale simulation technology provides a validation platform for long-term defense strategies, such as redundancy resource allocation and system recovery plans. Although system planning and recovery focus on longer time scales, they cannot be effectively implemented without short-term dynamic simulations. Finally, multi-time scale simulation technology is of crucial significance for the rapid response of the system.  
In practical operations, deliberate attacks are often sudden and dynamic. Attackers adjust their strategies dynamically based on the system's state, and existing defense strategies are often inadequate to resist such attacks. With the assistance of multi-time scale simulation technology, attackers' behaviors can be predicted, enabling rapid response and precise intervention in the event of an attack.
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 3 Computational Settings
### Computational Domain and Mesh Scheme  
The computational domain should contain the location of the reference mast (from where the relevant quantities of the inflow profiles can be obtained) and the area of interest for the numerical simulation, and, in addition, it should have enough long upstream and downstream sections to ensure that the inflow profiles and the hill-induced wake are fully developed. In the upstream, from the inlet to the target, the upstream sections can be  
Figure 3: Illustration of the measurement locations on Bolund island.  
Figure 2: Illustration of the geographical location of the meteorological masts on Askervein hill.  
divided into the following three categories: the inlet flow, approach flow, and incidence flow (as seen in Figure 4a), and to ensure that all three are consistent, i.e., the inflow profile does not decay, the appropriate boundary conditions, turbulence model, and wall function should be chosen.  
The computational domain and grid used in this paper are shown in Figure 4b-d, where the meshes around the mountain and near the ground need to be refined. In Case 1, the domain size is the same as the wind tunnel (5.8 m (L) \(\times\) 0.629 m (W) \(\times\) 0.9 m (H)), the height of the first grid is 0.1 mm, and the minimum horizontal grid size is 0.002 m. In Case 2, the domain size is 6 km (L) \(\times\) 6 km (W) \(\times\) 2 km (H), the minimum horizontal cell size is 7 m, and the minimum vertical cell size is 0.24 m. In Case 3, the domain size is 900 m (L) \(\times\) 800 m (W) \(\times\) 1000 m (H), the minimum horizontal cell size is 0.3 m, especially near the cliff, and the minimum vertical cell size is 0.12 m. In Case 4, the computational domain and grid scale are the same as in Case 3, and the hill was rotated by a certain angle to make the inlet stream parallel to the inlet plane. For these four cases, we have conducted corresponding grid independence analyses, and the similar grid schemes as mentioned above have been used in analogous studies [5; 10].
# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 4 Experiments and Analysis
### Evaluation Indicators  
To verify the performance of the algorithm, we used the mean average precision (_mAP_) as the evaluation indicator for all the experiments and used the average precision (_AP_) as the accuracy of recognizing a single category. _mAP_ is the mean of all categories' _AP_ values. The higher the _mAP_ value and _AP_ value, the higher the recognition accuracy of the model. The _mAP_ is expressed as follows:  
\[mAP=\frac{1}{n}\sum_{i=1}^{n}AP_{i} \tag{25}\]  
where \(n\) represents the total number of categories (_n_ = 7 in this dataset) and \(i\) represents the index of the category. The value of _AP_ is the area of the _PR_ (precision-recall) curve for each category. The _AP_ is expressed as follows:  
\[AP=\int_{0}^{1}P(R)dR \tag{26}\]  
where \(P\) represents the ratio of the number of true aircrafts detected to the total number of aircrafts detected, and \(R\) represents the ratio of the number of true aircrafts detected to all true aircrafts. These are expressed as follows:  
\[P=\frac{TP}{TP+FP} \tag{27}\]  
\[R=\frac{TP}{TP+FN} \tag{28}\]  
where _TP_ (true positive) indicates that the forecasted value is true and the actual value is true; _FP_ (false positive) indicates that the forecasted value is true and the actual value is false; and _FN_ (false negative) indicates that the forecasted value is false and the actual value is true. _TP_ represents the number of correctly detected targets, _FP_ represents the number of false alarms, and _FN_ represents the number of missing targets.
## 2 Fabrication and Characterization  
(Ba\({}_{0.2}\)Na\({}_{0.2}\)K\({}_{0.2}\)La\({}_{0.2}\)Bi\({}_{0.2}\))TiO\({}_{3}\) (BNKLBT) and (Ba\({}_{0.2}\)Na\({}_{0.2}\)K\({}_{0.2}\)Mg\({}_{0.2}\)Bi\({}_{0.2}\))TiO\({}_{3}\) (BNKMBT) ceramic samples were fabricated by conventional solid state reactions followed by a fast increment and decrement of temperature (15 \({}^{\circ}\)C/min) to avoid volatilization of the bismuth and sodium content. High purity Bi\({}_{2}\)O\({}_{3}\) ([PERSON], 99.9%), Na\({}_{2}\)CO\({}_{3}\) (Sigma Aldrich, 99.98%), K\({}_{2}\)CO\({}_{3}\) (Sigma Aldrich, 99.98%), La\({}_{2}\)O\({}_{3}\) (Aladin, 99.9%), BaCO\({}_{3}\) (Sigma Aldrich, 99.98%), MgO (Alfa Aesar, 99.99%) and TiO\({}_{2}\) ([PERSON], 99.9%) powders were weighted with the stoichiometric ratios of (Ba\({}_{0.2}\)Na\({}_{0.2}\)K\({}_{0.2}\)La\({}_{0.2}\)Bi\({}_{0.2}\))TiO\({}_{3}\) and (Ba\({}_{0.2}\)Na\({}_{0.2}\)K\({}_{0.2}\)Mg\({}_{0.2}\)Bi\({}_{0.2}\))TiO\({}_{3}\) and milled at 30 rpm in ethanol for 18 h. The mixed powders were dried in an oven at 100 \({}^{\circ}\)C overnight and then calculated at 800 \({}^{\circ}\)C for 3 h. The calcinated BNKLBT and BNKMBT powders were then ground, and ball-milled for 12 h at 30 rpm and dried before cold isostatically pressing the powders into disk shapes with a diameter of 11 mm under 200 MPa pressure. Afterwards, ceramics were placed in sealed crucibles with extra prepared powder around the disks to avoid Bi and Na volatilization during high temperature sintering. Finally, the compacted ceramic samples at both concentrations were sintered at 1250 \({}^{\circ}\)C for 4 h at a fast heating and cooling rate (15 \({}^{\circ}\)C/min).  
Structural phase transformation and phase impurity of fabricated BNKLBT and BNKMBT ceramic samples were investigated by X\(-\)ray diffraction (XRD, PANalytical, Netherlands) analysis with a scan rate of 0.02\({}^{\circ}\) s\({}^{-1}\) for a 20 scan range of 20-70\({}^{\circ}\). Structural confirmation of the fabricated ceramic samples was confirmed with Rietveld refinement of the attained XRD patterns of the ceramic using FULLPROF software. Morphologicalanalysis and 2-dimentsional (2D) colored elemental mapping of the synthesized samples were performed by field emission scanning electron microscopy (FE-SEM, FEI Quanta 200, Hillsboro, Oregon) at an acceleration voltage of 10 kV. Later, prepared ceramic samples were polished to a thickness of \(\sim\)0.7 mm and an Ag electrode was pasted on both surfaces of the ceramics to measure their dielectric and ferroelectric properties. Ferroelectric analysis with electric field-dependent polarization loops (\(P-E\) loops) was measured at a frequency 10 Hz using a ferroelectric tester (aixACCT TF Analyser 1000). Relative dielectric constant versus temperature (\(\epsilon_{r}-T\)) curves and dielectric loss (\(tan\delta\)) versus temperature curves at the frequency range of 100 Hz to 1 MHz were recorded using an HP4980A, Agilent LCR analyzer.
# Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches
## 3 Methods
### Feature Selection  
The main objective of feature selection is to reduce the number of features to increase the prediction accuracy of machine learning. Feature selection methods are classified as wrapper, filter, and embedding methods. Relieff, which is a filter method, was used as the feature selection method in this study. In the Weka interface, Relieff is represented by the term ReliefAttributeEval.  
The Relief algorithm was first developed by [PERSON] and [PERSON] [28] for use with discrete and continuous attributes; however, it could only be used for solving two-group classification problems. In 1994, [PERSON] [29] then proposed an extension of the Relief algorithm called Relieff for use with noisy or incomplete data; this algorithm could be applied to multi-classification problems and regression problems. [PERSON] repeatedly obtains samples from the training set and calculates the difference between instances to find the nearest neighbors. The Algorithm 1 Relieff is as follows.  
```
Input: for each training instance a vector of attribute values and the class value Output: the vector \(\mathsf{W}\) of estimations of qualities of attributes
1. Set all weighs \(W[A]:=0.0\);
2. for\(i:=1\) to \(m\)do begin
3. randomly select an instance \(R_{i}\);
4. find \(k\) nearest hits \(H_{j}\);
5. for each class \(C\
eq\) class(\(R_{i}\))do
6. from class \(C\) find \(k\) nearest misses \(M_{j}(C)\);
7. for\(A:=1\) to ado
8. \(W[A]:=W[A]-\sum_{j=1}^{k}diff\Big{(}A,R_{i},H_{j}\Big{)}/(m\cdot k)+\)
9. \(\sum_{c\
otin class(R_{i})}\big{[}\frac{p(C)}{1-p(Class(R_{i}))}\sum_{j=1}^{k}diff \Big{(}A,R_{i},M_{j}(C)\Big{)}\big{]}/(m\cdot k)\)
10. end;
```  
**Algorithm 1** Relieff:  
where \(W[A]\) represents the weight of feature \(A\), \(m\) represents the number of cycles of random training examples, \(R_{i}\) represents a randomly selected example, \(H_{j}\) (referred to as the nearest hits) represents the search for k-nearest neighbors from the same class, \(M_{j}(C)\) (referred to as the nearest misses) represents the search for k-nearest neighbors from each category, \(p(C)\) represents the proportion of the category, and \(p(Class(R_{i}))\) represents the proportion of randomly selected instances belonging to each class. \(W[A]\) is updated according to the values of \(R_{i}\), \(H_{j}\), and \(M_{j}(C)\). Finally, the appropriate features are selected  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**GameVariable** & **B1** & **B2** & **B3** & **B4** & **SP1** & **SP2** & **SP3** & **SP4** & **X1** & **Y** \\ \hline
1 & 31 & 5 & 1 & 14 & 7.2 & 2 & 3 & 12 & 1 & 0 \\ \hline
2 & 36 & 9 & 3 & 11 & 6 & 7 & 2 & 8 & 1 & 0 \\ \hline
4857 & 26 & 4 & 2 & 11 & 6 & 2 & 5 & 7 & 1 & 1 \\ \hline
4858 & 44 & 15 & 2 & 13 & 7 & 5 & 2 & 9 & 1 & 1 \\ \hline \hline \end{tabularaccording to the weights. The basic difference between ReliefF and Relief is the selection of \(k\) hits and misses; moreover, compared with Relief, ReliefF is more robust against noise.
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 2 Materials and Methods  
### Study Area  
The city of Yantai is located in the northeastern part of the Shandong Peninsula, China, between 119\({}^{\circ}\)34\({}^{\prime}\)-121\({}^{\circ}\)57\({}^{\prime}\) and 36\({}^{\circ}\)16\({}^{\prime}\)-38\({}^{\circ}\)23\({}^{\prime}\). It has a wide sea-front, bordering the Bohai Sea in the north, the Yellow Sea in the east, and the cities of Weifang, Qingdao, and Weihai. There are many small and medium-sized rivers in the city, including the Wulong, Daguija, and Xin'an Rivers. Located north of the Tropic of Cancer, this region has a typical temperate monsoon climate. Compared with the inland areas of the same latitude, it is characterized by a mild climate, moderate rainfall, and high humidity. The annual average precipitation is approximately 524.9 mm.  
The northern region of Yantai was studied, in which eight counties and municipalities are located, Laizhou, Zhaoyuan, Longkou, Penglai, Fushan, Zhifu, Laishan, and Muping. There are four ports along the coast: Yantai Port (or Zhifuwan Port), Penglai Port, Longkou Port, and Laizhou Port. The area is situated at the confluence of the Yellow Sea and the Bohai Sea, and the length of the coastline is approximately 600.40 km. The area that was studied includes the coastline and a 2-km coastal zone. Therefore, the surface area that was monitored was 795.06 km\({}^{2}\). In order to ensure the integrity of the surface features in the sea area, the whole area of islands, aquaculture areas, and structures within 2 km of the coastal waters and extending outwards continuously is included in the monitoring scope (Figure 1). By the end of 2017, the regional population reached 43.917 million, accounting for 67.13% of Yantai's population. This area had a GDP of 597.7 billion yuan, accounting for 85.11% of Yantai's GDP. This area is the port area that has the largest population and economic density in Shandong Province. Pervasive human interference from activities such as aquaculture and artificial island construction has had a noticeable impact on the changes of coastline and coastal zone in the past 30 years [18].