esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### First Validation Cycle  
The first validation cycle was based on the drag coefficients derived from the semi-empirical formula of [PERSON] et al. [11], as shown in Figure 7. These coefficients were applied to the OF DT of System 002. \(u_{\mathrm{stw}}\) was varied from 0.25 m/s to 1.0 m/s in no-wave conditions and with a simulation time \(t=2500\) s to reach a steady span.  
Based on Figure 19a and the comparison criteria given in Section 3.4, a validation criterion can be defined. Focusing on the \(u_{\mathrm{stw}}\) ranging between 0.75 m/s and 1.0 m/s for nominal and wide spans and between 0.75 m/s and 1.5 m/s for a narrow span, if the data points fall close to or inside the 15% increase area, the simulated curve can be considered as validated. A deviation percentage was also quantified. Considering this, the following findings can be identified:  
* The axial drag coefficient \(C_{D,\mathrm{OF},a}^{*}\) led to a DT estimation larger than the measured data in the narrow span. Specifically, the average deviation between the 15% increase area and real-world data was about 276%. The main phenomenon that was not captured by OF in this case was the shielding effect (cf. Section 2.3 for an explanation on this effect).
* The axial drag coefficient \(C_{D,\mathrm{OF},a}\) produced a 15% area that was close to the GPGP data points for the nominal span. Nevertheless, the majority of the data points were still outside of the area, and the computed mean deviation was about \(-3\%\) (minus sign meaning an underestimation of the ground truth). The expectation was that the second and third validation cycle curves will comply even better with the validation criteria. The reason behind this expectation is that \(C_{D,\mathrm{OF},a}\) was not optimized for the angles of attack to the flow and the twine-by-twine calculation had not been included yet.
* The axial drag coefficient \(C_{D,\mathrm{OF},a}\) produced a 15% area that was close to the GPGP data points for a wide span. Even some data points were inside the area, and the calculated mean deviation was just 1%. That means that the OF model might be considered as validated at this cycle for a wide span. Nevertheless, the expectation was that the 2 nd and 3 rd validation cycle curves would comply with the validation criteria in a more-conservative manner, following the same reasoning as for the nominal span.  
Figure 18: Dependence of the lift coefficient on the angle of attack. Median of the fluctuations is shown with standard deviation.
## 2 Materials and Methods
### Annotation Scheme
The below were the formulas:  
\[i_{t}=\sigma\Big{(}x_{t}\bullet w_{xh}^{j}+h_{t-1}\bullet w_{hh}^{j}+b_{h}^{i} \Big{)}, \tag{1}\]  
\[f_{t}=\sigma\Big{(}x_{t}\bullet w_{xh}^{f}+h_{t-1}\bullet w_{hh}^{f}+b_{h}^{f} \Big{)}, \tag{2}\]  
\[o_{t}=\sigma(x_{t}\bullet w_{xh}^{o}+h_{t-1}\bullet w_{hh}^{o}+b_{h}^{o}), \tag{3}\]  
\[\stackrel{{\rightarrow}}{{c}}_{t}=tanh(x_{t}\bullet w_{xh}^{c}+h _{t-1}\bullet w_{hh}^{c}+b_{h}^{c}), \tag{4}\]  
\[c_{t}=i_{t}\otimes\stackrel{{\rightarrow}}{{c}}_{t}+f_{t} \otimes c_{t-1}, \tag{5}\]  
\[h_{t}=o_{t}\otimes tanh(c_{t}), \tag{6}\]  
where \(\sigma\) is the sigmoid activation function; \(\otimes\) is a dot product; \(tanh\) is the activation function; \(i_{t}\),\(f_{t}\), and \(o_{t}\) represent the memory gate, forget gate, and output gate at time \(t\), respectively; \(c_{t}\) denotes the cell state at time \(t\); and \(h_{t}\) denotes the hidden state at time \(t\). The BiLSTM formulation is as follows:  
\[\stackrel{{\rightarrow}}{{h}}_{i}=LSTM\Big{(}\stackrel{{ \rightarrow}}{{h}}_{i-1},x_{i}\Big{)}, \tag{7}\]  
Figure 1:0  
Figure 1:1 The architecture of MLM. Figure 1:2  
In which, \(\overset{\rightarrow}{h_{i}}\) and \(\overset{\leftarrow}{h_{i}}\) represent the forward hidden layer vector and the backward layer vector, respectively, and \(\oplus\) denotes the joint operation.
## 4 Results
### Single-Objective Optimization
#### 4.1.1 Net Power Optimization  
This is carried out by solving Equation (24), using the decision variables and their values shown in Tables 2 and 3, respectively, on the GWO algorithm. The results obtained as shown in Table 4 depict how, in order maximize net power, the following should take place:  
* Minimize the compression ratio, pinch point temperature difference and inlet combustion chamber temperature;
* Maximize the inlet turbine temperature.
# Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges
## 5 Conclusions
\begin{tabular}{l l} AGB & Aboveground biomass \\ APAR & Absorbed PAR \\ AMSR2 & Advanced Microwave Scanning Radiometer 2 \\ AMSR-E & Advanced Microwave Scanning Radiometer for Earth \\ ASAR & Observing System \\ ATLAS & Advanced SAR \\ AVHRR & Advanced Topographic Laser Altimeter System \\ AMSR-E/2 & AMSR-E and AMSR2 \\ Tb & Brightness temperature \\ China-Brazil Earth Resources Satellite & CBERS-1 \\ DEM & Digital Elevation Model \\ DInSAR & Differential Interferometric Synthetic Aperture Radar \\ DMSP & Defense Meteorological Satellite Program \\ ERS & European remote sensing satellite \\ ETM & Enhanced Thematic Mapper \\ EVI & Enhanced Vegetation Index \\ FT & Freeze-thaw \\ FY & Feng Yun \\ GLAS & Geoscience Laser Altimeter System \\ GIMMS & Global Inventory Monitoring and Modeling System \\ GCOS & Global Climate Observing System \\ GNSS & Global Navigation Satellite System \\ GOES & Geostationary Operational Environmental Satellite \\ GRACE & Gravity Recovery and Climate Experiment \\ GBL & Great Bear Lake \\ GSL & Great Slave Lake \\ GPP & Gross Primary Productivity \\ ICESat & Ice, Cloud, and land Elevation Satellite \\ IMS & Interactive Multisensor Snow and Ice Mapping System \\ InSAR & Interferometric Synthetic Aperture Radar \\ ISRO & Indian Space Research Organisation \\ HKHT & Kush-Himalaya-Tibetan \\ LST & Land Surface Temperature \\ LAI & Leaf Area Index \\ LIDAR & Light Detection and Ranging \\ LUE & Light Use Efficiency \\ MBE & Mean Bias Error \\ MSG & Meteosat Second Generation \\ MWRI & Microwave Radiation Imager \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ MTSAT & Multifunctional Transport Satellites \\ NASA & National Aeronautics and Space Administration \\ \end{tabular}
## 5 Results  
The geochemical data, including the contents of major, trace and REE elements of 12 pegmatie samples, are listed in Table 2. The data and their petrogenic significance are summarized as follows.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **LJP-1** & **LJP-2** & **LJP-3** & **LJP-4** & **LJP-5** & **LJP-6** & **LJP-7** & **LJP-8** & **LJP-9** & **LJP-10** & **LJP-11** & **LJP-12** \\ \cline{2-13}  & \multicolumn{3}{c}{**Wall Zone**} & \multicolumn{3}{c}{**Core**} & \multicolumn{6}{c}{**Intermediate Zone**} & \\ \hline \multicolumn{13}{l}{_Major elements (wt\(\%\))_} \\ SiO\({}_{2}\) & 68.77 & 71.84 & 65.73 & 76.60 & 75.14 & 77.45 & 88.75 & 75.82 & 81.03 & 81.56 & 71.33 & 72.95 \\ Al\({}_{2}\)O\({}_{3}\) & 16.70 & 14.66 & 18.30 & 14.04 & 14.88 & 13.70 & 6.73 & 14.64 & 11.
## 4 Bayesian Game among Community Operators
### Distribution Algorithm  
Based on the formulated Bayesian game model, a distributed algorithm is proposed to search the Bayesian Nash equilibrium for community operators, which is presented in Algorithm 1. In the algorithm, \(N-1\) in step 4 represents \(N-1\) community operators except operator \(n\); optimization problem in step 7 is solved by interior point method. According to the process of algorithm, we can know that each community operator with each type has to constantly readjust its bidding strategy until the Nash equilibrium is reached. The dynamic bidding-making process can be reflected with following steps:  
1. Community operator \(n\) with type \(i_{n}\) initializes its bidding strategy;
2. Community operator \(n\) will optimize the bidding strategy of its opponents;On basis of the optimal strategy of opponents, community operator \(n\) will optimize its own bidding strategy;
4. Repeat step 2 and 3 until all operators' strategies is unchanged.  
Note that, the above steps are all executed by community operator \(n\), and other community operators will also have the same decision process.  
```
Input: Price policy, community operators' types, probability distribution Output: Bayesian Nash equilibrium for community operators
1 Initialization: Bidding strategy \(x_{n}\left(j_{n}\right)\) for all community operators. Repeat
2\(m=1\);
3 for\(m\leq N-1\)do
4\(j=1\);
5 while\(j\leq|J_{m}|\)do
6 Update community operator \(n\)'s strategy by solving problem (16) ;
7\(j=j+1\);
8
9 end for
10\(m=m+1\);
11 end for
12 Until all community operators have obtained the optimal strategy ;
13 Return community operator \(n\)'s bidding type strategy \(x_{n}\left(j_{n}\right)\).
```  
**Algorithm 1**Executed by each community operator \(n\in\mathcal{N}\)
### Paper Contributions  
This paper addresses the challenge of DASR maximization through power allocation optimization with LCT. To this end, a viable solution to design the training sequence, which is both low in complexity and suitable for LCT, is obtained. Additionally, a low-complexity power allocation optimization technique is employed to maximize the DASR of FDD MMIMO systems. The following is a summary of the main contributions of this research:  
* This paper addresses the challenge of channel estimation with a very large antenna and short coherence time with an objective to maximize the DASR.
* This paper proposes a low-complexity solution for CSI estimation by utilizing the statistical information of the channel covariance matrix, which is considered to be locally stationary and varying more slowly than the instantaneous channel.
* Unlike previous works that have considered mean square error (MSE) minimization criteria, see, e.g., [20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 39, 40, 41, 42, 43, 44], this paper investigates the maximization of the DASR in LCT, which is crucial for many wireless system applications.
* This paper proposes a power allocation optimization strategy that is based on dividing the energy nonuniformly between the data transmission and training sequence transmission with LCT, with an objective function to maximize the DASR of MMIMO systems with the FDD transmission mode.
* We derive an analytical closed-form expression for the DASR with a regularized zero-forcing precoder (ZFBF) using a theory based on the random matrix method.
* This paper conducts comparisons for the DASR performance results between the proposed power optimization method and the conventional method with uniform power allocation. The results show that the proposed low-complexity power allocation optimization approach markedly improves the DASR over the conventional method across a wide range of configurations considered. This success in maximizing the DASR over the conventional method is also demonstrated in the literature.  
DASR signifies the feasibility of applying the proposed method in practical systems utilizing URLLC and LCT scenarios.
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 4 Aircraft Wake Identification Results and Discussion
### Comparison Experiment of Different Recognition Models  
To verify the superiority of the proposed model with regard to wake recognition, the existing SVM, KNN, CNN models, and the deep learning recognition model, VGG16, were compared with the improved ParNet model [11; 12; 27; 28]. VGG16 inherits the advantages of AlexNet. Moreover, with small convolution kernels and deep network structures, its recognition performance is better than that of AlexNet.  
In this study, the parameters of the SVM and KNN were optimised using the grid search algorithm. The CNN's hyperparameters were set according to the neural network setting method in meta-learning. VGG16 used the transfer learning method to fine-tune the parameters for this recognition task. The wake identification effect of each model is shown in Table 4.  
As indicated by Table 4, the accuracy, precision, recall, and F1 value of the improved ParNet model are 98.91%, 99.06%, 98.90%, and 98.90%, respectively, which are superior to those of all other methods. Compared with the SVM, KNN, and traditional CNN, the aircraft wake recognition performance of the proposed model significantly improved. As compared with the method with the migration of VGG16 as the backbone network and fine-tuning, the proposed method achieved similar recognition performance with fewer parameters and computation. It also reduced the requirements of the hardware and made the model more economical.
## 2 Materials and Methods
### Study Design and Instrumentation
#### 2.2.1 Digital Goniometer  
The digital goniometer (Figure 1) is an electronic device designed to measure the rotational angle of the head, particularly in ophthalmology, where it plays a key role in the assessment of abnormal head posture. Equipped with high-precision sensors and a digital display, this tool allows for the accurate measurement of head rotation relative to a reference axis. During testing, the participant is asked to focus on a fixed point, enabling clinicians to assess eye alignment and detect deviations from the normal head posture. Compared to traditional goniometers, the digital version offers improved accuracy, minimizes manual errors, and facilitates the longitudinal monitoring of postural changes.
## 3 Simulations and Discussion
### Initial Analyses after Data Assimilation
Though DA, the wind structure and intensity of a TC vortex is only slightly adjusted by RO data assimilation. Observations that may provide or retrieve the wind information will be helpful for TC intensity forecasts, which constitutes the reason why the DVI is combined with RO data assimilation to spin up the TC in this study. These track forecasts for NoDA, GTS, REF, and BND compare well with those in [PERSON] et al. ((2014), Figure 10) using the one-way system, except the BND track is slightly better on day 3 but indeed worse on day 5. The intensity forecasts for GTS and BND are similar for both systems, but the two-way system produces a slower deepening on day 1 but a faster intensification on day 3. As shown in Figure 6, the simulated typhoon intensity for REF is much weaker than that for GTS and BND in days 2 and 3 using the two-way system, while it is similar in these three experiments using the one-way system. These differences might be partially attributed to the fact that the hybrid DA analysis is influenced by the updated background covariance computed by the ensemble forecasts using either MPAS or CWB GFS. Note that due to computational loading, the coarser resolution is applied for the ensemble members in this study. The background covariance structure may be further improved with the same higher resolution to improve the DA analysis ([PERSON] and [PERSON], 2016). For understanding the track responses for GTS, REF, and BND, Figure 7 shows the forecasted geometric height, temperature, and wind at 850 hPa at 0000 UTC 7 July 2016 (after 3-day forecasts) when their tracks begin to deviate greatly as shown in Figure 6a. The temperature presents a warmer intensity in the vicinity of the typhoon for the three experiments compared to the ERA-Interim temperature. The predicted typhoon for REF (Figure 7c) becomes much stronger at the end of the assimilation, but is still weaker than those for GTS and BND (Figure 7b,d), all more intense and closer to the best track than the ERA-Interim as also shown in Figure 6c. However, the environmental field including the western flank of the extended high for BND and GTS has an alignment more northward than that for REF, in association with more northward wind components mainly east of the inner typhoon. The extended high is also stronger in REF and produces more westward wind components near the north of the inner typhoon. Such combined effects may facilitate a more westward and less northward movement of the typhoon in REF, as shown previously by the actual typhoon movement. Figure 6: (**a**)The CWB best track (black) and the forecasted tracks for BND (pink line), REF (green line), GTS (blue line), and NoDA (red line) for Typhoon Neparatk. (**b**) as in (**a**) but for the evolution of the track errors with forecast time, and (**c**) as in (**b**) but for the central sea-level pressure of the typhoon.
## 1 Introduction
### Problem Description  
In a very general setting, path planning refers to finding a continuous mapping \(\sigma:[0,1]\to X\) (a path), in a given environment with known boundaries and obstacles embedded in a state-space X. Constraints on this mapping typically include:  
* Constraints on the initial state \(\sigma(0)\) (position, orientation, etc.) and the final state \(\sigma(1)\) (location of the target point);
* Staying within the boundaries and avoiding collisions with obstacles \(\sigma(\alpha)\in X_{free},\forall\alpha\in[0,1]\);
* Geometric constraints on the path, typically a maximum curvature.  
However, when moving obstacles are taken into account, a simple path planning algorithm (i.e., generation of a geometric path between the origin and the destination) cannot be used alone, and the time dimension must be added. In this case, the planning problem can not be solved in polynomial time, and becomes an NP-hard problem [7]. Correspondingly, the constraints change to:  
* Constraints on the initial state and the sub-goal state, considering the time dimension.
* Constraints on staying within the boundaries and avoiding moving obstacles;
* Differential constraints on the trajectory, typically upper bounds for speed, acceleration, steering, and steering rate.  
Figure 1: Perception and decision levels.  
This problem is now called trajectory planning. It can be broken down by using a \"path-speed\" decomposition approach, where the geometric path and the speed profile are computed separately, but this is not always possible or optimal. These real-time planning algorithms typically run continuously with a receding horizon, and must be robust enough to handle sudden changes in their environment. In simulation, these emergency situations can be used to test the algorithm's limits.
# Compact Midwave Imaging System: Results from an Airborne Demonstration
## 4 Discussion
### Planetary Boundary Layer  
Previous studies have identified the PBL height (PBLH) as a key length parameter for modeling cloud/aerosol entrainment, vertical diffusion, turbulence mixing, convective transport, and atmospheric pollutant deposition [13; 14; 15; 16]. As a satellite observable, CTH serves as a critical observational constraint to PBLH. In the case of marine boundary layer, stratocumulus clouds are capped by strong temperature inversion at the top of PBL. Therefore, the MBL CTH is a good measure of PBLH (e.g., [17; 18; 19]). CTH has also been used to infer the PBLH of stratocumulus-topped continental [20] and Arctic [21] boundary layers.  
It has been a great challenge for spaceborne sounding to accurately resolve the PBLH because it is often too close to the surface. Current observations of PBL depth from radiosondes remain sparse and a global dataset of PBL depth and its variations is needed.  
[PERSON] et al. [22] showed that the stereo CTH from Terra/MISR is able to capture the PBLH variation in a stratocumulus-to-trade-cumulus transition off the coast of California. PBL cloud observations from MISR also helped to better characterize Arctic clouds and their radiative impacts [23; 24]. Thus, a multi-angle and multi-platform (MAMP) stereo method was developed to overcome the correlated-error problem and successfully implemented for the MISR-GEO pairing [4]. A pairing between MODIS and GEO satellites was also developed to extend the coverage to both day and night with a wider swath [25]. However, [PERSON] et al. [25] also showed that the MODIS-GEO pairing suffers from stereo blind spots where the look angles for both platforms are parallel. Stereo blind spots would also occur in a VIIRS-GEO pairing. In contrast, CMIS provides a single integrated instrument that is well-suited to all of these pairings and without stereo blind spots.
## 3 Results and Analysis
### Effect of Water Content on Penetration Efficiency  
Regarding the energy consumption and utilization during the rock breakage process of the cutter, the specific energy (SE) used in previous works was also adopted in this paper to evaluate the penetration efficiency [6; 18; 27; 32]. Specific energy is defined as follows:  
\[SE=\frac{W}{V} \tag{9}\]  
where \(W\) is the consumed energy that can be calculated by Equation (7), and \(V\) is the rock chip volume.  
From the above formula, it can be found that the key indicators determining the specific energy are the consumed energy and the rock chip volume. Below is the analysis of the variation trend of the specific energy with the water content based on the aforementioned analysis of the consumed energy and rock chip volume. The trend of the specific energy was analyzed based on the water content. As shown in Figure 12d, when the water content was 0, the specific energy decreased from 8.92 in the first indentation to 7.57 in the second indentation, for a total reduction of 15.13%. As the water content increased, the specific energy was generally on a decreasing trend. When the water content was increased to 0.43%, the specific energy was only 3.42 in the first indentation and 1.63 in the second indentation, which were significantly lower than the corresponding values in the dry state. As the water content increased, the specific energy generally decreased. When the water content remained the same, the specific energy in the second indentation was always smaller than that in the first indentation. In other words, judging from the specific energy, which was used as the indicator to measure the penetration efficiency, the granite specimen with water content could be more easily broken. This indicates that the water content of rocks is a factor affecting the penetration efficiency of the cutter. The increase in the water content causes the rock breakage load of the cutter to decrease and promotes the propagation of rock cracks. The corresponding volume of rocks broken also increased, indicating that as the water content increases, rocks are more easily broken.