esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This work laid the basis for investigating possible roles of biomass-derived gasification char as a carbon source in electrospun carbon fibers. A correct preparation of the material consists in a combination of solvent cleaning, milling, and sifting. The milling process was proven to effectively reduce the average char particle diameter below the 32 \(\upmu\)m mesh used for sieving. Four different solutions were prepared for electrospinningning: 100% PAN; 90% PAN-10% biochar; 75% PAN-25% biochar; 50% PAN-50% biochar. All the solutions tested successfully produced a fiber mat. The materials produced were then processed through cyclization (5 h at 250 \({}^{\circ}\)C) and carbonization (1 h at 1700 \({}^{\circ}\)C). Biochar particles embedded in the material resulted in an average size that differed extremely from the fiber diameter. An increase in the biochar share in the electrospinning solution resulted in a final material where the filament part was less and less present due to the inclusions. Regardless of the bulk presence of the char inclusions, all the materials prepared stood the thermal treatments with no physical consequences. IR spectra showed a correct cyclization of the samples even if the biochar shares introduced new noises in the analyses. The original biochar showed significant Ca and K contents typical of vine pruning biomass. These elements drastically reduced their content during the high-temperature carbonization process. As concluding remarks, it is possible to observe that the biochar pores show walls as thin as the desirable final fiber is. There is then a chance for future works to further reduce the particle size. A possible solution takes advantage of cryogenic milling processes that should better perform in terms of final powder dimension.  
Conceptualization, A.S., S.P., F.R. and G.A.; methodology, F.R., A.B.A. and R.S.; validation, S.P.; investigation, A.S.; data curation, A.S., F.R., A.B.A., R.S. and G.A.; writing S.P., A.S. and G.A.; supervision, G.A. and F.R.; project administration, F.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: IR spectra of “cyclized” fibers.
## 3 Convexification and Discretization of the Re-Entry Trajectory Generation Problem
### Discretization of Dynamic Model  
The problem \(P_{1}\) is a representation in the continuous flight range domain, which is a model with infinite dimension. In order to further solve the problem, it needs to be discretized. First, set grid point locations of the generated trajectory in the flight range domain as \(S_{i}\), \(i=1,\ldots,m\) represents the serial number of the grid point, \(m\) represents the number of the grid points, \(S_{1}=S_{f0}\geq S_{i+1}\geq S_{i}\geq S_{m}=0\).  
Then the dynamic Equation (20) can be discretized using the Euler method.  
\[X_{i+1}=X_{i}+\Delta S_{i}\dot{X}_{i}=X_{i}+\Delta S_{i}\Big{(}A_{i}^{(k)}X_{ i}+B_{i}^{(k)}U_{i}+C_{i}^{(k)}\Big{)} \tag{36}\]  
where, \(\Delta S_{i}\) represents the projected range-to-go difference between grid point \(i\) and \(i+1\).  
At the same time, due to different iterations, the trust region size required of optimization may be different, so the variable trust region method is adopted in Equation (30) to improve the search range of solution  
\[\left\{\left|X_{j}-X_{j}^{(k)}\right|\leq\eta\xi_{x}\right. \tag{37}\]  
where \(\eta\) represents variable of trust region On this basis, the optimization objective \(J_{1}\) is further discretized to obtain a new optimization objective \(J_{2}\)  
\[J_{2}=J_{0}-\kappa_{3}\left(\sum_{j=0}^{N}\big{|}\dot{x}_{j}\big{|}\Delta S_{j}+ \sum_{j=0}^{N}\big{|}\dot{\beta}_{j}\big{|}\Delta S_{j}\right)+\kappa_{4}\eta \tag{38}\]  
where \(\kappa_{3}\), \(\kappa_{4}\) are optimization coefficients.  
Then discretize the constraints (31), (33) to further obtain the final optimization problem \(P_{2}\)  
\[\begin{array}{ll}P_{2}:\min&\begin{array}{c}J_{2}\\ \text{\emph{subject to}}:&\eqref{eq:P2},\eqref{eq:P3},\eqref{eq:P3}\\ &X\Big{(}S_{f0}\Big{)}=[r_{0},\phi_{0},\varphi_{0},\varphi_{0},\theta_{0}, \psi_{0}],\ v(s=0)=v_{f}\end{array}\\ \end{array} \tag{39}\]
## 7 Conclusions  
In this paper, we introduced pose refinement methodology and a golf swing analysis system based on explainable swing embedding for self-training. Our approach for pose refinement utilizes the changes in coordinates per frame for detecting biased pose joints. Because this method uses sequential coordinate information of the coordinates, we can apply it not only to 2D poses but also to 3D poses. Additionally, we demonstrated through these findings that we can refine the human pose estimation result by reducing the sharp changes in coordinates. Furthermore, we proposed a swing embedding method using the geometric information extracted from the swing pose. Our embedding method not only can compare the similarity of two golf swing poses but also can visualize the different points because the features of the embedding vector consist of intuitive information, such as the angle of the shoulder. Consequently, the case study showed that our swing guide system for self-training can appropriately suggest the specific body point that needs to be fixed to become more similar to the pro golfer's swing. Our proposed system can be utilized in an application service for a user who wants to study golf swing with a low-cost and time-efficient approach.  
Conceptualization, C.-Y.J.; methodology, C.-Y.J.; software, J.-H.K.; writing--original draft preparation, C.-Y.J.; writing--review and editing, D.-H.L.; supervision, D.-H.L.; funding acquisition, D.-H.L. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea government (MSIT) (No.RS-2022-00155885, Artificial Intelligence Convergence Innovation Human Resources Development (Hanyang University ERICA)), and a grant from the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No.NRF-2022R1F1A1073208).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
HPE Human Pose Estimation CNN Convolutional Neural Network
###### Abstract  
Remote sensing of high-latitude forests phenology is essential for understanding the global carbon cycle and the response of vegetation to climate change. The normalized difference vegetation index (NDVI) has long been used to study boreal evergreen needleleaf forests (ENF) and deciduous broadleaf forests. However, the NDVI-based growing season is generally reported to be longer than that based on gross primary production (GPP), which can be attributed to the difference between greenness and photosynthesis. Instead of introducing environmental factors such as land surface or air temperature like previous studies, this study attempts to make VI-based phenology more consistent with photosynthesis dynamics through applying a light use efficiency model. NDVI (MOD13C2) was used as a proxy for both fractional of absorbed photosynthetically active radiation (APAR) and light use efficiency at seasonal time scale. Results show that VI-based phenology is improved towards tracking seasonal GPP changes more precisely after applying the light use efficiency model compared to raw NDVI or APAR, especially over ENF.  
forest phenology; NDVI; light use efficiency; gross primary productivity 1
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## Abbreviations  
\begin{tabular}{l l} ADC & Analog-Digital Converter \\ BER & Bit-error rate \\ BPF & A Band Pass Filter \\ CDF & Cumulative Distribution Function \\ CFR & Crest Factor Reduction \\ CP & Cyclic Prefix \\ CPU & Central Processing Unit \\ CR & Clipping Ratio \\ DAC & Digital-Analog Converter \\ DL & Downlink \\ Ds & Delay Spread \\ EVM & Error Vector Magnitude \\ FFT & Fast Fourier Transform \\ FPGA & Field-programmable gate array \\ HF & High Frequency \\ IBI & Inter-Block Interference \\ ICI & Inter-Carrier Interference \\ IFFT & Inverse Fast Fourier Transform \\ IoT & Internet of Things \\ ISI & Inter-symbol Interference \\ LNA & Low Noise Amplifier \\ LOS & Line of Sight \\ LSTM & Long Short-Term Memory \\ LTE & Long-Term Evolution \\ MER & Modulation Error Rate \\ MIMO & Multiple Input Multiple Output \\ ML & Maximum Likelihood \\ MMSE & Minimum Mean Square Equalizer \\ MUF & Maximum Usable Frequency \\ NVIS & Near Vertical Incidence Skywave \\ OFDM & Orthogonal Frequency Division Multiplexing \\ OFDMA & Orthogonal Frequency-Division Multiple Access \\ PAPR & Peak-to-average Power Ratio \\ PN & Pseudorandom Noise \\ PSK & Phase-shift keying \\ QAM & Quadrature Amplitude Modulation \\ SC-FDE & Single-Carrier Frequency-domain Equalization \\ SC-FDMA & Single-Carrier Frequency Division Multiple Access \\ SDR & Software Defined Radio \\ SIMO & Single Input Multiple Output \\ SNR & Signal-to-noise ratio \\ UL & Uplink \\ USN & Ubiquitous Sensor Networks \\ USN & Ubiquitous Sensor Network \\ ZF & Zero Forcing \\ \end{tabular}
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
###### Abstract  
With the rapid advancement of aerospace technology, the maneuverability of spacecraft has increasingly improved, creating a pressing demand for star sensors with a high attitude update rate and high precision. Star identification, as the most complex and time-consuming algorithm of star sensors, faces stringent requirements for enhanced identification speed and an enhanced identification rate. Furthermore, as the space environment is becoming more complex, the need for star sensors with heightened detection sensitivity is growing to facilitate real-time and accurate alerts for various non-cooperative targets, which has led to a sharp increase in the number of high-magnitude navigation stars in the star catalog, significantly impeding the speed and rate of star identification. Traditional methods are no longer adequate to meet the current demand for star sensors with high identification speed and a high identification rate. Addressing these challenges, a voting-based star identification algorithm using a partitioned star catalog is proposed. Initially, a uniform partitioning method for the star catalog is introduced. Building on this, a navigation feature library using partitioned catalog neighborhoods as a basic unit is constructed. During star identification, a method based on a voting decision is employed for feature matching in the basic unit. Compared to conventional methods, the proposed algorithm significantly simplifies the navigation feature library and narrows the retrieval region during star identification, markedly enhancing identification speed while effectively reducing the probability of redundant and false matching. The performance of the proposed algorithm is validated through a simulation experiment and nighttime star observation experiment. Experimental results indicate an average identification rate of 99.760% and an average identification time of 8.861 milliseconds, exhibiting high robustness against position errors, magnitude errors, and false stars. The proposed algorithm presents a clear advantage over other common star identification methods, meeting the current requirement for star sensors with high star identification speed and a high identification rate.  
star sensor; star identification; star catalog partitioning; voting decision +
Footnote †: journal: Applied sciences  
Article
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 1 Introduction
Then, the reference signal and the echo signal of the corresponding order are subjected to Fourier transform and matched filter processing. The coherent integration of multiple echo signals is realized by the two-dimensional Fourier transform; that is, the twice coherent accumulation gain of the target can be obtained, while the noise and interference part can only obtain the twice incoherent accumulation gain. Thus, this method will greatly improve the output SNR of the target and the detection performance of the passive radar.
## 3 Results
### Test Quality Parameters of the Prediction Methods
#### 3.2.4 Neural Network Analysis  
On average, the MLP achieved a sensitivity of 75% and a specificity of 84% (Table 6). It could thus correctly identify approximately 12 of the 16 top performers and 126 of the 150 low performers. Taken together, these two parameters add up to 159%. The overall prognostic quality was thus 91%. If a test participant received a judgement as a later top performer, 33% of the predictions were correct. This means that, out of nine high-potential test participants, three were expected to reach the top level. Among the predicted low performers, only 3.1% of the predicted performance outcomes failed to apply. Thus, 3 out of 100 test participants reached the top level despite a negative individual forecast. Comparing the results with a random draw, the benefits of the MLP become apparent. The random sensitivity and specificity were 21.7% and 78.3%, and thus, the overall classification quality was 72.9%. Both the sensitivity and the specificity of the MLP clearly exceeded these random predictions. The Area Under ROC Curve was 0.831 (standard error = 0.056; 95% confidence interval: min = 0.721, max = 0.941).  
Figure 2: Discriminant analysis to predict later U13-U17 tennis performance group based on initial performances (U9; each full symbol represents ten children).
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.11 HydroCast [55]  
In this paper, Ref. [55] The authors proposed a pressures-based routing algorithm that optimizes the network performance by bypassing the routing holes, which they named the protocol HydroCast. For the protocol's architecture, the author suggested that there will be sinks on the water's surface and at various depths of the sea. The nodes underseas are fixed, and the ones above are mobile. The fixed sensors collect data and use a pressure sensor to find the location of the next node to forward the data. As previously discussed, the pressure sensors used a greedy algorithm to find the next node to forward the data, and it also used network hole bypass mode to bypass the holes and forward the data. Figure 6 shows the hole avoidance process.
## 2 Thermodynamics and Operational Conditions of the Proposed System
### Operational Conditions  
The heat transfer process should meet two requirements simultaneously. Firstly, the pinch point temperature difference (PPTD) should not be lower than 30 K in the modelling of ORC evaporator during the phase change process to ensure the feasible heat exchanger design in practical applications. Furthermore, the exhaust gas temperature should be higher than the acid dew point to avoid the possible corrosion in the evaporator (exhaust side), which is normally taken to be 393 K [40; 41].  
Figure 4 shows the evaporation process based on the PPTD method, which has been explained in our previous study [35]. During the evaporation process, the evaporation temperature of working fluid rises as the pressure increases. For a working fluid with higher critical temperature, when ORC operates at \(T_{3a}\), the relative exhaust gas temperature \(T_{6a}\) after heat transfer is above 393 K. In this case, the heat transferred in the evaporator will become smaller as the evaporation pressure increases, which would lead to a reduction of power output.  
However, for a working fluid with low critical temperature, the temperature during the evaporation process varies from \(T_{2}\) to \(T_{3b}\). As a result, the exhaust gas temperature would be lower than 393 K on the basis of the assumption that the PPTD is 30 K. To avoid a possible corrosion problem in the ORC evaporator, the outlet temperature of exhaust gas after heat transfer is set to be 393 K. In this case, the heat content in the ORC evaporator remains the same, even the evaporation pressure varies. On the other hand, as the evaporation pressure increases, the flow rate of working fluid decreases and the energy density becomes greater.  
A program developed on the basis of the MATLAB platform was adopted to analyse the thermodynamic performance. Four environmentally friendly working fluids--R1233 zd, R1234 zez, R245 fa and R245 ca--were considered as the ORC working fluids for the performance comparison, and the refrigerant R134a was used in the VCC. Their enthalpies and entropies of were obtained by using a database Refprop 9.0.  
Figure 4: The temperature-entropy diagram on both exhaust gas and working fluid during the heat transfer process based on the pinch point temperature difference (PPTD) method.
## 5 EI-RNN Optimization and Analysis
### EI-RNN Training
#### 5.1.3 Hyperparameter Tuning and Generalization  
After model creation with initial settings and train-debug cycles, we performed a hyperparameter search via the wandb tool. We chose the grid search method and observed the impact of the hyperparameter hidden units, batch size(for example, see Figure 11), and optimizer in various sweeps (for example, see. Figure 12a). We found the best batch size and best number of hidden units for each RNN and each dataset. Table 5 shows the variation in the hidden units for the UGRNN architecture on the SPHAR dataset. Increases in the number of hidden units improved performance but adversely affected the model size. An optimal choice had to be made. We chose 16 as the number of hidden units considering both performance and model size. Next, we found the best optimizer without any regularization. This was conducted for each of the RNN architectures for each dataset individually. Figure 11 corresponds to variations in batch size with the FastRNN architecture for the DSA19 dataset. We chose a batch size of 32 for this RNN and dataset. The optimizer hyperparameter check was conducted for each dataset. The choice of optimization technique significantly influenced the efficiency of the learning algorithm, as shown in Figure 12a. Figure 12b corresponds to the performance of the EI-RNNs on the DSA19 dataset with RMS Prop as the optimizer, with each of them having 16 hidden units. The approximate sensitivity of the hyperparameters under study is given in Table 6. Table 7 gives the list of optimizers corresponding to each dataset. Once we found the best set of hyperparameters, we observed the test error or generalization error. The ability of a model to perform well on previously unobserved data is called generalization [59; 60]. Regularization methods reduce this error. In our study, we found that dropout regularization reduced the generalization error significantly. Dropout is a regularization technique applied to only nonrecurrent matrices of an RNN [61].  
Figure 10: (a) High bias problem where training error is far from expected, an under-fitting case, and (b) high variance problem where training error is close to expected, but there is a large gap between training and validation error, an over-fitting case.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hidden Units** & **Test Accuracy** & **F1** & **Model\_SIZE (KB)** \\ \hline
128 & 0.94 & 0.94 & 14.16 \\ \hline
64 & 0.95 & 0.95 & 7.16 \\ \hline
32 & 0.94 & 0.95 & 3.66 \\ \hline
16 & 0.94 & 0.94 & 1.91 \\ \hline
8 & 0.91 & 0.91 & 1.04 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of variation in hidden units on performance and model size of UGRNN on SPHAR dataset.  
Figure 11: Impact of batch size variation on Fast RNN architecture using DSA19 dataset.  
Figure 12: (**a**) Validation accuracy variations for different optimizers on DSA19 dataset on GRU, and (**b**) performance of 6 different RNN architectures on the DSA19 dataset using the RMS prop optimizer.
## 5 Conclusions  
In this paper, motivated the by the need for speeding up ray-tracing simulations and efficient beam tracking in future mmWave/THz systems, we first investigated the correlational properties of the channel state information in terms of AoD, AoA, and path loss in practical outdoor deployment scenarios. Then, we proposed a new data representation that makes it feasible to utilize a large set of powerful ML-based video frame interpolation and extrapolation techniques without substantial modification. Finally, we demonstrated how to predict channel state parameters and reported the prediction accuracy.  
Our results show that the dependence in the AoA, AoD, and path loss is rather high, even at distances that are \(k\approx 300\)-600 times larger than the wavelength at 300 GHz. This implies that one may expect good interpolation and extrapolation results in ray tracing when the grid size is \(k\lambda\), which drastically improves the simulation times. From the beam-tracking perspective, this implies that the beam-tracking interval can be as long as \(t=k\lambda/v\), where \(v\) is the velocity of the UE. ML prediction results show that reliable prediction accuracy of AoA and AoD angles is feasible up to 60 cm distance with AoA being characterized by a higher MSE.  
Conceptualization, Y.K.; methodology, V.P., A.A., A.K. and Y.K.; software, V.P. and A.A.; validation, A.K. and Y.K.; formal analysis, V.P.; investigation, V.P. and A.A.; resources, Y.K.; data curation, V.P.; writing--original draft preparation, V.P. and A.A.; writing--review and editing, Y.K.; visualization, A.A. and V.P.; supervision, Y.K.; project administration, Y.K.; funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.  
The publication was supported by the grant for research centers in the field of AI provided by the Analytical Center for the Government of the Russian Federation (ACRF) in accordance with the agreement on the provision of subsidies (identifier of the agreement 000000D730321P5Q0002) and the agreement with HSE University No. 70-2021-00139.  
The original data of this contribution can be requested from the corresponding author.  
The authors declare no conflict of interest.