esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research on BPL Decoding Algorithm for Polar Codes Based on Information Geometry
### Path Selection Criterion Based on Soft Information Centroid
#### 3.2.3 Bit Error Rate Calculation  
Assuming that the decoding performance of the \(L\) factor graphs is the same, each factor graph has the same weight \(w\), and the soft information centroid is \(\frac{\sum\limits_{l=1}^{L}\lambda_{j}}{L}\). For any information bit, let us assume the transmitted codeword is 1. After decoding, the output soft information is \(\lambda\). If \(\lambda\leq 0\), the decoding is incorrect; if \(\lambda>0\), the decoding is correct. Assuming \(\lambda\) follows a normal distribution, the original BP decoding bit error rate is:  
\[p(\lambda\leq 0)=\Phi(\frac{0-\mu}{\sigma})=\Phi(-\frac{\mu}{\sigma}), \tag{18}\]  
where \(\Phi\) represents the cumulative distribution function of the normal distribution. When calculating the soft information output of \(L\) factor graphs using the soft information centroid, given that \(\sum\limits_{j=1}^{L}\lambda_{j}\) follows a normal distribution with parameters \((L\mu,L\sigma^{2})\), the bit error rate of BPL decoding employing the path selection criterion proposed in this paper is:  
\[p(\frac{\lambda_{1}+\lambda_{2}+\cdots+\lambda_{L}}{L}\leq 0)=p(\lambda_{1}+ \lambda_{2}+\cdots+\lambda_{L}\leq 0)=\Phi(\frac{0-L\mu}{\sqrt{L}\sigma})=\Phi(- \frac{\sqrt{L}\mu}{\sigma}). \tag{19}\]  
When the bit error rate is less than 0.5, \(\mu>0\), \(-\frac{\sqrt{L}\mu}{\sigma}<-\frac{\mu}{\sigma}\). Since the cumulative distribution function of the normal distribution is monotonically increasing, the bit error rate of BPL decoding using the path selection criterion proposed in this paper is less than the original BP decoding bit error rate. Figure 4: Flow diagram of polar code BPL algorithm based on soft information centroid. Assuming that the decoding performance varies across different \(L\) factor graphs, without loss of generality, we assume that the transmitted codeword is 1. Let \(Pe_{i}\) represent the bit error rate, then:  
\[Pe_{i}=p(\lambda_{i}\leq 0)=\Phi(\frac{0-\mu_{i}}{\sigma_{i}})=\Phi(-\frac{\mu_{ i}}{\sigma_{i}}). \tag{20}\]  
Assuming the factor graph decoding performance is ranked from best to worst, let \(Pe_{1}<Pe_{2}<\cdots<Pe_{L}<0.5\). At this point, \(w_{1}>w_{2}>\cdots>w_{L}\), \(\mu_{1}>\mu_{2}>\cdots>\mu_{L}{\sigma_{1}}^{2}<{\cdots}<{\sigma_{L}}^{2}\).
# 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition
###### Abstract  
Human activity recognition is an active field of research in computer vision with numerous applications. Recently, deep convolutional networks and recurrent neural networks (RNN) have received increasing attention in multimedia studies, and have yielded state-of-the-art results. In this research work, we propose a new framework which intelligently combines 3D-CNN and LSTM networks. First, we integrate discriminative information from a video into a map called a'motion map' by using a deep 3-dimensional convolutional network (C3D). A motion map and the next video frame can be integrated into a new motion map, and this technique can be trained by increasing the training video length iteratively; then, the final acquired network can be used for generating the motion map of the whole video. Next, a linear weighted fusion scheme is used to fuse the network feature maps into spatio-temporal features. Finally, we use a Long-Short-Term-Memory (LSTM) encoder-decoder for final predictions. This method is simple to implement and retains discriminative and dynamic information. The improved results on benchmark public datasets prove the effectiveness and practicability of the proposed method.  
action recognition; fused features; 3D convolution neural network; motion map; long short-term-memory +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 42; doi:10.3390/f11020042
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 5 Conclusions  
An improvement upon the Color Attenuation Prior-based dehazing method, termed ICAP, has been developed. By carefully examining the advantages and disadvantages of CAP, as well as other state-of-the-art haze removal algorithms, ICAP is proposed to overcome some of the existing limitations. First of all, owing to the lack of datasets containing pairs of hazy and haze-free images of real scenes, synthetic datasets were used to train the depth estimator. We, therefore, proposed the enhanced equidistribution, in lieu of the standard uniform distribution, to create the synthetic training dataset. This was validated in our previous work [29; 30], revealing that the new synthetic training dataset brings about a significant improvement in dehazing performance. Additionally, to reduce the computational complexity of the proposed algorithm, the fast and efficient quad-decomposition algorithm that operates on the luminance channel was utilized for atmospheric light estimation. As a result, the simple mHMF can be used to refine the depth map, instead of a very large guided image filter. Furthermore, the background noise issue that arose in the CAP was successfully traced to the noise in the saturation channel, which was filtered easily by a low-pass filter. Finally, the well-known color distortion existing in many contemporary haze removal methods also was resolved, by using the adaptive linear weight.  
ICAP exhibits good performance in comparison to other benchmarking methods for the task of visibility restoration. This has been verified through extensive experiments on both synthetic and real datasets. The quantitative results show that there is large room for improvement in the haze removal algorithm.  
Conceptualization, G.-D.L. and B.K.; data curation, D.N.; validation, D.N. and B.K.; writing-original draft, D.N. and B.K.; supervision, B.K.  
This research was supported by research funds from Dong-A University, Busan, South Korea.  
The authors declare no conflict of interest.
## 4 Verification and Analysis
### Registration Visualization
However, the standard ICP algorithm is greatly influenced by the accuracy of initial registration and poses a great impact on subsequent registration due to the poor accuracy of registration in the process of Monkey registration. In the case of direct registration with ICP, despite the fast rate, there is a large error, and in some cases, it falls into local optimization, failing to fulfill accurate registration. The standard RANSAC algorithm is required to transform and calculate a large amount of point cloud data, which makes its registration rate slower than that of the standard ICP algorithm. However, the proposed IWOA-RANSAC-ICP algorithm overcomes the shortcomings of both the above two algorithms and introduces intelligent optimization algorithms that can quickly achieve the initial registration of point clouds. The point cloud after initial registration subsequently provides a more reliable initial value for the improved ICP algorithm, and the normal vector constraint of key points  
Figure 5: Iteration curve of IWOA optimized RANSAC algorithm. Figure 6: Registration results of Bunny and Monkey. (**a**) Standard ICP algorithm; (**b**) Standard RANSAC algorithm; (**c**) Depth Filtering-ICP; (**d**) IWOA-RANSAC-ICP algorithm. is introduced into the ICP registration algorithm to avoid local optimization in the stage of accurate registration. On the whole, this method can achieve the optimum registration effect, but given the long registration process, its efficiency remains to be improved. The registration time of Bunny and Monkey using these four methods is shown in the following table (Table 1). As can be seen from the above table, the number of Bunny point clouds is small, and the ICP algorithm converges the objective function by repeated iterations of the points so that the corresponding points are gradually approximated to achieve accurate alignment quickly, and when the number of point clouds increases significantly, the time required by the ICP algorithm increases accordingly. Depth Filtering-ICP and IWOA-RANSAC-ICP take more time to align than the previous two due to the increased workflow of point cloud registration, and in the subsequent research, the focus is on improving and optimizing the algorithm to increase the speed of the alignment. The mean square error of the four methods is shown in Table 2 below. According to Tables 1 and 2, the registration speed of Bunny and Monkey using the standard ICP algorithm is fast but is also subject to a large root-mean-square error, which may be attributed to the great influence of the initial state on the registration. Additionally, the standard RANSAC algorithm is slow but accurate, and the Depth Filtering-ICP algorithm is better than the first two in some metrics. The IWOA-RANSAC-ICP algorithm is close to the standard RANSAC algorithm in terms of the registration rate, but it is more accurate with a minimal root-mean-square error.
# Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network
## 4 MTN Predictive Control Scheme with the Differential Term
### Stability Analysis  
**Theorem 1.**_Consider the quadratic performance index shown in Equation (13). Denote \(h_{\rm max}=\max_{k}(h(k))\) and \(h(k)=\left|\frac{\partial\hat{g}(k)}{\partial u(k)}\right|\). When \(\varepsilon\) satisfies \(0<\frac{1}{\varepsilon}<\frac{2}{h_{\rm max}^{2}}\), the closed-loop system is stable._  
**Proof.** Define \(V(k)=\frac{1}{2}e_{\rm C}{}^{2}(k)\). So:  
\[\begin{array}{ll}\Delta V(k)&=V(k+1)-V(k)\\ &=\frac{1}{2}[e_{\rm C}{}^{2}(k+1)-e_{\rm C}{}^{2}(k)]\\ &=\frac{1}{2}[e_{\rm C}(k+1)-e_{\rm C}(k)][e_{\rm C}(k+1)+e_{\rm C}(k)]\\ &=\frac{1}{2}\Delta e_{\rm C}(k)[e_{\rm C}(k+1)+e_{\rm C}(k)]\end{array} \tag{18}\]  
\(\Delta e_{\rm C}(k)\) can be denoted by [42; 43]  
\[\Delta e_{\rm C}(k)=-\frac{\partial\hat{g}(k)}{\partial u(k)}\Delta u(k) \tag{19}\]  
Substitution of (19) into (18) yields  
\[\begin{array}{ll}\Delta V(k)&=\frac{1}{2}\Bigl{[}-\frac{\partial\hat{g}(k) }{\partial u(k)}\Delta u(k)\Bigr{]}[e_{\rm C}(k+1)+e_{\rm C}(k)]\\ &=-\frac{1}{2}\frac{\partial\hat{g}(k)}{\partial u(k)}\Delta u(k)\Bigl{[}2e_ {\rm C}(k)-\frac{\partial\hat{g}(k)}{\partial u(k)}\Delta u(k)\Bigr{]}\end{array} \tag{20}\]  
According to Equation (14), we can obtain  
\[\begin{array}{ll}\Delta u(k)&=\frac{1}{\varepsilon}(r(k)-\hat{g}(k))\frac{ \partial\hat{g}(k)}{\partial u(k)}\\ &=\frac{1}{\varepsilon}e_{\rm C}(k)\frac{\partial\hat{g}(k)}{\partial u(k)} \end{array} \tag{21}\]Substituting (21) into (20), we can obtain  
\[\begin{array}{ll}\Delta V(k)&=-\frac{1}{2}\frac{1}{\varepsilon}[e_{\mathbb{C}}(k )]^{2}\Big{[}\frac{\partial\vartheta(k)}{\partial u(k)}\Big{]}^{2}\Big{\{}2- \frac{1}{\varepsilon}\Big{[}\frac{\partial\vartheta(k)}{\partial u(k)}\Big{]} ^{2}\Big{\}}\\ &\leq-\frac{1}{2}\frac{1}{\varepsilon}[e_{\mathbb{C}}(k)]^{2}\Big{[}\frac{ \partial\vartheta(k)}{\partial u(k)}\Big{]}^{2}\Big{(}2-\frac{1}{\varepsilon }\Big{]}\frac{\partial\vartheta(k)}{\partial u(k)}\Big{|}_{\max}^{2}\Big{)}\\ &=-\frac{1}{2}\frac{1}{\varepsilon}[e_{\mathbb{C}}(k)]^{2}\Big{[}\frac{ \partial\vartheta(k)}{\partial u(k)}\Big{]}^{2}\Big{(}2-\frac{1}{\varepsilon }h_{\max}^{2}\Big{)}\end{array} \tag{22}\]  
It can be found that \(\Delta V(k)<0\) if \(2-\frac{1}{\varepsilon}h_{\max}^{2}>0\) holds. That is, \(0<\frac{1}{\varepsilon}<\frac{2}{h_{\max}^{2}}\). By the Lyapunov stability theorem, the closed-loop system is stable.  
What needs to be noted is that for a quadratic performance index with a differential term, the differential term can help the system to act in advance, reduce the overshoot, and shorten the adjustment time. That is, it can reach the stable region as soon as possible.  
It should be noted that an arbitrary number of learning weighting factors was selected according to the stability condition. In an actual situation, the appropriate parameters should be selected according to the actual situation, and the stability condition should be satisfied. \(\square\)
## 4 Discussion
### Variable Shading vs. Constant Shading  
This subsection first compares a constant shading value during a year with a variable value of shading between months with higher shading during winter compared to summer. These values of shading induce a loss of 30% of PV energy for one year in Paris. The values are estimated by geometric modelling with 10 m height buildings and with 11 m of route width in an urban environment considering the angle of incidence of the sunrays. [PERSON] et al. [12] explained the variation in shading losses during a year and stated that the lower solar elevation in winter induces higher losses due to shading from buildings. Table 3 gives the shading values for a year in Paris. The values are between 21% and 69%. [PERSON] et al. (2023) [20] investigated solar irradiance through a non-uniform shading object, coupled with a fisheye camera recording on a vehicle roof. The shading values recorded are from 11%, 27% and 46%, respectively, in an open zone, a residential zone and a building zone. A 30% shading scenario could be considered a generalized mean value.  
Table 4 presents the simulation results with variable shading between months compared to constant shading in Paris. It shows an improvement between 0% and 9% of the energy used and of the distance covered by the VIPV. The improvement of life cycle assessment results is only between 1 and 2 kg CO\({}_{2}\)-eq.  
Another possibility to consider for the shading is different values between driving and parking. New simulated use cases are with shading losses equal to 26% during driving and 52% during parking, which are average yearly values for road and parking given by [PERSON] et al. [12].  
Table 5 presents the simulation results with shading differences between driving and parking as compared to a constant shading of 49% in Paris and Malaga for the three electrical architectures. The considered charge with the grid is every five nights.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**January** & **February** & **March** & **April** & **May** & **June** & **July** & **August** & **September** & **October** & **November** & **December** \\ \hline
69\% & 59\% & 29\% & 21\% & 23\% & 25\% & 24\% & 24\% & 23\% & 51\% & 69\% & 64\% \\ \hline \end{tabular}
\end{table}
Table 3: Variable shading losses between months in Paris.  
Table 5 shows the small influence of considering different shading losses between the driving and parking phases. This is because the use profile of passenger cars includes parking most of the day.
# Improving Graph Neural Network Models in Link Prediction Task via A Policy-Based Training Method
## 4 Experiments
### Influence of the Sampling Period  
In this experiment, we study the influence of the sampling period on the proposed PbTRM. The prediction performance of the GCN+PbTRM model is compared to its benchmarking GCN model by searching the \(T\) value in a range of {1, 2, 5, 10}. The \(T\) value for the benchmarking GCN model means that the negative samples in its training set are updated by randomly sampling unobserved edges after finishing \(T\) training epochs. To illustrate the effectiveness of the proposed PbTRM, the prediction performance of the benchmarking GCN model, whose \(T\) value is set as 1, is used as the baseline performance. The experimental results are illustrated in Figure 4a-d.  
Figure 4: The changes in prediction performance of GCN+PbTRM and benchmarking GCN models on different \(T\) values of a sampling period.  
On the Cora dataset, the AUC and AP values of the GCN+PbTRM model with the \(T\) value of 1 and 2 are higher than the baseline performance and fell to around the baseline performance when the \(T\) value is equal to 5. On the CiteSeer dataset, the AUC and AP values of the GCN+PbTRM model keep higher than the baseline performance when \(T\) is equal to 1, 2, and 5. Given these results, the proposed PbTRM is workable when the value of \(T\) is set to be a relatively low value. As the value of \(T\) increases, the training of the policy network in the negative sample selector becomes insufficient, which results in the poor prediction performance of the GCN+PbTRM model. The update frequency of negative samples in \(E_{train}^{reg}\) keeps decreasing with the increasing \(T\) value, which can be another reason for the decreasing prediction performance of both the GCN+PbTRM and benchmarking GCN models.
## 3 Results
### Relationship between Satellite Inversion Wind Field and Wind Energy Density over the Yellow Sea  
Figure 10 compares the 10 m wind fields derived from HY-2C and CFOSAT satellite retrievals with the wind power density simulated using WRF at various heights. The wind power density also increased as the 10 m wind speeds increased in HY-2C observations. When the 10 m wind speed was less than 9 m/s, it followed a cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the wind speeds. Due to the concentration of HY-2C observations on the Yellow Sea surface in the range of 6-10 m/s, the data points on the graph were relatively clustered, and the cubic relationship between the wind power density and wind speed was not apparent.  
Similarly, as the 10 m wind speeds increased in CFOSAT observations, the wind power density also increased. When the 10 m wind speed was less than 9 m/s, it followed a cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the wind speed.  
Figure 9: Comparison between HY-2C and CFOSAT inversions of the 10 m wind field over the Yellow Sea.
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 4 Discussion  
### Interannual Signal in GNSS Time Series  
Concerning the difference between the deterministic (_tiasd_) and stochastic (_cats_d_) accounting of interannual variations, we can point out some key elements. Even if our statistical method using the dispersion of instantaneous velocity provides more realistic uncertainties (more than 10 times larger) than that directly given by the WLS method, we also observe in Figure 6 that the uncertainties for _tiasd_ are three to four times smaller than those for the WSL + MLE method, which contain a complete stochastic part (WH + PL noise). Moreover, these small uncertainties are accompanied by important differences in  
Figure 11: Lomb–Scargle periodograms for the three loading models and the detrended MG3 GNSS solution. The periodograms of all stations were stacked and then divided by the number of stations in order to obtain mean periodograms for each solution. The vertical black lines indicate the solar cycle harmonics, and the vertical green lines indicate the draconitic harmonics.  
the velocity values compared to that estimated by CATS: the differences are nested in an interval of \(\pm 1.5\,\mathrm{mm/y}\), which is quite important considering the precision requirement on the terrestrial reference frame (such as ITRF) of \(0.1\,\mathrm{mm/y}\)[12].  
Even if it is mathematically correct to fit the time series with polynomials, this is clearly not suitable for geophysical interpretation. Interannual signals in GNSS time series are due to geophysical deformations in only few special cases: regions with melting ice caps or tectonic activity such as slow slip events [4; 74; 75; 76]. If some GNSS interannual variations have been related to a geophysical origin [44], several previous studies demonstrate that there is a limitation in interpreting this type of signal in terms of geophysics at large spatial scales [77]. Interannual variations in GNSS time series are properly captured by a spatially consistent PL noise model with a relatively large amplitude, regardless of the GNSS station location or the geophysical phenomena affecting them. Therefore, to avoid a misleading interpretation of the interannual signal as a deterministic signal, we do not recommend the use of polynomials in GNSS time series adjustment models. It still remains very difficult to distinguish the true geophysical signal from the noise contribution in the residual time series.
## 3 RL-Based Multimedia Traffic Routing Architecture
### Process Description  
To begin with, a customer requests to watch a video from a multimedia provider. The SDN switch sends a packet_In message request to the controller, which starts to monitor the network topology in the infrastructure plane by utilising the network awareness and the network statistics. In the meantime, the controller (on-the-fly) uses the video stream classifier module to check the packet type and decide which routing path should be chosen. There are several scheduled events for monitoring the network. Each event runs periodically based on the utilised topology. Three different network sizes have been used; small, middle and large scale (see Section 5.1). Several experiments under the operation of varying topology sizes have been carried out to select the correct values for each scheduled event which resulted in maintaining a full image of the network state to obtain accurate results. The monitoring step updates every 10 s under the employment of the small-scale network, 15 s when the middle-scale network is utilised, and 20 s when the large-scaletopology is used. It involves the network awareness module, which operates every 5, 6 and 8 s for small, medium and large scale networks, respectively and the network statistics module, which runs within the monitoring step. The monitoring periods include the computation of link delay, measured by implementing a python-based RYU application [32] that operates every 8, 10 and 15 s according to the network size. At this point, it is possible to measure the link-state metrics (i.e., available bandwidth, delay, jitter and packet loss rate), which are later fed as QoS parameters into the RL agent The measurement of these metrics is explained in detail in Section 4.1. After that, the network data processing and the network data repository are utilised to extract and log the QoS parameters. Based on the classification result, video flows are prioritised against background traffic flows. The application plane receives the topology information and QoS metrics, which allow applying intelligence to find the best route. Once the path is found, it will use the flow installation module, which starts to install the optimal route between the customer and the video provider. Figure 2 illustrates the flowchart of the described process.  
Figure 2: Flowchart of the proposed RL-based multimedia traffic routing.