esa-sceva/satcom-chunk-collection huggingface dataset:
# Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair
## 4 Results
### Impact on Quality
#### 4.3.2 Temporal Quality Change  
From Table 2, we concluded that the quality of KINS is better than KIsrc for low QP values, on average. However, from Figures 4 and 5, we observed the opposite in the first frames after keyframe insertion. That is, in the first frames, KIsrc is of a better quality than KINS. Additionally, we observed that using a higher QP resulted in more frames before the quality of CSsrc drops below the quality of CSNS.  
To analyze more thoroughly how long KIsrc has a better quality than KINS, Table 3 shows the average number of frames (after keyframe insertion) until KINS has a better VMAF score than KIsrc. In other words, it shows the number of frames before a quality inversion occurs between KINS and KIsrc.  
When using QPNS \(=\) QPCS \(=22\), the quality of KIsrc is only better than KINS for 6.1 frames, on average over all test sequences. In contrast, when using QPNS \(=\) QPCS \(=32\), it is better for 46.4 frames, on average. These results are in line with the observations of Figures 4 and 5. That is, a larger QP induces more frames in which KIsrc is of a better quality. Additionally, it can be observed that when QPNS \(\
eq\) QPCS, the number of frames is much higher than when QPNS \(=\) QPCS.  
The results from Table 3 can be used to decide whether it is better to use CSsrc or CSNS at keyframe insertion, in function of the number of remaining frames in the GOP of NSsrc. For example, if keyframe insertion is required for QPNS \(=\) QPCS \(=\) 32, and the next keyframe in NSsrc will be introduced in 16 frames, it is probably better to use CSsrc for keyframe insertion. In contrast, if the next keyframe will not be introduced for 500 frames, than it is probably better to use CSNS for keyframe insertion, as the average VMAF decrease is less in that case (see Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Average VMAF Score Decrease**} \\
**QPCS** & & & \multicolumn{3}{c}{**KIsrc**} & & \multicolumn{3}{c}{**KINS**} \\ \hline  & **QPNS** = & **22** & **27** & **32** & **37** & **22** & **27** & **32** & **37** \\ \hline
22 & & 0.38 & 0.60 & 0.35 & \(-0.52\) & **0.27** & 0.36 & 0.25 & 0.15 \\
27 & & 0.61 & **0.78** & 0.48 & \(-0.40\) & 0.59 & **0.60** & 0.44 & 0.28 \\
32 & & 1.35 & 1.41 & **0.90** & \(-0.08\) & 1.40 & 1.46 & **0.75** & 0.48 \\
37 & & 3.09 & 2.93 & 1.98 & **0.73** & 3.22 & 2.98 & 2.01 & **0.94** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average decrease in VMAF score.
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 3 Experiments
### Recognition of Pavement Construction Area Boundary
#### 3.3.1 Recognition of Straight Polygon Pavement Construction Area Boundary  
Three types of straight polygons PCA boundary shown in Figure 1a-c are all composed of straight line segments, so the recognition of the straight polygon PCA boundary can be regarded as identifying a straight line segment in the PCA boundary. Taking Figure 1b as an example, the GPS-RTK measurement errors mentioned in Section 3.2 were added and the proposed BSW was used for boundary recognition. As shown in Figure 10a, as long as there are more than two RP, the fitted function can be obtained regardless of the window size, and the accuracy of boundary recognition is high in terms of single-point positioning, as shown in Figure 10b.
# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 2 Methods
### Label Smoothing  
Label smoothing is a regularization method that allows for a reduction in overfitting and helps CNN architectures to improve their generalization capability. Label smoothing was introduced by [35], and it was shown to boost classification accuracy, adopting a weighted sum of the labels with uniform distribution instead of evaluating the cross-entropy with the \"hard\" labels from the dataset. \"Hard\" label assignment corresponds to binary labels: positive for one class and negative for all of the other classes. \"Soft\" label assignment gives the largest probability to the positive class and very small probability to other classes. Label smoothing is applied to prevent the neural network from being too confident in its prediction. By decreasing the model confidence, we prevent the network training from getting in deep valleys of the loss function [41]. Label smoothing can also be implemented by adding the negative  
Figure 6: Cyclical learning rate with triangular policy mode.  
entropy of the softmax output to the negative log-likelihood training objective, weighted by an additional hyperparameter [42; 43; 44].  
The CNN prediction is a function of the activations in the second to last network layer:  
\[p_{k}=\frac{e^{x^{T}w_{k}}}{\sum_{l=1}^{L}e^{x^{T}w_{l}}} \tag{3}\]  
where \(p_{k}\) is the probability the network classifies to the \(k\)-th class, weights and biases of the final network layer are given with \(w_{k}\), \(x\) is a vector of activations of the second-last network layer fused with '1' to consider the bias. If we train the network with \"hard\" labels, we intend to minimize the cross-entropy between the real labels \(y_{k}\) and the neural network's predictions \(p_{k}\), as follows:  
\[H(y,p)=\sum_{k=1}^{K}-y_{k}\log(p_{k}) \tag{4}\]  
where \(y_{k}\) is '1' for the correct label and '0' for the others. When train network with label smoothing with parameter \(\alpha\), what we minimize is the cross-entropy between the'smoothed' labels \(y_{k}^{LS}\) and the network predictions \(p_{k}\), smoothed labels are given with:  
\[y_{k}^{LS}=y_{k}(1-\alpha)+\alpha/K \tag{5}\]  
The smoothing technique is used in the proposed method aiming to prevent the neural network from becoming too confident in its predictions and, therefore, increase its robustness and predictive capabilities.
# Targeting KRAS in PDAC: A New Way to Cure It?
## 2 Clinical Status of PDAC
### Clinical Treatment of PDAC
#### 2.2.2 For Patients Who Are Unsuitable for Surgery  
Current screening for PDAC targets high-risk populations, and there is no effective screening for the general public due to the relatively low incidence in this population [39]. As a result, some patients have missed the resectable stage by the time PDAC is diagnosed. Coupled with the early onset of metastasis in PDAC, most patients have advanced-stage tumors at the time of diagnosis [40]. For PDAC patients with locally advanced or distant metastases, systemic chemotherapy is currently an effective therapy. The combination of gemcitabine and nab-paclitaxel, as well as FOLFIRINOX, is a commonly used chemotherapy regimen [37; 41]. Although a small percentage of patients with advanced PDAC will have their tumors shrink to an operable size after chemotherapy, the vast majority will not be curable. The primary purpose of systemic chemotherapy for PDAC patients with locally advanced or distant metastases is to slow disease progression and prolong life. A retrospective analysis of clinical cases showed that patients who were younger or in better overall physical condition benefited more from FOLFIRINOX, as evidenced by an increase in overall survival [42; 43]. Additionally, for patients who are not candidates for combination chemotherapy, gemcitabine monotherapy has improved their survival [44].  
Biomarker-targeted therapy is a novel treatment modality, and some progress has been made in the treatment of PDAC. Mutations in BRCA1 and BRCA2 genes are often found in breast cancer patients, and approximately 5% of pancreatic cancer patients also have mutations in these genes [5; 43]. Previous trials have shown the positive effects of PARP inhibitors in breast and ovarian cancer patients involving BRCA1 or BRCA2 mutations, with similar findings in pancreatic cancer patients [45; 46]. Results from the phase III POLO trial showed that the use of olaparib prolonged progression-free survival in patients with metastatic pancreatic cancer of the germline BRCA mutation compared to the placebo group [47]. In 2019, the FDA approved olaparib, a PARP inhibitor, for the treatment of germline BRCA-mutated metastatic pancreatic adenocarcinoma [48].  
Overall, although the clinical therapies for pancreatic cancer are being pursued, the gains have been relatively limited. On the other hand, research into novel therapies, such as targeted therapies and immunotherapies, has the potential to break through present therapeutic dilemmas. It is exciting to note that research on targeted drugs for KRAS, a member of the RAS gene family that has long been considered undruggable, has made tremendous breakthroughs in recent years and has benefited NSCLC patients [49; 50; 51]. It is clear that PDAC, as a tumor that also contains a high percentage of KRAS mutations, is likely to benefit from this.
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
###### Abstract  
Sometimes, it is impossible to conduct tests with the use of the GNSS system, or the obtained results of the measurements made differ significantly from the predicted accuracy. The most common cause of the problems (external factors, faulty results) are interference disturbances from other radio telecommunication systems. The subject of this paper is to conduct research, the essence of which is an in-depth analysis in the field of elimination of LTE interference signals of the GNSS receiver, that is based on the developed effective methods on counteracting the phenomenon of interference signals coming from this system and transmitted on the same frequency. Interference signals are signals transmitted in the GNSS operating band, and unwanted signals may cause incorrect processing of the information provided to the end-user about his position, speed, and current time. This article presents methods of identifying and detecting interference signals, with particular emphasis on methods based on spatial processing of signals transmitted by the LTE system. A comparative analysis of the methods of detecting an unwanted signal was made in terms of their effectiveness and complexity of their implementation. Moreover, the concept of a new comprehensive anti-interference solution was proposed. It includes, among others, information on the various stages of GNSS signal processing in the proposed system, in relation to the algorithms used in traditional GNSS receivers. The final part of the article presents the obtained research results and the resulting significant observations and practical conclusions.  
interference signals; study and analysis; LTE system; GNSS receiver
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 2 Model Description
### NGSO Satellite System Model
#### 2.3.2 Constraints  
The terminals of the NGSO constellation system are distributed in the satellite coverage. Due to the limit of the minimum elevation angle of a terminal, the maximum off nadir angle \(X_{max}\) of a satellite beam could be represented by the following formula:  
\[X_{max}=arcsin(\frac{R_{e}}{R_{e}+h}cos\epsilon), \tag{10}\]  
where \(\epsilon\) is the minimum elevation angle of a user terminal.  
The communication systems with the same frequency as that of the NGSO system also include GSO satellite systems, and the NGSO satellite system needs to avoid the interference to GSO satellite systems according with the the requirement of radio regulation article 22. Most of the NGSO satellite systems adopt the angular isolation to avoid interference with GSO systems, among which OneWeb's system adopts the progressive pitch to avoid interference, while the angle between Starlink satellites and the GSO arc needs to be larger than 22 degrees. Considering that the GSO exclusion zone could change the beam pointing distribution of NGSO satellites, the interference analysis of the RAS should also take the exclusion zone of GSO systems into consideration. Considering the satellite coverage and the GSO avoidance, the satellite beam pointing should meet the following constraints,  
\[arccos(cosAzcosEl)\geq X_{max}, \tag{11}\]  
\[\alpha(\gamma,Az,El)\leq\alpha_{th}, \tag{12}\]  
where \(El\) and \(Az\) are the elevation and azimuth angles of the NGSO satellite beam, and \(\gamma\) is the latitude of the NGSO terminal. \(\alpha\) is the GSO arc avoidance angle, which is the angle between an NGSO satellite and the GSO arc observed from the terminal, where the beam point to the terminal. \(\alpha_{th}\) is the threshold of the GSO arc avoidance angle. Because of the geometry of the NGSO satellite, the GSO arc, and the terminal location, \(\alpha\) is a function of the latitude of the satellite and the beam pointing.
# Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
## 3 H-Transformer
### Training Step  
Figure 1 shows the training step of the H-Transformer. In this step, the following modules are trained:  
1. **The linear module \(M_{L}\)** is estimated using the training set \(Z_{train}\). The objective is to find the SARIMA model's parameters that generate the best linear forecast \(\hat{L}_{train}\) for the training set. Their outputs are the linear forecast \(\hat{L}_{train}\) and the trained SARIMA;
2. **The nonlinear module \(M_{N}\)** is trained using \(Z_{train}\) and \(exo_{train}\) as inputs to predict the residual series \(E_{train}\) (Equation (4)). The output of the nonlinear module is the forecast \(\hat{N}_{train}\) and the trained Transformer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Source** & **Description** \\ \hline \(exo_{train}\) & Dataset & Training n-lagged exogenous data such as weather data. \\ \hline \(Z_{train}\) & Dataset & Training n-lagged target. \\ \hline \(\hat{L}_{train}\) & \(M_{L}\) & Forecast of the linear component generated by \(M_{L}\) from \(Z_{train}\) \\ \hline \(\hat{N}_{train}\) & Equation (4) & Nonlinear component generated by Equation (4) \\ \hline \(exo_{test}\) & Dataset & Testing n-lagged exogenous data. \\ \hline \(Z_{test}\) & Dataset & Testing the n-lagged target. \\ \hline \(\hat{L}_{test+1}\) & \(M_{L}\) & Forecast of the linear component of \(Z_{test+1}\) generated by \(M_{L}\) \\  & & using \(Z_{test}\) \\ \hline \(\hat{N}_{test+1}\) & \(M_{N}\) & Prediction of the nonlinear component of \(Z_{test+1}\) generated by \(M_{N}\) \\  & & using \(Z_{test}\) and \(exo_{test}\) \\ \hline \(Z_{test+1}\) & Equation (5) & Forecast of \(Z_{test+1}\) after the sum of the linear and nonlinear components \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of variables of the proposed hybrid system.  
In the first step of the system, the linear model \(M_{L}\) is trained based on the n-lagged target \(Z_{train}\) to predict the target one step ahead. The trained \(M_{L}\) then outputs the linear component of the \(Z_{train}\), \(\hat{L}_{train}\), which is used by Equation (4) to produce the residual series \(E_{train}\).  
\[E_{train}=Z_{train}-\hat{L}_{train}. \tag{4}\]  
The second step of the system trains a Transformer model \(M_{N}\) using the n-lagged exogenous data \(exo_{train}\) and \(Z_{train}\) to be able to predict the residual series \(E_{train}\). The output of this step is the forecast of the nonlinear component \(\hat{N}_{train}\).  
In the Transformer training step, a BHO [30] searches for the best parameters of the model and the best number of lags to be considered. The BHO finds the best model by minimizing the validation loss function. A summary of how the optimization works and how it was used in this work is further described in Section 4.2.
## 4 Experiment and Verification
### Verification of Validity  
Long-term data trends in the current and bearing temperature are extracted using SD. The results are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Excellent** & **Good** \\ \hline HDDD & 0.821 & 0.338 \\ \hline \end{tabular}
\end{table}
Table 2: Results of threshold calculation.  
Figure 11: Chart of component health status level changes.  
The long-term trends in the current and bearing temperature are normalized positively to the HDDD variation curve. A comparison of the normalized trends is shown in Figure 13. The similarity between the two sets of trends is evaluated using three measures: Pearson correlation coefficient, root mean square error (RMSE), and mean absolute error (MAE). The results of these calculations are summarized in Table 3.  
As can be observed from the calculations in Figure 13, there is a very high similarity between the trends in the complete long-term data extracted using SD and the trends in the HDDD based on the WD. Although slight variations can be observed in certain details, the  
Figure 12: Trend extraction based on the seasonal decomposition algorithm.  
Figure 13: Comparison of the two trends in the (**a**) current and (**b**) bearing temperature after positive normalization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Current** & **Bearing Temperature** \\ \hline Pearson correlation coefficient & 0.999 & 0.999 \\ RMSE & \(3.622\times 10^{-5}\) & \(3.273\times 10^{-5}\) \\ MAE & \(2.986\times 10^{-5}\) & \(2.652\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation of trend change similarity indicators.  
magnitudes of the RMSE and MAE, as presented in Table 3, are all on the order of \(10^{-4}\). The consistency of the change is also reflected in the results of the similarity metrics. The method presented in this research relies on a single indicator for the description of health changes. However, SD first generates model assumptions and parameter estimates for the overall data and then extracts accurate trends by processing the long-term data. The validation results, which indicate a high degree of similarity, fully demonstrate the validity and accuracy of the method.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 3 Maximum Likelihood Estimator  
Most previous research concentrates on the estimation of the target position with TDOA measurements. Due to the constrained conditions of target, previous algorithms are not suitable for jointly estimating the source position and velocity [29; 30] with TDOA measurements only. In this section, we propose an iterative maximum likelihood estimator for both position and velocity in the scenario of geolocation based on SAGI networks.  
Define the unknown parameter \(\theta_{2}=\left[\theta_{a}^{T},\theta_{nav}^{T}\right]^{T}\) which contains the locations of target \(\theta_{a}=\left[\alpha_{o}^{T},\hat{\alpha}_{0}^{T}\right]^{T}\) and observers \(\theta_{nav}=\mathbf{z}_{nav}^{o}\). Suppose \(\theta_{2,g}=\left[\theta_{a,g^{\prime}}^{T},\theta_{nav,g}^{T}\right]^{T}\) is the initial solution. The observation equation can be approximated as  
\[\Delta\mathbf{z}=\mathbf{z}-\mathbf{z}^{o} \tag{57}\] \[=\mathbf{z}-\Big{(}\mathbf{z}\Big{|}_{\theta_{2}=\theta_{2,g}} \Big{)}-\Big{(}\left.\frac{\partial\mathbf{z}}{\partial\theta_{2}}\right|_{ \theta_{2}=\theta_{2,g}}\Big{)}\big{(}\theta_{2}-\theta_{2,g}\big{)}\]  
The least squares solution of \(\delta\hat{\theta}_{2}\) is given by  
\[\delta\hat{\theta}_{2}=\left[\left(\left.\frac{\partial\ddagger}{\partial \theta_{2}}\right|_{\theta_{2}=\theta_{2,g}}\right)^{T}\!\!C^{-1}\!\left( \left.\frac{\partial\ddagger}{\partial\theta_{2}}\right|_{\theta_{2}=\theta _{2,g}}\right)\right]^{-1}\left(\left.\frac{\partial\ddagger}{\partial\theta _{2}}\right|_{\theta_{2}=\theta_{2,g}}\right)^{T}\!\!C^{-1}\big{(}\theta_{2}- \theta_{2,g}\big{)} \tag{58}\]  
where \(C\) is defined in (6), and \(\frac{\partial\mathbf{z}}{\partial\theta_{2}}\) is given by (28). The corrected estimation of \(\theta_{2}\) is  
\[\theta_{2}^{(1)}=\theta_{2,g}+\delta\hat{\theta}_{2} \tag{59}\]  
Substituting the corrected result into (57) by multiple steps of iteration, the estimation will converge and the amended variable \(\left\|\delta\hat{\theta}_{2}\right\|\) will be smaller than the threshold. Then \(\theta_{2}^{(n)}\) is the final estimation.  
The best localization accuracy of \(\hat{\overset{\star}{\Theta}}_{2}\) is  
\[MSE\big{(}\hat{\theta}_{2}\big{)}=\left[\left(\frac{\partial\ddagger}{\partial \theta_{2}}\right)^{T}\!\!C^{-1}\!\left(\frac{\partial\ddagger}{\partial\theta _{2}}\right)\right]^{-1} \tag{60}\]  
It can be easily shown that (60) is equivalent to (30). Hence, the accuracy of the proposed estimator can achieve the CRLB. The estimation of the target's position and velocity in (59) is modeled in the geodetic coordinate system. The transformation matrix from geodetic to ECEF is defined in (26).
# A Methodology to Improve Energy Efficiency and Sustainability in Urban Environments
## 4 Solar Potential Study
### Connection between Spatial Data and Solar Potential Values  
In the second part of the methodology, the focus was on making the solar potential data applicable and usable at the urban scale [46]. The aim was, therefore, to integrate the calculated solar potential values for each surface area of every building with the basic information provided in a source.osm file [47]. To achieve this, a code was developed in Grasshopper that allowed the grouping of the solar potential values for each building. The compiled list containing all the calculated values for each building was created. Subsequently, these data were integrated into the source.osm file, which served as the base for the urban model (Figure 12). This integration process facilitated the incorporation of solar potential information into the overall urban analysis database.  
### Connection between Spatial Data and Solar Potential Values  
In the second part of the methodology, the focus was on making the solar potential data applicable and usable at the urban scale [46]. The aim was, therefore, to integrate the calculated solar potential values for each surface area of every building with the basic information provided in a source.osm file [47]. To achieve this, a code was developed in Grasshopper that allowed the grouping of the solar potential values for each building. The compiled list containing all the calculated values for each building was created. Subsequently, these data were integrated into the source.osm file, which served as the base for the urban model (Figure 12). This integration process facilitated the incorporation of solar potential information into the overall urban analysis database.  
Figure 11: Calculation of geometric and solar potential data.  
Figure 12: Solar potential values for each surface area for an individual building.  
In order to have a single data value that could be easily read and utilized in other software, the calculated solar potential values for each building were summed up. This sum was then used to overwrite the source.osm file, which was enriched with the new simulated solar potential information for each building. This enabled the export of the information to various spatial data processing software, such as Qgis [48] (Figure 13). The significance of this process lies in the creation of a sort of solar cadaster, where data on the solar potential of all buildings are consolidated into a single database. This database format ensures that the solar potential information is accessible and readable for various stakeholders, including individual building owners and public administrations involved in urban planning and energy management.