esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna
## 2 Materials and Methods
### Accuracy Measures  
The positioning accuracy of the GNSS was determined using chosen accuracy measures which are especially important in the transportation industry because of the widespread use of GNSS [42,43,44]. Therefore, it is important to assess the accuracy of GNSS-based systems carefully and to ensure that they meet the required level of performance for a given application.  
In the case of measures of the accuracy of the 2D horizontal position, conversion to ENU (East, North, Up) coordinate system was performed, and the following coefficients were defined [1,2,29]:  
* DRMS (Distance Root Mean Squared)  
Figure 6: Field test stand.  
\[\text{DRMS}=\sqrt{\sigma_{\text{east}}^{2}+\sigma_{\text{north}}^{2}} \tag{1}\]
* 2 DRMS (Two Distance Root Mean Squared) \[2\text{DRMS}=2\sqrt{\sigma_{\text{east}}^{2}+\sigma_{\text{north}}^{2}}\] (2)
* CEP (Circular Error Probable) \[\text{CEP}=0.62\sigma_{\text{north}}+0.56\sigma_{\text{east}}\] (3) where: \(\sigma_{\text{east}}\)--standard deviation of Easting, \(\sigma_{\text{north}}\)--standard error of the Northing [1,2]. In turn, in the case of the 3D position, conversion to the ECEF coordinate system was performed, and the following accuracy measures were determined [1,2,45]: SEP (Spherical Error Probable) \[\text{SEP}=0.51\big{(}\sigma_{\text{x}}+\sigma_{\text{y}}+\sigma_{\text{z}} \big{)}\] (4)
* MRSE (Mean Radial Spherical Error) \[\text{MRSE}=\sqrt{\sigma_{\text{x}}^{2}+\sigma_{\text{y}}^{2}+\sigma_{\text{z}} ^{2}}\] (5)
* 90% SAE (Spherical Accuracy Standard) \[90\%\text{ SAE}=0.51\big{(}\sigma_{\text{x}}+\sigma_{\text{y}}+\sigma_{\text{z}} \big{)}\] (6)
* 99% SAE (Spherical Accuracy Standard) \[99\%\text{ SAE}=1.122\big{(}\sigma_{\text{x}}+\sigma_{\text{y}}+\sigma_{\text{z}} \big{)}\] (7)  
where: \(\sigma\)--standard deviation of the satellite pseudoranges, \(\sigma_{\text{x}}\)--standard deviation of the x-coordinate, \(\sigma_{\text{y}}\)--standard deviation of the y-coordinate, \(\sigma_{\text{z}}\)--standard deviation of the z-coordinate [1,2].  
Table 1 presents the probability values of the described accuracy measures for the 2D and 3D systems. Mentioned measure values with a higher probability value are more reliable indicators of the accuracy of the GNSS. It also explains why the comparisons of the accuracy of two or more GNSS should be made based on the same accuracy measures (different probabilities).  
Moreover, there are mathematical relationships between the individual measures of positioning accuracy that enable the conversion of values among themselves [2,45].  
\begin{table}
\begin{tabular}{c c c} \hline  & **Position** & **Probability** \\ \hline CEP & 2D & 50\% \\ \hline DRMS & 2D & 65\% \\ \hline
2 DRMS & 2D & 95\% \\ \hline SEP & 3D & 50\% \\ \hline MRSE & 3D & 61\% \\ \hline
90\% SAE & 3D & 90\% \\ \hline
99\% SAE & 3D & 99\% \\ \hline \end{tabular}
\end{table}
Table 1: Probability of selected two-dimensional (2D) and three-dimensional (3D) accuracy measures [2,45].
## 5 Conclusions  
We presented an extended thermal activated fission gas release model for UO\({}_{2}\) fuel. The relevant physical phenomenon addressed in this model involves the diffusion of fission gases from the inner region of a UO\({}_{2}\) grain to the grain boundary. It focuses on the resolution between the grain and the grain boundary, as well as the accumulation and saturation of gas at the grain boundary. This study considers three cases under steady-state conditions: a perfect sink with gas production, a perfect sink without gas production, and an imperfect sink. A perfect sink with gas production is viewed as steady-state, and a perfect sink without gas production is regarded are a transient operation of a LWR. In particular, transient operation belongs to typical Class-II incidents in LWRs. In addition, the effect of FGR resolution on the grain boundary was also considered.  
This model investigates how the fraction of fission gas release (FGR) varies over time, considering different factors such as the grain size, diffusion coefficient, temperature, and linear heat generation rate. Through the validation of the FGR model, several conclusions can be drawn. Gas atoms are release during transient operation faster than during steady-state operation, and the time of FGR is shorter than later in the model validation process when FGR fraction is equivalent. Fission gas will be released immediately during nuclear accidents. According to the results of the imperfect sink, it is evident that FGR is greatly influenced by resolution. The resolution will delay the onset time of FGR.  
Furthermore, it was observed that the FGR fraction increases with higher diffusion coefficients and temperatures, while it decreases with larger grain sizes and higher linear heat generation rates. The diffusion coefficient, which is influenced by the temperature and linear heat generation rate, plays a significant role in driving the variation of the FGR fraction. Therefore, the impact of the diffusion coefficient on the FGR fraction is essentially a result of the effects of temperature and the linear heat generation rate. Importantly, all the results obtained in this model align with existing theories and conclusions. Hence, the  
Figure 12: Gas release fraction versus time without gas production in this model and experimental measurements [14].  
effectiveness of this model is deemed acceptable based on its consistency with established knowledge and findings.  
Conceptualization, J.G. and W.Z.; Methodology, S.C.; Investigation, K.W.; Data curation, J.G.; Writing--original draft, J.G. and W.Z.; Writing--review & editing, S.C. and K.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (23 qnpy75).  
The authors declare no conflict of interest.
## 4 Evaluation and Discussion
### Performance of Forecasting Based on Augmented and Original Dataset
Moreover, the enhancement performance of the LSTM model utilizing augmented data improved by 34.49% in Los Angeles, 34.71% in San Diego, and 36.13% in San Francisco, respectively. Furthermore, in San Francisco, San Diego, and Los Angeles, the hybrid model CNN-LSTM model improved by 44.17%, 42.54%, and 42.31%, respectively. Furthermore, the forecasting of augmented data improved the performance of the hybrid model LSTM-CNN in San Francisco, San Diego, and Los Angeles by 44.46%, 43.91%, and 43.12%, respectively. Figure 6 compares the percentage improvement of the proposed models based on augmented vs. original data in terms of RMSE and MAE. This enhancement demonstrates that providing sufficient training data for the forecasting model has an impact on how well the proposed models perform. Consequently, training data augmentation techniques can help overcome overfitting issues in deep learning models and improve forecasting accuracy. * Augmenting training data to forecast solar radiation has profound scientific implications; it enables forecasting models to understand complex atmospheric processes better and improve decision making. More data allow for better training of deep learning models to capture patterns and relationships between meteorological features and solar radiation. In addition, missing solar radiation data are generated to provide temporal continuity of the time series data, resulting in more reliable predictions of solar radiation. * Finally, this study demonstrated the superiority of standard models utilizing augmented data on the original data in all cases, based on the comparability of the datasets (climatology, geography of the study area, and dataset size). As a result, the current study's findings are congruent with the benchmark study reported by [59]. This improvement illustrates that augmented data affect how well the deep learning models function. Deep learning models are critical for estimating solar irradiance, especially when dealing with a complex problem with a large amount of data. Furthermore, a hybrid model provides better accuracy than single deep learning models. However, it extracts temporal and spatial features from the data. In contrast to machine learning models, their efficiency remains constant as input data increase. Figure 7 shows a comparison of the accuracy of the proposed models based on augmented vs. original data in terms of RMSE for the target three locations. The RMSE improved by utilizing the augmented data for all three datasets compared to the original data for the deep learning model. A lower RMSE value means good model performance [23].
## 7 Methodology Applied in This Research
### Evaluation of the Hybrid IDS-Cisco Fiepower in a Live Office Network
#### 7.2.1 Case Study of Cisco ASA5516-X Fiepower  
The Cisco product used in this live environment markets itself as a leader in threat-focused next-generation security services. The Cisco ASA5516-X with Fiepower Services is centrally managed by the Cisco Fiepower Management Centre, which provides security teams with comprehensive visibility information and control over activity within the network [55; 56]. The engineering team of the SME has direct access to this Cisco security team together with any up-to-date patches and security features available.  
According to Cisco, their Firepower System can help in monitoring the network for traffic that could affect the availability, integrity, and confidentiality of a host and its data. By placing managed devices on key network segments, the device can examine the packets that traverse the network for malicious activity. The system has several mechanisms it uses to look for the broad range of exploits that malicious actors have developed.  
The system uses Intrusion Prevention System (IPS) to monitor the event and cut off any connections to the attacks. This information is a record of the date, time, the type of exploit, and contextual information about the source of the attack and its target. Managed devices transmit their events to the Firepower Management Centre (FMC) where data can be viewed, and the aggregated data can be gained for a greater understanding of the attacks against the network [57].  
The Cisco Firepower records these intrusion data as Impacts to the networks. The impact level indicates the correlation between intrusion data, network discovery data, and vulnerability information. It ranges from Impact Level 0 to Impact Level 4. The Firepower is also able to identify the IP addresses used by the receiving host involved in the intrusion event called Destination IP (Syslog: DstIP). The Firepower also provides information of Classifications where the rule is able to generate where the event belongs.  
The Firepower plays two important roles, one as the IDS and the other as the IPS thus completing a full IDPS integrated system. Through the Security Info Event Management (SIEM), the Firepower is able to produce data and alerts when intrusion is detected, send alarms, and prevent the intrusion.  
In the next section that follows, the Firepower is evaluated in this live environment and its results analysed. This environment was the operating system in the SME which is the industry party in this study. The results reflect empirical observation and analysis of data of the network. The results will be examined as to what is being captured and subsequently the method of approach used from a scientific model and how this is applied to a real-life business of IT management.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 5 Research Gaps and Future Direction
### Mathematical Models  
Mathematical models are common in optimizing ADR schemes. The models help in studying the different metrics and predicting the behaviour and then solving the optimization problem. The models can solve the problem of collision and duty cycle limitations for example. However, most models consider simple single gateway networks and do not support implementation with multiple gateways. Future work would entail optimising the placement of the multiple gateways and incorporating large complex networks in the models. Additionally, some models use complex combinatorial optimization problems which cannot be solved directly. Future work would be to find simpler optimization methods.
## 5 Experimental Validation  
### Experiment Platform and Data Description  
To further verify the presented IAVMD method, we process the experimental data collected from the rotor-bearing system in the vibration measurement laboratory at North China Electric Power University (NCEPU). The experimental device used in the testing is a Bently RK-4 test bench (see Figure 17), which mainly consists of a signal front adapter, speed-adjusting, and a bearing oil pump system. The specification of the data collection equipment is ZonicBook/618E. The eddy current sensor and key phasor transducer are mounted on both sides of the disk to collect the rotor experimental vibration signal and rotational speed signal, respectively. First, plastic rods near the revolving shaft were adopted to produce the slight local rub-impact fault under 1790 r/min (i.e., rotating frequency \(f_{1}=29.83\) Hz). Second, the rotor oil-whirl and oil-whip fault were generated by adjusting the device such as the oil pump, preload scaffold, and motor speed controller when the rotation speed was respectively set at 2600 r/min (i.e., rotating frequency \(f_{2}=43.33\) Hz) and 4500 r/min (i.e., rotating frequency \(f_{3}=75\) Hz). Note that the critical speed of oil-whip is about 1800 r/min (i.e., oil-whip frequency \(f_{w}=30\) Hz). Specifically, in this experiment, the rotor system operates at above twice the critical speeds, resulting in rotor oil-whip. The sampling frequency and data length during testing were set at 1280 Hz and 1024 points, respectively. Generally speaking, when the rotor shows a rub-impact fault, the expected main fault feature frequencies in the frequency spectrum are the rotating frequency \(f_{1}\), its harmonics (e.g., 2 \(f_{1}\), 3 \(f_{1}\), and 4 \(f_{1}\)) and sub-harmonics (e.g., 0.5 \(f_{1}\), 1.5 \(f_{1}\), and 2.5 \(f_{1}\)). Besides, as we know the rotor oil-whirl is also known as half-speed whirl. That is, the expected main fault frequencies in the frequency spectrum are half of the rotating frequency (0.5 \(f_{2}\)) and its related harmonics (e.g., \(f_{2}\), 1.5 \(f_{2}\), 2 \(f_{2}\), 2.5 \(f_{2}\), and 3 \(f_{2}\)) when the rotor shows oil-whirl. Similarly, the expected main fault frequencies in the frequency spectrum are the oil-whip frequency \(f_{w}\), rotating frequency \(f_{3}\), and their related harmonics (e.g., 0.5, \(f_{3}-f_{w}\), \(f_{3}+f_{w}\), 2 \(f_{w}\), + 1.5 \(f_{w}\), and 2 \(f_{3}\)) when the rotor shows oil-whip.
# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 4 Methodology
### PLDA Tree-Based Clustering Block
This choice implies:  
\[P\Big{(}\mathbf{\phi}_{j}|\mathbf{y}_{i}\Big{)}\sim\mathcal{N}\Big{(}\mathbf{\phi}_{j} |\mathbf{\mu}+\mathbf{V}\mathbf{y}_{i},\mathbf{W}^{-1}\Big{)} \tag{7}\]where \(\mathbf{\mu}\) is the speaker independent term, \(\mathbf{V}\) a low rank matrix describing the speaker subspace and \(\mathbf{W}\) a full rank matrix explaining the intra-speaker variability space. Similarly, the definition of \(P\big{(}\mathbf{y}_{i}|\mathbf{\Phi}_{ij}\big{)}\) according to the same model is:  
\[P\big{(}\mathbf{y}_{i}|\mathbf{\Phi}_{ij}\big{)}\sim \mathcal{N}\Big{(}\mathbf{\phi}_{j}|\mathbf{\mu}_{\mathbf{y}_{i}}(j), \mathbf{L}_{\mathbf{y}_{i}}(j)\Big{)} \tag{8}\] \[\mathbf{L}_{\mathbf{y}_{i}}(j)= \mathbf{I}+\mathbf{V}^{T}\sum_{k=1}^{j-1}\theta_{ik}\mathbf{W} \mathbf{V}\] (9) \[\mathbf{\mu}_{\mathbf{y}_{i}}(j)= \mathbf{L}_{\mathbf{y}_{i}}(j)^{-1}\mathbf{V}^{T}\mathbf{W}\sum _{k=1}^{j-1}\theta_{ik}(\mathbf{\phi}_{k}-\mathbf{\mu}) \tag{10}\]  
In addition, for the well-known closed form definitions, the choice for the FBPLDA model also makes the previously mentioned integral have a closed-form solution:  
\[P\Big{(}\mathbf{\phi}_{j}|\mathbf{\Phi}_{ij}\Big{)} \sim\mathcal{N}\Big{(}\mathbf{\phi}_{j}|\mathbf{\mu}_{i}(j),\mathbf{\Sigma}_{ i}(j)\Big{)} \tag{11}\] \[P(\mathbf{\Phi},\Theta)=\prod_{j=2}^{N}P\Big{(}\mathbf{\phi}_{j^{\prime}}\theta_{j}| \mathbf{\phi}_{1}^{j-1},\theta_{1}^{j-1}\Big{)}P(\mathbf{\phi}_{1},\theta_{1}) \tag{3}\]0 (12) \[P(\mathbf{\Phi},\Theta)=\prod_{j=2}^{N}P\Big{(}\mathbf{\phi}_{j^{\prime}}\theta_{j}| \mathbf{\phi}_{1}^{j-1},\theta_{1}^{j-1}\Big{)}P(\mathbf{\phi}_{1},\theta_{1}) \tag{3}\]1  
With respect to the label prior \(P\Big{(}\theta_{j}|\theta_{1}^{j-1}\Big{)}\), we opt for its definition applying the same distribution as in [51]. In this work, the authors make use of a modification of the Distance Dependent Chinese Restaurant (DDCR) process [52]. This process explains the occupation of an infinite series of clusters by a sequence of elements, assigning new elements to already existing clusters or creating a new group. The presented decomposition fits a unique tree structure in which nodes stand for assignment decisions, and leaves, all at depth \(N\), represent the possible partitions \(\Theta\) for the set of embeddings \(\mathbf{\Phi}\). Due to the high complexity of finding the optimal set of labels \(\Theta\)[53], this tree clustering approach follows a suboptimal iterative inference of the labels: We estimate the best partition \(\theta_{1}^{j}\) for the subset \(\mathbf{\phi}_{1}^{j}\) given a solution for a simplified problem and the set of labels \(\theta_{1}^{j-1}\) for the subset of embeddings \(\mathbf{\phi}_{1}^{j-1}\). This procedure is complemented by the M-algorithm [54], which simultaneously tracks the best \(M\) alternative partitions at each time. In our experiments, we opt to make use of a Fully Bayesian PLDA of dimension 100 and trained with all the available broadcast data, i.e., MGB and previous editions of Albayzin evaluations.
# Co-Design of Mechanical and Vibration Properties of a Star Polygon-Coupled Honeycomb Metamaterial
## 3 Stress-Strain Characteristics of the Metamaterials by Simulation and Experiment
Static compression experiments were conducted using the Instron Legend 2367 universal testing machine (UTM) (INSTRON Corporation, Boston, MA, USA), and Figure 3a illustrates the main components of the UTM. The load conditions of the experiments were the same as with the simulations, and the compression displacement was controlled from 0 to 15 mm with a velocity of 0.01 mm/s. However, some of the structures failed before reaching the final displacement, and this problem comes down to the material properties of the WL8000. The testing was stopped when the sample was damaged. Figure 3b-d show the compression histories of the IM, SPCHM, and SPCHM with copper pins generated from the simulations and experiments, respectively, and the small figures A-C are the shapes of the structures by simulation and experiment under different load conditions. As can be seen from the figure, the maximum errors of the three metamaterials between simulation and experiment occur at the end of the experiments. There is little difference in the overall stiffness value of the sample during elastic deformation in the simulation and experiment, and the root mean square errors of the three metamaterials are 5.7%, 6.5%, and 9.88%, respectively. The reason for the error is that, with the increase in the deformation load, the sample is slightly bent along the thickness direction in the experiment but it does not happen in the simulation, so this leads to the experimental value being slightly smaller than that of the simulation. In addition, the manufacture error of the sample is also one source of the testing error. From Figure 3b,c, the pressures to cause 1% deformation of the IM and SPCHM are about 16 kPa and 68 kPa, accordingly, and the mass of the IM and SPCHM are 0.96 kg and 1.26 kg, respectively. The stiffness of the SPCHM is improved by 325% compared to that of the IM, while the mass of the SPCHM is only 23.8%  
Figure 2: Simulation structures and finite element meshes. (**a**) Simulation structure of the IM; (**b**) simulation structure of the SPCHM; (**c**) simulation structure of the SPCHM with copper pins; (**d**) finite element mesh of the IM; (**e**) finite element mesh of the SPCHM; (**f**) finite element mesh of the SPCHM with copper pins. heavier than that of the IM, which indicates that the SPCHM shows an improvement in stiffness owing to the addition of rings. Therefore, the stable load-bearing capacity of the SPCHM increases with the improvement in stiffness. As drawn in Figure 3d, the pressure of the SPCHM with copper pins is up to about 110 kPa at the 1% deformation point and the mass of the SPCHM with copper pins is 5.49 kg. The stiffness of the SPCHM with copper pins is 62% higher than that of the SPCHM, and the mass of the SPCHM with copper pins is 335% heavier than that of the SPCHM. Although the SPCHM with copper pins provides the potential for heavier load-bearing applications, it requires a significant increase in mass.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 1 Introduction
[25] used a CNN (Convolutional Neural Network) to extract feature points from point cloud data for classification and recognition, offset the point cloud in the identification region, and converted the data for the use of localization algorithms by iterative methods. While these methods improve localization accuracy, they may not perform well in environments with sparse and unstructured feature points. This is because most deep learning-based learning methods are based on supervised learning, and their performance is closely tied to the dataset used for training. In [26], [PERSON] et al. introduced the foundational concepts of manifold-based UKF and conducted preliminary two-dimensional robot localization research and validation using depth cameras. Building on [PERSON]'s work in [27], UKF-M is based on the general Lie group structure in which the state is mapped from the manifold to the tangent space using a mapping rule to compute mean and covariance, resembling a normal distribution defined on Lie algebras, with transformations to and from the group using exponential mappings. [PERSON] et al. [28] proposed that using state points on the manifold can better determine the uncertainty caused by sensor-related system errors. To address the issue of robots not performing well in unstructured environments, in this paper we introduce three-dimensional state variables into the manifold space and utilize exponential mapping for spatial transformations. The conventional Lie group structure is modified to approximate the covariance calculation errors as group perturbations in order to better adapt to different observations. The main contributions of this work can be summarized as follows:  
* Introduction of a manifold-based filtering approach for localization (LIF-M) that is suitable for multi-sensor fusion scenarios while being adaptable to unstructured environments. Transformation of the three-dimensional sigma points of the state estimation into the manifold space while applying left and right multiplication methods at different stages of the algorithm to achieve improved localization results. * Design of a global auxiliary localization system to ensure algorithm stability.