esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 5 Results and Discussion  
### Model Comparison  
Two models were developed in this study, including a Multinomial Logistic Regression model and a Multi-Level Multinomial Logistic Regression model. For all two models,the target variable remained the same: Collision Severity, where four categories were retained: Minor Injury, Major Injury, PDO, and Fatality. The different categories of Collision Severity was examined using the term KABCO, which assessed and classified the severity of collisions if they led to Fatality, Major Injury, Minor Injury, or just Property Damage. PDO (Property Damage Only) was kept as the baseline in this modelling.  
After a comparative analysis between two models, Multinomial Logistic Regression (MLR) and Multi-Level Multinomial Logistic Regression (MLMLR), the model performance and complexity assessment were conducted based on their goodness of fit metrics--AIC and BIC. AIC and BIC served as guiding metrics, facilitating model selection. Lower AIC and BIC values signify a more streamlined and efficient data interpretation model. Specifically, an AIC or BIC difference of >10 is strong evidence toward the superior of the model with less AIC or BIC [47; 48].  
As shown in Table 2 for the Multinomial Logistic Regression model, the AIC and BIC are approximately 427,550 and 428,464, respectively. In contrast, the Multi-Level Multinomial Logistic Regression model exhibits a lower AIC (around 421,118) and BIC (about 422,365) values, indicating a better trade-off between fit and complexity. When interpreting the AIC and BIC, smaller values signify enhanced model performance in elucidating collision severity without overcomplicating matters. The analysis favours the Multi-Level Multinomial Logistic Regression model, which maintains a significant advantage with considerably lower AIC and BIC values (more than 10 units) than the Multinomial Logistic Regression model. This suggests that the MLMLR model adeptly captures intricate nuances in collision severity, making it the preferred model. Each model was built sequentially and significant variables with \(p\)-value less 0.1 were considered.
## 2 System Model
### Generalized Pointing Errors Model with Non-Zero Boresight
According to the analysis performed in [42], the [PERSON]'s distribution in (6) can be accurately approximated and simplified through the following modified Rayleigh distribution [42]  
\[f_{R,m}(R_{m})=\frac{R_{m}}{\sigma_{\text{mod},m}^{2}}\exp\left(-\frac{R_{m}^ {2}}{2\sigma_{\text{mod},m}^{2}}\right),\quad R_{m}\geq 0, \tag{7}\]  
where the modified jitter is expressed as [3; 46]  
\[\sigma_{\text{mod},m}^{2}=\left(\frac{3\mu_{x,m}^{2}\sigma_{x,m}^{4}+3\mu_{y, m}^{2}\sigma_{y,m}^{4}+\sigma_{x,m}^{6}+\sigma_{y,m}^{6}}{2}\right)^{1/3}. \tag{8}\]  
Consequently, the PDF for \(h_{p,m}\) is approximated as [3; 46]  
\[f_{h_{p,m}}\left(h_{p,m}\right)=\frac{\psi_{m}^{2}g_{m}^{-\psi_{m}^{2}}}{A_{0, m}^{\psi_{m}^{2}}}h_{p,m}^{\psi_{m}^{2}-1},\,0\leq h_{p,m}\leq g_{m}A_{0,m}, \tag{9}\]  
where in each link it holds that \(\psi_{m}=w_{eq,m}/2\sigma_{\text{mod},m}\), with \(\psi_{x,m}=w_{eq,m}/2\sigma_{x,m}\), \(\psi_{y,m}=w_{eq,m}/2\sigma_{y,m}\) and \(g_{m}=\exp\left(\frac{1}{\psi_{m}^{2}}-\frac{1}{2\psi_{x,m}^{2}}-\frac{1}{2 \psi_{y,m}^{2}}-\frac{\mu_{x,m}^{2}}{2\sigma_{x,m}^{2}\psi_{x,m}^{2}}-\frac{ \mu_{y,m}^{2}}{2\sigma_{y,m}^{2}\psi_{y,m}^{2}}\right)\)[39; 41; 46; 58]. Here, it becomes evident that the impact of pointing errors effect is getting stronger as the value of \(\psi_{m}\) parameter decreases. Conversely, for very large values of \(\psi_{m}\), pointing errors effect can be considered as a negligible one. Moreover, considering independent identical zero mean Gaussian distributions for both elevation and horizontal displacement with \(g_{m}=1,\quad\mu_{x,m}^{2}=0,\quad\mu_{y,m}^{2}=0,\quad\sigma_{x,m}^{2}= \sigma_{y,m}^{2}=\sigma_{s}^{2}\), the generalized PDF for pointing errors with NZB in (9), can be simplified to the one for zero boresight misalignments presented in [7].
# Hybrid Ring- and Tree-Topology RoF Transmission System with Disconnection Protection
## 1 Introduction
The new generation SBOADM can always drop down the desired downstream signals whether the signals are injected into the SBOADM in each direction and can upload and send back the upstream signals via the reversed optical pathway of the downstream signals. Not any active device can be utilized inside this new generation SBOADM; to extend the application of the passive SBOADM, a hybrid ring- and tree-topology RoF transmission system with self-disconnection protection that utilized the new generation SBOADM for hybrid TDM and WDM RoF transport systems was proposed and verified. Comparing to the OADM-, BOADM- or published SBOADM-based WDM transport systems, no additional backup fiber loop, SW or any other active device is deployed among the CO and BSs. This means that no power supply is enquired in the passive fiber loop to accomplish the data transmission among the CO and BSs. The operating expense can be reduced significantly. Our experimental results prove that no complicated network management is required to accomplish the quality of service when a fiber-link failure occurs in any point of the transmission path. The system manager can simply adjust the state of an SW deployed inside the CO to recover all broken network connections.
## 2 Dissolved Gas Analysis (DGA) Dataset
The data is distributed as 691 normal operation samples, 52 thermal fault samples, and 72 electrical fault samples for five types of combustible gases (H\({}_{2}\), CH\({}_{4}\), C\({}_{2}\)H\({}_{2}\), C\({}_{2}\)H\({}_{4}\), and C\({}_{2}\)H\({}_{6}\)). The CO and CO\({}_{2}\) data were not used due to the absence of such data in some of the sources and because they were related to paper degradation [20]. The histograms of the 691 normal concentration samples for a particular type of gas are shown in Figure 1. The concentration limit for each gas at the transformer's typical operating condition is shown by the dotted red vertical line. A condition that requires care may be indicated by values that exceed this limit. Figure 1: Distribution of the 691 samples classified as normal: (**a**) samples of H\({}_{2}\); (**b**) samples of CH\({}_{4}\); (**c**) samples of C\({}_{2}\)H\({}_{2}\); (**d**) samples of C\({}_{2}\)H\({}_{4}\); and (**e**) samples of C\({}_{2}\)H\({}_{6}\). In general, analysis of gases dissolved in insulating oil shows that power transformers function normally. Most of the hydrogen (H\({}_{2}\)) and methane (CH\({}_{4}\)) concentrations are below the 100 ppm and 120 ppm standards, respectively; therefore, they do not indicate impending electrical failures. Because the concentration of acetylene (C\({}_{2}\)H\({}_{2}\)) is less than the threshold limit of 1 ppm, there are no potentially harmful electrical arcs present. It is evident that certain samples of ethylene (C\({}_{2}\)H\({}_{4}\)) and ethane (C\({}_{2}\)H\({}_{6}\)) surpassed 50 ppm and 65 ppm in that order. However, the limits shown are references for evaluation, but the final decision on the condition of the transformer must consider the wider context, including age, loading, and rated power of the equipment. The analysis should be complemented with an expert assessment that considers all these factors. Figures 2 and 3 display the histograms for the 52 thermal fault samples and the 72 electrical fault samples, respectively. Except for a few acetylene samples of electrical failures, all the gas concentration samples are shown to be above the standard limits. The characteristics and nonlinear evolution of the samples are categorized as being in normal condition, electrical fault, and thermal fault, which are frequently different. It should be noted that a variety of factors could influence how early transformer insulation system problems manifest, including age, construction details, cooling methods, nominal power and voltage, and other factors.
###### Abstract  
An antenna's radiation pattern is dependent on its geometrical characteristics and its antenna's surroundings, materials, and geometries. As such, to predict the antenna's performance in complex environments, such as that of small antennas on large vehicles, it is necessary to obtain a model that represents such a full scenario, so that the simulation may be accomplished in the process of antenna design and placement. Due to the complex and electrically large nature of some electromagnetic problems, the detailed representation, even for a simplified model, may imply a large computational effort, both in terms of time and memory, is needed to perform the simulation. This paper evaluates how machine learning models can be used to mitigate the computational effort required to predict the behavior of antennas requiring complex modeling. It is proposed to start from a more simplified model of the electromagnetic structure to obtain a prediction for the correct solution, without needing to simulate the full structure in every iteration, and to combine this with prediction algorithms to obtain the solution of the full problem. The proposed solution uses convolutional neural networks (U-Net) of a certain accuracy to help with the correct placement of small antennas on autonomous vehicles. The standard approach requires the simulation of a full model at each test position, requiring high computational time and memory. With this new proposal, it is possible to analyze more positions and radiation patterns in a much shorter time, and with less memory, when compared with the solution from the full model. Along with this methodology for each simulation, a Bayesian optimizer is proposed to improve the search process for the best location, leading to a reduction in the required steps. This methodology was applied to support the correct positioning of a GNSS antenna with reference to a set of performance indicators required for autonomous vehicles, but it can be also applied to larger and more complex structures, allowing one to reduce the simulation time of a large electromagnetic structure and the search time for the optimum location.  
antenna placement; radiation pattern; autonomous vehicles; machine learning; Bayesian optimization; U-Net +
Footnote †: journal: Applied Sciences  
16 December 2022  
A Prototype Method Using Machine Learning to Aid Complex Electromagnetic Simulations Validated on a Vehicle Model. Appl. Sci.**2023**, _13_, 2197. [[https://doi.org/10.3390/app13042197](https://doi.org/10.3390/app13042197)]([https://doi.org/10.3390/app13042197](https://doi.org/10.3390/app13042197))
## 3 Analysis of Simulation Results  
### Working Mechanism of the Antenna  
The antenna proposed in this article is an improvement on the rectangular patch antenna. Introducing slots into the patch not only increases the current routes and reduces the dimensions of the antenna, but also achieves CP characteristics.  
For a further illustration of the operation of the antenna, Figure 8a displays the surface current distribution when the antenna operates at a 1.4 GHz frequency point. The current is mainly distributed in the left part of the patch, while the current in the GND is mainly concentrated near the right slot. Figure 8b reveals the current distribution when the antenna operates at the 2.45 GHz frequency point: the current is mainly concentrated in the center of the patch, while the current in the GND is concentrated near the right-hand slot. According to the planar inverted F-type antenna equations in Section 2[25], the current path increases due to the two F-shaped slots and three I-shaped slots in the patch. Therefore, combining the current distribution plot and the equations, the new resonant frequencies of the antenna can be calculated to be about 1.41 GHz and 2.35 GHz.
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel Segmentation
#### 4.2.2 Segmentation of Static and Dynamic Objects  
For navigation purposes, identifying static obstacles (such as furniture and walls) is relatively straightforward, while detecting and tracking dynamic obstacles is more complex. This process requires continuous monitoring of the environment and effective tracking of moving objects, to ensure safe and efficient navigation. Ref. [51] proposed a method for detecting and tracking dynamic objects based on an octree structure, first converting point cloud data into voxels, then constructing a short-term map through the octree, comparing the current scan with the short-term map, to detect dynamic voxels, and performing clustering and tracking. Ref. [52] developed a 3D perception system for dynamic urban environments, which is capable of detecting and classifying static and dynamic voxel obstacles. Ref. [53] proposed a model-free tracking method that can perceive and understand the motion in dynamic environments in real time. With the development of deep learning, methods combining voxel models with deep learning frameworks have emerged, effectively handling static and dynamic objects and classifying moving objects, such as cars and pedestrians, especially in dynamic road scenes. Here, we present the performance of these methods on the SemanticKITTI dataset (see Table 4):
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 4 Optimal Encoding Schedules in Partially Parallel Encoding
### Partially Parallel Processing in 5G NR LDPC Encoding
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Encoding Schedule**} & \multicolumn{2}{c}{\(N_{\text{CPC}}\)} & \multirow{2}{*}{\(N_{\text{CS}}\)} & \multicolumn{2}{c}{_Eff\({}_{CS}\) **(b/Cycle per CS)**} \\ \cline{2-3} \cline{5-8}  & **BG1** & **BG2** & & **BG1** & **BG2** \\ \hline Serial & 265 & 150 & 1 & 31.9 & 25.6 \\ Column based parallel & 27 & 15 & 46 & 6.8 & 5.6 \\ Row based parallel & 46 & 42 & 26 & 7.1 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of approximated hardware usage efficiencies of a flexible encoder for various encoding schedules and for the longest codeword (\(Z=384\)) in b/cycle per circular shifter used. Figure 5: Proposed encoding schedule for BG1 codes when four CSs are available. Numbers represent the ordinal of the clock cycle in which the corresponding CPM is processed. that happen in layered decoding [41] architectures because of read-after-write (RAW) data hazards [5; 11]. Although PCM has a structure designed to support decoder efficiency, it can be processed in a way that is optimal for the encoder. For example, the sixth-row group shown in Figure 5 uses almost the same information and core parity bits as the ninth-row group. A significantly higher efficiency would be obtained if the sixth and ninth rows are processed together. Consequently, instead of conventionally processing adjacent rows groups in parallel, the encoding can be accomplished by processing the rows that use the same or at least almost the same information and core parity bits. Therefore, the proposed encoding schedule can be optimized for better hardware efficiency. A method for obtaining the optimal schedule is described in Section 4.2.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations
### Ranging Performance
#### 3.3.3 Multipath Error  
Multipath signals (echoes) of which the delay time \(\tau_{M}\) is shorter than \(T_{c}\Big{(}1+\frac{d}{2}\Big{)}\) distort the correlation function between the direct signal and the local replica, resulting in a pseudorange error [44], where \(T_{c}\) is the chip width. Assuming that the receiver front-end bandwidth is infinite, the correlation function distorted by one multipath signal is expressed as  
\[R_{comp}(\tau)=R(\tau)+\alpha R(\tau-\tau_{M})\cdot\cos(\Delta\psi) \tag{12}\]  
where \(\alpha\) is the multipath-to-direct ratio (MDR) and \(\Delta\psi\) is the phase difference between the direct and multipath signals.  
The distorted correlation function deteriorates the code-tracking performance and its effect depends on the discriminator type. Assuming that the receiver front-end bandwidth is infinite and there exists a single multipath signal, the CELP and NELP discriminator outputs can be expressed as Equations (13) and (14), respectively [45]:  
\[\varepsilon_{MP,CELP}(\Delta\tau)=\left[R\bigg{(}\Delta\tau-\frac{d}{2}\bigg{)} +\alpha R\bigg{(}\Delta\tau-\tau_{M}-\frac{d}{2}\bigg{)}\cos(\Delta\psi) \right]-\left[R\bigg{(}\Delta\tau+\frac{d}{2}\bigg{)}+\alpha R\bigg{(}\Delta \tau-\tau_{M}+\frac{d}{2}\bigg{)}\cos(\Delta\psi)\right] \tag{13}\]  
\[\varepsilon_{MP,NELP}(\Delta\tau)=\left[R\Big{(}\Delta\tau-\frac{d}{2}\Big{)} +\alpha R\Big{(}\Delta\tau-\tau_{M}-\frac{d}{2}\Big{)}\cos(\Delta\psi) \right]^{2}-\left[R\Big{(}\Delta\tau+\frac{d}{2}\Big{)}+\alpha R\Big{(}\Delta \tau-\tau_{M}+\frac{d}{2}\Big{)}\cos(\Delta\psi)\right]^{2} \tag{14}\]  
where \(\Delta\tau\) is the code tracking error in chips.
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 10 Mechanism of Signal Transduction
In support of this model, authors show that RseA-RseB dissociation by LPS is inhibited in the presence of LptA, suggesting that the periplasmic accumulation of LPS, due to release from Lpt proteins in the periplasmic bridge, can promote the dissociation of RseB from RseA and cause RpoE induction. However, the validity of this model needs further studies as free LPS is not known to accumulate when the LPS core is truncated and severe defects in LPS, particularly upon accumulation in the IM when the Lpt system is depleted, also induce RpoE. Analysis of distribution of free RpoE vs. RpoE bound to the IM in \(\Delta\)_xuaA_ has revealed that the elevated RpoE activity can be attributed to increased release of RpoE from the IM in such mutants [87]. However, a joint signaling when LPS is defective via the Rcs system and RseB can amplify RpoE induction and hence these models are not mutually exclusive. In summary, comprehensive analysis of various LPS defects has revealed that RpoE activity is specifically induced when LPS biosynthesis or assembly is compromised by the activation of the _rpoE_P3 promoter with phosphorylated RcsB acting as an activator and the sensing of LPS defects occurs via the Rcs phosphorelay system (Figure 3).
# Study on the Mechanism of the Variable-Speed Rotor Affecting Rotor Aerodynamic Performance
## 1 Introduction
The study showed that in slow forward flight, blade dynamic pressure reduction is the main cause of reduction in the profile power and the total required power of the variable-speed rotor. In fast forward flight, the advancing blade compression loss and blade dynamic pressure reductions both decrease the profile power and the total required power. The hover case, which is dominated by the induced power, has not yet been discussed. Furthermore, considering that the variable-speed rotor is mostly applied to a long-endurance unmanned helicopter, whose weight could largely decrease in flight, the effects of rotor loading should be investigated when discussing the aeromechanics of the variable-speed rotor. This current work mainly focuses on studying the mechanisms of the variable-speed rotor affecting the rotor aerodynamic performance and contributes to revealing the various effect mechanisms under the conditions of various flight speeds, including hover, and rotor loadings. A dimensionless parameter analysis is applied in this current work. The prescribed wake method with BVI (blade vortex interaction) correction is applied to predict the rotor inflow to capture the non-uniform distribution of rotor inflow which is of significance when predicting the induced power. The Leishman-Beddoes dynamic stall algorithm [20; 21; 22] is applied to model the airfoil nonlinear aerodynamic behaviors for a variable-speed rotor. The rigid flap assumption is applied in the current work which limits the study of the variable-speed rotor dynamics, for example, the resonance issue and blade root loading analysis. A helicopter simulation code developed in-house is applied in the current work.
## 2 Methodology
### Proposed Discrete Wavelet Transform (DWT)
Moreover, selecting the correct threshold in wavelet denoising is crucial. If the threshold is too low or too high, the signal cannot be accurately estimated. The threshold \(\lambda\) can be expressed as follows [18]:  
\[\lambda=\varepsilon\times\sigma \tag{3}\]  
where \(\varepsilon\) represents the control coefficient and \(\sigma\) represents the mean square error. Furthermore, given the availability of different threshold functions, such as hard-threshold and soft-threshold denoising types, we can select the most appropriate function. The wavelet coefficient (c) is a function of time in terms of the oscillations [18]. Soft and hard thresholding is used in wavelet denoising, defined by Equations (4) and (5), respectively [18; 23]. \[\mathrm{c}\lambda= \begin{cases}\mathrm{sign}(\left|\mathrm{c}\right|-\lambda),\,if\,\left|\mathrm{c}\right|\geq\lambda\\ 0,if\,\left|\mathrm{c}\right|<\lambda\end{cases}  \tag{4}\]  
\[c\lambda=\left|x\right|= \begin{cases}\mathrm{c}\,,\,if\,\left|\mathrm{c} \right|\geq\lambda\\ 0,if\,\left|\mathrm{c}\right|<\lambda\end{cases}  \tag{5}\]  
where c represents the wavelet coefficient and \(\lambda\) is the threshold. In the soft threshold de-noising method, when the wavelet coefficient \(\left|\mathrm{c}\right|<\lambda\), the noise can be reset to zero, while when \(\left|\mathrm{c}\right|\geq\lambda\), the \(\left|\mathrm{c}\right|\) is subtracted by \(\lambda\). In the hard threshold denoising technique, when \(\left|\mathrm{c}\right|<\lambda\), the noise can be reset to zero, and when \(\left|\mathrm{c}\right|\geq\lambda\), the wavelet coefficient retains c [18; 23]. Shown in Figure 0(a) are the noise data before applying DWT, and Figure 0(b) presents the noise of the sensing signal, which is filtered and looks clear in different levels or stages. As shown in the figure, as the number of levels increases in the DWT denoising technique, the effectiveness of noise removal also increases, resulting in a clearer and more refined sensor signal. Conversely, reducing the number of levels may lead to less effective noise reduction and a signal that retains more noise artifacts. Indeed, they typically achieve a good, clear signal after applying DWT denoising at stage 4 or stage 5, or stage or level or layer 5, striking a balance between noise reduction and signal preservation.