esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
###### Abstract  
Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors' operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.  
autonomous vehicles; sensors; perception; weather; camera; LiDAR; RADAR; GNSS +
Footnote †: journal: Sensors  
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# Equivalent Electrical Circuit Modeling of CNT-Based Transparent Electrodes
## 3 Electrical Modeling of CNT Networks
### Percolation Theory for the Modeling of CNT/Polymer Composites Conductivity  
When conductive nanoparticles are deposited on a substrate, or a conductive filler is dispersed in an insulating polymeric matrix, the electrical behavior of the newly formed network switches from insulating to conductive when the so-called percolation threshold is overcome, i.e., the specific filler nanoparticles density at which a conductive path is established. In the case of CNTs used as nanofillers, the electrical conductivity of the nanocomposite \(\sigma_{nc}\) can be modeled following the classical percolation theory by [34]  
\[\sigma_{nc}=\sigma_{i}\bigg{[}\frac{\phi_{c}}{\phi_{c}-\phi}\bigg{]}^{s}\qquad \qquad\qquad\phi<\phi_{c} \tag{7}\]  
\[\sigma_{nc}=\sigma_{c}\bigg{[}\frac{\phi-\phi_{c}}{1-\phi}\bigg{]}^{t}\qquad \qquad\qquad\phi>\phi_{c} \tag{8}\]  
where \(\sigma_{i}\) and \(\sigma_{c}\) are the insulating phase and conductive phase dc conductivities, and \(\phi\) and \(\phi_{c}\) are the CNT volume fraction and the percolation threshold. The exponents \(s\) and \(t\) are usually extracted by fitting the experimental data and are related to the spatial configuration of the CNT networks.  
The influence of the CNT properties (such as length, aspect ratio, aggregation state) on the tube-tube interactions and on the tunneling effect, determining the percolation threshold and the overall conductivity of the material, was discussed by several authors [39; 40; 41].
## 5 Discussion
### Implementation Overview  
The localization system utilizes ceiling-mounted ultrasound beacons and accommodates robots of varying heights:  
* Beacons are mounted on the ceiling to provide consistent coverage and minimize obstructions.
* The system is designed to work with robots of different heights, enhancing its versatility.
* The plane offset between beacons and robots varies based on robot and ceiling height.
#### Challenges to Preprocessing of Multi-Site TBI MRI Data
We provide a comprehensive evaluation of the effects of intensity normalization and bias-field correction on the performance of automated brain extraction tools (BET and optiBIET) using a large, multi-site dataset of patients with TBI. This systematic analysis across 154 preprocessing pipelines and two MRI sequence types (T1-MPRAGE and T2-FLAIR) allows for robust comparisons and far-reaching insights. We identify specific parameter configurations of N3 bias-field correction that significantly improve brain extraction accuracy for TBI data, with notable differences between T1-MPRAGE and T2-FLAIR images. These findings can directly guide the optimization of preprocessing pipelines for TBI MRI analysis. We find that alternative BET configurations (BET \(|\) R and BET \(|\) B,f0.2,g0.3) outperform the widely used BET \(|\) B,f0.1 setting for TBI data. This highlights the need for TBI-specific validation of preprocessing techniques and suggests avenues for improving tools like optiBIET. We demonstrate the nuanced effects of multiple intensity normalization methods (Z-score, WhiteStripe, KDE-based) on brain extraction performance, providing a basis for further research. Collectively, our results delineate the complex interplay between intensity processing and brain extraction in the presence of TBI, offering valuable insights for the development of robust, optimized preprocessing pipelines tailored to the challenges of TBI MRI data. The detailed analysis of technique interactions and TBI-specific effects informs both the refinement of existing methods and the creation of new, specialized tools for TBI neuroimaging research.
## 3 Preliminary Calibration Attempt and Selection of Parameters for Optimization
### Summary of the Selected Parameters for Optimization  
With the heat loss and transfer parameters (\(\alpha_{loss}\), \(\alpha_{HTF}\)), the constants to represent the temperature distribution of the vapor vessel (k\({}_{1}\)-k\({}_{3}\)) and the two reaction rate constants (k\({}_{R,1}\), k\({}_{R,2}\)), seven parameters in total are selected for optimization by inverse modeling. The parameters and the considered ranges are listed in Table 5. It was found, that perme  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** & **Range Minimum** & **Range Maximum** \\ \hline Heat-transfer coefficient & \(\alpha_{HTF}\) & \(W/\text{m}^{2}\text{K}\) & 150 & 350 \\ Heat-loss coefficient & \(\alpha_{loss}\) & \(W/\text{m}^{2}\text{K}\) & 0 & 350 \\ Temperature distribution Param1 & \(k_{1}\) & \(\text{Km}^{-2}\) & \(-15\) & 15 \\ Temperature distribution Param2 & \(k_{2}\) & \(\text{Km}^{-1}\) & \(-20\) & 20 \\ Temperature distribution Param3 & \(k_{3}\) & K & 740 & 780 \\ Reaction coefficeent & \(k_{R,1}\) & \(\text{s}^{-1}\) & 0.1 & 20 \\ Reaction exponent & \(k_{R,2}\) & - & 0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters chosen for optimization.  
ability and heat conductivity do not influence the temperature profile. They are, thus, not considered for optimization but assigned to the tabulated values, see Table 2.
# Two-Parameter Probabilistic Model and Experimental Research on Micron Particle Deposition
## 4 Conclusions  
1. Combining a mathematical expression of the migration, collision, and deposition of micron particles in the gas pipeline with a simulation of the flow field, a two-parameter particle probability deposition model based on a collision probability coefficient is established, which can show the fouling characteristics of micron particles in the gas pipeline in detail, and an algorithm for the distribution of pollution particles considering the two deposition targets of the pipe wall and sediment layer is presented.
2. For 10 \(\upmu\)m particles, deposition in the gas transmission pipeline is influenced by turbulent diffusion and presents spot deposition and a random deposition distribution law on each wall of the gas transmission pipeline; 40 \(\upmu\)m particle deposition presents flake deposition and concentrates on the lower wall and the two side walls close to the lower wall; for the lower wall, the two-parameter particle probability deposition model simulated a peak deposition efficiency range of (13 D\(\sim\)28 D), and the peak deposition efficiency range of particles in the experimental simulation is (6 D\(\sim\)16 D), which is mainly caused by the difference in the wind field and test samples in the gas transmission pipeline simulation.
3. The proposed two-parameter particle probability deposition model is only based on the particle spherical hypothesis, which explores the migration and deposition characteristics of pollutant particles in a long direct gas pipeline, and it still has a large room for improvement in the analysis object and background conditions.
4. In the future, based on this model, the deposition law of mixed material and particle-size composite pollution particles under different gas pipeline backgrounds can be explored, and reference suggestions for the gas pipeline selection of different main pollution particles can be given. At the same time, the influence of pipe length, pipe type, and pipe material on the deposition characteristics of composite pollutants can be further studied by simulation and experiment, and optimization suggestions for a standard gas transmission pipeline in different environments are given.  
Conceptualization, C.N. and Z.Z.; methodology, C.N.; software, C.N. and J.Q.; validation, C.N., X.Y. and Z.Z.; formal analysis, C.N.; investigation, J.Q.; resources, Z.Z.; data curation, C.N.; writing--original draft preparation, C.N.; writing--review and editing, C.N. and J.Q.; visualization, X.Y.; supervision, J.Q.; project administration, J.Q.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Laboratory data will not be uploaded to the database for the time being. If necessary, please email the corresponding author for consultation.  
The authors declare no conflicts of interest.
## 4 Discussion
Comparing our results with the literature, a Chinese study investigating the efficacy of PET-CT in the detection of extrapulmonary tuberculosis found that of the 25 lymph nodesdescribed in patients with tuberculosis, the most commonly involved area was the cervical lymph node, followed by mediastinal involvement [21]. Considering the limitations of our study, its retrospective, single-center focused nature could reflect on the results. However, our hospital is an important hub in the fight against TB in our country, hence the high number of patients being diagnosed and treated in our center. Recent advancements in tuberculosis (TB) diagnostics, particularly for extrapulmonary TB (EPTB), include PCR-based methods like GeneXpert MTB/RIF Ultra, which provide the rapid and sensitive detection of _Mycobacterium tuberculosis_ in non-sputum specimens, even in resource-limited settings. Next-generation sequencing (NGS) has further enhanced the ability to identify drug resistance mutations, enabling tailored treatment approaches. Additionally, artificial intelligence (AI) has shown potential in diagnosing EPTB through an automated imaging analysis of CT and MRI scans, and AI-driven predictive algorithms support clinical decision making and early risk stratification. These technologies collectively improve diagnostic accuracy and accessibility for managing EPTB [31]. To enhance the management of extrapulmonary tuberculosis, future studies should focus on advancing molecular diagnostics, such as developing portable point-of-care tests and multi-target molecular assays while integrating host biomarkers for improved specificity. Innovations in sample processing and rapid culture techniques could significantly improve diagnostic accuracy for difficult-to-handle specimens. AI-assisted imaging and portable diagnostic tools may revolutionize field diagnostics, especially in resource-limited settings. Genomic and proteomic research could identify drug resistance patterns and biomarkers for diagnosis and prognosis. Therapeutic advancements, including tissue-penetrating drugs and adjunctive immunotherapies, are essential, alongside implementation research to integrate these technologies into existing TB programs. Epidemiological studies on extrapulmonary tuberculosis burden and its socio-genetic determinants will further guide targeted interventions.
## 5 Discussion
### Influence of the Calibration Set on the Model Performance  
If the calibration set can well represent the relation of the SOM content with VNIR spectra, then the prediction model calibrated using this calibration set will have a good prediction performance. Table 4 shows that the prediction models calibrated using the calibration sets selected by the classical C and KS methods lacked good prediction performance, whereas the prediction models based on the Rank-KS method could build models with considerably high \(R_{p}^{2}\) and RPD in the study area. This phenomenon was mainly due to the fact that the calibration set obtained utilizing the C or KS method was representative of either the SOM content or the spectrographic information. By contrast, the calibration set obtained utilizing the Rank-KS method was both SOM content and spectral information representative. These results also indicated that the component concentrations and spectrographic information should be simultaneously taken into account to obtain the representative calibration set to accurately build a VNIR-spectrum-based SOM prediction model. However, in comparison with the Rank-KS method, the MVARC-R-KS method could select the more representative calibration sets, which could calibrate the prediction models with the highest accuracy. This result could be attributed to the consideration of the impact of the surrounding environment and spatial heterogeneity on the samples in the MVARC-R-KS method, which were substantially consistent with real situations.  
In summary, the prediction models depended substantially on the selected calibration sets. A representative calibration set instead of a randomly selected calibration set should be obtained in advance to construct a prediction model with good performance. Hence,calibration set selection should be taken seriously during the construction of the prediction models. This study suggests that the MVARC-R-KS method [3] can be set as the optimal choice in the riverside region. This is because this method simultaneously considers the influence of multiple influential factors (i.e., chemical components, spectrographic information, and environmental factors), thereby making the VNIR-spectrum-based SOM prediction model calibrated by the selected calibration set an extensively applicable one.
## 4 Experiments and Results  
To verify the effectiveness of our proposed method, we conduct comparison experiments using the publicly available RSOD [38] datasets and DIOR [39] datasets with different versions of YOLO, some classical detection algorithms, and our proposed method. In this section, we present the datasets used, the evaluation metrics, the experimental procedures, and the experimental results.
# Veins Depth Estimation Using Diffused Reflectance Parameter
## 6 Results and Discussion  
The diffused reflectance parameter Rd obtained from the simulations is plotted alongside the total thickness of skin (inferred as depth of veins) in Figure 3. Furthermore, the value of Rd computed from MCML is used to calculate the vein depth using the mathematical model given in Equation (7). The analytically calculated value of 'z' is also plotted against Rd in the same figures. It can be noted that an increase in the thickness of layered medium gradually decreases the diffused reflectance. This is apparent from the fact that as the thickness of the turbid medium increases, the absorption of photons energy increases with the longer path travelled inside the medium. The results obtained from MCML and analytical calculation exhibit a marginal error in the corresponding vessels depth values. In order to perform sensitivity analysis of the proposed model, the veins depth is calculated with a change of 5% in the optical parameters. The optical parameters like \(\mu_{\text{a}}\), \(\mu_{\text{s}}\) and g tabulated in Table 1 can vary due to the physiological characteristics and temperature. In [15], it is stated that, the value of total attenuation coefficient which depends upon these optical parameters, can vary \(\pm\)5% to \(\pm\)10% of its original value. With this fact, the veins' depth values are calculated, and the results are plotted along with the previously calculated values of veins depth with original attenuation factor. The results given in Figure 3 as E.D +5% and E.D -5% show the drift from the actual value of veins depth against the corresponding value of Rd. The percentage error increased but the overall trends of the plots remain similar.  
In Figure 3 E.D \(\pm\)5% shows the calculated depth with \(\pm\)5% change in attenuation coefficients. MCML plot depicts the vessel depth used in simulations against the diffused reflectance calculated. Table 2 presents the comparison of proposed method to the state-of-the-art methods. The optical density-based method detailed in [13] show the maximum error rate of 30% in calculation of vessel depth. The proposed method shows less percentage error as compared to [13]. In [16], authors have used optical density-based approach to estimate blood 1 vessels depth. The mean error reported for 11 subjects is 4.5%, with the maximum error of 15.2% for the darker skin subjects. However, in [16] the experiments were performed for the vessel depth of 2 mm maximum while in the proposed method, the vessels depth is assumed to be 4.5 mm maximum.  
Figure 3: Vein’s depth calculated (E.D) from the mathematical model against the diffused reflectance computed MCML simulations with vessels depth varied from 1.6 to 4.5 mm. Horizontal axes shows vessels depth in mm and vertical axes shows the measured diffused reflectance Rd through simulation.
## 3 Problem Formulation and the Proposed Path Loss Model
Both \(\vartheta\) and \(b\) are also the network weights to be optimized. A training dataset of inputs is used to find the optimal weights in a supervised learning manner. The goal of optimization is to minimize the error provided by the mean squared error (MSE) function between the network output and the ground truth in the training dataset. The MSE function is given by:  
\[J(\mathbf{\Theta},\mathbf{B},\mathbf{\vartheta},b)=\frac{1}{M}\sum_{k=1}^{M}( \hat{y}_{k}(\mathbf{\Theta},\mathbf{B},\mathbf{\vartheta},b)-y_{k})^{2} \tag{7}\]  
where \(y_{k}\) is the path loss in the training dataset and \(M\) is the total number of recorders. Minimizing the above cost function can be performed iteratively with the use of the error backpropagation algorithm [20]. In this study, the well-known Adam optimizer was adopted. In addition, mini-batch training was used to compute the gradient of the cost function with respect to the weights. To combat overfitting, dropout layers were also imposed. Table 1 summarizes the hyper-parameters of the proposed path loss model. The purpose of this study is to predict path loss in urban environments; therefore, the cell radius, \(Q\), was assumed to be 500 m. By trial and error, the numbers of neurons in the hidden layers were selected as \(N_{1}=502\), \(N_{2}=128\) and \(N_{3}=8\).
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 3 Approximating Stationary Kernels
### Learning Locally Stationary Kernel, GaBaSR
#### 3.2.5. Complexity of GaBaSR  
* **Complexity of sampling all**\(Z_{i}\): The complexity of sampling one \(Z_{i}\) is \(O(KMd)\). Thus, the complexity of sampling all \(Z_{i}\) for \(i=1,2,...,M\) is bounded by \(O(KM^{2}d)\), where \(d\) is the dimension of the input vectors, \(M\) is the number of samples to approximate the kernel and \(K\) is the number of Gaussian's found by the algorithm.
* **Complexity of sampling all**\(\mathbf{\mu}_{k}\) and \(\Sigma_{k}\):
* **Complexity of computing**\(\mathbf{\mu}_{k}\): To take a sample we need to compute \((N_{k}\mathbf{\omega}_{k}\Lambda_{j}+\mathbf{\Lambda}R)(N_{k}\Lambda_{k}+R)^{-1}\) which takes \(O(d+d^{2})\). Also, we need to compute \((N_{k}\Lambda_{k}+R)^{-1}\) which takes \(O(d^{3})\), so the complexity is bounded by \(O(d^{3})\).  
**Complexity of computing \(\Lambda_{k}\)**: To take a sample we need to compute \((\beta W)^{-1}\), which takes \(O(d^{3})\). After after that we need to compute the inverse of a matrix of \(d\times d\) which takes \(O(d^{3})\), so this step is bounded by \(O(d^{3})\).
**Complexity of computing both \(\mathbf{\mu}_{k}\) and \(\Lambda_{k}\)** is bounded by \(O(d^{3})\). We need to take \(K\) samples, so sample all \(\mathbf{\mu}_{k}\) and \(\Sigma_{k},k=1,2,...,K\) is bounded by \(O(Kd^{3})\).
* **Complexity of \(P(\mathbf{y}|X,W_{-j},\mathbf{\omega}_{j}^{*})\)**: The complexity of computing \(P(\mathbf{y}|X,W_{-j},\mathbf{\omega}_{j}^{*})\) (doesn't matter if it is a regression or classification task) is bounded by \(O(NdM+M^{2}N+M^{3})\). Since the complexity of computing the matrix \(\Phi(X)\) is \(O(NdM)\); then, the complexity of computing \(V_{N}\) is \(O(M^{2}N+M^{3})\). This means that the complexity of taking \(M\) samples \((\mathbf{\omega}_{1},\mathbf{\omega}_{2},...,\mathbf{\omega}_{M})\) is bounded by \(O(NdM^{2}+M^{3}N+M^{4})\).
* **Complexity of one swap (loop) of the algorithm**: We sum the three complexities and we have: \(O(KM^{2}d+Kd^{3}+NdM^{2}+M^{3}N+M^{4})=O(M^{2}d(K+N)+Kd^{3}+M^{3}N+M^{4})=O(M^{ 3}N)\) because \(N>>M\).
* **Complexity of \(s\) swaps (loops) of the algorithm**: If we make \(s\) swaps, then the complexity of all the GaBaSR is bounded by \(O(sM^{3}N)\).
###### Abstract  
For the bidirectional wireless power transfer system of electric vehicles, the topology proposed in this paper includes a direct three-phase AC-AC matrix converter as the pre-stage main circuit, a bilateral inductor-capacitor-capacitor-inductor (LCCL) as the resonance compensation network, and a full-bridge converter as the latter stage circuit. According to the characteristics of the system topology, a new control strategy is proposed based on the de-re-coupling method. The control principle of the coordination work of the scheme is expounded, and the corresponding switch combination logic is designed. According to the three-phase voltage amplitude relationship at different stages, combined with the resonant frequency, the switch arms of the matrix converter are alternately controlled separately. The number of switching operations is reduced, the system efficiency and safety are improved, and the full range of soft switching operations of the converter is realized. The theoretical analysis of the bilateral LCCL resonance compensation network is carried out, and its constant voltage/current output characteristic and high power factor transmission characteristic are obtained. Finally, the effectiveness and feasibility of the bidirectional wireless power transfer system for electric vehicles proposed in this paper are verified by simulation analysis.  
AC-AC converter; bidirectional wireless power transfer; soft switching; bilateral LCCL compensation Article