esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting
## 2 Weighting Model of Longitudinal Profile
### Lateral Distribution Model of Wheel Trajectories
#### 2.1.3 General Model Parameters  
Based on the generation mechanism of wheel trajectory zones, it can be inferred that the lateral distribution of wheel trajectories is not only related to vehicle type but also influenced by factors such as vehicle traffic volume, traffic density, and vehicle behavior patterns. In mixed traffic environments, particularly within urban traffic flows, the diversity of vehicle types is more pronounced compared to highways, resulting in a higher complexity of the lateral distribution of wheel trajectories. Fortunately, the lane channelization effect has a significant impact on vehicles traveling within each lane, leading to relative uniformity of vehicle types within each lane.  
When considering a typical configuration of a three-lane road with one direction, we can assume that the leftmost lane is primarily used for passing at high speeds or overtaking vehicles, where narrow wheel track width vehicles, such as small cars, may predominate. This composition of vehicles tends to result in a lateral distribution curve of wheel trajectories that approximates a wheel track width of \(L=1.6\) m. The middle lane or travel lane, as the primary path for most vehicles, may contain a more diverse range of vehicle types. If we take medium wheel track width vehicles, such as some trucks, as representative, the lateral distribution curve of wheel trajectories may more closely approximate a wheel track width of \(L=1.8\) m. As for the rightmost lane, if it primarily serves slower-moving or heavier vehicles, such as wide-track cargo vehicles, the lateral distribution curve of wheel trajectories may trend toward a wheel track width of \(L=2.0\) m. In mixed traffic conditions, if the distribution of various vehicles across all lanes tends to be uniform, similar to the vehicle mix in the traffic lanes, the lateral distribution curve of wheel trajectories may converge toward an intermediate value, such as a wheel track width of \(L=1.8\) m, reflecting a more general characteristic of traffic flow. These hypothetical descriptions aim to provide a theoretical framework for understanding the impact of lane channelization effects on the formation of wheel trajectory zones, without being limited to strict classifications of specific vehicle types.  
In view of this, this study suggests fixing the parameter \(\mu\) (or \(L\)) when establishing the lateral distribution model of wheel trajectories \(f(x)\) for a specific lane, with the specific fixed values referenced in Table 2. The core rationale behind this recommendation lies in ensuring a unified benchmark for different equipment when conducting road surface inspections and processing elevation point cloud data through the fixation of parameters. By doing so, not only can the comparability of data be effectively enhanced, but it also lays a more solid and reliable foundation for subsequent data analysis and decision making.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 3 The Baseline Crossing Event
### BCE Signal Model  
A baseline crossing signal model was presented in a previous characteristic study [34]:  
\[s(t)=A_{BCE}(t)\text{cos}\bigg{(}\phi-\frac{2\pi f_{0}}{c}R_{\beta}\bigg{)} \tag{3}\]  
For a received signal, \(s(t)\), \(A_{BCE}(t)\) is the scattering signal envelope, \(\phi\) represents the scattering phase, \(c\) is the speed of light, \(f_{0}\) is the operating frequency, and \(R_{\beta}\) is the bistatic range. Generally, \(R_{\beta}=R_{Tx}(t)+R_{Rx}(t)-L(t)\)[35]. The derivation of the scattering envelope, \(A_{BCE}(t)\) is detailed in a Doppler phenomenology study [28]. Figure 2 resulted from minor changes to the distance measurements in [28], as the baseline midpoint is not considered in this study. The distances are determined by Equations (4) and (5):  
\[T_{\alpha}^{\theta}(t)=\text{tan}^{-1}\left(\frac{x(t)}{L(t)-y(t)}\right) \tag{4}\]  
\[R_{\alpha}^{\theta}(t)=\text{tan}^{-1}\bigg{(}\frac{x(t)}{y(t)}\bigg{)} \tag{5}\]  
In these equations, \(T_{a}^{\theta}(t)\) and \(R_{\alpha}^{\theta}(t)\) are the azimuthal look-angles from \(T_{x}\) and \(R_{x}\), respectively. \(x(t)\) and \(y(t)\) represent target position components in the bistatic plane, respectively. The bistatic plane is defined by the two-dimensional system in which \(T_{x}\), \(R_{x}\), and \(L\) lie. Similar changes were adapted to the elevation look-angles.  
Key simulation parameters were defined in [34], and adaptations were made to transmitter parameters to emulate the L1 C/A signal as defined in [36]. The 'z' variable shown in the legend represents the offset of the target's center of body of mass and the bistatic baseline. The blue plot displays no offset, and the orange plot displays an offset of 20 m. In both cases, as the target approaches or departs from the baseline, there is a brief increase in signal power. At the crossing point, there is a prominent decrease in power. Therefore, the BCE is characterized by a 'W' or 'U' shaped waveform, which is easily seen on magnitude plots in low-noise environments or by correlation with an inverse Ricker wavelet [37].
## 4 Discussion  
The ICF and ICF-CY are two common conceptual models used to assess and classify disabilities. Using survey questionnaires and manual methods to assess and classify disability is an expensive and time-consuming process [15]. Thus, a lack of experts can significantly increase costs and time. Specialized methods can help reduce the cost and time of the classification process. A standard dataset is essential for the learning phase when creating and training an expert system.  
In this research, the aim is to contribute to previous research and achieve optimal performance in binary and multi classes imbalance SCADI dataset, so a new expert system based on the ML models has been proposed for SCADI datasets, which includes data preprocessing, re-sampling, feature selection model, evaluation, and hyper-tuning parameters. The random over-sampler technique is used to overcome the imbalance problem in both binary and multi-classes SCADI dataset.  
Feature selection: It is a very useful preprocessing technique in medical applications because it not only reduces dimensions but also helps us identify the causes of disease [34].The wrapping method is highly computational but has high classification performance, while filters are known to be fast but less accurate. In this study, filter and wrapper methods are combined to provide an advanced multi-objective model. As shown in Figure 2, the optimal features for binary and multi-class datasets were reduced to 14 and 15, respectively, using hyper filter and wrapper methods compared to the literature. The overall performance of four ML models was tested on SCADI datasets processed via feature processing and resampling methods to evaluate the approach to quickly and accurately diagnose binary and multi-class SCADI datasets. The effectiveness of each ML model was evaluated using 10 different cross-validation methods. The grid search method was used to find the best parameters for four ML models. The experimental results show that the RF classifier based on Gini impurity performs the best on both binary and multi-class SCADI datasets.
# 5G-MEC Testbeds for V2X Applications
## 5 Summary of Available Tools  
The implementation of a 5G-MEC testbed needs SDKs and open source tools and has specific hardware requirements. Hardware components are crucial due to the computing power required by SDKs and applications. This section broadly discusses the open source tools used to implement the 5G-MEC system. In the context of the V2X applications, the available simulators are mentioned. The open source tools and SDKs are classified based on their role in the 5G-MEC V2X testbed.
# FE Analysis of Motorcycle Helmet Performance under Severe Accidents
## 3 Finite Element Model
### Headform and Anvils  
Since the headform and anvils can be ignored for the deformation and stress distribution during impact analysis, a rigid body is preferred to generate the finite element models. The headform was modeled to represent a motorcyclist's head, while the anvils were used as the objects that impact the helmet structure. Both components were created based on specified dimensions according to TIS 369-2557 standards [11]. The M-size headform was selected in this paper because it was the most compatible with the helmet size (Figure 4) and had a weight of 5.6 kg. The material model of MAT_020_RIGID was utilized to assign these components in LS-DYNA. Finite element models of all rigid components were also generated using solid elements. The details for each structure are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parts** & **Element Type** & **Number of Elements** & **Number of Nodes** \\ \hline Headform & Tetrahedral & 82,763 & 17,351 \\ Flat anvil & Quadrilateral & 2300 & 2886 \\ Kerbstone anvil & Tetrahedral & 4329 & 20,732 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the finite element model of the headform and anvils.  
Figure 3: Stress–strain curve of material models. (**a**) Shell; (**b**) foam; (**c**) comfort padding [38].  
Each component was separately discretized in the mesh for both rigid and deformable bodies. An assembly of all helmet components was then generated for model preparation before simulation. However, the motion constraints of a rigid body (translations and rotations) were defined in MAT_020, which was different from the deformable body in LS-DYNA.
# Modelling the Defect Processes of Materials for Energy Applications
## 4 Oxide Interfaces
That is, considering the experimental migration enthalpies between strained and unstrained YSZ, the enhancement would be less than 4 \(\times\) 10\({}^{4}\). Recent studies focus on the reduction of the activation enthalpy of oxygen migration and the impact of strain. This is because the enhancement of the ionic conductivity is defined by the ratio of the ionic conductivity in the strained (\(\sigma_{ion}\)) over the unstrained state (\(\sigma_{ion}^{0}\)), given by:  
\[\frac{\sigma_{ion}}{\sigma_{ion}^{0}}\approx exp\left(-\frac{\Delta H_{mig}- \Delta H_{mig}^{0}}{k_{B}T}\right)exp\left(\frac{\Delta S_{mig}-\Delta S_{ mig}^{0}}{k_{B}}\right) \tag{15}\]  
where \(k_{B}\) is Boltzmann's constant, \(T\) is the temperature, \(\Delta H_{mig}\) is the migration enthalpy and \(\Delta S_{mig}\) is the migration entropy. Using [PERSON]'s relation, the change in the activation entropy of migration \(\Delta S_{mig}\) is [104; 105]:  
\[\frac{\partial\Delta S_{mig}}{\partial p}=-\frac{\partial\Delta V_{mig}}{ \partial T} \tag{16}\]  
where \(p\) is the pressure when there is strain in the film. The intricacies of oxide interfaces have been thoroughly investigated recently [106; 107; 108; 109]. In a recent study, [PERSON] et al. [108] used ionic interfaces to form a non-stoichiometric oxygen vacancy concentration. In particular, the oxide nanobrush architecture they created led to a colossal oxygen vacancy concentration at the fluorite-bixbyite (CeO\({}_{2}\)-Y\({}_{2}\)O\({}_{3}\)) interface [108]. This is an example of how designed interfacial oxide architectures can in essence tune the formation and transport properties of intrinsic defects that are key for the development of advanced energy technologies. From a more fundamental material science perspective, the study of [PERSON] et al. [106] determined that tuning the non-stoichiometry in grain boundaries can be used to modify the transport properties. In particular, [PERSON] et al. [106] determined that strain induced defects in the grain boundaries of La\({}_{1-x}\)Sr\({}_{x}\)MnO\({}_{3\pm\delta}\) (LSM) enhance the oxygen mass transport and abate the electronic and magnetic order. This is consistent with previous work [111; 112; 113] but opposite to other oxides in which grain boundaries typically reduce oxygen diffusion [114; 115]. The aim of the use of designed interfaces and nanocomposites with complicated architectures and compositions is to be able to tune the physical properties of energy materials and to reduce the use of expensive and scarce materials (for example, rare-earth materials). This can be more difficult in superconducting materials where rare-earth atoms are fundamentally important [116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130].
# Finite-Time Height Control of Quadrotor UAVs
## 3 Inner-Loop Control Scheme Design
### Design of Super-Twisting Controller
\begin{array}{ll}\dot{V}_{2}(t)&=-|\mu(t)|^{-\frac{1}{2}}\bigg{(}\mathbf{\eta}(t)^{ T}\mathbf{T}_{2}\mathbf{\eta}(t)-2\xi_{2}(t)\overset{\cdot}{\mathbf{Q}}_{2}^{T}\mathbf{\eta}(t) \bigg{)}\\ &\leq-|\mu(t)|^{-\frac{1}{2}}\bigg{(}\lambda_{\min}\{\mathbf{T}_{2}\}\|\mathbf{\eta}(t) \|^{2}-2\xi_{2}(t)\mathcal{Q}_{2}\|\mathbf{\eta}(t)\|\bigg{)}\end{array}  \tag{25}\]  
According to Theorem 1, the observation error \(\xi_{1}(t)\) and \(\xi_{2}(t)\) will converge to a small neighborhood centered at the origin in finite time \(T_{m}\). That is, there exist \(|\varsigma_{2}(t)|\leq\frac{\dot{4 MQ_{1}}}{\lambda_{\min}\{\mathbf{T}_{1}\}}\) and \(\frac{\dot{4 MQ_{1}}}{\lambda_{\min}\{\mathbf{T}_{1}\}}\leq\|\mathbf{\eta}(t)\|\). As a result, when \(\|\mathbf{\eta}(t)\|\geq\frac{\dot{4 MQ_{1}}}{\lambda_{\min}\{\mathbf{T}_{1}\}}\) is satisfied, there is \(|\varsigma_{2}(t)|\leq\|\mathbf{\eta}(t)\|\). Since \(|\mu(t)|^{\frac{1}{2}}\leq\|\mathbf{\eta}(t)\|\) holds with \(\|\mathbf{\eta}(t)\|^{2}=|\mu(t)|+|\
u(t)|^{2}\), there exists  
\[ \begin{array}{ll}\dot{V}_{2}(t)&\leq-\|\mathbf{\eta}(t)\|^{-1}\bigg{(}\lambda_{ \min}\{\mathbf{T}_{2}\}\|\mathbf{\eta}(t)\|^{2}-2\mathcal{Q}_{2}\|\mathbf{\eta}(t)\|^{2} \bigg{)}\\ &=-\|\mathbf{\eta}(t)\|\bigg{(}\lambda_{\min}\{\mathbf{T}_{2}\}-2\overset{\cdot}{ \mathcal{Q}}_{2}\bigg{)}\\ &\leq-\bigg{(}\frac{\lambda_{\min}\{\mathbf{T}_{2}\}-2\mathcal{Q}_{2}}{\sqrt{ \lambda_{\min}\{\mathbf{T}_{2}\}}}\bigg{)}\sqrt{V_{2}(t)}\end{array}  \tag{26}\]  
Letting \(q=\frac{\dot{\lambda}_{\min}\{\mathbf{T}_{2}\}-2\mathcal{Q}_{2}}{\sqrt{\lambda_{ \min}\{\mathbf{L}_{2}\}}}\), select propitiate \(\alpha_{1}\) and \(\alpha_{2}\) to make \(\lambda_{\min}\{\mathbf{T}_{2}\}-2\mathcal{Q}_{2}>0\), then \(\dot{V}_{2}(t)\leq-q\sqrt{V_{2}(t)}\) holds true. According to Theorem 1, the system error \(\mathbf{\eta}(t)\) will converge to the neighborhood of \(\left\{\mathbf{\eta}(t)|\|\mathbf{\eta}(t)\|\leq\frac{4 MQ_{1}}{\lambda_{\min}\{\mathbf{T}_ {1}\}}\right\}\) within finite time \(T_{s}\). Thus Theorem 2 is verified. The convergence time of the inner-loop dynamics equation is calculated as follows:  
\[ \begin{array}{ll}T_{s}&\leq\frac{2\sqrt{V_{2}(0)}}{q}+T_{m}\\ &\leq\frac{2\sqrt{V_{2}(0)}}{q}+\frac{2\lambda_{\max}\{\mathbf{L}_{1}\}}{d_{1}} \ln\frac{\frac{d_{1}}{\lambda_{\max}\{\mathbf{T}_{1}\}}\sqrt{V_{1}(0)}+\frac{d_{2} }{\sqrt{\lambda_{\max}\{\mathbf{L}_{1}\}}}}{\frac{d_{2}}{\sqrt{\lambda_{\max}\{ \mathbf{L}_{1}\}}}}\\ \end{array}  \tag{27}\]  
\(\Box\)
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 3 Management of Acute Asthma Exacerbation
### Intravenous Salbutamol  
There are studies going back to the mid-1970s exploring the effect of IV \(\beta\)2 agonists for the treatment of acute asthma. However, it is unclear if IV \(\beta\)2 agonists offer additional bronchodilation beyond the use of regular nebulisation that leads to better outcomes. A meta-analysis conducted in 2001 involving 15 RCTs and 584 patients concluded that there was no significant evidence to support the use of IV \(\beta\)2 agonists over the nebulised form to improve bronchodilator response [48]. However only 20% (3/15) of the studies directly assessed the outcomes of using IV \(\beta\)2 agonists in addition to the nebulised form. It is also worth noting that these trials evaluated different age groups (adult vs. paediatric) and used different primary outcomes, and as such, only limited conclusions can be drawn from the pooled data. Hence, the role of adding the IV form to the nebulised treatment remains somewhat unclear.  
A subsequent meta-analysis was published in 2012 specifically exploring the addition of IV \(\beta\)2 agonists to inhaled \(\beta\)2 agonists in acute asthma [47]. This analysis included three RCTs involving a total of 104 patients: [PERSON] et al. (1997) [49] (29 children), [PERSON] et al. (2010) [50] (29 adults) and [PERSON] et al. (2007) [51] (46 children). Of these, only [PERSON] et al. (1997) supported the use of the IV form with evidence of shorter recovery time and slight benefit regarding pulmonary index scores [49]. It was noted however, that these advantages should be considered carefully with respect to increased side effects associated with IV \(\beta\)2 agonists. Currently, there is no evidence to support the use of IV \(\beta\)2 agonists in patients with severe asthma in the ICU. However, it is available as an additional bronchodilatortreatment in those with severe airflow obstruction, and it continues to be used as a rescue therapy in ICU patients [45].
## 2 Materials and Methods
### Post-Processing  
The output of each class in our model is a 2D mask depicting the target's predicted location, or OAR, in the same view as the input image. Sometimes, the predicted classes are not segmented properly, creating gaps inside the segmented regions. Thus, an automatic algorithm with a combination of post-processing techniques and rules was implemented. Firstly, the predicted mask is binarized with a pixels' threshold of 0.5. The optimal threshold was determined after an extensive analysis of binarized predicted outcomes. A hole-filling process then eliminates the faulty gaps inside the segmentation. However, the hole-filling method is not performed for the bladder because neighbouring organs are commonly in the inner area (i.e., the prostate).  
A series of rules is also helpful in eliminating misclassified pixels. The rules are set according to each organ's original position and hierarchy. For example, the bladder is localized at the top of the image, followed by the rectum. Similarly, the left femoral head could not be on the right side, and vice versa.  
Artifacts are a common issue in medical imaging [45]. The most common artifacts for the acquisitions with the SSFP sequence occur at extreme slices where the magnetic field is less homogeneous.  
In whole-volume segmentation, it is a common problem that the model predicts inaccurate segments because of these artifacts. Hence, the classified pixels were eliminated if any organ was shown there according to slice position. In the last step, interpolation proposed by [PERSON] et al. [46] was used to homogenize the whole volume in three dimensions. Figure 5 illustrates the prediction without post-processing and the results with post-processing.  
Figure 5: Automatic segmentation and post-processing. (**a**) The predicted mask which contains false-predicted pixels. (**b**) Final mask after the post-processing steps, where the wrong segment of the rectum is removed, and the bladder’s segmentation morphology was corrected.
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components
### Tunable Semiconductor Laser Absorption Spectroscopy (TDLAS) Method  
TDLAS technology has been valued for its advantages of fast response speed, high sensitivity, and high spectral resolution, which can realize on-site point-based and regional open greenhouse gas detection, especially for the monitoring and analysis of trace greenhouse gases such as SF\({}_{6}\). TDLAS technology uses a tunable laser source with a narrow linewidth to fully scan one or more absorption lines of a gas molecule. If multi-gas component detection is carried out, laser multiplexing is usually required. The theoretical basis of TDLAS technology is the Beer-Lambert Law. Gas concentration is measured by selecting gas molecules in a high-resolution spectrum to absorb laser energy, which attenuates the power of the laser. Then, the absorption attenuation of the laser signal during processing and acquisition is amplified to obtain the gas concentration.  
The optical cavity structure serves as the main part of the laser. [PERSON] et al. [46], based on theoretical analysis, selected optical cavity spectroscopy technology to develop a TDLAS detection system. The detection accuracy of HF is 3 ppmv. To obtain the spectral information in the SO\({}_{2}\)F\({}_{2}\) and SOF\({}_{2}\) detection bands, [PERSON] et al. [47] established an experimental platform for TDLAS based on mid-infrared spectroscopy. The cross-interference analysis of SF\({}_{6}\) characteristic components such as H\({}_{2}\)S, SO\({}_{2}\), CO, and SF\({}_{6}\) background gases shows that SO\({}_{2}\)F\({}_{2}\) can be detected when the output wavenumber is 1506.4 cm\({}^{-1}\). When the output wavenumber is 1340.2 cm\({}^{-1}\), the SOF\({}_{2}\) result can be detected. When the concentration of SO\({}_{2}\)F\({}_{2}\) gas is greater than 1.01 and less than 19.88 ppm, the maximum relative error is 2.044%. When the SOF\({}_{2}\) gas concentration is greater than 1.03 and less than 9.00 ppm, the maximum relative error is 0.728%, which has a good linear relationship.
## 5 Automatic Signal Recognition
### Validation
#### 5.2.1 Analysis of \(N=20\), \(T=1\) s  
The recognition results are averaged over three rounds and presented in the form of a confusion matrix in Table 2, where a row represents the actual technology type of a given RSSI trace and a column represents a technology type determined by the algorithm. For all three technologies, the probability of a true positive in the confusion matrix is above 90%, which proves that the predicted technology types are highly consistent with the actual ones. All RSSI traces of LTE signals are correctly classified, whereas 1.85% of the DVB-T and 1.85% of WiFi traces are falsely predicted as LTE, leading to in total 3.7% false positives for LTE. This indicates that the thresholds for determining \(\sigma_{\overrightarrow{R}}\) and \(p(\overrightarrow{\overrightarrow{n}})\) can be improved to more accurately distinguish LTE from the other technologies.  
In Table 2, the most prediction errors appear for the case of Wi-Fi. This is because Wi-Fi is an indoor technology, consisting of multiple transmitters spread over the measurement sites, making its RSSI most sensitive to the influence of locations. As a matter of fact, one measurement location is relatively far from the APs and has no work station in the immediate surroundings. Consequently, the RSSI traces obtained at this location have smaller standard deviations. When none of the traces from this location are included in the training data, the probability to classify them as noise increases, which explains the high false positive rate of noise. Likewise, when the training data primarily consist of RSSI obtained at a remote location, the percentage of noise samples in the training data rises, leading to a higher \(\theta_{p}\). The increased threshold is likely to exclude the traces obtained at busier Wi-Fi areas, causing them to be further classified as either LTE or unknown signal.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 2 Hydrogen Production Technologies
### Thermochemical Processes
#### 2.1.3 Autothermal Reforming  
Autothermal reforming combines the two previous processes to produce hydrogen, as represented in Figure 5. Both steam and oxygen are introduced into the reformer, and the oxidation and reforming reactions occur simultaneously. Thus, the exothermic partial oxidation provides energy to the endothermic steam reforming reaction [31].  
Figure 4: Simplified representation of the methane partial oxidation processes (adapted from [32]).  
Figure 3: Simplified representation of the steam methane reforming processes (adapted from [27]).  
Due to the overlapping of an endothermic and exothermic reaction, autothermal reforming demands lower energy than the SRM process, although it produces a lower hydrogen yield than SMR, but is still able to produce more hydrogen per unit of input fuel than the partial oxidation process [31].