esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement Setup  
For the derivation of the calculation method, the torque influence in this study was first investigated for roller dynamometer measurements. In this way, the tire noise can be evaluated without interference from wind noises. In order to achieve generalized results that can be applied to a variety of different automobile segments, three vehicles of differing sizes and segments were investigated. Table 1 shows the technical specifications of the considered vehicles.  
For each vehicle, measurements with various torque levels on the roller dynamometer were available. In each case, summer tires were used, which are delivered with the series production vehicle. The roller dynamometer measurements of the luxury vehicle were additionally carried out with treadless slick tires.  
The analyzed signals were recorded inside the vehicle using a microphone array consisting of six condenser microphones positioned around the driver's head area. For the analysis, the root mean square (RMS) of the microphone signals was evaluated.  
In order to avoid additional torque-dependent phenomena of all-wheel drive (AWD) vehicles, such as differing torque levels at the rear and front axle or differing tire specifications, only rear-wheel drive (RWD) vehicles were used for this investigation. The following Table 2 shows the torque levels of the electric machine at which the measurements were conducted. The first column presents the terminology used in this paper, and the following columns show the corresponding torque level of the investigated vehicles.  
The denominated torque levels indicate the torque of the electric machine up until reaching the power limit. After reaching the power limit (P, [W]), the torque of the engine (M, [Nm]) decreases with rising rotational speed (n, [min\({}^{-1}\)]) of the engine (see Figure 1 and Equation (1)). This aspect was considered in the following analyses conducted in this study.  
\[\text{P}=\text{M}\times\text{n} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Vehicle 1** & **Vehicle 2** & **Vehicle 3** \\ \hline Vehicle category & luxury & compact & sport utility vehicle \\ Max power & 245 kW & 150 kW & 150 kW \\ Max torque electric machine & 560 Nm & 310 Nm & 310 Nm \\ Drive configuration & RWD 1 & RWD 1 & RWD 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the technical specification of the investigated vehicles.  
Figure 1: Engine torque over the rotational speed of the engine.
# Question Difficulty Estimation Based on Attention Model for Question Answering
## 3 Attention-Based Question Difficulty Estimation  
This paper defines a question difficulty estimation as determining the optimal difficulty level \(y^{*}\in\mathcal{Y}\) of a given question, where \(\mathcal{Y}\) is a set of difficulty levels. It assumes that a question consists of a questionary sentence \(q\) and a set of information components \(A=\{a_{1},\ldots,a_{n}\}\). An information component can be a passage associated with \(q\), candidate answers in multiple-choice QAs, or a video-clip description in video QAs. Then, the difficulty estimation becomes a classification problem in which a classifier \(f(\cdot;\theta)\) parameterized by \(\theta\) determines \(y^{*}\) given \(q\) and \(A\). According to Figure 1, \(q\) is \"Why did [PERSON] experience culture shock in Japan?\" and the passage \"A Japanese student...\" and five candidate answers become the elements of information component set, \(A\). Then, the classifier \(f\) determines the question difficulty given \(q\) and \(A\).  
The proposed model of which architecture is given in Figure 2 implements \(f(\cdot;\theta)\) with two kinds of attention modules. It takes \(q\) and \(A\) as its input and encodes them using a pre-trained language model. Then, it represents the bi-directional relationships between \(q\) and every \(a_{i}\in A\) with the dual multi-head attention and the relationship among \(a_{i}\)'s with the transformer encoder. Indeed, the representation of the relationships are accomplished in two steps, since the relationship among \(a_{i}\)'s can be expressed after the relationships between \(q\) and every \(a_{i}\in A\) are all represented. After that, it predicts the difficult level of \(q\) using the relationships.
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Site** & **Morecambe Bay,** & **Gomso Bay,** & **Guif of Tonkin,** & **The Yangtze** & **Dongha** & **Tiaozini** \\  & **U.K. [12]** & **Korea [10]** & **Vietnam [6]** & **River Mouth [14]** & **Sandridges [15]** & **(Our Study)** \\ \hline Vertical accuracy & 40 cm & 10.9 cm & 14.4 cm & 25-76 cm & 47 cm & 30-40 cm \\ Horizontal accuracy & 50 m & \(-\)50 m & \(-\)30 m & \(-\)30 m & 60 m & 30 m \\ Time span & 3–4 years & 1–2 years & 1 year & 1 year & 1 year & month level \\ Stability & relatively stable & more stable & stable & relatively stable & extremely unstable \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of different tidal flats sites in topography monitoring according precious studies. flat areas with drastic changes. On the time axis, the topographic map is generated by the waterline method for half a year or one year. Within this period, the ANN model can provide assistance for topography monitoring. Through the integrated use of these two methods, we can achieve a long-term observation system that provides continuous DEMs of tidal flats. The model in this study also has some issues that need to further improve and be verified. Due to a lack of large-area measured data, we used the topographic results via the waterline method as training data for the ANN model. The simulated DEM of the ANN model has relatively good accuracy, which is also reflected by the reliability of the waterline method, and the waterline method's ability to effectively support ANN simulations. At the same time, the data source of the ANN model was remote sensing image data at a low tide level, which will inevitably affect the input parameters and simulated accuracy. Thus, in addition to selecting low-tide flat images, it is necessary to further explore the influence of the tide level when constructing a topographical simulation model in future studies. In addition, the error was larger in the area of tidal creeks, more measured data should be obtained and input into the model to improve the accuracy around this area.
# Reader-Tag Commands via Modulation Cutoff Intervals in RFID Systems +
## 3 Digital Baseband Modulation Termination
### DBMT Operation  
Once a reader command addresses a tag or multiple tags, backscattering is established to reply to the reader, and the EN signal is pulsed to initialize and enable the DBMT during backscattering. The EN pulse will reset the outputs (i.e., Qs) of the DFFs to zeros. The MT flag is triggered if the CW is off for more than two signal periods. The rationale behind selecting at least two signal periods is that tags are backscattering their data through pulse-interval encoding (PIE) with on-off keying modulation [16]. This PIE encoding involves changing the signal level between low and high intervals when backscattering 0 or 1 s, as shown in the illustration in Figure 6. Hence, since the tag may send 0 (represented as low-high) followed by 1 (represented by high-low), there might be two consecutive \"high\" intervals. During high intervals, the impedance of the tag (Zload in Figure 6) is set to reflect the CW signal, which means the rectifier harvests no power or a high level is shown at the output of the ED. Therefore, the case of sending 0 followed by 1 might indicate high output at the ED for an extended period, which might be interpreted as a CW cutoff. Therefore, MT is designed to be triggered after three consecutive low intervals in the ED. It is worth mentioning that if the tags use phase modulation (PM) [9; 15] for backscattering, it absorbs power during low and high periods of the encoded data. Therefore, one DFF can be utilized in the DBMT of such tags.  
Figure 4: CWAD DBMT circuit location within typical passive RFID components.  
Figure 5: DBMT module and its placement after the ED.  
In Figure 6, a timing illustration is provided for the primary control and output signals in DBMT for a tag during backscattering. The EN control signal initializes DBMT (i.e., resetting the DFF outputs). The tag is to send a stream of bits starting by 01100\(\cdots\). Sending the \"0\" bit is encoded by a low period followed by a high period. Conversely, to send this \"0\" bit to the reader, the antenna impedance (Zload) is changed to R during the low period to absorb the CW signal, then to \"0\" to reflect it. Note that when the CW is reflected, no power is rectified by the tag and the ED produces a high level (as the output of the ED is inverted). The first DFF will follow the ED after one clock cycle, the second DFF after two cycles, and the third DFF after three cycles. As in the PIE encoding, there will be no three successive high periods, and the DFFs will not simultaneously produce a \"1\" at their outputs (Qs).  
The reader may detect corrupted reflections due to interference (collision), then it will cut off CW (within the first period of the 4 th bit in Figure 6) for at least three clock cycles to cause the ED output to 1 and propagate it the outputs of the three DFFs. Once \(Q_{1}\), \(Q_{2}\), and \(Q_{3}\) are all \"1\"s, the MT flag is triggered, and the ongoing modulation will be terminated.
## 4 Discussion
Turning to the specific case of PSP, these exercise-induced neurobiological changes could have therapeutic implications. PSP is a rare and debilitating neurodegenerative disorder characterized by progressive impairment of balance, gait, eye movements, and cognitive function [62]. The neurodegeneration in PSP is thought to involve excessive oxidative stress, mitochondrial dysfunction, and impaired neuroplasticity [6; 63]. By enhancing neuroplasticity, promoting neurogenesis, and bolstering antioxidant defenses, exercise may help offset the neurological decline associated with PSP and potentially improve functional outcomes and quality of life for patients [64]. The exercise program, which included aerobic exercise in its main part, may prove effective in improving motor condition and functional ability. It targeted the most basic motor symptoms, resulting in some improvements. In the early stages of idiopathic Parkinson's disease, high-intensity interval training and progressive resistive strength training combined with movement strategies and education have been found to be effective for both motor symptoms such as gait and balance and falls rates [65; 66]. The rehabilitation program followed by PSP patients often is similar to that of idiopathic Parkinson's disease's because initial symptoms or difficulty with function and activity limitations can appear similar [11; 67]. As PSP progresses, the exercise programs often need to be adjusted to account for the changing functional abilities of the patient. However, as the disease advances and balance, gait, and physical function decline, more targeted exercise strategies may be required. Restorative exercise approaches, such as those incorporating core stabilization, postural control, and compensatory movement patterns, might be beneficial. Additionally, exercise programs may need to be adapted to the specific stage of the disease. In the later stages of PSP, when mobility is severely impaired, exercises focusing on maintenance of range of motion, seated exercises, and activities to promote respiratory function may be more appropriate. One limitation of the present study was the challenge in reaching general conclusions. Also, the use of TUG as the only tool to assess balance is a limitation of this study. However, the study's strength and differentiation was the use of MCIDv in the PSP population. To the best of our knowledge, no other study has demonstrated MCIDv in this clinical population until now. It is essential to acknowledge that MCIDv for various assessments, such as the FTSST in vestibular system disorders [33; 34], the TUG in postoperative patients with degenerative disc disease [35], and the HADS in cardiovascular disease [36], have been reported in different contexts. Future research using larger sample sizes should be conducted to assess the effect of exercise programs, particularly at early stages, on PSP with more specific measurement tools like the progressive supranuclear palsy quality of life scale.
## 5 Results
### Implication for Geological Carbon Storage and Naturally Fractured Reservoirs  
Central Luconia carbonate reservoirs are promising sites for carbon storage as they meet the critical criteria, such as being located within the seismic-free zone, possessing suitable geothermal conditions [2; 3], and experiencing minimal active faulting. Although active faulting is minimal, paleo-faults are prevalent within the Cenozoic sections, particularly in the Oligocene-Moocene sequences. Thus, analysing the fracture network characteristics in assessing the reservoir's carbon storage capability is crucial. Carbon storage in carbonate reservoirs must be evaluated while considering the presence of secondary karst-related porosity. Modelling a realistic fracture network that integrates secondary karst-related porosity, on the other hand, is difficult, especially when there is a lack of available borehole data and the carbon storage volume in the reservoirs needs to be estimated. By studying the outcrop analogues, the behaviour of small (centimetre) and intermediate (metre) scale fracture networks can be acquired by combining outcrop and DEMs. Simultaneously, their interaction with karst features can be physically observed in the field. Areas that appear to lack faults or fractures on the seismic scale may be fractured on smaller scales. The smaller-scale fracture network is predicted to have better connectivity than the seismic-scale fractures. Underestimation of the fracture network can occur if the sub-seismic scale fractures are overlooked.  
The apertures of the fractures primarily determine the permeability of the reservoirs. Fracture orientation and density also affect the permeability, albeit to a lesser extent. According to the 2D DFN models constructed in the Subis Limestone paleo-karst system, only a minor part of the total fracture network contributes to the connectivity. According to field observations, the degree of the fracture enlargement or aperture and secondary porosity (e.g., vugs, caves) is vital for predicting the behaviour of karstified fracture reservoirs, as the significant secondary porosity in the carbonates could be an option for the long-term storage of carbon dioxide [40]. The degree of fracture enlargement in offshore carbonates can be extracted from the borehole log images, well test analysis, and/or mud loss drilling information.  
The analysis provided in this analogue study can be further applied to support the quantitative characterisation of fractures in naturally fractured carbonate reservoirs at multiple scales to improve the qualitative interpretation of the fracture network.
# Transfer Learning Model for Crack Detection in Side SlopesBased on Crack-Net
## 4 Experiment
### Ablation Study  
Following the stability analysis that confirmed the reproducibility of our method, we now examine the individual contributions of each proposed component. This paper substantiates the optimization effect of each module through a series of progressive ablation experiments, as illustrated in Table 4.  
With the incorporation of FDNM alone, the AP increases by 3.3% to 88.4%, and the accuracy improves to 78.9%. This result indicates that the dynamic spectral decomposition strategy of FDNM effectively enhances the identification of fine crack features. Meanwhile, the FPS increases to 25.5, demonstrating that the FDNM not only improves detection performance but also accelerates inference speed. When the DFFM is used alone, the AP reaches 89.2%, and the OIS index shows a significant improvement. This finding verifies the optimization effect of the multi-region fusion block on cross-layer feature alignment. At the same time, the FPS increases to 26.7, further confirming that the DFFM enhances both accuracy and efficiency. After integrating both modules, the AP further increases to 91.2%. This result demonstrates that the combined optimization strategy in both the frequency and spatial domains significantly improves the continuity prediction of complex crack structures. The improvement is due to the complementary nature of the feature enhancement directions, which clarifies the regulatory mechanism of collaborative multi-scale feature fusion. Notably, the FPS of the full model reaches 27.9, indicating that the combined modules not only maximize detection accuracy but also achieve the highest inference speed among all configurations. These findings highlight that previous methods often overlook small cracks, focusing primarily on larger ones. The proposed approach, which enhances detailed crack features, then extracts and fuses them, leads to substantial improvements in crack detection.
## 2 Compact Model Development
### Model Formulation
In reverse bias, the model of the junction capacitance thus consists of two contributions:  
\[C_{jT}(V_{d})=C_{jm}(V_{d})+C_{jl}(V_{d}) \tag{5}\]  
where the first contribution, \(C_{jm}(V_{d})\), represents the contribution at medium reverse bias, described by the classical equation:  
\[C_{jm}(V_{d})=C_{j0m}\left(1-\frac{V_{d}}{V_{j}}\right)^{-M_{mr}} \tag{6}\]  
The second part, \(C_{jl}(V_{d})\), represents the contribution around and beyond the condition of complete depletion of the collector. It can be expressed as follows:  
\[C_{jl}(V_{d})=C_{j0l}\left(1-\frac{V_{d}}{V_{j}}\right)^{-M_{lr}} \tag{7}\]  
where \(C_{j0l}\) and \(M_{lr}\) can be calculated internally from the punch-through bias, \(V_{PT}=\frac{qN^{-}}{2e_{collact}}\cdot W_{collector}^{2}\) and \(M_{mr}\) respectively [18]. In standard compact modeling practices, the time derivatives of the charges are used for the capacitance model. Thus, in our case, the two contributing charges of the capacitance model are multiplied by smoothing functions (or weights) that allow a continuously differentiable transition across the two adjacent bias regions. Moreover, in our model we also calculate the electric field, \(E_{max}(v_{d})\), at the input of the collector which is included in the expressions of the tunneling currents. We can then use these expressions to calculate the transit time of electrons in the collector, \(\tau_{c}\), from their drift velocity, which can be expressed by the following empirical expression [19]:  
\[v(E)=v_{sat}(1+\frac{\frac{E}{E_{scale}}-1}{1+A\left(\frac{E}{E_{scale}} \right)^{t}}) \tag{8}\]  
where \(v_{sat}\) is the saturation velocity, \(E_{scale}=\frac{v_{sat}}{\mu}\), \(A\) and \(t\) are parameters to be determined from the material used. The parameters of Equation (8) can be determined from a fit on the data of each material used.
## 5 Validation of SRRV Deviation Identification Method with Field Measurement Data
### Field Measurement Data Analysis and Validation Result  
An analysis was conducted on the dynamic measurement data of the two sets of turnouts to verify the effectiveness of the SRRV deviation identification method based on the wheel-rail force trend component. The sampling frequency during the on-site dynamic testing of the measurement vehicle was 2000 Hz, and the operating speed was 275 km/h.  
Figure 14a shows the measurement results of lateral wheel-rail force on the switch rail side when the train passes through the turnout with SRRV deviation. From the graph, it can be observed that the lateral wheel-rail force exhibits peaks in both the switch area and the crossing area, with peak magnitudes of 14.94 kN and 9.51 kN, respectively. Figure 14b represents the measurement results of lateral wheel-rail force when the train passes through the turnout without SRRV deviation. In this case, the lateral wheel-rail force exhibits peaks in both the switch area and the crossing area, with peak magnitudes of 7.79 kN and 9.77 kN, respectively.  
The decomposition results of the 7 th-order trend component and detail components of the measured lateral wheel-rail force on the switch rail side when the train passes through turnouts with and without SRRV deviation are shown in Figures 15a and Figure 15b, respectively. From the graphs, it can be observed that the 7 th-order trend component of lateral wheel-rail force in the switch area increases and then decreases, with the degree of fluctuation in the 7 th-order trend component of lateral wheel-rail force in the crossing area being less pronounced than in the switch area. Compared to the dynamic measurement data of a normal turnout, the peak values of the trend component of lateral wheel-rail force in the abnormal area of the turnout with SRRV deviation are larger, indicating a more significant variation.  
The analysis of on-site measured data shows that DWT effectively separates the wheel-rail force trend component associated with track structure irregularities. The variation pattern of the 7 th-order trend component of lateral wheel-rail force obtained from the field measurements is consistent with the analysis results presented in Section 4. Therefore, this technique provides an accurate means of identifying structural defects in turnouts and is capable of mitigating the influence of various disturbances, such as turnout positioning and track geometry irregularities.  
Figure 14: The field-measured lateral wheel–rail force when the train passes through the turnout: (**a**) turnout with SRRV deviation; (**b**) turnout without SRRV deviation.
## 3 System Model
### Methodology at a Glance  
Instead of accounting for inherent dependencies between building positions and their shapes in regular urban deployments, we characterize LoS visibility regions in \(\Re^{2}\) located at the UAV flying altitude, \(h_{R}\), see Figure 1. Using these regions we then proceed by utilizing  
Figure 1: Illustration of the considered deployment.  
the tools of integral geometry to determine the probability that a random point in this plane is covered by at least one LoS visibility region immediately delivering the sought metrics of interest in a simple closed-form.
## 2 Problem Statement and Chosen Methodologies
### Probabilistic Methods
#### 2.2.2 Sobol Global Sensitivity Analysis  
The global sensitivity analysis (GSA) aims to quantify the sensitivity of a quantity of interest (QoI) with respect to each random variable (RV) over its entire varying range. Among many methods, the Sobol indices have received much attention since they can give accurate results for most models [27], and are adopted in the present work.  
The Sobol GSA is based on the variance decomposition of a random model output (\(\gamma\)). The first-order Sobol index of a variable \(x_{i}\) can be given as:  
\[S_{i}=\frac{Var[\mathbb{E}(Y\parallel x_{i})]}{V_{t}} \tag{4}\]  
where \(V_{t}\) is the total variance of the model output. For the \(Var[\mathbb{E}(Y\parallel x_{i})]\), the inner expectation operator \(\mathbb{E}(\cdot)\) is the mean of \(Y\) considering all possible \(x_{\_i}\) values while keeping \(x_{i}\) constant; the outer variance \(Var(\cdot)\) is taken over all possible values of \(x_{i}\). The \(S_{i}\) is a measure about the effect of the variable \(x_{i}\) alone.  
There is also the total Sobol index for a variable \(x_{i}\), which represents the sum of all the Sobol indices involving this variable. The definition can be written as:  
\[S_{Tl}=S_{i}+\sum_{i\
eq j}S_{ij}+\cdots\ S_{1,\ldots,M} \tag{5}\]  
where \(M\) is the number of all the random variables.  
Estimation of these Sobol indices (first-order and total order) can be achieved by constructing a triplet of matrices [28]; each matrix has \(M\) columns and \(N_{s}\) rows. In this work, the \(N_{s}\) is set as 10,000 following the suggestion of [29]. Application of the Sobol GSA to geotechnical engineering can be found in [30; 31; 32].