esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis of the Shear Lag Effect in Tube Structural Systems of Tall Buildings
## 2 Shear Lag Effect in Tube Structural Systems
The main idea is to see how the ratio of the layout dimensions of a rectangular shaped building affects the buildings behavior when subjected to strong winds. Even though the shear lag effect is not addressed in this paper per se, an insight is given into the optimal ratio of layout dimensions for a tall tube structure. In [23] besides the ratio of layout dimensions of a tube structure, the effect of column spacing is also taken into account. In their research it is concluded that the layout dimensions of a tube structure have no or minimal impact on the shear lag effect. Furthermore, it is stated that the column spacing has a greater impact in the negative shear lag area of the building than in the positive shear lag area of the building. In the end, the impact that diagonal bracing placement on the exterior of the structure has on the shear lag effect is examined. A similar analysis is conducted in [24] where the use of mega bracing in tall buildings is studied with the conclusion that the X pattern diagonal bracing shows the most promising results regarding the reduction of the shear lag effect. In this paper the impact of the negative and positive shear lag effect on a tall tube structure will be analyzed in detail. Using a computer-based model a parametric analysis will be conducted with a series of different parameters being varied with the main purpose of detecting the critical height of the tall building where the transfer from positive to negative shear lag occurs. Besides that, the impact of every parameter variation on the shear lag effect will be shown. The parameters that will be examined include the ratio of column spacing and column dimensions, the ratio of storey height and the height of connecting beams, the ratio of the layout dimensions of the entire building, and the type (profile) of horizontal load being used. In the end, the main goal is to find the optimal layout dimensions of a tall building with a tube structural system and the optimal dimensions of the structural elements that are used in it.
## Discussion  
### Overall Vegetation Carbon Source/Sink in the ARNWC  
In our study, we found that 98.99% of vegetation in the ARNWC showed a carbon sink from 2001-2018, which is in line with earlier findings [53; 16]. We observed that the overall vegetation in the ARNWC showed various degrees of carbon sinks. In addition, there are two noteworthy features.  
First, the vegetation within bare areas, which mainly consist of desert ecosystems, has shown a weak carbon sink in recent years. A recent study demonstrated that although there is currently not enough evidence to prove that desert ecosystems can form stable vegetation carbon sinks, an increased aboveground biomass of desert vegetation is still observed in other parts of the globe, mainly due to increased precipitation during the growing season, which promotes vegetation growth. This also occurred in the ARNWC (Figure 9), and CO\({}_{2}\) fertilization effects increased the carbon sequestration capacity of vegetation [54; 55; 56]. In addition, biocrust is widely distributed in arid deserts, and it can fix carbon dioxide, which is also an essential factor that influences the desert carbon cycle [57]. When the drifting sand in the desert is fixed by plants, it will gradually generate different stages of biocrust by the physical effects of dust fall and rainfall [58], and the newly generated biocrust can also be more convenient for herbaceous and woody plants to invade and grow [59], which may be another important reason why the desert is a carbon sink.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Type** & **Cropland** & **Foresitland** & **Grassland** & **Urban Areas** & **Bare Areas** & **Total** \\ \hline \multicolumn{6}{c}{Total NEP for LC1 (Tg C)} \\
2001 & 24.64 & 6.69 & 23.50 & 0.03 & 18.82 & 73.68 \\
2018 & 33.00 & 7.24 & 25.86 & 0.11 & 18.85 & 85.06 \\ Change & 8.36 & 0.55 & 2.36 & 0.08 & 0.03 & 11.38 \\ \multicolumn{6}{c}{Total NEP for L1 (Tg C)} \\
2018 & 26.04 & 7.61 & 23.06 & 0.11 & 18.63 & 75.45 \\ Change & 1.40 & 0.92 & \(-\)0.44 & 0.08 & \(-\)0.19 & 1.77 \\ Percentage for LC1 & 16.75\% & 167.27\% & \(-\)18.64\% & 100.00\% & \(-\)633.33\% & 15.55\% \\ \multicolumn{6}{c}{Total NEP for L2 (Tg C)} \\
2018 & 33.30 & 7.79 & 26.99 & 0.12 & 20.23 & 88.43 \\ Change & 8.66 & 1.10 & 3.49 & 0.09 & 1.41 & 14.75 \\ Percentage for LC1 & 103.59\% & 200.00\% & 147.88\% & 112.50\% & 4700.00\% & 129.61\% \\ \hline \end{tabular}
\end{table}
Table 8: Total NEP for 2001 and 2018 under different scenario designs.  
Second, the carbon source in ARNWC mainly occurs in high altitude mountains (Figure 10), including the Tianshan Mountains, Kunlun Mountains, and Qilian Mountains. Previous studies have also illustrated that as altitude increases, vegetation at middle and high altitudes is less disturbed by human activities. Furthermore, regions at these altitudes have an increase in rainfall and a decrease in evapotranspiration, which can encourage the area of vegetation carbon sinks to increase. However, at extremely high altitudes, temperatures that are too low inhibit vegetation growth, and vegetation will behave as a carbon source [60].
## 6 Discussion
#### Broader Implications and Future Directions  
The implications of XR technologies extend beyond the educational sector into areas like healthcare, where they can similarly enhance patient education and rehabilitation. Looking ahead, future research should focus on the long-term impacts of XR across different age groups and cultural contexts to understand better the scalability and effectiveness of these technologies. Additionally, exploring advanced features like AI-driven personalization and the integration of biometric data within XR platforms could further tailor educational experiences to individual learner needs. A cloud-based or HPC-based architecture would enable our system to perform computationally intensive tasks off-site, improving front-end performance behavior on less powerful devices.  
In summary, XR technologies hold significant promise for inclusive education, but their broader adoption and impact require continued exploration and thoughtful implementation.
###### Abstract  
Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime.  
ride-pooling; transport mode choice; stated choice experiment; nested logit nighttime 2025157774  
1
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 5 Simulation Results
### Tradeoff between PMEPR and PSLR  
In practical applications, low radar SNR loss and low range sidelobe levels are desirable for IRC systems. By selecting a specific \(\alpha\) under different backgrounds, one can obtain the corresponding waveform by using the proposed algorithm. Therefore, the impact of weight \(\alpha\) on the characteristic of the waveform needs to be analyzed.  
Since the PMEPR and PSLR in the objective function (21) are of different magnitudes, the weights need to be searched in a large range. We take the weight range as \([0.2,100]\) because it is sufficient to analyze the problem. To analyze the impact of weights on PMEPR and PSLR performance, a standard Monte Carlo technique with the same 3000 independent trails is employed for each weight, and the results are presented specifically in Figure 7.  
Figure 5: PMEPR reduction performance, \(\alpha=5\), where Algorithm 3 is from [31].  
Figure 6: PSLR improvement performance, \(\alpha=5\), where Algorithm 3 is from [31].  
As expected, both the PMEPR and PSLR decrease with the weight \(\alpha\) increasing, i.e., the optimal PMEPR is obtained with a large weight, while the optimal PSLR is obtained with a small weight. Moreover, the PMEPR of the IRC waveform for a large weight (\(\alpha>100\)) approximates the PMEPR obtained by only optimizing PMEPR [23], i.e., the PSLR is basically not optimized. Additionally, the PSLR of the IRC waveforms for a small weight (\(\alpha\leq 0.2\)) is very good, but the PMEPR is particularly poor, even worse than that of the initial waveform. At this time, we consider the obtained IRC waveform to be unqualified, because the meaning of PMEPR optimization is lost. Furthermore, note that individual initial waveforms in Monte Carlo with small weights may get trapped in local optima during the iterative process, resulting in the obtained IRC waveforms with suboptimal PMEPR and PSLR performance. Then, an important observation from Figure 7 is that there is a tradeoff between the two performance evaluation indicators of PMEPR and PSLR. Therefore, the optimal weights need to be selected to meet the requirements for the PMEPR and PSLR in practical applications.
## 4 Discussion
### Strengths and Limitations  
This study was strengthened by its inclusion of a well-defined CNSLBP sample with a matched HC group, as deemed paramount for the qualitative evaluation of EIH [20], as well as the comprehensive assessment of both pain sensitivity outcomes and systemic inflammatory markers. Additionally, the comparison of HIIT and MICT protocols provides important insights into the value of different exercise intensities [13].  
This study also has limitations. First, the small sample size and technical issues leading to incomplete data could have reduced the statistical power in detecting significant differences. However, statistical power remained sufficient to evaluate strong correlations (r > 0.600) [88]. Since all significant correlations in this study approached or exceeded this threshold, the data that were used to interpret these results remained valid. Nonetheless, these correlation analyses also showed some strong correlations (r > 0.600) that were not significant, which could be caused by the high variability in a small dataset [89]. Furthermore, not all confounders could be included in one all-encompassing partial correlation analysis due to limits in the remaining degrees of freedom of the analysis (minimum dF = 3). Bigger datasets could also enable a shift toward elaborate cluster analyses [90] to provide supportive information regarding different overarching patient profiles. Second, the MICT protocol was only performed by persons with CNSLBP, making a direct comparison with HCs related to this protocol impossible. Third, participants were not required to fast before testing, which could have influenced the inflammatory marker outcomes. Dietary intake and substances like coffee or medication may impact the levels of inflammatory markers such as IL-6 and TNF-\(\alpha\), potentially masking or altering the true effects of exercise on these markers [91]. Additionally, studies should incorporate a broader range of inflammatory markers and consider individual variability in inflammatory responses to exercise [28,71]. Third, some confounders (such as the DASS-21 subscores [92]) showed very low baseline scores, limiting their clinical relevance in this sample. A dataset with more pronounced scores for these factors might have more impact on the final results. Lastly, some data points were missing for both the pain processing (n = 3, 8%) and inflammation (n = 23, 29%) outcomes due to technical issues or limitations of the assessment sensitivity. Given the small sample size, we did not perform a formal sensitivity analysis or use imputation techniques. However, we did carefully examine the distribution of the missing data and found no significant profile differences or systematic patterns suggesting bias. As such, we ensured that the missing data did not meaningfully impact the overall trends in the results.
# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 3 Results and Discussion
For SS 304L, it has been found that ferrite numbers between 15 and 19 offer the best fatigue resistance [117]. A multi-scale model demonstrated the fact that the projected fatigue initiation life together with residual FL closely match the outcomes of the experiments for fatigue in steel bridges [118]. While a case study's findings illustrated that the initial crack size has a substantial impact on fracture reliability. Thus, it was advised to use the S-N curve technique throughout the design phase and the fracture mechanics strategy when the structure neared its end of design life [119]. Sequel to the evaluation of the impact of defects/inclusion on the fatigue behavior of steels. Findings show that for both materials used, the defect/inclusion predominated the fracture mechanism [122]. Meanwhile, the prediction of S-N curves for welded junctions by the SPDTRS-CS-ANN) coupled with Boxplot and Analytic Hierarchy Process (AHP) approach were found to have low experimental data error as illustrated in Figure 16, since when compared to the experimental data, the fatigue life forecasts under a given stress range fall within an error region of around \(\pm 1.2\)[96]. Interestingly, welders may swiftly estimate and control welding distortion and residual stress before the actual welding process with the use of this responsive MR-based user interface, and beginners can learn the connection between the welding variables and the stimulated residual stress as well as deformation [128]. Moreover, early detection of crack start and growth in ductile structural steels subjected to cyclic loads was a challenge that was solved using the contemporary AE approach [129]. A comparison of the machine learning attributes for this study can be found in Table 1.  
Figure 15: (**a**) Time-dependent fatigue reliability indices on RD joints for longitudinal strain measurements, (**b**) time-dependent fatigue reliability indices on RD joints for transverse strain measurements [91]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Taxonomy** & **Material** & **Input** & **Output** & **Welding Process** & **Application** \\ \hline Regression & & \multicolumn{4}{c}{Looding conditions [21], pavement temperatures and traffic levels [23], hourly averaged road temperatures and number of trucks [24], number of vehicles to failure of specimen N, maximum tensile stress \(\mathrm{
## 6 Results
### Experimental Results  
This section presents the VSI control of the experimental setup illustrated in Figure 3. The MPC is formulated as a QP problem, as described in Section 5, and implemented in C code using the OSQP solver [25; 37]. The RHC computes the MPC control input, which is transformed back to _abc_ coordinates (\(\mathbf{v}_{\text{abc}}\)). The inverter pulses are generated using the space vector pulse-width modulator (SVPWM) in the dSPACE system. The human-machine interface (HMI) displays the voltage magnitude, filter current, output current, and execution time of the MPC algorithm in the ControlDesk software, enabling the evaluation of the VSI's dynamic performance and computational cost.  
This work evaluates the performance of the OSQP solver under various important operating conditions. Specifically, it focuses on voltage reference tracking and the activation of constraints for modulated voltage and filter current. In all cases, the iterative computation of voltage actuation is performed with 10 iterations, allowing the OSQP solver to achieve the solution in an average time of 70 \(\upmu\)s, demonstrating its suitability for MPC implementation in VSI control with the target sampling time of 200 \(\upmu\)s. For simplicity in the experimental tests, the steady-state values in the MPC algorithm design are set to zero, i.e., \(\mathbf{\hat{x}_{s}=0}\) and \(\mathbf{\bar{u}_{s}=0}\). Although this decision slows the system's convergence to a steady state, offset-free performance is ensured by the integral action included in the control design.
# Feature-Level Camera Style Transfer for Person Re-Identification
## 3 Feature-Level Camera Style Transfer  
In the person re-ID problem, camera style variance often leads to differences in viewpoint, illumination, and background, making it hard to determine whether two images captured by different cameras are of the same person or not. Some researchers apply pose-guided image generation as data augmentation. However, the image-level generative model takes a huge amount of time and memory. In addition, the training process of the image generation model is separated from the re-ID model, which makes it hard to achieve optimal performance for both models. To overcome these shortcomings, a GAN-based feature-level Camera Style Transfer (CST) approach is proposed to alleviate the camera style variance in the person re-ID task.
## 3 Eigenvalue Extraction for Non-Autonomous Dynamic Systems
### The QR Method  
The QR method [26; 27] transforms a square matrix \([D]\) into the product of an orthogonal matrix \([Q]\) (for which its transpose is the same as its inverse, i.e., \([Q^{T}]=[Q^{-1}]\)) times an upper triangular matrix \([R]\), such that \([D]=[Q][R]\). In order to compute the eigenvalues  
Figure 2: Time evolution of the characteristic polynomial coefficients \(a_{n}\): Left column is the attached mass decrease from \(R=0.20\) to \(R=0.02\); right column is the attached mass increase from \(R=0.02\) to \(R=0.20\).  
of \([D]\in\mathbb{R}^{n\times n}\), one begins by setting an initial matrix \([D_{0}]=[D]\) and performing the following iteration cycle:  
\[\begin{array}{l}[D_{0}]\!=\![Q_{1}][R_{1}]\ \rightarrow\ [D_{1}]\!=\![R_{1}][Q_{1}]\\ [D_{1}]\!=\![Q_{2}][R_{2}]\ \rightarrow\ [D_{2}]\!=\![R_{2}][Q_{2}]\\ \vdots\\ [D_{n-1}]\!=\![Q_{n}][R_{n}]\ \rightarrow\ [D_{n}]\!=\![R_{n}][Q_{n}]\end{array} \tag{21}\]  
After a number of iterations, matrix \([D_{n}]\) converges to a lower triangular form:  
\[[D_{n}]=\begin{bmatrix}\text{X}&\text{X}&\ast&\ast&\ast&\ast&\ast&\ast\\ \text{X}&\ast&\ast&\ast&\ast&\ast&\ast&\ast\\ 0&0&\text{X}&\text{X}&\ast&\ast&\ast&\ast\\ 0&0&\text{X}&\text{X}&\ast&\ast&\ast&\ast\\ 0&0&0&0&\text{X}&\text{X}&\ast&\ast\\ 0&0&0&0&\text{X}&\text{X}&\ast&\ast\\ 0&0&0&0&0&0&\text{X}&\text{X}\\ 0&0&0&0&0&0&\text{X}&\text{X}\end{bmatrix} \tag{22}\]  
The eigenvalues of the original matrix system are now recovered in closed form for each of the \(2\times 2\) submatrices appearing along the diagonal. These are complex conjugate pairs and are given by  
\[\lambda_{m},\overline{\lambda}_{m}=0.5\ast\text{tr}\pm\left(0.5\sqrt{4\text{ det}-\text{tr}^{2}}\right)j,\ j=\sqrt{-1} \tag{23}\]  
for the \(m-th\) pair, where \(tr\) and \(det\) are the trace and determinant of the submatrix. As a convergence criterion one may define that the absolute value of both the trace and the determinant of any \(2\times 2\) submatrix coming from the last \((n-th)\) iteration \([D_{n}]\) is within \(\varepsilon=10^{-12}\) from the corresponding one computed from the immediately previous iteration \([D_{n-1}]\).
# A Novel Dual-Wavelength Method for Evaluating Temperature Effect in Fiber-Optic SPR Sensors
## 2 Material and Methods
### Temperature Effect on Metal Layer  
The theoretical analysis of the temperature effect on a metal layer is described as follows. Firstly, the complex and frequency-dependent dielectric function of a metal sensor layer can be presented by the Drude model [25] as Equation (1):  
\[\varepsilon(\omega)=1-\frac{\omega_{p}^{2}}{\omega(\omega+i\omega_{c})} \tag{1}\]  
where the \(\omega_{c}\) and \(\omega_{p}\) are the collision frequency and plasma frequency, respectively.  
Then, temperature (\(T\)) dependence of the plasma frequency (\(\omega_{p}\)) can be calculated according to the volumetric effects [26] as Equation (2):  
\[\omega_{p}=\omega_{p0}[1+\gamma_{e}(T-T_{0})]^{-1/2} \tag{2}\]  
where \(\omega_{p0}\) is the plasma frequency at a reference temperature \(T_{0}\) and \(\gamma_{e}\) is the expansion coefficient of the metal layer.  
In addition, the impact of temperature (\(T\)) on the collision frequency (\(\omega_{c}\)) occurs through two main factors: namely, phonon-electron scattering (\(\omega_{cp}\)) and electron-electron scattering (\(\omega_{ce}\)). Therefore, the combined effect \(\omega_{c}\) can be represented as Equation (3)  
\[\omega_{c}=\omega_{cp}+\omega_{ce} \tag{3}\]  
Figure 1: Illustration for SPR-based fiber-optical sensing.  
\(\omega_{cp}\) can be simulated with the Holstein model of phonon-electron scattering [27]:  
\[\omega_{cp}(T)=\omega_{0}\Bigg{[}\frac{2}{5}+4\bigg{(}\frac{T}{T_{D}}\bigg{)}^{5} \int_{0}^{T_{D}/T}\frac{z^{4}dz}{e^{z}-1}\Bigg{]} \tag{4}\]  
where \(\omega_{0}\) is a constant related to the electric conductivity of metal and can be determined from the static limit of the equation [28]. \(T_{D}\) is the Debye temperature of the metal.
2. \(\omega_{ce}\) can be presented by the [PERSON] model using the Born approximation and Thomas-Fermi screening of the Coulomb interaction [29] as:  
\[\omega_{ce}(T)=\frac{1}{6}\tau^{4}\frac{\Gamma\Delta}{\hbar E_{F}}\Bigg{[}(k_ {B}T)^{2}+\bigg{(}\frac{\hbar\omega}{4\pi^{2}}\bigg{)}^{2}\Bigg{]} \tag{5}\]  
where \(\Gamma\), \(\Delta\), \(\hbar\), \(E_{F}\), and \(k_{B}\) are the Fermi surface average of scattering probability, fractional Umklapp scattering, [PERSON]'s constant, Fermi energy, and Boltzmann constant, respectively.  
Besides, temperature effects also affect the thickness of the metal layer. According to [30], the metal film only expands in the normal direction with a corrected thermal-expansion coefficient. Hence, the temperature dependence of the thickness of the metal layer \(d\) can be expressed as Equation (6):  
\[d=d_{0}\big{[}1+\gamma^{\prime}(T-T_{0})\big{]}=d_{0}\bigg{[}1+\gamma\frac{1 +\mu}{1-\mu}(T-T_{0})\bigg{]} \tag{6}\]  
where \(\mu\) is the Poisson's number, \(\gamma\) is the linear expansion coefficient of the sensor metal, and \(\gamma^{\prime}\) is the corrected thermal expansion coefficient. \(T_{0}\) is the reference temperature as in Equation (2).