esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Tie Cable Replacement Method of Half-through Tied-Arch Bridge
## 4 Results
### Results of the Impact of Each Replacement Scheme on the Superstructure
#### 4.2.2 Bending Moment of Arch Rib  
The influence of the tie-cable replacement on arch rib bending moment is shown in Figure 8.  
It can be seen from Figure 8 that the bending moment of the main arch foot section S3 changes the most. It can also be seen that  
1. The bending moment of arch rib caused by the non-substitution method scheme 1 and scheme 3 changes greatly, while the temporary substitution method scheme has little effect on the bending moment of arch rib.
2. During the replacement of the tie cables, each scheme has little influence on the bending moment of the main arch-crown section S1 and L/4 of the main span section S2. The influence of each scheme on the bending moment of the side arch rib is relatively small, and the changes are within 1000 kN\(\cdot\)m.
3. The bending moment changes caused by scheme 5 and scheme 7 are nearly the same. The bending moment changes caused by scheme 6 and scheme 8 are nearly the same, and this value is about twice that of scheme 5 and scheme 7.
4. At the arch foot section S3, scheme 1 and scheme 3 with the non-substitution method cause the largest bending moment, followed by scheme 8 and scheme 6 with the replacement of two tie cables on the same side. Scheme 5 and scheme 7, with the replacement of one suspender on the same side, result in the minimum change in the arch rib bending moment.
## 4 Conclusions  
In this study, the effects of the technical characteristics of different LIDAR models and vehicle surface paints on laser intensity measurements were modeled, and the relationships between these factors were estimated. Among the models tested, including Gaussian Process Regression (GPR), Polynomial Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting, the GPR model achieved the highest accuracy with an RMSE of 0.83162 and an R\({}^{2}\) of 0.99245. To further improve model accuracy, Bayesian optimization was applied, resulting in a 5.16% reduction in the RMSE and a 0.68% increase in R\({}^{2}\) after hyperparameter tuning.  
To better align LIDAR intensity measurements with theoretical optical power calculations, a curve fitting method was implemented. The analysis focused on distances of 2.5 m and 5 m, using third-order polynomial functions to model the relationship between experimental intensity data and theoretical optical power values. Additionally, angles between 0\({}^{\circ}\) and 5\({}^{\circ}\) were considered to ensure compatibility with real-world traffic scenarios, reflecting typical vehicle detection conditions. The accuracy of the model was evaluated using the input parameters from the experiments conducted by [PERSON] et al. According to the results presented in Table 6, a high correlation was observed between the predicted and actual intensity values, with accuracy rates exceeding 98% for all surface types. At a distance of 2.5 m and an angle of 0\({}^{\circ}\), the highest accuracy was 99.941% for the SB-Gloss (Black) surface, while the lowest accuracy was 98.694% for the TCSRM-Gloss (Red) surface. At 5 m, accuracy rates increased further, with the highest accuracy recorded at 99.991% for the TSSM-Gloss (Silver) surface. To assess the model's performance under real-world traffic conditions, validation was conducted using the CUPAC dataset. A spatial filtering process was applied to isolate only the points corresponding to the vehicle, eliminating the effects of surrounding objects. This ensured that the LIDAR measurements exclusively captured signals reflected from vehicle surfaces. A comparison between the filtered laser intensity values and the model's predicted values yielded accuracy rates ranging from 98.195% to 98.891%. The highest accuracy was observed for the SB-Matte (Black) surface at 98.891%, while the lowest accuracy was recorded for the SMRTG-Gloss (Green) surface at 98.195%.  
The findings of this study demonstrate that the GPR model can generate consistent and reliable predictions across different surface types, distances, and angles. The proposed approach provides a strong foundation for enhancing LIDAR-based perception systems in autonomous driving applications.
## 2 Hierarchical Sources
### Entropy-Stable Hierarchical Sources  
The concept of entropy-stable source discussed in this section allows us to formally define the concept of hierarchical entropy function.  
For each \(j\geq 0\), define the finite set of probability vectors  
\[\mathbb{P}_{m}(j)\stackrel{{\Delta}}{{=}}\{p_{\lambda}:\lambda \in\Lambda_{j}(m,k)\}\]  
where the reader will recall that \(p_{\lambda}=\lambda/\|\lambda\|\). Note that the sets \(\{\mathbb{P}_{m}(j):j\geq 0\}\) are increasing in the sense that  
\[\mathbb{P}_{m}(j)\subset\mathbb{P}_{m}(j+1),\ \ j\geq 0 \tag{4}\]  
Let \(\mathbb{P}_{m}^{*}\) be the countably infinite set of probability vectors which is the union of the \(\mathbb{P}_{m}(j)\)'s.  
Suppose we have a hierarchical source \(\mathcal{S}=\{S(\lambda):\lambda\in\Lambda(m,k)\}\). For each \(j\geq 0\), let \(h_{j}:\mathbb{P}_{m}(j)\rightarrow[0,\infty)\) be the unique function for which  
\[h_{j}(p_{\lambda})=H(S(\lambda))/\|\lambda\|,\ \ \lambda\in\Lambda_{j}(m,k)\]  
Suppose \(p\in\mathbb{P}_{m}^{*}\). Because of the increasing sets property Equation (4), \(p\) is a member of the set \(\mathbb{P}_{m}(j)\) for \(j\) sufficiently large. Consequently, \(h_{j}(p)\) is defined for \(j\) sufficiently large, and so it makes sense to talk about the limit of the sequence \(\{h_{j}(p):j\geq 0\}\), if this limit exists. We define the source \(\mathcal{S}\) to be _entropy-stable_ if there exists a continuous function \(h:\mathbb{P}_{m}\rightarrow[0,\infty)\) such that  
\[h(p)=\lim_{j\rightarrow\infty}h_{j}(p),\ \ p\in\mathbb{P}_{m}^{*}\]  
and the function \(h\) (which is unique since \(\mathbb{P}_{m}^{*}\) is dense in \(\mathbb{P}_{m}\)) is called the hierarchical entropy function induced by \(\mathcal{S}\). Henceforth, the terminology \"hierarchical entropy function\" denotes a function which is the hierarchical entropy function induced by some entropy-stable hierarchical source.
# Marine Microalgae for Potential Lutein Production
## 8 Current Market Demand, Value and Sources  
Currently, the global lutein market is valued at EUR 255 million and is expected to reach EUR 409 million by 2027 [4]. Lutein appears as a slightly lower value carotenoid compared to the market price for astaxanthin. As per ICIS market research, the price for 100% pure lutein may range from EUR 1688.00 to EUR 2532.00 per kg, while the available products with dry forms of lutein may range from EUR 126.00 to EUR 253.00 per kg, and the products with the liquid forms of lutein may range from EUR 422.00 to EUR 590.00 per kg ([[https://www.icis.com/explore/resources/news/2003/05/16/195956/lutein-eyes-robust-growth-in-food-and-nutraceuticalals/](https://www.icis.com/explore/resources/news/2003/05/16/195956/lutein-eyes-robust-growth-in-food-and-nutraceuticalals/)]([https://www.icis.com/explore/resources/news/2003/05/16/195956/lutein-eyes-robust-growth-in-food-and-nutraceuticalals/](https://www.icis.com/explore/resources/news/2003/05/16/195956/lutein-eyes-robust-growth-in-food-and-nutraceuticalals/)) accessed on 12 September 2020). The growth of the global lutein market is driven by an increase in demand for healthy and organic food products and a surge in awareness towards dietary supplements. Furthermore, the rise in disposable income allows consumers to purchase healthy alternatives to regular food products.  
Asia Pacific is projected to account for 23.2% of the global market. Developing nations such as China and India are expected to observe high growth. High occurrence of eye disorders coupled with the growing demand for dietary supplements is expected to accelerate the need [92]. Dietary supplements held 29% of the overall market share in 2019 and are expected to keep their dominance over the forecast period. Europe held 36.2% of the industry in 2018 and is projected to grow significantly in the coming years [92].
# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 3 CI Estimation in Predictive Maintenance  
In real-world situations, machine learning or AI applications are often incorrect or unreliable because of various factors, such as insufficient or incomplete data, issues arising during the modeling process, or simply the randomness and complexities of the underlying problem. These problems are critical in estimating the RUL and constructing intervals for prognostic outcomes.  
CI presents a range of possible values derived from sample data, providing a measure of uncertainty around the prediction value that reflects the variability inherent in sampling [28]. CI estimation, presented in Equation (6), is divided into three key components: calibration, residual analysis, and margin estimation. Calibration involves adjusting or fine-tuning the model parameters to ensure the predictions align as closely as possible with the observed data, with the residual analysis focusing on examining the differences between the observed values and the model's predicted values [29]. At the same time, margin estimation refers to calculating the range around the predicted value in which the actual value is expected to lie, given a certain confidence level. The formula for a CI is as follows:  
\[CI=\hat{\theta}\pm z\cdot SE \tag{6}\]  
where \(\hat{\theta}\) is the sample estimate, \(z\) represents the critical value corresponding to the desired confidence level, and \(SE\) is the standard error of the estimate.  
In classical statistical learning models, like ARIMA (Auto-Regressive Integrated Moving Average), CI is estimated based on the assumption that residuals are usually distributed by calculating the standard error of the forecast from the fitted model and estimating the CI by adding and subtracting the product of the critical value and the standard error from the predicted value [30]. In probabilistic frameworks like Prophet, CI is estimated using simulation over multiple possible future scenarios by sampling from the calibration distribution and using those simulated forecasts to compute empirical percentiles, definingthe CI. In contrast, frameworks like CP present more flexible capabilities since they provide post-hoc uncertainty evaluation, adapting to any underlying forecasting model.  
In the following part of this section, a detailed analysis of Prophet and Conformal strategy is presented, highlighting their internal workflows and remarking on the pros and cons when applied for uncertainty quantification in the domain of PdM.
## Materials and Methods
### Remotely Sensed Data  
The data source for both sites is the Landsat 5 Thematic Mapper remote sensor, obtained from the USGS Global Visualization website ([[http://glovis.usgs.gov](http://glovis.usgs.gov)]([http://glovis.usgs.gov](http://glovis.usgs.gov))). The dates of the scenes for the South Texas site are 23 October 2009, 23 August 2010, and 11 September 2011. The dates for the Houston site are 22 August 2009, 25 August 2010, and 28 August 2011. Cloud-free anniversary dates were not available for either site; hence the next nearest dates were chosen. Archive maps from the US Drought Monitor (USDM) were used to verify drought conditions at the study sites. The USDM maps determined that there was a severe drought in southern Texas in 2009, but by 2010 there was little to no drought with higher than average rate of precipitation throughout the whole state. By 2011, exceptional drought conditions again persisted according to USDM for the vast majority of the state. Measurement of drought conditions is heavily dependent on land use/cover thematic categories, and as such an initial supervised classification was performed on the six remotely sensed scenes across both study sites using the maximum likelihood classifier. Training sites identified six land use/cover classes: urban, rocks/soil, forest, water, agriculture-1, and agriculture-2. The two classes of agriculture were identified as predominantly pasture and cropland respectively.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Novelty and Contribution
#### 1.3.5 Selective LIS Antenna Activation for Computational Efficiency  
* Previous LIS systems [14] activate all antennas, increasing computational complexity. We propose intelligent antenna selection, activating only the most effective elements.
* This technique lowers computational effort by selecting only the most effective antennas while keeping channel estimation accuracy high.
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Artificial Intelligence
A neuro-fuzzy modelling approach for CBM to focus on model comprehensibility with effective decision aid for domain experts with Kullback-Leibler mean information to evaluate and refine tuned rules with a couple of real-world applications in bearing failure and aircraft engine failure [66]. A condition-based preventive maintenance arrangement was presented for thermal power plants using a hybrid Petri net modelling method coupled with fault-tree analysis and parameter trend to perform early failure detection and isolation [80]. In a similar study, a procedure for diagnosis and CBM was provided for power transformers [81]. Recurrent neural nets and the neuro-fuzzy systems approach were developed for managing the CBM of a combined-cycle power plant at a medium-sized Italian refinery. In some situations, genetic algorithms and/or fuzzy logic could be used for condition mentoring and maintenance. There are no certain guidelines when it should be used. Although artificial intelligence is used frequently for condition monitoring and prediction, there are some reservations in applications [82]. To improve the anti-noise performance of the fault diagnosis model and the denoising performance, ref [83] studied a joint learning mechanism. Researchers have relied on several signal processing techniques, such as fast Fourier transformation (FFT) [84], empirical mode decomposition (EMD) [85], energy entropy (EE) [86], wavelet packet decomposition (WPD) [87], empirical wavelet transformation (EWT) [88], variation mode decomposition (VMD) [89].
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Technical Improvements
#### 3.3.3 Cell-Free Design  
Radical network changes are expected to take place in 6G systems to move away from the conventional operation methods in favor of achieving better network performance. One possible direction is redefining the concept of network cells to create what is known as cell-free networks or distributed MIMO. Generally speaking, the architecture of such a network is made up of a number of access points (APs) equipped with a certain number of antennas evenly spaced in the coverage area. All the APs are connected to a centralized processing unit for the coordinated serving of users. This depiction of networks is believed to have multiple benefits. In its ideal form, it will be able to maintain a uniform level of the quality of service among all users in the network, especially under massive MIMO systems [96]. Another benefit is combating the unfavorable effects of signal propagation, such as signal fading and shadowing. In an attempt to elaborate this network architecture, we explored the recent work of [97], where the scalability aspect of networks was under scrutiny. The foremost priority in that work was preserving a limited computational complexity under a huge number of users. In short, restraining each available pilot sequence to one user during the network's initial access procedure under _dynamic cooperation clusters'_ (DCCs) deployment was proposed. This was performed to minimize the unwanted pilot contamination phenomenon and to fix the signal processing complexity. The backbone of the DCC's principle of operation is distributed MIMO systems. The objective of DCCs is to form a cluster of serving APs around each user with minimum signal interference from other users. An extension of this work under the name of the _radio stripe system_ was described by [98]. Such a system considers integrating antennas and their own processing units inside active cables running on the edges of construction elements (such as fences, staircases, windows, etc). Some of the forecasted advantages of these systems are inexpensive and flexible cell-free MIMO network formation and enhanced system longevity.
## 3 Results
### Data Synthesis and Findings  
Only ODI3 were tested in at least four studies and qualified for the meta-analysis for the summary diagnostic accuracy measures (Table 2) [24; 26; 30; 33; 34; 39]. In four studies, oximetry data were acquired from the reference PSG [24; 26; 30; 34], while in two studies, oximetry data were obtained from home respiratory polygraphy [33; 39].  
Description of summary estimates for six oximetry-based studies using ODI3 are shown in Table 3. Sensitivity and specificity increase with pathological severity. Forest plots and SROC curves are shown in Figures 3 and 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**N**} & **First Author, Year of** & **Sample** & **Prevalence** & **Severity** & **True Positive** & **False Positive** & **False Negative** & **True Negative** \\ \hline \multirow{2}{*}{1} & [PERSON] et al., 2015 [24] & \multirow{2}{*}{176} & 83.0 & Mild OSA & 94 & 5 & 52 & 25 \\  & & 40.3 & Moderate OSA & 63 & 32 & 8 & 73 \\ \hline \multirow{2}{*}{2} & [PERSON] et al., 2019 [26] & \multirow{2}{*}{376} & 80.9 & Mild OSA & 182 & 10 & 122 & 62 \\  & & 37.5 & Moderate OSA & 98 & 25 & 43 & 210 \\  & & 21.3 & Severe OSA & 65 & 34 & 15 & 262 \\ \hline \multirow{3}{*}{3} & [PERSON] et al., 2015 [30] & \multirow{2}{*}{176} & 42.6 & Mild OSA & 59 & 20 & 16 & 81 \\  & & 40.3 & Moderate OSA & 49 & 19 & 22 & 86 \\ \hline \multirow{2}{*}{4} & [PERSON] et al., 2021 [33] & \multirow{2}{*}{39} & 53.8 & Severe OSA & 19 & 3 & 2 & 15 \\  & & 80.8 & Mild OSA & 181 & 10 & 134 & 65 \\  & & 37.4 & Moderate OSA & 102 & 27 & 44 & 217 \\  & & 21.3 & Severe OSA & 68 & 38 & 15 & 269 \\ \hline \multirow{2}{*}{6} & [PERSON] et al., 2017 [39] & \multirow{2}{*}{119} & 54.8 & Moderate OSA & 30 & 10 & 36 & 43 \\  & & 38.5 & Severe OSA & 29 & 11 & 17 & 62 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of studies included in the quantitative analysis.
# Asymmetric Total Synthesis of Ieodomycin B
## 3 Experimental Section
### (R,E)-N-Methoxy-3-((4-methoxybenzyl)oxy)-N,6-dimethylnona-6,8-dienamide (**12**)  
NaH (0.21 g, 5.28 mmol, 1.5 equiv, 60% in mineral oil) was added to the solution of compound **11** (0.80 g, 3.52 mmol, 1.0 equiv) in DMF (12 mL) at \(-\)15 \({}^{\circ}\)C and PMB-Br (1.03 mL, 7.04 mmol, 2.0 equiv, stabilized with 5 wt % K\({}_{2}\)CO\({}_{3}\)). The suspension was stirred at \(-\)15 \({}^{\circ}\)C for 1.5 h until it was observed via TLC that compound **11** was consumed; then, it was poured onto H\({}_{2}\)O (5 mL) and ether (3 \(\times\) 40 mL). The layers were separated and the organic layer was then washed with brine, dried over anhydrous Na\({}_{2}\)SO\({}_{4}\), filtered, and concentrated at reduced pressure. The residue was purified via silica gel column chromatography (petroleum ether/EtOAc = 10:1) to yield PMB ether **12** (0.82 g, 67%) as a yellow oil.  
\([\alpha]^{25}_{D}\) = \(-\)7.5 (\(c\) = 1, CHCl\({}_{3}\)); \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 7.26 (d, \(J\) = 8.0 Hz, 2H), 6.86 (d, \(J\) = 8.0 Hz, 2H), 6.48 (dt, \(J\) = 16.8 Hz, 10.5 Hz, 1H), 5.85 (d, \(J\) = 10.8 Hz, 1H), 5.08 (d, \(J\) = 16.8, 1H), 4.98 (d, \(J\) = 10.1 Hz, 1H), 4.47 (q, \(J\) = 10.8 Hz, 2H), 3.98-3.92 (m, 1H), 3.79 (s, 3H), 3.66 (s, 3H), 3.19 (s, 3H), 2.86 (dd, \(J\) = 14.6, 6.2 Hz, 1H), 2.48 (dd, \(J\) = 15.0, 5.3 Hz, 1H), 2.25-2.07 (m, 2H), 1.75 (s, 3H), 1.80-1.66 (m, 2H); \({}^{13}\)C NMR (CDCl\({}_{3}\), 100 MHz) \(\delta\) 159.1, 139.1, 133.3, 130.8, 129.4, 125.6, 114.7, 113.7, 75.6, 71.6, 61.3, 55.2, 37.2, 35.4, 33.1, 29.7, 16.6. HRMS (ESI): \(m/z\) calcd. for C\({}_{20}\)H\({}_{29}\)NO\({}_{4}\)Na [M + Na]\({}^{+}\) 370.1989, found 370.1989.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Relationship Between Evaluation Indicators and Forecasting Performance
#### 3.5.4 Implications for Multi-Regional Integration  
The analysis of evaluation indicators and time-varying weights yields several important implications for multi-regional load forecasting integration:  
1. Operational Regime Differentiation: The distinct differences in evaluation metrics between peak and non-peak periods justify our approach of calculating separate weights for different operational regimes rather than using a single static weight. This differentiation allows our fusion methodology to adapt to the changing characteristics and relative strengths of provincial forecasts throughout the day.
2. Provincial Specialization Recognition The substantial differences in weights between provinces (e.g., Shanghai and Jiangsu) demonstrate how our methodology effectively recognizes and leverages provincial specialization. Rather than treating all provincial forecasts equally, our approach appropriately emphasizes forecasts from provinces with stronger evaluation metrics, particularly during periods when their forecasting strengths are most relevant.
3. Temporal Adaptation: The evolution of weights over time shows how our methodology responds to changing operational conditions in real-time. This adaptive capability is particularly valuable during transition periods (e.g., morning ramp-up, evening peak) when load characteristics change rapidly and the relative strengths of different provincial forecasting systems may shift.  
These case studies confirm that the superior performance of our weighted fusion approach is not merely a statistical artifact but rather emerges from the methodology's ability to recognize and adapt to the complex relationships between regional forecasting characteristics, operational conditions, and temporal dynamics. By dynamically adjusting provincial weights based on a comprehensive evaluation framework, our approach successfully balances the diverse strengths and weaknesses of regional forecasting systems to achieve optimal integrated grid-level forecasts.
## 3 Linear Stability Analysis
### Stability Analysis of Homogeneous Traffic Flows of RV, ACC, and CACC Vehicles  
To compare the stability of homogeneous traffic flows of RVs (with standard response characteristics), ACC, and CACC vehicles, the critical stability curves for the three types of traffic flows are plotted in Figure 2. Evidently, the critical stability curves for the three types show similar trends. By comparison, the stability regions of these three types of traffic flows are in descending order: CACC vehicles > ACC vehicles > RVs. Among them, the instability region of CACC vehicles is significantly reduced compared with those of ACC vehicles and RVs. It can be concluded that considering the headway, the speed difference, and the acceleration of multiple front vehicles can significantly improve the stability of traffic flows. This means higher vehicle intelligence coincides with the higher stability of the operating state of the fleet.  
Figure 1: The critical stability curves for different driver types with different reaction characteristics.