esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the aerodynamic effects of the oblique enlarged hood with opening holes installed at entrances or exits of tunnels are studied by a numerical method, and the effects of the hood geometry on the compression wave and the MPW are obtained. Then, the hood installed at both side of the tunnel is optimized. The main conclusions are as follows:  
1. An oblique enlarged hood with opening holes at the tunnel entrance can reduce the MPW emitted from the tunnel by extending the rising process of the compression wave, which is divided into three stages corresponding to multiple peaks. The pressure gradient of the compression wave can be alleviated by properly increasing the oblique rate of the hood entrance and the cross-section area of the hood.
2. When the same type of hood is located at the exit of the tunnel, the MPW can be mitigated due to the relief effects of the opening holes and a larger spatial angle caused by the oblique exit. The amplitude of the MPW decreases with the increasing oblique rate of the hood exit and the number of opening holes in a certain range.
3. The MPW emitting from the tunnel can be greatly alleviated by the installation of the oblique enlarged hood with opening holes at both sides of the tunnel. Based on numerical simulation analysis, the tunnel hoods configuration that can satisfy the MPW mitigation requirement is obtained at the speed of 400 km/h. The optimized hood with \(l_{\text{h}}=\) 80 m, \(A_{\text{t}}=\) 155 m\({}^{2}\), \(k=\) 1.25, and \(n=\) 16 can reduce the MPW to satisfy the specification even at a speed of 500 km/h. The result can provide reference for the design of tunnel hoods at higher speeds.
4. However, the impact of oblique enlarged hood with opening holes located at the entrance and exit on aerodynamic effects such as transient pressure and passenger comfort will be further studied in the future.  
Conceptualization, W.M. and M.S.; methodology, M.S.; software, T.L.; validation, Y.F. and T.L.; investigation, Y.F.; data curation, Y.F. and T.L.; writing--original draft preparation, M.S.; writing--review and editing, W.M.; visualization, Y.F. and T.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Foundation of State Key Laboratory for Track System of High-Speed Railway (Grant No. 2021 YJ056).  
Not applicable.  
All data generated or analyzed during this study are included in this article and are available upon request by contact with the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company China Academy of Railway Sciences Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Functional Connectivity Analysis
#### 2.3.3 Localization of AD Using Homotopic Pair Connectivity  
To answer the secondary RQ, the four homotopic pairs were compared against each other to ascertain as to which areas displayed a significant connectivity difference between the two groups. To do this, the PLV values obtained from both subject groups in each of the above-mentioned homotopic pairs were compared using linear mixed effects (LME) regression models. LME was fitted in RStudio (v.2021.09.10) using the lme4() package [32] and was chosen for this analysis because the repeated measure from the homotopic pairs was correlated, violating the assumptions of other tests, such as ANOVA.  
Figure 2: Homotopic pair connectivity was examined in four mirror regions in the left and right hemispheres including (**A**) centro-parietal (C3-P3 and C4-P4), (**B**) fronto-central area (F3-C3 and F4-C4), (**C**) parieto-occipital (P3-O1 and P4-O2) and (**D**) centro-temporal (C3-T3 and C4-T4) connections.  
The analysis included two steps; first, the LME model was fitted with PLVs as the response variable and pair and group as predictors. Participants were included as a random factor in the model. The interaction term was included to prevent the overly enthusiastic outcome that there is a difference in connectivity between HC and AD for all pairs. Next, following verification of the main effects, post-hoc comparisons were conducted between pairs to examine which brain regions showed significant differences between the two groups. These steps were only applied to the frequency bands that showed a statistically significant difference between the AD and HC groups in at least one of the homotopic pairs in the 'Homotopic Pair Connectivity' analysis.
# The Biosynthesis and Medicinal Properties of Taraxerol
## 3 Medicinal Properties of Taraxerol
### Treatment for Neurodegenerative Diseases  
Taraxerol has also been extensively studied for its potential in treating neurodegenerative diseases. Cholinesterase enzymes were targeted by various target compounds in drug development to find possible treatments for neurodegenerative diseases, particularly Alzheimer's [111]. [PERSON] et al. (2004) found the potential of taraxerol for this purpose by inhibiting acetylcholinesteraseic (AChE) activity in a dose-dependent manner, with an IC\({}_{50}\) value of 33.6 \(\upmu\)g/mL [75]. This finding was supported by [PERSON] et al. (2014) in which taraxerol could not exercise its inhibitory effects at concentrations higher than 33.6 \(\upmu\)g/mL [71]. Nevertheless, at 50 \(\upmu\)g/mL, taraxerol exhibited inhibitory effects on butyrylcholinesteraseterase (BChE) with 98.4% inhibition [71]. The IC\({}_{50}\) of taraxerol against BChE was found to be at 17.8 \(\upmu\)M.  
Furthermore, taraxerol displayed high binding affinity to the monomers and mature fibrils of amyloid peptides, which are critical proteins associated with neurodegenerative disorders [111]. Taraxerol can completely assimilate into the human body and cross the blood-brain barrier, which are the two prerequisites for the development of a potent neurodegenerative drug [15]. In silico analysis of taraxerol affinity towards acetylcholinesteraseicase A and B revealed high affinity towards both enzymes through the formation of hydrogen bonds [71]. This might explain the ability of taraxerol to compete for the active site of acetylcholinesteraseicase, thereby exhibiting potential as a treatment for neurodegenerative diseases.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 3 Methodology
### Treewidth Algorithms  
The treewidth implementations and algorithms used were submissions to Track A of the 2017 Parameterized Algorithms and Computational Experiments (PACE) competition [16]. All solvers used are listed below with a brief description and a link to where the implementation can be accessed. We emphasise that, where solvers have multiple functionalities, we used used them in _exact_ mode. That is, we ask the solver to compute the true treewidth of the graph, not a heuristic approximation of it.  
* **tdlib**, by **[PERSON] and [PERSON]**, referred to as **tdlib**. This is an implementation of the algorithm that [PERSON] proposed [14] for the 2016 iteration of the PACE challenge [15], which itself builds on the algorithm by [PERSON] et al. [8]. Implementation available at github.com/freetdi/p17
* **Exact Treewidth**, by **[PERSON] and [PERSON]**, referred to as **tamaki**. Also an implementation of [PERSON]'s algorithm [14]. Implementation available at github.com/TCS-Meiji/PACE2017-TrackA
* **Jdrasil**, by **[PERSON], [PERSON] and [PERSON]**, referred to as **Jdrasil**[41]. Implementation available at github.com/maxbannach/Jdrasil  
Tamaki's original 2016 implementation [14] was initially considered but later excluded due to being dominated by other algorithms, that is, there was no problem instance where it was the fastest solver.
###### Abstract  
In this paper, we present an analysis of the effectiveness of various machine learning algorithms in classifying astronomical objects using data from the third release (DR3) of the Gaia space mission. The dataset used includes spectral information from the satellite's red and blue spectrophotometers. The primary goal is to achieve reliable classification with high confidence for symbiotic stars, planetary nebulae, and red giants. Symbiotic stars are binary systems formed by a high-temperature star (a white dwarf in most cases) and an evolved star (Mira type or red giant star); their spectra varies between the typical for these objects (depending on the orbital phase of the object) and present emission lines similar to those observed in PN spectra, which is the reason for this first selection. Several classification algorithms are evaluated, including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Gradient Boosting (GB), and Naive Bayes classifier. The evaluation is based on different metrics such as Precision, Recall, Fl-Score, and the Kappa index. The study confirms the effectiveness of classifying the mentioned stars using only their spectral information. The models trained with Artificial Neural Networks and Random Forest demonstrated superior performance, surpassing an accuracy rate of 94.67%.  
automatic spectral classification; machine learning; Gaia DR3; astronomical objects; planetary nebulae; symbiotic stars; red giants Article
## 2 Experimental Section  
### Characterization  
\({}^{1}\)H NMR (nuclear magnetic resonance) spectroscopy was performed using a Varian Inova 600 NMR (Palo Alto, CA, USA) and CDCl\({}_{3}\) or deuterated dimethyl sulfoxide (DMSO-\(d_{6}\)) as solvents, with calibrating to the chemical shift of CDCl\({}_{3}\) at 7.26 ppm or DMSO-\(d_{6}\) at 2.49 ppm (s: singlet; m: multiplet; br: broad). FT-IR (Fourier transform infrared spectroscopy) spectroscopy was performed using a Nicolet Avatar 320 FT-IR spectrometer (Nicolet, Madison, WI, USA) and a KBr disk; 32 scans were collected at a resolution of 1 cm\({}^{-1}\). A THF (tetrahydrofuran) solution containing the sample was cast onto a KBr disk and dried under conditions similar to those used for bulk preparation. The sample chamber was purged with N\({}_{2}\) to maintain film dryness. Gel permeation chromatography (GPC) was performed in tetrahydrofuran (THF; flow rate: 1 mL/min) at 40 \({}^{\circ}\)C using a system equipped with a Waters 515 pump (Milford, MA, USA), a Waters 410 differential refractometer (RI), a Waters 486 absorbance detector (UV), a Viscotek SEC-MALS 20 multi-angle light (LS) scattering detector (Malvern, Worcessthire, UK) and two PSS SDV columns (Linear S and 100 A pore size). Monodisperse polystyrene standards were used for calibrations. Conversions of monomers were monitored using a HP 5890 series II (Hewlett Packard, Palo Alto, CA, USA) gas chromatograph (GC), equipped with a flame ionization detector (FID), and employing a CNW CD-5 column (30 m) featuring a proper internal standard. Concentrations of copper were determined through atomic absorption (AA) spectroscopy using a PerkinElmer (Waltham, MA, USA) Analyst 400 apparatus. Three analyses were performed for each sample and averaged. The measured intensities were converted to copper concentrations using a six-point calibration curve derived from aqueous Cu(NO\({}_{3}\))\({}_{2}\) solutions at concentrations ranging from 0 to 5 ppm (slope: 0.0844; \(R^{2}\): 0.9997).
## 3 NDN Enabled Communication and Transport Research in V2X
### Caching of Content  
Caching is a very prominent research field in ICN/NDN. [PERSON] et al. provide a comprehensive overview on available caching strategies in ICN [23]. However, caching in vehicular networks is quite different from caching in conventional networks because  
**Data is twofold**: This means that data can be divided into a category of data which hardly becomes stale (e.g., multimedia data) and data with a short lifetime (e.g., situational data from other vehicles) or a short time span until they are useless.
**Vehicles move**: Therefore, data may become stale in terms of that it is no longer relevant to vehicles in communication range.  
Current literature provides multiple mobility-aware caching strategies for V2X scenarios. For instance, [24] propose a proactive caching policy taking content popularity and mobility of vehicles into account. However, the mobility prediction is based on the exhaustive availability of base stations and considers the probability of entering and leaving communication range of base stations. [PERSON] et al. [25] present an optimal content prefetching mechanism for NDN in vehicle to infrastructure scenarios. The authors present a binary optimization problem whose solution provides an optimal prefetching of content for known topologies (which in mobile scenario is only valid for a very short period of time, if at all). [PERSON] et al. [26] propose a community similarity and population-based cache policy for vehicle-to-vehicle scenarios. Their approach reduces cache redundancy based on content popularity and lowers cache replacement overhead by predicting content popularity over time. Recently, [PERSON] et al. [27] investigate ICN caching strategies for vehicular environments (Leave Copy Down (LCD), Move Copy down (MCD), copy with probability (Prob) and distance based probability caching (DProb)) using simulation studies.  
The discussed caching strategies do not consider vehicle mobility as well as data category to full extent. Some of them try to predict the mobility of the vehicles, the connection duration to other vehicles, and take into account whether the content received or stored content is still _popular_. However,none of the caching strategies separates the incoming data into data vital for (safety) assistance systems or relevant for autonomous driving and (conventional) data which is relevant for passengers, i.e., multimedia content. We suggest that data vital for vehicles must be cached differently than conventional data, as it becomes irrelevant quickly.
# Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020
## 2 Materials and Methods
### Changes in SOCD Trends and Attribution Analysis  
A Theil-Sen median trend analysis was utilized to investigate the SOCD of soil at a depth of 0-20 cm and 0-100 cm, i.e., its spatial distribution, temporal variation characteristics, and trend changes. Theil-Sen median trend analysis is a robust nonparametric statistical trend computation method which computes the time series of all \(n(n-1)/2\) paired combinations of data, and it is particularly effective for small series trend estimation [34; 35]. The slope of the Theil-Sen median can represent the increase or decrease in SOCD at the pixel scale for the years 2001, 2005, 2010, 2015, and 2020. Its calculation formula is as follows:  
\[S_{SOCD}=median\left(\frac{SOD_{j}-SOD_{i}}{j-i}\right),\ 2001\leq i<j\leq 20 20; \tag{4}\]  
\(S_{SOCD}\), which is used to quantify a monotonic trend, is the median of the slope of the \(n(n-1)/2\) data combinations, and \(SOCD_{i}\) and \(SOCD_{i}\) represent the SOCD values in years \(i\) and \(j\). When \(S_{SOCD}>0\), the SOCD of the time series shows an increasing trend; when \(S_{SOCD}<0\), the SOCD of the time series shows a decreasing trend.
## 2 System Model
### Dynamic JT CoMP Transmission  
JT CoMP allows TPs in UE's CoMP set to simultaneously transmit the desired data signal to it. Owing to the spatial separation of the transmit antennas, multiple versions of the desired signal will be received by the UE, which generates extra spatial diversity gain and strengthens the signal. According to information theory [18], the obtained data rate of a JT CoMP transmission to UE \(k\) on RB \(n\) is given as:  
\[R_{k}^{n}=b\log\left(1+\frac{\sum\limits_{m\in\mathcal{M}_{k}}\left\|\mathbf{ H}_{m,k}^{2}\mathbf{w}_{m}^{n}\right\|^{2}p_{m}^{n}}{\sum\limits_{m^{ \prime}\in\mathcal{M}\setminus\mathcal{M}_{k}}\left\|\mathbf{H}_{m^{\prime},k }^{n}\mathbf{w}_{m^{\prime}}^{2}\right\|^{2}p_{m^{\prime}}^{n}+\sigma^{2}}\right) \tag{2}\]  
where \(\mathbf{H}_{m,k}^{n}\) is an \(N_{\text{R}}\times N_{\text{T}}\) channel matrix between TP \(m\) and UE \(k\) on the \(n\)-th RB. \(\mathcal{M}\setminus\mathcal{M}_{k}\) is the complementary of \(\mathcal{M}_{k}\) in \(\mathcal{M}\), which includes all of the interfering TPs. \(\mathbf{w}_{m}^{n}\) is the precoding vector with dimensions of \(N_{\text{T}}\times 1\), which maps data stream \(s_{m}^{n}\) onto the transmit antennas of TP \(m\), and \((\mathbf{w}_{m}^{n})^{\text{H}}\mathbf{w}_{m}^{n}=1\), \(E\|s_{m}^{n}\|=1,\forall m,n\). \(p_{m}^{n}\) is the power used by TP \(m\) for transmitting on RB \(n\), and \(\mathbf{n}_{k}^{n}\sim\mathcal{CN}(\mathbf{0},\sigma^{2}\mathbf{I}_{N_{\text{ R}}})\) is the corresponding complex Gaussian noise vector. \(b\) represents the bandwidth of an RB, which is standardized to be 180 kHz in LTE-A systems [12].  
Equation (2) implies a static coordinated strategy where TPs in \(\mathcal{M}_{k}\) are all required to serve UE \(k\) all of the time. However, static strategies are not always optimal due to the time variation of wireless channels. To further improve the network performance, we use a dynamic JT CoMP strategy where a subset of each \(\mathcal{M}_{k}\) rather than \(\mathcal{M}_{k}\) is adaptively determined to perform JT CoMP transmission. Define a scheduling index \(\beta_{m,k}^{n}\in\{0,1\}\) to indicate scheduling results, where \(\beta_{m,k}^{n}=1\) means that TP\(m\) is chosen to transmit to UE \(k\) on the \(n\)-th RB. Then, the data rate of a dynamic JT transmission can be given as:  
\[R_{k}^{n}=b\log\left(1+\frac{\sum\limits_{m=1}^{M}\beta_{m,k}^{n}\left\|\mathbf{ H}_{m,k}^{2}\mathbf{w}_{m}^{n}\right\|^{2}p_{m}^{n}}{\sum\limits_{m^{\prime}=1}^{M} \left(1-\beta_{m^{\prime},k}^{n}\right)\left\|\mathbf{H}_{m^{\prime},k}^{n} \mathbf{w}_{m^{\prime}}^{2}\right\|^{2}p_{m^{\prime}}^{n}+\sigma^{2}}\right) \tag{3}\]  
where \(\sum_{m=1}^{M}\beta_{m,k}^{n}\geq 1\).
## 3 Results
### Radiomics Features Extraction  
Radiomics features of pre-p-ChT CT examinations showed significant differences between responders and non-responders for _Shape_, _GLCM_, _First order_, and _NGTDM_ features. In particular, the extracted radiomics parameters showed significant differences in _LeastAxisLength_ (_Shape_ feature; \(p\) = 0.017), _Cluster Shade_ and _Autocorrelation_ (_GLCM_ features; \(p\) = 0.007 and 0.005, respectively), _Skewness_ (_First order_ feature; \(p\) = 0.012), and _Strength_ (_NGTDM_ feature; \(p\)= 0.049). The diagnostic performance of these features was tested with ROC curves showing AUCs of 0.815 for _LeastAxisLength_, AUC of 0.907 for both _Cluster Shade_ and _Autocorrelation_, an AUC of 0.889 for _Skewness_, and an AUC of 0.815 for _Strength_ (all \(p\) < 0.011). Complete results are shown in Table 3 and Figure 3.  
\(\Delta\)Radiomics values showed significant differences between responders and non-responders for _Shape_, _GLRLM_, _GLSZM_, and _NGTDM_ features. Specific features include _MeshVolume_, _LeastAxisLength_, and _SurfaceVolume_ (_Shape_ features; \(p\) = 0.012, 0.036, and 0.020 respectively); _LongRunEmphasis_ (_GLRLM_ feature; \(p\) = 0.039); _LargeAreaLowGrayLevelEmphasis_ (_GLSZM_ feature; \(p\) = 0.017); and _Contrast_ (_NGTDM_ feature; \(p\) = 0.049). Further, ROC curve analysis showed significant results for the abovementioned \(\Delta\)Radiomics features with an AUC of 0.889 for _MeshVolume_, 0.833 for _LeastAxisLength_, 0.852 for _SurfaceVolume_, 0.889 for _LongRunEmphasis_, 0.833 for _LargeAreaLowGrayLevelEmphasis_, and 0.796 for _Contrast_ (all \(p\) < 0.007). Full results including sensitivity, specificity, and confidence interval (CI) are reported in Table 4 and Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Responders** & **Non- Responders** & **\(p\)** \\  & **n = 6** & **n = 9** & **p** \\ \hline
**R0 resection (n, \%)** & 6 (100\%) & 9 (100\%) & 1.000 \\ \hline
**Retrieved nodes (mean \(\pm\)SD)** & 26.0 (\(\pm\)10.5) & 24.2 (\(\pm\)7.3) & 1.000 \\ \hline
**Positive nodes (mean \(\pm\)SD)** & 2.7 (\(\pm\)6.5) & 3.4 (\(\pm\)3.9) & 0.388 \\ \hline
**Node ratio (mean \(\pm\)SD)** & 0.1 (\(\pm\)0.3) & 0.1 (\(\pm\)0.1) & 0.388 \\ \hline
**Lymphovascular invasion (n, \%)** & 2 (33.3\%) & 4 (44.4\%) & 1.000 \\ \hline
**Perineural invasion (n, \%)** & 1 (16.7\%) & 3 (33.3\%) & 0.604 \\ \hline
**ypTNM stage (n, \%)** & & & 0.774 \\
**yI** & 3 (50.0\%) & 3 (33.3\%) \\
**yII** & 2 (33.3\%) & 4 (44.4\%) & \\
**yIII** & 1 (16.7\%) & 1 (11.1\%) & \\
**yIV** & 0 (0.0\%) & 1 (11.1\%) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 4 Empirical Assessment Using Real-World Data
### Results
#### 4.3.3 Full Model Assessment  
The full model evaluation results, as depicted in the Table 6, underscore the efficacy of our proposed two-stage model in accurately predicting crash frequencies. The Poisson hurdle model and the negative binomial hurdle model serve as traditional benchmarks, with their respective RMSE and MAE indicating decent performance levels. The Poisson hurdle model reports an RMSE of 2.097 and an MAE of 0.527, while the negative binomial hurdle model shows an improvement, with an RMSE of 1.336 and an MAE of 0.511. The CatBoost model in its single-stage form further refines these metrics, achieving an RMSE of 1.197 and an MAE of 0.421, suggesting a more precise prediction capability. However, our proposed model outperforms all benchmarks, with the lowest RMSE of 0.978 and MAE of 0.346. These values indicate superior predictive accuracy, highlighting our model's robustness in dealing with the complexities of zero-inflated crash frequency data. The results suggest that the integration of the classification and regression stages enables our model to deliver highly accurate and reliable predictions.
# Validation of the Player Personality and Dynamics Scale
## 2 Materials and Methods
### Data Analysis  
To assess the reliability of the Player Personality and Dynamics Scale, McDonald's omega coefficient was used. This statistic is considered a superior alternative to [PERSON]'s alpha in many contexts, especially when alpha's assumptions are not fully met [26]. McDonald's omega accounts for the underlying factorial structure of the data, making it more suitable in cases where items are not strictly tau-equivalent (i.e., when items do not have equal variances).  
Additionally, the following goodness-of-fit indices were used:  
* **TLI (Tucker-Lewis Index):** This index evaluates model fit by comparing the variance explained in the proposed model to that explained in a null model. TLI values range from 0 to 1, with higher values indicating better fit. A TLI greater than 0.95 is generally considered indicative of good model fit.
* **CFI (Comparative Fit Index):** Similar to TLI, the CFI compares the fit of the proposed model against a null model. Values also range from 0 to 1, with values above 0.95 indicating good fit.
* **RMSEA (Root Mean Square Error of Approximation):** RMSEA assesses how well the model fits the data by comparing the observed covariance matrix to the expected covariance matrix. Lower values indicate better fit, with values below 0.05 suggesting a good fit, and values between 0.05 and 0.08 considered acceptable.
* **SRMR (Standardized Root Mean Square Residual):** This index measures the standardized difference between observed and expected covariances. Values below 0.08 indicate good fit.
* **CMIN (Chi-Square Minimum Discrepancy):** CMIN evaluates the sum of squared differences between observed and expected covariances. Ideally, the result should approach a value of 2 for a well-fitting model.
* **90% Percentile Confidence Interval:** This interval estimates the range within which the 90 th percentile of a population parameter (e.g., the mean) is likely to fall, based on the sample. For RMSEA, a model is considered well-fitting when the upper bound of this interval remains below 0.08.