esa-sceva/satcom-chunk-collection huggingface dataset:
# Transcriptome Landscape Variation in the Genus _Thymus_
## 2 Material and Methods
### Positive Selection Analysis  
In the positive selection analysis, the corresponding codon-based nucleotide alignments (single-copy genes) for five _Thymus_ species were obtained using PAL2 NAL [42] with the help of amino acid alignments. Next, a gene tree was constructed by RAxML [38] using a GTR + \(\gamma\) model for each alignment. In order to do tests for positive selection, the branch-site model was implemented in the CodeML program [43] to estimate the dN/dS (non-synonymous vs synonymous) substitution rates (\(\omega\) value) for each single-copy orthologous sequence individually. The CodeML analysis used an unrooted tree obtained from relative alignment. We executed CodeML four times to stipulate four separate wild species (_T. daenensis, T. lancifolius, T. persicus_, and _T. pubescens_) as the foreground lineages. Genes were deemed under positive selection if they yielded statistically significant results for the likelihood ratio test (LRT, \(p\)-value \(<\) 0.01) comparison between the null and alternative models. For each positively selected gene (PSG), functional information was deduced based on its ortholog in _Arabidopsis thaliana_. GO enrichment analyses of PSGs were conducted using the web-based agriGO [44] with the TAIR database, the singular enrichment analysis (SEA) method, 0.01 as the significance level, and 5 as the minimum number of mapping entries.
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 3 Power Optimization  
Power optimization refers to the allocation of optimal power to the users by the APs [46]. Let \(\rho_{max}\) denote the maximum transmit power of an AP \(a\). It is the AP that decides the power allocated to the different users depending on different parameters, such as user distances or user channel conditions. Let \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \bmbm{ }}}}}}}}}}}}}}}} =[\rho_{1}}]\) define the downlink power coefficients, with \(\rho_{u}\) denoting the power allocated to user \(u\) by all the serving APs. The effective SINR in the downlink depends on \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\bmbm{ }}}}}}}}}}}}}}}}} }\) and can be expressed as [45]  
\[\text{SINR}_{u}(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\bmbm{ }}}}}}}}}}}}}}}}}}}) }}\]\] \[\text{SINR}_{u}(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}}}}} \text{}})\text{D}}\text{SINR}_{u}(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}}}}} \text{D}})\text{SINR}_{u}(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{ }}}}}}}}}}}}}}}}}}}} \text{}})\text{D}}\text{SINR}_{u}(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\where \(\mu\) is a variable that determines the power allocation behavior. Another parameter \(\vartheta\) can be used to reshape the power allocation ratio between the users, known as the tuning parameter \(0\leq\vartheta\leq 1\)[47]:  
\[\rho_{u}=\rho_{max}\frac{\big{(}\sum_{a\in A_{u}}\beta_{ua}\big{)}^{H}\omega_{u} ^{-\vartheta}}{\max_{i\in A_{u}}\big{(}\sum_{a\in A_{i}}\beta_{ia}\big{)}^{H} \omega_{i}^{1-\vartheta}} \tag{26}\]  
Sum SE maximization power allocation--Here, the optimal power to be allocated to the users is selected such that the sum of the spectral efficiencies is maximized. The sum SE maximization power allocation is represented as  
\[\text{maximize}_{\textbf{w}\geq 0_{U}}\sum_{u=1}^{\text{U}}\text{log}_{2} \bigg{(}1+\frac{\text{D}_{u}\mathbbm{e}_{u}}{\mathbbm{Q}_{u}^{\text{I}} \mathbbm{e}+\mathbbm{c}^{2}}\bigg{)} \tag{27}\]
## 5 Discussion
### Comparative Performance of Machine Learning Models in SSM  
Performance comparisons among machine learning models have shown that the Random Forest (RF) algorithm delivers the highest performance in sinkhole susceptibility analysis. RF outperformed other popular models such as XGBoost and LightGBM by approximately 0.6%, achieving a test accuracy of 95.5%. Additionally, the RF algorithm  
Figure 11: Areal distribution of susceptibility classes.  
achieved the highest values in metrics such as precision (96.4%), specificity (96.5%), and F1 score (95.4%), producing more consistent results in predicting sinkhole formations compared to the other models. Comparing sinkhole susceptibility maps from previous studies in the literature that utilized machine learning models, it is noted that the RF model demonstrated high performance [7; 17; 25]. These results highlight that RF is an effective method for sinkhole susceptibility analysis, demonstrating high accuracy, low misclassification rates, and repeatability. Similarly, the literature indicates that RF, particularly in heterogeneous and complex datasets, has shown high performance [81; 97]. Although other models, such as XGBoost (98.3%) and LightGBM (98.5%), exhibited comparable performance, the consistency and interpretability of RF were the key advantages highlighted in this study. Moreover, the fact that all models achieved accuracy above 94% demonstrates that the selected methods generally possess high predictive capacity. The findings obtained in this study support the effectiveness of machine learning models in the creation of sinkhole susceptibility maps. Multicollinearity issues negatively affect the performance of machine learning models, while the variable selection process enhances model accuracy [98]. The 34 criteria used in the study were examined through multicollinearity analyses (VIF, TOL, and Pearson correlation), and 11 criteria with high linear relationships were removed from the model. By including only the significant independent variables in the model, this process enhanced both the overall accuracy of the model and the reliability of the results obtained through SHAP analysis. This method highlights the importance of thoughtfully selecting variables, particularly when modeling intricate karstic processes. In karstic formations, the complex relationships between topographic, hydrogeological, geological, and environmental factors make the variable selection process critical [2]. The results obtained from multicollinearity analyses in this study have made the model less complex and more interpretable. This, in turn, has enhanced the effectiveness of explainable AI methods, such as SHAP analysis.
# PC5-Based Cellular-V2X Evolution and Deployment
## 5 PC5-Based C-V2X Deployment in Taiwan
### Commercial Pilot  
This project [27] is initiated by the New Taipei City government under the city intelligent transport program. PC5-based C-V2X technology has been applied in an open road environment for the first time in Taiwan. The city cooperates with the Ministry of Transport to use an autonomous driving bus in Tamsui Town as an extension of the MRT Danshui line. After six months of system stability testing, the project has been in service since August 2020 [28]. The public is given free rides to experience the new technology. The New Taipei City government expects to make people more interested in the \"Smart Driving Bus\" concept.
## 3 Results
### Technical Validation
#### 3.1.1 Comparison of Representations  
To demonstrate the effect of object representation on classifier accuracy, we compare three computationally efficient representations for three classifiers in Figure 5. The experiment was done on MSI data with NDVI and NDTI indices as image bands on data consisting of relatively small parcels (\(20\ldots 50\) px) with \(B=50\) bins per band. The parameter \(B\) controls both the amount of information we can capture and the reliability of the estimate; with small \(B\) the estimation task is easy but a majority of discriminative information is lost, whereas with large \(B\) we retain all information but can no longer reliably estimate the density from small samples. The choice of \(B=50\) (resulting in \(B^{2}=2500\) bins in total) is motivated by Figure 5(a), which shows the accuracy as a function of the discretization level for the case of 50 training samples per class.  
Figure 5 reports the accuracy for varying sizes of training data for three different classifiers. The main observations for our MSI dataset are: (i) All forms of density estimates outperform naive summary statistics. The baseline of using an aggregate summary of all pixels, the median of NDVI and NDTI values, barely beats the random baseline of 33% classification accuracy, whereas all density estimates achieve accuracies between 40% and 60% depending on the case; (ii) direct multivariate estimates are at least as good as histograms and for some cases (SVC) better; (iii) GKDE performs as well as the multivariate histogram and sometimes (SVC and MLP for some training set sizes) marginally better. In summary, the results show that proper density estimators were preferable over both multivariate and marginal histograms as general representations. Even though there was no clear difference for one of the classifiers (RF), there were no cases where using GKDE would hurt.  
Figure 4: Gaussian kernel density estimate representations of polarimetric SAR intensity measurements for ploughed, grass- and stubble-covered fields. (**a-c**): Class-level density estimates from a large random sample of all pixels of all field parcels of a class. (**d–f**): Density estimates for single parcels of each class. The small red dots indicate individual pixels, with small jitter so multiple pixels with identical values are also visible.  
Figure 5: Density estimates, histograms and the median as representations for multispectral NDVI/NDTI data across three different classifiers. (**a**) SVC (**b**) MLP (**c**) RF. Each color corresponds to a representation, the line indicates the average over five random training sets evaluated on a single test set, and the shaded areas represent 95% bootstrapping-based confidence intervals.
## 4 Experimental Results Analysis
### Trajectory Analysis  
Figure 4 illustrates the multi-stage terminal trajectories of anti-ship missiles generated by the various algorithms. While all algorithms successfully achieved target destruction within the specified iteration limits, the trajectory produced by the MOPSO-h demonstrates a superior performance. The optimal solution suggests that a larger entry angle and more stable attitude of the anti-ship missile impose higher requirements on H6, thereby allowing the missile to allocate sufficient time for attitude adjustment. The seven trajectories, governed by Equations (12) and (13), exhibit smoothness without any abnormal vibrations, and the altitude during the cruise phase remains stable, which corroborates the reasonableness of the design of \(f(\mathbf{X},t)\).  
Furthermore, Figure 5 illustrates the trend of distance decay between the missile and the ship, revealing that the decline of \(d\) is nearly linear, which signifies that the missile's motion state remains highly stable, consistently approaching the target at maximum speed. Figure 6, derived from the optimal solution of MOPSO-h, presents the speed and overload variation curves of the anti-ship missile. The results indicate that the missile tends to execute turning maneuvers at maximum overload and stabilizes upon reaching the expected altitude, effectively suppressing structural failure while adhering to fundamental physical constraints.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **MOPSO-h** & **MOPSO** & **MOEA/D** & **NSGA-III** & **PSO** & **ACO** & **GA** \\ \hline MMD (m) & 2.34 (1) & 2.90 (5) & 2.40 (2) & 3.02 (6) & 2.88 (4) & 2.46 (3) & 3.27 (7) \\ ETP (\%) & 8.91 (2) & 9.88 (4) & 9.02 (3) & 10.38 (6) & 8.42 (1) & 10.70 (7) & 10.22 (5) \\ TLS (m/s) & \(-\)542.1 (4) & \(-\)548.9 (3) & \(-\)521.2 (6) & \(-\)628.5 (1) & \(-\)493.7 (7) & \(-\)526.6 (5) & \(-\)619.3 (2) \\ TLA (\({}^{\circ}\)) & 85.68 (2) & 85.23 (4) & 85.17 (5) & 86.64 (1) & 84.07 (7) & 84.99 (6) & 85.41 (3) \\ PSA (m) & 999.6 (5) & 952.3(4) & 797.3 (2) & 1197.1 (7) & 703.2 (1) & 839.0 (3) & 1185.8 (6) \\ CT (s) & 3184 & 3037 & 3058 & 3235 & 2856 & 2698 & 2763 \\ \hline Score & 14 & 20 & 18 & 21 & 20 & 24 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the algorithm performance indexes.  
Figure 3: Algorithm convergence plot.
## 2 Proposed PLL Architecture
### Phase Frequency Detector (PFD)  
A PFD compares the rising/falling edge difference between the reference and feedback signals; thus, it can extract not only the phase difference but also the frequency difference. As a PFD detects the phase difference between the reference and the feedback frequency, when there is zero phase difference, it may go into a dead zone. In this condition, the charge pump cannot be fully turned on. In the proposed PLL architecture, the PFD has a dead zone voltage of less than 10 pV, as shown in Figure 13; still, a delay block of the inverter chain is used, having a delay from 0.9 to 2.6 ns in reset to avoid/minimize the unwanted dead zone.  
Figure 11: (a) Prescaler logic cell; (b) multi-modulus divider architecture.  
Figure 12: Multi-modulus divider simulation (781 kHz–33 MHz).  
The charge pump requires 1 \(\upmu\)s for the Vctr settling after the PFD is off due to the unavoidable I_up and I_dn tail current mismatch. Figure 14 shows the delay setting and inverter chain delay to avoid the dead zone.
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 3 Results
### Monitoring Data Analysis
#### 3.2.5 Comparison between the Monitoring Devices and Rainfall  
A synthetic comparison between the monitoring results is advised if the monitoring data surpass the threshold of the management values to determine whether a disaster has struck or whether an abnormal phenomenon of a single device has happened. As Figure 18 shows, a comparison between rainfall and GPS displacement involves determining the relationship between cumulative rainfall and displacement, as well as the relationship between rainfall intensity and displacement speed. Figure 19 shows a comparison between the CCD images of the time periods with the greatest rainfall intensity, aiming to ensure the rainfall monitoring is mistake-free.  
During a typhoon and heavy rain, we can observe this system to determine the real-time accumulated rainfall intensity. This monitoring process demonstrated that there is no displacement of the ground or the slope of the retaining wall, which proves that our system is workable.
## 4 Proposed Robust Capon IOVP Beamforming
### Stochastic Gradient Adaptation  
We devise an SG adaptation strategy based on the alternating minimization of the Lagrangian in (32), which yields:  
\[\tilde{\mathbf{a}}_{d}[i+1]=\tilde{\mathbf{a}}_{d}[i]-\mu_{a}[i]\mathbf{g}_{a}[i], \tag{33}\] \[\mathbf{s}_{d}[i+1]=\mathbf{s}_{d}[i]-\mu_{s}[i]\mathbf{g}_{s}[i]\]  
where \(\mu_{a}[i]\) and \(\mu_{s}[i]\) are the step sizes of the SG algorithms, the parameter vectors \(\mathbf{g}_{a}[i]\) and \(\mathbf{g}_{s}[i]\) are the partial derivatives of the Lagrangian in (32) with respect to \(\tilde{\mathbf{a}}_{d}^{*}[i]\) and \(\mathbf{s}_{d}^{*}[i]\), respectively. The recursion for \(\mathbf{g}_{a}[i]\) is given by:  
\[\mathbf{g}_{a}[i]=\left(\frac{1}{\lambda_{\text{RCB}[i]}}\mathbf{S}_{D}^{H}[i]\mathbf{R}^ {-1}[i]\mathbf{S}_{D}[i]+\mathbf{I}_{D}\right)^{-1}\mathbf{S}_{D}^{H}[i]\tilde{\mathbf{a}}_{d }[i] \tag{34}\]  
where:  
\[\mathbf{g}_{s}[i]=\mathbf{a}_{d}[i]\tilde{\mathbf{a}}_{d}^{H}[i]\mathbf{r}_{d}[i]+ \tau_{d}[i]\mathbf{a}_{d}[i]\mathbf{a}_{d}^{H}[i]\mathbf{s}_{d}[i] \tag{35}\] \[\qquad\qquad\qquad+\lambda_{\text{RCB}[i]}\mathbf{\alpha}_{d}[i]\mathbf{ \alpha}_{d}^{H}[i]\mathbf{s}_{d}[i]\]  
and:  
\[\tilde{\mathbf{a}}_{d}=\sum_{d=1}^{D}\mathbf{q}_{d}\mathbf{s}_{d}^{H}\mathbf{a}_{d}=\mathbf{S}_{D} ^{H}\mathbf{a}_{d}\in\mathbb{C}^{D\times 1} \tag{36}\]  
\[\tilde{\mathbf{a}}_{d}=\sum_{l=1,l\
eq d}^{D}\mathbf{q}_{l}\mathbf{s}_{l}^{H}\mathbf{a}_{l}\in \mathbb{C}^{D\times 1} \tag{37}\]We denote \(\mathbf{\alpha}_{d}\in\mathbb{C}^{M\times 1}\) as the difference between the updated steering vectors and the assumed one. The scalar \(\tau_{d}\) is the \(d\)-th diagonal element of \(\mathbf{R}_{D}^{-1}\). The term \(\mathbf{r}_{d}\) denotes the \(d\)-th column vector of \(\mathbf{R}_{D}^{-1}\). The Lagrange multiplier obtained is expressed as:  
\[\lambda_{\text{\tiny{RCB}}}[i]=-\left(\mathbf{S}_{D}[i]^{H}\mathbf{\alpha}_{d}[i]\mathbf{ \alpha}_{d}^{H}[i]\mathbf{s}_{d}[i]\right)^{\dagger}\mathbf{R}_{D}^{-1}[i]\tilde{\mathbf{a }}_{d}[i]\mathbf{a}_{d}^{H}[i]\mathbf{s}_{d}[i] \tag{38}\]  
The proposed RCB-IOVP SG algorithm corresponds to (7) to (9) and (33) to (38). The calculation of \(\lambda_{\text{\tiny{RCB}}}\) requires \(MD+D^{2}+4M+D\) complex multiplications, and the computation of \(\mathbf{g}_{a}[i]\) and \(\mathbf{g}_{s}[i]\) needs \(D^{3}+MD+D\) and \(5M+D+2\) multiplications, respectively.
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
## 5 Analysis of RSS Measurements Using Bluetooth Mesh Network
### Path Loss Models
Technical details and performance studies of the RSS-based ranging and location fingerprinting methods can be found in [4; 5; 6; 7; 9; 10; 17; 27] and the references therein.
## 3 Case Study
### OSP in Seamless Condition  
#### 3.3.1 Selection of Target Mode  
For large hydraulic structures like arch dams, under the excitation of dynamic water pressure, the vibration is mainly along the river flow direction (\(y\)-direction). Without loss of generality, this paper focuses on the OSP of the downstream surface of the arch dam along the river flow direction [31]. MPF reflects the dynamic response amplitudes of all modes. When constructing a new modal shape matrix, it is advisable to prioritize the selection of modes with larger MPF. Traditionally, the first five modes of arch dams are commonly chosen as target modes [63]. In this section, based on the consideration of MPF, the first five modes with higher modal energy are selected to form a new modal shape matrix. This approach aims to obtain higher modal information by selecting a fewer number of modes whenever possible. As shown in Figure 7, the 1 st, 4 th, 10 th, 15 th, and 17 th modal orders of the arch dam exhibit significant MPF along the river flow direction. Therefore, these five modal orders are selected as the latest modal shape matrix. The corresponding mode shapes are illustrated in Figure 8.
## 3 Materials and Methods  
### Preprocessing of the Dataset  
A dataset of 116 chymotrypsin-related proteases, for which structural experimental information is available, was selected. This dataset is based on the one used in [34], from which proteins with sequence identity \(>\) 70% were removed. The dataset comprises serine proteases from bacteria, eukaryotes, archaea, and viruses, in addition to chymotrypsin-related cysteine proteases from positive-strand RNA viruses. The full list of the proteins' PDB IDs is given in Table S1. The structures were downloaded from the Protein Data Bank, and the coordinate files were cleaned-up from heteroatoms, from copies of the protein in the crystallographic cell, and from residue-configurations with low occupancy. The position of missing atoms was rebuilt and the protein conformations were optimized using the software FoldX 4 [59]. Non-terminal missing residues were modeled with MODELLER [60,61]. An analysis of the first 3 normal modes for each protein was run using an elastic network model with a cutoff of 10 A, in order to identify the problematic cases in which the flexible protein termini impaired the analysis of the motion of the protein core. Such analysis was conducted by visual inspection of the modes on the protein structures. In those cases, flexible tails were not considered in the following analyses, which thus focused on globular structures. Moreover, in the case of multi-domain structures, only the domain known to have protease activity was retained.
## 3 Overview of the Study Area and Data Sources
### Data Sources  
The data used in this study mainly includes four types: remote sensing data, land cover data, crowdsourcing data, and public management data. The first three are basic data with geospatial attributes, and the fourth category is text data for use in result validation. The main data and data sources are shown in Table 2.
## 4 Numerical Demonstration
### Verification  
#### 4.3.1 Verification of Calculation Scheme  
The partition scheme of the calculation face may have affected the calculation results of aerodynamic loads, e.g., the number of calculation faces along the circumferential direction and the number of segments along the longitudinal direction. The more the number of calculation faces, the higher the accuracy of calculation and the lower the efficiency of calculation. The maximum pitching moment and side force of the head carriage when the train was running on flat ground under crosswind were taken as the target indicators.  
Firstly, three schemes for dividing the segments along the longitudinal direction were set up for comparison, as shown in Figure 9a. The number of the circumferential calculation faces remained constant (\(k=24\)). It is shown from Figure 9a that the target indicator of the scheme with 20 longitudinal segments was in good agreement with that of the scheme with 30 longitudinal segments. A scheme with 20 longitudinal segments (\(n=20\)) was sufficient. Four schemes for dividing the circumferential calculation faces were then set up for comparison, as shown in Figure 9b. The circumferential calculation faces were subdivided into 6, 12, 24, and 48 pieces, respectively (i.e., \(k=6\), 12, 24, and 48, respectively). The number of the longitudinal segments remained constant (\(n=20\)). It is shown from Figure 9b that the target indicator converged when the number of circumferential calculation faces increased to 24. The scheme with \(k=24\) and \(n=20\) was appropriate.  
Figure 8: Outline of the wind–train–rail structure (WTRSDYNA) coupling solution.