esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Perspectives  
This study aimed to investigate the impact of mutations in Rub A1, Rub A2, and GLRx genes on _Pseudomonas aeruginosa_ (PAO1) by assessing gene expression, virulence trait production, susceptibility to ciprofloxacin (CIP), and susceptibility to a phage cocktail. The mutants exhibited altered gene expression profiles, with a notable decrease in genes associated with quorum sensing (QS). The study found that mutants generally displayed increased biofilm production, with unexpected results for Rub A1 and Rub A2 mutants, suggesting potential gene interactions influencing biofilms. Moreover, protease production significantly increased only in the GLRX mutant for higher CIP concentrations, potentially being linked to iron metabolism. Rhamolipid production decreased as expected, while pyocyanin production was lower in all mutants and further reduced by CIP. Additionally, the mutants showed increased susceptibility to the phage cocktail, indicating the relevance of these genes in PAO1 resistance to phage infections. These findings underscore the potential therapeutic significance of exploring these genes in combating _P. aeruginosa_ infections.  
S.S.: Research, software, original draft preparation; C.S.: Investigation, software, original draft preparation; M.C.D.: Investigation; M.V.: Investigation; S.L.: Investigation; R.F.: Investigation, review and editing; A.C.R.: Investigation; P.J.P.: Investigation; M.O.: Investigation; J.M.: Investigation; G.N.: Investigation; C.L.: Investigation; A.G.: Investigation; J.M.M.: Investigation; D.M.-M.: Investigation; A.C.P.: Conceptualization, investigation, review and editing, project administration. P.B.: Conceptualization, investigation, review and editing, funding and resources, project administration. All authors have read and agreed to the published version of the manuscript.  
**Funding:**: [PERSON] (PB) acknowledges on the behalf of the authors the support of _Fundacao para a Ciencia e Tecnologia_ (FCT), Portuguese Government, under the Strategic Project Reference: UID/BIM/04293/2013. PB was also supported by FEDER/02/SAICT/2020/072560. [PERSON] was funded by LaBMI, grant number PORTIC/IPP4 COVID/BI/2021/01. [PERSON] was funded by LaBMI, grant number PORTIC/LABMI/BII/2021/01. [PERSON] was funded by FCT, grant number 2021.09498.BD. [PERSON] was funded by FCT, grant number 2021.06521.BD.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Diagnostic Method for the Saturable Reactor Core Looseness Degree of Thyristor Converter Valves
## 4 Diagnostic Method for Saturable Reactor Core Looseness Degrees
### Diagnosis Process for Iron Core Looseness  
The basic idea of the entire diagnostic process is to first train the model, then validate the model and then to finally use the model that meets the requirements for testing. The saturable reactor experimental platform is used to obtain datasets and their looseness level labels under various operating conditions and iron core looseness modes. These data face the issues analyzed in Section 3.2 and therefore cannot be directly used. Further extraction of beneficial features is needed. At this point, the SST algorithm is applied to convert the vibration signals into two-dimensional images. The vibration characteristics are sharpened and highlighted. Moreover, the image is input into a CNN network with set basic parameters for learning and training. The obtained model needs to be confirmed through the validation set, otherwise the parameters of the CNN network will continue to be optimized. The final model (the one that meets the requirements) will be truly used to test its diagnostic performance.  
The specific steps for constructing a diagnosis method for the saturable reactor core looseness degree based on the SST and CNN are as follows:  
1. Set different core looseness degrees on the experimental platform. Collect vibration signals under varying operating conditions and denoise them.
2. Select appropriate mother wavelet functions and related parameters. SST is used to convert the vibration signals into two-dimensional images. These images are divided into training sets, validation sets and testing sets.
3. Construct a CNN model using the time-frequency spectrum of iron core vibration signals under different looseness degrees as input and construct a class matrix of iron core looseness as output. Configure the network parameters for training.
4. Use the validation set to test the trained CNN model. If it does not meet the recognition accuracy requirements, return to step 3 and change the parameters.
5. Finally, the testing sets are used to verify the performance of the trained model.  
Figure 7: The proposed CNN model structure.
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 3 Gait Recognition Using GERF
### Score-Level Fusion of Multiple SD-GERFs  
We considered only the largest eigenvector as a solution of the GERF in our previous paper [20]. However, we can exploit multiple largest eigenvectors to obtain multiple GERFs for better accuracy in practice. Particularly, in the case of the SD-GERF, responses for each GERF often localize to a certain part. For example, the single largest eigenvector is localized to the head, and the second-largest is localized to the leg (refer to the feature examples in Section 4.4). Therefore, a single SD-GERF cannot reflect information from the whole body and may result in low recognition accuracy. We therefore adopt multiple SD-GERFs corresponding to the \(N\) largest eigenvalues to cover the whole body and fuse results from the multiple SD-GERFs for better accuracy.  
As for the fusion scheme, we first compute dissimilarity scores for each SD-GERF between a probe and every subject in a gallery set and apply probe-dependent z-normalization [40] to the set of dissimilarity scores, i.e., linearly normalize the scores so that their mean and variance are 0 and 1, respectively. Once we obtain \(N\) z-normalized dissimilarity scores from \(N\) SD-GERFs, we fuse them by either of two simple, yet effective, score-level fusion methods [41], i.e., sum and min rule.
# Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
## 3 Experiments  
### Training  
The model was trained using Google Colab, which provides free access to powerful GPUs and requires no configuration. We used a notebook developed by Roboflow.ai [58] which is based on YOLOv5 [50] and uses pre-trained COCO weights. We added the rest area dataset and adjusted the number of epochs to be trained as well as the stack size to train the upper layers of the model to detect our classes. Training a model for 500 epochs takes about 120 min. The improvement in our model can be seen in the graphs in Figure 5, which display different performance metrics for both the training and validation sets.  
Figure 4: Examples of augmented training data illustrating the horizontal mirroring, resizing and changes of the grey scale.  
Figure 3: Examples of annotated images with bounding boxes for the three classes: _truck_front_, _truck_back_ and _car_.  
There are three different types of loss shown in Figure 5: box loss, objectness loss and classification loss. The box loss represents how well the algorithm can locate the centre of an object and how well the predicted bounding box covers an object. Objectness is essentially a measure of the probability that an object exists in a proposed region of interest. If the objectivity is high, this means that the image window is likely to contain an object. Classification loss gives an idea of how well the algorithm can predict the correct class of a given object.  
The model improved swiftly in terms of precision, recall and mean average precision before plateauing after about 150 epochs. The box, objectness and classification losses of the validation data also showed a rapid decline until around epoch 150. We used early stopping to select the best weights.
# Anisotropic Shear Strength Behavior of Soil-Geogrid Interfaces
###### Abstract  
This paper presents an experimental study on the anisotropic shear strength behavior of soil-geogrid interfaces. A new type of interface shear test device was developed, and a series of soil-geogrid interface shear tests were conducted for three different biaxial geogrids and three different triaxial geogrids under the shear directions of \(0^{\circ},45^{\circ}\) and \(90^{\circ}\). Clean fine sand, coarse sand, and gravel were selected as the testing materials to investigate the influence of particle size. The experimental results for the interface shear strength behavior, and the influences of shear direction and particle size are presented and discussed. The results indicate that the interface shear strength under the same normal stress varies with shear direction for all the biaxial and triaxial geogrids investigated, which shows anisotropic shear strength behavior of soil-geogrid interfaces. The soil-biaxial geogrid interfaces show stronger anisotropy than that of the soil-triaxial geogrid interfaces under different shear directions. Particle size has a great influence on the anisotropy shear strength behavior of soil-geogrid interfaces.  
geogrid; geogrid-reinforced soil; soil-geogrid interface; shear strength; anisotropy
## 3 Methodological Background
### Random Effect Bayesian Logistic Regression  
The random effects Bayesian logistic regression (RBLR) was applied in this study as one of the methods for modeling crash risks. The Bayesian logistic regression differs from the standard (frequentist) logistic regression in the treatment of coefficients--it assumes that the coefficients follow a random distribution, rather than being fixed. The main advantage of Bayesian approach is the ability to capture uncertainty (variability) in the measured phenomena that may not be captured in the sample. This is achieved by formulating the model parameters as random variables that follow assumed prior distributions derived from some (prior) information about the measured phenomenon. The resulting confidence region associated with each estimated parameter is equivalent to the confidence interval in standard regression analysis, but it also captures the unobserved variability in the model parameters, which may improve the accuracy of the estimates. As another advantage over the standard regression models, the Bayesian models can help avoid the odds ratio overfitting, especially in complex models with large number of parameters. problem.  
In this study of traffic crashes, the response variable is binary with two outcomes: \(y_{i}=1\) and \(y_{i}=0\). In the crash likelihood model, \"1\" represents a crash event and \"0\" represents a non-crash case. In the crash severity model, \"1\" represents an injury/fatal crash, and \"0\" represents a property-damage-only (PDO) crash. The probabilities associated with the binary events are \(p_{i}\) and \(1-p_{i}\), respectively. Thus, applying the [PERSON] theorem, the RBLR is formulated as follows:  
\[y_{i}\sim Bernoulli(p_{i}) \tag{1}\]  
\[logit(p_{i})=\beta_{0}+\beta_{j}X_{j} \tag{2}\]  
where the probability of each observation (\(y_{i}\)) is assumed to follow Bernoulli distribution, \(X_{j}\) is the vector of \(N\) explanatory variables (\(j=1\),..., \(N\)), \(\beta_{j}\) is the vector of random coefficients (slopes) associated with the explanatory variables, and \(\beta_{0}\) is the random intercept in the regression model. Both the intercept and slope coefficients follow specific prior random distributions.  
In general, depending on the corresponding prior probability distributions, the coefficients can be categorized into two groups: informative priors and non-informative priors. The informative priors are used when prior information about the estimated coefficients or their possible values are known, while the non-informative priors are used when little or nothing is known about the values of the coefficients except a general functional form of the prior probability distribution (most often Normal distribution).
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 2 Modeling
### Parameter Determination
#### 2.4.1 Temporal Model Determination
 \tag{10}\]  
The undetermined coefficients in the temporal map can be determined by using the LS method with the RRMSE as the evaluation standard. Figure 11 shows the temporal regression coefficients of the Dourbes station as an example. It can be seen that the temporal regression coefficients increase significantly at noon when the solar radiation is the strongest compared with other times.
## 5 Machine Learning Models
### Introduction to Random Forest (RF)  
Random forest (RF) is a well-known supervised learning machine learning (ML) method. It may be utilized in ML for both classification and regression tasks [84]. It is built on the notion of ensemble learning, which is the process of merging numerous classifiers to solve a complicated issue and enhance the model's performance. RF contains a number of decision trees on different subsets of the provided dataset, and takes the average to enhance the predicted accuracy of that dataset [85]. Random forest, unlike a single decision tree, aggregates predictions from multiple trees and determines the final output based on the majority vote. Increasing the number of trees in the forest improves accuracy and helps to prevent overfitting. Bootstrap aggregation or bagging is used to establish an ideal number of trees based on the size and type of the training data. The RF prediction may be stated as shown in Equation (7):  
\[f(x)=\frac{1}{T}{\sum\limits_{t=1}^{T}{f_{t}(x)}} \tag{7}\]  
where \(f(x)\) is the prediction based on RF from the total number of trees, i.e., \(T\), and \(f(x)\) is the prediction from each individual tree for a given input, \(x\). The RF algorithm is proven to be a superior tool in the application of seismic damage evaluation [50; 86; 87; 88]. As a result, the RF algorithm is utilized here to construct the CDAF spectra.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Five-Story**} & \multicolumn{3}{c}{**Ten-Story**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & \(RMSE\) & \(MAPE\) & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) & \(MAPE\) \\ \hline Training & 0.947 & 0.052 & 3.866 & 0.938 & 0.089 & 5.401 \\ Testing & 0.972 & 0.049 & 6.253 & 0.945 & 0.084 & 7.762 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Performance of ANN model.  
Figure 13: Simulated and ANN’s predicted CDAFs for (**a**) 5-story models and (**b**) 10-story models.
## 3 Experimental Validation
### Implementation Details
#### 3.2.3 Baseline  
The Convolutional Block Attention Module (CBAM) [23] was set as the baseline in our experiment. CBAM employs both channel and spatial attention layers. It is also a plug-and-play module that can boost the representation power of CNNs. CBAM-assisted CNNs showed consistent improvement on multiple benchmarks, including ImageNet-1K, MS COCO, and VOC 2007. In the experiments, the reduction ratio of CBAM was set to 16 for the channel attention layer, and convolutional kernel size was set to 7 in the spatial attention layer.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing
### Three-Dimensional Model Construction  
The tilt photography data are pre-processed through quality checks, the removal of unqualified photos, and the extraction of images of areas of concern. The preprocessed data are then imported into a computer, where a tilt model is generated using the ContextCapture 10.20 software. SVSGeoModeler is then used to monomerize the model's subsidiary structures and complete the mapping, resulting in a highly detailed large-scene 3 DRS model. Finally, the model is converted into the 3 DTiles format, providing a reliable data foundation for the platform (Figure 8).  
Component-level 3 DRS modeling is established based on indoor and multi-source sensor point cloud data with the Trimble SX10 3D scanner. The indoor 3D point cloud data are processed using Trimble Realworks and SketchUp. Trimble Realworks is used to denoise and align the point cloud, ensuring an alignment error of \(\leq\)2 mm and a confidence level of \(\geq\)50%. Noise from pedestrians and debris is segmented and removed, retaining only the main structure of the monitoring station. The optimized point cloud is then imported into SketchUp to create a 1:1 scale component-level 3 DRS model (Figure 9). The  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Number** & **Field** & **Data Type** & **Primary Key** & **Introduction** \\ \hline
1 & SN & Int & Not Null & Device Serial Number \\
2 & Temperature & Varchar & Not Null & Temperature \\
3 & Humidity & Varchar & Not Null & Humidity \\
4 & Pressure & Varchar & Not Null & Air Pressure \\
5 & Wind\_speed & Varchar & Not Null & Wind Speed \\
6 & Wind\_direction & Varchar & Not Null & Wind Direction \\
7 & Rainfall & Varchar & Not Null & Rainfall \\
8 & Pm 2.5 & Varchar & Not Null & PM2.5 concentration \\
9 & Getting & Datetime & Not Null & Data Receiving Time \\ \hline \end{tabular}
\end{table}
Table 3: Meteorological database table design.  
large-scale and component-level models are integrated using SVS-GeoModeler, and the resulting 3D data are optimized and sliced using Cesium. Finally, the sliced data are exported to the server for management and service deployment.  
To enable fast 3D scene loading on the web, tile division is improved by reconstructing the top layer and loading only the tiles within the current field of view, significantly reducing the number of requests. The Draco vertex compression algorithm is used to reduce vertex data storage and the ASTC algorithm is used for texture compression. Block-based compression ensures image quality while improving the compression ratio, enabling quick and efficient web-based model loading. Figure 10 shows the 3D model fusion results.  
Figure 8: Flowchart for constructing a large-scene 3 DRS model.  
Figure 10: Web platform to display 3D model fusion results.  
Figure 9: Component-level 3 DRS model.
###### Abstract  
In this paper, a control method for electric vehicles (EVs) participating in grid Frequency regulation is proposed. Firstly, considering dispatching large-scale electric vehicles, the K-means clustering algorithm is applied to cluster EVs with different battery state of charge and with different average vehicle daily travel miles. Then, for each class of electric vehicle group, a multi-objective optimization model considering reducing power imbalance and feeding the driving power demand for electric vehicles is proposed. Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is applied to solve the optimization model and obtain the best control parameters for \"virtual synchronous machine\", which is functioned as the power controller between EVs and the power grid. At last, based on a Monte Carlo sampling, the simulation analysis of 50 EVs with the normal distribution of battery state of charge and average vehicle daily travel miles is carried out by using the proposed method. The results show that the proposed method can effectively classify the electric vehicles with different battery state of charge and different average vehicle daily travel miles. The parameters of the power converter controller for different classes of electric vehicles are optimized considering power grid frequency, their battery state of charge and their average daily travel miles, so as to maintain the balance of power grid frequency, and to meet the power needs of EV daily drive.  
e pacs: +
Footnote †: journal: Applied Sciences
## 3 Results  
### Correspondence Between Canopy Height and VIs  
Figure 4 illustrates CHM5 for years 2000, 2016 and 2018 classified into four canopy height classes. Trees associated with CHM > 10 m tend to be upland forest classes and are located on terraces or elevated levees. This figure also shows the areal coverage of each canopy height class within surveyed areas A, B, C and D. From 2000 to 2018, there was a general increase in all categories of canopy height exceeding 1 m, with 52.8% of total land surface cover in 2000 compared to 66.2% for 2016-18.  
Based on the distribution of random points (excluding marsh areas with vegetation <1 m) illustrated in Table 1, both NDVI and EVI demonstrated a statistically significant correlation with CHM30, with greater correspondence to NDVI. Highest correlations between canopy height and vegetation indices were found in shrub swamp and upland forest, whereas treed swamp and marsh had much lower correspondence (Table 1, Figure 5). Confidence intervals (Figure 5) also demonstrate higher confidence and lower variability, especially in shrub swamps relative to other landcovers. The corresponding linear regression relationships show similar results and are presented in Figure 5. In shrub swamps, the 95% confidence intervals (CI) in this figure are the narrowest among the landcover classes for both NDVI and EVI, suggesting higher confidence in the trends.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Landcover Class**} & \multirow{2}{*}{**N**} & \multicolumn{2}{c}{**Correlation with CHM30**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \cline{3-6}  & & **NDVI** & **EVI** & **NDVI** & **EVI** \\ \hline Marsh & 261 & 0.05 & 0.11 & 0.425 & 0.090 \\ Shrub swamp & 881 & 0.65 ** & 0.59 ** & \textless{}0.001 & \textless{}0.001 \\ Treed swamp & 292 & 0.13 * & \textless{}0.14 * & 0.023 & 0.014 \\ Upland forest & 293 & 0.58 ** & 0.22 ** & \textless{}0.001 & \textless{}0.001 \\ All & 1727 & 0.63 ** & 0.31 ** & \textless{}0.001 & \textless{}0.001 \\ \hline \hline \end{tabular}  
* Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).  
\end{table}
Table 1: Spearman correlation results between CHM30 and Landsat VIs for 2016/18 over the main landcover classes.  
Figure 4: Canopy Height Models (CHM5) (5 m pixel size). “White” areas within each sample region, represent either CHM <1 m or non-dominant landcover classes. (**A–D**) label each of the lidar surveyed areas.