esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this study is to compare the classification accuracy depending on the number of texture features used. This study used 400 computed tomography (CT) images of trabecular spinal tissue from 100 patients belonging to two groups (50 control patients and 50 patients diagnosed with osteoporosis). The descriptors of texture features were based on a gray level histogram, gradient matrix, RL matrix, event matrix, an autoregressive model, and wavelet transformation. From the 290 obtained texture features, the features with fixed values were eliminated and structured according to the feature importance ranking. The classification performance was assessed using 267, 200, 150, 100, 50, 20, and 10 texture features to build classifiers. The classifiers applied in this study included Naive Bayes, Multilayer Perceptron, Hoeffding Tree, K-nearest neighbors, and Random Forest. The following indicators were used to assess the quality of the classifiers: accuracy, sensitivity, specificity, precision, negative predictive value, Matthews correlation coefficient, and F1 score. The highest performance was achieved by the K-Nearest Neighbors (K = 1) and Multilayer Perceptron classifiers. KNN demonstrated the best results with 50 features, attaining a highest F1 score of 96.79% and accuracy (ACC) of 96.75%. MLP achieved its optimal performance with 100 features, reaching an accuracy and F1 score of 96.50%. This demonstrates that building a classifier using a larger number of features, without a selection process, allows us to achieve high classification effectiveness and holds significant diagnostic value.  
Keywords:classification; machine learning; texture analysis; osteoporosis; spongy bone tissue image analysis; texture features +
Footnote †: journal: Applied Sciences
## 5 Feature Extraction  
CNN, such as the AlexNet and VGG-16 designs, are both commonly employed for image classification applications. VGG-16 is made up of 16 convolutional layers as compared with AlexNet's 5 convolutional layers and 3 fully connected layers. VGG-16 uses the highest filter size of 4\(\times\), while AlexNet utilizes the smallest filter size of 3 \(\times\) 3 [34].  
AlexNet has a smaller capacity than VGG-16 but is faster to train. Both architectures extract features from images by convolving over the image with a set of learned filters. These features are then passed through a series of fully connected layers, which perform classification [35]. The facial image processing pipeline consists of two steps: facial feature extraction and detection. The pre-trained AlexNet model was used to extract features from facial image data at the output layer of the chain. We selected AlexNet as the CNN model for feature extraction based on a recent study, which compares the CNN model to five  
Figure 1: Alexnet and VGG16 feature extraction and classifier application for masked datasets.  
well-known pre-trained models and shows that it appears to be good for age-invariant facial recognition systems [36].  
The first layer extracts the input layer's average activation. The average activation of a layer determines how active the neurons in the layer are and is often used to determine the overall importance of a neuron in a layer. By using the average activation of a layer, we can determine which neurons are the most important for classification.  
The second layer extracts the input layer's non-linearity. Non-linearity is determined by how much the input layer's activation changes when the input layer's weight is changed. This feature is important because it determines how well the neurons in the input layer map to the correct output category.  
The third layer extracts the output layer's correlation with the input layer. The correlation between two layers is determined by how well the output of one layer predicts the input of another layer. This feature is important because it determines how well the neurons in the output layer map to the correct input category [37; 38].  
These features were extracted from VGG-16 and AlexNet, and different classifiers were applied to the masked face dataset of facial expressions. In the next subsection, we discuss the different classifiers that are used in our experiments.
## 3 Results
### Spatial Variation of Chlorophyll Concentration across the Study Site  
A Landsat 8 image acquired in January 2014 was used to produce landscape scale mangrove leaf CC map. First, NDVI green was computed for the region of interest then, using Equation (1), CC was calculated for every pixel using the band math tool in ENVI 5.0. The same procedure was applied to the Landsat 8 images acquired at different dates throughout a complete annual cycle (Figure 9). Maps are able to show the spatial distribution of CC with a pattern that seems related to distance from water. Larger CC values are observed at the borders of the Ria Celestun, in petenes (characterized by circular shaped \"islands\" of vegetation), and flooded areas, with values decreasing towards the continent or the sea. With respect to the temporal variability, in general the maps depict an increasing gradient from April 2013 to November 2013 and a decreasing pattern form November 2013 to March 2014 (Figure 10).  
Figure 8: Observed against predicted CC using the leave-one-out cross-validation method. Each point represents an ESU. RMSE =15 \(\upmu\)g\(\cdot\)cm\({}^{-2}\), \(R^{2}=0.703\).  
Figure 10: Temporal variation in CC. The boxes represent each CC map. Each box embodies the first and the third quartile. The bold line represents the median while the dark dot represents the mean. Whiskers are located at 1.5 times the interquartile range and white dots denote outliers.  
Figure 9: Remote sensing-based maps of the spatiotemporal variation in CC (\(\mu\)g\(\cdot\)cm\({}^{-2}\)) estimated using the relationship between Landsat 8 NDVI green and Chl measurements obtained in the field. The non-mangrove pixels have been masked out.
## 4 Experimental Results  
In this section, we evaluate how the proposed framework enhances the performance of trajectory optimization in dynamic highway settings. Our agent is trained in the gym highway environment [28], featuring multiple lanes and nearby vehicles, requiring the generation of appropriate control inputs based on odometry information for both its own position and that of nearby vehicles. The objective in this environment is to maintain a target speed of 30 m/s (108 km/h) while staying within the target lane and avoiding collisions. The base RL algorithms are implemented using the open-source RL framework, stable-baselines3 [38] with PyTorch, and the open-source toolbox do-mpc [39] is used for the MPC calculation.  
To assess the effectiveness of our training method and driving framework, we implement CER in TD3 [40], which is considered the state-of-the-art deterministic policy gradient RL method. As the baseline method, we employ TD3 with the UVFA [13] structure for the goal-conditioned policy.  
In an ablation study, we explore the impact of the behavior policy in the training process by integrating expert demonstrations with the stored transitions in the buffer. Trajectories from the MPC controller are utilized for reward calculation, with these trajectories also serving as expert demonstrations integrated into the training process, denoted as CER[M]. While adhering to the network hyperparameters outlined in [40], additional buffers are employed for our configuration. To ensure training stability, the reward scale is clipped within the range of [\(-\)1, \(+\)1]. Each episode consists of 1000 steps, with termination triggered if the agent deviates off track or collides with another vehicle.  
This section is structured as follows: In Section 4.1, we assess whether CER enhances training performance and compare the performances of TD3 in the highway environment with and without CER. Section 4.2 provides a detailed analysis of trajectories and sub-goal decisions, while Section 4.3 presents an evaluation of action diversity in the trained agent.  
We demonstrate the adaptability of our hierarchical module to changes in the decision-making module in this section. Lastly, in Section 4.4, we conduct an ablation study to examine the effect of the behavior policy on the CER training agent.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 4 Experiments and Analysis
### Ablation Experiment of Two Metrics  
To verify the validity of the two metrics (RES and \(\delta\)) of the AWNet, we perform an ablation experiment on the UCMD dataset with noise rates of 20% and 30%, respectively. In the ablation experiments, the retrieval effects of the metrics RES and \(\delta\) alone as inputs of the AWNet are investigated. In addition, the retrieval effects of the three parameters prediction probability, entropy and standard deviation S, which make up RES and \(\delta\), are also examined alone as the input of the AWNet. Specifically, the prediction probability denotes the predicted probability of the model classifier (i.e., the output of the softmax output layer). Table 8 displays the retrieval performance of the above experiments. The results indicate that using both RES and \(\delta\) as inputs to AWNet can help the AWNet to better predict \(\alpha\) and \(\beta\), resulting in the highest retrieval accuracy. This finding confirms the validity of the two metrics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **mAP** & **Methods** & **mAP** \\ \hline CE & 80.41 \(\pm\) 0.52 & NCE + RCE [17] & 87.70 \(\pm\) 0.11 \\ MAE [13] & 86.53 \(\pm\) 0.13 & NCE + MAE [17] & 85.82 \(\pm\) 0.53 \\ GCE [14] & 87.97 \(\pm\) 0.48 & NFL + RCE [17] & 88.04 \(\pm\) 0.32 \\ RCE [15] & 86.39 \(\pm\) 0.37 & NFL + MAE [17] & 84.07 \(\pm\) 1.20 \\ SCE [15] & 86.51 \(\pm\) 0.19 & A-NCE + RCE (ours) & 88.37 \(\pm\) 0.29 \({}^{*}\) \\ ACE [16] & 86.52 \(\pm\) 0.33 & A-NCE + MAE (ours) & 88.95 \(\pm\) 0.45 \({}^{*}\) \\ AUL [31] & 86.74 \(\pm\) 0.82 & A-NFL + RCE (ours) & 88.76 \(\pm\) 0.25 \({}^{*}\) \\ AEL [31] & 85.79 \(\pm\) 0.08 & A-NFL + MAE (ours) & 88.49 \(\pm\) 0.26 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\)R represents the highest retrieval precision.  
\end{table} 
Table 6: mAPs (%) (mean \(\pm\) standard deviation) of comparison with robust losses on the NWPU dataset with 20% noise. Additionally, to assess the rationality of the pretraining epochs \(t_{p}\) in this paper, we conduct ablation experiments on the UCMD dataset with 20% noise. The pretraining epochs \(t_{p}\) are set to 1, 2, 3, 4 and 5, respectively. The experimental results are shown in Table 9. The results show that the retrieval performance achieved the best when \(t_{p}=3\). Too few pretraining epochs (e.g., 1) may lead to underfitting of the model and affect the reliability of RES and \(\delta\). Therefore, in other experiments, the pretraining epoch is set to 3. To determine the optimal structure for the AWNet, we evaluate the model performance with varying numbers of hidden layers and neurons per layer on the UCMD dataset with 20% noise. Specifically, we test the model's performance with one, two and three hidden layers and with 50 and 100 neurons per layer, respectively. The experimental results are presented in Table 10. The results indicate that using one hidden layer yields a better  
\begin{table}
# Numerical Investigation of Slurry Fracturing during Shield Tunneling under a Reservoir
## 2 Numerical Method
### Introduction of Node Enhancement Function  
In order to realize the simulation of the crack, the XFEM introduces the displacement enhancement function, including the conventional node deformation function and the enhancement function describing the displacement mutation at the crack [34]. The displacement vector function containing the displacement enhancement component can be expressed as:  
\[\mathbf{u}=\sum_{l=1}^{N}N_{l}(x)\left[\mathbf{u}_{l}+H(x)\mathbf{a}_{l}+\sum _{\alpha=1}^{4}F_{\alpha}(x)\mathbf{b}_{l}^{\alpha}\right] \tag{1}\]  
where \(\mathbf{u}\) is the approximation of a displacement vector, \(N_{l}(x)\) is the usual nodal shape function, \(\mathbf{u}_{l}\) is the nodal displacement vector describing continuous deformation in finite element analysis, \(\mathbf{a}_{l}\) is the displacement vector of the reinforcement nodes on both sides of the crack surface, \(H(x)\) is the jump function across the fracture surfaces, \(\mathbf{b}_{l}^{\alpha}\) is the displacement vector of the reinforcement node at the crack tip, and \(F_{\alpha}(x)\) is the asymptotic crack-tip function. The three terms on the right side of Equation (1) are used to describe the displacement of all nodes in the model, capture the displacement discontinuity between the fracture surfaces, and the displacement of the crack tip.  
The jump function \(H(x)\) can be given by Equation (2). It has a unit value and changes sign as it passes through the crack, while conveying discontinuity on the fractured surface.  
\[H(x)=\left\{\begin{array}{cc}1,&\text{if }(x-x^{*})\cdot\mathbf{n}\geq 0\\ -1,&\text{otherwise}\end{array}\right. \tag{2}\]where \(x\) is the Gaussian node, \(x^{*}\) is the point on the crack closest to the Gaussian node \(x\), and \(\mathbf{n}\) is the unit outer normal vector at point \(x^{*}\) on the crack.  
The crack tip enhancement function \(F_{a}(x)\) can be expressed as:  
\[\{F_{a}(r,\theta)\}_{a=1}^{4}=\left[\sqrt{r}\sin\frac{\theta}{2},\sqrt{r}\cos \frac{\theta}{2},\sqrt{r}\sin\theta\sin\frac{\theta}{2},\sqrt{r}\sin\theta \cos\frac{\theta}{2}\right] \tag{3}\]  
where \((r,\theta)\) is the polar coordinate system with the crack tip as the origin and \(\theta=0\) is the direction tangent to the crack. [PERSON] and [PERSON] give more detailed information on XFEM research results [35].
# Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?
## 4 Empirical Study
### Results  
Table 4 summarises the forecasting performance of the various models across all SKUs with respect to the different forecast horizons and error metrics. The best performing model \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Name**} & \multicolumn{2}{c}{**Seasonality**} & \multicolumn{2}{c}{**Covariates**} \\ \cline{2-5}  & **Usual** & **Trignometric** & **Raw** & **PCA** \\ \hline Univariate & & & & \\ ES & ✓ & & & \\ TBATS & & & ✓ & & \\ ARIMA & ✓ & & & \\ RegARIMAF & & ✓ & & \\ Ridge & ✓ & & & \\ RidgeF & & ✓ & & \\ \hline With explanatories & & & & \\ ESXPC & ✓ & & & ✓ \\ PCRegARIMA & ✓ & & & ✓ \\ PCRegARIMAF & & ✓ & & ✓ \\ RidgeX & ✓ & & ✓ & \\ RidgeFX & & ✓ & ✓ & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Models used in the analysis. in each column (horizon) is highlighted in boldface. The results are grouped by models with and without covariates, and the rows within each group are sorted by the MASE overall performance. We highlight some key observations in the table. First, irrespective of the error measure and the horizon, models with covariates perform substantially better. Both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over the univariate benchmarks. This is expected given the importance of special events, price information, and promotions in retail forecasting. Second, Ridge regression models perform very well overall, and particularly when explanatory variables are available to them. Third, the usefulness of the trigonometric representation depends on the model and error metric. Fourth, the results between MASE and RMSSE differ, which is unsurprising since the error measures focus on different parts of the distribution of the target variable. To better compare the models we provide in Figure 3 the models' mean rank and the results of the MCB test at a 5% significance level, for the horizon 1-13. The forecasts are ranked by their mean rank, which is reported next to their name. The best performing forecasts are at the lowest of the plot and surrounded by a greyed area. For any other forecasts overlapping this area there is no evidence of statistically significant differences. The lines surrounding the forecasts are the critical distances of the Nemenyi test, which function in the same way. The horizontal axis plots the mean rank of the forecasts. For both error measures, RidgeX ranks first. The difference in the top ranking between Table 4 and Figure 3 is attributed to the distribution of the forecast errors, as the mean rank is non-parametric and therefore resilient against outlying errors. Again, we find that models that include the covariates perform best. There is evidence that including these with a shrinkage estimator performs better than using PCA to compress them, although the PCRegARIMAF remains a strong contender. ESXPC trails other models with covariates, yet is substantially better than any of the univariate benchmarks. Last but not least, for
## 4 Results and Discussion
### RQ3: What Evaluation Methods Are Used to Assess the Found Architecture?
#### 4.3.2 Datasets
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Search** & \multicolumn{2}{c}{**CIFAR-10**} & \multicolumn{2}{c}{**STL-10**} \\  & **Strategy** & **IS \(\uparrow\) * & **FID \(\downarrow\) ** & **IS \(\uparrow\) * & **FID \(\downarrow\) ** \\ \hline AutoGAN [12] & RL & 8.55 \(\pm\) 0.10 & 12.42 & 9.23 \(\pm\) 0.08 & 31.01 \\ AGAN [34] & RL & 8.29 \(\pm\) 0.09 & 30.50 & 9.23 \(\pm\) 0.08 & 52.72 \\ E\({}^{2}\)GAN [36] & RL & 8.51 \(\pm\) 0.13 & 11.26 & 9.51 \(\pm\) 0.09 & 25.35 \\ AdversarialNAS [39] & Gradient & 8.74 \(\pm\) 0.07 & 10.87 & 9.63 \(\pm\) 0.19 & 26.98 \\ DEGAS [40] & Gradient & 8.37 \(\pm\) 0.08 & 12.01 & 9.71 \(\pm\) 0.11 & 28.76 \\ alphaGAN [14] & Gradient & 8.98 \(\pm\) 0.09 & 10.35 & 10.12 \(\pm\) 0.13 & 22.43 \\ DAMGAN [42] & Gradient & 8.99 \(\pm\) 0.08 & 10.27 & 10.35 \(\pm\) 0.14 & 22.18 \\ E-GAN [22] & EA & 6.9 \(\pm\) 0.09 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of results on CIFAR-10 and STL-10 dataset.
## 4 QS Inhibition in the Context of Fish Preservation
### QS Inhibiting Agents
The agents also cause the repression of QS-mediated activities [74]. The third mechanism of inactivation of the QS system in _Pseudomonas_ spp. blocks the signaling cascade by deactivating the downstream response regulators or other regulatory factors [21]. For instance, an efflux pump inhibitor PA\(\beta\)N reduces the accumulation of AIs in supernatants and significantly decreases the relative expression of the QS cascade (_pqaA_, _pqsR_, _lasl_, _lasR_, _rhll_, and _Rhll_) in _P. aeruginosa_. Limonene and \(\beta\)-caryophyllene from \begin{table}
\begin{tabular}{c c c c} \hline \hline
**QS Inhibiting Agent** & **Target Microorganism** & **Impact on Bacterial QS-Controlled Processes** & **Reference** \\ \hline _Piper nigrum_ L. EO & _P. psychrophilia_ & reduction of proteolytic and lipolytic activities & [66] \\ \hline _Ferula asafoetiida_ EO & _P. aeruginosa_ & Reduction of pyocyanin and elastase production; prevention of biofilm formation & [69] \\ \hline _Myrtus communis_ L. EO & _P. fluorescens, P. orientalis_ & Reduction in the EPS production & [52] \\ \hline _Origanum majorana_ EO & _P. putida_ & Prevention of biofilm formation & [70] \\ \hline _Juniperus phenicca_ EO & _P. fluorescens_ & Reduction of proteolytic and lipolytic activities & [5] \\ \hline \multirow{2}{*}{Cinnamaldehyde} & _P. fluorescens_ & Reduction of proteolytic activity; prevention of biofilm formation & [71] \\ \cline{2-4}  & & Reduction the EPS production and bacterial motility, prevention of biofilm formation & [54] \\ \hline \multirow{2}{*}{Garlic extract} & _P. aeruginosa_ & Reduction of rhamnoid production & [72] \\ \cline{2-4}  & & Reduction the RAN & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Impact of natural QS inhibiting agents on fish-associated _Pseudomonas_ spp. black pepper EO also exhibited the anti-QS and anti-efflux pumps of pseudomonads [66]. The mRNA transcript levels of autoinductor synthases, membrane fusion, outer membrane proteins, and transcription of repressor regulators MarR and TetR were observed in _P. psychrophilia_. In general, limonene and \(\beta\)-caryophyllene affected the functioning of the QS system in _P. psychrophilia_ and consequently reduced the spoilage potential [66].
# Computing Persistent Homology of Directed Flag Complexes
## 4 Materials and Methods
### Performance Considerations
#### 4.4.4 Apparent Pairs  
In Ripser, so-called apparent pairs are used in order to skip some computations completely. These rules are based on discrete Morse theory [25], but they only apply for non-directed simplicial complexes. Since we consider directed graphs, the associated flag complex is a semi-simplicial rather than simplicial complex, so we cannot use this simplification. Indeed, experiments showed that enabling the skipping of apparent pairs yields wrong results for directed flag complexes of certain directed graphs. For example, when applied to the directed flag complex of the graph in Figure 5, using apparent pairs gives \(\beta_{1}=\beta_{2}=0\) and not using apparent pairs gives \(\beta_{1}=\beta_{2}=1\), which is visibly the correct answer.  
Experiments showed that when applied to directed flag complexes that arise from graphs without reciprocal connections (so that the resulting directed flag complex is a simplicial complex), enabling the skipping of apparent pairs does not give a big performance advantage. In fact, we found a significant reduction in run time for subgraphs of the connectome of the reconstruction of the neocortical column based data averaged across the five rats that were used in [6], the Google plus network and the Twitter network from KONECT [11].  
**Author Contributions:** Conceptualisation, R.L.; Data curation, D.G. and J.P.S.; Formal analysis, D.G. and J.P.S.; Funding acquisition, R.L.; Methodology, D.L.; Project administration, R.L.; Resources, J.P.S. and R.L.; Software, D.L. and J.P.S.; Supervision, R.L.; Validation, D.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded in part by an EPSRC grant EP/P025072/--\"Topological Analysis of Neural Systems\".  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 5: A digraph for which using apparent pairs to compute the homology of the directed flag complex gives the wrong results.
# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 3 Methods
### Machine Learning Algorithms  
#### 3.3.1 Support Vector Machine  
Support Vector Machine (SVM) is to project nonlinear inseparable samples into high-dimensional space by taking advantage of the kernel functions and then locate the optimal separating hyperplane in the projection space by solving a quadratic optimization problem. Typical kernel functions of SVM including the following four kinds:  
1. Linear kernel functions [26]: \(k\left(x_{i},x_{j}\right)=\left\langle x_{i},x_{j}\right\rangle+c\)
2. Radial basis functions (RBF) [27]: \(k\left(x_{i},x_{j}\right)=exp\left[-\frac{1}{2\sigma^{2}}\left|\left|x_{i}-x_ {j}\right|\right|\right]\)
3. Sigmoidal kernel functions: \(k\left(x_{i},x_{j}\right)=tanh\left(k\left\langle x_{i},x_{j}\right\rangle+c\right)\)
4. Polynomial kernel functions: \(k\left(x_{i},x_{j}\right)=\left(Y\left\langle x_{i},x_{j}\right\rangle+\gamma \right)^{d}\)  
In this paper, we use the above four kernel functions in the following experiment. In addition, from the results of classification, we compared the performances of the four optimized kernel functions with each other.