esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Com
The DNN combined with two sequence prediction models, LSTM and a Transformer, was selected as a cascaded scheme. The DNN-LSTM model replaces the Transformer module in Figure 9 with a typical LSTM network. The DNN, LSTM, and Transformer use independent models without time label processing modules, so these models can only achieve eye diagram and COM value predictions. The best structural parameters of these models--such as the number of hidden layers and the hidden layer size of the LSTM, and the head number, vector dimension, and feedforward network dimension of the Transformer's attention mechanism--were all obtained through Bayesian optimization. As presented in Table 6, the cascaded models demonstrate superior predictive capabilities compared to the standalone models. Among the cascaded models, those incorporating a Transformer display the best predictive performance, indicating that the DNN-Transformer cascaded model is the optimal COM prediction model. As shown in Table 7, we have compared the DNN-Transformer model with the models presented in the Introduction section in terms of functionality. The traditional LSTM [20] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** & **MRE (\%)** \\ \hline DNN & \(6.9\times 10^{-2}\) & \(4.3\times 10^{-2}\) & \(7.8\times 10^{-3}\) & 6.5 \\ \hline LSTM & \(5.3\times 10^{-2}\) & \(4.4\times 10^{-2}\) & \(6.4\times 10^{-3}\) & 2.0 \\ \hline DNN-LSTM & \(5.0\times 10^{-2}\) & \(4.1\times 10^{-2}\) & \(6.0\times 10^{-3}\) & 1.8 \\ \hline Transformer & \(4.7\times 10^{-2}\) & \(3.7\times 10^{-2}\) & \(5.5\times 10^{-3}\) & 1.7 \\ \hline DNN–Transformer & \(4.6\times 10^{-2}\) & \(3.5\times 10^{-2}\) & \(5.1\times 10^{-3}\) & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predicted error values of different models. Figure 14: Comparison of predicted and actual equalizer parameter results. structure could not achieve prediction under physical parameter variables. Traditional machine learning models such as the SVM [13], RFR [12], and DNN [9] models have limited processing capabilities for sequences and thus cannot predict transient waveforms. The GNN-RNN [21] model simplifies the interconnected components and circuits, meaning that some of the actual parameters of links are lost and the performance is decreased. The above models could not achieve link optimization, and RFR [12] could only complete FD prediction. In contrast, the proposed DNN-Transformer is more practical. This model can effectively achieve these above behavioral tasks and performs better than other models in RMSE and MAE for COM value prediction. Due to the increased complexity of this model, its inference time is increased, but it still has a significant time advantage compared to traditional EM simulation whose single-solution time is several hours.
## 5 Conclusions
**Data Availability Statement:** The data that support the findings of this study are openly available at NASA PCOE Research Center at [[https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository)]([https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository)) (accessed on 7 July 2024). **Conflicts of Interest:** The authors have no conflicts of interest to declare that are relevant to the content of this article. **Abbreviations**  
EV **Electric vehicle**  
RNN **Recurrent neural network**  
LSTM **Long short-term memory**  
MLP **Multilayer perceptron**  
GRU **Gated recurrent unit**  
RBF **Radial basis function**  
SoC **State of charge**  
UKF **Unscented Kalman filter**  
CC-CV **Constant current-Constant voltage**  
FFNN **Feedforward neural network**  
EKF **Extended Kalman filter**  
AGSMO **Adaptive Gain Sliding Mode Observer**  
APF **Adaptive particle filtering**  
NARX **Nonlinear autoregressive architecture with exogenous inputs**  
RMSE **Root-mean-square error**  
RFR **Random Forest Regressor**  
TBCC **Temperature-Based Coulomb Counting**  
DOB **Disturbance observer**  
PF **Particle filter**  
MAE **Mean absolute error**  
RLS **Recursive least square**  
\(H_{\infty}\) **H-infinity**  
AEKF **Adaptive extended Kalman filter**  
SMO **Sliding Mode Observer**  
DNN **Deep neural networks**  
DTR **Decision Tree Regressor**  
ACKF **Adaptive curvature Kalman filter**  
**Nomenclature**  
\(Q(t)\) **Electric vehicle**  
\(Q_{n}\) **Nominal capacity**  
\(x_{i}\) **Input from neuron of hidden layer i and neuron of hidden layer j**  
\(w_{ij}\) **Weight from neuron of hidden layer i to neuron of hidden layer j**  
\(R^{2}\) **Coefficient of determination**  
\(C_{n}\) **Nominal battery capacitor**  
\(i(t)\) **Battery's current at time t**  
\(g\) **Activation function**  
\(b_{j}\) **Bias of the hidden layer neuron j**  
\(w_{p}\) **Weight matrix between a hidden layer and an input layer**  
\(b_{y}\) **Hidden bias**  
\(y_{k}\) **RNN cell output at step k**  
\(H_{k}\) **Current hidden state**
## 2 Materials and Methods  
ITO films were purchased from Merck (Darmstadt, Germany)and had a thickness of around 200 nm, deposited on a glass substrate. AZO films were deposited with atomic layer deposition at a thickness of 100 nm using a Beneq P400A reactor (Beneq, Espoo, Finland). The glass substrates were placed in a chamber where they were initially stabilized at temperature of 200 \({}^{\circ}\)C and pressure of 1 mbar for 4 h. The low vacuum was achieved using a rotary pump and the pressure was controlled using an MKS Baratron 626D sensor (MKS Instruments, Inc., MA, USA). The precursors of diethylzinc and trimethylaluminum were then alternated with distilled water over a period of 2 h. The opening times of the valves that supply the precursors and the distilled water were 0.3 and 0.5 s, respectively. The irradiation experiment was performed at the Polish Academy of Sciences Institute of Nuclear Physics in Krakow. The energy of proton beams was 226.5 MeV, and the fluence of protons was 10\({}^{11}\) cm\({}^{-2}\). Samples were placed on a holder in such a way that the beam was incident directly on the film. A Gantry station was used to perform the irradiation experiment, which was conducted in air ambience and at room temperature. The beam homogeneity over the irradiated area was 9.99%. The samples after irradiation are named with an H letter; therefore, before irradiation they are AZO and ITO, and after irradiation, AZO:H+ and ITO:H+. Characterisation of irradiated samples was carried out after transporting them to laboratories at the AGH University and to NIMP in Romania.  
Structural properties were studied with a Panalytical Empyrean diffractometer using a copper anode (\(\lambda\) = 0.154 nm); the parameters for grazing incidence X-ray diffraction measurement were: 2 \(\theta\) range 20-80\({}^{\circ}\), step 0.04\({}^{\circ}\), time per step 5 s, and omega angle 1\({}^{\circ}\). Homogeneity maps were measured with a [PERSON]. Woolam 2000 M spectroscopic ellipsometer. The measurement angles were 58\({}^{\circ}\), 62\({}^{\circ}\), and 66\({}^{\circ}\). To observe the microstructure with scanning electron microscopy, we used Phenom microscope from Phenom-World. Transmittance of films as measured with an Avantes Sensline Ava-Spec ULS-RS-TEC spectrophotometer and an Avantes AvaLight DH-S-BAL-Hal lamp (Avantes, Apeldoorn, The Netherlands).  
The investigation of chemical properties of the surface was conducted with X-ray photoelectron spectroscopy in an analysis chamber equipped with a 150 mm hemispherical electron energy analyser (Phoibos). The analyser operated in fixed transmission mode (FAT) with pass energy of 20 eV. The X-ray source was monochromatised Al K\(\alpha\) with 1486.6 eV energy, at 250 W power (12.5 kV \(\times\) 20 mA). The charging effects were compensated with a flood gun (1 V \(\times\) 0.1 mA) and with binding energy correction to the C 1 s contamination core level at 286.4 eV.
###### Abstract  
Accurately understanding the changes in global light-response parameters (i.e., maximum light use efficiency, LUEmax) is essential for improving the simulation of terrestrial ecosystem's photosynthetic carbon cycling under climate change, but a comprehensive understanding and assessments are still lacking. In this study, LUEmax was quantified using data from 23 global flux stations, and the change patterns in LUEmax across various vegetation types and climate zones were analyzed. The extent of significant increases or decreases in LUEmax during different phenological stages of vegetation growth was evaluated using trend analysis methods. The contribution rates of environmental factors were determined using the Geodetector method. The results show that the LUEmax values of the same vegetation type varied across different climate types. More variable climates (e.g., polar and alpine climates) are associated with more significant fluctuations in LUEmax. Conversely, more stable climates (e.g., temperate climates) tend to show more consistent LUEmax values. Within the same climate type, evergreen needleleaf forests (ENF) and deciduous broadleaf forests (DBF) generally exhibited higher LUEmax values in temperate and continental climates, whereas the LUEmax values of wetlands (WET) were relatively high in polar and alpine climates. The mechanisms driving variations in LUEmax across different vegetation types exhibited significant disparities under diverse environmental conditions. For ENF and DBF, LUEmax is predominantly influenced by temperature and radiation. In contrast, the LUEmax of GRA, WET, and croplands is more closely associated with vegetation indices and temperature factors. The findings of this study play an important role in advancing the theoretical development of gross primary productivity (GPP) models and enhancing the accuracy of carbon sequestration simulations in terrestrial ecosystems.  
maximum light use efficiency (LUEmax); spatial heterogeneity; impact of factors; global climate types +
Footnote †: journal: Physics Letters  
0000-0002-4000-0000][PERSON]  
1 School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (S.Z)1 School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, [EMAIL_ADDRESS] (S.Z)1 School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China192139000@ecut.
###### Abstract  
The removal of necrotic and vital pulp substrates and microorganisms and their toxins from the root canal system (RCS) has been found to be the basis for a successful endodontic treatment. In this study, our aim was to evaluate the effect of passive ultrasonic irrigation (PUI) on the elimination of the organic remnant tissue from infected, narrow and curved mandibular root canals during their instrumentation. For this purpose, mesibucocal canals from mandibular first molars were instrumented with the RaCe rotary system, using PUI activation or conventional irrigation (CI) and two apical diameters (#25 and #35). The root canal cleanness of the samples was evaluated by microscopy and using a modified [PERSON]'s ordinal scale. Parametric and non-parametric statistical analyses and principal coordinates analysis (PCoA) of the samples were performed. When PUI was used, there was a significant reduction of the organic remnant in the apical enlargement of 25 at 2 mm from the apex (\(p<0.001\)). After pooling the groups, regardless of the depth of the observation (2 and 4 mm from the apex), the pair #35 + PUI vs. #25 + CI showed statistically significant differences (\(p<0.001\)). The effect of PUI explained 65% of the overall variance when compared with the CI samples. The use of PUI reduced the organic material of narrow infected and curved root canals with an apical enlargement of #25 and #35. When PUI is not used, a biomechanical instrumentation up to a diameter \(\geq\)#35 is recommended.  
passive ultrasonic irrigation; apical enlargement; organic remnant +
Footnote †: journal: Applied Sciences
# Investigating the Influence of Feature Sources for Malicious Website Detection
## 5 Experimental Results--GAWAIN
### Model Performance
We attribute this to our preprocessing pipeline, where we have showed in earlier sections that we have removed certain type of bias. With newly constructed features, and with one of the most commonly employed models for classification purposes in XGBoost, we were only able to reach 83% indicating that the problem appears to be a lot more difficult. Considering how we cascaded the feature combinations, the introduction of embeddings on top of the _Baseline_ brings little improvements: going from 83.11% to 83.73%, then 83.69%, then 83.69%, then 83.69%, and finally to 83.79%. In the end, we have minor improvement that is being added from the embedding components. This suggests that the _Baseline_ category already contains enough context that overlaps with what embeddings are extracting from raw data. However, we believe that even this small increment is vital if the end goal is to detect as many malicious websites, potentially preventing future intrusions. We would like to stress again that with this new dataset, the problem has become much harder compared to other studies, and even the minor improvements are worth considering. Finally, we were able to get the best score of 84.27% by manually selecting certain features that are not part of an automated pipeline, but were part of a trial and error approach. In the next part, we point to an interesting result when it comes to individual feature contributions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Category** & **Individual** & **Combined** \\ \hline Lexical & **75.48** & N/A \\ \hline Host-based & 65.70 & **80.02** \\ \hline Content-based (Baseline) & 76.30 & **83.11** \\ \hline URL embeddings (Longformer) & & \\ all & 76.38 & 83.23 \\ PCA 10 & 75.41 & **83.73** \\ PCA 25 & 75.24 & 82.41 \\ PCA 50 & 75.25 & 82.09 \\ Chi-sq & 76.43 & 83.29 \\ \hline Image embeddings & & \\ all & 60.27 & 82.24 \\ PCA 10 & 62.57 & **83.69** \\ PCA 25 & 61.73 & 83.46 \\ PCA 50 & 61.16 & 82.82 \\ Chi-sq & 61.29 & 83.62 \\ \hline Content embeddings & & \\ all & 67.63 & 81.89 \\ PCA 10 & 67.71 & **83.74** \\ PCA 25 & 68.11 & 83.58 \\ PCA 50 & 67.76 & 83.06 \\ Chi-sq & 66.57 & 83.59 \\ \hline URL embeddings (Distilbert) & & \\ all & 76.62 & 82.47 \\ PCA 10 & 72.97 & **83.79** \\ PCA 25 & 73.24 & 83.57 \\ PCA 50 & 74.08 & 82.99 \\ Chi-sq & 75.12 & 83.64 \\ \hline Best Score (Feat selection) & N/A & **84.27** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test accuracy for XGBoost across different feature combinations. The individual column shows the accuracy when these features are used for training on their own. The combined column shows accuracy when combined with the preceding feature category in the table. Highlighted scores are the best score for that particular combination of categories.
## 4 Discussion
### Analysis of ACE Module Effectiveness  
The role of the proposed ACE module is to reduce the information loss of the crack features at the shallow feature extraction stage and to turn as many crack features as possible into abstract semantic information to pass to the involution operator in the deeper layer for semantic information enhancement. From the results of Section 3.4, it can be seen that the perceptual field of asymmetric convolution is different from that of convolution, and its special field of view matches the linear features of cracks, meaning that it can extract crack information from multiple aspects. However, due to the unique field-of-view mechanism of asymmetric convolution, some of the non-feature points are misjudged as points of interest for extraction, resulting in excessive noise after concatenation. To mitigate this effect, we promote the interaction and integration of information between channels by using large convolutional up-dimensioning to refine the crack features and attenuate the noise. From the results of Section 3.6, it can be seen that after adding the ACE module, the MIOU, MPA, recall, and F1 score of the model are improved by 0.0281, 0.18%, 0.0443, and 0.0209, respectively. This illustrates the effectiveness of the ACE module.
## 2 Materials and Methods
### Magnetic Resonance Imaging (MRI)  
All pigs underwent MRI procedures at PND 32 or 33 at the Beckman Institute for Advanced Science and Technology Biomedical Imaging Center using Siemens MAGNETOM Trio 3T (Siemens, Munich, Germany) equipment with a Siemens 32-channel head coil. Methods used were adapted from previous studies using MRI in pig [40; 41; 42]. Each pig underwent imaging protocols only once, and scans for each cohort of pigs were completed all on the same day. The pig neuroimaging protocol included three magnetization prepared rapid gradient-echo (MPRAGE) sequences and diffusion tensor imaging (DTI) to assess brain macrostructure and microstructure, respectively, as well as magnetic resonance spectroscopy (MRS) to obtain brain metabolite concentrations. In preparation for MRI procedures, anesthesia was induced using an intramuscular injection of telazol (50.0 mg of tiletamine plus 50.0 mg of zolazepam reconstituted with 5.0 DI water; Zoetis, Florham Park, NJ) administered at 0.07 mL/kg bodyweight, and maintained with inhalation of isoflurane (98% O\({}_{2}\), 2% isoflurane). Pigs were immobilized during all MRI procedures. Visual observation of each pig's well-being, as well as observations of heart rate, PO\({}_{2}\) and percent of isoflurane were recorded every 5 min during the procedure and every 10 min post-procedure until animals recovered. Total scan time for each pig was approximately 60 min. Imaging techniques are briefly described below.
# Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II
## 2 Path Optimization Problem Description
### Path Length  
In machining, the cutting tool needs to travel a number of cities, \(n\), in the shortest path. The path length between any two points in the \(x\) and \(y\) coordinates can be expressed using the Euclidean. Equation (4) calculates the distance for a path via connecting a sequence of points in a two-dimensional space, which is typically used to determine the shortest path that a tool must travel in applications like machining. Each point \(p_{i}\) in the sequence is represented as a coordinate \((x_{i},y_{i})\). \(D(p_{i},p_{i+1})\) represents the total distance traveled from the starting point \(p_{i}\) to the endpoint \(p_{i+1}\). \(x_{i}\), and \(y_{i}\) are the Cartesian coordinates of the \(i\)th point. \(x_{i+1}\) and \(y_{i+1}\) are the Cartesian coordinates of the point following \(p_{i}\).  
Therefore, for two sequential points (\(p_{i}=(x_{i},y_{i})\), \(p_{i+1}=(x_{i+1},\ y_{i+1})\)), the path length connecting these points can be calculated. The total traveling distance can be obtained through summing all of the ordered path lengths as expressed by (5).  
\[D(p_{i},p_{i+1})=\sum_{i=1}^{n-1}\sqrt{\left(x_{i+1}-x_{i}\right)^{2}+\left(y _{i+1}-y_{i}\right)^{2}} \tag{4}\]\[L(p)=D(p_{i},p_{i+1}) \tag{5}\]  
It is shown in Figure 1 that the path length is minimized by the shortest operator. This operator removes extra orange lines to obtain the shortest path. The updated lines in the new toolpath are illustrated in Figure 1b.  
PP also includes smoothness optimization. This is essential to validate it thoroughly, as any failure in the tool could result in significant damages, putting the rest of the mission at risk [49]. Path smoothness is a critical consideration in designing algorithms for PP to guarantee the safe navigation of robots or CNCs. It involves evaluating and optimizing the path to prioritize smoothness trajectories while achieving the desired objectives, such as minimizing travel time or energy consumption.
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
## 2 Related Work
### LES Simulation  
Large eddy simulation (LES) is a numerical method used to investigate turbulent flows and is particularly suitable for resolving flow phenomena. In LES, small eddies in the flow field are filtered through a filter function, while the large eddies and smaller eddies are directly solved using the NS equation. The additional impact of the small eddies on the large eddies is considered through sub-grid scale (SGS) models. For instance, [PERSON] [19] carried out LES simulations to analyze lift and drag around a cylinder and used SGS models to capture the detailed characteristics of the flow field. Researchers have also attempted to apply LES to practical engineering. [PERSON] et at. [20; 21] employed LES to study the diffuser of a centrifugal pump and analyzed the flow behavior and the interaction between guide vanes. They found that LES can improve the accuracy of numerical simulations when the working conditions deviate from the design. Scholars have also investigated two-dimensional and three-dimensional LES, highlighting the limitation of simplifying three-dimensional flows to two-dimensional, since it ignores spanwise fluctuations of large eddies and leads to inaccurate calculations. [PERSON] [22] investigated flows around a square column and found that the two-dimensional LES fails to reflect actual flow behavior. The above-mentioned research indicates that LES and detached eddy simulation (DES) methods have been gradually transitioned to engineering applications. With the computational requirements taken into account, direct application of high-precision numerical simulation methods in engineering is challenging. However, with continuous advancements in computer technology and simulation methods, LES simulations have played a significant role in exploring turbulent flows and solving practical engineering problems.
## 4 Results and Discussion
### Data Quality Improvement and Application of Results  
The effectiveness of CNN-based classification extends beyond feature recognition in pedestrian crash diagrams; it also plays a crucial role in improving data accuracy and completeness. In traffic safety research, inconsistencies in crash data--such as uncoded, missing, or incomplete entries--can introduce bias and reduce the reliability of findings. This study enhances pedestrian crash database quality by leveraging CNNs to generate and refine critical features, ensuring a more comprehensive and reliable research and policy development dataset.  
Figure 3: Computational time of all CNN models for all features’ classifications over 50 epochs.