esa-sceva/satcom-chunk-collection huggingface dataset:
# Sarcasm Detection Base on Adaptive Incongruity Extraction Network and Incongruity Cross-Attention
## 4 Experiment
### Baseline Models  
To assess our model, we compare it to the baseline models as follows:  
NBOW: In order to represent the complete sentence as a feature in subsequent tasks, this model employs the average of all word embeddings.  
TextCNN: [PERSON] [2] has proposed it as an upgrade to image convolution, which is specifically utilized for text feature extraction. With little hyperparameter adjustment and the use of static word vectors, simple TextCNN models may perform rather well  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Train** & **Dev** & **Test** & **Total** \\ \hline Twitter-G & 46,071 & 5118 & 3742 & 54,931 \\ Twitter-P & 8498 & 1062 & 1062 & 10,622 \\ Reddit-Pol & 20,843 & 2605 & 2605 & 26,053 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the dataset.  
when classifying text. Furthermore, if the word vectors are fine-tuned during training, the performance may be increased much further.  
Bi-LSTM: The so-called Bi-LSTM is a two-layer neural network. The first layer starts with the first word of the sentence, whereas the second layer starts with the last word and carries out the first layer's processes backwards. In order to capture context information in words, bidirectional LSTM outperforms unidirectional LSTM.  
SIARN: SIARN can learn an intra-attentive feature representation of the input text that may capture contradictory sentiments and incongruence. Previous sequence models, such as LSTMs, were unable to identify the interaction between word pairs.  
SMSD-BiLSTM: Self-matching networks are proposed by [PERSON] et al. [5] as a means of detecting incongruity on the basis of a modified co-attention approach. They also develop the compositional information of the text using a bidirectional LSTM.  
BERT: Transformers' BERT is a bidirectional encoder. In order to pre-train deep bidirectional representations from unlabeled text, it attempts to calculate criteria shared by left and right contexts. Therefore, state-of-the-art models for a variety of natural language processing tasks may be generated by fine-tuning pre-trained BERT models with a single additional output layer. BERT can provide superior semantic expression as compared to conventional word embedding techniques, and its feature representation may be adjusted according to the circumstances. The introduction of BERT is crucial historically for the study of several tasks in natural language processing.  
SDCK: SDCK was proposed by [PERSON] et al. [13]. They introduced commonsense knowledge into sarcasm detection for the first time and achieved state-of-the-art performance in 2021. It shows the importance of commonsense knowledge in sarcasm detection. However, he also has many shortcomings. He does not make full use of the text itself and commonsense knowledge.
## 2 Materials and Methods
### Numerical Model
#### 2.2.1 Solution Conditions  
The model's computational domain is selected based on the assumption that the inflow and outflow of tides at the open boundary are not affected by the bay coastline. After repeated debugging, it was determined that the calculation area of the model is the sea area enclosed by the line from the mouth of the Huahe River in Jiangsu to Yeonggwang in South Korea and the coastline. The calculation domain is about 617 km long from east to west and about 760 km long from north to south. In view of the tortuous coastline and complex nearshore terrain of the bay, the model uses an unstructured triangular grid with a good land boundary fit. The number of grid cells in the computational domain is 129,949 and the number of nodes is 69,083. To enhance the accuracy of the calculations, the grid in the southwest of Laizhou Bay and its vicinity is encrypted, and the maximum area of the encrypted grid is 5000 m\({}^{2}\). The model's solid boundary condition is defined such that the normal velocity perpendicular to the shoreline is zero. For the dynamic boundary conditions, particularly in the offshore area with numerous shoals, the dry/wet grid technology is employed to determine the boundary [25]. Regarding the open boundary conditions, the TPX08 global tidal model is utilized to calculate the open boundary tidal level due to its relatively high prediction accuracy in China's coastal areas [26].
## 4 Experiment and Results
### The Effect of Wall Thickness on the MPPT Signal  
Experiments were conducted on Group B, consisting of steel pipes with the same external diameters and different wall thicknesses, to verify the effect of the internal diameter of a pipe on the MPPT signal. The signals of axial cracks in the internal walls of the pipes of different sizes are shown in Figure 15. Their peak-to-peak values were extracted, and the obtained fitted curves are shown in Figure 16.  
As shown in the experimental results in Figure 17b, the signal amplitude of the defects at the same depth decreases with the increase in the pipe thickness. It can be inferred from the trend that when the wall thickness is less than 8 mm, the signal increases significantly with the increase in the defect depth. When the wall thickness is larger than 12 mm, the increase in defect depth may have little effect on the signal amplitude within a certain depth range.  
Figure 14: Experimental signals of group A. (**a**) Crack depth of 1 mm; (**b**) crack depth of 1.5 mm.  
Figure 15: Signals for pipes of different external diameters. (**a**) Simulation results; (**b**) experimental results.
## 5 Conclusions
**Acknowledgments:** We thank: [PERSON] for starting the experiment, [PERSON] for assisting on this project, [PERSON] for performing the greenhouse experiment, [PERSON] for help with the CH4, N2O and NH3 measurements, Slootsmid Mesttechniek B.V. (Borculo, The Netherlands) for the fertilization of the fields, and [PERSON] from the Arjan Prinsen Farm (APF) (Haarlo, The Netherlands) for the pilot fertilizers. **Conflicts of Interest:** The authors declare no conflicts of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Treatment**} & \multicolumn{3}{c}{**Product Applied**} & \multicolumn{3}{c}{**Total Applied**} \\ \hline \multicolumn{2}{c}{**Crop**} & **Soil** & & **AS** & **CAN\({}^{\star}\)** & **CaSO\({}_{4}\)** & **N** & **SO\({}_{3}\)** & **P\({}_{2}\)O\({}_{5}\)** & **K\({}_{2}\)O** & **MgO** & **Na\({}_{2}\)O** & **Cu** & **Zn** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Grass \\ 2021 \\ \end{tabular} } & \multirow{2}{*}{Sand} & CAN-S & - & 500 & 450 & 114 & 361 & \multirow{4}{*}{15} & \multirow{4}{*}{85} & \multirow{4}{*}{50} & \multirow{4}{*}{30} & \multirow{4}{*}{2} & \multirow{4}{*}{0} \\ \cline{3-4} \cline{6-11}  & & AS & 1910 & - & - & 111 & 325 & & & & \\ \cline{3-11}  & \multirow{2}{*}{Clay} & CAN-S & - & 450 & 410 & 103 & 328 & & & & \\ \cline{3-11}  & & AS & 1740 & - & - & 101 & 296 & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Grass \\ 2022 \\ \end{tabular} } & \multirow{2}{*}{Sand} & blank & 0 & 0 & 0 & 0 & 0 & & & & & \\ \cline{3-11}  & & CAN-S & - & 483/387 & 419/336 & 116/93 & 326/262 & 15/25 & 180/100 & 25/25 & 10 & 2 & 0 \\ \cline{3-11}  & \multirow{2}{*}{\
\begin{tabular}{c} Grass \\ 2022 \\ \end{tabular} } & AS & 1600/1282 & - & - & 116/93 & 326/262 & & & & & \\ \cline{3-11}  & & blank & 0 & 0 & 0 & 0 & 0 & & & & \\ \cline{3-11}  & \multirow{2}{*}{Clay} & CAN-S & - & 441/366 & 383/318 & 106/88 & 298/247 & 95/25 & 110/50 & 75/75 & 0 & 0 & 0 \\ \cline{3-11}  & & AS & 1462/1213 & - & - & 106/88 & 298/247 & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Maize \\ 2021 \\ \end{tabular} } & \multirow{2}{*}{Sand} & CAN-S & - & 600 & 530 & 137 & 427 & \multirow{4}{*}{0} & \multirow{4}{*}{50} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} & \multirow{4}{*}{0} \\ \cline{3-11}  & & AS & 2250 & - & 50 & 137 & 420 & & & & \\ \cline{3-11}  & \multirow{2}{*}{Clay} & CAN-S & - & 690 & 600 & 157 & 486 & & & & \\ \cline{3-11}  & & AS & 2570 & - & 55 & 157 & 478 & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Product dosage (kg ha\({}^{-1}\)) in the field demonstration (CAN\({}^{\star}\) is a commercial product: CAN supplemented with S) for the 1 st and 2 nd fertilization (given as: 1 st/2 nd), respectively, on 4 April 2022 and 16 May 2022.
## 2 Analysis of the Relationship between Beam Walk and DWS Signal
### Numerical Simulation Analysis of Beam Walk
Therefore, it is necessary to investigate when, under different propagation distances, the \(\pm\)300 nrad angular measurement error boundary can be met to achieve an angular range of \(\pm\)300 \(\upmu\)rad. Figure 6 indicates that the optimal propagation distance must not exceed 0.16 m, correlating with a beam walk of 48 \(\upmu\)m. An increase in the beam walk expands the angle measurement range, yet the measurement error surpasses \(\pm\)300 \(\upmu\)rad, a threshold that is impermissible. To ensure a measurement range beyond \(\pm\)300 \(\upmu\)rad amid beam walk, it becomes imperative to constrain the Gaussian flat-top beam's propagation distance. Nonetheless, achieving a propagation distance under 0.16 m for the Gaussian flat-top beam poses a significant challenge in practical engineering applications. Consequently, strategies to curtail the beam walk demand serious consideration. The angle measurement capacity of the DWS technique is inherently constrained. Figure 3 illustrates the correlation between the angle measurement error and beam angle when the beam walk equals =0 \(\upmu\)m. The data suggest an approximate angular measurement range \(\varphi\) of \(\pm\)400 \(\upmu\)rad, indicating that the linear region is adequate when both beams are aligned at the QPD's center. The analysis underscores the substantial nonlinearity of the DWS signal induced by beam walk, a factor that exacerbates the angle measurement error and restricts its range. To accommodate angle measurements across varying beam propagation distances, it is essential to minimize the beam walk distance generated by the pointing jitter of the Gaussian flat-top beam on the QPD surface. Figure 5: DWS error signals for various degrees of beam walk during light propagation over a 2 m axial distance.
# Titanium Dioxide in Chromogenic Devices: Synthesis, Toxicological Issues, and Fabrication Methods
## 2 Synthetic Routes and Fabrication Techniques for TiO\({}_{2}\)
### Oxidation Method  
The oxidation route requires the oxidation of Ti via the use of anodization or oxidants. In a typical route, TiO\({}_{2}\) nanorods on a Ti plate were synthesized using a 30 wt% H\({}_{2}\)O\({}_{2}\) solution at 353 K for 72 h. The formation of crystalline TiO\({}_{2}\) takes place by a dissolution precipitation mechanism. When inorganic salts (NaF, NaCl, NaSO\({}_{4}\)) are added, the TiO\({}_{2}\) crystalline phase can be tuned; F and SO\({}_{4}\) addition promotes the anatase synthesis, while formation of rutile is boosted by Cl [87]. Nanotubes were synthetized from a Ti sheet under a voltage between 10 V and 20 V in hydrogen fluoride. Additionally, oxygen, argon, and acetone are used as sources of Ti oxidation [88]. Rutile TiO\({}_{2}\) nanowires were achieved by thermal oxidation of titanium foil via the use of potassium hydroxide (KOH) in two steps. First, the reduction of KOH into droplets at 400 \({}^{\circ}\)C followed the oxide growth at 500-800 \({}^{\circ}\)C in the furnace. The nanowires were obtained at high concentration and showed a length between 500 and 600 nm [89].  
TiO\({}_{2}\) rutile monocrystalline nanowires by thermal oxidation were also obtained by [PERSON] et al. [90], showing an improvement of photocatalytic activity compared to a reference TiO\({}_{2}\) bulk sample.
## 3 Proposed System  
In recent years, various methods have been published for multiview action recognition, but very few are actually suitable for real-time applications due to their high computational cost. Therefore, the cost of the feature extraction and action classification has to be reduced as much as possible. The proposed system has been designed around these parameters. It uses the human silhouette as input for feature extraction and formulating the region-based novel feature descriptor. Human silhouette can easily be extracted using foreground detection techniques. At the moment our focus is not on foreground segmentation; rather, we are focused on later phases of the action recognition such as feature extraction and classification. Although the human silhouette is a representation that contains much less information than the original image, we show that it contains sufficient information to recognize the human actions with high accuracy. This work proposes a novel feature descriptorfor multiview human-action recognition based on human silhouette. When a silhouette is partitioned into radial bins, then region descriptors are formed which are represented as a triangle or quadrangle. Accordingly, it makes the pose description process simple and accurate. The descriptive detail of the proposed system is given in Algorithm 1 and block diagram is shown in Figure 1.  
We consider the centroid of a silhouette as a point of origin, and divide the human silhouette into \(R\) radial bins with the interval of same degree. Here the value of the \(R\) is selected as 12, which constitutes 12 radial bins with the interval of 30\({}^{\circ}\) each. This value has been selected with experimentation to cover the maximum viewing angles. This is unlike [31]--where the radial histograms were used as a feature descriptor--and [25], where the contour points of each radial bin were considered as features, and model fusion was used to achieve the multiview action recognition by obtaining key poses for each action through K-means clustering. Our method computes efficient and discriminative geometrical and Hu-moment features from each radial bin of the silhouette itself. Then, these features from all bins are concatenated into a feature vector for multiview action recognition using support vector machine.  
This approach has three major advantages. Firstly, it divides the silhouettes into radial bins, which cover almost all viewing angles, thus provides an easy way to compute the features for different views of an action. Secondly, it uses the selected discriminative features and avoids extra computation such as fusion or clustering as used in [25]. Thirdly, due to employing selected features from each bin, it does not require any dimensionality reduction technique.  
Figure 1: Block diagram of the proposed system.
# Effect of Bentonite Content and Hydration Time on Mechanical Properties of Sand-Bentonite Mixture
## 3 Results of Calcium Bentonite-Sand Mixture  
### Effect of Calcium Bentonite Content on Shear Strength  
Figure 3 shows the deviator stress-axial strain curves of the specimens with different bentonite contents under the same confining pressure and hydration time. The results indicate that the peak shear strength of the mixture specimen decreases with the increase in bentonite content, and the shear strength of the sand-bentonite mixture remains more than 90% of that of pure sand. The hydration and expansion of bentonite in the pores of sand make the direct contact of sand-sand interface change to the contact of sand-bentonite pore grout, which was thought to be the reason for the slight decrease in shear strength with the increase in bentonite content. Figure 3 also shows that the stress remained stable after reaching the peak value for the mixture specimen. This phenomenon was more obvious for specimens with 10% bentonite than that with 5% one. Figure 3 shows the effect of bentonite content on the stress-strain behaviors under the confining pressure of 200 kPa. It can be observed that the higher the bentonite content, the greater the residual strain. Figure 3 shows the effects of bentonite content on the stress-strain behaviors at confining pressures of 300 kPa and 400 kPa. Compared with confining pressures of 200 kPa, the strain-softening phenomenon of the pure sand specimen under confining pressures of 300 kPa and 400 kPa is less obvious, which results in the backward shift of the intersection point of stress-strain curves between pure sand specimens and sand-bentonite specimens. At the constant relative density of the skeleton, the compression of the pore water is limited in the process of saturation and consolidation of the sand-bentonite mixture, and part of the bentonite is gelled. With the increase in confining pressure, the dilatancy behavior of pure sand is inhibited, and the strain-softening trend is correspondingly limited, which leads to the delay at the intersection point of stress-strain curves of specimens with different bentonite contents.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Bentonite Content/\%** & **Confining Pressure/kPa** & **Hydration Time/h** \\ \hline
1 & 0 & 200 & / \\
2 & 0 & 300 & / \\
3 & 0 & 400 & / \\ \hline
4 & 5 & 200 & 48 \\
5 & 5 & 300 & 48 \\
6 & 5 & 400 & 48 \\
7 & 5 & 200 &
# Open DGML: Intrusion Detection Based on Open-Domain Generation Meta-Learning
## 5 Experimental Results
### Performance Comparison in Non-Open Domain  
In the non-open domain setting, we only consider the algorithm's performance on the current dataset, with no unknown target domain data involved. Table 3 shows the detailed comparison results of the four algorithms in the non-open domain.  
By observing the performance of each algorithm in the non-open domain, we find that with our algorithm, Open DGML, although not always the best in terms of individual accuracy and detection rate on these four datasets, the difference from the best results is not significant, and all values exceed 94%. Notably, its performance on the NDSec-1 dataset is far better than the other algorithms.  
Specifically, on the ISCX2012 dataset, HAST-IDS has the highest accuracy, and FC-Net has the highest detection rate. The accuracy of Open DGML is 99.13%, ranking second among the four algorithms, and its detection rate is 98.26%, also in second place. On the NDSec-1 dataset, Open DGML has the highest accuracy and detection rate, at 94.20% and 95.29% respectively, leading the second-best HAST-IDS in accuracy by 3.16% and the second-best CLAIRE in detection rate by 3.69%. On the CICIDS2017 dataset, CLAIRE has the highest accuracy at 98.01%, and FC-Net has the highest detection rate at 99.62%. Open DGML's accuracy and detection rate are both second, only 0.48% and 2.32% lower than the highest values, respectively. On the CICIDS2018 dataset, Open DGML's accuracy is 2.26% lower than the highest CLAIRE algorithm, and its detection rate is only 0.15% lower than the highest FC-Net.  
However, in terms of the average performance across the four datasets, our algorithm's accuracy and detection rate are better than the other three algorithms, with a 0.49% and 1.76% improvement over the next best algorithm, respectively. This indicates that our algorithm also performs well in the non-open domain, and achieves consistent results across multiple datasets (domains), due to its better generalization capability. It has a good initial model for domain transfer and can quickly adjust its parameters to adapt to the new data distribution after a few fine-tuning steps.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS
### Scheduling Optimization
#### 3.3.1 Solution of the Coding  
The first section is the coding of the problem's solution. This article divides the chromosome into four segments, corresponding to the four decision variables in the model mentioned above. As shown in Figure 6, assuming that there are four orders in the scheduling that contain a total of 17 transportation operations, the entire chromosome contains 68 genes, and each gene fragment contains 17 genes. The first part reflects the transportation operation's priority, decreasing from left to right, and the gene's code defines transportation operations related to \(O_{1}\) for each order. The genetic code is the same as their order number, so that all the genes related to \(O_{1}\) operations have the code \"1\" and subsequently the code \"2\" is given to all the genes related to \(O_{2}\), and so on. So the first \"1\" represents \(O_{11}\) and the first \"2\" represents \(O_{21}\). The second segment of the genetic code is the AGV number, and this genetic code defines which AGV will transport the corresponding transportation operations. The third segment represents the allocation of charging tasks, which are all Boolean variables. The last segment of the genetic code represents the amount of charge when the AGV is charging. For example, genetic number two corresponds to the transportation operation \(O_{12}\), which will be carried by the AGV numbered two. The AGV numbered two will go to the charging area to charge once before the transportation, and the charged power is 20.
# Analytical Calculation Model of the TV3-117 Turboshaft Working Regimes Based on Experimental Data
## Nomenclature  
The following parameters and variables are used in this research.  
**Nomenclature**  
**Description**  
\(P\)[HP]  
Shaft Power  
\(SFC\) [kg/HP/h]  
Specific fuel consumption  
\(NG\) [%]  
Gas generator shaft speed  
\(NTP\) [%]  
Free turbine shaft speed  
\(N\) [%]  
Working gas generator shaft speed  
\(F_{C}\) [kg/h]  
Fuel consumption  
\(F_{f}\) [kg/s] [kg/h]  
Fuel flow  
\(ff_{c}\) [-]  
Fuel flow coefficient  
\(\alpha_{ex}\) [-]  
Air excess  
\(M_{af}\) [kg/s]  
Mass air flow  
\(G_{f}\) [kg/s]  
Gases flow  
\(\pi_{Ct}\) [-]  
Overall total pressure ratio  
\(\pi_{Tt}\) [-]  
Turbine total pressure ratio  
\(\eta_{Ct}\) [-]  
Compressor total adiabatic efficiency  
\(\eta_{Tt}\) [-]  
Turbine total adiabatic efficiency  
\(\eta_{mG}\) [-]  
Gas generator mechanical efficiency  
\(P_{0}\) [\({}^{\circ}\)C]  
Standard pressure  
\(T_{0}\) [\({}^{\circ}\)C]  
Standard temperature  
\(P_{1t}\)[bar]  
Compressor inlet total pressure  
\(T_{1t}\) [K]  
Compressor inlet total temperature  
\(P_{2t}\) [bar a]  
Compressor outlet total pressure  
\(T_{2t}\) [K]  
Compressor outlet total temperature  
\(P_{3t}\) [bar a]  
Turbine inlet total pressure  
\(T_{3t}\)[K]  
Inlet turbine maximum temperature  
\(P_{4t}\) [bar a]  
Gas generator turbine inlet total pressure  
\(T_{4t}\) [K]  
Turbine outlet total temperature  
\(P_{45t}\) [bar a]  
Free turbine outlet total pressure  
\(T_{45t}\) [K]  
Free turbine outlet total temperature  
\(h_{a}(T)\) [K/kg]  
Air enthalpy polynomial function  
\(s_{a}(T)\) [K/kg]  
Air entropy polynomial function  
\(h_{gid}(T)\) [K/kg]  
Ideal gases enthalpy polynomial function  
\(s_{gid}(T)\) [K/kg]  
Ideal gases entropy polynomial function  
\(x_{gid}\) [-]  
Ideal gases mass participations  
\(x_{a,ex}\) [-]  
Air excess mass participations  
\(a_{i,g}\) [-]  
Gases enthalpy and entropy polynomial coefficients  
\(l_{Ct,id}\)[KJ/kg]  
Compressor total specific ideal work  
\(l_{Ct}\)[KJ/kg]  
Compressor total specific actual work  
\(l_{Tt,id}\)[KJ/kg]  
Turbine total specific ideal work  
\(l_{Tt}\)[KJ/kg]  
Turbine total specific actual work  
\(h_{1t}(T)\) [kJ/kg]  
Total specific enthalpy for station \"1\"  
\(h_{2t,id}(T)\) [kJ/kg]  
Total specific ideal enthalpy for station \"2\"  
\(h_{2t}(T)\) [kJ/kg]  
Total specific enthalpy for station \"2\"  
\(h_{3t}(T)\) [kJ/kg]  
Total specific enthalpy for station \"3\"  
\(h_{4t}(T)\) [kJ/kg]  
Total specific enthalpy for station \"4\"  
\(h_{45t}(T)\) [kJ/kg]  
Total specific enthalpy for station \"45\"  
\(P_{TP}\)[kW]  
Free turbine shaft power