esa-sceva/satcom-chunk-collection huggingface dataset:
# \"Blurry Touch Finger\": Touch-Based Interaction for Mobile Virtual Reality with Clip-on Lenses
## 5 Main Experiment: BTF-T, BTF-R and CONT
### Results and Discussion
#### 5.5.2 Object Size  
ANOVA showed a significant main effect for target object size (Figure 9) for all (selection time: [F(2,81) = 4.894, \(p\) = 0.010 *], distance error: [F(2,81) = 5.939, \(p\) = 0.004 *, number of unsuccessful selections: [F(2,81) = 103.10, \(p\) < 0.001 *]].  
Figure 9: Quantitative performance measures (average selection time, average distance error and average number of unsuccessful selections in percentage) among the three target object sizes. The asterisk mark (*) indicates statistically significant difference.  
Using the post hoc Tukey's Pairwise Comparisons, the selection were significantly faster with large targets (Large vs. Small: \(p\) = 0.008 *). Yet, the error distance when selecting large targets were significantly higher than when selecting small targets (Large vs. Small: \(p\) = 0.003 *).  
For the rate of unsuccessful selection, all sizes had significant differences (Large vs. Medium: \(p\) = 0.016 *, Large vs. Small: \(p\) < 0.001 *, Medium vs. Small: \(p\) < 0.001 *). However, for just BTF-R, there was no significant difference between the varied sizes (they existed only for BTF-T and CONT) (Figure 10). This is meaningful because there were no significant differences in the selection time between the BTF-R and the CONT condition. This indicates that if the target is small and the user needs to act precisely, BTF-R could be the way to go.  
Another interesting fact is that when using BTF-T, if the target objects were large, BTF-T was good enough to compete with the other two interfaces--equally accurate than BTF-R or CONT and faster than CONT but slightly slower than BTF-R (Figure 10). The Cohen's d values were around 0.34 for BTF-T vs. BTF-R and 0.76 for BTF-T vs. CONT for the selection time. Considering the fact that the size of the large targets in the experiment matches the smallest size required from the Android Design Guidelines, any size that is bigger than the \"large\" size in this experiment can benefit from using BTF-T.  
The results follow the usual speed-accuracy trade-off with respect to object size. The smaller the object is, more time was spent to select it, but with nearly equal accuracy for BTF-T (Figure 10) and better accuracy with BTF-R, by the visible target (and guiding cursor) and less fat finger problem.
## 3 Results
### Comparative Analysis with Deep Learning Approaches  
To accurately evaluate the performance of BO-Stacking-TabNet, we conducted a comprehensive comparative analysis against several prevalent deep learning architectures commonly used in precision agriculture. The models selected for comparison include CNN-RNN hybrid models, a standard Transformer-based regressor, and a ResNet-LSTM hybrid. All models were trained for 300 epochs on the same dataset to ensure a fair and direct comparison. The performance metrics, including Accuracy, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R\({}^{2}\)), are presented in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model Name** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Random Forest & 0.6321 & 0.6803 & 0.4654 & 0.4788 \\ Xgboost & 0.6509 & 0.6748 & 0.4807 & 0.4869 \\ Catboost & 0.6557 & 0.6747 & 0.4998 & 0.5023 \\ LightGBM & 0.6321 & 0.6337 & 0.4594 & 0.4605 \\ Gradient Boosting Tree & 0.6580 & 0.6751 & 0.5009 & 0.5030 \\ Ordinal Logistic & 0.6892 & 0.6615 & 0.5645 & 0.5490 \\ CORAL neural networks & 0.6954 & 0.6831 & 0.5622 & 0.5572 \\ TabNet & 0.7028 & 0.5710 & 0.5729 & 0.5716 \\ BO-Stacking-TabNet & 0.7241 & 0.6969 & 0.5912 & 0.5935 \\ \hline \end{tabular}
\end{table}
Table 5: Model performance evaluation based on spectral reflectance as input variables.  
The results clearly demonstrate the superior performance of our proposed model, BO-Stacking-TabNet. With an accuracy of 72.41%, our model significantly outperforms the ResNet-LSTM hybrid (68.60%), the Transformer Regressor (70.18%), and the CNN-RNN hybrid models (69.01%). This notable increase in accuracy underscores the effectiveness of our model's unique architecture in capturing the complex patterns and dependencies within the agricultural dataset.
# Space Charge Accumulation and Decay in Dielectric Materials with Dual Discrete Traps
## 4 Results and Discussion
### Time Dependent Free and Trapped Charge Densities at the Same Position
It indicates that during the charging process there is charge transfer from shallow traps to deep traps, so the charge decay becomes more difficult when the sample is charged for a longer time. Figure 6: Time dependent free charge density and trapped charge densities at the same position \(x=2\Delta x\) in the dielectric material subjected to the voltage of 6.0 kV (**a**), 7.5 kV (**b**), and 9.0 kV (**c**). The charge time-dependent space charge decay behavior was also observed in XLPE [11; 12] and LDPE [16; 17] by PEA techniques. [PERSON] et al. studied the space charge decay properties of virgin XLPE subjected to an electric field of \(5\times 10^{7}\) Vm\({}^{-1}\) for 2 h and 26 h at ambient temperature. The space charge distributions in the samples were measured and recorded by the PEA system. Then, the space charge density at the same position near the electrodes was extracted from the position distributions of space charges and plotted as a function of decay time. It was found that the space charge decay properties of the samples poled for various times are different. The space charges decayed slower for long poling duration. The experimental space charge decay results indicated that the charge trapping/de-trapping dynamic process of each trap center is coupled with the dynamic process of other trap centers. As shown by our analytical and numerical results in Section 2 and the BCT simulation results, the initial conditions for space charge decay processes are different for various poling durations.
## 4 Experiments
### Experimental Results
#### 4.5.1 Results on English  
Table 4 presents the comparison of GNNSDP and previous approaches on the test sets of the SemEval 2015 Task 18 English dataset. From the results, we have the following observations:  
* In the basic settings, GNNSDP achieves 1.8 and 1.3 averaged LF1 improvements on the ID and OOD test set over the previous best parsers.
* Adding both the character-level and the lemma embeddings, most models improve performance quite a bit generally. GNNSDP leads to 1.9 and 1.9 averaged LF1 improvements over the previous best parsers on the ID and OOD test sets.
* Adding BERT embeddings pushes performance even higher generally. GNNSDP outperforms the previous best parsers by 0.3 and 2.2 average LF1 improvements on the ID and OOD test sets, respectively.
* GNNSDP makes significant improvements on the PSD formalism, with 4.6 and 4.7 LF1 improvements in the basic settings, 4.6 and 5.1 LF1 improvements when character-level and lemma embeddings are added, 0.9 and 5.3 LF1 improvements when BERT embeddings are added on the ID and OOD test sets, respectively.
* The LF1 scores of all parsers on the PSD is lower than the other two formalisms. Table 5 shows part of contrastive statistics of three formalisms. We have noticed that the PSD formalism appears linguistically most fine-grained because it contains the most semantic labels and frames [35]. This makes PSD more challenging to predict. However, GNNSDP performs better than other first-order and second-order parsers, suggesting that higher-order information is beneficial for improving SDP.
* The performances of GNNSDP(GCN) and GNNSDP(GAT) are close in three embedding settings, demonstrating that both GCN and GAT are capable of capturing higher-order information.
## 5 Flight Verification and Data Analysis
### On-Orbit Parameters and Boundary Conditions  
After the module is launched into orbit, the basic performance parameters of the thermal management system are set up according to the indexes in Table 2, and the flow rate, working pressure, pressure difference and working temperature range of the thermal control bus are specified. It can be seen that the flow rate of TCB is not less than 600 L/h, in which 60 L/h should be distributed to the load equipment branch and the rest will be distributed to each payload rack through TCB in the module.  
Figure 17: Working principle of integrated thermal control system in module.  
According to the data, the air temperature in the module changes in a certain range during the flight phase of CSS on orbit; the reference temperature is 23 \({}^{\circ}\)C and varies from 19 to 26 \({}^{\circ}\)C. In addition, the platform provides cooling water of 16-22 \({}^{\circ}\)C for the cold side of liquid-liquid heat exchanger, and the flow rate is (500 \(\pm\) 50)L/h.
## 1 Introduction
### GPR--State-of-the-Art  
GPR is a non-invasive geophysical method that uses high-frequency electromagnetic waves to detect subsurface features by identifying contrasts in dielectric properties. Renowned for its high resolution, GPR excels in detecting underground cavities, fractures, water content, and structural features such as road layers. GPR's effectiveness in archeological surveys and geotechnical applications has been demonstrated [10]. They utilized drone-mounted and ground-based GPR systems to detect subsurface structures like cavities, clay-rock boundaries, and buried artifacts. Similarly, [PERSON] et al. [11] and [PERSON] et al. [12] highlighted its use in identifying structural weaknesses threatening road stability, while [PERSON] et al. [13] showcased GPR in investigating the internal structure of geological layers and their connection to rock discontinuities. Additionally, GPR has been instrumental in identifying air-filled cavities and structural discontinuities in karst systems [14]. Despite these successes, GPR faces challenges such as limited penetration depth in conductive soils and reduced performance in urbanized environments, where signal attenuation is significant. Innovations like drone-mounted systems and hybrid surveys combining ground-based and airborne data have expanded GPR's capabilities [15]. These advancements allow GPR to address complex subsurface conditions, making ita transformative tool for detecting underground cavities, mapping water content, and assessing structural features like road layers.
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Energy Model of the Differential Drive Wheeled Mobile Robot  
The main energy losses of the DDWMR consist of the losses inside the motors, the kinetic energy losses, the losses due to friction, and the losses in the electronics [22; 28]. The energy consumed by motors consists of two main parts, which are the energy transformed into robot kinetic energy and the energy to overcome traction resistance. In practice, the energy consumption is mainly transformed into robot kinetic energy.  
For the design of an energy-efficient controller, it is necessary to have an appropriate energy model. To simplify the energy consumption model, only the kinetic energy losses of the robot have been considered. This simplification is based on the hypothesis that  
Figure 3: Geometry of a differential drive wheeled mobile robot.  
accelerations and decelerations change the kinetic energy of the robot body. The kinetic energy of the robot at any time is [12; 22; 28]  
\[E_{\text{kinetic}}=\frac{1}{2}m[v(t)]^{2}+\frac{1}{2}I[\omega(t)]^{2} \tag{2}\]  
where \(m\) and \(I\) denote the mass and the moment of inertia of the differential drive wheeled mobile robot respectively; \(v\) and \(\omega\) are the linear velocity and the angular velocity of the robot at time \(t\).  
When implementing movement of the DDWMR in the software, the discrete time is used. Thus, the kinetic energy of the robot at time \(t_{i}\) is calculated by Equation (3). The difference in kinetic energy between the two states is calculated by Equation (4).  
\[E_{\text{kinetic}(i)}=\frac{1}{2}m[v(t_{i})]^{2}+\frac{1}{2}I[\omega(t_{i})] ^{2} \tag{3}\]  
\[\Delta E_{kinetic}(i)=E_{Kinetic(i)}-E_{Kinetic(i-1)} \tag{4}\]  
where: \(i=1\), \(2\), \(3\), \(\dots\), \(m\) (\(m\) is the number of sampling times).  
The robot motion may include acceleration, deceleration, and moving with constant velocity. It is assumed that kinetic energy is not recuperated and stored in the battery; thus, every acceleration followed by deceleration leads to energy loss. To sum up all those energy differences, the state of the robot is inspected and the total energy consumption is calculated. Therefore, in the simulation, the total energy consumption of the DDWMR is calculated based on Equations (3) and (4), the kinetic energy changes in the deceleration phase are considered as a loss.  
The Optimization of the Fuzzy Logic Controller Used for the Differential Drive Wheeled Mobile Robot  
To carry out the full optimization process for the FLC, first, the initial FLC is created to navigate the DDWMR to the reference point. Then, the initial FLC will be optimized based on the GA to automatically tuning the parameters of the Membership Functions (MFs) for the FLC so that the energy consumption of the robot is minimum.
## 6 Potential Anti-Virulence Agents for the Treatment of Cholera Disease  
[PERSON] and colleagues reported that MbaA, an inner membrane protein, plays a key role in the biofilm architecture of _V. cholerae_[72]. Their study showed that mutants lacking MbaA produce an increased amount of extracellular matrix, resulting in thicker biofilms with disrupted structure. This observation underscores the regulatory function of MbaA in biofilm development.  
_V. cholerae_ is a Gram-negative, facultative anaerobic bacterium and the causative agent of cholera, a severe diarrheal disease. In _V. cholerae_, similar to other organisms, c-di-GMP is involved in the regulation of multiple cellular processes including biofilm formation, motility, and virulence. MbaA and CdgC are GGDEF-EAL domain proteins that function as PDEs in _V. cholerae_, regulating biofilm formation and motility by modulating c-di-GMP levels [72; 73].  
Though MbaA contains a GGDEF domain, it lacks key residues required for diganylate cyclase (DGC) activity, reinforcing its role as a PDE [72]. [PERSON] et al. and [PERSON] et al. demonstrated that MbaA plays a role in gene regulation, specifically by repressing _vps_ gene expression in _Vibrio cholerae_[43; 74]. The _vps_ genes are crucial for producing Vibrio polysaccharide (VPS), a key component of the extracellular matrix that supports the formation of complex biofilm structures. By modulating _vps_ gene expression, MbaA influences biofilm development and structural integrity in _V. cholerae_.  
Similarly, CdgC, another EAL domain-containing PDE, influences motility and biofilm formation [73]. [PERSON] and [PERSON] showed that overexpression of CdgC leads to reduced _vps_ gene expression, affecting biofilm matrix production [75]. Additionally, strains overexpressing wild-type CdgC exhibit enhanced motility, whereas a mutated cdgC-AAL allele (disrupting PDE activity) fails to impact motility [43]. These findings underscore the role of MbaA and CdgC in regulating c-di-GMP levels, where mutations or overexpression result in distinct alterations in bacterial biofilm architecture and motility. Consequently, small-molecule inhibitors targeting MbaA, CdgC, or both could serve as potential anti-virulence agents for cholera treatment.
## 2 Methods
### Correction Technique of Displacement Signal Measured by UAV  
It can be seen from the previous section that the displacement time-history curve of the bridge can be obtained by tracking the target points in the video taken by the UAV through KLT, but the displacement signals contain the false displacement. Three techniques of eliminating the random signal caused by the UAV will be introduced in this section, including the two techniques used in this paper and the existing differential filtering technique.
###### Abstract  
An optical configuration of double-diffracted spatially separated heterodyne grating interferometer with a mechanical fixture was designed. To further investigate its features and provide robust measurements, the alignment tolerance in double-diffracted spatially separated heterodyne grating interferometer was qualitatively and quantitatively analyzed. Except for the offset error causing no influence on the interfering signal, the effect of the other four errors, roll, yaw, pitch angles, and stand-off error were geometrically analyzed and mathematically modeled. The simulation result quantified the position mismatches of output beams in a double-diffracted configuration and found the crucial structural parameters related to the intensity of interfering signals. Experiments based on the grating interferometer with a mechanical fixture and the same optical configuration built by independent optical components were implemented, whose results agreed with the simulation. Besides, the results showed that the proposed grating interferometer structure could tolerate the \(\pm\)1100 arcsec roll movement, \(\pm\)440 arcsec yaw movement, \(\pm\)280 arcsec pitch movement, and \(\pm\)0.6 mm stand-off error when -10 dB intensity loss is afforded.  
Keywords:grating interferometer; laser encoder; spatially separated heterodyne interferometry; alignment tolerance
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Trace and Revocation  
If an RSU or vehicle detects suspicious behavior from a malicious vehicle, it can submit the corresponding pseudonym \(AID_{v}^{f}\) to CA. Then, CA calculates \(ID_{v}^{f}=H(sk_{c}||r_{v}^{f})\oplus AID_{v}^{f}\) using the system secret key \(sk_{c}\), random number \(r_{v}\) and the pseudonym \(AID_{v}^{f}\) to obtain \(ID_{v}^{f}\) of the malicious vehicle.  
Figure 5: Authentication.  
Afterward, the CA removes \(\{ID^{f}_{v},AID^{f}_{v},r^{f}_{v},y^{f}_{v},r^{f}_{v},R^{f}_{Fv}\}\) from the CA's database, Subsequently, it adds the entity \(\{ID^{f}_{v},AID^{f}_{v},t^{f}\}\) to CA's revocation list \(REV\) to ensure that the malicious vehicle is no longer able to participate in the system.
## 3 Optimization Formulation
During the optimization, the designer specifies a required minimum buckling capacity \(\lambda_{stab}\). The penalty function \(p_{3}(\overline{\lambda})\) ensures that the final solution satisfies the prescribed stability requirement. Complementary to the penalty functions, bolt placement rules from EN 1993-1-8 [11] were integrated into the optimization process to ensure a code-compliant design. This is expressed in Equation (18), where \(e\) represents the edge distance from the bolt center, \(p\) denotes the spacing between adjacent bolt centers, and \(d_{b}\) is the nominal bolt diameter. Finally, Equation (19) defines the initial geometric imperfection applied to the structure. In this formulation, \(\mathbf{X}\) represents the perturbed geometry, composed of the perfect geometry \(\mathbf{X}_{0}\) and an imperfection term \(\Delta\mathbf{x}\). The imperfection \(\Delta\mathbf{x}\) corresponds to the superposition of selected buckling mode shapes \(\mathbf{\phi}_{j}\) obtained from LBA, each scaled by a prescribed amplitude \(\omega_{j}\). This approach allows multiple mode shapes to be considered simultaneously, enabling a more realistic representation of potential initial imperfections in the structural design.
# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 3 Proposed Method
### Introduction of Deformable Convolution  
The high-level CNN layers encode the semantic information over spatial locations [49], and different locations may correspond to objects with different scales or deformation. However, the receptive field size of the same CNN layer is invariable, which can affect the network to extract features. Specifically, in the application of crack segmentation, the  
Figure 4: An illustration of the context feature enhancement module.  
cracks have diverse morphologies and different orientations. Therefore, it is difficult for the traditional CNNs limited by fixed geometric structures to learn all the morphological features of cracks.  
To address this problem, we replace some of the standard convolutions in the encoder network with deformable convolutions, which can better capture various morphological information of cracks by adaptively adjusting the receptive field sizes. In the standard convolution, given the input feature map \(X\), for each location \(p_{0}\) on the output feature map \(Y\), we have  
\[Y(p_{0})=\sum_{p_{n}\in R}\omega(p_{n})\cdot X(p_{0}+p_{n}) \tag{1}\]  
where \(\omega(p_{n})\) denotes the weight of the convolution kernel at \(p_{n}\), \(p_{n}\) enumerates the locations in \(R\), and \(R\) is the full set of sampling points. The size of the sampling region of standard convolution is fixed and determined by the convolution kernel size. In deformable convolution, \(R\) is unchanged, but the sampling region is augmented by sampling offsets. As illustrated in Figure 5, the sampling offsets are obtained by a convolutional layer over the same input feature map. The spatial resolution of the output offset is the same as the input feature map, while its channel dimension is \(2N\), corresponding to \(N\) 2D offsets. Therefore, in deformable convolution, Equation (1) becomes  
\[Y(p_{0})=\sum_{p_{n}\in R}\omega(p_{n})\cdot X(p_{0}+p_{n}+\Delta p_{n}) \tag{2}\]  
where \(\Delta p_{n}\) is the sampling offset. The introduction of deformable convolution allows the network to adaptively adjust the sampling area and receptive field size to improve its ability to extract crack features.