esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Training Setup and Experimental Results  
Our proposed method was trained and tested using five-fold cross-validation. We conducted experiments to predict the beauty of faces using the four subsets or the entire set SCUT-FBP5500. The results of the average of all five-fold cross validations for the different subsets are shown in Table 3. In this setting, 80% of the images have a score, and the remaining 20% are unlabelled. The results show that merging multiple similarity graphs from the data space and the label space achieved the best performance compared to other methods using only a single graph. Moreover, using the Caucasian and Asian dataset, we can see that the more samples in the dataset, the better the performance in predicting the beauty of a face.  
Although it is not fair to compare a semi-supervised method with supervised methods, Table 4 shows such a comparison. The reason for this is twofold. First, there are almost only supervised methods for predicting the beauty of faces in the literature. Second, the supervised approaches can give an indication of the actual performance of the semi-supervised approach, especially when the number of labelled images is exactly the same.  
This table includes the MAE, the RMSE and the PC obtained with different supervised techniques. The same table also illustrates the performance with a recent semi-supervised approach. We can see that the proposed method performs well compared to many supervised techniques that use end-to-end deep learning paradigms. Moreover, our semi-supervised method outperformed a recent semi-supervised approach based on graph-based score propagation.  
Finally, Figure 5 shows the predicted scores for three face images. In this figure, we compare the predictions obtained by two semi-supervised techniques: the NFME method presented in [29] and the proposed method. The top row shows the ground truth scores. We can see that the predictions of our proposed method are closer to the ground truth than those of the competing method. We can also see that both methods accurately predicted the score associated with the average degree of beauty.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Dataset** & **Descriptor** & **Dimension** & **\# of Instance** \\ \hline \multirow{3}{*}{SCUT5500-AF} & VGG-face+fc6 & 4096 & \multirow{3}{*}{2000} \\  & Resnet50 & 2048 & & \\  & Geometric & 162 & \\ \hline \multirow{3}{*}{SCUT5500-AM} & VGG-face+fc6 & 4096 & \multirow{3}{*}{2000} \\  & Resnet50 & 2048 & & \\ \cline{1-1}  & Geometric & 162 & \\ \hline \multirow{3}{*}{SCUT5500-CF} & VGG-face+fc6 & 4096 & \multirow{3}{*}{750} \\  & Resnet50 & 2048 & & \\ \cline{1-1}  & Geometric & 162 & \\ \hline \multirow{3}{*}{SCUT5500-CM} & VGG-face+fc6 & 4096 & \multirow{3}{*}{750} \\ \cline{1-1}  & Resnet50 & 2048 & & \\ \cline{1-1}  & Geometric & 162 & \\ \hline \end{tabular}
\end{table}
Table 2: Face beauty dataset and image descriptors.
# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 2 Clutch Temperature Prediction Model Construction Based on Bi-LSTM and Migration Learning
### Model Construction  
The model construction involves the following steps:  
(1) Data Preprocessing: Experimental data from the Fuda (FD) clutch pressure plate are utilized. The data is divided into 10 parts, with 9 parts assigned to the training set and 1 part to the validation set. The data is also normalized to ensure consistent scaling.  
(2) Interactive Slider Implementation: During model training, sliders are introduced as an interactive approach to finely adjust parameter values and step sizes. This method enables precise control over parameter changes and leverages the correlation between data before and after clutch operations, thereby enhancing model accuracy.  
(3) Dropout Layer Integration: To prevent overfitting, a dropout layer is incorporated into the network architecture. This layer facilitates learning multiple independent sub-networks, reducing co-adaptation between neurons, and enhancing the model's generalization capability.  
(4) Loss Function and Evaluation Metrics: Mean_squared_error is chosen as the loss function, quantifying the difference between predicted and actual values through squared errors. This choice ensures a smoother loss value and avoids the cancellation of positive and negative errors. The model's performance is evaluated using metrics such as _MSE_ (Mean Squared Error), _RMSE_ (Root Mean Squared Error), _MAE_ (Mean Absolute Error), and \(R^{2}\) (\(R\)-squared). Mathematical expressions for these metrics are shown in Equations (1)-(4).  
\[MSE=\frac{1}{n}\sum\limits_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}},\,\in[0,+\infty) \tag{1}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}}},\,\in[ 0,+\infty) \tag{2}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{|y_{i}-\hat{y_{i}}|},\,\in[0,+\infty) \tag{3}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}}}{\sum\limits_{i=1 }^{n}{(y_{i}-\overline{y})^{2}}}\in[0,1] \tag{4}\]  
where \(n\) is the number of data, and \(\hat{y_{i}}\) is the mean of \(y_{i}\).  
(5) Model Validation and Output: Finally, the model parameters are fine-tuned using the validation set, and the optimized model is generated. This optimized model is then used to generate the fitted image for further analysis.
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Mission Profile
#### 3.1.1 Ascent  
With the vehicle leaving the launch pad, a trajectory has to be followed up to a phasing orbit, after which rendezvous and transfer manoeuvres are initiated. For Charon, a direct ascent was chosen, meaning that the vehicle follows a vertical flight for the first 100 m after which it starts pitching in a gravity turn that results in the vehicle reaching the phasing orbit. The gravity turn is designed in such a way that the maximum acceleration experienced by the crew corresponds to a maximum of four Martian-g's. The optimum ascent trajectory was determined by generating both a 2D point-mass simulation as well as 3D rigid-body simulation. The code written for these simulations is publicly available on GitHub [4]. The ascent profile, with the most important phases and altitudes that the vehicle goes through, can be seen in Figure 2. Moreover, the gravity field of Mars has been simulated using values of Sharaf and Selim [5], and the Martian atmosphere has been simulated following the same exponential model as [PERSON] and [PERSON] [6] (p. 3).  
Figure 1: Charon mission profile.
# Study on Dynamic Characteristics of the Bistable Nonlinear Damper
## 2 System Vibration Model
### Experimental Validation  
In order to validate the dynamic model established in this research, the testbench was installed, and the corresponding experiment was conducted. The testbench consisted of the base, the upper cover which acted as the main system and the bolts between them. The pretigthting torque could be adjusted by a torque wrench, shown in Figure 3. The vibration absorber was fixed on the top of the upper cover to suppress the vibration. The vibration absorber contained the beam, which acted as the spring component, and the permanent magnets, depicted in Figure 3. Two permanent magnets were used in the vibration absorber. One was pasted at the end of beam, the other was attached on the steel wall. The beam is made of aluminum alloy to reduce the effect of the beam's mass. An accelerometer was attached at the end of the beam, and the system was stimulated by a hammer with a force sensor. According to the collected data, the direct frequency response function (FRF) at the end of the beam was determined with the Metalmax system. The experimental result is shown in Figure 3. From the experimental FRF, the first nature frequency is 629 Hz.  
Figure 3: Experimental process. (**a**) Pretigthting torque adjustment; (**b**) Dynamic test of experimental setup; (**c**) Experimental result.  
The dynamic parameters of the interface between the upper cover and the base can be determined with the Yoshimura integral method. According to the dynamic model established in this research, the theoretical result of the first nature frequency is calculated as 611 Hz. The error between the experimental result and the theoretical one is 3%. The experimental result is slightly greater than the theoretical. That is because the nonlinearity of the magnetic force is ignored in the nature frequency calculation. The nature frequency calculation must be a linear calculation process with the nonlinear factor ignored. The actual stiffness factor is greater than that in the calculation, which causes the actual nature frequency to be greater than that in the calculation. However, the error between them is relatively small, and the dynamic model in this research is validated.
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 4 Antimicrobial Mechanism of D-Limonene
### Antibacterial and Antifungal Mechanism of D-Limonene
#### 4.1.4 Interference with Gene Expression  
As the research got deeper, the mechanism of action of D-limonene at the gene level was revealed. After D-limonene treatment, the expression of _dpeE1_, a gene downstream of the cell wall synthesis, and _clgR_, a gene that protects cell membrane integrity in _M. tuberculosis_, thereby disrupted cell integrity, as well as _Streptococcus pyogenes_ virulence-associated genes _covR_ and _sepB_ by 53% and 16%, respectively, and _covS_ and _mga_ being downregulated by 26% and 57%, respectively, with no significant difference in the expression levels of _srv_ and _luxS_, indicating that D-limonene reduced the surface-mediated virulence factors of _S. pyogenes_[109; 119]. And for fungi, it was shown that D-limonene induced disruption of the specific cell differentiation program of _C. parasilosis_, leading to apoptosis, and also arrested the cell cycle of _C. albicans_ in the G1 phase and the abundancies of 52 proteins were significantly changed (\(\geq\)two-fold), 33 of which were upregulated and 19 downregulated. Furthermore, qPCR demonstrated that _C. albicans_ cell wall and cell membrane damage stress genes (_KRE 9_, _ERG 11_), oxidative stress genes (_TRR 1_), nucleolus stress genes (_PRL 11_), and apoptosis-related genes (_CaMCA 1_) were overexpressed, indicating that D-limonene can induce apoptosis in _C. albicans_ cells through multiple pathways [85; 93].
# Numerical Study of Dig Sequence Effects during Large-Scale Excavation
## 1 Introduction
[22] used infrared thermography for the detection of potential leakages in underground diaphragm walls in foundation pits and investigated the infrared radiation characteristics of the leaking and potentially leaking parts of underground diaphragm walls through indoor simulation tests and on-site inspection methods. [PERSON] et al. [23; 24] conducted similar modeling tests to simulate the precipitation process to investigate the mechanism of pit deformation caused by pre-excavation precipitation. Through numerical simulations and experiments, [PERSON] et al. [25] carried out a study on the appropriate thickness of the piezometric head of an upper pressurized aquifer, where the base of the pit was located to avoid the problem of uplift at the base of the pit. [PERSON] et al. [26] simulated the mechanical properties of a pile support structure under different combinations of excavation depths, support pile burial depths, and excavation widths, and they investigated the performance of the support structure under different excavation sizes with the help of finite element software. In summary, many researchers have focused on the theoretical and technological aspects of safety control in the deep foundation pit excavation process, but so far, few studies have been conducted on the effect of foundation pit excavation sequence on the stability of DW structures, even though it is a critical factor. Additionally, with the complexity of urban construction, the foundation pit may have an irregular shape; currently, the deformation law for foundation pits with irregular shapes under different excavation methods is different from the deformation law of foundation pits with regular shapes. Taking the deep foundation pit of a metro project in Nanjing, China, as an example, we utilized a 3D fast Lagrangian analysis code (FLAC3D) to study the stability of both the deep foundation pit, shaped as an irregular large rectangle, and the DW structure under various excavation schemes to provide a reference for the excavation sequence of similar projects in the future.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 3 Methodology  
Figure 2 presents an MIS network, designated as DEFI-Net, which is founded on a dual-encoder interactive fusion approach. Distinct from conventional methodologies that employ Transformers and CNNs in parallel and directly harness the fused features, DEFI-Net not only amalgamates features from disparate encoders but also promotes interactive learning between the Transformer and CNN. During the encoding phase, we construct a parallel encoder featuring a three-layer pyramid structure that incorporates both CNN and Transformer technologies for extracting multi-scale features from the input image. Subsequently, the GLIL module is utilized to facilitate interaction between the Transformer encoder and the CNN encoder at each pyramid layer, with the learned features being transmitted to subsequent, deeper layers of the network. Concurrently, the global-local feature fusion (GLGF) module is applied to efficiently amalgamate features from both encoders. Additionally, skip connections are implemented to aggregate features across various scales, culminating in the decoding process to generate the final segmentation output.
## 4 Digital Watermarking System for JPEGs
### Methodology Rationale  
Why Fuse DCT and DWT?  
DCT coefficients offer excellent energy compaction and hence high resilience to JPEG quality scaling, while the DWT subbands retain the spatial correlation between blocks that survive geometric warps (rotation, scaling). Because the two transforms occupy orthogonal frequency supports, embedding the same payload across both domains yields complementary invariants: an attacker who cancels one domain must still cancel the other. In addition, the joint embedding capacity doubles (\(C=128\) bits), and under an i.i.d. bit-flip model, the probability that both domains incur a bit error rate exceeding 0.1 falls below \(3.2\times 10^{-4}\). This spectral separation ensures decorrelated failure modes across domains and directly supports the dual domain embedding strategy.  
The selection of DCT-DWT over alternative transform domains such as SVD or Contourlet derives from compatibility requirements with JPEG compression and forensic workflows. Although SVD provides exceptional stability for certain watermarking applications [24], its computation is prohibitively expensive for large forensic datasets. Similarly, Contourlet transforms offer directional selectivity advantages [5] but introduce complexity that conflicts with forensic processing time constraints.  
**Forensic integrity preservation:** A hybrid DCT-DWT domain technique was selected to insert watermarks directly into JPEGs in disk images. This choice preserves the overall disk structure, ensures compatibility with forensic tools, and supports precise leak source attribution. Modifying only the JPEG files, without altering the disk structure, preserves the evidentiary integrity of the image.  
**Robustness with imperceptibility:** The dual domain embedding strategy (DCT and DWT) was informed by an extensive review of the literature on watermarking. Studies consistently demonstrate that multi-domain approaches offer improved robustness against compression, filtering, geometric distortion, and noise injection without compromising visual quality or PSNR thresholds [19; 24; 25]. This balance is important in forensics, as the integrity of the evidence and the visual fidelity must be preserved.  
**Security and efficiency balance:** A consistent PRNG seed is applied to all images within an investigation, with unique watermarks assigned per recipient. This design reflects a deliberate trade-off between computational efficiency and security. It addresses the practical constraints of forensic workflows, where investigators must process large volumes of evidence efficiently and retain traceability.  
Unlike recent ML-based approaches that offer potentially greater adaptability [17; 19; 29], this method prioritises transparency and reproducibility, critical factors for court-admissible evidence. Similarly, while reversible watermarking techniques [18; 40] provide complete recovery capabilities, their computational requirements exceed the processing constraints of typical forensic investigations involving thousands of images.
## 7 Experiments and Results
### Results
#### 7.3.2 Human Evaluation  
We present human evaluation experiments to validate the results obtained with automatic evaluation metric. We used a pilot rating experiments that was used by [PERSON] et al. [16] in which participants were asked to rate translations using a 1 to 7 Likert scale. We evaluated the quality of the LA-MSA translation direction asking a group of 7 speakers of MSA and they are familiar with the Levantine Arabic (LA) to rate sentences produced by the Multi-Task NMT with POS system and Multi-Task NMT without POS system. We presented to the native speakers a source segment in LA and two translations in MSA. We randomly selected 40 segments and create two sub-sets of 20 segments each. We provide each annotator with a sub-set and ask them to rate the translations taking both fluency and adequacy into account using a Likert scale of 1 to 7. The average results obtained by each system in the pilot rating experiment are presented in Tables 4 and 5.  
The average results suggest that humans have a rather positive opinion about the translations produced by both systems. The average score for LA-MSA translation task obtained by the Multitask NMT+POS system was 5.9 whereas the Multitask NMT without POS obtained 1.4. Furthermore, the average score for MA-MSA translation task obtained by the Multitask NMT + POS system was 4 whereas the Multitask NMT without POS obtained 1.3. The outcomes of the pilot rating experiments  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Pairs** & **Embedding Type** & **Epochs** & **BLEU** & **Accuracy** \\ \hline NMT + POS\_LEV & LA-MSA & FastText & 90 & **0.43** & \\ NMT + POS\_LEV & MSA-ENG & FastText & 50 & 0.30 & \\ NMT + POS\_LEV & POS\_LEV & Random & 40 & & 98\% \\ NMT + POS\_MA & MA-MSA & FastText & 50 & **0.34** & \\ NMT + POS\_MA & MSA-ENG & FastText & 30 & 0.29 & \\ NMT + POS\_MA & POS\_MA & Random & 20 & & 99\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of Multi-Task NMT with POS in terms of BLEU score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Pairs** & **Embedding Type** & **Embedding Size** & **Epochs** & **BLEU** \\ \hline Single NMT & LA-MSA & Random & 150 & 120 & 0.17 \\ Multitask & LA-MSA & Random & 150 & 170 & 0.41 \\ Single NMT & MA-MSA & Random & 180 & 120 & 0.16 \\ Multitask & MA-MSA & Random & 180 & 230 & 0.30 \\ Single NMT & MSA-ENG & Random & 160 & 120 & 0.10 \\ Multitask & MSA-ENG & Random & 150 & 170 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of Multi-Task NMT with shared decoder in terms of BLEU score [11].  
confirm that the Multitask NMT+POS system produces a higher quality output than Multitask NMT without POS system for both tasks.
## 3 3D Dynamics Deduction
### Dynamic Equilibrium Equations
#### 3.3.4 Moment Balance around the Central Axis Perpendicular to the Orbital Plane  
The equilibrium position and the centripetal force vector of the mass center of the \(i\)-th ball in \(<x^{\prime\prime}y^{\prime\prime}z^{\prime\prime\prime}>_{C}\) is \(\hat{\textbf{\emph{r}}}_{Bi}^{\prime\prime}(i=1\sim n)\) (Equation (4)) and \(\textbf{\emph{F}}_{Bi}^{\prime\prime\prime}(i=1\sim n)\). \(\textbf{\emph{F}}_{Bi}^{\prime\prime\prime}\) can be converted by the \(\textbf{\emph{F}}_{Bi}^{\prime}\) (Equation (20)) and written as  
\[\textbf{\emph{F}}_{Bi}^{\prime\prime\prime}=\textbf{T}_{3}\textbf{T}_{2} \textbf{\emph{F}}_{Bi}^{\prime}=-m_{b}\omega^{2}\left[\begin{array}{c}Rcos \hat{\textbf{\emph{\beta}}}_{i}\big{[}\cos^{2}\hat{\textbf{\emph{\gamma}}}_{1} \cos^{2}(\hat{\textbf{\emph{\gamma}}}_{2}+\theta)+sin^{2}\hat{\textbf{\emph{ \gamma}}}_{1}\big{]}+\\ Rsin\hat{\textbf{\emph{\beta}}}_{i}sin\hat{\textbf{\emph{\gamma}}}_{1}cos\hat{ \textbf{\emph{\gamma}}}_{1}sin(\hat{\textbf{\emph{\gamma}}}_{2}+\theta)+\\ cos(\hat{\textbf{\emph{\gamma}}}_{2}+\theta)\hat{x}_{c}+sin\hat{\textbf{\emph{ \gamma}}}_{1}sin(\hat{\textbf{\gamma}}_{2}+\theta)\hat{y}_{c}\\ Rcos\hat{\textbf{\emph{\gamma}}}_{1}\big{[}sin\hat{\textbf{\emph{\beta}}}_{i}cos \hat{\textbf{\emph{\gamma}}}_{1}+cos\hat{\textbf{\emph{\beta}}}_{i}sin\hat{ \textbf{\emph{\gamma}}}_{1}sin(\hat{\textbf{\gamma}}_{2}+\theta)\big{]}+cos\hat{ \textbf{\emph{\gamma}}}_{1}\hat{\textbf{\emph{\gamma}}}_{b}\\ Rsin\hat{\textbf{\emph{\beta}}}_{i}sin\hat{\textbf{\emph{\gamma}}}_{1}cos\hat{ \textbf{\emph{\gamma}}}_{1}cos(\hat{\textbf{\emph{\gamma}}}_{2}+\theta)-\\ Rcos\hat{\textbf{\emph{\beta}}}_{i}cos\hat{\textbf{\emph{\gamma}}}_{1}sin(\hat{ \textbf{\emph{\gamma}}}_{2}+\theta)cos(\hat{\textbf{\emph{\gamma}}}_{2}+\theta)-\\ sin(\hat{\textbf{\emph{\gamma}}}_{2}+\theta)\hat{x}_{c}+sin\hat{\textbf{\emph{ \gamma}}}_{1}cos(\hat{\textbf{\gamma}}_{2}+\theta)\hat{y}_{c}\end{array}\right],\ i=1\sim n. \tag{27}\]  
When the system is in dynamic balance, the sum of moments in 3D for the central axis (\(\textbf{\emph{z}}^{\prime\prime}\)-axis) perpendicular to the orbital plane is zero. Thus, the dynamic equilibrium equation of the \(i\)-th ball around the \(\textbf{\emph{z}}^{\prime\prime\prime}\)-axis of the orbital plane can be obtained.  
\[\hat{\textbf{\emph{r}}}_{Bi}^{\prime\prime\prime}\times\textbf{\emph{F}}_{Bi}^{ \prime\prime\prime}\big{[}0\quad 0\quad 1\big{]}^{T}=0,i=1\sim n. \tag{28}\]  
\[\begin{array}{c}m_{b}R^{2}\omega^{2}\Big{\{}-cos\big{(}2\hat{\textbf{\emph{ \beta}}}_{i}\big{)}sin\hat{\textbf{\emph{\gamma}}}_{1}cos\hat{\textbf{\emph{ \gamma}}}_{1}sin(\hat{\textbf{\emph{\gamma}}}_{2}+\theta)+sin\hat{\textbf{\emph{ \beta}}}_{i}cos\hat{\textbf{\emph{\beta}}}_{i}\big{[}sin^{2}\hat{\textbf{\emph{ \gamma}}}_{1}-cos^{2}\hat{\textbf{\emph{\gamma}}}_{1}sin^{2}(\hat{\textbf{\emph{ \gamma}}}_{2}+\theta)\big{]}\Big{\}}\\ +m_{b}R\omega^{2}\big{\{}\hat{x}_{c}sin\hat{\textbf{\emph{\beta}}}_{i}cos(\hat{ \textbf{\gamma}}_{2}+\theta)-\hat{y}_{c}\big{[}cos\hat{\textbf{\emph{\beta}}}_{i}cos \hat{\textbf{\emph{\gamma}}}_{1}-sin\hat{\textbf{\
# Mapping Server Collaboration Architecture Design with OpenVSLAM for Mobile Devices
## 4 Experiments and Results
### Experimental Environments  
Through the mobile SLAM system proposed in this paper, we tried to verify by creating and localizing a map in the real environment. To this end, image data and IMU data were collected by capturing the experimental environment in advance.  
Figure 6: SLAM procedure by FastAPI-based mapping server.  
Figure 7: Interaction between mobile device and mapping server for performing SLAM based on mapping server.  
Figure 8 shows the indoor (office) experimental environment used for the experiment. The indoor space has an area of about 65 m\({}^{2}\), and it was captured using the camera of the Galaxy Note 20 Ultra 5G.