esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Lubrication Status  
During the disengaging process, the wet clutch goes through the boundary lubrication stage (C1), mixed lubrication stage (C2), and hydrodynamic lubrication stage (C3) in sequence (the division of T2 is marked in Figures 5f and 6). The variations of the contact and hydrodynamic torque in the disengaging process with different ATF temperatures are shown in Figure 6. When the hydrodynamic torque starts to increase, the boundary lubrication stage finishes. When the contact torque reduces to zero (less than 10\({}^{-6}\) N\(\cdot\)m), the mixed lubrication stage finishes. The end moment of the boundary lubrication stage \(t_{1}\) and the end moment of the mixed lubrication stage \(t_{2}\) with different ATF temperatures are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **T1** & **T2** & **T3** & **T4** & **T5** \\ \hline
**T/(\({}^{\circ}\)C)** & **60** & **80** & **100** & **120** & **140** \\ \hline \(\delta_{5}\)/(mm) & 0.2237 & 0.2338 & 0.2321 & 0.2298 & 0.2293 \\ \(\delta_{6}\)/(mm) & 0.1968 & 0.2069 & 0.2057 & 0.2035 & 0.2025 \\ NUC & 0.5825 & 0.5761 & 0.5774 & 0.5784 & 0.5782 \\ \(t_{1}\)/(s) & 1.575 & 1.568 & 1.563 & 1.560 & 1.558 \\ \(t_{2}\)/(s) & 1.674 & 1.631 & 1.610 & 1.599 & 1.591 \\ \(t_{3}\)/(s) & 1.745 & 1.674 & 1.640 & 1.621 & 1.610 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.  
As the ATF temperature increases from 60 \({}^{\circ}\)C to 140 \({}^{\circ}\)C, the end moment of the boundary lubrication stage brings forward from 1.575 s to 1.558 s, and the durations of the boundary lubrication stage are 0.075 s, 0.068 s, 0.063 s, 0.06 s, and 0.058 s, respectively. Similarly, the end moment of the mixed lubrication stage also brings forward from 1.674 s to 1.591 s, and the durations of the mixed lubrication stage are 0.099 s, 0.063 s, 0.047 s, 0.039 s, and 0.033 s, respectively; the durations of the hydrodynamic lubrication stage are 0.071 s, 0.043 s, 0.03 s, 0.022 s, and 0.019 s, respectively. Thus, the increase of ATF temperature accelerates the change of lubrication status between friction pairs during the disengaging process and shortens the duration of the hydrodynamic (shortened by 73.2%), mixed (shortened by 66.7%), and boundary (shortened by 22.7%) lubrication stages, respectively.
###### Abstract  
Characterizing the spatiotemporal patterns of ecosystem responses to drought is important in understanding the impact of water stress on tropical ecosystems and projecting future land cover transitions in the East African tropics. Through the analysis of satellite measurements of solar-induced chlorophyll fluorescence (SIF) and the normalized difference vegetation index (NDVI), soil moisture, rainfall, and reanalysis data, here we characterize the 2010-2011 drought in tropical East Africa. The 2010-2011 drought included the consecutive failure of rainy seasons in October-November-December 2010 and March-April-May 2011 and extended further east and south compared with previous regional droughts. During 2010-2011, SIF, a proxy of ecosystem productivity, showed a concomitant decline (~32% lower gross primary productivity, or GPP, based on an empirical SIF-GPP relationship, as compared to the long-term average) with water stress, expressed by lower precipitation and soil moisture. Both SIF and NDVI showed a negative response to drought, and SIF captured the response to soil moisture with a lag of 16 days, even if it had lower spatial resolution and much smaller energy compared with NDVI, suggesting that SIF can also serve as an early indicator of drought in the future. This work demonstrates the unique characteristics of the 2010-2011 East African drought and the ability of SIF and NDVI to track the levels of water stress during the drought.  
solar-induced chlorophyll fluorescence; drought; photosynthesis; East Africa; water stress; NDVI +
Footnote †: journal:  
Article Ecosystem Productivity and Water Stress in Tropical East Africa: A Case Study of the 2010-2011 Drought
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
###### Abstract  
Analyzing land use changes (LUC) in both past and future scenarios is critical to optimize local ecology and formulate policies for sustainable development. We analyzed LUC characteristics in Huaibei City, China from 1985 to 2020, and used the CLUE-S and PLUS models to simulate LU in 2020. Then, we compared the accuracy of the simulation phase and chose the PLUS model to project LU under four scenarios in 2025. The results showed the following: (1) Farmland and grassland areas decreased from 1985 to 2020, while forestland, water, and construction land increased. (2) The LU types in the region are explained well by the driving factors, with all receiver operation characteristic (ROC) values greater than 0.8. (3) The kappa indices for CLUE-S and PLUS analog modeling were 0.727 and 0.759, respectively, with figure of merit (FOM) values of 0.109 and 0.201. (4) Under the farmland and ecological protection scenario (FEP), farmland and forestland areas are protected, increasing by 1727.91 \(\mathrm{h}\mathrm{m}^{2}\) and 86.22 \(\mathrm{h}\mathrm{m}^{2}\), respectively, while construction land decreases by 2001.96 \(\mathrm{h}\mathrm{m}^{2}\). These results confirm that PLUS is significantly better than the CLUE-S model in modeling forestland and water, and slightly better than the CLUE-S model in modeling the rest of the LU type. Urban sustainability is strong in the scenario FEP.  
CLUE-S model; PLUS model; LUC; auto-logistic regression; scenario simulation +
Footnote †: journal: Applied Sciences
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 2 Materials and Methods
### Characterization of Materials  
The surfaces of the plasma-treated samples were observed under a scanning electron microscope (SEM, S-4800; Hitachi, Tokyo, Japan) and a scanning probe microscope (SPM, SPM-9600, SHIMADZU). The chemical composition of the surface coating was analyzed by X-ray photoelectron spectroscopy (XPS, Kratos analytical axis ultra DLD electron spectrometer; Kratos Instruments, Manchester, UK) with a monochromatic Al K\(\alpha\) X-ray source. Each sample was etched with Ar ions for 2 min (evaporation rate: 5 nm min\({}^{-1}\)) to remove surface contaminants.  
Contact angle measurements in ultrapure water were performed at room temperature using a video contact angle measurement system (VSA 2500 XE; AST Products, Tokyo, Japan).
# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 1 Introduction
Deep learning-based smoothers, while more flexible, require extensive labeled data for supervised training or risk mode collapse in adversarial setups. Additionally, many end-to-end smoothing networks do not explicitly enforce global trajectory consistency, leading to accumulated drift over long sequences. To address the aforementioned challenges, some researchers have attempted to jointly optimize video stitching and stabilization tasks to produce globally smooth and stabilized videos [1; 3]. However, these methods typically require designing joint optimization functions for overall camera trajectory modeling and performing multiple offline iterations, making them unsuitable for real-time applications. Stabstitch [12] proposed an unsupervised online video stabilization and stitching method primarily targeting inter-frame discontinuities after image stitching, assuming that the original video does not contain natural camera shake. Despite this, Stabstitch's reliance on post hoc smoothing limits its ability to correct misalignments introduced during stitching, and the assumption of negligible camera shake restricts its applicability to highly dynamic handheld conditions. To tackle the challenges of both natural camera shake and inter-frame discontinuities introduced by stitching, this paper presents a novel end-to-end online video stabilization and stitching framework based on unsupervised deep learning. The proposed method employs a recurrent neural network (RNN) with dual inputs--historical stabilized frames and original unstable frames--to achieve real-time video stitching and stabilization. During training, the overall network jointly optimizes three key modules: camera trajectory estimation, stitching trajectory estimation, and trajectory smoothing. This ensures both smooth trajectory generation and high-quality stitching. To mitigate cumulative errors caused by absolute coordinate modeling, a relative coordinate representation strategy is adopted, effectively reducing video distortion. Additionally, a dynamic attention mask mechanism is introduced, generating spatiotemporal weight maps based on foreground motion prediction to suppress interference from dynamic objects. The proposed method enables high-quality stitching and stabilization of multi-source handheld videos in real time, providing an efficient and cost-effective solution for video enhancement technology.
# Frame-Based and Subpicture-Based Parallelization Approaches of the HEVC Video Encoder
## 4 Results and Discussion  
In this section, we present the evaluation of the parallel algorithms detailed in Sections 2 and 3, in terms of parallel and R/D performance. In order to implement the algorithms presented in this work, we have modified the HEVC reference software HM v16.3 [18]. For the subpicture-based parallel algorithms, the OpenMP API v3.1 [19] has been used, whereas for the frame-based algorithms we have used MPI v2.2 [17]. The parallel platform used in our experiments is an HP Proliant SL390s G7 (a distributed memory multiprocessor with 14 nodes). Each node is equipped with two Intel Xeon X5660 and 48 GB of RAM. Each X5660 includes six processing cores at 2.8 GHz. QDR Infiniband has been used as the communication network. The video sequences used in the experimental tests and their characteristics are shown in Table 3. Four different values of the Quantization Parameter (QP) have been used in the experiments, ranging from low compression rates to high compression rates (22, 27, 32, 37).
## 3 Congestion Prediction Model
### Traffic Distribution  
Network traffic between the C section and other sections is estimated using the gravity model. As shown in Figure 5, when comparing the network traffic between zones 1 and 2 with that between zones 1 and 4, the network traffic between zones 1 and 2 is greater than expected. This is because, although the distance between zone 1 and 2 and that between zone 1 and 4 are the same at 500 m, the number of nodes in zone 2 is 20, which is double the 10 nodes of zone 4. In addition, the number of nodes located within zone 1 and 2 is the same as that within zones 1 and 3, but the 500 m distance between zones 1 and 2 is more than twice as distant than the 200 m between zones 1 and 3, so it can be expected that the network traffic from zone 1 to zone 3 is higher than the network traffic from zone 1 to zone 4. This is due to the impacts of scale and distance, and the gravity model reflects this, which allows for traffic prediction.  
The basic equation considering scale and distance impacts can be expressed as in Equation (7) below:  
\[T_{ij}=\frac{N_{i}N_{j}}{d_{ij}} \tag{7}\]  
* \(N_{i}=\) Node number in zone \(i\)
* \(N_{j}=\) Node number in zone \(j\)
* \(d_{ij}=\) The distance between zones \(i\) and \(j\)  
The basic equation is limited as it is considers the characteristics of a WSN, for which traffic comes into the sink from the source. In other words, it places constraints for zone-specific total arrival traffic (\(D_{i}\)). It must be transformed to satisfy the following constraint: the sum of the traffic (\(\sum_{i}T_{ij}\)) starting from all other zones arriving in zone \(j\) (sink) should be the same as the total amount of arrival traffic in zone \(j\) (\(D_{j}\)). This is shown in the following Equation (8):  
\[\sum_{i}T_{ij}=D_{j} \tag{8}\]  
The constraint is expressed in the following Equation (9):  
\[T_{ij}=O_{i}B_{j}D_{j}d^{\beta}{}_{ij} \tag{9}\]  
* \(B_{j}=\) Adjustment factor of destination zone \(j\).  
Figure 5: Gravity model example.  
The adjustment factor \(B_{j}\) may have different values for the destination zone in the Equation 9. Using Equations (8) and (9), the adjustment factor \(B_{j}\) can be derived as in the following Equation (10). Substituting Equation (11) into Equation (10), Equation (11) can be expressed as follows:  
\[B_{j}=\frac{T_{ij}}{O_{i}D_{j}d^{\beta}{}_{ij}}=\frac{\sum_{i}T_{ij}}{D_{j}\sum _{i}O_{i}d^{\beta}{}_{ij}}=\frac{D_{j}}{D_{j}\sum_{i}O_{i}d^{\beta}{}_{ij}}= \frac{1}{\sum_{i}O_{i}d^{\beta}{}_{ij}} \tag{10}\]  
\[T_{ij}=\mathrm{D}_{j}\frac{O_{i}d^{\beta}{}_{ij}}{\sum_{i}O_{i}d^{\beta}{}_{ij}} \tag{11}\]  
Thus, in order to obtain the traffic volume \(T_{ij}\), the adjustment factor \(B_{j}\) was first obtained by using Equation (10) and then, the traffic volume was calculated using Equation (10) or (11).
## 5 Conclusions  
The main aim of this research is to create a punctuation-restoration model using a transformer model. The transformer models evaluated in this study were BERT-based models, which have been used in many NLP problems. To the best of our knowledge, no research has been conducted to restore punctuation on social media. This study explores and evaluates the performance of different BERT transformer models using LSTM and GRU with a linear neural-network layer to restore punctuation of texts. Among all the models in this study, the LSTM+RoBERTaLARGE model produced the highest scores compared to other models for Amazon and TM review datasets. In the future, the dataset imbalance that resulted in high recall values for some classes and lower values for others (comma, period, question) will be investigated. Smoothening methods or any other method that can achieve a better result could be implemented in the future to mitigate this problem. In addition, the punctuation-restoration model will be integrated as one of the pre-processing tasks in sentiment analysis in order to simplify sentences and extract more insight from the text, in addition to other NLP processes, such as automatic speech recognition (ASR), machine translation, and text-to-speech (TTS) systems. They can also be applied to text-editing tools, such as grammar checkers and digital assistants, like chat bots, for clarity and coherence of generated responses. Also, this method can be applied to other languages, for example, the Malay language, which does not have many NLP resources, as most natural-language resources are currently intended for English. However, multilingual transformer models are available, which can be used for other languages, such as Malay. Therefore, the creation of this model for other languages is feasible.  
Conceptualization, A.M.B., K.S.M.A. and S.M.; Methodology, A.M.B. and K.S.M.A.; Vali-dation, A.M.B. and S.K.; Formal analysis, A.M.B. and J.K.; Investigation, J.K.; Resources, S.K.; Writing--original draft, A.M.B. and K.S.M.A.; Writing--review & editing, S.M. and S.K.; Visualization, J.K.; Supervision, S.M.; Project administration, K.S.M.A.; Funding acquisition, K.S.M.A. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Telekom Malaysia Research and Development Grant (TMR&D)(Grant Number: MMUE/210038 RDTC/211021).  
Figure 4: Proposed model comparison with existing models.
###### Abstract  
Volume loss is an important method to estimate ground movement during tunnelling. However, volume loss is usually estimated by empirical methods, especially for volume loss at the tunnel face, which is a three-dimensional problem. Based on the principle of minimum total potential energy, we proposed a semi-analytical method to predict the volume loss at the tunnel face and ground surface. The proposed method provides a more direct way of estimating volume loss at the tunnel face from an energy point of view. Moreover, a new deformation mechanism was designed to describe the ground movement before the tunnel face. Based on the proposed method, we investigated the influence of the support pressure, tunnel diameter, and tunnel depth on the volume loss at the tunnel face, and other parameters related to surface subsidence. The volume loss at the tunnel face decreased with the increase in the support pressure ratio and the slurry weight. The volume loss at ground surface was generally smaller than the volume loss at the tunnel face due to the soil compression during ground movement. The bigger the tunnel diameter, the bigger the volume loss at the tunnel face. However, the volume loss at ground surface may not increase with the increase in tunnel depth, because of the soil arching effect. Moreover, the deeper the tunnel, the more obvious the influence of the support pressure ratio on volume loss. Similarly, the bigger the tunnel diameter, the more obvious the influence of the slurry weight on the volume loss.  
semi-analytical method; ground movement; volume loss; minimum total potential energy Article
## 5 Conclusions and Future Work  
We propose an algorithm by designing a dual-branch adversarial data augmentation framework to obtain training samples with greater diversity and construct a robust network structure by adding a modified attention mechanism. As demonstrated by the above experiments, we effectively solve the problems of insufficient extraction of critical features and the lower accuracy of detection models. Compared with the current mainstream algorithms, the proposed model has higher robustness and can perform well in unpredictable data distribution. At present, the development of forged image detection algorithms is usually limited to some specific datasets. Although the performance of detection models on these datasets is excellent, it needs more practical effects and is challenging to meet the needs of landing applications. In order to ensure that detection models can achieve ideal results in the practical environment, we should construct more abundant deepfake data samples and improve the cross-database testing ability of the algorithm. The proposed algorithm effectively fills the gap in deepfake detection, where detection models cannot recognize forgery images that suffer from human or natural corruptions. In future research, we can design dual-stream convolutional neural networks to fully use the richer multimodal features in the forged images from the feature fusion perspective, exploring the essential differences between authentic and forged images to improve the generalization of the detection model.  
Conceptualization, D.W., M.C. and S.P; methodology, D.W. and S.P.; formal analysis, D.W. and M.C.; investigation, D.W., S.P. and W.Q; resources, D.W. and S.P.; data curation, D.W. and M.C.; writing--original draft preparation, D.W.; writing--review and editing, M.C. and L.L.; visualization, D.W. and S.P.; supervision, M.C. and L.L. All authors have read and agreed to the published version of the manuscript.  
Figure 9: The Grad-CAM visualizes the attention maps of images and explores the detecting regions of interest for the models.  
**Funding:** This research was funded by Double First-Class Innovation Research Project for People's Public Security University of China (No. 2023 SYL07).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository.  
**Acknowledgments:** All the face images used in this paper are from publicly available deepfake datasets, including DeepFakes, FaceSwap and FaceShifer.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Grasp Planning Based on Metrics for Collaborative Tasks Using Optimization
## 2 Grasp-Quality Metrics
### Heuristic Inertial Approach Metric  
Until this point, a pair of metrics based only on steady-state geometric properties have been considered. Now, regarding the post-grasping movement of an object, it is clear that the object's movement direction is unknown beforehand. However, the three axes (X, Y, Z) perpendicular to the main planes (\(YZ\), \(XZ\), \(XY\)) in space are considered as possible motion directions that the grasped object could implement, so there are dynamic inertial forces that would affect the moving-object grasp. Figure 4a shows the main rotation axis (_rot Axis_) considered in this metric for this grasp polygon over this desk example, which is formed by the point \(p3\) and the centroid \(C\) of the grasp polygon. \(p3\) is the feasible grasp point for a robot.  
Theoretically, the points that we called _cornerPoints_ are the possible furthest points of the object that generate maximum inertial moments around \(rotAxis\). We can translate this as the minimum distance of each one of these _cornerPoints_ until intersection with \(rotAxis\). \(Q_{InterYZ}\) is the minimum distance of the farthest point around \(rotAxis\) created by a specific grasp polygon \(p1\), \(p2\) and \(p3\) over \(YZ\) (Figure 4b). For a desk object, four perpendicular dotted lines are generated by the four corners of the object projection for the \(YZ\) plane.  
Figure 3: Extrapolated Metrics: (**a**) Area Polygon made up of three contact points; (**b**) Distance from Polygon Centroid to Object’s Center of Mass.  
Therefore, this heuristic quality metric aims to minimize the distance from the furthest point of the object to the rotation axis \(rotAxis\) attached to the grasp polygon in each one of the three main planes \(YZ\), \(XZ\), \(XY\), independently (\(Q_{InterYZ}\), \(Q_{InterXZ}\), \(Q_{InterXY}\)).  
\[Q_{InerYZ}=PlaneYZ(min(maxDistance(cornerPoints,rotAxis))) \tag{3}\]  
\[Q_{InerXZ}=PlaneXZ(min(maxDistance(cornerPoints,rotAxis))) \tag{4}\]  
\[Q_{InerXY}=PlaneXY(min(maxDistance(cornerPoints,rotAxis))) \tag{5}\]