esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Comparison of Different Deep Learning Architectures  
In Table 7, some important deep learning architectures are compared in terms of performance and computational requirement. These architectures are the basis of development of different deep learning models for video classification, and from this comparison, an estimation of the requirement of computational cost for each of these architectures can be drawn.  
\begin{table}
\begin{tabular}{c c} \hline
**Method** & **Accuracy** \\ \hline LRCN [48] & 82.9 \\ DT + MVSV [125] & 83.5 \\ LSTM–Composite [49] & 84.3 \\ FSTCN [126] & 88.1 \\ C3D [127] & 85.2 \\ iDT + HSV [128] & 87.9 \\ Two-Stream [61] & 88.0 \\ RNN-FV [129] & 88.0 \\ LSTM [50] & 88.6 \\ MultiSource CNN [130] & 89.1 \\ Image-Based [55] & 89.6 \\ TDD [35] & 90.3 \\ Multilayer and Multimodal Fusion [110] & 91.6 \\ Transformation CNN [131] & 92.4 \\ Multi-Stream [112] & 92.6 \\ Key Volume Mining [132] & 92.7 \\ Convolutional Two-Stream [62] & 93.5 \\ Temporal Segment Networks [39] & 94.2 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of video classification method on UCF-101.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Architecture Name** & **Parameters** & **Error Rate** & **Depth** & **Category** & **Year** \\ \hline \multirow{2}{*}{LeNet} & \multirow{2}{*}{0.060 M} & [dist]MNIST: 0.8 & \multirow{2}{*}{5} & \multirow{2}{*}{Spatial exploitation} & \multirow{2}{*}{1998} \\  & & MNIST: 0.95 & & \\ \hline AlexNet & 60 M & ImageNet: 16.4 & 8 & Spatial exploitation & 2012 \\ \hline ZfNet & 60 M & ImageNet: 11.7 & 8 & Spatial exploitation & 2014 \\ \hline VGG & 138 M & ImageNet: 7.3 & 19 & Spatial exploitation & 2014 \\ \hline GoogLeNet & 4 M & ImageNet: 6.7 & 22 & Spatial exploitation & 2015 \\ \hline \multirow{3}{*}{Inception-V3} & \multirow{3}{*}{23.6 M} & ImageNet: 3.5 & \multirow{3}{*}{159} & \multirow{3}{*}{Depth + width} & \multirow{3}{*}{2015} \\  & & multi-crop: 3.58 & & & \\ \cline{1-1} \cline{3-5}  & & Single-Crop: 5.6 & & & \\ \hline Highway networks & 2.3 M & CIFAR-10: 7.76 & 19 & Depth + multi-path & 2015 \\ \hline Inception-V4 & 35 M & ImageNet: 4.01 & 70 & Depth + width & 2016 \\ \hline Inception-ResNet & 55.8 M & ImageNet: 3.52 & 572 &
\begin{tabular}{c} Depth + width + \\ multi-path \\ \end{tabular} & 2016 \\ \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different deep architectures [127].
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 2 Related Work
### Architecture of Deep Neural Networks  
As a class of deep learning techniques, deep neural networks (DNN) is a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. In practice, DNN currently provides the best solutions to many problems in the field of computer vision, such as robust feature representation that is different from traditional manual feature engineering and generalizable discriminative algorithms. At present, there are many prominent architectures of deep neural networks, including classic convolutional neural networks (CNNs) [18] and generative adversarial networks (GANs) [19]. Among of them, since the emergence of CNNs as an initial deep architecture in image recognition, a number of modern variants based on the classic CNNs, e.g., ResNet [21], MobileNet [20], ShuffleNet [28] and DenseNet [29], have been proposed. The purpose of their architectural variations is to bolster the relevant performance (e.g., accuracy or robustness) by improving their own representational capability on specific computer vision tasks such as image classification and generic object detection. Moreover, there are several other prominent variant architectures that come from the classic GANs, e.g., DCGAN [22] and CycleGAN [30], which usually focus on some other specific vision tasks, such as image generation.  
Despite their great successes in numerous general applications, these notable architectures do not care about the particular issue on the vision-based waterline detection for inland marine USVs. In other words, none of the existing deep neural networks can be utilized directly to effectively detect the inland waterlines for USVs. Thus, in this paper, inspired by deep neural networks, we comply with the same architectural principles with modern deep model to customize a specific deep network for the waterline detection, and simultaneously exploit classical GANs to build an auxiliary deep network to work together with the previous one.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 4 Assessing the Level of Digital Twinning (LoDT)
### UNI-TWIN--The Unified Digital Twinning Assessment Model  
The proposed UNI-TWIN model for assessing the level of digital twinning of physical assets comprises nine dimensions: hierarchy, connection, synchronisation, geometric representation, non-geometric representation, intelligence, interface, accessibility, and autonomy. Each dimension has its own level of complexity, ranging from \"1\" (least complex) to \"5+\" (most complex). The \"5+\" rating is assigned to comprise cases that meet these criteria and those with even greater (and, sometimes, unforeseeable) complexity. According to the presented model, the following remarks can be established:  
* Since the concept of a \"Digital Twin\" requires a digital representation and a real-time data connection from the physical space to the virtual space, this interpretation can be associated with a minimum development level of 4 in the \"connection\" dimension and a level 2 in the geometric representation;
* Narrower interpretations of the term \"Digital Twin\" (e.g., [45]), such as the so-called \"true Digital Twins\" [91], which only admit automatic bidirectional connections between the physical and virtual spaces, correspond to a level 5+ classification in the \"connection\" dimension; \begin{table}
\begin{tabular}{l l} \hline \hline
**Dimension** & **Description** \\ \hline Hierarchy & The hierarchical level of the physical assets pertaining to the case \\ \hline Connection & The type of data connection between the physical and digital spaces \\ \hline Synchronisation & The frequency at which data is integrated into the digital space \\ \hline Geometric & The type of geometric representation of the physical space \\ \hline Non-geometric & The level of representation of non-geometric characteristics of the physical \\ representation & space that are relevant to the defined purpose \\ \hline Intelligence & The type of intelligence associated with data analysis \\ \hline Interface & How users interact with the information generated in the digital space \\ \hline Accessibility & The scope of users that access the information generated by the digital space \\ \hline Autonomy & The autonomy level of digital space in decision making \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Proposed model for assessing the LoDT. * The so-called \"Digital Shadows\" [45], which envision an automatic data connection only between the physical and virtual spaces, correspond to a level 4 in the \"connection\" dimension;
* \"Digital Models\" [45], \"Pre-Digital Twins\" [58], or \"Geometric Digital Twins\" [92], which may not have connections to the physical space or have only manual or semi-automatic ones, correspond to levels equal to or lower than level 3 in the \"connection\" dimension;
* The so-called \"Advanced Digital Twins\" or \"Intelligent Digital Twins\" [58], because of their advanced analytical capabilities, using cognitive models that attempt to replicate human intelligence (artificial intelligence), correspond to level 5+ in the \"intelligence\" dimension;
# Additive Manufacturing in Underwater Applications
## 4 Technologies
### 3D Printing Technologies
#### 4.1.3 Technologies Based on Liquid Filaments  
The other group of technologies, based on liquids, includes vat photopolymerization-stereolithography (SLA), material jetting (MJ), digital light processing (DLP), and others. As a printing material, this technology typically uses a liquid resin that is hardened under different actors--for example, UV light [16]. This group has not been yet tested for the technology of printing in situ in the underwater environment. Nevertheless, it has been used for the manufacturing of some elements for underwater applications, such as housing for monitoring elements that work underwater [45; 63], membranes for water treatment systems [64], or other advanced solutions such as an underwater magnetic nanofluid droplet-based generator (designed to convert the mechanical energy of sliding droplets in to electricity), which was inspired by shark skin [65].  
In this area, an interesting experiment was performed by [PERSON] et al. [63], who confirmed the possibility of using SLA technology aboard a moving vessel at sea [63]. Thanks to the application of passive stabilization to support SLA printing, it was possible to manufacture high-resolution models that were fully solid and impervious to water elements for underwater applications, including sealed housings [63]. This type of approach can be beneficial in supporting marine engineering development, especially shipboard  
Figure 3: The test setup. (**a**) Schematic diagram of the underwater printing system; (**b**) diagram of the underwater laser–material interaction process; (**c**) the actual manufacturing process of underwater LMD. Source: [49].  
maintenance, and other applications that require the fast repair of non-typical elements for marine applications, including underwater ones [63].
## 3 Results
### Module Preservation Analysis  
Module preservation examines how well-defined modules are conserved across ccRCC datasets. This analysis reflects the reproducibility of co-expression networks and biological relevance. Module preservation in the ccRCC S1 dataset was assessed from the S2-S4 datasets. Modules with a Z-score value greater than 10 were considered highly preserved, and larger modules represented stronger patterns of connectivity [23; 24]. Figure 4 illustrates the Z-score values of each module's genes from different stages. Seven modules were generated with high preservations of blue, brown, and red. Although the turquoise module had 2500 genes, it was not strongly consistent across all datasets and was excluded. The blue module showed the highest score value in all stages. The number of modules passing the threshold was almost equal across datasets, except for S3, which had a higher score value than S2, before somewhat decreasing in S4. This is perhaps because S4 included genes that were not expressed during earlier stages of ccRCC.  
Figure 3: Dendrogram of gene clustering and module identification for the ccRCC s1 dataset. Colors denote the gene co-expression modules associated with the sections of the dendrogram above.
## 3 Results and Discussion
### Total Volatile Fatty Acids (VFA) Production After Pretreatment and Dark Fermentation  
Figure 5 shows the volatile fatty acid (VFA) production after the following two sequential stages: (1) HTP of biomasses and (2) the subsequent dark fermentation (DF) process. As can be seen in Figure 5, HTP significantly increased the breakdown of biomass structures, thereby facilitating the VFA production during the subsequent DF.  
However, VFA concentrations after fermentation of 10 and 20 min (DF10 and DF20) exhibited similar values across all biomasses, as follows: OP (5.6 and 5.4 gH-Ac/L), SBP (6.9 and 6.7 gH-Ac/L), BSG (4.3 and 5.3 gH-Ac/L), and RH (0.9 and 0.8 gH-Ac/L), respectively. In fact, the maximum VFA concentrations obtained following pretreatment were detected after 30 min, with SBP yielding 6.6 gH-Ac/L, followed by BSG (5.4 gH-Ac/L), OP (2.5 gH-Ac/L), and RH (1.6 gH-Ac/L).  
Figure 5: Total volatile fatty acids (TVFA) of OP, SBP, BSG, and RH, expressed as gH-Ac/L, after and before DF. Note: T10:T20:T30 (Pretreatment at 10, 20, and 30 min); DF10:DF20:DF30 (dark fermentation of biomasses treated at 10, 20, and 30 min). Unt—Untreated.  
Furthermore, the subsequent DF of pretreated biomasses demonstrated a significant increase in VFA production. Accordingly, the maximum VFA was detected after seven days of dark fermentation, with SBP reaching 15.1 gH-Ac/L, followed by OP (9.0 gH-Ac/L), BSG (7.1 gH-Ac/L), and RH (3.8 gH-Ac/L).  
Many studies have shown that (HTP) is an effective method to increase the production of volatile fatty acids (VFAs) after dark fermentation. According to [PERSON]. [PERSON] et al. (2015), the VFA yield exhibited a 55% increase after 15 days of DF under the HTP at 160 \({}^{\circ}\)C for 30 min. In a similar study ([PERSON] et al., 2015), a 22% increase in VFA yield after HTP application at 132 \({}^{\circ}\)C for 27 min was obtained. In a subsequent study ([PERSON] et al., 2015), it was demonstrated that VFA production attained 5 g/L after a duration of 10 min of HTP at 190 \({}^{\circ}\)C.  
In general, in the present study, acetic acid was the predominant VFA produced from all evaluated biomasses, although with differing yields. Specifically, the acetic acid accounted for approximately 78% of the total VFAs produced from OP, 70% from BSG, 55% from SBP, and 31% from RH. However, the butyric acid was the second-most abundant VFA, representing 66% of them from RH, 45% from SBP, 25% from BSG, and 21% from OP.  
Hence, the present work has proved that acetogenic and butyrogenic pathways are the main routes for VFA production during DF of pretreated biomasses, with their predominance being contingent on substrate composition. In the case of SBP, the acetic and butyric pathways exhibited approximately similar activation, yielding a higher TVFA yield. In contrast, the fermentation of pretreated BSG and OP was predominantly driven by the acetogenic pathway, resulting in enhanced TVFA production. In addition, microbial metabolism within RH demonstrated a preference for the butyric pathway, as this route is longer and more complex. This led to a lower total acid yield.
# Segmented Hybrid Impedance Control for Hyper-Redundant Space Manipulators
## 5 Object Manipulation Control  
In this paper, we present a segmented hybrid impedance motion/force control method designed to achieve the primary objective of wrapping around an obstacle and establishing multiple points of contact. By creating these multiple contact points, we aim to securely grasp a free-floating obstacle in space with enhanced robustness. This approach not only facilitates effective manipulation of objects but also improves stability during the interaction process. Furthermore, the robotic arm will not be confined to its end-effector or object geometries.  
The object's attitude control is achieved through a passive mechanical constraint strategy rather than active force control. Specifically, the force applied constantly by the end-effector and the configuration of the HRSM around the object create a geometrically constrained system that inherently keeps the object's orientation to a minimum.  
In the following section, we present a detailed analysis of object position control during space manipulation. Before proceeding with the control formulation, it is essential to establish the concept of the virtual end-effector. Unlike traditional robotic manipulation, where a single end-effector interacts with the object, our snake-like configuration creates multiple interaction points with the target object. We define the virtual end-effector as the collective set of links and joints that maintain contact with the obstacle during manipulation.
# CAD-Based Feature Recognition for Process Monitoring Planning in Assembly
## 3 Concept
### Generation Of Product-Specific Monitoring Requirements  
The parameterization sub-module generates the requirements for process monitoring. Information from the recognition module and from the process plan is transferred to the monitoring templates to define the requirements. Allocation rules allow the templates to be filled with information about the part IDs, process types, and feature IDs. For this step, it is important for the process plan to contain information about the processes to be executed according to each part and part ID. The result of this step is a generated list of requirements for each process step of the preloaded process plan. This list can now be manually completed by the user to add additional features that were either not identified in the CAD file or defined in the process plan (e.g., the required torque for the screw and torque to be monitored).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Process Type**} & \multicolumn{2}{c}{**Part A**} & \multicolumn{2}{c}{**Part B**} & \multicolumn{4}{c}{**Parameter**} & \multicolumn{4}{c}{**Feature**} \\ \cline{2-11}  & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Descriptions**} & \multicolumn{2}{c}{**Volume (List of**} \\  & & & & & & & & & & **Positions and** \\  & & & & & & & & & & & & **Orienations)** \\ \hline \multirow{3}{*}{Screwing} & \multirow{3}{*}{Block A} & \multirow{3}{*}{23} & \multirow{3}{*}{Block B} & \multirow{3}{*}{24} & Torque & Nm & mechanical & / & / & / & / & Position Orientation \\ \cline{3-11}  & & & & Rotational & & & & & & & & \\  & & & & speed & & & & & & & & \\ \cline{3-11}  & & & & Angle & \({}^{\circ}\) & geometric & / & / & / & / & Position Orientation \\ \hline \multirow{3}{*}{Joining} & \multirow{3}{*}{Block A} & \multirow{3}{*}{23} & \multirow{3}{*}{Block B} & \multirow{3}{*}{24} & Contact & \multirow{3}{*}{m\({}^{2}\)} & \multirow{3}{*}{geometric} & / & / & / & / & Position Orientation \\  & & & & surface & & & & & & & \\ \cline{3-11}  & & & & Lead-in & & & & & & & \\ \cline{3-11}  & & & & & & & & & & & \\ \cline{3-11}  & & & & & & & & & & & \\ \cline{3-11}  & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Exemplary monitoring template structure for joining and screwing.
## 2 Materials and Methods
### Model Summary  
The proposed model for detecting deepfake videos, utilizing the DFDC dataset [17; 18], begins its process by splitting videos into frames. At this stage, videos are transformed into an analyzable format, making each frame suitable for deepfake detection. Frame extraction ensures consistent and structured data acquisition from videos. Subsequently, these frames undergo face detection using the Multi-task Cascaded Convolutional Network (MTCNN) algorithm, and their dimensions are resized to 224 \(\times\) 224 pixels. This preprocessing step standardizes input data for the model, aiming to enhance both accuracy and processing efficiency. The processed frames are prepared for the feature extraction phase.  
During feature extraction, the images are processed through a ViT-based model, specifically the CaiT. The CaiT effectively captures both global and local features of the images through self-attention and class-attention mechanisms, offering advanced hierarchical representations. This phase generates the fundamental data required for deepfake detection. The layer structure and processing steps of the CaiT enhance the model's strong feature extraction capabilities. The extracted features are subsequently fed into a Quantum Neural Network (QNN) model, operating with quantum simulation tools like Qiskit, PennyLane, and Cirq. Using quantum gates (Hadamard, RY, CX), this analysis enables advanced computations for deepfake detection.  
In the final stage, the model classifies the analyzed images as either \"Real\" or \"Fake\". This process combines the feature extraction capability of the CaiT with the analytical power of a QNN to achieve higher accuracy rates. The steps and operations of the model are illustrated in detail in Figure 1 as a flowchart.
## 3 Results and Discussion
### Characteriziaton of Phase Structure and Microstructure
From the above results, it can be concluded that the NiO sub-microspheres sample has the largest specific surface area and pore volume, which could be related to the smallest grain size of the NiO sub-microsphere sample, as observed in the TEM results (Figure 3). Combined with the above results, the synthesis route of mesoporous NiO hierarchical nanostructures can be summarized in Figure 6. By adjusting the ratio of Ni\({}^{2+}\)/HMT, the precursor with multi-morphologies can be obtained. When the ratios of Ni\({}^{2+}\)/HMT were 2:1 and 1:2, Ni\({}_{3}\)(NO\({}_{3}\))\({}_{2}\)(OH)\({}_{4}\) nanosheet and microsphere were obtained. Being calcined, they decomposed to the NiO phase and by-product gases such as NO\({}_{2}\), H\({}_{2}\)O, and O\({}_{2}\) by the following reaction [26]:  
\[\mathrm{Ni}_{3}(\mathrm{NO}_{3})_{2}(\mathrm{OH})_{4}\to 3\mathrm{NiO}+2 \mathrm{NO}_{2}(\mathrm{g})+2\mathrm{H}_{2}\mathrm{O}(\mathrm{g})+\frac{1}{2} \mathrm{O}_{2}(\mathrm{g}) \tag{1}\]  
When the ratio of Ni\({}^{2+}\)/HMT was 1:3, Ni(HCO\({}_{3}\))\({}_{2}\) sub-microsphere was obtained, which decomposed to the NiO phase and gases of H\({}_{2}\)O and CO\({}_{2}\) by the following reaction [27]:  
\[\mathrm{Ni}(\mathrm{HCO}_{3})_{2}\to\mathrm{NiO}+\mathrm{H}_{2}\mathrm{O}( \mathrm{g})+2\mathrm{CO}_{2}(\mathrm{g}) \tag{2}\]  
Figure 1:0 (**a**) N\({}_{2}\) adsorption–desorption isotherm curves of NiO nanosheet; (**b**) N\({}_{2}\) adsorption–desorption isotherm curves of NiO microsphere; (**c**) N\({}_{2}\) adsorption–desorption isotherm curves of NiO sub-microsphere; and (**d**) pore size distribution derived from the adsorption branches of the isotherm data of the as-prepared NiO nanostructures. The released by-product gases lead to the formation of a mesoporous structure in the NiO samples. To prove that the mesoporous structure was formed by calcination, the microstructure of an uncalcined microsphere precursor was studied by TEM, as shown in Figure S2. It clearly indicates the absence of mesoscale porosities in the uncalcined microsphere precursor. Thus, the calcination is helpful to the formation of the mesoporous structure.