esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 2 Materials and Methods
### Morphological Characterization of ZnO-CF\({}_{x}\) Nanocomposites  
TEM microscopy was performed with an FEI (Eindhoven, The Netherlands) Tecnai T12 instrument (high tension: 120 kV; filament: LaB\({}_{6}\)) on 30-nm-thick samples deposited onto a carbon-coated Cu grid (300 mesh, Agar Scientific, Essex, UK). The microscope was calibrated by using the S106 Cross Grating (2160 lines/mm, 3.05 mm) supplied by Agar Scientific. Alignments and astigmatism correction were carried out following the factory settings and fast Fourier transform processing, respectively.  
AFM micrographs were acquired on 150-nm-thick films with a Keysight AFM system Model 5500 (Keysight, Santa Rosa, CA, USA), in dynamic (\"tapping\") mode, in air, using polygonal Si probes with a typical tip radius of 8 nm (Nanoworld, Neuchatel, Switzerland) NCL-W Pointprobe(r). The nominal spring constant and resonance frequency were within the range of 31-71 N/m and 160-210 kHz, respectively. To evaluate surface roughness, the root-mean-squared roughness (RMS) on a sample's surface was determined within 10 \(\times\) 10 \(\upmu\)m\({}^{2}\) areas for each sample, as (1):  
\[RMS=\sqrt{\frac{1}{N}\sum_{j=1}^{N}r_{j}^{2}} \tag{1}\]  
where \(r_{j}\) is the single collected roughness data, and \(N\) is the number of experimental points. All AFM topographies were analyzed using the freely available software Gwyddion 2.41 ([[http://gwyddion.net/](http://gwyddion.net/)]([http://gwyddion.net/](http://gwyddion.net/))).
# Effect of Exposure Time on Thermal Behaviour: A Psychophysiological Approach
## 4 Data Analysis and Findings
### Overall Observations  
As per the setup of the experiment, it was expected of the test subjects to use the change of space as the initial tool of adaptation. Figure 4 below show the total duration of work inside the climate chamber (Sense Lab), breaks in the cool room and breaks on the outside of the facilities.  
Figure 4: Behaviour trends over the course of experiment.  
As it can be easily observed, there are no clear correlations between the temperature rise and duration/amount of breaks, which rejects the initial hypothesis of spatial adaptation. The authors explain this by means of psychological aspects; even though the test subjects were introduced to the facilities before the experiment started, and an entire first week was used solely to get the subjects used to the space (the data at the end was left out of the resulting dataset), there was still probably no sense of ownership of the space, routine, set expectations, familiarity/relationship with other people in the shared office (cool room), or so. This, in theory, most likely limited the free use of all available spaces and the subjects might have preferred to stay in the climate chamber longer, albeit the heat stress. The effect of similar psychological decision-making on heat tolerance is an aspect to be studied in the future.  
As for the other adaptive behaviour, change of the clothing was also constantly observed and marked. Figure 5 shows the variation of clothing values per participants, for each test day. Similar to the break pattern, there is no clear trend between the rise of the temperature and clothing decision. However, similar to the number of breaks taken during the day, the number of clothing changes presents a different characteristic behaviour for each participant.
## 3 Results and Discussions
### Fracture Surface Analysis  
Figure 12 shows the scanning electron micrographs, indicating the fracture surface of fatigue samples obtained from damaged parts of A357 aluminum semi solid materials. The fatigue failure is recognized by the metallographic appearance of the fracture surface, which starts with cracks initiation and is followed by its propagation. The fracture surface mainly shows two regions; a smooth region, which refers to brittle fracture by crack propagation, and a rough region, known by the presence of dimples, where the component indicates a ductile fracture manner. The crack initiation sites are  
Figure 11: Finite element (FEA) results obtained for the applicable parts with various designs proposed.  
Figure 10: FEA of trussed design; (**a**) von Mises stress plot, (**b**) total displacement plot, (**c**) other view of Von Mises (VM) stress plot.  
related to slip bands found in the alpha aluminum phase, as shown in Figure 12 for WC3 samples. Slip bands are regions where there is intense deformation due to the shear motion between crystal planes. The existence of slip bands in a local region leads to severe roughening of the surface; it leads to localization of plastic strain versus fatigue failure [1; 9; 23].  
Figure 12: SEM micrographs of fracture surfaces due to fatigue failure of A357 aluminum semi solid samples heat treated by multiple thermal aging cycles.  
The figure indicates the type of fracture, either ductile or brittle, for various thermal aging cycles investigated. For samples of WA0 and WC3, slip bands and beach marks were observed, indicating a complete fatigue failure and the absence of defects. In addition, the presence of dimpled structure, mostly for WC3 samples, indicates a ductile failure due to fatigue. A nearly defect-free microstructure for WC3, with the presence of slip bands and dimpled structure causing a ductile fatigue failure, can explain the high number of fatigue cycles for this thermal aging condition [1; 23]. Porosities, referred by arrows, were observed for the WA1 and WB5 samples; hence, it was rejected due to its very low fatigue life cycles. In addition, the WB5 showed the presence of oxides, referred by arrows, in aluminum matrix, which had negative effects on the microstructure characteristics and fatigue performance of alloys investigated. These defects may highlight the problems and difficulties related to the semi-solid casting technique, which requires high precision and sophisticated equipment. For the control arm sample of WB0, the observed microstructure indicates a fatigue failure with a brittle structure. This brittle microstructure indicates an inductile material; hence, the crack propagation was very fast, that slip bands, referred by arrows, could not be clearly observed as those in WA0 and WC3 samples.
## 4 Experimental Results and Hardware Implementation
MATLAB software is used to display simulation results from Equations (10)-(12) and the other reported methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compared Items** & **[PERSON] \& [PERSON]** & **[PERSON] \& [PERSON]** & **[PERSON]** & **Proposed** \\  & **[5,16]** & **[16]** & **[11]** & **Case 3** \\ \hline
**Compensation scheme** & Shift-and-Add & Constant & Shift-and-Add & **Fractional bits** \\
**Segment number** & 6 & 14 & 2 & **2** \\
**Significant fractional bit** & 7 & none & 7 & **none** \\
**Max. positive percentage error** & 0.9572\% & 1.2\% & 1.5054\% & **1.1551\%** \\
**Min. negative percentage error** & \(-0.5786\%\) & \(-0.1436\%\) & \(-0.0488\%\) & **\(-0.0512\%\)** \\
**Total percentage conversion error** & 1.5358\% & \(-0.1436\%\) & 1.5542\% & **1.2063\%** \\
**Area (\(\mu\)m\({}^{2}\))** & 6439.91 & 3319.75 & 5425.36 & **2122.24** \\
**Delay (ns)** & 6439.91 & 1.5 & 1.8 & **1.1** \\
**ADP** & 11,913.83 & 4979.63 & 9765.65 & **2334.46** \\
**ADP saving** & 0 & 58.20\% & 18.31\% & **80.41\%** \\
**eADP** & 182.9726 & 66.9063 & 151.78 & **28.1606** \\
**eADP saving** & 0 & 63.43\% & 17.05\% & **84.61\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of percentage conversion errors and results for proposed case 3 and [PERSON] and [PERSON]’s [5] six-region, [PERSON] and [PERSON]’s [16] 14-region, and [PERSON] and [PERSON]’s [11] two-region schemes. Figure 10: Circuit block diagram for hardware implementation.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 4 Experimental Setup
### Data Splitting and Initial Labeled Dataset  
Each experiment followed a well-defined process. First, we performed random division of the dataset into three subsets: a test dataset comprising 30% of the data, an initial labeled dataset consisting of ten randomly selected data instances, and an unlabeled data pool constituting approximately 70% of the data.  
Within each experiment, we conducted ten rounds, and each round involved the selection of ten labeled instances chosen at random. These labeled instances were used to train the initial model from scratch. Subsequently, based on different query strategies, additional unlabeled instances were selected from the unlabeled pool for annotation. Importantly, the instances in the unlabeled pool already had labels, allowing us to retrieve the label corresponding to each selected instance. These newly labeled instances were then appended to the labeled dataset and the model was retrained using the updated labeled dataset. This iterative process continued until the query budget was exhausted. The final results reported in our experiments are the average outcomes across all ten rounds.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Feature Name** & **Description** \\ \hline \multirow{8}{*}{PHEME} & Length of characters & The number of all characters contained in a post \\  & Number of words & The number of words contained in a post \\  & Count uppercase letters & Fraction of capital letters in the tweet \\  & Sentiment Score & Sum of \(\pm 0.5\) for weak positive/negative words, \(\pm 1.0\) \\  & Number of URLs & for strong ones \\  & Registration age & The number of URLs in a post \\  & Count followers & Number of users following current account \\  & Is verified & Is current account verified \\  & Statuses count & The number of tweets at posting time \\  & Sentiment positive words & The number of positive words in the text \\  & Sentiment negative words & The number of negative words in the text \\  & Contains multi mark & Contains a question mark?? \\  & Contains pronoun first & Contains a personal pronoun in 1 st person \\  & Contains pronoun second & Contains a personal pronoun in 2 st person \\  & Contains pronoun third & Contains a personal pronoun in 3 st person \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._Our experimental design incorporated a query budget, which was set to 200. This choice was informed by careful experimentation and empirical observations that suggested this budget as sufficient for ensuring that most models converge effectively. Unless otherwise specified, we maintained the remaining parameters of the query strategy in the active learning and machine learning models at their default values.
## 2 Conceptual Model and Problem Procedure  
The conceptual design of the proposed intelligent method for identifying and classifying internal electrical faults, external faults during the saturation of CT, and inrush currents in power transformers is shown in Figure 1.  
As depicted in this figure, the proposed approach can be implemented on an online platform through the measurement of voltage and current data at the installation site of power transformers within the power grid, even when the measured data are noisy or uncertain. In this design, the internal and external fault currents as well as the inrush currents of power transformers are initially measured by measurement units, which may contain noise or uncertainties. Subsequently, the sampled signals during various events are transferred to MATLAB software for detection and differentiation. At this stage, the features of the sampled signals are determined using advanced wavelet transform. Afterward, the real-time fault detection system, based on the trained advanced long short-term memory (LSTM) neural network, differentiates and identifies the type of fault occurring in the power transformer. The steps for implementing the proposed method are detailed in Algorithm 1 below.  
Figure 1: Conceptual Model of Proposed Scheme for Real-time internal fault detection of power transformer.  
```
1. Training Stage:
1.1. For k = 1:2000
1.2. If k \(\leq\) 500
1.3. Run the Simulation file for sampling the differential currents of external fault
1.4. SignalVector = Differential currents of external fault
1.5. ElseIf 500 < k \(\leq\) 1000
1.6. Run the Simulation file for sampling the differential currents of invush current
1.7. SignalVector = Differential currents of invush current
1.8. ElseIf 1000 < k \(\leq\) 1500
1.9. Run the Simulation file for sampling the differential currents of internal fault without CT saturation
1.10. SignalVector = Differential currents of invush current internal fault without CT saturation
1.11. ElseIf 1500 < k \(\leq\) 2000
1.12. Run the Simulation file for sampling the differential currents of internal fault with CT saturation
1.13. Call the Improved RTBSWT Function (\(x(t)\))
1.14. \(\sigma(\textit{W}_{x}(t))\)
1.15. Call Proposed LSTM Network Function \(i_{t}=\sigma(\textit{W}_{xi}x_{t}+\textit{W}_{hi}\textit{h}_{t-1}+b_{i})\)
1.16. \(\sigma(\textit{W}_{x}(t))\)
1.17. End \(\sigma(\textit{W}_{x}(t))\)
1.18. End \(\sigma(\textit{W}_{x}(t))\)
1.19. End Stage 2
2.10.
2.11. End Call RTBSWT Function (\(x(t)\))
2.12. End \(\sigma(\textit{W}_{x}(t))\)
2.13. End Stage 2
```  
**Algorithm 1** Procedure of the Proposed Framework for Internal Fault Detection in Power Transformers
## 4 Experiments
### Implementation Details  
**Data Split.** To ensure an equitable comparison, we employ a data partitioning methodology identical to that utilized in the experiments conducted by PraNet [51]. Specifically, the Kvasir-SEG dataset comprises 1000 polyp images, while ClinicDB comprises 612 images. From the Kvasir-SEG and ClinicDB datasets, we allocate 900 and 550 images, respectively, for the training set. The remaining 100 and 62 images are allocated for the test set. Furthermore, our model undergoes evaluation on three additional datasets not previously encountered: ColonDB, CVC-300, and ETIS.  
**Data Augmentation.** The colonoscopy polyp datasets have the following three characteristics: (1) significant variations in polyp size, including both large and small polyps; (2) close similarity in color between most polyps and the background leads to a diminished contrast between foreground and background, making segmentation of some samples challenging; (3) blurry imaging resulting from the rotating movement of the camera within the intestine, significantly increasing the difficulty of polyp detection. To tackle these issues, we introduced a series of random augmentation operations during model training: (1) random scaling by a factor of 0.7 to 1.3; (2) color augmentation following the method in [32]; (3) contrast enhancement sampled randomly within the range of [0.8, 1.2]; (4) Gaussian filtering using a \(5\times 5\) convolutional kernel. The purpose of Gaussian filtering is to simulate the blurriness caused by lens rotation, aiming to improve the model's ability to adapt to blurred samples. It is worth noting that random scaling involves operations on the shape of the image, so the corresponding pseudo-labels and bounding boxes also follow this operation.  
**Training Details.** Our algorithm is trained using the PyTorch framework version 2.3.0. For hardware, we leverage an NVIDIA Tesla A100 GPU equipped with 40 GB of GPU memory to train the segmentation model. Throughout the network training phase, mini-batch training iterations are conducted with a batch size set to 4. The entirety of the training process spans a total of 25 epochs. The model parameters are optimized utilizing the [PERSON] [9] optimization algorithm, initialized with a learning rate of 0.0001 and a weight decay of 0.00001.  
**Evaluation Metrics.** In line with the evaluation metrics used in Polyp-PVT [52] and HSNet [43], we employ the mean Intersection over Union (mIoU) and mean dice score (mDice) to assess the segmentation results in our experiments.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 2 Video Deepfake Detection with CNN-LSTM
### Convolutional Neural Network (CNN)  
Convolution, a mathematical process, involves the merging of two functions by shifting one across the other. At every overlapping point, the values of the functions are multiplied, generating a new function that illustrates how the shape of one is modified by another. It can be expressed mathematically as follows:  
\[(f\times g)(x)=\int_{-\infty}^{\infty}f(\tau)\ \cdot g(x-\tau)\ d\tau \tag{1}\]  
where  
* \(f(x)\) and \(g(x)\) are the input functions;
* \(\tau\) is the integration variable;
* \((f\times g)(x)\) is the convolution of \(f(x)\) and \(g(x)\) evaluated at \((x)\).  
This equation represents the operation of sliding \(g(x)\) along the \(x\)-axis, multiplying it with \(f(x)\) at each position, and then integrating it into all the possible positions. A CNN consists of three layers, including a convolution layer, a pooling layer and a fully connected layer which maps the extracted features into a final output [16], as shown in Figure 1.  
The convolutional layer performs most of the computational tasks in a Neural Network by applying a sliding window function, called a kernel or filter, to a 3D matrix of pixels representing an image. This process extracts features and recognises specific patterns, such as facial distortions [17]. Multiple kernels scan the image to generate new grids highlighting detected features. To introduce nonlinearity, Rectified Linear Unit (ReLU) transformations are applied, addressing the vanishing gradient problem by outputting zero for negative inputs and preserving positive values [18].  
The pooling layer aims to extract significant features from the convoluted matrix while reducing dimensionality and the number of parameters in the model [19]. It slides a filter across the input and applies an aggregation function, with max pooling selecting the maximum pixel value and average pooling computing the average [20]. Max pooling is generally preferred for its noise suppression, while average pooling reduces noise through dimensionality reduction [21]. Both methods help mitigate overfitting by summarising outputs and decreasing spatial representation size [20]. The fully connected layer maps input representations to the output by connecting all neurons from the previous and output layers [22]. It computes nonlinear combinations through matrix multiplication and biases and then applies an activation function for classification. This process generates probabilities for final label predictions, with binary classification commonly used in deepfake detection to label inputs as real or deepfake [23].
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Ship Detection Based on One-Stage Object Detection Model  
Video monitoring data provide real-time dynamic information about vessel movement, which is essential for waterway traffic situation awareness. To enable efficient and real-time vessel detection in a waterway, this section focuses on employing a one-stage object detection algorithm for vessel detection.  
The workflow of the one-stage object detection algorithm is illustrated in Figure 3. First, the input image is resized to a fixed dimension, followed by data augmentation to enhance the model's generalization capability. The image is then passed through the backbone network for feature extraction, generating multi-level feature maps that capture rich spatial and semantic information. These feature maps are subsequently processed via the neck network, typically utilizing a feature pyramid network (FPN) or path aggregation network (PAN) to integrate features from different scales and improve detection performance for multi-scale objects. In the detection head, the algorithm divides the feature map into multiple grid cells, with each grid cell responsible for predicting several anchor boxes and outputting the corresponding class probabilities, bounding box offsets, and confidence scores. These predictions are then refined through post-processing steps, such as non-maximum suppression (NMS), to select the final detection results. The loss function of the algorithm accounts for classification error, localization error, and confidence  
Figure 2: Multi-source data fusion framework: Video data are first captured using network cameras, and the target detection algorithm is enhanced to address the specific characteristics of inland waterway targets. Following detection and tracking, the vessel positions are identified. Simultaneously, AIS data are collected through reception equipment, filtered, and processed using a trajectory prediction algorithm to ensure temporal alignment with the video data. Finally, a fusion module integrates the AIS data and video data for a comprehensive analysis.  
error. By jointly optimizing these losses, the algorithm achieves efficient and accurate detection performance.
## 2 Internet of Vehicles and Smart Cities
### Main Challenges
#### 2.4.3 Service Management  
Given the large number of services that can be provided in IoV environments, it becomes a challenge for vehicles to manage services to obtain optimal solutions for them at a given point in time, minimizing costs and delivery time.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 5 Design of the DNN Accelerator and Acceleration Framework Based on FPGA
### Accelerator Design for Specific Application Scenarios
#### 5.2.3 FPGA Accelerator for Natural Language Processing  
Natural language processing (NLP) is a technology that uses the natural language used by humans to communicate with machines. It studies various theories and methods  
Figure 19: The Adaptive GNN accelerator (AGA) framework.  
that can realize effective communication between humans and computers using natural language, and it is also one of the important application fields of deep learning.  
In terms of accelerating NLP, the authors of [152] point out that although the language representation based on transformer [153] has achieved the most advanced accuracy on various NLP tasks, the deployment of large model networks has always been a challenge for resource-constrained computing platforms. Weight pruning is used to reduce weight parameters, which can effectively compress network models and improve network performance. In order to solve the problem of large storage demand and network performance degradation caused by a large number of parameters when RNN is applied to natural language processing, the authors of [154] focused on the computing resource demand of RNN and adopted fixed-point quantization technology in order to design an FPGA accelerator, which reduced the memory consumption by 90%, and the accuracy loss was less than 1%.  
Different from [154] on quantifying input data, some scholars have devoted themselves to NLP task optimization based on the BERT (bidirectional encoder representation from transformers) network model [155] and have adopted the idea of full quantization to a design accelerator. Not only input data but also weights, activations, Softmax, layer normalization, and all the intermediate results are quantified in order to compress the network and improve performance [156].  
Some scholars put forward an optimization scheme for heterogeneous NLP models that can automatically improve the performance of NLP models on CPU, GPU, and FPGA by identifying key performance operations and their performance problems in various NLP models [157]. In order to accelerate the inference of the BERT model, the authors of [158] proposed an FPGA-based overlay processor T-CPU for the inference of the BERT model in view of the large number of natural language models and the update of fast characteristics. T-CPU is composed of a data extraction unit, a matrix multiplication unit (MMU), an output control unit, a nonlinear vector memory (NVM) unit, and a nonlinear vector (NV) unit, which can efficiently execute various natural language processing models.