esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Motion Blur Using Multi-Layer Motion Vectors
## 5 Discussion  
In this section, we compare previously proposed motion blur generation algorithms with our method and discuss potential improvements and future work.  
The previously proposed methods can be broadly categorized into accumulation buffers, the use of AI, and post-processing. First, the accumulation method creates the motion blur effect by repeating the rendering in a single frame. This method produces the most ideal motion blur effect because it repeats the rendering. However, this method takes a long time to create the motion blur effect because it needs to go through many rendering iterations to reduce artifacts, which means it is not suitable for real-time rendering environments.  
Second, using AI is a fast way to create motion blur effects. However, it requires a large amount of data to train the agent, and it consumes a lot of resources to create the data. It can also produce artifacts in real-time rendering environments with many objects and rapid changes. Also, because the agent cannot accurately explore the processes it outputs, it takes a long time to correct errors when they occur.  
Finally, post-processing methods that use information from the current frame and previous frames to create motion blur effects are the most popular. Single-layer post-processing is a popular method, but if objects in the first layer move, it is not possible to create a motion blur effect that blends with objects in later layers. Many algorithms have been developed and proposed to deal with this problem, but they have limitations because they do not use the information provided by the real layer.  
Our method uses depth filling and stencil routing techniques to accurately capture multiple objects moving in different directions and at different speeds. It also uses motion vectors from multiple layers to reproduce blur close to the answer. This allows us to approximate the colors and details of the original scene more accurately, which are often lost in traditional methods. We have shown that our approach achieves higher SSIM values, indicating superior image quality, and maintains high frame rates, confirming its practical application in real-time rendering.  
In future work, we will investigate ways to improve the motion vector detection process and streamline the multilayer rendering process. If the efficiency of the motion vector detection process is increased, the denoising process can be eliminated. Also, due to the nature of our method, which uses information from multiple layers, resources are essentially consumed in drawing multiple layers. In the case of depth filling, for example, rendering must be repeated in the same frame to get as many layers as needed. Stencil routes are rendered once, but there is a process of sorting by depth, and additional denoising techniques must be used. By studying and applying these improvements, you can expect to increase the efficiency and speed of motion blur generation.  
Figure 16: Motion blur with different numbers of layers.
# Integrating Classical Preprocessing into an Optical Encryption Scheme
## 2 Background and Tools
### All-or-Nothing Transforms  
Let \(\ell,s\) be positive integers such that \(1\leq\ell\leq s\) and \(X\) be a finite set with \(|X|=v\). We say that \(\phi:X^{s}\longrightarrow X^{s}\) is an \((\ell,s,v)\)_-all-or-nothing transform_ (AONT) provided that all of the following holds:  
1. \(\phi\) is a bijection.
2. If any \(s-\ell\) of the \(s\) output values \(y_{1},\ldots,y_{s}\) are fixed, then any \(\ell\) of the input values \(x_{i}\) (\(1\leq i\leq s\)) are completely undetermined, in an information-theoretic sense.  
Following [20; 21], throughout we use a definition of AONTs in terms of the entropy function \(H\):  
Definition 1: Let \(X_{1},\ldots,X_{s},Y_{1},\ldots,Y_{s}\) be random variables taking values from the finite set \(X\), with \(|X|=v\). These \(2\)s random variables define an \((\ell,s,v)\)-AONT provided that the following conditions, are satisfied:  
1. \(H(Y_{1},\ldots,Y_{s}|X_{1},\ldots,X_{s})=0\),
2. \(H(X_{1},\ldots,X_{s}|Y_{1},\ldots,Y_{s})=0\)
3. For all \(\mathcal{X}\subseteq\{X_{1},\ldots,X_{s}\}\) with \(|\mathcal{X}|=\ell\), and for all \(\mathcal{Y}\subseteq\{Y_{1},\ldots,Y_{s}\}\) with \(|\mathcal{Y}|=\ell\), it holds that \[H(\mathcal{X}|\{Y_{1},\ldots,Y_{s}\}\backslash\mathcal{Y})=H(\mathcal{X}).\] (2)  
The definition of a linear AONT is the obvious one:  
Definition 2: Let \(X\) be a finite field. An all-or-nothing transform is _linear_ if each \(y_{i}\) is an \(X\)-linear function of \(x_{1},\ldots,x_{s}\).  
The following theorem provides a method to obtain linear AONTs [21]:  
Theorem 1: Let \(q\) be a prime power and \(M\in\mathbb{F}_{q}^{s\times s}\) invertible. Then \(M\) defines a linear \((\ell,s,q)\)-AONT  
\[\begin{array}{cccc}\phi:&\mathbb{F}_{q}^{s}&\longrightarrow&\mathbb{F}_{q} ^{s}\\ &x&\mapsto&xM^{-1}\end{array} \tag{3}\]  
if and only if every \(\ell\) by \(\ell\) submatrix of \(M\) is invertible.
###### Abstract  
For fault diagnosis, convolutional neural networks (CNN) have been performing as a data-driven method to identify mechanical fault features in forms of vibration signals. However, because of CNN's ineffective and inaccurate identification of unknown fault categories, we propose a model based on transfer learning with probability confidence CNN (TPCCNN) to model the fault features of rotating machinery for fault diagnosis. TPCCNN includes three major modules: (1) feature engineering to perform a series of data pre-processing and feature extraction; (2) transferring learning features of heterogeneous datasets for different datasets to have better generality in model training and reduce the time for modeling and parameter tuning; and (3) building a PCCNN model to classify known and unknown fault categories. In addition to solving the problem of an imbalanced sample size, TPCCNN self-learns and retrains by iterating with unknown classes to the original model. This model is verified with the use of the open-source datasets CWRU and Ottawa. The experimental results showing the feature transfer of heterogeneous datasets are of average accuracy rates of 99.2% and 93.8% respectively for known and unknown categories, and TPCCNN is then proven effectively in training heterogeneous datasets. Likewise, similar feature sets can also be applied to reduce the training of predicting models by 34% and 68% of the time.  
fault diagnosis; probability confidence; feature engineering; transfer learning; deep learning; convolutional neural networks 2022  
[PERSON], [PERSON][PERSON], [PERSON], [PERSON] A Novel Mechanical Fault Diagnosis Based on Transfer Learning with Probability Confidence Convolutional Neural Network Model. _Appl. Sci._**2022**, 12, 9670. [[https://doi.org/10.3390/app1299670](https://doi.org/10.3390/app1299670)]([https://doi.org/10.3390/app1299670](https://doi.org/10.3390/app1299670))  
Academic Editor [PERSON]
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Model Evaluation and Parameter Tuning  
The system environment used in this study is as follows:  
* Windows 10 operating system
* 16 GB RAM
* Intel Core i7 processor
* GPUs--Nvidia GeForce GTX 1080 Ti
* Deep learning libraries: TensorFlow 2.7, Transformers 4.48.1
* Additional evaluation libraries: Scikit-learn 1.0, PyArabic 0.6.14, lime 0.2.0.1  
The models were compiled prior to training. The Adaptive Moment Estimation (Adam) optimizer was employed due to its memory efficiency and notable effectiveness [65]. The loss function used was sparse categorical cross-entropy, appropriate for labels formatted as a sparse matrix.  
The tuning parameters were model-specific. Deep learning (DL) models were optimized by varying the number of hidden layers, whereas the transformer model was tuned based on batch size. This study employed EarlyStopping and ReduceLRonPlateau as regularization strategies to mitigate overfitting. Training ceased when the model achieved optimal performance based on the monitored parameters, before overfitting could occur.  
The EarlyStopping parameter monitored validation loss and stopped training if the loss remained constant or increased over eight consecutive epochs. The ReduceLRonPlateau technique reduced the learning rate by a factor of 0.1 if the loss value remained unchanged over two epochs.  
Table 2 presents the parameters used in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **Values** & **Tuning** \\ \hline \multirow{3}{*}{Transformer model} & Batch size & [32; 64; 128] & Yes \\  & Epoch & 50 & No \\ \cline{1-1}  & Learning rate & \(5\times 10^{-5}\) & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the proposed study for each model.  
Figure 9: The BiLSTM and BiGRU model architecture.
## 3 Results and Discussion
### Microstructure and Phase Characterization of the HPT Processed Composite
#### 3.1.2 SEM Analysis  
Following SEM analysis, the Al-Mg nanostructured composite was found to consist of an Al matrix with isolated Mg inclusions (Figure 2). Two interlayers were found between Al and Mg arrays with a different color contrast from that of the Al and Mg phases. As an outcome of EDX analysis, the chemical composition of the first layer, co-joined with Al, consisted of Al-60 at. % and Mg-40 at. %. The latter corresponds to the Al\({}_{3}\)Mg\({}_{2}\) intermetallic phase. The composition of the second layer, co-joined between the first layer on one side and Mg on the other side, consisted of about Al-40 at. % and Mg-60 at. %. This approximately corresponded to the Al\({}_{12}\)Mg\({}_{17}\) phase. The detailed element composition of the interlayers at the interphase boundaries between Al and Mg is provided hereafter. A direct correspondence between the stoichiometric phases' compositions and the atomic distribution of the obtained EDX cannot be observed due to the small size of the particles and their localized nature. This prevented the estimation of the components' atomic ratio with high accuracy.
## 2 Materials and Methods
### Medium and Test Conditions  
The studies were performed in nutrient rich artificial seawater. This consisted of modified Baar's medium (g L\({}^{-1}\)): MgSO\({}_{4}\) 0.2; sodium citrate 0.5; CaSO\({}_{4}\) 0.1; NH\({}_{4}\)Cl 0.1; K\({}_{2}\)HPO\({}_{4}\) 0.05; sodium lactate 3.5; yeast extract 1.0 added to 4 L of artificial seawater prepared according to ASTM 114-98 (g L\({}^{-1}\)) [43]; NaCl 24.53; MgCl\({}_{2}\) 5.2; Na\({}_{2}\)SO\({}_{4}\) 4.09; CaCl\({}_{2}\) 1.16; KCl 0.695; NaHCO\({}_{3}\) 0.201; KBr 0.101; H\({}_{3}\)BO\({}_{3}\) 0.027; SrCl\({}_{2}\) 0.0025, NaF 0.003 and high pure water.  
The test medium was distributed to eight 500 mL glass bottles. The pH was adjusted to pH 7, 8, 9 and 10 using 1 M hydrochloric acid or 1 M sodium hydroxide. The test medium was purged with nitrogen gas for 1 h and sterilized by autoclaving for 15 min at 121 \({}^{\circ}\)C.  
_Desulfovibrio vulgaris_ (ATCC\({}^{\circ}\)7757), a species of SRB, was used in this study (In Vitro Technologies, Victoria, Australia). The strain was stored at -80 \({}^{\circ}\)C in 15% glycerol. Bacteria were retrieved from stock and cultivated in 500 mL modified [PERSON]'s medium (Table 2) for 48 h at 37 \({}^{\circ}\)C under anaerobic conditions. After 48 h, 10 mL of bacteria culture medium was removed to determine bacterial concentration. The bacterial cells were harvested by centrifugation (5000 rpm, 10 min) and resuspended in 10 mL high pure water for enumerating bacteria cells following staining with 0.4% trypan blue and counting using a hemocytometer. Five mL of culture medium was added to each 200 mL glass bottle containing nutrient rich artificial seawater to give a final bacteria concentration of approximately \(2.23\times 10^{3}\) cells mL\({}^{-1}\) stock solution.  
Additionally, phosphate-buffered saline 1X (PBS) and 4% formaldehyde in PBS solution were prepared for staining bacteria with 4',6-diamidino-2-phenylindole (DAPI) and observing the biofilm by confocal laser scanning microscopy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & \\ \cline{2-10}
**Duplex Stainless Steel 2205** & **Fe** & **Mn** & **S** & **V** & **Si** & **Cr** & **Ni** & **Cu** & **Mo** \\ \cline{2-10}  & 66.32 & 1.68 & 0.05 & 0.11 & 0.43 & 22.1 & 6.12 & 0.30 & 2.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of DSS 2205.  
Figure 1: Experimental set up showing a three-electrode cell with nitrogen gas layer to maintain anaerobic test conditions.
## 3 Design Approach and Methodology
### Data Collection and Requirements Gathering  
To ensure that the participatory design approach and agile scrum methodology were correctly followed and implemented, the data collection process was divided into three sections: unit testing (cross-communication between teams); primary testing (meetings were regularly scheduled with clients and supervisors for progress updates and feedback); and secondary testing (requesting feedback from first-year students registered for anatomy modules in the following disciplines: medical bioscience, nursing, sports and exercise science, occupational therapy, physiotherapy, pharmacy, and dentistry). A Google Forms survey was provided to the students to evaluate their perception of the functionality, usability, and experience when using the _Anat_Hub_ application. Informed consent was obtained from the participants, using a Google Form, prior to accessing the survey. The teams, clients, and supervisors provided feedback throughout the application development period to ensure that the relevant 3D models used were presented and configured in an acceptable and realistic manner, while feedback was obtained from the students once the final application was launched. Frequent feedback was necessary to ensure that the aim and specific objectives for the application development process were met.
## 3 Results and Discussion
### Hardened Properties of OMGPC  
#### 3.2.1 Hardened Density of OMGPC  
Figure 8 presents the hardened density of OMPGC incorporating RESIN8 and FWG. The inclusion and increase in the content of RESIN8 lead to a decrease in the hardened density of OMGPC at increasing curing age. The reduction in hardened density is attributed to the lightweight properties of RESIN8, which are evident from the SG and bulk density, as presented in Section 3.1.3. Previous studies have established that the density reduction of concrete containing recycled waste plastic depends on the percentage substitution contents [60; 61; 62; 63]. The hardened density of OMGPC decreased by 1.1% and 1.4% after 28 d of ambient curing for R5% and R10% specimens, respectively, compared to the control specimen. The percentage reduction in the fresh density of the R5% specimen is similar to the percentage reduction in its hardened density.  
In contrast, the percentage reduction in the fresh density of the R10% specimen is higher than its hardened density. This could be attributed to the higher porosity of the OMGPC containing RESIN8 in its fresh state, in addition to the lightweight properties of RESIN8. However, the hardened density of OMGPC-containing RESIN8 ranges from 2232 to 2318 kg/m\({}^{3}\), classifying them as normal-weight concretes. In contrast to RESIN8 containing OMGPC, substituting MS with FWG results in an improved hardened density, which aligns with the previous investigation performed on these aggregates by [PERSON] et al. [64] and [PERSON] et al. [65]. The hardened density (at the 28-d) of G5% and G10% increased by 1.6% and 0.6%, respectively, compared to their control specimens. The hardened density of OMGPC containing FWG ranges from 2288 to 2324 kg/m\({}^{3}\) (normal-weight concrete), as depicted in Figure 8.
## 1 Introduction
However, the process of acquiring LST data from TIR observations often encounters obstacles due to the presence of cloud cover, which introduces widespread data gaps, affecting statistically more than 60% of the global extent [57]. This issue poses substantial challenges in analyzing the spatial and temporal variations of LST.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 4 System Verification
### Straightness Measurement  
The verification of the proposed straightness system was conducted by using a dual laser interferometer (5529A, Agilent Tech., Santa Clara, CA, USA) as a reference standard to simultaneously measured straightness errors of a three-axis machine tool (KSC-611, KENT, New Taipei City, Taiwan) with the measurement distance of 1000 mm in a well air-conditioned laboratory, as shown in Figure 13.  
The results of horizontal straightness measurements are shown in Figure 14. The average horizontal straightness errors measured by 5529A and the proposed system were found to be 7.2 \(\pm\) 0.2 \(\upmu\)m and 8.2 \(\pm\) 0.5 \(\upmu\)m, respectively. The results of vertical straightness measurements are shown in Figure 15. It was observed that the average vertical straightness errors measured by 5529A and the proposed system were 5.3 \(\pm\) 0.3 \(\upmu\)m and 9.2 \(\pm\) 1.7 \(\upmu\)m, respectively. The residual error of the horizontal straightness and vertical straightness are found to be \(\pm\)1.3 \(\upmu\)m and \(\pm\)5.3 \(\upmu\)m, respectively.
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Evaluation Measures
#### 4.2.3 Mean Average Precision (MAP)  
Based on \(P\) and \(R\) values, we also report results in terms of precision vs recall curve (\(P\)-\(R\) curve) and the mean average precision (MAP). The \(P\)-\(R\) curve represents the tradeoff between precision and recall for a given retrieval approach. It reflects more information about retrieval performance that is determined by the area under the curve. If the retrieval system has better performance, the curve is as far from the origin of coordinates as possible. The area between the curve and the \(X\)-\(Y\) axes should be larger, which is usually measured and is approximate to MAP [42]. In other words, the most common  
Figure 8: Class representatives from the RSSCN image dataset [33].  
way to summarize the _P-R_ curve in one value is _P-R_. _P-R_ is the mean of the average precision (_AP_) scores of all queries and is computed as follows:  
\[MAP=\frac{1}{|T|}\sum_{Q\in T}AP(Q) \tag{11}\]  
where \(T\) is the set of test images or queries \(Q\). An advantage of MAP is that it contains both precision and recall aspects and is sensitive to the entire ranking [43].
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods
### System Stability Evaluations
#### 2.5.2 Water Quality Data Stability Test  
To confirm the stability of water quality data from this system with fluctuating battery voltage, we measured the water quality data (pH, EC, DO, and water temperature) with a fully charged nickel-metal hydride (Ni-MH) battery set (six cells of a serially connected AA-type battery, eneloopTM BK-3 MCC; Panasonic Corp., Osaka, Japan). Each of the pH and EC sensors was immersed in a standard-solution (pH 4.01 or EC 1.41 dS m\({}^{-1}\)) bottle  
Figure 7: Flight stability test with the payload of the water quality monitoring system and reinforcing floats for aqueous operations. Upper: initial location of the drone system. Lower: on-flight location to be stabilized at ideally (initial location of each test). Lateral (\(y\)-axis) fluctuation was evaluated without alieron control. The threshold was set as 1.5 m by demarcating the test space with elastic strings. The time to deviate from the inner space (\(\pm\)1.5 m) was measured.  
(approximately 5 mL) with a water-tight screw cap. For the SplashDroneTM 4 system, the EC sensor was immersed directly in tap water for freshwater monitoring. The DO sensor was placed in approximately 1 L of circulating tap water with continuous aeration at approximately 1.5 L min\({}^{-1}\) of bubbling in an external circulator vessel and approximately 2.4 L min\({}^{-1}\) of circulation for more than 15 min. The water temperature sensor and the water-tight bottles for pH and EC measurement were also immersed in the same water. The water temperature values with the batteries were validated with the same measurement with a continuous power source (9.0 V DC output converted from 100 V AC domestic power source with an AC/DC adapter, GPE018A-090100-6; Succul Co., Ltd., Okayama City, Japan). Atmospheric pressure, relative humidity, and air temperature were measured using an atmospheric sensor (SEN-15440; SparkFun Electronics Inc., United States) with a continuous power source (via the USB port of a laptop computer connected to 100 V AC domestic power source) for estimating the saturated DO concentration and evaluation of stability in water temperature measurements. The data were recorded using computer software for real-time data/message visualization and storage (Processing Grapher ver. 1.3.5; [PERSON]) or using Tera Term ver. 4.106 (TeraTerm Project) at approximately 10 s intervals. The time to 5% deviation from temperature-corrected reference values by depleting the battery was measured as the lifetime of the system per power charge. Before starting the test, each water quality sensor was calibrated using the standard solutions.
## 3 Methodology  
The motivation of this study is to train a detection model that can overcome the adverse effects on object detection caused by rainy weather conditions while maintaining the robustness and reliability of the model. Considering many downstream tasks of object detection, as the objects to be detected are not included in commonly used pre-trained models, such as COCO dataset models only containing specific 80 categories, retraining the model in such cases is unavoidable. We were inspired by this to aim to train a detection model that can adapt to rainy weather conditions. However, images under rainy conditions are relatively scarce compared to those under normal weather conditions, both in quantity and diversity of objects. This limitation makes it challenging to use real images under rainy conditions for model training. Thanks to advancements in image augmentation and synthesis techniques, this study generates realistic rainy images as training data through these techniques.  
RTDETR and YOLOv8 were chosen as novel object detection models in this study. RTDETR combines Transformer and CNN into a hybrid detection model, inspired by the successful application of Transformers in the field of natural language processing. It transforms the object detection problem into an attention mechanism problem, modeling the relationships between objects in images through self-attention mechanisms. YOLOv8 directly predicts bounding boxes and class probabilities using deep CNN. It is an improved version of YOLOv5, optimized for network structure, data augmentation, and training techniques to achieve better detection performance. The choice of model significantly affects the detection accuracy of the detection model. Currently, common detection models under rainy conditions are mostly based on the YOLO series, with less exploration of Transformer-based models.  
Therefore, this study not only explores different image generation methods under rainy weather conditions but also analyzes two detection methods, RTDETR and YOLOv8, with vastly different model structures. We aimed to investigate the detection performance of different combinations of training data and models, attempting to propose a robust and reliable model training method to overcome the impact of rainy weather conditions on object detection accuracy. The research process is illustrated in Figure 1.