esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN
###### Abstract  
Upper gastrointestinal endoscopy is widely performed to detect early gastric cancers. As an automated detection method for early gastric cancer from endoscopic images, a method involving an object detection model, which is a deep learning technique, was proposed. However, there were challenges regarding the reduction in false positives in the detected results. In this study, we proposed a novel object detection model, U-Net R-CNN, based on a semantic segmentation technique that extracts target objects by performing a local analysis of the images. U-Net was introduced as a semantic segmentation method to detect early candidates for gastric cancer. These candidates were classified as gastric cancer cases or false positives based on box classification using a convolutional neural network. In the experiments, the detection performance was evaluated via the 5-fold cross-validation method using 1208 images of healthy subjects and 533 images of gastric cancer patients. When DenseNet169 was used as the convolutional neural network for box classification, the detection sensitivity and the number of false positives evaluated on a lesion basis were 98% and 0.01 per image, respectively, which improved the detection performance compared to the previous method. These results indicate that the proposed method will be useful for the automated detection of early gastric cancer from endoscopic images.  
gastric cancer; endoscopy; deep learning; convolutional neural network +
Footnote †: journal: journal: Medical Sciences, Fujita Health University, Toyoake 470-1192, Japan; [EMAIL_ADDRESS]
technology, has recently been confirmed to have high capability for image recognition by several studies that were conducted in the medical field [5; 6; 7; 8; 9; 10]. Therefore, we focused on the automated detection of GC in endoscopic images using a computer-aided diagnostic method based on deep learning technology.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 4 Proposed Deep Learning Model
### Final Detection and Output Generation  
After generating bounding boxes for detected vials, Non-Maximum Suppression (NMS) was applied to eliminate redundant detections. NMS operates by retaining the box with the highest confidence score while suppressing overlapping boxes based on the Intersection over Union (IoU) threshold, as shown in Equation (8):  
\[IoU(A,B)=\frac{|A\cap B|}{|A\cup B|}. \tag{8}\]  
In this study, a threshold of 0.5 for the Intersection over Union (IoU) was applied, indicating that detections exhibiting an overlap exceeding 50% were suppressed to ensure that only the most pertinent vials were preserved.  
Once finalized, the detected vials were rendered in the Mixed Reality environment, where they were annotated with bounding boxes and class labels. This enables real-time interaction, allowing medical professionals to efficiently identify and differentiate between vials, reducing the likelihood of medication errors.
## 4 Gain Correction
### Full-Consensus Algorithm
Namely, we have in this case the following model:_  
\[\hat{g}_{t+1}=\hat{g}_{t}+\delta\Delta x_{t}^{2}\alpha^{2}\Gamma\hat{g}_{t} \tag{19}\]  
_and we can prove--using the theory of time-varying linear stochastic systems and the theory of dynamic consensus schemes--that there exists such a \(\delta^{\prime\prime}>0\) that, for all \(\delta<\delta^{\prime\prime}\), the system asymptotically exponentially achieves consensus with regard to \(g_{t}\) in the mean square sense [60; 44]._  
In order to obtain a qualitative insight into the convergence properties of (17) under the presence of sensor nonlinearities, we apply at this point the line of reasoning based on Theorem 1, and introduce \(\hat{g}_{t}=\hat{T}^{-1}\hat{g}_{t}\), where \(\hat{T}=[\mathbf{1}\colon\hat{T}^{*}]\); here, the \(n\times(n-1)\) matrix \(\hat{T}^{*}\) satisfies \(\operatorname{span}\{\hat{T}^{*}\}=\operatorname{span}\{\hat{\alpha}^{2}\Gamma\}\). We obtain from (17) (keeping the definition of the matrix \(\Gamma_{t}^{w}\) in mind) the following characteristic terms in the resulting expression for \(\hat{g}_{t}\):  
1. \((\hat{T}^{-1}\alpha^{2}\Gamma\hat{T})\hat{T}^{-1}\hat{g}_{t}\),
2. \((\hat{T}^{-1}\alpha^{2}\Gamma\hat{T})\hat{T}^{-1}W_{t}\hat{g}_{t}\),
3. \((\hat{T}^{-1}\alpha^{2}W_{t}\Gamma\hat{T})\hat{T}^{-1}\hat{g}_{t}\),
4. \((\hat{T}^{-1}\alpha^{2}W_{t}\Gamma W_{t}\hat{T})\hat{T}^{-1}\hat{g}_{t}\). It is straightforward to verify that, in cases (a) and (b), the vectors resulting from the given expressions have zeros as their first components (according to Lemmas 1 and 2); meanwhile, in cases (c) and (d), the first elements of these vectors are, in general, nonzero, depending on \(W_{t}\). The first component of the transformed corrected parameter vectors is very important, since it is directly connected to the zero eigenvalue of \(\alpha^{2}\Gamma\)[33; 35; 36; 55]. Consequently, a comparison with Theorem 1 and the results from [52; 55; 34; 44] shows that the desired consensus cannot be reached, in general, with regard to all the elements of \(\hat{g}_{t}\), and that--even for small nonlinear terms, \(v_{t}\)--the whole system of recursions can become unstable. Observe that the existence of the time-varying terms \(x_{t}\) and \(v_{t}\) makes a reduction in \(\hat{g}_{t}^{i}\) to a single constant multiplied by \(\mathbf{1}\) impossible; this is required for achieving consensus (compare with (9)). **Remark 9**: _Note that the formal elimination of the nonlinear term in the sensor model leads to an algorithm for which convergence has been proved in [44]. The assumption \(v_{t}^{i}=0\) formally corresponds to the above term denoted as (a), when the consensus of the corrected gains is reachable._  
**Remark 10**: _Similarly to Remark 5, when \(\Delta y_{t}^{i}\approx\Delta y_{t}^{i}\) for all \(x_{t}\), we have \(\epsilon_{t}^{\Delta,ij}\approx\delta^{i}\Delta x_{t}-\delta^{i}\Delta x_{t}\), and we can again rely on the linear sensor model [56; 44; 34; 45; 46]._
# Development of a Mobile Buoy with Controllable Wings: Design, Dynamics Analysis and Experiments
## 4 Dynamics Simulation Results
### Dynamic Responses in Argo Mode  
Vertical dive and climb motion are important indicators to evaluate the performance of the proposed mobile buoy in Argo mode, which directly reflects the capability to observe the ocean in a vertical profile. In the ANSYS-Fluent simulation environment, the mobile buoy is set on the computational domain of the fluid with a net buoyancy mass \(m_{0}\in[-0.1\) kg, \(0.1\) kg] and a wing rotation angle \(\alpha_{w}\) of \(90^{\circ}\). At the beginning of the simulation, the attitude and net buoyancy of the mobile buoy are controlled by the controllable wings and buoyancy/gravity to dive vertically. After reaching the target depth, the buoyancy system is adjusted again to drive the mobile buoy climbing vertically. Figure 9a,b illustrate the fluctuations in forward and heave velocities during the diving and climbing phases. It can be found that the heave velocity changes periodically with the net buoyancy mass, but the forward velocity fluctuates around zero. This is mainly due to the fact that the wing is subjected to less lift in its upright position, resulting in the forward velocity being far away from the heave velocity. From Figure 9c-e, it can be found that although the buoy has pitching motion, its angular velocity, pitch moment and pitch angle are small and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mass of mobile buoy \(m_{t}\) (kg) & 14.7 \\ Inertia of the buoy with respect to \(y\)-axis \(I\) (kg\(\cdot\)m\({}^{2}\)) & 2.1 \\ Span of the buoy \(W\) (m) & 1.4 \\ Buoy fuselage length \(L\) (m) & 0.35 \\ Water depth \(H\) (m) & 10 \\ Fluid density \(\rho\) (g/cm\({}^{3}\)) & 1.025 \\ Buoy fuselage diameter \(d\) (m) & 0.35 \\ Nonuniformly distributed mass \(m_{av}\) (kg) & 3 \\ Net buoyancy mass \(m_{0}\) (kg) during the diving phase & 0.1\(\sim\)0.5 \\ Net buoyancy mass \(m_{0}\) (kg) during the climbing phase & \(-\)0.5\(\sim\)\(-\)0.1 \\ Displacement of the nonuniform hull-distributed point & 0.14 \\ mass \(m_{av}\) with respect to GC \(\mathbf{r_{av}}\) (m) & 0.14 \\ Variation range of the wing rotation angle \(\alpha_{w}\) (\({}^{\circ}\)) & \(-\)90\({}^{\circ}\)\(\sim\)90\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of mobile buoy used for dynamics simulation.  
Figure 8: Mesh independence verification, showing (**a**) forward velocity, (**b**) pitch angle.  
can be negligible, thus ensuring the stability. The motion trajectory during an operation cycle in Argo mode is illustrated in Figure 9f, and it can be clearly seen that the horizontal displacement is almost negligible compared with the diving depth; thus, the diving and climbing motion can be assumed to be performed in the vertical direction.
## 6 Strengths, Limitations, and Future Research Directions
### Future Research Directions
#### 6.3.4 Evaluation  
The adequacy of the metrics to the task and problem at hand is also another suggested line of future research. Most studies have used standard performance metrics (such as accuracy and F1-score) to evaluate the performance of deep learning architectures. Future research should consider using more diverse metrics that better capture the strengths and weaknesses of different architectures.
# Survey on Videos Data Augmentation for Deep Learning Models
## 3 Review on Video Data Augmentation
### Simulation
Autodesk Maya was used to render the synthetic videos of a camera moving in different static environments and with different illumination. Simulated data do not need to render the entire scene. Some methods superimpose new simulated actors or objects on top of existing videos. In [34], [PERSON] et al. generate an augmented synthetic dataset of video from surveillance cameras. The idea is to extract the background from a surveillance video sequence, calculate the 3D plane of the ground and superimpose 3D simulated pedestrians on top of it. This method is able to create a realistic video sequence, but it needs significant preprocessing to extract the background and calculate the 3D walkable area of the scene. The data generation model was tested in a person detection scenario using the Town Centre data set. A pretrained version of ResNet-101 and RPN+ networks was used as baseline. The two networks were then trained on the proposed synthetic dataset improving the average accuracy compared to the baselines (ResNet-101 39.09% \(\rightarrow\) 50.04%, RPN+ 20.77% \(\rightarrow\) 21.32%). A similar approach to generate video simulation for self driving cars (GeoSim) has been proposed by [PERSON] et al. [52]. The idea behind their work is to superimpose realistic dynamic objects (cars) on existing on-board camera videos in order to obtain novel synthetic data. The authors create a dataset of 3D dynamic objects from real images and LiDAR point clouds. Starting from camera video footage, LiDAR point clouds, and HD maps, GeoSim adds and moves the 3D models of the cars into the 3D representation of the source video. The 3D scene is then used to add the car model to the source video generating a new realistic synthetic sequence. In addition, the authors collected a real-world dataset from cameras mounted on a fleet of self driving cars (UrbanData). To test the synthetic data generation, they trained two semantic segmentation networks (PSPNet, DeepLabv3) on the UrbanData dataset, both with and without augmenting it with synthetic data. The networks trained on the augmented dataset obtained an increase in the mean IoU: PSPNet +1.8%; DeepLabv3 +0.2%. [PERSON] et al. [56] automatically estimate 3D human motions from videos and use that information to generate synthetic videos of realistic 3D human avatars performing those actions. The authors tested the data augmentation quality of their synthetic data training a 3D ResNet-50 network on the NTU RGB+D dataset of human actions. The network was tested with and without data augmentation. The model trained on real+synthetic data improved the mean accuracy over the one trained only on real data (74.8% \(\rightarrow\) 81.7%).
## 3 Results and Discussion
### Effect of Energy Source  
Three types of fuel (gas, coal and oil) were analyzed for the evaluation of the potential impact for the eight categories. Figure 5 shows the changes in the PEI output for each category and for the different fuels used in the large scale production of magnetite (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles via coprecipitation. It can be initially observed that the PEI outputs are low for each category, except for the AP category. However, it can be determined that the use of coal increases the impact in each category, especially in the AP category (\(3.4\times 10^{1}\)) because the coal releases more hydrogen ions than reacting with SO\({}_{2}\), causing acid rains [19]. Additionally, it can be evidenced that the use of the gas as the fuel for this process generates the smallest impacts in each category.
## 3 Experimental Setup
### Setup  
In order to reproduce the same experiments reported in the literature by [21; 22; 23], we split the ALL-IDB1 data set into three parts, namely the training, validation and testing set, with 70%, 20% and 10% of images, respectively. In order to favour reproducibility and preserve the same partitions for all the experiments that we carried out, the images were selected in lexicographic order. In addition, we used a stratified sampling procedure to keep the splits almost balanced. In this way the testing set presented 5 and 6 healthy and ALL images, respectively. Finally, the images are resized to match the input size requirements of the CNNs. The training process was run using the ADAM solver for 50 epochs, with a starting learning rate of \(1\times 10^{-4}\) and learn rate decay of 0.1 every 10 epochs. During training we used the cross entropy loss function and the best model for evaluation was chosen based on the lowest validation loss value (the minimum observed values were between \(1\times 10^{-3}\) and \(1\times 10^{-5}\)).  
Since the number of training images was limited and no image augmentation procedures were used, in order to avoid over-fitting, the regularisation factor L\({}_{2}\) was set to 0.1. All the experiments were conducted on the same machine that presented the following configuration: Intel(R) Core(TM) i9-8950 HK @ 2.90 GHz CPU with 32 GB RAM and NVIDIA GTX1050 Ti 4 GB GPU. Finally, the classification performance was evaluated by using five common metrics, Accuracy (A), Precision (P), Recall (R), Specificity (S) and F1-score (F1), which are computed as follows:  
\[A=\frac{TP+TN}{TP+FP+TN+FN}\hskip 56.905512 ptP=\frac{TP}{TP+FP}\hskip 56.905512 ptR= \frac{TP}{TP+FN}\]  
\[R=\frac{TN}{TN+FP}\hskip 56.905512 ptF=\frac{2 RP}{R+P}\]  
where _TP_ (True Positive) indicates the number of images correctly classified as positives, _FP_ (False Positive) provides the number of negative images wrongly classified as positives, _TN_ (True Negative) indicates the number of images correctly classified as negatives and _FN_ (False Negative) gives the number of positive images wrongly classified as negatives. Furthermore, to provide a visual explanation for the decisions made by the CNN-basedmodels, making them more transparent, we used the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm [12]. This produced a coarse localisation map that highlighted the important regions in the image used for predicting a specific concept.
## 2 The Proactive Routing Protocol
### Research Trends of OLSR Routing Protocol in MANETS  
Owing to the routing table, OLSR has a significantly smaller latency. It has the potential to simultaneously upgrade the routing table, on the other side [10]. Since the control messages are transmitted on a regular basis and do not need to be delivered in order, the link is dependable. Collecting data from Web of Science databases, a scientific metric assessment of research trends pertaining to the OLSR Routing Protocol and MANETS is shown in Figure 2, where it is clear that OLSR possesses far better performance than other protocols. High-density networks are best suited for OLSR since it prohibits packet transmission delays from lasting too long. Figure 3 demonstrates the fact that OLSR in MANETS is closely used for optimization and effective routing. The experiment is performed using the VOSviewer tool for empirical evidence landscaping adopting the OLSR and MANET keywords' epicenters from the WoS database having started in 2002 to 2022.  
According to Figure 3, research papers have steadily expanded every year since 2002. The total publications have risen dramatically from years 2007 to 2016, affirming that a lot of work is being accomplished in this discipline. The year 2013 achieved the highest publication rate among all years.
# Antimicrobials in Dentistry
## 2 Antimicrobials Used in Dentistry
### Antibacterial Monomers in Dental Restorative Systems  
Dental adhesive systems that exhibit intrinsic antibacterial properties are not reliant on the release of any antibacterial agents and either function through contact killing as in the case of quaternary ammonium salts or the presence of charged species [66, 67, 68, 69, 70]. There are a few QACs that have been investigated in experimental adhesive formulations (Section 2.2); however, the balance of bactericidal effects and properties of the polymerised systems needs further evaluation. Another approach, which has shown promising results but so far is limited to laboratory evaluation, is utilising (meth-)acrylate derivatives of molecules such as eugenol or N\({}_{\text{\tiny{i}}}\)N-dimethylammonium derivatives that have an established bactericidal effect and has been used as polymer therapeutics in dental applications (Figure 2) [71].  
A number of methacrylate derivatives based on mono, di and polysaccharide methacrylate monomers synthesised from glucose, sucrose and chitosan, respectively, with glycidyl methacrylate added to dental adhesive formulations have been reported to exhibit promising antibacterial action [72,73], with the sucrose-methacrylate derivative outperforming the others [74]. A decrease in bacterial metabolism was reported with sucrose-methacrylate that exhibited low toxicity and increased the bond strength after 6 months of storage. More recently, 5-(methacryloxy-2-ethoxaminocarbonyl)methyl-thiazol (MEMT) [75] and ester-free quaternary ammonium monomers based on methylacrylamides with side alkyl chain lengths of 9 or 14 carbons have been reported with antimicrobial properties [76] for potential use in dentistry. The antibacterial activity of MEMT-containing polymers was mainly associated with the release of unreacted monomer and surface adhesion on the immobilised MEMT units. Similarly, the methacrylamides exhibited their antimicrobial activity by virtue of the charge due to quaternary ammonium species. Another approach has also been recently reported that applies a dual strategy of bacterial gene modification with dimethyl amino hexadecyl methacrylate as an antibacterial monomer [77]. These systems have the potential to imbibe antimicrobial properties in dental materials; however, the following section focuses specifically on intrinsically antibacterial methacrylate derivatives of eugenol, which form comonomers in dental formulations that exhibit promising potential for imbibing antimicrobial properties.
## 5 Results
### Harmonic Emissions
The amplitude of this harmonic is highest at a supply voltage equal to 210 V  
Figure 3: Current harmonic spectrum for various supply voltage values for the: (**a**) CFL 20 W lamp; (**b**) LED 7 W lamp; (**c**) LED 8 W lamp. and decreases as the voltage increases. The severity of the harmonics starting from the 15 th one is lower than 3%, and they are more immune to supply voltage variation. The severity of harmonic disturbances contained in the current signal can be specified collectively by the total current harmonic distortion factor (\(THD_{i}\)), which can be calculated according to the equation:  
\[THD_{i}=\frac{\sqrt{\sum_{2}^{40}{{I_{h}}^{2}}}}{{{I_{1}}}}\cdot\text{100\%} \tag{1}\]  
where \(I_{h}\) is RMS current value of the individual harmonic component, e.g., \(I_{1}\)--RMS current value of the first (fundamental) harmonic. The value of the total current harmonic distortion factor (\(THD_{i}\)) is mainly determined by odd harmonics, especially those up to the 15 th order. Their share in the analyzed spectrum is equal to 99.85%. The value of even harmonics is relatively low (usually below 1%), therefore it can be neglected. The numerical indicator (\(THD_{i}\)) of harmonic current emission injected into the power grid by the connected individually lighting devises implies that the overall level of harmonic currents is influenced by a \(\pm\)10% variation of supply voltage. A small change is noticed for CFL 20 W, the THD varies from \(-\)2.9% to 4.1%. For LED 7 W the change is moderate (\(-\)6.2% to 8%) and for LED 8 W the change varies between \(-\)13.3% and 11.9% which is noticeable. For the selected LED 8 W lamp the maximum relative emission change is higher circa 12% compared to the emission generated by the same device supplied with the nominal voltage level. These statements are illustrated numerically in Table 2, and graphically in Figure 4.  
The voltage level variation within the permissible tolerance \(\pm\)10% in the public distribution system according to EN50160 standard [14] may cause such an increase in the \(THD_{i}\) factor that it will affect the assessment of the current harmonic emissivity according to the permissible levels formulated in EN61000-3-2 standard [16]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Supply** & **210** & **220** & **230** & **240** & **250** & **Lighting Equipment** \\ \hline \multirow{6}{*}{\(THD_{i}\) [\%]} & 104.7 & 105.6 & \multirow{2}{*}{107.8} & 110.2 & 112.3 \\  & (\(-\)2.88\%) & (\(-\)2.02\%) & & (+2.23\%) & (+4.17\%) \\ \cline{1-1}  & 15.1 & 15.8 & \multirow{2}{*}{16.1} & 17.1 & 17.4 \\ \cline{1-1}  & (\(-\)6.21\%) & (\(-\)1.86\%) & & (+6.21\%) & (+8.01\%) \\ \cline{1-1}  & 18.9 & 20.1 & \multirow{2}{*}{21.8} & 22.8 & 24.4 \\ \cline{1-1}  & (\(-\)13.30\%) & (\(-\)7.80\%) & & (+4.59\%) & (+11.93\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influence of the voltage supply variation on the \(THD_{i}\) factor value. Figure 4: Influence of the voltage supply on the \(THD_{i}\) factor value for selected lighting sources.