esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN
## 2 Methods
### Box Classification  
The detected candidate regions included many over-detected regions (FPs). These FPs may be recognized and reduced using a different approach from the segmented U-Net for segmentation. In the box classification part of the proposed U-Net R-CNN, FPs are eliminated from the candidate regions by another CNN (Figure 3).  
Figure 2: Architecture of U-Net.  
First, the input image was provided to U-Net and the output labeled image of U-Net was automatically binarized by [PERSON]'s method [20], followed by the labeling process to pick up individual candidate regions. The bounding box of the candidate region was then cut out and input to the CNN to classify the candidate region as GC or false positive. Finally, the regions that the CNN identified as GC were used as the final detection results.  
For the CNN architecture, we introduced VGG-16 [21], Inception v3 [22], and ResNet50 [23], as well as DenseNet121, 169, and 201 [24], then selected the best model by comparing them. These CNN models were pretrained using the ImageNet dataset, which has a much larger number of training image samples than our original dataset. For the classification of GC and FPs, we replaced the fully connected layers of the original CNN models with three layers having 1024, 256, and 2 units.  
For the input of the CNN, the image of the candidate region was resized to 224 \(\times\) 224 pixels and the optimization algorithm was the Adam algorithm with a learning rate of 0.0001 and 200 training epochs. For data augmentation, vertical and horizontal image flipping were performed randomly.
## 2 Literature Review
### Random Forest  
The ensemble approach, which combines different classifiers, has been proposed to improve the performance and accuracy of diabetes analysis and prediction. Random Forest is one such ensemble technique that combines the findings of individual decision trees [34]. It is made up of an ensemble of classifiers, each of which is a decision tree with nodes representing attributes. Random Forest typically works by using the bagging method to generate subsets of training data. Previous studies have shown that Random Forest has been used in many areas and has produced significant results.  
[PERSON] et al. [19] utilized machine learning models such as Gaussian Naive Bayes, Logistic Regression, K-nearest neighbor, Classification and Regression Trees (CART), and Random Forest to predict type 2 diabetes for the next six months to one year based on electronic medical records (EMR) data. The results showed that RF outperformed other models in predicting diabetes. [PERSON] et al. [20] used Random Forest to identify the most important attributes related to diabetes. The proposed model was compared to other machine learning models such as Support Vector Machines and Logistic Regression (LR) and produced significant results. RF outperformed other models in terms of prediction accuracy and estimated relevance of the attributes. [PERSON] et al. [22] used a Random Forest classifier for disease classification and tested it on 10 medical benchmark datasets. A feature ranking-based approach was developed and implemented for medical data classification and the proposed RF model produced promising results. Finally, [PERSON] et al. [21] used a random forest (RF) classifier to model changes in hypertension control in 1294 patients at Vanderbilt University Medical Center. They found that the RF model was able to accurately predict changes in hypertension control status. This research could be used to develop personalized hypertension management plans.  
Previous studies have shown that using a random forest (RF) model can be applied to prediction models in many areas. However, these studies have also revealed that the presence of outliers and imbalanced data in the training set can negatively impact the performance of the RF model. By removing outliers and balancing the training set, the RF model is expected to have improved classification accuracy.
## 5 Application of Self-Sensing Materials for
### Traffic Monitoring
Analyzing the voltage changes over time under bicycle loading, it was also found that the developed self-sensing pavement can be employed for axle counting, determining the distance between axles, and assessing speed. Recently, [PERSON] et al. [236] developed a lightweight self-sensing cementitious metamaterial with energy harvesting and sensing capabilities. The prototype was created by integrating electrically conductive concrete with a nonconductive polymer lattice. The operation of this multifunctional cementitious metamaterial is based on the concept of triboelectrification (contact-electrification). This innovative multifunctional material holds potential for revolutionizing pavement and seismic isolator applications. Given the patterns of voltage change observed, it was evident that the developed multifunctional cementitious metamaterial represents a significant advancement for traffic monitoring and SHM in transportation infrastructures. Although the bulk fabrication of multifunctional cementitious composites in the field remains one of the main challenges, the fabrication procedure proposed in a previous study by [PERSON] et al. [7] can be considered a promising step for the bulk fabrication of these multifunctional cementitious composites in the field. Considering recent advances in multifunctional cementitious composites, the fabrication of such composites for transportation infrastructures can be performed in pre-cast or cast-in-place forms [14].
## 4 Discussion
Further studies on the viability of these microbes in the presence of these agents may provide information that may clarify the distinction between microbial density and biofilm abundance. Comparing the three agents, it is evident that bromelain is more effective as a biofilm dissolution agent in at least two strains, ATCC 27853 and ATCC 31461 (66 and 75% BD MAX, respectively) (Table 3). Since bromelain possesses antibacterial properties, the dissolution or debridement of the biofilms may also be attributed to the reduction in bacterial colonies, while the biofilm reduction may be attributed to both the chemical dissolution activity on the polysaccharide and glycoprotein matrix together with antibacterial action on the bacterial colony; however, this needs further study to determine the presence and magnitude of both these properties of bromelain. All the three strains of _Pseudomonas aeruginosa_ were well affected by a combination of bromelain and NAC (BromAc), indicating BD MAX values of 80% and above (Table 4). This indicates that a suitable combination of these two agents would provide a good therapy to eradicate biofilm. Of note, strain PA01 3981 was most sensitive, with a BD MAX value of 97% at a combination of 25 mg/mL NAC + 25 mg/mL bromelain, with a ratio of 1000:1 on a weight basis. This may also indicate that the high dose of NAC required is reflective of the number of disulfide bonds present within the biofilm. The CI values for all effective combinations indicate very good synergy, with extremely high synergy in the case of strain PA01 3981. The other two strains reacted equally well compared to each other, with BD MAX values of 85-87%, although ATC27853 showed a BD MAX value of 87% with a combination of 15 mg/mL NAC + 25 mg/mL bromelain (ratio of 600:1 by weight) and for strain ATCC 31461 with a weight ratio of 1000:1 at a BD MAX of 85% (25 mg/mL NAC + 25 mg/mL bromelain). These variations may be related to the slight differences in percentage compositions of proteins, glycoproteins, and carbohydrates, as well as the abundance of biofilm secreted. Further, the low concentration of bromelain required with a relatively high (100-fold) NAC is indicative of the chemical reactivity of the two agents; bromelain is an enzyme that regenerates, whilst NAC, being an antioxidant, is used up during the degradation of the biofilm. The amount of NAC required may also reflect the abundance of disulfide linkages present within the biofilm. In the clinical setting, delivery of BromAc by either intravenous or oral route would be impractical, since bromelain is readily bound to blood albumin, macroglobulin, and other blood and cellular components, such that it would be impossible to achieve the effective dose used in this study [43]. However, although a high NAC dosage may be given by IV with proven safety, as in acetaminophen toxicity [44], it is envisaged that the medicament is more suitable for local delivery at the site of the mesh or other implants. The safety of BromAc via intraperitoneal delivery has been established both in vivo [45] and in clinical trials [38].
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### Features
#### 2.2.4 Total Deviation of CAV  
The total deviation of CAV is calculated by taking the absolute difference of the area under the \(CAV\) plots of an actual event and the corresponding linear state. The change of pattern in \(CAV\) time series and its linear counterpart provides useful information about the damage. However, the comparison at each point of observation makes it a very high dimensional feature. Hence, the area is calculated as a compact (low dimension) feature.  
\[\Delta_{CAV}=\frac{|A_{s}-A_{l}|}{A_{l}}\times 100\%, \tag{5}\]  
where \(A_{s}\) and \(A_{l}\) are the areas under the CAV plot of a structure and its corresponding linear system, respectively. This value was expected to increase with damage, while for an undamaged case, it is 0.0.
###### Abstract  
We previously compared time-domain diffuse optical spectroscopy (TD-DOS) with magnetic resonance imaging (MRI) using various water/lipid phantoms. However, it is difficult to conduct similar comparisons in the breast, because of measurement differences due to modality-dependent differences in posture. Dual-energy computed tomography (DECT) examination is performed in the same supine position as a TD-DOS measurement. Therefore, we first verified the accuracy of the measured fat fraction of fibroglandular tissue in the normal breast on DECT by comparing it with MRI in breast cancer patients (n = 28). Then, we compared lipid and water signals obtained in TD-DOS and DECT from normal and tumor-tissue regions (n = 16). The TD-DOS breast measurements were carried out using reflectance geometry with a source-detector separation of 3 cm. A semicircular region of interest (ROI), with a transverse diameter of 3 cm and a depth of 2 cm that included the breast surface, was set on the DECT image. Although the measurement area differed between the modalities, the correlation coefficients of lipid and water signals between TD-DOS and DECT were rs = 0.58 (\(p<0.01\)) and rs = 0.90 (\(p<0.01\)), respectively. These results indicate that TD-DOS captures the characteristics of the lipid and water contents of the breast.  
diffuse optical spectroscopy; time-resolved spectroscopy; breast cancer
## 3 Simulation Results
### Traditional Time-Domain Beamforming  
Figure 6 shows the simulated beamforming map obtained by the DAS beamformer using (4) before the first iteration. The hotspot in Figure 6 indicates the location of the dominant sound source, \(s_{1}\). Because the sound power level (SWL) of \(s_{1}\) was relatively higher than those of \(s_{2}\) and \(s_{3}\), the locations of the sound sources of \(s_{2}\) and \(s_{3}\) can barely be observed in Figure 6. Thus, the spatial information from the sound map and the preliminary estimation of \(s_{1}\) by time-domain DAS is regarded as an initial value, and it can be easily removed from the mixed signal by the proposed method. A virtual measurement signal is created such that the first dominant sound source, \(s_{1}\), does not exist.  
Figure 7 shows the beamforming maps obtained after removing \(s_{1}\). To verify the effectiveness of the denoising DNNs, the performance of the DAS beamformer with and without the DNNs was determined through controlled experiments as well. The location of the dominant sound source, \(s_{2}\), in a virtual upgraded sound area is shown in Figure 7a,b. The effectiveness of adding the DNNs before removing \(s_{1}\) was not remarkable in this step.  
To further separate the measured mixed signal, \(s_{1}\) and \(s_{2}\) were both removed by analogous processes, while Figure 8 shows the corresponding beamforming maps. In Figure 8a, which shows a beamforming map obtained without pre-processing by DNNs, the location of \(s_{3}\) cannot be identified successfully because of the interference caused by the error generated after removing \(s_{1}\) and \(s_{2}\) from the mixed signal. This problem was resolved by DNN pre-processing, as shown in Figure 8b, where the location of \(s_{3}\) in the virtual upgraded sound area can now be observed. The results of virtual sound area reconstruction and the corresponding source locations shown in Figures 7b and 8b verify the effectiveness of DNN denoising.  
Figure 5: Eight-channel measured mixed signal in the simulative study.  
Figure 6: Preliminary beamforming map by DAS.
# A Novel Hybrid CSP-PV Power Plant Based on Brayton Supercritical CO\({}_{2}\) Thermal Machines
## 2 Methodology
### Sizing of Heat Exchangers  
In the heat pump and heat engine, two types of heat exchangers have been used: hybrid (H\({}^{2}\)X) and printed circuit heat exchangers (PCHE). As explained earlier, H\({}^{2}\)X prevents potential clogging problems, while PCHEs are commonly employed in S-CO2 applications to withstand high pressure differences.  
An iterative design approach has been used to perform the sizing of the heat exchangers, based on the sub-heat exchanger methodology. Sizing calculations are greatly influenced by the significant changes in CO\({}_{2}\) properties, particularly when the operating conditions are close to the critical point. This method assumes a continuous variation of properties throughout the sub-heat exchangers that comprise the entire unit [30].  
Using the information supplied by the leading manufacturer of the PCHEs [31], the maximum plate size is 600 \(\times\) 1500 mm. Modules are built with the plates, assuming a core block with a maximum height of 600 mm. This configuration results in a total of 96,000 channels (48,000 per stream). This kind of design comes from the manufacturer [32], and the authors have previously employed it in a CSP project [22]. The channels where the fluid flows are semicircles with a 2 mm diameter and a 2.5 mm pitch. The thickness of the plates is 1.6 mm. Inconel alloy 617 has been recommended for both the receiver (REC) and high-temperature recuperator (HTR) due to the elevated temperatures [33]. Austenitic stainless steel 316L has been selected for the remaining PCHEs.  
The overall heat transfer coefficient for each sub-heat exchanger has been evaluated following the methodology described by [PERSON] [21]. The length of the heat exchanger is determined using the heat transfer across the surface area; consequently, the pressure drop along the entire channel is assessed for every stream. The total number of channels is iterated during the design process until the desired temperatures at each port are achieved. If the calculated maximum pressure drop exceeds or falls below the desired value, the number of tubes increases or decreases accordingly.
## 3 Results and Discussion
### Comparison  
A comparison of the proposed antenna system's performance with existing 5G antennas is presented in Table 2 below. The proposed antenna is tuned for the 4.7-6.5 GHz frequency band, thus having a fractional bandwidth of 32.72%, while the comparative studies of the proposed antenna with the other designs mentioned in [11,39] show that the presented designs have lower bandwidths of 46.96% and 11.11%, respectively. The proposed system also ensures an isolation of \(>\)10 dB; however, this is slightly less than the isolation of \(>\)17 dB and \(>\)20 dB realized in [11,39], respectively, but is adequate for MIMO operation. Furthermore, the ECC for the proposed designs is \(<\)0.03, which is very good MIMO diversity and is superior to \(<\)0.002 in [11] and \(<\)0.01 in [39]. The overall efficiency of the proposed antenna varies between 76.12% and 91.57%. However, compared to other equivalent designs of [11] (64-82%) and [40](53-86%), it can be concluded that the proposed antenna design is suitable for high-performance 5G applications. In summary, the proposed design solution provides a reasonable bandwidth, isolation, and efficiency that guarantee its competitiveness in the sphere of prospective next-generation wireless communication systems.
# Utilizing Constructed Neural Networks for Autism Screening
###### Abstract
Augmented Reality SGs for social-emotional communication, designed for adolescents with ASD, indicated the potential to assist in identifying comorbidity with ADHD symptoms [47]. A recent study conducted with the help of a mobile game-based health app captured video footage of 95 children involved in gameplay in their own homes [48]. The study compared the gaze patterns of TD children and those with ASD. The findings showed distinct differences in the way the two groups fixated their eyes, with children with ASD having their own specific visual scanning patterns. A deep learning model trained on gaze fixation had low ASD detection power. Evidence of digital healthcare addressing healthcare access disparities has been reported with an SG that uses naturalistic gaming data and a random forest classifier to classify ASD and TD children (AU-ROC = 0.745, recall = 0.769) [49]. On the same gameplay dataset, another study achieved 79% accuracy utilizing a convolutional neural network to classify children's audio as either ASD or TD [50]. In addition, by imitating a model's dancing moves using Kinect Xbox motion tracking, researchers using a support vector machine approach reported a valid biomarker: children with ASD showed worse imitation compared to TD children, which is also linked to more excellent core autistic symptoms and discrimination (accuracy = 87.2%) between the two groups [51]. Although machine learning (ML) is widely used in Serious Games (SGs), there is a need for more research that combines ML and SGs in the healthcare industry [52]. Most studies focus on rehabilitation to provide individualized and entertaining healthcare and less on prevention and diagnosis to address personalized screening, performance assessment, monitoring, risk modeling, and treatment response prediction [52; 53; 54]. Further, standardized diagnoses and objective evaluations are repeatable [42]. However, ML algorithms in SGs addressing communication, cognition, emotion, and behavior in children with ASD require further research to create personalized, effective, and successful clinical experiences [55]. This study aims to explore how neural networks can be developed to classify children into two categories: TD and children with ASD. The results can be utilized in an intelligent system with automated results to enhance assistance for healthcare professionals and upscale decisions and evaluations regarding screening procedures discriminating between TD children and those with ASD. The study used a new biometric dataset and created various neural networks with optimizers to categorize children automatically. The study continues with Section 2, which provides background knowledge on constructing neural networks and implementing optimizers. Next, Section 3 describes the research methods and the dataset. Section 4 presents the results of this study, including statistical analysis and experimental results. Finally, Section 5 presents the research's final findings, including limitations and ideas for future research.
# One-Photon-Interference Quantum Secure Direct Communication
## Appendix D Simulation Formulas for DL04 and MDI-QSDC Protocol  
The secrecy rate of DL04 protocol with INCUM is given by [26]  
\[C_{s}^{M}\geq\max\{Q_{B}[1-h(e)-h(\varepsilon_{x}+\varepsilon_{z})],0\}. \tag{103}\]  
In the simulation, the gain of [PERSON] \(Q_{A}\), [PERSON] \(Q_{B}\) and [PERSON] \(e\) is given by [26]  
\[\begin{split} Q_{A}&=\eta_{d}\eta_{c}+p_{d},\\ Q_{B}&=\eta_{d}\eta_{c}^{2}+p_{d},\\ eQ_{B}&=e_{d}\eta_{d}\eta_{c}^{2}+e_{0}p_{d},\end{split} \tag{104}\]  
where \(e_{d}=1.3\%\) is the intrinsic detector error rate, and \(e_{0}=1/2\) is the background error rate. For simplicity, we assume that the detection error rate \(\varepsilon_{x}=\varepsilon_{z}=\varepsilon\), and it is given by  
\[\varepsilon Q_{A}=e_{d}\eta_{d}\eta_{c}+e_{0}p_{d}. \tag{105}\]  
The secrecy rate of MDI-QSDC protocol with INCUM is given by [57]  
\[C_{s}=Q[1-h(e)-h(\varepsilon_{y})], \tag{106}\]  
where \(Q\) is the signal gain of Bob for message decoding, while \(e\) and \(\varepsilon_{y}\) represent the QBER and the detection error rate, respectively. And the gain \(Q\) is given by [57; 58]  
\[\begin{split} Q&=q_{\text{C1}}q_{\text{C2}},\\ q_{\text{C1}}&=\frac{\eta_{c}}{3}(G_{X}+G_{Z}),\\ G_{X}&=G_{Y}=\mathcal{P}_{12}^{-2}+\mathcal{P}_{14}^ {-+}+\mathcal{P}_{12}^{++}+\mathcal{P}_{14}^{++},\\ G_{Z}&=2(\mathcal{P}_{ij}^{HV}+\mathcal{P}_{ij}^{ HH}),\\ q_{\text{C2}}&=\eta_{c}+(1-\eta_{c})p_{d}.\end{split} \tag{107}\]Here,  
\[\mathcal{P}_{ij}^{HV}=\mathcal{P}_{ij}^{VH}=(1-\eta_{c})^{2}p_{d}^{2}(1-p_{d})^{2} +(1-\eta_{c})\eta_{c}p_{d}(1-p_{d})^{2}+\frac{1}{4}\eta_{c}^{2}(1-p_{d})^{2}. \tag{100}\]  
\[\mathcal{P}_{ij}^{HH}=\mathcal{P}_{ij}^{VV}=(1-\eta_{c})^{2}p_{d}^{2}(1-p_{d})^ {2}+(1-\eta_{c})\eta_{c}p_{d}(1-p_{d})^{2}+\frac{1}{2}\eta_{c}^{2}p_{d}(1-p_{d} )^{2}. \tag{101}\]  
\[\mathcal{P}_{12}^{-+}=\mathcal{P}_{12}^{+-}=\mathcal{P}_{34}^{-+}=\mathcal{P}_ {34}^{+-}=(1-\eta_{c})^{2}p_{d}^{2}(1-p_{d})^{2}+(1-\eta_{c})\eta_{c}p_{d}(1-p_ {d})^{2}+\frac{1}{4}\eta_{c}^{2}p_{d}(1-p_{d})^{2}. \tag{102}\]  
\[\mathcal{P}_{14}^{-+}=\mathcal{P}_{14}^{+-}=\mathcal{P}_{23}^{-+}=\mathcal{P}_ {23}^{+-}=(1-\eta_{c})^{2}p_{d}^{2}(1-p_{d})^{2}+(1-\eta_{c})\eta_{c}p_{d}(1-p _{d})^{2}+\frac{1}{4}\eta_{c}^{2}(p_{d}+1)(1-p_{d})^{2}. \tag{103}\]  
\[\mathcal{P}_{12}^{++}=\mathcal{P}_{34}^{++}=\mathcal{P}_{12}^{---}=\mathcal{P }_{34}^{---}=(1-\eta_{c})^{2}p_{d}^{2}(1-p_{d})^{2}+(1-\eta_{c})\eta_{c}p_{d}(1 -p_{d})^{2}+\frac{1}{4}\eta_{c}^{2}p_{d}(1-p_{d})^{2}. \tag{104}\]  
the QBER and the detection error rate is given by [57; 58]  
\[\begin{split} e&=\frac{e_{0}p_{d}+e_{\text{edd}}\eta _{c}}{\eta_{c}+(1-\eta_{c})p_{d}},\\ \epsilon_{y}&=\frac{e_{d}(1-2\mathcal{E}_{y})+ \mathcal{E}_{y}}{\sum\limits_{(i,j)\in\{(1,A),(2,3)\}}(\mathcal{P}_{ij}^{++}+ \mathcal{P}_{ij}^{--})+\sum\limits_{(i,j)\in\{(1,2),(3,A)\}}(\mathcal{P}_{ij} ^{+-}+\mathcal{P}_{ij}^{-+})}{4G_{Y}}.\end{split} \tag{105}\]