esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 3 Resonance Conditions
### Localized Surface Plasmon Resonance  
LSPR is an optical phenomenon that arises when the incident light wave is trapped within conductive nanoparticles with dimensions smaller than that of the wavelength of the light [41]. While SPs propagate along the continuous metal surfaces, LSPs are localized around the surface of the nanoparticles. Under the resonance condition, metal nanoparticles will present a strong absorption band of light that will enhance their damping amplitude [42]. Recently, the particle-optical wave excitation, which occurs in the visible wavelength region for noble metal nanoparticles with dimensions smaller than the wavelength of incident light, is reported [43].
## 2 Materials and Methods
### Experimental Design, Optimization and Validation of Optimized Conditions  
The extraction parameters were optimized through central composite response surface methodology. On the basis of preliminary results (data not shown), proper ranges of four independent variables including ultrasonic power (X\({}_{1}\)), sonication time (X\({}_{2}\)), ratio of water to raw material (X\({}_{3}\)) and extraction temperature (X\({}_{4}\)) were obtained, and then central composite design with three levels was  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{4}{c}{**Uncoded Variable Levels**} & \multicolumn{3}{c}{**Extraction Yield (\%)**} \\ \cline{2-7}  & **X\({}_{1}\): Ultrasonic** & **X\({}_{2}\): Sonication** & **X\({}_{3}\): Ratio of Water to** & **X\({}_{4}\): Extraction** & & \\  & **Power (W)** & **Time (min)** & **Raw Material (mL/g)** & **Temperature (\({}^{\circ}\)C)** & **Experimental** & **Predicted** \\ \hline
1 & 50.00 & 20.00 & 20.00 & 40.00 & 4.45 & 4.41 \\
2 & 150.00 & 20.00 & 20.00 & 40.00 & 4.70 & 4.66 \\
3 & 50.00 & 80.00 & 20.00 & 40.00 & 5.17 & 5.12 \\
4 & 150.00  
performed. The extraction yield of crude polysaccharide (Y) was taken as the dependent variable of design experiment. As shown in Table 1, the whole design was composed of 30 experimental runs carried out in random order, with six replications at the center point to estimate the repeatability of the method. An empirical second-order polynomial model was fitted to correlate the response and independent variables as follows:  
\[Y\left(\%\right)=\beta_{o}+\sum_{i=1}^{4}\beta_{i}X_{i}+\sum_{i=1}^{4}\beta_{ ii}X_{i}^{2}+\sum_{i<j=3}^{4}\beta_{ij}X_{i}X_{j} \tag{1}\]  
where \(Y\) (\(\%\)) is the dependent variable, and \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\) are the regression coefficients for model intercept, linear, quadratic, and interaction terms, respectively; while \(X_{i}\) and \(X_{j}\) are the independent variables.  
Experimental design, data analysis, and quadratic model building were conducted using the Stat ease Design Expert 7.0.0 statistical software (Stat-Ease Inc., Minneapolis, MN, USA). The significance of all the terms in the model was assessed by the probability (\(p\)) of 0.05. The adequacy of the model was checked accounting for determination coefficient (\(R^{2}\)), adjusted-\(R^{2}\), the coefficient of variance (C.V.%) and predicted error of sum of square (PRESS) [21].  
Numerical optimization was carried out to predict the exact optimum level of independent variables leading to the desirable response goal. In this regard, all the independent variables were kept within range, while the response was maximized. Additional triplicate experiments were carried out under optimal extraction conditions in order to determine the validity of optimized conditions. The average value of the validation experiment was compared with the predicted value of the developed condition in order to find out the accuracy and suitability of the optimized conditions [21].
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 5 Performance Evaluations
### Simulation Results
#### 5.3.3 Average End-to-End Delay Evaluation  
Another series of experiments are carried out to investigate the relationship between the typical end-to-end latency and the typical traffic rate. Comparisons are made between the HMBCR [10], ECRP [11], and EPSO-CEO [24] approaches for homogeneous and heterogeneous networks operating at varying traffic rates using the ERCP technique that was just proposed. The differences in the average end-to-end delay that occur with the average traffic rate are shown in Figures 5 and 6 for homogeneous and heterogeneous traffic patterns, respectively.  
The suggested ERCP has the lowest end-to-end latency compared to the HUMBCR, ECRP, and EPSO-CEO algorithms for both homogeneous and heterogeneous networks, as evident from the numbers below. It selects forwarding nodes that can send the packet through a more reliable connection, hence lowering the probability of packet loss and retransmission, which, in turn, lowers the delivery latency.  
As mentioned above, in the case of the HUMBCR, ECRP, and EPSO-CEO algorithms, packets cannot bypass the lossy links, which results in an increase in the end-to-end latency due to the retransmission of lost packets.  
Figure 4: Influence of increasing average traffic rate on packets’ delivery ratio for heterogeneous networks.  
Figure 3: Influence of increasing average traffic rate on packets’ delivery ratio for homogenous networks.
# Material Removal Model for Lapping Process Based on Spiral Groove Density
## 3 Experimental Results  
### Material Removal Rate  
In the lapping process, the groove density affects the material removal rate by varying the flow of the slurry at the contact interface and the contact pressure on the wafer. The simulation results from the material removal model and the experimental results are compared in Figure 8a. In the experimental results, the material removal rate is the lowest at the lowest groove density of 0.1 and drastically increases until the groove density is 0.3, indicating a noticeable difference between the material removal model and the experimental result in the groove density range of 0.1 to 0.3. When the groove density exceeds 0.3, the increase in material removal rate is proportional to the groove density and in good agreement with the material removal model. The difference between the material removal model and the experimental result in the groove density range below 0.3 can be attributed to the change in the contact interface condition, which was confirmed through the analysis of the frictional force occurring in the process.  
As shown in Figure 8b, which shows the relationship between the material removal rate and the frictional force, the material removal rate is linearly proportional to the frictional force, which is 40 N or less at the low groove density of 0.1 to 0.2. The frictional force is 60 N or more when the groove density is 0.3 or higher and also exhibits a significant difference at a groove density of approximately 0.3, similar to the material removal rate. Based on this, it is believed that when the groove density is lower than 0.3, the slurry at the contact interface brings the hydroplaning phenomenon into effect, and some or most of the load applied to the wafer is supported by the slurry, resulting in a lower frictional force. On the other hand, as the groove density increases, the load supported by the slurry decreases, while the load delivered to the abrasives increases, leading to a significantly higher material removal rate. Similar results were reported by [PERSON] et al. [26].  
Figure 8: (**a**) Comparison of the theoretical material removal rate with the experimental results according to groove density; (**b**) relation between material removal rate and frictional force.  
The theoretical values vary from the actual material removal rates because the effect of the dynamic pressure by the slurry was not considered in the material removal model, while all of the load applied to the wafer was assumed to transfer to the abrasives. However, the experimental values were in better agreement with the material removal model for groove density values higher than 0.3 due to the absence of the hydroplaning phenomenon. To confirm this, changes in the oil film thickness and frictional force with groove density were analyzed at the contact interface.
## 3 Results
### Segmentation Method Dependence Results  
The robustness of the MRI radiomic features was evaluated using a six-level scale (going from 0 to 5) and based on a combination of ICCs and the SD of the mean percentage differences between the three raters (i.e., auto, man1, man2). Percentage differences were evaluated according to Equation (6). Each feature was evaluated considering the ICC and SD. The score--from 0 (very high robustness) to 5 (very low robustness)--was assigned according to the conditions reported in Table 2.  
The values obtained are illustrated, for each of the five feature categories, in the following figures (Figures 7-11).  
Starting from the previously defined scale (see Table 2), an overall robustness value--defined according to Equation (7)--was computed for each radiomic category \(\Phi\) to summarize the robustness by feature category across the segmentation methods. This formula gives values between 0 (lower robustness) and 1 (higher robustness). Results are shown in Table 4. Instead, for each category of features, Table 5 displays the percentage of robust features (which have obtained a roughness index \(\geq\) 3) as the segmentation methodology varies.  
\[Robustness(\Phi)=\frac{\sum_{i\in\Phi}S_{\phi_{i}}}{maxValue\times|\Phi|} \tag{7}\]  
where:  
* \(\Phi\) is the radiomic category (\(\Phi\in\{GLCM,GLRLM,GLSZM,GLDM,NGTDM\}\));
* \(S_{\phi_{i}}\) represents the score on the scale described above (see Table 2) for the robustness of each feature \(i\);
* _maxValue_ represents the maximum possible value (i.e., 5);
* \(|\cdot|\) represents the cardinality of a given radiomic category \(\Phi\).  
Figure 11: Robustness of the NGTDM radiomic features obtained on DCE-MRI images using automated segmentation against two manual segmentation.  
Figure 9: Robustness of the GLSZM radiomic features obtained on DCE-MRI images using automated segmentation against two manual segmentations from two independent readers.
## 2 Materials and Methods  
### 3D-Printed Specimens Preparation  
As the test standard DIN EN ISO 178 requires the usage of test pieces with a length (l) height (h) ratio of l/h = 20 for 3-point bending tests on polymer materials, a virtual model of the specimens with the dimensions 2 mm \(\times\) 25 mm \(\times\) 40 mm was designed using the software 3D-Builder (Microsoft, Redmond, WA, USA). In total, 75 specimens (i.e., 15 for each of the 5 materials) were printed for 3-point bending tests and Vickers hardness tests. With regard to test groups, two different digital light processing (DLP) printers (group 1: NextDent 5100, Vertex-Dental B.V., Soesteberg, The Netherlands; group 2: ASIGA MAX, Pluradent GmbH & Co. KG, Offenbach, Germany), one desktop stereolithography (SLA) printer (group 3: Form labs Inc., Sommerville, MA, USA), and one liquid crystal display stereolithography (LCD-SLA) printer (group 4: Slash Plus, UniZ Technology LLC., San Diego, CA, USA) were used. The samples were produced using four different 3D printing resins, one for each printing machine (group 1: NextDent SG, Vertex-Dental B.V.; group 2: Optiprint Guide, dentona AG, Dortmund, Germany; group 3: Dental SG, Formlabs Inc.; group 4: zSG Amber, UniZ Technology LLC.). All of the resins mentioned before are authorized by the manufacturer for steam autoclaving and printing with the particular printer utilized. The resin used in group 0 (E-Guide, Envisiontec Inc., Dearborn, MI, USA) is not authorized by the manufacturer for steam autoclaving, but only for immersion disinfection. It was printed with the DLP printer authorized by the manufacturer (Micro Plus XL, Envisiontec Inc.).  
All specimens were printed such that the printing layers were parallel to the longer edge and perpendicular to the support structures and to the shorter edges, as recommended by [PERSON] et al. (2018).  
Five specimens per group (total \(n=25\)) were not subjected to autoclaving and were used as controls. Ten specimens per group were sterilized by two different vacuum steam autoclaving programs (Vacuklav 44-B, MELAG oHG, Berlin, Germany): 5 specimens at 121 \({}^{\circ}\)C, 1 bar, and 20.5 min (cycle 1), while the remaining 5 were sterilized at 134 \({}^{\circ}\)C, 2 bar, and 5.5 min (cycle 2). A summary is provided in Table 1.
## 4 The User Eye-Tracking Experiment
### Experimental Results
#### 4.5.2 A Heatmap Analysis and the Feasibility of Personalized Information Displays  
In Experiment 2, the VR interface was optimized based on the multidimensional sentiment model derived from affective computing. Eye-tracking technology was employed to record the distribution of users' fixation points in the optimized interface. Heatmap visualization was then used to analyze the allocation of user attention, allowing for an assessment of how personalized information presentation influences user engagement, historical understanding, and cultural experience.  
By observing and analyzing the heatmap results shown in Figure 8, key patterns emerged. In the Zhengyangmen scene, users' fixation points were primarily concentrated in the cultural and architectural modules, especially in text areas related to the historical background. This indicates that users were more inclined to focus on architectural history, cultural narratives, and their evolution over time.  
In general, the optimized presentation of personalized information based on UGC data enhances user engagement by shaping the distribution of their attention across different cultural heritage scenarios. In historical architectural settings (e.g., Zhengyangmen), users exhibit a stronger focus on architectural history and cultural narratives, while in culinary heritage settings (e.g., Quanjude), their attention shifts toward brand culture and service quality. This variation in the attention allocation suggests that scene-specific content optimization can effectively influence users' information engagement patterns, ultimately enhancing their cultural cognition and immersive experience in distinct historical and cultural contexts.
# An Enhanced YOLOv5-Based Algorithm for Metal Surface Defect Detection
## 1 Introduction
Facing the challenges of general object detectors and their backbone networks in small object defect detection tasks, as well as the difficulty in balancing the detection accuracy and efficiency of existing metal surface defect detection, a surface defect detection network called the Information Enhancement YOLOv5 network (IE-YOLOv5) is proposed in this paper. The standard convolution block in the neck module is replaced with a lightweight one in the IE-YOLOv5 network to construct a lightweight Neck module-GSConv, which can enhance the feature extraction efficiency. A slim neck structure with feature fusion based on the federated learning algorithm (FedFusion) [9], FF-Slim-neck, is proposed to optimize the Neck module. The Parameter-free Spatial Attention mechanism (PSA) is introduced into the YOLOv5 network to enhance the feature extraction capability, enabling swift and precise detection of defects on metal surfaces. In summary, the contributions of this study are as follows:  
1. The novel lightweight convolution module GSConv is employed to replace the standard convolution in the original YOLOv5 network's Neck module, which allows efficient preservation of hidden links for each channel and feature extraction efficiency improvement. 2. Utilizing the federated learning algorithm known as FedFusion, we introduce a feature fusion module designed to aggregate features from both local and global models at the feature extraction stage. Combined with GSConv, the FF-Slim-Neck structure is constructed to optimize the Neck module of the network and reduce additional computational costs. 3. A lightweight Parameter-free Spatial Attention mechanism called PSA is proposed to reduce network parameters and enhance network representation capacity. Additionally, the YOLOv5's CIoU loss function is optimized to EIoU loss function, which improves prediction accuracy and speeds up model convergence.
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
###### Abstract  
Early detection of caries is an urgent problem in the dental clinic. Current caries detection methods do not detect early enamel caries accurately, and do not show microstructural changes in the teeth. Optical coherence tomography (OCT) can provide imaging of tiny, demineralized regions of teeth in real time and noninvasively detect dynamic changes in lesions with high resolution and high sensitivity. Over the last 20 years, researchers have investigated different methods for quantitative assessment of early caries using OCT. This review provides an overview of the principles of enamel caries detection with OCT, the methods of characterizing caries lesion severity, and correlations between OCT results and measurements from multiple histological detection techniques. Studies have shown the feasibility of OCT in quantitative assessment of early enamel lesions but they vary widely in approaches. Only integrated reflectivity and refractive index measured by OCT have proven to have strong correlations with mineral loss calculated by digital microradiography or transverse microradiography. OCT has great potential to be a standard inspection method for enamel lesions, but a consensus on quantitative methods and indicators is an important prerequisite. Our review provides a basis for future discussions.  
optical coherence tomography; early enamel caries; quantitative assessment; quantitative indicators
## Chapter 1 Cyberattack Path Generation and Prioritisation for Securing Healthcare Systems  
### 1.1 Introduction  
The healthcare sector is becoming more digitally connected due to the advancement of technology, and so the potential risk of a cyber incident will increase. The connectivity of medical devices with other software and information communication technology (ICT) infrastructures poses potential risks. There is increasing concern that the connectivity of these medical devices will directly affect healthcare service delivery and patient safety, which is unique compared to traditional computing systems [1]. Research studies have shown that the number of hacking incidents reported in healthcare was 42% more in 2020 [2]. The healthcare information infrastructure is equipped with medical devices that require both physical and cyber interaction, which can create new attacker capabilities [3]. It is necessary to identify the possible attacks and related paths that can pose potential riskswithin the healthcare context. To our knowledge, this is the first study that focuses on the vulnerabilities related to healthcare devices and their dependencies on other information technology (IT) infrastructures to propagate an attack.  
The present paper illustrates an evidence-based attack path discovery method, considering the unique characteristics of the healthcare information infrastructure, such as assets, and its cyber and physical dependencies, vulnerabilities, threat actor and user profile, and indicator of compromise (IoC). There are three main contributions of this work. Firstly, the proposed approach includes a systematic process for attack path identification based on the assets, dependencies, and vulnerabilities. It adopts the existing standards, such as common vulnerabilities and exposures (CVE) and the common vulnerability scoring system (CVSS), to identify and analyse the vulnerabilities relating to the attack paths. These repositories contain huge amounts of data about the vulnerabilities. The identified attack paths are prioritised based on the IoC, which shows the evidence of any attack. Secondly, a knowledge base is developed that consists of rule-based reasoning to identify the possible attack paths. The rules are based on certain conditions that are necessary for a successful attack campaign. This allows us to determine possible attack paths so that appropriate control actions can be taken for securing the system. Finally, a real healthcare use case scenario that processes big data of healthcare applications is considered to validate the applicability of the proposed method. The results show that it is a practical approach for the attack path generation and determine the necessary areas that need adequate protection for the overall cyber security improvement.
## 4 Complexity Analysis of the VVC Decoder
### Correlation between the HGPP and EGPP  
In this subsection, the correlation between the HGPP and EGPP is presented. This analysis allowed us to identify the effect of the hardware resources on the performance of different decoding blocks. Table 4 presents the average processing times (in secs) of each decoding block over the HGPP and EGPP for the AI and RA configurations. The processing times of ED, TX, IP, DBF, and OT were roughly \(\times\)2 higher than the processing time of the EGPP with respect to the HGPP for both the AI and RA configurations. Moreover, the processing times of the ALF were \(\times\)7.1 and \(\times\)7.4 higher than those of the EGPP for the AI and RA configurations, respectively. Additionally, for the RA configurations, the EGPP required \(\times\)3.5 more time to process the EP. However, the fps values obtained over the HGPP for the AI and RA configurations were \(\times\)2.5 and \(\times\)3.3, respectively. These results show that the influence of the architecture impacts mainly the EP block for the RA configurations and the ALF block for the AI and RA configurations. Therefore, the acceleration process is addressed in the EP and ALF over the EGPP.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **ED** & **TX** & **IP** & **EP** & **DBF** & **ALF** & **OT** & **fps** \\ \hline \multirow{3}{*}{AI} & HGPP & 49.5 & 19.9 & 51.9 & 0.0 & 72.2 & 13.9 & 22.0 & 2.2 \\  & EGPP & 113.0 & 43.6 & 121.3 & 0.0 & 143.8 & 98.3 & 53.6 & 0.9 \\  & Ratio & 2.3 & 2.2 & 2.3 & n/a & 2.0 & 7.1 & 2.4 & 2.5 \\ \hline \multirow{3}{*}{RA} & HGPP & 12.4 & 5.7 & 6.5 & 31.7 & 28.2 & 10.5 & 9.7 & 5.6 \\  & EGPP & 30.3 & 13.5 & 16.2 & 111.6 & 61.8 & 77.8 & 21.6 & 1.7 \\ \cline{1-1}  & Ratio & 2.5 & 2.4 & 2.5 & 3.5 & 2.2 & 7.4 & 2.2 & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average processing times (in secs) for each decoding block, average global fps achieved and ratio between HGPP and EGPP.  
Figure 6: Average time distribution for different blocks of the VVC decoder (in %) over the EGPP.