esa-sceva/satcom-chunk-collection huggingface dataset:
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 4 Discussion
### Denoising Techniques and Segmentation Accuracy  
The results demonstrate that wavelet-based denoising improves the accuracy of retinal layer thickness computation across all layers, including those where Gaussian-filtered images showed discrepancies. However, this increased accuracy comes at the cost of significantly longer computation times. The wavelet-based approach requires approximately 10 s for denoising before segmentation can commence, compared to the much quicker Gaussian filtering.  
This study also notes that variations in normative retinal layer thickness values can be attributed to factors such as different OCT devices, regions of the retina scanned, and demographic variables like ethnicity, age, and sex. These factors must be considered when comparing results across different studies and populations. While wavelet-based denoising provides marginally better accuracy, the simplicity and speed of Gaussian filtering make it a viable option for many clinical applications, particularly where time and computational resources are limited.  
The overall segmentation results indicate that the graph-based algorithm performs efficiently even without advanced denoising techniques. This finding is consistent with previous literature on graph-based image segmentation algorithms, reinforcing the utility of Gaussian filters for preprocessing SD-OCT images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Retinal Layer**} & \multicolumn{2}{c}{**Manual Segmentation**} & \multicolumn{2}{c}{**Automated Segmentation**} & \multirow{2}{*}{**Mean Error**} & \multirow{2}{*}{**Accuracy (\%)**} \\  & **Segmentation** & & & \\ \cline{2-4}  & **Mean Thickness** & **Mean Thickness** & **Mean Thickness** & **Mean Error** & **Accuracy (\%)** \\  & \(\pm\)**SD (in \(\mu\))** & \(\pm\)**SD (in \(\mu\))** & & \\ \hline RNFL + GCL & 22.76 \(\pm\) 2.52 & 25.02 \(\pm\) 3.16 & \(-\)2.26 \(\pm\) 1.59 & 90.07 \\ \hline IPL & 7.24 \(\pm\) 2.81 & 5.40 \(\pm\) 2.79 & 1.84 \(\pm\) 1.64 & 74.58 \\ \hline INL & 6.46 \(\pm\) 1.12 & 5.94 \(\pm\) 1.10 & 0.52 \(\pm\) 0.69 & 91.95 \\ \hline OPL & 8.23 \(\pm\) 0.93 & 8.45 \(\pm\) 0.96 & \(-\)0.22 \(\pm\) 0.67 & 97.33 \\ \hline ONL + IS & 16.42 \(\pm\) 1.58 & 16.24 \(\pm\) 1.76 & 0.18 \(\pm\) 0.74 & 98.90 \\ \hline OS + RPE & 12.18 \(\pm\) 4.68 & 12.67 \(\pm\) 6.04 & \(-\)0.49 \(\pm\) 1.57 & 95.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results showing the layer-wise mean thickness values computed by the algorithm for manual and automated segmentations, the mean error, and accuracy of segmentation for each layer across 25 SD-OCT images.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Segmentation Technique**} & \multicolumn{2}{c}{**Mean Computation Time (in Seconds)**} \\ \hline Automated & 4.93 \\ \hline Manual & 578.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average time taken by the algorithm to segment the foveal and non-foveal image slices of SD-OCT images across the test dataset.
## 4 Experimental Results and Analysis
### Evaluation Indexes  
Precision, recall, and AUC are the three evaluation indexes used to quantify the relatively important nodes obtained by several algorithms in this work.  
Precision is mainly used to measure whether the \(top-L\) nodes in the results by the algorithm are predicted correctly. It is specifically defined as the proportion of correct predictions in \(top-L\) nodes among the predicted results. The formula is defined as:  
\[\text{precision}=\frac{N_{r}}{L} \tag{21}\]  
where \(N_{r}\) refers to the frequency at which the \(top-L\) nodes predicted by the algorithm occurred in the unknown important node set \(U\).  
Recall is mainly used to measure how many of the \(top-L\) nodes predicted by the algorithm are correctly predicted. It is specifically defined as the proportion of the number of unknown important nodes \(n_{r}\) found in the \(top-L\) nodes in the prediction results relative to all nodes in the unknown important node set \(U\). The formula is defined as:  
\[\text{recall}=\frac{n_{r}}{|U|} \tag{22}\]  
AUC is mainly used to measure the precision of the algorithm as a whole. The formula is defined as:  
\[\text{AUC}=\frac{0.5N_{1}+N_{2}}{N} \tag{23}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \(n\) & \(m\) & \(n_{1}\) & \(\overline{k}\) & \(C\) \\ \hline SARS & 224 & 2247 & 18 & 20.06 & 0.65 \\ Genepath & 6306 & 57,340 & 51 & 18.19 & 0.32 \\ Mouse & 1187 & 1557 & 67 & 2.62 & 0.09 \\ Yeast & 5093 & 24,743 & 1167 & 9.72 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic topological characteristics of real networks.  
The specific calculation process for AUC is as follows: one node is selected from the unknown important node set \(U\), and another is selected from the unimportant node set \(V_{2}\) in each experiment, and the relative importance scores of the two nodes are compared. If the two nodes receive the same score, then the score is recorded as 0.5 point; if the relative importance score of the node selected from the unknown important node set \(U\) is greater than that from the unimportant node set \(V_{2}\), then the score is recorded as 1 point. \(N\) represents the number of all node combinations from the two sets \(U\) and \(V_{2}\). After \(N\) independent experiments, the final AUC value is the sum of the scores of \(N\) experiments. Among them, the frequencies of getting 0.5 point and 1 point are \(N_{1}\) and \(N_{2}\), respectively.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 2 Background
### Network Slicing  
The 5G wireless technology enables the provisioning of network slices for use-case-specific connectivity requirements such as the required data rate, latency, reliability, availability, and number of devices to be supported. The NSSF is used to select network slices based on the customer's connectivity requirements. A slice type is identified using the Single-Network Slice Selection Assistance Information (S-NSSAI) identifier consisting of an 8-bit Slice/Service Type (SST) for the type of service, and an optional 24-bit slice differentiator to differentiate among slices of the same SST [16]. The 8-bit SST field gives room for hundreds of different slice types and further differentiation through the slice differentiator. Likewise, one user equipment can request an NSSAI consisting of 8 S-NSSAIs at the same time. Thus, devices from different customers can be provisioned with the requested slice types out of the standardized slice types, or by configuring the slice templates or defining new slice types altogether [17]. As a result, slice allocation between devices and slice types must follow one-to-one, one-to-many, many-to-many, or many-to-one relationships. For the scope of this paper, only the standardized slice types are included in the cost model, and no additional slices are defined. However, any number of slice types can be included in the model.  
Network slice provisioning can exercise different levels of isolation between the VNFs of different slices. For example, two use cases--a and b--can be served over the same physical infrastructure without logical isolation, where all VNFs are common; they can also be served with partial/full logical isolation, where some/all VNFs can be dedicated per slice. This is illustrated in Figure 1, where a separate VNF1 is used for each slice for partial isolation, but VNF2 and VNF3 are shared between slices; in the case of full isolation, each VNF is dedicated per slice. The level of isolation between VNFs can depend on use-case-specific technical requirements, data security, and the required flexibility in configuring and managing VNFs for each slice. Furthermore, the slices can consist of VNFs deployed in different physical locations such as the UP VNFs being deployed locally or in the edge cloud, while the CP VNFs can be deployed in an operator's central cloud.  
Different isolation options can be applied in the cost modeling. The dedicated/common VNFs translate to dedicated/common VMs, which in turn translate into servers, racks, cabling, switches, and the required software licenses, VNF licenses, electricity, cooling, and maintenance cost allocations per slice.
## 4 Results and Discussion
### Non-Linear Seismic-Induced Oscillations
#### 4.2.1 Seismic-Induced Oscillation of Single Channel System
The results illustrate that the increase of inlet loss coefficient will drive the system to show a smaller non-linear oscillation. Increasing the flow resistance at channel inlet will increase the pressure drop of heavy fluid region. Moreover, the increase of inlet flow resistance will also attenuate the seismic-induced oscillation of the system. Thus, the increase of the inlet loss coefficient would generate a stable effect on the seismic-induced oscillation. Figure 5: The influence of parameter on the seismic-induced oscillation: (**a**) the effect of operating state with uprating power; (**b**) the effect of inlet flow temperature. Figure 5(b) further evaluates the influence of the outlet loss coefficient (\(k_{e}\)) on the seismic-induced oscillation of a single supercritical heated channel. The analytical results show that a larger outlet loss coefficient will result in a more prominent seismic-induced oscillation. The increase of outlet loss coefficient will increase the pressure drop of light fluid region inside the channel. Our previous study [14] indicated that increasing the pressure drop in the light fluid area could destabilize the system. On the other hand, the increase of outlet flow resistance would also produce an attenuated influence on the seismic-induced oscillation. It seems that the unstable effect is dominant such that increasing the outlet loss coefficient (\(k_{e}\)) tends to destabilize the seismic-induced oscillation of this single heated channel system. Figure 7 explores the influence of the heated length (\(L_{H}\)) and channel diameter (\(D_{H}\)), respectively, on the transient oscillations of a single supercritical heated channel caused by vertical seismic acceleration wave in Figure 3(a). At a fixed uniform heat flux, a longer heating channel implies a higher input power, which will extend the length of the light fluid region. Moreover, lengthening the channel will enlarge the seismic-induced pressure drop estimated by Equation (8). Thus, the analytical results in Figure 6(a) show that a longer channel tends to destabilize the seismic-induced oscillation of single supercritical heated channel. On the other hand, at a fixed total flow rate, increasing the channel diameter will reduce the flow velocity; thereby a smaller channel pressure drop would stabilize the system. Therefore, Figure 6(b) illustrates that a larger channel diameter could generate a stable effect on the seismic-induced oscillation of a single supercritical heated channel.
# Review of Studies on User Research Based on EEG and Eye Tracking
## 4 Application Scope
### Industrial Products  
Eye tracking (ET) and electroencephalograms (EEGs) are used to evaluate the design and function of products in various fields such as transportation, furniture, clothing, and even packaging. These measurement indicators can predict quantifiable perceptual reactions, and provide a more objective and realistic understanding of users' aesthetic preferences. Currently, they are mainly used for the evaluation of design and function, helping decision makers to analyze users' real reflections on products, providing more intuitive and persuasive forms of expression for research results, and helping them to better understand users' attention to product functions, styling, and structure, thereby clarifying user demands. They can help product innovation or improvement at the design level, and can help researchers to better understand user or consumer feedback, preferences, and needs in market strategies, thereby improving user experience. They have an important significance and role in consumer decision making.
## 4 Discussion
### Limitations and Recommendations  
The limitation of this study is the reliance on CBCT imaging, which, while highly accurate, may not be accessible in all clinical settings, potentially limiting its generalizability. Additionally, this study's cross-sectional design provides a snapshot of PRG prevalence but does not allow for the assessment of how these anatomical features progress or impact periodontal and endodontic health over time. The relatively low prevalence of rarer groove types, such as Type 3B, may have reduced the statistical power to identify subtle associations with clinical outcomes.  
Future studies should explore larger, more diverse populations to validate these findings and investigate potential regional or genetic influences on PRG prevalence. The use of AI-based diagnostic tools could further enhance the detection and classification of PRGs, improving early diagnosis and treatment planning.
# Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis
###### Abstract  
Since machine vision systems (MVS) lead to a wide usage of monitoring systems for industrial applications, the research on the statistical process control (SPC) of image data has been promoted as an automated method for early detection and prevention of unusual conditions in manufacturing processes. In this paper, we propose a non-parametric SPC approach based on the 2D wavelet spectrum (WS-SPC) to extract the feature that contains the spatial and directional information of each subspace in an image. Using the 2D discrete wavelet transform and spectrum analysis, the representative statistic, the Hurst index, is calculated, and a single matrix space that consists of estimated statistics is reconstructed into a spatial control area for SPC. When a control limit is determined by the density of statistics, real-time monitoring based on WS-SPC is available for time releasing images. In the application, an analysis of wafer bin maps (WBMs) is conducted at a semiconductor company in Korea in order to evaluate the performance of the suggested approach. The results show that the proposed method is effective in terms of its fast computation speed and spectral monitoring.  
image processing; condition based maintenance; statistical process control; wavelet spectrum; wafer bin map +
Footnote †: journal: Applied  
13 December 2019; Published: 15 December 2019
###### Abstract  
This paper presents a potentiostat readout circuit with low-noise and mismatch-tolerant current mirror using chopper stabilization and dynamic element matching (DEM) for electrochemical sensors. Current-mode electrochemical sensors are widely used to detect the blood glucose and viruses in the diagnosis of various diseases such as diabetes, hyperlipidemia, and the H5 NI avian influenza virus (AIV). Low-noise and mismatch-tolerant characteristics are essential for sensing applications that require high reliability and high sensitivity. To achieve these characteristics, a proposed potentiostat readout circuit is implemented using the chopper stabilization scheme and the DEM technique. The proposed potentiostat readout circuit consists of a chopper-stabilized programmable gain transimpedance amplifier (TIA), gain-boosted cascode current mirror, and a control amplifier (CA). The chopper scheme, which is implemented in the TIA and CA, can reduce low frequency noise components, such as 1/f noise, and can obtain low-noise levels. The mismatch offsets of the cascode current mirror can be reduced by the DEM operation. The proposed current-mirror-based potentiostat readout circuit is designed using a standard 0.18 \(\upmu\)m CMOS process and can measure the sensor current from 350 nA to 2.8 \(\upmu\)A. The input-referred noise integrated from 0.1 Hz to 1 kHz is 21.7 \(\upmu\)BMS, and the power consumption was 287.9 \(\upmu\)W with a 1.8 V power supply.  
low-noise; mismatch-tolerant; electrochemical sensor; current mirror; chopper stabilization; dynamic element matching (DEM) +
Footnote †: journal: Applied
# Implementation and Security Test of Zero-Knowledge Protocols on SSI Blockchain
###### Abstract  
The problem of digital identity acquires more relevance every day in the eyes of a society that spends more and more time connected to the Internet. It has evolved throughout its history to reach a decentralized model known as Self-Sovereign Identity (SSI), which finds its natural tools in the blockchain technology and Zero-Knowledge Proofs (ZKPs). ZKPs, in this context, allow users to prove that their credentials are legitimate without revealing more information than is strictly necessary, and constitute one of the most promising areas of applied cryptography. In this work, an application is developed for the study of Zero-Knowledge Proof methods and, specifically, in their application for authentication in public-private key encryption systems. It focuses on the study of three ZKP protocols (Feige-Fiat-Shamir, Guillou-Quisquater, and Schnorr, which rely on the problems of large number factorizations and discrete logarithms for security) in the practical use-case where a prover wants to demonstrate knowledge of a private key for a public key without revealing the key itself. The application allows the user to modify the necessary parameters in each method to achieve a better understanding of their role in their safety and efficiency. Several types of attacks are carried out against the above-mentioned protocols to analyze their degree of security and what recommendations can be made to improve it.  
educational software; Self-Sovereign Identity; Zero-Knowledge protocols Article
## 3 Extraction of the Ground Vibration Components
### Comparison of EMD and MEEMD Based Long Period Component Extraction
#### 3.3.1 Extraction and Reconstruction of Long-Period Ground Vibration Components
Figure 11 shows the original ground shaking time course (ORG), the long-period component (LPC), and the short-period component (SPC) of three selected ground shocks: ILA056, TCU068, and CHY025. The three ground motions and their components act on Figure 4a, with the highest pier, No. 4, as the object of study, were used to study the displacement response under different pier heights. The displacement response of the bridge piers is shown in Figures 12-14. As shown in Figures 12 and 13, we used the two methods of extracting the long-period component and compared it with the original ground shocks. For the displacement of the piers from the impact of the long-period component, the moment of the maximum displacement was similar; in contrast, the short-period component and the original vibration had very different effects on the response of the structure. As shown in Figure 14, the long-period components extracted based on the two methods w \begin{table}
\begin{tabular}{c c c} \hline
**Ground Motion** & **Component** & **Tr (s)** \\ \hline \multirow{2}{*}{ILA056} & SPC & 1.31 \\  & LPC & 4.79 \\  & SPC & 0.703 \\  & LPC & 3.283 \\  & SPC & 1.215 \\  & LPC & 3.68 \\ \hline \end{tabular}
\end{table} 
Table 4: MEEMD-based separation of the reconstructed Tr. Figure 9: Comparison of the long-period component before and after basis correction: (**a**) Acceleration time course (TCU068 LPC); (**b**) Velocity time course (TCU068 LPC); (**c**) Displacement time course (TCU068 LPC). vibration response of the structure; however, the LPC obtained based on the MEEMD method was closer. Figure 10: Component time curve of TCU068: (**a**) acceleration (LPC); (**b**) acceleration (SPC); (**c**) velocity (LPC); (**d**) velocity (SPC); (**e**) displacement (LPC); (**f**) displacement (SPC). Figure 11: Time-range curves of acceleration components of three long-period ground shocks: (a) ILA056(ORG); (b) CU068(LPC); (c) ILA056(SPC); (d) TCU068 (ORG); (e) TCU068 (LPC); (f) TCU068(SPC); (g) CHY025(ORG); (h) CHY025 (LPC); (i) CHY025 (SPC). Figure 12: Displacement response of pier No. 4 with different pier heights (EMD): (a) ILA056; (b) TCU068; (c) CHY025. Long-period ground shaking has a large impact on the deformation of high-pier large-span bridges, and it is more intuitive to see that the long-period component plays a decisive role in the impact of long-period ground shaking on these structures, which can reflect or even represent the response characteristics of long-period ground shaking in long-period structures. The reasonable validity of extracting the long-period component in ground shaking based on the two methods was verified. Meanwhile, the MEEMD method was superior to the EMD method, and the long-period component extracted by the MEEMD method was more representative of the original ground shaking.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Channel Coding
#### 5.1.5 NPUSCH Format 2--Block Encoder  
The channel coding section for the NPUSCH format 2 includes the bit mapping only. Since the UCI data is transmitted via 1 bit, after the block coding module, it is represented by 16 identical bits. The procedure is presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Columns** & **Intercolumn Permutation Pattern** \\ \hline \(C_{subblock}^{TC}\) & \(<\)\(P(0),P(1),...,P(C_{subblock}^{TC}-1)\)\(>\) \\ \hline
32 & \(<\)0, 16, 8, 24, 4, 20, 12, 28, 2, 18, 10, 26, 6, 22, 14, 30, 1, 17, 9, 25, 5, 21, 13, 29, 3, 19, 11, 27, 7, 23, 15, 31\(>\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intercolumn permutation pattern for sub-block interleaver.
## 2 System Model of MC\(-\)ODBR\(-\)DCSK
### Chaotic Signal Generator  
Due to its noise-like characteristics, chaotic signals have good confidentiality as carrier signals, and have become a research hotspot in the field of communication. The chaotic signal generation model for chaotic digital modulation systems is generated by chaotic system mapping. The following will focus on several commonly used chaotic maps.
# Conductive Textiles for Signal Sensing and Technical Applications
## 7 Conclusions  
This review has highlighted various fabrication methods for conductive textiles with detailed information about conductive polymers. Research should be carried out to eliminate the disadvantages of various coating and printing methods of imparting conductivity. Graphene has become the center of fascination for researchers for its extraordinary properties. Smart textiles with electrical conductivity and signal-sensing capabilities are gaining more and more attention due to their various applications. E-textiles face many challenges, such as washability, power supply, product development, and commercialization, from users' perspective. There is a huge research gap in this aspect to investigate post-usage problems and the development of solutions.  
**Author Contributions:** Conceptualization, M.A.U. and A.S.M.S., Writing, M.K.H.K., M.T.S., H.R., M.R.R., M.T.A., M.H., M.R.K., T.R.R., F.A.T., I.H.S., A.W.F. and M.G.S.R., Review and Editing, M.G.S.R., M.A.U. and A.S.M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.