esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
System logs are widely used to analyze system running states in many fields and record events representing real-time system states. However, most log parsers do not exhibit both high accuracy and low running time. In this study, we proposed an efficient log parsing method with heuristic rules by clustering based on its length and first token (CFTL). CFTL first clusters log messages by the first token and the log message length, then uses specific  
Figure 9: Impacts of different LCS thresholds on accuracy and F1-measure.  
separation rules to divide the messages into groups, and finally obtains the corresponding log template of each log message. The proposed CFTL method was evaluated on seven datasets and compared with four state-of-the-art log parsing methods in terms of accuracy, F1-measure, and efficiency. The experimental results show that CFTL not only exhibits higher accuracy but also a lower running time. The number of log events parsed by log parsers of log templates generated by CFTL is also closer to the real count, and CFTL also exhibits a good accuracy rate for subsequent anomaly detection modules. In addition, CFTL is more lightweight with the most valuable parsing rules; thus, it is suitable for anomaly detection tasks. In future work, we will further investigate the proposed method from the perspective of multimodel integration and large-scale practical deployment to promote its deployment in practical application scenarios as soon as possible. In addition, the structure of application logs differs, and the applicability of CFTL in these application unstructured logs is not discussed in this study. Other logs can only provide very few heuristic rules. How to further optimize the proposed CFTL method to be suitable for application logs, audit logs, and other unstructured logs is also a future research direction. In addition, first determining which token is most relevant and then determining the cluster should be adopted as a leading policy, which will further extend the scope of application of the proposed approach.  
Conceptualization, J.H.; methodology, H.S.; software, L.L.; validation, J.H., and G.Z.; formal analysis, H.S.; investigation, G.Z.; writing--original draft preparation, J.H.; writing--review and editing, H.S.; visualization, L.L.; supervision, Z.G.; project administration, Z.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The States Key Laboratory of Air Traffic Management System, grant number SKLATM202306, and The Fundamental Research Funds for the Central one Universities of Civil Aviation University of China, grant number 3122023033.  
Not applicable.  
All data generated or analyzed during this study are included in this published article, and further requests can be sent to the corresponding author.  
Author [PERSON] was employed by the company China Electronics Technology Group Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Cooperative Power-Domain NOMA Systems: An Overview
## 1 Introduction
### Organization of the Paper  
The paper's structure is provided in Figure 3. All acronyms used in this study are listed in Table 1. The remaining portion of the paper is organised as follows. The NOMA's historical perspective is addressed in Section 2. The cooperative PD-NOMA variations of 5G are examined in Section 3. The outstanding concerns and research difficulties of NOMA are reviewed in Section 4, and the study concludes in Section 5.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Type** & [23] & [26] & [40] & [41] & [42] & [43] &
\begin{tabular}{c} **Our** \\ **Work** \\ \end{tabular} \\ \hline Physical layer security & ✓ & ✗ & ✗ & ✓ & ✓ & ✗ & ✓ \\ MIMO and mMIMO & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ ComP & ✗ & ✓ & ✗ & ✗ & ✗ & ✓ \\ Cooperative Network & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ mmWave & ✗ & ✓ & ✗ & ✓ & ✓ & ✓ \\ MEC & ✗ & ✓ & ✗ & ✓ & ✗ & ✓ \\ THz & ✗ & ✓ & ✗ & ✓ & ✗ & ✗ & ✓ \\ IRS & ✗ & ✓ & ✗ & ✓ & ✗ & ✗ & ✓ \\ ML & ✗ & ✓ & ✗ & ✓ & ✗ & ✗ & ✓ \\ Clustered NOMA & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ \\ Hybrid NOMA & ✓ & ✓ & ✓ & ✓ & ✗ & ✓ \\ TAS & ✓ & ✗ & ✗ & ✗ & ✗ & ✓ \\ Covariance shaping & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ \\ Channel estimation & ✗ & ✗ & ✗ & ✗ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comprehensive list of existing survey papers list that addressed the PD-NOMA and its comparison with our work.
## 3 Materials and Methods
### Comparative Models  
#### 3.4.1 Support Vector Machine  
The SVM model is a classifier based on Vapnik-Chervonenkis (VC) dimension theory and the principle of least structural risk [69]. The learning strategy of SVM is spacing maximization and the optimization algorithm for solving convex quadratic programming. Based on the limited information of the sample, the best compromise between the complexity of the model and the learning ability is sought in order to obtain the best generalization ability.  
SVM also includes kernel techniques, which enable it to become a virtual nonlinear classifier. For the nonlinear classification problem in the input space, it can be transformed into a linear classification problem in a dimensional characteristic space by nonlinear transformation.  
The kernel function is helpful in transforming the input samples into high-dimensional space so that they can be linearly classified. The kernel function usually has four kinds: linear (LN) polynomial (PL) radial basis function (RBF), and sigmoid (SIG). In this study, RBF was selected because the function has relatively good performance in solving nonlinear regression problems [70]. This is defined as follows:  
\[\text{K}\big{(}x_{i},x_{j}\big{)}=\exp\big{(}-\gamma\parallel x_{i}-x_{j} \parallel^{2}\big{)} \tag{10}\]
## 1 Introduction
Additionally, in the Philippines, identified relevant flood factors and judgments of decision-makers were analyzed using AHP judgments to weigh each parameter in estimating flood hazards in the study [38] at the central business district of Tugugerao City, Philippines. Another study was conducted in Infanta, Quezon Province, Philippines, aiming to give the municipality options and models for flood mitigation. The drainage system in said municipality is at risk of causing flood-related problems deliberating identified relative factors via AHP [6]. The evaluation of flood zones and flood problems for Davao Oriental, Philippines, were analyzed by the AHP and Maxent tool which reduce the subjectivity and uncertainty in selecting and weighting criteria [8]. The rareness of using AHP-based research made it easier to make a model of indecision without compromising the subjective and objective aspects of the assessment process [29]. Hence, the number of flood events in the Municipality of Odiongan that caused property damage to the community explicitly need the output of this research study. The results of this assessment will be used as the basis for the municipality's flood mitigation and risk management. Additionally, the information will useful in areas with similar topography and weather conditions.
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
###### Abstract  
The increasing demand for higher resolution and faster machinery in silicon wafer inspection is driven by the rise in electronic device production and the decreasing size of microchips. This paper presents the design and implementation of a device capable of accurately measuring the surface of silicon wafers using the stitching technique. We propose an optical system design for measuring the surface profile, specifically targeting the roughness and nanotopography of a silicon wafer. The device achieves a lateral resolution of 7.56 \(\upmu\)m and an axial resolution of 1 nm. It can measure a full 300-mm wafer in approximately 60 min, acquiring around 400 million data points. The technique utilized is a wavefront phase sensor, which reconstructs the surface shape using two images displaced a certain distance from the conjugate plane in the image space of a 4f system. The study details the calibration process and provides a method for converting local measurement coordinates to global coordinates. Quantitative phase imaging was obtained by using the wave front intensity image algorithm. The conclusive results validate the method different metrics over a wafer with bonded dies. In addition, the device demonstrates the ability to distinguish different dies that are thinned along with die-to-wafer bonding onto a carrier wafer to obtain the difference in coplanarity between the die and its surroundings as well as to detect defects during the die-to-wafer bonding. Lastly, the residual stress in the thin film deposited over the die is obtained using the Stoney model.  
wavefront sensor; silicon wafer; quantitative phase imaging; stitching Article
## 3 Results and Discussion
### Characterization of PDMSB-Grafted PS Nanoparticles
#### 3.5.3 DSC Investigations of PDMSB-Grafted PS Particles  
To gain first insights into the crystallization behavior of PDMSB chains attached on a curved substrate, the grafted particles were investigated by using DSC in a similar manner as the PDMSB homopolymers. PDMSB-grafted PS particles were heated to 473 K, and the temperature was held for 10 min to remove the thermal history. The sample was measured with a cooling rate of 30 K \(\text{min}^{-1}\) down to 253 K, followed by heating again to 473 K with a rate of 30 K \(\text{min}^{-1}\). The results are given in Figure 9 for sample PS(r)PDMSB-1, the sample with a higher content of PDMSB attached on the surface of the particles, and in Figure 10, the DSC results are given for sample PS(r)PDMSB-2.  
Figure 8: Scattering vector (\(q^{2}\)) dependence of the reciprocal average relaxation times, \(<\)\(\text  
Both DSC diagrams in Figures 9 and 10 show the glass transition caused by the cross-linked PS nanoparticles. The transition temperature was estimated to be 398 K, which is higher than the glass transition of PS homopolymers (380 K) due to the crosslinking of PS with DVB [58]. Also, during the cooling process, no crystallization peak could be observed, as is the case for PDMSB homopolymers, independently of obtained molar masses. This can be explained by either too few PDMSB chains attached at the surface of PS nanoparticles or a tremendous influence of the curved substrate both hampering the crystallization. In the subsequent heating process, a weak melting peak was observed at about 301 K, which is very close to the melting peak of PDMSB homopolymers (303 K). In contrast to PDMSB homopolymer investigations, only one melting peak was observed. This is because the diffusion of PDMSB chains attached to the surface is remarkably hindered, and the chains can only crystallize locally at the surface of PS nanoparticles. Hence, the recrystallization process is inhibited. Figure 10 is the DSC diagram of PS@PDMSB-2. From DLS results in the previous section, R\({}_{\rm h}\) for PS@PDMSB-2 could be determinated to 175 nm, which was much smaller than the value of PS@PDMSB-2 (209 nm). This means that there is even less crystallizable material at the surface. And indeed, DSC results show that after sample treatment, to obtain the same thermal history, there is no melting peak during the heating process for PS@PDMSB-2.
# ATWin: An Improved and Detailed Startup Model of TTP/C
###### Abstract  
TTP/C (Time-Triggered Protocol Class C) is a mainstream communication protocol commonly utilized in cyber-physical systems within the aerospace and automotive industry. Unfortunately, when it comes to the startup model, there are three issues in the standard of TTP/C (namely AS6003). Firstly, AS6003 only mentions a high-level specification, which leads to a gap between the standard and its implementation. Secondly, the standard startup model in AS6003 aggressively handles the multi-clique problem by dropping the first valid frame unconditionally without a contention-detecting mechanism, resulting in additional time consumption in some types of contention scenarios. At last, there is lack of the formal verification for the validity of the standard startup model with an arbitrary number of nodes and the formal derivation of its upper bound of startup time. To address these limitations, we propose a detailed and improved startup model named ATWin based on AS6003. It not only bridges the gap between the top-level standard and its implementation by supplementing the undefined details, but it also enhances the efficiency of the startup time by adding a contention-detecting strategy to the standard startup model. The ATWin model is developed as an open-source implementation for TTP/C's startup. We also formally demonstrate the validity of ATWin and deduce its upper bound of startup time with an arbitrary number of nodes in this paper.  
TTP/C; startup; the multi-clique problem; upper bounds of startup time; implementation
## 4 Time Gating and Demodulation Algorithms  
As noted in the discussion that relates Equation (10) to the output equation in Figure 6, the relevant approximations can be made only for certain periodic windows in time. Accordingly, the output signal at \(Z_{1}\) must be gated to be observable only at those times and suppressed at all other times. It amounts to imposing time-limited frequency shift on the received signals, the frequency shifts being the pseudo-Doppler shifts of the individual superposed OAM modes. This is recognized as the frequency-domain dual of the band-limited time-delay problem, the time-delay being a fraction of the sampling interval of a discrete-time signal [19].
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 4 Implementation and Experiments
### Implementation and Deployment of Our Prototype System  
Table 1 lists the hardware and software used in our prototype system. Our Mobile App is developed to run on Apple iOS. Mesh Beacons and Mesh Routers are implemented using the RedBear development board [33] because of its small size and the co-existence of BLE and Wi-Fi interfaces. The Application Server is an Ubuntu PC with a Node.js web server. The Cloud Database is implemented using Google Firebase [34], which also provides the user registration and authentication functionalities for our Mobile App. Our BLE beacon network is deployed on the 8 th floor of the engineering building  
Figure 6: The message format for Type 3 and Type 4 messages.  
in our campus. Figure 7 shows the floor plan where we deployed four Mesh Beacons (the blue ones labeled \(A\) through \(D\)) and one Mesh Router (the red one).  
First, we describe the user interface of the Mobile App because we use this to measure and display the response time. Three screenshots of our Mobile App are shown in Figure 8. After the user logs in and then manually presses the \"Locate\" button, the central \"Locationing\" circle with breathing light effect will appear as in Figure 8a. During this locating period, the Mobile App scans for the beacon messages sent by Mesh Beacons. As soon as a beacon message is received and the physical location is determined, the location name will show up as in Figure 8b, which is \"TKU Building E Location A\" in this case. Meanwhile, the Mobile App will broadcast the user's presence state into the Bluetooth beacon network. In response to the presence message from the Mobile App, the Mesh Router will send an acknowledgement message back to the Mobile App. When the acknowledgement message finally arrives at the Mobile App, a welcome message shows up and the check mark appears as in Figure 8c, meaning the whole process has now completed. Regarding the response time, the time (0.842 s) shown in Figure 8b refers to the period starting from the user pressed the \"Locate\" button, to the time when the physical location is determined at the Mobile App. Note that the screenshot in Figure 8c was captured in another test. The received time (1.983 s) shown in Figure 8c refers to the period starting from the completion of locating, to the instant that the Mobile App received the acknowledgement message from the Mesh Router.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Technology** \\ \hline Mobile App & Apple iOS version 10.2 \\ Mesh Beacon and Mesh Router & RedBear Duo development board (with Bluetooth V4.1 and IEEE 802.11n Wi-Fi) \\ Application Server & Node.js web server on Linux Ubuntu 14.04 \\ Cloud Database & Google Firebase \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware and software used in our prototype system.  
Figure 7: Deployment of BLE beacon network with 4 Mesh Beacons and 1 Mesh Router.
## 3 Methods
### Communication Model Design  
In this section, we investigate the inference offloading latency across IoMT devices, including interconnection communication latency and local communication latency. We model the two types of models separately and calculate the total communication latency at the end.  
In e-healthcare, we propose a system model for the IoMT that enables users to utilize medical LLMs. As depicted in Figure 1, each model operates independently on one IoMT device in this system, while possessing the capability to function cooperatively with other models to enhance performance. For interconnection among IoMT devices, there is a set \(\mathcal{N}=\{1,\dots,n,\dots,N\}\) of IoMT devices, which allows doctors and users to access e-healthcare services via wireless communication. In the interconnection communication model with \(\mathcal{N}\), the interconnection communication latency between device \(n\) and device \(n+1\) is denoted as \(l_{n}\). The interconnection communication latency between device \(i\) and device \(j\) is denoted as \(l_{ij}\). The interconnection communication latency is denoted as \(l_{n}^{com}\); the \(l_{n}^{com}\) can be calculated as  
\[l_{n}^{com}=d_{n}^{p}+d_{n}^{pr}+d_{n}^{q}+d_{n}^{w}, \tag{1}\]  
where propagation delay is denoted as \(d_{n}^{p}\), processing delay is denoted as \(d_{n}^{pr}\), queueing delay is denoted as \(d_{n}^{q}\), and waiting delay is denoted as \(d_{n}^{w}\).
## 3 Sepiolite Hybrid Network Properties  
### Filler Network Properties  
When a reinforcing filler is present in sufficient amount and it is well dispersed without zones lacking filler, a filler network is formed. Seen at the electronic microscope, it looks like a pattern of filler extended throughout the whole image, building up a single macroscopic structure out of an assembly of many nanometric particles. When a filled compound reaches the minimum concentration to form a particle network, materials properties change abruptly, and the obtained materials have hybrid characteristics between the filler and the matrix [24]. It is called a nanocomposite, because it combines the basic properties of its constituent like in macroscopic composite materials (reinforced concrete, prepegs etc.) but exploits an interaction at the nanometric level. This implies the formation of an extended interfacial area whose properties characterize the nanocomposite behavior, together with the particulate network structure.  
Figure 2: The crystalline structure of sepiolite, where thin phyllosilicate TOT layers are bonded by the edges in the typical checkerboard pattern. From ref. [23].  
In the effort of separating structure contribution from the interfacial behavior, few phenomenological studies attempted to correlate macroscopic mechanical properties with the nanocomposite chemical structure [25; 26]. To quote one for all, the work of [PERSON] is still considered the reference approach to explain network and interface effects on the behavior of a filled elastomer [27]. In brief, the shear modulus of a filled elastomer presents a low deformation regime (strain lower than 1% strain amplitude) where the filler network is intact and contributes to reinforcement. At the higher deformation regime (1-10% strain) the physical network contribution of the filler structure is gone, and only hydrodynamic and interface filler/rubber chemical interactions contribute to the mechanical reinforcement. The study of elastic modulus G\({}^{\prime}\) at low strain (G\({}^{\prime}\)0) and high strain (G\({}^{\prime}\)\({}_{\infty}\)) allows separating those contributions which can be related to the nanocomposite structure.  
The quantitative relation between particulate filler characteristics and consequent modulus enhancement is given by constitutive equations, the most popular being the Guth-Gold-Smallwood equation:  
\[\text{G}/\text{G}_{0}=1+0.67f\varphi+1.62f2\varphi^{2} \tag{1}\]  
where, G and G\({}_{0}\) are the initial moduli of filled and unfilled material, respectively, \(\varphi\) is the filler volume fraction, the quadratic term accounts for the mutual disturbance caused by the spherical particles and \(\hat{f}\) is a shape factor that allows the application of the equation to non-spherical fillers.
## 3 Results
### Influence of Different Cropland Use Transitions on GPP  
#### 3.2.1 Loss of GPP Due to the Cropland Non-Agriculturalization  
Throughout the study period, the non-agricultural area in Guangdong Province exhibited an overall decreasing trend (Figure 6), with a total decreasing area of 82.7 thousand km\({}^{2}\) and a non-agriculturalization rate of 1.53%. The regions mainly affected by non-agriculturalization are the PRD and WG, losing 28.7 thousand km\({}^{2}\) and 25 thousand km\({}^{2}\) of cropland, respectively. The cities with the largest non-agriculturalization areas are Zhanjiang, Qingyuan, and Maoming, while those with the highest non-agriculturalization rates are Guangzhou, Dongguan, and Zhongshan. Temporally, the most significant non-agriculturalization of cropland occurred from 1991 to 2000, reaching 37.8 thousand km\({}^{2}\), whereas 2010-2020 saw the least, with only 20.3 thousand km\({}^{2}\). This change is likely attributed to the stabilization of urbanization processes in Guangdong Province and a shift in economic development from extensive to intensive growth.  
From 1991 to 2020, the non-agriculturalization of cropland in Guangdong Province resulted in a total GPP loss of 34.11 million tons (Table 3). The spatial distribution of this loss is similar to that of non-agricultural cropland, primarily concentrated in the PRD and WG, losing 12.4 million tons and 10.56 million tons of GPP, respectively. There is considerable variation in GPP loss among different cities; Zhanjiang recorded the highest loss at 6.05 million tons, representing 17% of the province's total; whereas Zhuhai had the lowest at 0.15 million tons, reflecting the lowest per-unit area yield in the study region.  
Figure 5: (**a**) Spatial shift of cropland gravity center; (**b**) Spatial shift of population gravity center; (**c**) Spatial distribution of the annual average cropland use transition rate from 1991 to 2020; (**d**) Longitudinal and latitudinal shifts of cropland and population gravity centers.