esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Deformation Measurement of Laser Repair Substrate Based on Orthogonal Sampling Moire
## 4 Results and Discussion  
During laser repair, laser power, spot size and scanning speed affect the laser fluence input into the substrate. In addition, factors such as scanning path affect the thermal history in the process of repair and further result in different deformation and stress distribution [25]. In this paper, the impacts of scanning path and speed are studied; as shown in Figure 8, three scanning speeds of 5 mm/s, 10 mm/s and 15 mm/s are set for the four scanning paths. The width of a single cladding channel is about 3 mm, and the spacing between each cladding channel is 2 mm.  
Of the three different scanning velocities, the energy fluence is the highest when the laser moves at the speed of 5 mm/s. Additionally, the cantilever beam components after cladding in this case are shown in Figure 9. It can be seen that, even at the maximum energy fluence, the orthogonal grid lines on the side of the cantilever beam are still high-contrast, which proves the reliability of the prepared high-temperature grating.  
Figure 8: Four scanning modes in repair.  
Figure 7: Orthogonal grid lines photographed during repair. Gray value in line segment AB coincides well with the sinusoidal curve.  
The cladding results of four scanning paths at different scanning speeds are shown in Figure 10. Although the substrates after cladding with different repair parameters have nearly equivalent mass, the characteristics of deformation field vary a lot. In particular, when scanning along the \(y\) direction, it can be clearly seen that the slower the scanning speed is, the greater the warping deflection at the end of the cantilever beam is. However, this phenomenon is not evident when scanning along the \(x\) direction, which reflects the different stress distribution on the substrate under different scanning paths, resulting in different residual deformations. The details will be further discussed through the displacement and strain analysis below.  
Figure 10: Cladding results under different scanning paths and scanning speeds. (**a**) Unidirectional scanning in \(x\) direction, (**b**) serpentine scanning in \(x\) direction, (**c**) unidirectional scanning in \(y\) direction, (**d**) serpentine scanning in \(y\) direction.  
Figure 9: Grid lines state after cladding at 5 mm/s scanning speed.
## 3 Network Connectivity Analysis  
### Node Spatial Distribution  
Suppose the sensor nodes are scattered from the air and the process resembles Poisson arrival in a service queue. Thus we use a homogeneous Poisson point process with intensity \(\lambda\) to model the spatial distribution of the nodes. This process is defined as follows [26]:  
**Definition 1** Consider a measure space (\(X\), \(\Sigma\), \(\mu\)), where \(\mu\) is a \(\sigma\)-finite measure. A Poisson Point Process (P. P. P.) with intensity \(\lambda\) is a collection of random variables \(N(A,\omega)\), \(A\in\Sigma\), \(\omega\in\Omega\), defined on a probability space \((\Omega,\mathcal{F},\mathrm{P})\) such that:  
\(N(\cdot,\omega)\) is a counting measure on \((X,\Sigma)\) for each \(\omega\in\Omega\).  
\(N(A,\cdot)\) is the number of nodes in subarea \(A\) which follows Poisson distribution with mean \(\lambda(A)\):  
\[P(N(A)=k)=\frac{\lambda^{k}}{k!}\,e^{-\lambda(A)}\text{, all }\,A\in\Sigma \tag{1}\]  
with an expected value \(E(N)=\lambda(A)=\rho(A)\|A\|\), \(\rho(A)\) and \(\|A\|\) are node density and size of subarea \(A\) respectively.  
\(\bullet\) If \(A_{1}\), \(A_{2}\),... are disjoint sets then \(N(A_{1})\), \(N(A_{2})\),... are independent random variables:  
\[P(N(A_{1})=k_{1}\wedge N(A_{2})=k_{2}\wedge...\wedge N(A_{n})=k_{n})=\prod_{i =1}^{n}P(N(A_{i})=k_{i}) \tag{2}\]  
If \(\rho\) is constant over the entire infinitely large area, the process is homogeneous. In other words, the outcome of \(N\) only depends on \(\|A\|\) but not on its particular location or shape. Note that a homogenous Poisson point process can be regarded as a case of uniform distribution of \(k\) nodes in area \(A\), as \(k\) and \(\|A\|\) tends to infinity but the density \(\rho=k\|A\|\) remains constant.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Other Graphene ECG Electrodes
#### 2.8.1 GNR ECG Electrode  
Graphene nanoribbon (GNR) is an important type of graphene material. Its various edge structures can bring it rich optical, electrical, magnetic, and semiconductor characteristics [85]. [PERSON] et al. proposed a GNR electrode for ECG signal acquisition [86]. They synthesized GNR with good electrical and mechanical properties by unzipping the MWCNT through oxidation and reduction. Then, the biocompatible and water-soluble PVA base material was mixed with GNR to form a PVA/GNR composite, which was finally implemented in an ECG electrode. The PVA/GNR electrode shows excellent performance (59.99 dB signal-noise-ratio, 348.62 mV R-peak value) with only 1 wt% GNR, which shows the potential of GNR applied to low-cost graphene ECG electrodes.
# Firefly Optimized Resource Control and Routing Stability in MANET +
## 3 Design of Firefly Resource-Optimized Routing (FFROR) Technique
```
Input : Source Node '\(SN\)', Destination Node '\(DN\)', Intermediate Nodes \(MN_{1},\ MN_{2},MN_{3},MN_{4},MN_{5},MN_{6},MN_{7},MN_{8},MN_{9},MN_{10}\), Number of paths Output : Improves routing stability in MANET Begin Step 1: Define Objective function f(\(y\)); \(y=(y_{1},y_{2})\) \(f(y)=y_{1}=\) minimum energy consumption and \(y_{2}=\) minimum bandwidth utilization; Step 2: Create the initial population of fireflies \(y_{p}=(p=1,2,\ldots,n)\); Step 3: Calculate light intensity\(L_{1}\) at \(y_{p}\) determined by f(\(y\)) using (4) Step 4: Define absorption coefficient \(\gamma\) Step 5: while(\(t<Termination\)\(Criterion\)\(not\)\(met\)) Step 6: for\(p=1:\ n\) (all n fireflies) Step 7: for\(q=1:\ n\) (all n fireflies) Step 8: Calculate the distance \(r\) between firefly \(p\) and firefly \(q\) using (6) Step 9: if (\(I_{q}>I_{p}\)) Step 10: Move firefly \(p\) towards firefly \(q\) using (7) Step 11: endif Step 12: attractiveness changes with distance \(r\) via \(exp(-\gamma r)\); Step 13: Compute new solutions and update light intensity; Step 14: endfor\(q\) Step 15: endfor\(p\) Step 16: If Step 17: Rank fireflies according to the light intensity and find the best resource-optimized route path for DP transmission Step 18: endwhile Step 19: DP transmission from the source node to the destination node End
```  
**Algorithm 1**Firefly Algorithm for Resource Optimization
## 3 Multi-Dimensional Safety Risk Theory
### Evaluation of Multi-Dimensional Safety Risk Model  
1. Pareto rule  
The safety risk ratio characterizes the extent to which each element in the system contributes to the total safety risk value of the system, and from this, the critical safety factor in the system can be intuitively identified. According to Pareto rule [68; 69], when distinguishing safety-critical links, it can be considered that 80% of accidents are originated from 20% of dangerous sources. Therefore, the value of the safety risk ratio \(\eta_{i}\) is sorted in descending order, and the first 20% of the values of \(\eta_{i}\) are defined as safety-critical elements, and the last 80% are defined as general safety elements.  
2. ALARP principle  
As a project risk criterion generally adopted by domestic and foreign institutions, the principle of ALARP (As Low As Reasonably Practicable) [70; 71] sets two risk \"boundaries\" based on the value of safety risk and related experience: intolerable boundary and negligible boundary [70], meanwhile forming three risk region and level: serious risk region, ALARP region and negligible region, and the top extreme of the principle is \"accident\", and the bottom extreme is \"safety\". ALARP rule is shown as Figure 4. The values of the regions and boundaries of ALARP principle are all relative, and there is no standard of definition [72; 73]. In practice, expert evaluation method considering potential severity, propagation probability, and propagation time can be applied to determine final values of the boundaries [70; 74; 75]. Meanwhile, alternative values of the boundaries are also obtained. Finally, compared and analyzed results of final values and alternative values of boundaries, final results can  
Figure 3: The three-dimensional model space of risk factors.  
be determined. ALARP region means risk value in this region is reasonably acceptable. Therefore, according to the ALARP principle, this paper classifies risk value of each element into different regions, in order to make further research to propose preventive measurements, so as to reduce the risk level and improve system safety.
# Performance Evaluation of an API Stock Exchange Web System on Cloud Docker Containers
## 4 Resource Demand Prediction  
This section elaborates on the approach used for estimating **resource demand**. Resource demand refers to the average time that a request takes to receive service from a resource (CPU) in a system, and the total number of visits to the resource, excluding waiting times. Measuring resource demand accurately with existing monitoring tools is challenging.  
Numerous approaches for predicting resource demand have been proposed [16]. The precision of these predictions is significantly affected by the accuracy of the measurements. In our study, we used SEWS to analyse the response time and CPU utilization with realistic workloads generated by SELG. We measured all parameter values of SEWS using SELG and system tools while the system was running. Based on these values, we estimated the resource demand for each resource and one workload class. The estimation process uses data from container monitoring logs. However, the obtained values may differ from those obtained in native systems [5] since Docker containers are runtime instances of the corresponding images.  
In this study, the estimation process is crucial, and its accuracy depends on the monitored traces. We executed the **estimation process** on the prepared monitoring data, which consists of a set of measurements (output parameters) obtained by the SEWS benchmark.
## 6 Sequence-to-Sequence Models  
Sequence-to-sequence models are machine-learning architectures designed for various tasks that involve sequence processing. They take an input sequence, process it, and generate an output sequence. In general, input and output sequences could be of different lengths. Sequence-to-sequence models are applicable to a wide range of problems where both input and output data are structured as sequences. This is common in many NLP tasks. For instance, in machine translation, the input is a sentence--represented as a sequence of words in one language--and the output is a sentence in another language. In summarization, both the input and output are sequences of sentences, with the output being a more concise summary. Beyond NLP, sequence-to-sequence models are also employed in video processing, where an input sequence of frames is transformed into a new sequence, such as a sequence of motions. In bioinformatics, these models process sequences of DNA, RNA, or proteins. In smart homes, sequence-to-sequence models analyze sequences of sensor readings to predict sequences of activities performed by residents.  
Historically, statistical models were the first to be used, drawing inspiration from the noisy channel model in information theory. Their advantage lies in a modular structure, allowing different information sources to be added as new components. With the rise in computational power, neural encoder-decoder architectures emerged. The encoder processes the input sequence and compresses it into a fixed-length context vector, a numerical representation of the entire input. The decoder then uses this context vector to generate the output sequence. Early neural models primarily relied on recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks, due to their ability to manage long-term dependencies in sequences. They process input sequences sequentially. Nowadays, transformer architectures have largely replaced LSTMs because they enable faster training. Transformers process sequences in parallel, without the need for recurrence, and rely entirely on a self-attention mechanism, making them highly efficient, especially for long sequences.  
The pioneering application for all three sequence-to-sequence models was machine translation. Then, its use was expanded to many other areas. We mentioned a few earlier. In our research, architectures based on all three models, statistical, LSTM, and transformer were applied to speech normalization, which is also a sequence-to-sequence problem.
## 3 Materials and Methods
### Features
#### 3.3.4 Timing  
**Feature feasibility:** Packet timing and interval between packets from a network node is meaningful data for many ICS networks. The strict real time requirements of the system, especially in the device level of the network, make the timing statistics of the traffic stand out from typical application traffic.  
**Our data:** We analyzed packet intervals and timing of packets. The packet intervals had great variation and showed no simple patterns or regularity. No patterns or useful behavior was detected in packet timing. Analysis of the most active network nodes showed no exploitable characteristics in individual systems packet intervals. Further analysis is required for usability of timing features, especially for single network nodes.
## 6 Simulations Results
### Effect of ISR on Success Rate and RMSEs  
The simulation conditions were the same as in Section 6.1. Among them, only the JSR changed from 0 dB to 10 dB, and the step size was 1 dB. The relationship curve between success rate and JSR is shown in Figure 8, and the relationship curve between RMSEs and JSR is shown in Figure 9. With the increase in JSR, the success rate decreased, RMSEs increased, and A6 almost failed. Due to the lack of polarization information assistance, even in the cross terms, strong interference still affected the DOA estimation of weak signal. The success rates of A8 and A9 were always better than others, especially when the JSR was large. The RMSEs of A7 and A9 were always lower than others, especially when the JSR was small. A9 was overall optimal, with a high success rate and small RMSEs.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 4 Simulation and Evaluation
### Results of Power Allocation for SM-MPC
#### 4.3.1 SM-MPC Power Allocation Comparisons with MPC and SM Methods  
From Figures 7 and 8, it can be observed that for the SM method, the fixed fuel cell power leads to excessive hydrogen consumption most of the time. Compared to the SM method, MPC performs better in tracking the vehicle's power demand. However, the SM-MPC method outperforms the MPC method in tracking the vehicle's power demand. This is because, during low-power response, the MPC method stabilizes the fuel cell output, while during high-power response, the SM method is used, allowing the lithium-ion battery to absorb high-frequency power fluctuations. In other words, it extends the lifespan of the fuel cell stack.
###### Abstract  
Optical camera communication (OCC) is a technology in which a camera image sensor is employed to receive data bits sent from a light source. OCC has attracted a lot of research interest in the area of mobile optical wireless communication due to the popularity of smartphones with embedded cameras. Moreover, OCC offers high-performance characteristics, including an excellent signal-to-interference-plus-noise ratio (SINR), high security, low interference, and high stability with respect to varying communication distances. Despite these advantages, OCC suffers from several limitations, the primary of which is the low data rate. In this paper, we provide a comprehensive analysis of the parameters that influence the OCC performance. These parameters include the camera sampling rate, the exposure time, the focal length, the pixel edge length, the transmitter configurations, and the optical flickering rate. In particular, the focus is on enhancing the data rate, SINR, and communication distance, which are the principal factors determining the quality of service experienced by a user. The paper also provides a short survey of modulation schemes used in OCC on the basis of the achieved data rate, communication distance, and possible application scenarios. A theoretical analysis of user satisfaction using OCC is also rendered. Furthermore, we present the simulation results demonstrating OCC performance with respect to variations in the parameters mentioned above, which include the outage probability analysis for OCC.  
optical camera communication (OCC); performance improvement; camera sampling rate; SINR; shutter speed synchronization; outage probability; user satisfaction
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 3 Preliminary Knowledge and Research Motivation
### Research Motivation
As a consequence, the time for communication and confirmation between candidate nodes can be reduced, which can slightly reduce the delay of the whole system. Figure 6: Packet load in different areas. Figure 7: Energy consumption in different areas. COOR(P) method: This increases both the transmission radius and the transmission power of each node in the far-sink region to a certain extent, but maintains the _PAR_ of each hop unchanged. As the transmission radius of each node increases, the number of hops to the sink decreases but the distance to the sink remains same. Therefore, the goal of reducing delay and improving network reliability is achieved. Examples to illustrate how the COOR method can improve network reliability follow. Consider the same reliability of one-hop transmission, the relation between the end-to-end reliability and the number of routing hops are shown in Figure 8. Furthermore, the influence of single transmission success rate \(p\) with different size \(N\) of candidate nodes set can also be obtained. End-to-end reliability will gradually decrease as the number of hops increases. Simultaneously, both the reliability of single transmission and the size of candidate set have a positive promotion on end-to-end reliability. Accordingly, the COOR(R) method keeps _hops_ invariant and improves \(p\), while the COOR(P) method maintains the \(p\) unchanged and reduces _hops_. Therefore, the two methods increase the reliability of the WSNs from different perspectives. At the same time, it is obvious that if the transmission power of the node is increased too much, the energy consumption of node increases significantly, which will seriously shrink the network life, so how to calculate the optimal transmission power to fully utilize the remaining energy to enhance network performance, the core problem of the COOR scheme, will be explained in the next section.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for High-Throughput and Spatial Multi-Omics Integration
#### 2.3.6 Summary and Comparative Insights  
In summary, the platforms discussed--SM-Omics, OmicsSuite, Single-Platform Multi-Omic Profiling, CHOmics, and Majorbio Cloud--address the challenges of high-throughput and spatial multi-omics integration in unique ways.SM-Omics stands out for its automated workflows in spatial transcriptomics and proteomics, revealing tissue-specific heterogeneity in complex systems like tumors. OmicsSuite offers a GUI-based alternative for multi-omics integration, enabling users without advanced programming expertise to analyze large datasets. The Single-Platform Multi-Omic Profiling system unifies proteomics and metabolomics workflows, enhancing reproducibility for rare disease studies. CHOmics addresses the biopharmaceutical industry's needs, optimizing CHO cells for protein production using multi-omics approaches. Lastly, Majorbio Cloud offers a comprehensive, cloud-based solution for managing and analyzing multi-omics data, with an emphasis on collaboration and scalability. While each platform excels in specific applications, their varying levels of accessibility, specialization, and computational requirements should guide in selecting the most appropriate tool for study needs.
## 5 Conclusions  
The dynamic instability of axially-moving nanoplates resting on a viscoelastic foundation was investigated based on the nonlocal elasticity theory. The viscoelastic foundation, nonlocal effect and biaxial loadings of the nanoplate are considered in the established model to capture the size-dependent mechanical property. The main conclusions are drawn as follow:  
1. The viscoelastic foundation has a dominant impact on vibrations of the axially-moving nanoplate. The dimensionless natural frequencies of the axially-moving nanoplate resting on a viscoelastic foundation are complex numbers when the dimensionless moving speed C \(>0\).
2. The Winkler's modulus \(k_{ov}\) of the viscoelastic foundation significantly affects both the instability zone and the divergence speed. The Pasternak's modulus \(k_{G}\) of the viscoelastic foundation affects mainly the divergence speed. The damping \(c_{t}\) of the viscoelastic foundation has almost no influence on the instability zone or divergence speed.
3. Mode-couple flutter disappears when the axially-moving nanoplate rests on a viscoelastic foundation. This change of instability types is mainly influenced by the damping \(c_{t}\) of the viscoelastic foundation.
4. Small-parameter \(\alpha\) affects both the divergence instability zero and the divergence speed of the axially-moving nanoplates resting on a viscoelastic foundation. Such influences become more substantial when \(\alpha>0.02\).
5. The divergence instability is also quite sensitive to the biaxial force, which influences the divergence instability zero and the corresponding divergence speed.  
Conceptualization, J.D., D.Z., W.W.; Methodology, J.D., D.Z.; Writing--Original Draft Preparation, J.D., D.Z.; Writing--Review & Editing, J.D., W.W.; Funding Acquisition, J.D.  
This research was funded by the Natural Science Foundation of China (grant number 11702325 and 11302254) and the Natural Science Foundation of Hebei Province of China (grant number A2018210065).  
The authors declare no conflict of interest.
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 3 System Model and System Security
### System Security
#### 3.2.1 Secure VANETs Assumption  
* All OBUs and RSUs are registered with the TA. The TA is not feasible to be compromised in the system and can be fully trusted by all parties.
* RSUs are usually deployed in open unattended environments, which can be compromised by attackers or collude with each other. However, we assume that RSUs are monitored so that their compromise can be detected in a short time. As a result, at a given time slot, very few RSUs are compromised.
* OBUs have limited computing power and storage space while TAs have greater computational power and enough hardware.