esa-sceva/satcom-chunk-collection huggingface dataset:
# Sketch-Guided Topology Optimization with Enhanced Diversity for Innovative Structural Design
## 1 Introduction
This framework integrates two approaches to achieve human controllability: sketch-based constraints and length-scale control of geometric features. Free-hand sketches are decomposed into stroke elements and closed-shape elements, with two distinct constraints introduced for them accordingly: a stroke-based undirected structural boundary constraint and a closed-shape-based regional constraint. They are further integrated with length-scale control of geometric features to provide multifaceted control strategies. Each control strategy, with unique control characteristics for structural features, has a different impact on design quality and diversity, thereby enabling fine-grained and flexible human controllability. Additionally, the framework enhances solution diversity by introducing randomness through the perturbation of Fourier mapping frequencies and load condition parameters in the density-based neural implicit TO process. To reduce the computing cost caused by having multiple TO processes, this framework implements an adaptive parallel computing scale adjustment method. A case study of an innovative design of automobile wheel spokes is conducted, and comprehensive studies on human controllability and its impact on both design quality and diversity are carried out qualitatively and quantitatively. The main contributions of this paper are summarized as follows:  
1. Novel sketch-based constraints, including a stroke-based undirected structural boundary constraint and a closed-shape-based regional constraint, are proposed to enable sketch-based TO control. 2. Sketch-based constraints and length-scale control of geometric features are integrated, providing multifaceted control strategies to enable fine-grained and flexible control. 3. A diversifying method based on random sampling of Fourier mapping frequencies in a neural implicit TO process is proposed, and quantitative studies on different diversifying methods as well as the effects of human control strategies on design quality and diversity are conducted. 4. An adaptive parallel computing scale adjustment method is incorporated to improve the efficiency of multiple TO processes.
## 3 EDAS Prototype Specifications
### The EDAS Platform
#### 3.4.3 Adaptation Engine  
The _Adaptation Engine_ decides a mitigation action for a particular user and thing in a given risk context. It takes the risk information from the _Risk Analyzer_ and calls a runtime RDF/XML ontology. The proposed security adaptation ontology, shown in Figure 10, contains the security metrics, device capabilities and user preferences necessary to decide an optimized adaptation action from a pool of available actions. By optimized action, we refer to a response that is selected after assessing user, service and device requirements in a particular risk context. Entities and associated relationships in the ontology along with examples are detailed in our previous work [6]. The ontology entities are grouped into three contexts; user, thing and security. Each context captures the respective knowledge, as well as current runtime security settings necessary for deciding an adapted action. While selecting an action, the engine utilizes an Apache Jena-SPARQL API-enabled [17] script to query and update the stored ontology. Updating is performed to ensure that the ontology as a runtime knowledge platform is aware of all of the current configurations that may be required in possible succeeding adaptation decisions. The adaptation engine is activated when it receives a risk alarm from the risk analyzer. The alarm is a token detailing the risk components, _i.e._, the Threat-ID, Risk-Level, and Device-ID. The adaptation  
Figure 10: Security adaptation ontology. action is selected in a stepwise procedure as shown in Table 1. The table describes all of the signatures pertaining to the ontology elements, _i.e._, subjects, predicates and objects, which are accessed at each step of the procedure. The ontology is developed in an RDF/XML format using the Protege tool [18]. Different types of ontology elements used in the table are described as follows: a Class refers to a concept of interest in the ontology. Class Objects are the members of a class. Object Property is the relationship between one or more members of one class with one or more members of another class, and a Data Property refers to a particular attribute of a class object. Example objects and a description of classes used in the adaptation process are given in Table 2. For a detailed description of all of the concepts and relationships in the proposed ontology, refer to our previous work [6].
###### Abstract  
We propose a reconfigurable terahertz (THz) metamaterial (RTM) to investigate its multifunctional electromagnetic characteristics by moving the meta-atoms of split-ring resonator (SRR) array. It shows the preferable and capable adjustability in the THz frequency range. The electromagnetic characteristics of the proposed RTM device are compared and analyzed by moving the meta-atoms in different polarized transverse magnetic (TM) and transverse electric (TE) modes. The symmetrical meta-atoms of RTM device exhibit a resonant tuning range of several tens of GHz and the asymmetrical meta-atoms of RTM device exhibit the better tunability. Therefore, an RTM device with reconfigurable meta-atoms possesses the resonance shifting, polarization switching, electromagnetically induced transparency (EIT) switching and multiband to single-band switching characteristics. This proposed RTM device provides the potential possibilities for the use of THz-wave optoelectronics with tunable resonance, EIT analog and tunable multiresonance characteristics.  
metamaterial; terahertz; optical switching; electromagnetically induced transparency; resonator Article
## 3 Methodology
### Test of Dynamic Penetration  
Considering that bored piles are widely used at present, and pile base post-grouting technology is often used in practical projects, it is particularly necessary to detect the effect of pile base grouting. However, the conventional dynamic penetration method cannot test the strength of the soil before and after grouting under the same pile base. In order to solve this problem, this study will bury two pre-embed pipelines at the same time, and the changes in the bearing capacity before and after grouting can be tested on one pile.  
Dynamic penetration determines the mechanical properties of the soil according to the number of hammer blows required to penetrate the probe into the soil for a certain distance. Before and after grouting at the pile base, the strength of the soil at the base of the pile is very different. In this study, larger diameter pipelines will be buried in the bored pile in advance, as shown in Figure 6. The pre-embed pipelines can be used for a heavy hammer to penetrate the channel. The grouting reinforcement effect can be determined by the number of hammer blows in the dynamic penetration test before and after grouting.  
Figure 5: Transmitted wave test in the pile.  
Figure 6: Dynamic penetration device of pile base grouting effect test.  
The strength of the undisturbed soil before grouting and after grouting of the No. 1 pile, No. 2 pile, and No. 3 pile were tested by a heavy-duty dynamic penetration method. The specific parameters of heavy-duty dynamic penetration are shown in Table 2, and the characteristic value of the bearing capacity of the gravel foundation determined by the heavy-duty dynamic penetration number N63.5 is shown in Table 3.  
The main test steps are as follows:  
Pre-embed two pipelines in the pile when forming a pile; hammer the probe with a heavy hammer from a certain height in a pre-embedded pipeline, record the number of hammer blows and the cumulative penetration (the distance of the wire rope falling); grouting and maintenance at the pile base for 28 d; hammer the probe with the same heavy hammer from the same height in another embedded pipeline, and record the cumulative penetration required for the same number of hammers as in the previous step; and finally, compare the number of hammers and the cumulative penetration curve of the two embedded pipelines to evaluate the grouting effect. This part of the test photos is shown in Figure 7.
## 4 Experiments
### Combination of All Three Strategies  
When the original algorithm combined all three strategies, precision increased by 15.51%, demonstrating the synergistic effect of these strategies in image segmentation. Accuracy also improved, indicating reduced mis-segmentation while maintaining high precision. The recall rate increased by 16.72%, further confirming the enhanced recognition and segmentation capabilities of the algorithm.  
Notably, whether a single strategy is employed, two strategies are combined, or all three strategies are utilized together, experiments on image sample sets of varying scales consistently indicate that the processing speed of the proposed algorithm fluctuates by approximately one image per second, either faster or slower, compared to the Magefreehome algorithm. Given the substantial improvements in other performance metrics, this slight variation in processing speed is considered acceptable.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm Combination** & **Speed (Number/s)** & **Accuracy** & **Precision** & **Recall** \\ \hline Magefreehome & 32.14 & 78.32\% & 97.69\% & 74.76\% \\ Magefreehome + Strategy1 & 32.09 & 86.32\% & 92.31\% & 82.05\% \\ Magefreehome + Strategy2 & 33.44 & 83.49\% & 97.12\% & 75.94\% \\ Magefreehome + Strategy3 & 31.73 & 84.93\% & 99.04\% & 80.47\% \\ Magefreehome + Strategy1 + Strategy2 & 32.41 & 86.76\% & 94.23\% & 85.96\% \\ Magefreehome + Strategy1 + Strategy3 & 32.75 & 89.04\% & 96.15\% & 88.50\% \\ Magefreehome + Strategy2 + Strategy3 & 32.55 & 84.02\% & 98.05\% & 79.69\% \\ Magefreehome + Strategy1 + Strategy2 + Strategy3 & 31.38 & 93.83\% & 98.71\% & 91.48\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the Enhancement Degree of the Three Strategies in this Study’s Algorithm.
## 5 Results and Discussion
Comparison of the change-points in the trajectory on both HIE2 and HIE3 with the PGA and API are shown on Figures 10 and 11. There is a recognizable correlation of seismic activity and changes of movements on both devices. However, HIE2 shows more movement changes correlating with PGA than the HIE3 device. The most notable change-point probably influenced by the seismic activity occurred at the end of December 2013, when the strongest earthquake during the monitoring period (M = 5.1) also occurred. Other change-points presumably influenced by seismic activity happened in 14-Nov-2013; 23-Dec-2013; 24-Dec-2013; 25-Dec-2013; 28-Dec-2013; 16-Mar-2014; 04-Sep-2014; 30-Mar-2015; 08-Nov-2015; 13-Nov-2015; 21-Apr-2016; 08-Jun-2017; 14-Mar-2018; 14-Jun-2018; 15-Jun-2018 and 17-Oct-2018. In case of API there is not a clearly visible correlation with change-points, except in December 2013; July 2016 and April 2018 affecting the HIE3 device and July 2016 affecting the HIE2 device. This suggests that the nature of the measured movement has, more probably, an endogenous cause. These observed results are in good accordance with the analysis performed by [32]. In their study, a period from October 2013 to June 2014 was analyzed on the HIE3 device. Three major impulses were recognized (rainfall event in December 2013 and seismic events in December 2013 and March 2014). All these three impulses were also identified using this proposed methodology. Figure 10: HIE2—Time stamps of change-points (green lines) detected using the Ramer–Douglas–Peuckner (RDP) algorithm vs. PGA (**a**) and vs. antecedent precipitation index (API) (**b**). Red stars show the time stamps of five most significant changes of the trajectory. However, during most of the analyzed period, there is no clear correlation between change-points and seismic or rainfall activity. This can be caused by two reasons. Firstly, the seismic activity (PGA), which is presumed to be the main triggering factor of SAL movement [49], has ceased significantly since 2013, as is decreasing the average speed of movement of the SAL documented in this work; changes in the residuals are negligible. Secondly, the influence of precipitation (API) is probably negligible on the SAL behavior. Only three events were identified, mostly affecting the HIE3 device. This is probably caused by the presence of a shallower landslide block influenced by precipitation (see [32] for more details).
## 4 Approximation Algorithm
### Upper and Lower Bounds of Delay  
Before describing the approximation algorithm, we first need to determine the upper and lower bounds of the delay, as shown in Algorithm 1. We assume that the task set is initiated by device \(a\).  
From line 1 to line 2, we initialize the lower and upper bounds of the delay for all tasks on different servers to infinity (since it is not feasible to set the value to infinity in practice, we substitute it with \(10^{6}\) or a larger value), and set the lower bound of the virtual task \(s\) at device \(a\) to be 0. From line 3 to line 7, based on the task order in List I, we calculate the upper and lower bounds of the delay for each task on the execution node that satisfies its service requirements. This calculation is performed using a greedy approach, without considering the cost constraint. List I represents the task order obtained through a topological sorting algorithm, such as the Kahn algorithm, ensuring that all task dependencies are respected. Finally, at line 8, \(UB\) and \(LB\) are obtained as the delay bounds.  
```
1:Initialize \(T_{\min}(v,m)\leftarrow\infty\), \(T_{\max}(v,m)\gets 0\), \(v\in V,m\in M\).
2:Set \(T_{\min}(s,a)\gets 0\).
3:for\(j\in V\setminus\{s\}\) in List I do
4:for\(m\in M_{j}\)do
5:\(T_{\min}(j,m)\gets T_{j}^{m}+\min_{x_{i}\in M_{i}}\max_{i\in C(j)}\bigg{[} T(i,x_{i})+T_{ij}^{x_{i}m}\bigg{]}\). \(T_{\max}(j,m)\gets T_{j}^{m}+\max_{x_{i}\in M_{i}}\max_{i\in C(j)}\bigg{[} T(i,x_{i})+T_{ij}^{x_{i}m}\bigg{]}\).
6:endfor
7:endfor
8:\(LB\gets T_{\min}(t,a)\), \(UB\gets T_{\max}(t,a)\).
```  
**Algorithm 1**\(\text{FULC}(G(V,E),\text{List I})\)
## 4 Discussion
Laboratory scale calculations establish that the method of extraction by UAE would be the process with lower energy consumption and lower CO2 emission compared to extraction by maceration or Soxhlet, so it can be considered an \"environmentally friendly\" method, suitable to laboratory level, but scale-up proper for sustainable industrial application [60]. These methods are considered the most feasible and economically profitable in the large scale, due to the minimum detrimental effect on the extracted compounds, they are easy to install, with competitive energy costs that require low maintenance. However, it is important to optimize the variables implied in the process at laboratory scale, because the process is sensitive [61]. The main companies that have developed and commercialized large-scale ultrasound devices are Hielscher (Germany), offering ultrasonic probes with power range of 500 to 16,000 Watts; and REUS (France), that offer ultrasonic baths of 500 to 1000 L [60]. UAE represents an alternative for the extraction of phenolic compounds at industrial level, as it has been successfully applied in several large-scale experiments [62]. Figure 3: Effect of acoustic cavitation on plant cell wall during the extraction of bioactive compounds by ultrasonic assisted extraction method. (**a**) Cell wall during treatment, (**b**) cell wall after UAE process.
## 4 Hardware Design of Dual-Phase Sensitive Lock-in Amplifier Circuits
### Bandpass Filter Circuit  
The signal passes through the differential amplifier and enters the bandpass filter module, which filters out most of the interfering signals, reducing the load on the chip and improving the signal-to-noise ratio. As shown in Figure 5, the bandpass filter module consists of two amplifiers, which can meet the requirements of high \(Q\) to ensure excellent filtering effect, thus guaranteeing improvement in signal quality.  
When \(C_{1}=C_{2}=C_{3}\), \(R_{2}=R_{3}=R\), \(R_{4}=R_{5}\) in this circuit module, the transfer function \(T\)(s) is Equation (14):  
\[T(s)=\frac{s\frac{2}{RC}}{s^{2}+\frac{1}{R_{1}c}s+\left(\frac{1}{RC}\right)^{2}} \tag{14}\]  
The center frequency \(f_{0}\) and \(Q\) of the bandpass filter can be determined using the resistance and capacitance values, as shown in Equation (15). This calculation is based on the transfer function.  
\[f_{0}=\frac{1}{2\pi CR_{2}};Q=\frac{R_{1}}{R_{2}} \tag{15}\]  
In ECT detection, it is crucial to ensure that the frequency of the effective signal matches that of the excitation signal. For instance, if the excitation coil operates at a frequency of 1 MHz, then the eddy current generated on the surface of CFRP prepreg should also have a frequency of 1 MHz. Consequently, it is essential for the detector coil to detect an effective signal with an amplitude and frequency of 1 MHz as well. Therefore, maintaining an \(f_{0}\) identical to the excitation signal's frequency is imperative. However, aiming for high precision by setting a very high \(Q\) value (narrowing down the cut-off bandpass) can place excessive demands on electronic components. Hence, relying solely on a bandpass filter to eliminate interference signals becomes unfeasible. Additional modules such as multipliers and low-pass filters are required to further reduce noise.
## 1 Introduction
Fine-tuning the adaptive synchronization mechanism requires careful analysis and testing to ensure optimal performance and  
Figure 1: The main challenges of Physically Distributed Logically Centralized SDN control. responsiveness based on the distributed SDN controller deployed application requirements and characteristics. However, the challenges persist, such as optimizing SDN control techniques in complex, dynamic networks, where a control plane's configuration and forwarding mostly rely on human operation [21]. Recent distributed SDN consistency research that integrated network control with AI is much more flexible than earlier adaptive synchronization attempts since it automates policy learning in any network environment and makes no network assumptions [10]. The flexibility and robustness of the SDN network and its centralized nature can provide a global network view, driving the integration of SDN and AI concepts [10]. We review notable PDLC SDN solutions, including both AI and non-AI approaches from industry and academia. The focus of this work is on how these solutions tackle synchronization and consistency issues. We suggest a design framework that achieves an adaptive controller-to-controller synchronization rate, illustrating the design factors for a state consistency communication protocol. This will help optimize and reduce communication costs while ensuring precise and fast state synchronization amongst dispersed SDN controllers. This framework was developed following research and analysis of open problems and existing solutions. The remainder of this paper is organized as follows: Section 2 introduces Physically Distributed Logically Centralized (PDLC) SDN architecture and components. Section 3 discusses non-AI consistency solutions for Physically Distributed but Logically Centralized SDN controllers. Section 4 presents AI-based solutions for SDN consistency synchronization. Section 5 discusses a future direction: AI-based state consistency architecture. Section 6 concludes the paper.
# Air Pollution: Sensitive Detection of PM2.5 and PM10 Concentration Using Hyperspectral Imaging
## 1 Introduction
Although open-path spectrometers, such as OP/FTIR and QCLOPS, can perform the mobile detection of air pollution concentration along a path, they produce large errors due to the poor collimation of the infrared light source. However, this problem is solved by QCLOPS by using a QC laser, which is expensive and difficult to popularize. [PERSON] et al. used an airborne visible/infrared imaging spectrometer (AVIRIS-NG) with a coverage spectrum of 380-2510 nm to collect spectral images and analyze the concentration changes of methane (CH\({}_{4}\)) over Ahmedabad [26]. [PERSON] et al. collected spectral images over California landfills using AVIRIS-NG and used a linearized matched filter algorithm to analyze the distribution of pollutants [27]. In their approach, an imaging spectrometer is equipped with both image and spectrum information and can perform spectrum analysis on a certain area of an image to determine the air pollution concentration and analyze the distribution of pollutants. Therefore, the spectrometers are usually mounted on satellites or unmanned aerial vehicles that acquire a large area of spectrum data from high altitudes for analysis. Nevertheless, this approach uses passive sensing and is expensive, does not have high accuracy, and is thus difficult to employ in daily detection applications. Although the accuracy of the imaging spectrometer is lower than that of the other two methods above, it can obtain large-area measurements, and its accuracy can be improved with some additional data. Therefore, this study uses hyperspectral images to study air pollution and attempts to develop HSI technology. Cameras that do not originally have spectrum data are assigned spectrum information to significantly reduce the cost of the instrument. Then, the acquired ultra-spectrum images are analyzed for air pollution (i.e., PM10 and PM2.5) to increase the feasibility of daily detection.