esa-sceva/satcom-chunk-collection huggingface dataset:
# Out-of-Plane Experimental Study of Strengthening Slender Non-Structural Masonry Walls
## 2 Experimental Program
### Test Setup and Loading Protocol  
The out-of-plane cyclic quasi-static experimental investigations of slender non-structural walls were carried out at the Faculty of Civil and Geodetic Engineering of the University of Ljubljana in Slovenia. As shown in Figure 3, the test setup was prepared on a strong floor where the specimen was clamped into the rigid steel supporting frame, with the actuator mounted on the reaction wall. On two parallel frame columns, the top and bottom support of the wall specimen is provided by rigid steel elements and a U-shaped profile. As the experiment was a simulation of non-structural partition walls, the upper steel profile was placed on the top edge of the wall, without applying additional vertical loading.  
In order to prevent horizontal relative displacements and allow rotations at the supports i.e., at the contact between the specimens along the steel rail, wooden wedges were inserted in the spacing between them. The supports are shown in details **A** and **B** in Figure 3. The displacements of a specimen were recorded with seven LVDTs, which were placed along the specimen height.  
The main objective of the present study was to identify the behavior of non-structural URM walls under seismic out-of-plane loading and the efficiency of different remedial actions. Therefore, quasi-static cyclic out-of-plane tests were conducted by applying the horizontal displacement-controlled load at one-third and two-thirds of the specimen's height, as shown in Figure 3. The applied out-of-plane horizontal cyclic loading protocol shown in Figure 4 was performed with a stepwise horizontal displacement, increasing up to the limit state of failure of an individual specimen. Each loading step consisted of two load cycles in a positive and negative direction. In the extreme positions, at the maximum amplitude of the forced displacement, the specimen was held for 5 s. A similar protocol has been used in experiments conducted by other researchers [61].  
Figure 3: Quasi-static cycling test setup for out-of-plane testing of non-structural wall specimens.  
At the start of each individual experiment, the maximum displacement amplitude was increased by 2.5 mm in each step, up to a displacement amplitude of 15 mm. In the subsequent steps, the displacement amplitude was increased by 5 mm until the maximum amplitude of 50 mm was reached. For the last sequence of steps, the amplitude was increased for 10 mm until the limit state of failure of a specimen was reached. The load rate also increased stepwise from 0.5 mm/s at the beginning up to 5 mm/s at the end, when an amplitude of 60 mm (at a minimum) displacement was reached.
# Selection of Key Frames for 3D Reconstruction in Real Time
## 1 Introduction  
A detailed description of the environment in the form of images or 3D data is of major importance for a whole range of applications. For example, such data are used for planning and developing our transport infrastructure [1], as well as geomomitoring [2] or medical practice [3]. The data are collected by a wide variety of sensors. In most cases, these are light detection and ranging (LiDAR), cameras, or radar. Cameras play a prominent role in this context, as they can be designed to be very cheap and small and can therefore be used in almost any detection system--either as a stand-alone acquisition system or in combination with the previously mentioned sensors.  
When extracting or selecting images from videos at 30 frames per second, many images are created in a very short time. However, if one makes fast motions, translational, rotational, or even both, the frames can suffer from motion blur [4], as seen in Figure 1. Recordings of fast-moving objects can distort their shape and appear blurry. The problem can be simplified by ensuring that only static objects are in the recording. More simply, only the motion of the camera itself contributes to the distortion of the image. Moreover, during the video recording, one obtains many redundant images. At 30 frames per second, many of the images contain almost the same information, which is not necessary for further processes. Due to the high recording rate, the angular changes between two successive images are only marginal. With faster motions, this angle difference would increase,but the quality would suffer. If these images are used for further processes, such as 3D reconstruction, this would significantly increase the computation time. In addition, the detected features would lose accuracy when projected into three-dimensional space by less-accurate matching feature points if the images are distorted, due to faster motions during recording. If we could select images automatically, we would thereby reduce the computation time and expect a more accurate result. So how can one manage to extract key frames automatically and efficiently in real time to use the best-suited images for scene reconstruction, with respect to quality and redundancy?
# A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection
## 1 Introduction
Evolutionary algorithms (EAs) are known for their high computational complexity, which often limits their practical application despite their potential for performance improvement [26]. In this study, we propose a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) to address this issue. By leveraging feature correlation knowledge and a dynamically allocated competitive-cooperative mechanism, the KCCEA significantly reduces computational complexity while enhancing performance. This approach balances the trade-off between computational efficiency and performance gain, making it a valuable contribution to real-world applications where high-dimensional data and limited computational resources are common. The main contributions of this work can be summarized as follows:  
* We propose a feature grouping method based on correlation and, based on this, introduce a Knowledge-Guided Evolutionary Algorithm (KGEA). Initially, the correlations between features are assessed using Spearman's correlation coefficient. Then, using a predefined optimal threshold, features are grouped based on their correlations. Finally, the obtained grouping information serves as knowledge to guide the evolutionary process. In this way, the speed and quality of evolutionary feature selection can be significantly enhanced. * We design a Knowledge-Guided Competitive-Cooperative Evolutionary Algorithm (KCCEA) framework. Initially, a method based on allocation ratios allows the two algorithms to cooperate. Then, during the evolutionary process, the algorithms continually update the allocation ratio dynamically by assessing the success rates of producing superior offspring, allowing the two algorithms to compete. This method achieves an improvement in search speed and solution quality;
* To verify the performance of the proposed methods, we conduct a series of experiments. The experimental results demonstrate that the proposed methods effectively enhance the performance of evolutionary algorithms in feature selection tasks. The structure of this paper is as follows: Section 2 introduces the background. Section 3 describes the Knowledge-Guided Evolutionary Algorithm (KGEA). Section 4 presents the competitive-cooperative evolutionary mechanism (KCCEA). Sections 5 and 6 describe the experimental setup and results, respectively. Section 7 concludes the paper and outlines several themes for future research.
## 6 Experimental Results
### Benchmark Functions
 \tag{27}\]  
where \(u(x_{i},a,k,m)\) is equal to  
and  
\[y_{i}=1+\frac{1}{4}\left(x_{i}+1\right)\]  
Figure 13: 3D View of some unimodal benchmark mathematical functions [44]. **Multimodal test functions with fixed dimensions**:  
Shekel's Foxholes Function:  
\[f_{10}(X)=\left[\frac{1}{500}+\sum_{j=1}^{25}\frac{1}{j+\sum_{i=1}^{2}\left(x_{ i}-a_{ij}\right)^{6}}\right]^{-1} \tag{28}\]  
where  
\[a_{ij}= \begin{bmatrix}-32&-16&0&16&32&-32&...&0&16&32\\ -32&-32&-32&-32&-16&...&32&32&32\end{bmatrix} \]  
Six-hump Camel Back Function:  
\[f_{11}(X)=4x_{1}^{2}-2.1x_{1}^{4}+\frac{1}{3}x_{1}^{6}+x_{1}x_{2}-4x_{2}^{2}+ 4x_{2}^{4} \tag{29}\]  
Branin's Function:  
\[f_{12}(X)=\left(x_{2}-\frac{5.1}{4\pi^{2}}x_{1}^{2}+\frac{5}{\pi}x_{1}-6\right) ^{2}+10\left(1-\frac{1}{8\pi}\right)\cos x_{1}+10 \tag{30}\]  
Goldstein-Price Function:  
\[ \begin{array}{c}f_{13}(X)=\left[1+\left(x_{1}+x_{2}+1\right)^{2}\left(19-14x _{1}+3x_{1}^{2}-14x_{2}+6x_{1}x_{2}+3x_{2}^{2}\right)\right]\\ \left[30+\left(2x_{1}-3x_{2}\right)^{2}\left(18-32x_{1}+12x_{1}^{2}+48x_{2}-36 x_{1}x_{2}+27x_{2}^{2}\right)\right]\end{array}  \tag{31}\]  
Hartman's Family Function:  
\[f_{14}(X)=-\sum_{i=1}^{4}c_{i}\exp\left[-\sum_{j=1}^{n=3}a_{ij}\left(x_{j}-p_{ ij}\right)^{2}\right] \tag{32}\]  
Figure 14: 3D View of some multimodal benchmark mathematical functions [44]. \[f_{15}(X)=-\sum_{i=1}^{4}c_{i}\exp\left[-\sum_{j=1}^{n=6}a_{ij}\left(x_{j}-p_{ij} \right)^{2}\right] \tag{33}\]  
where \(a_{ij}\), \(c_{i}\) and \(p_{ij}\), for \(f_{14}(X)\), i.e., \(n=3\), are given in Table 4, and for \(f_{15}(X)\), i.e., \(n=6\), in Table 5.  
Regarding the second set of benchmark functions, all basic functions that have been used in the composition functions are shifted and rotated functions [43]. In addition, all composite functions are multimodal, non-separable, asymmetrical and have different properties around different local optima [43]. These properties create a high complexity in searching the optimum solution. Complete definitions of these functions are stated in the CEC 2017 Technical Report [43]. The number of dimensions and the search subset or range \((SeaSub)\) were \(n=10\) and \([-100,+100]\), respectively, for all composite functions utilized, as shown in Table 6. Figure 16 shows the 3D views of the benchmark composite functions chosen. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(i\) & \(a_{ij}\) & & & & \(c_{i}\) & \(p_{ij}\) & & & & \\ \hline
1 & 3 & 10 & 30 & 1 & 0.3689 & 0.1170 & 0.2673 \\
2 & 0.1 & 10 & 35 & 1.2 & 0.4699 & 0.4387 & 0.7470 \\
3 & 3 & 10 & 30 & 3 & 0.1091 & 0.8732 & 0.5547 \\
4 & 0.1 & 10 & 30 & 3.2 & 0.03815 & 0.5743 & 0.8828 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Values of \(a_{ij}\), \(c_{i}\), and \(p_{ij}\) for function \(f_{14}(X)\); \(n=3\) and \(j=1,2,3\). Figure 15: 3D View of some multimodal benchmark mathematical functions with fix dimensions [44].
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 3 Measurement Campaign
### Measurement System  
The acquisition system is built on Beckhoff voltage and current sampling cards and is configured to 5 kHz sampling frequency. Voltage acquisition cards feature an oversampling principle with  
Figure 1: One-line diagram of test network.  
16-bit resolution and measurement error not higher than 0.5% relative to full scale value specified for frequencies up to 5 kHz. Current analogue acquisition cards are characterized by differential input and 16-bit resolution with measurement error less than 0.3% relative to full scale. This accuracy is likewise applicable for the 5 kHz range. The monitoring and acquisition of the data are done through TwinCAT software.  
On the MV side, there are two voltage measurement points wired to VTs of Substation G and Primary Switchboard (Isolation Transformer). MV currents are measured through every substation as well as through Isolation Transformer. The accuracy class of these CTs is 0.2S providing superior performance at fundamental frequency. Since MV current transducers are typically not characterized for harmonic frequencies, a higher phase shift can be expected at the output. In this paper, we do not use harmonic phase angles of MV side currents. On the LV side, three voltage channels for every busbar are connected directly to the three phases at 250 V. Individual currents of every load are measured by means of Fluke i1000s AC Current Probes providing four-channel current input to sampling cards. The range selector of these current clamps was switched to 10 mV/A sensitivity with minimum measured current of 100 mA and maximum of 100 A. The basic accuracy at this range is 2% of reading for the frequencies up to 5 kHz with a phase shift of no more than 10 degrees. Within the reported frequency range, the linearity of measurements is ensured. On site, all the utilized measurement equipment had valid calibration certificates.  
As it can be seen, this measurement system is characterized by modular architecture and capable of recording synchronously voltage and current waveforms. The distributed clock principle with local clock on slave controllers and a master clock ensure the synchronization of all components of the system via unique EtherCAT technology. Additionally, this system is synchronized to the higher-level absolute time making use of external reference based on IEEE1588 PTP. This technique allows all the distributed clocks to be adjusted to a constant offset. By combining these two distinct synchronization routines, it was possible to achieve the distributed clock precision of better than 1 \(\upmu\)s.
# SPN-Based Dynamic Risk Modeling of Fire Incidents in a Smart City
## 3 Modeling Methodology and Analysis  
### Overview of the Modeling Framework  
The dynamic risk modeling framework integrates NLP, SPN, and Bayesian theory to assess urban natural fire risks (Figure 1). NLP extracts key event nodes and dependencies from unstructured texts, which are mapped into BPMN diagrams. These diagrams are converted into SPN models, incorporating time factors to capture dynamic changes. The model is analyzed for safety, boundedness, liveness, and reachability, with performance metrics calculated to evaluate its effectiveness. Empirical analysis validates its feasibility.  
Bayesian theory dynamically updates the SPN model using new observed data, ensuring real-time adaptability. The updated model identifies key risk nodes and their probabilities, supporting risk management and emergency response planning. This approach enhances urban resilience to natural fire threats by providing a clear methodology for understanding accident evolution and formulating prevention strategies.
# Durability Investigation of Fiber-Reinforced Functionally Graded Concretes in Cold Regions
## 2 Test Methods and Proportioning Model
### Impermeability Test
#### 2.3.2 Chloride Ion Penetration Resistance Test  
The rapid chloride migration coefficient method (RCM) was used to test chloride ion penetration resistance. The test was carried out with a cylinder of 100 mm diameter and 50 mm height, and the test time was 56 d. The test results are shown in Table 5.  
The test results show that the freeze-resistant and anti-cracking mass concretes' 28 d chloride ion diffusion coefficients were all less than 3.0 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s, of which #2 and #3 had the lowest diffusion coefficients. Compared with ordinary concrete of the same grade, the concretes with matching ratios #2, #3, #5, and #6 used in this test significantly improved the resistance to chloride ion attack, and effectively enhanced the durability of the concrete. It can be explained that the dense skeleton accumulation of mineral powder enhances the compactness of concrete, fills some of the pores, hinders the diffusion of chloride ions, and improves the impermeability of the concrete.
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 3 Description of the Communication System
### Receiver System
#### 3.2.1 Signal Conditioning  
The voltage induced in the receiving coil is in the order of millivolts, so it is necessary to carry out an amplification of voltage and current. In this stage, the receiving magnetic coil is connected to ground and to the input of the voltage amplifier circuit and the output is directly connected to the base of a 2N222 transistor directly configured to have current amplification.  
Figure 8: Receiver coil electrical circuit.  
Figure 7: ASK modulation tests.
## 4 Experiments and Analysis
### Experimental Data and Experimental Environment  
The method presented in this paper is embedded into the NewMap Spatio-temporal Big Data Center independently developed by the China Academy of Surveying and Mapping for experimental verification. The experimental data source is spatio-temporal big data from Dongying City, Shandong Province. The user task requests include \"spatio-temporal data querying\" and \"spatio-temporal data download\". Task requests are sent randomly without priority and require various system resources. The experimental environment is a service center set up on a single graphics workstation with the following specifications: Windows 10 operating system, Intel(R) Core(TM) i7-6700 HQ CPU with a clock speed of 2.6 GHz, and 64 GB of memory.
## 1 Introduction
Moreover, although a similar approach to the clustering algorithms is proposed in the existing methods, a new and efficient clustering pattern is implemented, as a tree structure is used. The BIRCH algorithm also uses a tree structure; however, when comparing the proposed method with BIRCH, we found that the matching of similar advertisements could be carried out faster and with higher accuracy when using the proposed approach. The contributions and innovations of this study can be listed as follows:  
* Analyzing the job postings in Turkish as an agglutinative language by using a stream clustering algorithm for the first time in the literature;
* Keeping these job postings, which are big and stream data, in a novel and more effective data structure than the others in the literature. In addition, this novel approach can be used in any area effectively;
* Discovering the fit positions of new job postings quickly to adapt them to this data structure for the first time in the job postings industry;
* Recommending up-to-date and significant qualifications for new job postings as a decision support system for the first time in the job postings industry  
To carry out these operations, first, we aimed to transform the unstructured job postings using natural language processing methods and to keep them in a data warehouse. Then, tests were conducted, comparing the three most effective data retention methods: traditional object relational database usage, stream clustering, and semantic networks. These approaches are presented in Section 3. All details of the study and the proposed algorithm are provided in Section 4. The obtained test and comparison results are also presented, along with graphics and tables, in Section 5.
## 4 Discussion
### Limitations  
#### 4.3.1 No External Context Consideration  
In some cases, plowed rice paddy and plowed field share very similar internal cultivation patterns, as shown in Figure 11, making them difficult to distinguish based solely on parcel-internal textures. In reality, a rice paddy is typically situated on lower ground to retain water, whereas a field is often located on slightly higher terrain to facilitate drainage. However, in this study's approach, all external background information is masked out so that the model trains only on the parcel's interior textures. As a result, crucial contextual cues--such as ground elevation and drainage conditions--are excluded, preventing the model from adequately separating plowed rice paddy and plowed field. This frequent confusion between the two classes suggests that supplementary strategies incorporating \"external background\" data may be necessary.
# A Unified PUF and Crypto Core Exploiting the Metastability in Latches
## 3 Physical Unclonable Function
### Analytical Model  
The analytical model of the PUF is based on the metastability response in latches. The metastability in latches has been studied for TRNG applications, presenting an analytical model depending on the mismatch and noise based on a loop gain model [17]. The voltage in the nodes \(V_{Q}\) and \(V_{QN}\) are modelled using functions depending on the opposite voltage node (\(V_{Q}=f(V_{QN})\) and \(V_{QN}=f(V_{Q})\)). In addition, the mismatch (\(\delta_{m(Q)}\) and \(\delta_{m(QN)}\)) and noise (\(\delta_{n(Q)}\) and \(\delta_{n(QN)}\)) are added to the response of each voltage node in (1).  
\[\begin{array}{l}V_{Q}=g(V_{QN})+\delta_{m(Q)}+\delta_{n(Q)}\\ V_{QN}=f(V_{Q})+\delta_{m(QN)}+\delta_{n(QN)}\end{array} \tag{1}\]  
When the latch is balanced, the \(\delta_{n(Q)}\) and \(\delta_{n(QN)}\) generate a random response. In addition, the \(\delta_{m(Q)}\) and \(\delta_{m(QN)}\) impose a bias in the latch response. The undesirable effects in each node generate voltage variations in each node. When the \(V_{Q}\) and \(V_{QN}\) exceed the maximum voltages tolerated (\(V_{(QF)}\) and \(V_{(QNF)}\)), the response of the latch is obtained. The result of the loop gain model approximates the response to (\(V_{Q}=(a*V_{QN})+b\)). The coefficients \(a\) and \(b\) are calculated using the \(V_{(QF)}\) and \(V_{(QNF)}\) values in (2), respectively.  
Figure 5: The PUF architectures based on RO, multi-modal RO, and latches.  
Figure 6: Metastability response in balanced and unbalanced latches.  
\[a=\frac{f(V_{QF})-V_{QNF}}{V_{QF}-g(V_{QNF})} \tag{2}\] \[b=\frac{V_{QF}V_{QNF}-f(V_{QF})g(V_{QNF})}{V_{QF}-g(V_{QNF})}\]  
Finally, the response of the latch in a metastable response is denoted by \([(a*V_{Q})+b]>[f(V_{Q})-\delta_{m(QN)}-\delta_{n(QN)}]\). The latch response depends on the relationship between each node's voltage, noise, and mismatch. Therefore, a PUF implementation needs to increase the process variations represented in the mismatch and reduce the noise introduced in the latch.
## 4 Multi-Hop Data Forwarding Strategy Based on Annulus-Sectors  
### Cluster Header Selection  
According to [36], it is known that when the CH is located near the center of the cluster, it can effectively balance the energy consumption as well as prolong the lifetime of this cluster. Thus, the definition of the \"Region for Candidate CHs\" (RCCH) is firstly described. For the \(j\)-th annulus-sector in the \(i\)-th annulus, the intersection of its symmetry axis and the middle line of this area is regarded as the center of the RCCH (the green region in Figure 4, and the radius of this RCCH is denoted as \(r_{i}\). It is easy to know that \(i\in[1,k]\) and \(j\in[1,n]\). All the cluster headers are selected from the nodes that are located in the RCCHs, and it is assumed that each node knows its own coordinates after network deployment. If the sensor node is located in the RCCH, it is regarded as the \"Candidate Cluster Header (CCH)\", otherwise it is called a \"Common Node (CN)\".  
\(W\) is defined as the weight of each CCH, and the value of it is calculated by Equation (3). \(\alpha\) and \(\beta\) are the adjustable parameters, and they meet \(\alpha+\beta=1\). \(e_{0}\) and \(e_{r}\) are defined as the initial and residual energy of the CCH respectively. Moreover, \(d_{locenter}\) is the Euclidean distance between the CCH and the center of the RCCH, and \(\delta\) is an adjustable coefficient.  
\[W=\alpha(e^{r}/e_{0})+\beta(\delta/d_{to~{}center}) \tag{3}\]  
At the beginning of each round of data collection, each CCH calculates its weight, and then the CCH with the maximal value of \(W\) in this RCCH is selected as the cluster head. Subsequently, the ID of this CH is broadcasted to all nodes in the same annulus-sector, and nodes that receive this message establish a one-hop communication link to the CH for uploading data. The definitions of data collection parameters are given in Table 2.