esa-sceva/satcom-chunk-collection huggingface dataset:
## Application of the Subspace-Based Methods
###### Abstract  
A large number of research studies in structural health monitoring (SHM) have presented, extended, and used subspace system identification. However, there is a lack of research on systematic literature reviews and surveys of studies in this field. Therefore, the current study is undertaken to systematically review the literature published on the development and application of subspace system identification methods. In this regard, major databases in SHM, including Scopus, Google Scholar, and Web of Science, have been selected and preferred reporting items for systematic reviews and meta-analyses (PRISMA) has been applied to ensure complete and transparent reporting of systematic reviews. Along this line, the presented review addresses the available studies that employed subspace-based techniques in the vibration-based damage detection (VDD) of civil structures. The selected papers in this review were categorized into authors, publication year, name of journal, applied techniques, research objectives, research gap, proposed solutions and models, and findings. This study can assist practitioners and academicians for better condition assessment of structures and to gain insight into the literature.  
subspace system identification; data-driven stochastic subspace identification (SSI-DATA); covariance-driven stochastic subspace identification (SSI-COV); combined subspace system identification; PRISMA; damage detection; vibration-based damage detection
## 6 Results
### Degradation Efficiency  
The contaminant breakdown performance is described based on Table 4, from which it is observed that, for the selected model pollutant (phenol), the plasma technology, depending on the DBD reactor setup and optimization conditions, can achieve up to 98% contaminant degradation efficiency (as illustrated in Figure 4). Such plasma performance on phenol closely compares with well-established chlorination and ozonation technologies at 99% and >99%, respectively. The other illustrative comparisons for the listed contaminants are also accordingly shown in Table 4.  
Figure 4: Contaminant removal efficiency by the different water treatment technologies considering phenol as a model pollutant.  
Figure 3: Energy efficiency associated with three different water treatment technologies when removing phenol as a contaminant.  
The operating parameters of the plasma reactor influence the performance outputs to a considerable extent, as demonstrated by [PERSON] et al. [107] during the study of the degradation of phenolic compounds by DBD plasma. For their study case, the plasma discharge voltage, initial contaminant concentration, treatment liquid flow rate, solution matrix parameters (conductivity and pH) and structure of the organic pollutants were observed to influence the removal efficiency of the DBD plasma system. [PERSON] et al. [37] complements the mentioned parameters with frequency (an electrical variable) and mode of contaminant exposure to the plasma. Thus, with well-optimised plasma treatment conditions and choice of suitable reactor structure, the plasma treatment can achieve close to 100% removal of pollutants. For example, [PERSON] et al. [108], during their study of the degradation of bisphenol A using non-thermal DBD plasma, reported 100% elimination of contaminants in the treatment liquid within 25 min using an investigated optimum plasma discharge voltage of 16.8 kV.
# On the Use of FOPID Controllers for Maintenance Phase of General Anesthesia
## 1 Introduction
A fractional adaptive high-gain controller was proposed in [12], where the assessment of the controller performance was carried out by considering four patients. Fractional-order closed-loop model reference adaptive control was presented in [13; 14], with the main aim of increasing the robustness of the system. Regarding FOPID controllers specifically, they were employed for DoH control in [15], where the isodamping approach [16] was used to tune a FOPI controller, and the methodology was evaluated by considering 24 patients. A more general framework also involving hemodynamic variables was considered in [17; 18], and the FOPID controllers were tested on the same 24 patients. Finally, a multivariable approach targeting the control of cardiac output and mean arterial pressure was presented in [19; 20; 21]. Despite some pioneering studies on the use of fractional control in anesthesia, it appears that a thorough analysis of the performance achievable with a FOPID controller and a rigorous comparison with PID controllers are still lacking. Indeed, a FOPID controller is more complex than a PID controller, and therefore, it should provide clear advantages over its integer-order counterpart to justify its adoption. In this paper, we provide such an analysis. In particular, we tuned both a PID controller and a FOPID controller by using genetic algorithms to minimize the worst-case integrated absolute error (IAE) on a dataset of 13 patients representative of a diverse population. Note that this approach has already been proven to be effective for PID controllers [3] and has also been confirmed by clinical results [22]. The obtained controllers were then tested on many virtual patients, generated with the Monte Carlo method, in order to consider both inter- and intra-patient variability. In addition, a FOPID controller was designed by also applying the isodamping approach [23] and tested on the same set of patients generated by the Monte Carlo method. In this way, the two typical options for the design of the FOPID controller were both considered. This paper is organized as follows. The patient model, the control architectures and the design of the controllers are explained in Section 2. The extensive simulation results are presented in Section 3 and discussed in Section 4. Finally, conclusions are drawn in Section 5.
## 4 Separation Methodology
### The Cases of Identifying Abnormal Conditions for Hourly Data  
The data of Building A in August 2018 are selected for the case analysis, as shown in Figure 12. The three cluster centers and eigenvalues, such as large and small thresholds, are shown in Table 5.  
The comparison diagram between the simulation conditions and the actual conditions for hourly data is shown in Figure 13, with 93.1% accuracy. It is divided into three states (closed, slightly open, and fully open) for daily data, but this is not necessary for the complex steps for hourly data. It can also be divided into two states (closed and open) without affecting the results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cluster Categories** & **Number of Samples** & **Eigenvalues** & **Power Consumption of Lighting (kWh)** & **Outdoor Temperature (\({}^{\circ}\)C)** \\ \hline \multirow{3}{*}{Cluster I: closed} & \multirow{3}{*}{47} & C & 1152.50 & 17.28 \\  & & MAX & 1282.65 & 24.55 \\  & & MIN & 805.47 & 3.30 \\ \hline \multirow{3}{*}{Cluster II: slightly open} & \multirow{3}{*}{27} & C & 1448.11 & 19.21 \\  & & MAX & 1686.75 & 24.83 \\  & & MIN & 1287.33 & 19.89 \\ \hline \multirow{3}{*}{Cluster III: fully open} & \multirow{3}{*}{13} & C & 2066.55 & 28.87 \\  & & MAX & 2280.90 & 32.05 \\ \cline{1-1}  & & MIN & 1787.04 & 25.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Eigenvalues of three clusters by the KNN method.  
Figure 12: Characteristic value recognition results.  
Figure 13: Comparison of identification results using the KNN method.
## 3 Methodology
### Path Planning Model
#### 3.3.3 Hotspot Identification  
Activity Hotspot Identification: By iterating through trajectory points, the degree of aggregation of these points serves as the criterion for determining the user's activity hotspots. \(F(c,i)\) represents the degree of aggregation of the \(i\) point and subsequent points in the \(c\) user's walking trajectory, and its calculation formula is as shown in Formula (1). When \(F(c,i)>M\), it is considered that point \(i\) and subsequent points form a hotspot area, the centroid of which is the activity hotspot \(k\), with the position information \(X_{ck}\) and time information including the arrival time and stay time represented by \(Y_{ck}\) and \(T_{ck}\), respectively; the previous trajectory and corresponding time are \(GW_{ck}\) and \(GT_{ck}\), respectively.  
\[F(c,i)=\sum_{j=i}^{N_{c}}\big{(}\prod_{k=i+1}^{j}I(c,i,k)\big{)} \tag{1}\]  
\(F(c,i)\) represents the number of location points that satisfy the distance constraint near \(i\), where \(I(c,i,n)\) indicates \(i+n\) and the average location information recorded from \(i\) to \((i+n-1)\). The calculation formula is shown in Equation (2).  
\[I(c,i,n)=\begin{cases}0,L\Big{(}\overline{w}(c,i,n),w_{c(i+n)}\Big{)}>L\\ 1,L\Big{(}\overline{w}(c,i,n),w_{c(i+n)}\Big{)}\leq L\end{cases} \tag{2}\]\(\overline{w}(c,i,n)\) represents the center of gravity of the subsequent \(i\) point positions of point \(n\), and the calculation formula is shown in Equation (3).  
\[\overline{w}(c,i,n)=\frac{1}{n}*\sum_{j=i}^{i+n-1}w_{cj} \tag{3}\]
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 3 Methodology
### Deep Learning Network Architecture  
In our research, we used a combination of a convolutional neural network (CNN) and bi-directional long short-term memory (Bi-LSTM) to train our proposed SafeNet model (Figure 4). The model's architecture comprises one-dimensional (1D) convolutional layers, a 1D Bi-LSTM structural layer, and a fully connected (FC) block. For feature extraction from the input data, the SafeNet model employs CNN layers, which are combined with Bi-LSTMs to facilitate sequence prediction. SafeNet accesses subsequences of main sequence as blocks, collects features from each block, and then transfers extracted features to the LSTM layer for interpretation. To enable the CNN model to read each subsequence in the window, the entire CNN model is wrapped in a Time Distributed layer. The extracted features are then flattened and provided to the Bi-LSTM layer for reading, and further features are extracted. The 1D convolutional layers are utilized to extract data features in concept, and then Bi-LSTM structures are employed to optimize feature extraction in sequential data. Finally, the classification probability is calculated using an FC layer. The loss function is categorical cross-entropy, and the optimization is performed using the Adam method [31]. For more details on the SafeNet network architecture, please refer to Text S4 (Supplementary Materials).  
The proposed model was developed in TensorFlow 2.0 with the Keras (v. 2.3.0) interface [32]. To facilitate fast training, all models were trained on a server equipped with two Intel Xeon E5-2650 v4 CPUs, 128 GB of RAM, and an NVIDIA GeForce RTX 2080 Ti graphics processing unit (GPU) [33]. Owing to the sensitivity of the proposed method to the chosen parameters, Bayesian hyperparameter tuning was utilized to determine the optimal settings [34] and was developed using the Hyperopt Python package [35]. The negative of the F-measure (F1) was utilized as the objective function's return value (loss) in this procedure. The procedure optimized hyperparameters based on their capacity to minimize an objective function by constructing a probability model based on the results of previous evaluations. Consequently, this model can be expected to perform better with fewer iterations than the random or grid searches would require. Table S1 (Supplementary Materials) summarizes the search space for SafeNet's important parameters. Each model was assigned a maximum of 100 iterations. DataSet S1 (Supplementary Materials) provides the hyperparametric optimization trial results for all the datasets used to train the SafeNet model and other benchmarking classifiers.
## 3 Results
### Horizontal Movements  
Estimated latitudinal movements of PSAT tagged white sharks along the Australian east coast spanned from Fraser Island (25\({}^{\circ}\) S) to southern Tasmania (44\({}^{\circ}\) S) (Figure 1). The estimated longitudinal range generally extended from the coast to approximately 154\({}^{\circ}\) E (Figure 1). One of the tracked sharks (shark 180) travelled further southeast towards New Zealand's South island, during the PSAT tracking period (Figure 1). It remained in waters approximately 500 km off southwest South Island New Zealand for 18 d, before travelling in a northern direction to an area west of the Lord Howe Rise, where it remained for approximately 18 d before returning to eastern Australia on a directed path.  
Seasonal movement patterns were apparent (Figure S2) with tagged sharks dispersing north and south along the NSW coast between June and August during the austral winter. In the austral spring (September to November) they further extended their latitudinal range, which reached a maximum in December, with sharks occurring across \(\sim\)2000 km of coastline from southern Queensland waters, along the NSW coast and into waters off southern Tasmania (Figure S2).  
Figure 1. Spatial scale of individual movements. Interpolated telemetry tracks of 12 juvenile white sharks tagged off eastern Australia from a combination of satellite-linked radio, pop-up satellite archival, acoustic and recapture records collected between 12 September 2017 and 1 March 2020. Positions are colour-coded by month. Arrows indicate tagging locations. Red square indicates the study area. ID numbers, sex and fork length of tagged sharks are indicated above each map. All maps were generated using the marmap package in R [37].
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 2 Related Work
### Authentication between Users and Drones  
Several researchers studied user authentication to allow only authorized users to access data available on remote drones. [PERSON] et al. [5] proposed a user authentication mechanism for drones that jointly uses the mobile device, password, and biometric of a user as multiple authentication factors. The proposed authentication mechanism is based on temporary credentials to ensure user anonymity in addition to restricting unauthorized access to drones. [PERSON] et al. [6] proposed a lightweight cryptographic protocol to provide mutual authentication and key agreement between users and drones by using only cryptographic hash functions and XOR operations. The proposed approach additionally requires the intervention of a control server. [PERSON] et al. [4] presented a comprehensive review of user authentication mechanisms to drones in IoD environments. These studies have mainly focused on obtaining the authority of controlling drones through authentication between drones and remote users via ground stations, which are assumed as trusted authorities. Unlike these approaches, the main focus of our work is to check whether a drone in the fly zone obtains the authorization of flight or not through the authentication between drones and ground stations.
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 2 Materials and Methods
### Machine Learning Pipeline for Classification
#### 2.3.1 Feature Selection Algorithms  
High dimensionality of the hyperspectral data is a critical factor that needs to be carefully considered when analyzing the hyperspectral data [17]. In this study, we propose two alternative techniques: (i) use the complete reflectance spectrum in order to exploit fully the information content of each band, and (ii) put forth a feature selection framework as a preprocessing tool that allows to reduce considerably the number of bands to be sent to the classification algorithms. In the second scenario, the feature selection framework is composed of two main steps: (1) an ensemble-based feature selection method (composed of sequential feature selector (SFS), feature importance score (FSI), recursive feature elimination (RFE)) are introduced to select a subset of feature bands containing the most significant features. This aims to eliminate the redundant and less relevant bands for the classification process. These three methods are a popular yet powerful feature selection paradigm due to their simplicity and effectiveness at appointing the most relevant features [29]. In this step, each feature selector provides a different subset of feature bands, which are simply aggregated by applying a majority-based vote mechanism. The bands with the most votes are chosen as the selected subset which will act as an accelerator for the successive step. (2) In the second step, the resulting subset of reduced bands with essential information content is subject to an intensive optimization search process using a proposed GA [30]. We exploit GA as a feature selection tool due to its customizability and it is characterized by its efficiency in exhaustively testing different sets of feature combinations [4]. Figure 3 illustrates an overview of the proposed framework.
# Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model
## 3 Methodology  
In this paper, we propose a method for power load forecasting based on LVMD-DBPCM load curve clustering, and the CNN-IVIA-BLSTM model is shown in Figure 6. First, data preprocessing is performed, and the influences of load are divided into continuous and discrete series. After normalizing the continuous sequences, they include load data, meteorological data (temperature, wind speed, and humidity), and economic factors. One-hot encoding is applied to discrete sequences, and the discrete sequences include the data types of workdays, weekends, and holidays. Load data decomposition and denoising with LVMD are performed on the preprocessed data. Then, the power load curve is clustered using the DBPCM algorithm. The processed data are extracted with a 1D CNN for feature extraction and finally, predicted with the IVIA-BLSTM model.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 5 Conclusions  
In order to explore the performance of convenient single RF link MIMO system, this paper aims to find the system-level simulation platform that can directly evaluate the components characteristic influence on the overall performance of the system. In SystemVue platform, the whole MIMO system is decomposed into four parts, including single RF link transmitter, channel and receiver, and the measured end. The characteristics of single RF link MIMO communication system was studied using the full system simulation platform, and the physical parameters effect of the switch parasitic antenna are analyzed. A new data recovery method, combined with the broken zero algorithm and minimum mean square error algorithm at the receiver, was proposed to reduce the system error rate. Compared with the equivalent fading model in [35], the BER will be decreased to \(10^{-4}\) using the algorithm mentioned above. This paper analyzes the simulation platform of single RF link MIMO system based on switched parasitic antenna and makes up for the disadvantages of high cost in experimental verification and greatly improves the efficiency of MIMO system, thus providing prospects for future research on the single RF link MIMO system.  
**Acknowledgments: This research was supported by the National Natural Science Foundation of China (Grant No. 61671180, No. 61501275, No. 61571155 and No. 61371044), the National Key Research and Development Program of China (2016 YFE0111100) and the Science and Technology Innovative Talents Foundation of Harbin (2016 RAXJ044).**  
**Author Contributions: [PERSON] set up experiments, then wrote the draft of this paper. [PERSON] helped with construction of the experiments and obtaining results, and he also put forward the idea. [PERSON] and  
Figure 13: The regularity of BER change with SNR under different algorithms.  
[PERSON] helped to analyze the results, and improved the English of this paper. All the authors wrote this paper together, and they have read and approved the final manuscript.  
The authors declare no conflict of interest.
###### Abstract  
This research presents an innovative methodology for accurately predicting spring-back tendencies in V-bending of SS400 steel sheets under elevated temperatures. The study leverages extensive tensile test data to determine parameters for pure isotropic and kinematic hardening laws at varying temperatures, crucial inputs for Finite Element Method (FEM) simulations. While using pure isotropic or kinematic hardening laws alone has limitations, especially at elevated temperatures, a hybrid approach is recommended for robust predictive models in ABAQUS 6.13 software. To address this challenge, a novel method is introduced, utilizing flow stress curve ratios between elevated and room temperatures as a function of equivalent strain to derive combined hardening law parameters. Rigorous comparison of simulation and experimental results confirms the model's effectiveness in predicting spring-back in the V-bending of SS400 steel sheets, particularly under elevated temperatures. This innovative approach enhances understanding of material behavior at high temperatures and improves predictive capabilities for designing and optimizing complex V-bending processes.  
tension test; elevated temperatures; V-bending; FEM; spring-back; SS400 steel sheet
## 3 Results
### Energy Dispersive Spectrum (EDS) Analysis  
In order to better understand the content changes of the elements of the paste after CO2 absorption by adopting ultrasonic vibration technology, EDS analysis was conducted with the needle-like production in cement paste without CO2 absorption, respectively and the cement paste with 1.32% CO2 absorption by mechanical agitation, as well as that with 1.32% CO2 absorption by ultrasonic agitation, as illustrated in Figure 14a-c. The contents of each element are shown in Table 3.  
Figure 14: EDS analysis.  
According to Table 3, the main elements of the measure points in Figure 14a-c are C, O, and Ca, respectively. Meanwhile, there are also some Si and Al elements. It can be obtained from Table 3 that the contents of C, Ca, and Si elements are increased, while that of Al element is obviously reduced when CO2 is absorbed by the cement paste under mechanical agitation. However, under ultrasonic agitation, the contents of C and Ca elements are increased more obviously and that of Al element continues to decrease.  
According to the above results, it can be concluded that with the absorption of CO2, the content of C element of the cement paste is increased and there are CaCO3 crystals in the cement paste. Moreover, due to the \"cavitation effect\" of ultrasonic agitation, CO2 is effectively dispersed in cement paste, leading to more CaCO3 crystals being produced by adopting ultrasonic agitation. Therefore, cement paste is able to absorb more CO2, thus forming more CaCO3, when ultrasonic agitation is applied compared with that when mechanical agitation is applied.
## 5 Experimental Results
### Characteristics of the Proposed Approach
#### 5.1.4 Characteristics: Main Findings  
The main findings of this section are the following:  
* For two knapsakos, \(n_{k}=2\):
* _PO-prob_ performs better in identifying extreme solutions. In the case of random selection, it also results in fragmented coverage of the Pareto-frontier. This tendency can, however, be repaired by using traditional non-dominated sorting during later generations, as in _PO-prob_*.
* _NSGA-II_ performs the best in covering the middle part of the Pareto-frontier. The behavior and performance of _NSGA-III_ and _PO-count_ are similar to that of _NSGA-II_.
* When \(n\_k>2\), the tendency changes:
* _PO-prob_ does not result in fragmented sets of solutions; this is observed for \(n_{k}\geq 3\) in our experiments.
* _PO-prob_ and _NSGA-III_ result in fewer extreme solutions than other algorithms; this is observed for \(n_{k}\geq 4\) in our experiments.
* Ranking based on probabilistic _PO_ results in a very low number of incomparable solutions, which is not the case for _PO-count_ and _PD_ sorting. This demonstrates the ability of the probabilistic _PO_ sorting to distinguish between the solutions and find the direction for further evolution.