esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Analytical Model for Bonding of CFRP Rod in Concrete Subjected to Cyclic Loads
## 2 Bonding of Embedded CFRP Rod in Concrete G40 and UHPFRC
### Material Properties
#### 2.2.2 CFRP Properties  
The application of Carbon-Fibre-Reinforced Polymer (CFRP) in structural engineering is highly regarded for its exceptional protective qualities and service life enhancement of structures exposed to severe conditions. Known for its efficient insulation against thermal  
\begin{table}
\begin{tabular}{c c c} \hline
**Material** & **UHFFRC** & **G40 Con.** \\ \hline Density (Kg/m\({}^{3}\)) & 2500 & 2439 \\ Weight Per Unit Volume (N) & 24,520 & 24,000 \\ Mass/Unit Volume (Kg/m\({}^{3}\)) & 2500 & 2400 \\ Modulus of Elasticity (GPa) & 45 & 35 \\ Poisson Ratio & 0.2 & 0.2 \\ Coefficient of Thermal Expansion (Millionths/\({}^{\circ}\)C) & \(14.8\times 10^{-6}\) & \(10\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 2: Concrete Material Properties.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Code** & **Type** & \(I_{f}\) **(mm)** & \(\mathbf{d_{f}}\) **(mm)** & \(\sigma_{f}\) **(N/m\({}^{2}\))** & **Shape** \\ \hline WSF0220 & Straight & 20 \(\pm\) 1 & 0.2 \(\pm\) 0.5 & 2500 & Straight \\ \hline \end{tabular}
\end{table}
Table 3: Property of Steel Fibre.  
Figure 2: Overall Testing Specim.  
fluctuations, moisture penetration, and aggressive chemical agents, CFRP serves as a vital material in safeguarding the integrity of infrastructural components. The manufacturing of CFRP involves an intricate process where carbon fibres are embedded within a resin matrix, the synergy of which imparts the composite material with its notable strength and durability characteristics.  
The unique properties of CFRP stem from the interplay between the high tensile strength of the carbon fibres and the binding capability of the resin matrix, which together contribute to the overall robustness and performance of the material under a variety of environmental stimuli. The comprehensive properties of CFRP, which include its mechanical strength, thermal stability, corrosion resistance, and fatigue endurance, are meticulously tabulated in Table 4. This tabulation serves not only as a point of reference but also as a testament to the capabilities of CFRP as a reinforcing and protective agent in modern construction practices.  
The integration of CFRP into construction not only extends the service life of structures but also offers a lightweight yet sturdy alternative to traditional materials. Its versatility is such that it can be applied in numerous contexts, ranging from the restoration of aging infrastructure to the enhancement of new, avant-garde architectural endeavours, effectively meeting both the corrective and prescriptive demands of the industry.  
Table 4 therefore is a crucial element of this study, encapsulating the quintessential properties of CFRP, which engineers and researchers can rely upon when designing structures to withstand the rigours of harsh environments and to optimise the durability and longevity of the built environment.
## 4 Results
### Dataset  
The dataset utilized in this study for experimental evaluation is the publicly available electricity consumption dataset from the Irish CER Smart Metering Project [31]. It includes daily electricity consumption records for approximately 5000 residential and commercial users. Details of Irish dataset are shown in Table 1.  
For the experiments, we use data collected over a 365-day period. We exclude users with missing values (NaN), correct erroneous data, and apply min-max normalization to preprocess the dataset [13]. To enhance detection performance, the detection window is set to cover 7 days (\(L=336\)) instead of a single day. We indeed assume that the distribution of electricity usage data exhibits periodicity within the 7-day detection window for each day. As shown in the Figure 4, we plotted the autocorrelation graphs for both normal and abnormal samples. It is clear that in the autocorrelation graph of the normal sample, the electricity usage data distribution shows periodicity, especially with similar usage patterns between consecutive days. In contrast, the autocorrelation graph of the abnormal sample shows almost no periodicity in the electricity usage data distribution. This provides factual support for our designed PE-DOCC. We believe that for legitimate users, their electricity usage data may exhibit periodicity between days, weeks, and months. However, considering that the time complexity of our designed CCPA is \(O(L\log L)\), to save computational costs while still capturing sufficient periodic information within the detection window, we have chosen a 7-day detection window. The electricity consumption data from 5000 residential users is split into training, validation, and test sets in a ratio of 8:1:1. The training set is treated as clean, normal data for model training. In the validation and test sets, 10% of the data is randomly selected and tempered using the attack models described in Section 4.2. The validation set is utilized for hyper-parameter tuning, while the test set is reserved for evaluating the model's anomaly detection performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Descriptions** & **Values** \\ \hline Number of smart meters & 25,730 \\ Time span & 14 July 2009–31 December 2010 \\ Recording interval & 30 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of Irish dataset.
## 3 Antenna Prototype and Measurements  
The antenna is prototyped on a FR-4 substrate of \(\mathrm{e_{r}}=4.6\) and \(\mathrm{tan}\)\(\delta=0.02\), as shown in Figure 11a, and assembled in the enclosure with the battery and all parts of the device. The PIN diode used in this prototype is the BAR65 which has a parallel reverse resistor higher than 10 K\(\Omega\) in the frequency band from 100 MHz to 1 GHz and a typical parallel capacitor of 0.5 pF in the frequency band from 100 MHz to 1.8 GHz.  
Figure 11: Prototyped tracker device: (**a**) prototyped antenna and the different parts of the connected device; (**b**) assembled connected device (tracker).  
Figure 10: Simulated radiation patterns: (**a**) at 401 MHz (OFF state); (**b**) at 868 MHz (OFF state); (**c**) at 466 MHz (ON state).  
The measurements are performed with the Tektronix TTR305A VNA. After calibrating the VNA with a UFL (SOLT) calibration kit, we measured the antenna input reflection coefficient at the reference plane, as shown in Figure 2, where an UFL connector is implemented. The measured and simulated results of the antenna input reflection coefficient after assembling all parts of the connected device, as shown in Figures 12 and 13 in OFF and ON states, respectively.  
The S11 of the antenna is measured in the ON and OFF states of the PIN diode, as shown in Figures 12 and 13, respectively. In the ON state, the measurement and simulation are in good agreement. However, in the OFF state, there is a shift between measurement and simulation results at both lower and higher frequencies.
## 1 Introduction
In [20], a hybrid control strategy was proposed to realize stable output voltage and quasi-constant bus voltage, which consists of the output voltage feedback loop and the bus voltage feedback loop. However, the influence of the grid voltage harmonics on grid currents is not considered in [18; 19; 20]. In order to eliminate the influence of the grid harmonics on grid currents of the Q1S AC-DC converter and fill in research gaps, in this paper, this problem is deeply researched and a grid current closed-loop control strategy for this converter is proposed. This paper has the following innovations and contributions. (1) The large-signal model of the Q1S AC-DC converter is built for the first time. (2) The influence of the grid voltage harmonics on grid currents is derived, which builds the theoretical basis for the upcoming solutions. (3) A novel grid current closed-loop control strategy is put forward by combining proportional-resonant (PR) controllers and odd-mode repetitive controllers (OMRC). The proposed closed-loop control strategy realizes the control of grid currents without steady-state error and eliminates the influence from grid voltage harmonics. The rest of this paper is organized as follows. Section 2 introduces the process of building the large-signal model of the Q1S AC-DC converter. In Section 3, the influence of grid harmonics on grid currents is discussed based on the built model. In Section 4, a novel compound grid current closed-loop control strategy containing a PR controller and an OMRC is proposed. Then, parameter constraints of the proposed compound controller are discussed. Section 5 shows the experimental results. Finally, the conclusion is presented in Section 6.
## 4 Classification Algorithms
### Support Vector Machine (SVM)  
A Support Vector Machine (SVM) is a classifier derived from statistical learning theory and was originally developed by [PERSON] [37,38]. SVM separates any two classes of interest by identifying an optimal linear separating hyperplane. A kernel function is used to project non-linearly separable classes from the original feature space to a higher dimensional space, where the non-linearly separable classes can be separated by a linear hyperplane. To choose the optimal separating hyperplane between two classes, SVM utilizes the maximum margin concept, which maximizes the margin between the separating hyperplane and the closest feature vectors. These feature vectors are called \"support vectors\", meaning that the position of other feature vectors does not affect the hyperplane. Selecting this particular hyperplane maximizes the ability of the SVM to predict the correct class of previously unseen samples. Furthermore, a soft-margin parameter is introduced in SVM to deal with outliers in the data, which allows some data points to violate the separation through the hyperplane without affecting the final result. Therefore, the soft-margin parameter determines a trade-off between the size of the margin and the hyperplane violations. As the SVM classifier is dependent on a distance measure, it is important to account for the normalization of the feature variables by their average variance or covariance. To apply SVM to multi-class problems, a simple and commonly used generalization method is to train multiple, one-versus-all classifiers. Optimal SVM parameters are selected according to a standard ten-fold cross-validation method during the training phase of the classifier. Using a \(k\)-fold cross-validation moreover decreases the risk of over-fitting [39].
# Optical Fiber Sensor with Stable Operating Point for AC Magnetic Field Measurement
## 4 Conclusions  
In this paper, an optical fiber Bragg grating sensor attached to a magnetostrictive rod that has a stable operating point for AC magnetic field measurement was proposed, fabricated, and characterized. The performance of the sensor was measured under different ambient temperatures and different heating laser powers, as well as different DC magnetic field intensities and different AC magnetic field drive currents and frequencies. The calibration results show that the sensitivities of temperature, heating laser power, and magnetic field intensity are 7.86 pm/\({}^{\circ}\)C, 0.604 nm/W, and 3.95 pm/mT, respectively. Dynamic measurement results confirm that the sensor can maintain a stable operating point using a PID control algorithm when the temperature is changed by more than 15 \({}^{\circ}\)C. Given that the sensor is insensitive to ambient temperature fluctuations, it is well suited for practical applications in AC magnetic sensing, e.g., for health monitoring of transformers and fault diagnosis of induction motors.  
Figure 7: Signals at different temperatures without (**a**) and with (**b**) the PID control.  
**Author Contributions:** Conceptualization, S.W. and S.H.; methodology, S.W. and S.H.; software, S.W., X.C. and H.L.; validation, S.W., L.L. and S.H.; formal analysis, X.C.; investigation, X.C., H.L. and S.W.; resources, S.H.; data curation, X.C.; writing--original draft preparation, X.C.; writing--review and editing, S.W., E.F. and S.H.; visualization, X.C.; supervision, S.W. and S.H.; project administration, S.H.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported partially by the Key Research and Development Program of Zhejiang Province (2021C03178), the Guangdong Innovative Research Team Program (under grant 201001D0104799318), the Ningbo Science and Technology Project (2021Z030), the National Key Research and Development Program of China (no. 2018 YFC1407503), the Ningbo Science and Technology Plan Project-Key Core Technology Emergency Tackling Plan Project (2020G012), the China Postdoctoral Science Foundation (no. 2018M642423), and the Shanghai Zhangjiang Science City Special Development Fund.  
**Institutional Review Board Statement** Not applicable.  
**Informed Consent Statement** Not applicable.  
**Data Availability Statement** Not applicable.  
**Conflicts of Interest** The authors declare no conflict of interest.
## 2 Related Work and Foundational Concepts
### NIST Risk Management Framework
#### 2.4.3 Selection Phase  
In the Selection Phase, a baseline of security controls is established based on a comprehensive evaluation of threats, vulnerabilities, and risks [33; 34]. This process begins with a thorough threat assessment to identify potential adversaries and attack vectors, followed by vulnerability assessments to pinpoint weaknesses in the system [33; 36]. The identified threats and vulnerabilities are then analyzed in a risk assessment to determine their likelihood and potential impact [35; 38]. Based on this analysis, appropriate security controls are selected to mitigate the identified risks [37]. AI enhances this selection process by analyzing vast amounts of threat intelligence data in real time and automating vulnerability assessments to provide a more dynamic and accurate risk picture [42; 43].
# Evaluation of Eccentric Compressive Behavior Between Active and Passive Preloading in CFST
## 1 Introduction
### Research Significance  
It is clear from the above literature that when preloads are applied to CFST columns in various ways, the effect on the behavior of CFST columns may be significant, even though the preload values may be small. However, only a few researchers have compared the behavior of CFST columns in different preloading systems in quantitative ways. [PERSON] et al. [11] made some progress on this issue, but their research was limited to axially compressed CFST columns. In practice, most CFST columns are eccentrically compressed. Therefore, in this study, the mechanical properties of eccentrically compressed short CFST columns with passive and active preloading systems were evaluated using finite element numerical simulation methods. First, the compressive load-carrying capacity and ductility of four representative CFST columns were compared. Then, the behavior of the concrete and the steel tubes in the preloading and service loading stages was investigated in-depth. Finally, the effects of the preloading method, eccentric ratio, concrete compressive strength, steel yield strength, and steel thickness on the load-carrying capacity and M-P curves of CFST columns were discussed.
## 3 Results
### Performance of Group of Features Extracted from All Channels without Feature Selection  
We evaluated classification performance corresponding to the three sets of features: complexity measures (9 features per channel), oscillatory power (10 features per channel), and a combination of the two (19 features per channel). Table 5 displays the performance of the three sets of features. Classification of seizure versus seizure-free reached an accuracy and F-measure of 90.68% and 91.05%, respectively, using the complexity measures, and 90.95% and 91.33%, respectively, using relative and absolute power in the alpha, beta, gamma, theta, and delta bands. A high AUC value of 95%, an F-measure of 91.41%, and an accuracy of 91.07% were obtained using the RF classifier when the entire set of features was given as classification input. RF and GBDT outperformed SVM and k-NN in all experiments. There was only a slight difference in performance between RF and GBDT.
# Multi-Wavelength Digital-Phase-Shifting Moire Based on Moire Wavelength
## 3 Experiments and Results
### Measurement
To demonstrate the measurement accuracy of two-image multi-wavelength digital-phase-shifting moire based on moire wavelength, a double-hemisphere object, with known hemisphere radii \(50.800\pm 0.015\) mm and distance between centres \(120.000\pm 0.005\) mm based on manufacturing specification and precision, was measured. To increase the measurement resolution and accuracy, the calibration was performed by projecting two binary grid patterns with smaller grid pitches (resulting in 8- and 10-pixel grid pitch in the captured images) than for the other objects. The moire wavelengths were \(\lambda_{1}=16.966\) mm and \(\lambda_{2}=21.035\) mm for the 8- and 10-pixel-pitch images, respectively. Two-wavelength temporal phase unwrapping was applied to the double-hemisphere object by projecting two binary grid patterns with the grid pitches used in the calibration, and capturing a single image for each pattern (8- and 10-pixel grid pitch) (Figures 14a and 15a). For each grid pitch, a moire pattern with high-frequency grid lines was generated by digital phase-shifting moire (Figures 14b and 15b), and a moire pattern was extracted by grid removal (Figures 14c and 15c). Two wrapped phase maps \(\Phi_{1}\) and \(\Phi_{2}\) (Figure 16a,b) were computed with moire wavelengths \(\lambda_{1}\) and \(\lambda_{2}\) (Section 3.2). The extended continuous phase map \(\Phi_{12}\) (Figure 17a) with extended beat wavelength \(\lambda_{12}=\frac{\lambda_{1}\lambda_{2}}{[\lambda_{1}-\lambda_{2}]}=87.720\) mm, which covers the entire range of depth, was computed from wrapped phase maps \(\Phi_{1}\) and \(\Phi_{2}\) (Figure 16a,b). To minimize the phase error caused by the noise in \(\Phi_{12}\), \(\Phi_{12}\) with no phase ambiguity was used as a reference to unwrap the phase map \(\Phi_{1}\) with smaller moire wavelength \(\lambda_{1}\), to obtain the unwrapped phase map \(\varphi\) (Figure 17b). Height measurement was performed using Equation (9) and least-squares fitted spheres to the measured 3D point cloud data (Figure 17c) had radii of 50.83 mm and 50.74 mm, and thus errors of 0.03 mm and 0.06 mm, respectively (sphere fitting standard deviations were 0.15 mm and 0.14 mm). The centre-to-centre distance between hemispheres was 119.81 mm, thus with an error of 0.19 mm. A sample cross-section of measured points with the true semicircles is shown in Figure 18, the measured points are close to the true values for most of the surface, except at the edges, which are typically unmeasurable, since the camera and projector optical axes are nearly parallel to the surface. Figure 12: Extended continuous phase maps: (**a**) Extended continuous phase map calculated from \(\Phi_{12}\) and \(\Phi_{23}\), (**b**) \(\Phi_{123}\) with high SNR and with extended beat wavelength \(\lambda_{123}\) calculated from \(\Phi^{{}^{\prime}}_{12}\) and \(\Phi^{{}^{\prime}}_{23}\), and (**c**) unwrapped phase map \(\varphi\). Figure 13: Point cloud representations of measured points of spatially isolated objects, mask, and manikin head.
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 6 Application Fields
Pharmaceutical research and manufacturing: IoT sensors improve quality control and regulatory compliance while ensuring ideal circumstances for medication development and manufacture. Clinical trials: The use of IoT devices increases the dependability of trial results and ensures the security of sensitive patient data by collecting precise, ongoing data from trial participants. Elderly care: Senior living facilities that use IoT-based monitoring systems encourage resident safety and well-being while securely storing data in the cloud for caregiver access. Health and wellness wearables: IoT devices monitor physical activity, sleep habits, and general well-being, enabling people to actively control their health. Emergency response and disaster management: IoT-enabled medical equipment enables healthcare professionals to remotely assess patient conditions during emergencies and natural catastrophes, facilitating quick response. Public health surveillance: IoT data gathering and analytics help to monitor population health trends, track disease outbreaks, and create efficient public health interventions. Data-driven decision-making: When paired with cloud-based analytics and artificial intelligence, IoT-generated data helps healthcare managers and providers make decisions based on the best available evidence. Healthcare facility management: IoT sensors track the health of medical equipment, the environment, and patient flow, enhancing both operational effectiveness and patient care. Precision agriculture and nutrition: IoT devices keep an eye on crop health and soil conditions to help produce foods that are nutrient-rich and encourage preventive healthcare. Medical imaging and diagnostics: Medical imaging equipment that is IoT-connected streamlines data collection and transfer, increasing the precision and speed of diagnostic procedures. Medical supply chain management: IoT sensors in the supply chain monitor the state of the equipment and medicines to ensure quality and avoid shortages. Medical training and simulation: Medical education and skill development are improved by IoT-driven simulation systems that provide realistic training experiences. Data privacy and security: Blockchain and other emerging technologies can improve the security and privacy of patient data, guaranteeing compliance with rules like HIPAA and GDPR. IoT, emerging technologies, and secure cloud-based solutions have the power to revolutionize healthcare delivery, enhance patient outcomes, increase operational effectiveness, and spur innovation across a range of healthcare-related industries.