esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation into Failure Modes of Low-Yield-Point Steel Plate Shear Walls
## 2 Horizontal Cyclic Loading Test
### Experimental Results
#### 2.2.3 Strain
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Levels**} & \multicolumn{6}{c}{**Infill Panel**} & \multicolumn{6}{c}{**Left Column**} \\ \cline{2-13}  & **Upper Right** & **Lower Left** & **Lower Right** & **Middle** & **Middle of Web** & **Lower of Web** & **Lower of Left Flange** & **Lower of Right Flange** \\ \cline{2-13}  & **F** & **R** & **F** & **R** & **F** & **R** & **F** & **R** & **F** & **R** & **F** & **R** & **F** & **R** \\ \hline
1 & -35 & 29 & -86 & 17 & 89 & -36 & -39 & 30 & 19 & -26 & -17 & 3 & -26 & 29 & -30 & 15 \\
2 & -69 & -41 & -10 & 34 & 105 & -45 & -76 & 80 & -50 & -40 & 4 & -49 & 88 & 34 \\
3 & -169 & 82 & -330 & 62 & 179 & -116 & -158 & 110 & 56 & -19 & -59 & 8 & -148 & 137 & -80 & 78 \\
4 & -325 & -22 & -338 & 147 & 179 & -225 & -140 & 36 & 74 & -172 & -70 & 25 & -173 & 187 & -16 & 195 \\
5 & -588 & -199 & -427 & 197 & 197 & -281 & -123 & 197 & 76 & -220 & -43 & 26 & -244 & 203 & -29 & 355 \\
6 & -798 & -255 & -446 & 302 & 200 & -468 & -244 & 297 & 77 & -328 & -44 & 192 & -244 & 273 & 91 & 504 \\
7 & -495 & -281 & -473 & 586 & 222 & -507 & -256 & 386 & 102 & -320 & 48 & 303 & -306 & 288 & 187 & 655 \\
8 & -498 & -301 & -477 & 731 & 175 & -673 & -286 & 211 & 120 & -644 & 411 & 349 & -442 & 209 & 290 & 671 \\
9 & -1130 & -4 & -548 & 1100 & 139 & -715 & -500 & 467 & -34 & -746 & -48 & 443 & 438 & 374 & 1468 \\
10 & -1257 & 232 & -700 & 1299 & 227 & -402 & -403 & -404 & 270 & -588 & -522 & 280 & -1516 & -204 & 834 & 1562 \\
11 & -330 & -443 & 182 & 180 & -126 & -248 & -241 & -240 & -250 & -581 & 180 & 254 & -672 & -190 & 1068 & 1109 \\
13 & 648 & 2308 & 345 & 136 & -437 & 225 & 580 & -1149 & -3811 & -2511 & -1755 & -205 & -803 & -1568 & 3117 & 1266 \\
14 & 1175 & 2708 & 103,157 & 4461 & 284 & 2570 & -3128 & -1562 & -4366 & -3033 & -4455 & -3000 & 241 & -1396 & 2077 & 2480 \\ \hline \hline \end{tabular} Note: F indicates Forward, whereas R denotes Reverse.  
\end{table} 
Table 5: Strain values at different locations for specimen 1 at each level of peak load(units: \(\mu\epsilon\)). opposite directions at different loading modes. The measured strains in the frame column were larger for reverse loading than those for forward loading in the last few levels. Figure 20: Variations of strains in the infill panel of specimen 1: (**a**) Strains at the upper-right corner of infill panel; (**b**) Strains at the lower-left corner of infill panel; (**c**) Strains at the lower-right corner of infill panel; (**d**) Strains at the middle of infill panel; (**e**) Strains of infill panel during forwarding loading; (**f**) Strains of infill panel during reverse loading.
## 3 Resources and Demand Assessment of the Case Study
### Economic Assignment Criteria
#### 3.5.3 Net Present Cost  
According to HOMER Pro, total NPC value signifies the project life cycle cost, and is computed using Equation (3). The cost includes the initial capital cost and the cash flow of the \(t\)-year over the factor (capital cost, fuel cost, operation cost, maintenance cost, replacement cost, etc.). Income contains electricity selling and salvage value after project life cycle.  
\[\begin{split} NPC=CF_{0}+\left\{\frac{CF_{1}}{\left(1+i\right)^{ 1}}+\frac{CF_{2}}{\left(1+i\right)^{2}}+\frac{CF_{3}}{\left(1+i\right)^{3}}+ \ldots+\frac{CF_{N}}{\left(1+i\right)^{N}}\right\}\\ =CF_{0}+\sum\limits_{t=1}^{N}\frac{CF_{1}}{\left(1+i\right)^{t}} \end{split} \tag{3}\]  
Figure 6: Frequency of the scaled load profile.  
\(CF_{0}\) is initial capital cost (USD); \(CF_{t}\) is cash flow of \(t\)-year (USD); \(t\) is number of year (yr); \(i\) is annual real interest rate (%); \(N\) is project lifetime (yr).
# On Site Investigation and Health Monitoring of a Historic Tower in Mantua, Italy
## 4 Continuous Dynamic Monitoring of the _Gabbia_ Tower
### Automatic System Identification  
As in the preliminary tests, the identification of modal parameters was carried out considering time windows of 3600 s, in order to largely comply with the widely-agreed recommendation [20] of using a minimum duration of the acquired time window (which should be 1000-2000 times the fundamental period of the structure) to obtain accurate estimates from output-only data. In fact, output-only methods assume that the (un-measured) excitation input is a zero mean Gaussian white noise and this assumption is as closely verified as the length of the (measured outputs) time window is longer.  
The modal parameters of the building were estimated from the measured signals using an automatic procedure [16], based on the covariance-driven stochastic subspace Identification (SSI-Cov) method [17; 18].  
In the SSI-Cov algorithm [17; 18], the covariance matrices of the \(m\) measured outputs are computed, for positive time lags varying from \(\Delta t\) to (\(2i{-}1\))\(\Delta t\), to fill a (\(mi\times mi\)) block Toeplitz matrix, that is decomposed to estimate stochastic subspace models--_i.e._, matrices \(A\), \(C\)--of increasing order \(n\). Once matrices \(A\), \(C\) of different order \(n\) are obtained, \(n/2\) sets of modal parameters are extracted from a model of order \(n\): natural frequencies and damping ratios are calculated from the eigenvalues of \(A\), whereas the mode shapes are evaluated from the product of \(C\) and the eigenvectors of \(A\).  
In the automated procedure [16], each stabilization diagram (where the modes associated to increasing model order are plotted together) was \"cleaned\" from certainly spurious modes firstly by detecting system poles with non-physical characteristics through a damping ratio check, and subsequently by excluding the poles whose mode shapes exhibit a high modal complexity [21]. Similar modes in the cleaned stabilization diagram are grouped together based on the sensitivity of frequency and mode shape change to the increase of the model order. Finally, the identified modal parameters are compared to the reference results, available from the preliminary test.  
In the present application, the time lag parameter \(i\) was set equal to 70 and the data was fitted using stochastic subspace models of order \(n\) varying between 30 and 120.
# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 2 System Model
### Anomaly Detection Mechanism  
Anomaly detection is a critical component of the proposed security framework, enabling the system to identify and mitigate the effects of malicious activities on communication signals [38, 39]. For this purpose, we employ isolation forest, a machine learning technique well-suited for detecting anomalies in high-dimensional data. Isolation forest operates by randomly selecting features and splitting values to create isolation trees. The assumption is that anomalies are few and different, leading to their isolation in fewer steps compared to normal instances. This method is particularly effective for our use case, as it can handle the complex and high-dimensional nature of the received signals in 6G networks. The isolation forest algorithm constructs \(t\) isolation trees, and the anomaly score \(s\) for a given point \(x\) is calculated as  
\[s(x,t)=2^{-\frac{E(h(x))}{c(u)}} \tag{9}\]  
where \(E(h(x))\) is the average path length from the root to the terminating node of \(x\) in the isolation trees, and \(c(n)\) is the average path length of unsuccessful searches in binary search trees, defined as  
\[c(n)=2H(n-1)-\left(\frac{2(n-1)}{n}\right) \tag{10}\]  
and \(H(i)\) is the \(i\)-th harmonic number. The anomaly score ranges from 0 to 1, with values close to 1 indicating anomalies.  
The isolation forest algorithm, as applied in our framework, effectively detects anomalies in the physical layer of 6G networks by identifying unusual signal patterns indicative of attacks such as spoofing, jamming, and eavesdropping. The method isolates data points by building multiple decision trees based on random feature subsets of the received signals. Anomalous signals, caused by attacks, are typically isolated with fewer splits, leading to shorter path lengths, while normal signals require more splits, resulting in longer paths.  
This method demonstrated a high level of accuracy in detecting anomalies during our simulations, particularly under noisy and complex channel conditions, providing real-time insights into potential threats. The key advantages of using isolation forest include its ability to manage high-dimensional data efficiently and its relatively low computational cost compared to more resource-intensive machine learning models. This makes it suitable for real-time applications, as shown by the reduced BER and improved detection accuracy in attack scenarios, particularly under jamming conditions. However, the method has its limitations. Although it performed well in detecting anomalies, its effectiveness can diminish when facing more sophisticated, blended attacks that may not fit the typical anomaly patterns. Additionally, the reliance on predefined parameters, such as the number of trees and sample size, may require fine-tuning to adapt to different network environments, potentially affecting scalability and robustness across the wider 6G use cases.
# A Review of Machine Learning Applications in Land Surface Modeling
## 5 Techniques of Machine Learning
### Artificial Neural Networks (ANN)  
ANN is the supervised learning approach to approximate relationships between input and output using interconnecting units called neurons. The development of ANNs was motivated by the functioning of the human brain. Like the brain, the connections between neurons decide the function of the network. Multiple inputs given to the neuron are assigned with different weights, calculated by minimizing a loss function, that collectively determines the importance of input signals [77]. This part is termed as 'training' and some popular training methods are backpropagation, evolutionary algorithm and gradient descent. Output signal is produced by the summation block, which adds all of the weighted inputs algebraically. The input and output layers are called 'visible layers' as they are directly connected to inputs and outputs, respectively. Hidden layers are the layers in the middle (see Figure 2). 'Activations' are the outputs sent to the (i + 1)th layer from the ith layer, which is calculated based on the transformation functions (such as sigmoidal, tanh, rectified linear units or ReLU) of combined input from (i - 1)th layer. 'Width' is defined by the number of neurons on a layer and 'depth' is defined by the number of layers in the network.  
Deep Learning (DL) is a suite of tools based on carefully designed large ANNs with multiple hidden layers. Compared to non-deep networks and traditional earlier-generation ML methods, DL is characterized by an abundance of layers to process the complex information in big data, addition of unsupervised learning units and effective regularization techniques. These allow automatic extraction of features from input data. By contrast, traditional ML models, such CART and SVM, need human construction of relevant input features for best results, which is often the most tedious step. Many of these conventional methods are also not suitable for generalization purposes and working with large data sets that DL can handle. Though DL-ANN are the only DL techniques used with land modeling so far, LSMs can be further benefitted from some of these techniques previously in water science, especially hydrology [78; 79] and envisage more potential uses for those in the future. For example, CNN can be used to estimate crop yields by merging LSM and satellite data. However, DL can face some of the issues like other ML methods, such as convergence of the learning process dependent on training data, hidden layers acting as a black box, etc.  
In addition to DL techniques, gaussian process emulation can be used for the simple representation of complex land-surface features and test different potential values of parameters. High-resolution land cover data can be obtained using Kernel Regression. Wavelet transform can also be used to quantify the information transfer between different model components of an LSM.
# An LLC Converter with Capacitive Insulation
## 1 Introduction
### Safety Capacitors  
Typically, for offline converters, safety capacitors are required to suppress the conducted interference due to the electromagnetic interference (EMI) in the alternating current (AC) input. The AC power input is divided into three terminals: line, neutral, and earth ground. The safety capacitors in the line-earth or neutral-earth and the primary side to the secondary side are referred to as Y capacitors, and these capacitors should be certified for safety certification [1].  
Y capacitors can be divided into four classes, Y1 to Y4, according to different withstand voltage levels. The smaller the number is, the higher the safety level, as shown in Table 1. Briefly, class Y1 and class Y2 are more commonly used: the Y2 capacitor, called Y2-cap, is used on line-earth or neutral-earth, and the Y1 capacitor, called Y1-cap, should be used on the primary side to the secondary side. In addition, the Y1 capacitor is marked with a rated withstand voltage of 250 V or 275 V. However, the peak pulse withstand voltage of the Y1 capacitor is as high as 8 kV, and the peak pulse withstand voltage of the Y2 capacitor is as high as 5 kV, as shown in Table 1.  
Many previous studies have presented circuit architectures using Class Y capacitors as isolation components and these capacitors replace transformers as energy transfer components. However, this is only for LED lighting [2; 3; 4; 5; 6; 7; 8] or electric vehicle chargers [9; 10], and is characterized by the fact that the secondary side is not in contact with the human body.
## 3 Proposed Crypto Processor Design
### Data Memory  
The data memory benefits keeping the initial, intermediate, and final generated results during and after the computation. In Figure 1, we used an \(11\times m\) size of a register array as a data memory, where 11 shows the total number of addresses and \(m\) specifies the number of bits stored on each address. Out of the eleven addresses, three addresses keep ECC curve parameters, i.e., \(x_{p}\), \(y_{p}\), and \(b\), where \(x_{p}\) is the \(x\) coordinate of initial point \(P\), \(y_{p}\) is the \(y\) coordinate of initial point \(P\), and \(b\) is the curve constant. In comparison, the remaining eight addresses hold the intermediate and final results to implement instructions of Algorithm 1. The inner structure of the data memory comprises two \(11\times 1\) sizes of multiplexers and one \(1\times 11\) size of a demultiplexer. The multiplexers read two 233 bit operands from the data memory, and a demultiplexer updates the value of the specified register for writing back. The control unit is responsible for managing the control signals inread/write operations. Note that the utilized data memory makes our architecture flexible, as users can load different curve parameters, i.e., \(x_{p},y_{p},\) and \(b\), and a scalar multiplier to our proposed processor for PM computation. In addition, the corresponding values or curve parameters for \(x_{p},y_{p},\) and \(b\) buffers are selected from a standardized NIST document [8].
## 5 Experiments  
In order to verify the VSP-SMEHSS proposed in this paper, a test rig was constructed. The TDDSC, DSC, active disturbance rejection controller (ADRC), and proportional-integral-derivative (PID) algorithms were utilized for the purpose of comparing results. Finally, the energy consumptions between the VSP-SMEHSS and the EHSS were compared.
## 2 Materials and Methods
### Postural Control  
Postural control was assessed by standing upright on a force platform (Muscle-Lab 4000e, Ergotest Technology, Langesund, Norway), facing 1.5 m away from the wall of a room. All walls, around the participant, were draped white, and a 1 cm red square was placed on the opposite wall at eye level. The feet were placed such that the medial malleoi were approximately 1.5 cm apart. The appropriate toe and heel positions were marked on the force plate to ensure a consistent position among the trials and over follow-up. During the tests, the arms were extended at the sides, and the knees were fully extended. The participant performed four trials in random order with a two-minute rest between each trial, and each trial lasted 30 s. The four trials were performed in static and dynamic conditions [45] with their eyes open (gazing at the red square) and with their eyes closed. The dynamic condition was represented by an external stimulus generated by a pendulum system. The pendulum, placed behind the subject, impacted the dorsal part in correspondence with the area between the inferior angle of the two scapulae, generating a slight perturbation. The stimulus was proposed three times every 7 s (7 s, 14 s, and 21 s) within the entire trial duration of 30 s [64]. Body sway (sampled at 100 Hz) was quantified by the center of pressure (CoP) displacements. For the analysis, the total length  
Figure 3: Representative determination of hip angle–angle diagram during gait (wTO—weakest toe off, wMKF—weakest maximum knee flexion, wMHF—weakest maximum hip flexion, wFS—weakest right foot strike, wMKF-C= weakest maximum knee flexion during the contact phase) and synchronized sEMG of the weakest leg muscles during stance phase. The orange dotted line in the angle-angle diagram represents stance phase.  
sway path and the right-left (R-L) and forward-backward (F-B) components of CoP were considered [45; 71]. The total path length was calculated using the following equation:  
\[Sway\ path\ lenght\ =\ \sum_{n\,=\,1}^{N\,=\,1}\sqrt{\left(FB_{n+1}-FB_{n}\right)^{2 }+\left(RL_{n+1}-RL_{n}\right)^{2}} \tag{1}\]  
where \(N\) is the total number of data points in the CoP displacement time series and \(n\) is a data point of interest [72]. Analysis of the data was performed using Muscle-Lab 4000e software (Bosco-System, Ergotest Innovation, Stathelle, Norway). In the static conditions, the path displacements of the CoP (in millimeters) were analyzed during the entire trial (30 s). In the dynamic conditions, three windows, each of 1.5 s, were analyzed; each of them began when the ground reaction force changed due to the external stimulus. The values obtained in the three windows were averaged and considered for analysis of the horizontal components of the displacement (R-L and F-B).
## 3 Electricity Distribution Network Development
### Distribution Network Planning  
ADN and microgrid planning principles and methods are reviewed in [29]. Accordingly, traditional distribution network planning (TDNP) focuses on the optimal sizing and location of distribution substations and feeders and other electrical components. The optimal sizing and location of the DERs can be seen as an extension of TDNP in its transformation to active distribution network planning (ADNP). The challenge with the TDNP is running several planning optimization problems, for example, multiple scenarios simultaneously. In the ADNP, multi-energy and active management strategies are integrated into the TDNP. The ADNP mainly involves the interactive information system, DER participation, and smart automation technology integration, highlighting the planning process. The main objectives are cost, technical features, and performance [29].  
The ADN is extended from the TDN with the load forecasting [30], the network planning [31; 32; 33; 34; 35], and the power management and control [36; 37]. The LV distribution networks are of great interest since they are significantly affected by the connection of DERs; hence, the development of the LV-specific tools is essential to manage and effectively control the LV distribution level. Integrating DERs into the electricity distribution networks raises issues such as uncertainties in various time-series of energy consumption or generation that have to be solved at the ADN planning stage. Spatial forecast, satellite maps, and the geographical information system (GIS) improve planning drastically [38]. Microgrids can be considered as a flexible resource in ADNP [39; 40].  
From the economic perspective, the methodologies for developing business cases for microgrids are presented, for example, in [41]. For the profitable microgrids creation, a method and a tool for microgrid planning are presented in [42] to decrease the microgrid development cost. In [40], the authors claim that the following high-level steps in the planning, deployment, implementation, and operation of microgrids are needed to succeed: (i) conceptual design, (ii) technical design, (iii) implementation, and (iv) operation and maintenance. In each step, a variety of tools are used. The authors convince that the optimum would be one ultimate microgrid software combining single tools in one platform.
## 2 Materials and Methods
### Characterization Techniques
#### 2.3.6 Essential Oil Loading/Release Profiles  
The functionalized nanostructures were loaded into a solution containing 750 uL of essential oil, 6 mL of dimethyl sulfoxide (DMSO), and 21 mL of phosphate-buffered saline (PBS). The plates were immersed in the solution under constant agitation at 50 rpm at a temperature of 25 \({}^{\circ}\)C for 24 h. After incubation, the plates were transferred to a PBS solution to evaluate the release concentration of each functionalization using UV-Vis spectroscopy. The essential oil was determined at a wavelength of 280 nm and measured at different time intervals. All experiments were conducted in triplicate. The same methodology was applied to control mineral oil.  
To determine the mechanism of release, five models were fitted to the release profiles and described using the following equations:  
Zero-order model:  
\[F=K_{0}\cdot t \tag{2}\]where \(F\) is the fractional drug release, \(K_{0}\) is the zero-order release constant, and \(t\) is the time release.  
First-order model:  
\[F=100\cdot\left(1-e^{-K_{1}\cdot t}\right) \tag{3}\]  
where \(F\) is the fractional drug release at time \(t\), and \(K_{1}\) is the first-order release kinetic constant.  
Korsmeyer-Peppas model:  
\[F=K_{KP}\cdot t^{n} \tag{4}\]  
where \(F\) is the fractional drug release, \(t\) is the release time, \(K_{KP}\) is the Korsmeyer-Peppas kinetic constant, and \(n\) is an exponent that characterizes the mechanism of drug release.  
Higuchi model:  
\[F=K_{H}\cdot t^{1/2} \tag{5}\]  
where \(F\) is the fractional drug release at time \(t\), and \(K_{H}\) is a Higuchi kinetic constant.  
Hixson-Crowell model:  
\[F=100\cdot\left[1-(1-K_{HC}\cdot t)^{3}\right] \tag{6}\]  
where \(F\) is the fractional drug release at time \(t\), and \(K_{HC}\) is the constant of incorporation, which relates surface and volume.