esa-sceva/satcom-chunk-collection huggingface dataset:
# Response of Corroded Steel Pipeline Without and with CFRP Reinforcement to Reverse Fault Movement
## 4 Results and Analyses
### Mechanical Response of Corroded Pipeline Crossing Reverse Fault  
#### 4.2.1 Mechanical Response of Pipeline with Corrosion at Hanging Wall  
Figure 5 shows the axial strain diagrams at the top of the pipeline with different residual wall thicknesses when the corrosion occurs at the hanging wall at a distance L = 3.2 m from the fault plane and 0.9 m fault displacement. Figure 5 shows that as the corrosion deepens, its influence on the pipeline strain increases within a certain range x = [17 m, 19.3 m] outside the corrosion area due to the reduced remaining wall thickness. As the pipe wall becomes thinner, the strain at the corrosion location exhibits an approximately linear increase (approximately 0.01 per every 10% reduction in thickness). When the residual wall thickness is 0.8 t, the tensile strain in the etched region exceeds 0.03 (i.e., failure).  
Figure 6 shows the von Mises stress diagram of the pipeline with different residual wall thicknesses when corrosion occurs at the hanging wall at a distance L = 3.2 m from the fault plane and 0.9 m fault displacement. Figure 6 demonstrates that the whole pipeline exhibits S-shape deformation and is obviously bent at two locations 3.2 m from both sides of the fault plane. With the stress concentration, the pipe wall is folded due to the pressure. Combined with analyses of Figure 5, the results reveal that local buckling is still the main failure mode of the pipeline crossing a reverse fault when the residual wall thickness is more than 0.8 t. With the deepening corrosion, the main failure mode will change from a single local buckling to a double form controlled by local buckling and tensile fracture when the residual wall thickness is less than 0.8 t. In other words, the increased corrosion weakens the seismic performance of the pipeline and promotes the development of multiple failure modes.  
Figure 4: A comparison of numerical calculation and [PERSON]’s test. (**a**) Local buckling of the steel pipeline. (**b**) The axial strain of the pipe’s top.  
Figure 5: The axial strain of the pipeline’s top with corrosion at the hanging wall. (**a**) The axial strain of the pipe’s top. (**b**) Enlarged image.
# Novel Instrument for Clinical Evaluations of Active Extraocular Muscle Tension
## 1 Introduction
In the current study, we developed a compact measuring device to non-invasively evaluate active EOM tension, which would facilitate objective and quantitative estimations of the EOMs. The proposed device employed a medical swab to transfer the EOM tension connected to the force sensor as a non-invasive medium. The tilting angle of the swab and EOM tension force were wirelessly transferred to a laptop for recording and real-time displaying of the measured values. We believe that this novel device is an easy-to-use tool for making quantitative measurements that could provide valuable information about the active tension in EOMs, making it suitable for use both clinically in strabismus management and in research into understanding its pathophysiology.
## 2 Methodology
### Procedure of Analysis  
A flowchart outlining the procedure of the proposed approach for estimating the modal parameters of a structure is presented in Figure 4. Given that the proposed method is applicable to both time-invariant and time-varying systems, the initial step involves generating a scalogram for the first mode of the system under consideration to determine whether the system exhibits time-varying behavior or not. To establish the level of decomposition \(k\), it is recommended to set it as the integer value of \((\ln(f_{s}/f_{1})/\ln(2)-1)\), where \(f_{s}\) is the sample frequency for the data, and \(f_{1}\) is the approximately estimated frequency of the first mode obtained from the scalogram of measured responses. As a result, the fundamental frequency resides within the preserved frequency range of subspace \(U_{k}^{(0)}\), facilitating the straightforward identification of modal parameters for higher modes through the utilization of signals in other subspaces.  
Figure 4: A flowchart outlining the procedure of the approach for estimating modal parameters.  
The identified modal parameters are associated with the eigenvalues and eigenvectors of \(\widetilde{\mathbf{A}}\), where \(\widetilde{\mathbf{A}}\) is an \(\overline{n}\times\overline{n}\) matrix. Many of these eigenvalues and eigenvectors correspond to spurious modes. The genuine mechanical modes are discerned using the stabilization diagram technique. Stable modal parameters are determined by comparing results obtained from five consecutive values of \(s\) (in Equations (23) and (24)), corresponding to the order of a time series model [54]. The outcomes are considered stable if the relative differences in the frequency and damping ratio of the desired mode are less than 1% and 10%, respectively, and the MAC values exceed 0.95.  
In the proposed procedure, the range of \(s\) (denoted as \([s_{\text{min}},\ s_{\text{max}}]\)) needs to be defined before establishing \(\widetilde{\mathbf{V}}_{l}\) and \(\widetilde{\mathbf{F}}_{l}\). The value of \(N\) in constructing Equations (17) and (29) is recommended to set \(N\Delta t\) equal to \(2/f_{1}\), ensuring that sufficient data points are used to identify both the first mode and other modes. The value of \(2\ L\) in the window \(\left[\overline{l},\ \overline{l}+2L\right]\) is set equal to \(N+s_{\text{max}}\).
## 1 Introduction
In novelty detection, new data are compared with a dataset that does not contain outliers to determine whether the new data are outliers. In SPC, the dataset collected in phase I may include data from both IC and OOC conditions, and the type of data cannot be known in advance. The aim of phase I is to identify the outliers so as to obtain a clean dataset. As such, phase I analysis corresponds with outlier detection. The outlier-detection method considered in this study requires defining a parameter called the contamination rate, which refers to the fraction of the total sample size that must be considered outliers. This parameter must be carefully tuned to achieve the best performance. Unfortunately, little research has been conducted to determine the contamination rate. The focus of this study is to propose a method that can accurately estimate the contamination rate to improve the performance of unsupervised algorithms. The method proposed in this paper consists of three main processes. First, the spline smoothing method is used to smooth a collected set of noisy profiles. Second, a procedure for estimating the proportion of outliers in the dataset is developed. A clustering algorithm is employed to obtain an initial cluster that includes most of the profiles from the IC process. Profiles in the normal cluster are used to determine a baseline profile, and a distance-based decision function is used to identify potential outliers and provide an approximate estimation of the contamination rate. Third, principal component analysis (PCA) is used to identify important features for conducting dimensionality reduction. Researchers in various domains have recognized the importance of high-dimensionality of data [15; 22; 23]. Several studies have suggested that data should be subjected to dimensionality reduction to decrease the number of coefficients that must be monitored. An outlier-detection algorithm is then employed to identify outlying profiles. Outlier detection can be carried out using several algorithms. In this study, we adopted three commonly used unsupervised algorithms, namely the Local Outlier Factor (LOF), Elliptic Envelope (EE), and Isolation Forest (IF) algorithms. The rest of this paper is structured as follows. Section 2 describes related work in the field of machine learning in profile monitoring. Section 3 provides a brief overview of anomaly detection and introduces some commonly used outlier-detection algorithms. Section 4 details the proposed method for phase I nonlinear profile monitoring. Section 5 describes the performance of the proposed method and compares it with related methods from the literature. Finally, Section 6 provides the conclusions of this study and recommendations for future research.
## 1 Introduction
[42] demonstrated that the longitudinal variations of the topside TEC are different from the corresponding longitudinal variations of electron densities around the F2 peak. [PERSON] et al. [43], reported that the crest of the equatorial ionization anomaly in the Eastern African sector is fully matured, thus yielding maximum values of TEC during the equinoxes (March/April and September/October) and minimum values of TEC in the solstice (June/July and November/December). They further reported TEC enhancements during the nighttime at stations away from the dip equator, showing that ionospheric plasma density diffuses from the dip equator and leads to the formation of EIA crests that can last for a few hours after local sunset time. As a result of the erratic and problematic nature of TEC variations near the magnetic equator, there is still a need for further studies to strengthen the understanding of TEC variability around the geomagnetic equator that eventually escalates the prediction capabilities of ionospheric models with appropriate mathematical implementations. Hence, this research was aimed to investigate and understand the local-time offsets and peculiar anomalies in TEC at different longitudinal sectors by analyzing them in terms of the diurnal, seasonal, and solar cycle variations in GPS-TEC. The overestimation and underestimation characteristics of the GIM and IRI-2016 models were also assessed by focusing on the equatorial and low latitudes throughout the study. Moreover, the seasonal discrepancies in the longitudinal variation of TEC, which are the focal points of research in aeronomyscience and model development nowadays, are addressed in this work. Section 2 deals with the data and methodology. In Section 3, we present the results from the current study and discussions. Finally, Section 4 highlights the concluding remarks drawn from the present study.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 5 Results and Discussion
### Grayscale Index  
The grayscale index is calculated based on the total grayscale value inside the ROI divided by the total pixels of the ROI. The grayscale value indicates the hardness of the tissues; if tissues are hard, they will be displayed as white, while the soft ones will be darker. The grayscale value is equivalent to amplitude values in the time series signal, and in this case, shows the intensity of the CT scan image of the object. Grayscale index values for patients with normal bone are expected to be higher than those of osteoporosis patients.  
Figure 3 illustrates the data sets from the experiment. Data sets consist of CT scan data of the pelvic and hip, and DEXA scans of the lumbar spine. The X-ray energy used for CT scanning is 400 times higher than that used in DEXA scanning. The result showed that the grayscale value tends to decrease from normal to osteoporosis bone, and the difference is quite significant. As expected, healthy bone is harder compared with osteoporoptic bone; thus, its appearance upon X-ray scanning will be displayed as brighter than osteoporosis. The grayscale index consistently decreases in both CT scans and DEXA images from normal to osteoporoptic bone.
## 4 Operation of the Proposed MMPC  
### Operation Control  
The main objectives of the control scheme of the proposed MMPC is to maintain the bus voltage at its desired value while ensuring corrective measures are taken to address the imbalance in the SoC of the batteries and/or available PV power among the PMs. To achieve this, a global controller (shown in Figure 8) has been proposed to determine the power contribution of each PM. It uses a weighting factor (K\({}_{\text{i}}\)) calculated according to Equations (1)-(3) and also considers the maximum power available from the PVs of all PMs. These are used to calculate PM's output voltage references V\({}_{\text{i-Ref}}\) accordingly. These are then passed to the local PM control loops which control the duty cycles of the switch Si3 (d\({}_{\text{i3}}\)) and the switch Si1 (d\({}_{\text{i1}}\)) in order to achieve the reference output voltage as can be seen in Figure 9. During the charging process of the battery module, based on the maximum possible charging power of PM's battery, each PM local controller determines the operating mode of its PV-converter (MPPT) either to work at or away from MPP in order achieve the module's reference output voltage requested by the global controller. The MPPT algorithm at the local control loop of the PM controls the current and accordingly the power of the PV-module through the duty cycle of the switch S3 (d\({}_{\text{3}}\)) independent of the output voltage (as long as it is larger than PV-module voltage), while the duty cycle of the switch S1 (d\({}_{\text{1}}\)) controls the output voltage of the power module based on Equation (11) for a defined d\({}_{\text{3}}\) (defined by the MPPT) and an actual value of battery voltage V\({}_{\text{batt}}\).  
Information about state of charge and state of health of the individual battery cells within the battery modules gathered by the battery management system (BMS) can be used to determine the charge/discharge power of each battery module at global control loop. The proposed converter reduced the required number of series-connected battery cells through the decentralized battery modules which can eliminate the need for cells balancing/equalization circuits as investigated in Reference [38]. If a fault in any one of the PMs prevents it delivering its desired power contribution to the bus, this imbalance will cause its output voltage to drop down to zero, causing the forward bias and therefore conduction of the diodes D1 and D2 as well as the anti-parallel (body) diode of S3 (that can be easily duplicated to grantee fault tolerance operation). This means there will be a natural bypassing ofthe faulty modules. Losing the voltage contribution of this faulty module will be detected by the global controller which will then modify the reference voltage of the remaining PMs in order to compensate for that fault and maintain the bus voltage at its reference value. These measures can provide a wide range of fault tolerance for the proposed power system: this is one of the important advantages of the proposed MMPC.
## 3 The Time Source Techniques
### Low Earth Orbit
#### 3.2.2 Accuracy  
Due to the shorter distance from LEO satellites to Earth, signal power is stronger, resulting in highly accurate timing. According to [50], a receiver can achieve sub-microsecond accuracy from a single signal burst. Reference [52] reports a positioning accuracy of 20 m indoors with timing accuracy within 1 \(\upmu\)s. Studies in [53; 54] measured and quantified the accuracy and stability of pulse-per-second (PPS) timing outputs from STL receivers by comparing them to UTC (NIST) and GPS. Results show that an uncalibrated STL receiver can provide sub-microsecond accuracy, with accuracy under 100 ns achievable through delay calibration. While the STL receiver offers superior short-term stability compared to the GPS Disciplined Clock (GPSDC), its stability decreases over longer periods, ranging from several hours to days. In general, LEO timing accuracy can reach 1 \(\upmu\)s, or even 100 ns with calibration.  
Figure 1: Iridium constellation orbits [51].
## 3 Results
### Case Study
#### 3.1.6 Forecasting Errors
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Hour**}} & \multicolumn{3}{c}{**16 January 2022**} & \multicolumn{3}{c}{**24 April 2022**} & \multicolumn{3}{c}{**17 July 2022**} & \multicolumn{3}{c}{**9 October 2022**} \\ \hline \multirow{3}{*}{**Hour**} & **LD** & **FLD** & **FLD\_C** & **LD** & **FLD\_C** & **LD** & **FLD\_C** & **LD** & **FLD\_C** & **LD** & **FLD\_C** \\  & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) & \(\left[\frac{\text{MWh}}{\text{h}}\right]\) \\ \hline  
M & 4911 & 4906 & 5018.18 & 6793 & 6827 & 6750.49 & 6737 & 6587.86 & 5914 & 5903 & 5993.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Historical load demand data and forecasted load demand values obtained using the proposed methodology and CENACE’s methodologies for weekend days across all four seasons. Table 8 displays the MAPE values for the forecasted load demand using the proposed methodology alongside the CENACE results for the three weeks illustrated in Figures 17-19. As shown in the first row, the MAPE for the Easter week is 1.25%, while the value calculated using CENACE's forecasted values is 2.49%, resulting in an improvement of nearly 50% in accuracy. Additionally, the third row presents the MAPE for the minimum load week, achieving a value of 1.14% compared to CENACE's forecasted value of 2.13%, yielding an improvement of more than 46% in accuracy. Finally, the second row shows the MAPE for the minimum load demand week, which is 1.15% compared to the 1.17% obtained from CENACE, resulting in an improvement of 1.7% in accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**21 May 2022**} & \multicolumn{3}{c}{**25 December 2022**} \\ \hline \multirow{2}{*}{**Hour**} & \(\begin{bmatrix}\mathbf{MDh}\\ \mathbf{h}\end{bmatrix}\) & \(\begin{bmatrix}\mathbf{MDh}\\ \mathbf{h}\end{bmatrix}\) & \(\begin{bmatrix}\mathbf{MDh}\\ \mathbf{h}\end{bmatrix}\) & \(\begin{bmatrix}\mathbf{MDh}\\ \mathbf{h}\end{bmatrix}\) & \(\begin{bmatrix}\mathbf{MDh}\\ \mathbf{h}\end{bmatrix}\) & \(\begin{bmatrix}\mathbf{MDh}\\ \mathbf{h}\end{bmatrix}\) \\ \hline  
M & 7510 & 7631 & 7430 & 4451 & 4440 & 4264.78 \\ \hline MAPE & & 1.32\% & 2.11\% & & 1.48\% & 4.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Historical load demand data and forecasted load demand values obtained using our methodology and CENACE’s methodologies for a holiday.
## 2 Methods for Determination of Lipophilicity
### Direct Experimental Determination of Lipophilicity
#### 2.2.7 Nano-Absorbent Based Method  
Nanoparticles with absorbent properties can be used for the determination of partition coefficient. One of these methods is based on the use of porous silica-encapsulated magnetic nanoparticles, which are preloaded with a known amount of \(n\)-octanol [48; 49].  
Figure 2: VALLME workflow (adapted from [45]). (Reprint with permission from [45], Copyright (2019) ELSEVIER).  
These nanoparticles are then dispersed into an aqueous phase containing the tested compound. The small size of the \(n\)-octanol droplets and the efficient mixing provided by the nanoparticles increase the interfacial contact area between the two phases, which significantly reduces the time required to achieve equilibrium. The magnetic properties of the nanoparticles allow the easy separation of phases. The partition coefficient is determined by measuring the concentration of the targeted analyte in the aqueous phase before and after partitioning.  
Despite being used for isotropic lipophilicity determination, nano-absorbent-based methods acquire particular relevance for the determination of anisotropic lipophilicity. Nano-sized artificial membranes, like liposomes [50] or micelles [51], act as absorbents enabling the direct determination of the lipophilicity in these biomimetic nonaqueous phase. The partitioning occurs just by mixing the artificial membranes with an aqueous solution of the compound to be tested. The major technical difficulty is the phase separation after reaching equilibrium. While \(n\)-octanol and water are two immiscible solvents, liposomes and micelles form a colloidal suspension in water. The most used techniques for phase separation are ultrafiltration [50] and ultracentrifugation [51]. The partition coefficient is determined by quantifying the compound present in the filtrate or in the supernatant.  
An alternative method for the determination of anisotropic lipophilicity is based on solid-supported lipid membranes (SSLMs). SSLMs are silica nanobeads coated with a liposomal membrane that was noncovalently bound to the bead [52]. SSLMs beads are added to an aqueous solution of the compound to be tested, and the mixture is incubated to allow the partition between the lipid and aqueous phase. Phase separation is performed by centrifugation with a regular bench centrifuge [52] or by a short filtration step [53]. SSLM silica beads are commercially available and offer a high throughput as the time-consuming ultracentrifugation or ultrafiltration steps are not required.
# A Cross-Layer, Anomaly-Based IDS for WSN and MANET
## 5 Experimental Setup
A static set of features collected from the MAC and network layers are used for the detection process using a C4.5 decision tree or random forest, as shown in Table 5. To avoid feature redundancy and for computational efficiency, a subset of the features is selected using a correlation-based feature selection technique called a correlation attribute evaluator, where the features are ranked according to their relative weights; the higher the weight, the more influential is the feature on the decision process. The top six features were chosen in every scenario for every NUT. The classifiers are validated using a ten-fold cross validation process. This process divides the labeled data into ten equal folds and uses nine folds for training and one-fold for testing. The process continues in a circular way, and resulting parameters are averaged over ten. At each reporting time (_Tr_), the sniffers generate a set of TN, TP, FP, FN, which constitutes the confusion matrix. The CCI which is the sum of the TP and TN, showed via the calculation of its variation in timely manner, to be a better measure for differentiating between malicious and normal nodes, this is mainly due to the labeling of the data set that is based on network behavior, by having all normal nodes for the normal labeling process, and three malicious nodes when obtaining the malicious labeling. This approach was meant to give higher robustness when having networks with different nodes functionalities or roles. \begin{table}
\begin{tabular}{c c} \hline \hline
**No. of Nodes** & **30** \\ \hline
**Field area** & 1000 m \(\times\) 1000 m \\
**Node speed** & 1 m/s, 5 m/s, 10 m/s 15 m/s \\
**Simulation time** & 2000 s \\
**Power levels** & 3 dBm, 7 dBm \\
**MAC layer protocol** & 802.11 b \\
**Routing protocol** & AODV \\
**Mobility model** & RWP \\
**Reporting time (Tr)** & 50 s, 100 s, 200 s \\
**Sampling time (Ts)** & 5 s, 10 s, 20 s, 40 s \\
**Nodes Under Test NUTs** & 13, 19, 21, and 23 \\
**Traffic source** & CBR (UDP) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation Parameters. A sample of performance measures were added in Appendix A. Those are related to the scenarios in Section 6. The results are related to one dedicated sniffer regarding two nodes, node 13 (normal) and node 21 (malicious). Three instances for each scenario are taken from the beginning, middle, and the end of the simulation.