esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
## 4 Results and Discussion  
The performance of the SABO-LSSVM model in predicting pillar stability needs to be evaluated from both vertical and horizontal perspectives. First, the impact of the SABO optimization process on the original model must be analyzed by comparing the average accuracy of the model before and after optimization to determine whether it had a positive or negative effect. Second, the LSSVM can be combined with various optimization algorithms. To assess the optimization ability of SABO compared with other algorithms, a comprehensive comparison of the performance of SABO-LSSVM with common optimization models is necessary.
## 2 Materials and Methods
### Reference Value Assignment
#### 2.2.4 Standard Uncertainty of the Assigned Value  
The standard uncertainty of the assigned value \(u_{\hat{x}}\) was calculated according to Equation (2) [15; 20] as follows:  
\[u_{\hat{x}}=\sqrt{\left(u_{\overline{x}_{PTI}}^{2}+u_{\overline{x}_{CRM}}^{2}+ u_{CRM}^{2}\right)} \tag{2}\]  
where the following definition applies:  
* \(u_{\overline{x}_{PTI}}\) is the standard uncertainty of the PT item assay, which is common to all the PT items tested in a round: \[u_{\overline{x}_{PTI}}=\sqrt{\left(\frac{s_{session}^{2}}{n}+\frac{s_{r}^{2}}{ nq}\right)}\] (3) where \(n\) is the number of sessions; \(q\) the number of replicates; \(s_{session}\) the standard deviation between sessions, which was obtained by the pooled standard deviation of the results from the analysis of the 3 PT items; and \(s_{r}\) is the standard deviation of the repeatability (obtained from an ANOVA where the session was considered as a random factor). \"Sample\" was not considered an uncertainty factor as it is a requirement that the PT item must be sufficiently homogeneous (which is verified by the homogeneity test). Therefore, this was considered a negligible factor.
* \(u_{\overline{x}_{CRM}}\) is the standard uncertainty of the CRM assay, and it is calculated using Equation (4) as follows: \[u_{\overline{x}_{CRM}}=\sqrt{\left(\frac{s_{session}^{2}}{n}+\frac{s_{r}^{2}}{ nq}\right)},\] (4) where \(s_{session}\) and \(s_{r}\) are obtained from an ANOVA where \"session\" is considered a random factor. Similarly to the PT item assay, CRM must be sufficiently homogeneous. Therefore, \"sample\" was not considered an uncertainty factor.
* \(u_{CRM}\) is the standard uncertainty of the CRM.  
This was derived from the data provided by the supplier of the reference solution, Lactrol(r) 530.  
The standard uncertainty of the assigned value \(u_{\hat{x}}\) can be disregarded if it satisfies the condition \(u_{\hat{x}}<0.3\hat{\sigma}\) as outlined in the ISO 13528:2022 standard [13], where \(\hat{\sigma}\) represents the standard deviation used for proficiency assessment in evaluating participants' performance via the z-score (ISO 13528:2022 [13]. Should this criterion not be fulfilled, the PT provider has the option to incorporate the actual uncertainty into the evaluation of participants' performance by employing the z\({}^{\prime}\)-score (ISO 13528:2022 [13]). Compliance with this uncertainty criterion for the assigned value was verified in each PT round.  
On the other hand, the different values for the combined standard uncertainty of the assigned value that were obtained depending on the various uncertainty sources considered were compared against the values estimated by the provider according to their design documentation [17]. The differences in the evaluation of the participants' proficiency were also estimated by comparing z\({}^{\prime}\)-score against z-score. For the calculations and the statistical analyses, MS Excel (Office 365) and the statistical software SPC for Excel 5.0.2.1 were used.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
###### Abstract
Furthermore, the established links between BS and MS following the beam search process are vulnerable to objects and obstacles that propagate in the direct path, thus creating a beam blockage problem. This impacts the signal level, causing it to drop below the receiver sensitivity. Consequently, channel capacity and spectral efficiencies are affected due to this link blockage, notably when radiating at pencil beamwidths that result in short channel coherence times. Additionally, the use of pencil beams results in significant challenges in the user detection process and is attributed to the low reflection coefficients for reflected mmWave signals in non-line-of-sight (NLoS) environments that results in a sparse structure. Along these lines, fast beam-tracking and handover solutions are required here to enhance the signal quality, compensate for the path loss and overcome link blockages and provide fast link restoration without dropping the communication sessions in the network. Multiple studies investigated the problem of beam recovery against link blockages in mmWave communications. These efforts often focus on beamforming designs, channel estimation, and out-of-band assistance. Namely, they assume that mmWave networks operate as a supplementary carrier to legacy microwave bands (NSA mode). Hence, these efforts do not support the SA operational mode of mmWave bands.
# Effects of On-Site PV Generation and Residential Demand Response on Distribution System Reliability
## 1 Introduction
### Literature Review
In [18], the climate change effects were taken into account for assessing reliability of the PV integrated power systems. Several positive and negative impacts of the roof-top PV systems on the reliability of distribution transformers were identified in [18]. The negative impacts of the roof-top PV systems according to penetration level were presented in [19]. Hybrid systems usually increase power system reliability, and, in this manner, [PERSON] and [PERSON] [20] studied the impacts of hybrid systems having different combinations of diesel/PV/battery systems on power system reliability. Markov model and frequency-duration (F-D) reliability techniques were used for assessing reliability indices, namely, loss of load probability (LOLP), expected energy not supplied (EENS), and mean downtime (MDT). In [21], optimum restoration strategies were generated together with the consideration of distribution system reliability assessment considering hybrid renewable DG systems. A time-sequential Monte Carlo simulation was used for evaluating distribution system reliability in the mentioned study. [PERSON] et al. [22] examined the effects of wind power integration on the reliability assessment of power systems. The authors in [23] constructed a power system reliability model by taking highly integrated wind farms into account and analyzed the results by an IEEE-RTS79 based case study through Monte Carlo simulation technique. However, any types of DR programs combined with RES integration under reliability assessment architecture were not considered in [22; 23].
## 3 Description of the Vector Control Structure for Induction Motor  
During the tests, the induction motor was controlled by a closed-loop vector control structure known as the Direct Field Oriented Control--DFOC. A block diagram of the control structure is shown in Figure 3.  
This method involves the independent control of the flux and torque of the machine by two decoupled control paths [6]. The implementation of this control method requires the transformation of the stator current vector to the synchronous reference frame (\(x\)-\(y\)) rotating with the speed of the rotor flux vector. The rotor flux is controlled by the \(i_{sx}\) component of the stator current, whereas the electromagnetic torque is controlled by the \(i_{sy}\) stator current component. The reference frame transformation requires the information about the actual position of the rotor flux vector. During the simulation and experimental tests, the current model was used for the rotor flux vector estimation [1]:  
\[\psi_{\mathbf{r}}{}^{\mathbf{i}}=\int\Big{(}\frac{R_{r}}{L_{r}}\Big{(}L_{m} \mathbf{i_{s}}-\psi_{\mathbf{r}}{}^{\mathbf{i}}\Big{)}+jp_{b}\omega_{m}\psi_{ \mathbf{r}}{}^{\mathbf{i}}\Big{)}\mathrm{d}t. \tag{17}\]  
Instead of the current model, the voltage model can be used in the vector controlled IM drives for rotor or stator flux estimation:  
\[\psi_{\mathbf{r}}{}^{v}=\frac{L_{r}}{L_{m}}\int\big{(}\mathbf{u_{s}}-R_{s} \mathbf{i_{s}}\big{)}\mathrm{d}t-\frac{w}{L_{m}}\mathbf{i_{s}}, \tag{18}\]  
This estimator directly depends on the stator resistance value. Therefore, it can be used to evaluate P-MRAS operation in the closed-loop mode.  
Voltage vector components were calculated using control signals and measured DC bus voltage from a Voltage Source Inverter (VSI):  
\[u_{sx}=\frac{2}{3}u_{d}\Big{(}S_{A}-\frac{1}{2}(S_{B}+S_{C})\Big{)}, \tag{19}\]  
\[u_{s\beta}=\frac{\sqrt{3}}{3}u_{d}(S_{B}+S_{C}). \tag{20}\]  
The PQ-MRAS estimator can operate in two modes (Figure 4):  
* Open-loop mode--independently of the control structure;
* Closed-loop mode--coupled with the control structure; the estimated parameters are injected to the control structure (flux estimator).  
Figure 3: Scheme of the DFOC system.
# Key Factors for a Successful Telemedicine Solution for Cardiovascular Diseases: A Systematic Review
## 3 Results
### Interventions

Table 3: Description of the studies relative to heart failure. participants with a sphygmomanometer that automatically uploaded readings to an app accessible by patients and clinicians. [PERSON] et al. [51] provided a wrist blood pressure monitor that sent daily readings via Bluetooth to a website. [PERSON] et al. [52] used a Sanjian Tech Co. digital bracelet and app for patients and healthcare providers to log and monitor data automatically. In atrial fibrillation studies, [PERSON] et al. [39] used an app and photoplethysmography (PPG) smart devices for heart rhythm monitoring, allowing remote or hospital-based management of critical events. [PERSON] et al. [42] had both intervention and control groups use an app to log heart rate, with the intervention group logging additional parameters like blood pressure, weight, glucose levels, oxygen saturation, and therapy adherence. [PERSON] et al. [45] provided an app for selecting and statistically analyzing triggers for atrial fibrillation episodes, offering probabilistic feedback on potential triggers. For heart failure, [PERSON] et al. [33] offered an app for daily logging of blood pressure, weight, medication, meals, and exercise, including informational content and direct Q&A with clinicians. [PERSON] et al. [34] provided a Samsung Galaxy Tab2, a UA-767 blood pressure monitor, and a precision scale, with data transmitted to a central system and alerts for out-of-range values. [PERSON] et al. [35] used a comprehensive system with a blood pressure monitor, scale, pill organizer, and wristband sensor, with data processed by a decision support system and displayed via a smartphone app installed on a Nokia 6 TA-1021 smartphone. [PERSON] et al. [36] used a digital scale and Samsung Galaxy Tab A to transmit weight data to an online decision support system, generating six possible alerts about weight fluctuation or low-level battery. [PERSON] et al. [40] combined educational and self-management toolkits with three home visits and an app, only available for experimental group B, for reminders, symptom logging, and communication with medical staff. [PERSON] et al. [41] provided devices for collecting blood pressure, weight, and electrocardiogram (ECG), which were connected via Bluetooth to a digital assistant transmitting data to a medical center. [PERSON] et al. [48] offered the OPTILOG device, a tablet connected to a scale featuring symptom monitoring, interactive education, and diuretic adjustments, with data shareable to medical practitioners at the patient's discretion. [PERSON] et al. [49] used an app for daily logging of weight, heart rate, blood pressure, and symptoms, with healthcare provider alerts for concerning data. [PERSON] et al. [50] provided a system that included an app for daily logging of health metrics, processing data online, and sending risk alerts to medical staff, with additional educational features and interactive content.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 4 Proposed Combination Approach for an IoT Intrusion Detection System  
Hybrid IDS was proposed to overcome the limitations of SIDS and AIDS by combining them to detect both known and unknown attacks. Innovative methods were utilized to merge the results of SIDS and AIDS. In our methodology, AIDS was used to identify zero-day attacks, while SIDS was used to detect known attacks. The boosting method was employed to combine the classifiers and reduce the combined model's bias. The Hybrid IDS comprises two stages: the SIDS stage and the AIDS stage, as shown in Figure 2. The objective of AIDS is to create profiles of normal node activity and raise an alarm if the difference between regular requests exceeds a predefined threshold for a particular observation. The profiles of nodes were generated by utilizing benign action records. The system then observes the traffic's behavior and compares the new records with the built profiles to identify any anomalies. When a malicious request is detected, the system stores it in the signature database to protect against similar attacks in future malicious activities. In other words, SIDS will have a suitable history of previously known attacks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Key References** & **Placement Strategy** & **Detection Techniques** & **Security Threat** & **Validation Strategy** \\ \hline [PERSON], [PERSON] and [PERSON] [22] & Centralized & AIDS & Botnet & Simulation \\ \hline [PERSON], [PERSON] and [PERSON] [23] & Hybrid, centralized, and distributed & Hybrid & Routing attacks & Simulation \\ \hline [PERSON] and [PERSON] [24] & Distributed & AIDS & Network attack & \begin{tabular}{c} Empirical (NSL-KDD \\ Dataset) \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [25] & Distributed & AIDS & Network attack & \begin{tabular}{c} Empirical (NSL-KDD \\ Dataset) \\ \end{tabular} \\ \hline [PERSON], [PERSON] and [PERSON] [26] & Distributed & Hybrid & \
\begin{tabular}{c} The botnet, Man in the Middle \\ \end{tabular} &
\begin{tabular}{c} Empirical \\ (UNSW-NB15) \\ \end{tabular} \\ \hline [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [27] & Centralized & AIDS & DoS attack & Simulation \\ \hline [PERSON], [PERSON], [PERSON] and [PERSON] [28] & Distributed & Hybrid & Sinkhole attacks & Simulation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the proposed research on IDSs for IoT.  
Figure 2: Anomaly Intrusion Detection System for the IoT ecosystem.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
## 3 Lyapunov Vector Field Guidance Framework
\right] \tag{8}\]  
Then for a stationary target, the course can be represented as  
\[\psi=a\tan(\frac{\dot{y}}{\dot{x}}) \tag{9}\]  
Referring to work of [PERSON] [12], we calculate the curvature by  
\[\kappa=\frac{\dot{\psi}}{s}=\frac{s\cdot c\cdot r_{d}^{3}((c^{2}-2)r^{2}+2r_{ d}^{2})-\dot{c}\cdot r_{d}\cdot r(r^{2}-r_{d}^{2})\sqrt{r^{4}+(c^{2}-2)r_{d}^{2} r^{2}+r_{d}^{4}}}{s(r^{4}+(c^{2}-2)r_{d}^{2}r^{2}+r_{d}^{4})^{\frac{3}{2}}} \tag{10}\]  
When \(c\) is a constant, Equation (10) reduces to  
\[\kappa=\frac{\dot{\psi}}{s}=\frac{c\cdot r_{d}^{3}((c^{2}-2)r^{2}+2r_{d}^{2}) }{\left(r^{4}+(c^{2}-2)r_{d}^{2}r^{2}+r_{d}^{4}\right)^{\frac{3}{2}}} \tag{11}\]  
It is worth noting that the traditional vector field is a special case where \(c\) is constant at 2, and the curvature of the classical vector field is always positive, as a monotone decreasing function of radical distance \(r\), which means that it spends a long time on the path toward the loiter circle, resulting in an increase in convergence time. All curves intersect at the point \((rd,\frac{1}{rd})\), which implies that the vector field with different \(c\) converge to the standoff circle, and the UAV flies steadily along the circle. Considering the maximum bank angle of the UAV, the maximum turning rate is limited, so the curvature is subject to a saturation constraint as  
\[|\kappa|\leq\frac{\dot{\psi}_{\max}}{s} \tag{12}\]  
As shown in Figure 5, when \(c\) is small enough, an extremum lies on the curve near to the desired radius. This extremum may conflict with the curvature constraint of the UAV, which will be discussed in the subsequent section.
## 2 The Effects on Amplitude Due to Interactions between Tones
### Analysis of the Effects of the Interaction between Tones
The cycle was repeated every three windows, as can be expected since the ideal period in this example (\(Tw=0.6\) s.) is three short windows. In Figure 2, minor amplitude changes in the position of the farthest 6 th harmonic can be seen. These changes are much smaller in the rest of the spectral bars, which only contain the received leakage (as can be seen in the resulting value of the interharmonic group gIH5). Therefore, the most important variations occur in the spectral bars corresponding to the harmonics closest to the interharmonics that generate spectral leakage when the DFT is applied to successive time windows. In these spectral bars, the leakage is projected on the fixed vector of the harmonic. The  
Figure 1: Example with signal composed by 5 th and 6 th harmonics and an interharmonic at 253.3 Hz: (**a**) waveform in the time domain, and (**b**) spectrum after using the discrete Fourier transform (DFT) in a single window of total aggregation time (\(Tw\)) = 0.6 s.  
Figure 2: DFT on successive windows of 0.2 s of the signal of the example of Figure 1: (**a**) modules obtained, and (**b**) vector evolution of the 5 th harmonic and its sum with the leakage received in the spectral bar of 250 Hz. spectral leakage phase is variable, so the resulting vector at the frequency of the harmonic can change considerably between successive collected windows, as illustrated in Figure 2b. It should be noted that the amplitudes of vectors represent the RMS values of the different measured frequency components. Since leakage reduction is not always possible, at least the RMS value found measuring the harmonic content of each spectral bar must be stable and as close as possible to the RMS value of all the components added in that bar (or common RMS, according to the standard IEC 61000-4-7, Appendix C.4). This common RMS is obtained as the square root of the sum of squares of the modules of the components \(\overline{x}\) and \(\overline{y}\) that add up in this spectral bar, \(\sqrt{x^{2}\,+\,y^{2}}\); in this example, \(\sqrt{13.2^{2}\,+\,4.05^{2}}=13.808\) V. As can be seen in Figure 2, the values of the amplitude of the resulting vector in each window were generally different from the correct common RMS value and were unstable.
## 4 Optimization Method
### Multi-Objective Optimization Using \(\varepsilon\)-Constraint Method  
According to [33; 34], the \(\varepsilon\)-constraint method is considered as one of the classic methods for multi-objective optimization. This method is in line with the exact methods. In addition to its efficiency and simplicity, this method is applicable to both convex and non-convex problems. The main idea of the \(\varepsilon\)-constraint method is to reformulate the multi-objective problem as a single-objective problem. Then, by iteratively solving the single-objective problem, a Pareto Front is obtained. In the following, the details of the \(\varepsilon\)-constraint method are explained [34].  
Considering a multi-objective problem (\(\Psi(X)\)), as shown in Equation (39), subjected to different constraints that should be optimized, the following steps should be taken.  
\[\Psi(X)=(f_{1}(X),f_{2}(X),\ldots f_{i}(X)),\qquad i=1,2,\ldots,n \tag{39}\]  
where \(f_{i}(X)\) denotes the \(i^{th}\) objective function and \(n\) shows the maximum number of existing objective functions.  
1. Each objective function (\(f_{i}(X)\)) is optimized with the existing constraints separately and the results are saved in a table, called the payoff table.
2. According to the priority of the objective functions, one objective function is selected as the main objective function. Then, the rest of the objective functions are treated as new constraints and added to the main constraints. It should be noted that except for the main objective function, if the goal is to minimize and maximize all the objective functions, then, \(f_{i}(X)\leq e_{i}\) and \(f_{i}(X)\geq e_{i}\), respectively. Also, \(e_{i}\) is a variable parameter.
3. In order to assign values to \(e_{i}\), the maximum (\(f_{i}^{max}\)) and minimum (\(f_{i}^{min}\)) values of each objective function should be considered, as shown in Equation (40). It should be noted that those values can be obtained from the payoff table. \[f_{i}^{min}\leq f_{i}(X)\leq f_{i}^{max}\] (40)
4. To generate different values for \(e_{i,n_{i}}\), Equations (41) and (42) are used to minimize and maximize the objective function, respectively. By dividing the domain of the \(i^{th}\) objective function into \(q_{i}\) equal parts using Equations (41) and (42), \(q_{i}\) different values are obtained for \(e_{i,n_{i}}\). It should be noted that \(n_{i}\) denotes the number of available generated values for \(e_{i,n_{i}}\). \[e_{i,n_{i}}=f_{i}^{max}-(\frac{f_{i}^{max}-f_{i}^{min}}{q_{i}})n_{i},\qquad n _{i}=0,1,\ldots,q_{i}\] (41)
## 2 Design Methodology
### Bands of the Multifrequency System  
The considered frequency bands to be attenuated are shown in Table 1. The study can be easily applicable to other bands. As a design strategy, the first two bands are grouped. Therefore, two main bands are addressed and implemented in an FSS composed of two resonators, one for each band.