esa-sceva/satcom-chunk-collection huggingface dataset:
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
###### Abstract  
Almost every year, earthquakes threaten many lives, so not only do developing countries suffer negative effects from earthquakes on their economies but also developed ones that lose significant economic resources, suffer massive fatalities, and have to suspend businesses and occupancy. Existing buildings in earthquake-prone areas need structural safety assessments or seismic vulnerability assessments. It is crucial to assess earthquake damage before an earthquake to prevent further losses, and to assess building damage after an earthquake to aid emergency responders. Many models do not take into account the surveyor's subjectivity, which causes observational vagueness and uncertainty. Additionally, a lack of experience or knowledge, engineering errors, and inconspicuous parameters could affect the assessment. Thus, a consensus-based Likert-LMPP (the Levenberg-Marquardt backpropagation algorithm) model was developed to rapidly assess the seismic performance of buildings based on post-earthquake visual images in the devastating Kahramanmaras earthquake, which occurred on 6 February 2023 and had magnitudes of 7.7 and 7.6 and severely affected 11 districts in Turkey. Vulnerability variables for buildings are assessed using linguistic variables on a five-point Likert scale based on expert consensus values derived from post-earthquake visual images. The building vulnerability parameters required for the proposed model are determined as the top hill-slope effect, weak story effect, soft story effect, short column effect, plan irregularity, bounding effect, heavy overhang effect, number of stories, construction year, structural system state, and apparent building quality. Structural analyses categorized buildings as no damage, slight damage, moderate damage, or severe damage/collapse. Training the model resulted in quite good performance (_mse_ = 7.26306 \(\times\) 10\({}^{-5}\)). Based on the statistical analysis of the entire data set, the mean and the standard deviation of the errors were 0.00068 and 0.00852, respectively.  
building damageability; consensus degree; Likert scale; Levenberg-Marquardt algorithm; ANN; backpropagation +
Footnote †: journal: Applied Sciences  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Frequency Characteristic by Exchange of Frequency Containment Reserves Control
### Frequency Characteristic  
Because of uneven supply and demand, the disconnecting of multiple utilities and parts of the power system result in frequency deviation and fluctuation from the nominal value. Following a frequency deviation, the frequency responses are divided into three phases: the arresting phase, the recovery phase, and the post-recovery phase. The frequency response in the power system is depicted in Figure 1[14]. The frequency shift in the power system is shown using the swing equation. The swing equation [15] is as follows:  
\[\frac{\mathrm{d}}{\mathrm{dt}}\mathrm{f}\times\frac{2\mathrm{H_{total}}}{ \mathrm{f_{0}}}=\frac{\Delta P}{\mathrm{S_{total}}} \tag{1}\]  
where f is the system frequency, Htotal is the system inertia constant, Stotal is the amount of generator capacity, and f0 is the nominal frequency.  
The inertia constant of the system is defined as follows:  
\[\mathrm{H_{total}}=\frac{\sum_{i=1}^{n}\mathrm{H_{i}}\mathrm{S_{i}}}{\mathrm{ S_{total}}} \tag{2}\]  
where Hi is each generating type's inertia constant, and Si is the generator capacity. Addressing the stability of the power system, the frequency nadir and the Rate of Change of  
Figure 1: Feature of frequency response.  
Frequency (RoCoF) value are the two most important considerations. The RoCoF and nadir value in the European power system are terminology used [16] to refer to the ENTSO-E requirements that are addressed during transient operations. According to [4], particular investigations are provided using the nadir frequency value for the greatest RES penetration in the Korean electrical system. Frequency stability is the retention of frequency values within standard limits. Discussions on the use of HVDC interconnectors for the control of the main frequency have been dominated by technical issues, such as setting the range of operation, power outages, and maximum discrete frequency variation of the HVDC connection. This study aimed to enhance the system's frequency stability after a cyber attack occurs by promoting the main frequency response through employing several HVDC link cooperation techniques.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 4 Improved Dynamic Window Approach
### Key Improvements to the Dynamic Window Approach  
#### 4.2.1 Global Optimization Strategy  
Standard DWA only takes the endpoint as the goal point in path planning. However, it is easy for this approach to get stuck in local optima or deviate from the optimal path when encountering situations such as U-shaped obstacles. To address this shortcoming, we combine DWA with PAGWO. The globally optimal path generated by PAGWO is decomposed into individual local goal points, which guide the robot via DWA toward these local goal points, thereby ensuring that the robot successfully traverses these points to reach the endpoint.
## 7 Limitations and Future Lines of Research
### Future Lines of Research  
* The application of meta-regression and moderator analysis is necessary to evaluate how contextual variables, such as country of origin, industry type and firm size, impact the competitiveness of manufacturing PYMES.
* The implementation of advanced natural language processing models, such as latent semantic analysis (LSA) or neural networks, is also recommended to improve the identification of emerging trends in the academic discourse on innovation and quality in PYMES.
* It is suggested that the role of government financing and technological support programs be analyzed, with the objective of identifying effective strategies that facilitate the digital transformation and competitive growth of PYMES in different economic and regulatory contexts.
## 2 Materials and Methods
### Electromyographic Measurements Procedure  
The sEMG data were registered bilaterally from the tight muscles (quadriceps-Q, hamstrings-H, and gluteal-G) using shorts similar to elastic sportswear with properly positioned pairs of bipolar surface electrodes for non-invasive assessment of the electrical muscle activity (Myontec Ltd., Kuopio and Suunto Ltd., Vantaa, Finland). The electrodes were placed on the inner surface of the shorts and recorded the mean rectified sEMG signal on the distal part of the tight muscles, and the reference electrodes were positioned longitudinally along the left and right lateral sides (above the tractus liothialis). The shorts provide reliable and repeatable data [17; 26; 27] with the intra-class correlation for Q, H, G measurements ranging from 0.83 to 0.87 [26]. It has to be highlighted that the shorts are unable to assess particular muscle excitation (e.g., biceps femoris) within a selected muscle group (e.g., hamstrings). For example, the electrodes around the H muscle area collected data from the semimembranosus, semitendinosus and both heads of biceps femoris. The raw sEMG signals were registered with a high sampling frequency of 1000 Hz and a frequency band of 50-200 Hz (\(-\)3 dB). Next, they were rectified and averaged for each 100 ms interval without overlapping. These 10 Hz data were stored in ASCII format in the memory module and transferred to the PC via custom software (Muscle Monitor, Myontec Ltd., Kuopio and Suunto Ltd., Vantaa, Finland) for analysis. From the 10 Hz sEMG data, the mean rectified value was calculated from a 1 s window during a constant torque signal. Six channels were recorded (three from each limb). To ensure proper signal conduction, the electrodes were moistened with tap water before donning the shorts. When wet, the membrane covering the electrodes prevents the electrode from drying out at the skin-to-electrode interface. The mean lower-limb sEMG activity imbalance represented a relative (percentage) ratio between lower limbs for each muscle group and was used for further analysis. Maximal voluntary isometric contraction (MVIC) measurement was performed according to the SENIAM procedure [28]. After the warm-up, participants were tested in MVIC of the studied muscles as described in Table 2.
## 4 Experiment and Result Analysis
### Results and Comparison
#### 4.4.2 Model Interpretability  
Model interpretability is an important task to get a better understanding of the reasoning behind the prediction models. As a comparison result, GBT and RF classifiers determined the best predictive models when combining with the SMOTE among all subjects. Thus, the most important features for SMOTE with GBT and RF models were provided as shown in Figures 6 and 7.  
To ensure model interpretability, features were sorted in descending order of their importance scores in model construction. According to the SMOTE with the GBT model, \"daily smokers at home\", \"age of smoking initiation\", and \"attendance in smoking cessation education\" were maintained as the most useful features, with scores of 0.089, 0.088 and 0.079, respectively, to predict the smoking cessation target. On the contrary, \"attendance in smoking cessation education\", \"daily smokers at home\" and \"household income\" features were highly important for constructing the SMOTE with RF model. The highly scored features enhance the rationale decisions in smoking-related health concerns and should be collected in smoking cessation data. Both of the predictive models were constructed by the same features, such as \"diabetes\" and \"hypertension\", with low importance scores. Therefore, this analysis is expected to improve the efficiency and effectiveness of healthcare decision support system in real-world adoption.  
Figure 5: Boxplot of the geometric mean over prediction models among women.
## 5 Results of the Proposed Model
### Comparison between Numerical and Experimental Results  
To validate the proposed model, this paper focuses on whether the numerical model can reproduce the pore pressure response measured in [8]. The simulated correlation between excess pore pressure \(\Delta p\) and the increase of penetration depth is compared with the tested result, see Figure 7a-d for the comparisons of PPT1-PPT4, respectively.  
All curves in Figure 7a-c show that \(\Delta p\) keeps increasing until the penetration depth reaches the depth of the observation point, and then gradually decreases. In this \(\Delta p\) trend, there is a good agreement between the tested and simulated results. Furthermore, this trend has been observed in studies of vibratory close-ended pile driving [16; 17; 19; 24]. Note that the \(\Delta p\) in Figure 7d continuously increases throughout the penetration process because the pile toe did not pass by PPT4.  
From Figure 7a-c, one can also notice that both the experiment and the simulation indicate that the maximum \(\Delta p\) of different observation points is related to their depths. Specifically, the maximum \(\Delta p\) increases with the depth of the observation point. This may be attributed to the increase of penetration resistance in the deeper soil. Large penetration resistance reduces the pile velocity, and thus the observation point is subjected to more vibrations, resulting in the increase of \(\Delta p\). A similar trend can also be found in the literature [16; 24], in which a more significant response of pore pressure occurs at a deeper point.  
Figure 6: Time histories of (**a**) penetration depth and (**b**) pile velocity.  
Figure 8 compares the distribution of \(\Delta p\) at different penetration depths \(L\) between experiment and simulation. Three key points can be seen in Figure 8. Firstly, all the maximum \(\Delta p\) occur near the pile toe. For example, the maximum \(\Delta p\) at \(L=8D\) occurs at the buried depth of \(8D\). Secondly, for a certain penetration depth, \(\Delta p\) increases with the buried depth when the observation point is above the pile toe and decreases with the buried depth when the observation point is below the pile toe. Finally, the maximum \(\Delta p\) at different \(L\) rises as the depth of pile toe increases. Generally, the proposed model is capable of capturing the essential aspects of the experiment result in terms of \(\Delta p\) distribution.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
###### Abstract  
At present, many path tracking controllers are unable to actively adjust the longitudinal velocity according to path information, such as the radius of the curve, to further improve tracking accuracy. For this problem, we propose a new path tracking framework based on model predictive control (MPC). This is a multilayer control system that includes three path tracking controllers with fixed velocities and a velocity decision controller. This new control method is named multilayer MPC. This new control method is compared to other control methods through simulation. In this paper, the maximum values of the displacement error and the heading error of multilayer MPC are 92.92% and 77.02%, respectively, smaller than those of nonlinear MPC. The real-time performance of multilayer MPC is very good, and parallel computation can further improve the real-time performance of this control method. In simulation results, the calculation time of multilayer MPC in each control period does not exceed 0.0130 s, which is much smaller than the control period. In addition, when the error of positioning systems is at the centimeter level, the performance of multilayer MPC is still good.  
articulated vehicle; path tracking; velocity decision; multilayer control; model predictive control Article
## 2 Materials and Methods
### Methods  
The primary parameter observed was ultimate tensile strength (UTS), defined as the maximum stress (in MPa) the implant could withstand before failure. Secondary parameters were elongation at break (in mm) and maximum force (in N).  
Mechanical testing conditions: Mechanical tensile tests were performed using a universal testing machine equipped with a 100 N load cell calibrated according to ISO standards. The tensile speed was set to 10 mm/min to simulate physiological strain rates relevant to neurosurgical applications. Each sample was tested in triplicate to assess within-sample repeatability and limit the influence of random measurement errors. Prior to testing, samples were conditioned for 24 h in a controlled environment to ensure constant hydration, simulating in vivo conditions. All values were digitally recorded at a sampling rate of 10 Hz.  
Environmental conditions: All mechanical tests were performed in a controlled laboratory environment maintained at a temperature of 22 \(\pm\) 1\({}^{\circ}\)C and a relative humidity of 50 \(\pm\) 5%, as recommended by ASTM D638 [13]. To replicate physiological conditions and account for the hygroscopic nature of collagen, samples were pre-soaked in phosphate-buffered saline (PBS) at 37\({}^{\circ}\)C for 60 min prior to testing. This ensured standardized moisture content and minimized variability in biomechanical response due to environmental fluctuations.  
Statistical justification for sample size: The minimum sample size required to detect a moderate effect size in a multiple regression with approx. 6 predictors, at a significance level (\(\alpha\)) of 0.05 and a power of 0.80 is approx. 97 valid observations. To account for potential sample loss due to test anomalies or exclusion of outliers, a total of 120 implants were tested and 97 were selected. This ensured statistical stability and improved the generalization of the machine learning model by maintaining appropriate data diversity and minimizing model overfitting.  
After the samples were boiled and measured, they were subjected to static and cyclic tensile tests on an Instron machine (Figure 3).  
Then, selected samples were analyzed under a microscope (Figure 4).
# Non-Interactive Decision Trees and Applications with Multi-Bit TFHE
## 4 Private Comparison of Integers  
This section presents our novel comparison algorithm for encrypted integers, which is the critical component of our privacy-preserving decision tree evaluation.  
We encrypt an unsigned integer by considering it a sequence of 4-bit integers or nibbles. The first nibble is the most significant, and the last nibble is the least significant. Each of these nibbles is encrypted separately using TFHE [3] encryption. The encrypted nibbles are then concatenated to form the encrypted integer.  
The 4-bit integers are encoded to the plaintext space \(\mathcal{P}=\mathbb{T}_{p}\), where \(p\mid q=2^{64}\). The value of \(p\) should be greater than 4-bits. As the leading bits, these extra bits accommodate the result of homomorphic operations such as addition. These leading bits are called padding bits [4] and are initialized to zero. The number of padding bits is denoted by \(\bar{\omega}\).
# Wireless Power Transfer Approaches for Medical Implants: A Review
## 1 Introduction  
In recent years, medical progress has evolved with an increased interest in instruments for sensing and controlling the specific functions of the brain. These medical instruments considerably decrease morbidity and improve the standard of life for certain patients. Sensor systems are now quite advanced but providing power to these devices is still a major challenge. The answer to this issue is using wireless power transmission (WPT) technologies for a range of biomedical implants. WPT is a secure and appropriate energy supply for recharging biosensors and electrical implanted devices as well as for data communication in these specific applications.  
WPT comprises two main methods: near-field and far-field transmission. The region is considered near-field if it satisfies two conditions: First, the distance between the transmitter and receiver coil (\(d\)) should be less than one wavelength (\(\lambda\)) at the operating frequency (\(d<\lambda\)), and second, the largest dimension of the transmitter coil (D) should be less than \(\lambda/2\). In contrast, for far-field D \(>\lambda/2\). Moreover, the near and far fields are defined in terms of the Fraunhofer distance (\(\mathrm{d_{F}}=2\mathrm{D^{2}}/\lambda\)); if the conditions \(\mathrm{d_{F}}\)\(>>\) D and \(\mathrm{d_{F}}>>\lambda\) are satisfied, the region is considered far-field.  
There are three major ways to accomplish a near-field WPT: (1) capacitive coupling based on electric fields; (2) inductive coupling based on magnetic fields; and (3) magnetic resonant inductive coupling, which include a resonant circuit in transmitter and receiver coils. Far-field WPT is also known as microwave coupling. Hybrid wireless power transmission (HWPT) includes both far-field and near-field WPT.  
The biomedical implants are intended to be used for biological studies, therapy, and medical diagnostics. Novel biological materials also provide additional biocompatibility and efficiency, as well as reduced expenses. Implantable medical devices (IMDs) can be classified into two primary categories based on their methodologies for the transmission of power. Transfer mechanisms such as inductive coupling, optical charging, and ultrasound are included in the first category. The second category is split into two subsections: batteries, such as lithium; and natural harvesting, including biofuel cell, thermoelectricity, piezoelectricity, electrostatic, and electromagnetic [1]. Various WPT techniques are reviewed in the literature. For instance, the ultrasound and inductive coupling methods were evaluated by [PERSON] et al. [2] and [PERSON] et al. [3], respectively. In this paper, common WPT approaches for IMDs, including inductive coupling, microwave, and ultrasound, are studied. HWPT systems, a mixture of two different methods, are also reviewed.
## Appendix A The Engineering Process Standards  
IEC 10303 and IEC 62264 describe procedures to manage heterogeneous information produced during a product's life cycle by creating models that can trace all relevant information in the digital thread for all the main engineering process phases: design, manufacturing, sales, operation, maintenance, and disposal. Both standards try to formalise the knowledge related to a product's technical data, forcing adopting users to translate information from generic concepts to more practical and customised ones [62].  
ISO 15926 has been developed to handle interoperability issues due to the different HW/SW technologies adopted in the digital thread infrastructure designed to collect and process the life cycle data for process plants, considering the critical issues for manufacturing organizations [63]. For example, different modelling languages are used at different life cycle stages. For instance, STEP's EXPRESS may be used at a detailed design stage, while UML may be used for initial design stages. In these types of cases, the ISO 15926 standard can be used to consolidate product information created using these different languages to build a coherent knowledge base.  
Building information modelling (BIM) technology is a recent development in the architecture, engineering, and construction industries. With BIM, developers can design accurate virtual models of buildings, which are known as building information models. These models can be used for the planning, design, construction, and operation of the facility. Thus, BIM helps different stakeholders (architects, engineers, and constructors) visualise what will be built in a simulated environment to identify any potential design, construction, or operational issues [33].  
Finally, the ISO/IEC 81346 standard \"Industrial Systems, Installations and Equipment and Industrial Products\"-Structuring Principles and Reference Designations\" provides a standardised format for plant structuring and generations of reference designations. For that, an object concept is introduced, where objects are defined as things with or without physical existence. Objects with physical existence include tanks, lamps, and valves. Nonphysical objects can be things that only exist as an assembly of sub-objects (System of Systems), such as a car, an engine, an air plan, or the identification of a set of information. Objects with or without a physical existence are treated in the same way for all concerns in the standard. The standard defines a base reference system for the plant structure based on three different object aspects: a location aspect, a function aspect, and a product aspect. The functional aspect describes what the object is intended to do. The location aspect describes the object's physical location inside the plant. The product aspect is defined in the standard by the phrase, \"an object does what it is intended to do\". Each aspect can be used as a base reference system that describes an object, and each object can be described via each base reference system simultaneously [64].
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 2 Design of Antenna Structure  
In this chapter, the authors introduce the compact multiband monopole antenna structure and its MIMO mutual coupling reduction method in detail, as well as their simulation results obtained using the full-wave simulation tool Ansys HFSS. The antennas' simulation conditions are printed on a single-layer FR4 board with a relative permittivity (\(\epsilon_{r}\)) of 4.4, a tangent loss (\(\delta\)) of 0.02, a substrate thickness of 1.6 mm, and a copper thickness of 35 um.