esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Frequency-Selective Output Constraint Algorithm for Active Vibration Control
## 1 Introduction
[PERSON] presented a CMD LMS (Constrained Minimal Disturbance Least Mean Square) algorithm to handle multiple constraints in both gain-constrained and power-constrained applications, with a faster convergence rate when compared with the leaky LMS algorithms [18]. It can also improve the frequency response performance, especially in colored noise environments. [PERSON] studied a 2 GD-FXLMS (two-gradient direction FXLMS) algorithm [19]. A stricter output constraint that restricts the instantaneous amplitude and average power of the output signal has been studied by using two gradient directions for the weights' updating. All the algorithms above are studied for a single frequency or broadband AVC or ANC applications. The algorithm for the sub-frequency output constraints has not been studied yet. In this paper, an algorithm that is subject to frequency-selective output constraint control is proposed. The NFXLMS (Narrowband Filtered-X Least Mean Square) algorithm is adopted to select the target sub-frequency component of the output signal while a modified rescaling algorithm has been studied and used to apply the amplitude constraints. The remainder of this paper is divided into four sections. Section 2 gives a brief analysis of the rescaling algorithm used by [PERSON]. The proposed algorithms based on the NFXLMS and the modified rescaling algorithm is developed in Section 3. Section 4 presents some numerical simulations for both single-frequency and multi-frequency cases. Section 5 briefly concludes the paper.
# Defining Breast Tumor Location Using a Four-Element Wearable Circular UWB MIMO Antenna Array
## 2 The Proposed Antenna Design
### Antenna Array Setup  
In this study, the human breast is surrounded by four antenna elements, with each antenna positioned at a distance of 8.6 mm from the breast skin. The bending radius of the antennas is not considered, and antennas 1 and 3 are positioned opposite to each other, while antennas 2 and 4 are also placed in opposition. Figure 8 provides a visual representation of the proposed detection scheme, showing the perspective and top-side placements of the antennas in relation to the breast phantom.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Tissues** & \(\boldsymbol{T}\left(\mathbf{mm}\right)\) & \(\boldsymbol{R}_{out}(\mathbf{mm})\) & \(\boldsymbol{R}_{in}\left(\mathbf{mm}\right)\) \\ \hline Skin & 5 & 60 & 55 \\ \hline Fat & 15 & 55 & 40 \\ \hline Glandular & 40 & 40 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dimensions of the various layers of the breast.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tissues**} & \multicolumn{2}{c}{**Dielectric Properties**} & \multirow{2}{*}{\(\boldsymbol{\rho}\) (kg/m\({}^{3}\))**} & \multirow{2}{*}{\(\boldsymbol{c}_{p}\) (J/K/kg)} & \multirow{2}{*}{\(k\) (W/K/m)} \\ \cline{2-2} \cline{5-6}  & \(\boldsymbol{\epsilon}_{r}\) & & & & \\ \hline Skin & 36 & 4 & 1085 & 3765 & 0.4 \\ \hline Fat & 9 & 0.4 & 1069 & 2279 & 0.3 \\ \hline Glandular & 11–15 & 0.4–0.5 & 1050 & 3600 & 0.5 \\ \hline Tumor & 50 & 4 & 1050 & 3600 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The suggested phantom’s electromagnetic parameters.  
Figure 7: (**a**) Phantom schematic, and dispersive properties of (**b**) glandular, (**c**) skin, (**d**) fat, and (**e**) tumor tissues.  
To facilitate the detection process, a mechanical system is proposed, resembling a rotating bed with a hollow semi-spherical Dacron cavity positioned appropriately to align with the patient's breast. As illustrated in Figure 8c,d, the proposed system is securely fixed within the Dacron cavity, while the bed is prepared for rotation. Once the tumor detection is confirmed, the system halts the rotation and begins to move longitudinally until the precise position of the tumor is identified. This mechanism allows for the accurate localization of the tumor within the breast.
# Development of Ground Special Vehicle PHM with Case-Based Reason Model
## 2 Framework Description
### Case Retrieval  
Case retrieval is a key process in case reasoning. This step assures the right case is collected with a proper retrieval approach. In this paper, the k-nearest neighbor method is adopted to calculate case similarity between the source case and target case.  
Let \(C_{i}\) and \(C_{j}\) denote \(i\)th case and \(j\)th case, respectively, then the similarity between the two cases can be calculated by using the similarity of their condition attributes, which can be described as below:  
\[sim\big{(}C_{i},C_{j}\big{)}=\left\{\sum_{q=1}^{m}w_{q}\times sim\big{(}x_{iq },\ x_{jq}\big{)}\right\}^{1/2} \tag{4}\]  
Equation (4) is a modified weight Euclidean formula. \(w_{q}\) is the weight of \(q\)th attribute. Attribute values that correspond to the feature are normalized to reduce the influence of different dimensions. Minimization-maximization is selected as the normalization method. The weight represents the importance of a condition attribute. The sum of each weight is 1, as below:  
\[\sum_{q=1}^{m}w_{q}=1 \tag{5}\]  
The greater the \(sim\big{\langle}C_{i},C_{j}\big{\rangle}\) value, the higher the similarity between case \(C_{i}\) and case \(C_{j}\). The method of similarity calculation is dependent on the types of condition attribute format. If condition attribute format is continuously varying real numbers, the similarity is calculated as below:  
\[sim\big{(}x_{iq},x_{jq}\big{)}=1-\big{|}x_{iq}-x_{jq}\big{|},q=1,2,\dots,\ m \tag{6}\]  
If condition attribute format is a discrete number, the similarity is calculated as below:  
\[sim\big{(}x_{iq},x_{jq}\big{)}=\left\{\begin{array}{l}1;x_{iq}=x_{jq}\\ 0;x_{iq}\
eq x_{jq}\end{array}\right. \tag{7}\]  
where \(m\) is the total number of attributes, \(1\leq q\leq m\). Through case retrieval, some of the source cases that are most similar to the target case can be selected. Then, the process of case reuse uses these selected similar cases to solve the target case. It uses the solutionattribute value of these selected cases to calculate the solution attribute value of the target case.
# Design of a Trusted Content Authorization Security Framework for Social Media
## 5 Discussion
### Privacy Protection  
The first principle is data minimization. The framework only collects necessary user information to perform its functions, such as authentication and content abstraction. It does not collect irrelevant personal information. When processing user data, appropriate anonymization and encryption technologies are adopted to protect users' identity information, for example, using hash functions to encrypt sensitive information. The framework gives users complete control over their data, including the right to access, correct, and delete data. Users can choose whether to participate in authentication and data collection  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Steps** & **Specific Measures** \\ \hline \multirow{4}{*}{Establishing filtering criteria} & Based on objective and impartial \\  & principles, such as community norms, \\  & laws and regulations or industry \\  & standards. \\ \cline{2-3}  & Clearly define standards \\ \hline \multirow{4}{*}{Ensure transparent filtering mechanisms} & Users understand the principles and \\  & operation of filtering decisions. \\ \cline{2-3}  & Provide channels for user participation \\  & and feedback. \\ \hline \multirow{4}{*}{Differentiating between different types of content} & Strict censorship of illegal content \\ \cline{2-3}  & A light touch on political views or \\  & controversial topics \\ \hline \multirow{4}{*}{Establish an independent monitoring and review mechanism} & Third-party organizations or committees \\  & are responsible for reviewing filtering \\  & decisions periodically \\ \hline \multirow{4}{*}{Continuous evaluation and improvement the filtering framework} & Regular audit standards \\ \cline{2-3}  & Gathering user feedback \\ \cline{2-3}  & Monitoring the effect \\ \hline \multirow{4}{*}{Raise user awareness through education} & Users understand how to identify false \\  & information or inappropriate content \\ \cline{1-1} \cline{2-3}  & Users understand their rights and \\ \cline{1-1} \cline{2-3}  & responsibilities on social media \\ \cline{1-1} \cline{2-3}  & platforms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key steps and specific measures to ensure freedom of speech.  
processes. In addition, the framework publishes its privacy policy, clearly explaining the methods and purposes of data collection, use, and sharing to users, ensuring that users fully understand the processing of their data. The framework conducts regular compliance reviews to ensure compliance with global privacy regulations, such as the European General Data Protection Regulation (GDPR) and other relevant regulations. At the same time, appropriate data security measures, including access control, encryption, and secure transmission, should be taken to prevent unauthorized access, disclosure, or tampering with user data. Finally, to further protect user privacy, the framework can provide anonymous posting options, enabling users to post content without exposing their identity.
# A Microscopic Traffic Model Considering Driver Reaction and Sensitivity
## 1 Introduction
Advanced Driver Assistant Systems (ADASs) are deployed in many new vehicles [30] and interest in automated driving (AD) is increasing [31]. Traffic safety and environmental impact can be examined using microscopic models, but the connection to real traffic is unclear [30]. However, they have been used in the Project for the Establishment of Generally Accepted quality criteria, tools and methods as well as Scenarios and Situations (PEGASUS) to address AD challenges [31]. Software such as SUMO, PTV Vissim, and CARLA has been developed to predict traffic behavior. SUMO is a multi-modal microscopic traffic simulator that can be used to model large traffic networks such as in cities [32]. It has been integrated with the ID model to examine features such as free and congested flow, but driver behavior is not considered [33]. SUMO can be used to investigate multi-lane streets with lane changing, intersections with streets having the same or different priorities such as right-before-left, and lane-to-lane connections [32]. Connected vehicles interact with their surroundings to ensure safety [34]. PTV Vissim [35] can be used to forecast freeway capacity and facilitate CAV deployment [36]. However, characterizing the interactions between autonomous and non-autonomous vehicles poses significant challenges [37]. The NGSIM dataset [38] is widely used in transportation research. However, it only focuses on human-driven vehicle behavior and traffic flow. Automated Driving System (ADS) data were introduced in [39] for CAV analysis such as the interaction between human-driven and ADS-equipped vehicles, car-following behavior, and traffic flow. CARLA is an open-source urban driving simulator designed to facilitate the development and validation of autonomous vehicles under different weather and lighting conditions [40]. A microscopic traffic model is proposed that models the acceleration exponent in the ID model [17] using driver reaction and sensitivity. Driver sensitivity is the reaction to changes in velocity and can be typical, sluggish, or aggressive. This variable exponent provides better stability and is more realistic than the fixed exponent in the ID model. Thisis because it is based on real traffic parameters. The performance of the proposed and ID models is evaluated on a single-lane circular road of length 1200 m. A circular road can be considered worst-case because there is no loss of vehicles. A platoon of 21 vehicles is considered for 150 s. The results obtained show that the changes in density with the proposed model are appropriate and the velocity and density evolve smoothly over time.
## 3 Results  
### Chemical Profiling of the Hydroalcoholic Extract  
The hydroalcoholic extract was fractionated by CPC in a total of 10 fractions eluted in increasing order of polarity. All fractions were analyzed by 1D and 2D NMR, but \({}^{13}\)C NMR data were first used to build the dereplication workflow. Bucketing of \({}^{13}\)C peaks across the ten fractions resulted in a table of 10 columns (ten CPC fractions) and 202 rows corresponding to the NMR buckets (\(\Delta\) 0.3 ppm) for which a \({}^{13}\)C peak was detected in at least one CPC fraction. This table was submitted to HCA. In this way, \({}^{13}\)C NMR signals belonging to the same molecule were aggregated as in a heatmap presented in Figure 4. For each cluster, database proposals were confirmed or re-oriented to the right chemical structures by rigorously scrutinizing \({}^{1}\)H-\({}^{13}\)C and \({}^{1}\)H-\({}^{1}\)H correlations in HSQC, HMBC, and COSY spectra. As a result, 28 _P. patens_ metabolites were unambiguously identified using this NMR-based identification process.  
As illustrated in Figure 4, the most polar fraction F10, which represented \(\approx\) 94% of the crude extract mass, was mainly composed of simple sugars (glucose, fructose, and saccharose), as well as amino acids, choline, \(\gamma\)-aminobutyric acid, and malic acid. The less polar fractions F01-F04, representing \(\approx\)1.5% of the extracted mass together, were characterized by the presence of the diterpene ceruchinol, as well as fatty acids in the mixture. Lactic acid and succinic acid were significant compounds detected in fractions F05-F09, recovered during the elution step and representing \(\approx\)5% of the extracted mass. Other minor compounds of fractions F05-F09 exhibited a high chemical diversity; they included, for instance, ocumaroyl-threonine, indole acetic acid, erythromycinolactone, niacin, leucine or leucine-beta, tyramine, uridine, and adenosine.  
The identification of gibberell members, family of phytohormones that control various developmental events, including elongation of stems, germination, dormancy, budding, flower development, and senescence of leaves and fruits [41], including Kaurane derivatives like ceruchinol may suggest a potential role in the maturation processes of _P. patens_. These compounds could act as precursors, laying the groundwork for the formation of the wax layer. Some fatty acids like C16 and C18 were also identified in our analysis.  
Another noteworthy discovery was the existence of indole-3-acetic acid (IAA), a type of auxin. The presence of this auxin is of particular significance, as it is recognized for its role in facilitating caulonema formation and maturation in mosses [24]. This multifaceted molecule may suggest a nuanced interplay of hormonal and structural factors that may collectively contribute to the intricate processes involved in the development of wax layers in _P. patens_.
## 4 Results
### Experiments  
#### 4.2.1 Trajectory Reconstruction  
The trajectory reconstruction process was executed first. The extracted metadata from the geo-tagged images were transformed into a trajectory. All the trajectory-related data were stored in DataFrames. Figure 8 depicts a part of the extracted data from the University Hill dataset using the Phantom 4 Pro drone and the drone's corresponding reconstructed trajectory.
###### Abstract  
The synchronization and control of chaos have been under extensive study by researchers in recent years. In this study, an adaptive Takagi-Sugeno-Kang (TSK) fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed, which is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing CMAC (SOCMAC), and a compensation controller. Specifically, SOCMAC, RCMAC, and adaptive laws are adopted so that the association memory layers of ATFSORC can be modulated in accordance with the layer decision-making mechanism in order to reduce the structure complexity and improve the control performance of ATFSORC. Moreover, the Takagi-Sugeno-Kang fuzzy rules are introduced to increase the learning speed of ATFSORC, and the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the proposed ATFSORC is applied to chaotic systems in order to validate its performance and feasibility. Several simulation schemes are demonstrated to show the effectiveness of the proposed method. Simulation results show that the proposed ATFSORC can obtain a favorable control performance when the chaotic systems are operated at different parameters. Specifically, ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC.  
c pacs: 041567
## 3 Materials and Methods  
### Experimental Setup and Test Procedure  
The impact tests were performed to estimate the propagation velocity of the elastic waves on a grid of predefined points on the sample surface. The tests were carried out using an instrumented impact hammer (model PCB 086D20) for the application of impulsive force on the material surface (Figure 2a) equipped with a dynamic load cell for the digital acquisition of the time-amplitude signal of the impact force, and high-sensitivity uniaxial piezoelectric accelerometers (10 V/g of sensitivity, model PCB 393B12) for the acquisition of the surface vibration induced by the hammer impact (Figure 2b). The data were recorded through an acquisition system (model imc C-SERIES) with a sample rate of 100 kHz, in order to catch the high velocity of the elastic waves.  
It should be noted that the propagation of the elastic waves in the material is strongly dependent on the physical and mechanical properties of the material crossed.  
The purpose of the tests was to obtain an estimation of the elastic properties of the analyzed material by the calculation of the propagation velocity of the elastic waves through it. As explained in the previous Section, different types of elastic waves can be studied, i.e., volume (P waves) and surface waves (S and R waves). The P waves are analyzed by means of the so-called direct test, with the hammer hitting on the surface of the solid and one accelerometer receiving the wave, as material vibration, measured on a point of the opposite side of the solid. The velocity propagation is calculated as the ratio between the points distance and the time interval taken by the wave from the impact to the receiver. The S and R waves are analyzed through indirect tests, with the hammer hitting on the surface of the solid and two accelerometers receiving the surface waves on two different points aligned with the impact position. In this case the velocity propagation is calculated as the ratio between the distance of the two receivers and the time interval taken by the wave to cover the distance between them.  
Following this approach, the tests allowed to estimate the elastic parameters of the medium starting by the assumption of homogeneous and isotropic properties. Working with heterogeneous materials, the procedure can be used to evaluate the elastic properties of the homogenized continuum if specific attention is paid to the microstructure dimensions and the distance on which the wave velocity is calculated.  
Figure 1: Schematic illustration of wave propagation in a half-space.
## 5 Estimation Equation for Maximum Load Bearing Capacity Considering Buckling Position
### Prediction of Buckling Position  
A formula for estimating the position where buckling deformation will occur in PCFST is proposed, using the radius thickness ratio \(R\), slenderness ratio \(\lambda\), concrete filling ratio \(L_{\text{c}}/L\), and axial force ratio \(n\) as parameters. The formula was obtained by linear discriminant analysis using the free statistical analysis software R, release 3.6.2, similar to the process of deriving the equation for estimating horizontal load bearing capacity. The formula for estimating buckling position using the various parameters and obtained by linear discriminant analysis is as follows:  
\[f=25\times R+0.40\times\lambda+3.97\times n-8.88\times\frac{L_{\text{c}}}{L}+0.04 \tag{5}\]  
Here, it is predicted that buckling occurs only at the section just above the concrete if \(f\) is positive and that buckling occurs at the base, or at two locations, at the base and immediately above the concrete, if \(f\) is negative. Thus, the relationship is as follows:  
\[f\begin{cases}>0&\dots\text{buckling only at section just above concrete}\\ \\ <0&\dots\text{at base, or at base and just above concrete}\end{cases} \tag{6}\]  
As representative examples, Figure 13 shows the relationship of the buckling position in the steel tube and the estimation formula for models C-009-020 and C-009-040. In the figure, open dots indicate cases where buckling occurred only at the section directly above the concrete and closed dots indicate cases where buckling occurred at the base, or at two locations as described above. The dashed line shows values from the formula for estimating buckling position. The figure shows that the buckling position is adequately classified. If the point is above the dashed line, buckling is predicted to occur at the base, or in the two locations noted earlier. Alternatively, if the point is below the line, buckling is predicted to occur only at the section directly above the concrete. The same tendency was also observed under other conditions, showing that the position where buckling of the steel tube occurs can be classified using this estimation formula.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 3 Data Monitoring
### Monitoring Equipment
#### 3.2.2 The Data Acquisition and Transmission Module  
The data acquisition and transmission module consisted of a comprehensive acquisition module and a DTU wireless transmission module. As shown in Figure 6, the comprehensive acquisition module had 32 measurement channels, and the extension cable of the sensor was connected to the corresponding channel to export the data. The DTU wireless transmission module mainly included a CPU control module, a wireless communication module, and a power module. As shown in Figure 7, by inserting a SIM card and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensor Number** & **Test Number** & **0 (MPa)** & **50 (MPa)** & **100 (MPa)** & **150 (MPa)** & **200 (MPa)** & **K** \\ \hline N5 & 1 & 0.0000 & 50.1052 & 99.9916 & 150.0968 & 199.9832 & 0.2188 \\  & 2 & 0.0000 & 50.1052 & 100.2104 & 150.0968 & 199.7644 & 0.2122 \\  & 1 & 0.0000 & 50.0792 & 99.9462 & 150.0254 & 200.1046 & 0.2122 \\  & 2 & 0.0000 & 49.8670 & 100.1584 & 150.2376 & 199.8924 & 0.2122 \\  & 1 & 0.0000 & 50.3040 & 99.9792 & 149.8640 & 199.9584 & 0.2096 \\  & 2 & 0.0000 & 50.0944 & 99.9792 & 150.0736 & 199.9584 & 0.2096 \\  & 1 & 0.0000 & 50.0460 & 100.0920 & 149.9185 & 199.9645 & 0.2195 \\  & 2 & 0.0000 & 50.0460 & 100.0920 & 149.9185 & 199.7450 & 0.2195 \\  & 1 & 0.0000 & 50.3040 & 99.9792 & 149.8640 & 199.9584 & 0.2096 \\  & 2 & 0.0000 & 50.0944 & 99.9792 & 150.0736 & 199.9584 & 0.2096 \\  & 1 & 0.0000 & 50.2272 & 99.9366 & 149.7600 & 199.9872 & 0.2304 \\  & 2 & 0.0000 & 49.9968 & 99.9936 & 149.9904 & 199.9872 & 0.2204 \\  & 1 & 0.0000 & 50.0400 & 100.0800 & 150.1200 & 199.9376 & 0.2224 \\  & 2 & 0.0000 & 49.8176 & 100.3024 & 150.3424 & 199.7152 & 0.2224 \\  & 1 & 0.0000 & 50.1860 & 99.9356 & 149.6852 & 200.0894 & 0.2182 \\  & 2 & 0.0000 & 50.0594 & 99.9356 & 149.6852 & 200.0894 & 0.2182 \\  & 1 & 0.0000 & 50.4212 & 99.9768 & 149.7488 & 199.9372 & 0.2164 \\  & 1 & 0.0000 & 50.6376 & 99.9768 & 149.3160 & 199.9536 & 0.2164 \\  & 1 & 0.0000 & 50.0400 & 100.0800 & 149.8976 & 199.9376 & 0.2224 \\  & 2 & 0.0000 & 50.0400 & 100.3024 & 149.8976 & 199.7152 & 0.2224 \\ Maximum error & & 0.000 & 0.6376 & 0.3964 & 0.3424 & 0.1384 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 5: Smart string-type steel stress meter.  
connecting to the GPRS network, users can connect to it and send the measurement data to their computer, enabling remote monitoring of the data. The DTU needs to be connected to the comprehensive acquisition module to achieve remote measurement of the data.