esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Numerical Study of the Jib Connection Frame of a Wheeled Crane
## 6 Conclusions  
This article presents a study of the stress and deformation to the jib joint frame of a wheeled crane, employing a combination of numerical simulation and experimentation. Furthermore, a methodology for calculating similar structures is proposed.  
1. A comparison of the numerical simulation with the test results and the calculation results reveals that the test results are largely consistent with the analysis results, thereby verifying the feasibility of the analysis method and allowing us to conclude that the damage to the connecting frame is a local stability damage.
2. A novel calculation method is put forth for the analogous K-type structure.
3. Finite element analysis is employed to examine the beam element and solid element models. It is determined that the beam element model is not as accurate as the solid model, yet it also meets the engineering requirements. Furthermore, the beam model and the solid model are consistent in terms of damage form and ultimate load. Therefore, the beam model can be utilized to enhance the efficiency of the improvement program.
4. Three improvement schemes are proposed for similar short-arm structures, including strengthening the transverse connection, changing the arrangement of the web in the lifting plane, and changing the arrangement of the web in the lifting plane. It is found  
Figure 25: Reinforced structure for lifting planes.  
that the double-X arrangement scheme can achieve the best results; however, given the increased material consumption that would result from it, the Z arrangement can be chosen as an alternative, which can also achieve good results.  
However, this study has some limitations. The current analysis is mainly based on static loading conditions, and the effects of dynamic loading (e.g., vibration and impact loading) on structural damage have not yet been considered. Future research can further explore the response characteristics of the structure under dynamic loading, evaluate the durability of the improved scheme in combination with fatigue testing, and consider expanding the research method to more complex coupled systems to provide a more comprehensive theoretical basis for the safe design and optimization of wheeled cranes.  
Conceptualization, E.Z. and B.G.; methodology, B.G.; software, B.G.; validation, B.G.; formal analysis, B.G.; investigation, E.Z.; resources, K.C.; data curation, B.G.; writing--original draft preparation, B.G.; writing--review and editing, K.C., E.Z. and B.G.; visualization, B.G.; supervision, K.C. and E.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Event-Related Coherence in Visual Cortex and Brain Noise: An MEG Study
## 2 Materials and Methods
### Analysis Pipelines in FieldTrip and Brainstorm
#### 2.4.2 Artifact Removal and Loading Data  
Accurate brain source analysis requires the correct integration of MEG data with structural magnetic resonance imaging (MRI) scans. Both software programs align all data by defining a subject coordinate system using three fiducial points, namely the nasion and left- and right-auricular points. Moreover, we complemented the alignment based on only three points by an automatic refinement procedure utilizing additional points on the scalp, marked using a 3D digitizer (Polhemus in the considered experiment).  
In FieldTrip, we used the \"Colin27\" head averaged template MRI [38] and adjusted it to the subject's head shape recorded by the Polhemus device. In Brainstorm, default anatomy was warped to fit the scalp shape of every subject with a 2% fit tolerance using digitized head points from the Polhemus device. After an automatic refinement of head points, the 50-Hz electrical power grid frequency and its harmonics were filtered using notch filters. The 56-ms trigger delay was corrected in the recordings. The recorded electrooculogram (EOG) and electrocardiogram (ECG) signals were used to automatically detect instances of eye blinks and cardiac activity in order to apply signal-space projection (SSP) methods to alleviate the respective artifacts.  
Well-defined artifacts such as eye blinks, cardiac activity, muscle contractions, and MEG SQUID jumps were detected semi-automatically using FieldTrip/Brainstorm functions or manual screening. Once artifacts were identified, depending on the artifact intensity, we either discarded trials that contained an artifact or applied linear projection to remove them.
## 2 Theoretical Background
### Enhancement of Fire Response Measures Using Cluster Flight System  
Setting an appropriate flight path is crucial for establishing firebreaks in areas affected by wildfires. In this process, it is essential to equip fire-extinguishing drones with RTK technology, a real-time positioning method, to realize accurate location measurement and improve the performance of fire extinguisher deployment. Fire-extinguishing drones are used to deploy multiple fire extinguishers for establishing firebreaks and preventing the spread of wildfires. RTK-guided drones can deploy fire extinguishers accurately, which increases the efficiency and precision of firebreak construction. RTK technology is an extension of the Global Navigation Satellite System (GNSS) (a system that receives signals from satellites to determine the position of the receiver). The GNSS determines receiver positions by receiving signals from satellites but various factors such as atmospheric errors and errors in satellite elevation angles reduce its accuracy.  
The precision of RTK technology is higher than that of basic GNSS positioning. The GNSS measures a position by receiving satellite signals, and its accuracy is in the order of meters because of atmospheric errors and errors in satellite elevation angles. Meanwhile, RTK technology calculates and corrects these errors by using real-time GNSS data received from a fixed reference point (base station), and its position measurement accuracy is in the order of centimeters for a moving receiver. Therefore, RTK technology is suitable for applications requiring enhanced accuracy, such as aerial photography [22].  
Furthermore, it is challenging to deploy prevention and response measures effectively by operating a single drone over a large area covered with wildfires. Therefore, a cluster-flight system with high autonomy and cooperative capabilities is needed. An outdoor group-flight system based on RTK-GPS, which provides precise location information at the centimeter level, can fulfill these requirements [23]. The RTK-GPS system enhances the flight and operational performances of drone clusters, which demonstrates its potential for use in various UAV applications. Therefore, the RTK-GPS-guided cluster-flight system is expected to play a crucial role in the advancement of spatial information and drone-based operations, which can greatly enhance the deployment of effective response measures in situations such as wildfires.
## 3 Results and Discussion
### Hardened Properties
#### 3.2.2 Water Absorption  
The water absorption of the tested mixes was determined in accordance with the Polish standard PN-B-06250:1988 [26]. Four specimens were prepared from each concrete series. The average values are represented in Figure 5.  
Figure 4: Compressive strength of the tested concrete mixes showing standard deviation.  
Mixes containing fly ash showed the lowest water absorption of all the tested mixes after 28 days of curing. It was observed that the higher the fly-ash content in concrete, the lower its absorption capacity. The absorption values of mixes containing waste-glass powder were at a similar level of ca. 7.4%.
## 2 Model and Algorithm
### Relation to Ordinary CS  
For this special form of sparse recovery problem Equation (1), it could be also tackled via ordinary CS algorithms. We discuss its relation to our proposed algorithm in this subsection.  
We rewrite problem Equation (1) as:  
\[v=\mathcal{A}\bigg{(}\begin{bmatrix}U&I\end{bmatrix}\begin{bmatrix}w\\ s\end{bmatrix}+\xi\bigg{)}+\epsilon \tag{4}\]  
Here \(I\) is the \(n\times n\) identity matrix. Let \(B=\begin{bmatrix}U&I\end{bmatrix}\) and \(x=\begin{bmatrix}w&s\end{bmatrix}^{T}\), so \(x\) is a \"\(K+d\)\"-_sparse_ signal. Then problem (1) converts to the classic CS model regardless of the effect of data noise \(\xi\).  
\[v=\mathcal{A}\big{(}Bx+\xi\big{)}\ +\ \epsilon\ \approx\ \mathcal{A}(Bx)\ +\ \epsilon \tag{5}\]  
As \(\mathcal{A}\) is a CS operator satisfying RIP, for example, a random Gaussian matrix, and \(B=\begin{bmatrix}U&I\end{bmatrix}\) is a fixed basis, it is well-known in CS theory that \(\mathcal{A}\) is largely incoherent with \(B\)[2]. Then, \(x\) can be exactly recovered via CS techniques given enough linear measurements, for example CoSaMP, so does \(s\).  
However, we want to point out that though ordinary CS could also estimate the sparse signal \(s\) if we make the proper linear algebraic transform as above, the proposed algorithm works better when the sparse signal is also corrupted by small noise \(\xi\) and not only the measurement noise \(\epsilon\). It is because in step 3 of Algorithm 1 we explicitly make the least square estimation to cancel the noise effect. We put the comparisons in the experiments section.
## 2 The Proposed Approach
### Formulation of the Optimal Task Placement Problem
#### 2.1.2 Optimal Task Position Determination  
The optimization process aims at improving the overall transmission of the joint speeds utilized, to achieve given end effector velocities.  
The objective function is therefore defined as the maximum minimum overall value of the MVR in the k points of the 8 paths:  
\[\mathrm{R_{v}}\left({}^{\mathrm{B}}\mathrm{P_{L}},\mathrm{\theta}_{-}\right)= \left\{\begin{matrix}\max_{\mathrm{j}}\left(\min_{\mathrm{i}}\left(\mathrm{r_{ v,i\mathrm{j}}\left({}^{\mathrm{B}}\mathrm{P_{L}},\mathrm{\theta}_{-}\right)} \right)\right)\\ 0,\quad\text{if}\quad\exists\left|\mathrm{J_{v,i\mathrm{j}}}\right|=0\end{matrix} \right.,\mathrm{i}=1,\ldots,\mathrm{k},\quad\mathrm{j}=1,\ldots,8, \tag{8}\]  
where \(\mathrm{R_{v}}\) is the objective function.  
Therefore, it is possible to derive the solution to the considered problem as:  
\[\left({}^{\mathrm{B}}\mathrm{P_{L}},\mathrm{\theta}_{-}\right)=\mathrm{argmax }\left(\mathrm{R_{v}}\left({}^{\mathrm{B}}\mathrm{P_{L}},\mathrm{\theta}_{-} \right)\right), \tag{9}\]  
As such, the method's outputs are the orientation and position of {L} with respect to {B}. Since the required end effector postures for each of the task frames with respect to {L} are _a priori_ given, deriving them requires a simple calculation of the respective transformation.
## 2 Materials and Methods
### Station-Based Drought Indices  
The station-based drought indices of PDSI, moisture anomaly index (Z-index) and SPI were selected for evaluating the remote-sensing based drought indices. The National Climate Data Center (NCDC) has made monthly Z-index, PDSI, and SPI records available from 1895 to the present day [26]. These _in situ_ indices have been used in various studies to evaluate remote-sensing drought  
Figure 1: Location of the study area. The study area was divided into different climate divisions and the US-ARM, US-Aud, US-KFS, US-FR2, US-Kon, US-Ro3 and US-Var are names of the flux tower sites. The black circles are their geographic positions.  
indices [7; 16; 20; 27; 28]. The PDSI and Z-index are calculated using a soil moisture balance algorithm that requires not only a time series of daily air temperature and precipitation data, but also information on the available water content of the soil. The Z-index for the month \(i\) is calculated basing the formula below:  
\[Z_{i}=d_{i}K_{i}, \tag{1}\]  
where \(Z_{i}\) is the Z-index for month \(i\), \(K_{i}\) is a weighting factor that is initially determined by an empirically derived coefficient \(K\prime\) using the formula below:  
\[K_{i}=\frac{14.2}{\sum D_{i}K_{i}}K\prime, \tag{2}\]  
where \(D\) is obtained using the calibration period by determining the of the absolute values for each month of the year. The relationship between PDSI and Z-index is as below:  
\[PDSI_{i}=\frac{Z_{i}}{3}+0.897 PDSI_{i-1}, \tag{3}\]  
where \(PDSI_{i}\) is the PDSI for month \(i\), \(Z_{i}\) is the Z-index for month \(i\), \(PDSI_{i-1}\) is the PDSI of the month \(i-1\). Complete details on how to calculate PDSI and Z-index are available in [PERSON] _et al._[4].  
The SPI is calculated by standardizing the probability of observed precipitation for any duration of interest (e.g., weeks, months, or years). Durations of weeks or months can be used to apply the SPI for agricultural or meteorological purposes, and longer durations of years can be used to apply it for hydrological and water management purposes [4; 29]. In this study, the monthly precipitation data was used and the 1-, 3-, 6-, 9-, 12-, and 24-month SPI were calculated for each climate division region (specified as SPI-1, SPI-3, SPI-6, SPI-9, SPI-12 and SPI-24, respectively, in this study).
## 3 Design of the Master and Auxiliary Compound Control
### Variable Universe Fuzzy PID Controller
#### 3.1.1 Adaptive Fuzzy PID Control Module
(3) Establishment of fuzzy control rules  
According to the influence of the parameters \(K_{p}\), \(K_{i}\) and \(K_{d}\) on the PID control output characteristics of the multi-channel concentrated liquid supply system, combined with the existing reference literature and the operating experience of the research platform, the PID parameter adjustment principle is designed, as follows. General principle: When the deviation \(e\) is large, the deviation should be eliminated as soon as possible under the premise of ensuring the stability of the system; when the deviation \(e\) is small, the stability of the system is the main thing. The fuzzy rules are shown in Table 4.  
(4) Defuzzification  
The centroid method is used to solve the fuzziness, and the precise adjustment value of the PID parameters is obtained. Then, the PID control parameters \(K_{p}\), \(K_{i}\) and \(K_{d}\) are obtained by adding the initial values of the PID control parameters. The calculation formula is shown below. \[\left\{ \begin{array}{l}K_{p}=K_{p0}+\Delta K_{p}\\ K_{i}=K_{i0}+\Delta K_{i}\\ K_{d}=K_{d0}+\Delta K_{d}\end{array} \right. \tag{22}\]  
where \(K_{p}\) is the scale factor, \(K_{i}\) is the integral coefficient, and \(K_{d}\) is the differential coefficient. \(\Delta K_{p}\) is the increment of the proportional coefficient, \(\Delta K_{i}\) is the increment of the differential coefficient, \(\Delta K_{d}\) is the increment of the integral coefficient, and \(K_{p0}\), \(K_{i0}\) and \(K_{d0}\) are the initial proportional coefficient, initial differential coefficient and initial integral coefficient, respectively. Figure 11: The membership functions: (**a**) Input variables \(e\), \(ec\); (**b**) Output variables \(\Delta K_{p}\), \(\Delta K_{i}\), \(\Delta K_{d}\).
## 2 Materials and Methods
### Outline of Computational Approach
#### 2.5.2 Phasor Particle Swarm Optimization (PPSO)  
PPSO, introduced by [PERSON] et al. [42], is a novel variant of the PSO algorithm designed to enhance performance, particularly for high-dimensional optimization problems. Inspired by phasor theory in mathematics, PPSO models the control parameters using a single phase angle, \(\theta_{i}\), for each particle \(i\), transforming the algorithm into a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic approach.  
The mathematical expressions of the PPSO algorithm, including Equations (9) and (10), were adapted from [42]. In standard PSO, the velocity update relies on control parameters such as the inertia weight \(\alpha\) and acceleration coefficients \(\beta_{1}\) and \(\beta_{2}\), which require careful tuning to balance exploration and exploitation. PPSO eliminates this need by incorporating these controls into periodic trigonometric functions, specifically \(\cos(\theta_{i}^{\tau})\) and \(\sin(\theta_{i}^{\tau})\), where \(\theta_{i}^{\tau}\) is the phase angle of particle \(i\) at iteration \(\tau\). The phase angle is initialized randomly in the range \([0,2\pi)\) and evolves over iterations, enabling adaptive search behaviors without explicit parameter adjustments [42].  
The velocity update in PPSO is defined for each dimension \(k=1,2,\ldots,K\) as Equation (9) [42]:  
\[S_{i,k}(\tau)=\left|\cos(\theta_{i}^{\tau})\right|^{2\sin(\theta_{i}^{\tau})} \cdot\left(Q_{i,k}^{\text{best}}(\tau)-Y_{i,k}(\tau)\right)+\left|\sin(\theta_{ i}^{\tau})\right|^{2\cos(\theta_{i}^{\tau})}\cdot\left(Q_{k}^{\text{global}}(\tau)-Y_{i,k}( \tau)\right) \tag{9}\]  
Here, the functions \(\left|\cos(\theta_{i}^{\tau})\right|^{2\sin(\theta_{i}^{\tau})}\) and \(\left|\sin(\theta_{i}^{\tau})\right|^{2\cos(\theta_{i}^{\tau})}\) replace the traditional acceleration coefficients \(\beta_{1}\) and \(\beta_{2}\), respectively. The inertia weight \(\alpha\) is set to zero in the basic PPSO formulation, simplifying the velocity update and emphasizing the influence of the personal best (\(Q_{i}^{\text{best}}\)) and global best (\(Q_{k}^{\text{global}}\)) positions. The position update remains consistent with standard PSO as Equation (10) [42]:  
\[Y_{i,k}(\tau+1)=Y_{i,k}(\tau)+S_{i,k}(\tau) \tag{10}\]  
The phase angle \(\theta_{i}^{\tau}\) is updated in each iteration to dynamically modulate the influence of the cognitive and social components. This adaptive mechanism effectively balances global exploration and local exploitation, reducing the risk of premature convergence to local optima, a common issue in standard PSO, especially in high-dimensional spaces. The optimization process iterates by updating particle positions and velocities based on the phase angle dynamics until a predefined stopping criterion is met, such as reaching a maximum number of iterations or achieving a sufficient fitness value for \(Q_{k}^{\text{global}}\)[42].
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## 1 Introduction
A mathematical analysis of the proposed control algorithm was performed in detail and compared to the previous method [38]. The proposed algorithm, the ESO-based PIDSMC, and the previous algorithm (the ESO-based SMC) were implemented on MATLAB/Simulink. Two different simulations were performed. Firstly, simulations were performed on a second-order system to validate the mathematical analysis of the algorithm in detail. Different cases were considered in the simulations based on the reference trajectory, and the performance and limitations of the algorithm were discussed in detail. In the second simulation, the algorithms were implemented on a two-DOF virtual robot manipulator using SimScape/MATLAB. The trajectory-tracking error of the proposed algorithm was smaller than that of the ESO-based SMC, and simulation results verified the proposed algorithm's performance. This manuscript is organized as follows. Section 2 presents the control design and a detailed analysis of the proposed control logic. Section 3 features the simulation and discussions. Finally, Section 4 concludes the paper.
# Making Sense of Language Signals for Monitoring Radicalization
## 3 Semantic Modeling
### Vocabulary for the Moral Foundation Theory (MFT)  
The popularity of LIWC has led to several LIWC-like dictionaries. One of such dictionaries is the Moral Foundations Dictionary (MFD) [59], which includes new annotations for morality. MFT [60] identifies six universal moral foundations that exist across cultures. Each foundation can be considered a vice or a virtue from a moral point of view. The six foundations (virtue/vice) that describe political views are:  
* Fairness, equality, or reciprocity/cheating: notions of rights and justice.
* Care/harm: compassion, protection of community members.
* Loyalty or ingroup/betrayal: respect for the norms of the group, patriotism, and sacrifice for the group.
* Authority or respect/subversion: obedience to authority figures and hierarchical structures.  
* Purity or sanctity/degradation: promotion of sacred values, chastity, control of one's desires.
* Liberty/oppression: feelings of reactance and resentment people feel toward those who dominate them and restrict their liberty.  
Moral dimensions are usually classified into _individualizing_ and _binding_ foundations [24]. Individualizing foundations (e.g., care/harm and fairness/cheating) deal with the roles of individuals within social groups. Binding foundations (e.g., loyalty/betrayal, authority/subversion, purity/degradation, and liberty/oppression) are related to the formation and maintenance of social groups.  
This article presents a proposal of a semantic vocabulary for morality annotations [51] which is composed of two main classes: Foundation, which represents an MFT foundation; and Polarity, to represent a polarity within a given foundation. Six entities of type Foundation have been defined: FairnessCheating, CareHarm, LoyaltyBetrayal, AuthoritySubversion, SanctityDegradation, and LibertyOppression. Polarity has two extremes, vice (Vice) and virtue (Virtue), although other classifications are possible through the use of the Polarity class. The two polarities are defined as opposites of each other, using the oppositeOf property in both instances. Moreover, a numeric value can be assigned to express this intensity (polarityValue). Positive values are typically associated with a virtue class, and negative values correspond to a vice. To tie these two concepts together, the vocabulary also includes the concept of a MoralValue, which is associated with a foundation (annotated through hasFoundation) and a polarity (through hasPolarity). The main elements of the vocabulary can be seen in Figure 3, and the  
Figure 2: SLIWC taxonomy, simplified, focusing on its connection to WordNet-Affect. Dimensions are represented in different font sizes to emphasize their hierarchical relation.  
annotation categories are detailed in Figure 4. An example of its use for the annotation of a text is shown in Section 3.4.