esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Algorithm for the Integration of Dynamic Equation  
According to the description in the previous sections, the algorithm for the integration of the dynamic equation in the modified form of Equation (18) is as follows:  
1. Input data for catenary and pantograph. Regarding the catenary: the number of spans of the cantons, lengths of spans, moments of inertia of the elements of the Furrer beam section (body of the beam and the contact wire), characteristics of the materials, the geometry of the sections, characteristics of the flanges, etc. The characteristics of the equivalent section of the beam are then determined according to Equations (5) and (6). For pantographs, the following are entered: number and type of pantographs, masses, spring stiffnesses, dampers, etc.  
Figure 7: Position correction functions of the contact element for linear slope.  
The setting of the different terms of the dynamic equation at the initial instant: the stiffness matrix, damping matrix, mass matrix, and external load vector, according to Equations (2)-(4) and (7). The matrix of restriction conditions at the initial position of the pantograph(s) is also established according to Equations (8)-(15).
3. The initial value of the integration variables is set to \(t_{0}=0\): coordinates of the nodes \(q_{0}\) and their derivatives, and the initial contact force \(\lambda_{0}\) according to Equations (23) and (24).
4. The integration cycle begins at the time of the differential Equation (18) in \(t_{n+1}\). The cycle varies for different time values with \(n=0,1,2\ldots\), adding in each cycle the value of \(\Delta t\) to \(t_{n}\): 1. Calculation of the coordinates corresponding to the positions of the catenary nodes and the pantograph masses \(q_{n+1}\), according to Equation (20).
2. Calculation of the position of the contact elements of the pantograph(s): \((y_{3})_{n+1}\) and \((y_{4})_{n+1}\) according to Equation (10). If the contact element is deactivated, its position coincides with that of the pantograph head mass; in this case, Equation (14) or (15) shall be used.
3. Calculation of the pantograph-catenary contact forces \((\lambda_{1})_{n+1}\) and \((\lambda_{2})_{n+1}\), according to Equations (21) and (22).
4. Setting the restriction conditions matrix \(\phi_{n+1}\), using Equations (8)-(15), according to the advance of the pantograph(s) along the line.
5. For the calculation of the forces, the positions of the contact elements, and the restriction conditions, the three cases of activation of the contact element are accounted for, according to the position of the pantograph along the line, as explained in Section 2.6.
6. The remainder of the terms of the dynamic Equation (18) is also set in \(t_{n+1}\): \(C_{n+1}\), \(K_{n+1}\), and \(R_{n+1}\), and, in particular, the pantograph stiffness matrix must be modified if there is a take-off or a coupling according to Equation (22).
7. Calculation of the velocity vector in the coordinates for the next integration cycle in \(t_{n+1/2}\) by the mid-pass correction of the velocity vector, using Equation (19).
## 4 Data Collection and Result Discussion
### Comparison with IGS Type Values  
Besides the internal repeatability accuracy of the experiment itself, an external critical antenna calibration accuracy assessment with other calibration methods would be necessary, e.g., results from the anechoic chamber. However, due to the limitations of the experimental setup, the absolute antenna phase center calibrated results from the anechoic chamber are unavailable. Therefore, the published IGS antenna files like igs05.atx or igs08.atx contain most of common geodetic absolute antenna phase data sets calibrated by other international institutions or research groups. The test receiver antenna corrections in the igs08.atx stemmed from robot measurements by the well-known company Geo\(+\). Referring to the calibration values from igs08.atx, we extended the assessments in Table 1 to Table 2. Assessments in Table 2 demonstrate that the calibrated values for the three types of antennas used in the experiments were estimated correctly and can be comparable, better than 0.5 mm, to those of IGS mean types in igs08.atx.  
Compared with the high accuracy calibration results in igs08.atx, the full derived PCVs in this paper show good consistency in the elevation range of 15-90\({}^{\circ}\) and \(\pm\)1 mm absolute antenna phase center calibration accuracy can be safely obtained, see Figures 8 and 9 (due to lack of space, only the example of DAY240-2014 is given). Some differences of 1-4 mm can be found close to horizontal reception angles below 15\({}^{\circ}\), possibly caused by the remaining multipath effects and bad antenna receiving  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Antenna Type** & \(\sigma_{N}\)**(mm)** & \(\sigma_{E}\)**(mm)** & \(\sigma_{U}\)**(mm)** \\ \hline TRM57971.00/NONE & 0.16 & 0.1 & 0.23 \\ TRM59800.00/NONE & 0.03 & 0.17 & 0.18 \\ LEIAR25/NONE & 0.03 & 0.07 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PCO solutions accuracy compared to IGS mean types.  
Figure 8: Differences of derived full PCVs (GPS L1 frequency) between the results of DAY3240-2014 and IGS standard values for TRM57971.00/NONE. An external consistency accuracy on the order of \(\pm\)1 mm in the phase pattern can be achieved from zenith 75\({}^{\circ}\) to 0\({}^{\circ}\), whereas the calibration accuracy at close to horizon typically fluctuated within 1–2 mm.  
performance at low elevations in most of cases. Nevertheless, the consistence of two individual calibrations within the range of 1 mm above 15 elevations demonstrate the good performance of our calibration procedure and the calibration strategies for low elevation need to be further investigated in the future.
## 4 Discussion
### TMDs Prevalence and Etiology
#### 4.1.7 Gene-Environment Interactions  
The interplay between genetic predisposition and environmental factors further complicates TMDs etiology. [PERSON] et al. demonstrated that individuals with specific COMT variants exhibit heightened susceptibility to TMDs when exposed to stress or orthodontic interventions [51; 55]. This highlights the potential for genetic screening and personalized orthodontic planning to mitigate risks in predisposed individuals.  
TMDs etiology reflects a complex interplay of biological, psychological, and behavioral factors. While orthodontic treatment does not appear to exacerbate TMDs symptoms, its interaction with genetic predispositions and developmental changes warrants careful consideration. Integrating genetic screening, psychological assessment, and behavioral interventions into TMDs management could enhance treatment outcomes and reduce long-term risks. Future research should focus on validating genetic markers, exploring epigenetic mechanisms, and further delineating the role of behavioral habits in TMDs progression.
## 5 Discussion
Firstly, the smart card data used were aggregated at hourly intervals, whereas public transport services typically operate at 5-30 min intervals, which may limitforecasting precision. Analyses based on minute-level data could enable more refined and detailed predictions. Secondly, although the deep learning models used in this study are effective in capturing short-term fluctuations, they exhibit limitations in modeling long-term trends and seasonal patterns. This limitation primarily stems from the relatively short time span of the dataset (1.5 years), which makes it difficult to identify statistically significant seasonal cycles. Classical ARIMA approaches tend to show limited performance when applied to high-frequency (e.g., hourly) and multivariate datasets, particularly in terms of accuracy, computational efficiency, and sensitivity to data quality [74]. Therefore, traditional models were not included in this study. In future studies, it would be beneficial to extend the dataset over multiple years and increase the data coverage, in addition to employing hybrid models that combine statistical seasonal forecasting methods with deep learning architectures, in order to better capture seasonality.
###### Abstract  
In this article, three distinct strategies for designing an Active Fault-Tolerant Control System (AFTCS) for Air-Fuel Ratio (AFR) control of an Internal Combustion (IC) engine in a process plant to avoid engine shutdown, are presented. The proposed AFTCS employs a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a Nonlinear Regression (NLR)-based observer model in the Fault Detection and Isolation (FDI) unit for analytical redundancy. A comparison between these three proposed techniques is carried out to determine the least expensive and most accurate approach. The results show that the nonlinear regression produces highly accurate results by consuming very low computational power, and its response time is also very low as compared to GA and PSO. The results obtained show that NLR requires 99.6% and 93.1% less computational time for throttle and MAP estimation, respectively, by reducing the estimation error to as low as 0.01. The simulation of the proposed system is carried out in the MATLAB/Simulink environment. The results prove the superior fault tolerance performance for sensor faults of the AFR control system, especially for the Manifold Absolute Pressure (MAP) sensor in terms of less oscillatory response as compared to that reported in existing literature.  
active fault-tolerant control; AFR control; fault detection and isolation unit; IC engine; nonlinear regression; particle swarm optimization; genetic algorithm; observer model +
Footnote †: journal: Applied Sciences
## 6 Implementation and Simulation
### Dec-Knn  
The results of the DEC-KNN approach are shown in Figure 9. The network will use only six cluster heads to cover the connection of all sensors. All the nodes will choose the nearest CH to be chosen; therefore, the cluster head selection was improved. The dead nodes will not be chosen as CHs in the network. Using the KNN algorithm to choose the best cluster head position for nodes to minimise energy consumption and maximise the network's lifetime also avoids any CHs overlapping in the same area.  
The simulation and analysis for the original DEC and DEC-KNN protocols were performed using MATLAB for the same parameters and initial conditions. The base station (BS) is always located in the centre of the network. In the original protocol, the network has 17 clusters, each consisting of nodes with varying energy levels that have been randomly sorted. The proposed approach containing CHs was organised into six clusters. Summation was performed for 50 randomly distributed nodes in the 100 m \(\times\) 100 m field.
# A New Generation of Fast and Low-Memory Smart Digital/Geometrical Beamforming MIMO Antenna
## 4 Numerical Evaluations  
The optimization of the phase delay factors is carried out as per Equation (18) obtained in this paper. MATLAB(r) programing language is used to evaluate the phase delay coefficients. Unfortunately, MATLAB(r) does not have an effective way to display three-dimensional solid shapes. Therefore, Wolfram Mathematica(r) is used to model the relevant three-dimensional solid beam pattern. Initially, the phase delay coefficients are calculated using the developed software code. Subsequently, these coefficients are used to display the azimuthal plane and three-dimensional solid shape radiation patterns.
# An Overview on Denial-of-Service Attacks in Control Systems: Attack Models and Security Analyses
## 3 Deterministic and Game-Theoretical Approaches to Denial-of-Service Attack Modeling
### Deterministic Attack Models with Average Duration and Frequency Constraints
In particular, \(\kappa_{\mathrm{D}}\) and \(\kappa_{\mathrm{F}}\) are utilized in [55] for determining the initial capabilities of the attacker and for obtaining conditions of stabilization with a controller that is designed through a linearization approach. For the multi-agent consensus problem discussed in Section 2.3, [7; 73] utilized the attack characterization with the conditions in Equations (15) and (16). It is observed in [72] that, with randomized transmissions, consensus in multi-agent systems can be achieved if the average attack duration is restricted as in Equation (15) regardless of the attack frequency. In the discrete-time setting, a similar modeling approach can be utilized. The attack intervals can be defined similarly through sequences \(\{a_{k}\in\mathbb{N}_{0}\}_{k\in\mathbb{N}_{0}}\) and \(\{\tau_{k}\in\mathbb{N}_{0}\}_{k\in\mathbb{N}_{0}}\). In particular, the \(k\)th attack interval is given by \(\{a_{k},a_{k}+1,\ldots,a_{k}+\tau_{k}-1\}\). Again, it is assumed that \(a_{k+1}>a_{k}+\tau_{k}\). Furthermore, average duration and frequency restrictions in Equations (13) and (14) as well as Equations (15) and (16) can be imposed in a similar way. For discrete-time networked control problems, [PERSON] et al. [25] and [PERSON] et al. [39] considered average duration and average frequency restrictions in obtaining sufficient conditions of stabilization over networks under DoS attacks. We note that there are other deterministic modeling approaches such as periodic DoS attacks considered in [49]. In particular, [PERSON] and [PERSON] [49] modeled DoS attacks in a general way as a pulse width modulated (PWM) signal. In this model, the attacker repeats cycles of jamming and sleeping. Furthermore, in the special case of periodic jamming, each cycle consists of \(T_{\mathrm{J}}\) seconds of jamming that is followed by \(T_{\mathrm{S}}\) seconds of sleeping. [PERSON] and [PERSON] [49] considered the setup where both \(T_{\mathrm{J}}\) and \(T_{\mathrm{S}}\) are unknown in the networked control problem. In addition, constraints on attacks were considered previously in a finite horizon problem by [PERSON] et al. [34]. There, attacks can happen at arbitrary time instants given that the total number of attacks does not exceed a threshold for a given finite time horizon. As we further discuss in Section 3.2, such constraints have also been used in game-theoretic approaches.
# A Low-Autogenous-Shrinkage Alkali-Activated Slag and Fly Ash Concrete
## 3 Results and Discussion
### Cracking Potential of Concrete  
The stress evolutions in the plain AASF concrete and AASFICMK concrete are shown in Figure 9. A sudden drop in the stress to around zero indicated the failure of the concrete due to tensile stress. It can be seen that the stress generated in AASFICMK was much lower than that in AASF. In the first 4 days, a small compressive stress was detected in AASFICMK due to the slight \"expansion\" of the concrete (see Figure 8). Afterwards, a tensile stress started to develop. The stress in AASFICMK was only 50% and 30% of the stress in the plain AASF concrete at the age of 7 days and 14 days, respectively.  
Figure 8: Autogenous shrinkage of AASF and AASFICMK concrete.  
Figure 9: Self-induced stress in AASF and AASFICMK concrete.  
According to the cracking time and stress rate, the cracking potential of AASF concrete was classified as \"moderate\" [22]. With the incorporation of SAPs and MK, AASFICMK concrete did not crack within 56 days. The stress rate after the first week reached below 0.01 MPa/day, indicating a very \"low\" cracking potential of the concrete [22].  
The superior workability, the high 28-day strength, and the low cracking potential indicate that AASFICMK concrete could be considered as a highly commercially competitive construction material. Furthermore, due to the very low cracking potential of AASFICMK concrete, there is a lot of room for further tailoring the current mixture design in order to reach optimal overall performance of the concrete for different applications. For example, for the cases where the autogenous shrinkage is not very critical, lower liquid/binder ratios, lower dosages of SAPs/MK or higher amounts of slag could be used, by which higher strength of the concrete can be easily achieved.
## 2 Related Works
### Traditional Graph Construction Methods  
The process of graph construction is momentous in graph-based SSL which mainly involves two steps: building the graph adjacency structure and calculating the graph edge weight. For building graph adjacency structure, k-nearest neighbors (KNN) and \(\varepsilon\)-ball neighborhood are the two most popular approaches [20]. As for graph weighting methods, [PERSON] et al. [21] use the Gaussian kernel (GK) function to calculate the edge weight, however if only a few labeled samples are provided, it will be hard to determine the hyper-parameters in the function [22]. [PERSON] et al. [22] propose a non-negative local linear reconstruction (LLR) to use the neighborhood information of each data point to construct a graph in order to derive a more reliable and stable way to construct the graph. First, they approximate the entire graph as a series of overlapping linear neighborhood patches, then they find the edge weight of each linear neighborhood patch, and then they aggregate all the edge weights together to form the edge weight matrix of the entire graph; [PERSON] et al. [23] consider that sparsity is essential for improving the efficiency of SSL algorithms. Therefore, they propose local linear embedding (LLE)-based weight which can capture the local geometric properties of hyperspectral data and is good for weighting the graph edge in a low-level computational cost. [PERSON] et al. [24] proposed nonnegative low-rank and sparse (NNLRS) approach to use both low-rankness of high dimensional data samples and the sparsity to construct a good graph. The obtained graph can capture the local low-dimensional linear structures of the data samples and the global cluster or subspace structures of the data samples.  
However, these traditional methods share the same disadvantages that they all have fixed manually tuning parameters. As a result, this category of graph construction methods are very sensitive to the data noise and parameter variations.
# Effects of Organophosphorus Flame Retardants on the Dissipation Factor of Flame-Retardant Polymers
## 3 Results and Discussion  
### Effect of OH on Df and Dk of Flame Retardants  
The Df values of two flame retardants are plotted against the frequency in Figure 2. Clearly, except at a resonant frequency around 11 GHz, DOPO-HM has much lower dielectric losses than DOPO-Me in the measured range. This is rather interesting because the hydroxyl group in DOPO-HM actually produces the sought-after effect of lowering the Df values. Perceptibly, it is attributed to hydrogen bonds formed between P=O and OH, which slow down a dipole reorientation of the polar groups in DOPO-HM under a fast alternating electric field [22]. As a result, the dielectric loss of DOPO-HM is decreased. This effect is similar to that of hydroxyl group on the dielectric loss of epoxy or hydroxylated polypropylene, where hydrogen bonds contribute to a reduction in the dielectric loss, although hydrogen bonds are produced by an extraneous additive in the latter cases [23; 24].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **PPO (g)** & **DVB (g)** & **DCP (g)** & **DOPO-Me (g)** & **DOPO-HM (g)** \\ \hline PPO & 20 & 1 & 0.4 & \\ \hline PPO-DOPO-Me & & & & \\ \hline
10\% & 18 & 0.9 & 0.36 & 2 \\
20\% & 16 & 0.8 & 0.32 & 4 \\
30\% & 14 & 0.7 & 0.28 & 6 \\ \hline PPO-DOPO-HM & & & & \\ \hline
10\% & 18 & 0.9 & 0.36 & 2 \\
20\% & 16 & 0.8 & 0.32 & 4 \\
30\% & 14 & 0.7 & 0.28 & 6 \\ \hline \end{tabular}
\end{table}
Table 2: Preparation formula of flame-retardant PPO.  
In addition, DOPO-HM produces smaller Dk values than DOPO-Me, which is also surprising. Low-Dk flame retardants are desirable in high-speed PCBs. On the basis of a density of 1.3855 g/cm\({}^{3}\), which is higher than 1.3248 g/cm\({}^{3}\) of DOPO-Me, DOPO-HM should have larger Dk values than DOPO-Me, since Dk is dependent on free volumes of molecules. An increase in free volumes decreases the dielectric constant of a molecule, owing to a reduced number of polarizable groups per unit volume [25]. Judging by its structural similarity but lower density, DOPO-Me presumably has greater free volumes than DOPO-HM. However, hydrogen bonds can cause a significant drop in permittivity [18] and this factor obviously exceeds the influence of free volumes. Therefore, besides a lower Df value, DOPO-HM also has a lower Dk value than DOPO-Me.
## 2 Input Data  
The selection of all input data described below is based on the requirement of continuous spatial (global) and temporal (1979-2020) coverage. The overview of the input data given here is rather concise, and a more detailed documentation [21] provides additional clarifications on their implementation, technical details, and example figures.
## 4 Results and Discussion  
### Bending Resistance  
The failure load of all beams is shown in Figure 9.  
As can be seen from Figure 9, due to existence of the notch, failure loads of the RPC beam and BFRPC beam are reduced by 27 kN and 7 kN, respectively. Under the influence of interfacial damage, the failure load of RPC beam and BFRPC beam are reduced by 51 kN and 48 kN, respectively. The results show that both notch and interfacial damage can reduce the bending resistance of RPC and BFRPC beam, and the interfacial damage has a greater impact on the bending resistance. The reason for this is that the notch accelerates the formation and expansion of the cracks and reduces the bending capacity of the beam. However, the interfacial damage directly causes the insufficient bonding force between the steel bar and the concrete, which reduces the local mechanical properties of the beam body in the damaged area. It is a macroscopic manifestation of the influence of large-area corrosion of the steel bar on the stress characteristics of the beam. Besides, the failure load of the BFRPC beam was greater than that of the RPC beam under the corresponding conditions, which indicates that BF can increase the failure load of RPC and reduce the adverse effects of the notch and interfacial damage on the failure load.  
Figure 8: Experimental setups for the bending test and AE test.  
Figure 9: Maximum bearing capacity.
## 2 Problem Formation  
To simplify the mode of the PMSM, let us first assume: the three-phase windings of the stator are evenly distributed, the saturation effect of the core is ignored, and air gap magnetic field is sinusoidal. The governing equations of PMSM under \(d\)-\(q\) reference frame can be described as follows:  
\[\left(\begin{array}{c}\dot{i}_{d}\\ \dot{i}_{q}\\ \dot{\omega}\end{array}\right)=\left(\begin{array}{ccc}-\frac{R_{s}}{L_{d}}&n _{p}\omega&0\\ -n_{p}\omega&-\frac{R_{s}}{L_{q}}&-\frac{n_{p}\psi_{f}}{L_{q}}\\ 0&\frac{n_{p}\psi_{f}}{J}&-\frac{B_{m}}{J}\end{array}\right)\left(\begin{array} []{c}i_{d}\\ i_{q}\\ \omega\end{array}\right)+\left(\begin{array}{c}\frac{u_{d}}{L_{d}}\\ \frac{u_{d}}{L_{d}^{2}}\\ -\frac{T_{s}}{J}\end{array}\right) \tag{1}\]  
where \(R_{s}\) is stator resistance (\(\Omega\)), \(\psi_{f}\) is rotor flux linkage (Wb), \(T_{L}\) is load torque (N/m). \(J\) is moment of inertia (Kg-m\({}^{2}\)), \(\omega\) is angular velocity(rad/s), \(B_{m}\) is viscous friction coefficient, \(u_{d}\) and \(u_{q}\) are \(d\) and \(q\) axis stator voltage (V), \(L_{d}\) and \(L_{q}\) are \(d\) and \(q\) axis induction (H), \(i_{d}\) and \(i_{q}\) are \(d\) and \(q\) axis stator current (A), \(n_{p}\) is the number of pole pairs.  
The current vector is decomposed into flux-producing component \(i_{d}\) and torque-producing component \(i_{q}\) by coordinate transformation, therefore \(i_{d}\) and \(i_{q}\) can be regulated separately. Here make \(i_{d}=0\), so that the electromagnetic torque of the motor can be controlled only by \(i_{q}\). Finally, the PMSM speed-regulation problem is simplified to \(i_{q}\) regulation. The general structure of PMSM speed-regulation system is shown in Figure 1. The PMSM system includes a speed loop and two current loops. Here a PI controller is adopted in \(d\)-axis current loop. In this paper, we mainly design controller and disturbance compensator for speed loop and \(i_{q}\) loop.