esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 CFD Simulation
### Numerical Grids and Boundary Conditions  
A hybrid grid was established, including a tetrahedral unstructured mesh between the model room and outfield boundary and a hexahedral structured grid inside the model room. To ensure the accuracy of the numerical simulation and simplify the complexity of the simulation calculation, the grid spacing in the model room was 0.1 m, and the grid spacing from the model room to the outfield changed from 0.1 m to 1 m at a rate of 1.1. As the high Reynolds number k-\(\varepsilon\) model and standard wall function method were used in  
Figure 5: Geometric models: (**a**) CV and (**b**) SV.  
this study, the grid near the wall was not encrypted, but the first node was arranged in the region where turbulence was fully developed [38]. The total numbers of cells in the CV and SV grids were approximately 1,910,000 and 1,610,000, respectively. A grid independence analysis was carried out by increasing the numbers of cells to 4.77 million and 4.01 million for the CV and SV grids when the window is the CWO, respectively. Simulation results based on the two numbers of cells in the grids showed that there were negligible differences in the ventilation rate, confirming that the grid independence test was satisfactory.  
The boundary conditions are shown in Figure 6. For the CV model, the velocity-inlet boundary condition was applied to the inlet surface of the outfield. The velocity of the outfield was fixed at 2.2 m/s according to the average wind speed in the transition season in Tianjin. The outflow boundary condition was applied to the outlet surface of the outfield. A symmetrical boundary condition was applied to the sides and top of the outfield. The bottom of the outfield and the wall of the room were set as the wall boundary condition. For the SV model, the boundary conditions of the outlet surface, sides, and top of the outfield were the same as those of the CV model. The bottom of the outfield and the wall and floor of the room were assigned a wall boundary condition with a fixed temperature. The velocity of the outfield was set to be 0.1 m/s to form a flow and realise the ventilation process. In the simulation validation, the measured value of the average outdoor air temperature was taken as the outfield inflow temperature boundary condition, and the measured values of the wall temperature of the room under different conditions were taken as the wall temperature boundary condition. According to the relevant research, when the outdoor air temperature was higher than 10 \({}^{\circ}\)C and the indoor temperature was higher than 22 \({}^{\circ}\)C, the window opening probability of occupants began to increase significantly [42]. Therefore, in further simulations, the temperature of the outfield inflow and the bottom of outfield was set to 10 \({}^{\circ}\)C, and the wall and floor temperatures of the room were set to 22 \({}^{\circ}\)C. For the velocity-inlet boundary condition, the method of turbulent intensity and viscosity ratio was specified. The turbulent intensity was 5%, and the turbulent viscosity ratio was 10.
## 4 Test Results and Discussion
### The Effect of Soil-Caisson Kinematic Interaction
#### 4.3.2 Effect on the Rotation Component
These observations show that the rotational response of the caisson foundation is lower than the free-field \"pseudo-rotation\" response, indicating the influence of the kinematic interaction. This finding aligns with the conclusions obtained from Figure 8a. Moreover, the amplitude of the transfer function \(\left|H_{cp}(\omega)\right|\) under the Chichi wave is smaller than that under the Kobe wave, which indicates that the transfer function is influenced by the dominant frequency of the table input wave. Additionally, as the peak acceleration of the table input wave increases, the value of the transfer function decreases, implying a greater discrepancy between the caisson rotation \(\theta_{cr}\) and the free-field \"pseudo-rotation\" \(\theta_{pr}\). The impact of the dominant frequency and peak acceleration on the transfer function is also due to their influence on the contact stiffness between the caisson foundation and the surrounding soil, as described in Section 4.3.1 affecting the propagation of the outgoing waves emanating from the oscillating caisson foundation. In this section, the relationship between the caisson foundation motion and the free-field ground motion is quantified by means of the horizontal transfer function \(\left|H_{gf}(\omega)\right|\) and the rotation transfer function \(\left|H_{cp}(\omega)\right|\), thus illustrating the influence of the SCDI effects on the motion of the caisson foundation, in which both transfer functions show frequency dependence, with amplitudes less than one, and the horizontal transfer function is close to unity at low frequencies, and the amplitude of the horizontal transfer function shows a tendency to decrease with increasing frequency, whereas the rotation transfer function amplitude starts from zero and increases with increasing frequency.
## 6 Numerical Results
### Impact of the Efficiency-Stability Coefficient \(\alpha\) on the Schedules
Then, it will be disrupted by the new jobs' arrival, with an appearance frequency respectively equal to 0.2, 0.5 and 0.6. Averages are presented in Table 9.  
The increase of \(\alpha\) helps the _TWWT_, since its average value decreases with \(\alpha\). However, the average of _TWCTD_ increases with \(\alpha\) since the stability has less priority. This behavior is more observed when \(p_{\theta}\) increases, i.e., when the number of appearing jobs increases. In the case when the system is more disrupted by the new jobs arrivals, the possibility \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\alpha\) & **Initial Schedule** & **(1)** & **(2)** & **2 nd Schedule** & **(1)** & **(2)** & **3 rd Schedule** & **(1)** & **(2)** \\ \hline
1 & & & & C-F-A-E-D-B & 42 & 11 & C-F-A-G-E-D-B & 49 & 17 \\
0.9 & & & & C-A-F-E-D-B & 42 & 6 & C-A-F-G-E-D-B & 49 & 12 \\
0.8 & C-A-E-D-B & 31 & - & C-A-F-E-D-B & 42 & 6 & C-A-F-G-E-D-B & 49 & 12 \\
0.7 & C-A-E-D-B & & & C-A-F-E-D-B & 42 & 6 & C-A-F-E-G-D-B & 50 & 9 \\
0.6 & & & & C-A-F-E-D-B & 42 & 6 & C-A-F-E-D-G-B & 51 & 7 \\
0.5 & & & & C-A-F-E-D-B & 42 & 6 & C-A-F-E-D-B-G & 52 & 6 \\ \hline \(\alpha\) & **4 th schedule** & **(1)** & **(2)** & **5 th schedule** & **(1)** & **(2)** & & & \\ \hline
1 & C-F-A-G-H-E-D-B & 63 & 29 & C-F-A-G-H-I-E-D-B & 84 & 47 & & & \\
0.9 & C-A-F-G-H-E-D-B & 63 & 24 & C-A-F-G-H-I-E-D-B & 84 & 42 & & & \\
0.8 & C-A-F-G-H-E-D-B & 63 & 24 & C-A-F-G-H-I-E-D-B & 84 & 42 & & & \\
0.7 & C-A-F-E-G-H-D-B & 66 & 15 & C-A-F-E-G-H-I-D-B & 90 & 24 & & & \\
0.6 & C-A-F-E-D-G-H-B & 69 & 9 & C-A-F-E-D-G-H-I-B & 96 & 12 & & & \\
0.5 & C-A-F-E-D-G-H-B & 69 & 9 & C-A-F-E-D-G-H-B-I & 99 & 9 & & \\ \hline \hline \end{tabular} (1): _TWWT_ (2): _TWCTD_  
\end{table} 
Table 8: The first five jobs disposition. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \(p_{\theta}\) = 0.2 & & & \(p_{\theta}\) = 0.5 & & & \(p_{\theta}\) = 0.6 & \\ \hline \(\alpha\) & **(1)** & **(2)** & **(3)** & **(1)** & **(2)** & **(3)** & **(1)** & **(2)** & **(3)** \\ \hline
0.5 & 60.9 & 9.4 & 35.15 of having high weight jobs increases, as well as the possibility to delay the low weight jobs. Hence, the increase of their weights in function of time is judicious. Other tests have been performed with \(p_{\theta}\) exceeding 0.6, but the MILP fails to solve the problem in a reasonable time.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 2 Blending Filter-Based Thruster Fault Detection  
### Equations and Model  
Ignoring the installation gap error of the thruster, the following can be assumed:  
* The RLV can be treated as an ideal rigid body and any elastic vibration can be ignored;
* The dynamic characteristics of the navigation system can be ignored, and the feedback state of the vehicle can be regarded as the ideal state.  
For longitudinal attitude control, it can be assumed that \(\beta\approx 0,\ \varnothing\approx 0\), and, ignoring the earth's rotation, the flight dynamics equations [21] can be written as follows:  
\[\left\{\begin{array}{l}\dot{\mathbf{h}}=V\sin(\theta-\alpha)\\ \dot{\mathbf{V}}=-\frac{D}{m_{b}}-\frac{\mu\sin(\theta-\alpha)}{r^{2}}\\ \dot{\mathbf{\alpha}}=q-\frac{1}{m_{b}V}+\frac{(\mu-V^{2}r)\cos(\theta-\alpha)}{ V2r}\\ \dot{\mathbf{\theta}}=q\\ \dot{\mathbf{q}}=\frac{M}{y}\end{array}\right., \tag{1}\]  
where \(h\) is the altitude of the flight vehicle, \(V\) is the velocity, \(\alpha\) is the angle of attack (AoA), \(\theta\) is the pitch angle, \(q\) is the pitch rate, \(m_{b}\) is the mass of the flight vehicle, \(L\) and \(D\) are the lift force and drag force, respectively, \(\mu\) is the product of the gravitational constant and the mass of the Earth, \(r\) is the distance from the flight vehicle to the center of gravity, \(I_{y}\) is the moment of inertia, and \(M\) is the pitch moment that is the input and is induced by the thruster.  
The thruster valve is normally used to control the thrust from the thruster. When the thruster valve is open, the thrust produced by the thruster is not adjustable, so according to the thruster valve switching state, the fault-free thruster has only two states: normal-rated thrust and no thrust. The mathematical description of this can be written as follows:  
\[F_{i}=\left\{\begin{array}{l}0,U=0\\ F,U=1\end{array}\right., \tag{2}\]  
where \(F\) is the normal rated thrust of the thruster, \(U\) is the switch command of the thruster valve that represents the control input, \(0\) is the command to turn off the thruster valve, and \(1\) is the command to turn on the thruster valve. In this paper, the control signal refers to the physical signal corresponding to the switch command of the thruster valve (control input).
## 3 Gaps in the Literature  
From the literature review in the previous section, few studies have focused on objectively measuring FD in OAs using sensors. Most of the studies available in the literature focus on collecting sensor data to assess or monitor cognitive decline rather than physical/functional decline. Additionally, previous research using sensors to assess SI have generally used only one sensor or modality to collect data from participants to track and monitor their activities of daily living. The single modality approach leads to one-dimensional (view of) data, which may not be sufficiently representative to assess SI and FD. To comprehensively assess SI and FD, a more holistic approach is required that can capture different types of data based on mobility, physiological indicators, indoor and outdoor motion, sleep, and social behaviors [17]. A suggested approach [17] is using multiple or multimodal sensors for collecting objective data from participants to comprehensively monitor a multitude of daily routines and behaviors. This multimodal sensor approach ensures more reliable data collection, as data are gathered from multiple sensors, reducing the risk of data loss due to sensor malfunction. Further, it can provide complementary information from different sensors, enhancing our understanding of SI and the functional recovery of OAs, and facilitating the development of generalizable predictive models in the future.  
Most studies have collected sensor data over a short duration, typically around one week, to monitor patients for SI [17]. This duration may be insufficient, as SI does not change drastically over time; it is not an instant event (i.e., a fall) [17]. Long-term studies spanning several months are more beneficial, as SI develops gradually. To enhance the accuracy of assessments and predictive algorithms, various types of objective data can be collected, including social behaviors, sleep metrics, mobility, indoor motion, GPS data, step count, and physiological data (e.g., heart rate). The aforementioned data types are aligned and correlated with the definitions of SI and FD and capture patients' indoor and outdoor behaviors, sleep patterns, and physiological and mobility indicators.  
The aim of this current study was to investigate the correlations between SI and FD using various clinical and sensor data, including sleep patterns, physiological indicators, mobility, and social interactions. Collecting data from various sensing modalities is crucial for understanding the complex behavioral, physiological, and social dynamics of OAs recovering from lower limb fractures in the community. To address this need, our team employed MAISON (Multimodal AI-based Sensor Platform for Older iNdividuals), a multimodal sensor system designed to collect data from multiple modalities [29]. The multimodal data are encrypted and stored in a private cloud [30]. This work is the foundational step to inform a clinically validated system to evaluate and assess OAs' social and physical mobility activities.
## 4 Numerical Implementation  
### Discretization and Computation Processes  
In this work, numerical implementation is realized in Matlab using self-compiled PD code, which can be simply described in the flow chart (seen Figure 5). The processes can be mainly divided into two parts: discretization and computation.  
Figure 5: Flowchart of the numerical implementation procedure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & \(g(\xi,\delta)\) & **Symbol** \\ \hline Constant [24; 37] & \(1\) & \(g_{1}\) \\ Exponential [38] & \(e^{-\frac{\xi}{2}}\) & \(\delta 2\) \\ Gaussian [39] & \(e^{-(\frac{\xi}{2})^{2}}\) & \(\delta 3\) \\ Semi-elliptical [38] & \(1-(\frac{\xi}{2})^{2}\) & \(\delta 4\) \\ Quartic polynomial [33] & \(1-(\frac{\xi}{2})^{4}\) & \(\delta 5\) \\ Parabolic [16] & \((1-(\frac{\xi}{2})^{2})^{2}\) & \(\delta 6\) \\ Sixth-order polynomial [26] & \((1-(\frac{\xi}{2})^{2})^{3}\) & \(\delta 7\) \\ Cosine [26] & \(\cos(\frac{\pi\xi}{2\delta})\) & \(\delta 8\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Eight types of influence functions.  
The discretization process starts with discretizing the object into material points with the uniform spacing, \(\Delta x\), and the governing equation in integral form (Equation (1)) can be converted into a finite sum form, as re-expressed as  
\[\begin{array}{rl}\rho_{i}\ddot{u}_{i}{}^{n}&=\sum\limits_{j=1}^{P}f(u_{j}{}^{n }-u_{i}{}^{n},x_{i}-x_{j})\Delta\overline{v}V_{ij}+b_{i}{}^{n}(x_{i},t)\\ &=\sum\limits_{j=1}^{P}f(\eta_{ij}{}^{n},\xi_{ij})\Delta\overline{v}V_{ij}+b_{ i}{}^{n}(x_{i},t)\hskip 28.452756 pt\forall x_{j}\in H_{x_{i}}\end{array} \tag{19}\]  
in which \(n\) represents the \(n\)-th time step, \(p\) denotes the amount of points in \(H_{x_{i}}\), \(u_{i}{}^{n}=u(x_{i},t=n\Delta t)\) and \(b_{i}{}^{n}(x_{i},t)\) stands for volume force density due to external load. \(\Delta\overline{v}V_{ij}\) designates the effective computational volume of \(x_{j}\) in \(H_{x_{i}}\) and \(\Delta\overline{v}\) is the volume correction factor as expressed in Equation (20).  
\[\Delta\overline{v}=\left\{\begin{array}{ccc}1&&\|\xi+\eta\|\leq\delta- \frac{\Delta\overline{v}}{2}\\ (\frac{\delta-\|\xi+\eta\|}{\Delta\overline{v}}+\frac{1}{2})&&\delta-\frac{ \Delta\overline{v}}{2}<\|\xi+\eta\|\leq\delta\\ 0&&else\end{array}\right. \tag{20}\]  
The computation process is the core part, which is accomplished by time step integration. The bond force and elongation are gained from calculating the displacement and velocity of points, so as to analyze the state of bonds and the point damage in each time step. For quasi-static problems, a load increment algorithm based on fictitious density is proposed (seen Step 8), which will be described in detail in Section 4.2.
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 3 Experiments
### Experiments Conducted on Manned Airborne SAR Images
#### 3.2.2 Experimental Results  
Different GCPs were selected to conduct accuracy comparison tests for the above models. For the airSAR-X-1 image, GCPs were chosen in quantities of zero, one (labeled C01), three (C01 to C03), six (C01 to C06), twelve (C01 to C12), and nineteen (LOOCV). For the airSAR-X-2 image, GCPs were selected in quantities of zero, one (D01), three (D01 to D03), six (D01 to D06), twelve (D01 to D12), and sixteen (LOOCV). The above five types of R-D refinement models were realized using different numbers of GCPs, and the geolocation accuracies of CPs for each model are indicated in Tables 3 and 4.  
Figure 4: Distribution of reference points and parts of airSAR-X images. (**a**) Distribution of reference points in the airSA-X-1 image. (**b**) Part of an airSAR-X-1 image. (**c**) Distribution of reference points in the airSA-X-2 image. (**d**) Part of an airSAR-X-2 image. The numbers in Figure (**a,c**) are serial numbers of the reference points.  
For the original, unrefined R-D model, the airSAR-X-1 image orientation accuracies regarding range/azimuth were 514.85/4.83 pixels, and the mean error absolute values for all CPs were 514.85/4.77 pixels. The airSAR-X-2 orientation accuracies were 514.14/4.42 pixels, and the mean absolute values were 514.14/4.39 pixels.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 1 Introduction  
### Motivation and Background  
The introduction of ever-increasing amounts of intermittent renewable generation, coupled with the increasing electrification of European societies, leads to an increased strain on the power grid and its operation [1; 2; 3]. In order to maintain high security of supply, it is paramount to evolve the tools used for power systems operations [4]. One such tool would be the ability to predict undesired events with sufficient prediction horizon to facilitate mitigating actions [5; 6; 7].  
The development of such tools is encouraged by recent advancements in data-driven techniques, machine learning (ML), available data volumes, and computational resources [8; 9; 10]. These algorithms can derive insights from data without being explicitly told what to look for in the vast data steams [11; 12], which is particularly beneficial in the domain of power system fault prediction. An explicit detailed modeling of the power system is cumbersome and would not encapsulate conditions the modeled does not know about, that could lead to faults, such ascing on transmission lines, faults in critical components or reoccurring abnormalities.  
Data driven methods are only as good as the data they rely on, and only have the capability to predict situations that the model have been trained on [13; 14; 15]. In a best case scenario, the models are trained on a complete and large dataset, and it can rely on theautomatic tuning of model parameters [16; 17]. This is, however, often not the case in real word applications. In the case of fault prediction in the power system, the number of faults occurring are very small compared to normal operating conditions [18].  
To achieve high performance with data driven methods, the analyst must therefore pre-process the data--essentially guiding the algorithms in selecting their focus. This type of pre-processing includes dimensionality reduction, feature selection, feature engineering, and rescaling of features and prediction targets [19; 20]. While there are aspects of an art (or, more precisely, intuition based on experience and domain knowledge) to these activities, they depend on an understanding of the behaviour of the underlying power system.  
This paper seeks to establish (aspects of) the statistical foundation of the behaviour of the power system at the levels of transmission and distribution, cf. Figure 1. By establishing the statistical and temporal behaviour of cycle-by-cycle voltage harmonics from three sites in the Norwegian grid, we derive implications for the data-driven modelling of power grid events. Although the data are sourced from the Norwegian grid, we expect results to apply to other national grids.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 2 Related Work
The source node stores the sequence numbers of the last packets sent and the last packets received in two separate tables. When the source node receives an RREP packet, it compares the sequence number in this packet with the stored numbers. If there is a match, the source node starts data transmission; otherwise, it will classify the RREP packet as malicious. The source node will send an alarm message to the other nodes to block the malicious node. [PERSON] and [PERSON] [31] designed a solution that adds an additional task for the nodes: checking the RREP packet before accepting it. When a node receives an RREP packet, it checks if the sequence number is higher than a threshold value. If the sequence number of the RREP packet is higher than the threshold, it is classified as malicious and the node that sent it is added to a blacklist. When a node has detected a malicious node, it sends an alarm packet to inform all neighbouring nodes. This solution has been found to increase the overhead. [PERSON] et al. [32] suggested a solution that entails analysing the RREP packets received. When a source node receives the first RREP packet, it does not react to it immediately but waits for some time to receive multiple RREP packets and then saves them in a table. The source node then analyses the stored RREP packets in the table and rejects those with very high sequence numbers. The nodes that sent these packets are marked as malicious. The source node will arrange the remaining RREP packets according to the sequence numbers of their destination nodes, and the one with the highest number will be selected. This mechanism increases the end-to-end delay as the source node has to wait some time to receive multiple RREP packets. [PERSON] [33] proposed a cluster-based trust mechanism where data are transmitted securely using encryption. The objective is to identify nodes which produce high volumes of traffic as anomalous, but measuring not only the number of packets, but also their quality. Comparison is made against Energy-Aware AODV and Secure AODV and shows a slight improvement in performance.