esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 3 Experiments and Results
### Synthetic Data
#### 3.1.1 Robustness to Roll Angle Noise  
Our proposed method uses the roll angle as the prior knowledge, and this is different from the P3P solver and RPnP solver when the performance of all the three methods is tested. In practice, the value of the roll angle is given by IMU and is not absolutely accurate. Hence, the roll angle noise might affect the accuracy of our proposed method and it is essential to analyze the robustness to roll angle noise.  
For synthetic data, we denote the three angles of orientation as \(a_{x}\), \(a_{y}\), and \(a_{z}\). When the orientation estimation is finished using our proposed method, we obtain three angles of the orientation \(a_{x}^{\prime}\), \(a_{y}^{\prime}\), and \(a_{z}^{\prime}\). Here \(a_{y}^{\prime}\) is the roll angle noise. Then, we calculate the total rotation error, rotation error around the \(X\)-axis, and rotation error around the \(Z\)-axis as follows.  
\[\begin{split}& E_{rotation}=\sqrt{\left(a_{x}-a_{x}^{\prime} \right)^{2}+\left(a_{y}-a_{y}^{\prime}\right)^{2}+\left(a_{z}-a_{z}^{\prime} \right)^{2}}\\ & E_{x-axis}=\left|a_{x}-a_{x}^{\prime}\right|\\ & E_{z-axis}=\left|a_{z}-a_{z}^{\prime}\right|\end{split} \tag{22}\]  
The roll angle is given by IMU, and its accuracy is better than 0.1\({}^{\circ}\)[50]. When we test the robustness to roll angle noise (i.e., noise in rotation around \(Y\)-axis), zero-mean Gaussian noise is added onto the roll angle and the noise deviation level varies from 0 to 0.1\({}^{\circ}\). Then, 50,000 trials are independently performed at each level of noise with unchangeable deviation. Then, the mean errors of the total rotation, and rotation around the \(X\)-axis and \(Z\)-axis, respectively, at each level of noise are reported in Figure 3.  
From Figure 3, it can be seen that as the noise in rotation around the \(Y\)-axis increases, so does the total rotation error, rotation error around the \(X\)-axis, and rotation error around the \(Z\)-axis. When the noise in rotation around the \(Y\)-axis increases to 0.1\({}^{\circ}\), the maximum error is 0.13\({}^{\circ}\) for the total rotation, 0.051\({}^{\circ}\) for the rotation around the \(X\)-axis, and 0.048\({}^{\circ}\) for the rotation around the \(Z\)-axis. The maximum errors show that the performance degradation caused by the roll angle noise is very slight, which means our proposed method has a good robustness to roll angle noise.
# Increasing Resilience of Production Systems by Integrated Design
## 2 Materials and Methods
### Rapid Response to Changing Requirements Using Skill-Based Control Systems and Virtual Commissioning
Figure 10 shows the timeline for the conventional procedure for the development of a system and in comparison, the timeline for the same procedures with virtual commissioning. For virtual commissioning, machine and plant models are developed that correspond to their real counterparts in terms of their interfaces, parameters, and operating modes. With the help of these virtual systems, realistic test and commissioning situations can be simulated, including all control functions, whereby the control system can be operated and examined on the digital twin in the same way as on the real machine. Although domain-specific behavioral models are occasionally used today in the production phase for control and regulation functions, the comprehensive system simulation models from engineering are usually not adopted in the real operating phase. In the context of the resilience of production systems, these models in combination with the possibilities of the virtual commissioning contributes to various aspects of resilience enhancement. Increasing the resilience of production systems not only requires the early recognition of the internal and external events that lead to disruptions in production processes, but also strategies to respond to them flexibly. For example, it is necessary to evaluate a suitable alternative in the event of a production equipment failure. In addition to manufacturing products of the same quality, the objective may also involve meeting deadlines or changing input materials, which can lead to necessary changes in the production processes. The adaptation of the production system must be planned and examined for its feasibility. If the machinery of a component manufacturer, for example, is virtualized, a virtual commissioning system can be used to assemble machines and systems from a library [33]. With this modular  
This way, it is possible to react much more quickly in the event of an occurrence and thus shorten the downtimes of production systems. Today, up to two thirds of the control software for manufacturing systems is used for error detection and handling [34]. The testing of the methods used for this can only take place after the hardware has been realized. Only then can the corresponding errors be provoked, and the reaction of the control system can be validated. In addition, errors that lead to the destruction of the system, impairment of the machine environment, or even injury to the machine operator cannot be checked on the real system. With the digital twin, on the other hand, it is possible to examine these error patterns in an early phase of the system development, even before its realization, and to incorporate the gained knowledge into the development of the system components and control system. In this way, the robustness of systems in the face of faults can already be increased during their development.
# Recurrent Neural Network Based Short-Term Load Forecast with Spline Bases and Real-Time Adaptation
## 2 Methodology
### Real-Time Adapted Forecasting  
The increasing use of renewable power sources has produced an increase in intermittency and a ramping in the net load profile that requires additional control efforts to maintain frequency quality. For a complete treatment for STLF, we also provide a real-time adaptive STLF procedure to help system operators with a detailed view into the real-time power system condition, so as to aid in their decision making. A quasi-real time RNN-based forecasting model (RNN\({}_{adp}\)) with the objective of providing short-term load forecasts is described below.  
1. Load Forecasts Interpolation  
In the first stage, the STLF results are interpolated to be a sequence with values in every 5-min period. The Cubic Hermite interpolation method is used to produce the load forecasts at 5-min intervals. This real time load data at 5-min intervals is then used in the second step to adaptively adjust the forecasting results.  
2. Adaptive Load Forecasting  
The correction value is the average difference between the actual and the forecasted load values in the past 15-min interval. In other words, it is the average of the three differences of the actual and forecasted load values calculated at 5-min intervals.  
3. Exponentially Weighted Average  
Finally, we use the EWMA to smooth the correction result. The exponential smoothing is given by the formula:  
\[\widetilde{y_{i}}=0.8y_{i}+0.2\widetilde{y_{i-1}} \tag{15}\]  
where \(y_{i}\) is the \(i\)th corrected value.
## 2 Material and Methods
### Path Planning Method Based on the Coupling of Flood and Road Network Information  
To determine the shortest path in the flood-road network information coupling model, the Dijkstra algorithm is employed [29; 30]. Starting from a designated origin, the algorithm follows a greedy approach to traverse the network, calculating the closest neighboring node to the current node while excluding previously visited nodes. The process terminates once the destination node is reached. The specific implementation steps are as follows:  
Step 1: Construct the flood evolution simulation model for the reservoir dam breach. Using the FLOW-3D v.11.2 flood evolution simulation and FlowSight post-processing, gather essential data such as the downstream flood depth distribution, the flow velocity distribution, the inundation extent, and the arrival time of the flood front.  
Step 2: Convert the road network into a mathematical model suitable for the Dijkstra algorithm using graph theory. Integrate the downstream flood evolution data with the transportation network, thereby constructing a coupled road network model that incorporates flood evolution information.  
Step 3: Extract the dynamic failure process of the road network from the flood-road network information coupling model. By incorporating the flood front arrival time, divide the road network failure process into several characteristic periods. For each period, reconstruct the road network topology and update the corresponding adjacency matrix.  
Step 4: Using the downstream flood evolution data, population distribution, and terrain information, identify the starting point \(s\) (affected area) and the destination \(e\) (shelter) for path planning. Load the appropriate road network adjacency matrix and apply the Dijkstra algorithm to optimize the shortest path, determining the optimal route to the destination for each time period.  
Step 5: Finalize the path planning process and develop an evacuation plan for the affected populations downstream of the dam breach.  
The implementation process of the path planning method based on the coupling of flood and road network information is illustrated in Figure 1.
# Wavelet Packet Method for Locating Critical Slip Surface Using the Strength Reduction Method
## 3 Wavelet and Wavelet Packet Transformation and Reconstruction
### Threshold Processing of Wavelet Packet Decomposition Coefficients  
After selecting the optimal wavelet packet tree, we can use a threshold to remove the noise in wavelet packet nodes. There are many threshold denoising methods in practice.  
The commonly used threshold functions are soft threshold and hard threshold, and the calculation formulas are as follows:  
Soft threshold:  
\[\hat{w}_{j,k}=\left\{\begin{array}{cc}\text{sgn}\Big{(}w_{j,k}\Big{)}\Big{(} \Big{|}w_{j,k}\Big{|}-\lambda\Big{)}&\Big{|}w_{j,k}\Big{|}\geq\lambda\\ 0&\Big{|}w_{j,k}\Big{|}<\lambda\end{array}\right. \tag{16}\]  
Hard threshold:  
\[\hat{w}_{j,k}=\left\{\begin{array}{cc}w_{j,k}&\Big{|}w_{j,k}\Big{|}\geq \lambda\\ 0&\Big{|}w_{j,k}\Big{|}<\lambda\end{array}\right. \tag{17}\]  
Based on Matlab software, we can use the \"ddencmp.m\" function to obtain the default threshold value of the signal data. The methods to obtain these values include the soft and hard threshold methods. We can also use the \"wpthoef.m\" function to deal with the threshold values of wavelet packet coefficients [57].
# Image Storage in DNA by an Extensible Quaternary Codec System
## 4 Discussion
The development of efficient, cost-effective quaternary error correction codes that address the unique challenges of DNA storage systems remains an important area for future research [22]. Future research can explore more quaternary-based encoding methods, building upon the ETQ framework. By synthesizing and sequencing the same dataset multiple times, researchers can analyze sequencing reads to identify patterns and correlations between DNA sequences and errors. This could inform updates to the 5-to-6 mapping scheme, enabling the selection of more stable and efficient base combinations. The ETQ system adopts a single-function encoding approach with clearly encapsulated modules, offering excellent extensibility and flexibility. Its modular design allows for the easy modification and replacement of components, as well as integration into larger systems. For instance, future studies could implement multi-layered encoding schemes on top of the quaternary Huffman coding framework, while simultaneously improving error correction strategies to further enhance storage density and error resilience. In terms of biotechnology, the development of DNA synthesis and sequencing techniques specifically tailored for DNA storage is essential. These advancements could improve synthesis and sequencing efficiency, significantly reducing the frequency of errors other than insertions. Additionally, designing reliable DNA storage systems requires careful consideration of data volume and sequencing technology to determine optimal encoding schemes and sequencing depths, ensuring accurate and robust data storage and retrieval.
# Numerical Modelling of a Floating Wind Turbine Semi-Submersible Platform
## 3 Results and Discussion
### Decay Test with Moorings  
To have more data to compare and to test the quality of the meshes, a decay test with mooring lines was performed. This gives us the possibility to, once again, comparing our simulations with the experimental tests and also to testing the same uncertainty with different numbers of DoFs active but with lower impact due to the existence of the mooring lines.  
Thanks to the mooring lines behaving symmetrically as springs for this particular set of tests, we were able to keep the symmetry plane in the computational tests. The lines are positioned with a gap of 90 degrees between them and are displaced 45 degrees to the x-axis. Moreover, the lines are parallel to the XY (horizontal) plane.  
The springs are connected to the same point of the platform as in the experimental tests and to the curved wall of the volume of study in the simulations, with the sole aim of maintaining the same position in relation to the platform.  
Because of the existence of the mooring lines in the experimental tests, which work to maintain the platform in the desired position, a lower effect of the DoFs that are locked in the simulations is observed, showing much better matching between the experimental and computational tests.  
Figure 9 shows the comparison over time of the decay test with mooring lines, in which a closer relationship between the two lines can be clearly seen. Moreover, the experimental signal keeps decreasing in the third period, which may be related to a decrease in the influence of the effect of roll in these tests.  
The computational signal maintains the same period throughout all the experiments, with a negligible error in the period of less than 1%, a value that can be seen in the decay tests without moorings as were shown previously in Figure 6.  
The tendency for larger amplitudes in the computational runs is again reproduced. The damping factor study is done for this type of decays, showing a much better relation than in the runs without moorings.  
Figure 10 shows that both fittings are much closer than in the comparisons without moorings. The slope of the experimental test is 0.0155, while that of the computational test  
Figure 8: Damping factor comparison with the data from the first periods in order to delete the more significant effect of DoFs that were not unlocked in the computational tests.  
is 0.022. This is contrary to the tendency found for the case without moorings, however, which shows less total damping because it stays below the experimental fit.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 3 System Model  
### Environment and Assumption  
In this section, we describe the environment and assumptions made in this work. There are \(n\) vehicles in the network and each one has a unique Veh. ID, i.e., a vehicle set \(V=\{v_{1},\ v_{2},\ldots,v_{n}\}\). Moreover, there are \(x\) road segments in the network and each one has a unique Road ID, i.e., a road set \(R=\{r_{1},\ r_{2},\ldots,r_{x}\}\). Load length and the number of lanes of a road \(r_{i}\) are denoted as \(L_{i}\) and \(LA_{i}\), respectively. We make the following assumptions in this paper. Firstly, there are no roadside units (RSUs) in the network. Hence, V2V communications are the only data dissemination mechanism. Secondly, a multifunctional on-board unit (OBU) with IEEE 802.11p functionality is equipped by each vehicle. The OBU also has a GPS receiver and a digital map which allows a vehicle to be continually aware of its own geographic position and surrounding street organization. Thirdly, a source vehicle that initiates a data packet is assumed to be aware of the destination vehicle's position. The location management issue is out of the scope of this paper and can be supported by Location Services [29]. Finally, periodical beacon messages broadcasted by vehicles comprise of their current driving information, including Veh. ID, position, direction, and Road ID.
## 4 Results and Analysis
### Results of High-Temperature Rutting Test  
High-temperature rutting tests were carried out on the heat-recycled asphalt mixture without RAP (NHMA); the heat-recycled asphalt mixture with 40% RAP (40%HMA); the waste engine oil, recycled asphalt mixture with 40% RAP (40%RHMA), and the carbon-nanotube-modified, waste engine oil, recycled asphalt mixture with 40% RAP (40%RHMA-1C, 40%RHMA-1.5C and 40%RHMA-2.5C). The results are shown in Table 5.  
Figure 6: (**a**) Stability results of 40%RHMA-1.5C; (**b**) flow value diagram of 40%RHMA-1.5C; (**c**) gross volume density of 40%RHMA-1.5C; (**d**) void ratio results of 40%RHMA-1.5C; (**e**) voids in mineral aggregate results of 40%RHMA-1.5C; (**f**) voids filled with asphalt results at 40%RHMA-1.5C.  
As can be seen from Table 5, the _DS_ of 40%HMA was much larger than that of NHMA. This is because the old asphalt binder in RAP increased the hardness of the recycled asphalt mixture, thus improving the high-temperature performance of 40%HMA. After adding the waste engine oil to 40%HMA, the waste engine oil restored some of the rheological properties of the old asphalt binder. The waste engine oil softened the old asphalt binder and thus reduced the high-temperature stability of 40%RHMA and made it more similar to NHMA. Due to the good mechanical strength of carbon nanotubes, the addition of carbon nanotubes to 40%RHMA increased the high-temperature deformation resistance of the recycled asphalt binder between aggregates. Carbon nanotubes played a \"reinforcing\" role\" between the waste engine oil, recycled asphalt binder and could effectively enhance the high-temperature stability of the waste oil, recycled asphalt mixture. Among them, 40%RHMA-1.5C showed the greatest improvement in high-temperature rutting resistance, which increased by 24.3% compared with 40%RHMA. However, the high-temperature stability of the waste engine oil, recycled asphalt mixture would have been adversely affected if too many carbon nanotubes were added. The rutting plate samples after the high-temperature rutting test are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(d_{1}\) **(mm)** & \(d_{2}\) **(mm)** & _DS_ **(times/mm)** \\ \hline NHMA & 3.5 & 3.9 & 1575 \\
40\%HMA & 1.9 & 2.09 & 3204 \\
40\%RHMA & 2.33 & 2.48 & 2440 \\
40\%RHMA-1C & 2.28 & 2.53 & 2515 \\
40\%RHMA-1.5C & 1.94 & 2.15 & 3034 \\
40\%RHMA-2.5C & 2.14 & 2.38 & 2633 \\ Specification & - & - & \(\geq\)1000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of high-temperature rutting test.  
Figure 7: (**a**) NHMA rutting plate sample after high-temperature rutting test; (**b**) 40%HMA rutting plate sample after high-temperature rutting test; (**c**) 40%RHMA rutting plate samples after high-temperature rutting tests; (**d**) 40%RHMA-1.5C rutting plate samples after high-temperature rutting test.
# Chronotype, Risk and Time Preferences, and Financial Behaviour
## Literature Review
### Conceptual Model and Hypotheses Development  
#### 2.2.1 Morning Larks and Delinquent Credit Card Payments (H1)  
The first goal of the present paper is to explore the relationship between morning-types and revolving credit card debt. In the literature, [PERSON] [33] reported that morning larks are prone to be more proactive than evening owls, which provides an inspiring starting point. In particular, proactivity is the willingness and capability to alter a situation to make it in one's favour [58], and it can be influential in a wide range of circumstances. For example, a proactive personality connects to job autonomy through self-efficacy [59]; proactive individuals attain more success in careers, earnings [60], and studying performance [61].  
Although few studies have examined the role of the circadian chronotypes in revolving credit card debt, by integrating the role of proactivity, it is reasonable to anticipate the negative association. On the one hand, morning-types are more proactive than evening-types, and on the other hand, proactivity describes the extent of which individuals anticipate, and minimize the negative influence of possible future problems, avoiding them completely if possible [62; 63]. At the same time, unpaid balances on credit cards represents such a negative potential problem that will increase consumers' interest rates if they do not pay off their monthly balances on time [64]. Therefore, we expect that morning larks are much less likely to be unable to pay off their monthly credit card balance entirely. Accordingly, we propose:  
**Hypothesis 1**.: _Morning-types are negatively associated with delinquent credit card payments. Specifically, compared to intermediate- and evening-types, morning larks are less likely to have unpaid credit card debt._
# Model for Determining Parking Demand Using Simulation-Based Pricing
## 3 Results and Data Analysis
### Simulations  
When simulating the number of parked vehicles in any category, STU, DMP, NMP, or CMP, all simulated demands will generally be denoted as \(Q_{sim}\) and calculated for simulated prices \(P_{sim}\) using the following:  
\[Q_{sim}\ =D\cdot e^{-\lambda\cdot P_{sim}}. \tag{21}\]  
The model assumes an exponential (non-linear) relationship between price and demand, with category-specific parameters calibrated from observed data. For natural demand categories, parameter \(\lambda\) is taken from (9), and for categories with chaotic demand, the parameter \(\lambda\) in (21) is applied as \(\mu\) calculated in (15), as explained in the following sections.  
For natural demand categories, the Formula (21) is calculated with the parameter \(\lambda\) from (9), and the parameter \(D\) is taken as \(D=N\) from (10). When simulating categories with chaotic demand, we also use (21), but now we take \(\lambda\) as \(\lambda=\mu\), and \(\mu\) is from (15). Parameter \(D\) is taken as \(D=K\), and \(K\) is from (18). Then, the elasticity (1) for function in (21) is negative in both simulating categories. Namely, simulating prices \(P_{sim}\) must be taken to ensure enough parking place according to condition (4).  
When simulating the number of users in any category, \(P_{sim}\) prices are again denoted as and calculated for the simulated number of users \(Q_{sim}\). The price that should be set for the corresponding simulated number of users is predicted with the following formula:  
\[P_{sim}=\frac{-1}{\lambda}.ln\frac{Q_{sim}}{D}, \tag{22}\]  
For normal demand, \(\lambda\) is from (9), and \(D=N\) from (10). If a chaotic change in demand is observed, the price is again proposed using (22), but now \(\mu\) from (15) is substituted in place of \(\lambda\) and \(D\) is replaced by \(K\) from (18).  
From the demand functions, natural (11) and chaotic (19), the total revenue function \(U(P)\) is obtained by multiplying price and demand:  
\[U(P)=P\cdot Q(P)=P\cdot D\cdot e^{-P\lambda}, \tag{23}\]  
where \(D\) and \(\lambda\) are substituted by the constants \(N,\ K,\) or \(\mu\) respectively. From this definition follows the Theorem on the price that maximizes revenue.  
The price that achieves maximum revenue, as defined by Formula (22) for normal demand, is as follows:  
\[P_{max}=\frac{1}{\lambda},\]  
and the maximum revenue is as follows:  
\[U\bigg{(}\frac{1}{\lambda}\bigg{)}=\frac{N}{\lambda e}.\]  
Proof.: A necessary condition for a maximum is that the first derivative of the function (16) with respect to the only variable equals zero:  
\[U^{\prime}(P)=N\cdot\Big{(}e^{-P\lambda}-\lambda Pe^{-P\lambda}\Big{)}=N\cdot e ^{-P\lambda}\cdot(1-\lambda P)=0. \tag{24}\]  
Thus, the statement of the theorem directly follows from (23), since \(N,\lambda\), and \(e^{-P\lambda}\) are positive, and if (24), then \(U^{\prime\prime}(P)=-\lambda\cdot N\cdot e^{-P\lambda}(1-\lambda P+1)<0\). The same applies analogously in the case of chaotic demand. \(\square\)