esa-sceva/satcom-chunk-collection huggingface dataset:
# Architectural Design for Digital Twin Networks
## 4 Functional DTN Implementation
### IRTF Mapping  
Our interpretation [16] of the Digital Twin Network was developed independently of the IRTF interpretation. However, there is a potential mapping between the two, as they  
Figure 2: Visual representation of the multi-faceted Digital Twin Network for Bluetooth Mesh.  
share many concepts, albeit with different terminology. Therefore, this section describes the mapping between the key elements of both interpretations.  
In our implementation, the application layer is a collection of diverse structures. It includes request-response queries, which are used to retrieve specific data points from the DTN, such as the current topology or the link quality between two nodes. Another component is recipes, which are essentially collections of queries designed to provide deeper insights into specific optimizations or configurations. Lastly, we have pipelines that group multiple recipes together; each of these pipelines is tailored for a specific application with the ultimate goal of optimizing outcomes. Hence, while we use the term recipe pipeline for application layer, both refer to the same concept, merely under different names. One of the key applications in our work is optimizing lighting performance, which is a central feature of Bluetooth Mesh. The focus of this pipeline is to minimize network delays to ensure that users do not perceive any noticeable lag, as delays above approximately 150 ms [32] are generally regarded as unpleasant. In addition to minimizing latency, the pipeline emphasizes reliability, ensuring that actions such as pressing a button result in immediate responses, such as the lighting turning on. Thus, the lighting performance pipeline is designed to optimize both responsiveness and reliability.  
The real network layer consists of a Bluetooth Mesh nodes, containing a single gateway that interacts with the DTN. This gateway forwards information from the DTN into the network and also forwards aggregated data back to the DTN. We use Message Queuing Telemetry Transport (MQTT) to bridge the gap between the application layer, the DTN and gateway, ensuring efficient and reliable data exchange across all components.  
The management of our DTN instance handles external requests and replies, catering to both applications and the network. Our manager serves as a combination of the data repository and management functions, streamlining the handling and storage of data. The data repository is an InfluxDB storage system where data can be extracted and temporarily stored as readable and interpretable files, enabling their use by models and Click simulators.  
Models within the DTN communicate with each other using these file structures, ensuring interoperability and efficient data flow. We have two main models at the center of our multi-faceted DTN: the Click simulator and the graph model.
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 4 Performance Evaluation
### Experimental Results  
Table 7 presents the experimental results. Here, for the training dataset, we used 86,400 samples, where 73,019 active states and 13,381 non-active states were detected.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameters** & \\ \hline \multirow{8}{*}{**Linear SVM**} & C & 10 \\  & class\_weight & None \\  & dual & True \\  & fit\_intercept & True \\  & intercept\_scaling & 1 \\  & loss & squared\_hinge \\  & max\_iter & 1000 \\  & multi\_class & ovr \\  & penalty & 12 \\  & random\_state & None \\  & tol & 0.0001 \\  & verbose & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **Observations** & **RMSE** \\ \hline Decision tree & 92\% & 75\% & 83\% & 86,400 & 0.3478 \\ KNN & 93\% & 95\% & 94\% & 86,400 & 0.2085 \\ Random forest & 96\% & 85\% & 90\% & 86,400 & 0.1813 \\ Linear SVM & 98\% & 99\% & 98\% & 86,400 & 0.0677 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model Comparison Result.  
From the table, we can observe that linear SVM achieved 98% accuracy in terms of F1 score in the classification of non-active states of machines, followed by kNN, random forest, and decision tree, which were only 94%, 90%, and 83%, respectively. We can also observe from the table that linear SVM also achieved the lowest error rate (i.e., 0.0677) compared with other methods. Linear SVM used two hyperplanes that separated the observations linearly, thus that there were no observations between them. Linear SVM performed better than other methods in our experiments as the dataset has been constructed using features that are suitable for creating linearly-separable observations. On the other hand, the random forest also performed well, which resulted in accuracy close to SVM in terms of precision. Random forest is a well-known knowledge-based ensemble method that overcomes the overfitting problem as well as errors due to bias in the decision tree and, therefore, yields high accuracy. As kNN is not a knowledge-based ensemble technique like the random forest, it achieves marginally less accurate results but still outperforms the decision tree in terms of precision, recall, and F1 score.  
Figure 8 demonstrates the visualized result of SVM. In Figure 9,b, we can see machine states for a single day. Here, the blue line indicates the actual machine state (in every second), and the red line indicates predicted machine states (in every second) using linear SVM. From the figures, it is clear that the prediction by linear SVM follows the actual machine state well. On the other hand, the result also suggests that there were some errors (e.g., approximately at 02:00, 04:00, or 05:30) in the prediction process. However, a closer look for these errors indicated that wrongly predicted observations were short-term (a matter of seconds) and, therefore, were insignificant to the overall prediction of non-active state predictions.
# A New Kinematic Model of Portable Articulated Coordinate Measuring Machine
## 3 Error Model and Calibration Algorithm
### Calibration Algorithm  
Calibration algorithm is supposed to as the main work for solving the generalized geometric error parameters in Equation (8). The selection of calibration algorithm lies in its convergence rate and identification efficiency. Although GA, PSO, modified SA and nonlinear least square method have been applied in calibrating the kinematic parameters of robot or PACMM, the above-mentioned  
Figure 4: Length gauge.  
calibration algorithms with respect to LM algorithm have slower convergence rate and poorer stability. Therefore, LM algorithm is selected as the calibration algorithm for calibrating the generalized geometric error parameters of PACMM. Its detailed calculation procedures are as follows:  
1. Set the initial estimation values vector \(\varepsilon^{(0)}\) of generalized geometric error parameters vector \(\varepsilon\), the initial damping factor \(\mu=\mu_{0}^{(0)}\), permissible error \(\varepsilon>0\), set the flag of the iteration step \(t=0\) and the growth factor \(\
u>0\), \(\
u=2,5\) or \(10\);
2. Calculate \(E^{(t)}=E(\varepsilon^{(t)})\), \(S^{(t)}=E^{(t)}T^{E}(t)\), \(J^{(t)}=\left[\frac{\partial E_{k}^{(t)}(\varepsilon^{(t)})}{\partial \varepsilon_{ij}}\right],(t=1,2,\cdots,n)\);
3. Solve \((J^{(t)}T^{(t)}+\mu^{(t)}I)\Delta\varepsilon^{(t)}=-J^{(t)}T^{E}(t)\);
4. Calculate \(\varepsilon^{(t+1)}=\varepsilon^{(t)}+\Delta\varepsilon^{(t)}\), \(E^{(t+1)}=E(\varepsilon^{(t+1)})\), \(S^{(t+1)}=E^{(t+1)}T^{E(t+1)}\);
5. If \(\|\Delta\varepsilon^{(t)}\|<\varepsilon\), vector \(\varepsilon^{(t)}\) is supposed to as the best estimate \(\varepsilon^{*}\), else \(t=t+1\);
6. If \(S^{(t+1)}<S^{(t)}\), then \(\mu^{(t+1)}=\mu^{(t)}/\
u\), go to Step 2, else \(\mu^{(t+1)}=\mu^{(t)}\
u\), go to Step 3.  
However, some generalized geometric error parameters resulting in the tiny contributions for the probe position errors of PACMM could not be calibrated. Therefore, these parameters are assumed to be the redundant parameters of generalized geometric error parameters for error model of PACMM. The redundant parameters \(\varepsilon_{21},\varepsilon_{31},\varepsilon_{32},\varepsilon_{34},\varepsilon _{35},\varepsilon_{41},\varepsilon_{46},\varepsilon_{51},\varepsilon_{52}, \varepsilon_{66}\) are eliminated by using the analysis method introduced by [PERSON] _et al._[30].
# Flying Ad Hoc Networks: A New Domain for Network Communications
###### Abstract  
The advent of flying ad hoc networks (FANETs) has opened an opportunity to create new added-value services. Even though it is clear that these networks share common features with its predecessors, e.g., with mobile ad hoc networks and with vehicular ad hoc networks, there are several unique characteristics that make FANETs different. These distinctive features impose a series of guidelines to be considered for its successful deployment. Particularly, the use of FANETs for telecommunication services presents demanding challenges in terms of quality of service, energy efficiency, scalability, and adaptability. The proper use of models in research activities will undoubtedly assist to solve those challenges. Therefore, in this paper, we review mobility, positioning, and propagation models proposed for FANETs in the related scientific literature. A common limitation that affects these three topics is the lack of studies evaluating the influence that the unmanned aerial vehicles (UAV) may have in the on-board/embedded communication devices, usually just assuming isotropic or omnidirectional radiation patterns. For this reason, we also investigate in this work the radiation pattern of an 802.11 n/ac (WiFi) device embedded in a UAV working on both the 2.4 and 5 GHz bands. Our findings show that the impact of the UAV is not negligible, representing up to a 10 dB drop for some angles of the communication links.  
d 2018183571 doi:10.3390/s1810357181035718
# Parallel Load-Bearing and Damping System Design and Test for Satellite Vibration Suppression
## 4 FE Modeling and Analysis for the Damping System  
### FE Modeling for the Damping System  
In order to improve the damping effect, dampers were installed on the PAF. The PAF had stringers, skins and four round brackets. With four brackets, the PAF was divided into three parts. Since the dampers could not cross the round brackets, they were installed on the top, middle and bottom parts of the PAF. If dampers are fixed in the top part or the bottom part, they are required to be connected with  
Figure 4: Parallel damping system [21].  
Figure 3: Single-Degree-of-Freedom (SDF) system: (**a**) SDF system schematic; (**b**) SDF system transmissibility.  
the satellite holder or the rocket connector, which would lead to some limitations, like the dampers' sizes or installation methods. The middle part is the most appropriate for installing dampers. The partial skins between the middle two round brackets were removed to install eight dampers. The dampers were attached to the brackets by hinges, and their element type was a \"damper\" of 1D. The damping coefficient was assigned. The damping force could be determined by the damping coefficient and the relative velocity between the two ends of the damper. The FE model is shown in Figure 5.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 2 Description of the Problem and Concept of the Solution  
The lift control system consists of one or more lifts operating within a multi-story building. In the latter case, it is referred to as group control [8]. The system registers lift calls from the floors and cabins and processes them according to a chosen strategy. Calls from the floors are generated by physical buttons located on the floors, sometimes distinguishing between requests to travel upwards or downwards. Inside the lift cabin, there are stop buttons that trigger calls to specific floors. The objective of the lift control system is to serve its passengers optimally in the following aspects:  
* The average and maximum wait times for passengers as they await the lift's arrival.
* The average and maximum travel times for passengers to reach their destinations.
* The distance traveled by each lift.  
Finding an optimal strategy in this context is part of a broader category of real-time transportation and demand-related problems, often referred to as online-dial-a-ride [9].
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation
### Obstacle Glancing Tangent Algorithm  
Figure 12(a) shows the two shortest paths that go from point \(A\) into the shadowed region behind the obstacle. Their first segment is always a tangent from \(A\) to the contour of the obstacle, either one of the tangents \(T_{1}\) or \(T_{2}\). The tangent \(T_{1}\) can be obtained as follows.  
1. Let \(B\) be any point on the contour of the obstacle as, for example, the point that is situated the farthest along the positive \(x\)-axis, as in Figure 12(b).
2. Find the pixel of the contour at which the angle \(BAC\) is maximum. The line \(AC\) is then the glancing tangent to the obstacle in the clockwise direction.  
Figure 12: Difference between the Bresenham and the line supercover algorithms.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 5 Results and Discussion
### Delay of Previous Flight  
Delay of the previous flight was a key influential factor on flight delays. In this subsection, we focus on the relationship between delay of the previous flight and delay propagation. Figure 13 depicts flight delay and delay of previous flight (the same flight register number) per hour for two different time frames (Figure 13a: 00:00-12:00, Figure 13b: 12:00-24:00), for PEK to HGH. Figure 13b shows a correlation between the flight delay and the delay of the previous flight during the period 12:00-24:00. After calculation, the Pearson correlation coefficient was 0.78, confirming a strong correlation. Figure 14 shows the average delay per hour of departure flights at PEK. Apparently, delays were higher in the afternoon and evening (12:00-22:00).  
Figure 13: Average delay per hour of flights from PEK to HGH and their previous flights; (**a**) 00:00–12:00, (**b**) 12:00–24:00.  
The marginal effect of features on the prediction results of the model was captured by partial dependence plot (PDP). Figure 15 shows the PDP of delay of previous flight, number of actual flights, and cruise pressure. The scale on the horizontal axis is the location of the concentration of values. Considering delay propagation and absorption, it should be noted that the interval between the previous flight and the current flight is included in Equation (1). In other words, the greater the interval between two flights, the smaller the delay of the previous flight. Figure 15 shows that when the delay of the previous flight was less than 22, the delay of the current flight decreased. This may be due to the long interval between the two flights, where the delay of the previous flight was largely absorbed and was not the main factor causing the current flight delay. However, when the delay of the previous flight increased from 22 to 50, the delay of the current flight increased because the interval between the two flights was too short for the delay to be absorbed.  
This suggests that airlines should increase the time interval between two flights using the same aircraft, but only for smaller airlines. From the airport's point of view, the flight turnaround time should be shortened as much as possible to reduce the impact of the delay of the previous flight on the subsequent flight. Moreover, scheduling flights in the morning is a good option, although the balance flight schedules must also be considered.  
Figure 14: Average delay per hour of departure flights at PEK.  
Figure 15: Partial dependence plots.
## 4 Procedure and Analysis of Range Differentiation in 3D MMW Imaging
For the given chirp bandwidth (\(\Delta f\) = 6.261 GHz) and sweep time (T\({}_{s}\) = 1 ms), the theoretical depth resolution was calculated as \(\delta R\approx 0.024\) m. A larger chirp bandwidth directly enhances resolution, as it allows for finer discrimination of beat frequencies [15]. However, achieving such a resolution requires maintaining strict linearity in the chirp signal. Non-linearities can distort the frequency spectrum and introduce errors in the beat frequency, leading to depth measurement inaccuracies. The theoretical depth resolution of approximately 0.024 m is influenced by factors including the chirp bandwidth and the response time of the GDD. To ensure accuracy, the chirp bandwidth was selected based on the transmitter's safe mode operation, which maintained linearity and stability in the frequency sweep. This linearity is essential to prevent distortion in the frequency spectrum and beat frequency errors. Through experimental validation, we achieved a practical depth resolution of 0.022 m, confirming the alignment with theoretical predictions and demonstrating the system's effective resolution limit. Additionally, the response time of the GDD is a critical factor. A longer response time could smear closely spaced depth features, making it difficult to resolve fine details in the object's surface. For the proposed system, the GDD's modulation frequency was carefully selected to ensure sensitivity while maintaining an adequate temporal resolution. The modulation frequency must strike a balance, allowing the system to accurately detect beat frequencies within the operational bandwidth of the detector. By optimizing these parameters--chirp bandwidth, linearity, and GDD response time--the system achieves a balance between depth resolution and overall imaging performance, making it suitable for future extensions, like FPA imaging in MMW systems. By extending this approach across the entire object surface, we can capture pixel-specific depth variations to construct a comprehensive topographical map of the object. This experiment serves as a foundational step in developing a full 3 DMW imaging system.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 4 An Objective Model for Coded Spatial Audio QoE Prediction
### Deriving Localization Accuracy from B-Format Ambisonic Audio  
Localization accuracy, (\(LA\)), can be computed as a weighted product of similarity between reference, \(r\), and test, \(t\), Ambisonic channels as follows:  
\[LA=\prod_{k=1}^{N}V(r_{k},t_{k})^{\lambda_{k}} \tag{2}\]  
where \(N\) is the number of Ambisonic channels, \(V(r_{k},t_{k})\) is the phaseogram similarity between \(k\)-th reference Ambisonic channel and \(k\)-th test Ambisonic channel as measured by the modified ViSQOLAudio algorithm and \(\lambda_{k}\) is an exponent (weighting factor) applied to the \(k\)-th phaseogram's similarity measure. Due to the symmetry of Ambisonic channels, it is possible to reduce the number of exponents as follows:  
Exponents related to horizontal channels:  
* \(\lambda_{1}=\lambda_{3}=\alpha\);
* \(\lambda_{4}=\lambda_{8}=\beta\);
* \(\lambda_{9}=\lambda_{15}=\gamma\),  
exponents related to mixed-orientation channels:
* \(\lambda_{5}=\lambda_{7}=\delta\);
* \(\lambda_{10}=\lambda_{14}=\eta\);
* \(\lambda_{11}=\lambda_{13}=\zeta\),  
and exponents related to vertical channels:  
* \(\lambda_{2}=\chi\);
* \(\lambda_{6}=\psi\);
* \(\lambda_{12}=\omega\),  
Resulting \(LA\) is a bounded similarity score between 0 and 1 where 1 is a perfect match.
## 4 Classification of Routing Protocols
### QoS-Aware Routing Protocols
#### 4.1.7 QoS-Aware Peering Routing for Reliability-Sensitive Data (QPRR)  
QPRR is another QoS-aware peering routing protocol, proposed by [PERSON] _et al._ in [39] designed to improve the EPR [37] by categorizing the patients' data into Ordinary Packets (OP) and Reliability Sensitive Packets (RSP). The routing architecture of this scheme is same as that of QPRD [38] shown  
Figure 8: Routing architecture for QPRD and QPRR [39].  
in Figure 8, redrawn from [39] with a minor change. The delay module is replaced by a reliability module, which is responsible for monitoring and calculating the link reliability between any two nodes.
## 3 Results and Discussion
### Quantitative Analysis of Soil Microstructure
#### 3.2.5 Directional Frequency of Pores  
In order to represent the changes in the distributional intensity of the unit body in a certain direction, 0\({}^{\circ}\)-180\({}^{\circ}\) is divided into \(n\) equal parts (locations), and the angle range of the representative director of each location is \(\alpha=180^{\circ}/n\). From this, the directional frequency of the \(i\)-th location unit body in the \(n\) locations ranging from 0\({}^{\circ}\) to 180\({}^{\circ}\) can be obtained:  
\[P_{i}(\alpha)=\frac{m_{i}}{M}\times 100\% \tag{4}\]  
where \(m_{i}\) is the number of elliptical units in the \(i\)-th location along the long axis direction, and \(M\) is the total number of units or pores. By changing the value in Equation (4), i.e., changing the number of divided locations, different distributions of directional frequency can be obtained. This study takes \(\alpha=10^{\circ}\).  
The distribution of pores in each direction can be seen from the wind rose diagram of the directional frequency of pores in Figure 10. The pores in the unconsolidated soil sample were uniformly distributed without noticeable directionality in all directions. After a time of consolidation, the directional frequency of pores at a certain location in the range of 0\({}^{\circ}\) to 180\({}^{\circ}\) increased significantly. The directional angles of the pores in group F, for example, were mostly concentrated between 70\({}^{\circ}\) and 100\({}^{\circ}\), indicating that consolidation caused the pores in the soil to develop in a certain direction and that the distribution of the pore was more directional. The weak connection between soil particles in some directions was disrupted under the action of consolidation pressure, making the connection in one direction significantly weaker than in other directions. Then, the soil structure would continue to break along this direction, extend to the entire section and eventually form a particular direction angle.  
Figure 9: The curve of soil pore diameter under different consolidation times.  
Furthermore, because of the varying locations of the scanned images of the samples, the directionality of the pores was not constant, and the final directional angles were not completely concentrated. This demonstrates that the direction of soil particles during the consolidation process was random and that the consolidation compression and even the damage of soil particles were unequal and unpredictable. In addition, the deformation was frequently concentrated in particular areas, whereas other areas rarely changed. As a result, it is necessary to further understand the arrangement law of soil particles after lateral consolidation in future research, which can be helpful to grasp the inherent law of soil deformation and stability under the effect of pile driving and soil squeezing. It also has practical and important engineering guiding significance for the safe protection of pile driving.
## 3 Key Technologies Achieved by the System  
### Sensor Data Compensation and Correction  
Accuracy of henhouse environmental parameter collection has important effects on the modeling and regulation of henhouse environment. Under the condition that system costs will not increase, software technology is used for data compensation and correction to raise data acquisition accuracy of the sensors. The CO\({}_{2}\) sensors directly output the digital quantity after filtering processing, and no follow-up process is needed.