esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Statistical Algorithm for Hovers  
After understanding the time history of the yaw angle and the PSD algorithm, this section will introduce a statistical algorithm for 360\({}^{\circ}\) hovers. Using the median-crossing retrieval and de-redundancy algorithms in Sections 3.1 and 3.2, we can obtain the zero-crossing mark set \(\hat{T}_{0}=\{\hat{t}_{1},\hat{t}_{2},\cdots,\hat{t}_{i_{t}},\cdots,\hat{t}_{i _{0}}\}\) that removed the redundant marks of \(\Psi_{0}\). For the next step, we need to further count the number of hovers with the aid of the sequence \(\hat{T}_{0}\).  
First, based on the elements in \(\hat{T}_{0}\), \(\Psi_{0}\) is segmented into \(\lfloor\hat{n}_{0}/2\rfloor\) semi-open intervals, where the symbol \(\lfloor\cdot\rfloor\) denotes a downward rounding operator. For \(\forall\{\hat{t}_{i-1},\hat{t}_{i+1}\}\subseteq\hat{T}_{0}\), the elements of the yaw angle within the segmental interval \([\hat{t}_{i-1},\hat{t}_{i+1})\subset\mathbb{N}*\) are as follows:  
\[\Psi_{si}^{0}=\{\psi_{0}(t_{i-1}),\psi_{0}(t_{i-1}+1),\cdots,\psi_{0}(t_{i+1} -1)\}\in\Psi_{0},\;i=2,4,\cdots,2\lfloor\hat{n}_{0}/2\rfloor. \tag{6}\]  
Figure 4: The functions of PSD: (**a**) marking the median-crossing points; (**b**) splitting signal into halves using the median.  
Figure 3: The redundant median-crossing points.  
Let \(A_{si}^{0}=\max(\Psi_{si}^{0})-\min(\Psi_{si}^{0})\), and define the set \(H\) for 360\({}^{\circ}\) hover maneuvers and set \(S\) for non-hovering maneuvers. Note that the angle change for a hover is 360\({}^{\circ}\), and a criterion for hover can be expressed based on this characteristic.  
Criterion 3: If \(A_{si}^{0}>360-c\), then the aircraft does one hovering action \(i\in H\); otherwise, \(i\in S\).  
In Criterion 3, 360 represents a hovering angle of 360\({}^{\circ}\), and \(c\) represents the threshold for deviation of the yaw angle; the purpose of this threshold is to avoid Criterion 3 failure due to sampling errors of the yaw angle recorded in a discrete manner. The meaning of Criterion 3 is that, for the zero-crossing mark \(\hat{t}_{i}\), if the amplitude fluctuation of the yaw angles near it is greater than the threshold (\(360-c\))\({}^{\circ}\), it is inferred that the aircraft has hovered once; otherwise, the action taken is not a hover, although the aircraft crosses the median.  
As mentioned above, if the length of set \(H\) is \(N_{H}\), then \(N_{H}\) is the number of hovers in a flight profile.
## 5 Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Actuals** & **Estimation** & **MAE** & **RMSE** & **Bias (\%)** & **SMAPE** \\ \hline GRU & 8469 & 8330 & 0.127 & 0.342 & \(-1.643\) & \(1.181\) \\ LSTM & 8469 & 8300 & 0.127 & 0.349 & \(-2.000\) & \(1.120\) \\ MBG/NBD & 8469 & 8035 & 0.109 & 0.286 & \(-5.123\) & \(1.972\) \\ BG/NBD & 8469 & 8018 & 0.109 & 0.286 & \(-5.321\) & \(1.972\) \\ Pareto/GGG & 8469 & 8972 & 0.116 & 0.299 & \(5.936\) & \(1.278\) \\ NBD & 8469 & 9072 & 0.116 & 0.287 & \(7.125\) & \(1.971\) \\ Pareto/NBD (HB) & 8469 & 10,414 & 0.126 & 0.303 & \(22.966\) & \(1.316\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of Results for Online Retail II data: Total number of actuals and estimations, MAE (Mean Absolute Error, RMSE (Root Mean Square Error), bias and SMAPE (Symmetric Mean Absolute Percentage Error. Figure 7: Aggregate weekly transactions and predictions for Online Retail II data. The dotted vertical blue line in the upper line chart separates the calibration and holdout periods. activity in the Online Retail II dataset. The BTYD models demonstrate higher accuracy in terms of MAE and RMSE due to their tendency to underforecast. This means that BTYD models often forecast better near zero transactions or logins, which is probably the main reason for this result. In relation to performance, both GRU and LSTM models demonstrated superior results in terms of bias and SMAPE compared to the BTYD models across both datasets. The GRU achieves slightly better results compared to the LSTM. Notably, these recurrent neural network models effectively captured the underlying patterns and trends inherent in the data.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
## 4 Concept
### Public Trusted Entities Repository  
The Public Trusted Entities Repository is a component that can be used _optionally_ by participants for publishing publicly which companies, organizations and TSAs they trust. A participant's trusted entities and TSAs are expressed as a separate set of entities and TSAs. Thus, for example, \(\phi(\mathrm{P_{A}})=\{\mathrm{P_{B}},\;\mathrm{P_{C}}\;,\ldots\}\) represents the trusted entities and \(\phi(\mathrm{P_{A}})=\{\mathrm{P_{B}},\;\mathrm{P_{C}}\;,\ldots\}\) the trusted TSAs of participant \(\mathrm{P_{A}}\). Using these two sets, it becomes possible for participants, analogous to the Web of Trust, to derive transitive trust relations by adding the TSAs of trusted entities to their own trusted TSAs or to look at which other  
Figure 5: Procedure for local data recording and signing.  
entities their trusted entities rely on in order to add their trusted TSAs to their own _trust list_ (c.f. Section 4.2).  
Example: If participant \(A\) trusts organization \(B\), then participant \(A\) can look up in the Public Trusted Entities Repository whether organization \(B\) has published which TSAs and organizations it trusts. If participant \(A\) finds an entry for organization \(B\), it can view its TSAs and add them to its own set of trusted TSAs.  
In this way, the process of finding trusted TSAs is simplified by allowing the participants to derive them via their relationships instead of having to check each TSA individually. At the same time, the repository should increase the number of trusted TSAs per participant to maximize the probability of a common intersection between participants. Nevertheless, the use of the repository is only optional, and participants are free to derive their list of trusted TSAs according to their own preferences.
# Estimation of Driving Direction of Traffic Accident Vehicles for Improving Traffic Safety
## 3 Proposed Method
### Driving Direction Estimation of Vehicles Involved in Traffic Accidents  
Finally, we calculate the angle between \((x_{acc},y_{acc})\) and \((x_{dst},y_{dst})\) to estimate the driving direction that includes the Northbound and Southbound directions.  
The arctan obtains the angle using the slope values of \(||x||\), \(||y||\) coordinates. However, when calculated by the slope \((x_{dst},y_{dst})\), the relative position of the destination location that changes according to the sign of the coordinate value is not reflected. As a result, the relative position (\(-\)_or_\(+\)) of \((x_{dst},y_{dst})\) in \((x_{acc},y_{acc})\) is not known precisely, so it is impossible to distinguish between Northbound and Southbound. Therefore, we use the _arctan2_ function, the inverse function of \(tan\), and provide the angle in the range of \(-\pi\) to \(\pi\) to accurately determine which quadrant the angle belongs to [29]. The angle \(\theta\) between the two points is defined as follows:  
\[\theta=\frac{arctan2(||x||,||y||)}{\pi}\times 180 \tag{1}\]  
In Equation (1), the value obtained from \(arctan2\) is in radians, so we multiply it by \(180/\pi\) to convert it to degrees. The distances between the latitudes and longitudes of the two points, \(||x||\) and \(||y||\), are calculated as follows:  
\[||x|| =x_{dst}-x_{acc} \tag{2}\] \[||y|| =y_{dst}-y_{acc}\]  
Finally, the final driving direction is determined as follows:  
\[direction=\begin{cases}N,&0\leq\theta<180\\ S,&-180\leq\theta<0\end{cases} \tag{3}\]  
In Equation (3), if \(\theta\) is between 0 and 180, it is Northbound; if it is between \(-180\) and 0, it is Southbound. Figure 4 illustrates how the driving direction is determined based on the angle between \((x_{acc},y_{acc})\) and \((x_{dst},y_{dst})\) in the first quadrant. The driving direction is Northbound for \((x_{acc},y_{acc})\) and \((x_{dst}^{1},y_{dst}^{1})\) in the first quadrant because their relative position is \((+,+)\) and for \((x_{dst}^{1},y_{dst}^{1})\) in the second quadrant because its relative position is \((-,+)\). On the other hand, the driving direction is Southbound for \((x_{dst}^{2},y_{dst}^{2})\) in the fourth quadrant because its relative position to \((x_{acc},y_{acc})\) is \((+,-)\). Finally, for \((x_{dst}^{2},y_{dst}^{2})\) in the third quadrant, the relative position is \((-,-)\), so the driving direction is Southbound.
# Design Optimization of a District Heating Network Expansion, a Case Study for the Town of Kiruna
## 5 Conclusions  
This paper has presented a methodology for the multi-objective optimization of the design of DH networks when the configuration of the new piping layout and the capacities of new heat production sites have to be decided. A modular modelling and simulation tool for complex meshed networks was coupled to an evolutionary algorithm to search for the Pareto front of the best compromises between the investment and operating costs. The real case of the expansion of the DH network in the town of Kiruna (Swedish Lapland) was used as an example of application.  
The results of the design optimization for this specific case have shown that:  
* The appearance of the set of the optimal design solution is quite scattered, due to the discrete nature of the decision variables of the optimization problems, and different groups of solutions can be identified according to the capacity of the new bio-oil boiler, which was among those decision variables.
* There is little variety in the optimal design configurations of the piping layout feeding the new user areas. Significant changes in the values of the operating and investment costs instead are due to different diameters of the piping segments, rather than variations in the piping layout.
* The minimum operating cost is obtained for a large capacity of the new bio-oil boiler that should be installed close to the new user areas. This means that a more distributed heat generation, resulting in lower thermal losses and requiring lower pumping power (in this case however, the investment costs are higher, due to the larger capacity of the new heat production site).  
The multi-objective nature of the design optimization problem set up with this methodology allows the decision makers to be aware of the whole spectrum of the best compromises between investment and operating costs. Then, the design solution to be adopted can be selected in a more  
Figure 8: The six piping layout configurations of the optimal design solutions along the Pareto front in Figure 6. The colours of the markers in Figure 5 correspond to the colours of the piping layouts in this figure.  
conscious way, perhaps also using a broader perspective including other aspects, compared to a case in which only one solution is obtained as the result of a single objective optimization procedure.  
This research has been supported by HLRC (Hjalmar Lundbohm Research Centre, founded by local mining company LKAB), by ATTRACT (Attractive, sustainable habitats in cold climates, a research program financed by VINNOVA, the Swedish innovation agency) and by ALICE (Attractive Living in Cold Climate, a research program financed by FORMAS, the Swedish Research Council). The authors would also like to thanks professor [PERSON] for his scientific guidance and valuable suggestions.  
The authors declare no conflict of interest.
###### Abstract  
Different versions of principal component analysis (PCA) have been widely used to extract important information for image recognition and image clustering problems. However, owing to the presence of outliers, this remains challenging. This paper proposes a new PCA methodology based on a novel discovery that the widely used \(l_{1}\)-PCA is equivalent to a two-groups \(k\)-means clustering model. The projection vector of the \(l_{1}\)-PCA is the vector difference between the two cluster centers estimated by the clustering model. In theory, this vector difference provides inter-cluster information, which is beneficial for distinguishing data objects from different classes. However, the performance of \(l_{1}\)-PCA is not comparable with the state-of-the-art methods. This is because the \(l_{1}\)-PCA can be sensitive to outliers, as the equivalent clustering model is not robust to outliers. To overcome this limitation, we introduce a trimming function to the clustering model and propose a trimmed-clustering based \(l_{1}\)-PCA (TC-PCA). With this trimming set formulation, the TC-PCA is not sensitive to outliers. Besides, we mathematically prove the convergence of the proposed algorithm. Experimental results on image classification and clustering indicate that our proposed method outperforms the current state-of-the-art methods.  
principal component analysis; dimensionality reduction; image processing; pattern recognition; clustering +
Footnote †: journal: Applied
# Prototype of 5G Integrated with TSN for Edge-Controlled Mobile Robotics
## 4 Discussion and Results
### Evaluation of the Implementation Regarding the Use Case
In this case, the robot controller in the factory cloud needs to transmit a halt command to the other mobile robot within 7 to 10 ms; otherwise, the glass will break. The aforementioned time period includes any processing time of factory cloud system, which needs to be accounted for determining the permitted latency for the 5G system to ensure the safe operation of mobile robots in coordination tasks. During the measurement test period, we have not experienced any latency exceeding the maximum latency bound, and both mobile robots stayed in operating mode during the execution of the task during the experimentations.
## 3 Results
### Difference in DC for Internet Activities
A declining trend in DC was observed as bandwidth increased, reflecting improvements in data transmission efficiency: at 40 MHz bandwidth, the DC for FTP downloads decreased to 66.2%, while the maximum DC for 4K video streaming and video calls dropped to 64% and 5.5%, respectively; for 80 MHz bandwidth, the DC further reduced for certain applications, with FTP download dropping to 57%, the maximum DC for 4K video streaming to 44.7%, and live streaming (Twitch) to 5.5%. However, applications like voice and video calls showed little to no DC reduction, and in some cases, DC even increased. This can be attributed to higher data demands induced by increased transmission rates, as network services dynamically adjusted parameters like video resolution and frame rate to maintain quality of service (QoS), consequently raising DC. At the highest bandwidth of 160 MHz, the DC for file transfers did not significantly decrease. This is because the WLAN throughput reached a bottleneck, limited by the wired Ethernet connection to the AP. Notably, under 40 MHz bandwidth, the theoretical maximum data rate for Wi-Fi 6 with four SS is 1147.2 Mbps [22], already exceeding the 1 Gbps Ethernet link used in this study. These DC results can be used as a reference to accurately assess the realistic EMF exposure under known Internet activities, e.g., the exposure of children in multimedia classrooms with live online courses, multi-person online meetings in a meeting room, etc. Figure 6: _Cont._
# Analytical MPC Algorithm Using Steady-State Process Model
## 4 Discussion and Conclusions  
It is worth noting that the computational complexity of both DMC and SSDMC controllers is similar. In the SSDMC algorithm, in addition to the operations performed in the DMC algorithm, only the slope of the steady-state characteristic (the derivative of a specific function) needs to be calculated additionally, which does not significantly complicate the proposed algorithm. Moreover, in the example under consideration, determining the derivative is particularly easy due to the relative simplicity of obtaining the analytical formula for calculating it.  
The main tuning parameter, \(\lambda\), is still available in the SSDMC controller and has the same meaning as in the standard algorithms. It decides on the size of the penalty for the control signal changes. However, inside the algorithm, it depends on the process gain at the current operating point to obtain the compact control law.  
The next main tuning parameter, the control horizon \(s\), does not influence the responses obtained as much as the parameter \(\lambda\). Moreover, its decrease slightly slows down the responses obtained, worsening the control quality. On the other hand, thanks to the formulation of the SSDMC algorithm in the form of the control law, the longer control horizon does not influence the numerical complexity of the algorithm.  
An important advantage of the proposed algorithm is its robustness to measurement noise, measurement delay, and the uncertainty of process parameters. Even when the parameters of the control plant were significantly changed, the controller managed to stabilize the reactor and maintain satisfactory performance.  
The use of a dynamic model in the form of the process step response and static characteristic of the process, combined in a skillful way in order to synthesize the MPC algorithm, resulted in a computationally efficient predictive control algorithm in which the main part of the calculations needed to determine the control value is performed offline  
Figure 9: Responses of the control system with the SSDMC controller to changes in the setpoint to \(\overline{C}_{B1}=1\) mol/L and \(\overline{C}_{B2}=1.25\) mol/L and to the change of the disturbance by 10% in the 6 th minute of the experiment. \(\lambda=10^{-4}\); \(s=4\); \(k_{1}\) changed to 55 \(L\)/h (\(k_{2}=100\) 1/h, \(k_{3}=10\) L/(h\(\cdot\) mol))—red lines; \(k_{2}\) changed to 90 1/h (\(k_{1}=50\) \(L\)/h, \(k_{3}=101\)/(h\(\cdot\) mol))—blue lines, \(k_{3}\) changed to 9 \(L\)/(h\(\cdot\) mol) (\(k_{1}=50\) \(L\)/h, \(k_{2}=100\) L/h)—magenta lines.  
as in a conventional, analytical DMC algorithm. However, note that despite the relative simplicity of the controller obtained and the relatively straightforward way of receiving it, it can offer much better control quality than the conventional DMC controller based on the linear model.  
This research received no external funding.  
The data are contained within the article.  
The author declares no conflicts of interest.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 4 Big Data Collection in LS-WSNs
### Architecture Based on Mobility
#### 4.2.1 Static Sink Architecture and Moving Sensor Nodes  
This architecture has the particularity that the sensors are mobile. This approach has been the subject of much work in recent years [48; 86; 87; 88]. By the fact that the sensors are mobile, this architecture considerably reduces the number of sensors to deploy. Communication in this architecture is direct. This architecture helps maintain good network coverage. Moreover, this architecture allows a balanced consumption of energy at the sensor level, and the amount of data collected by the sink is significant.  
Figure 16: Challenges of mobility in a WSN.  
Figure 15: Architecture with sensor nodes and several static sinks.  
For instance, these projects use this architecture in which sensor nodes move while the sink is fixed: the COW project [95] and the CENWITS project [96].
## 6 Validation in Real PR Trials
### Target BRCS and Coverage Estimation  
The Bistatic Radar Cross Section (BRCS) of a model similar to the car employed as controlled target in the first deployment was simulated in ANSYS HFSS employing a multi-bounce rays approach with the physical theory of diffraction (PTD) correction. The RCS for all possible incidence and reflection azimuth angles \(\phi\in[0,360]\) were simulated, fixing the elevation of incidence wave at \(\theta_{inc}=55^{\circ}\) (Hispast 30W-5 elevation) and the reflected one \(\theta_{inc}=90^{\circ}\) because the targets and the PR are approximately at the same altitudes. Figure 12a shows the model employed for simulating the BRCS and, as an example, the results achieved along all reflected angles for incidence \(\phi_{inc}=180^{\circ}\), that is incidence in the centre of the rear part of the car.  
The possible trajectories along the two main roads of both scenarios were analysed to calculate the change in azimuth incidence and reflection angles along them. Knowing those angles, the car BRCS along the roads can be estimated (Figure 12b,c), showing that, for these geometries, this kind of targets reflect more from their rear part, when they are moving away from the PR, than from their front part, when they are approaching.  
The coverage curves from the PR as a function of the target BRCS are shown in Figure 13. For coverage calculation, the sensitivity of the system was calculated from the theoretical Signal-to-Noise Ratio (SNR) required for detecting Swerling I targets with additive white Gaussian noise, and the noise factor of the receiver; propagation losses were also considered. The estimated angular coverage, in which the receiving antenna gain is in the range between the maximum measured and the one at the beamwidth edges (\(G_{max}\)-3 dB) is marked in Figure 11.
## 1 Introduction  
Digital technology in the dental field started with the first commercial chairside CAD/CAM system developed by Drs. [PERSON] and [PERSON] in the late 1980s and have since been evolving rapidly, especially for restorative dentistry techniques [1, 2, 3, 4]. The approach for planning complex dental care has changed with novel technologies in dentistry [5, 6]. Digital dentistry is currently used in both the clinic and laboratory to manufacture restorations faster, easier, more accurately, and with predictable results [7, 8, 9, 10]. Digital planning for complex dental care has improved these techniques because the clinician can also include extraoral soft tissue landmarks to obtain harmonious teeth, smile, and face elements [11, 12, 13].  
Facial scanners in dentistry allow fully digitizing procedures to obtain virtual patients and evaluate proposed dental care without the need to have them physically present [14, 15].  
Various 3D scanning methods have been used in dental applications; for instance, enhancing preoperative planning processes [6; 16; 17]. Three-dimensional reconstructions have been found to represent precise and dependable methods for facial evaluations, assisting clinicians in formulating successful treatment plans [15; 17]. Clinicians can examine the interaction between soft tissues and dental arches by combining a digital dental cast with a 3D facial image, thereby reducing the need for X-rays in some situations [15; 17]. Existing technology also enables the digitization of dental models in 3D space [17]. Indeed, to better evaluate, contrast, and forecast treatment outcomes before and after the intervention, three-dimensional scans of the facial soft tissues can provide clinicians with a more realistic portrayal of facial morphologies [16; 17].  
Overall advantages of incorporating extraoral facial imaging for dental care include reliability, nearly instantaneous capture time, acceptable accuracy, and life-like renderings. However, imaging also has disadvantages such as high cost, complex software setup, and a learning curve [18; 19; 20]. The integration of facial and intraoral scanners has shown promising results in in vitro studies, but few clinical reports have presented protocols for complex full-mouth rehabilitation (FMR). Therefore, this report aims to showcase the protocol for integrating a 3D facial scanner and chairside dental CAD/CAM system for the evaluation and execution of a tooth-borne FMR.
# Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems
## 3 Deep Learning-Based Channel Estimation
### Convolutional Neural Network-Based Channel Estimation
We stress that the loss function (37) shares the same training data with that in Equation (31), but the fine structure is different. Specifically, the instantaneous channels are stacked in the vector form in Equation (31), while it is arranged in a matrix form in Equation (37) to make use of the benefits of the CNN.