esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model of the Motor
Figure 4 represents the LSPMSM phase currents and angular frequency transient processes at \(T_{c}\) = 0 N\(\cdot\)m and \(F\) = 10\({}^{-4}\) N\(\cdot\)m/(rad/s). Figure 4a shows the time plots of the components of the stator current along the direct (d) and quadrature axis (q) in a coordinate system fixed relative to the rotor. Figure 4b shows the time plot of the mechanical angular frequency of the rotor. Figure 5 shows the directions of the d and q axes relative to the magnetization direction of the permanent magnets. The transient waveforms were selected for the study because these values (phase currents and angular frequency) can be easily measured in experiments with the real LSPMSMs. Figure 4 shows that the LSPMSM starting time was 0.7 s. The steady-state phase current amplitude was 1.58 A. The starting inrush current was 15.8 A. During the starting process, the motor reached the synchronous speed of 157.09 rad/s (1500 rpm) at 0.06 s for the first time; however, further, it accelerated to a higher speed because of inertia. Later, the motor decelerated as a result of synchronous torque action. These oscillations were repeated several times with the decreasing amplitude caused by dumping factors such as asynchronous torque and load frictional torque. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Motor rated power, kW & 0.55 \\ Motor rated line-to-line voltage, V & 380 \\ Root-mean-square (RMS) rated stator current, A & 1.11 \\ Rated power factor & 0.85 \\ Rated frequency \(f\), Hz & 50 \\ Pole pair number \(Z_{p}\) & 2 \\ Stator phase resistance \(R_{u}\), Ohm & 15.3 \\ Total direct inductance \(L_{d}\), H & 0.26 \\ Total quadrature inductance \(L_{d}\), H & 0.15 \\ Leakage direct inductance \(L_{u,d}\), H & 0.038 \\ Leakage quadrature inductance \(L_{eq}\), H & 0.051 \\ Rotor direct resistance \(r^{\prime}{}_{d}\), Ohm & 9.24 \\ Rotor quadrature resistance \(r^{\prime}{}_{d}\), Ohm & 10.1 \\ Permanent magnet flux linkage \(\lambda^{\prime}{}_{0}\), Wb & 0.76 \\ Motor inertia moment \(f\), kg\(\cdot\)m\({}^{2}\) & 0.003 \\ Bearing friction and windage coefficient \(F\), N\(\cdot\)m/(rad/s) & \(10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The LSPMSM parameters (rated power 550 W, rated speed 1500 rpm). Figure 3: Simulink model of the LSPMSM motor fed from 3-phase 380 V, 50 Hz grid. (**a**) General view; (**b**) load torque model.
# Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
## 3 Materials and Methods
### Node Willingness  
The core functionality of OLSR [7; 8] is the mechanism of optimizing topology control traffic based on the multipoint relay algorithm, as proposed by [PERSON] et al. [16]. If a node is selected as an MPR node, there is always a path to its two-hop neighbours. The choice of MPR in OLSR affects routing performance. It is proved that the calculation of the minimum MPRs is an NP-complete problem thus to calculate the MPR set is very difficult, and some heuristic algorithm must be used to find the approximate optimal solution [17]. OLSR uses HELLO messages to find its one-hop neighbours and its two-hop information. Also, it performs a distributed election of a set of multipoint relays (MPRs). In the HELLO message packet format as seen in Figure 1, there is a willingness field. It specifies the willingness of a node to forward traffic for other nodes which is a criterion for the MPR selection. The willingness of a node can assume any integer value from 0 to 7. Nodes, by default, have a willingness of WILL_DEFAULT. A node can change its willingness value as its conditons change, for example, being low on battery or permanent power or high capacity interference to other nodes. Table 2 shows the degree of willingness and its corresponding values.
## 2 Materials and Methods
### Implant Position Accuracy Measurement  
After the implant placement in each model, the corresponding scan body (CARES(r) RC Mono Scan Body, Straumann, Basel, Switzerland) was attached and tightened onto each implant using a screwdriver. The adaptability of the implant platform and the scan body was examined. An extra-oral scanner (D900, three shapes, Copenhagen, Denmark) was used to capture each model with the scan body. An STL file of all models with a scan body was then imported into coDiagnostiX(r) software version 9.7. In the program, the actual implant position was generated from the position of the scan body and superimposed onto the same original planned implant position. The deviation between the planned and placed implants was automatically linearly and angularly evaluated in the \"Treatment-Evaluation tool\" in the coDiagnostiX(r) software program. The values of the linear and angular deviations were recorded (Figure 5).  
Figure 4: Illustration of the surgical guide with a 5 mm diameter Straumann T-sleeve.  
The software automatically calculated the deviation of the placed implant in relation to the planned implant. The outcomes were recorded as follows (Figure 6):  
* Linear deviation from the implant platform: the linear displacement in mm between the planned and placed implants, measured at the center of the implant platform.
* Linear deviation from the implant apex: the linear displacement in mm between the planned and placed implants as measured at the center of the implant apex.
* Linear deviation of the implant depth: the depth deviation in mm between the planned and placed implants.
* Deviation of the implant axis or an angular deviation: the deviation in degrees (\({}^{\circ}\)) between the center axis line of the planned and placed implants.  
All measuring points were measured at the center of the implant. If the placed implant was shallower than the planned implant, the data were recorded as a negative value. If the implant was placed deeper than the planned implant, the data were recorded as a positive value.  
Figure 5: The 3D measurement of implant deviation in coDiagnostiX® software. Post-operative implant position (blue) merged with pre-operative implant position (red): (**a**) buccolingual view; (**b**) meso-distal view; and (**c**) 3D view.  
Figure 6: An illustration of the 3D measurements representing the implant deviation between the planned and the placed implant position.
# Two-Dimensional Profile Simulation Model for Five-Axis CNC Machining with Bull-Nose Cutter
## 2 Model of Surface Topography with a Bull-Nose Cutter
### The Principle of 2D Profile Measurement  
The profile method is major for evaluating surface topography, and it is widely used in machining. A 2D profile is obtained by intersecting a specified plane \(P\) with the surface. The plane \(P\) is determined by the 2D profile measurement line \(V_{L}\), which is a straight line that connects the start point \(P_{S}\) and end point \(P_{E}\), and the normal vector \(V_{n}\) of the nominal surface at the start point \(P_{S}\). Usually, \(V_{n}\) and \(V_{L}\) are not parallel or perpendicular. The normal vector \(n_{P}\) of the plane \(P\) is a unit vector. Thus, \(n_{P}\) can be expressed in the form below:  
\[n_{P}=\frac{V_{L}\times V_{n}}{|V_{L}\times V_{n}|}. \tag{5}\]  
The length \(L\) between \(P_{S}\) and \(P_{E}\) is determined according to the standard. The measuring direction \(V_{M}\) is on plane \(P\) and perpendicular to \(V_{L}\), and it is a unit vector to which the probe is parallel during measurement. Thus, \(V_{M}\) can be expressed in the form below:  
\[V_{M}=n_{P}\times V_{L}. \tag{6}\]  
Then, the points and vectors of 2D profile measurement are described separately for planes and free-form surfaces.  
For a plane surface, the start and end points are on the nominal surface. Thus, the 2D profile measurement line \(V_{L}\) is on the nominal surface, and it is also perpendicular to the normal vector \(V_{n}\) of the nominal surface at the start point \(P_{S}\). Therefore, the measuring direction \(V_{M}\) is parallel to the\(\ldots\)These points and vectors are shown in Figure 3.  
However, for a free-form surface, these points and vectors are different from those on a plane. First of all, points \(P_{1}\) and \(P_{2}\) are taken on the nominal surface to determine the 2D profile measurement line \(V_{L}\). The 2D profile measurement line \(V_{L}\) satisfies:  
\[V_{L}=\frac{P_{2}-P_{1}}{|P_{2}-P_{1}|}. \tag{7}\]  
Then, \(P_{1}\) is used as the start point of the 2D profile measurement line \(V_{L}\), and is denoted \(P_{S}\). The point along the 2D profile measurement line \(V_{L}\) with distance \(L\) from the start point \(P_{S}\) is the end point \(P_{E}\). Normally, \(V_{L}\) is not perpendicular to the normal vector \(V_{n}\) of the nominal surface at \(P_{S}\). These points and vectors are presented in Figure 4. It is worth noting that point \(P_{E}\) is not on the nominal surface if the surface is a free-form surface, as shown in Figures 4 and 5.  
The process of obtaining a nominal profile can be summarized as follows: the stylus parallel to the measuring direction \(V_{M}\) starts from starting point \(P_{S}\), follows the measuring line direction \(V_{L}\), obtains a surface profile of a certain length \(L\), and ends at the endpoint \(P_{E}\). The above describes the points and vectors of 2D profile measurement, which is the foundation of the simulation algorithm of the 2D profile.  
Figure 4: Nominal profile for a free-form surface.  
Figure 3: Nominal profile for a plane surface.
## 2 Related Work
### Summary  
These surface-based reconstruction methods [9; 15; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31] mostly depend on the accuracy of surface extraction. In complex indoor scenes, the efficiency of surface extraction is low and contains excessive noise. Line-based reconstruction methods [32; 33; 34; 35; 36; 37; 38; 39; 40] can be used to completely represent the geometric information; however, these do not contain semantic information and adjacency relationship of rooms, leaving the reconstructed models to be useful only for visualization. In order to address the above shortcomings, we propose an innovative approach combining the rich structure of 2D lines with 3D geometry of surfaces to automatically build 3D structured models using MLS point cloud data. The output structural model presents novel applications in signal simulation, including the capability of providing optimal locations for 5G small base stations in the future.
# A Novel Real-Time Virtual 3D Object Composition Method for 360\({}^{\circ}\) Video
## 4 Experimental Results
### 3D Composition Using Pre-Visualization System  
At the same time as the recording, a 360\({}^{\circ}\) video clip and the external parameters of the stereo vision were transmitted to the Unity 3D game engine to create a virtual camera for pre-visualization. Figure 9 shows the results of the pre-visualization of the indoor and outdoor scenes while recording the 360\({}^{\circ}\) video. The result displayed through the pre-visualization system was used to confirm the composition result. For the final video, further composition processes such as lighting, shadowing, and texturing in 3D software are needed.  
The final composition was conducted in 3 ds Max 2018. When the recording was finished, the 3 ds Max script, which included the trajectory information of the stereo vision, was immediately generated. It was used to create a virtual camera in the 3 ds Max virtual space. Figure 10 shows the rendered images and the final 3D composition images. No difference can be seen in the camera trajectory because it uses the same trajectory data saved from a real-time pre-visualization system. As a result, it does not need an extra process for extracting the camera-tracking data, and thus our proposed system is more time efficient than the traditional method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Position X (\%)** & **Position Y (\%)** & **Position Z (\%)** \\ \hline \multirow{2}{*}{Indoor} & Ground truth–raw trajectory & 11.81081 & 1.875021 & 0.547256 \\  & Ground truth–trajectory with Kalman filter & 2.6780439 & 0.432794 & 0.112748 \\ \hline \multirow{2}{*}{Outdoor} & Ground truth–raw trajectory & 1.740435 & 3.604414 & 0.147483 \\  & Ground truth–trajectory with Kalman filter & 1.537084 & 3.093616 & 0.077608 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Standard deviation in the comparison of the ground truth, raw trajectory data, and trajectory with the Kalman filter.  
Figure 10: _Cont_.  
Figure 9: Results of pre-visualization: (**a**) indoor and (**b**) outdoor.
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 4 Reconfiguration Formation Algorithm
### Peak-Search Method
#### 4.1.3 Optimum Decision Block  
In this block, link performance \(P_{I}\) is defined as the total links \(L_{I}\) in the connection link block over the average query hop \(H_{I}\) in the hop length block for various \(I\) layers.  
\[P_{I}\ =\ L_{I}/H_{I} \tag{3}\]  
Figure 6 shows the sampled average link performance of various layers for different sizes of network. There exists a maximum point as the number of layers either at Layers 3 or 4 for the smaller size of network and at Layers 4 or 5 for the larger size of network. To locate the maximum point, a peak-search method is designed in the optimum decision block.  
Initially, \(I\) is set as two and the \(P_{I}\) is calculated. Then the \(I=I+I\) for the connection link block and \(P_{I+I}\) are computed in the optimum decision block. When \(P_{I+I}>P_{I}\), the optimum decision block increases \(I\) by one and triggers the connection link block again. The link performance ratio of Layer \(I+1\) is computed until \(P_{I+I}<P_{I}\) and the decision is made. As a result, \(I\) is regarded as the optimum number of layers, and the algorithm is stopped.
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 2 Methods
### Image Processing
Raster layers representing the three spectral indices (Table 2) were calculated from each UAV image mosaic and from each satellite image. These were clipped to the boundaries of the study tree canopies that had been carefully delineated using the final image in each time series through digitisation in a GIS (ESRI, Redlands, CA, USA). In the final image, treated trees could be easily and precisely identified. The UAV imagery and the satellite imagery were digitised separately to account for some residual variation caused by differences in sensor geometry. The values for all pixels within the delineated boundaries for treated trees were averaged to provide a mean for each cluster at each acquisition date. For the control (untreated) plots pixels were averaged within the boundary of the original 35 tree plot. The spectral indices from both datasets were de-trended using the method described earlier [64]. Analysis of the UAV imagery indicated that there was a significant trend in the data that was not due to the herbicide-induced stress [12]. Once the background changes were accounted for, the trend in spectral indices in the treated tree clusters was more pronounced, suggesting that the background noise masking the spectral stress signal had been adequately controlled. Changes in spectral indices were defined using an approach developed in previous experiments [12; 51]. This approach defined a \"no-change\" zone between the 25 th and the 75 th percentile of a de-trended spectral index for a given observation at the start of the time-series. Subsequent observations of the same spectral index can be deemed to be stressed if the 75 th percentile of the spectral index values for a given observation drops below the lower bound of the \"no-change\" region defined at the start of the time-series. To compare the influence of spectral and spatial resolution directly on the detection of physiological stress the UAV imagery was resampled to the same spatial resolution as the RapidEye data (5 m). A cubic spline resampling was applied to all UAV images using the gdalwarp function of the geospatial data abstraction library (GDAL version 2.2.1) [65] using the standalone version of the GDAL software. The resulting images were manually checked for any visual artefacts associated with the resampling process. All imagery was found to be artefact free and spectral indices were calculated and extracted using the same processing chain applied to the original UAV imagery.
## 3 Proposed Method
### Ensuring Authorization Security  
In order to ensure the security of authorizations, we converted the authorization-query problem into partial MAX-SAT as follows: (1) authorization logics and constraints are transformed into hard clauses; (2) the updating algorithm is adopted to transform the requested permissions with different matches into soft clauses; and (3) recursive and encoding algorithms are utilized to satisfy all hard clauses and as many soft clauses as possible. First, we present the definition with respect to the partial MAX-SAT.  
**Definition 4**.: _(Boolean logical descriptions of partial MAX-SAT):_  
1. _Basic elements: some elements are presented using Boolean variables_ \(x_{1}\)_,\(x_{2}\),..._ \(x_{i}\)_,..._,_\(x_{i}\)_, or_ \(\
eg x_{i}\)_(the negation of_ \(x_{i}\)_), which is called a literal logic; the instance formalized as_ \(x_{1}\vee x_{2}\vee\ldots\vee x_{n}\) _using_ \(n\) _literals_ \(x_{i}\) _is called a clause; and sentence_ \(c_{1}\wedge c_{2}\wedge\ldots\wedge c_{m}\) _using_ \(m\) _clauses_ \(c_{i}\) _is called the conjunctive normal form, which is expressed as_ \(\text{cnf}(c_{1}\wedge c_{2},\,\ldots\,c_{m})\)_;_
2. _Hard clause:_ \(c_{1}\)_,_ \(c_{2}\)_,..._ \(c_{m}\) _are hard clauses and satisfiable, if and only if there exist truth assignments such that the value of_ \(\text{cnf}(c_{1}\wedge c_{2},\,\ldots\,c_{m})\) _is_ \(1\)_. All hard clauses are set as_ \(\text{HC}\)_;_
3. _Soft clause: the sentences that do not satisfy hard clauses are soft clauses. All soft clauses are set as_ \(\text{SC}\)_._  
To transform the authorization-query request into the partial MAX-SAT, we represent permission \(p\) and role \(r\) with literal \(\vec{p}\) and \(\vec{r}\) respectively, and we describe them and their logical negations as follows:  
1. \(\vec{p}\): \(\forall p\in P,\exists r\in R,\exists s\in S,p\in\cup_{\textit{rereles\_session}(s)}\textit{perms\_ session}(r)\);
2. \(\
eg\vec{p}\): \(\forall p\in P,\exists r\in R,\exists s\in S,p\
otin\cup_{\textit{rereles\_session}(s)}\textit{perms\_ session}(r)\);
3. \(\vec{r}\): \(\forall r\in R,\exists s\in S,r\in\textit{reles\_session}(s)\);
4. \(\
eg\vec{r}\): \(\forall r\in R,\exists s\in S,r\
otin\textit{roles\_session}(s)\).
# GNSS Positioning Using Mobile Devices with the Android Operating System
## 4 Results and Discussion
### Real-Time Kinematic Positioning
This means that also considering previous studies [18], not all GNSS receivers installed inside smartphones can provide the same results because the raw observations have different conditions in terms of noise and accuracy. For the future, it will be interesting to perform the same tests considering new GNSS chipsets and the employment of new GNSS constellations and signals.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 5 Results
### Signal-to-Noise Ratio Impact on Radar Sensing
#### 5.2.3 Root Mean Square Error of Range Estimation  
The range estimation RMSE is presented in Figure 11. The best-performing configurations are PRS-4-4-2-RES, PRS-2-2-2-RES, and PRS-6-6-2-RES, where the first two reach the minimum range estimation RMSE at \(-\)3 dB of the input SNR with simultaneous PNFR equal to \(\sim\)12.5 dB, and the last achieves it at \(-\)2 dB with PNFR at the level of \(\sim\)13 dB. It is noticeable that PRS-12-12-1-RES is the next-best configuration, together with PRS-6-6-1-RES and PRS-2-2-1-RES. Although the first configuration spans 12 symbols in a single slot, it has pilots only on every 12-th subcarrier. Despite that, all of them reached a minimal RMSE at \(-\)1 dB of the input SNR with simultaneous PNFR at a level of \(\sim\)12 dB. It should be mentioned that PRS-12-12-1-RES converged faster to its minimal RMSE than PRS-6-6-1-RES and PRS-2-2-1-RES. PRS-4-4-1-RES reached a minimal RMSE at 0 dB of the SNR and with PNFR at \(\sim\)13 dB. Similarly, in the case of velocity estimation, PRS-12-1-2-RES and PRS-12-1-1-RES perform the worst, with the first configuration reaching a minimal RMSE at 8 dB of SNR with PNFR at \(\sim\)13.5 dB and the second at 10 dB of SNR with PNFR at \(\sim\)13 dB.  
Figure 10: Root mean square error of velocity estimation for different PRS configurations (solid lines with asterisk markers) together with PNFR as a reference (dashed lines).
###### Abstract  
This paper investigates the use of micro-Doppler spectrogram signatures of flying targets, such as drones and birds, to aid in their remote classification. Using a custom-designed 10-GHz continuous wave (CW) radar system, measurements from different scenarios on a variety of targets were recorded to create datasets for image classification. Time/velocity spectrograms generated for micro-Doppler analysis of multiple drones and birds were used for target identification and movement classification using TensorFlow. Using support vector machines (SVMs), the results showed an accuracy of about 90% for drone size classification, about 96% for drone vs. bird classification, and about 85% for individual drone and bird distinction between five classes. Different characteristics of target detection were explored, including the landscape and behavior of the target.  
drone detection; UAV detection; bird detection; micro-Doppler; spectrogram; continuous-wave radar; target classification; time-frequency analysis +
Footnote †: This work was presented partially at [PERSON]; [PERSON]; [PERSON] Experimental analysis of micro-Doppler characteristics of drones and birds for classification purposes. In Proceedings of the SPIE Conference on Radar Sensor Technology XXVI, Orlando, FL, USA, 3-7 April 2022; doi: 10.1117/12.2622408.
## 2 Materials and Methods
### Validation  
A ten-day intensive field study in July 2012 was conducted to establish a rangeland degradation classification system for Linxia rangeland and to evaluate the accuracy of LSMA using the bare-land ratio data as a reference. During the fieldwork, 108 groups of ground truth spectral information were obtained and recorded. Except the points for endmember selection, 50 points were selected for validation. The accuracy of LSMA method was estimated by scatterplot correlation and linear fitting by comparing estimated bare-land rate in each point with recorded ground truth data (Figure 4). Meanwhile, the validation results for the other three years (1996, 2001, and 2006) were estimated by comparison with visual interpretation results and reference data in 50 pixels, which were evenly and randomly selected from the masked images.