esa-sceva/satcom-chunk-collection huggingface dataset:
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 8 Conclusions and Perspectives  
A special version of the degree-constrained Steiner problem is initiated in our paper. The degree bounds are the maximal capacities of the nodes and the nodes can be used several times in the spanning structure. The degree constraints must be satisfied for each occurrence of the nodes. This model can be applied to several scheduling and routing problems. It is demonstrated that the optimum of the minimum cost capacity degree-constrained partial spanning problem is a hierarchy. The problem is NP-hard. As an important result, necessary and sufficient conditions for the existence of the solution are formulated.  
An ILP-based computation of the degree-constrained minimum-cost Steiner hierarchies is also presented.  
For the cases where the number of nodes with a degree bound of one is less than three, we present an approximation of the analyzed NP-hard problem.  
The degree-constrained Steiner hierarchies are compared to the degree-constrained Steiner trees and also to the Steiner trees without constraint. The experiments also prove that, in some cases, degree-constrained Steiner hierarchies may exist where Steiner trees respecting the constraints do not exist. Moreover, even if trees that respect the constraints exist, it is possible that an optimal hierarchy with lower cost can be found. Future investigations are needed for the development of efficient heuristics and improved approximation ratios. Artificial Intelligence is a good candidate to create efficient solutions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
1 M1 & **ct(STT)** & **arc(STT)** & **ct(DCMStT)** & **arc(DCMStT)** & **ct(DCMStH)** & **arc(DCMStH)** \\ \hline
2 & 7.14286 & 3.42857 & 7.14286 & 3.4 & 7.14286 & 3.51429 \\
7 & 21 & 11.4324 & 21.9189 & 11.7297 & 21.4324 & 11.6486 \\
12 & 30.303 & 17.1515 & 31.5455 & 17.5152 & 31.0303 & 17.697 \\
17 & 42.0741 & 23.4074 & 44.2692 & 23.6538 & 43.8889 & 24.1852 \\
22 & 53.2222 & 29 & 56.4722 & 29.6667 & 54.8889 & 29.8333 \\
27 & 60.7353 & 33.0882 & 63.7941 & 33.6176 & 62.5294 & 34.3824 \\
32 & 69.0345 & 37.6552 & 71.963 & 38.1852 & 71.4286 & 39.3929 \\
37 & 76.2222 & 42.3611 & 81.9143 & 43.2571 & 79.7778 & 44.8333 \\
42 & 87.6364 & 46.6667 & 93.1935 & 47.6452 & 91.2727 & 49.1212 \\
47 & 92.2 & 50.9429 & 99.3824 & 51.5882 & 96.3714 & 53.5714 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results by varying \(|M|\).
###### Abstract  
In this paper, a nonlinear regression method called a support vector regression (SVR) is presented to establish the relationship between engineering ground motion parameters and macroseismic intensity (MSI). Sixteen ground motion parameters, including peak ground acceleration (PGA), peak ground velocity (PGV), Arias intensity, Housner intensity, acceleration spectrum intensity, velocity spectrum intensity, and others, are considered as candidates for feature selection to generate optimal SVR models. The datasets with both useable strong ground motion records and corresponding investigated MSIs in the Sichuan-Yunnan region, China, are all collected, and these 125 pairs of datasets are used for selecting features and comparing regression results. Nine ground motion parameters are selected as the most relevant features: PGA is the first fundamental one and PGV is the fifth relevant feature. Based on performance measures on the testing dataset, the best SVR model is given when the number of features is one all the way up to nine. According to predicted accuracy, SVR models with Gaussian kernel give much better MSI prediction than linear kernel SVR models and linear regression models. In particular, the Gaussian kernel SVR of PGA gives much higher MSI prediction accuracy than the linear regression model of PGV and PGA. The proposed SVR models are valid for MSI values from VI to IX, and they can be used for rapid mapping damage potential and reporting seismic intensity for this high-seismic-activity region.  
macroseismic intensity; ground motion parameters; support vector regression; Sichuan-Yunnan region; linear regression; PGA; PGV; instrumental seismic intensity +
Footnote †: journal: Applied  
1
# Research Progress and Prospects of Agricultural Aero-Bionic Technology in China
## 5 Discussion
In the face of a harsh operating environment, the operational efficiency of agricultural aircraft is inseparable from the support of communication, navigation, command, and other sensor equipment in the aircraft. At present, in the Chinese agricultural aviation aircraft, sensor equipment can complete the work of information collection and communication of the operating environment. However, the function of the sensor is relatively single, and the product has a large gap with biological perception in terms of power consumption, volume, and anti-interference in a complex environment. Therefore, in the future, it is necessary to improve the equipment manufacturing process, combine with bionic technology, strengthen the development of miniaturized sensor devices, and conduct in-depth research on integrated and intelligent sensor devices. 4. In-depth research on the mechanism of animal brain systems. Poor operating environment, electromagnetic interference, weak signal, and other factors usually affect the operating efficiency of agricultural aircraft, and even make it impossible to operate. Intelligent positioning and navigation technology obtains the surrounding environment information by imitating the mechanism of an animal brain system to construct cognitive maps or deep learning, so as to achieve the purpose of autonomous navigation and image recognition without relying on communication equipment. Agricultural aviation research on animal brain systems mainly focuses on deep learning, intelligent navigation, etc., but the current research is still at the theoretical stage, mainly because human beings have not yet fully understood the brain structure of animals, and the principle of cooperation between various cells and neurons in the brain needs to be further explored. In recent years, the combination of animal brain bionic research and robot learning has gradually become a new development trend, which provides a direction for the future intelligent development of agricultural aviation.
# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 2 Materials and Methods
### Model Evaluation  
Several well-known metrics to evaluate regression model performance were used, including the coefficient of determination (\(R^{2}\)), the root mean square error (RMSE), and the mean absolute error (MAE). \(R^{2}\) measures the percentage of variance in the dependent variable that can be described by the independent variables in a regression model [51]. The RMSE accounts for positive and negative deviations between predicted and observed values. Regarding the MAE, it is calculated as the average of the absolute differences between the predicted and observed values of the dependent variable. The average magnitude of errors performed by the model is measured in the units of the same order as that of the dependent variable and denoted as the MAE. While the RMSE takes the squared differences into account, the MAE does not which makes it robust in regard to outliers and insensitive to extreme errors. By considering the mean absolute difference between the observed and simulated values, the MAE provides the overall accuracy of the model, regardless of the direction of the errors [52].
# Sparsity-Robust Feature Fusion for Vulnerable Road-User Detection with 4D Radar
## 3 Method  
In this section, we introduce our novel Radar Pillar Feature Fusion Network. We base our approach on PointPillars [9] for 3D object detection in LiDAR point clouds, extending the network with our novel Sparsity-Robust Feature Fusion (SRFF). The propagation of sparse data through a neural network is heavily influenced by the receptive fields of individual layers. Later layers with a larger effective receptive field have more valid information within their feature maps [29]. For this reason, SRFF uses multi-scale features to enhance the network's ability to detect particularly small objects with few points, e.g., pedestrians, in sparse radar point clouds. The complete network architecture of our Radar Pillar Feature Fusion Network is shown in Figure 2.  
Figure 2: Representation of our Radar Pillar Feature Fusion Network. First, pseudo-images are created from the radar point cloud. Subsequently, the backbone is used to generate deep semantic features, which are, in turn, fused by the Sparsity-Robust Feature Fusion (SRFF) to ensure robust pedestrian detection by the anchor head.
# Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion
## 3 Method
### In-Vehicle Sensors  
Modern vehicles have several hundreds of built-in sensors and electronic control units (ECUs) which monitor and optimize all car functions. Typical operating conditions (such as fuel injection or braking), and ancillary functions such as entertainment and air conditioning are tasks controlled and managed by the ECUs. Inside a car, the ECUs are connected together by means of a standard communication bus called the controller area network (CAN) introduced in the early 1980 by Bosch GmbH [22]. The CAN bus implements generic communication functions, on top of which, in 2001, a higher-abstraction-level protocol called On Board Diagnostics (OBD) was introduced [23]. This protocol also defines a physical connector, available in each car manufactured since 2001,  
Figure 1: Algorithmic workflow of the proposed method.  
which can be used for connecting compatible instruments. Through the OBD connector, an external device can interact with the car's ECUs by reading and writing each exchanged message. Although it is technically possible to access any information generated by the car's ECUs, the OBD protocol (called OBD-II in the current version) defines a subset of all sensor signals which are available for external reading. Every car that complies with the OBD-II, and so allows complete access to the standard subset of information. These signals are primarily intended for emissions inspections including, for instance, vehicle speed, engine revolution speed, engine load, throttle position, fuel and air pressure, fuel and air temperature, and so on. Car manufacturers also define additional signals which can be read, such as brake pressure, steering angle, wheel speeds, etc., but, because these are not part of any standard, their availability is not guaranteed.  
Starting from the OBD-II available signals we chose only those most significant for the estimation of driving behavior. In particular, we focused on:  
* Vehicle speed.
* Engine speed.
* Engine load.
* Throttle position.  
The first three signals (i.e., vehicle speed, engine speed, and engine load) are indirectly related to some driver actions, while the last one directly describes the position held by the driver on the accelerator pedal which therefore expressly represents the will of the driver.
## 2 Experiments  
### Die Attachment Samples  
As shown in Figure 1a, the polished side of a square silicon die (5 mm \(\times\) 5 mm \(\times\) 1 mm) was coated with a 50 nm Ti and a 50 nm Ag metallization layer using magnetron sputtering technology. A Ti layer was used as an adhesive by reacting it with natural oxides on the wafers, and an Ag layer provided a covering layer for tight integration with sintered micron silver paste. For the die bonding substrate (10 mm \(\times\) 10 mm \(\times\) 2 mm), copper (Cu 99.9%) was selected to make it, and a 50 nm Ti layer designed to prevent oxidation and copper atoms from spreading to sintered Ag joints was sputtered on one side, followed by a 2 \(\upmu\)m Ag layer. The sandwich structure of the die attachment sample is shown in Figure 1b, where the die attachment structure was the micron silver paste with a thickness of 100 \(\upmu\)m. The sample was assembled by brushing the paste on the metallized substrate, placing the silicon wafer on the Ag paste with tweezers, and sintering in air at the temperature of 230 \({}^{\circ}\)C without pressure, as shown in Figure 1c.  
Figure 1: Details of die attachment samples: (**a**) Ti/Ag plated silicon dies; (**b**) the die attachment structure consisting of a Cu substrate, micron silver paste and one side polished die; (**c**) the curing process of micron silver joints.  
Material parameters of the micron silver paste are listed in Table 1, and the SEM image with spectrum spot and EDS analysis result are shown in Figure 2. It can be seen that the material consists of submicron silver particles and silver flakes and involves the negligible carbon content which shows a small peak.
## 4 Simulation Analysis of CRPDC
### Coil Currents under Different PWM Frequencies  
While the input PWM control signals with different frequencies are applied to the proposed CRPDC, the coil current response is simulated and analyzed, as shown in Figure 5. For comparison, the output currents calculated by the mathematical models are shown in Figure 5 as well. The frequencies of input PWM signals are set to be 2.0 kHz, 3.0 kHz, 4.0 kHz, and 5.0 kHz, respectively. The duty cycles of the input PWM signals are all set to be 50%. The duty cycle is defined as the proportion of the high-voltage time to the total time in one cycle. The energy-storage capacitance and the power voltage are configured to be 10 uF and 24 V. As can be seen from Figure 5, the desired coil current can be fulfilled by the CRPDC. The average currents of the proportional valve coil under different PWM frequencies are evaluated to be 1.31 A, 1.48 A, 1.57 A, and 1.64 A (results from the Multisim model), as shown in Figure 6a. The current ripples of the proportional coil under different PWM frequencies are determined to be 140.11 mA, 64.29 mA, 36.25 mA, and 23.10 mA (peak-to-peak values, results from the Multisim model in Figure 4a), as shown in Figure 6b. It is observed that while the frequency of the input PWM signal increases, the output current of the proportional valve coil rises slightly but the current ripple obviously decreases. While the proposed CRPDC works in the turn-on state and turn-off state, the resulting coil current can be described by Equations (5) and (22), respectively. According to Equations (10) and (11), the coefficients \(K_{1}\) and \(K_{2}\) of Equation (5) are influenced by the final coil current of the previous turn-off state. From Equations (23) and (24), we found that the phase \(\varphi\) and the coefficient \(A\) of Equation (22) are influenced by the final coil current of the previous turn-on state. The switch between the turn-on state and turn-off state will result in a steady-state coil current featured with a ripple. Hence, the average-current and current-ripple values of the resulting coil current are determined by both the circuit parameters and the switching frequency. The current ripple can be regulated by accommodating the input PWM frequency of CRPDC. Moreover, the simulation results are basically consistent with  
Figure 4: Multisim model of CRPDC: (**a**) Capacitive reservoir circuit; (**b**) current sampling circuit.  
the mathematical calculation results, which confirms the effectiveness of the established mathematical models.  
Figure 5: The resulting coil currents of CRPDC under different PWM frequencies: (**a**) 2 kHz; (**b**) 3 kHz; (**c**) 4 kHz; (**d**) 5 kHz.  
Figure 6: The current characteristics of CRPDC under different PWM frequencies (results from Multisim model): (**a**) Average current; (**b**) current ripple (peak-to-peak value).
## 4 Experimental Measurements  
### Characterization of the Channel  
Focusing on the channel variations, Equation (4) defines the factors that affect the received signal intensity. In the following, based on the analysis of these principles, we evaluate how the intensity changes as a function of the distance. This evaluation can only be performed if the channel is previously characterized to correlate with the intensity and  
Figure 7: Received digital signal, filtered signal, and processed signal for decoding.  
the distance. This detailed analysis allows us to understand the variations in the intensity at different points of the plane, taking into account the elevation angle of the photodiodes, which assists us in identifying areas with stronger signals and areas under shadows or weaker signals. Channel characterization is crucial for estimating the distance because it provides information concerning the conditions and changes in the channel that affect the signal processing. As a result, multiple surface contours are generated in Figure 8 to represent the intensity of each transmitter as a function of its position in the plane.  
Based on these results, the relationship curves between the distance of the transmitters and the intensity measured at the receiver are calculated. This process involves plotting all the collected points for each transmitter as a function of the measured distance. Notice that the resulting curves are essential for the distance detection algorithm, and are the foundation upon which the positioning process and orientation angle calculation are obtained. Therefore, ensuring the accuracy of this relationship plays a major role in the system's overall reliability.  
As illustrated in Figure 9, a relationship between distance and the received intensity signal level is observed throughout the plane. To achieve the highest possible accuracy, it was carried out with five straight lines that describe its behavior as a function of the obtained value. These equations are applied according to the received signal level, as shown in Table 4.  
The analysis of the behavior of the lateral photodiodes as a function of the distance enables one to determine the proximity to a light source when the upper photodiode cannot provide this information. This may occur, for instance, for large propagation distances, which limit the upper photodiode's detection angle, or when reflections or shadows appears in the propagation scenarios subject to blocking effects. The side photodiodes, distributed evenly around the semi-sphere, cover a \(360^{\circ}\) area. By analyzing the photodiode that detects the highest intensity, we can deduce its orientation relative to the light source. This method is employed only when the top photodiode fails to provide information from at least three light sources. Although accuracy diminishes with increasing distance, this approach offers an acceptable estimation of the position and extends the system's range.  
Figure 8: Surface curves of the intensity of each LED transmitter as a function of its position in the plane. The colors of the surface denote the Vrms, from 5 mV to 20 mV.
## 4 Time-Varying Filter  
In the previous section, we showed the way in which the coefficients of the oscillatory system affect the settling time. Our research aims to use this knowledge to reduce the transient state of the given system. We propose to vary the parameters of the system, in a pre-defined way and at a specified horizon. After reaching the horizon, the values of the system are set to be constant, to preserve the initial frequency characteristic demands.  
As the well-known transfer function is only defined for time-invariant systems, one can describe the time-varying system with a difference equation, which is defined as follows:  
\[\sum_{i=0}^{q}a_{i}(n)y[n-i]=\sum_{i=0}^{m}b_{i}(n)x[n-i], \tag{4}\]  
where \(a_{i}(n)\) and \(b_{i}(n)\) are the time-varying coefficients, \(y(n)\) is the output, \(x(n)\) is the input, \(q\) and \(m\) are the numbers of coefficients, \(i\) is the auxiliary index, and \(n\) is the discrete time-index. As this is a system which is recursive in nature, one must also remember to check the stability of the designed filter. In previous research, we proposed a method, based on [11; 12], to calculate the time-varying poles of the filtering structure and proposed a methodology for assessing the stability of the design.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 4 Calculation of the Contour Integral
### The Complex Logarithm
Resuming to the case in discussion, using \(F(\theta)\)=\(\ln\left(\mathrm{re}^{\mathrm{i}\theta}\right)\), then, by taking into account (13), the following holds:  
\[\lim_{\delta\to 0^{+}}\left[\ln\left(\mathrm{re}^{\mathrm{i}(\pi-\delta)} \right)-\ln\left(\mathrm{re}^{\mathrm{i}(\pi+\delta)}\right)\right]=\left[\ln (r)+\pi\mathrm{j}\  
makes it simple to assert that, for this case, we have:  
\[\lim\big{(}\max\{\arg(z_{2}-a)\}-\min\{\arg(z_{1}-a)\}\big{)}=-\pi, \tag{26}\]  
and consequently, the following always holds true:  
\[\arg(z_{2}-a)-\arg(z_{1}-a)<-\pi. \tag{27}\]  
Similarly, if we have the reverse situation,  
\[ \begin{cases}(z_{1}-a)\in Q3\\ (z_{2}-a)\in Q2\end{cases} , \tag{28}\]  
then, the following can also be demonstrated:  
\[\arg(z_{2}-a)-\arg(z_{1}-a)>+\pi. \tag{29}\]  
In summary, when NRA intersection occurs from \(Q2\) to \(Q3\) (\(+\)\(\bullet\)), we have the first case in (24) which, by using the equality of case 3 from (15), certified by (27), allows to state the following:  
\[ \begin{split}\int\limits_{[z_{1}z_{2}]}\frac{\mathrm{d}z}{z-a}& =\ln(z_{2}-a)-\ln(z_{1}-a)+2\pi\mathrm{j}\\ &=\left(\ln\frac{z_{2}-a}{z_{1}-a}-2\pi\mathrm{j}\right)+2\pi \mathrm{j}=\ln\frac{z_{2}-a}{z_{1}-a}\end{split}  \tag{30}\]  
Similarly, when NRA intersection occurs from \(Q3\) to \(Q2\) (\(+\)\(\bullet\)), we have the second case in (24), which allows us to state the following by using the equality of case 2 from (15), as confirmed by (29):  
\[ \begin{split}\int\limits_{[z_{1}z_{2}]}\frac{\mathrm{d}z}{z-a}& =\ln(z_{2}-a)-\ln(z_{1}-a)-2\pi\mathrm{j}\\ &=\left(\ln\frac{z_{2}-a}{z_{1}-a}+2\pi\mathrm{j}\right)-2\pi \mathrm{j}=\ln\frac{z_{2}-a}{z_{1}-a}\end{split}  \tag{31}\]  
In conclusion, and by comparing expressions (24) and (15), the following final result is obtained for any \(a\
eq z_{1},z_{2}\):  
\[\int\limits_{[z_{1}z_{2}]}\frac{\mathrm{d}z}{z-a}=\ln\frac{z_{2}-a}{z_{1}-a}. \tag{32}\]  
A non-null value for expression (32) as the indicator of point inclusion in polygons is precisely the methodology firstly used by the author in [25] (pp. 149-152), in the early 1990's, as mentioned before, and whose validity has just been formally demonstrated.