esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Experimental Results  
#### 3.4.1 Multi-Scale Localization Feature Pyramid Experiment Analysis  
In the CrossVis-based Weakly supervised benchmark network (CWVIS), the high-level features of FPN have the problems of lack of spatial location information and lack of semantic information caused by single receptive field. In order to solve these problems, this paper proposes MSL-FPN. The spatial information refinement of FPN output features is realized so as to enhance the correlation between FPN output features and low-level feature sets. As shown in Table 1, in this paper, we perform three ablation experiments on MSL-FPN and compare them with CWVIS, where sub1, sub2, and sub3 denote SIR-subI, SIR-subII, and SIR-subIII, respectively. The segmentation accuracy of CWVIS is 32.2%. On the basis of CWVIS, the average segmentation accuracy of SIR-subIII, SIR-subII, and SIR-subI on FPN features P3, P4, P5 is 34.3%, 35.8%, and 36.6%, respectively. Compared with the segmentation results using SIR only, it improves by 2.1%, 3.6%, and 4.4%, respectively. The experimental results show that the network complements the spatial position information and receptive field of high-level features by MSL-FPN, which effectively improves the average segmentation accuracy of the network.
###### Abstract  
Car-mounted measurements of radiofrequency electromagnetic exposure levels were carried out in a large area around Tokyo. Prior to the electric field (E-field) measurements using a car, the effect of the car body was evaluated in an anechoic chamber. The measurements between May 2021 and February 2022 were carried out within a radius of 100 km centering on Nihombashi, Tokyo, with a measurement distance of about 13,800 km. The measurement results were averaged in the reference area mesh (1 km\({}^{2}\)). It was found that the E-field strengths of FM/TV frequency bands are lower than that of mobile phone base stations. It was also found that the E-field strength of only the 5G frequency band is approximately 20-30 dB lower than that of all mobile phone systems. However, note that it is possible to depend on the data traffic of 5G. The E-field strength of all bands is higher in Tokyo than in other prefectures. Additionally, repeated measurements were carried out to investigate the reproducibility of the measured E-field. The standard deviation is less than 3 dB along the same route, and a similar tendency of E-field strength by the car to the time-averaged results of spot measurements in the past was confirmed. Finally, the relationship of E-field strength with population density was investigated. It was found that the E-field strength from mobile phone base stations has a positive relationship with population density.  
mobile phone base stations; car; 5G; RF exposure level; electric field strength; outdoor environment; population density +
Footnote †: journal: _electronics_
# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 4 Experimental Results and Analysis
### Ablation Studies
#### 4.2.4 Ablation Study on Dynamic Motion Model Components
This demonstrates that the three components address complementary aspects of motion modeling challenges in multi-object tracking.
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Metaheuristic Algorithms-Swarm Intelligence
#### 3.1.6 Cohort Intelligence (CI) Methodology  
Cohort Intelligence (CI) methodology is used to model the behaviour of candidates depending on the interaction among them to perform a common goal. The behaviour of each candidate is improved by looking at all other candidates in the cohort. The CI is used for coverage area in the operation of search and rescue by swarm robots, the roulette wheel selection method, and the median method. Also, a perturbation technique is used to solve the problem of getting stuck in non-convex obstacles by robots. Many cases were achieved, such as the No Obstacle Case (NOC), Stationary Obstacles Case (SOC), Single Dynamic Obstacle Case (SDOC), Multiple Dynamic Obstacles Case with the Same Velocity (MDOC-SV), and Multiple Dynamic Obstacles Case with Different Velocities (MDOCDV) [57].
## 3 Results
### Kinetic Studies Using MME and IMME Based on Simulated Values
A summary of the work carried out can be seen in the scheme presented in Figure 3.  
To exemplify the errors obtained when the initial velocity is assumed, theoretical values (same data points, product versus time) were obtained with Equation (9) (Table 2) and the arbitrary values of the product (P) introduced in the equation (assuming predefined kinetics parameters) in order to obtain time (t) values (K\({}_{\text{m}}\) = 500 uM; V\({}_{\text{max}}\) = 0.120 umol min\({}^{-1}\) mg\({}^{-1}\); K\({}_{\text{ic1}}\) = 100 uM; K\({}_{\text{i1}}\) = 50 uM; K\({}_{\text{ic2}}\) = 5 uM; K\({}_{\text{i12}}\) = 5 uM). It should be pointed out that ratios  
Figure 3: Scheme of theoretical framework developed in order to show that with the same data range, different kinetic models were obtained as a function of the chosen methodology (initial velocities or progress curve analysis). \(\mathrm{K_{m}/K_{ic1}=5}\) and \(\mathrm{K_{m}/K_{iu1}=10}\) are in the same order of magnitude of simulations presented in Figure 2b,c. Moreover, these ratios are in the same order of magnitude exhibited by enzymes with product inhibition [10, 11, 21]. With these theoretical data points (product vs. time), the study was carried out using the initial velocity methodology (MME) and compared with an IMME based on the same data range (linear phase of product vs. time), i.e., using the same simulated points. The best methodology (MME or IMME) will be the one that provides similar kinetic parameters with those utilised for initial data point generation. As can be seen, the different values for the kinetic parameters and the inhibition model thus discriminated (UCI/MME) do not match the initial constants used to obtain the values of the data set (product vs. time). The kinetic constants obtained by the MME methodology were: \(\mathrm{K_{m}=344.4}\)\(\mathrm{\SIUnitSymbolMicro M}\) instead of \(500\)\(\mathrm{\SIUnitSymbolMicro M}\); \(\mathrm{V_{max}=0.077}\)\(\mathrm{\SIUnitSymbolMicro mcol\min^{-1}\mathrm{mg^{-1}}}\) instead of \(0.120\)\(\mathrm{\SIUnitSymbolMicro mcol\min^{-1}\mathrm{mg^{-1}}}\); \(\mathrm{K_{ic2}=\infty}\) instead of \(50\)\(\mathrm{\SIUnitSymbolMicro M}\); and \(\mathrm{K_{iu2}=7.3}\)\(\mathrm{\SIUnitSymbolMicro m}\) instead of \(5\)\(\mathrm{\SIUnitSymbolMicro M}\). On the contrary, the IMME methodology allowed us to obtain identical kinetic parameters (Table 5) and a kinetic model (MI) chosen to generate simulated data points (product vs. time). It is evident that the MME methodology (initial velocity) cannot be used to obtain \(\mathrm{K_{ic1}}\) and \(\mathrm{K_{iu1}}\). In addition, this methodology also failed to discriminate the inhibition model for the second inhibitor (UCI instead of MI) and gave the kinetic parameters (Table 5) \(\mathrm{K_{m}}\) and \(\mathrm{V_{max}}\) with values lower than around \(30\%\). These results highlight the superiority of the IMME methodology.
## 3 Experiments and Results
### Motion Pattern Recognition Based on Multilayer Perceptrons
#### 3.1.3 Different Feature Parameter Sets  
In order to study whether it is possible to reduce the number of input parameters of the multilayer perceptrons model and to simplify the structure of the multilayer perceptrons model, four different feature parameter sets were used: i.e., (1) only RMS feature parameters; (2) only time -domain feature parameters (RMS, VAR, and ZC); (3) only frequency-domain feature parameters (MPF and MF); and (4) all the feature parameters (RMS, VAR, ZC, MPF, and MF). These sets were used and compared on the validation set to calculate the accuracy.  
Figure 9: Effects of trend term on classification accuracy. (**a**) Recognition results of unremoved trend term. (**b**) Recognition results of removed trend term.  
The steps for establishing the feature parameter set are the same. The sliding window length was 1024 ms, and the window interval was 50 ms. Seventy percent of the dataset was used for training, and 30% was used for testing. In the 70% of the data-set used for training, 90% of the data-set was used for training, and 10% of the data-set was used for verification. The classifier uses a three-layer multilayer perceptron. The activation function of the hidden layer selects the sigmoid function, and the activation function of the output layer selects the softmax function. In the cross-validation method, the number of hidden layer nodes was changed from 4 to 60, and the classifier model was trained separately. The number of hidden layers with the highest accuracy was selected as the final classification model. The final recognition results are shown in Table 2.  
It can be seen from Table 2 that, when the sliding window length is 1024 ms, the classifier using all the feature parameter sets (RMS, VAR, ZC, MPF, and MF) has the highest recognition accuracy, reaching 95.93%. The contribution of the time-domain feature parameters to the classification performance of the classifier was much larger than the frequency-domain feature parameters. However, at the same time, the contribution of the frequency-domain feature parameters to the accuracy of the movement pattern recognition of the lower limb cannot be ignored. After adding the frequency-domain features, the accuracy rate of the movement pattern recognition was further improved, reaching 95.93%. Judging from the time required to make a prediction using the multilayer perceptrons model, a small increase in the number of input features does not significantly increase the time complexity of the operation, and there is no significant difference in the recognition speed of human motion patterns.
## 4 Results and Discussions
### Discussions
#### 4.2.2 Classifier-Wise Discussion  
Referring to Table 6, Datasets 11, 12, 13, 14 and 15 responded well to both the classifiers and the results were almost comparable. It can be observed that all the datasets produced slightly higher results on the MLP classifier w.r.t. the SVM classifier. This shows the slight upper hand of the non-linear functional approach of the MLP network over the linear separation approach of SVM to solved the proposed multi-class classification problem.
## 5 Discussions of Simulation Analyses
### The Effect of Different Axes Distances on Temperature Distribution  
As shown in Figures 6 and 10, airflow interaction exists in the spaces between the two fans when the present axes distance is 400 mm. This phenomenon will aggravate flow turbulence and energy waste and is detrimental for system cooling. In order to decrease mixing airflow at the junctions of fans, the central axes distance of two fans will be changed to study its effect on temperature distribution, which is shown in Figure 15. Taking the upper fan group, for instance, the curve of the differences of average temperature between outlet and inlet sections versus distances of fan axes is shown in Figure 16. With the axes distance changing from 340 to 500 mm, the temperature difference firstly stays at about 22 K, then increases to a maximum value of 30 K at the distance of 440 mm. Afterwards, the temperature difference begins to decrease with increasing distance and drops down to 17.45 K at the distance of 500 mm. The reason for this is that if the distance of two fans is too long, the radiators will not be covered fully by the fans, especially in the spaces between the two fans; but if they are too close, the airflow interaction will strengthen at the edges of fans. As a result, both situations will lead to poor cooling effects and temperature increases. Hence, there is a best distance of the fan axes. In this cooling system, its optimal distance is 440 mm and the interval of the two fans is 120 mm.  
Figure 16: Temperature difference of the outlet and inlet in the torque converter radiator versus the axis distance of two fans.  
Figure 15: Schematic diagram of the cooling system with different intervals of the fan axis and thermal baffles.
## 4 Results
### Estimation of NSP Intensity in the Yucheng Reservoir Catchment Area  
The NSP intensity was estimated for the cultivated land area in the Yuecheng Reservoir catchment area (Figure 12a). The results indicated a concentration area of high source intensity of farmland NSP southwest of the Yuecheng Reservoir catchment area. The NSP intensity decreased gradually to the north and decreased sharply to the east. The overall trend corresponded to the topographic relief, cultivated land area of each county, crop yield, crop environmental conditions, and other factors in the region. The main vein of the Taihang Mountains runs through the middle of the Yuecheng Reservoir catchment area from north to south, and most of the area to the east of the Taihang Mountains is rocky with high mountains and deep valleys, exposed rocks, and little cultivated land. This leads to decreased farmland NSP from the middle to the East. The main factor affecting the gradual reduction of the NSP intensity from south to north in the central and western regions is the crop growth environment. Among these regions, the Changzhi, Lucheng, and Xiangyuan counties are basins with flat terrain and abundant rainfall, which are suitable for crop growth, and multiple crops are mostly concentrated in this area. In addition, the statistical yearbook data indicate that the cultivated land area and fertilization are higher in these counties than in other counties, which makes these counties more likely to serve as sources of pollution.
## 4 Proposed Model for BDS MEO/IGSO B1-I Self-Interference  
Based on Equations (20) and (23), the proposed model for BDS MEO/IGSO B1-I self-interference, or the aggregate equivalent white noise due to self-interference can be expressed as:  
\[\begin{array}{ll}I_{0,m}\\ =p_{m}+&\sum\limits_{i=1}^{n}\left(SSC_{B1-I}\right)_{m}^{i}\\ &i\
eq m\\ =p_{m}+\frac{2T_{c}^{2}}{3T_{b}\sin^{2}(\pi f_{m}^{i}T)}&\sum\limits_{i=1}^{n} \left\{\left(2046-C_{m}^{i}\right)\left[\sin^{2}\left(\pi f_{m}^{i}K_{m}^{i}T \right)+\sin^{2}\left(\pi f_{m}^{i}\left[20-K_{m}^{i}\right]T\right)\right] \right.\\ &\left.i=1\right.\\ &\left.i\
eq m\\ +C_{m}^{i}\left[\sin^{2}\left(\pi f_{m}^{i}\left[K_{m}^{i}+1\right]T\right)+ \sin^{2}\left(\pi f_{m}^{i}\left[19-K_{m}^{i}\right]T\right)\right]\right\} \end{array} \tag{24}\]  
where \(I_{0,m}\) is the aggregate equivalent white noise imposed on the \(m\)-th signal by self-interference, \(p_{m}\) is the received power at the antenna, \((SSC_{B1-I})^{i}_{m}\) is the SSC between the \(i\)-th and \(m\)-th signals, which is determined by differential Doppler \(f_{m}\) and differential time delay \(K_{m}^{i}\)\(T+C_{m}^{i}\)\(T_{c}\).  
Table 1 shows an example of how to use the developed analytical model, i.e., Equations (20) and (23) to predict \(SSC_{B1-I}\) and equivalent white noise \(I_{0}\).  
Note that the last line of Table 1 comes from the sum of \(I_{0}\) of PRN 8 and 9. At a specific time, there are 8 satellites visible to the receiver. In Table 1, the first column is the PRN number. The second column is the relative received power, relative to \(-160\) dBW. The third column is the true (geometric) range from satellite to the receiver. The fourth column is the true Doppler. These four columns are the parameters used to compute the fifth and sixth columns, i.e., \(SSC_{B1-I}\) and equivalent white noise \(I_{0}\), respectively. Since here it is assumed that PRN 6 is the desired signal, it is unnecessary to compute its own \(SSC\) and \(I_{0}\).  
Table 2 presents the procedure to compute \(SSC\) and \(I_{0}\). The procedure described in Table 2 is simply a re-iteration of the development of the model Equation (24).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**PRN** & **Relative Received Power** & **True Range** & **Doppler** & \(SSC_{B1-I}\) & \(I_{0}\) \\
**(dB)** & **(m)** & **(Hz)** & **(dB/Hz)** & **(dBW/Hz)** \\ \hline
66 & \(11.067\) & \(36\),\(868\),\(693\) & \(-563\),\(659\) & - & - \\
**88** & **11.255** & **36,071,721** & \(-\)**525.663** & \(-\)**62.7** & \(-\)**211.4** \\
**99** & **10.459** & **39,531,328** & **578.098** & \(-\)**76.6** & \(-\)**226.2** \\ Total & & & & & \(-\)211.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model utilization example.
## 3 Case Study
### Model Outputs  
This study aimed to develop a Bayesian network model and simple power regression with a power function equation to predict the key characteristics of a tugboat during the early design phase. Bollard pull (BP) and vessel speed (V) were selected as the input variables, with length, beam, draft, maximum draft, and power serving as the output variables.  
Moreover, Figure 12 illustrates the actual and predicted output values using a Bayesian network and power regression for length (L), beam (B), draft (T), and power (P). These are presented in plots depicting the conditional mean as a function of ballard pull (BP) and vessel speed (V).  
The results depicted in Figure 12 indicate that both the Bayesian network and power regressions yield moderate results compared to the actual data, confirming the coherence of the predictions. However, while the results for the ballard pull values between 60 and 80 metric tons are more accurate, deviations are observed for the maximum and minimum pull values.  
\begin{table}
\begin{tabular}{l l l l l} \hline  & \multicolumn{1}{c}{**Non-Linear**} & \multicolumn{1}{c}{**Coefficient of**} & \multicolumn{1}{c}{**Non-Linear**} & \multicolumn{1}{c}{**Coefficient of**} \\  & **Regression Group I Functions** & **Determination (R2)** & **Regression Group 2 Functions** & **Determination (R2)** \\ \hline Length [m] & \(BP=4.338\)\(L^{0.4477}\) & 0.4383 & \(V=0.319\)\(L^{1.757}\) & 0.4626 \\ \hline Beam [m] & \(BP=2.484\)\(B^{0.3747}\) & 0.6891 & \(V=1.497\)\(B^{0.817}\) & 0.2159 \\ \hline Draft [m] & \(BP=0.7528\)\(T^{0.4508}\) & 0.6064 & \(V=0.360\)\(T^{1.034}\) & 0.2129 \\ \hline Draft, Max. [m] & \(BP=0.79\)\(T_{max}^{0.4596}\) & 0.7527 & \(V=0.455\)\(T_{max}^{0.976}\) & 0.2266 \\ \hline Power [kW] & \(BP=73.15\)\(P^{0.9535}\) & 0.9368 & \(V=29.020\)\(P^{1.942}\) & 0.2656 \\ \hline \end{tabular}
\end{table}
Table 2: Non-linear regressions’ power functions and coefficients of determination of case study.  
Figure 11: Bayesian regression graphics for (**a**) training, (**b**) test and (**c**) total process, of the developed network.  
Figure 12: Comparison of the design parameters: actual values (_+_); Bayesian network predictions (‘o’, step curve); power regressions (power function curve).
## 7 More Results and Discussion  
Table 6 summaries the key parameter comparison between the initial algorithm, 3D-FEM, and experiment results. It can be seen that the analysis results of the TFPMDEIG match well with the experiment ones.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Definition**} & \multirow{2}{*}{**Parameter**} & **Initial Algorithm** & **3D-FEA** & **Experimental** \\  & & **Results** & **Results** & **Results** \\ \hline No load voltage (Amplitude) & \(E_{f}^{\max}[\mathrm{V}]\) & 171.35 & 169.36 & 164.78 \\ Main harmonic no load voltage (Amplitude) & \(E_{f1}^{\max}[\mathrm{V}]\) & 163.03 & 161.77 & 158.42 \\ Output voltage (Amplitude) for full load & \(V_{off}^{\max}[\mathrm{V}]\) & 101.23 & 105.32 & 98.30 \\ Main harmonic full load voltage (Amplitude) & \(V_{off}^{\max}[\mathrm{V}]\) & 98.99 & 102.05 & 96.19 \\ Full load current (Amplitude) & \(I_{off}^{\max}[\mathrm{V}]\) & 4.49 & 4.46 & 4.26 \\ Main harmonic full load current (Amplitude) & \(I_{off}^{\max}[\mathrm{V}]\) & 4.68 & 4.60 & 4.31 \\ THD * of no load voltage & THD & 28.12\% & 30.99\% & 29.06\% \\ THD output voltage in full load condition & THD & 24.52\% & 25.54\% & 15.49\% \\ THD output current in full load condition & THD & 24.52\% & 25.42\% & 15.19\% \\ Armature Resistance & \(R_{a}[\Omega]\) & 11.63 & - & 11.8 \\ Synchronous Reactance & \(\mathrm{X_{c}}[\Omega]\) & 12.51 & 13.63 & 14.11 \\ No load voltage (Effective value) & \(E_{f}[\mathrm{V}]\) & 115.28 & 114.02 & 112.01 \\ Output voltage (Effective value) for full load & \(V_{off}[\mathrm{V}]\) & 70 & 72.12 & 68.02 \\ \hline \hline \end{tabular}  
* Total Harmonic Distortion.  
\end{table}
Table 6: Key motor parameters.  
Figure 17: Experimental results: (**a**) no load voltage; (**b**) full load voltage; (**c**) full load current; (**d**) output power in full load.  
It can be seen that voltage THD or current THD is high. This phenomenon in the proposed generator looks normal, because the air gap length is high. To decrease THD, the generator must be made with a high number of poles.  
Figure 18 shows the variation of the output voltage versus load current. Figure 19 shows the output power versus the load current. With increasing load current, and approaching the rated current, the difference between the FEA results and the experimental results becomes larger. The 3-D FEM results and the experimental results are thus in good agreement, up to the nominal current. If the current is increased further than this value, then the errors become more substantial. The main contributors to the discrepancies between the FEA results and the experimental results are as follows: (a) weakening of the PMs; difference between the no-load voltages obtained from the 3D-FEA and the experimentally measured no-load voltage (Figure 19 for \(I_{out}=0\)) settles the weakening of the PMs; (b) armature resistance increases with the heat produced by passing current through the winding; (c) three dimensional leakage flux pathways. These pathways affect the synchronous reactance.
## 3 Wake-Up Radio Communication Protocols
### Passive Wake-Up Signal Packet
#### 3.2.1 Wake-Up Signal Frame Header  
The wake-up preamble is the first part of the frame header, which is responsible for generating the wake-up signal. The bits in the preamble are responsible for the transmitter and receiver bit interval synchronization. The SFD (start frame delimiter) allows the wake-up radio to know the actual frame header position and when exactly to start frame decoding.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Time-Slotted Protocols
#### 3.2.2 Channel-Aggregation Diversity-Based MAC Protocol  
Channel-Aggregation Diversity-based MAC protocol (CAD-MAC) [74] is centered on a novel differing qualities innovation called Channel-Aggregation Diversity (CAD). CAD permits the auxiliary node to use various channels in parallel for data transmission that are not being in use by PUs. It can productively distribute the upper-limited power resource for the chosen channels in view of the channel qualities through joint power-channel allocation and information radio. CAD-MAC offers two mutual power-channel provision schemes; one is the ideal allocation strategy through dynamic programming to maximize the transmission rate, and the other is a corresponding allocation strategy, which uses fractional programming for optimizing energy efficiency. In view of the joint power channel assignment approach, the node can use the channel to transmit many information packets in one transmission round.  
CAD-MAC utilizes the surely understood ON/OFF model to portray PUs' channel-utilization patterns, and all information channels are grouped into synchronized channel slots. CAD-MAC assumes all of the subsequent nodes in the CRAHN are synchronized and work with the same division of time as that of PUs to productively shield PUs from being meddled by SUs. In the CAD-MAC protocol, node pairs negotiate to access the opportunistic data channels through control packets in the transmission period, which are RTS, CTS and REServation (RES), respectively. As can be seen  
Figure 5: P-MAC time-channel domain structure. RES, REServation.  
from Figure 6, after a successful three-way handshake (RTS-CTS-RES), data channels and rates for transmission can be determined. Then, the origin-destination pair can transmit using the selected data channels.