esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tuning Method for Speed Tracking Controller Parameters of Autonomous Vehicles
## 4 Simulation Results and Discussion  
### Experiment Design  
In this study, a longitudinal PID control model for autonomous vehicles was constructed in the MATLAB/Simulink simulation (2019b) environment, with the model structure shown in Figure 3. The experiment focused on evaluating the longitudinal control performance of autonomous vehicles under varying road adhesion coefficients, selecting typical coefficient values of 0.5, 0.6, and 0.8 to simulate vehicle response behavior in low-, medium-, and high-adhesion environments. Through adjusting control parameters during the longitudinal acceleration and deceleration processes, the experiment analyzed the tracking performance of the controller's speed under different road conditions.  
Additionally, Table 1 presents the key parameters of the autonomous vehicle used in this experiment, including essential characteristics such as vehicle mass, providing an accurate physical foundation for the simulation model and ensuring the scientific rigor and reproducibility of the experimental results.  
To implement the longitudinal control simulation experiment, the desired speed is set in segments to simulate the processes of acceleration, constant speed, and deceleration. The desired speed is set as follows:  
* 0-6 s: The vehicle accelerates from 0 km/h to 40 km/h, simulating the start-up acceleration process.
* 6-9 s: The vehicle maintains a constant speed of 40 km/h for 3 s.  
\begin{table}
\begin{tabular}{c c} \hline \hline Mass & 1723 kg \\ Drag Coefficient & 0.3 \\ Vehicle Frontal Area & 2.5 m\({}^{2}\) \\ Air Density & 1.225 kg/m\({}^{3}\) \\ Rolling Resistance Coefficient & 0.015 \\ Gravitational Acceleration & 9.8 m/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle parameters.  
Figure 4: Optimization algorithm flowchart.  
* 9-15 s: The vehicle accelerates from 40 km/h to 80 km/h over 6 s, simulating the second phase of acceleration.
* 15-21 s: The vehicle maintains a constant speed of 80 km/h for 6 s.
* 21-24 s: The vehicle accelerates from 80 km/h to 100 km/h over 3 s, simulating the final phase of acceleration.
* 24-27 s: The vehicle maintains a constant speed of 100 km/h for 3 s.
* After 27 s: The vehicle begins to decelerate gradually from 100 km/h to 0 km/h, simulating the complete acceleration-deceleration process.
## 2 Results and Discussion
### VOC Metabolomes of the Chassis and Mutant Strains  
The VOCs released by \(\Delta\)_Talig4::neo_, \(\Delta\)_Talig4::neo\(\Delta\)_TaTS8::lph_, and \(\Delta\)_Talig4::neo\(\Delta\)_TaTS9::lph_ were collected at day 5 by the SPEM and identified by the GC-MS against the NIST database. As shown in Table 3, a total of 39 compounds were detected in \(\Delta\)_Talig4::neo_, including seven alcohols (81.89%), 17 sesquiterpenes (11.71%), four diterpenes (3.08%), six esters (1.17%), one each of fatty acid (1.72%), monoterpene (0.18%), alkane (0.11%), ketone (0.07%), and benzene (0.06%). In comparison to the high content and diversity of the VOCs  
Figure 5: Transcript levels of the _TaTS_ genes in the chassis strain \(\Delta\)_Talig4::neo_ and four _TaTS_ gene knockout mutant strains in day 3 upon _Foc_ induction. (**a**) \(\Delta\)_Talig4::neo_\(\Delta\)_TaTS3::lph_. (**b**) \(\Delta\)_Talig4::neo_\(\Delta\)_TaTS4::nb_. (**c**) \(\Delta\)_Talig4::neo_\(\Delta\)_TaTS8::lph_. (**d**) \(\Delta\)_Talig4::neo_\(\Delta\)_TaTS9::lph_. *, **, and *** indicate statistically significant differences at \(p\) < 0.05, \(p\) < 0.01, and \(p\) < 0.001, respectively. KO, knock out. produced by \(\Delta\)_Talig4:neo_, less volatile compounds (27 and 13, respectively) were produced in \(\Delta\)_Talig4:neo_\(\Delta\)_TaTS8:hph_ and \(\Delta\)_Talig4:neo_\(\Delta\)_TaTS9:hph_ (Figure 6). Notable differences were detected among the diversity and contents of the sesquiterpenes (RT at 18-24 min, Supplementary Figure S6). \(\Delta\)_Talig4:neo_\(\Delta\)_TaTS8:hph_ produced 14 sesquiterpenes with less relative content of 11.22%, and \(\Delta\)_Talig4:neo_\(\Delta\)_TaTS9:hph_ only produced two sesquiterpenes, i.e., 1H-benzocycloheptem-7-ol,2,3,4,4a,5,6,7,8-octahydro-1,1,4a,7-tetramethyl-cis-19 and cyclohexane (undetected in \(\Delta\)_Talig4:neo_), with the low content of 0.35%. Similar results have been reported in the mutant strain of _Trichoderma viirines_ with the deletion of the sesquiterpene synthase gene _vir4_, which produced 24 less sesquiterpenes [27]. In addition, \(\beta\)-cubenene, as the predominant sesquiterpene of \(\Delta\)_Talig4:neo_ (7.54%), was completely absent in the VOCs of \(\Delta\)_Talig4:neo_\(\Delta\)_TaTS9:hph_. This indicated that _TaTS9_ is the most critical gene for the biosynthesis of sesquiterpenes, especially \(\beta\)-cubenene. A sesquiterpene synthase from filamentous _Acremonium chrysogenum_ (_Ac_TPS3; KFH47841.1) has been identified through genome mining, and its product was functionally characterized to be (+)-cubenene [28]. However, _TaTS9_ shared very low similarity (<30%) to _Ac_TPS3, except for the conserved motifs DDXXD and NSE of the Class I terpene synthases. Therefore, it is inaccurate to predict the functions of _TaTS9_ through sequence similarity and homology modeling. Thus, biochemical characterization of _TaTS9_ through heterologous expression is crucial and necessary.
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
###### Abstract  
Carpolactam wastewater (WCP), which is generated during the production of caprolactam, contains high contents of NO\({}_{3}^{-}\) and inorganic P and is considered to be difficult to treat. In this study, _Arthrospira platensis_ was used to remove N and P from WCP. Culture conditions and wastewater addition were optimized to relieve the inhibition effects of WCP. The results show that _A. platensis_ growth and photosynthetic activity were inhibited depending on WCP concentrations. The inhibition rates were enhanced as the culture time increased under batch mode. However, the fed-batch mode significantly minimized the negative impact on _A. platensis_, which is beneficial for removing N and P from WCP by _Arthrospira_. After 10 d of cultivation of _A. platensis_ in a 25 L circular photobieroactor in fed-batch addition of WCP (1.25% mixed WCP (\(v/v\)) each day), the average biomass productivity reached 17.48 g/(m\({}^{2}\cdot\)d), the maximum protein content was 69.93%, and the N and P removal ratios were 100%. The accumulation effect of WCP inhibition on algal growth was not observed under this culture condition. Fed-batch cultivation of _A. platensis_ is a promising way for bioremediation of WCP with high N and P removal efficiencies and high value-added biomass production.  
caprolactam wastewater; _Arthrospira platensis_; nitrogen removal; phosphorus removal; fed-batch addition
# Fusion Classification Method Based on Audiovisual Information Processing
## 1 Introduction
Despite the widespread application of multimodal fusion classification technologies [17; 18; 19; 20; 21], during the complementary process of cross-modal data fusion, the heterogeneity generated due to the distinct semantic characteristics of each modality can lead to insufficient feature information in the fused data. To address the heterogeneity issue in multimodal data fusion, this paper proposes an audiovisual fusion-based target classification framework. The innovations of this paper are as follows. (1) The transformation method is used to convert the one-dimensional sequence of sound, which is transformed into a two-dimensional discrete sound spectrogram. This process visually visualizes the sound and obtains a more stable sound signal. Additionally, it solves the problem of the ineffective fusion of one-dimensional sound sequence and two-dimensional image sequence, enhances the complementary ability of sound and image, and improves the accuracy of object classification. (2) Under the condition of dual-channel input of sound and images, this study combines Convolutional Neural Networks (CNNs) with feature-level fusion. To address the misalignment issue between spectrograms and images during the fusion process, this paper introduces a self-attention mechanism module during feature fusion. This approach utilizes a weighted method to resolve the heterogeneity between the spectrogram and image fusion.
# Implementation of Explosion Safety Regulations in Design of a Mobile Robot for Coal Mines
## 3 Description of the Mobile Robot Telerescuer
### Operator--Robot Communication  
A reliable system has been designed for communication between the operator and the robot. The main communication channel is based on an optical fibre cable. In case the cable is broken, a backup wireless communication system is activated automatically. The wireless network is built during robot motion by units called motes that act as repeaters to achieve hundreds of meters wirelessly. The motes are located on the rear part of the robot and they are automatically dropped depending on the intensity of the wireless signal.
## 3 Channel Statistics
### Characterization of SOFS for Outdoor and Indoor Radio Propagation Channels
For the Case 3, when the measurement setup is indoor office environment and measurements are taken at 28 GHz, the normalized LCR and AFD against different values of received signal's envelope's threshold level is plotted in Figure 10a,b, respectively. An increase at a slow rate in the LCR can be observed with an increase in the threshold level up to a certain threshold level, and a converse behavior of LCR can be observed for higher values of the threshold level. However, LCR has a smaller value for NLoS scenarios as compared to the LoS scenario, for all the threshold levels. The AFD is observed to increase with an increase in threshold level. Moreover, a lower value of AFD is observed for the LoS scenario as compared to the NLoS scenarios. Spatial auto-covariance is plotted in Figure 11a for the considered three scenarios against the distance in spatial points of observations normalized with the wavelength (\(r/\lambda\)). The rate of drop in spatial auto-covariance for the LoS scenarios compared to the NLoS scenarios is observed to be higher along an increase in the distance between the spatial positions of observation. CD for different scenarios of indoor mmWave channels is plotted in Figure 11b. The coherence indicates the separation distance between two observation points such that the spatial auto-variance remains at least 50%. CD is observed to have higher value for the LoS scenario as compared to the NLoS scenarios. Dissemination of the conducted analysis for the specific scenarios of mmWave channels to the research community shall significantly help in an early convergence to establishing generalization in today's mmWave channel models [30]. Figure 8: LCR and AFD plotted against different values of maximum Doppler shift in (**a,b**), respectively; for different scenarios of indoor mmWave (Case 2 (b); 38 GHz) radio propagation environments. (\(m=1\), \(\rho=2\)). Figure 9: Spatial auto-covariance and CD for different scenarios of indoor mmWave (Case 2 (b); 38 GHz) radio propagation environments in (**a,b**), respectively. (\(m=1\), \(f_{d}=10\) Hz).
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 4 System Design of PPUR
### Security Analysis
#### 4.5.2 Active Attack  
An active adversary may modify or damage the encounter record information. PPUR can prevent the active attack. Firstly, in PPUR, two encountering nodes generate the summary of encounter record information by the hash function, which can prevent information from being tampered with and forged, so the integrity is ensured. Secondly, the encounter record information is signed by the encountering nodes' private keys, so the signature ensures the nonrepudiation of the message.
## 5 Results and Discussion
### Simulation Results between Different Algorithms  
This section tests the performance of the IUPSO algorithm in different heliostat field scenarios. Three scenarios with different cloud equivalent velocities (5.0, 6.0, and 10.0) from the former section are selected, and seven optimization algorithms are compared: IUPSO, Modified PSO, PSO, BA, GA, WOA, and RDA. The parameters of these parameters are set in Table 3. The simulation results are shown in Figures 10-13 and Table 4.  
In Table 4, \(Y_{i}\) and \(\hat{Y}_{i}\) denote the optimization results of different algorithms. From Figures 11-13, it is evident that with the population inheritance mechanism, IUPSO and MPSO outperform GA, BA, WOA, and RDA by providing a better initial solution for optimizing the aiming strategy. Specifically, IUPSO, through its custom update mechanism for heliostat individuals, surpasses Modified PSO in solution efficiency, reducing over 100 iterations on average in tests across three heliostat scenarios. While WOA achieves convergence in all scenarios due to its spiral update mechanism, its performance degrades as heliostat complexity increases. In contrast, IUPSO demonstrates consistent performance and robustness, proving its superior real-time aiming strategy for heliostats. Additionally, from Figure 13 and Table 5, Modified PSO, GA, and BA fail to converge within the iteration constraint period. Although RDA achieves convergence quickly, it falls into a local optimum with a significantly lower output of the optimal solution than WOA and IUPSO. WOA requires more than 70 iterations to complete the optimization compared to IUPSO, highlighting IUPSO's ability to converge quickly. It also suggests that GA and BA have weaker problem-solving skills, while Modified PSO and RDA may become trapped  
Figure 11: Comparison of different algorithms based on the equivalent velocities 5.0.  
Figure 12: Comparison of different algorithms based on the equivalent velocities 6.0.  
Figure 13: Comparison of different algorithms based on the equivalent velocities 10.0.  
in a local optimum. IUPSO, Modified PSO, and WOA outperformed classical algorithms such as BA, GA, and RDA, regarding MSE, RMSE, and MAE under the same iteration constraint in three test scenes. IUPSO and WOA demonstrated higher global optimization capabilities by completing optimization before the iteration constraints. RDA showed good local optimization capabilities, but its global optimization ability was weaker than the other algorithms, resulting in inferior optimality. Modified PSO had similar local optimization capabilities as IUPSO but was more robust, taking longer to optimize in more complex scenes and surpassing the iteration constraints.
# Can LiDARs Replace Meteorological Masts in Wind Energy?
## Abbreviations  
* Atmospheric Boundary Layer CFD Computational Fluid Dynamics CNR Carrier-to-Noise Ratio CW Continuous Wave DBS Doppler Beam Swinging DEL Damage Equivalent Loads DLC Design Load Case IEC International Electrotechnical Commission NTM Normal Turbulence Model PDF Probability Density Function RIX Ruggedness Index RMSE Root-Mean-Square Error VAD Velocity Azimuth Display
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 5 Experimental Evaluation
### Experiments on Real-World Bipartite Network
In DUS and Maria datasets, \(EQ_{b}\) is used as the evaluation standard, and CBG&BEN algorithm and DSNE algorithm are used for community detection. The results show that the \(EQ_{b}\) value of the MARIA dataset is better than that of the DUS dataset. Still, the MARIA dataset's \(D\) value is lower than that of the DUS dataset when D is used as the evaluation standard, which indicates that there are more edges between communities, but the community division is relatively broken. It is not very complete. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**SCBG**} & \multicolumn{2}{c}{**SW**} & \multicolumn{2}{c}{**CL**} & \multicolumn{2}{c}{**DUS**} & \multicolumn{2}{c}{**CRIME**} \\ \cline{2-11}  & \(EQ_{b}\) & \(D\) & \(EQ_{b}\) & \(D\) & \(EQ_{b}\) & \(D\) & \(EQ_{b}\) & \(D\) & \(EQ_{b}\) & \(D\) \\ \hline CBG\&BEN & 0 & 1 & 0.41 & 0.50 & 0.54 & 0.57 & 0.26 & 0.75 & 0.79 & 0.35 \\ DSNE & 0 & 0.5 & 0.41 & 0.67 & 0.54 & 0.66 & 0.26 & 0.76 & 0.8 & 0.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: DSNE algorithm and CBG&BEN algorithm are compared to use \(EQ_{b}\) and \(D\) as evaluation index on different datasets. Figure 9: Experimental results of three algorithms evaluated by \(D\) in 11 datasets.
## 4 Application in Chenxi Bridge  
### Original Data  
As for Chenxi Bridge, it has established a displacement and strain monitor system. And at the end of the bridge, a camera was installed. Figure 14a shows the full bridge, and Figure 14b shows the spot where the truck passes the bridge.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Load** & **Noise Type** & **Noise Strength** & **Remark** \\ \hline
7 & 10 & Uniform & 0.5\% & Mean of disturbance \\
8 & 10 & random & 1.0\% & Noise is 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation cases with a 0.5 mean noise.  
Figure 14: (**a**) A full image of Chenxi Bridge; (**b**) the timestamp of a truck passing the bridge; (**c**) the MIRAN-130 displacement sensor; and (**d**) the displacement sensors on site.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Noise Details** & **IL** & **AIL** & **AIL-Modified** \\ \hline
1 & & 1.2\% & 2.2\% & 1.8\% \\
2 & Moving-load noise & 2.2\% & 2.4\% & 2.2\% \\
3 & & 2.3\% & 1.8\% & 1.9\% \\ \hline
4 & Two point (3 m) & 1.5\% & 2.0\% & 1.5\% \\ \hline
5 & White noise & 6.3\% & 3.2\% & 2.2\% \\
6 & Mean zero & 15.3\% & 2.6\% & 2.7\% \\ \hline
7 & White noise & 45\% & 7.2\% & 2.3\% \\
8 & Mean 0.5 & 93\% & 10.3\% & 3.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The identification errors for the different methods.  
The displacement sensors are MIRAN-130, which was produced by the company of MIRAN (Shenzhen City, Cina) and it was shown in Figure 14c, and they were installed under the bridge, as shown in Figure 14d. It should be noted that the MIRAN-130 was calibrated before and after installation. The parameter of the MIRAN-130 was listed in Table 6. The raw displacement data can be obtained according to the timestamp in the figure. It should be noted that the range of the sample rate is 1-20 Hz, and, in the application, the sample rate was set to 10 Hz.  
In the present study, a subset of
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 6 Evaluation and Discussion of the Results
### Comparison with Other Approaches  
#### 6.3.1 Fixed Data Sizes  
Simulation results with node numbers from 100 to 1000 are presented in Figure 5. The results do not include the schedule transmission overhead, however, this is almost negligible compared to the total values. The overhead varies from 3% to 7% (worst case) of the total data collection time and energy consumption. As we can observe, the impact of the collision-free transmissions in the data collection time is obvious. The performance in terms of data collection time is at least 10 times higher using an Aloha MAC approach such as LoRaWAN. Slotted-Aloha performs better, however, still far from the collision-free method.  
In our simulations, all the approaches achieved a packet delivery ratio over 90% while the collision-free ones exceeded 95%. The Aloha approaches achieved those PDR values only at the expense of much longer data collection times. This shows the superiority of collision-free time-slotted communications as the number of nodes increases. Moreover, the scheduling of transmissions over different slots and SFs leads to much lower data collection time compared to TS-LoRa (non-scheduling approach). The improvement seems to be higher when the number of nodes increases. However, in terms of energy consumption, this action causes an increased trend in energy cost. This happens due to two reasons. Firstly, as the number of nodes increases, more and more nodes are switched to higher SFs. Higher SFs cause longer transmission times and, thus, higher energy consumption. Secondly, the nodes periodically turn on their radio to receive the sync packet from the gateway, increasing the average consumption during a round. Comparing TS-LoRa with the Pure Aloha MAC performance, we can see that the synchronisation overhead causes about 15% higher energy consumption (see Figure 4(b)). However, as we showed in one of our recent studies [31], the synchronisation cost is much lower than that caused by re-transmissions in an Aloha-based protocol.  
Figure 5: Data collection time and energy consumption for a scenario with fixed data size.
# Collision Avoidance Resource Allocation for LoRaWAN
## 5 Experimentation and Performance Results
### Simulator Validation  
We first design a LoRaWAN standard simulator in order to first validate its satisfactory operation.  
Figure 8 depicts the packet error rate for 50,000 devices with the objective of verifying the simulator behaviour compared with to the mathematical model proposed in [16].  
The green and red lines represent the results from simulations assuming a standard LoRaWAN network and from the aforementioned mathematical model, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SF** & **Gateway Sensitivity (dBm)** \\ \hline
12 & \(-142.5\) \\
11 & \(-140\) \\
10 & \(-137.5\) \\
9 & \(-135\) \\
8 & \(-132.5\) \\
7 & \(-130\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gateway Sensitivity for each SF.  
Figure 8: Comparison of packet error rate (PER) for 50,000 devices between results from simulations assuming a standard LoRaWAN network and from the theoretical mathematical model.  
Both results are similar, albeit there is a little difference owing to the mathematical model considering an infinite number of nodes. The maximum relative error is 3.98% for a network load of 150 packets/s, which validates the correct behaviour of our simulator.  
Afterwards, we modified the simulator with the new LoRa MAC proposal without first considering the propagation model depicted in Section 5.1, that is, assuming ideal propagation conditions. As an initial verification, we checked that the proposed design allows the simultaneous transmission of up to 48 devices without the existence of collisions. For this purpose, a simulation with exactly 48 modes generating traffic has been carried out.  
Figure 8(a) shows the number of collisions for each spreading factor with different network loads. Analysing the simulations, all the collisions were due to the border effect. These collisions mainly occurs in SF12 since the higher ToA of the transmissions increases the probability of collisions. As an example, in a simulation with a network load of 30 packets/s, 51,222 packets were sent and only 2365 collided (PER = 4.6%). 1485 of these packets were transmitted with SF12 (62.8% of the collisions).  
For this reason, the simulator was later modified so that nodes will avoid transmissions spread over two different time windows (i.e., starting on window \(k\) and finishing on window \(k+1\)). If this were to happen, nodes would postpone their transmissions to the beginning of the next window. As shown in Figure 8(b), when our solution to avoid the border effect is executed, the collision probability is null as expected.