esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a System for Verification of Automatic Guided Vehicle Routes Using Computer Emulation
## 3 Results  
The proposed system for verification of AGV routes using computer emulation is suitable for automatic logistics systems. The partner organisation for the research of this system is Asseco CEIT a.s., the most famous innovation of which is intelligent mobile robots which ensure reliable, efficient, and safe automatic logistics in industrial halls. The created system processes manufacturing data, reacts flexibly, and can make independent decisions. Information from the control system is processed through the AGV Monitor system application, displayed in Figure 9, which displays the processed information in a user-friendly form, mainly by graphically visualising the status and position of individual logistics resources. All information, actions, and changes to settings, are recorded and collected by the system and deposited into the documents for later analysis or subsequent optimisation of logistics processes.  
In the experimental design, a control system with a global control method was unjustified. It is an intelligent control with automatically generated inputs for transport that are applied with full automation. The control is able to decide and subsequently manage transport based on the states of the peripheries, the type of material transported, requirements from external equipment and systems, robotic workplaces, or manufacturing lines. Based on this information, it decides and subsequently manages the automatic import and removal of materials or finished parts to specified positions for a specific type of material or finished part. All this happens without the presence and intervention of the operator. If necessary, intelligent control can only control and prioritise a specific AGV tractor. It also allows a specific AGV of the tractor to check whether further transport is still needed on the track or if the AGV tractor is to return to the starting position. Control is an autonomous, flexible solution that requires a complete package of services installed on the server, thanks to which it can comprehensively automate logistically demanding processes, synchronise with the manufacturing process, and ensure the continuity of material flow. Intelligent conditional control evaluates the fulfilment of transport conditions based on information from the database. The database information obtained (such as information on the status of peripherals, types of material, and automatically generated requests from external equipment and systems) is used for immediate decision-making and subsequent control of the automatic import and removal of materials or finished parts to specified positions for a specific type of material or finished part. In this way, even more logistically demanding processes can be automated. Intelligent control also uses the functionality of so-called checkpoints, which are placed on the tracks. The control system determines and picks one of the various paths depending on external criteria, it may assess whether more transport is required, or it can send an AGV command to the tractor to return to the starting place.
## 3 Simulation and Discussions  
### Grid Model Simulation with Renewable Energy  
In order to study the proportion and spectral characteristics of distributed new energy sources on the low-voltage feeder, a simulation model with multiple renewable energy sources connected to the grid is built in Simulink, and the model structure is shown in Figure 2. The voltage level of the main network is 750 kV, and at the 35 kV bus, wind farms and photovoltaic farms are connected. The wind turbine is a double-fed asynchronous wind turbine with a rated output power of 1.5 MW, and the PV cell is SPR-415E-WHT-D with a rated output power of 10 MW. A three-phase RLC load is connected to the 10-kV low-voltage feeder with a load power of 200 MW. The detailed parameters of each part of the simulation are shown in the Table 1.  
In this paper, the fixed load power is 200 MW, and the change of the new energy share at the low-voltage feeder is achieved by changing the number of input units of the wind turbine and the number of components of the PV array. On the basis of the consideration of the actual situation, a total of 500 sets of simulations are performed, and the new energy share increases from 0.1% to 50% by 0.1% increments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value (pu)** \\ \hline Main grid voltage & 750 kV & Resistance of Transformer 1 (main grid) & 0.08 \\ DFIG output voltage & 575 V & Inductance of Transformer 1 (main grid) & 0.08/30 \\ PV output voltage & 10 kV & Resistance of Transformer 1 (new energy) & 0.025 \\ Load power & 200 MW & Inductance of Transformer 1 (new energy) & 0.025/30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the simulation.  
Figure 2: Simulation model.  
Through the power acquisition module, the PV output power (P_PV), the wind turbine output power (P_wind), and the load-side power (P_load) are obtained, and the new energy share ratio is calculated for each simulation as follows:  
\[\text{ratio}=\text{P\_PV}+\text{P\_wind}/\text{P\_load} \tag{16}\]  
At the same time, there is more noise in the electrical signal due to the actual measurement in the field, for example, white noise interference, among others. To simulate this situation, adding Gaussian white noise (i.e., superimposing a certain percentage of normally distributed noise signal in the output current) simulates white noise interference.  
As an example, the time and frequency domain diagrams of the single-phase current at the low-voltage feeder are shown in the Figure 3 (taking the A-phase current signal for analysis), taking the 10% and 40% sets of data. With the increase of the proportion of new energy, the distortion of current waveform has been improved, to a certain extent. It can be seen from Figure 3 that there are obvious harmonics at the wave crest and wave trough.  
However, it can be seen that the degree of signal distortion at the low-voltage feeder does not vary much when the new energy ratio varies, so it is not possible to quantitatively analyze it visually.
# A Phase Bias Compensation Method for Anti-Interference Antenna Arrays in RTK Positioning
## 2 Materials and Methods  
This section begins by establishing the signal model for antenna array anti-interference algorithms. Following this, we analyze the impact of anti-interference effects on RTK positioning accuracy, a key consideration for UAV operations in environments prone to electromagnetic interference. In Sections 2.4 and 2.5, we proposes the phase compensation method. First, we introduce the error analysis of the anti-interference algorithm in RTK positioning. Then, a bias compensation algorithm based on array weighted-coefficient conjugation for rough phase bias compensation. Finally, we propose a compensation algorithm based on the array anti-interference output signal for fine phase bias compensation.
###### Abstract  
This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times--up to 30%--and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.  
automotive electronic identification; biometric recognition; customs vehicle relocation; automatic scheduling; dynamic resource allocation +
Footnote †: journal: _Algorithms_
## 3 Results and Discussion
### Sensorial Analysis

Table 5: Evaluations of sensorial analysis by trained panellists, for meat quality characteristics in Mallorquina lambs suckling (ML), grazing (MP), supplemented with grain (MC); and Roja Mallorquina lambs supplemented with grain (RC). was probably due to the animals being young and lean and the fact that all the animals received mother's milk as part of the traditional production system. Regarding the texture properties, consumers of lamb meat expect a tender, juicy meat that feels soft on the palate. The hardness attribute in Mallorquina and Roja Mallorquina meat showed low levels (between 3.44 and 3.78). Although the animals had ample freedom of movement together with the mothers, the meat can be considered tender since the lambs were slaughtered at an early age and there was little development of the muscle tissue. Meat hardness in animal species is generally greater in older animals [57]. According to [PERSON] et al. [58], the variation in meat hardness values in terms of the slaughter weight depends mainly on the myofibrillar content rather than the total collagen content or its solubility, especially when the toughness of cooked meat is considered. The range of values for the final juiciness of the meat (range 6.82-7.31) was higher than those for initial juiciness (range 5.94-6.31). When the meat first enters the mouth, the juiciness evaluated (initial juiciness) was similar for all the groups of meat analysed, while the final juiciness scores were higher in the light lambs than in the suckling lambs. The in-creased levels of fat were also associated to significant increases in the final juiciness of the cooked meat. The best sensory traits observed in the Mallorquina and Roja Mallorquina breeds were linked to the friability and chewiness of meat, because scores above 7.2 points out of 10 were recorded. Significant differences in the chewiness of meat in different groups of lambs were not observed. The lowest values for friability were observed in meat from lambs raised using milk and grain (\(p<0.05\)). In general, regardless of the traditional production system for lamb meat from the Mallorquina and Roja Mallorquina breeds, the judges considered that the meat has high friability and requires little chewing to be swallowed.
## 4 Case Study of Crash Scenarios
### Simulation Environment  
We selected OMNeT++, Veins, and Simulation of Urban MObility (SUMO) tools to simulate our V2V/V2P network [50; 51; 52]. Vehicles are inserted at every 2 s. Pedestrians are inserted at every 1.6 s. Transmission power of Vehicles and VRUs is set to 20 mW. This allows the communication range of 400 m. We employ TwoRayInterferenceModel as a path loss model in order to achieve realistic path propagation of V2P and V2V networks. Table 2 provides the details of simulation parameters used in our scenario.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Road length & 1 km \(\times\) 500 m \\ \hline No. of vehicles & 120–150 \\ Max. vehicle speed & 13.89 m/s = 50 km/h \\ \hline No. of pedestrians & 102 \\ Max. pedestrians speed & 1.5 m/s \\ \hline No. of bicycles & 1 \\ Max. bicycle speed & 4.3 m/s \\ \hline Transmission power for vehicles & 20 mW \\ Transmission power for VRUs & 20 mW \\ Data rate & 6 Mb/s \\ \hline Vehicles beacon periodicity & 10 Hz \\ VRU beacon periodicity & 2 Hz \\ (for Active Mechanism) & \\ Beacon length & 1024 bits \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 4: Simulation Scenario.  
The four pre-crash scenarios have different warm-up periods and simulation run-times which are shown in Table 3. The warm-up period allows each scenario to get into typical traffic condition. The crash time indicates the simulation time when the vehicle and VRU pair crashes into each other.  
The simulation data is collected over 3 independent runs for every pre-crash scenario under active and passive configurations. Average values are then computed for evaluation of final results.
## 3 Results
### Kaempfer Uregulates GSH Activity  
Research has confirmed that cervical cancer patients have significantly lower levels of GSH. An increase in GSH level will be able to restore the antioxidant defense system in cervical cancer patients. Kaempfer-exposed HeLa cells demonstrated an increase in GSH activity to 1.9, 2.4 to 3.1 fold at 30, 40 and 50 \(\upmu\)M of drug at 48 h (Figure 5C).  
Kaempfer Suppresses Cell Growth, Survival and Inflammation by Regulating Aberrant MAPK, PI3K/AKT/mTOR, NF-kB and JAK-STAT Pathways  
Kaempferol suppresses NF-kB and JAK-STAT pathways to reduce inflammation in HeLa cells. The phosphorylation level of IKBa (P-Ser32), NF-kB (P-Ser536), TBK1 (P-Ser172) and HDAC4 (P-Ser632) were reduced significantly, whereas HDAC (P-Ser394) TAK1 (P-Ser412) exhibited marginal reduction [18]. Expression of phosphorylated proteins of JAK-STAT pathways such as Src (P-Tyr419), STAT1 (P-Ser727), STAT2 (P-Tyr689), STAT3 (P-Tyr705), STAT5 (P-Tyr694), TYK2 (P-Tyr1054) was also reduced after treatment (50 \(\upmu\)M) compared to control.  
Consistently, at the protein level MAPK and AKT pathways were also suppressed. Expression of phosphorylated proteins pertaining to MAPK pathways, such as MEK (P-Ser217/221), HSP27 (P-Ser82), RSK1 (P-Ser380) and Raf-1 (P-Ser301), was reduced, whereas P53 (P-Ser15) was found to be upregulate. Likewise, the phosphorylated proteins of AKT pathway, such as GSK3a (p-ser21), GSK3b (p-ser9), MTOR (p-ser2448), PRAS 40 (p-Ther246), BAD (p-ser112), PTEN (p-ser380), AKT (p-ser473), RPS6 (P-Ser235/236), also exhibited reduced expression, whereas p27 (P-Thr198) expression was increased compared to the untreated control HeLa cells (Figure 6A,B).  
Figure 5: **(A)** Differential protein expression of inflammatory cytokines (**A**) Images of nitrocellulose membrane demonstrating differential expression (**B**) Graph representing reduced expression of proinflammatory and chemokines, whereas anti-inflammatory cytokines expression was increased in kaempferol-treated (50 \(\upmu\)M) cells compared to DMSO control. **(C)** Graph showing an increase in total glutathione level in kaempferol-treated HeLa cells at 30, 40 and 50 \(\upmu\)M for 48 h. Data are presented as the mean \(\pm\) standard deviation of three independent experiments. Two way-ANOVA * = \(p\) < 0.05; ** = \(p\) < 0.01, *** \(p\) < 0.001.
## 5 Conclusions  
This article delves into the inherent uncertainties that exist in engineering algorithms, as well as the practical engineering constraints that arise during the design process. To address these challenges, the article proposes using the stochastic Kriging surrogate model. By applying this model, the basic mathematical problem can be formulated to perform a robust optimization design of the aerodynamic shape and trajectory of a pulse correction trajectory projectile. Based on the analysis conducted, the main conclusions drawn from this study are as follows:  
1. The results of this study demonstrate that both the DK and SK surrogate models accurately predict the performance of the pulse correction projectile during optimization. However, the SK model's ability to predict both the mean and variance of performance provides a significant advantage in addressing uncertainty design issues, compared to the DK surrogate model;  
Figure 21: Angle of attack versus time.  
Figure 20: Uncontrolled and controlled trajectory.  
2. In comparison to the original configuration, both the deterministic optimization results based on the DK model and the robust optimization results based on the SK model significantly improve the mean performance indicators of the pulse correction projectile's lateral correction ability and maximum range, while also meeting the flight stability requirements. However, when considering the range of the variable _aero_factor_, the robust optimization design proves effective in improving the projectile's performance divergence characteristics. As a result, it achieves a stable improvement in performance for the pulse correction projectile.
3. Using the non-dominated sorting MOSSA algorithm, a Pareto optimization scheme for the aerodynamic shape and exterior trajectory of pulse correction projectiles has been developed. Aerodynamic parameters were obtained through wind tunnel tests and CFD simulations, and the correctness of the optimization results was verified through trajectory numerical calculations. The optimization design method presented in this article exhibits strong optimization capability and practical engineering significance, effectively improving the projectile's performance and shortening the design cycle compared to deterministic optimization methods.  
Conceptualization, B.X. and C.D.; methodology, B.X. and Z.D.; validation, C.D.; formal analysis, B.X.; investigation, B.X. and W.Y.; resources, C.D. and Z.D.; data curation B.X. and Z.D.; writing--original draft preparation, B.X.; writing--review and editing, C.D.; visualization, W.Y.; supervision, Z.D.; project administration, Z.D.; funding acquisition C.D. and Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 12102201, 11802141 and 11772160.  
Not applicable.  
We wish to express our gratitude to the members of our research team, [PERSON], [PERSON], [PERSON].  
The authors declare no conflict of interest.
# An Intelligent Condition Monitoring Approach for
## 3 Condition Monitoring Approach for Shearing Machines
### Feature Extraction Method Based on WPT  
Feature extraction is applied to decrease the dimension of the original signal, keep and strengthen useful information, reduce or even eliminate interference information, and to highlight feature information by transforming the signal form. As is already known, noises of a shearing machine contain a lot of information on the health state. When the health state changes, the amplitude-frequency and phase-frequency characteristics in the frequency domain will change, and the energy in each frequency band will change to a different value. As mentioned above, WPT is suitable for analyzing nonlinear and non-stationary signals containing large amounts of details just like the noises of the shearing machine.  
The energy in every frequency band can be calculated by the wavelet packet coefficients. Then, the health state of the shearing machine can be recognized by analyzing the energy distribution. By decomposing the signal, the wavelet packet coefficients of every frequency band can be obtained. Hence, the subband signals of \(n^{th}\) level can be reconstructed with the wavelet packet coefficients. The energy of each band in the \(n^{th}\) level can be expressed as the square sum of the corresponding node's wavelet packet coefficients:  
\[E_{i}=\sum_{k=1}^{m}|c_{ik}|^{2},(i=0,1,\cdots,2^{n}-1), \tag{6}\]  
Figure 3: Structure of the backpropagation network.  
where \(E_{i}\) is the energy of the reconstructed signal \(s_{ni}\), \(c_{ik}\) the wavelet packet coefficient of \(s_{ni}\), and \(n\) the level of the wavelet packet decomposition. The total energy of the signal is calculated with:  
\[E_{T}=\sum_{i=0}^{2^{n}-1}E_{i}. \tag{7}\]  
Therefore, for a \(n\)-level WPT feature extraction, the energy feature vector is obtained by normalizing the energy of every frequency band in the \(n^{th}\) layer:  
\[T=\left[\frac{E_{0}}{E_{T}},\frac{E_{1}}{E_{T}},\cdots,\frac{E_{2^{n}-1}}{E_{T }}\right]. \tag{8}\]
## 3 Results and Discussion
### Selection of Spectral Ranges for MLR and PLS Models
For Raman and MIR spectroscopy, the predicted concentration courses of the analytes glucose, ethanol, and biomass were in good accordance with the reference values (HPLC, gravimetry). On closer examination, it can be seen that the MIR-ATR data showed smaller variations than the Raman curves. The temporal concentration courses using PLS models for the evaluation of Raman spectra exhibited less fluctuation than the corresponding MLR curves. One reason might be that the PLS evaluation was based on the complete spectral range and comprised more information. Consequently, interferences by particles, variations in sample composition, and temporal changes of the fluorescence background can better be compensated. Comparing the four multivariate methods, the 2D MLR data fluctuated most and showed larger deviations from the reference analytics. The temporal concentration courses generated by 4D MLR, PLS with PP, and PLS without PP were of similar quality.
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.2 Multi-Class Machine Learning Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Type** & \(\mathbf{s_{A}}\) **(\%)** & \(\mathbf{s_{P}}\) **(\%)** & \(\mathbf{s_{R}}\) **(\%)** & \(\mathbf{S_{F1}}\) **(\%)** \\ \hline Normal & 99.24 & 98.86 & 99.43 & 99.81 \\ \hline DoS & 99.85 & 99.86 & 98.85 & 99.85 \\ \hline Probe & 95.64 & 97.42 & 95.43 & 96.41 \\ \hline R2L & 84.13 & 91.23 & 83.23 & 87.07 \\ \hline U2R & 28.49 & 66.88 & 28.57 & 40.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results using Naïve Bayes classifier. The experimental results show that the proposed ADL algorithm together with the NB algorithm (see Table 6) has a greater improvement in the classification accuracy of R2L and U2R compared to the ACE algorithm, and the overall performance of the algorithm is also better. This indicates that the algorithm is more efficient in intrusion detection for small sample data. Also, the use of encoders is avoided, as is the possibility of increased computational complexity. However, the \(s_{F1}\) of the ADL algorithm is relatively low and could be improved in future work. Results showed that the classification accuracy of the ADL algorithm increased as \(\theta\) increased in [0.1, 0.5]. The accuracy tends to saturate when \(\theta\) is greater than 0.6, demonstrating the stability and feasibility of the proposed ADL algorithm. Meanwhile, comparison results proved that the ADL algorithm is better than the CFS feature extraction algorithm when the amount of sample data is large. Compared with the ACE feature extraction algorithm, the ADL algorithm improves the classification accuracy of R2L and U2R.