esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Challenges in Agricultural Vehicle Path Tracking Control
### Overall Challenges in Path Tracking Control  
Usually, the path-tracking control technology calculates vehicle motion parameters based on control algorithms, such as vehicle speed, steering angle, etc., so that the vehicle reaches and tracks the predetermined working path. The main challenges in the current path tracking control of agricultural vehicles include: (1) Although the existing kinematic model can achieve good results under low speed, there is a lack of further research on the dynamic vehicle model. (2) Most existing path-tracking control methods are based on linearized systems or independent of specific mathematical models without fully considering the influence of disturbances and uncertainties. (3) The existing research on path-tracking control methods mainly focuses on straight-line paths, but the tracking accuracy of curved paths, especially curved paths with variable curvature, needs to be improved to adapt to autonomous agricultural production. In the future, appropriate control algorithms should be developed and designed to realize the coordinated control of agricultural vehicles horizontally and vertically while coping with different complex working conditions.
## 4 Simulation and Evaluation
### Performance Evaluation
#### 4.2.2 Scalability  
The number of sensor nodes deployed in studying a phenomenon may be up to thousands or more. For some special application, the number may reach an extreme value of millions. The new routing algorithms must be able to work with such number of nodes. So it is very important and necessary to test the scalability of protocols for a larger scale of WSNs.  
Figure 4 shows the difference of average energy consumption of MHFR and LQER over time with k=9, node number from 0 to 1000. As the node number increases, the difference value between MHFR and LQER also increases, which indicates that LQER has a good scalability of energy efficiency. Results are similar in Figure 5, which shows the difference value of average energy consumption of MCR and LQER.
## 4 Conclusions  
The main objective of this paper is to leverage the inherent flexibility of cooling systems in the aquaculture industry in order to minimize daily electricity costs. To accomplish this, the XGBoost algorithm was employed to predict the power consumption of the primary cooling systems based on a limited set of available data. The results confirmed that the proposed method can accurately capture the power consumption patterns of the systems under study. Building upon this nonlinear relationship and in combination with the technical constraints of the pre-cooling unit, the optimal discharge temperature of water into the breeding pools was determined using a Bayesian Optimization approach. The findings demonstrate that the proposed model can significantly reduce total electricity costs compared to the BaU scenario, highlighting the potential for energy and cost savings in energy-intensive industries such as aquaculture. In addition, this study underscores the effectiveness of machine learning in optimizing industrial operations even in situations with sparse data. The same black-box modeling strategy can be extended to other energy-intensive systems beyond aquaculture, including district and urban cooling networks, commercial real estate HVAC systems, and cold-storage facilities, where flexible assets such as compressors, chillers, and pumps can be co-optimized for both operational efficiency and grid-interactive services. Finally, this work outlines a clear pathway for interdisciplinary impact, as follows:  
1. Utility and grid operators can integrate price-responsive industrial loads into ancillary services markets and congestion management strategies.
2. Policy-makers can design dynamic tariff schemes and demand response programs that incentivize and reward industrial flexibility.
3. Smart city planners and IoT/BMS integrators can adopt this workflow to coordinate distributed assets for enhanced efficiency and resilience.  
Future research will extend this framework to support multi-site coordination as well as co-optimization with on-site renewables and energy storage systems. This will further strengthen the connection between industrial flexibility, smart grid development, energy policy, and sustainable urban infrastructure.  
Conceptualization, S.G., H.G., A.A.-M. and B.B.-J.; methodology, [PERSON] and H.G.; software, S.G.; validation, S.G. and H.G.; formal analysis, [PERSON] and H.G.; investigation, [PERSON], H.G. and [PERSON]-M.; writing--original draft preparation, S.G. and H.G.; writing--review and editing, [PERSON], H.G., [PERSON]-M. and [PERSON]-[PERSON]; visualization, S.G.; supervision, A.A.-M. and B.B.-J.; funding acquisition, B.B.-J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the ELFORSK program under the Danish Energy Agency grant number ELF221-496872.  
**Data Availability Statement:** Data available on request due to restrictions (e.g., privacy, legal or ethical reasons).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Mechanical Parameters of Rock Mass Under the Influence of Freeze-Thaw Cycles
### Determination of Mechanical Parameters of Rock Mass Considering Freezing and Thawing Effects
A comparison between the cohesion (\(c\)) and internal friction angle (\(\varphi\)) obtained from conventional triaxial compression tests and those from the Hoek-Brown criterion revealed that the post-reduction cohesion measures approximately 1/5 of the initial value while the reduced internal friction angle constitutes about 3/5 of the original magnitude, which aligns well with the reduction coefficients recommended in the Chinese \"Standard for Engineering Rock Mass Classification\" (GB/T 50218-2014) [29], thereby validating the rationality of determining the geological strength index (_GSI_) through integrated field discontinuity surveys and acoustic wave measurements. Based on the Hoek-Brown strength criterion, the mechanical parameters of three types of rock masses under different numbers of freeze-thaw (F-T) cycles were determined. These parameters were subsequently incorporated into FLAC3D to numerically simulate slope stability under various F-T cycling conditions.
# Study on Seismic Performance of Steel Fiber Reinforced Concrete Pier Under Bending-Torsion Coupling
## 4 Parametric Analysis of Seismic Performance
### Influence of Torsion-Bending Ratio  
Keeping the height of the SFRC in the plastic hinge region constant, the skeleton curves of the piers under different torsion-bending ratios were compared, as shown in Figure 13a-e. To quantitatively analyze the influence of the torsion-bending ratio on the maximum bearing capacity of the specimens, the reduction ratio of the maximum bearing capacity of the specimens under the bending-torsion coupling effect compared to the no-torsion specimens was determined, as listed in Table 7.  
It can be observed that, under the bending-torsion coupling effect, the torsion effect on the bridge pier is more obvious as the torsion-bending ratio increases. The presence of torque will increase the strain demand on the pier section, resulting in the insufficient deformation capacity of the pier under shear and bending moments. The reduction ratio of the maximum bearing capacity of the bridge pier is accelerated as the torsion-bending ratio increases, while the trend of the decrease in bearing capacity is delayed by the addition of steel fibers.  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen Number** & **SFRC Height/mm** & **Torsion–Bending Ratio** \\ \hline RC-0.27 & 0 & 0.27 \\ SFRC300-0.27 & 300 & 0.27 \\ SFRC600-0.27 & 600 & 0.27 \\ SFRC900-0.27 & 900 & 0.27 \\ SFRC1200-0.27 & 1200 & 0.27 \\ RC-0.36 & 0 & 0.36 \\ SFRC300-0.36 & 300 & 0.36 \\ SFRC600-0.36 & 600 & 0.36 \\ SFRC900-0.36 & 900 & 0.36 \\ SFRC1200-0.36 & 1200 & 0.36 \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 13: Comparison of skeleton curves under different torsion–bending ratios. (**a**) RC; (**b**) SFRC with a height of 300 mm; (**c**) SFRC with a height of 600 mm; (**d**) SFRC with a height of 900 mm; (**e**) SFRC with a height of 1200 mm.  
When the torsion-bending ratio is 0.09, the reduction ratio of the maximum bearing capacity for both SFRC and RC pairs is relatively small. When the torsion-bending ratio is 0.17, the reduction ratio of the maximum bearing capacity of the RC pier is larger than that of the SFRC pier, indicating that SFRC materials can effectively improve the structural bearing capacity and reduce the decrease ratio in the structural bearing capacity under low torsion-bending ratios. When the torsion-bending ratio is 0.27, the bearing capacities of the RC pier and the pier with an SFRC height of 300 mm decrease obviously, while the bearing capacities of other specimens decrease by less than 10%. When the torsion-bending ratio is 0.36, the reduction ratio of the maximum bearing capacity of the RC pier and the piers with SFRC heights of 300 mm and 600 mm is about 20-30%, while that of piers with SFRC heights of 900 mm and 1200 mm is about 16%.  
It can be seen that the bending-torsion coupling has a significant effect on the bending and torsion bearing capacities of piers. When the SFRC reaches a certain height, the bearing capacity of the pier cannot be obviously improved by increasing the height of the SFRC.
# Integrated System for Official Vehicles with Online Reservation and Moving Path Monitoring
## 3 System Build-Up and Test Results
### Data Format Transmission Test  
In the database reorganization of transmitted GPS data, the GPS satellite time is out of synchronization with the server time. Thus, it is impossible to distinguish each GPS datum when reorganizing the data. Given this situation, the background database generates a set of random 32-character keys when the STB operates so that each datum can be distinguished. After repeating data format improvement, the STB uploads the data in the JSON format, as shown in Figure 23. After the backend server identifies the number of each group of data and the corresponding user, the device proceeds to the next step according to the return code of the backend server.  
Figure 21: Circuit diagram of the control board of a sub-cabinet.  
Figure 22: PCB layout of lower and lower layers of the control board of the key cabinet.  
The transmission format of the independent key locker transmits data every second in the JSON format to find out whether a registered user in the server uses a vehicle. This action is verified through the computer's serial transmission platform SSCOM. When accessing the background database system, it returns messages such as \"myregion\" (the company's area), \"myboxID\" (the cabinet), \"has_open\" (whether there is an open cabinet), and \"box_status\" (whether the cabinet has the key and STB). If no user uses the key and STB in a sub-cabinet, the return values of the \"has_open\" and \"box_status\" background database systems are both \"0\", as shown in Figure 24.  
When a user opens a cabinet, \"has_open\" becomes \"1\", indicating that the door handle of the cabinet is opened. If the STB is taken out and the RFID cannot read the information of the STB, \"box_status\" is displayed as \"1\", indicating that the STB is used by the user, as shown in Figure 25.  
Figure 24: SSCOM read and return data diagram (for not using a vehicle).  
Figure 23: GPS data upload format.
## 4 Conclusions and Future Work

Table 6: Comparison of our proposed SSAPM model with recent state-of-the-art methods, including DeepH and MAT-Transformer, on JARVIS-DFT and Zenodo datasets, demonstrating superior performance across all evaluation metrics. Figure 9: Error analysis for different models across datasets. feature extraction, noisy or incomplete data can still affect accuracy, particularly in cases of sensor calibration errors. The physics-informed constraints enhance interpretability but may limit flexibility in extreme material behaviors. Future research should focus on making SSAPM more computationally efficient, integrating advanced uncertainty quantification, and extending its applicability to complex material properties like anisotropic and phase-changing materials. These improvements will strengthen its role in real-world structural assessments. Conceptualization, Y.S. ; methodology, H.D.F. ; software, M.Z. ; validation, M.Z. ; formal analysis, H.D.F. ; investigation, Y.S. ; data curation, M.Z. ; writing--original draft preparation, Y.S., M.Z. and H.D.F. ; writing--review and editing, Y.S. ; visualization, H.D.F. ; supervision, M.Z. ; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors thank XYZ Laboratory for providing computational resources. The authors declare no conflicts of interest.
# Multi-Scale Convolutional Network for Space-Based ADS-B Signal Separation with Single Antenna
## 4 Experiment
### Comparison with Different Deep Models  
Three commonly used deep learning networks [18; 21; 22] are experimented on in our proposed dataset, and decoding accuracy and bit error rate (BER) are used as evaluation. The decoding accuracy is the percentage of the correctly decoded signals number in the total number of received signals, while the BER is the ratio of the erroneous bits' number to the total number of signal bits. The average decoding accuracy and BER of the four networks on 120,000 test samples are shown in Table 1. From the table, it can be observed that the transformer based method is better than Conv-TasNet and Bidirectional LSTM. Moreover, our proposed MConv-TasNet outperforms the transformer-based method, which verifies the effectiveness of our proposed method.
## 5 Temperature Field Analysis of Concrete Bridge Tower  
### Engineering Background  
The three-dimensional sunshine temperature field research was carried out by taking the ultra-high concrete cable tower of a bridge as a calculation case to analyze the distribution law of the sunshine temperature field of a concrete bridge tower. The concrete cable tower adopts a thin-walled hollow structure. The total height of the bridge tower is 242.8 m (the height from the cushion cap to the top of the tower). The wall thickness of the upper tower column is 1.1 m, and the wall thicknesses of the middle tower column and the lower tower column are 1.2 m. The specific structural form and size are shown in Figure 18. The concrete bridge tower is located at 116\({}^{\circ}\)20\({}^{\prime}\) E and 39\({}^{\circ}\)56\({}^{\prime}\) N. The material characteristic parameters required for the calculation of the temperature field of the bridge tower are shown in Table 3 below.  
Figure 17: Temperature change curve of the web on the sunward side and shaded side.  
In this paper, the time selected for the temperature field calculation of the bridge tower was 19 August 2022, and the temperature field results, calculated continuously three days before the calculation date, were used as the initial temperature field and imported into the calculation model to eliminate the impact of the initial temperature field on the calculation results. The atmospheric temperature data for the calculation period are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Order Number** & **Material** & **Parameter** & **Value** \\ \hline
1 & & Density (kg/m\({}^{3}\)) & 2650 \\
2 & & Elastic modulus (Pa) & 3.45 \(\times\) 10\({}^{10}\) \\
3 & Concrete (C50) & Poisson ratio & 0.25 \\
4 & & Thermal expansion coefficient & 1.0 \(\times\) 10\({}^{-5}\) \\
5 & & Thermal conductivity (W/(m\({}^{\circ}\)C)) & 2.0 \\
6 & & Specific heat capacity (J/(kg\({}^{\circ}\)C)) & 970 \\ \hline \end{tabular}
\end{table}
Table 3: Material property parameters.  
Figure 18: Schematic diagram of the bridge tower structure. (**a**) The elevation of the bridge tower; (**b**) Section A; (**c**) Section B.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 4 Experiments  
### Conventional Experiment  
#### 4.1.1 Experimental Setup  
In order to verify the performance of the SR and CD-SR methods, an experimental platform consistent with the simulation was established. As shown in Figure 11, the Olympus V316 20 MHz (Olympus NDT, Waltham, MA, USA) focused transducer was excited with an Olympus 5800 Pusler-Receiver (Olympus NDT, Waltham, MA, USA) and the signal was acquired with a Tektronix MDO3032 oscilloscope (Tektronics, Beaverton, OR, USA). The sample was composed of polystyrene sheets and silicon wafer, and the material properties are shown in Table 2. The reference echo is from the top layer of polystyrene sheet. The target echoes are the top layer echo \(x_{1}(t)\) (whose FWTM duration \(T\) is 127.7 ns) and the bottom layer echo \(x_{2}(t)\) of the silicon wafer. Three cases were designed by changing the thickness \(d\) of the silicon wafer. Figure 12a-c are the cases where the overlap degree \(\delta\) is \(-30\%\), \(50\%\) and \(60\%\), respectively.  
Figure 11: The 20 MHz experimental platform.  
Figure 10: Comparison of reconstructed echoes (SNR \(=-15\) dB).
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Optimization of the Fuzzy Logic Controller Based on the Genetic Algorithm
#### 4.1.2 Simulation Open-Course Code
Table 10 shows that the minimum value of the energy consumption has been obtained at the 200 th generation (E = 8.47 J), and the smallest value of the travel distance has been obtained at the 150 th generation (s = 46.07 m). However, this study mainly aims to minimize the energy consumption. Thus, the best results (in terms of fitness function value) which have been obtained are (E = 8.47 J) and (s = 46.10 m) at the 200 th generation. Figure 13 and Table 11 serve as the base for a comparison between the proposed controller (GA_FLC) with other previous published controllers. When the differential drive wheeled mobile robot is controlled by the GA_FLC to track the zigzag path, the energy consumption of the robot is much lower than the energy consumption of the robot controlled by any other used controller. In this case, the GA_FLC has saved more than 110.1% energy in comparison with the other controllers. In addition, Figure 13c,d also show that the linear velocity and the angular velocity of the robot controlled by the Circle-based controller, the Mathew-based controller, the Chaudhari-based controller, the Thoa-based controller, the Expert-based controller, and the Mohammadian-FLC have oscillated over time, which has led to the vibrations and jerks of the robot. However, this problem has been fixed quite well when the GA_FLC is used. Figure 13c shows that when the GA_FLC is used, the linear velocity is almost not changed, i.e., the linear velocity of the robot is almost constant during the movement, even when the robot moves through the corner. Moreover, Figure 13d also shows that the angular velocity of the robot controlled by the GA_FLC only changes the value at the time when the robot turns at each corner of the path, i.e., the robot only changes its orientation at the corner of the path. Therefore, the issues about the robot vibration and the robot jerk have been solved quite well by the GA_FLC. Besides, Figure 13a and Table 11 show that when controlling the differential drive wheeled mobile robot by the GA_FLC, the tracking error is quite small. This error is the smallest in all, except the error tracking of the robot when it is controlled by the Mathew_based controller. Furthermore, Figure 13e,f also indicate that the energy consumption of the DDWMR when it is controlled by GA_FLC is the smallest and it increases insignificantly from about 2 s to 40 s.  
As mentioned previously, the optimization of the FLC using the GA based on minimizing energy consumption criterion would be the prime objective of this study, thus from the analyses above, it can be concluded that the proposed controller (GA_FLC) is the best one. In other words, the GA_FLC is much better than others in terms of energy-saving and the other performances of motion control are still good.
## 5 Experiments
### Experimental Results and Analysis  
#### 5.2.1 Ablation Study  
To evaluate the effectiveness of each component of the proposed method, we conduct an ablation study on the Market-1501 dataset. Specifically, the backbone network in Figure 1 is used as the baseline. Sequentially, the EFEM and IREM are applied to the baseline, respectively, to review their respective contributions to performance improvement. Furthermore, the EFEM and IREM are applied to the baseline at the same time to demonstrate the synergy between the two modules. All experiments are carried out under the same training configuration to ensure a consistent basis for comparison.  
The ablation study results are summarized in Table 2. Compared to the baseline, both EFEM and IREM exhibit significant performance improvements, with EFEM enhancing Rank-1 accuracy by 1.3% and mAP accuracy by 1.8%, while IREM boosts Rank-1 accuracy by 2.6% and mAP accuracy by 2.9%. When employed concurrently, they lead to an increase in Rank-1 accuracy by 4.2% and mAP accuracy by 5.0%. This can be attributed to the fact that both EFEM and IREM focus on pedestrian areas, effectively alleviating the background differences in images of the same person captured by different cameras. In addition, the edge features and inter-part relationships are combined into pedestrian representation, which enriches the feature set and enhances the distinguishing ability of the model.
# Effect of Poultry and Goat Manures on the Nutrient Content of _Sesammum altatum_ Leaf Vegetables
## 4 Discussion
### Macro-Nutrients
#### 4.1.2 Calcium, Magnesium, and Potassium  
The general increase in Ca, Mg, and K concentration of _Sesamum altatum_ under manure-treated plants compared with control plants (Table 3) was probably due to nutrient release from poultry and goat manure (Table 2). However, Ca and Mg contents decreased but K content increased in _S. indicium_ plants subjected to poultry manure [29]. Calcium is an important nutrient for healthy bones, contraction and relaxation of body muscles, and coagulation [21; 30]. Magnesium is an enzyme co-factor in metabolic processes, protein synthesis, and like Ca, it also prevents bleeding disorders, degenerative diseases and immunological dysfunction [31]. Potassium also promotes good myosin-actin cycling and increases iron availability in the body, while it regulates the pH balance and neurotransmission [21].  
The interactions between season and different manure application rates were significantly different for the Ca, Mg and K contents when compared with the control (Table 4). A similar significant interaction was also reported in _Abelmoschus esculentus_ due to the effect of poultry manure application [16]. Goat manure applied at 2.5-12 t h-1 increased the concentration of Ca, Mg, and K in _Capsicum annum_ leaves during both planting seasons [21]. Again, Ca, Mg, and K contents were better in _C. gynandra_[22] and _Amananthus curentus_[32] plants treated with goat manure compared with the control.  
The highest Ca content recorded in 3 t ha-1 of poultry manure-treated plants in season one as well as 1 and 3 t ha-1 goat manure in season two (Table 4), which was possibly due to the high levels of calcium content recorded in poultry manure in the first season and goat manure in the second season (Table 2). Poultry manure with higher Mg content suppressed the Mg concentration in _S. altatum_ edible shoots, but lower rates of application showed improvements when compared with the controls of each season. Organic manure is used in crop production for its ability to increase harvestable yields and also to replenish soil nutrients for improved plant quality [12].