esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test results, and the time of an engine test is so long that the test results cannot meet the real-time requirement. Therefore, it is necessary to develop technology with high accuracy and a fast response to predict the in-cylinder pressure of diesel engines. In this paper, the in-cylinder pressure values of a high-speed diesel engine under different conditions are used to train the extreme gradient boosting model, and the sparrow search algorithm--which belongs to the swarm intelligence optimization algorithm--is introduced to optimize the hyper parameters of the model. The research results show that the extreme gradient boosting model combined with the sparrow search algorithm can predict the in-cylinder pressure under each verification condition with high accuracy, and the proportion of the samples which prediction error is less than 10% in the validation set is 94%. In the process of model optimization, it is found that compared with the grid search method, the sparrow search algorithm has stronger hyper parameter optimization ability, which reduces the mean square error of the prediction model by 27.99%.  
diesel engine; in-cylinder pressure; prediction; machine learning; swarm intelligence optimization algorithm
## 5 Results and Analysis
### Summary and Comparative Analysis  
Table 5 briefly summarizes the simulation results found about the performance of the OLSR, D-OLSR, ML-OLSR, and P-OLSR protocols in terms of PDR, latency, energy consumption, and throughput. The effectiveness of the four routing protocols was evaluated with regard to UAV mobility, scalability of UAVs, and allowable space situations, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Allowed Space.** & **OLSR** & **D-OLSR** & **ML-OLSR** & **P-OLSR** \\ \hline \(1\times 2\) & 39 & 40 & 41 & 40 \\ \(1.5\times 3\) & 35 & 35 & 37 & 36 \\ \(2\times 4\) & 30 & 31 & 33 & 32 \\ \(2.5\times 5\) & 26 & 27 & 29 & 28 \\ \(3\times 6\) & 23 & 24 & 26 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of the total number of data packets sent by four routing protocols during the allowed space scenario.  
Figure 16: Energy consumption vs. allowed space.  
Based on Table 5, we conclude that ML-OLSR, followed by P-OLSR, has the most outstanding performance across all three-use scenarios for a UAV that is used in search and rescue operations. The higher performance of ML-OLSR may be explained based on the mobility-aware algorithm and how it chooses appropriate MPR UAVs. This is the reason why ML-OLSR is so effective. In addition, it uses a load-aware algorithm, which ensures that only routes with a low volume of traffic are evaluated for the transmission of data packets to their final destination UAV. In the meantime, the P-OLSR protocol uses ETX measurements to predict the quality of the wireless link between UAVs to choose the optimal path.
# Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties
## 3 Controller Design  
### State Augmentation  
Considering that the integral action was able to handle set-point tracking and disturbance rejection issues, the output signal tracking error was defined as follows.  
\[\dot{x}_{e}(t)=r(t)-y_{p}(t) \tag{2}\]  
where \(r(t)\in\mathbb{R}^{l}\) is a bounded piecewise continuous tracking command.  
This integral error state was then appended to the plant model in Equation (1); therefore, the augmented dynamics were delivered as  
\[\left\{\begin{array}{l}\left[\begin{array}{c}\dot{x}_{p}(t)\\ \dot{x}_{e}(t)\end{array}\right]=\left[\begin{array}{cc}A_{p}&0\\ -C_{p}&0\end{array}\right]\left[\begin{array}{c}x_{p}(t)\\ x_{e}(t)\end{array}\right]+\left[\begin{array}{c}B_{p}\\ 0\end{array}\right]\left(\Lambda u(t)+\partial_{p}^{T}\Theta_{p}\Big{(}x_{p}(t) \Big{)}\right)+\left[\begin{array}{c}d_{p}(t)\\ 0\end{array}\right]+\left[\begin{array}{c}0\\ I\end{array}\right]r(t)\\ y_{p}(t)=\left[\begin{array}{cc}C_{p}&0\end{array}\right]\left[\begin{array}[] {c}x_{p}(t)\\ x_{e}(t)\end{array}\right]\end{array}\right. \tag{3}\]  
Define \(x(t)=\Big{[}x_{p}(t),x_{e}(t)\Big{]}^{T}\) and \(n=n_{p}+l\), so \(x(t)\in\mathbb{R}^{n}\). Equation (3) can be rewritten as  
\[\left\{\begin{array}{l}\dot{x}(t)=Ax(t)+B\Big{(}\Lambda u(t)+\theta^{T} \Theta(x(t))\Big{)}+d(t)+B_{r}r(t)\\ y(t)=Cx(t)\end{array}\right. \tag{4}\]  
where  
\[A=\left[\begin{array}{cc}A_{p}&0\\ -C_{p}&0\end{array}\right]B=\left[\begin{array}{c}B_{p}\\ 0\end{array}\right]B_{r}=\left[\begin{array}{c}0\\ I\end{array}\right]C=\left[\begin{array}{cc}C_{p}&0\end{array}\right]\]
## 4 Optimization Applications for SGs in Smart Manufacturing
### Smart Manufacturing Environment and Technology Importing  
Smart manufacturing is characterized by the use of highly automated production equipment, which is connected and communicated through the IoT. It is beneficial for data collection and big data analysis [6] and further AI learning (including machine learning and deep learning) to predict possible production conditions (e.g., using decision trees to predict categorical values, and using the self-organizing map to predict the physical quality of products manufacturing) and judge production operations (e.g., using additive models to correct short-term forecast errors during judgment) [74] to provide advanced manufacturing operations of self-perception, automatic decision-making, and automatic execution. Although different countries have emphasized different smart manufacturing technologies and applications, most of them have focused on cyber-physical systems, big data analysis, cloud computing, and energy saving [75; 76]. With advances in smart manufacturing technologies, highly automated equipment has been introduced, such as human-machine systems, robots, automated guided vehicles, and automated storage and retrieval systems [77; 78; 79]. Although smart manufacturing has made manufacturing processes more effective and efficient, these advanced processes through highly automated human-machine systems and high energy-consuming equipment consume increasing energy and cause increasing environmental issues [80].  
The SG is a key to the supply and management of energy in smart manufacturing [81]. As illustrated in Figure 3, a smart manufacturing framework based on the SG system [82; 83; 84; 85; 4, 82; 86; 4, 87; 88] consists of the following components:  
1. Smart manufacturing: The smart manufacturing environment includes human-machine systems, automated guided vehicles (AGVs), automated storage/retrieval systems (AS/RS), and other equipment, which are monitored by smart meters.
2. Power supply from power companies: The energy required for manufacturing processes is supplied by hydroelectric energy, wind energy, nuclear energy, and thermal power from the power companies in the SG.
3. Self-power supply: A lot of smart factories set up solar panels and energy generators on the SG to control the power supply independently.
# Biosensors-Based In Vivo Quantification of 2-Oxoglutate in Cyanobacteria and Proteobacteria
## Results
### The 2-OG Biosensors Are Responsive to 2-OG Changes In Vivo in Anabaena and E. coli
Three different concentrations of 2-OG were added (0.01 mM, blue line; 0.1 mM, green line; and 1 mM, red line) for F-mT_PII and (0.1 mM, blue line; 0.5 mM, green line; and 1 mM, red line) for F-mT_NtCa. (**C**) Time-lapse recordings of FRET ratio variations in _Anubena sp. strain PCC 7120 (Anubena)_ cells bearing the F-mT_PII 2-OG biosensor (solid lines) or F-mT_PII\({}^{\rm 88M}\) biosensor (doted lines) before (grey line) or after addition of different concentrations of dimethyl-2-OG (1 mM, green line; 10 mM, red line) in the culture medium; (**D**) Time-lapse recording of FRET ratio variations of _Anubena_ cells bearing the F-mT_PII\({}^{\rm 2}\)-OG biosensor (red) or F-mT_PII\({}^{\rm 88M}\) (grey) at different time points after combined-nitrogen step-down. The FRET ratio is referenced as 1 for the lowest value measured for each construction; (**E**,**F**) Time-lapse recordings of FRET ratio variations in _E. coli_ bearing 2-OG biosensor F-mT_NtCa (**E**) or F-mT_NtCa\({}^{\rm 88E}\) (**F**) (grey line), after addition of 10 mM of dimethyl-2-OG (blue line) or 10 mM of glucose (red line) in the culture medium. Data were recorded every 3 min for 30 min; (**G**) Time-lapse recording of the FRET ratio of _E. coli_ cells bearing the F-mT_NtCa 2-OG biosensor in presence of 10 mM of ammonium (blue line) or subjected to changes in ammonium concentrations. T1: red line, decrease of ammonium concentration from 10 mM to 2 mM; T2: addition (red line) or no addition (grey line) of ammonium to a final concentration of 10 mM) in the culture medium. Data were recorded every 10 min for 4 h. The experiments were repeated three times, and the mean value and the standard deviation are shown.
# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 8 Concluding Remarks and Future Trends
### Nomenclature  
\begin{tabular}{l l} AI & Artificial Intelligence \\ ABC & Artificial Bee Colony \\ ACO & Ant Colony Optimization \\ AD & Anomaly Detection \\ AM & Attention Mechanism \\ AML & Auto-ML \\ AnEn & Analog Ensemble \\ ANFIS & Adaptive Neuro-Fuzzy Inference Systems \\ ANN & Artificial Neural Networks \\ ARD & Automatic Relevance Determination \\ BE & Bagging Ensemble \\ BIPV/T & Building-integrated PV/T \\ BPNN & Backpropagation NN \\ BR & Bayesian Regression \\ BSM & Back-stepping Sliding Mode \\ BT & Bagged Tree \\ BTSMC & Backstep-ping terminal sliding mode control \\ CEEMD & Complementary Ensemble Empirical Mode Decomposition \\ CGSVM & coarse-Gaussian SVM \\ CI & Confidence Interval \\ CNN & Convolutional Neural Network \\ CPRS & Continuous Ranked Probability Score \\ CPV & Concentrating PV \\ DDPG & Deep Deterministic Policy Gradient \\ DELM & Deep Extreme LM \\ DIFPSO & Dynamic Factor PSO \\ DL & Deep Learning \\ DNN & Deep Neural learning/Network \\ DQN & Deep Q-network \\ DQR & Direct Quantile Regression \\ DT & Decision Tree \\ DWT & Discrete Wavelet Transform \\ ECBO & Enhanced Colliding Bodies Optimization \\ EI & Error Indicator \\ EL & Electroluminescence \\ ELM & Extreme Learning Machines \\ EM & Ensemble Methods \\ EMD & Empirical Mode Decomposition \\ E-MVFTS & Evolving Multivariate Fuzzy Time Series \\ FD & Fault Detection \\ FDD & Fault Detection and Diagnosis \\ FFNN & Feedforward Neural Network \\ FL & Fuzzy Logic \\ FLC & Fuzzy Logic Control \\ FNN & Fuzzy neural network \\ GA & Genetic Algorithm \\ GAN & Generative Adversarial Network \\ GFF & Generalized Feed-Forward \\ GLLR & Generalized Local Log-likelihood Ratio \\ GMPP & Global MPP \\ GPR & Gaussian Process Regression \\ GRNN & Generalized Regression Neural Network \\ GRU & Gated Recurrent Unit \\ HCPV & High CPV \\ HIF & High Impedance Fault \\ IFR & Infrared \\ \end{tabular}
## 2 Methodology
### Cluster Analysis  
#### 2.2.1 Descriptions and Definitions of Application-Oriented Hierarchical Cluster Analysis (HCA)  
As mentioned before, the _proximity matrix_ describes the proximity between objects to be clustered using a problem-specific metric. It is the only, and therefore crucial, input to a clustering algorithm. The following application-oriented hierarchical clustering represents an _exclusive_ (each object belongs to exactly one cluster with no overlapping), _intrinsic_ (unsupervised, no a priori partition) and _hierarchical_ (interlaces sequence of partitions) form of _object classification_. The following mathematical notations are explicitly taken from [PERSON] et al. (2019).  
Hierarchical cluster analysis (HCA) is a procedure for transforming a defined proximity matrix into a sequence of interlaced partitions.  
The n objects to be clustered are denoted by the set \(\chi\) where \(x_{i}\) is the \(i\)th object:  
\[\chi=\{x_{1},x_{2},\ x_{3}\ldots x_{n}\ \} \tag{1}\]  
A partition, \(\Gamma\) of \(\chi\), divides \(\chi\) into subsets \(\{C_{1},C_{2}\ldots C_{m}\}\), which satisfy the following expressions:  
\[C_{i}\cap C_{j}=\varnothing \tag{2}\]  
for all \(i\) and \(j\) from 1 to m, \(i\
eq j\), where \(\varnothing\) is the empty set,  
\[C_{1}\cup C_{2}\cup\ldots\cup C_{m}=\chi \tag{3}\]  
the union of all clusters results in the total quantity of all \(n\) objects.  
Thus, the hierarchical clustering algorithm yields a sequence of partitions in which each partition is nested into the next partition in the sequence. The process is repeated to form a sequence of nested clusters until a single cluster containing all \(n\) objects remains ([PERSON] and [PERSON], 2019). This approach is also called hierarchical _agglomerative_ clustering (HAC). The visualization of the clustering results is obtained through a special type of tree structure called a _dendrogram_. A dendrogram consists of layers of nodes, each representing a cluster. Cutting a dendrogram horizontally creates a clustering ([PERSON] and [PERSON], 2019). Therefore, a suitable 'cut-off' criterion, described later in detail in the present chapter, must be defined in order to achieve an optimum number of clusters for a given set of objects.  
Two specific hierarchical clustering methods can be distinguished, the _single-link_ and the _complete-link_ form. The sequences of clusterings created by these two methods depend on the proximities only through their rank order. _Single-link_ clusters are characterized as _maximally connected_ subgraphs, whereas _complete-link_ clusters are _maximally complete_ subgraphs ([PERSON] and [PERSON], 2019). In single-link clustering, the similarity of two clusters stems from the similarity of their most proximate similar objects. In complete-link clustering, the similarity of two clusters stems from the similarity of their most dissimilar objects. This diverse approach induces a different dendrogram; thus, a conscientious interpretation and analysis of the resulting clusterings are necessary.
# An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level
## 4 NSGA-III Algorithm
### Conception Introduction  
#### 4.1.1 Pareto Front  
1. Dominant relationship  
In multi-objective programming, there are dominant relationships and non-dominant relationships. The dominant relationship between the two optimization objectives is described as follows:  
In the minimum optimization problem with two objectives, the objective vector is composed of sub-vectors formed by each optimization objective:  
\[f(x)=(f_{1}(x),f_{2}(x)) \tag{32}\]  
For the two decision variables \(x_{a}\) and \(x_{b}\) in the population, there are:  
\[\begin{cases}f_{1}(x_{a})\leq f_{1}(x_{b})\\ f_{2}(x_{a})<f_{2}(x_{b})\end{cases} \tag{33}\]  
Or:  
\[\begin{cases}f_{1}(x_{a})<f_{1}(x_{b})\\ f_{2}(x_{a})\leq f_{2}(x_{b})\end{cases} \tag{34}\]  
Then we call \(x_{a}\) dominates \(x_{b}\), otherwise if there is:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Value** \\ \hline \(OC_{b}\) (the operating cost per mileage of a standby train set) & 1 for per kilometers \\ \(PT_{CAN}^{c,j}\) (the penalty time of cancelling a train) & 1440 min \\ \(SC_{b}\) (the seating capacity of the standby train set) & 1200 people \\ \(SC_{i}\) (the seating capacity of the train set) & 1200 people \\ \(EST_{j}\) (the extra stop time of trains) & 3 min \\ \(EDT_{j}\) (the extra departure time of trains) & 2 min \\ \(TST_{j}\) (the train stop time at stations) & 3 min \\ \(ADT\) (the acceptable maximum associated delay time) & 20 min \\ \(\alpha\) (the acceptable maximum coefficient of overcrowding on a train) & 1.2 \\ \(TDH\) (the minimum train delivery headway of two trains) & 4 min \\ \(COT\) (the operation time of train coupling) & 16 min \\ \(DOT\) (the operation time of train decoupling) & 10 min \\ \(SVT_{j}\) (the serving time of train set at stations) & 20 min \\ \hline \hline \end{tabular}
\end{table}
Table 4: Value of the parametric.  
\[\begin{cases}f_{1}(x_{a})<f_{1}(x_{b})\\ f_{2}(x_{a})>f_{2}(x_{b})\end{cases} \tag{35}\]  
Then we call \(x_{a}\) and \(x_{b}\) do not dominate each other and they are non-dominated.
2. Pareto rank and Pareto Front  
The NSGA-III algorithm classifies the population by calculating how many solutions dominate the current solution. A population may have multiple Pareto levels, as shown in Figure 4:  
As shown in Figure 4, the blue solutions form level 1, and the yellow solutions form level 2. In terms of dominant relationships, the solutions in level 1 do not dominate each other, and the solutions in level 2 do not dominate each other either. But the solutions in level 2 are dominated by some solutions in level 1. At the end of the iteration, the set of solutions with the highest Pareto level constitutes the Pareto front. In this example, level 1 is the Pareto front.
## 3 System Design
### Measurement and Control Unit  
The measurement and control unit is mainly composed of a capacitive sensing control circuit and a host computer software. The capacitive sensing control circuit detects the displacement signals of the TM by directly loading a 100 kHz sinusoidal modulation signal to the TM to form a resistor-capacitor (RC) based resonant capacitor bridge. The capacitor bridge includes the differential capacitance formed by the TM and the EH and the differential transformer.  
The basic measurement principle is shown in Figure 8, in which C1 and C2 are capacitors formed by the TM and the EH, \(C_{p1}\) and \(C_{p2}\) are the parasitic capacitor and resonance tuning capacitor in the circuit, respectively, \(C_{b1}\) and \(C_{b2}\) are the electrostatic feedback drive filter capacitors, \(L1\), \(L2\), and \(Ls\) are transformer inductances, and \(\mathrm{V_{d}}\) is the detection voltage of the differential capacitance. Table 2 shows the corresponding values of the capacitances and inductances.  
A high gain of the signal can be achieved by adjusting the RC bridge, working near the resonance point, reducing noise and improving the signal-to-noise ratio of the differential capacitor signal. Therefore, the capacitor inductor circuit has the same resonant frequency as the sinusoidal modulation signal, i.e., 100 kHz. The circuit parameters are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Content** & **Value** & **Tolerance** \\ \hline TM size & 40 mm \(\times\) 40 mm \(\times\) 6.4 mm & \(\pm\)0.002 mm \\ TM weight & 42 g & \(\pm\)1 g \\ Spacer thickness & 6.64 mm & \(\pm\)0.001 mm \\ Distance between TM and EH & 120 \(\upmu\)m & \(\pm\)0.003 mm \\ Area of electrode X1 & 473.56 mm\({}^{2}\) & 0.2\% \\ Area of electrode X2 & 236.98 mm\({}^{2}\) & 0.2\% \\ Area of electrode X3 & 236.86 mm\({}^{2}\) & 0.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the sensitive structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **Value** & **Comment** \\ \hline \(C1\), \(C2\) & 53 pF & Equilibrium value \\ \(C_{p1}\), \(C_{p2}\) & 1058 pF & Error: 10\% \\ \(C_{b1}\), \(C_{b2}\) & 1 nF & Error: 10\% \\ \(L1\), \(L2\), \(Ls\) & 4.5 mH & Error: 8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Capacitances and inductances list.  
Figure 8: Schematic of the capacitor bridge.  
The generated voltage signal is transmitted to the PID controller, and then a feedback voltage is obtained. This feedback voltage is applied to the electrodes through an electrostatic drive circuit to complete a closed-loop control. One end of the capacitive sensing circuit is connected to the sensitive structure through a coaxial cable, and the other end is linked to a host computer through a serial port. The software for measurement and control is developed through LabView, which can control the switching of different measurement modes, and collect and display the current measurement information in real-time.
## 4 Discussion
The RMSE and precipitation score results are averaged over all the analysis and forecasts during that time. Thus, statistical evaluation on the system is much more valid than just single analysis and forecast for the case study [49]. In all, assimilating satellite and radar data with POD-4 DEnVar method shows improvements when making a forecast on heavy rainfall. On the one hand, the flow-dependent increments are produced in the precipitation area according to the current circulation pattern. The better precipitation forecast in POD is attributed to the local change of dynamical and thermodynamic field. On the other hand, assimilation decreases the spin-up time for hydrometeor, which is important for the development of short-term heavy rainfall. Since the precipitation forecast is weaker than the observations in CON, assimilation help strengthens the precipitation, resulting in better forecast performance. Although this study shows encouraging results of simultaneously assimilating satellite and radar data with POD-4 DEnVar method, further work is still needed. Firstly, results from the evaluation of the assimilation system show that the benefits of assimilation are still not guaranteed all the time. Thus, more testing and additional cases are required to further evaluate the performance of the system. Secondly, satellite data has been assimilated in all-sky conditions [50; 51; 52], which is our further goal to assimilate satellite data in all-sky conditions with POD-4 DEnVar method. In addition, more efforts are needed to optimize the system in order to be applied in the daily weather forecast.
## 5 Discussion
### Sensitivity Analysis of Perturbation Strength  
When studying the impact of perturbation strategies on algorithm performance, the sensitivity analysis of perturbation strength, as a core parameter, is crucial. The perturbation strength is directly related to the ability of the algorithm to jump out of the local optimal solution and the breadth of the global search, so the careful analysis of its impact on the quality of the solution and the convergence speed is a key step in optimizing the performance of the algorithm.  
For concrete implementation, we first fixed the tabu tenure parameter to 1 and set a series of perturbation strength levels from 0.02 \(\times\) 1 \(V\)! to 0.1 \(\times\) 1 \(V\)! with a step size of 0.02 \(\times\) 1 \(V\)!, which were gradually applied to our local search algorithm. For each selected value of perturbation strength, we run the algorithm 10 times at each intensity and record the best solution and average solution obtained in each run in Table 3.  
Figure 5: Comparing the time needed to find the best solution between PCC\({}^{2}\)LS and other algorithms on common UDG instances.  
By comparing the number of best solutions obtained and the average solution quality under different perturbation strengths, we can observe the specific impact of the perturbation strength on the performance of the algorithm. Ideally, a moderate perturbation strength can significantly improve the algorithm's ability to jump out of the local optimal solution while avoiding the problem of degradation of solution quality or algorithmic efficiency caused by excessive perturbation. For example, when the perturbation strength is set to \(0.05\times\ |V|\), the algorithm shows better performance compared to other perturbation strengths and successfully finds smaller connected dominating sets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Problem**} & \multicolumn{2}{c}{**0.02 \(\times\) 1**_V_1} & \multicolumn{2}{c}{**0.04 \(\times\) 1**_V_1} & \multicolumn{2}{c}{**0.05 \(\times\) 1**_V_1} & \multicolumn{2}{c}{**0.06 \(\times\) 1**_V_1} & \multicolumn{2}{c}{**0.08 \(\times\) 1**_V_1} & \multicolumn{2}{c}{**0.1 \(\times\) 1**_V_1} \\ \cline{2-13}  & **Min** & **Avg** & **Min** & **Avg** & **Min** & **Avg** & **Min** & **Avg** & **Min** & **Avg** & **Min** & **Avg** \\ \hline  
*20** & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 2