esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, a new fractional-order (FO) \(\mathrm{PI}^{\lambda}\mathrm{D}^{\mu}\) controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order \(\lambda\) and the differential order \(\mu\) are introduced in the controller, and the two additional adjustable factors make the FO \(\mathrm{PI}^{\lambda}\mathrm{D}^{\mu}\) controller have better accuracy and robustness. Simulations are carried out for comparison with a ship's digital PID autopilot. The results show that the FO \(\mathrm{PI}^{\lambda}\mathrm{D}^{\mu}\) controller has the advantages of a small overshoot, short adjustment time, and precise control. Due to the uncertainty of the model parameters of USVs and two extra parameters, it is difficult to compute the parameters of an FO \(\mathrm{PI}^{\lambda}\mathrm{D}^{\mu}\) controller. Secondly, this paper proposes a novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO \(\mathrm{PI}^{\lambda}\mathrm{D}^{\mu}\) controller parameters. By dynamically changing the learning factor, the particles carefully search in their own neighborhoods at the early stage of the algorithm to prevent them from missing the global optimum and converging on the local optimum, while at the later stage of evolution, the particles converge on the global optimal solution quickly and accurately to speed up PSO convergence. Finally, comparative experiments of four different controllers under different sailing conditions are carried out, and the results show that the FO \(\mathrm{PI}^{\lambda}\mathrm{D}^{\mu}\) controller based on the IPSO algorithm has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control.  
underactuated surface vessels; fractional order \(\mathrm{PI}^{\lambda}\mathrm{D}^{\mu}\) controller; course-keeping; improved particle swarm optimization algorithm; autopilot +
Footnote †: journal: Applied Sciences
## Materials and Methods
### Algorithm Implementation  
#### 2.4.1 Empirical Algorithm  
Several studies have used empirical algorithms with single-band and band combinations (or dual-band and band combinations) [10, 30, 36, 38, 39, 40]. The results of these studies show that using band combinations for remote-sensing inversion of lake water quality is better than using single bands [41]. Moreover, band ratios are helpful for eliminating the noise of the reflected signal from the bottom of a water body, and some Landsat image studies have shown that the partial band ratio is effective for the prediction of water transparency in many lakes. However, it is difficult to manually identify the appropriate bands and band ratios for water transparency prediction [10, 30, 36, 38, 39, 40]. Therefore, stepwise regression was used in this study to identify bands and band ratios suitable for predicting regional water transparency.  
Stepwise regression is a simple and effective method with few variables in the regression equation, and the most significant variables are retained. This method is effective in practice and achieves high prediction accuracy for water transparency [32]. Furthermore, if there is a relationship (i.e., multicollinearity) between variables, stepwise regression can correct the multicollinearity to a certain extent. All variables were used as input parameters, including specific band reflectivity, band ratios, and other forms of algebraic combination [36, 42, 43]. We cannot intuitively assess the validity of any variable to explain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **1 Day** & **3 Days** & **7 Days** \\  & **(_n = 112_)** & **(_n = 254_)** & **(_n = 432_)** \\ \hline Min (m) & 0.18 & 0.18 & 0.15 \\ Max (m) & 7.50 & 7.50 & 8.00 \\ Mean (m) & 2.01 & 1.88 & 2.02 \\ Stdev (m) & 2.09 & 1.86 & 1.97 \\ CV & 1.04 & 0.99 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Secchi depth statistics of 1-day, 3-day, and 7-day time window matching data. Coefficient of variation (CV) = Stdev/Mean.  
SD. Hence, we used stepwise regression to obtain the multivariate linear equation that can best describe SD.  
Logarithmic transformation can provide more stable SD estimates than using measured SD values directly [36; 44]. To improve the generalization ability of the regression model, two variables were randomly selected as the initial variables, and the Akaike information criterion (AIC) [45] was used to evaluate the model's goodness-of-fit after introducing or deleting variables until the AIC value was no longer reduced significantly. The final results not only ensured an accurate interpretation of the data but also avoided overfitting.
## 3 Results and Discussion
### Macromineral Concentrations of Yellow-Fleshed Cassava Roots by Genotype
From these results, it could be inferred that most of the genotypes had higher levels of mineral density than the check genotypes, and they could be advanced to the next stage in the breeding program. Additionally, there were significant differences in the macroelement concentrations among the genotypes. Most of the values obtained for the macroelements for all the genotypes compared well with what most researchers reported. [PERSON] et al. [19] reported the macroelement content of the nine cassava varieties as follows: Potassium content ranged from 6810 to 12,200 mg/kg, and sodium ranged from 1720 (Nalumino variety) to 4590 mg/kg (Bangweulu variety). [PERSON] et al. [20] reported that the macroelements of the peel of the three cassava cultivars studied was a calcium content of 137.4 to 198.1 mg/kg), sodium of 137.0 to 142.3 mg/kg, magnesium of 128.0 to 148.1 mg/kg, and potassium of 787.2 to 1239.8 mg/kg. Besides, the authors also corroborated the finding from this study that cassava roots are rich in macroelements, especially potassium. However, the values of macroelements reported by [PERSON] et al. [21] for six varieties obtained from the Crop Research Institute of Council for Scientific and Industrial Research (CSIR) in Ghana were lower than those obtained in this study and reported. The reported calcium content ranged from 0.06 to 1.60 mg 100 g\({}^{-1}\), sodium ranged from 0.25 to 0.37 mg 100 g\({}^{-1}\), magnesium content ranged from 1.35 to 2.52 mg 100 g\({}^{-1}\), and the potassium content ranged from 0.25 to 0.36 mg 100 g\({}^{-1}\). The difference in the values could be due to the genotypic differences and the environment. This study used yellow-fleshed genotypes, but most of the authors have used white-fleshed varieties. This implies that yellow-fleshed varieties are rich in macroelements compared with white-fleshed varieties. Additionally, it was observed in this study that most of the genotypes had the lowest sodium content among the macroelements, which was not in agreement with what some authors ([PERSON] et al. [16]; [PERSON] et al. [21]; [PERSON] et al. [20]) reported for white-fleshed varieties. This could also be due to genetic variation in terms of yellow-fleshed versus white-fleshed varieties. However, [PERSON] et al. [22] and [PERSON] et al. [17] reported sodium as the lowest macroelement in cassava roots, as found in this study, which reported a sodium content of 3.6 to 3.9 mg 100 g\({}^{-1}\).
# A Comparison of Deep Learning Algorithms for Anomaly Detection in Discrete Mechanical Systems
## 5 Results  
The 240 damaged records were firstly analyzed via an EMA. However, the natural frequencies and mode shapes were found to slightly change for a damage of 10%, as shown in Figure 10.  
For damage of a higher degree, reported in Figure 11, the differences are appreciable, but the detection of the damage position is not straightforward, confirming the main issue described in the literature concerning the fact that vibration signals are a strong indicator of damage, but it is difficult to retrieve its location in this way [43].  
With this in mind, the anomalies set were pre-processed with the same scaler obtained for the training set and then fed to both the PINN-CAE and the DD-CAE. The MAE errors for each of the anomalies recorded and for each channel (accelerometer) were then compared to the previously found MAE test thresholds. Each record with a loss higher than  
Figure 11: Vibration modes of the structure for a 50% reduction in the stiffness value and for different positions of damage.  
Figure 10: Vibration modes of the structure for a 10% reduction in the stiffness value and for different positions of damage.  
the threshold is classified as an anomaly. Both the architectures detect all the time histories considered in the anomaly set as anomalies, confirming the expected higher precision of data-driven algorithms in detecting structural damage compared to conventional methods. In particular, the difference is extremely significant for the cases in which the extent of damage is not so high. Moreover, for each detected anomaly, the channel (corresponding to the accelerometer position, i.e., the floor) with the maximum value of the MAE loss was selected as the predicted damage position and compared with the real and known one. Then, for each damage extent in the range of 10-60% taken as a reference, the following accuracy indicator was evaluated for both the considered algorithms:  
\[\mathcal{A}=\frac{n_{d}}{n_{tot}}\times 100 \tag{16}\]  
where \(n_{d}\) is the number of anomalies with damage extent equal or higher than the reference value and whose position is correctly detected by the model, while \(n_{tot}\) is the total number of anomalies with a damage extent equal to or higher than the reference value. The results are reported in Table 5.  
It is possible to conclude that the PINN outperforms the results obtained with the purely data-driven approach, as expected.
# Application Layer Protocol Identification Method Based on ResNet
## 2 Related Work  
Protocol recognition is a crucial technology in network security, as it identifies the protocol types used in network traffic, thereby enhancing network security and management efficiency. With the advancement of the network era, protocol recognition methods have been continuously evolving and currently primarily include the following three approaches.
# Approximate Sensory Data Collection: A Survey
## 4 Query-Driven Approximate Data Collection Algorithms  
As important providers of sensory data sources to provide the sensory data, the WSNs and IoT systems also can be seemed as a database system, so that it is quite important for them to deal with various queries. Therefore, a group of data collection algorithms are designed accordingly. Since such data collection algorithms serve a certain query, the comment steps are as follows.  
Firstly, based on the inputted query and the precision requirements, a distributed query plan, which involved a number of sub queries, is determined and diffused in-network.  
Secondly, the proper sensory data set with a minimal size are retrieved by each sensor when it has received the corresponding sub query. Such partial results are transmitted and aggregated in the network toward the sink.  
The main challenges of the algorithms in this category are how to determine the proper sensory data set for each query and how to aggregate the partial results, and the main advantage of these algorithms is that the transmission cost is extremely small since they are designed with the purpose of serving specific queries.  
The existing works belonging to this category can be classified into two types, the sampling-based algorithms and the coding-based algorithms. The details are as follows.
## 3 Results
### Power Source  
#### 3.5.1 Generator Capacity  
The charging capacity of the solar panel is not absolute and should be evaluated based on the location of the panel (forest, open landscape), the seasons, and climate conditions (sunny or cloudy conditions). One solar panel can provide up to 150 W of charge to the generator, and if more charging power is needed due to higher energy demand from the generator, additional panels can be added and connected to the previous panel. According to our testing, the generator used in our study (1800 W) was able to power the whole system (including the laptop) without been charged for a period of \(\approx\)48 h prior to depletion. This period expanded to a full week when the software was run within the reader, bypassing the laptop and its power consumption. On a smaller scale, we found that the power required to activate four antennas and the computer running the ItemTest software was approximately 45-60 W according to the read on the generator's screen. However, at full capacity of 32 antennas and the software running within the reader (excluding the laptop), the unit consumed only 11-13 W and was easily self-sustained by recharging from the solar panel. It is strongly recommended to provide proper ventilation windows in the box's body for the generator to avoid overheating. Despite being relatively expensive (US $2500/Table 1), the generator is a good off-the-shelf solution to power the unit.
Some other works involve doping with S, P, and N grown with Co\({}_{9}\)S\({}_{8}\) nanoparticles (as a bifunctional catalyst) [22] or silylation by employing three distinct silica precursors (CO\({}_{2}\) adsorption of 3.5 mmol/g at 5 bar and 0 \({}^{\circ}\)C [23] or thiophene click reaction [24]. Finally, other works can be found by using MgO, rGO, and amorphous carbon to prepare sandwich-like structures (CO\({}_{2}\) adsorption up to 31.5 wt % at 27 \({}^{\circ}\)C, 1 bar CO\({}_{2}\) 22.5 wt % under the simulated flue gas) [25]. In most of these works it was concluded that the functionalization of 3D rGO materials by introducing various heteroatoms functional groups is a useful tool to improve the textural properties (porosity and SA) [18; 19; 23; 25], CO\({}_{2}\) capture capacity [18; 19; 21; 23; 25] and/or selectivity over N\({}_{2}\)[19; 21]. In our previous works, we prepared 3D rGO monolithic structures for CO\({}_{2}\) capture, functionalized either by a partial reduction of GO [9] or by functionalized polymer particles [8], achieving CO\({}_{2}\) capture up to 2.1 mmol/g and 3.97 mmol/g at 25 \({}^{\circ}\)C and 1 atm,respectively. The main disadvantage of these materials is their low fraction of micropores, which likely is the main disadvantage of achieving higher CO\({}_{2}\) capture. To increase the micropore fraction within the monolithic rGO materials, we functionalized the GO platelets with different silane coupling agents. Two aims were targeted, on the one hand, to introduce heteroatoms (S, O, N) as they have already been shown that their presence makes the structures more CO\({}_{2}\)-fillic, and on the other to form dense structures that will increase the formation of micropores during the creation of the monolithic structures. For that aim the following silane coupling agents were used: (i) (3-Mercaptopropyl)trimethoxysilane (TMOS) (functionalize with Si and S atoms), (ii) 1-[3-(trimethoxysil)propyl]urea (TMON) (functionalized with Si and N atoms), and (iii) Cyanomethyl [3-(trimethoxysil)propyl] trithiocarbonate (TMOSN) (functionalization with Si, N and S atoms). In Scheme 1, the chemical structures are presented. The monolithic structures were performed by chemical reduction of GO and modified GO with ascorbic acid (AsA) at two different analogies with the initial GO (0.5 and 2), produced at two different temperatures (60 \({}^{\circ}\)C and 90 \({}^{\circ}\)C). The different parameters (physicochemical, elemental, and porosity) that govern the adsorption capacity were deeply studied.
## 2 Materials and Methods
### Multi-Layer Perceptron
#### 2.3.2 MLP Model and Methodology  
In this study, an MLP model was designed for numerical data regression analysis to predict continuous values. The MLP model consists of multiple fully connected (Dense) layers, utilizing dropout and batch normalization techniques to improve the generalization performance and stabilize the training process.  
The input data are provided in vector form for each feature, and the first layer consists of 256 neurons with the ReLU activation function. Following this, batch normalization and 20% dropout are applied to prevent overfitting and maintain training stability. The second, third, and fourth hidden layers use 128, 64, and 32 neurons, gradually reducing the model's complexity. Each hidden layer similarly applies the ReLU activation function, batch normalization, and dropout. The final output layer uses a single neuron to produce the final predicted value, performing the regression analysis.  
Figure 2: Multi-layer perceptron model structure.  
Activation functions in neural networks, such as those used in the hidden layers of this MLP model, play a crucial role in determining the output of each neuron by introducing non-linearity into the model. Without activation functions, a neural network would behave like a simple linear regression model, limiting its ability to learn from complex data patterns. The rectified linear unit (ReLU) activation function, applied throughout the hidden layers in this model, is particularly popular due to its simplicity and efficiency in handling large networks. It outputs the input directly if it is positive or zero; otherwise, it is effective for large-scale neural networks. Additionally, more advanced activation functions, such as Leaky ReLU and exponential linear unit (ELU), can address specific challenges like the \"dying ReLU\" problem, but the ReLU function remains a standard choice due to its balance between simplicity and performance. The choice of activation function, as seen in the design of this MLP model, can significantly impact the training dynamics and the overall performance of the neural network.  
The model was trained using the Adam optimization algorithm, and the Huber loss function was adopted to ensure robust learning against outliers. R2 score and MAE were used as evaluation metrics to monitor the model's performance, with detailed training parameters shown in Table 1.  
During training, the R2 score callback was defined to record and output the R2 score and MAE for the training, validation, and test data at each epoch. The ModelCheckpoint callback was also used to save the model weights, demonstrating the best performance. After training was completed, the final R2 score and MAE for the test data were calculated, and the predicted values were adjusted within the range of actual values to evaluate the model's performance.
## 4 Data Reconstruction and Route Optimization
Next, on this basis, route optimization is further carried out based on the reconstructed weather results, and the optimization results are shown in Figures 17 and 18. As shown in Figures 17 and 18, in the low-resolution wave height and wind speed weather map, the red circle is expressed as the scope of ship safety field, the route shows that the ship is about to sail into the orange high-risk area. In this area, the wave height is about 3.5 m, and the wind speed is about 11 m/s. When the ship cannot withstand the sea conditions, the system often chooses to detour or drift, which significantly increases the navigation time and cost. However, after the super-resolution optimization of the weather map, the area corresponding to the same path becomes a yellow medium-risk area, the wave height is about 3 m, and the wind speed is about 10 m/s. This situation can ensure the normal operation of the ship's route and avoid unnecessary detour. It can be seen that by the super-resolution reconstruction of weather data, the accuracy of environmental information can be effectively improved, thereby reducing the time and cost of ship navigation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{t}\)\(\leq\)**0.05** & & \multicolumn{2}{c}{**SRResNet**} & \multicolumn{1}{c}{**SRNet**} & \multicolumn{1}{c}{**SRCNN**} \\ \cline{3-6}  & & & **Significance** & **Significance** & **Significance** \\ \hline \multirow{3}{*}{Wave Height} & \multirow{3}{*}{SRCBAM} & PSNR & True & True & True \\  & & SSIM & True & True & True \\ \cline{1-1}  & & GMSD & True & True & True \\ \cline{1-1}  & & FSIM & True & True & True \\ \hline \multirow{3}{*}{Wind Speed} & \multirow{3}{*}{SRCBAM} & PSNR & True & True & True \\ \cline{1-1}  & & SSIM & True & True & True \\ \cline{1-1}  & & GMSD & True & True & True \\ \cline{1-1}  & & FSIM & True & True & True \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test of significance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **PSNR** & **SSIM** & **GMSD** & **FSIM** \\ \hline \multirow{5}{*}{Wind Speed} & SRCBAM & 41.52 dB & 0.9797 & 0.0400 & 0.9997 \\  & 95\% Confidence Interval & 41.36–41.67 & 0.9792–0.9803 & 0.0391–0.0410 & 0.9997–0.9998 \\  & SRResNet & 41.12 dB & 0.9792 & 0.0406 & 0.9997 \\  & 95\% Confidence Interval & 4  
Figure 17: Comparison of route optimization results—wind speed. Figure 18: Comparison of route optimization results—wave height.
# Experimental Study of Tool Wear in Electrochemical Discharge Machining
## 2 Experimental Settings  
### Experimental Equipment  
The ECDM experimental system includes a power supply system, a machine tool system, a microelectrode system, and a machining control and monitoring system. The power supply system uses AN12010D-M single-pulse power supply (Ansi, Wuxi, China), which can provide 0-120 V voltage, frequency within 10 KHz and 0-100% duty cycle. The machine tool system is composed of a three-axis simultaneous machining platform, an air floatation test bed, a high-speed electric spindle, and fixtures. The air floatation test bed can maintain the stability of the machining platform and ensure the accuracy of the spindle at high speed. The microelectrode system includes a spiral tungsten carbide electrode, electrolytic cell, electrolyte, fixture, and a lifting platform. The machining control and monitoring system consist of the MP-C154 motion control card, A622 current probe (Tektronix, Shanghai, China), TBS1104 digital storage oscilloscope (Tektronix, Shanghai, China) and a B013 Supereyes (SuperEye, Shenzhen, China). The current probe was used to monitor the current signal in a real-time manner, and the data were output from oscilloscope. The key performance indicators of the digital storage oscilloscope include the 100 Mhz bandwidth, four channels and the 1 GS/s sampling rate. Supereyes is a portable digital microscope with 13 million pixels, which can achieve 1-2000 magnification. It is mainly used for process observation, image capturing, and tool (tool immersion depth) setting. As shown in Figure 2, the experiment was conducted in an environment with a constant temperature of 20 \(\pm\) 0.3 \({}^{\circ}\)C. The tool wear was observed using a NOVA NANOSEM 450 scanning electron microscope (Hillsboro, OR, USA). The BSM-220.4 electronic balance (ZhuoJing, Shanghai, China) was used to measure the amount of the cathode weight loss, which has an accuracy of 0.0001 g. Vernier caliper was used to measure the cathode length loss, and the precision was 0.02 mm.