esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mask-Based Adversarial Defense Scheme
## 4 Results
### The Main Result and Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**LeNet + MNIST**} & \multicolumn{3}{c}{**ResNet18 + SVHN**} \\ \hline
**Attack Method** & \(\mathbf{c}\) & **Attack** & **Defense** & **Improvement** & \(\mathbf{c}\) & **Attack** & **Defense** & **Improvement** \\ \hline Benign & - & & 96.13\% & & - & 91.65\% & \\ \hline FGSM L1 & 10 & 44.12\% & 68.94\% & 24.82\% & 15 & 50.47\% & 76.84\% & 26.37\% \\ FGSM L1 & 20 & 28.35\% & 49.50\% & 21.15\% & 20 & 45.85\% & 70.25\% & 24.40\% \\ FGSM L2 & 0.8 & 34.85\% & 58.60\% & 23.75\% & 0.4 & 53.17\% & 80.40\% & 27.23\% \\ FGSM L2 & 1.0 & 30.28\% & 52.82\% & 22.54\% & 0.6 & 46.77\% & 71.48\% & 24.71\% \\ FGSM Linf & 0.03 & 36.26\% & 61.82\% & 25.56\% & 0.01 & 57.12\% & 85.40\% & 28.28\% \\ FGSM Linf & 0.04 & 28.58\% & 50.31\% & 21.73\% & 0.02 & 46.06\% & 68.95\% & 22.89\% \\ BIM L1 & 4 & 8.98\% & 59.49\% & 50.51\% & 10 & 15.46\% & 80.37\% & 64.91\% \\ BIM L1 & 6 & 2.26\% & 44.17\% & 41.91\% & 12 & 11.54\% & 75.85\% & 64.31\% \\ BIM L2 & 0.2 & 16.55\% & 69.23\% & 52.68\% & 0.25 & 21.75\% & 84.40\% & 62.65\% \\ BIM L2 & 0.3 & 3.79\% & 49.04\% & 45.25\% & 0.3 & 16.59\% & 81.37\% & 64.78\% \\ BIM Linf & 0.015 & 11.72\% & 70.94\% & 59.22\% & 0.01 & 16.06\% & 86.31\% & 70.25\% \\ BIM Linf & 0.02 & 3.62\% & 56.33\% & 52.71\% & 0.02 & 2.58\% & 71.00\% & 68.42\% \\ PGD L1 & 4 & 16.04\% & 71.79\% & 55.75\% & 14 & 15.09\% & 84.14\% & 69.05\% \\ PGD L1 & 6 & 3.37\% & 54.75\% & 51.38\% & 16 & 12.11\% & 82.35\% & 70.24\% \\ PGD L2 & 0.2 & 22.98\% & 77.87\% & 54.89\% & 0.4 & 16.40\% & 84.22\% & 67.82\% \\ PGD L2 & 0.4 & 1.26\% & 48.85\% & 47.59\% & 0.5 & 12.17\% & 81.37\% & 69.20\% \\ PGD Linf & 0.015 & 11.64\% & 70.90\% & 59.26\% & 0.01 & 10.10\% & 84.45\% & 74.35\% \\ PGD Linf & 0.02 & 3.27\% & 56.38\% & 53.11\% & 0.02 & 4.69\% & 80.51\% & 75.82\% \\ CW L2 & 1.5 & 0.20\% & 93.39\% & 93.19\% & 1.8 & 21.70\% & 96.05\% & 74.35\% \\ DeepFool L2 & 0.6 & 32.62\% & 61.05\% & 28.43\% & 0.5 & 59.78\% & 87.09\% & 27.31\% \\ DeepFool L2 & 0.8 & 26.49\% & 51.39\% & 24.90\% & 0.7 & 53.87\% & 82.22\% & 28.35\% \\ DeepFool Linf & 0.05 & 24.77\% & 47.26\% & 22.49\% & 0.01 & 57.10\% & 88.06\% & 30.96\% \\ DeepFool Linf & 0.07 & 22.50\% & 39.76\% & 17.26\% & 0.02 & 46.39\% & 72.98\% & 26.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Defense against adversarial attacks by MAD for LeNet (with the MNIST dataset) and ResNet18 (with the SVHN dataset). From the results in Table 6, all methods have an acceptable accuracy on benign inputs. Nevertheless, regarding the list of tested adversarial attacks, MAD outperforms these state-of-the-art defense approaches in most cases.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Behaviour of IRP System in 12.7 Mm (0.5 inch) Discontinuous Host Pipe
#### 3.1.2 Debonding Modes of Adhesive Layer  
Table 8 demonstrates the different debonding mechanisms observed in three representative cases of the 12.7 mm (0.5 inch) discontinuous IRP system, with varying levels of adhesive strength. These cases were subjected to a fatigue load of 21.4 kN (4.8 kip) and a maximum load of 71.2 kN (16 kip). It is observed that the adhesive with a strength of 20 MPa (2.9 ksi) exhibits failure at a load of 28.5 kN (6.4 kip). In contrast, the adhesive with a strength of 40 MPa (5.8 ksi) remains intact until reaching the maximum load of 71.2 kN (16 kip). On the other hand, the adhesive with a strength of 5 MPa (0.7 ksi) shows early debonding occurring at a load as low as 8.5 kN (1.9 kip).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Load kN (kip)**} & \multirow{2}{*}{**Locations**} & \multicolumn{3}{c}{**Deflection mm (inch)**} & \multicolumn{2}{c}{**Difference with**} \\  & & & & **Benchmark** \\ \cline{3-6}  & & **5 MPa** & **40 MPa** & **20 MPa (Benchmark)** & **5 MPa** & **40 MPa** \\ \hline \multirow{3}{*}{71.2 (16)} & \multirow{3}{*}{1.7065} & 22.682 & 4.152 & \multirow{3}{*}{5.410 (0.213)} & \multirow{3}{*}{319\%\(\uparrow\)} & \multirow{3}{*}{23\%\(\downarrow\)} \\  & & (0.893) & (0.164) & & \\ \cline{1-1} \cline{3-6}  & & 11.949 & 2.272 & \multirow{3}{*}{2.914 (0.115)} & \multirow{3}{*}{310\%\(\uparrow\)} & \multirow{3}{*}{22\%\(\downarrow\)} \\ \cline{1-1} \cline{3-6}  & & (0.470) & (0.089) & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison between various levels of adhesive strength for the 12.7 mm (0.5 inch) discontinuous IRP system at a maximum load of 71.2 kN (16 kip).  
Figure 19: Lateral loading versus deflection of the 0.5 in discontinuous steel IRP system under different levels of adhesive strengths.  
Figure 20 illustrates the average length of debonding in the adhesive layer under a maximum load of 71.2 kN (16 kip) while comparing it across different levels of adhesive shear strength for a 12.7 mm (0.5 inch) discontinuous IRP system. The findings reveal a significantly higher rate of debonding for adhesives with lower shear strengths, with a ratio of 23.6 (slope 1 at 11.8 and slope 2 at 0.5), compared with adhesives with higher shear strengths. When the adhesive strength reaches 20 MPa (2.9 ksi), the debonding of the IRP system is significantly reduced.  
Figure 20: Average debonded length of the adhesive layer at the maximum load of 71.2 kN (16 kip) for 12.7 mm (0.5 inch) discontinuous IRP system versus different levels of adhesive shear strength.  
\begin{table}
\begin{tabular}{l l
## 4 Case Studies
### Test System Specification  
The IEEE-8 bus system and a real wind-farm-integrated substation named the Jhimpir power substation in Jamshoro, Pakistan, were considered testbeds for an evaluation of the performance of the proposed approach.  
The ANFIS-SARIMA model and HHO-LP optimization technique were implemented in a registered version of Matlab-2020(R2002a). The fault analysis was carried out on the Electrical Analysis Transient Program (ETAP) software. The operating system had a processor Intel(R) Core(tm) i5-4210 central processing unit (CPU) at 1.7 GHz-2.4 GHz with 4 GB of random-access memory (RAM).
# Efficient Removal of Heavy Metal Ions in Wastewater by Using a Novel Alginate-EDTA Hybrid Aerogel
## 3 Results and Discussion
### Adsorption Mechanism  
The variation tendency of the concentration of metal ions in the solution has also been monitored during the adsorption process in order to clarify the adsorption mechanism of the Alg-EDTA. It turns out that the amount of Pb\({}^{2+}\) (or Cd\({}^{2+}\)) in the solution gradually decreases in the progress of adsorption, whereas the amount of Ca\({}^{2+}\) gradually increases due to the ion exchange effect between Ca\({}^{2+}\) in Alg-EDTA and Pb\({}^{2+}\) (or Cd\({}^{2+}\)) in solution. However, after adsorption, the amount of Ca\({}^{2+}\) released from Alg-EDTA (0.041 \(\pm\) 0.002 mmol) is evidently smaller than the amount of adsorbed Pb\({}^{2+}\) or Cd\({}^{2+}\) (0.072 \(\pm\) 0.002 mmol), the ion exchange effect contributes approximately 56.9% of metal ions adsorption. It indicates that ion exchange effect is not the only approach in which Alg-EDTA binds to Pb\({}^{2+}\) (or Cd\({}^{2+}\)) ions. Therefore, we analyzed the XPS spectra of Alg-EDTA before and after adsorption. As shown in Figure 10A, the new Pb 4f and Pb 5 s peaks for Alg-EDTA loaded Pb\({}^{2+}\) verify that Alg-EDTA successfully loaded Pb\({}^{2+}\) after equilibrated with Pb\({}^{2+}\)-containing solution. The binding energy of the O element of Alg-EDTA shifts from 529.86 to 529.07 eV, it might be caused by the coordination effect between O atoms in Alg-EDTA with Pb\({}^{2+}\) ions [28]. Based on IR, XPS, and heavy metal ions concentration analysis of the Alg-EDTA sorbent before and after adsorption, we proposed a possible adsorption mechanism of the Alg-EDTA combines with heavy metal ions as depicted in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sorbent** &
\begin{tabular}{c} **Maximum adsorption** \\ **capacity for Cd\({}^{2+}\) (mg/g)** \\ \end{tabular} & **Adsorption Time (min)** & **Reference** \\ \hline
2-mercaptoethanolpropylrimidine functionalized & 112.5 & 30 & [17] \\ Mesoporous silica & 18.3 & 120 & [18] \\ \hline Acintivated carbon-zeolite composite & 161.8 & 1440 & [19] \\ \hline Mango peel waste & 68.9 & 60 & [20] \\ \hline Nano crystallite hydroxyapatite & 142 & 120 & [21] \\ \hline Natural zeolite & 130.4 & 1440 & [22] \\ \hline Clinoptilolite & 4.22 & 2880 & [23] \\ \hline Magnetic modified sugarcane bagasse & 123.6 & 60 & [24] \\ \hline Zeolite synthesized from perlite waste & 139.0 & 1440 & [25] \\ \hline Natural Egyptian bentonite clay & 8.2 & 60 & [26] \\ \hline L-cysteine modified bentonite-cellulose & 16.1 & 2880 & [27] \\ nanocomposite & 177.3 & 360 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 4: The adsorption capacity of different heavy metal ions sorbents for Cd\({}^{2+}\).  
Figure 10: (**A**) XPS survey of (**a**) Alg-EDTA and (**b**) Alg-EDTA loaded Pb\({}^{2+}\). O 1s spectra of Alg-EDTA (**B**) before and (**C**) after Pb\({}^{2+}\) adsorption.
# Improved Low Complexity Predictor for Block-Based Lossless Image Compression +
## 1 Introduction  
With the maturity of large-size display technology for cost reductions and the expansion of network communication bandwidth, high-resolution images such as 4 K and 8 K have become increasingly popular. Before compression, the size of high-quality images reaches several hundred megabytes. Therefore, lossless compression has become important.  
Lossless image compression is conducted in the prediction and entropy encoding parts. The prediction part, however, is not limited to lossless usage. Prediction is applied to intra-frames in video coding such as high-efficiency video coding (HVEC) [1]. The main purpose of prediction is to reduce the spatial redundancy of input images. Residues, the differences between the real pixel value and predicted value, have a small interval that originates from 0 if the predictor is precise enough. According to information theory [2], the more the residue is centralized, the lower the entropy.  
Lossless JPEG (JPEG-LS) with the median edge detector (MED) as its predictor [3] was renamed the LOCO-I algorithm, while context-based entropy coding was added after the prediction [4]. The predictor is simple and requires three of the causal neighbors of\(N\), \(W\), and \(NW\) (each representing North, West, and Northwest, respectively) and \(X\) as the predicted value (1). It uses the following equation:  
\[X\triangleq\left\{\begin{array}{ll}\min(W,N)&if\ NW\geq\max(W,\ N)\\ \max(W,N)&if\ NW\leq\min(W,\ N)\\ W+N-NW&otherwise\end{array}\right. \tag{1}\]  
The MED is based on the fact that \(X\) is always chosen as the median value of \(A\), \(B\), and \(A+B-C\). Despite its simplicity, the MED has a higher accuracy than the gradient-adjusted predictor (GAP), which is used in context-based adaptive lossless image coding (CALIC) [5]. GAP predicts values according to whether the vertical/horizontal gradients nearby exceed specific thresholds. Therefore, GAP cannot fit various inputs. Another renowned predictor is the edge-directed predictor (EDP), which predicts pixel values based on the assumption that natural images have the nth Markovian property. The predicted value is obtained by using the weighted sum of causal neighbors with the weighting vector solved using the least square (LS) method. Nonetheless, due to the heavy computational load of the covariance matrix, EDP is difficult to apply. Machine learning-based methods [6; 7] have higher accuracy compared to traditional predictor, they face a serious challenge in hardware implementation.  
There have been studies to improve the MED [8]. However, the algorithm involves k-mean clustering, which increases time complexity compared with the original MED. We re-examined the concept of the MED and proposed three methods.
# Physico-Chemical Characteristics of _Rosa canina_ L. Seeds and Determining Their Potential Use
## 2 Materials and Methods
### Mineral Content  
The content of potassium (K), calcium (Ca), magnesium (Mg), sodium (Na), iron (Fe), copper (Cu), manganese (Mn), boron (B), and zinc (Zn) was determined after ground and wet mineralization of rosehip seeds in analytically pure HNO3. Analyses were performed by atomic absorption spectrometry (AAS) according to the EN-ISO 6869:200018 method [22], with the use of a SOLAR 939 Unicam (Camberley, Surrey, UK) spectrometer. The results were expressed as mg/100 g of dry weight of seeds.  
Total Phenolics, Total Carotenoids, \(L\)-Ascorbic Acid Content, and Antioxidant Activity of Roselip Seeds  
One gram of powdered seeds was extracted with 10 mL of methanol (80%) in an orbital shaker (Daihan Scientific, Seoul, South Korea) at room temperature, 250 rpm, for 2 h. After filtration, the residue was re-extracted with 10 mL of methanol. The combined methanol extracts were stored at -18 \({}^{\circ}\)C until analysis.  
The content of phenolic compounds was determined based on the reaction with the Folin-Ciocalteu reagent [23]. Absorbance was measured spectrophotometrically at a wavelength of 765 nm (UV-2600i, Shimadzu, Kyoto Japan). The total phenolic content of rosehip seeds is expressed as \(\upmu\)g of gallic acid equivalents (GAE) per g fresh weight.  
The total carotenoid content in the rosehip seeds was determined by acetone-petroleum-ether extraction followed by spectrophotometric measurement. The absorbancewas measured at 450 nm with a UV-2600i spectrophotometer (Shimadzu, Kyoto, Japan) for the concentration of total carotenoids calculated as \(\upmu\)g/g fresh weight. All-trans-\(\upbeta\)-carotene (Sigma Chemical, St. Louis, MO, USA) was used as the standard.  
The content of L-ascorbic acid was determined by titration with 2,6-dichlorophenol according to the method described by [PERSON] et al. [24] The concentration of L-ascorbic acid was calculated from the volume of 2,6-dichlorophenol used for titration and expressed in milligrams per 100 g fresh weight.  
The antioxidant capacity of extracts was assessed based on the reduction of the synthetic and stable 2,2-diphenyl-1-picryhydrazyl (DPPH) free radical. The colorimetric method allows for the measurement of changes in the absorbance of a DPPH solution at a wavelength of 517 nm as the result of the antioxidant activity of the sample [25]. Absorbance was measured using a spectrophotometer (UV-2600i, Shimadzu, Japan). The results were expressed as \(\upmu\)mol Trolox (Sigma-Aldrich, St. Louis, MO, USA) per g fresh weight (FW) of rosehip seeds. The FRAP assay was conducted according to the method described by [PERSON] et al. [26]. Absorbance was measured with the spectrophotometer (UV-2600i, Shimadzu, Kyoto, Japan) at 700 nm. The results are expressed as \(\upmu\)mol Trolox g\({}^{-1}\) DW.
## 3 Methods to Address the Pipeline of End-To-End ADS
### Driver Assistance and Predicting Driver Patterns
#### 3.2.3 LSTM Based Models  
Currently, there are many approaches to road prediction using LSTM architectures for these types of forecasts. [PERSON] et al. [65] suggested spatiotemporal LSTM architecture for motion planning in AVs. It uses convolutional layers for feature extraction in an image, and it runs through an LSTM to find sequences in those images, something similar to a CRNN architecture. Once this data is acquired, a CNN architecture is applied to extract spatiotemporal information from multiple frames. Lastly, a fully connected layer is used to extract the trajectory taken by peripheral vehicles. This trained model can be of service for detecting multiple object trajectories. It is considered an end-to-end algorithm since all this raw data enters this architecture, and it outputs a solution. Additionally, it is quite demanding in terms of training and testing data.  
Figure 17: Basic Reinforcement Learning (RL) process, agent-environment relation (image under creative commons license).  
[PERSON] et al. [66] propose using an LSTM architecture for lane changing detection on highways, using data such as velocity and lateral displacement from vehicles. It was trained using the NGSIM US-101 dataset and trained two different LSTM models, one for lateral displacement and another for linear velocity. It performed very rapidly with 70 cm of error in displacement and 3 m/s error for speed, with 10 s ahead of raw predictions. This is a less direct approach since it requires the labeling of data. It can be assumed that they are using a Frenet like topology since they are only considering linear velocity and lateral displacement in their model.
## 1 Introduction
studied a fuzzy neural network energy management strategy for PHEV based on the adaptive neuro-fuzzy inference system (ANFIS) optimization algorithm on the ADVISOR software platform [23]. Based on the above recent researches, the traditional rule-based energy management strategy is not ideal. Fuzzy control is easy to understand and easy to implement in the control chip. Considering the implementation of the algorithm in vehicles and practical applications, the energy management strategy using fuzzy control combined with intelligent algorithm optimization has broad prospects in energy management systems of electric vehicles. Unlike the previous control methods, a neural network fuzzy energy management strategy (NNF-EMS) for HEVs is designed based on DCR, with the objective of addressing the inherent problems of traditional F-EMS, such as poor adaptability and lack of self-learning. The methods of neural network sample learning and characteristic parameter analysis are used to realize the recognition of driving cycles, and the recognition results are considered as the reference input of the fuzzy controller. Research shows that the proposed NNF-EMS can perform adaptive optimization of the fuzzy membership function and control rules under different driving cycles. Consequently, the proposed NNF-EMS has strong robustness and practicability under different driving cycles. In this paper, a novel NNF-EMS based on DCR for HEVs is designed to improve the adaptability of EMS to different driving cycles. The research ideas and arrangement for the rest of this paper are as follows: Section 2 introduces the traditional F-EMS of HEVs. Then, the driving cycle block and its definition are introduced, and the design method of NNF-EMS based on DCR is proposed in Section 3. In Section 4, the simulation verification of an HEV is designed and performed using different methods. Finally, the results and analysis are compared and illustrated in Section 4, followed by the conclusion and discussion in Section 5.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 2 Literature Review
### WMN Node Placement and Routing
#### 2.2.3 Wireless Mesh Routing  
As noted above, routing protocols for wireless meshed networks generally emphasise minimising the hop count of any route to the gateway in order to limit delays and optimise load. However, given the tight bond between the configuration of the network itself and how it is utilised to maximise capacity, many protocols have been developed to allow for adaptation to dynamic environments.  
In fully dynamic meshes, routing must consider the nature of the nodes themselves, which may move or leave the network over time. In such cases, a great deal of research has been performed to actively build and maintain routing infrastructures. The main metrics used, beyond hop count, include some variations of Expected Transmission Count (ETX) and Expected Transmission Time (ETT) [41], and a range of protocols have been developed based on proactive, reactive, or hybrid techniques. These include ad hoc algorithms like LQSR, flooding-based techniques such as OLSR and MMRP, or traffic-aware mechanisms such as AODV and DSR, among others [42]. There are also variations of the above that consider multi-path approaches or that emphasise load balancing. More recently, routing has also moved to consider centralised SDN-based approaches [6].  
However, in the case of FWA networks wherein routes can be considered relatively stable, many of these protocols are simply unnecessary. As with any wireless medium, there is always the potential for some intermittent interruption or interference on specific links, but this can largely be mitigated through the implementation of resilience measures such as denser meshing. Accordingly, routing in this space typically reverts to shortest path or distance vector algorithms such as some form of the Distributed Bellman-Ford or Dijkstra algorithms [26; 43].
## 4 Numerical Results
### Bubble Dynamics
Moreover, the spectral levels of Model-A are higher than those of Model-B in both numerical and experimental results. The actual number density of nuclei is known to be dependent on the air content rate in the cavitation tunnel. However, because there is no clear relationship between air content rate in the freshwater and the number density of nuclei, additional computations are carried out to investigate the effects of initial distribution of nuclei on cavitation noise of the underwater propellers. The considered cases are for the NCD of initial nuclei: 33.3% and 100% of seawater condition. It is noted that the case presented in Figure 22 is 10% of the seawater condition. The numerical results predicted using these distributions of nuclei are presented and compared with the experimental ones in Figure 23. In all the cases, the slope of spectrum is almost the same, and the acoustic level due to the cavitation flow of the Model-B is less than that of the Model-A. In the case using 33.3% of the seawater condition, there are good agreements in the frequency ranges over 3.5 kHz between the numerical and experimental results for both propellers. For more quantitative comparison, the overall sound pressure levels (OASPL) are computed in the given frequency range for the reference pressure of \(10^{-6}\) Pa. Table 7 compares the SPLs between the computed and measured data for Model-A and Model-B. It is found that the overall SPL predicted for the Model-B is less by 6.3 to 7.1 dB than that of Model-A, while the measured one for Model-B is less by 2.7 dB than that of Model-A. These results reveal that the current numerical method can adequately appreciate the effects of sweep angle on the cavitation inception and noise of the underwater propeller of the submarine. \begin{table}
\begin{tabular}{c|c c c c c c c c c} \hline \hline
**Initial Nuclei Radius (\(\upmu\)m)** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** & **Total** \\ \cline{2-10}
**Number of** & **Model-A** & 120 & 77 & 10 & 10 & 7 & 8 & 10 & 12 & 254 \\ \cline{2-10}
**Cavitation Bubble** & **Model-B** & 111 & 75 & 14 & 12 & 6 & 11 & 10 & 13 & 252 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The number of cavitation bubbles according to initial nuclei radius. Figure 23: Predicted acoustic pressure spectrum of Model-A and Model-B for different initial nuclei distributions: (**a**,**b**) 33.3% of number concentration density (NCD) of seawater condition; (**c**,**d**) NCD of seawater condition. Figure 22: Predicted acoustic pressure due to tip vortex cavitation in the time and frequency domain for (**a**) Model-A and (**b**) Model-B.
# Physics-Guided Neural Network-Based Feedforward Control for Seamless Pipe Manufacturing Process
## 3 Results  
### Neural Network Training  
In this section, we present the results of two neural network-based frameworks for the feedforward controller training process. For the benchmark model, a neural network trained on data collected from tests with various piston speeds and actuation forces is employed, which is an experimentally intensive process. To overcome this challenge, the training results of a physics-guided neural network are presented, incorporating our reduced-order actuator model for feedforward control, which is far less dependent on extensive training data.