esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Particulate Matter 2.5 Sensor Based on Surface Acoustic Wave Technology
## 2 Design and Simulation
### Pm\({}_{2.5}\) Deposition  
The upper hot source and the lower SAW sensor chip embedded in the air current microchannel form the plate-to-plate TP. As shown in Figure 6, the gas molecules coming from the hot plate have a larger kinetic energy than those coming from the cold plate [19]. Thus, the greater momentum received from the hot side causes a net force (thermophoretic force \(F_{\text{th}}\)) in the direction of decreasing temperature, leading to the overall movement of particles to the cooler side. The velocity resulting from the thermophoretic force is called thermophoretic velocity \(V_{\text{th}}\).  
Figure 4: Velocity field distribution of the virtual impactor.  
Figure 5: Collection efficiency curve of the virtual impactor.  
In the microscale TP, the particle deposition efficiency \(\eta\) can be written as [21]:  
\[\eta=\frac{S_{\text{cold}}V_{\text{th}}}{Q_{\text{in}}}=K_{\text{th}}\frac{vS_{ \text{cold}}\
abla T_{\infty}}{Q_{\text{in}}T} \tag{2}\]  
which is determined by the collection area of the cold plate (\(S_{\text{cold}}\)), the thermophoretic velocity (\(V_{\text{th}}\)) and the inlet flow rate (\(Q_{\text{in}}\)). \(v\) is the kinematic viscosity of the fluid, \(\
abla T_{\infty}\) is the temperature gradient, \(T\) is the particle temperature and \(K_{\text{th}}\) is the thermophoretic coefficient. Here, the \(K_{\text{th}}\) formula given by [PERSON] is adopted [22]. In Figure 7, the dimensionless \(K_{\text{th}}\) is plotted as a function of \(R\) in a semi-logarithmic coordinate, where the area in red dashed line represents the PM\({}_{2.5}\) area. With the decrease of particle radius, the \(K_{\text{th}}\) increases monotonously, and accordingly, the deposition efficiency will increase. Thus, when the deposition efficiency for particles of a certain diameter of 2.5 \(\upmu\)m is set to 100%, the TP dimensions can be deduced and drawn in Figure 8. The corresponding operating conditions were the temperature of the hot plate equaling 393.15 K, the temperature of the cold plate equaling 303.15 K and the \(Q_{\text{in}}\) equaling 6 mL/min. Besides, considering the surface size of a SAW sensor chip, the dimensions of the thermophoretic precipitator were 200 \(\upmu\)m \(\times\) 3.6 mm \(\times\) 6.3 mm.  
Figure 8: Thermophoretic precipitator dimensions for particles of 2.5 \(\upmu\)m diameter under 100% deposition efficiency condition.  
Figure 6: Schematic of the deposition principle of thermophoresis [20].  
Figure 7: Thermophoretic coefficient \(K_{\text{th}}\) with different particle radius \(R\) in a semi-logarithmic coordinate.
Molecules_**2012**, _17_, 3708-3722; doi:10.3390/molecules17043708  
_molecules_  
**ISSN 1420-3049**  
www.mdpi.com/journal/molecules  
_Article_  
**Analysis of Organic Volatile Flavor Compounds in Fermented Stinky Tofu Using SPME with Different Fiber Coatings**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
* School of Food and Chemical Engineering, Beijing Technology and Business University, Beijing 100048, China
* Beijing Key Laboratory of Flavor Chemistry, Beijing 100048, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 4 January 2012; in revised form: 27 February 2012 / Accepted: 13 March 2012 / Published: 26 March 2012_  
**Abstract:** The organic volatile flavor compounds in fermented stinky tofu (FST) were studied using SPME-GC/MS. A total of 39 volatile compounds were identified, including nine esters, seven alcohols, five alkenes, four sulfides, three heterocycles, three carboxylic acids, three ketones, two aldehydes, one phenol, one amine and one ether. These compounds were determined by MS, and conformed by comparison of the retention times of the separated constituents with those of authentic samples and by comparison of retention indexes (RIs) of separated constituents with the RIs reported in the literature. The predominant volatile compound in FST was indole, followed by dimethyl trisulfide, phenol, dimethyl disulfide and dimethyl tetrasulfide. In order to find a better extraction time, the extraction times was optimized for each type of SPME fiber; the results show that the best extraction time for Carboxen/PDMS is 60 min, for PDMS/DVB 30 min, for DVB/CAR/PDMS 60 min and for PDMS 75 min. Of the four fibers used in this work, Carboxen/PDMS is found to be the most suitable to extract the organic volatile flavor compounds in fermented stinky tofu.  
**Keywords:** fermented stinky tofu; analysis; organic volatile flavor compounds; solid phase microextraction
## 3 Background
### Literature Review Methodology
#### 3.1.1 Optimization Using Metaheuristics  
The significant increase of new metaheuristic algorithms in recent years has been critically addressed by some authors, such as [17], who points out a proliferation of new algorithms without adequate empirical validation, which hinders their evaluation and innovation. Despite these challenges, the emergence of new metaheuristics also presents opportunities, and in particular in the optimization of processes linked to new ML and DL models, where hyperparameter tuning, feature selection, and other optimization tasks are critical to improving model performance [4].  
As pointed out by [18], the use of metaheuristics such as the Whale Optimization Algorithm (WOA) is crucial for feature selection in the context of DDoS attacks, as it improves the classification process by selecting the optimal features for analysis, thereby enhancing the detection accuracy. Similarly, ref. [19] proposes the implementation of a hybrid model that combines the Particle Swarm Optimization (PSO) algorithm with  
Figure 2: Wordcloud of the 50 most used keywords in the reviewed articles.  
Long Short-Term Memory (LSTM) networks to reduce prediction errors and improve model accuracy in cloud environments. Furthermore, the same study highlights the use of the Firefly algorithm as another metaheuristic approach for feature selection; however, limitations were identified, particularly regarding its convergence, as the algorithm tended to become trapped in local optima and lacked historical data retention, adversely affecting its performance in DDoS attack detection scenarios.  
This theoretical basis establishes the importance and research interest in the use of metaheuristics in contexts related to machine learning and process optimization, and their application in feature selection and hyperparameter tuning in ML and DL models. This research will not delve into the implementation of metaheuristic algorithms, but its use is considered relevant and is presented as an opportunity for future research.
# Teleolocation--Remotely Operated Legged Robots
## 3 Experimental Protocol
### Experiment I--Telelocomotion Interface Types with Varying Traversal Complexity
#### 3.1.3 Evaluated Operator Interface Types  
In terms of operator input, three different hardware platforms were evaluated:  
1. standard computer keyboard, \(\mathbf{K}\);
2. standard gaming controller, \(\mathbf{J}\);
3. Sensable PHANToM Omni 3 DOF haptic device, \(\mathbf{H}\).  
Operator interface mode \(\mathbf{H}\) is that described in detail in Section 2.2.2, while the devices types of keyboard \(\mathbf{K}\) and controller \(\mathbf{J}\) are commonly used alternatives in teleoperation architectures. In all cases, the hexapod was maneuvered with the alternating tripod gait.  
Computer Keyboard, \(\mathbf{K}\)  
This operator interface is the most basic of the three tested, and involves just the use of a common computer keyboard. The user input mechanism relies solely on six keystrokes, as depicted in Figure 9a. With keys A and S, the operator is able to modulate step size for the predefined, alternating tripod gait sequence, and thus effectively change walking speed. Furthermore, the operator is able to translate the hexapod forward and backward with I and K respectively, and pivot left and right with J and L.  
Gaming Controller, \(\mathbf{J}\)  
This interface incorporates the use of a handheld gaming controller. The components utilized are the left and right joystick, as depicted in Figure 9b. In this setup, the step size or speed is modulated by how far the user pushes the left joystick forward or backward. The further the joystick is displaced, the faster the robot walks. The pivoting motion is controlled by the right joystick. Similar controllers are used to control the Endeavor's PackBot [57] and Auris Monarch surgical robot [58].  
Figure 8: Teleoperated locomotion tasks. (**a**) demonstrates a baseline case for which all useful interfaces should perform similarly, while (**b**) introduces challenging and non-ideal terrain. In (**b**), orange indicates ascending, purple indicates descending, red indicates flat, and white indicates goal.
## Appendix B Implementation of the Counter
### Extra Symbols  
A first possibility, that we followed in a previous version of the paper, is to duplicate the symbol alphabet \(\Sigma\) by adding a set of _extra tape symbols_\(\overline{\Sigma}=\{\overline{a}\mid a\in\Sigma\}\): a new symbol \(\overline{a}\), for any \(a\in\Sigma\) (including the blank \(\Box\)). In this way, when in a final state, the machine replaces any symbol \(a\) with the corresponding extra symbol \(\overline{a}\), and moves to the right. Dually, when in a source state, if the current cell contains an extra symbol \(\overline{a}\), the machine replaces the current symbol with the corresponding symbol \(a\) and moves to the right; otherwise, when the current symbol is \(a\in\Sigma\), it behaves as specified by the main transition function \(\delta_{0}\). The legal configurations are then restricted to three possible cases:  
1. \(\langle{a},{q},{\beta},{i}\rangle\)\(\langle\alpha\overline{\gamma},q_{t},\beta,i\rangle\)
3. \(\langle\alpha,q_{s},\overline{\gamma}\overline{\beta},i\rangle\)  
where: \(\alpha\beta\gamma\in\Sigma^{*}\), \(\overline{\gamma}\in\overline{\Sigma}^{*}\) is the sequence of extra symbols obtained by replacing any symbol \(a\) of \(\gamma\) with the corresponding extra symbol \(\overline{a}\); \(q\) is any state; \(q_{t}\) is a target state; \(q_{s}\) is a source state. It is readily seen that, to obtain an isomorphism between QTMs with extra symbols and QTMs with counters, it suffices to take the following bijection of configurations (where we use the same symbols as above):  
1. \(\langle\alpha,q,\beta,i\rangle\mapsto\langle\alpha,q,\beta,i,0\rangle\)
2. \(\langle\alpha\overline{\gamma},q_{t},\beta,i\rangle\mapsto\langle\alpha,q_{t},\gamma\beta,i-|\gamma|,|\gamma|\rangle\)
3. \(\langle\alpha,q_{s},\overline{\gamma}\overline{\beta},i\rangle\mapsto\langle \alpha\gamma,q_{s},\beta,i+|\gamma|,|\gamma|\rangle\)  
where \(|\gamma|\) denotes the length of \(\gamma\).
## 3 High Traffic Load Issues  
### Congestion and Queue Overflow in Convergecast Traffic  
Networks with convergecast traffic, such as those used for data collection applications, suffer from severe congestion problems [21]. These applications often have a convergecast behavior, where all of the data collected by all of the sensors in the network are routed to the sink. With high traffic load, the number of data packets traveling from leaf nodestowards the sink becomes higher, which leads to congestion mainly in nodes located near the sink. Congestion has negative consequences on network performance such as delay, queue overflow, and, ultimately, packet loss. When a network is congested, it is not simple for nodes to access the medium and transmit their data toward the sink because of high contention and collisions. In order to mitigate the impact of such congestion, a better distribution of the cells allocated to the nodes is necessary according to the node position within the network. In 802.15.4e networks with TSCH mode, a node \(n\) can transmit two types of data: periodic packets denoted by \(A_{l}^{W}(n)\) and event packets denoted by \(A_{l}^{V}(n)\).  
The total flow generated in a timeslot \(T\) by a node \(n\) denoted by \(A_{l}^{T}(n)\) is given by [22]  
\[A_{l}^{T}(n)=A_{l}^{W}(n)+A_{l}^{V}(n). \tag{1}\]  
The distribution of the number of data packets \(N(t)\) generated by each node in the network from the start of the slotframe to its end is calculated as follows [22]:  
\[P\{N(t)=n\}=\frac{(\lambda t)^{n}}{n!}e^{\lambda t}. \tag{2}\]  
We will assume that the queue associated with link \(l\) is denoted by \(Q_{l}(t)\) and \(E_{l}(t)\), the amount of data transmitted over a link \(l\) in timeslot t. With \(E_{l}(t)\leq Q_{l}(t)\) as a constraint, the evolution of the traffic queue for a specific node is then given by [23]  
\[Q_{l}(t+1)=Q_{l}(t)+A_{l}(t)-E_{l}(t). \tag{3}\]  
In [23], a constraint where the amount of data transmitted over a link must not exceed the queue to that link was taken in order to not have negative values in the queue evolution equation. \(A_{l}(t)\) denotes the amount of data generated by node n in timeslot t. The root maintains no queue as it is the destination node for all flows. The idea would therefore be to ensure that the nodes close to the root exhibit as little congestion as possible.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 7 Kaolin in Petrochemical Catalysis: From Fundamentals to Innovations
### Kaolin-Driven Catalysis in Petrochemical Processes
#### 7.4.3 Methanol-to-Olefin (MTO) Catalytic Reaction for Olefin Production
Recent advancements in hierarchical ZSM-5 materials, featuring both meso- and microporosity, enhance molecular transport while maintaining catalytic properties, highlighting their potential to improve MTO process efficiency and selectivity [234]. Modifying kaolin through treatments such as calcination and dealumination enhances its reactivity, facilitating the formation of ZSM-5's characteristic microporous structure. Initial findings suggest that kaolin-derived ZSM-5 exhibits comparable catalytic properties to conventional methods, offering a sustainable and economical alternative for industrial applications. Further research is needed to optimize these synthesis processes and fully realize kaolin's potential in producing high-quality ZSM-5 zeolites [235]. Studies from 2014 to 2019 have explored the synthesis and catalytic performance of ZSM-5 from kaolin for the methanol-to-olefin (MTO) reaction, revealing significant insights. [PERSON] et al. (2014) [236] highlighted the importance of binders in shaping zeolite catalysts, noting that kaolin and other clays enhanced macroporosity and mass transfer properties. They found that while binder-induced changes in acid site density and speciation affected intrinsic catalytic activity, they did not directly correlate with selectivity or catalyst lifetime. Notably, kaolin contributed to improved mechanical stability and catalytic performance. In 2017, [PERSON] et al. [237] synthesized nano-/micro-scale ZSM-5 from kaolin, demonstrating that nanosized ZSM-5 exhibited better coke tolerance, longer catalytic lifetime, and higher propylene selectivity compared to submicron and microsized samples. The enhanced performance of kaolin-derived ZSM-5 was attributed to the presence of elements like Fe, P, and Ti, which positively influenced catalytic stability and product distribution. In addition, [PERSON] et al. (2019) [238] investigated the effect of spray drying on ZSM-5-based catalysts, incorporating kaolin and other clays. They found that the nature of the selected clay significantly influenced the final acidity of the composite, impacting the MTO reaction pathways. The study underscored the critical role of slurry formulation and processing parameters in achieving optimal particle size and morphological characteristics, with kaolin-based composites showing promising catalytic performance due to their modified acidity profiles. Overall, these studies emphasize the potential of kaolin as a raw material in ZSM-5 synthesis, contributing to enhanced catalytic properties and offering a cost-effective alternative for MTO applications.
###### Abstract  
Zhaotong City in Yunnan Province is one of the largest apple growing bases in China. However, the terrain of Zhaotong City is complicated, and the rainy weather is more frequent, which brings difficulties to the identification of apple orchards by remote sensing. In this paper, an improved spectral angle feature is proposed by combining the Spectral Angle Mapper and Sentinel-1 data. Based on the Google Earth Engine and Sentinel image, a random forest classifier was used to extract apple orchards in the Ganhe Reservoir area, Zhaoyang District, Zhaotong City, which provides a theoretical basis for extracting the spatial distribution and sustainable development of the local apple industry. The classification results show that the improved spectral angle characteristics can improve the overall accuracy and F1 score of apple orchards. The RGB band combined with NDVI, GLCM, and improved spectral angle features obtained the most favorable results, and the F1 score and overall accuracy were 88.89% and 84.44%, respectively, which proved the reliability of the method in identifying apple orchards in Zhaotong City.  
crop classification; Google Earth Engine; random forests; spectral angle mapper; time-series analysis Article
# On-Board Road Friction Estimation Technique for Autonomous Driving Vehicle-Following Maneuvers
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(ADAS\) & Advanced Driver Assistance System \\ \(MPC\) & Model Predictive Control \\ \(ACC\) & Adaptive Cruise Control \\ \(AEB\) & Autonomous Emergency Braking \\ \(LQR\) & Linear Quadratic Regulator \\ \(TTC\) & Time To Collision \\ \(PID\) & Proportional-Integral-Derivative \\ \(SAE\) & Society of Automotive Engineers \\ \(ABS\) & Anti-lock Braking System \\ \(TRICK\) & Tire Road Interaction Characterization and Knowledge \\ \(MF\) & Magic Formula \\ \(ECUs\) & Electronic Control Units \\ \(CAN\) & Controller Area Network \\ \(GPS\) & Global Positioning System \\ \(SMC\) & Sliding Mode Control \\ \(MIL\) & Model In the Loop \\ \(SUMO\) & Simulation of Urban MObility \\ \(LPFVM\) & Lumped-Parameter Full Vehicle Model \\ \(FEM\) & Finite element Methods \\ \(c.o.g.\) & Center of Gravity \\ \(v\) & longitudinal velocity evaluated at the c.o.g. \\ \(a\) & longitudinal acceleration \\ \(\Omega\) & wheel speed \\ \(IA\) & inclination angle \\ \(v_{x_{CP}}\) & longitudinal velocity evaluated at the contact point \\ \(v_{x_{guide}}\) & spindle velocity \\ \(m\) & vehicle mass \\ \(g\) & acceleration of gravity \\ \(l_{f}\) & distance c.o.g. - front axle \\ \(l_{r}\) & distance c.o.g. - rear axle \\ \(L\) & wheelbase \\ \(\rho\) & air density \\ \(h\) & height c.o.g. \\ \(C_{x}\) & longitudinal drag coefficient \\ \(C_{z}\) & lift coefficient \\ \(I_{\omega}\) & moment of inertia about the wheel axis of rotation \\ \(I_{z}\) & wheel moment of inertia \\ \(R_{r}\) & rolling radius \\ \(A_{v}\) & master section \\ \(W_{f}\) & static load - front axle \\ \(W_{r}\) & static load - rear axle \\ \(\Delta F_{z}\) & load transfer \\ \(Fz_{zero_{i}}\) & aerodynamic down force \\ \(F_{i_{input}}\) & inertial force \\ \(F_{x_{i}}\) & longitudinal force \\ \(F_{x_{i}(ref_{x_{i}})}\) & longitudinal force evaluated on a reference road surface \\ \(F_{z_{i}}\) & normal force \\ \(H_{x_{actual}}\) & actual friction coefficient estimated \\ \(\mu_{x_{actual}}\) & actual friction coefficient estimated on a reference road surface \\ \(\tilde{\mu}_{x_{i}}\) & potential friction coefficient estimated \\ \(\lambda\) & slip ratio \\ \(e\) & distance error to the desired \(d_{des}\) \\ \(T_{b}\) & braking torque \\ \end{tabular}
# Thermal Analysis of Cornea Heated with Terahertz Radiation
## 2 Materials and Methods
According to published reports [5, 22, 24], convection boundary conditions at tear-air, cornea-tear, cornea-aqueous humor interfaces were assigned as \(h_{tear-air}=h_{Water-air}=20\,\mathrm{W/(m^{2}\cdot K)}\), \(h_{cornea-tear}=h_{Water-cornea}=500\,\mathrm{W/(m^{2}\cdot K)}\), \(h_{cornea-tumor}=h_{Water-cornea}=500\,\mathrm{W/(m^{2}\cdot K)}\), respectively. Initially, the tissue was assumed to be at a uniform temperature of 35 \({}^{\circ}\)C, and the ambient temperature of air (above the cornea) was kept at a constant value of 25 \({}^{\circ}\)C. Temperature at the bottom surface of the aqueous humor was also kept constant at 35 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c c c} \hline
**Property** & **Material** & **Value** & **Material** & **Value** \\ \hline Density \(\rho\) (kg/m\({}^{3}\)) & Water & 1000 & Cornea & 1060 \\ Thermal conductivity \(k\) (W/m/K) & Water & 0.578 & Cornea & 0.556 \\ Specific heat \(C\) (J/kg/K) & Water & 4180 & Cornea & 3830 \\ Absorption coefficient \({}^{1}\)\(\alpha\) (1/m) & Water & 24,067 & Cornea & 14,400 \\ Convection coefficient \(h\) (W/m\({}^{2}\)/K) & Water-air & 20 & Water-cornea & 500 \\ Film thickness \(t\) (\(\mu\)m) & Year film & 10 & Cornea & 600 \\ \hline \end{tabular} \({}^{1}\) Absorption coefficient is at a frequency of 1 THz.  
\end{table} 
Table 1: Physical and thermal parameters of tear and aqueous humor (modelled as water), and the cornea [5, 22, 24] used in the model.
# Correntropy-Based Constructive One Hidden Layer Neural Network
## 4 Proposed Method
### Learning from Data Samples
#### 4.4.2 Output Side Optimization
This process is iterated for each new node until the predefined condition is satisfied. The following proposition demonstrates that for each node, the algorithm converges. **Proposition 2**. : _The sequences \(\left\{\cap_{L}^{\beta}\big{(}\alpha^{t},W_{L}^{t}\big{)},t=1,2,\ldots\right\}\) and \(\left\{\mathcal{U}_{L}^{\beta}\big{(}\gamma^{t},\beta_{L}^{t}\big{)},t=1,2, \ldots\right\}\) converge._  
Proof. : From Theorem 5 and Proposition 1, we have \(u_{L}^{\beta}\big{(}\gamma^{t},\beta_{L}^{t}\big{)}\leq u_{L}^{\beta}\big{(} \gamma^{t+1},\beta_{L}^{t}\big{)}\)\(\leq u_{L}^{\beta}\Big{(}\gamma^{t+1},\beta_{L}^{t+1}\Big{)}\) and \(\left(\cap_{L}^{\beta}\big{(}\alpha^{t},W_{L}^{t}\big{)}\leq u_{L}^{\beta} \big{(}\alpha^{t+1},W_{L}^{t+1}\big{)}\right)\). Thus, the non-decreasing sequence \(\left\{u_{L}^{\beta}\big{(}\gamma^{t},\beta_{L}^{t}\big{)},t=1,2,\ldots\right\} \Big{(}\left\{u_{L}^{\beta}\big{(}\alpha^{t},W_{L}^{t}\big{)},t=1,2,\ldots\right\} \Big{)}\) converges since the correntropy is upper bounded. **Proposition 3**. : _When \(\Theta=I\), the output weight that is adjusted by the cornertropy criterion is equivalent to the output weight that is adjusted by the MSE-based method such as IELM._  
**Proof. ** Suppose that \(\Theta=I\), by \(\beta_{L}=\frac{E_{L}\Theta\epsilon_{L}^{\text{sim}(e)^{T}}}{\epsilon_{L}^{\text {sim}(e)}\Theta\epsilon_{L}^{\text{sim}(e)^{T}}}\), we have  
\[\beta_{L}=\frac{E_{L}\sigma_{L}^{\text{sim}(e)^{T}}}{G_{L}^{\text{sim}(e)} \sigma_{L}^{\text{sim}(e)^{T}}}.\]  
\(\square\)  
The training process of the proposed method is summarized in the following Algorithm 1 (C2N2). ```
training samples \(\chi=\{x_{i},y_{i}\}_{i=1}^{N}\) Optimal input and output weights \(\beta_{L},W_{L},L=1,...,L_{max}\) Initialization: Maximum number of hidden nodes \(L_{max}\), regularization term \(C^{\prime}\), maximum input side and output side iterations \(IT1,IT2\), error \(E_{0}=[y_{1},...y_{N}]\). For\(L=1:L_{max}\) Step 1: Calculate \(H_{L}\) and \(X\) For\(k=1:IT1\)  Update input parameters End Step 2: Calculate the hidden node vector, \(G_{L}^{sim(e)}\)by previous step For\(k=1:IT2\)  Update output weight End Update error as \(E_{L}=E_{L-1}-\beta_{L}^{sim(e)}G_{L}^{sim(e)}\) End
```  
**Algorithm 1** C2N2  
**Remark 1**: _The auxiliary variables \(\gamma_{i}\) and \(\alpha_{i},i=1,\ldots,N\) are utilized to reduce the effect of noisy data. For the samples with a high amount of error, these variables are very small; thus, these samples have slight effects on the optimization of the parameters of the network, which results in a more robust network._