esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN
## 5 Evaluation
### Setup  
We implement flow table management schemes as applications running on the Ryu controller. For the data plane, we use Mininet with Open vSwitch 2.13.1 to build the network topology. The topology of the experimental network is depicted in Figure 8. We use four attack hosts to generate flows to overflow the victim switch. The attackers use Scapy [32] to generate probing packets and the attack traffic. We also deploy one host to replay the packets from traffic traces [33] as benign background traffic. The flow table capacity is set to 1000.  
To handle the packet-in messages, we deploy the \(simple\_switch\_14.py\) application on the controller, which provides the basic forwarding function. The match field is configured as \(<\)src_mac, dst_mac, src_ip, dst_ip\(>\). For the random time mechanism, the controller assigns a value between 1 s and 15 s. For adaptive timeouts, we implement typical adaptive timeout algorithms, including Intelligent Timeout Master (ITM) [15], SmartTime [17], and TST [19] (For TST, we only implement the adaptive timeout module with the initial timeout of 1 s.) according to the corresponding papers.
## 5 Conclusions  
By analyzing time domain vibration signals of a harvester under operating conditions, it is found that their mean, variance and RMS changed greatly with time, and the vibration signals satisfied non-stationary random features.  
The analysis results of EMD showed that modal aliasing existed in the IMFs of 16 test points. The number of IMF components obtained by EEMD is less than for EMD. EEMD analysis decreased modal aliasing and increased accuracy.  
It was found that the relationship between frequency and the mass of the harvester is not obvious, and it can be considered that the frequency does not change with the mass when the feeding amount situation is small, the influence of weight change on frequency is relatively small.  
The value of the Euler distance between the EMD fitting equation and the SSI algorithm fitting equation is 446.7883, and the value of the Euler distance between the EEMD fitting equation and the SSI algorithm fitting equation is 417.2845. The main frequencies of EEMD have a higher fitting degree with modal frequencies identified by the SSI algorithm, and EEMD is more suitable for estimating the modal frequency of a harvester.  
Figure 10: Frequency fitting curves of the EMD, EEMD and SSI results.  
**Author Contributions:** Conceived of the proposed idea, Y.Y., X.L. and L.G.; designed experiment, Y.Y.; analyzed the data of experiment, L.G., Y.W., X.Y. and X.Z.; writing--original draft preparation, X.L., Y.L. and L.L.; writing--review and editing, Y.Y. and D.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** All of the authors would like to acknowledge the financial support from the National Key R&D Program of China (2021 YFD2000502) and China Postdoctoral Science Foundation (2022M711982).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Results and Discussion
### Water Absorption (WA), Solubility, and Swelling Degree
\begin{table}
\begin{tabular}{c c c c} \hline  & **Water Absorption (\%)** & **Solubility (\%)** & **Swelling Degree (\%)** \\ \hline WFF Control & 15.2 \(\pm\) 0.68 \({}^{\text{a}}\) & 54.55 \(\pm\) 0.89 \({}^{\text{a}}\) & 124.12 \(\pm\) 3.45 \({}^{\text{a}}\) \\ WFF-2.5 ss & 9.31 \(\pm\) 0.03 \({}^{\text{b}}\) & 48.04 \(\pm\) 1.35 \({}^{\text{b}}\) & 129.35 \(\pm\) 3.15 \({}^{\text{a}}\) \\ WFF-5 ss & 9.88 \(\pm\) 0.34 \({}^{\text{c}}\) & 47.66 \(\pm\) 1.48 \({}^{\text{b}}\) & 147.12 \(\pm\) 1.12 \({}^{\text{b}}\) \\ WFF-10 ss & 11.79 \(\pm\) 0.17 \({}^{\text{d}}\) & 51.79 \(\pm\) 0.24 \({}^{\text{c}}\) & 165.81 \(\pm\) 2.75 \({}^{\text{c}}\) \\ WFF-15 ss & 12.53 \(\pm\) 0.1 \({}^{\text{e}}\) & 51.13 \(\pm\) 0.34 \({}^{\text{c}}\) & 167.29 \(\pm\) 0.86 \({}^{\text{c}}\) \\ WFF-20 ss & 12.51 \(\pm\) 0.7 \({}^{\text{e}}\) & 50.11 \(\pm\) 1.15 \({}^{\text{b}}\),\({}^{\text{c}}\) & 176.96 \(\pm\) 1.15 \({}^{\text{d}}\) \\ WFF-11\%A & 13.81 \(\pm\) 0.06 \({}^{\text{f}}\) & 63.15 \(\pm\) 1.55 \({}^{\text{d}}\) & 185.79 \(\pm\) 0.98 \({}^{\text{e}}\) \\ WFF-1\%B & 11.78 \(\pm\) 0.09 \({}^{\text{d}}\) & 57.40 \(\pm\) 1.64 \({}^{\text{e}}\) & 179.95 \(\pm\) 0.72 \({}^{\text{f}}\) \\ WFF-2\%A & 10.56 \(\pm\) 0.25 \({}^{\text{g}}\) & 62.08 \(\pm\) 0.34 \({}^{\text{d}}\) & 143.30 \(\pm\) 2.98 \({}^{\text{b}}\) \\ WFF-2\%B & 9.32 \(\pm\) 0.14 \({}^{\text{b}}\) & 59.80 \(\pm\) 1.04 \({}^{\text{e}}\) & 134.64 \(\pm\) 1.36 \({}^{\text{g}}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Water absorption (%), solubility (%), and swelling degree (%) of all prepared WFF biocomposites with coffee silverskin particles and extracts. Mean values and standard deviation. Different superscripts indicate statistically significant differences (\(p\)\(<\) 0.05).
## 6 Conclusions  
Control methods for MPPT and for the entire grid-connected PV power generation system under PSC were proposed in this paper. An ANN was combined with the nonlinear controller of a DC/DC converter to obtain good tracking, especially in case of fast irradiance changes. Under the simulation conditions discussed in the paper, the following conclusions could be drawn:  
1. The PV output characteristics were modeled and analyzed under PSC, and the complex nonlinear multi-peak morphology of the PV array output was verified. It was found that the timings of irradiance changes might cause the failure of traditional MPPT methods or make it difficult to define the starting conditions, especially for MPPT algorithms based on voltage or current information.
2. Based on the above analysis, an artificial neural training network model was designed based on environmental variables and irradiance information, and GMPPT was realized efficiently and effectively using the hybrid method (ANN + AFL) for the DC/DC converter. A comparison of the proposed MPPT method with the conventional P & O method and the improved P & O method was illustrated through simulations. The GMPPT failure caused by the timing of the irradiance change was solved.
3. For the problems caused by the partial shading process, such as the MPP voltage of the PV output being shifted greatly, the static operating point of the system being changed, or the difficulty of designing a PI controller to eliminate the steady state under small-signal modeling, this paper proposed an AFL control method for nonlinear systems to accurately track the MPP without static errors. The control parameters were clear and easy to design and apply.  
This study was supported by the National Natural Science Foundation of China (51377007) and by the Specialized Research Fund for the Doctoral Program of Higher Education of China (20131102130006).  
[PERSON] and [PERSON] conceived the condition assessment models and prepared most of the manuscript. [PERSON] provided the required inputs and prepared parts of the manuscript. [PERSON] analyzed the data and checked the completed paper.  
The authors declare no conflicts of interest.
## 2 Related Work
### Dilated Convolution  
Starting with DRN [25], dilated convolution gained recognition. The proposed dilated residual network achieved better results compared to previous residual networks. The paper [26] further explores the interactions between dilated convolutions of different rates when extracting semantic information at different scales, achieving improved segmentation performance while maintaining the resolution. The paper [27] analyzed the need for the specific design of the dilation rates when using multiple dilated convolutions. The proposed dilation rates avoid redundant computations for the same position during the calculations of multiple dilated convolutions, allowing the network to obtain more semantic information. Smoothed Dilated Convolution [28] addressed the potential issue of gridding artifacts that can arise when using dilated convolutions by introducing techniques such as Group Interaction Layers and Separable and Shared Convolutions. The DeepLab [14; 15; 16] series fully leveraged the advantages of dilated convolutions to enable the network to learn diverse features and proposed various ASPP (Atrous Spatial Pyramid Pooling) modules to deepen the network and extract features at different scales.  
Dilated convolution has shown good results in the networks mentioned above. However, we believe that these modules are still not sufficiently intelligent and the computational cost is relatively high. We apply multi-scale dilated convolution to the network without altering the original feature size and incorporate attention learning along the channel dimension. This enables the network to capture features at multiple scales, while dynamically selecting and preserving the most crucial ones.
# Hybrid CMP Slurry Supply System Using Ionization and Atomization
## 5 Conclusions  
To reduce the slurry consumption in the CMP processes, we proposed a hybrid slurry supply system that combines the ionization and atomization systems. The experimental results of the CMP performed on Cu and SiO\({}_{2}\) NPWs measured the MRR and uniformity based on the different slurry supply systems. And step height reduction and dishing were measured using the hybrid and conventional systems in the Cu PW CMP. During the experiment, the reaction between the Cu surface and the slurry enhanced owing to the increased slurry activity in the ionization system. Additionally, the atomization system contributed to improving the uniformity and cooling effect. Thus, we confirmed that the MRR can be increased by 23% and NU can be decreased by 25% or more. Moreover, the dishing and pitch-size dependence can be reduced using the hybrid slurry supply system with minor modifications to the existing equipment. Consequently, the efficiency of the CMP process can be improved, and the slurry consumption can be reduced by increasing the MRR of the hybrid slurry supply system. In addition, the polishing quality can be enhanced by improving the uniformity and reducing the dishing and pitch-size dependence.  
Currently, in the advanced process control (APC) parameters on the CMP equipment, only the mechanical factors, such as multi-zone pressure [30], velocity [31], and conditioning [32] can be adjusted. To enhance CMP technology, chemical factors, such as a slurry must be considered in the APC parameters. The hybrid slurry supply system can be an actuator that can control the parameters, such as chemical activation, slurry distribution, and temperature. If the hybrid slurry supply system is applied to APC, the efficiency of the CMP process can be improved further by controlling both the chemical and mechanical factors.  
Conceptualization, D.S.L. and H.S.L.; methodology, D.S.L.; validation, D.S.L. and H.J.; investigation, D.S.L. and H.J.; data curation, D.S.L. and H.J.; writing--original draft preparation, D.S.L. and H.J.; writing--review and editing, H.J.; visualization, H.J. and S.H.J.; supervision, H.D.J. and H.S.L.; project administration, H.D.J. and D.S.L.; funding acquisition, H.D.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2019R1A2B5B01070489 and NRF-2018R1D1A1B07043169) and BB21+ Project in 2020.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 4 Computer Algorithms
### Calculation of max(\(L_{A}\)) and max(\(L^{\prime}_{A}\))  
The parameter \(L_{A}\) expresses the insertion loss between ports 1 and 2, namely, the filter attenuation. In a microstrip low pass filter, the parameter \(L_{A}\) presents a maximum value and then decays. This means that in the plot of the logarithmic curve of the parameter \(S_{21}\), the filter presents a decreasing attenuation up to a certain minimum point. The point of minimal attenuation is the maximum of the \(L_{A}\) curve, because it corresponds to the same curve but with the opposite sign; see Equation (18). Thus, a greater maximum in \(L_{A}\) suppresses better the high frequencies.  
The derivative of \(L_{A}\), with respect to frequency \(f\), is expressed by  
\[L^{\prime}_{A}=\frac{dL_{A}}{df}.\]  
This parameter is of interest because if the instant value of \(L^{\prime}_{A}\) is higher, then the amplitude of the filtered signal decays faster at high frequencies. Therefore, a higher value of max(\(L^{\prime}_{A}\)) is desirable.  
Although the derivative \(L^{\prime}_{A}\) is continuous, in practice is easier to approach this value with a discrete derivative of the form  
\[L^{\prime}_{A}=\frac{\Delta L_{A}}{\Delta f}.\]where \(\Delta f=f_{2}-f_{1}\) is a discrete increment in frequency, and \(\Delta L_{A}=L_{A}(f_{2})-L_{A}(f_{1})\). The Algorithm 2 is used to compute the values \(\max(L_{A})\) and \(\max(L_{A}^{\prime})\) of a filter.  
```
0:\(S_{21}\)
0:\(\max(L_{A})\) and \(\max(L_{A}^{\prime})\)
0:\(L_{A}(0)\gets 0\); \(\Delta L_{A}(0)\gets 1\)
0:\(f\gets f_{cj}\); \(i\gets 1\)
0:while(\(\Delta L_{A}(i-1)>0\)) do  Compute \(S_{21}(f)\)\(\triangleright\) from Equation (17) \(L_{A}(i)\leftarrow-20\log|S_{21}(f)|\) \(\Delta L_{A}(i)\gets L_{A}(i)-L_{A}(i-1)\) \(dL_{A}\leftarrow\Delta L_{A}/\Delta f\) \(f\gets f+\Delta f\); \(i\gets i+1\) endwhile \(L_{Amax}\leftarrow\max(L_{A})\) \(L^{\prime}_{Amax}\leftarrow\max(dL_{A})\)
```  
**Algorithm 2** Algorithm for computing \(\max(L_{A})\) and \(\max(L_{A}^{\prime})\)  
What this algorithm does is calculate \(L_{A}\) and its derivative \(L_{A}^{\prime}=dL_{A}\) for each discrete frequency value, from 0 up to the maximum frequency simulated in \(S_{21}\). Finally, \(L_{Amax}=\max(L_{A})\) and \(L_{Amax}^{\prime}=\max(dL_{A})\). These values are used to determine the maximum attenuation and the maximum rate of attenuation decay.
## 2 Existing Literature
### Model Specification  
To check the association among dependent and independent variables, the model follows the [PERSON] (2014) [63] specification to adopt the regression procedure. The multivariate regression model specification is as follows in its implicit forms as:  
\[\mathrm{GDPPC_{t}}=\mathrm{f}(\mathrm{EAP_{t}},\mathrm{EARP_{t}},\mathrm{EAUP_ {t}},\mathrm{EN_{t}},\mathrm{PG_{t}},\mathrm{RPG_{t}},\mathrm{UPG_{t}}) \tag{1}\]  
In Equation (1), \(\mathrm{GDPPC_{t}}\) indicates the gross domestic product per capita, \(\mathrm{EAP_{t}}\) represents the electricity access to the total population, \(\mathrm{EARP_{t}}\) indicates the electricity access to the rural population, \(\mathrm{EAUP_{t}}\) represents the electricity access to the urban population, \(\mathrm{EN_{t}}\) indicates the energy use, \(\mathrm{PG_{t}}\) show the population growth in Pakistan, \(\mathrm{RPG_{t}}\) represent the rural population growth, and \(\mathrm{UPG_{t}}\) indicates the urban population growth.  
\[\mathrm{GDPPC_{t}}=\Psi_{0}+\Psi_{1}\mathrm{EAP_{t}}+\Psi_{2}\mathrm{EARP_{t} }+\Psi_{3}\mathrm{EAUP_{t}}+\Psi_{4}\mathrm{EN_{t}}+\Psi_{5}\mathrm{PG_{t}}+ \Psi_{6}\mathrm{RPG_{t}}+\Psi_{7}\mathrm{UPG_{t}}+\mu_{t} \tag{2}\]  
By using natural logarithm to Equation (2), a log-linear model is as follows:  
\[\mathrm{InGDPPC_{t}}=\Psi_{0}+\Psi_{1}\mathrm{lnEAP_{t}}+\Psi_{2}\mathrm{lnEARP_ {t}}+\Psi_{3}\mathrm{lnEAP_{t}}+\Psi_{4}\mathrm{lnEN_{t}}+\Psi_{5}\mathrm{lnPG_{ t}}+\Psi_{6}\mathrm{lnRPG_{t}}+\Psi_{7}\mathrm{lnUPG_{t}}+\mu_{t} \tag{3}\]  
Equation (3) presents the log-linear form of the variables. \(\mathrm{lnGDPPC_{t}}\) represents the natural logarithm of the gross domestic product per capita; \(\mathrm{lnEAP_{t}}\) represents the natural logarithm of electricity access to the total population; \(\mathrm{lnEARP_{t}}\) represents the natural logarithm of the electricity access to the rural population; \(\mathrm{lnEAUP_{t}}\) represents the natural logarithm of the electricity access to the urban population; \(\mathrm{lnEN_{t}}\) represents the natural logarithm of energy use; \(\mathrm{lnPG_{t}}\) represents the natural logarithm of population growth in Pakistan; \(\mathrm{lnRPG_{t}}\) represents the natural logarithm of rural population growth; \(\mathrm{lnUPG_{t}}\) represents the natural logarithm of urban population growth; \(t\) is the time dimension; \(\mu_{t}\) is the error term; \(\Psi_{0}\) indicates the constant intercept; and the coefficients of the model \(\Psi_{1}\) to \(\Psi_{7}\) represent the elasticity for the longrun.  
Figure 8: Gross Domestic Product (GDP) Per Capita in Pakistan.
## 1 Introduction
Subsequently, a number of authors have used [PERSON]'s TSDT theory in the analysis of free vibrations and the dynamic stability [12; 13] of FGM plates, with or without the interaction of the plate and the elastic foundation [14]. The impact of temperature, plate geometry, and material on free vibrations was studied in [15]. In addition to TSDT, a special HSDT group of theories, which has been developed in order to exclude the need to use correction factors, includes HSDT theories with shape functions. In general, there are different types of shape functions. The initial idea of developing FGM was aimed at obtaining a material with high resistance to temperature gradient on one side and also good mechanical properties on the other side. For this reason, a number of authors have addressed the behavior problems of FG plates made of metal-ceramic constituents under mechanical and thermal, static, and dynamic loads, applying the theories mentioned above. The equilibrium and stability equations of thin, moderately thick, and thick FGM plates exposed to the impact of temperature have been considered in the area of linear [16; 17; 18] and nonlinear elasticity [19; 20]. The effect of uniform, linear, and nonlinear temperature change in the direction of plate thickness has been analyzed in dynamic problems [21; 22; 23]. The plate/foundation interaction and the impact of the elasticity of the foundation, modeled by the Winkler-Pasternak model, were analyzed by the authors in [24; 25; 26; 27]. The problem of constrained multi objective optimization performed for mass and material cost minimization as well as the minimization of stress failure criteria or maximization of natural frequency is studied in [28; 29]. A recent trend of research in the area of FGM is quantifying uncertainty [30; 31; 32; 33]. Ultimately, the final aim of all the previously mentioned research and studies is the application of FGM in various fields of engineering and industry. Although initially used as FGM material for thermal coating in spacecraft, due to their advantages over conventional materials, today FGM is increasingly being used in medicine [34], dentistry [35], the energy and nuclear sectors [36], the automotive industry [37], the military industry [38], optoelectronics [39], and others.
## 1 Introduction
### Our Motivations and Contributions
In our proposed schemes, the sink node with high-computing power and high-energy is used to determine the phantom node candidate set, which avoids the disadvantage of excessive communication overhead caused by flooding to determine the phantom node in previous traditional schemes;  
(2) The location of the phantom nodes is determined by our proposed schemes that are distributed throughout the network; which, in turn, improves the diversity of the phantom nodes in terms of location and the randomness of routing. In our proposed schemes, the routing path appears randomly across the entire network, which prevents the attackers from backtracking to the location of the source node via the routing path. Our proposed schemes avoid the defects that the phantom node locations in the traditional schemes are concentrated near the real source node and that the location privacy of the real source node cannot be well protected;  
(3) Our proposed schemes do not require the node to be equipped with a positioning module, such as GPS, which reduces the node cost and energy consumption, hence the application scenario of our proposed schemes are universal, and the source location privacy can be effectively and stably protected even in resource-constrained wireless network environments;  
(4) We conduct theoretical analysis and extensive experimental simulations to evaluate the performance of our proposed schemes. The simulation results further verify the effectiveness of our proposed schemes.
## 6 Results and Discussion
### OWC Performance Evaluation
#### 6.2.2 Performance With Real Wave Data  
For this study case, real surface elevation measurements of waves in Mutriku obtained by the Acoustic Doppler Current Profiler on 12 May 2014, from 00:00:00 a.m. to 00:00:50 a.m., shown in Figure 17, were considered.  
The flow coefficients of the OWC with real measured wave data input in the uncontrolled case, and with airflow control using the traditionally tuned PID (ZN-PID) and the optimized PID (SGHS-PID), are illustrated in Figure 18.  
Figure 18 shows that in the uncontrolled case, the flow coefficient exceeds the threshold value 0.3, which will provoke the stall effect in two time periods with strong waves. The first wave train is from 2 s to 11 s and the second strong wave train is from 37 s to 50 s. In both controlled cases, the flow coefficients were regulated below 0.3 thanks to both ZN-PID and SGHS-PID controllers, with a slight superiority for SGHS-PID.  
Figure 19 shows that in the uncontrolled case, the airflow speed continues to increase uncontrollably in both periods of the strong waves; however, in both controlled cases, the airflow speed was decreased thanks to both ZN-PID and SGHS-PID controllers, but with a slight superiority for SGHS-PID.  
Figure 17: Considered real wave data to the wave-to-wire model  
Figure 18: Flow coefficients of the OWC in uncontrolled and controlled cases.  
The obtained turbine torques of the PTO in the uncontrolled case, ZN-PID controller and SGHS-PID controller are shown in Figure 20a,c,e, respectively. And the generated powers for the uncontrolled case, ZN-PID controller and SGHS-PID controller are shown in Figure 20b, Figure 20d,f, respectively.  
As with regular waves, it may be observed that during the uncontrolled case, the torque has been affected by the stall effect, which reduces it in terms of average value. On the other hand, the airflow control manages to avoid the stall effect and increase the torques in terms of average values in both high wave regions. The resulting generated powers in the uncontrolled case is the lower than the powers generated when using the SGHS-PID and the ZN-PID in the proposed airflow control scheme.