esa-sceva/satcom-chunk-collection huggingface dataset:
# AccFlow: Defending against the Low-Rate TCP DoS Attack in Drones
## 2 Low-Rate TCP DoS Attack  
In this section, we provide a brief overview of the low-rate TCP DoS attack and its effectiveness in causing denial of service to legitimate TCP flows. The characteristics of an ideal low-rate TCP DoS attacking flow can be represented by a triple \(\{R,P,D\}\), where \(R\) indicates the peak data rate, \(P\) indicates the attacking period and \(D\) indicates the burst duration within one period, as illustrated in Figure 0(a). In order to overflow the network, \(R\) needs to be larger than the bottleneck link bandwidth. \(P\) should be small enough, compared to the RTTs of legitimate TCP flows, to attack most of the traversing flows. \(D\) is negatively correlated with \(R\) if the amount of traffic generated by the attackers in one period is fixed. A detailed discussion on the choice of \(R\), \(P\), and \(D\) can be found in [19].  
We set up simulations on the ns-3 platform to illustrate the effectiveness of low-rate TCP DoS attacks. We create a \"dumbbell\" network topology whose bottleneck link bandwidth is 10 Mbps, as illustrated in Figure 0(b). In this simulation setup, nine legitimate TCP flows and one attacking flow are traversing the bottleneck link. We configure the network so that each legitimate TCP flow is transmitting at 1 Mbps and the attacking flow triple \(\{R,P,D\}\) is (30 Mbps, 200 ms, 67 ms). Note that we scale down the bottleneck link bandwidth and flow rates in order to accelerate the simulation. As illustrated in Figure 1(a), without being attacked, all legitimate TCP flows fairly share the bottleneck link bandwidth and achieve their desired data rates. However, they are throttled to nearly zero throughput under attack, as illustrated Figure 1(b). Note that the time-averaged data rate of the attacking flow (around 3.5 Mbps) is far less than the bottleneck link bandwidth. Therefore, attackers use much less resources to achieve very effective DoS attacks.  
Figure 1: Attacking traffic and network topology.  
Figure 2: Effectiveness of the low-rate TCP DoS attack.
## 1 Introduction
Moreover, the complex coupling among the auxiliary components requires the thermal management system to be able to decouple the correlated control parameters to control the temperature accurately. This paper aims to develop a control model with the particle swarm optimization (PSO) algorithm and an artificial neural network (ANN) to improve the proportion integration differentiation (PID) control precision and dynamic response ability under changing load current and stack temperature, closer to real-life operating conditions. This paper first presents the stack performance and temperature characteristics experiments in Section 2. Then, in Section 3, a dynamic mechanistic model based on the fuel cell stack mechanism and the thermodynamic equations of auxiliary components and the control model for stack thermal management is developed with the PSO+ANN algorithms. The results and discussion of the control model using Simulink are presented in Section 4. Finally, conclusions can be found in Section 5.
## 5 Numerical Simulation and Discussion
### Simulation Results and Discussion  
#### 5.2.1 Step Changing Reference Voltage  
The voltage reference trajectory is employed to verify the robustness of all the above controllers as follows:  
\[V_{ref}=\left\{\begin{array}{ll}45,&\text{if}\quad 0\leq t\leq 0.25\\ 75,&\text{if}\quad t>0.25\end{array}\right.\quad(\text{V}) \tag{53}\]  
In this case study, the input voltage and the current load are kept constant at V and A, respectively. The output voltages of the FIBC, in the case of the step voltage reference change under the four considered control approaches, are illustrated in Figure 3. As shown in this figure, it can be recognized that all the controllers are able to maintain the output voltage at the desired level. Although the cascade control approach C4 with the two PI controllers adopted for current inner and voltage outer loops has the advantage of simplicity, it possesses the worst performance in both transient and steady-state regimes with the longest settling time compared to other control approaches.  
In addition, the system responses under the C3 and C2 control frameworks are significantly improved in comparison to the C4 control approach. However, it should be noted that the proposed control approach achieves the best regulation performance with a shorter settling time and without overshoot by means of the GSTA-based current loop and the GSTA-based observer in the voltage loop. The inductors currents of the FIBC under the four control approaches are presented in Figure 4. As shown in this figure, the inductor current can closely track the current reference generated by the voltage outer loop. However, from Figure 4d, it can be recognized that the GSTA-based controller achieves better control accuracy in comparison to the PI and STA-based controllers. The results confirm the robustness of the proposed control approach in the presence of the step voltage reference change.  
Figure 3: The output voltages of the FIBC under the four controllers in case of step voltage reference change.
# Simplified Plastic Hinge Model for Reinforced Concrete Beam-Column Joints with Eccentric Beams
## 3 Joint Shear Strength Degradation Model
### Plastic Hinge Model  
#### 3.2.1 Ultimate Point  
According to [PERSON] and [PERSON] [37], the joint plastic shear angle \(a_{j}\) is defined as follows: For interior beam-column joints,  
\[a_{j}=0\leq\frac{1}{1050}\left[\frac{1.1a_{c}(\beta_{c}+0.2)\Big{(}6-\beta_{ j}\Big{)}f^{\prime}_{\ c}(b_{s}h_{c})}{V_{u}-V_{T}}-16\right]\leq a_{mj} \tag{17}\]  
\[a_{mj}=0.03-\frac{10.2a_{j}}{\alpha_{i}f_{y}(1-0.45h_{b}/L_{s})(\kappa-0.13)(3 -5\kappa)}>0.01 \tag{18}\]  
\[\kappa=0.8\frac{h_{c}}{d_{b}}\frac{\sqrt{f^{\prime}_{c}}}{f_{y}}+0.05\leq 0.6 \tag{19}\]  
\[\beta_{c}=\frac{(A_{s}+A^{\prime}_{s})f_{y}}{0.85f^{\prime}_{\ c}b_{b}h_{b}} \tag{20}\]  
\[V_{u}=\big{(}A_{s}+A^{\prime}_{s}\big{)}f_{y}-P_{u} \tag{21}\]\[V_{T}=\min\left[A_{h}f_{yh},0.65A_{s}f_{y}\right] \tag{22}\]  
\[\alpha_{t}=0.24\left(1+\frac{A_{h}f_{yh}}{A_{s}f_{y}}\right)\leq 0.6 \tag{23}\]  
For exterior beam-column joints,  
\[a_{j}=0\leq\frac{1}{1050}\left[\frac{2.8\alpha_{c}(\beta_{c}+0.1)\left(3-\beta _{j}\right){f^{\prime}}_{c}(b_{s}h_{c})}{V_{u}-V_{T}}-8\right]\leq a_{mj} \tag{24}\]  
\[a_{mj}=0.03-\frac{9a_{j}}{\alpha_{t}f_{y}(1-0.45h_{b}/L_{s})(\kappa-0.12)(3-5 \kappa)}>0.01 \tag{25}\]  
\[\kappa=0.14\leq 1.56\frac{l_{dh}}{d_{b}}\frac{\sqrt{{f^{\prime}}_{c}}}{f_{y}}-0.06 \leq 0.5 \tag{26}\]  
\[\beta_{c}=\frac{A_{s}f_{y}}{0.85{f^{\prime}}_{c}b_{s}h_{b}} \tag{27}\]  
\[V_{u}=A_{s}f_{y}-P_{u} \tag{28}\]  
where \(\alpha_{c}\) is the strength increment factor due to the confinement effect of cross-beams (Figure 5); \(\beta_{c}\) is the compression zone depth ratio; \(\beta_{j}\) is the joint configuration ratio (\(=h_{b}/h_{c}\)); \(\kappa\) is the energy dissipation ratio of beam-column joints; \(l_{dh}\) is the development length of beam flexural bars anchored into the joint; and \(d_{b}\) is the diameter of beam bottom bars. Note that in the calculation of \(a_{mj}\), the initial value of \(a_{j}\) is used without consideration of the limitation of \(a_{mj}\).  
On the other hand, the beam plastic rotation angle \(a_{f}\) is defined as a function of the energy dissipation ratio \(\kappa\)[8; 9; 10; 37].  
\[a_{f}=\frac{3+5\kappa}{3-5\kappa}a_{j}\leq a_{mf} \tag{29}\]  
where \(a_{mf}\) is the maximum plastic rotation angle in order to consider the beam failure (\(=0.003\) to \(0.025\) rad) [16; 37].
## 4 Core Insights and Critical Reflections
### Expansion and Deepening of Application Fields
For example, in the surveying and mapping industry, UAVs can quickly obtain high-precision topographic maps and 3D models, which greatly improves the efficiency of surveying and mapping and reduces costs. In the power industry, UAVs can be equipped with visible light and thermal infrared sensors to inspect transmission lines and promptly identify potential safety hazards. With the continuous progress of technology and the deepening of application needs, the application fields of UAV-RS will further expand, providing more precise, efficient, and safe remote sensing information services for various industries.
# A Multi-Tier Security Analysis of Official Car Management Apps for Android
## 5 Low-Level Static Analysis
### Outdated Software Components Analysis
On the other hand, four apps employed an older version (v.1.9.1) of the JavaScript_[112] library. The aforementioned version is linked to a couple of CVEs, namely CVE-2015-9251 and CVE-2007-2379. The former refer to a XSS injection, while the latter to a JavaScript hijacking vulnerability. An equal number of apps were found vulnerable to an obsolete version (v3.0.0) of the _InAppBrowser_[113] plug-in, which provides the ability to launch a web browser within the app. Specifically, the superannuated version is linked to one critical severity CVE, namely CVE-2019-0219. This vulnerability may enable an attacker to execute arbitrary JavaScript code in the main app's webview. Moreover, a couple of apps were found to use an obsolete version (v1.2.8) of _zlib_[114], a data compression library. This version is associated with four critical or high severity CVEs, namely CVE-2016-9840 to CVE-2016-9843, referred to as \"Numeric Errors\". The same number of apps were vulnerable due to the use of a superseded version (v.2.2.4) of _expat_[115], a stream-oriented XML parser library. This outdated version is prone to a couple of high severity DoS vulnerabilities, namely, CVE-2018-20843 and CVE-2019-15903. The former can be exploited by using a large number of columns, potentially causing high CPU and RAM usage, while the latter refers to a heap-based buffer overflow attack. Lastly, a single app was using an outdated version (v7.60.0) of _libcurl_[116], a client-side URL multi-protocol file transfer library. This version is associated with three DoS-related CVEs, namely, CVE-2018-16842, CVE-2018-0500, and CVE-2018-16840. Furthermore, it is vulnerable to the high severity CVE-2019-5443, which allows for code injection. One app was found to incorporate an obsolete version (v1.5.0) of the _libjpeg_[117] library, used to read and write JPEG image files. With reference to CVE-2018-14498 and CVE-2018-20330, this version may be vulnerable to DoS attacks. Finally yet importantly, apps that belong to the same group seem to have an identical (BMW, Mitsubishi, FCA, PSA) or approximately identical (VW) pattern regarding the use of outdated libraries.
# Recognition of Vehicle License Plates Based on Image Processing
## 2 License Plate Recognition System
### Super Resolution  
#### 2.3.1 Super-Resolution Generative Adversarial Network (SRGAN)  
A generative adversarial network (GAN) is a deep learning model consisting of a generator and discriminant network. The delimiter learns to separate the actual data from the generated data, while the constructor learns in the direction that interferes with it. In this process, the generator expects to discover the manifold where the actual sample distribution exists. The unsupervised learning GAN has the characteristic of estimating the probability distribution of the original data, and allowing the artificial neural network to create the distribution. Figure 3 shows the network structure of SRGAN using two deep learning models with opposing relationships. This algorithm is used in paper [8].  
The generator's deep learning model receives and trains with low-resolution images. The generator inference results are fed into the discriminator and learned until the high  
Figure 3: SRGAN network architecture.  
Figure 2: (**a**) Input images; (**b**) license plate detection images.  
resolution image is determined. The learned generator makes it possible to reconstruct a low-resolution image to deceive the discriminator that determines whether it is a high-resolution image. Thus, the generator can perform a high-resolution restoration [11; 12].
## 4 Racing + CBR Module
### CBR Submodule  
The CBR submodule's aim is to find the most similar case with the new problem, regardless of which metaheuristic is used. As a consequence, the returned case includes the metaheuristic and the necessary parameters to use. Since the device must determine which procedure and parameters to use, this method seems to be appropriate for the problem at hand.  
The CBR submodule consists of a cycle similar to that described in Section 3.2.2. This cycle consists of four main processes: retrieve, reuse, revise and retain (see Algorithm 5). Note that, in the revise stage, there is communication with the multi-agent system in order to execute the new case and extract results of completion and execution times.  
```
newCase  Begin listCases \(\leftarrow\) retrieve(newCase) \(\triangleright\) retrieving phase bestCase \(\leftarrow\) reuse(listCases) \(\triangleright\) reusing phase \(solution\gets getSolution(bestCase)\) \(\triangleright\) get the solution for the best case \(sim\)BestCase \(\leftarrow\) getSimilarity(bestCase) \(\triangleright\) get the similarity revisedSolution \(\leftarrow\) revise(solution,simBestCase) \(\triangleright\) reusing phase \(results\gets executeCaseMAS(newCase,revisedSolution)\) \(\triangleright\) execute in MAS \(retain(newCase,revisedSolution,results)\) \(\triangleright\) retaining phase End
```  
**Algorithm 5** Case-based reasoning  
Whenever a new instance arises, the CBR submodule creates a new case, which is solved through the recovery of one or more previous similar cases. After obtaining a list with the most similar cases, the most similar case among all is reused and is suggested as a possible solution. In the next step, and before proceeding to the communication with the multi-agent system, the revision of the suggested solution is performed by adjusting and refining the parameters. Finally, the case is accepted as a final solution and retained in the case base as a new learned case.
## 4 Discussion  
The results of the derived optimal scenario were compared with the historical data for 2006-2013. The optimal scenario was selected as the one with the largest amount of electricity sales. Because the unit price of electricity in Korea changes every day, the maximum value of production does not lead to the maximum value of sales amount. Therefore, the electricity sales were set as the objective function to consider the economic aspect. The electricity sales for each scenario were calculated by multiplying the time series of electricity production calculated in HEC-5 by the average monthly sales unit price in the past. Figure 10 shows the comparison between the historical data of Hwacheon Dam and the simulation results. As shown in Table 4, it was confirmed that the Hwacheon Dam operated at a lower target water level than the previous data. In addition, since the target outflow discharge was operated at a high level, it was confirmed that a larger amount was discharged than the historical data.  
It was checked to see how effective the optimal operation results were compared to the past performance. The period for comparison is 2006-2013. Table 4 shows the results of comparing the power generation of all dams, which are operational goals, with the past performance. Hwacheon Dam and Chuncheon Dam, which were evaluated for their low resilience, produced less electricity than their past performance. However, the Uiam Dam and Cheongpyeong Dam, located downstream, produced more electricity than the historical data because a large amount of discharge was performed at a high dam water level. At this time, it was confirmed that the value of resilience was also highly evaluated. In the case of Cheongpyeong Dam, which is located the most downstream, about 50% more electricity could be produced. If the four dams were evaluated as a single power system, the power generation could be increased by 151,652 MWh. This is a result of an increase of about 19.83%. The operation of the hydropower reservoir considering resilience resulted in improved power production, which is the main performance. However, in this study, it cannot be said that all possible operating rules are reflected because it is simply a scenario  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dam**} & **Scenario** & **Power Generation** & **Flood** & **Drought** & **Resilience** \\  & **(WaterLevel/Outflow)** & **(MWh)** & **Performance** & **Performance** & **Performance** & **Resilience** \\ \hline Hwacheon & \(-\)120,000/+30 & 204,942 & 0.94 & 0.64 & 0.350 \\ \hline Chuncheon & +20,000/+60 & 120,451 & 0.93 & 0.74 & 0.300 \\ \hline Uiam & +10,000/+60 & 174,531 & 0.92 & 0.77 & 0.729 \\ \hline Cheongpyeong & \(-\)70,000/+60 & 416,556 & 1.00 & 0.94 & 0.858 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance Results of Hydropower Dam with Scenario.  
in which the target water level and outflow discharge are increased and decreased at a certain rate. Therefore, if research on random scenarios is added in the future, it is expected that the optimal operating rules for maximizing electricity sales will be obtained.
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 4 Simulation Results  
Firstly, the ABER performances of the proposed OSP-MSK over AWGN, Rayleigh fading channel, and Nakagami-m fading channel are evaluated analytically and via comprehensive Monte Carlo simulations. To compare meaningfully, the analytical formulas and the simulation results are assessed in both uncoded and coded scenarios. It should be mentioned here that the relation between the code symbol energy per \(N_{o}\) and the bit energy per \(N_{o}\) is expressed as follows [31]:  
\[\frac{E_{c}}{N_{o}}=\Big{(}\frac{n}{k}\Big{)}\frac{E_{b}}{N_{o}}, \tag{56}\]  
in which \(\big{(}\frac{n}{k}\big{)}\) denotes the channel encoder rate.  
Secondly, the ABER performance of the proposed MIMO OSP-MSK modulation is evaluated and compared to the ABER performance of the MIMO-CEM system using the traditional GMSK modulation, and by using the quadrature multiplexed Gaussian minimum shift keying (QMGMSK) modulation [32; 33]. The QMGMSK modulation is a special form of the MSK modulation that boosts up the spectral efficiency of the MIMO-CEM by reducing the main lobe spectrum (i.e., the bandwidth) of the MSK modulation. The ABER comparison is performed over the Rayleigh fading channel and under different scenarios based upon the simulation parameters listed in Table 1.  
Finally, the PSD of the proposed OSP-MSK modulation is evaluated analytically and via numerical simulation and compared with the PSDs of the above-mentioned schemes in order to evaluate the spectral capabilities of the proposed OSP-MSK modulation.
# Adaptive Trajectory Tracking Control for
## 3 Trajectory Tracking Control Design
### Surge Control Law  
In this subsection, a first-order sliding mode will be employed to design surge control law \(\tau_{u}\) to make make \(u_{e}\) converge. At the same time, neural network minimum learning parameter method is used to compensate unknown dynamics and time-varing disturbances online in real time.  
Define the first sliding surface  
\[s_{u} = u_{e}+\lambda_{1}\int_{0}^{t}u_{e}(\mu)d\mu \tag{14}\]  
where \(\lambda_{1}\) is a positive design parameter. Taking the time derivative of \(s_{u}\) along (13) produces  
\[\dot{s}_{u} = \lambda_{1}u_{e}+\frac{m_{22}}{m_{11}}vr-\frac{d_{11}}{m_{11}}u- \frac{1}{m_{11}}\Delta_{u}+\frac{1}{m_{11}}\tau_{u}+\frac{1}{m_{11}}b_{u}-\dot {u}_{d} \tag{15}\]  
Meanwhile, in order to ensure that the sliding surface can converge to zero within a finite time [38], we define that  
\[\dot{s}_{u} = -k_{ue}s_{u}-\eta_{u}\text{sgn}(s_{u}) \tag{16}\]  
where \(k_{ue}\) and \(\eta_{u}\) are two positive design parameters; \(\text{sgn}(\bullet)\) represents the symbol function [36].  
Therefore, the corresponding surge control law is selected as  
\[\tau_{u} = \tau_{ue}+\tau_{uw} \tag{17}\]  
where \(\tau_{ue} = m_{11}(-\frac{1}{2}s_{u}\hat{\phi}_{u}h^{T}h-\lambda_{1}u_{e}-\frac{m_{ 22}}{m_{11}}\tau+\frac{d_{11}}{m_{11}}u+\dot{u}_{d})\) and \(\tau_{uw} = m_{11}(-k_{ue}s_{u}-\eta_{u}\text{sgn}(s_{u}))\).  
Finally, the update law for the neural network minimum learning parameter method is taken to be  
\[\dot{\phi}_{u} = \frac{\gamma_{u}}{2}s_{u}^{2}h^{T}h-\kappa_{u}\gamma_{u}\hat{ \phi}_{u} \tag{18}\]  
where \(\gamma_{u}\) and \(\kappa_{u}\) are positive design parameters.  
Figure 2: Block diagram of trajectory tracking control strategy.
## 5 Conclusions  
The present research investigated the interaction effects of the color rendering index (CRI) and correlated color temperature (CCT) on user acceptance and explored the proper color attributes for human-centric lighting (HCL) in offices. User acceptance is considered an important quality measure for HCL in offices. Preference, visual comfort, satisfaction, and self-reported productivity scales were employed for user acceptance assessment. The following conclusions were obtained from the experimental results:  
LED lights with a CRI \(<\) 80 at 3000 K were considered to be more acceptable than LED lights with a CRI \(\geq\) 80 at 3000 K. By contrast, LED lights with a CRI \(\geq\) 80 at 6500 K were rated as more acceptable than LED lights with a CRI \(<\) 80 at 6500 K;  
At a CCT of 3000 K, LED lights with a CRI \(<\) 80 were rated as more acceptable than LED lights with a CRI \(\geq\) 80, indicating that the current recommendation for a CRI \(\geq\) 80 is inappropriate for HCL in offices. In addition, a recommendation for the CCT is needed, as CCT is an important factor in the color attributes of LED lights;For brightness perception, LED lights with a CRI \(<\) 80 were perceived to be brighter than LED lights with a CRI \(\geq\) 80 at both 3000 K and 6500 K. The results mean that LED lights with a CRI \(<\) 80 were perceived to be brighter than LED lights with a CRI \(\geq\) 80;  
LED lights with a CRI \(<\) 80 at 6500 K were regarded as brighter than LED lights with a CRI \(\geq\) 80 at 6500 K. However, the increase in the brightness perception of LED lights with a CRI \(<\) 80 at 6500 K was accompanied by lower acceptance. The analytical results showed that the LED lighting conditions were at the marginal point, where brightness perception no longer increases user acceptance. The findings indicated that the proper color attributes for HCL in offices should be determined in consideration of both user acceptance and brightness perception.  
The proper color attributes for HCL in offices should have an appropriate brightness level and consider user acceptance. This study is expected to be used as a basis for developing an LED lighting system for offices that takes into account user acceptance.  
Designing the research, conducting the survey, analyzing the data, writing--original draft preparation S.L. and H.C.Y.; writing--reviewing and editing, supervision, H.C.Y. Both authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2015R1D1A1A01058577) and by the 2021 Hongik University Research Fund.  
Data sharing not applicable.  
The authors declare no conflict of interest.