esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Performance Analysis
### Computation Cost  
We show the comparison outcomes in Table 3. Our proposed protocol is lightweight as compared to other related protocols. Therefore, we can demonstrate that the proposed protocol is suitable for vehicular cloud environment in VANETs.  
For comparing the computational cost, we define following notations. \(T_{bp}\), \(T_{bpsm}\), \(T_{MPH}\), \(T_{h}\), \(T_{sem}\) and \(T_{ea}\), which denotes the execution time of bilinear mapping, multiplication related to bilinear pairing, map-To-point hash, one-way hash, small scale multiplication related to elliptic curve cryptography (ECC), and addition related to ECC. We focus on time overhead in the process of authentication message generation and message verification. For rough estimation, we consider the existing results reported by [34]. The execution time of each operation is as following.  
* \(T_{bp}\): Time for bilinear pairing operation (\(\approx\)4.2110 ms)
* \(T_{bpsm}\): Time for small scale multiplication related to bilinear pairing (\(\approx\)1.7090 ms)
* \(T_{MPH}\): Time for map-To-point hash operation (\(\approx\)4.406 ms)
* \(T_{h}\): Time for one-way hash operation (\(\approx\)0.0001 ms )
* \(T_{sem}\): Time for small scale multiplication related to ECC (\(\approx\)0.0138 ms)
* \(T_{ea}\): Time for point addition related to ECC (\(\approx\)0.0018 ms)
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 6 Results
### Analysis of Transmission Costs  
We have analysed the radio transmission costs of the presented quantum immune cryptographic schemes and secrecy coding. Our figure of merit is the estimate of the total energy consumption of the transmitters and receivers, which is calculated by estimating the transmission times and multiplying them by the average power consumption of a pre-selected network interface card. Namely, we use the average power consumption of a Cisco Linksys Dual-Band WirelessN Express-Card WEC600N2 card as listed in [49]: the average power consumption of the transmitter is 2640 mW and the average power consumption of the receiver is 1980 mW.  
We assume that both legitimate devices, Alice and Bob, use radios compliant with the IEEE 802.11n standard in mixed mode. Furthermore, we assume that both radios use orthogonal frequency division multiplexing (OFDM) with 52 data subcarries and four pilot subcarriers, where the channel bandwidth is 20 MHz. Data bits are assembled into frames consisting of a 9-symbol preamble and a payload of up to 4096 OFDM symbols. The signalling speed is equal to 250 ksymbols/s, which implies that one symbol duration is four microseconds. We assume QPSK modulation with a 3/4 coding rate.  
The estimates of energy consumption used by radio transmission are presented in Table 4. The number of transmitted symbols is calculated by dividing the corresponding number of transmitted bits by the number of available data subcarriers (52) and the number of bits per modulation symbol (2) and accounting for nine extra preamble symbols. The average power consumption of a transmitter employing the secrecy-coding scheme is doubled to account for the additional energy consumption due to a need to generate a jamming signal.  
The energy consumption of radio units is not affected by transmission latency between Alice and Bob as long as there is no need to retransmit any messages. In other words, the maximum allowed transmission latency should not exceed channel coherence time. Channel coherence time is a statistical measure of the time duration over which the channel is essentially invariant, and it is approximately equal to the inverse of maximum Doppler frequency. For example, if we consider a carrier frequency of 2.4 GHz, a vehicular speed of 125 kph, we get a coherence time of about 3.6 ms. As shown in Table 4, transmission times for secrecy coding schemes are much shorter than 3.6 ms.
## 1 Introduction
For S\({}^{3}\)F-CTs, only after a wafer enters a PM can the PM start its processing activity, implying that the processing activities follow the robot tasks. As a result, it is important to carefully determine the robot task sequence [6; 21]. However, for the tools addressed in this work, except for the processing activities, the internal chambers of PMs should perform rotation operations such that the robot can correctly load wafers into PMs for processing. Notice that if a PM is still performing a rotation operation when the robot arrives at the PMfor loading or unloading, the robot has to wait. This implies that rotation operations might delay the robot task time, too. Therefore, for SF\({}^{3}\)-CTs shown in Figure 2, it is necessary to analyze how the rotation operations in PMs affect the robot task schedule. This makes the obtained results for S\({}^{3}\)F-CTs in the existing studies not directly applicable to SF\({}^{3}\)-CTs. Furthermore, RTCs and the requirement for the right use of clean and dirty fingers make the scheduling problem of SF\({}^{3}\)-CTs much more complicated. Note that, for the addressed tool, four wafers can be processed concurrently in a PM. However, each time, only two wafers can be loaded/unloaded, resulting in the increase in time delay of these wafers in a PM. Thus, how to reduce such a time delay is an important issue. Up to now, there is no research report on this topic, while practical applications require corresponding techniques to provide solution methods for that, i.e., there is a gap between the existing research achievements and the practical application demand. This motivates us to conduct this study. It should be pointed out that, as discussed above, although there are extensive studies on scheduling cluster tools with RTCs, the time delay caused by loading wafers into a PM more than once time makes the RTCs much more difficult to satisfy. Furthermore, due to the fact that the robot arm is restricted from performing certain tasks, the widely used scheduling strategies are not applicable, since the existing methods assume that there is no restriction on the robot arms. In summary, for the addressed problem, we face new challenges. With the new challenges, this work aims to examine new scheduling strategies and explore the effect of different scheduling strategies on obtaining feasible schedules. To do so, based on the properties of the system, four scheduling strategies are presented, and methods are developed to find feasible and optimal schedules. Then, experiments are conducted to test the performance of the methods on the capability for finding feasible and optimal schedules. The rest of this article is organized as follows. In the next section, a detailed description of the problem is given. Then, the properties of the system are analyzed in Section 3. In Section 4, linear programs are established to find optimal and feasible schedules based on the presented robot task sequences. In Section 5, comparison experiments are carried out to show the application of the proposed method. Section 6 concludes this work.
# A Systematic Review on Design Thinking Integrated Learning in K-12 Education
## 4 Discussion
### Learning Evaluation in DTIL  
#### 4.3.1 Dependent Variables  
Knowledge, skills, and dispositions are all covered by the dependent variables reported from the reviewed papers. For example, the existing empirical studies measured students' knowledge of relevant subject concepts, e.g., physics concepts of acceleration, Newton laws of motion, velocity, motion, energy, etc., [37], or the understanding of the mechanical concepts [43]. Some studies also checked students' understanding of design thinking (e.g., [39]). Various skills, like creativity, productive thinking, problem solving, and critical thinking, are evaluated as crucial learning effects. Except for the concepts and skills, students' performance on design thinking practice (including the design process and design work) has been paid close attention, and it is the most frequently evaluated one. This exactly confirms the practical orientation of design, or the non-verbal competence [15] that is required. Besides, many studies surveyed students' dispositions, as there exists a bi-directional relationship between emotion and design-based learning [12]. To sum up, in the DTIL context, design thinking is considered in many studies as both a dependent variable and partly as an independent one. Results of the learning effects indicate optimistic learning outcomes of students; additionally, the competences cultivated by the design thinking activities were often associated with the 21 st century skills, like communication, collaboration, creativity, and critical thinking [11; 27; 29; 40].
## 4 Conclusions  
In this study, we extracted FFL through various ethanol concentrations and tested a safety profile and potential bioactive properties without inducing mutagenicity up to a concentration of 5 mg/plate. Among the different extraction methods, the ethanol extracts of FFL exhibited greater antioxidant activities than the hot water extraction. The 70% ethanol extract exhibited the most potent antimutagenic activity, particularly when tested with a metabolically active enzyme system. The fractionation process identified the diethyl ether and butanol fractions as being particularly effective in suppressing the growth of mutated colonies, highlighting those such as essential oils, vitamins, alkaloids, and flavonoids as possible key active components contributing to these effects. The findings underscore the significant antioxidant and antimutagenic capabilities of FFL extracts, suggesting their promise for functional food development. Our future study will focus on the further isolation of diethyl ether and butanol fractions to verify the specific compounds responsible for the antimutagenic effects and to explore their applications associated with healthcare. Overall, our study demonstrates for the first time that FFL ethanol extracts possess potent antimutagenic activity, and these can be safely used in medicinal applications after further investigations.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14093927/s1](https://www.mdpi.com/article/10.3390/app14093927/s1)]([https://www.mdpi.com/article/10.3390/app14093927/s1](https://www.mdpi.com/article/10.3390/app14093927/s1)), Table S1: The mutagenicity activity of the FFL extracts in TA98 and TA100 strains.  
Conceptualization, H.-B.L.; investigation, C.-G.P.; resources, H.-B.L.; writing, C.-G.P.; writing--review and editing, H.-B.L. and C.-G.P. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.  
The authors declare no conflicts of interest.
## 3 Results and Analysis
### Analysis of Wind Characteristics  
Figure 4 shows the comparison of the fluctuating power spectral intensities in the three directions between the simulated and measured results. The generated spectrum fluctuates around the target spectrum, and there is no large deviation, demonstrating that the turbulence characteristics of the simulated fluctuating wind are in line with the measured actual typhoon. In addition, the longitudinal fluctuating energy is higher than the other directions, and the vertical energy is the lowest, which corresponds to the fluctuating wind speed in the three directions. In addition, the fluctuating wind using the Kaimal spectrum and Von Karman spectrum that are commonly used in the specification were also simulated, and compared with the results of the measured typhoon spectrum. At the hub height of the wind turbine, the turbulence intensities of the three fluctuating wind were 10.83%, 10.35 and 13.89%, respectively. In order to quantify the error of the numerical results, Table 2 shows the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Rated speed & 10.5 m/s & Hub height & 100 m \\ Number of blades & 3 & Blade length & 78 m \\ Diameter of rotor & 160.7 m & Tip prebending & 4.5 m \\ First-order natural frequency (out of plane) & 0.438 Hz & First-order natural frequency (in plane) & 0.826 Hz \\ Second-order natural frequency (out of plane) & 1.364 Hz & Second-order natural frequency (in plane) & 2.565 Hz \\ First-order natural frequency of tower & 0.217 Hz & Second-order natural frequency of tower & 1.321 Hz \\ \hline \end{tabular}
\end{table}
Table 1: 6 MW wind turbine parameters.  
Figure 3: Calculation zone and node partition.  
comparison of the turbulence intensities in three directions between the simulated and the measured results at the height of the anemometer. The results simulated by the proposed method in this paper are in good agreement with the measured ones. The most important longitudinal error for wind turbine dynamics simulation is only 1.7%, and the turbulent fluctuation is higher than the recommended spectrum in the standards; this difference reflects the particularity of typhoon. Figure 5 shows the fluctuating wind speed in three directions at the hub height of the wind turbine. The longitudinal inflow fluctuation reaches a value of 36.73 m/s. The horizontal and vertical fluctuation amplitudes are reduced, but the minimum vertical fluctuation amplitude still reaches to 13.06 m/s, which reflects the strong fluctuation of typhoon wind.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Direction** & **Measurement** & **Proposed** & **[PERSON]** & **Von Karman** \\ \hline x & 18\% & 17.7\% & 12.63\% & 15.45\% \\ y & 15\% & 14.3\% & 10.66\% & 12.36\% \\ z & 7\% & 5.8\% & 5.89\% & 7.87\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of turbulence intensity.  
Figure 4: Comparison of the power spectrum. (**a**) \(x\)-direction; (**b**) \(y\)-direction; (**c**) \(z\)-direction.
## 4 Experimental Results
### Ablation Studies  
In this section, we assessed the significance and impact of various image preprocessing techniques in the HaEx Segmentation and Macula Localization modules on the overall performance of the DME grading system. For the HaEx Segmentation module, consider a scenario where the original fundus images have poor contrast and varying illumination. Without preprocessing, the segmentation model struggles to distinguish HaEx from other retinal structures, resulting in suboptimal performance. Enhancing the quality of the input images using contrast enhancement (CE) and CIELAB preprocessing methods leads to more accurate segmentation. These results are shown in Table 2.  
Because input image quality is unknown, we cannot select an enhancement method for all possible input images. To alleviate the negative impact of the low-quality input image on the DME grading accuracy, we introduced the majority voting mechanism to the DME grading. Table 9 shows the DME grading accuracy and AUC obtained using two enhancement methods individually and without any enhancement method which are compared with the proposed majority voting. From Table 9, we can observe that the majority voting mechanism is necessary.  
In the Macula Localization Module, several preprocessing techniques were applied sequentially to the original fundus images for accurate detection of the macula area. This sequence of preprocesses is composed of IRHSF, histogram specification, CLAHE, and Otsu binarization in this order. It is not possible to eliminate one of these processes from the sequence because the output of the previous process is the input of the next process. Table 10 shows the percentage of Messidor images whose macular area can be detected with/without the sequence of preprocesses. From Table 10, we can conclude that the sequence of preprocesses is indispensable for macula area detection. If this sequence is eliminated, only 43.36% of Messidor images can be evaluated in the DME grading module.
## 4 Applications
### Enzyme-Free Biosensors  
#### 4.2.1 Ascorbic Acid  
The antioxidant activity of ascorbic acid (AA, also called vitamin C) can be used for the prevention and treatment of scurvy, common cold, mental illness, cancer, and AIDS. The concentration in human tissues is between 0.04 and 4 mM, while in food rich in vitamin C it is in the range of 0.005-2 mM [138; 139]. The detection of AA with high sensitivity is then needed for foods, drugs, and cosmetic applications.  
[PERSON] et al. developed an all-PEDOT:PSS OECT sensor for ascorbic acid detection [57], without the presence of either enzyme or other redox mediators; ascorbic acid was directly electrooxidized at the gate electrode. A limit of detection of 13 nM and a sensitivity of 4.5 \(\times\) 10\({}^{-6}\) A decade\({}^{-1}\) have been reached, with a linearity domain between 10\({}^{-6}\) and 10\({}^{-3}\) M.  
[PERSON] et al. [112] investigated the effect of the geometry of the OECT on the transistors performance. All-PEDOT:PSS screen-printed OECTs exhibited higher sensitivity, larger modulation and better LOD values (80 uM) with a small channel and gate areas ratio (smaller gate electrode than the channel). For low AA concentrations (10\({}^{-8}\) M to 10\({}^{-5}\) M) OECTs work in the non-Faradaic regime, where the polymer doping is coupled with the charging of an ionic double layer. For higher concentration (up to 10\({}^{-2}\) M) the OECTs operate in the Faradaic regime where doping/dedoping is coupled with the electro-catalytic oxidation of ascorbic acid by PEDOT:PSS at the gate electrode.  
Very recently (2018), a molecularly imprinted polymer (MIP)-based OECT sensor was designed to detect ascorbic acid [110]. The adsorption and the oxidation of AA occurs on the MIP-modified gate electrode, thereby improving the selectivity. A LOD of 10 nM and a sensitivity of 75 uA of channel current change per decade (within the range 1 uM-100 uM) were measured. The addition of common interferents (Asp, Gly, Glu, UA, H\({}_{2}\)O\({}_{2}\), etc.) gives rise to weak changes of the effective gate voltage and therefore weak drain current changes.
## 6 Results and Discussions
### Case 2: Non-Cascaded Non-Pumped STHTS Considering Transmission Losses [25]
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Independent Samples \(t\)-Test**} \\ \hline \multicolumn{1}{c}{**Levene’s Test for the Equality**} & \multicolumn{6}{c}{\(t\)**-Test for the Equality of Means**} \\ \multicolumn{1}{c}{**of Variances**} & & & & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{l} **Comparison** \\ _APSO** \\ \end{tabular} } & \multirow{2}{*}{**F**} & \multirow{2}{*}{**Sig.**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Sig.** & **Mean** & **Std. Error** & \multicolumn{2}{c}{**95\% Confidence Interval**} \\  & & & & & (2-tailed) & Difference & Difference & \multicolumn{2}{c}{**of the Difference**} \\ \cline{5-10}  & & & & & & & & **Lower** & **Upper** \\ \hline \multirow{2}{*}{\begin{tabular}{l} Equal \\ variances \\ (100 instances) \\ \end{tabular} } & \multirow{2}{*}{4.674} & \multirow{2}{*}{0.032} & \multirow{2}{*}{\(-\)1.133} & \multirow{2}{*}{198} & \multirow{2}{*}{0.259} & \multirow{2}{*}{\(-\)76.9991850} & \multirow{2}{*}{67.9525842} & \multirow{2}{*}{\(-\)211.0028687} & \multirow{2}{*}{57.0044987} \\  & & & & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{l} Equal \\ variances \\ not assumed \\ (200 instances) \\ \end{tabular} } & \multirow{2}{*}{14.501} & \multirow{2}{*}{0.000} & \multirow{2}{*}{1.605} & \multirow{2}{*}{398} & \multirow{2}{*}{0.109} & \multirow{2}{*}{77.2827} & \multirow{2}{*}{48.1474} & \multirow{2}{*}{\(-\)17.3722} & \multirow{2}{*}{171.93} \\  & & & & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{l} Equal \\ variances \\ not assumed \\ (100 instances) \\ \end{tabular} } & \multirow{2}{*}{\(-\)1.133} & \multirow{2}{*}{185.468} & \multirow{2}{*}{0.259} & \multirow{2}{*}{\(-\)76.9991850} & \multirow{2}{*}{67.9525842} & \multirow{2}{*}{\(-\)211.0585712} & \multirow{2}{*}{57.0602012} \\  & & & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{l} Equal \\ variances \\ not assumed \\ (200 instances) \\ \end{tabular} } & \multirow{2}{*}{1.1605} & \multirow{2}{*}{367.693} & \multirow{2}{*}{0.109} & \multirow{2}{*}{77.2827} & \multirow{2}{*}{48.1474} & \multirow{2}{*}{\(-\)17.3960} & \multirow{2}{*}{171.9615} \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SPSS results of the independent sample \(t\)-test showing the comparison between simple APSO and improved APSO on the non-cascaded non-pumped STHTS problem. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Interval** & **PT (MW)** & **Phyd (MW)** & **Ploss (MW)** & **Dis (acre-ft/h)** & **V (acre-ft)** & **Total Cost (S)** \\ \hline
1 & 833.91 & 377.4 & 11.399 & 2206 & 97,527.06 & \\
2 & 950.66 & 575.86 & 26.52 & 3192 & 83,222.43 & \\
3 & 813.76 & 293.10 & 6.87 & 1786.7 & 85,781.40 & \\
4 & 1091.94 & 753.47 & 45.41 & 4074.7 & 60,884.22 & \\
5 & 733.34 & 220.54 & 3.89 & 1426.1 & 67,770.92 & \\
6 & 851.08 & 466.313 & 17.395 & 2647.57 & 60,000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Power flow and cost optimization with the improved APSO algorithm implementation.