esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
In this section, we inspect the performance of the proposed EP-MAC protocol. We compare our proposed EP-MAC protocol with three other protocols: S-MAC, T-MAC and Intelligent Hybrid MAC (IH-MAC).  
The performance metrics used in the evaluation of EP-MAC protocol are the energy consumption, average delay and average delivery ratio.  
We have simulated with Castalia, a simulator for Wireless Sensor Networks and Body Area Networks [18] which is developed on the discrete event simulator OMNET\(++\)[19]. In the simulation setup, we take 100 nodes distributed in a 100 m \(\times\) 100 m area grid. The nodes considered here are static. The radio range is selected in such a way that all the non-edge nodes have eight neighbors. The sink node is chosen on the bottom left corner of the network grid. We evaluate the performance of our EP-MAC and compare with three other said protocols. We use four data slots in a frame for our proposed EP-MAC. The simulation parameters are given in the Table 1.  
Energy efficiency of sensor nodes for EP-MAC, T-MAC, IH-MAC and S-MAC are shown in Figure 6(a). We vary the packet generation interval in order to measure the performance for variable traffic load. We see that the average energy consumption per node of EP-MAC is less than the energy consumption of other three protocols both for low and high traffic load. It is because proposed EP-MAC protocol avoid redundant control signal (RTS/CTS) and work like TDMA as explained in Section 3.4 of this paper.  
Figure 6(b) shows the average packet delay for proposed EP-MAC and other three protocols. As the packet inter arrival time increases and reaches to a certain level the average packet delay of EP-MAC become steady. But the delay in EP-MAC is a little bit higher than the delay of the other three  
\begin{table}
\begin{tabular}{|l|} \hline
**Parameter Name Value** \\ \hline Channel band width 20 kbps \\ Data packet length 20 bytes \\ Transmission power 36 mW \\ Receive power 14.4 mW \\ Idle power 14.4 mW \\ Sleep state 15 μW \\ Frame length 1 s \\ (for S-MAC,T-MAC,IH-MAC) \\ Frame length (for EP-MAC) 4 s \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for the MAC protocol.  
protocols because it trades off latency for energy savings. But the delay performance of EP-MAC is better than the pure TDMA because of using the concept of parallel transmission which is explained in Section 3.3 of this paper.  
Figure 6(c) shows the average packet delivery ratio for our proposed EP-MAC protocol and the other three protocols. EP-MAC clearly outperforms the T-MAC, S-MAC and IH-MAC in terms of packet delivery ratio for different traffic load. This is due to using the concept of TDMA and parallel transmission together in the EP-MAC protocol.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 2 Materials and Methods
### Sensor Performance
#### 2.4.2. Carbon Monoxide Sensors  
To measure sensor response of the carbon monoxide sensors, a MAP-Pro Hand Torch Cylinder (BernzOmatic Company, Newark, NJ, USA) was used to generate carbon monoxide gas inside the chamber. Measurements from the CO sensors were compared to a Q-Trak Plus 8552 (TSI Inc., Shoreview, MN, USA) reference instrument. The Q-Trak is an electrochemical sensor and was calibrated with zero air (GASCO 103L-1, Oldsmar, FL, USA) and a 10-ppm calibration gas tank (GASCO 103L-375A-10, Oldsmar, FL, USA) before the experiment. The torch was operated for 150 s and measurements were collected as the CO slowly dissipated in the still air of the chamber. The CO sensor measurements were converted from mV to ppm using slope (0.358 ppm/V) and intercept (0.217 mV) values from earlier laboratory experiments [40]. In that study, [PERSON] et al. exposed three CO sensors at concentration levels of 2, 5, 10, 12, 15, 25, 30 and 50 ppm, and compared the results with a GrayWolf Advanced Pro (GrayWolf Sensing Solutions LLC, Shelton, CT, USA) reference instrument. The authors of that study then derived a linear regression model with high correlation (\(R^{2}\) = 0.998) between the CO sensor and the GrayWolf Advanced Pro measurements.
## 8 Result Analysis
### Communication Cost  
Table 4 lists the communication overhead between the device and the edge gateway for the proposed cross-domain handover authentication protocol and the comparison schemes. It is worth noting that Table 4 does not cover all methods listed in Table 3, as not all of the related works conducted experiments on communication overhead. As shown in Table 4, the proposed protocol involves the exchange of two messages. Specifically, the device sends the message \(\{TID_{i},W_{i},M_{5},T_{4}\}\) to the edge gateway, resulting in a communication overhead of 660 bits on the device side. In response, the edge gateway sends the message  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Device Side**} & \multirow{2}{*}{
\begin{tabular}{c} **Edge Gateway** \\ **Side** \\ \end{tabular} } & \multicolumn{2}{c}{**Overall**} \\  & & & **Computational** \\  & & & **Cost (ms)** \\ \hline [PERSON], [PERSON] et al. [4] & \(4T_{h}+2T_{mul}\) & \(5T_{h}+2T_{mul}\) & 2.284 \\ [PERSON], [PERSON] et al. [8] & \(7T_{h}+5T_{mul}\) & \(-\) & 5.353 \\ [PERSON], [PERSON] et al. [10] & \(3T_{h}+3T_{mul}\) & \(4T_{h}+5T_{mul}+3T_{add}\) & 1.467 \\ [PERSON], [PERSON] et al. [14] & \(4T_{h}+6T_{mul}+4T_{add}\) & \(-\) & 2.543 \\ [PERSON], [PERSON] et al. [16] & \(5T_{h}+9T_{mul}+4T_{add}\) & \(5T_{h}+7T_{mul}+3T_{add}\) & 4.808 \\ [PERSON] et al. [28] & \(2T_{h}+3T_{mul}+3T_{add}\) & \(-\) & 2.715 \\ [PERSON] [PERSON] et al. [30] & \(4T_{h}+6T_{mul}+3T_{add}\) & \(-\) & 5.412 \\ [PERSON], [PERSON] et al. [32] & \(4T_{mul}+3T_{add}\) & \(-\) & 5.250 \\ [PERSON], [PERSON] et al. [33] & \(5T_{mul}+2T_{add}\) & \(-\) & 2.733 \\ [PERSON], [PERSON] et al. [45] & \(6T_{h}\) & \(12T_{h}+4T_{mul}+T_{add}\) & 2.234 \\ [PERSON] et al. [44] & \(3T_{h}+4T_{mul}\) & \(2T_{h}+2T_{mul}+3T_{add}\) & 7.392 \\ Our Method & \(3T_{h}+2T_{mul}\) & \(4T_{h}+2T_{c}\) & 0.924 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computational overhead with related work.  
Figure 5: Comparison of computational cost with schemes [PERSON], [PERSON] 2024 [4], [PERSON], [PERSON] 2024 [8], [PERSON], [PERSON] 2024 [10], [PERSON], [PERSON] 2021 [14], [PERSON], [PERSON] 2022 [16], [PERSON], C 2024 [28], [PERSON], [PERSON] 2024 [30], [PERSON], [PERSON] 2024 [32], [PERSON], J 2023 [33], [PERSON], [PERSON] 2022 [45], and [PERSON], [PERSON] 2020 [44].  
\(\{M_{6},X_{m},T_{5}\}\) to the device, with a corresponding overhead of 410 bits. Therefore, the total communication overhead is 1070 bits. While our method may not be the most optimal in terms of communication cost, as shown in Figure 6, it provides a superior trade-off by achieving the best computational efficiency, making it highly suitable for resource-constrained IIoT environments.
## 4 Evaluation Method
### Comprehensive Evaluation and Decision Analysis
#### 4.2.3 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)  
The TOPSIS model serves as a method for decision-making that depends on a limited set of evaluations to determine how well an object corresponds with a specified objective [131]. The core principle behind this approach lies in ranking evaluation objects according to their proximity to both positive and negative ideal outcomes. As a technique for multi-objective decision analysis, it is widely utilized in GSEUUS. By establishing both ideal and anti-ideal solutions, the TOPSIS method enables a structured assessment of UUS development amidst varying geological contexts, thus laying a scientific foundation for urban planning and construction activities. The evaluation procedure starts with the normalization of the initial data matrix, considering both positive and negative ideal scenarios. Next, the distances that each evaluation item has from these ideal solutions, both positive and negative, are computed to assess their proximity to the ideal solution. Ultimately, the strengths and weaknesses are prioritized according to their level of proximity [132].  
The TOPSIS model's benefit is found in its capacity to efficiently handle multi-criteria decision-making challenges by assessing each option according to its proximity to the ideal positive and negative solutions. It is user-friendly, simple to grasp, and uncomplicated to calculate. On the other hand, a significant drawback is its susceptibility to the importance assigned to evaluation criteria; the reasonableness of weight allocation directly affects the results of the decision-making process. Conventional TOPSIS uses Euclidean distance as its measurement standard, which might not fully reflect the underlying trends within the evaluation data, consequently influencing the accuracy of the assessment (Figure 7).  
The TOPSIS method was utilized for evaluating the multi-layered UUS of Wuhan [70]. This evaluation involved the creation of a foundational layer assessment index system and a multi-layer transfer coefficient matrix, alongside the fuzzy analytic hierarchy process (FAHP) method to determine the significance of the evaluation indicators. A quantitative analysis of the UUS in the Wuchang Railway Station vicinity was then performed. In another analysis regarding the geological appropriateness of UUS in Hangzhou [133], the author applied an enhanced TOPSIS model, which was supplemented by gray correlation analysis, to identify implicit change patterns in the indicators and support the examination of data distribution under diverse informational contexts. This strategy effectively mitigatesthe disadvantages of Euclidean distance and addresses the inherent limitations found within the TOPSIS model itself (Figure 8a and Table 1).
## 2 Materials and Methods
### Biohydrogen Fermentability Test in Serum Bottles  
The isolated biohydrogen-producing bacterium was then used to test its fermentability in four different fermentation media with varying carbon sources. The four fermentation substrates were AFEX-pretreated OPEFB enzymatic hydrolysis, DAA-pretreated OPEFB molasses, synthetic defined media with glucose and xylose with 2:1 ratio and RCM without any modification. Accordingly, DAA OPEFB molasses is valuable to emulate the possible worst-case scenario for any OPEFB-pretreated samples due to a significant amount of dissolved furfural and serves as an opportunity to observe the abilities of locally isolated _Enterobacter_ sp. KBH 6958 to grow in slightly unfavorable conditions. On the other hand, the initial total carbohydrate (TC) for other media was adjusted to 7 g/L and supplemented with 50 mM phosphate buffer, 3 g/L yeast extract, 3 g/L NaHCO\({}_{3}\) and 0.5 g/L L-cysteine-HCl. Fermentability test was carried out in 50 mL serum bottles with 25 mL of working volume. The initial pH for both AFEX-pretreated hydrolysate and synthetic media was adjusted to 7.0 while the initial pH of DAA-pretreated OPEFB molasses was 9.0. The difference in initial pH is due to the furfural content in OPEFB molasses, which must be minimized to reduce the inhibition effect and a detailed explanation was described in previous work [13]. Next, nitrogen gas was sparged through the media for 5 min to create anaerbiosis for the fermentation. The isolated biohydrogen-producing bacterium was initially grown in RCM broth until the OD reached 1.0 \(\pm\) 0.1 before inoculation. Each bottle containing medium was inoculated with 10% (\(v/v\)) inoculum. Bottles were capped, clamped, and incubated for 72 h at 37 \({}^{\circ}\)C and 150 rpm. The biogas produced was sampled using disposable syringes and its composition was analyzed using GC.  
The metabolites in fermented broth from fermentability test were then analyzed using gas chromatography mass spectrophotometry (GCMS). The GCMS system (Bruker Scion SQ, USA) was equipped with 30 m Stabilwax(r) column (Restek(r), USA) (0.25 mm ID, 0.25 \(\mu\)m d\({}_{\ell}\)). The injection port temperature of GCMS was fixed at 200 \({}^{\circ}\)C and the initial oven temperature was maintained at 70 \({}^{\circ}\)C for 5 min, then raised to 180 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min and held for 5 min before ramped (10 \({}^{\circ}\)C/min) again to 195 \({}^{\circ}\)C and maintained for another 5 min. About 1 \(\mu\)L of filtered fermentation broth was used in each analysis.
# CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed
## 2 CFD Simulations and Energy Loss Model
### Energy Loss Model for High-Speed Centrifugal Pumps  
The external characteristic analysis model of the high-speed centrifugal pump is established by using the energy loss model (ELM). The total power of the pump is the sum of the mechanical loss power \(P_{m}\), volume loss power \(P_{v}\), hydraulic loss power \(P_{h}\), and useful power \(P_{u}\), and the mechanical power loss of the low specific speed pump is mainly the disc friction loss.  
The volume loss refers to the partial fluid in the centrifugal pump that will not be discharged from the pump due to the existence of the leakage channel, including three parts: the internal flow \(P_{v,b}\), the tip leakage of inducer \(P_{v,ind}\), and the tip leakage of impeller \(P_{v,imp}\). The volume loss of each part is composed of the input work, the product of leakage flow, and the differential pressure between the inlet and the outlet, as shown below:  
\[P_{v}=P_{v,b}+P_{v,ind}+P_{v,imp}=q_{b}\Delta p_{b}+M_{b}\omega+q_{tip}\Delta p _{tip}+M_{tip}\omega+q_{imp}\Delta p_{imp}+M_{imp}\omega \tag{12}\]  
The hydraulic power of the centrifugal pump is calculated by the following formulas:  
\[P_{h}=P_{h,ind}+P_{h,imp}+P_{h,dif}+P_{h,dif-}+P_{h,rot}+P_{h,out} \tag{13}\]  
The actual useful power of the centrifugal pump is calculated by the following formulas:  
\[P_{u}=\rho gQH \tag{14}\]  
\[H=\frac{P_{out}}{\rho g}-\frac{P_{in}}{\rho g}+\Delta z \tag{15}\]  
The total efficiency \(\eta_{all}\) can be divided into three parts: the mechanical efficiency \(\eta_{m}\), the volumetric efficiency \(\eta_{v}\), and the hydraulic efficiency \(\eta_{h}\). The mechanical efficiency is the ratio of the power minus the friction loss to total power. The volumetric efficiency is the ratio of the theoretical useful work to that without volumetric loss. The hydraulic efficiency represents the ratio of the actual useful power to the theoretical useful power considering the volume loss, as follows:  
\[\eta_{all}=\eta_{m}\eta_{v}\eta_{h}=\bigg{(}1-\frac{P_{m}}{P_{all}}\bigg{)} \bigg{(}\frac{P_{u}+P_{h}}{P_{u}+P_{h}+P_{v}}\bigg{)}\bigg{(}\frac{P_{u}}{P_{u} +P_{h}}\bigg{)}=\frac{P_{u}}{P_{all}} \tag{16}\]
# Integration of Functional Link Neural Networks into a Parameter Estimation Methodology
## 5 Conclusions and Further Studies  
There have been many studies to improve the RSM by combining statistical and mathematical techniques, but there are cases where particular data types are required or assumptions are made to define functions between mean, variance, and input elements. NNs, which have recently been widely applied in artificial intelligence, can present simple mathematical models (functions) using artificial neurons and determine unknown interactions between the input and output performance of a process without any knowledge of the principle.  
This study has described a functional-link-NN-based estimation method that offers an alternative RD technique without assumptions inherent in the conventional LSM-based RSM. Compared with the existing RD dual-response estimation approach, the proposed method provides significant advantages in determining the functional relationship between the control factors and output performances and the optimal solutions. The proposed dual-response estimation approach can be quickly and efficiently implemented using MATLAB (see Appendix B). Experimental results show that the proposed NN-based estimation method can achieve better solutions than the conventional LSM-based RSM in the EQL metric.  
In the future, the proposed functional-link-NN-based dual-response RD estimation approach will be extended to time series data and multiple-response optimization problems. In addition, we plan to search for the optimal structure by binary coding the neural network  
Figure 6: Criterion space of the two estimated functions: (**a**) conventional LSM; (**b**) proposed NN-based RD estimation model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Estimation Model**} & \multicolumn{3}{c}{**Optimal Factor Settings**} & \multirow{2}{*}{**Process Mean**} & \multirow{2}{*}{**Process Bias**} & \multirow{2}{*}{**Process Variance**} & \multirow{2}{*}{**EQL**} \\ \cline{2-2} \cline{5-7}  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & & & & \\ \hline LSM & 1.0000 & 0.07153 & \(-\)0.25028 & 494.67226 & 5.32773 & 1977.53266 & 2005.91742 \\ Proposed NN & 0.99999 & 0.99999 & \(-\)0.79327 & 496.39189 & 3.60810 & 450.64771 & 463.66611 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis results.  
structure with a genetic algorithm and conducting research on optimizing the weights of the neural network.  
Conceptualization, T.-H.L. and S.S.; Methodology, T.-H.L. and S.S.; Modeling, T.-H.L.; Validation T.-H.L. and S.S.; Writing--Original Draft Preparation, T.-H.L. and S.S.; Writing--Review and Editing, M.T., J.H.J., H.J., and S.S.; Funding Acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) (No. NRF-2019R1F1A1060067).  
The authors declare no conflict of interest.
## 3 Hardware Implementation  
The proposed secure communication systems are implemented on FPGA platform for different values of N (from N = 1 to N = 20). The main block in the studied systems is the 4D unified hyperchaotic system. The forward Euler integration method is used to determine the numerical solution of the 4D hyperchaotic system shown in (4).  
\[\begin{split} x_{1}[n+1]&=x_{1}[n]+h(a_{1}(x_{2}[n] -x_{1}[n])+x_{4}[n])\\ x_{2}[n+1]&=x_{2}[n]+h(a_{2}x_{1}[n]-x_{1}[n]x_{3}[n ]+b_{4}x_{2}[n])\\ x_{3}[n+1]&=x_{3}[n]+h(x_{1}[n]x_{2}[n]-a_{3}x_{3}[n ])\\ x_{4}[n+1]&=x_{4}[n]+h(0.2x_{4}[n]+0.1x_{2}[n]x_{3}[ n])\end{split} \tag{4}\]  
where \([n]\) and \([n+1]\) are the current and the next states, respectively. \(x_{1},x_{2},x_{3}\), and \(x_{4}\) are output state variables of the master system, \(a_{1}=45\), \(a_{2}=3\), \(a_{3}=10\), \(a_{4}=[13,27,36]\) are the coefficient parameters, and the initial conditions are \(x(0)=[1,1,1,1]^{T}\). The initial conditions of the state variables are \(x_{0}=[1,-1,1,-1]\), while the discretization step size is h = 0.001.  
Figure 4 shows the basic hardware blocks required for the implementation which are adders, subtractors, multipliers, and delays.  
Then, the hardware implementation of the N-levels cascaded chaotic-based secure communication system in Figure 3 is done by using the VHDL code. Each system is realized on the FPGA **Cyclon V** platform. We use the 32-bits fixed-point arithmetic representation for all the variables and constants for the FPGA implementation. This representation has 1-bit for the sign, 7-bits for the integer part, and 24-bits for the fractional part.  
In this paper, we measure FPGA resource utilization and maximum clock frequency, which are crucial metrics that influence the cost and performance of FPGA-based encryption systems, particularly as the number of encryption levels (N) increases. Finding the right balance between logic utilization and maximum frequency is key to optimizing the FPGA design. A design with low resource usage lowers the cost of hardware implementation, while a high frequency ensures the system's performance remains fast and efficient. By examining how these parameters change with N levels, the paper aims to identify the most cost-effective FPGA implementation that offers robust encryption with minimal resource overhead and maximal processing speed. This balance is vital for practical applications where both cost and performance are important constraints. Table 2 shows FPGA resources utilization of N-level chaotic-based secure communication system for varying numbers of levels (N) from 1 to 20. The maximum frequency for the studied systems was between 31.08 MHz when N = 1 and 29.69 MHz when N = 7. The utilization of FPGA resources increases with an increasing value of N, for number of chaotic systems levels greater thanN = 7, the system exceeds the capacity of the Cyclone V FPGA, which may require a more advanced FPGA model or additional resources to accommodate the implementation.
## 4 Results
### Computed Phenological Dates  
The automated computed phenological event dates for all the 18 cameras for each year of investigation as indicated by the automatically extracted greenness values are presented in Table 3. Figure 6 shows a comparison between the date of end of senescence for 2011 and start of leaf-up for 2012 observed through the automated method and visual inspection validation dates across the 18 cameras. In both cases a strong [PERSON]'s correlation coefficient, \(r\), was found (\(r\) = 0.7879, \(p\) \(<\) 0.001, date of end of senescence, 2011; \(r\) = 0. 9199, \(p\) \(<\) 0.001, date of end of start of leaf-up, 2012). Although the automated analysis estimated dates too early compared with visual inspection of the cameras' images, and others too late, overall the root-mean-square difference between the estimate dates using the automated and manual methods was five days. Compared with the date of start of green up, the date of end of senescence was less certain. This concurs with other studies (using satellite data) that suggest it is more challenging to measure this as a single date [44] but that this can be overcome using a different index of the data (e.g., green-red vegetation index (GRVI) [45]). In order to fully understand why there was error in the predicted dates the values of greenness would need disaggregating to the component vegetation types and further research into different methods of data smoothing and date extraction would be required (similar to that applied to satellite data [29,46]).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Camera No.**} & \multicolumn{2}{c}{**2011 Season**} & \multicolumn{2}{c}{**2012 Season**} \\ \cline{2-5}  & **Start of Senescence** & **End of Senescence** & **Start of Green-up** & **End of Green-up** \\ \hline  
ul & 23-Nov & 06-Mar & 11-May \\ \hline \hline \end{tabular}
\end{table}
Table 3: Automatically calculated phenological dates for the cameras that are shown Figure 1.