esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration
###### Abstract  
Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms' effectiveness through the design of a parallel integration model structure.  
Keywords:lightweight constrained iris; one-to-one certification; unsteady state texture; multi-algorithm parallel integration model; maximum trusted method +
Footnote †: journal:
## 2 Evaluation Method
### Test Conditions
#### 2.3.3 Target Position  
Figure 6 shows the distance conditions between the vehicle and the pedestrian target were defined as 5 m, 12.5 m, and 25 m. Except for the minimum distance of 5 m for measurement, the remaining distance conditions were considered to require a time of at least 1.5 s for the driver to avoid collision with the pedestrian target [26]. Here, Time to Collision (TTC) should be considered, which is often a very important variable in relation to FCW (forward collision warning) or CAS (collision avoidance system) [27; 28].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Weather Condition**} \\ \hline \multirow{2}{*}{**Weather Type**} & **Level** & **Description** \\  & **(Visibility Distance or Rainfall)** & \\ \hline \multirow{2}{*}{Clear} & More than 2 km & No climatic condition \\ \cline{2-3}  & 40 m & Dense fog \\ \cline{2-3}  & 100 m & Thick fog \\ \cline{2-3}  & 200 m & Fog \\ \hline \multirow{2}{*}{Rain} & 20 mm/h & Heavy rain \\ \cline{2-3}  & 50 mm/h & Violent rain \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weather conditions for the test.  
Table 2 shows a summary of the positions of pedestrian targets. In the case of 25 m, when driving at 60 km/h (the speed limit at the time of the test, which has been changed to 50 km/h after April 2021 in Korea), which was the speed limit on roads except for automobile-only roads in Korean cities, the TTC (time to collision) is the driving distance considering 1.5 s, and 12.5 m is the driving distance considering the TTC when driving at 30 km/h, which is 1/2 of the maximum speed, in fog with a visibility of 100 m or less.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 4 Calculation of the Contour Integral
### Parametric Definition of the Integration Path  
An observation that can be made about the technique described in the previous sections is that calculating the line integral for a function with a non-continuous anti-derivative requires care because of the sense of the integration path crossing the NRA, which results in different contributions, as seen. Anyway, for linear segments, the method just described circumvents that issue; however, for non-straight paths to be discussed further, that may not be so clear. Hence, let us adopt an alternative approach to obtain (32) by using parametric integration paths because that explicitly defines a path from a start to an end, along with the variation of the parameter that describes the curve. Let us use a parameter \(t\) that varies from \(0\) to \(1\) to cover the full path segment, and let us start with the case of linear segments. Being \(z_{1}\) and \(z_{2}\) the extremities of a linear path, any point of the path from \(z_{1}\) to \(z_{2}\) is given by:  
\[z=z_{1}+t(z_{2}-z_{1})\,,\,0\leq t\leq 1. \tag{38}\]  
After adjusting the integration variable, \(\mathrm{d}z=(z_{2}-z_{1})\mathrm{d}t\), the line integral can now be calculated as follows:  
\[\int\limits_{[z_{1}z_{2}]}\frac{\mathrm{d}z}{z-a}=\int\limits_{0}^{1}\frac{(z_ {2}-z_{1})\,\mathrm{d}t}{z_{1}+t(z_{2}-z_{1})-a} \tag{39}\]  
that results in:  
\[\bigg{[}\ln(z_{1}+t(z_{2}-z_{1})-a)\bigg{]}_{0}^{1}=\ln(z_{2}-a)-\ln(z_{1}-a), \tag{40}\]  
which is the same as (16), but shares the same risks of discontinuity because the anti-derivative is still a logarithm, despite the fact that the integration variable is now \(t\). Therefore, the solution is exactly the same as before, given in (22) and (23). In the case of NRA crossing, and depending on the sense (+o or +o), there will be a contribution of \(\pm 2\mathrm{i}\pi\) to the contour integral, or \(\pm 1\) in the NRA crossings counter.  
Although it could have been stated and adopted earlier, working for a segment \(\overline{z_{1}z_{2}}\) in respect to a point \(a\) is actually equivalent to working with segment \(\overline{(z_{1}-a)(z_{2}-a)}\) in respect to \(a=0\). So, from now onwards, when \(z\) points are used, it is assumed that they result from the original points subtracted from \(a\), i.e., \((z-a)\), and that the reference point in analysis is the system origin \((0,0)\).
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 3 Low Complexity Equalizers for UP-OCDM under Doubly Selective Channels
### MMSE Block Equalizer  
In this subsection, we propose the time-domain MMSE equalizer for UP-OCDM. With the UP-OCDM input-output relation under doubly selective channel expressed by (15), the linear block MMSE equalization can be written as  
\[\mathbf{\hat{d}}_{MMSE} =\mathbf{\Phi}^{H}\mathbf{\hat{s}}_{MMSE} \tag{17}\] \[=\mathbf{\Phi}^{H}\mathbf{H}^{H}(\mathbf{H}\mathbf{H}^{H}+\gamma^{-1}\mathbf{I}_{N}) ^{-1}\mathbf{r}\] \[=\mathbf{\Phi}^{H}(\mathbf{H}^{H}\mathbf{H}+\gamma^{-1}\mathbf{I}_{N})^{-1}\mathbf{H} ^{H}\mathbf{r},\]  
where \(\gamma\) equals to \(\sigma_{d}^{2}/\sigma_{w}^{2}\). Note that the matrix \((\mathbf{H}^{H}\mathbf{H}+\gamma^{-1}\mathbf{I}_{N})\) in (17) is Hermitian, and can be solved by LDL\({}^{H}\) factorization within \(\mathcal{O}(N^{3})\) flops [21]. Therefore, the computational complexity of the MMSE block equalizer for UP-OCDM system is \(\mathcal{O}(N^{3})\), which will be a significant burden for applications with large \(N\) such as broadcasting applications, or for energy-limited applications such as underwater acoustic communication.
# Two-Phase Deep Learning-Based EDoS Detection System
## 6 Result Analysis
### Performance Analysis at the Victim Server  
In this section, we present the experiment results of the CPU usage of a victim server under the control of our proposed EDoS detection mechanism to evaluate the efficiency of  
Figure 12: Compute usage according to attack rate.  
Figure 13: Memory usage according to attack rate.  
our proposed model at the service provider's end. We launched a TCP-SYN flooding attack with an attack rate of 7000 requests per second and a normal traffic rate of 400 requests per second during a 1-h period.  
During the first 5 min, we only launched normal traffic, after which, we launched a TCP-SYN flooding attack. Figure 14 shows the victim's CPU usage when protected by our proposed EDoS detection model and when not protected by our model. Without being protected by our model, the victim server has to handle a large number of abnormal requests, which makes the server consume a huge number of resources. When the TCP-SYN flooding attack is launched (at minute five), the CPU usage increases from 40% to over 50% and reaches 100% after 50 min. Most cloud-based systems set an upper threshold of 80% of the CPU usage to trigger another virtual machine. Therefore, without being protected by our proposed model, the cloud system will need a second virtual machine allocated 30 min after an attack, leading to an increase in the cost the user must pay to the cloud service provider.  
However, when being protected by our proposed model, the average CPU usage of the victim server is only approximately 40% during a 1-h period. This means the cloud user does not need to pay for a second virtual machine when being protected by our proposed model while under EDoS attacks.
# Electronic Sensing Technologies in Food Quality Assessment: A Comprehensive Literature Review
## 5 e-Tongue
In anperometric sensors, the working electrodes are modified to either oxidize or reduce the analyte, while the counter electrode is placed in a solution containing the analyte of interest. These sensors can detect and quantify specific analytes in gases, liquids, or solutions, offering high sensitivity and selectivity and a wide dynamic range [149,150]. Examples of electrode materials used in e-tongues and their applications are presented in the Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor Material** & **Detection Limit** & **Recovery Rate** & **Application** & **References** \\ \hline \(\beta\)-GICNT/rGO electrochemical & & & Quantitative content of & \\ sensor-based & 0.01–100 \(\upmu\)mol/L & 94.80–112.20\% & capsaicinids in soy & \\ rGO/PEI—CNTs/\(\beta\)-CD & & & sauce and roasted & \\  & & & meat products & \\ \hline FI-PAD working electrode Au, & & & Detecting the & \\ auxiliary electrode platinum wire, & 0.005 g/L & 93.00–109.5\% & concentration of & \\ reference electrode Ag/AgCl & & & chlorine ions in & \\  & & & raw milk & \\ \hline PPy-based voltameries & & & & \\  7 electrodes: & & & & \\ PPy/AQDS, PPy/SO\({}_{4}\), PPy/DBS, & & 91.3\% & & \\ PPy/FC, PPy/SF, PPy/FCN, and & & & & \\  & & & & \\   PPy/TSA & & & & \\ \hline Hydrogel containing mucin, NaCl & & Astringency & & \\ as an ion-transporting electrolyte, & & 29.3 mM–0.59 \(\upmu\)M at a & & \\ and chitosan/poly(acrylamide— & sensitivity of 0.2 wt\%\({}^{-1}\) & & Sensing an stringent & \\ acrylic acid) as the main 3D & & Bitterness & and bitter taste & [154] \\ structure maintaining the & & 63.8 mM–6.38 \(\upmu\)M at a & & \\ hydrogel network & & sensitivity of 0.12 wt\%\({}^{-1}\) & & \\ \hline  & & & \\  & & & & \\ \hline  & & & \\  & & & \\  & & & \\ \hline  & & & \\  & & & \\ \hline  & & & \\  & & & \\ \hline  & & & \\ \hline  & &  
The most notable disadvantages of the electronic tongue (e-tongue) include the requirement for sample preparation, particularly for solid foods such as meat and meat products. Additionally, the sensors have a short lifespan due to nutrient absorption, especially in the case of potentiometric sensors [15; 157].
## 5 Application Scenarios
Other IoT scenarios of interest are covered in [220,221], such as power line inspection, monitoring of cultural heritage sites, and smart farming [214,222], all involving the use of UAVs. An interesting perspective is provided in [206], which foresees the use of UAVs to provide near-user edge computing capabilities in IoT scenarios in which edge and cloud infrastructure may be unavailable, and satellites for cloud computing capabilities. Complementary solutions to UAVs, which fall into the category of LAPs, are described in the literature in the form of HAPs, such as balloons [198]; although less used in real deployments, they offer wider coverage and longer endurance. Because of those features, HAPs are preferred when it comes to providing reliable wireless coverage in large geographic areas [105]. Network architectures for SAGINs, thus involving LAPs, HAPs, and satellites at different orbits, are described in [197,223], emphasizing the achievable level of QoS. Three reference scenarios, i.e., search and rescue, surveillance and monitoring, and goods delivery, are mentioned in [223] involving a Flying Ad Hoc Network (FANET) and nanoSATs. The case of Non-Radio-Line-of-Sight (NRLoS) conditions in a dispersed FANET is covered in [213], foreseeing a back-haul via satellite to deliver data.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 3 Materials and Methods
### Our Proposed Method
#### 3.1.4 Features Extraction from AP  
As shown in Table 2, there are 19 parameters of AP waveform which belong to time-domain features, statistical features, frequency-domain features, and nonlinear features.  
In this paper, the time-domain and frequency-domain features are defined and calculated in the following description:  
AP is considered as time series \(X=[X_{1},X_{2},\dots,X_{n}]\). \(i\) is the subscript(index) number of the sampling point, \(n\) represents sampling number, the index of peak(valley) is \(p\), \(X_{p}\) is the value of AP at \(p\). The absolute value of amplitude change is defined as Amplitude = \(1\,X_{p}-X_{1}\,1\), \(X_{1}\) is amplitude at the start point. AP duration can be represented by the AP length \(n\). Rising duration is the length from the start position to the peak position of AP represented by \(p\). Decline duration is the length from the peak position to the end position of AP represented by \(n-p+1\). Rising slope is the ratio of amplitude to rising duration. Assume DP as the absolute difference between the value of peak and the value of the end of AP, then the decline slope is the ratio of DP to decline duration. AP area can be calculated by \(Area=\sum\limits_{i=1}^{n}|x_{i}|\).
## 4 Results and Discussion
### VR Shopping Model Improvement
#### 4.2.2 Improved Validation  
The experiment involved 25 participants, aged 18-30, who had previously participated in the eye-tracking experiment detailed in Section 3.2.1. The design and implementation environment were aligned with the previous experimental setup to ensure consistency. To verify the feasibility of integrating virtual reality technology with packaging using the virtual packaging multi-sensory evaluation model, participants evaluated the enhanced VR shopping system to determine its effectiveness in improving consumers' sensory experiences. All 25 participants submitted valid responses. According to Table 13, the improved VR shopping system received a score of 7.36, indicating a notable improvement in consumers' sensory experiences. Improvements were noted in operational behavior consistency, information transmission accuracy, control module rationality, tactile feedback, and packaging function accuracy, all of which had previously contributed to poor sensory experiences. Visual and auditory sensory experiences also improved. Participants' feedback confirms that the virtual packaging multi-sensory evaluation model effectively guides the application of virtual reality technology in packaging and enhances consumers' sensory experiences:  
\[X_{\text{improved}}=\sum_{i=1}^{17}G_{i}W_{i}=7.36\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria Layer** & **Sub-Criteria Layer** & **Score** \\ \hline \multirow{3}{*}{Interactive behavior (B1)} & Consistency of operational behavior (C1) & 7 \\  & Accuracy of information transmission (C2) & 6 \\  & Rationalization of the control module (C3) & 7 \\ \hline \multirow{3}{*}{Color transmission (B2)} & Rationality of color application (C4) & 7 \\  & Coordination of color and environment (C5) & 8 \\  & Replaceability of color groups (C6) & 7 \\ \hline \multirow{3}{*}{Sensory experience (B3)} & Hearing (C7) & 8 \\  & Vision (C8) & 9 \\  & Touch (C9) & 7 \\  & Smell (C10) & 3 \\  & Taste (C11) & 3 \\ \hline \multirow{3}{*}{Scene experience (B4)} & Adaptation of scene and packaging (C12) & 9 \\  & Scene immersion (C13) & 8 \\ \cline{1-1}  & Scene entertainment (C14) & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Virtual packaging multi-sensory evaluation model score.  
Figure 13: Design diagram of hand movement distance: (**a**) code diagram and (**b**) MaxDis script diagram.
# A New Orthogonal Least Squares Identification Method for a Class of Fractional Hammerstein Models
## 2 Model and Problem Formulation  
### Mathematical Background of Fractional Differentiation  
Generally, there are three widely used definitions of fractional calculus: the Grunwald-Letnikov (GL) definition [21], the Riemann-Liouville (RL) definition [22], and the Caputo definition [23]. Among these, the GL definition is the most frequently used due to its ease of implementation and programming. Therefore, this paper adopts the GL definition as the basis for investigation. The concept of fractional calculus can be expressed as  
\[\Delta^{\alpha}f(t)=\lim_{h\to 0}\frac{1}{h^{\alpha}}\sum_{j=0}^{\lfloor(t-t_{0})/h \rfloor}(-1)^{j}\left(\begin{array}{c}\alpha\\ j\end{array}\right)f(t-jh), \tag{1}\]  
where \(\lfloor.\rfloor\) denotes the floor operator, \(\Delta\) represents the differentiation operator \(\Delta=\frac{d}{dt}\), \(\alpha>0\) is the fractional order, and the binomial term \(\left(\begin{array}{c}\alpha\\ j\end{array}\right)\) is defined as  
\[\left(\begin{array}{c}\alpha\\ j\end{array}\right)=\begin{cases}1,&\text{for }j=0,\\ \frac{\alpha(\alpha-1)\cdots(\alpha-j+1)}{j!},&\text{for }j>0.\end{cases} \tag{2}\]  
Define \(w_{j}^{\alpha}=(-1)^{j}\left(\begin{array}{c}\alpha\\ j\end{array}\right)\), when \(t_{0}=0\), to numerically evaluate the fractional derivative; the parameter \(h\) in Equation (1) is substituted with the sampling period, and as a result, the limit operation is dropped  
\[\Delta^{\alpha}f(t)=\frac{1}{h^{\alpha}}\sum_{j=0}^{\lfloor t/h\rfloor}w_{j}^ {\alpha}f(t-jh)+O(h). \tag{3}\]  
In the above equation, the error term \(O(h)\) is directly related to the length of the sampling interval. Therefore, to ensure that the approximation error is negligible, the sampling interval should be sufficiently short.  
Under the condition that \(f(t)\) is relaxed at \(t=0\) (\(f(t)=0\) for all \(t<0\)), the Laplace transform of \(\Delta^{\alpha}\) is given by \(\mathcal{L}\{\Delta^{\alpha}f(t)\}=s^{\alpha}F(s)\)[24]. Therefore, for the following fractional mathematical model,  
\[y(t)+a_{1}\Delta^{\gamma_{1}}y(t)+\cdots+a_{L}\Delta^{\gamma_{L}}y(t)=b_{0} \Delta^{\beta_{0}}u(t)+b_{1}\Delta^{\beta_{1}}u(t)+\cdots+b_{K}\Delta^{\beta_ {K}}u(t), \tag{4}\]  
a symbolic representation of the above system can be established using the transfer function  
\[G(s)=\frac{B(s)}{A(s)}=\frac{\sum_{i=0}^{K}b_{i}s^{\beta_{i}}}{1+\sum_{j=0}^{L }a_{j}s^{\gamma_{j}}}. \tag{5}\]  
Additionally, if \(G(s)\) is commensurate of order \(\alpha\), it can be rewritten as  
\[G(s)=\frac{\sum_{i=0}^{k}\bar{b}_{i}s^{i\alpha}}{1+\sum_{j=0}^{l}\bar{a}_{j}s^ {j\alpha}}, \tag{6}\]  
where \(k=\frac{\beta_{K}}{\alpha}\) and \(l=\frac{\gamma_{L}}{\alpha}\) are integers; for \(\forall i\in\{0,1,\cdots,k\}\) and \(\forall j\in\{0,1,\cdots,l\}\), we have [16]  
\[\begin{cases}\bar{b}_{i}=b_{i}&\text{if }\exists i\in\{0,1,\ldots,K\}\text{ such that }i\alpha=\beta_{K},\\ \bar{b}_{i}=0&\text{otherwise},\\ \bar{a}_{j}=a_{j}&\text{if }\exists j\in\{0,1,\ldots,L\}\text{ such that }j\alpha=\gamma_{K},\\ \bar{a}_{j}=0&\text{otherwise}.\end{cases} \tag{7}\]
## 2 Experimental Section
### SPME TWA Extractions  
A continuous flow system (Figure 3) was first used to analyze and to optimize THM standard solution conditions for TWA sampling. The system was composed of a mixing compartment, and a series of connecting glassware components with sampling ports positioned to hold SPME fibers. The glassware was connected together using heat shrink tubing. The flow system also had an inlet and an exit port whereby a solvent would run through. The flow was controlled using a Dynamax model SD-200 solvent delivery system (Varian Instruments, Mississauga, ON, Canada) connected to the inlet port, and the flow rate was set to 10.00 \(\pm\) 0.1 mL min\({}^{-1}\) at 25 \({}^{\circ}\)C \(\pm\) 1.0 \({}^{\circ}\)C. Sample flowed out of the exit port through a loop and back into in the sampling reservoir. Sampling of the flow system was achieved through the use of six sampling ports in the glassware where the six SPME fibers could be inserted and tested simultaneously. The fiber needles had the springs removed in order to enable the fibers to be extended or retracted into the needle casing to specific distances, and held there for the duration of the sampling period. The continuous flow system could be connected to separate reservoirs that contained different mixes of THM standards. Sampling rates of 1, 2, 3, and 5 days, different fiber retracted positions (5, 10, 15, 20, 25 and 30 mm for different diffusion sampling lengths) and where the fiber was fully extended were studied and optimized to give best results for the six THM standards. A 1-ppm (mg: L\({}^{-1}\)) concentration was used to assess and optimize conditions. A concentration range of 0.02, 0.05, 0.1, 0.2, 0.5, 1, 2 and 5 ppm of the six THM standards were used to establish the calibration curve for TWA analysis.  
Based upon optimized conditions found from the continuous flow system results, TWA sampling of THMs from real water samples was undertaken in two ways: (1) by (triplicate) TWA-SPME sampling of a collected water sample from sealed 40 mL vials using similar optimized HS-SPME conditions but with the optimized TWA sampling time and fiber retraction distance; and (2) by (triplicate) on-site sampling. For (2), each of the SPME fibers was positioned at the optimized retraction distance into their SPME needle casing (see Figure 2a). Triplicate fibers were then attached to a stand and placed into the pond. In both ponds, the fiber stand was positioned at the same pond point and water depth (1/3 m below water surface) where other water samples (1) were collected. After the optimized extraction time (2 days) the fiber stand was then retrieved and the fibers were taken for subsequent GC/MS analysis under optimized GC/MS conditions.