esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Algorithm Design
### Establishment of Profit Function
#### 3.2.2 Price Function Improvement  
In the process of allocating spectrum for SUs, the allocated spectrum is equivalent to the goods in the competitive market. Therefore, the SUs will certainly need to spend the appropriate price. The traditional price function is expressed as follows [8]:  
\[c=\sum_{i=1}^{N}b_{i} \tag{7}\]  
In Equation (7), the price function entirely depends on the size of the total allocated spectrum of the SUs and it does not reflect the willingness of DC to share the spectrum with SUs of different priorities. In cognitive radio systems, SUs determines whether the data can be transmitted or not through detecting the relevant spectrum. Once the PU needs to use the spectrum, the SU must provide the spectrum immediately to avoid interference of PU, which may affect the PU communication. However, the traditional price function does not take this into consideration. SUs with different detection capabilities do not have priorities, which will affect the enthusiasm of SUs and weaken their detection capabilities. Subsequently, the detection performance of the whole system will be greatly reduced.  
In order to differentiate SUs, a new price function is proposed:  
\[c_{i}=b_{i}\frac{2}{1+e^{-\alpha(\frac{1}{\overline{\gamma}_{i}}-1)}}+\sum_{ j=1,j\
eq i}^{N}b_{j} \tag{8}\]The price function is based on the sigmoid function [16]. In Equation (8), we used \(w_{i}(w_{i}\in[0,1])\) to represent the detection capability of the SU in submitting the test results correctly at the DC. For example, \(w_{i}=1\) represents that the SU has the best detection capability and the test results are absolutely correct, which has the highest priority. At this point, the price function is reduced to the traditional price function. When the detection capability reduces, the priority level of SU will decrease, which will lead to an increase in the price of the allocated spectrum. When the detection capability of \(SU_{i}\) reduces to a certain extent, it will be marked as a malicious user by the DC and the price function reaches the maximum. Finally, \(\alpha\) represents the evaluation criteria for the SU's price and credibility.  
Equations (6) and (8) are substituted into Equation (2) to obtain a new profit function:  
\[U_{i}=\sigma_{i}\ln(1+r_{i}k_{i}b_{i})-b_{i}[\frac{2}{1+e^{-\alpha(\frac{1}{r_{ i}}-1)}}b_{i}+\sum\limits_{j=1,j\
eq i}^{N}b_{j}]-v(\frac{1}{2}b_{i}^{2}+b_{i} \sum\limits_{j=1,j\
eq i}^{N}b_{j}) \tag{9}\]  
The first item represents the revenue generated by \(SU_{i}\) by transmitting data traffic through the allocated bandwidth. The second item represents the cost of using the allocated spectrum for the \(SU_{i}\). The third item reflects the degree of interaction between the SUs.
## 3 Method
### Pose Refinement Process
#### 3.1.3 Rule-Based Outlier Detection  
The algorithms in Figure 4 are designed for rule-based outlier detection, specifically tailored to the unique features of a golf swing. The coordinate used in this paper is (0, 0) at the top left, with the y-value increasing as you go down, and the x-value increasing as you go to the right. At impact, the ball flies towards the upper right. Algorithm (a), termed the 'Inversion Detector', identifies instances where the left and right pose coordinates are swapped. Throughout the entire swing motion, the x-coordinate of the right ankle should never exceed that of the left ankle. Based on this observation, the Inversion Detector flags any instance where the right ankle's x-coordinate surpasses the left ankle's x-coordinate. Similarly, algorithm (c) identifies outliers in the lower body by examining the distance between the two ankles. If this distance becomes less than half of the expected separation, the swing is flagged as having an anomaly. Lastly, algorithm (d) focuses on wrist coordinates. A golf swing video shows a motion of holding a golf club with both hands from the beginning to the end of the swing, raising it clockwise, and then swinging it counterclockwise towards the ball. During the golf swing, both hands are always holding the golf club. Since the distance between the two wrists remains constant throughout the swing, any deviation in this distance range is considered an error, and the swing is flaggedaccordingly by this detector. The flags generated by algorithm (a) are addressed by directly converting the left and right joint coordinates, and the flags generated by algorithms (c) and (d) are deleted.
## 6 Performance Analysis  
In this section we evaluate the performance of SLP and SLP-E in terms of communication cost, computational cost, security analysis, and communication cost _vs._ security.
## 4 Experiments
### Quantitative Results  
We compared our method with a variety of state-of-the-art (SOTA) methods, and the results are shown in Table 2. Specifically, these methods can be divided into the following types. (1) _Output regularization methods._ Since self-distillation can be interpreted as a regularization of the output [15], the proposed CSD is compared with some output regularization methods. Among them, AdaCos [58] and Virtual-softmax [59] maximize the angular margins by adding a cosine distance constraint and an additional virtual class, respectively. Maximum-entropy [60] regularizes the entropy to maximize the entropy of the predicted distribution. The results show that our methods, as well as the other inter-sample self-distillation methods, outperform the baselines on all five datasets, mainly because of the inter-class relationship introduced by these methods through self-distillation. Moreover, the introduced inter-class relations are dynamically optimized and gradually approach the true data distribution during training, which greatly helps to improve the generalizability of the model. (2) _Generalized self-distillation methods._ A few of the most recent state-of-the-art models are compared here. Data distortion-guided self-distillation (DDGSD) [36] enforces consistent output between different data-enhanced versions. Be your own teacher (BYOT) [35] transforms the knowledge in the deeper layers of the network into shallower ones. DDGSD outperforms the output regularization method on TinyImageNet and MIT67, but it does not significantly improve the performance on the other three datasets. All the inter-sample self-distillation methods outperformed it, in contrast to inter-sample self-distillation. Although theye methods also tap inter-class relations, what they lack is a constraint on intra-class variation. (3) _Inter-sample self-distillation methods._ The existing two-sample self-distillation methods contribute significantly to image classification. CS-KD [15] randomly samples a batch of samples with the same label as the teacher in each iteration during training. BAKE [16] samples multiple batches of samples with the same label in each iteration and
# Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
## 2 Theoretical Background and the Related Work
### Computation of Criteria Weights Based on Entropy Measure  
To determine the criteria weights, we have used the entropy weighted approach. The entropy weighted approach measures the capacity of each criterion to contain decision information in order to estimate the relative importance of characteristics. The amount of entropy value reflects how unpredictable a message is. One could investigate the work of [PERSON] [48] and [PERSON] [49] for additional information.  
If \(\Pi=\left(\pi_{ij}\right)_{m\times p_{i}}\) is the decision matrix and \(w=(w_{1},w_{2},\ldots,w_{n})\) be the weight vector, and \(0\leq w_{j}\leq 1\) and \(\sum w_{j}=1\) are in relation to the \(m\) alternatives and \(n\) criteria then the weight \(w_{j}\), \(j=1,2,\ldots,n\) can be obtained as follows:  
Step 1: calculate \(\Omega_{j}=-\frac{1}{\log(m)}\underset{i=1}{\overset{m}{\underset{1}{\sum}} }p_{ij}\log(p_{ij})\), \(j=1,2,\ldots,n\) where \(p_{ij}=\frac{\pi_{ij}}{\underset{i=1}{\overset{m}{\underset{i=1}{\sum}}}\pi_{ ij}}\).  
Here, it is stated that \(\underset{p_{ij}\to 0}{\lim}p_{ij}\log p_{ij}\to 0\) ;  
Step 2: calculate \(\Psi_{j}=1-\Omega_{j}\), \(j=1,2,\ldots,n\);  
Step 3: calculate \(w_{j}=\frac{\Psi_{j}}{\underset{j=1}{\overset{m}{\underset{1}{\sum}}}\Psi_{ j}}\), \(j=1,2,\ldots,n\).
## 4 Discussion
#### Limitations  
Readers should consider some important limitations when interpreting the study findings. First, we acknowledge the potential for selection bias, since we were not able to make contact with phone calls before mailing the initial survey, which would have allowed us to minimize the exclusion of eligible participants. However, an online survey was used to collect data as it was the most realistic and feasible method during the COVID-19 pandemic. Online surveys could be administered in a time-efficient manner, minimizing the period it took to get a survey into the field and for data collection. In addition, respondents could answer at a convenient time for themselves. Second, we cannot assume that our study population is representative of the possible over-representation of health-conscious individuals and those more informed or concerned about the outbreak. However, students are, on average, more homogeneous than non-student participants [32], and we enrolled a very large sample of participants. Third, our study was a cross-sectional design rather than longitudinal, thus, we are unable to estimate changes in risk perception and practices over time. In addition, our study was restricted to university students from one region, and therefore, one must be cautious in interpreting the results as generalizable for students in the whole country. However, it is reasonable to suppose that the findings of the study may be representative of the Southern regions of Italy. Moreover, there is a possibility of social desirability bias on the adherence to NPIs, where the respondents may give more favorable responses about their practice of public preventive behavior. This bias was limited by guaranteeing participants that their responses would be anonymous and could not be traced back to them. Fourth, we did not collect data on students' living situations, and this missing information could have affected the adherence to NPIs, e.g., for students cohabiting with individuals with a weakened immune system or underlying medical conditions. Finally, we emphasize the aim of our study was descriptive, making the relationship between variables and outcomes of interest more tentative and we caution readers against drawing causal conclusions regarding the observed differences. However, this was not a primary goal of the study, since we aimed to assess knowledge about, the risk perception level of, and adherence to preventive and control measures for, COVID-19, among university students.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 3 Satellite Signals Accessible and Decodable for Educational Purposes  
Numerous satellites have been launched into space, each allocated to transmit data from sensors on different frequencies. Some of these data are available for worldwide reception and decoding. This research paper has compiled the key characteristics of signals from certain satellites that are easily accessible and decodable, making them suitable for educational purposes, and considering only satellites that downlink in the V, U, and L-band frequency ranges. This is because these frequencies are easier to find receivers for, easier to construct antennas for, and require less expensive equipment compared to receiving signals in the S-band and X-band, as shown in Figure 3 and Table 1.  
Figure 2: The ground stations and space classrooms: (**a**) space classroom, (**b**) control station in the space classroom, (**c**) ground stations for learning components.
## 3 Results
### Passive EQ--Distributed Parameters  
A solution to replace the lumped elements in the EQ is the use of transmission lines equivalent to the components. Some solutions were proposed [15; 16; 17; 18; 19], to include microstrip-based structures in the EQ form, due to size reduction in the mainline or wideband frequency response. Solutions for gaining EQ based on ceramic substrate with microstrip stepped impedance resonators also increase the bandwidth and input/output return loss as presented in [20]. If the resonant structure is replaced in the EQ with a composite right-/left-handed (CRLH) structure, a reduced size should be obtained as a shorter CRLH can realize the same resonant frequency compared to right-handed structure, in the traditional EQ [21].  
Solutions presented include an open stub in an RL EQ series structure [15]. The RL EQ placed in the middle of the microstrip line will perform the high-pass filtering. At low frequency, the inductor's impedance has lower value comparing to the termination resistor, but its impedance will dominate at high frequency [22]. This method can greatly alleviate the frequency dependent loss, transform the IL into a flat curve, and though reduce the ISI. Before the series resistance of the RL network in [15], it is proposed to connect an open  
Figure 24: Waveforms at the input and output: (**a**) RC EQ (**b**) RLC EQ and (**c**) Bridge T RLC.  
stub that will be equivalent to a LC-series resonator to shunt the equivalent reactance to the ground. The schematic is represented in Figure 25a. The open stub has to make the high-pass effect of EQ sharper, so the frequency response of the transmission line can rise earlier than in RL EQ [15].  
A new variant of this design is proposed below in Figure 25b. The IL at 10 GHz is two times lower comparing to the individual trace and the eye diagram is represented in Figure 25c. Dimensions of the transmission lines that replace the LC resonator equivalent to the open stub can be deduced via synthesis process for the chosen working frequency. Another solution may be suited from an optimization process. The values for widths and lengths of the lines are in Table 2.  
Fitting the input and output impedances of the circuit can further improve this design. The designed circuit has a return loss at 10 GHz of \(-\)53 dB, so better performance comparing to the purely lumped EQs. Furthermore, the EM analysis may give another approach and new perspectives for this network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transmission Line** & **TL1** & **TL2** \\ \hline Length (mm) & 5 & 4.43 \\ Width (mm) & 0.43 & 1.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensioning the open stub EQ.  
Figure 25: Open stub EQ: (**a**) splitted line, (**b**) pre-EQ with open stub, and (**c**) eye diagram—in blue is represented the eye diagram for the proposed EQ.
## 4 Conclusions  
The coating thickness in multilayer nitride was measured as 1.085 \(\upmu\)m, surface roughness as Ra = 1.327 \(\upmu\)m, and maximum roughness value as Rz = 6.43 \(\upmu\)m.  
For AA6063 and AZ91, in the Cr, CrN, TiCrN, and TiCrCN multilayer nitride coating, high-intensity diffraction peaks (211) were detected for Cr, (111) and (200) diffraction peaks were detected for CrN coating, (111), (200), and (222) diffraction peaks were detected for TiN, and (200) and (311) diffraction peaks were detected for TiCN.  
For the AZ91 base material, tensile and fatigue cracks started in notches, voids, or cracks on the surface. Cracks started from surface roughness in nitride coatings and spread and progressed in a similar way to the base material. With the multilayer hard nitride coating that fills the defects, notches, and gaps on the surface of the base material with low surface roughness, the tensile strength also increased in parallel with the fatigue strength. The fatigue limit value of the AZ91 base material increased from 69.09 MPa to 92.47 MPa. The multilayer nitride coating increased the tensile strength value of the base material from 137.89 MPa to 139.65 MPa.  
In the tensile and fatigue tests of the multilayer AA6063 nitride-coated AA6063 alloy, the crack initiation probably started from the coating-aluminum layer interface due to low adhesion and spread rapidly towards the aluminum layer after consuming the coating. With the nitride coating, the fatigue strength value of the AA6063 base material decreased from 79.71 MPa to 71.9 MPa and the tensile strength value from 129.35 MPa to 118.16 MPa.  
Consequently, although the Cr/CrN/TiCrN/TiCrCN multilayer coating increased the tensile and fatigue strength of the AZ91 Mg alloy, it reduced the tensile and fatigue strength of the AA6063 Al alloy. For the AZ91 alloy, multilayer nitride coatings represent an appropriate method to increase tensile and fatigue strength.  
Methodology, S.S. and F.K.; Investigation, R.Y. and F.K.; Resources, S.S.; Writing--original draft, R.Y.; Writing--review & editing, S.S. and F.K.; Supervision, R.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
Figure 9: Tensile fracture surface images of the specimens.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Dynamic Multi-Population Mutation Architecture-Based Equilibrium Optimizer (DMMAEO)
### Computational Complexity of D MMAEO  
For evaluating the runtime of algorithm, the computational complexity is employed as a vital metric in this paper. It encompasses two primary categories: a non-polynomial order and polynomial order. Comparatively, the non-polynomial order is regarded as inefficient, whereas polynomial order is regarded as efficient. This is due to the fact that the complexity of the non-polynomial order algorithm increases dramatically with the increase in the scale of the optimization problem. As a common method used by many researchers [29; 30; 31], big-O notation is adopted to assess the computational complexity.  
Figure 1: Flowchart of D MMAEO.  
The components of population initialization, equilibrium pool generation, particle concentration updating, and particle concentration evaluation determine the total computational complexity of EO. The population initialization needs the complexity \(O(ND)\); the fast sorting approach is adopted by the equilibrium pool generation process, whose worst-case complexity is \(O(g_{\max}N^{2})\); the complexity involved in particle concentration updating process is \(O(g_{\max}ND)\); and the complexity involved in particle concentration evaluation is \(O(g_{\max}N)\). Hence, EO has the complexity \(O(g_{\max}(N^{2}+ND))\). The DMMAEO additionally includes a dynamic multi-population guidance mechanism, a dynamic Gaussian mutation-based sub-population concentration updating mechanism, and a dynamic Cauchy mutation-based sub-population equilibrium candidate generation mechanism. The fast sorting method is also adopted by the dynamic multi-population guidance mechanism, which results in the worst-case complexity \(O(g_{\max}N^{2})\); the dynamic Gaussian mutation-based sub-population concentration updating mechanism requires complexity \(O(g_{\max}ND)\); and the dynamic Cauchy mutation-based sub-population equilibrium candidate generation mechanism requires complexity \(O(g_{\max}ND)\). According to the above results, the total computational complexity for DMMAEO is \(O(g_{\max}(N^{2}+ND))\).  
To summarize, the proposed DMMAEO and the canonical EO have the same computational complexity. This result indicates that the proposed DMMAEO does not introduce additional complexity to the canonical EO and is an efficient algorithm with polynomial order complexity.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 3 Hardware Design of RFID Reader/Router
### Hardware Design of Wireless RFID Reader/Router  
#### 3.2.1 Circuit Design of Radio Frequency (RF) Module  
RF communication circuit consists of micro controller and RF chip. Its main function is to communicate with the electronic tags for completing the exchange of the tag information. The core chip of RF module is Philips MF RC522. Electronic tags are M1 cards produced by Philips. Figure 4a shows the RF communication module circuit.  
Figure 3: Block diagram of the RFID reader/router.  
Figure 2: Communication structure of the proposed hybrid system.  
As shown in Figure 4a, the RF module transmits carrier energy signal which is 13.56 MHz and modulated by envelope signal to the antenna module through pins TX1 and TX2. The antenna is driven by an Electro Magnetic Compatibility (EMC) filter circuit consisted of L1, L2, C11, and C12, and a matching circuit composed of C5-C10. In the receiving circuit, the DC input voltage of the RX pin is stabilized at the pin VMID by C3 and R2, and its AC input voltage can be adjusted by R3 and C4.  
Figure 4: Schematic of the RFID reader/router (**a**) RFID reader system communication module and (**b**) ZigBee core control circuit.
## 3 Experimental Results and Discussion
### Pile-Soil Stress Ratio  
The variation curve of the pile-soil stress ratio with load is shown in Figure 5. In the first stage load, the pile-soil stress ratios of S1/S2 and S1/S6 are about 3.5, and the pile-soil stress ratio of S1/S8 is 6.1. The difference in pile-soil stress ratio is 2.5, indicating that S8 is subjected to less stress in the soil compared to S2 and S6. This may be due to the fact that S8 is minimally constrained by the piles, and a spatial soil arching effect occurs, causing it to settle more, which in turn contributes less to the upper loads and reduces stress. On the other hand, S2 and S6 are constrained by the piles on both sides, and the plane soil arch effect is weakened. As the load increases to 41.0 kPa, the pile-soil stress ratios of S1/S2 and S1/S6 are approximately 2.2 and 2.8, and the pile-soil stress ratio of S1/S8 is about 4.6. The stress ratio difference between pile and soil is reduced to 1.5. With the increase in load, the effect of the plane soil arch and the space soil arch is weakened. The reason is that the soil on the top of the pile is compressed, the soil particles are wedged tightly with each other, and the soil arch is formed within a certain range. When the soil arch effect reaches its maximum, the load is still increasing, resulting in the range of the arch foot increasing. At the same time, the soil between piles is continuously compacted, and the load borne by it gradually increases; thus, the pile-soil stress ratio decreases.  
Figure 4: Axial stress of pile; (**a**) P1. (**b**) The pile of P1–P5 under the 41.0 kPa load.  
As shown in Figure 5, the pile-soil stress ratio of S1/S2 in the transverse of the subgrade decreases from 3.5 with the increasing load and finally stabilizes at 2.1. The pile-soil stress ratio of S1/S6 in the longitudinal section of the subgrade slowly and continuously decreases from 3.5 to 2.7. The transverse pile-soil stress ratio is always smaller than the longitudinal pile-soil stress ratio, which indicates that the trapezoidal section distributes the upper loads to both sides of the embankment better. The pile-soil stress ratio for S5/S4 appears to be less than 1 due to the loading location, and S5 is less stressed due to the lack of direct-acting loading. Therefore, in the design of railway and highway foundations, zoning reinforcement of the GRPS embankment can be considered depending on the location and area of the upper loading.