esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Fuzzy Systems Based on Boolean Relations
## 5 Application Example of Architecture I: Nonlinear Plant Identification  
By the simplification procedure using [PERSON]'s algebra described in Sections 2 and 3, the compact structures are established in Section 4; therefore, this section shows the application of structure I for the identification of a nonlinear system.  
The identification process builds mathematical models of dynamic systems based on the real input observations, outputs, and disturbances. It also requires selecting the structure of the model [61; 62].  
The system given by Equation (50) is taken as a case study, which has been described in [22]. A stable functioning is guaranteed with \(u[n]\in[-2,2]\). The calculation of the training data is made using \(y[0]=0\), and \(y[1]=0\).  
\[y[n+1]=\frac{y[n](y[n-1]+2)(y[n]+2.5)}{8.5+y[n]^{2}+y[n-1]^{2}}+u[n] \tag{50}\]  
Figure 15 shows the architecture employed to identify the plant with one delay in the input and two for feedback; \(u\) represents the input while the output is \(y\) and \(z^{-1}\) represents a delay.
## 2 Related Work
### Flow-Based Attack Identification Methods  
Flow-based attack identification methods are mainly divided into flow statistical feature-based identification, flow raw feature-based identification, and flow raw data-based deep learning identification, depending on the features. They can capture the interaction pattern between hosts and mine the feature relation among packets. ZED-IDS [7] trained deep learning models based on statistical features generated by the CICFlowMeter to achieve the detection of potential zero-day attacks. FS-Net [8] used a recurrent neural network for feature learning based on the sequence of packet size in a flow. STIDM [13], on the other hand, classified attacks based on the sequence of packet size and time intervals using a combination of CNN and LSTM. Based on raw traffic, [PERSON] et al. [14] transformed the traffic into a two-dimensional matrix and used CNN to extract features automatically to mine the correlation features between different instances.  
However, the flow-based attack identification method has limitations. Because the representation of an attack behavior may be jointly determined by the features of multiple flows, focusing on a single flow only can make the feature representation less comprehensive and complete.
## 3 Experimental Study
### Signal Extraction  
The wire rope breakage and damage detection test bench is illustrated in Figure 6a. Rope buckles are created at both ends of the wire rope with broken wires and damage, and the wire rope is securely fastened to the test bench using the rope buckle. During the signal acquisition process, the tray is controlled to move along the guide rail, driving the sensor in the direction of the steel wire rope for damage detection. The sensor preprocesses the magnetic leakage signal collected by the circuit board and transmits it to the signal acquisition system. Figure 6b shows the signal acquisition system, which utilizes NI and LabVIEW to achieve real-time display and storage of the signal. Figure 7 shows the one-dimensional magnetic leakage signal of the steel wire rope collected by the focusing magnetic sensor.
# A DFB-SOA Based Optical Vector Network Analyzer for Characterization of Bandpass Optical Devices
## 2 Operating Principle
Following square-law detection at the photodiode, the resulting microwave photocurrent corresponding to the specific component can be extracted as  
\[i(\omega_{m})\propto C\cdot H(\omega_{c}-\omega_{m}) \tag{10}\]  
where \(C=4\pi^{2}A^{2}J_{0}(\beta)J_{-1}(\beta)G(\omega_{c})H(\omega_{c})\) represents a constant, derived from the multiplication of the magnitude response of the optical filter and the DFB-SOA at a specified frequency. As can be seen from Equation (10), to obtain the transfer function of the ODUT, it is possible to extract it from the detected microwave photocurrent, which is determined by  
\[H(\omega_{c}-\omega_{m})\propto i(\omega_{m})/C \tag{11}\]  
It can be clearly seen from Equation (11) that optical-to-electrical domain conversion response of the ODUT is characterized through frequency scanning utilizing an EVNA. The primary advantage of our novel OVNA lies in its capability to measure optical devices featuring a bandpass response, distinct from the traditional OSSB-based OVNA. Additionally, by mitigating the effects of adjacent high-order sideband beating, our novel OVNA system is capable of attaining measurements with enhanced precision.
# Optoelectronic Instrumentation and Measurement Strategies for Optical Chemical (Bio)Sensing
## 3 Spectroscopic Techniques
### Measurement of Luminescence Lifetimes
#### 3.3.2 Measurement of Lifetime in the Time Domain  
The measurement of lifetimes in the time domain consists of applying a pulse of light of very short duration to the sample, after which a decay curve is obtained that must be processed to obtain the lifetime. Analysis in the time domain is more complex than analysis in the frequency domain [45] due to the difficulty of extracting the time constant from an exponential setting [46]. One of the methods used for measuring lifetimes in the time domain is called rapid lifetime determination [RLD] [47], as shown in Figure 11.  
This method consists of measuring the areas \(A_{1}\) and \(A_{2}\) during the decay time of the luminescence and after the application of the excitation pulse. Assuming a monoexponential decay and identical length of the measuring windows, \(\Delta t\), the lifetime can be calculated by the expression:  
\[\tau=\Delta t/ln(A_{1}/A_{2}) \tag{10}\]  
where \(A_{1}\), \(A_{2}\) are the areas of intensity in the time increments. This scheme offers the advantage of eliminating the background luminescence, which is within the first 100 ns, before activating the CCD.
## 4 Experiments and Results
### Reconstruction of 2D NMR Spectra
Take the GB1 experiment as an example: With the parallel computing SLSMF, the time spent on reconstructing spectra only requires 4.6% of the computation of the SLS without parallelism. However, if the data dimension is too small, parallel computing is not recommended because it must pay for a certain time on switching the parallel pool, i.e., a mechanism used to control multiple processing cores in software. For example, for the reconstruction of the solid-state spectra, using the LRHMF only takes about 22 s, but it takes 26 s under parallel computing, whereas switching the parallel pool spends about 24 s. We also ran methods on the personal computer and the computation time, as reported in  
Figure 8: Peak intensity correlation between fully sampled spectrum and reconstructed spectra on the 2D \({}^{1}\)H-\({}^{15}\)N best-TROSY spectrum of ubiquitin. (**a-d**) are correlation plots of all peaks using LRHM, LRHMF, SLS, and SLSMF, respectively. (**e-h**) are correlation plots of low-intensity peaks using LRHM, SLS, and SLSMF, respectively. Figure 7: Peak intensity correlation between fully sampled spectrum and reconstructed spectra on the 2D HSQC of GB1. (**a-d**) are correlation plots of all peaks using LRHM, LRHMF, SLS, and SLSMF, respectively. (**e-h**) are correlation plots of low-intensity peaks using LRHM, SLS, and SLSMF, respectively. Figure 12. The calculation time with the personal computer also confirms that the proposed method can accelerate the reconstruction and can further reduce the time by parallel computing. Figure 11. The computation time of spectra reconstruction on a computational server using the LRHM, LRHMF, SLS, and SLSMF methods. **(a,b)** computation time of spectra reconstruction without and with parallel computing, respectively. Note: The switching on and off parallel pool takes an average of 24 s.  
Figure 10. The RLNEs versus times of different approaches. **(a–c)** are the RLNE curves of the 1D experiments marked with a dotted line in Figure 2(a), Figure 3(a), and Figure 4(a), respectively. Note: The point indicated by the arrow in the Figure is to update the outer subspace. Figure 9. Peak intensity correlation between fully sampled spectrum and reconstructed spectra of the solid NMR. **(a–d)** are correlation plots of all peaks using LRHM, LRHMF, SLS, and SLSMF, respectively. **(e–h)** are correlation plots of low-intensity peak using LRHM, SLS, and SLSMF, respectively. In the fast NUS NMR, high-fidelity reconstruction of the spectrum is most important, followed by the rest of other performances, such as the computation time. Our work is to design a new algorithm to reduce the computation time while achieving the best spectra. To emphasize our proposed method's advantages in both reconstruction quality and speed, in Table 5 we list the advantages and disadvantages of the comparison method.
# BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
## 2 Mathematical Model  
The orbits of maneuvering satellites will be altered because of the orbit maneuvering thrust, which cannot be expressed using the same set of orbital parameters. This problem could be solved completely if the orbit maneuvering thrust can be obtained or estimated, but it is difficult to be realized in real-time. Considering the advantages of SRIF, where the orbit could be estimated epoch-wisely, the orbit information is transferred to the next epoch through the state constraint equation. The systematic errors caused by orbit maneuvering thrust are thus absorbed by adjusting the prior covariance of the state constraint equation, and the processes are as follows.
## 4 Numerical Experimental Simulation and Analysis
### Application Testing  
In order to evaluate the effectiveness of the proposed G-Pre preconditioning algorithm in practical applications, a total of four static analysis models were selected in this paper; their detailed information is shown in Table 3. Figure 9 shows a visual schematic of each model, where the red arrows indicate the forces applied to the finite element model, and the black arrows indicate the fixed constraints of the model. It should be noted that while the scale of each model only represents the size of its basic constituent finite element mesh, there were also encrypted meshes within the different models that were smaller in size. A brief description of the different models is given below. Model 1 represents a simple cantilever beam with one end fixed and the other end subjected to a static load. Model 2 depicts a perforated plate structure with one end fixed and the other end subjected to a static tensile force aligned with the surface of the plate. Model 3 is a simple printed circuit board with two sides and a fixed through-hole; there is a static force acting on one corner of the board. Model 4 is a BGA circuit board with the bottom edge, holes, and two sides held stationary, and a static force acting on one side of the board.  
A comparison of solution times for the corresponding models is shown in Table 4. It can be observed that the characteristics of the G-Pre preconditioning algorithm were consistent with those analyzed in previous matrix experiments within the real simulation model. Firstly, it was evident that the G-Pre preconditioning algorithm improved the efficiency of solving the models. Furthermore, the optimization effect of the G-Pre preconditioning algorithm became increasingly prominent as the model size increased. Specifically, for Models 3 and 4, improvements of 1.29- and 2.41-fold, respectively, were achieved compared to ILU preconditioning. However, for small-scale simulation models, such as Model 1, the optimization effect brought about by the G-Pre preconditioning algorithm was not as significant. In fact, there was even a slight deterioration in solution efficiency  
Figure 8: Comparison of the accuracy of the results of solving different matrices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Model File Size** & **Nodes** & \
\begin{tabular}{c} **Matrix** \\ **Dimension** \\ \end{tabular} &
\begin{tabular}{c} **Non-Zero** \\ **Elements** \\ \end{tabular} & **Matrix File Size** \\ \hline Model 1 & 6 KB & 60 & 180 & 6298 & 190 KB \\ Model 2 & 965 KB & 10,863 & 32,589 & 1,996,613 & 67.4 MB \\ Model 3 & 20 MB & 463,874 & 532,146 & 38,501,924 & 1.34 GB \\ Model 4 & 205 MB & 3,648,656 & 4,824,972 & 371,365,956 & 13.6 GB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model information.  
when compared to ILU preconditioning. This suggests that the limitations of the G-Pre preconditioning algorithm in handling small-scale models may become more apparent in practical applications, highlighting an area for improvement in future research.
# Effect of Heat-Treatment Remediation on the Mechanical Behavior of Oil-Contaminated Soil
## 3 Results and Discussion
### Relationship between Pollutant Removal Percentage and Treatment Parameters  
According to Taiwan's regulations concerning soil and groundwater pollution remediation, the concentration of total petroleum hydrocarbons in soil must be below 1000 ppm. Hence, in this experiment, the removal percentage of 5000 ppm-contaminated soil specimens had to exceed 80%, while that of 10,000 ppm-contaminated soil specimens had to exceed 90% to meet regulatory standards. Figure 2a,b plots the removal percentage at various operating temperatures and times when 5000 ppm of gasoline or diesel was added. The results indicate that soil that contained 5000 ppm of contaminant must be treated at over 600 \({}^{\circ}\)C for 30 min to ensure that the removal percentage of gasoline or diesel reaches 80%. Figure 3a,b shows the results obtained for 10,000 ppm of gasoline or diesel. Although the removal percentage tended to increase with treatment time or temperature, a removal percentage of 90% could only be achieved at a temperature of 900 \({}^{\circ}\)C. [PERSON] et al. [21] used a fluidized bed incinerator to treat waste oil sludge, and the combustion efficiency was 98%-99%. Therefore, removal efficiency of more than 90% should be achieved by using high-temperature incineration to treat oil-contaminated soil. The removal efficiency was decided by treatment temperature and time. The results of this study also show that the removal efficiency increases with the operating temperature and time. In order to meet the standards of regulations, the operating temperature and time will be different for different oil-contaminated soil.  
Figure 2: \(Cont.\)  
Figure 3: \(Cont\).  
Figure 2: Removal percentage of 5000 ppm of pollutant with different treatment parameters. (**a**) Gasoline; (**b**) Diesel.
# Affordable Thin Lens Using Single Polarized Disparate Filter Arrays for Beyond 5G toward 6G
###### Abstract  
This paper proposes a novel design approach for a thin lens with the aim of overcoming fineness limits in the commercial millimeter wave printed circuit board (PCB) manufacturing process. The PCB manufacturing process typically does not allow the fabrication of metallic patterns with a gap and width of less than 100 \(\upmu\)m. This hampers expanding thin lens technology to 5G commercial applications, especially when such technology is considered for 60 GHz or higher frequency, which requires a finer gap and width of metallic traces. This paper proposes that problematic process conditions can be mitigated when a lens is designed by establishing single-polarized lumped element models where larger capacitance and inductance values can be obtained for the same patch and grid unit cells. While the proposed design technique is more advantageous at higher target frequencies, a 60 GHz application and a wireless backhaul system is selected because of a limited range of frequencies that can be measured by an available vector network analyzer. The required gap or width of metallic traces can be widened significantly by using the proposed single-polarized unit cells to acquire the same in-plane capacitance or inductance. This enables the lens operating at higher-frequency under the process limits in fabricable fine traces. Finally, the effectiveness of the simulated design procedure is demonstrated by fabricating a 60 GHz thin lens that can achieve a gain enhancement of 16 dB for a 4 x 4 patch antenna array with a gain of 16.5 dBi.  
lens; metasurface; millimeter wave antenna; polarization; spatial filter; transmit array Article
## 4 Conclusions and Implications  
According to the results of the conducted experiment and above analysis, the WSN adopted in this study is a convenient and useful tool to monitor the real-time environment conditions including temperature and humidity for eggs at consumers' home storage. Temperature, humidity and time of storage all have an influence on the freshness of eggs and there was no significant difference between n-3 PUFAs enriched eggs and conventional eggs. N-3 PUFAs enriched and conventional eggs stored under refrigeration may be kept for as long as approximately one month whereas stored at room temperature only 18 days. It can be concluded that to maintain the internal quality of eggs, refrigeration is strongly recommended for home storage and consumers in China should be educated about it.  
This research was conducted under the support of Research & Technology Innovation Team attached to Beijing Poultry Industry, China Modern Agriculture System. The authors would also wish to thank anonymous reviewers for their time and constructive comments.  
[PERSON] and [PERSON] designed the research and experiment. [PERSON] performed the experiment and analyzed the data. [PERSON] contributed to materials/analysis tools. [PERSON] and [PERSON] wrote the paper. [PERSON] contributed to the paper's modification and refinement.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Weight Loss**} & \multirow{2}{*}{**Loss Rate**} & \multicolumn{2}{c}{**HU \({}^{*}\)**} & \multicolumn{2}{c}{**VI \({}^{*}\)**} & \multicolumn{2}{c}{**Albumen**} & \multicolumn{1}{c}{**Her**} \\  & & & **Decline** & **YI \({}^{*}\) Decline** & **Date** & **Date** & **Date** & **Date** \\ \hline n-3 ref [(2)] & 2.5\({}^{\text{a}}\) & 4.2\% & 40.8\({}^{\text{a}}\) & 42.6\% & 0.2 \({}^{\text{a}}\) & 34.7\% & 0.9 \({}^{\text{a}}\) & 11.4\% \\ con ref [(2)] & 2.3\({}^{\text{a}}\) & 3.4\% & 38.4\({}^{\text{a}}\) & 42.4\% & 0.2 \({}^{\text{a}}\) & 40.0\% & 1.2 \({}^{\text{a}}\) & 14.2\% \\ n-3 room [(2)] & 3.8\({}^{\text{b}}\) & 6.2\% & 44.2\({}^{\text{b}}\) & 46.2\% & 0.3 \({}^{\text{b}}\) & 60.9\% & 1.0 \({}^{\text{a}}\).b & 12.1\% \\ con room [(2)] & 5.7\({}^{\text{b}}\) & 8.9\% & 42.2 \({}^{\text{a,b}}\) & 46.6\% & 0.3 \({}^{\text{b}}\) & 57.8\% & 1.3 \({}^{\text{b}}\) & 13.4\% \\ \hline \hline \end{tabular} \({}^{\text{a}}\) Means of 5 measurements; \({}^{\text{b}}\) The n-3 PUFAs enriched egg samples stored in refrigeration; \({}^{\text{b}}\) The conventional eggs samples stored in refrigeration; \({}^{\text{b}}\) The n-3 PUFAs enriched egg samples stored under room temperature; \({}^{\text{b}}\) The conventional egg samples stored under room temperature; Values with different letters (\({}^{\text{a,b}}\)) within the same column are significantly different (\(p<0.05\)); \({}^{\text{a}}\) See Table 2 for details.  
\end{table}
Table 4: The quality \({}^{\text{b}}\) changes of n-3 PUFAs enriched and conventional eggs under different conditions.
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.1 Traffic Engineering
For instance, if, for some data, the machine learning model predicts an erroneous output, then it can affect the load-balancing process negatively. However, if application policies were also involved, the effect of the erroneous knowledge could be reduced to some extent;
* Adaptive load-balancing: Adaptive load-balancing is the best approach suitable to be deployed in knowledge-defined networks, as it combines both static and dynamic load-balancing techniques to achieve optimum load distribution by considering present network status and application policies. For instance, in [447], the load status and dynamic weight of each controller are considered along with pre-defined load thresholds to adaptively achieve load-balancing. Some have used machine learning to predict link state and then used the predictions as weights to calculate the optimum path between network hosts [448]. A mechanism for load-balancing for distributed controllers that monitors the imbalance state of the entire network and uses resource consumption metrics for load-balancing to reduce communication among controllers is presented in [449]. The service-oriented load-balancing concept is a type of adaptive load-balancing scheme. In [450], data are collected in a cloud environment to measure the delay of packets, which are used to spread the workload equitably by comparing it with the round-robin scheduling policy of the applications. An application is used to classify services, and load is balanced with the objective of maximizing resource utilization and minimizing the response time of users, which is an adaptive load-balancing approach [451]. QoS Provisioning  
By allocating traffic based on QoS needs, applications may ensure that their QoS requirements are met [195]. Traffic that needs high bandwidth or low latency, for instance, might be automatically routed along pathways that have been optimized for those particular needs. Applications can be guaranteed to obtain the resources they require to operate well in this way. Due to the presence of a knowledge layer, knowledge about the current network conditions can be learned in real-time to provide the required resources to high-priority traffic, such as video streams [452]. A QoS-aware flow rule aggregation that considers both the flow rule capacity of the switches and the QoS requirements of applications has been utilized in IoT networks [225]. Some have classified network traffic into application-level QoS classes using machine learning, and a path is selected for routing those classified packets based on minimum average link occupancy times or maximum average path residual capacity [453].
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 3 Propagation Models
### 3 GPP Indoor Hotspot (InH) Model  
The 3 GPP channel modeling community carried out a study on channel models for frequencies up to 100 GHz, and it provides a path loss model for indoor environment in technical report TR 38.901 [51]. The report provides an indoor path loss model for two different types of conditions i.e., LoS and non-LoS (NLoS) condition, as given in Equations (10) and (11), respectively. The 3 GPP InH model consists of a distance and frequency dependent deterministic part and a random part that includes the impact of shadowing. In Equation (10), \(PL_{LoS,dB}\) is the path loss in \(dB\) for LoS condition, \(d_{3D,m}\) is the three dimensional distance between the two points in meters, \(f_{c,GHz}\) is the frequency of operation in \(GHz\), and \(\zeta_{LoS,dB}\) is the shadowing/slow fading factor in \(dB\) under the LoS condition, and the shadowing factor in LoS is modelled as a Gaussian random variable with  
Figure 2: Path loss for outdoor device-to-device (D2D) communication with different location percentage.  
zero mean and standard deviation \(\sigma_{LoS}\), where \(\sigma_{LoS}=3\) for \(1m\geq d_{3D,m}\leq 150m\). Moreover, in Equation (11), \(PL_{NLoS,AB}\) and \(\zeta_{NLoS,AB}\) are the path loss and shadowing factor in \(dB\) for NLoS condition, respectively, and \(\zeta_{NLoS,AB}\) is modelled as a Gaussian random variable with zero mean and standard deviation \(\sigma_{NLoS}\), where \(\sigma_{NLoS}=8.03\) for \(1m\geq d_{3D,m}\leq 150m\).  
\[PL_{LoS,AB}=32.4+17.3\log_{10}(d_{3D,m})+20\log_{10}(f_{c,GHz})+\zeta_{LoS,AB} \tag{10}\]  
\[PL_{NLoS,AB}=\max(PL_{LoS,AB},17.3+38.3\log_{10}(d_{3D,m})+24.9\log_{10}(f_{c, GHz})+\zeta_{NLoS,AB}) \tag{11}\]  
The 3 GPP report defines two types of indoor environments i.e., open and mixed indoor types, and the LoS probability is defined for each indoor environment types, as follows: