esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
The CSRZ achieved a significant improvement over RZ and MDRZ with the implementation of EOPM as shown in Figure 5. This is because the optical phase in a CSRZ signal is periodic at half the data rate and the interaction between the neighboring pulses is suppressed [20]. In other words, its carrier suppression and the \(\pi\) phase change between alternative pulses help to suppress the interference between the pulses [14]. This was previously observed and has been attributed to the fact that CSRZ is more tolerant to optical fiber nonlinearity [20; 22; 36]. On the other hand, it was reported in the literature that the performance of MDRZ in purely WDM systems outperformed the RZ modulation format [37]; however, in the proposed hybrid system using the EOPM module, the performance of the system based on the RZ format had better performance than the one based on the MDRZ format, as shown in Figures 3 and 4. The average BERs of channels 2, 8, and 13 at a transmitted power of 22 dBm for MDRZ were \(5.93\times 10^{-12}\), \(4.8\times 10^{-10}\), and \(2.1\times 10^{-12}\), and for RZ, they were \(9.82\times 10^{-14}\), \(2.75\times 10^{-10}\), and \(2.6\times 10^{-12}\), respectively. The EOPM module was used  
Figure 4: The Average BERs versus Transmitted Power for Four Random Channels (RZ). Figure 5: Comparison between Different Modulation Formats’ Performances for the Hybrid Optical CDMA/WDM System. to simultaneously modulate the signals' phase carried by DWDM channels to eradicate the intra-channel FWM effect. The eradication of intra-channel FWM occurs because its contributions coming from different pulses acquire a relative phase shift. In ref. [19] the optimum phase deviation of the EOPM module was set at \(2\pi/3\), which helped to destroy the interference between different intra-channel FWM contributions. Thus, the phase shift was responsible for reducing the intra- channel FWM effect, which in turn lowered the bit error. The effectiveness of the EOPM module was more significant in the case of the CSRZ modulation formats compared to RZ and MDRZ formats. These findings of the current study are consistent with those in ref. [20].
###### Abstract
We evaluate how sensitive the two methods are with respect to the starting points of the parameters and with what accuracy and precision these parameters can be estimated. In order to compare the two methods, we generate synthetic dielectric spectra by employing a single-pole Cole-Cole model, using data from the literature [24] as the true values for its parameters, and we use them as reference. The present work is an extended version of the conference paper [25]. In the previous work, only one set of intervals was considered for initial value estimations. Here, this is extended and completed by considering wider intervals and including also parameter \(\alpha\), which in [25] was practically fixed at the value 0.1. In addition, we also consider curve fitting applied to the Debye model in order to compare the two permittivity models as well. Finally, the fitting algorithms are equipped with proper constraints that allow the obtainment of algorithms with much better performance in terms of execution time and convergence. We consider first-order models to perform the comparison as they are a good trade-off between complexity and fitting capability. In [26], the dielectric properties of a blood plasma sample are fitted to single-pole, two-pole, and three-pole Cole-Cole models, and it was found that the single-pole model is sufficient to represent data with lower numbers of parameters and computational time. Furthermore, all blood glucose models we are aware of from the literature use Debye or Cole-Cole single-pole models.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
###### Abstract  
As rock fractures caused by micro-seismic events has potential safety hazards to underground workers, it is often necessary to accurately locate the micro-seismic source for hidden danger investigation. Micro-seismic data are generated in complex underground environments which are significantly affected by random noise. These data greatly influence subsequent micro-seismic source location, energy estimation, and disaster monitoring. In this paper, a new denoising method based on Time-Frequency Peak Filtering (TFPF) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is proposed for micro-seismic data. Micro-seismic data are decomposed into several Intrinsic Mode Functions (IMFs) by CEEMDAN. Then, discriminant factors are used to determine which IMF needs to be denoised by TFPF. Finally, the denoised result is reconstructed by inverse CEEMDAN. By comparing and analyzing different entropies, Fuzzy Entropy (FE) is selected as the best discriminant factor. The CEEMDAN-FE-TFPF denoising method can effectively avoid the influence of fixed window length of the conventional TFPF method. The effectiveness and superiority of this method are verified by experiments of synthetic and actual data.  
micro-seismic data; noise suppression; TFPF; CEEMDAN; entropy Article  
Random Noise Suppression Method of Micro-Seismic Based on CEEMDAN-FE-TFPF.
## 4 Direct Cooperation via In-Band Energy Level Patterns
### Advanced Frequency Selection  
The aforementioned cooperation techniques can be further enhanced by an advanced frequency selection mechanism. Wi-Fi systems already support DFS (Dynamic Frequency Selection) that can be used by adjacent and non-centrally coordinated APs to avoid interference. DFS is mandatory in the 5250-5350 MHz and 5470-5725 MHz Unlicensed National Information Infrastructure (U-NII) bands of unlicensed spectrum for radar avoidance [31]. Within 3 GPP, it has been agreed that a frequency selection functionality is an implementation issue and will not be part of the LTE specifications [32]. An advanced frequency selection mechanism will increase LTE fairness in the unlicensed spectrum and render it compliant with the regional regulations.  
Figure 10: LTE adjustable channel occupancy time and mute period.  
In the case of the cooperation technique that is described in Section 4.2, an eNB can sense the activity on different channels in the unlicensed spectrum. If an idle channel is available, it can decide to perform a new frequency selection and either start the transmission of the periodic synchronization pattern, if there is not already one, or it can be synchronized with another existing network that already periodically transmits a synchronization pattern. In order to avoid radio link failure among the eNB and the UEs that are attached to it, the eNB must notify the UEs about the new frequency that will be used, so they can perform the transition synchronously.  
The same frequency selection procedure can be adopted regarding the enhanced LTE LAA technique that is described in Section 4.3. An eNB operating in LTE LAA can sense multiple unlicensed channels in order to move to a less busy one. Similar to the first case, the eNB must notify the attached UEs about the new frequency and initiate the transition progress.
# Multidisciplinary Reliability Design Optimization Modeling Based on SysML
## 2 Related Works
### RBMDO
Uncertainty in engineering design has the following characteristics: first, there are many sources of uncertainty (many channels causing uncertainty generation), such as various uncertainties caused by dimensional errors, material properties, assembly errors, stress loads, etc., of products; second, there are various forms of uncertainty in the design process, such as aleatory uncertainty obeying statistical distribution, cognitive uncertainty with only partial fuzzy information, and time-varying uncertainty that considers time factors, etc. Unlike traditional deterministic design optimization, in RBMDO, continuous design variables and design parameters are considered to have stochastic, cognitive uncertainty or time-varying uncertainty. The design constraints consist of deterministic constraints and probabilistic reliability constraints. Under the condition that the reliability of the design constraint is greater than a given value, RBMDO is generally constructed as a problem of minimizing the total product cost or weight, etc. Reliability design optimization ensures that the reliability of the designed engineering system or product meets the given reliability requirement, thus reducing the probability of system or product failure to an acceptable level. A typical multidisciplinary reliability design optimization model with RBMDO containing stochastic uncertainty variables is shown in Equation (2) as an example [8]:  
\[\begin{array}{c}\underset{s.t. }{min}\ \ f(\mathbf{d}_{s},\mathbf{d}_{i},\mathbf{x}_{s}, \mathbf{x}_{i},\mathbf{p})\\ s.t.\ \Pr\Big{(}G^{(i)}(\mathbf{d}_{s},\mathbf{d}_{i},\mathbf{x}_{s},\mathbf{x}_{i},\mathbf{p},\mathbf{y}_{i })\geq 0\Big{)}\geq R_{i}\\ \ \ g^{(i)}(\mathbf{d}_{s},\mathbf{d}_{i},\mathbf{y}_{i})\geq 0\\ \ment of the discipline \(i\). \(g^{(i)}(\bullet)\) is the deterministic design constraint of the discipline \(i\). \(Pr\Big{(}G^{(i)}(\bullet)\geq 0\Big{)}\) is the probabilistic reliability non-failure model of the discipline \(i\), and \(Pr\Big{(}G^{(i)}(\bullet)\geq 0\Big{)}\geq R_{i}\) is the probabilistic reliability design constraint of the discipline \(i\). The reliability design optimization model with cognitive uncertainty and time-varying uncertainty is similar to Equation (2) and is not repeated here. From Equation (2), it can be seen that a typical multidisciplinary reliability design optimization problem includes optimization variables, optimization objectives, and optimization constraints, where optimization constraints are divided into two types: reliability constraints and deterministic constraints, and the difference between the two is whether the constraints contain uncertainty variables or not.
## 3 Results  
All performance metrics reported below refer to model evaluation on the independent test set, following an 80:20 train-test split with internal five-fold cross-validation on the training data.  
The random forest (RF) model was evaluated by accuracy, AUC, precision, recall, F1-score, specificity, and sensitivity.  
As summarized in Table 2, the RF model demonstrated optimal performance in predicting lymph node involvement using features extracted from specific sequences. Notable results included:  
* DWI nodule features: Accuracy of 67% and AUC of 0.83;
* T2-weighted PPAT features: Accuracy of 78% and AUC of 0.86;
* T2-weighted whole gland features: Accuracy of 78% and AUC of 0.97.  
When combining features from all three sequences for nodule evaluation, the accuracy increased to 88%, although the AUC was lower than that of individual sequences. However, integrating nodule features from all sequences with features derived from the T2-weighted whole gland and T2-weighted PPAT resulted in an overall accuracy of 89% and an AUC approaching 1.  
To rigorously compare model performance, [PERSON]'s test was employed to assess the sensitivity and specificity of the models by comparing the AUCs of their respective ROC curves. This non-parametric statistical test allowed for a robust evaluation of whether observed differences between models were statistically significant.  
The comparison of the best performances from nodule, whole gland, and PPAT analyses revealed that whole-gland evaluation had the greatest predictive impact, with a statistically significant difference (\(p\leq 0.05\)) when compared with nodule analysis alone. These findings highlight the importance of comprehensive gland assessment in predicting lymph node involvement in prostate cancer patients.
# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 5 Results  
### Performance Comparison  
As shown in Tables 3 and 4, entity prediction experiments are conducted on facts at future timestamps. The tables list the predictive performance of MSEN and the baseline models on ICEWS14 and ICEWS18 datasets. The results of baseline models in the table are from [15].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MRR** & **Hit@1** & **Hit@10** \\ \hline DistMult & 25.31 & 17.93 & 42.22 \\ ComplEx & 32.33 & 23.21 & 52.37 \\ RGCN & 28.14 & 19.43 & 46.02 \\ ConvE & 30.93 & 21.74 & 50.18 \\ RotatE & 27.53 & 18.60 & 47.62 \\ CyGNet & 37.65 & 27.43 & 57.90 \\ RE-NET & 39.86 & 30.11 & 58.21 \\ xERTE & 40.79 & 32.70 & 57.30 \\ TTfer & 41.73 & 32.74 & 58.44 \\ RE-GCN* & 41.99 & 32.93 & 61.92 \\ CENET & 41.30 & 32.58 & 58.22 \\ TiRGN* & 43.18 & 33.12 & 62.24 \\
**Our Method** & **46.68** & **35.29** & **68.96** \\ \hline \hline \end{tabular}  
* The mark
* in the table represents removing static information from models for fair comparison [15]. The best results are in bold.  
\end{table}
Table 3: Performance on the ICEWS14 dataset in terms of MRR (%), Hit@1 (%), and Hit@10 (%).  
In the experimental results, the MSEN model outperforms other baseline models. The results show the effectiveness of the MSEN model in predicting entities at future timestamps. Compared with TiRGN\({}^{\star}\), MSEN improves the MRR by 3.5 and 1.83 percentage points on ICEWS14 and ICEWS18, respectively.  
Probing deeper, we evaluated recall capabilities against TiRGN\({}^{\star}\) on ICEWS14. At recall@1, MSEN achieved 32.78%, while TiRGN\({}^{\star}\) achieved 34.88%. For recall@10, the models achieved near identical results, with MSEN at 61.98% versus 62.21% for TiRGN\({}^{\star}\). While marginally behind in recall@k, MSEN still demonstrates competitive retrieval ability.
## 2 Experiment Setup and Measurement of PD Data
### Fabrication of Artificial Defects in Epoxy Resins  
Epoxy resin is a general term for a large class of polymer materials. It is characterized by high plasticity and can be poured into differently shaped molds. Epoxy resins exhibit strong adhesion, low shrinkage, good chemical stability, and excellent mechanical and electrical insulation properties. As such, they are widely used in several fields, such as civil structure reinforcement, electronic component packaging, high-voltage insulation perfusion, waterproof engineering, adhesives, and industrial parts.  
Bisphenol A and polyoxypropylene diamine were used as the epoxy resin and curing agent, respectively, and were mixed at a ratio of 3:1. The mixture solidified after curing, allowing for the easy observation of internal changes. The mixture was poured into a square silicone mold to form epoxy resin samples with a length and width of 3.5 cm.  
The choice of a square shape for the mold offered some advantages. First, the square shape ensured that the image of the resin sample remained undistorted when viewed from the side, providing a clear and accurate representation of any defects. Second, the  
Figure 1: PD experimental setup of an epoxy resin with a void bottom.  
inner walls of the mold were designed to be smooth, which helped to obtain clean and well-defined PRPD patterns.  
Artificial defects (voids of 0.3 cm in epoxy resins) were fabricated using a controlled and deliberate process. Each sample included only one void. However, there still remained some very tiny bubbles that could be neglected. Schematic diagrams and photographs of the fabricated voids at different positions in the epoxy resin are shown in Figure 2a,b, respectively.  
Copper sheets were positioned at the top and bottom of the epoxy test samples to ensure proper connectivity of the electrodes with the lead wires on the high voltage (HV) and low voltage (LV) sides. As mentioned previously, these samples were immersed in a test oil tank during the experiment, and the entire setup was placed in an acrylic outer box, as shown in Figure 1.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 4 Preliminaries of the Security Protocol
### The Ring-LWE Encryption Scheme
#### 4.1.2 Key Generation  
Two polynomials \(r_{1}\) and \(r_{2}\) are sampled from \(\mathcal{X}_{\sigma}\) using a discrete Gaussian sampler. Then, compute  
\[\tilde{r}_{1}\gets NTT(r_{1}),\tilde{r}_{2}\gets NTT(r_{2}),\tilde{p} \leftarrow\tilde{r}_{1}-\tilde{a}*\tilde{r}_{2},\]  
where the Number Theoretic Transform \((NTT)\) corresponds to the Fast Fourier Transform \((FFT)\) when the primitive roots of unity are from a finite ring of integers instead of complex numbers.  
The private key is \(\tilde{r}_{2}\) and the public key is \((\tilde{a},\tilde{p})\).
## 4 Simulations and Results  
In this section both the accuracy and the reliability of the proposed technique are discussed for the reduction of sidelobe suppression of the OFDM signal. The performance comparison of the proposed technique has been done in terms of normalized power spectral density (PSD) with already existing techniques in this area. Several cases have been discussed on the basis of spectral white spaces and its bandwidth between spectral white spaces. Throughout the simulations, the number of samples \(M\) is taken as 501, while the number of frequencies \(N\) in Case I is taken as 16, in Cases II and IV as 32, while in Case III and Case V, it is taken as 16, 32, 64, and 128, respectively.
# Broadband Millimeter-Wave Front-End Module Design Considerations in FD-SOI CMOS vs. GaN HEMTs
## 1 Introduction
We, then, also compare our PAs with other FEMs designed in Si [24; 25; 26; 27; 28; 29; 30; 31] and GaN [32; 33; 34; 35; 36] technologies found in the literature, where [24; 25; 27; 29; 30; 35] are part of the SHF band, and [26; 28; 31; 32; 33; 34; 36] reach into the EHF band. The Si FEMs found in the literature commonly use device stacking along with power combining to reach greater Tx output power in both the SHF and EHF bands, but there are not many GaN FEMs that have been published, and none of them are seen to use device stacking like what we are reporting here in this work for GaN PAs. The paper is laid out as follows: Section 2 describes the materials and methods used to design, simulate, and/or measure the antenna arrays and PAs in our lab. Sections 3.1 and 3.2 present the simulation and/or measurement data we collected on our antenna and PA designs, and then in Section 3.3 we compare our work to others in the literature. In Section 4, we discuss the results, and then the work is concluded in Section 5.
## 2. Mature Technologies for Water and Wastewater Remediation
### Chlorination
#### 2.1.2. Chlorination Process  
Chlorine is a highly effective disinfectant, but its volatility leads to the production of DBPs (disinfection byproducts) when chlorine makes contact with organic matter. Furthermore, chlorine remains active in water for only 8 h after it is added. Chloramine, on the other hand, is a less effective disinfectant than chlorine. Hence, it is less reactive with organic matter and forms fewer DBPs. Chloramine remains in water for up to 8 days, which protects purified water from pathogens that may be introduced during the transportation process.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 3 Configuration Optimization
### Improved NSGA-III Algorithm  
The NSGA-III algorithm [22; 23] is an evolutionary optimization algorithm proposed by [PERSON] et al. to deal with multi-objective optimization problems. It improves upon the NSGA-II algorithm [24] by introducing a reference point mechanism, which retains  
Figure 3: Transferring configuration design.  
those individuals that are non-dominated and associated with the reference point. Compared to NSGA-II, the superiority of NSGA-III is mainly reflected in high-dimensional problems (objective function \(\geq\) 4), where the crowding distance sorting is not effective in maintaining population diversity. NSGA-III increases population diversity by directing the population towards a set of reference points that are uniformly distributed in the hyperplane.  
Although NSGA-III has improved population diversity compared with NSGA-II, there are still many problems when facing high-dimensional non-linear problems: (1) the traditional crossover has a weak search efficiency in high-dimensional problems; (2) although the reference-point mechanism can increase the diversity of the population, it sacrifices the speed of convergence; (3) the genetic algorithms are prone to exhibit \"premature convergence\" when applied to high-dimensional non-linear problems. Therefore, this paper improves the algorithm from three aspects according to the above problems of NSGA-III.
## 3 Advancements and Alternatives to Modal-Based Damage Identification
### Non-Modal Damage Sensitive Features
#### 3.3.1 Vibration Based Damage Sensitive Features
[PERSON] et al. [157] extended this concept through the incorporation of a cubic polynomial spline interpolation function, applied to the deformed shapes to extenuate deformations. This method allows spatial abnormalities to be detected without requiring a curvature calculation, which would innately contain errors via the double differentiation process. Higher interpolation errors indicate a greater likelihood of damage, which requires the IDDM to apply a probabilistic means of damagedetermination so that only interpolation errors that are greater than a pre-determined threshold are deemed as probable damage events. [PERSON] et al. [157] assessed the IDDM's performance against the MCM on a single span RC bridge under forced harmonic vibration subjected to incremental damage at various locations. The results showed that the IDDM is consistently capable of detecting, locating, and tracking the evolution of damage; however, its performance is dependent on the threshold value chosen and on sensor distribution. When compared to the MCM results, the IDDM fairs quite well. The MCM demonstrated good sensitivity to damage for the first two vibration modes, but became less accurate thereafter. This is most probably due to the requirement of a denser array of sensors for accurate modal curvatures at higher modes. The IDDM requires fewer sensors than the MCM by reliably detecting and locating damage. However, IDDM assumes that all sources of vibration will equally cause all locations to produce the same variation in interpolation error, which may not be suitable for all bridge applications.