esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A combination of far-field electrospinningning (FFES) and free-radical polymerization has been used to fabricate coated electrospun polymer fiber mats as a new type of biosensor platform. Poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) electrospun fibers were dip-coated with different compositions of poly methyl methacrylate-co-methacrylic acid (poly(MMA-co-MAA)). This synergistic approach utilizes large specific surface area of PHBV fibers and co-polymer coatings that feature an optimum concentration of surface carboxyl (-COOH) groups. The platform surface morphology, porosity and tunable hydrophobicity enhance biomolecular interactions via plurality of molecular forces. These customized fiber mats have been integrated into a newly designed 96-well plate called an \"intrant enzyme-linked immunosorbent assay\" or i-ELISA. I-ELISA allows colorimetric sandwich assay to be carried out without any modifications or additional steps in ELISA methodology. By introducing the fiber mats in fabrication of i-ELISA via extensions on the lid, we address some of the limitations of the previous designs while demonstrating an enhanced signal intensity up to 12 times higher than that of conventional assays. With improved sensitivity, specificity and accuracy in the detection of dengue virus, i-ELISA has proven to be a reliable platform for biomolecular recognition. The proposed fiber mat-assisted well plate in this study holds great potential as a universal approach for integration of different types of fiber mats with pre-designed specific properties in order to enhance the detection sensitivity of the assay.  
Bio-analytical assay; electrospun fiber mats; fiber-based analytical platform; protein immobilization; surface functional groups +
Footnote †: journal: Applied Physics Letters
## 3 Materials and Methods
### Addressing Imbalanced Data Classification
#### 3.3.1 Rationale for the Choice of Ensemble Classifiers
This shortcoming can originate from the complexity of the classifiers and may be analyzed by means of the bias-variance decomposition of the classification error [50; 51]. Although there are different definitions and measures of the bias and variance concepts, bias can be generally defined as a measure of the difference between the true and predicted values, while variance is the variability of the predictions of the classifier. Following the previous notation, if \(D\) is a classifier randomly chosen, \(X\) is a point in the feature space and \(\omega\in\Omega\) is the class label of \(X\), the posterior probabilities for the \(c\) classes given \(X\) are \(P(\omega_{i}\left|X\right)\) and the probabilities for any possible classifier \(D\) are \(P_{D}(\omega_{i}\left|X\right)\), \(i=1,\ldots,c\). Taking into account both probabilities, bias measures are based on comparisons between true not optimal to classify the examples; however, high variance can cause overfitting. Some causes of the variance are noise in the data, train sample and randomization. In imbalanced datasets, the noise in examples of the minority class has a great impact on the induced models and can be the cause of overfitting. As such, in these cases it is important to resort to methods that avoid this problem. Ensemble classifiers can also suffer overfitting because they make use of sampling. Several studies about the behavior of ensembles regarding bias and variance have been carried out, and most of them agree on the fact that Bagging reduces variance without increasing bias, which is usually low, too [48; 50; 51; 52; 53]. The same studies have found that boosting behaves differently over the course of its iterations. Bias is reduced during the first iterations, while the variance is mainly reduced during the last steps. AdaBoost is good at avoiding overfitting problems and reducing errors, even though the complexity of the induced classifiers gradually increases. Boosting and AdaBoost might lose performance on noisy data, especially for small datasets; however, bagging does not have this problem. Random forest can benefit from the advantages of bagging due to their common characteristics. On the other hand, it is admitted that random forest is relatively robust to outliers and noise. This is in part due to the way it generates the samples from both the feature set and the dataset. All these reasons have guided the proposal presented in this work, which combines the use of ensembles and dataset sampling.
# Seismic Resilience Evolution of Shield Tunnel with Structure Degradation
###### Abstract  
The structural performance of shield tunnel structures is highly susceptible to degradation under complex environmental loads, with the most common manifestation being bolt preload loss. In this study, a shield tunnel numerical simulation model was established to analyze the seismic response of shield tunnels with varying degrees of bolt preload loss. Firstly, the deformation patterns of shield tunnel structures under seismic loads were analyzed. Subsequently, ellipticity and joint opening were selected as seismic resilience assessment indicators based on the mechanical response. A seismic resilience assessment model was then established, including three states: normal state, affected state, and recovered state. The results show a direct relationship between the recovery capacity of tunnel structures and the initial performance of the lining structure, as well as the magnitude of the load. The lower the degree of structure degradation, the greater the structural recovery capacity. Additionally, there is a positive correlation between residual deformation and the initial performance loss of shield tunnel structures, as well as the intensity of seismic loads. This study contributes to enriching the theoretical framework for the seismic resilience assessment of shield tunnels, which have significant implications and provide valuable references for engineering safety.  
shield tunnel; resilience; seismic load; structural performance +
Footnote †: journal: Applied Sciences
## 4 Discussion
### The Interplay of IEQ, Energy Use, and Environmental Impacts
By enhancing their responsiveness to actual usage, buildings can optimize their energy efficiency while maintaining IEQ. Addressing this performance gap is essential for aligning energy use with the sustainability goals identified in the graph, ultimately fostering a more efficient and comfortable building environment. To bridge this performance gap, it is essential to implement more sophisticated control systems capable of dynamic adjustment to real-time occupancy and environmental conditions. Implementing a more refined Building Management System (BMS), besides the existing BEMS, with capabilities such as demand-controlled ventilation and zoned heating could significantly enhance its responsiveness [92; 93]. These systems can detect occupancy using sensors and adjust the energy usage accordingly, ensuring that only occupied areas receive heating, thereby optimizing energy efficiency. Additionally, integrating smart technologies, such as AI-driven analytics, within the BMS could enhance its predictive capabilities further, allowing the system to anticipate occupancy patterns and adjust its settings proactively [4; 92; 94; 93]. This would not only reduce unnecessary energy use but also extend the lifespan of the building's technical infrastructure. Moreover, fostering collaboration between the buildings' managers, occupants, and system designers would ensure that the control strategies aligned closely with the actual user behavior, further minimizing the discrepancies between the predicted and actual energy use. By embracing these advanced strategies and engaging in continuous monitoring, educational institutions can significantly close performance gaps, both achieving cost efficiency and reducing their environmental impact. Additionally, sustainable practices in educational buildings extend beyond immediate benefits. They set a precedent for environmental stewardship, influencing students and staff to adopt sustainable habits in their daily lives. Through a commitment to a comprehensive perspective that prioritizes the interconnectedness of energy, the environment, and occupants' health, educational facilities can become buildingsthat not only meet the performance targets but also foster thriving learning environments. This balanced approach will result in sustainable, resilient buildings that serve their users effectively while minimizing their ecological footprint.
## 2 Emerging Applications of 6G
### Video Streaming (4K, 8K)  
The 4K and 8K technologies were introduced as means of obtaining better-quality video output. They are enhanced video streaming standards compared to existing video streaming standards such as 720p and 1080p. For example, 1080p video supports 1920 \(\times\) 1080 pixels, while 4K supports a 4096 \(\times\) 2160 pixels resolution and 8K supports a 4-fold higher resolution compared to 4K. Nowadays, services such as Netflix, YouTube, AR/VR, and online gaming use 4K and 8K videos, as they provide better quality of service and experience for the users. With the appropriate encoding mechanisms, 4K and 8K can ensure not only high-definition video streaming but also low-latency live streaming over the Internet [18]. However, between the two technologies, 8K videos are more realistic due to their higher resolution of 7680 \(\times\) 4320 pixels, and result in lower latency compared to 4K [19], thereby facilitating more natural communication between hosts [20]. However, to achieve a high-definition streaming experience, latency levels lower than 60 ms [18] and bandwidth connections such as 10 Gbps are required [20]. Further, to achieve the scalable video streaming service with techniques such as multicast and storage closer to the user [21], a higher network bandwidth is required in the access networks.
## 3 Data Collection and Analysis
### Data Analysis
* The elapsed time thresholds should vary with the time of day and day of week (e.g., peak hours versus off-peak hours) because the passenger transfer time with different travel purposes is diverse, as shown in Figure 1.
* The elapsed time thresholds should be constant at the same time period at a specific station because the previous average transfer time and volumes are similar, such as peak hours on weekdays in Figure 1.  
In conclusion, the proposed transfer identification model should develop temporal-spatial elapsed time thresholds by considering station configurations, traffic patterns, and time periods, etc.
## 5 Conclusions  
For selective laser melting technology of metals, the process parameters, including laser power, scanning speed, powder layer thickness and hatch spacing have an important influence on the performance of metal parts. For the additive manufacturing of metal parts, how to quickly and accurately select the reasonable process parameters, avoid the generation of internal defects and obtain the best performance is an urgent problem to be solved at present. In view of the shortcomings of traditional performance prediction methods, considering the dynamics of the parameter value of absorptivity, which has an important impact on the prediction results, verification with numerical simulation and experimental results proves the feasibility and effectiveness of the proposed analytical method.  
1. Through the analysis of the four cases (A = 0.3, A = 0.5, A = 0.87 and of A calculated by the formula proposed in this paper), the temperature field distribution is calculated by the analytical solution presented in this paper. From the geometry of the melt pool, it can be seen that absorptivity has a significant effect on the melt pool configuration. Therefore, when performing thermal analysis, absorptivity cannot be regarded as a fixed value, but an experiment should be performed on absorptivity of the material to be printed. Based on the calculation of the obtained absorptivity, ideal process parameters will be quickly computed by the analytical solution, which can provide suggestions for production process engineers.
2. By comparing the analytical method, the experiment and the FE simulation, the results show that the analytical method for the prediction of the melt pool size is more consistent with the experimental results. Further, this method can quickly and accurately predict the thermal distribution of AM parts.
3. The analytical method is successfully applied to the prediction of the internal defects of the In718 by SLM, combined with the criteria of lack of fusion, keyholing and balling, the regime of the internal defects under different combinations of laser power and scanning speed is calculated, so as to obtain an ideal process parameter combination to avoid defects.  
Figure 8: The effect of laser power and scanning speed on defects of In718 by SLM.  
For the prediction of the keyholing regime, further in-depth discussions are still needed. In addition, the influence of the powder layer and hatching distance on the temperature field and the characteristics of the molten pool needs to be considered in the future.  
Methodology, H.Y. and S.W.; validation, Z.L.; investigation, S.W.; writing--original draft preparation, Z.L.; writing--review and editing, H.Y.; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Foundation of State Key Laboratory (Grant No. 9100419010) and Fundamental Research Funds for the Central Universities (Grant No. 2017 MS150).  
The authors declare no conflict of interest.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 2 Material and Methods  
### The UWB Life-Detection Radar Hardware System Design  
#### 2.1.1 The Golay Complementary Coded UWB Radar  
The UWB life-detection radar has obvious advantages in the ability of anti-interference and long-range resolution. The pseudo-random coded signal modulated radar transmits a pseudo-random coded signal and performs pulse compression on the radar echo in the receiver to obtain the impulse response function of the target, achieving large transmission power and high range resolution simultaneously.  
The choice of pseudo-random coded signal is a key issue in the design of this system. The existing coding methods include m-sequence, Gold sequence and Golay complementary sequence. Based on the previous research on the pseudo-random codes' technology [16; 27], the Golay complementary sequence is composed of coded signal A and coded signal B, and the autocorrelation functions of these two codes can eliminate the side lobes remarkably. The amplitude of signal peak can be double. The working process of the Golay complementary coded UWB radar is that the radar sequentially transmits the Golay coded signal A and the Golay coded signal B for the electromagnetic detection. The sum of two autocorrelations of Golay coded signal A and Golay coded signal B constitutes the radar's fast scanning time.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 3 Making Sense of Correlations
**(b)** cross-correlation functions between different elements of the ISI matrix \(\mathbf{R}_{0}^{(n)}\), and **(c)** cross-correlation between different ISI orders. The superscripts Re and Im denotes the real and imaginary parts of the coefficients, whereas the subscripts \(xx\), \(xy\), \(yx\), and \(yy\) denote the four components of a \(2\times 2\) ISI matrix. For example, the curve marked by \(R_{0,xy}^{\text{Re}}\), \(R_{0,yx}^{\text{Re}}\) denotes the cross-correlation between the real part of the off-diagonal elements of \(\mathbf{R}_{0}^{(n)}\). **(d)** normalized (Pearson) correlation of the phase-noise component in a multi-subcarrier system. The curves show the correlation between the phase noise affecting two tributaries within the COI, as a function of the frequency separation between them. Results are shown for 64-QAM modulation, and probabilistically-shaped (PS) 64-QAM. All of the results were obtained for a \(10\times 100\) km link of SMF. The auto-correlation and cross-correlation functions of figs. (**a–c**) were measured for a system carrying 15 WDM channels, whereas in fig (**d**) 21 WDM channels where used.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 3 System Overview  
In this section, we present the design details of SEZZ, a directed fuzzer employing sequence complexity estimation and two-stage seed scheduling under selective instrumentation for efficient crash reproduction. It overcomes the limitations of the existing works by guiding DGF to choose a better seed to quickly approach the target site and expose the vulnerability.  
The workflow of SEZZ is depicted in Figure 2. SEZZ contains two major components: function sequence complexity estimation and two-stage seed scheduling during fuzzing. First, the target point reachability analysis is performed on the program under test (PuT) source code. During the analysis, the PuT is compiled without instrumentation, and the generated CG and CFG during compilation is used to estimate reachability from the PuT entry point to the target site. Second, we conduct function sequence complexity estimation based on the CG and CFG selected by reachability analysis and store the result in a dictionary. During the fuzzing campaign, the scheduler evaluates the seed that triggered new coverage with the help of the dictionary. Third, to dynamically collect execution traces, we implement compile-time instrumentation. This lightweight instrumentation logs the runtime control flow while maintaining execution efficiency. Fourth, the instrumented binary enters the main fuzzing loop, where it continuously processes test cases under the guidance of our scheduling strategy. Fifth, upon discovering a seed with new coverage, the scheduler evaluates it as follows: If the seed triggers a program crash, a report is generatedfor triage. Non-crashing seeds are classified into reaching seeds or reached seeds according to the function call sequence, and the next seed is chosen to fuzz. Sixth, the fuzzer's mutation engine mutates the scheduled seed, generating new inputs to feed back into the testing loop. This process iterates until termination conditions (e.g., timeout) are met.
## 1 Introduction
The analysis is performed across a broader range of performance metrics than previous related studies [8, 11, 12, 14] including BER, energy efficiency, spectral efficiency, and time delay performances. Given the critical importance of energy in WSNs, the detailed modeling and analysis of communication (circuit and transmission) energy consumption and processing (CPU or central processing unit) energy consumption of WSN nodes in different operating modes, is another key contribution of this paper. Findings of this study can provide useful insights into certain performance aspects and identify promising solutions for future high data-rate and energy-efficient WSNs. The rest of the paper is organized as follows: Section 2 gives an overview of related works on performance evaluation of MIMO systems for WSNs. Section 3 introduces background concepts on cooperative virtual MIMO, V-BLAST, and multi-carrier modulation. In Section 4, the system model of the cooperative virtual MIMO WSN is presented. This is followed by the parametric modeling of performance parameters for virtual MIMO and SISO systems in Section 5. Evaluation results are then presented and discussed in Section 6. Finally, the paper is concluded in Section 7.
## 3 Framework
### Complexity Analysis  
Let \(G(V,E)\) be a graph representing the network topology. Each vertex \(v\in V\) represents a network node and for all vertices \(u\) and \(v\) in \(V\), the edge \((u,v)\in E\) if and only if the two nodes \(u\) and \(v\) are one-hop neighbors. One-hop coloring of \(G\) consists in coloring each vertex in \(V\) using the smallest number of colors and in such a way that two vertices with the same color have no common edge. The associated decision problem can be defined as follows. Let \(k\) be a positive integer strictly smaller than the cardinal of \(V\). Can \(G\) be colored with \(k\) colors in such a way that two nodes that are one-hop neighbors have not the same color? Clearly, the answer is yes if and only if \(G\) can be one-hop colored with \(k\) colors.  
**Lemma 2**: _The number of colors needed by a three-hop coloring of nodes in \(U\cup\{u_{0}\}\) in graph \(G^{\prime}\) is equal to \(n+1\), where \(n\) is the number of nodes in \(G\)._  
_Proof:_ From lemma 1, all nodes in \(U\cup\{u_{0}\}\) are one-hop or two-hop neighbors. Hence, no color can be reused with three-hop coloring. By construction of \(G^{\prime}\), the number of these nodes is equal to \(n+1\).  
**Lemma 3**: _Any color used for a node in \(V\) by a three-hop coloring of \(G^{\prime}=(V^{\prime},E^{\prime})\) cannot be used by any node in \(U\cup\{u_{0}\}\)._  
_Proof:_ Let us consider any node \(u\in U\cup\{u_{0}\}\) and any node \(v\in V\). By construction, the distance between \(v\) and \(u\), denoted \(d(v,u)\) meets \(d(v,u)=d(v,f(v))+d(f(v),u)\). From Lemma 1, \(d(f(v),u)\leq 2\). Hence \(d(u,v)\leq 3\). Hence, they should use different colors with three-hop coloring.  
We want to show that \(G(V,E)\) has a one-hop coloring with \(k\) colors if and only if \(G^{\prime}(V^{\prime},E^{\prime})\) has a three-hop coloring with \(k^{\prime}\) colors.  
Given a one-hop coloring of \(G\) with \(k\) colors, we want to show that there exists a three-hop coloring of \(G^{\prime}\) with \(k^{\prime}\) colors as follows. This three-hop coloring will use \(k\) colors for nodes in \(V\) and \(n+1\) colors for nodes in \(U\cup\{u_{0}\}\) according to Lemma 2. From Lemma 3, colors used in \(V\) cannot be reused in \(U\cup\{u_{0}\}\). It follows that there exists a three-hop coloring of \(G^{\prime}\) with exactly \(k^{\prime}=n+k+1\) colors.  
Now, let us assume that we have a three-hop coloring of \(G^{\prime}\) with \(k^{\prime}\) colors and we want to show that we can find a one-hop coloring of \(G\) with \(k\) colors. From Lemma 2, \(n+1\) colors are needed for three-hop coloring of nodes in \(U\cup\{u_{0}\}\). From Lemma 3, colors used in \(V\) cannot be reused in \(U\cup\{u_{0}\}\). Hence, \(k^{\prime}-n-1\) colors are used to color the nodes in \(V\). Since, any two nodes \(v1\) and \(v2\) in \(V\) that are one-hop neighbors in \(G\) are three-hop neighbors in \(G^{\prime}\), by construction of \(G^{\prime}\), we deduce that no two one-hop neighbors in \(G\) use the same color. Hence, we can find a valid one-hop coloring of \(G\) with \(k=k^{\prime}-n-1\) colors.