esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Pure Water Permeability and Antifouling Properties
The higher fouling rate in the bare Al\({}_{2}\)O\({}_{3}\) membrane results from the higher probability of pore fouling in the larger pores of the bare membrane than in the smaller pores of the TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane [23]. The previous results also confirmed that the membrane permeability decreased significantly after immobilizing a TiO\({}_{2}\) layer on the Al\({}_{2}\)O\({}_{3}\) membrane support, as shown in Table 1. The benefits of applying the POEM template become obvious upon exposing the membranes to UV illumination. In fact, no flux decline was observed for the POEM-directed TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane under UV illumination. In contrast, severe membrane fouling was observed in the bare Al\({}_{2}\)O\({}_{3}\) membrane under UV illumination, with a \(\sim\)51.2% decay in the permeate flux relative to that in the absence of UV illumination (2.57 vs. 1.25 LMH h\({}^{-1}\)). The higher fouling rate on the bare Al\({}_{2}\)O\({}_{3}\) membrane under UV illumination is attributed to the formation of a cake layer on the membrane surface [15]. Although photolysis phenomena are likely involved during organic degradation on the bare Al\({}_{2}\)O\({}_{3}\) membrane [24; 25], photolysis itself may not be sufficient to remove the cake layer on the membrane. \begin{table}
\begin{tabular}{c c c} \hline  & \multicolumn{2}{c}{**Permeability (L m\({}^{-2}\) h\({}^{-1}\) bar\({}^{-1}\))**} \\ \hline \(\Box\) & **Bare Al\({}_{2}\)O\({}_{3}\) Membrane** & **TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) Composite Membrane** \\ \hline
**Without UV** & 65.2 & 9.9 \\
**With UV** & 71.5 & 14.2 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of pure water permeability for bare Al\({}_{2}\)O\({}_{3}\) membrane and Poly(oxyethylene methacrylate) (POEM)-directed TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane during filtration (with and without ultraviolet (UV) illumination). Figure 8: Comparison of the membrane fouling rate for the bare Al\({}_{2}\)O\({}_{3}\) membrane and Poly(oxyethylene methacrylate) (POEM)-directed TiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) composite membrane during filtration in the presence (With UV) and absence (Without UV) of ultraviolet (UV) illumination (filtration time: 24 h).
## 4 Applications to Biomedical Devices
### Neck Orthosis  
A neck orthosis is an external medical device that has to support, prevent deformity, or correct the spine. It is used in treating specific pathologies. Rigid cervical collars available on the market are generally made of hard polyethylene covered by a soft pad. However, it is known that the use of these collars can be uncomfortable, restrictive, and poorly tolerated. Customization of the device together with appropriate material selection and geometry, realizable by 3D-printing usage, can help improve the performance of these devices mainly in terms of comfort.  
The customized orthosis was designed starting from the Computer Tomography (CT) scan of the neck of a volunteer subject. The final prototype was designed by introducing a pattern of voids in order to improve the breathability and decrease the overall weight.  
As a first step, a void-free neck collar was manufactured in order to assess the feasibility to reproduce the complex geometry of the model (Figure 6a). In a later step, a honeycomb pattern of voids was designed and a lighter collar was manufactured (Figure 6b,c). More details on the design of the model are available in Reference [16].  
The accuracy obtained for this model depends on the value of layer height. Excellent accuracy values (\(\leq\)0.1 mm) are obtained with a layer height of \(\leq\)0.25 mm. The superficial roughness values are related to the orientation of the orthosis with respect to the print bed. The best internal and external surface finish was obtained by printing the orthosis parallel to the printing bed. Lastly, the presence of numerous holes that guarantee the necessary breathability prevents the adoption of infill ratios higher than 50%.
###### Abstract
In the Methodology section, the research approach is detailed, including explanations of the dataset, preprocessing steps, and the feature selection techniques used. The Models section discusses the machine learning models and their hyperparameter tuning. Evaluation Metrics are explained to clarify the chosen assessment criteria. The Results section presents the outcomes of the experiments, and the paper concludes with the Conclusion and Future Work section, summarizing findings and proposing future research directions. The contributions of this paper are as follows:  
* A comprehensive evaluation of feature selection techniques across multiple models: This study offers a systematic comparison of six feature selection techniques across various machine learning models. By evaluating models with and without feature selection, it demonstrates the substantial impact these techniques have on classifier performance, especially for high-dimensional EEG data. * Insights into the differential impact of feature selection on linear and non-linear models: The findings reveal that feature selection techniques influence linear and non-linear models differently. Methods like RFE and Elastic Net enhance linear models by reducing feature redundancy, while techniques such as MI and mRMR are more effective for non-linear models that rely on capturing complex interactions. * Identification of effective combinations of feature selection techniques and classifiers: This study identifies promising combinations of feature selection methods and classifiers, showing how targeted feature selection can narrow the performance gap between simpler models and more complex approaches, especially in high-dimensional EEG datasets. * Impact on clinical diagnostics and computational neuroscience: By demonstrating the crucial role of feature selection in optimizing model performance, this research provides valuable insights that could advance clinical diagnostics and computational neuroscience. It supports the development of more accurate and interpretable machine learning models for depression detection, contributing to better diagnostic tools for mental health.
###### Abstract  
Integrated satellite multiple terrestrial relay network (ISMTRN) is a new network architecture that combines satellite communication with terrestrial communication. It both utilizes the advantages of the two systems and overcomes their shortcomings. However, security issues inevitably arise in the ISMTRN resulting from the broad coverage of the satellite beams and the openness of wireless communication. One of the promising methods to achieve secure transmission is covert communication technology, which has been a hot discussion topic in recent years. In this paper, we investigate the performance of covert communication in the ISMTRN with partial relay selection. Particularly, when the satellite transmits its signal to the user, we consider the scenario that the selected relay opportunistically sends covert information to the destination. Furthermore, the closed-form error detection probability and average covert communication rate are derived. Finally, numerical simulation results are provided to reveal the impact of critical parameters on system covert performance.  
Keywords:integrated satellite multiple terrestrial relay network; covert communication; error detection probability; average covert communication rate +
Footnote †: journal: Physics Letters A
###### Abstract  
The analysis of damage in cities after an earthquake to implement mitigation strategies of seismic risk is a complex job that is usually full of uncertainties. Numerous variables affect the final result of the observable damage in a set of buildings in an urban area. The use of methodologies capable of providing global explanations beyond the traditional undiscilplinary approach of disciplines, such as structural analysis, earthquake engineering, geotechnics, or seismology, can be very useful for improving the behavior of our cities against earthquakes. This article presents geostatistical post-earthquake analysis, an innovative approach in this field of research based on GIS spatial statistical tools to evaluate the importance of the different variables after an earthquake that may have caused damage in a city. This new framework will be applied to analyze, from a geostatistical perspective, the damage levels observed in the city of Lorca (Spain) after the earthquake of 2011; a case study where various studies have proposed different measures to mitigate the impact of future earthquakes as a consequence of focusing on different phenomena as the main variable for the damage produced. A bivariate GIS assessment will allow spatial correlation of the problems detected from a statistical point of view (inadequate design of buildings, age of the real estate stock, inefficient urban planning configurations, geological risk, etc.) and the different levels of damage that the technicians who participated in the post-earthquake phase evaluated in the city. The results obtained will allow one to hierarchize the importance of the different detected phenomena to prepare the city better against future earthquakes and to elaborate an improved seismic mitigation strategy.  
post-earthquake diagnosis; geostatistical evaluation; bivariate spatial analysis; building damage; seismic mitigation strategy +
Footnote †: journal: Applied
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 4 Experimental Results and Analysis
### Comparisons of UAV Measurement  
The uncorrected displacement measured by the UAV and the false displacement predicted by the CNN are shown in Figure 14. The figure shows that the uncorrected displacement curves measured by the UAV have serious drift.  
The true displacements of the structure are shown in Figure 15, which are obtained by subtracting the false displacement of the target points predicted by the CNN from the uncorrected displacement of the target points. The corrected displacement curve obtained by DIC has more drift than that by the KLT optical-flow method. In addition, it can be seen from Figure 15 that each wave of displacement time-history curves fluctuates at \(Displacement=0\). The greater the drift of the curve, the farther the mean line of each wave is from \(Displacement=0\). The drift degree of the displacement time-history curves can be represented by the root mean square (RMS [39]). The RMSs of the DIC and KLT optical-flow methods are 0.063 and 0.025, respectively, which illustrates the low tracking accuracy of DIC, and it proves the stability of the KLT optical-flow method.  
Figure 13: PSD obtained by DIC and KLT optical-flow method: (**a**) results of DIC; (**b**) results of KLT optical-flow method.  
Figure 14: Uncorrected displacement measured by UAV and false displacement obtained by CNN: (**a**) results of DIC; (**b**) results of KLT optical-flow method.  
Figure 15: _Cont._  
The above displacement signals in Figure 15 are processed into PSD curves, as shown in Figure 16. The natural frequencies extracted for the two displacement signals are 3.285 Hz and 3.286 Hz, respectively, the relative error is 0.04%. It shows that the KLT optical-flow method is feasible to replace DIC to extract the natural frequency of the structure under UAV.
# A Hybrid Discrete Memetic Algorithm for Solving Flow-Shop Scheduling Problems
## 3 A Review of Classic and State-of-the-Art Approaches
### Heuristic or Meta-Heuristic Algorithms
#### 3.2.5 Hybrid Approaches  
In [61], the authors tackle the flow-shop scheduling problem (FSSP) on symmetric networks using a hybrid optimization technique. The study combines the strengths of ant colony algorithm (ACO) with particle swarm optimization (PSO) to create an ACO-PSO hybrid algorithm. By leveraging local control with pheromones and global maximum search through random interactions, the proposed algorithm outperforms existing ones in terms of solution quality. The ACO-PSO method demonstrates higher effectiveness, as validated through computational experiments. Addressing the NP-hard nature of flow-shop scheduling problems, ref. [62] presents a computational efficient optimization approach called NEH-NGA. The approach combines the NEH heuristic algorithm with the niche genetic algorithm (NGA). NEH is utilized to optimize the initial population, three crossover operators enhance genetic efficiency, and the niche mechanism controls population distribution. The proposed method's application on 101 FSP benchmark instances shows significantly improved solution accuracy compared to both the NEH heuristic and standard genetic algorithm (SGA) evolutionary meta-heuristic. Ref. [63] addresses the flexible flow-shop scheduling problem with forward and reverse flow (FFSPFR) under uncertainty using the red deer algorithm (RDA). The study employs the Fuzzy Jimenez method to handle uncertainty in important parameters. The authors compare RDA with other meta-heuristic algorithms, such as the genetic algorithm (GA) and imperialist competitive algorithm (ICA). The RDA performs the best at solving the problem, achieving near-optimal solutions in a shorter time than the other algorithms.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Data Query Phase  
In the scenario of medical data sharing, DUs often need to perform multi-condition queries to obtain corresponding EMR's numbers, and then execute the chaincode in the data channel to obtain the storage information of EMR. Consider a case of a conditional query \(Q\): querying all medical data records generated in hospital \(H\) that include a specific diagnostic result \(DR\). After executing the query \(Q\), the query result \(R\) should satisfy the following conditions simultaneously:  
* The medical data records were generated in hospital \(H\).
* The diagnostic result of the medical data records include \(DR\).  
If the conditional query process is not optimized, in a Hyperledger Fabric blockchain-based system, it would require individual queries for each condition, followed by filtering out results that do not satisfy the other conditions one by one. Consequently, the final result set that satisfies all query conditions would exclude the vast majority of data. With an increasing number of query conditions, the system would need to access and parse a corresponding increasing number of blocks, leading to a significant increase in the total amount of data to be processed and resulting in more redundant data. This inevitably increases the time required for conditional queries, thereby reducing query efficiency and making the entire query process appear both time-consuming and inefficient.  
To address the above issue, this scheme proposes a method of creating composite key, which can significantly improve the efficiency of conditional queries.
###### Abstract  
This study focused on analyzing the impact of ground-penetrating radar (GPR) scan spacing on accurately assessing the reinforcement of concrete bridge girders, providing practical insights. A decommissioned bridge box beam was evaluated to unveil rebars and tendons' depth and spacing. The box beam was decommissioned from the West Virginia Division of Highways inventory. An innovative algorithm was developed to fully automate the analysis of survey grid data across all sides of the beam. Implementing this algorithm into a computer code has paved the way for comprehensive automation of GPR data analyses. Comparing GPR data analyses from various profile line offsets, this study assists in producing optimal protocols for inspecting box beams. Transverse profile line offsets between 4 in. and 24 in. yielded nearly identical results, setting a new standard for precision. Utilizing more than one longitudinal profile line was highly beneficial in accurately assessing prestressed concrete box beams. This research helps redefine bridge evaluation by precisely finding rebar spacing, concrete cover, and other internal characteristics. This study's findings offer invaluable advancements and equip state departments of transportation with the knowledge to accurately assess in-service concrete bridge box beams, empowering them to make informed decisions.  
reinforced concrete; prestressed concrete; corrosion; deterioration; bridge inspection; bridge assessment; nondestructive testing Article
## 6 MIMO Antenna Performance Enhancement Techniques
### Metamaterial  
Metamaterials are materials which can be designed manually by using two or more than two different materials which contain electromagnetic characteristics. Different types  
Figure 20: Designing and mutual coupling reduction technique of MIMO antennas, a summarized generic view.  
of metamaterials with the characteristics of single negative, electromagnetic, electromagnetic band gap, double negative, anisotropic, isotropic, terahertz, chiral, tunable, photonic, frequency selective surface-based, nonlinear, and tunable metamaterial. Using metamaterial, it is possible to have an antenna with low mutual coupling, high gain, bandwidth, and be compact in size [37; 88]. Slot elements are used to achieve enhancement in the impedance bandwidth using the coupling method in the radiator or patch or in the ground plane. The slot antenna have the capability to provide wide bandwidth, high gain, and high efficiency but have a high mutual coupling value [89]. Complementary split-ring resonators (CSRR) are used for isolation improvement, to perform iterating function, and to provide lower mutual coupling. CSRRs are also used to provide high efficiency and miniaturize the size of the antenna [88]. CSRR is made up of two concentric ring structures with slots opposite to each other. Frequency reconfiguration is based on switching techniques by using varactor diodes and MEMS switches, and PIN diode reconfigurability can be achieved to increase frequency range and enhance the value of the envelop correlation coefficient.
# Active Tendon Control of Stay Cable by a Giant Magnetotrstive Actuator Considering Time-Delay
## 6 Simulation for the Active Control of Cable with the Time-Delay
### Harmonic Excitation  
A harmonic excitation force is applied on the node of L/14 span, whose amplitude is 3N and frequency is set to the fundamental frequency of the cable. Other simulation conditions are same as the free vibration case. When the time-delay is set to 0.11 s, the displacement responses of the cable at mid-span are shown in Figure 7. The RMS of displacements in the four cases are 0.00615 m, 0.00173 m, 0.00258 m and 0.00179 m, respectively. For the three control strategies, the response reduction ratios of the cables at mid-span are 72.13%, 58.04%, and 70.89%, respectively.  
With the change of the time-delay, the reductions at mid-span of three control strategies under harmonic excitation are summarized in Table 3. The results indicated that the control effect of the cable vibration gradually decreases as the time-delay increases for the condition without the time-delay compensation. Notably, when the time-delay is increased to 0.15 s, the response of the cable under no compensation is divergence. While the method of time-delay compensation is applied, the reduction in cable still reaches 67.62%, even though the time-delay increased to 0.17 s.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time-Delay** & **Reduction without** & **Reduction without** & **Reduction with** \\
**Amounts** & **Time-Delay** & **Compensation of** & **Time-Delay** \\
**(s)** & **(\%)** & **Time-Delay** & **Compensation** \\ \hline
0.00 & 57.13 & & \\
0.05 & & 56.06 & 55.37 \\
0.07 & & 55.76 & 54.41 \\
0.09 & & 42.12 & 51.90 \\
0.11 & & 9.87 & 53.34 \\
0.13 & & \(-\)12.26 & 54.23 \\ \hline \end{tabular}
\end{table}
Table 2: Response reduction at mid-span of three control strategies under free vibration.  
Figure 6: The time history of displacement at mid-span under free vibration (0.11 s time-delay).
## System Description
### Different Behaviors of Relay  
In this paper, three types of relay behaviors are discussed:  
1. Cooperative Behavior: The relay decodes the received signal and forwards it to the destination.
2. Selfish Behavior: The relay refuses to cooperate with probability \(P_{off}\)[14], while selecting to cooperate with probability \(P_{on}\)\(\left(P_{on}=1-P_{off}\right)\).
3. Malicious Behavior: The relay forwards random noise instead of the received signals of the source.
## 3 Metasurface Antennas in Small Cavities for Telemetry Applications
### Cavity Diameter of 0.15 \(\lambda_{0}\)  
#### 3.1.1 Patch with Cavity Diameter \(a=20\) mm (0.15 \(\lambda_{0}\))  
Although an ideal simulation of a patch antenna within a cavity of diameter 20 mm can give good matching at 2.3 GHz (\(h_{c}=14\) mm, \(d=18.38\) mm, \(f_{x}=2.8\) mm), the realistic simulation, taking into consideration a glue layer of thickness 0.2 mm, showed that the resonance frequency shifts to a higher frequency and cannot be shifted back by increasing the patch size. With a diameter \(a=20\) mm, the realization of a patch antenna printed on RO3010 substrate is not feasible with the manufacturing process we use.
## 4 Experiments
### Evaluation Index
#### 4.2.2 Micro-F1 and Macro-F1  
The micro-F1 is the micro \(F1\) value. Considering the multi-label classification as n binary classifications, there are n confusion matrices. Then we can calculate the \(TP\) (True Positive), \(FP\) (False Positive), and \(FN\) (False Negative) of n categories. Thus we can sum up these metrics to get the overall \(TP_{all}\), \(FP_{all}\), and \(FN_{all}\), so as to find the overall Precision and Recall and then find the micro-F1 value. The calculation process is shown in Equations 5-6.  
\[Precision_{micro}=\frac{\sum_{i=1}^{n}TP_{i}}{\sum_{i=1}^{n}TP_{i}+\sum_{i=1}^{ n}FP_{i}} \tag{5}\]  
\[Recall_{micro}=\frac{\sum_{i=1}^{n}TP_{i}}{\sum_{i=1}^{n}TP_{i}+\sum_{i=1}^{ n}FN_{i}} \tag{6}\]  
Therefore, the \(F1\) value calculated based on the above \(Precision_{micro}\) and \(Recall_{micro}\) is the \(F1_{micro}\), as shown in Equation (7).  
\[F1_{micro}=2\cdot\frac{Precision_{micro}\cdot Recall_{micro}}{Precision_{ micro}+Recall_{micro}} \tag{7}\]  
In contrast, macro-F1 is precisely the arithmetic average of \(F1_{n}\) calculated by first finding the \(F1\) of all classes and then calculating it directly. The procedure is shown in Equations (8)-(10).  
\[Precision_{macro}=\frac{1}{n}\sum_{i=1}^{n}Precision_{i} \tag{8}\]  
\[Recall_{macro}=\frac{1}{n}\sum_{i=1}^{n}Recall_{i} \tag{9}\]  
\[F1_{macro}=\frac{1}{n}\sum_{i=1}^{n}F1_{i} \tag{10}\]  
Therefore, in the case of multiple classifications, it is necessary to decide depending on the number of different classes in the dataset:  
1. Typically, if the amount of data in the dataset is unbalanced for each class and all classes are equally important, then using macro-F1 will work; because macro-F1 is calculated by class and then takes the average F1 as the final value. In comparison, micro-F1 will approximate the F1 of the category with the largest amount of data under extremely unbalanced data, which is not a good measure of the overall effect of the model.
2. If we need to focus on weak categories in an unbalanced dataset, weighted-F1 should be utilized, which is a weighted average of macro-F1, and the weight is the weight of category numbers to the overall number.