esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Applications
#### 4.3.1 Microwave Absorbing FSSs
Ideal and multiband absorption are the chief objectives of such absorber. Nowadays, lossy FSS absorbers made of the resistive element and dielectric substrates are the top option for a broadband absorber. Their design is easy and makes use of simple elements whose impedance can be characterized by series RLC circuit, for instance, patches with square, interlaced, and ring [136]. Any element pattern can design such absorbers with the single layer; nevertheless, the pre-eminent bandwidth performing FSS microwave absorbers have not been known so far. A broadband lossy FSS absorber is reported in [27] with a combination of fractal square patch and criss-cross as seen in the inset of Figure 15b. It is observed from Figure 15b that, by increasing the thickness of FSS from 3 to 4 mm, the unit cell coupling can generate absorption nulls in the lower frequency and also improves the bandwidth of the lossy absorber. For comparison, Figure 15c gives the impedance of a 4 mm grounded substrate (obtained from transmission line method) and impedance of proposed FSS absorber (calculated according to the equivalent circuit model) with its periodicity, highlighting the resonances of the FSS and the absorber. Such a fractal FSS can solve the issue of tradeoffs between FSS thickness and the bandwidth, thereby allowing for the development of a thinner, broadband absorber for stealth technology. Polarization insensitive features are imperative in numerous EM applications and some microwave absorbers). Conventionally, good symmetric unit cells are designed for this purpose, such as a 90\({}^{\circ}\) rotational symmetry [137], four axis symmetry [27], or upper symmetry [138] in which periodic resonating elements resonate equally at the different polarization of incident waves. This strategy has also been exploited to realize tunable polarization in-sensitive absorber in reference [139] in which an inventive FSS absorber at 1.6-8 GHz is investigated. It has four axial symmetric shapes and includes an array of PIN diodes with bias lines of inductors. Figure 16b are shown the unit cell geometry with geometrical parameters and the topology of tunable FSS absorber. When the equivalent resistance of PIN diodes is reduced, gradually by increasing the bias voltage, multi-resonant tunable absorption peaks are created, as can be observed from the trend  
Figure 15: (a) Novel transmit/absorb FSS element (b) Broadband lossy FSS absorber in the inset and optimized reflectivity graph for 3 mm and 4 mm thickness (c) Impedances of 4 mm grounded substrate and proposed FSS highlighting the resonances and absorption nulls. graph that is drawn in Figure 16a. Various attempts by researchers have been set to enhance the performance of microwave absorbers and a comparison is provided in Table 7 explains the types of elements that are used.
## 3 Results and Discussion
### Physical-Chemical Characterization of the Waste Vitreous Enamels
#### 3.1.5 Results of Field Emission Scanning Electron Microscopy (FESEM)  
The FESEM analysis was conducted for all sampled waste vitreous enamels. Considering that, the investigated surface of the samples was similar for all batches, only representative batch 3 of WEP and WETM, are shown in Figure 4.  
Observation at a magnification of 5.00 kX of the microstructure of the generated waste materials shows the appearance of different sizes of particles mainly with polygonal form and pronounced edges in both samples. It is obvious that the application of different production methods does not affect significantly the density or the size and morphology of the particles. Smaller particles, more uniform particle distribution and higher porosity are characteristic of the WEP sample. On the other hand, the WETM sample show larger particles and bimodal particle distribution.  
It is known from the literature that smaller white particles of irregular shape and sharp edges, can correspond to fluxes or other components added to the frit [57]. Distinguishing the other particles with a more amorphous shape is difficult since some cover the others, but as mentioned earlier in the introduction, the components of the frit are mainly silica with additives such as alumina, flux and metals (Ti, Zn, Sb and Fe as adhesive agents) to provide chemical adhesion in the interphase between enamel and steel. Larger particles commonly correspond to the silica, while smaller ones correspond to the alumina, and other white particles with a polygonal form are probably belong to metal oxides. This analysis is in good correlation with results obtained by ICP, XRPD and FTIR analysis.  
Based on the literature [27], it is known that smaller particles can much more easily slip in the gaps between coarse aggregates and undergo pozzolanic reactions, which indicates that the WEP sample should possess better pozzolanic activity in comparison to WETM. These pozzolanic reactions are able to reduce the water and air pockets in the concrete matrices, which indicates that the concrete mixture containing WEP will have better properties and higher density compared to the mixture containing WETM.  
Figure 4: FESEM micrographs of the waste vitreous enamel (batch 3).  
Results of Determination of the Pozzolanic Activity, Parameters of Cement Paste, Mortar and Concrete Produced with the Addition of Waste Enamels
# A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction
## 3 Materials and Methods
### Data Pre-Processing of Stroke Dataset
#### 3.3.4 Normalization  
There are several features in the stroke prediction dataset, including various numerical values. A good model has the potential to learn to choose a relatively small weight when a feature's possible range of values is large. Similarly, a weight's reasonable value will be relatively large when the feature's possible values are small. As a result, the prediction process is impacted by these various feature values, and the training process moves slowly to attain the cost functions.  
To find a much more direct path to the global minimum of the cost functions, we need to rescale the features so that their ranges of values are comparable. Additionally, the training process needs to run quickly. We used Z-score normalization to implement feature scaling, which allows features with very different ranges of values to have similar ranges of values.  
In Z-score normalization, we calculate the feature's standard deviation and mean value, subtract each feature value from the mean, and divide it by the standard deviation. Equation (1) defines the Z-score normalization [23]. Consequently, if the feature value matches all feature values' mean, the stroke prediction dataset is normalized to 0. A number below the mean will be negative, while a number above the mean will be positive. Table 3 shows the dataset after applying the Z-score normalization.  
\[X_{norm}=\frac{(value-\mu)}{\sigma} \tag{1}\]  
where: \(\mu\) refers to the mean of the features, and \(\sigma\) refers to the standard deviation of the features calculated from the Z-score normalization.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Age** & \begin{tabular}{c} **Avg Glucose** \\ **Level** \\ \end{tabular} & **BMI** & \
\begin{tabular}{c} **Gender** \\ **Male** \\ \end{tabular} & **Hypertension\_1** & **Heart\_Disease\_1** & **Ever\_Married\_Yes** &
\begin{tabular}{c} **Work\_Type** \\ **Never\_Worked** \\ \end{tabular} \\ \hline
67 & 5.432367 & 3.600048 & 1 & 0 & 1 & 1 & 0 \\ \hline
61 & 5.309307 & 5.309307 & 0 & 0 & 0 & 1 & 0 \\ \hline
80 & 4.662684 & 4.662684 & 1 & 0 & 1 & 1 & 0 \\ \hline
49 & 5.143008 & 5.143008 & 0 & 0 & 0 & 1 & 0 \\ \hline
79 & 5.159745 & 5.159745 & 0 & 1 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The stroke prediction dataset after applying the one-hot encoding.
# Implementation of PMDL and DRM in OpenSees for Soil-Structure Interaction Analysis
## 5 Conclusions  
In this manuscript, we provide a detailed account of the methodology for integrating the DRM and PMDLs into OpenSees for two-dimensional problems, including a revised version of the DRM to accommodate inclined incident SV waves. A comprehensive verification process was conducted, encompassing problems involving both vertical and inclined incident SV waves in a two-dimensional domain. Furthermore, we provided practical examples examining the effects of seismic wave incidence angles on the response of single-cell and double-cell tunnels. From the comparisons and figures given in preceding sections, the findings of this study can be summarized as below:  
* PMDLs improve simulation accuracy in wave propagation by minimizing boundary reflections.
* In terms of resource efficiency, PMDLs are far more computationally efficient than extended domain methods (e.g., PMDLs requiring a single processor against Extended Domain Model requiring 20 processors for the same problem), thus balancing accuracy and efficiency.
* Regarding the analysis time required, PMDLs significantly reduce computational time when compared to traditional methods (e.g., with PMDLs, the analysis concluded in 35 min as compared to 250 min when the same problem was solved with Extended Domain Model).
* The coupled PMDL-DRM technique, when implemented in OpenSees, simplifies advanced SSI analyses in truncated domains.  
Overall, this research contributes to the field by providing a robust framework for simulating complex seismic wave interactions in 2D problems, paving the way for more advanced seismic analysis in civil engineering applications. While our study successfully implemented and validated the DRM and PMDLs, it is limited by the specific material properties and loading conditions used herein. Future research could explore different soil properties, loading conditions, and three-dimensional extensions of the coupled PMDL-DRM to further enhance its applicability.  
Figure 26: Acceleration histories for the control points of the double-cell tunnel; (**a**) horizontal acceleration at P1, (**b**) vertical acceleration at P1, (**c**) horizontal acceleration at P1, and (**d**) vertical acceleration at P2.  
**Author Contributions:** Conceptualization, S.U. and Y.A.; methodology, S.U. and Y.A.; software, S.U.; validation, S.U.; investigation, S.U. and Y.A.; writing--original draft preparation, S.U. and Y.A.; writing--review and editing, S.U. and Y.A.; visualization, S.U.; supervision, Y.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Innovation and Application of the Type III Secretion System Inhibitors in Plant Pathogenic Bacteria
## 3 Discovery of T3 SS Inhibitors in Plant Pathogens and Their Regulatory Mechanisms
### Salicylidene Acylhydrazides and Their Derivatives as T3 SS Inhibitors  
Salicylidene acylhydrazides are one of the earliest T3 SS inhibitors that were first identified through large-scale chemical screening approaches in _Yersinia_ spp. [139; 140; 141]. Later studies have shown that salicidene acylhydrazide family compounds actively against T3 SSs from other human pathogenic bacteria, including _S. enterica_, _Shigella_, _Chlamydia_, _P. aeruginosa_, and enteropathogenic _E. coli_[142; 143; 144; 145; 146; 147; 148; 149; 150]. However, whether salicidene acylhydrazides inhibit the plant pathogenic bacterial T3 SSs remained unknown. To address this question, [PERSON] and colleagues screened a small library of compounds. They found that several salicidene acylhydrazides repressed the expression of T3 SS genes, including the master regulator encoding gene _lrpL_, in _E. amylovora_[121]. Microarray analysis of _E. amylovora_ treated with compound 3, also known as ME0054 [benzoic acid N\({}^{\prime}\)-(2,3,4-trihydroxy-benzylidene)-hydrazide] [141], confirmed its inhibitory impact on T3 SS, as the majority of the 38 known T3 SS genes was downregulated [121]. In addition, compound 3 suppressed the production of EPS amylovoran, one of the major pathogenicity factors of _E. amylovora_[7; 151], and reduced the disease development of _E. amylovora_ on crab apple flowers [121].  
[PERSON] and colleagues recently applied a similar approach to the T3 SS of _R. solanacearum_ to discover T3 SS inhibitors [122]. Three synthetic salicidene acylhydrazide derivatives, ME0054, ME0055 [4-nitrobenzoic acid N\({}^{\prime}\)-(2,4-dihydroxy-benzylidene)-hydrazide], and ME0177 [2-nitro-benzoic acid N\({}^{\prime}\)-(3,5-dichloro-2-hydroxy-benzylidene)-hydrazide] [141; 152], were shown to inhibit the expression of T3 SS genes through the inhibition of the regulator encoding gene _lrpB_[122]. All three salicidene acylhydrazides could suppress the multiplication of _R. solanacearum_ in planta and protect tomato from bacterial speck caused by _P. syringae_ pv. _tomato_[122]. Since [PERSON] et al. reported that ME0054 and ME0055 are T3 SS inhibitors in _E. amylovora_[121], these findings suggest that salicidene acylhydrazides and their derivatives are effective against different plant pathogenic bacteria.
## 7 System Performance Optimization
### CSI-Based Localization for Wireless Transmitters
#### 7.2.2 Dimension Reduction Using PCA  
What is received at the receiver is a sequence of CSI values, and each CSI represents the amplitudes and phases on the group of subcarriers. In this paper, the dimensionality of CSI values is reduced by PCA, because PCA can recognize which subcarriers show the strongest correlation with the position, choose the most representative components from all CSI time series and remove the uncorrelated noisy components. The PCA-based dimension reduction includes the following steps.  
Processing:  
A matrix \(H\) presents the CSI time series measurements with \(30\times N_{TX}\times N_{RX}\) streams after noise removal. Every column of \(H\) represents the CSI measurements of each subcarrier, and the column of \(H\) will be \(30\times N_{TX}\times N_{RX}\). Then, the mean value of each column in \(H\) is calculated, and the corresponding mean values are subtracted in every column.  
Correlation calculation:  
Correlation matrix \(H_{c}\) is calculated as the following equation: \(H^{T}\times H\). After obtaining the correlation matrix, then the eigenvalues and eigenvectors of covariance will be also calculated.  
Main eigenvalues chosen:  
Eigenvalues are sorted from large to small, and we choose the matrix \(k\) number of eigenvalues. Then, the corresponding \(k\) eigenvectors will form an eigenvector matrix.
## 1 Introduction
Given the complexity of centralized control models, design difficulties, and low universality, the coordinated control method is mainly used for integrated design in VDM. The control of a chassis system should be based on system theory, and the design of the subsystem controller should be combined with the design of the whole chassis controller to obtain the best control performance for the chassis system. Considering the complexity of the vehicle chassis system, the current work takes CDC suspension as the research object on the basis of VDM to improve and optimize the overall control performance for vehicle chassis systems. This paper comprises five parts: introduction, basic handling stability control strategy of electronic stability program (ESP), design of multi-objective fuzzy continuous damping controller, simulation and result analysis, and conclusion. In Section 1, the research background and research status are introduced, and a brief overview of the overall layout of the paper is presented. In Section 2, the state space expression of lateral stability control is given by studying the control mechanism of the ESP. The ideal yaw angle rate and sideslip angle of the mass center are calculated by deriving the ideal monorail model with two degrees freedom. Finally, a direct yaw moment proportional-integral-differential (PID) control strategy based on yaw rate and centroid sideslip angle is proposed. In Section 3, the multi-objective fuzzy continuous damping controller based on ESP is proposed to achieve sky-hook control and thereby ensure passenger comfort while preventing rollover. The effect of the ESP can be improved by redistributing the vertical force at the same time. In Sections 4 and 5, the effect of the multi-objective fuzzy continuous damping control strategy is analyzed and verified through a sine wave steer input test, double line change (DLC) test, and fishhook test. The simulation results show that the multi-objective fuzzy control strategy is successful. The strategy can ensure the comfort of passengers under good driving conditions while ensuring strong adaptability and control effect under dangerous and extreme working conditions.
## 3 Results Analysis
### Experimental Results for Sleep Measurements
#### 3.2.2 CPU-Bound Load Test Results  
The distributions for the CPU-bound load are very similar to their respective distributions in the idle system scenario. The distributions (Figure 12) of _HPTSleep_ miss, as well as their medians, are in the same range on all five platforms: Host OS has a 102 ns median value of miss time (Figure 12a); Xen virtualized OS has 94.5 ns (Figure 12b); Virtual Box has 5.18 \(\upmu\)s (Figure 12c); QEMU-based OS has 137 ns (Figure 12d); and VMware ESXi has 114 ns (Figure 12e). Distributions of data with _uSleep()_ function are also close to each other: 54.7 \(\upmu\)s on Host OS, 99 \(\upmu\)s on Xen, 61.3 \(\upmu\)s on QEMU and 56 \(\upmu\)s on VMware ESXi hypervisor-based OS, except Virtual Box with 7.6 ms. Considering the CCDF in Figure 13, _HPTSleep_ produces much longer tails than _uSleep()_. Mean values are grouped together in the range between 0.1 and 1 ms, except the Virtual Box _uSleep()_ case, while median values are around 100 ns in the case of _HPTSleep_ (7.6 ms for Virtual Box) and 50 \(\upmu\)s in case of _uSleep()_. However, median values of _HPTSleep_ differ by 15% max, while medians of _uSleep()_ function differ by up to 45% (ignoring Virtual Box). In addition, the CCDF shows that outcomes of the _HPTSleep_ function have a miss time below 120 ns with a probability 0.999 for HostOS, Xen, QEMU, and ESXi. _HPTSleep_ for Virtual Box keeps to the right, because of the higher order of values in comparison to Host OS and Xen. QEMU and VMware ESXi behave almost the same as Host OS and Xen.  
Figure 11: CCDF representation of measurements of miss time for the idle testbed.  
Figure 12: Measurements of miss time for the CPU testbed on: (**a**) Host OS; (**b**) Xen; (**c**) Virtual Box; (**d**) QEMU; and (**e**) VMware ESXi.
## 4 QDAR Mechanism Overview  
### The QDAR Mechanism  
The QDAR mechanism is designed for the overall routing process. There are five phases in QDAR mechanism: data_ready phase, routing decision, interest phase, packet forwarding and acknowledgement. The data_ready phase is responsible for information collection, after which the sink node can get the fundamental information for routing decision. Then, the sink node determines the path according to QDAR algorithm. During the interest phase, the sink node sends a packet to the source node along the determined path. Thus, the path can be constructed. Packets are sent along the path thereafter as long as the acknowledgement phase is successful. Otherwise, the whole mechanism restarts. The main communication procedure is briefly depicted as Figure 1.  
Figure 1: Q-learning based delay-aware routing mechanism.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 1 Introduction
Each time after the state estimation, the next optimized waypoint is solved and becomes an index to the control algorithm so that the UAV can reach the desired position and minimize the target localization uncertainty. The main contributions of this paper can be enumerated as follows. 1. It is derived that the target localization using the UAV with a visual platform is a 3D bearing-only target localization problem. 2. A closed-form method based on EIF for the simultaneous target localization and UAV trajectory optimization problem is presented, which well decouples the target localization from the UAV trajectory optimization. By using the predicted estimate of the target estimate and taking account of the process noise in the trajectory optimization phase, the optimized next waypoint will become more effective than existing methods. 3. By analyzing the valid information of the information matrix, the position-related information matrix is extracted, which can facilitate the numerical computation for the optimization objective of the next waypoint. The remainder of this paper is outlined as follows. Section 2 introduces the system and principle of target localization, and derives that the target localization problem using the UAV with a visual platform is a 3D bearing-only target localization problem; Section 3 presents the simultaneous target localization and UAV trajectory optimization method based on EIF; Section 4 gives the simulations, field experiments and relevant discussions; and Section 5 concludes this paper and introduces the future work.
## 3 Methodology
### Procedure of Conflict Detection and Resolution
As shown in Figure 8, vehicle A goes along lane W2, which will occupy the first, second, third and fourth conflict points successively, and vehicle B goes along lane S2, which will correspondingly occupy the fourth, fifth, sixth and seventh conflict points. Each vehicle will occupy the conflict points on the path for a while and leave black time blocks on the graph. There will be no collision if they do not occupy the fourth conflict point at the same time. Otherwise, the overlapping areas will be marked red, representing a collision accident. The calculation method of the occupation time will be introduced later. The procedures of conflict detection and resolution are introduced as follows (as shown in Figure 9):  
* **Step 1:**: Assume that all the vehicles will communicate with the intersection manager in turn according to their arrival time after entering the communication area. Next, vehicles will report their motion information, including initial speed (in the range of 10 to 50 km/h), arrival time, and location via V2I technologies. At the same time, the intersection manager will send a speed regulation message to the vehicle, and the design speed is set to 30 km/h by default in order to keep a balance between efficiency and safety (speed limit is set to 50 km/h). * **Step 2:**: The intersection manager will predict the spatial-temporal trajectory of the vehicle and conduct a conflict detection procedure to check whether there will be a collision. (1) In the simulation, the vehicle will speed up or slow down to the speed designated by the intersection manager with a constant acceleration (1 m/s\({}^{2}\)) in the approach road. (2) It will keep the designated speed while going through the rest of the approach road and intersection. (3) The intersection manager will record the time occupied by the vehicle for each conflict and check whether it  
Figure 8: Samples of conflict-duration graph. (**a**) Collision-free; (**b**) collision. Figure 7: State of occupation. (**a**) Start of occupation; (**b**) end of occupation; (**c**) collision-free; (**d**) collision. conflicts with other vehicles. If there will be a collision, the intersection manager will adjust the initial default design speed to ensure safety. The conflict resolution method that is the calculation method of the modified speed will be introduced later. * **Step 3:** After a rigorous conflict detection performed by the intersection manager, the vehicle will follow the instructions that were proven safe to pass the intersection without collision. After that, it will speed up to the speed limit in the departure road to improve efficiency.