esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 4 CubeSat Design  
Current CubeSat design methods require a team of experts, who use their engineering expertise and decision to develop a spacecraft design from available COTS components. Such an approach may not result in an optimal design in terms of mass, volume, and power capabilities and can also miss innovative designs not thought of by the human design team. As such, the CubeSat design problem is modeled as a multidimensional multiple-choice knapsack problem. The goal is to effectively package subsystems from an inventory of COTS components within a specified mass and volume constraint such that the spacecraft can meet the defined mission goals. As a test example, the objective is to design a 3U nadir-pointing, earth-observing CubeSat in LEO. The CubeSat is composed of many components that can be broken down into six major subsystems: (a) structure, (b) command and data handling (on-board computer), (c) communication (transceiver and antenna), (d) power (solar panels, battery pack, and power management board), (e) attitude determination and control unit (reaction wheels, magnetorquers, sun sensor, accelerometer, star tracker, magnetometer), and (f) payload (camera and lens) [1]. At first, an inventory of all the components is created from major CubeSat vendors (GomSpace, ISIS, Nanoracks, EnduroSat, DHV, NanoAvionics, Maryland Aerospace, Blue Canyon, Clydespace, Pumpkin, Space Micro, Rincon Research) which is used as a database for the Knapsack Problem. To solve the MMKP, an EA is used where a candidate population is evolved for hundreds of generations using evolutionary operators like selection, crossover, and mutation. Each individual in the population is defined by a gene, \(\mathcal{G}\), which specifies what and how many components are to be packaged inside the spacecraft. This keeps the gene and design process simple and produces results fast using desktop computers. There are also other approaches to automated design, and these include the use of variable length generative coding schemes [28] that generate a constructive program to design the gene. Other bio-inspired approaches model morphogenesis [30]. An example gene structure is shown in Table 1. It consists of two parts: the first part is the subsystem specifier {sID, cID, aID, tID, bID, pID, adID, spID, caID} that selects components from the available database, and the second part selects the number of batteries {\(n_{b}\)} and the number and location of solar panels: body panels {b1, b2, b3, b4}, side deployed panels {ds1, ds2, ds3, ds4}, and top deployed panels {dt1, dt2, dt3, dt4}. Figure 3 shows an example of a phenotype of the CubeSat rendered in MATLAB VRML. A script for automated assembly of all the components inside the structure is written such that each component is modeled as a cube with its maximum dimensions, and a spacing of 5 mm between components is provided for the wiring space requirement.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
###### Abstract
* The antenna's overall volume is increased by the air gap between the substrates. This research presents a comprehensive optimization process for antenna design for 5G sub-6 GHz applications, focusing on multiple performance metrics. The innovative combination of fractal geometries and FSSs, SRRs, and comprehensive optimization sets a new standard for miniaturized, tunable, and high-performance antennas. The proposed FSS-based miniaturized fractal antenna demonstrates significant advancements in terms of size reduction, tunability, and performance enhancement, making it a promising solution for next-generation 5G applications. The research design is innovative, as the FSS is designed to be smaller than the main antenna size, making the antennas economically feasible. This research provides a comprehensive approach to antenna design, addressing the current limitations and setting a new benchmark in RF communication applications. The overall volume of the antenna is reduced by maintaining a lower air gap. This research contributes to tunability of the resonating frequency at the designed frequency by reducing the air gaps, while maintaining the antenna gain by balancing the tunability and gain enhancement conditions. The design complexity is reduced by designing the simplest FSS design structure and reducing the FSS size to be lower than the antenna size. This antenna is applicable not only for the sub-6 GHz band but also for C-band applications. This work is structured as follows. Section 2 explains the design's development. Section 3 outlines the parametric investigation and the measured and simulated results' analysis. Finally, the work is concluded in Section 4 by highlighting the antenna's limitations, applications, and future scope.
# Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization
## 4 Where Are the APs?  
One issue that may have dampened enthusiasm for the deployment of localization using Wi-Fi RTT is that the positions of the APs need to be known. Installation of APs may be based on a marked-up floor plan with the desired coordinates. But such plans may not be available, may not be accurate or may not be up to date, given that APs are replaced when faulty, and are moved when \"holes\" in Wi-Fi coverage are noted.  
Wi-Fi APs supporting IEEE 802.11 mc do have the opportunity to encode their own location and provide it to the UE using the Location Configuration Information (LCI) or the Location Civic Report (LCR) fields in the \"Measurement Report Element\" [32; 44], but setting these fields has not yet been widely supported by AP makers. This is about to change as major vendors such as HPE/Aruba [45], Cisco/Meraki [46], Juniper [47], Extreme Networks [48], and Zebra [49] start to support AP location information and automated determination of AP locations. These systems can use RTT ranging between APs, in addition to known coordinates of a small number of \"anchor\" APs (whose position may, for example, be obtained via some form of high-accuracy GNSS) [50].  
For APs in areas where GNSS is available (e.g., near the outside walls of buildings or under non-metallic roofs), it is also possible to obtain estimated positions using crowdsourced data such as those made available by Google's geolocation API [51]. There are some limitations to this, since what is reported is based in part on the weighted centroid of the GNSS-determined locations of smartphones that were near enough to the AP to receive a signal, not the location of the AP itself. This can be problematic when directional antennas (such as panel antennas) are used or when most of the pedestrian traffic is off to one side of the AP. Also, this service can only be provided for \"universally administered\" MAC addresses. Typically APs support several Basic Service Set Identifiers (BSSIDs), many of which are \"locally administered\". These are not recorded by the geolocation API because they are not globally unique.  
In any case, what is needed is an automated way of determining where the APs are.
# Improving VulRepair's Perfect Prediction by Leveraging the LION Optimizer
## 3 Methodology
### Improving VulRepair: ImpVulRepair  
To improve VulRepair, we constructed ImpVulRepair by looking at the hyperparameters of VulRepair. The hyperparameters we focused on were the optimizer, learning rate, weight decay, batch size, encoder and decoder block sizes, and momentum. These hyperparameters influence the learning process of a model. We adjusted the hyperparameters to improve performance and searched for a current optimizer. When choosing an optimizer, various options were explored, including the LION optimizer, as discussed in research articles such as [32]. The decision was made based on the characteristics described for each optimizer, assessing their efficiency and determining their compatibility with the T5 neural network.  
When selecting values for the other hyperparameters, we used a different approach. There are different methods for optimizing hyperparameters, such as manual search, random search, grid search, halving search, automated tuning, artificial neural network tuning, etc. [33]. We used a semi-random search along with information on optimization from a research paper by [PERSON] et al. [32] to evaluate the hyperparameter values. We used the evaluation loss from training as our guide when selecting or tuning the hyperparameters since testing could take more than 5 h. We selected hyperparameter values based on the lowest loss. Training also takes time. Although it may not be ideal, we used only one epoch to find the best hyperparameter values. We used this method when reviewing the learning rate, weight decay, batch size, encoder/decoder block sizes, and betas. This was not an exhaustive grid search, and there could be other combinations that yield a better model.
## 3 Results
### Multi-Block Analysis  
#### 3.3.1 SO-PLS-LDA Analysis  
The sequential data fusion model was built using data preprocessed by the optimal pretreatment selected in individual analysis: first derivative. Building SO-PLS-LDA models, the optimal number of latent variables, six for X\({}_{1}\) and seven for X\({}_{2}\), was selected based on a cross-validation procedure. The corresponding optimal classification model provided a sensitivity of 98.7% and a specificity of 98.0% for Class Sorrento and vice versa for Class Non-Sorrento (i.e., 98.0% sensitivity and 98.7% specificity). When this SO-PLS-LDA model was used to predict validation samples, the classification rates were extremely satisfying. In fact, it correctly classified all test samples except one belonging to Class Non-Sorrento. In Figure 6 the histograms representing the scores of the training and test samples along the canonical variate are displayed both as scatterplot (panel a) and as histograms (panels b and c). Taking a look at this graphical representation of the results, it appears that samples belonging to Class Sorrento (red bars) present negative value of the canonical variate score; on the contrary, Non-Sorrento  
Figure 5: PLS-DA analysis: Predicted Y vs. sample index. Legend: Class Sorrento: Red diamonds; Class Non-Sorrento: Blue squares. The black dashed line represents the threshold between the two classes (Sorrento for y > threshold; Non-Sorrento for y < threshold). Empty and filled symbols represent training and test samples, respectively.  
samples present positive or slightly negative values of CV1. The misclassified test sample is the Non-Sorrento object whose score on the canonical variate is at around \(-0.1\). VIP analysis has been carried out also on this model, following the procedure described in [58]. The selected variables are approximately the same highlighted before, so the discussion is not reported here; the graphical representation is displayed in Figure A2 in Appendix A.
## 2 Materials and Methods
### Behavioral Experiment  
Because the results of the previous experiment obtained different semiochemistry in the honeydew of aphids reared under aCO\({}_{2}\) and eCO\({}_{2}\) concentrations, we tested the hypothesis of a cascade effect on the searching and oviposition behaviors of an aphid predator. Young plants (i.e., phenological stage 10 on the BBCH scale) grown either under aCO\({}_{2}\) or eCO\({}_{2}\) conditions were isolated in plastic containers (50 cm\({}^{3}\)) filled with perlite:vermiculite substrate and 30 mL of water. The plants were covered with 35 \(\upmu\)L fresh honeydew that had been collected from the corresponding CO\({}_{2}\) treatment (see Section 2.2). This amount was previously shown to induce oviposition in hoverflies [16; 17]. According to our quantification assays, this volume represents a quantity of honeydew excreted by 50 adult aphids over 24 h. After honeydew deposition, plants were left over for 30 min to let the honeydew dry. The behavioral experiment started with the introduction of a single mated and gravid hoverfly female in a flight cage (25 \(\times\) 25 \(\times\) 65 cm; Bugdorm, Megaview Science, Taiwan) in the presence of two plants, one from each CO\({}_{2}\) condition. One hour after the beginning of the experiment, behavioral observations were made using the software The Observer XT (Noldus Technology(r), Wageningen, The Netherlands) for 30 min. During observations, the duration of the following behaviors were recorded: (i) random flight and landing on the cage; (ii) stationary-oriented flight near a plant; (iii) walking on a plant; and (iv) oviposition (the lowerly extended its abdomen and touched the plant stem or leaf with its ovipositor). Here again, the experimenter was considered naive, with no knowledge of the CO\({}_{2}\) condition that the plant and honeydew were obtained from. The number of eggs laid on each plant was counted for four hours after introducing the female. The experiments were conducted under laboratory conditions (T: 21 \(\pm\) 1 \({}^{\circ}\)C; RH: 60 \(\pm\) 10%; [CO\({}_{2}\)]: 450 \(\pm\) 50 ppm). Hoverfly females were 17 to 24 days old and had not previously been exposed to aphid products. The behavior of 20 females was recorded.
## 3 Experimental Setup
To determine the optimal bias voltages for different equalizers in this system, we incrementally increased the bias voltage from 3.5 V to 4.5 V during experiments. The PA-DFE, MA-DFE, PA-FFE, and MA-FFE were used to equalize the PAM4 signals under different bias voltages, which were captured in a back-to-back (BtB) case with an ROP of \(-22\) dBm. Figure 4b illustrates the bias voltage versus BER curves for different equalizers. It can be seen that the PA-FFE and MA-FFE achieve the lowest BER at a bias voltage of 4.175 V (orthogonal point: Q1), while the PA-DFE and MA-DFE reach their lowest BER at 3.825 V (non-orthogonal point: Q2). We define the extinction ratio of PAM4 as the ratio of the maximum amplitude to the minimum amplitude of the signal. To show the difference in extinction ratio more clearly, Figure 4c,d show the constellation diagrams of the received PAM4 signals at low rates when the bias points are Q1 and Q2. It can be seen that the amplitude of the PAM4 signal at the quadrature point Q1 is in the range of [0.5, 3], while the amplitude of the PAM4 signal at the non-quadrature Q2 point is in the range of [0, 3]. Therefore, the extinction ratio of the PAM4 signal at Q2 is higher. Although the non-orthogonal point introduces nonlinear distortion, the MA-DFE and PA-DFE can effectively compensate for signal nonlinear distortions, and thus the advantage of high extinction ratio is presented.
## 2 Vehicle Dynamics Model and Model Verification
### Fourteen-Degree-of-Freedom Vehicle Model
#### 2.1.3. Suspension Motion Equations  
Suspension model is shown in Figure 2. As shown in Figures 1 and 2, the body and wheel vertical motion equations are as follows:  
\[m_{s}(\dot{w}+pv-qu)=\frac{F_{vl1}}{D_{l1}}+\frac{F_{vr1}}{D_{r1}}+\frac{F_{vl2}} {D_{l2}}+\frac{F_{vr2}}{D_{r2}} \tag{8}\]  
\[\left\{\begin{array}{l}m_{ul1}\dot{w}_{ul1}=K_{ul1}(Z_{rl1}-Z_{ul1})+C_{ul1} (w_{rl1}-w_{ul1})-\frac{F_{vl1}}{D_{l1}}\\ m_{ul2}\dot{w}_{ul2}=K_{ul2}(Z_{rl2}-Z_{ul2})+C_{ul2}(w_{rl2}-w_{ul2})-\frac{F_ {vl2}}{D_{r2}}\\ m_{ur1}\dot{w}_{ur1}=K_{ur1}(Z_{rr1}-Z_{ur1})+C_{ur1}(w_{rr1}-w_{ur1})-\frac{ F_{vl1}}{D_{r1}}\\ m_{ur2}\dot{w}_{ur2}=K_{ur2}(Z_{rr2}-Z_{ur2})+C_{ur2}(w_{rr2}-w_{ur2})-\frac{F_ {vl2}}{D_{r2}}\end{array}\right. \tag{9}\]  
The force on the suspension by each wheel can be expressed by the following equations:  
\[F_{vl1}=K_{sl1}(Z_{ul1}-Z_{s}+L_{1}\sin\theta+n_{l1}\sin\varphi)+C_{sl1}(Z_{ul1 }-w+L_{1}q\cos\theta+n_{l1}p\cos\varphi) \tag{10}\]  
\[F_{vl2}=K_{sl2}(Z_{ul2}-Z_{s}-L_{2}\sin\theta+n_{l2}\sin\varphi)+C_{sl2}(Z_{ ul2}-w-L_{2}q\cos\theta+n_{l2}p\cos\varphi) \tag{11}\]  
\[F_{vr1}=K_{sr1}(Z_{ur1}-Z_{s}+L_{1}\sin\theta-n_{rl1}\sin\varphi)+C_{sr1}(Z_{ ur1}-w+L_{1}q\cos\theta-n_{r1}p\cos\varphi) \tag{12}\]  
\[F_{vr2}=K_{sr2}(Z_{ur2}-Z_{s}-L_{2}\sin\theta-n_{r2}\sin\varphi)+C_{sr2}(Z_{ ur2}-w-L_{2}q\cos\theta-n_{r2}p\cos\varphi) \tag{13}\]  
where \(m_{u}\) is the unsprung mass, and \(\theta\) is the pitch angle of the vehicle. \(K_{sij}\) and \(K_{uij}\) are the stiffness coefficients of the spring and the wheel, respectively. \(C_{sij}\) and \(C_{uij}\) are the damping coefficients of the shock absorber and the wheel, respectively. \(Z_{sij}\) and \(Z_{uij}\) are the sprung and unsprung mass displacement of each wheel. \(Z_{rij}\) is the road roughness of each wheel.
## 1 Introduction  
Diarrhea is a common and important disease which seriously affects the growth development and mortality of piglets, and restricts economic benefit of pig industry. Previous studies indicated that diarrhea of piglets is mainly related to the gut microbiota dysbiosis and intestinal barrier function [1; 2; 3]. Therefore, to promote development of intestinal barrier function is an important way to alleviate diarrhea and improve the growth performance of newborn piglets. Growing evidence has suggested that the gut microbiota plays an important role in the development of the intestinal barrier function [4; 5; 6]. Therefore, accelerating the colonization of gut microbiota of newborn piglets may be an effective method to promote maturity of intestinal barrier function, reduce diarrhea and improve the growth performance of piglets.  
Gut microbiota has many roles benefiting the host. Many studies revealed that the early postnatal period is an early intervening window for regulating colonization and development of gut microbiota [7; 8; 9]. Fecal microbiota transplantation (FMT) is gradually being used in improving growth and regulating the development of gut microbiota in newborn piglets. Recent studies supported that feeding crossbred newborn piglets with FMT suspension of healthy adult pigs significantly promoted the growth performance and intestinal barrier function of newborn piglets [2; 7; 10]. Research indicated that the colonization of gut microbiota follows patterns of \"first come, first served\", homologous bacteria are more likely to colonize the recipient's gut, and hypoxic intestinal environment is more conducive to the colonization of symbiotic bacteria [11; 12; 13]. Probiotics exhibit a profound influence on maintaining intestinal homeostasis, regulating gut microbiota and reducing diarrhea [11; 13]. _Saccharomyces boulardii_ (SB) is beneficial for gut health [14; 15; 16] and could reduce the abundance of intestinal aerobic bacteria [17; 18]. _Clostridium butyricum_ (CB) is known as a regulator for gut health, and the dietary supplementation of CB can improve early intestinal barrier function [19; 20; 21; 22]. However, whether SB or/and CB could improve the effect of early intervention remains elusive.  
Here, we hypothesized that SB and CB may improve the effect of FMT through oxygen competition and enhancing the intestinal barrier function. Therefore, the objective of this study was to investigate the effects of FMT combining CB and SB in early intervention on growth performance, diarrhea rate, intestinal barrier function, fecal microbiota and production of short-chain fatty acids (SCFAs) in piglets, under an antibiotic-free condition; in addition, we set up the antibiotic-treated group as a positive control group.
## 4 Voting Algorithm
### Basic Definition and Rules of the Voting Algorithm
#### 4.2.2 Rules for Weight Increase and Decrease in Security Weight Value  
The increase or decrease in security weights are mainly reflected in the user's contribution to the network security; that is, the user's contribution to the network security events can increase the user's security weight, while threatening behavior to the network security can reduce the user's security weight. The rules are as follows:  
1. When the user participates in the voting process or reports misbehaving and malicious behavior to the network actively, the security weight is increased by one at one time.
2. When publishing invalid or other prohibited content, the user's security weight decreases with the number of times. The first time security weight is reduced by one, the second by two, and so on (the number of times is recorded by SNR nodes).
3. If there are verified attacks on other users or SNR nodes in the network, the first-time attacking user's security weight is halved and warned, and the second time, the public key is directly revoked.
# Synergistic Effects of Fuel Components on Aromatics Formation in Combustion: A Review
## 2 Formation Mechanisms of Benzene and PAHs
### Formation Mechanisms of PAHs
#### 2.2.3 Formation Mechanism with Radicals
[69] investigated the reaction of the indenyl radical with acetylene and vinylacetylene. They found that when 1-indenyl reacted with methyl radicals, it resulted in naphthalene formation instead of following the HACA and HAVA mechanisms. This discussion explained that the CH\({}_{3}\) radical exhibits slower kinetics in occupying the phenanthrene armchair position compared to C\({}_{2}\)H\({}_{2}\), revealing that the HACA mechanism outcompetes MAC in the presence of acetylene. In the interstellar medium, characterized by a carbon-rich environment, ethynyl formation has been observed in the decomposition of acetylene, in contrast to the diffused interstellar medium, rendering the ethynyl addition mechanism more feasible [86; 87]. [PERSON] et al. [88] conducted a semi-empirical quantum theoretical investigation to explore potential energy surfaces for chemical pathways. Their study revealed that PAH formed through ethynyl addition exhibits the lowest energy. [PERSON] et al. [87] proposed a mechanism for ethynyl addition as a viable alternative to HACA for the formation of PAH at low temperatures, particularly if ethynyl addition occurs due to photoinduction. Additionally, a mechanism involving hydrogen abstraction and ethynyl radical addition was suggested for the formation of benzo(a)pyrene from chrysene and benzo(a)anthracene, as illustrated in Figure 11[89]. Figure 7.5 Hydrogen abstraction and ethynyl addition for benzo(a) pyrene formation (adapted from [PERSON] et al., 2021) [89]. The solid and dashed arrows indicate single\(-\)step and multi\(-\)step reactions respectively. Figure 7.6 MAC mechanism for methyl addition on phenanthrene [85] for pyrene formation [61]. The solid and dashed arrows indicate single\(-\)step and multi\(-\)step reactions respectively. 2.2.4. Hydrogen Abstraction Phenylacetylene Addition (HAPaA) Mechanism  
HACA and HAVA mechanisms have demonstrated limitations in describing the formation of pre-condensed aromatic hydrocarbons (PCAHs) across different temperatures. [PERSON] et al. [90] proposed the HAPaA mechanism while conducting their experimental and theoretical investigation of the reaction between phenylacetylene and 1-naphthyl/4-phenanthryl radicals. Their findings emphasized the efficiency of this pathway in PCAH formation. Experimental studies were conducted using synchrotron vacuum ultraviolet photoionization and examined the reactions of 1-naphthyl with phenylacetylene and phenanthryl with phenylacetylene. Additionally, quantum chemical calculations elucidated the pathway for PAH formation in both interstellar and combustion mediums, as depicted in Figure 7.7