esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Resolutional Analysis Results
### HMS System Resolution Analysis Based on RFP Extraction  
Similar to the RDG extraction, the RFP extraction for the reference SAR image and the de-speckled images were carried out and are presented in Figure 10. According to the results, it can be deduced that the RFP peak-to-peak difference between the reference image and the de-speckled samples are not only the same, but also have few differences in magnitude, with a value of 0.2 dB. In comparison to the RDG simulations and according to Figures 8 and 9, the RFP extraction is more robust to spatial de-speckling filter implementation than the RDG. In other words, the RDG or received 2-D backscattered data is more sensitive to the phase inconsistency and the HMS presence, while the RFP is less sensitive and mostly dependent on the system parameters. This means that radar spatial resolution can be easily affected by high-frequency errors such as geometric and radiometric loss, specifically while dealing with phase coherency, while the RFP as another system resolution criteria is less sensitive to the presence of such errors. Figure 11 also presents the RFP of the reference SAR image in the presence of real noise and noise-free conditions.  
Figure 8: Resulting RDG presentation (**a**) Reference, Frost and Gamma (**b**) Kuan, Lee (**c**) Lee sigma, Local adaptive median and Local sigma—resulting RDG differences (**d**) Frost, Gamma and Kuan (**e**) Lee, Lee sigma, Local adaptive median and Local sigma—RDG evaluation.  
Figure 9: Reference RDG evaluation in the presence of noise and compensated noise conditions.  
As can be seen from Figure 11, HMS also has an effect on the RFP, based on instantaneous changes. However, in comparison to Figure 9, there is less change to the magnitude. In brief, the RFP mainly deals with the system resolution while dealing with the waveform in the receive procedure, but the RDG mainly deals with the waveform while being transmitted and interacting with the CCT. As can be deduced from Figures 7-11, the HMS has adverse effects that are more important to the phase history of the backscattered data in comparison to their adverse presence while being processed as part of the signal in the IFA. The results also verify that undertaking CTT studies on aspects such as their complex values, behaviors and specifications in the presence of HMS is difficult, and involves errors that must be considered in comparison to the received signal or image processing.
## 4 Experiment and Result Analysis  
### Experimental Environment and Parameter Settings  
The experiment is based on the deep learning framework Pytorch, and the programming language is Python 3.6. In terms of experimental hardware, the CPU is Intel(R) Core(TM) i7-10750H CPU @ 2.60 GHz, the memory is 16 GB, the GPU is Nvidia Geforce RTX 2060, the video memory is 6 GB, and the CUDA version is 10.2. The prior probability \(p_{pr}\) was set to 0.5, and the final decision threshold of \(p(o)\) was set to 0.3. During training, the batch size was 8, the initial learning rate of the network was set to 0.0001, and the total iteration was 50. Adam and the cross entropy were used as the network optimization method and the loss function, respectively. The network was trained using a pretrained model.
# Effect of Different Drying Methods on the Quality of _Oudemansiella raphanipes_
## 3 Results and Analysis
### Effects of Different Drying Methods on the Volatile Substances of O. raphanipes
For hydrocarbons, more alkanes and olefins were detected in all three samples, and the response intensity of alkanes in the vacuum freeze-dried samples was higher, which may be due to the fact that with the loss of water in the samples, the volatile flavor components of olefins changed under the low-temperature and vacuum conditions, and the spatial structure changed [53], resulting in the generation of a variety of alkanes. The flavor threshold of hydrocarbons is generally high and has little effect on the overall flavor change of raw materials. However, some olefins have unique flavors, such as D-limonene, which can give a fresh orange aroma and lemon aroma to food products. In addition, longifolia alkene and \(\Delta\)-juniperene can contribute herbaceous and woody aromas, respectively. Finally, the three different drying treatments also yielded acid and benzene compounds and a range of other substances, including pyrazines, amines, furans, ethers, and naphthalenes. The response intensity of pyrazine compounds is higher after hot air drying and infrared radiation drying, which is due to the fact that infrared radiation drying makes the internal reaction of _O. raphanipes_ more intense, while the high temperature of hot air drying treatment makes it undergo the Meladic reaction, which generates pyrazine compounds with a nutty flavor. In addition, 2-n-pentylfuran contributes to the flavor formation of _O. raphanipes_ by providing a fruity aroma. PCA analysis was performed based on the identification results of GC-MS, and Figure 2 shows the differences between samples, with the first two principal components (PCs) accounting for 66.4% and 29.2% of the total variance, respectively, and a cumulative contribution of 95.6% for PC1 and PC2. From Figure 3, it can be further visualized that the volatile flavor substances in the _O. raphanipes_ treated by the three drying methods were different from each other. The volatile compounds of Sample A were mainly concentrated in substances with higher flavor thresholds such as aldehydes and ketones, while those of Sample B were dispersed in alkanes and acids, and most of the compounds with lower flavor thresholds, such as alcohols, alkanes, and esters, were mainly concentrated in Sample C, where the differences were more significant.
## 2 Time-Domain Finite Integration Theory (TD-FIT)  
Similar to the Yee-cell of FDTD method, in FIT, discretized electric field components are allocated along the edges of primal grid \(G\) while discretized magnetic field components are allocated along the edges of dual grid \(\widetilde{G}\). The hexahedral, orthogonal primal-dual grid pairs are staggered with each other by half a lattice length, as shown in Figure 3.
# Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols
### Source Tree Adaptive Routing (STAR)  
In a network context, such as the internet or an ad hoc network, the STAR protocol is intended to be utilized by mobile as well as stationary nodes. This is because the protocol was designed to be universally applicable. The parameters of the source routing tree are necessary for the neighboring routers of a STAR router. The source routing tree contains every connection that the router needs to have in order to communicate with any host or combination of hosts that are located on the internet or in the ad hoc network. After gaining knowledge of new destinations, the likelihood of loops, the chance of node failure, or the splitting of a network, a router will only update the source routing tree that it uses to route traffic. Because of this, the router's data transfer capability and power consumption will both be reduced.  
The STAR routing protocol can be implemented in a variety of distinct ways, such as with an ORA or a least-overhead routing strategy, to name just a couple of the available options. This is sometimes abbreviated to ORA, which stands for \"optimal routing approach\" (LORA). The first strategy takes into account several statistics in order to determine which links between nodes will be the most beneficial. The effectiveness of this method does notdepend on the total quantity of messages to be routed; rather, it is determined by how frequently the routing table is refreshed. The LORA technique, on the other hand, stays true to its name by putting an emphasis on the achievement of instantaneous message transmission at the price of optimality in accordance with the ORA requirements. This analysis concentrates on STAR-LORA since it offers a more direct comparison with on-demand routing protocols and is more interesting algorithmically. Additionally, this comparison can be made more easily.
# Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario
## 3 Analysis of Channel Characteristics
### Optical Path Loss
Compared to the physical size of the sub-gallery link (8 \(\times\) 10 m\({}^{2}\)), the large physical size is set in the simulation environment (10 \(\times\) 30 m\({}^{2}\)). As discussed in [29], the longer link spans means that the light rays take long paths to the RX, i.e., it is a higher OPL that affects the system's performance. Furthermore, a multitude of reflective surfaces in the IIoT scenario can also lead to fading due to multi-path propagation [30]. Meanwhile, we investigate how the different user heights affect the OPL for VLC channels in IIoT scenarios. The FI model is no longer suitable for fitting the OPL due to the random nature of the light under multiple TX cases. As a result of extensive simulation studies, the normal distribution is used to fit the OPL data [14]. Figure 3b gives the cumulative distribution function (CDF) fitting line of the OPL samples for the VLC channels in Road 1. The mean value of OPL (\(\mu\) = 37.33) at h = 2.5 m is smaller than that at h = 1.5 m (\(\mu\) = 37.84). This result indicates that the OPL increases when the user is set to be lower. The difference is due to the occlusion from surrounding objects, whose average heights are set to 2 m. At h = 1.5 m, most of the objects are higher than the users. The reflective multipath is blocked by the objects, resulting in heavy shadow fading [31]. Hence, the received optical power is weakened. Meanwhile, at h = 2.5 m, the surrounding objects are generally lower than the users. There is less occlusion to the light rays, and more multipaths can be received by the RX, i.e., smaller OPL at h = 2.5 m. Furthermore, the mean values of the OPL fitting results for VLC channels in the IIOT scenario are larger than that for the indoor hospital (60-70 dB) in the [14]. This can be explained by the number of LEDs deployed on the ceiling. Compared with the number of LEDs (15) setting in the [14], the IIoT environment in this paper is deployed with 21 LEDs, i.e., contributing much more MPCs received by the RX. Hence, the OPL is smaller in the IIoT scenario. Figure 3: (a) The distance dependence of OPL under single TX case; (b) the CDF of OPL under the multiple TX case in Road 1.
###### Abstract  
The braking energy can be recovered and recycled by the regenerative braking system, which is significant to improve economics and environmental effect of the hydraulic hybrid vehicle. Influencing factors for the energy recovery rate of regenerative braking system in hydraulic hybrid vehicle were investigated in this study. Based on the theoretical analysis of accumulator and energy recovery rate, modeling of the regenerative braking system and its energy management strategy was conducted in the simulation platform of LMS Imagine Lab AMSEim. The simulation results indicated that the influencing factors included braking intensity, initial pressure of the accumulator, and initial braking speed, and the optimal energy recovery rate of 87.61% was achieved when the parameters were 0.4, 19 MPa, and 300 rpm, respectively. Experimental bench was constructed and a series of experiments on energy recovery rate with different parameters were conducted, which aimed to validate the simulation results. It could be found, that with the optimal parameters obtained in the simulation process, the actual energy recovery rate achieved in the experiment was 83.33%, which was almost consistent with the simulation result. The obtained high energy recovery rate would promote the application of regenerative braking system in the hydraulic hybrid vehicle.  
energy recovery rate; hydraulic hybrid vehicle; regenerative braking system; braking intensity; initial pressure of the accumulator; initial braking speed; energy management strategy
## 1 Introduction
This article describes a technique to realize an UWB antenna array that has two wide rejection bands to eliminate interference signals from unwanted wireless systems that operate in the ultra-wideband frequency domain. This is accomplished by applying metamaterial (MTM) and EBG techniques. Application of MTM extends the operationally bandwidth of the array, and EBG is used to create notched bands at the interfering frequencies. The simulation results of the design were verified using two different 3-D full-wave electromagnetic tools.
## 2 Establishment of Disturbance Model
### Disturbance Model of Single Support Cylinder
#### 2.2.3 Support Stiffness  
When the support pressure of the upper and lower chambers and the volume parameters of the upper and lower chambers change, the support stiffness of the FSS changes.  
\[C_{K}=C_{1}+C_{2}+2C_{p}+C_{Bl}+2C_{Bs} \tag{23}\]  
where \(C_{k}\) is the total stiffness of the support cylinder, \(C_{1}\) is the stiffness of the upper chamber, \(C_{2}\) is the stiffness of the lower chamber, \(C_{p}\) is the stiffness of the beryllium bronze reed, \(C_{Bl}\) is the stiffness of the small diaphragm, \(C_{Bs}\) is the stiffness of the large diaphragm.  
\[\begin{array}{l}C_{1}=\frac{\Delta P_{i}S}{\Delta L}\\ C_{2}=\frac{\Delta P_{i}S}{\Delta L}\end{array} \tag{24}\]  
where \(\Delta P_{u}\) is the pressure change in the upper chamber, and \(\Delta P_{s}\) is the pressure change in the lower chamber.  
Based on the above, the relationship between temperature change and FSS performance under different air contents is shown in Figure 3.  
Figure 3: Perturbation affecting relationship.  
As shown in Figure 3, the hydraulic oil in the FSS contains air. During the working process, the change in ambient temperature will cause a change in the volume and pressure of the upper and lower chambers of the support cylinder. The air content in the working medium affects the change in volume and pressure. When the support shaft assumes different working positions, owing to the different volume capacities of the upper and lower chambers, the volume and pressure changes of the upper and lower chambers are also different. Changes in the volume and pressure of the upper and lower chambers of the support cylinder will cause changes in the support height, support pressure, and support stiffness, which will lead to the deformation of the supported optical mirror and affect the surface forming accuracy of the optical mirror surface.
## 3 Results and Discussion
### Molecular Docking of Abundant Phenolic Compounds
Comparatively, quercetin, hesperetin, myricetin, luteolin, and isorhamnetin, along with (-)-epicatechin, have lower binding affinities than 3-\(p\)-coumaoylquinic acid. The calculated glide energy of myricitrin, punicafolin, rutin, chlorogenic acid, acarbose, procyanidin B2, punicalagin, 3-feruloylquinic acid, diosmin, quercetin-3-O-arabinoside, naringin, 3-\(p\)-coumaroylquinic acid, myriectin, apigenin 8-C-glucose, quercetin, kaempferol, ellagic acid, carnosic acid, isorhametin, luteolin, hesperetin, (\(-\))-epicatechin, protocatechu acid, 3-O-sinapoylquinic acid, isorhametin, caffe acid, pyrogallol, \(p\)-hydroxybenzoic acid, taxifolin, \(p\)-coumaric acid, gallic acid and cinnamic acid in 5 NN8 were observed as 63.23, \(-\)71.81, \(-\)69.71, \(-\)58.68, \(-\)58.26, \(-\)56.13, \(-\)72.90, 59.38, \(-\)54.35, 51.48, \(-\)55. Figure 5: \(Cont\). Figure 5: Chemometrics analysis of phytochemicals in selected herbs and spices, including basil (B), mint (M), fenugreek (F), fennel (Fe), and coriander (Co). Debiased Sparse Partial Correlation Network (**A**), [PERSON]’s correlation heatmap (**B**), Partial Least Squares Discriminant Analysis VIP score (**C**), Correlation coefficients between rosmarinic acid (**D**). Debiased Sparse Partial Correlation Network Figure 5A clearly indicates a strong networking of rosmarinic acid, sagerinic acid and carnosol with all other abundant phytochemicals. [PERSON]'s correlation (Figure 5B) and correlation coefficients (Figure 5D) indicate that rosmarinic acid has the strongest interaction with 3-caffeoylquinic acid, caffeic acid, syringic acid, rosmanol and catechin. Figure 5C indicates the concentration and VIP score of individual phytochemicals in selected herbs and spices. Gallocatechin has the highest VIP score and is observed in higher concentrations in mint. It is observed that selected herbs and spices contain a diverse range of phytochemicals that are beneficial for human health.
## 2 Materials and Methods
### Predictive Algorithm Selection
#### 2.2.2 Performance Prediction Using Intelligent Algorithms  
Although CFD modeling provides valuable insights for analysis and optimization, conventional approaches often entail prohibitive computational complexity and time constraints. In contrast, artificial intelligence algorithms can establish input-output mappings without explicit reliance on physical laws, enabling rapid predictions with high accuracy [43,44]. Commonly employed algorithms include artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and extreme gradient boosting (XGBoost). To comparatively assess the predictive performance, regression models were developed using nine distinct algorithms. The coefficient of determination (R\({}^{2}\)) and mean absolute error (MAE), as formulated in Equations (14) and (15), were employed to evaluate the suitability of the regression equations.  
\[R^{2}=1-\frac{RSS}{TSS}=1-\frac{\sum(y_{\mathrm{i}}-y_{\mathrm{i}}^{*})^{2}}{ \sum(y_{\mathrm{i}}-\overline{y})^{2}} \tag{14}\]  
\[MAE=\frac{1}{\mathrm{N}}\mid(y-y^{*})\mid \tag{15}\]  
where \(RSS\) and \(TSS\) denote the residual sum of squares and total sum of squares, respectively; \(y_{\mathrm{i}}\) is the actual value, \(y_{\mathrm{i}}^{*}\) is the output value predicted by the artificial intelligence algorithms, and \(\overline{y}\) is the mean of the actual values.  
The coefficient of determination (\(R^{2}\)) ranges between 0 and 1, and when this value is closer to 1, the lower mean absolute error (\(MAE\)) indicates superior model fitness.
## 5 Proposed Multi-EC Enabled Air-Ground VN Scenarios
### ML for Computation Offloading in Multi-Service Multi-EC Vehicular Environments
#### 5.2.2 Possible Challenges  
The main challenges that need to be considered during computation offloading over a Multi-EC multi-service vehicular environment are described below.  
* **Resource limitations of EC nodes:** Each EC node can have limited communication, computing, and storage resources through which they can only serve a limited number of users with acceptable costs. Furthermore, they can hold a limited number of services that can be accessed by users mainly because of resource limits.
* **Transmission Distance vs. Capacity Tradeoff:** For the case of Multi-EC multi-service VNs, an important coverage-vs-capacity tradeoff needs to be considered, while selecting an EC platform and an amount to be offloaded. The nearby EC platforms, such as RSUs and UAVs, can have a limited capacity and coverage range. Therefore they can serve only a few users with acceptable latency. On the other hand, 5G BS and HAP can have powerful EC servers with a better coverage range. However, they are located at further distances from VUs and can have longer transmission distances with poor quality channel environments. Therefore, a proper EC selection needs to be done for taking advantage of EC benefits from different platforms.
* **Vulnerability of EC servers towards Third-party Attacks:** It is often the case that during computation offloading VUs shares a piece of sensitive information, including location, speed, possible directions/destination with the EC servers. If EC is vulnerable to third-party attacks, it can be dangerous to share such sensitive data with them, especially in the case of autonomous driving environments where an attacker can take control of vehicles and can unimaginable damages.
* **VUs Mobility and a Handover Latency:** In dynamic environments, like VNs, VUs mobility limits the computation offloading performance. The offloading process should be completed before VUs pass through the coverage range of an EC node, failure of which can add additional costs in terms of handover latency. VUs mobility can also impact the channel characteristics in terms of unstable channel behaviors, Doppler spread, etc, which can seriously degrade the offloading performance. In the past, researchers have adopted different approaches for modeling VUs mobility. In some cases, mobility characteristics are analyzed with mathematical frameworks (i.e., mobility models) with certain assumptions over VUs speed, directions, surrounding environments, etc. In some other cases, researchers have resorted to the trajectory predictions of VUs movements. In particular, the VUs mobility parameters can be used for trajectory prediction; thanks to this we are able to predict how the system resources can be used and in case reserve them. In the past, some authors have considered such approaches [49].
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 6 Experiment and Analysis
### Robust Analysis  
We studied the coverage completion time and coverage rate of GGC under the condition of no abnormality (failure of individual robots). In large-scale swarm robots, the failure of individual robots is inevitable. Therefore, in the case of a robot failure, it is necessary to analyze the robot's influence on the experimental results.  
The environment was kept consistent, the number of robot groups was set as 3, and the coverage distance was set as 6. In the gradient generation process and the self-organizing coverage process of the robot, 0, 1, 5, 10, and 20 robots were randomly set to fail, which made the robot unable to carry out any work. The experimental results obtained are shown in Figure 23. Figure 23a shows that in the process of gradient formation, a different number of individual robots cannot obtain the gradient of neighbor robots, so the gradient cannot be formed normally. The coverage rate curves shown in the figure roughly coincide. When there are 20 robots with failures, the early coverage speed is faster than the early coverage speed when the other number of robots fail, and this is because the failure robot is close to the leader robot, and the fault robot is at the edge of the swarm robot team. At this point, the gradient of the failure robot is infinite, so that it is in the coverage state first, which reduces the time it takes to move around other robots and faster enters the area to be covered. Figure 23b is the coverage rate and time curve of failures of individual robots in the coverage process. As can be seen from Figure 23b, the curves obtained in all abnormal cases are roughly coincident. When there are 20 robots with failures, the time of other robots moving around the failed robots is increased to some extent, therefore, when N is 20, the coverage speed of the swarm robot is slightly slower in the later stage. In general, the GGC algorithm has good robustness when the robot fails.