esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Correlation Analysis between Microbiota and Volatile Compounds
_Norank f_Mitochond_ correlated strongly positively with the following substances: Ethyl butyrate (V34) and Isopentyl hexanoate (V36). There was a strong positive correlation between _Leuconostoc_ and 1-hexanol in the fermentation of F15 (Figure 4D). _Pseudomonas_ and _Stenotrophomonas_ both had a strong positive correlation with the following substances: Isopentanol (V02), Isobutanol (V08), Ethyl caprylate (V19), Ethyl caproate (V24), Isoamyl acetate (V26), Phenethyl acetate (V33), and Ethyl butyrate (V34). _Lactobacillus_ and _Leuconostoc_ in wines degrade both citric acid and malic acid; therefore, they significantly affect the taste of wine and increase some aromas with a flavor complexity [40], indicating that the potential to produce aromatic substances by glycosidase activity [41] was a common core microorganism in the fermentation of 796, CEC01, CECA and F15. _Lactobacillus_ only appeared in the core microflora of CEC01 and CECA. According to the correlation data, _Lactobacillus_ and _Leuconostoc_ promoted the production of phenylethyl alcohol and phenylethyl acetate in the fermentation process of CEC01, which may bring a rose fragrance to the final wine [42]. _Pseudomonas_ is one of the most common bacteria in soil, which can also be separated from wine cork. It is related to the pyrazine compounds with a low sensory threshold, which may have a negative impact on wine flavor [43]. Pyrazine compounds were not detected in any samples in this experiment. _Pseudomonas_ was a common core microorganism in the fermentation of CEC01, CECA and F15. There was a strong positive correlation between _Pseudomonas_ and phenethyl acetate in the wine samples of CECA and F15. _Sphingomonas_ was a unique core microorganism in the fermentation of 796. _Sphingomonas_ can degrade carbon sources at an incubation temperature of 31 \({}^{\circ}\)C [44]. Earlier reports determined that _Sphingomonas_ positively correlated with fermentation rate [45], and _Sphingomonas_ can degrade aromatic compounds, which may lead to decreased methyl caprate and 4-ethylphenol [46]. In 796 fermented samples, this microorganism may have been related to the formation of such volatile substances as 3-Hexen-1-ol, Hexanal and 3-Octanone. In summary, the core microorganisms of different starters were different in the process of alcohol fermentation. Some microbes, such as certain bacteria, may have come from the vineyard environment. Although _Saccharomyces cerevisiae_ played a dominant role in the fermentation process, there were still some strains that may antagonize or cooperate with environmental microorganisms to inhibit or promote the synthesis of some aroma components [47]. The compositions of the core fermentation microorganisms of CECA and CEC01 were similar. The compositions of the core fermentation microorganisms of F15 and 796 were similar. This agreed with the PCA result of volatile aroma.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 2 Filters Integration, Topologies, and Impact in the Power Grid
### General Topolgies of Powerline Filters  
In terms of the safety rating (leakage current) and insertion loss, both are two topologies of PLC filters with and without Y capacitors, namely C\({}_{\text{y}}\), having a middle point connected to protective earth [21]; these types of filters are shown in Figure 3. All other types of passive filters are different arrangements starting from these two types of filters. The graph in Figure 4 shows the insertion loss (with symmetrical loading) of the two filters depicted in Figure 3, as simulated using SPICE (Simulation Program with Integrated Circuit Emphasis). The optional resistor has the function to dampen oscillations at resonant frequencies and to discharge the capacitors.  
Figure 3: Basic types of filters: (**a**) PLC filter without a Y capacitor and (**b**) PLC filter with a Y capacitor.  
Figure 2: PLC filters use cases in the smart grid.
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 4 Rockburst Mechanisms
### Rockburst Instability Mechanisms
This breakthrough in rock mechanics provided a better understanding of rock fracture mechanics [124] and revealed that the test machine's lower rigidity caused a rock specimen to fail violently compared to its unloading stiffness. [PERSON] utilized these findings to explain rockburst events in mines and shed some light on the phenomenon. The rigidity hypothesis provides insight into the cause of rockbursts and is useful in avoiding them in engineering projects. However, the hypothesis and its associated mechanism cannot fully describe the dynamic process of rockburst in the mechanical system. Additionally, the structural rigidity of mines included in the theory is unclear and lacks a suitable calculation method. Furthermore, it is challenging to determine the stiffness of mine construction after the peak strength is reached, which limits the applicability of the rigidity hypothesis. More complex instability phenomena arise from the interaction of rock-rock interfaces. If the properties of the surrounding media are different for the same rock mass, the failure sequence and mode might vary. Previously, only the quality of the failed mass and energy generated were considered. However, recent studies [124,125] have shown that the energy released during rock mass fragmentation is much greater than that from the failed mass alone. The energy source for rock mass failure in events such as rockbursts comes from both the failed mass and the surrounding media's elastic energy release. Further investigation into the interaction of rock-rock interfaces can provide important insight into the study of rockburst mechanisms.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design
#### 3.1.1 Haptic and Visual Feedback Design in Assistive Systems for Visually Impaired Pedestrians  
Haptic feedback serves as an important alternative mode for visually impaired individuals to perceive external information [24; 47]. Research on vibration-based feedback [42; 43; 44] has demonstrated varying tactile sensitivity across different body regions [80]. While studies have explored haptic feedback on various body locations, including the waist [81], arm [82], and leg [83], the chest and abdomen regions have shown particularly promising results. [PERSON] et al. discovered that these regions exhibit exceptional tactile recognition capabilities, with accuracy rates reaching 95% [84]. [PERSON] et al. further demonstrated that, by controlling the vibration frequency and contact area parameters, chest and abdomen-based feedback can effectively convey different levels of urgency perception [85]. Studies have also revealed that approximately 90% of legally blind individuals retain basic light perception abilities [2; 3], allowing them to detect changes in light characteristics [27; 28].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology Type** & **Haptic Feedback** & **Visual Feedback** \\ \hline Portable devices & Smart canes and handheld devices [49; 65; 66] & LED obstacle highlighting [78] \\ Wearable devices & Vibrating belts, backpacks, and wristbands [29; 67; 68] & LED guides and visual displays [27; 28; 69] \\ Specific applications & Street-crossing and traffic navigation [25; 30] & Navigation markers and enhanced visibility [73; 74; 75] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assistive technology applications for visually impaired individuals.  
[PERSON] et al. confirmed that head-mounted LED arrays could effectively convey information through controlled light patterns [27].  
Based on these findings, this study implements two parallel feedback schemes, as shown in Figure 1: a chest and abdomen vibration system that conveys urgency information through controlled frequency and contact area parameters, and a head-mounted LED array system that communicates the same information through adjustments in light frequency and illumination area. This dual-mode design aims to accommodate diverse user preferences and needs while ensuring consistent urgency perception across both feedback channels.
## 4 Discussion
### Uncertainty Analysis  
This study analyzed the key drivers of extreme WUE variations and the meteorological factors influencing interannual WUE changes from 2001 to 2020, revealing that VPD plays a significant role in driving WUE variation. However, the study has several limitations and uncertainties. First, WUE was calculated based on the ratio of GPP to ET, and the accuracy of the selected GPP and ET data introduces uncertainty into WUE estimates. Although the reliability of the data was validated by comparisons with flux stations observations, the limited scope and distribution of flux station still present uncertainty. The restricted coverage and number of flux stations may impact the precision of the data. The ET in the PML_V2 data is divided into several components: soil evaporation, vegetation transpiration, canopy interception, and water body snow/ice evaporation. When calculating ecosystem WUE, including certain types of ET may increase the uncertainty of the study. Currently, the separation of evaporation components requires further discussion. Given that canopy interception plays a relatively minor role in the carbon-water coupling of vegetation, we excluded this component to reduce potential biases in the study. The vegetation GPP and ET data simulated by the PML model are based on observed data, and thus contain some uncertainty. However, the PML_V2 dataset is one of the most physically comprehensive and high-quality datasets available. For these reasons, our study utilized the PML_V2 data while excluding canopy interception from the ET components. Second, the impact of human activities is complex, involving factors such as land use change, agricultural irrigation, afforestation, nitrogen deposition, and CO\({}_{2}\) fertilization, all of which can influence the results of our study [24; 67]. However, it is important to note that climate change itself already incorporates information about human activities, such as the warming effects caused by increased CO\({}_{2}\) concentrations. While some studies have explored the influence of human activities on WUE, our research was limited by a relatively short time series. Additionally, the study of CO\({}_{2}\) concentration and nitrogen deposition effects requires longer-term data. Therefore, this will be an important aspect for future research to address.
## 3 Background on DoS/DDoS Attacks and Defense Mechanisms  
In [14], different attack types, such as DoS/DDoS, reflection, IP-based DDoS reflection/amplification, and DDoS reflection, were evaluated in detail. An SIP server was attacked by using the IP spoofing technique and exploiting the capability to reflect SIP request/response messages. The \"Via\" and \"Record-Route\" SIP headers were targeted in the attack. It was proven that the attack could bypass existing security mechanisms. In this section, we describe possible defense mechanisms proposed in the existing works against DDoS attacks.  
Snort and Zeek are two open-source network security monitoring and intrusion detection system products. Unfortunately, due to the existing security flaws in the SIP protocol specifications, these solutions have limited capabilities against SIP-based attacks. They do not have any sophisticated capability to resist SIP-DRDoS attacks.  
Anomaly detection engine features are also available in several VoIP application firewalls, and VoIP IPS/IDS and SBC (session border controller) solutions. Because the SIP-DRDoS attack has the ability to overcome current security systems, new defense approaches that are specific to this type of attack need to be developed, as discussed in our previous paper [14].
## 3 Designed Antenna Performance  
To verify the designed antenna performance, the antenna was fabricated, measured, and compared with the simulation results. Figure 7 depicts the fabricated circular sector-shaped dipole antenna with the AMC. The circular sector shape combined with the meandered arm, which is an element of the dipole antenna, is symmetrically positioned. The AMC was fabricated by stacking four ferrite layers with a height of 2.5 mm to achieve a  
Figure 5: Three types of antennas with AMC: (**a**) reference; (**b**) with strip arms; (**c**) with meandered arms.  
Figure 6: Simulated VSWR characteristics of three types of antenna.  
10 mm height. The area between the dipole antenna and the AMC was implemented using a 1 mm thick styrofoam. The dimensions of the designed antenna are \(0.02\times 0.02\times 0.0007\)\(\lambda^{3}{}_{L}\) (\(\lambda_{L}\) is the wavelength at 14 MHz).  
Figure 8 presents the simulated and measured VSWR characteristics of the designed dipole antenna with an AMC. VSWR was measured by connecting the antenna's coaxial cable and Agilent 8510C network analyzer in an anechoic chamber. Both the simulated and measured VSWR results satisfy the characteristics below 3.5 in the 14-361 MHz HF/VHF/UHF bands and the trends of the two graphs is similar. The difference between the simulated and measured results is due to manufacturing and measurement errors.  
Figure 9 illustrates the simulated radiation patterns of a circular sector-shaped dipole antenna with the AMC structure at 14, 200, and 370 MHz in the xz and yz-planes. Because the AMC acts as a reflector, it causes the radiation pattern to be focused in the +\(z\)-axis. Additionally, due to the image current formed on the AMC's surface, the characteristic of the antenna do not deteriorate even though it is closer than \(\lambda_{L}/4\), which is the required  
Figure 8: Simulated and measured VSWR characteristics of the proposed antenna.  
Figure 7: Photographs of a fabricated circular sector-shaped dipole antenna with an AMC structure: (**a**) circular sector-shaped dipole antenna; (**b**) \(5\times 5\) AMC structure; (**c**) side view.  
separation distance from the general reflector. Thus, low-profile characteristics were achieved in HF, VHF, and UHF bands.  
Figure 10 depicts the simulated realized gain on the +\(z\)-axis of a circular sector-shaped dipole antenna based on the AMC structure. The main polarization characteristic of the proposed antenna is horizontal polarization and has a gain of \(-38\) to \(-1.75\) dBi in the operating bandwidth of 14 to 371 MHz.
## 3 Results
### Morphological Characterization of Metal Titanate Nanorods  
Figure 5 presents SEM images of the metal titanate nanorods before and after annealing.  
Top row images are referred to materials before annealing and bottom row images are referred to materials after annealing in an order of MgTiO\({}_{3}\), CoTiO\({}_{3}\), NiTiO\({}_{3}\), and ZnTiO\({}_{3}\). SEM images reveal homogenous and well-defined nanorods in all four samples before and after annealing. The nanorod precursors that have been created after the stirring process have smooth surface which after annealing gets rougher with smaller nanoparticles of irregular shapes and sizes attached on it. After annealing MgTiO\({}_{3}\) nanorods' average length is 1.5-2 \(\upmu\)m, CoTiO\({}_{3}\) is 2 \(\upmu\)m, NiTiO\({}_{3}\) is 1-2 \(\upmu\)m, and ZnTiO\({}_{3}\) is 3-4 \(\upmu\)m. The elementary analysis of the metal titanate nanorods is shown in Figure S3. In all samples peaks of Ti, O, and metal, with metal being either Mg, Co, Ni, or Zn, appeared in percentages matching with the chemical formula ATIO\({}_{3}\). Moreover, the absence of other peaks denotes the purity of the samples agreeing with the XRD and FTIR data.  
Figure 4: Raman spectra of metal titanate nanorods ATIO\({}_{3}\) (A = Mg\({}_{2}\) Co, Ni, Zn) after annealing process.  
Figure 5: SEM images of (**a**,**e**) referred to MgTiO\({}_{3}\), (**b**,**f**) to CoTiO\({}_{3}\), (**c**,**g**) to NiTiO\({}_{3}\), and (**d**,**h**) to ZnTiO\({}_{3}\) before and after annealing process, respectively. So top row images are referred to materials before annealing and bottom row images are referred to materials after annealing in an order of MgTiO\({}_{3}\), CoTiO\({}_{3}\), NiTiO\({}_{3}\) and ZnTiO\({}_{3}\).  
Figure 6 presents the TEM images of metal titanate nanorods after annealing. Verifying the SEM data, TEM images reveal homogenous nanorods with rough surfaces that seem to be composed of smaller, round in shape, nanoparticles that have aggregated with each other. The nanorods in MgTiO\({}_{3}\) and CoTiO\({}_{3}\) samples have a diameter of 500 nm. In NiTiO\({}_{3}\) the rods have a diameter of 200 nm, and in ZnTiO\({}_{3}\) the diameter is 250-300 nm.
## 3 Analysis and Discussion  
### Numerical Simulation  
The numerical simulation control block diagram was shown in Figure 2. The voltage data were predicted directly using the CNN algorithm. To further improve the correction accuracy, the correction using the CNN algorithm was followed by a further correction using the SPGD algorithm. The CNN algorithm was used for coarse correction and the SGPD algorithm for fine correction.  
The optimal set of hyperparameters was automatically chosen as the parameters of the convolutional neural network to minimize the loss value of the neural network testing dataset, including the optimal accuracy of the wavefront sensorless system based on the convolutional neural network with a Bayesian-based hyperparameter optimization model that used the testing set accuracy (root-mean-square error (RMSE)) of the convolutional neural network as the optimization target. In practice, the amount of hyperparameter regulation increased exponentially as a result of the excessive number of network layers. As a result, by stacking the same network blocks, the hyperparameters of each network layer were shared, and the amount of hyperparameter conditioning was decreased.  
Table 1 displays the hyperparameter selection range.  
The control group models included ResNet, DenseNet, etc. ResNet introduced residual blocks to address the gradient disappearance issue created by a high number of network layers, enabling layer stacking to increase the network accuracy. DenseNet built on ResNet's foundation by having a stronger generalization capability while maintaining Resnet's accuracy. Regularization and other aspects were improved in Inception v3, which lessened the problem of model over-fitting; using a minimal amount of data, U-Net excels at image segmentation. Inception v3 had enhanced regularization to lessen model over-fitting. U-Net has made significant progress in image segmentation, achieving excellent segmentation outcomes with modest datasets. Moreover, the concept behind EfficientNet-B0 was to create a standard convolutional network extension model in order to increase accuracy while using less computational power.  
Figure 2: The numerical simulation control block diagram. The CNN algorithm was used for coarse correction and the SGPD algorithm was used for fine correction.
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 3 Cognitive Radio Network Based on IoT
### Problems and Challenges in Cognitive Radio Based on IoT  
In order to fully benefit beyond what cognitive radio can provide IoT networks, some unsolved research issues have to be resolved. Many of these concerns are addressed in the next section.  
* Spectrum efficiency in the context of a network built on cognitive-radio-based internet-of-things nodes requires the optimization of a number of resources, including energy efficiency, transmission power, latency, and data throughput. The authors of [34] formulated an optimization problem that considers transmission rate, transmission power, and transmission delay in order to identify the best possible solution given specific limitations. They used a polyhedral branch to obtain a solution. The results demonstrate that transmission latency, power rate, and interference all rise in tandem with network and packet size. There is much work to be carried out on the formulation and solution of multi-objective optimization problems that take into account a wide range of variables.
* Energy efficiency is a major issue that must be resolved in CR-based IoT networks. The power consumption problem is exacerbated for energy-constrained nodes and battery-powered devices when IoT items with cognitive radio capabilities perform additional functions, primarily spectrum sensing. Energy harvesting as well as Cooperative Wire  
Figure 5: Challenge and issues for IoT in the monitoring environment.  
less Networks are among the methods offered to address energy efficiency challenges. The term \"energy harvesting\" refers to the practice of collecting and storing energy from renewable resources like the sun and the wind for later consumption [35]. When it comes to Green Computing and the practical use of the internet of things, energy harvesting is often regarded as one of the most enabling and important technologies. Using energy harvesting, the authors of [36] present a differential game model to handle the problem of resource allocation in cognitive wireless sensor network (WSN).
* Security is of paramount importance in CR-Based IoT networks, but it is also a difficult undertaking as, because of the inherent heterogeneity of most IoT products, each one must conform to its own standardized security requirements. Not all heterogeneous networks can be successfully implemented using these guidelines. Authentication, security assurance, and intrusion software are just a few of the privacy and security considerations that should be made when developing IoT systems [23]. There have been commendable attempts to address security concerns in several cognitive-radio-enabled IoT applications, as illustrated in Figure 6.