esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Bioactivity of _Chlorella vulgaris_ Compounds
### Antioxidant Activity
#### 3.1.1 Non-Enzyme-Promoted Antioxidants  
Chlorophyll, a key component of _C. vulgaris_, has been shown to scavenge free radicals, offering protection against oxidative stress and its associated health risks, such as neuroinflammation and cell damage [18; 59]. Carotenoids like lutein and \(\beta\)-carotene are also crucial non-enzyme-promoted antioxidants that protect cells, particularly in the skin and eyes, by neutralizing ROS [44; 54]. Lutein, in particular, has been linked to improved cognitive function and neuroprotection, suggesting its role in mitigating the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's [59; 60].  
The polyphenols found in _C. vulgaris_ enhance antioxidant defense mechanisms and protect neurons through both antioxidant and anti-inflammatory actions. Although _C. vulgaris_ contains relatively lower levels of omega-3 fatty acids like \(\alpha\)-linolenic acid compared to marine algae, these essential fats still contribute to brain health and help prevent cognitive decline [59; 60]. These combined non-enzyme-promoted antioxidant properties of _C. vulgaris_ not only support brain health but also protect skin cells from UV-induced damage, making this microlga a promising candidate in both neuroprotection and dermatological applications [61].
## 3 Impact of Cold Plasma on Packaging Properties
### Water Barrier Properties  
Water vapour permeability (WVP) of packaging material is an important parameter in determining the moisture barrier property of the packaging materials that affect the resulting shelf life and quality of the packaged food. The poor moisture barrier property of biopolymer-based materials is one of the major shortcomings in packaging applications [78; 111]. Different modification techniques are adopted to enhance the barrier property or at least avoid any drastic change in the permeability in order to augment the usage of biopolymers in food packaging applications.  
From the literature, as summarized in Table 2, the application of plasma onto film surfaces shows a range of possible effects. Some authors reported that plasma treatment decreased the WVP, while others observed that plasma treatment had no significant impact on the barrier property or in some cases increased the WVP. Cold plasma treatment decreased the WVP of starch/PCL and starch/PLA composite films by approximately 94% [112]. Due to the enhancement in adhesion properties caused by plasma treatment, different composite films like zein/chitosan [94], zein/PLA [46], and whey protein concentrate/wheat cross-linked starch [113] films also showed a significant decrease in WVP. [PERSON], [PERSON] [80] treated zein film using atmospheric cold plasma and reported that WVP decreased by approximately 24% as compared to control films. In general, plasma treatment of the film creates surface roughness and increases the tortuosity of the diffusion pathway of water resulting in the decrease in WVP [52; 94]. On the contrary, [PERSON], [PERSON] [79] reported that the WVTR increased when treated with plasma for two minutes or beyond due to the etching effect that facilitates water vapor transport.  
Studies demonstrating that no impact is caused by plasma on barrier properties include the work of [PERSON], [PERSON] [78]. They showed that plasma treatment with different gases like O\({}_{2}\), N\({}_{2}\), air, Ar, and ethanol-argon (EtOH-Ar) had no significant effect on the WVP of gelatin films. Similarly, [PERSON], [PERSON] [99] found no significant change in the starch film when treated with air and O\({}_{2}\) for 4, 8, and 12 min. Although the application of different gases increased the polar functional groups (like peroxide, carboxylic acid, and hydroxyl, C-N, C=N, and amide bonds) at the surface of the polymer and induced cross-linking resulting in higher cohesive energy density, it did not affect the WVP. This indicates that WVP is not only dependent on the surface properties but also depend on thermodynamic properties, vapour pressure, and concentration gradient across the film surface depending on diffusion and the solubility mechanism [52, 78, 83, 99].
## 5 Modeling and Analysis in Ansys
### Creating Geometry  
The IGBT structure, illustrated in Figure 3, begins at the bottom with a base plate that provides both mechanical support and thermal conductivity. Above this, a baseplate solder alloy (solder 1) is used to securely attach the layers above. Next is the lower copper layer (copper 1), which serves as a conductor and distributes heat evenly, followed by a ceramic layer that offers electrical insulation while maintaining thermal conductivity. Above this lies the upper copper layer (copper 2), another conductive layer that helps in electrical and thermal management. The die-attach solder alloy (solder 2) then connects the upper copper layer to the IGBT chip, the core active element of the device. Finally, aluminum bond wires are used to establish electrical connections between the chip and external circuitry. All these elements are located within a silicon gel, which helps in providing additional thermal conductivity and protecting the components. This multilayer design ensures efficient electrical performance, thermal management, and durability.  
It is important to note that there are two types of bond wires in the module. The first type, referred to as chip-level bond wires, connects the chips to the copper plates. The second type, known as terminal bond wires, links the chips or copper plates to the external connections. While both types have the same diameter of 400 \(\upmu\)m, the primary difference between them lies in their shape, with terminal bond wires being longer [25].  
The dimensions of the various components of the IGBT, including width, length, and thickness, are detailed in Table 2. These measurements are essential for accurate 3D modeling, ensuring the precise replication of the physical structure during the design and simulation processes. Noticed that solder 1, copper 1, and copper 2 are based on the nomenclature provided in Figure 3. Additionally, in Table 2, the symbols are defined as follows: 1 (in mm) represents length, w (in mm) represents width, and t (in \(\upmu\)m) represents thickness.  
Figure 3: Detailed layer composition of the IGBT module.  
Figure 2: FP50R12 KT4 IGBT power integrated module.  
Using these dimensions, a 3D model of the IGBT was created in ANSYS SpaceClaim, as shown in Figure 4. The model is constructed with a layered configuration to represent the entire structure. Notice that silicone gel was excluded from the 3D model, as its damping ratio remains constant for frequencies above 100 Hz. Since the failure model focuses on identifying failure locations and causes at frequencies above 100 Hz, this constant damping has no impact on the results. While the gel may extend the module's lifespan, it does not influence failure locations or causes [26].
# Effects of Wind Barrier Porosity and Inclination on Wind Speed Reduction
## 4 Discussion
This implies that the inclination may have a considerable effect on the range of wind protection, resulting in greater wind barrier efficacy. Therefore, the effectiveness of wind barriers may depend not only on their porosity but also on their inclination. Our results suggest that a wind barrier with a porosity of 50% and an inclination of 70\({}^{\circ}\) may provide the most effective protection for pre-maturity saplings against wind in vegetative windbreak forests, although, overall, all wind barriers with varying porosities and inclinations performed well. Despite our findings, several avenues of this study remained unexplored, which should be considered in future research. As the wind tunnel section in this study was relatively small, the height of the wind barriers was not tested, although past studies have reported that height significantly affects shelter efficiency (e.g., [9; 19; 41]). Wind barriers with lower porosity are laborious, time-consuming, and costly, although they exhibit a higher reducing effect on wind velocity [28]. To achieve an optimal and cost-effective wind barrier, it is recommended to explore a wider range of porosities and inclinations beyond those tested in this study while keeping the material cost similar. An array of wind barriers can facilitate the reduction in wind velocity for vegetative windbreak forest construction [9]. Single and multiple rows of vegetation can also considerably reduce wind velocity, depending on the vegetation species used, such as shrubs, coniferous species, and broad-leaved species [9; 10], or when combined with wind barriers [8; 42]. Thus, combining an array of wind barriers with structurally varying windbreak trees with different porosities and inclinations could further advance our understanding of the change in wind flow caused by different porosities and inclinations, and optimize vegetative windbreak forest management strategies for various environmental conditions.
## 2 Related Works
### MEC-Enabled UAV-Assisted VANETs  
The MEC technique have been widely applied to improve the successful task processing ratio in VANETs [38; 39; 40]. The MEC technique brings the computing ability and the storage resource to the edge of the network, which can enable the computing ability and the storage resource in close proximity to users. Therefore, the task offloading problem has been investigated in many researches in the context of VANETs [41; 42; 43; 44]. In those works, the main objective is to minimize task processing delay by selecting the optimal MEC server, while the load balancing is not taken into account. Aiming at this problem, the authors of [45; 46] focused on task offloading and execution utilizing the computing resources on vehicles to load balancing. Moreover, the works in [47; 48; 49; 50] proposed to reduce task processing delay by offloading computation-heavy and latency-sensitive tasks to nearby RSUs or UAVs for remote execution. Furthermore, in [51; 52; 53], the offloading problem was studied with an objective to minimize the weighted sum of mobile energy consumption for a multiuser UAV-assisted VANET. The work in [54] designed a new system model where UAVs can offload and/or share intensive computation tasks with other nearby network nodes. Then, the authors of [55] used the computation response time, the energy consumed for the computation, the cost of cellular communication, and the computation cost as the main system metrics to make any computation offloading/sharing decisions that optimized the system performance. The difference is herein we propose a MEC-enabled UAV-assisted VANETs architecture, based on which a number of vehicles are served by UAVs equipped with computational resources. Each vehicle has to offload its computing tasks to the proper MEC server on UAV due to the limited computation ability. In addition, we assume that each vehicle can only select one MEC server for computational offloading.
# A Study on the Effect of Gear Hobbing Process Parameters on the Residual Stress of the Tooth Root
## 2 Kinematic Relation of Gear Hobbing
\tag{5}\]  
According to the analysis of the kinematic relation of gear hobbing, there are three types of working motions involved: the rotary motion of the hob, the feed motion of the hob along the shaft of the gear, and the rotary motion maintaining the meshing relationship between the gear and the hob. The above formula indicates that the rotary motion of the gear is dependent on the rotary motion of the hob, so it can be treated as a variable with the hob speed. Reference [21] analyzed the cutting conditions of a radial-axial feed strategy in gear hobbing machining, revealing that increasing the cutting times (radial cutting depth), cumulative cutting length, and average cutting length would result in a higher tool load. This also indicates that the depth of radial cutting in hobbing machining will also affect the machining quality of the gear. Therefore, it can be concluded that the hob speed, axial feed speed, and radial cutting depth are the primary parameters affecting the hobbing process. Due to the gear transmission system tending to be lightweight, the bending strength of the tooth root becomes a critical constraint, and the fatigue fracture of the tooth root is actually more hazardous than the failure of the tooth surface. Additionally, the residual stress plays a significant role in affecting the bending fatigue performance of the tooth \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Parameter Symbol** & **Parameter Name** & **Parameter Symbol** \\ \hline Hob speed & \(\omega_{h}\) & Hob rotation phase angle & \(\varphi\) \\ Axial feed & \(f\) & Axial feed velocity & \(v\) \\ Hob radial mounting distance & \(\rho\) & Hob mounting angle & \(\delta\) \\ Axial feed displacement & \(\zeta\) & Phase angle of gear rotation & \(\psi\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relative position and motion parameters of hob and workpiece space. root. Therefore, the hobbing machining experiment focuses on investigating the impact of three process parameters (hob speed, axial feed speed, and radial cutting depth) on the residual stress of the tooth root.
## 2 Literature Review
### Feature Selection  
The increasing scale and diversity of datasets have elevated the importance of feature selection, leading to a heightened emphasis in this area. As a result, numerous studies are being conducted to address its significance. [PERSON] and [PERSON] (2021) carried out research using eight environmental datasets, encompassing approximately 6830 features and 18 feature selection techniques, employing filter, wrapper, embedded, and ensemble methodologies [20]. They identified the optimal method and feature sets by evaluating the average accuracy ranking and variance of each technique. [PERSON] et al. (2014) developed a hybrid algorithm that combines filter-wrapper techniques to improve performance in classification problems and executed feature selection [21]. Classifiers trained with datasets reduced by this hybrid model exhibited high balanced classification rate (BCR) performance during testing. [PERSON] et al. (2019) analyzed various datasets from a metallurgy company, conducting a study that integrated the remote memory reference (RMR) algorithm--a filter technique--with a rule-based model [22]. This approach resulted in reducing the feature space from the initial 47 datasets to the most significant 32 datasets. [PERSON] and [PERSON] (2019) performed feature selection on diverse real-world datasets using filter, wrapper, and embedded methods [23]. They then conducted a comparative analysis of the most effective feature selection methods for each sector, using metrics such as accuracy and F1_score. [PERSON] and [PERSON] (2021) researched using feature selection to decrease the processing load of the model and mine data streams in real time [24]. They proposed a wrapper-based particle swarm optimization (PSO), grammatical evolution (GE), and a hybrid PSO-GE model. The hybrid PSO-GE model demonstrated an 8.63% higher accuracy in feature selection compared to other models. Lastly, [PERSON], and [PERSON] (2022) provided an overview of feature selection techniques and instability of algorithms, offering solutions for various causes of instability [25].
## 5 Result and Discussion  
In this study, the characteristics extracted in the previous section were considered the neural network's inputs. Three neural networks were implemented to estimate the volume ratios of ethylene glycol, crude oil, and gasoil. By determining the volume ratio of the three mentioned products, the ratio of the fourth product, i.e., gasoline, could easily be calculated. Figure 5 and Table 1 show the structure of the implemented networks.  
Regression and error diagrams related to training, validation, and test data can be seen in Figures 6-8, presenting the accuracy of the designed networks. The most important criterion for evaluating the performance of artificial neural networks is the accuracy of prediction. Some of the most important prediction accuracy criteria calculated in this study are:  
Root Mean Square Error  
\[(RMSE)=\frac{\sum_{j=1}^{N}{(e_{j})}^{2}}{N}\] (6) Mean Absolut Error  
\[(MAE)=\frac{1}{N}\sum_{j=1}^{N}{|e_{j}|} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ANN Type**} & \multicolumn{3}{c}{**MLP**} \\ \cline{2-4}  & **Ethylene Glycol** & **Gasoil** & **Crude Oil** \\ \hline No. of input layer neurons & 5 & 5 & 5 \\ \hline No. of 1 st hidden layer neurons & 12 & 20 & 16 \\ \hline No. of 2 nd hidden layer neurons & 8 & 10 & 7 \\ \hline No. of 3 rd hidden layer neurons & 4 & 5 & 3 \\ \hline No. of output layer neurons & 1 & 1 & 1 \\ \hline No. of epochs & 650 & 800 & 550 \\ \hline Activation function used for each hidden neuron & Tansig & Tansig & Tansig \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specifications of the designed networks.  
Figure 5: The structure of the implemented MLP neural network for prediction of (**a**) ethylene glycol, (**b**) crude oil, and (**c**) gasoil volumetric ratios.  
where \(e\) is the error, \(y\) is the network output, and \(N\) represents the amount of data.  
Figure 6: Performance of the designed neural network to determine the volumetric ratio of ethylene glycol: (**a**) training data, (**b**) validation data, and (**c**) test data.  
Figure 7: Performance of the designed neural network to determine the volumetric ratio of crude oil: (**a**) training data, (**b**) validation data, and (**c**) test data.  
Table 2 shows the calculated errors for each of the predictor networks. The general process of determining the volume ratios of each oil product is shown in Figure 9.  
Figure 8: Performance of the designed neural network to determine the volumetric ratio of gasoil: (**a**) training data, (**b**) validation data, and (**c**) test data.  
Figure 9: The general process of determining the volume ratio of each oil product.
## 4 Calibration Methodology
### Building Calibration
#### 4.1.2 Model Evaluation  
Three metrics were used to assess the quality of building models: the NMBE, the coefficient of variation of the RMSE (CVRMSE) and the coefficient of determination \(R^{2}\)see e.g., [26]. Model acceptance is based on the threshold values recommended by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and the International Performance Measurement and Verification Protocol (IPMVP); see e.g., [26].
## 3 Pasteurization Targets and Regulations
### Regulatory Aspects of High Pressure Processed Foods  
International food regulation agencies propose 5-8-log\({}_{10}\) reductions (\(log\Big{(}\frac{N}{N_{0}}\Big{)}\)) in relevant microbial hazards for efficient pasteurization processes and the production of safe foods (EFSA, 2022) [5].  
In 2011 the Food and Drug Administration (FDA) Food Safety Modernization Act was implemented, aiming the shift of the focus of federal regulators from responding to contamination to preventing it, thus ensuring the safety of the United States food supply (Public Law, 2011) [40]. With respect to HPP, FDA and Health Canada have established a regulatory frame specifically for this technology application ([PERSON] and [PERSON], 2018) [41]. There is a need to demonstrate that a new HPP treatment achieves a specific log reduction in the pathogen(s) of concern. On the contrary, HPP is not specifically regulated in Europe, where a precautionary approach designates HPP products as Novel Foods, with the need to demonstrate safety beyond pathogen reduction. Thus, in Europe, HPP foods are considered novel and subsequently require pre-market approval, in particular HPP products of animal origin.  
The adoption of HPP in North America has been driven by a regulatory requirement for processors to apply a pathogen-reduction step in high-risk ready-to-eat (RTE) food production ([PERSON] and [PERSON], 2018) [41]. The pathogens of concern in RTE meats, fruits, and vegetable-based products are _Salmonella_, _L. monocytogenes_, and _E. coli_ O157:H7. The FDA designated a 5-log cfu/mL reduction in food by the HPP process. With respect to processed meat, the United States Department of Agriculture (USDA) Food Safety and Inspection Service (FSIS) published a directive identifying HPP as an intervention to control _L. monocytogenes_, thereby fulfilling Alternative 1 or Alternative 2 within RTE meat production. HPP can also be applied to control _E. coli_ O157:H7 on beef trim and _Salmonella_ on poultry. When HPP processing is applied as a pathogen reduction step, there is a need to define the processing parameters and the product formulation.
## 7 Simulation Results
As evaluation distances, short distances to the closest plane were used (\(z_{1}+0.1\) mm, \(z_{1}+0.5\) mm), in addition to the distances 20 mm, 50 mm and 150 mm. The results are depicted in Figure 10. It can be observed that, even for a distance \(z_{1}\) of 4 mm, the mean error levels drop below 0.92 dB for noise levels of \(-\)24 dB or smaller, even for the FT evaluation distance of 4.1 mm, which is extremely close to the plane of equivalent currents \(z_{1}\) (corresponding to a distance of \(\lambda/300\) from \(z_{1}\)). For FT evaluation distances further away (\(\lambda/60\) from \(z_{1}\) and further), the mean errors are below 0.76 dB. Note that a 4 mm distance at 10 GHz corresponds to the same electrical distance as 2 mm at 20 GHz. Figure 10: _Cont._  
Figure 9: Mean absolute error in psPD\({}_{n}\) (**a**) and psPD\({}_{tot}\) (**b**) for different noise levels (see legend) depending on distance. The plane of equivalent currents was placed at 2 mm for all cases. Mean values over all antennas are shown.
# Design of a New Non-Coherent Cross-QAM-Based \(M\)-ary DCSK Communication System
## 5 Conclusions  
In this paper, a new non-coherent XQAM-\(M\)DCSK system has been proposed by using an autocorrelator. The proposed framework not only retains the non-coherent characteristic of the DCSK system, but also can be utilized in the star QAM-MDCSK and SQAM-MDCSK  
Figure 5: PAPR performance of the XQAM-MDCSK and star QAM-\(M\)DCSK systems.  
Figure 6: BER performance of the proposed systems with autocorrelator and the systems with perfect CSI.  
systems. Moreover, the BER expression of the proposed XQAM-MDCSK system has been analyzed over a multipath Rayleigh fading channel. Compared to the star QAM-MDCSK system, the theoretical and simulated results show that the proposed XQAM-MDCSK system has better a BER performance and lower PAPR. In particular, the proposed XQAM-8 DCSK system can provide an approximately 2 dB gain over the star QAM-8 DCSK system, while the proposed XQAM-32 DCSK system can offer an approximately 1 dB gain over the star QAM-32 DCSK system. Furthermore, results show that the performance of the proposed system can approach that of the system with the perfect CSI.  
Conceptualization, G.C.; methodology, G.C.; software, Z.Y. and G.C.; validation, Z.Y.; formal analysis, Z.Y.; investigation, Z.Y. and G.C.; resources, G.C.; data curation, Z.Y. and G.C.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y. and G.C.; visualization, Z.Y. and G.C.; supervision, G.C.; project administration, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NSF of China grant number 62071129.  
The data used in this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.