esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Color Measurements  
In Table 1 and Figure 3, it is evident that the color of the WFFs underwent a change following the addition of coffee silverskin particles and extracts, allowing for an evaluation of the color characteristics of the films. The lightness (L*) values of the WFF composites were notably lower compared with the control WFF. A gradual decrease in lightness values was observed across all samples, with a reduction of 41.15% for the maximum concentration of CS particles and 7.6% for films containing CS aqueous extracts. Conversely, the a* values exhibited a gradual increase, with higher values noted for films with CS particles. In contrast, films with CS aqueous extracts did not show a significant increase in a* values. Regarding the b* and c* values of the WFF-ss samples, they initially doubled upon the addition of coffee silverskin particles to the matrix before eventually reaching the same values as the WFF control film. Contrariwise, the b* and c* values for films with CS extracts, progressively escalated and almost doubled for the WFF-2%B film. The b* values decreased when CS particles or extracts were added to the matrix compared with the WFF control film, by up to 43.02% for WFF-20s and 8.4% for WFF-2%B. The R% values decreased, as expected, when coffee silverskin particles and aqueous extracts were added to the wheat flour-glucose matrix. For all films, color strength (\(K/S\)) was inversely proportional to the reflectance at the maximum absorption wavelength (R%). Films with a higher content of coffee silverskin particles and aqueous extracts exhibited evaluatered color strength in comparison with the WFF control film. Elevated levels appeared in films with coffee silverskin particles as the increased addition led to agglomerations coating the wheat flour matrix. The increased addition of CS particles to the matrix led to a brownish film and yellowish appearance for films with CS aqueous extracts. This conclusion matched the a* and b* values. Similar outcomes were observed for PLA films with coffee silverskin  
Figure 2: SEM images of biocomposite wheat flour films with coffee silverskin aqueous extracts.  
particles, which led to a light brown color, which was likely due to the increase in the a* and b* values [29].
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Numerical Simulation Efforts of LPBF Defects  
Evaluating internal and external defect effects in LPBF-printed parts is especially crucial when dealing with lattice structures. Improper process parameters for LPBF-printed lattice structures can result in geometrical irregularities, including variable cross-sections, strut waviness, protrusions, cross-sectional eccentricities, missing/interrupted struts, an uneven junction material distribution, microporosities, and thermal cracking. Due to the complexities of the LPBF printing process and intricate lattice structures, it is inappropriate to assume that the printed lattice structure will mechanically behave precisely like the idealized CAD model the part was modeled after. The optimization of improper process parameters by repeatedly building parts is expensive and time-consuming, so numerical simulation techniques are often used to simulate the lattice's mechanical performance or behavior. Representative behaviors of the actual lattice structures and the defective effects can be achieved via scanning techniques such as XCT. Transmitted light microscopes have been utilized to develop reconstructed CAD models of printed parts. Numerical simulations of actualized LPBF-printed lattice structures can be classified under three categories, namely (1) mechanical simulations of reconstructed CT-scanned geometrical LPBF defects using FEM, (2) mechanical simulations of statistically driven LPBF defects using FEM, and (3) mechanical simulations of LPBF embedded defects using the finite element method (FEM).
## 2 Flow Loss Model  
The growth of boundary layer thickness along the blade surfaces is mainly ruled by the adverse pressure gradient which results from the profile velocity distribution. The deceleration on profile velocity is someway quantified by a diffusion parameter. Based on this concept a flow loss model was first proposed for axial compressor blades [12; 13]. A correlation specifically established for axial turbine blades was later presented by [PERSON] et al. [14] as given by Equation (1):  
\[\frac{\dot{\theta}}{l}=\frac{0.003}{1-1.4D_{tot}}, \tag{1}\]  
This correlation between a non-dimensional momentum thickness and a diffusion parameter was based on low speed turbine cascade tests [15], as well on design data of transonic turbine rotor blades [16]. The proposed parameter is called total diffusion factor, which takes into account the amount of deceleration in both pressure and suction surfaces, according to Equation (2):  
\[D_{tot}=\left(1-\frac{V_{p,\min}}{V_{1}}\right)+\left(1-\frac{V_{2}}{V_{s,\max }}\right), \tag{2}\]  
The momentum thickness is seen to slightly increase for low values of diffusion factor, but above about 0.4 the increase becomes sharp, indicating the imminence of separation. The momentum thickness given by Equation (1) is supposed to be valid for a Reynolds number of 400,000, with the blade mean-line curvature length \(l\) and the downstream velocity taken as the characteristic parameters. The final momentum thickness determined as the above description must be corrected with the local Reynolds number to the \(-1/5\) power, characteristic of the viscous losses under turbulent flow [17].  
The boundary layers developed along the end walls (at tip and hub surfaces) also have to be considered for a total loss assessment. In this case, furthermore, there is a transverse pressure gradient between pressure and suction surfaces [18], which meaningfully complicates the problem. A simple procedure is applied to bypass this issue: the end wall momentum thickness is estimated from the profile momentum thickness multiplied by the ratio of respective frictional areas. A tridimensional momentum thickness parameter [19] is then considered to add the contribution of the end wall boundary layers.  
From the momentum thickness determined as per the above, a total pressure loss coefficient can be obtained according to the procedure given by [PERSON] [20]. This step requires solving mass and momentum conservation equations for the flow from a station just between the trailing edges to a station downstream from the blade row. This way, the friction losses, the mixing of boundary layers with the free stream and the trailing edge blockage effect are all included. From the total pressure loss previously determined, the kinetic energy loss coefficient is readily obtained to allow the calculation of the velocity diagrams.
## 9 Conclusions and Future Work  
BYOD security requires applying access control policies at all three security levels: operational, tactical and strategic, considering privacy requirements. This study is the first systematic review of security policies based on the privacy criteria that they meet. It also examines current research and focuses on the following aspects: (1) categorising privacy and security requirements within bring your own device (BYOD) policies; (2) analysing advanced security policy technologies for BYOD, considering three layers of security, and assessing their alignment with privacy requirements; (3) identifying technological trends in the field; (4) evaluating the effectiveness of techniques to enhance privacy and security through various measures; and (5) identifying potential areas for future BYOD security and privacy research.  
In total, 74 articles were analysed based on SLR standard procedures to extract the key findings that underpin this study. Firstly, a taxonomy of security policy techniques and privacy requirements was introduced. According to the survey, BYOD security and privacy requirements can be divided into seven categories: confidentiality, integrity, availability, non-repudiation, authentication, privacy preservation, and attack detection. Secondly, the survey found that every criterion can be fulfilled with a specific technique, except for integrity, availability, non-repudiation, and privacy-preservation requirements, which were combined with other requirements. Third, having identified contemporary trends relevant to BYOD security, the techniques identified were classified according to their associated technological methods. Fourth, there was a discussion of the performance criteria used to evaluate the effectiveness of each technique. The review effort also showed the limitations of each of the methodologies. Lastly, for the benefit of academics interested in working on BYOD security and privacy, potential research areas were identified for future studies. For future studies, researchers are encouraged to focus on developing access control to address most issues in the BYOD environment. Utilising blockchain technology for security and emphasising context-aware access control to protect the privacy of the BYOD environment are recommended. The study also emphasises the need to consider privacy requirements such as integrity, availability, non-repudiation, and privacy preservation.  
Conceptualization, A.T.A.M.; methodology, A.T.A.M.; validation, A.T.A.M.; formal analysis, A.T.A.M.; investigation, A.T.A.M.; resources, A.T.A.M.; data curation, A.T.A.M.; writing--original draft preparation, A.T.A.M.; writing--review and editing, A.T.A.M.; visualization, A.T.A.M.; supervision, A.W.A.W. and M.Y.I.I. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the University of Malaya Impact Oriented Interdisciplinary Research Grant under Grant IIRG008 (A, B, C)-19 IISS.  
Not applicable.  
**Acknowledgments:** The authors of this paper appreciate the reviewers' observations, comments and suggestions for improving the manuscript's content.
## 2 Materials and Methods
### Mechanical Design
#### 2.3.2. Actuation Box and Robot Base  
The robot base consists of a metallic structure made of Bosch Rexroth 40 mm \(\times\) 40 mm profiles and two custom designed Ergal plates. The base supports the actuation box, which carries 12 linear actuators. Each linear actuator drives one module and is composed of a two poles brushed DC motor (SVTN B 01-3571) and a three stages planetary gearbox (SVTG B 36), both provided by Servotecnica. Figure 7 shows the CAD model of a linear actuator. Each motor is controlled by an EPOS4 50/8 motor driver produced by Maxon.  
Figure 6: Results of the FEM analysis on the 1 st module of the robotic arm. (**a**) shows the forces and constraint reaction forces acting on the module; (**b–d**) show the total resulting deformation in mm from different points of view, with a deformation scale factor equal to 71.  
Figure 7: CAD drawing of the linear actuator.  
The linear motion is obtained through a lead screw drive mechanism and is indirectly measured using an incremental encoder (HEDL-5540-A11), provided by Avago technologies, whose resolution is 500 pulses per turn. The gear ratio upstream of the screw is 211.5 and the pitch of the screw is 3 mm per turn. Figure 8 shows how the actuators transfer the motion to the joints via cables.  
The actuation box motion, whose limits are \([0,4\pi]\) rad, is driven by an eight poles brushless motor (EC45) and a planetary gearbox (GP42C), both produced by Maxon. An additional gearbox (SR MR V 50 UO3A), provided by the Rossi-Habasit Group, is mounted. The rotational motion is measured through an incremental encoder (HEDL 9140), provided by Avago technologies, whose resolution is 500 pulses per turn, and by an AkSim2 magnetic absolute encoder supplied by Renishaw, whose resolution is 18 bits with an accuracy of 0.0017 rad. The use of cable actuation moves the actuators, their drivers, the control system, and the power suppliers to a remote site removed from the extreme environmental conditions present at the inspection site, such as high temperatures. In addition, external covers can be fitted to the mechanism to provide additional shielding as required.
# Microbiome-Based Interventions for Food Safety and Environmental Health
## 4 Current Applications for Microbiome-Based Interventions
### Synbiotics in Food Safety  
Synbiotics are a blend of probiotics and prebiotics that have a positive effect on the host by enhancing the implantation and survival of live microbial strains in the gastrointestinal system [86]. They can work in synergy or complement each other to perform a variety of tasks, such as antioxidant activities, which can improve the health of the host [87]. The antioxidant effects of synbiotics are attributed to their activation of nuclear factors that enhance the expression of antioxidant enzymes, generate key antioxidant molecules, and neutralize harmful singlet oxygen and free radicals [87; 88]. When probiotic bacteria and prebiotic compounds are combined, antibacterial compounds like bacteriocin are released, which can stop the growth of harmful bacteria [82]. Due to their synergistic effects, synbiotics are expected to confer more benefits than probiotics or prebiotics alone [89]. Synbiotics have been shown to help prevent the aberrant growth of harmful bacteria in the intestine in animals. For instance, it has been discovered that _Campylobacter jejuni_, the causative agent of foodborne gastroenteritis in humans with poultry and poultry products serving as the primary source of infection, can be moderated using synbiotics. Galactooligosaccharides and the microencapsulated _Bifidobacterium longum_ subsp. _longum_ PCB133 strain were used in an in vivo experiment by [90] to evaluate the effectiveness of the symbiotic in grill chickens. Comparing the treated group to the control group that did not get any special treatment, the results showed a substantial drop in _C. jejuni_quantification and a significant increase in total bifidobacteria after two weeks of treatment. In an observational study, ref. [91] investigated the prevalence of infectious diseases in infants transitioning from exclusive breastfeeding to symbiotic-supplemented formula, finding reduced infection rates. Results from the study showed that compared to babies who received normal formula, the incidence of infectious disease and growth were found to be positively impacted by supplementation with symbiotic-enriched formula.
## 3 Results  
For each image, the networks were trained with the \(3\) different optimisation algorithms, \(3\) layer configurations and \(4\) epoch numbers, for a total of \(36\) different combinations. Thus for the \(6\) composite images there were \(216\) results. The misclassification of each segmentation was measured against the ground truth as the percentage of pixels classified incorrectly. These results are summarised in Table 1.  
The best results for each image were selected and compared against traditional methodologies and are shown in Table 2. The results are illustrated graphically in two ways. Figure 6 shows segmented the classes overlaid as different colours over the original textured images. Figure 7 shows correctly segmented pixels in white and the misclassified pixels in black.  
_Appl. Sci._**2019**, \(9\), 3900  
\begin{table}
\begin{tabular}{l l r r r r r r r} \hline \hline  & \multicolumn{3}{c}{**Method**} & \multicolumn{6}{c}{**Figures**} \\ \hline
**Layers** & **Optimisation Algorithm** & **Epochs** & **a** & **b** & **c** & **d** & **e** & **f** \\ \hline  
\hline Max & & & 71.9 & 80.3 & 80.3 & 82.3 & 80.3 & 94.1 \\ Mean & & & 10.4 & 30.7 & 39.4 & 33.7 & 35.6 & 30.7 \\ Min & & & 2.6 & 8.9 & 7.2 & 7.1 & 7.7 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative misclassification (%) results of the different U-Net configurations. (Bold and underline denotes the best result for each image).  
_Appl. Sci._**2019**, \(9\), 3900  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & \multicolumn{6}{c}{**Figures**} \\ \hline  & **a** & **b** & **c** & **d** & **e** & **f** & **Average** \\ \hline Co-occurrence [5] & 9.9 & 27.0 & 26.1 & 51.1 & 35.7 & 49.6 & 33.23 \\ Best in Randen [55] & 7.2 & 18.9 & 20.6 & 16.8 & 17.2 & 34.7 & 19.23 \\ \(p_{8}\)[52] & 7.4 & 12.8 & 15.9 & 18.4 & 16.6 & 27.7 & 16.46 \\ LBP [52] & 6.0 & 18.0 & 12.1 & 9.7 & 11.4 & **17.0** & 12.36 \\ Watershed [51] & 7.1 & 10.7 & 12.4 & 11.6 & 14.9 & 20.0 & 12.78 \\ MSBF [8] & 2.8 & 14.8 & 8.4 & 7.3 & **4.3** & 17.9 & 9.25 \\ U-Net [46] & **2.6** & **8.9** & **7.2** & **7.1** & 7.7 & 17.5 & **8.50** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative misclassification (%) results with co-occurrence [5], best filtering result from Randen [54], \(p_{8}\) and LBP [53], Watershed [51], Multiresolution sub-band filtering (MSBF) [8] and U-Net [46]. (Bold is the best for each image).  
Figure 6: (**a–f**) Results of the segmentation with U-Nets for the six texture arrangments. The misclassification (%) is shown in each case. The classes are shown as overlaid colours.
# Hybrid Precoding Design for Wireless Fading Channels
###### Abstract  
Precoding techniques are widely used to eliminate interference between multiple user signals in a wireless fading channel environment. The linear MMSE precoding technique, one of the most widely used techniques so far, has low computational complexity but has the disadvantage of relatively poor symbol error rate (SER) performance. The symbol-level precoding (SLP) technique, on which much research has been conducted recently, has excellent SER performance but has the disadvantage of being too computationally complex. In this paper, we propose a hybrid precoding technique that simultaneously applies SLP and MMSE precoding to appropriately adjust SER performance and computational complexity performance. If two different types of precoding techniques are applied simultaneously, interference may occur between signals to which different types of precoding are applied, which can significantly deteriorate SER performance. Therefore, in this paper, precoding is designed to prevent interference between the two signals using the null space of the channel matrices. Through computer simulation, the proposed scheme showed that its SER performance was superior to that of the linear MMSE scheme, and the computational complexity was much lower than that of the SLP scheme.  
symbol-level precoding (SLP); linear MMSE precoding; computation complexity; symbol error rate (SER) +
Footnote †: journal: Applied Sci. 2024, 14, 1883. [[https://doi.org/10.3390/app14051883](https://doi.org/10.3390/app14051883)]([https://doi.org/10.3390/app14051883](https://doi.org/10.3390/app14051883))
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Prediction Model Design  
#### 2.3.1 Model Variable Selection  
The first step in constructing a traffic conflict prediction model is to select appropriate input variables. Based on the above association rules, 11 key variables are selected as model inputs from the aspects of driving behavior, vehicle state, road condition environment, etc., as shown in Table 2. In addition, considering that traffic conflict is related to individual  
Figure 5: Structure of LSTM unit.  
factors such as a driver's reaction time and psychological state, the demographic variable of driver's age (DA) is included. Figure 6 shows the distribution characteristics of various variables. By comprehensively considering multiple influencing factors, such as person-vehicle-road-environment, internal and external causes of traffic conflicts can be described, laying a more comprehensive and reliable data foundation for the construction of the prediction model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **Variable Names** & **Symbol** & **Units** & **Data Sources** \\ \hline \multirow{6}{*}{Driving behavior} & Average speed & AS & km/h & On-board sensors \\  & Standard deviation of speed & 8 SD & km/h & Vehicle sensor \\  & Average time headway & AH & s & In-car sensors \\  & Standard deviation of & HSD & s & In-car sensors \\  & headway & & & \\  & Average lateral acceleration & ALA & m/s\({}^{2}\) & In-vehicle sensors \\ \hline \multirow{6}{*}{Road condition environment} & Road curvature & RC & 1/m & Road alignment \\  & Coefficient of road adhesion & RAC & Dimensionless & \begin{tabular}{c} Measurement condition \\ monitoring \\ \end{tabular} \\  & Road gradient & RG & \% & \begin{tabular}{c} Road alignment \\ measurement \\ \end{tabular} \\  & Visibility & V & m & Posted by Weather \\  & Wind Speed & WS & m/s & \
\begin{tabular}{c} Department \\ Issued by the Weather \\ Department \\ \end{tabular} \\ \hline Driver & Driver age & DA & years &
\begin{tabular}{c} Driver information \\ registration \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input variables of traffic conflict prediction model.  
Figure 6: Statistical distributions of input variables for traffic conflict prediction modeling.
# EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid
## 2 Smart Grid Network Architecture with EV Access
### The Service Layer  
The service layer consists of two parts: A traditional cloud layer and a temporary fog layer. The service layer can provide various V2G services to end-users of the user layer through SAP.  
Cloud computing (CC) is a kind of distributed computing technology. It splits a huge computational processing program into a myriad of smaller subroutines through the network and passes the processing results back to the user after calculation and analysis. We have integrated cloud computing and V2G systems to enable V2G system entities to handle more complex service applications.  
However, providing a reliable and low latency response V2G service is a new challenge. In detail, in V2G system, smart meters can collect large amounts of data from EVs, drivers, charging stations, and infrastructure, which requires the execution of data analysis tools on the cloud. Since this data is different in volume, velocity, and variety, storing, processing, and analyzing the data is a challenging task. Although SG can transfer all data to the cloud-based infrastructure for further processing, it is difficult for Cloud Computing (CC) to store and process these collected data in a short time. Therefore, we apply fog computing (FC) to solve these problems.  
Fog computing, as shown in Figure 3, is an integrated version of mobile cloud computing (MCC) and mobile edge computing (MEC) that demonstrates a better infrastructure that includes resources for network edge services to provide applications for future Internet and services [24]. Generally, it acts as a bridge between the terminal device and the cloud. FC collects data from SG entities and then stores and calculates them locally. The data is transferred to the cloud for storage after processing. It reduces the need for network bandwidth for overly large data and can help protect sensitive data by providing local analytics.