esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### The Mathematical Model  
The problem is formulated as a mathematical optimization model, namely the two-echelon vehicle routing problem with drop box facilities (2 EVRP-DF). This model is divided into two major parts, the first and the second echelon. The first echelon contains a collection of drop box facilities and one depot, which is represented by a set of \(M=\{1\ldots\ |\ M|\}\). The distance between nodes \(i\) to \(j\) is denoted as \(Cs_{ij}\), where \(i\) and \(j\) are part of \(|\ M|\). Travel costs from the first echelon will be multiplied by a fare \(R\), so that the total cost for the first echelon is calculated. In addition, the first echelon also adds the emission costs obtained from the total distance \(Cs_{ij}\) multiplied by the emission coefficient \(E_{v}\) (where \(v\) is the index of vehicles) and multiplied by the carbon tax \(T\). In the second echelon, there is a set of customer \(N=\{1\...\ |\ N|\}\). The customer is associated with demand \(D_{n}\) where \(n\in N\). The assignment of customers is limited by the capacity of the drop box facility \(\text{Q}_{\text{sm}}\), where \(m\in\{2\ldots|M|\}\). The vehicle capacity \(\text{Q}_{\text{v}}\) also limits route decisions to the first echelon where \(v\) is one of the vehicles, which is also represented by the set \(V=\{1\...\ |\ V|\}\). The following are the decision variables contained in the 2 EVRP-DF model.
## 3 Results and Discussion
### Phase II: Greenhouse Panel Study
#### 3.3.5 Mass Balance of Cu in Panel Study  
The total uptake of Cu by amelina plants in the panel was 9.2 mg (Table 4). In the control and amelina panels, 90 mg and 84 mg of Cu leached respectively, during all three leaching events combined. The total Cu in all surface runoff combined in control and amelina panels was 1786 mg and 123 mg, respectively. This result shows that a plant cover lowered the loss of Cu in surface runoff, which is otherwise high in unplanted stamp sand. However, similar to our observation in the column experiment, the majority of Cu was still held in the stamp sands in both control (76,565 mg) and amelina (78,545 mg) panels. During the six-month experimental period, uptake by amelina accounted for 0.01% of the total Cu. Leaching from the unplanted panel was 0.12%, whereas from the panel planted with amelina was 0.11%. The total Cu lost in runoff in the panel planted with amelina was 0.16%, whereas in the unplanted panel, the Cu loss by runoff was much higher, at 2.53%.
# Genetic Variation Affects the Anti-Melanogenic Efficacy of _Platycodon grandiflora_ Flowers
## 4 Conclusions  
This study highlights the potential of utilizing by-products from _P. grandiflora_ and underscores the important role of genetic diversity in enhancing their anti-melanogenic properties. The WF variety (WF_BU; IC50 = 241.58 \(\pm\) 8.82 ug/mL) demonstrated superior anti-melanogenic activity compared to the PF variety (PF_BU; IC50 = 427.97 \(\pm\) 20.26 ug/mL), primarily from higher levels of saponins such as PD3 and PD. These findings were supported by molecular docking analysis, which confirmed the strong TYR inhibitory effects of these saponins. Additionally, the study revealed increased expression of genes involved in saponin biosynthesis in the WF variety, further illustrating the impact of genetic diversity on phytochemical composition and pharmacological efficacy. These results suggest that by-products of _P. grandiflora_ may serve as valuable ingredients in the cosmetic industry, particularly for skin-whitening products. The importance of selecting specific genotypes to optimize the quality and efficacy of plant-derived products is evident. Future studies should focus on exploring the full range of bioactive compounds in _P. grandiflora_ and conducting clinical trials to ensure the safety and effectiveness of these compounds in cosmetic and therapeutic applications.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14166867/s1](https://www.mdpi.com/article/10.3390/app14166867/s1)]([https://www.mdpi.com/article/10.3390/app14166867/s1](https://www.mdpi.com/article/10.3390/app14166867/s1)), Table S1: Primer sequences for qRT-PCR analysis, Figure S1. Total saponin content in BuOH fractions.  
Conceptualization, E.K. and T.K.H.; Investigation, E.K.; Writing--original draft preparation, E.K. and T.K.H.; Writing--review and editing, E.K. and T.K.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-001, 1345370811).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compounds** & \begin{tabular}{c} **Lowest Binding Energy** \\ **(scal/mol)** \\ \end{tabular} & \begin{tabular}{c} **Mean Binding Energy** \\ **(scal/mol)** \\ \end{tabular} & \
\begin{tabular}{c} **Residues Involved in** \\ **Hydrogen Bond Interaction** \\ \end{tabular} & **Residues Involved in** & **Risi (uM)*** \\ \hline PE & \(-\)4.90 \(\pm\) 1.85 & \(-\)4.00 \(\pm\) 1.14 & Lys 4 &
\begin{tabular}{c} Lys 4, Val 7, Ile 237, Vel 240, Ile 243, \\ Val 285, Tyr 286 \\ \end{tabular} & 42.94 \(\pm\) 7.66 \\ PD3 & \(-\)9.39 \(\pm\) 0.11 & \(-\)8.72 \(\pm\) 1.26 & Asn 57, Arg 209 & Asp 55, Asp 57, Gln 158, Asp 183, & 26.99 \(\pm\) 19.91 \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Molecular docking analysis of _Platycodon grandiflora_ triterpen aponins with tyrosinase.
# Towards a Comprehensive Metaverse Forensic Framework Based on Technology Task Fit Model
## 3 Results and Analysis
### Measurement Model  
As highlighted in Section 2, this study leverages structural equation modeling for the evaluation of the framework. A SEM typically uses a measurement model to evaluate the correlation and covariances among constructs and then uses a structure model to test for causation [34]. The measurement model is, therefore, the initial step in analyzing the PLS-SEM data. In measurement models, reliability analysis is taken into consideration. In carrying out the measurement model, all constructs and their corresponding measurement items were analyzed in line with the theoretical model defined in Figure 4. The final outcome of the measurement model is presented in Figure 5. It was observed that all the items in all constructs had a factor loading \(\geq\)0.5 except TAC5, with 0.292 loading. Consequently, when the item was removed, as the construct had sufficient items, an improved factor loading was observed for other items in the construct (TAC). The path coefficients among the constructs also  
from 0.60 to 0.70 are considered acceptable, while values over 0.70 imply higher reliability. Table 7 shows the reliability metrics (Cronbach's alpha, composite reliability, and average variance extracted/explained) considered to evaluate the measurement model. The results (Table 7) demonstrate that all the constructs fall within an acceptable range, except for COM, which has a poor dependability value (low Cronbach's alpha, value of 0.55). To prove convergent validity, the average variance extracted (AVE) was also evaluated with a thumb rule of 0.5 value [38]. The computed AVE for all constructs (as shown in Table 7) was more than the 0.50 thumb rule, thus satisfying the requirement for accepting the measurement model. All measurement items generated factor loading higher than 0.5, as shown in Figure 5, further supporting the reliability of the measurement instruments. Additionally, convergent validity describes the degree to which each concept converges to explain the variance of its items, and average variance extracted (AVE) is the measure used to assess the convergent validity of constructs. Furthermore, discriminant validity is evaluated to explain the extent to which one construct is distinct from others in the structural model.
###### Abstract  
Many new consumer applications are based on the use of automatic speech recognition (ASR) systems, such as voice command interfaces, speech-to-text applications, and data entry processes. Although ASR systems have remarkably improved in recent decades, the speech recognition system performance still significantly degrades in the presence of noisy environments. Developing a robust ASR system that can work in real-world noise and other acoustic distorting conditions is an attractive research topic. Many advanced algorithms have been developed in the literature to deal with this problem; most of these algorithms are based on modeling the behavior of the human auditory system with perceived noisy speech. In this research, the power-normalized cepstral coefficient (PNCC) system is modified to increase robustness against the different types of environmental noises, where a new technique based on gammatone channel filtering combined with channel bias minimization is used to suppress the noise effects. The TIDIGITS database is utilized to evaluate the performance of the proposed system in comparison to the state-of-the-art techniques in the presence of additive white Gaussian noise (AWGN) and seven different types of environmental noises. In this research, one word is recognized from a set containing 11 possibilities only. The experimental results showed that the proposed method provides significant improvements in the recognition accuracy at low signal to noise ratios (SNR). In the case of subway noise at SNR = 5 dB, the proposed method outperforms the mel-frequency cepstral coefficient (MFCC) and relative spectral (RASTA)-perceptual linear predictive (PLP) methods by 55% and 47%, respectively. Moreover, the recognition rate of the proposed method is higher than the gammatone frequency cepstral coefficient (GFCC) and PNCC methods in the case of car noise. It is enhanced by 40% in comparison to the GFCC method at SNR 0 dB, while it is improved by 20% in comparison to the PNCC method at SNR \(-\)5 dB.  
robust automatic speech recognition; ASR; feature extraction; MFCC; RASTA-PLP; GFCC; PNCC +
Footnote †: journal: Applied Soft. 2019
# Mechanical Performance of Gilsonite Modified Asphalt Mixture Containing Recycled Concrete Aggregate
## 1 Introduction  
### Gilsonite Background  
Gilsonite is a natural fossil resource that was discovered more than one century ago, hence, it has been broadly studied. It is a type of natural asphalt (generally solid) derived from petroleum, which has a predominantly dark brown color, and is found widely around the world. It is naturally produced through the penetration of oil or petroleum in rocks under combined actions of heat, pressure, oxidation, and microbe activity throughout millions of years [1]. It is a hard and brittle material [2], but it can easily be crushed into powder. It is also scientifically named unitaite [3; 4; 5]. This resinous hydrocarbon has been evaluated and used in different industrial processes [3; 4; 6; 7]. Generally, it presents a carbon content superior to 70% and a low H/C ratio, which indicates a high degree of molecular condensation. It contains a large quantity of N, O, and S elements in which there are existing functional polar groups and a high presence of metallic elements. Upon carrying out SARA (Saturates, Aeromatics, Resins, Asphaltenes)-fractions, gilsonite presents a high content of asphaltenes [4; 8; 9; 10; 11; 12]. It is a material that is highly compatible with conventional asphalt binders [13; 14]. Gilsonite is soluble in aromatic and aliphatic solvents, as well as in petroleum derivates [4].  
In pavements, gilsonite is a material used to modify the properties of asphalt binders. In particular, it is used to increase the stiffness and thermal susceptibility [5; 15; 16]. Therefore, it helps to increase the resistance of hot-mix-asphalts (HMA) to the phenomenonof rutting in high temperature climates [1; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29], increasing the Marshall stability and stiffness under a cyclic load (e.g., resilient modulus and dynamic modulus) and resistance to permanent deformations. There have also been reports of improvements regarding moisture damage resistance [1; 2; 5; 16; 24; 28; 30] and indirect tension resistance [5; 31]. However, in low temperature climates, this may generate problems associated with thermal and fatigue-induced cracking [1; 32; 33; 34; 35]. Because of this, several studies have combined gilsonite with other conventional polymeric additives (e.g., styrene-butadiene-styrene (SBS), styrene butadiene rubber (SBR), and crumb rubber) in order to modify asphalt binders [1; 31; 36; 37]. Additionally, in high addition percentages, it can considerably increase the viscosity of the asphalt binder, negatively affecting mix temperatures (workability) and compaction, increasing energy consumption and environmental pollution [1; 2]. Despite the above, if the gilsonite dose is controlled up to a certain point, it may not exert a considerably negative influence upon the mentioned aspects [3; 38]. On the other hand, in comparison with other modifiers, gilsonite offers advantages in terms of lower costs and increased ease of use [1; 5; 21; 24; 32; 39].
## 2 Datasets and Methods
### Methods
#### 2.2.2 Growing Season Detection  
Growing season is the most active period in the phenology cycle of non-evergreen vegetation, hence it is often used to define developmental stages of vegetation [1]. Mean NDVI values during the growing season can be a better indicator for vegetation growth conditions than that of the whole year since it focuses on developmental stages with high activity and avoids spurious NDVI data outside the growing season, especially in winter [36]. To extract the growing seasons of vegetation across the country, we used a modified Harmonic ANalysis of Time Series (HANTS) algorithm [51, 52], which aims to obtain smoother time series by removing abnormal NDVI values and minimizing non-vegetation effects including clouds, aerosol, snow, _etc._ HANTS uses Fourier analysis iteratively and describes seasonal NDVI variation with several low frequency cosine functions. More technical details of HANTS algorithm can be referred in reference [1].  
Considering that the length, start and end of growing season have great impacts on the value of growing season NDVI, to avoid uncertainties in interannual NDVI variation resulting from different growing seasons in different years and grids, we separated the research area into 15 regions according to vegetation and climate types [53]. Each region with the same vegetation and climate type shares the same growing season calculated from the spatial and interannual averaged NDVI from 1982-2011.  
For non-evergreen vegetation, based on the HANTS smoothed NDVI series, we take the start of season (SoS) as the maximum of the first derivative (maximum change rate), following the method taken by [PERSON] _et al._[1] and [PERSON] _et al._[13]. The end of season (EoS) is defined as the time when NDVI value equals to or drops below the value at SoS. According to the distribution of SoS (Figure 2a) and EoS (Figure 2b), for most of non-evergreen vegetation across the country, SoSs vary from April to May and EoSs vary from October to December. The growing seasons of vegetation in southern China begin earlier and end later than those in northern China. It should be noted that for evergreen vegetation, high activity lasts throughout the year thus the growing season is taken as entire year.
# GE-YOLO for Weed Detection in Rice Paddy Fields
## 3 Results and Discussions
### Testing Under Different Lighting Conditions  
In real-world scenarios, lighting conditions can vary significantly. For instance, overcast weather or dusk can result in dim lighting, while intense sunlight can lead to overly bright conditions. To evaluate the robustness of our proposed model, we simulated different lighting scenarios by varying the illumination factor \(\alpha\) and assessed our model's performance under these conditions. The description of lighting simulation is as follows:  
\[I_{new}(x,y)=\alpha I_{new}(x,y) \tag{18}\]  
Here, \(I_{new}(x,y)\) represents the pixel value of the original image at position \((x,y)\). The illumination factor \(\alpha\) is used to adjust the brightness, where \(\alpha>1\) indicates an increase  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Gold-YOLO** & **GloU** & **EMA** & **mAP/\%** & **F1 Score/\%** & **GFLOPs** & **Params/M** & **Speed/FPS** \\ \hline YOLOv8 & - & - & - & 91.2 & 88.5 & 8.1 & 3.01 & 187.6 \\ YOLOv8 & ✓ & - & - & 92.1 & 89.3 & 17.6 & 8.06 & 96.0 \\ YOLOv8 & ✓ & ✓ & - & 92.2 & 89.4 & 17.6 & 8.06 & 92.5 \\
**YOLOv8** & ✓ & ✓ & ✓ & **93.1** & **90.3** & **17.6** & **8.06** & **85.9** \\ \hline
**Increase** & & & & **\(\uparrow\)1.9** & **\(\uparrow\)1.8** & **-** & **-** & **-** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of ablation experiments.  
Figure 9: Detection Results of GE-YOLO, where (**a**) are AS, (**b**) are PL, (**c**) are BS.  
in brightness, \(0<\alpha<1\) indicates a decrease in brightness, and \(\alpha=1\) means no change in brightness. \(I_{new}(x,y)\) represents the pixel value of the adjusted image at position \((x,y)\). Images under different illuminations are shown as Figure 10.  
We set multiple values of \(\alpha\) and obtained a series of test results as shown in Figure 11. It can be observed that our model remains stable when \(\alpha\) is within the range of 0.3 to 1.1, with the mAP value consistently above 90%. Although as \(\alpha\) increases further, the mAP value gradually declines, but it is still above 82%. This indicates that our model has good resistance to low light and moderate bright light conditions, demonstrating strong generalization performance.
# Novel Design of Expandable Spinal Cage for Efficient Lumbar Spine Fusion Operation
## 3 FEA for Mechanical Performance Verification  
### Verification Criteria  
In this study, mechanical performance of spinal cage was evaluated through FEA and experiments based on ASTM F2077 testing standards. ASTM F2077 includes tests for compression, compression-shear, and torsion. Since axial rotation in the lumbar spine is not as significant as in the cervical spine, torsion testing is often excluded for lumbar  
Figure 4: Expansion method of spinal cage. (**a**) Posterior and anterior in initial state. (**b**) Height extension. (**c**) Angle extension.  
cages. As this study focuses on a cage for lumbar applications, only compression and compression-shear tests were conducted.  
Through these tests, the load-displacement curve of cage is obtained. From this curve, stiffness is determined as the initial slope, yield load is defined as a load causing 2% strain or 2% permanent deformation, and ultimate load is defined as the maximum load applied. These standardized tests enable evaluation of cage's mechanical performance under consistent conditions, allowing for easy comparison between developed cage and existing designs.  
As shown in Table 1, allowable mechanical performance criteria identified through ASTM F2077 tests are defined by ISO 23089-2, international standard for spinal cage mechanical performance [30]. According to ISO 23089-2, the yield load of a spinal cage should meet or exceed the performance of the bottom 5% of FDA-approved spinal cages. Based on this standard, spinal cages should demonstrate a yield load of at least 6371 N in a compression test and at least 1996 N in a compression-shear test.
# Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition
## 5 Summary and Outlook  
### Conclusions  
In this article, a real-time gesture recognition system based on wearable motion sensors was built. The system is compatible with a custom-built smart ring as well as a smartwatch, both sending motion data to a smartphone, which is able to predict gestures with an accuracy of up to 98.8%. The foundations for building such a system and the associated challenges were examined. Related research covering similar challenges was organized and the relevant solutions were incorporated into the design of the system. Specifically, a minimum accuracy score of 92% was derived as a requirement for the machine learning process, various algorithms for processing the data were incorporated and the combination of accelerometer and gyroscope was selected to capture hand movements. The results show that the finger produces more information, which is consistent with the observations of [PERSON] et al. [10]. The information influences the data set by introducing more variance inside gesture classes, leading to machine models with lower accuracy. Additionally, the results clearly show that the activation mechanism affects the accuracy of the models. Since the mechanism allows for more freedom with respect to the ending point of the gesture, some gestures contain highly variant information, which decreases the model accuracy further. Additionally, the mechanism is not suited well for clap gestures, because the thumb is moved unwillingly when performing claps. This leads to an early deactivation of the sensors and thus, the _Clap Once_ and _Clap Twice_ gestures are confused frequently.  
The system considers 12 different gestures, which are performed with the hand. They were recorded beforehand with the support of 10 participants. Each participant recorded three differently data sets during two sessions. Two data sets have been recorded simultaneously, utilizing the sensors of the smart ring and the smartwatch, while sharing the smartwatch display as an activation mechanism to start and stop the sensors. The third data set has been recorded solely with the smart ring while utilizing a force sensitive resistor as an activation mechanism.  
Multiple features describing the signal were derived from each data set and sorted by importance using the Mutual Information score. Three different sized feature vectors were generated using this score and facilitated to train four different types of machine learning models. After an exhaustive parameter search, the best model for each data set was selected.  
Nonetheless, every data set resulted in a model that exceeds the defined minimum score of 92% accuracy. Therefore, the proposed system is able to compete with the results of related research.