esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Plant Stem Cells as Helpful Agents for Skin Disorders--A Narrative Review
## 7 Limitations  
Although plant stem cells could overcome conventional challenges associated with plant material, such as limited plant quantities, undomesticed species, and sometimes rare plant sources, they are not without limitations and drawbacks. Plant growth regulators are used for the induction and proliferation of plant stem cells, such as 2,4-D, BAP, Picoloram, Dicamba, Kinetin, and NAA, as listed in Table 1. Although these regulators are potent for plant cell induction and proliferation, some are hazardous to the environment and animal biological system--for instance, 2,4-D. Previous studies on 2,4-D (growth regulator and herbicide) revealed that it polluted the soil, air, and surface water and could cause neuropathologic effects in animal models [114; 115]. Therefore, additional efforts must be made to dispose of by-products and waste properly. In addition, [PERSON] and [PERSON] have recently reported that using 2,4-D at different concentrations leads to morphological changes in the plant model, BY-2 tobacco, and anomalous tumor-type proliferation growth [116]. Their research might explain the effectiveness of 2,4-D as an inductor and proliferatorator for plant stem cells. However, it also implies that the quality system frameworks are crucial. Regular testing should be done to ensure that there is no bioaccumulation of harmful and toxic compounds on the final product that may cause illness [117; 118].  
Research on the use of plant stem cells as an active ingredient is a relatively new trend in cosmetology. While skincare products containing plant stem cells have shown various advantageous benefits on aging skin, there are still numerous concerns that may restrict their usage. Although cosmetics do not include live stem cells, they utilize stemcell extracts to reduce wrinkles and enhance skin firmness through their action and antioxidant properties [43; 44]. These extracts are commonly found in cosmetic products, but their effectiveness may be hindered by limited transdermal penetration. To address this, further research is needed to optimize formulations and overcome the skin's natural barrier function. Additionally, the application of plant stem cells in clinical practice is a recent and emerging approach; thus, long-term side effects may still need to be discovered. Therefore, the effects of plant stem cells on aging skin still require further, better, and more profound research.  
The production of valuable extracts or secondary metabolites from plant stem cells requires fulfilling the quality system frameworks, including good manufacturing practices (GMP), good laboratory practices (GLPs), quality control (QC), and quality assurance (QA) [119]. Potential challenges during plant stem cell production include ensuring product consistency and purity, controlling microbiological contaminations, and ensuring product comparability [118]. Furthermore, ethical issues must be considered when the research progresses to trials using animal models or clinical trials [119]. All these practices are essential to ensure the safety of consumers.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Component** & \begin{tabular}{c} **Rank by** \\ **Proposed Model** \\ \end{tabular} & \begin{tabular}{c} **Conventional** \\ **RFN Method** \\ \end{tabular} & \begin{tabular}{c} **Normalized** \\ **Median** \\ \end{tabular} & \begin{tabular}{c} **Component** \\ **Proposed Model** \\ \end{tabular} & \begin{tabular}{c} **Rank by** \\ **RPN Method** \\ \end{tabular} & \begin{tabular}{c} **Conventional** \\ **Median** \\ \end{tabular} \\ \hline Feed Water & 01 & 03 & 01 & Drain pump & 14 & 10 & 16 \\ Pump & & & & & & & \\ Feed Water & 02 & 08 & 02 & Deaerator & 15 & 12 & 15 \\ Pump motor & & & & & & & \\ \hline Supply Water & & & & & & & \\ Temperature & 03 & 02 & 05 & Burner & 16 & 05 & 14 \\ Sensor & & & & & & & \\ \hline Return Water & & & & & & & \\ Temperature & 04 & 01 & 06 & \begin{tabular}{c} Induced Draft \\ (ID) Fan \\ \end{tabular} & 17 & 17 & 13 \\ \hline Header & 05 & 07 & 07 & Feed water hose & 18 & 06 & 12 \\ \hline Feed Motor & 06 & 04 & 04 & Shell & 19 & 24 & 10 \\ \hline Forced Draft & & & & & & & \\ (FD) Fan & 07 & 14 & 08 & Water Software & 20 & 21 & 21 \\ \hline Feed Water tank & 08 & 11 & 09 & \begin{tabular}{c} Intake vent/Air \\ vent \\ \end{tabular} & 21 & 20 & 22 \\ \hline Water Tubes & 09 & 09 & 03 & Furnace & 22 & 23 & 20 \\ \hline Combustion Chamber & 10 & 19 & 11 & \begin{tabular}{c} Mechanical dust \\ collector (MIC) \\ \end{tabular} & 23 & 25 & 24 \\ \hline Temperature & & & & & & & \\ regulator & 11 & 16 & 19 & Condensate filter & 24 & 18 & 25 \\ \hline Back flow & & & & & & & \\ \
\begin{tabular}{c} prevent valve \\ \end{tabular} & 12 & 22 & 17 &
\begin{tabular}{c} Water level \\ controller \\ \end{tabular} & 25 & 13 & 23 \\ \hline Strainer & 13 & 15 & 18 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparative analysis by Proposed Model, Conventional RPN, and Normalized Median method. Figure 2: The histogram of the component rankings of the boiler system by conventional method and proposed model. The discrepancy between the FMECA proposed, and the traditional FMECA are compared. From Figure 2, the ranking orders of the boiler system components have slightly deviated. This is because the proposed FMECA resolves many of the problems in the conventional FMECA, such as not considering vagueness and subjectivity in decision-making and enhancing consistency by including a large number of experts. Moreover, the ranks given by the proposed method and the normalized median method are similar and slightly deviated in some cases, which shows that the proposed ExJ-PSI model is effective.
## 4 Control and Actuation Strategies
### Closed-Loop Control  
The closed-loop control system follows the same procedure for imitating one cycle that was described in Section 4.1. The movement imitates the \"virtual\" muscle activation patterns (Figure 5) using three motors and two low-drift 9-axis BNO055 IMU (Inertial Measurement Unit equipped with an accelerometer, gyroscope, and magnetometer) sensors located at the base of the hip and knee (Figure 1).  
Each sensor underwent a calibration process recommended by the manufacturer using a series of sensor-fusion algorithms that blends the accelerometer, magnetometer and gyroscope data into stable three-axis orientation output [40]. For calibrating the position of the sensors, we used the dynamics captured by the open-loop system along with the simulation to find the model's limitations and available range of motion. This process executed a series of open-loop control motions for each stage of gait and used the motion capture recordings to correlate the angles of the compression elements with the IMU sensor readings. The sequential state machine requires a compensation for the forward kinematics and position. Here we monitor the hip (\(\theta_{hip}\)) and knee (\(\theta_{hac}\)) angles using the IMU sensors and update their values in the state machine at each time iteration (see the \"Control\" panel of Figure 4). The actuation strategy relies on tracking the angles using the IMUs, applied forward kinematics using the motor driver, then updated the target angle (\(\theta_{ref}\)) for the next stage of gait. This feedback loop incorporates a proportional controller that effectively manages the physical model to change the stages of gait once the system reaches a minimal steady-state error of 1.25% from the target position. In the event of an overshoot, the physical model is capable of correcting itself (Figure 4) when a gait stage has malfunctioned or is disturbed by an unanticipated outside force (i.e., pushing or pulling the structure out of the range of values).  
To further validate our simulation, we compared its power consumption with the physical prototype's (Figure 6C). The power consumption of the electrical motor is significantly larger than the other components. The microcontroller (290 mW) and sensors (25 mW) have a constant power requirement that was subtracted from the global power consumption to compute the power consumption of the motors. The model underestimates the idle power consumption, but it predicts well the power consumption during the third and forth stages of gait. The initial mismatch is most likely due to the differences between the ideal behavior of the OpenSim 3.0 muscles and commercial electrical motors at low speeds.
## 3 Proposed Method for Zenith Angle Estimation from Mixed Reflected Light
### Zenith Angle Estimation from Two Types of Reflected Light  
Based on Equation (1), the gradient magnitude of the object surface can be derived as follows [26]:  
\[|\
abla f|=\sqrt{p^{2}+q^{2}}=|\tan\theta| \tag{6}\]  
This equation indicates that the gradient magnitude of the object surface is related solely with the zenith angle. Therefore, the accuracy of the zenith angle directly influences the details of the reconstructed surface. The more accurate the zenith angle is, the greater the revealed detail is and the clearer the contours of the 3D image are.  
The light reflected from the target surface can be divided into specular and diffuse reflections. In the early stages of polarized 3D reconstruction research, many researchers relied primarily on specular reflection characteristics for target restoration. However, specular reflection exhibits strong directionality, and its degree of polarization has a multivalued relationship with the incidence angle, as shown in Figure 2. This complexity can lead to inaccurate 3D reconstruction results. To address these issues, subsequent research has proposed the use of the degree of polarization of diffuse light to obtain the incidence angle. In this process, light enters the object's surface and, after transmission through the object, exits into the air as diffused light. This light not only possesses polarization properties but also maintains a one-to-one relationship between its degree of polarization and the angle of incidence, as shown in Figure 3.  
Figure 2: The degree of polarization of the specular reflection in relation to the refractive index and the angle of incidence [27].  
When calculating the zenith angle, the effectiveness of the specular and diffuse reflection models depends on the type of reflected light. Under controlled laboratory lighting conditions--typically within a single darkroom environment--the light source is typically a point source. The target object is moderately sized and composed of uniform material, ensuring a short distance between the target and the camera with no interfering factors within the field of view. This setup helps ensure the uniqueness of the reflected light component. Given the limitations of specular and diffuse reflection models, this study proposes a method for solving the zenith angle from mixed reflected light, making it suitable for outdoor natural lighting conditions. The proposed method allows for the accurate calculation of the zenith angle while remaining robust against variations in the reflected light components. Thus, it achieves high precision in polarized 3D reconstruction.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 4 Simulation Results
### Complexity and Feasibility  
To assess the computational complexity of each model, we calculate the average elapsed time. The simulation platform is presented as: cpu intel i7-6800K and nvidia GTX-1080 Ti GPU.  
In Table 2, it's obvious that under the same conditions, the calculation time of different neural networks is proportional to its structural complexity. MLP network only needs 1.8 s to complete the classification, but it also has the lowest classification accuracy. CNN_SVM consumes the longest time, which is 15.4 s. Considering the accuracy of it fails to reach 99%, the efficiency of CNN_SVM needs to be improved. The Improved RNN network adopted in this paper consumes 14.1 s, Compared with Simple RNN and typical CNN, the improved model requires more computation time and higher computational complexity, because first, the initial network has a deeper network structure than the typical convolutional network, second, features can be extracted without manual operation and finally, the initialization of parameters expands the feature dimension and increases the computational cost, which is acceptable in practical system and can meet the requirements of forward error correction codes type classification.
## 2 ROP Prediction Based on Mechanical-Specific Energy Theory
### Drilling Rate Equations Based on Mechanical-Specific Energy Theory
\(EFF\) is an indicator that represents the performance of a mechanical system, typically used to measure losses in energy conversion processes. Mechanical efficiency is usually expressed as a percentage, indicating the ratio between the actual output power and input power. In this experiment, the data from laboratory quasi-triaxial rock mechanic experiments were used in conjunction with Equation (2). The values were regressed to obtain the mechanical efficiency. The maximum and minimum mechanical efficiency values for five neighboring wells (Fu4, Fu9, Fu10, Fu48, and Fu49) based on well logging data were expressed by the following equation:  
\[ \begin{cases} EFF_{min}=0.031 CCS+7.932\\ EFF_{max}=0.047 CCS+12.645\end{cases}  \tag{5}\]  
where \(EFF_{min}\) is the minimum mechanical efficiency, %; and \(EFF_{max}\) is the maximum mechanical efficiency, %. Taking the average of the equation above yields the mechanical efficiency for the target well:  
\[EFF=0.039 CCS+10.289 \tag{6}\]  
Mechanical-specific energy can represent drilling efficiency during the drilling process. At a constant mechanical drilling rate, a smaller mechanical-specific energy indicates that less energy is required to break a unit volume of rocks, indicating more reasonable drilling parameters. During the drilling process, the drilling pressure acts on the cutting teeth of the drill bit, causing them to penetrate the rock for rock breaking. The relationship curve between the mechanical drilling rate and drilling pressure is shown in Figure 1[24]. In an ideal scenario, ROP should exhibit a direct proportionality with WOB, implying that an increase in WOB should correspondingly enhance the ROP. However, in certain drilling conditions, as described in Stage 3, anomalies may occur. Instances like encountering bit balling, bottom hole pack-off, or drilling tool vibrations can lead to additional WOB being applied to the drill bit. Despite the increase in WOB, the ROP decreases due to the adverse effects of these conditions. This disruption signifies that the expected relationship of the mechanical-specific energy theory is disturbed in these specific circumstances. Therefore, in practical drilling operations, it is essential to adapt flexibly to various challenges to maintain the efficiency and safety of the drilling process.
## 5 Results and Discussions
### Skin Friction Coefficient, Local Nussl, and Sherwood Numbers  
Table 1 compares some results of the proposed scheme with those given in past research. The agreement of numerical values obtained in this research with those in past research can be seen, and this also verifies the Matlab code used for the computations performed in this research. Table 2 shows the numerical values of skin friction coefficient (excluding the Reynolds number) by varying the electric variable, Casson parameter, magnetic parameter, and inertia coefficient. The skin friction coefficient decays by the rising electric variable and escalates by enhancing values of the Casson parameter, magnetic parameter, and inertia coefficient. Table 3 displays the numerical values of the local Nusselt number (excluding the Reynolds number) with a variation of the thermophoresis parameter, Brownian motion parameter, Prandtl number, and Eckert number. The local Nussult number decays by increasing the thermophoresis parameter, Brownian motion parameter, and Eckert number, and it grows by rising values of the Prandtl number. The effect of the thermophoresis parameter, Brownian motion parameter, Schmidt number, and reaction rate parameter on the local Sherwood number (excluding the Reynolds number) is given in Table 4. The local Sherwood number declines by growing the Brownian motion parameter, whereas it escalates by increasing the thermophoresis parameter, Schmidt number, and reaction rate parameter.  
\begin{table}
\begin{tabular}{c c c c} \hline \(\lambda\) & **[PERSON] [43]** & **[PERSON] et al. [44]** & **Proposed** \\ \hline
0.0 & 1.0000 & 1.00000 & 1.002798 \\
0.5 & 1.2247 & 1.224747 & 1.228238 \\
1.0 & 1.4142 & 1.414217 & 1.418096 \\
1.5 & 1.5811 & 1.581147 & 1.585066 \\
2.0 & 1.7321 & 1.732057 & 1.736710 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed scheme with past research for finding numerical values of \(-f^{*}\left(0\right)\) using \(\beta=100,000,\ M=E_{1}=F_{r}=0,\ \text{and}\ N(no.\ of\ grid\ points)=30\).
# Class Activation Map Guided Backpropagation for Discriminative Explanations
## 6 Discussion  
The lack of category discrimination indicates that feature attribution methods can only reveal the features detected by the model in the image rather than focusing on those relevant to a specific category. This limitation hinders the understanding of the model's decision-making process. In this paper, we identify RectGrad's inability to produce category-discriminative saliency maps and propose CGBP to address this issue by enhancing category discrimination.  
CGBP improves RectGrad by utilizing the feature maps, which are the outputs of the last convolutional layer, to guide gradient selection at the spatial level. Class activation maps (CAMs), derived from these feature maps, are employed to highlight category-specific regions. However, the low resolution of CAMs limits their precision in guiding RectGrad, particularly when multi-category objects are closely positioned. To overcome this limitation, we introduce target and hostile class activation maps. The target saliency map emphasizes regions dominated by support for the prediction, while the hostile saliency map highlights regions dominated by rejection of the prediction. The final saliency map is generated by comparing and selecting pixels where the importance score in the target saliency map exceeds that in the hostile saliency map.  
To validate CGBP's enhanced category discrimination, we selected multi-category object images for visualization. The results show that Grad's saliency map is the noisiest, while IG, IDG, and GIG produce better results but require significantly higher computational costs. GBP and RectGrad tend to generate similar or even identical saliency maps. CGBP demonstrates superior category discrimination compared to other methods, particularly in cases such as the black swan and goldfish image, underscoring its effectiveness. Furthermore, quantitative results on three insertion metrics (SIC, AIC, and IS) across four models confirm that CGBP more accurately reveals the model's behavior. Notably, GBP achieves higher metric scores than some methods because it highlights all model-detected pixels, most of which support the top category. However, sanity check results indicate that GBP lacks sensitivity to model parameter changes, making it unreliable, while CGBP exhibits greater sensitivity. Overall, CGBP is category-discriminative and accurately reveals the model's behavior. Its applications in model debugging and medical image diagnosis further demonstrate its potential.
## 2 Materials and Methods  
### Ice cream Mixture Formulation and Processing  
Fresh _Tremella fviciformis_ (TF) was donated by Mid-Sum Biotechnology Co. Ltd. (Taichung, Taiwan, ROC). At harvest, fresh mushrooms were removed from the jar and cut into fruit body (100-120 g fresh weight/mushroom) and the base was sliced (10 g fresh mass/slice). The sliced bases were freeze-dried and ground into a powder with a milling machine (Retsch Ultra Centrifugal Mill and sieving machine, Haan, Germany), screened through 100, 200, and 300 mesh sieves, and then stored at 4 \({}^{\circ}\)C until use. Milk cream (Taiwan Branch of Singapore Business Pan-Asia Dragon PTE. Ltd., Taipei, Taiwan), emulsifier (imported from First Chemical Co., Ltd., Taipei, Taiwan, manufactured by Shiohama Co.,Japan), sucrose (Taiwan Sugar Co. Ltd., Tainan, Taiwan), sodium chloride (Taiyen Biotech Co., Ltd., Tainan, Taiwan), sodium carboxymethyl cellulose (imported by Da Kwan Trading Co. Ltd., New Taipei, Taiwan, manufactured by Nippon Paper Industries Co., Ltd., Tokyo, Japan), and milk powder and maltodextrin (Sally Biotech Co. Ltd., Changhua, Taiwan) were purchased from a local market.  
Fresh _T. fuciformis_ fruit body, _T. fuciformis_ sliced base, and 100, 200, and 300 mesh _T. fuciformis_ powders were used to make various ice creams, assigned as fresh fruit body (TF), fresh base (TFB), 100, 200, and 300 mesh _Tremella fuciformis_ powder (TFP) (Figure 1A). The ice cream production method is summarized in Figure 1B. Experimental ice creams were formulated as reported in Table 1. The 300 mesh _T. fuciformis_ powder was used to create different additive ratios in ice cream, assigned as 300 mesh TFP low ratio (0.4%), 300 mesh TFP medium ratio (0.9%), and 300 mesh TFP high ratio (1.4%). Ice cream mixes were prepared by using a batch ice cream maker (2 L capacity, Kaiser Kice-2030, China) for 60 min. Briefly, the TP sample was blended (90 \({}^{\circ}\)C hot water, 4 min), and then milk powder, sucrose, sodium chloride, emulsifier, and stabilizer were added and the mix was agitated for 1 min. Afterward, liquefied milk cream was added and the entire mixture was blended for 1 min using a mixer. The mixture was heated using a batch heater (30 secs at 90 \({}^{\circ}\)C). Then, the ice creams were packed in 500 mL paper cups and stored (-18 \({}^{\circ}\)C, 24 h) for hardening, followed by storing in a freezer (-18 \({}^{\circ}\)C) prior to analysis.  
Figure 1: _Tremella fuciformis_ sample (**A**) and ice cream production flow chart (**B**).