esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Results
### Building and Verifying the Deep Learning Model Architecture
Therefore, using 61 intervals could result in slight overfitting. The convergence speeds of accuracies of using four different intervals can also be observed from Figure 16. The accuracy convergence of 31 intervals and 61 intervals are very fast and close to each other. Figure 15: Granule size distributions divided into four different intervals: 16, 21, 31, and 61.  
learning model, a Monte Carlo dropout [67] was performed in a further analysis. For each test data in the 120-point test dataset, 100 forward passes were performed with dropout enabled. We ran a Monte Carlo dropout analysis with TensorFlow to take the averages of the max prediction probability (confidence score) and calculate the standard deviation (11) for uncertainty estimates for every test data as plotted in Figures 18 and 19. \[\sigma=\sqrt{\frac{1}{100-1}}{\sum\limits_{i=1}^{100}}{\left({{P_{i}}-\overline {P}}\right)^{2}}, \tag{11}\]  
where:  
\(P_{i}\) is the prediction obtained in the \(i\)-th forward pass of a test datum. \(\overline{P}\) is the mean prediction across all 100 forward passes of the same test data. In Figure 18, it is observed that the averages of max prediction probabilities (confidence scores) are greater than 0.75 for all 120 test data, and most of them are well above 0.85. In addition, it is observed that all standard deviations (uncertainties) are below 0.2 in Figure 19. Therefore, even considering the worst case where the largest standard deviation is subtracted from the smallest of the averages of max prediction probabilities, the max prediction probability is still above 0.55, which is still greater than the sum of the remaining  
Figure 19: The standard deviations (uncertainties) of max prediction probabilities of 120 test data. Figure 18: The averages of max prediction probabilities (confidence scores) of 120 test data. two probabilities of 0.45. Therefore, even if the selected deep learning model has uncertainties from dropout, it will not affect the consistency of prediction on the same test data in most cases. Consequently, the robustness of the selected deep learning model is assured. Furthermore, the overall average of prediction accuracy of 12,000 tests performed to run the above Monte Carlo dropout analysis is 96.67%. The effectiveness of the proposed image processing along with the selected deep learning model are both successfully verified.
# Freshness Monitoring of Packaged Vegetables
###### Abstract  
Smart packaging is an emerging technology that has a great potential in solving conventional food packaging problems and in meeting the evolving packaged vegetables market needs. The advantages of using such a system lies in extending the shelf life of products, ensuring the safety and the compliance of these packages while reducing the food waste; hence, lessening the negative environmental impacts. Many new concepts were developed to serve this purpose, especially in the meat and fish industry with less focus on fruits and vegetables. However, making use of these evolving technologies in packaging of vegetables will yield in many positive outcomes. In this review, we discuss the new technologies and approaches used, or have the potential to be used, in smart packaging of vegetables. We describe the technical aspects and the commercial applications of the techniques used to monitor the quality and the freshness of vegetables. Factors affecting the freshness and the spoilage of vegetables are summarized. Then, some of the technologies used in smart packaging such as sensors, indicators, and data carriers that are integrated with sensors, to monitor and provide a dynamic output about the quality and safety of the packaged produce are discussed. Comparison between various intelligent systems is provided followed by a brief review of active packaging systems. Finally, challenges, legal aspects, and limitations facing this smart packaging industry are discussed together with outlook and future improvements.  
smart packaging; intelligent systems; active packaging; sensors; indicators; RFID tags; nanoparticles; biosensors; humidity; oxygen; carbon dioxide +
Footnote †: journal: Applied Sciences
## 1 Introduction  
In an era of widespread global supply chain disruptions and acute volatility, demand forecasting has become more crucial than ever for any competitive business seeking to gain a firm foothold in the market [1]([PERSON], 2022). This is primarily because traditional methods for forecasting demand are not sufficiently effective in capturing drastically shifting consumer patterns [2]. Artificial intelligence (AI) provides a revolutionary alternative that offers the most accurate and agile forecasting models [2]. Compared to reliance on conventional methods, AI-based forecasting is far more accurate, as it transcends the limitations of even the most sophisticated traditional models [3]. Importantly, AI-based forecasting strategies are dynamic, with models that continuously adjust to market fluctuations, making them an ideal tool for ensuring the alignment of strategies with actual consumer behavior [4, 5]. Thus, AI not only enhances demand prediction but also introduces a new paradigm for developing forecasting strategies [6].  
This paper aims to advance existing research by addressing the following key questions: How does AI-driven automation enhance demand forecasting efficiency in supply chains? Which AI projects apply to demand forecasting in supply chains? What are the future research gaps, and how should these be addressed? This paper provides a comprehensive review of the literature on AI-driven demand forecasting in supply chains. The literature review for this study is thorough, integrating the foundations of AI used in the demand forecasting supply chain (DFSC) framework to propose an analytical framework that provides a comprehensive perspective on the AI-DFSC challenges. The paper utilizes both macroscopic visual analysis and micro-level content analysis for clarity. At a macro-level, it presents descriptive visual research findings related to topics, thematic progress, and thematic evolution in the general literature. At a micro-level, this paper focuses on conducting a detailed content analysis of the literature. Equally important, this study identifies promising future research trends and addresses the gaps that need attention in future studies. The remainder of this paper is structured as follows: the background of the research on AI-DFSC is presented in this section to establish the importance of this study. The data sources and the methods used in data processing are described in detail in Section 3. Section 4 presents a macroscopic structural analysis of AI-DFSC using visual analytics. Section 5 provides a thematic discussion of AI-DFSC in detail.
## 4 Results
### Precondition  
For a project to succeed, there are always certain preconditions with which it must comply. Usually, factors such as time, money and quality lay the foundation for success. To see what the added value of occupancy sensing is or can be, it is essential to gain insight into the basics. The following conditions emerged from the literature review: data maturity of the client organisation, human behaviour and quality of the sensors. The expectation was that the supplier and the users would provide other valuable information about essential preconditions. Therefore, the sub-question for this topic was, What are the preconditions for successfully using occupancy sensing?  
This question is answered from a supplier perspective and a user perspective. The suppliers know different customers, and the users can provide insight through their practical experience with the use of occupancy sensing. The answers from the respondents about the preconditions can be divided into two phases of occupancy sensing: the preparation phase and the operational management phase. The preconditions are therefore arranged within these two phases. The question is first answered from the supplier perspective and then from the user perspective. What is interesting about the sample is that the user-respondents were at very different stages in the use of occupancy sensing. One of the users was still very much in the beginning and was still determining the scope, and another user has been using occupancy sensing for years. The conversations with the users in different stages make it possible to look at the situation from multiple angles. The market is very much in development, and what did not work last year may now be resolved. Answering this sub-question will be completed by linking supplier and user and any gaps discussed before.  
Conclusions  
It appears that suppliers and users unanimously agree that an essential precondition for successful use of occupancy sensing is that it is known precisely what needs to be measured and what results the organisation wants to achieve. Knowing what one wants to measure is enhanced by a firm strategic alignment between the FM strategy and the corporate strategy and translation of this into concrete actions and targets at tactical and operational levels in the organisation. This indicates that more data-mature organisations will be better equipped to meet this precondition and the less data-mature organisations will have trouble defining the information needs as they lack a plan-do-check-act system and dashboard monitoring for their daily operations. From the user side, it is essential to take the time to look for a suitable supplier. From the supplier side, it is crucial to get all relevant stakeholders at the table in the first phase in order to ensure the system meets the demands of those that will use it. Of course, the interviewed suppliers did not say anything about this, but it appears from the users that not all suppliers can deliver what they say.  
That is why it is also essential to accurately record what is expected of the supplier and what has to be delivered.
## 2 Materials and Methods
### Machine Learning Model
#### 2.3.2 Multi-Objective Optimization  
A multi-objective optimization problem (MOOP) involves achieving optimal solutions from two or more conflicting objectives. Genetic algorithms (GAs) are evolutionary optimization techniques widely applied to such problems. The GA operates on the principles of natural selection inspired by [PERSON]'s theory, employing genetic operators including selection, crossover, and mutation [60]. In this study, the second-generation nondominated sorting genetic algorithm or NSGA-II is used as the multi-objective optimization method. NSGA-II represents an advancement over NSGA proposed by [PERSON] [61], recognized as a popular GA for generating the Pareto front in MOOP within engineering. NSGA-II employs two effective sorting principles: elitist nondominated sorting to rank solutions and crowding distance sorting to maintain diversity in the solution set.
## 4 Conclusions  
This study compared the relaxation modulus converted form dynamic modulus and creep compliance based on the same HMA specimens. To save testing costs and obtain more accurate viscoelastic parameters of asphalt mixture at a wider range, some conclusions can be illustrated:  
* For the transition of creep compliance to relaxation modulus, using the collocation method and least squares method is quite related. However, the collocation method is not as complex as the least squares method. Thus, the collocation method may be preferable in practice.
* The master curves of relaxation modulus converted from dynamic modulus and creep compliance do not present statistical difference at both short- and long-term time domain, and the major difference is observed at the transient time region.
* Potential reasons for the difference in the transient reduced time region may be attributed to the error in Prony series coefficients. The master curve pattern is sensitive to the Prony series coefficients. However, in the pre-smoothing and fitting process, the approximation is common, leading to loss of information. The most vulnerable time range of relaxation modulus may be the transient reduced time range.
## 2 Materials and Methods
### Corrosion Inhibition Efficiency Test of IL1 and IL2 by EIS Method  
A blank solution was prepared by providing a 1% NaCl solution, and 100 mL of 1% NaCl solution placed in a 250 mL beaker, which was equipped with a magnetic stirrer. Three types of electrodes were used in the experiment such as a carbon steel electrode (as a working electrode), a platinum electrode (as a supporting electrode), and a saturated calomel electrode (as a reference electrode) [11]. In the blank solution, CO\({}_{2}\) was flowed for 30 min (to make sure the CO\({}_{2}\) was saturated). Then, the Potentiostat was set in scanning the electrochemical impedance spectroscopy (EIS) method mode with a measurement frequency range of 10 kHz-100 MHz [35]. A Nyquist curve was produced first with a blank, then each of corrosion inhibitors (IL1 and IL2) was injected alternately by various concentrations of 20, 40, 60, 80, 100 ppm, respectively. The addition of each IL was started from the lowest concentration to the highest without changing the blank solution. In addition, the corrosion inhibition performance test was also carried out by the coating method. IL-based corrosion inhibitors were directly applied to the carbon steel plates until they were completely covered, then allowed to dry and stick on carbon steel surfaces. Meanwhile, the corrosion inhibition performance test was carried out by the EIS method until the Nyquist curve was obtained continuously.
# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 4 Conclusions  
In this study, an algorithm for retrieving high-spatial resolution AODs from HJ-1 A/B CCD data was proposed. The AOD was retrieved based on interband regression coefficients that varied by solar/view angle and land cover type and a model simulated aerosol lookup table. This algorithm provides a method for retrieving AODs from sensors without SWIR bands, which can be useful for various remote sensing sensors and application scenarios. The inversion results were validated with surface measurements and compared to the MODIS aerosol product. The results showed that the  
Figure 14: Density plots of the AOD retrieved from the HJ-1 A/B CCD data versus the MODIS aerosol product. (**a**) Comparison between Figure 12b,c; (**b**) comparison between Figure 12e,f. The color indicates the counts of data pairs falling in each grid.  
AODs retrieved by this method were superior to the MODIS aerosol product in terms of image quality. The validation results showed that the retrieved AODs were consistent with in situ measurements with an \(R\) of 0.866, RMSE of 0.167, MAE of 0.131, and EE of 53.9%. If the AOD of a specific point in the image was given, the AOD of the entire image could be much more accurately obtained by this method (\(R\) is 0.989, RMSE is 0.052, MAE is 0.042, and EE is 96.7%).  
[PERSON] designed the experiment; [PERSON] performed the analyses and wrote the manuscript.  
This research was funded by the National Key Research and Development Project (2016 YFA0602301), the National Natural Science Foundation of China (41601349), Jilin Province's Science and Technology Development Program (20180520220 JH), and the Fundamental Research Funds for the Central Universities (2412019 FZ003).  
The authors would like to thank the AERONET and POLDER groups for sharing their observation data.  
The authors declare no conflict of interest.
## 3 Dimensional Analysis
### Application of Numerical Analysis
#### 3.3.3 Validation of Levee Overtopping Risk Curve Using Numerical Analysis  
As shown in Figure 3, an overtopping risk assessment was performed using three dimensionless parameters (the Froude number, the ratio of superelevation to levee height, and the ratio of water depth to levee height) regardless of a levee's size or shape. To confirm the curve's reliability, FLOW-3D simulations were conducted on two levees with heights of 0.25 m for levee No. 1 and 0.50 m for levee No. 2 under overtopping conditions (Figure 8), with a Froude number of 0.63, a superelevation ratio of 0.3, and a water depth ratio of 0.8. By comparing these numerical results with the overtopping risk curve, it was determined whether each levee would experience overtopping based on consistent dimensionless criteria.  
In this study, two virtual levees were simulated using numerical analysis to examine overtopping behavior in a curved channel, thereby verifying the reliability of the levee overtopping risk curve. Consistent with the predictions of the overtopping risk curve, both levees experienced overtopping in the numerical simulations. Despite the difference in levee size, both exhibited similar overtopping patterns, enhancing confidence in the curve's reliability. Additionally, the dimensionless characteristics of levees were examined by comparing the distribution of water depth relative to levee height, based on the overtopping risk curve. Figure 9 shows the distribution of water depth for a small levee (No. 1) and a large levee (No. 2), revealing similar flow characteristics in both cases. Furthermore, as illustrated in Figure 10, an area-ratio analysis demonstrated that the difference in area ratios between the two levees remained below 3.8%, indicating minimal variation.  
Figure 8: Specification of the levee: (**a**) Small levee (No. 1); (**b**) Big levee (No. 2).  
Figure 7: Comparison of theoretical equations (ADWR) and simulation results.  
These results confirm that the overtopping risk curve can be consistently applied to levees of different scales to predict overtopping risk. By adopting a standardized, dimensionless-based approach, this method facilitates efficient evaluation of levee performance under various conditions and ensures reliable safety measures. Despite the differences in absolute height, the two levees exhibited nearly identical values for \(\Delta\)y/H and h/H, resulting in consistent overtopping behavior, as predicted by the risk curve. This suggests that the overtopping response is primarily governed by relative hydraulic conditions rather than absolute geometry.  
Additionally, the slight difference in area ratios observed in Figure 10 suggests minimal scale-dependent variation in the flow structure. These results emphasize the robustness and applicability of the proposed dimensionless overtopping criterion across different levee sizes, supporting the use of the risk curve as a predictive tool in practical design scenarios.
# A Low-Cost Terahertz Camera
## 2 Materials and Methods
At both interfaces, a certain fraction of the THz beam might be reflected, R\({}_{12}\) at the first surface and R\({}_{21}\) at the second surface, and so on. The reflectance and the transmittance can be calculated by multiplying the single contribution of the primarily reflected (or transmitted) waves. By considering N wafers, the reflectance and transmittance at normal incidence after the ith wafer are given by [19]:  
\[R=\prod\
olimits_{i=1}^{N}\frac{2R_{12}}{(1+R_{12})}(\frac{n_{2}}{n_{1}}|\mu^{2 })^{(i-1)} \tag{1}\]  
\[T=\left(\frac{n_{2}}{n_{1}}|\mu^{2}\right)^{i} \tag{2}\]  
Figure 1: (**a**) The experimental setup using the tilted-pulse-front pumping scheme for intense terahertz THz pulse generation, with a tilted angle of \(\theta_{\text{LN}}=63^{\circ}\) in a 1.3 mol. % MgO-doped stoichiometric LiNbO\({}_{3}\) (LN) crystal growth by Oxide Corp. The off-axis parabolas OAP1, OAP2, and OAP3 have focal lengths of 10 mm, 100 mm and 100 mm, respectively. (**b**) Shows the THz pulse with its Fourier transform in inset. Figure 2: (**a**) Shows the absorption coefficients of chalcogenide glass [17] and a high resistivity undoped silicon wafer as a function of THz frequency, respectively. (**b**) Transmission and reflection geometry inside a silicon wafer. with the Fresnel coefficients:  
\[R12=\left|\
u\right|^{2}=\ \left(\frac{n_{1}-n_{2}}{n_{2}+n_{1}}\right)^{2} \tag{3}\]  
\[\left|\mu\right|^{2}=\ \left(\frac{2n_{1}^{2}}{(n_{1}^{2}+n_{2}^{2})}\right) \tag{4}\]  
For one silicon wafer, using the refractive index of air n\({}_{1}\) = 1 and the refractive index of silicon n\({}_{2}\) = 3.418 we found R = 46.1% and T = 53.9% of the total average power. By example, for five silicon wafers and taking into account the multiple reflections and transmissions inside the wafers, the following relation can be used [19]:  
\[T=\left[T_{12}T_{21}+T_{12}R_{21}^{2}T_{21}+T_{12}R_{21}^{4}T_{21}+\ldots \right]^{5}=\left[\frac{1-R_{12}}{1+R_{12}}\right]^{5} \tag{5}\]  
for such case, a transmission of T = 4.55 % is estimated, which is approximately 135 uW in average THz power. In the case of chalcogenide glass, its refractive index and absorption coefficient are n\({}_{2}\) = 3.5 and \(\alpha\) = 88 cm\({}^{-1}\) at 1 THz [17], respectively. Undeniably, in comparison with silicon wafers, the losses in chalcogenide glass at 1 THz are dominated by its large absorption coefficient. For example, the absorbance A can be expressed in dB/cm as a function of distance x using this simple relation:  
\[A=\log(e^{\mu x}) \tag{6}\]  
and at 1 THz, adding to its Fresnel losses, a high absorption factor of 38 dB/cm in the chalcogenide lens prevents for a good THz transmission.