esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Design Parameters on Drying of Kiwi Fruit
## 4 Conclusions and Recommendations  
Drying conditions of the kiwi fruits were presented in a convective type air dryer at the air temperatures of 45 \({}^{\circ}\)C and air velocity of 0.5, 1 and 1.5 m/s and kiwifruit slice thicknesses of 4 mm and 8 mm. Some important results obtained from this study can be listed as follows;  
* The drying rate and the moisture content of the sample are affected by the thickness of the kiwifruit and the speed of the drying air. Increased drying air velocity and reduced fruit thickness cause drying time to be reduced.
* The lowest drying time (225 min) was obtained at 4 mm slice thicknesses and 1.5 m/s. The highest drying time (750 min) was obtained at 8 mm slice thicknesses and 0.5 m/s velocity.
* The drying was carried out to final moisture content of 10% from initial moisture content of about 80% (wb).
* Freshly harvested kiwifruit was successfully dried in the convective dryer at different speeds and at different thicknesses. It has been determined that working the convective dryer at different speeds is advantageous in terms of preserving brightness and color quality.
* By applying ANOVA to the test results it was found that, the most dominant parameter affecting the amount of moisture was drying time with 45.79%. Drying time was followed by air velocity with 29.54% and thickness of the slices with 17.32%.
* The taste quality of the samples are not tasted, hence this study gives only information about the drying process. Further nutrition analysis are recommended by the co-authors of this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & **Degree of Freedom** & **Sum of Square** & **Variance** & \multirow{2}{*}{**F**} & **Contribution** \\  & _(df)_ & **(SS)** & **(V)** & & **(\%)** \\ \hline A & 1 & 103.8263775 & 103.8264 & 41.06048 & 17.31623091 \\ B & 2 & 177.8696111 & 88.93481 & 35.17128 & 29.54121585 \\ C & 2 & 272.9478971 & 136.4739 & 53.9717 & 45.79426629 \\ Error & 12 & 30.34344542 & 2.52862 & 0 & 7.348286946 \\ Total & & 584.9873311 & & & \\ \hline \hline \end{tabular}  
* Significant at least 99% confidence level.  
\end{table}
Table 5: Results of variance analysis.  
**Author Contributions:** For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used \"conceptualization, F.O.; methodology, F.O.; investigation, F.O. and N.C.; writing--original draft preparation, N.C.; writing--review and editing, N.C.  
**Conflicts of Interest:** The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material
## 3 Results  
### Uniaxial Compressive Strength  
The strength of SWCPB directly affected ore body safety and continuity of mining. Figure 3 shows the curve strengths of SWCPB in groups A and B based on UCS test results.  
Results shown in Figure 3 indicated that the strength for three days in group A observed no obvious trend, while with seven days of curing, the strength decreased with the increase of limestone powder replaced. The strengths of A2, A3, and A4 were similar after 28 d. Similarly, for group B, the strength of B4 with 28 d was higher than B2 and B3. Comparing the strengths of group A with B, all strengths of group A were higher than those of group B. It was interesting that there was an apparent retardation in strength development as a result of cement substitution. According to [10], the hydration processes of stone powder cement tailing backfill can be divided into the following four stages: dissolution period, condensation period, infiltration period, and hardening period. At the early curing stage, the groups with cement substitution had less cement to increase the strength, and limestone powders just played a role as a crystal nucleus. However, at the later curing stage, hydration products reacted with SiO\({}_{2}\), which improved hydration reactions and strength.  
In addition, according to mining experience, the target strength of seven days is 0.5 MPa, while for 28 days the required strength is 1.3 MPa. As a consequence, the seven-day strength of all groups met the requirements, while the 28 d strengths of B2 and B3 did not meet mining strength requirements of backfill.  
Figure 3: UCS comparison of different mixture ratios of groups A and B.
# A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
## 5 Case Study: Successful Student Sessions in Coursera
### Case Study & Framework  
This study takes into account data gathered from a Coursera course held between 23 June 2017 and 14 April 2018. The course required a total of 17 h of time commitment and was divided into 6 modules, one for each week. In this research, 58 potential resources for interaction were considered, including 35 video lectures and 23 assessments. The decision to use this course for the case study was made for two reasons: first, ethics clearance had already been obtained on the data, as this is a secondary analysis of data previously used, and second, it was thought necessary to use pre-COVID-19 data so that it was not skewed by changes in MOOC use as a result of the pandemic. 15,420 people interacted with the platform during the observation period, of which 13,861 started the course during this time frame; we only keep the data of those who finished the course. Data was utilized in compliance with Coursera's Terms and Conditions of use for research in learning analytics and additional ethics clearance was provided by Pontificia Universidad Catolica de Chile. Prior to being provided to the researchers, the data was anonymized, so it was impossible to individualize particular users. The initial step in implementing the framework was to match Coursera's simple data model with the minimum data model proposed here. The Coursera data model had almost 75 tables. The Course Progress table was the most significant for this study since it tracked the course ID, the resource interacted with, the user who performed the interaction, the state (start/complete), and the date specifying when it occurred. However, because this table only contained IDs, it needed to be supplemented with the course information tables (Course Item Types, Course Items, Course Lessons, Course Modules, and Course Progress State Types) in order to establish the order of the resources within the course and obtaincontextual information about each resource. Table 3 shows the details of the fields in each of the tables used for the framework's application. The second step to apply the framework was to build the low-level event log, including the concept of session ID. To achieve so, an action was regarded to occur in the same session
## Materials and Methods
### Multi-Objective Prediction Method Based on the Conditional Copula Function  
The load prediction model proposed in this paper includes two main components. One component is the conditional copula interval ultra-short-term prediction method based on the discrete form, which uses load historical data after giving a set of prediction model parameters (\(K\), \(t\)), to establish the probability distribution function of the load in the period to be predicted by calculating the marginal distribution function such that the upper and lower bounds of the prediction interval under the given confidence level are obtained; the prediction of the entire prediction period is then completed using rolling prediction, and the interval prediction effect is measured by two evaluation indices containing PICP and PIAW. The second component is the NSGA-II multi-objective optimization method, which uses PICP and PIAW as the objective functions within the value range of the copula interval prediction model parameters \(K\) and \(t\) and finds the non-inferior solution set for predicting the parameters of \(K\) and \(t\). It is assumed that each evaluation index is equally important such that each index is endowed with 50% weights, and a set of optimal prediction model parameters is obtained by weighting calculations. The copula interval prediction is performed with the recently obtained parameter, and the results are compared with the prediction results of the currently used interval prediction methods.  
To better understand the detailed process of this prediction method, we give the specific implementation steps of the prediction method, as shown in Figure 1:  
Step 1: Using the historical load sequence, in the process of establishing conditional copula distribution function in discrete form, we first determine the value range of \(K\) (interval division number) and \(t\) (condition number) that is applicable to the current historical data.  
Step 2: We use two indicators, PIAW and PICP, to evaluate the accuracy of the conditional copula interval prediction results under different \(K\) and \(t\) values.  
Step 3: Taking PICP and PIAW as objective functions, we use the NSGA-II multi-objective optimization method to find the non-inferior solution set of the conditional copula prediction model parameters within the range of \(K\), \(t\).  
Step 4: We obtain a set of optimal parameters for the prediction model using the average weight method.  
Step 5: We use the optimal group of parameters of \(K\), \(t\) for conditional copula interval prediction and compare the prediction results with those of GPR, ANN and ELM (three commonly used interval prediction algorithm results) to verify the effectiveness of the proposed method.
# Data Hiding of Multicompressed Images Based on Shamir Threshold Sharing
## 3 Methodology
### Embedding Procedure
#### 3.1.2. Subsecret Embedding Stage  
The main idea of the DBS data hiding method is to directly replace the bitmap compressed by the smooth block with the secret message. If the difference between the high amount and the low amount is less than the preset threshold T, this block is considered  
Figure 3: Example for secret sharing stage.  
as smooth block. During decoding, the change of the bitmap does not have a significant impact on the visual quality of the entire image due to the closeness of the high and low gray levels.  
The binary of the first elements in \(X_{1}\), \(Y_{1}\), \(X_{2}\), \(Y_{2}\) are \(\{X_{1}\)\({}^{1}\), \(Y_{1}\)\({}^{1}\), \(X_{2}\)\({}^{1}\), \(Y_{2}\)\({}^{1}\)} = [[0000], [0101], [0001], [1010]], respectively. Set the threshold value to T = 10. Figure 4a is an original image block; the triplet of this image block after AMTC compression is [100, 95, BM], BM is shown in Figure 4b and H - L = 100 - 95 = 5 \(<\) T. Replace the bitmap BM with \(\{X_{1}\)\({}^{1}\), \(Y_{1}\)\({}^{1}\), \(X_{2}\)\({}^{1}\), \(Y_{2}\)\({}^{1}\)}, and the modified triplet is [100, 95, [0000 0101 0001 1010]], as shown in Figure 4c. The 2 nd, 3 rd and 4 th elements of \(X_{1}\), \(Y_{1}\), \(X_{2}\), \(Y_{2}\) are embedded in the compressed bitmaps of the four images in the same way, and so on, embedding all \(X\) and \(Y\) dimensions into \(n\) images, respectively.
# Multi-Agent Dynamic Fog Service Placement Approach
## 2 Related Work Review
What dynamic service placement algorithms set apart is that service discovery mechanisms are defined in such a way that available services are known to other services due to their interaction [26]. Each of the service (microservice) enquiries are dynamically updated in the service registry to determine availabilities. A service placement solving technique can also be offline and online [27]. In an offline technique, all the requirements and constraints are known in advance. To be more precise, it can be said that a placement decision is made in the compile time [28]. Meanwhile, online placement decisions are made in the runtime. It is, however, more beneficial to consider a placement as an online technique. It is more dynamic and capable of reacting to current changes in the infrastructure. Dynamic networks are the ones where both the mobile fog nodes and the end-users change their characteristics within the time, including network topology changes [29]. A fog computing infrastructure has to be mobility aware, but it is challenging due to its dynamics. Dynamicity can be related to the fog infrastructure dynamicity and to the application dynamicity [28]. Fog computing architecture is highly dynamic [20] and its nodes may join or leave the network any time. But the resources in that particular node may not be sufficient to host a requested service that leads to a lower QoS, longer response time,or even service failure. Applications, therefore, should be deployed/removed dynamically while considering the capabilities of the changing fog infrastructure [28]. Please see Table 1 for a review summary. There are numerous research papers trying to solve a service placement or an application placement using a dynamic approach. Some of them use a distributed control method instead of a centralized or federated one. Some of them focus on resilience. But there are almost none of them that are dedicated to a dynamic service placement in a distributed way with an intention to keep it more resilient due to a distributed approach.
## 3 Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class**} & **Selected** & \multirow{2}{*}{**Weight**} & \multicolumn{2}{c}{**Values of the Class Representative. Confidence Intervals and Band**} \\  & **Features** & & & **(_LBPH Case_)** \\ \hline \multirow{4}{*}{_OD_} & _ImG_ & 0.8 & [0.515; 0.594] \\  & _ConG_ & 0.9 & [0.392; 0.476] \\  & _FDG_ & 0.8 & [2.575; 2.613] \\  & _LBPH_ & 0.9 & [11381; 13000], [5095; 5813], [5141; 5512], [1395; 1941], [5609; 6320], [5842; 7123], [1926; 2231], [5225; 5836], [5361; 6010], [14276; 15261] \\ \hline \multirow{4}{*}{_MA_} & _ConG_ & 0.8 & [0.038; 0.063] \\  & _EnG_ & 0.8 & [0.701; 0.742] \\  & _LG_ & 0.8 & [0.081; 0.113] \\  & _LB_ & 0.8 & [0.049; 0.064] \\ \hline \multirow{4}{*}{_EX_} & _ImG_ & 0.8 & [0.431; 0.478] \\  & _ConG_ & 0.9 & [0.575; 0.627] \\  & _EnG_ & 0.8 & [0.789; 0.824] \\  & _MonG_ & 0.8 & [0.852; 0.901] \\  & _FDG_ & 0.8 & [2.491, 2.527] \\ \hline \multirow{4}{*}{_HE_} & _ImR_ & 0.8 & [0.679; 0.717] \\  & _FDR_ & 0.8 & [2.487; 2.525] \\  & _LR_ & 0.8 & [0.141; 0.163] \\  & _LB_ & 0.8 & [0.103; 0.126] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for class representatives. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Box**} & **SB1\_3** & **SB2\_3** & **SB3\_3** & **SB4\_3** & **SB5\_3** & **SB6\_3** & **SB7\_3** & **SB8\_3** & **SB9\_3** & **SB10\_3** \\ \hline Pixels & 1819 & 713 & 776 & 957 & 608 & 366 & 420 & 543 & 389 & 129 \\ Percent (\%) & 44.38 & 17.39 & 18.94 & 23.36 & 14.85 & 8.95 & 10.25 & 13.26 & 9.50 & 3.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Area and percentage of EXs for different segmented boxes. Figure 10: Example of images from MESSIDOR (**top**) and STARE (**bottom**) databases for the operating phase. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Box**} & **SB1\_4** & **SB2\_4** & **SB3\_4** & **SB4\_4** & **SB5\_4** & **SB6\_4** & **SB7\_4** & **SB8\_4** & **SB9\_4** & **SB10\_4** \\ \hline Pixels & 406 & 249 & 307 & 355 & 453 & 353 & 416 & 242 & 350 & 264 \\ Percent (\%) & 9.91 & 6.08 & 7.49 & 8.66 & 11.05 & 8.62 & 10.15 & 5.91 & 8.54 & 6.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Area and percentage of _HE_s for different segmented boxes.
# Addition of Bee Products in Diverse Food Sources: Functional and Physicochemical Properties
## 5 Meat Products  
Meat products and all foods of animal origin are considered very susceptible to being affected by microorganisms. The addition of propolis ensures microbial stability and food quality during storage [67]. In general, the fat content and composition profile in hams and sausages play a fundamental role in their technofunctional characteristics; however, during processing, lipids are vulnerable to oxidation affecting their structure and generating the presence of various harmful volatile compounds, such as aldehydes, which are responsible for meat products becoming rancid. Lipid oxidative stability is a challenge for the food industry [68]. The main findings regarding the effects of bee pollen and propolis addition regarding lipid oxidation, physicochemical composition, microbiological characteristics, and sensory quality are described in Table 2.  
The addition of propolisols extract (0.06%) in cured ham is an alternative method to control lipid oxidation, producing a decrease in odor [69]. A similar protective effect was found in salami samples (0.05% propolisols extract) in comparison to the positive control by using BHT (butylated hydroxytoluene) after 35 days of aging [73]. Further, burger meat with microencapsulated propolis (0.1 g/kg) added showed a better lipid oxidative stability than the use of a commercial additive (sodium erythorbate). The burger meat with added propolisols showed a lower TBA (thiobarbituric acid) value than control during storage. The maximum malonaldehyde value was reached in 14 days, whereas in the control sample, it was reached at 7 days. This could be due to the gradual release of
# Measuring Maritime Paint Thickness under Water Using THz Cross-Correlation Spectroscopy
## 2 Materials and Methods
### Reference Measurement  
The paint sample shown in Figure 3A was a 14 cm by 7 cm brushed steel plate with one layer of Jotacote Uni N10 Alu paint applied. The sample was provided by the company JOTUN. The expected dry film thickness (DFT) that was calculated by the amount of paint applied was 200 \(\upmu\)m.  
To get an accurate reference measurement for the correct dry film thickness (DFT), we performed a surface height and roughness 3D scan using an Alicona focus-variation microscope. A magnified 3D image of a scan across the sample is visible in Figure 3B and the average surface height along the line in Figure 3B was 230.0 \(\upmu\)m. The average surface height standard deviation, i.e., the roughness, was 9.4 \(\upmu\)m. Therefore, for a practical field implementation, the layer thickness accuracy needs only to be within 9.4 \(\upmu\)m.
## 6 Conclusions  
This paper investigates the trajectory tracking control problem in powered parafoil systems. To address the underactuation, coupling, and wind disturbance challenges during the control process, we propose an intelligent control method based on TD3-optimized LADRC. Initially, an eight-DOF model is established to simulate the actual parafoil system's flight state accurately. Subsequently, we address the underactuation issue by introducing a trajectory tracking guidance law. Moreover, we design two LADRC controllers to achieve decoupling between horizontal trajectory tracking and height control while enabling trajectory tracking control. To further enhance the trajectory tracking process, we employ the TD3 algorithm to obtain real-time parameters for the controller, thereby reducing tracking errors. The proposed method successfully achieves trajectory tracking control under various disturbance conditions, with its effectiveness duly validated.  
The proposed method in this paper effectively achieves trajectory tracking control for powered parafoils and is also instructive for the trajectory tracking control of other aircraft. However, during the TD3 optimization process, finding a good balance between the horizontal and height tracking states within the reward function is a significant challenge that warrants further consideration. Assigning excessively high rewards to either state can undermine the feedback loop of the other state during the training process. We intend to continue investigating this issue in our future research. Additionally, in this paper, the configuration of the action space is manually adjusted based on multiple simulations, lacking theoretical solid justification. Addressing this limitation is a crucial aspect that requires dedicated research efforts. Furthermore, this paper did not delve into a theoretical analysis of the actor-critic framework. Some insights on this can be found in refs. [38; 39; 40], and we plan to carry out further research in this direction in the future.  
Conceptualization, Y.Z., Z.C. and M.S.; methodology, J.T.; software, Y.Z.; validation, Y.Z., Z.F. and J.T.; formal analysis, Y.Z.; investigation, Q.S.; resources, Q.S.; writing--original draft preparation, Y.Z. and J.T.; writing--review and editing, Q.S. and H.S.; visualization, Y.Z.; supervision, Q.S. and H.S.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61973172, 61973175, 62003175, 62003177 and 62073177) and Key Technologies Research and Development Program of Tianjin (Grant No. 19 JCZDJC32800).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to copyright issues with co-developers.  
Author [PERSON] was employed by the company Silo AI. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Model Predictive Control for Power Optimization and DDPG-Based Optimization Scheduling
### Model Predictive Control for Power Optimization
#### 4.1.1 System Development  
Based on the actual flight state of the UAV, a multi-task hydrogen fuel cell UAV flight state model is developed. The flight state parameters mainly include flight speed, flight altitude, attitude, and so on. Then, a function relating the flight state \(x(t)\) to power consumption \(P(t)\) is established based on the following empirical formula:  
\[P(t)=f(x(t),u(t)) \tag{32}\]  
where \(x(t)\) is the flight state vector and \(u(t)\) is the control input vector.  
The flight state vector mainly includes flight altitude \(h(t)\), flight speed \(v(t)\), pitch \(\theta(t)\), and engine temperature \(T_{E}(t)\). The control input vector mainly includes thrust command \(u_{\mathit{ltttttttt}}(t)\) and pitch control \(u_{\mathit{pitch}}(t)\).  
The experimental data, including flight altitude, flight speed, pitch angle, and engine temperature, are used as the state vector. Due to the large amount of data, only the range is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Moment** **(min)** & **Flight Altitude** **(m)** & **Flight Speed** **(m/s)** & **Pitch Angle** **(\({}^{\circ}\))** & **Engine** **Temperature (\({}^{\circ}\)C)** \\ \hline
1–3 & 0–20 & 0–1 & 5–10 & 55–60 \\
4–18 & 20–50 & 1–1.5 & 3–5 & 50–55 \\
19–30 & 50 & 1.5–2 & 0–3 & 45–50 \\
31–40 & 50 & 1.5–2 & 0–2 & 50–55 \\
41–120 & 50 & 1.5–2 & 0–2 & 45–50 \\
121–140 & 50 & 1.5–2 & 0–2 & 50–55 \\
141–250 & 50 & 1.5–2 & 0–2 & 45–50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The range of flight state parameters.  
Figure 4: The MPC flowchart.