esa-sceva/satcom-chunk-collection huggingface dataset:
# Segregation Modeling in Stockpile Using Discrete Element Method
## 2 Numerical Model
### Input Variables and Numerical Parameters  
The following input variables were used in the study:  
* Coefficient of Uniformity (CU): Two different PSDs were tested and characterized through their CU (1:2.5).
* Feeding height (H): Different heights were tested to observe their influence on segregation [1]. Heights tested were 1 m, 2 m, and 3 m.
* Coarse to fine particle ratio: Two coarse/fine particle ratios were tested: 70/30% and 60/40%.
* Particle shape: Sphere-shaped particles were initially used and calibrated. Other particle shapes were subsequently simulated to analyze their influence on segregation.
* Number of particles: The number of particles was varied based on the PSD and the coarse-to-fine particle ratio used.  
Table 1 summarizes the input variables and the experimental plan (tests) simulated through the DEM. The fine particle sizes are defined based on the study; here, particles below 11.8 mm were defined as fine particles. The PSDs used in the tests are shown in Figure 2.  
Figure 1: Stockpile model example used in the simulation.  
Figure 2: Particle size distributions used in tests.  
Additionally, the software used the following parameters that had to be defined and calibrated:  
* Force fraction: parameter that multiplies the gravity force (9.8 m/s2) to calculate the force of adhesion (Sn) between particles.
* Adhesion distance: parameter that defines the distance of overlap between particles; used to determine the force of adhesion.
* Dynamic friction (\(\upmu\)D): parameter to calculate the friction between particles during movement.
* Static friction (\(\upmu\)S): parameter to calculate the friction between particles in static conditions.
* Rolling friction (\(\upmu\)R): parameter used to include a contact moment in the opposite direction of rotation. Some software parameters were defined based on similar studies [10; 11]:
* Elastic moduli: the stiffness of particles and surfaces.
* Density: the mass of a particle volume.
* Restitution coefficient: energy dissipated between contacts.
# Techno-Economic Analysis of a Solar Thermal Plant for Large-Scale Water Pasteurization
## 4 Cost Estimation Model  
The two parameters that are most commonly used to find the optimal configuration of solar thermal plants are the solar multiple and the thermal energy storage duration [44; 45; 46]. The solar multiple (SM) can be defined as the ratio between the effective aperture area of the collectors and that that would be required to obtain the target output power [44]. From its definition, it follows that SM \(>\) 1 represents an oversized solar field, while SM \(<\) 1 an undersized one. On the other hand, the thermal energy storage (TES) duration represents the time for which the storage system should be able to provide the required power without intervention of the primary energy sources (i.e., solar field or gas burner, in this case). Therefore, the heat storage system increases the flexibility of the plant. In the following sub-sections, we first detail the calculations of the different contributions to the total cost of the plant, and then discuss the possible plant optimization based on the previously mentioned parameters, namely the solar multiple and TES duration.
## Materials and Methods
### Model Validation and Evaluation  
The validation of discriminant models is of great importance to verify the performance of the classification models. The classification accuracies, representing the percentage of the sample classified correctly over all samples, were used to evaluate the model performances. Three methods were applied to verify the performances of the models. Firstly, the ten-fold cross-validation was utilized to optimize the model parameters during the calibration process. The training set was divided into ten equal-sized subsets, where each subset was determined by selecting every tenth sample in the training set, starting at samples numbered one through ten. Then, each subset was retained as the validation data to test the sub-model that was built by the other nine subsets. The sum of samples classified correctly from ten sub-models over the total number of samples in the training set was calculated. These ratios were used to find the optimal parameters, creating a model that could fit the data well in the training set. Secondly, an independent testing set worked as an unknown sample set to evaluate the model parameters that had been optimized by the training set [41]. Because the samples in the testing set were completely different from the training set, all models were trained in the absolute absence of the testing set [42]. In this way, it was more objective to examine how well the model could perform with the unknown samples. Thirdly, the permutation test (Y scrambling) was utilized to check the reliability of the models. It was supposed to verify whether the optimized parameters of a model were overfitting or not [43]. In a permutation test, the labels of cultivars were randomly assigned to all individuals as their response variables, and all models were then rebuilt on the new data set with the same optimized parameters. The resulting models should generate a much lower accuracy. This was because the relationship between the predictors and cultivar labels was broken by scrambling the response categories. Otherwise, it could be reasonably concluded that the high classification rate of previous models was obtained by a chance correlation or overfitting the calibration data. The average classification accuracy of one hundred iterations in the permutation test was reported in this study.
## 4 Modern Imaging Technologies  
### Portable Imaging Devices  
The development of portable dental imaging devices marks an important step towards expanding diagnostic access, especially in remote, underserved, or emergency settings. These compact, battery-operated systems are suitable for use in mobile dental units, with patients confined to their homes and during humanitarian missions, allowing clinicians to perform imaging on site without the need for patients to visit a dental clinic [7].  
Modern portable devices often feature wireless data transfer and cloud integration, enabling real-time image sharing with specialists and facilitating collaborative treatment planning. These capabilities are particularly valuable for remote consultations and telemedicine applications. Looking ahead, portable imaging systems are expected to incorporate AI-assisted diagnostics and potentially 3D imaging, further increasing their effectiveness in decentralized and field-based dental care [8].  
Figure 2: **(A)** Panoramic radiograph of a 38-year-old male showing a well-defined, round radiolucent area near the distal root of the right mandibular second molar (black arrow), initially suggestive of a cystic lesion. **(B)** Coronal CBCT section of the same region reveals intact cortical margins and no sign of a cyst, effectively ruling out pathology. **(C)** Panoramic reconstruction confirms complete resolution of the radiolucency, indicating that it was an artifact caused by contamination or improper handling of the phosphor storage plate. This case illustrates the importance of correlating panoramic radiographs with CBCT findings to avoid diagnostic errors and unnecessary treatment. Image courtesy of [PERSON].
## 3 Simulation and Analysis
### Bessel Curve Optimization  
In order to make the generated path conform to the kinematics law of the robot system and increase the stability of the system, the generated path needs to be smoothed. The path-smoothing effect of the simulation experiment under the three maps is shown in Figure 10.  
It can be seen from the figure that the third-order Bessel curve can deal well with the problem of planning-path twists and turns. Through the smooth processing of the planned path, the point of turning mutation in the path is replaced by a curve, which satisfies the kinematic law of the robot system and makes the autonomous movement of the robot more stable.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 4 Services in the Architecture
### Service Components on the Control Plane
#### 4.2.5 The Edge VPN Gateway  
To enable the automated establishment of data-center to data-center Transport Links, an Edge VPN gateway is used for this purpose. Because this gateway has the responsibility of establishing Transport Links, it needs a southbound configuration interface towards the network controller. This configuration consists of a tunnel interface that includes a pointer to a KMS server. However, the VPN configuration can also consist of a full IPsec tunnel configuration defined by the orchestration layer or it can also be configured manually. The network controllers southbound configuration interface to the External VPN gateway is a domain specific choice, where RESTconf [21] or Command Line Interface (CLI) are most common and recommended to use. Furthermore, if a VPN tunnel setup with a KMS server configuration is possible, this is announced over the Tier 1 BGP peering. The External VPN gateway contains BGP peering interfaces for all tiers. This enables both an exchange point of SFC routes between the Providers and it makes the VPN gateway capable of routing SFC packets.  
It is optional whether the Tiers 2 and 3 peerings are established over the VPN tunnel or if it is a multi-hop BGP peering. However, from a security perspective, the peering is more protected if it runs over the VPN connection. It is also optional if the Tiers 2 and 3 peerings run as one or two peering instances. Since they use different address families, it is preferable to use one peering. To enable the scaling of Tiers 2 and 3 routes, it is suggested to use a BGP route filter to only allow direct peers to be announced over the BGP link. This means that Provider A only receives Provider B SFIRs from the Provider B peering, where Provider B not will proxy Providers C SFIRs to Provider A. In summary, the Edge VPN gateway must be able to:  
* [noitemsep,nolistsep]
* Dynamically establish the Transport Link to other data-centers by terminating VPN interfaces,
* Route SFCs to the corresponding Transport Links,
* Filter SFIR and SFIR-E routes for relevant peers.
# Effect of Ultrasonic Treatment on the Quality of Pumpkin Juice Fermented by Yeast
## 1 Introduction  
Pumpkin, an important vegetable crop, is rich in nutrients and possesses significant healthcare and medicinal values. As a functional food, it is widely applied in industries such as the baking, beverage, meat, and dairy industries. Yeast, a single-celled fungus with unique biological fermentation capabilities, is commonly used as a natural leavening agent in the food industry. Yeast fermentation technology can not only significantly improve the structural properties of food, but also enhance the aroma compounds, leading to remarkable improvements in both food quality and flavor. For instance, [PERSON]'s study has shown that probiotic-fermented fruit and vegetable juices exhibit higher antioxidantactivity and an overall increase in phenolic compound content. [PERSON] [1] et al. found that the physical and chemical properties of ultrasonic-treated pumpkin juice were better preserved during storage compared to untreated samples. Therefore, ultrasonic treatment demonstrates broad application prospects in preserving bioactive substances and physicochemical properties, as well as extending the shelf life of fresh pumpkin juice. Consequently, investigating the ultrasound-assisted yeast fermentation of pumpkin juice holds significant benefits for multiple industrial sectors.  
Ultrasonic-assisted fermentation technology has emerged out of requirement and demonstrated great potential. In the deep processing of fruits and vegetables, ultrasonic waves can exert a variety of positive effects during the yeast fermentation process [2]. The cavitation effect of ultrasonic waves can disrupt the cellular structure of fruits and vegetables, making the nutrients inside the cells more readily soluble and thus easier to leach out [3]. The disruption of the cellular structure of fruits and vegetables via the cavitation effect of ultrasonic waves provides yeast with a more abundant fermentation substrate, thereby promoting the fermentation process. Some researchers, such as [PERSON] [4], have discovered that ultrasonic waves can significantly enhance the extraction of substances like phenols, anthocyanins, and aromatic compounds. In this study, the ultrasonic-assisted fermentation of pumpkin juice was carried out to reveal the potential advantages of ultrasonic technology and fermentation technology in improving the quality of pumpkin juice products, enhancing the sterilization effect, promoting the fermentation process, and improving the texture of foods. Our deep exploration of the process parameters and action mechanisms of ultrasonic-assisted fermentation technology provides a new development direction for the deep processing industry of fruits and vegetables and promotes the research, development, and promotion of healthy foods.
# Cybersecurity Access Control: Framework Analysis in a Healthcare Institution
## 3 Results  
The rising complexity and sophistication of cybersecurity threats, coupled with the widespread adoption of encryption, necessitate cybersecurity solutions to operate more granularly, moving closer to the individual host and segmenting at the micro level. Although effective to some extent, a PBSM still leaves a gap that indicates trends of advancing exploitation. Among the top 10 healthcare institutions breached in 2023, affecting over 112 million individuals, none implemented a ZTA [13]. ZTA solutions emphasize mitigation and detection mechanisms at the host level. Ensuring complete payload visibility (i.e., post/pre-encryption), runtime-based detections, user authentication, and anomaly detection.  
A phased approach to adopting a ZTA is recommended for enterprise-level organizations. ZTA comprises multiple components that can be deployed individually, allowing organizations to align their procurement strategy with their current financial capabilities. Moreover, phased implementation would enable the development of expertise among support personnel regarding the newly adopted solutions. Implementing a gradual approach enables the organization to harmonize the culture, centered on the open access of a PBSM, with the tenets of Zero Trust (ZT). The approach successfully introduces ZT cybersecurity measures at a pace manageable for the end-user population, thus minimizing the risk of adverse reactions.  
Further research initiatives are recommended with the infancy and present low adoption rate of ZTA. The survey for this study indicated a 33% adoption rate by healthcare organizations of 5000 to over 10,000 employees. Given that the nature of this study was exploratory research, as more organizations move towards a ZTA, the increase in qualitative and quantitative data will be substantial. Additionally, the increase in ZTA/Secure Access Service Edge (SAE) solutions to the market will shift the data's capabilities and financial aspects.  
Further research incorporating data sources from regions beyond the southeast or even the United States would enhance the insights into adopting a ZTA and a PBSM. This extends to various industries. Initiating studies across different sectors and examining the implementation of PBSM and ZTA would introduce additional perspectives and potential extraneous data variables, thereby enriching the understanding of ZTA and PBSM in the healthcare industry. Finally, a supplementary study employing additional vendor solutions to introduce varied cost and capability parameters or concentrating on contrasting a SASE deployment against a complete ZTA deployment provides an impartial and potential alternative recommendation to PBSM beyond solely ZTA.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Shoot Dry**} & \multicolumn{2}{c}{**Root Dry**} & \multicolumn{2}{c}{**Root**} & \multicolumn{2}{c}{**Root System**} & \multicolumn{2}{c}{**Root Mean**} & \multicolumn{2}{c}{**Root Surface**} & \multicolumn{2}{c}{**Root Specific**} & \multicolumn{2}{c}{**Root Density**} \\  & & \multicolumn{2}{c}{**Weight**} & \multicolumn{2}{c}{**Weight**} & \multicolumn{2}{c}{**Volume**} & \multicolumn{2}{c}{**Length**} & \multicolumn{2}{c}{**Diameter**} & \multicolumn{2}{c}{**Area**} & \multicolumn{2}{c}{**Length**} & \multicolumn{2}{c}{**Root Density**} \\ \cline{3-14}  & & & \multicolumn{2}{c}{**g**} & \multicolumn{2}{c}{**cm\({}^{3}\)**} & \multicolumn{2}{c}{**cm**} & \multicolumn{2}{c}{**mm**} & \multicolumn{2}{c}{**cm\({}^{2}\)**} & \multicolumn{2}{c}{**m/g**} & \multicolumn{2}{c}{**mg/cm\({}^{3}\)**} \\ \hline \multirow{5}{*}{First Experiment} & C & 1.33 & b & 0.10 & b & 1.33 & b & 190.66 & b & 0.94 & 56.38 & b & 22.68 & ab & 72.72 & bc \\  & VT & 1.48 & b & 0.10 & b & 1.67 & b & 235.71 & b & 0.93 & 70.12 & b & 25.67 & a & 60.20 & c \\  & Ap & 2.82 & ab & 0.21 & a & 2.21 & b & 293.84 & ab & 0.99 & 88.49 & b & 15.45 & bc & 94.97 & ab \\  & MC & 3.97 & a & 0.26 & a & 3.68 & a & 348.13 & a & 1.15 & 125.96 & a & 13.70 & bc & 73.38 & bc \\  & Bk & 3.42 & a & 0.26 & a & 2.41 & b & 209.03 & b & 1.25 & 78.18 & b & 8.58 & c & 105.97 & a \\  & C.V. (\%) & 44.55 & 39.94 & & 37.20 & & 28.49 & & 18.51 & 26.75 & & 38.95 & & 23.87 & a \\ \hline \multirow{5}{*}{Second Experiment} & C & 1.16 & c & 0.05 & b & 0.83 & b & 197.73 & b & 0.71 & 44.64 & b & 44.60 & 120.16 \\  & VT & 1.83 & bc & 0.08 & ab & 1.43 & ab & 467.49 & a & 0.69 & 90.57 & a & 46.40 & 107.79 \\  & Ap & 2.76 & ab & 0.11 & a & 1.63 & a & 375.04 & a & 0.76 & 87.13 & a & 35.33 & & 144.55 \\  & MC & 3.15 & a & 0.13 & a & 2.10 & a & 459.90 & a & 0.63 & 101.37 & a & 58.40 & 142.25 \\  & Bk & 2.45 & ab & 0.12 & a & 1.73 & a & 406.99 & a & 0.80 & 98.27 & a & 3  
In the first experiment, beetroot treated with Ap, MC, and Bk resulted in a greater shoot fresh weight and root dry weight compared to the control group but did not differ from the VT group (Table 5). In the second experiment, there were no differences between the treatments in beetroots. The treatments in carrots did not differ from each other in the first experiments but in the second, VT-treated carrots had more dry weight (Table 5).
## 3 Results
In multivariate analysis, oncologic history remained a significant independent variable associated with PSN growth (odds ratio, 7.2; 95% confidence interval, 1.72-29.98; \(p\) = 0.007). Figure 3: Bland–[PERSON] plot showing the variability of the linear mass density (LMD) measurements. The mean difference in the LMD measurements is represented by the solid blue line. The upper and the lower limits of agreement are represented by the purple dashed lines. SD, standard deviation; CR, coefficient of repeatability. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multicolumn{2}{c}{**PSN Outcome**} & \multicolumn{1}{c}{\(\chi^{2}\)**Test**} \\ \cline{2-3}  & **Growing (\(n\) = 33)** & **Nongrowing (\(n\) = 17)** & \(p\) **Value** \\ \hline Patient age & & & \\ \(\leq\)65 years & 14 (28) & 10 (20) & 0.276 \\ \(>\)65 years & 19 (38) & 7 (14) & \\ \hline Patient sex & & & \\ Male & 19 (38) & 5 (10) & 0.062 \\ Female & 14 (28) & 12 (24) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Associations between part-solid nodule (PSN) growth and independent variables (patient age, sex, smoking history, oncologic history, emphysema status and PSN lobe location). Figure 4: Growth pattern of the 33 growing part-solid nodules (PSNs) based on the linear mass density (LMD) changes during the follow-up CT scan. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CT Feature** & **Growing PSNs** & **Nongrowing PSNs** & \(p\) **Value \({}^{*}\)** \\ \hline Area (mm\({}^{2}\)) & 76.7 (45.1–96.1) & 45.6 (36.6–66.9) & 0.008 \\ Perimeter (mm) & 42.5 (34.3–49.6) & 30 (24.2–36.7) & \(<\)0.001 \\ Mean Feret diameter (mm) & 12.6 (9.4–13.7) & 8.6 (7.6–10.7) & 0.002 \\ Mean attenuation (HU) & \(-\)490.5 –(551—44.2) & \(-\)504.5 –(585.3—455.6) & 0.362 \\ Median attenuation (HU) & \(-\)521 – (594.4–476) & \(-\)548 (\(-\)618—499) & 0.566 \\ Modal attenuation (HU) & \(-\)716 –(756.5—647.5) & \(-\)674 –(765.0—574) & 0.401 \\ Standard deviation (HU) & 233 (204.4–255.9) & 204.8 (188.8–225.3) & 0.013 \\ Skewness & 0.7 (0.47–0.93) & 0.67 (0.33–0.90) & 0.846 \\ Kurtosis & \(-\)0.33 (\(-\)0.59–0.29) & 0.16 (\(-\)0.63–0.63) & 0.676 \\ LMD (mg/mm) & 35.3 (22.8–44.2) & 21.6 (18.1–30) & 0.007 \\ Circularity & 0.59 (0.46–0.66) & 0.76 (0.64–0.82) & \(<\)0.001 \\ Solidity & 0.78 (0.71–0.84) & 0.88 (0.82–0.93) & 0.001 \\ \hline \hline \end{tabular} \({}^{*}\) Mann-Whitney U-test. Data are presented as the median (interquartile range). LMD, linear mass density.  
\end{table} 
Table 3: Quantitative CT features calculated from the baseline CT images of growing and nongrowing part-solid nodules (PSNs).
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 6 Conclusions  
In this paper, we propose MSNUNet, a mixed noise removal method based on nested UNet and multi-scale feature extraction. In MSNUNet, the two-layer nested UNet structure can deeply extract multi-scale features from high-resolution images and aggregate these features more efficiently using the proposed MSCAM. This approach not only accurately models mixed noise by leveraging the richer local and global information in the original image but also effectively extracts important features of the image through CA, thus preserving more image texture details. The experimental results clearly demonstrate that MSNUNet can achieve leading quality measures and fine textures that outperform all other contemporary competing methods.  
In the future, we will further develop our work in two aspects. On the one hand, we will focus on developing a lightweight solution for image denoising models. Due to the limited cost of hardware devices in real-world applications, most of the current deep learning-based denoising models cannot be deployed on hardware. We will explore two alternatives to address and alleviate this issue. Firstly, we will design more efficient neural network components, such as DConv, to reduce computational complexity. Secondly, we will optimize existing models using currently available lightweight techniques. On the other hand, we plan to design a versatile model for low-level computer vision tasks.  
Conceptualization, J.J. and Y.C.; methodology, J.J. and L.L.; software, L.L.; validation, L.L.; formal analysis, Y.Z. and Y.C.; investigation, J.J. and L.L.; resources, Y.C.; data curation, Y.Z.; writing--original draft preparation, L.L.; writing--review and editing, J.J. and L.L.; visualization, Y.C.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the National Natural Science Foundation of China under Grant 62001236, in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 20 KJA520003, in part by the China Postdoctoral Science Foundation under Grant 2018M640441, and in part by the Six Talent Peaks Project of Jiangsu Province under Grant JY-051.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[https://paperswithcode.com/datasets](https://paperswithcode.com/datasets)]([https://paperswithcode.com/datasets](https://paperswithcode.com/datasets)) (accessed on 21 May 2023).  
The authors declare no conflict of interest.