esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Meshing  
The Fluent Meshing software was utilized for the process of meshing, and the specific reason for this preference is its capability to generate meshes using the poly-hexcore option. The poly-hexcore method is a hybrid approach that combines a combination of polyhedral and hexahedral element types and effectively addresses specific requirements by employing those types of elements. The polyhedral and hexahedral element types are illustrated in Figure 2. The poly-hexcore approach proves particularly advantageous in aerodynamic and hydrodynamic investigations, offering benefits such as reduced element count, faster convergence, and improved accuracy [19]. It is a useful technique for meshing complex geometries with boundary layer approximation, just as in the present study.  
Figure 1: Pictures of the selected geometry: (**a**) real image of model; (**b**) simplified geometry.  
Figure 2: Element types used in finite volume analysis.
## 2 Microwave-Assisted Plasma Combustion Technology  
### Microwave-Assisted Burner Structure  
The proposed microwave-assisted industrial-scale burner is an internal combustion, multi-stage ignition system for pulverized coal, in which a small portion of pulverized coal is combusted to serve as an ignition source for igniting a larger quantity of pulverized coal, thereby facilitating the formation of a stable and sustained flame required for efficient pulverized coal combustion. As shown in Figure 1, the three-stage ignition system generates plasma (1) in the first stage by ionizing air, used as the working gas, through microwave energy. The secondary air, supplied through a 150 mm diameter pipe connected to the waveguide (11), is regulated by an electronically controlled fan to adjust its flow rate. This air is directed toward the burner and delivered into the inner section of the coaxial line (2). The secondary air serves two primary functions: first, it facilitates the formation of a stable plasma flame; second, it prevents the high temperatures of the plasma flame from reaching the waveguide and the magnetron, thereby protecting the magnetron from potential thermal damage.  
Inside the coaxial line, a tungsten rod with a diameter of 5 cm is positioned (3). A second metal rod with a copper tip, housed within the igniter (6), is pneumatically actuated to move in and out through an opening at the top of the burner. When this rod strikes the tungsten rod, a spark is produced, initiating plasma formation. The position of the copper-tipped rod along the axis of the coaxial line is adjusted to optimize the conditions for stable plasma generation.  
A fan pressurizes the pulverized coal and primary air mixture, which is then supplied to the burner through a 300 mm diameter pipe. A damper (8), capable of adjusting to various angles on this pipe, divides the coal-air (black lined in Figure 1) mixture into two streams: one portion is directed to channel (4) to be ignited by the plasma flame, while the remaining portion is directed to channel (5). Upon entering channel (4), the coal-air mixture interacts with the plasma flame, initiating combustion and causing the flame to elongate. Subsequently, as the coal-air mixture introduced through channel (5) ignites within this extended flame (red lined in Figure 1), the overall flame propagates outward from the burner.  
A camera and a pyrometer (7), installed in the burner's port, provide real-time visual monitoring of the processes inside the burner and precise temperature measurements of the pulverized coal-plasma flame. The flow rates and temperatures of both the pulverized coal-primary air mixture and the secondary air are measured through measurement ports (9) and thermocouples (10), respectively, mounted on the pipe. In addition, coal samples taken from the measurement ports (9) can be used to determine the amount of coal combusted inside the burner.
## 2 Experimental Schemes
### Experimental Equipment and Procedure, Experimental Process  
Conventional triaxial tests on small-particle specimens were conducted using a fully automated, high-precision triaxial testing system (TAS-LF, Triaxial Automated System-Load Frame). Large-scale triaxial tests on larger-particle specimens were performed using a custom-developed YSZ-200 large-scale triaxial apparatus. The equipment used is shown in Figure 4.  
In the study of coarse-grained soil, relative density (\(D_{\text{r}}\)) is widely used as a control index for specimen preparation [40; 41; 42; 43].  
According to the test standard [38], the shear rate of the test is set to 0.2%/min, and the test confining pressure is 100 kPa, 200 kPa, 300 kPa, and 400 kPa. Specimens were labeled using the convention \"\(d\)-\(\sigma_{3}\)\", where \(d\) (mm) represents the glass bead diameter and \(\sigma_{3}\) (kPa) represents the confining pressure.  
After the specimen preparation was completed according to the aforementioned method, consolidated-drained (\(CD\)) triaxial shear tests were conducted following standard procedures. The confining pressure was applied to the specimen via the water in the confining chamber. Once the set confining pressure was reached, it was stabilized for 30 min before loading was initiated to induce shear deformation until specimen failure occurred. The failure criterion was defined as the maximum shear stress or the shear stress value corresponding to an axial strain of 15%.  
Figure 4: Experimental equipment.
## 3 Results and Discussion
### Convolutional Neural Network (CNN) for Image Segmentation
The resulting CNN images were 8-bit grayscale images in which grayscale values corresponded to the probability of being in the cell wall class. Finally, binary images were created by thresholding the CNN image with 0-127 grayscale values assigned to air and 128-255 values assigned to cell wall. In the absence of reference standards, it is not possible to quantify the accuracy and precision of a segmentation technique. However, an alternative evaluation approach is to use validation metrics for semantic image segmentation that compare ground-truth images with the resulting network segmentation. In this work, the intersection over union (IoU), which is a metric that quantifies the overlap between the classes in the ground-truth image and the network's resulting binary image [41; 42], was used to quantify the accuracy of CNN. For CNN image \(P\) and corresponding ground-truth image \(G\), IoU is calculated for each pixel class using the following:  
\[\text{IoU}=\frac{P\cap G}{P\cup G}(100\%) \tag{1}\]  
where \(P\cap G\) is the size of the area of overlapping intersection, and \(P\cup G\) is the size of the union [42]. IoU ranges from 0 to 100%, with 0% signifying no overlap and 100% signifying perfectly overlapping segmentations. The network testing data set consisted of the 3034 images reserved from the 14,010 generated ground-truth images. First, the IoU was calculated for both the air and the wood cell wall material classes. The mean IoU for a given image was then cal  
Figure 5: Convolutional neural network (CNN) architecture diagram used in this work. This CNN was an adaptation from the U-Net architecture designed by [PERSON] and coworkers [26]. culated by averaging the air and cell wall IoUs. For the full testing data set, an average IoU of 96% was calculated. This means that compared to the ground-truth images, 4% of pixels in the segmented CNN images were misclassified. Compared to the traditional histogram-based segmentation methods tested in the Supplementary Materials, the CNN mean IoU of 96% was much higher with none of the other full image methods reaching 50%. Compared to the literature, this value is also higher than the IoU of 92% reported for the U-Net CNN effectiveness for segmenting the cellular structure of HeLa cells [26]. The 96% IoU also compares favorably to the 73% to 96% IoUs reported for seven different U-net segmentation tasks in biomedical imaging [43].
# A Conceptual Design of Deployable Antenna Mechanisms
## 2 Materials and Methods
### Hierarchical Composition--Twelve Bays Composed of Three Mechanisms
#### 2.2.3 V-Folding Mechanism  
The V-folding mechanism, composed of two longeron linkages and one rotational joint, is designed to ensure the structural rigidity of the deployable mechanism, as shown in Figure 2c. Specifically, the middle section of the V-folding mechanism is constructed with hinges, allowing it to fold into a V-shape. As will be detailed later, CFRP (Carbon Fiber-Reinforced Plastic) is considered for the V-folding mechanism. The linkages (of the V-folding mechanism) made of CFRP are fixed to both ends of the hinge, and an extensible (elastic) cable passes through the interior linkages (see Figure A1 in Appendix A). This cable connects the V-folding unit to the top end of the longeron in the 6-bar linkage. This connection is likely the free end, creating infinite DoFs. Given these aspects, it is worth noting that the joint motion caused by the wire is kinematically similar to the pole shock-cord driving mechanism, in which the wire couples the pipes. This way, it is possible to ensure that the deployment process of the V-folding mechanism (which involves 3D spatial movements) proceeds smoothly without any mechanical interference due to DOF constraints. For these reasons, once the structure is fully deployed, a tension is applied to the wire, helping to engage the V-folding mechanism with the upper section of the 6-bar linkage.
## 4 Discussions
Non-UD coding becomes particularly efficient on securing large collections over power-limited devices, where the cost of encryption becomes heavy in terms of energy. This reduction also helps to increase the throughput of a security pipeline without a need to expand the relatively expensive security hardware. For instance, let's assume a case where the data is waiting to be processed by a hardware security unit. When the amount of data exceeds the capacity of this unit, a bottleneck appears, which can be resolved by increasing the number of such security units. However, adding and managing more security units is costly, particularly when the bottleneck is not so frequent, but only appearing at some time. An alternative solution is to use the proposed non-UD coding, where instead of expanding the security units, data can be processed appropriately while waiting in the queue, and the amount to be encrypted can be reduced up to desired level by applying the scheme recursively if needed. Notice that as opposed to previous selective encryption schemes, non-UD coding supports the security of the whole file instead of securing only the selected partitions. Besides massive multimedia files, small public key files around a few kilobytes that are used in asymmetric encryption schemes are also very suitable inputs for the non-UD coding. The exchange of public keys via symmetric ciphers can also benefit from the reduction introduced.
# Privacy Framework for the Development of IoT-Based Systems
## 6 Proposal of Privacy Guidelines
### Key Findings and Reflections  
The study highlights that guidelines such as \"Encrypted Data Communication\" (4.51) and \"Encrypted Data Storage\" (4.44) are considered highly important, reflecting participants' emphasis on data security and regulatory compliance. These high scores suggest a shared understanding among participants of encryption as a fundamental privacy measure in IoTSs.  
In contrast, medium-priority guidelines, such as \"Minimize Data Acquisition\" (3.86) and \"Query Answering Without Raw Data\" (3.95), were recognized for their privacy benefits but viewed as context-dependent. Their practical feasibility is often limited by specific IoTSs that require large-scale data collection for core functionalities.  
Lower-rated guidelines, like \"Minimize Knowledge Discovery\" (3.16) and \"Chain Aggregation\" (3.21), may have been perceived as too specialized or unclear in their implementation strategies. These lower ratings point to the need for clearer definitions or more adaptable versions of these guidelines to suit various IoT environments.  
In summary, this data-driven categorization provides a balanced and objective evaluation of the guidelines, emphasizing their real-world applicability and reflecting the collective insights of the participants. However, while this analysis provides valuable insights, it remains unclear why participants rated certain guidelines higher or lower. To gain a deeper understanding, future studies could incorporate qualitative methods, such as in-depth interviews or focus groups, to explore the motivations behind participants' choices. This qualitative data could reveal additional factors influencing guideline adoption, offering a more comprehensive view of the challenges and preferences in IoT privacy practices.
## 1 Introduction
This method has been used by several authors to obtain a set of time reparametrizations \(\Psi\) of the form \(dM=Q(r)d\Psi\), where \(Q(r)\) is called the partition function and \(\Psi=\Psi(M)\) is a \(2\pi\) periodic function of \(M\) that satisfies \(\Psi(0)=0\), \(\Psi(\pi)=\pi\). All these variables can be reduced to anomalies, including a normalization scale factor, so that they take values in \([0,2\pi]\) along one revolution. The classic mean anomaly \(M\), the eccentric anomaly \(g\), and the true anomaly \(f\) can be considered temporal variables. [PERSON] [14] defines the semifocal anomaly \(\Psi\) as the mean between \(f\) and \(f^{\prime}\), where \(f^{\prime}\) is the antifocal anomaly [15]. In the year 2017, [PERSON] introduced the biparametric family of anomalies \(\Psi_{\alpha,\beta}\), defined by [PERSON] [16] as  
\[K_{\alpha,\beta}r^{\alpha}r^{\beta}d\Psi_{\alpha,\beta}=dM\,. \tag{2}\]  
This family contains the anomalies defined by the previous time transformations. For \(\alpha=\beta=0\), we have the mean anomaly \(M\); for \(\alpha=1\), \(\beta=0\), the eccentric anomaly \(g\); for \(\alpha=1/2\), \(\beta=-1/2\), the generalized length of arc introduced by [PERSON]; for \(\alpha=3/2\), \(\beta=1/2\), the elliptic anomaly \(w\); for \(\alpha=1\), \(\beta=1\), the antifocal anomaly; and for \(\alpha=2\), \(\beta=1\), the semifocal anomaly. The biparametric family groups many anomalies used in celestial mechanics, although this is an empirical grouping. Notice that the anomaly \(\Psi_{\alpha,\beta}\) is symmetric to the axis of the ellipse when \(\alpha-\beta=1\). There are some interesting anomalies not included in this biparametric family [17; 18; 19; 20]. This paper is focused on studying a new geometric point of view on the biparametric family, which allows reinterpretation of its operation in causal terms, which would be a relevant fact. This section presents the background and the primary goal of this paper. In Section 2, we study the relation between the curvature of the revolution ellipsoid that contains the orbit and the vector radii \(r\,r^{\prime}\). From them, the biparametric family of anomalies is related through the vector radius \(r\) and the mean radius of curvature of the ellipsoid. In Section 3, we study several anomalies induced by the latitudes of the ellipsoid, in particular, the geodetic latitude \(\Psi\) and the geocentric latitude \(\Phi\), concluding in the first case that the anomaly induced by the geodetic latitude coincides with the semifocal anomaly and the anomaly induced by the geocentric anomaly is not included in the biparametric family of anomalies. Despite the above, we can generalize the relations obtained for the biparametric family to the other anomalies in some instances. In Section 4, we present several numerical examples that allow a better understanding of the results previously obtained. In Section 5, the main conclusions of this study will be discussed.
## 5 Communication Protocol Design
### PHY Layer Description  
The design of the proposed DS-CDMA PHY layer involved the definition of a suitable packet frame format and the accompanying spreading and modulation parameters. The corresponding transmitter and receiver architectures are directly affected by these decisions, therefore, their structure is also discussed in this section.
## 5 Conclusions  
This study analyzed the spatial and temporal changes of LCE based on carbon emission data and land-use data, and examined the effects of NTL, PD, and GPP on LCE. During the period of 2000-2019, the average value of LCE was 31.30 million t. Temporally, the total amount of LCE showed a significant increase from 2000 to 2019 (1.76 million t; \(p\) < 0.01). Meanwhile, the carbon absorption of grassland, forestland, and wetland showed a significant downward trend, while the carbon emissions in the construction land showed a significant increase (0.54 million t/a; \(p\) < 0.01). Spatially, regions where the LCE significantly increased occupied 71.20% of the total area, which was distributed in the central and eastern parts of the study area. Global Moran's I of LCE indicated that the LCE showed a significant positive spatial correlation, and had a remarkable aggregation state. The local spatial auto-correlation clustering results of the LCE were mainly represented by the H-H and L-L agglomerations. The H-H agglomeration area of LCE was distributed in the central urban agglomeration of the study area, and showed a downward trend from 2002 to 2019. The L-L agglomeration area was always distributed in the southwest part of the GZA with low carbon emissions. The correlation analysis showed that the average correlation coefficients between LCE and NTL, PD, and GPP were 0.13, 0.21, and \(-\)0.05, respectively, which showed a large spatial heterogeneity distribution characteristic. The MGWR analysis also indicated that the NLT and PD had obvious positive effects on LCE, especially for urban agglomeration areas, such as Xi'an and Baoji, while GPP has obvious negative effects on carbon emissions in most areas, mainly due to the carbon sink effect of ecosystems, such as forests and grasslands. The results of this study have an important reference value for the formulation of carbon emission reduction policies and the development of a low-carbon social economy in the GZA and even the whole Shaanxi province. Conceptualization, Y.L., Z.L. and P.H. ; methodology, [PERSON] ([PERSON]) and Y.Y. ; software, [PERSON] ([PERSON]) and Y.Y. ; formal analysis, [PERSON] ([PERSON]), Y.Y., W.Z. and Z.Z. ; data curation, Y.Z. ; writing--original draft preparation, Z.L. and S.J. ; writing--review and editing, Z.L. and S.J. ; visualization, Y.W. and Y.L. ; supervision, B.F.; project administration, [PERSON] ([PERSON]) and W.Y. ; funding acquisition, [PERSON] ([PERSON]) and W.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by Shaanxi agricultural rural economic and technological system construction project, grant number NYKJ-2020-YL-18, Startup fund for doctoral research of Northwest A&F University, grant number 2452020186, and Special project of science and technology innovation plan of Shaanxi Academy of Forestry Sciences, grant number SXLK2022-02-7. Not applicable. The data presented in this study are available on request from the corresponding author.
## 5 Research Results
### Evaluation of the Learning and Action Research Success Rate Results by After-Course Surveys  
For this purpose, the same questions which were designed to provide insight into theoretical practices and the application of each of the four MACH principles were used. As it was described thoroughly in Section 5.1, the questions had two parts: the first was a knowledge assessment where students choose one of four offered answers (one was correct). After completing the course, 32 out of 35 students (91%) identified the benefit of microservices correctly. All students correctly identified the primary functions of the API-first principle. On the topic of cloud-native development, 30 out of 35 students (86%) understood the concepts correctly and overcame misunderstandings about monolithic structures and on-premise servers. Finally, 33 out of 35 students (94%) understood the principles of headless architecture correctly. The second set of questions about confidence showed the following: Average confidence in microservice implementation raised to 4.6 out of 5. Confidence in the API-first principle increased to 4.4 out of 5, confidence in cloud-native development rose to 4.3 out of 5, and overall confidence in applying MACH principles increased to 4.5 out of 5. A summary of the trends showed significant knowledge gains across all MACH principles, with students demonstrating marked improvements in their understanding. Correct answers increased by an average of 57-66% post-course. In the API-first principle, most dramatic improvement was detected, with a 100% correct response rate post-course. Students' confidence in applying MACH principles improved by an average of 2.1 points on a 5-point scale. This trend indicates not only a better theoretical understanding, but also increased practical skills and readiness to implement these principles in real-world scenarios. These trends reflect the effectiveness of the course in addressing key learning objectives. The structured approach to teaching MACH principles, combined with practical exercises, likely contributed to these positive outcomes. To validate these trends, the paired \(t\)-tests, \(p\)-values, and Cohen's d were used. The results are provided in the Table 8.  
Figure 4: NFT marketplace. The t-statistic values for all MACH principles were extremely high, indicating a strong difference between the pre- and post-course confidence levels. All \(p\)-values were extremely low (close to 0), far below the typical significance threshold of 0.05. This suggests that the improvements in confidence levels were statistically significant. The Cohen's d values for all principles were very large, indicating a very strong effect size. This means the course had a substantial impact on improving students' confidence in applying MACH architecture principles. Regarding qualitative experience, we did not repeat questionnaires, and we decided to use reflective journal to extract details in with initial pre core qualitative research questions. An overview of the most important and most interesting questions is given in Table 9.