esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
For example, in [3] a smart production planning and control system was described but no attention was provided to the mentioned problem--human workforce. As well, Ref. [4] presented a decision support system for dynamic job-shop scheduling using real-time data with simulation but it only mentioned workstations with their operations but no employees who would perform the tasks in the workstations. Another study showed the results of the increased productivity of operators but did not mention how to select the employees, what their capabilities were to perform the specific tasks, or even how to solve situations when this employee was absent [5]. Based on these and other reviews, DSM DPP shows novelty in the field of production planning when human force is involved. One of the main components for this method to work is real-time data and there are many highly useful technologies in this field that do not require significant investments and can be easily adapted by SMEs. For example, the use of the Industrial Internet of Things (IIoT), sensors, blockchains, and data analytics can present the newest data, which is needed to optimize manufacturing processes and improve productivity based on the method. Similarly, cloud-based systems can store and analyze large amounts of data, enabling real-time decision-making without requiring significant investment in IT infrastructure. SMEs must adapt to the new Industry 4.0 technological advancements to stay competitive and effective in today's fast-paced and ever-changing manufacturing industry. The adoption of Industry 4.0 technologies can help to optimize production processes, reduce costs, and improve the quality of products, ultimately, leading to increased competitiveness and profitability. The article is presented in six sections. Section 1 is the Introduction, Section 2 covers the real-time data technologies that are adaptable for SMEs (IIoT, automatic quality control, and blockchains), Section 3 presents the required data for investigation and case study, Section 4 provides the main results, Section 5 offers a discussion on the existing limitations of this method, and Section 6 delivers the conclusions. Observations and data collection from the case study company were the key aspects of the whole research.
## 3 Results and Discussion
As shown in Table 1, from the perspective of the width and symmetry of the alveolar fissure between the design results of the appliance (this study) proposed in this paper and the clinical correction results, for samples 3, 4, 5, 8, and 11, the reduction of the width of the alveolar fissure was significantly greater than that of the clinical correction. For samples 1, 2, 3, 8, 9, and 10, the reduction of the width of the alveolar fissure was roughly equivalent to that of the clinical correction, and only the reduction of the width of the alveolar fissure in sample 7 was less than that of the clinical correction. For length similarity, it can be seen that, except for samples 1, 5, 9, and 10, the algorithm designed in this paper performed better than clinical correction in 11 samples. For samples 1, 5, 9, and 10, the algorithm designed in this paper performed only slightly worse than clinical correction. For MS, the algorithm designed in this paper had no significant clinical correction effect on the improvement of MS, but did not significantly reduce MS. According to Table 3, it can be seen that there was no significant difference between Clinical correction results and design result when conducting t-tests based on PP' and symmetry. Specifically, there was no significant difference in PP' (t = 0.195, \(p\) = 0.849), MS (t = \(-\)0.659, \(p\) = 0.825), or LS (t = \(-\)1.113, \(p\) = 0.292), and the means and standard deviations of both were roughly equivalent. Therefore, it can be concluded that the design algorithm model in this paper can effectively reduce the crack width and improve the symmetry, and its effect can be completely comparable to the effect of PNAM appliance made by clinicians, or even better than that of experienced clinicians in some samples. There are still some problems with this method that need further research and solutions: (1) Standardization of the upper jaw model making process. The algorithm in this paper is based on the upper jaw alveolar bone model for design, and if there are errors in the upper jaw model making process, it will lead to unclear feature points, which will affect the subsequent algorithm design for PNAM correction. (2) Design for the growth of the affected side of the model. This method currently designs rotation and internalization methods for the healthy side, but in an actual process, the patient's growth and development may lead to translation on the affected side. The translation distance on the affected side is related to many factors, such as the symmetry of the model and the patient's age. Further research will be conducted to quantify the amount of translation on the affected side. (3) Targeting unilateral complete cleft lip and palate. The current algorithm in this paper mainly targets unilateral complete cleft lip and palate, and further research will be conducted on bilateral complete cleft lip and palate.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 4 The Proposed MAC Address Randomization Covert Channel
### Covert Channel Metrics
#### 4.3.4 Covert Channel Jitter  
The jitter, in the network context refers to the delay variation of a frame over the channel, measuring the irregularity of frame arrival. It is expressed in Equation (4) in ms, where \(N\) is the frame number. The jitter plays a critical role, as it quantifies the variation in delay within the covert channel. By measuring jitter, we can assess how the delay fluctuates throughout the transmission, revealing periodic improvements or degradations in the network condition. This parameter is crucial for identifying variations in network behavior throughout the entire simulation:  
\[Jitter=\frac{Delay\ PR\ Frame_{N-1}-Delay\ PR\ Frame_{N}}{Simulation\ Time}\ [\text{ms}] \tag{4}\]
## 3 Results and Discussion
### Heat Transfer Mechanism of Phase Change Panels  
Figure 9 presents the temperature difference variation curves on both surfaces of phase change panels in experimental groups 1 and 2, with the heat transfer process of experimental group 1 annotated. A smaller temperature difference between the heated and unheated sides of the phase change panels signifies a faster heat transfer process and greater heat storage efficiency.  
During the solid-phase sensible heat storage stage, heat transfer occurs primarily through conduction. At this stage, the temperature has not yet reached the melting point  
Figure 9: The temperature difference variation curves on both surfaces of phase change panels in experimental groups 1 and 2 (the stages are set according to group 1).  
of the PCM. The thermal conductivity of the aluminum alloy and the PCM is the key factor influencing the temperature difference variation. Initially, the aluminum alloy on the heating surface absorbs heat, causing its temperature to rise. Subsequently, it transfers heat to the PCM. Due to the PCM's relatively low thermal conductivity, heat transfer to the unheated side occurs slowly, leading to a rapid increase in the temperature difference between the two sides of the phase change panel.  
During the latent heat storage stage, the heat transfer mechanism involves both the latent heat storage properties of the PCM and liquid-phase convective heat transfer. As the internal temperature of the phase change panel rises, the liquid phase volume of the PCM gradually expands. The buoyancy resulting from density changes causes the liquid PCM to move along the heating surface to the top, continuously cutting through the unmelted solid PCM until it is fully melted. This liquid-phase convective heat transfer accelerates the PCM's attainment of its melting point. Most of the heat transferred through the heating surface is absorbed by the PCM, causing the temperature on the unheated side to rise slowly. As the heating surface temperature continues to increase, the temperature difference between the two sides also increases.  
During the liquid-phase sensible heat storage stage, heat transfer occurs primarily through the convective heat transfer of the liquid PCM. At this stage, the PCM is completely melted. When the temperature of the heating surface reaches its upper limit, the convective heat transfer of the liquid PCM causes the temperature of the unheated side to rise rapidly, gradually reducing the temperature difference between the two sides. Once the phase change panel reaches thermal equilibrium, the temperature difference remains relatively constant.
## 3 Results and Discussion
### Structure and Morphology
Figure 2: XRD patterns of MRGO, MRGO@ZIF-7 and MRGO@ZIF-MIP. 1370 cm\({}^{-1}\)[65]. Compared with MRGO and MRGO@ZIF7-7, the D-band of RGO@ZIF7-MIP was the widest, indicating that the disorder of graphene layers gradually increased during the functionalization modification. By comparing the G-bands of the three materials, it can be seen that the G-peaks of both RGO@ZIF7-MIP and MRGO@ZIF-7 are blue-shifted compared to those of MRGO, indicating that there is an interaction between ZIF-7, MIP, and magnetic graphene, which provides evidence for the successful preparation of the various materials. In addition, the intensity ratio (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)) is characteristic of the extent of disorder present within the material [66]. The calculated I\({}_{\text{D}}\)/I\({}_{\text{G}}\) ratios for the three materials are 1.04, 1.07 and 1.1, respectively, which again indicate the increase in surface disorder of graphene materials. Figure 4b shows the magnetic hysteresis loop of MRGO, MRGO@ZIF-7, and RGO@ZIF7-MIP. All three materials have good magnetic properties, and the saturation magnetization intensity of MRGO is the largest (74.97 emu g\({}^{-1}\)). The magnetic intensity of the composites gradually decreases after the gradual modification of ZIF-7 and MIP on their surfaces. The saturation magnetization intensity of RGO@ZIF7-MIP is minimal (4.84 emu g\({}^{-1}\)) and still has the magnetic properties required for magnetic separation. Effective magnetic separation can be observed after about 80 s under the action of an external magnetic field, as shown in Figure 4b (bottom inset).
# Review of Photogrammetric and Lidar Applications of UAV
## 6 Discussion
### Discussion on Applications of UAVs
#### 6.2.3 Documentation of Historical Heritage
The measurements were carried out in Chiapas, Mexico. The article [134] discussed research on using UAVs for monitoring, change detection, and analysis of sites where archaeological excavations have occurred. The work was carried out in 2014, 2017, and 2020 in the ancient theatre of Uzuncaburc. Three-dimensional point cloud, orthophotomosaics, DEM, and 3D models were created from images. The 3D position accuracy calculated for the control point (CP) used in the four phases of the survey ranged from 5.8 mm to 33.5 mm. The research in the article [135] was devoted to lightweight and inexpensive UAV to be used for documenting and creating photorealistic 3D models of archaeological sites. The images were taken using a fixed-wing UAV and a DJI Phantom 4 with 12.4 Mpix and 24.1 Mpix RGB sensors (Sony A5100). The researched area was in Pakistan, where the Pakistan Memorial at Shakarparian Hills in Islamabad was monitored. The data were processed using the SfM method. A total of 336 photographs were taken from a 7 ha area, generating 17,174,268 points after processing. The texture was created with a GSD value of 1.2 cm. The article [136] focused on a topographic survey of an archaeological site using a Foxtech UAVocopter with a lidar Yellowscan Mapper sensor. The site selected for this study is located in the northern Sardinian region of Usini (Italy). As a result, a DTM was obtained and compared with the DEM. The authors of the article [137] discussed modern geomatic techniques for detecting and investigating emerging archaeological structures on the northwestern slope of Etna in Sicily. The survey was carried out using a DJI Phantom 3 Advanced with 12 Mpix camera. The flight was carried out at a height of 30 m AGL, and the flight was controlled usingthe Pix4D capture mobile application. Using GCP, a 3D model was georeferenced in QGIS software. The result was an ortho-mosaic and a DSM. The UAV application has its use in the field of cultural and industrial heritage. Archeology is a field in which UAVs are also closely connected and used primarily in the field of the geometrical documentation of archaeological sites (excavations), the creation of high-resolution digital models and 3D models, the monitoring of the crack patterns of historic structures, and 3D reconstruction of scenes.
## 4 Results
### Comparison and Verification of Typical Landslides
#### 4.3.1 Huijayuan Landslide  
The Huijayuan landslide is located in Miaoxi Village, Huangu Town, Ziyang County; the slope is 26\({}^{\circ}\), and the elevation is 112 m. The average annual precipitation is 1066.2 mm, mostly concentrated from mid- and late June to early October. This landslide is a thrust-type landslide, which can be detected by both data sources.  
A field survey shows that the landslide is covered with a large number of scattered gravel soil blocks. Point P1 is the back of the landslide (Figure 9d), where the soil is exposed, and obvious traces of decline can be observed. Small collapses can be seen everywhere on the slope body (Figure 9e), and there are many cracks in the side walls of the houses and the cement road (Figure 9f), which is consistent with the results from deformation monitoring. The deformation rates found from the two data types across the time coverage are different, as shown in Figure 9a,b: the annual deformation-rate range found by ALOS/PALSAR\(-2\) is \(-124\) mm\(\sim\)19 mm/year, while that found by Sentinel\(-1\)A is \(-94.5\) mm\(-13\) mm/year. The deformation ranges detected by Sentinel\(-1\)A and ALOS/PALSAR\(-2\) are nearly the same, but the latter can clearly detect more deformation details due to its superior resolution.
## 3 Procedure for Forensic Facial Soft Tissue Reconstruction in Detail
### Discussion of Methodological Foundations  
While the process of facial reconstruction inherently involves some degree of estimation, the core components of our method are grounded in established anatomical and statistical principles. The placement of the eyes, the reconstruction of the nose, the width  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Step** & **Software** & **Method and Literature** \\ \hline Digitalization of the & \multirow{2}{*}{Case 1: InVesalius v3.1 [37]} & Bone Segmentation: Image Threshold Segmentation [37] \\ \hline \multicolumn{3}{p{113.8 pt}}{} & Mesh Generation: Marching Cubes [37] \\ \hline \multicolumn{3}{p{113.8 pt}}{} & Case 2: Meshroom v2021.1.0 [38] & Photogrammetry \\ \hline Artifact removal & Blender v2.7 [43] & Manual Removal (Blender Edit Mode) \\ \hline \multirow{3}{*}{Placement of soft tissue markers} & Blender v2.7 [43] & (Custom Add-on: Enchanted Soft Tissue (ESTM)) & \\ \cline{1-1}  & \multirow{2}{*}{Blender v2.7 [43]} & \multirow{2}{*}{Manual Sculpting (Blender Sculpt Mode)} \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \hline Positioning of eyeballs & Blender v2.7 [43] & (Custom Add-on: Eye Add-on) & According to [PERSON] and [PERSON] [2; 67] \\ \hline \multirow{3}{*}{Mouth Reconstruction} & Blender v2.7 [43] & (Custom Add-on: Aooth Editor) & According to [PERSON] [68] \\ \cline{1-1}  & \multirow{2}{*}{Nose Reconstruction} & Blender v2.7 [43] & \multirow{2}{*}{(Custom Add-on: Aoothof the mouth, and the positioning and average thickness of soft tissue markers are all derived from well-documented anthropological studies and follow reproducible mathematical procedures. The anatomical landmarks used for soft tissue marker placement are clearly defined and leave minimal room for subjective interpretation. It should be noted, however, that the objectivity of these elements also depends on the quality, sample size, and demographic representativeness of the underlying anthropological datasets.  
Nevertheless, certain facial features remain less constrained by skeletal morphology. For instance, while mouth width and vertical positioning can be estimated based on dentition, the shape and fullness of the lips cannot be reliably inferred from bone structures due to the lack of direct anatomical correlates. Similarly, features such as the hairline and eyebrow shape exhibit significant individual variation and are only partially informed by underlying craniofacial structures. As such, these elements are reconstructed with greater artistic freedom, although we aim to minimize arbitrariness by adhering to population-level norms and anatomical plausibility.
## 4 Methodology
### Calibration of the AR Environment  
Tracking a real object in augmented reality and visualizing it in context with the real environment requires a correspondence between two different coordinate systems: one based in the real world and one based in the virtual world, which will be superimposed on the real world to provide the AR graphics. As outlined in Section 4.3, the drone's position is continuously estimated based on aggregated IMU readings and its heading is supplied directly by its onboard compass. Hence, the calculated pose is expressed in relation to a system of coordinates with the drone's starting location as an origin point and its \(y\)-axis aligned with the north.  
The HoloLens does not use its internal compass to orient its internal coordinate system, and the compass readings are not readily available to Unity apps. Therefore, the AR elements, including the virtual drone marker and the video panel, must be expressed in relation to the internal coordinate system, with an origin and y-axis direction equal to that at the user's location and heading, respectively, at the time of launching the application.  
In order to link the two coordinate systems and place the virtual drone marker in the corresponding position of the physical drone and, by extension, for the video panel to be displayed correctly in context with the environment, a calibration procedure should be performed. The calibration process aims to calculate the relative translation and rotation between the two systems. Even though both real and virtual objects are mobile in 3D, with six DoF, the drone's altitude is tracked and reported independently via its altimeter. Hence, this can be viewed as a 2D problem of axis rotation and translation.  
Keeping elementary linear algebra in mind [37], for two 2D coordinate systems with the same alignment (no rotation) and an offset of \(x^{\prime}_{0},y^{\prime}_{0}\), if \(x,y\) are the coordinates of a point in one system, then its coordinates in the rotated system will be:  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix}=\begin{bmatrix}x\\ y\end{bmatrix}+\begin{bmatrix}x^{\prime}_{0}\\ y^{\prime}_{0}\end{bmatrix} \tag{2}\]  
Meanwhile, for two 2D coordinate systems with the same origin point (no translation) and a rotation of \(\phi\), if \(x,y\) are the coordinates of a point in one system, then its coordinates in the rotated system will be:  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix}=\begin{bmatrix}cos(\phi)&sin(\phi)\\ -sin(\phi)&cos(\phi)\end{bmatrix}\begin{bmatrix}x\\ y\end{bmatrix} \tag{3}\]  
In the generic case of both translation and rotation, we have:  
\[\begin{bmatrix}x^{\prime}\\ y^{\prime}\end{bmatrix}=\begin{bmatrix}cos(\phi)&sin(\phi)\\ -sin(\phi)&cos(\phi)\end{bmatrix}\begin{bmatrix}x\\ y\end{bmatrix}+\begin{bmatrix}x^{\prime}_{0}\\ y^{\prime}_{0}\end{bmatrix} \tag{4}\]  
Hence, using the real-world drone and its virtual marker in the AR coordinate system as the common point, we can link the two coordinate systems following a calibration procedure. The following text describes both the default, manual calibration method and two vision-based alternatives.
# Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
## 4 Proposed Model and Improved Algorithm
### Experiment Results with Real Noisy Images  
In this section, we employed real-world noisy images to evaluate the performance of our proposed algorithm. The data were sourced from a benchmark image denoising database, PolyUDataset [34]. The data were acquired using a Sony A7 II ILCE-7M2 camera, manufactured by Sony Corporation in Tokyo, Japan, with an Aperture of f/4, Shutter Speed of 1/200 s, and ISO Value of 3200. The dataset was generated by capturing multiple shots of the same static scene and computing the average image to create the ground truth image for a more accurate assessment of denoising method performance. We selected one frame from these noisy images, with pixel dimensions of \(3008\times 1688\).  
Figures 13 and 14, respectively, display the ground truth image and the original noisy image. Singular Value Thresholding (SVT), Alternating Direction Method of Multipliers (ADMM), and the Self-adaptive ADMM (S-ADMM) technique were individually applied to process the noisy image, with results shown in Figures 15-17. The final mean square error and structural similarity index of the algorithms are presented in Table 5. These results indicate that our proposed S-ADMM method has the best performance.  
Figure 16: Denoising effects using ADMM algorithms (\(3008\times 1688\)).  
Figure 17: Denoising effects using S-ADMM algorithms (\(3008\times 1688\)).  
Figure 15: Denoising effects using SVT algorithms (\(3008\times 1688\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **SVT** & **ADMM** & **S-ADMM** \\ \hline MSE & 0.0119 & 0.1043 & 0.0093 \\ \hline SSIM & 0.80704 & 0.96089 & 0.96561 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MSE and SSIM from different algorithms (\(3008\times 1688\)).