esa-sceva/satcom-chunk-collection huggingface dataset:
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 4 Results and Discussion
### Sensitivity Analysis
As seen in Figure 10, the loss (MSE) decreases with epoch times increase. However, more epochs do not mean that the model is more accurate, as shown in Figure 9e. The overfitting phenomenon may happen when the epochs are exaggerated in neural networks. Such a phenomenon could occur in the MLP model as well. When it happens, the training epoch needs to be decreased so as to learn less noise and detail of the dataset evolution. It can be noticed that the impact of varying batch sizes and epochs on model performance is not as pronounced as the effects of varying the number of hidden layers, learning rate, and neurons. The training results of ANNs could be improved greatly by optimizing the hyperparameters, especially the value of hidden layers and neurons. In the case of this study, the optimization of learning rate, batch size, and epochs did not significantly improve the accuracy of the model. These sensitivity analyses of hyperparameters offer a reference tutorial for the ANNs tuning.
## Appendix B
### Data Synchronization Frequency  
Frame Synchronization: To achieve real-time interactivity, including ray detection and card simulation feedback, frame synchronization is employed. Unreal Engine aligns key states, like handle positions and card postures, with the Llama model and database at 60 Hz or 90 Hz, matching VR device refresh rates. However, this high-frequency synchronization can heighten network and computational demands. Thus, it is crucial to optimize data payloads by transmitting only essential state parameters, such as coordinates and operation identifiers.  
Card label synchronization is employed for non-real-time data, like narrative logic and card combination outcomes. A fixed interval, such as every second, is set for synchronization, which is triggered by key operations like the completion of a card combination. Unreal Engine transmits aggregated status data, including the current card set and plot progress node, to the Llama model and the database. This approach minimizes communication overhead.  
Card System Database Design  
Card Attribute Storage: Design the 'Card' table with fields including 'card_id' (unique identifier), 'card_name' (name),'mesh_path' (mesh resource path),'material_path' (material path), 'type' (character/scene/event/emotional clue), and 'description' (storytelling description). For dynamic attributes like temporary bonuses from card combinations, add a 'temp_attribute' field to store JSON key-value pairs (e.g., '[\"durability\": 5, \"effect\": \"Light up scene clues\"]).  
Combination Rule Storage: Create the 'CardCombinationRule' table with 'rule_id' as the primary key, and fields such as 'card_a_id', 'card_b_id' (IDs of the cards involved in the combination), 'combination_effect' (description of the effect, e.g., \"Generate new card C, play animation D\"), and 'is_valid' (rule status). Clearly define the card combination logic for seamless integration and verification by Unreal Engine and the Llama model, enhancing the system module's technical feasibility from interaction diagrams to communication protocols and synchronization mechanisms.
## 3 Results and Discussions
### Metabolic Pathways of the Key Aarcoma Compounds  
The changes in aroma characteristics may be influenced by several factors during storage. Geraniol and linalool, which are monoterpenes, serve as the primary constituents responsible for the pleasant floral and fruity scent found in plants. These compounds are synthesized in tea through the metabolic conversion of two primary precursors, namely isopentenyl diphosphate (IPP) and dimethylallyl pyrophosphate (DMAPP). The mevalonate pathway (MVA) plays a critical role in the synthesis of secondary metabolites, including sterols, sesquiterpenenes, and triterpenes within the cytoplasm and endoplasmic reticulum. The 2-C-methyl-D-erythmitol-4-phosphate pathway (MEP) and carotenoid cleav  
Figure 4: Based on OAV analysis of volatile compounds during RAPT storage. (**A**) Flavor wheel of 43 key volatile compounds in samples at different storage times. (OAV > 1). (**B**) Radar plots of flavor characteristics of RAPT during storage. (OAV > 1).  
age dioxygenase (CCD) are two key metabolic pathways involved in the biosynthesis of monoterpenes and oxidation of carotenoids, respectively [34]. It is hypothesized that changes in the metabolism of microorganisms during storage may result in changes in aroma characteristics. Trans-\(\beta\)-ionone and \(\alpha\)-ionone are secondary oxidation products derived from carotenoids, including \(\beta\)-carotene, \(\alpha\)-carotene, phytoene, lutein, and lycopene, which undergo oxidation by CCD to form primary oxidation products and then are further converted into secondary oxidation products [28]. Furthermore, the conversion of linolenic acid into (E,E)-2,4-heptadienal occurs through the involvement of leucanthocyanidin reductase (LOX) and glutathione peroxidase (HPL). Previous work has shown that LOX continues to facilitate the synthesis of aldehydes from fatty acids throughout the fermentation stage [35]. The storage of RAPT, in addition to being a natural aging and fermentation process, is also believed to contribute to the changes in aroma. These changes may be attributed to microbial metabolism, enzymatic digestion, and natural oxidation.
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 5 Discussions
### The Contribution of Each Component in the HC-MMK Approach
#### 5.2.2 Knowledge  
The importance of knowledge for producing land cover products has been well acknowledged [11; 16; 47; 48]. Subject to the availability, scale and quality of knowledge, at present, limited knowledge has been drawn upon to enhance the accuracy of land cover products, such as terrain knowledge [16; 49], spatial distribution knowledge [11], and temporal signature [18; 50; 51]. In this paper, a variety of available knowledge, including geographical rules, existing thematic maps, field samples and statistical information, were applied together in the HC-MMK approach. Each type of knowledge played a unique role and all knowledge together contributed to quality improvement of the CLC-SW2010 product (Figures 9 and 10). The attempt to draw upon of variety of knowledge in this study offered a recommendable method for other land cover mapping projects.
## 2 Problem Statement
### Channel Model  
In the actual scenario where UAV provides services to users, due to the lack of information such as the exact location, height, and number of obstacles, there may be two situations: line-of-sight (LoS) link transmission and non-line-of-sight (NLoS) link transmission. Given the randomness of LoS and NLoS links, this paper employs probabilistic LoS link channel for modeling. Given that the probability of an LoS link is contingent upon environmental, equipment, and UAV location and elevation angle factors [18], the expression is provided by [19; 20]:  
\[P_{LoS}=\frac{1}{1+\psi\exp(-\phi[\theta-\psi])} \tag{1}\]  
where \(\psi\) and \(\phi\) are the S-curve parameters, whose values are closely related to the environment type. \(\theta\) is the pitch angle of the air-to-ground link, then the pitch angle between the user \(i\) and the UAV \(\theta_{i}=\frac{180}{\pi}\times\sin^{-1}(\frac{h}{d_{i}})\), and \(h\) is the hovering height of the UAV. The distance between the user \(i\) and the UAV is \(d_{i}=\sqrt{\left(x_{i}-x_{\text{uav}}\right)^{2}+\left(y_{i}-y_{\text{uav}} \right)^{2}+h^{2}}\), where \((x_{i},\ y_{i})\) denotes the location of the user \(i\) and \((x_{\text{uav}},\ y_{\text{uav}},\ h)\) denotes the location of the UAV.  
As UAVs function as airborne base stations to offer services to ground users, the radio signals they emit propagate through free space. However, when reaching urban environments, these signals are subjected to masking and scattering induced by human-made structures, which introduces additional losses in the air-to-ground link. Therefore, the average air-to-ground path loss, expressed in decibels (dB), can be obtained and modeled as follows:  
\[PL_{\xi}=FSPL+\eta_{\xi} \tag{2}\]  
where \(FSPL\) denotes the free-space path loss between the UAV and ground users, while \(\eta_{\xi}\) represents additional path loss and \(\xi\in\{LoS,NLoS\}\).  
Due to the existence of both LoS and NLoS transmission scenarios, the probability of NLoS transmission can be denoted \(P_{NLoS}=1-P_{LoS}\). Hence, the link path loss model between the UAV and the user can be expressed as:  
\[PL=P_{LoS}\times PL_{LoS}+P_{NLoS}\times PL_{NLoS} \tag{3}\]  
where the average path loss for both \(LoS\) and \(NLoS\) links to ground can be represented as:  
\[PL_{LoS}(dB) =10\alpha\log(\frac{4\pi df_{c}}{c})+\eta_{LoS} \tag{4}\] \[PL_{NLoS}(dB) =10\alpha\log(\frac{4\pi df_{c}}{c})+\eta_{NLoS} \tag{5}\]  
Figure 2: UAV transmission time-slot model.  
Therefore, the link path loss between the UAV and the user is given by:  
\[PL(dB)=10\alpha\log(\frac{4\pi df_{c}}{c})+P_{LoS}\times\eta_{LoS}+P_{NLoS}\times \eta_{NLoS} \tag{6}\]  
It is evident that the average channel gain (expressed in dB) between the UAV and the device is denoted \(\overline{g}(dB)=-PL\). Consequently, in computing signal-to-interference-plus-noise ratio (SINR), the average channel gain is utilized to model both interference and desired links for all devices in UAV communication.
## 3 Antenna Response to Excitation  
The proposed antenna performance was analyzed with the aid of CMA in Section 2. This section validates the antenna performance in terms of reflection coefficient and impedance matching. Figure 7 shows the dimensions of the antenna structure. Generally, the monopole antenna is half-wavelength or quarter-wavelength; in the case of an antenna on the outer ring with a feed line (Ant1) (Figure 1a), the sum of the circumference of R1 and feed length LF is 13.83 mm which is almost equal to one wavelength. It resulted in resonance at 21.67 GHz, as depicted in Figure 8a. However, the bandwidth is very narrow due to inductive reactance. With another circular ring (R3-R4) added (Ant2) in Figure 2a, a resonance at 38 GHz was observed in Figure 8b, because the circumference length of R3 was one wavelength. Further addition of R5 and four equilateral triangles (Ant4) combined the previous resonances, consequently leading to a broad bandwidth. The impedance was finetuned by varying the ground length, leading to a final bandwidth ranging from 23 to 41 GHz, as shown in Figure 8d. The designed antenna's overall impedance (\(Z0\)) was \(\left(50\pm\mathrm{j}20\right)\Omega\). The structure resulted in a linear change in minor capacitive reactance to inductive reactance. This is due to a combination of capacitive and inductive modes, as presented in Figure 4c.  
The CMA analysis in Section 2 demonstrated that the surface current in the adjacent modes (Figure 5) had an orthogonal phase of 90\({}^{\circ}\), and the summation of these modes resulted in circular polarization. Four equilateral triangles embedded in the design generated this orthogonal phase difference. It perturbed the current flow such that the current in the left portion of the radiator underwent a phase change of +90\({}^{\circ}\) and in the right, of -90\({}^{\circ}\). Because of this differential phase change, the left portion of the radiator gave rise to left-handed CP (LHCP) and the right portion, to right-handed CP (RHCP). However, when the resultant tip of an electric field was observed in the far-field region [25], it was realized that the magnitude of the minor axis was lower than the major axis, consequently leading to elliptical polarization. Figure 9 illustrates this behavior of the LHCP and RHCP on the respective left and right portions of the antenna at 38 GHz.  
Figure 6: Resultant radiation patterns from surface current \(J_{n}\) at **(a)** 25 GHz, **(b)** 28 GHz, **(c)** 35 GHz, and **(d)** 38 GHz.  
Figure 8: Reflection coefficient and impedance curves of the (**a**) circular ring antenna (Ant1), (**b**) antenna with two circular rings (Ant2), (**c**) antenna with a circular patch at the center and two circular rings (Ant3), and (**d**) proposed antenna (Ant4).  
Figure 7: (**a**) Top and (**b**) bottom views of the proposed antenna. Its dimensions in mm are the following: R1 = 1.9, R2 = 1.7, R3 = 1.4, R4 = 1.2, R5 = 0.8, LF = 1.9, LW = 0.45, SL = SW = 6, STL = 0.7, STW = 0.2, LG = 1.7, and a = b = c = 1.1.
## 3 Results and Discussion
### Phospholipid Composition  
The phospholipid composition is an important indicator from the technological point of view because it will show the necessity of usage of the concentrated buttermilk for the production of dairy products. The phospholipid composition of buttermilk retentates is given in Table 4. The total phospholipids increased with the increase in VRR 1.8, 2.4, 2.6, and 2.8 times, respectively. The main components in the phospholipid fraction were phosphatidylethanolamine (23.3%) and phosphatidylcholine (21.8%), followed by phosphatidylinositol (18.8%) and sphingomyelin (13.9%). The authors in [44] established that the total phosphatidylethanolamine, phosphatidylcholine, phosphatidylserine, and sphingomyelin contents of buttermilk were 39.0, 8.9, 24.4, 8.3 and 19.3%, and those reported by [45] were 46.5, 9.6, 20.7, 9.3 and 13.9%. The authors in [1] also established that the main phospholipids in the raw material, intermediate and final products in the production of buttermilk quark were phosphatidylethanolamine and phosphatidylcholine, and total phospholipid content increased from 154 mg/100 g (in the buttermilk) to 232.2 mg/100 g (in the concentrated buttermilk) at double concentration. They established that the highest increase was observed for phosphatidylcholine and phosphatidylinositol at VRR 2. In our results at the same VRR, the highest concentrations were for phosphatidylcholine, lysophosphatidylethanolamine, and phosphatidylserine. The lowest increase was for diphosphatidylglycerol. The rise in the phospholipid concentrations together with the concentrations factors of the main components (Figure 4) demonstrates the suitability to use concentrated buttermilk by ultrafiltration in the production of dairy products to enrich them with biologically active substances.
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 2 Analysis of Key Technologies for the Transition from Open-Pit to Underground Mining
### Current Status of Slope Stability Research in Open-Pit Underground Mining
By combining numerical simulations, theoretical calculations, and field monitoring, mining engineers can more accurately assess crown pillar stability, providing reliable technical support for the transition from open-pit to underground mining. With ongoing technological advancements and the accumulation of practical experience, future research on crown pillar thickness will continue to deepen, offering more scientifically grounded and reliable solutions to support safe mining operations.
# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 5 Evaluation
We also show how the priority of packets influences their delivery ratio and delivery delay.
## 3 Results
### FLC Results  
The advanced control framework was tested in the same load conditions as with the P&O and SMC tests. Accordingly, significant improvements in the performance were accomplished; Figure 13 recaps the results. Despite this figure focusing on the main controlled results, atmospheric conditions were the same and are reflected in Figure 9, where the irradiation had less fluctuation than the previous cases.  
The PV controlled voltage that is shown in Figure 13b displays a different demeanour in comparison to P&O and SMC. Initially, FLC performed better due to the chattering reduction because the voltage amplitude was 0.11 V, which means that it was diminished by several orders of magnitude. Section 2.5 explained that the P&O demeanour dimmed the overshoots and undershoots due to the resistance change because of the chattering excess, which was roughly perceived with SMC and, in this case, it could be managed with FLC. During the increase, an overshoot with an amplitude value of 2 V was observed, which had a settling time near to 8 s. At the decline, the settling time was similar in the undershoot although the slope correction was straight with an amplitude of 4.7 V.  
As with SMC control, this framework is also an error-based approach where the results of this variable are displayed in Figure 13h. Subsequently, the values obtained were mostly averaged at 0 V except for periodical fluctuations, which reached an amplitude of 0.11 V and peaks of 7 V during the load rise. These were generated by the resistance changes but, in comparison to the previous analysed structure, the correction is slower.  
Figure 13: MPPT based FLC: (**a**) Resistance load (\(V_{out}/I_{out}\)); (**b**) PV voltage; (**c**) PV current; (**d**) PV power; (**e**) DC–DC output voltage; (**f**) DC–DC output current; (**g**) Duty cycle; (**h**) Error (\(V_{ref}-V_{pv}\)).
# Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery
## 2 Materials and Methods
### Haptic Device Integration  
The Omega 7 haptic device was integrated into the system to control the ATHENA virtual parallel robot and provide the required force feedback to the user.  
Omega 7 is a 7-DOF haptic device with hybrid architecture. It uses a Delta parallel architecture with 3 active DOFs used to provide force feedback. It also has a serial wrist with 3 passive DOFs used for orientation and a 1-DOF pinch used for the active function of the surgical instrument (i.e., gripping). The Omega 7 enables real-time control of the robot by continuously updating the end-effector position based on the user's movements, generating the \(X\), \(Y\), \(Z\), \(\psi\), \(\theta\), and \(\varphi\) coordinates (Figure 4).  
Both the robot and the haptic device positions are continuously monitored and processed in the MATLAB R2024B environment. The MATLAB R2024B application consists of three key modules:  
* Haptic Handler Module--Establishes communication with the haptic device, recording its position (\(X\), \(Y\), \(Z\)), orientation (\(\psi\), \(\theta\), \(\varphi\)), and gripper state. It also sets forces along the \(X\), \(Y\), and \(Z\) axes of the Omega 7 haptic device.
* Inverse Kinematics Module--Uses the output from the Haptic Handler, namely the desired change in orientation and position in terms of \(\Delta\psi\), \(\Delta\theta\), \(\Delta l_{ins}\), and \(\Delta\varphi\) determined using Equation (1) to compute new joint positions (\(q_{1}\), \(q_{2}\), \(q_{3}\), and \(q_{4}\)) that replicate the user's movements.  
Figure 4: Haptic device integration into AR-based simulator.  
* Input/Output Handler--Transmits the computed joint positions (\(q_{1}\), \(q_{2}\), \(q_{3}\), and \(q_{4}\)) to Unity via a.NET pipeline and receives the end-effector's position and orientation from the virtual environment in Unity. In the Unity environment, multiple scripts ensure accurate end-effector movement according to the haptic device's inputs:
* Input/Output Script--Receives joint positions from MATLAB R2024B to update the virtual robot and transmits the actual end-effector positions back to MATLAB.
* Virtual Robot Controller--Adjusts joint positions based on the mathematical model, ensuring precise replication of the user's haptic movements.
* Virtual Robot--The Unity object whose configuration is changed upon the provided values of the active joints (\(q_{1}\), \(q_{2}\), \(q_{3}\), and \(q_{4}\)).
* Image Processing Script--Processes the images from the HoloLens camera stream, selects every tenth frame, and resizes it to meet the AI model's input requirements.
* AI Force Prediction Script--Analyzes the images provided by the Image Processing Script for force prediction. The AI force prediction script estimates the applied force at the end-effector and sends the data back to the Haptic Handler in MATLAB R2024B, ensuring real-time force feedback for the user.
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
###### Abstract  
RPL (routing protocol for low-power and lossy networks) is an important candidate routing algorithm for low-power and lossy network (LLN) scenarios. To solve the problems of using a single routing metric or no clearly weighting distribution theory of additive composition routing metric in existing RPL algorithms, this paper creates a novel RPL algorithm according to a chaotic genetic algorithm (RPL-CGA). First of all, we propose a composition metric which simultaneously evaluates packet queue length in a buffer, end-to-end delay, residual energy ratio of node, number of hops, and expected transmission count (ETX). Meanwhile, we propose using a chaotic genetic algorithm to determine the weighting distribution of every routing metric in the composition metric to fully evaluate candidate parents (neighbors). Then, according to the evaluation results of candidate parents, we put forward a new holistic objective function and a new method for calculating the rank values of nodes which are used to select the optimized node as the preferred parent (the next hop). Finally, theoretical analysis and a series of experimental consequences indicate that RPL-CGA is significantly superior to the typical existing relevant routing algorithms in the aspect of average end-to-end delay, average success rate, etc.  
chaotic genetic algorithm; objective function; RPL; routing metrics +
Footnote †: journal:
## 6 Conclusions  
We proposed a GRU-based network bandwidth prediction model and a buffer-based ABR algorithm to enhance the QoE of video streaming applications. The proposed GRU model predicted network bandwidth with reasonable accuracy, with an average RMSE of 0.079 across scenarios involving pedestrian, bus, and train movement. Our buffer based ABR scheme yielded approximately 11% higher QoE compared with that of the existing BBA in various mobility scenarios. This implies that our scheme produces high-quality video for users, resulting in reduced rebuffering and video quality changes. Our key contributions include accurate bandwidth prediction using the GRU model, optimal video quality selection based on predicted bandwidth and buffer occupancy, and enhanced performance  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Proposed Technique** & **BBA** \\ \hline Average video quality & 3762 KB & 3668 KB \\ Number of video quality changes & 25 & 25 \\ Number of rebufferings & 28 & 119 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed scheme and buffer-based adaptive bitrate (BBA) algorithm in train scenarios.  
Figure 22: Comparison of buffer occupancy in train scenario 1.  
in networks with substantial fluctuations. Future work will focus on improving the QoE of real-time video-streaming applications in wireless networks.  
Conceptualization, J.W., S.H. and D.A.; methodology, J.W. and D.A.; software, J.W.; validation, J.W., D.K. and D.A.; writing--original draft preparation, J.W. and D.A.; writing--review and editing, J.W., S.H., D.K. and D.A.; visualization, J.W. and D.A.; supervision, D.K. and D.A.; project administration, S.H. and D.A.; funding acquisition, S.H. and D.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by ICT R&D program of MSIT/IITP [2021-0-00715, Development of End-to-End Ultra-high Precision Network Technologies]. This research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-005).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: