esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This model assesses the accuracy of awareness data based on the extent of awareness latency and subsequently determines the compensation weights for various resource prediction accuracy scenarios. A dynamic network resource allocation algorithm based on the accelerated alternating direction method of multipliers (ADMM) is proposed, which uses compensation data as an input with the optimization objective of maximizing the service resource coverage ratio (SRCR). Considering the differences in service priorities and resource requirements, the ability of resources between nodes to carry service was calculated, and the local variable initialization process of the ADMM was optimized to reduce search iterations, improve algorithm convergence speed, and achieve efficient network resource allocation. The main contributions of this paper are as follows:  
1. A network resource dataset that utilizes the different awareness latencies of each node as its temporal sequence was constructed. The changes in node resource occupancy and release were simulated based on the service arrival models. INT was used for resource awareness, and a dataset was compiled using the corresponding awareness latency as the time interval. The dataset served as the foundation for resource status prediction, data compensation, and resource allocation. 2. An awareness-prediction joint compensation model was designed. The LSTM is used to dynamically predict the onboard resource status for each node using the different awareness latencies between nodes as the prediction steps. An innovative method for constructing compensation parameters based on awareness latency and prediction accuracy is also proposed. This approach can compensate for the prediction results based on the resource prediction accuracy combined with awareness data, thereby improving the timeliness and accuracy of the data. 3. A dynamic network resource allocation algorithm based on the ADMM is proposed. The initialization process of the ADMM's local variables was refined based on the actual service carrying capacity of satellites, using compensation data as the input and with the optimization objective of maximizing the SRCR. This enhances the algorithm's convergence speed and enables efficient resource allocation. The remainder of this paper is organized as follows: Section 2 introduces the scenario of the LEO satellite network, service model, and resource model. Section 3 introduces resource state awareness based on INT and resource state prediction based on LSTM. Section 4 introduces the awareness-prediction joint compensation model. Section 5 proposes a dynamic network resource allocation algorithm based on accelerated ADMM. Section 6 validates the proposed method through simulation experiments. Section 7 provides a brief summary of the paper.
## 2 Materials and Methods
### Desorption and Regeneration Experiments  
After the adsorption experiment, the exhausted adsorbent was washed and dried overnight. Then it was shaken in a 100 mL flask which contained 30 mL HCl or HNO\({}_{3}\) solution with different concentrations. After the system reached equilibrium, the suspension was filtered, and finally, Eu(III) and La(III) content in the filtrate was determined. Subsequently, the adsorbent was neutralized by ultrapure water for several times and reconditioned for adsorption in succeeding cycles. The second adsorption experiments were carried out by using regeneration adsorbent under the optimum condition. The adsorption-desorption processes were repeated five times in this work.
## 5 Discussion of Results
The investigation of the fracture energy concerns the dissipation in the postpeak region, after the load-peak but just before the fail-load, when a flexural failure is starting and it is no longer possible to differentiate the shear mechanism. Additionally, in this case, a consideration of the different casting places is not possible because the number of specimens considered is not so significant. The shear strength is studied as the maximum stress that a material can withstand before failure and the shear strength ranges from 11 MPa to 14.5 MPa. Regarding the punching test, a total of 3 slabs were tested; all the specimens were cast in the laboratory and two notches on their surface were made so as to make two punching tests possible for each slab, so 6 punching tests were realized. The punching shear resistance was investigated according to the influence of the size parameter \(k\), which can be affected because of the slenderness of the specimens. Indeed, the theoretical results were obtained considering its limit value \(k=2\) and the actual value \(k=3\),58. The experimental values of the shear resistance have been calculated according to the loaded area's perimeter located at \(r=45\) mm and the control perimeter \(2d\) located at \(r=105\) mm from the centre of the loaded area. Comparing the experimental values obtained according to the control perimeter \(2d\) with the theoretical ones, they are closer to the value given by the size parameter \(k=3.58\) but values are still far. This means that \(k=2\) yields conservative theoretical values, whereas \(k=3.58\) produces an overestimation of the punching shear strength of the slabs. The average difference between the theoretical and the experimental values is approximately 14%. During the response of the slabs to the extreme load events, it was experimentally verified that the UHPFRCC panels can withstand a pressure wave from a stand-off distance of 1 meter up to 300 g of TNT with no visible damage and up to 800 g of TNT with visible frontal erosion but no scabbing from the distal face of the panel. Further, it was demonstrated that the ballistic system in terms of a wall 1.8 m in height can withstand a pressure wave from a stand-off distance of 3.4 m delivered by 3.6 kg of TNT. The resulting damage was visible on the distal face of the wall where the crack propagated approximately in the mid-span of the panels in the middle of the height of the wall. However, the wall remains compact and no scabbing occurred on the distal face of the wall, which is very important for personnel operating behind it. It was further verified that the wall withstood approximately 50 hits from a projectile fired from a short handgun and did not cause any damage to the back of the structure. Only small craters were visible on the front of the wall from the compression of the material upon the impact of the projectile. No material was released from the rear side of the assembly, which is the primary requirement for the safety of intervening units during emergencies.
# Lycopene: A Natural Arsenal in the War against Oxidative Stress and Cardiovascular Diseases
## 2 Discovery, Chemical Structure, Properties, Biosynthesis and Physiological Role of Lycopene
Because it is a substantial carotenoid in human blood, it can protect lipids, proteins, and DNA from oxidative stress [21]. Lycopene's antioxidant activity may be enhanced by the absence of the b ionone ring structure. Due to stereochemical variations, lycopene differs from other regularly eaten carotenoids in that it can only be found in particular subcellular locations. The human body directly absorbs a substantial amount of intact lycopene, which circulates through the body's plasma, liver, and peripheral organs. Figure 3: Biosynthesis of lycopene starts from central metabolite Acetyle-co-A, which is subsequently converted to IPP, GPP, GGPP, phytoene, and lycopene. It accumulates in the human tissues but is not evenly distributed [64]. Adipose tissues, adrenal glands, testes, and liver, for example, have larger concentrations, while the kidneys, prostate, lungs, and ovaries have lower concentrations [56]. Plasma lycopene has a half-life of 12-33 days in the human body [65]. Lycopene must be absorbed and incorporated into the plasma and tissues in order to be used as a dietary supplement. Tomato lycopene is not easily absorbed since it is integrated into the nutritional matrix. Clinical research demonstrates that heat-processed tomato products absorb lycopene more quickly than raw sources, and that adding oil increases absorption [66]. Isomerization of lycopene at low pH in the stomach has been described [67]. Before the lycopene is integrated into mixed micelles, it must be freed from the food matrix. Micelles include bile salts, cholesterol, and fatty acids from the meal and their amphiphilic shape aids in keeping the lipophilic nutrients soluble in the watery digesta [68]. The micelles approach the apical side of intestinal enterocytes' unstirred water layer, where lycopene diffuses passively over the apical membrane [69]. Lycopene is considered to be absorbed in the same way that dietary lipids are, via passive diffusion [70; 71]. According to research, the scavenger receptor class B type I (SR-BI) cholesterol membrane transporter aids in lycopene absorption. It has also been observed that various additional transporters are linked to lycopene absorption. [72; 73]. Once within the enterocyte, lycopene is bound with dietary lipids to form chylomicrons [69], which are then transported via the basolateral membrane, into the lymphatic system, and finally discharged into the circulation (Figure 4). Lycopene transport and distribution are aided by plasma lipoproteins. It continues to be found in the lipophilic area of lipoproteins, which is the hydrophobic molecule's core [18; 74]. Lycopene is mostly transported by low-density lipoproteins [75; 76]. Furthermore, cis isomers of lycopene have been found to have a stronger capacity to integrate into lipoprotein and other proteins than all trans isomers, owing to their shorter chain length [77].
# The Concept regarding Vehicular Communications Based on Visible Light Communication and the IoT
## 3 Implementation and Results  
The proposal was tested using various methods and tools based on the Linux operating system capable of intercepting data or connections, such as BackTrack and WireShark. These methods' connections and their traffic management were analyzed. These aspects are important and each type of amendment was staged, which was the purpose of the study, starting from the unstructured ones and then the structured, external, and internal ones. In vehicular networks in particular, we can experience unstructured threats from other users without a high level of training; these practices are undertaken only out of pure curiosity and their method of operation is extremely easy to identify. These types of penetration are carried out by users who know the methods of operation and the vulnerabilities of networks and later develop scripts capable of disrupting access to them.  
Vehicular network security and communications between infrastructure, pedestrians, cars, and intelligent traffic systems are extremely pressing topics for today's society. Attacks from the outside can be initiated at the level of intelligent transportation systems (ITSs) by capable individuals who gain access to the entire infrastructure, generating chaos and panic in addition to pursuing extremely well-defined goals of controlling certain areas of activity. Many of the attacks from outside target issues that are closely related to bank fraud, personal information, and the mining of confidential data. Analyzing the subject in detail, persons with hidden intentions could take control of autonomous vehicles, as well as intelligent traffic systems. Attacks of an internal type, however--and at this moment it is much too early to take these aspects into account--have more to do with the accuracy and degree of security established by existing users and the way they set up their accounts.
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 8 Using the Tables to Select the Most Suitable Model  
Depending on the problem you want to solve, you may either have specific requirements or be in an exploratory phase where you need to map the available models. This guide provides a structured way to use the tables, allowing you to iteratively filter out models that do not meet your criteria.  
If you have a specific problem, such as a **robotic arm with a monocular camera** that must operate in **real time** and **grasp a specific item** in a production line, you should define key parameters and their importance. Then, you can progressively filter models by examining different tables and eliminating those that do not meet your needs.  
The selection process follows an iterative approach where you can combine tables in any order that best suits your problem:  
1. **Identify Relevant Datasets:** If dataset selection is critical, start with Tables 1-3 to find datasets similar to your problem. If your dataset appears in Table Tables 4-6, which present the eight most common datasets, you can filter models accordingly. If not, external research may be required.
2. **Apply Key Model Constraints:** Depending on your requirements, filter models using criteria such as: * **Real-time performance:** Use Tables 12 and 13 if execution speed is a priority. * **Input type:** If your model requires specific input formats (e.g., single vs. multi-image, RGB vs. 3D integration), consult Tables 14-17.
3. **Search for Specialized Features:** If your task involves handling **symmetry, occlusion, or other specific challenges**, refer to Tables 18 and 19 This step helps further refine the selection based on advanced capabilities.
4. **Evaluate Model Strengths and Weaknesses:** To further eliminate unsuitable models, analyze their advantages and limitations from Tables 20 and 21.
5. **Compare Model Performance:** After filtering based on previous factors, use Tables 7-11 to compare model performance scores and select the best candidate for your application.  
The order of these steps is not fixed--you can adjust it based on your priorities. Each selection step filters out models that do not meet your requirements, ensuring an efficient and targeted search.  
Finally, for a deeper understanding of the underlying **model technologies and architectures**, you can refer to **Section**4. This information may be useful if specific architectures are better suited to your problem.  
By iteratively combining tables and filtering models at each step, you can efficiently identify the best model for your specific use case.
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 3 Results
### Distribution of Operating Cost Components
#### 3.1.5 Stockpiling  
A visual representation of the cost distribution for key components associated with stockpiling cost is available in Figure 6.  
The estimated stockpiling cost is primarily driven by maintenance and repairs, labor, equipment depreciation, and fuel, which collectively account for 83% of the total cost. In contrast, inventory management and storage are the smallest components, accounting for 10% and 4% of the total stockpiling cost, respectively.
## 2 Related Work
A real-world time series GAN (RTSGAN) extends the capabilities of GANs by addressing challenges like variable-length sequences and missing data [12]. It leverages an autoencoder to map sequences into a fixed-dimensional latent space, simplifying the generation process while preserving temporal dependencies. The framework employs a Wasserstein GAN (WGAN) for robust synthetic data generation and introduces RTSGAN-M for datasets with significant missing values. Evaluations of RTSGAN on datasets such as energy usage and stock prices show its ability to outperform benchmarks like TimeGAN and Causal Optimal Transport GAN (COTGAN) in both discriminative and predictive tasks. The method's flexibility makes it particularly suited for complex real-world scenarios, where incomplete data often pose significant challenges. TimeVAE introduces a variational autoencoder (VAE) framework specifically designed for time-series data, emphasizing interpretability and reduced training complexity compared to GAN-based methods [13]. By combining convolutional and dense layers with a probabilistic latent space, TimeVAE generates synthetic data that closely align with real distributions. The model's evaluation demonstrates its strength in capturing temporal patterns, particularly for datasets with sinusoidal and structured sequences. TimeVAE's simplicity and efficiency make it an attractive option for applications requiring fast, high-quality data generation, although its performance may decline with highly volatile data. The VAE-GAN architecture merges the strengths of VAEs and GANs to address limitations like mode collapse and data diversity [14]. The VAE component encodes input data into a latent space, while the GAN generator uses this representation to create synthetic samples. This approach allows VAE-GAN to generate more representative data distributions without extensive preprocessing or domain knowledge. VAE-GAN has been successfully applied to datasets related to energy and solar production, demonstrating superior performance in metrics like Kullback-Leibler divergence and Wasserstein distance compared to standalone GANs. Its ability to synthesize diverse and realistic data highlights its potential for applications in fields requiring robust and flexible data generation. The models discussed offer varied strengths and limitations depending on the application. Monte Carlo methods like fintech-kMC excel in simplicity and control but may lack scalability for large datasets. TimeGAN and RTSGAN address the complexities of temporal data with advanced architectures, while TimeVAE provides a more interpretable and efficient alternative. VAE-GAN offers a hybrid approach, balancing diversity and representational accuracy. Table 1 summarises the comparative advantages and drawbacks of these models.
## 4 The Dynamic Effects
### Frontogenesis Dynamic Effect
#### 4.2.1 Impacts on Frontogenesis
Low-level convergence, high-level divergence, and cross-front vertical circulation are all rather pronounced. By this time, the greatest virtual potential temperature perturbations have exceeded 4 K in both cases, which are still at a height of 6-8 km. The structure of the lager tendency of virtual potential temperature gradient matches with the latent heat structure (Figures 12 and 13e,f). In comparison, the region of virtual potential temperature perturbations over 4 K was slightly larger in P-case than in C-case. The tendency of virtual potential temperature gradient is still increasing from t = 18 h to 21 h at low levels in P-case. Nevertheless, the tendency of virtual potential temperature gradient has weakened slightly by t = 21h in C-case (Figure 13c,e). This shows a continuous intensification of low-level frontogenesis in P-case. However, in C-case, low-level frontogenesis weakens, and it is much weaker than P-case. Overall, under polluted conditions, aerosols have little effect on Mei-Yu front structure, but significantly increase the intensity of the low-level Mei-Yu front through its microphysical feedback interactions. To explore the physical processes behind the influence of aerosols on frontogenesis, we calculate frontogenesis function [80] using the outputs from C-case and P-case numerical experiments. The expression of frontogenesis function is given by:  
\[F=\frac{d}{dt}|\
abla\theta_{e}|=F_{1}+F_{2}+F_{3}+F_{4}, \tag{14}\]  
\[F_{1}=-\frac{1}{2}\frac{1}{|\
abla\theta_{e}|}(\
abla\theta_{e})^{2}\cdot( \frac{\partial u}{\partial x}+\frac  
the front (forming a cool pool). This is thermal frontogenesis. The other terms in the equation are all induced by the DDE of latent heat, belonging to dynamic frontogenesis. High aerosol concentrations influence frontogenesis through microphysical effect and DDE. On the one hand, enhanced latent heat release (heating and cooling) produce more intense warm and  
Figure 14: Vertical cross sections of zonal mean frontogenesis function at t = 21 h (Units: 1 k/100 km\(\cdot\)h\({}^{-1}\)) (**a**,**b**) \(F_{i}\) (**c**,**d**) \(F_{1}\) (shading) and \(F_{2}\) (contour); (**e**,**f**) \(F_{3}\) (contour) and \(F_{4}\) (shading). \(F\) represents the frontogenesis function; \(F_{1}\), \(F_{2}\), \(F_{3}\), and \(F_{4}\) represent horizontal divergence term, horizontal deformation term, tilting term, and diabatic heating term, respectively. The left panels show C-case, the right panels show P-case. cool pools, invigorating thermal frontogenesis. On the other hand, the latent heat feedback effect strengthens updrafts ahead of the front and downdrafts behind the front, invigorating convergence near the frontal boundary and, hence, dynamic frontogenesis. Briefly, during precipitation development and maximum precipitation periods, high aerosol concentrations in Mei-Yu systems strengthen both thermal and dynamic low-level frontogenesis through microphysical feedback, leading to an intensified low-level front.
# Transformation of Trigonometric Functions into Hyperbolic Functions Based on Cable Statics
## 4 Application of Hyperbolic Function Transformation
### Trigonometry
#### 4.4.1 Law of Sines and Cosines  
Below are the steps to solve a triangle with sides \(a=9\), \(b=5\), and \(c=12\), using the law of sines and the law of cosines. The angles are represented by \(\theta\), with \(A\), \(B\), and \(C\) denoting the vertices, which are reflected in the subscript of each angle, forming the following notation: \(\theta_{A}\), \(\theta_{B}\), and \(\theta_{C}\). The following conventions are adopted to represent the angles: \(\theta_{A}=\angle CAB\), \(\theta_{B}=\angle ABC\), and \(\theta_{C}=\angle BCA\). The triangle is shown in Figure 12. Thus, the angles satisfy the equation \(\theta_{A}+\theta_{B}+\theta_{C}=\pi\).  
The law of cosines is useful when the three sides of the triangle are known. It can be used to find an angle \(\theta_{C}\) when the three sides are known. The formula is:  
\[\cos\theta_{C}=\frac{a^{2}+b^{2}-c^{2}}{2 ab}. \tag{73}\]  
By substituting the values of the sides of the triangle, the following is obtained:  
\[\cos\theta_{C}=\frac{9^{2}+5^{2}-12^{2}}{2\times 9\times 5}=-\frac{19}{45}. \tag{74}\]  
Now, to find \(\theta_{C}\), the inverse cosine is taken:  
\[\theta_{C}=\cos^{-1}-\frac{19}{45}\approx 114.975^{\circ}. \tag{75}\]  
Now that \(\theta_{C}\) has been found, the law of sines can be used to find the angle \(\theta_{A}\) of the triangle. The law of sines states that:  
\[\frac{a}{\sin\theta_{A}}=\frac{b}{\sin\theta_{B}}=\frac{c}{\sin\theta_{C}}. \tag{76}\]  
This formula is used to determine \(\theta_{A}\), where \(\sin\theta_{A}\) is then isolated:  
\[\sin\theta_{A}=\frac{a\sin\theta_{C}}{c}. \tag{77}\]  
The known values are substituted:  
\[\theta_{A}=\sin^{-1}\frac{9\sin 114.947^{\circ}}{12}=42.833^{\circ}. \tag{78}\]  
The values are substituted to find \(\theta_{B}\):  
\[\theta_{B}=\sin^{-1}\frac{5\sin 114.947^{\circ}}{12}=22.192^{\circ}. \tag{79}\]  
The angles of the triangle are substituted into the law of sines:  
\[\frac{9}{\sin 42.833^{\circ}}=\frac{5}{\sin 22.192^{\circ}}=\frac{12}{\sin 114.9 75^{\circ}}. \tag{80}\]  
The values are substituted into the law of sines:  
\[13.238=13.238=13.238.\]  
Figure 12: Oblique triangle, showing its sides and angles.