esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Non-Spherical Earth Gravitational Perturbation  
The gravitational field of the Earth plays a significant role in satellite orbit determination. Accurately detecting the satellite's acceleration within the Earth's gravitational field is crucial for improving orbit determination precision. The spherical harmonic expansion of the gravitational field is an effective method for representing the Earth's gravitational potential and hence for calculating the satellite's acceleration. The spherical harmonic expansion of gravitational potential V is shown below [15].  
\[V(r,\theta,\lambda)=\frac{GM}{r}[1+\sum_{n=2}^{\infty}\left(\frac{R}{r}\right)^ {n}\sum_{m=0}^{n}(C_{nm}\text{cos}(m\lambda)+S_{nm}\text{sin}(m\lambda))P_{nm}( cos\theta)] \tag{8}\]  
\(r\) is the distance from the Earth's center; \(\theta\) is the geocentric latitude; \(\lambda\) is the geocentric longitude; \(GM\) is the Earth's standard gravitational coefficient; \(R\) is the reference radius (usually the Earth's average radius); \(C_{nm}\) and \(S_{nm}\) are the spherical harmonic coefficients, corresponding to the cosine and sine components, respectively. \(C_{nm}\) represents the symmetric part of the Earth's mass distribution, while \(S_{nm}\) represents the asymmetric part; \(P_{nm}\) is the normalized associated Legendre polynomial. Using the spherical harmonic expansion of the gravitational potential \(V(r,\theta,\lambda)\), the gravitational acceleration acting on the satellite can be obtained by computing the gradient of the gravitational potential: \(a(r,\theta,\lambda)=-\
abla V(r,\theta,\lambda)\). By substituting the spherical harmonic-expanded gravitational potential into this equation, the radial, tangential, and normal components of the gravitational acceleration in three-dimensional space can be computed from the gradient of the potential.  
Radial acceleration \(a_{r}\):  
\[a_{r}\ =\frac{\partial V}{\partial r} \tag{9}\]  
Tangential acceleration \(a_{t}\):  
\[a_{t}\ =\frac{1}{r}\frac{\partial V}{\partial\theta} \tag{10}\]  
Normal acceleration \(a_{n}\):  
\[a_{n}\ =\frac{1}{rsin\theta}\frac{\partial V}{\partial\lambda} \tag{11}\]  
By combining these components, the satellite's total acceleration at any given position can be determined.
## 1 Introduction
[15; 16; 17; 18] employed finite element software to examine the sound transmission loss (STL) of cross-sectional images; the result was very similar to the test values, demonstrating the correctness of the simulation. Some research established the simplified void models using acoustic-electric analogy theory or Helmholtz resonance theory [19; 20], but it is essential to acquire the material's properties and void characteristics, which were difficult to obtain. The mentioned researches focus mainly on the void characteristic, but the effect of the void on sound absorption has been less concerned, which is the key to revealing the noise reduction mechanism of PAC. In conclusion, previous research studies have provided most of the macroscopic indicators for the noise reduction design of PAC, and the relationship between void parameters and noise reduction impact has been investigated from a microscopic perspective. However, how to reveal the noise reduction mechanism of PAC with voids is still a challenge to be addressed. This research is aimed at solving the above problems. Two highlight points were proposed: one is the statistical models between void parameters of PAC and SAC and the other is the FEM established by CT images, which shows high accuracy in SAC prediction. Both two highlights are helpful to the further study of the sound absorption capability of PAC and useful to the practical design of low-noise pavement. In this paper, the relationship between the air voids and the sound absorption capability of PAC was examined. First, the transfer function method was used to obtain the SAC spectrum of PAC with different void contents. Second, the void structure, identified from CT scanning images, was used to analyze the microscopic parameters. Third, statistical models between microscopic parameters and SAC were established. Last, a finite element model based on CT section images was built to predict the SAC spectrum of PAC and explore the noise reduction mechanism of porous asphalt pavement at the microscopic scale.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 1 Introduction
### Contributions  
While advancements in forecasting energy demand is evident, there is a lack of studies to directly compare the performance of Artificial Neural Networks (ANN) and Random Forest (RF) models in HVAC systems in near-zero-energy buildings (nZEB) and heating applications across regions in Spain. The goal of this paper is to implement a MATLAB-based framework to accurately evaluate and contrast the ability of ANN and RF algorithms in different contexts. This study will evaluate the performance of both machine learning methods based on their accuracy and computational cost. It will examine both regional climates and building characteristics, as well as their effect on energy demand predictions and how this information can be useful for informing and improving energy management tasks in Spanish households. The main contributions of this paper can be summarized as follows:  
* It addresses the impact of Industry 5.0 on energy demand, specifically within the context of sustainability and meeting current market needs while minimizing environmental impact.
* It fills a gap in energy consumption forecasting by integrating artificial intelligence approaches, specifically Artificial Neural Networks (ANNs) and Random Forests (RFs), into the Industry 5.0 framework.
* It implements and compares ANN and RF models using real-world energy consumption data from two specific locations in Spain (LUCIA, FUHEM) using a householded code based on MATLAB.
* It evaluates and compares the performance of ANN and RF models in predicting energy demand using a comprehensive set of metrics, including Root Mean Square Percentage Error (RMSPE), Root Mean Square Relative Percentage Error (RMSRPE), Mean Absolute Percentage Error (MAPE), Mean Absolute Relative Percentage Error (MARPE), Kling-Gupta Efficiency (KGE), and the coefficient of determination (R\({}^{2}\)).  
This paper is organized as follows: Section 1 offers an overview, including background information, a review of the relevant literature, and the objectives of the study. Section 2 details the methodology, covering data collection, normalization techniques, mathematical modeling with Artificial Neural Networks (ANNs) and Random Forests (RFs), as well as the optimization processes. Section 3 presents the results along with their discussion. Finally, Section 4 provides the conclusions and suggests directions for future research.
## 4 Kinematics, Workspace, Singularity, and Simulation  
### Exoskeleton Range of Motion and Human Wrist Joint Motions  
The patient's upper limb is subject to kinematic constraints when using a wrist exoskeleton. If the wrist exoskeleton does not obstruct the operator's normal wrist motions or workspace, that is advantageous. Additionally, exoskeleton technology needs to be able to equal or surpass human capabilities. The human hand includes five fingers and a wrist joint. Two DOFs exist in the human wrist. Flexion, extension, radial deviation, and ulnar deviation are the four basic wrist movements. The normal range of motion (ROM) of the wrist is given in Table 2.  
Table 2 displays the maximum ROM limits for a human wrist joint as determined by the American Academy of Orthopaedic Surgeons (AAOS) for various wrist motions, as well as the achieved ROM and overall capability of the exoskeleton robot.  
The exoskeleton depicted in Figure 5 performs various wrist movements (Supplementary Video S1). The exoskeleton has a stand which rests on the table. The one end of the forearm (near to the elbow joint) rests inside the base platform. From the centre of the base platform, there is a circular extension to support or rest the forearm. This support can be further extended to the centre of the manipulator to hold the forearm from dropping down for paralyzed limbs. Also, a larger-sized stand can carry the maximum weight. Using a Velcro strap, the hand is attached to the palm splint. There are many openings in the palm splint to place the Velcro strap to adjust to different hand sizes. A user with the robot can be seen in Figure 5f.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Normal Wrist ROM** & **Achieved ROM** & **Device Capability** \\ \hline Flexion: 0–80\({}^{\circ}\) & Flexion: 0–31\({}^{\circ}\) & Flexion: 0–31\({}^{\circ}\) \\ Extension: 0–70\({}^{\circ}\) & Extension: 0–31\({}^{\circ}\) & Extension: 0–31\({}^{\circ}\) \\ Ulnar deviation: 0-30\({}^{\circ}\) & Ulnar deviation: 0–30\({}^{\circ}\) & Ulnar deviation: 0–38\({}^{\circ}\) \\ Radial deviation: 0–20\({}^{\circ}\) & Radial deviation: 0–20\({}^{\circ}\) & Radial deviation: 0–26\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: ROM of human wrist joint and exoskeleton device capability.  
Figure 5: Robot performing various wrist motions: (**a**) extension, (**b**) at home position, (**c**) flexion, (**d**) radial deviation, (**e**) ulnar deviation, (**f**) robot attached to the forearm.  
Table 2 displays the capabilities of the exoskeleton, the exoskeleton's achieved ROM, and its desired range. It is evident that for some wrist joint motions, the exoskeleton design satisfies the required workspace requirements. The device can only move the wrists 31\({}^{\circ}\) in the flexion and extension directions, that is, 49\({}^{\circ}\) and 39\({}^{\circ}\) less than what a human wrist joint can perform. As can be observed, the exoskeleton achieves 100% ROM while performing wrist ulnar deviation and radial deviation motions.
# A Matheuristic for Joint Optimal Power and Scheduling Assignment in DVB-T2 Networks +
## 4 A Matheuristic for DVB Design
### Evolution of the Population  
#### 4.2.1 Selection  
We select the individuals to be combined to give birth to the new generation by operating a _tournament selection_: given the current population \(POP\), as first step we create \(k>0\) subgroups by random selection of \(\lfloor\alpha\cdot|P|\rfloor\) individuals from \(POP\), with \(\alpha\in(0,1)\). As second step, we extract\(m<\lfloor a\cdot|P|\rfloor\) individuals offering the highest fitness value in every group. These extracted individuals are then combined to create the new generation by means of crossover.
# Evaluation of Tip Loss Corrections to AD/NS Simulations of Wind Turbine Aerodynamic Performance
## 4 Applying Corrections to AD/NS Simulation
### Application of New Correction  
The application of the new correction consists of two steps: The first is using factor \(F_{R}\) determined by Equation (34) and the second is using factor \(F_{S}\) determined by Equation (35).  
In the first step, an implementation similar to the Glauert-C correction is adopted, which is performed by replacing Equations (9) and (10) with  
\[\tilde{a}=a/F_{R}, \tag{56}\]  
\[\tilde{a}^{\prime}=a^{\prime}/F_{R}. \tag{57}\]  
A limiter of \(\tilde{a}=1\) is used when \(a\) is greater than 1. The angle of attack \(\alpha\) can then be determined by calculations using Equations (11)-(14).  
The second step is to correct the lift and drag coefficients by using Equations (42) and (43), the result of which is used to replace the \(C_{l}\) and \(C_{d}\) in Equations (18) and (19).
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
###### Abstract  
Real-world nonhomogeneous haze brings challenges to image restoration. More efforts are needed to remove dense haze and thin haze simultaneously and efficiently. However, most existing dehazing methods do not pay attention to the complex distributions of haze and usually suffer from a low runtime speed. To tackle such problems, we present an efficient re-parameterization residual attention network (RRA-Net), whose design has three key aspects. Firstly, we propose a training-time multi-branch residual attention block (MRAB), where multi-scale convolutions in different branches cope with the nonuniformity of haze and are converted into a single-path convolution during inference. It also features local residual learning with improved spatial attention and channel attention, allowing dense and thin haze to be attended to differently. Secondly, our lightweight network structure cascades six MRABs followed by a long skip connection with attention and a fusion tail. Overall, our RRA-Net only has about 0.3M parameters. Thirdly, two new loss functions, namely the Laplace pyramid loss and the color attenuation loss, help train the network to recover details and colors. The experimental results show that the proposed RRA-Net performs favorably against state-of-the-art dehazing methods on real-world image datasets, including both nonhomogeneous haze and dense homogeneous haze. A runtime comparison under the same hardware setup also demonstrates the superior efficiency of the proposed network.  
image dehazing; nonhomogeneous haze; re-parameterization network; residual attention block +
Footnote †: journal: Medical Image Analysis
# Scalable Fabrication of Si-Graphene Composite as Anode for Li-ion Batteries
## 2 Materials and Methods  
### Synthesis of Si-Graphene Nanocomposite  
Graphene nanoplatelets (Cheap Tubes Inc., Grafton, VT, USA), Grade 3, prepared by acid exfoliation, are multilayer graphene (4-5 layers) with a thickness of 8 nm, a purity >97%, and a surface area of 600-750 m\({}^{2}\) g\({}^{-1}\). Sodium dodecylbenzenesulfonate (SDBS) was purchased from Sigma Aldrich and functioned as a surfactant. Silicon Nanopowder (Si, 98+%, 50-70 nm, laser synthesized, polycrystalline structure) was purchased from US Research Nanomaterials, Inc., Huston, TX, USA. All the materials were used as received, without any pre-treatment.  
Briefly, the surfactant SDBS, graphene, and Si nanoparticles were sequentially dispersed in deionized water (DI water) with concentrations of 2.0 wt.%, 0.2 wt.%, and 0.2 wt.%, respectively. The dispersion was obtained using ultrasonication (Branson Model 450 Digital Sonifier with a 3/4\({}^{\prime\prime}\) disrupter horn). The power of the ultrasonication was set at 20% amplitude (about 40 watts) and the sonication time for each material is 20 min. During the dispersion of Si nanopowders, an ice bath was used to maintain the temperature of the fluid. After that, the fluid was transferred to a vacuum-assisted filtration setup and repeatedly washed with DI water to remove the excess surfactant. The filtered solid mixture was then taken out of the funnel and left in a vacuum oven at 80 \({}^{\circ}\)C for 12 h. The dried sample was then scraped off the filter and ground with a marble pestle and mortar. Figure 1 shows the schematic diagram of the preparation of the Si-graphene nanocomposite.
## 7 Discussion  
The costs of utilizing conventional concrete (CVC), grout-enriched roller-compacted concrete (GERCC), and vibrated grout-enriched CSGR, solely in terms of factory material costs (reference unit price: P.O 42.5 grade ordinary Portland cement 450 yuan/t, fly ash 120 yuan/t, high-efficiency water reducing agent 8000 yuan/t, air-entraining agent 13,000 yuan/t), excluding expenses like transportation and mixing, are compared in Table 12. From this comparison, it becomes evident that CVC and GERCC are the most expensive options, whereas vibrated grout-enriched CSGR stands out for its significantly lower cost attributed to the use of on-site gravel materials. The cubic meter cost of vibrated  
Figure 14: The impoundment of the flood control leveee to an elevation of 335 m.  
Figure 13: The installation of the T-shaped surface water stop: (**a**) transverse joint water stop (**b**) water stop between protection layer and toe slab (**c**) mortar cover protection.  
The vibrated grout-enriched CSGR is produced by grouting and vibrating directly onto the pre-paved CSGR material. In contrast to CVC, it plays a pivotal role in coordinating transition deformations at the interface between the impermeable layer and the main dam. This not only bolsters the continuity and integrity of the dam structure but also mitigates the local damage and stripping caused by abrupt shifts in material characteristics. Moreover, vibrated grout-enriched CSGR minimizes the variety of materials required at batching plants, thereby boosting production efficiency and decreasing construction interference. As a result, it emerges as an optimal choice for future CSGR dam protection and seepage control layers. However, ongoing research is essential to comprehensively understand its long-term material performance and engineering applications. The successful deployment of vibrated grout-enriched CSGR at the Tangba Protection Dam serves as a promising case study for similar projects.  
This study delves into the influence of slurry quantity on properties of vibrated grout-enriched CSGR material and construction efficacy, refining conventional joint sealing methods and investigating water sealing effectiveness. Nonetheless, challenges remain in utilizing this technology in 100-m-high dams, prompting the consideration of external flexible geomembrane installation for taller structures. Variations in geographical applicability underscore the need for further research into more frost-resistant types of cemented sand, gravel and rock. These considerations highlight avenues for future advancements in CSGR technology to enhance dam construction practices.
## 5 Conclusions  
In conclusion, the surface deformation of Tendurek volcano was determined using 150 Sentinel-1 SAR images captured between May 2018 and October 2022 using the SNAP-StaMPS integration. In this way, the most up-to-date and comprehensive analysis of the Tendurek volcano was carried out. Since the study area is a mountainous region with a high tendency to be affected by tropospheric delay, we applied two different atmospheric correction approaches. However, in our case, the GACOS atmospheric correction method produced larger standard deviation values for the maximum deformation rate. Therefore, we used the linear phase-based atmospheric correction approach. As a result, 10.6 \(\pm\) 0.76 and 10 \(\pm\) 1.18 mm/yr LOS velocities were determined from descending and ascending acquisition geometries, respectively, and updated deformation maps showing annual mean velocity changes for the region were created.  
This study confirms that the StaMPS-InSAR analysis on Sentinel-1 datasets has great potential for investigating surface deformations caused by volcanoes. In this context, the findings of this study may have important implications for authorities in disaster management, owing to the ability of this technique to achieve rapid results in large areas. However, this work can be improved in the future. For instance, surface deformations can be detected with higher sensitivity using a local atmospheric model and high-resolution InSAR images. It may also be useful to compare InSAR observations with ground measurements if ground-based monitoring instruments can be established in the future.  
Conceptualization, H.I.G., F.Y. and O.O.; methodology, H.I.G.; software, H.I.G.; formal analysis, H.I.G., F.Y. and O.O.; investigation, H.I.G., F.Y. and O.O.; data curation, H.I.G.; writing--original draft preparation, H.I.G., F.Y. and O.O.; writing--review and editing, H.I.G., F.Y. and O.O.; visualization, H.I.G. and O.O.; supervision, F.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 4 Results and Discussion
### Creep of RPC
#### 4.3.2 Effect of Steel Fibre on Creep of RPC  
Figure 7 shows variations in the stress-dependent strain, specific creep, and creep coefficient of RPC samples containing different steel fibre contents with time. In Figure 7a, the stress-dependent strain increases with increasing steel fibre content. This is because at the same axial stress ratio, the samples with more steel fibre content bear greater stress, causing more stress-dependent strain. As shown in Figure 7b,c, during the later period after loading, the specific creep and creep coefficient of the RPC samples decrease with increasing steel fibre content. For the samples without steel fibre, the ultimate specific creep is 8.95 \(\mu\epsilon/\)MPa. However, for the samples with 1% and 2% steel fibre content, the ultimate specific creep is 7.37 and 6.65 \(\mu\epsilon/\)MPa, respectively. The ultimate specific creep of the RPC samples with 1% and 2% steel fibre content are thus 17.7% and 25.7% less, respectively, than that of the RPC samples without steel fibre. For the samples without steel fibre, the ultimate creep coefficient is 0.40. However, for the samples with 1% and 2% steel fibre content, the ultimate creep coefficients are 0.35 and 0.32, respectively. This means that the ultimate creep coefficients of RPC samples with 1% and 2% steel fibre content are 12.5% and 20.0% smaller, respectively, than that of the RPC samples without steel fibre. The positive effect of steel fibre for reducing creep, particularly during the later period after loading, can be explained as follows:  
1. Even within the linear creep range, when the RPC carries a compressive load, microcracks can form and develop gradually inside the RPC due to the inhomogeneity of the RPC matrix. As the microcracks emerge during the later stage, the steel fibres passing through the microcracks can prevent the microcracks from developing further. Hence, the creep strain is reduced.
2. During the early period after loading, the steel fibre and RPC matrix produce a section slip. As a result, the inhibition effect of the steel fibre on creep is not significant. On the other hand, during the later period after loading, the slip between the steel fibre and RPC matrix tends to be stable. Hence, the ability of steel fibre to inhibit creep gradually appears.  
Figure 7: _Cont._