esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 3 Simulation  
According to [PERSON] et al. [32], the simulation of the magnetic field is obtained by using the mathematical expression of the field source with regular geometry distributed in space, such as the sphere model. This paper uses the sphere magnetic source model (referred to as the sphere combination model) to make its calculation, and the magnetic anomaly expression generated by a single-sphere magnetic source is as follows:  
\[\begin{array}{ll}\Delta T(x,y,z)=&\frac{\mu_{0}}{4\pi}\frac{m}{[x_{1}^{2}+y _{1}^{2}+z_{1}^{2}]^{3/2}}[(2z_{1}^{2}-x_{1}^{2}-y_{1}^{2})\sin^{2}I+(2x_{1}^{ 2}-y_{1}^{2}-z_{1}^{2})\cos^{2}I\cos^{2}D+\big{(}2y_{1}^{2}-x_{1}^{2}-z_{1}^{2} \big{)}\cos^{2}I\sin^{2}D\\ &-3x_{1}z_{1}\sin 2I\cos D+3x_{1}y_{1}\cos^{2}I\sin 2D-3y_{1}z_{1}\sin 2I\sin D ] &\text{where }\mu_{0}\) represents the vacuum permeability, \(D\) represents the declination angle of the magnetization intensity, and \(I\) indicates the inclination of the magnetic field and the inclination of magnetization intensity. \(m\) represents the magnetic moment, which has a relationship with the magnetic susceptibility \(J\): \(m=J\times\pi R^{3}\), and \(R\) represents the radius of the sphere. \(\Delta T(x,y,z)\) represents the magnetic anomaly at the position point \((x,y,z)\). The variable \((x_{1},y_{1},z_{1})\) is calculated via the formula \(x_{1}=x-x_{0},y_{1}=y-y_{0},z_{1}=z-z_{0}\), and \((x_{0},y_{0},z_{0})\) represents the center coordinates of sphere.
## 3 Results and Discussion
### Formation Mechanism of Walnut-like Superparamagnetic Microspheres
With moderate irradiation (e.g., S-3), the extent of polymerization increases, leading to thicker shells. At this stage, the walnut-like surface morphology begins to emerge, though the particles may still collapse due to their hollow interiors. Upon further increases in the irradiation time, the polymerization reaction reaches completion, leaving few unnected monomers and yielding solid MPCPs with a walnut-like morphology. When we replaced DVB with styrene, neither collapsed spheres nor solid spheres with walnut-like morphology were observed. Instead, we found broken hollow microspheres or intact, smooth spheres with a hole. This difference can be attributed to the higher solubility of polystyrene (PS) in styrene compared to poly(divinylbenzene) (PDVB) in DVB, which inhibits phase separation during polymerization. In contrast, the formation of the walnut-like morphology in the DVB system likely results from a combination of phase separation, contraction of the crosslinked PDVB network, and a high degree of polymerization that limits hollowing. Therefore, the walnut-like morphology is probably driven by the interplay of high polymerization degree, phase separation, and network contraction [23]. Further experiments are needed to fully validate this hypothesis. These could include increasing the polymerization time in the styrene system, partially substituting DVB with styrene, or initiating polymerization within the monomer droplet using an internal initiator such as AIBN (Azobisisobutyronitrile). These directions will be explored in future work.
# Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering
## 4 Experiment Details
### ML-1m  
The results of the prediction accuracy as obtained by different methods are shown in Figure 4.  
Figure 4: Prediction accuracy for the ML-1m data set, obtained using different comparison methods.  
Figure 3: Errors in predictions versus trust (or estimated trust) for the ML-100k data set, corresponding to different methods. (**a**) Mean estimated trust against signed error in rating prediction using M-Trust. (**b**) Estimated trust against signed error in rating prediction using S-Trust. (**c**) Estimated trust against signed error in rating prediction using W-Trust. (**d**) Initial trust against signed prediction error for I-Trust.  
As for the previous data sets, the M-Trust demonstrates the best performance, with significant performance improvements compared to the baseline methods: 5.47% compared to SVD, 4.7% compared to SVD++, 48.09% compared to SocialMF, and 480.78% compared to NGCF. In contrast to the results of the previous data set, S-Trust has a considerably better improvement than that of W-Trust. Its improvements against the SVD, SVD++, SocialMF, and NGCF methods are 0.78%, 0.05%, 41.50%, and 458.45%, respectively. W-Trust does not exhibit any improvement against SVD and SVD++; however, its improvement against the SocialMF and NGCF methods are 39.9% and 451.57%, respectively. I-Trust maintains its status as in the previous data sets, and has performance improvements of 2.64%, 1.88%, 44.11%, and 468.74% compared to SVD, SVD++, SocialMF, and NGCF, respectively.  
The results for the error-trust relationships, as shown in Figure 5, follow the same trends as those for other data sets.
# A Semi-Explicit Multi-Step Method for Solving Incompressible Navier-Stokes Equations
## 2 Governing Equations  
The Navier-Stokes equations for incompressible flow defined over domain \(\Omega\) with boundary \(\Gamma\) can be written as:  
\[\rho\frac{\partial\mathbf{u}}{\partial t}+\rho(\mathbf{u}\cdot \
abla)\mathbf{u}-\rho\
u\Delta\mathbf{u}+\
abla p =\mathbf{f}, \tag{1}\] \[\
abla\cdot\mathbf{u} =0, \tag{2}\]  
where \(\mathbf{u}\) is the velocity vector, \(p\) the pressure, \(t\) time, \(\mathbf{f}\) the body force, \(\rho\) the density and \(\
u\) the kinematic viscosity.  
For ensuring the well-posedness of the system defined by Equations (1) and (2), suitable boundary conditions must be specified. On the boundary \(\Gamma=\Gamma_{D}\cup\Gamma_{N}\), such that \(\Gamma_{D}\cap\Gamma_{N}=\emptyset\), the following conditions are prescribed:  
\[\mathbf{u} =\mathbf{u}^{pr}\;\;\text{at}\;\;\Gamma_{D}, \tag{3}\] \[\boldsymbol{\sigma}\cdot\mathbf{n} =\boldsymbol{\sigma}_{n}^{pr}\;\;\text{at}\;\;\Gamma_{N}, \tag{4}\]  
where \(\mathbf{u}^{pr}\) is the prescribed velocity, \(\mathbf{n}\) is the outer unit normal to \(\Gamma_{N}\) and \(\boldsymbol{\sigma}_{n}^{pr}\) is the prescribed traction vector. Homogeneous boundary conditions will be considered here for the sake of simplicity.  
We define symbolically the differential Navier-Stokes operator as:  
\[\mathcal{A}(\mathbf{u},p)=\left(\begin{array}{c}\mathcal{A}_{1}(\mathbf{u}, p)\\ \mathcal{A}_{2}(\mathbf{u},p)\end{array}\right), \tag{5}\]  
where:  
\[\mathcal{A}_{1}=\rho(\mathbf{u}\cdot\
abla)\mathbf{u}-\rho\
u\Delta\mathbf{u}+ \
abla p, \tag{6}\]  
\[\mathcal{A}_{2}=\
abla\cdot\mathbf{u}. \tag{7}\]  
Then, introducing the notation \(\mathcal{F}=\left(\begin{array}{c}\mathbf{f}\\ 0\end{array}\right)\), the governing equations can be written as:  
\[\begin{pmatrix}\rho\frac{\partial\mathbf{u}}{\partial t}\\ 0\end{pmatrix}+\mathcal{A}(\mathbf{u},p)=\mathcal{F}. \tag{8}\]
# Blockchain Integration in the Era of Industrial Metaverse
## 2 Historical Evolution of the Blockchain
### Blockchain 1.0  
In the early 1990s, research efforts were focused on the design and development of suitable frameworks for the digitization of transactions in an attempt to facilitate transactions in industries and institutions. The Blockchain 1.0 era is dated from 2008 to 2013. The research efforts yielded interesting results in the field of cryptocurrencies, so that users from both parties can trade without the need for traditional centralized systems [4]. However, one of the key issues following the introduction of digital transactions and cryptocurrencies is double spending. Double spending induces additional risks of fraudulent activities.  
Consequently, the first generation of blockchain, i.e., Blockchain 1.0, evolved on the principle of Digital Ledger Technology (DLT), which has enabled all the participants of a distributed network to keep track of a common record and thus eliminate certain challenges, such as double spending [5]. As a result, Bitcoin, the first type of cryptocurrency, has been developed and released by [PERSON] in 2008, based on the principle of decentralization, promising security, verifiability, and efficiency of digital transactions [6]. Taking into consideration the above-mentioned, the key benefits of Blockchain 1.0 can be summarized as follows:  
* Enhanced network reliability is achieved by masking the identity of users or organizations, thus increasing the privacy of users.
* Transparent communication between the network participants is possible as long as the users satisfy the conditions of the consensus mechanism.
* Operation costs are reduced due to the use of a decentralized and distributed network versus the higher cost of conventional systems.  
In Figure 5, the workflow of bitcoin transactions between two users is illustrated based on the framework of blockchain technology. The concept illustrated below involves the most simple form of transaction, i.e., the transfer of cryptocurrency from user X to user Y.  
Figure 4: Evolution from Blockchain 1.0 up to Blockchain 5.0.  
Despite the added value in digital transactions, the first generation of blockchain has unveiled some key challenges requiring further elaboration [7]. Among these challenges are:  
* For the validation of transactions, miners are obliged to work simultaneously in order to solve a cryptographic puzzle and adhere to the principles of the Proof of Work (PoW) consensus mechanism. By extension, multiple computing resources and increased computational time are required. Therefore, PoW is not efficient enough.
* Due to the increased computational time and computational resources required, delays in the execution of transactions are induced in the system, which leads to a less-than-optimal user experience.
* Security risks are still present, as there might be corrupt miners within the network who try to corrupt the ledger in an attempt to extract additional revenue/rewards.
###### Abstract  
Field emitters can be used as a cathode electrode in a cathodoluminescence device, and single-walled carbon nanotubes (SWCNTs) that are synthesized by arc discharge are expected to exhibit good field emission (FE) properties. However, a cathodoluminescence device that uses field emitters radiates rays whose intensity considerably fluctuates at a low frequency, and the radiant fluctuation is caused by FE current fluctuation. To solve this problem, is very important to obtain a stable output for field emitters in a cathodoluminescence device. The authors consider that the electron-emission fluctuation is caused by Fowler-Nordheim electron tunneling and that the electrons in the Fowler-Nordheim regime pass through an inelastic potential barrier. We attempted to develop a theoretical model to analyze the power spectrum of the FE current fluctuation using metallic SWCNTs as field emitters, owing to their electrical conductivity by determining their FE properties. Field emitters that use metallic SWCNTs with high crystallinity were successfully developed to achieve a fluctuating FE current from field emitters at a low frequency by employing inelastic electron tunneling. This paper is the first report of the successful development of an inelastic-electron-tunneling model with a Wentzel-Kramers-Brillouin approximation for metallic SWCNTs based on the evaluation of FE properties.  
Keywords:field emission; metallic single-walled carbon nanotube; high crystallinity; current fluctuation; inelastic electron tunneling; Fowler-Nordheim tunneling +
Footnote †: journal: Applied Physics Letters
## 6 Characterization of Surface Wave Modes  
In [27], surface susceptibilities and GSTCs are utilized to characterize guided waves along single-layer metafilms for both surface-wave mode and complex mode. However, most surface-wave applications have multiple-layer structures including a ground plane at bottom, like EBG [4; 5; 6] and modulated metasurface antennas [39; 40; 41]. Since GSTCs are only appropriate for single-layer surfaces, related methods derived in [27] cannot be utilized for the analysis of multiple-layer structures' surface-wave mode. In this section, a characterization method for surface-wave mode of multiple-layer surfaces will be derived, based on surface susceptibilities, sheet impedances and equivalent circuit model.
## 5 Experimental Results
### Ablation Experiment of Attention Mechanism  
For the information fusion method of the image modality and attentional pseudo-lidar modality, the following three groups of ablation experiments were carried out. In the first group, the two-modal information is fused directly without an attention mechanism. In the second group, after the region pseudo-lidar is multiplied by the attention map generated by the region pseudo-lidar, it is fused with the image information for the 3D object detection. In the third group, after the pseudo-lidar is multiplied by the attention map generated by the image region, it is fused with the image information for the 3D object detection. The experimental results are shown in Table 7. It can be seen from the table that the experimental performance of the first group directly fusing the two-modality data is the worst, and the experimental performance of the third group using the attention map generated by image region for object detection is the best because the image region is more accurate than the region pseudo-lidar, so the attention map generated by the former is also more accurate and the experimental performance is better.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(AP_{\text{M}}\)} & \multicolumn{3}{c}{\(AP_{\text{Dev}}\)} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline \(offset=(\triangle x_{1},\triangle y_{2})\) & 33.27 & 21.89 & 19.54 & 42.91 & 29.83 & 24.86 \\ \hline \(offset=(\triangle x_{1},\triangle y_{1})+(\triangle x_{2},\triangle y_{2})\) & 34.85 & 22.59 & 19.81 & 44.71 & 30.67 & 25.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study of offsets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(AP_{\text{M}}\)} & \multicolumn{3}{c}{\(AP_{\text{Dev}}\)} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline (1) Without attention & 22.27 & 18.35 & 16.54 & 29.81 & 23.65 & 20.86 \\ \hline (2) Image-based attention & 27.32 & 20.95 & 18.73 & 36.39 & 27.75 & 23.45 \\ \hline (3) Point cloud-based attention & 34.85 & 22.59 & 19.81 & 44.71 & 30.67 & 25.05 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study of attention mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(AP_{\text{M}}\)} & \multicolumn{3}{c}{\(AP_{\text{Dev}}\)} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline \(\alpha=0.1\) & 34.27 & 21.96 & 19.65 & 43.54 & 29.86 & 24.76 \\ \hline \(\alpha=0.2\) & 34.65 & 22.32 & 19.77 & 44.16 & 30.35 & 24.97 \\ \hline \(\alpha=0.25\) & 34.85 & 22.59 & 19.81 & 44.71 & 30.67 & 25.05 \\ \hline \(\alpha=0.3\) & 34.54 & 22.29 & 19.68 & 44.47 & 30.24 & 24.70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of auxiliary learning.
## 4 Computer Simulation
### Feasibility of Applying the Proposed Method to Realistic Illumination Models  
In the previous sections, the evaluation was conducted using a lighting setup consisting of a single LED without variations in nonlinear coefficients. However, in real-world applications, LED chips exhibit variations in nonlinear coefficients, and a single LED chip has a low luminous flux [21]. Consequently, LED lightings are typically composed of multiple chips, leading to a more complex nonlinear distortion. In this section, we evaluate the effectiveness of the proposed method under such realistic lighting conditions to verify its practical applicability. The simulation parameters are mostly inherited from the previous section, only with changes to \(E_{b}/N_{o}\) and the nonlinear parameters. Specifically, \(E_{b}/N_{o}\) is fixed at 20 dB, while the nonlinear parameters are randomly varied according to a normal distribution with a standard deviation of 0.04. The reason for setting the standard deviation to 0.04 is to assess the practicality of the proposed method under a relatively large variation. The deviation of 0.04 from the nonlinear parameter of white LEDs, \(\zeta=0.582\), nearly matches that of infrared (IR) LEDs, suggesting that such a large variation in standard deviation is unlikely in practical scenarios. Additionally, the simulations assume a three-path Rician fading environment with \(K=5\) dB. The BER characteristics of the two proposed methods under an increasing number of LEDs per lighting setup are shown in Figure 14.  
As shown in the simulation results, both proposed methods demonstrate a consistent reduction in BER as the number of LEDs increases. This improvement can be attributed to the statistical averaging effect inherent to the normal distribution. Even if a subset of LEDs exhibits significant nonlinear distortion, the collective contribution of multiple LEDs mitigates the impact of outliers through mutual compensation between highly nonlinear and nearly linear parameter sets. For nonlinear LEDs without compensation, the BER reduction  
Figure 14: BER performance of the proposed method in realistic lighting conditions.  
for both proposed approaches saturated at approximately \(10^{-2}\). However, after applying pre-distortion compensation using LSM, the BER was reduced to around \(10^{-4}\) for both methods, demonstrating the practical feasibility of the proposed approach. Moreover, the current results indicate that the proposed method incorporating FSS achieves better BER performance than the method without FSS when the number of LEDs is six or more, with the improvement saturating at 24 LEDs. Although the necessary number of LEDs may vary depending on the \(K\) and \(E_{b}/N_{o}\), in practical applications, the communication system can be designed by pre-assessing the method both used with and without FSS configurations to maximize the benefits of FSS.
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
###### Abstract
Subsequently, [PERSON] [22] (2022) proposed a well trajectory tracking control algorithm based on DDPG, and on this basis, the adaptive tracking control of well trajectories was realized by transfer learning. The experimental results show that the well trajectory tracking algorithm based on DDPG proposed by [PERSON] has strong anti-interference and is more in line with the actual well trajectory design. Aiming at the problem of the slow convergence speed of deep reinforcement learning models, [PERSON] [23] (2023) proposed a D3 QN model with an improved reward mechanism for wellbore trajectory optimization. The results show that the improved D3 QN algorithm not only has a fast convergence speed but also has strong adaptive ability. [PERSON] and [PERSON] et al. [24] (2023) proposed the use of the artificial intelligence (AI) framework to process Logging-While-Drilling (LWD) data in order to optimize a well trajectory in real time during the horizontal section of drilling and maximize oil and gas production. The research results show that compared with PPO, DDPG, TwinDDPG, and other deep reinforcement learning algorithms, wellbore trajectory planning based on artificial intelligence (AI) is better. [PERSON] [25] (2021), [PERSON] [26] (2023), [PERSON] [27] (2024), and others successively proposed a well diameter optimization control algorithm based on deep strong chemistry. Although their research achieved certain results in intelligence and adaptation, there are still defects in anti-interference. In the well trajectory planning task, reinforcement learning has the following advantages. Firstly, the agent can independently output control actions according to the current state and historical experience and realize the path self-guided generation. Secondly, it can adjust the direction according to the real-time state in the process of trajectory execution and has the ability to perform trajectory tracking and dynamic updating. Thirdly, the reward function is designed flexibly and can integrate various engineering constraints, such as curvature constraints, well inclination and azimuth requirements, geological obstacles, etc., to improve the robustness and generalization ability of the strategy. Therefore, this paper uses the TD3 algorithm to construct a trajectory design agent system and deeply study the key issues, such as state space construction, action strategy design, and re ward mechanism constraints, aiming to achieve the wellbore path planning ability of target-oriented, obstacle avoidance, and constructability and to verify its superiority and feasibility through experiments.
## 3 Application of Automatic Optimization System
### Sample Input Space Based on Latin Hypercube Sampling  
When the automatic optimization framework starts the execution, the first step is to initialize the sample population for the NSGA-II algorithm. In this study, Latin Hypercube Sampling (LHS) was used to hierarchically and uniformly sample the variable space, as shown in Figure 6. Compared to Monte Carlo sampling and random sampling, LHS results in a more uniform sampling of the entire variable space. This aids algorithms in covering all corners of the sample space during the iterative search process, avoiding local optima and facilitating the discovery of the global optimum. Through LHS, it is ensured that the optimization algorithm has better global search capabilities, increasing the likelihood of finding the optimal solution.  
This section introduces the sampling method chosen by the automatic system in its application. The selection of Latin Hypercube Sampling (LHS) allows the initial population of the optimization to consider sample points across the entire global space. This is done to maximize the likelihood of obtaining a globally optimal optimization result.
## 2 DMLS Technology  
The DMLS technology functions on the principle of direct laser agglomeration of metal powder and belongs to the Powder Bed Fusion (PBF) category according to the classification standards valid for the additive manufacturing. The development of the technology started in 1994 with active participation of the Rapid Product Innovations company and EOS GmbH company. The technology thus represents the first commercial method of manufacturing metal component parts by means of the additive manufacturing in a single process. Its application is especially in the prototype manufacturing as well as in the structure of tools, moulds, medical implants, and component parts used in aviation, in case of which high thermal endurance is required. There is no use of any binding substance or fusing agent necessary in utilization of the technology, yet the fine metal powder with particles of 20 microns diameter is clinkered via the high-performance fibre laser. The elimination of polymer binders prevents burning-up or infiltration by foreign particles and a steel part with the 95% density is formed. When compared to the Selective Laser Sintering (SLS) technology with the density being of 70%, then the aforementioned number is higher. Further advantages of the DMLS technology, when compared to the SLS technology, include a higher definition of details due to the utilization of thinner layers on the basis of material application with smaller diameter, which allows forming more complicated shapes of the part. The following materials belong to the group of materials available for the DMLS technology: titanium, bronze, aluminium, cobalt-chromium, stainless steel, and tool steel [2,3,14].