esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Satellite and Payloads
We suggest that extra caution is required when using the PAP, EFD, and TBB for scientific research. The scientific data are packed into HDF (Hierarchical Data Format) with the descending (orbit path from north pole to south pole) and ascending (from south to north) half-orbit data files. There are five levels of data in total based on the working principles of each payload, which are described as follows: Level 0: The data are reconstructed after a series of preprocessing, including frame synchronization, de-randomization, decoding, and de-formatting on the raw telemetry data. Level 1: The data obtained after general error elimination, physical unit conversion, format conversion, etc. based on the Level-0 data. Level 2: The calibrated physical values correspond to Level 1 data with orbit information after coordination system transformation and necessary data inversion. Level 2A: Only the EFD in the ULF band and GOR deliver the Level 2A data. For the EFD, Level 2A is generated after eliminating the \(\mathbf{Vs}\times\mathbf{B}\) effect in the ULF band, where vs. is the satellite's velocity and \(\mathbf{B}\) is the geomagnetic field. For GOR data, it is the TEC values obtained after conversion with precision orbit determination information on Level 2. Level-3: Time sequential data along satellite orbits generated after resampling, necessary spectral analysis based on Level-2 data. Level-4: The global interpolation maps of physical values from Level-2 data. The Level 2 data products that can be directly used for scientific application are accessible via the website to the international scientific community ([[http://leos.ac.cn](http://leos.ac.cn)]([http://leos.ac.cn](http://leos.ac.cn)) accessed on 26 December 2021).
## 2 Materials and Methods
### Machine Learning Regression Algorithms Evaluated for Digital SOC Mapping
QRF is a variation of RF that estimated the conditional quantiles of the response variable, operating similarly to RF; however, instead of averaging the predictions, it outputs the specified quantile estimate from each tree [50]. SVM for regression applied a margin-based approach, aiming to find the best hyperplane that minimizes prediction error while allowing for some deviations, effectively capturing non-linear relationships through kernel functions [51]. Similarly, KNN relied on the local structure of the data, predicting outputs based on the average outcome of the k-nearest data points. As another advanced ensemble method, XGB optimized tree learning with regularization and control overfitting [52], while ANN leveraged neuron layers to capture complex, non-linear patterns through numerous interconnected layers [53].
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 3 The Proposed Approach
### Hierarchical Baseline Stereo-Matching  
In this section, the hierarchical baseline stereo-matching process is introduced in detail. First, the background is masked out from light field raw images. Then, a macro-pixel map pyramid is constructed through Gaussian convolution by \(2\times\) downsampling. It is important to note that the Gaussian convolution is applied over \(3\times 3\) neighboring macro-pixels centered on each macro-pixel. In the experiment, the proposed pyramid has four layers, with the top layer consisting of \(92\times 49\) macro-pixels. The resulting disparity values of the low-scale layer are taken as prior knowledge input for the next layer, which can narrow the search range and improve matching accuracy while being less time-consuming.  
In each layer of the pyramid, three steps are performed: coarse matching, constraint enhancement, and fine matching.
## 3 Results and Discussion  
### bAuNPs Characterization  
#### 3.1.1 UV-Vis and TEM  
A few hours after adding chloroauric acid to the bacterial supernatant, the synthesis of nanoparticles was initially identified by the color change of the solutions to red (Figure 2a). The synthesis of bAuNPs was confirmed using UV-Vis spectroscopy, which revealed characteristic peaks in the wavelength range of 500-600 nm (Figure 2a). UV-Vis spectroscopy was employed to determine the concentration and aggregation of bAuNPs.  
The morphological features, including shape and size, were determined using TEM. TEM analysis (Figure 2b) demonstrated that under alkaline pH (9.0) and at 58 \({}^{\circ}\)C for 24 h, the gold nanoparticles exhibited a spherical structure and reduced sizes (30.862 \(\pm\) 13.48). The synthesis methodology of AuNPs follows two steps: (I) nucleation and (II) growth phase. Nucleation is the process in which a cluster, a particle from a new phase, is formed in a single-phase system. In the chemistry of AuNPs synthesis, these clusters are referred to as nuclei.  
The critical radius of these clusters is related to the smallest size at which a particle can remain in solution without redissolving. During the growth stage, more material is deposited on the cluster, increasing its size and transforming it into a nanoparticle. This is caused by the spread of growing species as well as surface processes [31]. At a high pH, the OH\({}^{-}\) ions present in the extract replace the C\({}^{1-}\) ions present in the AuCl\({}^{4-}\), preventing the possible subsequent growth of the nuclei due to repulsions between negatively charged ions of the cell extract and gold ions. This may explain why the bAuNPs synthesized at pH 9.0 maintain a small size and spherical shape [32].
## 3 System Model and Problem Formulation
### Energy Harvesting Model  
In each time block \(t\), the AP can transfer the RF signals at an energy transmit power \(P_{d}(t)\) to charge the mobile device [6]. The mobile device harvests the energy and stores it in the battery after conversion. Therefore, the energy harvested by the \(i\)-th mobile device is:  
\[E_{i,h}(t)=\eta_{i}T_{i,h}(t)P_{d}(t)g_{i,d}(t),\forall i\in K,\forall t\in \mathcal{T} \tag{14}\]  
where \(0<\eta_{i}<1\) represents the energy conversion efficiency.  
We combine the offloading computing energy and the local computing energy, the overall energy consumption at user \(i\) in the \(t\)-th time block is given by:  
\[E_{i,c}(t)=E_{i,loc}(t)+E_{i,off}(t),\forall i\in K,\forall t\in\mathcal{T} \tag{15}\]  
As each user can utilize the harvested energy for local and offloading computing to achieve self-sustainability, the energy consumed cannot exceed the harvested energy in each time block. Hence we have the following constraints:  
\[E_{i,c}(t)\leq E_{i,h}(t),\forall i\in K,\forall t\in\mathcal{T} \tag{16}\]
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 4 Test Scenarios and Validation Results
### Device Installation  
This first test scenario evaluates how effectively and accurately the system incorporates a newly added device into the smart home ecosystem. It specifically verifies the extensibility requirement, showing that the orchestrator can detect, register, and activate additional devices at runtime without significant manual intervention.  
In this trial, a simulated Smart Temperature device was introduced into the environment. To emulate an actual deployment, an MQTT client published a discovery message containing the device's identity, capabilities, and configuration details. Once this message was transmitted, the orchestrator's console logs confirmed proper receipt and parsing, as illustrated in Figure 6, indicating the system's ability to handle heterogeneous device announcements without requiring user-driven configuration steps.  
After processing the discovery message, the orchestrator proceeded to install and start the corresponding OSGi bundle for the Smart Temperature device in the Apache Felix runtime. As shown in Figure 7, the bundle was installed and became Active, signifying that the device's functionality was now available to other components. Simultaneously, the device service was registered in the OSGi Service Registry, and verified through the _scr:list_ command. Figure 8 confirms the presence of the service entry, ensuring that any orchestration logic, whether generated by AI or initiated by the user, can locate and interact with the Smart Temperature bundle.  
Taken together, this discovery and installation process demonstrates the orchestrator's ability to integrate new devices in real-time. It also avoids the need for a system-wide restart and preserves a high degree of modularity since each new device becomes available as an independent service. These results highlight how the proposed architecture addresses the extensibility criterion by managing device additions with minimal disruption to ongoing operations.  
Figure 6: MQTT discovery message received for a new Smart Temperature device, demonstrating real-time device onboarding via event-driven messaging.  
Figure 7: Console output confirming the successful installation and activation of the device bundle (Smart Temperature) into the Apache Felix OSGi environment.
# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
## Appendix B  
For the 2D axis-symmetric FEM model utilized in Sections 2.2 and 3.1, the preform billet was meshed using axisymmetric four-node elements with an element mesh density equal to 3 and average element side length equal to 0.41 mm. This parametric mesh allows a finer mesh on the corners to be obtained in contrast to the coarser mesh in the more uniform parts of the model, as shown in Figure A3a. The top die was modeled as flat and was set to move with a 1 mm/s speed.  
To evaluate the preform shapes for the literature forging case in Section 3.3, a total of approximately 32,000 tetrahedral elements were utilized for the meshing of the preform. The adaptive mesh was employed resulting in the regions of the model presenting small details in having an average mesh size equal to 0.815 mm whereas the uniform regions had an average mesh size equal to 2.004 mm. Both material properties and the plastic behavior of the considered plasticine were acquired from the literature [27]. Thanks to the symmetry of the forging shape, only one-quarter of the model was considered in all the 3D simulations, as shown in Figure A3b. For the above two FEM simulation models, the top and bottom dies were considered as rigid to focus on the metal flow of the preform shape. The friction coefficient was set to 0.2.  
For the definition of the FEM model for the piston head forging, a 3D thermo-mechanical FEM model was implemented in DEFORM 3D by considering the same mesh size utilized in the grinding tool-like shape model. The initial temperature of the preform, the die temperature, and the friction coefficient were equal to 350 \({}^{\circ}\)C, 280 \({}^{\circ}\)C, and 0.3 were those utilized in the real piston production. The AISI-H13 steel and the AA-6061 aluminum alloy were utilized for the modeling of the top and bottom dies and preform, respectively. The temperature-dependent properties and plastic behavior for both materials were acquired from the DEFORM material library and MATLAB(r) (Material Information Link and Database Service), which is largely employed for the material properties acquisition utilized in hot forging processes [32]. To determine the influence of the preform geometries on the die wear, the Archard wear model in Equation (A1) was included in the FEM simulation. The wear coefficients were calculated as 1.44 \(\times\) 10\({}^{-6}\) for \(K\), 1.63 for \(a\), 1.25 for \(b\), and 1.76 for \(c\) based on the lab-scale pin-on-disc experimental results.  
\[W=\int K\frac{P^{a}V^{b}}{H^{c}}dt\] (A1)
## 5 Experiments
### Interpretability Analysis (RQ4)  
To evaluate the interpretability of GLARA, we conduct a case study by visualizing a real recommendation scenario using the Last.FM dataset. As shown in Figure 3, we select a  
Figure 2: (a) Last.FM. (b) Movie-1M.  
specific user \(u_{57}\) and a recommended item \(i_{33}\), to illustrate the preference propagation path from historical interactions to the recommended item.  
Figure 3 presents the multi-hop structure of \(u_{57}\)'s interaction history, including user nodes (blue), item nodes (orange), and knowledge entities (green), along with their semantic clustering relationships. Figure 3b illustrates the semantic relations associated with this case and their corresponding virtual relation mappings, showing how original KG relations such as \(r_{15}\)and \(r_{30}\) are abstracted into the same virtual relation \(v_{0}\). These two original relations are both associated with \"awards\", reflecting a coherent semantic group. Similarly, relations aligned with \(v_{2}\) pertain to \"location\"-related semantics.  
We observe that the model clearly identifies preference propagation paths from historical items to \(i_{33}\) via semantically enriched virtual connections. Furthermore, through the attention mechanism, the model assigns higher weights to items or paths more aligned with the user's preferences. For instance, the attention score assigned to \(i_{33}\) indicates a stronger relevance to user \(u_{57}\), which implies that the model deems it more informative in capturing current interests.  
In Figure 3b, attention scores are displayed for each virtual relation during the embedding fusion process. These scores reflect the degree of attention user \(u_{57}\) pays to different semantic aspects when generating the recommendation. Notably, the model assigns a higher score to virtual relation \(v_{0}\)(awards), suggesting that awards are more influential than location information in this user's preference. This provides a clear semantic explanation for why item \(i_{33}\) is recommended--it matches the user's interest in award-related content.  
In addition, Table 4 shows the exposure frequencies of different virtual relations across the dataset. The distribution indicates that the use of virtual relations balances the exposure of originally sparse relations, helping mitigate the long-tail problem. This further confirms that VRKG not only improves model interpretability but also enhances knowledge coverage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Virtual Relation** & \(\mathbf{V_{0}}\) & \(\mathbf{V_{1}}\) & \(\mathbf{V_{2}}\) \\ \hline \hline Expouse Count & 14,145 & 12,493 & 4398 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shows the number of exposure of the virtual relations.  
Figure 3: Case study from the Last.FM dataset. (**a**) Shows user \(u_{57}\)’s preferences and clustering relationships for interaction items. Best viewed in color. (**b**) Shows the semantics of the relationships in the case study and the corresponding virtual relationships.
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 1 Introduction: Hadronization  
In deep inelastic scattering (DIS) [1], a high-energy incoming electron interacts with a valence quark in a nucleon through a virtual photon. If the energy transfer is sufficient, the quark is ejected from the nucleon. However, due to the principle of color confinement, the quark cannot exist in isolation in its colored state and must undergo hadronization, a transformation into a hadron, to remain color-neutral.  
The experimental data analyzed in this study come from semi-inclusive deep inelastic scattering (SIDIS), where both the scattered electron and the resulting hadrons are measured, supporting an exploration of the hadronization dynamics.  
The Lund String Model [2], developed at Lund University, is a theoretical framework for understanding hadronization. In this model, the quarks within a hadron are connected by a massless string of gluon field lines. As a quark moves apart from its partner, thestring stretches and stores energy. When the energy reaches a critical threshold, the string breaks, generating a new quark-antiquark pair. This model reflects the principles of asymptotic freedom and confinement inherent in quantum chromodynamics (QCD). The Lund String Model is incorporated into the PYTHIA event generator [3], a widely used tool for simulating hadronization in high-energy physics experiments.  
Hadronization in a nuclear environment, as opposed to a vacuum, involves additional interactions due to the presence of partons in the medium. The EG2 experimental data analyzed in this work include nuclear targets such as carbon (C), iron (Fe), and lead (Pb), representing \"cold QCD matter\". As the quark traverses the nuclear medium while hadronizing, it may interact with other partons, leading to energy loss. Understanding this energy loss is the focal point of this study, as it impacts the final state of the hadrons observed and provides insight into hadronization processes within a nuclear medium.
## 3 Solid-Supported Eosin Y
### Functionalized Eosin Y Materials in Photocatalytic Applications
The corresponding products were obtained with good-to-excellentyields, and the supported Eosin Y nanoparticles were removed by means of an external permanent magnet. The recovered nanoparticles where then used in subsequent runs up to eight times without Eosin Y leaching or without the chemical efficiency being affected; only a slight aggregation was observed in TEM imaging. To further explore the versatility of Eosin Y anchored to different materials, [PERSON] and [PERSON] exploited the well-defined dimension of metal-organic framework (MOF) pores as an anchor site for Eosin Y. A Zr(IV) framework was dye-functionalized and employed as a visible light heterogeneous photocatalyst in the functionalization of tertiary amines [36]. The application of MOF as catalysts for the synthesis of fine chemicals is not new [37], but in this application, the organic dye was covalently attached to the MOF by means of free amino groups available on the MOF surface. For this purpose, MOF UiO-66-NH\({}_{2}\) was selected and Eosin Y was anchored via an amide bond formation (Scheme 13). The newly created material EY@UiO-66-NH\({}_{2}\) (**21**) was able to efficiently catalyze the oxidation of tertiary amines to their respective iminium ions, which undergo a subsequent nucleophilic addition by several nucleophiles under the activation of visible green light. The photocatalytic cyanation of tertiary amines--as well as the addition of nitroalkanes, dimethylmalonate and triethyl phosphite to \(N\)-aryl tetrahydrooisquinolines--was accomplished with good results, showing that the catalytic efficiency of the heterogeneous photocatalyst was superior to that observed for unsupported Eosin Y. In addition, a recycling test showed that this new material can be recycled up to 12 times without exhibiting a decrement in terms of reaction yield. **Scheme 12. ** Iron oxide silica core shell nanoparticle NP Eosin Y-**(20)** applied in the photocatalytic heterogeneous oxidative coupling of N-aryl tetrahydroquinolines with different nucleophiles. **Scheme 13. ** Photocatalytic reactions promoted by solid-supported Eosin Y on a zirconium-cluster organic framework. Eosin Y was also covalently anchored to other backbones, such as graphene. Over the last decades, this interesting material has attracted increasing attention due to its outstanding mechanical strength and electronic and optical properties. In this sense, [PERSON] and co-workers used a reduced graphene oxide functionalized with a diethyleneamine linker to covalently tether Eosin Y to the surface, creating a new material of unique photocatalytic properties (Scheme 14) [38]. This new material (**22**) was then applied in the photocatalytic aerobic oxidation of \(\alpha\)-halo carbonyl compounds upon irradiation with a 24 W compact fluorescent lamp, leading to the formation of desired compounds with moderate yields. In addition, the recycling and reusing of the same material up to five times induced no significant drop in terms of its catalytic activity.
# Design of Capacitive Paddy Moisture Sensor Based on Electrical Impedance Spectroscopy Analysis
## 5 Conclusions  
Electrical impedance spectroscopy experiments of paddy variety Wu Yu Jing 3 (_Oryza sativa_ L.) were conducted. The paddy moisture detection model was established and the moisture of the paddy variety Nanjing 9108 (_Oryza sativa_ L) was detected by a designed paddy moisture sensor. The following conclusions can be made from this research.  
Paddy capacitance has a negative relationship with frequency. However, when the frequency is lower than 1 kHz, the capacitance fluctuates with increasing frequency in a low paddy moisture content, especially in low temperatures. The changing rate of capacitance to moisture gradually decreases as the frequency increases. In order to ensure the sensitivity, stability, and reliability of the paddy moisture sensor detection value, 2 kHz was selected as the excitation frequency for paddy moisture detection in this study. Two-thirds of the experimental data points (i.e., 55 experiments points) at 2 kHz were modeled by regression analysis and the R\({}^{2}\) of the paddy moisture detection model was 0.933. The remaining one-third of the experimental data points (i.e., 28 experiments points) were used to evaluate the paddy moisture detection models, and the standard deviation was 1.14%.  
The designed paddy moisture sensor can realize the detection of the paddy moisture content, but the detection error is relatively high, especially in the high moisture content. The standard deviation of detection residual was 1.23%, which is close to the standard deviation of 1.14% in the fitting test. The paddy moisture detection model needs to be optimized in the future to improve the paddy moisture detection accuracy.  
Conceptualization, Q.S., X.W., W.S., Z.L., T.T.; resources, X.W., W.S.; methodology, Q.S.; formal analysis, Q.S.; writing--original draft preparation, Q.S.; writing--review and editing, Q.S.; supervision, X.W.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Synergistic Innovation Center of Jiangsu Modern Agricultural Equipment and Technology (4091600010); and Science and Technology Support (Agriculture) Project of Jiangsu Province (BE2014367).  
This research was conducted in the Central Laboratory of the College of Agricultural Equipment Engineering. Thanks to my supervisor, [PERSON], for inspiration and guidance of my ideas and experiments and proof-reading the article.  
The authors declare no conflict of interest.  
Figure 8: Detection result of designed paddy moisture sensor.