esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optimization Method
\begin{table}
\begin{tabular}{c c c c c} \hline  & **HTL** & **LTL** & **HTL** & **LTL** \\ \cline{2-5}  & Cyclohexane & R245 fa & Toluene & R236 fa \\ \hline evaporation temperature/K & 480.3 & 349.0 & 440.3 & 378.9 \\ net power output/kW (literature [29]) & 64.0 & 47.2 & 32.6 & 64.6 \\ net power output/kW (present work) & 64.01 & 46.58 & 32.61 & 63.59 \\ relative error/\% & 0.0 & 1.3 & 0.0 & 1.6 \\ \hline  & **HTL** & & **LTL** & \\ \cline{2-5}  & Water & R123 & R236 fa & R245 fa \\ \hline evaporation temperature/K & 480.0 & 347.5 & 369.0 & 352.3 \\ net power output/kW (literature [30]) & 54.5 & 46.0 & 60.6 & 49.4 \\ net power output/kW (present work) & 54.52 & 45.21 & 59.32 & 48.53 \\ relative error/\% & 0.0 & 1.7 & 2.1 & 1.8 \\ \hline \end{tabular} Relative error = \(\mid\)data of present work–data of literatures \(\mid\)/data of literatures \(\times\) 100\%  
\end{table} 
Table 3: Data comparison between the present work and the literature. terminated, and the results are shown [6; 44]. A flowchart of the genetic algorithm is demonstrated in Figure 4.  
The genetic algorithm has been extensively used for optimization [6; 37; 45; 46; 47; 48; 49; 50; 51]. [PERSON] et al. [6] selected a net power output per unit heat transfer area and exergy destruction rate as the objective functions and optimized evaporation pressure, superheat degree, and condensation temperature for ORC using the genetic algorithm. [PERSON] et al. [46] set exergy efficiency as the objective function and performed turbine inlet pressure, turbine inlet temperature, and the fractions of the flow rate of the regenerative optimization for an ORC through the genetic algorithm. [PERSON] et al. [47] selected a ratio of the net power output to the total heat transfer area as the performance evaluation criterion and optimized key thermodynamic design parameters for the ORC through the genetic algorithm. Thus, the present work used the genetic algorithm in MATLAB for optimization, and the parameter setting of the genetic algorithm is presented in Table 5. The fluid properties are calculated using Refprop 9.0.
## 2 Blockchain-Based Data Storage Methods: On-Chain, Off-Chain, and Hybrid Approaches
### Off-Chain Data Storage Methods
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Data Storage Environment** & **Storage Mechanism** & **Description** & **Advantages** & **Disadvantages** & **Examples** \\ \hline \multirow{6}{*}{Centralized Data Storage Systems} & Cloud storage systems & Data are stored in central data centers & Fast access, high central data centers & Dependency on central authority, Reliability and scalability. Easy integration, wide range of services & Dependency on central authority, high cost. Data security depends on an external provider. Risk of failure due to centralized structure. \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \cline{1-1} \cline{2-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The table classifies off-chain data storage methods in blockchain technologies, distinguishing between centralized and decentralized (P2P) systems. It outlines their mechanisms, advantages, disadvantages, and real-world applications, addressing scalability, cost, and accessibility challenges. Figure 3: Off-chain data storage methods. Off-chain storage keeps data outside the blockchain while ensuring security and verifiability through cryptographic techniques. Methods include decentralized networks like IPFS, Arweave, and Filecoin, as well as blockchain-integrated cloud solutions. Transactions reference off-chain data via cryptographic hashes or smart contracts, preserving integrity and optimizing efficiency.
## 4 Conclusions  
The effects of different scanning parameters have been investigated in order to determine their optimal settings. The following conclusions can be drawn based on this study:  
* The resolution and shutter time, as well as the interactions between resolution and laser power, and the laser power and shutter time of the laser scanner, had a significant effect on the accuracy.
* The scanning resolution had a significant effect on the scanning time and the number of triangles in the case of the handheld laser scanner.
* The scanning parameters of the white light scanner did not have a significant effect on the accuracy. However, the resolution had a significant effect on the scanning time and the number of triangles.
* The optimized scanning parameters for the two scanners were identified as follows: Laser scanner: laser power = 50%, shutter time = 6 ms, resolution = 3 mm White light scanner: shutter time = 3 ms, resolution = 2 mm.
* The laser scanner demonstrated better accuracy than the white light scanner, whereas the white light scanner demonstrated better acquisition speed.
* The white light scanner is comparatively lighter, possesses higher portability, requires less setup time, and is easier to use than the laser scanner. It also exhibits a shorter processing time (owing to lesser triangles). However, the laser scanner provided better accuracy.  
The authors aim to study each scanner thoroughly in order to improve the quality of the acquired data. The key aspects such as holes, defects in borders, false points, etc., will be considered to refine the acquired data and hence reduce the processing time. The effect of various object materials such as transparent, fluorescent, shiny, glossy, etc., will also be investigated in the future work.  
The authors are grateful to the Deanship of Scientific Research, King Saud University for funding through the Vice Deanship of Scientific Research Chairs.  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] and [PERSON] designed and conducted the experiments, wrote the manuscript as well as reviewed it. [PERSON] supervised the work.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### MTT Assay for Cytotoxicity of Cold Plasma and PTS Treatment  
After 12 h, 24 h, and 48 h of treatment of the whole blood of 4 CLL patients and 4 sex- and age-matched healthy control subjects with cold plasma or PTS, the metabolic activity of WBCs was measured using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide; Sigma, St. Louis, Missouri, USA) assay according to the manufacturer's protocol. Briefly, the samples were centrifuged and the blood plasma was isolated. Next, RBCs were removed using lysis buffer added to the pellet, and WBCs were isolated and cultured in isolated blood plasma. MTT solution (at a final concentration of 5 mg/mL in PBS) was added to each well. Following incubation for 3 h at standard culture conditions, the supernatant was removed, and 150 \(\mu\)L of DMSO was added to each well. The absorbance was measured using a BioTek microplate reader (BioTek, Winooski, Vermont, USA) at 570 nm.
###### Abstract  
Gross primary productivity (GPP) quantifies the rate at which plants convert atmospheric carbon dioxide into organic matter through photosynthesis, playing a vital role in the terrestrial carbon cycle. Machine learning (ML) techniques excel in handling spatiotemporally complex data, facilitating accurate spatial-scale inversion of forest GPP by integrating limited ground flux measurements with Remote Sensing (RS) observations. Enhancing ML algorithm performance for precise GPP estimation is a key research focus. This study introduces the Random Grid Search Algorithm (RGSA) for hyperparameters tuning to improve Random Forest (RF) and eXtreme Gradient Boosting (XGB) models across four major forest regions in China. Model optimization progressed through three stages: the Unoptimized (UO) XGB model achieved \(R^{2}\) = 0.77 and \(RMSE\) = 1.42 g Cm\({}^{-2}\) d\({}^{-1}\); the Hyperparameter Optimized (HO) XGB model using RGSA improved performance by 5.19% in \(R^{2}\) (0.81) and reduced \(RMSE\) by 9.15% (1.29 g Cm\({}^{-2}\) d\({}^{-1}\)); the Hyperparameter and Variable Combination Optimized (HVCO) XGB model with selected variables (LAI, Temp, NR, VPD, and NDVI) further enhanced \(R^{2}\) to 0.83 and decreased \(RMSE\) to 1.23 g Cm\({}^{-2}\) d\({}^{-1}\). The optimized GPP estimates exhibited high spatial consistency with existing high-quality products like GOSIF GPP, GLASS GPP, and FLUXCOM GPP, validating the model's reliability and effectiveness. This research provides crucial insights for improving GPP estimation accuracy and optimizing ML methodologies for forest ecosystems in China.  
g 2025 16 518
# Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module
## 3 Disease Classification Models of Xception Fused by the SE Module
### SE_Xception Network Structure  
Conventional convolution considers all spatial information and channel information together. The depth-wise separable convolution used in Xception separates spatial convolution by channel and each channel with the same priority, so that the relationship between different channels is not utilized effectively. The SE module introduces attentions to different channels. Weighting on channels from the previous layer has the potential to improve depth-wise separable convolution. This study introduces the Squeeze-and-Excitation module into Xception to achieve channel weighting. Instead of simply adding SE modules to the network as separate modules, this paper proposes a network that deeply integrates the Xception network and the SE module, namely SE_Xception. In SE_Xception, depth-wise separable convolutions in Xception were reconstructed into depth-wise spatial convolution and point-wise convolution, then the SE module was inserted between depth-wise convolution and point-wise convolution. The feature channels from the low level were directly weighted and then the point-wise convolution was carried out in order to fully model and utilize the dependency relationship between feature channels. The fusion method, namely SE-DEEP block, is shown in Figure 3e. The author of SENet proposed four blocks to fuse the SE module into CNNs, and their experiments proved that standard SE block, SE-PRE module, and SE-Identity block had basically the same degree of improvement on the performance, while those of the SE-POST module were not as good as the aforementioned three schemes.  
To verify the effectiveness of the SE-DEEP block proposed in this paper, SE_Xception2 network was designed by referring to the network used in [PERSON]'s paper [36] based on the fusion of standard SE block with Xception for animal species recognition. In SE_Xception, the SE-DEEP block is inserted into the shallow and middle layers of the Xception network, as shown in Figure 4, where SE_SeparableConv in the figure is using the SE-DEEP block.  
Figure 3: SE-DEEP block: SE model deeply fused to depth-wise separable convolution block: (**a**) Standard SE block; (**b**) SE-PRE block; (**c**) SE-POST block; (**d**) SE-Identity block; (**e**) SE-DEEP block(proposed block).
## 3 Numerical Modelling
### Mesh Dependence Check and Validation  
The numerical model adopted different meshes in different simulation regions for the purposes of appropriately reducing the mesh number and increasing the computational efficiency. Structured mesh was used for the reservoir zone with a large size, while unstructured mesh with a relatively small size was used for the gate shaft to realize the mesh deformation and reconstruction. The other domains employed structured mesh and its size was between the two. Three different meshes were presented and mesh independence check was conducted. The simulation condition for the independence check was as follows: upstream water depth of 100 m and service gate height of 1.0 m. The panel pressure of the service gate was 38,288.6 kN in physical model test. The simulation results are shown in Table 2. From Table 2, as the mesh size decreased, the numerical simulation results showed little difference. The relative error between the simulation and the experiment was relatively small, indicating that the mesh size had no remarkable influence on the simulation result. In order to better adapt the mesh deformation of the gate structure boundary, a third mesh was chosen to carry out numerical simulations.  
Figure 3: Schematic diagram of the monitoring section.
## 4 Production, Applications and Toxicity  
Vertically aligned nanosheets of WSe\({}_{2}\) were synthesized and characterized by [PERSON] et al. [6]. Using atmospheric pressure CVD for the selenization of W thin films on a sapphire substrate, they developed an efficient and scalable growth technique. The nanosheets produced were demonstrated to be highly crystalline and well oriented, while mixing a minor phase with the most stable 2H polymorph.  
Nanowires made of Co, Fe, Ni, Py, and their alloys have interesting magnetic properties reviewed by [PERSON] [7]. As illustrated in his paper, complex and reproducible structures were obtained by electrodeposition techniques, including core-shell nanowires, multilayered nanowires, and 3D networks of interconnected nanowires. Applications of magnetic nanowires are presented, among which there are magnetotransport, magneto-thermoelectric effects, and spin valves. Interestingly enough, some structures present tunable magnetic properties.  
[PERSON] et al. [8] produced poly (L-lactic acid) (PLLA) nanofibers by electrospinning for energy harvesting applications. Using a battery of characterization techniques, they discovered a considerable improvement in the mechanical and piezoelectric properties of nanofibers annealed above the glass temperature that ensued from a much better crystallinity of the post-annealed samples.  
In their contribution, [PERSON] et al. [9] describe how the inclusion of different kinds of nanoparticles in a polymer can be exploited for producing nanocomposites presenting a high absorbance for microwaves. Improved electromagnetic shielding effectiveness has been achieved by combining sp\({}^{2}\) carbon nanostructures and magnetic nanoparticles, especially when resorting to a hierarchical organization of these nano-inclusions.  
Having used in vivo and in vitro approaches for many years, [PERSON] et al. [10] have been conducting research on the toxicity of carbon nanotubes for many years. They have demonstrated that the toxicity of nanotubes strongly depends on their size and shape. The mechanisms responsible for the development of a cancer affecting the mesothelium (malign mesothelioma), which up to now has been mostly due to asbestos exposure, have been investigated in animal models using needle-like nanotubes. The authors have discovered that carbon nanotubes, thanks to the broad range of their physico-chemical characteristics, offer several advantages over asbestos for the understanding and the therapy of mesothelioma.
# Mesoscale Modelling of the Mechanical Behavior of Metaconcretes
## 1 Introduction
[45] further explored this approach by numerically investigating dual-resonant aggregates in metaconcrete, revealing enhanced wave attenuation and tunable bandgap properties. In parallel, studies employing the Discrete Element Method (DEM) have also contributed essential insights. [PERSON] and [PERSON] [46] investigated the forced vibration-damping characteristics of metaconcrete at the unit-cell scale, optimizing aggregate selection to enhance vibrational mitigation. Additionally, [PERSON] and [PERSON] [47] examined nonlinear vibration behaviors of single aggregate systems within metaconcretes, demonstrating how resonator design can significantly impact dynamic performance. Notwithstanding these advances, existing numerical studies typically assume regularly arranged resonators, whereas practical castings are expected to exhibit a random spatial distribution of inclusions within the cementitious matrix. Furthermore, most investigations have primarily focused on predicting eigenfrequencies and associated band gaps as functions of resonator type and volume fraction, leaving the impact of resonator incorporation on the quasi-static compressive strength of metaconcretes largely unexplored. This study explicitly addresses this gap by investigating the mechanical response of metaconcrete under quasi-static loading conditions with randomly distributed resonators, thus establishing the novelty and relevance of the research approach. To this end, the present study introduces a mesoscale numerical model (the first of its kind dedicated to capturing the mechanical behavior of metaconcretes) that explicitly accounts for heterogeneous material distributions and interfacial mechanics. Following validation of the model against experimental data sets from the literature [36], the research team investigated the effects of varying resonator size, content, and distribution on static compressive performance. The study offers critical insights into these factors, laying the groundwork for the rational design of multifunctional metaconcretes that can mitigate dynamic hazards while maintaining a reasonable load-bearing capacity. This paper aims to shed light on the proper empirical studies conducted using a mesoscale numerical model to simulate the compressive response of MCs, organized into the following seven main sections. The first section provides a brief explanation of the reason behind the approach. In the second section, the authors describe the research design using a description of the method. The third section details the validation of the specific model. The fourth section presents the main findings of the empirical approach. The fifth section explains how to analyze the results to gain further information on the research issue. The sixth section summarizes the conclusions based on the results. Finally, the seventh section deals with research limitations and future directions.
# Evaluation of the GPS Precise Orbit and Clock Corrections from MADOCA Real-Time Products
## 3 Products Availability  
To investigate the availability of MADOCA products, the MADOCA recorded real-time products and the IGS final products were collected from 1 November 2018 to 31 January 2019. The three-month statistic results are presented in Figure 2. The red dots represent the satellite unavailability only in MADOCA real-time products, and the blue dots denote the satellite unavailability in both MADOCA and IGS final products.  
Figure 2: Unavailability of MADOCA real-time products for GPS satellites from 1 November 2018 to 31 January 2019.  
It should be noted that in early December 2018, there was a serious absence of MADOCA real-time products which was officially explained by JAXA as an unplanned stop providing MADOCA real-time products [17]. Furthermore, the G04 was always on unhealthy status in the period because no MADOCA and IGS final products were available for this satellite at all. In addition, the availability of the G18 was also not good, given the low-quality MADOCA real-time products. In the following availability and accuracy assessment, these two satellites were excluded from the statistics. Statistics show that the epoch availability of MADOCA real-time products during the three months was about 92.6% and the available number of satellites at almost all of available epochs can reach 30, which makes it possible to determine a high quality positioning geometry in real-time PPP applications.
## 2 Materials  
### Mechanized Mining Processes  
In mines where mechanized mining is extensively applied, the medium and deep hole mining method is predominantly adopted [27], as illustrated in Figure 1. Ore body mining constitutes a multi-process task, primarily encompassing rock drilling, charging, blasting, ventilation, support, ore drawing, and other operations. For a mining area, during the initial design phase, geological engineers can estimate the Designed Ore Mining Quantity (DOMQ/t). The production capacity (PC/(t/d)) is employed as a metric to evaluate the mining efficiency and production capacity of the stope. The key factors influencing the stope's mining efficiency and production capacity at different stages are outlined below:  
1. During the drilling stage, workers are required to operate the drilling rig to drill blast holes in accordance with the mining design. Given that the blast hole depth in medium and deep hole mining typically exceeds 15 m, the primary factor influencing mining efficiency at this stage is the drilling efficiency (DE, m/d).
2. In the charging stage, engineers utilize the charging jumbo to charge and detonate explosives within the blast holes. The charging and blasting efficiency (CBE, h/d) serves as the main influencing factor during this phase.
3. Following the blasting process, a significant amount of fumes is generated in the blasting area, necessitating ventilation for dispersion. In mines with an inefficient ventilation system, the ventilation time (VT, h/d) may exceed 12 h, thereby substantially impacting production efficiency.
4. In the support stage, due to rock mass damage caused by blasting, support operations become critical. If the surrounding rock is unstable, additional time will be allocated for support work. The support time (ST, h/d) constitutes the primary influencing factor during this stage.  
In the ore drawing stage, loaders are employed to shovel, transport, and unload the blasted ore. The ore removal efficiency (ORE, t/d) represents the main factor affecting mining efficiency at this stage.
* It is worth noting that in mechanized mines, the equipment maintenance time (EMT, d) of mining equipment cannot be overlooked, as it also plays a significant role in influencing production efficiency [28].
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 2 Network Architecture and Project's Main Achievements
### Achievements
#### 2.2.4 The SDN/NFV Domain  
SDN/NFV is one of the technology domains investigated in ALLIANCE. For this domain, we have proposed an overall solution where an SDN/NFV orchestrator controls an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure. In particular, three main achievements can be highlighted:  
1. The Non-Orthogonal Multiple Access (NOMA) modulation with multiband Carrierless Amplitude and Phase (NOMA-CAP) modulation format has been experimentally validated for the Passive Optical Network (PON) infrastructure using Radio-over-Fibre (RoF) technology and for split-enabled optical interconnects.  
To enabling an efficient Spatial Division Multiplexing (SDM), MultiCore Fibre (MCF)-based optical infrastructure have been deeply analysed with special emphasis on the optimal crosstalk level that optimises the aggregated capacity of MCFs.
3. Finally, we have designed and experimentally validated an AI-empowered control/management and orchestration framework allowing the automatic and autonomous deliver of network slices customised to the requirements of the vertical services and applications.