esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulated Results and Error Analysis
### Discussion and Analysis of Reconstruction Simulation Results
#### 3.3.2 Non-Axisymmetric Simulation Distribution
The cause is its high sensitivity to the initial value, which is the same as for the axisymmetric distribution cases. As explained, the MIARO algorithm has advantages  
Figure 14: Effect of noise properties for non-axisymmetric distribution on various algorithms. (**a**) ARE and standard deviation of temperature reconstruction results; (**b**) ARE and standard deviation of concentration reconstruction results. Figure 15: Effect of noise properties of non-axisymmetric models on various algorithms. (**a**) ARMSE and standard deviation of temperature reconstruction results; (**b**) ARMSE and standard deviation of concentration reconstruction results. over the MAART algorithm and the Landweber in this regard. From the simulation results, the conclusion is that the average relative errors of MIARO were reduced by 0.010 and 0.038; the standard deviations were brought down by 60.5% and 51.0% for temperature and concentration reconstructions, respectively. Using the proposed new algorithm, clear dynamic-flow-field boundaries can be obtained, and artifacts are eliminated, so improved measurement accuracy and robustness can be achieved. Its applicability in harsh environments will be conducive to the optimal design and combustion system monitoring and control.
###### Abstract
In addition, the augmentation of two regional navigation satellite systems (RNSSs), the Japanese Quasi-Zenith Satellite System (QZSS) and the Navigation with Indian Constellation (NavIC) for regional users and the LEO enhancing four-GNSS performance are also analyzed based on this method. In other words, we perform modeling to evaluate the performance of next-generation GNSSs, RNSSs, and the LEO augmentation for constellations on the global scale, demonstrating its superiority to the navigation constellations that are currently in operation.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 5 Conclusions and Outlook
### Future Outlook
(7) Cross-Scenario Adaptability: Building a Meta-Learning-Driven Adaptive Recognition Framework  
To enhance the scenario scalability of MFR systems, it is essential to develop dynamic adaptation mechanisms based on meta-learning. Meta-learning and few-shot learning techniques can construct a meta-feature library for modulation formats, enabling rapid adaptation to newly emerging modulation types through gradient update strategies. This addresses the challenge of zero-shot recognition across scenarios. For example, design a hierarchical meta-network (Meta-Net) that learns general modulation features at the base layer and quickly adjusts decision boundaries in the adaptation layer using a few samples from new scenarios. Further, combine this with knowledge distillation techniques to compress multi-scenario expert models into a lightweight unified model, effectively balancing model complexity and cross-scenario performance. Future VLC-MFR research needs to shift away from the traditional focus on optimizing a single performance metric and instead pursue a holistic optimal solution that integrates algorithms, data, hardware, and scenarios. Through interdisciplinary innovation and the construction of a standardized ecosystem, it is possible to bridge the gap between laboratory settings and real-world applications, providing intelligent modulation recognition solutions that are highly reliable and low-latency for 6G integrated optical and wireless communications.
## 3 Materials and Methods
### General Procedure for the Enzymatic Esterification of FFA and Fusel Oil in a Solvent-Free System  
The direct esterification of purified FFA from the enzymatic hydrolysis of USCO and dehydrated fusel oil was conducted in closed or open glass reactors (capacity of 100 mL with a height to diameter ratio of 2) in the absence or presence of molecular sieves (5% mass per mass of reaction mixture--5% m.m\({}^{-1}\)) containing 8 g of starting materials composed of stoichiometric FFA: fused oil molar ratio--1:1 (composed of 1.65 g or 23 mmol of fusel oil + 6.35 g or 23 mmol of FFA). The reaction systems were immersed in a temperature-controlled thermostatic water-bath at 40 \(\pm\) 1 \({}^{\circ}\)C under continuous mechanical stirring at 240 rpm containing a fixed biocatalyst concentration of 5 mg of protein per gram of reaction mixture (corresponding to 0.166 g of immobilized lipase or 0.14 g of soluble lipase Eversa(r) Transform 2.0 per gram of reaction mixture). Samples from the reaction mixtures (50 \(\mu\)L) were periodically withdrawn, diluted in 10 mL of hydrous ethanol solution (92.5% m.m\({}^{-1}\)), and titrated with a NaOH solution (40 mmol\(\cdot\)L\({}^{-1}\)) using phenolphthalein as an indicator. The FFA conversion percentage (Y\(\sim\)%) was determined as shown in Equation (2) [30; 62]. Ester production was performed with two replications, and experimental data are represented as mean \(\pm\) deviations. In this set of experiments, control assays were prepared with PSty-DVB beads, and no FFA consumption was detected under these experimental conditions, thereby confirming that all of the consumed FFA in the reaction was converted into monoalkyl esters.  
\[Y=\left(\frac{FFA_{0}-FFA_{t}}{FFA_{0}}\right)\times 100 \tag{2}\]  
where \(FFA_{0}\) and \(FFA_{t}\) are the initial and residual FFA concentration after a given reaction time \(t\) (mol\(\cdot\)L\({}^{-1}\)), respectively.  
Productivity (\(\mu\)mol.min\({}^{-1}\)-mg\({}_{\text{prot}}\)\({}^{-1}\)) was determined according to rearranged equation proposed by [44]--Equation (3):  
\[P=\frac{FFA_{0}\times\text{Y}}{t_{e}\times m_{prot.}} \tag{3}\]  
where \(FFA_{0}\) is the initial number of moles of FFA in the reaction mixture (23 mmol or 23,000 \(\mu\)mol), \(Y\) is average FFA conversion percentage at equilibrium (%), \(t_{e}\) is reaction time at equilibrium (min), and \(m_{prot}\) is the protein amount in the reaction mixture (mg).
## 4 Results and Discussion
### Comparison between S-GBS and W-GBS  
For S-GBS and W-GBS, a comparison is made by computing the 3-by-3 boreholes and the boundary conditions are given by Equations (15) and (16). In this scenario, the charging process is found to be different between the two systems, as shown during the spring and winter seasons in Figure 10. The contrast is particularly significant in the spring, where the near-borehole temperature in W-GBS is around 12% higher than the one in S-GBS. This is caused by the fact that the prescribed time-dependent boundary in W-GBS maintains at a constant heat transfer rate, rather than sinusoidal pattern, throughout the charging period. Although the predefined boundaries are taken in a seasonal resolution (S-GBS at a higher time resolution can have daytime/nighttime fluctuations in colder seasons [33; 41]), this comparison still views the energy supply and demand in the two systems. As a result, the  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Value** & **Unit** \\ \hline Inner radius, \(a\) & 0.0889 & m \\ Outer radius, \(b\) & 50 & m \\ Specific heat (ground), \(c_{p,ground}\) & 1100 & J/(kg\(\cdot\)K) \\ Specific heat (water), \(c_{p,water}\) & 4182 & J/(kg\(\cdot\)K) \\ Thermal conductivity (ground), \(k_{ground}\) & 2 & W/(m\(\cdot\)K) \\ Mass density (ground), \(p_{ground}\) & 2490 & kg/m\({}^{3}\) \\ Thermal diffusivity (ground), \(a_{ground}\) & \(7.3019\times 10^{-7}\) & m\({}^{2}\)/s \\ Undisturbed temperature, \(T_{0}\) & 463.01 & K \\ \hline \end{tabular}
\end{table}
Table 2: Prescribed geometrical and thermophysical parameters of the ground and working fluid (water). The listed values of parameters are consistent with the work [40].  
Figure 9: Comparisons between the analytical model and the field-scale experimental data from [40] regarding the temperature profile at a depth of 400 m.  
W-GBS can generally require more energy input than the S-GBS in terms of the charging process.  
As for the discharging time of the year, both systems demonstrate similar behaviors. A slight temperature distribution around the boreholes is observed during summer and fall in Figure 10, which can be explained by the energy residue after the charging process for the W-GBS. Such energy residue is accumulated near each borehole, and it takes time to be distributed to surroundings even after the charging period.
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 5 Experiment Results and Analysis
### Results on Simulation Datasets
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Runner** & **Kobe** & **Aerial** & **Crash** & **Traffic** & **Drop** & **Average** \\ \hline GAP-TV [13] & 28.46 0.898 & 26.45 0.848 & 24.82 0.838 & 25.05 0.828 & 20.89 0.714 & 33.79 0.963 & 26.58 0.848 \\ GMM-TP [11] & 29.56 0.899 & 25.13 0.788 & 26.26 0.800 & 25.71 0.868 & 25.08 0.821 & 34.34 0.964 & 27.68 0.857 \\ MMLE-MFA [9] & 29.37 0.895 & 24.63 0.756 & 26.55 0.848 & 26.11 0.860 & 22.67 0.728 & 35.88 0.971 & 27.54 0.843 \\ MMLE-GMM [9] & 31.98 0.927 & 27.34 0.829 & 28.01 0.882 & 26.93 0.830 & 25.69 0.825 & 40.28 0.988 & 30.04 0.880 \\ PnP-FFDNet [17] & 32.88 0.988 & 30.47 **0.925** & 24.02 0.814 & 24.32 0.838 & 24.08 0.833 & 40.87 0.988 & 29.44 0.889 \\ E2E-CNN [18] & 33.33 0.971 & 28.59 0.761 & 27.29 0.873 & 26.29 0.874 & 23.72 0.829 & 35.97 0.971 & 29.20 0.876 \\ Saideni’s [30] & 37.67 & **31.25** & **29.21** & 25.40 & **28.15** & 35.40 & 31.18 \\ RE\({}^{2}\)-Net [19] & 35.46 0.962 & 30.05 0.917 & 28.96 0.905 & **27.94** 0.917 & 26.62 0.898 & 42.11 0.991 & 31.86 0.935 \\ \hline Ours & **38.52 0.990** & 29.11 0.881 & 28.95 **0.942** & 27.47 **0.931** & 28.13 **0.951** & **42.35 0.996** & **32.42 0.949** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The PSNR in dB (left entry) and SSIM (right entry) metrics of different methods on six simulation datasets. The optimal results are shown in **bold**, and the second-best results are underlined. Figure 5: The reconstruction results of two stages in our network: (**a**) Overall structure reconstruction; (**b**) Incremental details reconstruction; (**c**) Final reconstruction. proposed network can effectively reconstruct the contour structures and detail information of the objects, and it has the best visual quality.
## 4 Experiments and Discussion  
### Experimental Settings  
To verify the effectiveness and efficiency of our work, four representative crack damage detection methods are compared with the proposed model. Specifically, these compared methods are the Otsu based crack- [8], the Canny based- [4], the SVM based- [11], and the DL based-crack detectors [26]. Among them, the first two models belong to the global analysis based crack detection model, and the latter two are categorized as the crack detection method by local analysis. What we should note, here, is that these compared crack damage detecting algorithms were programmed by ourselves, based on their original methods.  
For fairness, the presented crack detector, and all the compared ones, are programmed on the same computer (Win7 x64 system, Matlab2017b, Intel 2.40 GHz CPU, 64 GB RAM, GTX960 GPU). As for the local analysis based methods (including the presented method and the latter two compared ones [11; 26]), the same training samples and testing concrete image data were used.
## 3 Results and Discussion
### Cellulose, Hemicellulose and Lignin  
As validated by the crude fibre and the neutral detergent fibre method, fibre is the most abundant component in crude olive pomace and pulp fraction. Polymerised sugars compose fibre into cellulose and hemicellulose structures, which can be quantified by HPLC after acid hydrolysis. The cellulose and hemicellulose content of crude olive pomace samples were not significantly different (\(p<0.05\)). Hemicellulose (as the sum of xylose, arabinose, galactose and mannose) was a little more abundant in the pulp fraction (12% DW) than cellulose (10-11% DW), with xylose (8% DW) being the main hemicellulose sugar, followed by galactose (2%) (Table 1). The pulp fraction richness in xylose makes its potential application in the food industry even more feasible. Xylans have unique properties, like the capacity of interaction with bile and cholesterol, activation of colon peristalsis and important technological properties [41]. Taking into consideration that the olive endocarp is mostly lignified [3], the lignin content is high in pulp fraction (42-46%). Lignin is a macromolecule inert for colonic fermentation but can be able to release when linked to phenolic compounds with low molecular weight (bound phenolics) (e.g., vanillin and caffe acid). These phenolic compounds can play an essential role in gut health due to their antioxidant properties, antimicrobial and immunological activity [42; 43].
# On the Use of Deep Learning for Video Classification
## 1 Introduction  
The task of automatically classifying videos has become very successful recently. Particularly, the subject has drawn increased interest since deep learning models became an effective method for automatically classifying videos. The importance of the accurate video classification task can be realized by the large amount of video data available online. People around the world generate and consume a huge amount of video content. Currently, on YouTube only, over 1 billion hours of video are being watched by different people every single day. In recognition to the importance of the video classification task, a combined effort is being made by researchers for proposing an accurate video classification framework. Companies such as Google AI are investing in different competitions to solve the challenging problem under constrained conditions. To further advance the progress of the automatic video classification task, Google AI has released a public dataset called YouTube-8M with millions of video features and more than 3700 labels. All these efforts being made demonstrate the need for a powerful video classification model.  
An artificial neural network (ANN) is an algorithm based on interconnected nodes to recognize the relationships in a set of data. Algorithms based on ANNs have shown a greatsuccess in modeling both the linesar and the non-linear relationships in the underlying data. Due to the huge success rate of these algorithms, they are extensively being used for different real-time applications [1; 2; 3; 4]. Moreover, with an increase in the availability of huge datasets, the deep learning models have specifically shown a significant improvement in the classification of videos. This paper reviews studies based on deep learning approaches for video classification.
## 5 Advantages, Limitations, and Future Research Directions of the Model
### Model Limitations and Future Research Directions
#### 5.2.5 Influence of Image Size Generated by Stable_Diffusion Model on Image Quality  
The size of the generated image is one of the hyperparameters that can be set in the image generation model. This hyperparameter determines the quality of the generated image by affecting the size of the generated image. Figure 7 shows the images of different sizes that we generated using the same text prompt.  
From Figure 7, we can see that as the size of the generated image increases, the image is closer to reality, the details increase, and the image quality also improves. Looking at Figure 7b,c, we can see that the image quality of 768 \(\times\) 512 is not much improved compared with 512 \(\times\) 512, and both are very close to the actual image. Therefore, from the perspective of balancing image quality and computation, we choose to generate 512 \(\times\) 512 images to complete our experiment, which is also the size recommended by the authors. In order to further prove our conclusion, we generated a group of 256 \(\times\) 256 images using the emotion datasets and input them into the CLIP model for image classification. Compared with another group of 512 \(\times\) 512 images, the results are shown in Table 8: Size 256 and Size 512 Compared with Size 256, Size 512 has nearly 7% improvement, indicating that image size dramatically impacts model classification accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Accuracy** \\ \hline Iteration50 & 34.34\% \\ Iteration100 & 34.62\% \\ Size256 & 27.66\% \\ Size512 & 34.34\% \\ NSFW & 31.46\% \\ No\_NSFW & 34.34\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The CLIP model classifies the images generated by the stable_diffusion models with different settings.  
Figure 7: Pictures of different sizes generated according to “A strong lion walking slowly in the forest”. (**a**) generates an image with a resolution of 256 \(\times\) 256, (**b**) with 512 \(\times\) 512, and (**c**) with 512 \(\times\) 768.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 3 Grid Operations
### Grid Monitoring and Control in Operation
#### 3.1.2 Grid Control  
With the increasing integration of DERs and digital advancements, grid control is becoming more crucial in power distribution networks. The concept of grid-edge control is reported upon in [181], it proposes integrating traditional grid systems with the autonomous control of DERs to effectively address integration challenges.  
This approach encompasses various control architectures and operational layers, including hierarchical coordinated control strategies for PV inverters, as detailed in [119]. Such multi-layer mechanisms enhance voltage quality and reactive power management, illustrating the importance of SGs and digital tools like smart meters and vehicle-to-grid systems in grid control and operation and improving data acquisition and connectivity. Furthermore, Ref. [119] stresses the role of digital metering, communication technologies, and cloud computing in real-time grid monitoring and management. An optimized power control strategy for grid-connected PV inverters is introduced in [120], showcasing efficient inverter design, while [182] explores real-time electrical energy monitoring in distribution networks using modern metering devices and automated controls.  
These discussions highlight the critical role of advanced control strategies and digital technologies in enhancing grid efficiency, reliability, and renewable integration, propelling power distribution towards more sophisticated, reliable, and efficient solutions.
## 5 Conclusions  
In conclusion, the state-of-the-art scheme that applied K-means cluster algorithm in geometric shaping based on iterative polar modulation has been proposed, which could be employed in OINs. Even if the transmission distance is sufficient, the proposed scheme could perform better when K-means cluster algorithm in IPM is used, which opens up new applications for it in high-speed, long-distance optical fiber communication systems. We establish a 60 Gbps coherent optical transmission simulation system to verify the performance of our proposal. In terms of mutual information, whether in long/short-distance transmission, IPM could provide a large capacity for the transmission system. For the BER performance, the scheme of IPM-16 with K-means achieves 0.9 dB and 1.7 dB, respectively, compared with IPM-16 without K-means and QAM-16; the scheme of IPM-64 with K-means achieves 0.3 dB and 1.1 dB, respectively, compared with IPM without K-means and QAM 64; the scheme of IPM-256 achieves 0.4 dB, and 0.8 dB, respectively, compared with IPM 256 without K-means and QAM-256. The scheme of IPM with K-means is superior to the other two schemes, which behave as strong robustness to NLPN, according to the aforementioned results, whether in terms of BER or channel capacity. We consider that our proposed scheme will provide a new idea for developing transmission technology of inter-data center optical interconnection networks.  
Conceptualization, X.S. and Q.Z.; methodology, X.S. and Q.Z.; software, X.S. and Z.J.; validation, X.S., R.G. and D.G.; formal analysis, X.S. and Z.J.; investigation, D.G.; resources, Q.Z.; data curation, X.S. and Q.Z.; writing--original draft preparation, X.S.; writing--review and editing, X.S.; visualization, R.G.; supervision, Q.Z. and D.G.; project administration, X.X.; funding acquisition, Q.Z. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Project of China, under Grant 2019 YFB1803701, in part by the National Natural Science Foundation of China under Grant 61835002, 61727817.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors declare no conflict of interest.
# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 2 Related Work
### Community Detection  
When dealing with meso-scale topologies, it turns out to be necessary to define a strategy to partition the overall interaction network in homogeneous clusters. By doing so, we can better identify different cohorts of individuals sharing a common set of features.  
In complex networks, a widely popular way to address node clustering relies on community detection (CD) approaches. However, there is no single, standard definition of what a community should look like. Indeed, several algorithms have been proposed so far to efficiently partition graphs into connected clusters, often maximizing specifically tailored quality functions [45]. As regards echo chamber-related tasks, [PERSON] et al. [22], to discover the political structure of defined _retweet_ and _mention_ networks, rely on the _Label Propagation_ algorithm, i.e., assigning an arbitrary cluster membership to each node and then updating it according to the label shared by most of its neighbors. In [17], authors exploit the _Internal-Density_ CD approach, partitioning the Facebook pages graph using the Modularity function. Further, [PERSON] et al. [46] look for highly segregated communities of users in the Italian vaccination debate via the Infomap algorithm. The aforementioned works model the issue mainly identifying an accurate partition of nodes from a topological point of view, regardless of nodes homophily.  
From our perspective, echo chamber's members carry valuable semantic information (i.e., their opinion on the controversy) that has to be taken into account when computing cohesive communities. For such a reason, we will leverage a specific instance of the CD problem in the present work, namely _Labeled Community Detection_ (LCD). Formally, given a labeled graph \(\mathcal{G}\), LCD algorithms aim to find a node partition \(C=\{c_{1},\dots,c_{n}\}\) of \(\mathcal{G}\) that maximizes both topological clustering criteria and label homophily within each community.