esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Supervised Learning as Statistical Analysis for RVS
### Extra Tree  
Various generic randomization methodology like Bagging has been proposed [74; 77; 78], which have been proven for better accuracy when compared to other supervised learning classifiers like SVM. Ensemble methods, in combination with trees, provided low computational cost and high computational efficiency. Several studies focused on specific randomization techniques for trees based on direct randomization methods for growing trees [71].  
ET or Extremely Randomised Trees is also an ensemble learning approach and fundamentally based on Random Forest. ET can be implemented as ExtraTreeRegressor as well as ExtraTreeClassifier. Both methods hold similar arguments and operate similarly, except that ExtraTreeRegressor relies on predictions made by the tress's aggregated predictions, and ExtraTreeClassifier focuses on the predictions generated by the majority voting from the trees. ExtraTreeClassifier forms multiple unpruned trees [79] from the training data subsets based on the classical top-down methodology. Compared to other tree-based ensemble methods, the two significant differences are that ET splits the nodes by selecting the cut-points randomly and thoroughly. Moreover, it utilizes the complete learning sample (rather than a bootstrap replica) to build the trees.  
The prediction for the sample data makes the majority of the voting. While implementing ML algorithms that utilize a stochastic learning algorithm, it is recommended to assess them using k-fold cross-validation or merely aggregate the models' performance across multiple runs. To fit the final model, few hyperparameters can be tuned, like increasing the number of trees and the number of samples in a node, until the model's variance reduces across repeated evaluations. The ET classifier has low variance and has higher performance with noisy features as well.
## 5 Discussion  
The Li River Basin is located in southwest China and possesses a typical karst landscape, and WUE is particularly important for vegetation growth. In this study, the spatial and temporal variations in WUE in the basin from 2001 to 2020 were characterized, and their relationships with precipitation, air temperature, NDVI, LAI, and SPEI were investigated. This study showed that the WUE in the basin showed an overall downtrend at a rate of \(0.0072\) gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). Based on the analysis of meteorological factors, the effects of air temperature and precipitation on WUE were polarized. The air temperature was positively correlated with WUE, while precipitation was negatively correlated with WUE.  
Figure 12: Spatial distribution of correlation analysis between WUE and SPEI in Li River Basin from 2001 to 2020. (**(a)** Spatial distribution of correlation coefficients in the Analysis Results. **(b)** Spatial distribution of various significance Levels).  
Figure 13: Relationship between WUE and SPEI in Li River Basin from 2001 to 2020.  
In the past 20 years, the distribution and areas of vegetation in the basin showed a continuous growth trend, which was related to the implementation of local ecological management policies and projects. During the period from the \"10 th Five-Year Plan\" to the \"14 th Five-Year Plan\", the forest cover of the basin increased from 62.06% in 2000 to 71.87% in 2020. In the case of ET as the dominant factor of WUE in the basin, the ecological management of the basin increased vegetation cover and promoted the transpiration of vegetation, and an increase in ET further promoted a decrease in WUE, which was corroborated by the negative correlation between the NDVI, LAI, and WUE.  
In this study, MODIS data were used for estimating the WUE of the Li River Basin from 2001 to 2020. A limitation of our study is that this paper only analyzed the correlation between WUE and air temperature, precipitation, NDVI, LAI, SPEI, etc. However, in addition to the above data, solar radiation, carbon dioxide concentration, etc., also have some influence on WUE. In addition, some researchers conducted a comparative analysis using nine GPP and evapotranspiration ET products against observational data from eight flux towers, revealing significant variations in data outputs among different products when studying diverse ecosystem types [42]. In this study, the data were not measured by us, so there may be some errors, which may lead to some uncertainties in the results of this study. To ensure a further refinement of the experimental results regarding data accuracy, subsequent measurements of raw data and a comparative analysis need to be conducted.
# Ion Channel Properties of a Cation Channelrhodopsin, \(Gt\_\)Ccr4
## 3 Results
### Gt_CCR4 Is inactivated by 590 nm Light But Fully Reactivated by Blue Light  
As already shown in Figure 1A, one of the obvious characteristics of _Gt_CCR4_ is the small inactivation upon 530 nm illumination which is close to \(\lambda_{\text{max}}\) (525 nm). Here we compared the current shape by illumination of 530 nm and 590 nm light (Figure 5A-C). Repetitive illuminations of 530 nm light three times gave almost the same current shape, which has a high steady state level (Figure 5A). Upon illumination of 590 nm light, the current slowly decayed into a small steady state level, which was further reduced after a second illumination of 590 nm LED following several seconds of a dark period (Figure 5B,D). Even after 30 sec or 2 min, the inactivated current did not recover (Figure S1A,B). These observations suggest that _Gt_CCR4_ possesses a long-lived inactivated state which could accumulate in 590 nm light. Only illuminating with 530 nm allowed for a full recovery to the original steady state level (Figure 5C,E).  
Figure 5: _Gt_CCR4_ is inactivated by light with a longer wavelength. Standard solutions were used. Membrane voltage was clamped at \(-60\) mV. Photocurrent by 530 nm light (7.44 mW/mm\({}^{2}\)) in (**A**) reached a steady state level after transient peak current. Repetitive simulations gave almost identical current shapes. (**B**) Slow inactivation of the photocurrent was observed by illumination with 590 nm light (7.44 mW/mm\({}^{2}\)). The current was further reduced when illuminated twice with 590 nm light. (**C**) After 590 nm inactivation, 530 nm light fully reactivated the photocurrent to the original steady state level. (**D**) Photocurrent density from the measurement shown in (**B**). The photocurrent density upon exposure to the first 530 nm light (shown in G), and the second and third 590 nm light (\(\Upsilon\)), is shown. Ip; transient peak component. Iss; steady state component. (**E**) Photocurrent density from the measurement shown in C. The photocurrent density upon exposure to the first 530 nm light (shown in G), the second 590 nm light (\(\Upsilon\)), and the third 530 nm light (shown in G), is shown. (n = 4 cells).
## 3 Development of Wireless Laser Displacement Monitoring Equipment
### Indoor Static Loading Test for Verification  
#### 3.3.1 Experimental Setup  
To test the feasibility and accuracy of the above algorithm model, experiments were carried out in the laboratory for verification. Since the laser monitoring equipment is only a simple data acquisition, to realize the real-time upload of data, a networking equipment--gateway was needed, so the experimental equipment included: laser moni  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Laser Ranging Accuracy \(\Delta l\) (mm)** & **Displacement Measurement Accuracy \(\Delta d\) (mm)** & **\(\alpha\) (\({}^{\circ}\)C)** \\ \hline
1 & 1.0 & 0.10 & 5.7 \\
2 & 1.0 & 0.16 & 9 \\
3 & 1.0 & 0.20 & 11.5 \\
4 & 1.0 & 0.21 & 12 \\
5 & 1.0 & 0.26 & 15 \\
6 & 1.0 & 0.30 & 17.5 \\
7 & 1.0 & 0.40 & 23.6 \\
8 & 1.0 & 0.50 & 30 \\toring equipment, total station, gateway, loading equipment, microprism reflective film, etc. Among them, the basic performance parameters of the laser monitoring equipment wee the initially determined requirements (shown in Tables 4-6), which meet the proposed displacement monitoring requirements for short and medium-span bridges. The incident angle range of the microprism reflective sheet was 5\({}^{\circ}\)-40\({}^{\circ}\).  
The indoor verification process adopted static loading. The loading test beam was a hollow plate beam with a length of 20 m and 2 tons of concentrated load on the mid-span. The measurement and comparison equipment used was a dial indicator with an accuracy of 0.001 mm. The installation of the test equipment is shown in Figure 7.  
Figure 7: Test scene. (**a**) Installation of laser displacement monitoring equipment. (**b**) Microprism reflective sheet. (**c**) Dial indicator. (**d**) Schematic diagram of the experiment.
## 3 Results
### Aesthetic Properties  
The report for the Cention-N alkasite material's aesthetic properties showed that, at T0, 55 restorations (100%) qualified as clinically excellent, while at T1 (3 months) 45 restorations (81.82%) qualified as clinically excellent and 10 (18.18%) as clinically good. At T2 (6 months), 30 restorations (54.55%) qualified as clinically excellent; 23 (41.83%) as clinically good, one (1.81%) as clinically sufficient, and one failure (1.81%) was reported. At T3 (12 months), 26 restorations (47.27%) restorations qualified as clinically excellent, 26 (47.27%) as clinically good, one (1.81%) as clinically good, and two failures were reported (3.63%) (Table 3).  
The survey for the Fuji-IX GIC aesthetic properties showed that, at T0, 56 restorations qualified as clinically excellent (98.25%) and one (1.75%) as clinically good. At T1 (3 months), 37 restorations (64.91%) qualified as clinically excellent and 20 (35.08%) as clinically good. At T2 (6 months), 25 (43.85%) restorations qualified as clinically excellent, 31 (54.39%) as clinically good, and the first failure was reported (1.75%). At T3 (12 months), 20 restorations (35.09%) qualified as clinically excellent, 32 (56.14%) as clinically good restorations, three (5.26%) as clinically sufficient restorations, and the second failure was reported, for a total of two failed restorations (3.51%) (Table 3).  
The Kruskal-Wallis test indicated a statistically significant (K-W \(p\) < 0.001) decrease in the clinically excellent restorations made by using the Cention-N alkasite material, up to 47.27% at T3 (1 year).  
Regarding \"clinically excellent\" Fuji-IX GIC restorations, a statistically significant (K-W \(p\) < 0.001) decrease, up to 35.09% at T3 (1 year) was also observed.  
If the two materials are compared, a statistically significant difference favouring the Cention-N alkasite material was found at T1 (\(p\) < 0.05) whereas no statistically significant differences were observed at T2 and T3. Overall, there was no statistically significant differences in terms of aesthetic performance in a 12-month period between Fuji IX GIC and Cention-N alkasite material.
## 4 Proposed Solution
### DPoM File Fetching Algorithm  
The process of responding to user content requests is detailed in Algorithm 2. The algorithm initializes the MAN servers for content fetching and processing power for transcoding. At the request for a video content file \(f_{v}\) in quality \(q\) by user \(u_{k}\), DPoM first checks if the file is on the local MAN server \(n\) and serves \(u_{k}\) if it is available (\(\epsilon_{m_{n}}^{q_{f_{v}}}=1\)). If the desired video is not in cache at the local MAN server, the edge cache manager searches cooperatively with the other MAN servers and serves the user \(u_{k}\) if the file \(f_{v}\) is cached on any \(k\)-th neighbor MAN server (\(\epsilon_{m_{k}}^{q_{f_{v}}}\)=1; \(k\
eq n\)). To efficiently utilize the limited processing power of the MAN servers, if the requested quality \(q\) is within the cluster, there is no need for transcoding. However, if there exists \(f_{v}\) in a higher quality \(\hat{q}\) in the local \(n\)-th or alternate \(k\)-th MAN server, the file is transcoded to the desired quality and served to the user (\(\psi_{m_{n}}^{q_{f_{v}}}=1\); \(\psi_{m_{k}}^{q_{f_{v}}}=1\)). If the video is not on any of the servers clustered as neighbors, the algorithm presents a search request to the ROC.  
Since the ROC has a sufficiently large storage capacity, there is a higher probability that the file will ultimately be served from its server (\(\gamma_{m_{n}}^{q_{f_{v}}}=1\)). However, the central cloud is the last resort if the desired file is unavailable on the ROC server. The ROC always transcodes the requested file to the desired quality before forwarding it to the MAN server. Once a user \(u_{k}\) reaches the view-time threshold of the file \(f_{v}\), the remaining parts are transcoded by the ROC and sent to the transient cache of the user's local MAN server (\(f_{WT_{v}}\geq\lambda_{v}\)).  
```
1:File request \((f_{v})\) by a user \(u_{k}\).
2:Output : Cache Hit Rate \((H_{\text{C}})\), Latency \((L_{T})\), and Backhaul Traffic \((\text{T}_{B})\)
3:Initialize : Available processing power \(P_{n}^{u}\)
4:Initialize : Cache on MAN servers \(s_{m_{1}},\ldots,s_{m_{N}}\)
5:for\(n\in 1,\ldots,N\)do
6:for each request \(r_{u_{k}}\) do
7:if\(\varepsilon_{m_{n}}^{d_{f}}==1\) then
8:Serve \(u_{k}\) from MAN server \(n\)
9:else if \(\varepsilon_{m_{n}}^{d_{f_{v}}}==1\);\(k\
eq j\) then
10:Fetch \(f_{v}\) from MAN server \(k\) and serve \(u_{k}\)
11:switch (expression)
12:begin
13:case \(\gamma_{m_{n}}^{d_{f_{v}}}==1\) then
14: Transcode from \(\hat{q}\) to \(q\) and send \(f_{v}\) from the ROC
15:server to the MAN server \(n\)
16:break
17:else
18:case \(\phi_{m_{n}}^{d_{f_{v}}}==1\) and \(P_{\hat{q}\to q}\leq P_{n}^{u}\); \(\hat{q}>q\) then
19:Transcode \(f_{v}\) from \(\hat{q}\) to \(q\) on MAN server \(n\)
20:break
21:break
22:break
23:break
24:break
25:break
26:break
27:else
28:endfor
```  
**Algorithm 2** DPoM Fetching Algorithm
## 2 Literature Review
### Personalized Recommendation Systems  
The domain of personalized recommendation systems has undergone a significant transformation since its inception. What began as rudimentary algorithms have evolved into complex computational models that leverage machine learning, data analytics, and neural networks. This shift has fundamentally changed users' interactions with digital platforms, making personalized recommendations pervasive in today's digital experience. In the initial stages, recommendation systems largely relied on simple algorithms using user-item filtering techniques. These rudimentary methods, however, were quickly found to be lacking in scalability and adaptability. This led to the incorporation of machine learning algorithms that were adept at capturing the non-linear patterns in users' behavior, providing a nuanced approach to personalization.  
The utility of recommendation systems has spread across various sectors, from e-learning to crowdfunding platforms. In educational settings, algorithms analyze a multitude of factors, such as student engagement and performance metrics, to deliver personalized course suggestions [10]. In financial platforms such as crowdfunding, machine learning algorithms evaluate market conditions, investment risks, and historical behaviors to tailor investment suggestions [11].  
Recent advancements in machine learning techniques, particularly deep learning, have enabled these systems to analyze users' historical behavior and predict their future actions with greater accuracy [12]. Additionally, the rise of big data technologies has empowered these systems to process enormous datasets in real time, enhancing the efficiency and effectiveness of recommendations [13].  
However, the increasing capabilities of personalization also bring forth challenges related to data privacy. Given the sensitive nature of users' data, there is an urgent need for robust encryption methods and decentralized data storage solutions to safeguard privacy [14]. Ethical considerations around users' consent and data usage are also gaining prominence in research. Evaluation metrics for recommendation systems have undergone a paradigm shift. They are no longer solely focused on accuracy but now encompass fairness and interpretability, providing a more comprehensive assessment of a system's performance. Fairness addresses algorithmic biases, while interpretability focuses on the explainability of the recommendations.  
While advances have been made, challenges remain, notably in scalability and real-time recommendations. Potential solutions could include adopting more efficient algorithms, particularly those based on deep learning, and using high-performance computing resources.
# Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA
## 5 Numerical Results
According to [31], the computational complexity of CBF weights is \(\mathcal{O}(N_{T}^{3}\times U^{3})\); on contrast, the computational complexity is about \(\mathcal{O}(N_{T}\times U)\) of  
Figure 8: The influences of different user pairing schemes on spectrum efficiencies, where \(U=20\). Figure 9: Spectrum efficiency comparison between Rayleigh and Rician fading channels. OBF scheme. Compared to CBF scheme, the computational complexity is evidently reduced in OBF scheme, especially with the large numbers of antennas and users. Therefore, these is a trade-off between low complexity and high spectrum efficiency. We present the time of convergence for GA, GSP, VQ and the proposed schemes in Figure 11, it is found that the time of all schemes grows with the increased waiting users, and the time of the proposed scheme is lowest. For example, when \(U=20\), the time of the proposed scheme approximates 20 (s), and the GA scheme achieves approximately 36 (s). Moreover, with the increasing of \(U\), the gaps between the proposed scheme and other scheme raise, which validates our previous analyses. To compare the spectrum efficiency between Algorithms 1 and 3 visually, Figure 12 is presented. It is found that the spectrum efficiency of Algorithm 1 is higher than that of Algorithm 3. The reason is that the applied bipartite graph and distributed queue are suboptimal solutions of subcarrier scheduling and user pairing. Although there is a gap between Algorithms 1 and 3, the complexity of Algorithm 3 is lower than that of Algorithm 1. Therefore, Algorithm 3 is a feasible solution of the original resource allocation problem, which is a trade-off between complexity and spectrum efficiency. Figure 11: Time of convergence for GA, GSP, VQ and the proposed schemes, where \(\frac{R_{\text{total}}}{\sigma^{2}}=10\) dB. Figure 3:0 The comparison of spectrum efficiencies between CBF and OBF versus the increased number of waiting users.
## 2 Materials and Methods  
### Dataset Source  
Currently, no dataset specifically tailored for rock identification tasks exists, and data for ultra-fine-grained rock identification are even rare. To ensure the validity and reliability of our experimental samples, we sourced all thin rock section data from the science data bank [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. While attempting to minimize disparities and errors in data volume among different rock types, we selected limestone and sandstone, both possessing substantial data quantities, as the focal subjects of our study. For limestone nomenclature, we followed the revision scheme proposed by [PERSON] and [PERSON], predicated on [PERSON] [36; 37]. In contrast, the classification nomenclature for sandstone adheres to the classification method proposed by [PERSON] [38]. Data augmentation, a prevalent pre-processing technique in various deep learning tasks, enhances the diversity of training samples, reduces overfitting during the training process, and, consequently, bolsters the generalizability of neural networks. Upon segmenting the dataset into a training set and a test set, we applied standard augmentation techniques to the training set, including random cropping, random rotation, random horizontal flipping, etc. This approach ensured balanced data volumes in each category within the training set. Additionally, we utilized the dataset [39] published by Nanjing University in China as the test dataset for the generalization experiment. This dataset boasts significant hierarchy and extensive type coverage, comprising 108 rock types, which account for over 90% of commonly used classifications, making it ideal for verifying the model's generalizability. The data conditions for this experiment are detailed in Table 1.  
Figure 1: The ultra-fine-grained categories of 10 types of rocks. Due to the slight visual differences between thin sections of rocks from different categories, it is very challenging to distinguish the ultra-fine-grained categories of these 10 types of rocks. For example, the visual differences between quartz debris sandstone and debris sandstone, as well as debris quartz sandstone, are very subtle.
## 3 Results
### DCF Degradation in Aqueous Solution by EB Irradiation after Nanobubbling Process
The increase in TOC could be related to the higher amounts of organic matter containing carbon in the irradiated aqueous samples. Contrary to these results, [PERSON] et al. [18] reported 90% mineralization of 0.5 mM DCF using gamma radiation at 40 kGy and [PERSON] et al. [22] and [PERSON] et al. [21] observed TOC removal efficiencies lower than 6.5% at 2 kGy and 7.5 kGy, respectively. Figure 8: Total carbon (TC) variation with the absorbed dose under electron-beam irradiation with (Nano + EB) and without nano-bubble pretreatment (EB) (\(\rm{[DCF]}_{0}=125\) mg/L). Figure 7: Variation in inorganic carbon (IC) concentration with the adsorbed dose under electron-beam irradiation with (Nano + EB) and without nano-bubble pretreatment (EB) (\(\rm{[DCF]}_{0}=125\) mg/L). Looking at DCF's structure (Figure 9), it is possible that Cl was released from position \"1\" (blue color) of DCF and NH was released from position \"2\" (green color) of DCF, forming Cl- and NH4+ through related chemical reactions, and CO2 was formed from bond cleavage in position \"3\" (red color). The degradation of DCF under EB irradiation, EB irradiation together with nanobubbling, and the nanobubbling process alone were studied in flow system. Concerning nanobubbling alone, about 20% of DCF was removed after this process. In the case of the nanobubbling + EB process, where the DCF solution was irradiated just after nanobubbling, its removal efficiency was slight lower than that of the EB process alone at absorbed doses below 5 kGy (Figure 10). At 6 kGy, about 91% of the DCF seemed to be removed from the aqueous solution in both the EB and nanobubbling + EB processes (Figure 10).
## 3 Proposed Method
### Dynamic Bit-Level Encoding Algorithm (DEA)
To ensure a better understanding, the algorithm is not only presented through a flowchart and described step by step but is also illustrated with a small example below.