esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 2 Surface Modification of HNTs and the Relevant Properties
### Free Radical Modification  
The surface of HNTs contains hydroxyl groups that could react with monomer on the inner or outer surface. The functionalized HNTs have improved hydrophobicity and dispersibility in organic solvents. [PERSON] et al. (2017) prepared modified HNTs by grafting the polymethyl methacrylate (PMMA) via radical polymerization and then compounding with poly (vinyl chloride) (PVC) to form composites with higher toughness, strength, and modulus. The results showed that the modified HNTs have uniform dispersed in PVC aqueous solution. The modified HNTs could effectively improve the mechanical properties. [PERSON] et al. (2017) reported a kind of functionalized HNTs that were modified by polymers via atom transfer radial polymerization (ATRP) and cross-linked with polystyrene (PS) and polyacrylonitrile (PAN), respectively. The results indicated that the composites showed excellent wettability for entrap water droplets.
# Partial Order Rank Features in Colour Space
## 2 Background
### Ordering High-Dimensional Data  
The application of rank features to multi-channel images or higher dimensional data is hindered by the fact that there is no natural way of ordering multivariate data. It is certainly possible to provide a total order for a colour space; for instance, one could order RGB data lexicographically using the R channel as the primary sorting key, followed by G and finally by B. However, like other similar options, this has a disadvantage, namely, there are colours that are very close to each other in colour space, but very far in the order--and is therefore of limited practical interest (a sub-relation of the lexicographical order, the _product order_, is indeed of practical interest and will be discussed in detail in this paper, see Section 2.5.1). In general, it is best to resort to some sort of sub-ordering principle. These can be broadly divided in four categories [16]:  
* Marginal ordering (_M-ordering_).
* Reduced (aggregate) ordering (_R-ordering_).
* Conditional (sequential) ordering (_C-ordering_).
* Partial ordering (_R-ordering_).  
In marginal ordering, ranking is carried out on one or more components (_marginals_) of the multivariate data. Ranking colour data in the RGB space by the value of red is an example of M-ordering; lexicographical ordering is another one. Reduced (aggregate) ordering relies on converting multivariate data to univariate through suitable transformations. A common way to do this consists of establishing a reference point in the data space and using the distance from that point to rank the data. Conditional ordering occurs when we sort a random multivariate sample based on the corresponding (usually marginally-sorted) values of another sample. C-ordering is closely related to the concept of _concomitants_ in Statistics [17]. Partial ordering will be discussed in detail in Section 2.5.  
Interestingly, many of the common ways of dealing with order in colour space fall under the first two categories, i.e., marginal and reduced (aggregate) ordering. For instance, ranking based on intensity can be seen as a marginal ordering of the HSV space along the V axis; or as a reduced or aggregate ordering over the RGB space, where the aggregating function is the grey-level intensity. Other examples of aggregating orders will be given in Section 3.1.  
In this paper, we will focus on rank features based on _partial orders_. Before introducing these, we review recent approaches to using multivariate orders on colour images.
## 3 Results and Discussion  
A total of 100 VOCs were successfully identified, including 14 aldehydes, 19 alcohols, 16 esters, nine acids, 22 terpen, seven ketones, five furans and benzenes, four pyrazines, and two phenols, of which three VOCs (\(\alpha\)-Terpinene, geranial, and 2,3-diethyl, pyrazine) had not been previously reported in raisins (Table S1). The remaining 97 compounds were mentioned in different studies regarding raisin varieties, drying treatments, and storage [2; 3; 5; 13; 20; 21; 22]. The concentration (average \(\pm\) standard deviation) of both (free- and bound-form) VOCs of the different treats (AD, PAD, SD, and PSD) are presentedin Table S1. There were 99 and 77 compounds that existed in free- and bound-form, respectively. The additional 22 VOCs subsisted only in the free-form because they can not be linked to the sugar through the \"glycosidic bond\" deprived of similar groups [2].  
Overall, the concentration of bound-form VOCs in aldehydes, alcohols, esters, acids, terpenes, ketones, furans, benzenes, and pyrazines was significantly lower than free-form volatiles (Figure 1). The hydrolyzation of glycosylated bound compounds is the reason for less volatile content, which changes into an intense flavor [18]. Only the phenolic group had more content in bound-form as a result of the higher content of 4-vinylguaiacol. Some compounds such as 3-methyl-2-buten-1-ol, ethyl decanoate, and 2,3-diethyl, pyrazine only existed in bound-form, which might be due to the strong glycosic bounding of the sugar with hydroxide group. Among the bound-form volatiles, pentanal, hexanal, 3-methyl-1-butanal, nonanoic acid, acetic acid, and geranic acid had a concentration above 500 ug/g (Table S1). The concentration of all bound-form compounds was more plentiful in fresh grapes than dried, except styrene, 1-hexanol, and nonanal. The compounds such as octanal, methyl hexadecanoate, and 2-acetylfuran were only found in fresh grapes. At the same time, 1-butanol, ethyl octanoate, ethyl decanoate, (E)-2-nonenal, and (E,E)-2,4-heptadien were not identified in fresh grapes (Table S1).
## 3 Results
### Validating Gridded GPP Estimates Derived from GOSIF
GOSIF GPP with finer resolutions fully captured the spatial patterns and seasonal cycles present in the coarse-resolution (1\({}^{\circ}\)) data. For example, in United States and East Asia (Figure 5a,b), GPP exhibited the highest values in July, intermediate values in April and October, and the lowest values in January; in Australia (Figure 5c), the majority of grid cells are dominated by drylands and exhibited low GPP values throughout the year, while higher GPP in eastern and southeastern Australia in the wet season was also captured. The coarse-resolution GPP estimates had tremendous data gaps, while our ensemble GPP had globally continuous coverage and much finer spatial details. This finer-resolution GOSIF GPP dataset provides \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Biomes**} & \multicolumn{3}{c}{**8-day**} & \multicolumn{3}{c}{**Monthly**} \\ \cline{2-7}  & **R\({}^{2}\)** & **RMSE** & **Slope** & **R\({}^{2}\)** & **RMSE** & **Slope** \\ \hline ENF & 0.75 & 1.85 & 1.10 & 0.77 & 1.96 & 1.19 \\ \hline EBF & 0.51 & 2.69 & 0.84 & 0.47 & 3.00 & 0.68 \\ \hline DBF & 0.85 & 1.74 & 0.95 & 0.87 & 1.57 & 0.97 \\ \hline MF & 0.78 & 1.58 & 0.93 & 0.80 & 1.47 & 0.95 \\ \hline OSH & 0.75 & 0.79 & 1.21 & 0.86 & 0.62 & 1.35 \\ \hline WSA & 0.83 & 1.10 & 1.05 & 0.83 & 1.05 & 1.09 \\ \hline SAV & 0.65 & 1.52 & 1.03 & 0.69 & 1.33 & 1.06 \\ \hline GRA & 0.77 & 1.48 & 1.11 & 0.83 & 1.19 & 1.10 \\ \hline WET & 0.79 & 1.81 & 0.94 & 0.79 & 1.85 & 0.97 \\ \hline CRO & 0.62 & 2.91 & 1.13 & 0.66 & 2.72 & 1.22 \\ \hline All & 0.74 & 1.92 & 1.04 & 0.75 & 1.90 & 1.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Validation of the GOSIF GPP against FLUXNET GPP for each biome at both 8-day and monthly time steps. The full names of the biome types are given in Table S1. The statistical measures include coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE). The units of GPP are g C m\({}^{-2}\) d\({}^{-1}\). Figure 4: Validation of the interannual variability of GOSIF GPP: (**a**) coefficient of variation (CV) of tower GPP versus CV of estimated GPP for each site with each circle denoting the CV of each site; (**b**) the site-averaged tower GPP anomaly versus estimated GPP anomaly for each year with each circle denoting the anomaly of each year. a valuable opportunity to examine terrestrial photosynthesis at various scales from ecosystem to the globe.
## 3 Results and Discussion
### System Performance Comparisons of Batteries and SCs
Figure 7: Performance of PV-membrane system with fully charged Li-ion batteries (black curve) compared to the system with SCs and the charge controller (grey curve) on the “partly cloudy day”, and the results shown for **(A)** the motor power, **(B)** voltage of pump (V\({}_{\text{Pump}}\)), **(C)** voltage of PV equipped with battery (V\({}_{\text{PV\_Battery}}\)), **(D)** current of PV equipped with battery (I\({}_{\text{PV\_Battery}}\)), **(E)** SOC, **(F)** TMP\({}_{\text{RO}}\), **(G)** flux\({}_{\text{RO}}\), **(H)** permeate EC, **(I)** permeate production and **(J)** SEC. batteries overlaps with SCs if the batteries have small capacities (such as \(\sim\)1 Ah providing five min of power to the system). Therefore, it is recommended to employ Li-ion batteries instead of SCs as energy storage units in this PV-membrane system due to their high energy intensities, charge/discharge cycles and reduced costs per kW. It is intuitive that increasing the size of the energy storage units would provide power for longer periods of fluctuations. This would be a trade-off between the benefits of increasing the storage time vs. the added cost of the energy storage capacity. As indicated in a recent report, the capital cost of SCs was estimated to be relatively stable at US$ 1600/kWh [80]. The cost of Li-ion batteries reached US$ 135/kWh in 2020 and is projected to fall below US$ 100/kWh in 2024. This was attributed to the technological advancements and economies of scale [81]. The operation and maintenance (O&M) costs of the SCs were \(\sim\)US$1/kW-yr [80], while the O&M costs of the Li-ion batteries were in the range of US$ 6-14/kW-yr in 2017 [82], with further cost reductions anticipated, to attain US$ 8/kW-yr by 2025 [80]. Further work is required to (i) choose a better version of the charge controller for batteries with high voltage outputs, such as 48 Vdc, (ii) sense the preset voltage slopes for the charge controller to avoid the prompt discharging of SCs at the start of the day and (iii) examine the effects of integrating Li-ion batteries and SCs on the PV-membrane system performance and the overall improvements in water quality and quantity. An energy management system is required to distribute the energy flow among the pump, batteries and SCs to provide higher water quality and quantity.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 2 NB-IoT
### NB-IoT Key Technologies
#### 2.2.3 Coverage Extension  
Although the LTE can operate up to 144 dB in the maximum coupling loss (MCL), the NB-IoT was designed to satisfy 164 dB MCL for extended cell size in environments with high penetration loss. An additional 20 dB link budget can be obtained from the lower bandwidths and repeating transmission. LTE operates in PRB units of 180 kHz, but the NB-IoT can operate with 15 kHz and 3.75 kHz, and thus can take additional 14 dB and 17 dB gains in terms of power spectral density, respectively. Likewise, the retransmission count of up to 128 for the uplink and 2048 for the downlink allows to obtain the additional link budget.  
Figure 3: NB-IoT deployment scenarios.
## 2 Materials and Methods
### Model Architecture
#### 2.2.4 Adaptive Bagging  
Bootstrap Aggregating (Bagging) is an ensemble learning method that improves overall performance by combining the predictions of multiple models. In our segmentation task, we enhance the model's generalization ability and segmentation accuracy by training multiple U-Net models with random parameters. Each model is independently trained using different subsets of data obtained from the training set through Bootstrap sampling, ensuring diversity among the models. In the traditional Bagging method, all models' predictions are given equal weight, and the final prediction is based on a simple average of all the model predictions. However, the improved version of this study introduces an adaptive weight adjustment mechanism. The weight of each model in the ensemble is dynamically adjusted according to its performance on the validation set, thereby optimizing the overall predictive performance. The method for calculating the weights is as follows:  
\[\mathrm{W_{i}}=\frac{\mathrm{e}^{\mathrm{\pi(\beta\mathrm{P_{i}}+(1-\beta)q_{i} )}}}{\sum_{i=1}^{n}\mathrm{e}^{\mathrm{\pi(\beta\mathrm{P_{i}}+(1-\beta)q_{i} )}}} \tag{3}\]  
Figure 2: StyleGAN2-ADA model framework, where G denotes the Generator, D represents the discriminator, Augmentations refer to various augmentation techniques, and Compute D/G loss pertains to the calculation of the loss for the Generator or discriminator.  
Here, \(n\) represents the total number of models. The Intersection over Union (IoU) coefficient of each model \(i\) on the validation set is denoted as \(p_{i}\), and the Dice coefficient is denoted as \(q_{i}\). \(\beta\) is used to balance \(p_{i}\) and \(q_{i}\), and \(\alpha\) is a parameter that controls the degree of weight amplification. The exponential weighted value is denoted as \(e^{\alpha p_{i}}\), \(\sum_{i=1}^{n}e^{\alpha p_{i}}\) represents the sum of the exponential weighted values of all the models, and \(W_{i}\) is the final weight value of model \(i\).  
To avoid the situation where model weights are too high, this study introduces an L2 regularization term to optimize the model weights. The weight calculation formula after introducing the L2 regularization is as follows:  
\[W_{i}^{\prime}=\frac{W_{i}}{\sqrt{1+\lambda\sum_{j=1}^{n}W_{j}^{2}}} \tag{4}\]
## 3 Data Fusion and Multimodal Integration
### Data Fusion Techniques
#### 3.1.3 Challenges in Real-Time Data Fusion  
Real-time data fusion presents several challenges that must be addressed to ensure accurate and timely fault detection:  
* **Latency**: Low latency is crucial for detecting faults in real-time systems. However, the fusion process, particularly when handling large datasets from various modalities, can create delays. Minimizing this latency is essential, and it can be accomplished by optimizing the fusion algorithms and utilizing high-performance computing techniques [38]. \[\text{Latency}_{\text{total}}=\text{Latency}_{\text{sensor}}+\text{Latency}_{ \text{fusion}}+\text{Latency}_{\text{diagnosis}}\] (10) Each term denotes the duration allocated for sensor data collection, the fusion process, and fault diagnosis.
* **Data synchronization**: Data from various sensors may not always be synchronized, complicating the fusion process. Time-stamping, interpolation, and synchronization algorithms are essential to align the data streams before fusion can occur.
* **Noisy data**: Sensor data are often noisy in real-world environments, which can impact the accuracy of the fusion process. Filtering or denoising techniques are essential to ensuring that the data remain clean and reliable [39].
* **Scalability**: As the number of sensors or modalities increases, the complexity of the fusion system also grows. Efficient fusion algorithms are necessary to manage large datasets and ensure real-time processing capabilities.  
To make research more accessible and reproducible, there are several widely used benchmark datasets and open-source tools in the field of fault diagnosis. For example, the Case Western Reserve University (CWRU) Bearing dataset is often used to test models for diagnosing issues in rotating machinery. The NASA Prognostics and Health Management (PHM) Data Challenge datasets are also common, especially in studies focused on aerospace and industrial health monitoring. Additionally, there are other useful datasets like the MIMII dataset, which is designed for detecting acoustic faults, and the SEU vibration dataset, which is helpful for vibration-based fault detection. As for tools, open-source frameworks like ensorFlow 2.12, PyTorch 1.13, and Scikit-learn 1.1.3. are incredibly popular. These tools support the building, training, and deployment of machine learning and deep learning models. They come with ready-to-use modules and GPU acceleration, and they benefit from strong community support, all of which help researchers create reliable, scalable fault diagnosis systems more efficiently.
# Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios
## 4 Simulation Assumptions  
The performance of the proposed solution is assessed in a Matlab simulator with MR-DC capabilities, which is based on [34]. The introduced approach is compared with the 3 GPP solution based solely on RSRP, so the benefit of the proposed methodology can be highlighted. They are tested in a non-standalone dense urban scenario that correspond to a region of a real cellular network with 20 tri-sectional sites fulfilling 3 GPP specification [24]. Figure 2 depicts this scenario. It should be also noted that the eNBs and the gNBs are co-located in each site, so only small-cells are assumed. However, five different scenario setups are proposed to analyze the feasibility of the methodology. In this manner, a uniform distribution of users and services over the whole scenario is set in the first tests, so all regions have equal characteristics. However, the next four scenario setups will consist of four squares of the same size but different characteristics. In particular, an uneven distribution of services is set in the second set of tests. On the other hand, an unequal distribution of users is configured in the third set of tests, resulting in less bandwidth available per UE as density increases. In a similar manner, regions with different level of propagation losses are simulated in the fourth set of tests. Finally, last tests combine the features of the second, third and fourth configurations. Further details of each scenario are collected in Table 1, as well as a graphical representation is shown in Figure 3.  
The benefit of the proposed approach is analyzed in terms of MOS reported by users for each of the four services. They are real time video (RTV), video streaming (VS), whose functioning implies a non-real time service, file download (FD) and web browsing (WB) [35]. Their MOS values are computed based on the models introduced in [32]. Regarding FD and WB, it relies on the average session throughput. As for VS service, the MOS depends  
Figure 1: Block diagram of the proposed methodology.  
on the initial buffering time as well as the average stalling frequency and duration. Finally, the MOS of RTV relies on the playout time stored in the receiver buffer. Table 2 summarizes the main simulation parameters.  
Figure 3: Configurations of the scenario in second, third and fourth tests. (**a**) Second scenario setup. (**b**) Third scenario setup. (**c**) Fourth scenario setup.  
Figure 2: Dense urban scenario considered in this work.
## 2 Materials and Methods
### Accuracy Assessment of the HJ-1-Based Forest Map in 2010  
To assess the accuracy of forest classification, a confusion matrix was adopted to measure the agreement between our classification result and validation data. The user's accuracy, producer's accuracy, and overall accuracy were calculated. The overall accuracy represents the percentage of the points correctly identified [58]. The user's accuracy demonstrates the likelihood that a classified object matches the ground situation. The producer's accuracy is the proportion of object types that were correctly classified. Furthermore, the area proportion was considered to adjust the accuracy assessment results and evaluate the uncertainties of the forest products' accuracies and area estimates [59], based on a 95% confidence interval. The performance of the accuracy assessment was carried out with ArcGIS 10.0 [51].
## 3 Results and Discussion
### Optimization of the Small Coal Pillar Size  
As the lateral stress reduction zone of the goaf is 10.5 m when the roof cutting height top is 12 m, three schemes of coal pillars with different widths of 6 m, 5 m, and 4 m are selected for simulation. Figures 10 and 11 show that, the smaller the coal pillar size, the smaller the vertical stress in the coal pillar, and the peak stress is 4.58 MPa, 3.07 MPa, and  
Figure 8: Vertical stress cloud map of the surrounding rock after roof cutting: (**a**) roof cutting height of 6 m (cut to the subkey strata); (**b**) roof cutting height of 12 m (cut to the main key strata).  
Figure 9: Diagram of the vertical stress distribution of the lateral surrounding rock of the 1301 goaf after roof cutting: (**a**) roof cutting height of 6 m (cut to the subkey strata); (**b**) roof cutting height of 12 m (cut to the main key strata).  
1.79 MPa, respectively. When the width of the coal pillar is 4 m, its maximum bearing capacity is 1.79 MPa, which is far less than the applied overburden load, which is not conducive to the long-term stability of the coal pillar. The surrounding rock is easily broken, so the size of the coal pillar should be greater than 4 m. For small coal pillars of different sizes, there is no noticeable difference in the stress distribution at the 1203 working face. The stress concentration zone is 3-10 m away from the right side of the roadway and the stress peak value is 16.4-16.62 MPa.  
Figure 11: Diagram of the vertical stress distribution of the coal pillar of different widths.  
Figure 10: Three-dimensional (3D) curved surface of vertical stress of coal pillars of different sizes: (**a**) the width of the coal pillar is 4 m; (**b**) the width of the coal pillar is 5 m; (**c**) the width of the coal pillar is 6 m.  
The deformation curve of the roadway surrounding rock is shown in Figure 12. With the increase in coal pillar size, the moving amount of the two sides of the roadway increases and the moving amount of the roof and floor decreases. When the coal pillar size is 6 m, 5 m, and 4 m, the peak displacements of the two sides of the roadway are 309.98 mm, 255.79 mm, and 187.55 mm, respectively. The displacements of the roof and floor exhibit little change, and their peak values are 170.78 mm, 175.18 mm, and 171.51 mm, respectively. To prevent air leakage to the adjacent goat, a 5 m coal pillar is more conducive to fire prevention than a 4 m coal pillar.  
Considering the change law of stress in the coal pillar and the deformation characteristics of the surrounding rock comprehensively, it is reasonable to set a 5 m wide coal pillar under the premise of roof cutting and pressure relief treatment. This can ensure the stability of the roadway surrounding rock and save coal resources to the greatest extent.