esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Microstructure Analysis  
Figure 4 shows the XRD patterns of the Al powder, B\({}_{4}\)C particulates, and as-received B\({}_{4}\)C-Al composites prepared under different conditions. Apart from the original phases of Al and B\({}_{4}\)C, new peaks at 41.790\({}^{\circ}\) and 43.079\({}^{\circ}\) can be assigned to those from Al\({}_{76.8}\)Fe\({}_{24}\)[23]. Formation of the Al\({}_{76.8}\)Fe\({}_{24}\) phases in the composites is mostly originated from (1) contamination from the steel ball; and (2) the reaction between Al powder and the Fe\({}_{2}\)O\({}_{3}\) existed in the raw B\({}_{4}\)C powders. The heating temperature of B\({}_{4}\)C-Al composites was below the melting point of Al, hence there are no apparent ternary carbides and diborides in these composites [7; 13; 24].  
The SEM micrographs of various B\({}_{4}\)C-Al plates are shown in Figure 5. It is observed that the microstructure is significantly changed with the energy of ball milling. Both the 15 wt % and 20 wt % B\({}_{4}\)C-Al composites prepared by high-energy ball milling show good bonding between the Al matrix and the B\({}_{4}\)C particulates without apparent micro-voids at their interfaces (Figure 5c,d). However the composites prepared by low-energy ball-milling, voids and micro-voids were observed inside the matrix and at the matrix-particulate interfaces (Figure 5a,b).  
Figure 6 shows the neutron radiography images of the B\({}_{4}\)C-Al composites, in which the black areas correspond to the B\({}_{4}\)C particles or B\({}_{4}\)C agglomerates.  
Figure 3: Sintering schedule of B\({}_{4}\)C-Al composites.  
Figure 4: X-ray diffraction (XRD) patterns of the raw materials and B\({}_{4}\)C-Al composites.  
Figure 5: _Cont_.  
Figure 5: SEM micrograph of polished B\({}_{4}\)C-Al composites with various ratio of B\({}_{4}\)C: (**a**) 15 wt % B\({}_{4}\)C-Al prepared by low-energy ball milling; (**b**) 20 wt % B\({}_{4}\)C-Al prepared by low-energy ball milling; (**c**) 15 wt % B\({}_{4}\)C-Al prepared by high-energy ball milling; (**d**) 20 wt % B\({}_{4}\)C-Al prepared by high-energy ball milling.  
Macroscopic B\({}_{4}\)C agglomerates were clearly observed in the 15 wt % B\({}_{4}\)C-Al and 20 wt % B\({}_{4}\)C-Al composites prepared by low-energy ball milling, with estimated diameters from 0.5 mm to 1.5 mm (Figure 6a,b). As explained in the Introduction section, the microstructural non-uniformity of the B\({}_{4}\)C particles inside the Al matrix results in the inhomogeneity distribution of B-10, thus causing the image contrast in the neutron radiography images. The images obtained from the 15 wt % B\({}_{4}\)C-Al and 20 wt % B\({}_{4}\)C-Al composites (shown in Figure 6c,d) prepared by high-energy ball milling reveal that the B\({}_{4}\)C particles were dispersed uniformly inside the Al matrix, which is compared to those from the low-energy ball milling process.
# Preparation and Characterization of Polymer-Modified Sulphoaluminate-Cement-Based Materials
## 1 Introduction  
Polymer-modified cement-based materials are commonly incorporated into cement-based materials in the form of polymers and polymer fibers to form organic-inorganic composite materials, which have the strength of inorganic materials and the flexibility of organic materials. These composites exhibit high strength, wear resistance, corrosion resistance, and other desirable properties [1; 2; 3; 4]. However, their uniformity, adhesion, and compatibility with cement-based materials are often poor, leading to insignificant performance improvements [5; 6]. For instance, [PERSON] et al. [7] observed improved frost resistance but no significant enhancement in compressive strength or flexural strength when using polypropylene-fiber-modified cement-based composite materials for dams.  
By introducing organic-inorganic hybrid polymerization into cement-based materials, it is possible to synthesize polymers in situ from monomers, thus creating an organic polymer network structure. This process involves simultaneous crosslinking and entangling with the inorganic network structure formed by hydration products of cement, resulting in an organic-inorganic dual network structure. In comparison to the direct addition of polymers, this method offers fuzzy interface adhesion between modified cement-based materials through in situ polymerization, along with a dense and compatible structure that leads to significant performance improvement [8; 9; 10]. However, there is currently limited research on the mechanism behind modifying cementing material through in situ polymerization. Furthermore, most studies focus on single-factor additions rather than comprehensive impacts on material properties using polymers formed via this technique [11; 12; 13].  
To address the aforementioned deficiencies, this study utilized a polymer consisting of an acrylamide monomer, ammonium persulfate initiator, and N,N,N\({}^{\prime}\)-methylene bisacrylamide crosslinking agent to improve a sulfoaluminate-mud-based material. Sulfoaluminate cement is widely used and cost-effective in marine engineering [14; 15]. The experimental results were subjected to orthogonal experiments, range analysis, and variance analysis to determine the optimal level of combination using a comprehensive scoring method. Qualitative, quantitative, and mechanistic analyses of the polymer-modified sulfoaluminate-mud-based materials were conducted through SEM, XRD, and FT-IR tests.
# Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model
## 2 Related Works
### PM2.5 Concentration Prediction  
In recent years, researchers have introduced artificial intelligence methods and hybrid three-dimensional models for measuring air pollutants and achieved some results. Hybrid methods have good robustness, low risk, and strong adaptability [10; 11; 12]. In terms of artificial intelligence methods, [PERSON] et al. [13] developed a new fuzzy time series model based on the fuzzy K-Medoid clustering algorithm to predict SO\({}_{2}\) concentrations in Turkey. [PERSON] et al. [14] proposed a prediction method for PM2.5 concentration based on the LSTM and SVR hybrid model. By introducing relaxation variables into the SVR model, the LSTM model can correct large prediction errors, so to achieve better predictions.  
With more and more attention paid to air pollution, researchers have also proposed many spatiotemporal prediction models to predict air pollution. [PERSON] et al. [15] presented a new ensemble reinforcement learning gated unit model. The key of this model is to establish a sub-series forecasting model by the SAE-GRU method. SAE was used to obtain low-latitude features of PM2.5 data, and GRU was applied to finish PM2.5 sub-series forecasting. [PERSON] et al. [16] proposed a new air quality spatio-temporal prediction model to predict future air quality based on a large amount of environmental data. [PERSON] et al. [17] proposed a deep multi-output LSTM (DM-LSTM) neural network model that incorporates three deep learning algorithms to configure the model to extract the key factors in complex spatio-temporal relationships. [PERSON] et al. [18] proposed a hybrid model for spatio-temporal forecasting of PM2.5 based on graph convolutional neural network (GCN) and LSTM. [PERSON] et al. [19] aimed at the long-term prediction of PM2.5 concentration, considering PM2.5 spatio-temporal correlation between multivariate data, and a TSMN prediction model was proposed. The model constructs a local memory component and a neighborhood component to explicitly model the temporal and spatial dependencies. [PERSON] et al. [20] proposed an attention-based parallel network (APNet), which can extract short-term and long-term temporal features simultaneously, based on the attention-based CNN-LSTM multilayer structure to predict PM2.5 concentration in the next 72 h. [PERSON] et al. [21] proposed a PM2.5 prediction model based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), differential symbolic entropy (DSE), variational mode decomposition improved by the butterfly optimization algorithm (BVMD) and kernel extreme learning machine optimized by the crow search algorithm (CSA-KELM), which was named CEEMDAN-DSE-BVMD-CSA-KELM. [PERSON] et al. [22] proposed a hybrid machine learning model (WD-SA-LSTM-BP model) based on simulated annealing (SA) optimization and wavelet decomposition.
## 4 Verification of Zero-Energy Building Integrated Planning Methodology
### Verification of Building Orientation and Solar Panel Installation
In addition, to analyze the correlation between the direction of the building and the energy production, the energy production of a total of 36 orientations at 10-degree intervals from the south direction was identified. As shown in Table 13, the west has the highest energy production and the northeast has the lowest energy production. This is the effect of the BIPV installation, and the proportion of the BIPV module installed on the south side of the square building is larger than that on the east and west sides. The west side, where the south side receives sunlight until the latest, has the greatest power generation. The final energy consumption analysis, shown in Table 14 and Figure 11, revealed that large offices in the case of applying the heat transmission rate of the central region to the Daejeon region decrease in the order of Seoul, Daejeon, and Busan. This is due to local weather conditions, and the Busan area, which shows relatively high energy production conditions and relatively low basic energy consumption, is the most advantageous area, with a final energy consumption of 80% of that of Seoul. Finally, for large office buildings, the final energy consumption was estimated by subtracting the energy production from the energy consumption of the three areas in Seoul, Daejeon, and Busan (Table 15). Figure 11 shows the energy reduction as a result of the analysis of the final energy consumption derived by assuming that the energy produced by the rooftop PV and the BIPV module is consumed in the building. The energy consumption reduction ranges from 17% to 30%, due to differences in solar panel application boundary conditions. In addition, the energy reduction increases in the order of Seoul, Daejeon, and Busan, mostly due to the difference in energy consumption, with only minor difference in energy production in each region. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Contents**} & \multicolumn{2}{c}{**1 st**} & \multicolumn{2}{c}{**2 nd**} & \multicolumn{2}{c}{**3 rd**} \\ \cline{2-6}  & **Orientation** [\({}^{\circ}\)] & **Produced** & **Orientation** [\({}^{\circ}\)] & **Produced** & **Orientation** [\({}^{\circ}\)] & **Produced** \\  & & **Energy** [kJ/h] & & **Energy** & **Energy** & **Energy** \\ \hline Seoul & 90 & 600,004,585 & 100 & 599,863,957 & 80 & 597,264,743 \\ Daejeon & 90 & 752,933,650 & 80 & 752,350,262 & 100 & 749,617,380 \\ Busan & 90 & 747,771,046 & 80 & 745,898,465 & 100 & 745,453,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Building orientation with high energy production. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Contents** & **Area** & **Capacity** \\ \hline Rooftop PV & 70\% (Rooftop) & 256.5 kW \\  & 30\% (South facade) & 62.7 kW \\ BIPV & 30\% (West facade) & 41.8 kW \\  & 30\% (East facade) & 41.8 kW \\ Total & - & 402.8 kW \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Boundary condition for photovoltaic modules.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 4 Simulation Results
It is also important to note that the so-called regularization parameter is usually related to the value of SNR, i.e., the lower the SNR, the larger the regularization term should be [48]. In case of the KF-TOT, such a parameter (i.e., \(\zeta\)) is used in the initialization step, for the matrices \(\mathbf{R}_{\widehat{\mathbf{m}}_{2}}(0)\), \(\mathbf{R}_{\widehat{\mathbf{m}}_{2}}(0)\), and \(\mathbf{R}_{\widehat{\mathbf{m}}_{1}}(0)\). Hence, a simple and practical way to relate its value to the SNR level is \(\zeta=1/\text{SNR}\).
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Types of C-RAN
#### 2.4.1 Fully Centralized  
In this architecture, as shown in Figure 3a, layer 1 functionalities such as sampling, modulation, resource block mapping, antenna mapping, and quantization; layer 2 functionalities such as transport-media access control; and layer 3 functionalities such as radio resource control are located in the BBU [23]. It has the capability of supporting the multi-standard operation, expanding network coverage area, maximizing resource sharing, and providing multi-cell collaborative signal processing. Notwithstanding the significant benefits of this architecture, the load on the fronthaul link is substantial and the bandwidth requirement is very high.
###### Abstract  
Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works.  
Software Defined Wireless Sensor Networks (SDWSN); scheduling; secure routing; Moving Target Defense (MTD); network construction; clustering +
Footnote †: journal: _Future Internet_  
0202416386. [[https://doi.org/10.3390/fi16110386](https://doi.org/10.3390/fi16110386)]([https://doi.org/10.3390/fi16110386](https://doi.org/10.3390/fi16110386))
## 5 Method
### Feasibility Analysis  
#### 5.2.1 Application Feasibility  
Regarding the rescue area side, the UAV carries different mission payloads, such as a lift-off base station, infrared detection device, aerial photography equipment, etc., to perform tasks such as disaster intelligence reconnaissance, emergency communication, and material protection as required. The drones are connected to each other through a self-organized network, and the aggregation node is selected to connect with the 5G base station (gNodeB) according to the local conditions to access the 5G network. At the same time, intelligent devices, VR/AR terminals, IoT terminals, etc., in the mission area access the 5G network through the protocol converter on the base station side to realize application services for various rescue missions. On the edge side of the mission, MEC servers are co-located with the base station to simplify deployment, and on-demand networking is used to build an edge cloud for deploying AI large models and unmanned equipment control servers. The edge cloud is connected to the 5G macro base station in the rear command center through the fiber optic cable network, and the emergency rescue command center all adopts wireless access to the network. Emergency rescue command center. Located in the fixed rear, the computing, storage and power resources are sufficient and can be reliably guaranteed. Therefore, it facilitates the deployment of emergency rescue services such as drone control applications, AI large models, image rendering, and video rectification, and realizes business applications such as intelligence processing based on large models, situation analysis and visualization, the cooperative control of drones, and VR/AR remote support.
## 5 Numerical Experiments
### Managerial Insights on Analysis of Algorithms  
Based on the results of the classical dataset and relevant analysis, the main insights can be summarized as follows:  
1. **By adopting the GLB-oriented lower bound, the initial solution with high quality can be obtained efficiently.** The average GAP between the initial solution and the optimal solution is within 10%, and the computational time is less than 1 s. The GLB-oriented lower bound estimation process comprehensively considers the cost of the previous decision and the subsequent decision, and the computational time is shortened by decomposing the original problem with quadratic terms in both constraints and objective functions into a series of linear generalized assignment problems. Moreover, this method is sensitive to the locations of different types of nodes, and it is difficult to obtain the initial feasible solution for some instances.
2. **The GLB-oriented lower bound is relatively loose for the original problem. Although the GAP between the initial solution and the optimal solution is usually small, the convergence of branch- and-bound algorithm is slow due to the poor lower bound.** For Set 3, the initial solution of several instances has been the optimal solution, and the branch-and-bound process is mainly to improve the lower bound. However, the convergence process analysis shows that the lower bound is not always loose and needs a case-by-case evaluation.  
Figure 18: Convergence process of branch-and-bound algorithm.  
3. **The branch-and-bound algorithm combined with the GLB-oriented lower bound is suitable for solving small-scale examples, while the adaptive large neighborhood search algorithm with efficient initial solution can almost achieve the same effect as the solver**. For Set 3, the heuristic algorithm obtains optimal solutions in a relatively short time, and for Set 4, the solution with an average gap of 6% can be obtained within 80% of computational time of the solver. The GLB-oriented lower bound is the main reason to ensure an efficient solution. In some instances, even large neighborhood operations cannot improve the initial solution.
4. **Although lateral-transshipments will make transportation costs higher, it is still the reasonable choice under specific demand pattern**. In the sensitive analysis of demand, the proportion of lateral-transshipments volume for some instances up to 70%, and the overall proportion of EP and RP strategies is 40%-50%, which indicates the necessity of lateral-transshipments.
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 1 Introduction
[24] showed that the INC-ML tree was much less accurate than RAxML and often not as accurate as FastTree 2. Overall, their results of using Constrained-INC for gene tree estimation were disappointing. However, their study had several important limitations: (1) the study did not evaluate any other DTM method for gene tree estimation beyond Constrained-INC, (2) only a few variations to the parameters of the pipeline were considered, and (3) only one model condition had more than 1000 sequences. These limitations are significant, since other approaches for designing divide-and-conquer pipelines, along with the use of other DTM methods, might provide better accuracy, and it is also possible that the most important usage of DTM pipelines might be found on the largest datasets. Being motivated by the new DTM methods that have been developed in the last years, we aim to revisit the question of whether DTM pipelines can be useful in large-scale maximum likelihood gene tree estimation. We include three ML heuristics (RAxML-NG, IQ-TREE 2, and FastTree 2) and compare them to divide-and-conquer pipelines using the three current leading DTMs (TreeMerge, GTM, and Constrained-INC). We explore topological accuracy and runtime on simulated datasets with 1000 to 50K sequences. Our study shows that the improved DTM pipelines we developed for this study are more accurate than FastTree 2 and IQ-TREE 2 under the model conditions we explored (which constrain the running time and available memory to 64 GB). Furthermore, our new DTM pipelines are faster than RAxML-NG and they can analyze ultra-large datasets that IQ-TREE 2 or RAxML-NG either fail on or return a poor tree, potentially as a result of the limitation on computational resources. However, for those datasets small enough for RAxML-NG, the DTM pipelines that we developed do not reliably match or improve the accuracy of RAxML-NG, which suggests that further development is needed to achieve the goal of enabling fast, scalable, and highly accurate ML gene tree estimation. Therefore, our study also provides insight into design strategies for use in divide-and-conquer approaches to large scale phylogeny estimation, and it provides directions for future research. Finally, our study, although limited to a small number of model conditions, enables us to make some preliminary recommendations about method choice using existing methods and DTM pipelines.