esa-sceva/satcom-chunk-collection huggingface dataset:
# Community Detection Framework Using Deep Learning in Social Media Analysis
## 6 Conclusions  
This paper proposes a GCN-based framework for community detection in social media analysis. It can combine network topology features and communication text features to compute user features. For the automatic extraction of user features, we use the Node2 Vec method based on Word2 Vec, which can capture the connections between nodes and communicate text. Based on user features, GCN-based clustering is performed for community detection. Our proposed framework is evaluated on the Enron email dataset and shows superior performance on benchmark models. One of the novel contributions of this paper is the precise extraction and categorization of users from social networks. This framework can be further utilized to train models on a variety of data and purposes, including social activity detection and user classification. It can not only enhance practical applications in law enforcement but also enrich academic discussions on community dynamics and user behavior in digital environments. Furthermore, our proposed model demonstrates a modularity score that is 1.1% higher than the latest baseline model, indicating a significant improvement in community structure identification. This enhancement reflects our model's superior ability to capture the underlying community features compared to existing approaches.  
Community detection in social media analysis is still a relatively new area of research, and there are several avenues for future work. Our future work is to develop more sophisticated algorithms that can identify hidden communities and sub-communities within larger communities. Another potential direction is to incorporate more complex features, such as temporal information and textual content, into the community detection framework to improve its accuracy. Additionally, research could explore how to effectively incorporate domain knowledge and expert insights into the community detection process to increase its usefulness in real-world investigations. Finally, efforts could focus on improving the frameworks against various scenarios and datasets, especially datasets from different sources, to solve the limitations. It is believed that more comprehensive neural network architectures, such as transfer learning, possess the capability to effectively address these challenges. However, considerable experimental research still needs to be carried out to investigate the practical applications of these algorithms.  
A.S.: methodology, code, and writing; K.-P.C.: data preparation, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Polytechnic University 2024 University-level Research Program for Graduate Student Associate Supervisors (EGD24 DS10) and Young Teachers Funding Project of Shanghai (ZZEGD202412).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Inversion of Chlorophyll-a Concentration in Wuliangsu Lake Based on OGolden-DBO-XGBoost
## 1 Introduction
In summary, artificial intelligence algorithms have demonstrated great potential in inversion of Chl-a concentration in water bodies, but there are still some limitations, such as higher requirements for parameter tuning, easy overfitting, and higher computational cost. To address these problems, an improved OGolden-DBO-XGBoost model is proposed in this paper. The model improves DBO by introducing Obl and Gold-SA, which enhances the global search capability and convergence speed of DBO to search for better XGBoost parameters. This effectively prevents the overfitting problem while also improving the computational efficiency. Wuliangsu Lake is a river trace lake formed by the diversion of the Yellow River [13], which is the largest lake wetland in the Yellow River Basin, and has important values in ecology, economy, scientific research, and other aspects [14]. In the early years, human activities and climate change have led to the deepening of eutrophication and deterioration of water quality in the water bodies of Wuliangsu Lake [15; 16], which has caused serious impacts on the economy and ecological environment of the surrounding areas. In recent years, as an important part of the Inner Mongolia Autonomous Region's \"One Lake and Two Seas\", the environmental protection of Wuliangsu Lake has been highly valued [17], and the water quality and ecological environment have been improved, but it is still necessary to continue to monitor its water quality to ensure that the ecological environment of Wuliangsu Lake is continuously optimized. Therefore, this paper presents the application of an inversion model for Chl-a concentration in Wuliangsu Lake and use of OGolden-DBO to optimize the XGBoost parameters based on the measured Chl-a concentration data and the Sentinel-2 remote-sensing image data. The main work of this paper can be summarized in the following three points:  
1. Due to the fact that there are seasonal characteristics of the nutrient status of Wuliangsu Lake, this paper investigates the influence of monthly characteristics on the inversion of Chl-a concentration in Wuliangsu Lake. 2. Introducing the Obl to optimize the population initialization of the dung beetle optimization algorithm, which increases the diversity of the initial population and improves the global search ability of the dung beetle optimization algorithm. 3. Incorporating the Gold-SA to improve the dancing strategy of DBO, which promotes the exchange of information between the individuals and the best individual, and improves the local search ability of the algorithm.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.7 IoD Formation Performance  
Evaluating the IoD formation proves more effective, as it provides a comprehensive assessment of the entire group of drones within the IoD. The evaluation of the IoD formation involves calculating the average fitness value for the entire formation, which reflects the level of stability or cohesiveness achieved by the IoD formation.  
Again, SLR and RPD CIs and the different CIs of the whole formation are obtained for 30 replications, and the results are shown in the sixth frame of Figures 7, 8, 10 and 11.  
Furthermore, for SLR, the different CIs plotted in the sixth frame of Figure 8 indicate that IPSO and PSO are in close competition. They also indicate no significant difference between PSO and ABC. Thus, IPSO shows better SLR and provides shorter path length.  
The confidence interval and difference confidence interval of RPD shown in the sixth frame of Figures 10 and 11 illustrate that IPSO outperforms the other algorithms, while PSO and ABC are in close competition.  
To further investigate the performance of the presented algorithms, we compare the formation SLR and RPD for all algorithms, and the results are depicted in Figure 12.  
It can be observed that CIPSO outperforms PSO, ABC, and GA in terms of both SLR and RPD. However, when compared to IPSO, CIPSO shows slightly higher SLR and RPD. This observation suggests that IPSO-generated paths exhibit the least energy consumption, as the difference between these paths and straight lines is remarkably small, while GA shows the worst results.  
Overall, the results of the IPSO algorithm consistently outperform those of the other algorithms, demonstrating superior performance for the IoD formation. This observation aligns with the findings recorded in Table 3.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 6 Test Bench and Results
### LoRa Coverage  
To perform the coverage test, a mixed scenario was chosen. It contains rural and urban areas. The scenario is located on the Guadalfeo river in the coastal town of Motril (Granada, Spain). The gateway is placed in an elevated position in the city of this scenario. SF = 7 was used because it is the configuration that would offer us the results in the worst possible scenario, i.e., SF7 offers the shortest coverage [24]. Figure 16 shows the position of the gateway.  
Figure 16: Position of the Gateway.  
Figure 15: Website showing 4 nodes and the measured data.  
Using practical experiments, such as the ones shown in [45], we collected data of RSSI and SNR to determine the area that the network can cover and the quality of the received signal.  
Figure 17 shows the RSSI values positioned on a real image of the scenario under study. The dots colored in red are the zones with the worst RSSI and, the green colored dots are the zones with a high RSSI. According to the results, we can see that a single gateway is giving coverage of 4 km approximately.  
The RSSI values (see Figure 18) and SNR values (see Figure 19) are obtained based on the distance to the gateway. Values of received signal are compared to the Free Space Model [46; 47]. The Free Space Model provides a measure of path-loss when the transmitter and receiver are within line of sight (LOS) range without any obstacles between them. It is based on the Friis free space transmission equation. We use this model to compare our results with the theoretical ones. As can be seen, the results follow the tendency of this ideal model although its values are worse.  
Figure 17: Results of RSSI measured in the scenario.  
Figure 18: Results of RSSI as a function of the distance.  
We can observe that the coverage in the urban area is quite good when there is direct sight between the node and the gateway (i.e., in streets with few buildings in the Fresnel area between the devices). However, in the plain of the Vega, our coverage is very good, reaching values of \(-95\) dBm at two kilometers away from the gateway. Near the coast, we can also observe the influence of the buildings, which reduce the obtained RSSI level. The maximum coverage range is obtained in the valley, with 4 km and an RSSI of \(-115\) dBm.  
Regarding the SNR values, they are very dispersed. The SNR can worsen due to interferences of similar signals (for example, working on the same frequency band or modulation) or due to a malfunction of wires or devices. The presence of extremely high moisture levels in the environment could worsen the signal as well, although this issue was not studied for LoRa networks. In this case, we are sure that no other LoRa node is transmitting in this area. If no interfering signal is present and we assume that devices are properly working, one could think that the SNR can be affected by the presence of obstacles in the line of sight. Therefore, this behavior could be due to the influence of buildings and obstacles.
# Securing SDN-Based IoT Group Communication
## 2 Background  
### SDN and Bloom Filter-Based Forwarding  
Software-defined networking (SDN) [4] is a technology that allows a centralized entity, known as the \"network controller\" (or simply controller) to control programmable switches. SDN switches forward packets based on rules defined by the controller. In particular, in order for a switch to determine how to handle an incoming packet, it either queries the controller or uses a set of rules stored in the switch using a protocol such as OpenFlow [5].  
SDN can be used for implementing Bloom filter [6]-based packet forwarding [7]. This type of forwarding enables multicast communication in an efficient, fast, and stateless way. From a high-level perspective, the solution in [7] assumes that each outgoing interface of an SDN switch is identified by a bitstring identifier; then, it uses a Bloom filter to encode the identifiers of all interfaces through which a packet should be forwarded; finally, it stores this forwarding identifier in the IPv6 address field of the packet. SDN switches are preconfigured with rules that allow them to decide the outgoing interface of each incoming packet simply by \"ORing\" the packet's forwarding identifier with the identifiers of all outgoing interfaces.
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 3 Proposed Method
### Mimo-NOMA System  
Consider an uplink MIMO-NOMA system. In this system, a BS equipped with N antennas is employed. To mitigate near-field effects, users are required to maintain a certain distance from the BS. The BS receives signals simultaneously from K users, each transmitting with a single antenna, which can be written as  
\[\mathbf{\tilde{y}}=\mathbf{\tilde{H}}\mathbf{\tilde{x}}+\mathbf{\tilde{n}}\,, \tag{13}\]  
where \(\mathbf{\tilde{x}}\) represents the user's modulation symbols vector. \(\mathbf{\tilde{H}}\) represents the channel matrix during transmission, and \(\mathbf{\tilde{n}}\) represents the AWGN noise. We transform (13) into a real domain as  
\[\mathbf{y}=\mathbf{H}\mathbf{x}+\mathbf{n}\,, \tag{14}\]  
where \(\mathbf{H}\triangleq\begin{bmatrix}\mathcal{R}(\mathbf{\tilde{H}})&-\mathcal{S}(\bm {\tilde{H}})\\ \mathcal{S}(\mathbf{\tilde{H}})&\mathcal{R}(\mathbf{\tilde{H}})\end{bmatrix}\), \(\mathbf{y}\triangleq\begin{bmatrix}\mathcal{R}(\mathbf{\tilde{y}})\\ \mathcal{S}(\mathbf{\tilde{y}})\end{bmatrix}\), \(\mathbf{x}\triangleq\begin{bmatrix}\mathcal{R}(\mathbf{\tilde{x}})\\ \mathcal{S}(\mathbf{\tilde{x}})\end{bmatrix}\), \(\mathbf{n}\triangleq\begin{bmatrix}\mathcal{R}(\mathbf{\tilde{n}})\\ \mathcal{S}(\mathbf{\tilde{n}})\end{bmatrix}\). The real parts are written as \(\mathcal{R}(\cdot)\) and the imaginary parts are written as \(\mathcal{S}(\cdot)\). We have \(\mathbf{H}\in\mathbb{R}^{2N\times 2K}\), \(\mathbf{y}\in\mathbb{R}^{2N}\), \(\mathbf{n}\in\mathbb{R}^{2N}\) and \(\mathbf{x}\in\mathbb{R}^{2K}\). In this system, K users share the total power P, which can be divided into \(p_{1}\), \(p_{2}\), \(\cdots\), \(p_{k}\). The power allocation coefficient for user \(k\) is denoted as \(\alpha_{k}=\frac{p_{k}}{P}\), where \(\sum_{k=1}^{k}\alpha_{k}=1\). By monitoring the CSI during transmission, users can be arranged in ascending order based on the magnitude of the channel gain \(h_{k}\). Users with higher channel gain are assigned greater \(\alpha_{k}\), enhancing signal clarity during reception. However, \(\alpha_{k}\) cannot increase without limitations. Allocating excessive power to one user may distort the information of other users during transmission, leading to an overall increase in errors. Balancing efficiency and equity considerations, the power allocation factor should fluctuate within a certain interval. Detailed simulation results are presented in Section 4.4.  
To enhance transmission rates, reduce IUI, and lower BER, numerous researchers have explored user-matching strategies. In the downlink NOMA scenario, the BS possesses higher transmit power and greater computational capabilities. Utilizing user-matching strategies in the downlink can create larger power differences among NOMA signals in the same resource block, effectively reducing IUI. However, in the uplink NOMA system, terminal transmit power is constrained, making it challenging to achieve significant power differences through user-matching strategies.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 4 Network Flow Programming Models
### Special Classes of NFPs
#### 4.2.2 Assignment Problem  
This problem is very similar to the transportation problem except that the external flows are all +1 and -1. The only relevant parameter for the assignment model is the arc cost. Note that this model also has the bipartite structure, and an illustrative example is provided in Figure 5 which describes the network graph of a problem were a number of workers are required to be assigned to a number of jobs. The number of workers and jobs are equal, and the assignment is one-to-one. The decision variable for the assignment problem is denoted by \(x_{ij}\), which takes the value 1 if worker \(i\) is assigned to job \(j\) and 0 otherwise. The each arc has a cost, which represents the cost \(c_{ij}\) required for assigning \(i\) to job \(j\). The objective is to find the assignment that yields the minimum cost \(\sum_{i=1}^{m}\sum_{j=1}^{n}c_{ij}x_{ij}\). In case a problem is unbalanced (i.e., \(n\
eq m\)), it should be balanced by adding dummy nodes as explained in [24; 25]. The problem has a transportation tableau for which the entries are either 0 or 1. Although the assignment problem is a special case of the transportation problem, but the transportation simplex turns to be inefficient for the assignment problem. A well known algorithm that has a cubic polynomial worst cast time complexity, \(O\left(n^{3}\right)\), for the one-to-one balanced assignment problem is the _Hungarian Algorithm_.  
**Example for a Possible Application to WSN**: One example we can think of, in which the assignment problem could be relavant in a WSN, is the allocation of subchannels to pre-assigned links in the network, assuming we have frequency selective fading. The subchannels, thus have different gains for different links in the network at different carrier frequencies. A frequency selective fading environment could be a place that have so many reflectors, like inside factories with many metal surfaces (a typical place were WSNs can exist). In such a place the delay spread would be high yielding a frequency selective fading channel. An optimization problem would be needed to _assign_ the carrier frequencies (or subchannels) to the links. The cost for allocating a subchannel to a link can be the amount of attenuation experienced for the pair. The objective would be to minimize the overall attenuation in the network. Over here, we assume that we only assign one subchannel for each link, which means that the radio transmitter is a single carrier transmitter.  
Figure 5: NFP model of the assignment problem.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 5 Experimental Verification
### Separation Test
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **X** (\({}^{\circ}\)/s) & **Y** (\({}^{\circ}\)/s) & **Z** (\({}^{\circ}\)/s) \\ \hline
1 & 0.09 & 0.32 & 0.58 \\
2 & 0.15 & 0.35 & 0.57 \\
3 & 0.13 & 0.30 & 0.61 \\
4 & 0.11 & 0.33 & 0.63 \\
5 & 0.08 & 0.39 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Test value for the angular velocity. Figure 19: Separation process. (**a**) Initial state; (**b**) Intermediate state; (**c**) Popup state. Figure 20: Three-axis gyro within the satellite.
## 2 Results and Discussion
### Application to Real Urine Samples  
To test the applicability of the present methodology to real matrices, fifty-two urine samples from anonymous donors were analyzed, using GC-MS operating in the full-scan mode acquisition, to ensure possible positive identifications. Figure 6 depicts a chromatogram from a urine sample spiked at the 100.0 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm{\SIUnitSymbolMicro m}\) for the six TCAs (a) and a positive sample (b) for AMT with an amount of 158.87 \(\pm\) 1.93 \(\mathrm{\  
First, the obtained recovery yields and precision levels are much better or alike other analytical approaches, both in water and urine matrices. The attained analytical thresholds (LODs and LLOQs) compares favorably with the other proposed analytical methods. Unlike other methodologies already reported, the present work has the great advantage of encompassing therapeutic, toxic and lethal concentrations in its linear range. In short, the methodology proposed herein can be considered an alternative for the determination of the six TCAs involved in urine samples.
## 4 Numerical Examples
### The Backward Heat Conduction Problem (BHCP)  
A 1D benchmark BHCP is considered in the second example. The governing equation, as written as Equation (1), is considered. The boundary values on the right and left boundaries are described as Equations (19) and (20). The boundary data at final time is:  
\[u(x,\ t=T)=e^{-\sqrt{\frac{\omega}{2\omega^{2}}}x}\cos(\omega T-\beta x). \tag{23}\]  
The exact solution, as depicted in Equation (22), is adopted in the validation of BHCP. In this example, the thermal conductivity is 1, length of the space domain is 1, and \(\omega=1\). Since the assigned boundary data on the lateral boundaries are zero and the final time boundary data are small, this BHCP under investigation is a severely ill-posed case.  
To examine the stability of our approach, we consider the 1D BHCP with different final elapsed time. The collocation scheme for the BHCP is shown in Figure 10. From Figure 10, the final time boundary data are assigned on the top side of the 2D space-time domain. The right and left boundary data are then assigned on the lateral sides of the 2D space-time domain. The numbers of the boundary, source and inner points are 750, 150, and 900, respectively. Parameters including the terms of the SRPBF and the dilation parameter in this BHCP are considered to be 11 and 4.  
To illustrate the accuracy of our approach, the MAE and the RMSE of the computed results is evaluated by Equations (12) and (13). Table 3 shows the MAE and RMSE of our approach for solving the 1D BHCP with the consideration of different final elapsed time. The MAE of the numerical solutions with the consideration of final elapsed time is of the order of \(10^{-6}\) to \(10^{-8}\). The RMSE of the numerical solutions with the consideration of final elapsed time is of the order of \(10^{-8}\) to \(10^{-11}\). Figure 11 demonstrates the absolute error of our method with the consideration of different final elapsed time. From Figure 11, it seems that the recovered temperature with high accuracy may be yielded.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(N_{x}\) & \(N_{t}\) & **MAE** & & **RMSE** & \\ \hline \multicolumn{5}{c}{**Space-time Localized RBFCM [28]**} & **This Study** & **Space-time Localized RBFCM [28]** & **This Study** \\ \hline
40 & 80 & \(7.54\times 10^{-5}\) & \(3.11\times 10^{-6}\) & \(2.22\times 10^{-5}\) & \(1.47\times 10^{-8}\) \\
40 & 120 & \(3.23\times 10^{-4}\) & \(7.09\times 10^{  
Figure 11: The absolute error of the proposed method with the consideration of different final elapsed time: (**a**) T= 0.4; (**b**) T= 0.6; (**c**) T= 0.8; (**d**) T= 1.0.  
Figure 10: The collocation scheme for the BHCP.
## 4 Results and Discussion
### Computational Complexity of RFMLP Model  
The computational complexity of the proposed RFMLP model is estimated using the execution time on all three datasets, and results are given in Table 10. The execution time of the RFMLP is higher than RF, LR, and MLP, however, RFMLP usually takes less time than  
Figure 4: Performance comparison of all models on KDD-CUP 99 and NSL-KDD.  
Figure 5: The ROC curve of the proposed RFMLP model.  
Figure 3: Performance comparison of all models on STIN dataset.  
SVM. Given that the proposed model takes a slightly longer time for training and testing, the performance of the RFMLP is significantly higher than the machine learning models.