esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Comparison with ANL Experimental Data  
To verify the reliability of the analytical model, we compared the actual vehicle data on Company B's EV, provided by the ANL, with the SOC reduction and voltage-current increase and decrease in the test-driving mode.  
As the initial SOC in the actual experiment was 91.8%, the initial SOC in the analysis model was also set as 91.8%. Figure 8 shows that the changes in the SOC, voltage, and current while driving were consistent with the actual vehicle data on the amount of current; therefore, the reliability of the model was confirmed. Table 4 lists the performance values obtained from the analysis model. The relevant experiment and analysis assumed a room temperature condition; hence, there was a slight numerical difference in the authorized electronic fuel economy combining low- and high-temperature electricity.
# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 2 Background Knowledge
### Planned Event Prediction  
The planned events occur with known cause and timing. The time, location, and topics concerning those events are usually announced in advance through social media or other public platforms. Protests organized by political parties, labor, and student unions are the most common type among the panned events. Those who organize the protests would like to post the gathering date, time, and place as well as the specific demands on their websites or social media accounts in order to attract as many people as possible to obtain support. They assume that larger protests tend to be more disruptive and effective at conveying support compared to smaller ones. To mobilize a large number of participants, it is advisable to plan and publicize the protest's details in advance [11; 12; 13]. In this case, the planned events can be predicted by detecting the event occurrence and tracking critical indicators from news and social media rather than mining patterns from the previous data. For instance, [PERSON], et al. [14] developed a clustering technique based on spatiotemporal k-dimensional structure trees. This method was used to investigate the spatiotemporal patterns of conflict events that occurred in India during 2014 based on the Global Database of Events, Language, and Tone (GDELT) data. Twitter serves as a widely used social media platform that contains abundant information regarding planned events. [PERSON] and [PERSON] [15] introduced an Improved Deep Belief Neural Network (iDBNN) to predict protests using Twitter data, where the efficiency of the proposed method was validated with the case study of the 2019 Hong Kong protests. In addition to Twitter, Google Trends (GT) offers a valuable gateway to access extensive big data on various global topics. [PERSON] and [PERSON] [16] proposed a novel \"variance-in-time\" approach that utilized GT to predict the protests in the United States, contributing fresh insights into the specific domain of political protests.
## 2 Background and Setting
### Notation and Problem Definitions  
For an integer-weighted graph \(G=(V,E,W)\), we will denote \(n:=|V|\), \(m:=|E|\), and \(W_{e}\) the weight of an edge \(e\in E\) throughout the paper. Let \(\delta(v)\subset V\) be the set of edges incident on node \(v\), and \(\mathcal{N}_{G}(u):=\{v:uv\in E\}\) the neighborhood of \(u\in G\). Denote by \(d_{G}(u,v)\) the shortest-path distance in \(G\) from \(u\) to \(v\). For a graph \(G=(V,E,W)\), given two sets of nodes, \(U\) and \(U^{\prime}\), let \(\mathcal{P}_{G}(U,U^{\prime}):=\{uv\in E:u\in U,w\in U^{\prime}\}\) be the set of edges connecting \(U\) to \(U^{\prime}\). Let \(\mathcal{P}(U):=\mathcal{P}(U,U)\) as shorthand. All logarithms will be taken with respect to base 2, unless otherwise stated.  
Definition 2 (Steiner tree problem): Given a weighted, undirected graph \(G=(V,E,W)\), and a set of nodes \(\mathcal{Z}\subset V\), referred to as Steiner terminals, output the minimum weight tree in \(G\) that contains \(\mathcal{Z}\).  
Definition 3 (Approximate Steiner tree): For a Steiner tree problem with terminals \(\mathcal{Z}\) and a solution \(\mathcal{S}_{\text{OPT}}\) with edge set \(E_{\mathcal{S}_{\text{OPT}}}\), a tree \(T\) in \(G\) containing \(\mathcal{Z}\) with edge set \(E_{T}\), such that  
\[\sum_{uv\in E_{T}}W_{uv}\leq r\cdot\sum_{uv\in E_{\mathcal{S}_{\text{OPT}}}}W_ {uv}\]  
is called an approximate Steiner tree with approximation factor \(r\).  
Definition 4 (Directed minimum spanning tree problem (DMST)): Given a directed, weighted graph \(G=(V,E,W)\) and a root node \(r\in V\), output the minimum weight directed spanning tree for \(G\) rooted at \(r\). This is also known as the minimum weight arborescence problem.
## 3 Vision, Key Features and Challenges of 6G
### Vision and Key Features of 6G
#### 3.1.3 Green Network  
The 6G network is expected to meet the essential requirements for energy-efficient wireless communication globally. The green 6G network enables minimum energy utilization and helps achieve a peak data rate (THz) during signal transmission. A significant improvement in the energy efficiency of a network can be greatly experienced by incorporating different energy-harvesting techniques [154; 190]. This also helps facilitate green communication by reducing CO\({}_{2}\) emission. In addition, several communication techniques, e.g., D2D communication, massive multiuser multiple-input-multiple-output (MIMO), heterogeneous network (HetNet), green IoT, non-orthogonal multiple access, energy-harvesting communications, etc., may be adopted to facilitate green communication for future wireless networks [191; 192; 193].
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 4 Simulations and Remarks  
In power systems, various types of nodes require various requirements of reliability and latency. For example, the reliability and the latency of some typical services in power systems are listed in Table 1[19]. In this section, we utilize the Matlab software to investigate whether the proposed OFDM/FHMA system based on the mini-slot scheduling mechanism can meet these requirements under the multi-node access scenario. We list the setting of the simulation parameters as follows: the OFDM numerology (SCS parameter) was set to be 15 kHz; the length of a mini-slot was \(L=7\) OFDM symbols; the central frequency of the system band was set to be 2.5 GHz; the wireless channel follows the flat Rayleigh distribution with the parameter \(\Omega=1\). To show the advantage of our proposed system, the transmitted power of all services (equipments) was assumed to be always equivalent, and the proposed system suffers from an external jammer with a fixed signal-to-jamming ratio (SJR).
## 2 Overload in SAG Mills
### Physical Classification of Overloads  
In SAG mill operations, early identification of overload conditions is essential to maintain efficiency and prevent downtime. Composition, distribution, and mill filling levels significantly influence overload risk. On the other hand, monitoring power consumption can help detect initial overload stages, but its predictive capabilities are limited [8]. The following elements serve as key indicators of an overload scenario (Figure 2):  
* Feed Rate: High feed rates can lead to volumetric overloads.
* Feed Size (Coarse Granulometry): Larger feed size can strain the mill's grinding capacity.
* Pulp Density: This influences the grinding efficiency and power consumption.
* Ball Charge/Ball Size: Improper ball charge or size can reduce grinding effectiveness.  
Figure 1: Types of overloads on SAG mills.  
* Rotation Speed: Speeds that are either too high or too low can affect grinding efficiency; high speeds may not provide sufficient energy for grinding, whereas low speeds might not generate enough potential energy.
* Pulp Viscosity: Viscosity affects the flow through the mill and grinding efficiency.
* Grate Clogging: This leads to slower flow and accumulation, affecting discharge rates.
* Increased Energy Consumption: Higher energy use is often symptomatic of inefficiencies.
* Decrease Noise: This suggests changes in grinding dynamics.
* Pressure Increase: An increase can signal an overload condition.  
These indicators underscore the complexity of overload conditions in SAG mills, highlighting the need for a comprehensive monitoring strategy to effectively classify and address potential overloads. Our study is limited to the variables available in our dataset so that we will focus on these for analysis. Our proposed method uses a convolutional neural network (CNN) supported by physical information to forecast overloads. By incorporating selected physical rules derived from operational conditions, our approach aims to enhance predictive accuracy and reliability. This integration aligns the model with real-world SAG mill dynamics, enabling more precise overload predictions and providing operators with an intuitive and actionable tool for effective decision-making.
## 4 Experiments
### Results
#### 4.3.1 SORT and Deep-SORT Evaluation
An evaluation of SORT and Deep-SORT, where the data association threshold is modified, was also performed on the MOT17 dataset, whose results are presented in Figure 4. As expected, as the threshold value increased, the MOTA score decreased for the majority of the cost matrices. As observed, no threshold value was found to be suitable for all evaluated cost matrices. Hence, the best results were obtained using a threshold value of 0.3, which was thereafter used for all the evaluations. Table 4 presents the results attained on the ISR Tracking dataset. Due to the multi-class available on the ISR Tracking dataset, an evaluation on the SORT algorithm using and not using the class gate metric, to discard associations of objects with different object classes, was performed. Regarding the SORT's results, similar to the reported results on the MOT17 dataset, the proposed data association cost matrices outperformed the default IoU cost matrix. Moreover, the results of all evaluated data association cost matrices were slightly improved by using the class gate formulation, being able to reach the highest MOTA result with 91.02%. The \(A\) cost matrix using the class gate formulation was able to achieve the best result on the TP, FN, IDs, and MT with 29,785, 2799, 51, and 69.3%, respectively. The \(A\) data association cost matrix presents a significant improvement on the MT evaluation metric compared with the IoU cost matrix (61.7% to 69.3%),  
the \(E_{D}^{IoU}\) achieved the best TP, FN, IDs, and ML results. A significant improvement of the MT evaluation metric was attained with the \(A\) cost matrix. Overall, on both SORT and Deep-SORT algorithms, the proposed data association cost matrices outperformed the default \(IoU\) cost matrix. The \(A\) cost matrix achieved the highest values on MOTA and MT evaluation metrics, showing that it could be the most suitable data association cost matrix to use. Regarding those evaluation metrics, the Deep-SORT outperformed the SORT algorithm, with a highlight on the MT evaluation metric (69.3% to 78.7%). Moreover, promising results were reached by both methods on the ISR Tracking dataset. For the following evaluations, based on the reported results, only the following data association cost matrices using the class gate metric were used: \(IoU\), \(A\), and \(W_{M}\) on the SORT algorithm and the \(IoU\), \(E_{D}^{IoU}\), and \(A\) on the Deep-SORT algorithm.
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Performance Evaluation
#### 4.3.3 Comparison on the CEC 2005 Dataset
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Functions** & **LFA** & **FAtidal** & **VSSFA** & **GDAFA** & **WFA** & **CLFA** & **CFA** \\ \hline Sphere (2) & \(0.043\) & \(\mathbf{0.0}\) & \(-\) & \(-\) & \(-\) & \(\mathbf{0.0}\) \\ Rosenbrock (2) & \(1.34\) & \(0.0076\) & \(-\) & \(-\) & \(-\) & \(\mathbf{0.0}\) \\ [PERSON] (2) & \(0.4950\) & \(\mathbf{0.0}\) & \(-\) & \(-\) & \(-\) & \(\mathbf{0.0}\) \\ [PERSON] (10) & \(17.5\) & \(\mathbf{0.0}\) & \(\mathbf{0.0}\) & \(\mathbf{0.0}\) & \(0.0690\) & \(\mathbf{0.0}\) & \(\mathbf{0.0}\) \\ [PERSON] (10) & \(3.15\times 10^{4}  
all compared algorithms with the same evaluation criteria and parameter settings as described in the original paper [26]. Each algorithm is executed for 25 independent runs on each test function with \(n=10\) and 30, respectively. All compared methods are executed by being allowed to consume 1000, 10,000, and 100,000 FEs. We adopt the GAP performance measure proposed in the original paper [26] and it is defined as \(\text{GAP}=|\text{f}-\text{f}^{\bullet}|\) where f is the function value obtained by an evaluated method and f* is the global optimum value of the test function. Table 10 shows the mean minimum (Min.) and average (Avg.) of GAP and Merit of all compared methods over the 12 functions for \(n=10\). We observe that our CFA is less exploitative in small size CEC problems than the leading methods such as G-CMA-ES and L-CMA-ES, both of which are based on the covariance matrix adaptation evolution strategy (CMA-ES) [31] which updates the covariance of the multivariate distribution to better handle the dependency between variables. Though the CFA is less competitive in the mean Avg. GAP, it can deliver a quality Min. out of 25 independent runs. It suggests that the CFA can be executed multiple times and output the best value from those runs when tackling small size yet complex problems. This phenomenon is also revealed in the geometric mean of the merits (GMM). The GMM of the Min. function value is 1.099, 0.995, and 0.878 at 1000, 10,000, and 100,000 FEs, while the GMM of the Avg. function value gradually deteriorates from 0.963, 1.167 to 1.211 at the same FEs. As for high-dimensional and complex CEC problems with \(n=30\), the mean Min. and Avg. of GAP and Merit of all compared methods are tabulated in Table 11. It is seen from the GMM that our CFA is compared favorably to the other methods in both Min. and Avg. function value at all FEs check points. The prevailing exploration search conducted by CFA is due to its elements of AMP-responsive strategies, which are more effective when the problem is more complex and is presented in higher-dimensional space. The G-CMA-ES is again the best method since it excels in terms of GAP in most cases as compared to the other competing methods. It is worth further studying the possibility of including the CMA technique into the CFA to resolve the dependency between variables.
# BDCOA: Wavefront Aberration Compensation Using Improved Swarm Intelligence for FSO Communication
## 3 Simulation Results  
The BDCOA-based WA compensation is developed using MATLAB R2020b with the configuration of an i5 processor and 16 GB RAM. Table 2 shows the simulation parameters (i.e., Scenario 1) used for analyzing the BDCOA. The transmitter module of FSO has Continuous Wave (CW) laser source, where the data are produced using Pseudo-Random Bit Sequence (PRBS) generator, with a pulse generator based on NRZ and Differential Phase Shift Keying (DPSK) modulation. The SLAO has DM using Actuators with the SMF length of 500 m, while the receiver comprises a APD photodiode and Bessel-Thomson electrical filter. Here, Zernike polynomials are used to represent the WAs where refractive  
Figure 4: BDCOA Flowchart for optimum control signal.  
structure parameter is fixed as \(9\times 10^{-15}\) m\({}^{-2/3}\), that is turbulence intensity. This turbulence intensity leads to the moderate level of atmospheric turbulence. Further, the Kolmogorov model is considered as the turbulence model, which helps to simulate the random phase distortions generated by atmospheric turbulence. Accordingly, this Kolmogorov model considers an homogeneous and isotropic turbulent medium that interferes the broadcasting optical wavefront.
## 5 Results and Analysis  
### No Vibration Damping Material Test Analysis  
In this experiment, the loss detection sensor does not incorporate any vibration-damping materials. The impact plate is directly connected to the counterweight mechanism, and a vibration source frequency of 5 Hz was selected. During the experiment, the vibration of the impact plate was measured using a vibrating instrument. Additionally, the voltage conversion value was recorded using a charge amplifier. The data collected during the selected intermediate period are presented in Table 2.  
Finally, the collected vibration and voltage data were analyzed and processed, and the waveform is depicted in Figure 8. The analysis revealed that the vibration frequency of the stressed plate and the vibration source vibration frequency are closely aligned. The vibration amplitude is high, and the detection voltage amplitude of the charge amplifier is also high. In the absence of any vibration-damping material, the mechanical vibration interference was completely overwhelmed by the effective signal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (ms)** & **Vibration Amplitude (mm)** & **Voltage (V)** & **Frequency (HZ)** \\ \hline
150 & 1.56 & 2.62 & 5 \\
151 & 1.92 & 2.62 & 5 \\
152 & 1.92 & 2.83 & 5 \\
153 & 1.92 & 2.89 & 5 \\
154 & 1.90 & 2.69 & 5 \\
155 & 1.92 & 2.46 & 5 \\
156 & 1.92 & 2.36 & 5 \\
157 & 1.92 & 2.98 & 5 \\
158 & 1.90 & 2.62 & 5 \\
159 & 1.86 & 2.69 & 5 \\
160 & 1.93 & 2.78 & 5 \\
161 & 1.56 & 2.76 & 5 \\
162 & 1.92 & 2.96 & 5 \\
163 & 1.92 & 2.99 & 5 \\
164 & 1.92 & 3.03 & 5 \\
165 & 1.90 & 3.00 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Amplitude and voltage without damping material.  
Figure 8: Amplitude and voltage trend chart of non-vibration damping material.
###### Abstract  
Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. Despite earlier studies' good performance, these methods lack syntactic information to guide feature development for contextual semantic linkages in social media text. In this paper, we introduce an enhanced LSTM-based on dependency parsing and a graph convolutional network (DPG-LSTM) for sentiment analysis. Our research aims to investigate the importance of syntactic information in the task of social media emotional processing. To fully utilize the semantic information of social media, we adopt a hybrid attention mechanism that combines dependency parsing to capture semantic contextual information. The hybrid attention mechanism redistributes higher attention scores to words with higher dependencies generated by dependency parsing. To validate the performance of the DPG-LSTM from different perspectives, experiments have been conducted on three tweet sentiment classification datasets, sentiment140, airline reviews, and self-driving car reviews with 1,604,510 tweets. The experimental results show that the proposed DPG-LSTM model outperforms the state-of-the-art model by 2.1% recall scores, 1.4% precision scores, and 1.8% \(F1\) scores on sentiment140.  
Keywords:natural language processing; sentiment analysis; social media text; deep learning; attention mechanism +
Footnote †: journal:
## 5 Study 2: Resilience of Roof-Mounted Solar Panels to High Wind Loads
### Structural Integrity of Roof-Mounted Solar Panels  
With the escalating frequency of severe meteorological phenomena, a pressing need arises to ascertain the structural integrity of roof-mounted photovoltaic (PV) panels amidst the elevated wind velocities associated with such events. Existing research has primarily concentrated on the standalone performance of PV panels, overlooking the critical aspect of their interaction with the supporting roof. Incidents of PV racking system failures during extreme weather events underscore the imperative for refined installation practices and enhanced design methodologies, guaranteeing the resilience of the panels and their underlying roofs.  
Hence, the impetus behind this inquiry lies in scrutinizing the dynamic interplay of wind forces between residential roofs and solar panels, with a discerning eye toward potential consequences on roof configuration and PV system deployment for bolstered performance under extreme wind conditions. The specific focal points encompass an assessment of the influence of panel spacing and arrangement on the wind-induced loads acting upon the solar panels and the building's roof [58]. Concurrently, an evaluation was conducted to ascertain the adequacy of extant design protocols and regulatory standards pertaining to roof-mounted PV systems in regions characterized by high wind speed.
## 5 Discussion and Future Opportunities  
### The Selection of Models  
Currently, there is no consensus on which type of machine learning model yields superior evaluation results. In landslide susceptibility assessment, the selection of machine learning models plays a pivotal role in enhancing prediction accuracy and efficiency. Looking ahead, as technology continues to advance and data accumulate, we anticipate progress in the following areas:  
1. Utilization of Coupled Models: Ensemble learning, through the integration of predictions from multiple models, has the potential to enhance overall predictive performance [108]. Section 4.2.2 details the benefits of coupled models. Figures 4 and 6 illustrate two approaches to model coupling: Figure 4 shows the results of Model 1 being fed into Model 2 for further prediction, while Figure 6 depicts the evaluating factors being the input into different models, with the results of these models then being aggregated using methods such as weighted averaging or voting. Future research could delve into combining various types of machine learning models (such as decision trees, neural networks, support vector machines, etc.) to develop a more robust and reliable landslide susceptibility assessment model.
2. Integration of Multi-Source Data and Interdisciplinary Collaboration: Future studies can harness diverse data sources, including satellite remote sensing data, terrain data, meteorological data, etc., and collaborate with experts in geology, geography, meteorology, and related fields. By amalgamating data and expertise from various sources, more comprehensive and precise landslide susceptibility assessment models can be developed.
3. Advancement of Deep Learning Methods: With the enhancement of computing capabilities and the progression of deep learning technology, the utilization of complex models such as deep neural networks in landslide susceptibility assessment is poised for further expansion [3,144]. Future research can explore strategies to leverage deep learning methods to unearth the latent patterns and features in data, thereby enhancing the accuracy and reliability of landslide prediction. Deep learning models  
Figure 10: Schematic diagram of the feature-based domain adaptation principle [143]. (S\({}_{0}\): Source domain landslide; S\({}_{1}\): source domain non-landslide; S\({}_{T0}\): target domain landslide; S\({}_{T1}\): target domain non-landslide).  
possess strong feature extraction capabilities, enabling them to handle multi-source data and exhibit efficient pattern recognition abilities. Therefore, future research can explore the coupling between various deep learning models as well as the coupling between traditional machine learning models and deep learning models. Such an exploration can address weaknesses in the transparency, interpretability, and susceptibility to overfitting of these models, ultimately enhancing the accuracy and reliability of landslide prediction.
## 3 The Proposed Approach
### Cryptographic Verification  
The final layer of the proposed methodology involves the use of cryptographic techniques to verify the legitimacy of nodes flagged as suspicious. By employing public key infrastructure (PKI) and digital signatures, this component ensures that only authenticated nodes participate in the network, thereby safeguarding the integrity of data transmissions and preventing unauthorized access.