esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Particle Swarm Optimization (PSO)  
PSO is a stochastic optimization technique used to approach non-convex optimization problems. Within the PSO, the candidate solutions or particles move around the search space according to some rules regarding their position and velocity. Each particle's movement is influenced by its local best known position, and is also guided toward the best known positions in the search space [32]. Equations (10) and (11) are used for computing the velocity and position of a given particle, respectively. In this case, \(t\) is the number of the current iteration; \(\vec{x}_{i}\) and \(\vec{v}_{i}\) indicate, respectively, the \(i\)th particle's position and velocity; \(w(t)\) is the inertia weight; \(\vec{x}_{gBest}\) and \(\vec{x}_{pBest_{i}}\) are the historically best position of the entire swarm and particle \(i\), respectively; and \(c_{1}\) and \(c_{2}\) are the personal and global learnidng coefficients, respectively. Finally, \(r_{1}\) and \(r_{2}\) are uniformly distributed random numbers in the range \([0,1]\).  
\[\vec{v}_{i}(t+1)=w(t)\vec{v}_{i}(t)+c_{1}r_{1}\big{[}\vec{x}_{pBest_{i}}-x_{i} (t)\big{]}+c_{2}r_{2}\big{[}\vec{x}_{gBest}-\vec{x}_{i}(t)\big{]} \tag{10}\]\[x_{i}(t+1)=x_{i}(t)+v_{i}(t+1) \tag{11}\]  
Figure 1 depicts the flowchart of the implemented PSO. The algorithm begins with a random generation of candidate solutions or particles. Each particle is randomly located in the search space, bearing in mind the limits of the decision variables. As previously mentioned, every particle is associated with two vectors: one that indicates its position and another one that describes its velocity. These two vectors are updated for each particle in every iteration. In this updating process, the personal best and global best information are considered to update the new positions and velocity of the particles. The process stops once a given number of iterations has elapsed. In [33], the authors combine PSO with HS to approach high dimensional optimization problems. In this paper, a hybrid PSO-HS algorithm is also tested to solve the optimal coordination problem.
###### Abstract  
Piping systems are important nonstructural components of most types of buildings. Damage to piping systems can lead to significant economic losses, casualties, and interruption of function. A survey of earthquake disaster sites shows that suspended piping systems are flexible and thus prone to large deformation, which can lead to serious damage of the piping systems. The single-sided bounding tuned mass damper (PTMD), which is an emerging vibration suppression tool, has the potential to serve as a cost effective and non-invasive solution for the mitigation of vibration in suspended piping systems. The operating frequency of the single-sided PTMD can be tuned similarly to a tuned mass damper (TMD). The single-side PTMD also possesses high energy dissipation characteristics and has demonstrated outstanding performance in vibration control. One of the key factors affecting the performance of the PTMD is the damping material, and there is a constant search for the ideal type of material that can increase the performance of the PTMD. This paper explores the use of shape memory alloy (SMA) sponge as the damping material for two types (spring steel and pendulum types) of PTMDs to mitigate the vibration of a suspended piping system. The PTMDs are tested both in free vibration and in forced vibration. The results are compared with no control, with a TMD control, and with a viscoelastic (VE) material PTMD control. The results show that in free vibration tests, SMA-PTMDs attenuate the displacement of the piping system significantly. The time to mitigate vibration (i.e., reduce 90% of the vibration amplitude) is reduced to 6% (for spring steel type) and 11% (for pendulum type) of the time taken to mitigate vibration without control. In forced vibration tests, the overall magnitudes of the frequency response are also lowered to 38% (spring steel) and 44% (pendulum) compared to vibration without control. The results indicate that SMA has the potential to be a promising energy dissipating material for PTMDs.  
vibration control; bounding tuned mass damper; PTMD; viscoelastic material; shape memory alloy; SMA; suspended piping system +
Footnote †: journal: Applied Sciences
# Monitoring of [PERSON]'s Disease Based on Wireless Sensing Technology
## 5 The Data Processing
### Data Preprocessing
#### 5.1.2 Signal De-Noising  
The patient's actions mainly affect the low-frequency components of the wireless signal, so we needed to filter out the high-frequency noise generated by environmental noise, slight internal voltage fluctuations, etc. In this paper, wavelet transform was used to realize signal de-noising [25]. The \"wden\" function with one-dimensional noise reduction in the MATLAB toolbox was used. The main principle of this function is to filter out noise through threshold processing of the wavelet decomposition coefficient of the original signal.  
We used the \"sym8\" wavelet to decompose the original signal in 5 layers. The \"SimN\" (N = 2, 3,..., 8) wavelet has good symmetry, which can reduce the phase distortion during signal decomposition and reconstruction to a certain extent. At the same time, we applied heuristics to overcome the problem of noise distribution at each decomposition level. The signal waveforms of each action after de-noising by wavelet transform are shown in Figure 7; the larger the variance, the larger the information. We chose the subcarrier according to the principle of maximum variance [26].  
Figure 6: The original signal after removing the outliers.  
Figure 7: _Cont._
# A Study of a Gain Based Approach for Query Aspects in Recall Oriented Tasks
## 4 Experiments and Analysis
### Analysis of the Results
#### 4.3.4. Final Remarks  
In this last section of the analysis of the results, we want to briefly summarize our findings. As a remainder, we want to point out that, the GAIN measure proposed by [21], was originally used to estimate the missing information that the user could have gained, by using different subtopic formulations, showed in a user-interface. Although such task shares similar aspects with the one of predicting the recall, they are not fully overlapping. Our main contributions in this paper are:  
* First, adapting an already established technique to a different task. In this sense, to the best of our knowledge, this is the first effort in adapting the GAIN measure proposed by [PERSON] et al. [21] to the query formulation recall prediction task.
* Secondly, its \"mean\" version, which we refer to as \"Mean Gain\", is observed here for the first time, as a better adaptation of [21] to the predictive task.  
Finally, the Similarity-based Gain is a completely new contribution of this manuscript, which exploits similar elements to the gain measure proposed by [PERSON] et al. (2018).  
Table 4 shows that the similarity based gain has the overall best performance both compared to other gain based measures and traditional predictors (cfr. Table 3). Interestingly, while the original gain worsen with the increase of the cutoff (as observed both in Tables 2 and 3), both the mean based and the similarity one tend to improve their performance when the cutoff increase. The original gain suffers of the \"saturated gain\", as reported in Section 4.3.1, while our proposal (both mean and similarity) improve as new relevant information is added.
## 2 Theoretical Calculation of Additional Internal Forces in Composite Foundation Piles and Soil
### Method for Calculating the Additional Internal Forces of Target Piles and Soil  
The target piles and soils investigated in this study refer to all piles and inter-pile soils within the composite foundation, as illustrated in Figure 1. Additionally, the focus included the central pile and inter-pile soil located at the central position, as depicted in Figure 2.  
Figure 1: Schematic diagram of all piles and inter-pile soils in the composite foundation: (**a**) all piles and inter-pile soils; (**b**) all piles; and (**c**) all inter-pile soils.  
When calculating the additional axial force in the target pile, it was necessary to select all finite element units corresponding to the target pile. Different cross-sectional areas at various elevations were established for the target pile. The vertical normal stresses of the finite elements at the cross-section were then mapped onto the cross-sectional area. Subsequently, the sectional numerical integration method was employed to calculate the additional axial force and additional vertical internal force at the cross-section, as shown in Equation (1).  
\[F_{N}^{p}=\int\limits_{A_{z}}(\sigma_{z}^{p}-\sigma_{z}^{p0}) \tag{1}\]  
where \(F_{N}^{p}\) represents the additional axial force at the cross-section of the target pile; \(\sigma_{z}^{p}\) denotes the vertical normal stress on the cross-section of the target pile under the loaded condition; \(\sigma_{z}^{p0}\) represents the vertical normal stress on the cross-section of the target pile under the initial condition; and \(A_{z}\) is the cross-sectional area of the target pile.  
The method for calculating the additional vertical force in the target soil was the same as that for the pile, as shown in Equation (2).  
\[F_{N}^{s}=\int\limits_{A_{s}}(\sigma_{z}^{s}-\sigma_{z}^{s0}) \tag{2}\]  
where \(F_{N}^{s}\) represents the additional vertical force at the cross-section of the target soil; \(\sigma_{z}^{s}\) denotes the vertical normal stress on the cross-section of the target soil under the loaded condition; \(\sigma_{z}^{s0}\) represents the vertical normal stress on the cross-section of the target soil under the initial condition; and \(A_{s}\) is the cross-sectional area of the target soil.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 4 Design and Performance Simulation of the Adaptive RC-NB-LDPC Coding Scheme
### Design and Performance Simulation of the Adaptive RC-LDPC Coding Scheme
#### 3.2.1 Encode and update the cluster center  
When the image is transmitted in the wireless channel, the code rate allocation scheme is adaptively adjusted by the clustering of the current channel data, and the corresponding source and channel coding are performed by using the code rate allocation scheme. Finally, the real-time data of the channel at this time is used as training data to update the cluster center.
# Intelligence-Based Strategies with Vehicle-to-Everything Network: A Review
## 5 AI-Powered VANET Solutions
### Mobility Management  
The Internet of Vehicles permits dependable, low-latency, and high-bandwidth transmission. It exhibits a strong correlation between user portability and responses to barriers in complicated circumstances on highway frameworks, such as crossroads and traffic signs, and at RSUs located on highways or around an urban area. Significant impacts are observed on both the mobility concept and the overall efficiency of radio signals by such constraints [168]. The primary distinguishing feature of the vehicle is its high-speed mobility, which sets it apart from different cellular networks. Vehicle velocities may differ based on their surroundings [169]. While the motion of cars can be anticipated, the effect of motion on connection and network structure constitutes a significant obstacle in vehicular networks. As an illustration, a vehicle may swiftly join or depart from the community, resulting in regular modifications to the network's architecture. Formulatingapproaches to effectively regulating drivers' actions is paramount in tackling mobility concerns. CNNs and ANNs are suitable AI approaches for driving patterns and highway congestion. ANN and fog computing can be utilized for predicting mobility, particularly in vehicular networks based on Software-Defined Networking (SDN) [170; 171]. The KNN algorithm can forecast mobility and lane shifts by considering the nearness of one vehicle with comparable characteristics [172].
## 5 Discussion of the Application and Effectiveness of AR in Industrial Assistance and Training
However, without a systematic design approach, there is a risk that AR solutions are developed in an ad hoc manner, which could lead to inconsistencies, redundancies or even failures in their implementation. This lack of standardisation and consistency in the design of support instructions can limit the effectiveness of AR, wasting its potential and, in adverse situations, adding complications or risks to industrial processes. Conversely, in the field of safety training, AR is being integrated as a tool that can enhance the standard of training deemed effective in Industry 4.0. Virtual training systems have been developed that have enhanced the current methods of teaching safety protocols related to manufacturing processes [46; 50]. The adoption of panoramic AR viewers has enhanced the interactive user experience in safety training [58]. In terms of equipment operation, an AR system has been proposed that enables workers to immediately detect hazards associated with equipment and machinery in their work environment [74]. Furthermore, the utilisation of AR has enabled workers to gain enhanced capabilities for the identification of risks in real time. Research conducted by [PERSON] and [PERSON] [47] and [PERSON] and [PERSON] [58] has demonstrated the potential of AR in facilitating the improvement of risk identification and management, thereby contributing to the creation of safer workspaces. Although AR has been demonstrated to be effective in training and hazard detection, its capacity for early risk prevention in the context of Industry 4.0 has not yet been fully explored. Nevertheless, despite the progress made, occupational risk prevention involves not only the identification of hazards but also the anticipation and proactive management of potential risks before they become real threats. While AR has proven its efficacy in training and hazard identification, its potential for anticipatory risk prevention in the context of Industry 4.0 has not yet been fully realised. This gap in the application of AR for the proactive prevention of occupational hazards presents a significant opportunity for research and development. Industry could greatly benefit from AR systems that not only detect hazards but also propose early solutions and prevention strategies. Such applications could encompass a range of scenarios, from AR simulations that recreate various risk scenarios and provide real-time solutions, to systems that supervise and monitor the work environment and alert workers to potential risks before they become workplace accidents.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Data Preprocessing  
The primary objective of this survey is to evaluate and compare the performance of DL models against TBML methods for time series prediction. To maintain the integrity and relevance of the analysis, certain models were excluded based on their consistent underperformance or lack of applicability. Linear regression (LR) models, for example, are omitted due to their poor performance across most surveyed studies, apart from a single instance of Ridge Regression that showed promising results [27]. While this exception highlights potential avenues for future research, it should be noted that other Ridge Regression examples in the surveyed literature perform poorly [61]. Decision Tree (DT) models and Support Vector Machine (SVM) models are also excluded, as they consistently underperformed relative to other methods.  
Autoregressive Integrated Moving Average (ARIMA) models, including variants like Seasonal ARIMA (SARIMA), have historically been popular for time series forecasting. However, in all surveyed instances, these models are outperformed by either DL or TBML methods. Consequently, ARIMA-based models are excluded from this comparative analysis [6; 19; 33; 36; 48; 51; 55; 59; 60; 62; 65]. Adaboost, a tree-based ensemble algorithm developed in 1995, is similarly excluded due to its inferior performance when compared to similar modern TBML methods such as XGBoost, LightGBM, and CatBoost. In all reviewed cases, Adaboost either underperformed or matched the performance of these more advanced methods [30; 64; 66; 73].  
For this analysis, models are grouped into broader algorithmic categories to facilitate meaningful comparisons. Radial Basis Function Neural Network (RBFN) [6], Neural Network Autoregression (NNAR) [37], and Broad Learning System (BLS) [30] models are grouped with Feed-Forward Neural Networks (FFNNs), which also encompass MLP, ANN, and DNN architectures. While Convolutional Neural Networks (CNNs) are technically FFNNs, their unique operational characteristics justify separate analysis. Recurrent Neural Network (RNN) architectures, including GRU, LSTM, and Bi-LSTM, are grouped alongside DeepAR, an autoregressive recurrent neural network [78]. The TBML category encompasses XGBoost, LightGBM, CatBoost, Bagging, GBDT, and RF models.
# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Class** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Resnet-50} & MEL & 0.7602 & 0.8477 & 0.8223 & 0.8350 \\  & NVs & 0.8134 & 0.6952 & 0.7341 & 0.7146 \\  & BKL & 0.7798 & 0.8288 & 0.7955 & 0.8121 \\  & AK & 0.7742 & 0.8792 & 0.9041 & 0.8916 \\  & BCC & 0.8076 & 0.7466 & 0.8197 & 0.7831 \\  & VASCs & 0.8235 & 0.8108 & 0.9023 & 0.8565 \\  & DF & 0.8542 & 0.6355 & 0.8345 & 0.7350 \\ \hline \multirow{8}{*}{Densenet} & MEL & 0.8272 & 0.8766 & 0.8119 & 0.8442 \\  & NVs & 0.7961 & 0.7978 & 0.7783 & 0.7880 \\ \cline{1-1}  & BKL & 0.8013 & 0.8402 & 0.8656 & 0.8529 \\ \cline{1-1}  & AK & 0.8105 & 0.9184 & 0.8769 & 0.8976 \\ \cline{1-1}  & BCC & 0.8117 & 0.6875 & 0.8002 & 0.7438 \\ \cline{1-1}  & VASCs & 0.8432 & 0.8333 & 0.8053 & 0.8193 \\ \cline{1-1}  & DF & 0.9088 & 0.8634 & 0.8221 & 0.8427 \\ \hline \multirow{8}{*}{Inception-v2} & MEL & **0.8331** & 0.8757 & 0.8169 & **0.8463** \\ \cline{1-1}  & NVs & **0.8761** & **0.9104** & **0.7993** & **0.8548** \\ \cline{1-1}  & BKL & 0.7968 & 0.8264 & **0.8976** & **0.8620** \\ \cline{1-1}  & AK & **0.9014** & **0.9424** & 0.8901 & **0.9162** \\ \cline{1-1}  & BCC & **0.8203** & **0.7643** & 0.8105 & **0.7874** \\ \cline{1-1}  & VASCs & **0.8661** & **0.8645** & 0.8263 & **0.8454** \\ \cline{1-1}  & DF & **0.9142** & **0.9325** & **0.8658** & **0.8991** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification results obtained using the focal loss function. Significant values are in [bold]. Figure 11: Loss curves for classification results obtained using the focal loss function. (**a**) Resnet-50, (**b**) Densenet, and (**c**) Inception-v2. Figure 10: Accuracy curves for classification results obtained using the focal loss function. (**a**) Resnet-50, (**b**) Densenet, and (**c**) Inception-v2. Figure 12: Histogram of classification results based on focal loss. (**a**) Resnet-50, (**b**) Densenet, and (**c**) Inception-v2.
## 4 Experiment and Results
### Comparison With the Current Gesture Recognition Methods
Similar to the Ninapro DB5 results, for the BioPatRec DB2 dataset, our model maintained excellent classification performance, outperforming EMGHandNet by a substantial margin of 7.4%. For the BioPatRec DB1 and BioPatRec DB3 datasets, our model achieved remarkable classification accuracies of 91.4% and 91.3%, respectively, significantly surpassing Multi-View CNN. While our model may not achieve the absolute highest accuracy on certain databases, it consistently ranks second overall, comparable to the average accuracy of all other models, and this is accomplished with an impressively minimal parameter count.
## 2 Materials and Methods  
### Study Area  
The PLEEZ (Figure 1), with Poyang Lake (China's largest freshwater lake) as the core, one of the world's most important wetlands recognized by the IUCN, performs multiple ecosystem functions and is of great ecological, economic, social and international significance [35]. Covering 51,200 km\({}^{2}\), it encompasses 38 counties in 9 cities, including the provincial capital, Nanchang. Three subzones can be considered; starting from the center and moving outward, there are the core protection zone, the lakesbore zone, and the development zone. A humid subtropical climate causes abundant precipitation, with an annual average of 1600 mm. Poyang Lake mainly receives water from five rivers (the Gan, Xiu, Xin, Rao and Fu Rivers) and discharges into the Yangtze River in the north. Massive seasonal water throughput is a significant hydrological characteristic of Poyang Lake which manifests as frequent spring and summer floods and low water levels in autumn and winter.  
Encompassing 30% of the provincial land area, the PLEEZ supports nearly half of the population (approximately 20 million) and contributes over 60% of the economic output [36]. Frequent flooding has posed a constraint on local economic development, especially in the  
Figure 1: An overview of the study area of the PLEEZ.  
agricultural sector. Seventeen major flood events occurred here in 60 years, from 1959 to 2010 [37]. Disasters and uncertainty have continued to challenge local flood management, as severe flooding events occurred in four of the five years from 2016 to 2020. Particularly, in the catastrophic summer flood of 2020, the Poyang Lake Basin endured a devastating strike, with more than 7 million people being affected, over 185,000 ha of crops being destroyed and direct economic losses reaching approximately CNY 24 billion [38]. The authorities were unprepared to face the unanticipated widespread inundations despite past response experience and the highest level of control measures, which was attributed to the uncertainty caused by climate warming.  
A long-term increasing trend in the interannual variation in the highest flood stages and duration was determined through an analysis of the 60-year historical Poyang Lake flood records [37]. The estimates obtained by [PERSON] et al. [39] by using representative concentration pathways (RCPs) suggest an increase in flood magnitude and frequency until 2049. There is still a lack of feasible analysis frameworks to address the coming challenges related to risk and uncertainty, despite Poyang Lake basin being a research hotspot for remote sensing flood- and climate-induced hydrological impact [34; 38; 39; 40].