esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor
## 3 Results and Discussion
### Performance Evaluation
#### 3.3.2 Comparison of Classification Accuracy  
In this subsection, the classification accuracy and the average number of features selected by each algorithm are compared. The accuracy is the percentage of correctly classified test samples, and the average features selected (AFS) is the average number of features to which an algorithm reduces the dimensions of a dataset. AFS is computed by taking the average of the number of features selected by an algorithm in all 20 runs. The best accuracies obtained by each algorithm on all datasets are presented in Table 4. It is evident from the results that EO outperformed all other algorithms on all datasets. However, ASO performed equally well in three cases: DS1, DS5, and DS6. Simulated Annealing, on the other hand, was the worst performer on all datasets. It is important to note that the easiest dataset to classify was DS5, because all algorithms attained 100% on this dataset; however, SA was the only one that had an accuracy of less than 100% on this dataset. In this analysis, DS2 remained the toughest benchmark, on which the best performer could not obtain more than 82% accuracy.  
In addition to comparing best accuracies, the average accuracy of all runs along with standard deviations are compared in Figure 3. When comparing best accuracies, EO outperformed on all datasets, but due to inconsistent performance in different runs and a slightly higher standard deviation, EO was no longer the best performer on DS1, DS3, and DS6. However, its performance was comparable. On the other hand, ASO managed to outperform EO on DS1 and DS6, and both were equally good at DS2. Moreover, the performance of GSA was noticeably the best on DS3, whereas SA was still the worst performer on all datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **SA** & **GSA** & **SCA** & **ASO** & **HGSO** & **EO** \\ \hline DS1 & 0.01869 & 0.01758 & 0.0198 & **0.01157** & 0.0198 & **0.01157** \\ DS2 & 0.22692 & 0.20258 & 0.20092 & 0.19268 & 0.19888 & **0.18153** \\ DS3 & 0.13662 & 0.08635 & 0.10285 & 0.08712 & 0.11781 & **0.06985** \\ DS4 & 0.08574 & 0.04419 & 0.02946 & 0.05951 & **0.01532** & 0.01591 \\ DS5 & 0.02798 & 0.00453 & **0.00001** & 0.00407 & **0.00001** & **0.00001** \\ DS6 & 0.07777 & 0.00333 & **0.02515** & 0.02665 & 0.04863 & **0.02515** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best fitness values of all algorithms.  
Amazingly, ASO, which gave some good results in terms of best accuracy, was unable to minimize dimensions as superbly as the other algorithms did. ASO frequently provided double the AFS as the best AFS provided by any other algorithm, as seen in DS2, DS4, DS5, and DS6.
## Materials and Methods
### Characterization Techniques  
The scanning electron microscope (SEM) images of particles were taken on a JSM-IT800 instrument, JEOL, Akishima-shi, Tokyo, Japan operating with 5 kV. The X-ray diffraction (XRD) patterns of the compounds were acquired in the range of 10-90\({}^{\circ}\) on a D8 ADVANCE instrument, Bruker, Billerica, MA, USA with CuK\(\alpha\) radiation and a scanning rate of 3\({}^{\circ}\)/min. The Fourier transform infrared spectroscopy (FTIR) analysis of particles was recorded in KBr in the form of a compressed pellet in the range of 400-4000 cm\({}^{-1}\) on Vertex 70 V. Bruker, Billerica, MA, USA.  
Microwave absorption properties were characterized by measuring the magnetic and dielectric properties of the compounds in the frequency range from 0.1 GHz to 18 GHz, covering most of the microwave pollution frequency in our daily life, such as communication devices, satellite communications, radar, etc. [35]. The sample was prepared in the shape of concentric circles with a thickness of 3 mm, an inner diameter of 3 mm, and an outer diameter of 7 mm, and was tested using the coaxial method based on the ASTM D5568-2a standard [36], on an ENA Series Network Analyzer, N5080a Agilent, Santa Clara, CA, USA. As shown in Figure 2, the concentric circle sample was put on the coaxial airline (sample holder) and then accessed the coaxial transmission line through a connector to the Network Analyzer. Then, the reflection loss was calculated using transmission line theory. A detailed illustration of the fabrication and the characterization method is presented in Figure 2.
## 1 Introduction
Long-term flux measurements at three forest sites (one deciduous and two evergreen stands), as well as a time series of satellite observations were used to analyze the responses of annual NEP and GPP to multiple spring phenological indicators, including C-uptake period onset, spring temperature, and spring EVI. The main objectives of this study were: (1) to compare the potential of various spring phenological indicators for predicting annual C fluxes, and (2) to analyze the responses of different plant functional types to the relationship between C fluxes and these spring phenological indicators.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 3 Coordinated Control Strategy
### Modified Efficient Modal Control
#### 3.2.2 Introduced Damping Weight  
The novelty of this method with respect to EMC is to exploit the introduced damping as a weighting parameter. The introduced damping weight is calculated in Equation (11) as the ratio of the damping, introduced by the \(j\)-th actuator in the \(i\)-th mode, and the maximum damping introduced among any actuator-mode couple:  
\[w_{h_{i,j}}=\frac{h_{i,j}}{h_{max}} \tag{11}\]  
where \(h_{i,j}\) is calculated through Equation (8) and \(h_{max}=\max(h_{i,j})\).
# Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV
###### Abstract  
The coaxial Tri-rotor micro air vehicle (MAV) is composed of three coaxial rotors where the aerodynamic characteristics of is complicated in flight especially when the wind effect is introduced. In this paper, the hovering performance of a full-scale coaxial Tri-rotor MAV is analyzed with both the simulations and wind tunnel experiments. Firstly, the wind effect on the aerodynamic performance of coaxial Tri-rotor MAV is established with different rotor speed (1500-2300 rpm) and horizontal wind (0-4 m/s). Secondly, the thrust and power consumption of coaxial Tri-rotor (\(L/D=1.6\)) were obtained with low-speed wind tunnel experiments. Furthermore, the streamline distribution, pressure distribution, velocity contour and vortex distribution with different horizontal wind conditions are obtained by numerical simulations. Finally, combining the experiment results and simulation results, it is noted that the horizontal wind may accelerate the aerodynamic coupling, which resulting in the greater thrust variation up to 9% of the coaxial Tri-rotor MAV at a lower rotor speed. Moreover, the aerodynamic performance is decreased with more power consumption at higher rotor speed where the wind and the downwash flow are interacted with each other. Compared with no wind flow, the shape of the downwash flow and the deformation of the vortex affect the power loading and figure of metric accordingly.  
coaxial Tri-rotor MAV; horizontal wind; low-speed wind tunnel; numerical simulation +
Footnote †: journal: Applied Mechanics and Physics Letters  
1
Footnote 1: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China; [EMAIL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (Z.C.)1
# RF Energy Harvesting Wireless Communications:
## 4 Power Management in RF-EHWC
### Design Principles of Power Management in RF-EHWC
#### 4.1.3 Packet Deadline Constraint  
In some delay-sensitive applications, such as target detection and disaster warning, the sensing information must be delivered before a transmission deadline. How to minimize energy consumption while sending messages timely involves the third category of power management problem in RF-EHWC.  
Similar to the feasible data tunnel built in Figure 15a, we can build a feasible latency tunnel, where the upper bound is the cumulative data arrival. The lower bound of the tunnel is determined by the transmission deadline of data packets. As investigated in [58], if all data packets have the same deadline  
Figure 14: Step (**a**): Data transmission scheduling under the constraint of limited data storage, where \(D_{j}\) and \(t_{j}\) are the amount of data and the arrival time of data packet \(k\), respectively; \(D_{max}\) is the maximum capacity of the data storage; and \(r(t)\) is the instant transmission rate of an RF-EHWC device at time \(t\). Step (**b**): Schedule the energy request to minimize the energy cost subject to the least required energy obtained in Step (**a**). Here, vertical edges indicate energy harvests and incurs overhead for energy request.  
and the transmission follow an earliest-deadline-first service, then the lower bound of the tunnel can be obtained by simply shift the upper bound right by the timeline, \(\bar{t}\), as illustrated in Figure15b.  
\[\begin{split}\textbf{P3:}&\operatorname*{arg\,min}_{p( t)}\int_{0}^{t_{i}}p(t)\,\mathrm{d}t,\\ \textbf{s.t.}&\textbf{C:}&\sum_{j=1}^{t _{j}+\underline{t}\leq t_{i}}D_{j}\leq\int_{0}^{t_{i}}r(t)\,\mathrm{d}t\, \leq\sum_{j=1}^{t_{j}\underline{t}\leq t_{i}}D_{j},\qquad\qquad i=1,\ldots,N+1,\end{split} \tag{3}\]  
where \(D_{i}\) and \(t_{i}\) are the size and the arrival time of data packet \(i\), respectively; \(\bar{t}\) is the packet deadline constraint; and the transmission rate of the EHD \(r(t)\) and the transmission power \(p(t)\) are correlated through a power-rate function, \(p(t)=f(r(t))\). The right half of the constraint \(\boldsymbol{C}\) represents the data causality constraint and the left half indicates the constraint of packet deadline.  
In a more general case, an RF-EHWC system can generate data packets having different priorities and transmission deadlines. In this situation, \(\bar{t}\) in Figure 15b becomes a variable. The results obtained in [58] show that if we ignore the energy constraint, i.e., assuming an RF-EHWC device always has sufficient energy for transmitting the data at required rates, the optimal power management with dynamic delay constraint on each data packet is quite similar to the optimal power management with limited data storage obtained at Step (a) in Section 4.1.2. In other words, the profile of the cumulative data sent by the RF-EHWC device is graphically the tightest string in the feasible latency tunnel with a varying \(\bar{t}\).
## 5 Experiment and Result  
### Dataset  
The speech dataset used in this work were obtained from the public VoxCeleb1 audio files dataset. VoxCeleb1 [25] dataset contains 153,516 utterances which is collected from 1251 speakers. The utterances are extracted from different kinds of celebrities uploaded to YouTube. The ratio of male speaker in the dataset is balanced which is 55%. The speakers span a wide range of different ethnicities, accents, professions and ages. The VoxCeleb1 dataset has identification and verification split as shown in the Table 4 for speaker identification and verification, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Dataset Split**} & \multicolumn{2}{c}{**Number of Speakers**} & \multicolumn{1}{c}{**Number of Utterances**} \\ \hline \multirow{2}{*}{**Identification**} & Dev & 1251 & 145,265 \\ \cline{2-4}  & Test & 1251 & 8251 \\ \hline \multirow{2}{*}{**Verification**} & Dev & 1211 & 148,642 \\ \cline{2-4}  & Test & 40 & 4874 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Development and Test Split of VoxCeleb1 dataset for identification and verification.  
Figure 5: TitaNet Architecture [10].  
Although VoxCeleb1 dataset is not strictly clean from noises, we assumed it as clean dataset and generated noisy VoxCeleb1 datasets by adding a real-world noises (such as: Babble, Street, and restaurant noises) to the clean dataset. The noises used in this study is obtained from the source at [26].  
For speaker identification, firstly an original development and test split of the VoxCeleb1 dataset is mixed into one. Then the dataset is divided into training, validation and test split with the ratio of 80%, 10% and 10%, respectively. During speaker identification, randomly selected noise is added to each utterance of the training and validation split at the SNR level from \(-5\) dB to 20 dB. The speaker identification performance is evaluated by adding randomly selected noise to each of the test split utterances at the SNR level \(-5\) dB, 0 dB, 5 dB, 10 dB, 15 dB and 20 dB.  
For speaker verification, the original development and test split of the VoxCeleb1 dataset is used without change during our experiment. The training split consists of 148,642 utterances from 1211 speakers and the test split consists of 4874 utterances from 40 speakers which produces a total of 37,720 trials. During training for speaker verification, for each clean utterances, the noisy utterances are generated by adding randomly selected noises at the random SNR level from \(-5\) dB to 20 dB. The speaker verification performance is evaluated by using noise added verification test split. The randomly selected noise is added to each of the test utterances at the SNR level \(-5\) dB, 0 dB, 5 dB, 10 dB, 15 dB and 20 dB during evaluating for speaker verification.
# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 3 Main Work
### SCGFormer
#### 3.2.1 SGraAttention Module  
The SGraAttention module is equipped with two residual connections [49], primarily comprising a multi-head self-attention mechanism and two layers of semantic graph convolution, along with a LayerNorm (LN) layer [50] and a dropout layer [51]. As illustrated in Figure 2, the 17 joints undergo pre-encoding in the Chebyshev graph convolution layer before being fed into the SGraAttention module. The input feature vectors are initially normalized by the LayerNorm (LN) layer [50] and subsequently passed through the multi-head self-attention layer. The multi-head self-attention module employed here corresponds to the traditional Transformer [41] architecture. Initially, global feature information is extracted through the self-attention mechanism, as depicted in Figure 3a, aiding the model in comprehending contextual relationships among human joint points.  
Figure 3: Strategies for human skeleton feature extraction in the model structure. (**a**) the weight matrix V in self-attention is capable of capturing global information; (**b**) the adjacency matrix in semantic graph convolution, with constraints based on human body structure, is utilized to enhance local information; (**c**) the adjacency matrix in Chebyshev graph convolution, combining adjacency matrix information, is employed to broaden the receptive field, further strengthening local information. The thickness of the yellow arrows represents the strength of the correlation and the black arrow indicate the order of feature extraction.  
Figure 2: Structure of SGraAttention module.  
Following the self-attention mechanism, we eliminate the MLP [42] and introduce semantic graph convolution layers. While the MLP layer possesses robust data fitting capabilities, its excessive parameter count results in some neurons not contributing to the transmission of network information, leading to a wastage of spatial resources. Hence, we opt for semantic graph convolution layers as a replacement for the MLP. Semantic graph convolution layers are more suitable for handling graph-structured data like human skeletal data. Each layer enhances the local feature information of human joint points by learning correlated weights between joints, as illustrated in Figure 3b.  
The structure of the SGraAttention module is depicted in Figure 2, where 'h' represents the number of heads. The module initially utilizes a multi-head self-attention mechanism to capture the global information of joint points, followed by semantic graph convolution to refine the preliminary features of joint points.
## 2 Green Routing Algorithms in WSNs  
Among the factors that consume energy in WSNs, communication among sensors is the worst offender. The routing scheme determines the forwarding path between sender and receiver, and an efficient routing algorithm can clearly minimize the communication cost and maximize the lifetime of WSNs.  
In most existing surveys of green routing protocols in WSNs, routing algorithms are categorized based on the construction of the network, its topology, etc. However, there are few classifications according to the approaches that improve the energy efficiency of WSNs. In this paper, a novel classification is proposed as shown in Figure 1, which classifies the existing energy-efficient routing algorithms into three categories according to the energy-saving scheme, namely, setting special nodes, energy-efficient scheduling, and optimizing data flow. Specifically, setting special node-based routing algorithms include setting hierarchical nodes and special functional nodes. Energy-efficient scheduling-based routing algorithms can be classed into static node scheduling and mobile node scheduling. Optimizing data-flow-based routing algorithms can be classified into single path and multi-path routing schemes.
## 5 Results and Discussion
### The Influence of Categories  
Besides the training sample scale, the number of categories and the distribution of data in different categories will also affect the performance of the classifier. Four different kinds of classification cases are processed together by KNN and SVM. Case 1 is the original 4-category classification for GWC A, B, C and D. Cases 2 to 4 are three 2-category classifications, respectively, for classes A and B, classes B and C, and classes C and D. Figure 19 shows the OA differences between the two methods, i.e., the difference is equal to the OA of KNN minus the OA of SVM.  
In cases 1 and 3, the OA differences are both first less than 0 (SVM achieves a higher accuracy) and then greater than 0 (KNN achieves a higher accuracy) with the increasing of training data quantity,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Coefficients**} \\ \cline{2-4}  & \(OA_{opt}\) & \(R\) & \(M\) \\ \hline NN & 96.18 & 0.007754 & \(-8.232\) \\ SVM & 95.14 & 0.01294 & \(-7.353\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The fitting results.  
Figure 17: The exponential curve fit to the performance results of the KNN.  
Figure 18: The exponential curve fit to the performance results of the SVM.  
which is in line with the descriptions in Section 5.1 and 5.2. In Figure 19a, the zero crossing is about 240, and the quantities of four classes are 19, 92, 114 and 15, while, in Figure 19c, the zero crossing is about 20 and the quantities of classes B and C are 9 and 11. The total quantity of training data differs a lot from 240 to 20 in these two situations. The sizes of the smallest categories, 15 and 9 respectively, are relatively close. It appears that it is the quantity of data in the smallest category that matters rather than the total quantity of all training data.  
However, for case 2 and case 4, it is hard to single out a similar statistical characteristic due to the significant disparity in sample scale of different categories and the lack of data of classes A and D.  
Figure 19: _Cont._
## 4 Discussion and Conclusions
### Conclusions  
The analysis of the applicability of nighttime light image datasets in the extraction of impermeable surfaces in the southwest Karst mountains is of great importance for their use in topographically fragmented areas and the in-depth mining of nighttime light image data. This study used Luojia1-01 nighttime light data, NPP-VIIRS nighttime light data, and Flint-derived nighttime light data to divide the study area into different geomorphological regions for analysis. It further investigated the potential of different nighttime light image datasets for impermeable surface extraction in terrain fragmentation areas. This work can contribute to the subsequent extension of the application potential of nighttime light imagery, supports research to reduce the impact of global warming on humans, and is important for achieving sustainable development of the region.  
**Author Contributions:** Data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, Z.Z. and D.H.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China Regional Projects, grant number 41661088, and the Guizhou Province \"High-level Innovative Talents Training Program '100' Level Talents\" Project, grant number (2016)5674.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Durability of Mortars with Partial Cement Replacement by Recycled Brick Powder
## 3 Results
### Chloride Diffusion Resistance  
The results obtained from the chloride diffusion migration tests are presented in Figure 10. This figure also presents the thresholds established by other authors for classifying the material's durability based on its resistance to chloride diffusion [86; 87].  
The results obtained follow the trend observed in the previous tests. Using RBP as an SCM for OPC partial substitution reduced the apparent chloride diffusion coefficient. In this case, the optimal mixture was again the one with a 35% substitution, yielding an average value of 4.97 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s, which falls below the 5.00 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s threshold for classifying the material as highly durable. The value obtained for the C35 mix is four times lower than that of the reference mix (19.94 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s), indicating low durability. This trend in the results aligns with the observations from the accelerated carbonation study and is closely related to the accessible porosity data presented in Section 3.2. [PERSON] et al. reported a similar trend, noting a decrease of between 40% and 50% for substitution rates between 10% and 20% [92]. In contrast, [PERSON] et al. also observed a significantly greater variation, but in tests conducted at 1500 days of age [62].