esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Coal and Gangue Recognition Model Based on CAM-Hardswish with EfficientNetV2
## 4 Experimental Results and Analysis
### Comparison between EfficientNetV2-CAMHardswish and Mainstream Model  
To further evaluate the improved model's performance, its test accuracy, recall rate, F1 score, and training speed were utilized as evaluation metrics. These parameters were then used to compare the improved model quantitatively with a network model that has achieved exceptional performance in image classification in recent years. The results of this analysis are presented in Table 5.  
As shown in Table 5, the construction of the coal and gangue recognition network with EfficientNetV2-CAMHardswish architecture in this paper led to significant improvements in both the recognition accuracy and training speed. Consequently, the revised EfficientNetV2 network is an apt choice for actual coal and gangue production conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1 Score** & **Training Time** & **Inference Time** \\  & **\%** & **\%** & **\%** & **/h’min** & **/ms** \\ \hline EfficientNetV2-S & 94.26\% & 90.4\% & 0.942 & 1 h’6 min & 25.7 \\ EfficientNetV2-M & 94.57\% & 93.8\% & 0.962 & 2 h’13 min & 38.2 \\ EfficientNetV2-L & 95.0\% & 92.9\% & 0.96 & 3 h’45 min & 54.8 \\ EfficientNetV2-CAMHardswish & 98.24\% & 99.1\% & 0.989 & 48 min & 19.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model training speed comparison.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Attention** & **Activation** & **Accuracy** & **Recall** & **F1 Score** & **Training Time** & **Inference Time** \\  & **Mechanism** & **Function** & **\%** & **\%** & **\%** & **/h’min** & **/ms** \\ \hline EV2-SE & SE & ReLu & 94.26\% & 90.4\% & 0.942 & 1 h’6 min & 25.7 \\ EV2-CBAM & CBAM & ReLu & 97.45\% & 96.9\% & 0.973 & 1 h’31 min & 34 \\ EV2-L & CAM & ReLu & 97.87\% & 98.6\% & 0.984 & 1 h’35 min & 28 \\ EV2-CAM & EV2-CAM & & & & & \\ Hardswish & CAM & Hardswish & 98.24\% & 99.1\% & 0.989 & 48 min & 19.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of model performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1 Score** & **Training Time** & **Inference Time** \\  & **/\%** & **/\%** & **\%** & **/h’min** & **/ms** \\ \hline Vision Transformer & 94.31 & 94.2 & 0.952 & 1 h’17 min & 8.2 \\ MobileNetV3-large & 94.57 & 94.1 & 0.956 & 30 min & 8.5 \\ ConvNeXt & 94.0 & 93.8 & 0.954 & 1 h’8 min & 6.5 \\ EfficientNetV2-CAMHardswish & 98.24 & 99.1 & 0.989 & 48 min & 19.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model evaluation results.
## 3 Methodology
### Classification Methods
#### 3.3.4 Bidirectional Encoder Representations from Transformers (BERT)  
BERT, which has recently entered the literature, is a Transformer (deep learning model)-based machine learning model for NLP pretraining that was propounded in October 2018 by Google [58]. BERT uses the Transformer [59] architecture to learn word representations. Transformer is a new architecture for array modeling that can be incorporated into deep networks. Using only attention mechanisms, the Transformer learns about global dependencies between input and output [60]. BERT is used in two ways, one of which is the Masked Language Prediction approach. In this method, a few words of the input text are masked and then input into the BERT model, which is then used to predict the masked words. To predict a masked word, the BERT model considers the context of the unmasked words that precede and follow it. BERT can be applied in two phases: pretraining and fine-tuning. During pretraining, the model learns to recognize the input text data and its contextual relationships. In the fine-tuning phase, the model adapts to a specific task and refines its understanding to provide the best solution. By adding a new layer, a pretrained BERT model can be fine-tuned to achieve exemplary results [61]. BERT uses the same architecture in the pretraining and fine-tuning stages, and both stages are shown in Figure 7.  
Figure 6: Architecture of MLP. The BERT model consists of three parts: token placement, partition placement, and position placement [62]. In the model, sentences always start with [CLS] and end with [SEP]. The overall input text sequence is classified according to the output embedding associated with the [CLS] token. BERT has two different variants: Base and Large. BERT-Base consists of 12 layers, 768 hidden dimensions, and 12 attention heads, with 110M total parameters. On the other hand, BERT-Large consists of 24 layers, 16 attention heads, 1024 hidden dimensions, and 340M total parameters [55]. The Base and Large variants also have two different versions each: cased and uncased. In the uncased version, text is converted to lowercase before the word tokenization process. Conversely, the cased version is case sensitive [63]. These classification methods are summarized in terms of their advantages and disadvantages in Table 2 below.
## 4 Simulation Evaluation
### Evaluation of SC-Sched  
#### 4.2.1 Evaluation of Dealing with Frame Loss  
Here, we evaluate the function of consecutive transmissions and retransmissions.  
In Section 3.4, the number of WuC transmissions and the number of data retransmission slots are made adaptive to average SNR. As a comparison, we also evaluate another method, which is SC-Sched with fixed retransmission policies regardless of SNR. In this simulation, the number of sensor nodes is 600 and we do not use multiple channels.  
Table 4 shows the effect of the number of WuC transmissions and retransmission slots on three metrics.  
We first focus on the case of \"Fixed\". The frame delivery ratio increases with the number of WuC transmissions and data retransmission slots. On the other hand, total energy consumption and data collection time increase as well. That is, there is a trade-off between frame delivery ratio and both total energy consumption and data collection time.  
Next, the cases of \"Adaptive\" and \"Fixed\" are compared. When the number of WuC transmissions is 6 and the number of retransmission slots is 3, the frame delivery ratio of \"Fixed\" is lower than that of \"Adaptive\". Nevertheless, the total energy consumption and data collection time of \"Adaptive\" are much less than those of \"Fixed\". In summary, deciding the number of WuC transmissions and data retransmission slots adaptively per hop is effective in improving the frame delivery ratio while suppressing total energy consumption and data collection time.
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 3 Evaluation of Strain Determination Using RBFs
### Verification Examples of WAAM-Produced Cylindrical Components  
We are interested in comparing the properties of the RBF-approach with the classical triangulation concept. For this purpose, an analytical example of a surface deformation is considered, which is similar to a real sample produced by a WAAM-process. In a first step, however, pure tension and torsion of a cylindrical tube with a smooth surface is considered so that the order of possible additional errors due to the roughness of the specimens can be interpreted in the subsequent step.
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
## 2 System Environments and Distance-Based Registration
### Problem of Distance-Based Registration and Its Improvement  
Previous studies [11; 15; 16] used distance threshold \(D\), which is defined as the number of cells between the central cell of the LA and the nearest outer cell in which the UE should register its location, as shown in Figure 1.  
In this definition, the distance between neighboring cells is 1. If \(D=3\), then the LA consists of a central cell (ring 0), with six cells surrounding the central cell (ring 1) and 12 cells surrounding the cells belonging to ring 1 (ring 2). When the UE moves to the outer cell of the current LA to perform location registration, a new LA is set where the cell the UE registered becomes the new central cell of the new LA.  
In a hexagonal cell environment, assuming that an LA consists of \(D\) rings (ring 0, ring 1, ring 2,..., ring \(D-1\)), \(g(i)\), the number of cells in the \(i\)-th ring, can be expressed as follows:  
\[g(i)=\left\{\begin{array}{ll}1,&  
made up the LA through performance analysis. Through this process, we showed that an LA increasing on a cell-by-cell basis had a smaller signaling cost than an LA increasing on a ring-by-ring basis that previous studies considered. Thus, an LA should increase on a cell-by-cell basis in order to achieve optimal performance.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 5 Conclusions and Future Works  
In this paper, we have investigated the joint power allocation and power splitting problem in SWIPT aided OFDM-IDMA for power limited networks. The purpose is to minimize the total transmit power, subject to the requirements of both EH and ID functions. Due to the features of iterative MUD employed in OFDM-IDMA, both EH and ID requirements are affected by the number of iterations. Hence, we have formulated the power minimization problem into the one to jointly optimize the power allocation, PS ratio as well as the number of iterations among all RNs. We then decomposed the original problem into two sub-problems where the EH and ID requirements are considered independently. A sub-optimal algorithm has been proposed to approach the solution of the original problem, by iteratively solving these two sub-problems. Simulation results have confirmed that the performance can be greatly improved through the proposed algorithm.  
The future work lies in two aspects. First, in the proposed system perfect channel state information is assumed. Considering the practical scenario, further study on the robust design of the power allocation and splitting algorithm with respect to the channel estimation error needs to be addressed. Second, we will extend the proposed SWIPT aided OFDM-IDMA system into the multi-hop networks. In such a case, the information of SN will not be delivered to the RNs directly, but through the relaying of multiple intermediate nodes. The optimal path selection problem turns out to be an important issue to impact the energy efficiency of the whole system [30,31], which requires further investigation in the future.  
This work is in part supported by the National Natural Science Foundation of China (NSFC) under No. 61401254, 61371110 and 61671278, EPSRC TOUCAN Project under No. EP/L020009/1, and EU FP7 QUICK Project under No. PIRSES-GA-2013-612652.  
[PERSON], [PERSON] and [PERSON] conceived of the main algorithms. [PERSON] and [PERSON] deduced the main mathematical results. [PERSON] and [PERSON] provided some suggestions. [PERSON] and [PERSON] contributed to the simulations. [PERSON] and [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] revised it.  
The authors declare no conflict of interest.  
Figure 8: Iteration number needed for the proposed algorithm (\(\sigma^{2}=-112\) dBm \(K=4\,,8\,,16\) and BER targets \(=10^{-6}\)).
## 3 Methodology
### Spectral Water Index Methods
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Sensor** & **NDWI Equation** & **Symbol and Notation** \\ \hline \multirow{4}{*}{TM} & NDWI\({}_{T2,4}\) = (B\({}_{T2}\) - B\({}_{T4}\))/(B\({}_{T2}\) + B\({}_{T4}\)) & NDWI\({}_{T2,4}\) is the McFeeters’s NDWI for the TM sensor; NDWI\({}_{T2,5}\) \\  & NDWI\({}_{T2,5}\) = (B\({}_{T2}\) - B\({}_{T5}\))/(B\({}_{T2}\) + B\({}_{T5}\)) & and NDWI\({}_{T2,7}\) are the [PERSON]’s NDWIs for the TM sensor; B\({}_{T2}\), B\({}_{T4}\), \\  & NDWI\({}_{T2,7}\) = (B\({}_{T2}\) - B\({}_{T7}\))/(B\({}_{T2}\) + B\({}_{T7}\)) & B\({}_{T5}\) and B\({}_{T7}\) are Bands 2, 4, 5, and 7 of the TM sensor, respectively. \\ \hline \multirow{4}{*}{ETM+} & NDWI\({}_{E2,4}\) = (B\({}_{E2}\) - B\({}_{E4}\))/(B\({}_{E2}\) + B\({}_{E4}\)) & NDWI\({}_{E2,4}\) is the McFeeters’s NDWI for the ETM+ sensor; \\  & NDWI\({}_{E2,5}\) = (B\({}_{E2}\) - B\({}_{E3}\))/(B\({}_{E2}\) + B\({}_{E5}\)) & NDWI\({}_{E2,5}\) and NDWI\({}_{E2,7}\) are the Xu’s NDWIs for the ETM+ sensor; \\  & NDWI\({}_{E2,7}\) = (B\({}_{E2}\) - B\({}_{E2}\))/(B\({}_{E2}\) + B\({}_{E7}\)) & sensor; B\({}_{E2,8}\), B\({}_{E4,9}\) and B\({}_{E7}\) are Bands 2, 4, 5, and 7 of the \\  & STM+ sensor, respectively. \\ \hline \multirow{4}{*}{ALI} & NDWI\({}_{A4,6}\) = (B\({}_{A}\) - B\({}_{A4}\))/(B\({}_{A4}\) + B\({}_{A6}\)) & NDWI\({}_{A4,7}\) and NDWI\({}_{A4,7}\) are the McFeeters’s NDWIs for the ALI \\  & NDWI\({}_{A4,7}\) = (B\({}_{A4}\) - B\({}_{A2}\))/(B\({}_{A4}\) + B\({}_{A7}\)) & NDWI\({}_{A4,8}\) and NDWI\({}_{A4,9}\) and NDWI\({}_{A4,10}\) are the Xu’s NDWIs \\  & NDWI\({}_{A4,8}\) = (B\({}_{A}\) - B\({}_{A8}\))/(B\({}_{A4}\) + B\({}_{A9}\)) & for the ALI sensor; B\({}_{A4,6}\), B\({}_{A7}\), B\({}_{A8}\), B\({}_{A9}\), and B\({}_{A10}\) are Bands 4, \\  & NDWI\({}_{A4,10}\) = (B\({}_{A4}\) - B\({}_{A10}\))/(B\({}_{A4}\) + B\({}_{A10}\)) & 6, 7, 8, 9, and 10 of the ALI sensor, respectively. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Normalized difference water index equations and associated sensors (T: TM, E: ETM+, A: ALI). employed to form three NDWI models, namely, NDWI\({}_{\text{A4,8}}\), NDWI\({}_{\text{A4,9}}\), and NDWI\({}_{\text{A4,10}}\). Similarly, Bands 2, 5, and 7 of the TM and ETM+ sensors were used to form four NDWI models, namely, NDWI\({}_{\text{T2,5}}\), NDWI\({}_{\text{T2,7}}\), NDWI\({}_{\text{E2,5}}\), and NDWI\({}_{\text{E2,7}}\) (Table 2).
###### Abstract  
(1) Benign melanoses are a frequent issue in aesthetic dermatology. Solar lentigo, ephelides, cafe au lalit spots, and other melanoses represent a cosmetic issue for a growing number of subjects. The Q-switched 1064/532-nanometer (nm) laser may be considered the gold standard for management of these aesthetic issues. A new generation of Q-switched lasers, capable of concentrating the energy pulse in the spectrum of hundreds of picoseconds, is emerging, promising better results than previous ones. In this paper, we report the use of a Q-switched laser with a picosecond pulse to manage hypermelanoses. (2) Methods: 36 patients seeking melanosis removal were retrospectively enrolled at Magna Graecia University of Catanzaro. Treatment parameters, although variable, were the following: 1064 nm with a pulse duration of 450 picoseconds (ps) for dermtic lesions and 532 nm with 370 ps for epidermal lesions. Up to four treatments, with a minimum interval between laser treatments of 30 days, were performed. After the last session, patients' satisfaction was assessed at a three-month follow up with a Visual Analogue Scale (VAS). Two blinded dermatologists measured the aesthetic outcome using a five-point scale comparing pictures before laser sessions and during follow-up. (3) Results: 36 patients were enrolled; 23 were females (63.9%) and 13 males (36.1%). The mean reported age was 49.2 \(\pm\) 18.9 years. All participants were assessed with a complete/almost complete melanosis removal at the dermatological evaluation, with a mean VAS score of 9.39 \(\pm\) 0.90. (4) Conclusions: The Q-switched 1064/532 nm laser may be considered the main weapon in treating benign hypermelanosis. The picosecond pulse seems to guarantee better results than other devices. However, a clinical trial comparing Q-switched nanosecond pulse with picosecond pulse is necessary to confirm this study's findings.  
Q-switched laser; benign hyperpigmentations; melasma; solar lentigo +
Footnote †: journal: _Sci._  
Footnote †: journal: _Sci.  
Among these lasers, various papers suggest that the Q-switched laser, releasing energies in nano or picosecond, acts selectively on melanin, and does not cause any thermal damage on the surrounding tissues. Picosecond-pulsed Q-switched lasers release energy in a minimal amount of time and appear to be the most effective therapy [10; 11; 12].  
This study evaluates the safety and efficacy of hypermelanosis removal using a Q-switched picosecond 1064/532 nm laser.
# Effective Attention-Based Mechanism for Masked Face Recognition
## 5 Discussion  
MFR is a significantly challenging problem that is currently attracting substantial research interest in computer vision and the face recognition field. As the key features such as the mouth, nose, and chin are occluded by mask wearing, existing face recognition methods perform poorly. Further, the insufficient availability of training and validating datasets currently represents a major barrier to the adoption of deep learning approaches in MFR. Figure 7 illustrates the loss and accuracy curves of the model. The loss curve shows that the proposed model is learning from the data by trying to reach the minimum point and the accuracy curve still slightly increase until the last epoch. The experimental results show that the proposed method can achieve high performance in the verification task on simulated masked datasets. However, this method exhibited slightly decreased performance when evaluated on the real masked dataset due to the small size of the training real face data. By contrast, other methods exhibited a substantial decrease in performance when evaluated on the real masked dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **LFW\_m** & **AgeDB-30\_m** & **CFP-FP\_m** & **MFR2** \\ \hline CBAM & 98.66 & 94.45 & 96.15 & 95.50 \\ Backbone & 99.31 & 95.28 & 97.08 & 96.25 \\ Backbone + M\({}_{\text{channel}}\) & 99.35 & 95.53 & 97.47 & 96.50 \\ Backbone + M\({}_{\text{spatial}}\) & 99.38 & 95.58 & 97.38 & **96.75** \\ Backbone + M\({}_{\text{channel}}\) + M\({}_{\text{spatial}}\) & **99.43** & **95.86** & **97.74** & **96.75** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experimental results (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Training Set** & **LFW\_m** & **AgeDB-30\_m** & **CFP-FP\_m** & **MFR2** \\ \hline [PERSON] et al. [38] & WebFace-OCC & 97.08 & 87.18 & 86.07 & - \\ [PERSON] et al. [9] & VGGFace2-mini-SM & 97.25 & - & - & 95.99 \\ MFCosface [11] & VGG-Face2\_m & 99.33 & - & - & **98.50** \\ Proposed method & CASIA-Webface\_m & **99.43** & **95.86** & **97.74** & 96.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of face verification results (%) with different methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline LFW\_m & 99.41 & 99.26 & 99.56 & 99.40 \\ AgeDB-30\_m & 95.38 & 93.10 & 98.11 & 95.53 \\ CFP-FP\_m & 96.98 & 96.17 & 98.40 & 97.27 \\ MFR2 & 99.00 & 99.50 & 98.54 & 99.02 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of accuracy, precision, recall, and F1 score (%) using VGG-Face2_m.
## 2 Object Detection Algorithms
### RetinaNet  
[PERSON] et al. [17] propose another one-stage detector called RetinaNet. This model solves the imbalance problem directly connected to one-stage detectors such as YOLO or SSD. The issue is based on the generation of too many locations, in which an object could be present.  
[PERSON] and [PERSON] [18] analyzed the performance of the RetinaNet model, which is decomposed into several parts marked with an appropriate count of FLOPs. Experimental results indicated that the proposed technique, called LW-RetinaNet-v1, is characterized by higher mAP by 0.1% along with the drop of GFLOPs of 1.15 times with respect to the original value of 156 GFLOPs acquired from RetinaNet-800.  
[PERSON] et al. [19] aim at RetinaNet optimization to improve the detection accuracy of tiny objects. The research is based on anchor optimization that utilizes the Crow search algorithm to find ratios and scales during the training phase. Experimental verification on the VisDrone dataset has shown the progress of 4 mAP in contrast to standard RetinaNet characterized by fixed anchors.  
[PERSON] et al. [20] included RetinaNet in their research on the estimation of the maturity levels of broccoli crops from UAV input. Based on the research, RetinaNet demonstrates lower performance than Faster R-CNN, which is highlighted as the best, along with CenterNet.
# Multi-Objective Configuration Optimization of
## 6 Conclusions  
Taking compensation of wind power fluctuation, for example, the study developed the multi-objective configuration optimization of HESS to compensate the fluctuating power output of the intermittent DG, in which the HESS is composed of a supercapacitor and battery. For maximizing the stability of the wind power output with minimized HESS investment, along with fuzzy control that is used for HESS power allocation, a multi-objective model for optimal HESS configuration was established to decrease the installation, operation, and maintenance costs of HESS and to improve the compensation satisfaction rate of wind power fluctuation. With the application of BC-MOPSO, a set of Pareto solutions were identified to provide diverse alternative configuration solutions, from which the decision-maker can make a choice according to the requirements of investment and compensation performance. The Pareto front demonstrate the relation between the power fluctuation compensation effect and the total installation, operation, and maintenance costs of HESS based on which the compensated power quality and the HESS investment economy is compromised along with the configuration of the HESS. The simulation results also demonstrate that the power allocation strategy  
Figure 6: Compensation effect on wind power output by HESS. (**a**) Comparison between \(P_{WD}\) and \(P_{WD}\); (**b**) comparison between \(P_{WD}\) and \(P_{WDnf}\).  
Figure 7: SOC of the ESS.  
with the application of fuzzy control enables to effectively reduce the fluctuation of the output power. Meanwhile, the SOC of the ESS is controlled within a reasonable range, which avoids negative effect on its lifetime resulting from saturated and exhausted states.  
This work is supported by the National Natural Science Foundation of China under Grant No. 5160070515 and Hubei Provincial Engineering Research Center of Microgrid under Grant No. 2015 KDW09.  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] designed the application of BC-MPSO algorithm and reviewed and edited the manuscript. [PERSON] established the MOO model and realized the simulation platform. [PERSON] wrote the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.