esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 4 Optimization Design
### Prediction Model  
EBFNN is an approximate model with good flexibility and suitable for solving multi-dimensional problems. An elliptical basis function neural network was trained to predict the damping force and dynamic range of the MR damper. The relationship between the design variables of the approximate model and the target response can be written as  
\[y(x)=\widetilde{y}(x)+\varepsilon \tag{7}\]  
where \(y(x)\) is the actual value of simulation, \(\widetilde{y}(x)\) is the predicted value obtained in the approximate model, and \(\varepsilon\) is the error between the simulated value and the predicted value.  
Figure 10 shows the distributions of the damping force and dynamic range with different design variables obtained based on the elliptical basis function. The damping force and the dynamic range increased with the increase in shear effective length \(L_{1}\) and \(L_{2}\). To test the accuracy of the EBFNN prediction models, the 80 samples were used, and the predicted damping force and dynamic range agreed well with the FEA simulation.  
To verify the prediction precision of the proposed EBFNN models, five methods, namely the EBFNN, the RSM, the orthogonal polynomial, the Kriging method, and the radial basis function neural network were employed in this work. Table 3 lists the corresponding FEA results. The root-mean-square error (\(RMSE\)) and the determination coefficient (\(R^{2}\)) demonstrate the prediction model validity and evaluate the model precision for both responses, respectively. The upper limit value of \(RMSE\) to judge the model is 0.2, and the smaller the value, the smaller the error of the constructed approximate model. The lower limit of \(R^{2}\) for judging the model qualified is 0.9, and the closer its value is to 1, the closer the constructed approximate model is to the actual response. Moreover, the errors of EBFNN model with respect to the FEA results are lower than that of the other methods, indicating that the fitting accuracy of this model is the highest. Therefore, the following analysis and optimization would be conducted based on the EBFNN prediction model. The corresponding equation is defined as:  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{\left({y_{i}-\widetilde{y}_{i}}\right)^{ 2}}}} \tag{8}\]  
\[R^{2}=1-\frac{{\sum_{i=1}^{n}{\left({y_{i}-\widetilde{y}_{i}}\right)^{2}}}}{{ \sum_{i=1}^{n}{\left({y_{i}-\overline{y}_{i}}\right)^{2}}}} \tag{9}\]  
Figure 9: Main effect graph: (**a**) damping force; (**b**) dynamic range.  
Where \(y_{i}\) is the actual response value of the samples of part \(l\), \(\widetilde{y}_{i}\) is the predicted value of part \(i\) of samples in the response surface model (RSM), and \(\widetilde{y}_{i}\) is the average value of the actual response for the samples of part \(i\).
# Simulating Forest Dynamics of Lowland Rainforests in Eastern Madagascar
## 3 Results  
Within the 100 BTP plots, a total of 233 tree species belonging to 49 families comprised the 2847 trees measured in 2004 and 2005 (1797 among 50 plots in 2008). Stems per plot ranged from 16 to 53 for trees (mean = 28.47) and 22 to 224 for regeneration (less than 5 cm at DBH; mean = 94.19), with an average of 19.27 species per plot in 2004. In addition, species per plot averages fell from 19.27 per plot in 2004 to 15.71 per plot in 2005. The per plot averages decreased in 2005 to 22.27 for trees and 57.28 for regeneration. For the plots measured in 2008, the average number of trees per plot was 35.94, with 54.1 stems of regeneration. However, there was an increase in the average DBH of trees from 12.77 cm to 13.5 cm. Similarly, both total plot basal area (m\({}^{2}\)/ha) and average plot canopy cover (%) increased from 61.46 m\({}^{2}\)/ha and 79% in 2004 to 81.51 m\({}^{2}\)/ha and 83% respectively, in 2005. These trends are concurrent with dynamics that have been observed in other rainforests worldwide ([16; 24]), where local thinning occurs following recovery from the formation of canopy gaps by large-scale disturbances such as cyclones, including Cyclone Kesiny, which heavily impacted the area in and around BTP in 2002.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 2 System Description
### Mppsk Demodulation  
Special impacting filter is a special class of infinite impulse response (IIR) filters [19]. In very narrow passbands, Special impacting filter (SIF) has notch and select frequency characteristics. While filtering out noise, SIF can convert the phase jump of the MPPSK modulated signal into an amplitude impacting. Therefore, the modulation symbol can be demodulated by detecting the position of the impact at the demodulation terminal, which greatly simplifies the demodulation difficulty of the MPPSK system. The transfer function of SIF is given by  
\[H(z)=\frac{\sum\limits_{i=0}^{L}b(i)z^{-i}}{1+\sum\limits_{i=1}^{L}a(i)z^{-i}} \tag{2}\]  
This paper takes the filter having two conjugate poles and one zero point, where  
\[b_{0}=b_{2}=1;\]  
\[b_{1}=-1.9021496572560159,\]  
\[a_{1}=-3.6512241163814698,\]  
\[a_{2}=5.1727286626648894,\]  
\[a_{3}=-3.3577825365961242,\]  
\[a_{4}=0.84572301542400019.\]  
The designed filter is suitable for MPPSK signal whose sampling frequency is 20 times of carrier frequency. Amplitude-frequency response and phase-frequency response of the SIF are shown in Figure 2.  
Figure 1: The MPPSK Waveforms.  
In this paper, the demodulation method based on amplitude integral decision is adopted. MPPSK system structure is shown in Figure 3.  
The received MPPSK signal can transform phase jump into amplitude impact after passing through the impacting filter, and the envelope of the signal can be extracted after passing through the low pass filter for decision. The envelopes of \(M-1\) phase hopping positions are sampled and integrated over the symbol period to find the maximum value of the integral and the associated slot. If the maximum is below the threshold level, the decoder outputs a zero value. Otherwise, the decoder allocates the symbols represented by the time slots corresponding to the largest integrated value [18]. The threshold level of the symbol is the average of the integral value at the phase jump of the symbol and the integral value at the corresponding position of the 0 symbol. The waveform of MPPSK system is shown in Figure 4.  
Figure 4: Waveform of MPPSK system.  
Figure 3: MPPSK system structure.  
Figure 2: The MPPSK Waveforms.
## 4 Discussion
### Potential of OCO-2 SIF and MODIS VIs in Estimating the Autumn Crop Production of China
This result reveals that SIF can monitor crop dynamics better than VIs, which is in good agreement with the previous results [50; 51; 52; 53], suggesting that SIF is able to precisely track the seasonality of photosynthesis and NDVI is insensitive to seasonal changes in photosynthesis. This finding further indicates that it is feasible to estimate autumn crop production while using satellite-derived SIF products. Additionally, the performance is better and more reliable than VIs.
# Haze-Aware Attention Network for Single-Image Dehazing
## 4 Experiments
### Ablation Study  
We performed an ablation study on our HAA-Net using the Haze4k dataset, thus gradually beefing up the key parts of the model to really show how effective each module is, as shown in Table 2. We started with a \"Base\" model, which is a straightforward U-Net structure with basic \(3\times 3\) depth convolutions. When the HAAM was added to the model, its performance improved significantly, thus achieving a PSNR of 31.76 dB and an SSIM of 0.97. What is truly impressive is that the PCNR increased by 6.3 with the addition of the HAAM, thus demonstrating the effectiveness of the HAAM module for dehazing images. As we continued to enhance the HAAM, we observed the PSNR rising even higher to 32.32 dB, along with an SSIM of 0.98. When we integrated the MFEM with the HAAM, the model truly excelled, thus achieving a PSNR of 33.46 dB and an SSIM of 0.99. Furthermore, by incorporating SKFusion technology, we elevated the PSNR to a new peak of 33.93 dB while maintaining an SSIM of 0.99. These outcomes not only validate the effectiveness of the modules we have developed but also establish HAA-Net as a standout performer in the field of image dehazing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **PSNR (dB)** & **SSIM** & **\# Param** & **\# MACs** \\ \hline Base (U-Net) & 25.46 & 0.91 & 0.85 M & 13.35 G \\ Base + HAAM & 31.76 & 0.97 & 8.6 M & 122.29 G \\ Base + MFEM & 32.32 & 0.98 & 18.2 M & 61.90 G \\ Base + MFEM + HAAM & 33.46 & 0.99 & 18.6 M & 122.44 G \\ Base + MFEM + HAAM + SKFusion (Full) & 33.93 & 0.99 & 18.7 M & 122.48 G \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of our HAA-Net on the Haze4k Dataset [48].  
Figure 6: Visual results comparisons on real-world hazy images from the NH-HAZE dataset [49]. Zoom in for best view.
# An Overview of Smart Composites for the Aerospace Sector
###### Abstract  
The continuous evolution of aerospace technology has intensified the demand for innovative materials that enhance structural performance, fuel efficiency, and operational safety. This study conducts systematic bibliometric analysis using data from Scopus and the Web of Science, covering publications from the last decade. Smart composites have emerged as a transformative class of materials, integrating structural health monitoring (SHM), electromagnetic interference (EMI) shielding, and multifunctional capabilities such as self-sensing, self-healing, responsiveness to external stimuli, and adaptability to environmental conditions. Recent developments focus on nanotechnology, the additive manufacturing of smart materials, piezoelectric materials and sensors, as well as aerogels and ultralight structures. By analyzing the evolution of scientific contributions and identifying knowledge gaps, this review provides a valuable resource for guiding future advancements in smart composite materials for aerospace engineering.  
composites; aerospace; nanocomposites; SHM; bibliometric review 1
# Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
## 1 Introduction
All of the approaches mentioned have reported accuracies superior to 98% when tested with the MIT-BIH Arrhythmia Database, but few of them follow the recommendations of the AAMI in terms of the type of heartbeat to detect (in particular the classification of SVEB and VEB) and the use of quality measures in addition to overall accuracy (like sensitivity and positive predictivity) which can be distorted by the results of the majority class [4]. Even more importantly, none of the works that have reported the use of RF for heartbeat classification have followed an inter-patient paradigm where the signals used for the training phase are obtained from different patients respect to those used during the test phase. As noted elsewhere [4; 18], those limitations make it difficult to make fair comparisons between the different approaches reported in the literature. In this investigation, we explore the use of RF for performing heartbeat classification while following the AAMI guidelines and the inter-patient paradigm. The study is also focused on the selection of a reduced set of features that allows for optimizing the classification performance without implying a high computational complexity. For this, we consider a feature space composed of several characteristics already found to be relevant in other studies [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28] and also introduced some novel considerations for calculating normalized features. An optimal set of features is identified by using a selection approach based on the mutual information (MI) ranking criterion [28]. Results confirm that RF are an excellent tool for heartbeat classification and that few selected features of the ECG are enough to obtain state-of-the-art performance.
## 1 Introduction
### Mobile Social Network  
The Mobile Social Network (MSN), previously known as Pocket Switched Networks [7; 8], is a special type of DTN and can be viewed as one type of socially aware DTN. MSN is of growing significance as mobile devices (e.g., smartphones, wearable devices, etc.) proliferate. MSN enables users to share and exchange data between themselves in an established network without the presence of network infrastructure. Data transfer occurs upon opportunistic contacts between mobile devices. The data contents are shared without the need to download the content through the infrastructure. Mobile devices in MSN are largely influenced by user decisions and social behavior. A detailed state-of-the-art MSN is presented in Reference [9], along with the MSN design requirements and future developments.  
Figure 1: The Store-Carry-Forward message switching paradigm in DTN.  
MSN has received increased attention in recent years due to the rapid growth in the use of handheld devices and growing interest in social-based routing strategies. Some recent solutions [10; 11; 12] are proposed for MSN, but they are not multicast-oriented. Since MSN is human-centric and based on the interaction patterns of people and their social interests, it is one of the precursors in realizing the newly emerging Internet of People (IoP) paradigm. The IoP is a human and device-centric paradigm that views human and user devices as the key elements in the network. [PERSON] et al. [13] presented a comprehensive overview of IoP and outlined the open challenges in IoP realization.
## 3 Materials and Methods  
Thanks to newly available technologies, biomechanical data acquisition in sports can be done using optical or non-optical systems. Optical systems include optoelectronic systems (MoCap), which can be marker-based or markerless, such as the Kinect (RGB camera and depth sensor). Non-optical systems include inertial systems. The following considerations guided the choice of experimental settings reported in this paper. Firstly, effective motion tracking using an RGB camera is highly difficult due to quick motions, the presence of several people, and challenging lighting conditions in training rooms. However, deep learning techniques accurately identified persons in RGB movies. Secondly, MoCaps are computationally too expensive to offer the necessary precision for real-time sports. They demand the use of numerous synchronized cameras and the wearing of many markers, making them expensive and impracticable for use in sporting events. Furthermore, to avoid occlusions, the area between the sensor and the tracked person must remain empty, which is a substantial restriction in training facilities. The athlete also needs to stay within the depth camera's field of vision. Finally, monitoring joint rotation with depth sensors is challenging, which is crucial in sports [35]. On the other hand, IMUs, even though they must be placed on the athlete, they do not need a well-organized workspace to be used [4]. IMUs can measure acceleration and angular velocity, but because errors can accumulate during the integration of the acceleration data, they are far less helpful for monitoring position and velocity. For these reasons, magnetometer information is integrated using a sensor fusion technique, usually a Kalman filter. Moreover, IMUs have a greater sampling frequency, often between 50 and 400 Hz, as opposed to a typical depth sensor's 30 Hz. When compared to the visual data from the Kinect (RGB camera), the information provided by IMUs regarding acceleration, angular velocity, and the magnetic field is very different. Nevertheless, previous studies have found that both modalities help support real-time sports training. Therefore, when selecting the sensor for sports motion analysis, ease of use may be a deciding factor. It is worth noting that tracking direction with an IMU may be essential for assessing sports actions that involve rotation, such as fencing during the lunge [35].  
This section introduces the (i) study population, (ii) study design, and (iii) algorithm used for preprocessing and classification purposes. Specifically, Section 3.2 depicts the study population and inclusion criteria; Section 3.3 depicts the instrumentation used during data collection; Section 3.4 depicts the acquisition protocol; and Section 3.5 depicts the biomechanical data preprocessing to identify the data associated with lunge movement. Section 3.6 depicts the principal component analysis algorithm used for dimensionality reduction; Section 3.7 depicts the Machine Learning algorithm selected and used in this study and how Machine Learning algorithms were trained and tested. The data follow the logical flow represented in Figure 1.
## 5 Experiments and Results
### Evaluation Metrics
#### 5.2.3 Average LPIPS (Learned Perceptual Image Patch Similarity)  
The average LPIPS is a deep learning-based metric that assesses the perceptual similarity between two images, focusing on human-like visual perception rather than pixel-level differences.  
\[\text{LPIPS}(x,y)=\frac{1}{N}\sum_{i=1}^{N}d(\phi_{i}(x),\phi_{i}(y)) \tag{5}\]  
where  
* \(N\) is the number of image patches;
* \(\phi_{i}(x),\phi_{i}(y)\) are the feature representations of the \(i\)th patch of images \(x\) and \(y\), respectively;
* \(d\) is a distance function (e.g., Euclidean distance) that measures the difference between the feature representations.
###### Abstract  
The rapid air-to-ground search of injured people in the outdoor environment has been a hot spot and a great challenge for public safety and emergency rescue medicine. Its crucial difficulties lie in the fact that small-scale human targets possess a low target-background contrast to the complex outdoor environment background and the human attribute of the target is hard to verify. Therefore, an automatic recognition method based on UAV bimodal information is proposed in this paper. First, suspected targets were accurately detected and separated from the background based on multispectral feature information only. Immediately after, the bio-radar module would be released and would try to detect their corresponding physiological information for accurate re-identification of the human target property. Both the suspected human target detection experiments and human target property re-identification experiments show that our proposed method could effectively realize accurate identification of ground injured in outdoor environments, which is meaningful for the research of rapid search and rescue of injured people in the outdoor environment.  
air-to-ground search; human target; multispectral imagery; bio-radar +
Footnote †: journal: Medical Imaging
## 6 Conclusions  
This study presents empirical evidence supporting the effectiveness of a newly developed hybrid deep learning model, CNN-ResBiGRU, in accurately identifying gym exercise activities using multimodal EMG and IMU sensor data. The model integrates a CNN, residual connections, and BiGRU to capture spatial and temporal characteristics from the sensor data. Extensive experiments on the Myogym dataset, comprising data from 10 individuals performing 30 different gym activities, validated the superiority of the CNN-ResBiGRU model. The most promising results were obtained by combining IMU and EMG data from the Myo armband, leveraging 14 sensor channels (six for IMU and eight for EMG data). When integrating IMU and EMG data, the model achieves an accuracy of 97.29% and an F1-score of 92.68%, surpassing baseline models in various sensor modality scenarios.  
Rigorous ablation investigations provide insights into the contributions of components within the CNN-ResBiGRU architecture. Convolutional blocks capture spatial patterns, while the ResBiGRU block represents temporal relationships and long-term contextual information, which is particularly significant for EMG data. Examining multiple sensor modalities highlights the synergistic relationship between IMU and EMG data in gym activity detection, resulting in optimal performance by leveraging diverse information sources.  
The study's findings demonstrate the practical utility of the CNN-ResBiGRU model in fitness monitoring, tailored training, and injury prevention. Providing precise exercise histories empowers users to make informed decisions, set achievable goals, and visually track progress. The model's computational efficiency enables real-time deployment on embedded devices, making it suitable for remote monitoring and feedback systems.  
Conceptualization, S.M. and A.J.; methodology, S.M.; software, A.J.; validation, A.J.; formal analysis, S.M.; investigation, S.M.; resources, A.J.; data curation, A.J.; writing--original draft preparation, S.M.; writing--review and editing, A.J.; visualization, S.M.; supervision, A.J.; project administration, A.J.; funding acquisition, S.M. and A.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Phayao (Grant No. FF67-UoE-214); the Thailand Science Research and Innovation Fund (Fundamental Fund 2024); the National Science, Research and Innovation Fund (NSRF); and [PERSON]'s University of Technology North Bangkok (contract no. KMUTNB-FF-67-B-09).  
Not applicable.  
To clarify, our research utilizes a pre-existing, publicly available dataset that was collected and made available by University of Oulu Finland. The dataset has been anonymized and does not contain any personally identifiable information. We have cited the source of the dataset in our manuscript and have complied with the terms of use set forth by the dataset provider.  
**Data Availability Statement:** All data are presented in the main text.
## 4 Experimental Validation
### Constant Frequency Sinusoidal Load  
In the last experiment we aim to validate the proposed implementation when recovering a multi-frequency sinusoidal load. This loading case will activate fewer mode shapes compared to the previous case. The same beam is considered again, and all the parameters are kept unchanged from the last experiment with only replacing the load. The input parameters are shown in Figure 14. Also in the same figure we plot the identified load compared to the actual load. The plots also show the acceleration recovered by the sensor and used as an input for the algorithm. In the initial stage we can observe a small drift in the amplitude and the phase between the identified and the actual. This is stabilized in around 0.5s and soon after two cycles of loading. The errors obtained in this case are 2.64% and 0.9381 with the PREM and the ACM, respectively. This reflects the accuracy in identifying the load.  
Figure 13: Parameter setting of load identification(**top**) and Load identification of the first impact (**middle**) and second impact (**bottom**).