esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 4 Performance Evaluation and Lessons Learnt
### Results and Discussion  
#### 4.2.1 UWB Dataset Classification  
The UWB dataset contains Movement and Respiration data. The data is labeled based on detecting the movement of human in the shop floor or no movement. The value remains zero (0), as long as there is no movement. However, it gave values other than zero once movement is detected. On the other hand, the breathing value gives maximum of 40 (meaning 80 bpm if multiplied by 2) for normal healthy person. The labeling is such that, for bpm between 5-40, we return as normal, while declaring all bpm below as anomaly and needing emergency attention to check the health status of the workers in such factory. Similarly, if there are more than one (1) person in the shop floor, the breathing value is returned as zero since there is no case of isolated factory worker. The data labeled as one (1) if the movement is above 0 means that it is a normal condition; otherwise, it is labeled as zero (0), which means abnormal. On the other hand, if the respiration value is below 5 and above 20, it means abnormal and is labeled as zero, while, if the respiration value is between 5 to 20, it is labeled as normal or one. Figure 10 is the distribution pattern of the UWB dataset. It can be seen from the figure that both the respiration and movement data are randomly distributed.  
The basic LR algorithm function in Python is used by calling the LR function using Keras. After the simulation, the LR algorithm achieved 97.54% accuracy rate for Movement data, while, for Respiration data, it achieved 100% accuracy rate. So, on average, the LR achieved 98.77% accuracy rate. Similarly, we designed two different KNN algorithms. First KNN (KNN-1) consist of one neighbor, while, for the second KNN, (KNN-4) has 4 neighbors. Based on the simulations, KNN-1 and KNN-4 achieved 97.76% and 98.60% accuracy rate, respectively, for Movement data, while, for Respiration data, both KNNs achieved 100% accuracy rate. So, in summary, KNN achieved 99.32% accuracy rate. The accuracy for SVM, NB all were in the same range of 97%. Loss function plotted for given number of epoch is given in Figure 11. The CNN algorithm achieved 90.04% accuracy rate for Movement data, and 100% accuracy rate for Respiration data.  
Figure 10: Visualizing the UWB data output showing the movement and breathing data. The unit of the breathing data is breaths per minute (bpm).
## 6 Discussion and Future Research Directions
### Terrestrial Networks and Non-Terrestrial Networks Integration  
UAVs are expected to be extensively deployed in the 6G networks. They provide wireless services at high altitudes and can be critical in enabling the integration of terrestrial networks with non-terrestrial networks in achieving unrestricted connectivity in 6G. UAV leverages ML integration for network intelligence, which is a core feature of 6G networks. ML practical applications of the convergence of ML and UAV technologies include UAV detection, traffic control, UAV flight path planning, collision avoidance, network positioning, resource management, handover optimization, and mobility support in the airspace [6, 156, 159].  
For scenarios involving path planning and collision avoidance, in most ML-enabled UAV applications, the parameters to measure the model effectiveness usually include average coverage score (c), Fairness Index (f), normalized average energy consumption (E), and energy efficiency (r). In trajectory design and power allocation applications, ML enhances the network utility and system capacity of the system while reducing the computational complexity [155]. To achieve optimization in multi-UAV networks, where the number of UAVs is more than one, the multi-agent DRL methods (MADDPG, MADDPG-FP, and MAAC) are usually utilized [155]. ML-integrated UAV applications are also expected to address users' privacy and data security concerns and provide ultra support for low-latency communication in mMTC devices.
# A Review of Time-Scale Modification of Music Signals +
###### Abstract  
Time-scale modification (TSM) is the task of speeding up or slowing down an audio signal's playback speed without changing its pitch. In digital music production, TSM has become an indispensable tool, which is nowadays integrated in a wide range of music production software. Music signals are diverse--they comprise harmonic, percussive, and transient components, among others. Because of this wide range of acoustic and musical characteristics, there is no single TSM method that can cope with all kinds of audio signals equally well. Our main objective is to foster a better understanding of the capabilities and limitations of TSM procedures. To this end, we review fundamental TSM methods, discuss typical challenges, and indicate potential solutions that combine different strategies. In particular, we discuss a fusion approach that involves recent techniques for harmonic-percussive separation along with time-domain and frequency-domain TSM procedures.  
digital signal processing; overlap-add; WSOLA; phase vocoder; harmonic-percussive separation; transient preservation; pitch-shifting; music synchronization +
Footnote †: _Appl. Sci._**2016**, 6, 57; doi:10.3390/app6020057
## 2 Materials and Methods
### Pretreatment Transformations  
The basic pretreatment transformations for VIS-NIR are Savitzky-Golay (SG), first derivative (FD), second derivative (SD), standard normal variate (SNV), multiplicative scatter correction (MSC), logarithmic transformation (LG), mean centre (MC), and dislodge tendency (DT) [13; 14; 15; 16]. The SG method is very important to guarantee that the edge band is removed from the spectral curve, which has extensive noise, and this process improves the signal-to-noise ratio, enhances the function of the central wavelength point, and retains the peak characteristics of the original spectral signal to the greatest extent. FD and SD can both eliminate the effect of the linear baseline but result in amplified noise. SNV calibrates the effects of soil particle size and surface scattering [17]. MC and DT both reduce the spectral offset. Therefore, this manuscript combined the 29 pretreatment algorithms that are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Pretreatment Method** & **Abbreviations** \\ \hline Reflection spectrum without pretreatment method & RS \\ First derivative & FD \\ Second derivative & SD \\ Standard normal variate & SNV \\ Multiplicative scatter correction & MSC \\ Logarithmic transformation & LG \\ Mean center & MC \\ Dislodge tendency & DT \\ Dislodge tendency with standard normal variate & SNV + DT \\ First derivative with standard normal variate & SNV + FD \\ Second derivative with standard normal variate & SNV + SD \\ First derivative with multiplicative scatter correction & MSC + FD \\ Second derivative with multiplicative scatter correction & MSC + SD \\ First derivative with logarithmic transformation & LG + FD \\ Second derivative with logarithmic transformation & LG + SD \\ Savitzky–Golay & SG \\ First derivative with Savitzky–Golay & SG + FD \\ Second derivative with Savitzky–Golay & SG + SD \\ \hline \end{tabular}
\end{table}
Table 1: Pretreatment methods applied to the visible near-infrared (VIS-NIR) of soil samples.  
Figure 1: The indoor spectral acquisition system.
## 3 Results and Discussion
### Sensitivity Analysis of Plastic Ratio  
Figure 9 established that the annual cost decreased as the plastic ratio increased in all cases, but the CO2 emissions increased, indicating a trade-off relationship.  
Cases 2 and 4 have a larger rate of decrease in annual expenses than Cases 1 and 3. However, this was because the heat sales expenses were increasing due to the increase in the calorific value of waste due to the increase in plastics. In Cases 1 and 3, even if the amount of heat generated by waste increases, it could only be used for waste incineration power generation, which was less efficient than the case where heat was used, and electricity sales could only be carried out at 13 yen/kWh. Therefore, annual cost reduction did not increase.  
When the plastic ratio increased, CO2 emissions increased significantly because the plastic was incinerated in all cases. Therefore, it could be seen from this sensitivity analysis that considering methods other than thermal recycling of plastic waste for reducing CO2 emissions due to waste incineration was necessary.
# Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System
## 4 Intercrystal Scattering Rejection Algorithm
### Selection of Detector Window  
When considering the detector window width, cases in which \(\Delta\text{Det}>2\) can be disregarded due to their relatively low occurrence rate. To determine the optimal detector window width, a simulation is conducted using several models with different count rates. The findings demonstrate that the criterion of \(\Delta\text{Det}=1\) is preferred for systems with high count rates (more than \(10^{3}\)/s), for example, when the detected object is an empty container or is made of materials with low atomic numbers, whereas \(\Delta\text{Det}\leq 2\) should be selected for systems with low count rates. The justification for this recommendation is explained below.  
Judgment errors mainly come from the misjudgment of events from different photon decays and omission of true coincidence events, which are illustrated in Figure 12. The events marked as No.24446 and No.24440 come from different photon decays and would be incorrectly judged under the \(\Delta\text{Det}\leq 2\) criterion. However, the true coincidence events from the same photon, No.26578, might be omitted under the \(\Delta\text{Det}=1\) criterion. High count rates typically result in an increased frequency of events from different photon decays appearing in adjacent detector windows, in which the impact of a misjudgment is greater than that of an omission. Hence, the \(\Delta\text{Det}=1\) criterion proves effective in reducing misjudgments. Meanwhile, for conditions with low count rates, misjudgments rarely occur; therefore, the \(\Delta\text{Det}\leq 2\) criterion is utilized to mitigate the chance of overlooking true coincidence events.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 2 Related Works
### Adversarial Defense
#### 2.3.1 Adversarial Training  
Adversarial training serves as a defense mechanism to enhance resilience against adversarial attacks. This was achieved by incorporating adversarial instances during the model-training phase. As a defense method, it utilizes data augmentation to mitigate entropy loss in the face of adversarial attacks. As demonstrated by Equation (3), [PERSON] et al. [20] suggested adversarial training as a potential countermeasure for withstanding adversarial attacks. This strategy is implemented during model training and is categorized under defense methods that leverage data augmentation.  
\[\alpha\times L(\theta,x,y)+(1-\alpha)\times L(\theta,x_{adv},y) \tag{3}\]  
Equation (3) illustrates a training technique that incorporates both the original and adversarial examples by applying a weighted sum. The scale factor \(\alpha\) serves as a control mechanism to balance the training proportion between the adversarial examples and the original data, which is directly determined by the user during adversarial training. This method facilitates resistance to adversarial assaults by learning dual data distributions using a singular loss function. Additionally, the approach extends beyond simply adding adversarial examples to the original data; it also includes a process that disrupts the maximization phase inherent in adversarial attacks. Equation (4) [25] describes the external minimization process that equips the model with the capacity to generate precise predictions.  
\[min\delta_{\theta}L(\theta,x_{adv},y),wherex_{adv}=x+max_{\theta}L(\theta,x+ \delta,y) \tag{4}\]In particular, the adversarial training proposed by [PERSON] et al. [25] exhibited superior accuracy compared to conventional training when exposed to FGSM and PGD attacks, underscoring its resilience against adversarial attacks. Among the existing defense methods against adversarial attacks, adversarial training is the only fundamental technique that does not align with obfuscated gradients. Nonetheless, there are constraints associated with the efficiency of the learning duration because adversarial training necessitates additional learning phases.
## 4 Experiments  
### Dataset  
We took 1240 UAV images of Maebonsan recorded on 24 July 2019, and used 1032 for training and 208 for model validation. In order to improve the diversity of the training and validation dataset, five data augmentation methods, such as horizontal flips, vertical flips, and rotation in 90, 180, and 270 degrees, were applied to both the training and validation sets to obtain a final training set of 6192 images and a validation set of 1248. We aim to train robust semantic segmentation models for cabbage fields of different scales through data augmentations. Next, we composed three different testing sets to measure the generalization performance of the proposed method. The first testing set, denoted as the MBS dataset, consisted of 471 images taken from the Maebonsan area on 5 August 2019. Evaluation on the MBS dataset is necessary to measure how well our models generalize to data collected under different temporal conditions. The second testing set, denoted as the GNM dataset, consisted of 156 images taken from the Gwinemi area on 5 August 2019. Lastly, 143 images of the Anbanduck area on 6 August 2019, comprised the third testing set, denoted as the ABD dataset. Evaluations on the GNM and ABD datasets are necessary to measure how well our models perform on data collected under different spatiotemporal conditions.
###### Abstract  
Recently, significant research efforts have been directed toward leveraging Artificial Intelligence for sensor data processing and system control. In particular, it is essential to determine the optimal path and trajectory by calculating sensor data for effective control systems. For instance, model-predictive control based on Proportional-Integral-Derivative models is intuitive, efficient, and provides outstanding control performance. However, challenges in tracking persist, which requires active research and development to integrate and optimize the control system in terms of Machine Learning. Specifically, Reinforcement Learning, a branch of Machine Learning, has been used in several research fields to solve optimal control problems. In this paper, we propose an Extended Maximum Actor-Critic using a Reinforcement Learning-based method to combine the advantages of both value and policy to enhance the learning stability of actor-critic for optimization of system control. The proposed method integrates the actor and the maximized actor in the learning process to evaluate and identify actions with the highest value, facilitating effective learning exploration. Additionally, to enhance the efficiency and robustness of the agent learning process, we propose Prioritized Hindsight Experience Replay, combining the advantages of Prioritized Experience Replay and Hindsight Experience Replay. To verify this, we performed evaluations and experiments to examine the improved training stability in the MuJoCo environment, which is a simulator based on Reinforcement Learning. The proposed Prioritized Hindsight Experience Replay method significantly enhances the experience to be compared with the standard replay buffer and PER in experimental simulators, such as the simple HalfCheetah-v4 and the complex Ant-v4. Thus, Prioritized Hindsight Experience Replay achieves a higher success rate than PER in FetchReach-v2, demonstrating the significant effectiveness of our proposed method in more complex reward environments.  
actor-critic; reinforcement learning; replay buffer; hindsight and prioritized experience; reward +
Footnote †: journal: Applied sciences
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 3 Experiment and Results  
### Dataset  
In this paper, the performance of the algorithm is compared using the public dataset and the self-test dataset. The public dataset is derived from 12 sets of public data in the 2015 IEEE Signal Processing Cup [17], and each set of data in this dataset contains 2 channels of PPG signals, 3-axis acceleration signals, and 1 channel of Electrocardiogram (ECG) data, with a sampling frequency of 125 Hz. The PPG signals were acquired using pulse oximeters with 515 nm green LEDs. The subjects were all healthy males aged 18-35 years old. The data were recorded from subjects running on a treadmill over a 5 min period, with speed varying as follows: 30 s at 1-2 km/h, 1 min at 6-8 km/h, 1 min at 12-15 km/h, 1 min at 6-8 km/h, 1 min at 12-15 km/h, and finally, 30 s at 1-2 km/h.  
Eight sets of self-measurement data were collected by the laboratory equipment from subjects aged 18 to 35, and each set of data contained 4 channels of PPG signals, 3-axis acceleration signals, and 3-channel ECG signals. The sampling frequency of acceleration signals was 25 Hz, and the sampling frequency of all other signals was 250 Hz. Among them, the PPG signals and acceleration signals were collected by the smart wristwatch, and the ECG signals were collected by the chest strap, as shown in Figure 10. The Chest strap model is SIBET-JCY2.0, and the wristwatch model is MPPB-V1. The PPG data were collected using green LEDs at a wavelength of 540 nm. The data were collected under three distinct states: resting, walking, and running. Specifically, datasets 2, 6-8 correspond to the resting state, datasets 1 and 3 to the walking state, dataset 4 to the running state, and dataset 5 includes the transition from resting to walking and then to running.
## 3 ANSYS Analyses Results
### Solid Rotor Simulation Results
#### 3.1.2 5 mm Layered Hybrid Rotor Simulation Results  
The performance obtained with the rotor structure with a 5 mm CFRP outer layer is higher than the solid and 3 mm layered rotor structure. The simulation results given in Table 7 show that the RPM value that can be reached with the rotor with a 5 mm CFRP layer is 12.7% more than the solid rotor for mild steel. This ratio is 12.4% for titanium alloy. In terms of weight, the mild-steel rotor with a 5 mm CFRP layer is 11% lighter than the solid steel rotor. Similarly, the titanium alloy rotor with a 5 mm CFRP layer is 8% lighter than the solid titanium alloy rotor. This means that the energy that can be stored with the hybrid rotor is 12-14% higher. Visuals of the simulation results are given in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Amb. Temp.: 22 \({}^{\circ}\)C** & **Mild Steel** & **Mild Steel** & **Titanium Alloy** & **Titanium Alloy** \\ \(O\) 100 mm Rotor with_ & **+** & **+** & **+** & **+** \\
**5 mm CFRP Layer** & **5 mm CFRP** & **5 mm CFRP** & **5 mm CFRP** & **5 mm CFRP** \\  & **Yield Point** & **Rupture Point** & **Yield Point** & **Rupture Point** \\ \hline Speed (RPM) & 57,150 & 77,520 & 141,260 & 151,500 \\ Weight (kg) & 2.46 + 0.11 & 2.46 + 0.11 & 1.45 + 0.11 & 1.45 + 0.11 \\ Minimum (MPa) & 1.1 & 2.1 & 1.1 & 1.3 \\ Maximum (MPa) & 250 & 460 & 930 & 1070 \\ \hline \end{tabular}
\end{table}
Table 7: Rupture and yield stress values of 100 mm rotor with 5 mm CFRP layer.  
Figure 8: Simulation visuals: stress and rupture values for steel and titanium alloys with 3 mm CFRP layer.
# RAU-Net++: River Channel Extraction Methods for Remote Sensing Images of Cold and Arid Regions
## 4 Experiments
### Experimental Results
#### 4.5.7 RAU-Net++ Network Ablation Experiments on NWPU-RESISC45 Dataset  
Since the generalizability of the RAU-Net++ network cannot be verified by using only one Tarim River dataset, we used the river remote sensing dataset in the NWPU-RESISC45 dataset to perform ablation experiments as a way to verify the generalizability of the network. The results of the ablation experiments on NWPU-RESISC45 are shown in Table 11. From the experimental results, it can be seen that although the Recall of our proposed RAU-Net++ network decreases to 96.72%, the Precision reaches 98.21%, and its improvement compared to U-Net++ is 1.54%. Both ED and ED\({}^{\prime}\) are optimal. The above experiments demonstrate the overall effectiveness and generalization of the proposed model in river remote sensing images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Network** & **Precision (\%)** & **IOU (\%)** & **Recall (\%)** & **F1 (\%)** & **ED** & **ED\({}^{\prime}\)** \\ \hline U-Net & 97.90 & 96.57 & 99.12 & 98.51 & 1.393 & 0.0227 \\ U-Net plusplus & 99.41 & 98.84 & 99.60 & 99.51 & 1.407 & 0.0072 \\ U-Net plus plus + Vgg16 & 99.40 & 98.89 & 99.66 & 99.53 & 1.408 & 0.0069 \\ U-Net plusplus + Vgg16 + CBAM & 99.37 & 98.77 & 99.60 & 99.49 & 1.407 & 0.0074 \\ U-Net plusplus + Vgg16 + RAFF & 99.55 & 99.18 & 99.76 & 99.65 & 1.409 & 0.0051 \\ U-Net plusplus + Vgg16 + CBAM + RAFF & 99.60 & 99.20 & 99.74 & 99.67 & 1.410 & 0.0047 \\ U-Net plusplus + Vgg16 + CBAM & **99.78** & **99.39** & **99.71** & **99.75** & **1.411** & **0.0036** \\ + RAFF + Weight Loss & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \end{tabular}
\end{table}
Table 10: Tarim river mainstream dataset overall network ablation experiments results. The bolded section indicates optimal parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**(A, B, C, D)** & **Precision (\%)** & **IOU (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline (1, 2, 20, 0.1) & **99.80** & 99.35 & 99.67 & 99.73 \\ (1, 2, 20, 0.2) & 99.79 & 99.36 & 99.69 & 99.74 \\ (1, 2, 20, 0.5) & 99.79 & 99.37 & 99.70 & 99.74 \\ (1, 2, 20, 0.7) & 99.79 & 99.37 & 99.70 & 99.74 \\ (1, 2, 20, 0.8) & 99.78 & 99.38 & 99.70 & 99.74 \\ (1, 2, 20, 0.9) & 99.78 & **99.39** & **99.71** & **99.75** \\ (1, 2, 20, 1.0) & 99.74 & 99.37 & 99.69 & 99.74 \\ (1, 2, 20, 1.5) & **99.80** & 99.37 & 99.69 & 99.74 \\ \hline \end{tabular}
\end{table}
Table 9: Parameter adjustment experiment of Jaccard loss. The bolded section indicates optimal parameters.