esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results
### Visibility  
The visibility is another parameter resulted from the FDS numerical model simulation. The visibility is an important parameter for the people evacuation in case of fire scenarios. A visibility higher than 10 m is perceived as very good visibility, and visibility higher than 6 m is suitable for evacuation [34]. The visibility in the FDS depends not only on the amount of fuel burned but also on the type of the burned material (amount of smoke resulting from the complete combustion depends on material type). In all three cases, we considered in FDS that SOOT_YIELD = 0.05, meaning that 5% of the amount of combustible material is transformed into smoke particles. We used this percentage as an average value, corresponding to a fire in a civil building.  
The time variation of the visibility for the three ventilation scenarios (Figure 10) shows that the higher the exhaust air flow, the better the visibility, because the hot air filled with smoke particles is partially exhausted.  
For CALIB3, visibility is good during the entire study time interval (\(0\div 760\) s), stabilizing at about 6 m. On the contrary, the low ventilation rates (CALIB2) or no ventilation rates (CALIB1) lead to improper evacuation conditions in less than one minutes.  
The FDS numerical simulations present values for all parameters inside the entire analyzed domain. These parameters are used to observe the variation of the temperature and visibility along the entire longitudinal plane of the analyzed space (Figure 11). We note that the increase of the ventilation rate simultaneously leads to a general smaller temperature and general, slightly better visibility inside the analyzed space.  
Figure 9: Comparative chart of indoor temperature, point S24.
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
###### Abstract  
Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guaranting good classification performance in identifying typical electric loads.  
non-intrusive load monitoring: nonlinear devices; feature selection; machine learning; power definitions +
Footnote †: journal: Applied sciences
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 2 Preliminary  
### Monocular Depth Estimation  
The goal of monocular depth estimation is to retrieve the distance information using only a single image. It is a great challenge to do that because a pixel of a 2D image may match infinite physical points in the 3D space. However, humans can predict the distance by aggregating the surrounding visual cues even when only one eye is used. This fact suggests that the visual cues may imply distance information. Thus, MDE can be formulated as a problem of uncovering an underlying mapping between the visual cues and the distance information, which can be defined as follows:  
\[D=f(v_{1},v_{2},\ldots,v_{n}) \tag{1}\]  
where \(D\) is the distance, \(v_{1},v_{2},\ldots,v_{n}\) are visual cues, and \(f\) is a non-linear function. Visual cues, such as scale, shadow, texture, etc., are often fused into intermediate representations extracted by encoders, while decoders are used to gather the features and predict the distance information. Therefore, Equation (1) can be rewritten as (2):  
\[D=Dec(Enc(X)) \tag{2}\]  
where \(X\) is input image, \(Enc\) is an encoder, and \(Dec\) is a decoder. Most of the existing MDE methods exploit an encoder-decoder architecture consisting of CNN or Transformer, or a mixture of CNN and Transformer.
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 2 Literature Review  
### Current Developments in Machine Learning in Photonics  
The field of photonics has developed rapidly in recent years. Photonics has been combined with machine learning algorithms and optical systems to add new functions to optical systems and improve the performance of optical systems. This development has generated new research foci [1; 4; 18], such as the design and operation of pulsed lasers and the characterization and control of ultrafast propagation dynamics [18]. Computer calculation abilities improved dramatically after graphical processing units began to be used in nongraphic calculations, and this trend accelerated after the 2010s. This trend has resulted in engineers focusing on the development of electronic hardware accelerators of machine learning, such as Google's Tensor Processing Unit (TPU). Current machine learning processors are limited by the electricity required to process data when executing complicated operations. In general, more complicated data require more electricity and result in slower electronic data transmission. Therefore, past studies have used light to replace electricity for calculations. The neural network TPU uses photons instead of electrons to overcome these limitations and create a stronger and more energy-efficient AI [19]. In addition, AI plays a crucial role in the optical communication industry, such as optical network planning and operation in both transport and access networks [20]. Therefore, combining machine learning and photonics and improving hardware performance is a future research direction.  
Past AI deployment in photonics has spawned much research activity. AI has a certain synergy with photonics, especially in terms of power efficiency and parallelism. Two major directions exist in applying machine learning to photonics. One of the two main directions is using AI algorithms, implemented on conventional computers, to design optical structures and devices with improved, task-specific performance. The other perhaps more ambitious direction is the attempt to implement AI computation using optical systems rather than electronic ones [21]. Now that semiconductor features have shrunk to nanometer scale and room is running out for Moore's law to continue to hold, a new generation of integrated photonics could boost the speed and processing power of AI beyond what electronics can provide [15]. The combination of machine learning and photonics has considerable potential in technological development and market applications. This study used patent analysis to identify the technological positioning of technology developers. The following section explains the patent analysis method.
# Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications
## 2 Aluminum Plasmonics
### Treatment Mechanisms  
#### 2.2.1 Photothermal Therapy (PTT)  
Localized heating and control of photothermal energy in metallic nanoparticles generates several applications including nanoscale sensing [35; 36; 37], photothermal imaging [35], photothermal  
Figure 2: SEM images of (**a**) aggregated disk-shaped Al NPs obtained from a commercial Al foil; (**b**) _E. coli_ bacterial cells attached to Al NPs (Adapted from [PERSON] et al. [29]).  
therapy [36; 38; 39; 40], drug delivery [41], nanochemistry [42], and thermoelectric solar cells [43; 44; 45]. Au and Ag NPs have been extensively studied for the applications in photothermal therapy of cancer [46; 47; 48; 49; 50; 51; 52] and bacterial inactivation [53; 54; 55]. They operate based on the photothermal inactivation mechanism in which the plasmonic NPs absorb light in the visible or near IR spectral range and generate extensive heating which damages the adjacent cancer or bacterial cells.  
Al NPs have the potential to show similar effects with the advantages of a broad spectral range and low cost. Al NSs may also provide additional possibilities for synergistic treatment by the combination with other inactivation mechanisms based on UV irradiation, hot electrons, and chemical treatments. Aluminum, as an energetic material [56], has been a source of energetic NSs. Photooxidation of Al NPs by a Xenon flash lamp combined with plasmonic enhancement was used for photothermally activated ignition with high local temperature and local heat control [57]. Thermoplasmonic ignition of Al NPs by the melt dispersion mechanism also showed high local temperatures [58]. Due to large energy density, low ignition temperatures, and fast burning rates [59], Al NPs can also be used for propulsion systems and hydrogen generation. Due to strong plasmonic resonances in a broad spectral range, Al NPs can be utilized for solar light harvesting. Self-assembled Al NPs on 3D porous membranes were used for solar desalination [60].
## 2 Study Area and Data Properties  
This study was undertaken in an underground coal mine in Quang Ninh province, Vietnam. It lies geographically between longitudes 106\({}^{\circ}\)36\({}^{\prime}\)23\({}^{\prime\prime}\) E and 106\({}^{\circ}\)36\({}^{\prime}\)47\({}^{\prime\prime}\) E; latitude 21\({}^{\circ}\)04\({}^{\prime}\)10\({}^{\prime\prime}\) N and 21\({}^{\circ}\)04\({}^{\prime}\)30\({}^{\prime\prime}\) N (Figure 1). The study site shows a complex geological structure with many faults and folds. The results of stratigraphic studies indicated that the mine area consists of coal-bearing sediments of Hon Gai Formation (T3n-r hg) with a thickness of 600-800 m (Figure 1). The coal seams have different thickness alternating with layers of gravel, sandstone, sandstone, siltstone, and claystone. Coal in the study site belongs to anthracite to bituminous coal. It is black and has black and gray markings, glass or metallic luster. The coal is mainly homogeneous, brittle, and less cracked. The previous results indicated that the study site coal has a high calorific value, low volatile matter, and sulfur content, with an excellent response to industries.  
In this study, the database was collected from previous geological reports at the Mong Duong coal mine for many years [66]. The database consisted of the results of the proximate and ultimate analysis along with the gross calorific values from the coal samples taken at different coal seams in the mine. Sampling, processing, and analyzing methods were carried out by Vietnam's standards (TCVN) [67; 68; 69; 70]. In this study, the results of the proximate analysis for coal samples were used to predict the GCV. The values of the moisture content (M), ash (A), and volatile matter (VM) for each sample  
Figure 1: Location and geological conditions of the study area.  
were measured in the laboratory. According to [PERSON] et al. [71], fixed carbon (FC) was considered as an input variable for predicting GCV. However, FC can be calculated by the given equation of FC = 100 - (M + A + VM). Therefore, the correlation between FC and M, A, and VM is 100%. As per recommended in statistical techniques, high correlation parameters should be removed from the dataset to avoid effect on the accuracy of the models [72; 73]. [PERSON], [PERSON] [74] were also given similar recommendations and the FC was also not used in their study for predicting GCV. Therefore, FC was not used to predict GCV in this study. Herein, a dataset of 2583 coal samples used in this study included factors of M, A, VM, and GCV. A brief of the dataset used in this study is listed in Table 1. In addition, an illustration of the dataset used in this study is shown in Figure 2.
# Studies on a Piezoelectric Cylindrical Transducer for Borehole Dipole Acoustic Measurements
## 2 Principles and Numerical Analyses
### Transmitting Voltage Response (TVR)  
Based on our design, we fabricated and tested the transducer to compare with simulation results.  
As is shown in ref [18], the transmitting voltage response level is defined with the following formula:  
\[TVR=20\lg\frac{|p_{a}|D}{V}+120, \tag{9}\]  
where \(p_{a}\) is the pressure in the far field, usually far away from the source for about 3-5 wavelengths; \(D\) is the distance from the transducer to the receiver; and \(V\) is the excited voltage. The reference pressure in Equation (9) is taken to be \(1\times 10^{-6}\)\(P_{a}\cdot\)m/V.  
The comparison of the simulation and test results of TVR of the proposed transducers in water is shown in Figure 8. The solid black line and the dashed blue line represent the simulation and the test results of the proposed transducer, respectively.  
From the simulation of the transducer in the frequency range of 400-6000 Hz, it is shown that there are multiple resonance peaks in the curve. The four main resonant frequencies are 497 Hz, 980 Hz, 3698 Hz and 5000 Hz, respectively. The TVR values are 99 dB, 121 dB, 123 dB and 127 dB, respectively, corresponding to the four mode modes in the previous modal analysis.  
In the test measurement curve, there are mainly four resonance peaks, of which the frequencies are 450 Hz, 925 Hz, 3550 Hz and 4700 Hz, respectively, and TVR values of 101 dB, 121 dB, 133 dB and 127 dB, respectively. For the resonance frequencies, the relative error between the simulation and test results is less than 11%. For the TVR value, the relative error between the simulation and test results is less than 8%. Therefore, the numerical simulation result and the test result are in good agreement.
# Designing Audio Equalization Filters by Deep Neural Networks
## 2 Problem Statement  
Multi-point audio equalization is a very complex task: considering an environment with several sound sources and microphones, as depicted in Figure 1, a large number of impulse responses must be equalized, and the complexity of this problem increases with the number of sources \(\mathcal{S}\) and microphones \(\mathcal{M}\). Several optimization algorithms can be employed to generate filter coefficients able to obtain the desired frequency response at the microphone positions in a specific frequency range. In this work we compare our approach with state of the art methods to design the FIR filters offline. We assume, as in those works, that the listening environment is linear and time-invariant.  
The generated FIR filters \(g_{s}\), one for each sound source \(s\), are employed for pre-processing the input signal \(x\). The signal recorded at the \(m\)-th microphone is [24; 25]:  
\[y_{m}=\sum_{s=1}^{\mathcal{S}}h_{m,s}*(g_{s}*x)\quad m=1...,\mathcal{M}. \tag{1}\]  
The frequency response at the microphone is given by:  
\[Y_{m}(\omega)=|\mathscr{F}(y_{m})|\quad m=1...,\mathcal{M}, \tag{2}\]  
where \(\mathscr{F}\) is the Fourier transform operator.
## 3 Analysis Results
### Temporal Evolution of the Instantaneous Energy Level (IE) of IMFs  
The temporal evolution of IE with respect to the respective IMF is presented in Figure 8. As already confirmed from Figures 4-7, the seasonality in the IE variations is clearly observed only for low-mode IMFs (IMF-1 through IMF-4); note that the time with a local peaked IE level corresponds to every summer. Overall, from 2010 onwards, the IE level continues to increase after a long-lasting duration of low IE levels, except for the initial short-term peaks. This could be mainly due to a long-term warming trend [43].  
A noticeable intra-wave and inter-wave frequency modulation seems to emerge for higher mode IMFs (IMF-5 through IMF-10), and for the two modes of IMF-5 and IMF-6, the peak-to-peak timescale tends to get shorter from 2005 onwards. Since the timescale of these two modes is nearly one year, any forcing with an annual timescale could be related to this behavior. The so-called EJS SST index, computed by performing the area-weighted average on SST over the western half of the EJS (33-43\({}^{\circ}\) N, 128-136\({}^{\circ}\) E) [48], has shown a statistically significant peak at the annual timescale. That is, there could be a decadal change in SSTA variability in the western part of the EJS, including the EKB.
## 4 Results
### Analysis of Co-Integration
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multirow{2}{*}{**Trace Statistic Test**} & **Max-Eigen** & **MacKinnon** \\
**Variables** & & **Statistic Test** & **Critical Value** \\ \hline \(\Delta\ln(Urr)\), \(\Delta\ln(Urs)\), & & & \\ \(\Delta\ln(Exe)\), \(\Delta\ln(Fie)\), \(\Delta\ln(Emr)\), & & & \\ \(\Delta\ln(Hhr)\), \(\Delta\ln(Edc)\), & & & \\ \(\Delta\ln(Gtr)\), \(\Delta\ln(Bie)\), & 151.25*** & 190.20*** & \(p<0.01\) \\ \(\Delta\ln(Ker)\), \(\Delta\ln(Gmr)\), & \(\Delta\ln(Gmr)\), & & \\ \(\Delta\ln(Ecr)\), \(\Delta\ln(Eir)\), & & & \\ \(\Delta\ln(CO_{2})\), \(\Delta\ln(CH_{4})\), & & & \\ \(\Delta\ln(N_{2}O)\) & & & \\ \hline \hline \end{tabular} Source: Author’s estimate (2024). Note: (\(Urr\)) is the urbanization rate, (\(Urs\)) is the industrial structure, (\(Exe\)) is the export–import ratio, (\(Fie\)) is the indirect foreign investment, (\(Gep\)) is the government expenditure, (\(Emr\)) is the employment rate, (\(Hlr\)) is the health and illness rate, (\(Edc\)) is the education rate, (\(Gtr\)) is the green technology, (\(Bie\)) is the biomass energy, (\(Rer\)) is the renewable energy rate, (\(Gmr\)) is the green material rate, (\(Ecr\)) is the energy consumption, (\(Eir\)) is the energy intensity rate, (\(CO_{2}\)) is the carbon dioxide emissions, (\(CH_{4}\)) is the methane emissions, (\(N_{2}O\)) is the nitrous oxide emissions, *** denotes a significance, \(\alpha=0.01\).  
\end{table} 
Table 2: Analysis for the confirmation of the indicators’ properties. From Table 2, it can be observed that all indicators are suitable for constructing the Path-GMM-Nearest-Neighbor model, because each indicator shows co-integration. The Trace statistic test and Max-Eigen statistic test values exceed the MacKinnon critical value at a significance level of \(\alpha\) = 0.01, indicating that all indicators are appropriate for use in the Path-GMM-Nearest-Neighbor model. This confirms their suitability for analyzing the influence of relationships and for forecasting purposes.
# Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM
## 6 Experiments and Discussion
### Experiment 2: Effects of Hand Features in the Skeleton Data  
This experiment shows the influences among hand features from skeletons, as shown in Table 7. It has an important role in real-time applications with high requirements for the processing time.  
The model using motion features achieved 82.50% on the 14 classes and 72.85% on the 28 classes. When we added hand shape skeletons, our model could capture the local motion between the fingers, increasing our accuracy from 82.50% to 84.52% on the 14 classes and from 72.85% to 82.02% on the 28 classes.  
Figure 13: Confusion matrix of the best models using the singleshape feature on DHG 14 (accuracy of 92.26%) (**a**) and DHG 28 (accuracy of 88.33%) (**b**). The red circles are the false cases causing the confusion in recognition.  
Moreover, the joint point-cloud model could learn good features in 3D geometry and improve the performance of our model by 5.07% (14 classes) and 2.98% (28 classes). Finally, when we integrated normalized point features, we achieved good accuracy on the skeleton data, 93.45% (14 classed) and 90.11% (28 classes).  
In Figure 14, the red circle on the left confusion matrix points out the weakness of the model using the motion + skeleton shape feature. The model confused the grab and pinch gestures with the false cases being 27%. In contrast, the joint point-cloud was better with the false cases being 13%. Therefore, the combined results of the two models increased the accuracy from 84.52% to 90.59% (6.07% increase).  
Figure 14: Confusion matrix of prediction results using the MLF LSTM Conv1-2D model with the features motion + skeleton shape (accuracy of 84.52%) (**a**) and joint point-cloud (accuracy of 85.11%) (**b**) on DHG-14.
## 1 Introduction
[PERSON], [PERSON] [31] proposed a path-planning and formation control strategy for the two key problems of AUV collision avoidance and obstacle avoidance but lacked experimental proof of simulation in 3D space. This paper addresses the process of cooperative search and target capture in unknown waters by the formation of multiple AUVs, with the following main contributions:  
* For the formation avoidance problem in the process of searching for targets in unknown waters, a Formation Comprehensive Cost (FCC) model that balances the convergence time, transformation distance and load energy consumption is proposed to realize the collision avoidance of array-shaped obstacles. * The FCC model is introduced to the genetic algorithm to solve the assignment problem of capture position. * An improved neural self-organizing map (INSOM) algorithm is proposed to realize path-planning during the capture process, and the feasibility of the proposed scheme in different types of obstacle environments is verified by simulation experiments. The rest of the paper is organized as follows: Section 2 describes the general flow of the proposed formation capture scheme. Section 3 establishes the simulation environment model, describes the position change of each robot during the formation transformation using the formation parameter information matrix, and proposes an FCC model that balances the convergence time, transformation distance and load energy consumption to realize the formation change during the search process. Section 4 introduces the FCC model to GA to complete the allocation of capture positions within the formation and combines this with the INSOM algorithm to realize the rapid deployment of robots in collaborative capture. Section 5 shows and explains the simulation experimental results. Section 6 summarizes the proposed target capture scheme.