esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Construction projects in deep underground engineering are associated with the recording of massive amounts of diversified signals during real time and continuous microseismic monitoring given the complexity and specificity of the construction environment. Before the analysis of source information and further prediction of possible disasters, it is generally necessary to perform onset time picking and detection of microseismic signals. To improve the accuracy and efficiency of these tasks, this paper proposes an advanced deep dual-task neural network, which sequentially integrates the two processes. In this method, a score map is used to label the onset time of micro-fracture waveforms to improve the picking accuracy. The proposed model can simultaneously handle the onset time picking and detection tasks of microseismic signals to achieve optimal performance. Based on the similarity of data structures, the output from the onset time picking section is imported into the detection section to classify different types of microseismic waveforms. The onset time picking and detection procedures can be seamlessly integrated, where the score map of the onset time can help improve the detection accuracy. The results show that this method has a good performance for the onset time picking and detection of microseismic waveforms that are polluted by noises of various types and intensities. A comparison of the proposed method with existing methods and applications in underground engineering projects helped demonstrate the excellent performance of this method. The proposed approach can accelerate the automatic processing of microseismic signals and has significant potential for the exploration of seismology and earthquake research.  
deep learning; microseismic monitoring; dual-task neural network; onset time picking; waveform detection +
Footnote †: journal: applied sciences
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 3 Discussion
### DSA Data Analysis and Proposed Framework for Optimal Model Selection  
As the data characteristics can influence the model performance, a study is conducted based on a collected dataset of DSA. As the attention level toward signage can greatly influence the pricing decision, the dependency and relationship of the environmental factors related to the attention level should be carefully considered. The environmental factors included in this study are location, weather, and temperature. These factors have significant impacts on the attention level and the final pricing decision [2]. Table 11 shows two examples of how the environmental factors impact the attention level and consequently the pricing of the DSA.  
Realizing the environmental factors impacts on the pricing decision, the collected dataset is composed of three attributes including the temperature, air humidity level, and the popularity index of the surrounding business environment based on the two environmental factors, as shown in Table 11. They were collected from 13 locations of digital signage in Malaysia using data scraping methods from April to June 2021. The temperature and humidity data were obtained from OpenWeather API, while the popularity index was scraped from Google Maps.  
These data characteristics are used to measure the index representing the attention level, which indirectly affects the price forecast of the DSA. Thus, this study aims to investigate the collected DSA data characteristics daily, monthly, and as a whole. The signage being analyzed was selected randomly. The signage with ID 1000, which was located at Negeri Sembilan, Malaysia, with coordinates (101.9119, 2.70179) was investigated in the study. The results for other signages were not presented in this article to avoid repetitive information.
# Images Segmentation Based on Cutting the Graph into Communities
## 4 Computer Experiment  
A computer experiment tests an algorithm on color images with a color depth of 1 byte per pixel. We used a computer with a quad-core processor and a frequency of 2600 MHz to carry out the experiment. The software package was implemented in C++. The algorithm highlights a segment in the center of the image. The result was compared to a manually selected segment. The dependence of the results for the algorithm on its parameters was investigated in a computer experiment. The experiment was carried out on a collection of 100 images. The most accurate segmentation was performed at parameter values \(h\in[0.005,0.01]\). An example of algorithm operation at different values of parameter \(h\) and \(R=1\) is shown in Figure 2.  
Figure 1: Dependence of average edge weight in the community on the number of nodes included at different \(R\).  
Increasing the \(h\) parameter reduces the size of the segment. Two parameters were used to compare segmentation results numerically. The segmentation efficiency is equal to the relative number of pixels correctly assigned by the algorithm to the segment.  
\[Eff=\frac{N_{t}}{N_{0}}.100\%. \tag{7}\]  
\(N_{t}\) is the number of pixels that the algorithm correctly classified. \(N_{0}\) is the number of pixels in ground truth. The algorithm error is calculated as the ratio of the misclassified pixels number \(N_{e}\) to the total number of pixels in the segment defined by the algorithm \(N_{s}\).  
\[Err=\frac{N_{e}}{N_{s}}.100\%. \tag{8}\]  
Figure 2: Example of algorithm operation at R = 1 and different values of parameter h: (**a**) initial image, (**b**) ground truth, (**c**) h = 0.005, (**d**) h = 0.01.  
The calculation of these coefficients was performed on the image collection. An average value was calculated for these coefficients. At \(R=1\) and \(h=0.01\) the coefficients are \(Eff=47\%\), \(Err=4.1\%\). At \(R=1\) and \(h=0.001\) the coefficients are \(Eff=56\%\), \(Err=3.8\%\). Lowering the threshold parameter \(h\) improves the segmentation accuracy with a slight reduction in errors.  
The segmentation accuracy is improved by increasing the correlation radius \(R\). Examples of segmentation at \(h=0.007\) and different values \(R\) are given in Figure 3.  
Increasing the correlation radius \(R\) significantly improves segmentation efficiency (\(R=10\)\(Eff=67\%\), \(R=20\)\(Eff=69\%\), \(R=25\)\(Eff=83\%\), \(R=30\)\(Eff=87\%\)). Further, increasing the correlation radius does not change the segmentation efficiency. Relative segmentation error decreases as correlation radius increases (\(R=10\)\(Err=8\%\), \(R=20\)\(Err=7\%\), \(R=25\)\(Err=6.7\%\), \(R=30\)\(Err=6\%\)). The number of misclassified  
Figure 3: Examples of segmentation at \(h=0.007\) and different values \(R\): (**a**) \(R=10\), (**b**) \(R=20\), (**c**) \(R=25\), (**d**) \(R=30\).  
nodes is growing slowly. The number of nodes in the community increases rapidly. The ratio of these values decreases.  
Examples of segment highlighting in different images are shown in Figure 4.
# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
###### Abstract  
In 3D reconstruction tasks, single-sensor data fusion based on deep learning is limited by the integrity and accuracy of the data, which reduces the accuracy and reliability of the fusion results. To address this issue, this study proposes a multi-sensor data fusion method based on a self-attention mechanism. A multi-sensor data fusion model for acquiring multi-source and multi-modal data is constructed, with the core component being a convolutional neural network with self-attention (CNN-SA), which employs CNNs to process multi-source and multi-modal data by extracting their features. Additionally, it introduces an SA mechanism to weigh and sum the features of different modalities, adaptively focusing on the importance of different modal data. This enables mutual support, complementarity, and correction among the multi-modal data. Experimental results demonstrate that the accuracy of the CNN-SA network is improved by 72.6%, surpassing the improvements of 29.9% for CNN-CBAM, 23.6% for CNN, and 11.4% for CNN-LSTM, exhibiting enhanced generalization capability, accuracy, and robustness. The proposed approach will contribute to the effectiveness of multi-sensor data fusion processing.  
multi-model; data fusion; convolutional neural network; self-attention mechanism; deep learning +
Footnote †: journal: applied
## 2 Data  
### Landsat  
We acquired all high-quality Landsat images over Crete for the years 1984-2006 following [PERSON] et al. [49]. Crete is covered by six Worldwide Reference System 2 (WRS-2) scenes. We used the Level 1, Collection 1, Tier 1, terrain precision corrected dataset, comprised of Landsat 4, Landsat 5, and Landsat 7 imagery. Images with cloud coverage > 70% were discarded. A total of 2816 images were downloaded from Google's Landsat Cloud Storage-Bucket ([[https://console.cloud.google.com/storage/browser/gcp-public-data-landsat](https://console.cloud.google.com/storage/browser/gcp-public-data-landsat)]([https://console.cloud.google.com/storage/browser/gcp-public-data-landsat](https://console.cloud.google.com/storage/browser/gcp-public-data-landsat)), accessed on 18 September 2021) via the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE; v. 3.5) [59]. Figure 1 showsthe number of clear-sky observations for each pixel (after preprocessing, see Section 3.1.1), i.e., filtered for clouds, cloud shadows, and snow. In general, clear-sky data availability is very high due to remote sensing-friendly weather conditions in the Mediterranean. Fewer observations are available for mountainous areas due to orographic cloud formation, and potentially due to persistent false-positive cloud detections over the bright massifs, c.f. [60], which, however, is uncritical since these bare rock surfaces are out of the scope of the study at hand. Data availability for the southern part of the island is higher than for the northern part due to advective clouds from dominant northwestern low-pressure tracks [61]. In the central and most eastern part of Crete (Iraklio and Sitia provinces), data availability is highest due to the presence of lateral orbital overlaps, which effectively double observation frequency.
## 5 Simulation and Industrial Application
### Signal Acquisition  
To test the performance of the denoising methods, a pure sound signal sequence was needed first. Because there was much background noise in industrial field, it was extremely difficult to collect the pure sound. Moreover, sound signal of a machine consisted of many frequency components, so it was also hard to synthesize a representative series with practical meaning artificially. In this paper, the sound of a motor working in a soundproof room was recorded as the original signal. Concretely, the sound signal was acquired from soundproof testing branch, Jiangsu Key Laboratory of Mining Mechanical and Electrical Equipment. The walls of the testing room were constructed of a special acoustic insulating material and echo cancellation was designed in the testing process. An AC servomotor with the rated out power of 1 kW and the corresponding electrical system were installed in the laboratory. The schematic of the testing room and the experiment site are shown in Figure 5.  
Echo cancellation material was installed on the inner wall and acoustic insulating equipment was placed on the external wall. The motor, microphone and computer were fixed in the room with the length of 6 m, width of 5 m and height of 4 m. Operators controlled the motor outside the room. The sound signal was recorded by the microphone and then transmitted to the computer. Sampling frequency of the sound signal was 44.1 kHz. The experiment was conducted as follows: start the motor remotely and keep it in no-load operation, then stop the motor after 10 min, pre  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operating System** & **Windows 7 (64 bits)** \\ \hline CPU & Intel Xeon E5-2690 (8 cores, 2.9 GHz) \\ Memory & 16 GB (DDR3) \\ Hard disk space & SSD (512 GB) \\ Matlab version & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the workstation.  
Figure 5: **(a)** Schematic of the soundproof testing branch; and **(b)** The sound recorded in site.  
sound in the computer in wav format. Quantization was subsequently operated to convert the sound amplitude into the scope of [-1, 1]. Finally, a piece of relatively stable sound was extracted with the duration of 0.5 s, as shown in Figure 6.
## 4 Mathematical Formalism of Consensus Through Path-Laplacian Matrices
### Consensus Dynamics on Undirected Networks
#### 4.2.1 Continuous-Time Consensus  
In the first-order continuous-time model, each agent \(i\) adjusts its state with a rate proportional to the discrepancies with its neighbors' states. This is described by the following system of ordinary differential equations  
\[\dot{\phi}_{i}(t)=\sum_{j\in V_{i}}\left(\phi_{j}(t)-\phi_{i}(t)\right), \tag{7}\]  
where \(V_{i}\) denotes the set of neighbors of node \(i\) (all \(1\leq j\leq n\), \(i\
eq j\), such that \((i,j)\in E\)). Equivalently, in vector form, one can write  
\[\frac{d\boldsymbol{\phi}(t)}{dt}\ =\ -\,L\,\boldsymbol{\phi}(t). \tag{8}\]  
where \(\boldsymbol{\phi}(t)=[\phi_{1}(t),\ldots,\phi_{n}(t)]^{\top}\) and \(L\) is the Laplacian of the network [21]. Equation (8) is a linear system whose solution is \(\boldsymbol{\phi}(t)=e^{-Lt}\cdot\boldsymbol{\phi}(0)\). For a connected graph, \(L\) has a single zero eigenvalue and all other eigenvalues \(\lambda_{2},\ldots,\lambda_{n}>0\). In this case, the system (8) drives the agents to a consensus: \(\phi_{i}(t)\) converges to \(\phi_{0i}\) as \(t\to\infty\) for all \(i\). Indeed, one can show \(\lim_{t\to\infty}\boldsymbol{\phi}(t)=c\cdot\mathbf{1}\), where \(c\) is a constant equal to the average of the initial states \(\phi_{i}(0)\)[21; 41]. This average is an invariant of (8) because \(\mathbf{1}^{T}L=\mathbf{0}\), so \(\frac{d}{dt}\sum_{i}\phi_{i}(t)=c\cdot\mathbf{1}\).  
\(\mathbf{1}^{\top}\boldsymbol{\dot{\psi}}(t)=-\mathbf{1}^{\top}L\boldsymbol{\psi}(t)= \mathbf{0}\). The eigenvalues of \(L\) govern the convergence rate of continuous consensus; in particular, the algebraic connectivity) determines the slowest decay mode and hence the asymptotic convergence speed [7; 21].
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 4 Discussion  
Resources onboard are limited, which means the resources for satellite clusters, such as communication resources and computation resources, may have conflicting demands from the mission and negotiations. This causes failure of negotiations easily. Therefore we need a method to balance the gains from negotiation and negotiation delay. We proposed a method in which the negotiations are divided into three levels of granularity with different working modes. As shown the result, these three levels of granularity perform differently, which means this division is effective and can be used in different situations. Moreover, the satellite selects the best level onboard in real-time, and achieves better performance than each of the three levels. Additionally, the satellite can select the level onboard without many resources. The cost-effectiveness ratio of the satellite cluster is increased. It is worth noting that the three levels of granularity were designed by the author based on the author's task planning algorithm. That means the number and mode of each granularity may be different from those in other task planning algorithms.  
We used machine learning to train a neural network. As shown in the training result, the MSE of the neural network reaches 0.002, which is not very good for the training itself, and this may be caused by a small sample set of 112 kinds of the sample. Those kinds of samples are the statistical result from 13,608 samples. However, this error is satisfactory in application, because possibility does not require high accuracy. Nonlinear fitting and small sample learning may obtain better results, but may not change the selection of granularity.
## 3 Mathematical Formulation of the Reactive Power Supply
### The Cost of Reactive Power Generation of the Capacitors Installed on Buses  
The reactive power generation cost of the installed capacitors that is spent on the buses depends on the initial investment used to install the capacitance of interest on the bus. Also, to convert the costs to $/h, the lifetime of the capacitance per hour should be considered. Equation (3) can be used to calculate the capacitor bank cost.  
\[Cost_{QCap}=\sum_{i=1}^{N_{C}}\frac{QCap_{1}.c_{C}}{L.U} \tag{3}\]  
where \(N_{c}\) is the number of the capacitor, \(c_{c}\) is the price per kVAr of the installed capacitor and \(Q_{Capl}\) is the capacitance of the \(i^{th}\) capacitor. In addition, the parameter \(U\) is the performance factor and describes a ratio of the capacitor lifetime that is spent on generating reactive power. Also, \(L\) is the life time factor.
## 3 Analysis of Test Results
### Single-Factor Analysis
#### 3.2.3 Fluidity  
Based on Equation (4), the relationship curve between each factor and fluidity is shown in Figure 8. It can be observed that the influence of the muck size on the slurry fluidity is negligible. In contrast, the soil-binder ratio exerts a more substantial influence on the slurry fluidity compared to the water-binder ratio, binder-sand ratio, fly flash-cement ratio, and water-reducing admixture content. As the soil-binder ratio and, consequently, muck content rise, the slurry fluidity diminishes, aligning with the observations by [PERSON] et al. [28]. In essence, the viscosity of soil in the muck, larger relative surface area of clay particles, and their enhanced water adsorption capacity [31; 51], coupled with the angular and rough surface of silty clay particles [52], contribute to reduced slurry fluidity. A decrease in the water-binder ratio and water-reducing admixture content, and an increase in the binder-sand ratio, leads to a decline in the slurry flow rate. Additionally, as the fly flash-cement ratio increases, the slurry fluidity initially improves but subsequently declines.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### Traditional SLAM Classifications
#### 4.1.4 Collaboration Levels  
The level of collaboration distinguishes between single-agent and multi-agent systems. Single-agent systems involve SLAM performed by a single underwater vehicle, relying solely on its onboard sensors and processing capabilities [31]. Multi-agent systems, on the other hand, involve collaborative SLAM, where multiple robots share information to build a collective map and improve localization accuracy [47]. This collaboration often requires robust communication protocols to handle data exchange between agents, particularly in environments where acoustic communication is limited by bandwidth and latency [30].
## 3 Results and Discussion  
### Driving Kinetics  
Figure 7 shows a diagram of the _MR_ and drying time (min). The results showed that with increasing IR power, the rate of moisture removal from the hulls was faster and the drying time was reduced. Based on this, the minimum and maximum drying time were obtained in IR power of 0.49 and 0.32 W/cm\({}^{2}\), respectively. IR waves reduced the drying time and increased the drying rate due to the direct penetration of energy on the grain surface and its absorption as heat [27]. In other words, high IR power caused more mass and heat transfer and more severe moisture reduction [53]. In addition, the drying time of the products was decreased at higher inlet temperatures, so the longest and shortest process times were related to drying at 40 and 60 \({}^{\circ}\)C, respectively. Increasing the temperature of the product surface led to an increase in mobility of water molecules and as a result easier migration of these molecules from the internal parts to the product surface [54]. Increasing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Sign** & **Unit** & **Category** & **Min** & **Max** \\ \hline Inlet air temperature & \(T\) & \({}^{\circ}\)C & Input & 40 & 60 \\ Infrared power & \(P\) & W/cm\({}^{2}\) & Input & 0.32 & 0.49 \\ Drying time & \(DT\) & min & Input & 0 & 68 \\ Moisture ratio & \(MR\) & - & Output & 0.2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input parameters for ANN and ANFIS to predict the _MR_ of parboiled hulls.  
the temperature on the one hand increased the moisture absorption capacity of the air due to increasing the temperature difference between the air and the product and on the other hand caused faster heating of the product and better evaporation of water from it and ultimately reduced drying time [55]. [PERSON] et al., [40] in a study of drying the thin layer of sour cherry using IR-HA showed that by changing the infrared power from 500 to 1500 W, increasing the air temperature from 35 to 65 \({}^{\circ}\)C, the drying time in the length of the drying process was reduced. Similar findings were observed in the drying of mint leaves [15] and pumpkin [55] and potato [41].
## 2 Materials and Methods  
### Sample Preparation  
Glass-epoxy laminate (FR4), with a thickness of 1.50 mm (Satland Prototype, Gdansk, Poland), covered with a 35 \(\upmu\)m thick Cu layer, was used as a substrate. The surface finishing was prepared from the hot air leveling (HAL) Sn99 Cu0.7 Ag0.3 layer with a thickness of 1 \(\div\) 40 \(\upmu\)m. IGBT transistors NGB8207 BN (V\({}_{\text{CES}}\) = 365 V, I\({}_{\text{Cmax}}\) = 20 A, T\({}_{\text{L}}\) = 275 \({}^{\circ}\)C, package TO-263, ON Semiconductor, Phoenix, Arizona, USA) were soldered onto the substrates with SACX0307 (Sn99 Ag0.3 Cu0.7) paste (LOCTITE HF 212, Henkel Adhesives, Dusseldorf, Germany) (Figure 2).The solder joints were prepared using stencil printing with a stencil thickness of 125 \(\upmu\)m. To prevent a short circuit from forming during the soldering, the stencil aperture was narrowed by 10% over the thermal pad. The samples were soldered with different soldering ovens: a batch oven, convection reflow ovens 3-zones SMT 460C (Essemtec AG, Aesch, Switzerland), 4-zones (TWS AUTOMATION, Avenza, Italy), and a vapour phase soldering oven (ASSCON Quicky 450 VPS, Konigsbrunn, Germany). The thermal profiles of the soldering processes are shown in Figure 3, and a detailed description of them is given in Table 1. The thermal profiles were measured at a soldering pad of the PCB, typically at the terminals of the IGBT. For the purposes of comparison, the samples were soldered with hot air (Infrared Preheater Quick 854 ESD and Lead-Free Rework Station QUICK856 AE, Quick Soldering Co. Ltd., Changzhou, China). Five samples were fabricated in each oven. Single-factor variance analysis and [PERSON]'s test were performed using Statistica software (TIBCO Software Inc, CA, -USA) at a significance level (\(\upalpha\)) of 0.05.  
Figure 2: Test sample of NGB8207 BN transistors soldered onto FR4 substrate.