esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation
### Success Rate Measurements  
We analyzed the success rate of fire detection transmitted from the sensor nodes to the external monitoring server under two configurations: random node placement and REM-guided placement:  
* Success Rate: In the random placement setup, the system achieved an 87% success rate, with 65 successful detections out of 75 fire events. Failures were primarily due to packet loss and transmission delays caused by suboptimal node placement. In contrast, the REM-guided configuration achieved a 100% success rate, successfully transmitting all 75 detection's without loss.
* Transmission Latency: The average latency for transmitting alerts to the monitoring server was 400 ms in the random placement setup, attributed to inefficient multi-hop  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Setup** & **Total Packets Sent** & **Total Packets Received** & **Packet Loss (\%)** \\ \hline Without REM & 500 & 302 & 39.6 \\ With REM & 500 & 500 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Packet loss comparison for REM and non-REM setups.  
pathways and interference zones. In the REM-guided setup, latency was significantly reduced to 150 ms due to optimized node placement and interference mitigation.
* Packet Loss: Packet loss was a critical issue in the random placement setup, with an average loss of 13% per transmission. The REM-based configuration eliminated packet loss entirely, as validated by consistent successful transmissions.  
The results demonstrate the critical role of REM-guided node placement in enhancing the system's performance. The elimination of packet loss and reduction in latency validate the system's suitability for real-time fire detection in industrial environments. These findings emphasize the reliability of the proposed system in ensuring timely and accurate fire alerts, critical for mitigating risks in high-stakes industrial settings. To further support our conclusions, we include a summary as shown in Table 2 for clarity.
## 2 Computational Model and Solving Method
### Acoustic Field Solver  
In the process of sound propagation, the time of media compression and expansion is much shorter than that of heat conduction, the media has no time to exchange heat with the adjacent part, so the sound process is assumed to be isentropic [27; 29]. Additionally, the media used in the centrifugal pump is water, the kinematic viscosity is very small and can be neglected in calculation [30]. Under the inviscid and isentropic condition, the Powell vortex sound equation is.  
\[\frac{1}{c_{0}^{2}}\frac{\partial^{2}p}{\partial t^{2}}-\
abla^{2}p=\
abla \cdot(\rho(\overrightarrow{\omega}\times\overrightarrow{u})+\
abla(\rho \frac{u^{2}}{2})) \tag{1}\]  
The vortex sound equation comprises of time, spatial and source items from left to right.  
During the unsteady simulation process of acoustic field, the inlet and outlet surfaces are set to be absorption boundary conditions, and the total reflection boundary condition is applied to the rest boundaries. The absorption boundary and total reflection boundary conditions are defined as [31].  
\[\frac{1}{c_{0}}\frac{\partial p}{\partial t}+\
abla p\cdot\overrightarrow{n}=0 \ (\text{absorption boundary condition}) \tag{2}\]  
\[p=0\ (\text{total reflection boundary condition}) \tag{3}\]  
To solve Equation (1), the intermediate variable \"\(q\)\" is introduced. The Equation (1) can be written as,  
\[\frac{\partial p}{\partial t}=q \tag{4}\]  
\[\frac{\partial q}{\partial t}=c_{0}^{2}\star(\
abla\cdot(\rho(\overrightarrow {\omega}\times\overrightarrow{u})+\
abla(\rho\frac{u^{2}}{2}))+\
abla^{2}p) \tag{5}\]  
The Equations (4) and (5) are solved based on implicit finite volume method [28]. To ensure the rationality of the initial conditions, the initial conditions are defined as [28],  
\[q_{initial}=0,p_{initial}=0 \tag{6}\]  
where \(q_{initial}\) and \(p_{initial}\) represent the initial values of acoustic field. Figure 3 shows the calculation flow chart of flow and acoustic fields. In this study, the various rotational speed condition study is carried out to reveal the changing rules of the acoustic field distribution characteristics.
## 3 Results
### Analysis of Virtual Constellation Model Performance  
A large portion of L8 NDVI data in coastal areas is often missing due to heavy cloud cover (see Figure 3). Therefore, to aid in long-term continuous monthly monitoring of CW vegetation dynamics, a virtual constellation model was developed and applied to improve NDVI coverage.  
In order to justify the use of a machine learning approach, the virtual constellation model using a RF to synthesize the NDVI data from the three satellite sensors was compared with one based on a simple LR [54]. The test data were NDVI values for pixels randomly selected without replacement (\(n=830\),\(772\) total). For clarity, Figure 9 only shows the virtual constellation NDVI versus observed L8 NDVI for the test data from four months representing the four seasons (March, June, September, December), however the \(R^{2}\) and RMSE are calculated on the entire test set. Figure 9 (left) shows the predictions of RF  
Figure 8: Virtual Constellation performance for February 2018 and September 2016. White pixels in the project area indicate information loss due to clouds, shadows, or scene missing in that month.  
virtual constellation while Figure 9 (right) shows the predictions of the LR based virtual constellation. The RF has a more consistent linear trend across the plotted seasons and tighter agreement than LR. The RF has an \(R^{2}\) value of 0.88 and a positive linear trend while the virtual constellation-LR has a weaker \(R^{2}\) value of 0.26 and is more scattered around its linear trendline, with some visible disjoints between seasons. On the test sample, the RF has a RMSE of 0.0020 while LR has a RMSE of 0.1207. The \(R^{2}\) and RMSE values suggest that the RF not only outperformed LR, but was also able to accurately estimate the absolute magnitude of NDVI. The data shown in Figure 9 are also color coded by month to represent seasonal variations and to investigate the possibility of the model performing well as a whole while simultaneously performing poorly in any individual month (Simpson's Paradox). The four months shown in Figure 9 represent the seasonality of vegetation growth in this region. The colors are well distributed in the RF scatter plot indicating that the virtual constellation model is performing well in all individual months in addition to the entire time period.
# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 4 Results and Discussion
### Evaluation Metrics  
There have been a lot of evaluation metrics used for foreign exchange prediction but mean absolute error (_MAE_) and root mean squared error (_RMSE_) are the most common metrics [54; 55; 56; 57; 58]. These metrics can be evaluated as explained below.
## 3 Results and Discussion
### Effects of Domestic Cooking on Changes in the Contents of VOCs of Faba Leaves
#### 3.4.2 Aldehydes/Ketones/Esters  
Of the nine groups of VOCs in the fresh faba leaves, aldehydes (14.44%), ketones (4.78%), and esters (2.89%) were ranked second, third, and fifth regarding peak areas, respectively (Figure 5, Table S2). For aldehydes, 2,4-heptdalenal (4.18%), benzene acetaldehyde (3.77%), 2,6-nonadienal (E, Z) (2.08%), and 2-hexenal (E) (1.91%) contributed more than 80% of the total contents. Among the represented aldehydes, several components have been known to contribute to various smells, exhibiting 2,4-heptdalenal with the odorant of nuts and fats, benzene acetaldehyde with the flavor of hawthom, and 2-hexenal (E) with the smell of apples and fresh green leaves [36]. Moreover, 1-Penten-3-one is associated with fish and pungent smells, and hexadecanoic acid methyl ester is associated with fatty, oily, and waxy flavors, which form approximately 50% of the detected VOCs that are categorized as ketones and esters, respectively (Table S2) [36]. After domestic cooking, the amount in each group  
Figure 5: The profile of nine groups of VOCs detected in fresh and cooked faba leaves. Values of groups are the totals within each compound content.  
decreased, with a similar pattern being observed in that boiling caused the most significant reduction, followed by steaming, roasting, then microwaving (Figure 4, Table S2).
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 5 Results
### Optimal Model Validation Analysis
#### 5.2.2 Case Application Analysis  
In order to achieve dynamic monitoring of financial distress of listed companies and verify the stability and effectiveness of the constructed model, this article selected the financial indicators of Company A and Company B in the past 12 quarters as inputs. It substituted them into the financial distress warning model based on CNN-BiLSTM-AT that had been built. One hundred repeated experiments with different random numbers of seeds were conducted. In each experiment, the model predicts the probability, p, of two companies facing financial distress, with a \(p\)-value range of [0, 1]. According to the judgment criteria set by the model, when the \(p\)-value is greater than 0.5, it is judged that the listed company is facing financial distress; when the \(p\)-value is less than or equal to 0.5, it is judged that the company is in a normal operating state.  
Figures 11 and 12 show the financial distress warning model results predicting the probability of financial distress faced by Company A and Company B under 100 different random numbers, respectively. Among them, for Company A, it was predicted that the minimum probability value of financial distress faced by the company in 100 repeated experiments was greater than 0.5, indicating that the company was facing financial distress. In 100 repeated experiments, using Company B, the probability of the company facing financial distress was predicted to be
## 5 Conclusions  
We have presented the development of a SAW-based NO\({}_{2}\) sensor and its interface electronics for monitoring NO\({}_{2}\) gas within spacecraft. The sensor consists of a two-port SAW delay line with 2D sensing materials positioned between two IDTs. Single-layer graphene and MoS\({}_{2}\) are used as sensing materials for NO\({}_{2}\) detection due to their high surface-to-volume ratio, high mobility, and moderate bonding with target molecules. The interface electronics, which include two oscillators, a mixer, an LPF, a comparator, an FPGA, and a PC, feature a built-in compensation circuit to mitigate output frequency shifts caused by variations in ambient temperature and humidity. The sensor was successfully verified to operate effectively under oxygen-free conditions and UV-C radiation, making it highly suitable for NO\({}_{2}\) measurement in spacecraft.  
F.N.: conceptualization, methodology, data curation, formal analysis, writing--original draft. H.L.: conceptualization, methodology, data curation, formal analysis, writing--original draft. [PERSON] and [PERSON] contributed equally to this work as first co-authors. W.W.: data curation, funding acquisition. K.L.: conceptualization, methodology, writing--original draft, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and ICT (RS-2023-00278288 and RS-2024-00457846).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Numerical Validation of the Damping Estimation Methodology  
### Initial Considerations  
The present section aims to show the validation of the damping estimation methodology presented before in Section 2 based on a controlled numerical example. The validation, which is presented in detail in Section 3.3, consists of the estimation of pre-imposed damping coefficients using numerically simulated responses under railway traffic of a filler-beam type bridge belonging to the Portuguese railway network, the Casalheira bridge. The 3D numerical model of this bridge has been developed, calibrated, and validated in a previous publication from the authors [28], while the dynamic analyses were performed with a train-track-bridge interaction methodology developed by [PERSON] et al. [49]. To guarantee a comprehensive validation, several scenarios have been tested, includingtrain passages at different speeds and considering different track irregularities. Since the accuracy of the damping estimation based on railway traffic induced vibrations is strongly dependent on the correct evaluation of the free vibration period of the bridge, a sensitivity analysis has been performed and presented in Section 3.2 to estimate the moment in which the free vibration actually starts after the train leaves the bridge.
## 4 Experiments
### Experiments and Results
We conducted the experiments by increasing the number of skeleton joints to 14, 18, and 20 to select the most appropriate number for classifying the behavioral pose of the dog. Figure 10 shows the _accuracy_, \(F1\)_score_, and error rate according to the number of skeleton joints. In the experimental results, the compared models, including our proposed method, performed better with a higher _accuracy_ and \(F1\)_score_ and a lower error rate in Figure 10c, which contained the coordinate values of the ears, in comparison to Figure 10a,b, which did not contain the ears. Based on the experimental results, we proved that our proposed number of skeleton joints (20) for adding the ears and stifles improved the performance of the models compared to the conventionally suggested number of skeleton joints (14 joints and 18 joints). Therefore, we also used the same number of skeleton joints, i.e., 20, that were applied in other experiments. Furthermore, through the experiments, we confirmed that the ear movement and stifles of the dog are important clues for identifying its behavior. **Experiments 4. Analysis of joints that the CNN model learns mainly for each dog behavior. **  
To add reliability to the results derived by the CNN model, we conducted these experiments to analyze the joints that the CNN model mainly learns. As mentioned in Section 2, although the models of the previous studies showed good performance, they had a problem in that they do not provide a sufficient explanation for the derived results of the model. To solve this problem, we produced statistics by extracting the joints that the CNN model learns mainly for each behavior. Table 6 shows the statistics of the joints extracted from all behaviors. According to the experimental results, or Walking and Eating Off Ground, which are dynamic behaviors, many body joint movements were detected. In the relatively static behaviors, such as Standing on Two Legs, Standing on Four Legs and  
Figure 9: (a) [PERSON] et al. [7], 14 skeleton joints; (b) [PERSON] et al. [5], 18 skeleton joints; and (c) our method, 20 skeleton joints. Smelling, the similar proportion of body and face joints were detected or in some cases, face joints were detected more than body joints. For behaviors such as Convulsing, mainly face joints were detected as in those cases, as dogs move their face left and right violently. Through this experiment, it can be seen that although there is less information than the movement of the body joints, the movement information of the face joints is also included.
## 5 Dynamic Identification
### Identified Modes  
The most recurrent experimental mode was seen to be the one at 2.57 Hz. By way of example, the stabilization and clustering diagrams of the identification of a sub-signal are reported in Figure 12.  
The main identified modes are reported in Table 2 in terms of natural frequency and damping ratio. From Figure 12, it can be observed that several clusters are likely to indicate authentic modes. For instance, additional modes are detectable at 3.24 Hz and 5.67 Hz. However, it is worth pointing out that the results presented in this work descend from the assumption that the three blocks belong to the same dynamic system, and a safe attribution, in the presence of a limited number of sensors, will require an accurate mechanical FE  
Figure 11: Measured acceleration responses: (**a**) time\(-\)domain; (**b**) frequency\(-\)domain.  
Figure 12: Stabilization (**a**) and clustering (**b**) diagram of the identification performed on the sixth sub\(-\)signal of setup 1 of the entire Pavilion V, with evidence of the mode at 2.57 Hz.  
model to be calibrated. Due to the redundancy of the measured degrees of freedom with respect to the ones of the diaphragmatic model, the representation of the modal shapes would require an optimization problem to be solved, as reported in Section 6 for the first horizontal mode.
## 3 Topology Optimization Design of U-Frame of Space Electro-Optical Tracking and Pointing Turntable
### Model Optimization Pre-Processing  
To facilitate the subsequent topology optimization, the model is segmented into two regions: Design-region and Frozen-region. This segmentation aids in selecting the appropriate design regions. The U-shaped frame structure consists of 7075 aluminum alloy, with material parameters as follows: yield strength 455 MPa, Young's modulus 72 GPa, Poisson's ratio 0.33, and density 2810 kg/m\({}^{3}\).  
The U-frame is meshed with distinct elements in the non-design area and the optimized design area. For the non-design area, the 10-node tetrahedron element C3D10 is employed, whereas the 8-node hexahedron element C3D8R is utilized in the optimized design area. This meshing method is made to leverage the advantages of both types of elements: hexahedral elements are known for their computational efficiency and accuracy in capturing stress distributions in regular, structured regions, while tetrahedral elements provide flexibility in meshing complex geometries and regions with irregular boundaries. This combination ensures a balance between computational efficiency and the ability to accurately model complex structures, leading to more reliable optimization results. Upon  
Figure 6: Simplified mechanics model of the turntable U-frame.  
completion of the meshing process, the U-frame model comprises a total of 168,721 nodes and 111,491 elements. Figure 7 illustrates the grid model of the U-frame.
## 1 Introduction  
Austenitic stainless steel exhibits significant potential for application in various industries [1], including the aerospace and petrochemical sectors, owing to its remarkable corrosion resistance, high toughness, and favorable machinability. However, the inherent hardness, wear resistance, and fatigue resistance of this material impose considerable limitations on the service life of components fabricated from it. Ion nitriding technology offers several advantages by enabling substantial alterations in the nitriding temperature [2], which can optimize the microstructural characteristics and mechanical properties of the designated penetration layer. Glow ion nitriding, in particular, is characterized by rapid nitriding rates, minimal thermal deformation, and a robust connection between the surface layer and the substrate. Consequently, it is extensively employed to enhance the surface characteristics of stainless steel, thereby improving the surface hardness, wear resistance, and fatigue strength of stainless steel components [3]. Currently, an investigation into the ionic nitriding process of AISI 316L austenitic stainless steel (hereafter referred to as 316L stainless steel) has been conducted at low temperatures (approximately 500 \({}^{\circ}\)C) has been documented. However, while there are numerous studies in this area, the nitriding speed at low temperatures is significantly slower compared to that achieved through high-temperature ionic nitriding, resulting in only marginal improvements in surface hardness [4]. These limitations hinder the advancement and widespread adoption of nitriding surface treatment technologies for stainless steel [5]. To address these challenges, enhancing the diffusion rate of nitrogen atoms is essential for increasing the thickness of the nitriding layer on the surface of stainless steel, thereby improving its hardness and wear resistance. Cold deformation can introduce additional dislocations and defects within austenitic stainless steel, which serve as rapid pathways for atomic diffusion [6; 7; 8]. Therefore, this study investigates the combination of the room-temperature deformation of 316L stainless steel subjected to low-temperature ion nitriding technology. This study investigates the impact of different levels of compressive deformation on the microstructure and properties of 316L stainless steel subsequent to low-temperature nitriding, with the aim of accelerating the ion nitriding process and achieving performance levels of the low-temperature-nitriding-reinforced layer comparable to those obtained through high-temperature nitriding.
## 2 Materials and Methods  
### Materials  
The tested soils were carbonate sand and quartz fines. The specific gravity of the tested carbonate sand and fines was 2.77 and 2.63, respectively. The average grain diameter (\(d_{50}\)) of carbonate sand was 0.75 mm. The maximum grain size of the fines was smaller than 75 \(\upmu\)m, which are classified as fine particles according to ASTM Standard Practice for Classification of Soils for Engineering Purposes (D 2487). Two carbonate sand-fine mixtures were formed by mixing 20 and 40 wt.% carbonate sand with fines, which is termed as the carbonate sand content, representing the mass proportion of carbonate sand in the mixtures. In this study, the fine content remained greater than the carbonate sand content for the tested carbonate sand-fine mixtures. The grain size distributions of the soil mixtures are shown in Figure 1. It was found that the soil mixtures comprised gap-graded soils for carbonate sand contents between 20 and 40%.  
The maximum and minimum void ratios of carbonate sand-fine mixtures with different carbonate sand contents were obtained using the ASTM procedures specified in ASTM Test Methods for Maximum Index Density and Unit Weight of Soils Using a Vibratory Table (D 4253) and Test Method for Minimum Index Density and Unit Weight of Soils and Calculation of Relative Density (D 4254). The results are illustrated in Figure 2. It is observed that both the maximum and minimum void ratios tend to decrease first and then increase with the increase in the carbonate sand content. The threshold value of the carbonate sand content denoting a change in soil fabric from \"sand-in-fines\" to \"fines-in-sand\" is approximately 60%. Therefore, for the carbonate sand-fine mixtures studied in this research, it was assumed that fines played a dominant role in the mixture skeleton since the maximum carbonate sand content was much less than 60%.  
Figure 1: Grain size distributions of carbonate sand-fine mixtures.