esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware-in-Loop Modules for Testing Automated Ventilator Controllers
## 2 Materials and Methods
### Characterization of the OxyVent Module  
The system's behavior was characterized by using a series of baseline and oxygenation procedures. Of particular interest were how the DO saturation ceiling and DO absorption rate were affected by the supplied gas mixture and canister pressure. Each experimental run started by deoxygenating the water via a nitrogen flush, followed by bubbling various air-nitrogen ratios whilst simultaneously regulating the pressure within the canister. The change in DO with time was recorded and analyzed to develop characteristic functions of the system's response. Each test ran for a period of 5 min to allow sufficient time for the system DO to reach a reasonably steady state. Bubbled gas mixtures were tested in triplicate at a cage canister pressure of approximately 5 psi (\(\sim\)250 mmHg). To observe changes in the DO saturation and responsiveness with a dynamic pressure range, variable pressure tests were conducted with a selection of air-nitrogen ratios that began at an initial gage canister pressure of 0 psi, increasing to 5 psi, and then finally to 10 psi. The canister pressure was held at each step for 5 min, with tests run in triplicate.  
To further test the OxyVent module, a preliminary decision-table-based closed-loop control logic was conceived for managing the O\({}_{2}\) ventilator settings. Automated logic was based on the ARDSNet protocol developed by the NIH NHLI ARDS Clinical Network [40], which has been used for automating ventilator function in other studies [41] (Figure 4A). We used the lower PEEP and higher FiO\({}_{2}\) logic but added additional rules to further prioritize FiO\({}_{2}\) adjustments when SpO\({}_{2}\) fell below a certain threshold. Logic started on the first step in the ARDSNet table, and, if SpO\({}_{2}\) ever drifted below 90%, immediate setting adjustments would be performed to increase FiO\({}_{2}\) to 100% and then continue following PEEP adjustments based on the ARDSNet decision table. Once SpO\({}_{2}\) was above 96%, FiO\({}_{2}\) would decrease according to the ARDSnet decision table. Two sampling rates for the oxygen management logic were evaluated at 5 s and 60 s (Figure 4B).
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 7 Conclusions  
By exploring the kinetic energy of anchor wire breaking and ejection, the following conclusions were drawn.  
1. In accordance with the tensile test of anchor wires and the case analysis of anchor wire breaking and ejection, the strain energy density of anchor wire breaking and ejection was 27.528 \(\times\) 10\({}^{6}\) J/m\({}^{3}\), the breaking and ejection speed of the anchor wire that was 0.3 m in length was 48 m/s, and anchor wire breaking length was directly proportional to strain energy but inversely proportional to ejection speed.
2. The tensile test of protective devices indicated that in case of the ultimate stretching of a protective device with a spring wire diameter of 4 mm by 1.03 m, the absorbable energy was 1424 J. The absorbable energy was 1308 J in the event of the ultimate stretching of a protective device with a spring wire diameter of 5 mm by 0.83 m.
3. As revealed in the numerical simulation through ANSYS, after the protective device with a spring wire diameter of 4 mm was hit, the middle section of the spring was locked up upon contact with the anchor end, failing to provide full play to spring performance. After the protective device with a spring wire diameter of 5 mm was hit, the middle section of the spring did not experience tightening or did not get stuck, the effective elongation of the spring increased, and the ultimate impact load increased from 70 m/s to 160 m/s, meeting the field protection requirements.
4. The performance of protective devices can be further improved by increasing the elastic coefficient and diameter of the spring in a protective device.
5. The anchor cable protective device proposed in this paper has the characteristics of high safety and recyclability, which can provide an effective solution for the breaking protection of anchor cable wire in mine roadways.  
Conceptualization, F.G.; methodology, M.T.; validation, J.D.; formal analysis, F.G.; investigation, F.G.; resources, F.G.; writing--original draft preparation, F.G.; writing--review and editing, M.T.; supervision, M.T.; project administration, M.T.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Anhui Collaborative University Innovation Project (GXCT-2020-056) and the National Natural Science Foundation of China (52074008).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 Results
The embedding dimension \(m=5\) was chosen for the calculation of %DET, while the time lag \(\tau\) was set to 10. Table 2 presents the median of %DETs for all the subjects in the AP, ML, and V directions. In the AP direction, the amount of %DET is lower when the robotic walker is used. The Kruskal-Wallis ANOVA rejects the null hypothesis that the data in the AP direction under all three conditions comes from the same distribution (\(p=0.011\)), while the Kruskal-Wallis ANOVA does not reject the null hypothesis for the %DETs in the ML and V directions (\(p=0.69\) for the ML direction, and \(p=0.84\) for the V direction). Furthermore, the results of multiple comparison tests shows a significant difference in the median of the %DETs (AP direction) between conditions 1 and 3 (\(p<0.01\)). \begin{table}
\begin{tabular}{c c c c} \hline
**Measures** & **Condition 1 NW** & **Condition 2 RW** & **Condition 3 RWW** \\ \hline \%DET (AP) & 0.51 \(\pm\) 0.02 * & 0.49 \(\pm\) 0.03 & 0.46 \(\pm\) 0.02 * \\ \%DET (ML) & 0.49 \(\pm\) 0.08 & 0.46 \(\pm\) 0.05 & 0.44 \(\pm\) 0.07 \\ \%DET (V) & 0.68 \(\pm\) 0.04 & 0.69 \(\pm\) 0.03 & 0.68 \(\pm\) 0.03 \\ \hline \end{tabular}
\end{table} 
Table 2: %DETs for the acceleration data of all the subjects in three directions. Values are reported as median \(\pm\) median absolute deviation; * indicates a statistically significant difference in the %DET between conditions 1 and 3. Figure 4: LSEs of the trunk acceleration data for one subject in the anterior–posterior (AP) direction with different embedding dimensions (\(m=5,\dots,10\)) in (**a**) condition 1 and (**b**) condition 3. LSEs of the trunk acceleration data for another subject in the AP direction in (**c**) condition 1 and (**d**) condition 3.
###### Abstract  
Synthetic-Aperture Radar (SAR) has emerged as an important tool for monitoring tropical cyclones (TCs) due to its high spatial resolution and cloud-penetrating capability. Recent advancements in SAR technology have led to smaller and lighter satellites, yet few studies have evaluated their effectiveness in TC monitoring. This paper employs an algorithm for automatic TC center location, involving three stages: coarse estimation from a whole SAR image; precise estimation from a sub-SAR image; and final identification of the center using the lowest Normalized Radar Cross-Section (NRCS) value within a smaller sub-SAR image. Using three wide-swath miniaturized SAR images of TC Noru (2022), and TCs Doksuri and Koinu (2023), the algorithm's accuracy was validated by comparing estimated TC center positions with visually located data. For TC Noru, the distances for the three stages were 21.42 km, 14.39 km, and 8.19 km; for TC Doksuri--14.36 km, 20.48 km, and 17.10 km; and for TC Koinu--47.82 km, 31.59 km, and 5.42 km. The results demonstrate the potential of miniaturized SAR in TC monitoring.  
synthetic-aperture radar; Hisea-1; tropical cyclone; sea surface wind direction +
Footnote †: journal: Applied Sciences
# Underwater Image Super-Resolution via Dual-aware Integrated Network
## 4 Experiments  
### Experimental Setup  
Two open datasets were adopted to train our proposed work, namely USR-248 [52] and UFO-120 [53], as well as the EUVP dataset [52]. USR-248 was the first dataset made for the SR reconstruction task of underwater optical images, which uses 1060 pairs of underwater images for training and 248 pairs of samples for testing. UFO-120 contains over 1500 pairs of training samples and 120 pairs of testing samples. It is important to note that the LR samples in the USR-248 and UFO-120 datasets are acquired by artificial distortion. Both of them follow the standard procedures [54; 55] for optical/spatial image degradation and use manually labeled saliency mappings to create pairs of data.  
Adam optimization is utilized to minimize the objective function, with the parameters configured as follows: \(\beta_{1}=0.9,\beta_{2}=0.999\), and \(\varepsilon=10^{-8}\). The learning rate is initially set to \(10^{-4}\) and then decreased by 50% after 300 iterations. In the STL, the number of multi-heads is set to \(h=5\) and the number of channels is set to \(C=50\). The performance of image super-resolution (SR) was thoroughly assessed using three criteria: peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and the underwater image quality measure (UIQM). Our model is implemented using the PyTorch framework and was executed on an NVIDIA RTX 3080 GPU.
## 3 Methods
### Encoder
#### 3.2.3 Adaptive Attention-Based Fusion  
When fusing audio and visual features, we consider that, in practical applications, the audio and video modalities often contain complementary information. The adaptive attention mechanism effectively integrates information from both modalities. By adaptively adjusting the attention weights, the model can dynamically balance the importance of each modality, leveraging their complementary nature. This enables the model to selectively choose the optimal fusion strategy under different input conditions.  
Figure 5: Sparse attention and global attention weight distribution on specific heat maps (left: global attention; right: sparse attention).  
Figure 6: The model’s attention to each part of the image under the original image and different attention mechanisms (left: the original image; middle: global attention; right: sparse attention).  
Initially, the audio feature \(f_{a}\) and the visual feature \(f_{v}\) are fed as inputs into the multihead attention mechanism to obtain the audio-to-visual attention:  
\[\begin{cases}Q_{a}=f_{a}\cdot W_{a}^{Q}\\ K_{v}=f_{v}\cdot W_{v}^{K}\\ V_{v}=f_{v}\cdot W_{v}^{V}\end{cases} \tag{11}\]  
Score as:  
\[S_{a-v}=\frac{Q_{a}\cdot K_{v}^{T}}{\sqrt{d_{model}}} \tag{12}\]  
The attentional weights of audio features over visual features are as follows:  
\[\alpha_{a-v}=softmax(S_{a-v}) \tag{13}\]  
Get an output:  
\[O_{a-v}=\alpha_{a-v}\cdot V_{v} \tag{14}\]  
Then, the visual-to-audio attention is as follows:  
\[\begin{cases}Q_{v}=f_{v}\cdot W_{v}^{Q}\\ K_{a}=f_{a}\cdot W_{a}^{K}\\ V_{a}=f_{a}\cdot W_{a}^{V}\end{cases} \tag{15}\]  
Score as:  
\[S_{v-a}=\frac{Q_{v}\cdot K_{a}^{T}}{\sqrt{d_{model}}} \tag{16}\]  
The attentional weights of the visual features on the audio features are as follows:  
\[\alpha_{v-a}=softmax(S_{v-a}) \tag{17}\]  
The output is obtained as:  
\[O_{v-a}=\alpha_{v-a}\cdot V_{a} \tag{18}\]  
Two weight parameters, \(a_{w}\) and \(v_{w}\), are obtained and normalized via the Softmax function to determine the weights of the two modalities:  
\[\beta=softmax\bigg{(}\begin{bmatrix}a_{w}\\ v_{w}\end{bmatrix}\bigg{)} \tag{19}\]  
Finally, a weighted fusion of these weights is obtained:  
\[F=\beta[0]\times O_{a-v}+\beta[1]\times O_{v-a} \tag{20}\]  
The fused features undergo dimensionality reduction via a CNN layer.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 2 Materials and Methods
### Combined Anomaly and Trend Detection Model
#### 2.1.1 System Assumptions  
The presented model builds upon certain assumptions. Data ingested in the system must be collected from a setup with a constant robot trajectory and load. The system analyses only univariate time series data of one health indicator per axis derived from acceleration sensor data. A suitable HI is described for example in [3]. The HI exhibits stationary behaviour when the robot axis is in a healthy state. The considered time series can be subject to trends \(x_{trend}(t)\), seasonality \(x_{seasonality}(t)\), noise \(x_{noise}(t)\) and anomalies \(x_{anomaly}(t)\). Noise can be caused by changing environmental conditions or sensor effects. Trends can occur due to wear. Trends due to sensor drifts are prevented by the sensor setup or suitable data preprocessing (e.g., high pass filtering of the raw data). Seasonality can occur due to changing temperatures of the gears. These temperature changes lead to variations in the HI (for example, see [37]). These temperature changes result from varying utilisation in the production system. They could be caused for instance by a three shift working model with reduced utilisation during night shift. Summarising, this time series can be expressed as in Equation (1).  
\[x(t)=x_{trend}(t)+x_{seasonality}(t)+x_{noise}(t)+x_{anomaly}(t) \tag{1}\]
# Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers
## 2 Miniature Loudspeaker Model Considering Nonlinear Acoustic Damping
### ECM Model of Miniature Loudspeakers Considering Nonlinear Acoustic Damping  
The ECM model describing the full dynamics of a miniature loudspeaker is shown in Figure 3. In the electrical domain, the voice coil is modeled as an inductor \(L_{e}\) and a resistor \(R_{e}\). Mechanical resistance \(R_{m}\), moving mass \(M_{m}\), and mechanical stiffness \(K_{m}\) were used to model the moving part of the miniature loudspeaker. Force factor \(Bl\) represents the coupling  
Figure 2: Control volume used in nonlinear acoustic damping deviation.  
effect between the electrical and mechanical systems. In the acoustic domain, inductor \(M_{a}\) is employed to represent the acoustical mass of the air pushed by the diaphragm and dome. In particular, a nonlinear parameter \(R_{a}\) is used to model the nonlinear acoustic damping near the orifice of the miniature loudspeaker. The effective radiation area \(A\) of the diaphragm behaves as a transformer connecting the mechanical and acoustical domains.  
In the large-signal domain, mechanical stiffness \(K_{m}\), mechanical resistance \(R_{m}\), and force factor \(Bl\) are nonlinear and dependent on the displacement/velocity of the voice coil [11]. Based on the ECM model shown in Figure 3, the following loop equations can be obtained:  
\[u=R_{e}i+L_{e}\frac{di}{dt}+Bl(x)v \tag{4}\]  
\[Bl(x)i=M_{m}\frac{dv}{dt}+R_{m}(v)v+K_{m}(x)x+pA \tag{5}\]  
\[p=M_{a}\frac{d(vA)}{dt}+R_{a}(v)vA \tag{6}\]  
where \(u\) is the applied voltage, \(i\) denotes the current, \(x\) is the displacement of the voice coil, \(v\) represents the velocity of the voice coil, and \(p\) is the sound pressure on the diaphragm and dome. By substituting Equation (6) into Equation (5), we obtain  
\[Bl(x)i=M_{m}^{\prime}\frac{dv}{dt}+(R_{m}(v)+R_{ma}(v))v+K_{m}(x)x \tag{7}\]  
where \(M_{m}^{\prime}=(M_{m}+M_{a}A^{2})\) is the equivalent moving mass and \(R_{ma}(v)=R_{a}(v)A^{2}\) is the equivalent mechanical resistance. The loop equations can be discretized into the following forms according to Equations (4) and (7) using the forward Euler method:  
\[u[n]=R_{e}i[n]+L_{e}\frac{i[n+1]-i[n]}{T}+Bl(x[n])v[n] \tag{8}\]  
\[Bl(x[n])i[n]=M_{m}^{\prime}\frac{v[n+1]-v[n]}{T}+(R_{m}(v[n])+R_{ma}(v[n]))v[n] +K_{m}(x[n])x[n] \tag{9}\]  
with \(T\) being the discrete time interval.
# Study of the Process of Packet Arrival at a Multiservice Node
## 2 Mathematical Model of a Multiservice Node
### Two Models of the Arrival Process  
It is expedient to consider the request flow as a sequence defined on the \"Time\" axis, which is usually denoted by the letter \(t\). On this axis, it is possible to single out time moments, \(x_{i}\), at which requests arrive at the queuing system input. The \(x_{i}\) values correspond, for example, to those time moments when packets arrive at a multiservice node. The request flow is random. There are also deterministic request flows. For them, the values of \(x_{i}\) are given by the predetermined schedule. The analysis of such a request flow is not related to the study of random variables.  
Figure 2 shows five moments of time, \(x_{i}\), at which requests arrive at the queuing system input. The size of the \(i\)-th time interval between the moments of arrival of neighboring requests is \(t_{i}\). Therefore, \(t_{i}=x_{i}-x_{i-1}\) for all \(i\geq 1\).  
The model shown in Figure 2 contains all the necessary information about the arriving request flow under the condition that at any moment of time, \(x_{i}\), only one request may arrive. Otherwise, it is necessary to set the values of \(k_{i}\), which determine the number of requests arriving at the time moment, \(x_{i}\). For the model under consideration, \(k_{i}=1\).  
Let us suppose that, at time moments \(x_{1}\) and \(x_{4}\), two requests arrive at the queuing system input at once. The second request flow model should be used then. This is shown in Figure 3. The \"Number of Requests\" ordinate axis, which allows the specification of all the values of \(k_{i}\), is added to it. The second request flow model is rarely used in practice.  
Let us return to Figure 2. Statistical information on the \(t_{i}\) values allows the determination of the CDF \(A(t)\). Methods for solving this problem will be described below.  
Let us suppose further that, for the selected function \(A(t)\), there is a Laplace-Stieltjes transform [8], which is denoted as \(\alpha(s)\). Then, the average value of the interval size  
Figure 3: Second model of the arrival process.  
Figure 2: First model of the arrival process.  
between the arrivals of neighboring requests \(A^{(1)}\) is determined via one of the following two formulas:  
\[A^{(1)}=\int_{0}^{\infty}tdA(t),\ A^{(1)}=-\frac{d\alpha(s)}{ds},\ with\ \ s=0. \tag{3}\]  
The values of \(\lambda\) and \(A^{(1)}\), as stated above, are related to each other by a simple relationship:  
\[\lambda=\frac{1}{A^{1}}. \tag{4}\]  
The \(A(t)\) function can be built based on the measurement results that is the histogram \(a(t)\). The distribution \(A(t)\) will be then a stepwise function. In this case, there is always a Laplace-Stieltjes transform for it. It is obvious that when using the Laplace-Stieltjes transformation, the calculation of the value \(A^{(1)}\) and other characteristics of the random variable is simplified due to the use of differentiation instead of integration.
## 3 Literature Review  
### Formation of DK  
This section overviews the formation process of DK, starting with kaolinite mineral and covering the production process and relevant chemical reactions, and ending with the by-product DK. Kaolinite, with the chemical formula \(\text{Al}_{2}\text{Si}_{2}\text{O}_{5}\text{(OH)}_{4}\), is a clay mineral consisting mainly of layered octahedral alumina (\(\text{AlO}_{6}\)) and tetrahedral silica (\(\text{SiO}_{4}\)) sheets (1:1 lattice). These are linked through hydrogen bonding between hydroxyl groups from the octahedral Al-OH sheet and basal oxygen atoms of the adjacent tetrahedral Si-O sheet, as well as covalent oxygen sharing between the tetrahedral and octahedral sheet within each 1:1 layer unit [13]. Figure 2 shows the structure unit of the sheets, and Figure 3 shows schematically the 1:1 lattice structure of kaolinite. During calcination at temperatures ranging at 550-900 \({}^{\circ}\)C dehydroxylation of kaolinite occurs [14; 15]. As a result, chemically bonded water is released, and hydroxyl groups (-OH) in the octahedral alumina sheet are broken [16; 17]. The crystalline structure collapses, transforming into a new disordered phase called \"Metakaolin\", a highly reactive, amorphous pozzolan with the chemical formula \(\text{Al}_{2}\text{Si}_{2}\text{O}_{7}\)[5; 15]. Equation (1) describes the chemical reaction of metakaolin formation:  
\[\text{Al}_{2}\text{O}_{3}\cdot 2\text{SiO}_{2}\cdot 2\text{H}_{2}\text{O}( \text{kaolinite})\overset{\text{A,550-900}\ ^{\circ}\text{C}}{\rightarrow}\text{Al}_{2}\text{O}_{3}\cdot 2 \text{SiO}_{2}(\text{metakaolin})+2\text{H}_{2}\text{O}(\text{g}) \tag{1}\]  
At temperatures above 900 \({}^{\circ}\)C, a different molecular arrangement occurs, leading to the formation of an intermediate aluminosilicate spinel-type phase with the approximate composition \(2\text{Al}_{2}\text{O}_{3}\cdot 3\text{SiO}_{2}\), which precedes the crystallisation of mulite (\(3\text{Al}_{2}\text{O}_{3}\cdot 2\text{SiO}_{2}\)) [14; 15].  
When used in the alum industry, the metakaolin thus formed undergoes acid leaching with sulphuric acid to produce aluminium sulphate (alum), a process that also generates a solid by-product material rich in silica (\(\text{SiO}_{2}\)) known as de  
Figure 3: 1:1 lattice structure of the kaolinite mineral.  
Figure 2: (a) Tetrahedral silica and (b) octahedral alumina structural units.  
(DK) [18; 19]. Equation (2) describes the chemical reaction of the dealuminated metakaolin using sulphuric acid [20]:  
\[\mathrm{Al_{2}O_{3}\cdot 2 SiO_{2}+3H_{2}SO_{4}+48H_{2}O\ ^{ yields}\ \mathrm{Al_{2}\left(SO_{4}\right)_{3}\cdot 51H_{2}O\ (liquid\ \mathrm{alum})+2 SiO_{2}}} \tag{2}\]  
It is important to note that the efficiency of the alum extraction reaction is not 100%; as a result, some alumina stays unreacted. That is why the X-Ray Fluorescence of any dealuminated metakaolin shows around 6% to 8% of alumina [20].
# An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning
## 4 Simulation and Result Analysis
### Experimental Results
#### 4.2.3 Test Error Distribution  
We make statistics on the test angle error of the actual samples and draw the distribution diagram, as shown in Figure 10. Figure 10a shows the test angle error distribution of the model before transfer learning, and Figure 10b shows the error distribution after transfer learning.  
We can observe from Figure 10 that before transfer learning, the test angle errors are mostly distributed between \(-50^{\circ}\) and \(50^{\circ}\), and there are some large angle errors below \(-100^{\circ}\) or over \(100^{\circ}\). After transfer learning, the error is mainly distributed between \(-1^{\circ}\) and \(1^{\circ}\), and the maximum angle error of the model is reduced between \(-5^{\circ}\) and \(5^{\circ}\). We can infer from the test result that our error calibration method based on transfer learning greatly reduces the overall DOA estimation error.