esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 3 Experimental Verification
### Column Concentration Inversion under Different Environments  
#### 3.3.1 Inversion of Gas Column Concentration under a Stable Environment  
Under relatively stable conditions in the laboratory, a gas column concentration inversion experiment was carried out with the surface source blackbody as the background. The experimental equipment is shown in Figure 8.  
During the experiment, the surface source blackbody was adjusted to 55 \({}^{\circ}\)C, and SF\({}_{6}\) gas with 99.99% concentration was released between the surface source blackbody and the imaging system through the gas path. The gray level image of the target scene was obtained through the reference spectral channel when the SF\({}_{6}\) gas was released, and a background temperature image and a background gray level image under the target spectral channel were obtained by combining the mapping relationships. As shown in Figure 9, Figure 9a is the grayscale image under the reference spectral channel, and because it is not affected by SF\({}_{6}\) gas in the reference spectral channel, the gas distribution cannot be observed; Figure 9b is the temperature distribution image; and Figure 9c is the grayscale image under the target spectral channel obtained through mapping.  
The absorbance distribution of the target gas can be calculated according to the gray image of the gas under the target spectral channel and the gray image of the background radiation obtained by mapping. We inserted the absorbance distribution into the calibration curve of the gas column concentration to invert the gas column concentration distribution. The results are shown in Figure 10, where Figure 10a is the absorbance distribution image and Figure 10b is the column concentration distribution image. From the column concentration distribution image obtained from the inversion, it can be clearly seen that the column concentration of the target gas shows a distribution trend of being high in the middle and low at both ends. The measured gas distribution accords with the result of Gaussian distribution fitting, and the background absorbance and column concentration  
Figure 8: Experimental device for inversion of gas column concentration in the laboratory.  
Figure 9: (**a**) Gray image of reference spectral channel; (**b**) temperature distribution image; (**c**) gray image of inversion.  
outside the target gas are close to 0. The gas path diameter of the released gas is 8 mm, the concentration of the released gas is approximately 100% and the pressure of the exhaust port is about 0.125 mpa. Therefore, when the gas is released into the atmosphere and the pressure is reduced, the column concentration of the gas is about 9871.67 ppm\(\cdot\)m, and the peak of the column concentration obtained by inversion is 10,258 ppm\(\cdot\)m, with a relative error of 3.9%. The inversion results are relatively close to the actual situation and have good inversion accuracy.
# SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements
## 2 Data Sources and Preprocessing
### Data Preprocessing  
In this study, the radar backscatter (\(\sigma^{0}\)) measurements from the HSCAT, ASCAT, and CSCAT scatterometers are first projected onto polar ice maps with a resolution of 25 km. The sizes of the Arctic and Antarctic ice maps are 448 \(\times\) 304 and 332 \(\times\) 316, respectively. Figure 1 shows the number of ASCAT \(\sigma^{0}\) data in each pixel of the (a) Antarctic and (b) Arctic ice maps on 4 January 2020. The overlying black lines are the land boundary. We can clearly see that the number of observations is relatively lower at lower latitudes.  
In addition, radar backscatters from ASCAT and CSCAT scatterometers are measured at various incidence angles (as shown in Table 1). The backscattering characteristics of sea water and ice have different responses to incidence angle. Thus, the dependence of \(\sigma^{0}\) data on incidence angle can also be used as a characteristic parameter in the classification of sea water and ice. In practice, a linear function of incidence angle (\(\theta\)) is usually used to express the incidence angle dependence [12]:  
\[\sigma^{0}(\theta)=\sigma^{0}_{40}+K*(\theta-40^{\circ}) \tag{1}\]  
where \(\sigma^{0}\) is in decibels; \(\sigma^{0}_{40}\) denotes the backscatter coefficients at an incidence angle of 40\({}^{\circ}\); and \(K\) is the slope of the linear function and gives the dependence of the backscattering coefficients on the incidence angle.
## 4 Discussion
### Seasonal and Climatic Effects on ET\({}_{0}\) Estimation  
Our study found that ET\({}_{0}\) in arid and semi-arid climate was more affected by air temperature, while solar radiation affected ET\({}_{0}\) more in humid and semi-humid climate (Figure 7). Several previous literature also found that the sensitivity of meteorological factors to ET\({}_{0}\) varied across climatic types [51,52,53,54]. When seasonal ET\({}_{0}\) was compared, the  
Figure 6: (**A**) Coefficients of determination (\(\tau^{2}\)) between meteorological factors and ET\({}_{0}\), (**B**) box chart showing the statistical distribution of \(\tau^{2}\) for meteorological factors.  
accuracy of ET\({}_{0}\) estimation decreased in autumn and winter seasons, especially for BP models in TC and PM zones. In Shaanxi Province, Northwest China, six sites were selected to represent semi-humid to arid climates to compare the performance of BP models and similar results were observed to our study. It was found that average relative error (ARE) was greater than 12% in autumn and winter seasons, especially for arid climate in North Shaanxi province [55]. In central China, worse model performance was also found during autumn and winter seasons, while the ARE was smaller (<9%) for a subtropic monsoon climate whose air temperature and relative humidity were relatively higher [56]. The reason why ET\({}_{0}\) estimates had greater discrepancy in cold seasons and areas might be attributable to its large \(\Delta\)T as indicated by the extremely low correlation with \(\Delta\)T (\({}^{\circ}\)C) in temperate continental and mountain plateau zones.
## 4 Range-Based Algorithms
### Multi-Lateration/Triangulation  
Based on the ranges, angles, or range differences, a set of equations can be constructed which usually result in an over-determined set of non-linear equations. For N number of BS position at \(S_{i}=[S_{ix},S_{iy}]^{T}\) and the target at location \(X=[X_{x}X_{y}]^{T}\), we can write  
\[d_{i} =||X-S_{i}||+\sigma,\quad i=1,2,\ldots,N\,, \tag{2}\] \[\delta d_{i} =||X-S_{i}-S_{1}||+\sigma,\quad i=2,...,N\,,\] (3) \[a_{i} =\arctan(\frac{X_{y}-S_{iy}}{X_{x}-S_{ix}})+\sigma,\quad 1,\ldots,N\,, \tag{4}\]  
where \(\sigma\) is the measurement error, while \(d_{i}\), \(\delta d_{i}\), and \(a_{i}\) are the range, range-difference, and angle measurements. Geometrically speaking, in the simplest scenario, having the range from target to three Base stations (BS) in 2D, the intersection of three circles with the centre of the BS and radius of the measured range will be the solution (see Figure 1). In 3D, the intersection of four Spheres is the response. For TDOA, the hyperbolas can be constructed, with the focus being the two BS (see Figure 2). In the AOA framework, the angles measured with the help of the geometric properties of the triangle could determine the target location (see Figure 3). In an ideal case, these methods intersect in one single point. However, this never happens in the real world, which gives rise to several scenarios [22].  
Figure 1: Tilateration: localisation based on the range from 3 anchors (TOA, RSS).  
The set of equations can then be solved either in approximated closed-form or iteratively. Closed-form formulation leads to an easy, low-complexity solution. For example, in [23], a closed-form algebraic solution for target localisation for both trilateration (three reference points) and multi-lateration (more than three reference points) is solved. The uncertainty of each information piece is considered by adding a variance matrix to the equations. However, it assumes the known covariance matrix error, which is not realistic. In an extended version of this method [24], after applying the standard multi-lateration procedure, if the solution lies within the reference node positions, it is considered the final solution. Otherwise, the algorithm keeps searching for the solution inside the zones that are determined within the reference nodes based on the strength of the RSS level (the  
Figure 3: Triangulation: localisation based on AOA from 3 BS.  
Figure 2: Localisation based on TDOA, the intersection of the hyperbolas.  
zone closer to the base station from which the strongest RSS level is read has the highest probability of containing the solution). In the search process for a solution inside a zone, virtual positions are defined inside zones, and then the final target location is selected based on the distance between the first estimation and the virtual positions.
# Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
## 2 Materials and Methods  
### Received Signal Strength Indicator and Channel State Information  
The principle of wireless indoor behavior detection is to transmit the generated wireless signal through multipath transmission. Reflection and scattering will cause multiple superimposed signals to be received in an indoor environment. These signals are physically affected by human behavior in the transmission space and generate various environmental characteristic information. Therefore, the information extracted from multipath superimposed signals can be used to identify human behavior [18].  
The most common data sources for the device free gesture recognition systems based on Wi-Fi signals are the Received Signal Strength Indicator (RSSI) and CSI [19]. RSSI is the most widely used signal indicator for wireless devices [20]. It describes the attenuation experienced during the propagation of a wireless signal. In the wireless sensor link, the RSSI of the wireless sensor unit will be changed with the movement of the person. In other words, the movement of the person can be detected based on the change of RSSI. Since the RSSI information is easy to capture, RSSI was used for hand gesture recognition in the early days [21]. The RSSI is a kind of coarse-grained information, mainly from the superimposition result of the receiver during signal transmission. It is affected by the multipath effect and environmental noise, so it has large fluctuations and poor stability [2].  
RSSI only reflects the total amplitude of multipath overlap on the media access control (MAC) layer, while CSI is more fine-grained subcarrier information of the physical layer. For a multiple-input multiple-output (MIMO) wireless technology in combination with orthogonal frequency division multiplexing (OFDM) Wi-Fi system, CSI is mainly derived from the sub-carriers decoded by the OFDM [22]. It can effectively eliminate or reduce the interference caused by the multi-path effect. CSI contains amplitude and phase information under different sub-carriers, and each sub-carrier does not interfere with each other. Thus, CSI is more sensitive and reliable than RSSI. It has higher detection accuracy and sensitivity, so it can achieve more detailed motion detection [23].  
A set of CSI data can be obtained from each received data packet of a wireless network card compatible with the IEEE 802.11n protocol standard. The amplitude and phase of a sub-carrier on the CSI data are shown in Equation (1):  
\[H(f_{k})=\|H(f_{k})\|e^{jLH(f_{k})} \tag{1}\]  
where \(H(f_{k})\) is the CSI of the sub-carrier with a center frequency of \(f_{k}\), \(\|H(f_{k})\|\) and \(\angle H(f_{k})\) are the amplitude and phase of the center frequency of \(f_{k}\), respectively. They are the most important information in CSI data, and \(k\) represents the total number of sub-carriers.
# Estimation of Changes in Sediment Transport along the Free-Flowing Middle Danube River Reach
## 4 Results
### Correlation between the Minimum Water Level and Cross-Sectional Area Changes at Gs
Finally, the correlation between the percentages of the observed cross-sectional area increase (morphological variable) and the minimum water level (hydrological variable) is presented in Figure 9. The cross-sectional area increase is calculated compared with the reference year, i.e., the first year with available bathymetric data, as indicated in the caption of Table 3. There is a linear correlation between the two variables, and the decrease in the low water level follows the incision of the riverbed. Values of regression coefficients are provided in Table 4. Figure 9 confirms what was already presented in Figures 5 and 6, i.e., that the highest rate of water level decrease is in Baja (rkm 1478.70) and that \(Z_{\min}\) decreases at a rate of 0.12 m\({}^{2}\) with a percentage increase in the cross-sectional area, with a coefficient of determination of R\({}^{2}\) > 0.6 (Table 4). The rate of \(Z_{\min}\) decrease is almost the same as in Mohacs (rkm 1446.8) and Bezdan (rkm 1425.59). It is approximately 0.03. However, the coefficient of determination in Bezdan (R\({}^{2}\) = 0.62) is significantly greater than that in Mohacs (R\({}^{2}\) \(\approx\) 0.09). The trend in Apatin (rkm 1401.90) decreases for the entire period (1964-2016), i.e., the slope of the trendline is positive but very small (\(a\) = 0.004). Moreover, the coefficient of determination is very low (R\(  
These longitudinal profiles of \(A\) were used to estimate the 25-year long-term sediment transport, presented in Figure 11. The largest multiannual sediment transport rate (\(Q_{s}\)) (around 65\(\cdot\)10\({}^{3}\) t/year) is on the Hungarian side between Baja and Mohacs, where the aggradation of the riverbed is evident (Figure 10). The most intensive degradation of the riverbed is downstream of Bezdan and resulted from a multiannual sediment transport rate of approximately 45\(\cdot\)10\({}^{3}\) t/year. Generally, downstream of Mohacs, the sediment transport rate is negative, meaning there is a continuous uptake of sediments from the riverbed, i.e., the incision of the riverbed. The uptake rates are mostly greater than 10\(\cdot\)10\({}^{3}\) t/year, and between some surveillance cross-sections, they reach approximately 25\(\cdot\)10\({}^{3}\) t/year.
# Study on Reinforcement Learning-Based Missile Guidance Law
## 2 Problem Formulation  
### Equations of Motion  
The most considerable location for a homing missile is the inside of the threshold that switches the INS (Inertial Navigation System)--based-guidance phase into the homing phase, which is the inner space of the gradation-filled boundary in Figure 1. \(\phi_{0}\) is the initial approaching angle, and the angular initial conditions for simulations will be set by using a randomly generated approaching angle as a constraint condition, which is as follows:  
\[\lambda_{0}=\left\{\begin{array}{l}\phi_{0}+\pi,\ (if\ \phi_{0}<\pi)\\ \phi_{0}-\pi,\ (if\ \phi_{0}\geq\pi)\end{array}\right. \tag{1}\]  
\[\psi_{0}=\lambda_{0}+L_{0} \tag{2}\]The heading of the missile is assumed to be parallel to the velocity vector, and the acceleration axis is attached perpendicular to the missile velocity vector. It is assumed that there is no aerodynamic effect nor dynamic coupling. That is, the mass point dynamic model will be applied. The equations of motion are as follows:  
\[R=\left|\overset{\rightarrow}{R}_{c}\right|\text{, }V_{m}=\left|\overset{ \rightarrow}{V}_{m}\right| \tag{3}\]  
\[L=\lambda-\psi=\arctan\left(\frac{\det[\overset{\rightarrow}{V}_{m}\vdots \overset{\rightarrow}{R}_{c}]}{\overrightarrow{V}_{m}\cdot\overset{ \rightarrow}{R}_{c}}\right) \tag{4}\]  
\[\dot{\psi}=\frac{a_{m}}{V_{m}} \tag{5}\]  
\[\Omega=\frac{V_{m}}{R}sin(\lambda-\psi) \tag{6}\]  
where \(R\) is the relative distance from the missile to the target, \(\lambda\) is the line of sight (LOS) angle, \(\psi\) is the heading of the missile, \(L\) is look-angle, \(V_{m}\) is the speed of the missile, and \(a_{m}\) is the acceleration applied to the missile. The acceleration of a missile is assumed to have a reasonable limitation of \(-10g\) to \(10g\), where \(g\) is the gravitational acceleration. The symbol '\(\cdot\)' between two column vectors indicates a concatenation that joins two vectors into a square matrix.  
The Zero-Effort-Miss (ZEM) is an expected miss distance if there will be no further maneuver from the current location [10]. The concept of this will be used to interpolate the minimum miss distance at the terminal state to compensate for the discreteness of the simulation. The ZEM* is the ZEM when the last maneuver command is applied, and we defined it as shown in Figure 2.  
\[\overset{\rightarrow}{R}_{d}=\overset{\rightarrow}{R}_{f-1}-\overset{ \rightarrow}{R}_{f} \tag{7}\]  
\[ZEM^{*}=\left|\overset{\rightarrow}{R}_{d}\right|\sin\left|\arctan\left( \frac{\det[\overset{\rightarrow}{R}_{f-1}\vdots\overset{\rightarrow}{R}_{d} ]}{\overrightarrow{R}_{f-1}\cdot\overset{\rightarrow}{R}_{d}}\right)\right| \tag{8}\]  
where \(\overset{\rightarrow}{R}_{f}\) and \(\overset{\rightarrow}{R}_{f-1}\) are \(\overset{\rightarrow}{R}_{c}\) at the final state and \(\overset{\rightarrow}{R}_{c}\) at the very former state, respectively.  
Figure 1: Geometry of a planar engagement scenario.
###### Abstract  
As a multimodal fusion task, audio-visual segmentation (AVS) aims to locate sounding objects at the pixel level within a given image. This capability holds significant importance and practical value in applications such as intelligent surveillance, multimedia content analysis, and human-robot interaction. However, existing AVS models typically feature complex architectures, require a large number of parameters, and are challenging to deploy on embedded platforms. Furthermore, these models often lack integration with object tracking mechanisms and fail to address the issue of the mis-segmentation of unvoiced objects caused by environmental noise in real-world scenarios. To address these challenges, this research proposes a lightweight audio-visual segmentation framework incorporating an audio-guided space-time memory network (AG-STMNet). First, a mask generator with a scoring mechanism was developed to identify sounding objects from generated masks. This component integrates Fastsam, a lightweight, pre-trained, object-aware segmentation model, with WAV2 CLIP, a parameter-efficient audio-visual alignment model. Subsequently, AG-STMNet, an audio-guided video object segmentation network, was introduced to track sounding objects using video object segmentation techniques while mitigating environmental noise. Finally, the mask generator and AG-STMNet were combined to form the complete framework. The experimental results demonstrate that the framework achieves a mean Intersection over Union (mIoU) score of 41.5, indicating its potential as a viable lightweight solution for practical applications.  
A Article  
A Lightweight Framework for Audio-Visual Segmentation with an Audio-Guided Space-Time Memory Network. _Appl. Sci._**2025**, _15_, 6885.  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# Analysis of Volatile Compounds in Coffee Prepared by Various Brewing and Roasting Methods
###### Abstract  
Volatile compounds of coffee brewer under various roasting conditions and by different brewing methods were analyzed. Green coffee beans (_Coffea anubia_) were roasted at 235 \({}^{\circ}\)C for 13 min, 240 \({}^{\circ}\)C for 15 min, and 245 \({}^{\circ}\)C for 17 min. Roasted coffee beans were ground into particles of three different sizes (710, 500, and 355 \(\upmu\)m) and brewer by an espresso coffee machine and the cold brewer method. Three types of water (filtered, tap, and bottled) were used for coffee extraction. SPME-GC-MS results indicated that increasing the roasting temperature and time increased the levels of 2,2\({}^{\circ}\)-methylene-bis-furan, guaicaol, and 4-ethylguaicaol (\(p<0.05\)) and decreased the levels of furfural (\(p<0.05\)). Grind size was inversely proportional to the measured signal of volatiles by GC-MS (\(p<0.05\)). The measured GC/MS intensities of 2-methylpyrazine, 2,5-dimethylpyrazine, and 2-methoxy-4-vinylphenol were significantly higher in coffee brewer with filtered water (\(p<0.05\)) than tap and bottled water. 2-Methylpyrazine, 1-methylpyrrole, and 2-acetylfuran were the most abundant components in the cold brewer. Overall, roasting conditions and extraction methods were determined to be significant factors for volatile compounds in coffee. This is the first study showing the analysis of volatile compounds in coffee according to various types of water and extraction methods, such as espresso and cold brewer coffee.  
volatiles; coffee; brewing; roasting; grinding Article
## Abbreviations  
The following abbreviations are used in this manuscript:  
GEO  
MIMO  
Multiple-Input Multiple-Output  
FFT  
Fast Fourier Transform  
PMF-FFT  
Partial Matched Filter-Fast Fourier Transform  
XFAST  
Extended replica Folding Acquisition Search Technique  
SNR  
Signal to Noise Ratio  
ISI  
Intersymbol Interference
## 5 Conclusions  
In this study, we analyzed the temporal-spatial trends, transition characteristics of \(ET_{0}\) and identified the main reasons for the change of \(ET_{0}\) in different stages over the TP based on data from 73 weather stations during 1970-2018.The main conclusions are:  
1. There is an abrupt point in the TP's annual \(ET_{0}\) series around 1997. The \(ET_{0}\) decreased remarkably at a rate of \(-25.9\) mm/decade before 1997, while increasing significantly at a speed of \(31.1\) mm/decade after 1997;
2. Second item: before 1997, \(T_{mean}\) and \(RH\) increased significantly while \(U_{2}\) decreased significantly and \(RS\) decreased insignificantly. After 1997, \(T_{mean}\) and \(U_{2}\) increased insignificantly, \(RH\) and \(RS\) decreased significantly;
3. The sensitivity analysis of each climate factor to \(ET_{0}\) indicated the \(ET_{0}\) on the TP is most sensitive to \(RS\), followed by \(RH\), \(U_{2}\) and \(T_{mean}\);
4. Third item: from 1970 to 1996, \(U_{2}\) was the most important meteorological factor contributing to the decline of \(ET_{0}\), followed by \(RH\), \(T_{mean}\), and \(RS\). During 1997-2018, the decreasing of \(RH\) was the dominant factor causing an increase in \(ET_{0}\). The increase of \(U_{2}\) and \(T_{mean}\) also intensified the increase of \(ET_{0}\) to a certain extent.  
**Author Contributions:** Conceptualization, S.H. and T.Z.; data curation, R.G.; formal analysis, R.G.; funding acquisition, S.H.; methodology, P.B.; resources, T.Z.; visualization, R.Z.; writing--original draft, S.H. and R.G.; writing--review and editing, S.H., T.Z., P.B. and R.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 41501027, the Beijing Outstanding Young Scientist Program, grant number BJJWZYJH01201910028032.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** There are no relevant statements for this research.  
**Acknowledgments:** The authors would like to express their sincere thanks to National Natural Science Foundation of China (41501027) and Beijing Outstanding Young Scientist Program (BJJWZYJH01201910028032) for their valuable support. The authors also would like to thank the peer reviewers for their valuable suggestions and comments on improving this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.