esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 2 Study Area and Data  
### Study Area  
The Yinanhe forest is located in the northern part of Heilongjiang Province, China. The Yinanhe forest belongs to the subtemperate zone and possesses a humid climate, with an average annual temperature of \(-2\ ^{\circ}\)C, a maximum temperature of 38 \({}^{\circ}\)C, and a minimum temperature of \(-42\ ^{\circ}\)C. The average elevation is 656 m. The vegetation belongs to the Lesser Hinggan flora and primarily includes cold hardy plants. On 27 April 2009, a forest fire broke out in the Yinanhe forest. The fire was located at \(48^{\circ}41^{\prime}\)N latitude and \(128^{\circ}12^{\prime}\)E longitude (Figure 1).
## 3 Asset Localization
### Other  
Apart from these widely used technologies, a couple of other solutions can be found in asset localization in IIoT systems. LOS/NLOS classification is shown in [97] for the less-common IEEE 802.15.4 systems, using SVM, random forests and neural networks. In [98], acoustic localization technology was employed in an IIoT underwater wireless sensor network, and linear regression was applied to predict the accuracy of node localization.  
An increasing number of studies that analyze the applicability of the so-called device free localization (DFL), which provides user or asset location without any hardware attached, have been published. There are a couple of machine learning techniques utilized in those algorithms, e.g., block-sparse coding with the proximal operator [99] or Bayesian methods [100]. A useful summary of this topic and recent state-of-the-art can be found in [101,102].
###### Abstract  
The pose measurement and motion estimation of long-distance space CubeSats is a difficult problem for autonomous rendezvous and track maintenance. In this work, an approach is proposed for the pose estimation of CubeSat based on the micro-Doppler effect by a laser radar system. Parameters such as spin rate and attitude angle can be estimated using data extracted from the returned signal. By utilizing threshold processing, edge extraction of time-frequency (TF) images can be achieved effectively. When the target size is unknown, the double edge ratio method (DERM) is suggested to obtain the attitude angle. The simulation results show that when the noise is not too high, the spin rate and the attitude angle can be acquired precisely. Our experiment indicates that the error for the estimation of spin and pose by our approach fulfills the demand in practice.  
lidar; micro-Doppler effect; pose estimation; CubeSat
## 2 The Model  
Users are represented as vectors of \(L\) factors that represent the possible topics on which they can express an opinion, so that user \(i\) corresponds to \(U_{i}=\left(u_{i}^{(1)},u_{i}^{(2)},\ldots,u_{i}^{(L)}\right)\), with \(-1\leq u_{i}^{(k)}\leq 1\), \(1\leq k\leq L\) and \(1\leq i\leq N\), where \(N\) is the total number of users. Similarly, since posts might contain opinion on a range of those topics, they are represented as vectors of \(L\) components as well: \(P_{n}=\left(p_{n}^{(1)},p_{n}^{(2)},\ldots,p_{n}^{(L)}\right)\) with the same range \(-1\leq p_{n}^{(k)}\leq 1\) but with \(1\leq n\leq M\), where \(M\) is the total number of messages.  
The opinion \(\Omega_{in}\) of user \(i\) about post \(n\) is computed as  
\[\Omega_{in}=\tanh\left(\frac{\beta}{L}\sum_{k}u_{i}^{(k)}p_{n}^{(k)}\right).\]  
The factor \(\beta\) modulates the nonlinearities of the system. If \(\beta\) is large, the system is basically a Heaviside function, if \(\beta\) is small the system is essentially linear. In the following, we shall deal only with the linear case:  
\[\Omega_{in}=\frac{1}{L}\sum_{k}u_{i}^{(k)}p_{n}^{(k)}.\]  
Given the matrix \(U\) of users' factors, such that \(U_{ik}=u_{i}^{(k)}\) and a set of \(M\) messages such that a single post can be written as \(P_{nk}=p_{n}^{(k)}\), we compute the opinion matrix \(\Omega\),  
\[\Omega=\frac{1}{L}UP^{T}.\]
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 2 Phased Antenna Arrays
This interactive feature allows for monitoring the optimization progress while enabling researchers to make informed decisions regarding parameter adjustments or algorithm modifications during runtime. Such detailed insights into the optimization convergence can serve to enrich the methodology and improve its effectiveness in practical applications [46, 47, 48]. Consider an antenna array consisting of \(N\) identical radiating elements. Each element of order \(n\) has its own radiation characteristic function \(f_{n}(\theta_{n}^{\prime})\) and is fed by the current \(\overline{I}_{n}=A_{n}\exp(j\varphi_{n})\). The electric field produced by the \(n^{\text{th}}\) element of the antenna array at a point P, located in its far radiation zone, is then written as follows:  
\[\overline{E}_{n}=\overline{I}_{n}e^{\text{i}\mathbf{r}_{\mathbf{n}}\cdot \mathbf{u}_{\mathbf{r}_{n}}}f_{n}(\theta_{n}^{\prime})\mathbf{u}_{\mathbf{E}_{ n}} \tag{1}\]  
with \(k=\frac{2\pi}{\lambda}\), \(\mathbf{u}_{\mathbf{E}_{n}}\) being a unit vector in the direction of the \(\overline{E}_{n}\) field produced by the \(n^{\text{th}}\) element and \(\theta_{n}^{\prime}\) the observation angle in the local coordinate system of the \(n^{\text{th}}\) element, while \(\mathbf{r}_{\mathbf{n}}^{\prime}\) is the position vector of the far point P in the local coordinate system of the \(n^{\text{th}}\) element. The total electric field radiated by the antenna array is the sum of all elementary fields \(\overline{E}_{0}\), \(\overline{E}_{1}\),..., \(\overline{E}_{N-1}\). Consequently, when the directions of observation are equal, the distances \(\mathbf{r}_{\mathbf{n}}^{\prime}\) are considered equal to the distance \(r\) between the origin of the global coordinate system and the observation point P. In terms of phase, the difference in path between \(\mathbf{r}_{\mathbf{n}}^{\prime}\) and \(r\) is rewarded by \(\Delta_{m}\approx-r_{n}\cos(\theta_{n})\), as shown in Figure 1.  
The resulting field is expressed as follows:  
\[\overline{E}(r,\theta)=\sum_{n=1}^{N}\overline{E}(r_{n}^{\prime},\theta_{n}^{ \prime}). \tag{2}\]  
Figure 1: Radiation of an element in an antenna array.
## 3 Results and Discussion
### Exergy Analysis
On the contrary, the exergy destruction percentage of the evaporator decreased from 19.78% to 7.97% mainly due to the rise of the evaporation pressure and the reduction of the heat transfer temperature difference between air and refrigerant. In the gas cooler the exergy destruction decreased gradually due to the reduction of the compressor discharge temperature. Furthermore, the CO\({}_{2}\) entropy difference between the inlet and outlet of the EEV decreased gradually with rise in NRC, thus the exergy destruction percentage also showed a slight downward trend. The exergy destruction percentage of the gas cooler and EEV corresponded to 22.06-27.08% and 24.13-29.63%, respectively. It could be concluded that insufficient refrigerant charging in the investigated air conditioner unit seriously affected the thermodynamic performance of the indoor and outdoor HXs, whereas slight overcharge was found to have little effect on the thermodynamic performance of each component. Finally, the obtained results suggested the need to improve the efficiency of the CO\({}_{2}\) compressor. Figure 6: The effect of normalized refrigerant charge (NRC) on exergy efficiency (n) and total product exergy rate (\(\dot{E}_{P,tot}\)) corresponding to the optimal COP at ambient temperatures of 35 \({}^{\circ}\)C, 38 \({}^{\circ}\)C and 40 \({}^{\circ}\)C.  
Figure 7: The effect of discharge pressure (P\({}_{\rm dis}\)) on (**a**) exergy efficiency (\(\eta\)) and total product exergy rate (\(\dot{E}_{P,tot}\)) as well as on (**b**) total power input (\(\dot{W}_{tot}\)) and total exergy destruction rate (\(\dot{E}_{D,tot}\)) at ambient temperature of 35 \({}^{\circ}\)C.
# A Nonbinary LDPC-Coded Probabilistic Shaping Scheme for a Rayleigh Fading Channel
###### Abstract  
In this paper, a novel, nonbinary (NB) LDPC-coded probabilistic shaping (PS) scheme for a Rayleigh fading channel is proposed. For the NB LDPC-coded PS scheme in Rayleigh fading channel, the rotation angle of 16 quadrature amplitude modulation (QAM) constellations, 64 QAM constellations and 256 QAM constellations are optimized by the exhaustive search. The simulation results verify the information-theoretical analysis. Compared with the binary LDPC-coded PS scheme for Rayleigh fading channel, the proposed NB LDPC-coded PS scheme can improve error performance. In summary, the proposed NB LDPC-coded PS scheme for Rayleigh fading channel is reliable and thus suitable for future communication systems.  
probabilistic shaping; nonbinary LDPC; Rayleigh fading +
Footnote †: journal:  
0000-0001-6000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000  
In this paper, we propose a NB LDPC-coded PS scheme for Rayleigh fading channel and optimize the rotation angle of QAM signals by the exhaustive search. The theoretical average mutual information (AMI) analysis and simulation error performance show the reliability of the proposed scheme.  
The innovation of this paper is shown as follows.  
* A NB LDPC-coded two-dimensional PS scheme for Rayleigh fading channel is proposed. Compared with the binary LDPC-coded two-dimensional PS scheme in [7], the proposed NB LDPC-coded two-dimensional PS scheme can obtain coding gain.
* A NB LDPC-coded two-dimensional QAM PS scheme for Rayleigh fading channel based on signal space diversity is proposed. Compared to the NB LDPC-coded two-dimensional QAM PS scheme for Rayleigh fading channel without rotation, the proposed NB LDPC-coded two-dimensional QAM PS scheme for Rayleigh fading channel based on signal space diversity can obtain diversity gain.  
The rest of the paper is organized as follows. In Section II, the system model of the NB LDPC-coded PS scheme for Rayleigh fading channel is proposed. AMI analysis of the proposed PS scheme for Rayleigh fading channel is discussed in Section III. In Section IV, simulation results are shown. Conclusions are given in Section V.
# Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
###### Abstract  
Currently, the evaluation of fruit ripening progress in relation to physicochemical and texture-quality parameters has become an increasingly important issue, particularly when considering consumer acceptance. Therefore, the purpose of the present study was the application of rapid, nondestructive, and conventional methods to assess the quality of banana peels and flesh in terms of ripening and during storage in controlled temperatures and humidity. For this purpose, we implemented various analytical techniques, such as attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy for texture, colorimetric, and physicochemical features, along with image-analysis methods and discriminant as well as statistical analysis. Image-analysis outcomes showed that storage provoked significant degradation of banana peels based on the increased image-texture dissimilarity and the loss of the structural order of the texture. In addition, the computed features were sufficient to discriminate four ripening stages with high accuracy. Moreover, the results revealed that storage led to significant changes in the color parameters and dramatic decreases in the texture attributes of banana flesh. The combination of image and chemical analyses pinpointed that storage caused water migration to the flesh and significant starch decomposition, which was then converted into soluble sugars. The redness and yellowness of the peel; the flesh moisture content; the texture attributes; Brix, and the storage time were all strongly interrelated. The combination of these techniques, coupled with statistical tools, to monitor the physicochemical and organoleptic quality of bananas during storage could be further applied for assessing the quality of other fruits and vegetables under similar conditions.  
banana fruits; ripening progress; image analysis; quality properties; Fourier transform infrared spectroscopy; statistical analysis Article Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 4 Experiments and Results
### Datasets
#### 4.1.1 BraTS2023-GLI Dataset  
The BraTS2023-GLI dataset [22, 24, 25, 26] contains a total of 1251 brain MRI images from 1134 patients. Each image has four modalities: T1, T1 Gd, T2, and T2-FLAIR, along with three segmentation targets: WT (Whole Tumor), ET (Enhancing Tumor), and TC (Tumor Core). The image resolution is \(240\times 240\times 155\) voxels in nii.gz format. The ground truth data are created after pre-processing, which included being co-registered to the same anatomical template, interpolated to the same resolution (1 mm\({}^{3}\)), and skull stripped.
# Structural Assessment of Endodontic Files via Finite Element Analysis
## 2 Materials and Methods
### Finite Element Modeling of Physical Tests
#### 2.2.4 Cyclic Fatigue Test Modeling:  
* The high-definition model  
The model includes an endodontic file where the jig part has 90 degrees curvature and 6 mm radius and the cylinder part has a groove of 5 mm radius and 1.5 mm width. The linear elastic material model for steel with 210 GPa Young's modulus and 0.3 Poisson's ratio is used for the jig and cylinder. The cylinder and the jig are modeled with first order brick elements, whereas the file is modeled as described in Section 2.2.2 (Figure 11).  
The prescribed displacement boundary conditions are applied to the endodontic file's end surface via a kinematic coupling element. The jig and the cylinder's outer surfaces are fixed.  
* The Idealized model  
Considering that the test apparatus components (jig and cylinder) are made of steel, which is 5 times stiffer than NiTi, the deformation of the apparatus is estimated to be negligible as compared to that of the endodontic file. Consequently, the simulated canal formed by the apparatus geometry is simulated by analytical rigid surfaces (Figure 12).  
The finite element simulation involved the following steps:  
1. Endodontic file insertion: The file is inserted to a depth of 22 mm from the top of the jig.
2. Endodontic file rotation: After insertion, the file's axial position is fixed, and it is rotated around its axis until the dissipated energy stabilizes.  
Figure 11: Cyclic fatigue test configuration model.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## Methods
### Proposed Predistorter for MIMO-GFDM Systems
The signals received in the form of vectors for each antenna at the \(i\)-th subcarrier may be written as follows [41]:  
\[ \begin{array}{ll}\Upsilon^{NL}(n,i)=K_{0}\mathcal{H}(n,i)X(n,i)+\mathcal{H} (n,i)D(n,i)+\mathcal{N}(n,i),\end{array}  \tag{21}\]  
where \(\mathcal{H}(n,i)\) is the transfer matrix of channels in the frequency domain, \(X(n,i)=[X_{1}(n,i)\dots,X_{Nt}(n,i)]^{T}\) is the vector containing the signal transmitted by each antenna, and \(\mathcal{N}(n,i)\) is the Gaussian noise obtained using the FFT process of the thermal Gaussian noise with zero mean and \(\sigma^{2}\) variance. The steps of the predistorter on the MIMO-GFDM system are summarized in the Algorithm 1 as follows:  
```
1:\(\mathbf{d}\leftarrow\mathbf{b}\)\(\triangleright\) mapping 16 OQAM
2:\(\mathbf{A}\leftarrow\mathbf{g}\)\(\triangleright\) pulse shaping RRC
3:\(\mathbf{x}\leftarrow\mathbf{A}\)\(\triangleright\) output GFDM
4:for\(\mathbf{k}\) = 1: \(\mathbf{K}\)do\(\triangleright\) per Tx branch
5:\(x_{k}(\mathbf{n})\leftarrow\rho(n)e^{i\phi(n)}\)\(\triangleright\) input predistorter
6:\(r_{k}(\mathbf{n})\gets A^{-1}[\rho(n)]\)\(\triangleright\) amplitude predistorter
7:\(\theta_{k}(\mathbf{n})\leftarrow\phi(t)-\Phi[r(t)]\)\(\triangleright\) phase of predistorter
8:\(u_{k}(\mathbf{n})\leftarrow r_{k}(\mathbf{n})e^{i\theta_{k}(n)}\)\(\triangleright\) output predistorter
9:\(R_{k}(\mathbf{n})\leftarrow\mathcal{A}[r_{k}(\mathbf{n})]\)\(\triangleright\) amplitude HPA
10:\(\psi(n)\leftarrow\theta_{k}(n)+\phi[r_{k}(n)]\)\(\triangleright\) phasa HPA
11:\(s_{k}(\mathbf{n})\leftarrow R_{k}(n)e^{i\phi(n)}\)\(\triangleright\) output HPA
12:endfor
```  
**Algorithm 1** Modelling of Predistorter for MIMO-GFDM Systems
# Numerical Analysis for Ground Subsidence Caused by Extraction Holes of Removed Piles
## 4 Conclusions  
In this study, a three-dimensional elasto-plastic consolidation analysis was performed to examine the effect of the extraction holes of removed piles on the static properties of the surrounding ground.  
The results obtained from the analysis are as follows.  
1. If an extraction hole of a removed pile is left as it is, large ground subsidence will occur near the extraction hole of the removed pile, especially at the lower part of the clay layer near the hole.
2. A cavity grows in the ground around the extraction hole of the removed pile due to the formation of the extraction hole of the removed pile, and the ground subsidence increases with time. In addition, as the number of extraction holes of removed piles increases, the depression phenomenon progresses and countermeasures are required at an early stage. Therefore, it is considered important to inject the filler simultaneously with the formation of each extraction hole of a removed pile.
3. In the process of pulling out a pile, the suction force acts on the surrounding ground, so the amount of subsidence on the surrounding ground decreases.
4. The greater the number of extraction holes of removed piles, the greater the compressive stress acting on the extraction holes of removed piles after the pile removal. For this reason, the filler should exhibit strength early as the number of extraction holes of removed piles increases.
5. When there are two and three extraction holes of removed piles, there is no difference in the compressive stress acting on the extraction holes of removed piles. Therefore, it is thought that the compressive stress acting around each extraction hole of removed pile is caused by the surrounding ground conditions.
6. When there is only one extraction hole of a removed pile, there is a difference between the compressive stress concentrated in the extraction hole of a removed pile on the ground's surface and the compressive stress acting near the lower part of the clay layer. On the other hand, when there are two or three extraction holes of removed piles, the difference in compressive stress acting on the extraction holes of removed piles due to the depth is small. From this, it can be said that as the number of extraction holes of removed piles increases, the necessity to inject a filler that exhibits uniform strength up to the deepest part increases.  
Not applicable.  
The authors declare no conflict of interest.
# Characteristics of Summer Flash Drought and Its Effect on Maize Growth in Liaoning Province, China
## 2 Materials and Methods
### Methods  
#### 2.2.1 Evaporative Demand Drought Index  
The EDDI was derived by sorting the cumulative values of evaporative demand (\(E_{0}\)) within a set time scale, constructing the distribution probability of \(E_{0}\), and normalizing the distribution [16].  
\(E_{0}\) was estimated with the Penman-Monteith model using the following formula (Equation (1)):  
\[E_{0}=\frac{0.408\Delta}{\Delta+\gamma(1+C_{\mathrm{d}}U)}(R_{n}+L_{n}-G)\frac {86400}{10^{6}}+\frac{\gamma\frac{C_{\mathrm{a}}}{T_{\mathrm{air}}}}{\Delta+ \gamma(1+C_{d}U)}U\frac{\varepsilon_{\mathrm{s}nt}-e_{a}}{10^{3}}, \tag{1}\]  
where \(E_{0}\) (mm\(\cdot\)d\({}^{-1}\)) is the atmospheric evaporative demand; \(\Delta\) (Pa\(\cdot\)K\({}^{-1}\)) is the slope of the saturated vapor pressure-temperature curve at the 2 m air temperature (K); \(\gamma\) (Pa\(\cdot\)K\({}^{-1}\)) is the psychometric constant; \(U\) (m\(\cdot\)s\({}^{-1}\)) is the wind speed at 2-m height; \(R_{n}\) (W\(\cdot\)m\({}^{-2}\)) and \(L_{n}\) (W\(\cdot\)m\({}^{-2}\)) are the net incoming shortwave and longwave radiation, respectively; \(G\) (W\(\cdot\)m\({}^{-2}\)) is the downward ground heat flux; \(T_{air}\) is the air temperature (K); \(\varepsilon_{sat}\) (Pa) and \(e_{a}\) (Pa) are the saturated and actual vapor pressures, respectively; and \(C_{n}\) and \(C_{d}\) are constants, with values of 900 and 0.34, respectively.  
Then, the cumulative distribution probability of \(E_{0}\) at a certain time was calculated, as shown in Equation (2):  
\[\mathrm{P}(E_{0i})=\frac{i-0.33}{n+0.33}, \tag{2}\]  
where \(P(E_{0i})\) is the empirical probability of \(E_{0i}\), which is aggregated across the period of interest; \(i\) is the rank of the aggregated \(E_{0}\) in the historical time series (\(i=1\) for maximum \(E_{0}\)); and \(n\) is the number of observations in the series being ranked.  
The EDDI was calculated as follows (Equations (3)-(5)):  
\[\mathrm{EDDI}=\mathrm{W}-\frac{C_{0}+C_{1}W+C_{2}W^{2}}{1+d_{1}W+d_{2}W^{2}+d_ {3}W^{3}}, \tag{3}\]  
\[W=\sqrt{-2 ln\big{[}P\big{(}E_{0i}\big{)}\big{]}}\ \ \mathrm{P}(E_{0i})\leq 0.5, \tag{4}\]  
\[W=\sqrt{-2 ln\big{[}1-P\big{(}E_{0i}\big{)}}\ \ \mathrm{P}(E_{0i})>0.5 \tag{5}\]  
where \(C_{0}=2.515517\), \(C_{1}=0.802853\), \(C_{2}=0.010328\), \(d_{1}=1.432788\), \(d_{2}=0.189269\), and \(d_{3}=0.001308\).  
A negative EDDI value indicates wet anomalies, a positive EDDI value indicates drier than normal conditions, and drought intensity increases with an increasingly positive EDDI.  
The percentile values of the EDDI in a time series were used to classify drought categories (Table 1). The EDDI percentiles greater than 70% were divided into five drought categories as follows: weak drought 70-80%, moderate drought 80-90%, severe drought 90-95%, extreme drought 95-98%, and exceptional drought > 98% [12].  
Figure 1: Distribution of 52 meteorological stations and maize planting areas in Liaoning Province, China.