esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Image Reconstruction Algorithms for Diffuse Optical Tomography
## 1 Introduction
### Bayesian Approach  
The measurements are always contaminated with additive random noise. Thus, the forward equation with a given optical property can be written as \(\mathbf{M}=\mathbf{F}(\mu)+\varepsilon\), where \(\varepsilon\) is the random additive noise and \(\mu\) is the given optical property. Owing to the randomness of \(\varepsilon\), \(\mathbf{M}\) is a random variable with a conditional (prior) probability density function \(p(\mathbf{M}\,|\,u)\). Assuming \(\mu\) and \(\varepsilon\) are stochastically independent, the joint probability is formulated as \(p(\mathbf{M},\,\mu)=p(\mathbf{M}\,|\,u)\cdot p(\mu)=p(\mu\,|\,\mathbf{M})\cdot p (\mathbf{M})\), which is [PERSON] rule.  
In the Bayesian approach based on the above formula, image reconstruction is to find \(\mu\) maximizing the conditional (posterior) function, \(p(u\mid\mathbf{M})=p(\mathbf{M}\mid\mu)\cdot p(\mu)\) as a likelihood function under the condition of \(\mathbf{M}\) determined, i.e., \(p(\mathbf{M})=1\). When \(\varepsilon\) is Gaussian random noise with a covariance matrix \(W\) and an average of zero, the log-likelihood of the posterior function is equivalent to Equation (1) with \(p(u)=\exp(-\lambda\cdot R(\mu))\). Assuming that \(\mu\) is a Gaussian random variable with a variance \(1/\lambda\), the image reconstruction incorporates classical Tikhonov regularization.  
By choosing the probability density function, the Bayesian approach can take advantage of regularization methods and prior knowledge for image reconstruction, as shown in previous studies [31; 32; 33]; some Bayesian approaches are introduced in the following sections.  
The image reconstruction method can be characterized by the methods selected for the forward and optimization processes, particularly the equations employed in the forward process and the cost functions with a certain regularization and use of prior information in the optimization process. Several studies on image reconstruction algorithms are categorized and introduced in the following sections from the viewpoint of the forward and optimization processes.
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 3 NB-IoT Downlink System Model
### Analytical Downlink Signal Model
The mapping will start from the initial slot and will stop at the second slot in the corresponding subframe. While the NPDSCH will not contain the BCCH, the subframe will be repeated for \(\min(M_{rep}^{NPDSCH},4)-1\) additional subframes before proceeding with the mapping of \(y^{(p)}(\cdot)\) in the next subframe. An inverse DFT (IDFT) operation is applied after the physical resource mapping to convert the frequency domain data into time-domain signal. The time-domain continuous signal \(\mathbf{x}_{l}(t)\) in OFDM symbol \(l\) on antenna port \(p\) in NB-IoT downlink slot is defined in [38] by  
\[\mathbf{x}_{l}(t)=\sum\limits_{k=-\lfloor N_{k}^{RB}/2\rfloor-1}^{\lceil N_{k }^{RB}/2\rceil-1}a_{k^{(-)}\rfloor}^{(p)}\cdot e^{j2\pi(k+1/2)\Delta f(t-N_{CP },T_{s})} \tag{10}\]  
for \(0\leq t<(N_{CPJ}+N)\times T_{s}\), \(k^{(-)}=k+\lfloor N_{sc}^{RB}\rfloor\),\(N=2048\), \(\Delta f=15\) kHz, and \(a_{kJ}^{(p)}\) is the content of resource element (\(k\),\(l\)) on the antenna port \(p\). According to release 13, only normal cyclic prefix (CP) length \(N_{CPJ}\) is allowed, which is inherited from existing LTE. After generating the time dimension baseband signal according to Equation (10), an RF front end is applied to upconvert the signal. This upconverted signal is transmitted through the multipath wireless fading channel, and the delay spread of this fading channel is considered smaller than the added cyclic prefix length to protect from the intersymbol interference. At the receiver, the transmitted signal is collected from several wireless multipath links with their individual additive noise and finally composed together as the received signal. The channel impulse response (CIR), along with the convolution of the transmitted signal and additive noise, represents the received signal as shown below:  
\[\mathbf{y}(t)=\mathbf{x}_{l}(t)\otimes\mathbf{h}(t)+\mathbf{n}(t) \tag{11}\]  
where \(y(t)\) and \(n(t)\) represent the received signal and additive white Gaussian noise (AWGN) of zero-mean with the variance \(\sigma_{n}^{2}\), respectively. The CIR with \(L\) distinct complex taps of the multipath wireless fading channel is denoted by \(\mathbf{h}(t)\) and can be expressed by the following equation:  
\[\mathbf{h}(t)=\sum\limits_{i=0}^{L-1}\beta_{i}\delta(t-\tau_{i}) \tag{12}\]  
where \(\tau_{i}\) and \(\beta_{i}\) indicate the delay and attenuation of fading channel at the \(i^{th}\) path, respectively. Hence, the delayed version of the noisy received signal will be represented as  
\[\mathbf{y}(t)=\sum\limits_{i=0}^{L-1}\beta_{i}x_{l}(t-\tau_{i})+\mathbf{n}(t) \tag{13}\]  
At the receiver end, the CP is removed first, and the reverse operation of the NPDSCH is performed along with the channel estimation and equalization with the help of a known NRS.
###### Abstract  
This study aimed (1) to explore the association between body composition [fat-to-muscle ratio (FMR), waist circumference (WC), and body mass index (BMI)] with motor competence (MC) and physical fitness (PFI) in 7-year-old children; (2) to ascertain whether FMR modifies the associations between MC and PF. A total of 164 children (7 yr) were included. Anthropometric (fat and muscle mass were calculated with TANITA), motor competence (CAMSA test) and physical fitness (1/2 mile run, handgrip strength, 10 \(\times\) 5, standing long jump, and sit and reach tests) variables were collected. FMR, WC and BMI correlated negatively with MC (all between r \(\leq\)\(-\)0.333 and \(-\)0.183 and \(p\) < 0.05) and PFI (all between r \(\leq\)\(-\)0.410 and \(-\)0.246 and \(p\) < 0.05) in all children. However, the associations were stronger for FMR. In linear regressions analyses, only FMR was independently and negatively associated with MC and PFI, explaining 12.6% and 20.9% of its variance, respectively. Positive correlations between PFI and MC existed. Also, the association was not modified by FMR levels, since in both groups (above and below FMR median), the associations were maintained (\(p\) < 0.0001). However, the \(t\)-test showed that children with higher FMRs have lower levels of MC (\(p\) = 0.005) and PFI (\(p\) < 0.0001). FMR may be the best body composition parameter related to MC and PFI. Furthermore, the association between MC and PFI is not modulated by FMR but children with higher FMRs show lower levels of MC and PFI.  
body composition; fat mass; motor competence; muscle mass; physical fitness
## 5 Experiment
### Comparative Experiments of New Algorithms in Recent Years
#### 5.5.2 Compared with IBOA  
We tested SGPVEFA using the same environment maps from [26], comparing the results with those from [26]. The IBOA parameters were set as follows: a maximum of 50 iterations, a population size of 4000, and the two path simplification strategies proposed in [26]. On a complex obstacle map of 40 \(\times\) 40, the paths found by the two algorithms are illustrated in Figure 21, and the path lengths and node counts are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Environment** & **Algorithm** & \multicolumn{3}{c}{**Path Length**} & \multicolumn{3}{c}{**Number of Nodes**} \\ \hline \multirow{3}{*}{1} & SGPVEFA & \multicolumn{3}{c}{40.076} & \multicolumn{3}{c}{6} \\ \cline{2-9}  & \multirow{2}{*}{IABC} & Best & Mean & Std. & Best & Mean & Std. \\ \cline{3-9}  & & 41.245 & 41.245 & 0 & 11 & 11 & 0 \\ \hline \multirow{3}{*}{2} & SGPVEFA & \multicolumn{3}{c}{52.92} & \multicolumn{3}{c}{10} \\ \cline{3-9}  & \multirow{2}{*}{IABC} & Best & Mean & Std. & Best & Mean & Std. \\ \cline{3-9}  & & 55.639 & 55.714 & 0.3246 & 18 & 18.933 & 0.2537 \\ \hline \multirow{3}{*}{3} & SGPVEFA & \multicolumn{3}{c}{27.024} & \multicolumn{3}{c}{5} \\ \cline{3-9}  & \multirow{2}{*}{IABC} & Best & Mean & Std. & Best & Mean & Std. \\ \cline{3-9}  & & 27.462 & 28.795 & 0.9889 & 8 & 10.4 & 1.6316 \\ \hline \multirow{3}{*}{4} & SGPVEFA & \multicolumn{3}{c}{28.724} & \multicolumn{3}{c}{9} \\ \cline{3-9}  & \multirow{2}{*}{IABC} & Best & Mean & Std. & Best & Mean & Std. \\ \cline{3-9}  & & 28.501 & 29.614 & 0.4733 & 12 & 15.033 & 1.2726 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of path length and number of nodes with IABC.  
Figure 21: Comparison of paths found in complex environments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Path Length** & **Number of Nodes** \\ \hline SGPVEFA & 58.289 & **10** \\ IBOA & 61.016 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of path length and number of nodes with IBOA.  
Experiments show that SGPVEFA still has advantages over IBOA with the combined path simplification strategy in terms of path length and number of path nodes, which makes the path searched by SGPVEFA smoother and with fewer turns.
# Impacts of Lightning-Induced Overvoltage on a Hybrid Solar PV-Battery Energy Storage System
## 4 EMTP-RV Model  
### Hybrid Solar PV-Battery Energy Storage System  
The 1 Megawatt (MW) solar PV system was modeled based on real applications in Puchong, Malaysia, with an additional ideal battery energy storage system using Electromagnetic Transient Program--Restructured Version (EMTP-RV) software. The system consisted of 42 solar PV arrays at 250 W per module, 42 string inverters, as well as ideal battery energy storage, transformer, and grid components. A block diagram is presented as shown in Figure 6.
## 3 Results
### Spatial Variability  
Generally, the performance of the daily, monthly, and annual precipitation estimated by the GPM in all locations did not significantly differ from the measurements. The GLDAS underestimated daily precipitation in CNR by about 14% and overestimated daily and annual precipitation in PRR (21%), CCR (17%), and RDP (23%). The TRMM overestimated daily precipitation in PRR (22%) and RDP (15%). The GPCP overestimated annual precipitation in PRR (25%) and RDP (28%). The MERRA product underestimated daily, monthly, and annual precipitation from 19.3% to 35.4% in all but three stations that include MTP, GBC, and SJRC, where there was no significant difference when compared with the measurements (Figure 5). The performance of individual precipitation products in terms of spatial variability was detailed below.  
Figure 4: Taylor diagrams showing the comparisons of (**A**) daily (mm day\({}^{-1}\)), (**B**) monthly (mm month\({}^{-1}\)), (**C**) annual (mm year\({}^{-1}\)) measured and estimated precipitation as well as (**D**) the performance diagram of GLDAS, MERRA, TRMM, GPM, and GPCP products in the state of Mato Grosso, Brazil. In panels A, B, and C, the Pearson correlation coefficient (r) is represented by the azimuth angle (black outlines), the Root Mean Square Error (RMSE) is represented by the red dashed outlines, and the standard deviation (SD) was indicated on the x and y axes, respectively. In panel D, the probability of detection (POD) is represented on the x and y axes; the BIAS was indicated by the dashed black lines; and the critical success index (CSI) is represented by continuous black lines. For (**A**), the daily values for the entire study period were averaged, while for (**B**), the total monthly values for all months during the study period were averaged, and the annual values in (**C**) were the annual values averaged for all years during the study period.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 4 The Proposed Scheme
### Successful Access Probability of Wi-Fi Stations  
In the subsection, we examine the successful access probability of Wi-Fi stations. In Wi-Fi systems, stations compete with each other for the channel access opportunity to transmit packets. The station will invoke a back-off mechanism if the channel is busy or a transmission collision occurs. The back-off process will continue until the station successfully sends a packet or reaches the maximal number of retransmissions.  
The above situation is called the saturation back-off mechanism, as shown in Figure 5. Assume that a station's packet transmission experiences a back-off delay \(d\) smaller than the maximal saturation back-off, \(D\). We denote the probability as \(P(d<D)\), where \(d\) is the back-off delay [22]. After this interval, the packet can be successfully transmitted. Therefore, the Wi-Fi successful transmission probability can be defined as the probability that \(d<D\), as indicated in (3).  
\[P(d<D\mid N_{STA})=\sum_{i=0}^{Ry}\sum_{j=0}^{W_{i}}P(d<D\mid i\ col,\ j\ slots)\cdot P(j\ slots \mid i\ col)\cdot P(i\ col) \tag{3}\]  
where \(Ry\) is the retry limit and \(W_{i}=\sum_{k=0}^{i}(CW_{k}-1)\) is the accumulated size of the contention window for the \(i\)-th retransmission.  
During the saturation back-off delay \(d\), collisions may happen \(i\) times. The whole backoff procedure may take the total number of \(j\) slots which is the sum of the number of slots for each backoff before succeeded transmission. As a result, the probability of the successful transmission of Wi-Fi in (3) can be divided into three parts. \(P(i\ col)\) represents the probability of \(i\) collision. \(P(j\ slots\mid i\ col)\) represents the probability that the sum-up numbers of back-off slots are equal to \(j\) when there are \(i\) collisions. \(P(d<D\mid i\ col,\ j\ slots)\) is the probability of \(d<D\) with \(i\) collisions and \(j\) back-off slots [11]. The detailed derivation and evaluation of these terms are given in Appendix A.  
The probability given in (3) serves as an important indicator regarding the loading of the Wi-Fi system. It will be used to decide on an adequate \(\delta\) value to optimize the time-division multiplexing between LTE and Wi-Fi.  
Figure 4: Flow chart for the evaluation of an optimal \(\delta\).  
Figure 5: The saturation back-off delay.
## 4 Conclusions  
The present paper mainly proposes a noise separation technique using the phase conjugation method to overcome the difficulty of separating strong coherent intrinsic noise of multiple transformers in a substation. The accuracy and practicability of the noise separation technique are investigated by point source simulation, noise separation experiment in a semi-anechoic chamber, and a transformer noise separation test in a substation. The main conclusions are:  
1. Point source simulation reveals that the separation error originates from the reconstruction error of phase conjugation on the source information; gives the optimal phase conjugation array parameters for noise separation, which is that the array aperture is 1.5 times the sources distance and the array element spacing is less than the minimum half-wavelength; and provides practical application advice for phase conjugation array setup, which is that the array aperture is greater than the sources' distance (i.e., 4 m for this example) and the array element spacing is less than 4\(\lambda_{\min}/7\) for a 2 dBA error tolerance.
2. The semi-anechoic chamber noise separation experiment and transformer noise separation test in a substation utilize the noise separation technique proposed in this paper to realize the separation of the respective noise source signals in an ideal laboratory environment and an actual engineering environment. The experimental results satisfy the engineering error requirements, and the test results are in accordance with the distribution law of the noise source sound field, i.e., the accuracy and practicability of the noise separation technique is verified by the experiment and test.
3. The test results can be used to guide transformer vibration and noise reduction, which can greatly reduce the cost of designing vibration and noise reduction. The noise separation technique using the phase conjugation method proposed in this paper is of vital importance for engineering applications.  
**Author Contributions:** Conceptualization, S.F. and S.L.; methodology, J.L.; software, J.L.; validation, J.L., L.L. and S.F.; formal analysis, J.L.; investigation, L.L.; resources, S.F. and S.L.; data curation, J.L. and L.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L.; visualization, J.L.; supervision, S.F. and S.L.; project administration, J.L. and L.L.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Science and Technology Project of China Southern Power Grid (Grant No. GDKJXM20220790).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** Thanks for the contributions of [PERSON] in paper writing and project administration.
# A Study of Emergency Aircraft Control During Landing
## 6 Control System  
### Preparation of Simulink Models  
For testing of longitudinal and lateral control channels, previously discussed state-space models were combined into two separate Simulink models with a linear state transition corresponding to the change in landing gear position. At the moment of transition between states, coefficients of state and control matrices changed according to (15).  
\[\mathrm{A_{t}}=(1-\mathrm{lg})\cdot\mathrm{A_{\mathrm{\mathrm{\mathrm{lg}}}0}} +\mathrm{lg}\cdot\mathrm{A_{\mathrm{\mathrm{\mathrm{lg}}}1}} \tag{15}\]  
During the transition in the aircraft's longitudinal channel, there is a change in non-zero steady-state flight parameters values, including equivalent airspeed, angle of attack, and pitch angle. This means that a disturbance of the steady state occurs when the landing gear is being extended or retracted. This situation was also taken into account in the Simulink models. The disturbance associated with a linear transition between two known object dynamics was modelled as an instantaneous parameter deviation from the steady state, which was being added to a vector of state variables. As an example, the equation describing an instantaneous deviation of the equivalent airspeed \(\mathrm{U_{\mathrm{EAS}}}\) is provided in (16)  
\[\frac{\mathrm{d}\mathrm{U_{0}}}{\mathrm{dt}}=\frac{\mathrm{d}\mathrm{lg}}{ \mathrm{dt}}.\Big{(}\mathrm{U_{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{ \mathrm{     \mathrm{      }}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}\] \] \}\] \}\}\] \}\}\ \}\ \}\ \ \ \ \ \ \ \ \related difficulties include the relatively long response of the object, the strict limitations of the engines' operating range, variable dynamics related to the airframe configuration, as well as the previously mentioned cross-coupling of the control channels. Concurrently, the very occurrence of a failure, the so-called \"freeze\" of controls, imposes a control realisation at a predefined steady state equivalent airspeed. Its value for the selected aircraft in a researched configuration is almost twice the recommended approach speed. This means that during an emergency landing, the landing gear structure will be subjected to loads for which it was not originally designed. To prevent a crash landing, the emergency system has to guarantee a precise touchdown.  
The quality of setpoint (e.g., glideslope angle) tracking is, among other things, related to the controller speed. The process of determining the LQRI controllers' settings was divided into two stages. The first stage investigated the maximum control speed that could be achieved with zero overshoot in both control channels. The second stage consisted of experimentally selecting settling times to minimise the interaction between the control channels.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 10 cm Depth in Bangladesh
#### 5.1.2 Three-Day Lead-Time Forecast
\begin{table}
\begin{tabular}{r c c c c c c c c c c c} \hline \hline
**Model** & **Time Window Size** & \multicolumn{2}{c}{**One**} & \multicolumn{2}{c}{**Two**} & \multicolumn{2}{c}{**Three**} & \multicolumn{2}{c}{**Four**} & \multicolumn{2}{c}{**Five**} \\ \hline \hline \multicolumn{1}{c}{**Scenarios**} & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** \\ \hline \multirow{10}{*}{Hybrid} & MEAN & 1.947 & 0.910 & 1.913 & 0.913 & 1.886 & 0.914 & 1.864 & 0.917 & 1.855 & 0.917 \\ \cline{2-11}  & MEAN-SUN & 1.922 & 0.912 & 1.910 & 0.914 & 1.866 & 0.916 & 1.843 & 0.918 & 1.830 & 0.919 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.868 & 0.916 & 1.839 & 0.919 & 1.820 & 0.921 & 1.800 & 0.922 & 1.796 & 0.922 \\ \cline{2-11}  & MEAN-HUM-RAIN & 1.891 & 0.914 & 1.852 & 0.917 & 1.828 & 0.919 & 1.810 & 0.921 & 1.802 & 0.921 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.866 & 0.916 & 1.837 & 0.919 & 1.820 & 0.920 & 1.802 & 0.922 & 1.794 & 0.922 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.851 & 0.918 & 1.819 & 0.920 & 1.805 & 0.921 & 1.797 & 0.922 & 1.782 & 0.923 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.865 & 0.916 & 1.835 & 0.919 & 1.818 & 0.920 & 1.797 & 0.922 & 1.790 & 0.923 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.852 & 0.917 & 1.825 & 0.920 & 1.807 & 0.921 & 1.792 & 0.922 & 1.782 & 0.923 \\ \hline \multicolumn{1}{c}{**MEAN**} & 1.944 & 0.910 & 1.905 & 0.919 & 1.874 & 0.916 & 1.880 & 0.918 & 1.837 & 0.919 \\ \cline{2-11}  & MEAN-SUN & 1.913 & 0.912 & 1.900 & 0.915 & 1.845 & 0.918 & 1.821 & 0.920 & 1.806 & 0.921 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.856 & 0.917 & 1.819 & 0.922 & 1.796 & 0.922 & 1.776 & 0.924 & 1.768 & 0.925 \\ \cline{2-11}  & MEAN-HUM-RAIN & 1.874 & 0.915 & 1.827 & 0.921 & 1.801 & 0.921 & 1.784 & 0.923 & 1.774 & 0.924 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.850 & 0.918 & 1.817 & 0.921 & 1.795 & 0.922 & 1.777 & 0.924 & 1.766 & 0.925 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.834 & 0.919 & 1.797 & 0.922 & 1.779 & 0.924 & 1.768 & 0.925 & 1.753 & 0.926 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.843 & 0.918 & 1.810 & 0.922 & 1.791 & 0.923 & 1.77 & 0.924 & 1.761 & 0.925 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.829 & 0.919 & 1.797 & 0.922 & 1.777 & 0.924 & 1.762 & 0.925 & 1.752 & 0.927 \\ \hline \end{tabular}
\end{table} 
Table 3: Conf.