esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Description & Mathematical Modeling
### Mathematical Modeling  
Parameters:  
\(J_{j}\): Job \(j\), \(j\) = 1, 2, \(\dots\)\(n\), of which \(n\) indicates the quantity of Jobs;  
\(pc_{j}\): \(J_{j}\) delay penalty coefficient;  
\(N_{j}\): size of \(j_{j}\), \(j\) = 1, 2, \(\dots\)\(n\);  
\(D_{j}\): Delivery date of \(J_{j}\), \(j\) = 1, 2, \(\dots\)\(n\);  
\(s_{j}\): Single piece volume of \(J_{j}\), \(j\) = 1, 2, \(\dots\)\(n\);  
\(O_{ji}\): \(i\)th process of \(J_{j}\), \(i\) = 1, 2, \(3,\dots\)\(G_{j}\), \(G_{j}\) is the number of process for \(J_{j}\);  
\(M_{p}\): Equipment \(p\), \(p\) = 1, 2, \(3,\dots\)\(m\), of which \(m\) indicates the quantity of equipment;  
\(B_{p}=\left\{ \begin{array}{c}1,\;\;if\;M_{p}\;\mbox{\it{is Heat Treatment Machine}};\\ 0,\;\mbox{\it{Otherwise}};\end{array} \right.\);  
\(T_{ji}\): Theoretical single piece processing time of \(O_{ji}\) (machining process);  
\(HT_{j}\): Theoretical heat processing time of \(J_{j}\);  
\(X_{jip}=\left\{ \begin{array}{c}1,\;if\;O_{ji}\;\mbox{\it{can be processed on $M_{p}$}}\\ 0,\mbox{\it{Otherwise}}\end{array} \right.\);  
\(K_{ji}=\left\{ \begin{array}{c}1,\;if\;O_{ji}\;\mbox{\it{is next process of the}}\\ \mbox{\it{heat treatment process}}\\ 0,\mbox{\it{Otherwise}}\end{array} \right.\);  
\(R_{jk}=\left\{ \begin{array}{c}1,if\;J_{j}\;\mbox{\it{and}}\;\mbox{\it{has can be combined for}}\\ \mbox{\it{processing at the same time}}\\ 0,\mbox{\it{Otherwise}}\end{array} \right.\);  
C: Theoretical volume of heat treatment equipment;  
Decision variables:  
\(S_{ji}\): \(O_{ji}\) processing start time (machining process);  
\(E_{ji}\): \(O_{ji}\) processing end time (machining process);  
\(PT_{ji}\): Actual processing time of \(O_{ji}\) (machining process);  
\(E_{j}\): \(J_{j}\) completion time;\[ID_{j}=\left\{ \begin{array}{c}1,\,if\,\,E_{j}>D_{j},\\ 0,\,Otherwise\end{array} \right. ;\] \[TD_{j};\,\,J_{j}\text{ delay penalty; }B_{q};\,\text{ batchs }q,\,q=1,\,2,\,\ldots\,n,\,q\text{ is the number of heat treatment equipment processing batches;}\] \[Y_{jq}=\left\{ \begin{array}{c}1,if\,\,J_{j}\,\,exist\,\,of\,\,B_{q} \\ 0,\,Otherwise\end{array} \right. ;\] \[N_{jq}\text{: the count of }Y_{jq};\] \[V_{jip}=\left\{ \begin{array}{c}1,\,if\,\,O_{ji}\,\,is\,\, processed\,\,by\,\,M_{p},\\ 0,\,Otherwise\end{array} \right. ;\] \[C_{q}\text{: The actual occupied volume of the }q\text{th heat treatment;}\] \[S_{q}\text{: The actual starttime of the }q\text{th heat treatment;}\] \[T_{q}\text{: The actual process time of the }q\text{th heat treatment;}\] \[E_{q}\text{: The actual endtime of the }q\text{th heat treatment;}\] \[U_{jip-f^{\prime}i^{\prime}p}=\left\{ \begin{array}{c}1,if\,\,O_{ji} \,\,is\,\,the\,\,next\,\,\,process\,of\\ O_{j^{\prime}i^{\prime}}\,\,processed\,\,on\,\,M_{p}\\ 0,\,Otherwise\end{array} \right. ;\]  
The flexible job-shop scheduling problem with heat treatment process studied herein has three scheduling targets, namely minimizing maximum makespan of workpiece (Equations (1) and (4)), maximizing average utilization rate of heat treatment equipment volume (Equation (2)) and minimizing delay penalty of workpiece (Equation (3)).
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 5 Results
### Experiment on the Influence of Iterative Initial Location  
The following simulation experiments are used to compare the effect of the initial location of the iteration on the performance of the algorithm. The location of the interference source is 125\({}^{\circ}\) E, 30\({}^{\circ}\) N, and the TDOA measurement errors are independent and identically distributed random variables distributed uniformly over \([-0.5\ \upmu\)s, 0.5 \(\upmu\)s]. The number of Monte Carlo is 500. The time difference measured at three consecutive times is used to remove mirrored points for target localization. The time interval is five minutes, and the error of the initial iteration value is the root mean square error of the prior longitude and  
Figure 4: Experiments on the influence factors of location error. (**a**) Location errors versus the number of the location moments; (**b**) location error versus the length of location time.  
latitude deviation of the first iteration. Figure 5 shows the relationship between the location error of single-moment location, multi-moment moment, and the precise positioning method based on multi-moment measurement with the iterative initial value error when the iterative initial value error is between 0\({}^{\circ}\) and 5\({}^{\circ}\). It can be found that, when the initial location error of the iteration increases gradually, the location error of the single-moment location degrades seriously. The multi-moment location method is basically not affected by the initial location error of the iteration, but with a poor performance compared with the single-moment method when the initial location error of the iteration is less than 3\({}^{\circ}\). On the basis of multi-moment localization, the proposed method can obtain better location accuracy than the other two methods.
## 3 Linear Deviations of the Four Scanner Configurations
### Linear Deviations of the Scanner (d) ba-ab  
(1) Using Figure 2(d1), from Appendix D.1, with Equations (A41), (A42), and (A45), the maximum linear deviation (obtained for the \(\varphi=0\) case) is  
\[y_{dP}^{max}=\frac{(b\cdot tan\theta_{1}\cdot tan\epsilon_{2}+e\cdot tan \epsilon_{2}^{\prime})\cdot cos\theta_{2}\cdot cos\epsilon_{3}^{\prime}+b\cdot sin \theta_{2}\cdot sin\epsilon_{3}^{\prime}}{cos\big{(}\theta_{2}+\epsilon_{3}^{ \prime}\big{)}}, \tag{29}\]  
where the angles \(\epsilon_{2}\), \(\epsilon_{2}^{\prime}\), and \(\epsilon_{3}^{\prime}\) are given by Equation (A46).  
From Figure 2(d1), the maximum linear deviation from the second diopter perpendicular on the O.A. to the screen (for the \(\varphi=0\) case) is  
\[y_{dS}^{max}=(L-l_{3}Q)\cdot tanD_{d}^{max}, \tag{30}\]  
therefore, using Equation (A44),  
\[y_{dS}^{max}=\bigg{[}L-\big{[}b(1+tan\theta_{1}\cdot tan\epsilon_{2})+e\cdot tan \epsilon_{2}^{\prime}\big{]}\cdot\frac{sin\theta_{2}\cdot cos\epsilon_{3}^{ \prime}}{cos\epsilon_{4}}\bigg{]}\cdot tanD_{d}^{max}. \tag{31}\]  
where the angular deviation \(D_{d}^{max}\) is given by Equation (5).  
(2) Using Figure 2(d2), from Appendix D.2, with Equations (A41), (A42) and (A50), _the minimum linear deviation_ (obtained for the \(\varphi=\pi\) case) is  
\[y_{dP}^{min}=e\bigg{[}tan\epsilon_{2}^{\prime}+\big{(}1-tan\epsilon_{2}^{ \prime}\big{)}\frac{sin\theta_{2}}{cos\epsilon_{4}}\bigg{]}+b\cdot tan\theta_ {1}\cdot tan\epsilon_{2}\cdot\bigg{(}1-\frac{sin\theta_{2}}{cos\epsilon_{4}} \bigg{)}, \tag{32}\]  
where the angles \(\epsilon_{2}^{\prime}\) and \(\epsilon_{3}^{\prime}\) are given by Equation (A46), and \(\epsilon_{4}\) is given by Equation (A51).  
From Figure 2(d2), the minimum linear deviation from the second diopter perpendicular on the O.A. to the screen (also for the \(\varphi=\pi\) case) is, with the same angles,  
\[y_{dS}^{min}=\big{(}L-l_{3}I_{4}\cdot cos\epsilon_{3}^{\prime}\big{)}\cdot tanD _{d}^{min}, \tag{33}\]  
therefore, using Equation (A48),  
\[y_{dS}^{min}=\bigg{[}L+\big{[}b\cdot tan\theta_{1}\cdot tan\epsilon_{2}-e \big{(}1-tan\epsilon_{2}^{\prime}\big{)}\big{]}\frac{sin\theta_{2}\cdot cos \epsilon_{3}^{\prime}}{cos\epsilon_{4}}\bigg{]}\cdot tanD_{d}^{min}, \tag{34}\]  
where the angular deviation \(D_{d}^{min}\) is given by Equation (5).  
For identical prisms, _the radius of the blind spot_ is, from Equation (32),  
\[\Delta_{d}=b\cdot tan\ \theta\cdot tan\epsilon_{2}+e\cdot tan\big{[}arcsin(n \cdot sin\epsilon_{2})\big{]}. \tag{35}\]  
In the particular case of the small angles approximations, this expression becomes  
\[\Delta_{d}^{lin}=(n-1)(e-b\theta/n)\theta. \tag{36}\]
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
###### Abstract  
Rice is a staple food in many Asian countries, but its production requires a high water demand. Moreover, more attention should be paid to the water management of rice due to global climate change and frequent droughts. To address this problem, we propose a rice water stress identification system. Since water irrigation usually affects the opening and closing of rice leaf stomata which directly affects leaf temperature, rice leaf temperature is a suitable index for evaluating rice water stress. The proposed rice water stress identification system uses a CNN (convolutional neural network) to identify water stress in thermal images of rice fields and to classify the irrigation situation into three classes: 100%, 90%, and 80% irrigation. The CNN was applied to extract the temperature level score from each thermal image based on the degree of difference between the three irrigation situations, then these scores were used to further classify the water-stress situation. In the experiments in this study, we compare CNN classification results without considering the degree between each class. The proposed method considerably improves water stress identification. Since rice leaf temperature is relative to air temperature and is not an absolute value, the background temperature is also important reference information. We combine two different methods for background processing to extract more features and achieve more accurate identification.  
rice water stress; thermal image; convolutional neural network +
Footnote †: journal: Medical Imaging  
1
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## 2 System Model
### Uplink Data Transmission
Therefore, we consider \(S_{m}=[s_{1,m},\ldots,s_{K,m}]\), which is a binary matrix with size \(N_{r}\times K\). The main aim for considering this matrix is to describe the state for each antenna at \(m_{th}\) AP depending on if it is connected to \(k_{th}\) RF chain or not, whereas ones denote the selected antennas, while deactivated antennas are depicted as zeroes in \(S_{m}\). Additionally, if \(n_{th}\) antenna at \(m_{th}\) AP is not connected to \(k_{th}\) RF chain, that does not mean this antenna will not be connected for other RF chains. Thus, \(\Delta_{m}\) is generated by element-wise multiplication of each column vector of \(S_{m}\) by each column \(\hat{\Delta}_{m}\), where \(\hat{\Delta}_{m}=[\Omega_{1,m},\Omega_{2,m},\ldots,\Omega_{N_{Q_{m}}}]\) as expressed below  
\[\mathcal{Z}_{m}=S_{m}\odot\hat{\Delta}_{m}. \tag{14}\]  
Consequently, \(\hat{W}_{m}\) is converted to \(W_{m}\) as follows  
\[W_{m}=S_{m}\odot(\hat{\Delta}_{m}\omega_{m}). \tag{15}\]  
Thus, \(w_{BB,k,m}\) can be obtained with the help of the obtained optimal \(W_{m}\)[32]. The uplink spectral efficiency (SE) can be obtained based on similar analysis techniques, such as [6, 22, 28, 33, 34]. The quickly fading random variables in complex numbers are independent, and they characterize the propagation model between UEs and APs. Cell-free massive MIMO systems with perfect CSI have a known capacity in some cases [35], while the ergodic capacity is unknown in the case of imperfect CSI. However, the SE can be analysed by using standard-capacity lower bounds in this system [7, 30]. Thus, the uplink spectral efficiency for \(m_{th}\) AP (SE\({}_{m}\)), which is measured in bits per second per Hertz, is obtained as follows  
\[\text{SE}_{m}=\sum_{k=1}^{K}\text{SE}_{k,m}=\frac{\tau_{c}-\tau_{p}}{\tau_{c}} \sum_{k=1}^{K}\log_{2}(1+\text{SINR}_{k,m}), \tag{16}\]  
where \(\text{SINR}_{k,m}\) denotes the effective instantaneous signal-to-interference-plus-noise ratio, which can be given as [22, 31]  
\[\text{SINR}_{k,m}=\frac{\rho|w^{H}_{BB,k,m}W^{H}_{m}\tilde{h}_{k,m}|^{2}}{\rho \sum_{k\
eq k}^{K}|w^{H}_{BB,k,m}W^{H}_{m}\tilde{h}_{k,m}|^{2}+\sigma^{2}\|w^{ H}_{BB,k,m}W^{H}_{m}\|^{2}}. \tag{17}\]  
Therefore, the total uplink SE for all APs is expressed as  
\[\text{SE}=\sum_{m=1}^{M}\text{SE}_{m}. \tag{18}\]
## 6 High Frequency Characterization and Modelling
### High-Frequency Characterization of UTC Photodiodes  
#### 6.2.1 Extrinsic Elements  
The pads and the coplanar waveguide (CPW) transmission lines have been modeled using a network of lumped elements located in the extrinsic part of the model. The extrinsic circuit includes the series resistance \(R_{p}\), the inductance, \(L_{p}\) and the capacitance, \(C_{p}\).  
In order to extract the values of these parasitic lumped elements, UTC-PD test structures in open (Figure 15a) and short (Figure 15b) configurations are characterized. One-port S-parameter measurements were performed up to 40 GHz with an RF power of -25 dBm using a vector network analyzer (VNA). Figure 16 shows the Smith chart depicting the measured reflection coefficients for the open and short test structures. From the measured real (Re) and imaginary (Im) parts of the \(Y\) (in S) and \(Z_{11}\) (in \(\Omega\)) parameters of the open and short structures, the values of \(C_{p}\) (in F), \(L_{p}\) (in H) and \(R_{p}\) (in \(\Omega\)) can be extracted using the following expressions:  
\[C_{p}=\frac{Im\big{(}Y_{open}\big{)}}{\omega},\ L_{p}=-\frac{1}{Im\big{(}Y_{ short}\big{)}\omega}\ \text{and}\ R_{p}=Re\big{(}Z_{11\_short}\big{)} \tag{11}\]  
The average values of resistance \(R_{p}\), \(C_{p}\) and \(L_{p}\) extracted at high frequency are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(C_{p}\) (ff) & \(L_{p}\) (pH) & \(R_{p}\) (\(\Omega\)) \\ \hline
26 & 61.5 & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Extracted parameter values of the UTC-PD de-embedding test structures.  
Figure 15: RF test structures with Ground-Signal-Ground (G-S-G) pads: (**a**) open and (**b**) short.
# Bayesian Updating for Random Tensile Force Identification of Ancient Tie Rods Using Modal Data
###### Abstract
Artificial Neural Networks are used to develop a robust methodology for the tensile axial force and the stiffnesses of the boundaries accounting for the irregular cross section along the beam model and all the information given by the experiments in [26]. Recently, the structural identification problem of ancient tie rods accounting for uncertainties is solved using a finite dimensional model, finite numbers of random variables, and Monte Carlo simulation in [27]. A suitable error function between the numerical natural frequencies and the target experimental values is set up and minimized, changing the main features of the assumed random models providing the tensile axial force probability estimate. To overcome the above mentioned drawbacks, this paper proposes to identify the random features of all the tie rods' uncertain quantities using numerical structural analysis and a Bayesian updating framework using dynamic data. Bayesian inference is a fundamental concept in probability theory, providing an efficient framework for updating beliefs or making predictions around the unknown/uncertain parameters based on new measurements, explicitly handling for modeling and measurement uncertainties. At its core, Bayesian inference uses the well known Bayes' theorem [28] to calculate the probability over an hypothesis or a parameter space given observed data. The use of dynamic data to update the initial hypotheses over the input parameter of a computational structural model was firstly introduced in [29; 30], becoming an actual research topic to solve the structural inverse problems (i.e., numerical model updating) of different types of structure (i.e., steel structures, masonry buildings, timber buildings, dams, wind turbines) [31; 32; 33; 34; 35; 36; 37; 38; 39]. In this paper, a Bayesian updating framework is used to solve the structural identification problem estimating the uncertainties associated with the computational model input parameters of axially loaded beam-like structures using natural frequencies as a reference. After discussing the mathematical setting used for the Bayesian updating and the description of a suitable computational model, a global robust sensitivity analysis using Sobol decomposition is carried out in order to estimate the influence of each computational model input parameter on the natural frequencies. The obtained results are used to properly select the updating parameters and then different cases are considered to assess the efficacy of the proposed Bayesian procedure. It is demonstrated that the proposed approach is able to provide the tensile axial force probability density function estimate.
###### Abstract  
Low Earth Orbit (LEO) constellations support the transmission of various communication services and have been widely applied in fields such as global Internet access, the Internet of Things, remote sensing monitoring, and emergency communication. With the surge in traffic volume, the quality of user services has faced unprecedented challenges. Achieving accurate low Earth orbit constellation network traffic prediction can optimize resource allocation, enhance the performance of LEO constellation networks, reduce unnecessary costs in operation management, and enable the system to adapt to the development of future services. Ground networks often adopt methods such as machine learning (support vector machine, SVM) or deep learning (convolutional neural network, CNN; generative adversarial network, GAN) to predict future short- and long-term traffic information, aiming to optimize network performance and ensure service quality. However, these methods lack an understanding of the high-dynamics of LEO satellites and are not applicable to LEO constellations. Therefore, designing an intelligent traffic prediction model that can accurately predict multi-service scenarios in LEO constellations remains an unsolved challenge. In this paper, in light of the characteristics of high-dynamics and the high-frequency data streams of LEO constellation traffic, the authors propose a DST-LEO satellite-traffic prediction model (a dynamic spatio-temporal low Earth orbit satellite traffic prediction model). This model captures the implicit features among satellite nodes through multiple attention mechanism modules and processes the traffic volume and traffic connection/disconnection data of inter-satellite links via a multi-source data separation and fusion strategy, respectively. After splicing and fusing at a specific scale, the model performs prediction through the attention mechanism. The model proposed by the authors achieved a short-term prediction RMSE of 0.0028 and an MAE of 0.0018 on the Abilene dataset. For long-term prediction on the Abilene dataset, the RMSE was 0.0054 and the MAE was 0.0039. The RMSE of the short-term prediction on the dataset simulated by the internal low Earth orbit constellation business simulation system was 0.0034, and the MAE was 0.0026. For the long-term prediction, the RMSE reached 0.0029 and the MAE reached 0.0022. Compared with other time series prediction models, it decreased by 22.3% in terms of the mean squared error and 18.0% in terms of the mean absolute error. The authors validated the functions of each module within the model through ablation experiments and further analyzed the effectiveness of this model in the task of LEO constellation network traffic prediction.  
satellite Communications and Broadcasting Department, 54 th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China; [EMAIL_ADDRESS] (K.L.); [EMAIL_ADDRESS] (S.L.)
## 3 Error Correction Codes in Wireless Sensor Network  
Error control coding (ECC) introduces redundancy into an information sequence \(u\) of length \(k\) by the addition of extra parity bits. Several different types of ECC exist, but we may loosely categorize them into two divisions: (1) block codes, which are of a fixed length \(n_{C}\), with \(n_{C}-k\) parity bits, and are decoded into one block or codeword at a time; (2) convolutional codes, which, for a rate \(k/n_{C}\) code, input \(k\) bits and output \(n_{C}\) bits at each time interval, but are decoded in a continuous stream of length \(L>>n_{C}\). Block codes include repetition codes, Hamming codes [17], Reed Solomon codes, and BCH codes [18]. Short block codes like Hamming codes can be decoded by syndrome decoding or maximum likelihood (ML) decoding by either decoding to the nearest codeword or decoding on a trellis with the Viterbi algorithm or maximum a posteriori (MAP) decoding with the BCJR algorithm. Algebraic codes such as Reed Solomon and BCH codes are decoded with a complex polynomial solver to determine the error locations. Convolutional codes are decoded on a trellis using either Viterbi decoding, MAP decoding, or sequential decoding.  
Another categorization is based on the decoding algorithms: (1) non-iterative decoding algorithms, such as syndrome decoding for block codes or maximum likelihood (ML) nearest codeword decoding for short block codes, algebraic decoding for Reed Solomon and BCH codes, and Viterbi decoding or sequential decoding for convolutional codes; (2) iterative decoding algorithms, such as turbo decoding with component MAP decoders for each component code, and the sum product algorithm (SPA) [19] or its lower complexity approximation, min-sum decoding [20], for low density parity check codes (LDPCs). The non-iterative decoding category may be further divided into hard and soft decision decoders; hard decision decoders output a final decision on the most likely codeword, while soft decision decoders provide soft information in the form of probabilities or log-likelihood ratios (LLRs) on the individual codeword bits. Viterbi decoding can be either hard decision or soft decision, with a \(2 dB\) gain in performance for soft decision decoding. Category (2) are all soft decision algorithms by nature, as iterative decoding requires soft information as _a priori_ input for each iteration. Iterative decoding algorithms provide significant coding gain, at the cost of greater decoding complexity and power consumption. With the recent technological advancements, all these ECC techniques can be used in WSN. However, LDPC code is considered in this paper as an ECC tool at WSN for its superior error correcting capabilities.
## 2 Principle
### Phase Unwrapping with One Coded Pattern  
To improve the measurement depth range, we utilized an additional coded pattern to provide more information for fringe order determination. Assume that the camera captures an object placed at the depth plane \(x_{min}\), there exists a one-to-one mapping between the camera sensor and the projector sensor, and the minimum fringe order \(k_{min}\) can be uniquely defined on the projector sensor. Figure 5 illustrates the main idea to determine the fringe order \(k\), in which line \(k_{min}\) plots the minimum fringe order, the line \(C\) plots the code word at depth plane \(z\), and line \(k\) plots the corresponding fringe order. The relationship between the three variables can be described as:  
\[k=C+N*ceil\left(\frac{k_{min}-C}{N}\right) \tag{10}\]  
For example, at point D, \(k_{min}-C<0\), thus \(k=C\); at point E, \(0<k_{min}-C<N\), thus \(k=C+N\); at point F, \(N<k_{min}-C<2*N\), thus \(k=C+2*N\). Similarly, the above equation must satisfy the following condition:  
\[0\leq k-k_{min}<N \tag{11}\]  
In other words, the measured objects should be close to the depth plane z\({}_{min}\) within \(2\pi N\) in phase domain. Through the above analysis, the proposed method raises the measurement depth range by \(N\) times compared with the traditional method.  
Figure 4: Phase unwrapping using the minimum phase map \(\Phi_{min}\).  
Figure 5: Fringe order determination using the minimum fringe order \(k_{min}\).