esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Training and Simulation
### Training Process of the MARL-Based Controller
The control frequency for the agents was set at 100 Hz, and each episode was set to last a maximum duration of 20 s, with a time step of 0.01 s. The size of the experience buffer was set at \(1\times 10^{6}\), utilizing the PER \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline \(N_{staps}\) & 2000 & Maximum time steps per episode \\ \hline \(y_{\max}\) & 10 mm & Threshold for lateral displacement termination \\ \hline \(\psi_{\max}\) & 10 mrad & Threshold for yaw angle termination \\ \hline \(T_{\max}\) & 2000 N-m & Maximum torque output for actuators \\ \hline \(\eta_{11}\) & 2 & Front IRW lateral displacement reward weight \\ \hline \(\eta_{12}\) & 1 & Front IRW yaw angle reward weight \\ \hline \(\eta_{21}\) & 0.5 & Rear IRW lateral displacement reward weight \\ \hline \(\eta_{22}\) & 0.2 & Rear IRW yaw angle reward weight \\ \hline \(\eta_{13}\) & 0.05 & Front IRW torque control reward weight \\ \hline \(\eta_{23}\) & 0.02 & Rear IRW torque control reward weight \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 6: Flowchart of the training process. scheme, where experiences are retained or discarded based on their TD priorities. The hyperparameters for the MARL algorithm training are detailed in Table 2.
## 4 Target Recognition System  
### System Architecture  
Target recognition system consists of two parts, namely data acquisition part and data processing part. Data acquisition part is divided into three units: sensing unit, wireless unit, PC monitor terminal unit. When the target enters into the detection area, the infrared signals are emitted by the human body can converge to PIR sensors through the aluminum mask and Fresnel lens, and then they are turned into an electrical signal. After that, the electrical signal is filtered and amplified. It is converted into digital signal by the A/D converter circuit. Through the point to point data transmission between Nrf24L01 wireless unit and wireless unit of gateway, data will be transferred to the PC software by the gateway based on the TCP/IP protocol. In this paper, the Qt4.7.4 software platform is used to develop the PC software primarily designed for data reading, storing and waveform drawing. The process of data collection is shown in Figure 6.  
The data processing part is mainly composed of three parts: feature extraction, feature matching and feature determination. In the feature extraction unit, this paper adopts FFT, STFT analysis method, WT and WPT, which are respectively used to extract the characteristics of sample data. Then, we can use SVM algorithm to classify and match the sample data in order to get a preliminary recognition rate. Furthermore, we can also analyze the effect on recognition rate caused by different installation height of the sensor and the distance between the human target and the PIR sensor node. Based on these factors, the FCEM is used to fuse the recognition data from four different sensors for the final recognition results.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Node Ranking Based on Network Resource Attributes and Topology Attributes
#### 4.1.5 Node Ranking Strategy  
Our node ranking strategy combines all of the above four attributes. We rank the nodes as follows:  
\[S(v_{i})=\alpha LR(v_{i})\cdot DC(v_{i})+\beta GR(v_{i})\cdot CC(v_{i}). \tag{16}\]  
In this strategy, we have integrated the local resources, global resources, local topology attributes, and global topology attributes. It can systematically evaluate nodes in the physical network and slice requests. \(\alpha\) and \(\beta\) are used to weigh the relative importance of local attributes and global attributes of the network.
## 2 Materials and Methods
### Crop Row Recognition and Navigation Line Extraction
#### 2.2.4 Crop Row Navigation Line Extraction  
To enable autonomous navigation of agricultural robots between intercropped crop rows, a navigation path extraction method based on the angle bisector principle was proposed. Geometrically, the bisector ensures an evenly balanced orientation between adjacent crop rows, reducing abrupt directional changes and improving the smoothness and stability of the navigation path. This method integrates the centerline information from both the left and right sides of soybean and maize rows to generate a stable and guidance-effective navigation path.  
First, the centerlines of the crop rows on both sides of the mobile platform's operating direction are extracted. For the soybean rows, the average slope of the left and right centerlines is calculated to construct a navigation line \(Y_{1}\), expressed as \(y_{1}=k_{1}x_{1}+b_{1}\). Similarly, for the maize rows, the average slope of the left and right centerlines is calculated to construct another navigation line \(Y_{2}\), given by \(y_{2}=k_{2}x_{2}+b_{2}\).  
To further generate a unified navigation path, the intersection point (\(x_{0},y_{0}\)) between \(Y_{1}\) and \(Y_{2}\) must be calculated. The computation is as follows:  
\[\left\{\begin{array}{l}x_{0}=\frac{b_{2}-b_{1}}{k_{1}-k_{2}}\\ y_{0}=k_{1}x_{0}+b_{1}\end{array}\right. \tag{10}\]  
The slope of the average navigation line, denoted as \(m_{0}\), is calculated based on the slopes of the navigation lines corresponding to the soybean and maize crop rows. The calculation formula is as follows:  
\[m_{0}=\frac{k_{1}+k_{2}}{2} \tag{11}\]  
Figure 8: Extraction of crop row centerlines based on real field LiDAR data.  
Finally, using the intersection point \((x_{0},y_{0})\) as a reference point and combining it with the average slope \(m_{0}\), the final navigation path \(Y_{\text{avg}}\) is determined using the point-slope form. The expression is as follows:  
\[y-y_{0}=m_{0}(x-x_{0}) \tag{12}\]  
The results of navigation line extraction are shown in Figure 9, where the green lines represent the fitted centerlines of the soybean and maize crop rows and the red line indicates the extracted navigation path.
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 2 Related Work
### SAR Imaging of mmWave  
The SAR imaging approach, which refers to the relative motion between radar and target, then uses signal processing techniques to achieve imaging. In the simplest SAR imaging, the target does not move, and the radar moves to produce a virtual array with a large aperture; for example, [11] used a rail system to drive the radar motion while the 2D (Two-Dimensional) target was fixed on a stand, and eventually, the collected data was processed to obtain the final 2D imaging results. The literature [21; 22], on the other hand, used a more powerful radar with a longer rail to achieve imaging of a 3D (Three-Dimensional) target. In addition, [23] extends the range of motion of the radar from two-dimension to three-dimension, and as a result, different virtual arrays are obtained, deriving imaging algorithms adapted to the new arrays. There is also CSAR (Circular Synthetic Aperture Radar), the radar of which rotates along a cylindrical trajectory, which was used in the literature [24; 25; 26]. Compared to the common SAR, ISAR (Inverse Synthetic Aperture Radar) allows the target to move as well. The authors of [27] let the target knife rotate while combining the traditional SAR, derived the imaging algorithm in this scenario, and obtained different imaging results at different moments. The authors of [28] investigated imaging under irregular curved motion trajectories. Our work's advantages are shown in Table 1.  
In this paper, the SAR approach is still used for imaging in order to achieve the goal of high resolution and make the algorithm simple, and instead of using the MIMO approach as  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source** & **Antenna Mode** & **Eliminate Ghost Image** & **Different-Distances Projection** \\ \hline This Work & SISO & Yes & Yes \\ [13] & MIMO & Yes & No \\ [16] & SISO & No & Yes \\ [12] & SISO & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compared to some related works.  
in the literature [13], the SISO approach is chosen for the simplicity of implementation. The problem of ghost image is also solved under SISO using the relevant algorithm. In addition, there are more studies on MIMO imaging; for example, the authors of [29] explored the imaging problem of special cross-shaped MIMO arrays and designed a corresponding imaging algorithm to accomplish target reconstruction. However, there are fewer studies on SISO.
# Robust Design of 3D-Printed 6-18 GHz Double-Ridged TEM Horn Antenna
## 6 Conclusions  
A 3D-printed double-ridged TEM horn antenna with an operating bandwidth of 6-18 GHz is designed using a new robust design method in this paper. The method is realized using a GSO algorithm and an OA matrix. The convergence speed of the GSO is faster than that of the GA or PSO, and is less likely to fall to the local optimum. OA matrix with two strengths and three levels is used to effectively reduce the simulation time. The GSO algorithm and OA matrix are employed to renew the antenna parameters and to evaluate the robustness of the parameters, respectively. The cost function and robustness are defined considering the reflection coefficient and gain of the horn antenna used in this design. As a result of the robust design process, the robustness of the optimized antenna is approximately 12.4% higher than that of the initial antenna. The frame of the robustly designed TEM horn antenna is built using a 3D printer of the SLA type, and its surface is plated with nano silver. Good agreement between the simulated and measured results with regard to the reflection coefficient and radiation characteristic is achieved, proving the feasibility of the proposed robust design method. Therefore, the proposed method with 3D printing technology is very useful and suitable for the robust design of a lightweight horn antenna via a rapid fabrication process available at a low cost. In addition, the proposed TEM horn antenna can be feasibly employed for radar and communication systems which require wideband capabilities.  
The presented work was carried out in collaboration of all authors. [PERSON] performed the simulations. [PERSON], [PERSON], K.-Y.L., K.-Y.J. and [PERSON] participated to the conception, fabrication and experiment. [PERSON] wrote the paper which was edited by all co-authors.  
This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2014R1A5A1011478).  
The authors declare no conflict of interest.
# Scene Text Recognition That Eliminates Background and Character Noise Interference
## 5 Conclusions  
This research presents an approach for scene text recognition based on ENBC. To begin, the dataset required by ENBC is built through preprocessing, and a synthetic scene dataset, text-image, including 180,000 scene text images and label pictures, is created. The ENBC network was then proposed, in which the encoder obtained features of different scales via ASPP dilated convolution with different expansion rates to expand the receptive field, allowing it to more effectively capture the character feature area, eliminate character noise interference, and improve character shape features. The decoder combined the high-level character feature information after upsampling with the low-level character feature information in the backbone network, separated the foreground characters from background interference, removed background noise, and produced the reconstructed picture. Finally, the recognition improvement module improves character context modeling by taking into account both forward and backward information from the text sequence. Experimental findings on synthetic and actual datasets demonstrate that, when compared to other popular approaches, ENBC has the highest accuracy on scene datasets and an outstanding recognition effect for text recognition. This technique effectively addresses the issue that previous models fail to properly reduce background noise and character noise when directly processing scene photos. However, in practice, the model in this study does not fully utilize the semantic information of characters due to the issue that the curvature of the text is too great and the characters are absent from the natural scene image. When visual information is absent and certain characters are erroneously identified, we will continue to investigate context semantic information modeling of the visual model's output in order to increase recognition accuracy.  
Conceptualization, S.T., S.L., Z.J. and K.L.; Methodology, Y.C.; Software, Y.C.; Validation, Y.C.; Formal analysis, Y.C.; Investigation, Y.C.; Resources, Y.C.; Data curation, Y.C.; Writing--original draft, Y.C.; Visualization, Y.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the Xi'an Science and Technology Plan Project with the grant number [23 KGDW0032-2022]. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data is contained within the article. The authors declare no conflict of interest.
# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
## 4 Linear Massive MIMO  
In this section, the concept of linear massive MIMO detection is discussed. Assuming a multi-user MIMO base station has \(q\) antennas and is serving \(p\) single antenna users \(p\leq q\). Assuming a frequency-flat channel, \(p\) users and \(q\) base station antennas produce a matrix (H), which may be written as follows.  
\[H=\begin{bmatrix}h_{11}&\cdots&h_{1j}\\ \vdots&\ddots&\vdots\\ h_{i1}&\cdots&h_{ij}\end{bmatrix},\]  
where channel gain or channel impairment is given from the jth transmit antenna to the ith receive antenna. The symbol vector s can be formed after the \(p\) users transmit their symbols individually towards the base station, \(\mathrm{s}=[\mathrm{s}_{1}\mathrm{s}_{2}\mathrm{\cdot}\mathrm{\cdot}\mathrm{ \cdot}\mathrm{\cdot}\mathrm{s}_{\mathrm{p}}]^{\mathrm{T}}\).  
The base station receives \(\mathrm{r}=[\mathrm{r}_{1}\mathrm{r}_{2}\mathrm{\cdot}\mathrm{\cdot}\mathrm{ \cdot}\mathrm{r}_{\mathrm{q}}]^{\mathrm{T}}\), which is corrupted by channel interference [35]. The relation between s and r can be written as follows.  
\[r=Hs+n. \tag{7}\]  
The MIMO link response is expressed as a set of linear equations. The receiver will only determine the transmitted data after solving these linear equations. The stability of the solution will depend on the condition number (\(\kappa(\mathrm{H})\)) of the matrix 'H'. The condition number quantifies the two important problems in MIMO transmissions: undesirable signal correlation and noise. High \(\kappa(\mathrm{H})\) is bad for the solution (ill-conditioned matrix) [36].  
Massive MIMO offers high energy efficiency and spectral efficiency compared to previous technologies [33]. Figure 5 depicts the massive MIMO with additive white Gaussian noise (AWGN). The nature of high frequencies attracts the path loss effects, which will be overcome by concentrating the transmitted power in a particular narrow area by growing the number of antennas. Massive MIMO will enable us to eliminate small-scale fading, and there will be only slowly changing, large-scale fading [33]. The transmission matrix, often called channel state information (CSI), signalizes the channel condition at the time of signal transmission, signifying the good or bad behavior of the channel [37]. In SISO, the knowledge of CSI is less influential because steady-state SNR characterizes it. The CSI varies quickly in case of rapidly fading channels, and MIMO has been employed to fragment the channel variations into spatially separated sub-channels. Thus, intelligent system design is possible because of CSI knowledge.
## 1 Introduction  
Thin-film solar cells with Cu(In,Ga)Se\({}_{2}\) (CIGS) absorber layer are one of the most promising photovoltaic technologies, with reported highest efficiency over 21% [1]. A crucial factor that determines the efficiency of CIGS thin-film devices are the optical properties of the absorber material [2]. This chalcopyrite compound possesses an excellent absorbing ability in the wide range of electromagnetic spectrum (typically 400-1200 nm, depending on the precise gallium amount within the material), making it ideal for the application in thin-film photovoltaics, despite its polycrystalline nature.  
To reduce the fabrication costs and deposition time, there is a need to reduce the thickness of the absorbing CIGS layer from current widely used 2 um to several hundreds of nm. However, in sub-micrometer thin CIGS solar cells additional efficiency issues emerge, such as back-contact recombination and decrease of optical path which reduces the absorption of the incoming photons [3]. Back-contact recombination can be limited by the choice of the back-contact material, surface modification or grading of gallium concentration throughout the absorber layer [4]. In order to improve the harvesting of the available photons that reach the cell, the employment of light management methods are of a great importance.  
Two groups of methods are the most commonly used for enhancing the absorption within the solar cell's absorber layer: texturing and use of nanoparticles embedded within the device. Texturing of solar cell layers causes a change in optical paths of the incoming light, which can modify the reflection on the cell's surface as well as on the interface between the cell's layers [5,6]. Texturing of the front side of the device is commonly used for reduction of overall reflectance of the cell, thus increasing the number of photons reaching the absorber material [7].  
The use of both metal and dielectric nanoparticles in light trapping applications have been extensively studied recently [8,9]. With a proper choice of size (within the visible spectrum range) and material, the employment of metallic and dielectric nanoparticles may exhibit light scattering properties through effect of plasmon resonance or dielectric scattering.  
Regarding the CIGS based solar cells, there are already some promising works published using scattering on the embedded dielectric nanoparticles [10], and texturing of the transparent front electrode [11]. The difficulty of stable integration of plasmonic metal nanoparticles was recently published [12], which makes the achieving of the proposed structures such as in [13] more plausible.  
In this simulation work we explore the possibilities of light-trapping effects achieved by texturing of the back (molybdenum) electrode of the CIGS thin-film solar cell. Similar techniques have been used for the efficiency improvement in thin film silicon solar cells [14, 15, 16]. These simulations should give an insight in the range of the texturing features' sizes, thus reducing the need for costly experimental works, giving the experimental focus to the best-resulting configurations.
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 3 Results
The remaining 8% of the flaring volume is observed at midstream and downstream facilities, with a 2 BCM peak in 2015-2016 and the lowest volume, 1.4 BCM, in 2019.
## 3 OTFS Channel Estimation
### Observation Model for Channel Estimation  
As in [11], channel estimation is performed considering the input-output relation in Equation (9), rewritten as  
\[\mathbf{y}=\mathbf{R}\big{(}\boldsymbol{\tau},\boldsymbol{\
u}\big{)}\mathbf{ g}+\mathbf{w}, \tag{14}\]  
where \(\mathbf{g}=[g_{1}\,g_{2}\,...\,g_{P}]^{T}\in\mathbb{C}^{P\times 1}\) is the channel gains vector and \(\mathbf{R}\big{(}\boldsymbol{\tau},\boldsymbol{\
u}\big{)}=[\mathbf{r}_{1}\, \,\mathbf{r}_{2}\,...\,\mathbf{r}_{P}]\in\mathbb{C}^{MN\times P}\) is a matrix called constituent delay-Doppler parameter matrix (CDDPM). Each column of \(\mathbf{R}\big{(}\boldsymbol{\tau},\boldsymbol{\
u}\big{)}\) is related to a different path and is given by  
\[\mathbf{r}_{i}(\tau_{i},\
u_{i})=\mathbf{G}_{i}(\tau_{i},\
u_{i})\mathbf{x}. \tag{15}\]  
Given the input-output relation in Equation (14), the maximum likelihood (ML) estimate of the unknowns is given by  
\[(\mathbf{\hat{g}},\,\hat{\boldsymbol{\tau}},\hat{\boldsymbol{\
u}})=\arg\min_{ \mathbf{g},\,\mathbf{r},\boldsymbol{\
u}}\|\mathbf{y}-\mathbf{R}\big{(} \boldsymbol{\tau},\boldsymbol{\
u}\big{)}\mathbf{g}\|^{2}. \tag{16}\]Although the minimization problem depends on three parameters, it can be simplified performing a two-step ML estimate of the unknowns as  
\[(\hat{\mathbf{\tau}},\hat{\mathbf{v}})=\arg\max_{\mathbf{\tau},\mathbf{\
u}}\Phi\big{(}\mathbf{R} \big{(}\mathbf{\tau},\mathbf{\
u}\big{)},\mathbf{y}\big{)}, \tag{17}\]  
and  
\[\hat{\mathbf{g}}=\Big{(}\mathbf{R}^{H}\big{(}\hat{\mathbf{\tau}},\hat{\mathbf{v}}\big{)} \mathbf{R}\big{(}\hat{\mathbf{\tau}},\hat{\mathbf{v}}\big{)}\Big{)}^{-1}\mathbf{R}^{H} \big{(}\hat{\mathbf{\tau}},\hat{\mathbf{v}}\big{)}\mathbf{y}. \tag{18}\]  
Here, the cost function \(\Phi\big{(}\mathbf{R}\big{(}\mathbf{\tau},\mathbf{\
u}\big{)},\mathbf{y}\big{)}\) is computed as  
\[\Phi\big{(}\mathbf{R}\big{(}\mathbf{\tau},\mathbf{\
u}\big{)},\mathbf{y}\big{)}= \mathbf{y}^{H}\mathbf{R}\big{(}\mathbf{\tau},\mathbf{\
u}\big{)}\Big{(}\mathbf{R}^{H} \big{(}\mathbf{\tau},\mathbf{\
u}\big{)}\mathbf{R}\big{(}\mathbf{\tau},\mathbf{\
u}\big{)} \Big{)}^{-1}\mathbf{R}^{H}\big{(}\mathbf{\tau},\mathbf{\
u}\big{)}\mathbf{y}. \tag{19}\]  
The DDIPIC algorithm proposed in [11] performs an iterative maximization of the cost function in Equation (17) in order to jointly estimate delays and Dopplers of the paths. The operation of the algorithm is summarized below.
## 2 Methodology
### Decomposition Methods
#### 2.1.2 Wavelet Packet Transform  
The wavelet transform (WT), first proposed by [PERSON] [50] to study data compression in image coding, texture discrimination and fractal analysis, is a multi-scale signal processing method with good time-frequency localization features. However, this approach simply decomposes the low frequency sub-series of the original signal and ignores the analysis of the sub-series with high frequencies, which makes it more suitable to process nonstationary and instantaneous signals instead of gradually-changed signals. Thus, the wavelet packet transform (WPT) was developed, based on the WT, to overcome this limitation. Similar to the WT, the WPT decomposes the original signal into a low frequency coefficient (called approximation) and a set of high frequency coefficients (called details). By contrast, the WPT can further convert each detail into another approximation and another detail. Taking three decomposition level as an example, we illustrate the comparison of the decomposition trees of WT andWPT in Figure 1. As to the WPT, the final decomposition results of three level cover one approximation, L, and seven details, denoted by the H1, H2,..., H7, respectively.