esa-sceva/satcom-chunk-collection huggingface dataset:
# Sand Removal Mechanism of a High-Speed Roller Bit with Helical Sealing
## 4 Results  
### Feasibility Study  
#### 4.1.1 Numerical Simulation  
The velocity field in Figure 8 shows that the fluid entering the sealing chamber can directly flow through the gap, and the velocity direction is from the upper part to the lower part of the flow channel. Other fluid flows in a clockwise direction in the helical flow channel. As the fluid flows to the gap,the tangential component of the velocity is from top to bottom, which indicates that the spiral pump works. Meanwhile, the overall flow direction is from the upper end to the lower end of the flow channel, which is consistent with the direction of helical sand removal.  
The pressure field presented in Figure 9 shows that the pressure gradually decreases from the upper end to the lower end of the flow channel, and the high pressure concentrates in the upper part of the flow channel. The pressure is lowest (9.94 MPa) at the lower end and highest (10.8 MPa) at the upper end. Additionally, the inlet pressure at the upper end is 11 MPa. It is suggested that the screw has the functions of pumping sand and reducing the upper pressure. Moreover, mud debris is subjected to a downward force and removed from the upper end to the lower end. The pressure curve shows that the trend of pressure distribution is a decrease from top to bottom, which is convenient for sand removal. Sections labeled a-e in Figure 10 at the turning point of the screw pitch are studied. The curve of the pressure change rate can be obtained from the pressure curve; i.e., the value obtained by the first-order differential of the pressure distribution reflects the rate of change in pressure at a certain axial position. The pressure increases with the axial distance when the pressure change rate is positive and decreases with increasing axial distance when the pressure change rate is negative. A lower change rate of pressure thus results in a faster pressure drop and better sand removal. The pressure change rates at axial sections b, c, and d rapidly decrease to a minimum within a relatively small axial distance and more rapidly than those at other axial sections. It is suggested that each stage of the screw promotes a pressure drop and the removal of sand. Helical structure parameters (i.e., the screw pitch, screw angle, and sealing gap) have an important effect on the pumping capacity and sand removal performance. Further research is needed.  
Figure 8: Velocity field.  
Figure 11 shows the detention of rock debris. The black line outlines the three-dimensional model of the flow channel, the blue line represents the trajectory of particle movement in the sealing gap, and the green line represents the trajectory of particle movement in the whirlpool. Most of the debris is removed directly through the sealing gap. Little of the debris is in a swirling flow state in the channel. The maximum retention time (0.0406 s) is short, indicating the rapid removal of sand.  
Figure 11: The retention time of debris particles.  
Figure 9: Pressure field and pressure curve.
# Learning Output Reference Model Tracking for Higher-Order Nonlinear Systems with Unknown Dynamics +
## 4 Validation Case Studies
### ORM Tracking for a Linear Process
the control input \(u_{k}\) is \(\tilde{u}_{k}^{*}=\tilde{C}^{*}(\mathbf{x}_{k}^{E},\boldsymbol{\pi}_{j})=- \frac{1}{2\pi\iota_{j}}[\pi\gamma_{j,r},\pi_{9,j},\pi_{10,j}]\mathbf{x}_{k}^{E}\). This improved linear-in-the-state controller is embedded in the linear system of equations (12) that is solved for every iteration of IMF-AVI. Each iteration produces a new \(\boldsymbol{\pi}_{j+1}\) that is tested on a test scenario where the uniformly random reference inputs have amplitude \(r_{k}\in[-1;1]\) and switch every 10 s. The ORM tracking performance is then measured by the Euclidean vector norm \(\|y_{k}^{m}-y_{k}\|_{2}\) while \(\|\boldsymbol{\pi}_{j+1}-\boldsymbol{\pi}_{j}\|_{2}\) serves as a stopping condition when it drops below a prescribed threshold. The practically observed convergence process is shown in Figure 2 over the first 400 iterations, with \(\|\boldsymbol{\pi}_{j+1}-\boldsymbol{\pi}_{j}\|_{2}\) still decreasing after 1000 iterations. While \(\|y_{k}^{m}-y_{k}\|_{2}\) is very small right from the first iterations, making the process output practically overlap with the ORM's output. _Comment 7_. : For LTI processes with an LQR-like c.f., an LTI ORM and an LTI generative reference input model, linear parameterizations of the extended Q-function of the form \(Q(\mathbf{x}_{k}^{E},\mathbf{u}_{k})=\Phi^{\top}(\mathbf{x}_{k}^{E},\mathbf{u} _{k})\boldsymbol{\pi}\) is the well-known [9] form \(Q(\mathbf{x}_{k}^{E},\mathbf{u}_{k})=[(\mathbf{x}_{k}^{E})^{\top},(\mathbf{u} _{k})^{\top}]\mathbf{P}[(\mathbf{x}_{k}^{E})^{\top},(\mathbf{u}_{k})^{\top}]^ {\top}\) of the quadratic Q-function, with parameter \(\boldsymbol{\pi}=vec(\mathbf{P})\) being the vectorized form of the symmetric positive-definite matrix \(\mathbf{P}\) and the basis function vector \(\Phi^{\top}(\mathbf{x}_{k}^{E},\mathbf{u}_{k})\) is obtained by the nonrepeatable terms of the Kronecker product of all the Q-function input arguments.
# Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths
###### Abstract  
The feasibility of implementing digital predistortion for next-generation wireless communication is faced with a dilemma due to the ever-increasing demand for faster data rates. This causes the utilized bandwidth to increase significantly, as seen in the 5G NR standard in which bandwidths as high as 400 MHz are utilized. Hence, the development of new predistortion techniques in which the forward and feedback paths operate at lower sampling rates is of utmost importance to realize efficient and practical predistortion solutions. In this work, a novel predistortion technique is presented by which the predistortion is divided between the digital and analog domains. The predistorter is composed of a memoryless AM/AM gain function that is implementable in the analog domain, and a nonlinear model with memory effects in the digital domain to relax the sampling rate requirements on both the forward and feedback paths. Experimental validation was carried out with a 20 MHz and a 40 MHz 5G signal, and the results indicate minimal linearization degradation with a sampling rate reduction of 50% and 30%, respectively. This sampling rate reduction is concurrently applied in the digital-to-analog converter of the forward path and the analog-to-digital converter of the feedback path.  
5G communications; digital predistortion; memory effects; nonlinear distortions; power amplifiers 2024243439. [[https://doi.org/10.3390/s24113439](https://doi.org/10.3390/s24113439)]([https://doi.org/10.3390/s24113439](https://doi.org/10.3390/s24113439))
# Stability Analysis of Jinchuan Hydropower Station Hydraulic Tunnels during Excavation and Unloading
## 5 Conclusions  
In this study, a 3D MS monitoring system was built to keep track of the micro-fracture signals of the rock mass during the excavation and unloading of the spillway tunnel in  
Figure 17: Comparison between numerical modeling results and site failure at stake 0 + 450 m of the spillway tunnel.  
the Jinchuan Hydropower Station. The 3D finite-difference numerical modeling software FLAC3D was used to simulate the site excavation process. Moreover, the numerical modeling results were compared with the monitoring results and site surveys to determine the failure mechanisms of the surrounding rock during the excavation and unloading and the following conclusions were drawn:  
1. The study focused on the temporal and spatial evolution characteristics of MS events brought on by the excavation and unloading of the spillway tunnel. When the causes of MS event concentration under various working conditions were combined with the site construction conditions, it was determined that the main causes of MS events were construction disturbance and an unfavorable geological structure, which damaged the surrounding rock and made it vulnerable to macroscopic deformation.
2. In the tunnel at shallow burial depths, there was only a slight stress concentration at some right-angle intersections of the free face due to the low level of ground stress, and the minimum principal stress of the shallow surrounding rock was directed at the free face, while the direction of the maximum principal stress was parallel to that of the tunnel axis, thus helping ensure tunnel stability. In the deeper part of the tunnel, the stress concentration became obvious due to the increased level of ground stress, and the maximum principal stress was nearly perpendicular to the tunnel axis, thus undermining the stability of the tunnel surrounding rock.
3. By comparing microseismic monitoring results, numerical simulation, and conventional monitoring, it is found that there is a good spatial correspondence between microseismic monitoring and numerical simulation results, as well as good temporal correspondence between numerical simulation results and changes in stress gauges, which proves the feasibility of the comprehensive research method of microseismic monitoring combined with numerical simulation. It can provide reliable technical support for assessing the stability of the surrounding rock.  
Methodology, Y.S.; writing--original draft preparation, Y.Z.; writing--review and editing, H.M.; project administration, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (42177143,42 277461) and the Science Foundation for Distinguished Young Scholars of Sichuan Province (2020)DJQ0011).  
Not applicable.  
Data Availability StatementData are based on field monitoring and can be obtained from the corresponding author via email if necessary.  
The authors declare no conflict of interest.
# Experimental Study on the Damage of Optical Materials by out of Band Composite Laser
## 3 Experimental Study of Composite Laser Damaged Flat Window
### Short-Pulse Laser Damage Experiment  
A short-pulse laser damage window experiment was performed using the 1-on-1 method. The experimental results are shown in Table 7.  
Observing the damage phenomenon, it was found that, unlike the long-pulse damage, the coating damage caused by the short-pulse laser was shallower than that of the long pulse. Under the experimental conditions of this paper, only the Ge window showed slight substrate damage at the laser energy of 160 mj, though it showed no body damage (Figures 7 and 8). This was because the short-pulse laser damaged the material for a short time, mainly due to the effect of the laser electric field, so it mostly appeared as film damage. Non-linear effects (such as non-linear absorption, non-linear refractive index, and self-focusing effects) induced by high electric fields when the high-power lasers acted on the windows were the main causes of damage. Since the laser intensity in the short-pulse laser irradiation area reached the order of GW/cm\({}^{2}\), the self-focusing effect appeared. By combining  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Single-Pulse Energy (mJ)** & **Silicon Window Damage Effect** & **Germanium Window Damage Effect** \\ \hline
65 & N/A & N/A \\
90 & N/A & N/A \\
115 & Optical Coating damage & Optical Coating damage \\
135 & Optical Coating damage & Optical Coating damage \\
160 & Optical Coating damage & Substrate damage \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results of the short-pulse laser.  
Figure 5: Damage phenomenon of the Ge window with long-pulse laser irradiation.  
Figure 6: Damage phenomenon of the Si window with long-pulse laser irradiation.  
the above three experimental results, it could be seen that short-pulse lasers outside the band could only cause mild film damage, while long-pulse lasers could cause body damage.
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## 2 Related Work  
This section presents related studies, surveying the state of the art of intrusion detection in IoT environments, with emphasis on the methods based on machine learning techniques.  
Nowadays, the Internet of Things (IoT) has spawned a new ecosystem of connected devices, and an increasing number of organizations are using it to improve their performance, e.g., to operate more efficiently or to improve decision making. However, while the IoT has gained popularity, security challenges pose a significant barrier to widespread adoption and deployment of these devices. The security vulnerabilities introduced by complexity and interconnectivity of IoT devices and applications pave the way for the development of increasingly sophisticated anomaly detectors. Over the last few years, the use of machine learning to aid security and anomaly detection in IoT environments has become extremely important to face their security issues [9] and to develop subsequently appropriate lines of defense [10]. [PERSON] et al. [11] surveyed some challenges and issues for the design and the deployment of IoT applications.  
Since intrusion detection in the IoT domain is increasingly addressed through machine learning and (deep) neural networks, many ready-to-use public intrusion detection **IoT datasets** have been produced to support the testing of new designs. Most of these datasets are collected in synthetic environments--across various IoT domains--under normative conditions and multiple intrusion scenarios. They attempt to emulate real network traffic, and they do not contain any confidential data. Popular public IoT intrusion detection datasets are MedBIoT [12], N-BaloT [2] and IoTID20 [13].
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Cross-Domain Recommendation
#### 3.3.2 Dual-Target CDR  
Single-target approaches can only utilize auxiliary information from a richer domain to assist a sparser domain. However, the richness of various types of information varies across different domains. If these types of information can be effectively harnessed, it is possible to enhance the recommendation performance in all domains simultaneously, rather than limiting improvements to a single target domain. To address this, dual-target CDR and multi-target CDR have been recently proposed to enhance recommendation performance across dual/multiple domains. To achieve cross-domain recommendations for dual-target domains, researchers have begun to develop various models. These models often incorporate advanced techniques such as graph models and attention mechanisms to facilitate knowledge transfer between different domains.  
Research on recommender systems that operate across multiple target domains has been increasingly explored in depth. [PERSON] et al. [39] were pioneers in that they introduced a dual-target-domain CDR framework called DTCDR. This framework capitalizes on multisource information, ensuring that more detailed embeddings are produced for both users and items. To further integrate this information, the authors employed multitask learning techniques to merge the embeddings of overlapping users.  
Building upon this foundation, [PERSON] et al. [126] delved deeper into the embeddings of overlapping users. They created two distinct heterogeneous graphs utilizing ratings and content information from both domains to produce more representative user and item embeddings. Taking hyperparameters and data sparsity into consideration, they achieved more accurate embeddings for these users. Subsequently, the DDTCDR model presented in [40], another dual-target domain CDR framework, took a different approach, emphasizing the bidirectional implicit relationships between users and items. It utilized implicit orthogonal mapping to learn user preferences, allowing for the bidirectional transfer of user embeddings between two domains.  
Furthermore, in [127], the authors incorporated graph embedding techniques. They not only utilized connectivity of the user-item graph within a single domain via a novel feature propagation layer but also facilitated bidirectional knowledge transfer between the two domains by employing common users as bridges. Moreover, distinct from prior cross-domain collaborative filtering methods, they integrated both shared user features and domain-specific attributes during the transfer process.  
In summary, these studies were dedicated to optimizing the performance of dual-target-domain recommender systems. Their collective efforts have contributed valuably to enhancing the accuracy and robustness of cross-domain recommender systems.
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 3 Fractal Reflectarray Antennas
### Review of Existing Fractal Reflectarray Elements  
Currently, the phase tuning mechanism of reflectarray antennas is realized by changing one or more geometrical/electrical parameters of the unit cell.  
Various approaches have been presented in the literature, which can be essentially classified as follows [33; 34; 35; 38; 39; 40; 41; 42]:  
* variable-size patches, in which the reflection phase is controlled by varying the patch size around its resonant size;
* fixed-size patches, in which the phase-tuning mechanism is based on the use of a variable-length coupled element, such as a line and/or slot, embedded in the same cell;
* active patches loaded with reconfigurable devices or materials, such as varactors, PIN diodes, or liquid crystals [38; 39; 40; 41; 42].  
The next subsections provide a brief review of the existing fractal reflectarray elements based on the first two approaches.
## 2 Materials and Methods
### qNMR Calculations  
The standard solution for qNMR analyses is 10.0 mg of maleic acid (MA) per 100 mL in D\({}_{2}\)O solution.  
Baijiu Sample: The \({}^{1}\)H NMR data for quantitation of the volatile components found in Baijiu were obtained on 175 \(\mu\)L of Baijiu, which was placed in a 5 mm tube with a 100 \(\mu\)L aliquot of standard maleic acid solution (equivalent to 10 mg). The calculation utilized to quantify these components was:  
\[\text{Comp (mg/L)}=0.995\times 10\text{ mg }\times((\text{I}_{\text{comp}}/\text{N}_{ \text{comp}})/50)\times(\text{Mw}_{\text{comp}}/116.1)\times(\text{1,000,000}/175)\]  
where Comp is the calculated weight of the component in mg/L, which is derived from the weight of maleic acid (MA) internal standard (10 mg); I\({}_{\text{Comp}}\) = integration of component resonance; N\({}_{\text{Comp}}\) = number of protons integrated; I\({}_{\text{MA}}/\text{N}_{\text{MA}}\) = 50 (MA integral set as 100); Mw\({}_{\text{Comp}}\) = molecular weight of component molecule; Mw of MA = 116.1 amu; and the 1,000,000 mL /175 mL factor rectifies the volumetric component of the calculation to allow mg/L to be calculated. A value of 0.995 represents the 99.5% purity of the maleic acid standard.  
Once the mass of ethanol per liter is known from the above qNMR calculation, the volume that this mass represents is calculated using a density of ethanol at 20 \({}^{\circ}\)C (0.7892 g/mL). The volume of water per liter is calculated by subtracting the volume of ethanol per liter (modified by 3%, which is a typical volume contraction for spirits in the 30-60 %ABV range). Using these values, the mole fraction of ethanol is calculated. Tables and plots are available showing the relationship between the mole fraction of ethanol and the volume contraction that occurs upon mixing ethanol and water. The ethanol content is then calculated by applying this contraction on a percentage basis to the theoretical volume calculated from the mass of ethanol in the sample.
## 2 Materials and Methods
### Identification of the Constants of GTN Model
#### 2.3.4 Polynomial Regression Method  
In this method, the objective function is approximated by a polynomial of second order as below [28]:  
\[OBJ(x)\cong a_{0}+\sum_{i=1}^{n}a_{i}x_{i}+\sum_{i,j=1}^{n}b_{ij}x_{i}x_{j}, \tag{21}\]  
In which, \(n\) is the number of constants of GTN damage model, and \(a_{0}\), \(a_{i}\)_and_\(b_{ij}\) are the polynomial coefficients. The GTN model has 10 constants \(\left(\begin{matrix}\sigma_{y}&\varepsilon_{y}&n&q_{1}&f_{0}&f_{c}&f_{f}&f_{N}& \varepsilon_{N}&S_{N}\end{matrix}\right)\). \(\sigma_{y}\), \(\varepsilon_{y}\) and \(n\) are obtained from stress-strain curve of plain specimen obtained from quasi-static tensile test. The optimum quantities of \(q_{1}=1.5\), \(S_{N}=0.1\) and \(\varepsilon_{N}=0.3\) are taken from literature [29; 30; 31; 32]. Therefore, four constants remain to be determined for structural steel ST37. The polynomial of second order for four variables has 15 coefficients as follows:  
\[\begin{split} OBJ=a_{0}+a_{1}x_{1}+a_{2}x_{2}+a_{3}x_{3}+a_{4}x_ {4}+a_{5}x_{1}^{2}+a_{6}x_{2}^{2}+a_{7}x_{3}^{2}+a_{8}x_{4}^{2}+a_{9}x_{1}x_{2 }+\\ a_{10}x_{1}x_{3}+a_{11}x_{1}x_{4}+a_{12}x_{2}x_{3}+a_{13}x_{2}x_{4}+a_{14 }x_{3}x_{4},\end{split} \tag{22}\]  
Figure 12: A schematic of six sample points selection using LHS method.  
In relation to Equation (22), we have: \(x_{1}=f_{c}\), \(x_{2}=f_{0}\), \(x_{3}=f_{n}\), \(x_{4}=f_{l}\). To optimize this relation, the 15 coefficients should be identified. Therefore, a system of 15 equations was needed to be solved simultaneously to obtain the coefficients.  
To do this, 15 numerical simulations of tensile test using GTN damage model with 15 different sets of constants sets (which have been obtained using LHS design) were performed and the quantities explained in Equation (20) were measured. The system of equations can be written in matrices as shown below.  
\[\left|\begin{array}{ccccccccc}1&f_{c}{}^{(1)}&f_{0}{}^{(1)}&f_{n}{}^{(1)}&f_ {f}{}^{(1)}&\ldots&f_{n}f_{0}{}^{(1)}&f_{f}f_{0}{}^{(1)}&f_{f}f_{n}{}^{(1)}\\ 1&f_{c}{}^{(2)}&f_{0}{}^{(2)}&f_{n}{}^{(2)}&f_{f}{}^{(2)}&\ldots&f_{n}f_{0}{}^{( 2)}&f_{f}f_{0}{}^{(2)}&f_{f}f_{n}{}^{(2)}\\ &&&\ldots&&&\\ &&\ldots&&&\\ 1&f_{c}{}^{(15)}&f_{0}{}^{(15)}&f_{n}{}^{(15)}&f_{f}{}^{(15)}&\ldots&f_{n}f_{ 0}{}^{(15)}&f_{f}f_{0}{}^{(15)}&f_{f}f_{n}{}^{(15)}\\ \end{array}\right|\left|\begin{array}{c}a_{0}\\ a_{1}\\ \ldots\\ a_{14}\\ \end{array}\right|=\left|\begin{array}{c}OBJ^{(1)}\\ OBJ^{(2)}\\ OBJ^{(15)}\\ \end{array}\right|, \tag{23}\]  
Having obtained the coefficients of Equation (23), and having optimized the equation using genetic algorithm, the constants of GTN model which are the variables of the optimization problem, were determined. The results are given in Table 2.