esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Investigations Focusing on Flow Regime Characteristics
### Investigations with Single-Sized Non-Spherical Particles
#### 3.2.3 Modeling Studies  
To better estimate the influence of particle shape, based on [PERSON]'s method [78; 79], the \(V_{\text{T}}\) for non-spherical particles was suggested to be modified as follows [80]:  
\[V_{\text{T}}=K_{\text{T}}V_{\text{TS}} \tag{6}\]  
where \(K_{\text{T}}\) is a correction function associated with \(\phi\) and the Reynolds number of particles determined by their \(d_{\text{ev}}\) and \(V_{\text{T}}\)[78; 79]; \(V_{\text{TS}}\) is the terminal velocity of spherical particles sharing the same \(d_{\text{ev}}\) with non-spherical ones [68].  
Further, in light of the aforementioned experimental analysis, it was found that during the particle-releasing stage, additional inter-particle interactions resulting from the shape-relevant characteristics would lead to less significant pool convection through the reduction of the particle releasing rate, which was already considered in the term \(v_{\text{pr}}\) in the base model. Moreover, considering the falling process of particles into the pool, it can be understood that such inter-particle interactions would restrict the intensity of pool convection or, in other words, result in a more significant effect for particle inertia. In order to take this influence into account for the base model of Equation (5), an extension scheme, \(K_{\text{NS}}\), was proposed [68]:  
\[I=K_{\text{NS}}\left[K_{\text{B}}\times\frac{\left(\frac{v_{\text{pr}}}{v_{ \text{c}}}\right)^{a}\left(\frac{H_{\text{W}}^{2}}{LW}\right)^{b}}{\left( \frac{\rho_{\text{p}}-p_{1}}{\rho_{1}}\right)^{c}\left(\frac{h_{\text{V}}V_{ \text{T}}}{a_{1}^{\prime}}\right)^{d}}\right] \tag{7}\]  
where  
\[K_{\text{NS}}=\frac{1}{1.0+s_{1}\frac{\left(1-\phi\right)^{s_{2}}}{\left(Re \right)^{s_{3}}}} \tag{8}\]  
Here, \(s_{1}\), \(s_{2}\), and \(s_{3}\) are empirical constants (see Table 3); \(Re\) is the Reynolds number of falling particles determined by \(Re=\frac{v_{\text{pr}}d_{\text{in}}}{\mu_{1}}\). Concerning the reasonability of the proposed \(K_{\text{NS}}\), it can be understood that if particles are non-spherical (i.e., \(\phi\) is smaller than 1.0), \(K_{\text{NS}}\) becomes smaller than 1.0, leading to a smaller value of index \(I\), indicating the possible emergence of a larger-numbered regime; while if particles are spherical (i.e., \(\phi\) is 1.0), \(K_{\text{NS}}\) becomes 1.0, and thus, the extended model will return to the base model developed in accordance with spherical particles (i.e., Equation (5)). As illustrated in Figure 15b, by using the extended model (i.e., Equation (7)), an extended regime map is attained for both spherical and non-spherical cases. According to the regime map depicted in Figure 15b, the reliability of the predicted results from the extended model is observed to be validated. On this basis, the potential for extending the applicability of the base model on DBF behavior under more realistic accidental situations was displayed to some degree.
## 3 Dynamic Modeling of CDPDR
### Kinematic Model
\begin{aligned} \begin{pmatrix}c_{ix}^{C}-k_{ix}^{C} \end{pmatrix}\begin{pmatrix}p_{ix}^{C}-k_{ix}^{C}\end{pmatrix}+\begin{pmatrix} c_{iz}^{C}-k_{iz}^{C}\end{pmatrix}\begin{pmatrix}p_{iz}^{C}-k_{iz}^{C}\end{pmatrix}= r_{pully}^{2}\\ \begin{pmatrix}c_{ix}^{C}-k_{ix}^{C}\end{pmatrix}^{2}+\begin{pmatrix}c_{iz}^{C}- k_{iz}^{C}\end{pmatrix}^{2}=r_{pully}^{2}\\ c_{iy}^{C}=k_{iy}^{C}\end{aligned} \right. \tag{5}\]  
The cable length is further obtained as  
\[l_{i}=\left\|\mathbf{l}_{i}^{C}\right\|=\sqrt{\big{(}\mathbf{c}_{i}^{C}-(\mathbf{x}_{PC}^{ C}+\mathbf{R}_{P}^{C}\mathbf{p}_{i}^{P})\big{)}^{T}\big{(}\mathbf{c}_{i}^{C}-(\mathbf{x}_{PC}^{ C}+\mathbf{R}_{P}^{C}\mathbf{p}_{i}^{P})\big{)}} \tag{6}\]  
Further, the derivative of Equation (4) is obtained:  
\[\dot{l}_{i}\mathbf{u}_{i}^{C}+l_{i}\mathbf{u}_{i}^{C}=\dot{\mathbf{c}}_{i}^{C}-\dot{\mathbf{x} }_{PC}^{C}-\left(\dot{\mathbf{R}}_{P}^{C}\mathbf{p}_{i}^{P}+\mathbf{R}_{P}^{C}\mathbf{p}_{i}^{P}\right) \tag{7}\]  
where \(\mathbf{u}_{i}^{C}=\mathbf{l}_{i}^{C}/l_{i}\) is the unit vector of the length of the \(i\)-th cable. Multiply both ends of Equation (6) at the same time by the vector \(\left(\mathbf{u}_{i}^{C}\right)^{T}\). According to the derivation formula  
Figure 2: Reference frames and kinematic parameters of CDPDR. \(\dot{R}_{p}^{C}\mathbf{p}_{i}^{p}=\omega_{PC}^{C}\times\left(R_{P}^{C}p_{i}^{p}\right)\) of the rotation matrix and the properties of the vector mixed product, the cable velocity can be further obtained as  
\[\dot{l}_{i}=-(\mathbf{u}_{i}^{C})^{T}\dot{\mathbf{x}}_{PC}^{C}-\left(\left(R_{P}^{C}p_{ i}^{p}\right)\times\mathbf{u}_{i}^{C}\right)^{T}\mathbf{\omega}_{PC}^{C}=-\left(\left(\mathbf{u}_ {i}^{C}\right)^{T}\quad\left(\left(R_{P}^{C}p_{i}^{p}\right)\times\mathbf{u}_{i}^{ C}\right)^{T}\right)\left(\dot{\mathbf{x}}_{PC}^{C}\right) \tag{8}\]  
where \(\mathbf{\omega}_{PC}^{C}= \begin{bmatrix}0&\dot{\theta}&0\end{bmatrix} ^{T}\) is the angular velocity vector of the motion platform. \(\dot{I}\) is defined as the cable velocity vector matrix. Further rearranging Equation (8) can obtain  
\[\dot{\mathbf{l}}=-\dot{\mathbf{J}}\dot{\mathbf{X}}_{PC} \tag{9}\]  
where \(\dot{\mathbf{J}}\) is the kinematic Jacobian matrix of the CDPDR. \[\ddot{\mathbf{J}}= \begin{bmatrix}\left(\mathbf{u}_{1}^{C}\right)^{T}&\left(\left(R_{ P}^{C}p_{1}^{p}\right)\times\mathbf{u}_{1}^{C}\right)^{T}\\ \left(\mathbf{u}_{2}^{C}\right)^{T}&\left(\left(R_{P}^{C}p_{2}^{p}\right)\times \mathbf{u}_{2}^{C}\right)^{T}\\ \left(\mathbf{u}_{3}^{C}\right)^{T}&\left(\left(R_{P}^{C}p_{3}^{p}\right)\times \mathbf{u}_{3}^{C}\right)^{T}\\ \left(\mathbf{u}_{4}^{C}\right)^{T}&\left(\left(R_{P}^{C}p_{4}^{p}\right)\times \mathbf{u}_{4}^{C}\right)^{T}\end{bmatrix}  \tag{10}\]  
Then, by taking the derivation of Equation (9), the relationship between the acceleration of the cable movement and the movement of the motion platform can be obtained  
\[\ddot{\mathbf{l}}=-\ddot{\mathbf{J}}\dot{\mathbf{X}}_{PC}^{C}-\dot{\mathbf{J}}\dot{\mathbf{X}}_{PC} ^{C} \tag{11}\]  
where \(\dot{\mathbf{J}}\) is the derivative of the kinematic Jacobian matrix, which is  
\[\dot{\ddot{\mathbf{J}}}=
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
###### Abstract  
Long-Range Wide Area Network (LoRaWAN) is a fast-growing communication system for Low Power Wide Area Networks (LPWAN) in the Internet of Things (IoTs) deployments. LoRaWAN is built to optimize LPWANs for battery lifetime, capacity, range, and cost. LoRaWAN employs an Adaptive Data Rate (ADR) scheme that dynamically optimizes data rate, airtime, and energy consumption. The major challenge in LoRaWAN is that the LoRa specification does not state how the network server must command end nodes pertaining rate adaptation. As a result, numerous ADR schemes have been proposed to cater for the many applications of IoT technology, the quality of service requirements, different metrics, and radio frequency (RF) conditions. This offers a challenge for the reliability and suitability of these schemes. This paper presents a comprehensive review of the research on ADR algorithms for LoRaWAN technology. First, we provide an overview of LoRaWAN network performance that has been explored and documented in the literature and then focus on recent solutions for ADR as an optimization approach to improve throughput, energy efficiency and scalability. We then distinguish the approaches used, highlight their strengths and drawbacks, and provide a comparison of these approaches. Finally, we identify some research gaps and future directions.  
adaptive data rate; algorithm; Internet of things; LoRa; LoRaWAN; LPWAN +
Footnote †: journal: Sensors
# A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs
## 5 Our Presented HM-ACOPSO Algorithm  
In this section, we will illustrate our presented HM-ACOPSO algorithm. HM-ACOPSO mainly contains four phases: Clustering; mobile agent shortest moving sequence searching based on ACO; further improvement of the scheduled path using anchor nodes based on PSO; and communication range adaption for energy balancing and anchor nodes merging for decreasing the sojourn time of the mobile agent. During the initial phase of the network, sensors exchange their own information that includes location and sensor ID with their neighbors in the range of communication.
###### Abstract  
Due to the relative motion between transmitters and receivers and the multipath characteristic of wideband underwater acoustic channels, Doppler and channel estimations are of great significance for an underwater acoustic (UWA) communication system. In this paper, a preamble signal based on superimposed linear frequency modulation (LFM) signals is first designed. Based on the designed preamble signal, a real-time Doppler factor estimation algorithm is proposed. The relative correlation peak shift of two LFM signals in the designed preamble signal is utilized to estimate the Doppler factor. Moreover, an enhanced channel estimation algorithm, the correlation-peak-search-based improved orthogonal matching pursuit (CPS-IOMP) algorithm, is also proposed. In the CPS-IOMP algorithm, the excellent autocorrelation characteristic of the designed preamble signal is used to estimate the channel sparsity and multipath delays, which are utilized to construct the simplified dictionary matrix. The simulation and sea trial data analysis results validated the designed preamble, the proposed Doppler estimation algorithm, and the channel estimation algorithm. The performance of the proposed Doppler factor estimation is better than that of the block estimation algorithm. Compared with the original OMP algorithm with known channel sparsity, the proposed CPS-IOMP algorithm achieves a similar estimation accuracy with a smaller computational complexity, as well as requiring no prior knowledge about the channel sparsity.  
Doppler estimation; channel estimation; improved orthogonal matching pursuit; superimposed linear frequency modulation signal; underwater acoustic communication +
Footnote †: journal: Marine Science and Engineering  
0000-0001-938-2281]Chenglei Lv  
1 College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; [EMAIL_ADDRESS] (C.L.); [EMAIL_ADDRESS] (Q.S.); [EMAIL_ADDRESS] (L.X.)12 Interdisciplinary Student Training Platform for Marine Areas, Zhejiang University, Hangzhou 310027, China 3
## 4 Conclusions  
Inter-basin water diversion is a vital way to alleviate the conflict of supply and demand of water resources, and accurate hydraulic modelling is essential to guarantee its smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilities and equipment, it is difficult to obtain accurate and effective measurement data to establish models. Therefore, based on data-driven methods, this research diagnosed and restored the important parameters of the water conveyance system, such as data for the elevation of pipeline and water level data; the major conclusions are as follows:  
1. In this paper, the identification model of abnormal elevation data was obtained by combining expert data annotation and an SVC classifier, and the identification accuracy of abnormal data was as high as 91%.
2. The single abnormal data and the continuous abnormal data in the data for the elevation of pipeline were restored using the interpolation method and MLR method, respectively. The restored data are consistent with the judgment of expert knowledge and can be used for the establishment of a water conservancy model.
3. For multi-dimensional water level data, K-medoids combined with the 3-sigma method was established to identify the outliers. The water level data were divided into three classes, and the abnormal data under each mode were well identified.
4. The GBDT water level data restoration model was established, which restored the outliers in a predictive manner. The MAPE in the test set of the three water level classes was 0.003%, 0.025% and 0.091%, respectively, indicating that this model has the best accuracy compared with other models and can perform data restoration work well.  
Figure 13: Model performance on the class 3 water level testing set.  
**Author Contributions:** Conceptualization, M.L. and X.L.; methodology, M.L.; software, M.L. and G.X.; validation, M.L.; formal analysis, M.L.; investigation, M.L.; resources, G.X. and X.L.; data curation, M.L.; writing--original draft preparation, M.L.; writing--review and editing, all authors; visualization, M.L.; supervision, G.X. and X.L.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Fundamental Research Program of Shanxi Province (grant number 20210302124645), the Scientific and Technological Innovation Plan Project of Higher Education Institutions in Shanxi Province (grant number 2021L019), and the school-level fund of Taiyuan University of Technology (grant number 2022 QN052).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiment and Result Analysis
### Implementation Details
#### 4.2.3 Training Settings  
To compare with the proposed RT-TrAdaBoost approach, experiments are also conducted on the more popular convolutional neural network transfer learning (CNN-TL) approach and the AdaBoost approach based on ensemble learning. The CNN-TL has a total of four one-dimensional convolutional layers, four pooling layers and one fully connected layer. The convolutional layer filters are 38, and the kernel size is 32. Both the AdaBoost approach and the RT-TrAdaBoost approach have 15 CART base learners, each with a maximum depth of 5 layers. In particular, as mentioned in Section 2.3, the AdaBoost approach assumes that all data available for training is identically distributed. Therefore, in the case that the target domain samples are insufficient to train the RUL prediction model alone, the target domain and source domain data are combined to train the AdaBoost.
# Vision-Based Path Guidance to Achieve Dies-Free Roller Hemming Process
## 2 Experimental Apparatus and Methodology
### 3D Surface Reconstruction and Coordinate Calibration Methodology
According to the readings of the gauge module, one then proceeds to fine-tune the end position of the robot arm to confirm the alignment with the calibration module. After which process, the transformation matrix \(\overset{W}{O}\boldsymbol{T}\) describing the transformation from the world frame \(\{W\}\) to the robot frame \(\{O\}\) can then be derived from the current posture of the robot arm. The last step is the calibration of the hand-eye relationship. The purpose of this is to calibrate the conversion relationship between the sensors and the end frame of the robot, so that in the future, the data can be directly converted into the world frame through the robot's posture during the sensing process. As shown in Figure 10, the calibration method of this item needs to align the sensor module with the optic calibration plate in the center of the calibration device, and it uses the device's black and white checkerboard grid points to perform vision correction and calculate the 3D conversion relationship with the optic calibration plate. Next, through the known position of the optic calibration plate in the world frame, the conversion relationship \(\overset{W}{L}\boldsymbol{T}\) of the sensor relative to the world frame can be derived. In the meantime, not only are the postures of the robot recorded, but the conversion relationship \(\overset{W}{H}\boldsymbol{T}\) of the end-effector of the robot under the world frame is calculated and obtained at the same time. Using the following formula, the hand-eye relationship \(\overset{H}{L}\boldsymbol{T}\) can then be further derived. After calibrating the hand-eye relationship, the points scanned by the sensor can be transformed from the sensor frame into the world frame. \[\overset{W}{H}\boldsymbol{T}=\overset{W}{O}\boldsymbol{T}\overset{O}{H} \boldsymbol{T}, \tag{3}\]  
\[\overset{H}{L}\boldsymbol{T}=\overset{W}{H}\boldsymbol{T}^{-1W}\boldsymbol{T}= \overset{H}{W}\boldsymbol{T}^{W}\boldsymbol{T}, \tag{4}\]  
Figure 9: Photographs showing the calibration procedure by manipulating the robot to calibrate (**a**) the tool center point (TCP) with different postures and (**b**) the world frame with the same posture.
## 1 Introduction
According to the difference between the resonant capacitor and the coil connection, it can be divided into four basic compensation circuits; namely, series-series (SS), series-parallel (SP), parallel-series (PS), and parallel-parallel (PP) [19; 20; 21]. It is stated in the literature [17; 19; 20; 21; 22; 23] that these traditional methods are affected to varying degrees by the load and coil coupling coefficients. In the SS structure, the resonant capacitance of the transmitting end does not change with the load or the coupling ratio. In the SP structure, the resonant capacitance of the transmitting end changes with the coupling rate. In the PS and PP structures, the resonant capacitance of the transmitting end changes with the load and the coupling ratio. In the IPT system of the [24; 25], the inductor-capacitor-inductor (LCL) resonance compensation circuit is adopted at the transmitting end. At the resonant frequency, the transmitting end coil exhibits a constant current characteristic, which does not change with the change of the load and the coupling ratio, and the transmitting end always works at the maximum excitation state; the receiving end uses a parallel resonant network. However, when the receiving end adopts the parallel resonant network, the coil contains reactive components, and the reactive component changes with the load and the lateral and longitudinal offset, and is fed back to the transmitting end through mutual inductance. For this reason [26] proposed a bilateral LCL type compensation circuit to solve the shortcomings caused by the traditional compensation method, so that the system has constant voltage constant current output characteristics. However, its compensation inductance is large and uneconomical, and the component loss is large. In view of the above problems, this paper proposes a new type of electric vehicle wireless charging and discharging system based on direct three-phase AC-AC matrix converter, which can realize the conversion of low-frequency alternating voltage to high-frequency alternating voltage without any intermediate energy storage components. At the same time, for the traditional capacitor-inductor series/parallel compensation affected by the load and coil coupling coefficient, the structure of the bilateral LCCL resonant network is proposed. Combined with the above system structure, a new control strategy is proposed. The idea of de-re-coupling is adopted to reduce the number of switching times and make the switch work in the soft switching state at the same time, reducing switching loss and improving system efficiency, thereby achieving the purpose of charging and discharging electric vehicles. The structure of this paper is as follows: the second section introduces the topology of the proposed system; the third section introduces the control strategy adopted by the proposed system; the fourth section theoretically analyzes the bilateral LCCL resonant networks; the fifth section gives simulation analysis; and finally, the sixth section gives the conclusion.
# Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems
## 2 Related Works  
In previous works, a myriad of strategies have been presented to enhance the efficacy of covert communication across diverse scenarios. These approaches span a spectrum, including the utilization of relaying networks [24; 25], multiple interference networks [26; 27], and multi-user networks [28]. Particularly noteworthy is the observation by [PERSON] et al. [29], who recognized the potential of the intelligent reflecting surface, also referred to as the reconfigurable intelligent surface (RIS), in amplifying the received signal at the intended receiver while concurrently attenuating the signal at potential eavesdroppers. In their system model, [PERSON] covertly transmits messages by either reflecting her signal to [PERSON] or introducing additional noise to confuse potential eavesdroppers through IRS devices. Recent studies [30; 31; 32] have built upon this foundation, demonstrating that exploiting  
Figure 1: IRS-enabled covert communication in multi-sensor systems.  
uncertainty about noise can effectively enhance covert performance in IRS covert communication. Furthermore, research has delved into the optimization of transmission power and reflection beamforming in IRS networks [30; 33; 34; 35; 36; 37]. Additionally, covert communication within Unmanned-Aerial-Vehicle-mounted IRS (UIRS) communication systems has garnered attention in recent investigations [38; 39].  
In [40; 41], the spectrum and time resources have been adopted to improve the performance of covert communication. Moreover, in [8], the authors have proved that phase resources can also be utilized to enhance the performance of covert communication. They extended IRS covert communication to the complex Gaussian channel and introduced a novel codebook, i.e., the \(N\)-BPSK codebook. This codebook is derived from the traditional BPSK codebook, and a simple reflect operation allows obtaining the full phase gain. This gain is crucial as traditional IRS covert communication requires biased reflection operations for each symbol, imposing high hardware demands and increased complexity on the IRS [8].  
While their work and findings are significant, their analysis of IRS covert communication is limited to scenarios with symmetric noise, specifically where the noise variances on the real and imaginary axes are equal. However, in multi-sensor scenarios, channel noise is typically asymmetric due to the dispersed positions of sensors and varying signal transmission intervals. The performance boundaries of covert communication under such conditions merit further investigation.