esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Mobile wireless sensor networks (MWSNs) are a kind of wireless sensor network. Usually they are composed of any minority or majority of mobile sensor nodes in a monitoring area, and they can form an arbitrary network topology among the nodes. The main difference with a static wireless sensor network is the mobility support of the nodes. After one-time deployment of a static sensor network, the positions of nodes are fixed, and centralized control can be realized. However in the MWSN, usually there are some nodes joining and exiting the network, in addition, the mobility of nodes makes the deployment area dispersive, so only distributed control can be realized [1; 2]. MWSNs not only have the characteristics of traditional WSNs, but also have their own advantages, which makes them have wide application prospects in military applications, environmental monitoring, medical care and other related fields [3]. On the other hand, the mobility of nodes also brings some problems. The topology of the network changes frequently and the communication links are not stable, which can interrupt the data transmission or decrease the amount of data transmitted. The identity authentication after movement and the large energy consumption are the main bottleneck for the application of MWSNs. Therefore, research on an efficient topology control algorithm for MWSNs has great significance [4; 5]. It can make nodes adjust their own working state according to the environment changes and adapt to the dynamic environment, so it can make the WSN support node movement better, and also extend the lifetime of the network, and improve the communication capacity [6].  
Aiming at addressing some of the problems of MWSNs, like the frequent topology changes, large energy consumption and security weakness, a kind of efficient Topology Control algorithm support for nodes Mobility (TCM) is proposed in this paper. A Particle Swarm Optimization (PSO) clustering algorithm based on multi-objective optimization was used to solve the problems of uneven clustering and energy consumption in the topology construction phase. In the topology maintenance stage, aiming at the mobility of the nodes, a digital signature authentication method based on Error Correction Code (ECC) and the soft handover communication mechanism were adopted. This can increase the amount of data transmitted and extend the network lifetime on the basis of considering the establishment of secure communication between the nodes.  
The structure of the rest of the paper is as follows: Section 2 introduces some work related to the research and compares several classical algorithms for topology control. Section 3 describes the TCM method in detail, and in Section 4, some simulations have been carried out and the TCM method is compared with some other methods. Finally, Section 5 concludes this paper.
## 2 Methods
### Ray Tracing Optimization Design  
A 2D grating array is used as a diffraction waveguide system for coupled gratings, and 2,000,000 rays need to be traced to ensure the accuracy of the results. In order to improve the efficiency of ray tracing and to ensure the smooth iteration of the objective function, the ray tracing is optimized.  
Figure 4: Two-dimensional grating ray tracing.  
The multi-order diffraction behavior of a 2D-EPE coupler is simulated by Zemax OpticStudio. The relative energy carried by each diffraction order of a 2D grating is obtained from the diffraction efficiency calculated by RCWA. For a grating surface with three diffraction orders, a light beam carrying 10 mW of energy is incident with three diffraction orders of 1 mW, 4 mW and 5 mW, respectively. The function of probabilistic splitting is applied in the custom object and when a light carrying 10 mW of power is incident on the 2D-EPE coupler, the energy efficiency distribution of the diffraction order is used to determine the probability of tracing the secondary according to the diffraction order. As shown in Figure 5, the probabilities of occurrence of 1, 4, and 5 mW are 0.1, 0.4, and 0.5, respectively; each incident ray striking the 2D grating surface will trace only one outgoing ray with the same power of 10 mw as the incident ray but with different angles and according to the probabilities of 0.1, 0.4 and 0.5.  
To ensure the accuracy of the image simulation, Zemax OpticStudio is used to trace \(10^{6}\) rays in one iteration cycle. The Monte Carlo method is used to simulate the case of multi-order grating diffraction, using only uniformly distributed random samples. The implementation is as follows: first, a random number seed is defined, and a uniformly distributed random number S in the range of 0 to 1 is generated using this seed. Then, a state vector is defined based on the diffraction efficiency, which can determine the state of the random number and, thus, the probability of tracing the ray. Following the previous example, if the diffraction efficiencies of three diffraction orders are 0.1, 0.4, and 0.5, respectively, then the generated state vector is L = [0, 0.1, 0.5, 1.0]. Then, the random number S is compared with the state vector L: if S < L[1] and S > L[0], the traced ray will be the diffraction order with efficiency 0.1; if S < L[2] and S > L[1], the traced ray will be the diffraction order with efficiency 0.4; if S < L[3] and S > L[2], the traced ray will be the diffraction order with efficiency 0.5.
## 2 MEMS Sensor-Based Localization Using the Data and Model Dual-Driven Structure
### Data and Model Dual-Driven Mode  
In this part, a unified data and model dual-driven (DMDD) structure is constructed that contains the integration of the INS mechanization, magnetic vector, and DLSE result, which can realize an accurate trajectory estimation result under complex motion modes and interfered environments. The state vector of presented DMSS is modeled as [22]:  
\[\delta\mathbf{x}=\left[(\delta\mathbf{p}^{n})_{1\times 3}\ \ (\delta\mathbf{v}^{n})_{1 \times 3}\ \mathbf{\phi}_{1\times 3}\ \ (\mathbf{e}_{g}^{b})_{1\times 3}\ \ (\
abla_{a}^{b})_{1\times 3} \right]^{T} \tag{5}\]where \(\delta p^{n}\), \(\delta v^{n}\), and \(\phi\) indicate the state vector of the position, velocity, and attitude using the n-frame, respectively. \(\epsilon_{g}\) and \(\
abla_{a}\) are the biases of the gyroscope and accelerometer, respectively. The relationship of the state update of DMDD is modeled as:  
\[\begin{bmatrix}\delta\mathbf{p}^{n}\\ \delta\mathbf{v}^{n}\\ \dot{\mathbf{\phi}}\\ \dot{\mathbf{\varepsilon}}_{g}^{b}\\ \dot{\
abla_{a}}\end{bmatrix} \tag{6}\]  
where \(\mathbf{f}^{n}_{sf}\) indicates the acceleration vector under n-frame, \(\tau_{bg}\) and \(\tau_{ba}\) indicate the correlation time parameters, and \(\mathbf{w}_{eg}\) and \(\mathbf{w}_{\
abla a}\) represent the state noise of \(\mathbf{w}_{\epsilon}\) and \(\mathbf{w}_{\
abla}\), respectively.  
The original INS mechanization is subjected to the cumulative and divergence errors affected by low-cost sensors, thus cannot be applied individually. In the proposed DMDD structure, the DLSE predicted velocity is applied as the observations:  
\[\left\{\begin{array}{l}\delta\mathbf{Z}^{n}_{\mathbf{p}}=\mathbf{v}^{n}_{DSE}-\mathbf{v}^{n} _{\text{INS}}\\ \delta\mathbf{Z}^{n}_{\mathbf{p}}=\mathbf{p}^{n}_{DISE}-\mathbf{p}^{n}_{\text{INS}}\end{array}\right. \tag{7}\]  
where \(\mathbf{v}^{n}_{DISE}\) and \(\mathbf{p}^{n}_{DISE}\) represent DLSE predicted walking speed and location observations, \(\mathbf{v}^{n}_{\text{INS}}\) and \(\mathbf{p}^{n}_{\text{INS}}\) indicate the walking speed and location calculated by the INS mechanization.  
To decrease the effect of the distorted local magnetic field, in this work, the magnetic observation among the recognized quasi-static magnetic field (QSMF) is extracted for modeling the difference vector between the first epoch data [5]:  
\[\delta\mathbf{Z}^{n}_{\mathbf{m}}=C^{b}_{n,k}\cdot\mathbf{m}^{b}_{k}-C^{b}_{n,1}\cdot\mathbf{m} ^{b}_{k,1} \tag{8}\]  
where \(C^{b}_{n,1}\) and \(\mathbf{m}^{b}_{k,1}\) indicate the attitude matrix and magnetic data collected from the first QSMF epoch, respectively, and \(C^{b}_{n,k}\) and \(\mathbf{m}^{b}_{k}\) indicate the attitude matrix and magnetic data collected from the following QSMF epochs, respectively.
## 1 Introduction and Motivation
### Ensemble Methods
Furthermore, our use of a simple training process is more efficient than baseline training schemes. The proposed scheme simultaneously applies oversampling to minority classes and undersampling to majority classes in order to correct for imbalances in the number of instances associated with the various classes. Training is conducted in three rounds. The first round involves training the network using sampled data with a large ratio of oversampling. The second round uses sampled data with a small ratio of oversampling. The third and final round uses normal data without oversampling or undersampling. The proposed cost-sensitive algorithm also includes a novel loss function (referred to as a balanced focus loss function), which automates the assignment of costs in accordance with the probability that a given class will occur in the batch sample. We employed the PointNet deep network as our learning model for its ability to deal with point cloud data directly, thereby enhancing computational efficiency by eliminating the need for pre-processing. The mean intersection over union (mIoU) test accuracy results obtained using PointNet training were as follows: XYZRGB data (91%) and XYZ data (86%). The mIoU test accuracy results obtained using the proposed scheme were as follows: XYZRGB data (98%) and XYZ data (93%). Our prediction results are clearly more accurate and stainless than the baseline results. The goal of this paper is to improve the segmentation deep network using imbalanced point cloud data. Our main contributions are as follows. * A novel two-stage scheme is proposed, which combines the hybrid-sampling method and the balanced focus loss function to improve object segmentation using imbalanced point cloud data. * A novel two-stage scheme outperforms either sampling or loss function technique for the imbalanced problem. * The mIoU test accuracy results obtained using the proposed method outperforms the baseline method (PointNet) by 6%. The remaining sections are organized as follows. Section 2 describes in detail the proposed framework using hybrid-sampling and balanced focus loss function. Section 3 outlines experiment results. Concluding remarks and future research are presented in Section 4.
# Achievable Rate Maximization for Multi-Relay AF Cooperative SWIPT Systems with a Nonlinear EH Model
###### Abstract
Using this parametric nonlinear EH model, [26] proposed a joint power control and time allocation scheme for the MIMO SWIPT systems to maximize the system throughput, while [27] explored the rate-energy region for the SWIPT system in the MIMO broadcasting channel, and [28] proposed a jointly optimal design of the PS ratios, artificial noise covariance, and transmission precoding matrix to optimize the secrecy energy efficiency of the secure SWIPT systems. Another nonlinear EH model described by piece-wise linear function was considered in [29; 30; 31], respectively, for the outage capacity calculation of AF relaying systems, the outage performance analysis of DF relaying systems, and the BER expressions derivation and diversity order analysis of AF relaying systems.
## 4 Conclusions  
It is concluded that the tooth-added metamaterial superstrate structure with the connected interior and exterior patch provides the multiband operation with a healthy gain. The measured and fabricated results are compared for verification. The system's performance is analyzed by varying the substrate material, inserting and removing the tooth in the split-ring resonator, and connecting and disconnecting the interior and exterior patch. The results are compared, in terms of many bands, \(\boldsymbol{S_{11}}\), the voltage standing wave ratio and the bandwidth for the proposed four structures, by changing the substrate material Rogers RT Duroid 5880 and FR4. The results are compared with earlier published work. The presented design represents seven frequency bands of operation, a gain of 8.57 dB, and a maximum reflectance response of \(-33.79\) dB. The presented structure is appropriate for numerous wireless communication applications, such as radar surveillance, satellite communication and weather monitoring.  
Conceptualization, K.A. and S.K.P.; methodology, M.A. and S.L.; software, S.L. and K.A.; validation, S.L., K.A., S.K.P. and M.G.D.; formal analysis, A.A. and M.A.; investigation, A.A. and M.G.D.; resources, K.A., A.A. and M.A.; writing--original draft preparation, All authors.; writing--review and editing, A.A. and S.K.P.; visualization, K.A. and M.A.; supervision, A.A. and S.K.P.; project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data will be made available at a reasonable request to the corresponding author.  
The authors declare no conflict of interest.
## 5 Conclusions  
In this work, we established a theoretical analysis model by considering all necessary factors during the transmission of a URLLC message, including the channel quality indicated by SNR, finite coding length, modulation scheme, delay and reliability requirements as well as information length and duplicated transmission. Network calculus was then applied to reveal the connection between the URLLC \((k,d,\epsilon)\)-tuple, modulation and coding scheme, channel quality and number of duplicated transmission, where the error probability, the maximum delay, the minimum bandwidth as well as the effect of duplicated transmissions were analyzed. The analysis helps to find the theoretical bounds, such as maximum delay given the allocated bandwidth and minimum required bandwidth given  
Figure 8: Capacity region (\(k=256\) bits, \(d_{0}=1\) ms, \(\epsilon_{0}=10^{-3}\)).  
delay and reliability constraint. Configurations defined in the 5G NR network were used to obtain numerical results.  
The adaptive MCS selection thresholds and admission region under certain delay and reliability constraint are presented and discussed. In addition, the theoretical analysis is validated by comparing with the simulation results yielded from the system-level simulation platform. The presented analysis framework can be extended to other scenarios, and the presented results for URLLC traffic can be applied by the network to conduct adaptive MCS selection and resource allocation.  
Conceptualization, Y.G.; Formal analysis, Y.G.; Methodology, Y.G.; Validation, [PERSON] and H.Y.; Writing--original draft, Y.G.; Writing--review & editing, [PERSON] and L.C. All authors have read and agreed to the published version of the manuscript  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
### Related Works
[37] studied massive access in CF m-MIMO IoT, addressing challenges in active user detection and channel estimation by proposing a structured sparsity-based generalized approximate message passing (SS-GAMP) algorithm. Their work compared cloud and edge computing paradigms, demonstrating that edge computing could reduce processing latency while maintaining similar performance levels. [PERSON] et al. [38] investigated the optimization of CF m-MIMO for IoT by developing power control algorithms that enhance both SE and EE. Their work focused on the uplink transmission scenario, where IoT devices require optimal power allocation to maintain reliable connectivity under stringent energy constraints. The authors proposed max-min power control algorithms leveraging random matrix (RM) theory, which provided accurate SINR approximations based on large-scale fading coefficients. They also introduced a neural network (NN)-based power control algorithm for the downlink, which significantly reduced computational complexity while achieving near-optimal power allocation. Their results demonstrated that machine learning-based power control in CF m-MIMO networks enhances scalability by reducing computational overhead at APs without degrading system performance. [PERSON] et al. [39] explored the integration of RIS and backscatter devices (BD) in CF m-MIMO symmetric radio (CF-m-MIMO-SR) to improve spectral efficiency and energy efficiency in IoT systems. Their study analyzed the performance trade-offs between RIS-aided and BD-aided CF-m-MIMO-SR systems, focusing on how RIS can mitigate the double fading effect inherent in BD communications. The authors derived closed-form SEexpressions for different levels of cooperation among AP and investigated various signal cancelation schemes based on available CSI. Their simulation results demonstrated that RIS significantly improves the SE of the backscatter link due to its ability to control reflection elements, whereas BD-aided systems require additional signal processing for enhanced direct-link performance. Their findings highlight the potential of RIS in CF-m-MIMO-SR networks but emphasize the need for optimized signal processing techniques to maximize performance across both backscatter and direct links.
# Acoustic Emission Wave Velocity Measurement of Asphalt Mixture by Arbitrary Wave Method
## 4 Measurement Results of AE Wave Velocity
### AST Method  
Two AE sensors were arranged 50 mm away from both ends of the asphalt mixture beam, and the distance between the two sensors was 200 mm. Sensor 1 was used for pulse excitation and sensor 2 received the pulse. One pulse signal was excited each time, and the excitation was conducted 10 times in total. Then, sensor 2 was used for pulse excitation, and sensor 1 received the pulse signal, which was also excited 10 times. Therefore, 20 AE waveform signals were collected by the AST method. The time duration of each pulse was 5 \(\upmu\)s and the period was 50 \(\upmu\)s. The time interval between pulse signals was 1000 ms.  
Because the pulse signal excited by the sensor is very strong, the signal will crosstalk to the receiving channel, so the system cannot correctly identify the first arrival time of the AE wave. Therefore, first of all, the collected signal waveform was played back, and the correct threshold was set. Then, the crosstalk signal was blank processed, so that the system can accurately identify the first arrival time of the AE wave. Finally, the AE wave velocity was calculated. Figure 7 shows the AE waveform of the crosstalk signal before and after the blanking process.  
In order to avoid systematic errors, the first and last AST test results were eliminated, and the wave velocities calculated from the remaining 16 AST tests were taken as the initial wave velocities of AE. Table 2 shows the initial wave velocity of the asphalt mixture beam measured by the AST method.  
\begin{table}
\begin{tabular}{c c c} \hline
**Lead Breaking Position 1 (mm)** & **50** & **150** \\ \hline \multirow{6}{*}{\(v_{p}\) (m/s)/Threshold correction value (mV)} & 3191/25 & 3260/33 \\  & 3225/40 & 3296/26 \\  & 3157/23 & 3225/27 \\  & 3124/20 & 3333/13 \\  & 3030/26 & 3333/26 \\  & 3061/14 & 3703/5 \\  & 3092/14 & 3370/30 \\  & 3016/15 & 3658/11 \\  & 3254.6 \\ \hline \end{tabular} 1  
\end{table}
Table 1: Measurement results of the wave velocity by the PLB method.
## 4 Modeling and Analysis
### Metrics Definition
#### 4.1.2 Packet Delivery Ratio (PDR)  
PDR is the percentage of packets received successfully by all relay nodes in branches over the total number of packets sent by Hunter. In this paper, we focus on the selection of the relay node, thus only the failure caused by the collision in CTB contention is considered. This means that the packet fails to be delivered if any relay node on one branch cannot be selected successfully.  
In the following subsection, since the exponent-based partitioning method will affect \(T_{\text{part}}\) and \(T_{\text{con}}\) in each phase, \(\beta\) (the three metrics determinate \(v\)), and PDR in the whole procedure, we only develop the analytical models for the four metrics of our general relay-node selection on intersection.
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
###### Abstract
Some researchers have also used mixed inorganic binders to improve the properties of saline soils, such as using cement lime to stabilize saline soils [21; 22]. Lime undergoes an ion-exchange reaction with the ions in the saline soils, causing the saline soils to solidify [23]. [PERSON] investigated the strength properties of steel slag, cement, and kaolin composite binder under different clay fractions, moisture contents, and curing times, and they found that the composite binder could effectively enhance the strength of saline soils and provide properties similar to those of hydraulic soil [24]. In cold regions, the combination of freeze-thaw cycles and salt leads to the destruction of cement, soil, interfacial zones, and pore structure, resulting in the deterioration of the durability of stabilized soils [25]. Polyacrylamide (PAM) is a linear, water-soluble organic polymer containing a large number of amide groups [26]. The amide groups form hydrogen bonds that can strengthen the soil by increasing the cohesion between soil particles [27; 28]. PAM is widely used in applications such as preventing irrigation-induced erosion and improving the permeability of agricultural soils, as well as for stabilizing subgrade and highway roadbeds in road engineering. However, it is also susceptible to cracking due to the drying and degradation of aggregate soil stability [29; 30]. [PERSON] [31] investigated the basic physical properties, densities, microstructures, and crack patterns of untreated and PAM-stabilized soil samples and found that PAM improved the physical properties and densities of stabilized soil. [PERSON] et al. [32; 33] found that the strength and wind erosion resistance of arid soil stabilized with PAM were significantly improved. However, the \"shell\" formed by the polymer can break down in harsh environments, reducing the strength of the stabilized soil [34]. PAM is commonly used to improve agricultural soils, but few studies have investigated its feasibility for stabilizing saline soils for use in roadbeds. This study compared the stabilization mechanisms, mechanical properties, and frost resistance of sulfate saline soils stabilized by lime, FA, and PAM. Sulfate saline soils and three binding-material-stabilized saline soil specimens were prepared separately and tested for unconfined compressive strength (UCS), splitting strength, and freeze-thaw cycles. Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetry (TG), and X-ray photoelectron spectroscopy (XPS)were used to investigate the micromorphology and characteristics of the three binder-stabilized saline soils. The results of this study can provide theoretical and technical support for the use of stabilized saline soils as roadbeds in Xinjiang.