esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Protocols for Low Power and Lossy Networks in Internet of Things Applications
## 4 Enhanced Routing Solutions for Internet of Things Networks
### Mobile Nodes' Support
The congestion level is obtained by the ratio between the queue length of the node and its maximum queue. The mobility of the node, defined as \(\beta\) and computed by QuickBeta, is calculated in accordance with the changes in the neighbor table. Thus, the \(\theta\) value is automatically adjusted for different traffic loads and topology changes provoked by mobility. \(\theta\) also defines the switching of the routing strategy of BRPL. When its value is equal to one, BRPL realizes the packet forwarding using only the objective function of RPL. Otherwise, when \(\theta\) is nearest to zero, BRPL tends to perform the forwarding task inspired by backpressure routing concepts. This dynamic strategy allows BRPL to achieve a significant packet loss reduction as detriment of an increased end-to-end delay. An energy-efficient and mobility-aware routing protocol based on RPL was presented in [81]. The proposal, named EMA-RPL, considers low power networks composed by static nodes and MNs. To avoid the broken routes caused by the nodes' movement, the MNs are always defined as a leaf in the RPL tree. This feature prohibits MNs from performing the data message routing received from other nodes. MNs send and receive data messages through a static node, named the Associated Node (AN). Moreover, an AN is responsible for monitoring, predicting the movement, and defining a new AN to the MNs. The EMA-RPL functioning is divided into three different phases: mobility detection, reaction and prediction, and notification. The first phase is started when an MN moves away from its AN. The AN performs monitoring and detects the movement of MN nodes based on the RSSI value measured when data messages are received. If the measured RSSI is lower than a predefined threshold, the reaction and prediction phase is triggered. In this phase, the AN sends control messages searching for a new static node able to be used as the AN of the MN. When the new AN is found, the notification phase is started to inform the MN. Thus, the previous AN sends a control message to the MN informing about the change. After receiving this message, the MN should confirm the message reception and perform the AN change on its routing table. By avoiding MN sending several control messages monitoring its movements, EMA-RPL can reduce the energy and computational resource usage of mobile devices. However, in a dense and high mobility scenario, this approach can overload the static nodes and reduce the network performance. Furthermore, the proposal requires several changes in the structure of RPL control messages, which can make the interoperability of the EMA-RPL difficult with other approaches and the standard RPL implementation. Table 3 presents a summary of the above-mentioned routing solutions for mobile nodes' support.
## 3 Experiments
### Evaluation Metric  
In all the experiments we used the Mean Absolute Error (MAE) as the metric to quantitatively evaluate the performance that is defined as follows:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|t_{i}-y_{i}|, \tag{8}\]  
where \(N\) is the total number of images, \(t_{i}\) and \(y_{i}\) are the true and predicted numbers of cells in the \(i\)-th test image, respectively. We prefer MAE over MSE because MAE could be easily interpreted (the same unit as the original cell count, while MSE provides the squared error).
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 5 Discussion  
The PAOC technique could be regarded as an improved OC technique. The original OC technique only uses the forward offset correlator for discrimination. As the forward offset increases, the multipath mitigation performance of the OC technique improves, while its thermal noise performance deteriorates. To resolve this contradiction, the PAOC technique uses the prompt correlator to assist the offset correlator for discrimination. Specifically, a PAOC complementary filter is designed to combine the discrimination results of the prompt and offset correlators. Compared with the original OC technique, the PAOC technique has less thermal noise loss and reduces the risk of losing lock in the tracking loop. This suggests that the PAOC technique can use a larger forward offset to obtain better multipath performance in practice.  
The PAOC technique is more suitable for low-dynamic and stationary GNSS receivers. Experimental results in this paper show that the PAOC technique with a smoothing time of 0.2 s can effectively reduce thermal noise errors. Using a longer smoothing time can further reduce thermal noise errors, but the smoothing time is preferably no more than 1 s to maintain the dynamic performance. In practice, both the forward offset and smoothing time of the PAOC technique can be flexibly configured.  
The PAOC technique is easy to implement in existing GNSS receivers and has low computational complexity. Compared with the OC technique, the PAOC technique adds a complementary filter. The calculation rate of the complementary filter is typically between 50 and 1000 Hz, while the calculation rate of correlators is equal to the signal sampling rate, which is usually greater than 10 MHz for BOC signals. Therefore, the calculation amount of correlators is much larger than that of the complementary filter. Compared with the DBT method, the PAOC technique additionally uses two offset correlators. Considering that the DBT method employs eight correlators, using the PAOC technique in the DBT loop increases the computational burden by about 25%.  
As an internal-receiver multipath mitigation technique, the PAOC technique proposed in this paper does not use additional sensors. If auxiliary sensors, such as the inertial navigation systems, are available, the receiver status can be better detected so that appropriate parameters can be selected for the PAOC technique to optimize the multipath and noise performance. Moreover, the PAOC technique can be used together with pre-receiver and post-processing techniques to further improve the multipath performance of GNSS receivers.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 2 The Plane Rotation Model of UHS
### RCS Calculation Model of UHS
To verify the accuracy of the proposed method in this paper, we compared the RCS calculation results of the proposed method with those of the finite element method for eight different groups of parameters, as shown in Table 1.  
Table 1 shows that the maximum error of the calculation results between the proposed method in this paper and the FE-based method is 2.6%, which can meet the calculational accuracy of engineering.
## 2 Acoustic Communication System Model  
The communication system can be divided into analog communication system and digital communication system according to the signal processing technology. The acoustic communication model involved in this paper is a digital communication system, and the model is shown in Figure 1.  
Figure 1 is a digital communication system model, and the transmitter is composed of a source encoder, source encryption device, error-correcting encoder, and modulation transmitter. The function of the source is to realize the conversion of the outgoing message to the level signal. The signal output from the source is the baseband signal, and then the baseband signal enters the source encryption device and the error correction encoder with the appropriate conversion process to meet the needs of channel transmission characteristics. During this process, the modulator modulates the signal into the suitable frequency band and sends the modulated signal to the receiving end by the channel transmission. Then the receiving end demodulator demodulates the received signal to obtain the baseband signal, the baseband signal is error-corrected decoding to remove the signal redundancy, the source decrypt the operation and send it to the sink, and the sink finally transforms the level signal back to the original sending message.  
As the model shown in Figure 1, the communication channel is air, the source is from the operation instruction of the mobile user, the sink is the processor of the novel vehicle terminal, and the corresponding remote control operation is executed according to the demodulated instruction information.
## 2 Research Methodology
### The Delphi Survey Method  
The Delphi method was developed in the 1950s to obtain reliable consensus opinions from a group of experts through a series of questionnaires [55; 56; 57]. It has been applied in many health-related fields, including clinical medicine and public health research [58; 59].  
In the Delphi method, questionnaires are scored anonymously, and participants are encouraged to add new ideas, amend existing responses, or suggest the removal of redundant answers across several iterations until a consensus is reached [52; 60]. Typically, a carefully selected anonymous panel of experts undergoes two to three rounds of structured surveys, concluding when consensus is achieved [61; 62].  
The Delphi method is a commonly used research technique, widely regarded as a part of survey research [63; 64]. This paper chooses the Delphi method not only because it is extensively applied in health science research to determine priorities and reach consensus on important issues, addressing fundamental problems in healthcare [65], but also due to its features of anonymity, controlled feedback, flexible statistical analysis options, and the ability to gather participants from different geographical regions [66]. These characteristicsenable experts from diverse fields to conduct a comprehensive and multi-faceted evaluation of the research subject.  
The number of members in a Delphi panel is generally between 8 and 20 [65]. In the study by [PERSON], [PERSON], [PERSON], and [PERSON], the number of Delphi experts was 12 [67]. In the study by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], the number of Delphi experts was 15 [68]. To maximize the diversity of the sample, in this Delphi survey, twenty experts were invited to participate, including eight industrial designers with over 5 years of experience, two physicians with over 10 years of experience, four web designers with over 5 years of experience, and six interaction designers with over 5 years of experience. The survey was conducted anonymously using the Questionnaire Star (wenjuanxing: [[https://www.wjx.cn/accessed](https://www.wjx.cn/accessed)]([https://www.wjx.cn/accessed](https://www.wjx.cn/accessed)) on 15 April 2024) software over two rounds on 20 April 2024 and 23 April 2024, with consensus among the experts being reached by the end of the second round.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 4 Numerical Simulation and Analysis
### System Reliability Analysis with a Special Trigger Strategy Considered  
Load-sharing based reliability analysis has been presented in Section 4.1 and 4.2, both of which focus on the effects of the load-sharing mechanism on the circuit subsystem. In this section, we will further consider the trigger subsystem. Without loss of generality, we choose the linear rule with the parameter \(b=1\) as the basic load-sharing mechanism. On the one hand, as shown in Figure 5, the reliability of the linear rule with the parameter \(b=1\) falls sharply after 14 modules failed; on the other hand, in terms of the performance of the pulse output, each level can tolerate 5 failure modules at most. Therefore, we set the module failure threshold \(k\) as 5.  
Figure 6 presents the current trigger strategy used in the LTD system introduced in Section 2.1. 24 trigger units generate 240 pulse voltages and thus give the triggering command to 120 modules in 1 branch. Specifically, each trigger unit controls the discharge process of 5 modules among which 2 modules belong to a level while the others belong to the remaining level respectively.  
Figure 5: Reliability degradation process of different load-sharing rules.  
Figure 7 shows the comprehensive impact of the pre-specified working coefficients and the trigger unit reliability on the LTD system reliability. The horizontal axis represents the pre-specified working coefficients, and the vertical axis represents the LTD system reliability. Besides, different curves are corresponding to different triggering units. As can be seen from Figure 7, when the pre-specified working coefficient is lower than a certain value around 0.65, the system reliability keeps steady with the increase of the working coefficient and a rather high reliability can be obtained while the trigger unit reliability is more than \(1\times 10^{-3}\); but conversely, the system reliability rapidly declined and it is difficult even almost impossible to keep system reliability at a high acceptable level no matter how high the trigger unit reliability is.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 3 Design of a Vision-Based Deep Learning Loading-Arm Positioning System
### Description of the Positioning System
#### 3.1.2 Positioning Strategy  
The positioning strategy is as follows:  
* Coarse docking stage: The positioning system uses monocular camera A to collect images of the environment, and it uses the YOLOv8 model to detect the flange in the image and extract its characteristic information, as shown in Figure 8a. Combined with the monocular visual positioning algorithm, the spatial position of the target flange and the movement trajectory of the loading arm are calculated, and the attitude of the loading arm is dynamically adjusted using a kinematic model to quickly approach the target flange, as shown in Figure 8b;
* Fine docking stage: The positioning system combines the data from monocular camera B and ultrasonic sensors to further achieve high-precision alignment of the flange. Camera B captures a front view of the flange; extracts feature information through instance segmentation, using the YOLOv8 model; and calculates its 3D coordinates using a monocular visual positioning algorithm, as shown in Figure 8c. At the same time, the ultrasonic sensor provides real-time edge distance information to supplement the data in the blind spots of vision, ensuring that the loading arm can dock accurately under external interference conditions, as shown in Figure 8d.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 3 Methodology
### Model Development
#### 3.7.3 Reproductivity  
To ensure reproducibility, our study meticulously details all the parameters and configurations used across various models. In our study, we meticulously documented all parameters and configurations for each model to ensure reproducibility.  
**Random forest:** The model utilized 200 trees with a maximum depth of 20 and a minimum sample split of 5. This balance aims to optimize complexity against the risk of overfitting.  
**XGBoost:** The model was configured to achieve optimal performance with 300 estimators, a learning rate of 0.1, a max depth of 3, and a subsampling rate of 0.8. These settings were refined through an extensive GridSearchCV process involving 405 fits.  
**Extra trees regressor:** This was operated with 200 trees and a maximum depth of 10. This model demonstrated its efficacy by achieving an R\({}^{2}\) score of 0.93, reflecting its capability to manage high-dimensional and complex datasets.  
**Neural network**: The network was configured with varying layers and parameters across different scenarios. Notably, in one scenario involving advanced dropout and batch normalization settings, it reached an R\({}^{2}\) score of 0.88, showcasing its capacity to adapt and model complex interactions effectively.  
Comprehensive pseudo-code and algorithmic configurations are provided in [[https://github.com/bandpey65/Crime-Analysis-git](https://github.com/bandpey65/Crime-Analysis-git)]([https://github.com/bandpey65/Crime-Analysis-git](https://github.com/bandpey65/Crime-Analysis-git)), accessed on 2 April 2025, allowing other researchers to replicate or build upon our findings effectively.
## 3 Experiments
### Experiments on Public Datasets
#### 3.2.3 Ablation Studies  
The innovation of this work lies in proposing a knowledge distillation algorithm for global and local relationships. In order to verify the effectiveness of the proposed method, detailed ablation experiments were conducted on three public datasets. The knowledge distillation algorithms using only global and local relationships are denoted as Global and Local, respectively, and their fusion is denoted as Global-Local. The classification accuracies of these methods on 5-way-1-shot and 5-way-5-shot tasks are shown in Table 1.  
The results in Table 1 indicate that for both 5-way 1-shot and 5-way 5-shot tasks on all datasets, the classification accuracy of Global-Local is consistently higher than that of Global and Local. The experiments demonstrate that global and local relationships are complementary, and their fusion can extract richer image features. Therefore, the knowledge distillation algorithm based on global and local relationships can further improve the performance of knowledge distillation.
# A Novel Fiber Optic Sensor for Microparticle Velocity Measurement Using Multicore Fiber
## 5 Conclusions  
We used an MCF to design a low-cost and compact probe for single microparticle velocity measurement in 2D space. We translated the MCF into spatial filters peaking up spatial frequency components of the moving particle's shadow image. Velocity and direction of the single particle were successfully obtained. When the particle movement speed was located between 12 mm/s and 24 mm/s, the measurement error was less than 2%. The total cost of the senor is 3000-5000 US dollars. In future work, the sensing system could be further simplified by reducing the number of devices, such as PD, EDFA and splitter.  
Writing--original draft preparation, X.M.; writing--participated in the experiment and collected data such as photos, Z.S.; writing--proposed idea and analyzed the experimental data, H.L. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the International S&T Cooperation Program of China (2011 DFA11780), the National Natural Science Foundation of China (51567011, 11804133), and Jiangxi Provincial Natural Science Foundation (20181 BAB206034).  
The authors declare no conflict of interest.
## 3 Aim, Materials, and Methods
### Integrated Intelligent Perception Mode of \"Sky Air and Ground\"  
Integrated sensing mode is an advanced technology independently developed by China for the monitoring and warning of natural disasters. This technology combines positioning and communication technologies, such as communication satellites, Beidou satellite communication, and remote-sensing satellites, to achieve the network interconnection of satellite communication, UAV sensing, and ground command systems. By applying this technology, the disaster site can be comprehensively, deeply, and real-time sensed and monitored. Additionally, the \"Sky-ground\" integrated sensing mode integrates multiple services including satellite portable stations, remote-sensing monitoring, and Beidou navigation communication to enhance the efficiency and accuracy of disaster monitoring. Through this mode, real-time information about the disaster site can be obtained including topography data analysis along with rescue progress updates enabling the visual monitoring of on-site conditions. This mode not only provides robust support for rural natural disaster analysis but also offers theoretical insights into important technical challenges like intelligent perception (Figure 2).  
Figure 1: Basic architecture of intelligent emergency management mode for rural natural disasters. Source: Own processing according to the literature.
###### Abstract
To characterize the damage degree in rocks under cyclic loading, researchers have tried to employ different physical parameters to quantitatively describe the damage degree in rocks [14; 22; 25; 26; 27], such as elastic modulus, residual strain, dissipation energy, and acoustic emission cumulative counts, and have established the relevant damage constitutive models. However, in these studies, rocks are assumed to be isotropic and the anisotropic characteristics of rocks are not taken into account. In contrast, there is relatively little research on the mechanical behavior of rocks considering their anisotropic characteristics under cyclic loading. [PERSON] et al. [28] conducted conventional triaxial cyclic-loading tests on layered marble and studied the effects of different bedding angles on the deformation, crack propagation and energy evolution characteristics of marble. Employing the equal plastic strain cyclic-loading and unloading tests with acoustic-force joint measurement, [PERSON] et al. [29] analyzed the influence of foliation angles on the characteristic strength values, shear strength parameters, and longitudinal wave velocity to explore the anisotropic characteristics of biotite quartz schist. [PERSON] et al. [30] experimentally studied the anisotropic mechanical behavior of columnar-jointed rock masses under conventional triaxial cyclic loading by analyzing the influence of joint inclination on rock deformation, characteristic strength values, and failure modes. They found that the anisotropic mechanical properties of rock masses were caused by the combined action of structural anisotropy and stress anisotropy. However, to date, the fracture mechanism of transversely isotropic rocks under cyclic loading remains poorly understood. In this study, we carried out monotonic-loading and cyclic-loading tests on a typical transversely isotropic rock, slate, to investigate its mechanical properties, and monitored the internal damage process of the rock with the assistance of an AE system, and finally analyzed the microscopic fracture mechanism by obtaining the microscopic morphology of the fracture surfaces of the rock through the SEM technique. The paper is structured as follows. First, the materials, equipment, and scheme of the test are introduced, followed by an analysis of the mechanical parameters. Subsequently, the evolutions of the secant modulus and energy density of specimens during cyclic loading are investigated, and the damage variable of the rock is defined and calculated based on the dissipated energy. Afterwards, the acoustic emission characteristics under monotonic loading as well as cyclic loading are analyzed, and the characteristics of acoustic emission counts and cumulative counts are compared under different loading conditions and different loading-foliation angles. Finally, the fracture modes are analyzed with respect to both macroscopic and microscopic aspects to reveal the fracture mechanism of transversely isotropic rocks.
# Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers
## 3 Distribution of the SINR
The set \(\Omega_{m,l}\) is defined as  
\[\Omega_{m,l}{=}\Bigg{\{}\mathbf{i}=[i_{1},i_{2}]^{T}\in\mathbb{Z}^{2};\sum_{j =1}^{2}i_{j}{=}l-1,i_{m}=0,i_{j}\geq 0\ \forall j\Bigg{\}}. \tag{19}\]  
Since \(\bar{k}_{k}\sim\mathcal{CN}(0,1)\), \(|\bar{k}_{k}|^{2}\) is an exponential RV with unit mean. Then, the right-hand side of Equation (11) is the sum of \(R\) independent exponential RVs with different means, which follows Generalized chi-squared distribution [24]. Therefore, the PDF of \(\gamma\) is given by  
\[f_{\gamma}(x)=A\sum_{m=1}^{2}\sum_{l=1}^{t_{m}}\frac{B_{m,l,k,\delta}}{(t_{m}- l)!}\chi^{\prime}{}^{m-1}e^{-\frac{x}{2m}}. \tag{20}\]  
In the following section, based on the derived SINR distribution, we will discuss the performance of the coding massive multiuser MIMO system.