esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Analysis
### Correlation Method  
The incoming data sample can be written as:  
\[\rm{s_{l}}=(Ad_{l}c_{l}+n_{l})e^{[(\frac{2\pi d_{l}}{f_{s}})]+\varphi]} \tag{1}\]  
where A is the signal amplitude, \(\rm{d_{l}}\in(-1,\,1)\) the modulation data, \(\rm{c_{l}}\in(-1,\,1)\) PN-code, \(\rm{f_{d}}\) residual frequency and \(\rm{f_{s}}\) sampling frequency, \(\varphi\) random carrier phase, \(\rm{n_{l}}=n_{l,c}+j\rm{n_{l,s}}\) additive Gaussian white noise (AWGN) with zero mean and variance \(\rm{\sigma^{2}}\).  
The incoming samples are parsed into blocks of size \(\rm{2N}\times 1\). Two channels of incoming data samples in terms of vectors are \(\rm{s_{0}}=[s_{\tau+mN+1},s_{\tau+mN+2},\ldots,s_{\tau+mN+2N}]^{T}\), \(\rm{s_{l}}=[s_{\tau+mN+2N+1},s_{\tau+mN+2N+2},\ldots,s_{\tau+mN+4N}]^{T}\). Partitioning the samples into blocks with FN, folding the blocks F times to produce new blocks with length N and  
When the signal segment is indeed contained within the extended segment, the coherent term can be shown to be a Gaussian variable with a mean of N and variance of \(\mathrm{(F-1)\times NA^{2}}\), that is:  
\[\sum_{j=1}^{F}\sum_{l=1}^{2N}\mathrm{Ad}_{lCj}e^{[(\frac{2\pi l_{d}}{t_{s}})]+ \varphi]}c_{jN+1+\delta}\ \sim\psi\left(N,(F-1)NA^{2}\right) \tag{4}\]  
When the signal segment is not contained within the extended segment, the distribution is as follows:  
\[\sum_{j=1}^{F}\sum_{l=1}^{2N}\mathrm{Ad}_{lCj}e^{[(\frac{2\pi l_{d}}{t_{s}})]+ \varphi]}c_{jN+1+\delta}\ \sim\psi\left(0,FNA^{2}\right) \tag{5}\]  
The probability distribution is obtained by the virtue of the central limit theorem under assumptions for large N.  
The noise is treated as AWGN with zero mean and variance \(\sigma^{2}\), so the summation of all the multiplication between noise and PN-code, that is, the AWGN term in correlation result, follows:  
\[\sum_{j=1}^{F}\sum_{l=1}^{2N}n_{2N+l+r}e^{[(\frac{2\pi l_{d}}{t_{s}})]+\varphi ]}c_{jN+1+\delta}\ \sim\psi\left(0,F\mathrm{N}\sigma^{2}\right) \tag{6}\]  
We can see from the formulas that the number of folds mainly effects the intensity of noise. It is the reason for the SNR deterioration of folding methods. Given the distribution of coherent integration and noise, we can subsequently infer the theoretical detection probability below.
## 3 Proposed Methodology
### Computational Complexity Analysis  
This section assesses the computational complexity of each proposed algorithm. The lightweight encryption algorithm is chosen for user authentication due to its simplicity, speed, and security (with a computational complexity of '\(O(n,q)\)', where '\(q\)' represents the point \((x,y)\)). The mud ring algorithm is utilized for switch selection and state update, offering an optimal solution with a computational complexity of '\(O(n^{2})\)'. The BSHNN handles packet classification, and the Enhanced EAWNB deals with suspicious packet classification. The derivations of computational complexity are expressed in Equations (8)-(10).  
\[T(n)=2T(n/2)+n^{2} \tag{8}\]  
\[=n^{2}+2\big{(}2T(n/2)+n^{2}/4\big{)} \tag{9}\]  
\[=O\big{(}n^{2}\big{)} \tag{10}\]  
where '\(T(n)\)' represents the total work. The BSHNN for packet classification requires '\(O(n^{2})\)' computational complexity, yielding a minimum complexity per round of '\(O\big{(}n^{(log_{2}n)}\big{)}\)'. EAWNB requires '\(O(S)\)', where '\(S\)' is a nearly constant scale variable for all iterations. The aggregate processing duration of suggested BSHNN is significantly reduced, ensuring efficient and accurate results for applications, particularly due to sparse vectors in the total capacity.
## 2 Vehicle Departure Characteristic Analysis  
The exit of a parking lot plays an important role in connecting the internal roads of the parking lot with the external roads on the ground. As shown in Figure 1, the traffic flow on the external road tends to be saturated. In this situation, the departing vehicles in the exit lane of the parking lot are affected by the ground-level vehicles and associated intersection signal. Since it is difficult to merge into the external road, a queue is generated in the exit lane of the parking lot. As a result, the external road becomes a bottleneck. As time goes on, the queue traces back to the entrance and internal roads of the parking lot, which affects parking and regional traffic. As a node for the transition between underground static traffic and ground-level dynamic traffic, the entrances, exits, and management equipment of parking lots can limit or divert traffic flow. Therefore, we make full use of the entrance and exit of the parking lot to reduce the impact of the departure from super-large parking lots on the surrounding roads.  
Dynamic traffic on the ground and static traffic underground interacts with each other. The ground-level roads around parking lots can be influenced by the departure vehicles. During peak hours, vehicles often wait in the entrance and exit lane of the parking lot, which also affects the dynamic traffic on the ground-level roads. It can be seen that the separate management and control of surface ground-level traffic and underground static traffic cannot meet traffic control requirements. Thus, it is necessary to coordinate these control approaches to ensure driving efficiency.  
The problem related to traffic coordination control is commonly solved using ramp control [19]. The method takes the on-ramp of an expressway as the research object. Under the normal and continuous operation of the vehicles on main roads, the real-time traffic flow of the upstream and downstream of the ramp is coordinated, and the optimal off-ramp mediation rates for different upstream and downstream flows are calculated. In this way, the vehicles on the auxiliary road can enter the main road one by one, which achieves good control results. The scenario in which parking lot vehicles enter the ground-level road via the exit is highly similar to that of vehicles on the auxiliary road entering the main road via the ramp. This study introduces the coordinated control concept of ramp control to formulate intelligent control strategies for vehicle departure to avoid traffic congestion and improve driving efficiency.
# A Case Study of Floating Offshore Super-Long Steel Pipeline Combing with Field Monitoring
## 3 Monitoring Instruments and Monitoring Programs
### Monitoring Programs  
#### 3.2.1 Arranging the Number of Tugboats  
The water flow force on the steel pipeline was estimated to determine whether the number of floating tugboats was sufficient. The calculation method of water flow force was referred to in the literature of [PERSON] and [PERSON] (2017), and the calculation formula of the maximum water flow force of the pipeline Section 1 is as follows:  
\[F_{D}=1/2C_{D}\rho u^{2}S \tag{1}\]  
where: \(F_{D}\) is water flow force, N; \(C_{D}\) is the drag coefficient, which is 1.2; \(\rho\) is the density of water, which is 1000 kg/m\({}^{3}\) according to the test data. \(u\) is the water flow velocity, and 0.35 m/s is taken according to the test data. \(S\) is the area facing the flow (the submergence depth is 0.4 \(D\), \(D\) is the diameter, m), m\({}^{2}\).  
It is generally believed that 1 KW = 1.36 horsepower, and the drag force per 1 KW is 199.92 N. Therefore, one 700-horsepower tugboat and three 120-horsepower tugboats were equipped in the floating transport process of pipeline Section 1, and the maximum towing force was 155,812.2 n > 33,633.6 n, which met the requirements of floating transport towing force.  
During site construction, the tugboats should be arranged uniformly with the pontoons as far as possible, and rotated from the downstream to the transverse direction with a larger radius of curvature. Under the condition of overcoming buoyancy resistance, the tugboats kept the same power pushing on the buoyancy tank to avoid excessive deformation and 3 PE destruction caused by uneven thrust. Each tugboat was equipped with a walkie-talkie and 1-2 tugboats were reserved to respond to abnormal situations timely.
## 2 Project Overview
### Model Establishment  
Numerical simulation was conducted using ABAQUS2023 finite element software, taking into account factors such as grouting pressure, equivalent layer thickness, tunnel spacing, and soil chamber pressure. To eliminate the influence of boundary effects, the calculation model has a length of 150 m in the x direction, 100 m in the y direction, and 80 m in the z direction. The lower surface and surrounding boundaries of the soil model adopt vertical constraints, while the upper surface adopts free boundaries. The surface of the bridge pier has free boundaries. Considering that the pile foundation of the high-speed railway bridge is an end-bearing pile with no contact surface between the pile side and the surrounding soil, the bottom of the pile end and the bearing layer soil adopt a tangential rough normal \"hard\" contact model. A small-radius stacked tunnel underpass high-speed rail model was constructed to simulate the shield tunneling construction process, as shown in Figure 3. The basic model material parameters are shown in Table 2.
# A Novel Chipless Hybrid RFID Sensor for Metal Crack Detection
###### Abstract  
RFID technology has been widely researched and used for structural health applications because of its compact, wireless, and scalable nature. This technology is divided into chipped and chipless sensors. Chipped sensors are costly due to their chipped tags, have narrowband operations, and contribute to shortcomings in detection capability. Chipless tags provide real-time monitoring of cracks in harsh environments like high-temperature areas and high electromagnetic interference areas. This paper presents a design of a novel chipless hybrid circular-hexagon sensor that uses the frequency signature-based method for metal crack detection and characterization using wideband frequency. This sensor is small in size (16 mm \(\times\) 16 mm \(\times\) 1.4 mm) and easily mountable in hard-to-reach areas. It is a low-cost, passive chipless sensor that can wirelessly monitor the cracks in metallic structures. The radar cross-section of the chipless tag shows a shift in the resonant frequency of the tag under crack and no crack conditions. Key contributions of this work are that through simulations and experimental investigation, the tag is shown to be able to detect mm-scale cracks, validating the concept and correlating the presence and size of the cracks based on the shift in resonant frequencies in which a pair of Vivaldi antennas are used as a transmitter and receiver to connect to the VNA. The designed small sensor tag is tested in a benchtop setup with no prior calibration, imitating the real-time environment conditions for crack detection.  
radio frequency identification; crack detection; passive sensor; chipless; structural health monitoring; antenna tag 2020 IEEEexample:BSTcontrol
## 3 Results and Discussion  
### Results of Linear and Nonlinear Regression Based Models  
Different linear and nonlinear regression-based models were also applied in the current study. These equations which were developed by least square technique to drive regression coefficients using the training data set are listed in Table 3. XLSTAT software was used for the development of these equations. The performance evaluation parameter values are listed in Table 3 for all developed equations. Four statistical performance assessment statistics, CC, RMSE, MAE, and SI, were used to compare the performance of the MLR and MNLR based models (Table 3). According to the Table 3, the Equation (14) based model with higher value of CC (0.9492) and lower value of SI, MAE, and RMSE (0.1218, 5.4797%, and 6.9755%, respectively) has better results in comparison to other MLR and MNLR based models. Figure 3 provides agreement plots among observed and predicted relative energy dissipation (%) by Equation (14) (logarithmic regression) based models for training and testing stages, respectively. As observed from the graph that Equation (14) based model considerably overestimates the low (\(<\)40) and high (\(>\)90) DE/E\({}_{\max}\) values. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Range**} & \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(H_{\text{dam}}\)/\(d_{c}\)} & \multirow{2}{*}{\(\alpha\)} & \multirow{2}{*}{\(\Delta\)E/E\({}_{\text{max}}\) (\%)} & \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(H_{\text{dam}}\)/\(d_{c}\)} & \multirow{2}{*}{\(\alpha\)} & \multirow{2}{*}{\(\Delta\)E/E\({}_{\text{max}}\) (\%)} \\ \cline{2-2} \cline{6-10}  & & & & & & & & **Testing Data Set** & \\ \hline
**Minimum** & 4.0 & 1.945 & 10.0 & 0.1239 & 4.0 & 1.945 & 10.0 & 0.0308 \\ \hline
**Maximum** & 75.0 & 37.26 & 50.0 & 92.63 & 75.0 & 37.26 & 50.0 & 92.63 \\ \hline
**Mean** & 27.0 & 11.57 & 28.49 & 57.68 & 27.0 & 11.61 & 29.04 & 57.34 \\ \hline
**Standard** & \multirow{2}{*}{15.3217} & \multirow{2}{*}{7.043} & \multirow{2}{*}{14.078} & \multirow{2}{*}{22.17} & \multirow{2}{*}{15.14} & \multirow{2}{*}{7.063} & \multirow{2}{*}{14.10} & \multirow{2}{*}{22.28} \\
**Deviation** & & & & & & & & \\ \hline
**Kurtosis** & 0.2455 & 2.081 & \(-\)1.269 & \(-\)0.3790 & 0.3720 & 2.059 & \(-\)1.291 & \(-\)0.3704 \\ \hline
**Skewness** & 0.7987 & 1.344 & 0.1373 & \(-\)0.6655 & 0.8477 & 1.348 & 0.1052 & \(-\)0.6635 \\ \hline
**Confidence** & \multirow{2}{*}{0.5964} & \multirow{2}{*}{0.2741} & \multirow{2}{*}{0.5480} & \multirow{2}{*}{0.8628} & \multirow{2}{*}{0.8604} & \multirow{2}{*}{0.4014} & \multirow{2}{*}{0.8015} & \multirow{2}{*}{1.266} \\
**Level (95.0\%)** & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Used data and their characteristics.
## 1 Introduction
[17] used adaptive clustering and segmented point clustering, combined with lateral distance and point-to-line distance methods, to optimize the feature points, and fitted the crop row centerline using linear regression based on the Huber loss function. Addressing the extraction of vegetable seedling rows in thefield, [PERSON] et al. [18] used the Random Sample Consensus (RANSAC) algorithm and the least squares method. Among these methods, clustering and least squares are effective techniques for row line fitting, providing important references for improving the accuracy and robustness of crop row extraction and fitting. Notably, research on sugarcane row detection is still in its infancy. Compared to conventional row crops such as rice and corn, sugarcane exhibits greater variability in row and plant spacing during cultivation and often displays an irregular distribution pattern, which poses significant challenges for accurate row detection. Furthermore, sugarcane leaves and stalks frequently cross and overlap extensively during growth, creating a complex occlusion structure [19] that further complicates image feature extraction. In some studies that improve upon YOLO-based algorithms, enhancements in detection accuracy often come at the cost of increased computational overhead, thereby raising the hardware requirements for agricultural equipment. To address this challenge, this paper proposes SugarRow-YOLO, a sugarcane detection model based on YOLOv11n, which effectively improves detection performance without significantly increasing the number of parameters or computational complexity. This optimization not only aligns with the deployment needs of modern agricultural equipment but also enhances the accuracy of target detection. Compared with YOLOv11n, SugarRow-YOLO mainly makes the following improvements: 1. WTConv is used to replace the deep convolutional layers in the YOLOv11n backbone network, which not only expands the model's receptive field but also reduces over-parameterization. 2. The original multi-head attention in the C2 PSA module was replaced with the Inverted Residual Mobile Block (iRMB) inverted residual block attention mechanism to improve the model's ability to capture the crop's location, geometry, and spatial relationships. 3. By using the UIOU loss function, the model effectively addresses the issues of missed detections and false positives commonly encountered with dense and overlapping objects. With the above enhancements, SugarRow-YOLO is capable of accurately locating sugarcane plants. Subsequently, the spatial distribution of the detected sugarcane is processed using the DBSCAN clustering algorithm and spline curve fitting to achieve precise identification of sugarcane rows.
## 1 Introduction
The equidistance method selects vertices by setting a fixed side length between them. The ray-barrier method achieves vertex selection by setting the angle threshold between adjacent sides, which is not suitable for polygons with uniform distribution of vertex angles. The vertical distance tolerance method achieves simplification by controlling the vertical distance between simplified and original envelopes, which is a local algorithm and not suitable for envelopes with little different side lengths and vertex angles. The Douglas-Peucker algorithm is an improved version of the vertical distance tolerance method, and is a global algorithm rather than a local algorithm. The above methods have their respective advantages, weaknesses, and applicability. They are all designed to simplify geographic information data, such as administrative boundaries and contour lines [28; 30; 31]. The feasibility of simplifying polygons formed by buffer analysis using the above methods has not been evaluated. The objective of this study was to develop a simplification model of the distribution envelope formed by buffer analysis. The model obtained in this paper can effectively simplify the distribution envelope vertices, reducing the calculation pressure associated with drift prediction and improving the efficiency of green tide disaster emergency decision-making. The paper is organized as follows: Section 2 describes the study area, as well as the data and their processing. Section 3 proposes the method for building a simplification model and model assessment. Section 4 presents the simplification model, precision evaluation and applications. Section 5 gives the simplified envelopes analysis, methods comparison analysis, and drift prediction analysis. Section 6 includes the conclusions.
# Optimizing the Quality of Service of Mobile Broadband Networks for a Dense Urban Environment
## 4 Results and Discussions  
An analysis of the existing performance parameters based on statistical functions and swarm intelligence was executed. In order to ensure that the network is providing a high-quality user experience, the network KPIs being considered are analyzed by the use of MATLAB to check for their consistency with national and international specifications. All the measurements from the three areas were tabulated and detailed in comma-separated values (csv). During the analysis, a fraction of the data collected was missing. In the bid to execute efficient analysis, it is common for researchers to attempt replacing missing data using the piecewise cubic Hermite interpolating polynomial (PCHIP) algorithm and other error analysis techniques. In this analysis, care is taken to ensure that the data is used as obtained to avoid wide disparity between the original results and possible alterations made during the analytical process of introducing approximated block values into the entire process.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 2 Related Work
#### 3.2 Data Forwarding Mode  
From Section 3.1, the predicted location is obtained. As shown in Figure 1, Node A is the current node and Node D is the destination node. The distance from Node B to line AD is as follows:  
\[d_{B-AD}=\frac{|(D_{2}-x_{2})B_{1}+(x_{1}-D_{1})B_{2}+(d_{1}-x_{1})(x_{2}-x_{1 })|}{d_{0}} \tag{19}\]The formula of the relative deflection angle is:  
\[\alpha=\arcsin\frac{d_{B-AD}}{d},\;\alpha\in[0,\frac{\pi}{4}] \tag{20}\]  
Calculating the metric value of the candidate node based on the predicted location information. Then, the candidate node with the smallest metric will be selected as the next-hop data forwarding node. The metric value calculation formula is as follows.  
\[\varsigma_{j}=\tau_{a}\frac{d}{d_{0}}+\tau_{b}\frac{4\alpha}{\pi}+\tau_{c}(1- \frac{n}{N}) \tag{21}\]  
where \(\tau_{a}\), \(\tau_{b}\) and \(\tau_{c}\) are distance factor, angle factor and neighbor node factor. The \(d\) is the Euclidean distance between the candidate node and the destination node, \(d_{0}\) is the Euclidean distance between the current node and the destination node, and \(\alpha\) is the deflection angle of the connecting line between the alternative node and the destination node, \(n\) is the number of candidate nodes, and \(N\) is the total number of network nodes. Select a node that is closer to the destination node to reduce the number of route hops. The neighbor nodes with the smallest deflection angle are selected to reduce the appearance of boundary effect. Select a node with an alternate node to reduce the probability of encountering routing holes. Different decision factors have different effects on the routing effect.  
According to the NT table, the metric \(\varsigma_{j}\) of each node is calculated, and the node with the smallest metric be selected as the next-hop data forwarding node. The data forwarding mode can effectively reduce the packet loss rate of the boundary, avoid excessive consumption of edge nodes, and reduce the probability of encountering routing holes.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 8 Future Research Directions
### Energy Harvesting Techniques  
There is a need to investigate novel material for UAV's batteries and new energy harvesting solutions to get full potential of UAVs in extended time missions [120]. Battery charging time and weight are also some future concerns. Thus, there is a need to find lightweight batteries that can significantly enhance UAVs flight time and support longer distances. Efficient techniques to control battery power consumption for IoT terminals in UAVs should be addressed in future studies.
# Mass Flow Scaling of Gas-Assisted Coaxial Atomizers
## 4 Results and Discussion
### Significance of Liquid Mass Flow Scaling Rules  
Figure 3 depicts the effect of an increase in the liquid mass flow on the resulting droplet size as a radial distribution (left) and high-speed camera images (right) while operating nozzle N1.  
As expected, for an increment in the liquid mass flow \(\dot{M}_{liq}\), the Sauter mean diameter is increased significantly over the entire measured radial distribution. In particular, an increasing droplet size was detected near the centerline of the spray. The high-speed camera images shown in Figure 3 (right) underpin the results of the quantitative measurement technique, as the centerline of the spray reveals a huge quantity of large droplets at high liquid mass flows, even though a high gas velocity of \(v_{gas}\approx 178\) m\(\cdot\)s\({}^{-1}\) is applied. The remaining large liquid droplets originate from the incomplete primary breakup of the liquid jet near the nozzle orifice, due to the insufficiently high aerodynamic forces of the gas phase [17]. To reduce the droplet size for high liquid mass flow, two different approaches can be selected: (i) increasing the gas velocity up to sonic speed for a constant nozzle geometry; or (ii) adaptation of the nozzle geometry without significant further increase in gas velocity. The first approach is limited by sonic speed. Additionally, relevant process conditions, e.g., reaction zone position, residence time, and flow field should not be affected by nozzle scaling to guarantee reliable process operation. Beyond that, the effect of gas velocity on resulting droplet size levels off with increasing gas velocity [9; 45].  
Those drawbacks can be avoided over a large range of liquid mass flow rates by adapting the nozzle geometry, as described in Section 2.