esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of the Attacks on CC  
The CC is defenseless in the face of several attacks that provide serious security dangers. The threat of an attack is determined by the target of the adversary's attack. The CC service availability can be disrupted partially or entirely for a short or long period. The attacks of CC can be classified into four categories based on the attack's target, as shown in Figure 1. The categories are suffocative attacks, protocol attacks [41], and two new categories proposed in this survey paper: EDoS and Permanent Economic Attacks. Table 4 shows the relationship of these attacks and two questions: _Q1--What are they doing?_ and _Q2--How does the victim become paralyzed?_  
It attempts to destroy or modify hardware or component. For example, one-shot attacks exploit applications' firmware upgrade mechanisms to inflict harm remotely via various means, such as phishing.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline
**Attacks on CC** & **What Are They Doing?** & **How Does the victim Become Paralyzed?** \\ \hline Suffocative Attacks & They utilize tremendous traffic to saturate the target’s infrastructure-level bandwidth. & The attack generates huge traffic to deny access to the resource (e.g., Web service). The attack volume is generally calculated in packets/sec or bit/sec. \\  & Example: TCP flood, user datagram protocol (UDP) Flood/Storm, DNS amplification, network time protocol (NTT) amplification, and Internet Control Message Protocol (ICMP) flood or Ping flood [42]. \\ \hline Protocol Attacks & Attacks that make a target inaccessible by exploiting the vulnerabilities of the infrastructure-level bandwidth of the victim (in the network layer and transport layer protocol stack). \\ \hline Economic Denial (EDoS) & Attacks that exploit CC’s resource utilization, aiming to impact the CSP or CCC’s financial bottom line (e.g., to increase the bill), but not its physical network or server resources. It forces CSP to keep increasing its resource utilization to fulfill the SLA of its clients to the point it is economically non-viable or unable to sustain further demand for services. \\ \hline Permanent Economic Attacks & It can also be found locally on CC devices or other devices, vulnerable or misconfigured firmware, and binaries. It targets the firmware, deletes all files, and reconfigures network settings. If a system has not been sufficiently patched and upgraded, the probability of failure increases. Firmware is deliberately changed to corrupt the machine as a whole. \\ \hline \end{tabular}
\end{table}
Table 4: Attacks on CC based on two questions.  
Figure 1: Proposed taxonomy of attacks, DDoS attacks, and DDoS attack detection approaches on CC.
## 6 Conclusions  
In this paper, we designed a small dual-band, dual-polarized, implantable antenna for an implantable medical monitoring system. We analyzed the operating principle of the antenna in detail and obtained a good set of antenna parameters. To verify the actual radiation capability of the antenna, a prototype was made and measured in biogel and minced pork. The results of the measurements reveal that the \(-10\) dB bandwidth covers the 1.4 GHz WMTS band as well as the 2.45 GHz ISM band very well, and the radiation patterns have good symmetry. As its size is only 47.4 mm\({}^{3}\) and it has good radiation characteristics, this antenna is a fine product and candidate for use in implantable medical equipment.  
Methodology, X.X. and Z.S.; Software, X.X. and Y.W.; Formal analysis, X.X. and Y.W.; Resources, Z.S.; Writing--original draft, X.X.; Writing--review & editing, Z.S.; Supervision, Z.S.; Project administration, Z.S.; Funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China, grant number 62261052; in part by the Xinjiang Uygur Autonomous Region Natural Science Foundation General Project, grant number 2022D01C424; in part by the Tianchi Talent Project in Xinjiang Uygur Autonomous Region, grant number 510523005137; and in part by the Xinjiang University Youth Scholar Funding Program, grant number 620321063.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are unavailable due to privacy or ethical restrictions.  
**Acknowledgments:** Thank you for the experimental equipment provided by the Institute of Micro Nano and Radio Frequency at Hebei University of Technology.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Transmitter** & \\ \hline Operating frequency & 1.4 GHz & 2.45 GHz \\ Tx power \(P_{t}\) (dBm) & 4.24 & 5.02 \\ Tx antenna gain \(G_{t}\) (dBi) & \(-18.8\) & \(-19.1\) \\  & **Receiver** & \\ Rx antenna Gain \(G_{r}\) (dBi) & 2.15 & 2.15 \\ Polarization & CP & CP \\ Ambient temperature \(T_{0}\) (K) & 293 & 293 \\ Receiver noise figure NF (dB) & 3.5 & 3.5 \\ Boltzmann constant \(k\) & \(1.38\times 10^{-23}\) & \(1.38\times 10^{-23}\) \\ Noise power density (dB/Hz) & \(-199.95\) & \(-199.95\) \\  & **Signal Quality** & \\ Bite rate \(B_{r}\) (Mb/s) & 1 & 1 \\ Bite error rate & \(1.0\times 10^{-5}\) & \(1.0\times 10^{-5}\) \\ \(E_{b}/N_{0}\) (ideal BPSK) (dB) & 9.6 & 9.6 \\ Coding gain (dB) & 0 & 0 \\ Fixing deterioration \(\mathrm{G_{d}}\) (dB) & 2.5 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of the link budget.
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 3 Methodology  
Based on the above analysis, aiming at the problems of the present mainstream detection methods, such as the struggle to capture effective lane features in complex scenarios with a weak appearance, complicated model predictions and high computation costs, and being limited to fixed structures, this paper proposes a lane detection method of parallel multi-scale feature aggregation based on key points, FPLane. An overview of the method is shown in Figure 2. It takes the lane images obtained by vehicle sensors as the input, and outputs multiple groups of dense key points that represent the final  
Figure 1: Illustration of representative networks that rely on the high-to-low and low-to-high series framework. (**a**) U-shaped mirror symmetric framework. Hourglass [26] and its variants [25]. (**b**) Asymmetrical framework with heavy high-to-low and light low-to-high. The high-to-low process is based on a large backbone, and the low-to-high process is light [24]. (**c**) High-to-low and low-to-high framework with intermediate supervision [24] or fusion strategy [28; 29].  
prediction of lane geometry. FPLane mainly consists of three parts: MFANet, global key point detection and grouping key points with association embedding. The functions of each part are as follows.
# Computer-Aided Surgical Simulation for Correcting Complex Limb Deformities in Children
## Materials and Method
### From CT to 3D Digital Model  
A conversion procedure was applied to transform the CT scan into a 3D model [9]. CT scans in a digital imaging and communications in medicine (DICOM) format were imported to the imaging software InVesalius v3.1 (CTI: Center for Information Technology \"[PERSON]\", Campinas SAO, Brazil, 2017) [10] and rendered to generate virtual reconstructions of the selected structures. Care was taken in setting the right density range, in order to highlight the specific region and create the 3D model, eliminating unnecessary anatomical parts. The use of Invesalius, generated 3D surfaces in standard triangulation language (STL). The meshes generated were corrected using MeshLab v2016.12 (ISTI-CNR \"[PERSON]\", Pisa, Italy, 2016) [11], an open-source software that enables cleaning, remodeling, and remeshing of the structure. The use of MeshLab is essential to make the 3D surface hollow, and consequently, lighter. The structure obtained with MeshLab have holes, cavities, and, in general, imperfections. In order to remove them, another free 3D modeling software called Meshmixer v2017 (Autodesk, San Rafael, CA, USA, 2017) [12] was used. In the end, a Boolean operation was performed with Blender v2.8, (Blender Foundation, Amsterdam, The Netherland, 2017) [13] and the actual simulation was carried out in a parametric environment using Creo v6, (PTC--Parametric Technology Corporation, Needham, MA, USA, 2019) [14]. This first part of the reconstruction process was essential to achieve a good geometrical quality and a small amount of faces, in order to allow it to be opened in a parametric CAD without any decrease in performance. The process is summarized in Figure 1.
## 1 Introduction
However, no one has considered the problem of the coordinated control of trajectory tracking and vehicle stability under influencing factors such as parameter perturbation, uncertainty interference, time delay, and actuator saturation constraints in the vehicle dynamics model. Indeed, the traditional first-order sliding mode (FOSM) control methods are able to improve the system performance issues of parameter perturbation, uncertainty interference and time delay. However, undesirable chattering always exists due to the fact that a sliding mode controller itself contains one or more discontinuous sign functions [11].The chattering phenomenon is harmful to control systems. As described in [12], it may induce fatigue in mechanical parts, and can destroy the system in a short time. In fact, as an alternative method, the second-order sliding mode (SOSM) can be used to reduce the chattering problem [13; 14; 15]. The spiral sliding mode (SSM) algorithm is a kind of SOSM algorithm, which has strong robustness to uncertain factors such as unmodeled dynamics, parameter perturbation, and external disturbance, etc. In this paper, the adaptive spiral sliding mode (ASSM) is proposed for the trajectory tracking control of intelligent electric vehicles. This paper is organized as follows: In Section 2, the vehicle dynamics model and trajectory tracking model are established. The upper controller based on the ASSM is designed in Section 3, and the lower-level controller of the control system is also designed based on the optimal longitudinal force distribution algorithm in Section 3. In Section 4, co-simulation experiments based on CarSim and Simulink are shown. The paper ends with the conclusions.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 3 Transpiler-Based Design Model for Back-End Layers
### Relationship with Other Architecture Designs
#### 3.7.2 Mvc  
The proposed design can be used in conjunction with a Model-View-Controller (MVC) pattern to provide a more robust and scalable architecture. In the MVC pattern, the model represents the application's data and business logic, the view represents the presentation of the data, and the controller handles the user's input and updates the model and view accordingly [4].  
Using the proposed design, the back-end layer can be designed to provide a standardized interface for the front-end layer, which can be developed using an MVC pattern. The transpiled code can be used as the model layer, with the front-end layer acting as the view and controller layers. The back-end layer can expose the necessary methods to the front-end layer via nest implementations, allowing the front-end layer to easily access business logic methods.  
The use of this combination provides several benefits, including increased scalability, modularity, and separation of concerns. It allows for the development of the front-end layer using a well-established and widely used design while providing a standardized back-end layer that can be easily updated and modified as needed. This approach promotes code reusability and reduces the complexity of the overall architecture, making it easier to maintain and scale the applications.
# Unconstrained Estimation of Multitype Car Rental Demand
## 3 Hybrid Forecasting  
### Selection of Forecast Method  
For demand forecasting, there are three methods: quantitative analysis, qualitative analysis, and decision analysis. Quantitative analysis relies on a large amount of historical data and can be divided into time series forecasting, which uses time to organize data, and the causal analysis method, which uses relationships to organize data. The time series method is the most commonly used, in which the Holt-Winters model and moving average method are representative. Time series forecasting can predict future demand, but it cannot explain the reason. Causal analysis commonly uses regression analysis and simulation methods. The causal analysis method uses the causal relationship between data to look for changes and is generally used for macro prediction.  
Qualitative analysis mainly relies on expert knowledge and experience to evaluate and does not involve quantitative analysis. Delphi and judgments fall into this category. Although this kind of method operates simply, the subjectivity is too strong and the effect is also not favorable. The decision analysis method combines quantitative and qualitative methods. At present, the market survey and randomness method are more commonly used, but the research is not very mature. The quantitative analysis method is commonly used to compare the above three methods.  
The Holt-Winters model is a kind of time series forecasting model that avoids the deficiencies of the moving average method. It uses a cubic smoothing equation to make different data have different weights, and the predicted value is the weighted sum of the previous data sequence. [PERSON] et al. deemed the availability of the basic neural network more useful than traditional forecasting methods (moving averages, exponential smoothing, linear regression, etc.) by comparing the mean absolute percentage error [38].  
The backpropagation (BP) neural network is a widely used nonlinear forecast method that can simulate the neural structure of the human brain and solve more complicated problems. The BP neural network can use its own nonlinear characteristics to simulate the development trend of the data, without requiring an assumption function. When the prediction accuracy is reached, the future demand can be predicted according to the learning situation. However, the BP neural network relies on the initial conditions and is prone to fall into the local optimal solution. Therefore, the single prediction method has inevitable defects, and the error accuracy may not reach the conditions for actual use. This paper intends to use the hybrid forecasting to combine weights of multiple forecast methods and improve the forecasting accuracy.
# Efficient Coverage Path Planning for a Drone in an Urban Environment
## 5 SST--An Efficient Heuristic Algorithm for the GPP Problem
### Overview  
Together with being safe and complying with time constraints, the planned trajectory has to maximize the relevant information collected while following that information, allowing better decision-making regarding which landing spot to select. Since any point in the free area \(\pi_{f}\) is an appropriate candidate for a trajectory waypoint, selecting a subset of waypoints that maximizes the information among infinitely many possibilities is a great challenge.  
To tackle this problem, a pre-processing stage has been added with the aim of finding a finite, narrow set of candidate waypoints \(\mathcal{V}\) that will serve the task of trajectory planning throughout the entire mission. Considering this set of candidate waypoints, the planning stage includes three main steps: Score-Select-Traverse (SST).  
1. **Score--**Assign each candidate waypoint a score that represents some measure of its corresponding footprint.
2. **Select--**Select a subset of waypoints to form the trajectory.
3. **Traverse--**Determine the visiting order to minimize travel time.  
The detailed algorithms for the pre-processing and planning stages are presented in Algorithms 3 and 4, respectively, and further discussed in the following sections.  
Figure 3: Exhaustive search—a single iteration. (**a**) Probability map together with obstacles (black). The radius of each circle is proportional to its probability. (**b**) The discrete map was obtained by applying the threshold and the cluster centers (orange) are surrounded by a footprint-size circle. (**c**) The complete graph and the trajectory visiting all cluster centers. Green vertices represent the initial position, and gray vertices belong to the visibility graph.  
```
1:A preliminary probability map \(\mathcal{P}\), the minimum flying altitude \(h_{min}\), obstacle locations in \(h_{min}\)\(\mathcal{O}_{h_{min}}\)
2:A set of candidate waypoints \(\mathcal{V}\), a visibility graph \(\mathcal{VG}\)
3:\(POI\leftarrow\text{extractPOI}(\mathcal{P},\mathcal{O}_{h_{min}})\)
4:\(bandwidth\leftarrow\text{calcFootprintSize}(h_{min})\)
5:\(\mathcal{V}\leftarrow\text{ClusterPOI}(POI,bandwidth)\)
6:\(\mathcal{VG}\leftarrow\text{PlaneSweep}(\mathcal{O}_{h_{min}})\)
7:return\(\mathcal{V},\mathcal{VG}\)
```  
**Algorithm 3**SST--Pre-Processing  
```
Input: A probability map \(\mathcal{P}\), first waypoint \(w_{0}\), mission constraints \(\tau_{max}\), visibility graph \(\mathcal{VG}\)
1:A trajectory \(T\)
2:\(\mathcal{G}^{h}\leftarrow\text{scoreAndSelect}(\mathcal{P},w_{0},\tau_{max}, \mathcal{VG})\)
3:\(visit\_order\leftarrow\text{solveOLTSP}(\mathcal{G}^{h},I,R)\)
4:\(T\leftarrow\text{post\_processing}(visit\_order)\)
5:return\(T\)
```  
**Algorithm 4**SST--Planning
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 6 Conclusions  
In this paper, we devise and evaluate a sensor-scheduling algorithm for multi-target tracking in WSNs. Firstly, we consider decision indicators, including target type, degree of deviation, node density, and energy level, to construct a target priority function, in which EWM is used to determine the weights of indicators. Then, we propose a Q-learning-based task-allocation mechanism, where sensors are mapped as agents and the tracking status of targets is mapped as environments. Each agent can observe the environment state and select its action using an improved exploration-exploitation policy. By using the proposed sensor-scheduling algorithm, the system can reasonably assign tracking tasks to sensors, thereby improving tracking accuracy and energy efficiency. Simulation results indicate that the proposed algorithm outperforms other algorithms in terms of tracking accuracy and energy efficiency.  
[PERSON] developed the basic idea, devised main methodology, worked on simulations and wrote this paper; [PERSON], [PERSON], and [PERSON] investigated related work and discussed the feasibility of the algorithm; [PERSON] improved this paper; [PERSON] and [PERSON] are experts in the application research of wireless sensor networks; [PERSON] reviewed this work with extreme care; [PERSON] supervised this work with extreme care. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key R&D Program of China under Grant #2021 YFE0111900.  
**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.  
Figure 9: FND time steps vs. number of targets.
## 6 Simulation Setup
### Simulation Methodology  
The simulation steps of AFB-GPSR were built and executed using the OMNeT++ simulator and the INET framework. The OMNeT++ platform built in C++ is one of the most popular software simulators, which also include NS-2, NS-3 and OPNET, as it has the features of modules connected by gates with a graphical user interface (GUI) [60; 61; 62]. Figure 7 demonstrates the simulation system's configuration for verifying the proposed AFB-GPSR protocol. Figure 8 depicts the required project architecture of the simulation model.  
To verify the proposed AFB-GPSR routing protocol, the OMNeT++ simulation was run with various network sizes (node densities) and node-motion speeds in addition to various transmission power values. The experimental simulations were conducted to investigate: (i) GPSR routing protocol with fixed beacon interval times (1 s and 2 s), (ii) AODV routing  
Figure 6: Inputs and output membership function (**a**) node speed; (**b**) one-hop neighbors’ density; (**c**) link reliability; (**d**) output beaconing interval.  
protocol, and (iii) the proposed AFB-GPSR in case of adaptive beacon interval. Figure 9 shows the required network settings to establish and implement the configurations in OMNeT. The sources and destinations were chosen at random from the experimental scenario's nodes. In addition, the Random Waypoint (RWP) model is considered for the nodes' movement, where every node in the simulation environment can choose a random destination and move directly toward it. After arriving at its destination, it comes to a halt for a predetermined amount of time, identified as a pause time, and the procedure is repeated iteratively.
## 2 Materials and Methods
### External Load Monitoring  
Before the start of each game, a microsensor inertial measurement unit (IMU) (Kinexon Perform IMU, KINEXON Precision Technologies, Munich, Germany) was placed in a leather case with a clip that attached to the waistband of each participant's team uniform [50, 51]. The IMU was placed at the center of mass (COM), which was determined by placing the clip case at the intersection of the axial and sagittal planes in a straight line with the iliac crest on the posterior side of the body, as described by [PERSON] [51]. Positioning the accelerometer closer to the COM provides greater accuracy in quantifying physical work, as it is less sensitive to vertical vector motion noise resulting from upper body motions such as scapular oscillation, arm oscillation, torso flexion, and vector quantities that represent the total dynamic acceleration of the body [51]. Across all games, microsensor data were recorded via IMU microsensors and downloaded after each game to a system computer for analysis using Kinexon software (Kinexon Perform IMU 12.0, KINEXON Precision Technologies, Munich, Germany). The IMU microsensor included a 3-axis accelerometer with a range of \(\pm\)16 G at 1 KHz (provided with 100 Hz), a 3-axis gyroscope with a range of \(\pm\)4000 deg/sec at 200 Hz, and a 3-axis magnetometer with a range of \(\pm\)16 \(\upmu\)T at 100 Hz. EL metrics included accumulated acceleration load (AAL), also referred to as Player Load (within-device CV = 0.91-1.05%, between-device CV = 1.02-1.90%) [38, 43, 50, 52, 53, 54], which was calculated as the square root of the sum of the squares of the instantaneous rate of change of acceleration on each of the three vectors (X, Y, and Z axes) and divided by 100 [51, 55]. Data were expressed in arbitrary units (AU) [38, 43, 50, 52, 53]. Mechanical load (ML) is derived by accumulating all the instantaneous acceleration and deceleration samples in the x and y planes. Jump load (JL) is derived from the equation JL = M \(\times\) g \(\times\) vertical displacement, where M is the mass (kg), g is the gravitational constant (m/s\({}^{2}\)), and vertical displacement is the jump height (meters). Jump events were also identified and quantified using Kinexon's athlete tracking system, which integrates high-resolution positional and inertial sensor data. The system calculates jump height and frequency based on vertical acceleration and displacement parameters derived from the inertial data. These calculations are processed through Kinexon's proprietary software algorithms. While the specific computational formulas are not publicly available due to intellectual property protections, the detection of jumps is based on identifying characteristic vertical movement patterns exceeding predefined thresholds within the acceleration data. Distance in speed zones was categorized as in the following zones: 1. low (\(\leq\)5.04 km/h), 2. medium (5.04-10.8 km/h), 3. high (10.8-18.72 km/h), and 4. very high (over 18.72 km/h). These zones are similar to those used in basketball research [56, 57].