esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Analysis and Security Characteristics of Carrier-Based Aircraft Arresting in Yaw Condition
###### Abstract  
In order to research the safety characteristics of carrier-based aircraft in yaw arrest, a complete dynamic model of the arresting system of a certain type of aircraft was developed to understand more about its dynamic properties. Based on the discrete kink-wave model, a simulation of centering arrest was conducted. The simulation results were compared with experimental data from the United States (US) military standards, demonstrating that the basic changing laws are almost the same. On the basis of centering arrest, a simulation of yaw arrest was carried out. The results show that in yaw state, the difference in the lengths of the arresting cables on either side of the hook is smaller in the early stage after the hook hangs on the rope, which leads to little influence on load fluctuation produced by the kink-wave. With the increase in arresting distance, the difference in the lengths of the arresting cables on either side becomes larger, resulting in a situation in which the cable tension on the departure side will gradually become greater than that on the opposite side. In this situation, yaw landing has a negative impact on the characteristics of arresting safety, and the excessive yaw angle causes the aircraft to rush out of the safe landing area.  
arresting system; kink-wave; yaw landing; dynamics; safety characteristics +
Footnote †: journal: Applied Sciences
# Safety, Security and Privacy in Machine Learning Based Internet of Things
###### Abstract  
Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.  
i 1 (2022) 15 July 2022 29 July 2  
spoofed the source IP addresses to attack a location and the legitimate users struggled to access their IoT devices due to the systems' vulnerabilities. Machine Learning algorithms have a unique way of solving complex problems that are implemented in many real-time applications [10]. The basic aim of machine learning algorithms is to improve the performance of any task through training and experience, and it can assess the large dataset with an extraordinary analytical ability in any cyber-physical system [11; 12]. It has been used in many fields, including autonomous vehicles, medicine, image recognition, etc.  
The research contributions of this survey are as follows:  
* A detailed discussion on the attack surfaces, vulnerabilities, and security threats of IoT Systems: we discuss various attack surfaces on IoT devices, i.e., network, physical service, cloud service, application interfaces and web service.
* Detailed discussion and comparison of existing surveys, including an in-depth statistical overview of recently published articles on different ML techniques for IoT security.
* Incorporation of recent surveys on financial losses by breaches in the security of IoT systems, including the trend in the number of IoT devices in future.
* Presentation of the latest research challenges and future directions for IoT security based on ML.  
The rest of the paper is organized as follows: Section 2 presents the related works on the topic. Section 3 demonstrates the safety and security threats in IoT. Machine learning techniques and their applications in IoT are presented in Section 4. Issues, research challenges, and future research directions are mentioned in Section 5. Section 6 presents a discussion of the observations and results. Finally, the conclusion is given in Section 7.
## 5 Conclusions  
In this study, a new CLQ evaluation method was developed by integrating multi-source remote sensing data to reveal the spatiotemporal patterns of the CLQ in Guangzhou from 2010 to 2018. The results showed that CLQ in Guangzhou continuously improved during the study period. The amount of high-quality cultivated land increased, while that of medium- and low-quality cultivated land decreased. CLQ in the eastern and northern parts of Guangzhou continuously improved, while that in the western and southern areas initially decreased and then improved. However, CLQ was low in the central region throughout the study period. Soil fertility size, high productivity capacity, and stable productivity capacity were the main obstacles for the improvement of CLQ in Guangzhou. Accordingly, we proposed a series of targeted measures, such as adopting reasonable crop rotation, strengthening crop management, and constructing well-facilitated farmland. The CLQ evaluation method proposed in this study is suitable for macro-regional scale research. The evaluation results provide clear guidance for further improvement of CLQ in Guangzhou.  
Conceptualization, D.D.; methodology, D.D. and J.S.; software, D.D. and S.L.; validation, D.D. and L.F.; formal analysis, H.C.; investigation, D.D., S.L. and X.S.; resources, D.D. and X.S.; data curation, F.Z. and L.X.; writing--original draft preparation, D.D.; writing--review and editing, D.D. and X.S.; visualization, W.Y.; supervision, X.S. and P.Y; project administration, X.S.; funding acquisition, P.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant no. U1901601, 41921001, and 41901227), the National Key Research and Development Program of China (grant no. 2019 YFA0607401) and the Technology Innovation Center for Land Engineering and Human Settlements, Shaanxi Land Engineering Construction Group Co., Ltd. and Xi'an Jiaotong University (Grant No. 2021 WHZ0072).  
Not applicable.  
We thank the anonymous reviewers for the provided comments and suggestions that have helped us to improve the paper.  
The authors declare no conflict of interest.
# End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe
## 1 Introduction  
### Context  
The life cycle assessment (LCA) is an acknowledged method for assessing the environmental impacts of vehicles over their entire life cycle. Due to its inner nature, the LCA comprises evaluating the environmental impacts related to the end-of-life (EoL) stage.  
Nevertheless, the assessment of the EoL stage raises a methodological issue in the LCA regarding the system boundary definition [1]. Quantifying the benefits and burdens from material production and recycling at the EoL stage necessitates addressing the issue of their allocation between connected product systems. Unfortunately, several allocation methods are used in the literature to solve this issue, resulting in a high degree of uncertainty on the effect of EoL on the cradle-to-grave impact.  
At the same time, increasing regulatory requirements for material recovery is making end-of-life vehicle (ELV) management a global concern in the automotive industry [2, 3, 4, 5]. With reference to road vehicles, starting from 1 January 2015, the 2000/53/CE directive of the European Union set the objective for ELVs to be reused and recovered for at least 95% of their mass, with no more than 10% of that mass being recovered as energy [6]. Moreover, since landfill poses a substantial environmental risk due to the presenceof polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) in the biogas produced by landfilled compounds [7], no more than 5% of the ELVs mass should be landfilled as the last preferred option. In accordance with the 2000/53/CE directive and the ISO 22628 standard [8], ELVs should be processed through a three-step waste management scheme: depollution, dismantling, and shredding. A fourth stage, which we referred to as the postshredding stage, was included in this study. According to [7], at the end of the third stage, 20-25% of the output consists of the so-called automotive shredder residue (ASR). Although the ASR was largely landfilled prior to 2015 due to its heterogeneous and complex matrix [7; 9], in more recent years, to meet the requirements of the 2000/53/CE directive, postshredder technologies were prioritized in an effort to improve the recovery of materials and energy from ASR [7].  
Due to the need to be compliant with a mandatory waste management scheme, the existence of several allocation methods, the complexity of the vehicle material breakdown, and the scarcity of primary data, modeling the EoL stage is one of the main challenges in the LCA of vehicles.
## 4 Results
### Cluster Analysis of Small- and Medium-Sized Enterprises
#### 4.1.3 Results of Cluster Analysis  
Based on the performed PCA to select the clustering variables and to determine the optimal number of analysis clusters, a k-means cluster analysis was performed. The calculations were performed in software R, using factoextra and cluster packages. The results are summarized in Table 3, which shows that four rather consistent clusters with 32 to 65 elements were created. Table 3 reports the values within sum of squares (WSS), maximum within cluster distances as diameter, average distance within clusters (which should be as small as possible), and separation with minimum distances of a point in the cluster to a point to another cluster reported for each cluster.  
The graphical depiction of the cluster analysis is shown in Figure 2, which shows the results of the cluster analysis in relation to factor variables (VPi4 Level 1, Level 2, and Level 3) in 3D and then in 2D space. It is also evident from Figure 2b that the dimensions of the variables have different directions, confirming their independence. However, it is also evident that there is some overlap in the clusters. This overlap is also explained by the fact that the factor variables are partly complementary and reflect the levels of Industry 4.0. Achieving the second level is conditional by the first level; the achievement of the third level is partly conditional by reaching the first and second levels.  
Figure 1: Optimal number of clusters methods results: (**a**) Elbow method and (**b**) Silhouette method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** \\ \hline Within Sum of Squares & 57.6031 & 61.8557 & 73.8122 & 33.4195 \\ Diameter & 3.9006 & 3.7375 & 3.5976 & 2.7862 \\ Average Distance & 1.7915 & 1.4810 & 1.4024 & 1.1872 \\ Separation & 0.2584 & 0.3370 & 0.2584 & 0.3583 \\ Cluster Size & 32 & 49 & 65 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of k-means clustering.  
Table 4 shows results of k-means clustering according to the centroids average characteristics of the Industry 4.0 levels in individual clusters. Cluster 1 is strongly represented by the first and second levels of the VPi4 index. This means that Cluster 1 contains the enterprises with the highest industry implementation rate of 4.0 on average. Cluster 2 only shows a high rate for Factor 3, which is a certain extension of the implementation of Industry 4.0 technologies. Cluster 3 achieves positive values only for the first level of the VPi4 index. So far, its average enterprise achieves only a certain initial industry-leading implementation level of 4.0. The latest cluster, Cluster 4, contains the lowest average values at almost all levels. On average, it is an enterprise with a very low implementation rate of Industry 4.0.
## 2 Autonomous Global Positioning Method
### Number of Solutions to Global Positioning Method Equation System
Figure 4: The color map obtained by superimposing the contour lines representing solar altitude angles in Figure 3 onto the contour lines in Figure 2.  
When looking at a yearly time frame, which represents one full orbit of the Earth around the sun, the changes in the solar declination become relatively apparent, requiring detailed examination. Astronomical data indicate that the solar declination varies throughout the year from \(-23^{\circ}27^{\prime}\) to \(23^{\circ}27^{\prime}\). It is zero during the vernal and autumnal equinoxes and reaches its maximum and minimum values at the summer and winter solstices, respectively. Figure 5a-d show the global distribution of the number of solutions obtained by the autonomous global positioning method at the solar declinations of zero, \(5^{\circ}\), \(10^{\circ}\), and \(23^{\circ}27^{\prime}\) at the UTC of 21 h and \(E_{\omega}=169.8116/60/60\) h, respectively. In Figure 5, the white regions denote areas with a single solution, whereas the black regions indicate areas with two solutions. As shown in Figure 5a, when the solar declination is zero, there is only one global solution. In all other scenarios, there are areas with two solutions. It is important to understand that Figure 5 depicts the projection of the Earth's three-dimensional spherical shape onto a two-dimensional surface, which can introduce certain distortion; thus, the visible size of the black regions in Figure 5 might not accurately reflect their actual surface area on the Earth.
## 1 Introduction
### Results of this paper  
In this paper we try to assess the impact of locality on the algorithmic design of important computational tasks, like dominating set, connected dominating set, vertex cover, and independent set, in wireless networks. For arbitrary locality distances we show the first lower bounds for possible approximation ratios of local algorithms for all problems mentioned above in the setting of location aware nodes.  
We also investigate the power of algorithms with very low localities. It turns out that despite the fact we are looking only at locality one we can still design constant ratio approximation algorithms for dominating set, independent set, and vertex cover. We prove that for connected dominating set there is no constant ratio algorithm with locality distance one. But we present such an algorithm with locality distance two.  
In the proofs for the lower bounds we mostly employ unit disk graphs on a line (unit line graphs). In order to assess our bounds for each problem we present a local algorithm with locality one in unit line graphs. These algorithms achieve significantly better approximation ratios than the local algorithms for general unit disk graphs with this locality.  
All results including trade-offs between approximation ratios and bounds are presented in Tables 1 and 2.  
\begin{table}
\begin{tabular}{|l|c|c|c|} \hline Problem & UDG & ULG & Lower bound for \\  & one hop & one hop & locality \(k\) \\ \hline \hline Dominating Set & \(12\cdot OPT\) & \(3\cdot OPT\) & \(1+3/(2k+3)\) \\ \hline Independent Set & \(1/1801\cdot OPT\) & \(\lfloor 1/2\cdot OPT\rfloor\) & \(1+1/k\) \\ \hline Vertex Cover & \(6\cdot OPT\) & \(2\cdot OPT\) & \(1+1/k\) \\ \hline \end{tabular}
\end{table}
Table 1: Results for low localities presented in this paper. Note that the bound for dominating set depends on the congruence class of \(k\). The bound given in this table represents the worst case.  
\begin{table}
\begin{tabular}{|l|c|} \hline Connected Dominating Set & \\ \hline UDG one hop & no local constant ratio algorithm \\ \hline UDG two hops & \(216\cdot OPT\) \\ \hline ULG one hop & \(6\cdot OPT\) \\ \hline Lower bound for locality \(k\) & \(1+1/k\) \\ \hline \end{tabular}
\end{table}
Table 2: Results for connected dominating set presented in this paper.
## 6 Conclusions  
This paper explores the effect of symmetrical fissure morphology on the mechanical properties of hole-containing sandstone by developing models using granular flow program PFC2D. Furthermore, several key conclusions about the relationship between fissure morphology and sandstone strength exist.  
1. The deterioration effect of symmetric fissures on hole-containing sandstone can be controlled by increasing the fissure dip angle, suppressing the stress drop phenomenon. However, increasing the fissure length exacerbates the deterioration effect. The experimental findings indicate that the peak strength and elastic modulus of symmetrical fissure-hole sandstone are positively correlated with the fissure dip angle and negatively correlated with the fissure length within the experimental range.
2. The effect of a symmetrical fissure dip angle on the displacement field near the hole decreases with increasing dip angle while increasing fissure length exacerbates the effect of a fissure on the displacement field; the opposing transverse displacement of particles is the dominant factor for local tensile failure of the specimens and fissure penetration with the hole; as the fissure inclination increases, three damage patterns of compression, slip, and spalling occur in the hole in turn.
3. The symmetrical fissure-hole sandstone failure modes under conventional triaxial compression are all mixed shear-tension failures. As the angle between the fissures and the principal vertical stress increases, the degree of tensile failure weakens while the degree of shear failure increases. Moreover, the location of crack initiation shifts from the fissure tip towards the hole wall.
4. During the crack development stage of symmetrical fissure-hole sandstone specimens, the extension of the stress concentration zone drives rapid crack growth. It exhibits a stress drop in the macroscopic mechanical properties, followed by the evolution of the stress field with loading and rapid expansion of microcracks, eventually leading to destabilization and damage of the specimen.  
Methodology, H.G. and Z.W.; validation, P.S. and Z.Z.; investigation, Z.W. and L.X.; data curation, B.F. and P.S.; writing--original draft preparation, H.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 3 Adaptive Horizon Parameters
### Principles for the Horizon Parameter Settings  
At each sampling point, the MPC-based method obtains an optimal control sequence [\(u_{1}\), \(u_{2}\),..., \(u_{\mathrm{Nc}}\)] distributed within the control horizon. The control variables of the control sequence keeps constant between sampling intervals, and only changes at sampling points within the control horizon. The basic principles for the horizon parameters are listed as follows:  
* Sampling interval \(t_{\mathrm{s}}\). The length of sampling interval \(t_{\mathrm{s}}\) should fulfill the requirements of measurement collection, data processing and optimization, which change little during the entire receding horizon optimization process. A constant value \(t_{\mathrm{s}}=10\) s is applied in the proposed scheme, which also matches the value used in the coordinated secondary voltage control in operation in practical systems [22].
* Control horizon \(t_{\mathrm{c}}\). With a constant sampling interval, the optimal length of the control horizon is related to the voltage evolution and response feature, which varies over the receding horizon optimization process. In this paper, the system voltage is predicted using a linear model as is introduced in Section 2. The sensitivity feature changes little if no disturbances occur during the optimization process [21], and the solved control variation is proportional to the detected voltage deviation. When severe voltage deviation is detected, the length of the control horizons should be short enough to meet the requirement of the voltage recovery speed and online computation time, and should be long enough to deal with modeling inaccuracies and measurement noise. As the receding horizon optimization progressed, the controlled voltages are gradually closer to its desired range, and smaller control variations are needed to accomplish the control objective. In this situation, shorter control horizon parameters could reduce settling times of the optimization process and computational effort.
* Prediction horizon \(t_{\mathrm{P}}\). The system response caused by the control actions are predicted within the prediction horizon, thus the length of the prediction horizon should be greater or equal to the length of the control horizon. When the controlled voltages are gradually stabilized to the steady state values, the necessity to consider changes happening beyond the control horizon is reduced from a computational viewpoint. Therefore, at the beginning of the MPC implementation, the length of the prediction horizon is set longer than the length of control horizon. As the receding horizon optimization progressed, the length of the prediction horizon gradually approaches the length of the control horizon, which could also save unnecessary computation time
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 2 Related Works
### Deep Learning Approaches  
Convolutional Neural Networks (CNNs) and their variants such as YOLO and Faster R-CNN have been extensively used for object detection tasks in agriculture, including tomato ripeness detection. These models have proven effective in overcoming environmental variabilities such as changes in lighting and complex backgrounds [42]. Specifically, YOLO-based frameworks have been shown to provide a good balance between detection accuracy and computational efficiency, making them suitable for real-time applications in agricultural settings [43].  
Multi-Feature Fusion and Attention Mechanisms: A promising direction in tomato ripeness detection is the integration of multi-feature fusion networks. By combining color, texture, and shape features, models can better differentiate between ripened, unripe, and diseased tomatoes, resulting in improved classification accuracy under various environmental conditions [44]. Additionally, attention mechanisms have been introduced to focus on relevant features while suppressing irrelevant background noise, further enhancing detection performance in complex scenes [45].  
Real-Time and Robust Detection: With the increasing demand for autonomous agricultural machinery, real-time ripeness detection models have become essential. Efficient architectures such as YOLOv5 and its variants have been optimized for low-latency inference, ensuring high-speed tomato ripeness detection without compromising accuracy.  
These models are particularly valuable for robotic harvesting systems, where quick decision-making is crucial [46].  
The proposed YOLO-PGC framework builds upon these advances by introducing novel modules such as PSSD, CIF2M, and GHVC. These modules are designed to specifically address the challenges of tomato ripeness detection in dynamic agricultural environments where issues such as occlusion, complex backgrounds, and varying illumination are prevalent. The PSSD module enhances feature representation adaptively, while the CIF2M strengthens feature extraction under challenging conditions, and the GHVC module mitigates the effects of occlusion through innovative attention mechanisms.