esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the initial investigations of hazard assessment for the Cortinas sector at a 1:25,000 scale, researchers validated the hypothesis that the terrain must be under specific magnitudes of conditioning factors for an instability event to occur, which does not depend only on certain critical thresholds of rainfall and earthquakes. This process was termed accumulated geotechnical deterioration (AGD). A larger-scale analysis of geotechnical criteria was conducted based on data obtained from direct exploration and geophysics, examining conditions prior to slope failure, immediately after failure, and several years later. The results showed that, indeed, the terrain experienced an increase in deterioration that, under the influence of a critical rainfall event, led to failure; however, the stability of the terrain later recovered to the point that failure did not occur under similar rainfall events. In accordance with this, a novel method of analysis is established herein at a 1:5000 scale, which uses AGD to predict instability events by calculating the safety factor derived from the application of the infinite slope formula in a GIS framework. By incorporating specific variables and field measurements, a better approximation for the prediction of an instability event can be achieved. This study identifies the innovative factors of this method and provides a baseline for future research.  
landslide; cumulative geotechnical deterioration; prediction; slope; hazard zoning Article
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Pursue Mobility Model (PMM)
#### 6.4.3 Throughput  
Figure 19 provides an overview of throughput in a mobile network environment utilizing the PMM model, highlighting scenarios both with and without the Hello Flood attack. It features eight lines representing different combinations of mobile and attacker nodes, with percentages varying from 10% to 40%. Mobile nodes represent scenarios free of attack, whereas attacker nodes represent those exposed to it. As observed in the figure, there is a consistent trend of throughput decreasing with the increase in the number of nodes, hinting at a potential congestion issue. Moreover, networks lacking attack nodes generally sustain higher throughput levels compared to those with attackers. Additionally, an increase in the percentage of mobile nodes typically coincides with a decrease in throughput, indicating that mobility has an impact on data transmission efficiency. Notably, the inclusion of attacker nodes exacerbates the decline in throughput, highlighting the adverse impact of malicious activity on network performance.  
The integration of the PMM with the Hello Flood attack leads to a notable decrease in network throughput through several interconnected mechanisms. Firstly, the structured movement patterns inherent in the PMM model can create congestion points within the network as nodes pursue a target node. This congestion can lead to packet collisions and increased transmission delays, thereby reducing overall throughput. Additionally, the Hello Flood attack inundates the network with excessive traffic, saturating network channels and exacerbating interference, which further slows down packet transmission  
Figure 18: E2 ED without and with attack using PMM.  
and diminishes throughput. Secondly, the movement patterns introduced by the PMM model may introduce routing instabilities, leading to suboptimal routing decisions and increased packet loss. When combined with the disruption caused by the Hello Flood attack to routing protocols, the impact on throughput is magnified. Moreover, the increased network activity resulting from both the PMM model and the Hello Flood attack may lead to heightened contention for limited network resources such as bandwidth and processing power. This conflict increases the workload on network components, resulting in higher transmission delays and reduced throughput. Additionally, the Hello Flood attack may introduce additional processing overhead and network congestion, further impeding packet transmission and reducing throughput.
## 3 Test Results and Analysis
### Analysis of Influencing Factors
#### 3.3.3 Effect of Eccentricity  
The ultimate load capacity of the two groups of specimens is shown in Figure 10, which shows that the eccentricity has a significant effect on the ultimate load capacity of the specimens. From the damage characteristics, the lateral displacement in column II-PSRPC-1 was 35.2 mm when the ultimate load capacity was reached, and the lateral displacement of II-PSRPC-1 was 35.2 mm when the ultimate load capacity was reached. The damage pattern of the specimens did not differ much, but the ultimate loads did vary considerably. The greater the eccentricity, the greater the width of the main crack and the greater the number of cracks in the specimens.  
Figure 10: Ultimate load–eccentricity curve.  
Figure 9: Ultimate load–steel content relationship curve.  
4 Calculation of the Axial Compression Load Capacity of PSRPC Columns Based on Hoop Restraint Action
# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
## 5 Proposal of SADFIR
### Saderik Network Topology Management Phase  
The proposed SADFIR routing algorithm formulates unique coordination between infrastructure layer network topology and application layer path selection re-computation to develop an industrial level solution. The re-computation to establish reconfiguration of network topology demands periodic updates through the topology discovery mechanism. More comprehensive network updates assist the SADFIR routing algorithm in generating precisely accurate policy responses from the upper layer and distributed SDN controllers play a vital role in managing these computational tasks. The distributed SDN controllers follow network topology discovery through the state-of-the-art SDN controllers like POX and NOX SDN controllers [36; 37]. The OpenFlow Discovery Protocol (OFDP) is implemented with a unique design extension in IoT based networks. These OFDP coordinate with the existing Link Layer Discovery Protocol (LLDP) [38] to manage these tasks at both southbound and northbound. The coordination of OFDP and LLDP is generally referred to as Topology Discovery (TD).  
The proposed SADFIR utilizes these TD hello messages to extract particular network information at infrastructure layer such as; nodes residual energy, distance to BS, a mutual distance of nodes from previous Cluster-Head (CH), epoch information, RSSI level among neighbors and ID of serving SDN controller. This critical information is responded by IoT devices to TD iterative broadcasting by distributed SDN controller at BS. IoT network devices have heterogeneous residual energy resources; that is why distributed SDN controllers need to collect the information from all alive nodes and share to understand the status of the nodes. If a consolidated database of all distributed SDN controllers misses any entry of previous rounds entry table, then it is considered as a dead node. Conventional static network environments utilize the TD messages mentioned above, otherwise in mobility-based scenarios that demand more frequent localization information. The frequency of TD exchange rate in a static environment depends upon the duration of one period. The SADFIR-Visor holds the authority to alter the TD interval rate, and SADFIR-Visor manages this iteration according to the scalability of the network.
# Research on the Calculation and Analysis of Ski-Track Areas Based on Laser Point Clouds
## 2 Ski-Track Data Pre-Processing  
The best ski-track plane should be fitted in advance and all the point cloud data of the ski track should be projected onto the fitted plane to determine the edge of the ski track accurately to calculate the area.  
Currently, least squares fitting is the most common and easiest method of fitting a plane; however, the accuracy of least squares fitting is easily affected by noise. With the random sampling consensus algorithm (RANSAC) [26], the influence of noise can be eliminated through an iterative fitting method, which greatly improves the accuracy of the fit. As shown in Figure 1, the flow of the random sampling consensus algorithm is as follows:  
1. Based on mathematical knowledge, a plane can be fitted with at least three points, so, first, three points are randomly chosen and then the plane model parameters \(A\), \(B\), \(C\), and \(D\) are calculated based on the plane Equation (1).  
\[A\cdot x+B\cdot y+C\cdot z=D \tag{1}\]  
2. As a result of testing the plane model estimated in Equation (1), the result error is calculated and the error is compared with the set error threshold. If the error is less than the set error threshold, then the point is considered an inner point and the number of interior points under this parameter model is counted and recorded. Moreover, the distance threshold (T) and assurance (A) are set at 1 cm and 0.7, respectively, during the plane-fitting process.  
3. Continue with steps 1 and 2. In the event that the number of inliers in the current model exceeds the maximum number of inliers saved, update the model parameters; the reserved parameters are always those with the most inliers.  
4. Iterate continuously until the threshold is reached and then find the model parameters with the highest number of inliers. Then use the inliers to estimate the model parameters again to obtain the final parameters.  
By using the derived projection formula, the projected point of any point cloud on the plane can be easily calculated using the characteristic that the projected vertical line is parallel to the fitted plane normal vector. As a result, the entire point cloud only needs to be projected onto the fitted ski-run plane by repeating the process. There is a large overlap between the final projected surface and the real ski run, so the projection surface can be approximated as the ski-run surface It is implemented in the PCL and can, therefore, be accessed directly during use.
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 2 Motivation and Main Methodology
### Motivation  
The existing research on flight decision making mainly focuses on the mission decision making of the multirotor--for instance, path planning--but there has been little research on safety decision making. In the aspect of multirotor flight evaluation and decision making, research that considers both the flight attitude and fault state is relatively lacking. To solve the above problems, this paper proposes a flight strategy safety decision-making mechanism for multirotors that considers the real-time environment and the flight state of the multirotor, processing and fusing multiple types of information. According to the different failure levels of the multirotor--for instance, when the evaluation result of the failure level is \"danger\" or even \"disaster\"--the flight strategy is adjusted to realize the flight decision. Therefore, the motivation of this paper is to establish a multirotor aircraft flight strategy decision-making method considering the real-time environmental information and flight state constraints to ensure the flight safety of the multirotor. When the safety level of the multirotor is low, the flight strategy should be decided immediately.
# Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n
## 4 Experiment and Result Analysis
### Dataset Description  
The dataset used in this experiment is from a seamless fabric factory, containing a total of 580 images covering different types of seamless fabric patterns and textures. The LabelImg tool is used to accurately label the collected images. In order to improve the generalization ability of the model, various data augmentation methods are used to expand the image dataset, including image flipping, translation, rotation, and adding noise, which are randomly combined and applied. Through these technical means, more diverse training samples can be generated, effectively avoiding overfitting problems and improving the robustness of the model. Finally, the dataset is expanded to 4184 images and randomly divided into
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 4 Research Methodology
### Hybrid Discrete Wavelet-CNN Method  
The hybrid method consists of two main blocks, DWT analysis and a CNN model. For concluding an inference model, it is necessary to train the model with pairs of batches of DWT-analyzed signals and the corresponding labels for normal and faulty operation. The DWT analysis block is utilized for the feature extraction of valuable information from the time series. The features could be generated from the coefficients of each level. In more detail, these features could be statistical values, such as variance, standard deviation, mean, median, and the 70 th percentile or just the raw values of the coefficients. Data preparation is needed to prepare a training dataset that is employed to train a CNN model.
## 6 Ablation Experiments
### Effectiveness of MLC Module  
To discuss the effectiveness of the MLC module, we compared two settings: (1) WSA + GA module; (2) MLC + WSA + GA module. MLC is a multi-branch structure, and each branch consists of a layer of 1D convolution with different kernel sizes. Table 5 shows the comparison results on the THUMOS14 test set. It can be seen that MLC + WSA + GA had a +1.1% [EMAIL_ADDRESS] improvement compared to only using the WSA + GA module, suggesting that the MLC module can provide complementary information with WSA + GA. As the number of branches increases, the accuracy further increases, with only a small increase in the model parameters. However, when using a larger kernel size (k_s = 13), the effectiveness of MLC seems to become weaker (\(-\)1.4% in [EMAIL_ADDRESS]). Further expanding the kernel size (e.g., k_s = 15) leads to greater performance degradation. Multi-scale local features play an important role in enriching the feature details, but the range of the temporal receptive field must be controlled within a certain range. Beyond a certain range, the module's attention is distracted, not only being unable to capture long-range features, but also unable to focus on the local context.
## 5 Issues and Recommendations
### The Complexity of the Y-Fe-O Phase  
During the deposition process, secondary phases such as yttrium oxide (Y\({}_{2}\)O\({}_{3}\)), ferromagnetic (Fe\({}_{2}\)O\({}_{3}\)), and multiferroic perovskite (YFeO\({}_{3}\)) may develop [145]. Using a deposition and post-deposition annealing (PDA) technique, multiple groups have demonstrated the presence of these secondary phases during the formation of polycrystalline YIG on silicon and quartz substrates. In addition, the formation of Fe, Y, and O\({}_{2}\) vacancies can occur when YIG films are formed by PLD at low oxygen pressure [137]. Relationships between the material structure and growth conditions have not yet been thoroughly investigated. Consequently, future research should focus on the associations between material structure and growth circumstances.
## 4 Dynamic Vibration Test of Primary Mirror Assembly
The relative error of the natural frequency in the X, Y, and Z directions is small, which proves that the simulation analysis results of the primary mirror assembly are accurate. The connection reliability of the mirror assembly in the three directions is high, and the dynamic stiffness is good.
# Evolution towards Smart and Software-Defined Internet of Things
## 4 Smart Software-Defined IoT
### Network Functions Virtualization  
SDN has brought, to quite a large extent, the flexibility and scalability desired by IoT networks that were traditionally lacking. However, technology today is evolving at a rate faster than was possible before. The needs and desires of IoT architectures are changing accordingly. The networks demand to be stretched to even greater extents while enhancing security. To cater to these evolving requirements, SDN can be embedded with virtualization technologies to further strengthen and increase the parameters of the IoT networks, discussed so far, in addition to providing the functionalities of a multi-tenant environment, which was originally not feasible with SDN alone. In this section, we will study the NFV based SDN IoT architectures and analyze them accordingly.  
[PERSON] et al. in [10] explains that traditional architectures of IoT fail to meet the current requirements at various domains and, hence, need to be enhanced. SDN and NFV's integration to IoT brings about interoperability, discoverability, security, efficiency, management, and the desired scalability. Existing business models are infused with SDN and virtualization to see an increase in profitability trends.
## 3 Genetic Algorithm for VNF-Mapping in Cloud/MEC RAN-Based 5G Architectures  
### Network Architecture  
We consider a Cloud/MEC RAN architecture based on the one proposed in Reference [12] which is shown in Figure 1. In this architecture, RRHs connect via optical networks (using dedicated links or PON) to an AO. The AO hosts a MEC server to bring cloud services and function virtualization closer to the end user. This server will host instances of VNFs, including vBBUs. Since the closest AO to the RRH hosts the vBBU attending the RRH, we reduce the physical distance between both elements, ensuring that the stringent 5G latency requirement is fulfilled. We distinguish between two kinds of AOs: HD-AO (High Demand AO) and LD-AO (Low Demand AO). HD-AO serves a higher number of average end users than LD-AO and, consequently, the MEC servers at the HD-AO will be equipped with higher computing resources than the servers at the LD-AO. That definition is also compatible with the division in macrocells and microcells in current mobile networks. Lastly, each AO can connect to the CO via dedicated optical links, optical networks featuring Wavelength Division Multiplexing (WDM) like in References [6; 8; 9] or even PON technology. We assume that the CO hosts IT resources to deploy VNF instances, but it will not host virtual BBUs. This architecture does not consider splitting the BBUs into DUs and CUs [8; 9]. However, it is completely compatible with that architecture by simply hosting the DUs in the AOs while the non-time sensitive CU can be hosted in the CO.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 3 Measurement System
#### Automatic Gain Control  
Figure 4 illustrates three of the \(H_{\text{a}}\) distributed single antennas. During the first TDMA slot, antenna 1 transmits while all other antennas are in receiving mode. Given the relative distance between antenna 1 and antennas 2 and \(H_{\text{a}}\), the latter may require maximization of its receive gain. In the subsequent TDMA slot, the next antenna in the sequence transmits and the rest assume receiving roles. In this TDMA slot, antenna \(H_{\text{a}}\), positioned closer to the transmitting antenna, might experience analog to digital converter (ADC) saturation due to the preset gain of the receivers. This elementary example of a realistic scenario illustrates the need for an AGC. Due to the TDMA structure and how the antennas are distributed in space, the gain must be estimated and set within a couple of microseconds. Therefore, the AGC is implemented in the FPGA on board the radio to minimize latency. The implemented AGC is inspired by [17; 29].
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 3 The Baseline Crossing Event  
In FSR, the detection scheme is dependent on the modulation of signal amplitude that occurs as the target approaches, crosses, and departs the baseline. We will title this occurrence as a baseline crossing event (BCE). In the following sections, we explore the signal characteristics of a BCE in the time and frequency domains.
## 3 Damage Indexes Effectiveness
### Considered Case Study  
The reference case study is a four-storey RC residential building in L'Aquila (AQ) (an area of high seismicity in Italy). It has a rectangular floor plan (21.05 m \(\times\) 11.45 m). The first floor is 3.40 m high, while the inter-floor height for the rest of the building is 3.05 m (Figure 2). The one-way beam and block flooring system consists of RC beams and hollow bricks with a 5 cm thick RC overlay. The floor spans along the X-axis. The longitudinal and internal transverse RC beams are 25 cm deep; the depth of the transverse beams and edge beams varies depending on the configuration under consideration. The infill panels of the perimetral frame consist of two layers of hollow bricks (12 cm + 12 cm).  
Concrete and steel materials with the following mechanical properties are considered as concrete C25/30 (f\({}_{\rm{ck}}\) = 25 N/mm\({}^{2}\), f\({}_{\rm{cd}}\) = 14.2 N/mm\({}^{2}\)) and steel B450C (f\({}_{\rm{yk}}\) = 450 N/mm\({}^{2}\), f\({}_{\rm{yd}}\) = 391 N/mm\({}^{2}\)). The existing building was designed for gravity loads only, whereas for the code-compliant cases, seismic actions were also considered. The applied loads are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Load**} & **G\({}_{\rm{1}}\)-Floor** & \multirow{2}{*}{**G\({}_{\rm{2}}\)-Floor**} & \multirow{2}{*}{**Q-Floor**} & **G2-Rof** & **Q-Rof** & **G2-Staircase** & **Q-Staircase** & **G2-Infill** \\  & & & & & & & \\ Value & 3.20 & 3.10 & 2.00 & 1.60 & 2.18 & 0.80 & 4.00 & 3.50 \\ \hline \end{tabular}
\end{table}
Table 3: Permanent and live load applied to the structure (in kN/m\({}^{2}\)). The infill load takes into account 15% of opening. Note: G refers to dead loads while Q for live loads.  
For an initial assessment and development of methods and indicators that can accurately represent the state of the building after a seismic event, a 2D frame was extracted and considered for performing non-linear dynamic analysis: frame X1 highlighted in Figure 2. A planar frame analysis helps to avoid the influence of torsional phenomena on the interpretation of the damage indexes.