esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Malicious X.509 Certificates' Detection
## 6 Experiment  
### Data Collection  
To obtain models with better generalization performance and stability, we feed many data to models, especially deep learning models. The truth is that the number of malicious certificates is far less than the number of benign certificates. Therefore, the principle of use is collecting plentiful malicious certificates and a corresponding number of benign certificates. We acquire malicious certificates from two sources, and one is Uniform Resource Locators (URLs) of phishing websites provided by PhishTank (PhishTank: [[https://www.phishtank.com/](https://www.phishtank.com/)]([https://www.phishtank.com/](https://www.phishtank.com/))). The other is fingerprints of malicious certificates collected by the SSLBL project (SSLBL: [[https://sslbl.abuse.ch/](https://sslbl.abuse.ch/)]([https://sslbl.abuse.ch/](https://sslbl.abuse.ch/))). Connecting URLs with HTTPS in port 443 and obtaining possible certificates sent from servers during the process of ServerHello, we collect 1711 malicious certificates. We apply fingerprints provided by the SSLBL project as searching keys to search at crt.sh (Crt.sh: [[https://crt.sh/](https://crt.sh/)]([https://crt.sh/](https://crt.sh/))) and censys.io (Censys: [[https://censys.io/](https://censys.io/)]([https://censys.io/](https://censys.io/))) to get certificates in PEM format. We finally obtain 611 malicious certificates in this way. The total number of malicious certificates we collect is 2322. Compared with malicious certificates, benign certificates are adequate and easier to obtain. We leverage part domains of the Alexa top 1 million sites (Alexa rank: [[https://www.alexa.com/topsites](https://www.alexa.com/topsites)]([https://www.alexa.com/topsites](https://www.alexa.com/topsites))) as seeds to get their certificates with the help of HTTPS protocol. The number of benign certificates we collect is 11,909, which is imbalanced. To make fair use of malicious certificates, we select 9288(4 \(\times\) 2322) benign certificates whose domain names have a high Alexa rank for different experiments. Considering to train models with great generalization ability, we adopt to re-sampling malicious certificates, which makes data balance in training and validation. To make it easier for reproduction, we release the features of all malicious and benign certificates on our website (Our website: [[https://github.com/fight-think/features-extraction](https://github.com/fight-think/features-extraction)]([https://github.com/fight-think/features-extraction](https://github.com/fight-think/features-extraction))).
# SmartX Multi-View Visibility Framework
###### Abstract  
Modern information communication technologies (ICT) infrastructures are getting complicated to cope with the various demands needed to accommodate the emerging technology paradigms such as cloud, software-defined networking (SDN), and internet of things (IoT). Visibility is essential for the effective operation of such modern ICT infrastructures to easily pinpoint server faults, network bottlenecks, and application performance troubles. Even though many conventional monitoring solutions are now available, multi-layer visibility is still limited when operating such complicated infrastructures. To address this particular limitation, a futuristic multi-layer visibility framework denoted as SmartX multi-view visibility framework (MVF), is proposed for unifying the visibility of underlay, physical and virtual resources, flow, and workload layers. To unify multi-layer visibility, this paper presents a comprehensive extension of SmartX MVF with flow-centric visibility for simultaneously monitoring physical-virtual resources, flows classification, and visualization to eventually assist secured operation of SDN-enabled multisite cloud infrastructure. Flow-centric visibility design has five main components (1) a lightweight network packets-precise flows visibility collection component, (2) a visibility data aggregation and tagging component, (3) a multi-layer visibility data integration component, (4) a non-learning-based network packets flows classification component, and (5) a visualization component. Furthermore, a prototype implementation of SmartX MVF with flow-centric visibility is deployed in an SDN-enabled multisite cloud playground to verify the proposed multi-view visibility of fine-grained flow-aware physical-virtual resources.  
Keywords:software-defined networking; cloud computing; future internet testbed; flow-centric visibility; interactive visualization; monitoring technology +
Footnote †: journal: Applied
## 3 Multi-Link Channel Access Schemes and Coexistence Issue
### Multi-Link Channel Access Schemes of EHT  
**Asynchronous Operation (Async)**. The MLO functionality of EHT allows an asynchronous multi-link channel access scheme in which each of the STAs belonging to an MLD performs a channel access process (backoff procedure, BO) over its own link independently. Therefore, as illustrated in Figure 4a, the BOs of different links may finish at different times, and the transmissions over these links will be asynchronous. In addition, downlink and uplink frames can be transmitted simultaneously over multiple links.  
This Async scheme is ideal for STR MLDs. However, if it is applied to a non-STR MLD, as shown in Figure 4b, a transmission on a link will appear as interference beyond the ED threshold on other links, thus causing them to sense a busy medium and freeze their BOs until the transmission ends. Therefore, for a non-STR MLD, Async significantly degrades the ability to utilize multiple links.  
Figure 3: Spectral mask requirements of an IEEE 802.11 transmitter.  
**Synchronous Operation (Sync)**. The synchronous multi-link channel access scheme of EHT is illustrated in Figure 5. In this scheme, individual BOs are performed on all links. If a link finishes its BO early, it waits until the other links have also finished their BOs. If the channel of a link that has been waiting after finishing its BO becomes busy, the link must rerun its BO (the contention window (CW) remains unchanged).  
The Sync scheme is specifically designed for non-STR MLDs but is also applicable to STR MLDs. Because it only allows simultaneous transmission initiation on all links, it is unaffected by RF power leakage between links and thus operates similarly in STR and non-STR MLDs. However, it may not utilize multiple channels as optimally as Async since all links must have backoff counts of zero when their individual channels are idle.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation  
### Simulation Setup  
The performance evaluation of the proposed CAA-ERPL protocol against energy-efficient RPL(E-RPL) is done using a Contiki Cooja simulator [16; 34]. Sky motes are taken for the entire simulation. Sky motes have a 16-bit MSP430 MCU, 10 kB RAM, 48 kB ROM, a cc2420 802.15.4 radio transceiver, an external Flash memory, and a temperature, humidity, and brightness sensor [34]. Sensor nodes are located within the transmission range of every aggregator that comes under one region. The forest area is divided into multiple regions of unequal size. The aggregator is responsible for collecting and aggregating the data for forwarding to border router. The data are transmitted periodically from the sensor nodes every 60 s. The simulation is done by varying the number of nodes in the network as 10, 20, 30, 40, and 50. The simulation parameters are listed in Table 1.  
Figure 3: Parent selection based on ETX and energy.
# Study on the Co-Evolution Mechanism of Key Strata and Mining Fissures in Shallow Coal Seam Mining
###### Abstract  
Shallow coal seam mining makes the evolution form of mining fissures in rock and soil layers diversified, which leads to the easy penetration of mining fissures as the main channel of water, sand inrush, and air leakage. In order to reveal the co-evolution mechanism of broken rock beam structure and mining fissures in key strata, taking Hanjiawan Coal Mine as the research background, the relationship between mining fissures and rock beam structure, fissure activation period, propagation characteristics, and connectivity of working face was studied by means of field observation, physical similarity simulation, and theoretical derivation. The research shows that the fracture structure of key strata in shallow coal seam mining mainly includes hinged rock beam and step rock beam structures. Through the analysis of the rock beam structure, we found that the types of mining fissures in the overlying strata of key strata were up and down I-I and I-II mining fissures, and the heights of fissure development were 44.38 m and 98.35 m, respectively. The key block rotation made the mining fissures undergo five dynamic activation processes. The calculation formula of the fissure activation cycle was established, and the rock breaking angle, mining fracture lag distance, and fissure penetration discriminant were obtained and verified by field measurement results.  
shallow coal seam; key strata; rock beam structure; mining fissures; co-evolution
## 2 Related Work and State-Of-The-Art Technologies
### State-Of-The-Art Technologies
#### 2.2.3 Container Orchestration--Kubernetes  
But how can an application consisting of many microservices be managed and controlled? To do this, orchestration technologies come into play. According to [34], Kubernetes is a platform for automating deployment, scaling, and management of containerized applications. Together with Docker, Kubernetes is used to deploy applications (and therefore also microservices) in containers across clusters of computing nodes. It contains tools for orchestration, e.g., handling a set of executables as one application, service discovery (e.g., identifying instances of a certain type and checking their health for failure tolerance) and load balancing. By using Kubernetes, one or more containers can be logically associated with a pod that is the basic scheduling unit for Kubernetes to deploy an application part (e.g., consisting of one or more services having a close relationship) as a set of Docker imagesand manage such application parts. Pods can share resources needed by application parts across the associated containers (e.g., a certain file storage provided by a distributed file system or a certain network address). Kubernetes can automatically find a machine that has enough free computational capacity for a given pod and launches the associated containers. To avoid conflicts, each pod is assigned a unique IP address, enabling applications to use different ports under the same IP address. Kubernetes offers availability and quality checks for containers in order to heal failed containers in pods through its automatic failure recovery actions [35]. As presented in [36], compared to Docker Swarm, which is another container orchestration platform [37], Kubernetes is more powerful and provides immense scalability and automation at the same time. Additionally, the performance of Kubernetes currently surpasses that of Docker Swarm.  
By using Kubernetes for the framework, containerized executables running in Pods managed by deployments on the computing cluster can be managed and controlled easily and efficiently.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 6 Experiment Results and Analysis
### Comparative Experiment with Classical Algorithm  
To validate the superiority of the improved YOLOv5 model in bearing defect detection, it was compared with three mainstream object detection models. The experimental results are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **mAP/\%** \\ \hline Faster-RCNN & 88.1 & 86.3 & 89.6 \\ Cascade-RCNN & 91.2 & 89.5 & 90.3 \\ YOLOv3 & 86.7 & 83.1 & 85.3 \\ ours & 97.5 & 96.5 & 98.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison experiment results of mainstream algorithms.  
Figure 19: (**a**) Original YOLOv5 defect detection results. (**b**) Improved YOLOv5 defect detection results.  
The results in the table indicate that the improved YOLOv5 model outperforms other mainstream algorithms in terms of accuracy, recall rate, and average precision. This demonstrates the superiority of this approach in surface defect detection on bearing components.
## 5 Performance Evaluation Results and Discussion
### Effects of Misbehaving REPs and Mitigation of the Effects by S-REPs
If we assume that two S-REPs are deployed for each P-REP, the ratio of cellular traffic in the proposed method to cellular traffic in the existing method will be \(1.5(=\frac{(1+2)}{(1+1)})\) times as large as that in Figure 17. Namely, the ratios become about 1.5 for 250 MHs, 0.93 for 500 MHs, and 0.7 for 750 MHs in the pedestrian case, and the ratios become about 1.4 for 250 MHs, 0.92 for 500 MHs, and 0.7 for 750 MHs in the vehicle case. On the other hand, in Figure 19, one S-REP per P-REP reduces the number of different successful bids to 6 or less in the 5% misbehaving hosts case, which seems more probable than the 30% misbehaving hosts case. Based on these points, the demerit of the increase in the cellular traffic outweighs the merit of reducing the number of different successful bids when increasing the number of S-REPs per P-REP from one to two.
## 2 Methodology
### Life Cycle Assessment Method
#### 2.2.4 Life Cycle Impact Assessment (LCIA)  
The life cycle impact assessment (LCIA) stage is a prominent stage where the potential environmental impacts of a process are evaluated. This stage involves projecting the inventory data, which includes all the inputs and outputs collected during the LCI stage, into environmental impact categories. In the current study, two impact assessment methods were utilized, namely, the ReCiPe Midpoint (H) 2016 and ILCD 2011 Midpoint Methods. The ReCiPe Midpoint (H) 2016 method is a popular LCIA methodology that includes 18 impact categories employed to render water and energy consumption into environmental impacts [11; 12]. The Midpoint characteristic for both methods was selected over the Endpoint method as the former provides more depth of information (rather than breath) with lesser statistical uncertainties. The ILCD 2011 Midpoint Method is a standardized approach within the International Reference Life Cycle Data System (ILCD) framework for evaluating environmental impacts at Midpoint levels. The ILCD builds on ISO 14040 and 14044 standards [9; 13] which affords a general basis for reliable and robust life cycle data [14]. Our selection of both the ReCiPe and ILCD methods was intended to be complementary. The ReCiPe method predominantly focuses on assessing potential impacts in a limited number of environmental categories, but it affords a thorough analysis of specific environmental factors.
## 2 Methodologies  
This section presents, in addition to an introductory subsection of the physics of a catchment, the methodologies and procedures applied for: the evaluation of empirical relationships between potential evapotranspiration and SMC through linear regression; the analysis of seasonal and annual precipitation trends over the past twenty years; application of Monte Carlo approaches for generating synthetic profiles of precipitation, direct runoff, and SMC. Satellite data of hourly precipitation (mm/h) and of daily (hourly values are not currently available) soil moisture content (SMC), expressed as soil saturation fraction (dimensionless), are used; these data are taken from the MERRA-2 product, one of the NASA's databases having a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) (latitude and longitude, respectively) [29].
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 3 System Model  
We consider a wireless sensor network consisting of \(N\), \(N\in\mathbb{N}\) sensor nodes, which observe a common metric \(X\) (state update information such as temperature, humidity, and speed) characterized by a Gaussian process with zero-mean and variance \(\sigma_{X}^{2}\) (the assumption is based on the pervasiveness that a large number of phenomena are distributed in normal form, as is assumed in [23; 24; 25]), as illustrated in Figure 1. Each node's observation is transmitted through an orthogonal channel, which follows block Rayleigh fading. For ease of manipulation, we assume that the observation experience independent channel gain denoted as \(|h_{i}|^{2}\sim\exp(1)\), channel noise \(Z_{i}\) with zero-mean and variance \(\sigma_{Z_{i}}^{2}\). The received signal for the \(i\)-th node \(Y_{i}\) is expressed as:  
\[Y_{i}=h_{i}X+Z_{i}, \tag{1}\]  
where \(h_{i}\) can be obtained by transmitting pilot signal which is used for channel estimation.  
Then, the observations are transmitted to the FC. Considering that the ability of signal processing, collecting, and transmission are different among the facilities, we assume the transmitting time from sensor nodes to the FC obeys exponential distribution with mean \(1/v\) and is independent and identically distributed (i.i.d.) across the sensor nodes. Specifically, the transmission is organized in rounds where the FC waits for observations  
Figure 1: Signal model of the proposed system.  
from \(N\) sensors in each round, and it begins a new round at the exact moment when the previous round is complete. At the FC, selection combining (SC) technology is employed to better control the fidelity of the received data.
## 4 Results and Discussion
### Software Analysis-Optimization and Cost Analysis
#### 4.5.2 CFD Analysis  
In this study, to verify the infection spread on the residential floors of buildings through refuse chutes as claimed, a computational fluid dynamics (CFD) analysis was conducted using the Ansys Workbench Fluid Flow (Fluent) module. Prior to conducting this analysis, the flow model depicted in Figure 16, which shows the directions of air inflow and outflow, was utilized. In this context, considering a flow rate of 900 m\({}^{3}\)/h, the air velocity was defined as 1.3 m/s at inlet-1, inlet-2, and inlet-3. The air temperature was set to 283 K at inlet-1 and to room temperature (297 K) at inlet-2 and inlet-3.  
In the mesh configuration of this model, the \"inflation option\" was set to \"total thickness\", with the maximum thickness value set at 8 mm. The mesh design consisted of a total of 28,999 finite elements and 6409 nodes. Additionally, the \"Element Quality\" command was used to assess the mesh quality, as illustrated in Figure 17.  
Figure 16: Fluent model.  
To solve the flow analysis, an initial iteration count of 200 was set, and convergence was observed, allowing the results to be reliably utilized. The iteration result graph is presented in Figure 18. Additionally, to visualize the flow velocity, both contour and streamline results were displayed in the analysis. The contour flow velocity result is shown in Figure 19a, and the streamline flow velocity result is depicted in Figure 19b. According to these results, as claimed in this paper, the infection-laden air from the trash room and the residential floors (floor-1 and floor-2) spread almost unimpeded to the upper floors (floor-3). This indicates that, without intervention, the infection risk could reach a dangerous level. Therefore, it is recommended to consider parameters such as the current and renewed design for infection proposed in this paper.  
Figure 17: Element quality.  
Figure 18: Graph of the iteration result.
## 6 Simulation Results
### Simulation Results of WFRT
#### 6.1.1 Disconnection of GS-VSC\({}_{1}\)  
First trial associates with the disconnection of the grid side converter. The dc voltage and power profiles of WFRT are shown in Figure 9. GS-VSC\({}_{1}\) power dropped to zero from \(-0.6\) pu because of the immediate disconnection as this VSC station experienced a symmetric fault on the ac side, causing excessive power in the dc grid. Promptly, dc-link voltage builds up to 1.25 pu while, at \(t=2.1\) s, V\({}_{\mathrm{dc}}\) drops to 1.07 pu as droop control increases power transfer through GS-VSC\({}_{2}\), GS-VSC\({}_{3}\), and GS-VSC\({}_{4}\), supplied by the WF-VSCs. Wind power extraction stays unchanged. The permissible range of \(\pm 10\%\) is violated for dc-link voltage. WFRT does not devise any protection to tackle with such circumstances, which is a drawback.
# VIIRS Day/Night Band--Correcting Striping and Nonuniformity over a Very Large Dynamic Range
## 1 Introduction
### On-Orbit DNB Calibration Process  
Since the DNB calibration process is related to the root causes of striping, we describe it in detail here. Data that are collected by VIIRS on-orbit and processed on the ground to produce radiometrically calibrated and geolocated radiances are called the Sensor Data Record (SDR), or alternately the Level 1 product. The SDR is produced by the Interface Data Processing Segment (IDPS) that is part of the Common Ground System developed under contract by Raytheon for NOAA [9]. The DNB is just one of 22 VIIRS bands, but because of the complexity of the DNB electronics described above, the DNB calibration process is by far the most complex. In addition to the IDPS DNB SDR, NASA Land Science Investigator-led Processing System (Land SIPS) in coordination with the Suomi-NPP VIIRS Calibration Support Team (VCST) produces a DNB Level 1 product using a similar calibration process. Land-SIPS system is maintained separately from IDPS in order to develop new algorithms and to support reprocessing of data [10]. There have been some major changes to the DNB calibration, and both the original process and the recent changes are described here since both are relevant and depend on when the SDR used for imagery is created.
# Reducing the Uncertainty of Lidar Measurements in Complex Terrain Using a Linear Model Approach
## 2 Materials and Methods
### Test Site and Measurement Campaign
#### 2.1.4 Data Filtering and Selection  
In order to compare the different wind field reconstruction methods with the reference data from the met mast, these must be prepared and filtered in the pre-process. It is necessary to filter the data in order to accurately compare the different systems. The process of filtering and selection is shown in Figure 5. Data from the lidar and met mast are treated differently:  
**Lidar:** The high-resolution recorded lidar data are processed with the wind field reconstruction methods: The last N data points are used for each time step. These methods determine both the wind velocity components \(u,\ v,\ w\) and statistical parameters (\(CNR_{mean}\), \(CNR_{m}in\) and \(CNR_{max}\)) for the carrier-to-noise ratio (CNR). The CNR parameters are still required for later data processing. The horizontal wind speed is calculated from the wind speed components \(u,\ v\) (Equation (2)).  
Figure 4: Measurement trajectories for (**a**) the SWE Scanner and (**b**,**c**) the Galion lidar.  
These data are now subjected to a CNR filter to exclude samples outside of the CNR range. It is important that both the \(CNR_{m}in\) and the \(CNR_{max}\) are within the CNR limits. Values outside these limits are not taken into account for further consideration. From these data the 10 min statistics are calculated and selected.  
\[v_{h}=\sqrt{(u^{2}+v^{2})} \tag{2}\]  
**Met mast:** The recorded data is first subjected to a plausibility test where the system checks that values are within a realistic range. If data is available from a second sensor at nearly the same height a comparison of the data from those sensors is also made, e.g., between the cup anemometer at 100 m and the horizontal wind speed from the sonic at 98 m. Then the 10 min statistics are calculated, and the data is selected according to the selection criteria in Table 1.  
The directional dependency of the lidar met mast data is checked in a different way, represented by the green path in Figure 5.  
Figure 5: Steps in processing and selecting data from the lidar and met mast.  
After filtering and selection, both data streams will contain gaps. Only lidar and met mast measurement data that are available at the same time step are used.