esa-sceva/satcom-chunk-collection huggingface dataset:
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 4 Principles and Optimization of the YOLOv5 Model  
The YOLOv5 [14; 15; 16] network is widely used in the field of object detection, and is characterized by fast detection speed, high accuracy, and low computational complexity. It can meet the basic requirements for bearing image defect detection. Therefore, YOLOv5 is adopted as the detection algorithm in this paper. Figure 6 shows the architecture of the YOLOv5 network. The network is mainly composed of four parts: the input end (Input), the backbone network (Backbone), the neck network (Neck), and the detection network (Head).  
This paper optimized the original YOLOv5 network architecture to improve the accuracy of bearing defects detection. This included incorporating a coordinate attention mechanism into the backbone network, adding an adaptive fusion module to the neck, and replacing the loss function. The improved network structure is illustrated in Figure 7.  
Figure 5: Collection of magnetic trace images.
## 3 Theoretical Model
### The Raman Response Function Solved Using the Multiple Vibrational Mode Model  
Raman-induced changes in the spectrum and temporal envelope of an ultrashort pulse are governed by the time-domain Raman response function of the material. Previous researchers have made significant contributions to the development of models for the Raman response function. In 1989, [PERSON] et al. [34] reported the Raman response function expression of fused silica by approximating the Raman gain coefficient spectrum as single Lorentzian function, wherein a simple damped harmonic oscillation can be expressed by Equation (2).  
\[h_{R}(t)=\frac{\tau_{1}{}^{2}+\tau_{2}{}^{2}}{\tau_{1}\tau_{2}{}^{2}}\exp(-t/ \tau_{2})\sin(t/\tau_{1}) \tag{2}\]  
Here, \(\tau_{1}\) = 12.2 fs, and \(\tau_{2}\) = 32 fs. This model uses three parameters to provide the correct location and peak value of the dominant peak in the Raman gain spectrum. Because of its simplicity, it has a great fit with the experimental result within 100 fs, and this simple model has been widely adopted by other researchers [35; 36; 37]. However, the Raman response function in Equation (2) only considers the main peak of the Raman gain spectrum of fused silica and ignores the influence of other small Raman gain peaks; it does not provide a correct quantitative description of Raman-induced phenomena and leads to difficulty in comparing theorical and experimental results. To address this limitation, [PERSON] et al. [20] proposed a multiple vibrational mode model. The Raman gain spectrum of the materials were regarded as having a multi-peaked structure, and multiple Gaussian functions were used to reproduce the Raman gain spectrum. The model was adapted an  
Figure 1: Experiment setup for SC generation in the Ge–Sb–Se SIF.  
intermediate-broadening model using Lorentzian and Gaussian convolutions, as shown in Equation (3).  
\[h_{R}(t)=\sum_{i=1}^{n}\frac{A_{i}^{\prime}}{\omega_{p,i}}\exp(-\gamma_{i}t)\exp( -\Gamma_{i}^{2}t^{2}/4)\sin(\omega_{p,i}t) \tag{3}\]  
Here, \(A_{i}^{\prime}\) is the amplitude of the \(i\)th vibrational mode, and \(\omega_{p,i}\), \(\gamma_{i}\), and \(\Gamma_{i}\) are related to the Gaussian component position, Lorentzian FWHM, and Gaussian FWHM, respectively. Figure 2 demonstrates the comparison of Raman response functions obtained from the single Lorentzian model [38] and the multiple vibrational mode model [31] as well as using the experimental results provided in Ref. [39] for fused silica. It is evident that the Raman response function generated using the single Lorentzian model gradually tends to 0 after 150 fs, whereas the Raman response function generated using the multiple vibrational mode model consistently matches the experimental results.
## 3 Methodology
### Fault and Fracture Extraction  
Interpreters usually manually mark the fault on the seismic section to carry out fault interpretations, which is a very difficult and time-consuming process to accurately interpret a fault. The uncertainty in manually interpreting faults and fractures is very high. The automatic extraction of faults and fractures can minimize human interference by providing the maximum proficiency and accuracy in extracting the faults [78]. Automated fault extraction technology allows interpreters to comprehend a fault and mark selections amid the extracted surfaces instead of generating surfaces. However, the outcome of the automatic fault extraction technology is constrained by various factors. These factors have a directinfluence on the extraction of faults and fractures, and the factors are patch downsampling, minimum patch size, connectivity constraint, extraction background threshold, extraction sampling threshold, extraction sampling distance, and deviation from a plane.  
Downsampling is effective in controlling the density of the various points of every patch. The minimum patch size sets the minimum number of points for fracture patches. The extraction background threshold and extraction sampling threshold control the least threshold signal value for the extraction and estimation of fracture patches. The extraction sampling distance controls the least distance by defining the radius amid extraction points. The deviation from a plane (DFAP) controls the orientation and deviation of a fracture from the surface of the plane. The DFAP plays an essential role in distinguishing fractures. A higher value of the DFAP decreases the possibility of extracting more fractures and vice versa. A DFAP of 9 was used in the study because the fractures merged into each other when a higher value of the DFAP was used.
## 3 Background and Preliminaries
### Anomaly Detection in Wireless Sensor Networks
#### 3.3.1 Definitions and Basic Concepts  
Anomalies are defined in [22] as \"patterns in data that do not conform to a well-defined notion of normal behavior\". Another definition in [21] as \"an observation that appears to be inconsistent with the reminder of a dataset\". Anomalies might appear in data for different reasons, such as malicious activity, for example, cyber-attacks, cards frauds, breakdown of the system or terrorist activity, but the common feature of all of these reasons is that they are of interest to be analyzed [22].  
In WSNs, anomalies can be defined as those significant deviations in the sensing data measurements from the normal sensed data profile [22]. These anomalies occur due to several reasons and among them are: errors in the measurements caused by faulty sensor nodes, some noise gained by external factors, actual events because of the changes in the sensed environment, or malicious attacks launched by compromised sensor nodes. Anomaly detection as defined in [22] refers to the problem of finding patterns in data that do not match with the well-established and expected behavior.
# The Sun and Space Weather
###### Abstract  
The explosion of space weather research since the early 1990s has been partly fueled by the unprecedented, uniform, and extended observations of solar disturbances from space- and ground-based instruments. Coronal mass ejections (CMEs) from closed magnetic field regions and high-speed streams (HSS) from open-field regions on the Sun account for most of the disturbances relevant to space weather. The main consequences of CMEs and HSS are their ability to cause geomagnetic storms and accelerate particles. Particles accelerated by CME-driven shocks can pose danger to humans and their technological structures in space. Geomagnetic storms produced by CMEs and HSS-related stream interaction regions also result in particle energization inside the magnetosphere that can have severe impact on satellites operating in the magnetosphere. Solar flares are another aspect of solar magnetic energy release, mostly characterized by the sudden enhancement in electromagnetic emission at various wavelengths--from radio waves to gamma-rays. Flares are responsible for the sudden ionospheric disturbances and prompt perturbation of Earth's magnetic field known as magnetic crochet. Nonthermal electrons accelerated during flares can emit intense microwave radiation that can drown spacecraft and radar signals. This review article summarizes major milestones in understanding the connection between solar variability and space weather.  
solar eruptions; solar flares; coronal mass ejections; geomagnetic storms; solar energetic events; coronal holes; corotating interaction regions 2022
## 3 Results and Discussion
For chlorophylls evaluated over the cultivation time, all cultures supplemented with glycerol showed a reduction in pigment concentration after 8 h of cultivation (Figure 2). Despite the different light supply times, the pigments were influenced by the presence of glycerol in the medium. According to [PERSON] et al. [10], the use of an organic carbon source in cultures results in a reduction in pigments in the biomass due to the preference of algae to consume the organic carbon source, as it is easier for cells to obtain energy in this way than through the process of photosynthesis. The addition of glycerol to the culture medium modified the fatty acid composition of the _Spirulina_ sp. LEB 18 biomass by increasing the unsaturated acid content in all studied photoperiods. In the dark, neither the control nor the culture with the organic carbon source showed higher proportions of unsaturated fatty acids. Palmitic acid (16:0) and oleic (C18:1w9c) were predominant in the _Spirulina_ sp. LEB 18 biomass during the different photoperiods with glycerol supplementation. These results were in accordance with those of [PERSON] et al. [10] and [PERSON] et al. [33], who cultured _Spirulina_ species microalgae in medium supplemented with glycerol and observed an increase in unsaturated fatty acids with a predominance of C16:0 and C18:1w9c (Table 2). Figure 2: Variation in chlorophyll content over time for cultivation of _Spirulina_ sp. LEB 18 with different photoperiods in (**a**) cultures supplemented with glycerol and (**b**) controls.
## 7 Future Perspectives and Emerging Trends
### Advancements in Sensing Materials and Nanofabrication Techniques  
Advancements in sensing materials and nanofabrication techniques are expected to play a significant role in improving the performance and functionality of nanosensors for bridge maintenance. Researchers are exploring new materials, such as 2D materials, metal-organic frameworks (MOFs), and nanocomposites, that offer unique sensing properties and improved stability [100].  
Furthermore, innovations in nanofabrication techniques, such as additive manufacturing (3D printing) and nanoscale assembly processes, enable the creation of complex and customizable nanosensor structures [101]. These techniques allow for the integration of multiple sensing modalities, such as optical, electrical, and mechanical sensing, into a single sensor device, enhancing the range and accuracy of measurements.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 3 The Architecture and Application Scenarios of Satellite Edge Computing
### iSat System
#### 3.1.3. Uploading Apps  
Apps, as the soul of iSat, can be uploaded according to demands. According to the characteristic of satellite applications, apps can be divided into two types based on CPU and FPGA.  
It needs two parts to upload apps, i.e., terrestrial-satellite communication module and on-board apps reconfiguration module. The terrestrial monitor station will send uploading instruction, the file of apps and related configuration file to satellite by terrestrial-satellite communication module. The on-board apps configuration module can support uploading, verification, registration, running and management of apps. The module is designed as shown in Figure 4.  
The module consists of three parts: app list, library, and configuration management. Each iSat has an app list that is a collection of all apps for the satellite. The app list can be represented by the following expression: \(S_{i}=\Sigma[\text{App}_{i}(PS_{i},MS_{i},PLA_{i},at_{i},et_{i},dt_{i})]\), where \(S_{i}\) denotes the app list of iSat \(i\), \(\text{App}_{i}\) denotes the app \(i\), \(PS_{i}\) denotes the process scale of app \(i\), \(MS_{i}\) denotes the memory scale of app \(i\), \(PLA_{i}\) denotes the programable logic area, \(at_{i}\) denotes the arrival time of app \(i\), \(et_{i}\) denotes the end time of app \(i\), and dti denotes the duration time of app \(i\). apps can be pre-configured or uploaded by the ground  
Figure 4: The on-board apps configuration module.  
monitor station after the satellite enters the orbit. According to the relevant description of all apps of the iSat, a task scheduling module sorts and schedules the apps to obtain an execution queue. Then the loading and running of the two types of apps through the placer and loader.  
The library is used to store validated executable files and bitstream files. There are two purposes for designing the library. One is to improve the reliability of application uploading. Satellite orbiting will cause terrestrial-satellite communication to be intermittent. The apps before storage will be verified to avoid damage to the satellite caused by incomplete and incorrect files. The other is to reduce the dependence of satellites on the ground monitor system. Upon losing connection to the ground, iSat can load apps on demand from the library.  
For iSat application \(\text{App}_{i}\), there is a separate process corresponding to it. According to the value of the \(PLA_{i}\) in \(\text{App}_{i}\), the running environment to which the task loader loads the task is determined. The loader will load the app to the appropriate running environment, i.e., when \(PLA_{i}\) is equal to zero, the app can be run directly on the satellite operating system; when \(PLA_{i}\) is not equal to zero, the app needs to be placed into the reconfigurable FPGA resources.  
The on-board app reconfiguration module is managed by a house-keeping system to increase system reliability and autonomy.
## 3 Method
### Multi-Scale Non-Cooperative Human Object Location and Matching
#### 3.2.2 Multi-Frame Human Target Matching Strategy  
Human target matching aims to associate identities to specific targets after localization between temporal neighboring frames in a video. The whole process can be divided into two subtasks: prediction and matching.  
As shown in Figure 5, a Kalman filter [31] is introduced to predict the location of the target appearing in the frame \(k\) according to the target-related parameters captured in frame \(k-1(k>0)\). If the target in frame \(k\) can be successfully detected, the target information detected in frame \(k-1\) is associated with the target information in frame \(k\).  
State feature vector \(X\) is as in Formula (3), where \(u\) and \(v\) denote the horizontal and vertical coordinates of the centroid of each detection target, \(s\) and \(r\) denote the size and aspect ratio of the bounding box area of the detection target, and \(\dot{u}\), \(\dot{v}\), and \(\dot{s}\) represent the velocity components of the centroid coordinates as well as the speed of change of the bounding box area. The motion of the human body in the video is considered as a uniform motion with time interval 1. The relationship between the state variables in the \(k\) and \(k-1\) frame can be expressed as shown in Formula (4). The a priori estimated equation of state \(X_{k}\) and observation equations \(Y_{k}\) are established on this basis, as shown in Formula (5). \(X_{k}\) and \(X_{k-1}\) are the state values at the current moment versus the previous moment. \(A\) and \(B\) are the state transfer matrix and the input modulation matrix. \(Q_{k}\) and \(R_{k}\) are the system noise and observation noise at moment \(k\), respectively. The coordinates of the position of the center point can be obtained as the distance between the target and the observation point, denoted as the observation matrix H.  
\[X=(u,v,s,r,\dot{u},\dot{v},\dot{s},\dot{r})^{T} \tag{3}\]  
\[u_{k}=u_{k-1}+\dot{u} \tag{4}\]  
\[v_{k}=v_{k-1}+\dot{v} \tag{5}\]  
The Kuhn-Munkres algorithm [32] is introduced to complete the matching task. It is done by calculating the IoU value and cosine similarity of the same target between neighboring video frames to create a cost matrix.
# Indoor Occupancy Sensing via Networked Nodes (2012-2022): A Review
## 6 Accuracy and Suitability Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solution** & **ADPs** \\ \hline \multirow{6}{*}{[31]} & **Accuracy:** 71.0\% \\  & **Information requirement:** Prior probabilities for Bayesian model were calculated using four weeks of historical data. \\  & **Execution Platforms:** Samsung SmartThings Hub \\  & **Sensor observation rate:** 60 s \\  & **Sensor Failure rate:** High MTBF as per datasheet for ZMOTION® ZEPIR0 AA PIR sensor \\  & **Detection Area:** 403 ft\({}^{2}\)/node \\ \hline \multirow{6}{*}{[32]} & **Accuracy:** 98.4\% \\  & **Information requirement:** Labeled dataset for ML \\  & **Execution Platforms:** ARM based Beaglebone SoC \\  & **Sensor observation rate:** 30 s \\  & **Sensor Failure rate:** Unspecified PIR sensor \\  & **Detection Area:** 49 ft\({}^{2}\)/node \\ \hline \multirow{6}{*}{[33]} & **Accuracy:** 97.9\% \\  & **Information requirement:** Labeled dataset for ML \\  & **Execution Platforms:** PC/Server \\  & **Sensor observation rate:** 60 s \\  & **Sensor Failure rate:** PIR Sensor MTBF unknown (Phidgets 1111 IR Motion Sensor) \\  & **Detection Area:** 28 ft\({}^{2}\)/node \\ \hline \multirow{6}{*}{[35]} & **Accuracy:** 87.6\% \\  & **Information requirement:** Labeled dataset for ML \\  & **Execution Platforms:** Arduino Black Widow single-board MCU, MATLAB on PC/Server \\  & **Sensor observation rate:** 60 s \\  & **Sensor Failure rate:** Unspecified PIR sensor \\  & **Detection Area:** 430 ft\({}^{2}\)/node \\ \hline \multirow{6}{*}{[34]} & **Accuracy:** 75.0\% \\  & **Information requirement:** Labeled dataset for ML \\  & **Execution Platforms:** HOBO U series event loggers, MATLAB and WEKA on PC/Server \\  & **Sensor observation rate:** 5 min \\  & **Failure rate:** Unspecified PIR sensor \\  & **Detection Area:** <50 ft\({}^{2}\)/node \\ \hline \multirow{6}{*}{[5]} & **Accuracy:** 96.2\% \\  & **Information requirement:** Sensor data for correlation evaluation, Labeled dataset for ML \\  & **Execution Platforms:** Onboard SoC (EFR32 BG13, Silicon Labs) onboard nodes, Edge AI (Raspberry Pi 4) \\ \cline{1-1}  & **Sensor observation rate:** 60 s \\ \cline{1-1}  & **Sensor Failure rate:** >10,000 h (Panasonic® EKMB1391111K) \\ \cline{1-1}  & **Detection Area:** 364 ft\({}^{2}\)/node \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ADPs conformance for each application area. Figure 3: Taxonomy of occupancy sensing methods. _Comments on Solution Conformance to the Claimed Application Areas_  
Ref. [31]: Not feasible for the author-claimed _Energy and Space Utilization_ application as the solution accuracy does not meet ADP accuracy criteria, i.e., 71% < 90%. It is a data-driven method, thus has a pre-requisite of historical data collection before its deployment.
## 5 Proposed Protocol
### Registration  
After the initialization of the network, it is time to enroll. The CNs request registration by forwarding IDCNi, IDcardCni, and Time information to BS. Then, the smart contract (Algorithm 1) in the BCN is triggered. First, the Time information is checked. Next, it is checked whether the CN is registered in the BCN. Finally, IDCNi and PBKBS are used to authenticate the IDcard. If verification takes place in all of these processes, the CN will be registered with the BCN.  
```
if time_check = error; Return error();  if IDCNi_check = error; Return error();  if IDcardCni_check = error; Return error();  register(IDCNi, IDcardCni);  end
```  
**Algorithm 1** CN registration  
Although smart contracts can be used in CN-BS/BCN communications, these transactions were carried out with ECC since SN-CN communications have insufficient storage, processing capacity, and energy. Elliptic Curve Cryptography is preferred because it provides high security with low transaction costs [60; 61; 62; 63]. Each SN in the WSN is a member of only one CN. The SNs transmit IDSNi, Messagenecrypted, and Time information to predetermined CNs. The CNs first check the time information. Then, using the node's own PVKSNi, they verify the encrypted message with PBKSNi. If there is no problem so far, the CN sends IDCNi, IDSNi, IDcardCni, and Time information to the BS and requests the SN's registration request. Then, the smart contract (Algorithm 2) in the BCN is triggered. First, the Time information is checked. Next, whether the CN and SN are registered in the BCN is checked. Finally, IDCNi and PBKBS are used to authenticate the IDcard. If verification occurs in all of these processes, the relevant SN is registered with the BCN. In Figure 4, the sequence diagram of the registration part is given.  
Figure 3: Sequence diagram of the initialization.
###### Abstract  
Geopolymer concrete, also kNown as an earth-friendly concrete, has been under continuous study due to its environmental benefits and a sustainable alternative to conventional concrete construction. The supplies of many source materials, such as fly ash (FA) or slag (SG), to produce geopolymer concrete (GPC) may be limited; however, quarry rock dust (QRD) wastes (limestone, dolomite, or silica powders) formed by crushing rocks appear virtually endless. Although significant experimental research has been carried out on GPC, with a major focus on the mix design development, rheological, durability, and mechanical properties of the GPC mixes; still the information available on the structural behavior of GPC is rather limited. This has implications in extending GPC application from a laboratory-based technology to an at-site product. This study investigates the structural behavior of quarry-rock-dust-incorporated fiber-reinforced GPC columns under concentric and eccentric loading. In this study, a total of 20 columns with 200 mm square cross-section and 1000 mm height were tested. The FA and SG were used as source materials to produce GPC mixtures. The QRD was incorporated as a partial replacement (20%) of SG. The conventional concrete (CC) columns were prepared as the reference specimens. The effect of incorporating quarry rock dust as a replacement of SG, steel fibers, and loading conditions (concentric and eccentric loading) on the structural behavior of GPC columns were studied. The test results revealed that quarry rock dust is an adequate material that can be used as a source material in GPC to manufacture structural concrete members with satisfactory performance. The general performance of the GPC columns incorporating QRD (20%) is observed to be similar to that of GPC columns (without QRD) and CC columns. The addition of steel fibers considerably improves the loading capacity, ductility, and axial load-displacement behavior of the tested columns. The load capacities of fiber-reinforced GPC columns were about 5-7% greater in comparison to the CC columns. The spalling of concrete cover at failure was detected in all plain GPC columns, whereas the failure mode of all fiber-reinforced GPC columns is characterized with surface cracking leading to disintegration of concrete cover.  
geopolymer concrete columns; quarry rock dust; steel fibers; eccentricity; ductility
## 3 Results
### Effect of VOCs Emitted by Naganishia sp. on B. cinerea  
To evaluate the effect of VOCs emitted by _Naganishia_ sp. on _B. cinerea_, assays were conducted to analyze mycelial growth, germination, and sporulation (Figure 3). After two days of cultivation, mycelial growth of _B. cinerea_ was slower, in the presence of VOCs produced by _Naganishia_ sp., than the control. On the fourth day of cultivation, in the presence of VOCs, the growth of _B. cinerea_ stopped (Figure 3A). VOCs emitted by _Naganishia_ sp. inhibited 32.8% the conidia germination of _B. cinerea_ (Figure 3B) and reduced sporulation by 70.6% (Figure 3C).