esa-sceva/satcom-chunk-collection huggingface dataset:
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 4 Examination
### Data Collection  
To create bolt tightening image data for evaluation, the inside of a box girder at an actual bridge construction site was photographed. A video camera was used to take a series of images of the sides and bottom of the bolt tightening points in the box girders so that the images could be captured in a single video. The video was taken at the closest distance where all the horizontal rows of bolts at the bolted points could be captured, but the distance was approximately 30 cm to 50 cm because the number of bolts in a row varies depending on the location. There was only one type of bolt to be photographed. There was no lighting equipment installed on the bridge girder; thus, we used handheld lighting equipment to illuminate the bolt tightening points to be photographed. The lighting equipment was not fixed but placed next to the video camera and moved with the video camera. The video was shot with a JVC GZ-RY980 camcorder in 4K quality. The video used in this paper has a shooting time of 23 s, and the number of bolts in the video is 362. All the bolts in the video were visually confirmed to be tightened properly by the operator.
## 3 Examples of QS-Based Activities of _Pseudomonas_ spp. Affecting Spoilage of Fish
### QS and Proteolysis  
_Pseudomonas_ spp. isolated from fishery products displayed proteolytic activities accompanied by an increase in amino acids and volatile sulfur compounds such as mercaptans and H\({}_{2}\)S [32]. The proteases of fish-borne _Pseudomonas_ spp. are serine, thiol, or metalloproteases stabilized by Ca\({}^{2+}\). They exhibit low activation energies compared to the classical trypsin protease [36]. Proteolytic enzymes degrade the fish muscle and connective tissue, facilitating bacterial penetration to deeper structures and causing textural changes in fish [37].  
AHLs have been identified in proteinaceous foods and were correlated with the proteolytic activity of microflora [34]. AHLs were found in rainbow trout fillets contaminated by _P. fluorescens_ and _P. putida_[13]. Moreover, _P. fluorescens_ exhibited significantly higher proteolytic activity when exogenous C4-HSL and C6-HSL were added to the culture [30]. A significant increase in total volatile basic nitrogen (TVB-N) (from 5.21 to approximately 60 mg N/100 g) during storage was observed with the addition of C4-HSL and C14-HSL. These AIs had a stimulatory effect on the _aprX_ metalloprotease gene expression and proteolytic activity of _P. fluorescens_ (by 63 and 52%, respectively) [38]. In _P. psychrophilia_, _P. orientalis_, and _P. fluorescens_, changes in QS systems involving downregulation of AHL and the PQS molecules resulted in inhibition of proteolysis (approximately 15 to 30%) along with downregulation of genes encoding metalloproteases [39]. A significant inhibition by about 40% of extracellular protease activity was finally confirmed by [PERSON] et al. [35] in \(\Delta\)luxl and \(\Delta\)luxR mutants of _P. fluorescens_.
###### Abstract  
In industrial quality control, especially in the field of manufacturing defect detection, deep learning plays an increasingly critical role. However, the efficacy of these advanced models is often hindered by their need for large-scale, annotated datasets. Moreover, these datasets are mainly based on RGB images, which are very different from X-ray images. Addressing this limitation, our research proposes a methodology that incorporates domain-specific self-supervised pretraining techniques using X-ray imaging to improve defect detection capabilities in manufacturing products. We employ two pretraining approaches, SimSiam and SimMIM, to refine feature extraction from manufacturing images. The pretraining stage is carried out using an industrial dataset of 27,901 unlabeled X-ray images from a manufacturing production line. We analyze the performance of the pretraining against transfer-learning-based methods in a complex defect detection scenario using a Faster R-CNN model. We conduct evaluations on both a proprietary industrial dataset and the publicly available GDXray dataset. The findings reveal that models pretrained with domain-specific X-ray images consistently outperform those initialized with ImageNet weights. Notably, Swin Transformer models show superior results in scenarios rich in labeled data, whereas CNN backbones are more effective in limited-data environments. Moreover, we underscore the enhanced ability of the models pretrained with X-ray images in detecting critical defects, crucial for ensuring safety in industrial settings. Our study offers substantial evidence of the benefits of self-supervised learning in manufacturing defect detection, providing a solid foundation for further research and practical applications in industrial quality control.  
defect detection; manufacturing; optical quality control; deep learning; self-supervised learning  
X-ray Images. _Appl. Sci._**2024**, _14_, 2785. [[https://doi.org/10.3390/app1407285](https://doi.org/10.3390/app1407285)]([https://doi.org/10.3390/app1407285](https://doi.org/10.3390/app1407285))
# Low-Complexity Hybrid Optical OFDM with High Spectrum Efficiency for Dimming Compatible VLC System
## 4 Numerical Results
Furthermore, in contrast to HACO-OFDM scheme, while it has to detect ACO-OFDM signal first and then demodulatethe PAM-DMT signal, our proposed scheme can be detected upon utilizing the same receiver as the DCO-OFDM scheme, beneficially decreasing the complexity of receiver. Therefore, the proposed DCHO-OFDM scheme may be further cooperated with DCO-OFDM regime for a VLC system to satisfy the different transmission and illumination requirements, without increasing the complexity at the receiver side. Figure 5: The performance of dimming level \(\eta\) vs duty cycle \(\rho\), with various values of scaling factors. Figure 6: BER performance of the proposed DCHO-OFDM scheme under different variances of signal.
## 3 Results and Discussion
### Activation Energies of Diffusion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Substance**} & **Molecular** & **Molecular** & **Temperature** & **Activation** & **Pre-Exponential** & \multirow{2}{*}{**Method**} \\  & **Volume** & **Weight** & **Range** & **Energy** & **Factor D\({}_{0}\)** & & \\ \hline  & **(Å3)** & **(g/mol)** & (\({}^{\circ}\)C)** & **(kJ/mol)** & **(cm2/s)** & \\ \hline Methanol & 37.2 & 32.0 & 0–90 & 4.9 & \(1.4\times 10^{-8}\) & Permeation, below Tg \\ Ethanol & 54.0 & 46.1 & 0–90 & 37.3 & \(5.1\times 10^{-4}\) & Permeation, below Tg \\ Acetone & 64.7 & 58.1 & 85–105 & 7.6 & \(1.3\times 10^{-7}\) & Desorption \\
1-Propanol & 70.8 & 60.1 & 40–90 & 59.6 & \(2.2\times 10^{-1}\) & Permeation, below Tg \\
1-Butanol & 87.6 & 74.1 & 40–90 & 91.1 & \(2.6\times 10^{5}\) & Permeation, below Tg \\ Ethyl acetate & 90.6 & 88.1 & 85–105 & 123.9 & \(1.2\times 10^{9}\) & Desorption \\ Chlorobenzene & 97.6 & 112.6 & 85–105 & 171.3 & \(1.8\times 10^{15}\) & Desorption \\ Toluene & 100.6 & 92.1 & 85–105 & 180.6 & \(2.1\times 10^{16}\) & Desorption \\ Styrene & 111.8 & 104.2 & 80–100 & 180.9 & \(8.2\times 10^{15}\) & Desorption, below Tg \\ Styrene & 111.8 & 104.2 & 100–115 & 162.6 & \(2.5\times 10^{13}\) & Desorption, above Tg \\ Styrene & 111.8 & 104.2 & 85–105 & 189.8 & \(1.3\times 10^{17}\) & Desorption \\ \(n\)-Octane & 146.6 & 114.2 & 80–100 & 252.1 & \(7.0\times 10^{25}\) & Desorption, below Tg \\ \(n\)-Octane & 146.6 & 114.2 & 100–115 & 191.3 & \(2.3\times 10^{17}\) & Desorption, above Tg \\ Phenyl cyclohexane & 174.0 & 160.3 & 85–105 & 396.8 & \(1.6\times 10^{45}\) & Desorption \\ Benzophenone & 174.4 & 182.2 & 85–105 & 315.5 & \(1.5\times 10^{34}\) & Desorption \\ \(n\)-Decane & 180.2 & 142.3 & 80–100 & 336.7 & \(2.2\times 10^{37}\) & Desorption, below Tg \\ \(n\)-Decane & 180.2 & 142.3 & 100–115 & 220.6 & \(1.2\times 10^{21}\) & Desorption, above Tg \\ \(n\)-Dodecane & 213.8 & 170.3 & 80–100 & 416.4 & \(1.6\times 10^{48}\) & Desorption, below Tg \\ \(n\)-Dodecane & 213.8 & 170.3 & 100–115 & 243.1 & \(8.1\times 10^{23}\) & Desorption, above Tg \\ \(n\)-Tetradecane & 247.3 & 198.4 & 80–100 & 489.1 & \(1.5\times 10^{58}\) & Desorption, below Tg \\ \(n\)-Tetradecane & 247.3 & 198.4 & 100–115 & 256.4 & \(3.4\times 10^{25}\) & Desorption, above Tg \\ \(n\)-Hexadecane & 281.0 & 226.5 & 80–100 & 525.8 & \(1.4\times 10^{63}\) & Desorption, below Tg \\ \(n\)-Hexadecane & 281.0 & 226.5 & 100–115 & 244.3 & \(4.4\times 10^{23}\) & Desorption, above Tg \\ \(n\)-Octadecane & 314.6 & 254.5 & 80–100 & 504.9 & \(8.5\times 10^{59}\) & Desorption, below Tg \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Activation energies of diffusion \(\mathrm{E_{A}}\) and pre-exponential factors \(\mathrm{D_{0}}\) for GPPS.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 4 Results
### CO\({}_{2}\) Concentration Prediction  
Figure 7 compares the performance of MLTs in predicting CO\({}_{2}\) concentration in the living room based on IAQ monitoring in the kitchen. The following performance metrics were used: R\({}^{2}\) (Figure 7a), MAPE (Figure 7b), and RMSE (Figure 7c). Their median values are shown in Table 3.  
As can be seen in Figure 7a, among MLTs used to predict CO\({}_{2}\) concentration, linear methods MLR and MLP2 showed the highest coefficients of determination (R\({}^{2}\) = 0.51 and R\({}^{2}\) = 0.52, respectively). Yet, these values were low in a general sense, and they indicated the poor quality of prediction models. In the case of the naive approach, R\({}^{2}\) = 0.38 was still smaller. Coefficients of determination for nonlinear MLTs (R\({}^{2}\) = 0.05-0.16) were so small that they precluded the use of these models for predicting CO\({}_{2}\) concentration. Interestingly, as indicated in Figure 7b, the relative errors of CO\({}_{2}\) concentration prediction using linear methods (MAPE = 34.34-36.40%) were similar to those for nonlinear methods (MAPE = 29.11-39.69%). In addition, MLTs outperformed the naive approach  
Figure 6: Comparison of the MLTs’ performance in predicting living room relative humidity based on IAQ monitoring in the kitchen. The metrics are (**a**) R\({}^{2}\), (**b**) MAPE, and (**c**) RMSE. The dots indicate the means.  
(MAPE = 66.1%). Similar to the case of MAPE, the RMSE of CO2 concentration prediction using linear methods (RMSE = 265-277 ppm) was similar to that obtained using nonlinear methods (RMSE = 252 ppm-360 ppm), and all of them were smaller than the error offered by the naive approach, with RMSE = 491 ppm (see Figure 7c). In summary, it should be underlined that MAPE errors of tens of percentages, as well as RMSE errors of several hundred ppm, are large and not attractive results. Thus, based on the performed analysis, the prediction of CO2 concentration in the living room, based on IAQ monitoring in the kitchen, was ineffective while using MLTs trained on the data collected in different apartments.
## 2 Data and Methods
### Data
#### 2.1.2 CALIOP/CALIPSO Aerosol Profiles and MACv2 Aerosol Climatology  
To get the full image of the aerosol loading and optical properties in the area, aerosol extinction coefficient profiles at 532 nm (Extinction_Coefficient_532_Mean) from the Cloud-Aerosol Lidar with Orthogonal Polarization onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite (CALIOP/CALIPSO) version 4.20 (v4.20) monthly level-3 product were acquired along with AOD, single scattering albedo (SSA) and asymmetry parameter (ASY) data at 8 wavelengths (350, 450, 550, 650, 1000, 1600, 2200 and 3000 nm) from the Max Planck Aerosol Climatology version 2 (MACv2) [54]. Similarly to Aqua, CALIPSO, also part of the A-train satellite formation (until September 2018), has been flying at a sun-synchronous polar orbit since April 2006. It crosses the equator at around 13:30 LT shortly after Aqua and has a 16-day repeat cycle [55]. CALIOP onboard CALIPSO is an Nd:YAG elastic backscatter and polarization lidar transmitting a linearly polarized laser light at 532 nm and 1064 nm [56]. CALIOP provides profiles of aerosol- and cloud-related properties from the ground up to 30 km [55]. The daytime (temporally coincident with MODIS/Aqua) CALIOP/CALIPSO data used here cover the period 6/2006-12/2019 being available at a 2\({}^{\circ}\) (latitude) \(\times\) 5\({}^{\circ}\) (longitude) horizontal resolution and a 60 m vertical resolution (up to 12 km). The level-3 data are compiled from level-2 CALIOP/CALIPSO extinction coefficient data after applying various quality filters [57; 58]. The MACv2 climatology data are available at a 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) spatial resolution and refer to the year 2005. MACv2 is compiled by fusing observational data from the Aerosol Robotic Network (AERONET) and the Marine Aerosol Network (MAN) with simulations from 14 different AeroCom (phase1) models [54].
# Fabricating Sustainable All-Cellulose Composites
## 4 All-Cellulose Composites
### Biocomposites  
Biocomposites have at least one of their constituents (either reinforcement or matrix) derived from biomass [154]. Already in the early 1940s [PERSON] created natural fibre reinforcement biocomposites using soy-based plastics to reduce the weight of the cars [155]. However, it was not until recently that bio-based automotive parts caught popularity among the car manufacturers due to increasing emission control and sustainability concerns [156].  
The most commercially important biocomposites are made by combining man-made and biobased sources. Commonly, matrices are petrochemically derived thermoplastic polymers, such as polypropylene or polyamide, or thermoset polymers, such as unsaturated polyester or epoxy resins; meanwhile, the reinforcement is formed using a variety of biobased sources, such as natural cellulosic plant-based fibres or wood pulp [157, 158]. The use of wood and plant fibres in composites is motivated not only by their sustainability characteristics, but also their good performance, low cost, low density, safe handling, high electrical resistance, good acoustic insulation and the fact that they do not require moulding equipment [159, 160]. A major drawback of natural plant-based fibres compared to man-made fibres is the hydrophilic nature of cellulose. Water absorption by cellulose fibres can result in aging, poor mechanical properties and reduced dimensional stability of the composite. Plant fibres also naturally have poor fire resistance, and they degrade when heated to over 200 \({}^{\circ}\)C. They are also easily susceptible to biological degradation [138, 160].  
Nanocellulose, which is used as a raw material especially for ACNC, can be obtained by two approaches (i) top-down and (ii) bottom up. The top-down approach involves the disintegration of lignocellulosic biomass into nanoscale cellulose. In the bottom-up approach nanoscale cellulose can be produced via fermentation of low weight sugars by cellulose-producing bacteria, such as from the Acetobacter species. This type of nanoscale bacteria (BC) is pure cellulose and does not include hemicellulose, lignin, or pectin [161]. When cellulose-based composites are produced from less purified sources, such as wood pulps or other plant fibres, hemicellulose and lignin are always present to some degree, and these can act as impurities which often results impairing properties of the composite [30].
# A Review Paper on \"Graphene Field Emission for Electron Microscopy\"
## 2 Development of a Single-Tip Cathode Cold Field Emission Source  
### Theory of Cold Field Electron Emission  
Electron sources are essential tools for investigation into a broad range of field emission devices [27; 28; 29; 30; 31]. For electron microscopy applications, electron sources can be divided into two main categories, known as thermionic and field emission [32]. A thermionic electron gun uses Joule-heating induced thermal excitation to allow electrons to overcome the energy barrier between the metal tip and the vacuum. The electron beam that it produces is inherently incoherent, having a source size in the tens of microns range, a relatively large energy spread (2 eV) and poor brightness (\(\sim\)10\({}^{6}\) Am\({}^{-2}\)sr\({}^{-1}\)V\({}^{-1}\)). A CFE electron gun overcomes these limitations by decreasing the virtual source size to about 3 nm (by significantly decreasing the emission tip to about 100 nm radius) and the energy spread to about 0.3 eV (operating the source at room temperature), in which electrons tunnel from a solid surface to the vacuum under a high local electric field [1]. The mechanism of the electron emission process is based on the Fermi-Dirac distribution for a free electron gas in the potential energy barrier between the metal tip and the vacuum [33].  
[PERSON] and [PERSON] first found a classical theory describing the behavior of field emission currents from a metallic surface in 1928 [34]. In the theory, the relationship between the emission current (\(I\)) and the tip field strength (\(F\)) is expressed as,  
\[I=A\frac{1.5\times 10^{-6}}{\phi}F^{2}\exp\left(-\frac{6.44\times 10^{9}\phi^{ 1.5}}{F}\right), \tag{1}\]  
where the constant \(A\) has the dimension of area m\({}^{2}\), \(\phi\) is the work function in eV, and \(F\) is the local electric field on the cathode tip, given by \(F=\beta V_{\text{ext}}/d\), with \(\beta\) being the field enhancement factor, \(V_{\text{ext}}\) the external applied voltage, and \(d\) the anode to cathode tip spacing. The cathode tip is geometrically sharp, and the local electric field (\(F\)) is intensified by a factor of \(\beta\) on the tip surface. To achieve cold field electron emission, \(F\) is typically around few volts per nanometer. A plot of \(\ln\left(I/V_{\text{ext}}^{2}\right)\) against \(1/V_{\text{ext}}\) will have a slope of \(m=~{}-\left(6.44~{}\times 10^{9}\phi^{1.5}d/\beta\right)\), the well-known Fowler-Nordheim (FN) plot.
# Effects of Corner Set--Backs on Wind Loads and Wind Induced Responses of Rectangular Tall Buildings
## 3 Analysis of Test Results
### Base Moment  
#### 3.3.1 Base Moment Coefficients  
Similarly, only the mean and RMS base moment coefficients at along-wind direction, the RMS across-wind base moment coefficients are analyzed in this paper. The corresponding base moment coefficients are calculated with following equations:  
\[C_{MD}=\frac{\overline{F_{MD}}}{BH^{2}q_{H}},\ C_{MD}^{\prime}=\frac{\sigma_{MD} }{BH^{2}q_{H}} \tag{7}\]  
\[C_{ML}^{\prime}=\frac{\sigma_{ML}}{BH^{2}q_{H}} \tag{8}\]  
in which, \(C_{MD}\) is mean base moment coefficient in along-wind, \(C_{MD}\) and \(C_{ML}\) are the RMS along-wind and across-wind base moment coefficients, respectively. \(\overline{F_{MD}}\), \(\sigma_{MD}\) and \(\sigma_{ML}\) are the mean base moment in along-wind, RMS base moment in along-wind and across-wind, respectively. \(B\) is the breadth of the windward of the model, \(D\) is the depth of the side ward of the model, and \(H\) is the height of the model.  
Figure 11 presents the variations of base moment coefficients with the corner set-back rate. Since the aspect ratio (Depth to Breadth) of the benchmark building model is 2/3, several outcomes from previous studies [15; 20; 21] and design codes [16; 22] on the base moment coefficients of this typical tall building have also been listed for comparison. From Figure 11a, it can be found that as the rate of corner set-back increases, the mean along-wind base moment coefficient first decreases and then increases. The minimum value 0.338 was observed when the rate of corner set-back is 10%. This variation trend is in line with the trend of the mean drag force. The RMS base moment coefficients of along-wind direction decrease as corner set-back rate increases, although there is a tiny increase when the rate of corner set-back is 20%. When the rate of corner set-back exceeds 5%, the RMS along-wind base moment coefficients nearly reach stable state and keep at about 0.051. With regard to the RMS across-wind base moment coefficients, it is found from Figure 11c that the stipulated values from the Chinese design code [16] and suggested values from the previous studies [20; 21] are conservative. The value provides by the Japanese design code is more reasonable to describe the across-wind loads. The RMS base moment coefficient in across-wind decreases from 0.102 to 0.031 when the rate of corner set-back increases from 0% to 20%, indicating that larger rate of corner set-back could be effective in reducing the loads of rectangular high-rise buildings in across-wind.  
Figure 11: Cont.
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 4 Energy Efficient Techniques in the IoT
### Energy Efficient Techniques at Sensing Layer
#### 4.1.1 Energy Efficient Sleep/Wakeup Techniques
Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing (EESACS) [33] has been suggested to optimize energy consumption in IoT applications like water distribution monitoring, intelligent transport systems, smart cities, etc., by activating only a small subset of sensors to sense and transmit in every timeslot, using a scheduling approach based on compressive sensing. Further solutions based on dynamic programming are suggested, after decomposing the scheduling problem into sub-problems. If sensor \(s^{i}\) is chosen as activated \(m\) are the additional activated sensors out of sensors \(s^{i+1}\) to \(s^{N}\) where \(s^{i}\) and \(m\) additional sensors are connected, then the maximum residual energy of all possible subsets of \(m\) activated sensors is formulated as:  
\[E\Big{(}s^{i},m\Big{)}=\left\{ \begin{array}{ll}\max\limits_{s^{i}\in K^{ \prime}_{-(s^{i})}}\big{\{}E\big{(}s^{j},\ m-1\ \big{)}+v^{j}\ \big{\}}&\text{if}\ K^{\prime}_{-(s^{i})}\
eq\varnothing,\\ -\infty&\text{otherwise}\end{array} \right. \tag{2}\]  
where \(v^{j}\) is the normalized residual energy of sensor \(s^{j}\) and \(K^{\prime}_{-(s^{i})}\) is the set of sensors in the DNS of \(s^{i}\) except sink node \(s^{r}\). Analysis of the connection between network lifetime and energy consumption has not been done through the proposed technique, and this technique was not tested in various IoT applications such as tunnel and railway monitoring.