esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera
## 3 Width Measurement Using Image Processing
### Overview of Indoor Measurement Experiments
#### 3.1.2 Crack Measurement Limits  
In many countries, the standard for the assessment of states regarding crack widths is stipulated as 0.5 mm or smaller for the prevention of corrosion, the improvement of durability, and aesthetic reasons. ACI-318 regulations stipulate the standard as 0.41 mm for indoor exposure, 0.3 mm for outdoor exposure, and 0.2 mm for extreme environments. British standards (BS) 8110 stipulate that cracks should not exceed 0.3 mm in a general environment, and South Korea stipulates five levels of standards for the assessment of states  
\begin{table}
\begin{tabular}{c c c c} \hline
**Trial** & **Manufacturer** & **Focal Length (mm)** & **Width (mm)** & **Resolution (million)** \\ \hline CC50 & Canon & 50 & 22.1 \\ SC50 & & 50 & 0.1, 0.3, and 0.5 & 61.0 \\ SC100 & Sony & 100 & 0.1, 0.3, and 0.5 & 61.0 \\ SC135 & & 135 & & \\ \hline \end{tabular}
\end{table}
Table 1: Measurement experimental variables.  
Figure 3: Test image.  
regarding the maximum crack widths in visual inspections of various structures. Among them, in the case of grade B, the standards are presented as 0.1 to 0.3 mm for reinforced concrete and as smaller than 0.2 mm for prestressed concrete. In this study, the allowable limit for crack width detection and measurement was set to 0.1 mm to 0.3 mm, and an experimental plan was established for the selection of optimum image measurement equipment and the determination of shooting conditions and image processing techniques, such as the degrees of recognition of cracks according to working distances and the extraction of cracks with limits.
## 2 Modeling
### Path Planning Environment Modeling
(**b**) Regional Grid Marine Environment Map with a resolution of 25 m \(\times\) 25 m. In the picture, the white meshes represent the feasible region, while the black meshes are a barrier. \begin{table}
\begin{tabular}{c c} \hline \hline
**Plotting Scale** & **Depth Contour Interval** \\ \hline \(\geq\)1:150,000 & 0/2/5/10/20/30/50/100/200/500 \\ \(\geq\)1:1,000,000 and \(\leq\)1:150,000 & 0/5/10/20/30/50/100/200/500/1000 \\ \(\leq\)1:1,000,000 & 0/10/50/200/500/1000/2000/4000/6000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Interval table of electronic navigation chart (ENC) with different scales. accurate discrete depth points are chosen to be processed to obtain a raster prediction depth, and then evaluates the depth risk of the planned path. Spline function method with obstacles is utilized to interpolate from discrete water depth points. In this method, the two-dimensional minimum curvature spline method is used to interpolate points into rater surface, and the generated rater surface passes through all of the input depth points from electronic navigation charts. As shown in Figure 5c,d, the prediction depth generated by the spline function with obstacles can be well-matched with the contour line. Through this method, the water depth of each grid in feasible space can be obtained, and then the water depth risk of each grid can be calculated, as detailed in Section 3.2.
# Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
## 2 Data Analysis
### Rainfall Data Analysis  
China's meteorological departments classify rainfall according to its intensity [25]. Rainfall intensity refers to the amount of rainfall per unit period of time, which is generally expressed by the depth of rainfall per unit time. According to the difference in unit time, there are several different classification methods of rainfall levels; meteorological departments generally set the unit time as 1 h, 12 h, and 24 h. The classification standards of rainfall grades in China are shown in Table 2:  
Raw meteorological data include important parameters such as hourly atmospheric pressure, temperature, mean relative humidity, mean wind speed, and rainfall. The influence of factors other than rainfall on travel demand is small, so is not considered. This paper focuses on the influence of rainfall on online ride-hailing travel demand characteristics, so only hourly rainfall data and the date and time of the corresponding data collection were extracted. The longitude and latitude of Haikou meteorological station selected in this paper is 110.25\({}^{\circ}\) and 20\({}^{\circ}\). In order to facilitate the research, it was assumed that rainfall was uniformly distributed during the data collection period.  
Additionally, combined with rainfall intensity and online ride-hailing travel data, the comparison of hourly travel demand was made between 19 May 2017 (Friday with rain), 26 May 2017, and 12 May 2017 (both Friday with no rain). As shown in the figure below, the black column indicates the intensity of rainfall, while the black broken line indicates the number of online ride-hailing trips per hour on 19 May.  
It can be seen in following Figure 3 that, on this day, the rainfall intensity at 17:00 and 18:00 in the evening was relatively high, reaching the level of moderate rain. Compared with the other two days, the travel demand for online ride-hailing at this time also significantly  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rainfall Level** & **1 h Rainfall (mm)** & **12 h Rainfall (mm)** & **24 h Rainfall (mm)** \\ \hline Light rain & \(\leq\)2.5 & \(\leq\)4.9 & \(\leq\)9.9 \\ Moderate rain & 2.6–8 & 5–14.9 & 10–24.9 \\ Heavy rain & 8.1–16 & 15–29.9 & 25–49.9 \\ Torrential rain & \(\geq\)16.1 & \(\geq\)30 & \(\geq\)50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification standards of rainfall grades in China.  
Figure 2: Daily number of online ride-hailing trips.  
increased. According to the travel characteristics of online ride-hailing, this paper proposes an improved model based on the GRU model, which takes into account the factors of rainfall intensity and rest days.
## 3 Sea Surface Sun Glint Suppression and Tiny Human Object Detection Based on Visual Saliency
### Sea Surface Sun Glint Suppression and Object Saliency Enhancement
#### 3.2.1 Glint Suppression Based on HSV-Gray Method
\begin{split} GC[I]_{\text{p}}=\sum\limits_{\text{q}\in S}G_{ \sigma}(\|\text{p}-q\|)I_{\text{q}}\\ G_{\sigma}(r)=\frac{1}{2\pi\sigma^{2}}\exp\left(-\frac{r^{2}}{2 \sigma^{2}}\right)\end{split}  \tag{1}\]  
where \(G_{\sigma}(r)\) denotes the 2D Gaussian kernel, \(\sigma\) is a parameter defining the standard deviation, and \(S\) represents all the possible image locations. The weight and the spatial distance can be changed by adjusting the Gaussian kernel size (\(r_{\text{max}}\)) and the standard deviation (\(\sigma\)). The edge and saliency of an object are lost with high \(r_{\text{max}}\) and \(\sigma\) because averaging is performed over a much larger area. Then, the image is blurred to obtain a smooth pixel surface (\(P_{\text{guass}}\)). \[ \begin{split} H_{\text{RGB}}=H(I_{\text{RGB}})\\ P_{\text{HSV-gray}}=Gray(H_{\text{RGB}})\end{split}  \tag{2}\]  
\[ \begin{split} P_{\text{guass}}=GC(H_{\text{RGB}})\end{split}  \tag{3}\]  
\[ \begin{split} P_{\text{saliency}}=255\frac{P_{HSV-gray}-P_{ \text{guass}}}{\text{max}\big{(}P_{HSV-gray}-P_{\text{guass}}\big{)}}\end{split}  \tag{4}\]  
As shown in Equation (2), the HSV mapping function, H(I), is applied to the visible image (\(I_{\text{RGB}}\)) to obtain the mapped HSV image (\(H_{\text{RGB}}\)). The mapped gray intensity values (\(P_{\text{HSV-gray}}\)) are calculated with a common grayscale function. The representations of \(P_{\text{HSV-gray}}\) are shown as the first column in Figure 5. Large-scale Gaussian Convolution is applied to the maritime UAV images according to the object size, and the standard deviation (\(\sigma\)) is much larger than the latter. The 4\(\sigma\) Gaussian kernel size is applied to obtain the Gaussian pixel surface (\(P_{\text{guass}}\)) given in Equation (3). The value of \(\sigma\) is about 2 or 3 times that of tiny objects. In this study, we set \(\sigma\) = 40. The pixel surface results are shown in the second column in Figure 5. The difference between \(P_{\text{HSV-gray}}\) and \(P_{\text{guass}}\) is  
Figure 4: The relationship between \(P_{\text{HSV-gray}}\) and pixel intensity values in the different RGB channels. object saliency, \(P_{\text{saliency}}\), given in Equation (4). The representations of \(P_{\text{saliency}}\) are shown in the third column in Figure 5.  
As shown in Figure 5, \(P_{\text{HSV-gray}}\) shows that the color characteristics of an object can be effectively highlighted, and \(P_{\text{gauss}}\) demonstrates that the high pixel values around objects can be effectively suppressed with large-scale Gaussian Convolution. Finally, \(P_{\text{saliency}}\)-3D illustrates that the pixel intensity values of background such as the sea surface and sky can be significantly suppressed and tiny human targets highlighted.
# A Robust Localization System Fusion Vision-CNN
## 3 System Overview and Methodology  
### System Overview  
The system framework of this paper is shown in Figure 1, which is mainly composed of three parts. The first part is to collect the image and 2D laser information, and then train the convolutional neural network together with the prior map information (which can be obtained through the SLAM method). The second part is to use the trained neural network to provide rough pose estimation and then use the branch and bound method to speed up the search for accurate pose when the robot is in the stage of pose initialization or pose tracking failure (such as the kidnapping problem). The third part is to fuse the information of the IMU and the wheel odometer in the robot tracking stage to obtain the predicted pose of the robot. Then, the correlation sampling method is used to obtain the optimal estimated pose of the robot, and the nonlinear optimization method can be further used to obtain a higher-precision pose.
###### Abstract  
Corrosion can hurt the quality and service life of a workpiece, which could create potential safety hazards. However, improving the workpiece's surface integrity through surface treatment could improve the effect of corrosion resistance. To study the effect of surface integrity on the corrosion resistance of low-alloy steel for hydrogen reactors, electrolytic corrosion experiments were carried out on specimens that were processed using different grit sandpapers or using different shot peening pressures using a self-built electrolytic platform. The influence regulation of initial surface roughness and surface residual stress on the corrosion rate of the low-alloy steel for a hydrogen reactor under different lengths of corrosion time, surface roughness after corrosion, and the tensile property degradation of the specimens after 4 h of corrosion were analyzed, respectively. In this paper, based on experimental research, we obtain the influence regulation of the processing parameters on the corrosion resistance of low-alloy steel for hydrogen reactors and provide processing parameters that could improve the corrosion resistance of low-alloy steel, which guides the corrosion resistance processing of hydrogen production reactors. It was found through experiments that with an increase in the initial surface roughness of the specimens, the corrosion rate of the specimen tends to decrease with the increase in corrosion duration; the surface roughness of the specimen after corrosion first increases and then decreases with the increase in corrosion time; and the tensile strength of specimen with the initial surface roughness of Ra 0.168 \(\upmu\)m is relative good after 4 h of corrosion. With the increase in residual compressive stress on the surface of the specimen, the corrosion rate of the specimen decreases with the increase in corrosion time; the surface roughness of the specimen after corrosion first increases and then decreases with the increase in corrosion time; and the tensile strength of specimen with the surface residual stress of \(-\)335.64 MPa is relative good after 4 h of corrosion.  
surface integrity; hydrogen production reactor; electrolytic corrosion; corrosion rate; tensile strength +
Footnote †: journal: Applied Sciences
# Laboratory Test of a Vehicle Active Noise-Control System Based on an Adaptive Step Size Algorithm
###### Abstract  
NVH (noise, vibration, and harshness) is a key factor affecting vehicle comfort. Compared with traditional sound absorption and isolation methods, active noise control (ANC) offers a significant advantage in solving the problem of low- and medium-frequency noise from road surfaces. However, the classic filtered-x least mean squares (FxLMS) algorithm is ineffective in terms of adapting to different road noises to ensure a stable noise reduction effect when facing the complex and changeable noise environment of moving vehicles. Therefore, an adaptive step size algorithm (ASSFxLMS) is proposed in this paper, which can adjust the step size according to the size of the reference signal to ensure the stability of the adaptive process. In order to improve the performance of the algorithm, a particle swarm optimization algorithm is also used to automatically adjust the parameters, so that the step size of the adaptive algorithm always maintains a relatively ideal size. The simulated pulse noise of standard SaS distribution was used as the reference signal for the simulation. The simulation results show that compared with other algorithms, the proposed algorithm under different degrees of pulse noise conditions, noise reduction stability, and noise reduction amplitude are improved. In order to further verify the feasibility of the algorithm in vehicle road noise reduction, this paper also conducted a hardware-in-the-loop noise reduction experiment in the laboratory, employing the road noise data collected by the real vehicle. Under different interior noise conditions, the proposed active noise-control system has a maximum noise reduction effect of 12 dB for low-frequency noise below 100 Hz.  
active noise control; adaptive step size; particle swarm optimization algorithm; vehicle noise
# Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study
## 6 Conclusions  
This paper presented a DL-based method for dual-tracer [\({}^{18}\)F]FDG and [\({}^{11}\)C]MET dynamic PET image separation. The method employed a 2D convolutional encoder-decoder to separate dynamic dual-tracer images acquired in a single scan to infer static single-tracer images. Our simulation results showed that the proposed DL-based method using different numbers of training pairs (up to 80) outperformed the CM-based method at the pixel level for short time delay intervals \(\Delta t\) \(\leq\) 15 min and even for simultaneous injections (\(\Delta t\) = 0 min).  
Conceptualization, methodology, investigation, and formal analysis: B.P. and A.J.R.; software, data curation, validation and visualization: B.P.; writing--original draft preparation, B.P.; writing--review and editing, B.P. and A.J.R.; supervision, P.K.M. and A.J.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the EPSRC programme for Next Generation Molecular Imaging and Therapy with Radionuclides [EP/S032789/1, 'MITHRAS'] and by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank the Wellcome Multiuser Equipment Radioanalytical Facility funded by Wellcome Trust (212885/Z/18/Z), and the EPSRC programme for Next Generation Molecular Imaging and Therapy with Radionuclides (EP/S032789/1, 'MITHRAS'). This research was funded by EPSRC grant number EP/S032789/1. For the purposes of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Accepted Author Manuscript version arising, in accordance with King's College London's Rights Retention policy.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l} PET & Positron Emission Tomography \\ \([^{18}\)F]FDG & 18F-Fluorodeoxyglucose \\ \([^{11}\)C]MET & 11C-Methionine \\ TACs & Time-Activity Curves \\ AIF & Arterial Input Function \\ PCA & Principal Component Analysis \\ rXGBoost & Recurrent Extreme Gradient Boosting \\ ROI & Region of Interest \\ DL & Deep Learning \\ CED & Convolutional Encoder-Decoder \\ GAN & Generative Adversarial Network \\ WM & White Matter \\ GM & Grey Matter \\ TM & Tumour \\ MLEM & Maximum-Likelihood Expectation Maximization \\ GT & Ground-Truth \\ NF & Noise-Free \\ MSE & Mean Square Error \\ WNLS & Weighted Non-Linear Least Squares \\ NRMSE & Normalised Root Mean Square Error \\ SD & Standard Deviation \\ FI & Frame Integrated \\ FWHM & Full Width at Half Maximum \\ \end{tabular}
## 4 System Model
### DMPF Scheduler Module
#### 4.1.1 Request Analyzer  
SDN controller forwards the receiving DASH client request to the Request Analyzer Component (RAC). RAC collects the received requests from the DASH clients and performs some pre-processing on the collected requests to prepare them as an input parameter for the DMPF scheduler. RAC gives important meta-data on the video files in DASH servers. The meta-data includes the server list, file segments stored, layers, and their properties, such as each layer size and bit rate. In our work, we consider only the bit rate of each SVC layer.
# A Python-Based Indoor Channel Model with Multi-Wavelength Propagation for Color Shift Keying
## 3 Channel Characterization Metrics
### Inter-Channel Cross-Talk Matrix  
VLC systems utilizing WDM techniques are subject to inter-channel interference, primarily due to the non-ideal spectral emissions of the LEDs and the spectral responsivity of the photodetectors (PDs). In the specific case of CSK modulation, the use of three distinct color channels in both the transmitter and receiver (typically red, green, and blue) introduces interference. For instance, when the green channel is activated to represent a particular symbol in the constellation, the receiver may not solely detect the signal in the green channel, as the red and blue channels may also capture part of the emitted radiation.  
Estimating the inter-channel interference in WDM-based communication systems is essential for maintaining signal integrity, optimizing system performance, and reducing bit error rates. Accurate estimation allows for better channel separation, improved spectral efficiency, and enhanced modulation schemes such as CSK. Additionally, it enables the design of more advanced receivers capable of mitigating interference, helping to ensure reliable communication. In VLC systems where communication and illumination coexist, estimating the interference is crucial for balancing lighting quality and efficient data transmission. The system's inter-channel cross-talk is determined by computing the inner product between the spectral power distribution received on the active area and the responsivity of the photodetector for each central wavelength, as follows:  
\[h_{m,n}=\int_{\lambda}R_{m}(\lambda)P_{R,n}(\lambda)d\lambda \tag{16}\]  
where \(h_{n,m}\) represents the optical-to-electrical conversion of the n-th LED radiation that arrives at the m-th photodetector. The interference cross-talk matrix is defined in Equation (17).  
\[H=\begin{bmatrix}h_{1,1}&h_{1,2}&\cdots&h_{1,n}\\ h_{2,1}&h_{2,2}&\cdots&h_{2,n}\\ \vdots&\vdots&\ddots&\vdots\\ h_{m,1}&h_{n,2}&\cdots&h_{m,n}\end{bmatrix} \tag{17}\]  
The spectral distribution of the received power from each LED is estimated by assuming a Gaussian shape:  
\[P_{R,n}(\lambda)=\frac{P_{R,\lambda_{cn}}}{\sqrt{\pi\cdot\sigma_{n}^{2}/2}}e^ {-\frac{1}{2}\left(\frac{\lambda-\lambda_{cn}}{\sigma_{n}}\right)^{2}} \tag{18}\]  
where \(\sigma_{n}\) represents the full width at half-maximum, \(\lambda_{cn}\) is the central wavelength of the LED, and \(P_{R,\lambda_{c}}\) is the received power at the central wavelength.
## 5 Fuels from Isoprenoid and Fatty Acid Pathways
### Yeast Biofuels from Fatty Acids
cerevisiae_, researchers are on a quest to identify different yeasts with the capability to feed on various carbon sources (like lignocellulosic), high fatty acid producing ability, and yeasts which are easy to handle at industrial scale. _Yarrowia lipolytica_, a yeast with enhanced flux of acetyl-CoA, is being considered as a better model for the biosynthesis of fatty acids and biofuels. In a study, genes like ACC1 and DGA1 were upregulated in _Y. lipolytica_, leading to enhanced lipid synthesis (\(-\)0.3 g/g) [100]. By knocking out the \(\beta\)-oxidation pathway as well as inserting lipoxygenase from soybean, _Y. lipolytica_ was engineered to produce pentane (a short-chain alkane) [101]. _Y. lipolytica_ was engineered for upregulating stearoyl-CoA desaturase, DGA1, and ACC1 genes to circumvent the highly regulated metabolism of FAs. This alteration resulted in the enhanced production of lipids (\(\sim\)55 g/L) [102]. A few strains of _Y. lipolytica_ were also metabolically engineered to synthesize alkanes, FAEE, TAG, FOH, and FFA. In a study, directing different enzymes (like MmCAR, acyl-CoA/acyl-ACP-related enzymes, and AtfA) to different cellular compartments also enhanced the alkane (23.3 mg/L) and FAEE (142.5 mg/L) production. Moreover, by engineering substitute pathways for acetyl-CoA in cytosol, greater amounts of TAG (66.4 g/L) and FFA (9.67 g/L) were produced. Along with enhanced alkane, FAEE, TAG, and FFA production, the same study also reported the production of FOH (2.15 g/L). The FOH production was achieved by the expression of the heterologous genes FAR from _Marinobacter aquaeolei_ and fadD from _E. coli_[103]. Consuming lignocellulosic biomass, engineered _Y. lipolytica_ produced methyl ketones. This was achieved by localizing enzymes related to the methyl ketone pathway (bacterial) like FadM, FadB, ACO etc., in peroxises, and knocking out POT1 from its chromosomal DNA. This alteration has yielded about 315 mg/L of methyl ketone [104].
## 2 Concept
### Single- and Multi-Target Scenarios
#### 2.2.1 Scenario I  
Scenario I represents the very basic radar scenario: A single target is located within the main beam of one antenna. In the displayed case, target #1 is located in the main beam of antenna #2. Hence, the corresponding time-domain RX signal can be set up to be:  
\[s_{\mathrm{r1}}(t)=s_{\mathrm{t}}(t)*h_{\mathrm{sys}}(t)*h_{\mathrm{fs}}(t- \tau_{\mathrm{I}})*\sigma*h_{2}(t) \tag{7}\]  
Figure 2: Illustration of different radar scenarios occurring for coded RX- channel overlay operation.  
By means of adequate signal processing, target #1 can be easily found within the radar diagram and the subsequent spectral analysis will deliver the unique filter coding function \(H_{2}\) of the corresponding radar sub-channel.