esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 5 Conclusions  
In this study, we propose a Dual-Branch Luminance-Chrominance Attention Network (DBLCANet-HCIE) for low-light hydraulic concrete image enhancement. The network decomposes the task into two subtasks: luminance correction and chrominance restoration. The LAB employs global-local hybrid attention to enhance luminance and preserve structural details, while the CRB integrates CDB and FEDEB to suppress noise and enhance subtle chrominance and defect textures. Each branch integrates localized supervision modules to accelerate model convergence and generate intermediate enhancement results, complemented by a feature fusion block optimized for YCbCr space integration. Training was guided by a composite loss function combining structural preservation constraints and perceptual optimization. Extensive quantitative and qualitative evaluations on both the LOLv1 dataset and our custom LLHCID demonstrated the potential of the proposed framework for effective low-light hydraulic concrete image enhancement; our model achieved a PSNR of 28.61 dB and an SSIM of 0.9574 on hydraulic concrete images. Furthermore, real-world defect detection results show that the enhanced images significantly improve detection accuracy, highlighting the model's potential for practical structural inspection under low-light conditions.  
Future research will aim to extend the proposed method to a wider range of infrastructure types and to explore more advanced deep learning techniques to further improve enhancement performance. Additionally, efforts will be made to develop lightweight model architectures to facilitate real-time image enhancement on inspection devices with limited computational resources.  
Conceptualization, Z.P. and L.L.; methodology, Z.P.; software, Z.P., C.C., G.Z., J.J. and Z.T.; validation, Z.P., C.C., G.Z., R.T. and Z.T.; formal analysis, Z.P., L.L., R.T. and S.Z.; investigation, Z.P., R.T., G.Z., Z.T. and S.Z.; resources, Z.P., L.L., C.C., R.T., M.W., J.J., S.Z., Z.T. and Z.L.; data curation, Z.P., C.C., G.Z., R.T., M.W., S.Z. and Z.T.; writing--original draft preparation, Z.P., L.L., C.C., M.W. and Z.L.; writing--review and editing, Z.P., C.C., G.Z., J.J. and Z.L.; visualization, Z.P. and R.T.; supervision, Z.P. and L.L.; project administration, Z.P. and Z.L. funding acquisition, L.L. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number U21A20157.  
Not applicable.  
Figure 14: Comparison of low-light hydraulic concrete defect detection.
# Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
## 2 Preliminary Information  
### Extended Kalman Filter (EKF)  
The range between a ship and the AUV is not accessible at time step 0, due to long-term drift of the AUV. Thus, this paper uses the RPEKF to decrease the range estimation error. The RPEKF is composed of many independent EKFs, such that every EKF has a different initial range estimation.  
As the preliminary information of this paper, we introduce the EKF in [34] briefly. We consider discrete-time systems. \(dt\) indicates the sampling duration of our discrete-time system. At sample-index \(k\), we can define a nonlinear process model as  
\[\mathbf{X}_{k+1}=f(\mathbf{X}_{k})+\mathbf{w}_{k}^{p}. \tag{1}\]  
Here, \(\mathbf{X}_{k}\) denotes the state vector at sample-index \(k\). Also, \(\mathbf{w}_{k}^{p}\) is a Gaussian noise with mean \(\mathbf{0}\) and covariance matrix \(\mathbf{Q}_{k}\).  
At sample-index \(k\), we can define a nonlinear measurement model as  
\[\mathbf{z}_{k}=h(\mathbf{X}_{k})+\mathbf{w}_{k}^{m}. \tag{2}\]Here, \(\mathbf{w}_{k}^{m}\) is a Gaussian noise with mean \(\mathbf{0}\) and covariance matrix \(\mathbf{R}_{k}\).  
Let \(\hat{\mathbf{X}}_{k|k}\) denote the state vector of the EKF, utilizing all measurements up to sample-index \(k\). In addition, \(\mathbf{P}_{k|k}\) denotes the error covariance of \(\hat{\mathbf{X}}_{k|k}\). The EKF is composed of the prediction step and the measurement update.  
Using Equation (1), we predict the state vector of the EKF as  
\[\mathbf{X}_{k}^{-}=f(\hat{\mathbf{X}}_{k|k}). \tag{3}\]  
We then predict the error covariance matrix as  
\[\mathbf{P}_{k}^{-}=\mathbf{F}_{k}*\mathbf{P}_{k|k}*\mathbf{F}_{k}^{T}+\mathbf{ Q}_{k}. \tag{4}\]  
Here, \(\mathbf{F}_{k}\) is the partial derivative of \(f(\mathbf{X})\) with respect to \(\mathbf{X}\) evaluated at \(\mathbf{X}=\mathbf{X}_{k|k}\).  
Using the measurement \(\mathbf{z}_{k}\) in Equation (2), the state vector of the EKF is updated as  
\[\mathbf{X}_{k}^{-}=\mathbf{X}_{k}^{-}+\mathbf{K}_{k}(\mathbf{z}_{k}-h( \mathbf{X}_{k}^{-})). \tag{5}\]  
Here, we use  
\[\mathbf{K}_{k}=\mathbf{P}_{k}^{-}\mathbf{H}_{k}^{T}(\mathbf{R}_{k}+\mathbf{H} _{k}\mathbf{P}_{k}^{-}\mathbf{H}_{k}^{T})^{-1} \tag{6}\]  
where \(\mathbf{H}_{k}\) is the partial derivative of \(h(\mathbf{X}_{k})\) with respect to \(\mathbf{X}_{k}\) evaluated at \(\mathbf{X}_{k}=\mathbf{X}_{k}^{-}\). The error covariance is updated using  
\[\mathbf{P}_{k}^{-}=(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k})*\mathbf{P}_{k}^{-}. \tag{7}\]  
Then, we update both the state vector and its error covariance using  
\[\hat{\mathbf{X}}_{k+1|k+1} =\mathbf{X}_{k}^{-}.\] \[\mathbf{P}_{k+1|k+1} =\mathbf{P}_{k}^{-}. \tag{8}\]
## 3 Results and Discussion
Similarly, investigating the effects of the DR percentage against Re for various additives is depicted in Figure 5b. From the observation, all the materials investigated reduced drag. Apart from this observation, all their DR efficacy increased with the corresponding Re increase. However, the best performance was observed with the complex mixture. From Figure 5b, PIB at 150 ppm and SDS at 1000 ppm showed a linear trend. Apart from this quality, they both increased DR as Re increased. However, their performances were not the same. This figure shows that the polymer exhibited an improved DR with an initial value of 8.78% at \(Re\) = 302,227 to a maximum value of 19.197 at \(Re\) = 453,341. For the surfactant, SDS at 1000 ppm was less efficient compared to the polymer. For the surfactant, even at 1000 ppm, DR with an initial DR of 3.9 at \(Re\) = 302,227 to a maximum value of 8.03 at \(Re\) = 453,341. This behavior further confirms the initial observation that the surfactants are less effective than the polymers. Lastly, observing the complex mixture of the duo gave a direct trend, although DR increased as \(Re\) increased. However, various observations were made in the trend as well. From the commencement of the experiment at approximately \(Re\) of 302,227 to 362,673, an initial increase in the DR effect with the \(Re\) was observed. However, the DR values were observed to be the same at approximately \(Re\) of 362,673 to 392,896. At this point, the materials have not been completely stretched to make any reasonable effect. However, DR at approximately \(Re\) = 423,118 continuously increased from a value of 18.17 to 21.455 until the end of the experiment. Hence, the complex has been completely stretched and the synergistic action of the two additives is best felt at this point. The stander error and stander deviation were calculated by repeated the all runs for five times and the average value of torque and %DR were considered. Their values were tabulated in Tables S1-S9, which are available online at www.mdpi.com/2076-3417/6/12/355/s1. Figure 6 shows the complete interaction as a result of TEM, where the aggregate is formed by the surrounding polymer chain (small disc) to the threadlike micelles of the surfactant (large disc) Similar observations were made in [40], while working with a complex mixture of polyacrylamide (PAM) and sodium dodecyl benzene sulfonate (SDBS) in an RDA. Figure 5: Comparison of (**a**) torque; and (**b**) DR performance for polymer, surfactant, and their complex with different concentrations using smooth disk.
# Apollonian Packing of Circles within Ellipses
## 2 Materials and Methods
### Apollonian Circle Packing within an Ellipse  
#### 2.2.1 Point-to-Ellipse Distance  
The proposed approach to Apollonian circle packing within an elliptic domain is based on the evaluation of the distance between a point \(P\) and an ellipse \(\mathcal{E}\), owing to the necessity of satisfying a circle tangency condition on the outer elliptical boundary. In [2; 16], a special \"distance revealing\" type of parametrization is described; in addition, several algorithms for the numerical computation of the point-to-ellipse distance have been proposed and are easily available as open access contributions [17; 18]. In the case under analysis, the distance must only be evaluated for points within the elliptic boundary, and the algorithm must be chosen to provide an estimation of the distance value and, at the same time, the determination of point \(\overline{E}\) of the ellipse, which is at a minimum distance from \(P\). For this reason, a solution based on golden section search or the Fibonacci search algorithm [19; 20] is sufficient for our purposes, conveying high reliability and, although characterized by a linear convergence rate, being suitable to be applied to other convex figures, prior appropriate parametrization of the closed curvilinear boundary.
## 2 Materials and Methods
### Color Measurements  
The evaluation of the colorimetric parameters was performed according to the CIEL*a*b* color space. The colorimetric parameters L* (lightness), a* (red-green chromaticity coordinate), and b* (yellow-blue chromaticity coordinate) were measured using a Konica-Minolta (Tokyo, Japan) (model CM2500D) portable spectrophotometer with aperture diameter of 3 mm, standard illuminant D65, observation angle of 10 \({}^{\circ}\), and specular light included. Before measurements, the equipment was calibrated with the black and white standard plates that accompanied the equipment. The chroma (C* coordinate or saturation) and the hue angle (h\({}^{\circ}\)) were calculated, respectively, with Equations (7) and (8) [28]. The five colorimetric parameters were used to classify the wood color according to the cluster classification proposed by [PERSON] and [PERSON] [28].  
\[\text{C}^{*}=\left[\left(\text{a}^{*}\right)^{2}+\left(\text{b}^{*}\right)^{2} \right]^{0.5} \tag{7}\]  
\[\text{h}^{\circ}=\tan^{-1}\left(\frac{\text{b}^{*}}{\text{a}^{*}}\right) \tag{8}\]  
The samples were measured before (D, Figure 2) and after (E, Figure 2) thermal modification, where the former measurements represented the untreated treatment. The readings were taken at three points of each sample (Figure 3) on the tangential surface closer to the bark.  
The variation in the colorimetric parameters (\(\Delta\text{L}^{*}\), \(\Delta\text{a}^{*}\), and \(\Delta\text{b}^{*}\)) was calculated based on the deduction of the readings before and after the thermal modification, and the total color difference (\(\Delta\text{E}^{*}\)) was calculated with Equation (9), modified from [29]. \(\Delta\text{E}^{*}\) was qualitatively classified according to [PERSON] et al. [29], representing levels of visual perception (Table 3).  
\[\Delta\text{E}^{*}=\sqrt{\left(\Delta\text{L}^{*}\right)^{2}+\left(\Delta \text{a}^{*}\right)^{2}+\left(\Delta\text{b}^{*}\right)^{2}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemical Characteristic Evaluated** & **Observation** & **Reference** \\ \hline Total extractives & Use of toluene instead of benzene & T 264 cm-97 [22] \\ Extractives & Extraction with acetone and dichloromethane & T 204 cm-97 [23] \\ Insoluble Lignin & Insoluble Klason lignin & Gomide and Demuner [24] \\ Soluble Lignin & Acid soluble lignin & Goldschmid [25] \\ Holocellulose & Reaction with sodium chlorite and acetic acid & [PERSON], [PERSON], and [PERSON] [26] \\ Alpha-cellulose & Reaction of the holocellulose with 17.5\% & \\ Alpha-cellulose & sodium hydroxide & Adaptation of T 203 cm-99 [27] \\ Hemicellulose & Obtained by the difference between & - \\ holocellulose and alpha-cellulose & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of chemical analyses.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 2 Related Work
### Load Monitoring Approaches  
The process of load monitoring is conceived to facilitate the processes of identifying and monitoring main loads in the household [21; 25; 43]. Literature has identified two main categories to classify the methods to manage such processes: methods based on hardware, known as intrusive load monitoring (ILM), and those based on software, i.e., non-intrusive load monitoring (NILM) [44; 45].  
Most of the research work have been oriented to NILM, mainly motivated for its low-cost implementation and easy deployment requirements, using only one single point of sensing. This was the method selected in [19; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. In contrast, in some countries, the access to smart meter is still limited. The main challenges lie in regulation and implementation issues. Moreover, high-resolution data cannot be achieved with most current commercial smart meters today having complexity in setup, data storage, and cost. On the other hand, IoT technology has been gaining increasing popularity becoming an affordable option to overcome the difficulty of implementing NILM solutions. However, different requirements must be considered regarding data resolution, accuracy, real-time operation, and the number of devices to be covered, which also have been openly discussed in literature [16; 18; 22; 43].
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 3 Results
### Comparison of Single-Date NDVI Models with DHI According to Species Richness Metrics
\begin{table}
\begin{tabular}{l c c c c c c c}  & **Climate Only** & **Climate +** & **Climate +** & **Climate +** & **Climate +** & **Climate +** \\  & **NDVI 6-Mar** & **NDVI 26-Jun** & **NDVI 29-Aug** & **NDVI 14-Sep** & **NDVI 30-Sep** & **DHI** \\ SR Woodland & 23 & 33(34) & 42(40) & 42(43) & **45(46)** & 44(41) & 43 \\ SR Farmland & 6 & 15(12) & **35(36)** & 32(29) & 30(29) & 26(20) & 21 \\ SR Generalist & 8 & 12(13) & 7(13) & 12(11) & 12(13) & **14(12)** & 13 \\ SR Total & 16 & **20(22)** & 18(18) & 19(17) & 19(19) & **20(19)** & **20** \\ \hline \end{tabular}
\end{table} 
Table 2: Explanatory performance (%D\({}^{2}\)) of the bird-habitat models using climate factor only, climate and NDVI variables (_average_ and _standard deviation_ per plot) for each time period, or climate with DHI variables. The models are based on 759 bird plots using GAM. Response variables are species richness (SR) for four groups of birds (woodland, farmland, generalist and all the species). Bold numbers represent best performance for each bird group. Results are provided for both NDVI at 250 m and 500 m spatial resolution (out and in brackets respectively). Figure 4: Relationship between species richness (SR, \(y\)-axis) of woodland birds (**left**), farmland birds (**center**), and total richness (**right**) with the _average_ NDVI variable (\(x\)-axis) at the best time period. NDVI values (250-m product) were rescaled with a multiplicative factor of 10,000. The national spatial distributions of woodland and farmland species richness were predicted using their respective best model, showing a consistent pattern over France. Where the farmland bird species richness is high, the richness is low for woodland birds (Figure 6). In terms of absolute value, caution must be taken because of projection uncertainty in some areas (limited bird plots in mountain and Mediterranean regions used for model calibrations) [46]. Figure 5: Predictive performances (i.e., [PERSON]’s rank correlations _Rho_ between observed and predicted values) of the bird-habitat models based on climate factor only, climate with NDVI variables for each time period, climate with ensemble NDVI (consensus approach), or climate with DHI. The models are based on 759 bird plots using GAM and NDVI at 250-m spatial resolution. The response variables are species richness (SR) for four groups of birds (woodland, farmland, generalist and all the species). The predictive performances were calculated by 3-fold cross-validation with 100 repetitions. The average values of _Rho_ are provided above each boxplot. Different letters indicate that _Rho_ values are significantly different between datasets.
## 4 Implementation and Results  
### System Configuration  
The implementation environment was configured for experimentation, as shown in Figure 5. The AWS cloud environment used for testing was implemented using both hardware and software, as shown in Table 2. The main purpose of this experiment was to measure and compare the CPU overhead, execution time of the machine learning, and memory usage. The analysis results with and without the Docker Container are shown.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Resource** \\ \hline Cloud & Amazon Web Service \\ \hline Region & ap-northeast-2 \\ \hline Service & EC2 \\ \hline OS & Amazon Linux \\ \hline Kernel & Linux \\ \hline Instants type & T2.medium \\ \hline Key Pairs & RSA \\ \hline CPU & 2 \\ \hline Storage & SSD(gp2) 25G \\ \hline MEM & 4G \\ \hline \end{tabular}
\end{table}
Table 2: Cloud server computing environments.  
Jupyter Notebook: as shown in Figure 6, the convenience of the CLI is enhanced by registering the Jupyter Notebook with Jupyter.service for running automatically when Amazon EC2 boots.  
As shown in Figure 7, Jenkins is a tool that automates a build distribution, and the user can check the execution time of the batch jobs. Jenkins is automated to run the Docker Build/Run--a machine-learning defect determination application shown in Figure 8--and push it to the Docker Hub server, which is an image registry [37].  
Figure 5: Docker Container-based machine-learning pass/fail inspection system on AWS cloud server environment and a docker-based monitoring system, and image registry interconnection.  
Figure 6: Jupyter Notebook Interactive.
## 3 Experiment and Discussion
### Reliability of the Proposed Method and the Designed System  
To further demonstrate the reliability of the proposed measurement method, cutting edge radius of a diamond tool with a different nose radius of 2 mm, which was referred to as Diamond Tool 2, was also evaluated.  
Diamond Tool 2 was mounted on the nanoindentation system to replicate its cutting edge on the copper workpiece with various indentation depths. The AFM with Cantilever 1 was, then, applied to measure the profiles of the actual and replicated cutting edges. The command displacement of Diamond Tool 2 actuated by the FTS was also set from 50 to 300 nm with an interval of 50 nm. However, the corresponding indentation depths were evaluated to be 35, 45, 77, 104, 127, and 163 nm according to the outputs of the inside and the outside sensors, which were different from those by the previous diamond tool with a nose radius of 1 mm.  
The measured results of Diamond Tool 2 are summarized in Figure 13. The average radius of the diamond tool cutting edge was evaluated to be 41.29 nm with a standard deviation of 0.66 nm. The standard deviation of the evaluated uncertainty values was calculated to be 0.032 nm. The difference between the actual cutting edge radius by the proposed measurement method (\(R_{\text{tool}}\)) and the cutting edge radius imaged directly by the AFM (\(R_{\text{tool}\_\text{m}}\)) of Diamond Tool 2 was evaluated to be 7.36 nm. This value was also close to the nominal AFM tip radius of 7 nm. The reliability of the proposed method was, thus, verified from the measurement results of Diamond Tool 2.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 4 Data Aggregation Using Compressed Sensing Theory
### Compressed Sensing Theory
#### 4.2.2 Sparse Matrix Representation  
A sparse matrix is matrix representation which contains the sensor data d of the network nodes N. It is an orthogonal domain representation [31]. The data aggregation operation performs a regular interval, 't'. The sparse matrix at parent node's' representation is given in Equation (1).  
\[s=r_{i,t}\times d_{i,t} \tag{1}\]  
where \(r_{i,t}=\{r_{1,t},r_{2,t},\ldots r_{i,t}\}\), \(r_{i,t}\) is a random number and \(d_{i,t}\) is a sensor data of respective time interval.
## 2 Literature Review
### Crack Recognition Model  
#### 2.2.1 Image Processing-Based Model  
With the development of SAR technology, infrastructure settlement in large areas can be effectively monitored. Nevertheless, settlement information alone is insufficient to support experts in assessing the settlement status of infrastructure. Concrete structures play an essential role in infrastructure, but cracks are inevitable due to uneven settlement factors, construction quality, and other reasons. If cracks are allowed to develop, the durability and serviceability of the infrastructure can be severely compromised.  
Manual inspection was a very common and traditional method for crack detection [43]. This method, however, is labor-intensive, error-prone, vulnerable to traffic threats and subjective. In addition, surveying engineers often move between work areas, often in heavy traffic areas, so their life safety is also a concern [44]. Crack detection is a hot research topic. Many studies have been conducted on the manual investigation of cracks. Traditional methods include image processing and deep neural networks and support vector machines [45; 46]. The early use of computer vision technology to detect damage is the mainstream way to detect cracks with a high recognition rate of surface defects (e.g., cracks and corrosion). There are four edge detection methods that are commonly used: wavelet transform, Fourier transform, Sobel and Canny [47].  
[PERSON] et al. [48] proposed a computer vision-based pavement crack detection algorithm, developed an automatic crack recognition software system based on the proposed algorithm, and verified the effectiveness of the method. [PERSON] et al. [49] proposed a new full image processing method for the detection of cracks in concrete surfaces under various conditions. The method utilizes GP-assisted SACIF to develop a robust image filter (multi-sequence image filter) for crack detection. It automatically sets several simple image filters in multiple sequences by SACIF, then iterates and searches for the best combination to obtain the best multi-sequence image filter. [PERSON] and [PERSON] [50] used the improved Canny algorithm to judge the presence or absence of cracks, which performed well in removing noise and preventing edge loss. At the same time, the crack recognition rate of the algorithm has also been significantly improved.  
However, the literature review found that edge detection is a complex problem since the results are largely influenced by the noise generated when using image processing, mainly due to complex illumination, distortion and background, and without optimization of its solution. One of the most effective ways to surmount these problems is the use of noise reduction methods, but with little success.