esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing CNT Loading in Antimicrobial Composites for Urinary Tract Application
## 2 Materials and Methods
### Surface Characterization  
#### 2.3.1 Textural Properties  
The evaluation of the textural properties of CNT samples (CNTs-O and CNT-BM) was based on the N\({}_{2}\) adsorption isotherms, determined at \(-\)196 \({}^{\circ}\)C with a Quantachrome NOVA 4200e multi-station apparatus (Quantachrome Instruments, Boynton Beach, USA). The surface areas of the CNT samples were determined according to the Brunauer-Emmett-Teller method (SBET), and the total pore volume (V\({}_{\text{P}}\)) was determined from the amount of N\({}_{2}\) uptake at a relative pressure close to unity, namely at p/p\({}_{0}\) = 0.99 [27]. The pore size distribution was calculated using the non-local density functional theory by applying the kernel file provided by Quantachrome's software, where a cylindrical-pore model is assumed.
# Usability Testing of Mobile Applications:
## 3 The Theory of Mobile Usability  
### Usability Conceptualization  
In light of a recent study [27], the most widely accepted definition of usability is that provided in the ISO 9241-11 standard, which states that usability is \"the extent to which a system, product or service can be used by specified users to achieve specified goals with effectiveness efficiency and satisfaction in a specified context of use\" [28].  
At this point, of an obvious nature, a question arises: how does one understand context? Context can be understood as a carrier of information about the environment, place, time, and situation in which an entity currently exists [29]. Here, an entity is a user who deliberately interacts with a mobile application. With the ubiquity of mobile devices (GPS devices) [30] and Internet connectivity (public Wi-Fi hotspots, home Wi-Fi, LTE 4G, and 5G) [31], the ability to incorporate this type of information is common and in many domains has become even an imperative to use [32]. In summary, context in mobile systems can be divided into three categories [33]:  
* external, independent, and valid for all interested users (e.g., current weather, dangerous events, time, etc.);
* location, refers to information about the user's point of interest (e.g., traffic jams, road conditions, parking space, restaurant reviews, etc.);
* user-specific, related to the user's attributes, beliefs, activities, and interests (e.g., gender, age, nationality, religion, etc.).  
Incorporating such context in mobile applications significantly enhances the quality of service in terms of perceived usefulness by making our everyday environments increasingly intelligent [34].
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 3 Analysis and Discussion of Experimental Results
### Experimental Results and Analysis
#### 3.3.3 Comparison and Analysis of Performance Metrics for Different Models

Table 4: Comparison of performance indexes of different models on Dataset A.  
slightly lower than some models, MCD-Net excels in accuracy and robustness, particularly in handling complex backgrounds and randomly distributed corrosion areas, significantly improving detection accuracy and reliability. Compared to other hybrid CNN-Transformer architectures like Segformer and Swin-U-Net, MCD-Net further enhances the integration of local and global features. First, by introducing the Transformer into the convolutional encoder-decoder network, MCD-Net captures fine-grained corrosion features and enhances the ability to capture long-range dependencies through the self-attention mechanism. Second, the attention-based feature fusion method allows MCD-Net to dynamically adjust feature weights, effectively suppressing noise interference and excelling in corrosion edge detection. Additionally, the score-based multi-scale feature enhancement method proposed in this paper expands the receptive field and reinforces key features, enabling MCD-Net to demonstrate greater robustness and accuracy when handling multi-scale corrosion areas. Although MCD-Net's FPS of 20.12 is slightly lower than some lightweight models, its significant improvements in accuracy and robustness make it more suitable for real-world engineering corrosion detection tasks, particularly in situations where high precision and reliability are required. Compared to other traditional CNN models and hybrid architectures, MCD-Net not only achieves improvements in accuracy but also demonstrates significantly enhanced robustness in complex scenarios, striking a good balance between real-time performance and accuracy. Dataset B comparison results are presented in Table 5, showing similar findings.
## 3 Experiments, Results and Discussion
### Effects of Magnetic Particle Concentration on Magnetic Particle Indication Images
By controlling the optimal magnetic field strength and magnetic particle concentration, the reliability and consistency of automated magnetic particle inspection can be guaranteed. After processing the image information collected by the high-resolution CCD of the high-definition camera, the change in the concentration of the magnetic powder can also be monitored to form a closed-loop control. This article does not consider the detection conditions under extreme conditions, such as extremely low temperatures (below \(-20\)\({}^{\circ}\)C) and extremely high temperatures (above \(80\)\({}^{\circ}\)C); in these cases, the viscosity cannot be ignored. This limitation shall be overcome in future studies.
## 2 Theory and Simulation of Five-Frame Variable Phase-Shifting Method for SD-OCT
### Simulation
#### 2.2.1 CCA Suppression Ratio  
In order to quantify the complex conjugate suppression, the suppression ratio is the most important parameter for complex conjugate removal methods. Here, we defined the CCA suppression ratio as the difference between the amplitudes of the signal and complex conjugate artifact. Figure 3 showed simulated CCA suppression ratio values for FIP and FVP methods. As indicated in Figure 3, the CCA suppression ratios for FIP and FVP methods are 65.0 dB and 87.6 dB, respectively. The reason why the CCA suppression ratio of FVP method is higher than that of FIP method is that the polychromatic error of FIP method is higher than that of FVP method. Figure 4 demonstrated the correlation of polychromatic error and bandwidth of light source. It showed that the polychromatic error increases as the wavelength is far away from the center wavelength. For example, if the wavelength is 820 nm which is away from the center wavelength by a value of 30 nm, we could calculate the actual phase shift according to Equation (7) as \(\varnothing(2\times\pi/820)=\frac{\varnothing\times 2\times\pi/820}{2 \times\pi/850}\). Therefore, the phase-shifting error could be calculated by \(\varnothing_{error}=\frac{\varnothing\times 2\times\pi/820}{2\times\pi/850}-\varnothing\). When the phase shift \(\varnothing=\pm 90^{\circ}\), the phase-shifting errors for \(90^{\circ}\) and \(-90^{\circ}\) phase shifts reach \(3.29^{\circ}\). The error value will be \(6.58^{\circ}\) for \(180^{\circ}\) and \(-180^{\circ}\) phase shifts. The FIP method cannot completely remove CCA due to the influence of polychromatic error since the five phase shifts are correlated with the center wavelength  
Figure 2: Interferometric signal generated by a light source with a center wavelength of 850 nm and a bandwidth of 75 nm.  
and they result in phase-shifting errors. However, for the FVP method, we used variable phase shifts corresponding to the wavelengths instead of an invariant phase shift correlated with center wavelength and therefore, the polychromatic error is 0 in theory.
# Improving Water Efficiency in a Municipal Indoor Swimming-Pool Complex: A Case Study
## 2 Materials and Methods
### Water Efficiency Determination  
The water flow rates in the taps and showers were obtained by recording the duration of filling a specific volume \"X\" in a 10 L capacity bucket. Next, with the aid of a calibrated beaker, the final volume of water in the bucket was determined. The volumes of flushing cisterns were measured by registering the maximum level, producing a discharge, and then filling the cistern manually with a calibrated container until the restoration of the initial level. To obtain the water flow rates in the urinals, their outlets were covered with adhesive tape. Simultaneously, when the flow meter was activated, the discharge duration was measured in seconds. Finally, the water in the outlet was measured using a calibrated beaker. Concomitantly, to determine the water use behavior and patterns, a survey (Appendix A) was addressed to 70 swimming pool users to calculate the total average flow of each water device. Considering the obtained results for the water consumption, the flow rates of these water devices were adjusted to the reference values of class \"A\" water efficiency (the highest efficiency) of the Portuguese labeling system [29]. Nevertheless, when used in public buildings, their application is not very strict for health reasons, to ensure the proper functioning of drainage systems and adequate levels of user comfort [7]. Therefore, when possible, suggestions for more efficient devices were proposed to achieve higher water  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **Competition** & **Learning** \\ \hline Air temperature \({}^{\circ}\)C & 30 & 30 \\ Air humidity (\%) & 50–60 & 50–60 \\ Dimensions (L \(\times\) W) (m) & 25 \(\times\) 17 & 16.6 \(\times\) 10 \\ Base area (m\({}^{2}\)) & 425.0 & 166.6 \\ Minimum depth (m) & 1.8 & 0.7 \\ Maximum depth (m) & 2.0 & 1.3 \\ Water volume (m\({}^{3}\)) & 890.0 & 160.0 \\ Water added (m\({}^{3}\)/day) & 0.7 & 0.3 \\ Filter type & Pressure, closed & Pressure, closed \\ Number of filters & 2 & 2 \\ Filter diameter (mm) & 2000 & 1400 \\ Type of filter bed & sand & sand \\ Filtration surface (m\({}^{2}\)/ft\({}^{2}\)) & 3.1/33.8 & 1.5/16.6 \\ Filtration speed (L/min/m\({}^{2}\)) & 500 & 500 \\ Flow rate (L/min) & 1566.7 & 766.7 \\ Water residence time (h) & 4.2 & 2.1 \\ Water temperature (\({}^{\circ}\)C) & 26–28 & 26–28 \\ Water conductivity (\(\mu\)S/cm) & 190 & 200 \\ pH & 7.8 & 7.7 \\ Water turbidity (NTU) & \textless{}0.5 (l.q.) & \textless{}0.5 (l.q.) \\ Total chlorine (mg Cl\({}_{2}\)/L) & 1.1 & 1 \\ Free chlorine (mg Cl\({}_{2}\)/L) & 0.9 & 0.8 \\ Combined chlorine (mg Cl\({}_{2}\)/L) & 0.2 & 0.2 \\ Trihalomethnes \({}^{*}\) (\(\mu\)g/L) & \textless{}7 (l.q.) & \textless{}7 (l.q.) \\ \hline \hline \end{tabular}  
\end{table}
Table 1: Swimming-pool’s characteristics and mean values of some water parameters (Source: Braganca Municipality).  
efficiency. The proposals considered the Portuguese labeling system recommendations concerning public buildings (efficiency, price, and ease of installation).
## 4 Experiment
### Datasets and Evaluation Indicators  
#### 4.2.1 Datasets  
The public datasets used for evaluating the model in this paper mainly include the RESIDE dataset and the I-HAZE dataset. RESIDE is a large synthetic dataset divided into five subsets. We selected the Outdoor Training Set (OTS) and the Synthetic Outdoor Testing Set (SOTS) for experimentation. OTS contains 2061 clear outdoor images and 72,135 hazy images. Each clear image corresponds to 35 blurred images with different atmospheric light values and scattering coefficients. SOTS contains 500 indoor clear images and 500 clear outdoor images, along with their corresponding hazy images. The I-HAZE dataset is used to evaluate the performance of the algorithm in a real hazy environment, using an artificial fog device to generate real fog in an indoor setting.  
Currently, there are many indoor and outdoor hazy image datasets based on land, covering different scene types. However, there are hardly any publicly available datasets specifically for maritime hazy images. To address this issue, we used a hazing algorithm to fog natural images and constructed a maritime dehazing dataset called the Maritime Dehaze Dataset (MDD). To prevent overfitting, we collected maritime images from different scenes. Various visual scenes in the maritime domain are covered comprehensively in MDD, including inland waterway shipping, ports, near-shore coastal areas, watercraft, and buoy small target detection. In the MDD, there are a total of 2142 images for training and 200 for testing.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Image Preprocessing  
The five temporal images of Sentinel-1A SAR acquired for this study were automatically preprocessed using the Sentinel Application Platform (SNAP) 2.0 software [43]. SNAP is a common architecture for all Sentinel toolboxes, and is ideal for all earth observation processing and analysis. The images were first calibrated to sigma naught (\(\phi^{0}\)) backscatter coefficients using Equation (1). The calibrated images were then automatically terrain corrected using range Doppler terrain correction (producing 10 m square pixel resolution images), reprojected to the Universal Transverse Mercator (UTM) zone 51 N, and speckle filtered using a 7 \(\times\) 7 gamma map filter with 3 looks [44; 45]. Using the 9 June image as master, all the Sentinel-1A images were co-registered on-the-fly in SNAP based on binomial interpolation, using 200 ground control points (GCPs).  
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cloud-free images [46] as the one used herein. The atmospherically corrected Landsat 8 image was subset to the areal extent of the test site. This image subset was geometrically corrected using 30 GCPs located at major intersections or easily identifiable features across the study area, and a root mean square error (RMSE) of less than 0.5 pixel (<15 m) was recorded. A 4-band spectral subset comprising the green (0.53-0.59), red (0.64-0.67), NIR (0.85-0.88) and SWIR 1 (1.57-1.65) bands was then generated. These 4 multispectral bands were selected for the derivation of optical indices that will be combined with the temporal Sentinel-1A data for the land-cover mapping in this study.  
The final preprocessing involved a PAN-sharpening of the 30 m Landsat OLI image (selected 4 bands) to the 10 m square pixel resolution of Sentinel-1A. PAN-sharpening involves the merging of a panchromatic image (PAN) with a multispectral image (MSI) inorder to increase the spatial resolution of the latter, while simultaneously preserving its spectral information [47]. In this study, both datasets (Sentinel-1A as PAN and Landsat 8 as MSI) have been geo-referenced and projected to the same cartographic plane (UTM 51 N, WGS 84). The Gram-Schmidt Spectral Sharpening method was used to sharpen the multispectral Landsat data with the higher resolution Sentinel-1A data. Gram-Schmidt is an effective multispectral image sharpening method as it uses the spectral response function of a given sensor to estimate what the panchromatic data look like. It has been recommended for most remote sensing applications [47; 48] and is therefore, utilized in this study.
# Super-Resolution Reconstruction of Depth Image Based on Kriging Interpolation
## 1 Introduction
Numerous interpolation methods are in common use, such as inverse distance weighted interpolation [20], Kriging interpolation [21; 22], minimum curvature interpolation, the Sheppard method, natural neighbour interpolation [20], nearest-neighbour interpolation, multiple regression interpolation, the radial basis function method, the linear interpolation triangular network method, and the moving average method. The classical super-resolution algorithm for images introduces the Kriging interpolation algorithm by exploiting the fact that a depth image is also a distance image. In this study, the fractional-order calculus operator is introduced into the super-resolution reconstruction process of depth images, and the fractional-order differential mask is constructed to obtain high-frequency information such as image edges for originally low-resolution images. In addition, fractional-order edge matching is used to improve the accuracy of the Kriging interpolation process, and the fractional-order differential kernel is used to construct and improve the Kriging interpolation diffusion function. The experimental results show that the algorithm effectively improves the super-resolution reconstruction and enhances the subjective and objective outputs of the reconstruction process compared with the traditional algorithm.
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 2 Materials and Methods
### Image Multi-Labeling Based on a Pre-Trained CNN
However, it is not appropriate for image multi-labeling because we expect many neurons to output the value one and hence the sum will not be equal to one. Thus, we use other activation functions, such as linear or sigmoid, in the output layer and optimize the mean squared error (MSE) loss function, similar to deep models for regression problems. The linear activation is more appropriate because it has a derivative equal to one, which is preferable for the backpropagation algorithm used during training. Furthermore, the linear function is not limited to the interval \([0,1]\) like the sigmoid function, so each output neuron is free to estimate values below or above the correct outputs of zero or one. To train the neural network, we use the back propagation technique and minimize the following cost function [35]:  
\[\mathcal{L}_{2}(\theta_{NN})=\frac{1}{2n}\sum_{i=1}^{n}\sum_{k=1}^{C}\|\mathbf{y}_ {i}\ -\ \theta_{NN}(\mathbf{x}_{i})\|^{2}+\ \frac{\lambda}{2}\left(\ \sum_{l=1}^{H}\|\mathbf{W}^{(h)}\|_{F}^{2}+\sum_{o=1}^{C}\|\mathbf{W}^{(o)}\|_{F}^{2}\right) \tag{5}\]  
where \(\theta_{NN}(\mathbf{x}_{i})\) is the output of the NN for an input \(\mathbf{x}_{i}\), \(\mathbf{W}^{(h)}\) and \(\mathbf{W}^{(o)}\) are the weights of the hidden layer and the output layer, respectively, and \(H\) is the size of the hidden layer. The first term of (1) refers to the mean squared error between the estimated labels and the training labels, while the second term is the weight decay penalty that helps to prevent overfitting. Note also that \(n\) is the number of training samples, and \(C\) is the output vector size (the number of objects to be recognized). The estimation of the vector of parameters \(\theta_{NN}=\{\mathbf{W}^{(h)},\mathbf{W}^{(o)}\}\) of the NN starts by initializing the weights to small random values. The cost (1) is then minimized with a min-batch gradient descent algorithm [36]. At test time, we feed each test image to the CNN and assume that an output node \(j\) having a predicted value larger than a given threshold (for example, 0.5) indicates the presence of the object \(j\). We call this parameter the presence threshold \(T_{p}\).
## 2 Ultrafast Deep-UV and XUV Beamline
Figure 1a, Layerte GmbH, Mellingen, Germany) with a hole of 1.0 mm diameter, through which the XUV and residual NIR pulses can pass, whereas >90% of the DUV beam is reflected off. The XUV beam can pass through the aperture without clipping. Thin metallic filters such as Al and Zr (wheel of metallic filters in Figure 1a) allow the XUV pulse to be transmitted while blocking the remaining small portion of the DUV and fundamental NIR pulses. The residual NIR in the DUV arm is effectively suppressed by utilizing the reflectivity properties of the UV separators and dielectric mirrors (R > 99% at 270 nm, R < 10% at 405-850 nm) with a total of nine reflections from the DUV optics, resulting in NIR suppression of more than 99.99%. The thin metal filters in the XUV arm (150-300 nm thickness) suppress the NIR and DUV transmission by at least nine orders of magnitude, allowing for essentially perfect separation of the DUV and XUV beams for pump-probe experiments. The DUV and XUV pulses are then combined again at the second perforated dichroic beam separator (DBS2 in Figure 1a), which has a 1.5 mm diameter hole. The combined beams are then focused by a toroidal mirror into the interaction chamber where the interaction region is located between the repeller and extractor plates of a velocity map imaging (VMI) spectrometer [26]. In the interaction chamber, the sample under study can be injected either through a supersonic gas jet from the top of the target chamber or through an effusive gas jet which is integrated into the repeller plate of the VMI spectrometer.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture  
This work proposes an Edge AI-based anomaly detection framework tailored for smart home environments. The system ensures real-time data processing, enhances privacy, and optimizes energy efficiency while maintaining high anomaly detection accuracy. This architecture follows a multi-layered approach, incorporating IoT sensors, edge computing for AI-driven anomaly detection, optional cloud integration, and a user alerting system. Our proposed system architecture draws inspiration from several key studies:  
* **IoT Network Anomaly Detection in Smart Homes Using Machine Learning**: This study presents a machine learning-based approach for detecting anomalies in smart home IoT networks, utilizing Random Forest and decision tree classifiers. The methodology and results informed our selection of efficient models suitable for edge deployment [5].
* **Enhancing Smart Home Security**: Anomaly Detection and Face Recognition: This research study explores the integration of deep learning models for anomaly detection and face recognition within IoT devices, highlighting the importance of real-time, on-device processing to enhance security and privacy in smart homes [17].
* **Edge AI for Smart Home Applications**: This article discusses the application of Edge AI in smart homes, emphasizing its role in enabling functionalities like motion detection and anomaly detection without relying on cloud services, thereby ensuring data privacy and reducing latency [3].
## 5 Adaptive Double-Precision Gesture Interaction  
### Double-Precision Pointing Technique  
Position-based pointing techniques require a transfer function to map input motion captured by the device to cursor motion. The most common transfer function uses Control-to-Display (CD) gain, defined as the ratio between cursor movements, usually measured in terms of linear distance and input change.  
The research work of [PERSON] et al. has theoretically proved that the single-mode remote pointing technology is not suitable for large-scale display wall systems, so a double-precision technical framework supporting the acceleration function was proposed. The double-precision pointing technology divides the pointing task into two stages: (1) the coarse positioning stage, where the user controls the interactive object to quickly approach the target; (2) the precision positioning stage, where the user can precisely control the interactive object to move to the target area.  
Both coarse and precise modes can be absolute or relative. Absolute controls are variable speed and easy to use, as the previous cursor position does not affect the input movement, i.e., the cursor can be teleported. They have a fixed gain, however, and thus have limited precision. The pointer acceleration transfer function and wider CD gain range can be applied to the relevant controls, but the offset between the input and cursor position may increase, resulting in variable speed action.  
Currently, most research work on double-precision pointing technology uses absolute control for coarse positioning, relative control for precise positioning, and an explicit switching method to switch between coarse and precise modes. We believe that using an explicit switching method will affect the fluency of the user interaction process. In addition, it is necessary to define new interactive gestures or use other devices to realize mode switching, which will increase the user's burden. Therefore, we have designed an adaptive mode switching method based on the moving speed of user interaction gestures.