esa-sceva/satcom-chunk-collection huggingface dataset:
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 3 Methods  
### Architecture of the Network  
Figure 1 displays the comprehensive architecture of the neighborhood normal vector cues-based multi-view stereo network. As with other techniques, our approach utilizes a multi-scale feature pyramid network to acquire multi-scale feature information from images captured through different viewpoints. The information entering the network is N image information from varying viewpoints, represented as \(\{I_{i}|i=1...N\}\), where \(I_{1}\) signifies the target image, while \(\{I_{i}|i=2...N\}\) pertains to the source images obtained from the surrounding viewpoints. In addition to this, as input, we require the intrinsic matrix \(K_{i}\) and extrinsic matrix \([R_{i}|T_{i}]\) of each image, which can be calculated via the Structure From Motion (SFM) methodology. Our network model has three stages, as indicated in Figure 1, and the feature map dimensions used in each stage are \(R^{\frac{H}{4}\times\frac{W}{4}\times 16C}\), \(R^{\frac{H}{2}\times\frac{W}{2}\times 4C}\), and \(R^{H\times W\times C}\), correspondingly. The generated feature maps are then fed into the stereo-matching module at each stage, culminating in the development of higher-resolution depth prediction outcomes through iterative means.  
As showcased in Figure 2, distinct feature maps from varying perspectives undertake differentiable homographic transformations towards the target view through depth sampling, which in turn ensures view consistency for different feature vectors. The cost between N views is determined and further regulated by a 3D U-Net network. Moreover, the application of softmax weighting is conducive to the depth information of the output result, while normalization is initially performed on the normal vector information, followed by a weighted sum based on probability. Ultimately, the depth map and normal map of the current view image can be procured.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Manganese-Doped Copper Oxide Films  
The manganese (Mn)-doped CuO films were deposited on glass substrates [167]. Experimental findings described plate-like morphology and a monoclinic structure. The shapes of the nanostructures changed and the band gap increased when the concentration of Mn increased.
## 1 Introduction
Although there is a significant difference between their learning algorithms and the currently used algorithms, the core idea of learning by interactions and experience is the same. The three attempts shared the same idea of maximizing a cost function and learning to select the controller parameters to achieve the best performance. The learning algorithm implemented by [13] was able to achieve near optimal results compared with the Linear Quadratic Gaussian (LQG) under idealized conditions. Reference [15] had the same approach, but they introduced a new learning scheme, which allowed the controller after learning to work in certain conditions where the traditional LQG controller resulted in an unstable system. They also tested the learning method in a real vehicle, but with a semi-active suspension system installed, and they showed promising experimental results. A more recent successful attempt was the study done by [16] where they applied a stochastic real-valued reinforcement learning control to a non-linear quarter model. A similar approach to the one considered in this research was conducted by [17]; the actor critic networks were trained by the policy gradient method, and the controller was tested to some extent with the same road profile considered in this study. They compared their work with the passive suspension system and showed 62% improvement. In this paper, an online deep reinforcement learning controller is developed using the TD advantage actor critic algorithm. The controller is applied to a quarter model active suspension system to investigate the performance of this learning-based algorithm against the Linear Quadratic Regulator (LQR) and the optimum Proportional-Integral-Derivative (PID). In order to handle possible system uncertainties, the algorithm is integrated with a Recursive Least Squares method (RLS) for online estimation of system damping coefficients.
## 1 Introduction  
Properties and behavior of soils are determined by the following factors: fabric, mineralogy, and pore water. Due to the interdependence of individual factors, it is difficult to determine their interaction using only traditional statistical methods [1]. Classical constitutive modeling based on the elasticity and plasticity theories is unable to properly simulate the behavior of geomaterials. This is due to the formulation complexity, the idealization of the behavior of the material, and excessive empirical parameters [2]. Therefore, in many papers, artificial neural networks (ANNs) have been proposed as a reliable and practical alternative to modeling the constitutive monotonic and hysteretic behavior of geomaterials [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28].  
This article concerns the use of artificial neural networks (ANNs) to determine the value of the damping ratio (D) of clay soils. Parameter D is one of the basic soil parameters. It characterizes the behavior of soil subjected to dynamic loads, such as the foundations of machines, the traffic of city trans and subways, and the driving of precast pile or sheet pile. These geotechnical problems are associated with significant damping, so its impact has to be taken into account in the dynamic analysis [29]. Unfortunately, the damping ratio, although important, is not easy to determine either on the basis of empirical formulas or in laboratory tests. The authors present a model of an artificial neural network (ANN) and compare the appropriateness of its application with empirical formulas described in the literature. The ANN model was developed on the basis of a series of laboratory tests of the damping ratio performed in the Resonance Column manufactured by GDS Instruments Ltd. Neural networks were to increase the accuracy of prediction. They provide an attractive solution for determining the behavior of a complex system and they are widely used for nonlinear pattern recognition and regression.  
The paper also presents the procedure of creating an artificial neural network (ANN) model to determine the value of the damping ratio (D) of clay soils. The main purpose of this paper is to compare the appropriateness of ANN model application with empirical formulas described in the literature, and it also presents a method of selecting the structure of the ANN with the best predictive quality. To determine the architecture of the neural network, the following were determined: the number of input variables, the number of output variables, the number of hidden layers, the number of neurons in hidden layers, the type of activation function in the neurons of hidden layers and the output layer, and the number of learning epochs [30]. The Python programming language and the following libraries: TensorFlow, Keras, NumPy, Pandas, and Scikit-Learn, were used to create the ANN model. This article can be used as a guideline for creating other ANN models in the field of geotechnics.
## 1 Introduction
In cryosphere areas, the atmosphere is typically dry and does not contribute to satellite measurements [23], which can produce more uncertainties and biases that could limit our understanding of hydro-meteorological studies. Therefore, the accuracy of these products is questionable [24]. Thus, the credibility of GPPs should be evaluated against an appropriate dataset before it can be reinforced as input. Their credibility is beneficial for developers and users, where developers can diagnose the problems from feedback and improve the quality of products. On the other hand, analyzed results will enable the data users to consider the possible uncertainties related to a specific dataset and select an appropriate product for particular applications in a specific area of interest. Since the launch of GPPs, the majority of these products have been assessed based on their suitability and usability for a particular region. Most of these assessments are carried out in different parts of the world. Still, very few studies are conducted for the Hindukush, Karakorum, and Himalayas (HKH), including the upper Indus Basin (UIB) region [2; 11; 25; 26]. This region is termed the third pole as it stores the largest amount of (frozen) water after polar regions, which feeds the rivers with fresh water and accommodates millions of people living downstream. Previous studies revealed the mass balance of this region had been significantly altered by significant atmospheric warming, which has also impacted the water resources of countries downstream [26; 27; 28]. Thus, precise and accurate precipitation measurements are required to develop actionable, sustainable water resource management policies. Henceforth, this study is carried out (1) to provide more information about bias and uncertainties in datasets, specifically in the glaciated mountain region. Therefore, the primary purpose of our research is (2) to evaluate the performance and differences of the newly launched Multi-Source Weighted-Ensemble Precipitation (MSWEP) parallel to four other GPPs over the JRB. The selected four products are European Atmospheric Reanalysis (ERA5), APHRODITE, Princeton Global Meteorological Forcing Dataset for Land Surface Modeling (PGMFD), and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). We aimed (3) to recognize the datasets with the best performance combined with available gauged data to procure finer quality data than individual products. Such rectified precipitation products are essential for operational applications and ongoing research. The study findings would be significant in understanding the accuracy and error characteristics of multiple products for algorithm developers and users that could be used as baseline information for similar research, particularly in snow-fed mountainous watersheds.
###### Abstract  
In the field of modern navigation and positioning, the ground-based eLoran system, serves as a vital backup to the global navigation satellite system (GNSS), which is crucial for numerous key applications. Signal demodulation, integral to eLoran's precision timing and information transmission, significantly affects system performance. Aiming at the pulse position modulation (PPM) characteristics of eLoran signals, this paper introduces an innovative phase spectrum smoothing demodulation (PSSD) algorithm, crafted to improve demodulation performance under complex noisy and interference-laden conditions. Following a systematic review of existing demodulation techniques in eLoran, this paper details the theoretical foundation, key steps, and significant impact of parameter selection for the PSSD algorithm. Then, the unique advantages in dealing with noise, continuous wave, and skywave interference are analyzed and verified. Through extensive experimental validation under various SNR and interference conditions, the PSSD algorithm shows significant superiority in demodulation performance compared with the traditional envelope phase detection (EPD) algorithm. The effectiveness of the PSSD algorithm in interference mitigation and its stable performance across diverse conditions confirm its potential to meet the high-precision timing requirements of eLoran systems, contributing to the advancement of modern communication systems.  
eLoran; phase spectrum smoothing demodulation; signal reception processing; modern communication systems +
Footnote †: journal:  
Article
# Blockchain-Based Distributed Patient-Centric Image Management System
## 5 Evaluation
### PCIM System Analysis
#### 5.3.1 Evaluating the Image Access Time  
For this experiment, we set up the conventional cloud-based PACS (C-PACS) using PostDICOM [35], which uses Amazon S3 to store the medical images. We evaluated the efficiency of image access time by comparing the PCIM storage system with the C-PACS based on two parameters: the number of submitted images, and the size of the stored medical images. The measurement of the performance was based on the following metrics: upload and download time of medical images. We obtained anonymized sample images from the DICOM library dataset [30] to verify the image access time in Seconds. The experiment was performed on our local computer with an internet download speed of 272.19 MBps and an upload speed of 55.92 MBps. The medical image size range from 1-100 MB is uploaded to our distributed IPFS network and conventional cloud network and then downloaded the images to the local computer. The upload time for PCIM is observed when medical images get stored into the IPFS network and download time is observed during the medical images accessed using the IPFS hash value stored in the blockchain. Figure 10 shows the time taken to upload and download the medical images using the PCIM and C-PACS storage. The line graph shows that the proposed system takes less than 1 s to upload 4-115 medical images and takes 5.31 s to upload 100 MB of medical images. Whereas, the C-PACS system takes nearly 8 s for uploading 10 MB of images and it continues to increase as the image size reaches 100 MB. The bar graph in Figure 10 shows that within 6.19 s 1142 medical images are downloaded by the proposed system and C-PACS takes 19.43 s to download similar number of images. As evident from the figures, we can see that the medical image access time of the PCIM storage network outperforms the conventional cloud system. As expected, the proposed approach is faster due to its distributed operations. The traditional storage system is a complex process due to the centralized server, queued transactions, and privacy issues. In general, the experiments show that the IPFS based storage solution is robust and possible to access all the images faster and without any interruptions.
# Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application
###### Abstract  
Debris detection and classification is an essential function for autonomous floor-cleaning robots. It enables floor-cleaning robots to identify and avoid hard-to-clean debris, specifically large liquid spillage debris. This paper proposes a debris-detection and classification scheme for an autonomous floor-cleaning robot using a deep Convolutional Neural Network (CNN) and Support Vector Machine (SVM) cascaded technique. The SSD (Single-Shot MultiBox Detector) MobileNet CNN architecture is used for classifying the solid and liquid spill debris on the floor through the captured image. Then, the SVM model is employed for binary classification of liquid spillage regions based on size, which helps floor-cleaning devices to identify the larger liquid spillage debris regions, considered as hard-to-clean debris in this work. The experimental results prove that the proposed technique can efficiently detect and classify the debris on the floor and achieves 95.5% percent classification accuracy. The cascaded approach takes approximately 71 milliseconds for the entire process of debris detection and classification, which implies that the proposed technique is suitable for deploying in real-time selective floor-cleaning applications.  
Convolutional Neural Network (CNN); Support Vector Machine (SVM); floor-cleaning robot; object detection; debris classification; SSD MobileNet +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### GP Fitness Functions  
The GP-OLS MATLAB toolbox [38], with a modified fitness function, has been used for GPs training. In particular, three fitness functions are considered. In the case of the supervised approach, the accuracy of k-NN is considered [31]. In the case of the unsupervised approach, the fitness function is equal to the mean value of the silhouette coefficient computed on the data partitions provided by the k-means algorithm. The silhouette coefficient, defined in [29] and given in Equation (3), measures the similarity of a point to the points belonging to the same cluster compared with points belonging to the other clusters.  
\[Fit=\frac{1}{N}\sum_{i=1}^{N}s_{i} \tag{3}\]  
where  
1. \(s_{i}=\frac{a_{i}-b_{i}}{\max\{a_{i},b_{i}\}}\) is the silhouette value of the point \(i\)
2. \(a_{i}=\frac{1}{|\mathcal{C}_{i}|-1}\sum_{j\in C_{i},i\
eq j}d_{i,j}\) is the average distance from the ith point to the other points in the same cluster as \(i\)
3. \(b_{i}=\min\limits_{k\
eq i}\frac{1}{|\mathcal{C}_{k}|}\) is the minimum average distance from the ith point to points belonging to other clusters
4. \(N\) is the total number of observations  
The silhouette value ranges from \(-1\) to \(1\). A high silhouette value indicates that \(i\) is well matched to its cluster and poorly matched to other clusters. The clustering solution is appropriate if most points have a high silhouette value.  
The fitness function used to construct an optimal HI, as shown in Equation (4), consists of three terms: monotonicity, trendability, and prognosability.  
\[Fit=w_{1}Mon+w_{2}Trend+w_{3}Pogn \tag{4}\]  
where \(Mon,\ Trend,\ Progn\) are calculated by Equations (5)-(7), respectively [39]; \(w_{1},\ w_{2},\ w_{3}\) are the weights associated with each HI evaluation criteria, and \(\sum\limits_{i}w_{i}=1\).  
\[Mon=\frac{1}{N_{\text{F}}}\left|\sum_{i=1}^{N}\left(\frac{n_{i}^{+}}{n_{i}-1}- \frac{n_{i}^{+}}{n_{i}-1}\right)\right| \tag{5}\]  
\[Trend=min\left(|corcoeff_{ij}|\right),\ i,j=1,\ldots,N_{\text{F}} \tag{6}\]\[Progn=\exp\left(-\frac{std\Big{(}HI_{fail}\Big{)}}{mean\Big{(}\Big{|}HI_{start}-HI_{ fail}\Big{|}\Big{)}}\right) \tag{7}\]  
where \(N_{F}\) is the number of run-to-failure trajectories, \(n_{i}^{+}(n_{i}^{-})\) indicates the number of observations characterized by a first positive (negative) derivative, \(corrcoeff_{ij}\) is the linear correlation coefficient between the \(i\)th and \(j\)th run-to-failure trajectory.  
The other parameters adopted for all GPs are summarized in Table 6.
## 3 Proposed Joint Traffic Prediction and Handover Design Framework
### MDP Framework for Satellite Handover  
Based on the system model established in Section 2, we formulate the problem as an MDP, where each TTG is considered an agent that makes decisions based on the state information and selects optimal actions. An MDP is typically defined as a tuple \((\mathcal{S},\mathcal{A},\mathcal{R},\gamma)\) that describes the agent's sequential decision\(-\)making process. The following discussion provides a detailed explanation of these components:  
1. State space \(\mathcal{S}\): The state space for each TTG is constructed based on the current link status and the load conditions derived from the predicted traffic. For TTG \(k\), each state \(s^{t}\in\mathcal{S}\) is represented as a matrix \(\mathbf{S}\in\mathbb{R}^{M_{k}^{t}\times\gamma}\), where each row corresponds to one of the \(M_{k}^{t}\) visible satellites. The seven columns capture the satellite's service status \(s^{t}_{k,m^{\prime}}\), elevation angle \(\theta^{t}_{k,m^{\prime}}\), distance \(d^{t}_{k,m^{\prime}}\), remaining service time \(T^{t}_{k,m^{\prime}}\), SNR SNR\({}^{t}_{k,m^{\prime}}\), normalized demand weight \(l^{t}_{m^{\prime}}\), and computed load information \(\Psi^{t}_{k,m}\). Notably, the load information is obtained by combining the predicted traffic demand from Section 3.1 with the real\(-\)time satellite state through Equation (18), enabling each TTG to make decisions that are both adaptive and anticipatory.
2. Action space \(\mathcal{A}\): The action space defines the handover decisions available to the TTG in each time slot. Each action \(a^{t}\in\mathcal{A}\) specifies the satellite selected by the TTG for handover.
3. Reward function \(\mathcal{R}\): The reward function quantifies the immediate reward given by the environment after the TIGs take action \(a^{t}\) in state \(s^{t}\). This reward comprehensively considers the handover cost, the coverage quality of the TTG, and the load balancing between satellites, which is expressed as \[r^{t}=\sum_{k=1}^{K}\Bigl{(}\Lambda_{k}^{t}+\beta\cdot\text{SNR}_{k,m}^{t}+ \gamma\cdot\Psi_{k,m}^{t}\Bigr{)}.\] (25)
4. Discount factor \(\gamma\): This determines the weight of future rewards relative to immediate rewards, with \(\gamma\in[0,1]\). A higher \(\gamma\) value places greater emphasis on long\(-\)term rewards.
## 3 Methodology
### Surface Reconstruction  
After approximating each row of the depth image as a curve, it becomes possible to evaluate the surface value at any given point. To assess the surface at coordinates \((x,y)\), the first step is to determine the two neighboring rows of the depth image using Equation (10). The deprojection of the 3D point cloud into the depth image is shown in Figure 5.  
\[y=\{y\in\mathbb{R}\mid 0\leq y\leq h\} \tag{9}\]  
Here, \(h\) represents the height of the depth image.  
\[j=\lfloor y\rfloor \tag{10}\]  
The point at \((x,y)\) lies between the \(j\)-th and \((j+1)\)-th rows of the depth image. Consequently, the values of the approximated curves for these two rows are evaluated at position \(x\). A linear interpolation is then performed between the two estimated values to obtain the interpolated measured value \(r\) at the specified point \((x,y)\). Therefore, the interpolated value of the depth image at any given point \((x,y)\) can be obtained using Equation (11).  
\[r(x,y)=r_{j}(x)+(y-j)(r_{j+1}(x)-r_{j}(x)) \tag{11}\]  
Hence, the evaluation of the scatter depth image captured by a depth camera can be performed at any desired resolution, ensuring that outliers and invalid data are not a concern.  
Figure 5: Deprojection of 3D point cloud into depth image.  
Although the interpolated data exists in the depth image space, it needs to be transformed into \((x,y,z)\) space to represent a 3D point cloud for further utilization. To achieve this transformation, a mapping function provided by the manufacturer of the depth camera is employed.  
Subsequently, the points must be connected to form a cohesive surface. Each pixel in the depth image corresponds to a point in the 3D point cloud, and the connectivity data between neighboring points is preserved from the depth image. Consequently, it is possible to identify neighboring points in the depth image and establish a quad in 3D space. It is worth noting that the pixels \((i,j)\), \((i+1,j)\), \((i+1,j+1)\), and \((i,j+1)\) constitute a rectangle in the depth image. Consequently, their representation in the 3D point cloud also forms a quad in the same sequential order. By combining these quads, a continuous and hole-free surface of the area captured by the depth camera is constructed, effectively representing the shape of the soil in the excavation area as shown in Figure 6.  
Figure 6: Weaving points to generate a surface. (**a**) Evaluating points on approximated curves. (**b**) Linear interpolation of neighbor curves. (**c**) Forming surface from adjacent interpolated points.  
The proposed method offers a high-speed approach to surface reconstruction as it operates without the need for optimization. Moreover, it is reliable and deterministic, relying solely on a series of mathematical formulas rather than random numbers for optimization. Consequently, it consistently produces the same results for a given input. Additionally, it guarantees that the generated surface is free of holes, which is a crucial characteristic of this surface reconstruction approach.