esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hydrological Applications  
Since 1972 (launch of the first Earth Resources Technology Satellite, ERTS-1), scientist have used remotely-sensed data from different sensors to characterize, map, analyze and model the state of the land surface and surface processes. With the help of new algorithms, new hydrological information were extracted from remotely-sensed data and used in hydrological and environmental modeling. These new information and hydrological parameters have increased our understanding of the different hydrological processes by helping in quantifying the rate and amount of water and energy fluxes in the  
Figure 2: Impervious surface images of Indianapolis derived from a Terra’s ASTER image of June 16, 2001.  
environment. The ability of these sensors in providing various spatiotemporal scales data has also increased our capability in looking into one of the challenges of environmental modeling, mismatch between scales of environmental process and available data.  
The role of remote sensing in understanding hydrological processes and fluxes across different spatial and temporal scales can be tremendous, if appropriate spatial and temporal resolution remotely-sensed data are available under ranges of bands. With the availability of large volumes of remotely-sensed data, geographical information system (GIS) tools to manipulate, process, store and retrieve such data and efficient computing system, the application of remote sensing to water resources has been increasing in recent years.  
The application of remote sensing in water resources research and management mainly lies in one of the three categories: mapping of watersheds and features, indirect hydrological parameter estimation and direct estimation of hydrological variables.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 2 Theoretical Background
### AI Training Model  
AI algorithms that classify and predict specific values based on images generally use a CNN structure. A CNN is an artificial neural network comprising one or several convolutional layers and traditional artificial neural network layers, capable of extracting high-dimensional image information compared to conventional image processing methods. Unlike a fully connected neural network, a CNN maintains the shape of the input and output data for each layer, effectively recognizing features from adjacent images while preserving image spatial information. Using shared parameters for filters, it requires significantly fewer learning parameters compared to a general artificial neural network. Image classification and prediction algorithms based on CNN structures include Visual Geometry Group (VGG), ResNet, and MobileNet.  
VGG [32] is distinguished as VGG16 or VGG19 according to the number of convolutional layers, with the model structure comprising C and E, as shown in Figure 5. It repeatedly applies convolutional layers with filtering techniques to the input image, the ReLU nonlinear activation function, and pooling layers that reduce the size of the image data. Then, the data are vectorized through the fully connected (FC) layer and normalized through the softmax activation function.  
ResNet (Residual Network) [33], unlike VGG, is composed of blocks, as shown in Figure 5. By residual mapping, which adds values that have passed through convolutional layers to those that have not, it mitigates overfitting and parameter vanishing problems as the layer depth increases [20]. ResNet's layer structure uses convolutional layers with 7 \(\times\) 7 filters and 3 \(\times\) 3 max pooling.  
Figure 4: Example of super resolution application [31].  
MobileNetV2 [34] is a neural network designed for efficient computation in resource-constrained environments such as smartphones and other mobile devices, reducing the number of parameters and computation requirements compared to traditional CNNs. MobileNet applies depth-wise separable convolutions, as shown in Figure 5, and calculates spatial direction depth-wise convolutions and channel direction point-wise convolutions separately before combining them. Because different filters are applied to each channel, the number of output channels remains the same as the number of input channels.
## 2 Materials and Methods
### KB System
#### 2.1.3 Knowledge Representation Phase  
The knowledge representation phase begins with the definition of a predefined form for each DFW rule. It refers to the description of knowledge intended to be understandable for designers and engineers and then facilitates the implementation of corrective design actions. This phase encompasses the logical definition of DFW guidelines (syntax) and related information (e.g., suggestions about design changes to guarantee product manufacturability). A standard form, one for each design rule, is defined according to a given taxonomy and syntax, which are necessary to keep consistency among different guidelines. In this manner, the rules contain the same level of detail and information that can be manipulated by the mechanical designer during the product development process. DFW guidelines syntax requires mandatory and optional information. Mandatory information provides the minimum set of data to perform a design improvement. The data are (i) the design action to perform (verb) and (ii) the subject which requires modification (name). Optional information provides additional data that allow for the clarification of the context in which the design action is needed. These data include (i) the manufacturing process, (ii) the type of feature involved, (iii) the type/family of part, and (iv) the type of material.  
To give a detailed understanding of the knowledge representation phase, Figure 3 presents the DFW guideline syntax and an explanatory picture illustrating what to do and what to avoid. The same example is reported here concerning the W025 DFW rule: \"Avoid weld beads cross in arc welding.\"
# Depth Image Super Resolution Based on Edge-Guided Method
## 4 Conclusion and Future Works  
Conventional SR methods can cause edges to be blurred and jagged. Aiming at solving this problem, this paper proposes an edge-guided SR method. First, high-quality edge information is reconstructed based on generating a dictionary from pairs of HR and their corresponding LR edge patches. Then, with the guidance of these recovered edges, the SR depth image is interpolated by a joint bilateral filter. The guidance of high-quality edge information can improve the performance of SR algorithm resulting in sharper SR depth image. The quantitative and qualitative analyses of the experimental results showed the superiority of the proposed technique over conventional and state-of-the-art techniques.  
There are still some shortages of the proposed method. The running time is higher than some methods shown in Table 5. The process of dictionary pair requires acquiring a database from external HR-LR images. In the future, we will further improve the proposed method in the following ways. (1) Database Construction: We will construct an image pyramid by interpolating the inputted image  
Figure 6: Visual comparisons of “tsukuba”: (**a**) ground truth (reprinted with permission from [38]); (**b**) [PERSON][25]; (**c**)[PERSON] [7]; (**d**) [PERSON][6]; (**e**) [PERSON][8]; and (**f**) our method.  
across different scales, and then database can be extracted from image pyramid. (2) Dictionary Training: We will use an optimal approach to train a sparse dictionary so that the running time can be reduced.  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of China (Nos. 61751203, 61603066, and 61300015), the Program for Dalian High-level Talent's Innovation (2015R088), Program for Changjiang Scholars and Innovative Research Team in University (No. IRT_15R07), and Program for Liaoning Distinguished Professor.  
**Author Contributions:** [PERSON] wrote this manuscript. [PERSON], [PERSON] and [PERSON] contributed to the writing, direction, and content, and revised the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Implementation
### RAM consumption  
The third factor is RAM consumption, which is also an important criterion. Figure 9 shows the results obtained over the 100 execution times, and demonstrates the low memory consumption of our approach. This low resource usage makes our solution particularly suitable for IoT environments, where memory and energy optimization are essential.
# Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera
## 3 Methodology and Design
### Point Cloud Data Preprocessing
#### 3.6.2. RANSAC to Remove Outliers  
Based on the above discussion, the complexity of acquiring tread point cloud data is relatively low, allowing the use of the RANSAC algorithm [33] to detect and remove outlier data caused by measurement errors and mismatches. This algorithm can extract two planes from the working plane: one containing in-area points (points conforming to the model) and another containing out-of-area points (points not conforming to the model) within the surface point cloud data of the tread.  
The method treats all point clouds as a complete dataset and employs random sampling to mitigate the impact of noise on segmentation outcomes. From this full dataset, a subset is randomly selected. A specific model is derived from this subset, and the deviation of sample data points from the model is computed using minimal variance. Points with deviations below a predefined threshold are classified as in-model data points and recorded. Those exceeding the threshold are categorized as out-of-model data points. This process iterates across all data points, progressively accumulating in-model data points. Once enough points are classified as in-model, the best estimated model parameters are selected, and the in-model data points are retained to achieve point cloud segmentation [34]. The detailed process is illustrated in Figure 10.  
Figure 9: Overlay screening: (**a**) original image; (**b**) region of interest; (**c**) binarized mask layer.  
The constraint of the RANSAC algorithm to limit the number of iterations of the optimal model [35] is described by Equation (3):  
\[1-P=(1-W^{n})^{k} \tag{3}\]  
where \(P\) is the probability that a data point is a local point, \(W\) is the proportion of local points in the point cloud data point set, \(n\) is the number of data points needed for each iteration of the model, and \(k\) is the number of model iterations.
## 4 Adaptive Transmission Strategy Design and Outage Probability Analysis  
In this section, after the best relay selection, the design of the adaptive transmission strategy and the analysis of outage probability are given.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 4 Hardware Evaluation for Iterative Decoding of Product Codes  
The performance of the LDPC codes is affected by decoding algorithms and quantization methods [19; 24]. Furthermore, in the case of extremely low error rate, software simulation is not practical for it will need a long period of simulation time [26]. Therefore, the hardware platform is built in this paper. On one hand, the hardware platform can accelerate the performance evaluation of the proposed iterative decoding methods. On the other hand, the quantization methods for specific decoding algorithms and the feasible implementation architecture are also evaluated [5; 13; 14]. In this section, the encoder and decoder of the product code are implemented, and the corresponding performance evaluation results using the hardware platform are presented.
# From Modeling to Failure Prognosis of Permanent Magnet Synchronous Machine
## 3 Hidden Markov Model  
### The Prognostic Strategy  
A prognostic strategy, using HMM, will be developed to detect the presence of fault at its early stage [28]. Extracted features of data coming from torque, temperature and vibration sensors will be the input of the prognostic HMM. The output of the model will be the prognostic decision that will state if the machine is at its healthy or faulty state. If it is at a faulty state, it will give the type of fault and the machine's remaining useful life. The chart in Figure 18 represents the prognostic strategy and its tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Torque** & **Temperature** & **Vibration Displacement** \\ \hline Machine with magnet crack & 15\% & \(-\)0.02\% & \(\pm\)384\% \\ Machine with short circuit in one slot & \(-\)28\% & 27\% & \(\pm\)158\% \\ Machine with 10\% eccentricity fault & 11\% & 0\% & \(\pm\)216\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage of parameter deviation for different types of fault.  
Figure 17: Vibration in time domain (machine with turn-to-turn short circuit in one slot).  
In the beginning, the raw signal coming from torque, temperature and vibration sensors is collected. The selected features, for each dataset, will be extracted over one machine revolution. Those features will be the input of the prognostic model, as will be explained in the following sections. This prognostic approach will answer the following question: 'what is the state of the machine?'. If the machine is healthy, we stop. If the machine is faulty, the type of fault, demagnetization, eccentricity or turn to turn short circuit, will be identified and localized. Then, the remaining useful life of the damaged component will be estimated.
## 3 The Diurnal Reference Check (DRC)
### Formulation of the DRC  
In summary, the Diurnal Reference Check is formulated as follows.  
Each daily segment is checked with the following conditions:  
\[D<5\,\mathrm{K} \tag{3}\]  
\[T_{L4}-1.7S_{L4}<T_{NIGHT}-d(D)<T_{L4}+1.5S_{L4} \tag{4}\]  
If at least one of the two conditions (3) or (4) is not met, the whole segment is rejected. If conditions (3) and (4) are both satisfied, then individual \(T_{IS}\)'s within a given segment are checked for outliers with the following condition:  
\[a(D)-\,\alpha b(D)<T_{IS}-T_{NIGHT}<f(D)+\alpha c(D) \tag{5}\]  
In (5), \(a(D)\) and \(f(D)\) are functions of \(D\) corresponding to curves 4 and 3 in Figure 7b; \(b(D)\) and \(c(D)\) are functions of \(D\) corresponding to curves 4 and 3 in Figure 7c; and \(\alpha=2\) is an empirical coefficient. The \(T_{IS}\) is rejected if the condition (5) is not met.
## 3 Identification Algorithm
Then, the parameter vector estimate \(\hat{\mathbf{\theta}}_{k}\) can be recovered according to Equation (19),  
\[\hat{\mathbf{\theta}}_{k}=\hat{\mathbf{\Phi}}_{l_{2}}^{-1}\hat{\mathbf{\theta}}_{n,e}. \tag{32}\]  
The estimates of the intermediate variables \(x_{i,k}(t)\) can be refreshed by \(\hat{\mathbf{\theta}}_{k}\) as shown in Equations (15) and (16). Equations (9), (11)-(13), (15), (16), (18), (23), (25), (27), and (29)-(32) form the auxiliary model-basis pursuit de-noising iterative (AM-BPDNI) algorithm for the MISO-OE system. The implementation procedures are listed as follows:  
1. Collect the input-output data \(\{u_{i}(t),\,y(t)\colon i=1,2,\cdots,r;\,t=1,2,\cdots,m\}\) and set the parameter estimation accuracy \(\varepsilon_{0}\). 2. Construct the stacked output vector \(\mathbf{Y}\) by Equation (9). 3. Initialize the iteration: let \(k=1\) and \(\hat{x}_{i,0}(t)\) be random sequences. 4. Construct the information matrix \(\mathbf{\Phi}_{k}\) by Equations (11)-(13) and normalize \(\mathbf{\Phi}_{k}\) by (18). 5. Form the vectors and matrix \(\mathbf{z}_{n}\), \(b\), \(b\) and \(\mathbf{C}\) by Equations (23), (25), (27), and (29) and formulate the QP by Equation (30). 6. Call the function to obtain the optimum solution \(\hat{\mathbf{z}}_{n}\) and compute \(\hat{\mathbf{\theta}}_{n}\) by Equation (31). 7. Set a threshold to obtain \(\hat{\mathbf{\theta}}_{n,e}\) and recover the parameter vector estimate \(\hat{\mathbf{\theta}}_{k}\) by (32). 8. Compare \(\hat{\mathbf{\theta}}_{k}\) with \(\hat{\mathbf{\theta}}_{k-1}\): if \(\|\hat{\mathbf{\theta}}_{k}-\hat{\mathbf{\theta}}_{k-1}\|>\varepsilon_{0}\), update the auxiliary model outputs \(x_{i,k}(t)\) by Equations (15) and (16) and go to Step 4. Otherwise, stop the iteration and obtain the parameter vector estimate \(\hat{\mathbf{\theta}}\). The unknown time-delay of each input channel can be estimated according to the location of zero-blocks and the number of zeros in \(\hat{\mathbf{\theta}}\). It can be seen from Equations (3) and (6) that there are \(2r\) zero-blocks in \(\hat{\mathbf{\theta}}\). Denote the number of zeros in each zero-block by \(z_{i}\)\((i=1,2,\cdots,2r)\). Then, time-delays can be estimated by  
\[\hat{d}_{i}=z_{2i-1},\ i=1,2,\cdots,2r. \tag{33}\]
## 3 Non-Thermal Technologies in Food Processing
### Ultrasounds
#### 3.1.3 Application in Cereal Product  
For flour dough and bakery products such as bread, crackers, biscuits, wafers, and batters (pancakes, donuts), researchers have experimented with variations in texture, density, and volume; furthermore, ultrasound treatment has brought enhancements in visual (aspects, color), sensory characteristics, and digestibility [101, 102]. [PERSON] et al. have evaluated the physicochemical properties of germinated dehulled rice flour and its energy requirement in germination under US treatment. It has been seen that the treatment transforms the surface microstructure of rice, which facilitated moisture transfer during steam cooking, and it enhances starch hydrolysis and the glucose content [103]. For buckwheat grains, the US impact is considerable, since it modifies the functional properties of the resulting flour. Only 15 min of US treatment raises the water absorption index and the swelling power of the flour along with the water solubility index and insoluble polyphenols content. In addition, a redness and yellowish enhancement in all processed samples has been observed [104].  
[PERSON] et al. investigated the impact of US on the functional properties and structural characteristics of gluten. They have analyzed the textural and cooking peculiarities of noodles prepared with different amounts of US pretreated gluten. The noodles with less pretreated gluten showed similar cooking and textural characteristics to commercial ones. If treated with an increasing number of ultrasonic frequencies, the solubility, water-holding capacity, and oil-holding capacity of gluten rose substantially [105].
# Quality Parameters of Wheat Bread with the Addition of Untreated Cheese Whey
## Materials and Methods  
### Breadmaking and Storage  
Three different flour compositions were used, corresponding to the most common types of bread on the market: (i) wheat flour type 70%/whole wheat flour (1:1) (F70/WWF), (ii) wheat flour type 70%/wheat flour type M (1:1) (F70/FM) and (iii) straight wheat flour type 70% (F70). The composition of flours used was as follows: (1) F70: moisture 12.5%, protein 12%, carbohydrates 74.3%, fat 0.7%, ash 0.5%, (2) FM: moisture 12.5%, protein 14%, carbohydrates 71.9%, fat 0.8%, ash 0.8%, and (3) WWF: moisture 13.5%, protein 11.5%, carbohydrates 71.2%, fat 2%, ash 1.8%. Conventional wheat bread of the same flour composition prepared using water was taken as the controls. A home-type bread maker was used for the preparation of all types of bread. The mixing time was 25 min, and the baking time was 60 min at 200 \({}^{\circ}\)C.  
Bread was prepared using 400 g wheat flour, 15 g sugar, 7.5 g salt, 8 g commercial dry yeast, 30 mL olive oil, and 240 mL (for F70/WWF and F70 breads)/260 mL (for F70/FM bread) water or untreated cheese whey. Cheese whey was supplied by DODONI DAIRY Co. Ioannina, Greece. The whey used was the by-product of cheesemaking on that particular day and transferred to the laboratory in a portable ice box within 20 min. The whey composition was as follows: moisture 93.5%, dry matter 6.0%, lactose 4.5%, total protein 0.8%, whey protein 0.65%, ash 0.55% on an as-is basis. The total amount of whey used was (240 + 240 + 260) mL for the preparation of 3 loaves of bread per replicate \(\times\) 2 replicates = 740 \(\times\) 2 = 1480 mL.  
Experimental and control breads were stored in low-density polyethylene plastic bags in the laboratory at 24 \(\pm\) 1 \({}^{\circ}\)C for up to 6 days. Sampling was carried out on days 0, 2, 4, and 6 (and 1 h after preparation, time required for bread to cool down). The experiment was terminated when samples were no longer sensorily acceptable in any way (objectionable appearance: mold growth, texture, flavor).
###### Abstract  
To effectively manage a river system, systematic tracking and diagnosing the change and risks of a river system are essentially required to efficiently conserve or restore its conditions. Hence, this study focuses on how to integrate current status assessment, trend prediction, and cause diagnosis in river health to guide early warning decision-making in river protection and management. This study has presented a three-phase approach by coupling spatial with nonspatial information in a highly systematic and reliable way, and an early warning system has been designed. In phase I, the current health status is assessed and nowcasted by using the order degree of each indicator. In phase II, health predictors, including the single perspective-based health index (HI) (e.g., water quality index (WQI) and index of biotic integrity (IBI)) and multi-perspective-based health index, have been forecasted under normal conditions or emerging conditions using predictive models. In phase III, key causal factors threatening the river health have been identified to enable early notification and to address unexpected events before occurrence. Although different modeling methods can be used in each phase to demonstrate this concept, we tested the model of partial least square regression (PLSR) associated with time series. Additionally, the three-phase approach has been integrated with geographic information system (GIS) and a decision support system (DSS) to develop a river health prediction and early warning system (RHP-EWS), an automatic prediction and decision-making tool. This tool was implemented to deal with the landing of typhoon \"Maria\" in 2018 into the Shanxi River watershed in China. Because of the timely responses and decisions, the drinking water supply was not influenced. However, the models should be extended to other river systems for testing and improvement at different temporal or spatial scales.  
**Keywords:** river health; trend prediction; key causal factor identification; early warning system; PLSR
## 3 Results
### Technical Analysis
#### 3.2.7 Assessing Efficiency and Improvements  
When demonstrating the efficiency of the proposed methods described in their papers, researchers chose one or more of the following four strategies enumerated below to compare the results obtained using their control methods with the results obtained when (1) no control system was employed or no PQ improvement device was utilized [87], (2) a conventional PI, PID, or control method was used as the control system [111], (3) a simple (less efficient) version of the proposed method was employed [130], or (4) other methods proposed in similar studies from the literature were used [131]. The chart in Figure 9 illustrates the yearly distribution of the efficiency demonstrations of the four strategies enumerated above. As some papers contained more than one testing strategy [131], the number of methodologies is greater than the number of papers.