esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Reduction Circuit Based on Binary Tree Path Partition on FPGAs
## 7 Conclusions  
In this paper, we propose a novel reduction circuit based on binary tree path partition. It can process any number of data sets, and each data set can contain any number of elements. We introduced BTPP by taking the adder as the reduction operator as an example. BTPP divides the input data into multiple data groups, and each data group contains \(p\) elements. The \(p\) elements are added through a binary tree to get the partial result, and the final result is obtained by adding these partial results. After all operations are software pipelined, the circuit only needs to use a \(p\)-stage pipeline adder to perform these operations. We implemented BTPP on FPGAs. Compared with other methods, BTPP has the least area-time product.  
Funding acquisition, Z.H. and J.C.; Investigation, L.T.; Methodology, L.T.; Project administration, G.C.; Writing--original draft, L.T.; Writing--review & editing, Z.H., G.C., Y.Z. and J.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61704173, 61901440), the Beijing Municipal Natural Science Foundation (Grant No. 4202080), and the One Hundred Person Project of the Chinese Academy of Sciences.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Multiplier + Adder** & **Multiplier + BTPP** \\ \hline bit-width & 64 & 64 \\ Freq. (MHz) & 294 & 285 \\ Slice LUTs & 835 & 1541 \\ Slice Reg. & 1744 & 2430 \\ DSPs & 14 & 14 \\ BRAMs & 0 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Two circuits for matrix-vector multiplication are implemented on Xilinx Artix-7 XC7A100T.  
Figure 14: The comparison of execution time between the design using BTPP and the design not using BTPP. The number of rows of the matrix is 15 and the number of columns varies from 15 to 30.
## 3 Proposed Approach  
The proposed approach consists of a path tracking module based on NMPC (see Section 5.2) which uses 10 steps ahead to predict the future state and control inputs. The full system architecture is presented in Figure 1.  
For accurate state estimation, in this study, we used an extended Kalman filter (EKF) to fuse odometry data of multiple algorithms. The visual-inertial Odometry (VIO) algorithm fuses data from the visual odometry (VO) algorithm with data from the inertial measurement unit (IMU). The output of the VIO is the position and orientation data of the vehicle in three-dimension (3D). Simultaneously, the position data and the orientation data of the vehicle, in two-dimension (2D), are generated by a wheel odometry processor unit that consists of rotary encoders and a data processor. In the next step, the data provided by the two sources are sent to the final position and orientation estimator that is an extended Kalman filter in the robot localization package (see Section 4.4). The last localization unit does both the frame transformation and sensor fusion. The unit provides the position data and orientation data of the ground vehicle with high accuracy. The generated data are used as input signals for the NMPC algorithm. The controller generates control commands that are sent to the low-level control in the electronic control unit (ECU) of the vehicle. In the next three sections, we discuss the theory, design, and implementation of each element in the introduced system architecture.
## 2 Materials and Methods
### Cropland Mask  
To derive a general cropland mask for the period of 2000-2013, MODIS Collection 5 annual land cover product (MCD12Q1) from 2001 to 2012 was obtained from the MODIS website ([[http://modis.gsfc.nasa.gov/](http://modis.gsfc.nasa.gov/)]([http://modis.gsfc.nasa.gov/](http://modis.gsfc.nasa.gov/))). The classification accuracy of the cropland type around the world was greater than 70% [43], which is satisfactory for the analyses of this study. In order to create an annual crop mask, two land cover types were extracted from the land cover type 1 based on the IGBP global vegetation classification scheme; cropland (code = 12) and cropland/natural vegetation mosaic (code = 14). The general cropland mask for the period of 2000-2013 was created by using any pixel that belonged to either code 12 or 14 in all years. As the resolution of MCD12Q1 is 500 m, the general cropland mask was resampled using the NEAREST method of the ArcGIS software to the same resolution as MODIS FAPAR. The cropland mask for Alberta (AB), Saskatchewan (SK) and Manitoba (MB) is shown in Figure 1a. The Canadian Prairie Provinces consists of the largest crop area in Canada and accounts for about 85% of the national total harvested area ([[http://www.statcan.gc.ca/](http://www.statcan.gc.ca/)]([http://www.statcan.gc.ca/](http://www.statcan.gc.ca/))). In general, the croplands on the Canadian Prairies are predominantly rainfed [44]. Canola, spring wheat, barley, and oats are the four major annual crops in this region [44]. The region is covered by three agro-climate zones; the sub-humid zone, the semi-arid zone and the arid zone (Figure 1b) [19]. The soils are dominated by Brown Chernozems in the arid zone, Dark-Brown Chernozems in the semi-arid zone and Black and Dark Gray Chernozems and Gray Luvisols in the sub-humid zone [13; 45; 46].
## 4 Identity Management and Access Control Based on Blockchain under Edge Computing for IIoT
### Registration and Authentication Based on Self-Certified Public Key  
Under edge computing, a large number of manufacturing sensors and controlling devices have low computing and communication capabilities and cannot perform large amounts of computation and communication. Therefore, lightweight authentication mechanisms need to be used. Thus, we use the self-certified public key-based system (SCKBS) to implement registration and authentication of network nodes [42].  
The SCKBS system is similar to the identity-based public key system (IBS). The public key itself has an authentication function. Although it also depends on the key distribution center (KDC), the KDC does not directly generate user private keys, but only generates partial private keys corresponding to user identities. The user himself combines some private keys to obtain the private key.  
The specific registration and certification process are as follows. Let \(E\) be the elliptic curve over the finite field Fp of order \(q\), \(P\) be a base point of the order of the prime number \(n\) in \(E(Fp)\), \(U\) and \(V\) be the edge centers in the blockchain network, \(H\) be a Hash function that maps a finite binary string to an integer set [2,\(n\)-2], KDF be a key derivation function, MAC be a message authentication code, and \(\parallel\) denote a bit string connection.  
Edge center node authentication: Let (\(q_{\text{CA}}\),\(Q_{\text{CA}}\)), respectively, be the private key and public key of the cloud computing center, \(q_{\text{CA}}\) be a random integer, and there is an equation of \(Q_{\text{CA}}=q_{\text{CA}}P\)The process of describing edge center \(U\) in a blockchain network to obtain its implicit certificate is as follows:  
1. The node \(U\) randomly selects the integer \(g_{U}\in[2,n-2]\), calculates \(G_{U}=g_{U}P\), and sends \(G_{U}\) and its identity \(ID_{U}\) to the cloud computing center.  
2. If both \(G_{U}\) and \(ID_{U}\) are valid, the cloud computing center randomly selects the integer \(g_{\text{CA}}\in[2,n-2]\), calculating \(G_{\text{CA}}=g_{\text{CA}}P\) and \(B_{U}=G_{U}+G_{\text{CA}}\). The implicit certificate of node U is \(IC_{U}=(Q_{\text{CA}},ID_{U},B_{U},t_{U})\), where \(t_{U}\) represents the expiration time of the certificate. CA calculates \(e_{U}=H(ID_{U})\) and \(s_{U}=g_{\text{CA}}e_{U}+q_{\text{CA}}\) mod n, then sends (\(s_{U}\),\(IC_{U}\)) to node U.  
3. After node U receives (\(s_{U}\), \(IC_{U}\)), it computes \(e_{U}=H(ID_{U})\), \(q_{U}=s_{U}+g_{U}e_{U}\) mod n, and \(Q_{U}=q_{U}P\). (\(q_{U}\),\(Q_{U}\)) is the long-term private key and public key of node U, then U verifies whether equation \(Q_{U}=e_{U}B_{U}+Q_{\text{CA}}\) holds. If the above equation holds, then node U considers the implicit certificate \(IC_{U}\) to be correct.  
As a result, each edge center can perform authentication registration and obtain its public and private key pairs and corresponding implicit certificates. The implicit certificates of every node are stored in blockchain, and they are managed by cloud computing centers or edge centers.
## 2 Background and Related Works  
### Matrix Factorization for Recommender Systems  
There are various paradigms that can be used to implement recommender systems [11]. These include collaborative filtering (CF), content-based filtering, and hybrid filtering, the last of which combines multiple paradigms to improve recommendation accuracy and overcome the limitations of individual approaches.  
Content-based filtering is a commonly used approach in which recommendations are made based on the similarities between the features of an item and a user's preferences [12]. One example would be promoting French restaurants to customers who have expressed an interest in French cuisine. This method, however, has problems in capturing the delicate relationships between consumers and eateries.  
On the other hand, CF is a technique frequently employed in recommender systems that predicts a user's preferences based on the preferences of other users who have similar tastes [13]. One of the most effective recommendation techniques among CF-based methods is matrix factorization (MF) [10; 14; 15].  
In its simplest form, matrix factorization for recommender systems represents both users and items as vectors of latent factors, which are derived from the user-item rating matrix. When there is a strong correlation between the latent factors of a user and an item, it results in a recommendation of that item to the user [16]. Unconstrained gradient descent is typically used to learn the feature components of the user and item factor matrices [17].  
MF has been utilized in several domains, such as recommending movies, suggesting music, and providing restaurant recommendations [6]. The flexibility of matrix factorization to handle huge and sparse datasets distinguishes it from other collaborative filtering systems [16]. On large and sparse datasets, other collaborative filtering techniques, such as k-nearest neighbor (k-NN) and user- or item-based collaborative filtering, may not perform as well due to the sparsity problem [5].  
Matrix factorization also has an advantage in handling implicit feedback, like user views or clicks, as well as explicit feedback, such as ratings or reviews. This feature enables matrix factorization to capture a broader range of user preferences and behaviors, resulting in more precise and diverse recommendations [16].  
Typical matrix factorization models include non-negative matrix factorization (NMF) [18], singular value decomposition (SVD) [19], and optimized singular value decomposition (SVD++) [16]. NMF decomposes the original matrix into two smaller non-negative matrices [18]. SVD is a widely used algorithm for solving real and complex metrics, as well as for rate estimation and canonical correlation analysis [20]. SVD++ [19] is an advanced version of the SVD model that takes into account implicit feedback. Implicit feedback includes information on the user's preference history, which provides details on the items for which the user has expressed a preference, either explicitly or implicitly.
## 2 Theoretical Background
### Parametric Method
In addition, cost estimation using a parametric method involves a series of calculations using parameters, and in parametric estimation using this method, the extraction of valid parameters plays a decisive role in accurate construction cost estimation. Therefore, in order to develop a cost prediction model using a parametric method, it is necessary to extract a diverse range of parameters according to the parameter selection conditions, and the final test model should be selected through comparative evaluation of the models composed of the selected parameters. The parameter selection conditions must consider two aspects. First, it is necessary to select parameters according to the explanatory power (strong or weak) for the construction cost. The linear regression model that is the basis of the parametric method presupposes a linear relationship between the parameters, and the correlation coefficient can be used to determine the explanatory power of the relationship. Considering this point, parameters are first selected in the model development stage by comparing the explanatory power of the parameters according to the correlation coefficient. Second, it is necessary to select parameters considering multicollinearity in the multiple regression model. In general, in the model development process, the influence of a specific independent variable on the dependent variable is measured using regression coefficients, whereas the values of all other independent variables are fixed. However, when there is a strong linear correlation between independent variables, the typical analysis using regression coefficients causes collinearity. Here, multicollinearity refers to highly linear correlations between three or more independent variables [7] that violates the premise of regression analysis. When this situation occurs, it should be solved to perform appropriate regression analysis. In general, solutions to multicollinearity include data supplementation, removal of some of the highly correlated independent variables, ridge regression, linear combination of independent variables, and principal component analysis. Among these approaches, by removing variables when selecting parameters, factors with relatively small correlation coefficients are removed among factors of information that cannot be expressed in the planning stage or incorporated into BIM and influencing factors that cause multicollinearity. In this manner, a cost prediction model utilizing a dataset with preprocessed data and the application of parameters derived through the above process is developed.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
###### Abstract
In some research, the computational fluid dynamics (CFD) method was applied to predict nitric oxide (NOx) production [12; 13]. Due to a mismatch between the flow and chemical reaction in the field of time and length scale, the computational fluid dynamics (CFD) method may entail a substantial computational burden or pose challenges in terms of precision. The chemical reaction networks (CRNs) method has successfully improved the prediction of nitric oxide (NOx) emissions by sacrificing part of the accuracy in the field of flow [14; 15; 16; 17]. The accuracy of nitric oxide (NOx) prediction relies on ensuring a precise temperature and reactant concentration, which could be achieved through the utilization of the chemical reaction networks (CRNs) method, as supported by comprehensive reaction mechanisms. Moreover, by combining information from the computational fluid dynamics (CFD) method, the chemical reaction networks (CRNs) method could improve the accuracy of nitric oxide (NOx) emissions with only an increase in simulation time. This method was applied in several research studies and showed applicability with the experiment results [18; 19; 20]. Therefore, the CRNs method combined with the computational fluid dynamics (CFD) method would predict the nitric oxide (NOx) concentration in a flow field. This article mainly focuses on the prediction method of nitric oxide (NOx) production in different combustion chambers. Through the review of the methods that are mainly applied in current situations, the advantages and disadvantages were compared, and we discussed those appropriate application scenarios. The empirical formula provided a large scope of application due to easy access. With the help of artificial intelligence, this method's application could be extended further in practical use with accuracy improvement. The computational fluid dynamics (CFD) method can capture the combined behavior of flow, temperature, and reactant concentration. In the prediction of nitric oxide (NOx) generation field, the simulation by the direct numerical simulation method (DNS) is limited due to its enormous calculation cost. The other computational fluid dynamics (CFD) methods were restricted by the accuracy of their simplified combustion model, with 10-20% deviation. Moreover, the computational fluid dynamics (CFD) method would be applied only to the types of fuels that contain a finite number of reactants and reaction mechanisms, which would not be suitable for the complex components of fuel. The chemical reaction networks (CRNs) method focuses on the chemical reactions by sacrificing the flow of information, and the chemical reaction networks (CRNs) method is more suitable than computational fluid dynamics (CFD) for the simple flow. The chemical reaction networks (CRNs) method should be combined with computational fluid dynamics (CFD), as the prediction of NOx would be improved by coupling both advantages.
###### Abstract  
Various watermarking algorithms have been studied to better enable the copyright protection of remote sensing images. The robustness of such algorithms against image cropping attacks has subsequently been verified mainly by various experiments. However, to date, the experimental results are subject to the differences in experimental factors and computational resource costs. Hence, the study presented in this paper proposes a robustness computation model of watermarking remote sensing images in terms of the image cropping attack. The robustness computation model consists of three parts: analysis principles, an evaluation index, and a computation method. The robustness analysis principles are provided based on the salient features of watermarking remote sensing images and attacking properties. A probability-based evaluation index is then defined to more comprehensively measure the robustness of different algorithms. The computation method developed in this study is based on permutations and the inclusion-exclusion principle to theoretically calculate robustness. The experiments conducted to verify the effectiveness of the computation model, revealed true closeness between both the calculated and experimental results. Finally, the relationships between the robustness and the different parameters used in the watermarking algorithms are investigated by using the proposed computation model.  
digital watermarking; robustness; computation model; image cropping 1
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM) Coupled with Energy-Dispersive X-ray Spectroscopy (EDS)  
SEM characterization of the PdAS(x)-MA catalysts showed well-defined polymeric microspheres (Figure 2), which indicates the importance of toluene in determining the hydrophobic properties of the materials during synthesis, with the formation of well-defined and uniform oil droplets being observed [52]. The diameter particle size distribution (Figure S1) obtained by counting more than 600 particles directly from the SEM micrographs gave a narrow size distribution, and allowed estimation of the standard deviation of the particle size, as shown in Table 1. As indicated, an increase in the mean diameter was observed from 184 to 272 \(\upmu\)m, which was attributed to the increase of PdAS MCM in the resin formulation. The EDS results are shown as an inset in the SEM micrographs of Figure 2 and confirm the presence of Pd on the surfaces of the synthesized PdAS(x)-MA resins.  
Figure 1: Thermogravimetric profiles of PdAS(x)-MA catalysts. (**a**) PdAS(1)-MA, (**b**) PdAS(2)-MA, (**c**) PdAS(5)-MA, and (**d**) PdAS(10)-MA.  
Figure 2: SEM energy-dispersive X-ray spectroscopy (SEM-EDS) micrographs analysis. **(a)** Pd-AS(1)-MA (b) Pd-AS(2)-MA (c) Pd-AS(5)-MA, and **(d)** Pd-AS(10)-MA.
## 4 Theoretical Framework
### Service Quality Assessment Process  
For service providers, establishing a comprehensive inspection mechanism can ensure the quality of service and project processes. In the business model of manufacturing and OEM, productivity and yield rate are essential indicators of service performance [60]. Further, the reflection efficiency after a problem occurs will affect the overall quality. In the production process, systemic problems lead to misconfiguration of software and hardware, which affects shipments and causes the company to suffer losses. For instance, if the specifications of the shipped materials do not match the finished product because of material system problems and the system does not analyze and intercept, the materials may be scrapped or even delay delivery. This theoretical framework puts the checking mechanism in the center of the structure to determine the problems in service definitions and system construction. Although many systems are developed using test-driven design (TDD) concept at the preliminary stage of development, other requirements are usually derived from the project entering the market evaluation stage [52]. As the business grows, the initial design is no longer sufficient to support the latest requirements [61]. Adding the proposals of second or third parties to the framework increases the possibility of obtaining an optimal solution. Based on the TDD core concept, this evaluation system will analyze the system's reliability and predict with the AI model to optimize the service system. In the review process, second or third parties can use the evaluation results to add or improve related functions or issues during the system revision process. Companies can maintain market competitiveness by adjusting corresponding strategies based on predictive analysis through step-by-step simulation testing and the introduction of AI analysis models.
# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 4 Validation Test
### Test Setup  
To validate whether the proposed method can apply FE model updating accurately, a lab-scale validation test was conducted. A three-story building model with equally distributed masses connected by aluminum columns was chosen as the validation structure. The total height of the structure is 920 mm and the width is 240 mm. In order to generate large displacement of the structure when excitation load is applied, the thickness of the wall is 1.2 mm. Figure 5 shows an overview of the lab-scale validation test. To validate the performance of the FE model updating, this study conducted an experiment twice: (1) using an undamaged structure, and (2) by changing one of the aluminum columns to a damaged column. The column between the first and second floors was assumed to be damaged by drilling five holes into the column. The model was attached to a unidirectional shaking table with a maximum displacement of 100 mm. Band-limited white noise (BLWN) was adopted as an input motion to provide energy within a wide range of frequencies. Consumer-grade cameras are used to record the displacement output of the model and the shaking table. These cameras are oriented perpendicular to the motion axis as shown in Figure 5. A smartphone camera (Samsung GALAXY S8) was used to record the video with a frame rate of 60 fps and a resolution of 1080p (1920 pixels \(\times\) 1080 pixels). In order to improve the accuracy in detecting features necessary for displacement measurements, a fixed white background is used behind the experiment setup. The recorded video was  
Figure 4: Fitness values at each generation.  
converted into grayscale to improve the accuracy of detecting the features required for displacement measurements.