esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a multiple artificial neural networks (MANN) method with interaction noise for estimating the occurrence probabilities of different classes at any site in space. The MANN consists of several independent artificial neural networks, the number of which is determined by the neighbors around the target location. In the proposed algorithm, the conditional or pre-posterior (multi-point) probabilities are viewed as output nodes, which can be estimated by weighted combinations of input nodes: two-point transition probabilities. The occurrence probability of a certain class at a certain location can be easily computed by the product of output probabilities using [PERSON] theorem. Spatial interaction or redundancy information can be measured in the form of interaction noises. Prediction results show that the method of MANN with interaction noise has a higher classification accuracy than the traditional Markov chain random fields (MCRF) model and can successfully preserve small-scale features.  
data interaction; neural networks; spatial categorical variables; transition probabilities +
Footnote †: journal:  
0000-0001-5000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-00000-0000-00000-00000  
in the MANN training processes. The originality of the proposed solution lies in that no additional assumptions are made about the relationship between the multi-point pre-posterior probabilities and two-point transition probabilities, which takes a step forward with respect to the existing methods. In addition, we have also proposed a method for measuring the spatial interaction effects. The remainder of this paper is organized as follows. We give an overview and formulate the method of MANN with interaction noise in Section 2. To demonstrate our model, an artificial and a real-word case study have been carried out in Section 3. Conclusions and directions for future research are presented in Section 4.
# Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
## 2 Additive Manufacturing Parts Inspection
### Convolutional Neural Network (CNN) Architecture  
Several CNN models were explored using our dataset to obtain the optimal hyper-parameters of the model. Figure 3 presents the final schematic framework after several experiments. The overall schematic model is composed of feature extraction and classification. To make this work in a self-contained way, the fundamentals of our CNN model will be briefly described below. First, there were \(M\) depth images, \(X_{m}\), and the images were scaled to \(224\times 224\) pixels with grayscales after the data augmentation process, and then fed into the first convolutional layer with the kernel size of \(5\times 5\) for feature extraction. In order to model non-linearities of the mapping between input and output, the Rectified Linear Unit (ReLU(\(x\)) = max (\(0\),\(x\))) [21] was used in each convolutional layer operation. Moreover, a max pooling layer of \(2\times 2\) was followed by each convolutional layer. The max pooling layer substituted the activation in a sub-region of the feature map with the maximum value in that region. The pooling layer downsampled the previous feature map.  
Figure 3: Final schematic of the convolutional neural network (CNN) model for autonomous recognition of LMD build parts quality.  
Figure 2: Data augmentation: (**a**) origin image, (**b**) rotation, (**c**) flipping, (**d**) crop, (**e**) adding Gaussian noise, and (**f**) adding blur.  
After the feature extraction, the classification module took the \(28\times 28\times 128\) feature maps and flattened them as a 512 feature vector. Then fully connected (FC) layers were applied to densify the 512 feature vector to the dimension of 64 and \(C\), where \(C\) is the number of categories in our dataset. The vectors of \(C\) dimensions (\(\left[V_{1},\,V_{2},\,\dots,\,V_{c}\right]\)) were computed the predicted probability of a class using Softmax function [21] Equation (1) and transformed to the output.  
\[P\;(y_{m}=c\big{|}X_{m})=\frac{\exp(V_{c})}{\sum_{c=1}^{C}\exp(V_{c})} \tag{1}\]  
where \(P(y_{m}\;=\;c\big{|}\,X_{m})\) was the predicted probability of a sample \(X_{m}\) being class \(c\).  
During the training process of our CNN model, the difference between the true class and corresponding predicted class were calculated by the cross-entropy loss function as in Equation (2). Through the optimization of parameters \(\omega\) in the network, our target was to minimize the loss function for the training dataset X.  
\[L(\omega)\;=\;\sum\
olimits_{m=1}^{M}\sum\
olimits_{c=1}^{C}y_{mc}\log(P(y_{ m}\;=\;c\;|\,X_{m})) \tag{2}\]
## 4 Connector Behavior Specification
### Using Buffers for Replies and Requests  
We use request and reply buffers for the specification of merge behaviors. We model the _reply merge_ operation required in the \"single requester-multiple providers\" scenario (see Figure 1) using buffers dedicated to providers. We store the replies from providers in the _reply buffers_. Then, we operate a merge strategy over the elements in the reply buffers to combine the replies into a merged reply.  
Similarly, we model the _request merge_ operation required in the \"multiple requesters-single provider\" scenario (see Figure 5) using buffers dedicated to requesters. In this case, we store the requests from multiple requesters in the _request buffers_. Then, we operate a merge strategy over the elements stored in the request buffers to combine the requests into a merged request. We define two types of buffers for requests or replies:  
* _Single-copy buffers_ are for storing a snapshot copy of requests or replies in which subsequent requests or replies overwrite the previous ones.
* _FIFO buffers_ are for storing subsequent requests or replies using first-in, first-out ordering.  
For example, in the case presented in Figure 4c, we store the three replies returned from the providers in three separate single-copy buffers. Then we iterate over each buffer and combine the returned replies into a merged reply.  
Figure 6a shows requests received from multiple requesters. In this example, we store the requests in single-copy request buffers. After receiving requests from all requesters, we combine the requests stored in buffers as a merged request and forward the merged request to the providers. Figure 6b shows another example that involves a single requester with a single FIFO buffer. In this example, we store the subsequent requests received from the requester in the FIFO request buffer. When the FIFO buffer becomes full, we combine the requests stored in the buffer as a merged request and then send that request to the connected providers.  
Figure 6: Merging requests using buffers: (**a**) Storing requests from multiple requesters in single-copy request buffers. (**b**) Storing subsequent requests from a requester in a FIFO request buffer.  
Examples in Figure 4a,b do not require buffers since the former connector discards all replies, and the latter chooses one of the replies and sends it back to the requester. Consequently, connector behaviors for these cases will not include the behavior for merging the replies.
## 3 JPEG Image Compression and Proposed Artifact Reduction Scheme by Deep Convolutional Network
Different from the previous approach [51] where zero-padding is not adopted, in the proposed network, all the filters whose size is larger than 1 adopt the zero-padding strategy to maintain the size of the reconstructed images unchanged. Since the features extracted by the first layer are in low quality, the second convolutional layer is used for feature enhancement, and, with \(7\times 7\) filter size, it generates 32 feature maps. Because more nonlinear mappings are involved, more potential features can be extracted, and the third layer is used for nonlinear mapping. With \(1\times 1\) filter size, it generates 16 feature maps. The last layer is used for image reconstruction and the corresponding filter size is \(5\times 5\). After the first three convolutional layers, features in the inputs are successfully extracted and enhanced by optimizing the output of the network to generate the final recovered image. The operation of these layers can be formulated as:  
\[F_{i}(\mathbf{I})=ReLU(W_{i}*F_{i-1}(\mathbf{I})+B_{i}) \tag{10}\]  
where \(i\) indicates the \(i\)th convolutional layer; \(F_{i}(\mathbf{I})\) and \(F_{i-1}(\mathbf{I})\) are the outputs of the \(i\)th and \((i-1)\)th convolutional layer, respectively; and \(W_{i}\) and \(B_{i}\) represent the weights and biases for the \(i\)th layer, respectively. Since there is no activation function after the last conventional layer, the final restored images can be expressed as  
\[F_{4}(\mathbf{I})=W_{4}*F_{3}(\mathbf{I})+B_{4} \tag{11}\]  
In addition, aiming to learn the optimal values for the weight and bias of each layer, the L2 distance between the reconstructed hologram images \(F(\mathbf{I};\Theta)\) and corresponding uncompressed ground truth hologram images \(\mathbf{I}_{GT}\) needs to be minimized. Given a set of \(m\) uncompressed images \(\mathbf{I}_{GT}^{i}\), the corresponding compressed images \(\mathbf{I}_{i}^{i}\), the Mean Squared Error (MSE) loss function is:  
\[Loss(\Theta)=\frac{1}{m}\sum_{i=1}^{m}\|F(\mathbf{I}^{i};\Theta)-\mathbf{I}_{ GT}^{i}\|_{2}^{2} \tag{12}\]  
where \(\Theta\) contains the parameters of the network, including both weights and biases. The network weighting parameters are trained separately for phase-only holograms generated at different distances. Hence, the artifacts caused by JPEG compression in phase-only holograms such as high frequency noise and blocking effect can be suppressed by the proposed convolutional network.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Non-Concentrating Systems (NCC)  
Non-concentrating collectors usually consist of flat mirrors oriented to the equator line with a predetermined inclination angle depending on the installation place latitude. They can be static or movable systems, and are generally on a single axis.  
Their main advantages are: (1) simple design and low fabrication cost when compared to curved mirrors [65], and (2) they work with both direct and diffuse radiation. Also, they present high optical and quantum efficiency but usually do not heat water efficiently, which can be an advantage depending on the particular application. The main drawback is that they are generally designed for laminar flows, for which mass transfer rates are known to be suboptimal [19]. Among the non-concentrating systems, two deserve particular attention: (1) inclined plate collectors (IPC) and (2) water-bell photoreactors.
# Seismic Response Mitigation of Base-Isolated Buildings
###### Abstract  
Earthquake response mitigation of a base-isolated (BI) building equipped with (i) a single tuned mass damper at the top of the building, (ii) multiple tuned mass dampers (MTMDs) at the top of the building, and (iii) MTMDs distributed on different floors of the building (d-MTMDs) is studied. The shear-type buildings are modeled by considering only one lateral degree of freedom (DOF) at the floor level. Numerical approach of Newmark's integration is adopted for solving the coupled, governing differential equations of motion of 5- and 10-story BI buildings with and without TMD schemes. A set of 40 earthquake ground motions, scaled 80 times to get 3200 ground motions, is used to develop simplified fragility curves in terms of the isolator maximum displacement. Incremental dynamic analysis (IDA) is used to develop simplified fragility curves for the maximum target isolator displacement. It is found that TMDs are efficient in reducing the bearing displacement, top floor acceleration, and base shear of the BI buildings. In addition, it was noticed that TMDs are efficient in reducing the probability of failure of BI building. Further, it is found that the MTMDs placed at the top floor and d-MTMDs on different floors of BI buildings are more efficient in decreasing the probability of failure of the BI building when compared with STMD.  
B 2020, 10, 1230; doi:10.3390/app10041230
# An Efficient NIDPS with Improved Salp Swarm Feature Optimization Method
## 4 Results and Discussion
### Feature Selection Using ISSA Method  
The dynamic feature selection is applied to the preprocessed dataset using the ISSA method as explained in Section 3.2. However, stopping conditions of the ISSA method vary for binary and multiclass feature sets to fine-tune the ISSA method while training. The training of ISSA simply could be explained as finding the optimal and best location in the search space of the ISSA method. However, the optimal or best search space in terms of feature selection is known as the best and most accurate performance achieved subset of features. This is the fitness criteria of the ISSA method, to find the best combinations of features with the highest accuracy. To find the best subset of features from given feature sets, the ISSA method did the training. The training graph on minimization of loss is maintained at a maximum of 100 epochs. In binary class data, the loss does not decrease while applying 100 epochs, therefore it stopped on 50 epochs as the optimal stopping condition, and the result is founded. At this stage, it selected 15 features. In the case of multiclass problems as classes were 10, the loss was also not in decimal values. Although 100 epochs are used as maximum training conditions for the ISSA method, as a result, it is given 14 features as optimal ones for classification. The training graphs for both binary and multiclass classifications are shown in Figure 2.  
The left side for feature selection-based loss value is showing that it started from 0.062 and stopped to consist of a similar value for many epochs as 0.048. The right side of the training graph for multiclass data is showing that the loss value started from 0.2050 and ended at 0.1850 value while the value lastly decreased when moving from 80-100 epochs. It is observed that the binary class loss ranges in between 0.0 decimal values whereas, in the case of multiclass, the loss ranges from 0.2 to 0.18, this is due to the multiclass and binary class type of features data. The multiclass dataset loss fell to less low as compared to the binary class as there are only two types of classes to distinguish. The optimal fine-tuning parameters applied for both multiclass and binary class data feature selections are described in Table 2.  
Table 2 shows that 10 salp individuals are assigned to subset feature sets from the original given features. The final selected features range changes for multiclass as 14 features and 15 features for binary classification. The 50 and 100 values are given as maximum epochs, where they could be changed if the dataset size and classes change. The number of folds used while testing on KNN methods for training and testing of feature subsets with salps is 5 and the K value as neighbor assigning is also 5.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 3 Methodology  
### Research Framework  
Before introducing the proposed methodology, the overall research framework is depicted as follows:  
It can be seen from Figure 2 that the research framework contained three steps. In Step 1, the raw taxi GPS data are processed based on different passengers' pick-up locations, in which taxi demands are generated. Then, taxi demands of specific zones on each weekday are input into the matrix-based structure. In Step 2, the values in the matrix-based structure are randomly deleted according to the specific missing pattern. Then, an imputation method is proposed to generate the estimated values, and several performance evaluation criteria are selected. In Step 3, the imputation results and comparison results are illustrated, respectively.
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
###### Abstract
Our method involves three main components: (1) a generator component, which uses the Transformer to simulate the normal patterns of time series data; (2) a discriminator component, which can capture the intrinsic characteristics of the real-time series data to learn the boundary between normal patterns and anomalous patterns; and (3) an anomaly detection, which can identify the anomaly data with the trained Generator and Discriminator components. The main contributions of our work are summarized as follows:  
* We propose a new model, TGAN-AD, that incorporates Transformer into the GAN framework. TGAN-AD makes use of Transformer to capture the contextual information of the time series data for the subsequent GAN framework. * We use three public datasets and six baseline methods to comprehensively evaluate TGAN-AD anomaly detection performance. Compared with the baseline methods, TGAN-AD showed the best performance. We further provide some insights on the use of GANs for anomaly detection on time series data.
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 1 Introduction
It must also be emphasized that asphalt concrete produced by the dry or wet methods is generally comparable in its volumetric properties and mechanical performance [3; 25]. Most studies on the use of plastic waste considered the use of plastic-modified bitumen produced via the wet process. They predicted the mechanical behaviour of asphalt mixes based on the rheology of the modified binder [26; 27; 28; 29]. Differently, this study aimed to evaluate the asphalt concrete when plastic waste is added via the dry process as a bitumen extender. Therefore, the mechanical behavior of the asphalt mixtures was evaluated using performance tests over the mixes (cracking and permanent deformation resistance, and water sensitivity) instead of predicting it from bitumen rheology. A performance-based mix-design procedure is crucial for designing pavement layers with plastic-waste-modified asphalt concrete because this material is not conventional asphalt concrete. This project involved the study of asphalt concrete incorporating four different types of plastic waste: ABS (acrylonitrile butadiene styrene), HDPE (high-density polyethylene), HDPE500, and LDPE(uw) (low-density polyethylene from urban waste). The laboratory plan allowed the conclusion that the types of plastic waste added as bitumen extenders may reduce the workability of the asphalt concrete if the handling temperature and the quantity of plastic waste added are not adequate. In addition, since the level of uncertainty of the materials' behaviour is higher than usual, the mix-design procedures should be based on fundamental tests involving mechanical performance evaluation.
## 1 Introduction
[PERSON] and [PERSON] [47] evaluated the applicability of time series MODIS 250 m NDVI data for large-area crop-related LULC (land use/land cover mapping on the U.S. Central Great Plains. [PERSON] et al. [48] estimated the phenological metrics in French deciduous forests using MODIS-derived EVI, NDVI, and WDRVI time series. [PERSON] et al. [49] used MODIS NDVI time series to forecast wheat and maize yields on the Tisza River catchment and reported crop statistics. These methods show the valuable role of NDVI time series data in qualitative analysis. In this study, NDVI time series is closely related to crop yield (related to the sum of vegetation gross primary production). [PERSON] et al. [50] used satellite-based time series observations (including NDVI time series) and four process-based terrestrial biosphere models to identify and understand the changes of terrestrial GPP in Asia and obtained credible results. [PERSON] and [PERSON] [43] observed a remarkable relationship between NDVI and SOC storage. [PERSON] et al. [4] concluded that NDVI is highly predictive of SOC contents that reflect vegetation productivity and biomass. These findings indicate that a strong correlation exists between NDVI and SOC, which may be deeply connected in plain areas. Thus, the current research attempted to extract the valuable information of NDVI time series data in the digital soil mapping of SOC and determine the optimal model for predicting SOC in a plain region on the basis of previous studies. This study represented a new concept that providesa convenient predictive method to capture unavailable soil property information in a plain region, especially when the soil on earth surface is covered by vegetation. Jianghan Plain is one of the important food production regions in China. The topography of Jianghan Plain is dominated by plains, and its topographical relief is uniform. Honghu City, which is located in the middle of Jianghan Plain, was selected as the study region. NDVI maps from January to December were used as the auxiliary variables, and five methods, namely, stepwise linear regression (SLR), ordinary kriging (OK), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN), were applied as the predictive methods. The objectives of this study were to (1) discuss the correlation between SOC and NDVI time series data, (2) explore the feasibility of using NDVI time series data in SOC mapping, and (3) compare the differences of the digital mapping of SOC through different predictive methods.
## 3 FE Model Updating with Experimental Modal Data  
### Manual Tuning  
A first refined finite element model was built by using the FE software ANSYS [39]. The box girders, deck slab, and side barriers were all simulated with three-dimensional tetrahedral solid elements, known as Solid-92. Material properties, as listed in Table 2, which are typical of prestressed concrete, were assigned to the model. In particular, a reduction factor was applied to the elasticity modulus (and the shear modulus) of the side barrier. On-site observation found that high uncertainty existed in the connectivity between side barriers and the bridge deck and those between adjacent side barrier assemblies, which varied among different locations. The fifteen percent reduction was assigned by assumption, since the exact stiffness contribution of the side barrier to the whole structure was difficult to be modeled accurately. Moreover, the supports were simply represented by rigid constraints and the transverse slope of the bridge deck surface was neglected. See Figure 6a. In comparison to the experimental results, the numerical modal characteristics were not satisfactory for the initial model. First, as shown in Table 3, the relative difference of the natural frequencies was higher than 5% in absolute values for some modes. Secondly, one of the modes that was identified by the experiments was missed in the numerical prediction. Thirdly, a significant discrepancy could be found with respect to the cross-sectional deformation in the mode shape (see, for instance, Figure 7).  
Figure 5: Comparison of the experimental and numerical mode shapes: (**a**) those of the updated FE model (n3); (**b**) those of the identified results (WL3).  
In order to improve the numerical results, the following aspects were considered in the manual tuning of the FE model: (1) the geometry of the FE model was re-built by taking into account the superelevation of around 5%; (2) the cast-in-situ close-ends of the box girder at each side were modeled; (3) the neoprene supports were simulated by means of springs (Element COMBIN-14), instead of rigid constraints. See Figure 6b. Material properties are however unchanged from the initial model. Table 3 lists the modal results of the initial and tuned FE models with comparison to their experimental counterparts. The manually tuned model provided better modal results as compared to the initial model. In particular, one of the identified modes that was missed (mode No. 4) was found by the tuned model. Moreover, with respect to the modal deflection of the transversal cross section, the numerical results of the tuned model were closer to the experimental results than those of the initial model (see Figure 7).