esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Input Data** & **DL Methods** & \begin{tabular}{c} **Classification** \\ **Problem** \\ \end{tabular} & **Results** \\ \hline Proposed DeepFCM & Polar maps + clinical & DeepFCM & CAD\(\backslash\)No-CAD & Accuracy: 0.83 \\ \hline [PERSON] et al. [18] & SPECT & \begin{tabular}{c} RGB–CNN \\ (hand–crafted) \\ \end{tabular} & CAD\(\backslash\)No-CAD & \begin{tabular}{c} Accuracy: 0.93 \(\pm\) 0.28 \\ AUC: 0.936 \\ \end{tabular} \\ \hline [PERSON] et al. [2] & Polar maps & \begin{tabular}{c} RGB–CNN \\ (hand–crafted) \\ \end{tabular} & CAD\(\backslash\)No-CAD & Accuracy: 0.92 \\ \hline [PERSON] et al. [20] & Polar maps & VGG–16 & CAD\(\backslash\)No-CAD & \begin{tabular}{c} Accuracy:0.74 \\ Sensitivity 0.75 \\ Specificity: 0.73 \\ \end{tabular} \\ \hline [PERSON] et al. [50] & Polar maps + clinical & \begin{tabular}{c} CNN (Inception V3) \\ + Random Forest \\ \end{tabular} & CAD\(\backslash\)No-CAD & \begin{tabular}{c} Accuracy:0.78 \\ Sensitivity:0.77 \\ Specificity:0.79 \\ \end{tabular} \\ \hline [PERSON] et al. [21] & Polar maps & \begin{tabular}{c} Graph CNN \\ (hand–crafted) \\ \end{tabular} & CAD\(\backslash\)No-CAD & \begin{tabular}{c} Agreement rating \\ (Segment-by-segment): 0.83, \\ Sensitivity: 0.47, Specificity: 0.7 \\ \end{tabular} \\ \hline [PERSON] et al. [51] & SPECT & VGG–19 & CAD\(\backslash\)No-CAD & \begin{tabular}{c} Accuracy: 0.93 \\ Sensitivity: 1.0 \\ Specificity: 0.86 \\ \end{tabular} \\ \hline [PERSON] et al. [24] & Gray SPECT images & \begin{tabular}{c} 3D-CNN \\ (hand–crafted) \\ \end{tabular} & CAD\(\backslash\)No-CAD & \begin{tabular}{c} Accuracy: 0.87 \\ Sensitivity: 0.81 \\ Specificity: 0.92 \\ \end{tabular} \\ \hline [PERSON] et al. [52] & Stress–only SPECT & ResNet-34 & CAD\(\backslash\)No-CAD & AUC: 0.872 \(\pm\) 0.002 \\ \hline [PERSON] et al. [47] & Polar maps & CNN & CAD\(\backslash\)No-CAD & \begin{tabular}{c} Accuracy: 0.7562 \\ Sensitivity: 0.7856 \\ Specificity: 0.7434 \\ \end{tabular} \\ \hline [PERSON] et al. [53] & Polar maps + clinical (angina symptoms, age) & CNN & \
\begin{tabular}{c} Probability of CAD \\ in the left anterior \\ artery, left \\ circumflex artery, \\ and right coronary \\ \end{tabular} &
\begin{tabular}{c} Probability of CAD \\ in the left anterior \\ artery, left \\ Cer-vessel AUC: 0.89 \\ Per–patient AUC: 0.95 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of previous related studies with the proposed framework DeepFCM. transformative step forward in enhancing the accuracy and reliability of CAD diagnosis, ultimately improving patient outcomes and streamlining everyday clinical routines.
# Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)
## 2 Image Preprocessing
### Inverse Perspective Transformation  
Due to the perspective effect of the camera, the road image appears larger in the near vision and smaller in the far vision. A distorted road image is shown in Figure 1. To obtain the real road information, the road image needs to be processed by means of inverse perspective transformation [19]. After the transformation, the perspective effect of the camera is eliminated. The parallel lane line is shown in [20]. The perspective of the coordinate is shown in Figure 3.  
Figure 1: Effect of histogram equalization processing: (**a**) Before processing; (**b**) After processing.  
Figure 2: Image comparison of the raw and local OSTU segmentation: (**a**) Raw image; (**b**) Image of local OTSU segmentation.  
Because affine transformation is based on the corresponding relationship of image coordinate transformation, the transformation from inverse perspective to aerial view can be performed [21]. The principle is as follows: the raw image is set as \(U(u,v,w)\), and the transferred image is \(I(x,y,1)\); the image coordinate, which is transferred by the transformation matrix, is \(U^{\prime}(u^{\prime},v^{\prime},w^{\prime})\). The alpha and beta angles represent the pitch and deviation angles of the on-board camera, respectively. The raw image coordinate system is the reference coordinate system, so w is set as 1. The expressions for x and y are as follows:  
\[x=\frac{u^{\prime}}{w^{\prime}} \tag{3}\]  
\[y=\frac{v^{\prime}}{w^{\prime}} \tag{4}\]  
The matrix of transformation takes the following form:  
\[\mathrm{T}=\left[\begin{array}{ccc}t_{11}&t_{12}&t_{13}\\ t_{21}&t_{22}&t_{23}\\ t_{31}&t_{32}&t_{33}\end{array}\right] \tag{5}\]  
The aerial view transformation is expressed as:  
\[U^{\prime}=TgU, \tag{6}\]  
where \(\mathrm{T}=\left[\begin{array}{cc}t_{11}&t_{12}\\ t_{21}&t_{22}\end{array}\right]\) denotes the linear transformation, the inverse transformation is \(T_{2}=\left[\begin{array}{cc}t_{13}&t_{23}\end{array}\right]^{T}\), and \(T_{3}=\left[\begin{array}{cc}t_{31}&t_{32}\end{array}\right]\) represents the image transition.  
Therefore, x and y can be calculated using Equations (7) and (8), respectively, with the value of \(t_{33}\) being 1:  
\[x=\frac{u^{\prime}}{w^{\prime}}=\frac{t_{11}u+t_{12}v+t_{13}}{t_{31}u+t_{32}v+ t_{33}} \tag{7}\]  
\[y=\frac{v^{\prime}}{w^{\prime}}=\frac{t_{21}u+t_{22}v+t_{23}}{t_{31}u+t_{32}v+ t_{33}} \tag{8}\]  
According to the equations, as long as the coordinates of four noncolinear points in the image are found, the transformation matrix \(\mathrm{T}\) can be obtained. Then, each point coordinate in the aerial view, corresponding in the original image, can be solved using Equation (6). Figure 4 shows the aerial view obtained from the affine transformation; those that are marked in Figure 4 are the selected coordinate points.  
Figure 3: Comparison of the image perspective: (**a**) Image of inverse perspective; (**b**) Image of aerial view.
## 4 Hybrid Covering Location Problem Formulation
### MCLP-Sclp  
This model's structure exploits the coverage concept of covering problems in a contrary format to HCLP model. In MCLP-SCLP, it is supposed that facilities obey the coverage concept of MCLP having specified numbers as strategic decisions, while the minimum number of modules has to be determined  
Figure 2: The structure of different possible hybridization models. SCLP: set covering location problem, MCLP: maximal covering location problem, and HCLP: hybrid covering location problem.  
by SCLP in order to provide full coverage for each point from each module type as tactical decisions. The mathematical model of MCLP-SCLP has a similar formulation to MCLP-MCLP for facility location part, but the difference is in constraints (12) that should be replaced with constraints (19) that indicated each module type should be assigned to the opened facilities in a way that can provide full coverage for each demand point in each tactical period of strategic period. As a result, the objective of the MCLP-SCLP maximizes the profit obtained from the income of covering demand points and the cost of assigning the modules. The mathematical formulation of MCLP-SCLP is as follows:  
\[\max\sum_{i\in I}\sum_{j\in I}\sum_{k\in I}\sum_{t\in\mathcal{T}}\sum_{t\in \mathcal{T}}a_{jlt}g_{ijt}d_{jlt}x_{ijlt\tau}-\sum_{i\in I}\sum_{l\in I}\sum_ {k\in K}\sum_{t\in\mathcal{T}}b_{jlt\tau}y_{ijkt\tau} \tag{18}\]  
\[\sum_{i\in I}\sum_{k\in K}\epsilon^{\prime}_{ij}y_{iklt\tau}\geq 1 \tag{19}\]  
\[\forall j\in J,l\in L,t\in T,\tau\in\mathcal{T}_{t}\] (17), (8)-(11), (13)-(15).  
where \(b_{ilt\tau}\) is the cost of assigning the size \(k\) of module \(l\) to the facility \(i\) at tactical period \(\tau\) of strategic period \(t\), and \(\epsilon^{\prime}_{ij}\) is the binary parameter, which is 1 if \(dis_{ij}\leq S\) and 0, otherwise.
## 3 Results and Discussion
### Simulations  
Simulations were performed to further understand the effects of gold nanoparticles decorated micro-pyramid array substrate. We calculated the local electric field of the gold nanoparticles decorated micro-pyramid structure using a commercial the finite element method (FEM) software (COMSOL Multiphysics). We set the diameter of gold nanoparticles to 20 nm and pitch between nanoparticles to 22 nm. Figure 8 shows a cross-sectional view of the electric field distribution between two micro-pyramids with an incident light wavelength of 633 nm. In this figure, the normalized electric field represents the ratio between the electric field excited by the local surface plasmon resonance and the background electric field. We can clearly see that a strong electric field is formed around gold nanoparticles, especially between nanoparticles. V-shaped caves formed between the pyramids can also help to enhance the electric field by concentrating the input of light energy. This micro/nano hierarchical structured substrate helps to form many strong hot spots. Since the SERS enhancement effect is proportional to the fourth power of the local electric field intensity [20], these hot spots have a very good enhancement effect on the SERS signal.
## 2 Design of the Joint Module
### Joint Part Selection  
The main parameters for the proposed self-driven AACMM are listed below. (1) The total length is no less than 1.2 m. (2) The angular accelerations of Joints 1 and 2 do not exceed 10\({}^{\circ}\)/s\({}^{2}\). Those of Joints 3 and 4 do not exceed 20\({}^{\circ}\)/s\({}^{2}\). Those of Joints 5 and 6 do not exceed 30\({}^{\circ}\)/s\({}^{2}\). (3) The load on Joint 6 is the constant force trigger probe. The load on other joint modules comes from the weight attached to its output end to the probe part. Analysis of the driving force of the joint model for the  
Figure 1: Structure of an articulated arm coordinate measuring machine (AACMM).  
Figure 2: Joint model for a self-driven AACMM [31].  
self-driven AACMM can be conducted by referring to the following dynamics equation from the field of robotics [32]:  
\[\mathbf{Q}=\mathbf{M}(\mathbf{q})\bar{\mathbf{q}}+\mathbf{C}(\mathbf{q},\dot{ \mathbf{q}})\dot{\mathbf{q}}+\mathbf{F}(\dot{\mathbf{q}})+\mathbf{G}(\mathbf{q}), \tag{1}\]  
where, \(\mathbf{q}\) is the position vector of the joint, \(\dot{\mathbf{q}}\) is the joint velocity vector, \(\bar{\mathbf{q}}\) is the joint acceleration vector, \(\mathbf{M}(\mathbf{q})\) is the mass matrix, \(\mathbf{C}(\mathbf{q},\dot{\mathbf{q}})\) is a matrix related to Coriolis acceleration and centripetal acceleration, \(\mathbf{F}(\dot{\mathbf{q}})\) is a vector related to viscous friction and coulomb friction, \(\mathbf{G}(\mathbf{q})\) is a moment vector by the presence of gravity, and \(\mathbf{Q}\) is the generalized moment vector.  
Based on the low movement speed of the arm, \(\mathbf{C}(\mathbf{q},\dot{\mathbf{q}})\) in Equation (1) can be ignored. The influence of the friction term \(\mathbf{F}(\dot{\mathbf{q}})\) can also be ignored. Therefore, Equation (1) can be simplified as follows:  
\[\mathbf{Q}=\mathbf{M}(\mathbf{q})\ddot{\mathbf{q}}+\mathbf{G}(\mathbf{q}). \tag{2}\]  
Equation (2) indicates that the driving moment required by the self-driven joint module is related to its own mass, rotational angular acceleration and load. The required joint's moment can be expressed by  
\[\mathbf{Q}=\mathbf{M}_{\mathbf{M}}+\mathbf{M}_{\mathbf{j}}, \tag{3}\]  
where, \(\mathbf{M}_{\mathbf{M}}\) is the static moment on the joint introduced by its load and \(\mathbf{M}_{\mathbf{j}}\) is the moment required to change the motion state of the joint itself and load.  
Based on Equation (3) and the AACMM structure, design, and component selection were carried out by calculating each step from the probe to the base in a preliminary study [31]. Table 1 lists the design parameters, and load moment for each joint, and minimum moment required for the motor and reducer. Table 2 lists the parameters of the brushless motor and reducer.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 2 IoT Tide Gauge Network Design
### Sigfox Wide-Area Network  
The Sigfox module used has a carrier frequency of 868 MHz (working region EU) with an antenna power gain of 2 dB. The Sigfox network permits a maximum of 140 messages of 12 bytes to be sent each day. For instance, the data package could be made up of two integers (taking one byte each) and five floating-point numbers (taking two bytes each). This restriction for the UK is due to European Telecommunications Standards Institute regulations, which only permits devices on these frequencies to send messages 1% of the time (36 s in each hour). The typical communication range is around 10 km in urban areas and 40 km in open countryside.  
Data from the IoT devices are delivered to the Sigfox Cloud (see the schematic of the overall network in Figure 1) and redistributed from there to various servers for processing and display. For example, web servers show the tabulated data and graphical outputs; and a computer-server prepares inputs for the waterline algorithms in combination with the marine X-band radar data to produce beach morphology DEMs.
## 2 Materials and Methods
### Turbidity Compensation  
#### 2.5.1 Double-Wavelength Method (DWM)  
Turbidity absorbs visible light, and the absorbance curve changes with the change of turbidity value, which affects the determination of COD in water by spectrophotometry. It is important to compensate for turbidity. The traditional method uses the absorbance at 546 nm to predict the turbidity value. This method is simple, but the accuracy needs to be improved. In order to improve the accuracy, a dual-wavelength method (DWM) was proposed to determine the turbidity values in this paper. Because turbidity has an absorption in the visible light band. The DWM uses the slopes of the lines connecting the absorbance values at 400 nm and 600 nm to predict the turbidity values. As shown in Figure 6, the slopes of the lines and turbidity values have a good linear relationship, which can be used to calculate the turbidity values.
# Blockwise Frequency Domain Active Noise Controller Over Distributed Networks
###### Abstract  
This work presents a practical active noise control system composed of distributed and collaborative acoustic nodes. To this end, experimental tests have been carried out in a listening room with acoustic nodes equipped with loudspeakers and microphones. The communication among the nodes is simulated by software. We have considered a distributed algorithm based on the Filtered-x Least Mean Square (FxLMS) method that introduces collaboration between nodes following an incremental strategy. For improving the processing efficiency in practical scenarios where data acquisition systems work by blocks of samples, the frequency-domain partitioned block technique has been used. Implementation aspects such as computational complexity, processing time of the network and convergence of the algorithm have been analyzed. Experimental results show that, without constraints in the network communications, the proposed distributed algorithm achieves the same performance as the centralized version. The performance of the proposed algorithm over a network with a given communication delay is also included.  
active noise control; distributed networks; acoustic sensor networks; filtered-x least mean square +
Footnote †: journal: Applied Sciences
## 3 Theories and Methodology
### The Methods of Locating Strong Electromagnetic Pulses
Should the count of events in \(S\) exceed a threshold N (set at 50), a clustering process is initiated to delineate \(C_{i}\) clusters (as illustrated in Figure 2, Cluster 1 through Cluster 5). If the number of lightning flashes within these clusters surpasses a value n (set at 5), it is postulated that the event may originate from the centroids of these clusters (as depicted in Figure 2). 3. In instances where \(C_{i}\) equates to 1, the event's geographic location is presumed to be the cluster's centroid. Conversely, if \(C_{i}\) exceeds 1, the International Reference Ionosphere model is initially applied to calculate the \(VTEC_{i}\) at the IPP for each distinct cluster centroid  
Figure 2: Geolocation of single-satellite detection events via the CAL method. relative to the satellite. Subsequently, the \(VTEC_{i}\) values are transformed into \(STEC_{i}\) using the satellite's zenith angle \(\delta_{i}\) in relation to various IPs (as shown in Figure 2). Ultimately, the cluster center that exhibits the minimal divergence between \(STEC_{i}\) and the event's \(STEC_{dec}\) is identified as the event's geographic location. In scenarios where multiple cluster centers are essentially aligned on concentric circles, indicating that the incidence angles of the satellite relative to the cluster centers are nearly identical, the aforementioned method may not effectively discern the event's geographic location. The aforementioned process of ascertaining the geographic locations of single satellite-detected events through clustering analysis and the ground-based lightning location network observations encapsulates the essence of the CAL method. Furthermore, once the two-dimensional coordinates of trans-ionospheric pulse pairs (TIPPs) are established, the radiation height \(H_{e}\) can be calculated based on the TIPPs' reflection mechanism. This relationship is given by the formula:  
\[H_{e}=\frac{c\tau\text{cos}\,\theta}{1-\sin 2\theta} \tag{4}\]  
In this equation, \(c\) denotes the speed of light, and \(\tau\) represents the interval between the two pulses within a TIPP, as depicted in Figure 3d.
## 3 Current Developments
### Contributor Analysis
#### 3.2.3 Motivation, Behavior and Gender Dimensions
Oftentimes, only a few contributors collect the majority of data volume in a predefined area [112] or entire country [21]. [PERSON] and [PERSON] [119, 136] attempted to identify explicit social networks between contributors based on this assumption. The results showed that most collaboration in OSM is purely incidental and that data contributions are mainly done in isolation. In 2009, a first attempt was made to classify VGI project members into different overlapping contributor categories, such as neophyte, interested amateur, expert amateur, expert professional and expert authority [7]. For OSM, the contributors were oftentimes classified or sorted based on their contribution patterns. [PERSON] and [PERSON] [112] and [PERSON] and [PERSON] [72] grouped the members of the OSM project based on the number of contributions or created objects to analyze the different groups in more detail. [PERSON] and [PERSON] [119] classified the OSM contributors of the London (UK) area into four distinct groups, highlighting that the majority of the contributors edited the geometry of objects or their corresponding attributes, but in many cases not both. [PERSON] _et al_. [131] utilized a clustering method based on the contribution and feature types of each contributor to identify different patterns in mapping behavior. As a result, contribution profiles, such as \"Premium Creator\", \"Highway Mapper\" or \"All-Rounder\" were created.
# Text Normalization Using Encoder-Decoder Networks Based on the Causal Feature Extractor
## 3 Experimental Results
### Experimental Setup  
Three different experiments have been performed using different subsets of the filtered database, in order to analyze different aspects of the proposed model. The first two datasets are used to test and compare different models, whereas the latter is used to train the final model. Table 1 shows their name, training time, number of training/test/validation samples, and how the samples were selected; _random_ means that they were randomly taken, and _shortest_ that the elements with shortest outputs were selected. In all the cases, the validation and test size is 1/5 of the training size, and all the sets are disjointed.  
Training time is a key parameter in most deep learning systems, since it can determine the practical feasibility of a given method. Thus, accuracy is closely related with computational efficiency. For this reason, the comparison in the datasets is done by setting the duration of the training process, rather than fixing the number of training iterations or until reaching convergence.  
For the execution of the experiments, all the computations were done in a remote server via secure shell and distributed between three NVIDIA GeForce GTX1080 GPUs (each experiment using a single GPU), in a computer with an i7-5930K Intel(R) CPU, 12 effective threads (6 with hyperthreading), and 600 GB hard disk drive. Regarding the software, the code was mainly written in Python v.3.6, making use of the Pytorch v0.4 framework to build the neural models [26], as well as OpenNMT [27]. The latest is a neural machine translation toolkit used to speed up the process of solving and testing different problem solutions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Duration** & **Training Size** & **Test/Validation Size** & **Selection** \\ \hline E1 & 1 h & 50,000 & 10,000 & shortest \\ E2 & 12 h & 50,000 & 10,000 & random \\ E3 & 22 h & 1,000,000 & 200,000 & random \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the datasets used in the experiments. Name: Dataset identifier. Duration: Time used for training the models. Training size: Number of samples used for training. Test/validation size: Number of samples used for test and for validation. Selection: Sample selection criteria.  
Figure 8: Pseudocode of the approximate randomization test. R is the number of repetitions selected. Adapted from [24].