esa-sceva/satcom-chunk-collection huggingface dataset:
# Gyro Error Compensation in Optoelectronic Platform Based on a Hybrid ARIMA-Elman Model
## 3 Related Knowledge
### Elman Model  
An Elman neural network [13; 14; 15] is a kind of recursive network, which can approximate arbitrarily nonlinear functions with arbitrary precision. For the reason that a new layer is added to the hidden layer of the feed-forward network as a one-step delay operator to achieve memory ability, it has good approximation ability and dynamic characteristics. Additionally, it is well adaptive and self-organizing. Therefore, Elman networks have a wide range of applications, in areas such as function approximation, pattern recognition, and data compression.  
An Elman neural network is a feedback network, which can be regarded as a BP network with a local memory unit and local feedback connection. It consists of an input layer, several hidden layers, and an output layer. Each node in hidden layer has a corresponding associated layer node connection. In theory, a three-layer Elman network can approximate any nonlinear function with arbitrary precision. However, when there are too many neurons in the hidden layer, the convergence speed of the network is slow and the generalization ability of the network is poor. The generalization ability (also called comprehensive ability) refers to the capability that training with fewer samples enables the network to achieve the required accuracy in a given area, or using less training samples to give the network an appropriate output for untrained inputs. Therefore, a neural network without generalization capability has no practical value. According to the dynamic characteristics of inertial device error, this paper will use a four-layer Elman neural network; its structure is shown in Figure 4.  
The output of the hidden layer is \(O^{1}(k)\), that is, \(O^{1}(k)=f(W^{0}X+B^{0})+W^{2}O^{1}(k-1)\). \(O^{2}(k)\) is the output of the neuron, which can be expressed by \(O^{2}(k)=g(W^{1}O^{1}(k)+B^{1})\). In addition, \(W^{0},W^{1},W^{2}\) is the corresponding weight matrix. \(B^{0}\), \(B^{1}\) is the corresponding threshold.  
As can be seen from Figure 4, the Elman network consists of an input layer, a hidden layer, an associative layer, and an output layer. The difference between an Elman neural network and a BP network lies in the existence of its associated layer nodes. Each hidden layer node has a corresponding associated layer node connection, and the weights of the links are adjustable. The output of the association layer node of an Elman neural network actually plays the role of storing the internal state of the network. The connection between the association layer and the middle layer is similar to the state feedback inside the system. The number of hidden layer nodes is the same as the number of  
Figure 4: Structure of an Elman neural network.  
nodes in the associated layer. Through the output of the associated layer node, the Elman network can store not only the current sequential input data, but also some of the past information in the sequential input data.
## 3 Materials and Methods
### Feature Extraction of EMG  
Each participant's data included basic demographic information such as gender, age, weight, and height. This basic information was useful in the initial analysis to understand the overall characteristics of the participants. The primary focus of this study was on EMG measurements, with 16 EMG signals recorded for each participant. EMG provides valuable insights into the electrical activity of muscles, offering information related to muscle dysfunction. Based on this, for each filtered EMG signal, we extracted computational features, including the minimum peak (min) and maximum peak (max) of muscle activity exceeding 3 SD above the resting activity levels, as well as the total average (avg) and total sum (sum) of activity during the entire gait cycle. Here, the resting activity levels are defined as the average muscle activity during a 100 ms period while the participant is standing still before the gait cycle begins [53]. For example, as illustrated in the bottom panel of Figure 1, using the Lt. VMO as a reference, within a single gait cycle, there are two peak points above the baseline muscle activity, with the larger peak defined as the max and the smaller peak defined as the min. As a result, a total of 64 feature variables were derived for each participant (16 EMG signals \(\times\) 4 derived features). These features were chosen because they provide a numeric representation that is both representative and intuitive. Unlike a power spectrum analysis, which may be less straightforward for non-specialists, these features offer an easily interpretable summary of muscle activity. This makes them more accessible in clinical settings where practitioners may not have specialized expertise. Additionally, these features are computationally efficient to extract during pre-processing, which helps streamline the analysis process and enhances the overall efficiency of the model.
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 8 Performance Evaluation and Simulation  
The performance evaluation of the proposed scheme is paralleled with four different routing techniques called as E-HARP [27], PCRP [20], EERP [35] and EB-MADM [31] by using the NS-2  
Figure 5: Flowchart of EHCRP.  
simulator. For the comparison, the unit of time is taken in rounds. In a single round each sensor senses a data packet and forwards it ahead. The total time is measured between the first round and before finishing the last round. This scheme is checked with other contemporary methods in terms of residual energy, network lifetime, stability, end-to-end delay and network throughput.  
The topology used for proposed WBAN scheme has a total of ten SNs and two sink nodes, called the central node coordinator (CNC). These nodes are placed on various parts of the human body, as shown with details in Figure 3. These deployed tiny sensors sense human-related medical data such as glucose, BP, EMG, ECG and other important medical parameters. These sensed data are then forwarded to the CNC for further required processing. Details of the simulation are given below in Table 3.
## 2 Evaluation of Pavement Crack Damage by Deep Learning
### Outline of Convolutional Neural Network
#### 2.1.1 The Convolutional Layer  
The convolutional layer performs the operation of convolving the filter with respect to the provided input, and thus this layer has the function of identifying local characteristics. The basic structure of the convolutional layer is shown in Figure 2. Assuming that the size of images inputted to the convolutional layer is \(W\times H\times K\), the size of the filter is given by \(w\times h\times K\times L\). \(W\) is the image width, \(H\) is the image height, \(K\) is the number of channels of the image inputted to the convolutional layer, \(w\) is the width of the filter, \(h\) is the height of the filter, and \(L\) is the number of filters.  
If the pixel value of the input image is expressed using \(x_{ijk}\) with the index \((i,j)\) (\(i=0\),..., \(W-1\), \(j=0\),..., \(H-1\), \(k=1\),..., \(K\)) and the pixel value of the filter using \(h_{pdl}\)  
Figure 1: Basic structure of convolutional neural network (CNN) (C = convolutional layer, \(P=\) pooling layer, and \(F=\) fully connected layer).  
Figure 2: Schematic of the calculation performed in the convolutional layer.  
(\(l=1\),..., \(L\)) with the index (\(p\), \(q\)) (\(p\) = 0,..., \(w-1\), \(q\) = 0,..., \(h-1\)), the convolution operation is expressed with Equation (1):  
\[u_{ijl}=\sum_{k=1}^{K}\sum_{p=0}^{w-1}\sum_{q=0}^{h-1}x_{i+p,j+q,k}h_{pqkl}+b_{l} \tag{1}\]  
where \(u_{ijl}\) is the pixel number of the output image and \(b_{l}\) is bias.  
Next, we applied the activation function \(f\) to \(u_{ijl}\) obtained with Equation (1) as shown in Equation (2):  
\[z_{ijl}=f(u_{ijl}) \tag{2}\]  
While activation functions such as sigmoid function, tanh, normalized first-order function called ReLU, and soft sign function have been proposed so far, ReLU which was reported to be the most suitable was used in this study [29]. ReLU is expressed using Equation (3):  
\[f(u)=\max(u,0) \tag{3}\]  
The output image resulting from these analyses will be used as input for the next layer.
## 3 Results and Discussion
### Identification of Volatile Compounds in Six Citrus EOs
\begin{table}
\begin{tabular}{l l l c c c c c c} \hline  & **Compounds** & **CAS** & **RI 1** & \multicolumn{3}{c}{**HYEO**} & \multicolumn{3}{c}{**GFEO**} \\ \cline{4-9}  & & & **CP** & **MADH** & **SCC** & **CP** & **MADH** & **SCC** \\ \hline
7 & Bergaptene & 484-20-8 & 2062 & - & - & - & \(0.095\pm 0.010\) & - & - \\ \hline
8 & Osthole & 484-12-8 & 2143 & \(0.203\pm 0.053\) & - & - & \(0.800\pm 0.110\) & - & - \\ \hline
9 & Isoautapetene & 1088-17-1 & 2246 & \(0.153\pm 0.035\) & - & - & \(1.431\pm 0.263\) & - & - \\ \hline
10 & Meranzin & 23971-42-8 & 2258 & \(0.129\pm 0.028\) & - & - & \(1.759\pm 0.976\) & - & - \\ \hline
11 & Auraptenol & 51599-35-4 & 2298 & - & - & - & \(0.213\pm 0.036\) & - & - \\ \hline \end{tabular}  
* The RI values were obtained from AromChemBase (Alphasoft, Alpha M.O.S., Toulouse, France): “-” means not detected by GC-MS; CP, cold pressing: MADH, microwave-assisted hydrodistillation; SCC, spinning cone column.  
\end{table} 
Table 1: _Cont._associating nootkatone as a characteristic aroma compound of GF [34]. Therefore, HY peels may be a viable and sustainable source of natural nootkatone production, which is highly demanded by the flavor, fragrance and cosmetic industries [35]. Other compounds detected in six EOs included two terpene oxides (i.e., (_E_)-limonene oxide, cyanophyllene oxide), n-hexadecanoic acid and multiple coumarins (i.e., 7-methoxycournarin, 7-hydroxycournarin, bergaptene, osthole, isoauraptene, meranzin and auraptenol). All the coumarins detected were significantly more abundant in GF EO than in HY EO.
## 3 Numerical Method
### Validation of OHAM  
Validation of the OHAM technique is an essential part of a theoretical study. The aim of validation is to ensure the computations are reliable. For this purpose, we considered a non-linear system [42; 55] to evaluate our semi-empirical technique. Indeed, OHAM is known for its easy implementation, effectiveness for solving non-linear systems, and producing efficient results with minimum computations. The non-linear value system is given as follows:  
\[\begin{gathered} g^{{}^{\prime\prime}}(z)-zf^{{}^{\prime}}(z)+g(z )=y_{1}(z),\\ f^{{}^{\prime\prime}}(z)-zg^{{}^{\prime}}(z)+g(z)f(z)=y_{2}(z) \quad 0\leq z\leq 1\end{gathered} \tag{31}\]  
subject to the following boundary conditions:  
\[\begin{gathered} g(0)=g(1)=0,\\ f(0)=f(1)=0,\end{gathered} \tag{32}\]  
where \(y_{1}(z)=z^{3}-2z^{2}+6z\) and \(y_{2}=z^{5}-z^{4}+2z^{3}+z^{2}-z+2\) the exact solutions of the systems are \(g(z)=z^{3}-z\) and \(f(z)=z^{2}-z\). By applying OHAM scheme Equation (31) subject to the given boundary conditions Equation (32) to evaluate solutions denoted by \(g_{OHAM}\) and \(f_{OHAM}\). The effectiveness of OHAM is validated using absolute errors \(|g_{Exact}-g_{OHAM}|\) and \(|f_{Exact}-f_{OHAM}|\), the results of \(g(z)\) and \(f(z)\) for various values of \(z\) are also compared with [56]. Tables 2 and 3 verifies that accuracy is achieved even with the second order approximations.  
Figure 2: Schematic representation for the MHD Hiemenz flow analysis of nanofluid over a porous wedge.
## 2 The Process Plant Inspection and Maintenance System
### Classification of Current Plant Maintenance Strategies
As researchers have shed light on these domains, a holistic perspective emerges, guiding the path toward optimized, reliable, and efficient plant operations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Maintenance Strategy** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Limitation**} \\ \hline \multirow{6}{*}{PdM} & \(\bullet\) & Uses data and analytics to predict & \multirow{6}{*}{\(\bullet\)} & \multirow{6}{*}{Rquires significant investment in} \\  & \(\bullet\) & equipment failure before it occurs & & data collection and analytics \\  & \(\bullet\) & Allows for targeted maintenance, & & infrastructure \\  & & reducing downtime and costs & & \multirow{6}{*}{\(\bullet\)} & \multirow{6}{*}{May not be accurate in predicting} \\  & \(\bullet\) & Can improve equipment reliability & & all types of equipment failure \\  & & and safety & & \\  & & \(\bullet\) & Reduce the likelihood equipment & & \\  & & failure by performing regular & & maintenance tasks if equipment is in \\  & & maintenance tasks & & good condition \\ PM & \(\bullet\) & Can extend the lifespan of & & Difficult to predict when \\  & & equipment & maintenance is needed without \\  & & Cost-effective compared to & & maintenance is needed without \\  & & corrective maintenance & & prior knowledge of failure modes \\  & & \(\bullet\) & May result in unnecessary \\  & & maintenance tasks if equipment is in & & \\ CBM & & good condition & & \\  & & Difficult to predict when & & \\  & & maintenance is needed without & & \\  & & prior knowledge of failure modes & & \\  & & \(\bullet\) & Focuses on identifying critical & \\  & & equipment and failure modes & & \\  & & Uses data-driven approaches to & & data collection and analysis \\  & & optimize maintenance strategies & & May not be effective in predicting \\  & & \(\bullet\) & Can improve equipment reliability & & all types of equipment failure \\  & & and safety & & \\  & & \(\bullet\) & Addresses equipment failures as & \\  & & they occur & & \\  & & Can improve equipment reliability & & and increased costs \\  & & and safety & & \\  & & \(\bullet\) & Allows for immediate response to & \\  & & equipment failures & & \\  & & \(\bullet\) & Focuses on improving overall & \\  & & equipment effectiveness and & & \\  & & productivity & & \\  & & Involes all employees in & \\  & & maintenance activities & & \\  & & \(\bullet\) & Can improve equipment reliability & \\  & & and safety & & \\  & & \(\bullet\) & Can reduce maintenance costs and & \\  & & increase productivity & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and limitations of different maintenance strategies.
# Make Patient Consultation Warmer: A Clinical Application for Speech Emotion Recognition
## 3 Results
### SER Result Comparison  
Table 3 provides the comparison between manual emotion labeling and SER on the emotions of doctors and patients. Most of the samples were identified as neutral emotions. According to Table 2, the manual emotion labeling results differed slightly from the SER results. For example, in the fourth second, the patient's emotion was sad according to the manual label but was identified as neutral in SER. A comparison between the manual labeling and SER results for 30 clinical speech files revealed a classification accuracy of 88.6%, indicating that the SER model developed in this study is effective in emotion recognition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Neutral** & **Angry** & **Happy** & **Sad** & **Total** \\ \hline Neutral & 299 & 0 & 22 & 7 & \\ Angry & 0 & 91 & 5 & 0 & \\ Happy & 16 & 9 & 63 & 1 & \\ Sad & 0 & 0 & 0 & 97 & \\ \hline Accuracy & 94.9\% & 91.0\% & 70.0\% & 92.4\% & 90.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: SER model emotion categorization results on all the data sets (90.2% accuracy).  
Figure 7: ROCs of all the data sets. Blue: Neutral. Green: Angry. Dark Yellow: Happy. Purple: Sad.
# Technological Development and Application of Photo and Video Theodolites
###### Abstract  
Theodolites are fundamental geodetic measuring instruments for all practical geodetic tasks, as well as for experimental geodetic scientific purposes. Their development has a long history. Photo and video theodolites represent the advanced development of classic theodolites. Development started in 19 th century, but only in the last 15 years has commercial application been achieved in the geodetic profession. The latest development, called image-assisted total stations (IATS), is a theodolite which consists of a classic robotic total station (RTS) with integrated image sensors. It was introduced in the early 2000s. With the development of theodolites, their application became much wider; today, they can be used for structural and geo-monitoring, i.e., for the determination of static and dynamic displacements and deformations of civil engineering structures such as bridges, dams, wind turbines, and high buildings, as well as natural structures, such as mountain slopes. They can be implemented in geodetic monitoring systems, which are an integral part of engineering structural diagnosis, and they provide essential information about the current condition of the structure. This paper describes the technological development of photo and video theodolites divided into phases according to the main innovations in their development. The application of modern video theodolites (i.e., IATS) is presented through several experimental studies that were performed. The procedure of conducting measurements with this kind of instrument, as well as the analysis of acquired data and achieved results, is elaborated. Lastly, the authors conclude, according to the achieved results, that IATS can today be used for determination of quasi-static and dynamic displacements with small and high amplitudes.  
photo theodolite; video theodolite; IATS; technological development; displacement and deformation monitoring; structural monitoring; geo-monitoring 2021
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 2 Related Works
### Transform Computation  
As seen in the previous equations that represent higher-order motions, they result in a motion vector that depends on the position within the block. While implementations are very good at computing predictions with a constant motion vector (and especially for integer motion vectors as they are simple copy and paste), they are not designed for a constantly changing motion vector.  
In the proposed affine motion compensation in JEM [7], this problem is avoided by using constant motion vectors for blocks of \(4\times 4\) samples. However, this also means it is not true affine motion compensation.  
In Reference [8; 9], the authors suggest doing a \(1/16^{th}\) sample interpolation using a eight-tap filter. While it is quite slow, as the gradient method converges quickly towards the optimal value, it does not add too much additional burden to the encoder.  
In Reference [10], because the method requires to evaluate more transforms, the interpolation is faster, using the quarter sample interpolation from HEVC and using bilinear interpolation between the four surrounding samples. To avoid the need for computing the interpolation many times, the interpolated samples are stored in a buffer for each reference picture.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 4 Data-Cleaning Methods  
This study focuses on DQ problems in a real-world big data platform for a city public bus transportation system in China. The corresponding dataset was collected from July 2021 to February 2022. The size of the dataset is 364.6 GB, and the record number of the dataset is 544.48 million. Details about this dataset are listed in Table 2, in which the second column is the table name, the third column is the number of attributes, and the fourth is the description for each data table. In this paper, we will systematically discuss the DQ problems related to the aforementioned six DQ categories. Firstly, we introduce redundant data, conflicting and erroneous data, and the corresponding data-cleaning methods. Secondly, we describe noisy data in GPS trajectories and propose a solution for noise filtering. Thirdly, we illuminate missing values in the spatiotemporal information of bus arrivals and departures and propose a solution to repair these missing values. We then introduce the pipeline and workflow of noise filtering and map matching in Spark.
## 2 Synthesis and Characterization of MIP
### Polymerization Technique
#### 2.2.3 Precipitation Polymerization  
Precipitation polymerization is another direct and straightforward method for the synthesis of MIP beads, which are typically nanometer-sized and allow for the synthesis of uniform spherical particles (diameters typically less than 1 \(\upmu\)m). However, it requires a significant amount of templates. Precipitation polymerization is a high yield polymerization process that requires one preparative step and is a surfactant-free process that involves polymerizing monomers in dilute solutions (without overlap or coalescence) and the precipitation of the polymer particles. The entropic precipitation of nanogel (seed) particles, followed by a continuous capture of oligomers from solution, is the most common way for the particles to proliferate. Compared to other polymerization techniques, such as bulk polymerization, this approach requires a high amount of solvents. It should also be highlighted that multiple parameter, including the polarity of the solvent, reaction temperature, and stirring speed, influence the size of the particles formed. Therefore, reaction conditions should be carefully monitored [55]. The general scheme for the precipitation polymerization technique is shown in Figure 4.  
In precipitation polymerization, the monomer and initiator can be dissolved in the reaction medium, however, in the reaction mixture, the polymer is insoluble. Therefore,  
Figure 4: General scheme of polymerization techniques used for the preparation of MIP [63; 64].  
the polymer will precipitate out of the reaction system when a solid granular product has been formed. Precipitation polymerization is an efficient technique for synthesizing imprinted polymer microparticles with desirable properties regularly. The SEM images of the polymer synthesized using the precipitation polymerization technique is illustrated in Figure 3C.  
[PERSON] et al. [13] studied molecularly imprinted gallic acid polymers produced by precipitation polymerization. The interaction between the template and monomer was achieved using a non-covalent method throughout the synthesis process. Gallic acid was used as a template and acrylic acid as a functional monomer in the polymerization process. [PERSON] et al. produced microspheres with smooth, clean surfaces and appropriate particle sizes for their investigation. The study revealed that the procedure was straightforward and has several advantages over the suspension polymerization technique because stabilizers are not used during the polymerization process. Furthermore, the precipitation polymerization technique contains no suspending agent or dispersion, overcoming the problem of the polymerization system's high viscosity. When compared to MIPs made via suspension polymerization, the resulting polymers have high purity.