esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Preprocessing
### Data Standardization  
Some of the feature data of the WDBC dataset are shown in Table 2, from which it can be seen that there are differences in the magnitude of each feature, and if not standardized, direct experiments will lead to the inability to obtain the real results of the research object. In order to reduce the impact of the data dimension on the model, the data need to be processed dimensionlessly. Commonly used dimensionless processing methods are min-max (normalization) and \(Z\)-score standardization [48]. In this paper, based on the characteristics of the WDBC dataset, Z-score standardization is applied to the data.  
In order to improve the effectiveness of the breast cancer classification model and protect the patient's privacy, first, a hybrid feature selection method that can effectively eliminate redundant variables and select the optimal features is proposed. Second, the LR model is optimized using the BGD algorithm to minimize the loss function of the model. Finally, a logistic regression optimization model based on the hybrid feature selection and differential privacy is proposed by adding the Gaussian noise mechanism on this basis.
# Spectrogram Classification Using Dissimilarity Space
## 2 Proposed Approach
### Siamese Neural Network Training  
The SNN, described in more detail in Section 3, is trained to compare a pair of spectrograms by returning a measure of their similarity. Algorithm 3 presents the pseudocode for this phase and corresponds with step 1 of Algorithm 1. The SNN architecture is defined in steps 2 and 3 of algorithm Algorithm 3. Steps 5-8 are repeated for each training iteration. Step 5 extracts randomly \(batchSize\) spectrograms pairs from the training set using the function getSiameseBatch. Step 6 feeds the pairs  
Figure 1: Proposed approach scheme.  
to the network and computes loss and gradients for gradient descent. Steps 7 and 8 use the gradients and loss to update the weights of the fully connected layer and the twin subnetworks.  
```
1:Training image (\(trainIngs\)), training labels (\(trainLabels\)), batch size (\(batchSize\)), and iterations (\(numberOfIterations\)).
2:Trained SNN (\(tSNN\)).
3:functiontrainSiamese
4:\(subnet\leftarrow\texttt{network}([inputLayer,...,FullyConnectedLayer])\)
5:\(fcWeights\gets randomWeights\)
6:foriteration\(\leftarrow\) from 1 to \(numberOfIterations\)do
7:\(X1\), \(X2\), \(pairLabels\leftarrow\texttt{getSiameseBatch}(trainIngs,trainLabels,batchSize)\)
8:\(gradients,loss\leftarrow\texttt{evaluate}(subnet,X1,X2,pairLabels)\)
9:\(\texttt{update}(subnet,gradients)\)
10:\(\texttt{update}(fcWeights,gradients)\)
11:endfor
12:return\(tSNN\gets subnet,fcWeights\)
13:endfunction
```  
**Algorithm 3** Siamese training pseudocode  
Note: in the case where the SNN fails to converge on the training set, training is rerun.
# Piercing and Surface-Crack Defects in Cold Combined Forward-Backward Extrusion
## 1 Introduction  
The cold extrusion operation, which is an essential scheme of the cold forging process, has been broadly used in forming fasteners [1] and automotive parts [2], while the hot extrusion process is widely used in forming lightweight sections/profiles [3; 4; 5]. Figure 1 shows the cross-section of a support pin [1] with a crack defect by cold extrusion. The inner side of the extruded cup has to be threaded for its final usage. Figure 2 shows the sequence of a cold forging part [2] with cracks appearing on the side surface during the second forming scheme. The extruded rod was thread-rolled in the separate stage.  
There are forward and backward extrusions [6] in the application of cold extrusion. The so-called combined forward-backward extrusion (CFBE) process, when the two unidirectional extrusion schemes are used simultaneously, is shown in Figure 3. The metal flow tends to be complex and difficult to predict in the CFBE. Piercing and surface-crack defects are phenomenal and to be investigated in this work.  
[PERSON] et al. [7] investigated the characteristics of warm forming of AZ31B magnesium alloy with the CFBE process. They found that the crack was initiated and developed easily under the low static pressure condition. They also concluded that the low static pressure was attributed to the fact that the size of the forward extruded part was close to that of the backward extruded cup. [PERSON] et al. [8] further examined the effects of process parameters on the forming characteristics of AZ31B magnesium alloy in CFBE at warm temperatures. They concluded that the extrusion load increases as the forming temperature decreases, or as the punch speed increases, or backward extrusion ratio (BER) becomes large.  
In our previous work on the forming of a support pin [1] with the CFBE process, surface-cracks occurred on the extruded part and sometimes piercing defects were observed. The size of the forward extruded pin was not close to that of the backward extruded cup, but the surface-cracks and even piercing defects still occurred. This outcome indicates that the fracturing mechanism of the CFBE has not been fully comprehended. Therefore, in this work a series of the CFBE tests with various combinations of the forward extrusion ratio (FER) and the backward extrusion ratio (BER) were conducted. A forming limit diagram, detailed with the defects on the forward extruded pin or the backward extruded cup, was developed to provide a conception in choosing appropriate extrusion ratios in forming fasteners with such pin-and-cup features. In particular, the finding of the forming defects on the backward extruded cup has not been reported from the literature.  
Figure 1: A crack defect of support pin by cold combined forward-backward extrusion (CFBE) [1].  
Figure 2: (**a**) Cracks appearing on side surface of a cold forged part; (**b**) sequence of cold forging operations [2].
# Massive MIMO Indoor Transmissions at 38 and 65 GHz Applying Novel HBF Techniques for 5G
## 3 Methodology  
The throughput was deduced with MATLAB software using the Monte Carlo method. Firstly, the physical layer was implemented according to the 5G NR Standard specifications [25]. Then, the HBF algorithms were implemented. Finally, the real channel frequency responses, obtained in the measurement campaign described in the previous section, were introduced in the software.  
The following section presents the implemented algorithms (Section 3.1) and the physical layer parameters (Section 3.2) that are needed to understand the throughput analysis.
## 1 Introduction  
The distillation column is characterized by a large scale, continuity and multiple variables. It is widely used in chemical production and separation processes [1, 2]. Many control parameters must be regulated in order to maintain the balance of the operation process [3, 4, 5, 6, 7]. When the operating parameters change or malfunction occurs, the distillation column loses its original balance and enters an abnormal operation state [8], which threatens the safety of the chemical production process if it is not solved in time. Therefore, it is important to develop quick fault diagnosis method of fluid flow in columns to reduce the probability of accidents [9].  
At present, the monitoring methods of chemical equipment mainly include ray scanning and ultrasound techniques [10, 11]. However, these methods are not the ideal choices for column operation monitoring because of their high cost of operation and restrictions on application conditions. Recently, the rapid development of processing technology has promoted the application of acoustic signal in various fields [12, 13, 14]. As a new technology, acoustic detection technology has been successful in the detection of chemical dynamic equipment [15, 16].  
Compared to the obvious mechanical acoustics of dynamic equipment, as static equipment, the distillation column has less obvious acoustics. The acoustics of fluid flows in sieve tray distillation columns are affected by numerous factors, such as the gas-liquid two phase interaction, and fluid interaction with the column wall [17, 18]. These interactions are the main source of acoustics from the operation of the distillation column. Different operating states correspond to various acoustic characteristics. The feature database can be constructed by extracting acoustic features in different flow states. Based on the database, the unknown states can be recognized by a machine learning classification algorithm.  
In this work, the microphone array was installed on the outer wall of the sieve tray distillation column to collect the acoustic signals of different flow states. Then, the acoustic signals were preprocessed [19] and the characteristic parameters of the acoustic signals were extracted [20]. Next, the acoustic feature database containing the flow states label was constructed. Finally, the acoustic method based on the k-NN algorithm was used to identify the fluid flow states. The acoustic method could judge the operation states, which provided a strong guarantee for the safe operation of the distillation column.  
This paper is divided into six sections. Section 2 introduces the initial state and text process of the experiment and test equipment. Section 3 presents the feature extraction method of an acoustic signal. Section 4 describes the theory of the k-NN algorithm and the selection process of the \(k\) value. Section 5 shows the experimental process and analyzes the experimental data. Section 6 offers a synthesis of the objective and importance of this work.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 2 Literature Review
### The Measurement Stage
#### 2.1.2 Parking Space Marking-Based Sensors  
These types of sensors are all image sensors. Their primary function is to recognize the markings that define a parking space. Along with other rapid advances in automotive electronics, vehicle-based imaging systems advanced from monocular cameras [7; 8; 9; 10; 11] to AVM systems [12; 13; 14; 15; 16]. As a result, the advantages of an AVM system are its wider visual range compared to monocular cameras and its ability to help drivers identify surrounding parking space markings and obstacles. However, because both monocular cameras and AVM systems are image sensors, they are relatively sensitive to lighting problems.
## 2 Materials and Methods
### Device Fabrication  
For the fabrication of OFETs, highly \(p\)-doped Si wafers were used as gate electrodes and substrates. After piranha cleaning and consecutive DI water washing of the substrates, the GO solutions were spin-coated onto them at 1200 rpm for 60 s and cured at 120 \({}^{\circ}\)C for 30 min. Then, 0.7 wt % PS solution in toluene was spin-coated onto the GO films at 4000 rpm for 45 s, and cured at 90 \({}^{\circ}\)C for 20 min. A 50 nm-thick pentacene film, serving as the active layer, was deposited onto the GO/PS dielectric at a rate of 0.2 A s\({}^{-1}\) by means of an organic molecular beam deposition system. The film deposition conditions were kept identical for the film analysis and the device. Finally, Au source/drain electrodes with a thickness of 100 nm each were deposited using a thermal evaporator (Figure 1b). Electrical measurements were conducted using Keithley 2400 and 236 source/measure units. The electrical properties were evaluated in the dark under ambient conditions. The device parameters were calculated on the basis of the transfer curves in the saturation regime by using the equation \(I_{\text{d}}=(W/2L)\cdot\mu\cdot C_{\text{i}}\cdot(V_{\text{g}}-V_{\text{th}})^ {2}\); here, \(I_{\text{d}}\), \(\mu\), \(V_{\text{g}}\), and \(V_{\text{th}}\) are the drain current, field-effect mobility, gate voltage, and threshold voltage, respectively; \(W\) and \(L\) are the channel width and length, respectively; and \(C_{\text{i}}\) is the capacitance per unit area of the GO/PS gate insulator.
# Health Symptom Checking System for Elderly People Using Fuzzy Analytic Hierarchy Process
###### Abstract  
The ever-escalating rise in numbers of the aging population has preempted a revolutionary change in the healthcare sector and serves as a major counterpoint to modern life in the 21 st century. Increasing demand being placed on the health sector is almost certainly an inevitable process. However, providing appropriate healthcare services is requisite for senior citizens who suffer from various health issues and conditions. To minimize these health risks, we derived an intuitive technique for determining the incongruuity of health symptoms by using a symptom checker, which is embedded into a versatile mobile app named Help-to-You (H2U). The designed app helps the users and carers to determine and identify conceivable reasons for elderly ailments and to assist users in deciding when to counsel a health practitioner. The intention of this empirical study was to further analyze and foresee certain variations of infections based on the symptoms accounted for by the patient. The recommended solution consolidated conceptual design with multi-criteria decision analysis (MCDA) technique and an analytic hierarchy process (AHP) with fuzzy weights to deal with the uncertainty of imprecision and ambiguity resulting from various disease factors. Experimental results verified the effectiveness of the proposed model, subsequently providing a variety of life assistance services.  
symptom checker; mobile app; H2U; healthcare service; MCDA; fuzzy AHP; decision support 2018  
16 January 20184 April 20184  
have concentrated on the quality of functional aspects of life assistance services that accentuate comfort and adequacy based on the concept of smart home.  
MCDA fuzzy AHP, an intuitive diagnosis system platform in the symptom checker, incorporates knowledge of health issues in order to minimize uncertainty in making decisions based on the reported health symptoms. The proposed system aims at helping elderly patients and their caretakers to enrich the knowledge of the patients' health problems. This developed system can help guide the users as to in which departments of the hospital to be counseled in emergency circumstances. This research develops a structured decision model, which delivers the most suitable approaches for assessing multiple-criteria issues of healthcare confronted by the elderly.  
The novelty of the developed system was to propose a health symptom checker that can assist users and caregivers in comprehending the uncertainty degree of health issues, consequently keeping them fit and healthy. Furthermore, the presented work was not only confined to the elderly, but was also extended to other age groups by expanding the symptoms and alternatives in the database.  
This paper is organized as follows. In Section 2, MCDA fuzzy AHP is used for the multiple criteria decision-making method. Section 3 describes the approach used in the symptom checker for healthcare. The experimental results and discussion from case studies are presented in Section 4. The final section concludes the study.
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 2 Industrial Motivation  
In a manufacturing environment, it exits a vast number of potential disruptions that negatively affect the system's performance such as failures, wear, shortages, defectives, etc. Among these disruptions, machine failures are the most frequent problem observed in manufacturing systems. Furthermore, more realistic models are conceived at considering that machine reliability does depend on the degree of utilization of the machine leading to define operation-dependent failures, as indicated by [PERSON] [15]. Although, such type of failures is common in production systems, they are rarely considered by researchers and practitioners. Additionally, during the last years the focus has been on the dependence between productivity and the failure rate, and just some works have studied the connection between operation-dependent failures and deterioration, as in [PERSON] et al. [14]. Nevertheless, in modern production systems, deterioration is a common industrial phenomenon. Hence, this observation raises the question of whether at considering a deterioration process, the production at higher rates may accelerate the machine degradation, indicating a dependence between deterioration and several system's performance indices such as product quality, reliability, safety, etc. Conversely, producing at low rates may contribute to an increase of shortages and incur economic losses. Thus, given the dependency of the involved cost and productivity, a trade-off is implied, and it would be advantageous to reduce the production rate from its maximum value to a more profitable level to reduce for instances the increase of defective units and failures.  
The model presented in this paper has many applications especially in industries characterized by deterioration, where the production system is subject to random failures and repairs, defective quality is present and their production rates can be controlled. In particular, in situations where the production system deteriorates over time such as the automotive sector, pharmaceutical, semiconductor industries, etc.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 3 Design and Implementation
### System Implementation
#### 3.2.2 Fog Server
* Database: PostgreSQL is used as the database of the proposed fog server. PostgreSQL is a powerful object-relational database. * Security Module  
Generally, this module is responsible for the encryption and the verification algorithms. This module is not covered in this paper as it is beyond its scope. * Message broker  
A module can connect with another module through the corresponding queue. Modules can fire the message and continue performing their functions without the need for waiting for the response. A module will be notified to receive messages (if any) in an event-driven manner which in turn increases the performance of the proposed fog server. Several JMS APIs can be employed for implementation, such as open JMS, rabbitMQ, and ActiveMQ. In this work, ActiveMQ is utilized for implementing the message broker.
# Chemical Composition of Volatile and Extractive Components of Canary (Tenerife) Propolis
## 2 Results
### Composition of Volatile Components of Propolis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **Sample** & \\
**Monoterpene/Monoterpenoids, Including:** & \multirow{2}{*}{RIExp} & \multirow{2}{*}{RI\({}^{Lit}\)} & **Pr-1** & **Pr-2** & **Pr-3** \\ \cline{4-5}  & & & **27.07** & **55.84** & **38.10** \\ \hline
**Diterpene hydrocarbons, including:** & & & **0.40** & N.d. & N.d. \\ \hline unidentified diterpene C\({}_{20}\)H\({}_{32}\) & 1762 & - & 0.15 & N.d. & N.d. \\ \hline unidentified diterpene C\({}_{20}\)H\({}_{32}\) & 1805 & - & 0.25 & N.d. & N.d. \\ \hline
**Aliphatic acid, including:** & & & **7.3** & **1.94** & **6.23** \\ \hline formic acid & 538 & 535 & 2.78 & trace & 1.32 \\ \hline acetic acid & 626 & 616 & 3.03 & 1.94 & 4.64 \\ \hline isobutyric acid & 765 & 762 & 0.72 & trace & N.d. \\ \hline isovaleric acid & 842 & 848 & 0.32 & N.d. & 0.14 \\ \hline
2-methylbutanoic acid & 862 & 868 & trace & N.d. & 0.13 \\ \hline hexanoic acid & 983 & 989 & 0.27 & N.d. & N.d. \\ \hline
**Aliphatic alcohol, including:** & & & **4.87** & **0.82** & **1.75** \\ \hline ethanol & 480 & 484 & 2.64 & 0.03 & 0.81 \\ \hline isopentanol & 730 & 734 & 0.26 & trace & 0.53 \\ \hline
2,3-butanediol, isomer 1 & 737 & 734 & 0.22 & N.d. & 0.15 \\ \hline
3-methyl-2-buten-1-ol (prenol) & 770 & 771 & 0.81 & N.d. & N.d. \\ \hline
2,3-butanediol, isomer2 & 782 & 779 & 0.52 & N.d. & 0.35 \\ \hline
1-hexanol & 866 & 870 & 0.44 & N.d. & N.d. \\ \hline
1-heptanol & 970 & 968 & N.d. & 0.13 & N.d. \\ \hline
1-octanol & 1070 & 1070 & N.d. & 0.40 & N.d. \\ \hline
**Esters, including:** & & & **2.96** & N.d. & N.d. \\ \hline \(n\)-propyl propionate & 810 & 808 & 0.16 & N.d. & N.d. \\ \hline \(n\)-butyl butanoate & 997 & 998 & 0.32 & N.d. & N.d. \\ \hline \(n\)-butyl hexanoate & 1193 & 1192 & 1.68 & N.d. & N.d. \\ \hline ethyl octanoate & 1199 & 1198 & 0.17 & N.d. & N.d. \\ \hline glycerol 1,2-diacetate? & 1355 & - & 0.41 & N.d. & N.d. \\ \hline \(n\)-hexyl hexanoate & 1387 & 1387 & 0.22 & N.d. & N.d. \\ \hline
**Aliphatic carbonyls, including:** & & & **20.13** & **12.76** & **8.90** \\ \hline acetone & 500 & 501 & 2.73 & 2.31 & 2.28 \\ \hline isopentanal & 648 & 648 & trace & N.d. & N.d. \\ \hline Acetol (hydroxyacetone) & 667 & 673 & N.d. & N.d. & 0.48 \\ \hline acetoin (3-hydroxy-butanone) & 722 & 722 & 0.51 & N.d. & 1.00 \\ \hline
3-methyl-2-butenal (prenal) & 780 & 776 & 0.79 & 0.34 & 0.26 \\ \hline hexanal & 801 & 801 & 1.97 & 071 & N.d. \\ \hline
2-hexenal & 851 & 853 & N.d. & 0.36 & N.d. \\ \hline
3-heptanone & 885 & 890 & N.d. & N.d. & 0.22 \\ \hline heptanal & 902 & 902 & 1.12 & 0.80 & 0.19 \\ \hline _trans_-2-heptenal & 954 & 956 & N.d. & 0.13 & N.d. \\ \hline
6-methyl-5-hepten-2-one & 986 & 987 & 0.28 & 0.24 & 0.66 \\ \hline octanal & 1002 & 1004 & 0.52 & 1.68 & 1.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 4 Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter
### Prediction  
Based on Equation (17), the state estimation and error covariance are expressed by the following:  
\[\mathbf{\hat{X}}_{k-1/k-1}=\left[\begin{array}{c}\mathbf{\hat{x}}_{k-1/k-1}\\ \vdots\\ \mathbf{\hat{x}}_{k-d/k-1}\\ \mathbf{\hat{x}}_{k-d-1/k-1}\end{array}\right] \tag{23}\]  
Figure 3: Cost and weight functions of a DCS kernel.  
\[\begin{array}{l}\textbf{P}_{k-1/k-1}=\left[\begin{array}{cccc}\textbf{P}^{xx}_{k -1,k-1/k-1}&\cdots&\textbf{P}^{xx}_{k-1,k-d/k-1}&\textbf{P}^{xx}_{k-1,k-d-1/k-1} \\ \vdots&\ddots&\vdots&\vdots\\ \textbf{P}^{xx}_{k-d,k-1/k-1}&\cdots&\textbf{P}^{xx}_{k-d,k-d/k-1}&\textbf{P}^ {xx}_{k-d,k-d-1/k-1}\\ \textbf{P}^{xx}_{k-d-1,k-1/k-1}&\cdots&\textbf{P}^{xx}_{k-d-1,k-d/k-1}&\textbf {P}^{xx}_{k-d-1,k-d-1/k-1}\end{array}\right]\end{array} \tag{24}\]  
The predicted state \(\textbf{\hat{X}}_{k/k-1}\) and corresponding covariance \(\textbf{P}_{k/k-1}\) are determined by the following:  
\[\textbf{\hat{X}}_{k/k-1}=\left[\begin{array}{cccc}\textbf{\hat{x}}_{k/k-1} \\ \textbf{\hat{x}}_{k-1/k-1}\\ \vdots\\ \textbf{\hat{x}}_{k-d/k-1}\end{array}\right] \tag{25}\]  
\[\textbf{P}_{k/k-1}=\left[\begin{array}{cccc}\textbf{P}^{xx}_{k,k/k-1}& \textbf{P}^{xx}_{kk-1/k-1}&\cdots&\textbf{P}^{xx}_{k,k-d/k-1}\\ \textbf{P}^{xx}_{k-1,k/k-1}&\textbf{P}^{xx}_{k-1,k-1/k-1}&\cdots&\textbf{P}^ {xx}_{k-1,k-d/k-1}\\ \vdots&\vdots&\ddots&\vdots\\ \textbf{P}^{xx}_{k-d,k/k-1}&\textbf{P}^{xx}_{k-d,k-1/k-1}&\cdots&\textbf{P}^ {xx}_{k-d,k-d/k-1}\end{array}\right] \tag{26}\]  
According to Appendix A.1, the cubature points are generated according to \(\textbf{\hat{X}}_{k-1/k-1}\) and \(\textbf{P}_{k-1/k-1}\) as follows:  
\[\textbf{\hat{x}}_{k,k-1|k-1}=\text{Trans}\Big{[}\textbf{\hat{X}}_{k-1/k-1}, \textbf{P}_{k-1/k-1}\Big{]} \tag{27}\]  
The predicted state and corresponding covariance matrix are expressed as Equations (28)-(31).  
\[\textbf{\hat{x}}_{k|k-1}=\sum_{i=1}^{2n_{q}}w_{i}f\Big{(}\textbf{\hat{x}}_{i, k-1|k-1}\Big{)} \tag{28}\]  
\[\textbf{P}^{xx}_{k,k/k-1}=\sum_{i=1}^{2n_{q}}w_{i}\Big{(}f\Big{(}\textbf{\hat{ x}}_{i,k-1|k-1}\Big{)}-\textbf{\hat{x}}_{k|k-1}\Big{)}\Big{(}f\Big{(}\textbf{ \hat{x}}_{i,k-1|k-1}\Big{)}-\textbf{\hat{x}}_{k|k-1}\Big{)}^{T}+\textbf{Q}_{k-1} \tag{29}\]  
\[\textbf{P}^{xx}_{k,k-s/k-1}=\sum_{i=1}^{2n_{q}}w_{i}\Big{(}f\Big{(}\textbf{ \hat{x}}_{i,k-1|k-1}\Big{)}-\textbf{\hat{x}}_{k|k-1}\Big{)}\Big{(}\chi_{i,k-s|k -1}-\textbf{\hat{x}}_{k-s|k-1}\Big{)}^{T},s=1,2,\cdots,d \tag{30}\]  
\[\textbf{P}^{xx}_{k-s,k/k-1}=\Big{(}\textbf{P}^{xx}_{k,k-s/k-1}\Big{)}^{T},\,s= 1,2,\cdots,d \tag{31}\]