esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
This paper provided a distributed training algorithm for ESN, a type of RNN, and also a widely used algorithm of RC. The distributed optimization method is based on P-PSO, and was implemented on the Spark framework. For applications with huge amounts of data distributed and stored, the traditional centralized training mode may be ineffectual; our proposed platform, however, could handle these issues effortlessly.  
We tested our algorithm and platform using artificial datasets, a real-world dataset and an image dataset, with several different volumes and different training nodes. (1) For small datasets (<3 \(\times\) 10\({}^{4}\)), a relatively low number of nodes would obtain a good result, regardless of the training time or training error. (2) For large datasets (1 \(\times\) 10\({}^{5}\)-1 \(\times\) 10\({}^{6}\)), a big node number appears more advantages for the training time. For huge datasets (>3 \(\times\) 10\({}^{6}\)), the biggest node number is best. However, the marginal efficiency of increasing nodes becomes lower. (3) In situations of 48 nodes, when the training dataset grows 10\({}^{3}\) times (from 1 \(\times\) 10\({}^{4}\) to 1 \(\times\) 10\({}^{7}\)), the training time of our framework only increases 7.4 times. (4) Compared to traditional PSO, ADMM, CNN and P-PSO algorithms, the performance of our proposed algorithm was excellent in most cases, apart from the accuracy of image recognition being a bit lower than in CNN, due to its unique network architecture.  
The test results show the good performance of this proposed algorithm and platform, while ensuring the training accuracy, and the training time spent is also very satisfactory, which allows this platform to be widely applied, especially for extremely large-scale datasets in the Big Data era.  
Nevertheless, several issues are still worth investigating further. For real-world data, the stability of the training results is not as good as that for artificial datasets. The internal mechanism is still valuable to explore. Meanwhile, it was noticed that for huge datasets, the time spent on reading data (Stage 1) was obviously long. Besides, the ability to handle image data (or high-dimensional data) needs to be strengthened. We will focus on the above questions in future research.  
This work is supported by the Fundamental Research Funds for Central Universities (No. 2015 XS/2). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper. The authors declare no conflict of interest.  
Figure 12: Training error comparison between traditional PSO, ADMM, CNN and P-PSO–based distributed ESN algorithms. A typical length of data size (1 \(\times\) 10\({}^{5}\)) is selected for the four datasets.
# Vibration Analysis of a Piezoelectric Ultrasonic Atomizer to Control Atomization Rate
## 2 Ultrasonic Atomizer Fundamentals
### Approximate Plate Theory  
It has been demonstrated that the mechanical vibrating characteristics of the meshed circular thin plate in an ultrasonic atomizer controls the rate atomization [17]. There are two kind of vibrations that can be used to study the mechanical behavior of a homogeneous circular thin plate--namely, free and forced vibrations. In the case of the atomizer, the theoretical study is focused on a circular thin plate clamped on its edge. The analytical solution using the plate theory gives natural frequencies [18]:  
\[f_{nm}=\frac{ah}{2\pi R^{2}}\sqrt{\frac{E}{12\rho(1-v^{2})}} \tag{5}\]  
where \(R\) is the radius of a circular plate with thickness \(h\), \(\rho\) is the density, \(E\) is the Young's modulus, \(v\) is the Poisson's ratio, and \(\alpha\) is a constant that depends on the number of nodal diameters (diameter lines that remain without displacement \((n)\)) and the number of nodal circles (concentric circumferences without displacement \((m)\)).  
Figure 2 shows the behavior of the natural frequencies as a function of the thickness of the plate, as well as the elasticity modulus, respectively, for these modes. The frequency dependence on the thickness is linear, while a nonlinear behavior is observed as a function of the elastic modulus. It is expected that the addition of the micro-apertures to a homogeneous plate can have an inverse effect on the elastic modulus according to the rule of mixtures [19]. Using the volume fraction of the openings, the results show a decrease in the effective elastic modulus as a function of the size of the micro-openings affecting the dynamic response of the system. Therefore, a more complete dynamic and mechanical model of the atomizer should include the aperture shape and distribution of micro holes on the meshed thin plate.  
However, including these variables makes the system too complex to be easily calculated through analytical methods. Thus, an FE model might provide a better approximation to understand the mechanical behavior of the atomizer and its effect on the atomization rate.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 7 Real-Time Inference  
The proposed data-driven PHM methodology has been implemented, in real time, to provide a multi-step ahead (MSA) battery SOC estimation in [36]. The real-time framework is capable of providing an advisory window for the system status and provided consistent results to forecast a SOC horizon of up to thirty timesteps ahead. For comparison, the proposed data-driven bidi-LSTM PHM has been implemented and tested on two drive cycles datasets, the neural network (NN) and the \"HWFTa\" (Highway Fuel Economy Test Cycle).  
The referred method was implemented in two hardware platforms, with standard testbeds available:  
* Raspberry Pi 4 (RP4): using a Broadcom BCM2711 SoC with a 1.5 GHz 64-bit quad-core ARM Cortex-A72 processor, with 1 MB shared L2 cache.
* Ultra96-V2 (U96): running a quad-core 1.5 GHz ARM Cortex-A53, with 1 MB L2 cache.  
ARM processors are the most widely used architecture for embedded systems due to their low power consumption, high performance, and cost-effectiveness. The Ultra96-V2 (U96) is a MPSoC (multi-processor SoC) and has a wealthy set of processors and a FPGA. The MPSoC is a good candidate to fulfill the requirements of a mixed-criticality system, as implemented in [48]. The algorithms were executed on the referred ARM processors. Figure 7 shows the results for the selected drive cycles.  
It can be depicted by the chart that the fastest real-time execution of the proposed data-driven MSA PHM method takes around 1.3 s to run on U96 and 2.2 s on RP4.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 4 Key Capabilities for Resilience in IoMT
### Authorization and Access  
Proper authorization and access is critical for a resilient IoMT network, as these are required to ensure security and privacy in the IoMT network. Different encryption techniques are used in networks to provide security. Blockchain technology has the critical feature of authorization and access to provide a role- or network-based access into the network. The distribution of authority can also be achieved by decentralizing the control and access to the different access levels of users [29].
## 3 Results and Discussion
### Properties of Slag Cements (SCs) manufactured by 40% Slag
#### 3.3.1 Compressive Strength  
The early, late and long term compressive strengths of the slag cements (SCs) with 40 wt.% slag are presented in Table 10. The obtained compressive strengths of all the slag-cements ranging between 35.4 and 49.2 MPa are within the limits of EN 197-1 [17]. The results indicate that the compressive strengths of the slag cements are not solely affected by the FeO content of used slags in agreement with the findings of CPCs. The highest strengths for SCs with 40% slag, for both air-cooled and water-cooled slags, are obtained for the case of slag 4-2 in agreement with the results of the CPCs with 20% slag. In fact, as it is illustrated in Figure 10, the slag cement manufactured with 4-2b slag exhibits such a considerable increase in the long term compressive strength development that after 90 days, the values attained by OPC are reached.  
Figure 9: Effect of Blaine on strength development of: (**a**) OPC; (**b**) slag 4-2a; (**c**) slag 5a.  
In Figures 11 and 12 the normalized late (28 days) and long term (90 days) compressive strengths of the prepared cements in relation to their slag content are presented for the mixtures produced with the air and water-cooled slags, respectively. As normalized strength the following strength ratio was defined: Compressive strength obtained by the mixture where slag was used/ Compressive strength obtained by the corresponding OPC. Overall, it can be observed that the increase in the slag content results in the decrease of the strength. Nevertheless, the contained C\({}_{2}\)S of the slags results in a smoother decrease in case of long term strengths.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Slag**} & \multirow{2}{*}{**Grinding Time (min)**} & \multicolumn{6}{c}{**Compressive Strength MPa**} \\ \cline{3-8}  & & **1 Day** & **2 Days** & **7 Days** & **28 Days** & **60 Days** & **90 Days** \\ \hline – & 75 & 17.3 & 28.3 & 41.6 & 57.9 & 63.1 & 64.4 \\ –* & 95 & 21.0 & 32.7 & 45.7 & 58.7 & 61.4 & 64.5 \\
3-1a & 102 & 12.0 & 18.5 & 28.2 & 36.7 & 43.0 & 46.2 \\
3-2a & 95 & 12.2 & 16.8 & 24.4 & 37.5 & 41.2 & 43.4 \\
4-2a & 90 & 9.3 & 16.5 & 25.5 & 39.7 & 49.0 & 51.1 \\
5a & 90 & 10.8 & 15.0 & 23.3 & 36.8 & 41.5 & 45.8 \\
3-1b & 100 & 10.6 & 15.4 & 24.2 & 36.6 & 43.6 & 47.7 \\
3-2b & 100 & 10.3 & 15.2 & 23.9 & 35.4 & 40.0 & 39.4 \\
4-1b* & 101 & 11.4 & 18.4 & 28.5 & 38.5 & 42.0 & 45.0 \\
4-2b & 102 & 11.2 & 17.8 & 29.8 & 49.2 & 58.4 & 65.0 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Compressive strength and grinding time of manufactured SCs (40% slag). Blaine 4500 cm\({}^{2}\)/g, w/c: 0.5.  
Figure 10: Comparative compressive strength development of OPC and slag cement manufactured with 40% slag 4-2b, Blaine: 4500 cm\({}^{2}\)/g.
###### Abstract  
The traditional hearing aid fitting method, which mainly relies on the audiologist, is time-consuming and messy. To improve this situation, a self-fitting algorithm based on an improved interactive evolutionary computation (IEC) algorithm and expert system, which enables the patients to fit the hearing aid by themselves, is proposed. The algorithm takes the band gain as the fitting target and uses the patient's subjective evaluation to iteratively update the algorithm parameters based on the improved IEC algorithm. In addition, a real-time updated expert system is constructed to assist in the optimization of the initial and iterative parameters of the fitting based on the patient's audiogram and personal information. To verify the performance of the algorithm, a self-fitting software for the hearing aid is designed. Through this software, the test signal is generated for the patient to evaluate the audio quality on a five-level scale. Based on the evaluation results, the algorithm iteratively optimizes the algorithm parameters until the patient is satisfied with the generated audio. Compared with the fitting algorithm based on Gaussian processes algorithm or the interactive evolutionary algorithm, the average subjective speech recognition rate of the proposed algorithm increase at least 11%. The average recognition rate for environmental sound is also improved by at least 2.9%. In addition, the fitting time of the proposed algorithm is shortened by at least 10 min compared to others two algorithms.  
hearing aid; self-fitting; interactive evolutionary computation; expert system +
Footnote †: journal: Applied Sciences
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 5 Challenges
### Deformable Objects  
The 6D pose estimation of deformable objects is a huge challenge in the field, because the posture of the objects is unpredictable and there are many ways for the objects to deform. Thus, lots of conditions need to be taken into consideration, resulting in pressure on the algorithm and calculation.  
[PERSON] et al. [92] proposed a novel method that was able to classify and estimate the categories and poses of deformable objects. They simulated the deformable objects and obtained the depth images from different viewpoints in different postures as a dataset. By extracting features and using deep learning, they set up a codebook for the object, which could be used in training process. This method uses learning-based approaches and sets up a two-layer framework to solve the problem of 6D pose estimation. Lots of storage space is needed, but the method would be robust in such complex situations. In [93], a predictive and model-driven approach was proposed to solve this problem. A dataset was built up by using the picked-up garments in multiple poses under gravity. The dataset was organized in an efficient way, increasing the speed of the searching process. The proposed method constructed a fully featured 3D model of the garment in real time and used volumetric features to obtain the most similar model in the database in order to predict the object category and pose. Accurate model simulation could also be used to optimize the trajectories of the manipulation of deformable objects. [PERSON] et al. [94] proposed a four-step method to solve this problem of grasping clothes by using a point cloud. Firstly, the instance segmentation was performed, and then a wrinkledness measure was implemented to robustly detect the graspable regions of the cloth. Next, the identification of each individual wrinkle was accomplished by fitting a piecewise curve, and finally a pose for each detected wrinkle was estimated.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 4 Proposed Method: Adopting a Signal-Processing Technique  
When data are recorded as a time series signal, they are usually collected by a specific configuration of a source-receiver arrangement, followed by signal processing to enhance the signal-to-noise ratio. Many techniques have been developed to understand the data for better interpretation, including signal attribute analysis. Signal attributes are generated or extracted from the original signal data by applying specific mathematical expressions to bring the signal data from one domain into other attribute domains. Each attribute has different functionality and characteristics that help in understanding the target object's behavior.  
In general, some attributes that can be extracted from signal data are based on amplitude, frequency, and phase. Amplitude-based attributes provide information on medium properties. Meanwhile, frequency-based attributes indicate the geometry of the object and some absorption properties of the medium. The instantaneous phase attribute is based on the signal phase and is not dependent on amplitude. The usage of phase attributes is more to identify the features such as discontinuity of medium. The phase attribute generally indicates the geometry of the subsurface object, not the type or presence of internal properties of the medium.
# A Novel Plug-in Board for Remote Insect Monitoring
## 2 Materials and Methods
### Controller Unit  
The members of the Raspberry Pi \"family\" and microcontrollers are widely used devices for remote sensing in agriculture [17; 18]. Raspberry Pi devices are single-board computers from the Raspberry Pi Foundation. Since its first appearance (2012), numerous new Raspberry Pi devices have been released. They can be categorized into three model types: A, B, and Zero [19]. The core in all members of these three categories is an SoC with more CPUs (Central Processing Units) and memory. All models also have a dedicated camera port, as well as several GPIO (General-Purpose Input/Output) pins that can be used to control a wide range of connected devices.  
Although Raspberry Pi computers have several advantages, selecting an appropriate controller unit for the trap is not an easy decision because single-board computers and microcontrollers have different features. Single-board computers have relatively high processing power, physical memory, and storage capacity, but microcontrollers use less energy than a single-board computer uses. Table 1 shows a comparison between the main features of the Raspberry Pi Zero W and the Arduino Uno (ATmega328P) to illustrate the differences between a microcontroller board and a single-board computer.  
Figure 1: Front (**left**) and back (**right**) sides of the sensing device.  
In our sensing device, we have chosen the Raspberry Pi Zero W (Zero W 2 is also compatible) as a controller unit, which is one of the smallest members of the Raspberry Pi family. The main reason for our decision was the possibility of the high-level Python programming provided by the Linux operating system. It allows the easy transfer of deep learning algorithms. However, it also has disadvantages that need to be taken into consideration in the circuit design. From the sensing device point of view, we have collected the pros and cons of the Raspberry Pi Zero W computer. Its advantages are:  
* Relatively high processing power;
* Relatively high main memory;
* Large background storage;
* Wide range of interfaces (I2C, SPI, UART, CSI, etc.);
* High-level programming environment;
* Small size.  
Its disadvantages are:  
* Lack of deep sleep mode;
* Lack of a real-time clock (RTC);
* Lack of analog capabilities (e.g., analog-digital converter);
* Only one UART interface.  
Fortunately, all of the above disadvantages can be handled by external components. Therefore, the Raspberry Pi Zero can be an efficient controller unit without any restrictions.
## 4 Parametric Study
### Effect of Relative Density on Dynamic Behaviour of Pile  
Repeated analyses were carried out to investigate effect of soil relative density on the dynamic behavior of pile for various soil relative densities of 30%, 50%, and 80%. Figure 15 depicts the peak bending moment profiles of three systems for two different input accelerations. As shown in the figure, the bending moment profiles are almost identical for all cases, although the soil relative density is significantly different in each case. Figure 16 depicts the maximum lateral displacement profiles of the pile. It shows a similar pattern to that of the peak bending-moment profiles. This is quite a different trend from the previous result of the weight of the superstructure. In this part, the analysis condition was identical for all cases except soil relative density. Therefore, the induced inertial force was constant in every system and only the kinematic force induced by soil deformation was different. This means that the kinematic force is relatively not significant in dry sand.  
Several meaningful characteristics of dynamic soil-pile-structure interaction in dry sand were estimated through a series of numerical analyses, which were described in this section. In dry sand, the effect of the inertial force induced by the superstructure is dominant, whereas the effect of the kinematic force induced by soil movement is not as significant. This kind of dynamic characteristic must be properly integrated when the seismic design of the pile foundation embedded in dry sand is determined.  
Figure 15: Peak bending moment envelopes obtained from three different relative densities. (**a**) Input acceleration: 0.13 g; (**b**) Input acceleration: 0.25 g.
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 4 Experiments
### Common Sense Concepts vs. Formal Concepts  
We also compare our SFCA with an external information-based method: Commonsense-Aware Knowledge Embedding (CAKE) [19] framework. The comparison results are obtained by combining uniform sampling [10] and self-adversarial sampling [15] with the KGE model TransE [10] and RotatE [15]. Table 6 presents the link prediction evaluation results on the three datasets. By comparison, our SFCA has an average increase of 147.54% (45.13 points) and 9.34% (3.33 points) higher than CAKE in the MRR indicators of different baselines on the two datasets of FB15K237 and NELL-995, respectively. On DBpedia-242, in the best effect on each indicator of different baselines, the highest is 14.47% (2.3 points) higher than CAKE, and the lowest is 3.36% (1.5 points) lower than CAKE. These results prove formal concepts are more effective in most cases than common sense concepts for the KGC task.
## 6 Speech Quality and Intelligibility Evaluation
### Objective Evaluation
#### 6.1.2 Hearing Aid Speech Quality Index (HASQI)  
The HASQI can predict the sound quality performance of device according to the hearing threshold of the hearing impaired individual. Moreover, the HASQI is the product of two independent indexes. The first component, called \(Q_{nontin}\) captures the effects of noise and nonlinear distortion. The second component is \(Q_{lin}\), which captures linear filtering and spectral changes by targeting differences in the long-term average spectrum [37]. Both elements are calculated from the output of the auditory model to quantify specific changes in the clean reference signal and the enhanced signal by:  
\[Q=0.336Q_{nontin}+0.501Q_{nontin}^{2}+0.001Q_{lin}+0.16Q_{lin}^{2} \tag{15}\]
## 2 Methods  
### Governing Equations  
This study employs a hydrodynamic/acoustic splitting method to solve the compressible Navier-Stokes equations. Similar approaches have been successfully applied in previous studies [17, 18, 19, 20, 21, 22]. This method first decomposes the total flow variables into incompressible and perturbed compressible components as follows:  
\[\begin{array}{l}v_{i}=V_{i}+v^{\prime}_{i}\\ p=P+p^{\prime}\end{array} \tag{1}\]  
where \(v_{i}\), \(V_{i}\) and \(v^{\prime}_{i}\) represent the total, incompressible, and perturbed compressible velocity components, respectively, while \(p\), \(P\) and \(p^{\prime}\) represent the total, incompressible and perturbed compressible pressures, respectively. By substituting Equation (1) into the fully compressible Navier-Stokes equations and following the linearization procedure of [PERSON] and [PERSON] [23], the compressible Navier-Stokes equations split into incompressible Navier-Stokes equations and linearized perturbed compressible equations (LPCE):  
* Incompressible Navier-Stokes equations: \[\begin{array}{l}\frac{\partial V_{i}}{\partial t}+\frac{\partial V_{i}V_{i} }{\partial x_{i}}=-\frac{1}{\rho_{0}}\frac{\partial P}{\partial x_{i}}+\gamma _{0}\frac{\partial^{2}V_{i}}{\partial x_{i}\partial x_{i}}\\ \end{array}\] (2)
* Linearized perturbed compressible equations (LPCE): \[\begin{array}{l}\frac{\partial v^{\prime}_{i}}{\partial t}+\frac{\partial v ^{\prime}_{i}V_{i}}{\partial x_{i}}+\frac{1}{\rho}\frac{\partial^{\prime}P}{ \partial x_{i}}=0\\ \frac{\partial p^{\prime}}{\partial t}+V_{i}\frac{\partial p^{\prime}}{ \partial x_{i}}+\gamma P\frac{\partial v^{\prime}_{i}}{\partial x_{i}}+v^{ \prime}_{i}\frac{\partial p}{\partial x_{i}}=-\frac{dP}{dt}\end{array}\] (3) where \(\rho_{0}\) represents incompressible flow density, \(v_{0}\) is the kinematic viscosity, and \(\gamma\) is the ratio of specific heats. By solving these two equations, the incompressible and the perturbed compressible variables are determined, and the total variables are obtained by substituting them back into Equation (1). This hydrodynamic/acoustic splitting method has been successfully verified in simulating acoustics in human phonation against fully compressible flow solutions and theoretical data [24].  
The vocal fold dynamics are solved using the Navier equation:  
\[\rho\frac{\partial^{2}d_{i}}{\partial t^{2}}=\frac{\partial\sigma_{ij}}{ \partial x_{j}}+\rho f_{i} \tag{4}\]  
where \(\rho\) represents tissue density, \(d\) is displacement, \(\sigma\) is stress, and \(f\) is the body force per unit mass.