esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Motivation and Methods
### Data Processing for Automatic Refocusing of OCTA Data  
For digital refocusing, the propagation distance \(d\) between the focal plane and the original de-focal plane was determined by evaluating the contrast of B-scan OCTA images. One of the traditional ways to determine the optimal distance is to ascertain the \(d\) value that gives the best contrast of the digitally refocused image. To achieve this, the image contrast is evaluated while iteratively entering multiple \(d\) values and simulating a wave propagation. However, the OCT image of the mouse brain shows a rather monotonous structure, as shown in Section 3.2, making determination of optimum propagation distance \(d\) difficult. To address this problem, B-scan OCTA images were employed in evaluating the contrast comparison. In addition, to find a more reliable \(d\) value, a region of interest (ROI; square window) was set inside the B-scan OCTA image. The image with the largest sum of differences of pixel intensities in the B-scan direction (X direction) was judged as the image with the largest contrast. Figure 2 shows the digital focusing process to obtain OCTA images with optimal contrast.  
1. Four B-scan OCT images were Fourier transformed along the B-scan direction; they were then multiplied with the potential propagation distances (certain \(d\) value); lastly, an inverse Fourier transform was performed. This process was repeated over the depths in the ROI while changing the potential propagation distances consisting of an array of the numbers in the range of \(-1.5\) mm to \(+1.5\) mm. This procedure corresponds to the process number 1 in Figure 2.
2. The digitally propagated four B-scan OCT images were used to create a single OCTA image using Equation (1), and then the contrast of the OCTA image was evaluated using Equation (3). Prior to contrast evaluation, a Gaussian blurring was applied to the OCTA image to remove noise due to speckles. The size of the filter was \(1\times 5\) px. In this process, it is evaluated only for the OCTA images in the ROI that cover a certain depth (xz direction).
3. Upon repeated execution of the first and the second processes, an optimum propagation distance, \(d\)-max--which denotes maximum contrast in the OCTA images--was automatically searched and re-entered. Finally, with the found \(d\)-max, the optimum or the maximum contrast OCT and OCTA images were reconstructed.
4. The same process was performed with a series of the B-scan OCT images along C-scan direction to get a resolution-improved volume OCT/OCTA image set.
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 2 Federated Learning
### Why FL for Maritime Environments?  
Maritime environments have some unique characteristics when compared to other Industry 4.0 settings, rendering the adoption of FL-aided solutions vital [13]. More specifically, various maritime services must be provided in highly mobile and geographically dispersed topologies, comprising ships and port facilities. FL is decentralized by design, allowing models to be trained locally on each edge node without requiring constant connectivity to a central server. For example, FL can be significantly useful in UAV-aided remote [14] or offshore maritime environments with limited or intermittent wireless connectivity, significantly reducing the need for data transfer towards centralized premises and leading to energy efficiency gains for the aerial and the maritime nodes [14]. On the contrary, in more static Industry 4.0 scenarios, as is the case for factories, the advantages of FL might not be pronounced as much, since reliable power supply and wireless connectivity are usually guaranteed.  
Another critical aspect is related to data privacy and regulatory compliance, considering the international nature of maritime operations, adhering to various regulations, data protection laws and standards. Here, FL can address data privacy concerns by allowing individual vessels or port facilities to maintain control over their data while contributing to global model updates [15]. In addition, the maritime industry involves different stakeholders, including vessel operators, port authorities, and regulatory bodies, and FL can foster knowledge sharing and global model improvement without neglecting data ownership and operational autonomy. Nonetheless, other industrial settings do not have to deal with possibly conflicting regulations and data locality needs, since the desired service might not impose stringent privacy constraints, only involving national stakeholders.  
Finally, in intelligent maritime transportation systems, real-time decision-making must be supported for collision avoidance and route optimization purposes, especially when unmanned vessels are used. FL promotes computation task offloading by distributing the task among the edge nodes. In this way, the possible resource and energy constraints of respected models are more efficiently updated. Moreover, FL offers high scalability, as it does not require a centralized infrastructure to handle data from a diverse set of maritime nodes [16].
# Early Risk Prediction of Diabetes Based on GA-Stacking
## 4 Results  
### Data Set and Data Preprocessing  
The dataset used in this paper is 8787 desensitization physical examination data from Qingdao CDC. Each piece of data in the dataset includes the desensitization information of the user, such as sex, date of birth, date of physical examination, body temperature, breathing rate, pulse rate, and other individual examination items, as show in Table 1. At the same time, in order to verify the generalization ability of GA-stacking, the early-stage diabetes risk prediction dataset published by UCI [3] is used, as show in Table 2. It has 520 instances and 16 attributes related to diabetes, such as age, sex, polyuria, polydipsia, etc.  
Before classification, we performed data preprocessing, including data cleaning, coding, discretization, normalization, and dataset division.  
1. Data cleaning: Before data modeling, data cleaning can make the model more effectively extract the actual group characteristics. For the physical examination dataset, mode imputation was used to fill the samples with a few missing attribute columns. Samples with more than 10 attribute columns missing were deleted.
2. Data encoding: One-hot encoding was performed on the sex attribute to make the calculation of the loss function more reasonable and improve the accuracy of the model.
3. Data discretization: In the physical examination data, some samples were at the same age. After discretization, the model will be more stable. Moreover, the risk of fitting will be reduced.
4. Data normalization: After the dataset was normalized using the Equation (8), the convergence speed and accuracy of the model could be effectively improved.  
\[x^{\prime}=\frac{x-\mu}{\sigma} \tag{8}\]  
where, \(\mu\) is the mean value of the age, and \(\sigma\) is the standard deviation.  
Dataset division: If the output data of training the primary learner is used to directly train the mate learner of stacking, it will cause the risk of overfitting. Therefore, we used a five-fold cross-validation method to process the two datasets. Firstly, datasets are divided into training dataset and testing dataset in 7:3. Then, the training dataset is divided into five equal parts, of which four-fold data are used to train CNN and SVM, and the remaining one-fold data are predicted and classified by the primary learner as the training dataset of the meta learner. For the testing dataset, the information extracted by the primary learner five times are averaged as the input of the mate learner for classification output.
###### Abstract
Although the solid mathematical basis for generalizing the MAR to diffraction problems concerning arbitrary open two-dimensional structures (2D) was constructed nearly 30 years ago (see, in particular [24; 25]), the practical realization of the appropriate algorithms started only recently. From the list of recent applications of the generalized MAR we extract two papers which are of direct relevance to the present paper: the first [26], which demonstrates the fruitfulness of this approach to impedance calculations for transmission lines with an adjustable inner conductor; and the second [27] which contains the detailed deduction of the solution to the problem of electromagnetic plane wave scattering by arbitrary two-dimensional cavities. This paper [27] and the more recently published work [28], which addresses finding complex eigenvalues of slotted cylindrical cavities of arbitrary shape, contain a description of the necessary mathematical tools, numerical schemes, and related material which we use in the present paper. In fact, we obtain the solution to the more general problem when some or all of the conductors are slotted cylinders of the arbitrary cross section. They may be placed at any arbitrary point of two-dimensional space; the configuration is defined by the relative location of the conductors. In particular, the elements of this general solution may be found in [29]. The process of obtaining the general solution is labour-intensive and lengthy, so we omit the full details of its deduction and draw on previous publications [27; 28; 29] for an abbreviated outline of the solution algorithm in order to focus on numerical results and their discussion. Keeping that in mind, in the overwhelming majority of practical cases, the bounding contours of the waveguide (outer conductor) and the inner conductors are described by closed and smooth contours, it is more instructive to describe the solution for this case first. The general solution for finding the complex eigenvalues for slotted waveguides with inner metallic inserts will be presented in a subsequent paper. It should be pointed out that the solution obtained and the proof of its mathematical correctness are comprehensively discussed in previous publications related to slotted cylinders of arbitrary cross section, including references [24; 25]. The present paper deals with the less complex problem of closed contours. The mathematical background of this problem does not require special mathematical skills and its solution will be readily accessible to most of the electro-engineering community.
# Modal Analysis and Optimization of Tractor Exhaust System
## 1 Introduction
Modal testing is usually used to verify the accuracy of finite element analysis. Considering the economy and accuracy of test analysis, how to determine the optimization object and design plan is always an important issue. Before the finite element model is updated, a modal test is performed to extract the modal parameters from the real structure [23]. The modal test is an important step in the updating process of the finite element model. Sometimes, researchers and engineers encounter difficulties in directly drawing the model in finite element analysis software due to the complexity of the structure's geometry. There are many studies in the literature that report such an approach. In these studies, exhaust systems were modeled using CATIA V-5 software and transferred to finite element analysis software [24; 25]. [PERSON] et al. [26] aimed to perform finite element modeling of the exhaust structure and use a model updating approach to improve its dynamic behavior. [PERSON] et al. [27] analyzed the vibration characteristics of an automobile exhaust system using finite element software. Vibration mode diagrams were obtained in three different ways: natural, partially constrained, and fully constrained. One of the most common problems encountered in tractors with vertical exhaust systems is the breakage and deformation of the exhaust system due to heavy working conditions. Since experimental studies to investigate the origin of such failures are costly and time-consuming, computer-aided engineering analyses are often preferred. [PERSON] and his colleagues [28] performed modal analysis of the connecting rod, which is an important part of the automobile engine dynamic system, with the ANSYS program. They determined six points on the connecting rod and determined the magnitudes of the deformations and which frequency values caused these deformations. They also created and displayed graphical representations of natural frequencies and deformation values. [PERSON] et al. [29] used three-dimensional modeling software to construct a chassis model of a rice planter, and ANSYS software was used for stress analysis. Then, the modal eigenfrequency and vibration mode of the planter chassis were obtained. The obtained data were compared with the modal test results to demonstrate the effectiveness and accuracy of the finite element modal analysis method. The aim of this study is to solve the vibration problem of the exhaust system of a 90-horsepower tractor with a vertical exhaust system. The exhaust system was designed with PTC Creo V8.0.6.0 and subjected to computer simulations using ANSYS WB(r) software. The vibration behavior of the exhaust system at different frequencies was investigated during the simulation process. Based on the obtained results, necessary improvements were made to the exhaust system and a new design was created. This new design was verified both by computer simulations and experimental measurements. The new exhaust system prototype was produced and the success of the design was tested in practice.
## 3 Scenario and System Model  
### Scenario Description  
We consider the positioning in a general indoor scenario, such as a factory, as shown in Figure 1. A number of Base Stations (BSs) are deployed on the ceiling of the factory with a certain area, and each BS consists of one microcell with an omni-directional antenna towards the ground. The dense clutters are randomly distributed, and heavy NLOS propagations exist in such complex scenarios. The clutter in the scenario can be considered as machinery, assembly lines, and storage shelves. UEs are randomly distributed in the factory while receiving the reference signal, e.g., PRS sent by BSs and process channel estimation for deriving CIR.
## 3 Results and Discussion
### Morphology  
An FE-SEM analysis was conducted to examine the surface morphology of the pure polymers and the CNT-incorporated polymer materials, with the aim of evaluating the uniformity of the CNT dispersion. Figure 1a presents the SEM image of the pure PS, revealing characteristic features of a typical radical polymer, along with the presence of partially porous regions. In the case of the PETE, as shown in Figure 1d, no distinct porous structure was observed, unlike with the PS. Instead, the surface appeared relatively smooth and uniform. The morphologies of the PS and PETE containing 2.0 wt% MWNTs are shown in Figure 1b,c,e,f at 50\(\times\) and 100\(\times\) magnifications. The fibrous structure of the MWNTs is clearly visible, and it can be observed that the polymer matrices uniformly cover the MWNT surfaces, indicating that a well-blended polymer structure formed between the two components. The fibrous morphology of the MWNTs can be more distinctly observed in the PS sample than in the PETE sample, indicating a relatively clearer exposure of the nanotube structure within the PS matrix. Additionally, an FE-TEM analysis was performed to observe the morphology of both the pristine polymer and the CNT-incorporated polymer materials. In Figure 2a and c, the morphologies of the pristine PS and PETE are shown, respectively, while Figure 2b,d present the structures of the MWNT/PS and MWNT/PETE composites with a 2.0 wt% MWNT content. In both Figure 2b,d, the shape of the CNTs is clearly visible. In the case of MWNT/PETE, the polymer matrix appears to be more uniformly attached to the MWNT surface than in the case of MWNT/PS. This uniform interaction is expected to contribute to the lower surface resistivity observed in MWNT/PETE.  
\begin{table}
\begin{tabular}{c c c} \hline  & **PS** & **PETE** \\ \hline Mn (g/mol) & 6600 & 2640 \\ Mw (g/mol) & 16,900 & 4250 \\ D & 2.56 & 1.61 \\ \hline \end{tabular}
\end{table}
Table 1: GPC data of synthesized PS and PETE.  
Figure 1: FE-SEM images of samples: **(a)** PS at 50\(\times\) magnification, **(b)** 2.0 wt% MWNT/PS at 50\(\times\), (c) 2.0 wt% MWNT/PS at 100\(\times\), (d) PETE at 50\(\times\), (e) 2.0 wt% MWNT/PETE at 50\(\times\), (f) 2.0 wt% MWNT PETE at 100\(\times\).
## 3 System Model and Symbol Definitions
### mMTC Signaling Overload  
Signaling overload and battery lifetime are two of the leading IoT problems. The application server needs to trigger the device by the control messages, but the proportion of the control messages and the transmitted data is inefficient. The EPC is not affordable when it transmits a large number of messages at the peak time. The traffic model combines the payload size and protocol header overhead. When the devices are triggered, the signaling considers the total header, so we ignore the payload size. The protocol stack includes the Constrained Application Protocol (CoAP), Datagram Transport Layer Security (DTLS), User Datagram Protocol (UDP), and Internet Protocol (IP).  
With the massive and ever-growing number of MTC devices, reducing the signaling message is more important than ever. This will solve the server congestion problem and reduce the energy consumption of MTC devices in the B5G network. The mMTC devices are put on the mobile network and respond to the server's request automatically. The packet size varies between 4 and 21 bytes. The energy-saving mechanism can extend the life of device batteries by reducing the frequency of replacements.
## 2 Development of Conductive Mortar
### Experimental Procedure
#### 2.1.2 Specim Preparation  
Figure 1 shows the prepared specimens. Specimens were cup-shaped with a top diameter of 15 cm and a height of 20 cm. Ordinary Portland cement and standard sand were selected while adding various admixtures, i.e., activated carbon, bentonite, zeolite, and geopolymer, and chemical agents, i.e., sodium hydroxide, calcium hydroxide, lithium hydroxide, and sodium chloride. The four-probe system suggested by [PERSON] was designed to measure resistivity [31]; four probes were evenly spaced 1 cm apart.  
The mix design is given in Table 1. The primary mixing ratio of cement and sand by weight for general mortar was 1:2, and we reduced the amount of sand while increasing the amount of admixture for the conductive mortar. The admixture ratio is defined as the volume of admixture divided by the volume of admixture and sand. Different admixture ratios of 100%, 50%, 30%, and 10% were selected while the amount of cement was fixed. The amount of water was determined by conducting a flow test and depended on the characteristics of the admixture.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Admixture**} & \multicolumn{4}{c}{**Mixing Ratio (g)**} & \multicolumn{4}{c}{**Unit Content of**} & \multirow{2}{*}{**Admixture**} \\  & & & & & **Water in Concrete** & \\ \cline{2-7}  & **Cement** & **Sand** & **Admixture** & **Total Weight** & **Ratio (\%)** & **Water (g)** & \\ \hline General mortar & 250 & 500 & 0 & 750 & 18 & 135 & 0 \\ \hline \multirow{4}{*}{Activated carbon} & 250 & 0 & 165 & 415 & 44 & 181 & 100 \\ \cline{2-7}  & 250 & 250 & 82 & 582 & 25 & 145 & 50 \\ \cline{2-7}  & 250 & 350 & 49 & 649 & 20 & 131 & 30 \\ \cline{2-7}  & 250 & 450 & 16 & 716 & 17 & 119 & 10 \\ \hline \multirow{4}{*}{Bentonite} & 250 & 0 & 286 & 536 & 56 & 300 & 100 \\ \cline{2-7}  & 250 & 250 & 143 & 643 & 31 & 199 & 50 \\ \cline{2-7}  & 250 & 350 & 85 & 685 & 24 & 163 & 30 \\ \cline{2-7}  & 250 & 450 & 28 & 728 & 18 & 130 & 10 \\ \hline \multirow{4}{*}{Zeolite} & 250 & 0 & 365 & 615 & 33 & 200 & 100 \\ \cline{2-7}  & 250 & 250 & 182 & 682 & 22 & 150 & 50 \\ \cline{2-7}  & 250 & 350 & 109 & 709 & 18 & 130 & 30 \\ \cline{2-7}  & 250 & 450 & 36 & 736 & 16 & 120 & 10 \\ \hline \multirow{4}{*}{Geopolymer} & 250 & 0 & 365 & 615 & 33 & 200 & 100 \\ \cline{2-7}  & 250 & 250 & 182 & 682 & 21 & 143 & 50 \\ \cline{1-1} \cline{2-7}  & 250 & 350 & 109 & 709 & 18 & 125 & 30 \\ \cline{1-1} \cline{2-7}  & 250 & 450 & 36 & 736 & 15 & 107 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix design.  
Figure 1: Prepared cup-shaped mortar specimens for resistivity measurement.  
The weight of chemical agents was set to be 1% of the weight of the cement, and the weight of the chemical agents was not included in the mix design. The flow test also determined the amounts of water for the cases with chemical agents, but the amounts were the same as those without chemical agents.
# Review of V2X-IoT Standards and Frameworks for ITS Applications
## 3 Commercial V2X Products
### Commercial V2X Devices
In contrast, mobile devices are powered from smart devices or other external rechargeable batteries. It should also be noted that there are no specific, stringent requirements for the casings of mobile and OBU devices, while the casings of the RSU devices must be compliant with NEMA, IP67 or other environmental protection standards. As mentioned earlier, practically all commercial V2X devices are dedicated to DSRC communication technologies and do not support C-V2X. This, in turn, restricts further research into the hybrid architectural solutions of these technologies and the development of new V2X systems. The exception could be considered a Qualcomm Snapdragon 602 Automotive Platform and Qualcomm Snapdragon 820 Automotive Platform [75; 76], which have LTE, Wi-Fi/DSRC communication modules. However, there are several chipsets of transceivers for these communication technologies: QCA6574, WTR3925, WTR4905 and others. Perhaps for the last-mentioned reasons, Qualcomm develops C-V2X 9150 chipset that will support the C-V2X with the integrated global navigation satellite system (GNSS), and will have an application processor running the ITS stack [77]. ZTE has already announced its future communication with Qualcomm, developing a new generation of C-V2X solutions [78].
# Speech Intelligibility Analysis and Approximation to Room Parameters through the Internet of Things
###### Abstract  
In recent years, Wireless Acoustic Sensor Networks (WASN) have been widely applied to different acoustic fields in outdoor and indoor environments. Most of these applications are oriented to locate or identify sources and measure specific features of the environment involved. In this paper, we study the application of a WASN for room acoustic measurements. To evaluate the acoustic characteristics, a set of Raspberry Pi 3 (RPi) has been used. One is used to play different acoustic signals and four are used to record at different points in the room simultaneously. The signals are sent wirelessly to a computer connected to a server, where using MATLAB we calculate both the impulse response (IR), and different acoustic parameters, such as the Speech Intelligibility Index (SII). In this way, the evaluation of room acoustic parameters with asynchronous IR measurements two different applications has been explored. Finally, the network features have been evaluated to assess the effectiveness of this system.  
WASN; room acoustics; impulse response; speech intelligibility index; room parameters estimation +
Footnote †: journal: _Applied_