esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Verification of Manufactured Prototype  
Static compression shear static torsion test, and static torsion test were performed in order to identify the possible movements in the human spine, and a dynamic compression test was performed to confirm the problems caused by the cyclic loading, referencing ASTM-F2077, an international standard of the spinal fusion cage. In order to identify the likelihood of subsidence of the fabricated prototype, a subsidence test was performed with reference to international standard ASTM F2267, and an MTS 858 Bionix (MTS System Corp, Eden Prairie, MN, USA) was used for the mechanical load. To compare the performance with the fusion cage, commercial PEEK materials were compared with the fusion cage (LP Cage, Medyssey, Chungcheongbuk-do, Korea) and titanium alloy material fusion cage (LT Cage, Medyssey, Chungcheongbuk-do, Korea). For accurate comparison, the lengths and widths of all three products were kept consistent, and both of the selected fusion cages were approved by the Korean Food and Drug Administration and the US FDA 510K. Their results were normalized with the composite fusion cage and compared.  
Figure 4: (a) Implementation of lumbar treatment model, (b) Physiological loading applied to the developed lumbar treatment model.  
The jig was designed to transfer the force to the fusion cage according to the standards of ASTM F2077 and F2267. The jig was designed and manufactured for compression, torsion, subsidence and shear according to the direction of each force. For each test, stainless steel was used for the static test, poly acetal was used for the dynamic test, and PU (Polyurethane) foam material was used for the subsidence test (Figure 5).  
Static compression, compression shear, and subsidence tests were performed six times and averaged by applying loads (load, N) and displacement at a rate of 25 mm/min to obtain the data. Torsional strength (torque, Nm) and angle (angle, degree) were obtained by applying static torsion at a rate of 60 degrees/min, and the measurement was conducted six times to obtain data, and data were averaged. In the dynamic compression test, the R-ratio was set at 10% and 5,000,000 cycles were tested at a frequency of 5 Hz. The applied load was determined by the static compression load value, and the applied load was decreased step by step. Fracture and deformation were confirmed [14; 15].
## 3 Methodology
### Components of an MA-Based TRM  
The components of an MA-based TRM can be mostly put into three categories as layers, nodes and linkages. The definition of dimensions in MA was introduced to represent the layers in a TRM. The layers can be divided into technology, product, service and market layers, which are each listed above the previous layer. For example, the mechanism dimension is linked with the technology layer, the structure dimension is linked with the product layer, and the energy dimension is linked with the technology and product layers. There is a timeline covering all these layers to provide a criterion for identifying the interconnections among different layers. Generally, the left-hand side of a layer indicates point of the past, as the right-hand side of the layer indicates the latest point or a future time. In this study, the researchers chose the technology and product layers as the target layers to indicate the validity of the proposed approach.  
The nodes located in each layer are the units of innovation concepts. The technology nodes in the technology layer indicate one technology or several technologies as a group, which consist of shapes in  
Figure 2: Research framework.  
MA. The technology shapes in the morphology matrix are the concepts subordinate to the dimensions in the category forming the technology nodes. Not very differently, every product node is located in the product layer, which indicates a product or a group of products that could be presented in the form of a configuration in MA. The configurations in MA that outline a product or technology indicate combinations of different shapes.  
A linkage indicates a relationship between nodes. There are two kinds of linkages. The first linkage is between two nodes in the same layer, and the second is between two nodes in different layers. The direction of the developed technology can be found and technological similarity degrees can also be measured through the linkages between technology nodes. Moreover, through the connections of product nodes, the change and similarity of the products can be seen.
# Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets
Learning trends and characteristics of fault data can be challenging if the initial fault data are insufficient. This paper proposed a TC-CNN model to address the above problems. The TC-CNN model discovers more information by simultaneously extracting fault features in the frequency domain and time-frequency domain of the vibration signal. Feature discovery engineering is the focus of fault diagnosis. As long as the separability of data features is good enough, it is easy to obtain good results no matter how strong the data imbalance is. Adding fault features or making them easier to learn is another effective way to solve problems. Compared with the DAE model proposed in [16], the proposed method can simultaneously extract fault features in two feature domains and reduce the difficulty of fault diagnosis by increasing the dimension of features [17] used EMD for feature extraction, and time-frequency features were expressed as a set of intrinsic pattern functions (IMFs). The difference of the proposed method is that: GST is used to extract time-frequency features and represent them in the form of images, which takes advantage of the convolutional structure of CNN in image feature extraction. Summarizng the main contributions of this paper: (1) A TC-CNN model based on 1D-CNN and 2D-CNN is proposed, which increases the dimensionality of extracted features by extracting features in both the frequency domain and time-frequency domain. (2) FFT is used to extract the frequency spectrum as the input of 1D-CNN, and GST is used to extract the time-frequency image as the input of the 2D-CNN. (3) Feature extraction is carried out in two CNN channels, respectively. The splicing of the two feature vectors is performed in the fusion layer. The features learned from frequency spectrums and time-frequency images are fused and incorporated into the training and learning process of TC-CNN. The rest of this article can be summarized as follows: Section 2 introduces the two feature extraction methods used in TC-CNN. Section 3 introduces the theory related to CNN, and the framework of the TC-CNN model is given. Section 4 verifies the performance of TC-CNN through several experiments. Moreover, the proposed TC-CNN model is compared with the approximate and existing models to show its advantages. Section 5 summarizes the limitations of the proposed method and future research priorities.
## 1 Introduction
The preprocessing step improves the PSNR and contrast enhancement. There are three main objectives for implementing this improvement process:To analyze brain MRI and retinal images for early disease diagnosis. 2. To validate the proposed McCann Retinex algorithm to handle low-varying contrast and noise issues, as well as to observe the impact of the technique on postprocessing. 3. The noise factor always impacts the quality of image processing and machine learning methods, and the main objective is to analyze the impact of noise on the retinal color fundus images and brain MRI. A plethora of algorithms for brain tumor classification have been developed for this purpose [26]. These algorithms and many other algorithms [27; 28], however, have limitations in that the appropriate abnormal region of the tumor is not classified due to the low-varying contrast [22], and this challenging issue can be solved by implementing the image enhancement technique as a preprocessing step for segmentation (postprocessing) for disease analysis. In this research work, we deal with the image enhancement technique to analyze the FAZ region and MR brain images, because most of the small vessels are dropped, it is difficult to analyze the small vessels of the FAZ region; further, some of the brain MRIs contain low contrast, and it is difficult to detect a very small brain tumor. This step is known as preprocessing. We implement postprocessing to analyze the effect of the preprocessing on retinal blood vessel extraction and brain tumor detection. The paper is organized as follows. The related work is discussed in Section 2. The proposed method is explained in detail in Section 3. The database information and measurement parameters are explained in Section 4. Section 5 contains the results and discussion. Finally, Section 6 provides the conclusion and future research directions.
## 2 Background
### Contention Window Adaption  
In IEEE 802.11 networks, it is extensively accepted that the backoff algorithm plays a significant role in achieving a high throughput and less medium access delay [14; 15]. The IEEE 802.11 adopts a binary exponential backoff algorithm by default. As described in Section 2.1, when collisions happen, the BEB scheme simply exponentially doubles CW value to avoid repeated collisions, while it always resets the CW value to \(CW_{min}\) after a successful transmission, assuming that the network  
Figure 3: IEEE 802.11 ah RAW structure.  
is no longer congested. The fundamental problem is that BEB has no perception of the channel state, thus the algorithm does not know how to obtain an appropriate CW value to provide a better network performance.  
Thus, considerable effort was devoted to improve the efficiency of the IEEE 802.11 backoff protocol. Several feedback-based schemes [14; 15; 16; 17; 18; 19] have been proposed for adapting the station backoff to the present network conditions. In [16], an additional control is introduced on frame transmission for adaptation of CW according to the present network congestion. Further, the authors of [17] tuned CW by runtime estimation of the congestion condition and network status. In [14], the authors proposed a solution CCCW, which dynamically adjusts the CW size in both of saturated and unsaturated traffic conditions. The CW adaptation process in CCCW aims to achieve the optimal throughput. Recently, the authors of [15] proposed a delay-aware CW scheme adaption scheme called D2D, which tunes CW by the present delay level and channel congestion status of the network. However, these CW adaption schemes do not consider the influence of error-prone channel on the CW adjustment. On the other hand, several schemes [20; 21] proposed the optimal configuration of the CW parameters in EDCA according to a predefined set of performance criteria. However, the theoretically derived optimal values rely on the actual measurement of network parameters such as number of contending stations, which is difficult to measure precisely in the real dynamic wireless environment.
## 3 Experiment
Therefore, the Naive Bayes model resulted in parameter vectors \(\boldsymbol{\mu},\ \boldsymbol{\sigma},\ \boldsymbol{\mu}^{\prime}\, \boldsymbol{\sigma}^{\prime}\) with dimensions of 50 for the following probability densities:  
Figure 3:2  
Figure 3:3  
The present study utilized the parameter vectors obtained from assuming a Gaussian distribution for \(P(\boldsymbol{X}\,|\,y)\) to classify signals into either flame or UVB lamp classes based on the probability of the spectrum power vector \(X\) belonging to each class, utilizing [PERSON] theorem. The Naive Bayes model was found to be an effective approach for classifying signals based on their Fourier spectrum analysis results. The results of this study demonstrate the potential of using the SVM classifier and Naive Bayes classifier based on Fourier spectrum analysis to distinguish between signals from a flame and a UVB lamp. The classifiers were trained using 1000 samples of both the flame and UVB lamp and were evaluated with 500 samples of each. By using the SVM classifier with the penalty parameter \(C=10\), the accuracies obtained in the testing phase were 95.15% and 91.25% for flame and UVB lamp classification, respectively. Meanwhile, the results of performing the Naive Bayes classifier were 95.45% and 93.65% for flame and UVB lamp classification, respectively. These findings suggest that the SVM classifier and Naive Bayes classifier are reliable methods for accurately differentiating between signals from a flame and a UVB lamp based on their Fourier spectrum analysis results. The high accuracies achieved in this study offer a promising solution to the problem of false alarms in fire detection systems. By minimizing false alarms, the proposed system has the potential to save lives and minimize property damage. Additionally, the use of Fourier spectrum analysis to extract features of the flame and UVB lamp signals offers a robust and effective method for fire detection. Future research could further investigate the effectiveness of this approach in more complex environments, such as those with multiple sources of UV radiation or in the presence of smoke. Additionally, exploring the potential for integrating this system with other fire detection methods could lead to even greater accuracy and reliability in fire detection.
## 3 Statistical Methods
### Goodness-of-Fit Test
#### 3.2.4 Exponential Distribution  
Exponential distribution is one of the continuous probability distributions and is primarily used to model the time intervals between events [40]. In other words, when events occur at a constant rate, the time intervals between those events follow an exponential distribution. Additionally, the exponential distribution possesses the memoryless property, which makes it widely used in reliability analysis due to its mathematical simplicity and well-established methods for statistical estimation and hypothesis testing compared to other distributions. The exponential distribution has only a single parameter, which makes the calculation of the pdf and cdf relatively simple compared to other distributions. The single parameter is the scale parameter \(\lambda\), which represents the average rate of event occurrence. The pdf and cdf of the exponential distribution are expressed as follows [40]:  
\[D_{E}(x)=\lambda\cdot\text{exp}(-\lambda x)\text{ for }x\geq 0 \tag{15}\]  
\[F_{E}(x)=1-\text{exp}(-\lambda x)\text{ for }x\geq 0 \tag{16}\]  
For a more detailed understanding of the exponential distribution, readers are encouraged to refer to the relevant literature [24; 40].
## 3 The Proposed Method and Scenarios
### Overview of the System  
This section delineates the outlines of the proposed system, comprising its entity functions, an overview of the steps, and an overview of the authentication and blockchain part. The methodology integrates Kerberos authentication with the blockchain framework for the preservation of vehicles' authentication messages [21]. By employing blockchain technology, a possible decrease in handover time can be achieved as a result of its decentralized characteristics. Following the vehicle's completion of the initial authentication phase, there is no need to establish a connection with the Kerberos server when transitioning to the handover phase. The functions of entities, an overview of the phases, and an explanation of the authentication part and blockchain part are presented in the following sub-sections.
# Temporal Stability and Practical Relevance of Velocity and Velocity-Loss Perception in Back Squat
## 1 Introduction
asked participants to verbally report when they thought their velocity, compared to the first repetition, had dropped by 20% and 40% [21]. PVL accuracy was calculated as the difference between the number of repetitions at which the participant reported 20% and 40% PVL and the actual repetitions corresponding to those velocity losses [21]. Lastly, [PERSON] et al. asked participants to stop the sets when they thought their %VL was between 15% and 30% and thereafter calculated the percentage of sets stopped correctly [24]. As with PV, it has been proven that PVL accuracy also increases after familiarization, although the familiarization protocols used were shorter than those used for PV [22]. Finally, PVL accuracy seems to be related to the number of repetitions performed, where an increase in repetitions corresponds to an increase in the PVL error [20; 21; 22]. Despite these issues, some studies conclude that PVL can be implemented as a VBT monitoring tool whenever LPTs are limited or not available [21; 24]. Although the research conducted so far is encouraging, no study has explored the possibility of combining PV and PVL simultaneously. Thus, it is important to highlight that using both velocity and %VL in combination is essential for accurately prescribing VBT. Therefore, the first aim of this study was to investigate the possibility of using both PV and PVL simultaneously, and whether the combined use of these two parameters influences the accuracy of one (PV), the other (PVL), or both. Furthermore, as mentioned previously, it is well established that familiarizing subjects with PV and PVL enhances accuracy levels; however, it remains unclear whether these improvements are maintained over time and, if so, for how long. Therefore, the second aim of the present study was to assess whether the accuracy of PV and PVL remains stable after four weeks, during which participants trained using classical PBT rather than velocity-based training, and without receiving any form of velocity feedback during their sessions.
## 6 Application Case Study  
We now present an application case study using DDNB, implementations of sample application services that benefit from the use of DDNB architecture.  
_Nodehome_ is a development environment for blockchain-based applications and also an operating platform for running developed services on blockchain. Even without a complex understanding of blockchain, developers are able to implement their own services according to the API rules  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SN** & **Forged** & **1** & **3** & **9** \\ \hline
1 & - & - & - \\
5 & 262 & - & - \\
10 & 133 & 458 & - \\
20 & 129 & 387 & 777 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average time (in seconds) to blacklist forged nodes.  
Figure 8: Completion time of blacklisting (mutual verification process) with varying number of forged nodes—results from 10 independent experiment runs.  
(Terminal Layer \(\leftrightarrow\) Service Layer) provided by the _Nodehome_ platform [40]. Developers need not worry about the terminals on which they run their services, nor how to prepare the hardware and network infrastructure for their blockchain. If you can provide hardware that can run pre-developed services, you can share the profits generated by services on _Nodehome_ platform. _Nodehome_ platform is equipped with various blockchain based means to ensure trust, such as proof of assets, preservation of goods, movement of goods, registration of property, evidence of actions, and protection of information. Many blockchain-based services are currently being implemented and executed on the _Nodehome_ platform, and the information and assets are exchangeable between different services. _Nodehome_ platform is serviced currently in the form of a testnet, and web pages ([[https://nodehome.io/](https://nodehome.io/)]([https://nodehome.io/](https://nodehome.io/))) provide block explorers that provides detailed information about blocks, addresses, and transactions. In addition, a simple guide for architects, developers, and users is provided as well.  
As shown in Figure 9, a few client applications are also available. _NH Token Wallet_ is a wallet service that manages tokens issued from various applications in the DDNB's Service Layer (of course, some applications may not provide token-related services intentionally). _fMusic_-music streaming service is one of the practical applications built on DDNB. Since it is provided as a distributed service contrasting to existing services, it is able to connect singer-to-listener without sound production or distribution company. Not only this simple difference, but in intellectual property or derived user data perspective, it can also seek several new attempts that have not been possible before.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## 2 Materials and Methods
### Methods  
To develop the proposed pedestrian navigation system (PNS) the following steps are needed:  
* realizing an attitude and heading reference system (AHRS) that relies on the data of the inertial sensors;
* identifying each step performed by the user as accurately as possible;
* estimating the step length;
* defining the algorithm for estimating the direction of motion;
* integrating the data from inertial sensors with the global navigation satellite system.  
In the following sections, each of these points will be examined in depth in order to provide a clear understanding of the used methods.
## 2 Synthesis and Characterization of Monoliths
### Molecularly-Imprinted Monoliths  
The molecularly-imprinted technique (MIT) is one of the most promising techniques for preparing polymers with the desired and predetermined selectivity and provides specific binding sites or catalytic sites in the molecularly-imprinted polymer (MIP). Molecularly-imprinted polymers (MIPs) are synthetic materials with recognition sites that specifically bind target molecules in mixtures with other compounds. In contrast to classical SPE sorbents used for clean-up procedures, MIPs are more selective and allow the elution of analytes from the cartridges, nearly free from co-extracted compounds. Several polymerization methods can be used to obtain MIPs for SPE. Traditionally, MIPs have been prepared by bulk polymerization, because it does not require sophisticated instrumentation and because the reaction conditions can be easily controlled. Although this procedure is tedious and time consuming, it is the most widely-used method for the preparation of MIPs [25]. MIT has appeared as an interesting solution to solve the problem of the recognition ability using conventional SPE materials. In recent years, a number of analytical methods utilizing MIT have been applied for the analysis of residues in food, and existing methodologies have been improved [26,27]. Nevertheless, there is a growing interest in alternative routes for preparing MIPs to better control morphology and, thus, to explore new applications [27]. Recent advances in MIT include the development of monolithic columns for chromatographic separations and the development of new options for the extraction and microextraction of analytes [1,15]. Monolithic imprinting, as one of the methods for preparing MIP, combines the advantage of monolithic column and molecular imprinting technology [28]. MIP monoliths can not only concentrate, but also selectively separate the target analytes from real samples, which is crucial for the quantitative determination of analytes in complex samples.