esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Onset Cycle 1 and Offset Cycle 10  
A total of 14 studies reported offset cycle 10 values and 13 studies reported onset cycle 1 values for adults with typical voices under the age of 65 years. The distribution for offset cycle 10 ranged from \(-1.10\) to \(0.05\) ST with a median value of \(-0.6\) ST, whereas onset cycle 1 values ranged from 1.68 to 3.82 ST with a median value of 2.60 ST. See Figure 3 for a boxplot distribution of RFF values for adults with typical voices.  
Mean offset cycle 10 values were consistently lower for individuals with voice disorders when compared to those of typical voices. Individuals with adductor laryngeal dystonia had reported values of \(-1.20\) ST [2; 4], and those with PD exhibited \(-1.90\) ST [49] and \(-2.20\) ST [5]. Of the studies that reported values in patients with VH--including both phonotraumatic lesions and non-phonotraumatic VH subgroups--offset cycle 10 ranged from \(-1.76\) ST to \(-0.80\) ST, with a median value of \(-1.35\) ST [6; 13; 24; 27]. Two studies completed formalized receiver operating characteristic (ROC) curves to determine the RFF value that would most accurately distinguish between typical voices and those with VH. [PERSON] et al. [27] identified a value of \(-0.56\) ST (97% sensitivity, 90% specificity), whereas [PERSON] et al. [6] reported lower cut-off values of \(-1.92\) ST (VH subgroups vs. controls), though the sensitivity of these values were low, ranging from 29-43%.  
Onset cycle 1 trended lower in individuals with voice disorders but showed substantial overlap with the range of values reported by those with typical voices. For example, studies reporting on adductor laryngeal dystonia hovered around 2.60 ST [2; 4] and those with PD ranged from 1.75 ST [5] to 2.70 ST [49]. Studies on patients with VH were similar, reporting a range of 1.18 to 2.54 ST, with a median of 1.90 ST. ROC analyses showed close-to-chance detection rates [6; 27], indicating a poor ability to discriminate between those with and without VH.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 2 Background and Related Work
### Blockchain Platforms
#### 2.2.5 Other Popular Blockchain Platforms  
There are several other well-known BC platforms including IoT Application (IOTA), Libra, EOS.IO, IoT Chain, IoTeX, HDAC, Atonomi, and Hydrachain [10; 57; 58].  
* IOTA: IOTA is a block-less distributed ledger based on the DAG. The tangle that records the transactions in the IOTA DAG is known as the tangle. IOTA can execute a greater number of microtransactions per second without incurring a charge. A new transaction in IOTA must use a PoW method to validate and approve two prior transactions. As the number of users in the IOTA network rises, the tangle becomes more efficient, quicker, more reliable, and secure.
* Libra: It is a decentralized BC that enables cryptocurrency and has a consistent value supported by low-volatility reserves such as fiat money. This platform was created to assist unbanked people with quick, safe, and scalable financial services. Libra's unified auditing services for controllers and validators represent one of its most important features. Libra includes a native programming language called Move that allows the creation of customized transactions and smart contracts that are secure, flexible, and verifiable.
* EOS.IO: It is a new BC protocol that eliminates transaction fees and can handle millions of transactions per second. For enterprise-level DApps, the EOS BC architecture allows both vertical and horizontal scalability. High TPS, low latency, high TPS, enhanced parallel performance, and high sequential performance are all promising aspects of EOS. Inter-BC communication is also supported by EOS (IBC). The consensus algorithm used by EOS is delegate proof-of-stake (DPoS).
* IoT Chain: It is a platform designed to provide a light system for IoT devices' security and scalability demands. To achieve lightning-fast performance, IoT Chain combines a directed acyclic network with the practical Byzantine fault tolerance (PBFT) consensus method.
* IoTeX: It is a BC-in-BC platform for M2M transactions that require privacy; the architecture consists of a public Rootchain and Subchains, which are managed by the Rootchain. Subchains are made up of either private or public BC and are responsible for controlling groups of linked devices. Subchains communicate with one another via establishing cross-BC transactions with the Rootchain.
* HDAC: IoT contracts and M2M transactions are handled via a Multichain platform. The consensus method for this platform is ePoW, an energy-efficient variant of PoW.
* Atonomi: Atonomi is a platform built on the Ethereum framework that provides immutable identity management services to help develop safe, trustworthy IoT devices.
* Hydrochain: It is an Ethereum platform extension that allows constructing a private ledger.
## 3 Analysis and Discussion
### Analysis of Time-Frequency Characteristic Parameters
#### 3.2.4 Other  
As shown in Figure 3b, log Mel-frequency spectral coefficients (MFSC) are features created during extracting MFCCs. A study [61] has shown that MFSC features yield better  
Figure 9: Validation accuracy of each model with different hand-crafted feature parameters: (**a**) The effects of inverse spectrum boosting and pre-Emphasis; (**b**) influence of temporal first and second derivatives. _Note_: (1) learning rate = 0.001, dropout = 0.5; (2) lifter: sinusoidal filter applied to produce filtered MFCCs and \(n=26\); (3) pre-emphasis: pre-emphasis applied on the signal and \(\mu=0.97\); (4) MFCCs + D: MFCCs with 12 coefficients and energy along with their temporal first derivatives; (5) MFCCs + D + D: MFCCs with 12 coefficients and energy along with their first and second derivatives.  
results in the language processing task. Hence, we compare the effects of MFCCs and MFSC on the \(M_{0}\) model, and the results are shown in Table 3. It can be seen that the network with MFSC features has a longer calculation time, while accuracy is not significantly improved (about 0.5%), and needs to show better performance on small network structures. As a result, MFCCs are used as the input of model.
## 4 Results and Analysis  
### The effect of TCC and Cause Analysis  
The comparison of the RWT results under different scenarios from physical models were shown in Table 1 and Figure 6. For different _Pr_ values, RWTs were increased by 6.8-8.3 \({}^{\circ}\)C under S1 thermal distribution and 2.3-3.5 \({}^{\circ}\)C for S2 distribution. TCC had a significant effect on RWT, but the degree of improvement was related closely to the thermal stratification of the reservoir.  
Figure 4: Comparison of measured and simulated temperature distributions in front of the TCC. (1 m from TCC). (**a**) S1 distribution; (**b**) S2 distribution.  
Figure 5: Comparison of measured and simulated temperature distributions in front of outlet (0.3 m from the outlet). (**a**) S1 distribution; (**b**) S2 distribution.  
In order to reveal the reason why TCC has a good effect on RWT, baseline scenarios A0 and A1 were analyzed primarily to make a contrast with others. The water upstream of the dam was subjected to a pulling force as a function of the discharge through the power station and to buoyancy differences caused by density stratification [36]. When the thermal stratification of water was strong, vertical mixing was inhibited, and the flow field in front of the dam was characterized by stratified water intake. As shown in Figure 7a, the water flow was stratified, and the pulling due to discharge of water only drove the water within a certain vertical range. However, when the TCC was set as shown in Figure 7b, the flow field changed dramatically. The flow of the water in front of and above the TCC was strengthened and the longitudinal velocity of all sections were enhanced. Then the major plume of the faster water from upstream flowed across the top of the TCC and plunges (mainly) into the layers level with the outlet. Between the outlet and the TCC, the water pulled by the discharge was still on the same horizontal line as the outlet, with an enlarged range of influence. However, it could also be concluded from the results of the physical model tests and numerical simulation that the lateral gradient of the water temperature and lateral flow velocity in the water were small and negligible. Therefore, the analysis below was focused on the action in the longitudinal and vertical directions.  
Figure 6: Results of physical model tests. (**a**) S1 distribution; (**b**) S2 distribution.  
Figure 7: _Cont._
# Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles
## 1 Introduction
### Transfer Learning and Pre-Trained CNNs  
Transfer learning reuses the knowledge gained from a previous task in a new deep learning model. Usually, deep learning requires a large amount of data to achieve good results. However, it is often difficult to gather enough data, especially in the medical field. Therefore, transfer learning enhances the learning process when the dataset has limited samples [24]. A pre-trained model is a model that was created and trained to solve a problem that is related to the target task. For example, in image classification tasks, such as flower image classification, we can use VGG19, which is a pre-trained CNN used to classify  
Figure 3: The general structure of a CNN.  
images that was trained on a huge image dataset called ImageNet [25]. Table 2, presents the main information about the pre-trained CNN that was used in this paper [26; 27]. This information includes the model name, the number of layers, the top-1 accuracy of the model in classifying ImageNet data, and the year the model was established. The top-1 accuracy checks if the class with the highest probability is the same as the target label [28]. All of the models were trained on ImageNet. The ImageNet is a large dataset of 14,197,122 annotated images belonging to more than 1000 categories [29].  
Many studies on applying machine learning and deep learning to classify breast density from mammogram have been conducted. However, we could not find a study that used local data from Saudi Arabia. Moreover, according to [30; 31], breast density and ethnicity are associated. Therefore, an automated model to classify breast density trained on local mammogram datasets would be valuable. The main contribution of this paper is developing a breast density classification model that is suited for the local population in Saudi Arabia. The model is based on pre-trained CNNs and weighted average ensembles and applied to the KAMC dataset. The rest of this paper is divided as following, Section 2 explores recent related works that classify breast density from mammogram using deep learning techniques. Section 3 outlines our methodology. Section 4 presents and discusses our results. Finally, a conclusion is offered in Section 5.
## 3 Frequency Variations in Tuning-Fork Oscillators
### Oscillator Categories  
The resonance frequency of a crystal unit can vary with temperature. Depending on the method of dealing with the frequency _vs._ temperature characteristic, [PERSON] [4] classifies crystal oscillators in four categories: Crystal Oscillator (XO), Temperature-Compensated Crystal Oscillator (TCXO), Oven-Controlled Crystal Oscillator (OCXO) and Microcomputer-Compensated Crystal Oscillator (MCXO).  
Tuning-fork oscillators, which are a particular case of Crystal Oscillators (XO), are the most commonly used ones in WSNs. However, they are very sensitive to temperature variations. Their clock stability is about \(\pm 20\) ppm. This kind of oscillator has a quadratic dependency on temperature, which enables the application of a correction factor to improve the clock accuracy. Consequently, it is possible to reduce temperature effects by means of software mechanisms, making them a perfect solution for low-cost WSN motes.  
TCXO and OCXO are less used in WSNs, due to their economic costs, footprint and energy consumption. TCXO crystal units use the information provided by an internal thermistor to generate a correction voltage that is applied to a voltage-variable reactance in the crystal network. The reactance variations produce frequency changes proportional to the temperature changes. In TCXO, the clock stability is in the range of \(0.5\)-\(5\) ppm. Their internal components to compensate the frequency with temperature raise their price to about US$1-\(100\)[13]. In OCXO, the crystal unit and other temperature-sensitive components of the oscillator circuit are maintained at a constant temperature inside an oven, providing a clock stability of \(0.001\)-\(1\) ppm. For this purpose, a very precise encapsulation and isolation is required, which raises their price up to US$\(200\)-\(2,000\)[13].  
Finally, there are certain microcomputer-controlled oscillators available with \"self-temperature sensing\", which compensates for temperature variations in the frequency. These oscillators are known as MCXO (Microcomputer-Compensated Crystal Oscillator), with a clock stability of \(0.05\)-\(2\) ppm. The use of a microcomputer increases their size and economic costs (about US$\(<1,000\)[4]), making them inappropriate for their use in low-cost WSNs.
## 4 Discussion
The method of solving constitutive parameters of different components respectively has the merits of less virtual fields, large-scale linear equations and better solutions, because the solutions of the elastic constants acquired by the method of solving all components simultaneously had a negative value. From Figures 6c and 9f, it can be seen that the strain of the top layer is greater than the bottom layers, which indicates that the top layers are softer. Therefore, the image of strain distribution may be very useful for the diagnosis of diseases, as it shows the relative stiffness and boundaries of different components of soft tissue. As a high-resolution laser interferometry imaging technique, the signal to noise ratio of OCT images decreases with the increases of depth as shown in Figure 8b, and the imaging is easy to be disturbed. In this experiment, only a thin portion of \(\sim\)0.56 mm of the pork image was processed, as the correlation coefficient becomes too low anywhere below. As mentioned above, the OCT images are generated by laser interferometry. The scattering characteristic and anisotropy factor of the tissue influences the size and distribution of speckles of OCT images [29] and the DVC calculation. The size of OCT speckle was determined by OCT resolution, optical devices and microstructure of tissues [30; 31]. To improve the accuracy of the DVC based 3D OCE deformation measurement, the influence of OCT speckle properties needs to be studied further. [PERSON] et al. [32; 33] and [PERSON] et al. [34] proposed a more sensitive displacement and strain detection method using complex cross-correlation. The sensitivity can be within tens of nanometers and tens of micro-strains, which improves about tenfold the DVC method. In [32; 33] out of plane motion was measured from 2D images; in [34], particularly, 3D displacements were estimated from 3D OCT volumes. The using of complex data for correlation calculation may be also helpful for improving DVC calculation. The pork tissue tested in this paper is assumed to be layered isotropic elastic and homogenous based on the strain maps shown in Figure 9. However, soft tissue is usually anisotropic. DVC has the advantage of obtaining the displacement and strain tensors of anisotropic materials, although the algorithm may need to be further improved. The VFM may become very complicated though. Image segmentation methods may be used to differentiate between various components of heterogeneous material, so that the VFM can be simplified. If the constitutive model of the tested tissue is too complicated or even unknown, finite element method updating or other inverse problem solving may be a good choice.
# Heterogeneous Distributed Big Data Clustering on Sparse Grids
## 6 Results
### Datasets and Experimental Setup  
In all of our experiments, we used synthetic datasets with clusters drawn from Gaussian distributions with randomly drawn means \(\mu_{i}\) and equal standard deviation \(\sigma\). The \(\mu_{i}\) thus are the cluster centers. We normalized the datasets to \([0.1,0.9]^{d}\). As this moves data points sufficiently towards the center of the domain, we can use a sparse grid without boundary grid points.  
The parameters used to generate the datasets are listed in Table 3. The datasets with 100 clusters are challenging, as the density estimation needs to correctly separate 100 high-density regions in a setting of moderately high dimensionality. Furthermore, to make it possible to assess the quality of the clustering, we generated the node-level dataset so that the clusters are well-separated by forcing a minimum distance of \(7\cdot\sigma\) between the cluster centers. We verified that the noise connects all clusters in the unpruned \(k\)-nearest-neighbor graph.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & **Type** &
\begin{tabular}{c} **Cores/** \\ **Shaders** \\ \end{tabular} & **Frequency** & **Peak (SP)** & **Mem. Bandw.** & **Machine Balance** \\ \hline Tesla P100 & GPU & 3584 & 1.3 GHz (boost) & 9.5 TF & 720 GB s\({}^{-1}\) & 12.9 FB \({}^{-1}\) \\ FirePro W8100 & GPU & 2560 & 0.8 GHz (max) & 4.2 TF & 320 GB s\({}^{-1}\) & 13.2 FB \({}^{-1}\) \\
2 xeon E5-2680v3 & CPU & 24 & 2.5 GHz (base) & 1.9 TF & 137 GB s\({}^{-1}\) & 14.0 FB \({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hardware platforms used in the distributed and node-level experiments. We list the frequency type that best matches our observations during the experiments.  
We use synthetic datasets for several reasons. First, clustering problems have no general well-defined solution [10]. To estimate the performance of our approach, we need access to the \"ground truth\" of the underlying class labels. For real-world datasets this is typically missing or has been reconstructed by manual and error-prone labeling. Here, we use the adjusted Rand index (ARI) to measure the quality, which requires a reference clustering. Second, we want to ensure that the data set is challenging for our approach. The clusters' densities are ensured to be of maximum dimensionality. In particular, their dimensionality cannot be reduced in contrast to many real-world datasets. Furthermore, they form the worst case for the sparse grid density estimation [22]. Finally, competitive clustering performance for real-world datasets has already been shown for smaller datasets without distributed computing in previous work [21].  
The experiments depend on a set of parameters which are shown in Table 4. The ARI is a quality measure for clustering and is addressed in Section 6.4. In all of our experiments, we used single-precision floating-point arithmetic.
# Mdpi
## 2 System Model
### Transmission Using IDWT  
First, a serial-to-parallel (S/P) block converts the binary information source \(x[n]\) into \(M\) parallel subchannels, where we denote \(X_{k}[n]\) as the information source on the \(k\)-th subchannel. Next, \(X_{k}[n]\) is assumed to be mapped into the data symbol \(S_{k}[n]\) with a pulse amplitude modulation (PAM) scheme by a mapper. Then, an IDWT block converts the carriers to the time domain data \(s[n]\). The IDWT for baseband communication is the synthesis side of a cosine modulated filter bank (CMFB). The IDWT block depicted in Figure 1 is detailed in Figure 2. IDWT operations involve the application of \(M\) synthesis filters. Each input signal is up-sampled using factor \(M\) and passed through a set of synthesis filters. Without loss of generality, we consider the baseband transmission scenario, in which the number of subchannels \(M=512\) and the length of prototype filter \(N=gM\). Note that \(g=4\) is the overlapping factor, which implies that the symbol duration in the wavelet-OFDM system is extended  
Figure 1: Proposed system model. DWT = discrete wavelet transform; IDWT = inverse discrete wavelet transform; IN = impulsive noise; S/P = serial-to-parallel; P/S = parallel-to-serial; AWGN = additive white Gaussian noise; and MUX = multiplexer.  
over \(g\) consecutive symbols so that a guard interval is not used. We use \(F_{k}(z)\) to denote the Z-transform of the \(k\)-th synthesis filter \(f_{k}[n]\), which can be described as follows:  
\[f_{k}[n]=\sqrt{\frac{2}{M}}\cdot p[n]\cdot\cos\left[\left(k+\frac{1}{2}\right) \frac{\pi}{M}\left(n+\frac{M+1}{2}\right)\right]\cos(\theta_{k}), \tag{1}\]  
where \(k=0,\ldots,M-1\) is the index of the subchannel, \(p[n]\) is the prototype filter with coefficients given in [11] (Annex B), and \(\theta_{k}\) equals 0 or \(\pi\). By adding the \(M\) output of the synthesis filters, we have the channel inputs \(s[n]\) at the output of the IDWT block.
## 6 Results and Analysis
### Packet Delivery Ratio  
Figures 6 and 7, present the packet delivery ratio for the networks experiencing a gradual increase in the number of selfish and deceitful nodes with and without the deployed reputation model. When the performance of the networks shown in Figure 6 is analysed, a gradual increase in the number of selfish nodes operating in the networks led to a decrease in the packet delivery ratio of the networks. As observed in Figure 6, selfish nodes do not participate in route discovery processes, which exempts them from being presented packets for forwarding to other nodes in the network. Hence, there will be a high demand on the good nodes to forward packets for other nodes, such that the good nodes will be heavily relied on to deliver more and more data packets to the required destinations. This constitutes a huge task for the services these good nodes can offer to other nodes in the network. Thus, there is a possibility that some packets may be lost, and packet collision may also increase, which could also lead  
to further decline in the packet delivery ratio. The slightly improved packet delivery ratio recorded by the deployed reputation model is a result of the punishment module, which eliminates and isolates the detected selfish nodes and ensures that only reliable nodes (good nodes) use the network resources to transmit packets, which are delivered to the required destinations.  
Similarly, from Figure 7, it is observed that as the number of deceitful nodes continues to increase in the network (2, 4, 6, 8,..., 12), the packet delivery ratio of the network gradually decreases. This is expected as a result of the smaller number of good nodes forwarding data packets and more data packets being dropped by these deceitful nodes.  
It is also observed from Figure 7 that the deployment of the reputation model improved the packet delivery ratio of the network. For all of the cases whereby a decline in the packet delivery ratio occurred as a result of more deceitful nodes, the networks with the reputation model registered a much improved  
Figure 6: Packet delivery ratio for networks experiencing an increase in selfish nodes.  
Figure 7: Packet delivery ratio for networks experiencing an increase in selfish nodes.  
packet delivery ratio, which is from 15%-20% in some cases. This improved packet delivery ratio recorded by the reputation model is mostly a result of more data packets being delivered due to the isolation and prevention of these deceitful nodes accessing the limited resources. Furthermore, the improved packet delivery ratio may also be a result of reduced packet collision in the network due to a few good nodes contending for limited bandwidth.