esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this study, rotordynamic analysis is performed using a simple structural model for the polymer layer of gas foil-polymer bearing (GFPB) composed of an accumulated bump foil and a polymer layer with high structural damping. The simple model that considers the elastic behavior of a cylinder-shaped polymer layer is introduced, and the structural stiffness of the layer is estimated based on Hooke's law for differential elements in the layer. In addition, the simple model is coupled with the structural stiffness of the bump foil in consideration with a series relationship, which represents the structural model of GFPBs. A GFPB with thickness of 2 mm is fabricated, and the structural model is validated via static-load deflection tests for the GFPB. As a result of model validation, the proposed model is found to be effective in predicting the elastic behavior under the lightly loaded condition of GFPB. Next, the static performances of GFPBs, namely, gas-film pressure, thickness, and journal positions with respect to different polymer layer thickness, are analyzed to evaluate rotordynamic stability of GFPBs. The results indicate that high thickness yields an increase in damping and a decrease in cross-coupled effects. Specifically, in this study, 3 mm-thick polymer gives the best stability performance given the predicted effective damping results. As a result, this work provides a reasonable model for structural elasticity of GFPBs and lays a foundation for the widespread use of GFPBs.  
gas foil bearing; polymer; rotordynamics; bearing force coefficient; structural stiffness Article  
Rotordynamic Analysis of Gas Foil-Polymer Bearings Based on a Structural Elasticity Model of Polymer Layer along with Static-Load Deflection Tests. _Appl. Sci._**2021**, _17_89. [[https://doi.org/10.3309/app11041789](https://doi.org/10.3309/app11041789)]([https://doi.org/10.3309/app11041789](https://doi.org/10.3309/app11041789))  
Academic Editor: [PERSON]  
Received: 4 January 2021  
Accepted: 10 February 2021  
Submitted: 18 February 2021  
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**Copyright:** 2021 by the authors. License MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Proposed Methodology
### Customization of Loss Functions  
#### 3.3.1 Classification Stage  
In the first stage of the proposed two-part model for crash frequency analysis, we confront the significant challenge of data imbalance, which is a common issue in various fields, particularly pronounced in traffic accident analysis. This imbalance arises due to a disproportionate representation of \"non-crash\" instances compared to \"crash\" instances, leading to a skewed dataset where the majority class overwhelmingly outnumbers the minority class. Such a scenario can severely bias a predictive model towards predicting the majority class, thereby diminishing its performance in accurately identifying crash occurrences. To address this challenge, a custom loss function that specifically targets the imbalance problem is incorporated in the binary classification CatBoost model. The mathematical foundation of the proposed approach is rooted in modifying the traditional log-loss, a widely used metric in binary classification tasks, to account for the class imbalance. The log-loss for a binary classifier is defined as follows:  
\[\text{L}(\text{y},\text{p})=-\frac{1}{N}{\sum_{i=1}^{N}}[y_{i}\text{log}(p_{i })+(1-y_{i})\text{log}(1-p_{i})] \tag{7}\]where L is the log-loss, \(N\) is the number of observations, \(y_{i}\) is the actual label of the \(i^{\text{th}}\) observation, and \(p_{i}\) is the predicted probability of the \(i^{\text{th}}\) observation belonging to the positive class.  
To customize this loss function for handling data imbalance, we introduce sample weights that are inversely proportional to the class frequencies. Given a dataset with classes \(C_{j}\), where \(C_{0}\) represents the majority class (non-crash) and \(C_{1}\) the minority class (crash), the number of instances in each class is denoted by \(n_{C_{0}}\) and \(n_{C_{1}}\), respectively. The sample weight for an instance belonging to class \(C_{j}\) is computed as follows:  
\[w_{C_{j}}=\frac{1}{n_{C_{j}}} \tag{8}\]  
This weighting scheme ensures that the loss contribution of each sample is inversely proportional to the prevalence of its class, thereby amplifying the model's focus on the minority class. The weighted log-loss for a binary classification model can be expressed as follows:  
\[L_{weighted}(\text{y},\text{p})=-\frac{1}{N}{\sum_{i=1}^{N}w_{C_{j}}[y_{i} \text{log}(p_{i})+(1-y_{i})\text{log}(1-p_{i})]} \tag{9}\]  
This formulation allows for a more balanced treatment of classes, enabling the model to improve its sensitivity to the minority class without losing specificity to the majority class.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 2 Material and Methods
### Vital Sign Detection Using a Mono-Static UWB Life-Detection Radar
#### 2.2.4 Algorithm for Scanning Operating Mode  
For the quasi-static trapped human beings with quasi-periodic but weak respiration, we denote \(S_{i}(k,m)\) and \(S_{j}(k,m)\) as the output slow-time range matrices obtained from the \(i\)th and \(j\)th (\(i\
eq j\)) observation points after eliminating the time-invariant clutter/interference, respectively. Due to the fact that the cross-correlation function of the non-periodic noise is prone to zero, the cross-correlation between \(S_{i}(k,m)\) and \(S_{j}(k,m)\) is applied to improve the low signal-to-noise ratio (SNR). However, the quasi-periodic component and its harmonic components contained in the slow-time signal \(S_{i}(k,m)\) and \(S_{j}(k,m)\) are still preserved. Assume that the size of \(S_{i}(k,m)\) and \(S_{j}(k,m)\) are \(K_{u}\times M_{a}\) and \(K_{v}\times M_{b}\), respectively. The cross-correlation function \(R_{ij}(u,v,m)\) is defined as:  
\[R_{ij}(u,\ v,\ m)\ =\ \mathbf{E}\big{\{}S_{i}(k_{i=u}\,\ m_{a})\!\cdot\!S_{j}(k _{v}\,\ m_{b})\big{\}} \tag{10}\]  
\[R^{AN}_{\ ij}(u,\ v,\ m)\ =\ \mathbf{max}_{m}\big{\{}R_{\bar{y}}(u,\ v,\ m)\big{\}} \!\cdot\!\mathbf{AN}_{m}\big{\{}R_{\bar{y}}(u,\ v,\ m)\big{\}} \tag{11}\]  
where the range cell index \(k_{u}\in[0,\ K_{u}-1]\) and \(k_{v}\in[0,\ K_{v}-1]\), the slow-time cell index \(m_{a}\in[0,\ M_{a}-1]\), \(m_{b}\in[0,\ M_{b}-1]\) and \(m=m_{a}-m_{b}\). Then, the AN method is applied to \(R_{ij}(u,\ v,m)\) so that the weak quasi-periodic component of the quasi-static trapped human being can be further enhanced. We denote the output result of the AN method as \(R^{AN}_{\ ij}(u,\ v,m)\). The Fourier transform of \(R^{AN}_{\ ij}(u,\ v,m)\) is taken in each slow-time dimension, and the maximum corresponds to the quasi-static trapped human being, i.e., \((u_{\mathrm{i}max}\!,\!v_{\mathrm{i}max})\) indicates the possible range location of the quasi-static trapped human being depending on the different \(i\)th and \(j\)th observation points:  
\[\big{(}u_{\mathrm{i}max},v_{\mathrm{i}max}\big{)}\ =\ \mathbf{argmax}_{uv} \Big{\{}\mathrm{FFT}_{m}\Big{\{}R^{AN}_{\ ij}(u,\ v,\ m)\Big{\}}\Big{\}},\ u\ \
eq\ v \tag{12}\]
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 4 Network Model CNN-ST  
The network model CNN-ST is proposed in this paper, for a detailed extraction of the features of signals and for accurately recognizing the signal types. Firstly, the model framework of CNN-ST is described, and the motivation for choosing the CNN-ST model is explained. Next, the network model CNN-ST is designed. Finally, the swin transformer is utilized for enhancing the feature representation ability and reducing the loss of detailed information of dual-component radar signals under different SNRs.
## 2 Problem Formulation and Models
### System Model  
The targeted system is depicted in Figure 1, which includes three entities: IoT devices, edge devices and the IoT device owner.  
* **The IoT device owner**, who possesses the pre-trained CNN model, wants to perform the inference of the data collected by IoT devices for purposes such as event detection and object identification without revealing any sensitive information.
* **IoT devices** are considered clients that collect private data and conduct CNN inference. IoT devices are assumed to not be powerful enough to execute CNN inference locally in real time, though they may have some limited computational capability.
* **Edge devices** are deployed in a one-hop communication distance to the IoT devices. They are equipped with considerable computing resources and can perform inference tasks in real time. For inference outsourcing, IoT devices first transmit the CNN model to the edge device before the inference task starts.  
For simplicity, we use \(C\) and \(S\) to denote an IoT device and an edge device, respectively. We consider that there exists a direct wireless communication link between \(C\) and \(S\). The link quality is stable, and the bandwidth is comparable with contemporary home or commercial WiFi networks (e.g., hundreds of Mbps in each direction). We assume each IoT device is equipped with some storage capacity (e.g., tens of GB).
## 3 Materials and Methods
### Screening Strategy  
Our literature review targeted relevant studies and peer-reviewed articles in English from January 2017 to September 2024. Title and abstract screening of 133 articles resulted in the exclusion of 102 articles that were either out of scope, not relevant to the study, or did not meet the following criteria:  
1. Primary focus on TCO and related parameters: Studies that lacked a substantial emphasis on TCO and parameters were excluded. We specifically did not prioritize research that does not concentrate on the cost calculation, prediction, and optimization of economic aspects for CWPs and HVAC systems.
2. AI methods: studies that did not significantly focus on AI and ML-based prediction and optimization strategies were excluded.
3. Duplicate removal: Duplicate records of the combined results were removed. Only journal articles and review articles were included.  
This refined selection process ensured the inclusion of studies directly relevant to our objective of aiding financial decision making in CWPs.
## 3 Setup
### Measurement Data  
Afterwards, the corrected voltage signal and the measured current signal are Fourier transformed to \(\underline{V}\) and \(\underline{I}\). As only one frequency is applied at a time, only this frequency has to be evaluated. To save computational effort, the Goertzel algorithm can therefore be used [34]. Finally, the impedance \(\underline{Z}\) of frequency \(k\) is calculated by  
\[\underline{Z}_{k}=\frac{V_{k}}{\underline{I}_{k}}. \tag{22}\]  
Several impedances for different frequencies are measured quickly one after the other and can be combined to a spectrum. Figure 16 shows some example measured spectra data. Between the measurements, the cell was discharged for 45 min, therefore the spectra are not exactly identical. It can already be seen here that especially the low-frequency components, which are found on the right-hand side, are influenced by the state of charge.  
Figure 15: Adjusted voltage signal of an impedance measurement after subtracting the linear DC voltage function.
Furthermore, the analysis methodology proposed in this paper can also be used to design the ERS-TQZS VI established by other types of QZS structures. The subsequent sections of this paper are structured as follows. In Section 2, the mathematical model of the ERS-TQZS VI and the analytical solution method are introduced. In Section 3, detailed numerical analysis of the ERS-TQZS VI and comparisons with TQZS VI and TL VI are presented. This includes the energy harvesting and low-frequency vibration control potential of the ERS-TQZS VI, the influence of the mechanical parameters, and the RLC circuit parameters. In Section 4, a concise discussion of the main results is provided. In Section 5, some conclusions are given.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Encryption Performance
#### 4.1.3 Correlation of Adjacent Pixels Analysis  
The degree of correlation between adjacent pixels in an image is represented by the correlation of adjacent pixels. A meaningful image has a high degree of correlation between adjacent pixels in all directions. When encrypting an image, breaking strong pixel correlation is an important part of the image encryption algorithm. An effective image encryption algorithm should attempt to minimize the correlation between adjacent pixels and try to achieve zero correlation. Usually, the encrypted image has a low correlation coefficient, so it looks like a noisy image. The correlation of adjacent pixels can be obtained with Equation (7).  
\[C=\frac{\sum_{i=1}^{N}\Big{(}x_{i}-\frac{1}{N}\sum_{i=1}^{N}x_{i}\Big{)}\Big{(} y_{i}-\frac{1}{N}\sum_{i=1}^{N}y_{i}\Big{)}}{\sqrt{\sum_{i=1}^{N}\Big{(}x_{i}- \frac{1}{N}\sum_{i=1}^{N}x_{i}\Big{)}^{2}\times\sum_{i=1}^{N}\Big{(}y_{i}-\frac {1}{N}\sum_{i=1}^{N}y_{i}\Big{)}^{2}}} \tag{7}\]  
From the image's grayscale pixel value matrix, \(N\) pairs of adjacent pixels are selected at random, where the pixel values of two adjacent pixels are represented by the parameters \(x_{i}\) and \(y_{i}\).  
In order to verify that the POB has the property of re-encryption, we analyze and compare the correlation of adjacent pixels between the original image and the image after re-encryption by the POB system. First, 6000 adjacent pixel pairs are randomly selected from the original image and the re-encrypted image in the horizontal, vertical, and diagonal directions; then, the correlation of these adjacent pixels is calculated, and simulation tests are carried out. Figure 9 shows the correlation of adjacent pixels between the four original ROS images and the re-encrypted images. It is obvious from the figure that the correlation coefficient of the original image is close to 1, indicating that there is a strong correlation between adjacent pixels, while after re-encryption, there is a lower correlation between adjacent pixels in the image.  
Figure 8: The gray histograms of the original and encrypted images. (**a**) Brain 01; (**b**) Brain 02; (**c**) Pulmonary 01; (**d**) Pulmonary 02.  
Figure 9: _Cont_.
# Shunt Damping Vibration Control Technology: A Review
## 7 Challenges in Shunt Damping Vibration Control
### Noise and Acoustic Control
[136] also used the negative impedance converter to realize the sound absorption of a finite micro-perforated panel. Because the influence of the noise and acoustic is up to several thousand hertz, the resonant shunt is limited for such a large range bandwidth. If a piezoelectric and an electromagnetic transducer are employed, the negative impedance shunt may provide a feasible method to reduce the transmission loss. For an electromagnetic transducer, the influence from the inductance increases dramatically when the frequency is up to kilohertz. In this case, the influence from the resistance is very small. Then the negative inductance shunt can be used to control high frequency noise. Therefore, the negative impedance and adaptive negative impedance shunts have great potential in noise and acoustic control engineering.
# Contrast-Enhanced Ultrasound Imaging Based on Bubble Region Detection
###### Abstract  
The study of ultrasound contrast agent imaging (USCAI) based on plane waves has recently attracted increasing attention. A series of USCAI techniques have been developed to improve the imaging quality. Most of the existing methods enhance the contrast-to-tissue ratio (CTR) using the time-frequency spectrum differences between the tissue and ultrasound contrast agent (UCA) region. In this paper, a new USCAI method based on bubble region detection was proposed, in which the frequency difference as well as the dissimilarity of tissue and UCA in the spatial domain was taken into account. A bubble wavelet based on the Doinikov model was firstly constructed. Bubble wavelet transformation (BWT) was then applied to strengthen the UCA region and weaken the tissue region. The bubble region was thereafter detected by using the combination of eigenvalue and eigenspace-based coherence factor (ESBCF). The phantom and rabbit in vivo experiment results suggested that our method was capable of suppressing the background interference and strengthening the information of UCA. For the phantom experiment, the imaging CTR was improved by 10.1 dB compared with plane wave imaging based on delay-and-sum (DAS) and by 4.2 dB over imaging based on BWT on average. Furthermore, for the rabbit kidney experiment, the corresponding improvements were 18.0 dB and 3.4 dB, respectively.  
Keywords:ultrasound contrast agent imaging; bubble wavelet transform; eigenspace; coherence factor +
Footnote †: journal: Applied Soft
## 2 Model and Formulations
### Vibration Transmittance of Finite Structures
The QZS resonators with different bandgaps are alternately periodically distributed on the baseplate, as shown in Figure 10. In order to suppress the high vibration response at the frequencies of 7 Hz and 21 Hz in Figure 8, we set the parameters \(\gamma_{1}=0.5\), \(\gamma_{2}=0.561\), and \(\gamma_{3}=0.576\) according to Equations (5) and (18). The other parameters are consistent with those of the calculation model corresponding to Figure 8.  
Figure 9: Schematic of the initial compression regulator of spring \(k_{n}\). The comparison of vibration transmittances with different resonator arrays attached to the plate is shown in Figure 11. In the figure, model-2 represents the calculation model corresponding to Figure 8 with damping loss factors \(\eta_{1}=\eta_{2}=0.1\), and model-3 is the system shown in Figure 10 with damping loss factors \(\eta_{1}=\eta_{2}=0.1\). For comparison purposes, a plate with the same mass load as the QZS LR plate, named model-1, was also constructed. As can be seen from Figure 11, compared with model-1, the \(TL\) of model-2 has two obvious bandgaps around 45 Hz and 200 Hz, while model-3 not only exhibits two vibration transmittance attenuation bands near the above two frequencies but also exhibits two additional vibration attenuation bands around 7 Hz and 21 Hz. The vibration transmittances at 7 Hz and 21 Hz are reduced by 32 dB and 20 dB, respectively. The main vibration modes below 70 Hz are effectively controlled by the differentiated design of the bandgaps without adding additional mass load, which proves that the QZS resonator has broadband ultralow-frequency vibration suppression performance.
## 5 Co-Digestion of Australian RMP Waste  
The Australian RMP industry is both waste- and energy-intensive and processors have an interest in reducing both of these factors. Anaerobic digestion of the waste produced in an RMP facility alone is insufficient to meet the energy demands of the facility. Co-digestion with residues from co-located industries may be a solution to both convert waste materials and improve the offset of energy demands from fossil fuels. [PERSON] et al. [54] determined the aggregate quantities of waste organics from the on-farm dairy and pork, dairy processing, beef feedlot and red meat processing industries and compared the biogas energy potential with the industry energy consumption. On-farm dairy and pork and beef feedlots produce large quantities of waste which carry a correspondingly large energy potential that is not required on-site. This represents an opportunity for co-located industries to develop synergistic partnerships in which waste can be contributed to anaerobic digestion systems at RMP facilities, resulting in waste disposal for one industry and energy production and offsets for the other. This would have the added benefit of reducing greenhouse emissions from the waste generated in these industries.  
Co-digestion carries additional benefits to RMP facilities. The C:N ratio of abattoir wastewaters are typically low due to the high nitrogen content. [PERSON] et al. [28] measured C:N at 2.7:1 which is considerably lower than the optimal of 40:1 and indicates that the reactor is at high risk of ammonia inhibition. The addition of feedlot manure, for example, with a C:N of 9-15 will improve the quality of the substrate for anaerobic digestion. There is also the potential to produce a substrate with a more balanced TE profile. [PERSON] et al. [28] demonstrated that RMP wastewater was considerably lacking in trace elements to sustain long-term digestion performance (Table 3). By comparison, many of these trace elements can be found in much higher concentrations in feedlot manure [55] and would contribute to improved digester performance and longevity.  
Co-substrate selection is likely to be determined based on the type of pre-existing technology available onsite, but co-digestion opportunities may warrant the installation of new and more appropriate technology which may enable treatment of currently under-utilised substrates. In the RMP sector, paunch (stomach contents) of slaughtered cattle consists primarily of grass and is commonly disposed of via three routes [56]: (1) removal of paunch and other solids from the site (2) material is composted on-site with use on-site and; (3) material is composted on-site and used off-site. Similarly, spent bedding from the pork industry is typically a straw or saw dust which is also commonly composted. These substrates typically have a high solids content and may perform well as co-substrates in a dry digestion technology such as continuous plug-flow digestion.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
###### Abstract  
In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices' transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05).  
game theory; constrained stochastic game; energy; energy harvesting; Internet of Things (IoT) +
Footnote †: journal: Sensors