esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Discussion  
Deep learning is essentially to build a probability distribution model driven by data. Therefore, as the deep-learning-network architecture becomes deeper, the quantity and quality of training data will have a greater impact on the performance of the model. However, in the imaging process of microorganism images, the amount of satisfactory data is relatively small due to some objective reasons, such as the impurities in the acquisition environment, uneven natural light and other adverse factors, which leads to insufficient training and poor performance in various tasks. Though the proposed PID-Net has excellent segmentation and counting performance for images with dense tiny objects, there still exists some mis-segmentation, causing the decrease of counting accuracy. Several incorrect segmentation results are shown in Figure 10.  
There are three main problems for segmentation and counting, which are illustrated in Figure 10. The blue circle refers to the situation of under segmentation--that is, the neighbor yeast cells cannot be segmented, and the edges cannot be detected. Due to the counting method is based on the eight neighborhood search algorithm, the situation leads to under estimation of the real count of yeast cells. The green circle refers to a part of background is classified as yeast cells. As shown in Figure 10, most of the images are full of dense tiny yeast cells with irregular shapes, and the limitation of small dataset leads to inadequate training.  
Therefore, the background between yeast cells with irregular shape is easily classified as a yeast cell, which results in over estimation of the real count of yeast cells. On the contrary, the red circle represents the part of yeast cell is classified as background. There are 1 to 256 yeast cells with different sizes in a single image in this dataset, and thus the shape and size of yeast cells have a great difference. Therefore, the tiny yeast cell between the larger cells has a great similarity with the background, which is difficult for models to discriminate especially in a small dataset. The situation leads to under estimation of the real count of yeast cells.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training Time** & **Mean Training Time** & **Test Time** & **Mean Test Time** \\ \hline PID-Net & 10,438.86 & 7.10 & 454.68 & 0.93 \\ U-Net & 6198.00 & 4.21 & 257.64 & 0.53 \\ Swin-UNet & 7884.36 & 5.36 & 319.50 & 0.65 \\ Att-UNet & 6983.58 & 4.75 & 296.64 & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The summary of computational time (in seconds).  
The situations of adherent yeast cells and mis-segmentation lead to counting error. Moreover, the training data is limited due to the small dataset; therefore, the models cannot be trained perfectly. The small dataset is a limitation of the yeast counting task. However, despite some cases of mis-segmentation, most of the yeast cells in the test dataset could be detected and segmented with other cells. The segmented region might be small but has little effect on the counting results calculated using the eight neighborhood search algorithm.
## 3 Supervised Machine Learning
### Support Vector Machines  
Another widely used condition monitoring set of ML algorithms are the support vector machines (SVM). This is a set of supervised models used for regression, novelty detection tasks, feature reduction, and SVM, which is preferable in classification objectives [63]. In linear classification, each datapoint is represented as a vector in \(n\)-dimensional space (\(n\)--the number of features). Each of these points belongs to only one of two classes. Figure 7 shows an example of data classification.  
In the picture, two data classes are represented: Class 1 (triangles) and Class 2 (squares). The aim is to separate these points by a hyperplane of dimension (\(n-1\)), ensuring a maximum gap between them. There are many possible hyperplanes. Maximizing the gap between classes contributes to a more confident classification and helps to find an optimal hyperplane. As shown in Figure 8, to detect the optimal hyperplane, it is essential to find support vectors that can be defined in as closer position to the hyperplane as possible.  
Figure 6: Decision tree diagram.  
Figure 7: Possibilities in the finding of the optimal hyperplane.  
In addition to linear classification, SVMs can deal with non-linear classification using the kernel trick, also known as the kernel machine. As shown in Figure 9, the processing algorithm is similar to the linear one, but the kernel function replaces the datapoints.  
SVM is a good solution when there is no initial information about the data. This method is highly preferred because of the little computation power needed to produce results with significant accuracy. Although kernel machine is a great advantage of SVM, its managing is a complicated task. Moreover, it can take a long time to make large amounts of data processed, so SVM is not preferable in large datasets.  
Supervised ML approaches are widely applicable for condition monitoring of electrical machines. Many relevant kinds of research can be found in the literature. The authors in [64] proposed a new signal processing method for fault diagnosis of low-speed machin  
Figure 8: Support vectors and optimal hyperplane in linear classification.  
Figure 9: Support vectors and optimal hyperplane in non-linear classification.  
ery based on DT approaches. In [65], the authors applied statistical process control and supervised ML techniques to diagnose wind turbine faults and predict maintenance needs. The researchers in [66] presented a semi-supervised ML method that uses the DT algorithm's co-training to handle unlabeled data and applied to fault classification in electric power systems. In [67], the authors proposed a RUL prediction method of lithium-ion batteries using particle filter and support vector regression.
## 3 Results
### Nitric Oxide (NO) Measurement  
A dose-response evaluation of each extract was performed in a macrophage cell line using the same range of concentrations used for 3T3 cells (Figure S2). Similar patterns were found in both cases: extracts with a higher concentration of ethanol in the extraction procedure (E1 and E2) showed higher cytotoxic effects when compared with the other four extracts. For the hydroethanolic extracts with 25% ethanol, the highest decrease in cell viability was reached for the concentration of 4 mg\(\cdot\)mL\({}^{-1}\) (\(p<0.0001\)) with less than 20% of cell viability. The same trend was found for aqueous extracts, but although the concentration of 4 mg\(\cdot\)mL\({}^{-1}\) was significantly different from the control, cell viability percentages for that concentration were still high (above 70%).  
For each extract, all the concentrations above 90% cell viability were used to analyze the nitric oxide production and consequently the anti-inflammatory potential (Figure 3).  
Figure 2: Photoprorection assay using the closest concentration to 100% of cell viability identified for each extract of _Grateloupia turuturu_. Cells were subjected to UV radiation in the presence and absence of extracts until IC\({}_{50}\) of control without extract was reached to evaluate the photoproduction potential. Control of cell viability is represented as a dashed line. A one-way ANOVA followed by [PERSON]’s multiple comparisons test was performed to evaluate the significant differences between the extracts and the control (\({}^{*}p<0.05\), \({}^{***}p<0.001\), \({}^{***}p<0.0001\)). Values presented are the mean of 3 independent assays.  
Comparing with the control (cells subjected only to LPS solution), two extracts showed significant differences: E3 at 0.25 mg\(\cdot\)mL\({}^{-1}\) (\(p\) < 0.05), reducing the NO production 20%, and E6 at 0.01 (\(p\) < 0.01) and 0\(\cdot\)25 mg\(\cdot\)mL\({}^{-1}\) (\(p\) < 0.0001), reducing 27% and 38.3%, respectively. However, higher concentrations increased the NO production on macrophages cells.
# Fault Detection of Resilient Navigation System Based on GNSS Pseudo-Range Measurement
## 2 Methods
### Factor Graph Model
#### 2.2.2 Factor Nodes  
Several factor nodes equations are presented in this section.  
1. Prior factor; The prior factor information represents a set of previous information, which is defined as \[f^{\text{Prior}}(v)=d(v)\] (1)  
Generally, in the navigation system, we assume that the prior factor obeys the Gaussian distribution; then we can construct the prior factor model \(f^{\text{Prior}}()\) through the mean \(\mu_{v}\) and variance \(\Sigma_{v}\).
2. IMU factor; The measurement of IMU \(\mathbf{z}_{i}^{\text{IMU}}=\left\{\boldsymbol{\psi},\boldsymbol{w}^{\text{ b}}\right\}\) is related to the time period between \(\boldsymbol{x}_{i}\) and \(\boldsymbol{x}_{i+1}\) according to the frequency. The navigation estimation of strapdown inertial navigation can be realized by different integral processing methods, which will not be described in detail in this paper. The quaternion method is used to estimate the carrier's position, speed and attitude. The factor node model of INS is represented by the following formula \[\boldsymbol{\hat{x}}_{i+1}=h(\boldsymbol{x}_{i},\mathbf{z}_{i}^{\text{IMU}})\] (2)  
In principle, the form of system state equation represents an IMU factor \(f^{\text{IMU}}\). The system transfer matrix and covariance can be obtained based on a nonlinear equation about navigation state of position, velocity and attitude [24]. The state \(\boldsymbol{x}_{i+1}\) is calculated from the state and the measurement at time \(i\). Through the pre-integration method, the continuous IMU measurements over a period of time can be combined into a single factor. The transition matrix is given by  
\[\left[\begin{array}{c}\boldsymbol{p}_{i+1}\\ \boldsymbol{v}_{i+1}\\ \boldsymbol{\varphi}_{i+1}\end{array}\right]=\left[\begin{array}{c} \boldsymbol{v}_{i}\cdot\Delta t+\frac{1}{2}(\Delta t)^{2}g+C_{b}^{n}\cdot \Delta\boldsymbol{p}+\boldsymbol{p}_{i}\\ g\cdot\Delta t+C_{b}^{n}\cdot\Delta\boldsymbol{v}+\boldsymbol{v}_{i}\\ C_{b}^{n}\cdot\Delta\boldsymbol{\varphi}+\boldsymbol{\varphi}_{i}\end{array}\right] \tag{3}\]  
Figure 2: Schematic diagram of factor graph navigation framework.  
where \(\Delta t=t_{i+1}-t_{i}\) is the vpdate period. \(g\) is the gravity vector. \(C_{b}^{n}\) is the attitude transformation matrix. \(\Delta p\), \(\Delta v\), \(\Delta\varphi\) are the increment of position, velocity and attitude, and they are obtained by \(z_{i}^{\text{IMU}}\). The covariance of transition matrix is derived by system noise.
3. Other sensor factors; The other navigation sensor factor node is given by \[z_{i}^{\text{sensor}}=h^{\text{sensor}}(\kappa_{i})+n^{\text{sensor}}\] (4) where \(z^{\text{sensor}}\) is the measurement of the navigation sensor, \(h^{\text{sensor}}\) is the measurement function, and \(n^{\text{sensor}}\) is the measurement noise, which is white noise in a Gaussian system. The measurement noise of GNSS \(n^{\text{GNSS}}=[n_{p}^{\text{GNSS}},n_{\triangledown}^{\text{GNSS}}]\) is position and velocity measurement noise.
## 3 Communication Protocols in IoMT
### Network Layer  
The majority of protocols at this layer are based on the IEEE 802.15 standard [17]. The network layer is in charge of sending and receiving medical data. As a result, this layer acts as the foundation for the healthcare platform's architecture. Network security is a huge problem in healthcare [18], since these network devices and communication lines/channels transmit sensitive data. At this layer, the most popular protocols for IoMT are WiFi and ZigBee. Bluetooth is also utilized; however, it is used less frequently since it cannot reach large areas such as hospitals. Technologies such as LoRaWAN and 6 LoWPAN (IPv6 over Low-power Wireless Personal Area Networks) related to Wireless Sensor Networks [19] can also be utilized in some cases. Conventional cellular communication technologies (such as 3G/4G/5G or GPRS-General Packet Radio Service) can be utilized for data transfer over a considerable range.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure
#### 3.2.2 Provision of Lightweight and Loosely Coupled Microservices  
Loosely coupled, lightweight services must be provided that can be reused for the construction of different applications. These microservices come in packages (e.g., virtual machines and containers) with all the system libraries and dependencies they need to run. They can be selected from public repositories, which facilitates the composition of services in a fast and flexible way. Many of the tools and technologies for containerization and virtualization provide public repositories. Examples of these are Vagrant Cloud [47], Image Server for LXC/LXD [48], Docker Hub [49], Singularity Hub [50].
# An Optimized Fractional-Order PID Horizontal Vibration Control Approach for a High-Speed Elevator
## 5 Conclusions  
To suppress elevator vibration caused by rail irregularities, a FOPID controller optimized based on MOGA is proposed for elevator car systems using ARG active guide shoes. The main conclusions are as follows:  
1. Considering elevators' vibration characteristics during actual operation, a dynamic model of the elevator with the car body and frame separated is established. White noise with a low-pass filter is used to simulate rail irregularities, and the simulation experiment is more accurate.
2. An optimization design model for elevator vibration suppression is established, and MOGA is used to optimize the FOPID controller to limit the control cost and ensure the feasibility of the designed controller.
3. From the results, the horizontal acceleration of the system has been reduced by 68% under optimized FOPID control compared to no control and reduced by about 25% compared to the optimized PID control, fully demonstrating the effectiveness of the designed controller.  
Conceptualization, R.T., C.Q., M.Z., S.X., J.T. and C.L.; Methodology, R.T., C.Q., S.X., J.T. and C.L.; Software, M.Z.; Validation, R.T. and M.Z.; Investigation, S.X.; Resources, M.Z. and J.T.; Data curation, J.T.; Writing--original draft, R.T. and C.Q.; Writing--review & editing, C.Q.; Project administration, C.L.; Funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Natural Science Foundation of China (Grant No. 51935007) and Shanghai Municipal Science and Technology Major Project (2021 SHZDZX0102).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **C2** & **PID** \\ \hline \(J_{1}\) & \(1.519\times 10^{4}\) & \(2.0229\times 10^{4}\) \\ \(J_{2}\) & 77.7123 & 80.2407 \\ Relative displacement of car & 0.0019 & 0.0022 \\ frame bottom (\(X_{d}\)) & 2.3772 \(\times 10^{-4}\) & \(3.2882\times 10^{-4}\) \\ Relative deviation (\(X_{c}\)) & 0.1240 & 0.1652 \\ Horizontal acceleration of the & 464.9079 & 489.1896 \\ car body (\(\tilde{x_{2}}\)) & 10.6733 & 126.8436 \\ Control force (\(F_{3}\)) & 431.7551 & 429.4039 \\ Control force (\(F_{4}\)) & 10.3735 & 40.8524 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of corresponding indicators between PID and FOPID(C2).
## 3 Effects of Freeze-Thaw Cycles on Degradation of Performance
### Dynamic Indirect Tensile Stiffness Modulus  
The relationship between dynamic indirect tensile stiffness modulus and freeze-thaw cycles was shown in Figure 9. Figure 9 indicates that the dynamic indirect tensile stiffness modulus of asphalt mixture was continuously reduced and the loss ratio caused by damage was continuously increased under freeze-thaw cycles. The stiffness modulus decreased rapidly in the early stage of freeze-thaw cycles, and the performance degradation rate slowed down after 10 freeze-thaw cycles. After 20 freeze-thaw cycles, the NBAM group was significantly better than AM group. The loss ratio of stiffness modulus for AM caused by damage under 20 freeze-thaw cycles is 53%, and for NBAM is 45%. The low-temperature crack resistance of AM group was significantly reduced under freeze-thaw cycles. The stiffness modulus of NBAM group was 20% higher than AM group. The stiffness modulus of NBAM group increased by 41% compared to AM group after 20 freeze-thaw cycles. The freeze-thaw resistance of NBAM group was obviously better than AM group. The freeze-thaw cycles reduced the low-temperature indirect tensile stiffness modulus of asphalt mixtures. The addition of nano-TiO\({}_{2}\)/CaCO\({}_{3}\) and basalt fiber reduced the sensitivity of the low-temperature crack resistance to freeze-thaw cycles.
## 2 Different Methods of Hydrogen Generation
### Latest Hydrogen Production Process Advances
#### 2.3.1 C-Zero Technology  
A new technology dubbed the C-Zero process offers a compromise option for countries that really heavily on exporting fossil fuels while also allowing them to adhere to green energy goals, particularly the creation of hydrogen without carbon dioxide. This technology (Figure 9) utilizes thermocatalysis to transform natural gas into turquoise hydrogen and solid carbon via methane pyrolysis and heat.  
\[\mathrm{CH_{4(g)}+heat\to 2H_{2}+C_{(s)}} \tag{6}\]  
This could be a very interesting technology for energy transition [44]. This technology focuses primarily on resolving the problem of CO2 emission, but the leaks of methane during the distribution process remain a problem that should be solved, because this release of methane into the atmosphere is 80 times more powerful in warming plants than CO2. According to the International Energy Agency, 1 ton of methane may be regarded as equal to 28 to 36 tons of CO2 in the atmosphere. There is a recent advanced technology that can detect leaks of methane, but we cannot completely remove them [45]. The disadvantage of this process is that it needs heat to drive its reaction. That heat can be produced in four ways, as follows. (1) Burn a little bit of natural gas to drive the reaction so that this process does not have zero emissions, but the CO2 released in this process would still be a 75% reduction compared to traditional steam methane. (2) Use about a third of the hydrogen  
Figure 8: Recent hydrogen production technologies [36; 37; 38; 39; 40; 41; 42; 43].  
Figure 7: Current and projected hydrogen production technologies [35].  
produced to drive this reaction. The goal of all recent technologies is to increase the yield of hydrogen production, and using it again is not a good idea. (3) Use electric heating by renewable energy, but the intermittence of this solution can provide a problem. (4) Its climate value depends on providing clean energy for pyrolysis and physical carbon storage, but using a hybrid system that mixes natural gas, hydrogen, and electrical solutions as a source of heat and works with them all at the same time for greater efficiency, especially if there is not much electrical energy available, can resolve this problem [46].  
Producing hydrogen from natural gas costs about $1.5/kg, while pure hydrogen costs about $5/kg. As a result, 1% of hydrogen in 2021 was produced through electrolysis [47]. With global economies aiming to become carbon neutral, green hydrogen has emerged as a promising alternative; however, production costs must be reduced to make it economically feasible for countries worldwide. The DOE announced the Hydrogen Shot initiative, which seeks to reduce the price of pure hydrogen to $1/kg in 2050 [48]. As such, we still need something new and innovative to properly produce it.
## 3 Architecture and Design Methodology of the LHM-N Model
### Design Methodology of the LHM-N Model
#### 3.2.2 Design Methodology
Since the fading channel \(h\) is a complex coefficient with real and imaginary parts, the conjugate of \(h\), that is \(h^{\star}\) together with a square of \(h\) modulus, \(|h|^{2}\), is applied to obtain the signal equalization \(z\), given as:  
\[z_{1}=h_{1}^{\star}y_{1}=|h_{1}|^{2}x+h_{1}^{\star}n_{1} \tag{6}\]  
\[z_{2}=h_{2}^{\star}y_{2}=|h_{2}|^{2}x+h_{2}^{\star}n_{2} \tag{7}\]  
Finally, the combined received signal \(y_{c}\) is given by:  
\[y_{c}=z_{1}+z_{2}/|h_{1}|^{2}+|h_{2}|^{2} \tag{8}\]  
Then the transmitted coded symbol is estimated from the combined received signal using the Viterbi decoding algorithm together with a soft detection metric, such as the maximum likelihood detector (MLD). The MLD is computed based on the squared free distance \((d_{free}^{2})\), which is the smallest Euclidian distance of the coded symbol sequence to the all-zero symbol sequence bits [45]. This process of estimation with detection will account for the sent TCM convolutional coded bits and constellation mapping of the 16-QAM. Since the transmitted coded signal is over a Rayleigh fading channel distribution, the error performance of the 16-QAM signal, that is, the symbol error rate of the QAM signal \((SER_{(QAM)})\) in the Rayleigh fading channel, is given by:  
\[Pr_{NPF}=1-Pr_{PF} \tag{2}\]0  
where \((\gamma)\) is the signal-to-noise ratio (SNR), \(\tilde{\gamma}\) is the average SNR, \(P_{SER_{QAM}}\) is the symbol error probability of the QAM signal, and \(f_{\gamma}(\gamma)\) is the probability density function (PDF) of \((\gamma)\). Hence, the SNR, \((\gamma)\) is given as  
\[Pr_{NPF}=1-Pr_{PF} \tag{2}\]1  
\[Pr_{NPF}=1-Pr_{PF} \tag{2}\]2  
where \(E_{S}\) is the symbol energy per bit, and \(N_{O}\) is the Gaussian noise coefficient. Then, considering the SNR combined signal \(\gamma_{c}\), the PDF of \(\gamma_{c}\) is given by  
\[Pr_{NPF}=1-Pr_{PF} \tag{2}\]3  
where \(N_{n}\) is the number of receiving antennas. Hence, the \(P_{SER_{QAM}}\) for the combined signal in a Rayleigh fading channel is given by \[P_{SER_{QAM}}=\int\limits_{0}^{\infty}\frac{a}{n}\left\{ \begin{aligned} &\frac{\exp\left(-b\frac{\gamma_{cs}}{2}\right)}{2}-\frac{a\exp \left(-b\gamma_{cs}\right)}{2}\\ &+(1-a)\sum\limits_{i=1}^{n-1}\exp\left(-b\frac{\gamma_{cs}}{s_{ i}}\right)\\ &+\sum\limits_{i=n}^{2n-1}\exp\left(-b\frac{\gamma_{cs}}{s_{i}} \right)\end{aligned} \right\} \tag{13}\]  
Applying trapezoidal simplification gives  
\[P_{SER_{QAM}}=\frac{a}{n}\left\{ \begin{aligned} &\left(\frac{1}{b\tilde{ \gamma}+2}\right)^{N_{n}}-\frac{a}{2}\left(\frac{1}{\tilde{\gamma}+1}\right)^ {N_{n}}\\ &+(1-a)\sum\limits_{i=1}^{n-1}\left(\frac{s_{i}}{b\tilde{\gamma}+s _{i}}\right)^{N_{n}}\\ &+\sum\limits_{i=n}^{2n-1}\left(\frac{s_{i}}{b\tilde{\gamma}+s_{ i}}\right)^{N_{n}}\end{aligned} \right\} \tag{14}\]  
where  
\[a=1-\frac{1}{\sqrt{M}},\;b=\frac{3}{M-1},\;s_{i}=\frac{2\sin(i\pi)}{4n},\]  
The next section provides the implementation in MATLAB.
## 3 Analysis of Results and Mechanisms
### Very Low-Frequency Wideband Characteristic Dispersion Relation Curve
It is noteworthy that the forbidden band of the second flexural BG ranges from 1116 Hz to 12,576 Hz, with a bandwidth of 11,460 Hz, which is of great practical significance for the expansion of engineering applications in the field of low-frequency vibration damping of metamaterials and other fields. And the transmission of the AREM is also smaller in the same finite period for the four structures, indicating that the AREM can realize an excellent vibration suppression effect in fewer periods. Figure 3: The dispersion relation curve of BG characteristics of WREM. The brown scatter represents the imaginary part of the reduced wave vector and the green and blue lines represent the real part of the reduced wave vector, where the green line indicates the flexural vibration mode, and the blue line represents the longitudinal vibration mode.