esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Organization of Cloud Patterns in a Catalog
### Existing Patterns for Cloud Offerings  
While cloud types and cloud service models describe the cloud environments, cloud offerings describe the behavior of services available in them. We differentiated these offerings into three subclasses as depicted in Figure 5.  
Figure 4: Icons of the Cloud Types and Cloud Service Model Patterns (adapted from [1]).  
Figure 5: Icons of Cloud Offerings (adapted from [1]).  
_Cloud compute offerings_ provide resources to handle the actual computation workload of applications. They are often hosted in an _elastic infrastructure_ that allows them to be flexibly scaled. Further, we differentiated between _low-available_ and _high-available compute nodes_ since this resource property significantly affects how applications using these resources have to be built.  
The second class of cloud offerings are _cloud storage offerings_ that can be used to store data in the cloud. We described _relational data stores_--a table-based store that ensures a defined data schema [28] as well as a certain data structure, _blob storage_--a data store for large unrelated files, _block storage_--a cloud storage service that can be used similar to physical hard drives; and _NoSQL storage_--another table-based data store relaxing data consistency and schema regulations. Consistency behavior of these storage services, which can be _strict_ or _eventual_, was also described in a pattern-form.  
The third cloud offering class consists of _cloud communication offerings_, which can be used to exchange data between distributed applications or their individual application components. We described messaging services as well as their behavior that has a significant impact on the application using messaging. We included existing patterns for _reliable messaging_[19], also called transactional messaging and _messaging systems_[19] not offering transactional behavior. Further, we included patterns for the delivery behavior, which may be either _exactly-once_[19] or _at-least-once_[19] referring to delivering a sent message once and only once or possibly multiple times respectively.
## 5 Simulation and Experiment Study
### Experiment Study  
Apart from the simulation study, the proposed PACFTB control strategy was also verified in hardware experiment platform based on dSPACE DS1104 control board, which is shown in Figure 10. As can be seen from Figure 10, the platform contained a LIM with encoder, a drive board, a dSPACE DS1104 control board and a PC. When the experiment platform began to operate, the Simulink model of the proposed controller was compiled and loaded to the control board which realized the function of calculation and generation of three-phase PWM signals. The deadband was set as 5 \(\upmu\)s and the sample period of speed and current control was 400 \(\upmu\)s and 200 \(\upmu\)s, respectively.  
The desired speed was set as 0.8 m/s and the control objective was to track the desired speed with quick response and little error. The parameters of the controller were set as: \(k_{1}=2000\), \(k_{2}=200\), \(k_{3}=700\), \(k_{d}=k_{q}=1\), \(\gamma_{1}=\gamma_{2}=\gamma_{3}=0.2\), \(\gamma_{4}=5\times 10^{6}\), \(m_{1}=m_{2}=m_{3}=m_{4}=0.001\).  
Based on the adjustment parameters, the control performance could be achieved, which is shown in Figures 11 and 12. It is shown in Figure 11 that the speed of PACFTB tracked the reference well with quick response and little tracking error. In contrast, the PI control was applied to the experiment as well for speed tracking of LIM. It can be seen that the speed under PI control reached the reference first after 0.1 s while the speed under PACFTB mae it before 0.1 s, which shower better dynamic performance of the proposed PACFTB controller. Furthermore, the chattering at the stable state of PI control was a little more than that of PACFTB controller, which demonstrated better static performance of the proposed PACFTB controller. Thus, the proposed PACFTB control strategy possessed sound static and dynamic performance according to the discussion of experimental results.  
Figure 10: Hardware experimental platform based on dSPACE.
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 5 Proposed Fault Diagnosis and Severity Analysis Method
### Fault Diagnosis Process  
Once the rolling bearing has failed, the vibration signal of the rolling bearing is decomposed and restructured by the WPT. The WPEE of each node is calculated to generate the fault feature vector of the vibration signal. The mRVM classifier is established by the feature vectors extracted by different fault samples. The fault feature vector is fed into the mRVM to identify the fault type. The flow chart of the proposed fault diagnosis method is shown in Figure 3. The process of the fault diagnosis method is divided into two parts: The training process and the testing process.  
In the training process, the task is to determine the threshold of the SampEn for fault detection and train the mRVM multiple classifier. The main steps of the training process are as follows:1. Collect sufficient vibration samples of the rolling bearing under different fault conditions and construct the vibration sample set;
2. For each vibration sample, calculate the SampEn value and determine the fault detection threshold;
3. In the vibration sample set, calculate the WPEEs of vibration samples under different fault conditions and generate the feature vector set under different fault conditions;
4. Establish the mRVM classifier by using the feature vector set.  
The main steps of the testing process are as follows:  
1. Acquire the vibration signal of the rolling bearing and partition it into non-overlapping segments with the setting length N;
2. Calculate the SampEn value of the vibration signal by the fast algorithm of the sample entropy according to Section 2.2;
3. Compare the SampEn value of vibration signal with the threshold value to determine whether the rolling bearing has faults or not. If the SampEn value is smaller than the threshold value, this result suggests that the rolling bearing is running at normal conditions; jump to step (1) to continue collecting the vibration signals of the rolling bearing. In contrast, if the SampEn value is larger than the threshold value, then proceed to step (4);
4. Decompose the vibration signal by the WPT, calculate the WPEE of the reconstructed signal of each node in the last level, and generate the fault feature vector;
5. Input the fault feature vector into the trained mRVM to identify the fault type;
6. Output the fault diagnosis result.  
Figure 3: Flow chart of the proposed fault diagnosis method.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 4 Performance Analysis  
In this section, we analyze the exact BER expression for both the conventional NOMA and the proposed NOMA. In this paper, it is assumed that the data for user 1 and user 2 are modulated by QPSK. Each user's symbol is detected as one of \(\{00,01,10,11\}\), assuming that the priori probabilities of each symbol are equal, and each symbol is modulated according to QPSK as \(\{\sqrt{1/2}+j\sqrt{1/2},-\sqrt{1/2}+j\sqrt{1/2},\sqrt{1/2}-j\sqrt{1/2},- \sqrt{1/2}-j\sqrt{1/2}\}\).
## 5 Results  
### Application of Particle Filtering  
Changes in CPCI can be predicted by updating the time-series regression analysis model using the particle filtering technique. The measured data collected by section and age are used to update the prediction model used in this study. Figure 4 shows the prediction curves for each age across 208 sections (2.08 km) with 7 points and 261 sections (2.61 km) with 5 points, where CPCI prediction ranges at the time of completion were assumed for the three cases of CPCI (4.2, 4.4, and 4.6) as follows: 4.2-5.0 for 4.2, 4.4-5.0 for 4.4, and 4.6-5.0 for 4.6, where, to clarify the meaning of the prediction curve of CPCI, the baseline for maintenance application was indicated as well (4.0 for preventive maintenance, 3.25 for  
Figure 3: Basic prediction curve by condition history for concrete pavement. (**a**) CPCI prediction, basic model (using 7 points); (**b**) CPCI prediction, basic model (using 5 points).  
reactive maintenance, and 2.5 for reconstruction). As a result of the analysis, the narrower the setting range for the initial CPCI prediction value (the uncertainty section), the higher the prediction accuracy for the predicted points (30 years old and 27 years old) for both 7-point and 5-point sections.  
Figure 4: Comparison of detected data and particle filtering prediction. (**a**) Initial CPCI 4.2 (prediction curve using 7 points); (**b**) initial CPCI 4.2 (prediction curve using 5 points); (**c**) initial CPCI 4.4 (prediction curve using 7 points); (**d**) initial CPCI 4.4 (prediction curve using 5 points); (**e**) initial CPCI 4.6 (prediction curve using 7 points); (**f**) initial CPCI 4.6 (prediction curve using 5 points).  
Table 2 presents the prediction accuracy for the six cases shown in Figure 4. Prediction accuracy can be expressed along with the concept of relative error. Please refer to Equation (8). \(\varepsilon_{R.E}\) denotes the relative error rate between the actual data and the predicted data, and \(CPCI_{predicted}\) and \(CPCI_{detected}\) denote CPCI predicted through particle filtering and actual CPCI, respectively.  
\[\begin{array}{cc}\text{{Prediction accuracy}}\left(\%\right)&=1- \varepsilon_{R.E}=1-\frac{\left|{CPCI_{predicted}-CPCI_{detected}}\right|}{CPCI_{detected}}\end{array} \tag{9}\]
# Hydrogen Production from Offshore Wind Parks: Current Situation and Future Perspectives
## 2 System Components
### Electrolyzer Technologies
#### 2.2.2 Proton Exchange Membrane Electrolyzers  
PEMELs are more recent than AELs and come with several advantages, such as much faster start-up times, higher current densities which lead to smaller electrolyzer footprints, higher hydrogen purity (\(>\)99.8%), operation beyond nominal power, and higher output pressure [11; 12; 13].  
The report in [11] offers a comparison between the main features of PEMEL and AEL in 2017 and projected to 2025. With regard to the AEL (PEMEL figures in brackets) efficiency, an increase from the current 65% (57%) to 68% (64%) in 2025 is expected. At the same time, a decrease in the AEL (PEMEL) CAPEX from the current 750 \(\epsilon\)/kW (1200 \(\epsilon\)/kW) to 480 \(\epsilon\)/kW (700 \(\epsilon\)/kW) is foreseen by 2025. As far as the electrical consumption is concerned, some improvements are also to be expected. Currently, an AEL (PEMEL) consumes about 51 kWh (58 kWh) of electricity to produce 1 kg of hydrogen. The figure is foreseen to drop to 49 kWh/kg (52 kWh/kg) by 2025.  
When combined with a renewable power source, the ability to easily adjust the power to suit the conditions and a quick start-up time are two great features that allow this technology to extract the most out of intermittent power sources. During periods of shutdown, low amounts of energy are required to maintain system operation [11; 12]; this is an important fact to consider if the electrolyzer is to be kept offshore or if it will not be grid-connected. Furthermore, a backup power source must be provided if coupled with a renewable power source, since the intermittent nature of this type of power does not guarantee the necessary energy during shutdown periods.  
Despite PEMELs having made significant progress in recent years in efficiency, output pressure, ramp up and ramp down times, and CAPEX, they are still considerably more expensive than AEL and do not have the same longevity [12]. The main reason for the highprice is the significant amount of platinum needed to build the stack of the electrolyzer. The efficiency curve in Figure 1 is a typical efficiency curve of a generic electrolyzer. It is based on the average of efficiency curves of several commercial electrolyzers as taken from [11].
## 4 Overview and Development of Deep Learning Framework
### Deep Learning Framework Overview
#### 4.1.3 TensorFlow-TensorRT and TensorRT  
TRT is a deep learning framework optimization engine that can help improve deep learning services by optimizing learned deep learning models and improving inference speeds in NVIDIA GPUs by tens of times. Models generated through deep learning frameworks such as Caffe, PyTorch, and TensorFlow are optimized through TRT and re-recognized on NVIDIA GPU platforms (TESLA T4, JETSON TX2, TESLA V100, etc.). TRT supports models learned in most deep learning frameworks and supports optimal deep learning model acceleration. Because it supports C++ and Python at the API level, it can be easily used by developers in the deep learning field without much knowledge of CUDA or GPU programming. In addition, it builds a runtime binary to automatically use the optimal computational resources supported by GPUs, which makes it easy to improve latency and throughput, enabling the efficient execution of deep learning applications and services [30].  
The fusion of TensorFlow and TRT optimizes and executes compatible subgraphs, creating a model for TensorFlow to execute the rest of the graphs. In other words, TensorFlow's extensive and flexible functions can continue to be used, and TRT parses the model and applies optimization to the graph portion as much as possible. The processing process of TensorFlow and TF-TRT is shown in Figure 6.  
Figure 6 shows a comparison of the inference workflow of the basic TensorFlow and TRT. TensorFlow workflow typically loads the saved model and executes inference using TensorFlow runtime. For TF-TRT, TRT optimization is applied to the model's TRT support subgraph. Optionally, several additional steps are included, including pre-building the TRT engine. The precision mode is used to represent the mixing precision (e.g., float32,  
Figure 5: TensorFlow-Lite architecture and processing process.  
Figure 6: The workflow comparison processing process of TensorFlow and TensorFlow-TensorRT.  
float16) that can be used to implement TF-TRT operations. Then, in the saved model, a convert object is created to obtain transformation parameters and inputs. In TensorFlow 2.0, TF-TRT only supports models stored in the TensorFlow SavedModel format. Next, when the converter convert() method is called, TF-TRT converts the graph by replacing the TRT-compatible part of the graph with TRT EngineOps. The TRT execution engine must be built on a GPU of the same device type as the device type on which the inference will be run, because the build process is GPU-specific. For example, if you generate TF-TRT based on the T194 GPU used by Jetson AGX Xavier, it will not work with T4 GPU of other Jetson equipment [31].
# Bell-Clauser-Horne-Shimony-Holt Behavior Under Quantum Loss and Decoherence
## 4 CHSH Experiment with Quantum Imperfections  
Here, we come to the main part of our investigation. Consider the aforementioned CHSH setup in which [PERSON] and [PERSON] choose between two measurements each, and they perform these measurements on a pair of quantum bits. Instead of having a perfect entangled resource, we assume that each of the qubits goes through a quantum channel, which can model, e.g., the noisy transmission of the quantum bit from the source of entangled quantum bits to the parties.  
We assume the channel acting on [PERSON]'s qubit and the channel acting on [PERSON]'s qubit to be independent. That is, we neglect potential correlated imperfections at the locus of the generation of the qubit pair. It is plausible to assume that the loss and imperfections during the travel of each qubit to the given party, or its storage at the parties, can be described with a pair of independent channels.  
Independent channel pairs can be described with the direct product of their Choi matrices, as described in [27] in more detail. Thus, the scenario we consider is the following. We assume that the source generates the entangled state \(\Phi^{(+)}\) of Equation (10). Then, the qubits are passed to [PERSON] and [PERSON], going through the independent quantum channels \(\mathcal{E}_{\text{[PERSON]}}\) and \(\mathcal{E}_{\text{[PERSON]}}\). Then, [PERSON] and [PERSON] carry out the measurements like in the original CHSH scenario. The respective behavior is calculated as in the ideal case using Equation (14), but instead of \(\varrho^{(|\Phi^{(+)}\rangle)}\), we use the density matrix  
\[\varrho^{\text{ent}}=(\mathcal{E}_{\text{[PERSON]}}\otimes\mathcal{E}_{\text{[PERSON] }})\Big{(}\varrho^{(|\Phi^{(+)}\rangle)}\Big{)}, \tag{24}\]  
where the action of \(\mathcal{E}_{\text{[PERSON]}}\otimes\mathcal{E}_{\text{[PERSON]}}\) is calculated using Equation (18), using the product of the respective qubit Choi matrices.  
Let us now perform the calculations for the selected particular channel combinations. For the rest of the paper, for the sake of notational convenience, we introduce the following notations:  
\[x=\sqrt{(1-p_{1})(1-p_{2})}, \tag{25}\] \[c=\cos(\pi/8)=\sqrt{2+\sqrt{2}}/2,\] (26) \[s=\sin(\pi/8)=\sqrt{2-\sqrt{2}}/2,\] (27) \[cs=\sqrt{2}/4, \tag{28}\]  
thus, \(c^{2}-s^{2}=2 cs\). In the case of \(x\), the parameters \(p_{1}\) and \(p_{2}\) stand for the parameter of the channel on [PERSON]'s and [PERSON]'s sides, respectively, regardless of whether the channel is amplitude- or phase-damping. As we will be in the need of them later, we will specify the right eigenvalues of the channel matrices \(T^{(XY\to RS)}\), too. It is important to note that all the \(T^{(XY\to RS)}\) matrices describe a no-signaling behavior: they obey the no-signaling conditions in Equation (7) with the respective inputs and outputs.
## 2 Experimental Program
### Testing Procedures  
The workability of the fresh FS-GPC mixes was measured by the slump cone test as per ASTM C143/C143M-12. The mechanical properties of the mixes were evaluated by compressive, split tensile, and flexure strengths, and static modulus tests. The compressive strength test was performed on 150 \(\times\) 150 \(\times\) 150 mm cubes following BS EN 12390-3:2009 at the age of 3, 7, 14, 28, 56, and 90 days as shown in Figure 5. The split tensile strength tests were performed on 150 \(\times\) 300 mm cylinders according to ASTM C496/C496M-11 at the age of 28 days as shown in Figure 6. The flexural strength test was performed on 100 \(\times\) 100 \(\times\) 400 mm prisms after 28 days of curing following the ASTM C78/C78M-16 as shown in Figure 7. The static modulus of FS-GPC mixes was determined by conducting the tests on 150 \(\times\) 300 mm cylindrical specimens according to ASTM C46
## 7 Results and Discussion
### Fracture Locus  
Four parameters of the MMC criterion (\(K\), \(n\), \(C_{1}\), \(C_{2}\)) in Equation (9) were calibrated to generate a 3D fracture locus. Matlab2021-a was used for calibration. \(K\) and \(n\) were calibrated by fitting the data measured in the uniaxial tensile test to the curve generated from the power law. \(C_{1}\) and \(C_{2}\) were calibrated using least-squares optimization. These approaches minimize the cumulative error between the constructed 3D fracture locus and the measured data. The calibrated values are listed in Table 4. The optimized mean squared error (\(MSE\)) was 0.04476.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(\overline{\varepsilon}_{f}^{FEA}-\overline{ \varepsilon}_{f}\right)^{2} \tag{11}\]  
In Equation (11), \(\overline{\varepsilon}_{f}^{FEA}\) is the equivalent strain at fracture computed from the FE analysis. \(\overline{\varepsilon}_{f}\) is the equivalent strain at the fracture obtained from the 3D fracture locus. '\(n\)' denotes the number of specimens used for uniaxial tensile and fracture tests. The 3D fracture surface constructed in this manner is shown in Figure 8a.  
For comparative purposes, a 2D fracture using the Johnson-Cook (J-C) fracture criterion [9] was also constructed. The J-C fracture criterion is expressed as follows:  
\[\overline{\varepsilon}_{f}(\eta)=d_{1}+d_{2}exp(-d_{3}*\eta) \tag{12}\]  
The parameters (\(d_{1}\), \(d_{2}\), and \(d_{3}\)) were calibrated in the manner mentioned above. The calibrated values are listed in Table 5. The optimized mean squared error (MSE) was only 0.02494. Figure 8b shows the J-C fracture locus (2D). As shown, the J-C fracture locus is applicable when predicting the fracture of materials at high and very high stress triaxialities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(K\) (MPa) & \(n\) & \(C_{1}\) & \(C_{2}\) \\ \hline
976.2 & 0.1299 & 0.1226 & 558.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters used in the MMC fracture locus (3D).  
Figure 8: Fracture surface (locus) of 3.0 wt% silicon steel (**a**) 3D; (**b**) 2D.
## 2 Materials and Methods  
Three kinds of powders were used to prepare a magnetic sheet. Fe-6.5 wt.% Si powder (average particle size = 40 \(\upmu\)m), manufactured by Atmix Inc., and fine iron powder (average particle size = 3 \(\upmu\)m) were used as raw materials, and Fe nanopowders (average particle size = 100 nm) were used to effectively fill the void space between the coarse Fe-6.5 wt.% Si powder and fine iron powder. Nanopowders were surface-treated by using an epoxy-based silane coupling agent from Shinetsu Inc(Tokyo, Japan). To achieve uniform surface treatment, a silane solution using ethanol and distilled water was prepared (Figure 1). The prepared silane solution was added to the nanopowder for a mixture of 1% silane solution, which was then mixed with a pre-mixer for 1 h. The surface-treated nanopowders were subjected to dehydration condensation at 3 \({}^{\circ}\)C for 24 h, thereby preparing surface-treated nanopowders for producing a magnetic sheet [20].  
After mixing each powder at a constant mixing ratio, a magnetic slurry was prepared by using an epoxy binder and a dispersant. The prepared slurry was dispersed and mixed at a frequency of 64 Hz for 3 min, using a resonance acoustic mixer. The magnetic slurry produced was made of a magnetic sheet, using a tape casting process (Figure 2).  
The microstructures of the magnetic sheets were analyzed by scanning electron microscope (SEM, JSM-7100F, JEOL Ltd., Akishima City, Japan). The surface characteristics of the magnetic sheets were analyzed by using a surface profiler (Dektak 150, Veeco Tucsom Inc., Oyster Bay, NY, USA). The density and volume fraction of the magnetic sheets were evaluated by electric balance (OHAUSS Adventure, Mettler Toledo Inc., Columbus, OH, USA), using the Archimedes method. Transmission electron microscope images were taken on a JEOL 4010 (TEM, JEOL Ltd., Akishima City, Japan). Permeability of toroidal was evaluated by LCR meter (Agilent E4980A, Agilent Inc., Santa Clara, CA, USA).  
Figure 1: Schematic diagram of the process of bonding a silane coupling agent to a nanopowder surface.