esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Modelling Rationale
### Modelling Assumptions
#### 5.3.4 Traffic Scheduling Assumptions  
Since we want to quantify the reliability of TSN and PTRF networks in general, we also want to abstract most details related to the traffic scheduling. Nonetheless, certain aspects related to the traffic are relevant for the design and evaluation of the models. The most important aspect is that we only consider time-triggered periodic messages since in many applications this type of traffic is usually subject to the most stringent reliability requirements. Thus, all the traffic in our model is transmitted periodically and the period is defined in a per-stream manner.  
Another important aspect related to the transmission of frames is the passage of time. In Section 3 we propose to use the cyclic queuing and forwarding standard [23] to schedule frames. As we explain in Section 3, CQF schedules frames so that they traverse one and only one hop in each TAS window. Therefore, we have decided to use the TAS window duration as the main time reference for the models. Since the duration of the TAS window in a device depends on the traffic that traverses it, in our model each stream can have a different TAS window duration. Nonetheless, to reduce the complexity of the models we have decided that the TAS window duration must be the same in all the paths of a stream.  
Finally, we make two more assumptions related to the traffic. We assume that all the messages fit within one frame, which is a reasonable assumption for most time-triggered traffic with high reliability requirements. Second, the period must always be higher than the end-to-end delay of a frame. All these assumptions prevent us from modelling networks with certain characteristics regarding traffic scheduling. Nonetheless, since we want to measure the reliability of TSN and PTRF networks from a general point of view, these limitations do not reduce the relevance of the results of our comparison. All of these assumptions allow us to model TSN and PTRF approaches A and B using Discrete-Time Markov Chain (DTMC), where the passage of time is associated with the TAS window.
# A Simulation Study on Pressure Control in Oil Well Drilling Using Gain-Scheduled PID Controllers
## 4 Results
### GS Controller and MRAC: Implementation  
This section is a comparison of the results obtained from the simulation of two adaptive controllers, using Gain Scheduling (GS) and the reference model (MRAC), both implemented considering even the delay time in the modeling of the process and tested at different operating points (or depths).  
The structure of the GS controller, according to Figure 6, was implemented using the tools of the Simulink application, through the Lookup Table command, where we inserted the values of two proportional integrative and derivative hooks for different operating points, obtained according to Table 4. This command identified the system and made the values of the PID controller parameters modified and thus compensated for the change in the process, such as changes in the operating points. For this reason, it was important to fine-tune two parameters of the PID controller.  
Also, for comparison and analysis purposes, the reference model adaptive controller (MRAC) based on the [PERSON] theory was used [36]. Also, due to the presence of the delay time in the system, a Smith predictor was added so that the output signal of the system was divided into the drilling system signal without the delay time and a signal with the delay time. Afterwards, we connected these two signals and fed them back to the controller.  
The structure of the MRAC adaptive controller, according to [PERSON], is presented in Figure 7, and the values of the controller parameters (\(\gamma\)) and the parameters of the reference model, obtained from [16], were implemented in the Simulink platform.  
As already presented, in Table 4, the \(k_{p}\), \(t_{i}\), and \(t_{\text{d}}\) drills are shown for different drilling depths (500, 1000, 2000, 3000, 4000, and 5000 m). The GS controller methodology consisted of specifying all previously obtained data and storing this information in a table and using it to compensate for the instabilities of the system response. The control BMI with a filter (BMI+2 GL) developed by [31] was chosen for the start of this table and used in simulations of different depths. Next, we compared the results of this simulation with the results obtained with the adaptive controller developed by [37].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Depth [m]**} & \multicolumn{3}{c}{**Controller Parameters**} \\ \cline{2-4}  & \(k_{p}\) & \(t_{i}\) & \(t_{d}\) \\ \hline
500 & 0.0010 & 11.363 & 0.0238 \\
1000 & 0.0021 & 11.3617 & 0.0254 \\
2000 & 0.0041 & 11.361 & 0.0263 \\
3000 & 0.0061 & 11.3605 & 0.0268 \\
4000 & 0.008 & 11.36 & 0.0272 \\
5000 & 0.0099 & 11.36 & 0.0276 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gains specified for the GS controller.  
Figure 6: Structure of GS controller in Simulink language.  
Figure 7: Structure of MRAC in Simulink language.
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
## 3 Hardware Design
### Programmable Gain Amplification Module  
The programmable gain amplification module is a key component of echo reception pre-processing because it can efficiently adjust the amplitude of the echo signal to an appropriate range while improving the SNR. Figure 5 shows that our module comprises four submodules--pre-differential amplification, continuous gain amplification, two-stage amplification, and post-differential amplification.  
Figure 4: The calculated parameters of the band-pass filter circuit.  
Figure 3: Structure diagram of the analog band-pass filter circuit.  
In our design, we selected an instrument amplifier in the pre-differential amplification submodule to amplify the differential signal and subsequently convert it into a single-ended one. The two-stage amplification submodule can amplify the signal by 0 or 30 dB under the control of an analog switch, which is used to expand the gain range of the programmable gain amplification module. The on-off of the analogue switch is controlled by CPLD. The post-differential amplification submodule converts the single-end signal into a differential one, which is then used for transmission, achieving high-performance ADC.  
The continuous gain amplification submodule has AD603 as its core, and the gain is precisely controlled by a voltage, which can continuously change within a certain range. The control voltage is obtained after the front-end controller CPLD performs digital-to-analog conversion, operation, and filtering on the decoding result of the FPGA command. The operation circuit converts the 0-2.5 V voltage output from the DA conversion to the allowable input range (\(-\)500-500 mV) of the AD603 control voltage. The filter circuit uses a second-order active low-pass filter with a cut-off frequency of 112 kHz to filter out high-frequency noise.
## 2 AnFIS-PID
### Implementation
#### 2.2.1 Input Layer  
In this paper, the inputs to the ANFIS-PID controller are the error (\(E\)) between the target temperature and the actual temperature, and the amount of change between two adjacent errors (\(EC\)). In order to optimise the problem of reduced control accuracy due to the inability to perform decimal operations when using FPGAs, the input values \(E\) and \(EC\) are expanded by a factor of 10 for the overall calculation and restored at the final result.
# Dilatant Nature of Sand Shear Strength
## 3 Shear Strength of Sand for TXC, BXC, and TXE
### Drained Triaxial Extension  
For the drained triaxial extension (TXE), [PERSON]'s equation (Equation (2)) is correct for \(\phi^{\prime}_{\mu}<\phi^{\prime}_{f}<\phi^{\prime}_{cv}\)[21]. As in TXC, by comparing the FSC and the Rowe's stress ratios at the failure states, we can write:  
\[\textit{sin}\phi^{\prime}_{f}=\frac{3-\left(\delta\varepsilon_{v}/\delta \varepsilon_{a}\right)_{e}}{3-\textit{sin}\phi^{\prime}\left(\delta\varepsilon _{v}/\delta\varepsilon_{a}\right)_{e}}\textit{sin}\phi^{\prime}_{cv} \tag{30}\]  
The sand strength and dilatancy were not analyzed by [PERSON] [7] for TXE.  
On the basis of the analysis of some of the results of the experimental TXC and TXE tests presented in the literature [8; 57; 58; 59; 60; 61] (Figure 9), it is proposed that, for the same initial density and stress level at failure:  
\[\left(\delta\varepsilon_{v}/\delta\varepsilon_{a}\right)_{c}=-2(\delta \varepsilon_{v}/\delta\varepsilon_{a})_{e} \tag{31}\]For TXE, the friction angle can be calculated from the FSC equation (Equation (16)) by using Equations (13) and (15) and the values \(D_{e}\), \(A_{e}^{o}\), \(\theta_{e}\), \(\theta_{e}\), \(\alpha_{f}\), and \(\beta_{f}\) given in Table 1. The experimental and theoretical friction angles (\(\psi_{e}^{\prime}\)) for some TXE tests are shown in Figure 10.  
Most of the values of \(\psi_{e,exp}^{\prime}-\psi_{e,theor}^{\prime}\) are in the range of \(\pm 2^{\circ}\). By using the FSC and the proposed relationship (Equation (31)), the difference between the \(\psi_{e}^{\prime}\) and the \(\psi_{e}^{\prime}\) can easily be calculated (Figure 11).  
The difference \((\psi_{e}^{\prime}-\psi_{c}^{\prime})\) is only slightly influenced by the critical state angle. For \(\left(\delta\varepsilon_{v}/\delta\varepsilon_{a}\right)_{c}=-2\left(\delta \varepsilon_{v}/\delta\varepsilon_{a}\right)_{e}>-0.6\), the \(\psi_{e}^{\prime}\) value is slightly smaller than the \(\psi_{c}^{\prime}\). For \(\left(\delta\varepsilon_{v}/\delta\varepsilon_{a}\right)_{c}=-2\left(\delta \varepsilon_{v}/\delta\varepsilon_{a}\right)_{e}<-0.6\), the \(\psi_{e}^{\prime}\) is higher than the \(\psi_{c}^{\prime}\). The difference between the \(\psi_{e}^{\prime}\) and the \(\psi_{c}^{\prime}\) increases with a decrease in the strain increment ratio, and for \(\left(\delta\varepsilon_{v}/\delta\varepsilon_{a}\right)_{c}=-2\left(\delta \varepsilon_{v}/\delta\varepsilon_{a}\right)_{e}\), it reaches about \(5.5^{\circ}\). It is generally accepted that the friction angle for TXE is slightly higher than that for TXC [15; 51; 62; 63].  
Figure 9: Relationship between the strain increment ratio at failure for triaxial compression and triaxial extension.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
###### Abstract  
Space-based radar sensors have transformed Earth observation since their first use by Seasat in 1978. Radar instruments are less affected by daylight or weather conditions than optical counterparts, suitable for continually monitoring the global biosphere. The current trends in synthetic aperture radar (SAR) platform design are distinct from traditional approaches in that miniaturized satellites carrying SAR are launched in multiples to form a SAR constellation. A systems engineering perspective is presented in this paper to track the transitioning of space-based SAR platforms from large satellites to small satellites. Technological advances therein are analyzed in terms of subsystem components, standalone satellites, and satellite constellations. The availability of commercial satellite constellations, ground stations, and launch services together enable real-time SAR observations with unprecedented details, which will help reveal the global biomass and their changes owing to anthropogenic drivers. The possible roles of small satellites in global biospheric monitoring and the subsequent research areas are also discussed.  
synthetic aperture radar; small satellite; biospheric monitoring; satellite constellation 26 June 2020; 2 August 2020; Published: 7 August 2020
# LSH Models in Federated Recommendation
## 2 Related Works  
### Neural Cooperative Filtering and Differential Privacy  
Deep neural networks are widely used to represent the relationship between the mapping functions and the hidden factor of users and items. Three models named Generalized Matrix Factorization (GMF), Multilayer Perceptron (MLP), and Neural Matrix Factorization (NeuMF) are proposed, in which, GMF can learn the linear characteristics in the recommendation model, while MLP excels at getting more detailed and nonlinear characteristics. Neural network-based collaborative filtering (NCF) utilizes implicit feedback, meaning the historical interaction of users with the items. This approach avoids revealing user rating, but the historical interaction records of users are necessary during training. The deep interest network (DIN) [5] is evolved by importing the attention layer between the embedded layer and the perceptron layer. The deep interest evolution network (DIEN) [6] incorporates sequential models to simulate the evolution of users' preferences. In contrast, capsule networks are explored to extract users' diverse interests, and label-based attention mechanisms are also proposed in multi-interest network with dynamic routing (MIND) [7].  
The parameters of the federated recommender system models are encrypted by privacy protection methods during the gradient updates [10]. It is unnecessary to exchange local parameters of different recommendation services respectively and to train the global model directly in plain text, which effectively reduces the privacy risks during the aggregation process. During the aggregation of federated learning, users' information can also be restored based on model parameters or gradient information. [PERSON] [10] and [PERSON] proposed that simple federated learning without privacy-preserving mechanisms still suffers from gradient information attacks, which can leak private information. To avoid this kind of attack, differential privacy (DP) is combined into the model via adding noise [11]. The DPGAN framework proposed by [PERSON] [12] makes use of DP to invalidate GAN-based attacks when inferring the training data of users in deep learning networks. [PERSON] [13] proposed improved collaborative filtering algorithms applying DP and obtained a blinded item-item covariance matrix by adding Laplace noise to count and sum up the user rating data. And K-Nearest Neighbor (KNN) and Singular Value Decomposition (SVD) algorithms have been implemented based on the above scheme. The DPFedAvgGAN [14] framework also has been proposed in federated learning. The input layer data of the model are divided into two parts, the matrix factorization (MF) and the multi-layer perceptron (MLP). In the MF part, the input users' data and items' data are mapped into the user hidden vectors and item hidden vectors. In the MLP part, the input user data and item data also do the same mapping. The MF part and the MLP parts share the same embedding layer and learn the embedding layers separately with two different models providing better flexibility.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### AIDA Method
#### 4.4.1 Step 1--MST Computation  
In this step, a single minimum spanning tree (MST) based on SMs geographic coordinates is computed by an Euclidean minimum spanning tree (EMST) dual-tree Boruvka algorithm [40]. The MST computation considers all the unconnected smart meters of the iteration.  
The goal of using an MST formed by SM coordinates (SM-based MST) is to obtain the shortest path between a meter and its nearest neighbors. We can identify feasible communication paths between these elements from the virtual connections obtained by the MST. Doing so minimizes the number of analyses between each meter and the meters around it, as it limits the analysis to the smart meters connected by the MST edges. This strategy is useful for applying the method in large-scale scenarios.
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 2 Literature Review
A KPI for the power increase using the ANN models was compared against the KPI of ISO 19030, revealing superior prediction accuracy. On the other hand, the monitoring of fouling status employing DDMs was explored in [22]. Specifically, this work employed ML algorithms to predict propulsion power and quantify the performance degradation due to fouling using high-frequency data from a containership spanning 12 months. The Days Since Cleaning (DSC) feature was introduced to monitor fouling. To determine the best performing model, four ML algorithms were evaluated: K-nearest neighbors, decision tree, random forest, and ANN. The hyperparameters of each model were tuned using grid search and cross validation. The best performance was achieved by random forest, with a mean absolute percentage error (MAPE) of 1.17% on the test set. The gain in prediction accuracy was examined when DSC and significant wave height features were included. In [17], the number of days passed since the last hull maintenance was incorporated as a feature to account for the time-dependent nature of biofouling growth and its effect on vessel performance. Furthermore, [23] examined the performance degradation expressed by speed loss using a two-stage optimization algorithm that combined a genetic algorithm (GA) and long short-term memory (LSTM) network. Specifically, the GA algorithm was used simultaneously for the optimization of the internal parameters of the LSTM models, such as the number of neurons and the batch size, but also for feature selection. The model predicted a significant speed loss of 1.26 kn within 17 months of ship operation. Finally, [24] developed an advanced approach for the selection of features by implementing domain knowledge as well as correlation and recursive feature elimination with cross-validation (5 folds) to evaluate the importance of variables with the target value. The study presented a model for power prediction, which consisted of three sub-models corresponding to propeller rpm, hull fouling, and environmental conditions. Emphasis was given to the fouling sub-model that predicted hull roughness considering idling times and sea temperature. A comparison of this approach with several data-driven models (such as ridge, ANN, and random forest) using the same input variables was also carried out.
## 6 Application of the Optimization Techniques: Routing Protocols
### Hierarchical Routing Protocols
#### 6.3.3 TEEN (Threshold Sensitive Energy Efficient Sensor Network Protocol)  
TEEN [23] is other hierarchical protocol for reactive networks that responds immediately to changes in the relevant parameters. In this protocol a clusters head (CH) sends a hard threshold value and a soft one. The nodes sense their environment continuously. The first time a parameter from the attribute set reaches its hard threshold value, the node switches on its transmitter and sends its data. The nodes then transmits data in the current cluster period if the following conditions are true: the current value of the sensed attribute is greater than the hard threshold, and the current value of the sensed attribute differs from sensed value by an amount equal to or greater than the soft threshold. Both strategy looks to reduce energy spend transmitting messages.  
The main drawback of this scheme is that, if the thresholds are not reached, the nodes will never communicate; the user will not get any data from the network at all and will not come to know even if all the nodes die. Thus, this scheme is not well suited for applications where the user needs to get data on a regular basis.
## 6 Conclusions  
In conclusion, we have demonstrated an active switching of extremely high-Q Fano resonances by utilizing the VO\({}_{2}\)-implanted THz symmetry-broken double C-shaped MMs. Due to the IMT characteristic of VO\({}_{2}\), the active switching of Fano resonances can be realized at a very low external heat pump (68 \({}^{\circ}\)C), which is only a little higher than the room temperature. The simulation results confirmed the highly temperature-sensitivity nature of the Fano resonances of the proposed metadevice, which is difficult to be achieved in other devices. At the same time, using the cooling process, we can completely switch off the excited Fano resonances. It should be highlighted that the regulation processes of the Fano resonances base on the means of heating and cooling are quite different, and it is more difficult to switch off the Fano resonances by using the cooling process. In order to clarify the basic properties and internal physical mechanism of the thermal controlled Fano resonances, we simulated the surface current and electric field distributions at the corresponding resonant frequencies for the low (45 \({}^{\circ}\)C) and high (70 \({}^{\circ}\)C) temperatures. To further optimize the Q-factor and modulation depth of the Fano resonances at high temperature (70 \({}^{\circ}\)C), we have investigated the influence of asymmetric parameter (the offset length) on the spectral response, which show a significant compromise between the Q-factor and the modulation depth. The resonant linewidth of Fano 2 at 1.160 THz is 0.015 THz and the extremely high Q-factor is 98, which is one order of magnitude higher than that of traditional MMs. For the technologically difficult THz region, the designed thermally-induced Fano resonance MMs would open up avenues for advanced design and realization of high-performance photonic devices, such as ultra-sensitive sensors, modulators, and low threshold switching.  
Investigation, J.M. and Z.-H.W.; Methodology, J.M., Z.-H.W. and H.L.; Software, J.M. and Z.-H.W.; Supervision, Y.-X.F. and Z.-Y.T.; Writing--original draft, J.M., Z.-H.W. and H.L.; Writing--review and editing, Y.-X.F. and Z.-Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Dean Project of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### PV Simulation Software Tested  
The performance models tested in this work fall into categories of commercially available (licensed), freeware, and open source. A description of each software used in the comparison is provided in Table 1. Seven of the models use a 2-D VF method to calculate \(G_{POA,Rear}\) and one model uses a 3-D RT-based method. The 3-D RT method can simulate detailed features of the mounting structure, such as the torque tube and mounting rails, whereas the 2-D VF method simplifies the structural geometry to drastically reduce computation time. The 3-D RT simulations performed in this work required more than 12 h to complete a full-year simulation without a cumulative sky approach. In contrast, the 2-D VF simulations required between 10 s and 5 min to complete a full-year simulation of one PV system. Specifically, annual simulations were completed in less than 10 s in PlantPredict (First Solar Inc., Tempe, AZ, USA), PVsyst (PVsyst SA, Satigny, Switzerland, System Advisor Model (SAM) (National Renewable Energy Laboratory, Golden, CO, USA), and SolarFarmer (DNV GL, Bristol, UK). Annual simulations in bifacialvf (National Renewable Energy Laboratory, Golden, CO, USA), pvfactors (SunPower Corporation, San Jose, CA, USA), and MoBiDiG VF (ISC Konstanz, Konstanz, Germany) were completed in \(<\)1, \(<\)3, and \(<\)5 min, respectively. Simulations were performed using a Dell 7400 (Round Rock, TX, USA) laptop with an Intel i7-8665U processor (Santa Clara, CA, USA) and 16 GB of RAM, or similar.  
We believe that the software shown in Table 1 are representative of the state-of-the-art tools used within the industry and research community to simulate bifacial PV performance. We do not report on the useability of the different PV software, as this was outside the scope of the present work and is currently being assessed by the members of working group 3 (WG3) within the Pearl PV CA16235 project funded by the European Cooperation in Science and Technology (COST) program. There are several instances in Table 1 where multiple software use the same model for a particular modeling step. For example, all software use the Perez diffuse model to transpose horizontal solar radiation data to the plane of array (\(G_{POA,Front}\)). It has been demonstrated by [28] that the implementation of the same model can vary among software, which will be considered in the results section that follows.