esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Concept Alignment Example Setup and Modeling
### SoS Modeling for ATG  
The modeling of the SoS for the fault-based ATG was similar to the one defined for the simulation analysis. In order to allow the tool to automatically search for test cases, the SoS model should be set up to include elements capturing some analysis-specific concepts such as the fault or accident events. Figure 5 summarizes the main modeling activities and artifacts required for the successful execution of the ATG process. The modeling flow was as follows:  
1. Control architecture identification: The SoS architecture was modeled in terms of CSs and messages exchanged. For each system, an internal decomposition could be performed to better identify the responsibilities of safety control at lower hierarchical levels.
2. Control events and interconnection definition: For each system, the subset of received and sent events relevant to the safety of the SoS was identified. These events represented the messages and control actions that were needed to coordinate the CS in order to mitigate the effects of a dangerous event.
3. System's behavior: The nominal behavior of each system was modeled by describing how it reacted to hazardous events or other systems interactions. The modeled behavior represented an abstraction of the behavior of the CS. For each system, the possible faults were identified and the behavior of the system in their presence was captured (error model). The system's behavior may contain or not a description of the fault detection, identification, and recovery mechanisms.
4. Unsafe state or fault detection monitors: The hazard conditions representing unsafe states that may lead to accidents and losses were identified in case a scenario-based generation (or scenario-based ATG) had to be performed. In case a fault injection for fault detection had to be performed, the detection mechanisms were modeled.  
Figure 5: Modeling activities flows for ATG and its relationships to the analysis tool.
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 3 Construction of Communication Network Model for WAMS
### Backup Path Method Architecture Design  
The wide-area measurement system communication network plays a pivotal role in facilitating essential operations such as data transmission and command issuance between the PMU and PDC. In order to guarantee the dependability of data transmission in the wide-area measurement system communication network. This paper introduces SDN into the communication network of the wide-area measurement system with the objective of separating the control plane and data plane of the aforementioned system. The controller is responsible for designing the optimal backup path for the network topology, taking into account the real-time status of the network topology operation. This ensures the stable transmission of phasor data in the network topology. The configuration of the backup path algorithm is illustrated in Figure 1.  
As illustrated in Figure 1, the backup path design method based on BPDM-GCN comprises three distinct planes: the data plane, control plane, and application plane. The data plane encompasses a range of devices, including OpenFlow switches, PMUs, PDCs, and other similar apparatuses. The OpenFlow switch directs the phasor data generated by the PMU to be transmitted to the PDC in accordance with the flow table items issued by the SDN, and transmits the instructions determined by the PDC to the PMU. The control plane primarily consists of the SDN controller. Its key functions include real-time monitoring of link statuses within the data plane, transmitting this status information to the BPDM-GCN agent in the application plane, and determining the transmission path for phasor data based on the link weights provided by the BPDM-GCN agent. This path is then configured into flow table entries and forwarded to the OpenFlow switch. In the application plane, the BPDM-GCN agent acquires network topology details such as link transmission delay, bandwidth utilization, and packet loss rates from the SDN controller. Following iterative training, it outputs action values, specifically the link weights, which define network topology adjustments.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
###### Abstract  
The agri-food sector is in constantly renewing, continuously demanding new systems that facilitate farmers' work. Efficient agricultural practices are essential to increasing farm profitability, and reducing water consumption can be achieved by real-time monitoring of water needs. However, the prices of automatic systems for collecting data from several sources (soil and climate) are expensive and their autonomy is very low. This paper presents a low-consumption solution using the Internet of Things (IoT) based on wireless sensor networks (WSNs) and long-range wide-area network (LoRaWAN) technologies. By means of low-power wide-area network (LPWAN) communication, a farmer can monitor the state of crops in real time thanks to a large number of sensors connected wirelessly and distributed across the farm. The wireless sensor node developed, called BoXmote, exhibits very low power, since it has been optimized both in terms of hardware and software. The result is a higher degree of autonomy than commercial motes. This will allow the farmer to have access to all of the information necessary to achieve an efficient irrigation management of his crops with full autonomy.  
LoraWAN; Internet of Things (IoT); agriculture 4.0; wireless sensor network; energy consumption +
Footnote †: journal: Sensors
###### Abstract  
The issue of detecting objects bottoming on the sea floor is significant in various fields including civilian and military areas. The objective of this study is to investigate the logistic regression model to discriminate the target from the clutter and to verify the possibility of applying the model trained by the simulated data generated by the mathematical model to the real experimental data because it is not easy to obtain sufficient data in the underwater field. In the first stage of this study, when the clutter signal energy is so strong that the detection of a target is difficult, the logistic regression model is employed to distinguish the strong clutter signal and the target signal. Previous studies have found that if the clutter energy is larger, false detection occurs even for the various existing detection schemes. For this reason, the discrete Fourier transform (DFT) magnitude spectrum of acoustic signals received by active sonar is applied to train the model to distinguish whether the received signal contains a target signal or not. The goodness of fit of the model is verified in terms of receiver operation characteristic (ROC), area under ROC curve (AUC), and classification table. The detection performance of the proposed model is evaluated in terms of detection rate according to target to clutter ratio (TCR). Furthermore, the real experimental data are employed to test the proposed approach. When using the experimental data to test the model, the logistic regression model is trained by the simulated data that are generated based on the mathematical model for the backscattering of the cylindrical object. The mathematical model is developed according to the size of the cylinder used in the experiment. Since the information on the experimental environment including the sound speed, the sediment type and such is not available, once simulated data are generated under various conditions, valid simulated data are selected using 70% of the experiment cylinder data. The selected simulated data are used to train the model. Randomly selected experiment cylinder data, which are 70% of the total experimental cylinder data, and the rock measurement data are used to test the model. This process is repeatedly carried out 1000 times. The results show that the proposed method is effective under the circumstance where experimental data are not sufficient and a mathematical model is available for a target.  
target detection; logistic regression model; active sonar; shallow water 2018 8 116 10.3390/app8010116
# MFC/IMC Control Algorithm for Reduction of Load Torque Disturbance in PMSM Servo Drive Systems
## 4 Theoretical Analysis  
Theoretical analysis of a typical velocity control loop found in PMSM servo drives was conducted in order to demonstrate the benefits of the proposed method. In the following section, the classical and MFC/IMC velocity control loops are compared with regards to sensitivity to the disturbance caused by an external load. Parameter values of the PMSM model were presented in Appendix A in Table A1.  
In this section, an implicit conversion from time domain \(t\) to complex numbers domain (Laplace operator \(s\)) for all the previous equations is assumed; applying Laplace transform is a typical approach for solving linear systems. Later, when the numerical analysis is provided, a switch to the frequency domain is made. In this case, the Laplace operator is substituted, i.e., \(s=j\omega\). This approach allows for analyzing the entire system in its working frequency and does not require multiple simulations as the analysis in time domain would.
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Chemical  
A chemical reaction actuator is to use the mechanisms of chemical combustion or explosion to convert chemical energy into mechanical energy for actuation purposes. These materials are mostly gases with a high-volume energy density, such as alkanes and other hydrocarbons. During the process of reaction, a large amount of gas is generated in such a soft robot. After the expansion of gas, the air pressure rises and ejects the robot to a designated position.  
[PERSON] et al. designed a soft robot that can be activated by butane combustion to complete unfettered jumping movements [163]. This robot autonomously jumps up to 0.76 m high (six body height) and demonstrates directional jumping of up to 0.15 m (0.5 body length, 20% of jump height) laterally per jump. In addition, [PERSON] et al. presented a three-legged robot that burns methane and explodes to jump [164]. It uses electric sparks to ignite methane gas to achieve rapid combustion, releases a large amount of gas (71 kPa), and causes the internal gas network to make a jump action. In each explosion, the robot jumps to a height of 30 cm, and the jumping speed reaches 3.6 m/s. A chemical catalytic decomposition of a pneumatic robot jointly developed by [PERSON] et al. is controlled by microfluidic logic [165]. It automatically adjusts the fluid flow to catalytically decompose the vehicle's single propellant fuel supply. The gas (approximately 50 kPa) produced by the decomposition of the fuel expands the fluidic network downstream of the reaction site (expected to expand to 160 times its original volume), thereby causing movements.  
There are also other innovative chemical reaction actuation mechanisms applied to soft robots, such as electrolysed water actuators. These types of robots use electrolysed water to decompose hydrogen and oxygen, push the robots to swim forward when exhausting backwards, and change the speed by adjusting their voltage. Nevertheless, the chemical reaction actuation mechanisms exhibit the following limitations:  
1. Chemical soft robots include reaction devices, which are difficult to miniaturise;
2. Some robots driven by explosions are designed for jumping purposes, but the positioning accuracy of the robots is not high.
## 2 Materials and Methods
### CS Simulation Framework Based on LUCC
#### 2.3.4 Accuracy Verification  
The model's accuracy was assessed by comparing the 2020 modeled LU results with the actual LU data for that year. An overall accuracy of 93.68% was achieved, with a  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline  & & & & **ND** & & & & & & & & & & & **EP** & & & \\ \hline Type & A & B & C & D & E & F & A & B & C & D & E & F & A & B & C & D & E & F \\ A & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 1 & 1 \\ B & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 \\ C & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 \\ D & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ E & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 0 \\ F & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular} Note: A represents arable land; B stands for woodland; C denotes grassland; D corresponds to water areas; E signifies construction land; F indicates other land types. A value of 1 signifies that the conversion between them is possible, while 0 denotes that conversion is not allowed.  
\end{table}
Table 3: Scenario of the cost matrix setup.  
kappa coefficient of 0.834, signifying a high degree of validity (values above 0.80 are typically considered valid). These results demonstrate a strong correspondence between the predicted and actual spatial distribution of LU. The model efficiently captures the spatial-temporal patterns of LU alterations, meeting the study's objectives.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### Verification Techniques
#### 2.3.2 Generating Model NBRCS  
A forward model for the CYGNSS observatory (generating \(\sigma_{\text{mod}}^{\text{o}}\) from wind data) can be challenging. Operationally, CYGNSS uses a GMF LUT that maps between the two quantities. However, for the purposes of this analysis, we prefer to use a physically representative forward operator that represents the physical dynamics of the roughening of the ocean surface due to locally driven winds.  
For each sample matched up, \(\sigma_{\text{mod}}^{\text{o}}\) is generated by computing a spectrally corrected ocean surface MSS from an ERA5-forced [37] WaveWatch III wave model as described in [38]. Because the resolution of ERA5 is much coarser than CYGNSS Level 1 observations in both space and time, the \(\sigma_{\text{mod}}^{\text{o}}\) is matched up with \(\sigma^{\text{o}}\) via a tri-linear interpolation across three dimensions (latitude, longitude, and time).  
Therefore, for every track of data, there are observations from two different CYGNSS observatories and two modeled NBRCS from reanalysis data.
## 3 Measurement Results
### Tests of Medical Devices
#### 3.1.3 Insulin Pump--Medtronic MiniMed Veo Paradigm 554  
The standard therapy for type I diabetes is the administration of insulin. An insulin pump consists of a battery-operated pump that administers insulin directly (patch pumps) or via a catheter and cannula into the patient's body. The operating principle is similar for all insulin pumps. A small motor (usually a stepper motor, in some cases, also a servo motor) pushes the plunger of an insulin ampoule forward via a threaded rod so that the stored insulin is delivered. Insulin is injected into the patient's body every few minutes, which means regular start-and-stop operation for the motor. In insulin pump therapy, there are two main classes of insulin, bolus and basal insulin. Bolus insulin is fast-acting insulin that is often used before meals and at moments of extremely high blood glucose. Basal insulin, often referred to as background insulin, is longer acting and helps to keep blood glucose levels constant day and night. A representative of an insulin pump [9; 10; 26; 27] that has been studied is the Medtronic MiniMed Veo Paradigm 554. The pump delivers units (U) of insulin. Here, 100 U of a U-100 insulin correspond to 1 mL. The pump reservoir holds 176 U (around 1.75 mL) of insulin. Medtronic Paradigm insulin pumps also maintain a patient's blood glucose targets in two ways, by either delivering a continuous, minimal dose of insulin (basal mode) or a higher dose of insulin (bolus mode) when needed. Both modes can be examined separately. As an example, the basal mode is described in more detail in the following. The pump delivers 0.025 U for basal rates in the range of 0.025 U h\({}^{-1}\) to 0.975 U h\({}^{-1}\) and 0.05 U for basal rates in the range of 1 U h\({}^{-1}\) to 9.95 U h\({}^{-1}\).  
Figure 9 shows measurements at a flow rate of 0.025 U h\({}^{-1}\) (0.25 uL h\({}^{-1}\) or 4.167 nL min\({}^{-1}\)), which is probably very close to the lowest limit of the dynamic gravimetric method. The figure clearly shows the pulsed flow rate described by [PERSON] et al. [28], which represents a constantly delivered volume over a certain time interval. In this case, the pump delivers volume increments of 0.025 U at a flow rate of 0.025 U h\({}^{-1}\), which corresponds to one increment per hour, the smallest possible setting of the insulin pump.  
Figure 8: Typical calibration curve as produced by the HydroGraph software during a test. The red curve shows the instantaneous flow rate, the blue curve the average flow rate and the green curve the delivered volume.
## 4 Experiments
### POD-MCNN Model Performance
#### 4.3.1 Reconstructed Performance on Test Data  
In this section, we evaluate the predictive performance of the network model on the test data. Three cases are selected in the test set to test the model. Figure 11 shows the network model prediction results, numerical simulation results, and absolute error distribution at \(Z=350\) mm under the three cases. It is obvious that the CFD results and the neural network output look very consistent, so we plot the error. In the error chart, the bluer the color, the smaller the error, and the redder the color, the greater the error.  
In addition to this, we calculated the mean absolute error between the predicted results and the CFD calculated results. The results are shown in Table 2. It can be seen from the data in Table 2 that the average temperature MAE of the three cases is only 1.4045 and the average pressure MAE is only 0.5140, and the error is negligible in engineering applications. The network model performs well on the flow field prediction task on the test data.  
For a working push-plate kiln, the trend of temperature changes is important. Figures 12 and 13 show the comparison between the neural network prediction results and the CFD results of the temperature and pressure trend at \(Z=350\) mm and Y = 550 mm. It can be seen from the figure that the data trend of the model output is consistent with that of the CFD simulation and almost overlaps.  
\begin{table}
\begin{tabular}{c c c} \hline
**Case** & **MAE t** & **MAE p** \\ \hline case 1 & 0.9144 & 0.4492 \\ case 2 & 1.5922 & 0.7042 \\ case 3 & 1.7068 & 0.3887 \\ \hline \end{tabular}
\end{table}
Table 2: MAE for three cases on the test data.  
Figure 10: Relationship between the number of basic modes and reconstruction error.  
Figure 11: Temperature and pressure field prediction results for three cases at Z=350 mm. (**a**–**c**) is the temperature field prediction result of case1–case3, and (**d**–**f**) is the pressure field prediction result of case1–case3.  
Figure 12: Temperature trend prediction results for three cases on test data.  
Figure 13: Pressure trend prediction results for three cases on test data.  
After training, the model performs well on the temperature and pressure field prediction tasks of the test data, and can predict the flow field data of the test set accurately.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 3 System Model and Performance Metrics
### Performance Metrics
#### 3.2.1 Throughput  
Throughput is the rate of effective transmission of a signal in a wireless communication system, which is measured in bits per second (bps). To obtain the individual UE\({}_{w,s}\) achievable rate in the NOMA system, the following mathematical representation is applied [27]:  
\[R_{w,s}=B_{scl}log2\bigg{(}1+\frac{P_{w,s}H_{w,s}}{1+\sum_{l=1}^{w-1}P_{l,s}H _{w,s}}\bigg{)}(\text{bps}) \tag{6}\]  
The NOMA system sum rate is obtained by adding the corresponding individual throughputs as in Equation (7).  
\[R_{sum}=\sum_{s=1}^{S}\sum_{w=1}^{W}R_{w,s} \tag{7}\]
# Data Exchange in Cluster Structure for Longevity of IoT
## 2 Related Work  
### Wireless Sensor Networks  
A WSN is configured with small sensor nodes fabricated with low specification CPU, memory, a low-speed wireless communication module, and low-capacity battery. They are deployed in buildings, roads, mountains, and various environments and collect information from their surroundings. The collected data are transmitted to a specific destination via the sink node. According to the types of measured data, they are called a biosensor node, chemical sensor node, magnetic sensor node, light sensor node, motion sensor node, etc. The WSNs installed with various sensor nodes can be utilized for monitoring weather and temperature-humidity changes, surveillance of wild animals, control of vehicular traffic, etc. [6; 7].  
The sensor nodes are classified into a sink node and a general-purpose sensor node. A general-purpose sensor node obtains information from its surrounding environment and transmits it to the sink node. The sink node performs the role of a gateway to not only send the received information to an external network, but also transmit external requests to the corresponding sensor nodes. Since the deployed sensor nodes have limited power, memory, and other low-level resources, many methods for effective data transfer have been researched. One of them is an ad-hoc network that configures the network quickly, preserves low data transfer rates, and can support multiple sensor nodes in a loosely coupled style. In order to operate wireless sensor nodes effectively under limited power resources, TinyOS is proposed, which chooses an event-driven operation and state transition machine method [8]. Furthermore, SOS [9] provides a runtime upgrade function for applications areas and kernel areas. MANTIS [10] has been proposed on the microscopic threads. NanoQplus [11] has been studied as the operating system for WSNs. Furthermore, various operating systems for WSNs have been researched [12; 13].