esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation
### Problem Formulation  
In this paper, we focus on the UAV-assisted VANETs problem by jointly considering the task offloading, the resource allocation, and the security assurance that aims at minimizing the task processing delay. We formulate the optimization problem as follows:  
\[\begin{split}\text{P1}:\min_{\mathbf{x},\lambda,\mathbf{f}}T( \mathbf{x},\lambda,\mathbf{f})=\min_{\mathbf{x},\lambda,\mathbf{f}}\sum_{i=1 }^{N}T_{\Xi_{i}}\\ s.t.\text{~{}C1.1}:T_{\Xi_{i}}\leq T^{th},~{}\forall i\in\mathbf{N} \\ \text{~{}C1.2}:\sum_{j=1}^{M}\mathbf{x}_{i,j}=1,~{}\forall i\in \mathbf{N}\\ \text{~{}C1.3}:x_{i,j}\in\{0,1\},~{}\forall i\in\mathbf{N},~{} \forall j\in\mathbf{M}\\ \text{~{}C1.4}:0\leq\sum_{i=1}^{N}x_{i,j}f_{i,j}\leq F_{j},~{} \forall j\in\mathbf{M}\\ \text{~{}C1.5}:0\leq f_{i,j}\leq F_{j},~{}\forall i\in\mathbf{N},~{} \forall j\in\mathbf{M}\\ \text{~{}C1.6}:0\leq\sum_{j=1}^{M}x_{i,j}\lambda_{i,j}\leq 1,~{} \forall i\in\mathbf{N}\\ \text{~{}C1.7}:0\leq\lambda_{i,j}\leq 1,~{}\forall i\in\mathbf{N},~{} \forall j\in\mathbf{M}\\ \text{~{}C1.8}:R_{e}\leq R_{th},~{}\forall i\in\mathbf{N},~{} \forall j\in\mathbf{M}.\end{split} \tag{15}\]  
In (15), C1.1 ensures that the local computation time \(T_{i,j}^{loc}\) and the processing delay of the MEC server on UAV \(j\)\(T_{i,j}^{UAV}\) cannot exceed the task \(\Xi_{i}\) maximum allowed latency \(T^{th}\). C1.2 and C1.3 indicate that each vehicle selects only one MEC service for task offloading. C1.4 and C1.5 guarantee that the total amount of computing resources allocated by the MEC server to all tasks cannot exceed the total computation capacity of this MEC server. C1.6 and C1.7 represent that the total offloaded task \(\lambda_{i,j}d_{i}\) of vehicle \(i\) cannot exceed 1. C1.8 guarantees the security of the transmission.
# A High Performance 0.3 V Standard-Cell-Based OTA Suitable for Automatic Layout Flow
## 2 Replica Bias Approach to Control the Static Output Voltage of Inverters  
This section briefly reviews the standard-cell-based replica-bias control loop presented in [52] to accurately set the output static voltage of the inverter gates exploited as basic amplifier (BA) cells [52].  
The replica-bias control loop is depicted in Figure 1 and is composed of an instance (\(I_{0}\)) of a reference inverter taken from the standard-cell library of the target technology. The input of the inverter \(I_{0}\) is biased at a voltage equal to the analog ground \(AGND\) (i.e., \(V_{DD}/2\)). The output static voltage of \(I_{0}\) is compared with a reference voltage \(V_{ref}\) (usually equal to \(V_{DD}/2\)) through the standard-cell-based error amplifier composed of the inverters \(I_{2,3,4,5}\) (depicted in green in Figure 1). The generated output voltage, namely \(Ctrl\), is used to close the negative feedback loop through another instance \(I_{1}\) of the reference inverter. Referring to the approach presented in [52], the BA cell is implemented by the inverters \(I_{0}\) and \(I_{1}\) (depicted in pink in Figure 1). The control voltage \(Ctrl\) is then applied to all the BA cells exploited for analog design in order to guarantee a well-defined output static voltage of the BA cells in spite of PVT variations [52]. The reference inverter can be thought to be the minimum-sized inverter taken from the standard-cell library. However, since in the context of analog design, minimum-sized inverters exhibit very high mismatch (resulting in unacceptable offset for most analog applications), the reference inverter is here defined as an inverter gate taken from the standard-cell library whose area is the minimum one that allows it to achieve an acceptable matching performance for the specific analog design. For example, referring to a 130-nm CMOS technology, the area of the reference inverter to be used to design standard-cell-based OTAs is typically 10 times larger than the area of the minimum-sized one (e.g., \(\times 20\) instead of \(\times 2\)).
# Performance-Based Analysis of Single-Layer Cylindrical Steel Reticulated Shells in Fire
## 3 Air Temperature Field Analysis During a Fire
### FDS Model Verification  
In order to verify the accuracy and applicability of FDS for fire simulation analysis, this paper carried out a comparison between the results of FDS simulation and fire test results. The test was carried out in a large space steel factory building located in Xuzhou, China. The length of the steel structure is 60 m. The width of the structure is 30 m. The highest and the lowest part of the roof is 9 m and 8 m, respectively. There are two \(5\times 4\) m\({}^{2}\) doors at each of the two short sides and one \(5\times 4\) m\({}^{2}\) door at each of the two long sides of the structure. The geometry of the test model is shown in Figure 3. There are eight oil basins used as the fire source. The calorific value of diesel was 42,600 kJ/kg, the combustion efficiency and mass loss rate of diesel were 0.027 kg/s and 93.9%, respectively. The heat release rate of fire source is 1080 kW. There are 18 thermocouples arranged at the top of the fire source, starting from 0.4 m above the fire source with an interval of 0.2 m, which is used to record the temperature of flame and plume during the test.  
The FDS model is established as shown in Figure 4. The number of grid is \(150\times 300\times 45\), the mesh size is 0.2 m \(\times\) 0.2 m \(\times\) 0.2 m. The door opening, windows, fire source area, and thermocouples are defined according to the test situation. According to the test, the initial temperature is 16 \({}^{\circ}\)C and the simulation duration is 1400 s. The FDS simulation results and the test results are compared by the temperature measured at the location 4m above the fire source as shown in Figure 5.  
Figure 4: Fire Dynamics Simulator (FDS) Model.  
Figure 3: Geometry of the steel factory building.  
The temperature value of FDS simulation is basically consistent with test result. The overall changing trend of flue gas temperature is similar to the test results as well. However, there are some differences between simulation and test results. The temperature obtained by FDS simulation is relatively large and higher than the measuring temperature in the test because of the following reasons: (1) the air leakage and environmental temperature fluctuation are inevitable in the test, which will make the test values lower than the simulation results. (2) Incomplete combustion of fuel exists in the test and the heat release rate decreases gradually, and (3) there are measurement errors in the test. From the above analysis, it is can be seen that the numerical value and variation trend of simulation and test results are relatively similar. Therefore, the accuracy and reliability of the simulation method can be verified.  
In order to study the influence of different fire source locations on the internal temperature field of the reticulated shells, three different fire source locations were set up, as shown in Figure 2. For accuracy, the air temperature fields inside the reticulated shells of the three fire source locations were calculated.
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 3 Problem Transformation and Decomposition  
This section presents a distributed solving approach for coordinated space-ground multiresource scheduling for remote sensing missions. Firstly, the mixed-integer linear programming problem P1 is converted into a linear programming problem through variable relaxation. Then, the relaxed problem is decomposed into independent subproblems by duplicating the global variables and establishing local variables for each system.
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
###### Abstract
This makes it possible to real-time GNSS water vapor monitor at a standalone station, which is not dependent on the Internet, which can better compensate the sparse observation for depopulated area. Recently, some researchers have conducted water vapor retrieval using the PPP-B2b products (e.g., [27; 28]). Results show that the performance of PWV from the PPP-B2b service can achieve about 4 mm compared to reanalysis products [29; 30], which is generally consistent with those from traditional IGS RTS products [31]. This manifests the feasibility of further application of water vapor derived from the PPP-B2b service in meteorological fields. However, current studies about water vapor monitoring using the PPP-B2b service are generally conducted based on simulated real-time PPP mode (i.e., post-processing using real-time products), while its practical performance remains unclear. In operational meteorology, water vapor monitoring should be conducted continuously and automatically with experiment and testing in practical situation. Therefore, current studies based on simulated real-time processing cannot fully provide robust reference for assessing the performance of the PPP-B2b service for water vapor monitoring. In this regard, evaluating the actual performance of water vapor monitoring based on the BDS-PPP B2b service is of vital importance before its wide promotion. To achieve this purpose, a continuously operated water vapor monitoring system (here after called monitoring system), which consist of a GNSS receiver and an Industrial Personal Computer (IPC), was established in Wuhan, China. This monitoring system was operated same as other meteorological stations automatically in practical situation. Based on observations of 25 days, PWV data retrieved in NRT mode were evaluated to veritably illustrate the ability of water vapor monitoringusing the PPP-B2b service. The conclusion of this paper is expected to provide scientific reference for developing future water vapor observation platforms.
# Review of Energy Management Systems in Microgrids
## 1 Introduction
These systems aim to operate the microgrid at maximum efficiency by monitoring the power output of generation resources, weather forecasts, load demand, and real-time energy prices. The cost of deployment and data rate are the main factors influencing the choice of communication technologies for microgrids in remote, residential, and rural areas. WiFi, Bluetooth, Z-wave, and Zigbee are used as communication technologies in such microgrids. Passive optical networks, 3G, and 4G technologies are also used in microgrids used in public services. These communication technologies are used by routers at distributed energy resource and load ends to communicate with the local controller and microgrid central controller. Arduinos and Rasberry PI are two examples of inexpensive  
Figure 1: Energy management in microgrid units. embedded systems that can be used to implement local controllers. These technologies are designed to collect information from smart meters and monitoring sensors and carry out local control operations to protect consumer privacy. Data from SCADA, HMI, and local controllers are used by the microgrid central controller to drive energy management operations. The primary criteria used to choose the best solution approaches for these energy management operations are computational time complexity and convergence to the optimal solution based on merits [16]. The constant change of wind speed and solar irradiation values in renewable energy sources used in microgrids negatively affects system security and increases energy costs. The stochastic behavior of renewable energies, especially wind and solar, increases the need to find the optimum operation of the microgrid. The optimal operation of a typical microgrid aims to simultaneously minimize operating costs and accompanying emission pollutants over the daily planning horizon. By managing these energy resources in microgrids, it is aimed to increase the reliability and stability of the system while maintaining the balance between supply and demand [17]. This article provides an overview of microgrid energy management systems, outlining both their benefits and drawbacks. To control the unpredictability and erratic nature of renewable energy resources and load demand, a summary of different uncertainty measurement techniques is provided. A literature review and an investigation of the application of energy management techniques with varying goals in microgrids are also provided. Lastly, some thoughts about potential future directions and practical applications are given.
## 4 Research Directions for System Designs and Optimization Concepts
### Main Technical Challenges in System Design
As discussed above, the complexity of networks and channels can affect the corresponding optimization objectives, which results in additional non-convex optimization problems that are hard to address. The major challenge in the existing signal processing methods is the computational cost of implementing optimal secure precoding and beamforming schemes in practice. When considering the challenges and problems that are associated with system design, potential research directions and corresponding enhancement schemes can be identified and evaluated by reaping the benefits of ML and AI enabling technologies, which are discussed in detail in Section 6.
## 3 Applications of L-Band SAR Data in Forestry
### Moisture  
[PERSON] et al. used an airborne L-band microwave radiometer to study the effect of forest cover on soil water estimation in Australia and utilized the L-MEB zero-order radiative transfer model to simultaneously estimate soil water and vegetation optical depth [19]. [PERSON] et al. found that SAR signal was highly correlated with the optical depth, roughness and canopy density of vegetation by using SMOS for large-scale moisture estimation, and the Hr value of the spatial pattern of soil moisture was correlated with land-cover types. Their results demonstrated that the evergreen broad-leaved mixed forests and the deciduous coniferous mixed forests had higher values of Hr, ranging from 0.32 to 0.39, desert, shrub and bare soil had lower values of Hr, ranging from 0.14 to 0.16, and the Hr values of grassland and tundra cultivated land changed to between 0.20 and 0.23 [20]. [PERSON] et al. used SMAP data and a multi-temporal dual-channel retrieval algorithm (MT-DCA) to estimate the optical depth, moisture and reflectance of large-scale vegetation [21]. [PERSON] et al. studied the relationship between optical depth, penetration and temperature of vegetation [22]. Since the L-band is sensitive to moisture, it is often used to estimate forest moisture. [PERSON] et al. used L-band radiometer towers in Red Oak forests in Massachusetts, USA, to prove that the optical depth of vegetation measured by microwave radiometers is correlated with the amount of water in vegetation [23]. These studies indicate that the optical depth of vegetation is an important index to evaluate the microwave signal transmission process in forest canopy.
# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 3 Proposed Framework
### Robustness Analysis of Angle Features
#### 3.3.2 Translation Invariance  
Translation involves the parallel displacement of a target point along the X, Y, or Z-axes within the OXYZ plane. In the context of a patient's medical examination, it refers to bodily movements such as flexion, extension, lateral shifting, as well as anterior and posterior shifting.  
**Theorem 2**.: _Let the image of any \(\Delta\)ABC in the target surface \(\pi\) after the camera imaging be \(\Delta A^{\prime}B^{\prime}C^{\prime}\); when the target \(\Delta\)ABC translates arbitrarily along the x-axis, y-axis, or z-axis, the image angles \(\angle A^{\prime}B^{\prime}C^{\prime}\), \(\angle B^{\prime}A^{\prime}C^{\prime}\), \(\angle B^{\prime}C^{\prime}A^{\prime}\) do not follow the target changes in position. Hence, this property is referred to as the translation invariance theory._  
Proof of Theorem 2.: Establish the coordinate system shown in Figure 4, set the image of triangle \(ABC\) in image plane \(\pi_{1}\) as \(\Delta A^{\prime}B^{\prime}C^{\prime}\), move triangle \(ABC\) to \(A_{1}B_{1}C_{1}\) along the y-axis, and the image of triangle \(A_{1}B_{1}C_{1}\) is \(A^{\prime}_{1}B^{\prime}_{1}C^{\prime}_{1}\).  
Figure 4: The imaging model describes the motion of an object along the y-axis.  
According to the principle of pinhole imaging, triangle \(ABC\) is similar to triangle \(A^{\prime}B^{\prime}C^{\prime}\). The following Formula (2) represents this relationship:  
\[\Delta ABC\approx\Delta A^{\prime}B^{\prime}C^{\prime} \tag{2}\]  
When triangle \(ABC\) moves to \(A_{1}B_{1}C_{1}\) along the y-axis, triangle \(ABC\) will not be deformed due to parallel movement, so there is a congruent relationship between triangle \(ABC\) and triangle \(A_{1}B_{1}C_{1}\). There is the following Formula (3):  
\[\Delta ABC\cong\Delta A_{1}B_{1}C_{1} \tag{3}\]  
Triangle \(A^{\prime}_{1}B^{\prime}_{1}C^{\prime}_{1}\) is the image of triangle \(A_{1}B_{1}C_{1}\), and so, there is the following Formula (4):  
\[\Delta A_{1}B_{1}C_{1}\approx\Delta A^{\prime}_{1}B^{\prime}_{1}C^{\prime}_{1} \tag{4}\]  
Then, it can be concluded that triangle \(A^{\prime}B^{\prime}C^{\prime}\) and triangle \(A^{\prime}_{1}B^{\prime}_{1}C^{\prime}_{1}\) have a similar relationship. There is the following Formula (5):  
\[\Delta A^{\prime}B^{\prime}C^{\prime}\approx\Delta A^{\prime}_{1}B^{\prime}_{1 }C^{\prime}_{1} \tag{5}\]  
Thus, the image angles \(\angle A^{\prime}B^{\prime}C^{\prime}\), \(\angle B^{\prime}A^{\prime}C^{\prime}\), and \(\angle B^{\prime}C^{\prime}A^{\prime}\) do not change as the target moves along the y-axis. Similarly, when the target moves along the x-axis and z-axis, the image angle will not change either. \(\square\)
## 4 Performance Analysis of Islanding Detection Methods
### Load Type  
The construction and type of load have a significant influence on the operating voltage of the system and the performance of the islanding detection method. For instance, if the system is connected to a parallel RLC load, it will be difficult to detect the islanding condition efficiently, especially when the DG and load powers are matched. On the other hand, if the system is connected with more non-linear loads, the non-detected zone will be reduced, and the efficiency of the islanding detection method will be increased [53].
###### Abstract  
As a promising industrial wireless network technology, 5G URLLC is playing a key role in industrial control systems which employ different kinds of industrial control protocols. This leaves a significant challenge in how to adapt 5G URLLC with existing industrial control protocols and develop novel industrial wireless controllers. Motivated by this, this paper investigates the protocol adaptation and experimental evaluations for the development of a 5G-based industrial wireless controller prototype. Specifically, using the globally used industrial protocol Modbus as an example, we first deeply analyze the characteristics of the data packet format and the communication time sequence of Modbus RTU, Modbus TCP, and 5G protocols. Then, we propose a protocol resolution and conversion scheme for the protocol adaptation between Modbus RTU/Modbus TCP and 5G. Furthermore, we develop a wireless programmable logic controller (PLC) prototype system based on 5G. To evaluate the protocol adaptation scheme and the developed prototype, we perform extensive experiments to test the reliability and latency. The results demonstrate that the latency of the protocol adaptation scheme is smaller than that of protocol transparent transmission without resolution and conversion, while the reliability loss is not large. Moreover, the speed and reliability for the protocol adaptation between Modbus TCP and 5G are better than those between Modbus RTU and 5G.  
protocol adaptation; 5G; Modbus; end-to-end delay; industrial wireless controller; PLC
# On the Feasibility of Wireless Multimedia Sensor Networks over IEEE 802.15.5 Mesh Topologies
## 5 Performance Evaluation
### Latency
This holds true even for \(\text{AO}=\text{WO}-1\), where the _inactive duration_ will still be long enough. According to Table 5, for \(\text{AO}=\text{WO}-1\), the _inactive duration_ is as big as the active duration and thus adequate for transmission. In conclusion, for a fixed value of WO, the latency remains almost constant except for the two following cases:  
* For \(\text{AO}=1\) the _active duration_ is too short and, given that the contention for the medium is too high, few nodes are able to reserve a slot for transmission and soon the network experiences congestion collapse. Packets are stored in queues (of infinite size) and many of them, due to the failure of the CSMA-CA mechanism (no successful transmission after the maximum number of retries), are discarded and will not reach the destination. The latency measured corresponds to the few packets that are successfully received. * For \(\text{AO}=2\), the contention is still high (the _inactive duration_ is not long enough yet) but packet loss is not as important as in the previous case (though still present). Therefore, the average delay represented is higher since the final packets (with higher latency) experience less congestion than for \(\text{AO}=1\). However, the latency measured is also slightly higher than for AO values greater than 2 because of the network congestion, as the transmission of packets may require more than one _wakeup interval_.
## 5 Simulation Results
### Evaluating the Impact of the Density of Vehicles  
Figure 5 shows the simulation results when varying the number of vehicles. We selected 100, 200, 300, and 400 vehicles (_i.e._, 25, 50, 75, and 100 vehicles/km\({}^{2}\)). Table 4 shows some of the parameters used for the simulations; the rest of parameters are the following: the roadmap used is Rome, the radio propagation model used is RAV, vehicles follow the Krauss mobility model, there are 3 warning mode vehicles, the periodicity of messages is 1 message per second, normal message priority is AC0, the broadcast scheme applied is eMDR, and the channel bandwidth is 6 Mbps.  
As expected, the warning notification time is lower when the vehicle density increases. When simulating with 400 vehicles, information reaches about 60% of the vehicles in only \(1.3\) s, and the propagation process is completed in \(2.4\) s.  
Table 8 shows the percentage of blind vehicles and the number of packets received per vehicle when varying the density of vehicles. The behavior in terms of percentage of blind vehicles highly depends on this factor. This characteristic is explained because the flooding propagation of warning messages works better with higher vehicle densities. As for the number of packets received per vehicle, this number highly increases when increasing vehicle density.  
Figure 4: Evolution of the warning message dissemination process in the Rome scenario after 20 s, when using (**a**) the TwoRay Ground and (**b**) the RAV model.  
Figure 6 shows the number of warning messages received in each area when simulating 100 and 400 vehicles, respectively. When only 100 vehicles are simulated the dissemination process presents a very slow progression. If the simulations include 400 vehicles, the dissemination process is able to reach a wider area of the scenario since finding appropriate rebroadcasting nodes becomes easier.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vehicles** & **\% of Blind Vehicles** & **Packets Received** \\ \hline
100 & 76.63\% & 197.37 \\
200 & 60.92\% & 229.07 \\
300 & 36.40\% & 432.60 \\
400 & 21.01\% & 949.40 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Blind vehicles and packets received per vehicle when varying the density of vehicles.  
Figure 5: Warning notification time when varying the density of vehicles.