esa-sceva/satcom-chunk-collection huggingface dataset:
# RCS and Aero Surfaces Control Allocation Research on RLV's Re-Entry Phase
## 3 Control Allocation Scheme Design
### Daisy-Chaining Allocation  
The daisy-chaining allocation scheme is to divide the actuators into groups with different priorities. When the current group in use cannot meet the requirements, the next group with lower priority will be enabled. Figure 2 is the structure diagram of this daisy-chaining allocation scheme.  
Since the output of the RCS is discrete, in most cases it means \(\mathbf{M}_{RCS}\
eq\widetilde{\mathbf{M}}_{RCS}\). Considering AS can compensate the control allocation error caused from RCS's discrete working mode, this paper prefers the group of RCS with the higher priority. Thus, the daisy-chaining allocation function is  
\[\begin{cases}\widetilde{\mathbf{M}}_{RCS}=\mathbf{M}_{c},\mathbf{M}_{R}( \widetilde{\mathbf{\delta}})=0&(\mathbf{M}_{c}<k_{m}\mathbf{M}_{RCS}^{\text{max} })\\ \widetilde{\mathbf{M}}_{RCS}=\mathbf{M}_{RCS}^{\text{max}},\mathbf{M}_{R}( \widetilde{\mathbf{\delta}})=\mathbf{M}_{c}-\mathbf{M}_{RCS}^{\text{max}}&( \mathbf{M}_{c}>k_{m}\mathbf{M}_{RCS}^{\text{max}})\end{cases} \tag{10}\]  
where \(\mathbf{M}_{R}(\widetilde{\mathbf{\delta}})\) is the nonlinear fitting function given by Equation (2) [19], \(\mathbf{M}_{RCS}^{\text{max}}\) is the maximum moment of RCS, \(k_{m}\) is the safety coefficient, which is normally defined as \(0<k_{m}<1\).  
Compared to the first scheme, this scheme also has simple structure and does not need more than one controller for the heterogeneous actuators, but introduces the physical constraints into the allocation, which makes its computation more complex. However, the solution for this scheme is not the optimal, since the control efficiency, rate constraint and other constraints of each actuator are not considered. Moreover, with a prioritized RCS, the allocation scheme will lead to more fuel consumption than the proportional one.
# Configurable Hardware Core for IoT Object Detection
## 3 Hardware Core for Object Detection with YOLO  
Figure 2 presents the detailed architecture of the proposed core for the execution of the CNN base network of tiny versions of YOLO.  
The architecture uses direct memory access (DMA) to read and write data from the main external memory. Bypassing the CPU for the memory accesses frees the CPU to execute other tasks during data transfers.  
The data from the main memory are stored in distributed on-chip memories. There are three sets of memories in the architecture: one for input feature maps, one for output feature maps, and another for weight kernels and biases. The memories include a set of address generators that send data in a particular order to the cores to be processed.  
The computing cores are organized in a two-dimensional matrix to enable intra- and inter-parallelism, as discussed in Section 2.4. Cores in the same line receive the same input feature map but different kernels. Cores in the same column receive the same kernels but different input feature maps.  
The results from each core line are written into output memory buffers and then transferred back into main memory through the DMA.  
The dataflow of the accelerator is divided into three phases: _memory read_, _compute_, and _memory write_, as is presented in Figure 3.  
Figure 2: Block diagram of the proposed accelerator of the CNN network of Tiny-YOLO.  
In the _memory read_ phase, the data are transferred from the main memory to the IFM memories. In this phase, the read operations from the main memory and the writes to the memories are controlled by external address generator units (AGUs).  
The _compute_ phase sends the IFM and the weights from on-chip memories for computation, computes the layers in the custom cores, and writes the result in the OFM memories. In this phase, all the internal AGUs control the reads from the IFM memories, the operations at the cores, and the writes to the OFM memories.  
In the _memory write_ part, the data from the OFM memories are transferred back to the main memory. An external AGU controls the read accesses to the OFM memories and the write transfers to the main memory.  
This architecture allows the pipelining of consecutive dataflows. The execution of a second dataflow starts after the _memory read_ phase of the first dataflow is completed. At this point, the accelerator executes the _memory read_ phase of the second dataflow and the _compute_ phase of the first dataflow simultaneously.
# Performance Analysis of Cooperative Non-Orthogonal Multiple Access in Visible Light Communication
## 2 System Model
### Cooperative PD-NOMA  
Consider a system model with the main light source at the ceiling and \(M\) users. Cooperative PD-NOMA concept is divided into two phases: the direct transmission phase and the cooperative phase.
# Numerical Simulation and Optimization of the Airflow Field of a Forage Drum Mower
###### Abstract  
The repeated cutting of forage can cause grass breakage and affect the performance of the forage drum mower in harvesting forage. Also, it is worth paying attention to the effect of airflow around the cutter on the cutting and feeding processes. To explore the characteristics of the airflow field around the cutter and optimize the key parameters of the airflow, an analysis of the airflow field of the forage drum mower equipped with twisted blades, tilting discs, and guide plates was conducted through numerical simulation. Furthermore, an orthogonal experiment was carried out by using the numerical simulation model. According to the experimental results, the optimal velocity of the airflow for gathering, lifting, and feeding was reached when the disc speed was 2000 r/min, the blade twist angle was \(8^{\circ}\), the disc tilt angle was \(4^{\circ}\), and the number of guide plates was 2. On this basis, a prediction model of airflow parameters was constructed, and the parameters of airflow around the cutter were measured on a test bench. According to the measurement results, the results of prediction by the model were consistent with the simulation results. Also, compared with the data of airflow measurement, the average error of the model prediction value was \(-5.83\%\) for the velocity of the gathering airflow, \(2.37\%\) for the velocity of the lifting airflow, and \(4.20\%\) for the velocity of the feeding airflow, which demonstrates the reliability of the simulation results and prediction model. The results of this research provide a practical reference for the optimal design of the forage drum mower.  
forage drum mower; double-drum cutter; airflow field; numerical simulation +
Footnote †: journal: Applied Sciences
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
## 4 Performance Evaluation  
In this section, we present the effectiveness of the DCO and determine the proper values of MAC parameters by conducting experimental simulations. In the simulation, MAC configurations are employed for the Wi-Fi backscatter devices only. We compare the throughput performance between the Wi-Fi backscatter devices and the legacy Wi-Fi devices to observe their channel occupancy variations. In the following subsections, we describe the simulation setup and discuss the results of the simulation in detail.
## 2 Research Methodology
### CFD Analysis
#### 2.2.2 Governing Equations  
Fluid flow in the molten metal is assumed to be Newtonian, incompressible and laminar. The two phases (solid and liquid) are solved in a single computational domain. The governing equations describing the heat and mass transfers during the gas tungsten arc welding processes are as follows:  
Figure 3: Dimensions of computation domain and the mesh used in CFD (unit: mm): (**a**) dimensions of the computation domain; and (**b**) mesh divisions in CFD.  
The continuity equation:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u)}{\partial x}+\frac{ \partial(\rho v)}{\partial y}+\frac{\partial(\rho w)}{\partial z}+S_{m}=0 \tag{1}\]  
The momentum equation, in the \(x\) axis:  
\[\frac{\partial(\rho u)}{\partial t}+\frac{\partial(\rho uu)}{\partial x}+\frac {\partial(\rho vu)}{\partial y}+\frac{\partial(\rho wu)}{\partial z}=\frac{ \partial}{\partial x}\bigg{(}\mu\frac{\partial u}{\partial x}\bigg{)}+\frac{ \partial}{\partial y}\bigg{(}\mu\frac{\partial u}{\partial y}\bigg{)}+\frac{ \partial}{\partial z}\bigg{(}\mu\frac{\partial u}{\partial z}\bigg{)}-\frac{ \partial P}{\partial x}+S_{x} \tag{2}\]  
The momentum equation, in the \(y\) axis:  
\[\frac{\partial(\rho v)}{\partial t}+\frac{\partial(\rho w)}{\partial x}+\frac {\partial(\rho vv)}{\partial y}+\frac{\partial(\rho wv)}{\partial z}=\frac{ \partial}{\partial x}\bigg{(}\mu\frac{\partial v}{\partial x}\bigg{)}+\frac{ \partial}{\partial y}\bigg{(}\mu\frac{\partial v}{\partial y}\bigg{)}+\frac{ \partial}{\partial z}\bigg{(}\mu\frac{\partial v}{\partial z}\bigg{)}-\frac{ \partial P}{\partial y}+S_{y} \tag{3}\]  
The momentum equation, in the \(z\) axis:  
\[\frac{\partial(\rho w)}{\partial t}+\frac{\partial(\rho uw)}{\partial x}+ \frac{\partial(\rho vw)}{\partial y}+\frac{\partial(\rho ww)}{\partial z}= \frac{\partial}{\partial x}\bigg{(}\mu\frac{\partial w}{\partial x}\bigg{)}+ \frac{\partial}{\partial y}\bigg{(}\mu\frac{\partial w}{\partial y}\bigg{)}+ \frac{\partial}{\partial z}\bigg{(}\mu\frac{\partial w}{\partial z}\bigg{)}- \frac{\partial P}{\partial z}+S_{z} \tag{4}\]  
The energy equation:  
\[\frac{\partial(\rho H)}{\partial t}+\frac{\partial(\rho uH)}{\partial x}+ \frac{\partial(\rho vH)}{\partial y}+\frac{\partial(\rho wH)}{\partial z}= \frac{\partial}{\partial x}\bigg{(}k\frac{\partial T}{\partial x}\bigg{)}+ \frac{\partial}{\partial y}\bigg{(}k\frac{\partial T}{\partial y}\bigg{)}+ \frac{\partial}{\partial z}\bigg{(}k\frac{\partial T}{\partial z}\bigg{)}+S_ {H} \tag{5}\]  
where \(u,\ v\) and \(w\) are the components of velocity in three directions; \(\rho,\ P,\ H,\ k\) and \(\mu\) denote the density, pressure, enthalpy, thermal conductivity and viscosity, respectively; and \(S_{m},\ S_{x},\ S_{y},\ S_{z}\) and \(S_{H}\) denote the source terms of the continuity equation, momentum equations, and energy equation, respectively. In the present study, \(S_{m}\) is zero because the mass interchange between the computational zone and the environment is negligible in the GTAW welding process. The source terms for the energy equation and the momentum equations are detailed below.
# Advances in Deep Space Probe Navigation
## 2 An Overview of Published Articles
### Celestial Navigation and Positioning Technology  
X-ray pulsar-based navigation (XNAV) is a navigation method that estimates the position and velocity of a spacecraft using the X-ray radiation from pulsars. Flight experiments on the Insight-Hard X-ray Modulation Telescope (Insight-HXMT) and Neutron Star Interior Composition Explorer (NICER) have successfully verified the feasibility of using XNAV for a single spacecraft. Ref. [4] derives a pulsar-based navigation method that uses the pulse phase delay between spacecraft for spacecraft in formation. Moreover, a direct estimation method for pulse phase delay, which is independent from the pulsar template, is proposed. The verification of simulation data of the Crab pulsar and real data of the same pulsar obtained from Insight-HXMT and NICER show that the proposed method is feasible, and the proposed direct estimation method has higher computational efficiency, reducing CPU time costs by about 36.99%.  
Ref. [5] proposes a dynamic phase comparison algorithm for planar direction finding on a high-speed moving satellite radio receiver, treating the moving antenna as equivalent to single-baseline array antennas. Based on a phase interferometer algorithm, this algorithm adjusts the baseline length according to the frequency measurement module and the satellite's high-speed motion to avoid phase ambiguity indirectly. By integrating the traditional amplitude comparison algorithm based on orthogonal dipole antennas, a dynamic fusion direction-finding method is proposed. Simulation shows that the proposed algorithm covers a broader range of direction finding, achieves higher accuracy, and can effectively solve the problem of angle ambiguity.
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.4 Average Delay  
Here, the average latency of each protocol is investigated by increasing the number of communication nodes used at one time, with all other parameters kept constant, to find out the capacity of each communicating data packet within the network. The average delay of our protocol is shown in Figure 9, along with four other current protocols.  
Figure 8: Network throughput vs. traffic load.  
The graph shows, among other things, that the average delay time grows in linear proportion to the overall quantity of nodes. Otherwise, the average delay time for each of the four operations has an inverse relationship with the total number of nodes. The average length of a delay decreases as the number of nodes increases. The average network latency of our protocol is much lower than the CAPC, CSMA, SFAMA, and TCPC protocols in a network with the same 60 nodes, with reductions of 82.7%, 81.7%, 81.2% and 80.4%, respectively. Furthermore, compared to the T-LOHI protocol, our protocol has a somewhat reduced average network latency (2.3% lower). As the number of nodes increases, there is a linear relationship between the number of packets from nodes and potential collisions. Therefore, the CAPC, CSMA, SFAMA, and T-LOHI protocols all see an increase in their average delay period. Compared to the CAPC and CSMA protocols, the SFAMA and T-LOHI protocols demonstrate delayed changes in reaction to changing network circumstances because of the concern for managing time and space uncertainty and minimizing collisions and retransmissions in their design. Additionally, the overall network delay period noticeably increases when the total number of nodes rises in the CSMA protocol. This is mainly because multi-hop situations include several request-to-send (RTS) frames, which might overlap and lead to delays when there is ambiguity about both space and time. Through simulations, we show that the average delay of our proposed MAC grows more slowly as the number of nodes in the total increases compared to other similar and related existing protocols. In particular, our MAC protocol is somewhat better than the T-LOHI scheme and much better than the CAPC, SFAMA, and CSMA schemes in terms of average delay time.
## 5 Results and Discussion
### Significance of the CVROSS
#### 5.3.3 Green Business Model Using the IoT  
Considering the findings from implementing the proposed system, it can be concluded that the proposed system has advantages with respect to energy-saving, time efficiency and better roadside occupancy. For example, if there are always many trucks loading and unloading on Monday mornings, some logistics companies can plan to change their schedules in order to load or unload goods at other times, to prevent waiting at times of traffic congestion. Thus, the system is able to facilitate traffic and fleet management by self-regulation. The interface and related information can be further amended and transferred to a mobile application to enhance the transparency of roadside activities. Via self-regulation by road users and logistics companies (taking advantage of information and communication technologies), the system relieves traffic congestion, achieves an efficient road network and facilitates the development and management of a reliable and intelligent transport system. This work is not only beneficial to property management companies and drivers, but also has a positive influence on Hong Kong society, fostering an environmentally friendly and safe atmosphere in roadside operations. By adopting the CVROSS, companies could save on costs and labour power for managing roadside activities, and thus business profitability could be improved.
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 3 Sensor Introduction  
### Sensor Design  
Figures 1 and 2 present the schematic diagram and the photo of FBG strain hoop sensor, respectively. This sensor is comprised of an FBG sensor, two capillary copper tubes, three gripper blocks and a steel tube. The fiber in both sides of an FBG is packaged with the epoxy resin in the two capillary copper tubes. The steel tube is used to protect the fragile bare fiber from damage. Meanwhile, the steel tube is mounted on the outer surface of the pipeline, providing a track for the capillary copper tubes slide in it. The two gripper blocks fixed on the steel tube are temporarily jammed in an anchorage member when bonding the steel tube to the outer surface of the pipeline, ensuring that the steel tube can be closely mounted onto the pipeline surface. After bonding the steel tube to the pipeline, the two gripper blocks at the movable end and the fixed end are jammed in an anchorage member (as shown in Figure 2). The movable end shifts position when a circumferential deformation happens, which, in turn, drags the gripper block, causing deformation in the grating area and the gripper tubes.  
If the fiber packaged in the FBG strain hoop sensor is not tightened under working conditions, there may be no wavelength variation when pipeline leakage happens during its service. Therefore, a pre-tension mechanism (presented in Figure 3) was developed to load pre-stressing to the fiber and fix the FBG strain hoop sensor simultaneously. In practical application,  
Figure 1: Schematic diagram of a fiber Bragg grating (FBG) strain hoop sensor.  
caused by the pre-tension system is recommended to be restricted under 1 nm so that the FBG will not be easily destroyed during testing.
## 5 Conclusions  
The purpose of this study is to present an energy-aware QoS MAC protocol that maximizes energy efficiency while taking into account multiple levels of data packet priority and multi-hop routing. In the proposed EQPD-MAC protocol, the use of multi-hop routing makes the network deployment more practical and realistic. EQPD-MAC transmits the higher priority packets first, dynamically adjusts listen and sleep time, and incorporates a proven routing technique. A simple and efficient cross-layer communication technique is provided consisting of the Application, Network, and MAC layers. The findings reveal that the EQPD-MAC protocol reduces the power consumption of the sensor nodes while maintaining its network functionality for a longer period than the other investigated protocols. EQPD-MAC protocol outperforms in terms of average energy consumption of the sensor nodes and the sink node, the network throughput, and the PDR. The proposed EQPD-MAC gives the best performance, especially with a high number of sensor nodes. The proposed MAC protocol decreases the energy consumption of sensor nodes by up to 30.3%, the consumption per bit by up to 29.6%, the energy consumption of sink nodes by up to 27.4% and increases the network throughput by up to 23.3%. It also provides an acceptable latency for priority packets while maintaining the lowest energy consumption.  
Conceptualization, A.N.S. and M.D.; methodology, A.N.S. and M.D.; validation, A.N.S., M.D. and S.S.; resources, S.S. and N.T.T.H.; writing--original draft preparation, A.N.S.; writing--review and editing, M.D., S.S., A.A.A., N.T.T.H. and G.M.S.; funding acquisition, S.S. and G.M.S.; supervision, M.D. and A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the AQUASENSE project, which received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 813680.  
This research study was jointly supported by the graduate assistantship program at Universiti Teknologi PETRONAS and the AQUASENSE project. AQUASENSE has received funding from the European Union's Horizon 2020 research and innovation programme under the [PERSON] grant agreement no. 813680.  
The authors declare no conflict of interest.
# Recognition of Vehicle License Plates Based on Image Processing
## 2 License Plate Recognition System
### Distortion Correction
#### 2.4.3 Random Sample Consensus (RANSAC)  
After the morphological operation, the linear approximation is used to detect the quadrangle corresponding to the license plate area. Finding an appropriate linear model for the pixels \((x_{i},y_{i})\) of the extracted image means the process of minimizing \(a\), \(b\) in the following equation.  
\[E=\sum_{k=1}^{N}\left(y_{i}-(ax_{i}+b)\right)^{2} \tag{3}\]  
The least-square solution can be obtained by calculating the pseudo-inverse matrix, as shown below.  
\[\left[a\ b\right]=\left[y_{1}\cdots y_{i}\cdots y_{N}\right]\left[\begin{array} []{ccc}x_{1}&1\\ \vdots&\vdots\\ x_{N}&1\end{array}\right]\left(\left[\begin{array}{ccc}x_{1}&\cdots&x_{i}& \cdots x_{N}\\ 1&\cdots&1&\cdots&1\end{array}\right]\left[\begin{array}{ccc}x_{1}&1\\ \vdots&\vdots\\ x_{N}&1\end{array}\right]\right) \tag{4}\]  
In the above calculated model, the results will depend on the quality of the input data. Thus, we apply the RANSAC algorithm to obtain the optimal solution without including bad data into the model through random sampling [15].  
Figure 5: (**a**) Input image; (**b**) Otsu binary image.  
Figure 6: (**a**) Input image; (**b**) Result image after morphology calculation.  
First, we randomly select five points, the minimum data for determining the elliptic model parameters. For five points, the model is obtained using the least-squares method stated earlier. For all data in the image, the number of data is calculated, where the distance between the ellipse and data is less than a predetermined allowance. The data contained within the tolerance is called an inlier, while the data outside the boundary is called an outlier. When the proportion of the inlier in the total data exceeds a certain threshold, the model parameters are determined again by only the inlier. If the percentage of inlier is below a certain level, the previous process is repeated by randomly selecting data.  
Each linear approximation technique is an application of the RANSAC line-fitting technique. This is a technique for selecting a reliable pixel with an approximate equation. It is approximated without being affected by other linear components that can be approximated. RANSAC line fitting is applied to the outermost coordinates adjacent to the line \((x_{i},y_{i})\). The second approximate line equation is obtained in this process. By repeating this process (Figure 7b), four approximate linear equations and four feature points are obtained.  
The next step is to measure the angle of the corners of the license plate-shaped rectangle. If this square corner is not 90\({}^{\circ}\), it determines that the license plate is distorted, and performs the perspective transformation. Figure 8 shows the image of measuring the angle of the license plate edge.
# Valorization of Cassava By-Products: Cyanide Content and Quality Characteristics of Leaves and Peel
## 3 Results and Discussion
### Quality Assessment and Cyanide Level of Cassava Leaves following Screw Pressing
These findings confirm that simple screw pressing of frozen leaves is a promising method to obtain a press cake with considerably lower cyanide content. While some researchers have reported on the screw pressing of fresh cassava leaves, the pressing of frozen cassava leaves remains relatively unexplored, adding further novelty to this research. However, even after pressing, the cyanide level in the press cake is still above the safe limit of 10 ppm set by the FAO/WHO (2018). Therefore, to reduce the additional cyanide content in the press cake and make it valuable for use as food or feed, further processing is required. In contrast, the juice fraction can be used to extract linamarin or HCN, which have numerous industrial applications including pesticides, chemical synthesis, dyes, synthetic fiber production, plastics, mining, electroplating, and fumigation.