esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Applied to Latency Reduction for the Tactile Internet
## 6 Implementation Description
### Feedback Force (FBF-HSD)  
As illustrated in Figure 4 the hardware associated with the slave device (HSD) implements the feedback force via the FBF-HSD module. The FPGA-implemented circuit of the FBF-HSD module is designed to work with six input signals and three output signals. Among the six input variables, \(x^{OBI}[F32](n)\), \(y^{OBI}[F32](n)\) and \(z^{OBI}[F32](n)\) represent the spatial position of the closest object to the SD tool and the other three \(x^{ENV}[F32](n)\), \(y^{ENV}[F32](n)\) and \(z^{ENV}[F32](n)\) represent the spatial position of the SD tool in the ENV module. The three outputs \(F_{x}^{HSD}[F32](n)\), \(F_{y}^{HSD}[F32](n)\) and \(F_{z}^{HSD}[F32](n)\) represent the touch of the tool on the object. The variables \(h_{x}\), \(h_{y}\) and \(h_{z}\) represent the elasticity coefficients associated with the object. All FBF-HSD module calculations are performed in parallel.  
Based on Equation (60), the algorithm used for calculating \(F_{x}^{HSD}[F32](n)\) was implemented in FPGA according to the generic circuit illustrated in Figure 29. The circuit was designed to work with two inputs signals \(x^{OBI}[F32](n)\) and \(x^{ENV}[F32](n)\) and one variable \(h_{x}\).  
The calculation of \(F_{y}^{HSD}[F32](n)\), based on Equation (61), was implemented in FPGA according to the generic circuit illustrated in Figure 30. The circuit was designed to work with two input signals \(y^{OBI}[F32](n)\) and \(y^{ENV}[F32](n)\) and one variable \(h_{y}\).  
The generic circuit shown in Figure 31 was implemented in FPGA to perform the calculation of \(F_{z}^{HSD}[F32](n)\) and it is based on Equation (62). The circuit was designed to work with two input signals \(z^{OBI}[F32](n)\) and \(z^{ENV}[F32](n)\) and one variable \(h_{z}\).
# Parameter Calibration and System Design of Material Lifting System for Inclined Shaft Construction
## 2 Lifting System Parameter Calibration Method
### Motor Parameter Calibration  
According to the Safety Requirements for Mine Hoists and Mine Hoisting Winches (GB20181-2006), the maximum speed of the hoisting system should be no more than 5 m/s when the mine car is used to hoist the materials, and no more than 7 m/s when the skip is used to hoist the materials. Therefore, the hoist speed is in principle calibrated according to the maximum speed of no more than 5 m/s. For the convenience of calculation, the motor type is determined as a permanent magnet synchronous motor.  
The maximum speed \(V_{m}\) is as in Equation (7):  
\[V_{m}=\frac{\pi Dr}{60i} \tag{7}\]  
where \(i\) is the reduction ratio; \(r\) is the motor speed (\(r/\min\)).  
Motor power \(N\) as in Equation (8):  
\[N=\frac{KF_{j}V_{m}}{1000\eta} \tag{8}\]  
where \(K\) is the motor power spare factor; \(\eta\) is the motor drive efficiency.  
The permissible input power \(PC\) of the motor reducer is as in Equation (9):  
\[PC=P\times R\times S \tag{9}\]  
where \(P\) is the motor power (KW); \(R\) is the working condition factor, take 1.5-2; \(S\) is the safety factor, take 1.5-2.  
The physical diagram of the hoisting system motor is shown in Figure 2.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation
### Trade-Offs and Inefficiencies
#### 5.5.1 Potential Latency Issues with Raspberry Pi  
The Raspberry Pi 4 serves as the central processing unit for running the YOLOv5 model in the system. Despite its versatility and cost-effectiveness, the Raspberry Pi 4 is inherently resource-constrained compared to dedicated GPUs or high-performance edge computing devices. This introduces several trade-offs:  
* Processing Speed: The Raspberry Pi 4 processes images at a slower rate than GPUs, potentially resulting in latency during fire detection and alert generation. While YOLOv5's lightweight design mitigates some of this delay, real-time performance may still be impacted in scenarios with high input frequency or complex backgrounds [22].
* Thermal Management: Continuous operation under heavy computational loads can lead to thermal throttling, reducing performance over time. Although the system incorporates cooling mechanisms, prolonged usage in high-temperature industrial environments may exacerbate this issue.
* Scalability Constraints: As the number of sensors or the frequency of image captures increases, the Raspberry Pi's limited computational power may struggle to handle the load, necessitating distributed processing or hardware upgrades for larger-scale deployments [13].
# Transient Responses of Repeated Transverse Impacts on Beams
## 5 Results and Discussion
### Transient Impact Behaviors
#### 5.3.3 Accumulated Permanent Indentation  
As shown in Figure 13a,b, the accumulated permanent indentation of the last impact for every impact velocity are analyzed. The accumulated permanent indentations predicted by eigenfucntion expansion method and spring-mass method are compared with the experimental results. The plots show that the accumulated permanent indentation predicted by eigenfucntion expansion method agree well with experimental results than spring-mass method. It means that the wave propagation has obvious influence on the  
Figure 11: Contact force-Indentation relations by eigenfunction expansion method and spring-mass method (with Thornton contact model): (**a**) the 1 st impact, \(V_{01}\) = 0.990 m/s; (**b**) the 91 st impact, \(V_{091}\) = 2.656 m/s.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Theoretical Methods**} & \(\mathbf{P}\) & \(\mathbf{W}\) & \(\mathbf{\Delta E}\) & \(\mathbf{U_{a}}\) & \(\mathbf{P}\) & \(\mathbf{W}\) & \(\mathbf{\Delta E}\) & \multirow{2}{*}{\(\mathbf{U_{a}}\) (N.m)} \\  & **(N.s)** & **(N.m)** & **(N.m)** & **(N.m)** & **(N.s)** & **(N.m)** & **(N.m)** & **(N.m)** \\ \hline \multirow{4}{*}{Eigenfucn expansion method Spring-mass method} & \multicolumn{4}{c}{\(V_{01}\) = 0.990 m/s} & \multicolumn{4}{c}{\(V_{091}\) = 2.656 m/s} \\ \cline{2-9}  & 1.235 & 0.383 & 0.678 & 0.295 & 3.282 & 0.708 & 4.873 & 4.165 \\ \cline{1-1}  & 1.404 & 0.496 & 0.681 & 0.185 & 5.125 & 1.789 & 4.240 & 2.451 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impulse (\(P\)), contact work (\(W\)), energy absorption of the beam (\(U_{a}\)) and kinetic energy loss of the sphere (\(\Delta E\)) of eigenfunction method and spring-mass method for the first and 91 st repeated impact.  
accumulated permanent indentation of repeated impacts. Figure 13a,b shows that the accumulated permanent indentation predicted by eigenfunction expansion method is maximally 44.46% and 69.97% less than those predicted by spring-mass method for UC model and MYC model, respectively. It means that by using different local contact models, large discrepancies can be obtained on simulating impact behaviors of eigenfunction expansion method. This demonstrates the importance of selecting appropriate local contact models.
## 5 Simulation Analysis  
### Parameter Setting  
At present, the main LEO constellations in the world include SpaceX, Oneweb, and Telesat, with no loss in generality. Since the orbital altitude of the Telesat system is in a middle position among many LEO constellations and its high inclination orbit is conducive to covering challenging environments, such as polar regions, we use the 99.5\({}^{\circ}\) inclination orbit constellation of the Telesat system for simulation and analysis. The main parameters of the Telesat system are shown in Table 1.  
For IMU model parameters, we set the relevant parameters of the accelerometer and gyroscope according to the requirements of a low-cost consumption level or medium level. Among these parameters, the scale factor and cross coupling error of the accelerometer and gyroscope are set at 100-1000 ppm, while the accelerometer bias is set in the range of 10-100 mg, the gyroscope bias is set in the order of \(10^{-7}\), and the quantization noise is set as \(10^{-4}\)m/\(s^{2}\)[33]. Regarding the UKF, considering the uncertainty of each axis, we set the initial uncertainty of position and velocity in each axis as 1 m and 0.01 m/s, respectively, and the initial uncertainty of the acceleration level and gyroscope axis are set as \(2.9420\times 10^{-4}\) and \(4.848\times 10^{-9}\), respectively. For the convenience of analysis, we assume that the initial attitude error, initial position error, and initial velocity error of the UKF are 0. In addition, we set the initial position of the aircraft as follows: Latitude, 50.4250\({}^{\circ}\) north; longitude, -3.5958\({}^{\circ}\) east; and altitude, 10,000 m. The initial velocity along the longitudinal axis of the carrier coordinate system is 200 m/s; the initial roll angle, pitch angle, and yaw angle are 0\({}^{\circ}\), 0\({}^{\circ}\), and 90\({}^{\circ}\), respectively; and the initial errors are all set to 0. At the same time, there are two 45\({}^{\circ}\) turns in the opposite direction during the flight of the carrier. The time in the simulation parameters is 418 s, and the sampling interval is 0.01 s.
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 4 The New Clustering Routing Approach
### Operation Step  
The operation step is the process where the CH nodes are selected to produce an optimal network structure by the LS-II algorithm. In this stage, once the optimal set of CHs has been selected, the information is transmitted to the BS. Each round, the data transmission is verified. One round refers to the invested time in which the information is transmitted from generical nodes to CHs and then to the BS. The network structure is updated in each round by executing the LS-II scheme in order to identify the optimal structure. The mechanism to select the CHs and the optimal network structure is explained in the following subsections.
# Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks
## 4 Simulation Results and Discussion
For example, when the reconstructed signal is required to be accurate as much as possible, it is suitable to increase the number of selected sensor nodes and apply Bayesian estimation with an optimized selection scheme to reconstruct the signal. On the contrary, when the reconstruction error is not limited strictly, it is better to decrease the number of selected sensor nodes and use the OMP algorithm with an optimized selection scheme to reconstruct the signal for prolong  
Figure 1: Reconstruction error versus different number of selected sensor nodes. Figure 3 shows the remaining energy of the sensor network versus the number of selected sensor nodes. It can be observed that the remaining energy of sensor network grows when the number of selected sensor nodes increases. We can see that the optimized sensor nodes' selection scheme has more energy efficiency than the conventional sensor nodes' selection scheme. For example, when the number of selected sensor nodes is 30, the optimized scheme makes full use of 2.3 times energy to prolong network lifetime more. In Figure 4, the remaining energy of each sensor node is provided. The number of selected sensor nodes is set as 25. It can be seen that the remaining energy of each sensor is homogeneous for the optimized sensor nodes selection scheme. However, for the conventional sensor nodes selection scheme, the remaining energy of each sensor is disorderly. The remaining energy of some sensor nodes is few, and it is adequate for many sensor nodes. Thus, the use of random node selection scheme leads to the wasting of total energy. Figure 3: Remaining energy versus different number of selected sensor nodes. Figure 2: Network lifetime versus different number of selected sensor nodes. Figure 5 shows the energy efficiency versus different number of selected sensor nodes. The definition of energy efficiency is the proportion of network lifetime and the total energy cost. It represents the average time of information acquisition per unit energy. When the number of selected sensor nodes increases, the energy efficiency is decreasing because, as the number of selected sensor nodes grows larger, the energy cost in each information acquisition is also increased. The performance with the optimized sensor nodes selection scheme is better than the conventional sensor nodes selection scheme. By using the same energy cost, the proposed scheme can achieve more information acquisition times compared with the traditional scheme. Therefore, it can prolong the network lifetime.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## Network Model and Problem Analysis
### Problem Analysis  
The MPR selection mechanism is central to the OLSR protocol. Its purpose is to ensure that nodes in the network periodically send topology control information to all two-hop neighbors to quickly acquire the overall network topology while minimizing the retransmission of topology control packets, thereby reducing network control overhead [18; 19; 20]. However, the traditional MPR selection algorithm's preference for nodes with high coverage can lead to redundant MPR nodes, which increases the flooding of TC messages and consequently causes exponential growth in the network overhead. Given the limited channel resources, this heightens the risk of network congestion, potentially leading to the accumulation and expiration of control packets, route failures, or even network paralysis. Hence, MPR sets should be chosen based on the necessity of connection. If other MPR sets' two-hop neighbors also cover a node covered by an MPR set's two-hop neighbor, it indicates that this node is redundant in the MPR set and should be removed.  
The traditional OLSR protocol uses only hop count as the standard for route establishment and employs the Dijkstra algorithm to compute and generate node routing information. This approach needs to pay more attention to the quality of links in real  
Figure 1: LoRa distributed multi-hop network model.  
environments, making it challenging to accommodate situations with high traffic volumes or uneven network conditions. This can result in untimely route updates, link breaks, and traffic congestion. Therefore, considering only hop count may not optimize path selection [21; 22; 23]. LoRa communication parameters should be allocated according to different service requirements to adapt flexibly to various service scenarios. Considering that terminal devices in a distributed network may act as relay devices for multiple services during transmission, route establishment should dynamically select the optimal route for data transmission based on communication delay constraints, link reliability constraints, and node load conditions, aiming to achieve load-balanced routing optimization.
## 4 Results and Analysis
### Results of Simultaneous Equations Model
#### 4.2.2 Impact of Air Pollution on Industrial Structure  
Investigating the industrial development of air pollution in the Beijing-Tianjin-Hebei region is a matter of particular concern in this paper. The results of the simultaneous equations show that, under the same conditions, every increase in PM2.5 concentration will increase the proportion of secondary industry by about 0.08%-0.28%. Comparing the coefficient estimation results (0.08%-0.28%) of the simultaneous equations in Table 4 with the coefficient estimation results (0.09%-0.21%) of the single equation, it can be found that the 3 SLS estimate for the PM2.5 concentration in the simultaneous equation model has a larger range of variation compared with the estimation result of the single equation model, indicating that the impact of the air pollution assessment on secondary industry is uncertain. The results of the simultaneous equations for the development of tertiary industry and air pollution indicate that, under the same conditions, for every 1% increase in PM2.5 concentration, the proportion of tertiary industry will decrease by about 0.13% to 0.31%. Comparing the coefficient estimation results (0.13%-0.31%) of the simultaneous equations in Table 5 with the coefficient estimation results (0.08%-0.22%) of the single equations, it can be found that compared with the estimation results of the single equation models, the estimated value of 3 SLS for PM2.5 concentration is higher and more significant, which indicates that the single-equation model underestimates the negative impact of increased PM2.5 concentration on the development of tertiary industry. To sum up, PM2.5 concentration can have a significant impact on the regional industrial structure. The specific performance lies in the fact that reducing PM2.5 concentration can promote a decrease in the proportion of secondary industry and increase the proportion of tertiary industry, thereby achieving industrial upgrading.  
For the control variables that explain industrial development in the simultaneous equation model, the estimation results of the coefficients are in line with expectations. Fixed asset investment, technological innovation, and opening up to the world indeed play an irreplaceable role in the industrial development of the Beijing-Tianjin-Hebei region, but it has a different impact on secondary industry and tertiary industry. Specifically, the increase in fixed asset investment and opening up to the world has played a positive role in the development of secondary industry, and technological innovation has promoted the development of tertiary industry.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### CFAR Detector Based on K-Distribution
#### 2.2.1 Parameter Estimation on K-Distribution Model  
The probability statistical model of the K-distribution [28] is as follows:  
\[p(x)=\frac{2}{a\Gamma(v)}\Big{(}\frac{x}{2a}\Big{)}^{v}K_{v-1}\Big{(}\frac{x}{a} \Big{)}, \tag{2}\]  
where, \(\Gamma(\cdot)\) is the Gamma function; K\({}_{v-1}(\cdot)\) is the second type modified Bessel function of order \(v\)-1; a is the scale parameter; and, \(v\) is the shape parameter. For most sea clutter, the shape parameter values range is \(0.1<v<\infty\). When \(v\)\(\rightarrow\)\(\infty\), the distribution of the clutter is close to the Rayleigh distribution.  
Subsequently, we chose the statistical estimation method to estimate the parameters. From the K-distribution model expression, the r-moment of the K-distribution can be obtained, as shown in Equation (3):  
\[\mu_{r}=E[x^{r}]=\frac{\Gamma(v+0.5r)\Gamma(1+0.5r)}{\Gamma(v)}(2a)^{r}. \tag{3}\]  
According to the properties of the Gamma function presented in Equation (4), we can obtain Equation (5).  
\[\Gamma(x+1)=x\cdot\Gamma(x), \tag{4}\]  
\[z_{r}=\frac{\mu_{r+2}}{\mu_{r}}=(2+r)a^{2}(2v+r). \tag{5}\]  
It can be seen from Equation (5) that the moment estimation method only requires that multiple \(z_{r}\) groups be calculated. For sea clutter samples \(\{x_{i};i=1,2,\cdot\cdot\cdot,N\}\), the k-order sample moment of the K-distribution is expressed by Equation (6):  
\[\mu_{k}=\frac{1}{N}\sum_{i=1}^{N}x_{i}^{k}. \tag{6}\]  
Random sample moments are used instead of overall moments and an inverse transformation is performed to obtain the scale parameter \(a\) and shape parameter \(v\).
## 4 Object Classification Network DSDCN-ResNet
### ResNet in SAS Image Classification  
In recent years, convolutional neural networks have shown excellent performance in object classification tasks. Compared with shallow neural networks, deep neural networks have more nonlinear mapping structures, and with the deepening of network layers, their nonlinear expression ability is stronger, which is more beneficial for abstract feature acquisition. However, the increase of deep network layers brings the problem of gradient extinction. The residual module in ResNet networks allows for shortcut connections between convolutional layers, which can effectively avoid this problem in backpropaga  
Figure 5: Sampling position of DSDCN on objects in SAS Image. (**a**) Real object A. (**b**) Real object B. (**c**) Real object C. (**d**) Real object D. The red point is the position of the sampling points of deformable convolution.  
tion [34]. The structure of the residual module is shown in Figure 6, where \(x\) is the input and \(F(x)\) is the residual mapping. In Figure 6a, the output of the residual module is  
\[H(x)=F(x)+x, \tag{3}\]  
when \(F(x)=0\), and the residual module realizes identity mapping. The output of layer \(L\) is:  
\[H(x_{L})=x_{l}+\sum_{i=l}^{L-1}F(x_{i}), \tag{4}\]  
In Figure 6b, when the input and output dimensions of the residual block are different, a convolutional layer is added to the shortcut connection to perform a linear transformation to obtain a valid output. The model learns only the input and output of the residual blocks during training and directly transfers the error to the upper layer through a shortcut connection, which has good performance in image feature extraction [35].  
Since the residual blocks in ResNet use traditional convolution kernels with fixed sizes and shapes, which can only achieve the extraction of fixed local features, the original ResNet cannot be directly applied to the object classification of SAS images.
## 5 Conclusions  
We identified a major quantitative trait locus (QTL), _qSCM4_, controlling the resistance to lodging, the number of grains per panicle, the number of primary and secondary branch, basal culm diameter, and basal culm wall thickness in multiple years and multiple environments. The physical interval of _qSCM4_ was narrowed to 58.5 kb including 11 genes by using a residual heterozygous derived segregant population and a BC3F2 segregant population. DNA sequencing revealed the most likely candidate gene for _qSCM4_ was _Os04g0615000_, which encoded a functional protein with serine and cysteine structural domains. There were 13 DNA variations between LTH and SN265 in this gene, including a fragment deletion, two base changes in the 3' UTR region, six base changes in the exons, and four base changes in the introns. A near-isogenic line carrying _qSCM4_ showed improved lodging resistance through increasing stem thickness by 25.3% and increasing stem folding resistance by 20.3%. Additionally, it was also discovered that _qSCM4_ enhanced the primary branch per panicle by 16.7%, secondary branch per panicle by 9.9%, and the number of grains per panicle by 14.7%.  
Conceptualization, S.J. and X.Z.; methodology, X.Y., J.W. and L.W. ([PERSON]); software, M.Z., M.L., L.C. and R.L.; validation, G.L., Z.C., Y.L. ([PERSON]) and L.W. ([PERSON]): formal analysis, S.J.; investigation, B.S.; resources, S.J. and Y.L. ([PERSON]); writing--original draft preparation, X.Y. and S.J.; writing--review and editing, S.J.; funding acquisition, X.Y., X.Z. and S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Heilongjiang Province of China, Grant/Award Number: YQ2019C020; Fundamental Research Funds for the Research Institutes of Heilongjiang Province, Grant/Award Number: CZKYF2021B009, CZKYF2022-1-B004; National Natural Science Foundation of China, Grant/Award Number: 32071889; Heilongjiang Province Agricultural Science and Technology Innovation Project, Grant/Award Number: 2020 FJZX049, 2020 FJZX003, 2021 CQJC003, 2021 QKPY009; The Opening Project of the Collaborative Innovation Center Co-sponsored by Liaoning Provincial Government and Ministry of Education for Northeast Japonica Rice Genetic Improvement and High Efficiency Production, Grant/Award Number: KF2022-08.  
The authors declare no conflict of interest.