esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Analysis of Measured Fatty Acid  
According to the statistics, 135 samples were classified as grade1 (suitable storage) and their fatty acid values were in the range of 12.11-25.0 mg/100 g; 104 samples were classified as grade2 (mildly unsuitable storage), and their values were in the range of 25.13-35.0 mg/100 g; and 46 samples were classified as grade3 (severely unsuitable storage), and their values were in the range of 35.15-67.90 mg/100 g. The statistical results of the measured fatty acid values of paddy are shown in Table 1. Among them, the mean and variance of the calibration set sample (n = 190) were 24.15 and 10.67 mg/100 g, while those of the test set sample (n = 95) were 22.47 and 9.27 mg/100 g, respectively. In terms of mean and variance, the results of the two datasets were relatively close, which indirectly indicated that the division of the dataset was more reasonable. In addition, the range of fatty acid content in the test set (12.98-60.21) was precisely covered by the calibration set range (12.11-67.90), which met the basic modeling requirements, further indicating that the division of the sample dataset had a certain degree of rationality. Furthermore, the frequency plot of the samples of both the calibration and test sets based on their measured fatty acid values is shown in Figure 2. From Figure 2, it can be seen that most paddy samples were in a suitable or mildly unsuitable state for storage (\(\leq\)25.0 mg/100 g), with only a small number of samples having a fatty acid value greater than 35.0 mg/100 g and being in a severely unsuitable state. The overall data distribution of the three class samples was significant and could serve as a reference for classification models. However, there were easily mixed status samples at the threshold edges of 25 mg/100 g and 35 mg/100 g, which may affect the performance of the model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Calibration Set** & **Test Set** \\  & **(n = 190)** & **(n = 95)** \\ \hline Mean & 24.15 & 22.47 \\ Variance & 10.67 & 9.27 \\ Maximum & 67.90 & 60.21 \\ Minimum & 12.11 & 12.98 \\ Range & 12.11–67.90 & 12.98–60.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured values of fatty acid in paddy based on the calibration and test sets.
# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
## 4 Impact of Quantization of Bluetooth RSSI Values
### Statistical Analysis
However, these results do not consider an in-depth analysis of RSSI distribution. Observations from datasets of 3 ft (Sit and Stand) histograms indicated that the different distribution shapes of the RSSI occurred for varying poses. The reason is that the upper and lower bounds of measurable RSSI at each distance cause other forms of distributions. Figures 4 and 5 illustrate slightly skewed RSSI distributions measured from 3 ft (Sit and Stand). Two samples of RSSI histograms were collected from 3 ft (Sit) and 3 ft (Stand) in Figures 4 and 5. This study compared the sitting pose with the standing posture for three minutes at 3 ft--the histogram of 3 ft (Sit) is shown in the left-skewed RSSI distribution in Figure 4. The histogram of 3 ft (Stand) is shown as an almost normal-skewed RSSI distribution in Figure 5. The authors of [32] report a normal distribution where the measurement in that study is taken inside an office room. However, the fact is that people's poses can influence the distribution of RSSI. The samples are predominantly concentrated between \(-65\) dBm and \(-50\) dBm in the sitting pose and between \(-60\) dBm and \(-50\) dBm in the standing pose. In other words, different poses slightly change the distribution of RSSI. RSSI distribution is closer to the normal distribution in the standing posture. In addition, it can be observed from Figures 4 and 5 that different postures cause different ranges of variation in RSSI, which can cause inconsistencies to occur in mean RSSI values of these two poses, which may form their respective characteristics. From the above analysis, we conclude that there is a combination of mean value and skewness characteristics of RSSI in different poses. In the case of close distance, the current posture of ship passengers can be identified based on the mean and skewness of RSSI.
# Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings
## 2 Physical Models and Numerical Approach  
### Large-Eddy Simulation  
In this study, the LES method is used to simulate the wake turbulence of the wind turbines. The incompressible fluid is divided into large-scale and small-scale eddies by filtering, which are calculated, respectively. The resolved-scale (large-eddy scale) dynamics are obtained by solving the spatially filtered, incompressible Navier-Stokes equations, while the small scales are resolved implicitly by the sub-grid scale (SGS) model. It consumes fewer computing resources than direct numerical simulation (DNS), but can achieve higher accuracy than Reynolds-Averaged Navier Stokes (RANS) simulations [23].  
The filtered momentum equation is [24]:  
\[\left\{\begin{array}{l}\frac{\partial a_{i}}{\partial t}+\frac{\partial\left( a_{i}a_{j}\right)}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial p}{\partial x_{i}}+ \
u\frac{\partial^{2}a_{i}}{\partial x_{i}a_{j}}-\frac{1}{\rho}\frac{\tau_{ ij}}{\partial x_{j}}+\frac{1}{\rho}\mathbf{f_{i}}\\ \frac{\partial a_{i}}{\partial x_{i}}=0\end{array}\right. \tag{1}\]  
where \(u\) is the filtered velocity, \(\rho\) is the filtered pressure, \(t\) is the time, \(\rho\) is the air density which is about 1.225 kg/m\({}^{3}\), \(\
u\) is kinematic viscosity, \(\tau_{ij}\) refers to the sub-grid scale (SGS) stress, \(\mathbf{f_{i}}\) is the force exerted by the actuator line turbine model.  
The simulations in the present study are carried out with SOWFA, which is developed by [PERSON] et al. [12; 13] based on OpenFOAM v-2.4.x. The pressure-implicit with splitting of operators (PISO) method is used for solving the momentum equation. Time and space discretization are performed in the Backward format and Gaussian linear format.
## 3 Results
### Stripline Incorporating the Proposed Design Methodology
#### 3.2.3 The Optimal Design of the Density of Air Cavities  
For the proposed design methodology, the density of air cavities is directly related to the reduction in loss. To investigate this relationship, results for different values of \(s_{\text{a}}\) are compared, while keeping other parameters constant, as shown in Figure 10a,b. It can be observed that when \(s_{\text{a}}\) increases from 0.3 mm to 0.6 mm, the return loss improves by an average of 6 dB, while the attenuation constant slightly deteriorated by 3.84% on average. This suggests that denser air cavities further reduce the equivalent loss tangent,  
Figure 8: Attenuation constant comparison for the stripline.  
Figure 9: Return loss comparison for the stripline.  
although sparser cavities offer the advantage of an improved return loss due to fewer discontinuities in the transmission line. Therefore, a compromise between cavity density and fabrication constraints is recommended. Additionally, it is noteworthy that doubling \(s_{\text{a}}\) results in only a slight variation in the performance, indicating that the proposed design methodology has a broad fabrication tolerance, making it suitable for implementation with many standard processes.
# Self-Learning Power Control in Wireless Sensor Networks
## 6 Experiments
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Frequency channel & \(f\) & 26 \\ Distance transmitter-receiver & \(d\) & 8 m \\ Packet’ payload size & _payload_ & 8 Byte \\ Inter-arrival time & \(\mu\) & 3 s \\ Number of packets in a window & \(N\) & 10 \\ Duty cycle & \(dc\) & 38.4\% \\ \hline Number of transmission power levels & \(n_{ptx}\) & 8 \\ Transmission power & \(P_{tx}\) & \(0,-1,-3,-5,-7,-10,-15,-25 dBm \\ Current draw (transmit mode) & \(i_{tx}\) & 17.4, 16.6, 15.2, 14, 12.8, 11, 9.9, 8.5 mA \\ Current draw (receiver mode) & \(i_{rx}\) & 18.8 mA \\ Current draw (idle mode) & \(i_{idle}\) & 20 uA \\ Current draw (sleep mode) & \(i_{sleep}\) & 1 uA \\ Voltage supply & \(V\) & 3 V \\ \hline Number of states & \(n_{s}\) & 64 \\ Number of maximum retransmissions & \(n_{rdr}\) & 3 \\ Number of maximum CCA attempts & \(n_{CCA}\) & 4 \\ \hline Learning factor & \(\alpha\) & 0.8 \\ Discount factor & \(\gamma\) & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters and settings used in the experiments.
###### Abstract  
Ensuring high reliability and low latency poses challenges for numerous applications that require rigid performance guarantees, such as industrial automation and autonomous vehicles. Our research primarily concentrates on addressing the real-time requirements of ultra-reliable low-latency communication (URLLC). Specifically, we tackle the challenge of hard delay constraints in real-time transmission systems, overcoming this obstacle through a finite blocklength coding scheme. In the physical layer, we encode randomly arriving packets using a variable-length coding scheme and transmit the encoded symbols by truncated channel inversion over parallel channels. In the network layer, we model the encoding and transmission processes as tandem queues. These queues backlog the data bits waiting to be encoded and the encoded symbols to be transmitted, respectively. This way, we represent the system as a two-dimensional Markov chain. By focusing on instances when the symbol queue is empty, we simplify the Markov chain into a one-dimensional Markov chain, with the packet queue being the system state. This approach allows us to analytically express power consumption and formulate a power minimization problem under hard delay constraints. Finally, we propose a heuristic algorithm to solve the problem and provide an extensive evaluation of the trade-offs between the hard delay constraint and power consumption.  
ultra-reliable and low-latency communication; hard delay constraints; short-packet transmission; finite blocklength coding; cross-layer scheduling +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion  
### Influence of Ground Thermal Properties on Heat Exchange Phenomena  
During a preliminary phase of analysis to understand the extent of the variation in thermal outputs associated with the considered WBHE technologies, a sensitivity analysis was performed on the different underground thermal properties. Starting from the analysis of Equation 6, describing the thermal resistance due to conductive heat transfer in the rock, we proceeded to the graphic elaboration of curves of the variation of the resistance parameter over time with the values of density (\(\rho\)), volumetric heat capacity (\(\rho\)c\({}_{\text{s}}\)) and the thermal conductivity of rock (\(\lambda\)c\({}_{\text{s}}\)) change (Figure 6a-c). Moreover, an evaluation of the impact of thermal diffusivity value (\(\alpha\)) variation in the \(R_{\text{s}}\) parameter was reported in Figure 6d.  
Different input values associated with thermal properties subject to the performed sensitivity analysis were chosen in order to properly cover a significant range for the types of geological formations involved (sedimentary rocks) [28; 29]. The proposed analysis has been useful for understanding how thermal outputs change as a consequence of the variations in the input data of ground thermal properties. The ground thermal properties directly influence ground resistance values (\(R_{\text{s}}\)), as highlighted in Equation (6). The highest wellhead temperature variation is caused by changes in thermal conductivity values. The implementation in the proposed models of proper values of geological formation thermal properties (case studies-specific stratigraphy) turns out to be fundamental for improving the quality of the performed heat exchange analysis.
## 1 Introduction
NDI control produces many different forms by adjusting the controller structure or combining with other controllers. The NDI control based on L1 control is a representative direction. Ref. [24] proposed a new adaptive NDI control method based on L1 adaptive structure, called L1 Adaptive Nonlinear Dynamic Inversion (L1-ANDI), which can overcome the influence of interference and model uncertainty while ensuring the required dynamic performance. Ref. [25] also tried to introduce an L1 adaptive structure into NDI control to realize an adaptive DNI control method of an L1 adaptive structure with excellent dynamic performance and strong robustness. Combining it with a sliding mode control is another idea. Ref. [26] proposed a fault-tolerant control method based on an adaptive sliding mode, which can reduce the influence of time delay and sensor intermittent fault on the system and improve the stability of the inner loop controller. Ref. [27] combined baseline NDI control with advanced robust adaptive integral sliding mode (ISM) control to achieve acceptable flight quality at a high angle of attack under the influence of unknown disturbances and aerodynamic uncertainties. Adaptive NDI control is widely used in damage reconstruction. Ref. [28] proposed a reconfigurable nonlinear dynamic inversion (RNDI) controller, which combines a nonlinear disturbance observer (NDO) and improved NDI control for attitude control of structurally damaged aircraft. Adaptive NDI control is also used in variable structure aircraft. In Ref. [29], an NDI flight controller is designed for the flight control of the swept wing morphing aircraft. Additional feedback is added to the nonlinear cancellation channel to improve the control law, which eliminates the influence of interference force, interference torque, and nonlinear cancellation error to a certain extent. NDI control is also used in the controller design of small UAVs [30]. In the engineering field, the traditional control method is often used. In past research, many results focused on the improvement of control effects through adaptive control methods. Most of these results remain in the theoretical research stage, and the engineering application value is not high. This paper innovatively combines the frequency domain recursive aerodynamic parameter identification method with the NDI control method commonly used in the engineering field and realizes the adaptive NDI control method based on the identification results. On the one hand, this method introduces adaptive control to improve the control effect of the controller in the face of model uncertainty. On the other hand, the recursive identification method can ensure the real-time effect of identification and meet the needs of engineering applications. Compared with previous studies, the method proposed in this paper takes into account the needs of engineering control methods while improving the flight control effect, and has a good improvement in control effect and practicability.
## 6 Conclusions  
In conclusion, we have presented a weakly-coupled double bow-tie multi-ring elliptical core multi-mode fiber capable of supporting 22 independent eigenmodes across the C+L+U band. The proposed fiber structure, incorporating bow-tie air holes, a multi-ring elliptical core, and bow-tie stress-applying areas, exhibits enhanced modal birefringence and effectively separates adjacent eigenmodes. The proposed fiber design utilizes a multi-ring elliptical core created by inserting a high-index ring and adding an outer trench, resulting in improved separation and transmission between adjacent eigenmodes. The bow-tie air holes lead to enhanced modal birefringence and improved separation of adjacent eigenmodes. The bow-tie stress-applying areas induce stress on the fiber, further enhancing modal birefringence and improving polarization maintaining performance.  
Through parameter optimization, the proposed fiber achieves a \(\Delta n_{\text{eff}}\) between adjacent eigenmodes larger than 3.089 \(\times\) 10\({}^{-4}\) over the whole C+L+U band. The fiber has a high effective area (66.8-96.39 \(\upmu\)m\({}^{2}\)), low differential mode delay (\(-\)15.41-12.4 ns/km), and negligible bending loss (\(<\)10\({}^{-4}\) dB/m). Overall, our simulation results suggest that the proposed DBT-MREC-MMF holds great promise for applications in eigenmode-division multiplexing transmission, offering significant bandwidth improvements and increased transmission capacity. By analyzing and optimizing the structural parameters, the proposed fiber has potential applications in eigenmode-division multiplexing transmission to increase bandwidth and improve transmission capacity.  
Conceptualization, F.R. and Y.C.; methodology, F.R.; software, Y.C.; validation, X.L.; investigation, Y.C.; data curation, Y.L. and D.Z.; writing--original draft preparation, Y.C.; writing--review and editing, X.L.; supervision, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (FRF-TP-19-016A2).  
Not applicable.  
The data presented in this study are available upon reasonable request.  
The authors declare no conflict of interest.
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 2 Model
### Measurement Model  
In this subsection, we will introduce the radar measurement model. Let us denote the measured set of multipath echo as  
\[\mathbf{Z}=\{\mathbf{z}^{j}\}_{j=1}^{N_{m}}, \tag{10}\]  
where \(N_{m}\) is the number of detections, and \(\mathbf{z}^{j}\) is the measurement vector composed of TOA and DOA pairs as  
\[\mathbf{z}^{j}=[\tau^{j},\phi^{j}]^{T}. \tag{11}\]  
Figure 1: Ray-tracing simulation examples: (a) 3D ray-tracing; (b) 2D ray-tracing.  
The radar detection process will produce false alarms and noise; therefore, the measured set \(\mathbf{Z}\) is not the same as the theoretical multipath parameter set \(\mathbf{H_{x}}\) of the target. The measurement set consists of two parts denoted by  
\[\mathbf{Z}=\mathbf{C}\cup\mathbf{Z_{x}}, \tag{12}\]  
where the first part \(\mathbf{C}\) denotes false alarms generated from clutter, and the second part \(\mathbf{Z_{x}}\) denotes measurements from the detected multipath components of target \(\mathbf{H_{x}}\).  
The clutter \(\mathbf{C}\) is modeled as a Poisson point process (PPP) with Poisson rate \(\lambda\); thus, the number of clutter measurements is a Poisson distribution with the probability density function (PDF) given by  
\[p(|\mathbf{C}|=n)=e^{-\lambda}\frac{\lambda^{n}}{n!},n=0,1,\cdots \tag{13}\]  
Each clutter measurement \(\mathbf{c}\in\mathbf{C}\) is randomly distributed in the measurement space independently with the PDF given by  
\[p(\mathbf{c})=\frac{1}{V}, \tag{14}\]  
where \(V\) is the volume of the measurement space. The measurement vector \(\mathbf{z}\) is specified by the radar TOA and DOA range as  
\[\tau\in[\tau_{min},\tau_{max}],\phi\in[\phi_{min},\phi_{max}], \tag{15}\]  
thus the volume \(V\) is derived as  
\[V=(\tau_{max}-\tau_{min})\cdot(\phi_{max}-\phi_{min}). \tag{16}\]  
The second part \(\mathbf{Z_{x}}\) is detections from the target. Each path of multipath echos from the target either generates one measurement with detection probability \(P_{D}\), or becomes a missed detection with probability \(1-P_{D}\). The measurement noise for each detected path is modeled as an additive zero-mean Gaussian white noise \(\mathbf{w}\); therefore the detected set \(\mathbf{Z_{x}}\) is the union of detections of each path expressed as  
\[\mathbf{Z_{x}}=D(\mathbf{h_{x}^{1}}+\mathbf{w})\cup\cdots\cup D(\mathbf{h_{x} ^{N_{p}}}+\mathbf{w}), \tag{17}\]  
where \(D(\cdot)\) denotes for the detection process which is a Bernoulli distribution with probability parameter \(P_{D}\) denoted as  
\[\begin{cases}\Pr(D(\mathbf{h_{x}}+\mathbf{w})=\{\mathbf{h_{x}}+\mathbf{w}\}) =P_{D},\\ \Pr(D(\mathbf{h_{x}}+\mathbf{w})=\varnothing)=1-P_{D}.\end{cases} \tag{18}\]
## 5 Experimental Results  
### Analysis of Deformation Characteristics of Tunnel Anchorage Surrounding Rock  
According to the multi-point displacement meter test results of D2, the deformation of its free face significantly increases with the increase in load, as shown in Figure 11a. Under the design load, the maximum deformation of the free face is 0.046 mm. Under 3.5 times the design load, the maximum deformation of the free face is 0.158 mm. Under 7 times the design load, the maximum deformation of the free face is 0.509 mm. With the increase in load, the deformation of the free face increased by 3.4 times and 11.1 times, respectively, and the larger the load, the greater the growth rate of deformation.  
According to the monitoring data of the inclined holes C1 and C2 between the double anchors, the model anchor shows significant horizontal displacement deformation on the front and rear anchor surfaces during the load application process. Under the design load, the maximum deformation is 0.18 mm. Under 3.5 times the design load, the maximum deformation is 0.24 mm. Under 7 times the design load, the maximum deformation is 0.57 mm. As the load increases, the horizontal deformation of the rock mass between anchors increases by 1.3 times and 3.2 times, as shown in Figure 12.  
Figure 10: Loading jack system.  
The surrounding rock between anchors also shows weak horizontal deformation below the anchorage. The front anchor surface exhibits obvious horizontal displacement within the range of 3.0-6.5 m depth, while the rock mass above 3.0 m in depth shows backward tilting deformation, with a horizontal displacement range of about twice the width of the anchor body. The rear anchor surface shows overall the horizontal deformation from the anchor to the ground rock mass.  
According to the analysis of the deformation monitoring results of the sliding micrometer at the free face, front anchor face, and rear anchor face from H1 to H4, as the load level increases, the deformation at the free face and front anchor face will gradually increase. The deformation trend of the axis of the rear anchor surface is relatively disorderly. Overall, the deformation of the lateral rock mass is slightly greater than that of the upper rock mass of the anchor plug, as shown in Figures 11b and 13.  
Figure 11: Deformation characteristics of tunnel anchorage surrounding rock. (**a**) Deformation of the free face of the D2 surrounding rock between anchors; (**b**) deformation of the sliding micrometer on the rear anchor surface.  
Figure 12: Cumulative deformation curves inside the hole. (**a**) C1 Rear anchor surface; (**b**) C2 Front anchor surface.
# Communication Protocols of an Industrial Internet of Things Environment: A Comparative Study
## 7 Discussion
This led to a unified message structure, unified data processing, and hence the interoperability problem was solved. In conclusion, the choice of solution is totally dependent on the requirements of the industrial application. The MODBUS TCP builds the IIoT environment if publish-subscribe communications are not required, representing a secured solution that fulfills the requirements of most IIoT applications. If M2M communications are required, MQTT can be used, although it doubles the payload of the publish message in order to solve the interoperability problem.