esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Gough-Stewart Platform Dynamics
#### 2.7.2 Wind Load Aerodynamics  
Climatic conditions at the outdoor antenna introduce exogenous (disturbance) forces to the system throughout the large area of the antenna plate, i.e., mainly wind-induced aerodynamic generalized forces \(\tau_{D}\). Assuming that the wind velocity is modeled in world-frame coordinates as \(\tau_{w}=\begin{pmatrix}u_{w},&0,&0\end{pmatrix}^{T}\), then the wind-induced torques depend on the geometry and orientation of the antenna. Consider, without loss of generality, that \(\tau_{D}\) arises at a pressure point where all forces are concentrated as follows  
\[\dot{f}_{a}^{(w)}=\frac{1}{2}\rho\begin{pmatrix}A_{x}(y)\;C_{D}\;|u_{w}|\;u_{ w}\\ -A_{y}(y)\;C_{L}(y)\;|u_{w}|\;u_{w}\\ A_{z}(y)\;C_{L}(y)\;|u_{w}|\;u_{w}\end{pmatrix}\;\in\mathbb{R}^{3} \tag{30}\]  
where \(\rho\) is the air density, \(A_{i}(y)\) is the projection of the antenna area in the directions \(x,y,z\):  
\[A_{i}(y)=\max\bigl{(}A\bigl{|}\lambda_{a_{i}}(y)\bigr{|},A_{0}\bigr{)},\quad \forall\;i=\{x,y,z\};\]  
with \(A_{0}\) representing the antenna's minimal lateral exposed area, and \(C_{D}\) and \(C_{L}\) are the drag and lift coefficients, respectively, which are typically dependent on the angle of attack of the aerodynamic surface \(\alpha_{a}\). For simplicity, consider a circular flat plate to represent the wind current opposing force at the antenna, implying a constant drag coefficient of \(C_{D}=2\) and a variable lift coefficient \(C_{L}=C_{L}(\alpha_{a}(y))\) with an aerodynamic angle of attack \(\alpha_{a}(y)\) dependent on the antenna's attitude as follows  
\[C_{L}(y) =\begin{cases}0.1\alpha_{a}&\text{if }\alpha_{a}\in(-20^{\circ},20^{ \circ})\\ 0&\text{otherwise}\end{cases}\] \[\alpha_{a}(y) =\pi/2-\arcsin(c_{a}c_{\beta})=\pi/2-\arcsin(\lambda_{\alpha_{a}})\]Therefore, a simplified expression for the aerodynamic wind disturbance wrench \(\mathbf{F}_{D}\,\in\,\mathcal{F}\subset\mathbb{R}^{6}\) at the antenna, produced by (30), computed in terms of the platform's base coordinates yields to  
\[\mathbf{F}_{D}=\left(R_{w}^{0\,T}\mathbf{f}_{a}^{(w)},\ \ \ R(q)d_{p/p}\times R_{w}^{0\,T}\mathbf{f}_{a}^{( w)}\right)^{T} \tag{31}\]  
where \(d_{p/p}\in\mathbb{R}^{3}\) is the distance of the pressure point in the antenna _w.r.t._ the platform. The mapping between this aerodynamic wrench at the platform frame and the damping generalized forces in (29) is obtained after the virtual work principle as \(\mathbf{F}_{D}=A^{T}(\mathbf{z})\tau_{D}\). Therefore,  
\[\tau_{D}=A^{-T}(\mathbf{z})\mathbf{F}_{D} \tag{32}\]  
models a simplified approximation for the generalized aerodynamic forces produced by bounded persistent wind.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### IDEASSat Satellite Subsystems
#### 2.1.2 Thermal Control Subsystem (TCS)  
IDEASSat's thermal control design was based on passive temperature control. According to the thermal analysis and the recommendations of the sub-system datasheets, some of the sub-systems were to be augmented with heat conducting metal for use as  
Figure 1: IDEASSat component arrangement.  
heat distributors or polyimide film heaters to maintain temperatures within the operating temperature range. Take the battery module as an example, since the battery operating temperature range was 0-40 \({}^{\circ}\)C, a thin film heater was added as a heat source in the design of the battery module. Considering that the battery itself was a stable heat source while operating, a pressurized heat dissipation pad was also added between the battery and the securing fixture as a passive heat dissipation element. In the case of the S-band transmitter, an aluminum block was added to serve as a heat conductor between the transmitter and the spacecraft chassis. The UHF transceiver was directly mated to the inside of the spacecraft chassis to facilitate heat conduction.  
All sub-systems were equipped with temperature sensors, allowing FSW to determine the temperature status of each sub-system and switch the corresponding operation mode.
# Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
###### Abstract  
This paper focuses on optimizing the long- and short-term planning of the perishable product supply chain network (PPSCN). It addresses the integration of strategic location, tactical inventory, and operational routing decisions. Additionally, it takes into consideration the specific characteristics of perishable products, including their shelf life, inventory management, and transportation damages. The main objective is to minimize the overall supply chain cost. To achieve this, a nonlinear mixed integer programming model is developed for the multi-echelon, multi-product, and multi-period location-inventory-routing problem (LIRP) in the PPSCN. Two hybrid metaheuristic algorithms, namely genetic algorithm (GA) and multiple population genetic algorithm (MPGA), are hybridized with variable neighborhood search (VNS) and proposed to solve this NP-hard problem. Moreover, a novel coding method is devised to represent the complex structure of the LIRP problem. The input parameters are tuned using the Taguchi experimental design method, considering the sensitivity of meta-heuristic algorithms to these parameters. Through experiments of various scales, the hybrid MPGA with VNS indicates superior performance, as evidenced by the experimental results. Sensitivity analysis is conducted to examine the influence of key model parameters on the optimal objective, providing valuable management implications. The results clearly validate the efficacy of the proposed model and solution method as a reliable tool for optimizing the design problem of the PPSCN.  
perishable products; supply chain network; location-inventory-routing problem; genetic algorithm; hybrid metaheuristic algorithms 20231510711.  
1
## 3 Results
### Correlation Analysis between Radiance and the Dst Index during Auroral Events
Considering these findings, for this particular auroral disturbance event, the NO 5.3 \(\upmu\)m radiance at the altitude of 170 km exhibits the strongest correlation with the Dst index (R\({}^{2}_{\text{adj}}=0.88\)). The relationship between the two can be represented by the following equation:  
\[\text{Rad}=(-2.26232\text{e}-06)*\text{Dst}-(2.26337\text{e}-05) \tag{9}\]  
Although Equation (9) presented here only applies to the results at 55\({}^{\circ}\)S 160\({}^{\circ}\)W, 170 km point for this particular aurora event, the high correlation coefficient of 0.88 indicates the feasibility of establishing a parameterized formula between the Dst index and the NO 5.3 \(\upmu\)m radiance. This relationship has significant implications for future research on predicting the intensity range of the NO 5.3 \(\upmu\)m emission in auroras using the Dst index.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 3 Human Pose Estimation Method by Progressive Feature Fusion
### Loss Function  
In the proposed RGB-D network, designed for robust pose estimation under occlusions, a loss function is defined for each branch at every stage during training. At Stage \(r\), Branch 1 utilizes the loss function \(f^{r}_{\mathbf{L}}\), Branch 2 employs \(f^{r}_{\mathbf{S}}\), and Branch 3 applies \(f^{r}_{\mathbf{D}}\). The loss functions \(f^{r}_{\mathbf{L}}\), \(f^{r}_{\mathbf{S}}\), and \(f^{r}_{\mathbf{D}}\) are formulated utilizing the L2 norm as follows:  
\[\begin{split} f^{r}_{\mathbf{L}}&=\sum_{c=1}^{C} \sum_{P}\mathbf{W}(\mathbf{p})\cdot\left\|\mathbf{L}^{r}_{c}(\mathbf{p})- \mathbf{L}^{*}_{c}(\mathbf{p})\right\|_{2}^{2}\\ f^{r}_{\mathbf{S}}&=\sum_{j=1}^{J}\sum_{P}\mathbf{W }(\mathbf{p})\cdot\left\|\mathbf{S}^{r}_{j}(\mathbf{p})-\mathbf{Q}^{*}_{j}( \mathbf{p})\right\|_{2}^{2}\\ f^{r}_{\mathbf{D}}&=\sum_{j=1}^{J}\sum_{P}\mathbf{W }(\mathbf{p})\cdot\left\|\mathbf{D}^{r}_{j}(\mathbf{p})-\mathbf{Q}^{*}_{j}( \mathbf{p})\right\|_{2}^{2},\end{split} \tag{3}\]  
where \(\mathbf{W}(\mathbf{p})\) is a binary mask for a pixel \(\mathbf{p}\in\mathbb{R}^{2}\), which prevents loss contributions from false-positive predictions at joint locations that are not annotated in the ground truth (GT); \(\mathbf{L}^{r}_{c}\) is the vector fields predicted for limb \(c\) in Branch 1 at Stage \(r\); \(\mathbf{S}^{r}_{j}\) and \(\mathbf{D}^{r}_{j}\) are the confidence maps predicted for joint \(j\) in Branch 2 and Branch 3 at Stage \(r\), respectively; and \(\mathbf{L}^{*}_{c}(\mathbf{p})\) and \(\mathbf{Q}^{*}_{c}(\mathbf{p})\) are the ground truths of the confidence map and vector field, respectively.  
The total loss function \(f\) for training the proposed RGB-D network is formulated as follows:  
\[f=\sum_{r=1}^{R}(f^{r}_{\mathbf{L}}+f^{r}_{\mathbf{S}}+f^{r}_{\mathbf{D}})+ \sum_{r=R+1}^{T}(f^{r}_{\mathbf{L}}+f^{r}_{\mathbf{S}}). \tag{4}\]  
The total loss function \(f\) sums the vector field loss (\(f_{\mathbf{L}}\)) and the confidence map losses (\(f_{\mathbf{S}}\) and \(f_{\mathbf{D}}\)) over all stages. Since Branch 3 is not present after Stage \(T\), the \(f_{\mathbf{D}}\) term is excluded from the loss from that point onward.
## Materials and Methods
### Remote Terminal Unit
#### 2.4.1 Microcontroller  
The MCU is the central component of the RTU. The MCU used by the RTU is a dsPIC33 EP256 MU806, which is a 16-Bit digital signal controller designed for motor control. The MCU computes the position of the sun by using an astronomical solar position algorithm and calculates the angles of the heliostat to move it to the desired position according to OS received from the SU. Finally, the MCU controls the orientation of the heliostat using two pulse width modulation (PWM) signals that command the DC motors, comparing the desired position with the current position using the rotary encoders.
## 4 Results
### Signal Degradation Due to Foliage
In order to understand the effect of the obstruction on satellites/signals from other constellations, skyplots of signal strength indicators for C1C and C7Q signals of Galileo satellites E04, E07, E08, E11, E13, E15, E21, E24, E31, E34 and E36 are plotted, as shown in Figure 8 below. The Galileo signals appear to be quite robust, and although the signal strength is lower at the azimuthal range between 300 and 330\({}^{\circ}\), the signals are received throughout the period. Figure 5: Skyplot showing the visibility of the satellites during 2:00–3:00 h on 4 th and 5 th September 2022. (The satellite numbers G02, G04 etc. in this case mean the GPS satellite numbers. The numbers on the skyplot indicate increasing values of azimuth in the clockwise direction and increasing values of elevation inwards in the plot). Figure 6: Plot of SSI for code measurements vs azimuth for GPS satellite G04 (during the stationary period).
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 6 Numerical Studies
### JBG Optimization  
Based on the formula (10), we proceed with the JBG optimization. We begin by investigating the impact of the blocklengths on the average PAoI, assuming a fixed group size. Figure 4 shows the average PAoI \(\tilde{A}=\tilde{A}(K,L_{1},L_{2})\) obtained from (10) for varying blocklengths \(L_{1}\) and \(L_{2}\), with the group size fixed at \(K=2\) and the average SNR set to \(\rho=5\) dB. The figure shows that the average PAoI initially decreases and then increases as the blocklengths increase, attaining a minimum at an intermediate point. The circular  
Figure 3: Comparison between analytical and simulation results when \(L_{2}=200\) and \(K=2\). Results are presented for different SNR settings: \(\rho=1\) dB (**top**), \(\rho=3\) dB (**middle**), and \(\rho=5\) dB (**bottom**).  
marker on the surface in Figure 4 indicates the minimum point found through an exhaustive search, where \(\bar{A}\) reaches its minimum value of \(7.60\times 10^{3}\) at \((L_{1},L_{2})=(118,226)\). The star-shaped markers on the \(x\)- and \(y\)-axes denote the solutions to (19) and (20), showing that \(L_{1}^{*}(2)=118\) and \(L_{2}^{*}(2)=226\), which match the exhaustive search results.  
Now, we vary the group size \(K\) within the set \(\mathcal{K}\) and compute the optimal blocklengths \(L_{1}^{*}(K)\) and \(L_{2}^{*}(K)\) in (11) for each \(K\) by solving (19) and (20). Figure 5 presents the results, where \(L_{2}^{*}(K)\) increases with \(K\), whereas \(L_{1}^{*}(K)\) remains nearly constant with a slight decrease as \(K\) increases. This behavior arises because joint packet coding is performed at the sink node for second-hop transmission, making the blocklength \(L_{2}\) strongly dependent on the group size.  
The use of \(L_{1}=L_{1}^{*}(K)\) and \(L_{2}=L_{2}^{*}(K)\) minimizes the average PAoI when the group size is fixed at \(K\). The resulting average PAoI \(\bar{A}_{\text{min}}(K)\) is obtained from (12) and shown in Figure 6 for \(K\in\mathcal{K}\). The average PAoI is minimized at \(K=2\) for each SNR \(\rho\in\{1,3,5\}\) dB, meaning the optimal group size is \(K^{*}=2\) for these values of \(\rho\).  
Figure 4: Average PAoI \(\bar{A}=\bar{A}(K,L_{1},L_{2})\) in (10) for varying blocklengths \(L_{1}\) and \(L_{2}\), with the group size fixed at \(K=2\) and \(\rho=5\) dB.  
This result in Figure 6, combined with that in Figure 5, determines the optimal solutions \((K^{*},L_{1}^{*},L_{2}^{*})\) for the control variables \((K,L_{1},L_{2})\), as presented in Table 2. The minimum average PAoI achieved with \((K^{*},L_{1}^{*},L_{2}^{*})\) is calculated using (15) and given by \(\bar{A}_{\min}\in\{1.38\times 10^{4},1.01\times 10^{4},0.76\times 10^{4}\}\) for \(\rho\in\{1,3,5\}\) dB. Here, \(\bar{A}_{\min}\) decreases with \(\rho\) because a higher SNR improves the PER, allowing for a shorter blocklength.
## Materials and Methods
### Mapping Various Rice Disease Infested Areas
#### 2.4.1 Spectral Features for Mapping Diseases  
Considering the potential pathological impact of disease infestations mentioned above, six vegetation indices (VIs) that related to plant growth, vegetation coverage, and radiant absorption of pigments were selected for characterizing the biophysical variations caused by individual infestation: Normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI); Triangular vegetation index (TVI); Re-normalize difference vegetation index (RDVI); Modified Simple Ratio (MSR); and Structural Independent Pigment Index (SIPI). Among them, the NDVI is the best known VI for mapping the amount of green biomass in vegetation of low- to moderate density [47]; the SAVI is developed to minimize soil influences on canopy spectral, which has the potential of observing damaged fields with different proportions of soil exposure [48]; the TVI indicates the radiant energy absorption of chlorophyll [49]; the RDVI modified the NDVI to make the index more sensitive to high LAI values [50]; the MSR has great sensitivity to green LAI and resistant to atmosphere effects; and the SIPI indicates the ratio of carotenoids and chlorophyll a at the canopy scale. The definitions of these VIs in detail are listed in Table 2.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 6 Conclusions  
In this paper, we propose a novel secure time synchronization protocol against Sybil attacks for the centralized network employing a public neighbor forwarding mechanism based on reference broadcast, i.e., RSTS. The simulation results are given to show that RSTS is valid against Sybil attacks as well as manipulation attacks. Specifically, RSTS invalidates the potential malicious attacks at the message level instead of isolating the suspicious nodes. Moreover, using non-adjacent two-hop neighbors as the master nodes greatly shortens the synchronization path and thus invisibly improves the network synchronization accuracy.  
Although analysis and simulation results have proven RSTS's security, there are still multiple future research directions. First, it should be noted that RSTS secures the network synchronization at the cost of improving communication overhead, we would like to balance the communication overhead and security. Second, it would be interesting to devise a more complex tree topology constructing method with RSTS and expand RSTS's core idea to existing distributed synchronization protocols.  
Figure 7: Performance of FTSP, FTSP with threshold detection, and RSTS under Sybil attacks. The representation of the lines is the same in (**a–c**). (**a**) FTSP; (**b**) FTSP with threshold detection; and (**c**) RSTS.  
**Author Contributions:** Conceptualization, Z.W. and C.Y.; methodology, Z.W. and D.S.; software, D.S. and C.Y.; validation, Z.W., D.S. and C.Y.; formal analysis, Z.W., D.S. and C.Y.; investigation, Z.W. and D.S.; resources, Z.W.; data curation, D.S.; writing--original draft preparation, Z.W., D.S. and C.Y.; writing--review and editing, Z.W. and C.Y.; visualization, D.S.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 62002140), the Natural Science Foundation of Jiangsu Province (Grant No. BK20200887), and the Doctor's Program of Entrepreneurship and Innovation in Jiangsu Province.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Simulation Results
### Speed Regulation at Sudden Load Case  
Figure 7 shows that the CVOA-based PID controller can track the reference speed rapidly compared to the GA-based PID controller and HS-based PID controller. Besides, the CVOA-based PID controller can absorb the sudden load disturbance at an instant of 0.05 s in a short time.  
Table 4 summarizes the performance of each control technique. It can be noted that the CVOA-based PID controller has the least rise time and settling time. Also, the steady-state of the proposed CVOA is very small compared to other techniques algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Technique** & _Kp_ & _Ki_ & _Kd_ \\ \hline GA-based PID controller & 40.325 & 50.23 & 0.401 \\ HS-based PID controller & 90.564 & 66.365 & 0.352 \\ CVOA-based PID controller & 85.144 & 70.365 & 0.121 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The obtained parameters of each control technique.  
Figure 7: The speed of BLDC motor at speed regulation with sudden load test.  
Figure 8 demonstrates the corresponding controller output of each control technique. It is clear that the CVOA-based PID controller can detect rapidly any change in BLDC motor dynamic. In addition, the CVOA-based PID controller has a step change value at a sudden load test.
# The Use of Underwater Gliders as Acoustic Sensing Platforms
## 2 The Petrel II Glider and the Sea Trial
### Experiment Description  
In April 2018, a Petrel II glider was deployed in a sea trial near the Mariana Trench. In this experiment, only a single SCH was mounted on the tail of the glider. The total number of dives during the experiment was 32, which took approximately 55 h. The depth of the glider operation ranged from 0 to 800 m. The main purpose of the experiment was to study the characteristics of the self-noise and to preliminarily verify the ability of the glider to serve as a platform to study underwater noise. The location of the experiment is shown in Figure 2.  
In August 2018, the second sea trial was held in the South China Sea. The glider was modified to reduce the self-noise and two SCHs were mounted at the front and tail. The total number of dives during the experiment was 31, which took about 92 h. The depth of the glider operation ranged from 0 to 1000 m. The main purpose of the experiment was to verify the underwater signal detection and target azimuth estimation capability of the glider. Two different acoustic sources were deployed and various acoustic signals were transmitted, such as pulse cosine wave (PCW) signals, M-sequence, multitone, and linear frequency-modulated (LFM) signals.
## 2 Materials and Methods
Finally, an annealing process is performed in a box furnace at 450 \({}^{\circ}\)C for 1 h to reduce overall device resistance and restore the crystal structure, completing the device fabrication. All processes were conducted in the cleanroom facility at the Nano Fab of Hallym University. The electrical performance of the fabricated device was evaluated using the B1500A Semiconductor Device Parameter Analyzer (Keysight Technologies, Santa Rosa, CA, USA), with probes connected to the gate, source, drain, and substrate. The measurement software used was Keysight EasyEXPERT (version 6.4.2312.1715), and the collected data were analyzed and visualized using Microsoft Excel (version 2506) and OriginLab Origin (version 8.5). Table 1 presents the voltage conditions applied during the program (PGM) and erase (ERS) operations. In this study, to effectively represent synaptic weight, the PGM and ERS operations were each repeated 20 times. During the 20-cycle experiment, the ERS operation was conducted by applying a gate voltage (V\({}_{\text{gs}}\)) of \(-\)6 V for 10 s. Additionally, the substrate voltage (V\({}_{\text{sub}}\)) was incrementally increased by 1 V per cycle, ranging from 1 V to 20 V, for a total of 20 repetitions. The source voltage (V\({}_{\text{ss}}\)) and drain voltage (V\({}_{\text{ds}}\)) were maintained at 0 V throughout this process. Conversely, the PGM operation was conducted by applying V\({}_{\text{gs}}\) of 6 V. This voltage was applied twice at 1 s intervals, increasing from 1 s to 10 s, resulting in a total of 20 repetitions. Throughout this process, V\({}_{\text{sub}}\), V\({}_{\text{ss}}\), and V\({}_{\text{ds}}\) were maintained at 0 V. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Gate Voltage** & **Substrate Voltage** & **Source Voltage** & **Drain Voltage** \\  & **V\({}_{\text{gs}}\)** & **V\({}_{\text{sub}}\)** & **V\({}_{\text{ss}}\)** & **V\({}_{\text{ds}}\)** \\  & & **V\({}_{\text{sub}}\)** & **(Fixed)** & **(Fixed)** \\ \hline PGM operation & 6 V, 1–10 s & 0 V & 0 V & 0 V \\ ERS operation & \(-\)6 V, 10 s & 1–20 V & 0 V & 0 V \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Voltage applied during program and erase operations. All figures perform operations according to the following table.