esa-sceva/satcom-chunk-collection huggingface dataset:
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 3 Energy-Throughput Efficient Cross-Layer Solution Using MPNMRA for WUSNs
### System Model  
A Modulation  
In MI communication, it has been observed that there are fewer variations in the channel compared with electromagnetic (EM) waves [23]. However, it is important to consider the energy consumption in energy-restricted MI-based underground sensor networks (MI-USNs) when employing advanced modulation techniques [20]. For underground  
Figure 4: Structure of MI waveguide with 3D coils [22].  
Figure 3: Structure of MI waveguide technique [22].  
communication in MI-USNs, various modulation techniques can be chosen based on their suitability and simplicity. Examples of such modulation techniques include binary frequency shift keying (BPSK), binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and 16-quadrature amplitude modulation (16-QAM).  
In the context of modulation techniques, let us consider a connection between nodes \(i\) and \(j\). The adopted modulation technique is represented by \(m_{ij}\in\mathcal{M}\), where \(\mathcal{M}\) is the set of available modulation techniques. The modulation spectrum efficiency is denoted by \(\eta(m_{ij})\), which represents the amount of information that can be transmitted per unit of bandwidth using the modulation scheme \(m_{ij}\). To evaluate the performance of a particular modulation scheme \(m_{ij}\) over the link between nodes \(i\) and \(j\), we can obtain the bit error rate (BER), denoted by \(\psi_{ij}^{m_{ij}}\). The received signal-to-noise ratio (SNR) plays a crucial role in determining the BER for a specific modulation scheme over the link \(i-j\). By considering the SNR and modulation schemes, one can assess the BER and make informed decisions regarding the choice of modulation technique for efficient and reliable communication in MI-based underground sensor networks.
# Fault-Tolerant Tripod Gait Planning and Verification of a Hexopod Robot
## 3 Planning Method of F-TT Gait
### Measure to Improve the Stability of F-TT Gait  
In Section 3.2, when a malfunctioning robot performs the F-TT gait, it has the two states of stability and instability. As the hexapod robot moves, the COG of the hexapod robot will move out of the stable region composed of the supporting legs, resulting in the hexapod robot toppling. Locomotion moment, as shown in Figure 6b,c and Figure 8c, due to the forward movement of the body, causes the COG to gradually move out of stability. Aiming at the instability state of the robot, a measure is proposed to solve the problem of the ineligible stability margin of the hexapod robot with one leg fault: coxa initialization angle adjustment. An initial value of coxa joint angle is preset to increase the support area. For the legs involved in the unstable support state, the support area can be increased by selecting the appropriate coxa initialization angle within the allowed space. Figures 9 and 10 demonstrate the initial attitude with or without coxa initialization angle adjustment. The figure on the left shows the initial attitude without coxa initialization angle adjustment while the initial attitude with coxa initialization angle adjustment is shown on the right.  
The calculation of coxa initialization angle uses the Monte Carlo method to generate the random matrix of coxa initialization angle, restricting the adjustment range to \([-20^{\circ},30^{\circ}]\). Based on the planned gait parameters, the position of the foot ends of the next supporting phase is a known quantity. The stability margin is calculated using Equation (1). The solution with the stability margin greater than 0 is regarded as the feasible solution, and the evaluation index is defined as:  
\[\rho=\sum\
olimits_{i=1}^{n}\psi_{i}^{\prime\,2} \tag{17}\]  
where \(\psi_{i}^{\prime}\) is the coxa initialization angle of the leg \(i\). The minimum value of the evaluation is regarded as the adjustment angle. Figure 11 shows the method to obtain the adjusted angle.  
When a fault is detected, the robot resets to its original attitude and uses the coxa initialization angle adjustment method to calculate the angle of the initialization angle of the coxa joint. In a very short time, each leg is raised and swung to the initialization angle of the coxa joint to obtain a higher stability margin, and then begins to enter the fault-tolerant trip gait stage. By the measure of coxa initialization angle adjustment, the motion of Figures 6 and 8 translates to that shown in Figures 12 and 13, respectively. It can be seen that the stability margin is increased, and the COG is in the interior of the supporting triangle, which meets the static stability requirements.  
Figure 8: Transfer order of legs in fault tolerant tripod gait in leg 2 fault state.  
Figure 11: The method to obtain the adjusted angle.  
Figure 10: Initial attitude of leg 2 fault.  
Figure 9: Initial attitude of leg 1 fault.
# Automated Generation of Daily Evacuation Paths in 4D BIM
## 4 Framework and Algorithms for Automated Generation of Evacuation Paths in 4D BIM
### System and Algorithm Development
#### 4.2.2 A* Searching Application for Path Finding  
This study utilizes the A* search algorithm to calculate the shortest path from workers' locations to secured exits in a construction site. Associated job spaces were defined and searched for by drawing a rectangle grid mapped on the entire area of the job site, with 10 feet of node spacing. For each BIM model of four scenarios, the grid nodes were configured according to the distinct site conditions of the particular work day. The nodes in the grid consist of four types: A start node, an exit node, a blocked node (non-navigable), and a regular (non-blocked and navigable) node. Start nodes and exit nodes denote the workers' and exit locations, respectively. Blocked nodes correspond to inaccessible regions such as within 10 ft of installed columns, the inside areas of the steel stack and rebar stack, or areas directly underneath ongoing roofing activities including a scaffolding structure. Non-blocked nodes correspond to all other accessible regions.  
Based on the navigation grid, A* path planning finds an optimal path between a start node and an exit node that passes through non-blocked nodes and avoids blocked nodes. It starts with a single node (a start node) and keeps expanding the path by adding nodes until it generates a complete path to the exit node. As shown in Figure 3, 16 immediate neighbors of the current node (as shown in Figure 3) were considered to expand the path.  
From the 16 neighbors, blocked nodes and previously explored nodes are removed and the rest of the nodes are evaluated based on the heuristic function F(n).  
\[\text{F(n)}=\text{G(n)}+\text{H(n)}\]  
where G(n) = distance from start node to node n, H(n) = straight distance from node n to exit node.  
Figure 3: A grid node with neighbors.  
Figure 2: Custom 4D BIM platform for proposed path planning integration.
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 3 The Design of DAHC Protocol
### The Adjustment of Cluster Density
Therefore, the RSSI value of the nodes in the list must be greater than \(-87\) dBm. With the list, the operation is performed according to the order. In this process, there are two principles. One is when the CH prepares to remove a node, it judges whether the cluster density will be less than the average value \(N_{0}\) if the node is removed. If it is, the node will not be eliminated; otherwise it will be eliminated. Another is when the CH prepares to accept a node, it judges whether the cluster density will be more than the average value \(N_{0}\) if the node is accepted. If it is, the node will not be received and go back to the original cluster; otherwise it will be received. According to these two principles, we can reduce the burden of the cluster, so that the cluster density in the network can be kept within a reasonable range, and the energy load of the cluster can be balanced.
# AR Displays: Next-Generation Technologies to Solve the Vergence-Accommodation Conflict
## 4 Overview of Proposed Next-Generation 3D Display Solutions
### Light-Field Displays  
Light-field displays are an intermediate link to holographic displays. In contrast to a true holographic approach, the wavefront or actual light field is approximated by multiple directional views, which offers monocular focus cues, as well as the ability to show angle-dependent lighting effects, such as glare. As with digital holography, this is a computational-type display. Nonetheless, in case of wearable displays, the ability to correctly simulate angle-dependent optical properties of surfaces would not be a major advantage, as the eye position is fixed rather and the view typically changes with head and body movements recorded and fed to a rendering pipeline through positional tracking systems. Moreover, with light-field technology, there are several key challenges. First of all, to represent a light field truthfully, the discretization degree has to be rather high, meaning that multiple views are required, which becomes computationally challenging as the number of views increase. Consequently, this might interfere with real-time capability. A further challenge is the required data bandwidth to transfer all the light field information, which in recent times does not appear to be a problem of the scale previously thought, due to the high compressibility of light field data [22]. Nevertheless, the most substantial issue concerns the physical representation of light fields. Generally, the concept includes utilization of a microdisplay with overlaid lenslet array [23]. For example, Nvidia had investigated such an approach for use in near-eye displays already by 2013 [24]. The limiting factor for such a case is image resolution, as the total available pixels have to be divided into sub-portions of considerably low resolution to represent each view. An alternative that does not sacrifice image resolution has been proposed by Swiss startup, Creal3D, which utilizes time-multiplexed multiple pin-light sources for illumination of a reflective spatial light modulator to create full resolution time-multiplexed views [25]. The number of views that can be shown is limited by capabilities of a spatial light modulator. Nonetheless, from the practicality point of view--due to unrestricted image resolution among true light-field solutions for wearable displays--this appears to be the most promising.
# Multifunction RF Systems for Naval Platforms
## 4 MFRF System Prototype Development Programs
### InTop
#### 4.2.3 EW/IO/Comms ADM  
The EW/IO/Comms ADM utilized one array set per quadrant of surveillance volume, where each array set consisted of a Rx and Tx AESA [16]. Each Tx array provided up to four independent beams, while each Rx array supported from four to 16 independent beams through the use of four Rx channels per element, as was the case with the AMRFC testbed design.  
The specifications of the Tx AESA and associated channel components were driven mainly by the EA function requirements, the most important being an operating frequency range from C band to K\({}_{\text{a}}\) band, and sufficient EIRP to provide self-protection for a platform with a large RCS. The design also included an interface with the ship's ES system, the information from which was used by the EA function to track hostile emitters in angle, and aid in design of the jamming techniques.  
The IO functionality was provided through interfaces to the ship's signal exploitation equipment that provides threat identification information. The EA function incorporated this information in its response. The communications requirements for the ADM included two independent legacy system X-band CDL links, at least four independent TCDL links, and a \(\mathrm{K_{u}}\)-band network communications waveform within each quadrant covered by an array set.
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
## 3 Baseband Signal Generation METHOD
### Data Bits Design
#### 3.3.5 The Design of BCH-2  
Since the data bits to be checked are 26 bits, if adding the check bits, the total number of the bits is 38. First, add 12 zeros after 26 bits of the second protected data, then the check bit will be gotten by XOR with the codeword polynomial.  
First, find the information polynomial:  
\[\mathrm{m}_{2}(\mathrm{x})=\mathrm{m}_{25}\mathrm{x}^{25}+\mathrm{m}_{24} \mathrm{x}^{24}+\cdots+\mathrm{m}_{0} \tag{8}\]  
Second, find the codeword polynomial and determine by the look-up table method:  
\[\mathrm{g}_{2}(\mathrm{x})=\mathrm{x}^{12}+\mathrm{x}^{10}+\mathrm{x}^{8}+ \mathrm{x}^{5}+\mathrm{x}^{4}+\mathrm{x}^{3}+1 \tag{9}\]  
As the STM32 microcontroller is a 32-bits for parallel processing, the check polynomial can be obtained after an XOR operation.  
\[\mathrm{r}_{2}(\mathrm{x})=\Big{(}\mathrm{x}^{12}\mathrm{m}_{2}(\mathrm{x}) \Big{)}\mathrm{mod}\ \mathrm{g}_{2}(\mathrm{x}) \tag{10}\]  
If the number of the check bits is not enough for 12, the result is the check digital by preceding 0s at the beginning of the code until the number of check bits is 12.  
The specific implementation procedure in the STM32 microcontroller is as follows:  
1. Define variables and assign initial values to them according to the coding rules for satellite emergency position indicating radio beacons.
2. Calculate the second protected data, and define this data using 64-bit data. Then shift left 12 bits.
3. Calculate the codeword polynomial.
4. Take the data with the same number of the information polynomial as the polynomial to perform XOR operation with the codeword polynomial. Then, take the obtained results as the higher bits of the new information, and act the part without XOR operation in the original data as the lower bits of the new information polynomial.
5. Continue to take the data with the same number of bits as the codeword polynomial, and continue to perform the XOR operation until the final data is less than the number of bits of the codeword polynomial.
6. If the resulting number of data bits is less than 12, this number is the BCH checksum bit.  
\begin{table}
\begin{tabular}{c c c} \hline
**Bit** & **The Present** & **Value** \\ \hline
0–3 & Fixed bits & 1101:pass \\
4 & Position data & From Internal Navigation Device \\
5 & Aux device & 1: 121.5 MHz homer \\
6 & Latitude Offset Sign & \\
7–11 & Latitude Offset Minutes & \\
12–15 & Latitude Offset Second & \\
16 & Longitude Offset Sign & \\
17–21 & Longitude Offset Minutes & \\
22–25 & Longitude Offset Second & \\ \hline \end{tabular}
\end{table}
Table 3: The bit of the second protected data field.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 3 Hybrid RF-FSO Systems Techniques and Their Advanced Achievements
### Radio-over-FSO and Integrated FSO-RF Antenna Systems
[88], the comparison of link selection algorithms for a hybrid RF-FSO network was investigated. In Ref. [89], a hybrid RF-FSO antenna was designed and tested, under different atmospheric conditions, with a high-speed access network. Figure 21 shows the geometry of the dual-band composite RF-FSO antenna design. Based on the concept of a Cassegrain structure, a dual-purpose antenna was developed with a greater feed and with simple rear accessibility [90]. The RF transmission and reception systems were equipped with traditional hyperbolic and parabolic reflectors. As depicted in Figure 21, positioning the optical Tx and the Rx antennas at the shadow region of the RF system avoids the blocking of the RF signals by optical beams. To avoid EMI and for flexibility, optical fibers were used to transfer the Tx/Rx signals from optical unit to the external electronic units, while an RF electronics unit was connected via a waveguide.
## 2 Resources and Methodology
### Hardware and Software Resources  
This research is supported by the interaction between P3-DX robotic units and RBP computing platforms. The core hardware component used in this work is the RBP 4 Model B, a compact and cost-effective electronic board with the following notable specifications: a Broadcom BCM2711 quad-core Cortex-A72 (ARM v8) 64-bit SoC running at 1.8 GHz; a VideoCore VI GPU compatible with OpenGL ES 3.1; 8 GB of LPDDR4-3200 SDRAM; and robust connectivity options including dual-band (2.4 GHz and 5.0 GHz) IEEE 802.11 ac wireless and Gigabit Ethernet. Its computational capabilities, extensive library support, active development community, and affordability make it well-suited for electronic control system design. To complete the development environment, the Raspbian operating system was installed along with the specific MATLAB 2024a toolboxes: MATLAB Support Package for RBP Hardware and Simulink Support Package for RBP Hardware.  
For the robot under test, the P3-DX was selected [41]. It is a wheeled differential drive robot widely adopted in both education and research contexts. Although the robot is equipped with multiple sensors, this work focuses solely on odometry data to capture its current linear and angular velocities. The robot is integrated with an additional RBP as  
Figure 1: Key elements of the development environment used by authors for the nonlinear trajectory tracking of the P3-DX robot. The Electronic Control Unit (ECU-RBP) and the Robot Digital Twin (DT-RBP) are implemented in RBP 4 (model B).  
a digital platform that enables wireless communication with the control node (ECU-RBP) and provides an electronic interface to the robot's firmware.  
For Computer Aided Control Design Tool, Matlab/Simulink was used. Simulink Coder [42] deserves special mention, as it enables the automatic generation of C and C++ code from Simulink models. This capability makes it a widely used tool in embedded system development, especially for applications such as hardware-in-the-loop (HIL) testing.
# Novel MIMO Antenna System for Ultra Wideband Applications
## 1 Introduction
The antenna elements are positioned in a plus-shaped arrangement to provide pattern and polarization diversity [19; 22]. It is observed from the presented results that the designed MIMO antenna operates from 2.77-12 GHz. Furthermore, the presented MIMO antenna did not utilize any isolation enhancement technique, which makes it simple and it can easily be fabricated through low-cost fabrication techniques.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 5 Performance Evaluation
### Comparison with Existing Algorithms
#### 5.2.2 Impacts of the Number of Targets  
We evaluate the target number's impact in a scenario with 300 nodes and the packet generation probability of 0.3. We investigate the network lifetime variation when the number of targets increases from 100 to 300. To do so, we first generate 300 targets randomly in the network region. After that, the number of targets is adjusted by subtracting 50, 100, 150, and 200 targets at random from the initial set. The results are presented in Figure 8. As shown, Fuzzy Q-charging performs much better than the other algorithms. As expected, as the number of targets increases, the network lifetime achieved by all algorithms drops. The reason for this is because when the target number increases, the volume of traffic on the sensors increases as well. When the number of targets is significantly large (i.e., more than 250), the energy consumption rate of sensors becomes too high. As a result, the MC is unable to charge the sensors in time. As a consequence, no algorithm can considerably increase the network lifetime.  
When compared to the no-charge approach, Fuzzy Q-charging extends the network lifetime by more than 9.8 in all scenarios. Fuzzy Q-charging has average performance gaps of 6.3, 6.0, and 16.4 when compared to GSA, INMA, and RMP-RL, respectively. In the best case, the performance gaps of Fuzzy Q-charging to GSA, INMA, and RMP-RL are 12.6, 11.5, and 33.9, respectively. Compared to Q-charging, Fuzzy Q improves the network lifetime by 1.9 times in average and 3.1 times in the best case. The improvement of Fuzzy Q-charging is because Fuzzy Q-charging favors the sensors with more essential roles in covering targets and transferring data to the base station. It can hence maintain the essential sensors' lifetime and ensure all monitored targets. Other algorithms do not concurrently consider the target coverage and connectivity constraints. Therefore, the essential sensors may not be charged in time, causing some targets to be unmonitored.
## 2 Optimization Model
### Set of Constraints
#### 2.2.2 Power Flow Equation  
The active and reactive power flows transmitted through a specific distribution line, along with their corresponding maximum capacities, can be represented as follows:  
\[p_{lh}^{s}=\text{real}\big{(}v_{kh}y_{l}^{s}\left(v_{kh}-v_{mh} \right)^{s}\big{)},\forall\;l\in\mathcal{B},\;\forall\;h\in\mathcal{H}, \tag{4}\] \[p_{lh}^{r}=\text{real}\big{(}v_{mh}y_{l}^{s}\left(v_{mh}-v_{kh} \right)^{s}\big{)},\forall\;l\in\mathcal{B},\;\forall\;h\in\mathcal{H},\] (5) \[q_{lh}^{s}=\text{imag}\big{(}v_{kh}y_{l}^{s}\left(v_{kh}-v_{mh} \right)^{s}\big{)},\forall\;l\in\mathcal{B},\;\forall\;h\in\mathcal{H},\] (6) \[q_{lh}^{r}=\text{imag}\big{(}v_{mh}y_{l}^{s}\left(v_{mh}-v_{kh} \right)^{s}\big{)},\forall\;l\in\mathcal{B},\;\forall\;h\in\mathcal{H},\] (7) \[\|p_{lh}^{s}+jq_{lh}^{s}\|\leq s_{l}^{\max},\;\forall\;l\in \mathcal{B},\;\forall\;h\in\mathcal{H}\] (8) \[\|p_{lh}^{r}+jq_{lh}^{r}\|\leq s_{l}^{\max},\;\forall\;l\in \mathcal{B},\;\forall\;h\in\mathcal{H} \tag{9}\]  
where \(v_{kh}\) and \(v_{mh}\) are the complex voltages, \(y_{l}\) is the admittance of branch \(l\), and \(s_{l}^{\max}\) is the maximum apparent power flowing through the line.
## 4 Conclusions
The second generation of Fengyun geostationary meteorological satellites (FY-4) carried the advanced geostationary radiation imager and the geostationary interferometric infrared sounder. The former has 14 infrared channels, while the latter can detect the temperature and humidity of the vertical atmosphere. The data may provide more information for earthquake prediction;
3. The new prediction method that combines remote sensing data and surface observation data should be explored. The anomaly extraction algorithm in this paper can be used for time series analysis of other remote sensing data, and the statistical method can be used for other types of wide-field and long-time spatial-temporal data. Some parameters may need to be adjusted for using other data. Conceptualization, G.C. ; methodology, F.C. ; investigation, Y.Y. ; writing--original draft preparation, Y.Y. ; writing--review and editing, F.C. ; supervision, G.C. ; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This research received no external funding. Not applicable. **Acknowledgments:** Thanks to the National Satellite Meteorological Center of China for providing the data of the FY-2G satellite. **Conflicts of Interest:** The authors declare no conflict of interest.