esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Electromagnetic Radiation SAR of a Wearable Medical Device  
A wearable ECG sensor detects body information through an electrode, and, after information processing, communicates the information to the other terminal wirelessly. It can be drawn from the working principle of a wearable ECG sensor that apart from the electromagnetic radiation generated from the detection process, additional electromagnetic radiation is produced from the wireless communication process, which is worthy of consideration. The simulated liquids of muscle and heart were tested at 2450 MHz, with the test arrangement adequately reflecting a real working scenario of a wearable ECG sensor; the sensor was directly pasted onto the surface of the test phantom. The device was powered on and started normal operation, refer to Figure 4a. The electromagnetic radiation SAR distribution in key parts of the human body under normal transmission power was tested and charted. To reflect the superimposed effect of electromagnetic radiation SAR at the junction of multiple medical devices, the electromagnetic radiation SAR value of the human body when both cell-phone and sensor were in operation was tested.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (MHz)** & **915** & **1850** & **1880** & **2450** \\ \hline Deionized water & 50.31 & 54.23 & 56 & 56 \\ Tween & 48.43 & 45.27 & / & / \\ Oxidized mineral oil & / & / & 44 & 44 \\ Ethyl diglycol & / & / & / & / \\ Triton X-100 & / & / & / & / \\ Glycerol Diacetate & 1.35 & / & / & / \\ DGBE & / & / & / & / \\ NaCl & / & 0.50 & / & / \\ Additive and salt & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 2: Formulas and permittivity of simulated trunk tissue fluids satisfying target dielectric parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency (MHz)** & **Parameter** & **Error (\%) (\(\pm\)5\%)** \\ \hline \multirow{2}{*}{915} & \(\epsilon\) & \(-0.7\%\) \\  & \(\sigma\) & \(-0.5\%\) \\ \hline \multirow{2}{*}{1850} & \(e^{\prime}\) & \(-2.3\%\) \\  & \(\sigma\) & \(0.5\%\) \\ \hline \multirow{2}{*}{1880} & \(e^{\prime}\) & \(-2.4\%\) \\  & \(\sigma\) & \(2.4\%\) \\ \hline \multirow{2}{*}{2450} & \(e^{\prime}\) & \(0.2\%\) \\  & \(\sigma\) & \(2.9\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison for errors between permittivity and conductivity of simulated body liquids under different frequencies and reference values.  
The calculations established the relation between local SAR value of trunk muscular tissue and the distance from the wearable device to the skin surface. The results showed that as the distance from the antenna of the wearable device to skin increased, local SAR value decreased. Moreover, the electric field intensity distribution also confirmed that the SAR value decreased as the distance from the radiation source to head increased, as is shown in Figure 4. It can be seen that the maximum three-dimensional electric field intensity was 30 V/m; electromagnetic radiation SAR value at the surface was 0.961 W/kg, and the value was 0.37 W/kg at a depth of 6 cm from the surface.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
These characteristics make ultra-wideband the ideal choice in automation applications, as well as applications that deal with fast movement of objects to be tracked and high-speed applications [8]. Due to the large frequency range where UWB transmissions are allowed (3.1-10.6 GHz), the transmission power was limited to \(-41.3\) dBm to avoid and reduce interference with waves of other technologies operating within the same frequency range. Figure 1 summarizes the main characteristics of UWB and other RF technology frequencies and power spectral densities. Several keywords were used to import the related research works--such as \"UWB, Positioning, Localization, Tracking, IPS, RTLS, RTTS\"--from several databases, including IEEE, Elsevier (Scopus and ScienceDirect), Clarivate Web of Science (WoS), PubMed, and the ACM Library, during the past five years between January 2019 and June 2024. More than 270 research papers were retrieved from the aforementioned sources to examine their suitability for inclusion in this study. The inclusion/exclusion criteria were based on several factors related to the nature of the system used and the modernity of the work. The importance of the present work lies in providing a comprehensive view of indoor positioning systems operating with ultra-wideband technology in real time, and enabling researchers and engineers interested in this type of system to identify the available commercial units and their technical specifications, as well as the methods, techniques, and algorithms that enable the reader to reduce the effort and time expended in developing the relevant systems.
## 3 Test of the Surrounding Rock
### Comparative Analysis of In Situ Test Data
#### 3.4.2 Comparative Analysis of Deformation Modulus  
The comparison results of the borehole deformation method and pressure plate test at different depths are shown in Figure 8. The results of the borehole deformation method are similar to the load plate test results. The average value of the deformation modulus is 4.71 GPa in the pressure plate test. The average value of the deformation modulus measured by the drilling method near the same elevation (borehole depth is 30.9 m) of the pressure plate test point is about 4.6 GPa. This result indicates that the borehole deformation method and load plate test can be used to confirm each other. It further demonstrates the reliability of the deformation modulus obtained by the borehole deformation method.  
Figure 8: Comparison between borehole deformation method and pressure plate test.  
Figure 7: Borehole TV integration comparison.  
According to [PERSON] [23], it is known that the deformation modulus of the rock mass is related to the compressional wave velocity as a power function. The data measured in the field are fitted to the power function, as shown in Figure 9.  
The following is the fitted correlation between the longitudinal wave velocity of the acoustic wave in a single hole of the peolitic siltstone and the deformation modulus of the peolitic siltstone caused by the drilling method:  
\[E_{\mathrm{s}}=0.9225V_{p}^{1.462} \tag{3}\]  
where \(E_{\mathrm{s}}\) is the rock mass deformation modulus (GPa), \(V_{p}\) is the velocity of the single-hole acoustic p-wave in a rock mass (km/s).  
The deformation modulus is derived by fitting the formula based on the results of longitudinal wave velocity tests at different borehole depths. The comparison with the deformation modulus measured by the drilling method is shown in Figure 10.  
Figure 10: Comparison of fitting results and borehole deformation method.  
Figure 9: Relationship between modulus and wave velocity.  
The fitting results are similar to some extent. Combined with the wave velocity test, the average value of the deformation modulus for the weakly weathered peilitic siltstone can be computed to be substantially over 1.58 GPa.
## 4 Results and Discussion
### Results of Dust Removal Efficiency
#### 4.2.2 Relationship between Wind Speed and Dust Removal Efficiency  
During the experimental process, the speed of the air flow is regulated by adjusting the speed of the fan motor through the control system inverter. The working pressure and volume of the ultrasonic atomizer and the liquid gas ratio of the pressure sprayer remain unchanged. Dust removal efficiency and resistance of SCDC under different wind speeds were measured with constant water supply and pressure. The results are shown in Figures 12 and 13.  
As presented in Figure 12, the dust removal efficiency increases with the raising of wind speed. When the wind speed is over 14 m/s, the total dust removal efficiency increases slightly. On the contrary, the respirable dust removal efficiency decreases significantly. The main reason is that the wind speed is too high so that the dust-laden air stays in the cyclone for a very short time. Thus, a small part of the dust is carried away with the air flow. Dusty water mist particles cannot settle completely through centrifugation.  
Figure 12: Relationship between wind speed and dust removal efficiency.  
Figure 13: Relationship between wind speed and resistance.  
As shown in Figure 13, the resistance of the dust collector also increases with the improvement of wind speed. When the wind speed reaches 16 m/s, the resistance increases rapidly as the wind speed continues to increase.  
According to the above description, there is an optimum range of wind speed for SCDC, whose value is 14-16 m/s. Given this situation, the resistance of the dust collector is about 800-1000 Pa; the total dust removal efficiency is greater than 99.8%; and the respiratory dust removal efficiency is more than 97%.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 4 Modeling and Control
### Dynamic Modeling
#### 4.1.1 Example Controller and Simulation  
From this model, a suitable controller for the system can be prototyped. As a showcase of the capabilities of this model-based design (MBD) approach and to eventually use the model for quick prototyping, a tracking algorithm based on route points was implemented. Waypoints are represented by latitude and longitude coordinates \((lat_{k},lon_{k})\) for \(k\ =\ 1,2,\ldots,n\) representing an ordered database of points, as seen in Equation (16). Two PID-based (P and PD) controllers were used for orientation and speed (through the rudder and the propeller). The control architecture is depicted in Figure 9.  
Figure 9: StriderV1.0 controllers block diagram in MATLAB.  
The desired orientation angle \(\psi_{d}(t)\) from the position of the vessel \((x(t),y(t)\) regarding the inertial frame) was obtained from the expression in Equation (17). The calculation of the desired angle does not take into consideration the landing platform heading. Moreover, the calculation of a final heading when reaching a point was not considered in this work as a line-of-sight guidance approach was used. The expression has been written in latitude and longitude terms, coinciding with the inertial frame shown in Figure 4, following the NED convention.  
\[wpt.pos=(lat_{0},long_{0}),(lat_{1},long_{1}),\ldots,(lat_{n},long_{n}) \tag{16}\]  
\[\psi_{d}(t)=atan2(long_{k}-long(t),lat_{k}-lat(t)) \tag{17}\]  
where \(lat(t)\) and \(long(t)\) are current the latitude and longitude of the vessel and \(lat_{k}\) and \(long_{k}\) de selected waypoint coordinates. Once a waypoint has been reached, the next waypoint (\(k+1\)) is selected, resulting in a new \(\psi_{d}(t)\). For this purpose, the concept of circle of acceptance is adopted. Once the vessel is within a circle of radius \(\rho_{0}[m]\)m the next waypoint is selected.  
The simulation of the model was implemented using V-REP while the control architecture was built in MATLAB/Simulink. Figure 10 shows a screenshot of the simulation environment (which is also able to simulate UAVs and interaction between both robots).
## 5 DASH7 Communication System Implementation
### The Receiving Process
#### 5.2.4 Decoding  
The first stage after the symbol synchronisation is the binary conversion process. The _Binary Slicer_ block in Figure 14 converts every negative symbol value to a 0 b and the rest  
Figure 13: Demodulation process. The phase is extracted from the data signal and consecutively accumulated while high-frequency noise is filtered out.  
to 1 b. After converting the symbols to bits, the correlation process will start between the received bits and both the preamble and the sync word. The _Correlate Access Code-Tag_ block provides a tag that indicates the location of the preamble and the sync word in the data stream. The tags will be exploited by the _DASH7_demod_py_bb_ block. The _py_bb_ in the block name indicates that the block is implemented using Python code, and it receives and delivers binary data. The _DASH7_demod_py_bb_ block decodes the received packet. These are the bits after the sync word tag with PN9 sequences. After the decoding process, the block deploys a CRC16 to validate the correct reception of the received packet.  
The output of the decoding process is shown in the GNU Radio Console, which states the number of the packet, the packet length, the calculated CRC check, and the message in decimal and hexadecimal form, as can be seen in Figure 15. Note that the payload size of a DASH7 packet is longer due to extra obligatory fields of the other layers.  
Figure 14: Final GNU Radio flowgraph, where the demodulated samples are time-synchronized and put through a matched filter. After this step, a tag is added when a correlation is found to a predefined preamble and sync word sequence. Finally, the message is PN9-decoded, a CRC check is applied, and the output will be printed to the console window.  
Figure 15: Decoded output of a DASH7 packet with a length of 25 bytes as seen in the console window of GNU Radio. The packet has a payload of three bytes [0x00, 0 xAB, 0 xCD] followed by two CRC bytes.
## 3 Results and Discussion
### Effect of Solution pH  
In the adsorption process, the pH of the solution controls the surface charge of the adsorbent and the degree of ionisation of the adsorbate. The influence of pH on the adsorption of CV dye and Cr (VI) was carried out in the pH range of 2.0 to 12.0 for CV and 2.0 to 10.0 for Cr (VI), keeping the other parameters constant. Figure 8 shows that the removal rate of CV dye increased gradually from 2 to 12 and then decreased with increasing solution pH, while that of Cr (VI) increased more rapidly with increasing solution pH from 2 to 6 and then decreased due to deprotonation of functional groups on the CAC surface. The maximum retention of CV and Cr (VI) was found at pH 9 and pH 6, respectively. These pH values were chosen as the optimum pH for the subsequent adsorption experiments.  
Figure 7: Adsorption isotherm of iodine on the two activated carbons.  
This behaviour can be illustrated based on the point zero charge of ACSCGs, which was found to be equal to 8.2. The surface of the adsorbent became more and more negative as the pH increased and, consequently, the electrostatic attraction forces between the CV cations and the ACSCGs surface became more and more important. The decrease in removal above pH 10 can be interpreted by the competition between the dye cations and the Na\({}^{+}\) cations from NaOH. In this context, [PERSON] et al. [78] indicated that Na\({}^{+}\) ions surround the adsorption sites, which weakens the electrostatic attraction forces between the adsorbent and the adsorbate. On the other hand, the adsorption of dye cations at low pH can be explained by the existence of dispersive \(\pi\)-\(\pi\) interactions between the delocalised \(\pi\) electrons on the surface of basic activated carbons and the free electrons of the aromatic rings of the dye [73].  
For Cr (VI) adsorption in the pH range studied, ACSCGs was protonated and the two predominant forms of Cr (VI) were the hydrogen chromate ion HCrO\({}_{4}\)\({}^{-}\) (at pH between 1.0 and 6.0) and the chromate ion (pH above 6.0). As the pH of the aqueous solution increased, the predominant ions (HCrO\({}_{4}\)\({}^{-}\)) were transformed into the chromate ions CrO\({}_{4}\)\({}^{2-}\). The CrO\({}_{4}\)\({}^{2-}\) ion has two negative charges, which leads to an increase in electrostatic attraction and, consequently, an increase in the adsorption capacity of Cr (VI) is observed. Furthermore, the decrease in removal at higher pH is related to the increase in negative charge density on the carbon surface due to the progressive deprotonation of the surface sites. Moreover, this optimal adsorption around neutral pH is economically favourable as it does not require another neutralisation step of the solutions at the end of the process. Similar results have also been reported by [PERSON] et al., who studied the adsorption of methylene blue on activated carbon prepared from two biomasses [79], and by [PERSON] et al. in their studies on the removal of Cr (VI) ions on activated carbon prepared from longan seeds [80].  
Figure 8: pH effect on the adsorption of Cr (VI) and CV by ACSCGs.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 4 Q-Learning-Based Position-Aware Routing Protocols in FANETs
### Reward Function Learning for QL-Based Geographic Routing Protocol (RFLQGEO)  
[PERSON] et al. [50] proposed the reward function learning for a QL-based geographic routing protocol (RFLQGEO) that provides less retransmission, lower average end-to-end delay, and higher PDR compared with QGEO. RFLQGEO utilizes the inverse RL concept to design the routing decision by exchanging the hello packet, which accelerates the learning process with less communication overhead. RFLQGEO has three components: the location information module, QL routing module, and reward function learning module. The location information module uses hello packets to share the position obtained by the GPS, link condition, residual energy level, link error, location errors, and Q-value with neighboring UAVs. The QL module updates the Q-value using a reward function that jointly considers the distance between two UAVs directed to the destination sink node, PTT including the MAC and transmission delay, link and position error, and void area avoidance feature.  
_Advantages_: RFLQGEO outperforms QGEO in terms of designing the reward function for better learning by including the distance progress in the direction of the sink and void area avoidance component in the reward function.  
_Limitations_: The limitations of RLQGEO are that it does not consider the adaptive control of the hello interval, mobility control mechanism, and exploration and exploitation method to avoid local optima during the learning process. Additionally, the residual energy of the UAV node is not considered in the reward function.
## 3 Results
### Study Characteristics
#### 3.3.4 Machine Learning Implementation  
The implementation of machine learning algorithms revealed a preference for established classification methods. Random Forest was the most frequently employed algorithm (RF, \(n\) = 7; [7,8,20; 21,25; 26]), followed by Support Vector Machine (SVM, \(n\) = 4; [18; 19; 20; 22]). More specialized approaches included Hidden Markov Models (HMM, \(n\) = 2; [27,28]) and Artificial Neural Networks (ANN, \(n\) = 2; [17,19]), alongside clustering techniques such as K-means (\(n\) = 2; [21,25]) and Self-Organizing Maps (S-OM, \(n\) = 2; [17,21]).  
Prediction accuracy varied considerably based on the specific application. Physical activity classification achieved accuracy rates ranging from 59.1% to 86.4% [7,18,19], while sleep prediction demonstrated higher accuracy rates, reaching up to 96.2% [24]. Activity type recognition showed moderate success rates between 69.7% and 82.6% [17].
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
###### Abstract  
The demand for integrated telecommunication network infrastructure has increased, and 100 Gbps optical transceivers are a critical part of this infrastructure. In this paper, an efficient firmware design scheme is proposed for a 100 Gbps C form-factor pluggable (CFP) optical transceiver based on the multi-source agreement standard for optical transceivers. In the proposed method, a field programmable gate array (FPGA) approach is used to integrate the CFP communication interface and register structure, and a micro controller unit (MCU) is employed to implement the operation of the CFP optical transceiver. This paper also proposed monitoring techniques using high-order polynomials for accurate optical power monitoring of CFP optical transceivers. To ensure that the implemented firmware satisfies the proposed design scheme, an actual testbed was constructed and the performance of the firmware was evaluated. The results demonstrate that the proposed design scheme not only satisfies standard items but also achieved an average monitoring accuracy of more than 90%. In addition, the proposed scheme can be applied to 200/400G CFP optical transceivers in the future. By implementing the CFP firmware using the proposed method, manufacturers can ensure quality while reducing cost and development time.  
C form-factor pluggable (CFP); multi-source agreement (MSA); field programmable gate array (FPGA); firmware; optical transceiver +
Footnote †: journal: Applied Physics Letters B
## 5 Conclusions  
1. This paper presented an effective numerical approach for static and dynamic behavior simulation of steel frames with semi-rigid joints based on the Member Discrete Element Method (MDEM). In the MDEM, a structure is discretized into a set of finite rigid particles, as well as geometric nonlinearity and fracture behaviors can be naturally captured. A virtual spring element without actual length is applied to simulate the semi-rigid connection. On this basis, the modified formula of the contact element stiffness at the semi-rigid connection is derived. Finally, the numerical approach proposed is verified by complex behaviors of steel frames with semi-rigid connections such as geometric nonlinearity, snap-through buckling, dynamic responses and fracture. In addition, compared with other numerical approaches taking the FEM as a representation, the approach proposed is simple and feasible for simulating the semi-rigid connections because the zero-length spring element is not directly involved in the calculation.
2. The comparison of the analysis results of the proposed approach and the existing researches shows that the modified MDEM can accurately capture linear and nonlinear behaviors of semi-rigid connections. Some common conclusions can be drawn as follow: the semi-rigid connections may significantly reduce structural stiffness, and structural bearing capacity under static loading will be overestimated if the semi-rigid connections are ignored; When the frequency of dynamic load applied is close to structural fundamental frequency, resonance occurs in the frames with rigid or linear semi-rigid connections, but not in the frame with nonlinear semi-rigid connections, the reason is that the hysteresis damping of the nonlinear connection causes energy dissipation. Fracture behavior analysis also indicates that frames with semi-rigid connection possess more anti-collapse capacity.
3. The MDEM can avoid the difficulties of finite element method (FEM) in dealing with strong nonlinearity and discontinuity. A unified computational framework is applied for static and dynamic analyses. The method is simple and generally good, which is an effective tool to  
Figure 24: Failure processes of the steel frame with rigid connection.  
Figure 25: Failure processes of the steel frame with semi-rigid connection.  
investigate complex behaviors of steel frames with semi-rigid connections. In the follow-up study, material nonlinearity will be taken into account to simulate the collapse process of steel frames with semi-rigid connections under strong earthquake or impact loading action.  
This work is supported by the National Science Found for Distinguished Young Scholars of China (Grant No. 51125031) and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (CE02-1-31).  
[PERSON] and [PERSON] modified the Member Discrete Element Method, analyzed the numerical results, and wrote the paper.  
The authors declare no conflict of interest.