esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 4 More Modern Approaches with Larger Domains of Validity  
We have presented the two classical approximations: the perturbation theory (PT) and the Kirchhoff approximation (KA). In the view of main application to scattering from ocean, it is important to cite the two-scale roughness theories [101; 102; 103; 104]. They consist in assuming that scattering can be considered as the combination of two phenomena: that produced by large facets and well approximated by KA and that due to small roughness and approximated by PT. One major inconvenient of this technique is that it requires to fix a wavenumber parameter [105] to separate the two scattering domains.  
Figure 7: Ultrasonic inspection of a planar steel component (orange box) immersed in water (water path 25 mm) at frequency = 2 MHz (wavelength \(\lambda\) = 0.75 mm): Bscans simulated with KA for a (**a**) smooth and (**b**) a rough entry surface (RMS surface height \(\sigma\) = 1 mm and correlation lengths \(L_{x}=L_{y}=10\) mm). Scattering patterns (maximum of the echographic signal versus observation angle) (**c**) for different roughnesses (smooth, 10 \(\upmu\)m, 0.1, 0.5 and 1 mm) and (**d**) for three different generated rough surfaces of the same statistical properties (roughness \(\sigma\) = 0.2 mm and correlation lengths \(L_{x}=L_{y}=10\) mm).  
The small slope approximation, described in the next section, is a much more convenient technique to link the classical approximations and it reduces to their results in some limiting cases.  
In this section, some sound propagation models which have been notably devised for underwater acoustic applications and providing a better validity notably for grazing observation angles are studied. Underwater acoustic propagation depends on many factors. When sound propagates underwater the sound intensity decreases when increasing the propagation distance. Propagation loss is a quantitative measure of the reduction in sound intensity between two points, normally the sound source and a distant receiver. Modeling the acoustic reflection loss [106] at the rough ocean surface needs simulating forward scattering for incident grazing angles and is of interest for sonar especially for shallow oceans or oceans with a surface duct (sound waves trapped in a duct layer below the sea surface) [106]. Several models described hereafter (small slope approximation--SSA, and parabolic equation--PE) were notably designed to handle grazing angles. The integral equation (IE) method is also used as reference solution for validating SSA and comparing it to other approximations as KA and geometrical optics (GO).
## 2 Problem Background
### Unequal Buffer Capacities  
The second factor that may lead to a scenario-specific performance is due to unequal buffer capacities on nodes. Although the resources on portable handheld devices are roughly homogeneous in terms of energy and processing power, the amount of storage space users may be willing to allocate to the technology could be motivated by the benefits they derive from incentive schemes. Unfortunately, allocating different amounts of storage space may bring about false negatives on nodes with higher buffer capacity. Suppose two nodes, A and B, with buffer capacity of 100 MB and 10 MB have a buffer occupancy of 20% and 80%, respectively. This means that A and B have 20 MB and 8 MB of their buffers occupied, respectively. While A may have actually expended more energy in sending and receiving messages than B, most existing solutions will identify a lower burden on the former. For instance, with CCAF, which considers traffic load (i.e., burden) as the ratio between remaining storage and total storage capacity, nodes with more buffer capacity will be subjected to more burden. Similarly, by directing messages towards nodes with less buffer occupancy, CAFE will subject nodes with more buffer capacity to unfair treatment.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 3 LoRaWAN Overview
### LoRa Physical Layer  
LoRa modulation is Semtech's (Camarillo, CA, USA) proprietary Chirp Spread Spectrum (CSS) technology. Since the operational principles of LoRa modulation have been covered extensively in the literature [3; 4; 5], we will only focus on the influence of the transmission parameters on the effective bit rate of the modulation, its resistance to interference noise, power consumption, and its link-budget. The key transmission parameters that nodes can control are the transmission frequency channel (\(F_{c}\)), the bandwidth (\(BW\)), the spreading factor (\(SF\)), the coding rate (\(CR\)), and the transmission power (\(P_{Tx}\)).  
LoRa transmissions can be modulated by selecting an \(SF\) between \(SF7\) and \(SF12\). An increment in the selected \(SF\) increments the receiver's sensitivity, thereby increasing the link-budget. However, this also increases the time-on-air (\(ToA\)) of a packet, which increases power consumption and collision probability and decreases the data rate and throughput of a node. LoRa transmissions using different \(SF\)'s are semi-orthogonal to each other; thus, networks can utilise \(SF\) variation to increase its capacity.  
The centre frequency (\(F_{c}\)) is the carrier frequency used to modulate LoRa packets. Packets sent between nodes and gateways can be spread out on different centre frequencies/frequency channels. Nodes need to adhere to the maximum transmit duty cycle relative to the sub-band used and local regulations. In [2], the LoRa Alliance, the non-profit association responsible for the LoRaWAN standard, specifies that LoRaWAN devices should select a frequency channel pseudo-randomly, increasing frequency diversity and thereby increasing the interference robustness.  
\(BW\) specifies the range of frequencies around the \(F_{c}\) used for transmitting, thereby indirectly specifying the rate of change in the frequency of the chirp. A higher \(BW\) translates to a higher data rate and a lower receiver sensitivity (i.e., a lower link budget). LoRaWAN messages are typically transmitted with a \(BW\) of either \(125\,\mathrm{kHz}\) or \(250\,\mathrm{kHz}\), depending on the selected datarate. Lastly, LoRa packets are transmitted at a specific \(CR\), which specifies the Forward Error Correction (FEC) rate. Increasing the \(CR\) increases the packet size, and consequently increases the ToA. However, it also decreases the packet's susceptibility to burst noise, which decreased the Packet Error Ratio (PER) (percentage of transmitted messages incorrectly received by the receiver).  
Selecting the correct transmission parameters is important, as the ToA impacts the node's throughput due to the duty cycle limitations implemented in the ISM band. In Europe, duty cycles are regulated by Section 4.3.3 of the ETSI EN300.220 standard [6]. This standard defines the following sub-bands and their prescribed duty cycles:  
* 863.0-868.0 MHz: 1%
* 868.0-868.6 MHz: 1%
* 868.7-869.2 MHz: 0.1%
* 869.4-869.65 MHz: 10%
* 869.7-870.0 MHz: 1%
## 5 Case Analysis of Load-Sharing Performance
### Influence of Load-Sharing Performance on Vibration of Gearbox  
Considering the load-sharing performance of the star gearbox, the vibration of the star gearbox under different load coefficients was analyzed. The inner gear was taken  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Standard Installation**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Installation Error** \\ **with the Sun Gear** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Installation Error** \\ **with the Inner Gear** \\ \end{tabular} } \\ \hline load & Internal & External & Internal & External & Internal & External \\ coefficient & 1.164307 & 1.164307 & 1.231918 & 1.231930 & 1.232545 & 1.232509 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sun gear–planet gear external meshing load coefficient (installation error).  
Figure 9: Influence of load-sharing coefficient on gear train efficiency.  
as the research object, and the vector displacement of the internal gear ring's barycenter, indicating the gear vibration varying with the speed, is shown in Figure 10. The vector displacement of the peaks in the curve increased by 2.92% and 3.05%, respectively, when the load coefficients were 1.2319 and 1.2325, compared with 1.1643. The conclusion is that the vibration of the gearbox is increased due to the uneven distribution of the five shunt loads. Therefore, improving the load-sharing performance of the gear train can not only improve the efficiency, but also reduce the vibration of the gearbox.
## 5 Applications
### Medical Applications
Figure 12 shows measurements taken on two samples of CO\({}_{2}\) with different isotope concentrations using PAS with a current modulation (\(\pm\)5%) induced laser wavelength modulation. The blue line represents CO\({}_{2}\) in natural abundance (98.42% \({}^{12}\)CO\({}_{2}\), 1.10% \({}^{13}\)CO\({}_{2}\)), the red line a mixture with slightly less \({}^{13}\)CO\({}_{2}\) (98.46% \({}^{12}\)CO\({}_{2}\), 1.06% \({}^{13}\)CO\({}_{2}\)). The difference in \({}^{13}\)CO\({}_{2}\) concentration results in distinct differences in the measured signal. This test yields a detection limit of about 5 ppm for \({}^{13}\)CO\({}_{2}\), thus allowing for detection of variations in \({}^{13}\)CO\({}_{2}\) concentration on the order of 1% [70]. Figure 11: Emission spectrum of a DFB laser diode emitting around 2,043 nm wavelength. The SMSR of the device is \(>\)48 dB. Since the level of exhaled nitric oxide is an important indicator for various respiratory diseases, NO is another breath gas often associated with laser based measuring. Due to its strong absorption features in the mid-infrared region above 5 \(\upmu\)m, several NO measurement techniques have been developed using DFB QCL devices [73; 74]. Such lasers have also been successfully implemented in PAS setups, combining the advantages of PAS detailed above with the long mid-infrared wavelength range accessible to DFB QCL [75; 76].
# Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC
## 6 Conclusions  
For the amphibous multi-rotor UAV, the water-air transition presents a significant challenge due to changes in buoyancy and added mass as well as the impact of the \"water surface effect\". To address this challenge, this paper proposes the use of the conventional ADRC control algorithm, which has shown strong robustness in aircraft control, to design the controller for the amphibious multi-rotor UAV and conduct a preliminary exploration of cross-media control research. Kinematic and dynamic models of the amphibous multi-rotor UAV were established, and the hydrodynamics of the underwater vehicle were analyzed to construct a cascaded active anti-interference controller using the ADRC algorithm. Simulation results demonstrate that the ADRC algorithm provides better control performance and robustness than SMC and PID in the cross-media control process. Experimental verification is then carried out to verify the usability of the ADRC algorithm in actual control. The results show that during the water discharge process, although the amphibious multi-rotor UAV experiences significant disturbance, the ADRC algorithm demonstrates smaller attitude fluctuations compared to SMC and PID, as well as better control performance and robustness. These results verify the superiority of the ADRC control algorithm and demonstrate its potential for use in the design of a cross-media controller for the amphibious multi-rotor UAV.  
Conceptualization, L.T.; Methodology, S.L.; Validation, L.L.; Resources, J.H.; Writing--original draft, L.T.; Writing--review & editing, Z.Q. and H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science Fund for Excellent Young Scholars of Heilongjiang Province under Grant YQ2020F007, the National Natural Science Foundation of China under Grant 6191101340, the China Postdoctoral Science Foundation funded under Grant 2018M631930, and Fundamental Research Funds for the Central Universities under Grant 2022 FRFK060007.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
### The Issue of Cable Placement in Stabilized Backfill
They estimated the possible reduction in the service life of the power cable line at nominal load but did not take this phenomenon into account when calculating the ampacity of the power cable line. The research results presented in this article constitute a significant extension of the content included in the authors' article [3], commenting on the problem and indicating possible solutions for cables laid in thermal/stabilized backfill. Based on further, extensive analyses, the authors have introduced a method to calculate ampacity, taking into account the cross-sectional area of the cables and the dimensions of the stabilized backfill. Thanks to the implementation of advanced numerical models and calculations, a mathematical relationship for a correction factor is proposed that allows for the determination of the ampacity of cables. As a result of the authors' approach, it is possible to maximize the transmission potential of cables for their given cross-sectional area and stabilized backfill dimensions. The authors' proposal will have a positive impact on the reliability of cable systems. The authors' approach is an alternative to other solutions and a contribution to the current state of knowledge. This is very important for design practice, because proper modelling of ground properties has a significant impact on the thermal parameters of cable lines and, therefore, their ampacity [33]. The rest of the article is organized as follows: Section 2 applies to the power cable system without stabilized backfill and presents the computer model of the analyzed system, assumptions for simulation calculations, and visualization of the temperature fields for the cable system for various parameters. Section 3 contains the results of relevant computer tests on the ampacity of power cables placed in the ground with stabilized backfill. This section also includes a discussion of the results as well as a mathematical relationship summarizing the obtained results. Section 4 provides an overall summary of the article and related conclusions.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 2 Processes
### Air Sparging  
Air sparging is an innovative and successful remediation technique that uses physical stripping (volatlinsation) to remove volatile organic compounds (VOCs) and promote aerobic biodegradation in groundwater and soil by pumping a gas, usually air, into an area below the water table via injection wells [330; 331; 332]. The process of volatilisation is intensified by diffusion and mixing with aerated water [333; 334]. The use of air sparging has grown rapidly since its testing in the mid-1990s under the auspices of the U.S. Air Force Research Laboratory, Airbase and Environmental Technology Division, and Tyndall AFB. These entities funded a project that encompassed both laboratory and field testing to refine the concept of air sparging [335].  
Since its introduction, it has been the most widely used method for hydrocarbon contaminated sites, and is still popular today. However, there are still unresolved issues that pose challenges and affect performance, such as the accurate distribution of air in the target treatment zone and the relationship between the size of the contaminant source and air distribution, as well as reducing the risk associated with working with compressed air, which can have serious consequences if not handled properly. Normally, macro-bubbles are used for air purification, but the use of micro- and nano-bubbles (MNB) opens a new chapter in improving the effectiveness of air purification and leads to higher concentrations of dissolved oxygen [336; 337]. Incorporating short pulses of high air pressure also improves the overall treatment by increasing the zone of influence and air permeability [338]. A number of mathematical models can be found in the literature that simulate the transport of air and pollutants by air sparging [339; 340; 341; 342; 343; 344; 345].
## 5 Conclusions and Discussion  
We processed one month of observations from a very limited number of nine stations in the iGMAS tracking network for the precise orbit determination of BeiDou-3 satellites. For comparison, the observations from 52 MGEX stations were processed for the precise orbit determination of BeiDou-2 satellites. A two-step method was implemented in the routine software package developed by the Analysis Centre at the Institute of Geodesy and Geophysics, Chinese Academy of Sciences. Internal consistency and SLR validations were conducted, and the orbit radial component can reach an accuracy on the level of 1 decimeter and 4 decimeters for BeiDou-3 IGSO and MEO, respectively. The orbit radial component can reach an accuracy on the level of 1 decimeter and 0.5 decimeters for BeiDou-2 IGSO and MEO, respectively.  
At present, the orbit determination performance for BeiDou-3 satellites, particularly for global BeiDou-3 MEO satellites, remains inferior to that of BeiDou-2 satellites and other GNSS constellations such as GPS and GLONASS. This problem has been attributed to the lack of a well-distributed worldwide tracking network for BeiDou-3 satellites, particularly MEO satellites. Further efforts may involve two aspects: deployment of a larger global tracking network for BeiDou-3 satellites and more accurate dynamic models for BeiDou-3 satellites.  
For BeiDou-3 satellites, a relatively small number of ground tracking stations were deployed. With a larger and more well-distributed global tracking network, which can provide better coverage for all BeiDou-3 satellites, particularly for global MEO satellites, the accuracy of BeiDou-3 satellite POD will be improved. Meanwhile, more ground tracking stations and precise-orbit products will be notably helpful with the SRP modeling for BeiDou-3 satellites in the near future [23].  
This work was supported by National key Research Program of China \"Collaborative Precision Positioning Project\" (No. 2016 YFB0501900), the National Nature Science Foundation of China (Grant No. 41231064, 41674022, 41304034, and 41574033). We also acknowledge the funding support of the State Key Laboratory of Geodesy and Earth's Dynamics (Institute of Geodesy and Geophysics, CAS) (No. SKLGED2014-3-1-E). The authors would like to extend their sincere gratitude to iGMAS for providing the observation data. The authors are thankful to the ILRS for providing the BeiDou satellite SLR observation data. The authors are grateful to the IGS Multi-GNSS Experiment ([[http://mgex.igos.org](http://mgex.igos.org)]([http://mgex.igos.org](http://mgex.igos.org))) for supplying the related information about the BeiDou Navigation Satellite System. [PERSON] and [PERSON] provided the initial idea for this study. [PERSON] and [PERSON] conceived and designed the POD experiments. [PERSON] and [PERSON] analyzed the experiment results. [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 4 Experimental Results
### Basic Performance Test
#### 4.1.1 Performance Test of the Hybrid Controller without Initial Error  
Assuming there is no initial error in the various trajectories, the typical iterative learning controller (17) directly controls the rotation speed of the two wheels to solve the trajectory tracking problem.  
**Situation 1:** The control law of the corresponding gain matrix is described as follows:  
\[L_{1}(k)=\frac{1}{R_{rs}}\left[\begin{array}{cc}\cos(q)&\sin(q)&B_{rs}\\ \cos(q)&\sin(q)&-B_{rs}\end{array}\right];L_{2}(k)=0;\;\;\;L_{3}(k)=0; \tag{45}\]  
where \(R_{rs}=R_{c}\) represents the actual radius of the wheels, and \(B_{rs}=B_{c}\) represents the actual distance between the driving wheels.  
The trajectory tracking process and the final result of the mobile platform after \(~{}100\) iteration process are shown in Figure 3. By comparing the tracking results, we found the following:  
1. The system can directly control two driving wheels to track the specified trajectories without initial error using the iterative learning controller.
2. Selecting the midpoint of the two driving wheels as the control variable can effectively achieve different trajectory tracking tasks.
# Double-Consensus Based Distributed Optimal Energy Management for Multiple Energy Hubs
## 4 Application Examples
### Time-Varying Demand  
This case study focuses on testing the capability of the proposed DDC algorithm to handle changes in load demand. The initial conditions are the same as those in case study 1, and the load demand changes 2 times during the simulation. At \(t_{1}\) (\(k\) = 1000), the total power and heat load demands decrease by 20%, while at \(t_{2}\) (\(k\) = 2000), they are increased by 20%. The simulation results are shown in Figure 5. It can be observed from Figure 5a,b that neither the electricity nor heat mismatch can be maintained at a zero state after \(t_{1}\), because of the reduced power and heat load demands. By continually running the proposed DDC algorithm, each energy hub gradually reduces its electricity and gas inputs to meet the new supply-demand balance, as seen in Figure 5e,f; meanwhile, the electricity and heat multipliers decrease in response to the change seen in Figure 5c,d. After about 300 iterations, i.e., at \(k\) = 1300, the electricity and heat mismatches converge to zero again, and not only the electricity Lagrangian multipliers, but also the heat Lagrangian multipliers, converge to new common values. This implies that the new supply-demand balance and the optimization goal have been fulfilled. Furthermore, the balance is broken at \(t_{2}\) due to the increased load demands. From Figure 5a-f, it can be seen that after about 300 iterations, i.e., at \(k\) = 1300, the electricity and heat mismatches go to zero, both the electricity and heat multipliers increase and converge to new optimal values, and each hub increases its energy input to compensate for part of the increased load, finally converging to the new solutions. Based on the aforementioned discussion, it can be concluded that the proposed DDC algorithm can respond automatically to converge to new optimal solutions after each load change. This also means that the proposed DDC algorithm works properly under time-varying demand.