esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Learning Based Underwater Robotic Thruster Fault Detection
## 4 Propulsion System Online Modeling
### Propulsion System Online Modeling Process  
The implementation process of online modeling of the underwater robot propulsion system proposed in this paper is as follows.  
Step 1: During the operation of the underwater robot, obtain online propeller control and speed data. In order to improve the accuracy of modeling, the data to be acquired include a certain amount of change in thruster control.  
Figure 4: Particle position and learning center distribution.  
Step 2: Estimate the time delay of the propulsion system in the current state, according to the acquired control variable and speed data.  
Step 3: According to the estimated time delay \(\hat{\tau}\), realign the propeller control variable and speed data:  
\[\left\{\begin{array}{l}U_{t}=(u_{1},u_{2},\cdots,u_{N-t})\\ V_{\hat{\tau}}=(v_{\hat{\tau}+1},v_{\hat{\tau}+2},\cdots,v_{N})\end{array}\right. \tag{14}\]  
Step 4: Use the multi-center PSO algorithm with memory capability to optimize the thruster model order \(q\) and data volume \(w\) used for modeling, where \(1\leq q\leq q_{\text{max}}\), \(w_{\text{min}}\leq w\leq w_{\text{max}}\). When evaluating the fitness of particles, use the following data sets:  
\[\left\{\begin{array}{l}U_{\hat{\tau},w}=(u_{N-\hat{\tau}-w+1},u_{N-\hat{ \tau}-w+2},\cdots,u_{N-\hat{\tau}})\\ V_{\hat{\tau},w}=(v_{N-w+1},v_{N-w+2},\cdots,v_{N})\end{array}\right. \tag{15}\]  
Establish the rotational speed model \(\hat{v}=F_{q,w}(u)\) through polynomial fitting and evaluate the model using the mean absolute percentage error (MAPE):  
\[f_{q,w}^{\text{MAPE}}(U_{t,w},V_{t,w})=\frac{1}{N_{\hat{\tau},w}}\sum_{i=1}^{ N_{i,w}}\left|\frac{v_{i}-\hat{v}_{i}}{v_{i}}\right| \tag{16}\]  
Then, the fitness value of the particle whose current position is in \((q,w)\) is:  
\[f_{q,w}=\text{max}(f_{q,w}^{\text{MAPE}}(U_{\hat{\tau},w},V_{\hat{\tau},w}),f_ {q,w}^{\text{MAPE}}(U_{test},V_{test})) \tag{17}\]  
Among them, \((U_{test},V_{test})\) are the public test data taken from \((U_{\hat{\tau}},V_{t})\).  
Step 5: According to the optimal modeling parameter \((q^{*},w^{*})\) obtained under the current data set through polynomial fitting, a control variable--speed model \(\hat{v}=F_{q^{*},w^{*}}(u)\) is established.  
Through the above-mentioned steps, a control variable--rotation speed model, which can describe the propeller in the current operating state, can be established, in an online manner, for subsequent speed prediction and fault detection.
## 4 IoT Deployment Opportunities in Developing Countries
### Leveraging Unlicensed LPWANs for IoT Deployments
#### 4.2.3 Device Classes  
There are three medium access control (MAC) operating classes of LoRaWAN IoT device layers specified in the LoRaWAN standard to satisfy certain applications with trade-offs between latency and energy utilisation. Each device class is distinguished by design in the energy consumption profile which relates to the time (duty cycle) it takes their radio receivers to receive a LoRaWAN message. LoRaWAN devices are limited by regulation to transmit under 1% of the time, to manage the spectrum and reduce collisions. The three classes are based on the same physical layer characteristics [53]:  
Class A is a generic mode in LoRaWAN IoT modules that are battery powered, energy efficient, and are based on the ALOHA operational principle of uplink transmission predetermining downlink transmissions. Without a transmission in the uplink channel, downlink transmissions will be queued and delayed (latency introduced) because the uplink transmissions initiate the two possible downlink transmission windows. In this case, the devices only receive downlink packets in two short-lived windows, RX1 and RX2, immediately after the uplink transmission, TX1. The duration of the downlink receive windows is configurable. The receive delay parameter by default according to the LoRa Alliance parameter is a 1 s interval for maximum efficiency depending on the SF applied. The received delay is region-specific and reconfigurable through MAC layer commands. An acknowledged uplink transmission prevents packets retransmission, thereby saving energy. If there is no acknowledgement received, a re-transmission takes place with an increased spread function.  
Class B devices are higher power consumption LoRa devices, designed to increase the receive windows of downlink transmissions that are scheduled without requiring an uplink transmission [54]. The synchronisation message from the gateways that increased the UEs energy demand allows downlink re-transmissions to be rescheduled. In this case, a gateway can multicast a message to the IoT devices within the network.  
Figure 3: Secure LoRaWAN Architecture: (**a**) typical LoRaWAN architecture; and (**b**) LoRaWAN implemented on The Things Network.  
Class C devices are application dependent (for use-cases where a fixed power source is available). Typically, non-battery powered IoT modules can efficiently receive and transmit at any time. Class C LoRa devices continuously listen for downlink messages, and therefore are not suitable for power-constrained use-cases. A suitable application of this class is the traffic and packing systems where a power supply is fundamentally required for the system to function.
## 4 Results and Analysis
### Field Measurement Results and Analysis  
#### 4.2.1 Pre-Stack Combination Denoising  
The noise suppression of seismic data is a key link in data processing. Linear interference waves, such as surface waves and multiple refraction waves, and random interference waves are mainly developed in the study area, which are the main noise interference affecting pre-stack migration imaging. According to the characteristics of the interference wave in the area, the combined denoising method is used to suppress the interference wave and improve the signal-to-noise ratio of the reflected wave. For linear interference such as surface waves and multiple refraction, the t-x domain subtraction method is used to denoise step by step in the common shot point domain, common detection point domain,  
Figure 8: Kirchhoff pre-stack depth migration imaging results: (**a**) with the addition of model constraints; (**b**) without the addition of model constraints.  
and common offset domain. For random noise, adaptive high-frequency noise attenuation technology is used to effectively suppress high-frequency environmental noise and burst noise. The combined denoising results are shown in Figure 9. The seismic interference wave is effectively suppressed by the combined denoising method, the reflected wave signal-to-noise ratio is significantly improved, and a better amplitude-preserving denoising effect is achieved.
## 5 Sensitivity Analysis of AV to Situations and Events for the Generation of Concrete Scenarios
#### 5.1.2 Sensitivity Distribution Function on Logical Situations  
The sensitivity distribution function \(q\left(Y_{j}\right)\) is obtained by normalizing the AV sensitivities to the logical situations. This distribution function makes it possible to draw, one after the other, the logical situations which will be used to generate the concrete scenarios for subsequent simulations. As we can see, with such a distribution, the logical situations with high sensitivity values will also have high probability values and will have higher occurrences during the sampling. Thus, the sensitivity distribution function is based on an importance function that allows us to prioritize the sampling of logical situations \(Y_{j}\) for which the a priori sensitivity value of the AV is high.  
The probability distribution of \(q\left(Y_{j}\right)\) is defined as follows:  
\[\begin{cases}\text{q}\left(Y_{j}\right)\ \epsilon\ [0,1]\\ \sum_{Y_{j}}\text{q}\ \left(Y_{j}\right)=1\end{cases}\]  
Let \(S_{j}\) be the sensitivity value of the AV to the logical situation \(Y_{j}\) and let \(\text{W}=\sum_{Y_{j}}S_{j}\) be the normalisation factor. Thus, the sensitivity or importance distribution function can be defined as follows:  
\[q\left(Y_{j}\right)=\frac{S_{j}}{W}\]  
We can check that \(\sum_{Y_{j}}q\ \left(Y_{j}\right)=\sum_{Y_{j}}\frac{S_{j}}{W}=1\).  
We have defined how to lead a sensitivity analysis on the logical situations. However, in order to generate scenarios, it is also necessary to create events. In this respect, as for the logical situation, a sensitivity analysis of the events will be introduced in order to draw events according to their potential impact on the safety of the AV.
## 3 Materials and Methods
### Multi-Next-Hops Approach Proposed for FzGR
#### 3.1.2 Load Balancing Approach  
The main role of load distribution is to use the elected paths for transporting packets from the source to the destination in a balanced way. By applying the multi-next-hop algorithm to the FzGR protocol, as illustrated in Figure 3, the packets are distributed to several direct links, i.e., direct neighbors of each sending node, so the process is repeated for each participating node until the destination final is reached. In order to balance the load between these different links, we propose to integrate the WRR weighted round-robin [43] scheduling mechanism into MNH-FGR to support multiple heterogeneous links. Indeed, this algorithm requires additional information for the selection. This information was derived from the fuzzy logic applied in FzGR. In addition, WRR is able to balance the load between several heterogeneous links. Thus, if the weights of the bonds are different, WRR can avoid the overuse of the high-weight bonds and the underutilization of the low-weight bonds.  
Recalling that a path is composed of several hops, each hop represents only the link between the sender and its immediate neighbor. In addition, each link is affected by a weight that represents an integer value indicating its QoS capability. In our diagram, we calculate the pi weight of \(Li\) by the fuzzy approach presented in [9].  
The algorithm returns multiple and weighted links. In this case, the links with lower weights could carry a larger number of packages than those with a high weight. In other words, the number of packets allocated to links is limited by the weights of these links. The traffic load distribution, based on the WRR principle (see Algorithm 1), is performed by  
Figure 2: Adaptive GRS policy for MNH-FGR.  
each sending node in order to distribute the packets in the n paths to the destination node D as a function of the parameter weight _pi._
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios
## 2 System Operation Principle  
The basic geometry of a PR based on a space-borne IoO is depicted in Figure 1. The acquisition system is usually composed of two independent channels: one is the reference channel, for acquiring the direct signal from the satellite, \(s_{ref}(t)\); and the other is the surveillance channel, in charge of recording target echoes from the AoI, \(s_{surv}(t)\).  
A traditional PR bases its operation in the coherent processing of both acquired channels. After a preprocessing stage, that reduces DPI and Clutter interferences, a Cross Ambiguity Function (CAF) between the reference and surveillance signals is computed according to Expression (1).  
\[S_{CAF}[m,p]=\sum_{n=0}^{N-1}s_{ref}^{*}[n-m]\cdot s_{surv}[n]\cdot\exp^{- \beta\pi\frac{\xi}{N}n} \tag{1}\]  
The presence of a target echo in the surveillance signal gives rise to a local maximum at a bistatic range-Doppler shift pair (\(m\),\(p\)) related to its location and movement with respect to the PR geometry.  
In the specific case of GPS satellite based PRs, a deeper analysis of the transmitted signals and PR geometry should be carried out to allow the correct definition of the PR processing scheme.  
GPS operation is based on the continuous and simultaneous transmission of GPS signals from 24 satellites in a medium earth orbit (MEO), in three different frequency bands (L1, L2, L5). In this work, civil signals in the L1 band are considered as illumination sources. All GPS L1 signals share the same carrier frequency in a Code Division Multiple Access (CDMA) method requiring a signal reconstruction stage to isolate the selected reference signal. In addition, the platform location in an MEO generates a time variant geometry that should be considered in reference signal reconstruction and PR measurement interpretation. Both issues are considered in the following subsections.
## 3 Establishment of CPPS Real-Time Hardware-in-the-Loop Co-Simulation Platform
### Modeling and Simulation of Information Communication System  
Mainstream information communication simulators can be divided into two types: commercial simulators represented by OPNET (developed by riverbed, San Francisco, CA, USA) and QualNet (developed by SCALABLE Network Technologies, Inc., Culver City, CA, USA), and open source simulators represented by NS2 and SSFNET. Generally, commercial simulators are expensive and relatively enclosed, while offering complex overall modeling support. In contrast, open-source simulators are freely available and are open source; however, their structure and function are simplified when compared with real-world situations. When it comes to the co-simulation of a CPPS, the following functions of communication network simulators are required: compatibility with hardware-in-the-loop, detailed modeling of the electric power communication network, and the real-time performance of the simulation. In consideration of these factors, the OPNET Modeler was chosen to model the information communication system in our research.  
The OPNET Modeler is a discrete-event network simulator that analyzes and tests the performance of a communication network. Users can carry out high-fidelity modeling, scalable simulations, and detailed analysis through its three-layer modeling mechanism: network modeling, node modeling, and process modeling.  
OPNET provides three ways to communicate with the exterior environment: high-level architecture (HLA), the external simulation access-application programming interface (ESA-API), and system-in-the-loop (SITL). Of these three methods, SITL is the most current and fastest way to map multiple physical network interfaces to different network addresses in the virtual network. Through this technology, OPNET and the exterior environment can be made into a unified entity. Therefore, SITL was chosen as the interface between OPNET and the other simulators.
## 5 Simulation Experiment and Analysis
### Scenario Two  
In this experiment, the packet loss rates of the four algorithms were compared. The ratio of the number of data packets lost during the data transmission to the total number of packets sent by the source node is the data packet loss rate. Scenario two is different from scenario one in that the quantity considered for comparison is measured as a function of time. From Figure 9, we can intuitively see that the four algorithms' packet loss rates are very close for the first 8 s, but as time passes, the packet loss rates of QOS-POS and ACORC become higher than that of the algorithm proposed in this paper. The rate for the TANARP algorithm is lower than that for the proposed algorithm, but the difference is very small, and the packet loss rates of all four algorithms increase over time. Eventually, the packet loss rates of the algorithms stabilize: that of the ACORC algorithm tends toward 55%, that of QOS-POS tends toward 47%, that of MPASR is close to 40%, and that of TANARP is close to 39%.  
The main reason for the behavior described above is that over time, the energies of the nodes decrease, and some nodes die prematurely because of a lack of energy, which leads to an increase in the packet loss rate. The proposed algorithm comprehensively considers the node reputation, residual energy and delay. Therefore, to a certain extent, the residual energy of each node should be similar, thereby minimizing this effect. However, the node reputation value is given priority over the other two factors, so the algorithm may choose some nodes with high reputation values but low energies. The TANARP algorithm primarily considers the node energy and does not incorporate safety factors, which is the reason why the loss rate of the proposed algorithm is slightly higher than that of the TANARP algorithm.  
The data packet loss rates of the four algorithms for different numbers of nodes are shown in Figure 10. From this graph, we can see that when the number of nodes is small, the data packet  
Figure 9: Comparison of packet loss rates over time.  
loss rates of the three algorithms are not very large, but as the number of network nodes increases, the communication traffic will surge.  
In this case, because network congestion results in a significant increase in the number of packets lost, the data packet loss rate of the ACORC algorithm is significantly greater than those of the TANARP, QOS-PSO and MPASR algorithms. When the number of nodes reaches 100, the packet loss rate of ACORC is 51%, that of QOS-PSO is 37%, and that of TANARP is 35%, whereas that of MPASR is only 31%. For a fixed number of malicious nodes, as the total number of nodes increases, the proposed algorithm can still maintain a low packet loss rate; this finding confirms the feasibility of this algorithm.
## 5 Analytical Validation and Comparison with Existing Systems
### Atmospheric Attenuation Validation  
The analytical model for atmospheric attenuation integrates frequency-dependent dry-air and wet-air absorption coefficients as follows:  
\[A_{\text{atm}}(f,d)=(\alpha_{\text{dry}}(f)+\alpha_{\text{wet}}(f))\cdot d, \tag{21}\]  
Figure 11: Outage probability vs. distance for different SNR thresholds.  
where \(\alpha_{\text{dry}}(f)\propto f^{2}\) and \(\alpha_{\text{wet}}(f)\propto f^{1.6}\).  
A comparison was conducted for frequencies of 24 GHz, 28 GHz, and 32 GHz over distances ranging from 10 km to 3000 km. Figure 12, shows close alignment between the proposed model and ITU-R tabulated values, with a mean absolute error (MAE) of less than 0.35 dB.
# Application of HY-2B Satellite Data to Retrieve Snow Depth on Antarctic Sea Ice
## 5 Conclusions
All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China under Grant No. 42076235, the Excellent Teacher Training Program of Anhui Province under Grant No. YQZD2023009, and the Quality Engineering Project for Anhui Higher Education Universities under Grant No. 2023 jyxm0164. The data are available to readers by contacting the corresponding author. The authors would like to thank the Chinese National Satellite Ocean Application Service (NSOAS) and the U.S. National Snow and Ice Data Center (NSIDC) for providing HY-2B SMR brightness temperature data and the GCOM-W1 AMSR-2 snow depth product used in this study. We acknowledgement the Chinese Arctic and Antarctic Administration and the Polar Research Institute, as well as the members of the R/V Xuelong of the 35 th Chinese Antarctic Expedition for their great support and assistance during the Antarctic sea ice and snow in situ survey. The authors declare no conflicts of interest.
## 4 Derivation of Energy Efficiency Expression
### Network Power Consumption  
Generally, the BS power consumption comprises static power consumption and transmit power consumption [35]. The static power consumption is caused by signal processing, battery backup, as well as site cooling, and independent with the BS transmit power consumption. The transmit power consumption is determined by the transmission power of this BS and the load-dependent power consumption coefficient of this BS which is denoted as the number of its serving UEs. Define \(P_{m,s}\) and \(P_{p,s}\) are the static power consumption of each MBS and each PBS, respectively. With FeICIC scheme, the power consumptions of each MBS in PSF and USF can be expressed as \(P_{m}^{PFS}=P_{m,s}+N_{pm}\rho P_{m}\) and \(P_{m}^{UFS}=P_{m,s}+N_{um}P_{m}\), respectively. Similarly, the power consumptions of each PBS in PSF and USF can be given as \(P_{p}^{PFS}=P_{p,s}+N_{pp}P_{p}\) and \(P_{p}^{UIFS}=P_{p,s}+N_{up}P_{p}\), respectively.  
In PFS, the unit area mean power consumption \(P^{PFS}\) can be expressed as:  
\[P^{PFS}=\lambda_{m}P_{m}^{PFS}+\lambda_{p}P_{p}^{PFS}=\lambda_{m}P_{m,s}+\lambda _{p}P_{p,s}+A_{pm}\lambda_{u}P_{m}+A_{pp}\lambda_{u}P_{p}. \tag{11}\]  
Similarly, the unit area mean power consumption in UFS \(P^{UIFS}\) can be obtained as follow:  
\[P^{UIFS}=\lambda_{m}P_{m}^{UIFS}+\lambda_{p}P_{p}^{UIFS}=\lambda_{m}P_{m,s}+ \lambda_{p}P_{p,s}+A_{um}\lambda_{u}P_{m}+A_{up}\lambda_{u}P_{p}. \tag{12}\]Hence, the network power consumption can be expressed as:  
\[\begin{split} P^{total}&=\theta P_{PFS}+\left(1-\theta \right)P_{UIFS}\\ &=\lambda_{m}P_{m,s}+\theta\left(A_{pm}\lambda_{u}\rho P_{m}+A_{ pp}\lambda_{u}P_{p}\right)+\lambda_{p}P_{p,s}+\left(1-\theta\right)\left(A_{um} \lambda_{u}P_{m}+A_{up}\lambda_{u}P_{p}\right).\end{split} \tag{13}\]
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 6 Results
### Mobility Evaluation  
Figure 7 visualizes the connections between every transmission location and the currently serving cell, as well as the respective cell sector boundaries. Starting at the north side and moving counterclockwise, the first UE positions (i.e., red dots) are located within sector 1, which is the sector of the serving cell. When the UE moves outside sector 1, the cell reselection takes place and in most cases the adjacent cell corresponding to sector 2 becomes the serving cell. After leaving sector 2, the UE remains connected to the cell related to sector 2 and never connects to the cell covering the area of sector 3. Instead, we observe serving cells from other eNBs for short amounts of time (i.e., cell sectors 4 and 5). Thus, only two out of three cells of the center eNB are used.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Path Loss Model** & **Mean Error [m]** & **Median Error [m]** & **95 th Percentile [m]** \\ \hline Hata & 325 & 272 & 783 \\ COST-231 & 327 & 276 & 780 \\
**3 GPP AH-macro** & **320** & **259** & **790** \\
3 GPP AH-pico & 330 & 279 & 776 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Location estimation errors for different urban path loss models of an RSS-based ranging algorithm using a single NB-IoT antenna and cell sector.  
Figure 6: Location estimation errors for every outdoor RSS-based localization algorithm when using a single NB-IoT base station antenna. The left box plot shows the proximity algorithm results. The box plot in the middle shows the errors of the ranging algorithm with the 3 GPP AH-macro path loss model. The right box plot shows the estimation errors of the validation fingerprinting data set using the optimal parameter configuration.  
The time to select another serving cell varies from 51 s to 104 s, with a mean cell reselection time of 73 s. Within these periods, it is not possible to send NB-IoT messages. Furthermore, the UE remains connected to the cell covering sector 2 for more than four minutes after leaving that sector, without ever connecting to the cell corresponding to sector 3.