esa-sceva/satcom-chunk-collection huggingface dataset:
# Modulation Recognition System of Electromagnetic Interference Signal Based on SDR
## 6 Conclusions  
Considering the problem of modulation type interference signal recognition, a modulation mode recognition system of electromagnetic interference signal based on software radio was designed. The system receives the interference signal through HackRF One hardware, and realizes the frequency and symbol rate estimation, clock synchronization, classification recognition, and phase correction of the received signal in the GNU Radio software platform. Finally, the recognition results are intuitively reflected through a domain waveform diagram and constellation diagram. The results show that the system can quickly and accurately identify the modulation type of the electromagnetic interference signal. Compared with the traditional communication signal modulation identification method, the identification result is more real-time and more suitable for practical engineering applications.  
All authors have the same contributions. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on Key Methods of Internet of Things Security Situational Awareness in Open Pit Mines (LJKMZ2020677).  
This paper did not use publicly available datasets.  
The authors declare no conflict of interest.
## 6 Conclusions  
Developing a smart city is our primary objective, and fog computing is a critical component of a smart city. We investigated several fog computing issues and found that latency and delay are difficult challenges. We picked VANETs to deploy fog computing because VANET applications are time-sensitive. Our proposed model decreased a significant amount of the latency and delay, and enhanced the communication efficiency between vehicles, as shown in the results.  
This experimental work investigated several effective measures to reduce the delays and latency of the vehicular data transmission carried out with the fog computing model. Data transfer between vehicles with the same network-based connectivity was helpful for to perform data computation and transfer at faster rates by computing them at the nearest fog nodes. The inclusion of 5G-based MEC servers helped us to switch over the user's network connectivity with closer servers, and it reduced the queuing process by transferring data and requests to the available servers.  
We proposed a fog computing model that enhances communication efficiency and decreases data latency by 20% compared to the cloud computing architecture, and decreases the processing delay by 35% compared with the utilization of cloud computing architecture. Experimentally, we demonstrated the effectiveness of this model based on the delay and latency that occurred during vehicular data transmission, and it was performed even in a low networking environment. The effects of the high processing time were also evaluated analytically and agreed with several mathematical analyses.  
In future research, we are planning to extend our work in terms of scalability by gradually increasing the number of requests sent by the VANET-enabled vehicles to evaluate  
Figure 6: Comparison of latency: fog vs. cloud.  
the performance of the model under increased load. we will also be upgrading towards IoV, as IoV has emerged as the successor of VANETs and removes the VANETs' limitations. Moreover, research work on IoV's energy consumption, communication costs, and the installation of fiber optics between fog premises is included in our plan.  
Conceptualization, A.M.F., M.A.A. and S.I.H.; methodology, A.M.F.; software, A.M.F.; validation, A.M.F.; formal analysis, A.M.F.; investigation, A.M.F.; writing--original draft preparation, A.M.F.; writing--review and editing, A.M.F. and S.M.I.; supervision, M.A.A. and S.I.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 4 Experiments
### Experiments on Imagenet Categories
#### 4.2.3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Classic**} & \multicolumn{4}{c}{**Generalized**} \\ \cline{2-9}
**Method** & \multicolumn{2}{c}{**hit@3**} & \multicolumn{2}{c}{**hit@5**} & \multicolumn{2}{c}{**hit@5**} & \multicolumn{2}{c}{**hit@10**} \\  & **per-cls** & **per-ins** & **per-cls** & **per-ins** & **per-cls** & **per-ins** & **per-cls** & **per-ins** \\ \hline SJE & 25.26 & 27.42 & 35.36 & 37.56 & 6.14 & 7.39 & 10.83 & 11.89 \\ SJE + CI & **35.95** & **36** & **47.06** & **46.04** & **8.06** & **10.28** & **12.79** & **14.7** \\ \hline LatEm & 24.64 & 26 & 32.85 & 34.32 & 4.72 & 5.28 & 7.36 & 7.67 \\ LatEm + CI & **30.15** & **30.59** & **41.05** & **39.66** & **6.19** & **7.92** & **9.19** & **10.45** \\ \hline ConSE & 25.91 & 25.7 & 36.16 & 34.36 & 5.51 & 5.76 & 8.18 & 8.09 \\ ConSE + CI & **33.74** & **31.65** & **45.27** & **42.23** & **6.84** & **7.78** & **9.4** & **9.89** \\ \hline GCN & 29.24 & 26.34 & 40.01 & 37.59 & 7.34 & 6.38 & 12.29 & 10.3 \\ GCN + CI & **36.43** & **34.13** & **47.15** & **44.94** & **10.11** & **9.85** & **16.4** & **15.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top-n prediction results of HM\({}_{path}\) by applying contextual inference (CI) to the baselines.
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 3 Implementing Collaborative UVs for the OWT IMR Operations
### Collaborative Strategies to Prolong the Operation Time
Using a capacitive power transfer technology [128], a UAV only needs to land on a designated platform, without the need for precise orientation or alignment. This could significantly reduce complex control strategies and mechanisms just for the purpose of battery alignment to initiate the charging process. Furthermore, a wide charging area is made possible with this wireless charging technology. For a UAV docking on a USV [52], this technology can further ease the process, as swaying effects on a USV make docking strategy even harder when compared to docking on land. The wireless charging method for UAVs is further applied to prolong the UAV flight time by placing multiple landing pads along a flight path [129]. This strategy is useful for UAVs operating for long-distance surveying or in wide area coverage. Almost all automatic charging strategies can be implemented by USV to power up a UAV; however, the most practical solution would be the wireless charging method due to its simplified mechanical arrangement which does not require a UAV to properly align or be fitted to a charger. The wireless charging method uses inductive coupling to enable flexible contact between a UAV and a charging platform. Due to this practicality, a USV can easily charge a docked UAV on its charging platform without the complex control of automated mechanical parts.
# Characterization of Ultra-High-Dose Rate Electron Beams with ElectronFlash [PERSON]
## 3 Results
### Measurements of the Percentage Depth Dose (PDD) Curve and of the Dose Profiles  
Figure 6 (red dots) reports the experimental data of PDD measured along the beam axis as a function of the depth in the PMMA phantom, for A-d30 and A-d120, and for electron beams with the energy of 5 MeV. Figure 7 reports the data relative to the case of 7 MeV. Data concerning the depth-dose curves were normalized to their maximum value at a depth of 12 mm for the beam energy of 7 MeV and of 7 mm for the beam energy of 5 MeV.  
Starting from the data of the PDDs, the lateral dose profiles, at the points R100, R80, and R50, were obtained as shown in Figures 8 and 9 for the energies and applicator cases considered in this analysis. The lateral dose profiles were investigated at 12 mm (R100), 17 mm (R80), and 21 mm (R50) depth for 7 MeV and at 7 mm (R100), 11 mm (R80), 14 mm (R50) depth for 5 MeV. The measured and simulated profiles were normalized to the maximum dose value. We observe a very good flatness over almost the entire field of radiation for A-d120. The flatness also remains fairly good for the case of A-d30.  
Monte Carlo simulation results for PDDs and lateral dose profiles are shown for comparison on the same figures (Figures 6-9) (continuous blue lines).  
Figure 5: Dose per pulse measurements with film at the end of the A-d30 (**left**) and A-d120 (**right**), for 5 MeV electron beam (red) and 7 MeV (green), as a function of the pulse width. The dashed lines show the linear fit.  
Figure 6: Measured (dotted red line) and simulated (continuous blue line) PDD in the PMMA phantom obtained for the 5 MeV electron beam collimated with the A-d30 (**left**) and A-d120 (**right**), applicators.  
Figure 8: Measured (dotted line) and simulated (continuous line) transverse profile in the PMMA phantom obtained for the 5 MeV electrons beam collimated with the A-d30 (**left**) and A-d120 (**right**) applicators. The figures show the profiles obtained at 7 mm (R100), 11 mm (R80), and 14 mm (R50) depth.  
Figure 7: Measured (dotted red line) and simulated (continuous blue line) PDD in PMMA phantom obtained for the 7 MeV electrons beam collimated with the A-d30 (**left**) and A-d120 (**right**) applicators.  
Figure 9: Measured (dotted line) and simulated (continuous line) transverse profile in the PMMA phantom obtained for the 7 MeV electrons beam collimated with the A-d30 (**left**) and A-d120 (**right**) applicators. The figures show the profiles obtained at 12 mm (R100), 17 mm (R80), and 21 mm (R50) depth.  
To evaluate the goodness of the agreement between the measured and simulated curves, a gamma index analysis [30] was carried out. The best agreement was obtained with the minimum value of 3% 3 mm acceptance criteria, as reported in Table 5 for PDD and in Table 6 for transverse profiles.
## 2 Literature Review
### Hydrogen as an Aviation Fuel: Promise and Challenges
Burning hydrogen in a gas turbine enables high thrust levels and produces only water vapor and minimal nitric oxide and nitrogen dioxide (NO\({}_{\mathrm{x}}\)) emissions as byproducts. However, this approach presents fuel injection, flame stability, and thermal management challenges [30]. A fuel cell, similar to a battery, offers high efficiency and near-zero emissions but is limited by power density and weight constraints [31]. These two pathways offer more flexibility in aircraft design, enabling direct combustion using the existing jet turbines, electric power from hydrogen fuel cells to reduce noise, or hybrid propulsion systems enabling long-range electric vertical take-off and landing (eVTOL) designs [32]. However, both approaches to extracting energy from hydrogen require significant technological advancements in fuel storage, energy conversion, thermal regulation, and safety management to reach large-scale commercial deployment.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 4 Proposed Intrusion Detection Algorithms for Smart Cities Network
### Multipath-Based IDS (MBIDS)
```
1:\(s_{1}\ldots s_{n}\in S\)
2:\(CH_{1}\ldots CH_{h}\in H\)
3:\(sn_{1}\ldots s_{n}\in SN\)
4:Repeat
5:if(\(s_{i}\) ready to send)
6:\(s_{i}\;\stackrel{{\text{RREQ}}}{{\rightarrow}}CH_{j}\)
7:\(CH_{j}\;\stackrel{{\text{RREQ}}}{{\rightarrow}}Neigh\big{(}CH_{j} \big{)}\)
8:\(CH_{j}\;\stackrel{{\text{RREP}}}{{\leftarrow}}Neigh\big{(}CH_{j} \big{)}\)
9:\(s_{i}\;\stackrel{{\text{Msg(Data)}}}{{\rightarrow}}CH_{j}\)
10: Send (Msg)
11: Send (Ctrl)
12:Endif
13:\(\forall\;\mathbf{sn_{i}}\;\in\;SN\)
14:\(Receive\) (Msg(data))
15:\(Receive\) (Msg (Ctrl))
16:if(Compare(Msg (data),Msg (Ctrl)) = true)
17:\(Alert()\)
18:Endif
19: Until the transmission process is completed
20:End
```  
**Algorithm 1**Multipath-Based IDS (MBIDS)  
As can be seen in Algorithm 1, MBIDS involves certain overhead, which is the control message that has to be sent along with the message and through another route. The control message, in our experiments that will be shown in later sections, contains a small number of fields, which are sender ID, size, control route information, packet information route, and checksum. The control message has no payload information, and it is also a default message with any data message to be sent from any of the sensor nodes; therefore, the overhead is the only control message that will be sent through another route. To easily recognize the overhead of the MBIDS algorithm, the payload of any WSN could take the values of 120, 60, 40, 30, 24, and 20 bytes, and the control message is only 8 bytes [3]. Therefore, the overhead varies with the payload size of the message to be sent to the sink node; while in the worst case, the overhead is 40% when the payload is 20 bytes only, in the best case, the overhead is almost 6% when 120 bytes are used as message payload. In our case, the best case has been utilized where the 20 bytes payloads are not practically used in real network scenarios. However, security always comes with a cost, and it is a tradeoff between network overhead and network disruption. There are other overheads that have been noticed during simulating the algorithms, which are the packet loss and network energy consumption. These overheads have been described later in the experiment section.
## 3 Simulation and Experimental Verification
### Case 3: A Freight Locomotive Wheelset Bearing Signal with Compound Faults
Similarly, the filtered signal generated from the IFB and its corresponding SES are same with Figure 17b,c. Hence, both the FK and the Autogram only identifies the outer race fault in this case and fail to detect the compound faults completely. The results indicate that the Mkurtogram is more effective to deal with the signals with compound faults than FK and Autogram.
## 2 Numerical Approach
### Fluid Properties and Two-Phase Modeling
#### 2.3.4 Boundary Conditions and Grid Resolution  
The governing equations for the two models are solved with the following boundary conditions:  
* At the inlet (\(z=0\)): \[w=w_{in},u=v=0,\quad T=T_{in}=293K\] (27)
* On the pipe wall (\(r=R=D/2\)): \[u=v=w=0,-k_{eff}\frac{\partial T}{\partial t}|_{r=R}=q_{w}\] (28)
* At the pipe outlet, the gauge pressure is set equal to zero and all the normal diffusion fluxes and the mass balance correction are applied.  
Several different grid distributions were tested to ensure the independence of the numerical results to the grid size. A structured mesh is used throughout the domain, with 140 nodes in the circumferential direction, 220 in the radial direction and 800 in the axial direction. A grid refinement close to the wall and in the pipe entrance is deemed necessary, where the highest velocity and temperature gradients occur (see Figure 2). This mesh grid provides grid-independent solutions for all cases studied.  
Figure 2: Schematic view of the mesh grid: (**a**) in a given cross-section; and (**b**) along the axial direction.  
The calculations are performed in parallel using Mammouth Parallel 2 of the Calcul Quebec cluster with 2 nodes having each 8 processors. The convergence is typically reached after 4000 iterations corresponding to a CPU time of about 6 h.
# The Long and Winding Road to Cardiac Regeneration
## 4 Biomaterials
### Synthetic Biomaterials
#### 4.1.5 Poly(l-lactide) (PLA)  
Poly(l-lactide) (PLA) is a biodegradable polymer derived from lactic acid.  
PLA possesses desirable physical features, including high mechanical strength and tunable degradation rates. It can be easily processed into various forms such as films, fibers, and scaffolds and has shown excellent biocompatibility and low cytotoxicity. Cells can adhere, proliferate, and differentiate on PLA surfaces, promoting tissue regeneration. The degradation product of PLA, lactic acid, is naturally occurring and can be metabolized by cells without causing adverse effects [211,212].  
PLA scaffolds can guide the formation of functional cardiac tissue by promoting cell alignment, extracellular matrix production, and tissue remodeling.  
Several studies have explored the potential of PLA in creating cardiac patches, biogeochemical heart valves, and tissue-engineered blood vessels. PLA-based scaffolds have demonstrated promising results in promoting cell proliferation and differentiation, enhancing cardiac function, and supporting tissue regeneration [213].
# Smart Low-Cost Control System for Fish Farm Facilities
## 4 Proposal for Smart Low-Cost Control Systems for Aquaculture
### Proposal for Smart Low-Cost Control Systems for Aquaculture Facilities with Cages in the Sea
#### 4.1.4 Communication Protocol and Communication Technologies  
A group-based collaborative protocol for fish farming monitoring is proposed. Group-based networks provide high scalability to the wireless network. The proposed system takes advantage of collaborative networks and collects all the information sensed from the nodes and the wireless network parameters to monitor the environment, detect abnormal situations, predict future scenarios, and control the actuators in the network.  
Concerning the communication technology, a combination of a WiFi network, which acts as a local network, and a LoRa network are used to transmit the data from the sensor nodes to the DB. The local WiFi network connects all the fish farm facilities' sensor nodes and actuator nodes. A specific buoy is deployed in the facilities, which acts as a gateway connecting with a LoRa network, identified as the green buoy in Figure 12. The possible need for underwater communication and including underwater acoustic modems [113] has been considered. In a recent publication, LoRa connectivity has reached 7 km over the sea [114].
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 2 Radar Signal Modeling  
To investigate the potential of the new C-band SAR Sentinel-1 sensor for monitoring frozen soils in an agricultural context, the radar backscattering coefficients of frozen soils were simulated using the Integral Equation model [8; 9] and a dielectric constant model of frozen soil. In these simulations, only the case of bare soils was considered.
# Nonuniform Background Correction for Wide-Field Surveillance Camera
## 4 Experiments and Discussions
### Algorithm Time  
In order to compare the calculation time of different methods, all methods implemented in MATLAB R2012a and the PC specifications include an i5-6500u (2.30 GHz) with 8 GB of main memory. The image size is 6 K \(\times\) 6 K pixels. The calculation time of different methods is shown in Table 4. Different algorithms are based on the improvement of morphological filtering. From the calculation time, it can be seen that there is little difference between the latest INTHT [29] algorithm and our algorithm. But our algorithm has a higher accuracy of nonuniform background correction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **New** & **INTHT** & **The Proposed** \\  & **Top-Hat** & & **ENTHT Method** \\ \hline Time(s) & 14.644995 & 18.537087 & 20.092629 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculation time of different algorithms.  
Figure 9: Background correction results of different algorithms under the influence of stray light. (**a1–a4**) Original surveillance images, (**b1–b4**) Correction results of the new top-hat transform, (**c1–c4**) Correction results of the INTHT algorithm, (**d1–d4**) Correction results of the proposed ENTHT algorithm.
## 3 Problem Statement  
Space missions, particularly to distant objects in space, need efficient propulsion systems to cut down on energy consumption and path planning. A path, as the movement of an object over a given period, is essential when analyzing spacecraft mechanics and efficiency during a mission. NASA's Juno space probe, launched into space in 2011 with the directive of researching Jupiter, provides the framework for evaluating paths in terms of mathematical analysis methods such as Hamiltonian mechanics. Evaluation of precise orbits and detection of alterations are crucial to examining the orbits of spacecraft and mission planning performance. Traditional statistical analysis in view of highly complex and dynamic trajectory data from Juno's flight is at a disadvantage when identifyingmeaningful alterations in orbits. The primary hurdle lies in creating a robust framework for analysis that is strong enough to consistently monitor and comprehend such orbit modifications under the transient environment of space.  
This paper attempts to address the problem of identifying significant trajectory changes in Juno's mission from higher-order derivatives of its position vector. By applying the Hilbert transform to the amplitude fluctuations of the velocity, acceleration, jerk, and snap, this study is trying to identify extreme events (EEs) of trajectory changes. Furthermore, the integration of these findings into a learning model facilitates adaptation to non-stationary challenges, thus enhancing our forecasting and comprehension of trajectory modifications in space flights. The new technique described here also improves our understanding of spacecraft dynamics and the overall research on deep-space exploration and navigation.
## 2 Test Method
### Resistivity Change  
Resistivity is used to represent the resistance characteristics of soil, and the resistivity formula [22] is  
\[\rho_{0}=R\frac{BH}{L}\]  
In the formula, \(\rho_{0}\) is the resistivity of the soil sample, \(R\) is the average resistance measured in the test, \(B\) is the width of the test container, \(H\) is the height of the test container, and \(L\) is the length of the test container.  
The water content of soft clay was 35-55%, and the change of resistivity without grouting and filled with water and FeCl\({}_{3}\) solution near the anode at normal temperature is shown in Figure 14. The resistivity changes near the anode without grouting and FeCl\({}_{3}\) solution filling at low temperatures are shown in Figure 15.  
According to Figures 14 and 15, one can see that  
1. Low temperature can significantly improve the resistivity of soft clay. When the anode was not grouted, the resistivity of soft clay under a low-temperature environment  
Figure 14: Variation of resistivity under different working conditions at normal temperature.  
Figure 15: Variation of resistivity under different working conditions at low temperature.  
increased by 1.513-4.449 \(\Omega\)-m compared with that under a normal temperature environment, and the resistivity increased by 29.7-93.4%. When the FeCl\({}_{3}\) solution was poured into the anode, the resistivity of soft clay at low temperature increased by 1.757-3.999 \(\Omega\)-m compared with that at normal temperature, and the resistivity increased by 37.9-87.1%.
2. Whether at normal temperature or low temperature, anode filling with FeCl\({}_{3}\) solution can effectively reduce the resistivity. At normal temperature, when the water content was 35%, the resistivity of FeCl\({}_{3}\) solution decreased the most, by 0.455 \(\Omega\)-m, with a decrease of 8.9%; at low temperature, when the water content was 40%, the resistivity of FeCl\({}_{3}\) solution decreased the most, by 0.699 \(\Omega\)-m, and the decrease was 7.7%.
3. An anode filled with water cannot effectively reduce the resistivity at normal temperature, especially when the moisture content of soft clay is high, and even the resistivity will be increased.