esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 4 Threat Models and Attack Scenarios
### GPS Spoofing  
Civil GPS is an extensively used protocol. GPS is used to find the location of the drone or UAV. GPS is a broadcast system only, and it tracks the drone with the help of satellites and measures the time of flights of the data signals. GPS works on the trilateration principle, in which the drone receives the signal from satellites in the form of place and time (sent and receive). In a GPS spoofing attack, the GPS receiver is made to believe that the drone is located differently than its actual physical location. Recent drones have inbuilt GPS sensors for location guidance and tracking for various missions with other features such as location hold, altitude hold, return to home function, etc. The GPS being unencrypted makes drones prone to a GPS spoofing attack. The adversary sends false or modified GPS signals to the drone. A commonly employed method known as a replay attack involves capturing and replaying the signal received from a satellite, introducing an additional delay. This technique needs real-time visibility of the satellites and a transmitter with sufficient power to overpower the direct signals received from the satellite. By manipulating the observed time-of-flight of the signal, a receiver can be deceived into believing it is located at a greater distance from the satellite than it is. Table 3 illustrates GPS frequency bands and their usage in different fields [25].  
Another GPS-based attack is GPS jamming. Disconnecting the receiver from the authentic satellite can be an easy way. However, under the Wireless Telegraphy Act, it is an offense to \"knowingly use\" such a device to block GPS signals. Recently, a GPS jamming attack caused 46 drones to plummet during a display over Victoria Harbour [100]. These attacks can be detected by verifying the claimed position of satellites with various techniques, such as remote attestation and periodic or random location checking.  
Figure 3: Jamming and spoofing attacks.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 4 Discussion
### Results from Outdoor Short-Medium Distance Experiment  
For this experiment in outdoor scenarios, we obtained \(\overline{\theta}\), \(\delta\), and jitter as a function of the distance and bandwidth, therefore we decided to represent the obtained results as 3D surfaces, as Figure 11 shows. Figure 11a,c,e show the \(\overline{\theta}\), \(\delta\), and jitter results, respectively. Hence, in order to show which protocol has a better performance, Figure 11b corresponds to a top view of Figure 11a, Figure 11d corresponds to a bottom view of Figure 11c, and Figure 11f corresponds to a bottom view of Figure 11e, the better protocol corresponds to the one that encloses a long area. Additionally, the left side of the yellow surface in all the subfigures inside Figure 11 corresponds to outdoor scenarios with short  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Generic TCP**} & \multicolumn{2}{c}{**TCP-NACK**} \\ \cline{2-5}
**BER** & - & - & - & - \\  & \(\mathbf{\theta}\) **(Mb/s)** & \(\mathbf{\eta}\mathbf{\delta}\) **[\%]** & \(\mathbf{\theta}\) **(Mb/s)** & \(\mathbf{\eta}\mathbf{\delta}\) **[\%]** \\ \hline
0.5\% & 1.53 & 30.6 & 1.79 & 35.8 \\
1\% & 1.27 & 25.4 & 1.46 & 29.2 \\
5\% & 0.52 & 10.4 & 0.74 & 14.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Emulated link results.  
Figure 10: Short distance results: (a) \(\mathbf{\theta}\); (b) \(\delta\); (c) jitter.  
distances (to 300 m) by using WiFi Technology, and the right side to medium distances (from 300 m to 826 m) by using WiMAX technology. Figure 11b,d,f suggest that TCP-NACK has best performance than the Generic TCP in the majority of the cases. We observed that TCP-NACK had a better performance in terms of throughput, delay and jitter. We found that the algorithm TCP-NACK, by not decreasing the congestion window, as we could observe in Figure 11, for WiFi technology TCP-NACK in mean has offer an improvement over the 70% in terms of throughput, delay and jitter, meanwhile for WiMAX technology over the 70% for throughput and jitter, and almost 100% in terms of delay.
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
## 4 Experiment Results and Discussion
### Experiment 2
The minimum intersection activation value is the most efficient amongst the available paths. The objectives of lowering the cost must be considered to identify the feasible paths, even though the cost value that can be used is limited to 100. The  
Figure 11: Constraint two of feasible paths. Figure 10: Constraint one of feasible paths. results shown in the previous graphs demonstrated that the presented routing algorithm, which is based on an intersection GA-based routing algorithm operation, can achieve high overall performance for all the feasible paths. The graphs showed that the majority of the cost values for all available paths to the same destination are less than the maximum limit of the cost value, resulting in better results and more efficient path selection than the maximum cost value. However, the selected paths using intersection GA-based routing algorithm without priority have 50% of the maximum constraint value. Compared with scenarios without priority, intersection GA-based routing algorithm with priority increases the efficiency of path selection by 20%. Thus, the efficiency of the selected path increases as the number of nodes increases, and the performance of the algorithm improves as the number of nodes increases. The evaluation experiment outcomes indicated that the proposed intersection GA-based routing algorithm is trustworthy enough to find an efficient path based on multiple constraints. The results of these experiments confirmed that the efficiency of the available paths based on the two constraints can be improved considerably by employing the proposed intersection GA-based routing algorithm during the iterations. The chromosome intersection operation is one of the most important operations in the GA process. On the basis of genetic-based routing algorithm, this study investigated the effect of chromosome intersection method. The chromosome intersection method depends on the content of the intersection point, whereas the standard crossover operation depends on the location of the crossover point.
# Propagation Characteristics of Modulated EHF Signal in the Wake Region of Plasma Sheath
## 5 Conclusions  
This article studied the total transmission coefficient and phase shift characteristics of EHF signals in the tail channels of a hypersonic vehicle. In addition, the BER characteristics of EHF signals with different modulation in the tail channels are analyzed. The results indicate that the total transmission coefficient and phase shift are directly affected by channel and carrier frequency. Generally speaking, as the angle between the tail channel and the tail of the vehicle increases, the attenuation of the EHF signals in the tail channel decreases and the phase shift fluctuates more drastically. By comparing the BER characteristics of  
Figure 4: (**a**) The change of BER with the threshold, (**b**) schematic diagram of 2 FSK non-coherent demodulation, (**c**) the waveforms of the nodes in the schematic diagram of 2 FSK non-coherent demodulation.  
Figure 5: (**a**) The maximum electron density of different tail channels at a specific time, (**b**) the channel length of different tail channels at a specific time.  
EHF signals modulated by 2 ASK, 2 PSK, 4 QAM (QPSK), and 2 PSK in tail channels, it is found that, when the communication system served a hypersonic vehicle that selected 2 PSK modulation with a carrier frequency of 140 GHz or 225 GHz, or 4 QAM (QPSK) modulation with a carrier frequency of 140 GHz, and the tail channel with an angle between 50\({}^{\circ}\)and 60\({}^{\circ}\) to the tail of the vehicle, better communication BER performance can be achieved. This is the first result of the communication performance analysis of EHF signals with different modulation modes in different channels at the tail of the vehicle, which is helpful in solving the \"blackout\" problem when relay communication is adopted in engineering. Nevertheless, a multipath effect may exist in the wake region communication process, because the wake region is much larger than the aft region. The influence of multipath on the BER characteristics of EHF signals needs to be further studied.  
Conceptualization, Y.W. and K.Y.; methodology, K.Y. and X.Y.; software, X.Y. and J.X.; validation, X.Y. and Y.L.; formal analysis, X.Y; investigation, X.Y. and K.Y.; resources, Y.W.; data curation, Y.W; writing--original draft preparation, X.Y. and K.Y.; writing--review and editing, Y.W and Y.L; visualization, X.Y. and J.X.; supervision, Y.W. and K.Y.; project administration, K.Y. and Y.L.; funding acquisition, Y.W. 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. 61861031.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### SMC Results  
As mentioned earlier, the second structure to be contrasted was a first order SMC; this configuration was explained in Section 2.6. The load setup was the same as that used in P&O in terms of resistance for disturbance simulation. The electrically acquired variables are summarized in Figure 12. Certainly, the atmospheric conditions were different because the tests had been carried out in diverse surroundings; these were shown in Figure 10, where the radiation and temperature values were higher than they were in the previous test.  
Figure 11: MPPT based on P&O: (**a**) Irradiation (W/m\({}^{2}\)); (**b**) Temperature (\({}^{\circ}\)C); (**c**) Resistance load (\(V_{out}/I_{out}\)); (**d**) PV voltage; (**e**) PV current; (**f**) PV power; (**g**) DC–DC output voltage; (**h**) Duty cycle.  
With regards to the PV voltage, the first feature to highlight is the chattering; this reduction is visible in Figure 12b, where the amplitude was reduced to nearly 1 V without resistance changes, which is three times less in contrast to the P&O results. As a consequence, the second considered characteristic is a fairly visible overshoot through an amplitude of 3.7 V, whereas the undershoot reached nearly 7.2 V as Figure 12b shows.  
It is important to notice that the voltage error was acquired in this case because the P&O algorithm control action is based on perturbation, whereas SMC is an error-based controller. The acquired error values and robustness of this scheme are reflected in Figure 13h, where prompt corrections (that reached a maximum of 7.6 V) along the dynamical changes are a main advantage of this scheme. However, the inconvenience is the error amplitude, which reaches 1.2 V on average.  
Figure 12: MPPT based on SMC: **(a)** Irradiation (W/m\({}^{2}\)); **(b)** Temperature (\({}^{\circ}\)C); **(c)** Resistance load (\(V_{out}/I_{out}\)); **(d)** PV voltage; **(e)** PV current; **(f)** PV power; **(g)** DC–DC output voltage; **(h)** Duty cycle.
# On-Orbit Polarization Calibration for Multichannel Polarimetric Camera
## 4 Experimental Verification
### Accuracy Verification for Polarization Calibration  
The accuracy of the polarization calibration is verified by a reference source with a variable polarization degree. It was made up of two parallel glass plates. Its polarization degree can change from 0 to 35%. We placed it between the integrating sphere light source and the simulated polarization detection system, and changed the polarization degree several times. The accuracy verification system is shown in Figure 10.  
The incident light is a partial polarized light, whose theoretical polarization degree has been known. The accuracy of the polarization calibration is verified, by comparing the calibration result to the theoretical polarization degree. The deviation statistics of the verification result is shown in Table 1.  
The deviation statistics show that the relative deviation of the polarization calibration is 0.8% (670 nm), which is higher than the POLDER (2%) [9], but lower than the APS (0.2%) [12]. Polarization accuracy is one of the key factors in polarization measurement. It is generally accepted that when the target polarization degree is 0.2, the polarization measurement accuracy is better than 0.005, and the polarization data has higher sensitivity, which can significantly improve the inversion accuracy [25]. It shares the same calibration assembly with the radiometric calibration, and it doesn't need the extra polarized light source or large polarizer, which is suitable for current polarization remote sensing detection.
## 2 Methodology
### VMD Module
The augmented Lagrangian function for VMD is formulated to minimize the bandwidth of the Intrinsic Mode Functions (IMFs) while constraining the sum of all IMFs to  
\[\mathcal{L}(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum_{k=1}^{K}\parallel \partial_{t}\bigg{[}\bigg{(}\delta(t)+j\frac{t}{\pi}\bigg{)}*u_{k}(t)\bigg{]} e^{-i\omega_{k}t}\parallel_{2}^{2}+\left|\left|X(t)-\sum_{k=1}^{K}u_{k}(t) \right|\right|_{2}^{2}+\left\langle\lambda(t),X(t)-\sum_{k=1}^{K}u_{k}(t)\right\rangle \tag{6}\]  
Here, \(\alpha\) represents the penalty parameter, and \(\lambda(t)\) is the Lagrange multiplier used to ensure that the sum of all IMFs matches the original time series. Step 3: Iterative Optimization Process. A dual optimization strategy is used to iteratively update the estimation of modes \(u_{k^{\prime}}\) their center frequencies \(\omega_{k}\), and the Lagrange multiplier \(\lambda\) until the solution converges. Step 3.1: Initialization. \(\{u_{k}\},\{\omega_{k}\},\lambda\) is initialized, and the iteration counter \(n=0\) is set. Step 3.2: Iterative Updates and Convergence Check. For each iteration \(n\), the following updates are performed:  
1. Mode \(u_{k}^{n}+1(\omega)\) is updated:  
\[u_{k}^{n+1}(\omega)=\frac{f(\omega)-\sum_{\
eq k}u_{i}^{n+1}(\omega)-\frac{ \lambda^{n}(\omega)}{2}}{1+2\alpha\big{(}\omega-\omega_{k}^{n}\big{)}^{2}} \tag{7}\]  
Here, \(f(\omega)\) represents the Fourier transform of the original signal. 2. The center frequency \(\omega_{k}^{n+1}\) is updated:  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega\left|u_{k}^{n+1}(\omega) \right|^{2}d\omega}{\int_{0}^{\infty}\left|u_{k}^{n+1}(\omega)\right|^{2}d\omega} \tag{8}\]  
This update helps estimate the central frequency of each mode to ensure that it accurately captures the frequency characteristics of the IMF. Step 3.3: The Lagrange multiplier \(\lambda^{n+1}\) is updated:  
\[\lambda^{n+1}(\omega)=\lambda^{n}(\omega)+\gamma\bigg{(}f(\omega)-\sum_{k=1} ^{K}u_{k}^{n+1}(\omega)\bigg{)} \tag{9}\]  
The parameter \(\gamma\) controls the adjustment of the Lagrange multiplier, aiding in the convergence of the solution. Convergence check: after each iteration, it is checked whether the solution has converged by evaluating the relative change:  
Figure 2:0  
If the convergence criterion is met, the process terminates; otherwise, \(n\) is incremented, and the process continues. Step 4: Extraction of IMFs. The output consists of \(K\) IMFs \(u_{k}(t)\) that collectively represent the original signal. These IMFs are the core components that highlight differ ent intrinsic frequency bands of the time series data, making the signal analysis more interpretable and effective for further modeling. The VMD algorithm effectively deconstructs complex signals into simpler components that can be analyzed and modeled more easily. In the context of subway passenger flow forecasting, this decomposition allows for a refined analysis of the data's inherent features, such as daily patterns, peak periods, and anomalies, thereby enhancing the forecasting model's ability to capture and predict future trends based on historical data.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 2 Signal Model  
### Single Antenna Receiver  
In order to study the dual source problem we consider a receiver with a single antenna able to collect both a LOS signal (signal labelled 0) and a single NLOS reflection (signal labelled 1), which may be reflected for instance in a ground, sea or building surface (see Figure 1). Here we assume a purely specular reflection with no scattering effect as considered in [28; 41]. Note that this model is also related to the multipath model used in the GNSS literature ((6.42) [42]).  
We consider that a transmitter \(T\) and a receiver \(R\) have uniform linear motions such that the positions can be described as \(\mathbf{p}_{T}(t)=\mathbf{p}_{T}+\mathbf{v}_{T}t\) and \(\mathbf{p}_{R}(t)=\mathbf{p}_{R}+\mathbf{v}_{R}t\), where \(\mathbf{p}\) and \(\mathbf{v}\) are the position and velocity vectors, respectively and \(t\) is the time. Under such conditions, the distance between \(T\) and \(R\) can be approximated by a first order distance-velocity model [20]:  
Figure 1: Geometry of the problem, with \(h\) the altitude of the receiver and \(e\) the elevation angle of the transmitter seen from the specular point.  
\[\|\mathbf{p}_{TR}(t)\|\triangleq\|\mathbf{p}_{R}(t)-\mathbf{p}_{T}(t-\bar{\tau}(t)) \|=c\bar{\tau}(t)\approx d+vt,\text{ and }\bar{\tau}(t)\approx\tau+bt,\ \tau=\frac{d}{c},\ b=\frac{v}{c}, \tag{1}\]  
where \(d\) is the absolute distance between \(T\) and \(R\), \(v\) is the radial velocity between \(T\) and \(R\), \(\tau\) is the time-delay due to the propagation path and \((1-b)\) is the dilatation induced by the Doppler effect and \(c\) the speed of light in vacuum.
# Multiple PUE Attack Detection in Cooperative Mobile Cognitive Radio Networks
###### Abstract  
The Mobile Cognitive Radio Network (MCRN) are an alternative to spectrum scarcity. However, like any network, it comes with security issues to analyze. One of the attacks to analyze is the Primary User Emulation (PUE) attack, which leads the system to give the attacker the service as a legitimate user and use the Primary Users' (PUs) spectrum resources. This problem has been addressed from perspectives like arrival time, position detection, cooperative scenarios, and artificial intelligence techniques (AI). Nevertheless, it has been studied with one PUE attack at once. This paper implements a countermeasure that can be applied when several attacks simultaneously exist in a cooperative network. A deep neural network (DNN) is used with other techniques to determine the PUE's existence and communicate it with other devices in the cooperative MCRN. An algorithm to detect and share detection information is applied, and the results show that the system can detect multiple PUE attacks with coordination between the secondary users (SUs). Scenarios are implemented on software-defined radio (SDR) with a cognitive protocol to protect the PU. The probability of detection (PD) is measured for some signal-to-noise ratio (SNR) values in the presence of one PUE or more in the network, which shows high detection values above 90% for an SNR of -7 dB. A database is also created with the attackers' data and shared with all the SUs.  
cognitive radio network; spectrum management; primary user attack; network security +
Footnote †: journal:  
0000-0001-6600-0001-6600][PERSON]. [PERSON]  
1 School of Telecommunications Engineering, Universidad Santo Tomas, Bogota 110311, Colombia; [EMAIL_ADDRESS]
## 2 Broadband System
### Mobile Network Evolution
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Features** & **4G** & **5G** & **6G** & **Reference** \\ \hline \multirow{3}{*}{Use cases} & \multirow{3}{*}{MBB} & \(\bullet\) eMBB & \(\bullet\) LDHMC/uMUB & \multirow{3}{*}{[223,224,226]} \\  & & \(\bullet\) eMBB & \(\bullet\) eMBB/CacC & & \\  & & \(\bullet\) mMTC & \(\bullet\) ELRLC & \(\bullet\) ELRLC/mDRLLC/EDuRLLC & \\  & & & & \(\bullet\) umMTC & \\ \hline \multirow{3}{*}{Applications} & \(\bullet\) Mobile Internet & \(\bullet\) V2X & \(\bullet\) Internet of Bio-Nano-Things & \multirow{3}{*}{[223,226,227]} \\  & \(\bullet\) Mobile pay & \(\bullet\) V2X & \(\bullet\) Télectronic & \\  & \(\bullet\) High-definition videos & \(\bullet\) High-definition videos & \(\bullet\) UHD videos & \(\bullet\) Fully automated driving & \\  & \(\bullet\) Vogue & \(\bullet\) IoT & \(\bullet\) Space travel & \\  & \(\bullet\) Mobile TV & \(\bullet\) Smart city/factory/home & \(\bullet\) Holographic verticals and society & \\  & & \(\bullet\) Wearable devices & \(\bullet\) Deep-sea slightseems & \\  & & & \(\bullet\) Industrial Internet & \\ \hline \multirow{3}{*}{Network characteristics} & \multirow{3}{*}{Flat \& All-IP} & \(\bullet\) Virtualization & \(\bullet\) Slicing & \multirow{3}{*}{[223,226,227]} \\  & & \(\bullet\) Cloudization & \(\bullet\) Intelligentization & \\  & & \(\bullet\) Slicing & \(\bullet\) Cloudization & [223,226,227] \\  & & \(\bullet\) Softwarization & \(\bullet\) Virtualization & \\ \hline Peak data rate & 100 Mb/s & 20 Gb/s & \(\geq\)1 Tb/s & [223,224,228,229,230,231] \\ \hline Experienced data rate & 10 Mb/s & 0.1 Gb/s & 1 Gb/s & [223,228,231] \\ \hline Spectrum efficiency & \(1\times\) & 3\(\times\) that of 4G & 5–10\(\times\) that of 5G & [223,229] \\ \hline Network energy efficiency & \(1\times\) & 10–100\(\times\) that of 4G & 10–100\(\times\) that of 5G & [223,229] \\ \hline Area traffic capacity & 0.1 Mb/s/m\({}^{2}\) & 10 Mb/s/m\({}^{2}\) & 1 Gb/s/m\({}^{2}\) & [223] \\ \hline Connectivity density & \(10^{5}\) devices/km\({}^{2}\) & \(10^{6}\) devices/km\({}^{2}\) & \(10^{7}\) devices/km\({}^{2}\) & [223,224,228,229,230,231] \\ \hline Latency & 10 ms & 1 ms & 10–100 \(\mu\)s & [223,224,229,231,232] \\ \hline Mobility & 350 km/h & 500 km/h & \(\geq\)1000 km/h & [223,229] \\ \hline \end{tabular}
\end{table} 
Table 7: Requirements of 4G, 5G, and the envisioned 6G.
# A Method to Obtain the Transducers Impulse Response (TIR) in Photoacoustic Imaging
###### Abstract  
Photoacoustic tomography (PAT) is an emerging imaging technique with great potential for a wide range of biomedical imaging applications. The transducers impulse response (TIR) is a key factor affecting the performance of photoacoustic imaging (PAI). It is customary in PAI to assume that TIR is known or obtain it from experiments. In this paper, we investigate the possibility of obtaining TIR in another way. A new method is proposed to extract TIR from observed optoacoustic signal (OPAS) data, without prior knowledge, as a known condition. It is based on the relation between the OPAS data and the photoacoustic pressure signal (PAPS) at transducer positions. The relation can be expressed as a homogeneous linear equation. The TIR is solved by solving the homogeneous equation. The numerical test verifies the effectiveness of the presented method. This article also discusses the effect of calculation parameters on the extracting precision of TIR.  
photoacoustic imaging; deconvolution; least squares; impulse response +
Footnote †: journal: Applied sciences  
0000-0002-4149-22000