esa-sceva/satcom-chunk-collection huggingface dataset:
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 3 System Design and Methodology
### GNSS BPSK Jamming
* **Husban Zino Mini Pro**: While stable during hovering, it lost control in motion, drifting until it collided with obstacles, and did not initiate an automatic landing. * **ZLL SG 108**: Similar to the Autel Evo Nano+, it experienced total instability and descended rapidly, triggering an automatic landing. These results indicate that while DJI models react with some stability, other models, like the Autel Evo Nano+ and ZLL SG 108, demonstrate severe instability under spoofing conditions. Additional testing of spoofing techniques is planned, particularly to quantitatively assess the required transmit power versus distance, consideration of NFZs by different drones, and the use of GNSS systems other than GPS, namely Galileo and Glonass.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Other Works Analyzing Different Aspects of LoRaWAN
#### 3.3.2 Security  
One of the crucial aspects within the LoRaWAN implementation is security. Faced with this situation, many authors have carried out different studies on the security of LoRaWAN.  
Several of the articles theoretically carry out the analysis of vulnerabilities present in LoRaWAN devices. In [53], the different risks that IoT devices have in different areas such as smart cities, home automation, and agriculture, among others, are discussed. The different threats that exist in the different layers are also analyzed, e.g., sensor layer and network layer, among others. Four different technologies, i.e., blockchain, fog computing, edge computing, and machine learning, are proposed to increase the level of security in IoT devices. On the other hand, in [54], the vulnerabilities present in various network technologies such as LoRaWAN, ASV, and ZigBee are analyzed. Most of the attacks consisted of replay attacks, eavesdropping, and bit-flipping attacks.  
Additionally, in [48], the privacy implications of a drop in signal strength when a vehicle obstructs the sensor devices of a LoRaWAN-based smart parking lot are exploited. In this work, it is indicated that through an offside-channel attack, the attacker can collect data passively from great distances. Based on the use of supervised machine learning techniques based on neural networks, the attacker can estimate parking occupancy with an accuracy of up to 97%, while the random forest approach achieves an accuracy of more than 98%.
###### Abstract  
An adaptive-uniform-experimental-design-based fractional particle swarm optimizer (AUFPSO) with non-linear time-varying evolution (NTE) is proposed. A particle swarm optimizer (PSO) is an excellent evolutionary algorithm due to its simple structure and rapid convergence. Nevertheless, PSO has notable drawbacks. Although many proposed methods and strategies have enhanced its effectiveness and performance, PSO is limited by its tendency to fall into local optima and its tendency to obtain different solutions in each search (i.e., its weak robustness). Introducing fractional-order calculus in PSO (FPSO) can correct the order of the velocity derivative for each particle, which enhances the diversity and algorithmic effectiveness. This study used NTE of the order of the velocity derivative, inertia weight, cognitive parameter, and social parameter in an FPSO used to search for a global optimal solution. To obtain the best combination of FPSO and NTE, an adaptive uniform experimental design (AUED) method was used to deal with this essential issue. The AUED method integrates a uniform layout with the best combination phase and a stepwise ratio to assist in selecting the best combination for FPSO-NTE. Experimental applications in 15 global numerical optimization problems confirmed that the AUFPSO-NTE had a better performance and robustness than existing PSO-related algorithms.  
Keywords:uniform experimental design; fractional-order particle swarm optimizer; non-linear time-varying evolution; parameter optimization +
Footnote †: journal: Applied Soft. 2019, 9, 5537
## 5 Conclusions  
The DL-LET joint, which utilizes a layer of flexible H18 aluminum foil to increase the tensile stiffness of the LET joint, was proposed. The kinetostatic model for the DL-LET joint was given, and the equivalent bending stiffness was derived. The model was verified using finite element analysis (FEA). The results showed that DL-LET and LET joints have similar bending stiffness, while the tensile stiffness of DL-LET joints is several times larger than those of LET joints, which was further validated by tensile tests. Finally, the DL-Triple-LET joint was presented, and its performances were compared to the Triple-LET joint. The DL-LET joint is vulnerable to delamination of the two layers. Furthermore, this idea can also be used to combine different materials, such as more flexible materials such as polypropylene, and can also be used for a combination of multiple materials. The idea of combing two materials provides a new way to compliant joint design. Meanwhile, it provides an effective way to improve the tensile stiffness of joints. For future work we will develop variable stiffness techniques of this joint, and explore its application in a robot arm considering both kinetostatic and transient behaviors. Figure 16: Comparison of FEA results of DL-Triple-LET and Triple-LET joints, (**a**) Bending deformation and (**b**) Tensile deformation. Figure 15: Tensile deformations of DL-Triple-LET and Triple-LET joints obtained by FEA. **Author Contributions:** This paper has six authors. The authors made most of the contributions regarding conceptualization, development of theory, validation, verification of the analytical methods, discussion of the results, as well as the final manuscript. Individual contributions are as follows: introduction, methodology, visualization, and original draft preparation: B.M., N.L. and Y.Y. ; review, editing, and formal analysis: B.L., L.Q. and G.C. ; software, validation: N.L. and Y.Y. ; project administration, supervision, and funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors would like to thank the support provided by the National Natural Science Foundation of China (Nos. 51475037 and U1913213). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** Data will be available through the corresponding authors:  
[EMAIL_ADDRESS] ([PERSON]). **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
## 4 Numerical Simulation
### Initial and Boundary Conditions  
Two types of conditions were adopted in the modeling procedures, initial and boundary conditions. The initial condition was assigned to the impactor with a velocity of 1.38 m/s when it touches the spherical ball, as displayed in Figure 13a. All the nodes of the impactor and spherical ball could move only in the Z-direction. All the nodes of the hollow steel ring were prevented from movement in all directions, while all the nodes of the lower base of the ECC specimen were prevented from moving in Z-direction, as shown in Figure 13b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ECC2-0** & **ECC2-0.5** & **ECC2-1** & **ECC2-1.5** \\ \hline Tensile strength (MPa) & 4.8 & 5.9 & 8.1 & 6.5 \\ Compressive strength (MPa) & 70.1 & 69.6 & 68.7 & 66 \\ Elastic modulus (GPa) & 19.86 & 19.08 & 18.96 & 18.92 \\ Poisson’s ratio & 0.185 & 0.183 & 0.17 & 0.14 \\ Density (kg/m\({}^{3}\)) & 1808 & 1736 & 1758 & 1790 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of different engineered cementitious composite (ECC) specimens.
## 3 Simulation Analysis
### CEEMDAN-SVD-TE Simulation  
To further study the possible influence of noise on the transfer-entropy calculation results and to clarify the usefulness of the joint CEEMDAN-SVD-TE method, the same set of Gaussian white noise was added to the signals \(A\) and \(B\) with correlation coefficient \(\mu\) of 0.8; the signals \(A\) and \(B\) were noise-reduced with the help of CEEMDAN-SVD method, and the transfer-entropy curves of \(A\) and \(B\) signals before and after noise reduction were compared. Here, take the \(A\) signal as an example to show the effect of CEEMDAN-SVD noise reduction, the power spectrum of the \(A\) signal before and after noise reduction can be seen in Figure 6.  
Figure 6 shows that the 25 Hz characteristic frequency of the signal after noise addition is drowned by the high-frequency white noise, and the high-frequency white noise in the original signal can be effectively filtered out with the help of the CEEMDAN-SVD method. After CEEMDAN-SVD noise reduction, the characteristic main frequencies of the pure signal (25 Hz and 200 Hz) are highlighted again.  
The transfer-entropy curves of \(A\) and \(B\) before and after noise reduction are presented in Figure 7. As shown in the figure, without noise reduction of \(A\) and \(B\), the values of \(T_{A\to B}\) and \(T_{B\to A}\) are interleaved and converge, indicating that the characteristic information of the signals may have been submerged by the high-frequency noise and the information-transmission effect between the signals is weakened. According to Table 2 and Figure 7, the \(ITR_{A\to B}\) obtained from the signals of \(A\) and \(B\) without noise is 93.57%,the \(ITR_{A\to B}\) obtained from the signals of \(A\) and \(B\) after noise reduction is 90.22%, and the \(ITR_{A\to B}\) of \(A\) and \(B\) without noise reduction is 12.08%. These results indicate that the \(ITR\) can reflect the information-transmission strength between two signals, but the noise in the signals will weaken the transfer effect between the signals and have a negative effect on the computation results of transfer entropy. Using the CEEMDAN-SVD method can prevent the noise from the interference of the signal transmission direction analysis and effectively improve the accuracy of information-transmission-direction identification.  
Figure 6: Comparison of the power spectrum before and after noise reduction for the noise-added signal \(A\).  
In summary, the transfer-entropy algorithm can effectively determine the transmission source of the signal, but its results are easily affected by noise signals. Integrating the CEEMDAN-SVD noise-reduction method with the transfer-entropy algorithm can effectively reduce the negative impact of noise on the transfer-entropy calculation results and obtain real and reliable transmission-path results.
## 4 Results and Discussion
### Analysis of the Measured Back Pressure
H Pipe exhibited 0.280 kPa, which was approximately 2.8% lower, and X Pipe exhibited the lowest pressure of 0.256 kPa, which was approximately 11.1% lower. The experimental results confirmed the characteristics of the pressure change according to the difference in the confluence geometry. Therefore, the measured back pressure results were compared with reference to [PERSON]'s theorem. The H pipe showing the maximum pressure in Figure 6A,B is predicted to flow the exhaust gas at the slowest flow rate. In particular, the exhaust gas flowing from each exhaust pipe has a reduced flow velocity due to the disturbance of the flow resistance, such as a vortex generated at the confluence point. As a result, the pressure increased by 32.6% more in measurement position 2 than in measurement position 1. The X pipe with the lowest pressure was expected to flow the exhaust gas at a fast and stable rate. In particular, the exhaust gas flowing from each exhaust pipe naturally flowed to the confluence point, and the decrease in flow velocity was minimized. As a result, the pressure decreased by 14.8% more in measurement position 2 than in measurement position 1. The Y pipe showed a higher pressure than the arithmetic mean of the X pipe. The exhaust gas is expected to flow at a fast and stable flow rate inside the exhaust pipe. However, when the exhaust gas flowing from each exhaust pipe naturally passed through the confluence point, the pressure increased due to the reduced flow area. As a result, the pressure increased by 31% more in measurement position 2 than in measurement position 1.
## 6 Experiment  
To ensure thorough testing of FLuaScan, three evaluation questions are proposed.  
* **Correctness**: What is the number of true positives generated by FLuaScan? (Section 6.1);
* **Effectiveness**: Can FLuaScan detect vulnerabilities in real-world devices? (Section 6.2);
* **Practicability**: How are the time overhead and actual user experience of FLuaScan? (Section 6.3).  
To bolster the credibility of the experiment, we painstakingly curated a diverse test set consisting of multiple devices sourced from various vendors. As shown in Table 3, the test set comprises firmware from 13 devices obtained from 7 distinct vendors. The final row documents the count of Lua files present within the firmware. The test set has an average of 147 Lua files per image, satisfying the requirement of the research objective for multifile detection. In order to evaluate the advancement of FLuaScan, we also compared it with current state-of-the-art tool TscanCode in the experiment.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 1 Introduction  
UWA communication systems are widely regarded as one of the most challenging communication environments due to the extreme conditions found underwater. These challenges include severe transmission loss, multipath propagation, Doppler shifts, and complex ocean noise [1]. The underwater channels often experience doubly spread effects, resulting from time and frequency dispersion, which pose major challenges and require the development of robust receiver designs [2]. Unlike traditional wireless channels, the underwater environment presents unique obstacles, such as fluctuating sound speed profiles, extensive multipath spread, and rapidly changing propagation conditions [3]. This makes it difficult to design robust communication systems, as the characteristics of the channels are highly dynamic and depend on the specific conditions under the ocean. A common approach to address the challenges in UWA communications is the use of orthogonal frequency division multiplexing (OFDM) [4]. OFDM has shown promise as a robust modulation technique suitable for complex underwater environments [5, 6]. OFDM is a popular multicarrier transmission technique that divides the bandwidth into several narrower subcarriers, reducing the susceptibility to multipath fading and frequency-selective fading. The low symbol rate in OFDM also allows the use of guard intervals between symbols, which helps mitigate inter-symbol interference (ISI). This makes OFDM especially useful in UWA communications, where multipath propagation and time-spreading are significant issues. However, the performance of OFDM systems in UWA communication is highly dependent on accurate channel estimation, which is crucial for reliable data recovery. Pilot-assisted channel-estimation algorithms typically estimate the channel by transmitting pilot symbols with data subcarriers [7]. The following subsections delve into a comprehensive review of literature centered on conventional OFDM channel-estimation methods and a deep neural network (DNN)-based OFDM receiver, aligning with the direction of our proposed methodology.
### Motivation of Study  
During the pandemic, chest X-rays and computer tomography (CT) had the potential for the clinical diagnosis and evaluation of COVID-19 patients. These approaches are useful for doctors performing diagnosis and therapy. [PERSON] and [PERSON] used automatic detection with a bidirectional LSTM network and deep features [8]. They relied on the Bi-LSTM network inside the deep feature and then compared it with the deep neural network of the fivefold cross-validation approach. Their model was trained using 100 epochs and 32 batch sizes. The fourth section of their model was designed to handle the activation function, with two neurons representing the classes COVID-19 and no-finding [8]. A radiological study found a diagnostic tool for COVID-19. However, it can also be used for measuring the chronicity of diseases or complications in patients [9]. Most researchers rely on the visual interpretation of chest X-ray images, which requires an expert such as a radiologist or medical doctor. The densities of chest X-rays might vary depending on the severity of the disease. Figure 1 shows the denseness of chest X-ray images.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Approach** & **Advantages** \\ \hline \multirow{3}{*}{Chest X-ray diagnosis} & The expeditious identification and assessment of \\  & a substantial patient population within \\  & a constrained timeframe. \\ \hline \multirow{3}{*}{Chest X-ray images} & The aforementioned data may be obtainable from various \\  & hospitals and clinics responsible for their management. \\  & Individuals can be diagnosed with COVID-19. \\ \hline \multirow{3}{*}{Light chest X-ray detection system} & Implementing the light chest X-ray detection system can \\  & potentially mitigate widespread infection via timely \\  & diagnosis. The physician may request that the patient \\  & engage in self-isolation. \\ \hline \multirow{3}{*}{Disease complication} & Chest X-rays are a valuable diagnostic tool for \\  & investigating and monitoring numerous diseases, \\ \cline{1-1}  & including those associated with COVID-19. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chest X-ray identification advantages.  
Figure 1: Chest X-ray images of patients with various diseases (COVID-19, pneumonia, and tuberculosis).
# Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem
## 2 An Overview of the Techniques Used for Underwater Concrete Crack Detection
### Other Methods: Including the Use of Fiber-Optic Sensors, Thermal Imaging, and X-ray Imaging  
Rendering water is a key component in creating natural scenes. In [46; 47], we offered a method for creating accurate underwater optical effects using graphics hardware utilizing a \(Z\)-buffer, a stencil buffer, and an accumulation buffer. Additionally, ref. [48] provided a strategy for evaluating the statistical distribution of water's level slopes using flash photography. Researchers introduced a temperature tracer approach for fracture identification in sub-sea concrete structures based on heat transport theory in [49; 50; 51]. A method for controlling concrete cracking in underwater marine structures using basalt fiber was presented in [6].  
The study presented in [52] developed a structural health monitoring tool that considered the reciprocal relationships and priority weights of different structural distresses to assess underwater structures. Cracking and collapse behaviors of an undersea shield tunnel's segmental liner construction exposed to a wrecked high-speed train collision were explored in [53]. In [54], researchers looked at the chaotic actions of arched concrete barriers subjected to underwater explosions. In addition, a system using a single camera deployed in a vehicle or robot was proposed in [55] to process a sequence of images and estimate crack dimensions.  
Due to uneven lighting and considerable noise issues, detecting and classifying underwater dam breaches is difficult, and only a few approaches are adequate for this purpose. One study [56] presented a dodging algorithm to eliminate uneven illumination, based on an investigation of the statistical aspects of dam crack pictures, employing the regional features of block images and the global features of related domains. In [57], a simpler approach for estimating the wear life of fractured concrete below water was developed, utilizing a changed design code that took into account the crack's reduction in stress amplitude. Ref. [58] studied crack identification and categorization strategies based on crack kinds and then implemented [PERSON]'s base thresholds method for crack detection, which was then utilized to construct the suggested crack detection system.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 3 3 GPP Release 16: First 5G Upgrade  
Known as the \"first 5G upgrade\", 3 GPP Release 16 significantly expanded the scope and functionality of 5G by enhancing existing features, addressing new use cases, and enabling greater flexibility and efficiency for various industries.
# Intensified Pozzolanic Reaction on Kaolinite Clay-Based Mortar
## 3 Results and Discussion
### Investigation on Strength Reduction  
Unlike the other samples, the compressive strength of SF_15 steadily decreased after three days (Figure 8). To investigate possible reasons of the strength reduction, pore structures of the samples are additionally discussed in this section. As can be observed in Figure 5, the pore size distributions of Ref and SF_7.5 were almost unchanged between 14 and 91 days, which means that their pore structures stabilized at 14 days. On the other hand, in the case of SF_15, the first peak of pore diameters moved from 9 to 17 nm between 28 and 91 days (Figure 5c). This movement means that the pores around 10 nm expanded in size (by approximately double), possibly due to the accelerated drying shrinkage with a fine pore structure by excessive SF addition [15; 49]. Similarly, [PERSON] has reported that the addition of SF has a significant influence on the drying shrinkage of cement mortar due to the strong pozzolanic reaction and pore size refinement mechanism, especially with 15% (by wt % of cement) SF addition. In his study, the shrinkages of cement mortars with 0%, 10%, and 15% of SF showed 75.7, 303, and 812 \(\upmu\)m/m at 28 days, and 114, 756, and 1030 \(\upmu\)m/m at 60 days [50].  
In summary, there is an optimum SF to binder ratio in the developed kaolinite clay-based mortar considering its long-term strength. Greater SF addition above the optimum ratio can have a negative effect on the strength. Thus, based on the conducted experiments herein, it can be concluded that 7.5% of SF to binder ratio is more suitable than 15%, considering the long-term performance and economic point of view.