esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security  
When performing tasks, UAVs must communicate frequently with ground control stations, other UAVs, and external networks, which encounter various security threats, such as sensed data leakage, radio interference, identity authentication failure, non-encryption, and UAV SON vulnerabilities. If these threats are maliciously exploited, they could lead to mission failure and jeopardize personnel safety, compromise privacy, and even pose risks to national security.  
This chapter explores various aspects of AI techniques for UAV communication security, including sensing security, radio anti-interference, identity authentication, cryptography, and UAV SON security technologies. Sensing security ensures that the data collected by UAV sensors are accurate and reliable, forming the foundation for all subsequent security measures. Radio anti-interference helps maintain the integrity of communications by preventing eavesdropping and ensuring that the transmitted data reach the destination intact. Once the data transmission is secured, identity authentication protocols verify the identities of the communicating parties, preventing unauthorized access. Cryptographic techniques further enhance security by encrypting the data, ensuring confidentiality and integrity. Finally, SON security addresses the dynamic nature of UAV networks, protecting against advanced threats such as routing and node-based attacks and ensuring overall security. We analyze existing technologies in these areas and their current state of application in UAV communications and explore how AI techniques can empower these technologies to increase the overall security of UAV systems. By systematically elaborating these contents, we discuss the potential and application prospects of combining LLMs with key UAV security technologies.
## 4 Results
### Spatial-Temporal Characteristics  
#### 4.3.1 Analysis of Bus Headway  
1. Daily variation  
Figure 5 shows the daily variation of bus headway at different bus stops at each hour of a day. Bus stops were selected at equal intervals in a sequence. A conclusion could be drawn that a small headway usually occurs from 7:00 to 10:00 and 17:00 to 19:00, while a large headway frequently occurs from 13:00 to 15:00 and 21:00 to 24:00. Specifically,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Trip** & **Line** & **Line** & **Bus** & **Station** & **Station** & **Arr** & **Lea** & **Pre** & **Next** \\
**Id** & **Id** & **Dir** & **Num** & **Index** & **Id** & **Time** & **Time** & **StationId** & **StationId** \\ \hline
650c8 & 1130 & down & BS05265D & 1 & B\_ZS0165 & 20 April 2019 & 20 April 2019 & NULL & B\_ZS0029 \\
650c8 & 1130 & down & BS05265D & 2 & B\_ZS0029 & 20 April 2019 & 20 April 2019 & 20 April 2019 & B\_ZS0165 & B\_ZS0033 \\
2 aa9 & 1130 & down & BS05265D & 2 & B\_ZS0029 & 13:13:12 & 13:13:49 & - & - \\ - & - & - & - & - & - & - & - & - \\
650c8 & 1130 & down & BS05265D & 20 & B\_SH0033 & 20 April 2019 & 20 April 2019 & 20 April 2019 & B\_SH0035 & B\_SH0045 \\
2 aa9 & 1130 & down & BS05265D & 20 & B\_SH0033 & 13:49:36 & 13:50:18 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data samples of AVL.  
Figure 4: The process of bus operation during peak time and off-peak time: (**a**) During peak time from 7:00 to 9:00; (**b**) During the off-peak time from 13:00 to 15:00.  
the minimum headway was 277 s, which occurred from 7:00 to 8:00 at stop 10, and the maximum headway appeared from 23:00 to 24:00 at stop 10 with 1383 s.  
The result also demonstrates that the small headway of stops 10, 20, and 30 occurred from 16:00 to 17:00, while the later bus stops such as stops 40 and 50 had a small headway from 18:00 to 19:00, proving that the small headway caused by former bus stops is continuously transmitted to the following buses.  
2. Variation in different time types  
The average bus headway in different time types is presented in Figure 6, including four time periods, namely morning peak on weekdays (7:00-9:00), evening peak on weekdays (17:30-19:30), off-peak time on weekdays, and rest days. The headway during the morning peak on weekdays was the most unstable, followed by the evening peak on weekdays. The most stable headway usually occurred on rest days, with an average value of 450 s. Bus headway fluctuated relatively gently during rest days and showed a flat peak on weekdays compared with the morning and evening peak times.  
Figure 5: Daily variation of bus headway.  
Figure 6: Average headway at bus stops in different time types.
# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
## 3 Longitudinal Speed Tracking Controller Design  
The simplification of vehicle longitudinal dynamics and powertrain system in terms of their strong non-linear characteristics produces the model uncertainty, and the changes of vehicle characteristics, for example the vehicle transmission efficiency, produce the vehicle state uncertainty; we call them internal disturbances. In addition, the various driving conditions and the noise of sensors produce the external disturbances. This study regards these uncertainties as the \"total disturbances\" during the autonomous driving process. Thus, we created an ADRC controller with the ESO, which can timely estimate the \"total disturbance.\" The total disturbance is then treated as an additional vehicle state to be corrected for in the feedback process. The performance of the ESO is limited by the sampling rate, so a model-based feed-forward control is used to improve its performance. The feed-forward control is actually a gain-scheduled proportion control, as the proportion varies with the desired acceleration, preview time, etc. Therefore, feed-forward control is an effective improvement of ADRC in the transient process in the case of operating condition variations under limited sampling rate. Figure 3 shows the structure of the longitudinal controller.  
The entire controller consists of an ADRC controller and a model-based feed forward controller (MFC). ADRC observes the total disturbances using the ESO, and it also estimates the vehicle speed and acceleration. The MFC calculates the desired vehicle acceleration based on a time-preview of target speed profile. In addition, a throttle-brake switching module is included in the controller to avoid frequent switching between the throttle and brake actuators and to ensure driving comfort.
## 2 Results and Discussion
### Expression Analysis of Main Transcription Regulators  
Next, nine families of transcription regulators were studied as they are involved in virulence, biofilm formation, and development (Table 2). For each family (Table 2), the biological role and a selection of transcription factors further analyzed for each category are reported, along with their Gene_id, average reads value (considering the RNA-seq experiment), the Protein_id, and the average read values found in the RNA seq experiment. For each of these genes, specific primers were designed, and their expression was studied by real-time quantitative PCR (RTqPCR) in the presence of five different biofilm modulators: 340 mM NaCl, 680 mM NaCl, 1 M NaCl, 50 mM MgSO4, and 250 mM EDTA. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Transcription** & **Role of the Transcription** & **Protein Name** & **Gene_ID** & **Average Value * & **Protein_ID** \\ \hline
**AbrB** family & Essential for cell survival, & & & & \\ transcriptional & spore formation, and biofilm & AbrB-06010 & JUK32\_RS06010 & 5239 & WP\_204137943.1 \\ regulator & development [51] & & & & \\ \hline
**YebC/PmpR** family & Probably involved in & & & & \\ DNA-binding & regulation of quorum sensing, & & & & \\ transcriptional & and virulence, regulates EPS & & & & \\ regulator & and LPS production to form & & & & \\ \hline  
hline
**TetR/** & Regulation of essential & & & \\ \hline
Factors belonging to the AbrB family and the Yebc/PmpR family, which play an essential role in biofilm formation, show a lower expression level (Figure 4) when NaCl was added at the highest concentration [51; 52]. TetR/AcrR regulates essential processes, including metabolism, virulence, resistance, and biofilm formation [53; 54]. The selected genes belonging to the TetR/AcrR family exhibited opposite characteristics depending on the gene being considered (Figure 4); _tetR_-01335 and _tetR_-14575 were strongly overexpressed, while the expression of _tetR_-16580, _tetR_-19750, _tetR_-23190, _tetR_-11335, and _tetR_-13695 was repressed with respect to the control condition. All LuxR family genes, which play a key role in quorum sensing in Gram-negative bacteria, and regulate virulence, motility, and biofilm formation [55; 56], were repressed. Similarly, those of the LysR, GntR, and Crp/Fnr protein families, all involved in virulence processes [57; 58; 59; 60], were downregulated compared to the control. The XRE protein, which was suggested to be implicated in virulence [61], decreased with increasing NaCl concentration, remained nearly constant in the presence of 50 mM of MgSO\({}_{4}\), and increased if 250 uM of EDTA was introduced into the culture medium. BolA factor, which regulates the transcription of several genes coding for proteins implicated in the transition between the planktonic stage and biofilm development, spore formation, and cell survival [62], was repressed in all the conditions tested when compared to the control. Differences in gene expression due to the presence of MgSO\({}_{4}\) were displayed only for a few genes. Searching for the RNA-seq data for the EPS genes, only the expression
## 5 Experiments and Analysis
### Sensitivity Analysis  
#### 5.3.1 Sensitivity Analysis for Learning Rates  
Figure 2 illustrates the convergence performance of the MATD3-TORA algorithm in a multi-agent environment under varying learning rate configurations. In actor-critic algorithms, the learning rates of the policy network (actor) and the value network (critic), denoted as \(\alpha_{actor}\) and \(\alpha_{critic}\), play a pivotal role in ensuring stability and convergence. Typically, these learning rates require meticulous tuning to adapt to diverse learning environments and tasks. For instance, \(\alpha_{actor}\) and \(\alpha_{critic}\) can be set to 0.1 and 0.5, smaller values such as 0.01 and 0.02, or even 0.001 and 0.002. As depicted in the figure, all three learning rate combinations achieved convergence. However, when \(\alpha_{actor}\) and \(\alpha_{critic}\) were set to 0.1 and 0.5 or 0.01 and 0.02, the algorithm converged to suboptimal solutions. This is primarily attributed to excessively high learning rates, which result in large update steps during iterations, causing the algorithm to overshoot the optimal solution. In contrast, when \(\alpha_{actor}\) and \(\alpha_{critic}\) were set to 0.001 and 0.002, the algorithm converged to superior results. This is because a moderate learning rate allows the algorithm to explore the solution space more thoroughly, enabling the discovery of better strategies. This analysis underscores the importance of carefully selecting learning rates to balance exploration and exploitation, thereby enhancing the algorithm's ability to identify optimal policies in complex multi-agent environments.
## 2 Principle of Tilt Active Vibration Isolator  
The tilt active vibration isolator, in our design, is composed of a tilt-sensitive probe, a capacitive position transducer measuring the tilt motion of the VP, and a piezoelectric servo-controlled actuator driving the VP back to its former central position. The schematic diagram of the tilt active vibration isolator using a VP and PZTs is shown in Figure 1. The VP is a kind of ordinarily structured physical pendulum suspended by a flat elastic spring. The two opposite capacitance plates with a uniform shape are made by plating silver onto the hollow and colummed ceramic substrate, and a pair of differential capacitances are formed via the two opposite capacitances, which are separated electrically from both each other and the central pendulum body. The piezoelectric actuator includes two PZTs that are connected via the output of their driving circuit and placed under a screw supporter of the device's base.  
The VP will depart from its former central position due to tilt vibration acting on the supporting frame. Then, a gap variation between the VP and two capacitances will be produced and measured by a bridge-type capacitance transducer, which is a kind of capacitance sensor, the electric diagram of which is given in Reference 14. To ensure the precise operation of the piezoelectric servo-controlled actuator in a large displacement range, a digital and parallel controller, consisting of a hysteresis inverse model predictor and a PID adjuster, is adopted. This guides the driving circuit to generate the proper voltage to be applied to the two piezoelectric transducers. The digital and parallel controller is implemented by the field programmable gate array (FPGA) in order to easily adjust the controller's parameters. Finally, the VP is driven back to its former central position by the servo-controlled actuator, and the supporting frame also returns to the horizontal state. This means that the supporting frame is immune to the tilt vibration of the ground base. For example, the ground tilting to the right causes the frame to tilt to the right, as shown in Figure 1, and the feedback control circuit drives the PZT to tilt the frame to the left so as to balance the tilt, such that the frame returns to the horizontal state.
## 2 System Model  
The system model is described in Figure 1b. At the transmitter, we consider a binary independent and identically distributed (i.i.d.) Bernoulli (\(p\)) source sequence \(\mathbf{s}\) with entropy  
\[H=-p\log_{2}p-(1-p)\log_{2}(1-p), \tag{1}\]  
where \(p<1/2\). The source is first compressed by an un-punctured PLDPC code and then encoded by another punctured PLDPC code. The encoded sequence is binary-phase-shift-keying (BPSK) modulated into \(\mathbf{x}=(x_{1},x_{2},\dots,x_{m})\), where \(m\) is the length of modulated symbol sequence. The OD-ISI MR channel can be modeled as a discrete channel with ISI memory. For transmission over a OD-ISI MR channel, the output of the channel \(\mathbf{y}=(y_{1},y_{2},\dots,y_{m})\) can be expressed as a PR polynomial plus a Gaussian noise, which is given by  
\[y_{i}=\sum_{j=0}^{k}h_{j}x_{i-j}+n_{i}, \tag{2}\]  
where \(i=1,2,\dots,m\), \(k\) is the length of the channel memory, \(h_{j}(j=0,1,\dots,k)\) are the tap coefficients corresponding to the OD-ISI MR channel. \(n_{i}\) is the Gaussian noise with zero mean, variance \(N_{0}/2\) and \(N_{0}\) denotes the noise power-spectral density. In this paper, we primarily consider the extended class IV PR (EPR4) channel (i.e., \(h_{0}=1,h_{1}=1,h_{2}=-1,h_{3}=-1\)), which is commonly used in OD-ISI MR systems research.  
At the receiver, the decoder structure is processed iteratively by a three-stage serially concatenated framework, which contains a BCJR detector, a channel decoder and a source decoder. Different from the traditional Turbo equalization in Figure 1a, the residual redundancy left by source coding can be exploited by the three-stage serially concatenated Turbo equalization to further resist the ISI and improve the error performance of the system. As shown in Figure 1b, firstly, the BCJR detector uses its a priori information input to provide extrinsic information, which is passed to the channel decoder as a priori information. Secondly, the extrinsic information is exchanged between the channel decoder and the source decoder, which consists the JSCD iteration. After the JSCD iteration, finally, the output extrinsic information of the channel decoder is passed back to the BCJR detector as a priori information for further Turbo equalization iterations. Unlike the conventional Turbo equalization, in each Turbo equalization iteration of this system, the extrinsic information is encoded in the channel decoder. The decoder is then encoded in the channel decoder, which is encoded in the channel decoder, which  
mation is exchanged not only between the BCJR detector and the channel decoder, but also between the channel decoder and the source decoder. Here, the BCJR detector and the decoders are implemented by the BCJR algorithm and the belief propagation (BP) algorithm, respectively.
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 1 Introduction
Most of the previous research mainly focused on the migration of data to cloud platforms and lacks an end-to-end integrated model. Based on this, this study designed a broadband magnetotelluric control system based on an IoT cloud platform. In this system, instruments interact with the cloud platform in real time, and users interact through a web interface, enabling the monitoring of instrument status through various devices.
# Transformer-Based Distillation Hash Learning for Image Retrieval
## 5 Conclusions  
This paper proposes a Transformer-based image hash learning with a knowledge distillation framework. By combining the self-attention mechanism of the Transformer model, the image hash code is enabled to be global and unique. At the same time, this advantage is instilled into the efficient lightweight model by knowledge distillation, thus reducing the computation and having the advantage of the Transformer's features. Experimental results on MIRFlickr-25K and NUS-WIDE datasets show that our approach can effectively improve the accuracy and efficiency of image retrieval.  
Funding acquisition, C.W. and W.Y. ([PERSON]); Methodology, Y.L.; Resources, W.Y. ([PERSON]); Writing--original draft, X.Q.; Writing--review & editing, W.Y. ([PERSON]) and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the China University Industry-University-Research Innovation Fund (Grant No. 2021 FNA03001), the Research Project on Postgraduate Education and Teaching Reform of Xi'an University of Posts and Telecommunications (Grant No. YJGJ202034), the fellowship of China Postdoctoral Science Foundation (Grant No. 2020M683695 XB), Shaanxi Provincial Philosophy and Social Science Research Project in Major Theoretical and Practical Issues (Grant No. 2022 ND0181), and Xi'an Social Science Planning Fund Project (Grant No. 22 LW44).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 4: Performance curves of retrieval results on the NUS-WIDE dataset: (**a**) Precision using hash lookup within Hamming radius 2; (**b**) Precision–recall curve for 48 bits; (**c**) Precision curve for 48 bits; (**d**) Precision–recall curve for 24 bits; and (**e**) Precision curve for 24 bits.
# Biomechanical Optimization of Lumbar Fusion Cages with a Porous Design: A Finite Element Analysis
## 3 Results
### Endplate Stress  
The maximum von Mises stress on the L3 and L4 endplates was evaluated under the same motion modes and the results were illustrated in Figures 7 and 8, and the recorded data are listed in Table S4 and Table S5, respectively. For the L3 endplate, the C-Cage induced the highest stresses across all motion modes, peaking at 58.78 MPa in L-Bending and 51.05 MPa in R-Bending, reflecting significant mechanical loading that could compromise endplate integrity. The P-Cage reduced these values to 39.51 MPa and 38.91 MPa, respectively, indicating that personalized morphology mitigates stress to some extent. The G-Cage further lowered stresses to 32.16 MPa in L-Bending and 28.74 MPa in R-Bending, while the V-Cage achieved the most substantial reductions, with stresses of 28.33 MPa and 25.62 MPa, representing decreases of 51.8% and 49.8% compared to the C-Cage. A similar trend was observed for the L4 endplate, where the C-Cage recorded a maximum stress of 51.05 MPa in R-Bending and 50.16 MPa in L-Bending, contrasted by the values for the V-Cage of 21.08 MPa and 18.23 MPa, representing reductions of 58.7% and 63.7%, respectively.  
Stress contour maps (as shown in Figure 8) highlight that the C-Cage generated localized high-stress zones on both endplates, particularly at contact points, increasing the risk of endplate failure and subsidence. In contrast, the V-Cage promoted a more even stress distribution, likely due to its biomimetic porous structure, which mirrors trabecular bone and optimizes load dissemination. The G-Cage and P-Cage exhibited intermediate stress profiles, with the former benefiting from its porous design and the latter from its tailored fit, though neither matched the efficacy of V-Cage. These findings suggest that the V-Cage not only withstands lower internal stresses but also significantly alleviates  
Figure 6: The comparison of the stress for cages on six motion modes (**a**) maximum stress and (**b**) stress distribution on the cage surface.  
the mechanical burden on the endplates, a critical factor in preventing subsidence and enhancing fusion outcomes.
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
## 3 Dynamic Analysis of Simplified Models of Single-DOF and Two-DOFs  
In order to clarify dynamic behavior mechanisms of TSS and to explore the influence of various parameters on dynamic responses, Equations (15)-(17) need to be simplified, assuming that the tether length is fixed when system configurations remain fixed.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 3 Results  
### Fire Damage Assessment  
The spatial and temporal patterns of post-fire vegetation succession are determined by the climate conditions, the surrounding vegetation condition and topography. The burn severity influences the spatial configurations of forest patches, which, in turn, influence the ecological processes of post-fire recovery and succession. Hence, we must assess the burn severity to accurately track vegetation regeneration. The DNBR is the difference between the pre-fire NBR values and immediate post-fire values; this difference enabled us to identify the burn severity. We classified the burn severity in the study areas into four classes by using the DNBR method. Figure 3 shows one burn area's DNBR spatial pattern as computed via Landsat images. The pre-fire spatial pattern of the land cover and the post-fire burn severity classification result in 2000 are illustrated in Figure 3a-c. The classification values, burned areas and percentages are illustrated in Table 2.