esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the development of underwater exploration, underwater networking is in urgent demand. At present, underwater wireless optical communication (UWOC) is primarily based on line-of-sight (LOS) communication links. However, the underwater environment is so complicated that LOS communication links are easily affected by a couple of factors such as air bubbles, turbidity, oceanic turbulence, and so on. We put forward novel UWOC links using the total reflection at the air-water interface, which can mitigate those phenomena. This paper aims to investigate a UWOC link based on the total reflection at the air-water interface. In our work, we achieved the maximum data rate of 300 Mb/s and a bit error rate (BER) of 3.10 \(\times\) 10\({}^{-3}\) under the forward error correction (FEC) with the total reflection angle of 7\({}^{\circ}\). Furthermore, we verified the performance of the total reflection-based UWOC links at the air-water interface in the presence of waves and evaluated the impact on the UWOC links when the frequency and amplitude of the waves varied.  
underwater wireless optical communication; micro-LED; air-water interface +
Footnote †: journal: _Photonics_
## 3 Results  
### Drag Coefficient Variation by Ball  
Figure 3 shows the drag characteristic curves of the respective balls based on their panel orientation. The panel orientation of the respective balls was divided into two faces (Face A and B), and the respective drag coefficients were measured for each face. Most notably, the drag generated for the three types of balls varied substantially according to the ball type. The results showed that drag on the traditional Mikasa (MVA200) and Molten (V5M5000) balls varied significantly depending on the orientation of the ball panels (Figure 3c-f). The new volleyball for the 2020 Tokyo Olympics (V200W), on the other hand, showed relatively small drag variation with changes in panel orientation (Figure 3a,b).  
Figure 2: Volleyballs used in the experiment and their panel orientation settings. In this figure, (**a**,**b**) represent the Face A and B orientation of the new Mikasa volleyball (V200W), respectively; (**c**,**d**) represent the Face A and B orientations of the old Mikasa volleyball (MVA200), respectively; and (**e**,**f**) represent the Face A and B orientation of the old Molten volleyball (V5M5000), respectively.  
The critical Reynolds number of the respective balls was also found to vary according to the orientation of the panels. The moment at which the flow state of the ball spin changes from laminar to turbulent is called a transition, and the Reynolds number of this transition is called the critical Reynolds number (\(Re_{cr}\)). The \(Re_{cr}\) of the new Mikasa volleyball (V200W) was \(\sim\)\(2.9\times 10^{5}\) (\(Cd\approx 0.17\)) for Face A and \(\sim\)\(2.7\times 10^{5}\) (\(Cd\approx 0.15\)) for Face B. The \(Cd\) value of Face A of the new volleyball was particularly high relative to the other balls. The \(Re_{cr}\) of the old Mikasa volleyball (MVA200) was \(\sim\)\(2.7\times 10^{5}\) (\(Cd\approx 0.14\)) for Face A, and \(\sim\)\(3.1\times 10^{5}\) (\(Cd\approx 0.13\)) for Face B, indicating that the \(Cd\) value of Face B of the old Mikasa ball was low relative to the other balls. The \(Re_{cr}\) of the old Molten volleyball (V5M5000W) was \(\sim\)\(2.2\times 10^{5}\) (\(Cd\approx 0.16\)) for Face A and \(\sim\)\(2.8\times 10^{5}\) (\(Cd\approx 0.14\)) for Face B.
## 3 Materials and Methods
### Radiochemistry
#### 3.3.3 Analytical HPLC  
Samples prepared in manual radiolabeling experiments were analyzed on two different analytical HPLC systems (Knauer, Berlin, Germany) using identical analytical columns (MultoKrom(r) 100-5 C18 250 \(\times\) 4.6 mm) and solvents (0-10 min: 32.5% MeCNaq + 0.2% HCOOH, 10-15 min: 80% MeCNaq + 0.2% HCOOH; flow rate: 1.5 mL/min). Each system was equipped with a UV-Detector coupled in series with a LB 500 NaI radioactivity detector (Berthold Technologies, Bad Wilbach, Germany). Since HPLC analysis with post-column injection demonstrated near-quantitative recovery of radioactivity from the column, RCCs were calculated based on the summed integral of all peaks [36]. To account for variable signal strengths and slight differences in the retention time between the systems/columns (ca. 6%), all chromatograms were normalized to the highest peak and the time scale was adjusted with an appropriate correction factor.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 3 The Proposed Approach
### Macro-Pixel Map Construction  
Firstly, the decoding and calibration methods proposed by [PERSON] et al. [5] are used to generate the sub-aperture images \(LF_{left}(u,v,x,y,3)\) and \(LF_{right}(u,v,x,y,3)\). Then, several white images are captured to calculate the average white image. Light field raw images are then obtained by demosaicking the Bayer-pattern raw images. Finally, the micro-lens grid spacing is transformed and aligned to the integer pixel grid spacing by locating the centers of each micro-lens.  
Because the micro-lens grid is hexagonally packed, decoding leads to noisy pixels with vignetting and bias near the edges of micro-lenses [5]. Hence, we ignore the edge pixels in each micro-lens to construct the macro-pixel map \(\mathbf{I}_{MP}(X,Y)\), where \((X,Y)\) is the coordinate index of each micro-lens.  
For the lab-developed light field camera, the resolution of the micro-lens raw image is \(4454\times 6664\), and the decoded light fields are \((9,9,729,452,3)\). One macro-pixel is then constructed by extracting the central \(7\times 7\) pixels from each micro-lens.
## 1 Introduction
Similarly, NNs [11] and other adaptive techniques [8]--such as GA, SMCs [11], and fractional-order controllers [12]--offer robustness and adaptability for nonlinear systems but suffer from drawbacks like high computational complexity, convergence issues, chattering effects, and challenging parameter tuning. The main goal of this paper is to retain the functionality and effectiveness of \(D\) without compromising the simplicity of the controller. This study also aims to develop a simple control strategy that avoids the complexity of advanced methods while preserving the \(D\) term, unlike filtering approaches that often suppress it. Furthermore, \(D\) continues to present challenges (including _Ltd-D_ and _D-kick_) that must be overcome. Current solutions primarily focus on attenuating the control signal or applying tuning techniques that fail to address these issues holistically, as previously mentioned. To bridge this gap, this study proposes a novel controller, the \(PI\)-\(D^{\
ot{E}}\) controller, which leverages adaptive control theories and self-calculation of the exponential term \(D\). Unlike traditional approaches, this controller will not rely on training, rule-based systems, complex computations, or filters. Instead, it will employ adaptive control theoriesto dynamically adjust \(D\) through self-tuning mechanisms, including self-calculation of its exponential, effectively enhancing performance without significantly increasing complexity. The proposed \(PI\)-\(D^{\text{E}}\) controller aims to enhance the performance of \(D\) by effectively reducing noise sensitivity and managing abrupt SP changes. It seeks to prevent system destabilization and output signal saturation, providing a robust and efficient alternative to conventional \(PID\) tuning methods. In addition, it is hoped that this controller will become a tool that can be implemented in a wide variety of control systems. Furthermore, this work focuses on the development of an algorithm validated through the simulation and analysis of transient and steady-state responses. The letter \"/E\" is used in this article as a symbolic notation to represent the dynamic combination of proportional and derivative effects in the proposed controller. This choice is inspired by its origin as a ligature of the vowels \"a\" and \"e,\" which conceptually parallels the adaptive fusion of two classical control components. Thus, its use not only provides a compact representation of the algorithm but also conveys the integrated nature of these control actions. The rest of this article is organized as follows. Section 2 presents the \(PI\)-\(D^{\text{E}}\) controller. Section 3 evaluates its performance on a first-order and inverted pendulum-cart system. Section 4 describes the application of \(PI\)-\(D^{\text{E}}\) with a detailed analysis of its response in time and under disturbances. Section 5 concludes the study with discussions. Finally, Section 6 proposes the conclusions and opportunities for future improvements.
## 4 Results and Discussion
### Future Work  
Although the proposed user-centered data privacy protection framework, based on large language models and user attention mechanisms, demonstrated excellent performance in multiple experiments, there are still certain limitations and areas for improvement. Firstly, differential privacy techniques, while adding noise to ensure privacy, may negatively impact model accuracy. Future research should explore methods to mitigate the adverse effects of noise on model performance while maintaining privacy protection. Secondly, the complexity of the user attention mechanism increases computational costs; therefore, more efficient algorithms should be developed to reduce computational overhead and enhance real-time processing capabilities. Furthermore, the experimental data primarily focused on the financial and facial recognition domains; it is necessary to extend the scope to more diverse fields to verify the method's applicability and generalizability. In terms of theoretical analysis, the design of uni-loss needs further refinement and optimization tailored to specific application scenarios to enhance adaptability and generalization capabilities. Additionally, the application of the user attention mechanism in multimodal data processing should be explored, and its architectural design should be optimized. In the context of model training and optimization, more advanced optimization algorithms should be investigated to improve training efficiency and effectiveness. Moreover, the explainability and transparency of the models are crucial issues that need attention; integrating explainable AI techniques can enhance user trust. While the experiments were primarily conducted on high-performance computing platforms, future work should consider deployment and optimization in resource-constrained environments to broaden the applicability and practical feasibility. Finally, ensuring the compliance and legality of the methods is essential. Continual improvement and optimization of the privacy protection framework, based on user feedback, are necessary to provide solutions that better meet user expectations and needs.
# Modular Spiral Heat Exchanger Thermal Modelling
## 2 Materials and Methods  
### Geometry Model  
The usual SPHE configuration consists of one layer, here referred to as a module, of double spiral walls, which create two separate channels for the hot and cold streams. The hot stream enters the central core of the exchanger and the cold stream is introduced at the periphery of the studied SPHE (Figure 1a). This stream configuration provides a counter flow of streams.  
The other SPHE configuration investigated in the study is a two-module SPHE. It is composed of two one-module SPHEs connected in series with a counterflow stream configuration (Figure 1b). The two modules of the exchanger are connected by a shared end wall with openings at the peripheries of the hot and cold channels, respectively, which allows for their shortest possible serial connection.  
The heat exchanging spiral walls are configured such that the stream channels start and end at the same angle, thus producing the same number of turns, as shown in Figure 2.  
Figure 1: Schematic configurations of SPHE: (**a**) Single module SPHE. (**b**) Two-module SPHE.  
Figure 2: SPHE channel geometry configuration and depicted model parameters.  
The wall geometry of the SPHE investigated is composed of an Archimedean spiral, defined by equation  
\[r=a\phi+r_{s}, \tag{1}\]  
where \(\phi\)--the angular coordinate, \(r_{s}\)--the starting radius of a spiral (at \(\phi=0\)), and \(a\)--the parameter of the Archimedean spiral defined by \(a=w/\pi\), with \(w\) being the width of the hot and cold channels, respectively.  
The differential length of the spiral arc heat transfer surface area is expressed by  
\[dl=\sqrt{r^{2}+\left(\frac{dr}{d\phi}\right)^{2}}\,d\phi=\sqrt{r^{2}+a^{2}}\,d\phi. \tag{2}\]  
Utilizing Equation (1), introducing the turn number coordinate from the spiral start \(\varphi=\frac{\phi}{2\pi}\), relative to the spiral starting radius parameter \(z_{s}=\frac{r_{s}}{2\pi a}\), the differential arc length of the spiral, starting at a radius of \(r_{s}\), is represented by  
\[dl=4\,\pi^{2}\,a\sqrt{(\varphi+z_{s})^{2}+\frac{1}{4\,\pi^{2}}}\,d\varphi. \tag{3}\]  
The numerical model developed here was compared to the results published for the geometry by [PERSON] et al. [9], as shown in Figure 3a. The results for a two-module (2M) SPHE, with an equivalent surface area, are presented. The equivalent 2M SPHE is shown in Figure 3b in comparison to the SPHE geometry provided by [PERSON] et al. [9].
## 5 Autonomous Vehicle Vulnerabilities  
Autonomous vehicles, despite their potential to revolutionize transportation, are not immune to the growing complexity of cyber threats due to their dependence on advanced digital technologies [69]. Potential hackers may exploit these cyber vulnerabilities, creating a threat to the safety and privacy of individuals, regardless of whether they are motivated by malicious goals or simple curiosity [69]. Detailed analyses of these cyber threats can be discovered on informative platforms such as HackerNoon [70].  
The following sections highlight some of the prominent attack vectors that could potentially compromise the integrity of autonomous vehicles:  
* Key fob hacking: Remote vehicle access and ignition made possible by key fob technology can be compromised. By using devices to strengthen the signal transmitted by a vehicle's key transponder, hackers can gain unauthorized entry and even remotely start the vehicle [71].  
Figure 5: Schematic representation of the collision prediction algorithm.  
* Control area network (CAN) bus attacks: The CAN bus, which operates as the electrical network that connects the various electronic control units (ECUs) within a vehicle, is an attractive target for hackers. By exploiting vulnerabilities in the CAN bus, hackers can take control of fundamental vehicle functions such as braking and steering [71].
* Entertainment system hacking: Given its connection to the CAN bus, a vehicle's entertainment system can provide a back door for hackers, which, once breached, could potentially grant a hacker full control over the vehicle's systems [71].
* Adversarial machine learning techniques: Autonomous vehicles rely heavily on machine learning algorithms to interpret sensor data and make operational decisions. By employing adversarial machine learning techniques, such as evasion or poisoning attacks, hackers can manipulate sensor data, causing the vehicle to make faulty and potentially hazardous decisions [72].
* User data theft: Considering the plethora of user data stored in autonomous vehicles, these vehicles become prime targets for cyber criminals. A hacked vehicle can lead to significant privacy violations and impose safety risks to the driver and others on the road.
* Remote vehicle hijacking: In a potentially dangerous scenario, hackers might gain remote control of a self-driving car, causing passengers to experience difficulty.
* Denial-of-service (DoS) attacks: The launching of DoS attacks against the vehicle's systems could lead to system shutdown or failure.  
The extensive range of potential attack vectors underscores the imperative to enhance cyber security measures in autonomous vehicles. A compromised vehicle becomes a significant threat to all road users, necessitating immediate rectification of these vulnerabilities for public safety [69]. To fully understand the scope of these vulnerabilities, it is necessary to delve into the hardware aspects, specifically focusing on critical components such as the onboard diagnostic port (OBD) and the engine control unit (ECU).
## 4 Discussion
### Limitations  
A potential limitation of this investigation's findings is the manually corrected, gold-standard brain extractions. As previously described, these brain extractions are a benchmark used to evaluate the accuracy of automated brain extraction pipelines. Our gold-standard extractions were generated from an initial extraction by optiBET or BET | B,f0.4 and then manually corrected using various techniques until they were of sufficient quality. Therefore, DSC scores could be skewed in favor of pipelines that are more similar to the optiBET and BET | B,f0.4 control pipelines. However, because subsequent manual corrections were made for all images, the impact of this bias on our results should be negligible. Regardless, we would like to acknowledge the potential for this bias and address its two consequences on the interpretation of this study's findings. First, as evidenced by the inter-rater reliability issues associated with manual tracings of the brain, specific brain structures, and brain lesions from TBI, there is no single \"correct\" brain extraction [16; 42]. Rather, there is a range of acceptable brain extractions that can all be considered correct. Consequently, any change in the extracted brain resulting from the addition of an intensity processing procedure could be interpreted as a decrease in accuracy, while in actuality, it could simply reflect a difference in brain extraction that is not decidedly better or worse. Second, significant increases in a pipeline's accuracy from the addition of an intensity processing procedure are especially noteworthy. This is because the pipeline is showing enhanced accuracy despite bias possibly favoring its control, suggesting that improvements in accuracy from the added intensity processing were precisely in line with the manual corrections or were robust enough to transcend the inherent bias potentially in the gold-standard extractions.
## 2 Novel Contributions of the Current Paper
### Solutions Proposed  
This research proposes a novel feature-based authentication mechanism that integrates PUFs and efficient cryptographic algorithms to enhance security at the dynamic collaborative edge. PUFs provide a lightweight yet robust security solution, while cryptographic algorithms ensure secure computation and communication. The proposed feature-based approach focuses on PUF data security, privacy, and reliability, making it a suitable and efficient option for secure authentication in resource-constrained and heterogeneous edge environments.  
* PUFs provide hardware-level uniqueness, making each device tamper-resistant and unclonable. PUF-based authentication eliminates the need for storing cryptographic keys, reducing attack vectors such as key theft or tampering.  
* ML models enhance authentication by detecting anomalies, improving PUF response stability, and adapting to changing environmental conditions. ML-based feature extraction ensures accurate authentication while maintaining lightweight computation.
* The proposed authentication protocol is designed to be energy-efficient, lightweight, and secure, minimizing computational overhead on constrained devices. It ensures fast authentication while preserving strong cryptographic security.
* The protocol optimizes challenge-response communication to minimize data exchange, reducing bandwidth consumption. Using vectorized challenge-response representations, the system reduces authentication latency without compromising security.
## 4 Experimental Verification
### Field Test in Underground Coal Mine
#### 4.4.1 Sample Processing and Transfer Learning  
An industrial camera was used to collect a total of 244 images of on-site pressure relief holes, and some samples are shown in Figure 16. The improved SinGAN model was used to expand the image samples. Each real image generated 49 images, and finally, 12,200 images were obtained. Then, the sample data was divided into ten parts. and ten-fold cross validation was also used to test the performance of the network. The average value of ten results shall be taken as the final testing result.  
In the CNN model, the front and latter layers of the network model usually have different functions. The front layer is responsible for extracting image features, such as shape, color, etc. The latter layer is responsible for allocating corresponding weight factors according to different characteristics, and has decision-making power. The objects extracted from the front layer feature are allowed to be different data. If the data of the latter layer are different, the configured weight factors are also different. Therefore, for similar sample data, we can refer to the idea of transfer learning, use the front layer with a fixed network model, and only train the latter layer to achieve the target recognition task.  
In this experiment, the sample data of the pressure relief hole in the coal mine were very similar to the sample data collected on the ground, so transfer learning was used to improve the performance of the Faster R-CNN model. The specific operation is as follows: only train the parameters of the Softmax classifier in RPN and ROI Pooling, and fix the parameters of other modules unchanged. The specific training parameters are the same as those shown in Table 2.
# Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs
## 5 Performance Simulation and Analysis  
In this section, we evaluate the performance of the OCMR protocol. Firstly, we discuss the parameters of the simulation setting. We compare OCMR with VBF, HH-VBF, and QELAR, which are frequently used in UASNs to demonstrate the performance of the proposed OCMR. There are three performance metrics considered in our simulations: PDR, average end-to-end delay, and average energy consumption. To prove the effectiveness of the proposed protocol, we modify the node density, the node maximum moving velocity, and the node initial energy to analyze three performance metrics.
## 5 Conclusions  
In this paper, a resource allocation scheme was studied for a multi-D2D communications underlaying cellular network, where each D2D pair can share subcarrier resources with multiple CUs. We formulated an optimization problem which maximized the sum spectrum efficiency of the heterogeneous network while guaranteeing a positive system gain and the rate requirement of CUs. A heuristic greedy algorithm was designed to make the subcarrier matching between CUs and D2D based on maximum sum spectrum efficiency, and then an efficient power allocation algorithm for each individual D2D pairs was developed based on the Lagrangian dual method. The simulation results showed that our proposed scheme can significantly improve the sum spectrum efficiency of the heterogeneous networks.  
This work is sponsored by National Natural Science Foundation of China with Grant No. 61373042, and by the Natural Science Foundation of Hubei Province under Grant 2014 CFB880.  
**Author Contributions:** Yong Zhang and Fangmin Li initiated and discussed the research problem; Yong Zhang conceived and developed the methods; Yong Zhang and Mohammed Abdulaziz Aide Al-qanes performed the experiments and prepared the figures; Yong Zhang and Xidao Luan analyzed the data; Yong Zhang prepared and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.