esa-sceva/satcom-chunk-collection huggingface dataset:
# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
## 3 Results and Discussion
### Comparison of Capsule Network-Based Features and Hand-Crafted Features  
ABPCaps automatically generates and extracts critical features from peptide sequences. Compared to a number of hand-crafted features, the capsule network has a more powerful feature learning capability. For a more intuitive demonstration of the effectiveness of ABPCaps, we applied the dimension reduction technique based on t-distributed stochastic neighbor embedding (t-SNE) [52] to visualize the differences in amino acid coding between ABPCaps and traditional handcrafted features on test sets. The t-SNE plots in Figure 3 show that ABP and non-ABP can be easily separated by the features based on ABPCaps, but they cannot be well distinguished by the hand-crafted features. This result suggests that ABPCaps features can better capture the characteristic features of ABP sequences.  
Figure 3: Visualization of five hand-crafted features and features extracted by ABPCaps. The figure shows a comparison of five hand-crafted features and features extracted by ABPCaps, with ABP and non-ABP shown in blue and yellow colors, respectively. This visualization highlights the superior discriminative ability of the ABPCaps model in identifying ABP, thereby aiding in the diagnosis and treatment of related medical conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & **AUROC** \\ \hline iAMPpred & 77.28\% & 88.86\% & 65.13\% & 0.7517 & 0.7942 \\ ClassAMP & 61.28\% & 40.11\% & 49.95\% & 0.4450 & 0.5737 \\ AMPfun & 78.78\% & **94.64\%** & 65.66\% & 0.7753 & 0.8170 \\
**ABPCaps** & **93.33\%** & 90.95\% & **91.72\%** & **0.9134** & **0.9289** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of previous work on ABP prediction. The table summarizes the performance of different ABP prediction models proposed in previous studies in terms of accuracy, recall, precision, F1-score, and AUROC. The evaluation metrics are calculated on the independent test set. The best performance in each category is highlighted in bold.  
To further evaluate the performance of ABPCaps, four basic machine learning-based classifiers, including KNN, RF, SVM, and Gaussian naive bayes (GNB), were used to compare the performance of hand-crafted features and features extracted by ABPCaps on five hand-crafted features, ACC, DPC, PAAC, CKSAAGP, and PHYC. Accuracy, F1-score and AUROC were chosen as evaluation metrics. The results in Figure 4 show that the features extracted by ABPCaps outperform the other five hand-crafted features for all four classifiers, especially for KNN and GNB. This suggests that the features learned by ABPCaps would be more effective for the identification of ABPs, as well as being more suitable for general classification. In short, ABPCaps is a powerful tool that can automatically generate and extract critical features from peptide sequences using a capsule network.
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 3 The Modulation Recognition Method Based on Deep Learning
### Application of Convolutional Neural Network in Modulation Recognition
#### 3.1.2 The Modulation Recognition Method Based on Convolutional Neural Network
The relevant underlying features within and between antennas can be extracted under multi-scale signals through the cubic convolution filter of the network. Through experimental simulation, MONet can achieve 95% recognition accuracy under the condition of 0 dB. (2) Higher-order cumulants  
Research on signal modulation identification based on higher-order cumulants was carried out as early as 1992, when the author [53] used higher-order cumulants as relevant features and used decision trees to make judgments, resulting in the identification of modulation patterns. Subsequently, many scholars began to use high-order cumulants as features for modulation classification [54; 55; 56] and achieved good results. After the development of deep learning, some scholars combined deep learning with high-order cumulants to study a new AMR algorithm. [PERSON] proposed a CNN-based CO-AMR method [57]. The author used CNN to extract the high-order cumulant features of the signal in all antenna datasets and identify sub-results. All sub-results were combined, and finally, using decision rules (direct voting and weighted voting) to complete the classification. Experiments showed that when the signal-to-noise ratio is greater than 0 dB, the recognition accuracy of the algorithm can reach 100%, but at \(-\)5 dB, the recognition rate is only 82%. Therefore, it is necessary to further improve the recognition rate under a low signal-to-noise ratio. From the above literature survey, it can be seen that because CNN is more suitable for extracting information from images, there are not many studies on using CNN to directly extract signal sequence features. Relatively speaking, the ability of RNN to extract features directly from the signal sequence is stronger than that of CNN. Therefore, this paper will introduce the modulation recognition algorithm based on RNN in the next section.
## 1 Introduction
### Objective  
The purpose of this paper is to study the effect of several types of jamming attacks (in particular, random and reactive) in a WSN. More specifically, we shall study how network performance degrades, when subject to jamming and when using any of the following routing protocols: _ad hoc_ on-demand distance vector (AODV), dynamic source routing (DSR) and the multi-parent hierarchical (MPH) protocol [5,6]. Network, MAC and energy performance are captured in terms of several proposed metrics.  
Throughout our research, we have found both that MPH is more resilient to jamming attacks, as well as to adverse conditions of the wireless environment in that it offers a better network performance (overhead and energy consumption) than DSR and AODV. This is both because MPH encapsulates the attack to a smaller network segment better and because of the self-configuring capabilities of MPH, derived from a combination of a periodic and proactive route update. Thus, at least for certain scenarios, there is room for using alternate protocols to AODV or DSR. Our results ultimately motivate further research in studying other network metrics that enable, in the context of MPH, a timely detection of jamming and other kinds of attacks.  
We describe the organization of the rest of the paper. In Section 2, we present related work. Section 3 reviews jamming attacks. Section 4 discusses protocols, jamming and metrics analysis. Section 5 shows the description of simulator parameters and network architecture. Section 6 analyzes the results and discussion. Section 7 explains different spatial node distributions. Finally, conclusions are given in Section 8.
# A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information
## 1 Introduction
As illustrated in Figure 1, the model first uses the proposed pedestrian region to generate a series of candidate regions with possible pedestrian targets from the original image, and each region has a corresponding confidence score. Second, the pedestrian recognition network is used to judge whether each candidate region contains pedestrian targets. The pedestrian recognition network contains visual features, pedestrian poses, pedestrian attention, and classification modules. The visual feature module is responsible for extracting the visual feature descriptions of the candidate regions. The pedestrian pose module is used to extract the pose feature descriptions based on an improved OpenPose [16] framework. The pedestrian attention module is used to extract attention information, and the classification module is responsible for fusing the visual features and pedestrian pose descriptions with the attention mechanism. The main contributions of this study can be summarized as follows:  
1. We developed a pedestrian detection method that integrates an attention mechanism with visual and pose information and includes pedestrian region proposal and pedestrian recognition networks, effectively addressing occlusion and false detection issues. 2. We found that gesture information and an attention mechanism, as supplements to visual description, are key in pedestrian detection. 3. In this study, experiments on the public datasets Caltech [17] and CityPersons [12] were conducted to validate the effectiveness of the proposed network, which fuses an attention mechanism with visual and pose information.
## 1 Introduction
### Motivation  
Wasm has been nothing short of revolutionary in the realm of web development. Originally designed as a high-performance target for languages such as C, C++, and Rust, it effectively bridges the divide between JavaScript's agility and the demand for rigorous low-level computation in web applications. Its attributes include the following. (1) Compact Binary Format: Wasm's concise binary representation not only ensures a reduced file size, optimizing loading times, it offers rapid parsing and execution, which is pivotal in environments such as IoT devices where resources are limited. (2) Near-Native Execution Speed: Wasm promises execution at near-native speed, a feat that ensures intensive computational tasks can be executed seamlessly, enhancing the user experience and ensuring that IoT devices can process information swiftly. (3) Language Interoperability: the ability to compile code from multiple languages into Wasm means that developers are not shackled to a single language. This adaptability is crucial, especially in the diverse ecosystem of IoT, where devices might have been developed using various programming paradigms. (4) Sandboxed Security Model: Wasm's execution within a sandboxed environment offers a robust security paradigm, ensuring code confidentiality and integrity. This is particularly vital in the IoT spectrum, where devices often lack in built-in security features and can be frequent targets for attacks.  
As the realm of IoT burgeons with billions of interconnected devices, the quest for efficient, secure, and universally compatible technological foundations is paramount. The unprecedented scaling of IoT accentuates the need for solutions that are not just energy-efficient but compact, secure, and maintainable. Herein lies the potential of Wasm, with its unique strengths positioning it as an invaluable asset in the IoT toolkit. It is puzzling, considering its evident potential, that there remains a conspicuous absence of extensive literature focusing on the fusion of Wasm and IoT. While the intersection of these two burgeoning technologies possesses the potential to redefine our technological landscape, the lack of thorough research and understanding has left many nuances unexplored. Our motivation stems from this very gap. We embark on a journey to deeply understand, dissect, and then articulate the synergies between Wasm and IoT. This paper aims to be a beacon, shedding light on Wasm's architecture, its symbiotic relationship with IoT, the tools paving its developmental pathway, the intricacies of its security model, and the vistas it promises to unlock in the near future.
## 2 Method and Theory
### Experimental Components  
Our experimental setup includes several key components, as outlined below:  
* Pump source: A multimode fiber-pigtailed diode laser (AeroDIODE 808 LD-2-0-0), emitting at 808 nm with up to 5 W of optical power, was used to pump the Nd: YVO\({}_{4}\) gain medium. The laser's output was focused onto the Nd medium through a collimator focus module (Edmund Optics #88-181 & 88-186), which ensures efficient coupling.
* Gain medium: The Nd: YVO\({}_{4}\) gain medium has an input facet that is high-reflection (HR) coated at 1064 nm (reflectivity > 99.9%) and high-transmission (HT) coated at 808 nm (transmission > 95%). The output facet is anti-reflection (AR) coated for 1064 nm (transmission > 99.8%).
* Dichroic mirror: A custom-fabricated dichroic mirror, made by the National Research Council, is used to transmit 99.3% of 1064 nm light and reflect 70% of 1541 nm light, ensuring effective coupling of the pump and signal beams into the nonlinear medium.
* PPLN crystals: Three MgO-doped PPLN crystals, each with a 5 mol.% MgO doping concentration and fabricated in-house, were used for DFG. The crystals had lengths of 29 mm, 40 mm, and 50 mm and a periodicity of 30.51 \(\upmu\)m. The crystals were 1 mm thick and had poling duty cycles of 50%. These crystals were mounted to a copper heat sink with a thermoelectric cooler (TEC) that provided precise temperature control with an accuracy of 0.05 \({}^{\circ}\)C. The input facets of the PPLN crystals were AR coated for both 1064 nm and 1541 nm (transmission > 99.7%), while the output facets were AR coated for 1064 nm and approximately 3.5 \(\upmu\)m (transmission > 95%).
* Output coupler: A custom-made CaF2 plano-concave mirror with a 100 mm radius of curvature was used as the output coupler. It was HR coated for 1064 nm (reflectivity = 99.85%) and AR coated for \(\sim\)3.5 \(\upmu\)m (transmission = 92%).
* Beam monitoring: The DFG power was measured using a thermal power sensor (Thorlabs S401C) after the pump and signal beams were filtered out using a germanium (Ge) filter (Thorlabs WH91050-C9).  
Additionally, a tunable C-band laser (Agilent 8164B) around 1541 nm was amplified through a polarization-maintained EDFA (CivilLaser EDFA-C-BA-26-PM). The output was collimated with a Thorlabs F110 APC-1550 collimator, and a 100 mm focal length plano-convex lens (Thorlabs LA1050-C) focused the beam into the center of the PPLN. A half-wave plate (Thorlabs WPHSM05-1550) was inserted between the focusing lens and collimator to adjust polarization. This arrangement was simulated in Zemax, predicting an \(\sim\)80 \(\upmu\)m beam waist radius at the PPLN center.
## 4 Simulation Results
### Numerical Simulation Results
#### 4.1.1 Simulation Setup  
1. The dynamic model parameters setup. In the simulation, the formation system consists of three small spherical robots with the same dynamic model parameters. The concrete value of the parameters were identified in reference [20].  
2. Constraints setup. The maximum thrust of a single water jet thruster is 3 N. According to the \"H\"-shaped distribution of the four thrusters, the control input vector needs to satisfy the following equation:  
\[\left[\begin{array}{c}-6\\ -3\\ -0.5\end{array}\right]\leq\boldsymbol{\tau}\leq\left[\begin{array}{c}6\\ 3\\ 0.5\end{array}\right]. \tag{39}\]  
The control input increment vector satisfies the following constraint:  
\[\left[\begin{array}{c}-0.3\\ -0.3\\ -0.1\end{array}\right]\leq\Delta\boldsymbol{\tau}\leq\left[\begin{array}{c}0. 3\\ 0.3\\ 0.1\end{array}\right]. \tag{40}\]  
3. Parameters of the VS-ESO-MPC setup. The sampling period is \(T=0.05\)s. The control horizon \(N_{c}\) is almost half of the \(N_{p}\). According to the experience and the simulation results, \(N_{p}\) is set to 25 and \(N_{c}\) is set to 15.  
4. Formation reference path setup. In a multi-small spherical robot formation system, the formation path is calculated by the planning layer based on the actual environment. The planning layer sends a series of discrete points along the formation path to the spherical robot. In order to be closer to the real experiment, a series of discrete points on the \(x-y\) plane is set. Then, the cubic spine is adopted to compute the reference state trajectory, which includes the position and yaw angle. The discrete formation reference point is set as \(position_{X}=[-4,-2,0.0,2,4,6,10,12,15,18]^{T}\) and \(position_{Y}=[1.2,0.6,0.0,1.5,3.8,5.0,3.0,2.0,-2,1]^{T}\). The formation trajectory is based on the above discrete points and is shown in Figure 9. Figure 9b shows the heading angle after equal-length resampling. The reference trajectory in the depth is set as \(z_{d}(t)=0.5t\).
# Layered Double Hydroxides as Next-Generation Adsorbents for the Removal of Selenium from Water
## 5 Layered Double Hydroxides: Evolution and Future Research Directions
More recently, LDH have been explored in biomedical fields as drug carriers, owing to their ability to release drugs in a controlled manner. The 2020s continue to see innovations in LDH-based technologies, further enhancing their roles in energy storage and conversion, biomedicine, catalysis, gas adsorption, and environmental remediation. Although the future is hard to predict, trends are likely to emphasize smart materials with adaptive properties, sustainable technologies, and advanced biomedical applications. We can expect innovations in integrating LDH with artificial intelligence for smart sensors, as well as a focus on recycling and reusing LDH to reduce environmental impact. High-performance energy solutions will also be a key area, with developments aimed at improving energy storage and conversion. In terms of LDH as adsorbents for the removal of selenium from water, we can expect advancements in several key areas. Current research suggests that researchers are likely to develop LDH with enhanced selectivity for selenium ions, improving their efficiency in removing selenium even in the presence of other competing ions. There will be advancements in functionalizing LDH by incorporating specific functional groups or hybridizing them with other materials to boost their adsorption capacity and kinetics. Another trend is the emphasis on improving the regeneration and reusability of LDH, making them more cost-effective and sustainable for long-term water treatment use. The use of nano-formulations of LDH is anticipated to increase as these materials offer greater surface area and improved adsorption efficiency for selenium at lower concentrations. Additionally, integrating LDH with advanced technologies, such as redox treatment or membrane filtration, is likely to enhance the overall efficiency of selenium removal. Lastly, future research will also focus on assessing the environmental impact of LDH-based adsorbents, including their disposal and the lifecycle implications of their use in water treatment applications. The gradual evolution of LDH, from their discovery to their widespread application across various fields, along with future research trends, is illustrated in Figure 4.
###### Abstract  
Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the Istanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs.  
photogrammetry; UAV; RTK; PPK; PPP-AR; Structure-from-Motion; GCP
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 3 Signal-Processing Techniques  
### Online Data Acquisition  
Regardless of the type of output an accelerometer produces, a data processing unit is necessary to automatically collect and display the readings in real-time and provide information for online processing within the control electronic unit.  
This usually involves selecting a suitable microcontroller or a programmable logic controller (PLC) with the required peripheral interface for the sensor, as in [45]. Almost all microcontrollers available today can interface with accelerometer sensors since they all come equipped with analogue-to-digital converters and high-speed SPI and I2C interfaces.  
Figure 12: Inductive coupling disturbance of accelerometer sensor signal.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 2 Literature Review
### UAV Trajectory Optimization  
While UAVs offer significant advantages in flexibility and mobility, their potential can only be fully realized when their flight paths are optimally designed. Most conventional UAV-assisted localization studies assume fixed or circular flight paths, which may not be suitable for real-time or environment-aware applications. Trajectory optimization plays a vital role in improving localization accuracy while minimizing flight time and energy consumption. [PERSON] [8] demonstrated that optimizing the UAV's trajectory using RSSI-based feedback significantly improves the accuracy of radio source localization. [PERSON] et al. [6] proposed a cooperative multi-UAV placement algorithm for AOA-based localization, implicitly addressing spatial trajectory decisions. [PERSON] et al. [9] further explored cost-efficient trajectory design using TSP-based optimization under limited observation constraints. These studies highlight the growing importance of incorporating trajectory planning into the localization framework. However, most existing approaches treat signal features and flight paths as separate elements. In contrast, our study jointly considers them by integrating RSSI, AOA, and EOA into a unified optimization framework where UAV flight paths are dynamically designed to reduce estimation error.