esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Types of Scanning Patterns
### Square Spiral Scanning  
#### 3.3.1 Square Spiral Scan Trajectory  
The square spiral combines the properties of both spiral and raster scanning. It begins from the region of high probability and extends outward toward the region of low probability of finding the receiver satellite in the uncertainty area. The scan is performed in steps with the step size \(n\), which is defined by Equation (12). The scanning movement involves scanning straight lines, which take \(90^{\circ}\) turns relative to the previous path segments. Figure 3a,b show the square spiral path scanning the uncertainty region spanning \(\pm 250\,\upmu\)rad in both the horizontal and vertical directions, with a beam divergence of \(\theta_{b}=25\,\upmu\)rad and overlap factor values \(a=0\) and \(a=0.2\).  
If the uncertainty area spans from \(-u\) to \(+u\), it can be divided into five regions, as shown in Figure 4. In each loop, these five movements are repeated: first in the increasing horizontal (\(x\)) direction, then in the increasing vertical (\(y\)) direction, followed by decreasing \(x\), decreasing \(y\), and increasing \(x\).  
For any point in the \(M\)-th loop of the square spiral, the sum of all loop lengths before it can be computed using [41]  
\[S_{M-1}=8n\frac{M(M-1)}{2}. \tag{30}\]  
If the coordinate point of interest is in domain 1 of the uncertainty area, then  
\[M=\Big{\lfloor}\frac{x}{n}\Big{\rfloor}. \tag{31}\]  
If the coordinate point of interest is in domains 2, 3, 4, or 5 of the uncertainty area, then  
\[M=\frac{\max(|x|,|y|)}{n}. \tag{32}\]  
Figure 3: Square spiral path covering the uncertainty area. (**a**) Square spiral path with overlap factor = 0. (**b**) Square spiral path with overlap factor = 0.2.  
The cumulative distance \(L_{\rm ss}\) to any point falling in each domain can be computed using the following equation, as derived by the authors in [41]:  
\[L_{\rm ss}(x,y)=\left\{\begin{array}{ll}S_{M}+x-Mn&\mbox{for domain 1}\\ S_{M-1}+Mn+y&\mbox{for domain 2}\\ S_{M-1}+3 Mn-x&\mbox{for domain 3}\\ S_{M-1}+5 Mn-y&\mbox{for domain 4}\\ S_{M-1}+7 Mn+x&\mbox{for domain 5}\end{array}\right.. \tag{33}\]
## 1 Introduction
Convolutional neural network (CNN) has local perception and parameter sharing and can learn different levels of spatial feature information. This weight-sharing network structure reduces the complexity of the network model, reduces the number of weights, and can effectively learn the corresponding features, while avoiding the complex feature extraction process. However, continuous convolution and pooling operations in the network destroy the original spatial information and reduce the resolution, which decreases the classification accuracy of picture. The fully convolutional network (FCN) [15] converts the fully connected layer at the end of the convolutional neural network into a convolutional layer. This makes it possible for the convolutional neural network to achieve pixel-level classification. Combining fully convolutional neural network and ELM (extreme learning machine) [16] has achieved the pixel-level classification of hyperspectral images. We propose a classification structure based on the DeepLab v3+ network for the problem of how to use the limited hyperspectral image data to fully mine the feature information, thereby improving the accuracy of hyperspectral image classification. The main contributions of this article are summarized as follows:  
1. We utilize DeepLab v3+ as the neural network structure to extract spatial domain features and merge them with spectral domain features. DeepLab v3+ is one of the fourth-generation DeepLab series of semantic segmentation networks developed by Google and has the best comprehensive performance so far. We are the first to apply the latest version of the DeepLab network to the hyperspectral image classification task. 2. We use the PCA method to reduce the dimensionality of the original hyperspectral image. In the spatial feature extraction stage, we select the first three principal components and the first principal component as training data and labels [16] to solve the dimensionality problem of the hyperspectral imagery. 3. We select different classifiers such as SVM, KNN, etc., to complete the task of hyperspectral image classification. We test and compare the classification accuracy under different conditions.
# CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks
###### Abstract
In addition to these factors, resources are constrained in WSNs, such as processing power, available memory, and battery power. Resource constraints, node overload, error-prone communication links, and irregular upstream traffic can ultimately lead to network congestion. Congestion significantly reduces network performance and results in increased packet loss. Moreover, in some industrial applications of wireless sensor networks, such as power transmission line monitoring systems [14], sensor nodes can be powered by power lines or radio frequency waves [2; 15], so the above-mentioned energy-efficient routing algorithms cannot achieve the desired results [15]. Meanwhile, when WSNs are used in information acquisition and monitoring systems, the proliferation of sensor-generated data may lead to network congestion due to unexpected events in the perception area, seriously reducing network performance, which affects the quality of service [16]. Therefore, to ensure the quality of information acquisition, it is necessary to design a specialized WSN routing algorithm to reduce network congestion [17]. Based on the above observations, in order to alleviate network congestion and reduce data transmission delay, we propose a congestion-aware routing algorithm (CARA). The CARA algorithm senses the congestion status of nodes and their neighboring areas, and uses the multi-attribute method to make routing decisions. Our main contributions are summarized as follows:  
* CARA proposes a node queue model to detect the level of load on the node. * CARA considers the geographical location relationship between the nodes and sink, the cache state of the node itself, and the network's local congestion state; establishes routing evaluation parameters; and optimizes routing decisions to alleviate congestion. * CARA optimizes packet transmission paths, bypasses congestion nodes, reduces packet drop rates, and improves network reliability based on multi-attribute decision-making principles. The rest of the paper is organized as follows. In Section 2, an overview of related research works in the literature on congestion routing is presented. The CARA algorithm is described in detail in Section 3. The relevant simulation results and their analyses are discussed in Section 4. Section 5 concludes the paper.
###### Abstract
In answering the question, the following contributions were identified: (a) development and implementation of a lightweight periodic scheduler tailored to the specific needs of real-time biomechanical feedback applications and adaptable for other real-time data acquisition tasks, (b) a wearable RTBF system with flexible sensor sample streaming capabilities covering a range of possible needs in sports and rehabilitation, (c) determination of the upper limit of the sampling frequency for the specific hardware we used with different formats and numbers of samples in a packet, (d) analyzing the correlations between sampling frequency and the duration required to execute all processes involved in sensor data acquisition, packet assembly, and transmission, taking into account the number of samples contained within each packet and the structure of the formatted sensor data values.
# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 3 Methodology  
This section presents a network-based solution for the analysis and comparison of as-built and as-designed BIM, as illustrated in Figure 1. The input data comprise the as-designed BIM and the as-built BIM. As-built BIM is automatically reconstructed from point cloud data acquired from practical field laser scanning. As the most popular data format for BIM, the IFC data format [38] facilitates interoperability between various software platforms and is widely supported by many market-leading BIM software vendors. The network construction layer starts with the attributes extracted from IFC documents, and then the nodes and links are generated based on the calculation and analysis of attributes. The analysis and comparison will be conducted through the network analysis method, and practical implications and useful insights can be generated to guide the management of as-built BIM.  
To further enhance the effectiveness of this network-based framework, advanced algorithms such as machine learning and artificial intelligence can be integrated into the process [36]. For instance, Convolutional Neural Networks (CNN) can be employed to process and analyse the point cloud data [39], identifying areas where the actual construction deviates from the design with high precision. Moreover, integrating this network-based approach with real-time data acquisition systems can provide continuous monitoring and updating of the as-built BIM. Utilising Internet of Things (IoT) devices and sensors on con  
Figure 1: The network science-based framework for BIM analysis and comparison.  
struction sites can offer real-time feedback and adjustments, ensuring that any deviations are promptly addressed [40].
## 2 Problem Statement
* **Sensor-stream item**: \(\mathbf{V}_{i}\) values are generated by one specific sensor located at \((L,L)\) on the right top corner (the opposite side of sink node), for convention we use \(\mathbf{V}\) to represent the stream generated. For each stream, we process one stream item \(\mathbf{V}=\{\mathbf{V}_{1},\ldots,\mathbf{V}_{n}\}\) where the amount of data stored before the data sent is \(|\mathbf{V}|=n\). The generation is continuous at regular intervals (periods) of time. We consider gaussian data (\(\mu=0.5\) and \(\sigma=0.1\)) sent in bursts. * **Quality of a sample**: To assess the impact of data reduction on data quality, based on decision \(D\), we consider two rules: \(R_{dst}\) and \(R_{val}\). The rule \(R_{dst}\) aims at identifying whether \(\mathbf{V}\) and \(\mathbf{V}^{\prime}\) data distributions are similar. To compute this distribution similarity (\(\mathbf{\Upsilon}\)), we use the Kolmogorov-Smirnov test [39]. The rule \(R_{val}\) evaluates the discrepancy among the values in sampled streams, _i.e._, if they still represent the original stream. To quantify this discrepancy (\(\mathbf{\Phi}\)), we compute the absolute value of the largest distance between the average value of the original data, and the lower or higher confidence interval values (\(95\%\)) of the sampled data: \[\mathbf{\Phi}=\max\{|v_{low}-avg_{g}|,|v_{hig}-avg_{g}|\}\]in which the pair (\(v_{low};\,v_{hig}\)) is the confidence interval for the sampled data and \(avg_{g}\) is the average (mean value) of original data [36]. These rules help us to identify the scenarios where our sampling algorithm is better than simple random sampling strategy. These assumptions are considered in the whole paper. For instance, the routing algorithm is shortest path tree, the stream item is the set \(\mathbf{V}=\{\mathbf{V}_{1},\ldots,\mathbf{V}_{n}\}\), and so on. In the next three sections, we answer the questions addressed in _Problem definition_ by presenting the reduction design in real-time applications, the analytic model that estimate the ideal sample size \(|\mathbf{V}^{\prime}|\), and the data-centric reduction algorithms.
# All About Audio Equalization: Solutions and Frontiers
## 3 Parametric Equalizers  
The parametric equalizer is the most powerful and flexible of the equalizer types. Midrange bands in a parametric equalizer have three adjustments: gain, center frequency, and quality factor \(Q\) (or bandwidth). A parametric equalizer allows the operator to add a peak or a notch at an arbitrary location in the audio spectrum. At other frequencies, far away from the peak or notch, the parametric equalizer does not modify the spectral content, as its magnitude response there is unity (0 dB). Adding a peak can be useful to help an instrument be heard in a complex mix, or to deliberately add coloration to an instrument's sound by boosting or reducing a particular frequency range. Notches can be used to attenuate unwanted sounds, including removing power line hum (50 Hz or 60 Hz and sometimes their harmonics) [25] and reducing feedback [26]. To remove artifacts without affecting the rest of the sound, a narrow bandwidth would be used.  
A single section of a parametric equalizer is created from a second order peaking/notch filter, or in certain cases, a first or second order shelving filter for the lowest and highest frequency bands. When multiple sections are used, they are always connected _in cascade_ so that the effects of each subfilter are cumulative on a decibel scale. In this section, we derive the transfer functions for the first and second order shelving filter and for the second order peaking/notch filter.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security
### UAV Radio Anti-Interference  
The open nature of the wireless communication channels in UAV systems increases their vulnerability to external interference signals [169]. For example, an illustration of a jamming attack is presented in Figure 14. In low-altitude communication scenarios, on the one hand, it is necessary to ensure the security of data transmission through wireless channels and reduce the risk of interference. On the other hand, ensuring the security and reliability of the data transmitted between the network elements and the platform system is necessary. Smart antenna technology is a revolutionary advancement in wireless communication, significantly improving the anti-interference capabilities of UAVs by adjusting the weighting value of the antenna elements to form a directional beam via AI algorithms.  
Figure 13: LLM-based spectrum sensing for UAV security.  
[PERSON] et al. proposed a 3D robust beamforming method for supporting UAVs via integrated sensing and communication (ISAC) technology and introduced a dynamic beamforming method for UAV-enabled vehicle-mounted networks [170]. In 2024, [PERSON] et al. proposed a collaborative and secure relay communication method for UAV clusters, which uses distributed antenna arrays and beamforming techniques to resist the collusion of eavesdroppers, effectively improving the security of data transmission [171]. [PERSON] et al. discussed how to enhance the defense capabilities of UAVs through smart antenna systems and ML algorithms. Smart antenna systems provide directional gain and interference suppression, and ML algorithms identify and mitigate spoofing signals efficiently [163]. [PERSON] et al. studied the application of smart antennas in UAV communication, optimizing the power model of UAVs by integrating AI algorithms to improve their communication efficiency and security in complex environments [172].  
However, antennas designed by traditional AI algorithms may lead to uneven signal propagation, affecting the communication quality of UAVs. UAV communication requires the real-time adjustment of beam direction and transmission strategy, and smart antenna technology may produce ineffective continuous signal transmission, affecting the range time.  
LLMs can address complex multiconditional optimization problems and contribute better design schemes by comprehensively considering the frequency, bandwidth, size, material, and other parameters. Through the prediction capabilities of LLMs [173], the sleep and wake-up strategies of UAVs can be optimized to reduce unnecessary communication and energy consumption while ensuring security. In addition, in multi-UAV scenarios, LLMs can coordinate the beam direction and transmission strategies of multiple UAVs through distributed optimization algorithms.
## 1 Introduction
Specifically, we use these two types of CF methods to obtain user-item representations from different perspectives, and then concatenate the representations learned from the two methods to integrate them into a common deep collaborative filtering framework, so as to obtain a more robust user-item pair joint representation for more accurate prediction. In addition, before inputting the user-item representation vectors into the MLP, we input them into the attention layer, which adaptively assigns weights through the attention mechanism to focus their attention on specific parts. When the latent representation vector of user-items is compressed into a prediction vector, different parts make different contributions. The higher the weight is, the more information the corresponding factor has to recommend. Hence, the attention layer can better capture the hidden information in implicit feedback and improve the expression ability of DNN. Overall, our main contributions are as follows:  
1. We propose a ranking recommendation model, DACR, with the characteristics of fusion MLP and traditional MF, which learns the low-rank relationship for user-items and the mapping functions between user-item representations and matching scores from different perspectives. 2. We integrate the attention mechanism to capture the hidden information within implicit feedback. 3. We research the effect of the dimension of the prediction vectors and the negative sampling ratio on personalized ranking performance through detailed experiments. 4. We conduct numerous and detailed experiments on two public datasets to demonstrate the effectiveness of the DACR model and discuss some possibilities for improvement. The rest of the paper is structured as follows: We review the related works in Section 2 and present our method in Section 3, including details of the network structure and processing of the input data. Section 4 presents the experimental results, and Section 5 concludes the paper and discusses future work.
# Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes
###### Abstract  
To solve the limitations of these models for submicron materials like electrospun nanofiber membranes, a numerical simulation was used to construct a three-dimensional model closer to the actual structure to explore the filtration resistance and efficiency of these membranes. Based on the actual polydisperse electrospun nanofiber filter, the three-dimensional structure (fiber diameter 280 nm-1300 nm, thickness 0.0150 mm-0.0240 mm, and solid volume fraction 11.3-17.7%) was reconstructed by GeoDict software. The filtration resistance was simulated with the FlowDict module (surface velocity 6.89 cm/s, 20 \({}^{\circ}\)C), and the filtration efficiency was calculated with the FilterDict module (2.5 \(\upmu\)m particles, tracking 20,000). The results are compared with the experimental values, [PERSON] empirical formula, Happel model, and Kuwabara model. The results show that the simulated values of filtration resistance are generally higher than the experimental values (deviation \(\leq\) 20%), among which the simulation and experiment have the highest consistency, followed by the [PERSON] formula (such as the relative error of 41.62% at 9% spinning solution concentration), and the Kuwabara model has the largest error (59.86%). The simulated value of filtration efficiency is higher than the experimental value (deviation \(\leq\) 7%), because the model assumes that the particles adhere directly after contacting the fiber, and the actual sliding off is not considered. This study confirms that numerical simulation can efficiently predict the filtration performance of electrospun nanofiber membranes. Therefore, it provides a basis for optimizing material structure by adjusting spinning parameters and promoting the engineering application of submicron filter materials.  
electrospinning nanofibermembranes; filtration performance; numerical simulation Article  
1
## 4 Conclusions  
The design of the RF section of a ladder-shaped multibeam extended interaction klystron operating at 4 kV has been presented. The ladder-type intermediate and input/output resonant cavities operating at the Ka-band are compatible with 3D printing or microfabrication techniques. The RF section has been designed and analyzed using the commercially available 3D electromagnetic simulation software. The dimensions of the RF cavity have been optimized for getting different cavity parameters, such as the resonant frequency and the R over Q. The 3D PIC code simulation results of the proposed RF section have been reported with the estimation of the bandwidth, output power, and gain. The bandwidth of the device is found to be 510 MHz with a stable output power of 375.97 W. These devices can be utilized in high-data-rate wireless communications, such as airborne RADAR, 5G communication, etc.  
All authors contributed to the conceptualization, methodology, analyses, and review and editing. The design and validation were done by [PERSON] The formal analysis was done by [PERSON] and [PERSON]. [PERSON] and [PERSON] prepared the draft manuscript. [PERSON] worked under the guidance of [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by IIT Guwahati Technology Innovation and Development Foundation sponsored by the Department of Science and Technology (DST) grant number [TIH/TD/0303] and The APC was funded by [TIH/TD/0303].  
The authors would like to thank the IIT Guwahati Technology Innovation and Development Foundation sponsored by the Department of Science and Technology (DST) for providing financial assistance to carry out the research work.  
The authors declare no conflict of interest.
# Pedestrian-Crossing Detection Enhanced by CyclicGAN-Based Loop Learning and Automatic Labeling
## 3 System Architecture
### Data Collection from Real-World Driving  
This study aims to enhance the development of the pedestrian-crossing detection system that assists drivers in rapidly identifying pedestrian crossings in real-world driving scenarios. To ensure the training data accurately reflect the driver's visual experience, image collection is conducted during actual driving rather than relying on secondary sources such as Google Street View. Images obtained from online sources often differ in angle, perspective, and context and thus fail to represent the visual conditions encountered  
Figure 1: System architecture.  
by drivers on the road. Moreover, real-world driving across different regions exposes the vehicle to a variety of road designs and local traffic scenarios--such as white-and-green-striped crossings or diagonally painted crossings near school zones--which are rarely represented in publicly available datasets. These rare but critical cases are essential for improving the robustness and adaptability of the detection model. Consequently, our data collection strategy emphasizes capturing authentic road images during on-road driving, supplemented by synthetic images generated through CycleGAN under varying environmental conditions.  
To replicate the driver's perspective, data acquisition devices are mounted with consideration of the average adult driver's eye level, which is approximately 130 cm above the ground. A dashcam is installed behind the rearview mirror of the vehicle, slightly offset to the right and positioned at the standard eye level of the driver. In addition, a smartphone is mounted on the front windshield by a passenger, aligned straight forward at a height of approximately 116 cm from the ground. These two recording devices are selected to approximate the driver's field of view from both central and slightly offset perspectives. During data collection, the vehicle travels at a consistent speed of 40 km/h, maintaining a minimum following distance of 20 m to ensure safety and clear visibility. This setup is designed to maximize the number of pedestrian-crossing instances captured in a single driving session. Together, the vehicle, dashcam, and mobile device constitute the data collection module within the overall system architecture.