esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 3 Methods and Result
### Image-Based Product Quality Inspection and Feature Matching Techniques  
#### 3.1.1 Rotation and Scale Invariance in Image Comparison Using SIFT Algorithm  
As shown in Figure 5, after loading two images, the SIFT (scale-invariant feature transform) algorithm is used to detect keypoints within the images and calculate descriptors for those keypoints. SIFT is an algorithm designed to detect keypoints that are invariant to scale, rotation, and illumination changes, enabling it to reliably find consistent features even under various transformations [38]. The first step of the SIFT algorithm is to locate keypoints in the scale space. This is achieved by using a Gaussian filter to process the image at different scales, allowing the detection of important keypoints by identifying extrema (maxima and minima) at each scale. The process of finding keypoints in the scale space, where a Gaussian blur is applied, utilizes the difference of Gaussian (DoG) method [39]:  
\[\mathrm{D(x,y,\sigma)}=(\mathrm{G(x,y,k\sigma)-G(x,y,\sigma)})*\mathrm{I(x,y)} \tag{1}\]  
Here, \(\mathrm{G(x,y,\sigma)}\) represents the image with Gaussian blur applied at scale \(\sigma\), \(\mathrm{I(x,\,y)}\) is the original image, and \(\mathrm{k}\) denotes the scale factor. In this process, the differences between images at each scale are computed to identify extrema (maxima/minima), which are detected as keypoints [39; 40; 41]. By calculating the gradient magnitude and orientation of the pixels surrounding each keypoint, a principal orientation is assigned to each keypoint to ensure rotational invariance. The gradient magnitude \(\mathrm{m(x,y)}\) and orientation \(\mathrm{\theta(x,y)}\) are computed using the following equations:  
\[\mathrm{m(x,y)}=\sqrt{\mathrm{L(x+1,y)}^{2}-\mathrm{L(x-1,y))}+\left(\mathrm{ L(x,y+1)-\mathrm{L(x,y-1  
Here, \(\rm p_{i}\) and \(\rm q_{i}\) represent the iii-th components of the two descriptors, respectively. The smaller the Euclidean distance, the more similar the two descriptors are considered, enabling the matching of keypoints between the two images. Once the matching is completed, the rotation angle between the two images can be estimated, and this angle is calculated using the following equation:  
\[\theta=\tan^{-1}\left(\frac{y_{2}-y_{1}}{x_{2}-x_{1}}\right) \tag{5}\]  
Here, (\(\rm x_{1}\), \(\rm y_{1}\)) and (\(\rm x_{2}\), \(\rm y_{2}\)) represent the coordinates of the matched keypoint pairs in the two images, respectively. Using this equation, the rotation angle between the two images can be estimated, allowing for rotation correction and image restoration based on this information. Additionally, by calculating the distance between the matched keypoints, the scale variation between the two images can be assessed, enabling the determination of whether the image has been scaled up or down.
## 4 Materials and Methods
### Data Processing and Statistical Analysis  
Raw data was processed by the Compound Discoverer 3.0 (Thermo Fisher Scientific) software with aims to identify metabolites present in the samples. Detected metabolites with signal-to-noise >3 and peak intensity >1,000,000 were subjected to analysis. Intensity tolerance was set as 30%, and RT tolerance as 0.2 min. QC-based area was used for correction (min 50% coverage, max 30% RSD in QC, normalization by constant mean). A data filtration step removed 49% of the 7907 features and 29% of the 6114 features in positive and negative ionization modes, respectively. After peak alignment, gap filling was applied to fill missing values by a very small peak at the level of spectrum noise for the compound. The obtained table with accurate masses of annotated compounds was inserted into SIMCA 15 (Umetrics), where principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were performed using unit variance (UV) scaling. Statistically significant compounds in the projection used in OPLS-DA models were selected using Variable Importance in Projection (VIP) scores >1 and then filtered, taking into account the biological relevance and consistency of the fragmentation spectrum with databases. Pathway analysis was carried out with MetaboAnalyst 4.0, using Homo sapiens Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway database. The analysis used the hypergeometric test as an enrichment method and relative-betweenness centrality for topology analysis, and the results were presented in the form of scatter plots.
# ML-Based Pain Recognition Model Using Mixup Data Augmentation
## 3 Methodology
### Preprocessing  
We performed a series of preprocessing steps to prepare the electrodermal activity (EDA) signal data to be then used for pain recognition by the machine learning model. These steps were carried out using the neurokit2 library and involved loading the raw EDA signal, cleaning it, and decomposing it into tonic and phasic components. These steps are described in detail below.  
1. Data Loading: The raw EDA signal data were initially read from the BioVid Heat Pain Database (BVDB). These data were available in an Excel file. The file contained a column labeled \"gsr\" representing the recorded galvanic skin response (GSR) data, which were extracted for further processing.
2. Signal Cleaning and Decomposition: The EDA signal was processed using the following operations: * Filtering: The EDA signal was low-pass-filtered with a Butterworth filter, applying a cut-off frequency of 0.2 Hz. The objective of filtering is to remove the high-frequency noise from the EDA data, preserving the low-frequency components that are indicative of physiological arousal. * Segmentation: The filtered EDA signal was segmented into fixed-length windows of 4.5 s each, with a shift of 4 s from the onset of each segment. This segmentation structure allows for the extraction of response windows aligned with expected physiological changes following a stimulus. * Tonic and Phasic Component Extraction: The EDA signal was further decomposed into its tonic (baseline) and phasic (response-related) components. The tonic component reflects the baseline level of EDA over time, while the phasic  
Figure 3: Heat stimulus and pauses between stimuli.  
Figure 2: Elicited pain levels.  
component captures rapid, event-related fluctuations. This decomposition provides two distinct features for distinguishing between baseline physiological activity and stimulus-related responses, providing two distinct features for the machine learning model.  
The preprocessed signal using the above steps is shown in Figure 4.
###### Abstract  
Image segmentation plays a key role in remote sensing, particularly in landslide image segmentation. Remote sensing of landslide images is challenging due to their single category but complex detailed features, making it difficult to determine landslide boundaries and extents. Traditional segmentation methods often yield poor results for such images. To address these challenges, we propose the Large Kernel Nested UKAN (LKN-UKAN). The key contributions and findings are as follows. (1) We embed a Tokenized KAN Block (Tok-KAN) in U-Net++ to enhance complex feature modeling, leveraging Tok-KAN's strengths in nonlinear modeling and relationship capture. (2) We design a Dual Large Fusion Selective Kernel Attention (DLFFSKA) module to improve global perception and contextual information capture. (3) We apply transfer learning to transfer feature-rich remote sensing image features to landslide data, significantly improving segmentation performance. The experimental results demonstrate that the LKN-UKAN achieved significant improvements in remote sensing landslide image segmentation compared with state-of-the-art methods, particularly in terms of boundary accuracy and feature representation.  
Keywords:image segmentation; remote sensing landslide images; nonlinear learning; attention mechanism; transfer learning _Article_
## 3 Framework for Automated Earthwork Network Architecture (ENA)  
### Overview  
The proposed framework (Figure 1) aims to provide automated earthwork quantity estimation with high accuracy rates. The framework comprises three key modules: Topological Feature Collector, ENA Model Development, and Performance Evaluation.  
Module-1 (Topological Feature Collector) extracts and organizes critical earthwork features from road cross-sectional designs or similar datasets. This module collects relevant topological data such as cut and fill areas, slope information, and subgrade conditions, converting them into a structured and trainable format. The data typically come from unstructured sources like CAD drawings, representing informal data commonly used in civil engineering. The key role of this module is to pre-process and tokenize the input data, transforming it into feature sequences that can be efficiently fed into machine learning models. This step is essential to ensure that the subsequent models can effectively learn from the topological features and make accurate predictions.  
Module-2 (ENA Model Development) involves the design, training, and implementation of different deep learning models, including M1 (Multi-Layer Perceptron-based ENA), M2 (LSTM-based ENA), M3 (Transformers-based ENA), and M4 (LLM-based ENA) architectures. This module utilizes the pre-processed data from the Topological Feature Collector to train these models in predicting earthwork quantities. The primary objective here is to develop models that can accurately classify and estimate quantities from the topological features extracted. LLM-based models are particularly emphasized in this study as they are designed to handle sequences of tokens, making them highly effective for processing informal and unstructured data. Each model's architecture is fine-tuned to optimize performance, considering factors like loss reduction, accuracy, and convergence speed.  
Figure 1: Framework for automated Earthwork Network Architecture.  
Module-3 (Performance Evaluation) assesses the accuracy and efficiency of the developed ENA models. In this phase, the models are tested on both training and test datasets to evaluate their ability to predict earthwork quantities. The evaluation metrics include loss (how well the model's predictions match actual values), Quantity Takeoff Classification accuracy (QTCacc), and model performance in terms of convergence speed. Additionally, the resource consumption (e.g., GPU usage) and computational efficiency of each model are considered. This module ensures that the developed models not only perform well on the training data but also generalize effectively to new, unseen datasets, making them applicable in real-world civil engineering scenarios. Special attention is given to comparing the performance of LLM-based models with traditional deep learning models like the MLP and LSTM, highlighting the advantages of using LLMs for complex, unstructured data. Figure 1 depicts the overall flow of the proposed framework, and detailed descriptions of each module are provided in the following section.
## 5 Proposed Protocol
### Initialization  
At this stage, the BS makes all the nodes ready. Each node has its own unique ID. The ID of the sensor node i is specified as IDSNi. The ID of cluster node i is specified as IDCNi, while the ID of the BS node is specified as IDBS. The BS generates public and private keys for SN-CN and itself. The public and private keys of the BS node are specified as PBKBS, PVKBS, the public and private keys of the CN node are specified as iPBKCNi, PVKCNi, and the public and private keys of the SN node i are specified as PBKSNi, PVKSNi.  
Keys are used to verify the integrity of messages sent during verification and registration. The ID and PVK information of the sensor and cluster nodes are transmitted in the form of ID, and PVK in a secure environment to be stored at the relevant nodes. IDcard is produced for CNs. IDcard includes a signature created using IDCNi, PVKBS, and the Elliptic Curve Digital Signature Algorithm (ECDSA). As it is known, the Elliptic Curve Digital Signature Algorithm (ECDSA) is a Digital Signature Algorithm (DSA) that uses elliptic curve encryption. In Figure 3, the sequence diagram of the initialization part is given.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 2 Related Work
### Knowledge Flow in Video Temporal Grounding  
The utilization of knowledge flow in multimodal tasks is gaining momentum, predicated on the concept of disseminating knowledge across different models or modalities to augment collective model capability [25]. Present investigations can be bifurcated into two principal streams: knowledge distillation and knowledge transfer. Via knowledge distillation, the expertise of a large model is conferred upon a more compact model, ensuring the latter maintains commendable performance while remaining computationally efficient [26]. By integrating both local and global modality information and applying distillation techniques, [PERSON] et al. improved the feature representation of video understanding models, thereby achieving greater accuracy in temporal localization tasks [16]. Other works leverage the robust representational capacity of large language models (LLMs) to generate weakly supervised pseudo-labels via distillation, thereby offering a novel optimization pathway for VTG tasks [15]. Furthermore, [PERSON] et al. introduced a distillation approach employing multiple teacher models to collectively steer the student model's learning, significantly boosting temporal localization accuracy for VTG [9]. Diverging from the principles of knowledge distillation, knowledge transfer focuses on facilitating cross-modal information exchanges that foster cooperative learning among distinct modalities. [PERSON] et al. proposed a vision-prompted knowledge transfer method that integrates visual and textual modality features, significantly enhancing multimodal reasoning in video question answering tasks [17]. [PERSON] et al. devised a dynamic knowledge transfer mechanism that enables cross-modal knowledge sharing between visual and textual predictors, effectively bolstering temporal localization and answer generation in video question answering tasks [18]. Although existing knowledge flow methods moderately improve model performance, most studies are confined to unidirectional knowledge transfer (e.g., from large to small models) or cross-modal knowledge migration, failing to fully explore the potential of bidirectional cooperative learning and thus limiting the depth and breadth of knowledge sharing. Additionally, the phenomenon of gradient contention within multi-model collaborative learning undercuts the advantages of coordinated optimization, thus imposing further limitations on knowledge-sharing efficacy and holistic performance improvements.
# Experimental Investigation on Effect of Temperature on FDM 3D Printing Polymers: ABS, PETG, and PLA
## 1 Introduction
In aerospace, 4D-printed structures can adapt to changing environmental conditions, enabling shape-changing wings or morphing structures. In the medical field, 4D-printed implants and scaffolds with controlled drug release capabilities have been explored for personalized medicine and tissue engineering applications [24]. Architectural applications include adaptive structures and responsive facades that can self-assemble or change shape based on environmental factors. Prior research has not compared different 3D printing materials with each other for 4D applications. In this paper, we focus on a comprehensive experimental effort to understand the behavior of commercially available FDM 3D printing filaments (ABS, PETG, and PLA). Based on the current studies conducted in the field, a comprehensive study that focuses on all three different types of filaments, with different ranges of temperature stimuli, with the analysis of not only radius of curvature but also velocity, is conducted. This paper is novel since it is focused on answering the following questions: (a) Which of the filaments respond more drastically to increased temperature? (b) Does cyclic heating change the material properties of the filaments? (c) Can we develop a model that can predict the radius of curvature of a flat 3D-printed part after exposing it to heat? To answer these questions, a set of samples are 3D-printed using ABS, PETG, and PLA with consistent geometry. Each sample is heated and cooled three times for the three different temperatures of 85 \({}^{\circ}\)C, 105 \({}^{\circ}\)C, and 125 \({}^{\circ}\)C. During the heating and cooling phases, the motion of the 3D-printed parts is captured using a camera. The videos are image-processed by providing quantitative Cartesian coordinates to the end points of the curled parts. For each sample, the displacement and velocity in both x and y coordinates are captured and analyzed. Additionally, the radius of curvature for each sample is captured.
## 3 Results and Discussion
### Design Review with Back-Analysis
#### 3.3.2 Sensitivity and Parameter Analysis
It was also determined that the effect of the GSI value on displacement was greater in moderately weathered units than in completely weathered units. Additionally, it is clear that shear stress is controlled by unit weight in completely weathered rocks. Notably, defining a low GSI value for a moderately weathered rock mass reduces shear stresses considerably. The GSI and disturbance factor have a great impact on the factor of safety, regardless of the weathering degree. While the GSI value directly affects the factor of safety in both directions, it is effective only when the disturbance factor is reduced by 25%. These findings reveal that as the weathering degree decreases, the effect of the GSI on displacement and shear stress increases noticeably. As a result, GSI values were reviewed since we found that they were effective for determining displacement and stability on weathered rock slopes.
# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 1 Introduction
### Related Works
#### 1.1.3 Approaches Based on Different Sensors  
An alternative homing method (non vision-based) was proposed by [14]. The proposed method in [14] uses electromagnetic waves emitted and received by means of large coils placed in both the docking station and the AUV. The overall system makes it possible to compute bearing to the dock at distances up to 35 m in seawater. Bearing was also employed by [15] using an USBL (ultra-short baseline) sensor carried on the vehicle. The control law derived just has to ensure that the bearing angle is null along the trajectory to home. However, it is well known that, in USBL systems, the angle resolution decreases with the distance and the sensor may not be omnidirectional, thus being possible to measure angles and ranges only when the beacon is located at a given angular position, with regard to the USBL sensor. In [16], an extremum-seeking algorithm was introduced to find the maximum approach rate to a beacon. Nevertheless, the method in [16] requires initialization otherwise the AUV may be driven to a stable equilibrium point in the opposite direction of the beacon. In works such as [17], a priori information on the area is used to generate an artificial potential field combined with a sliding mode control law to home an AUV to its docking station.
# The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures
## 2 Materials and Methods
### Collection of COVID-19 Samples
#### 2.1.2 Human Participant Recruitment and Follow-Up Health Survey  
People recruited on social media (from July 2020 to April 2021) were asked to participate in the study by wearing a T-shirt overnight if they had been tested for COVID-19 within the previous 48 h. Participants filled out a survey to determine their eligibility. Data collected included: age, type of COVID-19 test performed, whether the test was performed within the preceding 48 h, COVID-19 test results (positive/negative/inconclusive) and when the results were received, presence of symptoms in the past 7 days, COVID-19 vaccination history, contact information, T-shirt size, how they heard about the study, and their preferred method of communication. Potential candidates were disqualified if they were younger than 18 years of age, not living in the United States, allergic to cotton, did not provide contact information, were tested via rapid antigen COVID-19 test instead of a PCR test, or if they were not tested for COVID-19 within the past 48 h.  
Eligible participants were shipped an unwashed cotton T-shirt, assigned a case identification number (case I.D.), and were categorized as: positive symptomatic, positive asymptomatic, negative symptomatic, or negative asymptomatic. Informed consent was obtained via electronic signature (Qualtrics) or a scanned copy of the consent form with a written signature. Participants provided a de-identified copy of their PCR test results.  
Following receipt of the T-shirt and instructions (wear it overnight without washing it or wearing any other type of nightshirt under it), participants were asked to complete a health survey confirming test results and symptomology, including duration that the shirt was worn, use of any fragrances, any humans or animals with whom they shared their bed, a rating of their current stress level on a scale of 1 (little) to 10 (maximal), current or past medical conditions, and current medications. The results of this health survey can be found in the supplementary information (Tables S1 and S2). The term \"symptom count\" is used to reference the number of separately accounted symptoms disclosed by the participant; for example, a participant who noted coughing and headaches as symptoms would have a symptom count of two.
# Investigation of IEEE 802.16e LDPC Code Application in PM-DQPSK System
###### Abstract  
With the development of the Internet and information technology, optical fiber communication systems need to meet people's information demand for large capacity and high speed. High-order phase modulation and channel multiplexing can improve the capacity and data rate of optical fiber communication systems, but they also bring the problem of bit error. To improve the transmission quality and reliability of optical fiber communication systems, forward error correction (FEC) coding techniques are commonly used, which serve as the fundamental approach to enhance the quality and reliability of fiber optic communication systems, ensuring that the received data remain accurate and reliable. The FEC in optical fiber communication systems is divided into three generations. The first generation FEC is mainly hard decision codewords, represented as RS code. The second generation FEC is mainly cascaded code, which stands for interleaved cascaded code. The third generation of FEC mainly refers to soft decision codes, which are represented as low-density parity-check (LDPC) codes. As a kind of FEC, LDPC codes stand out as pivotal contributors in the field of optical communication and have gained remarkable attention due to exceptional error correction performance and low decoding complexity. Based on IEEE802.16e standard, LDPC code with specific code length and rate is compiled and simulated in MATLAB and VPItransmissionMaker 10.1 and successfully incorporated into polarization multiplexed differential quadrature phase shift keying (PM-DQPSK) coherent optical transmission system. The simulation results indicate that the bit error rate (BER) can be reduced to \(10^{-3}\) when the optical signal-to-noise ratio (OSNR) reaches 14.2 dB, and the BER experiences a reduction by nearly three orders of magnitude when the OSNR is 17.2 dB. These findings underscore the efficacy of LDPC codes in significantly improving the performance of optical communication systems, particularly in scenarios demanding robust error correction capabilities. This study provides valuable, significant results regarding the potential of LDPC codes for enhancing the reliability of optical transmission in real-world applications.  
optical fiber communication; FEC; IEEE802.16 eLDPC; PM-DQPSK; OSNR