esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design
### Page-Based Parallel Write Module  
Through careful analysis of ZRAM, we found that ZRAM cannot handle new requests asynchronously. In other words, a request cannot be processed until the previous request is complete. Moreover, the time-consuming part of the whole process is the data compression and write operations, which are less sensitive to latency than read operations. Therefore, we decouple the process flow and construct a page-based parallel write module, as shown in Figure 5. For a new request, the QZRAM main thread preprocesses it, encapsulates it into the data required by workers in the postprocessing, adds it to the page request queue (First In, First Out), and allocates a worker from the thread pool to postprocess the request. Later, the worker retrieves the request from the shared queue and continues the process. This means that workers perform the compression operation and return asynchronously after the completion of the entire procedure, notifying memory reclamation that the processing of the current page has been completed.  
**Preprocessing:** For a new request, the main thread of QZRAM checks the flag bit of the current request to determine whether it is a delete operation. The request is then traversed and parsed at the virtual page granularity and sliced into the page collection because QZRAM's processing granularity is exactly the page size such as 4 k (1024 bytes). For the pages in these page sets, the first QZRAM main thread calculates the virtual page's QZRAM in-device index, in-page offset, and data length; the second QZRAM main thread incorporates in-device indexes, in-page offsets, data lengths, and other data structures needed for postprocessing into one object of page_list. QZRAM main thread will then add page_req to page_list and continue parsing the remaining physical pages until all the physical pages for the current write request have been parsed. Finally, the QZRAM main thread adds page_list to the FIFO (First In, First Out) queue req_list and allocates worker threads from the thread pool to continue the postprocessing work.  
**Postprocessing:** Once the workers begin running, they loop through the req_list queue to obtain the requests encapsulated by the main thread and then perform write operations for each page in the request. The worker obtains the workflow on the current CPU and uses the compression function of the compression module through the transparent compression entry provided by zstream (compression stream). After the zstream submits the compression request, it goes to sleep for a short time until the compression is complete or times out. Once the compression module returns, the worker immediately resumes and executes the rest of the processing flow. When the process is complete, the worker thread notifies the memory reclaim that the swap-out request processing for the current page has been completed. If the worker accesses the request queue and finds that there are no pending requests in the queue, the worker actively goes to sleep and releases the CPU, waiting for the main QZRAM to wake up and reenter the execution state.  
Figure 5: Page-based parallel write module.
## 2 Problem Description  
### Colored Noise  
Colored noise represents a class of random signals that deviate from the uniform power spectral density of white noise. This term encompasses noise signals whose power spectral density (PSD) varies as a function of frequency, leading to a non-flat spectral profile when plotted on a logarithmic scale.  
Colored noise can be subclassified based on the specific pattern of its PSD variation across frequencies. Pink noise exhibits a power spectrum that decreases linearly with frequency, adhering to a \(-\frac{1}{f}\) power law, resulting in equal power per octave. Brown noise, also referred to as red noise, demonstrates a PSD that decreases quadratically with frequency, conforming to a \(-\frac{2}{f}\) power law, thus emphasizing the lower frequencies more prominently. Blue noise, distinguished by its increasing PSD with frequency, often following a steeper power law compared to pink noise, concentrates its energy in higher frequencies. Violet noise, the most steeply increasing in PSD with frequency among the colored noises, is concentrated in the very-high-frequency range.  
In this paper, the signal captured by the array is significantly disrupted by colored noise. We show the vector components of the disrupted signal matrix and the frequency spectrum of corresponding noise for analysis. As depicted in Figure 1, all visualized values are linearly represented. The four subplots on the left show the results of signal components being disturbed by red, blue, pink, and purple noise, respectively. The horizontal axis represents the number of snapshots, i.e., time variation, and the vertical axis represents the amplitude values of complex signals at different snapshots. The four corresponding subgraphs on the right show the spectra of red, blue, pink, and purple noise, respectively. Due to the application of Fourier transform for frequency domain transformation, the curves exhibit symmetry. The horizontal axis represents frequency in hertz (Hz), and the vertical axis represents the amplitude distribution of the signal at different frequencies in decibels (dB). Colored noise presents different spectra, which means that different filtering algorithms need to be applied to different types of noise. Therefore, our goal is to apply an adaptive filter to preprocess the signal.
## 4 Results and Discussion
### Research Limitations and Prospects  
The major limitations of this research include two facets, including (1) good classification results compared to the studies, which include LiDAR data combined with very high-resolution satellite imagery [76; 88; 89; 90], and (2) uncertainty concerning the reference data since traditional mapping of urban green spaces (UGS) relies on field measurements, which can be time-consuming, and UGS can change quickly over time [30]. Therefore, two heatmaps were generated to identify and visualize newly discovered allergenic zones. Figure 4 clearly shows some areas not included in the reference dataset (Figure 4; first row). Later, they were detected through various classification scenarios (Figure 4; second row). The reference data were retrieved from the Web Map Service (WMS) of the Green Cadastre application, and the inventory of allergenic tree species included only the public area. A subtraction of the previously described heatmaps (i.e., ground points map minus CS3 map) resulted in a discrepancy map that shows newly detected zones where allergenic trees are located (Figure 4; third row).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**MDA**} & \multicolumn{2}{c}{**MDG**} & \multicolumn{2}{c}{**VSURF**} \\  & **OA** & **K** & **OA** & **K** & **OA** & **K** \\ \hline \multirow{3}{*}{**CS3**} & RF & 69.07 & 0.59 & 68.66 & 0.58 & 69.68 & 0.60 \\  & XGB & 67.79 & 0.57 & 68.46 & 0.58 & 68.81 & 0.58 \\ \cline{1-1}  & MLP & 62.92 & 0.51 & 59.68 & 0.46 & 57.82 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy results for classification scenario 3, based on different FS methods.  
Although airborne sensors currently obtain imagery with superior spectral and spatial resolution, the unique potential of satellite sensors for conducting recurrent observations of the same area presents promising prospects for enhanced multi-temporal data acquisition and vegetation phenology-based mapping [13]. The integration of satellite imagery with additional data types, such as LiDAR or aerial photography [88; 91], can further augment these capabilities. Furthermore, these datasets need to be used with deep learning (DL) methods, e.g., [PERSON] et al. [89] used high spatial resolution imagery in combination with LiDAR data for tree species classification in a complex urban environment, along with a DenseNet DL classifier. For the classification of eight tree species, DenseNet significantly outperformed RF and SVM regardless of training sample size.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Connectivity  
Connectivity is one major goal of node clustering in CR-WSNs. Ideally, a CM is able to communicate with its CH, either in one hop or in multiple hops, in intra-cluster communications. In inter-cluster communications, nodes can communicate via gateway nodes or via CHs.
## 5 Experiments and Results
### Experimental Results  
To validate the viability of our proposed framework for analyzing UAV imagery crack datasets, this study employed four prominent deep learning algorithms (Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) for conducting pavement crack object detection and a comparative analysis. The experiments were conducted using identical hardware and software environments, with consistent iteration numbers, training datasets, validation datasets, and test datasets. The results were evaluated based on the model performance during execution, the recognition accuracy of the models, and variations in crack category classification.  
Figure 9: Loss plot of the YOLOv5s model (the optimal iterations number is 200).
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
###### Abstract
The symbols \(\mathbb{R}^{1\times C}\) and \(\mathbb{R}^{C}\) denote the space of \(C\)-real-number row vectors and the space of \(C\)-real-number column vectors, respectively. The symbol \(\mathbb{R}^{H\times W\times C}\) denotes the space of 3D arrays (of real numbers) whose height, width, and channel number are equal to \(H\), \(W\), and \(C\), respectively. Tables 1 and 2 summarize the main symbols used in this paper. \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Definition** \\ \hline \(\mathbf{g}^{(n)}\in\mathbb{R}^{C}\) & A vector of channel-wise statistics according to the local feature maps \(\mathbf{A}^{(n)}\) at the \(n\)-th branch. \\ \(\mathbf{h}^{(n)}\in\mathbb{R}^{C}\) & A vector of channel-wise statistics according to the addition of \(\mathbf{g}^{(n)}\) and \(\mathbf{u}\) at the \(n\)-th branch. \\ \(\mathbf{s}^{(n)}\in\mathbb{R}^{C}\) & A vector of channel weights for the local feature maps \(\mathbf{A}^{(n)}\) at the \(n\)-th branch. \\ \(\mathbf{u}\in\mathbb{R}^{C}\) & A vector of channel-wise statistics according to the pre-merged feature maps \(\mathbf{B}\). \\ \(\mathbf{v}\in\mathbb{R}^{C}\) & A vector of channel weights for the pre-merged feature maps \(\mathbf{B}\). \\ \(\mathbf{A}^{(n)}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of local feature maps at the \(n\)-th branch. \\ \(\mathbf{B}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of pre-merged feature maps. \\ \(\mathbf{P}^{(n)}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of channel-weighted feature maps according to the local feature maps \(\mathbf{A}^{(n)}\) at the \(n\)-th branch. \\ \(\mathbf{Q}^{(n)}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of channel-weighted feature maps according to the addition of \(\mathbf{P}^{(n)}\) and \(\mathbf{B}\) at the \(n\)-th branch. \\ \(\mathbf{R}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of channel-weighted feature maps according to the pre-merged feature maps \(\mathbf{B}\). \\ \(\mathbf{X}^{(n)}\in\mathbb{R}^{1\times W\times C}\) & A 3D array of sensor data at the \(n\)-th branch (obtained from the \(n\)-th IMU). \\ \(\mathbf{Y}^{\delta\delta}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of channel-weighted and merged feature maps, which is the output of the MSE feature fusion with global channel attention. \\ \(\mathbf{Y}^{\delta\epsilon}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of channel-weighted and merged feature maps, which is the output of the MSE feature fusion with local skip connections. \\ \(\mathbf{Y}^{\delta\epsilon}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of channel-weighted and merged feature maps, which is the output of the MSE feature fusion with local skip connections. \\ \(\mathbf{Y}^{\delta\epsilon\epsilon}\in\mathbb{R}^{1\times L\times C}\) & A 3D array of channel-weighted and merged feature maps, which is the output of the MSE feature fusion. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of main symbols used in Sections 4, 5.1 and 5.2.
## 1 Introduction
Due to its good physical, chemical and mechanical properties, it has been also used in manufacturing of bio-composites, lightweight and insulating concretes and insulation mats in the construction industry. It ensures better elasticity and higher bearing capacity than solid wood, thus it can easily replace the traditional oriented strand board (OSB)-gypsum-water/air barrier-insulation wall composition [22; 25]. Hemp based materials are used either raw (shive or fiber, thermal insulation) or processed (together with glues, polymer matrices or other cohesion improving agents). Hemp shive and silica sol based novel building composites were investigated using gas chromatography-mass spectrometry (GC-MS). The yield of total extractives reached 6.23% (dry wt.%) followed by low density and enhanced water resistance of a composite [26]. Hemp shive mixed with slaked lime (calcium hydroxide) provides a building material which is solid, durable, soundproof, mold resistant and highly usable bio-based insulation. To become a building and construction material, hemp shive requires minimum processing. Its use represents a more environmentally-friendly way comparing to traditional building materials based on iron or glass, leading to buildings' ecological balance improvement throughout their entire life cycle. Furthermore, lime-hemp materials contribute to CO\({}_{2}\) emissions reduction. CO\({}_{2}\) is absorbed while hemp is planted as well as during the process when slaked lime is curing into limestone (calcium carbonate) again [22; 27; 28]. Since VOCs presence in indoor air is a case of concern (regarding sick building syndrome), confirmation of VOC emissions from building products is needed [16]. The aim of this study was to determine hemp shive (_Cannabis sativa L_.) VOC emissions before and after simple chemical treatments, and to compare VOC emissions intensity with spruce strands' (_Picea abies_).
## 3 Discussion
### Limitations and Future Prospects  
Compared to univariate and multivariate regression models, the random forest algorithm demonstrates higher robustness and predictive ability. It is better equipped to handle nonlinear problems and accurately depict the relationship between grassland biomass and modeling variables [49; 50]. However, these machine learning models have some limitations and uncertainties. The random forest algorithm prediction is the average of all decision trees, which tends to converge to the mean of the training data, leading to an overestimation or underestimation of AGB [51]. The physical mechanisms of the random forest algorithm for estimating AGB are unclear, but ecosystem models and physical models can better explain the spatiotemporal variations in AGB [52]. Furthermore, the fragmentation of altitude and grassland type also affects the layout of sampling points and impacts the accuracy of the estimation model [53]. Xinjiang's grassland AGB exhibits significant spatial variation owing to its intricate topographical and climatic conditions. This study considered seven variables and future research should incorporate additional indices such as vegetation index variables [50; 53] and climate variables [54] to improve the accuracy of the model.  
To better understand the distribution and growth conditions of different grassland types in Xinjiang, it is crucial to continue collecting AGB data and increase the sample size. A combination of machine learning, ecosystem, and physical models should be explored, and more drivers should be screened for different grassland types to develop high-precision AGB inversion models. This will help generate more accurate spatiotemporal distribution maps of AGB in Xinjiang, which is crucial for long-term monitoring and inventory of grassland resources.
# Automatic Medical Image Segmentation with Vision Transformer
## 4 Experiments and Discussions
### Experimental Setting
#### 4.1.1 Baselines  
For the experiments on infection region segmentation, the proposed network is compared with traditional medical segmentation models such as U-Net [2], Attention U-Net [36], and Inf-Net [60]. This benchmarks the proposed method against widely used medical segmentation architectures, including a standard U-Net baseline and prior attention-based models.
# Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers
## 2 Problem Definition  
Although both estimations of MLC and DLC are crucial tasks, this paper focuses on the MLC performed at on-ramp as shown in Figure 1. As the driver on the acceleration lane must perform lane-changing before reaching the end of the lane, it is expected that dangerous lane changes can sometimes occur even when the driving safety is not ensured. Moreover, drivers on the mainstream lane recognize that vehicles on the acceleration lane must perform a lane change. Therefore, the interaction between drivers should be thoroughly discussed focusing on MLCs at the on-ramp.  
Figure 2 depicts our target situation. The lane-changing vehicle is defined as the target, and it is represented in red. The proposed method monitors the closest vehicle in the front and behind the target on the mainstream lane. The preceding vehicle on the mainstream lane is defined as the lead, and the following vehicle on the mainstream is defined as the rear. The two vehicles are indicated in blue. The target vehicle should conduct lane-changing to the mainstream lane before reaching the end of the acceleration lane. Drivers may perform a lane change after they decide the goal where the vehicle cuts-in. In this paper, the front space of the lead vehicle is defined as \(A\), the space between the lead and rear vehicles is \(B\), and the behind space the rear vehicle is indicated as \(C\) as shown in Figure 2.  
Lastly, \(D\) indicates that the target vehicle maintains the current lane. When the \(D\) is estimated as a goal, it means that the target driver keeps the current lane until the driving safety is ensured. The target driver should consider the interaction with the lead and rear vehicles and assess the collision risk with them. The proposed method starts the goal estimation when the target vehicle appears on the acceleration lane. The estimation is performed at each time step until the target vehicle crosses the center line between the acceleration and mainstream lanes.  
This paper excludes the case that there is no vehicle on the mainstream lane since there is no interaction in this situation. The reference distance from the target is set to 50 m. If there is no vehicle within the distance from the target, the case is excluded. However, if either vehicle exists, the case is included in the consideration.
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 2 Materials and Methods
### Study Area and Road Units  
This study was conducted on Zhongda Road, located in Zhongli District, Taoyuan City. Zhongda Road spans from Henan Road to Zhongzheng Road, with a total length of 570 m. The road consists of two directional lanes, each measuring 3.6 m in width, resulting in a total pavement area of 4104 square meters (Figure 2a).  
To facilitate the analysis of the Pavement Condition Index (PCI), the road was divided into roadway units of 100 m each. For segments shorter than 100 m but greater than 50 m, they were categorized as independent roadway units. Considering both the forward and reverse directions, a total of 12 roadway units were defined for this study. The inspection start and end points are illustrated in Figures 2b and 2c, respectively.  
Figure 1: Flowchart illustrating the procedure for calculating the Pavement Condition Index (PCI) adapted from ASTM D6433-23 [1]: (**a**) the process of collecting basic road data and identifying distress types and severity levels; (**b**) calculation of distress density and deduct values (DVs), followed by adjustment to corrected deduct values (CDVs); (**c**) selection of the high corrected deduct value (HCDV) and computation of the final PCI score.
## 4 Methods That Use Large Language Models
### LLM-Based Fake News Detection
#### 4.3.3 Contextual Analysis  
Understanding the context of news articles is crucial for effective fake news detection. Various innovative approaches have been developed to leverage the capabilities of large language models (LLMs) for contextual analysis in this domain.  
In the work by [PERSON] et al., the Adaptive Rationale Guidance (ARG) network is proposed for fake news detection by combining the advantages of small and large language models [75]. The goal of ARG is to enable small language models (SLMs) to select useful rationales as references for final judgments. To achieve this, ARG encodes the inputs using SLMs, which utilize LLM-generated informative rationales on news content from various perspectives, such as textual description, commonsense, and factuality, to improve performance. The small and large LMs collaborate via news-rationale feature interaction, LLM judgment prediction, and rationale usefulness evaluation, allowing for rich interaction between news and rationales. The obtained interactive features are aggregated to make a final judgment. Additionally, a rationale-free version, ARG-D, was developed for cost-sensitive scenarios without querying LLMs, demonstrating the superiority of both ARG and ARG-D in experiments.  
[PERSON] et al. introduced SheepDog, a style-agnostic fake news detector robust to news writing styles [76]. This detector achieves adaptability through LLM-empowered news reframing, which customizes each article to match different writing styles using style-oriented reframing prompts. The style-agnostic training enhances the detector's resilience to stylistic variations by maximizing prediction consistency across these diverse reframings, focusing on content rather than styling.  
[PERSON] et al. developed DELL, a framework that identifies three key stages in misinformation detection [77]. First, LLMs generate synthetic reactions to new articles to represent diverse perspectives and simulate user-news interaction networks. Second, LLMs generate explanations of news articles for proxy tasks, refining feature embeddings with these explanations. Finally, DELL employs three LLM-based strategies to selectively merge the predictions of task-specific experts, enhancing calibration. Extensive experiments on seven datasets with three LLMs demonstrate that DELL outperforms state-of-the-art baselines by up to 16.8% in macro F1-score.  
Table 3 presents a comprehensive comparison of studies that employ LLMs for Fake News Detection.