esa-sceva/satcom-chunk-collection huggingface dataset:
# Origin and Function of Structural Diversity in the Plant Specialized Metabolome
## 1 Introduction
It has therefore been proposed that a primary function in the redox chemistry of specialized metabolites is in accordance with weak selective constraints on the carbon structure, and high structural diversity as a result [18]. To help readers form an opinion on the above, seemingly contradicting theories, we review here three main sources of structural variation in the plant specialized metabolome. First, we describe the major pathways, each starting from a few distinct primary metabolism precursors and leading to four distinct basic carbon skeleton core structures: polykketides and fatty acid derivatives, terpenoids, alkaloids and phenolics. Second, we address the structural diversity due to modifications to the core skeleton, such as changes in hydrogenation level, hydroxylations and methoxylations, specifically for flavonoids, and we discuss the impact of these modifications on flavonoid antioxidant activity. It should be noted here that, in other classes of specialized metabolites, other modifications of the core skeleton, such as the cyclization or ring cleavage, the addition or removal of skeletal atoms or bond migration, are observed but will not be addressed here. Third, we address the role of so-called \"decorative\" reactions, conjugations and polymerizations in the plant specialized metabolome. Interpreting the results of recent metabolite profiling studies in wild type, mutant and transgenic plants, we show how the combination of substrate promiscuity of enzymes, multiple decorations and oxidative coupling can lead to a seemingly endless variation in structures in the plant specialized metabolome.
# Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data
## 2 Data Acquisition and Processing
### Data Processing
#### 2.2.3 Data Filtering  
During the process of data acquisition, multiple factors may lead to ranging errors in the LiDAR device. Firstly, multipath reflection echoes may cause errors. Secondly, errors can arise from the equipment calculation process or from the vibrations generated by the vehicle-mounted structure due to road bumps. These errors manifest as abnormal points that deviate from the object's surface and may impact the detection results. Therefore, it is necessary to eliminate them as much as possible during the preprocessing. Various denoising methods for point cloud data are available, including the Laplacian denoising algorithm [22], the denoising algorithm based on moving least squares (MLS) [23], the denoising algorithm based on the mean curvature flow [24], and the denoising method based on neighborhood filtering [25,26]. Although each method has its advantages, the total amount of point cloud data collected in this research is too large, typically comprising more than 500,000 points within a range of about 10 m. Consequently, the efficiency of these methods is insufficient to meet the research needs.  
Therefore, this study employed a statistical filtering approach for each frame of the point cloud data. Typically, a single frame of the point cloud data consists of no more than 4500 points, with each point lying on the same plane. To circumvent the computational challenges arising from the disorderliness of the point cloud data, the positions of these points were sequenced and calculated accordingly. The proposed approach is based on the following principle: a specified number of adjacent points are sought for each point, and the average distance from each point to its adjacent points is calculated. The mean and standard deviation of these average distances is then computed. Since the distance distribution between two points in the point cloud generally follows a Gaussian distribution, a point can be considered as noise if its average distance to neighboring points exceeds the maximum distance. In such cases, the point is removed from the data. The calculation of the maximum distance is presented in Formula (3):  
\[L=M+k\times\sigma \tag{3}\]  
Figure 3: Coordinate system and rotation relationship. (**a**) LiDAR coordinate system. (**b**) Vehicle coordinate system. (**c**) Coordinate system rotation relationship.  
where \(L\) is the maximum acceptable distance between two points, \(M\) is the mean of the average distance between the points, \(k\) is the standard deviation amplification coefficient, and \(\sigma\) is the standard deviation of the average distance between the points. In order to retain accurate data as much as possible, the value of \(k\) was taken as 3.
## 3 Results
### Model-Data Agreement on Monthly and Seasonal Time Scales
The negative biases were improved using the two-leaf Farquhar model with reduced RMSE, especially in the deciduous forests and shrubs. The average biases increased from \(-\)35.2(\(\pm\)24.0) to \(-\)0.8(\(\pm\)21.5) \(\mu\)gC\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) and RMSE decreased from 43.3(\(\pm\)35.8) to 39.0(\(\pm\)32.4) \(\mu\)gC\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) during daytime daytime (6:00-18:00) for forests and shrubs in summer (Figure 4a,b). However, a significant  
Figure 4: Boxplots of bias and root-mean-square error (RMSE) for seasonally averaged diurnal composites simulated by the LUE-SW and the Farquhar model: (**a**) evergreen forests; (**b**) deciduous forests and shrubs; and (**c**) grasslands. improvement by the two-leaf model did not perform in the grasslands (Figure 4c). Note that the estimates by both models had great RMSE during the midday of spring and fall for all biome types, and during the midday of winter for evergreen forests. These results suggest that, whether model structures are complex or simple, the photosynthetic models could not well capture change of GPP consistently in spring and fall for forests and shrubs, and seasonal change for grasslands.
## 2 Named Data Networking and Its Role in IoT Smart Environments
### NDN in a Nutshell
#### 2.1.1 Data Plane  
As shown in Figure 1, each NDN node maintains three tables at the forwarding plane (also known as, the data plane).  
The Content Store (CS) is used for caching incoming data packets. Depending on the scenario and the available storage resources at the node, several decision policies can be implemented, ranging from the simple caching-everything-everywhere strategy to the probabilistic caching and the more advanced popularity-based autonomous or cooperative mechanisms [15]. The caching policy is always coupled with a replacement strategy that is applied when the storage space is full and a new item must be cached. The default replacement policy in NDN is Least Recently Used (LRU).  
The Pending Interest Table (PIT) tracks the transmitted interests together with the incoming/outgoing interfaces. This forwarding state is maintained until the data packets consume the interests, a lifetime expires, or a negative acknowledgement (NACK) is received to advertise an error that occurred along the path.  
The Forwarding Information Base (FIB) maintains the outgoing interface(s) per named prefix. Multiple interfaces may exist per prefix, ranked by routing preferences. Interfaces are called _faces_ in the NDN context. In particular, two face abstractions are identified: the _NetDeviceFace_, which enables communication with the underlying physical networks, and the _AppFace_, which enables communication with the applications.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 1 Introduction
Nevertheless, these studies only consider the impact of communication performance on information fusion without the collaborative optimization of joint radar-communication electromagnetic domain resources. Given the above research background, this article focuses on the JRCO algorithm in the context of the passive localization of targets by DAR. A fully distributed JRCO algorithm was proposed to achieve optimal localization performance under communication performance constraints. Compared with traditional methods, The proposed method could significantly improve the target localization performance of the UAV swarm. The main contributions of this article are as follows:  
1. A target localization system model for DAR is proposed. Compared with traditional DAR system models, the proposed system model focuses on considering the impact of UAV communications coverage on radar detection performance, the impact of UAV communications coverage on the topology of the UAV communication system, and the impact of the UAV location error on target localization performance. 2. A fully distributed JRCO algorithm is proposed. The main idea of this distributed algorithm is to enable each UAV to simultaneously sense the information broadcasted by neighboring UAVs in each time slot and make decisions simultaneously, enabling the joint optimization of radar and communication system parameters, fully utilizing the electromagnetic domain's freedom. Meanwhile, compared to centralized algorithms, distributed algorithms can continue to perform tasks in the event of a few node failures, avoiding the extreme situation of system paralysis caused by a few key node failures. 3. Compared with the current research of [PERSON] et al., numerical simulations have demonstrated that the proposed algorithm can achieve better target localization performance under communication constraints. The article was organized as follows: Section 2 introduces the DAR system model under a passive localization background. Section 3 proposes a fully distributed JRCO algorithm and explains the details of each model. Section 4 proves the effectiveness of the proposed algorithm compared to traditional radar domain resource allocation methods through numerical simulations. Section 5 summarizes the entire work and provides prospects for research in the future.
## 3 Related Work
It also suffers from cell overlap and slot contention in dense deployments, particularly when multiple links compete for limited slotframe space. A\({}^{3}\) introduces traffic adaptivity using EWMA-based load estimation but lacks topology-aware slot segmentation. Its adaptation strategy ignores node depth and subtree size, leading to inefficiencies in multi-hop networks. These shortcomings highlight the need for a solution that integrates hop-based slot allocation, traffic prediction, and link quality awareness to enhance latency, delivery ratio, and energy efficiency in dynamic network environments. Existing autonomous and adaptive scheduling approaches, in worst-case scenarios, may require multiple slotframes to achieve end-to-end packet delivery. Nodes with heavy traffic load result in queue overflow, higher latency, and contention [29; 30; 31]. A 6 TiSCH low-latency autonomous scheduler [32] has been developed to schedule cells for all nodes at a given RPL level in a particular segment. A traffic-aware autonomous scheduler [33] assigns supplementary cells for the heavily loaded nodes. Though they improve performance compared to the existing Orchestra and ALICE schedulers, they lack adaptability to changing topology and traffic. Like Orchestra and ALICE, they also allocate single cells per link in every slotframe, leading to contention probability and packet drop. Ultimately, there is further scope for improvement in the scheduling techniques for reliable data transmission in time-constrained heavy-load networks. The algorithm proposed in this work addresses these challenges by adaptively allocating cells and segmenting slotframes based on traffic demands while also incorporating link performance metrics. It creates a schedule based on the network topology and node density at a specified RPL level.
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 5 Computational Validation  
Two efficient metaheuristics were used to show the efficiency of the proposed GNDO algorithm at locating and sizing PV generation units in monopolar DC distribution networks. The metaheuristics were the Chu and Beasley genetic algorithm (AGCB) and the discrete-continuous vortex search algorithm (DCVSA) [22]. The computational implementation of the metaheuristic optimizers was carried out in MATLAB using our scripts. Regarding the software, Matlab version R2021b was used in the 64-bits version of Microsoft Windows. Hardware-wise, an Intel(R) Core(TM) i7-7700 HQ CPU 2.80 GHz computer with 24 GB of RAM was used.  
A population size of 10 individuals, 1000 iterations, and 100 repetitions of each algorithm was used in order to make an adequate comparison between the proposed GNDO approach and the CGBA and the DCVSA.
# EGMStream Webapp: EGMS Data Downstream Solution
###### Abstract  
The European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service (CLMS), provides free pan-European ground motion data to support local and regional ground deformation analyses. To enhance the accessibility and usability of EGMS products, a new webapp, EGMStream, has been developed using Python and JavaScript for downloading and converting EGMS data. This revised and updated version improves the functionality and performance of the original R-based desktop tool, avoiding the need for a standalone software installation. Users can now simply access the webapp with an internet connection. In addition, the web version enhances data processing by leveraging high-performance server-side computing without relying on personal computer resources. The EGMStream webapp offers advanced features, including the parallel processing of large datasets and extraction of converted EGMS data for areas of interest (AoI) in various GIS-compatible formats. The transition from standalone software to a cloud-based system streamlines the integration of EGMS data into existing workflows, broadens user accessibility, and supports large-scale geospatial analysis. Consequently, this shift promotes the dissemination of these relevant and free available measurement data to a wider audience, including non-expert users.  
EGMStream; webapp; InSAR; ground deformation; EGMS +
Footnote †: journal: Geosciences
# Automatic Construction of Fine-Grained Paraphrase Corpora System Using Language Inference Model
## 4 Automatic Relabelling with Fine-Grained Paraphrase Relations  
We construct the corpora from existing datasets containing sentence pairs with all fine-grained paraphrase relations. These include the language inference datasets as well as paraphrase datasets. After examining the size and sentence relations in various datasets, we focus the construction efforts on two language inference datasets: MNLI and SNLI and three paraphrase datasets: MRPC [1], QQP [12] and STS-B [13].  
**MRPC** The Microsoft Research Paraphrase Corpus [1] consists of 5000+ sentence pairs extracted from Web news. The sentences pairs are manually annotated with a binary label to indicate positive or negative paraphrase relation. The dataset has around 33% negative samples and the others are all positive.  
**QQP** The Quora Question Pairs [12] is a dataset with more than 400,000 question pairs released by the question-answer community Quora. The question pairs sharing the same semantics are annotated as duplicate based on Quora's merging policy. Similar to MRPC, the data is unbalanced, in which 63% are negative duplicates.  
**STS-B** Instead of using binary label representing the semantic equivalence of the sentence pairs, the semantic textual similarity benchmark [13] uses a similarity score ranged from 0 to 5. The corpus is extracted from news headlines, video and image captions data. Similar to MRPC and QQP, each pair is human-annotated.  
**SNLI** The Stanford Natural Language Inference (SNLI) corpus ([18]) is a collection of sentence pairs labelled for three classes including entailment, contradiction, and semantic independence. SNLI is large corpus resources which has 570,152 sentence pair. In addition, all of its sentences and labels were written by humans in a grounded, naturalistic context. There are four additional judgements for each label for 56,941 of the examples. Of these, 98% of cases emerge with at least three annotator consensus.  
**MNLI** The Multi-Genre Natural Language Inference (MNLI) corpus ([19]) is 433,000 sentence pairs from a broad range of genres of written and spoken English, annotated with sentence inference classes and balanced across three labels. In the corpus, each premise sentence is derived from one of ten sources of text, which constitute the ten genre sections of the corpus while each hypothesis sentence and pair label was composed by a crowd worker in response to a premise. The corpus is modelled on the SNLI corpus but differs in that it covers a range of genres of spoken and written text and supports a distinctive cross-genre generalisation evaluation.  
Table 5 summarises the label types of a different dataset.
## 4 Results and Discussion  
### Material Distribution with Induced Vibrations  
A visual comparison is made between parts printed with and without induced vibrations. The images taken at the micro-metrology station are used for this comparison (Figure 4). The two photos in Figure 4 are taken at the same magnification level of two different samples--one with and one without vibration. The images clearly show the pores in the parts printed without vibration. When vibrations are introduced, materials are redistributed, resulting in a reduction in the size of the pores.
# Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
## 6 NN Architecture Experiments
### Phase Three  
While the results of Phase Two were promising, there was still significant error in the estimates of the warping functions. To further improve the performance, it was determined to assist the NN by decomposing the problem into smaller pieces by clustering. To this end, several NNs were trained, each to be responsive to data pertaining to a particular cluster, where clustering was accomplished using \(k\)-means clustering. Different numbers of clusters were tried: \(k=4,5,6,10,15,20\). It was found that \(k=6\) clusters gave good performance. Figure 14 shows the \(k=6\) clusters for the selected spectral data from the 600 training phrases. The six clusters are shown using different colors.  
In the training stage, data from each of the \(k\) clusters were used to train \(k\) NNs. In the synthesis stage, for each segment of speech data it was determined which cluster the associated feature vector best matched, and the \((\mathbf{a}_{F},\mathbf{b}_{F})\) warping functions were obtained from the corresponding NN. These were used to provide the spectral warping.  
Figure 15a,b show how the clustering NN learned the paths \(\mathbf{a}\) and \(\mathbf{b}\), respectively, at 5000 training iterations. The DW path (truth) is shown in blue and the NN-learned path is in orange. At this point, the final sound at the 5000 training iterations is much better than the final sound produced from Phase Two, with little signal processing artifacts. Figure 16 shows the MSE between the true values of the warping paths (\(\mathbf{a}\) and \(\mathbf{b}\)) and the estimated paths for each of the six clusters. Interestingly, the learning demonstrated by the MSE varied from cluster to cluster. The MCD value for this phase is reported in Phase Three row of Table 2.  
Figure 14: Clustering of spectral information from 600 phrases (\(k=6\)).  
Figure 13: Mean squared error learning curve, Phase Two.
# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
## 5 Conclusions  
As worker safety is a major concern on construction sites, this study considered helmet detection as a computer vision problem, and proposed a deep learning-based solution. Existing studies have struggled in detecting objects from low-light images and smaller objects (due to the larger distance between the camera and workers). Therefore, a YOLOv5x-based architecture for automatic detection of safety helmets on construction sites was proposed to ensure worker safety.  
This study used different versions of YOLO architecture, YOLOv3, YOLOv4, and YOLOv5x, to detect safety helmets due to their proven accuracy in object detection tasks. Among them, YOLOv5x achieved the best mAP (92.44%) in detecting smaller objects and objects in low-light images, thereby showing its efficacy in safety helmet detection.  
Despite the significant outcomes achieved by YOLOv5x-based architecture, it also possesses several limitations. The proposed deep learning model struggled to perform in some scenarios (e.g., with an obstacle in front of helmets, and objects identical to helmets). Training the model with more images, including the above-mentioned scenarios, could potentially increase the model's efficacy. Moreover, in the future, more safety tools could be added for detection, such as vests, gloves, and glasses, to ensure greater safety for workers.  
Funding acquisition, F.M.-D.; Investigation, A.H.; Methodology, A.H.; Validation, A.H. and F.M.-D.; Writing--original draft, A.H.; Writing--review & editing, F.M.-D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable  
The dataset used in this article is openly available in the MakeML at [[https://makeml.app/datasets/hard-hat-workers](https://makeml.app/datasets/hard-hat-workers)]([https://makeml.app/datasets/hard-hat-workers](https://makeml.app/datasets/hard-hat-workers)).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Related Work** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** & **mAP (\%)** & **Accuracy (\%)** \\ \hline [PERSON] et al. [18] & 95.00 & 77.00 & 85.05 & 36.82 & X \\ \hline [PERSON] et al. [20] & X & X & X & 83.89 & X \\ \hline [PERSON] et al. [30] & X & X & X & 86.55 & X \\ \hline [PERSON] et al. [32] & X & X & X & 72.30 & X \\ \hline [PERSON] et al. [34] & X & X & X & 88.10 & X \\ \hline [PERSON] et al. [35] & 96.20 & 96.20 & 96.20 & X & 94.47 \\ \hline Proposed Study & 92.44 & 89.24 & 90.81 & 92.44 & 92.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Proposed Model with other state-of-the-art models Based on Deep Learning methods.  
**Acknowledgments:** Acknowledgment to the Bolsa de Investigacao (B.I.) within Project BASE: Banana Sensing (PRODERAM20-16.2.2-FEADER-1810). Acknowledgment to the LARSyS (Projeto--UIDB/50009/2020) for funding this research.
## 2 Materials and Methods
### Physical Parameter Measurements
#### 2.4.3. Color Analysis  
The color of both stems and florets of the cauliflower was determined before and after processing, and during each day of sampling (day 0, 14, 28, 42), using VeriVide's DigiEye system (VeriVide Ltd., Leicester, UK). This system was equipped with a DSLR camera (Nikon D90, Tokyo, Japan) and it captured an image of 4288 \(\times\) 2848 pixels with a resolution of 96 dpi. Before each measurement, the camera was white balanced and calibrated with the color chart provided with the equipment. The L\({}^{*}\), a\({}^{*}\), and b\({}^{*}\) components were recorded at a D65 standard illuminant. Each piece of the sample was photographed in a light cabinet on a black background. The captured images were analyzed according to the CIELAB color scale using DigiEye 2.9 software, with the use of the \"free form tool\" because of the irregular shape of each piece. Data were obtained from 18 measurements on each sample. The L\({}^{*}\) a\({}^{*}\) b\({}^{*}\) color space was used for determination of the color. Further, the total color difference (TCD) of the samples before (control) and after processing and storage were calculated as (Equation (3)) [24]:  
\[\text{TCD}=\sqrt{\left[\left(\text{L}_{0}^{*}\;-\;\text{L}^{*}\right)^{2}\;+ \;\left(\text{a}_{0}^{*}\;-\;\text{a}^{*}\right)^{2}\;+\;\left(\text{b}_{0}^{* }\;-\;\text{b}^{*}\right)^{2}\right]} \tag{3}\]  
where L\({}_{0}\)\({}^{*}\), a\({}_{0}\)\({}^{*}\), and b\({}_{0}\)\({}^{*}\) were the values for the untreated sample (control).