esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 1 Introduction
Traditional dynamic channelization algorithms can solve this problem by measuring parameters after signal reconstruction, but signal reconstruction and quadratic parameter estimation will consume computing resources [20]. The dynamic channelization algorithm based on sub-band spectrum detection does not need signal reconstruction [30; 31], but the frequency estimation accuracy could be better due to different applications. In the field of digital channelization receivers, much literature focuses on a single digital channelization structure, and there are few studies on the combination of digital channelization structure and parameter measurement after channelization [17; 24; 32]. In [32], [PERSON] et al. proposed an efficient non-critical sampling channelized digital receiver structure and combined digital channelization technology with an instantaneous frequency measurement (IFM) algorithm. Based on the IFM results, the algorithm completes channel arbitration and determines whether the narrowband signals distributed in different channels belong to the same signal. Then, the narrowband signal parameters are merged according to the channel arbitration result. The algorithm has high computational efficiency and can adapt to wide instantaneous bandwidth. However, when the SNR of the input signal is lower than 5 dB, the estimation accuracy of the chirp signal parameters is low. In addition, the channelization sample rate cannot precisely match the prototype filter bandwidth and cannot be minimized because the channel number is multiple of the decimation number. This paper proposes a digital channelized receiver architecture for wideband low SNR chirp signal detection. A non-critical sampling channelization structure based on weighted overlap-add (WOLA) is adopted, reducing the channel sample rate as much as possible under the condition that there is no blind zone and the signal spectrum is not aliased. In addition, an adaptive threshold generation algorithm is proposed to detect signals without prior information. Moreover, an improved CORDIC based IFM algorithm is proposed, improving low SNR chirp signals' frequency estimation accuracy. The instantaneous frequency is used for channel arbitration, and a channel arbitration logic is proposed to complete the cross-channel combination of wideband signals. Simulation shows that the proposed algorithm is reliable and robust for low SNR and wideband chirp signal detection. The rest of the paper is organized as follows. Section 2 presents the principle of channelization and the channel division method. Moreover, a digital channelized receiver architecture is also proposed. In addition, each part of the receiver architecture is introduced in detail. In Section 3, the reliability and robustness of the proposed receiver architecture are verified by simulation experiments, and the computational costs are analyzed. Finally, a conclusion is drawn in Section 4.
# Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection
## 5 Conclusions  
The particle size distribution is closely related to the light scattering characteristics of fire smokes and interference aerosols. In order to develop fire detectors with higher alarm accuracy, an experimental study on the particle size distribution is conducted for characterizing typical fire smokes and interference aerosols. A comprehensive aerosol experimental platform is set up to measure the aerosol particle size distributions. The particle size distributions of typical aerosol samples are measured in high resolution. A primarily analysis of our experimental results indicates that the particle size distribution of the fire smoke we measured is mostly on the sub-micron scale, while that of several common non-fire aerosols is mostly on the micron scale, which may be useful for discriminating fire smokes from interference aerosols in general scenarios. With detailed information on the particle size distribution, more distinguishing statistical characteristics for fire smoke identification could be studied in the future. Furthermore, the light scattering characteristics can be further inferred based on the Mie theory for improving the fire smoke identification ability of the optical fire smoke detector.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Median Particle Size (\(\upmu\)m)** & **Standard Deviation** \\ \hline A2 & 3.2198 & 1.5129 \\ A1 & 2.4152 & 1.5394 \\ JIS & 1.4896 & 1.4801 \\ Fire test spray & 7.2575 & 1.9974 \\ Sunscreen spray & 41.726 & 1.3151 \\ Gel & 3.3891 & 1.1598 \\ Yunnan Baiyao & 27.358 & 1.4405 \\ Perfume & 43.772 & 1.2347 \\ Freshener & 39.816 & 1.5268 \\ Humidifier & 9.4085 & 1.9872 \\ Mosquito & 39.902 & 1.4046 \\ repellent perfume & 4.2877 & 1.2939 \\ Water spray (fog cabinet) & 4.2877 & 1.2939 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical parameters of particle size distribution of typical interference aerosols.  
**Author Contributions:** Conceptualization, W.-H.D.; methodology, W.-H.D.; validation, W.-H.D.; formal analysis, S.W.; investigation, S.W.; resources, S.W.; data curation, X.-E.S.; writing--original draft preparation, X.-E.S.; writing--review and editing, T.D.; visualization, X.-E.S.; supervision, T.D.; project administration, T.D.; funding acquisition, T.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China, grant numbers 2021 YFC3001700, 2021 YFC3001701, 2021 YFC3001600, the National Natural Science Foundation of China (NSFC), grant number 62101199, and the Fundamental Research Funds for the Central Universities of HUST, grant number 2020 kfyXJJJS102.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We thank [PERSON] and [PERSON] for their help in the design of the aerosol analysis platform and experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Empirical Study on 5G NR Cochannel Coexistence
## 2 Related Work
The proposals are divided into three sections: inter-cell coordination, advanced receiver and sensing techniques. For instance, clustering, scheduling and resource allocation, power control, beamforming, UL/DL configurations are solutions proposed based on inter-cell coordination. Advanced receiver and sensing solutions proposed are interference suppression, maximum likelihood, interference cancellation and listen-before-talk (LBT) techniques. The paper of [19] also deals with a survey of specifications based on signaling for Cross Link Interference (CLI) mitigation. Authors in [20] present enhancements of 5G NR TDD operation and performance to mitigate CLI between neighboring cells. A reinforcement learning algorithm adjusts the TDD configuration for both macro and indoor deployments considering URLLC and eMBB traffic coexistence based on the new features introduced in 3 GPP NR Release-16 [21] to manage CLI, i.e., CLI-RSSI (received signal strength indicator), CLI SRS-RSRP (sounding reference signal-reference signal received power) and RIM (remote interference management). More recently, authors in [22] highlight the importance of UL traffic for industry use cases. Based on the coexistence issue and the traffic requirements of private networks, potential solutions are presented related to the coexistence issue, e.g., UL MIMO (multiple-input and multiple-output), carrier aggregation, full-duplex operation, or symbol blanking. Other recommendations are to implement different TDD configurations to minimize possible interference and the introduction of balanced or UL oriented TDD patterns. Most of the existing coexistence studies are based on theoretical and simulation analysis, especially in the context of cellular networks. Despite several deployment scenarios envisioned for 5G NR and the increasing popularity of 5G non-public networks, no systematic experimental investigations on coexistence behavior have been conducted that quantify over-the-air performance impact in real deployments. In [2], we describe our measurement methodology and initial results on the cochannel coexistence performance between an indoor non-public 5G NR network and an outdoor 5G NR network. In this article, we present a deep analysis of the cochannel coexistence results. Moreover, we present results on mitigation through link robustness of the observed performance degradation caused due to the coexistence issue.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Swainsonine  
Swainsonine (Swn), an indolizidine alkaloid (Figure 1), is known as a mannosidase inhibitor that impacts glycoprotein processing and induces lysosomal storage disease. Due to this activity, Swn is under consideration in cancer chemotherapy [92]. It was originally observed in several plant species due to its toxicity to livestock, but later found to be also produced by fungi [93]. Some controversy exists concerning the valid taxonomic name of these fungi; hence, herewith we use _Embellisia oxytropis_, which is the currently accepted epithet in Mycobank [94].  
The first attempt towards elucidation of Swn biosynthetic pathway revealed the existence of multiple genes in the genome of the producing fungi [69]. Five genes were annotated potentially operating in Swn biosynthesis, namely saccharopine dehydrogenase (SDH), thought to be a key gene of this pathway, saccharopine oxidase (FAP2), polyketide synthase (PKS), pyrroline-5-carboxylate reductase (PSCR), and cytochrome P450 (P450).  
Later on, the comparative genomics of the known Swn producers (Table 1) enabled identifying the Swn gene cluster [70]. Genome sequence analyses revealed that these fungi share orthologous Swn gene clusters, which include a multifunctional \(swnK\) gene containing predicted adenylylation and acyltransferase domains along with their associated thiolation domains, two reductase domains, and a b-ketoacyl synthase domain.  
Recent transcriptome analysis of the wild type _E. oxytropis_ and a mutant disrupted at the saccharopine reductase gene enabled screening of differential genes involved in Swn biosynthesis [71]. The biosynthetic pathway of Swn in the fungus was defined to include two branches of P6C and P2C. Delta1-piperidinee-2-carboxylate reductase, lysine 6-dehydrogenase, and saccharopine oxidase/L-pipecolate oxidase were part of P6C, while 1-piperidinee-2-carboxylate/1-pyrroline-2-carboxylate reductase (NAD(P)H) and delta1-piperidinee-2-carboxylate reductase were part of P2C, and saccharopine reductase was involved in both branches. The transcriptome data indicated that hydroxylation of 1-indolizidineone into Swn may be catalyzed by hydroxymethylglutaryl-CoA lyase rather than by PKS and P450, as earlier WGS studies proposed.
# Learning-Based Optimisation for Integrated Problems in
## 3 Methodological Preliminaries
### Agents' Setup  
The central challenge in IFT optimisation is to design a model that integrates heterogeneous agent behaviour, capable of reflecting multi-level decision-making processes, on different time scales and with different objectives. For instance, after a shipper decides to send cargo to a receiver, the rail, road, and terminal agents need to schedule their operations to ensure seamless transport. In this context, hybrid approaches are required to model multiple agents interacting together. Generally, designing the operational, highly dynamic transport problem as Multi-Agent RL (MARL) results in a nonstationary environment, reducing convergence and requiring more training steps. From a modelling standpoint, designing such systems offers two choices: either implementing single-agent RL with a centrally trained policy (e.g., [76]) or Multi-Agent RL, where agents learn heterogeneous or shared policies (e.g., [77]). The former approach may result in a larger state-action space, but the training is less computationally demanding. In contrast, the latter approach involves only formulating state actions for each agent, but this also leads to additional dynamics and therefore hampers convergence [78]. The decision regarding the policy update, whether to update only one model with all observations from the environment or to update multiple encapsulated models independently with partial information, is problem-specific and needs to be evaluated at the beginning. Alternatively, agents can operate and gather information independently in the environment, using a shared policy. Centralised Training Decentralised Execution (CTDE) MARL demonstrates a good ability to address highly dynamic Multi-Agent environments, enabling agents to act heterogeneously based on the global problem context [79; 80; 81]. A further challenge in MARL is to assign rewards to individual actions among multiple heterogeneous agents (credit assignment problem). Due to the collective decision-making, it is often unclear how to design and distribute rewards among many independent agents. Consequently, sophisticated reward distribution techniques are needed. We refer to the following studies that highlight reward shaping and reward distribution as critical steps: [77; 82; 83].  
More recent studies in this field explore Hierarchical Reinforcement Learning (HRL), where an agent (upper policy) at the strategic or tactical level learns to delegate tasks to lower-level agents (lower policies) at the operational decision level. HRL shows great potential, especially for integrated optimisation problems as it can naturally model the asynchronous decision-making between multiple agents [84]. Figure 3 illustrates three main strategies for the MARL setups, which can potentially model integrated decision-making in IFT.
# Remote Sensing Analysis of Geologic Hazards
## 3 Landslides
### Process Analysis and Susceptibility  
Three works are dedicated to the analysis of triggering factors and susceptibility to landslides and rock slopes. The surveys were conducted from ground, satellite or combined approaches.  
[PERSON] et al. [30] studied the spatiotemporal evolution pattern and driving mechanisms of landslides in the Bailong River Basin (China), where a strong earthquake occurred in 2008. They analyzed the period 2007-2020, mapping the occurred landslides using optical images from various sources and measuring the landslide deformation using MTInSAR. They identified three stages of landslide triggering: the earthquake (2008), the coupled earthquake-rainfall (2008-2017), and the rainfall (2017-present) driving stages. In particular, they observed that the landslides in the limestone area were more responsive to the earthquake, while the loess-phyllite-dominated sectors were mainly controlled by rainfall.  
The study of [PERSON] et al. [31] was located in Colombia between 2016 and 2019. They used an inventory of 346 rainfall-induced landslide events and the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset, which combines satellite-based rainfall data and ground-based gauge measurements to produce global rainfall data at a 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) resolution. They formulated new dynamic variables based on the rainfall occurrence and amount during dry and wet periods, and determined an original landslide triggering factor (LTF). Comparing their LTF with canonical event-duration threshold, they found that LTF performed better in 81% of cases.  
[PERSON] et al. [32] analyzed point clouds (PCs) of rock slopes acquired by a terrestrial laser scanner (TLS) in British Columbia (Canada). They partitioned the PCs into voxels based on local dimensionality, orientation, and topology and built an automatic decision tree that utilized geometrical, topological, and contextual information and enabled the classification of a multi-hazard railway rock slope into classes involved in landslide risk management. Their results demonstrated precision similar to more complex machine learning algorithms and manual knowledge-based analysis.
## 2 Related Studies
### Preliminaries  
This section presents some definitions that are relevant to the current research for a better understanding of the common phrases.  
1. **Co-citation Networks** are networks constructed according to the citation relationships between articles. If two publications have been cited on the same paper, this can be called co-citation. Since the two papers cannot cite each other at the same time, these networks are directed.
2. **Co-word Networks** reflect the frequency of co-words by exploring which papers have two keywords that appear simultaneously, which is also known as the keyword co-occurrence frequency. Co-word analysis is the process of discovering and visualising the interactions between keywords by studying the strengths of two keywords co-occurrence in two different publications. Keywords are especially powerful, as they are used by scholars and researchers to communicate the essence of an article; co-word analysis can thus investigate the network of ideas, scholarly topics, and research trends in a given discipline.
3. **Co-authorship Networks** as a commonly used ASN, each node in these networks refers to an author, while every edge denotes a co-authored relationship. Co-author networks can be studied from multiple perspectives, which is relevant to the vast majority of disciplines. Moreover, the rapid evolution and adoption of technology means that scientists can collaborate more easily across geographical boundaries and no longer need to be physically co-located at the same institution. Collaboration behaviors based on co-author networks can thus be studied by scholars, while collaboration teamwork has emerged as a new research pattern.
4. **Scientific Collaboration Network** is a form of social network in which the nodes represent scholars, and the links represent co-authorships. As such, if two authors have co-authored at least once, then there will be a link between them [12].
5. **Scholar Similarity Measurement** is based on the publication of two scholars and their behavior. The key concept is that if two scholars publish at same venues, or behave similarly in some aspect, they are more likely to be similar [12].
6. **Scientific Collaborator Recommendation** is the process of suggesting the suitable collaborators to a specific author. Potential collaborators are chosen based on their similarity and ranked according to how similar they are to the target authors [12].
## 6 Assigning the Most Accurate Value for the Initial Flow Rate Parameter  
Assigning an accurate value of initial flow rate (q\({}_{\text{i}}\)) is crucial to the success of production history matching as q\({}_{\text{i}}\) is always a main parameter in every DCA model. Initial flow rate could be assumed as the average production rate for the first month, first two months, first three months, or other values. Additionally, q\({}_{\text{i}}\) could be assumed as the maximum flow rate reached. The maximum flow rate was reached within the first three months of production in almost all the wells as shown in Figure 8. Production history matching using the entire production data was done on all the 1216 wells in this study while varying the value of q\({}_{\text{i}}\) to observe the change in accuracy of the fitted models with different q\({}_{\text{i}}\) values. Four different cases were investigated: (1) assuming q\({}_{\text{i}}\) was equal to the maximum flow rate reached, (2) assuming q\({}_{\text{i}}\) was equal to the average flow rate for the first month, (3) assuming q\({}_{\text{i}}\) was equal to the average flow rate for the first two month, and (4) assuming q\({}_{\text{i}}\) was equal to the average flow rate for the first three months.  
Figure 8: Average time to reach maximum flow rate for all wells from each formation.  
Figure 7: Example of production history matching using different production history data with the SEPD model for a well in the Niobrara formation.
The objective of the present study was to evaluate the effects of moderately high temperature (MHT) on solanesol accumulation in tobacco leaves and to determine the underlying regulatory mechanisms, by comparing solanesol contents, and net photosynthetic rate, and through RNA-sequencing-based transcriptome analysis of tobacco leaves under normal temperature (NT) and MHT conditions. This study indicates that solanesol accumulation in tobacco leaves can be manipulated through the regulation of the environmental temperature and revealed the effect of MHT on the transcription of genes essential to solanesol biosynthesis, thus establishing a basis for further elucidation of the molecular mechanism of temperature regulation of solanesol accumulation.
## 4 Results
### Experimental Results  
The model's loss and Intersection over Union (IoU) metrics in the training and validation sets are comparatively evaluated. As shown in Table 1, with increasing iterations, CellGAN gradually achieves a stable accuracy across all datasets. Due to dataset scale differences, smaller datasets like MoNuSeg and GlandCell can converge after 400 training rounds. In comparison, larger datasets like rice leaf cells and Nuclei require 1000 rounds to obtain stable results. Only in the rice leaf cell dataset can it be seen that the validation set IoU is higher than the training set; in the other three datasets of MoNuSeg, Gand-Cell, and Nucleus, the validation set IoU is lower than the training set, especially for the small datasets of MoNuSeg and GlandCell, as well as the simple texture Nucleus dataset, exhibiting a certain degree of overfitting during training.  
Figure 7 presents the segmentation results of several validation set samples to demonstrate the segmentation effect visually. The segmentation results for the rice leaf cell, GlandCell, and Nucleus datasets are relatively ideal, aligning well with the labels. However, in the MoNuSeg dataset, although the cell locations can be roughly located, the cell sizes cannot be accurately segmented due to the insufficient model generalization ability caused by the small dataset size.  
This study predicted the test set images and statistically analyzed the relationship between the proportion of correctly predicted cell pixels (t1) and the proportion of cell pixels in the label (t2), as shown in Figure 8. For the rice leaf cell and Nucleus datasets, this method has small RMSE and MRE values, with \(R^{2}\) approaching 1, indicating minor relative errors, and a good fit between the predicted data and the true data, while in the MoNuSeg and GlandCell datasets, there are more outliers, and the network fitting is slightly worse.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Training Set IoU** & **Validation Set IoU** \\ \hline MoNuSeg & 89.11\% & 77.50\% \\ GlandCell & 94.11\% & 83.52\% \\ Nucleus & 92.06\% & 86.08\% \\ RiceCell & 91.50\% & 93.21\% \\ \hline \end{tabular}
\end{table}
Table 1: Training and Validation Results of the Proposed Model on Different Datasets.  
Figure 7: Recognition Effects on Four Different Datasets.