esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization Analysis of PAP\({}_{\text{UAE}}\) and PAP\({}_{\text{HWE}}\)
#### 3.3.2 Molecular Weight Analysis  
The characterization of polysaccharides with different molecular weights is important to determine the structure-activity relationship of polysaccharides [25]. The weight-average molecular weight (Mw), the number-average molecular weight (Mn), and polydispersity (PD, Mw/Mn) of the products obtained using HWE and UAE were compared. The chromatograms of PAP\({}_{\text{HWE}}\) exhibited a single peak, indicating that there was no aggregation and the sample was homogenous. The Mw, Mn, and PD values for PAP\({}_{\text{HWE}}\) were measured to be \(1.113\times 10^{6}\) Da, \(3.931\times 10^{5}\) Da, and \(2.831\), respectively. The ultrasonic treatment also produced a single chromatographic peak, although a sharp decrease in molecular weight was observed. The Mw, Mn, and PD for PAP\({}_{\text{UAE}}\) were determined to be \(6.225\times 10^{5}\) Da, \(2.138\times 10^{5}\) Da, and \(2.912\), respectively (Figure 4). The reason for the decrease in molecular weight was that ultrasonic waves could induce the degradation of polysaccharides due to the fluid shear forces from the collapsing cavitation [26].  
Figure 4: Size-exclusion chromatography combined with laser light scattering (SEC-LLS) chromatogram of the extracted polysaccharides: PAP\({}_{\text{HWE}}\) (**A**) and PAP\({}_{\text{UAE}}\) (**B**) (HWE: hot water extraction, UAE: ultrasound-assisted extraction).  
Figure 3: FT-IR spectra of extracted polysaccharides.
# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 3 Thinger.io Overview
### Research Efforts at Thinger.io
#### 3.1.3 Interoperability  
One of the key benefits of Thinger.io is the possibility of being interoperable with other platforms and applications. That means that any device is accessible, both for sensing and actuating, from standard REST APIs, hiding the complexity of the underlying protocol optimizations between devices and the server.  
The REST endpoints to talk with devices are not defined statically, or manually set in the server. Instead, they are dynamically extracted from the device model definition when it is connected to the cloud. So, a set of resources defined in the device code can be accessed in real time from the generated REST endpoints. Figure 3 illustrates this use case, where a device contains a set of sensors (temperature, humidity, luminosity) that are defined in the device model. Using the Thinger.io client libraries, the device can connect to the Thinger.io cloud, using efficient bidirectional communications. Once the device is connected, the resources defined in the model can be accessed in real time from  
Figure 2: Encoding size comparison between different formats. Thinger.io uses a PSON encoding scheme to improve memory footprint, save bandwidth, and reduce power consumption in devices.  
the automatically generated REST endpoints, like _/device/temperature_, that executes the corresponding resource to obtain the current sensor reading.  
There are other use cases where applications require periodical updates, i.e., sampling accelerometers in real time, or giving temperature updates every second. Polling this kind of data over REST APIs is not efficient, as it generates too much overhead in the communication, and adds extra latency as it is necessary to issue a request every time a resource update is necessary. Thinger.io also provides mechanisms for subscribing to resource updates over standard Websockets. Thus, an external client can open a Websocket to the platform, and then, subscribe to a resource at a requested interval.  
This approach differs from other IoT solutions, especially from HTTP-based solutions where devices just push data to the cloud, and an external client consumes this information. In such a case, there is no real-time interaction between the external client and the device, as it is limited by the push or poll synchronization periods configured in the device. It also differs from MQTT platforms, as MQTT does not allow data models to be defined that can be exposed directly to standard REST APIs.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 2 Literature Review  
### Recent Advances in Entity-Relation Extraction  
#### 2.1.1 Pipeline Methods  
Current entity-relation extraction methods can be divided into two main categories: pipeline methods and joint extraction methods. In pipeline methods, researchers first build an entity recognition model using manually designed feature extraction and kernel function methods and then construct a model that can recognize semantic relations based on entity pairs to achieve relation extraction between entities [4]. With the rapid development of deep learning techniques in recent years, some end-to-end deep learning models have gradually dominated this field. Research related to deep learning-based named-entity recognition (NER) and relation extraction (RE) has achieved substantial progress [5; 6; 7; 8]. [PERSON] et al. [9] modeled the NER problem by leveraging relationships between words, proposing a new data-encoding pattern and a novel model architecture, which performed excellently on nested, non-nested, and discontinuous datasets. [PERSON] et al. [10] proposed a CNER model based on text similarity and mutual information maximization, where potential word boundaries are identified by calculating text similarity, and mutual information between characters, potential word boundaries, and sentences is maximized to enhance input features. Additionally, a specific network architecture was designed for structural enhancement. [PERSON] et al. [11] enhanced dialog-based relation extraction by predicting the speaker and trigger words. Speaker prediction captures features of the speaker who presents relevant entities, while trigger word prediction captures contextual information between entity relations, thus enabling relation extraction.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 1 Introduction
### Irradiated MoS\({}_{2}\) Materials  
Defects are created in MoS\({}_{2}\) few-layers when the layers are fabricated by exfoliation technique and vapor deposition growth when layers are transferred from substrate to substrate, and placed under electromagnetic radiation and energetic particle fluxes. The defects typically deteriorate the device quality of MoS\({}_{2}\) layers. On the other hand, defects in MoS\({}_{2}\) also have a beneficial impact on material properties. For instance, defects have been shown to be the dominant dopant in MoS\({}_{2}\), with natural defect variation allowing \(n\)-type and \(p\)-type regions to coexist across distinct regions of the same sample [73]. The sulfur vacancies can lower Schottky barrier heights of MoS\({}_{2}\)[73]. Therefore, it is crucial to fully understand defects in MoS\({}_{2}\) to develop a reliable MoS\({}_{2}\)-based electronics. In this paper, irradiation-related defects of MoS\({}_{2}\) few-layers are focused on. Up to now, MoS\({}_{2}\) 2D layers have been irradiated under different irradiation sources. Table 1 lists some typical irradiation effects of MoS\({}_{2}\) materials. The details are summarized in the following sections. The particle irradiation and electromagnetic irradiation will be reviewed first on structural properties and defects, then theoretical explanations of the irradiation mechanism are discussed. The irradiation-induced band structures, electric, catalytic, and magnetic properties will be summarized at the end. To understand the irradiation-induced defects well, the synthesis/preparation details of the as-prepared MoS\({}_{2}\) layers will be described in each section in addition to irradiation.
## 3 Compression Algorithm Design
### Utilization of Data Statistical Knowledge  
Usually statistical knowledge is available when time-series data are measured and transmitted through wireless sensors. A method can be used based on earlier observations, but since data are changing over time this knowledge can be of questionable value as far as the compression effectiveness is concerned.  
Therefore, in the proposed scheme, the previously observed frequency values are exploited to effectively update Huffman Code tables for the values to follow. Initially, the differential signal is produced, as most likely it has values that can be more effectively compressed. Following the initial phase, the differential signal is separated in fixed size packets. In the first packet, since there is no statistical knowledge of the data, the method is using the table from the LEC algorithm. However, in each following packet the statistical knowledge from previous data is used to create on the fly an adaptive Huffman Code table. The alphabet of numbers is divided into groups, the sizes of which increase exponentially. Each new differential signal value observed, leads to the appropriate group's frequency increment. When the processing of a data packet ends, the frequencies of each group are used to extract the possibilities of a value that belongs to that group. The blocks are sorted in descending order by their possibilities and a binary tree is created.  
After the formation of the Huffman Code table for the following packet, the current frequency table is element wise multiplied with a factor varying between 1 and 0. Therefore, as this parameter is approaching 0 the degree by which history (i.e., frequencies observed in previous packets) is taken into account in the next Huffman Code table diminishes. Therefore, if \"0\" is selected only the frequencies of the cur-rent packet are used; if 1 is selected the frequencies of every previous packet are equally used in the encoding of the next packets [12].  
Figure 1: Real-time huffman scheme flow chart.
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 4 Discussion  
### Water Extraction Methodologies in the Kashgar Region  
This study evaluated the performance of five water body extraction methods: Mahalanobis distance, minimum distance, maximum likelihood, support vector machine (SVM), and random forest (RF) algorithms within the Kashgar region. The results indicated that the Mahalanobis distance, minimum distance, and maximum likelihood methods yielded suboptimal overall accuracy, user accuracy, and Kappa coefficients, primarily due to misclassification of water bodies. These methods exhibited limitations in distinguishing water bodies from spectrally similar features such as shadows and glaciers. While SVM demonstrated relatively higher accuracy, its requirement for feature normalization during data preprocessing rendered it computationally intensive for the study's extensive temporal and spatial scales. In contrast, the RF algorithm achieved a favorable balance between accuracy and processing efficiency, proving to be the most suitable method for large-scale water body extraction in the Kashgar region.
# Target Detection of Passive Bistatic Radar under the Condition of Impure Reference Signal
## 3 Proposed Target Detection Method under the Condition of Impure Reference Signal  
To overcome the negative impact of the impure reference signal on target detection, a novel target detection method based on fusion feedback and reference signal purification is proposed. The detailed process is introduced as follows.
## 3 Results and Discussion
### Effect of Nitrogen Treatment with Admixtures on Surface Energy  
Calculations of surface energy showed that the plasma treatment significantly increased the polar part of the surface energy by approximately 10-14 mNm\({}^{-1}\), whereas the dispersive part was unaffected. Thus, surface energy was increased by the DBD treatment from an untreated value of around 32 mNm\({}^{-1}\) to values in the range of 42 to 46 mNm\({}^{-1}\). However, there were no significant differences in the polar part, the dispersive part or the total surface energy between the treatment in pure nitrogen and the treatments in nitrogen with the different gas admixtures. This effect was also independent of the concentration of the admixture over the range tested. The results for the different gas admixtures and concentrations are shown in Figures 7-9. The increase of the polar part of the surface energy is likely to be a result of the incorporation of electronegative chemical groups on the surface of the treated BOPP [38; 39].  
Figure 6: Level average water contact angles for array factors gas gap, dielectric thickness and current during the DBD treatment of BOPP in pure nitrogen.  
Figure 8: The polar part, the dispersive part and the surface energy of the BOPP films treated by the DBD system with admixtures of nitrous oxide.  
Figure 7: The polar part, the dispersive part and the surface energy of the BOPP films treated by the DBD system with admixtures of acetylene.  
Figure 9: The polar part, the dispersive part and the surface energy of the BOPP films treated by the DBD system with admixtures of carbon dioxide.  
To investigate the stability of the surface energy of the treated BOPP films, also called hydrophobic recovery or aging behaviour [40; 41], the contact angle measurements were repeated every 24 h over 30 days for the samples treated in pure nitrogen. The results in Figure 10 indicate that the surface energy of the BOPP films is substantially reduced 30 days after the plasma treatment with the DBD system. The polar part decreases from approximately 12 mNm\({}^{-1}\) to 5 mNm\({}^{-1}\) while the dispersive part is not affected. The total surface energy 30 days after the plasma treatment is only slightly higher than the untreated sample. The hydrophobic recovery of the plasma-treated films is related to the rearrangement of the polymer chains at the surface, which carry the electronegative chemical groups. The rearrangement leads to the reorientation of the electronegative chemical groups from the surface towards the bulk of the polymer and thus to the reduction of the polar part of the surface energy.
# User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning
###### Abstract  
In this paper, we investigate a user pairing problem in power domain non-orthogonal multiple access (NOMA) scheme-aided satellite networks. In the considered scenario, different satellite applications are assumed with various delay quality-of-service (QoS) requirements, and the concept of effective capacity is employed to characterize the effect of delay QoS limitations on achieved performance. Based on this, our objective was to select users to form a NOMA user pair and utilize resource efficiently. To this end, a power allocation coefficient was firstly obtained by ensuring that the achieved capacity of users with sensitive delay QoS requirements was not less than that achieved with an orthogonal multiple access (OMA) scheme. Then, considering that user selection in a delay-limited NOMA-based satellite network is intractable and non-convex, a deep reinforcement learning (DRL) algorithm was employed for dynamic user selection. Specifically, channel conditions and delay QoS requirements of users were carefully selected as state, and a DRL algorithm was used to search for the optimal user who could achieve the maximum performance with the power allocation factor, to pair with the delay QoS-sensitive user to form a NOMA user pair for each state. Simulation results are provided to demonstrate that the proposed DRL-based user selection scheme can output the optimal action in each time slot and, thus, provide superior performance than that achieved with a random selection strategy and OMA scheme.  
NOMA-based satellite networks; delay QoS limitation; user pairing 2023  
232370623390/s231670623
## 2 Materials and Methods
### Equilibrium Experiments
#### 2.5.3 The Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy  
To investigate the dye adsorption mechanism by the anion exchanger, the attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FT-IR) technique was used (Cary 630, Agilent, Santa Clara, CA, USA). The spectra of anion exchangers before and after DY50 sorption were recorded in the range of 600-3500 cm\({}^{-1}\).
## 3 Case Study  
### CFD Numerical Simulation  
#### 3.1.1 Determining the Number of BSCN Grooves  
Based on the optimization results of the genetic algorithm, the number of grooves in the inlet and outlet sections of the BSCN was determined by combining the terms in the following Formula (21), as shown in Figure 9. The models of the BSCN and conventional straight cone nozzle were established.  
\[\left\{\begin{array}{c}\quad\textit{n1}_{\textit{max}}\times(\textit{s1}+ \textit{w1})\leqslant\textit{L1}\\ \quad\textit{n3}_{\textit{max}}\times(\textit{s3}+\textit{w3})\leqslant \textit{L3}\end{array}\right. \tag{21}\]  
In this formula, n1 and n3 are the maximum positive integers, respectively; w1 and s1 are the groove width and slot pitch of the inlet section of the BSCN, respectively; and L1 is the length of the inlet section of the BSCN. w3 and s3 are the groove width and slot pitch of the outlet section of the BSCN, respectively, and L3 is the length of the outlet section of the BSCN.  
Figure 8: Groove width and slot pitch change under different groove depths: (**a**) inlet section of the BSCN and (**b**) outlet section of the BSCN.  
Figure 9: The number of circular ring grooves in the inlet and outlet sections.
# Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
###### Abstract  
_Peer-to-Peer (P2P)_ networks have emerged as potential solutions to issues that cause inefficient download times in networks because they can use the resources in the entire network, allowing nodes to act both as servers and clients simultaneously. Commonly, P2P networks use _radio frequency (RF)_ to communicate among nodes; however, _light fidelity (LiFi)_ has been developed as an alternative to _wireless fidelity (WiFi)_ systems due to some advantages such as great speed (up to 1 Tbps), a high level of security, and less saturated channels in unlicensed bandwidth, making it ideal for RF-sensitive environments and networks with static nodes, since LiFi systems require a high node alignment level to enable efficient communication. In this article, we develop a mathematical model that captures the dynamics of LiFi- and WiFi-based P2P networks in static environments to allow for adequate LiFi links in managed conditions where the content is distributed from a single source node to the rest of the peers through different architectures. For the services considered in our work, we compare the performance of P2P networks using WiFi 7 and LiFi 2.0 technologies to provide clear quantitative numerical results that allow for adequate selection between these systems. Also, we validate our mathematical results through extensive simulations.  
P2P; LiFi; decentralized; teletraffic; CTMC; analysis +
Footnote †: journal:  
Article Performance Analysis of P2P Networks with Light Communication Links: The Static Managed Case
# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 2 Materials
### CarbonTracker  
NOAA built the world's first business-oriented global carbon assimilation system CT (CarbonTracker) in 2007. CT is an atmospheric assimilation model used to study the temporal and spatial variation characteristics of carbon flux [27; 28]. It is used to estimate the variation in surface CO\({}_{2}\) absorption and release over time. CT includes an ocean module, a fire point module, a fossil fuel module, a land surface ecosystem module, and an atmospheric transmission module. It obtains the best state of each module through the data assimilation method. CT provides an effective scientific and decision-making tool for estimating carbon flux, but it relies too much on surface observation data. In areas with few surface observation data, the accuracy of CT still needs to be improved. In recent years, with the support of the EUROCOM (European atmospheric transport inversion comparison) framework, scientists strive to achieve the accuracy and consistency of regional high-precision carbon flux estimation, so as to provide strong support for the assessment of climate impact. The CT XCO\({}_{2}\) datasets used are from NOAA CT2019B ([[https://gml.noaa.gov/ccgg/carbontracker/CT2019B/](https://gml.noaa.gov/ccgg/carbontracker/CT2019B/)]([https://gml.noaa.gov/ccgg/carbontracker/CT2019B/](https://gml.noaa.gov/ccgg/carbontracker/CT2019B/)), accessed on 1 December 2021).  
The introduction of data from OCO-2, GOSAT and CarbonTracker is shown in Table 1.
## 2 System Architecture Framework
### Dense Mapping and Octree Mapping  
This research incorporates a dense mapping thread into the ORB-SLAM3 architecture to attain high-precision dense mapping. This thread employs depth data from RGB-D cameras to produce dense point clouds with the addition of new keyframes, delivering high-resolution, three-dimensional representations of all pixels using spatial coordinate computations. Furthermore, point cloud data are tinted with RGB information to maintain visual environmental characteristics. The system employs an octree data format to enhance the storage and management of dense point clouds. The octree efficiently compresses dense point cloud data by hierarchical voxel partitioning, preserving high-precision spatial representation and markedly enhancing system adaptability and real-time performance in intricate situations.
## 2 Asymmetric Turbo Code (ATC)  
The ATC encoder is composed of an interleaver \(\pi\), and two different recursive systematic convolutional (RSC) encoders, \(\mathrm{B_{1}}\) and \(\mathrm{B_{2}}\), as shown in Figure 1. The \(\mathrm{g_{a}(F)/g_{b}(F)}\) represents the generator polynomial of RSC encoder, where \(\mathrm{g_{a}(F)}\) and \(\mathrm{g_{b}(F)}\) denote the feed-forward and feedback encoding polynomials, respectively.  
The input information sequence \(\mathbf{q_{1}}\) and interleaved sequence \(\mathbf{q_{1}^{I}}\) are encoded by \(\mathrm{B_{1}}\) and \(\mathrm{B_{2}}\) into parity sequences \(\mathbf{p_{1}}\) and \(\mathbf{p_{2}}\), respectively. Then, these sequences are combined with the input sequence \(\mathbf{q_{1}}\) to generate the entire ATC codeword \((\mathbf{q_{1}},\ \mathbf{p_{1}},\ \mathbf{p_{2}})\).