esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Approach for Modelling Tree Phenology in Mixed Forests Using Remote Sensing
## 2 Materials and Methods  
### Study Area and Data Acquisition  
#### 2.1.1 Phenological Observations  
We used data on direct phenological observations of the timing of first leaf unfolding (hereafter termed as start of season, SOS) and general leaf colouring (hereafter termed as end of season, EOS) of selected tree species of the National Phenological Network (NPN) of the Slovenian Environment Agency. Slovenia is characterised by fairly large gradients of climatic factors due to its position between the Alps, the Mediterranean and continental Europe [43; 44]. Consequently, a great variety of forest habitats, from the lowlands to the high mountains, can be found in the country [45]. Six broadleaved tree species/groups with more than ten years of available phenological observations provided by the NPN were included in this study: European beech (_Fagus sylvatica_), silver birch (_Betula pendula_), lime tree (mainly _Tilia platynphyllos_), common ash (_Fraximus excelsior_), oak (pedunculate oak and sessile oak, i.e., _Quercus robur_ and _Quercus petraea_, respectively) and aspen (_Populus tremula_). The phenological data were collected at 17 NPN phenological stations which are distributed throughout Slovenia (Figure 1).  
Tree selection and observations were made in accordance with national guidelines [46], which in general follow the World Meteorological Organization (WMO) guidelines for phenological observations [9]. The monitoring of the different phenological events was  
Figure 1: Phenological stations of the Slovenian National Phenological Network (NPN) of the Slovenian Environment Agency. The stations represented are only a subset of stations with observations of nine years or more for at least one of the tree species of interest.  
performed on single individual mature trees for each tree species in natural populations, but more tree species at the same phenological station could be included. The observed trees could be selected in forests (seldom pure), at the forest edge, and in the open area. In spring, the observed period of leaf unfolding and leaf development was approximately four weeks, and during this time, the observations were performed daily. The SOS was recorded when the first regular surfaces of leaves became visible in three to four places on the observed tree. The phase corresponds to Biologische Bundesanstalt, Bundesortenamt und Chemische Industrie code 11 (BBCH11) [47]. The EOS was recorded when at least 50% of the leaves turned yellow on the observed tree (BBCH code 94). Each observed tree was precisely geolocated (longitude, latitude) using a GPS, and station characteristics were noted (elevation, slope, and aspect). The altitude of observed trees ranged from 53 to 820 m above sea level. The period of observation was from 2004 to 2019 for _Fagus sylvatica_ and _Quercus_ sp. at some stations, and from 2010 to 2019 for the other combinations of stations and species.
## 4 Performance Evaluations
### Experimental Environments
#### 4.1.1 Sign Language Recognition Model
The batch size was set to 4, considering the computing environment used in the experiment. The learning rate was set to \(1\times 10^{-3}\), a commonly used value. The optimizer was set to SGD. By leveraging the capabilities of the Video Swin Transformer, our performance evaluations aim to provide evidence that the recognition accuracy of the model can be improved by the proposed dynamic sampling rate adjustment method compared to methods proposed in previous research. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Configurations** \\ \hline Loss Function & Label smoothing cross-entropy \\ \hline Learning Rate & \(1\times 10^{-3}\) \\ \hline Optimizer & SGD \\ \hline Epochs & 200 \\ \hline Batch Size & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameters and their settings to train the sign language recognition model.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 4 Future Trend of ML in IVLC
### Intelligent Physical Layer
#### 4.1.3 Modulation Format Recognition  
With the continuous improvement of the transmission capacity, the future VLC networks must be a mixture of multiple transmission rates and multiple modulation formats, thus modulation format recognition (MFR) will become an essential part of the overall communication system. In the early years, traditional machine learning methods, such as decision trees and SVM [97], were applied in VLC format recognition. The drawback of these solutions is that they rely on manual feature extraction, which leads to a lack of flexibility and portability.  
Recently, deep learning is widely used in pattern recognition because of its ability to mine useful feature information at a deeper level. DNN is able to extract deeper layers of the signal by stacking layers of hidden layers [98]. However, as the number of hidden layers in DNN increases, the structural complexity increases. Therefore, it is especially important to design the network structure of DNNs rationally. CNN performs local feature extraction of information by setting appropriately sized convolutional kernels, and subsequently achieves classification through fully connected layers [99; 100]. The feature-extraction part is mainly composed of convolutional and pooling layers, and the recognition and classification part is the same as the fully connected layer of the BP neural network. Other schemes that combine format recognition with deep learning, such as RNN [101], PNN [102], etc., have been gradually proposed.  
It can be foreseen that due to the complexity of visible communication channels and the diversity of modulation formats, there will be an urgent need for machine learning-based modulation format technology. AI-driven MFR will effectively improve the recognition rate and accuracy, accelerating the pace of visible light communication applications.
# Improving Sustainability of Steel Roofs: Life Cycle Assessment of a Case Study Roof
###### Abstract  
In New Zealand (NZ), steel roofing is the most common roofing type for residential buildings. However, the environmental impact of steel roofs are not negligible. Steel roofs account for up to 30% of the upfront embodied carbon for a typical NZ house, highlighting the importance of investigating and reducing the environmental impacts of steel roofs in meeting the net-zero carbon goal of the NZ government. There are several research gaps in investigating the impacts of steel roofs, such as the variation in emissions between local and imported steel coil products, the unavailability of life cycle assessment (LCA) data, and additional impacts from ancillary items. Therefore, this study performed an LCA of a case study roof in NZ to investigate these gaps using GaBi Ts software. It was found that the overall impacts from the steel roofs, including the ancillary items, were 12 kg CO\({}_{2}\)-eq/m\({}^{2}\), where the ancillary items accounted for less than 10% of total emissions. The prepainted steel coils manufactured globally had less than 70% emissions when compared to the emissions of locally manufactured prepainted steel coils. In addition, the roll forming processes had an insignificant effect on overall emissions, whereas the transportation of steel coils had a notable impact.  
life cycle assessment; global warming potential; steel roofs; GaBi Ts; New Zealand +
Footnote †: journal: Applied Sciences
# Optimizing Perishable Product Supply Chain Network Using Hybrid Metaheuristic Algorithms
## 2 Problem Description
### Assumptions  
This study is based on the following assumptions:  
* Each retailer is served by one vehicle for one perishable product at any period.
* The demands of retailers are independent and follow specific probability distributions.
* Each product in the system has a predetermined shelf life.
* The number of DCs and their capacities are limited.
* The vehicle routing problem is specifically defined between the DCs and retailers.  
* The number and capacity of vehicles are predetermined.
* Shortage considerations are not included.
## 6 Complexity Analysis
### Accuracy
#### 6.3.1 Datasets  
The DNSA encounters rapid growth in both high- and low-resource languages due to its continuous extraction of news articles from multiple sources. As an example, the DNSE collects approximately 400 Urdu news articles from five different sources, 180 Arabic news articles from three sources, and 700 English news articles from ten online sources on a daily basis.  
To evaluate the proposed similarity measures, the DNSA selects datasets based on the heading or title of news articles, focusing on currently hot topics from a general pool. Table 5 provides a summarized overview of the datasets used in the evaluation process and Table 6 is used to analyse the similarity based on human-based observation.  
To evaluate the comprehensive overall impact of the proposed similarity measures, a dataset consisting of 282 news articles is utilized. These news articles are sourced from two online television broadcasters, namely Geo and Samaa News, and are available in both English and Urdu languages. The dataset includes 152 Urdu news articles and 130 English news articles that were selected from a general pool. For further details on the news articles used in the empirical evaluation, please refer to Table 7.
## 4 Conclusions  
In this study, an upscaled, high-flow-rate, ten-layer DBD reactor, capable of treating gas at a flow rate of 1000 L/min, was designed and fabricated for high-flow-rate gas treatment, especially for controlling indoor air quality. The design methodology for developing high-flow-rate DBD reactors, including CFD simulations, flow path optimization via practical and OES measurements, and electrical and spectroscopic characterizations, provided fundamental knowledge for the real-life application of DBD technologies, especially for environmental protection (exhaust purification, outdoor and indoor air pollution mitigation, wastewater treatment, CO\({}_{2}\) reduction, etc.). Notably, both the CFD simulations and practical velocity measurements confirmed remarkable improvement in the airflow uniformity in the multilayer DBD configuration owing to the incorporated airflow diffuser and perforated plates. The velocity uniformity in the ten-layer DBD reactor was significantly improved from 1.38 m/s to 0.13 m/s, considering the standard deviation of the ten flow channels in the practical measurements. Moreover, multi-gas (air, Ar, He, N\({}_{2}\), O\({}_{2}\), and CO\({}_{2}\)) plasma ignition and high-flow-rate plasma generation of up to 1000 L/min were achieved with the upscaled ten-layer DBD reactor, and this observation validated its effectiveness under multi-gas conditions and at high flow rates. Furthermore, spectroscopic characterization and gas temperature evaluation of the air plasma at high flow rates were also carried out, which confirmed that the optimum working flow rate was 680 L/min (in terms of emission intensity), and the gas temperature at the plasma-on condition was maintained at (low values) 51.85 and 42.41 \({}^{\circ}\)C at 50 and 1000 L/min, respectively.  
However, the treatment characteristics and performance of the ten-layer DBD reactor for indoor air pollutants, such as VOCs, were not investigated in this study. Future investigations will be focused on the characterization of VOC decomposition, such as toluene treatment, along with by-product analysis for environmental risk assessment and the decomposition of multiple VOCs or their mixtures for further refinement of high-flow-rate DBD reactors.  
Conceptualization, M.X., Y.M., Z.L., Y.F., Y.S., T.Z. and A.O.; methodology, M.X., Y.M., Y.F., T.Z., Y.S. and Z.L.; validation, M.X., Y.F. and Z.L.; formal analysis, M.X.; investigation, M.X., Y.F. and Z.L.; resources, M.X., Y.F., T.Z., Z.L. and A.O.; data curation, M.X., Y.F. and Z.L.; writing--original draft preparation, M.X.; writing--review and editing, M.X., Y.M., Z.L., Y.F., Y.S., T.Z. and A.O.; visualization, M.X.; supervision, A.O.; project administration, A.O.; funding acquisition, M.X. and A.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by JST SPRING, grant number JPMJSP2106, and by the Cooperative Research Project of the Research Center for Biomedical Engineering.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
## 2 Numerical Aspects and Experimental Designs  
### The Numerical Model and Data Assimilation System  
This study applies a non-hydrostatic atmospheric model, the MPAS version 5.2 for DA cycling (developed earlier) and version 6.1 for the free (main) forecast. MPAS was jointly developed by the climate modeling group at Los Alamos National Laboratory and the National Center for Atmospheric Research (NCAR) [1]. The global model MPAS has unstructured centroidal Voronoi meshes with C-grid staggering of state variables, allowing variable-resolution grid meshes with enhanced resolution at targeted regions. For the experiments in this study, the physics schemes for the \"mesoscale reference\" suite are used, which include the new Tiedtke scheme for cumulus convection [33], the WRF Single-Moment 6 (WSM6) for cloud microphysics [34], the Noah land surface model [35], the Yonsei University (YSU) for the planetary boundary layer parameterization [36], and the Rapid Radiative Transfer Model for General Circulation Model (RRTMG) for longwave and shortwave radiation [37].  
NCEP developed the GSI DA system, which can assimilate traditional data, satellite data, radar data, and GPS RO data [6; 38]. The GSI system was formulated as a hybrid DA system in 2012 (e.g., [39; 40]), which combines the advantages of 3 DVAR on static/climatological background errors (BEs) and EnKF on flow-dependent BEs. The GSI system has been implemented and operationally executed at CWB. More details about the GSI system can be found in [PERSON] et al. [22]. Ref. [22] conducted cycling DA with the GSI system and global forecast model at the CWB, which was coordinated with the MPAS simulation for free forecasts.  
In this study, we followed [22] but carried out the GSI DA system with the MPAS model for both the cycling DA process and the free forecast. This MPAS-GSI system was developedby NCAR but was not previously documented in the peer-reviewed literature. The primary development challenge of the MPAS-GSI system was appropriately handling various grid meshes: MPAS has an unstructured mesh, while GSI can only produce analyses on a Gaussian grid with regular intervals between latitude and longitude points. Accordingly, the MPAS-GSI system involves an interpolation step, where prior (before assimilation) states are interpolated onto a user-specified Gaussian grid, and the analysis increments are computed within GSI on this Gaussian grid. The increments are then interpolated back onto MPAS's mesh and added to the prior to complete the DA update on MPAS's native mesh. In addition, variable transforms are applied to convert MPAS's prognostic variables [1] to those needed by GSI. MPAS-GSI consists of a variational component as well as an ensemble square-root Kalman filter [41] and is capable of assimilating the full set of observations operationally assimilated at NCEP.  
In this work, a recentering process to update the ensemble mean for the EnKF ensemble members is adopted. Use of MPAS-GSI in the DA system is different from [22], in which CWB's global spectral model was used in the DA system.
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 4 Simulation Study
### The Effect of the Node Mobility  
We performed the simulation under the scenario where some nodes move. To model the mobility of the node, we introduced the following parameters; the fraction of mobile nodes among the whole nodes (\(\sigma\)), the speed (\(v\)) and direction (\(\theta\)) of movement. We considered several values of \(\sigma\) ranging from zero to one to observe the effect of the degree of node mobility. Once a node is determined as a mobile node, its moving speed (\(v\)) and direction (\(\theta\)) were randomly determined initially and maintained as constant until the end of simulation time. For each mobile node, we set the value of \(v\) with a random variable uniformly distributed between 1 and 4 m/s by considering the speed of walking or running, which is reasonable for the service scenario shown in Figure 1. Similarly, we set the value of \(\theta\) randomly between 0 and \(2\pi\). Figure 15 shows \(\overline{F}_{val}\) and \(\overline{T}_{dis}\) of our proposed mechanisms when \(N_{node}\) was 100 and \(\sigma\) was 0.0, 0.2, 0.8 and 1.0. In all the mechanisms (DRBF, ADRBF, DRBF\({}_{\text{RQ}}\), and ADRBF\({}_{\text{RQ}}\)), both values of \(\overline{F}_{val}\) and \(\overline{T}_{dis}\) were hardly affected by the change of \(\sigma\), i.e., the performance of the proposed mechanism was hardly degraded due to the node mobility. Meanwhile, the results in Figure 15 confirm again that (i) the re-queuing scheme is effective to improve the reliability of flooding at the cost of traffic dissemination time; compared to DRBF and ADRBF, DRBF\({}_{\text{RQ}}\) and ADRBF\({}_{\text{RQ}}\) increased \(\overline{F}_{val}\) by about 50 frames and also increased \(\overline{T}_{dis}\) by about 1.5 and 0.6 s, respectively, (ii) ADRBF\({}_{\text{RQ}}\) compromises the reliability and delay of flooding; compared to DRBF\({}_{\text{RQ}}\), ADRBF\({}_{\text{RQ}}\) decreased \(\overline{F}_{val}\) by only 1.3% but decreased \(\overline{T}_{dis}\) by more than 18%.  
Figure 14: The effect of the duplication ratio approximation on the number of valid frames (\(\overline{F}_{val}\)) and traffic dissemination time (\(\overline{T}_{dis}\)).
# Design of Wideband Decoupling Antenna Array for 5G Smartphones at N77/N78/N79/WLAN 5 GHz Bands
###### Abstract  
We propose a broadband decoupled antenna pair for 5G mobile terminals. The broadband decoupled design of this antenna pair is based on the characteristic mode theory (CMA) and defective ground structure. First, CMA is applied to obtain the characteristic current distribution of the antenna, then the characteristic current sensitive regions are optimized to make the antenna introduce new modes and obtain a wide bandwidth. After that, an antenna pair is added with defective ground structure to obtain a wideband decoupled antenna pair that has small size and high isolation. Next, an eight-element MIMO antenna system is constructed with the obtained broadband decoupled antenna pair, and a composite decoupling technique consisting of defective ground structure (DGS) and decoupling strip is applied to the two antenna pairs on the same side of the bezel to improve the isolation. The simulation and prototype test results show that the eight-element MIMO antenna with \(-10\) dB bandwidth of 3.28-5.85 GHz mainly covers the N77/N78/N79/WLAN 5 GHz band, and the antenna pair are only 1.6 mm apart with good isolation (\(-16.7\) dB), the ECC is less than 0.01, and it has a good total efficiency at the main operating frequency. Finally, the effect of a user's hand on the antenna is briefly analyzed to verify the robustness of the proposed MIMO antenna system.  
antenna array; characteristic mode (CM); DCS; multiple-input multiple-output (MIMO) +
Footnote †: journal: _sciences_
# Review on Structural Health Evaluation with Acoustic Emission
## 1 Introduction  
Nondestructive evaluation (NDE) of various structures, large and small, has been the primary target of acoustic emission (AE) technology along with its uses in materials research. The first success of AE technology was achieved at Aerojet for the inspection of Polaris missile chambers in the 1960s [1]. Further works continued for nuclear and chemical pressure vessels and tanks [2, 3]. For industrial AE applications to fiber reinforced plastics (FRP) vessels, the Committee on Acoustic Emission for Reinforced Plastics (CARP) was instrumental in code development, culminating in ASME Boiler and Pressure Vessels Codes and ASTM standards. See [PERSON] et al. [4] and four following articles in the special issue of Journal of AE in 1989. A wide range of AE applications have been compiled in the AE volume of the _Nondestructive Testing Handbook_[5], while many articles appeared in conference proceedings and in Journal of AE [6]. [PERSON] [7] and [PERSON] [8] reviewed various applications of AE to infrastructures and to structural diagnosis, respectively. Two recent review articles [9, 10] on bridge and pressure-vessel inspection are noteworthy in connection to the topic of this introduction. Another review was published as a Sandia report, comparing AE with other methods for structural health monitoring (SHM) in evaluating damages to a full-scale wind turbine blade, and demonstrating the advantages of AE over others [11]. The present author also prepared survey papers on structural diagnosis [12] and on composites [13]. A comprehensive monograph by [PERSON] [14] on SHM of aerospace composites appeared recently and AE monitoring for SHM was covered in depth. AE uses in SHM have fully integrated acousto-ultrasonic methods, taking advantage of piezoelectric wafer active sensors (PWAS). A review paper by [PERSON] [15] covering the roles of guided waves in SHM is also useful. With the wealth of these available resources, this article will focus on those topics not addressed adequately elsewhere.
## 2 Theoretical Basis
### MOPSO-h Algorithm and Applications
#### 2.2.2 Design Variables  
The primary objective of optimizing the terminal trajectory for multi-stage anti-ship missiles is to enhance the precision of the final strike within a constrained time-frame. By integrating Formulas (3) through (11), the control coefficients at each stage are formulated, and the control variables requiring optimization are combined to establish the ideal input for the MOPSO-h algorithm. Following this process, the theoretical variable dimensions identified for optimization amount to 20, designated as \(\mathbf{X}=[H_{1},H_{2},H_{3},R_{1},R_{2},\ldots,R_{6},K_{1},K_{2},\ldots,K_{11}]\). To mitigate the issue of prolonged algorithm optimization time resulting from high-dimensional variables, certain parameters are selected manually based on tuning experiments and expert insights. This selection is guided by the essential operational requirements for anti-ship missiles during low-altitude penetration, high-altitude reconnaissance, and vertical landing angle strikes, which collectively define the parameter set \(H_{1}\)= 10 m, \(H_{2}=1000\) m, \(H_{3}=800\) m. Furthermore, considering the diminishing influence of distance threshold during terminal penetration phases as the target proximity decreases and the inherent strong overload capacity of anti-ship missiles enabling greater maneuverability at closer ranges, the distance thresholds for Phase 1 to Phase 5 are sequentially established as \(R_{1}=25\) km,\(R_{2}=20\) km, \(R_{3}=17\) km, \(R_{4}=14\) km. Additionally, given that stages 1, 3, and 5 involve constant-altitude cruising modes where rapid adjustments to the azimuth angles are critical, parameter \(K_{1}=1\) is appropriately defined. Ultimately, the actual design variables involved in the optimization process are identified as \(\mathbf{X}=[R_{5},R_{6},K_{2},\ldots,K_{11}]\), which constitutes a 12-dimensional variable.