esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sentence-in-Noise Test
Listeners were asked to orally repeat everything they heard as accurately as possible and were encouraged to guess when uncertain. There was no time limit for responses. Based on the listener's answer, the tester indicated correctly recognized words on the computer. Correct recognition of 3, 4, or 5 words within a sentence was designated as a correct response and accurate word recognition of only 0, 1, or 2 words within a sentence was marked as incorrect. A two-down, one-up tracking procedure was used to estimate the SNR corresponding to the 70.7% sentence recognition on the psychometric function [53]. Specifically, the SNR step (initially 25 dB) was reduced by a factor of two following two correct responses or one incorrect response until the second reversal (i.e., the step size was reduced to 12.5 dB and then to 6.25 dB). For the next (3 rd) reversal, the SNR step was reduced by a factor of 1.41 (\(\sqrt{2}\)) to 4.43 dB, and for the subsequent reversals (4 th-6 th), the SNR step was reduced by a factor of 1.19 (\(\sqrt{2}\)) to 3.72 dB. SRTn was calculated as the arithmetic mean of the last four reversals.
## 3 Numerical Implementations and Algorithms
### Additional Solid Routines
As discussed above, quasi-static studies are the next step in investigating computational solid dynamics. In contrast, a real dynamic case may still require supplementary efforts. Moreover, complex solid constitutive laws, such as non-linear ones, must be tested and contrasted with the adapted reference results. In addition, the extension to 3D should be natural and in consequence could produce relevant results. In this work, ideas related to solid distribution functions were given, but they undeniably need further work so as to gain a deeper comprehension of solid equilibrium. For all of these reasons, we aim to share the presented advances for the benefit of the community. Conceptualization, R.N. ; Funding acquisition, L.N. ; Investigation, T.M. ; Methodology, T.M. and R.N. ; Project administration, L.N. ; Software, T.M. ; Validation, G.C. and L.N. ; Writing--original draft, T.M., R.N. and L.N. ; Writing--review and editing, T.M., R.N., G.C. and L.N. All authors have read and agreed to the published version of the manuscript. This work has been partially funded by the French National Research Agency (Agence Nationale de la Recherche) via the LBSMI project ANR-15-CE19-0002. This support is gratefully acknowledged. Not applicable. Data Availability Statement: Data are available from reasonable request to the authors. The authors declare no conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} BE & Boltzmann Equation \\ BGK & Bhatnagar, Gross and Krook \\ CFD & Computational Fluid Dynamics \\ FDM & Finite Difference Method \\ FEA & Finite Element Analysis \\ FEM & Finite Element Method \\ FPD & Fundamental Principle of the Dynamics \\ FSI & Fluid Structure Interaction \\ FVM & Finite Volume Method \\ GPU & Graphics Processing Unit \\ LBE & Lattice Boltzmann Equation \\ LBGKE & Lattice Boltzmann-BGK Equation \\ LBM & Lattice Boltzmann Method \\ LBMS & Lattice Boltzmann Method for Solids \\ LGCA & Lattice Gas Cellular Automata \\ MPM & Material Point Method \\ MRT & Multiple Relaxation Times \\ TRT & Two Relaxation Times \\ NRMSE & Normalized Root Mean Square Error \\ NSE & Navier-Stokes Equations \\ NSF & Navier-Stokes-Fourier \\ PaLaBoS & Parallel Lattice Boltzmann Solver \\ PDE & Partial Differential Equations \\ QE & Quasi-Equilibrium \\ REV & Representative Elementary Volume \\ RMSE & Root Mean Square Error \\ SPH & Smoothed Particle Hydrodynamics \\ SI & International System of Units \\ WENO & Weighted Essentially Non-Oscillatory \\ WSS & Wall Shear Stress \\ \end{tabular} 

\begin{tabular}{l l} \multicolumn{2}{l}{**Nomenclature**} \\ \(\rho\) & mass density \\ \(D\) & physical space dimension \\ \(f\) & density distribution function over velocity space \\ \(\xi\) & microscopic velocity of particles \\ \(\upsilon\) & macroscopic speed field \\ \(u\) & macroscopic displacement field \\ \(\sigma\) & Cauchy stress tensor \\ \(\varepsilon\) & linearised strain tensor \\ \(B\) & left Green-Cauchy strain tensor \\ \(H\) & Gibbs
## 5 Result and Analysis
Whereas at the same BER of \(10^{-3}\), the transmission latency or delay was 45 ms in the conventional QAM protocol. This validates the lower latency or delay in the LHM-N transmission than in the conventional QAM protocol. Figure 6: Comparative analysis of BER for LHM-N and the conventional QAM protocol. Figure 7: Comparative analysis of the throughput for LHM-N and the conventional QAM protocol. Figure 9 shows comparisons of the protocol overhead for packet forwarding transmission. From the three evaluated algorithms, the PSFE algorithm outperforms TCP Reno and TCP Cubic algorithms with very low protocol overhead throughout the simulation runtime during the packet-forwarding transmission. The PSFE has a maximum attainment of 0.5 protocol overhead. This is the least attainment of protocol overhead compared with TCP Reno and TCP Cubic, whose maximum protocol overheads are 0.9 and 0.8, respectively. Hence, the TCP Reno and TCP Cubic encountered higher protocol overhead due to the frequent packet forwarding in their algorithm's criteria. This causes excessive overhead for the packet header and trailer, which can lead to packet loss errors, sluggish network, congestion, and high energy consumption on the device. However, the PSFE maintains conditional properties based on a given threshold for packet forwarding. This allows only accumulated packets to be forwarded based on the conditional criteria leading to minimal protocol overhead. The minimal protocol overhead can be attributed to enabling congestion avoidance in packet-forwarding transmission. Figure 8: Comparative analysis of latency (delay) for LHM-N and the conventional QAM protocol. Figure 9: Comparisons of protocol overhead for packet-forwarding transmission.
# Real-Time Adjustment Method for Metro Systems with Train Delays Based on Improved Q-Learning
## 5 Case Study  
### Data Input  
To assess the effectiveness and efficiency of the algorithm, this study selects Shenzhen Metro Line 14 as a case study, as illustrated in Figure 7. The experimental scenario in this research involves the operation of 16 trains, with a total of 17 physical stations along the metro line. The train headway, representing the time interval between consecutive trains, is set to 257 s. Additionally, all trains are assigned the same block running time level in all sections, ensuring uniformity in their travel durations. Furthermore, each train adheres to the scheduled station dwelling time, maintaining consistency in the time spent at each station. The data used in the case study can be found in Supplementary Materials.  
The testing period spans from 7:00 AM to 10:00 AM. This timeframe is selected based on Figure 8, which illustrates a substantial passenger flow during the morning peak hours. It is during this period that train headways are relatively short, meaning that delays occurring within this timeframe will have a significant impact on train operations.
## 2 WSN for Monitoring of Column Shortening in High-Rise Buildings
### 2.2. Server and Monitoring Software  
All data measured by the sensors installed at the site are transmitted to the server through the WSN system. This server can be located either at the construction site or off-site, where it can simultaneously receive and manage data transmitted from numerous construction sites.  
The server comprises data reception module, database module, and situational awareness module. The data reception module receives data sent by the master node through a CDMA modem and transmits it to a database module. The database module is where all the information handled by the web-based management system, as well as the data transmitted by master node, are stored. This is also where the various internal procedures related to data storage and program operations are registered. Through the database module, the managing program can conduct data queries and the data can be stored and managed according to the type. The situational awareness module analyzes received data regularly to evaluate structural safety and risk. When the level of measured structural response is larger than the established acceptance criteria, the module sends a message through Short Message Service (SMS), e-mail, _etc_. The situational awareness module also has a regular analysis and alarm function regarding the condition of sensors and nodes so that action can be taken promptly.  
As shown in Figure 2, the server can be connected, anywhere and anytime, to check the situation at the site in real-time through the web-based managing program. Because the data received by the server is accessible online, the situation at the site can be monitored in real-time without the necessity of further action. For the convenience of site management, the server can also manage several sites where wireless sensor systems are installed and many users can connect to the server simultaneously.
# A Hybrid Deep Learning Model for Protein-Protein Interactions Extraction from Biomedical Literature
## 5 Discussion  
Recently, DL models have been shown to be superior to traditional ML models in many NLP tasks. In PPI extraction tasks, the performance of a DL-based method is mainly affected by the architecture of the DL models and the feature embedding methods. In this study, we compared different architectures of DL models, including CNN, LSTM, and hybrid models, and found that the proposed bidirectional LSTM+CNN model is superior to the other DL models for PPI extraction. As more complex network architectures (e.g., a deep hybrid model with more layers or a deep reinforcement learning model) have not been considered in this study, there is still space for further improvement of the DL model architecture.  
In addition to the DL model architecture, the feature embedding methods also have an effect on the classification performance. In DL models, the input semantic and syntactic features are embedded in their distribution representations for feature learning. Previous studies have experimented with some different feature embeddings (such as ngram, part-of-speech (POS), word position, and dependency path). In this study, the stacked pretrained word embedding and SDP embedding are fed into a two embedding channel (word embedding and SDP embedding) architecture, such that the model can extract the feature information more accurately. With more feature channels, an improved performance can be expected.  
The difference in the text preprocessing strategies (e.g., protein entities are generalized with special symbols or protein IDs, utilizing different tokenization and parsing tools, and utilizing filtering rules or not) and other experimental settings (e.g., hyperparameter settings and evaluation metrics) make it difficult to compare the performance of the DL models directly. This study did not directly compare the results with other related studies. Instead, under the same experimental environment, our experiments covered all of the DL models that have been applied in previous studies [24; 25; 26; 31; 32]. It would be more objective to compare the performance of different DL models directly. The experiments of this study used two standard PPI datasets, and a more robust DL model can be expected when using large-scale training data.  
DL models are generally opaque, meaning that although they can produce accurate predictions, it is not clear how or why a given prediction is made. This study investigated the hidden features learned by different DL models by reducing feature dimensions and visualizing these features. This would help us to compare the classification performance of these hidden features trained by different  
Figure 6: The F1-score increasing trends of the DL models as the number of epochs increase, (**a**) F1-score vs Epochs on Bioinfer; (**b**) F1-score vs Epochs on Aimed  
DL models in an approximate way. However, how to connect an input with the hidden features and how to control them during training stage are still challenging problems.
## 3 Field Testing Results on Performance
### Stability  
After the aforementioned performance testing, stability testing was conducted in the severe environment at the Heihe Pipeline Construction Site (Long Town, Heihe City, Heilongjiang Province, China). In an outdoor environment where the temperature is between \(-15\)\({}^{\circ}\)C to \(-20\)\({}^{\circ}\)C, the testing lasted a week. The testing scene is shown in Figure 12, where the thicknesses of the pipelines (#LY3407358) are 21.4 mm. In this testing, the inspection robot is required to operate in a long pipeline with multiple challenges: horizontal sections, horizontal curved sections, curved pipe climbing sections, and sections with ice. In addition, the inspection robot must have a continuously clear recording inside the pipeline.  
Figure 13 demonstrates the scene of the robot deployment and operation. There are two climbing sections (\(14^{\circ}\) and \(17^{\circ}\)) in the testing, as shown in the gyroscope data Figure 14. Overall testing results show the velocities and currents of four motors during this testing (see Figure 15). From these results, the developed inspection robot can generate a uniform motion through the regulation of the motor currents by the control system when facing the aforementioned challenges.  
Figure 12: Testing scene at the Heihe Pipeline Construction Site.  
Figure 13: Robot deployment and operation.  
For the performance of the inspection system in this testing, the information of pipelines and the scratch positions/ranges can be clearly captured, as shown in Figure 16.  
Figure 16: Captured information and scratch in pipelines.  
Figure 14: Incline angle detected from gyroscope.  
Figure 15: Testing results at the Heihe Pipeline Construction Site.
## 2 Materials
### Data Preprocessing  
The climatic variables were first calculated and aggregated to a 16-day interval from 25 May (or 24 May) to 2 December (or 1 December), resulting in 12 time-series input features (Table 2). Then, the climate and satellite data were unified into 500 m spatial resolutions and masked by the cropland layer derived from the MODIS Land Cover Type product (MCD12Q1 v6), which has a spatial resolution of 500 m and can be obtained from the Google Earth Engine (GEE) platform. We did not use a specific crop type classification map since the rice-wheat rotation accounts for about 85% of the cultivated land in the Indo-Gangetic Plains [42]. Therefore, rice is the dominant crop of the Kharif season in the study area, and the croplands of MODIS can effectively represent the distribution area of rice. In addition, we did not use the spatial production allocation model (SPAM) data, which had specific crop type maps, because it was only available in 2005 and 2010, while the cropping area changed from 2001 to 2016. The eight sequential variables (i.e., NDVI, EVI, NIRv, SIF, Tmax, Tmin, Grad, and Pr) were aggregated spatially to the district level. Finally, a total of 96 features were applied to build models, consisting of eight sequential variables with 12 time intervals for each.  
Figure 1: The location and topographic maps of the Indian Indo-Gangetic Plains. The topographic data from GEBCO gridded bathymetry data ([[https://www.gebco.net/](https://www.gebco.net/)]([https://www.gebco.net/](https://www.gebco.net/)) (accessed on 26 September 2022)).
## 5 Conclusions & Future Work  
In this study we have extensively summarized common issues that affect communication performance in LoRaWAN networks addressed in the literature. Based on this summary we conducted simulation experiments in order to break down widespread issues into crucial factors. We summarized our findings and proposed some safe pathways to deploy a LoRaWAN network and proposed a network design process which is based not only on the network constraints but also in application ones.  
In general, clustering algorithms that improve LoRaWAN communication performance is an open issue. Several studies are proposing novel approaches based on neural networks and machine learning techniques [33; 34; 35; 36]. We plan to extend our work on clustering nodes of a LoRaWAN network by applying our findings within a clustering algorithm, so we test it in real networks. We believe that a methodology which will be able to estimate the required number of gateways in an area we want to cover, along with their optimal position, can enable scalable network deployments with efficient budget allocation.  
In addition, we believe that extensive improvements to the LoRaWAN deployment process are expected once our approach is facilitated along with recent published methodologies on planning and deployment that take into account cost and security constraints [24].  
Finally, in order to further facilitate the network design and deployment process, we are working on an adaptive end system for range testing. This tool will be suitable for the evaluation of LoRaWAN coverage on various terrains, for the estimation of the number of gateways in an area that we want to cover, as well as their position.  
Conceptualization, M.F., S.P. and E.K.M.; Data curation, M.F.; Formal analysis, M.F.; Funding acquisition, S.P. and A.M.; Investigation, M.F. and M.K.; Methodology, M.F. and S.P.; Project administration, A.M.; Resources, M.F.; Software, M.F., M.K. and N.A.; Supervision, A.M.; Validation, M.F. and S.P.; Visualization, M.K.; Writing--original draft, M.F.; Writing--review & editing, M.F. and S.P. All authors have read and agreed to the published version of the manuscript.  
This research was co-funded by \"GSRT\" with code No. 2018 EE01300000, \"Action for Research in the Agri-Food Sector of Crete\" is part of Sub-Project 2, INTERVENTION B \"Pilot implementation of new agricultural production standards\" of the project \"Flagship Research Action of National Scope for the Utilization of new Technologies in the Agri-Food Sector\".  
Not applicable.  
Data Availability StatementData presented in the paper are available on request from the corresponding author S.P.  
The authors are grateful to all participants who volunteered in the study.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Summary and Conclusions
The comparison results of the buoy measurements and ASCAT observations show a better performance under low-to-moderate wind speed conditions. This study provides an important foundation for the construction of future Chinese operational GNSS-R constellations for the observation of typhoons. A GNSS-R constellation can largely increase the capability of the spatial and temporal coverage, thereby enabling near-real-time typhoon observations and forecasts. Furthermore, all objectives mentioned above could be realized by the low-cost GNSS-R technique. However, the forerunner BF-1 A/B twin satellites still require further work. In the future, the attitude information should be studied and introduced to optimize the quality control process. This would definitively improve the performance of the empirical GMF on sea surface wind speed retrieval. Moreover, a study on the sea surface wind speed under heavy precipitation is required to improve the NBRCS behavior for high wind speeds. Finally, assessment of other applications is required for the validation, such as the inland performance to guide the future constellation mission design. Conceptualization, C.J., X.N., C.D., F.L., and G.D.; methodology, C.J., X.N., and F.L. ; software, C.J. and F.L. ; validation, C.J. ; formal analysis, C.J. ; investigation, C.J. and F.L. ; resources, C.J., X.N. ; data curation, C.J. ; writing--original draft preparation, C.J. ; writing--review and editing, X.Y. ; visualization, C.J. ; supervision, C.D. ; project administration, F.L. and G.D.; funding acquisition, C.D. This work was supported in part by The National Key R&D Program of China under Grant 2017 YFB0502800, and in part by the National Natural Science Foundation of China under Grant 61527805, 41575048, 41175023 and 41871268). The authors would like to thank Dr. [PERSON] and the operation team of China Meteorological Administration for their great contribution to the BF-1 project, and the anonymous reviewers for their hard work. C-2015 ASCAT data are produced by Remote Sensing Systems and sponsored by the NASA Ocean Vector Winds Science Team. Data are available at www.remss.com. Reanalysis data available from ECMWF were used in this research. The authors are also grateful to NOAA/NDBC for making available TAO buoy measurements at its web pages. The authors declare no conflict of interest.
# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 3 Data Selection and Preprocessing  
### Data Sources  
The experimental data in this paper is from the public dataset UK-DALE [36] and WikiEnergy [37]. The UK-DALE dataset is a public access dataset from the UK, and the sampling frequency is \(1/6\) Hz. The WikiEnergy dataset was produced by the Pecan Street company in the UK and contains the data of nearly 600 households. It includes the total power consumed by each household over a period of time and the power consumed by each individual electrical appliance. The sampling frequency of the dataset is \(1/60\) Hz [38]. Kettles, air conditioners, fridges, microwaves, washing machines, and dishwashers were chosen as non-intrusive load disaggregation tasks for the following reasons: (1) The power consumption of these electrical appliances is a large proportion of the total power consumption. They are representatives of electrical appliances. (2) Electrical appliances with low frequency and minimal power consumption used in the data are easily disturbed by noise and not easily disaggregated. (3) The power consumption of these six electrical devices includes mode disaggregation from simple to complex.
## 4 Results and Discussion  
### The Results of the ADI Prediction Models  
As elaborated in Section 3, the prediction models are developed under two scenarios to evaluate their generalizability and adaptability under varying data conditions. In Scenario One, features are collected from all ten base stations in the Jaffna district, providing a diverse and comprehensive dataset that captures a wide range of network behaviors and atmospheric conditions. This setup is aimed at building models capable of recognizing generalized patterns across a broader geographic area. In contrast, Scenario Two focuses on a localized dataset, using features from only a single base station--Jaffna Town--to assess the model's performance in a constrained, site-specific environment. This comparison helps determine whether accurate predictions can still be achieved with limited, location-specific data, which is often the case in real-world deployments.
# Assessment and Categorization of Biological Effects and
## 3 RF-EMF Exposure  
Applications of non-ionizing RF-EMF, frequencies from 100 kHz to 300 GHz (see Figure 1b) have continuously increased for almost six decades. This involves medicine (e.g., magnetic resonance imaging and RF ablation), manufacturing (e.g., heaters and solders), home usages (e.g., child video display unit and Wi-Fi), protection and navigation (e.g., radar and RFID) and particularly in telecommunications (e.g., TV transmitting and  
Figure 1: EMF frequency and wavelength ranges (**a**) ionizing and non-ionizing, (**b**) RF.  
mobile phones). This growth signifies that great amounts of the population are undergoing exposure to RF-EMF and worry has been inflated concerning public health concerns due to such exposure.