esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Evaluation  
### Data Collection  
This study filtered the data of 2013 and 2014 from the Electronics category of Amazon Datasets ([[https://jmcauley.ucsd.edu/data/amazon/](https://jmcauley.ucsd.edu/data/amazon/)]([https://jmcauley.ucsd.edu/data/amazon/](https://jmcauley.ucsd.edu/data/amazon/)), accessed on 31 July 2020) for use as the dataset for the experiment. When analyzing the contents of user reviews, if the number of text reviews by users is too small to accurately analyze user preferences, sentiments, and semantics according to different aspects, it may affect the accuracy of rating prediction. Therefore, users with more than nine reviews and ratings, and products with more than five reviews, were selected for analysis. The filtered dataset consisted of 3409 users and 3440 products, with 46,279 reviews and ratings.  
To conduct the experiment evaluation, 75% of the dataset was employed as a training set to train the feature vectors of the users and products, and then build a rating prediction model. In total, 5% of the dataset was used as a validation set to train the parameters of the proposed methods. The remaining 25% of the dataset was used as a testing set to verify the accuracy of the rating predictions. In data pre-processing, text pre-processing, such as word breaking, sentence breaking, useless word removal, punctuation removal, and stemming are performed in review articles. In aspect detection, we set each aspect as containing 30 feature words. The feature word vector is a pre-trained GloVe word vector [30] with 50 dimensions. In addition, the dimensionality of the aspect sentiment vector and aspect semantic vector were both set as 50 dimensions.
## 2 Materials and Methods
### Statistical Analysis  
The data collected were processed using IBM SPSS statistical software (Version 25, IBM Corporation, New York, NY, USA). The intraclass correlation coefficient was performed to calculate Interrater and Intrarater reliability. The normality of the data was verified with the Shapiro-Wilk test. Student's \(t\)-test was used to identify statistically significant differences between the final discrepancy index score obtained using digital versus manual techniques  
\begin{table}
\begin{tabular}{c c c} \hline
**Section** & **Diagnostic Elements** & **Measurement Technique** \\ \hline The final discrepancy index score & Sum of the different sections & The points obtained for each section of the discrepancy index were added. \\ \hline Overjet & Digital/dental casts & Measured from the buccal surface of the most lingualized lower tooth to the middle of the incisal edge of the most vestibularized upper tooth. (Figures 1A and 3A) \\ \hline Overbite & Digital/dental casts & Measured from the incisal edge of the upper tooth to the incisal edge of the lower central or lateral tooth. (Figures 1B and 3B) \\ \hline Anterior open bite & Digital/dental casts & Measured from canine to canine, taking into account a ratio of \(\geq\)0 mm. \\ \hline Lateral open bite & Digital/dental casts & Each maxillary posterior tooth was measured at a ratio of \(\geq\)0.5. \\ \hline Crowding & Most crowded arch & The most crowded arch was measured from the mesial contact point of the right first molar to the mesial contact point of the left first molar. (Figure 1D–F and Figure 3D–F) \\ \hline Occlusal relationship & Digital/dental casts & The molar angle classification was used for each side of the model. \\ \hline Lingual posterior crossbite & Digital/dental casts & Each maxillary posterior tooth was measured where the maxillary buccal cusp is \(>\)0 mm lingual to the buccal cusp tip of the opposing tooth. \\ \hline Buccal posterior crossbite & Digital/dental casts & Each maxillary posterior tooth was measured where the maxillary palatal cusp is \(>\)0 mm buccal to the buccal cusp of the opposing tooth. (Figures 1C and 3C) \\ \hline Cephalometrics & Digital and printed radiographs & ANB, SN-MP and \(\overline{1}\) to MP angles. \\ \hline Other & Models, radiographs, photographs & Other conditions, which increase treatment complexity. \\ \hline \end{tabular}
\end{table}
Table 1: The discrepancy index score procedure.  
and for the scores of the 10 sections that comprise this index. \(p\) values \(\leq\) 0.05 were considered significant. Kappa coefficient tests were also used to evaluate the concordance of the categorical measurements obtained from the instrument. The Bland-Altman method was used to determine the agreement of continuous measurements obtained from the final discrepancy index score using digital versus manual techniques.
## 2 Materials and Methods
### Machine Learning Models  
Before discussing the experimental design, it was necessary to briefly explain the ML algorithms used. As mentioned above, the five algorithms employed were KNN, LR, SVM, ANN, and RF. These models were trained for binary classification to distinguish between patients with confirmed TB and those without TB.  
KNN is an instance-based learning classifier that does not attempt to build a general internal model. Instead, it stores instances of the training data and classifies new samples based on the \(k\) (a specified number) nearest neighbors. Classification is determined either by a majority vote among the nearest neighbors or by assigning weights inversely proportional to the distance from the point being classified [43].  
The regression task developed by the LR model consists of optimizing a cost function. It models the likelihood of a binary outcome (in this case, TB detection) using a logistic function [40].  
For ANN, different architectures can be applied, In this study, a single hidden layer with a set of neurons was used. Each neuron performs a weighted sum of its inputs and applies an activation function, which adds nonlinearity to its output. The model learns the optimal weights from the training set and generalizes them to new inputs [44].  
Another approach for classification can be implemented through the use of SVM. This model aims to maximize the margin, defined as the distance between the separation hyperplane and the closest labeled training samples, known as support vectors [45]. These support vectors contribute to the distinguishing between the TB-detected and non-detected classes.  
The hyperparameter optimization was performed using a grid search, systematically evaluating different parameter combinations to identify the best configuration based on training performance for each model. For the SVM, the parameters tested included the regularization parameter C, kernel type, and gamma. The KNN model was optimized by varying the number of neighbors, weight function, algorithm type, and leaf size. In the case of the LR model, the regularization parameter C and optimizer were adjusted. For the ANN, the optimization process involved tuning the number of neurons and layers, batch size, number of epochs, activation function type, and learning rate. Finally, for the RF model, the number of estimators and the sample-splitting strategy were explored.  
Finally, the RF model is an ensemble training method that combines multiple decision trees, each trained independently. While a single tree may not be sufficient for accurate classification, RF improves performance by aggregating multiple weak classifiers to enhance prediction accuracy [46]. All models were implemented using the _Scikit Learn_ library, except for ANN, which was implemented using the Keras library and its version 2.7.0 rc0 [47]. For each case, the hyperparameters were optimized through a grid search strategy, where different values were tested, and the best-performing configurations were selected.
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Laser Integration, Reliability and Performance  
#### 2.2.1 External Light Source vs. Integrated Light Source  
The reliability of the laser source is the most important requirement for the high-temperature operation of silicon photonic optical transceivers, yet the integration of reliable light sources is also one of the most challenging aspects of silicon photonic optical transceivers. The three main options for optical sources are (a) an integrated laser; (b) a board-mounted source, but external to the transceiver; and (c) a front-pluggable laser source, as shown in Figure 5. In order to minimize power consumption and cost, the  
Figure 4: IOCore micro-transceiver: (**a**) photo of proprietary transmitter multimode optical interface [3]; (**b**) top photo of complete IOCore micro-transceiver; (**c**) photo of proprietary receiver multimode optical interface; (**d**) cross-section of transmitter section with untapered waveguides (“optical pins”); (**e**) cross-section of receiver section with tapered waveguides (“optical pins”).  
laser should be integrated into or onto the silicon photonic chip as shown in Figure 5a. The disadvantage of this is that the laser is not field-replaceable and therefore must be extremely reliable in the densely populated component environment of the chip. In order to reduce the risk of transceiver failure due to failure of the laser source, many silicon photonics solutions opt for an external light source (ELS) scheme, whereby the CW laser is not integrated but rather located outside the transceiver assembly. This allows the laser to be in a more temperature-controlled environment. Some higher margin solutions may use Peltier coolers to ensure low-risk and stable operation of the laser source, but uncooled on-board (Figure 5b) or front-pluggable (Figure 5c) modules are likely to become more commercially viable, especially if they are field-replaceable.  
Figure 6 compares a multimode silicon photonic micro-transceiver with an integrated laser (Figure 6a) to a single-mode silicon photonic transceiver with an external laser source (Figure 6b). External laser source systems typically require polarization-maintaining fibers (PMFs) to ensure low loss coupling of CW light to the transceiver modulator and will include the additional components of a second package for the laser; therefore, they represent a more expensive solution with higher power consumption.
## 2 Recent Works
### Our Research  
Unlike the existing works described above, our research is based on a dynamic UAV scenario, taking into account the coverage of the UAVs and geographical fairness over the entire target area. In addition, this article considers the lifetime of the system, which is the total time from the release of all UAVs to the end of any UAV power outage, because in some harsh environments, the UAV fleet cannot meet the conditions required to return to the starting point for charging and then returning for service. This article also considers the issue of using solar energy to charge UAVs, which has an impact on the selection of appropriate flight trajectories for UAV groups. The joint optimization problems discussed in this paper are evidently unlike those studied in the related works introduced above.
# Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things
###### Abstract  
Software Defined Networking (SDN) provides a new perspective for the Internet of Things (IoT), since, with the separation of the control from the data planes, it is viable to optimise the traditional networks operation management. In particular, the SDN Controller has a global vision of the network of sensors/actuators domain, allowing real-time network nodes and data flows reconfiguration. As a consequence, devices, usually facing limited communications and computing resources, are relieved of the route selection task in a distributed and, thus, suboptimal way. This paper proposes a SDN-IoT architecture, specifically focusing on the Controller design, which dynamically optimises in real time the end-to-end flows delivery. In particular, the dynamic routing policy adaptation is based on the real-time estimation of the network status and it allows jointly minimising the end-to-end latency and energy consumption and, consequently, to improve the network life time. The performance of the proposed approach is analysed in terms of the average latency, energy consumption and overhead, pointing out a better behaviour in comparison with the existing distributed approaches.  
software defined networking; green internet of things; routing strategies; energy awareness; sensors and actuators domain +
Footnote †: journal:  
_Future Internet_
## 4 Materials and Methods  
### Carbon Fluxes and Climatic Data  
Daily GPP data and climatic data were retrieved from the FLUXNET 2015 Tier 1 dataset ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 10 May 2022). The FLUXNET 2015 dataset provides in situ estimates of carbon, water, and heat fluxes between ecosystems and atmosphere of 212 sites across the globe, which are determined using the eddy covariance technique. The eddy covariance data for all the sites were processed by consistent and uniform procedures including quality control, gap-filling, and net ecosystem exchange (NEE) partitioning [63]. NEE measurements were partitioned into GPP and ecosystem respiration (ER). GPP based on nighttime partitioning method (GPP_NT_VUT_REF) was used in this study [64]. To avoid spurious effects caused by low-quality data, sites having at least 5 years with high-quality carbon flux data (NEE_VUT_REF_QC > 0.75 in a year) above 30\({}^{\circ}\) N were selected. In addition, only sites with mean start of greenup (SOG, defined below) occurring during spring (April-June) were included in this study. At last, 40 sites (417 site-year data) were selected to be analyzed, including 14 DBF sites, 10 ENF sites, 3 MF sites, 3 GRA sites, 8 CRO sites, and 2 WET sites (Figure 6, Table 1).  
To analyze the relationship between Vgreenup and climatic variables, this study also collected the daily climatic data including TA, PRE, SW, WS, and VPD provided by the FLUXNET sites. Considering the unavailability of SM at most sites, the daily root-zone SM was extracted from Global Land Evaporation Amsterdam Model (GLEAM) v3.5a to reflect the soil water condition in root zone of each site ([[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/)), accessed on 15 May 2022) [65; 66]. The depth of the root zone is different for low vegetation (0-100 cm) and tall vegetation (0-250 cm). The GLEAM v3.5a dataset was produced by combining satellite and reanalysis data, which provides temporally/spatially continuous SM at spatial resolution of 0.25\({}^{\circ}\) (\(\sim\)25 km), spanning from 1980 to 2020. The overall performance accuracy of GLEAM SM products is considerably high [43]. The mean values in spring were calculated for all climatic factors.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 3 Satellite Ground Verification System and Algorithm Hardware Acceleration Strategy
### Composition of Ground Verification System  
We constructed a ground verification system for testing before porting it into the satellite to verify the feasibility of our scheme. All of our experiments were carried out in this simulated environment. Figure 5 shows the system schematic.  
The main components of the system were the OBC (on-board computer), POBC (on-board data processing computer), GPS (global positioning system) module, switch module, camera, image simulator, and simulated ground measurement and control system. As the control center of the system, the OBC runs the satellite service software and controls the entire satellite. It sends the status and telemetry information from the satellite to the simulated ground measurement and control system, and it communicates with the GPS module through the serial port. The POBC receives the camera's image data through the camera link, processes it as required, and then transmits it via ethernet. The simulated ground measurement and control system displays the satellite telemetry signal and status parameters. The GPS module provides latitude and longitude information.  
With this system, the information flows from the camera, which collects images of the image simulator, to the POBC through the camera link for processing and storage, before finally travelling to the simulated ground measurement and control system where the data are displayed.
# Named Entity Recognition Networks Based on Syntactically Constrained Attention
## 4 Materials and Methods
### Case Study  
To specifically demonstrate the advantages of SCNA-Net over the baseline approach [1], a case study was conducted. As shown in Figure 5, in the first context, the baseline approach identifies all of \"Dallas Lutheran School\" as a location category, which is clearly incorrect; the first \"Dallas Lutheran School\" should be represented as an organization or governing body entity. In the second context, the baseline approach identifies all of \"Starbucks\" as organizational or management entities, but the first \"Starbucks\" category here should be location. In comparison, SCNA-Net is able to identify and correctly classify all of these entities. This is because, compared to the baseline approach, SCNA-Net incorporates  
Figure 4: Effect of syntactic information on NER performance.  
syntactic information contained in the text into the attention mechanism, allowing the model to expand its view of the phrase range, as well as to analyze the entity type of each word in the text more comprehensively, based on word dependencies. For example, in the second case, if the model could not link \"Starbucks\" and \"at downtown\" at the end, it would be difficult to distinguish the specific type of the entity.
## 5 Results of the Survey
### Results Related to AI
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**To What Extent Do You Teach AI in the Following Applications?** & **Not at All** & \begin{tabular}{c} **To a** \\ **Small** \\ **Extent** \\ \end{tabular} & \begin{tabular}{c} **To Some** \\ **Extent** \\ \end{tabular} & \begin{tabular}{c} **To a** \\ **Accate** \\ **Extent** \\ \end{tabular} & \begin{tabular}{c} **To a** \\ **Great** \\ **Extent** \\ \end{tabular} & \
\begin{tabular}{c} **To a Very** \\ **Great** \\ **Answer** \\ \end{tabular} &
\begin{tabular}{c} **No** \\ **Answer** \\ \end{tabular} \\ \hline Manufacturing processes monitoring & **44.9\%** & 13.0\% & 13.0\% & 10.1\% & 5.8\% & 7.2\% & 5.8\% \\ Deliveries & **65.2\%** & 4.3\% & 13.0\% & 1.4\% & 2.9\% & 4.3\% & 8.7\% \\ Computer vision & **44.9\%** & 13.0\% & 13.0\% & 10.1\% & 5.8\% & 7.2\% & 5.8\% \\ Scheduling problems & **65.2\%** & 4.3\% & 13.0\% & 1.4\% & 2.9\% & 4.3\% & 8.7\% \\ Predictive maintenance & **42.0\%** & 7.2\% & 15.9\% & 4.3\% & 10.1\% & 14.5\% & 5.8\% \\ Quality problems & **40.6\%** & 13.0\% & 14.5\% & 14.5\% & 5.8\% & 1.4\% & 10.1\% \\ Supply chains management & **42.0\%** & 15.9\% & 13.0\% & 11.6\% & 4.3\% & 5.8\% & 7.2\% \\ Anomaly detection & **46.4\%** & 11.6\% & 11.6\% & 17.4\% & 1.4\% & 2.9\% & 8.7\% \\ Optimization & **47.8\%** & 18.8\% & 7.2\% & 7.2\% & 4.3\% & 1.4\% & 13.0\% \\ Cognitive systems & **31.9\%** & 11.6\% & 18.8\% & 10.1\% & 15.9\% & 2.9\% & 8.7\% \\ Autonomous systems & **34.8\%** & 23.2\% & 8.7\% & 5.8\% & 13.0\% & 8.7\% & 5.8\% \\ Robots & **40.6\%** & 13.0\% & 11.6\% & 7.2\% & 10.1\% & 11.6\% & 5.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Teaching the applications of AI, 100% = 69. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**To What Extent Do You Use** & & & & & & & \\ **the Following** & & & & & & & \\ **Software/Environment to** & & & & & & & \\ **Teach AI?** & & & & & & & \\ \hline ATTECH SPHINX & **81.2\%** & 2.9\% & 0.0\% & 1.4\% & 1.4\% & 0.0\% & 13.0\% \\ Statistica & **71.0\%** & 4.3\% & 1.4\% & 2.9\% & 4.3\% & 1.4\% & 14.5\% \\ Matlab & **34.8\%** & 11.6\% & 8.7\% & 10.1\% & 11.6\% & 17.4\% & 5.8\% \\ MS Excel & **36.2\%** & 21.7\% & 10.1\% & 8.7\% & 8.7\% & 2.9\% & 11.6\% \\ Scilab & **75.4\%** & 7.2\% & 1.4\% & 2.9\% & 0.0\% & 1.4\% & 11.6\% \\ RStudio & **66.7\%** & 8.7\% & 1.4\% & 2.9\% & 2.9\% & 1.4\% & 15.9\% \\ SWI Prolog & **63.8\%** & 10.1\% & 2.9\% & 1.4\% & 7.2\% & 4.3\% & 10.1\% \\ PyCharm & **58.0\%** & 13.0\% & 5.8\% & 5.8\% & 4.3\% & 4.3\% & 8.7\% \\ Spyder & **58.0\%** & 10.1\% & 2.9\% & 1.4\% & 10.1\% & 2.9\% & 14.5\% \\ Visual Studio Code & **52.2\%** & 8.7\% & 5.8\% & 11.6\% & 5.8\% & 2.9\% & 13.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Software/environment in AI teaching, 100% = 69.
# Prediction of Temperature Distribution for Previous Cement Concrete Pavement with Asphalt Overlay
## 5 Conclusions  
To obtain the one-dimensional distribution of the time-dependent temperature, the Gaussian quadrature equation was used to resolve the Laplace inverse transform and Laplace transform. Based on a system of multilayered pavements, this study subsequently came up with an analytical solution of this temperature field. Then, discrete least-squares approximations of the interpolatory trigonometric polynomials were used to fit several specific measured climatic factors regarded as the surface boundary conditions, i.e., the measured solar radiation intensity and air temperature. The solution process was simplified by the deduction of the relationship between the first layer and the constants of integration for the \(j\)th layer. The analytical solution can be obtained easily through solving the inverse Laplace transform using the Gaussian quadrature formula.  
It can be obtained from the comparison between the predicted and measured temperatures that the maximum absolute errors and the maximum mean absolute error values in the existing cement concrete pavement with an asphalt overlay at various depth locations are below 2.89 \({}^{\circ}\)C and 1.58 \({}^{\circ}\)C, respectively, which means that the one-dimensional analytical solution proposed in this study can lead to a reasonable and highly accurate temperature distribution with time dependence for a period of 24 h in both summer and winter conditions. Further, the prediction of multilayered pavement temperature profiles can be promoted by this analytical solution based on the solar radiation intensities and air temperatures.  
It is noted that the developed analytical temperature model was validated with relatively hot weather data. Future research will be conducted to validate the accuracy of the temperature prediction for pavements at a high latitude, where the local solar radiation is relatively sluggish and the local air temperature is relatively low.  
[PERSON] and [PERSON] conceived the study and developed the analytical model; [PERSON] and [PERSON] took part in field test and acquired the data; [PERSON] and [PERSON] contributed to the data analysis and interpretation; [PERSON] also carried out the parameter estimations; and [PERSON] provided the required resources and validated results. All authors contributed to the writing, review and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Fund for Basic Scientific Research of Central Colleges, grant number 300102219519; The Guangdong Provincial Transportation Science and Technology Project, grant number 2017-02-003; The Science and Technology Project from the Affairs Center for Highway of Guangdong province, grant number 2017-15.  
The authors declare that there is no conflict of interest.