esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Modelling Techniques
#### 3.3.6 Probabilistic, Fuzzy, and Hybrid Models  
Bayesian networks: Bayesian networks have been applied to enhance the modelling in complex multimodal contexts. One study incorporated a hybrid deep restricted Boltzmann machine with Bayesian network methodologies to improve facial expression recognition and multimodal teaching behaviour analysis, showcasing the potential of probabilistic reasoning in data fusion tasks [71]. Another study applied Bayesian Ridge Regression (BRR) alongside other regression models to analyse collaborative problem-solving scores, highlighting its role in capturing dependencies among multiple variables in learning scenarios [67].  
Fuzzy logic: fuzzy logic has been incorporated to address the inherent uncertainty and imprecision in human behaviour and emotion recognition. One study employed fuzzy mathematics in the evaluation framework for English translation teaching, enhancing the interpretability of emotion recognition models in classroom contexts [90]. Similarly, fuzzy conceptions were used for classifying musical intelligence in a study that combined AI and multimodal techniques to support music education, demonstrating fuzzy logic's role in handling vague or complex categorisations [80].  
Latent Profile Analysis (LPA): LPA can serve as a statistical approach to uncover distinct behavioural patterns among students over time. In one study, LPA was used alongside HMMs to analyse students' time-variant behaviours based on multimodal data, facilitating the identification of behavioural profiles and their association with learning outcomes [97].  
Hybrid models: hybrid modelling techniques have been increasingly adopted to address the complexity of multimodal educational data. One study introduced a behavioural disengagement detection framework that integrated deep language models with facial expression and gaze features, achieving higher accuracy than unimodal approaches [100]. Another work employed a hybrid deep restricted Boltzmann machine, combining deep learning with Bayesian networks and entropy minimisation to improve facial expression recognition in teaching scenarios [71]. Similarly, a hybrid architecture combining TCNs, Transformers, and CNNs was proposed to overcome limitations of individual models in student engagement detection [72].
# Differential Ultra-Wideband Microwave Imaging: Principle Application Challenges
## 3 Device Requirements for Differential Imaging
### Pulse and M-Sequence Radar  
The basic structure of pulse and M-sequence are depicted in Figure 13. The pulse radar (Figure 13A) is based on a pulse shaper, which launches periodically sub-nanosecond pulses triggered by a clock generator of repetition rate \(\tau_{R}^{-1}\). The data capturing is organized via subsampling in order to reduce the sampling rate of the ADC (Analog to digital converter). In the shown example, sequential sampling is used, by which one data sample per launched pulse is captured. By a programmable delay line, the sampling point is moved over the time interval of interest \(\tau_{ROI}\leq\tau_{R}\). The step size of the delay variation determines the equivalent sampling rate \(\Delta\tau=f_{eq}^{-1}\), which has to meet the Nyquist sampling criteria for the sounding signal. This is not required from the actual sampling rate. For modifications of the sampling principle (e.g., interleaved sampling) see [1; 41]. The peak power of the transmitted signal \(\hat{P}\), its average power \(P\), and the achievable bandwidth \(B\) (note \(B\) is a two-sided bandwidth) are as follows:  
\[\hat{P}\sim V_{0}^{2};\;\;P\sim\frac{\tau_{0}}{\tau_{R}}V_{0}^{2};\;\;B\;\tau_{ 0}\approx 1. \tag{33}\]  
In the case of the M-sequence radar the sounding signal is generated by a fast linear feedback shift register (LFSR) of length \(m\), which is pushed by a microwave source of frequency \(f_{c}\). It provides \(N_{m}=2^{m}-1\) chips per period. For data gathering, an interleaved subsampling approach is used. Due to spectrum limitations below \(f_{c}/2\), the equivalent sampling rate is preferentially selected to \(f_{eq}=f_{c}\) so that a binary divider can simply control the sampling [1]. Transmitter power and double-sided bandwidth are as follows:  
\[\hat{P}\approx P\sim V_{0}^{2};\;B=f_{c}=\frac{1}{\tau_{0}}. \tag{34}\]  
However, the received signal \(b(t)\) is mostly worthless as it leaves a chaotic impression. Therefore, one has to take its circular cross-correlation \(c_{bm}(t)\) with the M-sequence code \(m(t)\) for further data processing [1] in order to get the wanted impulse response function as follows:  
\[c_{bm}(t)=\int\limits_{0}^{\tau_{R}}b(\xi)m_{c}(\xi+t)\mathrm{d}\xi\,. \tag{35}\]  
Figure 13: Block schematic of pulse radar (**A**) and M-sequence radar (**B**). Both principles are working in the baseband. For extensions to bandpass principles see [1; 41]. Note that the actual sounding signal of an M-sequence radar is the bandlimited M-sequence \(m_{b}(t)\).
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## 3 Results  
At the end of the 2019 HWT, we analyzed over 61 written descriptions, 138 daily survey responses, and 36 weekly survey responses. Through this process, we sought to identify case studies to discover how the data were used and where they succeeded or failed. Additionally, we used the survey results to gain a more general understanding of the utility of NUCAPS products in severe weather forecasting and identify areas of improvement, both in product design and its application.
## 2 Literature Review
### Mathematical Statistics  
For statistical-based methods, [PERSON] et al. [9] proposed a data-driven autoregressive integrated moving average (ARIMA) approach for short-term prediction. Building upon this, [PERSON] et al. [10] introduced a seasonal autoregressive integrated moving average (SARIMA) model for vacant parking space prediction. Although ARIMA and SARIMA are simple and do not rely on external variables, they are not suitable for nonlinear time series data, and parameter estimation can be challenging [11]. Additionally, [PERSON] et al. [12] proposed a successful parking probability prediction algorithm, which can improve the accuracy of parking space prediction after short-term or long-term driving. In another study, [PERSON] et al. [13] created a parking demand prediction model based on the distribution of parking arrival and departure patterns. They proposed a parking trend-based method to determine the prediction interval and used curve fitting and the method of undetermined coefficients to calibrate parameters and predict parking demand. [PERSON] et al. [14] presented a framework based on continuous-time Markov M/M/C/C queues for vacant parking space prediction.  
Subsequently, [PERSON] et al. [15] used linear interpolation as a benchmark and employed a distance-weighted interpolation algorithm for parking prediction. [PERSON] et al. [1] also used a Kalman filter in a study to predict on-street parking availability, though the use of the Kalman filter did not result in significant statistical improvement. Additionally, [PERSON] et al. [16] employed time-varying analysis of historical data and modeled the discrete occupancy of parking lots as a nonstationary Poisson process, proposing an economically efficient parking spot search method. However, statistical-based methods primarily focus on theoretical analysis and are not suitable for nonlinear time series data, making them ill suited for complex real-world parking scenarios [17].
# Improving Throughput of Mobile Sensors via Certificateless Signature Supporting Batch Verification
## 1 Introduction
Signatures that support batch verification can solve this problem. Namely, the signatures on the data collected from various sensors and other equipment can be aggregated in a certain node before being transmitted to the center and can later be verified together. The framework is shown in Figure 1.
## 5 Social Big Data Analysis Applications
### Opinion Mining from Social Media Data
#### 5.2.4 Use Cases and Results  
The effectiveness of the proposed methodology was assessed on a real-world case study, aimed at analyzing the polarization of a large number of Twitter users during the 2016 US presidential election. In particular, our analysis focused on data collected for 10 US swing states: _Colorado_, _Florida_, _Iowa_, _Michigan_, _Ohio_, _New Hampshire_, _North Carolina_, _Pennsylvania_, _Virginia_, _and Wisconsin_. The reason behind the choice of these states is linked to their greater political uncertainty, which implies a marked strategic importance, as their votes are more likely to be the deciding factor in a presidential election. We leveraged the Search Twitter API for the extraction of tweets published in a given area or place through geo-referencing, collecting about 820,000 tweets posted by 140,000 users.  
Table 2 provides a comparison between the results obtained by IOM-NN, the average opinion polls collected before voting, and the real results of the political event. The achieved results show the greater accuracy of IOM-NN, which correctly identified the winning candidate in 8 out of 10 cases IOM-NN allows the analysis of a much larger number of users at a lower cost, with respect to opinion polls, by exploiting the information-rich contents published by social media users.
## 3 Results and Discussion
### Analysis of Environmental Properties  
The temperature conditions, TPH concentration and treatment time are major factors for the remediation of lubricant-oil-contaminated soil in this study. With the treatment of the contaminated soil (initial TPH concentration of 26,063 mg/kg) at 400 \({}^{\circ}\)C and the treatment times of 5, 10 and 15 min, the residual TPH concentration was measured as 5490, 2866 and 1846 mg/kg, respectively, indicating removal efficiencies of 78.3%, 88.7% and 92.7%, respectively. In the test at 500 \({}^{\circ}\)C, with an initial TPH concentration of 25,325 mg/kg and treatment times of 5, 10 and 15 min, the residual TPH concentration was measured at 3626, 1995 and 1383 mg/kg, respectively, indicating removal efficiencies of 85.7%, 92.2% and 94.5%. In the test at 600 \({}^{\circ}\)C, with an initial TPH concentration of 26,600 mg/kg and the treatment times of 5, 10 and 15 min, residual TPH concentration was measured at 2512, 652 and 38.2 mg/kg, indicating removal efficiencies of 90.6%, 97.6% and 99.7%, respectively.  
As shown in Figure 5 and Table 5, the removal efficiency increases as the thermal desorption temperature and treatment time increases, and it was found that the concentration level and condition of treated soil with even elapsed time of 15 min is restored to almost initial conditions.  
Based on the test results (Figure 6), we can assess the optimal operating conditions (target temperature and treatment time required for residual TPH concentration or removal efficiency) which satisfy the remediation period, as well as the remediation cost in the real site.
# Interpretability in Sentiment Analysis: A Self-Supervised Approach to Sentiment Cue Extraction
###### Abstract
This enables our framework to interpret sentiment classification models in scenarios where explicit annotation is lacking, effectively facilitating sentiment cue extraction. Importantly, this methodology transcends the boundaries of local interpretability techniques, offering a global interpretability approach. Such a global perspective allows for a more holistic and comprehensive understanding of the model's decision-making process across various instances, rather than being confined to localized, instance-specific explanations. To the best of our knowledge, ours is the first work to combine Monte Carlo methods with self-supervised learning to address the global interpretability issue in binary text classification [10; 11; 12]. The key contributions of our research are as follows. * We propose a Self-Supervised Sentiment Cue Extraction (SS-SCE) method. This approach, inspired by the concept of interpretability in text classification models, accomplishes the extraction of sentiment cues from texts under conditions of scarce labeled data through a global interpretability analysis of the text classification models. * We have developed a pseudo-label generation scheme for sentiment cue extraction models. This scheme selects appropriate mask sequences as pseudo labels for the sentiment cue extraction model based on the prediction results of a trained text classification model. Furthermore, we enhance the efficiency of pseudo-label generation by employing a Monte Carlo Sampling strategy. * We have introduced the Mask Sequence Interpretation Score (MSIS) metric, designed to evaluate generated mask sequences based on the prediction results of a text classification model, thereby providing a basis for the generation of pseudo labels. Empirical evidence demonstrates the effectiveness of our MSIS metric. The remainder of this paper is organized as follows: Section 2 discusses related work, providing background on sentiment analysis, self-supervised methods for information extraction, interpretability in machine learning, and the use of Monte Carlo methods. Section 3 details our methodology, explaining the sentiment cue extraction process, the use of Monte Carlo sampling, label sequence selection, and the sentiment cue extraction algorithm. In Section 4, we present our experimental setup, the datasets used, and a thorough evaluation of the performance of the SS-SCE framework. This includes an in-depth analysis of our results and a comparative study with state-of-the-art interpretability methods. Finally, Section 5 concludes the paper, summarizing our key findings, discussing the implications and potential applications of our work, and suggesting avenues for future research.
The presented antenna has dual band notch features at 3.5 GHz and 5.2 GHz by embedding a slot and stub on the radiator. The coupling of the antenna is reduced by introducing the strip on the ground plane. An orthogonally arranged two-port circular monopole UWB antenna with dimensions of 25 \(\times\) 39 mm\({}^{2}\) is illustrated in [18]. The presented antenna has dual notches at 5.4 GHz and 7.5 GHz by engraving L-shaped slots onto the radiator. The U-shaped branches entrenched in the ground plane reduce the mutual coupling. The electromagnetic band gap (EBG) structure that separates the two CPW-fed UWB antennas to enhance the isolation (>20 dB) is presented in [19]. The presented design has dimensions of 35 \(\times\) 32 mm\({}^{2}\) with the S11 curve ranging from 3.4 GHz to 10.4 GHz. According to the literature, most MIMO antennas use DGS to improve isolation, and the interelement separation is rather large. This work describes a unique two-port UWB MIMO antenna with a decoupling mechanism as a neutralization line. Impedance matching for the UWB spectrum is performed using a hybrid structure composed of a circular monopole with a rectangle as a radiator and a decreased ground plane. The lowered ground plane creates a local current channel, which aids in achieving a broader impedance bandwidth. U-shape slots are carved on the radiator to accomplish dual band-notching at WLAN and X-band frequencies. The band-notched monopole UWB antenna is horizontally recreated with a much less than quarter wavelength separation (i.e., 3.8 mm or 0.04 \(\lambda\), \(\lambda\) is computed using a lower frequency of 3.4 GHz). The coupling effect among the elements is reduced using a neutralization line. The projected antenna has physical dimensions of 21.5 \(\times\) 28 \(\times\) 1.6 mm\({}^{3}\) (0.24 \(\times\) 0.31 \(\times\) 0.01 \(\lambda^{3}\)) with an impedance bandwidth of 112% (3.4 to 12.2 GHz) and isolation greater than 16 dB throughout the operating frequencies. Furthermore, the projected antenna is investigated for characteristic mode analysis (CMA), MIMO diversity characteristics, and time-domain features. For experimental validation, the proposed design is printed on an FR4 substrate (\(\epsilon_{r}=4.4,\ \tan\delta=0.02,\ h=1.6\ \text{mm}\)).
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 3 Results and Discussion
### Analysis of the Survey
#### 3.5.2 Usability  
Usability was assessed through several dimensions, including posture improvement, fatigue level, perceived inefficiency, movement constraint, and overall helpfulness. The exoskeleton received a posture improvement rating of 5.55 out of 10, highlighting its effectiveness in enhancing physical alignment. The participants reported a fatigue level (tiredness) of 2.64 out of 10, indicating that the exoskeleton helped to significantly reduce fatigue during use. Perceived inefficiency, or time wasted, was rated very low at 1.86 out of 10, suggesting that the exoskeleton did not significantly hinder productivity. However, movement constraint received a score of 3.09 out of 10, indicating that some users felt restricted in their movements while using the device. Overall, the helpfulness of the exoskeleton was rated 5.41 out of 10, reflecting a positive impact on task efficiency but also highlighting areas for improvement in reducing movement constraints.
## 5 Conclusions  
The GF-4 satellite, a geosynchronous orbit optical satellite with a high resolution of 50 m, was first applied to detect the Chla changing details in Bohai Sea. A newly built Chla inversion model PMS-1 for the GF-4 PMS sensor was established. The new model PMS-1 can extract Chla in the Bohai Sea well with the correlation coefficient R\({}^{2}\) = 0.97, the total average relative error of 37.42% and the root-mean-square error (RMSE) of 29.61%.  
The high Chla concentration is mainly located in coastal areas, such as the top of Laizhou Bay, Bohai Bay and Liaodong Bay, with the value being around 14 \(\upmu\)g/ L. The low Chla concentration is mainly located in the Bohai Strait and northern Yellow Sea, with the value being around 5 \(\upmu\)g/L. Generally, the mean value of Chla showed a general trend of being high in the offshore area, and low in the distant sea.  
Furthermore, by taking full advantage of the continuous observation of the geostationary orbit satellite, GF-4 with a high resolution sensor PMS of 50 m, can effectively detect short-term change (changes within 10 min) in Chla concentration. The changes mainly appear at the southwest and northeast costal area as well as in the center of the Bohai Sea with the change value of around 3 \(\upmu\)g/L. This short-term change (changes within 10 minutes) in Chla concentration is mainly caused by ocean currents and winds. Therefore, the advantage of GF-4 can be applied to make more subtle monitoring of marine environmental factors.  
Conceptualization, L.C.; methodology, software, validation, writing--original draft preparation and editing, L.C. and J.B.; supervision, project administration, D.T.; investigation, data curation, M.Z., R.Y. and S.H.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This work is jointly supported by the following research projects: Research Project of Zhejiang Department of Education (Y201840279); The research is partly supported by National Key Research and Development Program (2016 YFC1401605); Guangdong Key Laboratory of Ocean Remote Sensing(South China Sea Institute of Oceanology Chinese Academy of Sciences) (2017B030301005-LORS2001, 2019 BT02H594); The open Foundation from Marine Sciences in the First-Class Subjects of Zhejiang Province (11104060218); Science and Technology Innovation Activity Plan and New Talent Plan for Zhejiang University Students in 2020 (2020R411004).  
The data product of the GF-4 satellite was obtained from the website: [[https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)]([https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)). The authors wish to thank the National Satellite Ocean Application Service, MNR of PRC for the data support. Thanks to [PERSON] for providing technology help.  
The authors declare no conflict of interest.
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
###### Abstract  
In this work, a Global Navigation Satellite System (GNSS) buoy that utilizes a Virtual Base Station (VBS) combined with the Real-Time Kinematic (RTK) positioning technology was developed to monitor water surface elevations in estuaries and coastal areas. The GNSS buoy includes a buoy hull, a RTK GNSS receiver, data-transmission devices, a data logger, and General Purpose Radio Service (GPRS) modems for transmitting data to the desired land locations. Laboratory and field tests were conducted to test the capability of the buoy and verify the accuracy of the monitored water surface elevations. For the field tests, the GNSS buoy was deployed in the waters of Suao (northeastern part of Taiwan). Tide data obtained from the GNSS buoy were consistent with those obtained from the neighboring tide station. Significant wave heights, zero-crossing periods, and peak wave directions obtained from the GNSS buoy were generally consistent with those obtained from an accelerometer-tilt-compass (ATC) sensor. The field tests demonstrate that the developed GNSS buoy can be used to obtain accurate real-time tide and wave data in estuaries and coastal areas.  
GNSS buoy; VBS-RTK positioning; real-time water surface elevations; tides; significant wave height; zero-crossing period; directional wave spectrum +
Footnote †: journal: Sensors
## 4 Model Application
### Model Optimization  
As part of the demonstration, a life cycle optimization model was used to study the vehicle replacement cycle to find the pattern that minimizes environmental impact for the short and long-run replacement policies. One of the more important objectives was to investigate how the optimal replacement pattern would be affected by the differences in maintenance and repair environmental profiles and adjust the replacement policies accordingly. The short and long-run replacement policies may represent a different approach to the problem of vehicle replacement by two different groups of individual consumers that use different replacement cycles. A large group of individual vehicle owners uses long replacement cycles, which is consistent with the economically ideal replacement policy for a vehicle. However, a portion of vehicle owners are willing to pay a substantial premium to own a new vehicle. The average age of passenger cars in Luxembourg is 6.7 years, while, for example, in Poland it is 14.5 years. To what extent do the different vehicle replacement cycles meet the Sustainable Development Goals?  
In this study, the potential environmental effects of replacing a EURO 3 class Ford Focus car with a newer model in a EURO 5 standard are studied. According to the assumptions of the model, retiring the vehicle and introducing the new vehicle occur simultaneously. The vehicle ownership limit is equal to the average lifetime of a car in the EU, which is currently 11.5 years. Optimization to minimize the cumulative value of the ecoindicator (EI-Pt) is considered in the give time horizon. Assuming that the average annual transport distance of passenger vehicles in the EU is 14,000 vkm, the results can easily be scaled to the mileage of the car. The impacts of replacing two fuel versions of vehicles are assessed: diesel and petrol. The study takes into account the higher fuel efficiency and lower emission factors of the new car and the service scenarios presented in Table S6 of the Supplementary Materials.