esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning Framework for Student Retention Policy Development: A Case Study
## 4 Experimental Results and Discussion
### Comparison with Existing Systems
\begin{table}
\begin{tabular}{p{56.9 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Study** & **Dataset Size** & **ML Models Used** & **Best Performing Model** & **Features Used** \\ \hline
**Our Case Study** & \
\begin{tabular}{l} 20,974 students (1 university, multiple departments) \\ \end{tabular} &
\begin{tabular}{l} CatBoost, RF, MLP, SVM, LR, KNNs, NB, CARTs \\ \end{tabular} & F1-score: 82\% & Demographics, enrolment, academic performance \\ \hline
[5] & University data (various features) \\  & 63,912 students &
\begin{tabular}{l} Stacking Classifier, \\ AdaBoost, XGBoost, KNNs, DT, LR \\ \end{tabular} & Accuracy: 88.41\% & First-semester academic performance, demographic data \\ \hline
[11] & University dataset 1712 students \\ \hline
[12] & Course level data from e-learning system (261 students) 2016–2020 &
\begin{tabular}{l} NB, RF, MLP, LR, SVM, DT \\ \end{tabular} & F1-score: 91\% & Coarse access, assignments, tests \\ \hline
[13] & University dataset (multiple departments) &
\begin{tabular}{l} XGBoost, LightGBM, RF, \\ MLP, SVM, Extra Trees \\ \end{tabular} & Accuracy: 96.5\% & Academic, personal, family, psychology, assessments \\ \hline
[15] & Academic information system of \\  & Sahmybook University \\  & 20,050 students between 2010 and 2022 & \
\begin{tabular}{l} LR, DT, RF, SVM, DNN, \\ LightGBM \\ \end{tabular} & F1-score: 84\% &
\begin{tabular}{l} Demographic, book loan \\ information, scholarship \\ information, counsel \\ information, extra course \\ information, academic status \\ information, grade information \\ \end{tabular} \\ \hline
[17] & \begin{tabular}{l} 500 students from the 2021 \\ academic year \\ \end{tabular} & \
\begin{tabular}{l} MLP, SVM, KNNs, \\ Gradient Boosting, RF, LR \\ \end{tabular} & F1-score: 82\% &
\begin{tabular}{l} Student demographics, \\ academic data, attendance data, and socioeconomic variables \\ \end{tabular} \\ \hline
[18] & Private university in Malaysia \\  & 197 students between 2006 and 2011 & LR & F1-score: 81\% &
\begin{tabular}{l} Demographics, academic \\ records, and surveys for \\ gathering information about \\ student’s socioeconomic \\ background and psychological \\ factors \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of the existing student dropout prediction studies.
## 2 Extraction Methods
### Non-Conventional Extraction
#### 2.2.2 Ultrasonic-Assisted Extraction  
Ultrasonic-assisted extraction (UAE) is performed using a high-intensity region of ultrasound (10-1000 W/cm\({}^{2}\)). Sonication and acoustic cavitation take place in the main body for UAE effects [41]. Sonic waves form gas bubbles by creating areas of alternating pressure in the medium. At the critical points of these gas bubbles, rapid condensation occurs and the energy inside the bubbles release. This creates shock waves with a large amount of heat and energy, which then physically affect the sample [42; 43]. UAE facilitates solvent penetration and mass transfer. It therefore reduces the solvent usage and extraction temperature and it increases extraction rate and yield [44]. Compared to conventional extraction methods, UAE leads to a higher amount of and more stable polymeric anthocyanins in anthocyanin-rich fruit extracts [45].  
Ultrasound pretreatment mixed with conventional extraction showed different results depending on the blackcurrant sample types: frozen, lyophilized, and over-dried [33]. Ultrasonic treatment increased total anthocyanins yield by 20% from freeze-dried samples but showed no effect on frozen or over-dried samples [33]. Additionally, UAE did not show an enhanced extraction efficiency of anthocyanins from blackcurrants in another two studies, which used frozen samples [34; 35].
## 3 Experimental Research and Analysis
### Model Evaluation Summary  
In the case of darker environments, higher demands are, therefore, placed on the accuracy and stability of the model. Through the above experimental comparisons and analyses of object recognition images and heatmaps under the same scenarios, it can be concluded that HSS-YOLO outperforms YOLO11 in both dim and other environments in terms of object recognition and heatmap quality. Therefore, compared to YOLO11, HSS-YOLO demonstrates better accuracy and stability.
## 3 Experimental Results
### Automatic Conference/Meeting Minute Generation System  
Figure 6 shows the graphic user interface (GUI) of the proposed AMMGS. As shown in Figure 6a, area A displays real-time speech waveforms, area B displays meeting keywords, and area C displays speech recognition results. The buttons from left to right are play, stop, download, and close. As a user presses the play button, the system will start playing the speech data and perform speech recognition and speaker identification, as shown in Figure 6b. Area A shows the speech waveform; the title displays the identified speaker, and text area C shows the speech recognition results. Figure 6b shows an example where signals are identified frame by frame; the identified speaker is [PERSON] (marked by a rectangle), as shown in the title of this figure, while the speech recognition result is presented below.  
Users can download the conference/meeting minutes by pressing the download button. The system will pop up the archive screen, and the user can select the file storage location, as shown in Figure 6c. Then, specify the file stored in the path and confirm that the download is completed, as shown in Figure 6d. The conference/meeting minutes contain the speech recognition results and the identified speaker.  
The hyperlink to demonstration videos of the proposed conference/meeting minute system is given below:  
[[https://www.youtube.com/watch?v=r_QWkh5](https://www.youtube.com/watch?v=r_QWkh5) Wcml]([https://www.youtube.com/watch?v=r_QWkh5](https://www.youtube.com/watch?v=r_QWkh5) Wcml) (accessed on 23 June 2024).  
As generative AI technology is developing rapidly, OpenAI can be integrated into the proposed system in the future. Users only need to apply for an API key from OpenAI in San Francisco, California, USA, to use the text summation function. The summarize_text function can generate the text summation result, which is helpful in enhancing the focus of the meeting. An example of a meeting's text summation is shown in the appendix (Table A4).  
Figure 5: Confusion matrix of identified speakers by the Speaker YAMNet, where the blue and light orange colors denote correctly and falsely identified results.  
Google API can recognize multiple languages so that it can generate recognition results. After collecting the speech data of multilingual speakers, through the transfer learning method introduced in Section 2.1, we can also recognize multilingual speakers, and this system can be developed into a multilingual conversation log generator.
## 5 Conclusions  
To maintain the stability of power transmission during information transmission, a bidirectional communication based on the DPSK modulation method was proposed in this paper. The analysis of steady-state and transient circuit models indicated the potential for simultaneous power and information transmission. Then, considering the interaction of information transmission, the primary and secondary communication modulators sharing with power modulators were designed. The demodulator and half-duplex communication protocol were implemented by analog circuit and software, respectively. Both simulation and experimental results showed that the effectiveness of the proposed DPSK communication method, and the maximum data rate reached up to 50 kbps. During the communication process, the power and information transmission was quasi-independent. In the future, the designed DPSK modulation communication module will be integrated into controller to track maximum power efficiency and regulate the power demand of load.  
[PERSON], [PERSON], and [PERSON] conceptualized the main idea of this research project; [PERSON] and [PERSON] designed and conducted the experiments; [PERSON] and [PERSON] checked the results; [PERSON] and [PERSON] wrote the whole paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results for the Research Questions
### RQ4: What Are the Methods That Have Been Used for Expert Finding Systems?
#### Hybrid Methods  
authors of references [56; 57] combined content-based recommendation algorithms into a social network-based collaborative filtering system to find experts from social networks. Also, the authors in reference [55] proposed a model based on computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating data from heterogeneous sources. The authors in reference [68] combined a document-based model (generative probabilistic models) and community-sensitive AuthorRank in their system. Additionally, the authors in reference [42] combined cluster-based expert selection with a collaborative filtering algorithm to solve the problems of scalability and noise caused by the collaborative filtering algorithm. The authors in reference [17] combined a cluster-based language model (CBDM), Bayesian smoothing with the Dirichlet prior technique, and a vector space model to develop a category-based and question-dependent approach for finding experts in question and answer communities. Moreover, the authors in reference [70] integrated the Dempster-Shafer theory of evidence and [PERSON]'s entropy with a multisensor data fusion framework to develop an expert finding system in academia. Also, the authors in reference [16] proposed a learning framework to predict the ranking of experts in question and answer communities. In their framework, they combined probabilistic dynamic expert profiling, topic modelling, and a document-centric model. In online communities, the authors in reference [82] presented a hybrid method for expert finding. Their method was based on combining content analysis (based on concept map) and social network analysis (based on PageRank algorithm) and social network analysis combined with email data mining to develop a personalized expert recommender tool [87]. Moreover, reference [79] proposed a model based on integrating Term Frequency and Inverse Document Frequency (TF-IDF) and the document language modelling approach to find experts in enterprise. Additionally, reference [25] proposed a context-aware researcher recommendation system based on combining content based, the social network-based and the sum method. In order to find experts for a certain topic, the authors of reference [88] combined topic-aware model (TAM) and context-aware model (CAM) to propose a topic-specific contextual expert finding method. Also, reference [53] combined social network analysis and semantic concept analysis to propose a network based researcher recommendation approach to enhance the efficiency of personalized researcher recommendation. The authors of reference [54] proposed a novel model called Friend Recommendation Model (FRM) by integrating Filtering Out Model (FOM) and Linear Combination Model (LCM).
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 3 Computational Results
### Relationship between Effective SAR Volume and Thermal Time Constant  
Figure 8 shows the relationship between the effective SAR volume and thermal time constants of the skin at the same intensity of RF exposure in rat and human models. Figure 8a shows the effective SAR volume for each exposure scenario. The effective exposure volume increased with decreasing frequency and increasing antenna-head separation. Figure 8b shows the relationship between the thermal time constant in the skin and the effective SAR volume. As shown in Figure 8b, the thermal time constant depended on the head shape rather than on the frequency, especially at frequencies of 6 and 10 GHz. As expected, the thermal time constant increased as the effective SAR volume increased in all models.  
Figure 7: Thermal time constants of (**a–c**) skin (\(\tau\) in Equation (9)) and (**d–f**) brain (\(\tau+\tau_{D}\) in Equation (9)) in human models for each peak SAR averaged over 10-g tissue. The distances between the skin surface and the dipole antenna \(d\) were set to (**a,d**) 5, (**b,e**) 15, and (**c,f**) 25 mm. Each point represents the average value and error bar showing the standard deviation (\(n=4\)). For comparison, the temperature elevation of rat brain tissue was also plotted at the 5-mm distance.
# Speed-Flow-Geometric Relationship for Urban Roads Network
## 4 Results
### Model Validation  
Two groups of traffic volume (veh/h for Model A and pcu/h for Model B) were used in the model development. The estimated average travel speed (ATS) was compared for three cases including the observed onsite average travel speed (ATS) using the moving observer method (MOM), estimated average travel speed (ATS) in Model A and estimated average travel speed (ATS) in Model B. Statistical analysis using paired _t_-test was performed to evaluate any significance difference among the samples means between these three cases. Table 6 shows the results of paired _t_-test. The detail result of paired _t_-test is shown in Appendix A.  
The result in Table 6 clearly shows the complete absence of any significant difference between Group 1 and Group 2 for three surveyed urban roads groups such as [M0, NL1, SF0], [M0, NL1, SF1], and [M1, NL2, SF0]. Therefore, paired _t_-test was conducted between estimated average travel speed (ATS) using Model A and B. Among these three surveyed urban roads groups, the [M0, NL1, SF0] and [M1, NL2, SF0] group displayed some significant differences between the average travel speed (ATS) values estimated using Model A and B. In addition, Model B that considered heterogeneous traffic volume revealed better results, indicating the stronger impacts of the urban roads segment geometric and traffic features on the average travel speed (ATS) for the studied two surveyed urban roads groups. However, for the surveyed urban roads group [M0, NL1, SF1], the paired _t_-test between the estimated average travel speed (ATS) using Model A and Model B in Group 3 showed no significant difference, thus adopting Model A for these surveyed urban roads group. For the surveyed urban roads group [M0, NL2, SF1] and [M1, NL 2, SF1], only Group 1 disclosed insignificant variation between the measured the average travel speed (ATS) using moving observer method (MOM) and the estimated average travel speed (ATS) using Model A, whereas Group 2 indicated a substantial difference for these two values, thus adopting Model A for these two surveyed urban roads groups.
## 4 Description of the System
### Incorporation of New Components
#### 4.2.2 Camera  
This image sensor contains 23 infrared LEDs for night vision with a maximum range of 20 m (refer to Figure 4). The control of gain, brightness, and clarity is automatically achieved. Because it is an external component, it can withstand adverse environmental conditions (dust and water); as a result, it has an IP66 certification. In the developed prototype, installation was performed with the following specifications:  
* Location: in the center of the upper front part of the vehicle, at an elevation that varies between 3.2 and 3.7 m, depending on the model of the bus.
* Viewing angle: 45\({}^{\circ}\).
* Focal length: 8 mm.
* Inclination of the camera: 15\({}^{\circ}\)-20\({}^{\circ}\).
* Light sensitivity of the sensor: 0.05 lux (night vision).
* Image resolution: \(640\times 480\) pixels.
## 2 HRF Modeling of DC Microgrid  
DC microgrid HRFs are usually arc high resistance faults. DC arcs can be series or parallel arcs, the latter of which develop when the two potentials change after damage to the insulation. The arc resistance grounding is parallel and is affected by the arc voltage, arc current, discharge gap, and arc length, among other factors. The main arc models are the Cassie and Mayr models; the latter simulates arc ignition and extinguishment. The Cassie model can handle the continuous arc burning of a DC. Arc resistance is affected by the energy relationship between the arc voltage and arc current.  
In Equation (1), \(Q(\mathrm{MW}\cdot\mathrm{S}=\mathrm{M}\mathrm{J})\) is the energy stored in the arc, \(P(\mathrm{MW})\) is the dissipation power, and \(u(\mathrm{kV})\) and \(i(\mathrm{kA})\) are the arc voltage and current, respectively. Equation (1) shows that arc energy is gradually dissipated during combustion.  
\[\frac{\mathrm{d}Q}{\mathrm{d}t}=u\cdot i-P \tag{1}\]  
Equation (1) can be transformed to quantify the relationship between the rate of change of arc unit conductivity \(g\) and that of arc power:  
\[\frac{\mathrm{d}Q}{\mathrm{d}g}.\frac{\mathrm{d}g}{\mathrm{d}t}=\frac{1}{P^{-1 }}(\frac{g\cdot u^{2}}{P}-1) \tag{2}\]  
\[\frac{1}{g}\cdot\frac{\mathrm{d}g}{\mathrm{d}t}=\frac{1}{g\cdot P^{-1}.( \frac{\mathrm{d}Q}{\mathrm{d}g})}(\frac{g\cdot u^{2}}{P}-1) \tag{3}\]  
Let \(\tau=g\cdot P^{-1}.\frac{\mathrm{d}Q}{\mathrm{d}g}\); Equation (3) can now be simplified to yield Equation (4):  
\[\frac{\mathrm{d}\ln g}{\mathrm{d}t}=\frac{1}{\tau}(\frac{g\cdot u^{2}}{P}-1) \tag{4}\]  
Let \(E\) be the arc voltage gradient, \(P_{0}\) the power emitted by the arc per unit volume, and \(\sigma\) the arc conductivity. These parameters are related Equation (5):  
\[E^{2}=\frac{P_{0}}{\sigma} \tag{5}\]  
Substituting Equation (5) into Equation (4) yields Equation (6):  
\[\frac{\mathrm{d}\ln g}{\mathrm{d}t}=\frac{1}{\tau_{c}}(\frac{u^{2}}{E^{2}}-1) \tag{6}\]  
where \(\tau_{c}\) is the time constant of the Cassie model. Equation (7) shows the Cassie model arc resistance:  
\[R_{arc}=\mathrm{e}^{\frac{1}{\tau_{c}}\int{(1-\frac{u^{2}}{E^{2}})\mathrm{d}t}} \tag{7}\]  
During an HRF, an arc occurs after the breakdown of the air gap between the line and ground; the arc resistance is in series with that of the ground. In the Cassie model, arc combustion stabilizes when the voltage between the arc and ground reaches a certain value. As the arc and grounding point resistance are connected in series, the grounding medium consumes some energy, rendering the arc current small and thus increasing the overall arc resistance.
Remote Sens._**2013**, \(5\), 2200-2218; doi:10.3390/rs5052200  
_Remote Sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
Exploring the Potential for Automatic Extraction of Vegetation Phenological Metrics from Traffic Webcams  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 Electrical Systems and Optics Division, Faculty of Engineering, University of Nottingham, University Park, Nottingham NG7 2 RD, UK; E-Mails: [EMAIL_ADDRESS] (J.A.C.); [PERSON]J.)  
2 School of Geography, University of Nottingham, University Park, Nottingham NG7 2 RD, UK; E-Mails: [EMAIL_ADDRESS] (D.S.B.); [PERSON]ESS] (K.L.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-115-846-7453.  
_Received: 14 March 2013; in revised form: 2 May 2013 / Accepted: 2 May 2013 / Published: 10 May 2013_  
**Abstract:** Phenological metrics are of potential value as direct indicators of climate change. Usually they are obtained via either satellite imaging or ground based manual measurements; both are bespoke and therefore costly and have problems associated with scale and quality. An increase in the use of camera networks for monitoring infrastructure offers a means of obtaining images for use in phenological studies, where the only necessary outlay would be for data transfer, storage, processing and display. Here a pilot study is described that uses image data from a traffic monitoring network to demonstrate that it is possible to obtain usable information from the data captured. There are several challenges in using this network of cameras for automatic extraction of phenological metrics, not least, the low quality of the images and frequent camera motion. Although questions remain to be answered concerning the optimal employment of these cameras, this work illustrates that, in principle, image data from camera networks such as these could be used as a means of tracking environmental change in a low cost, highly automated and scalable manner that would require little human involvement.  
**Keywords:** phenology; traffic camera; network; image processing; automation; climate change
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
###### Abstract  
Coverage maintenance is a bottleneck restricting the development of underwater acoustic sensor networks (UASNs). Since the energy of the nodes is limited, the coverage of UASNs may gradually decrease as the network operates. Thus, energy-saving coverage control is crucial for UASNs. To solve the above problems, this paper proposes a coverage-control strategy (referred to as ESACC) that establishes a sleep-wake scheduling mechanism based on the redundancy of deployment nodes. The strategy has two main parts: (1) Node sleep scheduling based on a memetic algorithm. To ensure network monitoring performance, only some nodes are scheduled to work, with redundant nodes in a low-power hibernation state, reducing energy consumption and prolonging the network lifetime. The goal of node scheduling is to find a minimum set of nodes that can cover the monitoring area, and a memetic algorithm can solve this problem. (2) Wake-up scheme. During network operation, sleeping nodes are woken to cover the dead nodes and maintain high coverage. This scheme not only reduces the network energy consumption but takes into account the monitoring coverage of the network. The experimental data show that ESACC performs better than current algorithms, and can improve the network life cycle while ensuring high coverage.  
underwater acoustic sensor network; coverage maintenance; memetic algorithm; sleep-wake scheduling +
Footnote †: journal: Sensors
# Application of RFID to Soil-Erosion Research
###### Abstract  
Soil erosion is a problem of global significance; yet obtaining data on rates of soil erosion is not straightforward. Radio-Frequency Identification (RFID) offers the prospect of improvements in estimates of these rates through better understanding of the movement of individual soil particles. We report on two laboratory experiments to measure the travel distances of surrogate soil particles during erosion events. In the first experiment, we used RFID tags that need to be placed within a reader for identification. In the second, we used tags with a booster antenna that allowed remote detection. The recovery rate of particles in the first experiment was 81.7%. Most likely, lost particles were buried by other moving sediment. The distribution of travel distances is well fitted by a Burr 4-parameter distribution which lends support to its use for modelling travel distances of eroded particles. In the second experiment, the average distance from which particles could be detected was 62 mm and the average error in locating particles was 17.9 mm. We attribute much of this error to the assumptions that the detection field is a circle on the ground with the reader at its centre, and that the same detection range exists for all positions of the reader. RFID is shown to provide insights into the movement of individual soil particles, but improvements are needed in both particle detection range and modelling of soil particle movement before the full potential of RFID for measuring soil erosion can be realized.  
Keywords:soil erosion; particle tracking; RFID +
Footnote †: journal: Applied Physics Letters