esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
## 2 Research Background
### Personalized Path Planning Based on Student Characteristics  
Most personalized learning paths help to improve learning efficiency [12]. The personalized learning path planning method based on student characteristics considers characteristics such as learning style, and preference is the essential parameter. Researchers rely on tests or questionnaires to collect learner characteristics [13; 14; 15], such as students' learning goals [16], learning styles [7; 17], and preferences [7; 18], to construct learner models. For example, Vanitha et al. considered learners' learning goals and knowledge levels to be significant elements for path planning [19]. Rohloff et al. emphasized the need for path planning and course recommendations based on diverse learning goals in MOOC learning scenarios [16]. Nabizadeh et al. used a depth-first search algorithm to locate as many course sequences as possible by combining the learning goals with a knowledge map [9]. Yang et al. proposed an attribute-based ant colony algorithm to recommend suitable learning objects based on learning styles and a learner's knowledge level [20].  
The aforementioned studies show that constructing a personalized learning path based on the learners' characteristics is valid and suitable. Nevertheless, constructing personalized learning paths based on learners' characteristics is prone to overlooking the logic of knowledge. Learners are prone to encountering challenges in grasping the whole knowledge structure during the learning process.
## 7 Proofs of lemmas in Section 2
The conclusion of this lemma implies R2. [Proof of Lemma 4.] Cover affects only the sequence of balls \(b_{0},b_{1},...,b_{k}\) as referred in its description. By construction the new balls cover entire curve segment \(\sigma(x,y)\) and vertex balls are not changed. So, C1 and the first part of C2 are satisfied. To prove the second part consider a ball \(b_{i}=B(c_{i},r_{i})\). We have following cases:  
Case 1 (\(i=0\)): We need to examine the effect of the new ball \(b_{1}\) onto \(b_{0}\). First notice that \(\mathrm{seg}_{\sigma}(b_{0})\) cannot contain \(c_{1}\) by construction. Therefore, C3.a holds trivially. If \(b_{1}\) is not created with the end game, the radius of \(b_{1}\) is \(\alpha\) and \(d(c_{0},c_{1})<r_{0}+\alpha/3<r_{0}+r_{1}/3\) where \(r_{1}=\alpha<r_{0}\) by construction. This satisfies C2. If \(b_{1}\) is created with the end game, then two cases occur. If \(b_{1}\) is the enlarged aiding ball \(\beta_{1}\), its radius is \(2\alpha/3\). Then, \(d(c_{0},c_{1})=r_{0}<r_{0}+r_{1}/3\). This satisfies C2. In the case where \(b_{1}\) is not the enlarged aiding ball, its radius is \(\frac{7}{6}\alpha\). We can similarly argue that \(d(c_{0},c_{1})<r_{0}+\alpha/3=r_{0}+\frac{2}{7}r_{1}\). So, \(b_{0}\) satisfies C2. Case 2 (\(i=k\)): In this case the center \(c_{k-1}\) of \(b_{k-1}\) cannot lie in \(\mathrm{seg}_{\sigma}(b_{k})\) by construction. So, C3.a holds trivially. The ball \(b_{k-1}\) is necessarily created with the end game. If \(b_{k-1}\) is enlarged \(\beta_{k-1}\), \(d(c_{k-1},c_{k})<r_{k}+\alpha/3=r_{k}+r_{k-1}/2\) satisfying C2. In the case where \(b_{k-1}\) is not the enlarged aiding ball, its radius is \(\frac{7}{6}\alpha\). Then, \(d(c_{k},c_{k-1})<r_{k}+\alpha=r_{k}+\frac{6}{7}r_{k-1}\) satisfying C2. Case 3 (\(i\
eq 0\) and \(i\
eq k\)): If \(b_{i}\) is adjacent to \(b_{0}\) and \(b_{k}\), it satisfies C2 by arguments in previous two cases. Also, \(b_{i}\) is not large enough to contain \(c_{0}\) or \(c_{k}\). Hence C3.a also holds. Now consider the case where \(b_{i}\) is adjacent to another ball \(b\) where \(b\
eq b_{0}\) and \(b\
eq b_{k}\). If neither \(b_{i}\) nor \(b\) is created by end game, we have \(d(c,c_{i})\leqslant r_{i}+\alpha/3=r_{i}+r_{i}/3\) satisfying C2. Also, in this case \(d_{\sigma}(c,c_{i})\geqslant\frac{4}{3}\alpha=\frac{4}{3}r_{i}\) which satisfies C3.a. We are left with the case when either of \(b\) and \(b_{i}\) is created with the end game. With similar arguments, one can check that C2 and C3.a hold in these cases too.
## 3 Results and Discussion
### Packed Bed Column Performance
The breakthrough curve, which describes the behavior of the adsorbate concentration concerning time for the pollutant load leaving the bed, was determined at the best conditions previously specified to achieve maximum removal in the packed column configuration and operation of the adsorption tests in order to establish the useful life and breakthrough or saturation point of the bed over 8 continuous hours [75], where the saturation of the biomass in the adsorption column was observed for an initial concentration of 100 mg/L. Figure 6 shows the maximum adsorption capacities in the packed bed system of Cr (VI) and Ni (II) on YSR and PSR calculated with Equation (3). Better performance of YSR for the removal of the two metals was observed, which could be attributed to its larger pore size and volume (Table 4) for PSR. Other authors found Ni (II) adsorption capacity of 54 mg/g on a _Bacillus_ sp. bio composite supported on alginate and revealed that the increase in bed height and initial concentration resulted in a higher adsorption rate and higher biosorption rate [76]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Bed Height (cm)**} & \multirow{2}{*}{**Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**PSR**} & \multicolumn{2}{c}{**YSR**} \\ \cline{3-6}  & & **Cr (VI)** & **Ni (II)** & **Cr (VI)** & **Ni (II)** \\ \hline
6.5 & 33 & 65.67 & 98.13 & 79.91 & 94.31 \\
10.0 & 40 & 82.45 & 98.28 & 99.91 & 99.03 \\
3.0 & 40 & 63.51 & 94.81 & 99.36 & 81.75 \\
11.5 & 55 & 94.78 & 96.73 & 99.91 & 91.60 \\
6.5 & 55 & 92.37 & 97.20 & 98.85 & 97.85 \\
1.6 & 55 & 54.87 & 97.13 & 97.58 & 81.49 \\
10.0 & 70 & 94.78 & 97.15 & 99.49 & 95.52 \\
3.0 & 70 & 89.19 & 95.00 & 99.36 & 95.25 \\
6.5 & 76 & 95.42 & 97.01 & 99.91 & 93.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Removal efficiencies of Cr (VI) and Ni (II) on PSR and YSR in a continuous system. Figure 6: Experimental packed-bed column capacity. \(n=1\). It is emphasized that the present study was limited to adsorption experiments of Cr (VI) and Ni (II) ions on two materials obtained as rejection product of the starch-obtaining process, using yam and plantain as raw materials, in a batch and continuous system. Desorption cycles were not carried out. However, the use of hydrochloric and nitric acids in concentrations ranging from 0.1 to 1 M was studied to evaluate the reuse of adsorbents and recovery of heavy metals from lignocellulosic matrices [32]. However, the final disposal of saturated biomasses with heavy metals remains a challenge. Thus, different immobilization techniques based on cement or brick have been studied as an attractive solution to this problem. In addition, the presence of soluble organic compounds in treated water presents another field for future research. A considerable difference was observed between the maximum experimental capacity obtained by the filter (Figure 6) and the calculated by [PERSON]'s model (Table 9).
# A 60 GHz CMOS I/Q Receiver for High-Speed Wireless Communication System
### Design of 60 GHz Single-to-Differential Amplifier  
The single-to-differential amplifier is designed to drive to the RF port of the double-balanced mixer because of the single-ended output at the LNA. The schematic of the 60 GHz single-to-differential amplifier is shown in Figure 3. It consists of a Marchand balun, a cascode amplifier with switched resistive feedback networks. The Marchand balun is used to generate the differential signal. The use of the capacitors in the balun makes the size of the balun compact. The input impedance of 100 \(\Omega\) is chosen at the input of the Marchand balun because two balun ports for I/Q mixers are connected to the output of the single-ended LNA. The simulated phase and amplitude mismatches of balun are <1 degree and ~0.2 dB, respectively, at 57-62 GHz. The 2-bit resistive feedback gain control is used with the PMOS switch to increase the gain control range. The cascode amplifier with the resistive feedback is achieved with the gain control of 4 dB for the linearity. The transformers of XFM\({}_{1}\) and XFM\({}_{2}\) are used at input and output for matching, which results in making the accurate differential signal and the compact chip size. To improve the phase and the amplitude balance, the transformer with the shunt capacitor (C\({}_{3}\)) is implemented at the load.  
Figure 2: Schematic of 60 GHz low noise amplifier.
# Development of Open-Source Tools for Event-Based Hydrological Modelling Using GIS and Python
## 4 Conclusions  
In this manuscript, we have presented a set of QGIS-based tools for processing geographical information and producing outputs tailored for running hydrological models. The outputs are perfectly suited for developing tools in any common programming language since they are provided in plain text.  
The methods presented are based on easily available information from satellite imagery so they can be performed with no relevant limitations. The integration between QGIS outputs and hydrological modelling in common programming languages provides particular versatility. The methodological framework we provide opens up opportunities to perform tailored modelling tools which represents a step forward in comparison to existing encapsulated analysis packages. Both the ability of QGIS to process spatial information and the ability of Python to develop enhanced calculation routines are boosted with the proposed method, which will maximise the performance of the hydrological analysis carried out.  
It is important to note that the hydrological model simulations included in this study are intended solely to demonstrate the applicability of the proposed GIS--Python framework for spatial data processing and hydrological model input. Calibration and validation of these models were not performed, which is beyond the scope of this methodological contribution.  
Future developments should be aimed at providing automatic tools for performing the tasks performed manually, developing self-checking routines for avoiding common errors in processing the information. While the absence of user-friendly interfaces in Python programming can prevent the extensive spread of the method, some tuned GUIs could be developed for addressing some specific tasks. However, the programming operation should be addressed in a pure coding environment to avoid limiting the efficacy of the proposed framework.  
**Author Contributions:** A.F.A.-N., writing--review and editing, writing--original draft, visualisation, validation, methodology and investigation. E.S., writing--review and editing, writing--original draft, visualisation, validation, methodology and investigation. A.B., writing--review and editing. M.A.P., writing--review and editing. J.M.M., writing--review and editing. S.Z., writing--review and editing, writing--original draft, supervision, methodology, investigation, funding acquisition, data curation and conceptualisation. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is part of the projects TED2021-131520B-C21 and TED2021-131520B-C22, funded by the MCIN/AEI/10.13039/501100011033 and the UE \"NextGenerationEU\"/PRTR.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We want to acknowledge the cooperation and help from the Spanish Confederation Hidrografica del Ebro for providing us with such valuable data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Improved Penzien Model
### Parameter Determination of the Improved Penzien Model
#### 2.3.3 Bending Stiffness of Pile Cap  
The improved Penzien model adopts combined piles, so it is necessary to add a bending resistance spring in the center of the pile cap. Assuming that the pile cap has a unit angle of rotation (\(\varphi\) = 1) in the plane, the axial force of each pile acts on the platform to form a bending resistance stiffness \(k_{\varphi}\).  
\[k_{\varphi}=\sum_{i=1}^{Q}x_{i}N_{i}=\sum_{i=1}^{Q}x_{i}\frac{E_{b}A_{b}}{l}x_ {i}=\frac{E_{b}A_{b}}{l}\sum_{i=1}^{Q}x_{i}^{2} \tag{23}\]  
where \(Q\), \(A_{b}\), and \(E_{b}\) represent the number of the single pile, the section area of the single pile, and the elastic modulus of piles, respectively. \(N_{i}\) is the axial force of the single pile \(i\). \(x_{i}\) is the horizontal projection of the distance from the axis of the single pile \(i\) to the center of the pile cap. \(l\) denotes the distance between the bottom of the pile cap and the fixed end of the pile.
## 4 Experimental Results
### Results of TIR Ship Detection
#### 4.2.2 Visual Comparison to TIR Ship Target Detection Baseline Methods
In the IFCM method, the Gaussian filter and top-hat transform are firstly used to smooth background, and then the modified fuzzy C-means clustering (FCM) is applied for ship target segmentation. It uses nonlocal spatial information and spatial shape information effectively to improve the performance of FCM to efficiently suppress noise interference, as depicted in Figure 14g2,g4, g8, g9. However, the top-hat transform and unsupervised fuzzy clustering are both highly sensitive to sea clutter and tail waves, as presented in Figure 14g1,g3,g5-g7. The CVM extracts ship targets through seeking the closed contours of relatively even regions by curve evolution and iterative convex optimization, and it can successfully segment ship targets with clear contour information, as presented in Figure 14h1,h4,h7,h8. Whereas, because heavy clutter can destroy the topology structure of ship targets and the islands may also have strong contour information, the detection results of CVM method will be greatly affected by heavy sea clutter and islands, as Figure 14h2,h3,h5,h6,h9 demonstrates. The HCST is a non-linear histogram curve transformation for maritime target detection based on background modeling, and the effect is excellent for targets with obvious local contrast, as depicted in Figure 14i4,i7-i9. However, for the environmental maritime conditions with multiple background types, the HCST method obtains poor detection performance, as presented in Figure 14i1-i3,i5,i6. It is noteworthy that those compared methods all fail to detect ship targets for Figure 14a3,a5,a6,a8,a9. Nevertheless, from Figure 14j1-j9, it can be seen that the proposed method can precisely detect all the ship targets with lowest false alarms and is more robust than other compared eight methods. This robustness is attributed to the dual approach with reasonable integration of multiple features after gray-level morphological reconstruction, including intensity, local contrast, contour, and shape features. Considering the intensity and local contrast of TIR ship targets, the IFSM, and BCSM are fused to differentiate potential ship targets and suppress heavy sea clutter. Additionally, considering the contour and shape features of TIR ship targets, the STAEM and the statistical shape knowledge constraint are introduced to characterize true ship targets and eliminate residual non-ship targets. Besides, a dual approach is adopted to simultaneously detect both bright and dark ship targets in TIR image. Figure 14: _Cont._
## 5 Conclusions  
Optimization of HPVP-GTAW process parameters, such as variable polarity frequency \(f_{\rm L}\), pulse frequency \(f_{\rm H}\), and a dimensionless factor \(\psi\) calculated by background current \(l_{\rm b}\), peak current \(l_{\rm p}\) and pulse duration \(\delta\) for AA 5456 aluminum alloy welded joints, have been systematically investigated. The following conclusions are drawn from the results of this study:  
1. Regression models, which took tensile strength, yield strength, percent elongation, and ratio of reduction in area as the response functions of process parameters \(f_{\rm L}\),\(f_{\rm H}\), and \(\psi\), were developed through least square estimation. The regression equations were proved to be effective by checking the coefficient of correlation \(r^{2}\) and confirmatory experiments.
2. The optimum parameters of HPVP-GTAW for AA 5456 aluminum alloy were obtained by analyzing the response surfaces and the contour plots of tensile properties based on regression equations. The optimum parameters were \(f_{\rm L}\) 1 kHz, \(f_{\rm H}\) 20 kHz and \(\psi\) 0.6 (i.e., \(I_{\rm b}\) 75 A, \(I_{\rm p}\) 180 A, and \(\delta\) 0.2).
3. The maximum tensile properties were obtained with the above optimum parameters, and the microstructural observation illustrated that there was a great deal of second-phase \(\beta\) (Mg\({}_{2}\)Al\({}_{3}\)) uniformly distributed in the weld zone, which was beneficial to the improvement of the mechanical properties of AA 5456 aluminum alloy welded joints.  
Figure 12: XRD analysis of weld zone.  
Figure 13: Tensile fracture graph (SEM) with optimum parameters: (**a**) \(\times\)200; (**b**) \(\times\)500.  
**Acknowledgments:** The authors would like to thank all the members in Welding Manufacturing Research Group in Beihang University. The work was supported by the National Natural Science Foundation of China under Grants No. 51675031, 50975015, and 51005011.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 1 Introduction
A set of experiments were carried out for comparing the combinations of method across features: (i) optimizing thresholds with Bayesian optimization for KL-GMM, (ii) reporting two naive systems as baselines for class imbalance, (iii) evaluating feature-method combinations on test data, and (iv) averaging results to identify the most suitable features for SCD. (v) comparing the relation between maximum probability of speaker change given feature change with results of the method. The rest of the paper is organized as follows: Section 1 provides the background of the SCD task and introduces the main purpose of this work. Section 2 reviews the existing studies related to SCD. Section 3 describes the methods used in this study and presents the various features along with their statistical comparisons. Section 4 discusses the experiments and reports the results. Section 5 concludes this work with possible future work.
## 3 Methodology  
### Performance Evaluation Methods  
The performance evaluation of the presented system involves its thermal, electrical, and shading performances. Simulations are used to investigate the influences of tank installation height (i.e., the vertical distance between the collector outlet and tank bottom) and the panel tilt angle on system performances.  
The operation strategy directly affects the simulation process of the thermosyphon solar water heater system. According to the domestic hot water usage habits of Chinese households, the hot water produced during the day is mainly used for the shower at night. Thus, we consider that the hot water production of 120 L in the day can be entirely consumed at night. Auxiliary heat is provided by gas water heater when insufficient solar radiation cannot make the tank water temperature reach 45 \({}^{\circ}\)C.  
In this study, the daily/annual thermal efficiencies of the system (\(\eta_{th}\)) are defined as:  
\[\eta_{th}=\frac{Q_{u}-L}{R_{T}}, \tag{5}\]  
where \(Q_{u}\) is the daily/annual useful thermal energy from PV/T panel(MJ), \(L\) is the daily/annual heat losses (MJ), \(R_{T}\) is the daily/annual solar radiation on panel (MJ).  
The annual electrical efficiency (\(\eta_{e}\)) is defined as:  
\[\eta_{e}=\frac{E_{out}}{R_{pv}}, \tag{6}\]  
where \(E_{out}\) is the annual electric energy output (kWh), \(R_{pv}\) is the annual solar radiation on PV cells (kWh).  
Considering that electrical energy is high-grade energy, primary energy-saving efficiency is proposed to evaluate the comprehensive performance of the PV/T system, and is given by [16; 17]:  
\[\eta_{f}=\eta_{th}+\lambda\frac{\eta_{e}}{\eta_{p}}, \tag{7}\]  
where \(\eta_{f}\) is the annual primary energy-saving efficiency, \(\lambda\) is the packing factor of PV/T panel, \(\eta_{p}\) is the heat-to-electricity conversion efficiency of conventional power plant, and its value is taken as 38%.  
The shading performance of the PV/T panel is evaluated through the shading coefficient which is defined as [18]:  
\[SD=\frac{Q_{S}}{Q_{l}}, \tag{8}\]  
where \(SD\) is the shading coefficient of window, \(Q_{S}\) and \(Q_{l}\) are respectively the total solar heat gains through the window with exterior shading and the window without exterior shading in a cooling season (MJ). The simulations of the total solar heat gains through the window were performed using DesignBuilder which is the first comprehensive user interface for the EnergyPlus dynamic thermal simulation engine [19].  
Figure 3: Cross-section and structure of water-based PV/T collector.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 6 Conclusions  
In this paper, we proposed, implemented and evaluated a lightweight slice-based QoS manager for non-3 GPP IoT scenarios, having in mind the Industry 4.0 and digital farming use cases with specific QoS requirements. Our solution was implemented as a component in SOFTWAY4 IoT considering the best effort, traffic policing, IntServ and DiffServ QoS models. The validation and evaluation were carried out in a real experimental environment. The results obtained showed that our solution has great potential for IoT deployments, which can provide the desired bandwidth for each slice (as virtual network partitions) according to the QoS requirements defined for each use case.  
Our solution, as a proof of concept, is feasible, and we can go further mainly in the evolution of the solution, as well as the development of an API to automate the process.  
As future work, besides the evolution of the slice-based QoS management, we are looking to deploy it as a pilot on a new version of the SOFTWAY4 IoT, considering multiple gateways, centralised management and orchestration. We are working on the integration of IoT non-3 GPP network access to the 5G core by SOFTWAY4 IoT. We also intend to evaluate our solution regarding other performance metrics as well.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Slice ID** & **Queue ID (Name)** & **Defined Maximum Bandwidth** & **Match: DSCP Value (Decimal)** \\ \hline
1 & 1 (A) & 15 Mbit/s & 10 \\ \hline
1 & 2 (B) & 5 Mbit/s & 18 \\ \hline
2 & 1 (A) & 20 Mbit/s & 10 \\ \hline
2 & 2 (B) & 10 Mbit/s & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Two slices’ DiffServimplementation details.  
Figure 19: Two slices’ DSCP marking: total achieved bandwidth in each slice.  
**Author Contributions:** Conceptualisation, A.O.-J. and K.C.; methodology, A.O.-J., K.C., F.S. and W.M.; software, A.O.-J. and K.C.; validation, A.O.-J., K.C., F.S. and W.M.; investigation, A.O.-J., K.C., F.S. and W.M.; resources, F.S. and W.M.; writing--original draft preparation, A.O.-J.; writing--review and editing, A.O.-J., K.C., F.S. and W.M.; supervision and project administration, K.C. and A.O.-J.; funding acquisition, F.S. and K.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by Fundacao para a Ciencia e a Tecnologia (FCT) under Portugal 2020, the European Regional Development Fund (ERDF) and Rede Nacional de Ensino e Pesquisa (RNP), Brazil.  
**Acknowledgments:** We would like to thank [PERSON] for his valuable contributions to this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.