esa-sceva/satcom-chunk-collection huggingface dataset:
# Recommender System for University Degree Selection:
## 2 Related Work
### Application of Recommender Systems in Educational Settings
However, collaborative filtering does not perform well in specific domains, particularly in educational contexts where students have diverse capabilities, learning curves, and core strengths. The sparsity of data matrices indicating users' degrees of collaboration further degrades the performance of collaborative filtering approaches in educational recommender systems [27]. To address this, researchers have developed hybrid recommendation frameworks that combine multiple methods to enhance robustness and accuracy. A context-aware recommender system based on fuzzy logic was proposed to generate personalised recommendations by assessing each user's performance [28]. This model calculates the required contexts for applying fuzzy logic based on the learners' assessment scores and the time to complete the assessment. Additionally, novel fuzzy-based recommender systems leverage family tree-based fuzzy inference systems to capture critical concepts in user profiles and the available courses, improving neighbours' semantic and content similarities for personalised recommendations [19]. Ontology-based approaches have also been explored to develop recommendation engines. These approaches transform human-generated information into machine-readable language, which is then used in machine learning-based solutions. For instance, ontology-based recommender systems have been proposed to better guide students in higher education systems by assessing their strengths, weaknesses, interests, and abilities. These systems collect data explicitly from user profiles and implicitly from surveys conducted with graduate students. Machine learning methods create and cluster profile models of graduate students, which are then used in hybrid recommendation engines [29]. Furthermore, deep learning-based recommendation frameworks have been developed for e-learning platforms. These models use K-Nearest Neighbour (KNN) and collaborative filtering approaches to find similar students with comparable learning capabilities. Other frameworks incorporate sentimental and cognitive analysis and learning style categorisation to generate personalised course recommendations [30]. Overall, intelligent, personalised course-advising models and hybrid recommendation systems combining collaborative and content-based filtering, optimised with genetic algorithms, have significantly improved recommendation reliability and performance. These advancements in recommendation system technologies offer practical applications for students and educational institutions, enhancing the guidance provided to students in their course registration processes [31]. However, the course recommendation approach differs from that of recommending a study programme; therefore, considering context affects the recommender system's structure.
# Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion
## 4 Methodology
### Analysis Base Construction  
Algorithm complexity vulnerability is a vulnerability related to algorithm logic design. This vulnerability has nothing to do with specific programming language features, so this type of vulnerability may appear in different languages, such as Java, Python, etc. Here, we mainly discuss Java language, for other program languages, simply replacing the JAR file with the corresponding source code or other code files, following the same follow-up process and method, ACV detection can also be achieved. Language-independent intermediate representation is an abstract and programming language-independent representation of programming code. By converting the source code into a language-independent intermediate representation, we can eliminate the characteristics strongly related to the language, focus on the language independent algorithm logic and call structure, and help analysts analyze vulnerabilities more efficiently.  
We propose two ways to support the analysis of ACV:  
* Code analysis based on the Soot framework [34; 35].  
Figure 2: Overall architecture.  
Using the Soot framework as a foundation, we proposed a new intermediate representation analysis framework to support the visualization of function call structures in graph format. Our framework supports the analysis of bytecode in JAR files, generating language-independent intermediate representations, and constructs dependency and call relationship lists. These relations are then stored and represented using a graph database. By leveraging the code graph, we can easily identify structural features associated with ACVs, such as direct recursion and indirect recursion as shown in Figure 3, aiding in the construction and summarization of vulnerability models, further supporting the transfer of models to different programming languages [36].
## 3 Results
### Spacing Metric  
The spacing metric assesses the uniformity of solution distribution along the Pareto front (Figure 3). A consistently low spacing value suggests a well-spread set of solutions, while higher values indicate clustering or uneven distribution.  
In the early generations, the spacing metric exhibits fluctuation, with a peak at generation 4, indicating uneven distribution as the algorithm explores the solution space. As the algorithm progresses, the spacing metric decreases, particularly around generations 9, 10, and 11, reflecting a more uniform distribution of solutions as NSGA-II converges toward a well-distributed Pareto front.  
In later generations, a slight increase in the spacing metric is observed, specifically around generations 13 to 15. This fluctuation is likely influenced by genetic operations, such as crossover and mutation, that introduce variability to maintain diversity. The spacing metric is sensitive to the specific set of solutions in each generation, which can lead to  
Figure 2: Hypervolume progression across generations of NSGA-II.  
minor fluctuations as the algorithm refines the Pareto front. Overall, the decreasing trend in spacing values suggests that the algorithm achieves a balanced distribution over time.
## 4 Future Research Directions
### Open Research Direction for DSM
#### Load Profile Modeling  
DSM is the art of managing consumers' load profiles. Demand during peak hours and increases in the electricity price are associated with the load profile. Moreover, system instability and the mismatch between generation and utilization are also dependent on the load profile. Therefore, modeling of the load profile is the key factor in DSM. While designing the DSM scheme, it is mandatory to model the load profile of each consumer before DSM scheme implementation. Therefore, the development of DSMs based on the load profile could be another research direction.
## 4 Selecting the Straight Line Segments
### Algorithm for Selecting a Pair of Straight Line Segments
Method 1 is used to compute the depth after the wrong matching points have been deleted by the above algorithm. Method 2 is used to compute the depth by the longest straight line segment directly. The measurement errors of the two kinds of methods are showed in Table 2. There are 3 objects whose depth should be computed in the given images, and it is shown in Section 5.2. We called the 3 objects _obj1_, _obj2_, _obj3_, respectively. In method 2, the error percentage of depth measurement of _obj2_ is 22.91%, because 1 or 2 points of the two endpoints of the longest straight line segment are matched wrong. It is a very big error. But, in method 1, the error is decreased sharply, because the 3 rd-longest straight line segment was used to compute the depth of object by the above algorithm. Although its length is not the longest, it is the best choice to compute the depth information. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & \multirow{2}{*}{**GT 1**} & \multicolumn{4}{c}{**Method 1**} & \multicolumn{4}{c}{**Method 2**} \\ \cline{3-11}  & & \(Ang_{max}\) & \(i\)-th 2 & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \(\mathbf{MD 3}\) & \(\mathbf{EP 4}\) & \(\mathbf{L_{max}}\) & \(\mathbf{MD 3}\) & \(\mathbf{EP 4}\) \\ \hline obj1 & 3565.38 & 0.35 & 1 & 269.00 & 229.34 & 3470.07 & 2.67\% & 269.00 & 229.34 & 3470.07 & 2.67\% \\ obj2 & 3389.00 & 2.62 & 3 & 141.41 & 121.31 & 3620.50 & 6.83\% & 190.09 & 154.59 & 2612.66 & 22.91\% \\ obj3 & 3758.01 & 0.55 & 1 & 302.35 & 257.87 & 3477.98 & 7.45\% & 302.35 & 257.87 & 3477.98 & 7.45\% \\ \hline \hline \end{tabular}  
* GT is the ground truth of the depth of object; 2 the \(i\)-th means that the \(i\)-th longest straight line segment was used to compute the depth of object; 3 MD is measured depth; 4 EP is error percentage.  
\end{table} 
Table 2: Measure result comparison of the two kinds of methods (unit: distance—mm; length of line segment—pixel; angle—degree. \(D=600\) mm, \(T_{a}=3^{\circ}\)).
For example, the visible algorithm has been used for water vapor retrieval for sensors such as the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experience 2 (GOME-2) [14; 19], the NIR algorithm has been applied to the water vapor retrieval for sensors such as the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Medium Resolution Spectral Imager (MERSI)-2 [20; 21], the TIR algorithm has been used for water vapor retrieval for sensors such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Advanced Very High Resolution Radiometer (AVHRR) [12; 22], and the MW algorithm has been applied to water retrieval for sensors such as the Microwave Radiation Imager (MWRI) and the calibration microwave radiometer (CMR) [13; 23]. On the whole, among the four algorithms, the NIR algorithm has the highest accuracy and the MW algorithm has the strongest applicability. In this work, the NIR algorithm is selected for water vapor retrieval of PSAC according to the band setting of PSAC. In this study, we use the NIR algorithm to retrieve CWV from PSAC onboard HJ-2 satellites and evaluate the accuracy of retrieval algorithm based on ground-based data. The retrieval results can be used for atmospheric correction of data from other sensors onboard HJ-2 satellites and provide data support for studies related to weather forecasting and climate change. The paper is structured as follows. The data used are introduced in Section 2 and the CWV retrieval algorithm is presented in Section 3. Then, the CWV retrieval results and analysis are shown in Section 4. After that, the limitations of this study are discussed in Section 5. Finally, the paper is summarized in Section 6.
# Research on the Impact of BDS-2/3 Receiver ISB on LEO Satellite POD
## 1 Introduction
[PERSON] and [PERSON] found a clock offset and TGD bias between BDS-2 and -3 [39; 40; 41]. The clock bias was similar to each receiver, while the B1I-B3I TGD bias depended on the receiver type. [PERSON] held the point of view that the clock offset and TGD bias should be regarded as the ISB (intersystem bias) of BDS-2/3 while solving the combined BDS-2/3 position solution. Some investigators studied the joint BDS-2/3 precise point positioning (PPP), whose precision would be approved by estimating the intersystem bias of BDS-2/3 [42; 43; 44; 45]. And the joint BDS-2/3 precise time transfer also benefits from the intersystem bias [46]. In the background above, it is meaningful and necessary to investigate the effect of the signal difference of BDS-2/3 on LEO POD. This paper first analyses the reason for receiver ISB between BDS-2 and BDS-3 and gives the measurement equation. Then, this paper collects the Tianping-1B onboard GPS and BDS-2/3 measurements for the experiments of BDS-3-based POD and the multi-GNSS-based POD.
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 2 System Overview
### Software Design  
Figure 5 shows the overview of software design, and each rectangle represents a thread. A real-time operating system named FreeRTOS is utilized for the proposed multi-function hydrophone, because it is easy to add different components, such as file system, transmission control protocol/internet protocol (TCP/IP) stack, etc. Moreover, FreeRTOS is a preemptive scheduling system, which is fast to respond to different events. In the proposed multi-function hydrophone design, the Fat32 file system is used. As a result, the computer can read the files directly. In the TCP/IP stack, file transfer protocol (FTP) service, Web service, user datagram protocol (UDP) communication, and TCP communication are easily configured and used. The main thread is used for interaction between threads and users, the controlling data are used for creating or terminating the four sub-threads. Specifically, the \"raw data storage\" thread, the \"raw data transmission\" thread, and the  
Figure 4: The MCU and its peripherals.  
\"DSSS demodulation\" thread are mutually exclusive, that is to say, only one thread can be executed at one time. In the following, the four sub-threads are introduced.
###### Abstract  
The high number of ontologies available on the web to date makes it increasingly difficult to select appropriate ontologies for reuse. Many studies have attempted to provide support for ontology selection and ranking; however, the existing studies provide support for ontology ranking from an objective perspective as opposed to a subjective perspective. They do not take into account the qualitative aspects of ontologies. Furthermore, the existing methods have a limited focus on group environments. In this paper, a multi-criteria decision-making approach is presented for ontology ranking with the development of an enhanced model combining the ELECTRE II model with the Z-Probabilistic Linguistic Term Set (ZPLTS). The ZPLTS-ELECTRE II model enables decision-makers to model ontology ranking problems using both numerical and linguistic data. Furthermore, the newly proposed model provides support for ontology ranking in group settings, with an emphasis on modeling the differing levels of credibility of decision-makers using the ZPLTS, which allows decision-makers to not only specify their opinion but also specify their level of credibility. The model was applied to rank a set of mental health ontologies obtained from the BioPortal repository. The results showed that the method was able to rank the ontologies successfully. The results were further compared with the traditional ELECTRE II and the PLTS ELECTRE II methods, displaying superior modeling capabilities. This paper demonstrated the effectiveness of the newly proposed ZPLTS-ELECTRE II model for ontology ranking in a real-world context, but the method is not constrained to the ontology ranking domain; rather, it may be applied to other real-world decision problems as well.  
ZPLTS-ELECTRE II; ELECTRE; ontology ranking; Z-number; probabilistic linguistic term set; multi-criteria decision-making Article
## 5 Conclusions  
We found a strong and complementary potential for both the ArborCam airborne sensor and the PlanetScope constellation for mapping and estimating the total aboveground carbon content and the intra-annual dynamics of a saltmarsh ecosystem. We found the potential to improve the accuracy of aboveground carbon estimates by developing species-specific calibration models reliant on accurate saltmarsh species distribution maps. Total carbon estimates in saltmarsh must also consider the temporal dynamics of saltmarsh over longer time periods (possibly up to one year) to account for seasonal and intra-annual variations in the spectral response recorded by satellites over saltmarsh ecosystems. Future research will focus on improving our understanding of the drivers of intra-annual variations in the spectral response of saltmarsh vegetation, in particular the roles of rainfall and groundwater, and on better distinguishing short-term fluctuations in vegetation spectral response from longer-term trends. An improved understanding of the spatiotemporal dynamics of saltmarsh ecosystems is critical to achieving more accurate monitoring and estimates of blue carbon using optical remote sensing.  
Conceptualization, E.W.-C. and K.P.D.; methodology, E.W.-C., K.P.D. and E.B.; formal analysis, E.W.-C., K.P.D., P.B. and N.H.; investigation, E.W.-C. and K.P.D.; resources, K.P.D., P.B., N.H. and E.B.; writing--original draft preparation, E.W.-C. and K.P.D.; writing--review and editing, E.W.-C., K.P.D., P.B., N.H. and E.B.; visualization, E.W.-C.; supervision, K.P.D. and E.B.; project administration, K.P.D. and E.B.; funding acquisition, E.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Australian Research Council, grant number IC170100023.  
We would like to thank the NSW National Parks and Wildlife Service for providing permission to access the field site.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. PB and NH are employees of ArborCarbon.
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 3 Verification According to European Standards
Flexural buckling verification of beam-column with open double-symmetric cross-section has to satisfy two expressions according to Eurocode 9:  
\[\left(\frac{N_{Ed}}{\chi_{y}\;\omega_{x}\;N_{Rd}}\right)^{\xi_{yv}}+\frac{M_{y,Ed}}{M_{y,Rd}}\leq 1, \tag{3}\]  
\[\left(\frac{N_{Ed}}{\chi_{z}\;\omega_{x}\;N_{Rd}}\right)^{\eta_{c}}+\left( \frac{M_{z,Ed}}{M_{z,Rd}}\right)^{\xi_{zx}}\leq 1, \tag{4}\]  
The hollow cross-sections and tubes have to be verified according to the design equation:  
\[\left(\frac{N_{Ed}}{N_{b,Rd,min}}\right)^{\eta_{c}}+\left[\left(\frac{M_{y, Ed}}{M_{y,Rd}}\right)^{1.7}+\left(\frac{M_{z,Ed}}{M_{z,Rd}}\right)^{1.7} \right]^{0.6}\leq 1.00, \tag{5}\]  
The beam-columns with open double-symmetric or monosymmetric cross-sections have to be verified for lateral torsional buckling about the weak axis of cross-section according to the following expression:  
\[\left(\frac{N_{Ed}}{\chi_{z}\;\omega_{x}\;N_{Rd}}\right)^{\eta_{c}}+\left( \frac{M_{y,Ed}}{\chi_{LT}\;\omega_{xLT}\;M_{y,Rd}}\right)^{\gamma_{c}}+\left( \frac{M_{z,Ed}}{M_{z,Rd}}\right)^{\xi_{zx}}\leq 1, \tag{6}\]  
\[\omega_{x}=\frac{\omega_{0}}{\chi+(1-\chi)sin\frac{\eta_{c}}{\gamma_{c}}}, \tag{7}\]  
\[\omega_{xLT}=\frac{\omega_{0}}{\chi_{LT}+(1-\chi_{LT})sin\frac{\eta_{c}}{ \gamma_{c}}}. \tag{8}\]  
where: \(\xi_{yv}=0.8\) or alternatively \(\xi_{yv}=\xi_{0}\chi_{y}\), but \(\xi_{yc}\geq 0.8\); \(\eta_{c}=0.8\) or alternatively \(\eta_{c}=\eta_{0}\chi_{z}\), but \(\eta_{c}\geq 0.8\); \(\xi_{zc}=0.8\) or alternatively \(\xi_{zc}=\xi_{0}\chi_{z}\), but \(\xi_{zc}\geq 0.8\); \(\eta_{0}\), \(\xi_{0}\), \(\gamma_{0}\) are defined in the Section 6.2.9.1 of the standard; \(N_{Ed}\), \(M_{y,Ed}\), and \(M_{z,Ed}\) are the design values of the compression force and the bending moments about the \(y\)-\(y\) and \(z\)-\(z\) axis in the verified cross-section; \(N_{Rd}\), \(M_{y,Rd}\), and \(M_{z,Rd}\) are the design values of resistances to normal force and bending moments about the \(y\)-\(y\) and \(z\)-\(z\) axis in the verified cross-section; \(N_{b,Rd,min}=\min(\chi_{y}\omega_{x}N_{Rd}\); \(\chi_{z}\omega_{x}N_{Rd})\) depending on the direction of buckling \(\Psi_{c}=\max(1.3\chi_{y};0.8)\) or \(\Psi_{c}=\max(1.3\chi_{z};0.8)\) depending on direction of buckling; \(\omega_{x}\) and \(\omega_{xLT}\) are the coefficients taking into account the distribution of the secondary bending moment along the member;\(x_{s}\) is the distance between support or point of inflection in the case of elastic flexural buckling and the point of verification;  
\(\omega_{0}\) is the coefficient taking into account the effect of cross welds, for a cross-section with no cross welds \(\omega_{0}=1\); \(\gamma_{c}=\gamma_{0}\), \(\chi=\chi_{y}\), or \(\chi_{z}\) are the reduction factors due to flexural buckling depending on the direction of buckling and \(l_{c}\) is the flexural buckling length.
## 5 Evaluation
### Experimental Results
#### 5.2.2 Fair Scheduling Policy in Dynamic Scenarios  
To test the performance of FBAC in dynamic networks, we selected the bandwidth tracking data of real networks to simulate the dynamic changes of bottleneck links to maximize the diversity of network states during the experiment. Considering the randomness of video stream requests in the real network, in this experiment, the three clients participating in the experiment no longer request videos simultaneously but choose random times to randomly order videos in the video dataset.  
As shown in Table 1, compared with the other three algorithms, the FBAC algorithm improves fairness by 13%, 11%, and 10%, respectively. The FBAC algorithm focuses on the long-term QoE fairness of video streams, so it has an advantage in dealing with dynamic network environments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **QoE Efficient** & **QoE Fairness** \\ \hline MPC & 43.3 & 0.76 \\ Pensieve & 45.1 & 0.79 \\ Bola & 43.9 & 0.78 \\ FBAC & 49.5 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of under dynamic bandwidth.  
Figure 4: (**a**) Evolution of bandwidth in MPC (**b**) Evolution of QoE in MPC.  
Figure 5: (**a**) Evolution of bandwidth in FBAC (**b**) Evolution of QoE in FBAC.  
In addition, the average QoE efficiency of FBAC is improved by 13%, 11%, and 9%, respectively, compared with the other three algorithms. To explore how FBAC can improve the overall QoE efficiency, we collected the video quality, re-buffering time, and video smoothness of each scheme in the experimental process. The experimental results are shown in Figure 6, and drew the following conclusions. FBAC can help clients with re-buffering risk to quickly establish buffers, which effectively reduces the re-buffering time of clients and enables video streams to establish a global buffer pool by using shared bottleneck links. This brings another benefit: the clients in the same bottleneck link can obtain higher overall video quality. This is because the perceived quality of the video is a convex function. When bandwidth is allocated from a client playing high-quality video to one playing low-quality video, the experience quality lost by the client playing high-quality video is lower than that gained by the client playing low-quality video.  
Furthermore, to verify the impact of global parameters on the distributed algorithm, we trained a version of UN-FBAC that does not use global parameters in the same environment and evaluated it in a dynamic scene. The experimental results are shown in Figure 7. UN-FBAC is inferior to the full version of FBAC in terms of QoE efficiency and QoE fairness. By introducing global parameters as the input parameters of the neural network, the QoE fairness of the FBAC algorithm can be improved by 10%, and the overall QoE efficiency can be improved by 8%.  
Figure 6: Various indicators of QoE in dynamic bandwidth scenarios.