esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Structure after Oxidation
#### 3.3.3 Surface Analysis  
The oxides on the surface of the HfCo, (HfCo)\({}_{90}\)(NbMo)\({}_{10}\), and (HfCo)\({}_{75}\)(NbMo)\({}_{25}\) alloys were identified by XRD analysis. After 1 h of oxidation (Figure 5a), the surface of the HfCo alloy was covered by two different types of a HfO\({}_{2}\) oxide, denoted as HfO\({}_{2}\)-14 (ZrO\({}_{2}\)-b-prototype; mP12) [47] and HfO\({}_{2}\)-225 (CaF\({}_{2}\)-prototype; cF12) [48]. The most intensive [PERSON]'s peaks belonged to HfO\({}_{2}\)_225, while HfO\({}_{2}\)_14 presented a series of low-intensity reflections.  
In the (HfCo)\({}_{90}\)(NbMo)\({}_{10}\) and (HfCo)\({}_{75}\)(NbMo)\({}_{25}\) alloys, four different oxides were found, namely CoO (NaCl-prototype; cF8) [49], HfO\({}_{2}\)_14, Nb\({}_{2}\)Co\({}_{4}\)O\({}_{9}\) (Nb\({}_{2}\)Mn\({}_{4}\)O\({}_{9}\)-prototype; hP30) [50], and MoO\({}_{3}\) (MoO\({}_{3}\)-prototype; oP16) [51] (Figure 5a). CoO dominated in both alloys, while, the intensity and number of the Bragg's peaks corresponding to other three oxides in the (HfCo)\({}_{75}\)(NbMo)\({}_{25}\) alloy were notably higher compared to the (HfCo)\({}_{90}\)(NbMo)\({}_{10}\) alloy.  
After 100 h of oxidation (Figure 5b), CoO became the dominant oxide in the HfCo alloy; traces of HfO\({}_{2}\)_14 were also detected. While CoO still prevailed in the (HfCo)\({}_{90}\)(NbMo)\({}_{10}\) alloy and no new oxides were identified, we observed the increased intensity of HfO\({}_{2}\)_14 and Nb\({}_{2}\)Co\({}_{4}\)O\({}_{9}\) and the appearance of several new low-intensity reflections belonging to MoO\({}_{3}\). In the (HfCo)\({}_{75}\)(NbMo)\({}_{25}\) alloy, we revealed the dominance of Nb\({}_{2}\)Co\({}_{4}\)O\({}_{9}\), HfO\({}_{2}\)_14, and MoO\({}_{3}\). The intensity of the diffraction maximums related to CoO were significantly lower compared to those of 1h-oxidation (Figure 5b).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Area of EDS Analysis**} & \multirow{2}{*}{**Hf**} & \multirow{2}{*}{**Co**} & \multirow{2}{*}{**Nb**} & \multirow{2}{*}{**Mo**} & \multirow{2}{*}{**O**} \\ \cline{2-2} \cline{5-6}  & & & & & \\ \hline \multicolumn{5}{c}{**HfCo**} \\ \hline OL & 0.8 & 40.1 & - & - & 59.1 \\ IRZ\_1 & 18.2 & 13.8 & - & - & 68.0 \\ IRZ\_2 & 21.7 & 22.0 & - & - & 56.3 \\ Co-rich layer & 16.3 & 71.9 & - & - & 11.8 \\ \hline \multicolumn{5}{c}{**HfCo**} \\ \hline OL & 0.3 & 42.4 & 0.7 & 0.4 & 56.2 \\ IRZ\_1 & 14.5 & 6.8 & 2.4 & 1.2 & 75.2 \\ IRZ\_2 & 20.0 & 21.0 & 0.9 & 2.2 & 55.9 \\ Co-rich layer & 10.9 & 66.1 & 2.4 & 2.8 & 17.8 \\ \hline \multicolumn{5}{c}{**HfCo**} \\ \hline Top of the IRZ & 13.6 & 12.6 & 5.4 & 0.9 & 68.6 \\ Middle of the IRZ & 10.2 & 12.7 & 4.2 & 3.3 & 69.6 \\ Bottom of the IRZ & 12.6 & 2.8 & 3.5 & 4.4 & 76.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average chemical compositions of the characteristic layers formed in the cross-sections of the HfCo, (HfCo)\({}_{90}\)(NbMo)\({}_{10}\), and (HfCo)\({}_{75}\)(NbMo)\({}_{25}\) alloys oxidized at 1000 \({}^{\circ}\)C for 100 h.
## 3 Electromagnetic Force and Torque  
### Permanent Magnet Array Model  
To get the mathematic model of the electromagnetic force and torque, the gap magnetic field generated by the Halbach permanent magnet arrangement in Figure 2 needs to be calculated first. To simplify the analysis, the following assumptions are made:  
* The relative permeability of the iron yoke has a value of infinity;
* The magnet array has a periodicity over the \({}^{m}\mathbf{x}\) and \({}^{m}\mathbf{y}\) direction and the magnetization value of the permanent magnet is not changed;
* Ending effect is neglected.  
The analysis model of the Halbach magnet array, the three-dimensional space is divided into two regions, region I is in the air and the permanent magnets in region II are located in between \(-h_{m}\leq{}^{m}\mathbf{z}\leq 0\). For region II, the permeability \(\mu=\mu_{0}\mu_{r}\), where \(\mu_{r}\) is the relative permeability of the permanent magnets, \(\mu_{0}\) is the permeability of free space.  
A magnetic scalar potential \(\varphi\) is introduced in both charge free regions to solve the problem. The magnetic scalar potential in the air (regions I in Figure 3) is governed by [PERSON]'s equation and in the permanent magnets by [PERSON]'s equation [4,27].  
The boundary conditions are as follows. The subscript I and II denote the air region and the magnets region respectively, and \({}^{m}x\), \({}^{m}y\), \({}^{m}z\) mean the components of the three directions.  
\[\varphi_{1}(^{m}x,^{m}y,+\infty)=0\
## 2 Background and Related Works
### Student Outcomes  
Outcome-based education (OBE) has emerged as a new school of thought in education and has recently enjoyed wide acceptability and adoption [12]. This educational paradigm shifts the focus of the teaching and learning process from the traditional teacher objectives to the so-called student outcomes. In simple terms, student outcomes refer to the knowledge, skills, and values to be attained by the students at the time of graduation or at end of a course [13]. The outcomes, representing the targeted competencies, might be defined and measured at the course level, i.e., course outcomes, or program level, i.e., program outcomes. Essentially, course outcomes enable the accomplishment of program outcomes, and their alignment (i.e., courses to program) is performed in a critical activity referred to as curriculum mapping. Computerized tools were developed to assist in realizing the OBE goals [14] and effectively document the educational assessment activities [15]. Their usefulness could be extended by incorporating intelligent models that can prognosticate the attainment of learning outcomes during academic terms.  
Measuring student outcomes in higher education indisputably brings about various benefits, including the establishment of program expectations for students and course instructors, the practical assessment of the quality of courses and programs, and the provision of key success indicators of the program, among others [7; 16]. Several quality assessment instruments, e.g., [17], and quality assurance frameworks, e.g., [18], were proposed to realize the outcome-based education philosophy and acquire program accreditations. Moreover, the ability to forecast the attainment of student outcomes adds further invaluable advantages, such as the ability to introduce corrective interventions to the learning processes. However, few works surveyed the intelligent prediction of student outcomes. Furthermore, the factors and attributes that impact educational outcomes are still vague. Studies suggest that these factors range between academic factors, e.g., teaching quality [19] and online engagement [20], and non-academic traits, e.g., family engagement [21] and student motivation [22]. In this work, we aim, through a systematic survey, to understand the landscape of student outcomes prediction using data mining and machine learning, identify the main challenges hindering the prediction of student outcomes, and propose relevant recommendations.
## 4 Discussion
Furthermore (considering that stability balls are not seating devices but training ones), some safety considerations have to be carried out regarding stability ball sitting practice, which may potentially lead to falls and increase the level of seating discomfort [17; 21]. Core (abdominal and back) muscle activation while sitting on a dynamic chair was found comparable to that on a stability ball [22]. On this basis, and taking into account the number of positive issues about dynamic sitting mentioned above, some tools have been proposed to promote trunk posture, movement and exercise during prolonged sitting [5; 15; 23; 24]. From our point of view, thinking about a novel seating device to be conceived in line with the dynamic and \"stability ball\" sitting approaches but able to overcome (almost in part) their limits (such as safety and comfort), we developed the \"EnergyBack\", which is a novel dynamic stool with an unstable base of support given by a ball positioned between the seat and the stool base (see Figure 1). The moving seat is fixed to the stool base by means of four elastic ropes, which enable two-dimensional swinging of the seating area and allow the dynamic stool components (seat, ball and base) to be centered on the same main vertical axis. The dynamic stool base has an adjustable height. The \"EnergyBack\" was built mainly with the aim to promote the preservation of physiological sagittal spinal curvature (thoracic kyphosis and lumbar lordosis) during sitting posture. The current findings validate the first aim of this device. Indeed, even if none of the sitting conditions (dynamic and conventional) was able to match completely with the \"ideal\" spinal posture (natural spinal curves on the sagittal plane evaluated in standing), considering their mean values (see Table 2), we argue that thoracic kyphosis and lumbar lordosis measured in the dynamic sitting condition (\"EnergyBack\" device) get significantly closer to the sagittal curvatures of the spine evaluated in standing posture than those assessed in the conventional sitting condition (standard stool). Thus, we claim for a better trunk posture alignment during sitting given by the dynamic stool than the standard one in our healthy volunteers. This validation study has some limitations. First of all, the design was not prospective and considered only the \"short term\" adaptations of trunk posture induced by the \"EnergyBack\" dynamic stool. So, we cannot infer its effects on trunk posture in terms of prolonged sitting and routine use (e.g., back pain prevention). Furthermore, we included only healthy people, so we cannot draw any conclusion about the effects of our dynamic stool on patients with back pain. Third, we did not test sitting posture during some daily common tasks such as reading, writing by hand, calling or typing on the keyboard. Fourth, no instrumental kinematic (e.g., motion analysis) and neurophysiological (e.g., surface electromyography) evaluation was carried out. Fifth, we did not include any evaluation of compliance with the novel dynamic stool tested in this study.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Microsatellites
#### 3.3.1 ICEYE-X1/X2 (85 kg)  
Developed by ICEYE in Finland and launched in 2018, ICEYE-X1 is known as the first SAR in microsatellite class with a design mass of 70 kg as well as the country's first commercial satellite [109]. In its launch configuration, ICEYE-X1 has a size of 0.7 m (H) \(\times\) 0.6 m (W) \(\times\) 0.4 m (D); its X-band antenna has a length of 3.25 m when deployed [110; 111]. With 2 to 3 years of designed life, ICEYE-X1 has a similar shape as the Micro XSAR proto-flight model or the SmallSat InSAR concept. Compared to this proof-of-concept version, ICEYE-X2 features increased form factors (0.8 m \(\times\) 0.8 m \(\times\) 0.6 m before deployment, 85 kg) to improve the SAR performance, as shown in Figure 18. The RF peak power of ICEYE-X2 is 4 kW, more than twice the ICEYE-X1 capacity, and its SAR imagery resolution also improved from 10 m to a sub-meter level [112]. The fast design iteration can be seen from the launch dates of the two satellites, January 2018 and December 2018, separated by less than a year. The follow-on satellites, ICEYE-X4 and ICEYE-X5, have identical design specifications as ICEYE-X2 and have been launched together in July 2019, which is probably the first case of launching multiple SAR satellites simultaneously. The ICEYE-X3, also called Harbinger, is a military-purpose variant featuring field-effect electric propulsion and high-rate laser communications capabilities whose payload operations demand 3 kW peak power. Harbinger is no longer classified as a microsatellite because its launch mass is 150 kg falling into the minisatellite class. All ICEYE spacecraft except X3 use Sun-synchronous orbits with inclination values of 97.56\({}^{\circ}\) or 97.77\({}^{\circ}\); ICEYE-X3 instead uses an inclined orbit with 40.0\({}^{\circ}\) for increased visit frequencies in mid- to low-latitude regions [113]. The fully-blown constellation, with a total of 18 ICEYE satellites launched by 2021, will provide an average of 3-h revisit interval around the globe to observe sea ice movements, marine oil spills, and illegal fishing vessels. In addition to its fast design iteration, the fact that ICEYE is the first Finnish commercial satellite exemplifies a strategy of jumpstarting the SAR satellite development, instead of optical/infrared observation satellites, for Earth remote sensing and its data commercialization.
## 4 Conclusions  
This study investigated the tribological properties of aluminum nanoparticle additives on glycerol solutions with the following conclusions obtained:  
Figure 12: Stribeck curve for different rotational speeds.  
Figure 13: SEM micrograph of aluminum nanoparticles (**a**) before; (**b**) after experiment No. 10; (**c**) before the experiment using optimal concentrations and (**d**) after the experiment with optimal concentrations.  
* Through ANOVA analysis, the three control factors (concentration of nanoparticles, concentration of SDS, and water content of glycerol) have 98.05% and 95.64% of confident level effects on the COF and wear rate. According to the RSM analysis, the regression equation of the model fits well with the experiment data.
* The most significant factor effect on both COF and wear rate was the DI water content in glycerol, followed by concentration of aluminum nanoparticles, and lastly the SDS content.
* The application of multi-objective optimization has shown that the best set of parameters was 0.6667 wt % for aluminum, 2 wt % for SDS, and 10 wt % DI water content of glycerol--both the wear rate and COF were the least when using the abovementioned parameters.
* The images from SEM and EDS observation showed that the collar friction traces became fewer and lighter when the optimal concentration of nanoparticles was added to the lubricant. The bearing ball effect of nanoparticles and the deposition of metal film on the wear surface were identified as the mechanisms that contributed to the reduction of the COE and wear rate.  
The authors are very grateful to the Department of Mechanical Engineering of National Kaohsiung University of Applied Sciences and Center for Micro/Nano Science and Technology of National Cheng Kung University for providing equipment and technical support.  
[PERSON] designed and performed the experiments, analyzed the data, and wrote the paper. [PERSON] provided and developed the research orientation and, in general, helped to improve the quality of the work.  
The authors declare no conflict of interest.
# Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges
## 2 Fog and Edge Computing: Modelling and Simulation Challenges  
The increasing ubiquity of mobile technologies and low-cost connected sensors has resulted in a deluge of computational and networking end points at several orders of magnitude than previous decades. Conventional computing infrastructures, including cloud computing, leverage geographical centralised data centres using relatively homogeneous commoditized hardware. Such infrastructures were not designed to cater for the processing, storage and data generated by billions of distributed end points operating in often dynamic environments with intermittent network connectivity. Unsurprisingly, service providers have faced unprecedented challenges planning and managing for greater demands while meeting minimum service levels. Fog computing has emerged to complement cloud computing. As discussed earlier, fog computing is located between smart end-devices at the edge of networks and traditional cloud or data centres [4]. It plays an important role in reducing network congestion and facilitating location awareness, mobility support, real-time interactions, scalability and interoperability [8; 22]. In contrast, edge computing, in its purest sense, defined by the exclusion of cloud and fog, and is limited to a small number of peripheral network layers [4]. Both fog and edge computing present significant challenges for service providers and researchers including application architecture design and deployment, infrastructure and network management, mobility, resource management, and scalability.
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 7 Experimental Results
### Structured Sparse Matrix Multiplication
(2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Stream-K: Work-Centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU. In Proceedings of the 28 th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, Montreal, QC, Canada, 25 February-1 March 2023; pp. 429-431. * [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON] [PERSON] Sparse matrix-matrix multiplication on modern architectures. In Proceedings of the 2012 19 th International Conference on High Performance Computing, Pune, India, 18-22 December 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 1-10. * [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON]; [PERSON] Cusparse library. In Proceedings of the GPU Technology Conference, San Jose, CA, 20-23 September 2010. * [PERSON] (2012) [PERSON]; [PERSON] _Cusp: Generic Parallel Algorithms for Sparse Matrix and Graph computations_, Version 0.3.0; 2012. * [PERSON] and [PERSON] (2011) [PERSON]; [PERSON] _Thrust: A Parallel Template Library_; GPU Computing Gems Jade Edition, 359; 2011. Available online: [[https://shop.elsevier.com/books/gpu-computing-gems-jade-edition/hwu/978-0-12-385963-1](https://shop.elsevier.com/books/gpu-computing-gems-jade-edition/hwu/978-0-12-385963-1)]([https://shop.elsevier.com/books/gpu-computing-gems-jade-edition/hwu/978-0-12-385963-1](https://shop.elsevier.com/books/gpu-computing-gems-jade-edition/hwu/978-0-12-385963-1)) (accessed on 10 December 2023). * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. In-place matrix transposition on GPUs. _IEEE Trans. Parallel Distrib. Syst._**2015**, _27_, 776-788. [CrossRef]
* [PERSON] and [PERSON] (2010) [PERSON]; [PERSON] Understanding throughput-oriented architectures. _Commun. ACM_**2010**, _53_, 58-66. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] A Many-Core Machine Model for Designing Algorithms with Minimum Parallelism Overheads. In _Parallel Computing: On the Road to Exascale_; IOS Press: Amsterdam, The Netherlands, 2016; pp. 35-44. * [PERSON] (2014) [PERSON] Florida Sparse Matrix Collection. 2014. Available online: [[http://www.cise.ufl.edu/research/sparse/matrices/index.html](http://www.cise.ufl.edu/research/sparse/matrices/index.html)]([http://www.cise.ufl.edu/research/sparse/matrices/index.html](http://www.cise.ufl.edu/research/sparse/matrices/index.html)) (accessed on 1 January 2023). **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
## 3 Results and Discussion
### Zeta Potential of the Peptide Fractions  
The net negative charge of each peptide fraction was determined from its zeta potential and the value of zeta potential determined surface phenomena occurring at the interface. The zeta potential values of the three peptide fractions of different molecular weights are presented in Figure 2. There was a clear trend of the potential increasing from -12.91 \(\pm\) 2.04 mV for P1 to -5.83 \(\pm\) 0.97 mV for P3. As the molecular weight of the peptide fraction increased, the absolute value of the zeta potential decreased. This can be attribute to the changes in the surface charge distribution of the native protein on the basis of its molecular rearrangement during hydrolysis and the repulsive intermolecular electrostatic force increased with the increase in the degree of hydrolysis (DH). Furthermore, the molecular weight of hydrolysate fraction decreases with the increase of DH under certain hydrolysis conditions and it indicated that low molecular weight peptide fraction with higher DH exhibited the higher surface charge. The surface charge of the peptides was conducive to the stability of the particles except when the particle size could not be balanced with electrostatic repulsion [16; 21; 22]. The low molecular weight peptide fraction P1 exhibited the strong zinc chelation capacity, this can be due to the high electrostatic repulsion at P1 surface, reflected by high magnitude zeta potential, which inhibit intermolecular aggregation leading to small peptide particles. The iron chelation capacity of sea cucumber ovum hydrolysates, as well as the stability of their iron complexes, was influenced by their net negative charge [23]. The zeta potential of the peptides indicated that they possessed different degrees of stability in aqueous solution, which affected their affinity for metal ions and the stability of their metal complexes [24]. The low molecular weight of peptide fraction with higher surface charge is considered to be related to the strong zinc chelation capacity and stability of the zinc-peptide complexes.  
Figure 1: Zinc chelating capacity of three molecular weight peptide fractions; bars in each chart with different letters represent significantly different mean values with P \(<\) 0.05.  
Figure 2: Zeta potential of three molecular weight peptide fractions; bars in each chart with different letters represent significantly different mean values with P \(<\) 0.05.
## 2 Related Work
### Object Detection and Semantic Mapping
Related to our approach, the authors in [32] perform cluster detection based on a cost function with different features, to learn online how to detect humans. These works seek to reduce the error in detection by learning from information sensed previously. The observations are compared against the stored models at a cost of an increase in the computation time. Additionally, these contributions focus only on the detection of single elements, e.g., people in indoor environments, without building a complete semantic map. However, methods based on deep learning require high-end, powerful hardware, often with a dedicated GPU, to perform the computation in real-time. To increase the computation effort, alternative approaches can be used paying attention to the physical characteristics of the objects to be detected. Thus, methods identifying legs [33], faces [34], or human poses [35] have been proposed. Their reduced computational effort makes these approaches suitable for semantic mapping on more common robotic platforms at the cost of a decrease in accuracy. In this work, we show that lightweight methods can still provide good accuracy even with low-density information.