esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion  
The main results of the work performed are as follows:  
(1) An analysis of scientific publications devoted to the use of semantic technologies for modelling educational programmes was carried out. Typical disadvantages of the proposed ontological models are revealed. They mainly consist of the fact that the developed models are poorly adapted for using logical inference tools, and the practice of such application has not been investigated. In addition, the existing models are constructed mainly using a limited set of roles (relationships) that reflect only the structure of the educational programme. They do not describe the relationship between concepts over time and the content of the educational process.  
(2) An ontological model of the educational programme has been developed, reflecting the studied courses in terms of the skills they form and the entrance requirements (pre-requisites) for the student's qualification. The model provides for the interrelationships of training periods over time, allowing to operate with sequences of study courses.  
(3) SPARQL queries have been developed that allow analysing the educational programme for consistency of the input requirements of the courses and the skills of the student formed during the previous training period. The result of the queries is a subset of skills that relate to the input requirements of a certain training period, but were not formed during previous training periods.  
The conducted research made it possible to answer the research questions posed.  
1. Is it possible to build, using an object-oriented approach, a detailed ontological model of an educational programme that combines courses, skills, and training periods? The answer to this research question is: YES, we did it. It should only be noted that for real cases (eg a four-year educational programme with a large number of courses, skills and training periods), the developed models are very complex and difficult to use.  
Figure 16: List of skills that cause inconsistency in the educational programme.  
* Is it possible to automate the analysis of the consistency of course prerequisites and learning outcomes of an educational programme and its ontological model with the use of appropriate software? The answer to this question is also: YES. Specialized software was used in the work not only to build the model, but also to explore it quite easily and automatically. A special query language for logical reasoning was used: SPARQL.
* Can the developed ontological model of the educational programme be easily integrated with the Learning Management Systems software? The answer to this question is: NO. LMSs mostly use relational database management systems in the data layer. Meanwhile, SPARQL queries cannot be easily implemented in this technology, i.e., in Structured Query Language (SQL). The implementation or integration of both technologies will require separate research.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### PV-DT Based on Developmental Stage
#### 3.1.4 PV Power Generation Forecasting  
Ref. [52] developed an algorithm for power forecasting of wind and PV named wPnet and implemented it in real-time for existing wind and PV installations using a DT. The role of the DT was to estimate the uncertainties and instabilities in power generation in real-time and apply a corresponding control strategy. The DT in [53] was applied for the now-casting of PV power using genetic algorithms. Now-casting is highly important in energy scheduling at the utility-scale level and is used mostly for National Grid energy planning. It is the prediction of energy on a short-term basis, usually up to 2 h in advance. The authors of [54] implemented a DS for power forecasting in DTs using a hybrid tree-based ensemble learning model. A multi-vector deep learning approach for Integrated Energy Systems (IES with PV sources included) was applied in [55] for day-ahead forecasting of energy supply and demand under various uncertainties posed by renewable energy components. This effectively reduced the operation cost of the associated microgrid or mains utility grid.
# Knowledge-Based Perturbation LaF-CMA-ES for Multimodal Optimization
## 4 Experiment and Analysis
### Parameters Analysis  
The configuration of parameters is crucial for achieving optimized results in algorithms. However, determining the optimal parameter settings can be challenging. By analyzing statistical data, the impact of parameters on performance can be assessed throughout the optimization process. In this part, the DOE method is applied to control the parameters of the proposed KbP-LaF-CMAES in the test suite of CEC2013. Three typical parameters mainly influence the capacity of the KbP-LaF-CMAES. These parameters are MainEvals (Evaluation number of LaF strategy), PertFac (Factor of perturbation), and PopFac (Factor of parent population).  
A 1/2 fractional factorial DOE was conducted for these parameters. The parameter choices are shown in Table 1: MainEvals \(\in\) [0.3, 0.5, 0.7, 0.9], PertFac \(\in\) [0.5, 1, 1.5, 2], and PopFac \(\in\) [0.1, 0.25, 0.5, 0.75]. A variety of dimensions (D = 30, D = 50, and D = 100) were selected as the dimensions of the test space to enhance the statistical significance. The orthogonal array of the parameter configurations is shown in Table 2. For each parameter group, the algorithm was run 10 times independently on all of the 28 functions of CEC2013.  
The ranks of these parameters are presented in Table 3. The MainEvals parameter is the most critical for performance. For each parameter combination in the orthogonal array, the average value (AVE) across all dimensions and the total average value (TAVE) are displayed in Table 4.  
According to the experimental results, the performance variation with these parameters is shown in Figure 6. The analysis indicates that the parameter _mainEvals_ is the most influential among the three parameters, with an optimal value of 0.3. The _mainEvals_ parameter determines the proportion of LaF in the algorithm, which is a powerful exploration strategy in KbP-LaF-CMAES. In contrast, _PertFac_ and _PopFac_ are relatively minor parameters but still affect population diversity, which, in turn, influences the quality of the exploitation process. Based on Table 3 and Figure 6, the parameter values for KbP-LaF-CMAES are as follows: _mainEvals_ = 0.3, _PertFac_ = 1.5, and _PopFac_ = 0.5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor Level** & **1** & **2** & **3** & **4** \\ \hline _mainEvals_ & 0.3 & 0.5 & 0.7 & 0.9 \\ _PertFac_ & 0.5 & 1 & 1.5 & 2 \\ _PopFac_ & 0.1 & 0.25 & 0.5 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The choices of the parameter.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Parameters**} & \multicolumn{2}{c}{**AVE**} & \multicolumn{2}{c}{**TAVE**} \\  & _mainEvals_ & _PertFac_ & _PopFac_ & **30D** & **50D** & **100D** & **Total** \\ \hline
1 & 1 & 1 & 1 & 6.
###### Abstract
To dynamically generate the prescheduling and rescheduling solutions of a sustainable hybrid flow shop, a GA-based dynamic scheduling optimisation with variable priorities is proposed [13]. In addition, for multiobjective SS problems, the literature has mostly adopted hybrid methods with GA. [PERSON] [10] established a multiobjective GA with a tabu-enhanced iterated greedy local search strategy to resolve SS problems. Previous studies have mostly focused on improving model accuracy by the modified GA to optimise multiple objectives in the economic and environmental aspects of SS. However, few studies have addressed multiobjective problems in three dimensions of SS by GA. Therefore, this study utilises an improved GA to optimise multiobjective scenarios in three dimensions to address the SSP by considering SS. After that, this study adopts decision-making analysis methods to further accurately optimise and select an ideal scenario. The purpose of this study is to propose an intelligent scheduling system for addressing the SSP by considering SS (ISS-AFLCCS) to achieve balanced development of the economy, environment, and society of SS. The main contributions of this study are presented as follows: (1) generate a deeper analysis and discussion on the balance relations of the three dimensions of SS; (2) propose a hybrid approach using an improved GA to optimise multiple objectives of scheduling scenarios and adopt the TOPSIS method with AHP to further precisely optimise and select a best-ideal scenario; and (3) under every electricity consumption strategy, selecting a corresponding optimal scheduling scenario of electric and fuel vehicle combinations can provide precise guidance for enterprise managers. The next section is a literature review, including the theoretical background, proposed methods and solved problems. The model is constructed in Section 3. The proposed hybrid method and the framework for solving the model are described in Section 4. The case information and analysis results are presented in Section 5. A more detailed discussion of the analysis resultsis provided in Section 6. The conclusions, important findings, limitations and possible future research directions are summarised at the end of the paper.
## Materials and Methods
### Characterization  
The crystal structure and phase purity of the powdered samples were analyzed by X-Ray Diffraction (XRD) using a PANalytical Empyrean Alpha-1 X-Ray Diffractometer (XRD) (Almelo, The Netherlands) and the measurements made at the University of Antioquia Colombia (Malvern, UK, Cu Ka\({}_{2}\) radiation, \(\lambda\) = 1.54442 A), operating at 40 mA with a step of 0.05\({}^{\circ}\) in 50 s, the 2\(\theta\) angle range was 5-80\({}^{\circ}\). Rietveld analysis was performed with the refinement program General Structure Analysis System (GSAS). Infrared (IR) spectra have been recorded with a IRPrestige-21 Spectrophotometer (Shimadzu, Kyoto, Japan, and the measures carried out in Cucuta, UFPS Colombia). The spectral resolution was set at 8 cm\({}^{-1}\). The powder samples were studied in KBr pellets. Raman spectroscopy was carried out with 633 nm line of a He-Ne ion laser at room temperature with a (The Jobin Yvon T64000 Raman system with a triple spectrometer, Horiba, Kyoto, Japan and the measures in UFC Fortaleza, Brazil) equipped. Morphologies of the samples were studied by Field Emission-Scanning Electronic Microcopy (FE-SEM). Images were obtained with a ZEISS Instruments and Systems (ZEISS, Oberkochen, Germany) working at 5.0 kV and 200.0 nA; the samples were coated with a 10 nm thick Pt layer. The surface area, diameter and pore volume were obtained from the Brunauer-Emmett-Teller (BET) method using a Micromeritics ASAP 2020 (Micromeritics, Norcross, GA, USA) apparatus. The reflectance measurements were obtained from Diffuse Reflection Spectra (\(DRS\)) with a UV-2600i: UV-vis spectrophotometer from Shimadzu (Kyoto, Japan). Photoluminescence (PL) measurements for possible effects of Mn inclusion were conducted using FluoroMax-4P apparatus (Horiba, NJ, USA) with \(\lambda\) = 250 nm. Dynamic Light Scattering (DLS) technique was used to analyze the coloidal stability of the samples. The measurements were carried out at room temperature using Malvern Zetasizer Nano (Malvern, UK).
# Assistance of a Person with Muscular Weakness Using a Joint-Torque-Assisting Exoskeletal Robot
## 3 Real-Time Gait Detection Method
### Gait Detection Algorithm  
In this section, the gait index derivation from the measurement of the foot sensor-pad during walking is introduced. In the case of people with paralytic gait, the swing motion is performed only when the center of gravity is stable. Therefore, the timing of the initial swing depends on the intention of the person. Robotic support for incomplete paraplegics should also be applied when the person intended. Otherwise, the operating force will cause unstable movement of humans and robotic systems. In this paper, the walking intention is monitored in real-time through the gait index analysis method using the foot sensor pad.  
Figure 5: Gait patterns of a leg: one gait cycle is from when the foot initially contacts the ground to when the foot contacts the ground again. DS1 and DS2 mean the first and the second double limb support phases, respectively.  
Swing phase of a leg can be determined as \(\mu_{swing}\):  
\[\mu_{swing}(t)=\begin{cases}1,&\text{if }f_{toe}(t)<s\text{ and }f_{het}(t)<s\\ 0,&\text{otherwise}\end{cases} \tag{1}\]  
where \(f_{toe}\) and \(f_{het}\) are the measurements of the toe and heel sensor pads, respectively, and \(s\) is the threshold to check that the sensor is pressed. Figure 6a shows the swing phases of both leg while walking, and \(\mu_{swing}=0\) represents the stance phase. As described in Section 3.1, the gait can be classified into four phases. To switch the control scheme according to the gait phase, it is necessary to detect the moment of phase change.  
The falling edge of \(\mu_{swing,right}\) represents the initial contact event of the right leg, which is equivalent to Gait index \(=1\), the rising edge of \(\mu_{swing,left}\) is equivalent to Gait index \(=2\), the falling edge of \(\mu_{swing,left}\) is equivalent to Gait index \(=3\), and the rising edge of \(\mu_{swing,right}\) is equivalent to Gait index \(=4\). The derived gait index of walking is shown in Figure 6b.
## 1 Introduction  
Cosmetics or coating formulations consist of inorganic and organic compounds, among which titanium dioxide (TiO\({}_{2}\)) is commonly used as an inorganic pigment or filler. As TiO\({}_{2}\) scatters or blocks ultraviolet (UV) rays by remaining on the skin or substrate surface, TiO\({}_{2}\)-based formulations have been widely used as sunscreen-functioning products. Moreover, TiO\({}_{2}\) is less irritating to the skin than organic compounds, such as avobenzene, and cosmetic products containing TiO\({}_{2}\) have been used for sensitive or infant skin [1; 2].  
In TiO\({}_{2}\)-based formulations, the UV ray blocking depends on the stability of the TiO\({}_{2}\) dispersion [3; 4]. TiO\({}_{2}\) particles generally aggregate in media owing to the van der Waals forces between the particles. This aggregation behavior prevents the particles from spreading evenly in the dispersion, thus reducing the UV-blocking efficacy [5]. As a strategy to enable particles to move far apart, adding a surfactant or dispersant is required to reduce the aggregation behavior, reaching a suitable rheological or light scattering property of the particle dispersions for practical usage. For example, through the adsorption of ionic surfactants or dispersants on the surface of particles, an electrical double layer is formed at the solid-liquid interface, and electrical repulsion prevents aggregation [6; 7]. Alternatively, the steric hindrance of the adsorbed polymeric dispersant inhibits the aggregation of TiO\({}_{2}\) particles [8; 9; 10].  
With regard to polymer adsorption on TiO\({}_{2}\) particles, [PERSON] et al. revealed the effect of the architectural dispersant structure of poly(acrylic acid) (PAA) on the degree of TiO\({}_{2}\) dispersion quality [11]. The polymers were synthesized in linear, single-branched, short-armed combs, long-armed combs, and star architectures. The relationship between the degree of dispersion and the dispersant structural architecture was investigated, and it was concluded that linear PAA was the most effective for TiO\({}_{2}\) adsorption and dispersion. Accordingly, the effect of the chemical structure of the surfactant or dispersant on the dispersion quality of TiO\({}_{2}\) is important for the interpretation and designing of TiO\({}_{2}\)-based formulations.  
Herein, we investigated the effect of the ionic character of a surfactant on the dispersion stability, viscosity, morphological characterization, and UV-blocking efficacy of a TiO\({}_{2}\) water-in-oil (W/O) formulation. We interpreted the correlation between the dispersion stability and UV-blocking efficacy. We further investigated the effect of the alkyl chain length of the anionic fatty acid used as a surfactant on the TiO\({}_{2}\) dispersion performance, based on stability, viscosity, and UV-blocking efficacy.
# Adam Algorithm with Step Adaptation
## 5 Conclusions  
We have developed a number of new gradient-type algorithms with step adaptation, intended for the minimization of functions with gradient interference; these are the gradient method with step adaptation (H_GR), momentum method with step adaptation (H_MOMENTUM) and Adam algorithm with step adaptation (H_ADAM). Such algorithms are used for training neural networks, where a gradient on a limited number of randomly selected data is used to perform individual steps of the minimization algorithm.  
One of the challenges for effective minimization is the conditionality of the problem. In order to study the effect of the conditionality on gradient-type methods, we have developed several functions with conditionality characteristics that do not depend on the problem dimension. Since the number of gradient method iterations depends to a greater extent on the degree of problem conditionality, we can conduct research on problems of low dimension, which enables us to reduce the computation time and thereby expand the range of issues under study.  
To analyze the stability of the algorithm, we selected optimization criteria to observe the algorithm's operation over a long period of increasing the accuracy of function minimization. We investigated the well-known Adam algorithm and the proposed algorithms with step adaptation. All the used test problems were intended to study the convergence rate of the algorithms under the conditions of a single local minimum with a given degree of conditionality. It can be concluded that the step adaptation proposed in the work can be useful in combination with the gradient method, momentum and Adam algorithms. At the same time, it turned out that the structure for obtaining the descent direction in the Adam algorithm was the most effective for organizing an algorithm with step adaptation. In future research, we plan to expand the list of algorithms to study the possibility of proposed step adaptation and to extend the algorithms to a wider range of applied problems.  
Conceptualization, V.K.; methodology, V.K. and E.T.; software, V.K.; validation, L.K. and E.T.; formal analysis, L.K.; investigation, V.K.; resources, V.K. and L.K.; data curation, E.T.; writing--original draft preparation, V.K.; writing--review and editing, E.T. and L.K.; visualization, V.K. and E.T.; supervision, L.K.; project administration, L.K.; funding acquisition, L.K. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Ministry of Science and Higher Education of the Russian Federation, project no. FEFE-2023-0004.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 6 Emerging Human-AI Collaboration Problems in RSA
### Emerging Problem 7: Stabilizing Live Camera Feeds for Task-Specific Needs  
The quality of the live camera feed, especially the steadiness and clearness of the video, can greatly affect the RSA performance in hazard recognition [109; 110], and thus, it is considered as one of the main factors determining the safety of blind users [10]. The problem of unstable camera feed is identified as G2.(5) in Table 1. Prior works [11; 94] found that the camera should be located appropriately to maximize vision stability. Since there is no camera stabilizer for either handheld mobile devices or glasses in RSA service, the live camera feed is easily affected by the camera motion and becomes unstable. It is reported that agents are more likely to experience motion sickness when the users are not holding the camera (e.g., smartphone hanging around the user's neck) [43]. Based on the significance of stabilizing live camera feeds, we regard it as an emerging research problem that can be addressed by human-AI collaboration.  
From the perspective of AI techniques, video stabilization methods [100] could offer possible solutions to this problem. These methods usually first analyze the video frames to  
Figure 3: Expanding the field of view with fisheye lens. Here, we attached a fisheye lens to the rear-facing camera of an iPhone 8 Plus.  
estimate the camera motion trajectory. For a shaky video, the camera motion is also oscillatory. Then, a low-pass filter is applied to smooth the camera motion. Based on the smoothed camera motion and the original shaky video, a new stabilized video can be synthesized. In this pipeline, the strength of stabilization does not have an objective criterion.  
In terms of human-AI collaboration, the stabilization of the live camera feed needs the agent's input on the strength of stabilization, which corresponds to the parameters of the low-pass filter. For various RSA tasks, the needed strengths of stabilization are different. For example, in a navigational task when the user is walking, the goal of stabilization is to reduce the video shaking and generate a pleasing video feed for the agent; in a text reading task when the user is trying to stay static, the stabilization should make the text display as still as possible. It is worth noting that the captured frame image can be blurry due to the camera shake. Based on the display effect of video stabilization, the agent can further adjust the strength of stabilization to achieve the optimal effect for a specific task. In addition, the study of Problem 7 (stabilizing live camera feeds) can also benefit Problem 3 (recognizing digital content) and Problem 4 (recognizing texts on irregular surfaces).