esa-sceva/satcom-chunk-collection huggingface dataset:
# The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios
## 4 Results
### Answers to the Research Questions  
The answer to the first research question (\"Are data generated by students watching educational videos perceived as useful by teachers in small scale learning scenarios?\") is affirmative: all interviewed teachers state that they find the presented graphs useful, and they would like to have them for all educational videos they use in their classes, although they do not see changes or improvements to be made to the videos as a result of the information obtained from the data. There is no clear explanation for this fact. One possible reason may be that the analysed videos were the result of work by teachers with a long experience in the creation of educational videos and, as such, they do not present points to be improved because of the obtained information. Another possible explanation may be that the timeframe of the interviews did not provide sufficient time for interviewees to reflect on the obtained information and conceptualize improvements to the videos. In any case, while the results of this research are encouraging on this point, more work is needed. This may come as an expansion to include more videos in the research, possibly created by less experienced teachers, or on expanding interviews on the videos that have already been analysed in this fork, for example.  
The answer to the first part of the second research question (\"Is there a minimum number of video playbacks to record so that the presented data are perceived as useful by teachers to enhance the learning process?\") is also affirmative: all interviewed teachers needed a minimum amount of data in the presented graphs in order to consider them useful. Regarding the second part of the question (\"In the affirmative case, what is that minimum?\"), in the case of short-to-medium length videos that minimum is achieved in less than 70 playbacks. In the case of longer videos, that minimum is achieved in less than 225 playbacks. In our cases, this corresponds to between one and three editions of a course, depending on cohort size, which could be considered as an initial estimate for when it is worth setting up a data collection infrastructure.  
Regarding the classification of the analysed videos, according to [PERSON] [21], e.g., lecture-based; enhanced containing worked examples; containing just worked examples; those focusing on problem solving; those combining problem solving with receptive viewing, there is not enough collected data to make any statements regarding differences in navigation patterns according to those classifications. This suggests a future line of work expanding the research to more videos, so that enough data are collected.
# Acting Instead of Reacting--Ensuring Employee Retention during Successful Introduction of i4.0
## 2 Literature Review
### Industry 4.0  
The term \"Industry 4.0\" (originally \"Industrie 4.0\") was first coined publicly by the German government in the year 2011 [15]. The term described neither existing developments nor current research. Together with representatives from business, politics, and academia, the government initiated the concept of i4.0 to enhance the competitiveness and innovation of Germany's domestic manufacturing industry [16]. The concept of i4.0 as the fourth industrial revolution, was, in fact, predicted ex-ante rather than recognized ex-post [17]. This is unique because the other three great industrial revolutions were always named, described, and analyzed in the aftermath. \"Even though Industry 4.0 is one of the most frequently discussed topics these days, I could not explain to my son what it really means\", said Audi's production site manager [16]. As a consequence, many studies still claim that there is no generally accepted definition for i4.0 [16; 18; 19].  
In order to ensure a common understanding, we will share short definitions (according to [20]) regarding the core drivers of the four industrial revolutions. The first revolution (1784) was based on water energy and steam engines to power machines. The second revolution (1870) used electrical energy to enable mass production by continuously driven conveyor systems/belts. The third revolution (1969) used programmable logic controllers (PLC) and IT systems for the automation of complete value-added processes. The fourth revolution (today) uses these four main levers: Internet of Things (IoT), Industrial Internet of Things (IIoT), Cloud-based Manufacturing (CbM), as well as Smart Manufacturing (SM) [20; 21]. While i4.0 does not revolve around a single innovation that enables the shift, the four main levers comprise various numbers and categories of technologies, such as Big Data analytics, Artificial Intelligence (AI), Cyber-Physical Systems (CPS), and decentralized systems [1; 19]. The i4.0 paradigm promotes the connection of sensors, devices, and enterprise assets, both to each other and to the internet [22].  
The main difference between i4.0 and Computer-Integrated Manufacturing (CIM) is the concern of the human role in the production environment [20]. In i4.0, human workers play an important role in carrying out production, while CIM considers production without workers [20; 23]. This underlines the central role of human resources in value-creation processes in the era of i4.0. Accordingly, these human performance factors should be considered appropriately in change management.
## 3 Results and Discussion
### Discussion  
The methodology applied during the development of evolutionary models stays in accordance with the Cross Industry Standard Process for Data Mining (CRISP-DM) protocol [27]. Therefore, the high quality of the models is assured. The obtained errors, below 8.3% for FPF and 8.2% for ED, in the 10-fold cross validation protocol suggests that the developed models exhibit significant extrapolation ability. With the latter confirmed, the models for ED and FPF could be used for prospective development of pulmonary drug delivery dosage forms, with models acting as decision support systems (DSS). This research demonstrates that the above presented models perfectly balance predictability with transparency [28; 29]. Moreover, the GP derived models are transparent and easily validated (Equations (4) and (5)).  
The model analysis suggests the highest impact of API to excipient ratio (Figure 5A) on the final aerosolization properties. These findings are in accordance with work of [PERSON] et al. [30], who observed higher ED values for spray-dried powders with lower API content. Moreover, positive impact on the FPF value with increasing amount of excipient was reported by [PERSON] et al. [31] for spray-dried particles composed of salbutamol sulfate and lactose. It was also noted that decreasing total feed concentration resulted in the higher FPF. [PERSON] et al. [32] observed influence of feedstock concentration on particles surface characteristic and powders aerodynamic properties. Low feedstock concentration led to more corrugated particles and resulted in the higher FPF, which corresponds with our analysis (Figures 5B and 6A). Ethanol content in the spray drying feed rate was also reported as a factor influencing particles morphology and ED [33], which confirms the validity of the selection of this variable by evolutionary algorithm (Equations (4) and (5), Figures 5C and 6A).  
The limitations and the possibility to extend the presented model should also be pointed out. First of all, the discussed effects are valid for the substances applied in the experimental setup; therefore, in the case of other drugs and excipients it may differ in magnitude and direction. There is a possibility that increasing the other API concentration may result in higher ED or FPF experimental value, because feedstock and concentration effects are dependent on chemical-physical properties of the API, such as cohesiveness, hygroscopicity, and solubility. The same is with different spray drying setup including the type of apparatus, nozzle, and mechanism of aerosol generation, they may modify temperature impact on ED and FPF. Despite some limitations on the direct use of proposed models, it seems that the presented methodology could help in the development of optimal pharmaceutical formulation and understand underlying phenomena. Presented models could be improved and expanded in the future whereas methodology and workflow may remain unchanged.
## 4 Results and Discussions
### Throughput Comparison  
Figure 4 presents a comparison between the proposed algorithm and the baseline algorithm in terms of the system throughput versus the system load. As can be seen in Figure 4, the proposedalgorithm provided better system throughput performance than the baseline algorithm did, particularly when the system load was heavy. For example, the system throughput of the proposed algorithm was about 12% better than that of the baseline algorithm when the system load was equal to 1. This was because by aggregating two CCs for transmission, the proposed scheduler had a better capacity to avoid/skip temporarily faded users when the system load was larger. To put that another way, the proposed scheduler is better able to achieve location (spatial) diversity. In summary, by aggregating multiple CCs for transmission the proposed algorithm provides clear improvements in system throughput, especially when the system load is large.  
Furthermore, that the throughput of the system is significantly better when the proposed algorithm (as opposed to the baseline algorithm) is used suggests that a greater amount of data can be transmitted in an equivalent amount of time, even as the amount of energy consumed remains almost the same, due to the fact that each RB is provided with the same amount of output power for transmission, with that output power being low in comparison to the input power level [24]. Accordingly, the proposed algorithm's efficient design not only allows it to adequately use spectrum resources for the purposes of transmissions, but also gives it greater energy usage efficiency than that provided by the baseline algorithm. With these points in mind, it can be concluded that the proposed algorithm serves as an efficient means by which to provide great spectral efficiency, even as it also addresses the problem of energy efficiency to some degree.
## 4 Implementation and Discussion of Results
### Selecting the CNN Parameters  
We build a CNN model with three convolution layers and two fully linked layers in this model. For the nonlinear transformation, we employ ReLU as the activation function, and two fully linked layers are constructed. We use data from 1 April 2020 to 31 May 2021, to train the CNN model. The model trained for 500 epochs and detected convergence after 300 epochs because the loss does not reduce much when the number of epochs exceeds that threshold.
## 3 Numerical Proof of the Theory
### Model Assessment at 1-Hour Sampling
The estimates accuracy can be further increased if \(\overline{\tau}_{a}\) is calculated as the theory prescribes, i.e., as a weighted average with respect to the Sun elevation angle. Figure 2: Graphical presentation of the relationships between the physical quantities involved in Equation (8): (**a**) Diffuse fraction \(k_{d}\) with respect to clearness index \(k_{t}\); (**b**) diffuse fraction \(k_{d}\) and clearness index \(k_{t}\) with respect to the relative sunshine \(\sigma\). Post-processed data from hDATA are used. At the sunny-sky limit \(\sigma=1\), Equation (8) reduces to \(k_{d}=1-\overline{\tau}_{a}/k_{t}\). Figure 3b also displays estimated (assuming hourly mean for \(\overline{\tau}_{a}\)) vs. measured \(k_{d}\) in this case. The model performance drops by half: \(nMBE=0.099\) and \(nRMSE=0.125\). A possible cause could be the presence of clouds close to the Sun or a thin layer of clouds covering the Sun generating the so-called cloud enhancement events [33]. At the overcast limit \(\sigma=0\), Equation (8) estimates always \(k_{d}=1\), which is theoretically sound (when the Sun is covered by clouds the global solar irradiance equals diffuse solar irradiance). The measurements of \(k_{d}\) experience values closer but different from one. Figure 4 shows a histogram of measured \(k_{d}\) for \(\sigma=0\). Majority of the values are clustered between 0.99 and 1.01, approx. 80%). The statistical indicators \(nMBE=0.009\) and \(nRMSE=0.013\) substantiate the model's assumption \(k_{d}=1\) for \(\sigma=0\). Summing up the results and computing the statistical indicators of accuracy over the entire database we obtained: \(nMBE=0.043\) and \(nRMSE=0.058\). This is a remarkable result, emphasizing the high-performance of Equation (8) in capturing the reality.
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
## 4 Conclusions  
The CubeSat missions have attempted to determine their precise orbits combining dynamic models and dual-frequency GNSS observations. While such kind of reduced-dynamic orbits can reach high accuracy of centimeters for large LEO satellites, the limited size, power, and capacity of CubeSats always set strong constraints on the availability of data and different resources, thus limiting the achievable accuracy of the determined orbits. As an important step at the planning phase of the CubeSat missions, a proper compromise needs to be carefully considered between these constraints and the orbital accuracy, so that limited resources can be better managed and saved for other tasks of CubeSat missions under the required POD accuracy.  
In this contribution, with the focus put on the dual-frequency postprocessed CubeSat POD, a detailed sensitivity analysis was performed between the orbital accuracy and different constraints, i.e., the data continuity, the data completeness, the observation sampling interval, the attitude information, the latency of the required orbital products, as well as the processed arc length. It was found that the accuracy degradation of the orbits could be large when several constraints are overlapped with each other, especially when one of the constraints is related to very low duty-cycle, e.g., below 40%. It was also found that the accuracy influences of the observation sampling interval is related to the sampling interval of the GPS satellite clock products used for the processing. In case that the data is otherwise tracked in good condition after considering the assumed constrains, the 3D orbital accuracy for arc lengths of 6 h to 24 h should generally be within or around 1 dm when applying the IGS final products or products from the IGS RTS, provided that the duty-cycle is not lower than 40% and the observation sampling interval is not larger than 60 s.  
[PERSON] and [PERSON] designed the work, [PERSON] and [PERSON] contributed to the data processing, [PERSON] analyzed the data, [PERSON]; [PERSON]; [PERSON]; and [PERSON] contributed to the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
The work is funded by the Australian Research Council Discovery Project: Tracking Formation-Flying of Nanosatellites Using Inter-Satellite Links (DP 190102444).  
We would like to thank West Virginia University for providing the GNSS data of CubeSat STF-1.  
The authors declare no conflict of interest.
# Convergence and Stability of Explicit and Implicit Schemes in the Simulation of the Heat Equation
## 2 The Heat Equation  
In engineering problems, it is common to find mathematical models that include partial differential equations [26; 27; 28; 29]. The analytical solution provides a better understanding of the behavior of some phenomena since it can be determined at any instant of time. In general, it is not possible to determine this solution due to the non-linearity of the equations that constitute the mathematical model or due to the domain where the model is studied. Numerical methods in science and engineering provide a tool that allows translating mathematical models into computational procedures, whose results can be contrasted with the analytical solutions, in those cases where they exist. In the following, an algorithm for solving partial differential equations is shown, using the one-dimensional diffusion equation as a model problem. The choice of the same one is made based on its multiple applications in problems of mechanics using the finite difference method to determine the numerical solution of the diffusion equation using an explicit method and the implicit CN method [30; 31].  
The one-dimensional 1D heat equation is expressed by Equation (1)  
\[\frac{\partial\varnothing}{\partial t}=\alpha\frac{\partial^{2}\varnothing}{ \partial x^{2}}\qquad\quad 0\leq x\leq L,\quad\quad t\geq 0 \tag{1}\]  
where \(\varnothing=\varnothing(x,t)\) is the temperature and \(\alpha\) is a constant coefficient. Equation (1) is a model of transient heat conduction in a metallic segment of length L (physical domain). The material property \(\alpha\) is the thermal diffusivity. In a practical calculation, the solution is obtained only for a finite time, tmax.  
The solution to Equation (1) requires the specification of boundary conditions at \(x=0\) and \(x=L\), and initial conditions at \(t=0\). The initial and boundary conditions are [32; 33]:  
\[\phi(0,t)=\phi_{0},\ \phi(L,t)=\phi_{L}\ \phi(x,0)=f_{0}(x) \tag{2}\]  
Other boundary conditions exist, represented by the gradient of the function at the ends of the thin bar [34; 35], or mixed conditions are also considered in the simulations. To keep the presentation as simple as possible, only the conditions in Equation (2) are considered in this article (Figure 1).
# Nutritional Composition and Biological Properties of Sixteen Edible Mushroom Species
## 4 Sixteen Edible Mushrooms of Greece  
### Agaricus bisporus  
_Agaricus bisporus (A. bisporus)_, commonly known as the button mushroom, is an edible species of the family Agaricaceae, accounts for 15% of the total mushroom production worldwide, and is cultivated throughout Europe and North America [5]. Dry matter (DM) of cultivated and wild-growing _Agaricus_ mushrooms varies and ranges between 83 and 285 g/kg [15]. Proximate compositions of A. bisporus have described moisture (91-92 g/100 g DM), ash (0.9-1 g/100 g DM), energy (29-31 kcal/100 g DM), protein (29.14 g/100 g DM), carbohydrate (51.05 g/100 g DM), and fat (1.56 g/100 g DM) [2]. The main amino acids found in _A. bisporus_ are aspartic acid, histidine, glutamic acid, lysine, and serine [4]. The lipid content of _A. bisporus_ is low with the main fatty acids being linoleic acid (61.8-67.3% of the total fatty acid content) and palmitic acid (12.7-14.7% of the total fatty acid content) [16]. Furthermore, nutrients such as phenolic compounds have been identified and may be responsible for the antioxidant properties of several mushroom species [17]. Specifically, _A. bisporus_ contains _trans_-cinnamic acid (9.4 mg/100 g DM) and chlorogenic acid (5.8 mg/100 g DM) [17]. The main polysaccharides are D-glucans, while chitin and other heteropolysaccharides are found in small amounts [4]. When it comes to the analyses of the University of Thessaly, the macronutrient content was found to be similar (protein 25.1 g/100 g DM, carbohydrate 52.7 g/100 g DM, and fat 0.9 g/100 g DM). Furthermore, these mushrooms are considered to be good sources of vitamins B1, B2, B3, niacin, folate, B12, D2, and ergosterol (biological precursor to vitamin D2), although the content of these vitamins varies depending on growing conditions [18]. Regarding mineral content, _A. bisporus_ has been described as a good source of K, Fe, Zn, Cu, Na, Se, Co, and Mn [19]. In addition, _A. bisporus_ has been reported to possess antioxidant, anti-diabetic, and antibacterial properties, possibly due to its polysaccharide and phenolic content [18]. Specifically, a study evaluated the antioxidant ability of _A. bisporus_ polysaccharide extracts using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and the results showed that at 250 ug/mL the extract exhibited 86.1% free radical scavenging activity, which was significantly higher (\(p<0.01\)) than BHT (83%) [20]. In addition, it has been reported that oral administration of high doses of _A. bisporus_ extract could lead to decreased severity of streptozotocin-induced diabetes in Sprague-Dawley rats. The rats were fed _A. bisporus_ powder (200 mg/kg of body weight) for three weeks, which led to significantly reduced triglyceride concentration (39.1%), plasma glucose concentration (24.7%), aspartate aminotransferase (15.7%), and alanine aminotransferase (11.7%) [21].
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
### Metal-Organic Frameworks: Promising Materials for Lithium-Sulfur Batteries Applications
#### 1.2.1 Overview of Metal-Organic Frameworks  
As illustrated in Figure 2, the chemical composition, surface area, and pore size of the synthesized MOFs can be precisely controlled through human intervention during the synthesis process [34; 35]. Generally, the structure and properties of metal-organic frameworks (MOFs) can be readily tailored by manipulating various synthetic parameters. These parameters encompass the selection of raw materials, such as metal precursors and organic ligands, the synthesis conditions including temperature, pH, and ionic concentration, as well as the employed synthesis methods, such as hydrothermal synthesis or microwave irradiation [36]. Another valuable strategy is post-synthetic modification (PSM), a routine method for functionalizing organic ligands and incorporating secondary metal nodes [37]. The tunable nature of MOFs renders them suitable for integration into supercapacitor applications and rechargeable batteries. Furthermore, the robust framework derived from MOFs can endure the physical stresses associated with the insertion and extraction of metal cations during operation.
## 2 Related Work
### Centralized Algorithms  
A family of centralized anti-collision protocols includes the Neighbor Friendly Reader Anti-collision (NFRA) series, consisting of NFRA, NFRA-C, NFRA-AIC, and Xia-NFRA.  
NFRA (Nonlinear Frequency Response Analysis) [27] is a TDMA-based anti-collision protocol designed for dense RFID networks. It utilizes a polling server as a command distributor to schedule reader communications, authorizing a single reader to read tags at a specific time. The NFRA algorithm assumes the presence of a centrally deployed query server, ensuring its messages reach the entire deployment domain. This server control allows readers to operate concurrently without collisions. NFRA adopts a dual-channel strategy, where readers only receive the command signal from the query server and do not respond.  
NFRA-AIC (Nonlinear Frequency Response Analysis Adaptive Interrogation Capacity) [28] is an anti-collision algorithm that incorporates adaptive read functionality based on the NFRA system. The key feature is that the query time of a reader is determined based on the number of tags within its range. NFRA-AIC utilizes subrounds to supplement and allocate a time slot after the Access (AC) frame to the first Operation Complete (OC) frame, enabling readers that finish reading tags to release the assigned resource.  
NFRA-C (Nonlinear Frequency Response Analysis with Counters) [29] extends the NFRA system by introducing a timer to capture the track record of successful Collision Resolution Tag (CRT) interrogations made by the reader. At each OC frame, the reader broadcasts timers to detect the presence of neighboring readers when encountering a beacon collision. By comparing the timers of colliding readers, the reader with the minimum timer value is granted the CRT interrogation in the current turn.  
Xia-NFRA (Xia. Nonlinear Frequency Response Analysis) [30] is an anti-collision algorithm designed specifically for densely populated RFID networks with mobile readers. In this approach, the server waits for a short period to discover the proximity of any reader. During this period, the reader broadcasts a beacon message according to its randomly selected number of periods (TS) and registers the total number of received single-beacon and multiple-beacon messages.