esa-sceva/satcom-chunk-collection huggingface dataset:
# Stochastic Model Driven Performance and Availability Planning for a Mobile Edge Computing System
## 5 Evaluation of MEC Availability
### Case Studies
#### 5.3.2 Transition Sensitivity Analysis  
For the sensitivity analysis, the MTTF and MTTR parameters were varied by five values within a range defined by the maximum and minimum values (50% plus and minus the default value). Table 8 presents the components and their respective sensitivity indices that cause significant impacts on the availability of the system. The most significant components will be exposed and discussed below. The failure and recovery times of the containers were the components that most impacted the availability of the base model; this can also be seen by looking at Figure 16a, so we can consider the containers as essential components of the system. Data are processed in the containers. The longer they stay out of operation, the more impaired is the efficiency of the system. Right after that are the failure times of _slave server_ components (OS, DD, and HW). As these components are parts that \"support\" the containers, their uptime has a significant impact on the uptime of the containers. The operating times of _master server_ components come subsequently. If the _master server_ fails, no data will arrive to be processed on any _slave server_. As aforementioned, the sensitivity analysis results in the base model were considered to generate an extended model. In the extended model, there was an inversion of indexes order regarding the component's relevance. By adding the hybrid redundancy strategy, the sensitivity index of the _slave server_ can be significantly reduced. Given the redundancy, when any component of _slave server 01_ fails, _slave server 02_ will be activated. The components of the _master server_ ended up becoming more relevant as they did not have a redundancy mechanism. The indexes were equal to the first analysis in the base model. Right after that, we have SWITCH_TIME, which is essential for defining the time it takes to initialize _slave  
Figure 16: Availability and downtime of the two architectures varying the number of containers. server 02_, becoming a relevant variable. The less relevant transitions were those related to failure of hardware (HWSF_MTTF) and operating system (OSSF_MTTF) in idle state. These transitions have very high values. Other components will fail many times before reaching that time. Figure 17 shows in more detail the impact of the three most essential components of each model. As can be seen in Figure 17a-c, varying the parameters influences availability up to a certain point. All base model results are below 99% availability, with the exception of the CTS_MTTR transition (Figure 17b). The results of the extended model present much higher values compared to the base model (see Figure 17d-f). The results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model.
## 2 Materials and Methods
### Obtained Spectral Data--Initial Analysis  
All data obtained in the laboratory setup (Figure 1) were collected in the spectral range of 350-750 nm, with a resolution of 0.5 nm. The amount of data collected in a real stand, for example, by a sorting machine, requires large calculation capacities when the whole range is analyzed. Researchers have tended to apply the method in commercial designs with VIS illumination; therefore, we needed to decrease the amount of analyzed data to ensure a reasonable processing time. From the whole spectral range registered by the spectrometer, we chose the wavelengths that correspond to commercially available lasers, such as diode-pumped solid-state (DPSS) lasers. The DPSS lasers were chosen because of their potential applicability in sorting and detecting systems, and their lasing parameters, stability, and relatively small dimensions. The available wavelengths of the DPSS lasers that are within the measurement range used for eggshells are 457, 473, 501, 515, 523, 526, 532, 543, 556, 561, 589, 593, 660, 671, 690, and 729 nm. The incandescent lamp used in the laboratory setup had a significant drop in intensity for wavelengths lower than 450 nm, which is the main reason for the lower limit on DPSS wavelengths because, based on the experiment, no reliable data were to be obtained from that region. Table 2 shows the exact wavelength and its full width at half maximum (FWHM) together with the assigned name, symbolizing the input variable.  
Figure 2 visualizes an example of data for ten samples from each measurement category.
## 1 Introduction and Related Work
It is worthwhile to note that this article extends our preliminary works proposed in [29; 30] and, to the best of our knowledge, it includes the most comprehensive set of linguistic features used to develop an automatic text classification system for the purposes of Italian language learning and teaching. In particular, with respect to [12; 29], we include additional features such as discursive features as well as the recently introduced morphological complexity index [31]. Moreover, in [30] only the support vector machine model was investigated, while in this work we include a comprehensive comparison among ten different classification models widely used in the machine learning literature. Importantly, note that neither this work nor the previously mentioned and related works consider the semantic content of a text as important for the task of measuring its linguistic complexity. In fact, a text can be linguistically difficult or easy independently from its semantic content. This aspect rules out the nowadays popular semantic methodologies like word and sentence embedding techniques [32; 33; 34] or most of the transfer learning approaches [35; 36]. The rest of the article is organized as follows. Section 2 introduces the main architecture of the system, while Section 3 describes the collected dataset of texts. Text preprocessing operations are described in Section 4 and the features computation procedures are detailed in Section 5. The classification models adopted in this work are described in Section 6, where also the settings of their hyper-parameters are discussed. Experimental results are analyzed and discussed in Section 7. Finally, Section 8 concludes the article by outlining possible future lines of research.
## 1 Introduction
For GPS BLOCK IIF satellites, a significant variation of the L5 phase biases, mainly due to thermal variation, compared to the L1 and L2 biases has been observed [11; 12]. To investigate the effects of different assumptions regarding the time stability of phase biases further, two methods are implemented and compared. In one approach, all phase biases are assumed to be static parameters, excluding satellite GPS L5 phase biases, whereas in the other approach, a separate epoch-wise phase clock is estimated for all receivers and satellites. The main purpose of this contribution is to analyze the quality of forward-filtered satellite clocks and biases for PPP-RTK and to compare the effects of different assumptions regarding the temporal stability of phase biases on the quality of the point position. Furthermore, a flexible method to introduce reparameterized ambiguities comparable to wide-lane and narrow-lane ambiguities into the functional model is presented, and the time until an ambiguity-fixed solution can be found is assessed. Ambiguity resolution is performed with the Ratio Test Integer Aperture estimation approach using the algorithm as described in [13]. When compared to the most common approach using the Success Rate and a Ratio Test, this approach should yield an integer fixed solution faster, as also investigated in the previously mentioned study.
## 4 Experiment and Result Analysis
### Experimental Environment and Training Parameter Settings  
The following hardware configuration was used for training: Intel Xeon(R) Silver 4210 CPU @ 2.20 GHz \(\times\) 40, NVIDIA GeForce RTX 2080 Ti. The software environment was Ubuntu 18.04.6 LTS, Python version 3.8, Pytorch 2.3.0 framework, GPU acceleration library is Cuda 12.1 version, and the training parameters are listed in Table 2.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### The Relevance of Cyber Resilience in Various Segments  
In the literature, there are several studies on the cyber resilience of organizations, whether they are public or private and in a wide range of segments: aviation, critical infrastructure, the financial system, health, industry, supply chains, among others. In recent years, there has been an increase in the number of cyber-attacks targeting Industrial Control Systems, which are also used in critical infrastructure systems [34]. Cyber threats represent a strategic risk and require joint work between the IT and business areas to make effective decisions, since many failures stem from organizational issues rather than technical problems [5].
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
## 3 Results and Discussion
### Fungi Cultivation with Culturing System  
Prior to biological experimentation, the lab-on-chip was assembled with the culturing system and partly covered with parafilm (gas inlet and outlet) in order to confine the evaporation of the medium from the channels while also creating a semi-permeable membrane, thanks to which an appropriate gas environment was ensured.  
The duration of _F. culmorum_ culture on-chip was up to 14 days. First, the dried fungi spores were introduced into the chip chamber, and next, the area covering mostly the connecting channels was selected for observation as the central, most interesting part of the LOC. On the third day of the culture, germination of the spores could be observed, being the typical time at which this process may appear [49]. In the following days, a mycelium of _F. culmorum_ was found to develop correctly and no changes were observed in the structures  
Figure 9: Microscope image of the fungi spores on-chip (_F. culmorum_) showing the location of the culture relative to the connecting channel.  
of the fungal hyphae compared to the reference culture on the Petri dish. Moreover, a fusiform shape of the mycelium, imitating a cobweb, could be noticed herein, which is a typically occurring pattern for this kind of fungi [49]. Ultimately, a physiological, notable growth of fungi species on-chip could be observed, suggesting appropriate performance of the LOC platform.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample photos of cultures conducted on the LOC in simulated microgravity and on the reference LOC. The tests were performed at least 5 times and similar results were obtained. Scale bar—100 \(\mu\)m.  
The table summarising the fungi development in terrestrial conditions with a view of the defined culture day is shown below (Table 3).
# Time to Capture a Moving Target Travelling along a Circular Trajectory
## 3 Results
### Fit of the Fitts' Model for Moving Targets with the Same Speed  
It was tested whether the traditional Fitts' model could be applied in a situation where the speed of the moving target is the same. The data collected at each three target speeds were examined to see if they could be explained by the traditional Fitts' model. The results of the linear regression analysis are shown in Equations (5)-(7). The \(R^{2}\) values of the linear regression model were 0.92 or higher. This means that the traditional Fitts' model has explanatory power for the targets moving at the same speed. However, there is a question of how to represent the targets moving at different speeds with a single model.  
\[\text{MT}_{v=0}=160.91+111.96 log_{2}\ (2A/W)\ R^{2}=0.93 \tag{5}\]  
\[\text{MT}_{v=100}=20.66+146.40 log_{2}\ (2A/W)\ R^{2}=0.92 \tag{6}\]  
\[\text{MT}_{v=200}=58.60+130.30 log_{2}\ (2A/W)\ R^{2}=0.96 \tag{7}\]  
Figure 4: Main effects of the speed of moving target (**left**), the target width (**center**) and the distance between home position and moving target (**right**) on the target selection time.
## 3 Results and Discussion
### Socio-Economic Analysis of Hydrogen Production Pathways
#### 3.3.3. Levelized Cost of Hydrogen (LCOH)  
The LCOH is the average cost to produce one kilogram of hydrogen over the entire life of a production plant. It includes both operating costs (OPEX) and capital costs (CAPEX), adjusted over time using a capital recovery factor (CRF) (Equation (15)):  
\[\text{LCOH}=\frac{\text{CAPEX}\times\text{CRF}+\text{OPEX}}{\text{Annual H}_{2}\ \text{Production}} \tag{15}\]  
where \(\text{CRF}=\frac{i(1+i)^{n}}{i(i+1)^{n}-1}\) (capital recovery factor), \(i=\) discount rate (assumed 8%), and \(n=\) plant lifetime (25 years).  
The LCOH is a key indicator for comparing hydrogen production methods and assessing their economic feasibility. A lower LCOH means a more cost-effective hydrogen supply, making it essential for planning large-scale, sustainable energy systems.  
Figure 7 summarizes the comparative techno-economic evaluation of the hydrogen production pathways, including biogas reforming, biomass gasification (small-scale), and biomass gasification (large-scale), highlighting significant differences in the capital expenditure (CAPEX), operating expenditure (OPEX), and levelized cost of hydrogen (LCOH). Biogas reforming shows a moderate CAPEX of approximately USD 2400/kW, reflecting the maturity and standardization of reforming technology. However, its OPEX is the highest among the three options, reaching around USD 0.75/kg H\({}_{2}\), likely due to the relatively high cost and variability in biogas feedstock and the operational demands of reforming processes. Consequently, biogas reforming results in the highest LCOH at about USD 5.2/kg H\({}_{2}\), making it the least economically favorable option in this comparison. On the other hand, biomass gasification at a small scale exhibits the highest CAPEX, approximately USD 3000/kW, attributed to the lack of scale economies and the complexity of biomass handling systems. Despite this, its OPEX is moderately lower, at around USD 0.60/kg H\({}_{2}\), leading to a reduced LCOH of about USD 4.0/kg H\({}_{2}\) compared to biogas reforming. The most economically viable option is biomass gasification at a large scale, which benefits from significant cost reductions due to scale. This pathway demonstrates the lowest CAPEX (approximately USD 2200/kW) and OPEX (around USD 0.45/kg H\({}_{2}\)), resulting in the most competitive LCOH of approximately USD 2.8/kg H\({}_{2}\).  
Figure 7: Comparative socio-economic metrics for biogas reforming and gasification.  
These findings underscore the critical role of scale in enhancing the economic performance of biomass-based hydrogen production technologies. While small-scale systems offer flexibility and decentralized applications, the cost advantages of large-scale biomass gasification suggest it holds the greatest promise for cost-effective hydrogen generation.
# Multi-Sensor GA-BP Algorithm Based Gearbox Fault Diagnosis
## 3 Simulation Analysis
### DS Evidence Synthesis and Decision-Level Fusion  
A single sensor in traditional fault diagnosis methods can lead to a lack of comprehensiveness of data. Hence, multi-sensor testing of gearboxes is needed. In order to perform fault diagnosis on fault data collected from multiple sensors, the decision-level fusion of the multi-sensor local diagnostic results obtained by the GA-BP algorithm using a DS-theoretical approach would be able to capture the multidimensionality of the data and improve the diagnostic accuracy.  
In addition, the GA-BP algorithm allows for the fault diagnosis of gears from the fault data collected by each sensor. The DS theory was used to fuse the local diagnostic results of each sensor at the decision level. The identification framework needs to be established first based on DS evidence theory. Furthermore, since there are four fault states, the identification framework is \(\Theta\)= \(\{\text{A},\text{B},\text{C},\text{D}\}\), corresponding to bearing fault states: Normal, 0.007 inches, 0.014 inches, and 0.021 inches, respectively. An objective body of evidence needs to be identified, with the body of evidence E = {E1, E2} in order to achieve effective fusion of diagnostic results The E1 represents the bearing fan-end sensor and E2 represents the bearing base-end sensor. The fusion rule of Equation (8) was then used to fuse the local diagnostic results of the two sensors to obtain the fused bearing fault diagnosis rate. The fault diagnosis results before and after fusion are shown in Figure 9.  
According to Figure 9, it can be concluded that the accuracy of the two bodies of evidence is 99.92% and 99.93% before the fusion. The accuracy after fusion was 99.99%. The accuracy after adopting fusion theory was significantly higher than that before fusion and more stable, compensating for the low accuracy of the traditional single fault diagnosis method to a large extend.  
Figure 8: Accuracy of the three algorithms.
# A Novel Fuzzy Kernel Extreme Learning Machine Algorithm in Classification Problems
## 3 Experimental Results and Analysis

Table 3: Performance comparison of various classification methods. Figure 10: Preprocessing steps for face recognition—color image to grayscale conversion and face detection. In the classification phase, the initial parameters were chosen by the user. For example, the number of neurons in the HL was set to 5, in LR it was 0.25, the minimum error was 0.001, and the maximum iteration number was 250. After training the database, the test image was classified using the FWMHKELM classifier. The results are presented in Table 4.  
As can be seen in Table 4, the FWMHKELM algorithm demonstrates better classification performance compared to the ELM and WNN algorithms. However, its training time is longer than that of the other algorithms. This increased training time can be attributed to the use of fuzzy rules within the model. To evaluate the classification performance of FWMHKELM, we calculated the accuracy, precision, and recall parameters. The accuracy value of the test is calculated by comparing the test data with the results predicted using real labels. The average accuracy value was calculated as 80.6%. Figure 12 graphically shows the comparison of all algorithms categorized by the accuracy, recall, and precision parameters. The study shows that our proposed FWMHKELM algorithm showed better results than the ELM and WNN. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Accura. (\%)** & **Train Time (s)** & **Test Time(s)** \\ \hline ELM & 77.8\% & 155 & 2.85 \\ WNN & 72.2\% & 242 & 5.16 \\ Hybrid Algorithm (FWMHKELM) & 80.6\% & 313 & 5.26 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics comparison for various methods of face recognition. Figure 11: Analysis of real and imaginary components of frequency response with Log Gabor filter with respect to phase angle (\(\theta\)).
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
## 3 Batteryless Energy Harvesting Systems
Different low-energy communication technologies have been applied in batteryless WSNs, such as Bluetooth Low Energy (BLE) [3] or LoRaWAN, probably the most popular technology within low-power wide-area networks (LPWAN) [32].
# Behavior of Astrocytes Derived from Human Neural Stem Cells Flown onto Space and Their Progenies
## 3 Results
### Proliferation of Newly Formed Astrocytes Derived from NSCs Flown to Space  
Three weeks post-flight, NSCs progenies derived from SPC-NSCs were cultured in 10% FCS to direct their specification to the astrocyte phenotype. The vast majority of astrocytes derived from SPC-flown NSCs did not proliferate. Nonetheless, a few cells divided in an interval of 17 h per microscope field (Figure 5).  
The proliferation of astrocytes is essential for normal brain development, while in the adult brain astrocytes exhibit little or no proliferation [11]. Nonetheless, upon injury or disease, astrocytes respond with a phenomenon known as astrogliosis, and in this stage they regain their proliferative state [16]. However, even in this state of prominent reactive astrogliosis, astrocytic proliferation is modest at best, with a proliferation rate of 1.1% being labelled as quite high for acute demyelinating lesions [17]. The extent of proliferation of astrocytes in nonneoplastic circumstances is therefore expected to be quite low, with proliferation rates lower than 1% of the total astrocyte population being quite normal.  
Figure 4: View of NSCs cultured in DF-10% FCS. Virtually all cells in these cultures were positive for (**A**) S-1008 and (**B**) glial fibrillary acidic protein (GFAP). Cells appeared to emanate from a flat layer positive for both markers, and as they were farther from that layer, they extended radial glia-like cell processes (arrows). Scale bar = 50 μm.
## 2 The Phytochemistry of _Artemisia annua_ L. (Qinghao)
### Monoterpenoids
#### 2.5.4. Bicyclic Monoterpenes
A \(\beta\)-pinene synthase, which converts GPP to both (-)-\(\alpha\)-pinene (**347**) and (-)-\(\beta\)-pinene (**356**), has recently been described from _A. annua_[131]. \(\alpha\)-Pinene (**347**) is known in both its (+)- and (-)-enantiomeric forms from this species [37]. \begin{table}
\begin{tabular}{|l|l|l|l|} \hline
**Name** & **Alternative Name(s)** & **CAS Number** & **References** \\ \hline \(\alpha\)-Pinene (**347**) & 2,6,6-Trimethylbicyclo[3.1.1]heptan-2-ene & [80-56-8] & [19, 22, 25, 28, 29, 32-34, 7785-26-4] & [19, 22, 25, 28, 29, 32-34, 7785-26-4] & [19, 22, 24, 25, 28, 29, 32-34, 7785-20-8] & [19, 22, 25, 28, 29, 32-34, 7785-26-4] & [136, 40-45, 50, 51, 66, 73, 74, 112, 113, 120, 132] \\ \hline Verbenyl acetate (**348**) & Verbenol acetate & [33522-69-9] & [23] \\  & 2-Pinen-4-ol, acetate & & \\ \hline _trans_-Chrysanthenol (**349**) & & [38043-83-3] & [43] \\ \hline _cis_-Chrysanthenyl acetate (**350**) & & [67999-48-8] & [19] \\ \hline Myrtenol (**351**) & 2-Pinen-10-ol & [515-00-4] & [19, 23, 32, 41, 43, 66] \\  & & [564-94-3] & \\ \hline (-)-Mytrenyl acetate (**352**) & (-)-O-Acetylmyrtenol & [36203-31-3] & [23] \\ \hline Verbenone (**353**) & 2-Pinen-4-one & [80-57-9] & [19, 25, 34, 43, 45] \\ \hline Chrysanthenone (**354**) & 2-Pinen-6-one & [473-06-3] & [32, 43, 66] \\ \hline Myrtenal (**355**) & 2-Pinen-10-alal & [23727-16-4] & [66] \\ \hline \(\beta\)-Pinene (**356**) & 6,6-Dimethyl-2-methylphenicyclo[3.1.1]-heptane & [127-91-3] & [19, 22, 23, 25, 26, 27, 28, 132, 32, 33, 34, 36, 38, 40, 43, 45, 51, 66, 73, 74, 112, 113, 120, 132] \\ \hline (-)-_trans_-Pinocarveol (**357**) & 2(10)-Pinen-3-ol & [547-61-5] & [19, 23, 25, 32, 33, 40, 43] \\  & & [3917-59-7] & \\ \hline _cis_-Pinocarveol (**358**) & Ispinocarveol & [6712-794-] & [30, 43] \\  & 2(10)-Pinen-3-ol, _cis_- & [5947-36-4] & \\ \hline Pinocaryl acetate (**359**) & 2(10)-Pinen-3-ol, acetate & [1078-95-1] & [30] \\ \hline Pinocarvone (**360**) & 2(10)-Pinen-3-one & [30460-92-5] & [19, 23, 25, 28, 30, 32, 33, 42, 43, 45, 66] \\ \hline 3-Pinanol (**361**) & 2,6,6-Trimethylbicyclo[3.1.1]heptan-3-ol & [25465-95-6] & [30] \\ \hline \(\beta\)-Pinene oxide (**362**) & 2,10-Epoxypinane & [6931-54-0] & [19] \\ \hline Bicyclo[3.1.1]heptan-3-one, 2,6,6-trimethyl-4-methylene- (**363**) & & [62594-31-4] & [30] \\ \hline \end{tabular}
\end{table} 
Table 27: Pinane Monoterpenoids (2,6,6-Trimethylbicyclo[3.1.1]heptane, 9 CI). Only two caranes (3,7,7-trimethylbicyclo[4.1.0]heptane, 9 CI): \(\alpha\)-carene ([13466-78-9]) (**364**) [32,41] and 2-acetyl-3-carene ([40824-36-0]) (**365**) [23] are known from _A. annua_: the majority of monoterpenes which incorporate an additional three-membered ring belong to the thujane class (Table 28). Two of the most abundant thujanes are sabinene hydrate (**373**) [34] and sabinol (**371**) [43], both of which can be present at around levels of 7% of the total essential oil.