esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Emulator
#### 3.1.2 FFDNet  
Since the modulation bandwidth of a single LED is limited, the use of m-MIMO LED and PD arrays are expected to substantially increase the capacity and transmission rate of VLC systems. However, due to the complexity of VLC channels, it is extremely difficult to estimate the m-MIMO channel matrix, which requires deep learning methods. Fast and flexible denoising convolutional neural network (FFDnet) is used for channel estimation in millimeter-wave communication recently [46; 47], which is also applicable in VLC [45]. As an image denoising tool using machine learning, FFDnet is able to recover the input noisy channel matrix into an almost noiseless channel matrix. Compared with the minimum mean square error (MMSE) method, the FFDnet has a stronger denoising effect, which can increase the peak signal-to-noise ratio (PSNR) of the recovered channel matrix image. Unlike the nonlinear channel modeling in point-to-point high-speed VLC links, the channel matrix is treated as an image and processed using machine learning methods of image processing, which is of great importance in channel estimation of m-MIMO-VLC channels.
## 6 Case Study
### Results and Discussion
#### 6.2.2 Prediction Module  
The Monte Carlo simulation model was run for 100,000 iterations, as increasing the number iterations beyond 100,000 slowed the execution speed without resulting in a notable impact on output results. The total duration and cost associated with the requirement reporting process was calculated using the probability distributions defined for each report. In each iteration, random numbers were sampled from the preparation time distributions of each report type, and a total reporting duration (or total cost) was achieved as the cumulative time (or cost) of all reports for that iteration. Total reporting duration (or cost) values of each iteration were then used to form a distribution of total reporting time, as shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Report Name** & **Frequency** & **Resources** &
\begin{tabular}{c} **Time (Minutes)** \\ **Min, Most Likely,** \\ **Max** \\ \end{tabular} \\ \hline Daily Update: & & 1 Safety Coordinator & \\ work plan and & Daily & 1 Project Manager & \\ estimated progress & & 4 Superintendents & 45, 60, 75 \\ \hline Equipment Log & Bi-Weekly & 1 Project Coordinator & 90, 120, 150 \\ \hline Installation Work & Bi-Weekly & 1 Project Controller & \\ Package Report & & 1 Scheubler & 210, 240, 270 \\  & & 1 Project Manager & \\ \hline
3-Week Look-Ahead & Bi-Weekly & 1 Project Control & \\ Schedule & & 1 Scheubler & 360, 420, 480 \\  & & 1 Project Manager & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sample of report preparation-associated input data.  
The mean value for the cumulative report preparation time was 5083 labor-hours (\(\sigma=142\)) for the project life cycle. The simulation was then run again using an average rate of 60 CAD per hour; here, the mean value of the total cost associated with the reporting process was calculated to be $304,939 (\(\sigma=88538\)), as shown in the predicted cost distribution in Figure 12. Notably, including individual labor rates for each personnel type will increase the accuracy of the framework's results.  
The distribution of report preparation time between various personnel types is summarized in Figure 13. The plurality of the cumulative report preparation time (31%) was associated with the project manager, who must review and approve most reports. Based on the mean cumulative duration of 5083 h (Figure 11), the project manager is expected to spend an estimated 1576 h (or, assuming a 9-h work day, 175 days) completing reporting requirements. With a provided project duration of 4400 h, the project manager is estimated to be performing reporting activities 36% of the time. Similarly, two other highly utilized resources were the project control team (28%) and scheduler (28%), who are responsible for collecting, merging, and overseeing the preparation of various report types. Together, these two resources will spend an estimated 2846 h (or 316 days) completing reporting requirements--equal to 32% of their time.  
Figure 11: Predicted cumulative report preparation time as a distribution.  
Figure 12: Predicted cumulative cost of reporting as a distribution.
## 9 Metabolic Features and By-Products from _Candida stellata_ Activity
### Biotechnological Application of Extracellular Enzymes Secreted by Candida stellata
#### 9.3.1 Pectinases  
Pectic substances are the major component of the plant cell wall and comprise a network in which cellulose microfibrils are linked [95]. The high viscosity of pectin prevents juice extraction, clarification, and filtration when it is dissolved after berry crushing. Furthermore, pectin impedes the phenolic and aroma compounds' diffusion into the must during wine fermentation [94]. Thus, pectinases such as polygalacturonase, pectin lyase, pectin methyl esterase, and polygalactosidase have the capacity to reduce the molecular size of pectin polymers by cleaving neutral side chain residues, facilitating the pressing and filtration processes of wines and ciders [96; 97]. In addition to their use in winemaking, these enzymes are also utilized in oil extraction [98], coffee and cocoa curing [99], the extraction and clarification of fruit juices, and the retting of textile fibers [100].  
Several authors have reported the production of polygalacturonase and pectin methyl esterase by _Candida_ in wine [4; 101; 102]. In a study realized by [PERSON] et al. [103], _C. stellata_ CLI 920 strain, which was isolated during spontaneous fermentation in Malvar (_Vitis vinifera_ cv. L.) must, produced the highest quantity of pectinases (polygalacturonases) in comparison with other non-_Saccharomyces_. This pectinase activity of _C. stellata_ CLI 920 could be correlated with the higher galacturonic acid content observed into the oligosaccharides fraction of the wine produced with this strain alone [104]. Also, polygalactosidaseosizes enzymes produced by _C. stellata_ together with exo and endoglucases are important in the degradation of the \(\beta\)-glucas by _Botrytis cinerea_[2].
# Investigations on Factors Affecting 3D-Printed Holes Dimensional Accuracy and Repeatability
## 1 Introduction
Layer thickness, printing speed, number of shells, and hole axis orientations were the parameters selected in the current study as having the potential of influencing the dimensional accuracy, at the same time affecting the printing time; with a tradeoff between the dimensional accuracy and manufacturing time and cost being often required. The most influential of these factors was searched for. Moreover, as no research was found on the dimensional accuracy of holes across different materials, 3D printers, and slicers, this aspect was also investigated as being meaningful for the delocalized production approach where the same stl file of a part is sent for 3D printing in different manufacturing sites that are using diverse equipment and slicing software, raising the question if the achieved accuracies are comparable. Another objective of the research was to understand if the medical decontamination process had influenced the accuracy on the directly 3D-printed holes. Thus, 3D printing points-of-care (3 DP-POCs), space, or other remote zones can be some of the beneficiaries of the results of this research, as locations in which the production of customized parts, spare parts, or low-volume parts takes place [5; 23], with the manufacturing accuracy-related knowledge being relevant for obtaining reliable products.
# AI-Driven Waste Management in Innovating Space Exploration
## 3 Traditional Waste Management Practices in Space  
Traditional waste management in space missions, particularly on the ISS, adapts Earth-based techniques for microgravity. Waste is collected, compacted, and stored, with future missions considering jettisoning logistics modules or gasification. In situ resource utilisation (ISRU) aims to use local materials, exemplified by NASA's Mars 2020 Perser-verance rover producing oxygen from Martian CO\({}_{2}\). RASSOR, a robotic miner, extracts resources from lunar and Martian regolith (NASA Kennedy Space Center, Merritt Island,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Waste Processing Technology** & **Energy Requirements (kWh per ton)** \\ \hline Plasma Arc & 500-1000 \\ \hline Pyrolysis & 200-500 \\ \hline bioreactors & 50-150 \\ \hline Mechanical Compaction & 10-50 \\ \hline Incineration & 300–600 \\ \hline Waste Type and Corresponding Management Techniques \\ \hline Waste Type & Management Techniques \\ \hline Organic Waste & bioreactors, Composting \\ \hline Inorganic Waste & Mechanical Compaction, Pyrolysis \\ \hline Packaging Waste & Mechanical Compaction, Plasma Arc \\ \hline Human Waste & bioreactors, Advanced Filtration Systems \\ \hline Chemical Waste & Incineration, Chemical Neutralization \\ \hline Electronic Waste & Recycling, Plasma Arc \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy requirements for waste processing systems.  
FL, USA). Proposed missions target water ice extraction from lunar craters for life support. Experiments like EPICS enhance water conversion to oxygen and hydrogen, which is crucial for long-term habitation. The Archinaut project explores on-orbit manufacturing using materials from celestial bodies, showcasing 3D printing potential in space [52; 53; 54].
## 5 Experimental Comparison with HS Variants and Well-Known EAs
### Comparison with Other Well-Known EAs  
In this subsection, the proposed aHSDE algorithm is compared with other state-of-the-art evolutionary algorithms (non harmony ones), including Adaptive Particle Swarm Optimization (APSO) [43] and Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) [44]. The experimental results (mean best and standard deviation of multiple runs) of different algorithms are all collected with the maximum function evaluation number DIM * 10000 respectively, all of which are summarized in Table 3. The best \"mean\" result for the same function is highlighted in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**aHSDE**} & \multicolumn{4}{c}{**HS(DIM = )**} & \multicolumn{4}{c}{**HS(DIM = )**} & \multicolumn{4}{c}{**SGHS(DIM = )**} & \multicolumn{4}{c}{**IGHS(DIM = )**} \\ \cline{3-14}  & & **vs** & **10** & **50** & **100** & **10** & **50** & **100** & **10** & **50** & **100** & **50** & **100** \\ \hline \multirow{3}{*}{3 Unimodal Functions} & + & 3 & 3 & 3 & 3 & 2 & 3 & 3 & 2 & 3 & 3 & 3 & 3 \\  & – & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \multirow{3}{*}{13 Simple Multimodal Functions} & + & 6 & 11 & 12 & 5 & 11 & 13 & 8 & 11 & 10 & 9 & 13 & 13 \\  & – & 4 & 1 & 1 & 6 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\  & – & 3 & 1 & 0 & 2 & 1 & 0 & 4 & 2 & 3 & 4 & 0 & 0 \\ \hline \multirow{3}{*}{6 Hybrid Functions} & + & 4 & 6 & 6 & 4 & 6 & 5 & 5 & 6 & 5 & 6 & 6 & 6 \\  & – & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & – & 2 & 0 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 \\ \hline \multirow{3}{*}{8 Composition Functions} & + & 4 & 6 & 6 & 4 & 5 & 6 & 6 & 4 & 6 & 4 & 6 & 7 \\  & – & 2 & 1 & 2 & 1 & 2 & 2 & 1 & 3 & 2 & 3 & 0 & 0 \\  & – & 2 & 1 & 0 & 3 & 1 & 0 & 1 & 1 & 0 & 1 & 2 & 1 \\ \hline \multirow{3}{*}{30 All Functions} & + & 17 & 26 & 27 & 16 & 24 & 27 & 22 & 23 & 24 & 22 & 28 & 29 \\  & – & 6 & 2 & 3 & 8 & 3 & 2 & 2 & 3 & 2 & 3 & 0 & 0 \\ \cline{1-1}  & – & 7 & 2 & 0 & 6 & 3 & 1 & 6 & 4 & 4 & 5 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall statistical comparison among aHSDE and HS, improved harmony search (IHS), self-adaptive global-best harmony search (SGHS) and IGHS on CEC2014.  
Observed in Table 3, it can be seen that APSO, CMA-ES and the aHSDE perform best on 7, 4 and 19 benchmarks respectively from the 30 benchmark functions. It should be further noted that APSO outperforms the aHSDE on eight problems and CMA-ES outperforms the aHSDE on six functions among this IEEE CEC 2014 benchmark suite. Therefore, generally speaking, the aHSDE significantly outperforms APSO and CMA-ES on most of the benchmark functions. However, it should be especially noted that APSO outperforms CMA-ES and the aHSDE on the composition functions, which indicates that APSO is promising for the composition, or the highly complex problems. Comparatively speaking, the aHSDE has better overall performance on multiple types of problems.
## 2 Materials and Methods
### Data Analysis Methods
#### 2.4.2 Frequency Domain Analysis  
The frequency domain amplitude spectrum was obtained using fast Fourier transform. Since the sampling frequency of the microphone was 48 kHz, the upper frequency limit of the spectrum was 24 kHz in theory, but in the actual measurements, the upper frequency limit in the spectrum may be three- to four-times lower than the sampling frequency, and the upper frequency limit of the signal in this paper was 16 kHz-17 kHz. The Fourier transform equation is  
\[Y_{i}(f)=\sum_{n=1}^{N}y_{i}(n)e^{-i\frac{2\pi f}{N}n},f=1,2,\cdots,N \tag{8}\]  
The frequency interval 0-16 kHz of the amplitude spectrum was divided into 16 frequency bands in the order from small to large, and the length of the band was 1 kHz and was recorded as \(E_{j}\). The energy of the band \(E_{j}\) is defined as the sum of the frequency amplitude of the band and the formula is  
\[E_{j}=\sum_{f=1000(j-1)}^{1000j}Y_{i}(f),1\leq j\leq 16 \tag{9}\]  
The band vector \(T\) was constructed and normalized for 16 bands of energy, which was used to represent the energy share of different bands, where \(E\) is the sum of energy in the frequency band 0-16 kHz, \(E=\sum_{1}^{16}E_{j}\), and the expression is  
\[T=[\frac{E_{1}}{E},\frac{E_{2}}{E},\cdots,\frac{E_{16}}{E}] \tag{10}\]
## 3 Results
### Partial Order Ranking of Grassland Conversion out Drivers Between 2000 and 2020  
#### 3.2.1 Partial Order Ranking of Grassland Conversion out Drivers in the Period from 2000 to 2010  
In an attempt to identify major drivers for GCO, we systematically analyzed the layers, chains, and structures to evaluate and rank the impact of the four driving factors on GCO.  
Regarding the effect of the weather variable-related driver group on GCO in the period from 2000 to 2010, a total of five levels were identified (Figure 5a). Weather variables have the strongest effect on GCO in DMJ, HJ, DLT, WS, EKTO, and EKT; Figure 5a indicates that all six of these counties are located in the upper level of the Hasse diagram. Meanwhile, weather variables have the smallest effect in WC, QSH, and HL, which are located in the lower level (the lowest level in Figure 5a). Overall, weather variable drivers have the largest effect on GCO in Ordos and the smallest effect in Hohhot.  
Figure 4: Schematic diagram of GCO and GCI analysis. **(a)** GCO and GCI from 2000 to 2010; **(b)** GCO and GCI from 2010 to 2020.  
Figure 5b and Table S3 show that GCO was least strongly affected by human drivers in the western region of Ordos, including EKT, ETKQ, and HJ. Human activities also had the smallest effect on DMJ, which is part of Baotou. The major land-use type in these four counties is grassland. The effect of population is larger than that of sheep density in HH, BT, and DS, and the population effect is decreased as follows: {HH > BT > DS}. Meanwhile, the effect of sheep density is larger than the population in the following counties: WC, TKT, TMZ, QSH, HL, TMY, GY, HJ, DLT, YJHL, WS, and ETKQ. A total of 74 related chains were identified, and the above counties are a \"sheep-density-dominant chain\" (Table S4).  
For the economic driver group, six levels were identified in this period (Figure 5c). From a spatial perspective, the economic effect was larger in four of the six counties in the Hohhot region (HH, TKT, TMZ, and HL), the eastern four counties of Ordos (DS, DLT, ZGE, and YJHL), and two counties in the southern part of Baotou (BT and TMY) (the upper level of the Hasse diagram in Figure 5c). Meanwhile, economic factors have a smaller effect in the western region of Ordos (ETKQ, ETK, and HJ).  
Figure 5: Hasse diagrams constructed based on different combinations of driving factors (2000 to 2010).  
Regarding the urbanization effect during this period, a total of three levels were identified; this means that the partial order relationship for each county is simple. Figure 5d shows that urbanization has the strongest effect in four counties in Hohhot (HH, WC, TKT, and HL), three counties in Baotou (BT, TMY, and GY), and only one county in Ordos (DS). This result indicates that urbanization has the largest effect in Hohhot, and then in Baotou, while it has the smallest effect in Ordos. Specifically, urbanization has the smallest effect in the eastern region of Ordos: HJ, WS, ETKQ, and ETK. Overall, the urbanization effect was larger around the three major cities of HBO (HH, BT, and DS).
## 4 Implementation and Experiments
### Implementation
#### 4.1.2 The SFWC Implementation  
The implementation of the proposed SFWC is explained in the following paragraphs.  
Web Page Retrieval  
The first step retrieves a web page from an input seed URL or from a queue of URLs. This module implements two methods to select the set of seed URLs as input for the proposed SFWC: (i) querying a search engine about a topic and (ii) randomly selecting a set of seed URLs from the input corpus. In the first case, the Google search API was used to query and retrieve seed URLs. The API allows for setting up a personalized Google search engine to query. For the experiments, the first five page results from Google were collected (50 URLs). In the second case, the same number of URLs (50 URLs) was randomly selected from the input corpus as in the first case.  
HTML Content Extraction  
The second step was implemented with the Java library Jsoup. The library contains functions to extract the content of predefined HTML tags, e.g., the \(<p>\) tag defines a paragraph. Jsoup is used to retrieve the text enclosed by this tag.  
Topic Classifier  
The Stanford Core NLP tool was used to analyze the web pages content, defining the PoS Tag, lemma, and entity label. DBpedia Spotlight was used to define the semantic annotation for each noun. The enriched nouns are used to compute the similarity of the web page content against the KRS. In this case, if the web page content is similar to the KRS content, the web page is stored in a repository of related web pages.  
URL Extraction  
The crawler frontier was implemented as a queue of URLs, arranged in accordance with the breadth-first algorithm.
## 2 The Antenna Pointing Control System  
### Mechanical Antenna Pointing Control  
As shown in Figure 2, there are two methods of directing antennas: electronic scanning and mechanical scanning. The merits and demerits of both methods are summarized in Table 1. Whereas electronic scanning has been investigated for directing antennas in HAPS [12; 13], mechanical scanning has usually been studied only in combination with electronic scanning [14; 15]. In our study, by focusing on the gain fluctuation and pointing accuracy in relation to weight, we studied the antenna pointing control system based on mechanical scanning with a two-axis gimbal system [16].  
Figure 1: A HAPS-based wireless relay system.
## 4 Security Benefits of Directional Antennas in WSNs
### Sybil Attack
The identification of Sybil attack is based on directional information of the exchanged messages, coupled with the public key cryptography and hash function applied to the same messages.
# Noise Attenuation Based on Wave Vector Characteristics
## 1 Introduction
In addition, ground roll easily loses its original elliptical polarization during propagation owing to the anisotropy and inhomogeneity of the near surface. Therefore, extra constraints need to be added into polarization filtering to improve discrimination among different polarization modes. In this study, we develop a noise attenuation method based on mean and median filtering of wave vectors, which, in turn, may be thought of as evolved from the pioneering works of [PERSON] and [PERSON], [PERSON] et al., and [PERSON] [5; 26; 27]. The proposed method is implemented in moving windows at different scales in the time domain. At each time sample, we consider the wave vector to be composed by the valid-signal vector, the ground-roll vector, the random-noise vector and other noise vectors (e.g., the vector of non-ground-roll related coherent noise). We devise two different smoothing approaches to separate the ground-roll polarization and the body-wave polarization from the random noise background. Our filter does not depend on ellipticity or directionality and does not require setting up any kind of data covariance matrices or complex traces. Tests of our noise attenuation method show it to be effective with numerical and field data examples.
###### Abstract  
Supporting simultaneous access of machine-type devices is a critical challenge in machine-to-machine (M2M) communications. In this paper, we propose an optimal scheme to dynamically adjust the Access Class Barring (ACB) factor and the number of random access channel (RACH) resources for clustered machine-to-machine (M2M) communications, in which Delay-Sensitive (DS) devices coexist with Delay-Tolerant (DT) ones. In M2M communications, since delay-sensitive devices share random access resources with delay-tolerant devices, reducing the resources consumed by delay-sensitive devices means that there will be more resources available to delay-tolerant ones. Our goal is to optimize the random access scheme, which can not only satisfy the requirements of delay-sensitive devices, but also take the communication quality of delay-tolerant ones into consideration. We discuss this problem from the perspective of delay-sensitive services by adjusting the resource allocation and ACB scheme for these devices dynamically. Simulation results show that our proposed scheme realizes good performance in satisfying the delay-sensitive services as well as increasing the utilization rate of the random access resources allocated to them.  
M2M communication; delay-sensitive devices; ACB mechanism; resource allocation; Markov chain +
Footnote †: journal: Sensors
## 7 Performance Evaluation and Simulation Results
#### 7.1.1 Very Low Social Media Interaction  
Node's Density = 100 Nodes/km\({}^{2}\)  
We can see in Figure 4 that the values for the percentage of packet losses are around 21%, for all values of \(w_{TS}\). There is no much variation in packet losses when we vary the weight \(w_{TS}\) in the computation of the multimetric score to arrange the available paths, see Equation (9). This has sense because although we increase \(w_{TS}\), the qualification of the \(\overline{TS_{k}}\) parameter of the paths (see Equation (8)) will be very low since we are in a scenario with very low tie strength between users. This means that only QoS parameters have importance in the computation of \(MScore\), see Equation (9)). Actually, the \(\overline{TS_{k}}\) measured in the paths \(k\) taken by packets from source to destination (see blue line in Figure 4), for all the cases was always 0 in all the simulations. Regarding the average end-to-end packet delay, it is around 0.7 s for all \(w_{TS}\) values.  
To sum up, in this scenario with very low social media interaction among users, the forwarding paths were formed by nodes with no tie strength between pairs of nodes. Thus, this scenario will be a reference to compare the rest of the scenarios where the social media interaction among users increases.