esa-sceva/satcom-chunk-collection huggingface dataset:
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 5 Experimental Results
### Testing the Obstacle Detection in Accordance with EN14752 Standard  
To meet the requirements of the EN14752 standard, forces above the sensitivity level (100 N) acting on an object must be measured and should not be applied for more than 1 s. Therefore, the door-closing program was programmed to open the door when an object was detected. The hard-object detection experiment was again performed with the new software. Figure 17a,b reveals that the force on the object reached the threshold at sample 340. At sample 341, the peak force on the object reached 183 N, and W9 and peak were 60.2 and 14.3, exceeding the hard-object detection threshold. Therefore, the algorithm halted the motor and initiated door opening. As the door moved in the opposite direction, the force on  
Figure 16: Results of hard-obstacle detection experiment. **(a)** W9 and peak amplitudes showing a sharp rise at detection point; **(b)** motor current and force signals illustrating the increased current and force that triggered the detection condition and halted the door-closing program.  
the object decreased; at sample 344, it dropped below the sensitivity threshold. The object thus experienced a force greater than 100 N for five samples. Each sample represented 0.0512 s; therefore, the program responded in less than 0.3 s and the proposed obstacle detection algorithm successfully met the requirements of the EN 14752 standard.  
The tests in this study were performed using a metal spring as the soft object and a rectangular plastic block as hard object. To expand the scope, future experiments will be performed with objects of varying sizes (small, medium, and large). We will include these results in the next phase of our research.  
The proposed system improves safety and prevents potential injuries by using torque command to control the DC motor torque, which limits the pinching force. In addition, it does not depend on sensitive-edge sensors, which are prone to failure and may result in false obstructions. By applying a sliding window and time-frequency domain approach, this system ensures more accurate detection, minimizing potential injury risks. This system tested the object detection force levels according to EN14752 and it meets the safety standard. To the best of our knowledge, none of the existing methods in the literature tested their  
Figure 17: Hard-object detection and door opening. (**a**) W9 and peak; (**b**) motor current and force signal.  
system performance with EN14752 without sensitive-edge sensors. This is a novel and significant contribution of the proposed system.
## 2 Materials and Methods  
### Test Materials  
The soil used in this experiment was collected from a construction site in Ankang City, Shanxi Province. The sampling depth was 2 m. Expansive soil from Ankang City, located in a renowned expansive soil region south of the Qinling Mountains, was chosen for its high expansiveness and plasticity, making it an ideal subject for studying the properties and engineering challenges of such soils. The particle size distribution of the soil was obtained using the BT-9300 ST from Liaoning Dandong Baxter Instrument Co. (Dandong, China) laser, as shown in Figure 1. Within the framework of the Unified Soil Classification System (USCS) [33], the expansive soil, characterized by a liquid limit of 58.41%, a plastic limit of 33.73%, and a plasticity index of 24.68%, was classified as a high-plasticity clay (CH). Furthermore, this soil exhibited a free swell ratio of 77%, categorizing it as a soil of medium expansiveness. The basic physical properties of the soil were measured in accordance with the \"Standard for Test Methods of Soil Engineering\" (GB/T 50123-2019) [34]. More detailed basic physical properties of the soil are shown in Table 1. The mineral composition of the test soil was determined by an X-ray diffraction (XRD) semi-quantitative analysis, in which the content of montmorillonite was 24% and the content of illite was 20%, as shown in Table 2.  
The lignin fibers are grayish-white, neutral in pH, insoluble in water, and environmentally friendly. They have good hydrophilic properties. These fibers are organic fibers extracted from chemically treated natural wood, which have good chemical stability and corrosion resistance, as illustrated in Figure 2a. The lime used in the test contains calcium oxide at a concentration of \(\geq\)98%, as depicted in Figure 2b.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**Liquid** **Limit **/(\%)** & **Plastic **Limit (\%)** & **Plasticity (\%)** & **Maximum Index** & **Optimal Dry Density (\%/g/cm3)** & **Optimal Moisture Content/(\%)** & **Free Swelling Ratio/(\%)** \\ \hline
58.41 & 33.73 & 24.68 & 1.57 & 22 & 77 \\ \hline \end{tabular}
\end{table}
Table 2: XRD mineral composition of Ankang expansive soil.  
Figure 1: Particle gradation curve of Ankang expansive soil.
## 1 Introduction  
Green synthesis is defined as an alternative synthesis method for substances that offers a reduced negative impact on the environment. Plants are known as nature's \"chemical factories\". Therefore, out of all of the biological alternatives present, plants and plant extracts seem to be the best option. They are cost efficient and low maintenance. Materials such as silver [1; 2; 3; 4], gold [5; 6], titanium [7] and silica [8; 9; 10; 11; 12; 13; 14; 15; 16; 17] can be extracted from agricultural products. Furthermore, plant-mediated nanoparticles are preferred, as they are cost effective, environmentally friendly and safe for therapeutic use in humans [1; 6]. The extraction of raw materials from agricultural wastes is considered to be green synthesis, as these waste products are otherwise left unused and cause pollution if not treated properly.  
Silica is one of the most abundant elements found in the world. It can exist in gel, crystalline or even in amorphous forms in nature, depending on the way it is produced [13]. The standard form of crystalline silica found in nature is quartz. Silica has been found to be a versatile material, and is heavily utilized in various fields and for various applications, such as in petroleum refining, ceramics, anticorrosion agents, cosme products and as a catalyst in chemical reactions [10; 12; 18]. The use of commercialized high purity silica is quite costly, and is unsuitable in most industrial applications because of its high melting point [19; 20]. Therefore, lots of methods have been explored to synthesize the raw materials of SiO\({}_{2}\), as they are suitable for various applications and are cost-efficient at the same time.  
Several past studies have proven that silica can be extracted from various agricultural wastes, such as rice husk, rice straw, sugarcane bagasse, corn cob and palm ash [8; 11; 12; 13; 15; 16; 17; 21; 22; 23; 24; 25; 26; 27; 28]. However, information regarding its synthesis from coconut husk waste is limited. This work can be considered novel, because there are only a few published analyses related to coconut husk ash (CHA) being used as a source of silica. Therefore, it is sensible to add a more extensive analysis to this work in order to realize the full potential of CHA when used as source of silica.  
Generally, coconuts are consumed worldwide as food, drink or as other products. The husks are typically discarded as a waste material, either by burning or disposal in waste disposal sites. Without proper treatment or disposal method, these waste materials will result in various environmental problems that can lead to a multitude of health issues. In this study, the silica extracted from CHA was originally planned to be used as a silica substitute for the preparation of a ZnO-SiO\({}_{2}\) composite. The ZnO-SiO\({}_{2}\) composite is a versatile material and is often used in optical applications. The physical and optical properties of these materials were explored accordingly.
# Nutritional Composition and Biological Properties of Sixteen Edible Mushroom Species
## 4 Sixteen Edible Mushrooms of Greece
### Ganoderma lucidum  
Ganoderma lucidum(G. lucidum) belongs to the family Ganodermaaceae and is most popular for its medicinal importance rather than its nutritional benefits since it has a hard texture and bitter taste [62]. It has been used in traditional Chinese and Japanese medicine as a herbal remedy for hundreds of years [63]. Regarding its composition, it has been found that _G. lucidum_ consists of 44.95% carbohydrate, 15.75% protein, 14.81% crude fiber, 12.99% moisture, and 4% ash [62]. When it comes to the analyses of the University of Thessaly, the most important outcome is not the percentage of protein (19.2%), carbohydrates (57.85), and fat (2.1%), but the high percentage of unsaturated fat which reaches 78.3% of the total fat content. More than 279 bioactive compounds have been identified in _G. lucidum_, including terpenoids (meroterpenoids, lucidenic acids, ganoderic acids) and polysaccharides (\(\beta\)-D-glucans, \(\alpha\)-D-glucans, \(\alpha\)-D-mannans) [63; 64; 65]. Regarding its pharmacological value and biological properties, _G. lucidum_ has exhibited antioxidant [66], anti-diabetic [67], anti-cancer [68], anti-inflammatory [69], and cardioprotective properties [70]. Furthermore, polysaccharides from _G. lucidum_ attenuate the production of pro-inflammatory cytokines and could have neuroprotective effects [69]. In addition, _G. lucidum_ polysaccharides possess immune-modulating effects through the activation and the expression of cytokines associated with inflammatory response (such as interleukin-1, interleukin-6, and tumor necrosis factor-\(\alpha\)) or anti-tumor activity (such as interferon-\(\gamma\) and tumor necrosis factor-\(\alpha\)) [68]. According to EFSA, 150-350 mg daily intake powder helps in the reduction of cholesterol levels and immunity. Lastly, _G. lucidum_ presents a unique and scarce combination: classified as a medicinal mushroom and as an adaptogen herb, which enhances the body's resistance to many environmental, chemical, and biochemical factors at no cost to the operation [71].
# Detecting Phishing Domains Using Machine Learning
## 6 Findings and Analysis  
To identify the most accurate machine learning model for detecting phishing domains, this paper employed an experimental approach using four ML techniques: SVM, ANN, RF, and DT. With a total of 11,055 data instances, the UCI dataset was utilized for experimentation. Thirty features were used for evaluating the dataset, and the 31 st feature was used as the output. Table 3 displays the outcomes of the simulation with the true positive rate (TPR), false positive rate (FPR), true negative rate (TNR), and false negative rate. Moreover, a five-fold cross-validation method was employed for the classification  
Figure 7: Model’s flowchart.  
procedure. The 10-fold cross-validation approach was used to locate a greater performance accuracy dataset. Cross-validation is a predictive performance model evaluation technique used to check a machine-learning algorithm's performance in generating predictions on newer data on which it has not been trained. The examination of the confusion matrix is the basis for the classification technique's result performance.  
The results are shown in Table 4. The RF model provided the highest detection accuracy rate at 97%, followed by DT at 96%, ANNs at 95%, and SVM at 94%. Figure 8 depicts these results. Finally, Table 5 compares the RF model to the state-of-the-art results in the literature.  
Table 5 lists other research dealing with phishing attacks and crucial information about different machine-learning techniques. Three solutions based on ensemble learning, including the bagging, boosting, and stacking methods, were developed by [PERSON] et al. [42]. They combined their classifiers to attain a 95.4% accuracy rate in their results. [PERSON] et al. [55] proposed a new method for detecting phishing websites by scanning the source code of the related website's HTML page for linkages. They achieved a 96% accuracy rate. The researchers in [71] suggested three meta-learner models using ForestPA; the suggested meta-learners are efficient, according to their experimental data, with the lowest accuracy  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Measure** \\ \hline SVM & 94.66 & 93.9 & 96.6 & 95.3 \\ \hline ANN & 95.5 & 95.6 & 96.3 & 96 \\ \hline RF & 97.3 & 96.9 & 98.2 & 97.6 \\ \hline DT & 96.3 & 96.5 & 96.8 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results in (%).  
Figure 8: Proposal evaluation results.  
at 97.4%. The accuracy values in this paper vary from 0.95 to 0.97%, except for [PERSON] et al. [71], who got 97.4%, but this model takes longer to train and implement than RF and DT classifiers.
## 2 Scope: Specifying a Satellite for Archaeology
This specification is discussed in further detail below. Using the priorities defined above, the following engineering specification was generated for a satellite system for archaeology:  
* Coverage must extend to all of Scotland;
* For eastern and southern Scotland (Figure 2) the maximum revisit time should be three weeks, with a two-week revisit interval desirable, to ensure crommark formation, which is time variant, can be captured;
* A GSD of <1 m is required, with 0.5 m desirable, in recognition of the scale of anticipated features;
* Imagery should be multi-spectral (including at least Red, Green, Blue and Near-Infra-Red bands);
* The system must operate for a minimum of five years, to allow for observations during both wet and dry years;
* Usability in regions of similar climate/land environment is desirable. Based on the engineering specification, some high-level system requirements are identified that will constrain the space system design. Firstly, the requirement to provide a GSD of <1 m requires a low-Earth orbiting (LEO) spacecraft, as spacecraft in low altitude orbits will produce lower GSD data. LEO spacecraft are generally defined as having mean orbit altitudes of 300-1000 km and will orbit the Earth with a period of approximately 90 min. Geosynchronous and geostationary spacecraft have an orbit period that is the same as the Earth's speed of rotation about its axis, allowing for continuous observations over a given longitude. However, these spacecraft operate at approximately 36,000 km  
Figure 2: **(A)** The location of the study area in north-west Europe. **(B)** Map of Scotland, with yellow tone indicating the generalised extent of arable ground, which is a specific focus of interest for repeat imaging. The locations of Figures 3, 5, 6 and 7 are shown, referring to issues discussed below. altitude, and hence would be unable to provide the GSD required. Furthermore, an imager with a low GSD will generally have a small field of view, meaning that it may be necessary to use a constellation of spacecraft to achieve the desired revisit times. A greater number of spacecraft would tend to imply a higher cost system, and so the space system design discussed below will attempt to reduce costs by employing the minimum number of spacecraft. However, space system cost depends on a range of factors and is challenging to estimate due to the limited public availability of past mission costs. As such, any cost estimates will be subject to significant uncertainties. On the basis of the defined requirements, existing space systems are firstly assessed against the specification (Section 3), which is followed by discussion of a bespoke space system design (Section 4).
## 5 Results and Discussion
### RQ1: What State-of-the-Art Methods Exist for Optimizing LoRaWAN Energy Efficiency?
#### 5.1.3 State-of-the-Art Ensemble Techniques  
Various studies have explored the use of ensemble, ML, and AI-based approaches to address performance degradation, EE, and reliability issues in LoRaWAN networks. Techniques such as Ensemble Learning, DRL, and MAB algorithms have been used to improve overall network performance under conditions of congestion and interference. These approaches have demonstrated significant improvements and are summarized in Table 6.  
It has been observed from the literature that RL techniques have been widely adopted by various authors for EE LoRaWANs. DRL, DQN, generic RL, FRL, and MARI are among these novel techniques followed by DL algorithms and models. RL techniques mainly focused on optimization of network parameters, while DL techniques were used for tasks like predictive resource allocation and optimizing transmit power. Supervised learning models are applied to predict outcomes based on labeled data, such as packet arrival time. Supervised learning models were typically used for analyzing data without predefined labels, such as grouping devices based on similar behavior or optimizing transmission settings without prior knowledge of the optimal configurations. Ensemble  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (s)** & **Year** & **Technique Used** & **Model** & **Application** \\ \hline [PERSON] et al. [39] & 2024 & FRL and Network & Neural Model with 2 & Adaptive resource allocation and \\  & & Slicing (NS) & Hidden Layers & prioritization in \\ [PERSON] et al. [40] & 2019 & RL, Deep RL, & & \\  & & Q-Learning, & RL techniques & LoRa-based networks \\  & & and SARSA & & \\ [PERSON] et al. [41] & 2023 & Multi-agent RL & MARI & EE in WUSNs integrated \\  & & & & with LoRaWAN \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (s)** & **Year** & **Technique Used** & **Model** & **Application** \\ \hline [PERSON] et al. [31] & 2020 & ML & k-means clustering, & Real large-scale LoRaWAN network \\  & & & LSTM NN, DTs & EE and network performance in \\  & & & & LoRaWAN with Clock \\  & & & & skew estimation \\ [PERSON] et al. [42] & 2023 & ML & ML clustering (K-means) & A LoRaWAN network infrastructure \\  & & & & in smart cities. \\ [PERSON] et al. [43] & 2021 & ML & SVR, DNN & Long distance Network, LPWANs \\  & & & & connected to the IoT \\ [PERSON] et al. [44] & 2024 & ML and DL & ARIMA, ANN, SVM & Accuracy in LoRaWAN \\  & & & & in outdoor devices \\ [PERSON] et al. [25] & 2019 & Supervised Learning & Decision Tree Classifier (DTC), SVM & Large geographical area in LPWANs \\ [PERSON] et al. [45] & 2019 & K-means clustering- & K-means clustering & Large-scale LoRa networks \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of ML and Supervised and Unsupervised learning techniques.  
methods combined multiple ML techniques to improve prediction accuracy, robustness, and reliability. Figure 4 presents the distribution of techniques and models in the literature.
## 5 Data Transmission Tests
### Gateways Installation  
Earlier on, in Section 3, it was claimed that two Gateways would be employed. Indeed, such a choice was made so as to improve the reliability of the link by establishing a space diversity scheme at the receiver side. In addition to it, LoRaWAN protocol itself provides for a time diversity scheme, that is ensured by the laws on temporal occupancy of the transmission band [33], and for a frequency diversity scheme, that is implemented by switching the carrier frequency in a pseudo-random fashion for each transmission. In so doing, a more robust broadcast is achieved.  
The Gateways along with their antennas were ashore installed on top of a knoll, whose altitude is 6 m above sea level, which belongs to the proprietary terrain of the aquaculture partner company. With a view of better exploiting the first Fresnel zone, the receiver antennas were placed on a 7.2 m pole raised on top of the knoll. In particular, they were installed on the pole upper extreme by mounting them on a 2 m bar fastened to the pole forming a cross. Hence, by summing up the altitude of the knoll and the length of the mounting pole, the receiver antennas were located at a height of \(h_{RX}=13.2\) m above sea level. As it will be seen later on, such a set up was far from being sufficient to allow for the 60% clearance of the first Fresnel zone, but, in order to keep the installation procedures as simple as possible, the antennas could not be placed any higher since only a temporary installation was planned for these early days of the validating experimentation phases of the project.  
The Gateways were implemented by means of a RAK831 produced by the RAKWireless, which is a multi-channel LoRaWAN concentrator, driven by a Raspberry Pi 3 model B. Figure 6 shows the Gateways setup on top of the knoll.  
Figure 6: Ashore setup of the Gateways.  
Two directional helical antennas having a gain \(G_{RX}=14\,\mathrm{dBi}\) were selected for the Gateways. They were in turn connected to the Gateways via two 15 m long coaxial cables having a total loss of \(L_{RX}=4.06\,\mathrm{dB}\) each, that was measured by exploiting a vector network analyzer. Finally, Internet connectivity was guaranteed by a 4G LTE router since there were no other opportunities in loco. This slightly slowed down the communication between the Gateways and the network server introducing a minimal amount of latency: nevertheless it was not an issue at all.