esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Inlet Configuration and Parametric Design of the Cowl
### Parametric Design of the Cowl  
The parametric design of the cowl should be performed under certain geometric restraints. To ensure that the mass flow ratio (\(\varphi\)) remains constant, the positions of the start-point C and the end-point E of the cowl are unchanged. To ensure the smooth transition of the cowl and the downstream isolator, the cowl curve should keep horizontal at the throat. As shown in Figure 2, a third quasi-uniform B-spline curve is adopted to perform the parametric design of the cowl configuration (blue curve). The cowl is determined by the control polygon CF\({}_{1}\)F\({}_{2}\)E (denoted by red lines). The fixed points C and E are the start and end points of the cowl, respectively. Point F\({}_{1}\) can move along the extended line of CF\({}_{1}\), where the angle between line CF\({}_{1}\) and the horizontal direction is denoted as \(\delta\). Point F\({}_{2}\) can move along the horizontal direction, and its radial coordinate is identical to that of Point E, thereby ensuring that the cowl is horizontal at E. In this study, the distances from C to F\({}_{1}\), and from E to F\({}_{2}\) are denoted as \(L_{1}\) and \(L_{2}\), respectively. With a given group of \(L_{1}\), \(L_{2}\), and \(\delta\), a corresponding cowl curve can be acquired. Under the proposed parametric design, \(\delta\) is the cowl lip angle. To simplify the study, \(L_{1}\) and \(L_{2}\) are set as \(0.32R\) and \(0.25R\), respectively, and the inlet configurations with different cowl lip angles are acquired by changing the value of \(\delta\).  
Figure 1: Studied model and Mach number contour under design point: (**a**) Schematic of the inlet configuration and flow structure; (**b**) Mach number contour.  
Figure 3 illustrates the cowl profiles with cowl lip angles of 0\({}^{\circ}\), 6\({}^{\circ}\), and 12\({}^{\circ}\) obtained by the parametric design. Cowl curves with different cowl lip angles have the same start and end points and remain horizontal at the throat, proving that the proposed method satisfies the geometric constraints of the inlet. Since the B-spline possesses satisfactory local control, the cowl lip angle mainly affects the shape of the front part of the cowl. For \(\delta=0^{\circ}\), the cowl curve is relatively gentle at the beginning, and gradually protrudes as it progresses downstream. With the increase in the cowl lip angle, the cowl increasingly protrudes at the beginning, but gradually deflects inward as it progresses downstream, and the duct contracts rapidly.
## 6 Discussion  
DnM people are an integral part of society, and the system presented in this manuscript aims to help them communicate with NDnM people. In this manuscript, machine learning is applied to develop a two-way system. The system is computer-based, where the DnM interface is provided so that their hand gestures are acquired and converted into text and then voice. The NDnM people are provided with an audio interface in which their voices are acquired and then converted into text. The system uses deep learning to detect and process hand gestures, which are implemented using a Leap device. Language detection is achieved through supervised machine learning. The system also provides speech-to-text-to-speech conversion, which is a useful feature for NDnM people. Due to this option, NDnM people do not need any training or are not required to know sign language.  
Many existing similar systems only focus on DnM people, where the interface is provided for them so that their hand gestures are detected. These systems expect NDnM people to learn about the detection of hand gestures and the interfaces provided to them. It is not easy for NDnM people to learn sign language, hence reducing the effectiveness of this system. The proposed system provides a complete solution to the problems faced by both DnM and NDnM people.  
The system is presented through hardware and software block diagrams in the manuscript. The system is validated through four experiments in which system performance is evaluated. The system performance is evaluated using several experiments, namely detection of similar hand gestures, low-resolution gesture images, inaccurate gestures, speed of making hand gestures, etc.  
The system is designed with a Leap motion device, and the results are encouraging. Using a COTS device means that there is no need for extensive validation and verification to convert a prototype into a product. The other advantage is that there is no calibration required compared to other custom-made devices, which are normally based on sensors connected to a glove and require continuous maintenance to make sure the sensors are working correctly. Upgradation of custom-made devices is also a challenge where the availability of components has been a problem recently.  
Another feature of the proposed system is that the end user is not expected to undergo rigorous training. Ease of use is a key feature of this research. Another key feature is that DnM individuals can also use this system to communicate with each other. In this mode, the system will acquire the data, which will help improve the performance of the machine learning algorithm. The software application is easy to install and doesn't require any licensing. The software processes new data and stores them in the database.
## 5 Applications of BSNs
### State-of-the-Art Research on BSN Applications
#### 5.1.4 Man-Machine Interfaces  
Along with the development of behavioral recognition technology, BSN devices can be used to replace traditional man-machine interfaces, such as keyboards, mice, joysticks, and touch screens, and they can also be used to design somatosensory control systems. In addition, BSN technology can assist handicapped people in daily life. In Reference [147], researchers propose some intra-body communication (IBC) applications. For instance, an IBC-enabled sensor embedded inside the shoes of a blind person can send voice information to the user, telling him or her the current location, identifying doorways or crosswalks. IBC-enabled eye-glasses can help deaf people comprehend audio broadcast announcements, by displaying texts converted from sound with the help of IBC-enabled speakers, Reference [148] presents a hand-gesture-based glove for facilitating communication among people with speech and hearing-impaired disabilities. In the system, a wireless sensor glove equipped with five flex sensors and a 3D accelerometer is used as input device. By integrating the speech synthesizer into an automatic gesture recognition system, user's hand gestures can be translated into sounds. In addition, Reference [149] proposes a method using visual implant technology to recover visual perception of the blind or patients with retinal degeneration, which is also an application of BSN in the man-machine interface field.
# Investigation of Energy Harvesting Capabilities of Metglas 2605 SA1
## 2 Magnetic Behavior
### Result and Discussion
Modeling results and comparisons with experiments are depicted in Figure 7 for Metglas 2605 SA1. As previously noted, the experimental magnetic flux density as a function of the magnetic excitation (\(B(H)\) curves) shows a limited hysteretic behavior, confirming the anhysteretic modeling approach. It can be noted that the unified model encompassing both tensile and compressive stresses could fit well with the experiment results, not only for the linear part in the low magnetic field range but also considering the saturation part for higher magnetic field levels. Finally, a good agreement between experimental and modeling results can also be observed as the stress is varied, although some slight discrepancies can be observed in the mid-field region at the maximal tensile stresses of 18 MPa and 30 MPa, as well as for \(-\)18 MPa compressive stress, and at high excitation values for the maximal tensile stress value of \(-\)30 MPa. However, these discrepancies are rather limited, so that the proposed unified model provides a relevant theoretical framework for the energy conversion ability assessment of Metglas.
## 1 Introduction  
Essential oils, which were obtained from several plants, have been considered to be potent fumigational insecticides that can be alternatives to the currently used manmade fumigants, including phosphine and ethyl formate, against stored product insects [1; 2; 3]. As some stored product insects have developed strong resistance to the currently used fumigants, such as phosphine, essential oils, having different target sites of insecticidal action and presumable selectivity for certain insects [4; 5], might help to overcome the problem of resistance.  
Recently, these essential oils have been introduced for their fungicidal activities for the control of agricultural pathogenic fungi, such as _Aspergillus flavus_, _A. niger_, _A. ochracous_, _Colletotrichum acutatum_, and _Fusarium granimearum_[6; 7; 8]. Therefore, these plant oils can be used for dual purposes by controlling both insect pests and agricultural pathogenic microbes.  
Mycotoxins produced by fungi spread steadily, contaminating agricultural foods and foodstuffs around the world, and there are several technologies that are available that use chemical and biological treatments and physiological applications to protect the foodstuffs from fungal toxins and to degrade the toxins before they reach the consumer [9; 10]. Interestingly, some essential oils can suppress mycotoxin contamination by inhibiting fungal growth and toxin production. Eucalyptus essential oils and cinnamon essential oils are potent inhibitors of _A. flavus_ growth and suppressors of aflatoxin production [11; 12]. Their primary constituents are 1,8-cineole (or eucalypt) and _t_-cinnamaldehyde, and their antifungal and antiflatoxigenic activities have been reported previously [12; 13]. A previous report demonstrated a dramatic reduction of the expression of some genes that are involved in the biosynthetic pathways of aflatoxin B\({}_{1}\) by cinnamaldehyde [14]. However, no reports have described the mode of fungicidal action for the antiflatoxigenic activity of 1,8-cineole. Additionally, the antifungal and antiflatoxigenic properties of essential oils and their primary constituents need to be reconsidered before the introduction of these chemicals in agricultural industries, because the indefinite mode of antiflatoxigenic action may be a major obstacle for their use in controlling _A. flavus_ growth and aflatoxin production.  
In the present study, 1,8-cineole and _t_-cinnamaldehyde were used to treat _A. flavus_ and to examine the growth and suppression of aflatoxin production. Aflatoxin production was quantitatively determined while using high performance-liquid chromatography (HPLC) with a fluorescence detector. An RT-PCR technique was employed to understand how these chemicals decrease the expression of genes that are involved in the aflatoxin biosynthetic pathway, in order to comparatively measure the gene expression levels between the samples and the controls.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Experimental Setup
#### 3.4.2 Simulation Parameters  
According to the anomaly definition, the dataset has 8.59% type 1 and 7.22% type 2 anomalies. In total, the anomalous data across the dataset is 15.78%. Figure 11 depicts the labelled data according to the definition. Hence, it implies that the available dataset is imbalanced, with the majority consisting of normal class conditions.  
We implemented WA techniques in the pre-processing stage using the rolling and replace function in the pandas DataFrame library. The standardization was performed via the StandardScaler() function, available from the sklearn library. Next, the simulation implemented principal component analysis through a function provided by sklearn. Table 2 describes all necessary values for the simulation. Although we did not systematically tune the parameters, we determined the best values from an informal  
Figure 11: The first two principal component plots of the labelled dataset.
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
###### Abstract
In the context of disaster management and Early Warning systems, the IoT provides the means for widespread environmental monitoring from different data sources, low latency communications and real-time data processing, which enable the generation of accurate and timely warnings in the case of disaster occurrence or forecasting. This work presents a review on the architectures and the requirements of IoT solutions used in EW systems. In particular, the paper will first introduce a general IoT architecture and some general concepts and requirements for IoT-based EWSs, and then focus on four different natural disasters: floods, earthquakes, tsunamis, and landslides. For each of them the review will present a set of use cases that focus on or optimize some IoT-related aspects (such as radio coverage, energy consumption, fault tolerance, latency, and real-time data processing) required for the optimal operation of each specific EW context of use. The main contributions of the review article are the following: (i) it describes a generic architecture for an Early Warning system based on the IoT; (ii) it systematically determines which are the most used solutions in the four use cases examined and it highlights possible trends and gaps; and (iii) it provides suggestions for future research in this field and some recommendations to be able to satisfy the main requirements of such a system, based on the reviewed papers and the authors' experience in this field. Compared to other reviews, surveys, or papers containing literature overviews that focus on a particular type of disastrous event or a single category of hazards, such as [17; 18; 19; 20; 21; 22; 23], this paper takes into account multiple natural disasters highlighting the differences from one case to the other, in term of IoT architectures and systems requirements such as the required latency constraints. While some reviews, such as [20; 21], more explicitly focused on prediction algorithms, Machine Learning prediction models and Computer Vision, or post-disaster management, the main focus of this work is on the adopted IoT solutions and their possible optimizations, including system requirements, communication protocols, data processing, and sensor network topology aspects. The paper is structured as follows. In the next section, we explain the methodology used to compile the review paper and we introduce the Research Questions (RQs) addressed by the paper. In Section 3, a generic IoT architecture is introduced, alongside a set of requirements for an EWS. Sections 4-7 answer the aforementioned RQs by analysing papers and up-to-date solutions for the four natural disasters that have been taken into consideration. In Section 8, we provide some recommendations about the future research on the topic of IoT solutions for Early Warning systems. Lastly, in Section 9, we discuss the main findings of the paper.
## 2 Study Area, Dataset, and Methodology
### Correlation Analysis and Trend Analysis
#### 2.5.2 Trend Analysis  
To identify long-term changes in NDVI, a linear trend analysis was applied to calculate the rate of NDVI change over time [44; 45] using Formula (2). The regression coefficient a of the trend represents the rate of change in NDVI over time x.  
\[a=\frac{\sum_{i=1}^{n}i\times NDVI-\frac{(\sum_{i=1}^{n}i)(\sum_{i=1}^{n}INDVI_{i })}{n}}{\sum_{i=1}^{n}i^{2}-\frac{(\sum_{i=1}^{n}INDVI_{i})^{2}}{n}} \tag{2}\]  
where n is the study period duration, and NDVI\(i\) represents the NDVI value in year i. Positive values of a indicate an increasing trend, while negative values indicate a decreasing trend.  
Figure 2: Workflow diagram.  
In the trend analysis, the changes in NDVI over time can be categorized into the following five classes: Significant Decrease, Moderate Decrease, No Significant Change, Moderate Increase, and Significant Increase [46]. Classification is based on the value of the trend coefficient a and its corresponding \(p\)-value. The criteria for each category are provided in Table 2.  
This classification facilitates the quantitative analysis of both the spatial and temporal trends of vegetation in Aksu and helps elucidate the extent to which climate factors and human activities influence changes in NDVI.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 5 Introductory Synthetic Tests
### Academic Examples  
When we want to study our model properties with the numerical schemes, it comes in handy to find an analytical solution for the feature descriptor, first. For this, we need a more practicable setting than the centaur shape.  
We will use the 1D damped wave equation \(u_{tt}+2 ku_{t}=u_{xx}\) on the interval of \(x\in\left[-\frac{k}{2},\frac{k}{2}\right]\), periodic boundary conditions and initial velocity \(u_{t}(x,t)=0\), for this purpose. The value 2 in this equation will make the calculation of the analytical solution easier to execute. In the following, we will outline the extraction of the solution, for the sake of completeness. In doing so, we want to be as general as possible and as detailed as necessary.  
With the method of separation of variables and the condition,  
\[1<\frac{2\pi}{kL} \tag{43}\]  
we receive the analytical solution \(u(x,t)=\sum_{n}u_{n}(x,t)\) via  
\[u_{n}(x,t)=e^{-kt}A_{n}\bigg{(}\cos(\alpha_{n}t)+\frac{k}{\alpha_{n}}\sin( \alpha_{n}t)\bigg{)}\cos(\beta_{n}x) \tag{44}\]  
The condition (43) is needed to generate an oscillating solution that may give a meangirl result for generating a feature. Furthermore, we set \(\alpha_{n}=\sqrt{\beta_{n}^{2}-k^{2}}\) and \(\beta_{n}=\frac{2\pi n}{L}\). In the standard shape-matching scenario, we would use \(u^{0}(x)\) or \(u^{\mathcal{C}}(x)\) as initial conditions \(u(x,0)\). Since we would have to Fourier transform these initial conditions, and this will not make the solution more pleasant, we choose the cosine function \(u(x,0)=\cos(x)\) as the initial condition instead. With this choice and \(L=2\pi\), we can determine the parameter \(A_{n}\) in Equation (44):  
\[\cos(x)=u(x,0)=\sum_{n}A_{n}\cos(nx) \tag{45}\]  
which implies  
\[A_{n}=\begin{cases}1&\text{if }n=1\\ 0&\text{if }n\
eq 1\end{cases} \tag{46}\]  
In the end, we get the analytical solution for our academic example  
\[u(x,t)=e^{-kt}\bigg{(}\cos(\sqrt{1-k^{2}}t)+\frac{k}{\sqrt{1-k^{2}}}\sin( \sqrt{1-k^{2}}t)\bigg{)}\cos(x) \tag{47}\]  
From this equation, by choosing a specific \(x\), we can also obtain the feature descriptor. We choose the centre of the domain \(x=0\), meaning  
\[f_{x=0}(t)=u(x,t)|_{x=0}=e^{-kt}\bigg{(}\cos(\sqrt{1-k^{2}}t)+\frac{k}{\sqrt{1 -k^{2}}}\sin(\sqrt{1-k^{2}}t)\bigg{)} \tag{48}\]  
Setting \(k=0\) will generate the solution and feature descriptor for the wave equation. As we did earlier, we will write _analytical (damped) feature descriptor_ for the feature descriptor obtained from the analytical solution of the (damped) wave equation.  
In Figure 2 we already presented the solution \(u(x,t)\) and the feature descriptor of this example. The solution \(u(x,t)\) is presented as a red-blue surface. Reddish parts are greater than zero, and bluish parts are less than zero. Additionally, we plotted the feature descriptor \(f_{0}\) as a solid black line. For the evaluation, we have chosen the time increment \(\tau=0.1\) and the damping constant \(k=\frac{\tau}{2}\).
# Novel Materials as Exogenous Carbon Sources for Denitrifying Biofilters
## 2 Materials and Methods  
### Materials  
Greenhouse wastewater from hydroponic tomato farming was used for the study (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **Arithmetic Mean** & **Minimum Value** & **Maximum Value** & **Standard** \\  & & & & **Deviation** \\ \hline pH & 6.37 & 6.20 & 6.63 & 0.13 \\ DO (dissolved oxygen) [mg O\({}_{2}\)/L] & 8.94 & 8.59 & 9.15 & 0.18 \\ Temperature [\({}^{\circ}\)C] & 22.20 & 21.00 & 23.50 & 0.77 \\ EC (electrical conductivity) [mS/cm] & 6.707 & 6.401 & 7.391 & 0.279 \\ TOC (total organic carbon) [mg/L] & 10.02 & 0.75 & 16.62 & 4.45 \\ COD [mg O\({}_{2}\)/L] & 34.96 & 25.60 & 49.20 & 7.91 \\ Nitrates [mg N/L] & 710.0 & 618.0 & 920.0 & 76.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and chemical properties of wastewater from soilless tomato cultivation (number of data points n = 10).  
The effluent was treated in fixed-bed biofilters. Four types of media (carbon sources) were used: beech chips, Oxytree chips (_Paulownia Clon_ in vitro 112), biobased polybutylene succinate derivative BioPBS FZ91 PB (51% carbon from biofuels, according to ASTM D 6866 [30]), and BioPBS FD92 PB (35% carbon from biofuels, according to ASTM D 6866).
## 2 Cable Model and Effective Length in 3D Twisted Conductors
### The Effective Length \(l_{\text{twist}}\) in 3D Models
#### 2.3.1 Numerical Calculation of the Effective Length \(l_{\text{twist}}\)  
Let \(\lambda\) be the linear distance along the \(z\)-axis for a full turn, and \(r\) be the radial distance of a given conductor from the central axis as shown in Figure 5. Then, a single conductor's helix length \(l_{\text{twist}}\) for one turn can be approximated by the Pythagorean relation:  
\[l_{\text{twist}}\ =\ \sqrt{\lambda^{2}+(2\pi\,r)^{2}}. \tag{2}\]  
In multi-layer or multi-segment cables, \(r\) may differ among conductors. Tighter twisting (small \(\lambda\)) increases the ratio \(l_{\text{twist}}/\lambda\) more substantially, raising the DC resistance because \(R_{\text{DC}}\propto length\).  
Figure 5: Helical path of length \(l_{\text{twist}}\) for one 360\({}^{\circ}\) revolution, with lay length \(\lambda\) along the \(z\)-axis and distance \(r\) normal to the \(z\)-axis.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 4 Hyperbranched Polymer Toughening
### Summary  
Hyperbranched polymers (HBPs) enhance epoxy resins by improving toughness, reducing viscosity, and increasing processability without compromising thermal resistance. Due to their compact structure, HBPs have lower viscosity compared to thermoplastic resins, making them suitable for low-viscosity epoxy resin composite manufacturing technologies. However, the complex synthesis and high cost of HBPs limit their widespread application in the toughening of epoxy resins. Therefore, developing simple and efficient synthesis methods for HBPs is a key focus for future research.