esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a New Bilayer Multipole Electromagnetic Brake System for a Haptic Interface
## 2 Modelling of a Magnetic Brake
### Power Consumption  
The designed multi-layered magnetic brake is operated using DC input power which is supplied to the individual solenoid coil in each layer. The solenoid coils are arranged in such a way that each pole finds its neighboring poles with inverted coil orientations. As a result, the current passing through one coil generates a flux that superimposes the flux in other poles. Since each layer of the brake can be individually actuated, all the coils in one layer have the same construction, number of turns and current.  
The total power consumption by the brake is given by  
\[P_{cons}=6\left(l_{i}^{2}R_{i}+l_{o}^{2}R_{o}\right), \tag{15}\]where \(I_{i}\) and \(I_{o}\) are the DC currents through the solenoid coils of inner and outer layer, respectively. \(R_{i}\) and \(R_{o}\) are the resistances of the coil wire in the corresponding layers and can be calculated by the relation  
\[\begin{split} R_{i}&=\rho\frac{(L_{man})_{i}N_{i}}{A_{ wire}}\\ R_{o}&=\rho\frac{(L_{man})_{o}N_{o}}{A_{wire}},\end{split} \tag{16}\]  
where \(\rho\) and \(A_{wire}\) are the resistivity and cross-sectional area of solenoid coil wire, respectively. \(\left(L_{man}\right)_{i}\) and \(\left(L_{man}\right)_{o}\) are the mean lengths of wire of solenoid coils in inner and outer layer, respectively, and can be calculated from the relation  
\[L_{man}=2(U+D+b), \tag{17}\]  
where \(U\), \(D\) and \(b\) are the lengths along the perimeter of the center of solenoid coil, as shown in Figure 5a, with the values calculated geometrically for the inner layer solenoid as  
\[\begin{split} U_{i}&=\left(r_{i_{\max}}+\frac{w_{ i}}{2}\right)\theta_{w_{i}}\\ D_{i}&=\frac{1}{\cos\theta_{C}}\left(\left(r_{i_{ \max}}+\frac{w_{i}}{2}\right)\cos\theta_{w_{i}}-\left(r_{i_{\min}}-\frac{w_{i} }{2}\right)\right)\\ b_{i}&=r_{i_{\min}}\tan\theta_{C}+\frac{w_{i}}{2} \left(\frac{1}{\cos\theta_{C}}-\tan\theta_{C}\right)\\ \theta_{w_{i}}&=\theta_{C}+\sin^{-1}\left(\frac{w_{ i}}{2r_{i_{\max}}+w_{i}}\right),\end{split} \tag{18}\]  
where \(w_{i}\) is the width of solenoid coil in the inner layer. Similarly, the mean length for outer layer solenoid can also be calculated using Equation (18).  
The number of turns of solenoid coil can be calculated by estimating the number of circles of cross-sectional area \(A_{wire}\) that can fit in an available area--width \(w\) and length \(L_{coil}\) of a solenoid coil, as shown in Figure 5b, and expressed by the relation  
\[N=\frac{wL_{coil}\delta}{A_{wire}}, \tag{19}\]  
where \(\delta=\pi/\sqrt{12}\) is the coil packing factor that considers the free spaces between the wrapped wires [19].  
Figure 5: Schematic diagram for a single solenoid coil. (**a**) Radial dimensions; (**b**) cross-sectional dimensions.
# Exploring the Potential of Microextraction in the Survey of Food Fruits and Vegetable Safety
## 1 Introduction
### Food Safety--Major Contaminants of Fresh Fruits and Vegetables and Their Toxicity
#### 1.1.2 Antibiotics  
Antibiotics are widely used around the world to control plant diseases. Agricultural antibiotics are applied at very low concentrations, and their quantity per unit area is much lower than that of other conventional pesticide chemicals [23]. However, antibiotics can exert continuous selective pressure on soil microbiomes for extended periods of time, leading to the selection of microorganisms with antibiotic-resistance genes [24]. These genes can be transmitted to potential pathogens through horizontal gene transfer mechanisms, which prolongs the dissemination of antibiotic resistance in the environment [23; 25]. Additionally, fresh edible vegetables could potentially serve as reservoirs of antibiotic resistance [26; 27]. Humans can also be exposed to antibiotics through the potential prevalence of antibiotic resistance, due to selective pressure on bacteria of clinical importance [27; 28]. The most commonly used antibiotics in plant agriculture are streptomycin and oxytetracycline (Figure 1).  
Streptomycin is an aminoglycosatile antibiotic that is naturally produced by soil actinomycetes. It is used in the United States of America (USA) to control fire blight, which is an important disease in Rosaceae members. Minor uses of streptomycin include controlling plant bacterial diseases in floriculture as well as on potato tubers, tobacco seedlings, and other vegetable seedlings in the field or greenhouse [23; 29]. This antibiotic is bactericidal and acts by irreversibly binding to the bacterial ribosome, thereby blocking protein synthesis [30; 31].  
Oxytetracycline, a tetracycline antibiotic produced naturally by _Streptomyces rimosus_[32], is primarily used to manage fire blight and other serious diseases in fruits (pears and apples as well as stone fruits such as peach and nectarine) and vegetable crops [23; 31]. In rare instances, it can be used internally in plants to treat lethal yellowing diseases on palm and elm trees, as well as other diseases caused by phytoplasmas. This antibiotic is bacteriostatic and inhibits the multiplication of bacterial cells by binding reversibly to the bacterial ribosome, thus, blocking protein synthesis [31; 32].
# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
## 2 Fundamentals
### Color Models  
Due to the difficulties in finding appropriate, accordant names for colors, color models were introduced throughout the 20 th century. Color models are systems that describe colors using numerical values instead of linguistic terms. The most influential one is the Munsell color system, created in 1913 by Professor [PERSON] as a rational way to describe colors [29]. The fundamental premise of the [PERSON] color system is the distribution of colors along three perceptually uniform dimensions: hue; value; and chroma. The placement of the colors was decided by measuring human responses, instead of trying to fit a predefined shape, thus obtaining an irregularly shaped cylindrical space that avoids distorting color relations [30]: in this space, hue is measured in degrees around horizontal circles, chroma goes radially outwards from the neutral vertical axis, and the value increases vertically from 0 (black) to 10 (white). The irregular shape of the Munsell color space allows for wider ranges of chroma for specific colors, which follows the physics of color stimuli and the nature of the human eye. This non-digital color system is the most-used in psychology and soil research [31; 32].  
With the rise of computer operations, digital color models were created to suit the new tasks better. The main digital color spaces are RGB, HSL/HSV, and CIELAB. A visual representation of these can be seen in Figure 1C. RGB was created in 1996 for internet and display purposes: in this space, colors are represented as three coordinates corresponding to red, green, and blue values. Despite being the most widely used color space in digital applications, the colors it can represent are limited, device-dependent, and discordant from human perception and interpretation. By contrast, HSL/HSV spaces were designed circularly, to better align with human perception of color-making attributes: these spaces consist of a three-dimensional cylindrical transformation of the RGB primaries into a polar coordinate system, obtaining hue, saturation, and value (in HSV) or lightness (in HSL). Finally, CIELAB is a uniform color space, based on human perception, that achieves the best compromise between complexity and accurate color representation by adopting the concepts of the Munsell color system. In CIELAB, the geometrical distance between any two points represents the perceived color differences without forcing the color relationships into strict geometrical shapes: this makes CIELAB more perceptually linear and decorrelated than the alternatives [33; 34; 35].
# Mycotoxin Level in Winter Wheat Grain as Impacted by Nitrogen and Manganese Fertilisation
###### Abstract  
A field experiment with winter wheat (_Tritcium aestivum_ L.) cultivation was conducted at the Research and Education Centre in Tomaszkowo, Poland (53\({}^{\circ}\)72\({}^{\prime}\) N; 20\({}^{\circ}\)42\({}^{\prime}\) E) in the years 2013-2016. Fertilisation with nitrogen at 150 and 200 kg ha\({}^{-1}\) and foliar application of manganese at 0.5 and 1.5 kg ha\({}^{-1}\) were the research factors. Wheat infestation by _Fusarium_ spp. was determined by the habitat conditions during crop growth. Neither nitrogen nor manganese fertilisation affected the presence of _Fusarium_ spp. symptoms on wheat ears, but the infestation intensity decreased with increasing nitrogen and manganese content in the grain. Only the level of deoxynivalenol (DON) was correlated with _Fusarium_ spp. infestation. Increasing the nitrogen fertilisation rate from 150 kg ha\({}^{-1}\) to 200 kg ha\({}^{-1}\) resulted in higher grain contamination with toxins. Supplementation of nitrogen fertilisation with manganese reduced the number of mycotoxins in wheat grain. The grain yield was mainly affected by the varied weather conditions during the wheat-growing periods. Neither nitrogen nor manganese fertilisation differentiated the wheat grain yield. The objective of this study was to examine the impact of the weather conditions and nitrogen and manganese fertilisation on the grain yield, occurrence of _Fusarium_ head blight and mycotoxin level in winter wheat grain.  
nitrogen; foliar fertilisation with manganese; mycotoxins; DON; NIV; ZEA; MON 2023, 13, 10086. [[https://doi.org/10.3390/app131810086](https://doi.org/10.3390/app131810086)]([https://doi.org/10.3390/app131810086](https://doi.org/10.3390/app131810086))
## 3 Parameter Analysis  
### Influence of the Slope Angle of Reef Bottom Layer on Site Responses  
In this section, the influence of slope \(\theta\) on reef island response is studied. Here, the reef platform width of \(L=500\) m is taken as an example, and \(\theta\) is 30\({}^{\circ}\), 45\({}^{\circ}\) and 65\({}^{\circ}\). The middle point of the reef platform width is taken as the origin of the horizontal coordinate, and an observation point is taken every 10 m. The horizontal coordinate corresponding to the reef platform width is \(-250\)\(\sim\)250 m, where \(x=\pm 250\) m is the corner point where the reef platform connects with the slopes on both sides.  
We define the ratio of the peak response of each point on the surface of the reef-island site to the peak response of the free-field ground surface as the site amplification factor \(A_{c}\).  
\[A_{c}=\frac{max(|a(x,\ t)|)}{max\Big{(}a_{free}(x,t)\Big{)}} \tag{2}\]  
Figure 4: Correctness verification by comparing with the theoretical solution [35].  
where the variable \(x\) is the horizontal coordinate of every observation point; \(a\) is the acceleration time series on the surface of the seabed and reef island in Figure 2; \(t\) is the time factor. The subscript \"_free_\" represents the ground response of the free-field site, i.e., the model only contains the seawater layer and the horizontal seabed layer without the reef island terrain.
## 4 The Factors for Degrading Performance of Localization
### Spatiotemporal Asynchronization  
Spatiotemporal asynchronization refers to the discrepancy in the clock pulse and relative position. The discrepancy in the clock pulse can occur when the system clock pulses of the anchor and devices are not the same. This causes a difference in time standards of the wireless signal, which impairs the localization performance.  
Next, the discrepancy in relative position can occur when the signal is transmitted from a moving transmitter or when the receiver is moving relative to the transmitter. In such cases, the received signal may arrive at different times and from different directions, causing errors in the estimation of the transmitter location. This can significantly affect the localization performance, as it can result in inaccurate range and angle measurements, which are used to determine the location of the transmitter.
## 2 Related Work
### SAGIN Architecture  
SAGIN refers to the integration and synergy of systems from multiple domains--space, air, and ground--to form a wide coverage area and a high-speed network that enables more efficient communication and data exchange. It is a proposed solution to address the growing demand for enhanced communication and information-sharing capabilities. SAGIN requires the use of various technical means, such as satellite communications, unmanned aerial vehicles, and ground sensors, to enable collaborative operations and data sharing between different platforms. By sharing data and information in real time, situational awareness can be improved, decision-making can be optimized, and mission effectiveness can be enhanced.  
Over time, the SAGIN architecture has rapidly evolved, and various projects, such as the global information grid (GIG), have been proposed and widely deployed [16]. [PERSON] et al. [3] described the communication network design and resource allocation algorithm  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Literatures** & **Game** & **ML** & **RO** & **SO** & **DRO** & **Mean Risk-Aware** & **SAGIN** \\ \hline [12] & ✓ & & & & & & & \\ [4,7] & & & ✓ & & & & & \\ [13] & & ✓ & & & & ✓ & & \\ [11] & & & & & ✓ & & & \\ [6] & & & & ✓ & & & ✓ & \\ [14,15] & & & & & & ✓ & & ✓ \\ [5] & & & & & ✓ & & ✓ \\ RaDROO & & & & & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical methods comparison in the relevant literatures.  
of SAGIN in a high-dimensional network environment. Likewise, SAGIN can be invoked in MEC environments and exhibits excellent performance in unique environments, including deserts and disaster scenarios. [PERSON] et al. [17] considered the fine-grained offloading problem and caching problem and proposed the SAGIN framework, which supports edge computing.  
The impact of SAGIN on computing task offloading is multifaceted and can be summarized as follows:  
* Cross-platform efficiency: Through the integration and collaborative operations achieved by SAGIN, different tasks can be offloaded and migrated between different platforms, thereby improving the efficiency of task execution.
* Increased flexibility through edge-cloud collaboration: In the SAGIN network, tasks can be dynamically allocated and scheduled, allowing them to be offloaded to the most suitable platforms based on their computational requirements. This improves resource utilization.  
Overall, SAGIN has a positive impact on the field of task offloading, as it can improve task execution efficiency, safety, flexibility, and coverage. An ECN built on the SAGIN framework will be more able to fully exploit its capabilities by consolidating available resources to provide computing resources for tasks within this network environment.
## 2 Literature Review
### Enhancing the User Interaction Experiences in Heritage Site  
One of the most interactive experiences for heritage sites can be via virtual tours. Three-dimensional models created from laser scanning and photogrammetry can be integrated into virtual tour platforms, allowing users to explore Ad Deir in a highly immersive manner. Visitors can navigate through the site from the comfort of their homes, experiencing the monument from different perspectives and angles [15]. Also, heritage BIM data can enhance virtual tours by providing interactive elements within the 3D model. Users can click on specific architectural features or historical artifacts to access additional information, such as historical context, construction techniques, or conservation efforts [37]. This interactive approach fosters engagement and deepens users' understanding of the site's significance. Moreover, virtual tours can incorporate guided narratives or audiovisual presentations, offering curated experiences led by experts or historians [38]. These narratives can provide insights into the historical context of Ad Deir, highlighting key architectural features, cultural significance, and stories behind the monument's creation.  
Three-dimensional models and heritage BIM data can serve as valuable educational resources for students and educators [39]. Schools and universities can integrate these digital assets into their curricula, allowing students to engage with cultural heritage in a dynamic and interactive manner [40]. Educational modules can cover topics such as archaeology, architecture, history, and conservation, providing a holistic understanding of Ad Deir and its place in history. Moreover, the educational applications can enable students to interact with 3D models of Ad Deir, encouraging hands-on exploration and experimentation. Virtual labs or workshops can simulate archaeological excavations, architectural analysis, and conservation practices, empowering students to become active participants in heritage preservation efforts. Furthermore, gamified experiences can make learning about cultural heritage more engaging and enjoyable. Educational applications can incorporate game elements, challenges, and quizzes based on Ad Deir's history and architecture. By gamifying the learning process, these applications can capture the attention of younger audiences and instil a sense of curiosity and appreciation for cultural heritage [41].
## 4 Campi Alto di Norcia: Description of the Historical City Centre
### General Features and Structural Aggregates (SAs)  
The building heritage of Campi Alto di Norcia covers an area of approximately 3.5 ha with 32 masonry aggregates (SAs) resulting in a total of 75 structural units (SUs), including three churches (i.e., Madonna della Piazza, Sant'Andrea and Santa Maria delle Grazie), fully destroyed by the 2016 earthquake sequence (Figure 2). According to the site morphology, SAs develop on three different levels, perpendicular to the slope of the hill on which the settlement is located. The first level has the entrance in correspondence of the downstream road and the top floor at the level of the upstream road.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Earthquake Date** **(yyyy-mm-dd)** & **Origin Time** **(UTC)** & **Magnitude** **(Mw)** & **Ejecture** **Area** & **Hypocentral** **Depth (Km)** & **Latitude** **(N\({}^{\circ}\))** & **Longitude** **(E\({}^{\circ}\))** \\ \hline
2016 08 24 & 01:36:32 & 6.0 & Accumoli (L) & 8.0 & 42.70 & 13.23 \\
2016 08 24 & 02:33:28 & 5.4 & Norcia (U) & 7.5 & 42.79 & 13.15 \\
2016 10 26 & 17:10:36 & 5.4 & Visso (M) & 8.7 & 42.88 & 13.13 \\
2016 10 26 & 19:18:05 & 5.9 & Visso (M) & 8.0 & 42.91 & 13.13 \\
2016 10 30 & 06:40:17 & 6.5 & Norcia (U) & 9.0 & 42.83 & 13.11 \\
2017 01 18 & 10:14:09 & 5.5 & Montereale (A) & 10.0 & 42.53 & 13.28 \\
2017 01 18 & 10:25:23 & 5.4 & Montereale (A) & 9.0 & 42.49 & 13.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the main seismic events of the earthquake swarm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Earthquake Date** **(yyyy-mm-dd)** & **Origin Time** **(UTC)** & **Magnitude** **(Mw)** & **East–West** **(pga)** & **North–South** **(pga)** & **Up–Down** (pga)** \\ \hline
2016 10 26 & 17:10:36 & 5.4 & 0.713 & 0.341 & \(-\)0.429 \\
2016 10 26 & 19:18:06 & 5.9 & 0.644 & 0.308 & 0.489 \\
2016 10 31 & 07:05:45 & 4.0 & 0.180 & 0.087 & \(-\)0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Max PGA (% g) recorded by the accelerogram of the Campi Station in the three main directions for different seismic events. The minus signs of the last column highlight the upward seismic acceleration.  
The different levels and SAs are then connected through an internal organized system of staircases. The construction heritage of Campi Alto di Norcia is made up of both isolated masonry buildings and SAs arranged in rows, following the topography of the land. The gross building total volume and area are respectively about 15,000 m\({}^{3}\) and 3065 m\({}^{2}\).  
The SAs differ in terms of number, shape and height of SUs constituting them. In volume, isolated buildings cover the 29% of the whole stock (resulting in a volume of about 4243 m\({}^{3}\)), while external (end position) and internal (in between) SUs cover, respectively, the 44% and 27% of whole constructions (i.e., 6577 and 4035 m\({}^{3}\)). The number and height of the floors, the maintenance conditions and the materials adopted (both for vertical and horizontal structures) are different for each structural unit.
## 4 The Proposed DC-CAAE Method
### The Basic Principles of Deep Clustering  
The hidden features extracted from the CAAE model represent good key features for expressing network traffic data. To further optimize the encoder parameters, we propose a new optimization goal using the _joint analysis_ method. For k-means clustering, the core  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Layer** & **Encoder** & **Generator** & **Discriminator** \\ \hline Input & Input & Input & Input \\ \hline Layer 1 & Conv2d (1, 8, 3, 2, 1) & Linear (100, 1000) & Linear (100, 1000) \\ \hline Layer 2 & Conv2d (8, 16, 3, 2, 1) & Linear (1000, 288) & Linear (1000, 1000) \\ \hline Layer 3 & Conv2d (16, 32, 3, 2, 0) & ConvTranspose2d (32, 16, 3, 2, 0) & Linear (1000, 1) \\ \hline Layer 4 & Linear (288, 1000) & ConvTranspose2d (16, 8, 3, 2, 1) & None \\ \hline Layer 5 & Linear (1000, 100) & ConvTranspose2d (8, 1, 3, 2, 1) & None \\ \hline Output & Output & Output & Output \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of the CAAE model.  
Figure 5: The framework of the deep clustering algorithm.  
idea is to make the points within the cluster as close to the center point \(C\) as possible. Its clustering objectives can be expressed as:  
\[L_{C}=\sum_{i=1}^{n}\lVert E(\mathbf{x_{i}})-C_{i}\rVert^{2}, \tag{6}\]  
where \(i\) represents the cluster category. To ensure the fairness of the experiment in this paper, the clustering method uniformly used the k-means clustering method based on the Euclidean distance. The loss function of the whole DC model consists of the error of the autoencoder in the CAAE and the error of the k-means clustering. The joint loss function is:  
\[\begin{split} L_{joint}=&(1-\lambda)L_{BCE}+ \lambda L_{C}\\ =&(1-\lambda)\left(-(\mathbf{x}^{\prime}\log\mathbf{x}+(1- \mathbf{x}^{\prime})\log\left(1-\mathbf{x}\right))\right)\\ +&\lambda\left(\sum_{i=1}^{n}\lVert E(\mathbf{x}_{i})-C _{i}\rVert^{2}\right),\\ &\text{s.t. }\lambda\in[0,1]\end{split} \tag{7}\]  
when \(\lambda=1\), the DC model does not calculate the autoencoder loss, it only uses the cluster loss to adjust the neural network parameters. When \(\lambda=0\), the DC model does not calculate the cluster loss, but only uses the autoencoder loss to optimize the network parameters. When \(0<\lambda<1\), the deep clustering uses both the autoencoder loss and cluster loss to constrain the convolution neural network parameters, which ensures the similarity between the input and output layers of the autoencoder while keeping the clustering's resulting clusters relatively close.