esa-sceva/satcom-chunk-collection huggingface dataset:
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 2 Overview of Space and Communications Systems  
### Satellites Evolution  
In the 1950s, the Soviet Union launched Sputnik I, the first artificial satellite that orbited the Earth. This milestone marked the beginning of a competition between two powerful countries that had, as one of its consequences, an accelerated technological development in aerospace sciences. Satellites created in the decades after the beginning of this competition were designed for very specific missions, and each development had its own subsystems--energy, command and data handling, attitude control, etc.--which allowed the particular requirements of a given project to be met. Such a design methodology involved an extremely expensive process due to the constant iterations necessary to create a new device, and the difficulties in reusing previous versions and designs.  
One early shift occurred when, due to the use of modular systems, the main bus was designed to be flexible and reconfigurable according to the goal of each mission. As a result of reducing the costs in developing one unit, constellations of these spacecraft began to be feasibly designed and used by those countries and companies that could afford the still enormous cost of development. Depending on the configuration, these formations could increase the instantaneous global coverage and reducing revisiting times, among others benefits.  
A large number of satellites now dot the skies for diverse applications such as navigation, imaging, meteorology, and communications. Some of the most significant applications are the following:  
* Positioning systems: constellation of satellites located in medium height orbits (approximately 20,000 km) that make it possible to determine the position of an object on the Earth's surface in a given coordinate system. There are several systems belonging to different countries, namely: GPS (USA--24 satellites), global orbiting navigation satellite system (GLONASS) (Russia--24 operational satellites), GALILEO (European Union--24 satellites), BEIDOU (China--17 operational satellites).
* Earth observation: Several satellites with a wide variety of cameras in different spectral bands have been sent to space. Defense and security, cartography, and meteorology are some of the disciplines that have benefited from these types of missions.
* Communications: Satellite systems that provide voice services, satellite television service, and narrowband/broadband connectivity through standalone satellites or constellations.  
The Union of Concerned Scientists (UCS) maintains a count of operational satellites orbiting the Earth. In Figure 2 we illustrate the distribution, according to the country of origin and the type of orbit, for the 1957 active satellites reported up to 30 th November 2018 [15].
## 4 Results and Discussion
### Backscattering Simulations
This may indicate that the modified backscattering model has a higher sensitivity to relatively sparse vegetation conditions. The result demonstrated that the use of vegetation cover fractions could separate the scattering mechanisms of the fractions of vegetation cover and bare soil cover, and the accuracy of the backscattering modeling was significantly improved. The problem of sparse vegetation cover and the strong effect from bare soil patches could be minimized by implementing the modified model. To understand the behavior of the model under different vegetation conditions, the results achieved terms of prediction accuracy (RMSE) and the goodness of fit (\(R^{2}\)) are reported in Table 2. As observed in Table 2, backscattering modeled by the modified model demonstrated a significant improvement over the basic model under relatively sparse vegetation conditions, possibly suggesting that the modified model had a higher sensitivity to relatively sparse vegetation conditions. However, the accuracy did not vary significantly under the relatively density vegetation conditions. It may be because that the backscattering contribution of bare soil surfaces was a very small component in the total backscattering under the relatively density vegetation conditions. Figure 2: Scatterplot illustrating the relationship between the measured backscattering from images and predicted backscattering values from the vegetation backscattering model described in Section 3.2.1 for **(a)** HH polarization and **(b)** VV polarization. Figure 3: Scatterplot illustrating the relationship between the measured backscattering from the images and predicted backscattering values from the modified vegetation backscattering model described in Section 3.2.2 for (**a**) HH polarization and (**b**) VV polarization. To demonstrate the spatial performance of the proposed method in this study, the error of model simulation was defined as the absolute difference between the modeled and the Radarsat-2 SAR observed backscattering (dB):  
\[error=\left|\sigma^{o}_{model}-\sigma^{o}_{SAR}\right| \tag{13}\]  
where \(\sigma^{o}_{model}\) and \(\sigma^{o}_{SAR}\) are the backscattering of the modeled and the observed backscattering, respectively. Figure 4 shows the error map on 4 August 2013 for HH polarization. The areas with layovers and shadows were masked out in the process. Model errors for most of the study area were lower than 2 dB, indicating that the model simulation worked well in these areas. However, in the areas with high relief and steep slopes, model errors could be higher than 6 dB, suggesting that soil moisture estimation by model inversion was not reliable in these areas.
## 2 Research Methodology
### Extraction Criteria and Primary Results  
The chosen studies were clustered after a three-pass approach, which was extract, classify, and store. Table 1 highlights the criteria for the exclusion and inclusion process.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Exclusion Criteria** & **Inclusion Criteria** \\ \hline Publications came from predatory journals or conferences. & \multirow{2}{*}{Journals’ and conferences’ credibility.} \\ Published before 2012. & & Published after 2012. \\ Studies not written in English. & \multirow{2}{*}{Studies written in English.} \\ The studies did not address smart card & & The studies directly addressed smart card \\ RFID security. & RFID security. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Exclusion and inclusion criteria.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Database** & **All References** & **After 2012** & **After 2017** &
\begin{tabular}{l} **English Only** \\ **and after 2017** \\ \end{tabular} \\ \hline Google Scholar & 877 & 556 & 217 & 186 \\ Scopus & 52 & 31 & 13 & 13 \\ ACM library & 18 & 14 & 6 & 6 \\ IEEE Xplore & 1 & 1 & 1 & 1 \\ SpringerLink & 147 & 99 & 52 & 52 \\ Total & 1095 & 701 & 289 & 258 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Keyword 1 results.  
The number of results found after a full extraction was 7484 based on the date of publication (2017-2022). Only 2138 out of 7484, which represents 28.56% of the existing references, were investigated in this SLR:  
* Another metric was taken into consideration, which was the language utilized by the authors. Thus, only results written in English were included. Due to this metric, the number of explored studies decreased from 2138 to 1590, which represents 74.36% of the results we obtained after the first extraction.
* Checking for duplicates in the 1590 studies included in the SLR should be performed to avoid a study being double-analyzed or -checked. As a result, the number of studies decreased from 1590 to 915 (57.54%).
* Last, but not least, an extraction built on the title, abstract, and scope of research was deemed for the sake of organizing the work. Further, to further scrutinize the existent implementations that had a quite similar vision as ours, 202 out of the 915 (22.07%) were selected to be utilized to accomplish the intended SLR (first full extraction). The distribution of these studies by year is shown in Figure 1 and Table 4.
## 3 Neighbor Verification through Low-Power Beacons
### Attacker Model  
Before delving into the details of the proposed scheme, we specify the adversary model as follows.  
* The attacker is stationary and located on the roadside [6; 11], which will be the most frequent attack scene. The stationary attacker is identified in [PERSON] et al. [6] as the most threatening attack for its low complexity of implementation and execution.
* Before transmission, the attacker can tweak the data obtained from sensors or from the in-vehicle networks such as the controller area network (CAN) bus.
* The attacker has a valid security credential [11]. So, the attacker can correctly encode invalid position data.
* The attacker can increase the Tx power or use directional transmission to affect farther vehicles.  
Note that the neighbor check using whispers is focused on the safety-related events that take place in the local neighborhood of each vehicle. To be precise, the local neighborhood is the two-hop range of the whispers (\(=2\ d_{W}\)). Thus the check completely prevents the attackers from placing \"ghost vehicles\" unless the attacker comes into the two-hop distance of the whispers. Then for the attackers that are indeed within the two-hop distance, we can develop an extension of the whisper scheme to further remove the attacks. For most safety-critical events, the attack prevention within \(2\ d_{W}\) will be sufficient, as they take place in close proximity, e.g., forward collision. There will be safety applications where vehicles need to heed messages from long distances, but they are beyond the scope of this paper  
Figure 1: Low-power beacon checking setup.
# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 4 Experiments
### Qualitative Evaluation  
Figure 6 illustrates the qualitative results of our and other methods on the synthetic datasets. Our method outperformed the other methods in areas such as detail (the area surrounded by the short green line), glass reflections (the area surrounded by the short blue lines), and translucent material objects (the area surrounded by the short yellow lines). Furthermore, as illustrated in Figure 7, our model achieved impressive results even in real-world scenarios, despite only being trained on synthetic data.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Metrics**} \\ \cline{3-7}  & & **MAE (\(\downarrow\))** & **RMSE (\(\downarrow\))** & **>1 (\(\downarrow\))** & **>3 (\(\downarrow\))** & **>5 (\(\downarrow\))** \\ \hline \multirow{5}{*}{OmniThings} & Crown360 [27] & 2.566 & 4.900 & 48.051 & 21.648 & 13.410 \\  & OmniMVS-ft [8] & 3.330 & 6.949 & 43.811 & 24.603 & 17.512 \\  & CasOmniMVS & **1.156** & **3.016** & **23.355** & **7.664** & **4.363** \\  & CasOmniMVS-ft & 1.516 & 3.610 & 31.262 & 11.309 & 6.373 \\ \hline \multirow{5}{*}{OmniHouse} & Crown360 [27] & 1.399 & 2.921 & 29.947 & 8.905 & 5.036 \\  & OmniMVS-ft [8] & 0.618 & 1.626 & 9.698 & 3.436 & 2.055 \\  & CasOmniMVS & 0.793 & 1.777 & 15.178 & 4.490 & 2.321 \\  & CasOmniMVS-ft & **0.451** & **1.075** & **7.164** & **1.841** & **1.029** \\ \hline \multirow{5}{*}{Sunny} & Crown360 [27] & 1.352 & 2.896 & 33.407 & 9.253 & 4.971 \\  & OmniMVS-ft [8] & 0.597 & 2.453 & 7.766 & 3.700 & 2.504 \\ \cline{1-1}  & CasOmniMVS & 1.216 & 3.205 & 23.269 & 8.469 & 4.808 \\ \cline{1-1}  & **CasOmniMVS-ft** & **0.446** & **1.570** & **6.807** & **2.191** & **1.277** \\ \hline \end{tabular}
\end{table}
Table 4: Quantitative results (index) on the synthetic datasets. The qualifier ‘\(>\)\(n^{\prime}\) refers to the pixel ratio (%) whose error is larger than n. The best results are in bold. ‘\(\downarrow\)’ indicates that the smaller the value, the better.  
Figure 6: Qualitative results on the synthetic datasets. Visualizing the inverse of the depth, with a minimum depth of 1.65 m.
# Routing Strategies for Isochronal-Evolution Random Matching Network
## 4 Two Routing Strategies
### Planning Algorithm for the LDPMH  
A binary search tree for the planning LDPMH (which is abbreviated to LDPMH-BST) is shown in Figure 2. The node of an LDPMH-BST represents the vertex of an IERMN. In an LDPMH-BST, a parent's left child is itself, and its right child is the vertex to which it is connected during the corresponding slot. The edge between a parent and its left child indicates that this vertex has decided to refuse to send packets. The edge between a parent and its right child indicates that this vertex has decided to send packets.  
The \(\lambda\)-th level of an LDPMH-BST is denoted by \(LDPMH-BST(\lambda)\); the number of the nodes in \(LDPMH-BST(\lambda)\) is denoted by \(|LDPMH-BST(\lambda)|\); the \(\xi\)-th node from the left in \(LDPMH-BST(\lambda)\) is denoted by \(LDPMH-BST(\lambda,\xi)\); and the root of the LDPMH-BST is denoted by \(LDPMH-BST(0,1)\). The temporal length of the path from the vertex that is represented by \(LDPMH-BST(0,1)\) to each vertex that is represented by the node in \(LDPMH-BST(\lambda)\) is \(\lambda\) slots. The planning algorithm for the LDPMH based on the building of an LDPMH-BST is shown in Algorithm 2.  
Once the number of the destination vertex, \(j\), has been found in \(\mathbf{X}\) by Steps 5-7, the construction of the LDPMH-BST stops immediately, according to Steps 12-14. Therefore, the slot that is represented by the level at which the leaves of the LDPMH-BST are is the first to reach \(v_{j}\). Then, at least one LDP is obtained by Steps 17-18. After that, the LDPMH can be selected from all of the LDPs.
# 3-D Integral Formulation for Thin Electromagnetic Shells Coupled with an External Circuit
## 5 Numerical Examples
### Validation Through an Academic Example  
In the first example, a thin magnetic conductive disk is considered with the circular conductor fed by a voltage of 1V and a frequency of 10 Hz (Figure 3).  
The device parameters are presented as below:  
* Thin disk:
* Electrical conductivity \(\sigma_{disk}=6\times 10^{7}\) S/m
* Permeability \(\mu_{r}=200\)
* R = 1 m, \(e=50\) mm
* The skin depth \(\delta=1.45\) mm is smaller than the disk's thickness  
* Electrical conductivity \(\sigma\) conductor = \(5.79\times 107\) S/m
* h = R/4 = 0.25 m  
This academic example is solved by three numerical methods, where the first one is the axisymmetric FEM, the second one is a shell element formulation implemented in 3D FEM method [5] and the last one is the proposed integral method with the surface elements. In order to valid our method and to compare different approaches, we mainly focus on the computed current in the conductor and the magnetic field in the air region close to the device (calculated on the path AB, for A (0.25; 0; 0.1) and B (0.25; 0; 0.25)).  
Let us note that the problem must be meshed very finely to have an accurate result with the FEM 3D (Table 1 and Figure 4) because of the high variations of the fields around the conductor. The current values greatly vary according to the number of the elements.  
If the axisymmetric FEM is considered as our reference, the coupling integral method leads to an error of 0.1% (Table 1). Our method leads to more accurate results than the same shell element formulation but considering the air region treated with the 3D FEM.  
Figure 3: Thin magnetic conductive disk and conductor.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
## 4 Discussion  
### Z-Resolution  
In this study, the OSSs were oriented vertically at 270\({}^{\circ}\) and the AL measurements represented the accuracy and precision in the \(Z\)- (or vertical) axis. The greatest magnitude of error and CoV values were observed in AL measurements, and the proportion of splints that were within the clinically acceptable range was considerably lower when compared with the ICW and IMW. This indicates that the greatest inaccuracy and variation was seen in the \(Z\)-axis, which is similar to the findings from other studies that also reported on variation in the \(Z\)-direction being more pronounced than in the \(X\)- or \(Y\)-direction [17]. Another study conducted by [PERSON] et al. demonstrated that orthodontic aligners printed vertically at 90\({}^{\circ}\) to the build platform were the most accurate, with this being the only group that printed within the clinically acceptable tolerance ranges in comparison to the 7 other orientation groups in this study [24].  
One possible source of the greatest variation in the \(Z\)-axis is the motorised stepper motor controlling the print platform height of the 3D printer, which elevates the build plate as each layer is laid. Given that the greatest inaccuracy and variation were seen in the \(Z\)-direction, it may be prudent to advise that the splints are printed flat relative to  
Figure 4: Scatter plots to show the mean value and the spread of the data collected for (**a**) intercanine width (ICW), (**b**) intercolor width (IMW), and (**c**) arch length (AL) of the upper arch.  
the printer base plate, ensuring that the supports are clear of the fit surface, in order to minimise the effect of the \(Z\)-axis variation found.
## 3 Results
### ISL Range Bias Estimation against Weighting Approach and Ground Station Sets
#### 3.2.1 Orbit Estimation Errors
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Scenario Name** & \
\begin{tabular}{c} **ISL Range Bias** \\ **Value [cm]** \\ \end{tabular} &
\begin{tabular}{c} **Weighting** \\ **Method** \\ \end{tabular} & **Station Set** \\ \hline (2.1) & 44G-0.0-F & 0.0 & Forstner & 44 (global) \\ (2.2) & 44G-0.0-N & 0.0 & Nominal & 44 (global) \\ (2.3) & 44G-1.0-F & 1.0 & Forstner & 44 (global) \\ (2.4) & 44G-1.0-N & 1.0 & Nominal & 44 (global) \\ (2.5) & 16G-0.0-F & 0.0 & Forstner & 16 (global) \\ (2.6) & 16G-0.0-N & 0.0 & Nominal & 16 (global) \\ (2.7) & 16G-1.0-F & 1.0 & Forstner & 16 (global) \\ (2.8) & 16G-1.0-N & 1.0 & Nominal & 16 (global) \\ (2.9) & 16E-0.0-F & 0.0 & [PERSON] & 16 (Europe) \\ (2.10) & 16E-0.0-N & 0.0 & Nominal & 16 (Europe) \\ (2.11) & 16E-1.0-F & 1.0 & Forstner & 16 (Europe) \\ (2.12) & 16E-1.0-N & 1.0 & Nominal & 16 (Europe) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of simulation scenarios used for the ISL range bias estimation check. Thus, relatively small changes in e.g., measurement accuracy might impact the orbit estimation error.
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 3 Methodology
### Label Attention Extractor  
Building upon the achievements of [PERSON] [31] and [PERSON] [8] et al. in capturing labeled word vector features, we introduce an attention mechanism to create a labeled attention extractor. This extractor enhances the focus on both intention and slot representations. Specifically, the two fully connected layer weight matrices are initialized as weight matrices for intentional and slot filling representations, respectively, as follows:  
\[W_{s}\in M^{d\times n^{S}} \tag{1}\]  
\[W_{I}\in M^{d\times n^{I}} \tag{2}\]  
where \(W_{s}\) denotes the weight matrix of the slot filling representation, \(W_{I}\) denotes the weight matrix of the intent representation, \(M\) denotes matrix, \(n^{I}\) denotes the number of labels for the intent, \(n^{S}\) denotes the number of labels for the slots, and \(d\) denotes the hidden layer dimension.  
First, the output vectors of the extractor are initialized through matrix multiplication and then activated via the Leaky ReLU activation function to obtain the label importance distributions of the intent and slot representations, the formulas of which are as follows:  
\[IntentScore=f(W_{I}\cdot v_{I}) \tag{3}\]  
\[SlotScore=f(W_{S}\cdot v_{S}) \tag{4}\]  
where \(v_{I}\) denotes the intent vector features, \(v_{S}\) denotes the slot vector features, \(\cdot\) denotes matrix multiplication, and \(f(\cdot)\) denotes the activation function of Leaky ReLU, which aims to alleviate the neuron death problem of zero-value gradient by introducing a small positive slope that allows a non-zero output for negative input values. \(IntentScore\) and \(SlotScore\) denote the labeled importance distribution of intents and slots, respectively.  
Finally, the label importance distributions are weighted with the corresponding original fully connected weight matrices to obtain the final intent representation and slot representation, the formulas of which are as follows:  
\[F_{I}=IntentScore+W_{I} \tag{5}\]  
\[F_{S}=SlotScore+W_{S} \tag{6}\]  
where \(F_{I}\) denotes the intent representation and \(F_{S}\) denotes the slot representation.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 1 Introduction  
The _Citrus_ species (Rutaceae) is the largest-spread fruit crop in the world, grown in more than 140 countries, of which China, Brazil, and the USA are the top producers [1, 2]. They are widely used in the food sector, consumed as a fresh product for their strong fragrance, and in pharmaceutical, medicinal, and cosmetic fields thanks to the beneficial properties of their biologically active constituents [3, 4]. In fact, multiple effects such as antioxidant, anti-inflammatory, anticoagulant, and anticancer qualities have been documented. Even essential oils (EOs), usually obtained from the citrus peel, being rich in secondary metabolites [5], are widely used in the food industry for conservation purposes or as additives to enhance the aroma [6], as well as in aromatherapy [7].  
[PERSON]'s hand is the common name of _C. media_ var. _sarcodactylus_ (Siebold [PERSON]) Swingle (Rutaceae family). Native to northwestern India, it is a small tree with long and irregular branches full of thorns, known to produce peculiarly shaped fruits(up to 30 cm long), segmented into finger-like protrusions, characterized by a white, non-bitter, pulp-free mesocarp (albedo). Externally, the yellowish-orange exocarp (flavedo) is highly fragrant when ripe [8]. Both parts are edible, and are used to prepare desserts, drinks, candies, and sauces [9]. Recently, [PERSON]'s hand fruits have been frequently used in many Japanese restaurants in combination with peaches to serve a fruit salad, and more occasionally as ingredients to formulate crisps and cookies. In general, in the eastern countries where they are known as fingered cirrons or as Fo Shou, the fruit is traditionally used in folk medicine. For example, in China, [PERSON]'s hand is appreciated as a tonic, antispasmodic, antiemetic, and expectorant, while in Taiwan it is used for the treatment of stomachache, headache, edema, rheumatism, infectious hepatitis, and arthritis. It is also taken as an adjuvant in the treatment of respiratory tract infections, asthma, and hypertension [10; 11; 12]. Some biological activities, including anti-inflammatory, antimicrobial, antioxidant, cytotoxic, and hypoglycemic effects of [PERSON]'s hand extracts or EOs obtained from different parts of the plant (fruits, leaves, peels, roots, stems) have been reported [10; 13; 14; 15; 16; 17]. Likewise, its chemical composition has been indicated as showing the presence of classes of compounds such as coumarins, flavonoids, phenolic acids, terpenoids, and steroids [10; 18].  
In our study, we chemically investigated exocarp EO and hydrolate (Hy), as well as exocarp and mesocarp methanol extracts (EEX and MEX), never characterized up to now, with the aim to deepening the knowledge of the secondary metabolites (volatiles and not volatiles) of [PERSON]'s hand, as well as its bioactive potential.