esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 2 Materials and Methods
### The Fixed Solution
We can calculate \(\mathbf{M}_{\text{DD}}\) based on the transition matrix \(\mathbf{D}\) and the part corresponding to the SD ambiguity in \(\hat{\mathbf{P}}\), as Equation (29) shows:  
\[\mathbf{M}_{\text{DD}}=\mathbf{D}\hat{\mathbf{P}}_{\text{xx}}\mathbf{D}^{\text{T}}. \tag{29}\]  
When it comes to \(\mathbf{M}_{\text{INS,DD}}\), let us take \(\mathbf{v}^{\text{n}}\) as an example. The covariance matrix that reflects the relationship between \(\mathbf{v}^{\text{n}}\) and the SD ambiguity vector \(\mathbf{\kappa}\) can be formulated as Equation (30) shows:  
\[\mathbf{M}_{\theta\mathbf{\kappa}}=E[(\hat{\mathbf{v}}^{\text{n}}-E(\hat{\mathbf{v}}^{\text{ n}}))(\hat{\mathbf{\kappa}}-E(\hat{\mathbf{\kappa}}))^{\text{T}}], \tag{30}\]  
where the notation \(E\) denotes a function to calculate the mean value. \(\hat{\mathbf{v}}^{\text{n}}\) is the posterior estimation corresponding with the corrected INS velocity, which helps us obtain Equation (31):  
\[\hat{\mathbf{v}}^{\text{n}}-E(\hat{\mathbf{v}}^{\text{n}}) =\hat{\mathbf{v}}^{\text{n}}_{\text{est}}-\delta\mathbf{v}^{\text{n}}-E (\hat{\mathbf{v}}^{\text{n}}_{\text{est}}-\delta\mathbf{v}^{\text{n}}) \tag{31}\] \[=-\delta\mathbf{v}^{\text{n}}.\]  
The covariance matrix that reflects the relationship between \(\delta\mathbf{v}^{\text{n}}\) and the SD ambiguity \(\mathbf{\kappa}\) can be formulated as Equation (32) shows:  
\[\hat{\mathbf{P}}_{\theta\mathbf{\kappa}} =E[(\delta\hat{\mathbf{v}}^{\text{n}}-E(\delta\hat{\mathbf{v}}^{\text{n}} ))(\hat{\mathbf{\kappa}}-E(\hat{\mathbf{\kappa}}))^{\text{T}}] \tag{32}\] \[=E[(\delta\hat{\mathbf{v}}^{\text{n}})(\hat{\mathbf{\kappa}}-E(\hat{\mathbf{ \kappa}}))^{\text{T}}]\] \[=-\mathbf{M}_{\theta\mathbf{\kappa}}.\]  
Equation (32) builds the connection between \(\mathbf{M}_{\theta\mathbf{\kappa}}\) and \(\hat{\mathbf{P}}\). Readers can understand this equation in the following way. Assume we directly set \(\mathbf{M}_{\theta\mathbf{\kappa}}\) as \(\hat{\mathbf{P}}_{\theta\mathbf{\kappa}}\). The SD to DD transition will not change the positive or negative nature of the value. We can calculate the fixed correction for velocity following Equation (27). As we neglect the inconsistency between \(\mathbf{M}_{\theta\mathbf{\kappa}}\) and \(\hat{\mathbf{P}}_{\theta\mathbf{\kappa}}\), we should compensate for the inconsistency when we calculate the fixed solution based on the float solution. Specifically, we can not directly subtract the fixed correction from the float velocity solution, as Equation (27) shows. Instead, we should add the fixed correction to the float velocity solution to obtain the fixed velocity solution.
## 2 CSES Satellite  
The China Seismo-Electromagnetic Satellite (CSES, also called ZH-1) was launched successfully on 2 February 2018. This satellite aims to preliminarily explore the characteristics and mechanisms of seismogenic ionospheric responses during seismic activities based on real-time in-situ monitoring of irregularities in the state of electromagnetic environments in space [30]. The CSES is a sun-synchronous satellite orbiting at a height of 500 km with a descending node of 14:00 local time (LT) and its revisiting period is 5 days. Eight scientific payloads are onboard the CSES: an electric field detector (EFD), a search-coil magnetometer (SCM), a high-precision magnetometer (HPM), a plasma analyzer package (PAP), a Langmuir probe (LAP), an energetic particle detector (HEPP), a GNSS occupation receiver (GNSS), and a three-band beacon (TBB). More details on these payloads can be obtained by referring to the corresponding literatures [31; 32].  
Concerning the scientific payloads of the LAP and the PAP onboard the CSES, the LAP predominantly measures the in-situ electron density and its temperature and the PAP measures ion densities (H\({}^{+}\), He\({}^{+}\), and O\({}^{+}\)) and their flying speeds. Two kinds of utilized data sampling working modes are employed here: a survey mode with a low sampling rate and a burst mode with a high rate. The former is working all around the world except areas where the main seismic belts and China lie, when the later measuring mode works. Corresponding to these working modes, data sampling rates are 1 s and 0.5 s for the PAP and 3 s and 1.5 s for the LAP, respectively [23].
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 3 Our Proposed Approach
### Vehicle Position on the Map  
After the implementation of the closest point map-matching method, we estimate the final position of the vehicle that is the position relative to the median lane. Figure 6 clearly illustrates the relation between the center vehicle and the median lane. The estimate distance of the center of vehicle relative to the median lane is formulated as follows:  
\[\mathbf{d}_{m}=\frac{LaneWidth_{m}}{(x_{2}-x_{1})_{px}}\ \mathbf{d}_{px} \tag{5}\]  
where \(\mathbf{d}_{m}\) is the estimate distance of the center vehicle relative to the median lane in _meter_, \(LaneWidth_{m}\) is the width of the lane in _meter_, \((x_{2}-x_{1})_{px}\) is the width of the lane in _pixel_, and \(\mathbf{d}_{px}\) is the estimate distance of the center vehicle relative to the median lane in _pixel_ (Figure 6).  
Using the estimated distance between the center of vehicle relative to the center lane, the lane-level localization is performed as shown Figure 7.  
Figure 6: Description of how the deviation distance between the center of vehicle and middle lane is obtained.  
Figure 7: Illustration of estimating vehicle’s position based on map-matching.
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## Appendix A Weierstrass Elliptic Curve
### Jacobi Quartic Elliptic Curve  
An elliptic curve in the Jacobi quartic form has the parameter \(a\) and coordinates \(x,y\) that satisfy the following equation:  
\[y^{2}=x^{4}+2 ax^{2}+1 \tag{11}\]  
The projective coordinates represent \(x,y\) as \(X,Y,Z\) to satisfy the following equations:  
\[x=X/Z\quad\text{and}\quad y=Y/Z^{2}\]  
The equivalence class containing \((X,Y,Z)\) is  
\[(X:Y:Z)=(\lambda X,\lambda^{2}Y,\  
\(A_{2}=X_{2}^{2},\quad C_{2}=Z_{2}^{2},\quad D_{2}=A_{2}+C_{2},\quad B_{2}=(X_{2}+ Z_{2})^{2}-D_{2},\)  
\(E_{2}=B_{2}+Y_{2},\quad A_{1}=X_{1}^{2},\quad C_{1}=Z_{1}^{2},\quad D_{1}=A_{1}+C_ {1},\)  
\(B_{1}=(X_{1}+Z_{1})^{2}-D_{1},\quad E_{1}=B_{1}+Y_{1},\quad H=A_{1}A_{2},\)  
\(I=B_{1}B_{2},\quad J=C_{1}C_{2},\quad K=Y_{1}Y_{2},\quad F=J+H,\quad F=2I.\)  
This formula requires 8 field multiplications and 6 field squarings. We can identify vulnerabilities of Type 1 and Type 2 during the computations of \(P_{1}+P_{2}\) for \(P_{1}\
eq P_{2}\) and \(P_{1}=P_{2}\).  
**Type 1 vulnerability**: Let us consider the computation \(B=[Y_{1}]\cdot[Y_{2}]\). In this formula, it is computed as \([Y_{1}]\cdot[Y_{1}]\) for \(P_{1}=P_{2}\), whereas it is computed as \([Y_{1}]\cdot[Y_{2}]\) for \(P_{1}\
eq P_{2}\). Similarly, in \(D=[Z_{1}]\cdot[Z_{2}]\) and \(F=[X_{1}]\cdot X_{2}]\), these are computed as \([Z_{1}]\cdot[Z_{1}]\) and \([X_{1}]\cdot[X_{1}]\) for \(P_{1}=P_{2}\), respectively. Thus, we can distinguish between \(P_{1}=P_{2}\) and \(P_{1}\
eq P_{2}\) using ROSETTA.  
**Type 2 vulnerability**: Let us consider the computations \(A=[Y_{1}]\cdot[X_{2}]\) and \(C=[Z_{1}]\cdot[Y_{2}]\). If \(P_{1}=P_{2}\); then, \([Y_{1}]\cdot[X_{1}]\) and \([Z_{1}]\cdot[Y_{1}]\) are computed. Thus, they have the same operand \(Y_{1}\) when \(P_{1}=P_{2}\) but not when \(P_{1}\
eq P_{2}\). Similarly, considering \(C=[Z_{1}]\cdot[Y_{2}]\) and \(E=[X_{1}]\cdot[Z_{2}]\), the multiplications for \(C\) and \(E\) have the same operand \(Z_{1}\) for \(P_{1}=P_{2}\) and different operands for \(P_{1}\
eq P_{2}\). Also, the multiplications for \(A\) and \(E\) have the same operand \(X_{1}\) for \(P_{1}=P_{2}\). Therefore, we can distinguish between \(P_{1}=P_{2}\) and \(P_{1}\
eq P_{2}\) using HCCA.  
By Algorithm 2, to use unified point addition on the Jacobi quartic elliptic curve, the two inputs of step 8 are expressed as follows:  
\[\begin{cases}P_{1}=(X_{1}:Y_{1}:Z_{1}),P_{2}=(r_{1}X_{1}:r_{1}^{2}Y_{1}:r_{1}Z _{1})&\text{If }k^{\prime}=0,\\ P_{1}=(X_{1}:Y_{1}:Z_{1}),P_{2}=(r_{2}X_{2}:r_{2}^{2}Y_{2}:r_{2}Z_{2})&\text{ If }k^{\prime}=1.\end{cases} \tag{10}\]  
where \(r\
eq 1\). It is sufficient to secure against Type 1 and Type 2 vulnerabilities by applying \(w\)RPC to unified point addition. The application of \(w\)RPC to unified point addition is evaluated in Table 10. Table 10 shows that vulnerabilities of Type 1 and Type 2 no longer exist.
## 3 Methods
### Supervised Learning Based Methods
#### 3.1.1 Support Vector Machine (SVM)  
SVM models the relationship between input features and continuous targets using a linear function in a high-dimensional feature space [18]. The optimization problem is formulated as  
\[\min_{\mathbf{w},b,\xi,\xi^{*}}\frac{1}{2}\parallel\mathbf{w}\parallel^{2}+c\sum_{i=1 }^{N}\left(\xi_{i}+\xi_{i}^{*}\right) \tag{1}\]  
subject to  
\[\begin{array}{c}y_{i}-\left(\mathbf{w}^{\mathsf{T}}\phi(x_{i})+b\right)\leq \epsilon+\xi_{i}\\ \left(\mathbf{w}^{\mathsf{T}}\phi(x_{i})+b\right)-y_{i}\leq\epsilon+\xi_{i}^{*} \end{array} \tag{2}\]  
where \(w\) is the weight vector, \(b\) is the bias term, and \(\phi(x_{i})\) represents the nonlinear mapping of the input \(x_{i}\) to a higher-dimensional space. The parameters \(\xi\) and \(\xi^{*}\) are slack variables that allow some flexibility by tolerating prediction errors within a margin \(\epsilon\). The regularization parameter \(C\) controls the trade-off between minimizing model complexity (i.e., \(\parallel\mathbf{w}\parallel\)) and the amount of tolerated error. This framework makes SVM robust against overfitting, especially when dealing with high-dimensional data.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
###### Abstract  
The Internet of Things (IoT) networks are the most prone to internal as well as external attacks in addition to energy consumption problems. Conventional security solutions are not able to address these issues effectively due to the limited resources of sensor nodes participating in IoT communications. In this work, an Efficient and Secure Fog Based Routing Mechanism (ESFRM) is proposed to protect the network from faulty internal as well as external attacks. Every node participating in IoT communications calculates the comprehensive trust value of the next intermediate node which is the addition of direct trust, indirect trust and energy trust values before forwarding the data. This comprehensive trust value is then compared with the comprehensive threshold trust value to decide whether the particular node is a rogue node or a valid normal node. Further, an enhanced RSA (Rivest, Shamir, Adleman) algorithm is implemented to provide three levels of data security from Cluster Head (CH) to fog node, from fog node to cloud server and directly from CH to cloud server. For this purpose, an efficient CH selection technique is also proposed in this work. The proposed methodology is compared with the Secure Energy-efficient Fog-based Routing (SEFR) protocol and Trust-aware Secure Routing Protocol (TSRP). The evaluation results show that the proposed ESFRM outperforms the conventional schemes with respect to energy consumption, malicious node detection and transmission rate.  
Internet of Things (IoT); cloud-native fog computing; advanced algorithms; wireless communications; security in IoT  
62F99; 65C99
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 6 Results
### Ebro River--(Tudela) Spain 2018  
The CEMS system was activated for this event on 13 April 2018 [63]. The flood delineation map generated by using a semi-automatic approach for the COSMO-SkyMed images acquired on 13 April 2018 at 17:45:11 UTC and 17:45:16 UTC) and by means of visual interpretation due to the poor radiometric quality of the COSMO-SkyMed data acquired on 13 April 2018 at 18:09:16 UTC and 18:09:21 UTC was considered as the reference data for this case study. The estimated geometric accuracy was 5 m [63]. The flood extension analysis involved a comparison between this map and the SAR-RST-Flood one generated by analyzing a Sentinel 1 image acquired on 13 April 2018 (Figure 7).  
UAf exceeded 0.94, indicating reasonably accurate flood extent mapping, the PAf value was 0.69 and K was 0.84 (Table 6). The two outputs (Figure 7) appear to be very similar, indicating the high reliability of the RST approach.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Event 4: Tudela Inundation 13 April 2018** & \multicolumn{3}{c}{**Reference Data**} & \multicolumn{3}{c}{**Accuracy Matrix**} \\ \hline  & Flood & Not Flooded & UA\({}_{\text{f}}\) & UA\({}_{\text{nf}}\) & PA\({}_{\text{f}}\) & PA\({}_{\text{nf}}\) & K \\
**RST-SAR-FLOOD** & Flood & 23,728 & 6963 & 0.94 & 0.95 & 0.69 & 0.76 & 0.75 \\  & Not Flooded & 1102 & 253,972 & 0.94 & 0.95 & 0.69 & 0.76 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for Ebro River (Tudela) event (13 April 2018).  
Figure 6: Flood map of the Ebro River (Zaragozza) event (2 February 2015).
## 5 Integrating Language Modeling with STEP
### Topic Modeling  
Inaccurate annotations during the concept extraction phase may result from mapping parts of a phrase to ontological concepts, where the union of these concepts does not represent the underlying term; as illustrated by the \"tibial shaft\" example, combining concepts representing \"tibial\" and \"shaft\" does not accurately convey the semantics of \"tibial shaft\". In order to capture the semantics of phrases more accurately and to capture terms and phrases that may not have been annotated, we propose incorporating two of the _Statistical Language Modeling_ techniques [23], _i.e._, _Topic Modeling_[24] and _N-gram Modeling_[25], with the _concept extraction_ module of the STEP methodology.  
Incremental and collaborative refinements to content in collaboration platforms, including micro-contributions in the biomedical domain, usually contain discussion on a variety of topics; in order to discover the _abstract topics_ and the _hidden thematic structure_ of micro-contributions, we have performed topic modeling on all contributions made by an author. Topic models are algorithms for discovering the _main themes_ that pervade a large and otherwise unstructured collection of documents. Topic models can organize the collection according to the discovered themes. Topic modeling algorithms can be applied to massive collections of documents and adapted to many kinds of data. Among other applications, they have been used to find patterns in genetic data, images and social networks [26]. We have opted for the _Latent Dirichlet Allocation (LDA)_ topic model, which allows documents to have a mixture of topics [27]. The intuition behind LDA is that documents exhibit multiple topics; e.g., a discussion regarding _Achondroplasia_, a disorder of bone growth, in _SKELETOME_[28], will most likely include information about the possible causes of the disease such as inheritance and genetic mutation, genes and chromosomes, treatment options, medication, _etc._ LDA is a statistical model of document collections that tries to capture this intuition. A topic is defined to be a distribution over a fixed vocabulary. For example, the \"genetics\" topic has words about genetics (such as FGFR3 gene, chromosome, _etc._) with high probability. The assumption is that these topics are specified before any data has been generated [26]. Each micro-contribution made by an expert is seen as an _exhibition of these topics in different proportion_. We then annotate the defined topics and words included in those topics to derive domain-specific concepts from domain ontologies.
## 2 Results
### Analysis of Aroma Components in Golden Mulberry Fruit Across Different Harvesting Stages  
A total of 67 volatile flavor substances were annotated by GC-MS analysis in four different periods of golden mulberry fruits, among which aldehydes were the most abundant (16), followed by alcohols (15), esters (12), alkanes (5), ketones (4), furans (4), acids (3), alkenes (3), dimethyl hydroxylamine, 1,2,4-triazole, 1-methyl-1,2,4-triazole, 4-methoxybiphenyl, allyl butyl ether, and other substances. As shown in Table 5, there were significant differences in the types and composition of volatile substances annotated in the mulberry fruits harvested from different periods. A total of 52 volatile compounds were annotated in T1, 46 in T2, 47 in T3, and 41 in T4, among which methyl nonanoate, allyl oxalate, methyl palmitate, methyl caproate, cis-2-penten-1-ol, 1-penten-3-ol, 2-methoxy-pentane, 1-penten-3-one, n-hexanal, nonanal, 2-hexenal, 2-pentylfuran, 2-butyltetrahydrofuran, and other volatiles were annotated at all four maturity levels. Substances specific to the T1 period included tetrahydrofurfuryl acrylate, 2-methyl-3-buten-1-ol, n-pentanol, 1-azabicyclo [3.1.0] hexane, trans-2-pentenal, 2-methyl-2-buten-2-ol, n-pentenyl alcohol, and n-pentanol. Pentenal, 2,2-dimethyl-4-ocreal, and propionic anhydride were the most abundant endemic substances, but their content accounted for only 2.4% of the total content in the T1 period of harvesting. 4,5-dimethyl-2-hepten-3-ol, 6,6-dimethyl-1,3-heptadien-5-ol, 3-methoxy-1-hexene, and dimethyl hydroxylamine were endemic to the T2 period and accounted for 6.7% of the T2 period's total content. The content of 2-methylcyclopentanol and 2-methylhept-6-en-3-one in the T3 period accounted for only 1.9% of the T3 period, and no specific substances were found in the T4 period, indicating that the relative content of the specific flavor substances annotated in the T4 period was relatively little and might show little influence on the flavor of the mulberry fruits. Figure 1: Effects of maturity on antioxidant capacity of golden mulberry. (**A**) ABTS, (**B**) FRAP, and (**C**) DPPH. Different letters indicate that significantly difference at 0.05 level.
# Performance of Numerically Optimized Tuned Mass Damper with Inerter (TMDI)
## 2 Modelling  
### Primary Structure  
The primary structure is modelled by the modal component that needs to be mitigated by the damper. Therefore, the model is given by the following single-degree-of-freedom system [40]  
\[m_{1}\ddot{x}_{1}+c_{1}\dot{x}_{1}+k_{1}x_{1}=f_{ex} \tag{1}\]  
\[c_{1}=2\;\zeta_{1}\;m_{1}(2\;\pi\;f_{1}) \tag{2}\]  
\[k_{1}=m_{1}(2\;\pi\;f_{1})^{2} \tag{3}\]  
where \(m_{1}\) denotes the modal mass; \(c_{1}\) is the viscous damping coefficient, which is given by the damping ratio \(\zeta_{1}\); \(k_{1}\) is the stiffness coefficient, which results from the eigenfrequency \(f_{1}\); \(\ddot{x}_{1}\), \(\dot{x}_{1}\), \(x_{1}\) describe the modal acceleration, velocity and displacement; and \(f_{ex}\) is the excitation force. The chosen primary structure modal parameters represent typical values of a tall building that may be susceptible to wind excitation and therefore may require a  
Figure 1: (**a**) Multi-degree-of-freedom system with TMDI at the anti-node position of the first bending mode. (**b**) Conventional TMDI with target structural mode (modeshape factor 1). (**c**) Classical TMD with target structural mode (modeshape factor 1). (**d**) TMDI with serial arrangement of stiffness, inerter and viscous damper with target structural mode (modeshape factor 1). (**e**) Two half TMDs with target structural mode (modeshape factor 1).  
mass damper (Table 1). Notice that the choice of the structural modal parameters does not influence the outcome of the comparative study under consideration. However, the selection of the modal parameters influences the optimum mass damper parameters that are given for all considered mass damper topologies.
## Materials and Methods
### Evaluation  
The performance of the proposed method is evaluated by comparing the pre-trained models with different metrics. The quality of the learning algorithms is generally evaluated by analyzing how well they perform on a test data [38]. The first one is the Receiver Operating Characteristic Curve (ROC), known as Area Under the Curve (AUC), which is a widely used performance measure in the supervised classification, and it is based on the relation between sensitivity and specificity [39]. For this work, was used a generalization of the AUC for multiple classes. This function defined by [PERSON] and Till [40] performs multiclass AUC, and it is composed by the mean of several AUC values. A data frame passes as predictor, and the columns have to be named according to the levels of the response.  
The sensitivity or recall corresponds to the accuracy of positive examples, and it refers to how many examples of the positive classes were labeled correctly; this can be calculated with Equation (1), where \(TP\) refers to the true positives, which are the number of instances that are positive and are correctly identified, and \(FN\) represents the false negatives, which are the number of positive cases that are mis-classified as negative.  
\[Sensitivity(Recall)=\frac{TP}{TP+FN}. \tag{1}\]  
Specificity corresponds to the conditional probability of true negatives given a secondary class, which means that it approximates the probability of the negative label being true; it is represented by Equation (2), where \(TN\) is the number of true negatives or negative cases that are negative and classified as negative, and \(FP\) is the number of false positives, defined by the negative instances that are incorrectly classified as positive cases.  
\[Specificity=\frac{TN}{TN+FP}. \tag{2}\]  
In general, sensitivity and specificity evaluates the effectiveness of the algorithm on a single class, positive and negative respectively.  
Commonly, accuracy is the most used metric to evaluate the classification performance. In the evaluation stage, the accuracy was calculated every 20 iterations. This metric calculates the percentage of samples that are correctly classified, and it is represented in Equation (3):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}. \tag{3}\]  
Precision, defined as the number of true positives divided by the number of true positives plus the number of false positives, is given by Equation (4). This measure is about correctness, i.e., it evaluates  
Figure 3: Representation of transfer learning and classification phase.  
the predictive power of the algorithm. Precision is how \"precise\" the model is out of those predicted positive and how many of them are actually positive.  
\[Precision=\frac{TP}{TP+FP}. \tag{4}\]  
F-score is determined as the harmonic mean precision and recall, as shown in Equation (5). It focuses on the analysis of positive class. A high value of this metric indicates that the model performs better on the positive class.  
\[F-score=2*\frac{Precision*Recall}{Precision+Recall}. \tag{5}\]