esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 CCSDS 131.2-B Protocol for High-Rate Telemetry
### Receiver Side
For its very low performance, it is impossible to use the SW EGSE both for testing real transmitters and for receiving data on ground stations. In particular, the SW EGSE is capable of decoding a single Codeword (8100 symbols) in, at minimum, more than 3 s, making its use unfeasible for any real-use case (in comparison, a HW receiver as the Cortex can decode about 60 k Codewords per second). The poor performance of the SW EGSE is due to the implementation of the EGSE itself, which was not explicitly developed for reaching high performance, but also due to the implicit limitation of SW implementation on Matlab. In recent years, the evolution of general-purpose processing HW has increased software performance. Applications like synthesizers, modulators/demodulators, and encoders/decoders can now be implemented in software thanks to the use of ad hoc embedded systems or high-performance processors. This is the case for the so-called Software-Defined Radios (SDRs), which represent radio communication systems that exploit SW to implement components that are usually implemented on FPGAs or ASICs, such as filters, amplifiers, modulators, and demodulators. This approach has gained traction also in the space telecommunication sector [21; 22; 23; 24]. A significant help to SDR performances has been given in recent years by General-Purpose Graphics Processing Units (GP-GPUs). In this scope, we have worked on the design and development of an SDR receiver, exploiting GP-GPUs to implement a system compliant with the CCSDS 131.2-B standard. The SDR approach offers manifold advantages with respect to the HW approach such as extensibility, flexibility, cheaper development and maintenance, upgradability, and potential future development [21; 25]. The next section describes the development of the SW-based receiver, focusing on the implementation choices carried out to obtain good performance in terms of data-rate and error correction. Such performances are discussed and evaluated in Section 4.
# Structural Topology Design Optimization Using the Binary Bat Algorithm
## 5 Numerical Examples
### The Pined-Roller Plate Problem  
The problem definition is illustrated in Figure 7. This problem is similar to the problem in Section 5.3. However, the pinned support on the right side is replaced by a roller support. A unit point load is applied vertically downward at the half-length of the bottom boundary. This problem was previously solved by [PERSON] et al. (2018) using the Ant Colony algorithm with a mesh size of (3200 elements). However, the compliance of the final design was not provided.  
Table 5 shows the layouts obtained after the optimization process and the minimum compliances of the SIMP method, the BBA without symmetry, and the BBA with symmetry. The symmetry is taken around the axis parallel to the \(y\)-axis passing through 1 on the \(x\)-axis. Again, the minimum compliances of the proposed bit-array representation are slightly better the SIMP method but with no blurry boundaries and the final design configurations are different.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Mesh Size: \(100\times 50\)** & **Compliance (c)** \\ \hline BBA with symmetry & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 7: Problem definition of the pinned-roller plate: design domain, loading, and supports.  
According to the results discussed in Section 5.1, Section 5.4, and Section 5.5, the BBA with symmetry is the best among the three methods because the number of design variables in the symmetrical problems is reduced to half. Therefore, the number of possible combinations is also reduced to half.
## 3 Results
### Transport of Cells  
Experiments were performed to demonstrate the effectiveness of our approach to transporting cells. Yeast cells with diameters ranging from 4 to 6 \(\upmu\)m were used.
## 5 Result and Discussions
### Evaluation of Trained Neural Network Model
#### 5.2.1. Trained NN Prediction Evaluation Parameters  
We evaluated obtained output of trained NN with help of formula, shown in Equation (8) and error rate shown in Equation (9), also used by [32].  
\[Accuracy(UF)=\frac{\text{Correctly Predicted}}{\text{Total Predicted}}*100 \tag{8}\]  
\[Error\ Rate=\frac{\text{Wrongly Predicted}}{\text{Total Predicted}}*100 \tag{9}\]  
* Accuracy Rate for Favorable class; Prediction = 88/89 \(\times\) 100 = 98%  
Figure 19. Gradient graph of 1 st training round NN.  
Figure 20. Training Round 2 and 6 MSE with performance values.  
* Accuracy Rate for Unfavorable class; Prediction = 116/145 \(\times\) 100 = 80%
* Error Rate for Favorable class; Prediction = 1/89 \(\times\) 100 = 1.122%
* Error Rate for Unfavorable class; Prediction = 29/145 \(\times\) 100 = 20%
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 3 Research Methodology
### Geographical-Optimal-Similarity Model Prediction
#### 3.4.2 Similarity Assessment  
The similarity between an unknown observation location \(\mathrm{v}_{\beta}(\beta=1,\cdots,n)\) and a known observation location \(\mathrm{u}_{\alpha}(\alpha=1,\cdots,m)\) was assessed according to geographical configuration attributes. The similarity calculation is expressed as follows:  
\[S(\mathrm{u}_{\alpha},\mathrm{v}_{\beta})=P\big{\{}E_{i}(e_{i}(\mathrm{u}_{ \alpha}),e_{i}(\mathrm{v}_{\beta}))\big{\}} \tag{6}\]  
where \(E_{i}\) is the calculation function for the \(i\) covariate, and for continuous variables, Equation (7) was used. \(P\) is the minimum value selection function:  
\[E_{i}(\mathrm{u}_{\alpha},\mathrm{v}_{\beta})=\exp\left(-\frac{\big{(}e_{i}( \mathrm{u}_{\alpha})-e_{i}(\mathrm{v}_{\beta})\big{)}^{2}}{2\big{(}\sigma^{2} /\delta(\mathrm{u}_{\alpha},\mathrm{v}_{\beta})\big{)}^{2}}\right) \tag{7}\]  
where \(\sigma\) is the standard deviation of the explanatory variable \(X_{i}\), including all unknown and observed positions, and \(\delta(\mathrm{u}_{\alpha},\mathrm{v}_{\beta})\) is the root mean square error (RMSE) between all unknown locations \(\mathrm{v}_{\beta}\) and the observed location \(\mathrm{u}_{\alpha}\).
## 5 RC-LARR: RSU-Assisted Cloud-Based Location-Aware Hybrid Routing
The installation of a high-speed cloud server in control plane layer 2 facilitates the containment of all information on RSUs, specifically the global view of the topology. When the sender requires the route to a specific destination node and the RSU determines that the destination is not listed in its routing table, it initiates a request to the server to locate the appropriate route. The server maintains a comprehensive understanding of the network's topology, allowing it to compute the optimal route. This information is then disseminated to all RSUs, which subsequently update their own routing tables and transmit the route to the relevant nodes. Figure 11 illustrates the details of the proposed routing protocol. The different phases of the proposed routing scheme are discussed in the following section. Figure 2:2 Working flow of proposed routing protocol. Figure 2:3 Communication between source node and destination node when the destination is not in the range of neighboring RSUs.
## 2 Compressive Sensing and Measurement Matrix  
### Compressive Sensing (CS)  
The signal \(x\in R^{N}\) is called the \(K\)-sparse signal if it only has \(K(K\ll N)\) nonzero coefficients. The measured signal \(y=\Phi x\) is obtained by compression and projection. \(\Phi\in R^{M\times N}(M\ll N)\) is called the measurement matrix, where \(M\) is the number of measurements. Using the measured signal \(y\) and the measurement matrix \(\Phi\), the original signal \(\hat{x}\) can be recreated by the reconstruction algorithm through the process depicted in Figure 1[28].  
Figure 1: Compressive Sensing process of signal (**a**) compression of signal; (**b**) reconstruction of signal [28].  
Since signal \(x\) is sparse and its recovery involves inversion, signal reconstruction can be regarded as the search of the solutions with the least number of non-zero coefficients from all possible solutions, i.e., the solutions that minimize \(l_{0}\) or \(l_{1}\) norm below:  
\[\min\lVert s\rVert_{l_{0}}s.t.y =\Phi x,\] \[\min\lVert s\rVert_{l_{1}}s.t.y =\Phi x\]  
In summary, CS takes place in the following steps: a suitable measurement matrix is constructed when sparse representation or compressibility of the original signal under a certain sparse basis is possible; the original signal of higher dimension is projected onto low-dimensional space using this matrix; the original signal is restored accurately or with high probability from the observed data with a reconstruction algorithm.
## 1 Introduction
Common accidents mainly include furnace explosion, coking in the furnace, and explosion in the flue. The furnace tube is damaged, the material in the tube leaks into the furnace, fire occurs, and the fire or explosion caused by overheating. In addition, the burner of heating furnace is damaged, the refractory insulating material in the furnace is impaired. The environment is seriously polluted, and the heating furnace has exploded. In numerous literature descriptions, furnace explosion was the most common, destructive, and extremely serious accident. In the past, there have been few literature studies on accident cases. Therefore, we used the furnace explosion accident as an example to analyze and improve the understanding of the explosion accident of the heating furnace. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Date of** \\ **Incident** \\ \end{tabular} } & \multirow{2}{*}{**Location**} & \multirow{2}{*}{
\begin{tabular}{c} **Specific** \\ **Details** \\ \end{tabular} } & \multicolumn{2}{c}{**Explusion Details**} & \multicolumn{1}{c}{**Injuries/**} \\ \cline{3-5}  & & & **Cause** & **Consequence** & **Fatalities** \\ \hline
2 & & & & & \\ November & Vernon, CA & \begin{tabular}{c} Used for \\ smelling lead \\ \end{tabular} & \begin{tabular}{c} Weakened furnace wall (holes) \\ compromised the integrity of \\ the furnace \\ \end{tabular} & \
\begin{tabular}{c} Explosion spewing hot \\ lead slag and dust \\ \end{tabular} &
\begin{tabular}{c} 1 employee \\ received 2 nd and \\ 3 rd degree burns \\ \end{tabular} \\ \hline
16 March & Cucamoga, CA & Electric arc & \begin{tabular}{c} Attempted maintenance on a \\ water leak from the furnace, \\ loud popping noise resulted \\ in explosion \\ \end{tabular} & \
\begin{tabular}{c} Explosion resulting in \\ hot steam and flying \\ debris \\ \end{tabular} &
\begin{tabular}{c} 1 severe injury, \\ 2 minor burns \\ and cuts \\ \end{tabular} \\ \hline
10 March & Midlothian, TX & Melt shop & \
\begin{tabular}{c} Furnace was lifted forward to \\ begin tapping \\ when the hydraulic nose \\ cylinder failed \\ \end{tabular} &
\begin{tabular}{c} Molten steel leaked out \\ of slag door, slag pot \\ overfilled and an \\ explosion occurred \\ \end{tabular} & 1 employee was killed \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Furnace explosion accident case summary in U.S.  
We integrated the furnace explosion case and analyzed the relevant factors comprehensively that may cause fire and explosion during the operation of the furnace. The explosion factors of the heating furnace system were established by fault tree analysis method, and qualitative analysis was carried out to provide a basis for diagnosis and prevention of furnace explosion. The results of this study could provide the operators and managers with further understanding of the elements of furnace explosion and substantially diminish the chance of accidents in the future.
## 1 Introduction  
The determination of the attitude (i.e., the orientation with respect to a given frame of reference) of satellites and orbiting objects is one of the most important tasks for present-day space safety [1; 2; 3]. The ever-increasing quantity of space debris, therefore, imposes an increasingly pressing need to evaluate the trajectory and effects of these potentially dangerous objects. Regarding this critical issue, it is essential to remember that in 2014 the European Commission, conscious of the present urgency, undertook the development of a European network of sensors for the surveillance and tracking of orbiting objects and initiated a specific SST (Space Surveillance and Tracking) support framework program. Italy, Germany, the U.K., France, and Spain joined the program and constituted, with SatCent, the front desk for SST services, the EUSST Consortium.  
In this frame, with several thousand objects orbiting around Earth, having a tool similar to a radar system that could help to track objects and determine their attitude and re-entry trajectory is therefore of primary importance. In order to envisage the trajectory of these objects, one has to know their attitude to evaluate the effects of the atmospheric drag on the trajectory itself, also associating radar measurements with other tracking systems such as the optical system, for example, LED (Light Emission Diodes) [4; 5; 6; 7], or light-curve acquisition systems [8; 9; 10] or magnetometer data [11]. Knowing the attitude using radar systems, therefore, becomes one of the fundamental tasks for the detection of space debris, as demonstrated by the recent case of the Chinese Space Station Tiangong-1 [12].  
To this aim, the bi-static reflection method, also known as NRL arc measurement, was used in order to determine the reflectivity of several objects that can be assimilated to space debris or to an orbiting satellite, relating the reflectivity to the object's attitude. NRL arch is the industry standard for testing the reflectivity of materials. Originally designed at the Naval Research Laboratory (NRL), the NRL arch enables fast, repeatable, and non-destructive testing over a wide range of frequencies [13; 14; 15]. The experimental reflection results have been related to real radar tracking conditions. The reference radar for tracking orbiting satellites has a frequency of 400 MHz.  
In the NRL system, two antennas are used, one for transmitting and the other for receiving signals, from a vector network analyzer (VNA), and microwave reflectivity can be measured at different angles of incidence, simulating different attitude positions. Several objects were built in aluminum, in scale with respect to a real satellite, in order to evaluate the reflection in different conditions of attitude and over a wide range of frequencies, between 12 and 18 GHz, relating the results to the real frequencies and dimensions of use.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## Mining Closed Frequent Itemsets with Map-Reduce
### Experiments
#### 3.3.2 Setup  
We ran all the experiments on the Amazon Elastic Map-Reduce [31] infrastructure. Each run was executed on sixteen machines; each is an SSD-based instance storage for fast I/O performance with a quad core CPU and 15 GB of memory. All machines run Hadoop version 2.6.0 with Java 8.
## 3 Results
### Principal Components Analysis and Cluster Analysis
They also exhibit the same characteristic bands at 1458 and 1380 cm\({}^{-1}\), but the band at 1740 cm\({}^{-1}\) was reduced in intensity. This is clearly seen when compared to the spectrum of lavender (Figure 3b). For oregano essential oil, the signature band for carvacrol is located at ~810 cm\({}^{-1}\), indicating C-H out-of-plane bending [26; 27]. Oregano (phenol type) essential oil contains 60-80% phenolic carvacrol according to the manufacturer's claim. The band near 1380 cm\({}^{-1}\) has been assigned to the symmetrical CH\({}_{3}\). Isopropyl groups produce double bends near 1370 and 1380 cm\({}^{-1}\) (e.g., tetrahydrorolinalool, tetrahydrorogeraniol and their acetates). The gem dimethyl (R\({}_{1}\)R\({}_{2}\)C(CH\({}_{3}\))\({}_{2}\)) group present in the in \(\alpha\)- and \(\beta\)-pinene also produces this doublet. \(\alpha\)- and \(\beta\)-pinene are two structural isomers, bicyclic monoterpenes, present in many essential oils. The most important bands for the Asteraceae family cluster were at 726, 1465 and 1733 cm\({}^{-1}\), together with the area between 850 and 920 cm\({}^{-1}\). Chamomile, marigold and tagetes were very close together, while echinacea was apart from the group (Figure 2b), perhaps because the peak at 1700 cm\({}^{-1}\) is smaller in intensity (Figure 3c). Chamomile has a double C = O band at 1711 and 1735 cm\({}^{-1}\). Methylene =CH\({}_{2}\) out-of-plane deformation is seen in the region around 890 cm\({}^{-1}\). \(\beta\)-pinene, however, absorbs at 875 cm\({}^{-1}\) with an increased intensity that is typical for strained ring structures with an exocyclic =CH\({}_{2}\) group [22]. The intensity of this band in myrcene is high, due to the conjugation with the vinyl group, which shows enhanced intensities for the out-of-plane deformation frequencies. Myrcene is a typical example of an unsaturated acyclic hydrocarbon, and is detected in chamomile flower essential oil [28]. A loading plot identified significant bands at 1159 and 1743 cm\({}^{-1}\) for the last cluster containing pure essential oils of arrica, primrose and frankincense (Figure 3d). The strong absorption at 1160 cm\({}^{-1}\) occurs due to the presence of lipids and alcohol groups (stretching of C-O and bending of C-OH) [23]. Frankincense essential oil contains diterpene alcohols, primrose oil contains high content of polyunsaturated fatty acids, while arrica contains sesquiterpene lactones and triterpenenedi esters (arnidiol/faradiol). Although alcohols show two strong bands, the band in the 1300-1450 cm\({}^{-1}\) region is usually overlapped by CH\({}_{2}\) and CH\({}_{3}\) absorptions, while the second band, assigned to the C-O stretching vibration, is in the 1000-1150 cm\({}^{-1}\) region. Primary alcohols like nerol and geraniol, due to the presence of a double bond and secondary alcohols, absorb about 50 cm\({}^{-1}\) lower [22].
# Seismogram Rearrangement as a Perspective Basis for Defining New Attributes
## 3 Results and Analysis
### Attributes from Rearranged and Ordered Fluctuations
#### 3.2.3 Seismogram Duration Based on Ordered SFs  
The Section 2.6.2 explained how the peaks of ordered SFs form a scatter diagram for the calculation of linear regression to find out the seismogram duration (see Figure 10).  
A seismogram is separated into two sets of SFs, positive and negative ones which means that one gets two scatter plots from peaks of positive and negative SFs. The elongated distribution of points in these scatter plots simply appeals for the calculation of linear regression. Therefore, one gets two regression lines: regression line + and regression line -. Because of the peak amplitude increase along the sequence of ordered SFs, the regression  
Figure 17: Variations in each difference function \(\varepsilon_{\text{Q}}(n)\) indicate the asymmetry profile of the assigned seismogram, given by its RSN. Each difference function is drawn with a 500% extended ordinate scale for better insight. Red lines designate positive differences between corresponding R\({}_{\text{Q}}\)-envelopes, and blue lines stand for negative differences.  
line + is inclined to _t_-axis and crosses it at a certain point (\({T_{1}}^{+}\) in Figure 19). The similar thing is for \({T_{1}}^{-}\).  
The duration, \({T_{D}}\), is determined simply as:  
\[{T_{D}}=\left({T_{0}}-{T_{1}^{+}}\right)+\left({T_{0}}-{T_{1}^{-}}\right)=2{T_{ 0}}-\left({T_{1}^{+}}+{T_{1}^{-}}\right) \tag{31}\]  
A very apparent attribute (general measure) of seismogram's symmetry can be defined directly from Figure 19 as a difference in overall durations of SF*s and SF\({}^{-}\)s:  
\[{A_{\Delta}}=\frac{{T_{1}^{+}}}{{T_{1}^{-}}}=\frac{{\sum_{k}{\Delta_{k}^{+}}}} {{\sum_{k}{\Delta_{k}^{-}}}} \tag{32}\]  
Figure 19: The nodes of R -envelope’s polygonal line, i.e., peaks of approximating triangles, can be considered as scatter plots, both for positive and negative SF envelopes. Calculating linear regression for both means putting a line through each of the scatter clouds. These are regression line + and regression line \(-\), which cross the \(\tau\)-axis at points \({T_{1}}^{+}\) and \({T_{1}}^{-}\). In this way, we get the effective duration of each R-envelope SF sequence, in combination, yields the duration of the seismogram, which stems from the whole waveform.  
Figure 18: (**a**) The regular R\({}_{Q}\)-envelope and the random N\({}_{Q}\)-envelope formed on ordered EFs over positive SFs. (**c**) The same as in (**a**) but over negative SFs. (**b**) The random difference function \(\eta_{Q}(n)\), depicted by the black solid line as the geometrical difference between red and blue solid lines, shows the random asymmetry of the seismogram but given by ordered EFs.
## 2 Constitutive Model for Undrained Clay
### Solving the Fourth-Order Algebraic Equation  
For the implementation of this material model with the ABAQUS software, the fourth-order (quartic) equation of Equation (8) needs to be solved each time the software calls the material model (for every integration point of each element), and a solution procedure needs to be included in the computer code to solve the equation. The general quartic equation (GQE) of Equation (8) can be converted to the depressed quartic equation (DQE)of Equation (17) using the method described by [40], by a change of variable in the form of \(\lambda=y-\frac{C_{2}}{4C_{1}}\).  
\[y^{4}+py^{2}+qy+r=0 \tag{17}\]  
Therefore, the coefficients of DQE are related to GQE as follows:  
\[p=\frac{8C_{1}C_{3}-3C_{2}^{2}}{8C_{1}^{2}} \tag{18}\]  
\[q=\frac{C_{2}^{3}-4C_{1}C_{2}C_{3}+8C_{1}^{2}C_{4}}{8C_{1}^{3}} \tag{19}\]  
\[r=\frac{16C_{1}C_{2}^{2}C_{3}-64C_{1}^{2}C_{2}C_{4}-3C_{2}^{4}+256C_{1}^{3}C_{ 5}}{256C_{1}^{4}} \tag{20}\]  
The quadratic equation is categorized into two cases. The biquadratic case corresponds to \(q=0\), and the Ferrari case corresponds to all cases in which \(q\
eq 0\). Using the coefficients of the DQE, the resolvent cubic (RC) equation is obtained from  
\[\xi^{3}+2p\xi+\Big{(}p^{2}-4r\Big{)}\xi-q^{2}=0 \tag{21}\]  
The roots of the RC equation are calculated by the method described by [41]. [PERSON] et al. [40] developed the following general solution to the quartic equation. The set of non-zero roots of the RC is defined as \(S_{\xi}=\{\xi_{1},\xi_{2},\xi_{3}\}-\{0\}\), and a parameter \(\alpha_{\xi}\) is defined as  
\[\alpha_{\xi}=\begin{cases}0&\text{if }S_{\xi}=\varnothing\\ -\frac{2q}{\sqrt{\xi}}&\text{if }S_{\xi}\
eq\varnothing\end{cases} \tag{22}\]  
The four roots of the GQE can be calculated as  
\[\lambda=\frac{\xi\sqrt{\xi}\pm\sqrt{\xi\alpha_{\xi}-2p-\xi}}{2}-\frac{C_{2}}{ 4C_{1}} \tag{23}\]  
If \(S_{\xi}=\varnothing\), \(\xi=0\) is used in Equation (23); otherwise, \(\xi\in S_{\xi}\), and \(\xi=\pm 1\).  
The computer code developed in this paper for the von Mises material primarily uses Equation (23) for solving Equation (8). However, in some cases, when the quadratic equation becomes a Ferrari case with root multiplicity, the Fortran implementation of Equation (23) added small imaginary parts to the roots, which prevented the computer code from recognizing the real roots. For those cases, the alternative method shown in Table 1, also available in [40], is used by the material code.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition 1** & **Condition 2** & **Solution** \\ \hline \(p^{2}=-12r\) and \(27q^{2}=-8p^{3}\) & \(q>0\) & \(\lambda_{1}=\lambda_{2}=\lambda_{3}=\sqrt{-\frac{p}{6}}-\frac{C_{2}}{4C_{1}}\) \\  & & and \(\lambda_{4}=-\sqrt{-\frac{3p}{2}}-\frac{C_{2}}{4C_{1}}\) \\ \hline \(q<0\) & \(\lambda_{1}=\lambda_{2}=\lambda_{3}=-\sqrt{-\frac{p}{6}}-\frac{C_{2}}{4C_{1}}\) \\  & & and \(\lambda_{4}=\sqrt{-\frac{3p}{2}}-\frac{C_{2}}{4C_{1}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solution of the quartic equation for Ferrari case with root multiplicity [40].