esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seismological Data and Results
### Seismicity Results
#### 2.1.2 The 2015 Lefkada Sequence
A final significant M5.2 event occurred on 21 October 2020 in the same region, after a period of quiescence. The 2018 Zakynthos mainshock-aftershock sequence was preceded by several foreshocks since the beginning of the year in the vicinity of the mainshock. An M4.8 event had occurred at the northern end of group #4 on 21 February 2018 and another Mw4.8 event was recorded half an hour before the mainshock. Earlier activity was also documented near the mainshock's region between September 2016 and March 2017, in at least three outbreaks, while a smaller one occurred in August 2017. Focal mechanisms of the 2018 Zakynthos aftershock sequence involved both low-angle and steeper strike-slip faulting [29]. The latter is mainly related with the northern subclusters, which also present a form of SW-NETending streaks, similar to the cluster at the southern end of group #4. The kinematics of  
Figure 5: Temporal evolution of the seismic sequence on Lefkada Island (spatial group #3) for events with \(M\geq 2.3\). Panels are similar to Figure 4.  
the mainshock and many of the major aftershocks are consistent with SW-NE-trending compression occurring in this region that is found in a transition zone between strike-slip faulting in the north (CLTFZ) and to the east (WAFZ in the NW Peloponnese, e.g., the 2008 Andravida earthquake [21,22]) and the northwestern end of the Hellenic Arc subduction zone. The temporal evolution of the post-seismic activity indicates that even towards the end of 2021 the seismicity rate had still not returned to its background level, despite the magnitude threshold being set as high as 2.5, quite above the average \(M_{\rm c}\) = 2.0 (Figure 6).
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 2 Grid-Forming Inverter Modelling and Overload Mechanisms
### Overload Mechanisms in GFM Inverters
#### 2.2.2 Inertial Power
Alternatively, an overload mitigation strategy can be proposed to ensure safe operational limits by reducing the inertia and ensuring the proper damping of the system. A trivial solution to avoid overload due to inertial power is to remove the inertia of the GFM inverter, making \(H=0\). However, this would increase the dynamics of the PSL, potentially leading to harmful interactions with electromagnetic transients, synchronous generators, mechanical modes, or internal controllers. Hence, a minimum inertial contribution is usually requested. Overload strategies should be tuned to ensure that the equivalent inertia during overload mitigation is not lower than this minimum threshold. In this work, the minimum inertia contribution is determined by the maximum natural frequency, \(\omega_{n}\), of the closed loop poles of the \(\Delta P/\Delta\omega_{g}\) transfer function. A maximum limit of 5 Hz is recommended according to [33]. Under these conditions, the minimum inertia constant is estimated according to (11). For a \(K_{t}=4\), this constant should be at least 0.64 s. The minimum inertia and the damping of the system during overloading determine the maximum peak power that the GFM inverter experiences under certain RoCoF with an overload mitigation strategy. This allows designers to either (1) adjust the active power thresholds to keep the total power within rated limits or (2) oversize the inverter to accommodate the transient demand. \[H_{min}=\frac{K_{t}\omega_{b}}{2\omega_{n}^{2}} \tag{11}\]
## 2 Background and Related Work
### Deep-Learning Background
#### 2.1.5 LSTM  
The LSTM was proposed by [26] as a variant of the vanilla RNN to overcome the vanishing or exploding gradient problem by adding the cell state to the hidden state of an RNN. The LSTM is composed of a cell state and three gates: input, output, and forget gates. The following equations describe the LSTM architecture.  
The forget gate \(\mathbf{f}_{t}\) determines which information is input to forget or keep from the previous cell state  
Following [26], the gates decide which information to be forgotten or to be remembered; therefore, the LSTM is suitable for managing long-term dependencies and forecasting time series with different numbers of time steps. Further, it can generalize and handle noise, distributed representations, and continuous values well.
## 5 Major Equipment and Technologies
### Advanced Machines Used to Advance Agriculture
Further, it is expected that about 40,000 unmanned driving, completely autonomous cars (level 5), and tractors will be sold by 2038 [109]. However, most farmers cannot afford such advanced farming machines, and most tractor service providers and manufacturers operate far below their potentials. Considering these challenges, for example, Hello Tractor has developed a solution to these problems. The company has developed a low-cost monitoring device that can be installed on any tractor, providing powerful software and analysis tools [113; 140]. The benefits of this device are twofold: it ensures that most growers can afford the tractor's total cost while monitoring the condition of the tractor and reports if any problems occur. The software connects tractor owners to farmers who need tractor services, just like Uber's tractors. Another major example is Case IH's Magnum series tractors, which use on-board cameras and light detection and ranging (LiDAR) sensors to detect the object that helps avoid collision and control accidents caused by agricultural vehicles [141].
## 3 Results
### Analysis of Used Engine Oil Fungal Degradation  
The percentage composition of the engine oil used by fungi was compared between T0 and T45 for each fungal strain by GC/MS analysis. The peaks detected on the chromatogram, indicative of the various compounds present in the used engine oil, were initially grouped into the macroclasses \"Aliphatic\", \"Aromatics\", and \"Other Hydrocarbons\" in order to identify the most degraded families of compounds. The three strains of _T. harzianum_ (F12, F26, and F58) all significantly modified the composition of the used engine oil macroclasses (MANOVA or PERMANOVA, \(p<0.05\)) (Figure 3). _T. asperellum_ F1020 was the only strain that did not significantly modify the oil composition (Figure 3). The compounds were then grouped into the following microclasses: C\({}_{1}\)-C\({}_{20}\) aliphatics, C\({}_{20}\)-C\({}_{50}\) aliphatics, BTEX, Alkyl Benzenes, Alkyl Indenes, Alkyl Tetralines, Alkyl Biphenyls, Polycyclic  
Figure 1: Phylogenetic tree based on maximum likelihood analysis of the internal transcribed spacer (ITS) sequences. _Nectria estromatica_ was used as the outgroup. Maximum likelihood bootstrap values greater than 70% are given at the nodes. The four strains used in this work are highlighted in green.  
Aromatics, and Other Compounds. The only fungal strain that produced significant changes in these groups after 45 days of action was _T. harzianum_ F26 (PERMANOVA, \(p\) < 0.05, Figure 4), which reduced the presence of BTEX from 4.33% \(\pm\) 0.38% to 2.98% \(\pm\) 0.50% and that of Alkyl Benzenes from 14.45% \(\pm\) 0.87% to 10.99% \(\pm\) 1.57% significantly, while C\({}_{1}\)-C\({}_{20}\) compounds increased significantly from 3.44% \(\pm\) 0.17% to 4.49% \(\pm\) 0.14% (two-tailed Mann-Whitney \(U\) test, \(p\) < 0.05) (Figure 5). The chromatograms are shown in Figures S1 and S2.
# Characterization of the Key Aroma Compounds in Different Aroma Types of Chinese Yellow Tea
## 3 Results and Discussion
### Key Aroma Compounds in Different Aroma Types of Yellow Tea  
It is well known that the characteristic aroma of yellow tea is formed by a series of volatile compounds with a certain composition and proportion. The characteristic aroma mainly depends on the concentrations of aroma compounds and their OAVs. OAV is usually used to evaluate the contribution of the aroma compound to the odor of tea [13]. Therefore, the aroma attributes and OAVs of all the identified volatile compounds were calculated and listed in Table S4. Sixty-one volatile compounds were identified with OAV \(>\) 1 in at least one aroma type of yellow tea. Twenty-four volatile compounds with OAVs \(>\) 1 were common in all yellow tea samples, and 12 (linalool, phenethyl alcohol, 1-octen-3-ol, \(\alpha\)-ionone, \(\beta\)-ionone, dehydro-\(\beta\)-ionone, (_E_,_E_)-2,_4-heptalien, phenyl acetaldehyde, methyl anthranilate, ethyl hexanoate, myrcene, and \(\alpha\)-muurolene) had OAVs \(>\) 10. Most of aroma compounds with high OAVs (OAV \(>\) 10) possess 'floral', 'fruity', 'citrus', 'green', and 'fatty' odors, which endow yellow tea with clean and pure scents and play a major role in the formation of a variety of aroma types of yellow teas [6]. Figure 2: PCA (**A**) and HCA (**B**) of the different aroma types of yellow teas.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Derivation of the Maximum Correntropy Criterion  
Based on a specified joint distribution function \(F_{pq}(p,q)\), the correntropy criteria quantify the degree of resemblance involving two independent variables \(P\in R\) and \(Q\in R\). The following is a mathematical definition of the idea of correntropy:  
\[V(P,Q)=\mathrm{E}[\theta(P,Q)]=\int\theta(P,Q)dF_{PQ}(p,q) \tag{16}\]  
where \(\theta\) refers to a translation-invariant kernel that satisfies [PERSON]'s theorem and \(E[\cdot]\) represents the expectation operator. The Gaussian kernel is a commonly selected kernel function in correntropy applications.  
\[\theta(P,Q)=H_{\tau}(f)=\exp\biggl{(}-\frac{f^{2}}{2\tau^{2}}\biggr{)} \tag{17}\]  
where \(\tau>0\) represents the correntropy's kernel bandwidth.  
Due to the restricted size of samples used in real-world applications, the joint distribution function \(F_{PQ}(p,q)\) is typically unknown. Consequently, the associated entropy is customarily approximated as follows:  
\[\hat{V}(P,Q)=\frac{1}{N}{\sum_{i=1}^{N}{\rm{G_{\tau}}}(f(i))} \tag{18}\]  
where \(f(i)=p(i)-q(i)\), let \(\{p(i),q(i)\}_{i=1}^{N}\) be the N samples drawn from \(F_{pq}\).  
The Taylor series is used to enlarge the Gaussian kernel, as shown below:  
\[V(P,Q)=\sum_{n=0}^{\infty}\frac{(-1)^{n}}{2^{n}\tau^{2n}n!}\mathrm{E}\Bigl{[} (P-Q)^{2n}\Bigr{]} \tag{19}\]  
The related entropy is a calculated value determined by adding together all of the even-order error variables, with the weighting of each variable being influenced by the kernel bandwidth parameter.  
With MCC, the goal function can be expressed in the following way when dealing with inaccurate data \(\{f(i)\}N_{i}=1\):  
\[J_{\rm MCC}=\frac{1}{N}{\sum_{i=1}^{N}{\rm{G_{\tau}}}(f(i))} \tag{20}\]
## 5 Numerical Results  
To evaluate the performance of the proposed algorithm in terms of different aspects, such as the communication symbol error rate and radar detection performance, numerical examples are provided in this section. The radar performance for the detection of moving weak targets includes both the OFDM intra-frame response and inter-frame integration response within one subfragment. Subsequently, we compare these different approaches.
## 4 Results and Discussion
### Association Rules among Multiple Elements
#### 4.2.3 Controls of Geological Features
The locations of rules [As (HH)] \(\Rightarrow\) [Sb (HH)], [Zn (HH), Ag (HH)] \(\Rightarrow\) [Cd (HH)], and [Cu (HH)] \(\Rightarrow\) [Co (HH)] are related to Pt\({}_{\text{t}}\)b, as shown in Figures 21-23. The association rules of sulfophile elements, e.g., [As (HH)] \(\Rightarrow\) [Sb (HH)], [Cd (HH)] \(\Rightarrow\) [Zn (HH)], [Zn (HH)] \(\Rightarrow\) [Cd (HH)], [Cd (HH)], Ag (HH)] \(\Rightarrow\) [Zn (HH)], [Cd (HH)], As (HH)] \(\Rightarrow\) [Zn (HH)], [Zn (HH), Sb (HH)] \(\Rightarrow\) [Cd (HH)], [Cd (HH)], Sb (HH)] \(\Rightarrow\) [Zn (HH)], and [Zn (HH), As (HH)] \(\Rightarrow\) [Sb (HH)], are mainly distributed in the Proterozoic Baishahe Formation (Pt\({}_{\text{t}}\)b), the Late Triassic Elashan Formation (T\({}_{\text{3}}\)e), and the Middle Triassic genadorite (T\({}_{\text{2}}\)\(\gamma\)\(\delta\)). The Middle Triassic magmatism resulted in the intrusion of the middle Triassic Kekesai Sequence granite and the Late Triassic Zamari Sequence granite, which provided conditions for enrichment of many sulfophile elements in the study area, especially represented by the rule [Zn (HH), Ag (HH)] \(\Rightarrow\) [Cd (HH)] (Figure 22). Therefore, the Middle Triassic magmatism provided a heat and material source to enrich elements and is an important geological unit for aggregating sulfophile elements. Cu mineralization often occurs in the contact between the Early Permian magmatic rocks and surrounding rocks, such as the Proterozoic Baishahe Formation (Pt\({}_{\text{t}}\)b), forming the Keregoau East copper occurrence and the Hariza copper deposit. The rules of [W (HH)] \(\Rightarrow\) [Cu (HH)], [Cu (HH)] \(\Rightarrow\) [Co (HH)], [Bi (HH)] \(\Rightarrow\) [Cu (HH)], [Cu (HH), La (LL)] \(\Rightarrow\) [Co (HH)], and [Co (HH), Sb (LL)] \(\Rightarrow\) [La (LL)] related to Cu HH and Co HH clustering also have high density in the Early Permian granite quartz diorite (P\({}_{\text{1}}\)\(\gamma\)\(\delta\)o), especially [Cu (HH)] \(\Rightarrow\) [Co (HH)] (Figure 23). Therefore, we may infer that a coexisting relationship between Cu and other elements developed in the Early Permian granite quartz diorite. Figure 20: _Cont._  
Figure 20: Total number of points and density of each mined rule in the main lithostrata. Figure 21: Rule {As (HH)} \(\Rightarrow\) {Sb (HH)} overlayed with the main lithostrata.
## 4 Experiments and Evaluations
### Evaluation Results  
The results of the experiment with 10 vulnerable devices are shown in Figure 7. The horizontal axis of each graph is time (seconds) and the vertical axis is the number of devices infected by each worm. For \(n=100\) and \(\ell=180\) (Figure 7e), the malicious botnet was eliminated in 500 s; for \(n=100\) and \(\ell=300\) (Figure 7f), in 394 s; for \(n=160\) and \(\ell=180\) (Figure 7h), in 361 s; and for \(n=160\) and \(\ell=300\) (Figure 7i), in 170 s. On the other hand, for \(n=50\) and \(\ell=180\) (Figure 7b), the white-hat worm was able to infect multiple devices but could not maintain that state and failed to disinfect the malicious botnet. Also, for \(n=50\) and \(\ell=60\) (Figure 7a), \(n=50\) and \(\ell=300\) (Figure 7c), \(n=100\) and \(\ell=60\) (Figure 7d), and \(n=160\) and \(\ell=60\) (Figure 7g), the white-hat worm was unable to infect multiple devices and failed to disinfect the malicious botnet.  
Next, the results of the experiments with 30 vulnerable devices are shown in Figure 8. As in Figure 7, the horizontal axis of each graph is time (seconds) and the vertical axis is the number of devices (units) infected by each worm. For \(n=50\) and \(\ell=180\) (Figure 7(b)), the malicious botnet was eliminated in 965 s, for \(n=100\) and \(\ell=180\) (Figure 7(e)) in 542 s, and for \(n=100\) and \(\ell=300\) (Figure 7(f)), in 1610 s. On the other hand, for \(n=50\) and \(\ell=180\) (Figure 7(b)), \(n=100\) and \(\ell=180\) (Figure 7(e)), and \(n=100\) and \(\ell=60\) (Figure 7(d)), the malicious botnet could not be disinfected because the white-hat worm failed to infect multiple devices. For \(n=160\) and \(\ell=60\) (Figure 7(g)), \(n=160\) and \(\ell=180\) (Figure 7(h)), and \(n=160\) and \(\ell=300\) (Figure 7(i)), the white-hat worm was able to infect multiple devices but failed to disinfect the malicious botnet. The repeated increase and decrease in the number of white-hat worms can be attributed to the fact that multiple white-hat worms that started operating at approximately the same time ended their operation due to lifespan, or that multiple devices were infected and started operating simultaneously by the white-hat worms. In addition, the time it took to disinfect the last malicious worm and for the malicious worm to infect the last device may be due to the fact that the search area was so large that it took a long time to find it.  
Figure 8: \(Cont.\)  
Figure 7: Experimental results of modifying each parameter of the white-hat worm using 10 vulnerable devices.
# Measurement of Unit Weight of Dry Sand Using Piezoelectric Sensor
###### Abstract  
Recently, an electro-mechanical impedance (EMI) technique has found wide application in the monitoring of concrete structures. This EMI technique was employed for measuring the unit weights of various grain sizes of sand and the relevant process is reported herein. A piezoelectric sensor (PZT, i.e., lead zirconate titanate sensor) was imbedded into small, medium, and large-sized grains of the Nakdong river sand and a surcharge was applied. The effect of an increase in unit weight owing to the surcharge was investigated in terms of the resonant frequency (peak frequency) and corresponding conductance at resonant frequency (peak conductance). A ceramic-coated PZT sensor was used to prevent the occurrence of a short circuit. The measured peak frequency and conductance from the PZT sensor were correlated with the unit weight of sand. With an increase in the unit weight of sand, the peak PZT frequency was found to have increased, however, the peak conductance was seen to have decreased. The peak PZT frequency and conductance were seen to be strongly correlated with the unit weight of Nakdong river sand except for the peak conductance of large grain sized sand.  
electro-mechanical impedance; piezoelectric; frequency; conductance; unit weight; sand +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Normalized Results  
To summarize the results more clearly, the optical transmittance, sheet resistance, and stiffness values are normalized with the value of two-layer graphene set to 1. Figure 13 and Table 5 show the normalized values of the optical transmittance, sheet resistance, and stiffness, respectively. From 0.2 to 1.0 mg/mL of AgNW concentration increasing, the optical transmittance and sheet resistance reduce to 79% and 16% of the original two-layer graphene value, respectively. The stiffness of the composite increases at first and then becomes stable after 0.6 mg/mL with a value of approximately 4.6 times higher than two-layer graphene.
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 3 Image Space Multidirectional Occlusion Shading Model
### Occlusion Computation
#### 3.3.2 Partial Occlusion  
For partial occlusion, we could use an opacity buffer in addition to the depth buffer in order to compute how much the light intensity is transported through a transparent medium, similarly to the front-to-back ray-casting computation starting the ray marching from a pixel \(P\). This additional opacity buffer is not necessary if we use the opacity approximation, which is introduced in Section 3.5, since there is only one parameter required per pixel and it can be stored in the A component of the color buffer (RGBA).  
Figure 4: Comparison of uniform (**a**) and nonuniform (**b**) sampling along the path \(r(t)\). The gray lines indicate the occluded paths. Coarse sampling might not be able to find the occlusion as shown in (**a**), where the third path is supposed to be occluded. The green dots are the occlusion test points, and the blue dots are the light samples.  
Figure 5: Different occlusion effects on the test data. Local illumination is applied to the opaque object in (a). Full occlusion (FO) is used in (**b**–**c**), and partial occlusion (PO) is shown in (**d**–**f**). \(a_{th}\) is used in (**f**) to apply ISMOS only on an opaque blue object. \(a_{app}\) indicates the approximated opacity. \(a_{th}\) is a threshold alpha value, which is used in Section 3.6.  
Once the ray hit a transparent medium, we accumulated the amount of occlusion for the \(i\)th light sample as follows.  
\[O_{oci}=(1-O_{oci})\cdot\alpha_{t}+O_{oci}, \tag{4}\]  
where \(\alpha_{t}\) is the opacity value at \(r(t)\) and \(O_{oci}\) is initialized as zero. We stored multiple depth values per pixel in the depth buffers to handle transparent layers. In this paper, we used four front-most depth layers since our main interest lied between the front transparent layer and the back opaque layer. More detail about the multiple depth layers is described in Section 3.6. The termination conditions for the ray marching were: (i) \(r(t)\) in Equation (1) was not placed between \((0,0,0)\) and \((1,1,1)\); (ii) \(O_{oci}\) was one; and (iii) \(t\) was one. The total occlusion from \(N\) light samples was computed as follows.  
\[O_{cc}=\frac{\sum_{i}O_{oci}}{N}. \tag{5}\]  
The final color was computed using Equation (3). Figure 5d presents an example of partial occlusion. The occlusion was computed on the front layer in the example; therefore, the occlusion was not visible behind the transparent object.
## 5 Experimental Results
### Comparison with Different Variations on LCA  
Table 3(a), Table 3(b) and Table 3(c) show the compression ratio, the maximum memory usage, and the compression time within the variations of LCA, respectively. In Table 3(a) and Table 3(c), we can see that the improved encoding brings more efficient compression than the naive encoding, and the processing time of the improved encoding is almost the same as that of the naive encoding. On the other hand, regarding the grammar size, there is really not much difference between the online and offline algorithm. From Table 3(b) and Table 3(c), the running time of **LCA-online** is almost the same as that of **LCA-offline** and a bit slower than **LCA-fast**. However, we recall that the compression speed of **LCA-offline** depends on the I/O time of the computing environment; and **LCA-fast** always needs more memory space than the input text. In addition, **LCA-online** has an advantage to enable us incremental compression.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Source** & & **online:I** & **LZW** & **gzip -9** & **bzip2 -9** & **Re-Pair** & **LZMA** \\ \hline
**Repetitive Text** & **Size (bytes)** & & & & & & \\ _Escherichia\_Coli_ & \(112,689,515\) & \(0.11\) & \(0.14\) & \(1.56\) & \(0.16\) & \(1.65\) & \(1.76\) \\ _Para_ & \(429,265,758\) & \(0.11\) & \(0.16\) & \(1.57\) & \(0.15\) & \(1.21\) & \(1.35\) \\ _Cere_ & \(461,286,644\) & \(0.091\) & \(0.19\) & \(1.48\) & \(0.15\) & \(1.10\) & \(1.19\) \\ _influenza_ & \(154,808,555\) & \(0.082\) & \(0.13\) & \(0.38\) & \(0.35\) & \(0.67\) & \(0.35\) \\ _coreutils_ & \(205,281,778\) & \(0.14\) & \(0.18\) & \(0.12\) & \(0.20\) & \(0.94\) & \(0.37\) \\ _kernel_ & \(257,961,616\) & \(0.14\) & \(0.20\) & \(0.11\) & \(0.15\) & \(0.80\) & \(0.72\) \\ _einstein.de.txt_ & \(92,758,441\) & \(0.12\) & \(0.14\) & \(0.12\) & \(0.33\) & \(0.63\) & \(0.15\) \\ _einstein.en.txt_ & \(467,626,544\) & \(0.11\) & \(0.16\) & \(0.11\) & \(0.34\) & \(0.67\) & \(0.16\) \\ _world\_leaders_ & \(46,968,181\) & \(0.076\) & \(0.10\) & \(0.089\) & \(0.11\) & \(0.50\) & \(0.21\) \\ \hline
**General Text** & **Size (bytes)** & & & & & & \\ _ENGLISH_ & \(209,715,200\) & \(0.21\) & \(0.22\) & \(0.18\) & \(0.16\) & \(3.42\) & \(0.92\) \\ _XML_ & \(209,715,200\) & \(0.18\) & \(0.15\) & \(0.06\) & \(0.23\) & \(1.76\) & \(0.42\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
By these results, **LCA-online** has sufficient performance compared with the offline version, and the proposed encoding is very effective at CFG representation.