esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SSC and Equivalent White Noise Level
### Data Bits Aligned  
When the data bits modulated on the undesired and desired signal are aligned, i.e., \(\Delta=0\), the situation is illustrated in Figure 4.  
In this case, ACF's of the undesired and desired signals become identical over any arbitrary integration interval. The variance of the correlator output could be represented as:  
\[\begin{array}{ll}E\Big{\{}|y_{k}|^{2}\Big{\}}&=\int_{kT_{b}}^{(k+1)T_{b}}\int _{kT_{b}}^{(k+1)T_{b}}R_{11}(s,t)R_{22}(s,t)e^{j2\pi fs}e^{-j2\pi ft}dsdt\\ &=\int_{kT_{b}}^{(k+1)T_{b}}\int_{kT_{b}}^{(k+1)T_{b}}R_{22}(s,t)e^{j2\pi fs}e^ {-j2\pi ft}dsdt\end{array} \tag{11}\]  
the last line of which comes from the fact that the ACF's of the incoming and local code are identical: both of them are unity pulse trains. As derived in Appendix B, substituting Equation (6) into Equation (11), we have:  
\[\begin{array}{ll}E\Big{\{}|y_{k}|^{2}\Big{\}}&=\ TT_{c}\Big{[}\frac{\sin(\pi ft _{c})}{(\pi ft_{c})}\frac{\sin(\pi ft_{b})}{(\pi ft)}\Big{]}^{2}\\ &=(T_{b})^{2}\frac{T_{c}}{T}\Big{[}\sin c(fT_{c})\sin c(fT_{b})\big{]}^{2}\end{array} \tag{12}\]  
where:  
\[\sin c(t)\triangleq\left\{\begin{array}{ll}1&t=0\\ \frac{\sin(\pi t)}{\pi t}&t\
eq 0\end{array}\right. \tag{13}\]  
is the sampling function. On the other hand, assume that the correlator shown in Figure 1 was driven by additive Gaussian white noise with power spectral density \(I_{0}\), then its output should have zero mean and a variance of:  
\[E\Big{\{}|y_{k}|^{2}\Big{\}}\triangleq I_{0}T_{b} \tag{14}\]  
which is the product of the power spectral density and equivalent bandwidth \(1/T_{b}\). Comparing Equations (12) and (14), and an equivalent white noise with power spectral density \(I_{0}\) is obtained defined as:  
\[I_{0}=\frac{E\Big{\{}|y_{k}|^{2}\Big{\}}}{T_{b}}=\frac{T_{c}}{T}T_{b}[\sin c( fT_{c})\sin c(fT_{b})]^{2} \tag{15}\]  
Figure 4: Undesired and desired signals with navigation data bits perfectly aligned.  
because this equivalent white noise generates the same variance values as a true interference does at the output of the correlator. Based on this definition, we can further define the spectral separation coefficient [17] for B1-I as \(SSC_{B1-I}\):  
\[\begin{array}{ll}I_{0}&\triangleq P_{R}\cdot\frac{T_{c}}{T}T_{b}[\sin c(fT_{c} )\sin c(fT_{b})]^{2}\\ &\triangleq P_{R}\cdot SSC_{B1-I}\end{array} \tag{16}\]  
where, \(P_{R}\) is inserted for the more general case where an undesired signal has an arbitrary power level in watts.
## 4 Results and Discussion
### Results
#### 4.1.4 Results on Time-Series Composition  
Time-series composition is an efficient method when dealing with cloud and shadow contamination. Also, contrastive learning was used in this study to extract inherent time-series features from incomplete time-series observations. This experiment aimed to determine if time-series composition is essential for contrastive learning. In the Dijon research region, 5-day raw time-series features and n-day (\(n\) = 10, 20, 30, 40, and 60) composited spectral features were, respectively, supplied into the proposed contrastive-learning-based feature representation framework. Table 5 presents their classification accuracy using feature representation with different composition periods.
## 2 Background and Related Work
### Attacks in DT-IoV  
DT-IoV routing protocols require a degree of cooperation between nodes belonging to it to deliver their destination messages. However, nodes might misbehave. Nodes' misbehavior may significantly affect the network performance, as shown in [7; 8], a significant problem to be studied in the context of DT-IoV.  
One of the most studied attacks is the black-hole attack. The impact on the network that results from this type of attack can vary, affecting the normal functioning of routing protocols in DT-IoV [9]. Not only the impact black-hole attacks can have in a network but also the fact that it is commonly studied within the research community shows how important it is to prevent this attack. This article will focus only on black-hole attacks aiming to identify nodes that perform such attacks and decrease their network effect.
## 2 Network Model and Underwater Acoustic Propagation
### Transmission loss  
The acoustic signals propagate differently in shallow water and deep sea. The shallow water refers to water with depth lower than 100 m [16]. In shallow water, acoustic signals propagate within a cylinder bounded by the sea surface and sea floor [25]. The transmission loss for shallow water can be expressed as [26]:  
\[TL=10\log\textit{d}+\alpha\textit{d}\times 10^{-3} \tag{2}\]  
where \(d\) is the distance between source and receiver expressed in meters, \(\alpha\) is the absorption coefficient with the unit _dB/km_, and \(TL\) is in _dB_.  
In deep sea, transmission loss is caused by spherical spreading and absorption. The loss can be expressed as [26]:  
\[TL=20\log\textit{d}+\alpha\textit{d}\times 10^{-3} \tag{3}\]  
in _dB_.  
Equations (2) and (3) indicate that transmission loss are mainly caused by distance dependent attenuation and frequency dependent absorption both in shallow water and deep sea.  
The frequency dependent absorption coefficient \(\alpha\) is calculated by [PERSON]'s expression in [22] for frequencies above a few hundred Hertz as:  
\[\alpha=\frac{0.1f^{2}}{1+f^{2}}+\frac{40f^{2}}{4100+f^{2}}+2.75\times 10^{-4}f^{ 2}+0.003 \tag{4}\]  
where \(\alpha\) is in _dB/km_ and \(f\) is the frequency in kHz.  
For lower frequencies, \(\alpha\) is expressed as:  
\[\alpha=0.11\frac{f^{2}}{1+f^{2}}+0.011f^{2}+0.002 \tag{5}\]  
where \(\alpha\) is in _dB/km_ and \(f\) is the frequency in kHz.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
### State of the Art  
Lately, several farm management platforms and technologies have become available to support precision and smart agriculture applications; below are some indicative platforms in English available in the market:  
* Climate FieldView is an integrated digital platform that collects and analyzes field data, helping farmers make more informed decisions regarding crop management, planting, and harvesting. The available tools allow farmers to manually delineate management zones ([[https://www.fieldview.com.au/](https://www.fieldview.com.au/)]([https://www.fieldview.com.au/](https://www.fieldview.com.au/)) (accessed on 11 August 2023));
* Granular's Farm Management Software (FMS), credited as the first cloud-based mobile-centric program of its kind, offers an intuitive breakdown of everything a farmer needs to consider, from financial to soil management to operations. The platform is mostly oriented toward sensors and smart agriculture ([[https://www.corteva.com/resources/media-center/granular-provides-new-digital-nitrogen-management-options-to-farmers.html](https://www.corteva.com/resources/media-center/granular-provides-new-digital-nitrogen-management-options-to-farmers.html)]([https://www.corteva.com/resources/media-center/granular-provides-new-digital-nitrogen-management-options-to-farmers.html](https://www.corteva.com/resources/media-center/granular-provides-new-digital-nitrogen-management-options-to-farmers.html)) (accessed on 11 August 2023));
* Farmers Edge, a comprehensive smart agriculture platform that includes field-centric data collection, satellite imagery, variable rate technology, and weather analytics to optimize farm operations ([[https://farmersedge.ca/](https://farmersedge.ca/)]([https://farmersedge.ca/](https://farmersedge.ca/)) (accessed on 11 August 2023));
* Agworld is a collaborative farm management platform that allows farmers, agronomists, and other stakeholders to work together on planning, budgeting, and reporting farm activities.
## 3 Results and Discussion  
### Spectral Modal Analysis  
After the eigenvalue analysis, the main vibration modes were determined, which can be seen in Table 1.  
Six main modes are identified, covering 93% of the total moving mass. Specifically, modes one on the \(X\)-axis and two on the \(Y\)-axis are of particular interest. These findings show the importance of these modes in structural analysis, highlighting their relevance for the accurate understanding and prediction of the dynamic behaviour of buildings, i.e., Figure 4.  
After performing the modal analysis, a modal superposition was carried out, as previously discussed in the materials and methods section. For a viscous damping ratio of 5%, displacement values were obtained, both in the \(X\)-axis, as shown in Figure 5A and in the \(Y\)-axis, as shown in Figure 6A. On the \(X\)-axis, the highest displacement occurred on the roof surface, with a value of 1.9452 cm, and for the \(Y\)-axis, it was 1.9142 cm.  
For a high ductility (25%), the obtained values were: for X = 1.1232 cm (Figure 5B), and for Y = 1.1053 cm (Figure 6B).  
The most notable difference in the presented results lies in ductility. A structure with higher ductility will exhibit superior damping, resulting in more efficient behaviour during seismic events. In this regard, displacements will be reduced.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No Mode** & **Frequency (Rad/s)** & **Frequency (Cycle/s)** & **Period (s)** \\ \hline
1 & 14.3953 & 2.2911 & 0.4365 \\
2 & 14.5378 & 2.3138 & 0.4322 \\
3 & 17.1032 & 2.7221 & 0.3674 \\
4 & 45.9558 & 7.3141 & 0.1367 \\
5 & 46.4144 & 7.3871 & 0.1354 \\
6 & 54.3164 & 8.6447 & 0.1157 \\ \hline \end{tabular}
\end{table}
Table 1: Main vibration modes. Eigenvalue analysis.  
Figure 4: **(A)** Vibration mode 5: Flexure in Y. Frequency = 7.3871 cycle/s. (**B**) Vibration mode 6: Torsion in Y. Frequency = 8.6447 cycle/s.
# Study on the Dynamic Generation of Subcooled Water Using a Compact Heat exchanger
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.1.3 Influence of Specific Flux \(i\)  
When considering the condition settings for the dynamic generation of subcooled water, the flow rate suitable for the dynamic generation of subcooled water in a compact heat exchanger is investigated according to the energy conservation relationship. To reduce the parameter variables, the specific flux \(i\) and the working fluid temperature difference between the two sides of the heat exchanger are taken as the objects of analysis, where the specific flux \(i\) is the ratio of the flow rate of NFL to tap water. For each of the different subcooling degrees (2 K, 2.5 K, 3 K), 30 experimental data sets are taken. For either tap water or NFL, a slight change in the flow rate at the inlet will cause a change in the temperature of the inlet and outlet on both sides of the heat exchanger, i.e., a change in the temperature difference. When the flow rate ratio is set in the range of 1.3-1.9, the temperature change of the tap water between the inlet and outlet of the heat exchanger is between 5 and 6 K, and that of the NFL is between 3 and 4 K, as shown in Figure 6. When the subcooling degree of the subcooled water increases, \(i\) tends to change more slowly, while for the working fluid moving in and out of the heat exchanger, the change in the temperature difference also slows. This occurrence indicates that as the subcooling degree increases, the range of suitable tap water flow is reduced. When the flow rate is high, the disturbance is large; when the flow rate is low, the time of flow through the heat exchanger increases, and this also increases the level of uncertainty and the ice nucleus growth time. Therefore, it is necessary to choose the most suitable specific flux size. As can be seen in Figure 6, ideal specific flux \(i\) for a large subcooling degree in the dynamic generation of subcooled water is around 1.5.
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
###### Abstract  
The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems' development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment.  
nonlinear-flight-mechanics; neural networks; guidance, navigation, and control; machine learning; model; matlab-simulink +
Footnote †: journal:  
_Article_
## 2 Results and Discussion
### Storage Stability  
Because immobilized microbial cells have the advantage of a long period of use, the storage stability of _Agrobacterium_ sp. IFO 13140 cells immobilized on loofa sponge and kept at 4 \({}^{\circ}\)C for 300 days was evaluated. Curdlan production remained constant over time, with an average value of 13.05 g/L, and no statistically-significant difference was observed compared to that obtained without storing cells (13.95 g/L). Therefore, immobilized cells retained their metabolic activity during the storage periods investigated (60, 120, 180, 240 and 300 days), demonstrating a high storage stability. The average yield was 26.10%.
## 2 Materials and Methods
### Genomic Characterization
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Species** & \multicolumn{2}{c}{**Culture**} & \multicolumn{4}{c}{**GenBank Accession Numbers 2**} \\  & **Collection 1** ** & **ITS** & _BenA_ & _CaM_ & _rpb_2 \\ \hline _Penicillium_ & CBS 291.48 & AF033483 & AF674402 & KU896821 & JN406584 \\ _Penicillium blankericum_ & CBS 143044 & LT899762 & LT898227 & LT899758 & LT899760 \\ _Penicillium fimosum_ & CBS 142991 & – & LT898273 & – & – \\ _Penicillium_ & CBS 142992 & LT899782 & LT898285 & LT899766 & LT899800 \\ _Penicillium_ & CBS 138225 & KJ775686 & KJ775179 & KJ775413 & MN969124 \\ _Penicillium mexicamum_ & CBS 138227 & KJ775685 & KJ775178 & KJ775412 & MN969127 \\ _Penicillium mexicamum_ & Y20P-5 & CQQ40471 & CQ30423495 & CQ319459 & — \\ _Penicillium mexicamum_ & **MUM 23.42** & **P736069** & **P7405214** & **P7421213** & **P7421215** \\ _Penicillium mexicamum_ & **MUM 23.43** & **PP376070** & **PP405215** & **PP421214** & **PP421216** \\ _Penicillium paradoxum_ & CBS 527.65 & EF669707 & EF669683 & EF669692 & EF66967 \\ _Penicillium crystallization_ & CBS 479.65 & AF033486 & EF669682 & FJ530973 & EF669669 \\ _Penicillium maladdorum_ & CBS 490.65 & AF033485 & EF669681 & FJ530972 & EF669672 \\ _Penicillium scirsis_ & FMR 18076 & LR884497 & LR884494 & LR884496 & LR884495 \\ _Penicillium empirosom_ & CBS 142990 & LT899781 & LT898238 & LT89765 & LRS979765 & LRS979979 \\ _Penicillium bovinfinosus_ & CBS 108285 & AF26347 & KJ83446 & FJ530989 & JN406649 \\ _Penicillium turbutum_ & CBS 383.48 & AF034454 & KJ834499 & KJ8096853 & JN406556 \\ _Penicillium maldriti_ & CBS 347.61 & AF033482 & KJ834470 & EU644076 & JN406561 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sequence data of _Penicillum_ species used in the phylogenetic analyses. The new data generated during this study are pointed out in bold. Blast2 Go analysis [51; 52] to acquire InterPro protein information, combine Gene Ontology (GOs) terms, and perform GOSlim and enzyme mapping analysis. Carbohydrate-active enzymes were identified utilizing the dbcan3 web-server [53], while biosynthetic gene clusters (BGCs) were scrutinized using the antiSMASH web server v.7.1.0 [54]. Furthermore, the BlastKoala mapping tool [55] was employed to reconstruct metabolic pathways and correlate the function of each gene product against the Kyoto Encyclopedia of Genes and Genomes (KEGG). The AsgeneDB [56] was screened with DIAMOND v.2.0.15 [57] (identity = 50%, evalue < 10\({}^{-4}\)), in order to obtain information regarding the species arsenic resistance gene contents and metabolic peculiarities. Additional data visualization and plotting were conducted using SRPlot [58].
# A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design
## 3 Methodology  
### Solution Model  
For each algorithm the same solution model was applied. The generic structure of the solution model is shown in Figure 1. Thus, in order to implement an algorithm, the following steps are taken:  
* For all node pairs in set \(\mathcal{N}\), a set of transponders \(\mathcal{T}\) must be selected that are capable of meeting the demands for the node pair. The set is a 4-element vector \(\mathcal{T}=\{n\cdot t_{1},n\cdot t_{2},n\cdot t_{3},n\cdot t_{4}\}\), where the first element is the number of 40G transponders used, the second element is the number of 100 G transponders used, the third element is the number of 200 G transponders used and fourth element is the number of 400 G transponders used. For example, \(\mathcal{T}=\{2,1,0,3\}\) means that this set consists of two 40 G transponders, one 100 G transponder and three 400 Gb transponders. The set of transponders is chosen from four precalculated solutions, generated using linear programming.
* For each selected transponder a path should be determined, through which it will transmit data. The path \(p\in\mathcal{P}\) is selected randomly by drawing one of the \(k\)-element set of predefined paths.
* Choosing path is necessary to be able to assign each transponder \(t\in\mathcal{T}\) a unique piece of bandwidth \(s\in\mathcal{S}\) for data transmission. This is done using a heuristic RMLSA (routing modulation level and spectrum allocation) algorithm [14] that sequentially handles each demand and uses longest path first (LPF) sorting.
## 1 Introduction  
Compounds with perovskite structure with a general formula ABO\({}_{3}\) have been of continuous importance for many years [1]. The properties of perovskites depend critically on the nature of the metal atoms in the A and B sites. As a consequence, substitutions on the A and/or B sites allow varying their properties over wide ranges, which makes them adaptable to various technical requirements. Perovskites have numerous applications such as dielectric resonators, filters and antennas in a vast field of domains like wireless communication devices, radar, direct broadcasting satellites and global positioning systems [2; 3; 4]. Tantalum-based ceramics such as Ba\({}_{3}\)ZnTa\({}_{2}\)O\({}_{9}\) (BZT) and Ba\({}_{3}\)MgTa\({}_{2}\)O\({}_{9}\) (BMT) are frequently used as materials for dielectric resonators, despite the elevated tantalum price. As a consequence, a considerable amount of research is nowadays focused on the search for alternative ceramic materials providing comparable dielectric proprieties at lower costs. Applications as dielectric resonators require materials with a high quality factor at the resonance frequency (\(Q_{f}\)), a high dielectric constant (\(\varepsilon_{r}\)) and a near-zero temperature coefficient of the resonance frequency (\(\tau_{f}\)) [2; 5]. In this context, Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) (BCZN) ranks among the best oxide materials with regard to applications such as dielectric resonators in the range of microwave frequencies due to its high \(Q_{f}\) value (\(\sim\)56,000-85,000 GHz), its high \(\varepsilon_{r}\) (\(\sim\)34.5) and its small \(\tau_{f}\) (\(\sim\)0) [6; 7]. BCZN-based ceramics have therefore been suggested as a promising alternative to tantalum-based ceramics for applications in the microwave range. However, the dielectric properties of these ceramic materials are dependent on their density [8]. In addition, it is well known that BCZN-based ceramics are difficult to densify below 1450 \({}^{\circ}\)C [9]. In order to improve the sinterability of BCZN ceramics, we have successfully used the aqueous mixing technique-assisted solid-state method for the preparation of Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) powders, which were sintered at 1350 \({}^{\circ}\)C for 30 h in air [10]. As a continuation of our previous work, the present paper describes improvements of the microwave dielectric proprieties of BCZN ceramics achieved by a supplementary annealing process, which is added to the synthesis procedure described previously. In this article, we study the effect of this additional annealing step on the structure, microstructure, and microwave dielectric properties of BCZN ceramics and compare the results with those obtained without this annealing. It will be shown that this second annealing step leads to a \(\sim\)28% increase of the \(Q_{f}\) value. The improvement is explained by cation ordering within the crystal, as revealed by X-ray diffraction (XRD). The relationship between the heat treatment and the BCZN characteristics will be discussed in detail.
## 3 Results and Discussion
### Device Extraction Efficiency  
The same extraction fibers were used to absorb the volatile components emitted by the flower for 15 min. The parallel experiments--the optimum design with/without using a fan--were performed to evaluate the device's extraction efficiency. The selected GC-MS analysis of volatile compounds (one of three replicates) is shown in Table 3.  
Figure 2: Chromatograms of volatile compounds from live water lily extracted by using two fibers. Upper part is the PDMS result, and the lower part is the PDMS/DVB result. According to the results in Table 3, 33 compounds were identified by GC-MS. Among them, benzyl acetate (10.4%), pentadecane (15.5%), 6,9-heptadecadiene (40.1%), and 8-heptadecene (15.3%) were the most abundant volatile compounds of live water lily. Further, the fan also affected the analysis. Because the mass transfer of VOCs was sped up by the airflow from the fan, the peak areas obtained from the two experiments were significantly different, as shown in Table 3. This demonstrated that the modified HS-SPME is a rapid and useful method to analyze VOCs emitted from live water lily flowers. There have only been a few studies on volatile components from water lily. In addition, our study is the first to analyze the volatile compounds from a live water lily flower. The main VOCs in this study, including benzyl acetate, pentadecane, 6,9-heptadecadiene, and 8-heptadecene, were also found in other studies. [PERSON] et al. also identified pentadecane, 6,9-heptadecadiene, and 8-heptadecene from tea infusions of water lily flowers (_Nymphaeaceae_, 21 tropical water lily cultivars and 12 hardy water lily cultivars) [29]. [PERSON] et al. found that benzyl acetate is one of the most abundant VOCs emitted by living flowers of _Jasminum sambac_ (L.), at around 10.56-14.30 mg/kg FW (fresh weight) in opening to fully opened flowers [30]. Therefore, the four main VOCs we analyzed in a live water lily flower were also