esa-sceva/satcom-chunk-collection huggingface dataset:
# The Volatile Compounds Change during Fermentation of _Saccharina japonica_ Seedling
## 4 Discussion
[32], which demonstrated that fermentation with _Saccharomyces cerevisiae_ effectively eliminated the fishy odor from _Bangia fusco-purpurnea_ and _Gracilaria lemaneiformis_, respectively, confirming the potential of _S. cerevisiae_ fermentation to enhance the flavor profile of various seaweed species such as _S. japonica_ seedlings. This finding is partially consistent with that of [PERSON] et al. [17], who found that the fishy odorant 1-octen-3-one disappeared from kelp after fermentation with _S. cerevisiae_, accompanied by the formation of 3-octanone. 1-Octen-3-ol, hexanal, heptanal, nonanal, and 2,4-heptadienal, which have been reported as the fishy odorants [16; 23; 31], are mainly derived from the oxidation of unsaturated fatty acids [37]. Unsaturated aldehydes and ketones, such as 1-octen-3-one, 2,6-nonanal, 2,4-decadienal, and 2-nonenal, have been identified as the contributors to the fishy odor of kelp [17]. Microbial fermentation has been utilized to deodorize seaweed, fish, and other seafood [38; 39; 40], as enone reductase catalyzes the reduction of unsaturated bonds [17]. In addition, the reduction of fishy odor is related to the reactions of ester synthesis, dehydrogenation, reduction, and deformylation-oxygenation catalyzed by enone reductase, oxidases, aldehyde deformylating oxygenase, aldehyde dehydrogenase, ester synthase, aldosterone reductase, alcohol dehydrogenase, epoxide hydrolase, dehydrogenase, and acyltransferase [23]. Moreover, the disappearance of 1-octet-3-ol may be related to the catalysis of enzymes such as ADH, ALDH, ES, and ATF, which can convert 1-octet-3-ol to esters [41]. Enzymes could catalyze the hexanal to alcohol and esters through the reactions of reduction, dehydrogenation, and ester synthesis [42]. In addition, 1-nonanal could be converted to fatty acids and enol through ester synthesis, dehydrogenation, and reduction reactions in yeast [43]. Thus, the removal of the fishy odor and the enhancement of the overall aroma profile could be attributed to the bioreactions involving reduction, dehydrogenation, and ester synthesis during fermentation with _S. cerevisiae_. This will facilitate the understanding of how microorganism metabolism enhances the flavor of seaweeds, such as _S. japonica_ seedlings. However, it is still unclear as to what the transformation pathways are for the eliminating or removing the fishy odor. Future research is required to explore how the fishy odors evolve during the fermentation process with _S. cerevisiae_.
## 2 Biosynthesis and Key Regulatory Networks of Volatile Terpenoids
### Regulatory Determinants of Volatile terpenoid Biosynthesis
#### 2.2.1 Key Enzymes
TPSs, serving as terminal rate-limiting enzymes in downstream terpenoid metabolic pathways, are classified into eight distinct subfamilies, designated TPS-a through TPS-h. Each subfamily plays specialized catalytic roles in regulating the biosynthesis of structurallydiverse terpenoids. Specifically, TPS-a primarily governs sesquiterpene biosynthesis, while TPS-b and TPS-g are responsible for cyclic and acyclic monoterpene formation. TPS-c, TPS-e, and TPS-f catalyze the production of GPP, gibberellins, diterpenes, monoterpenes, and sesquiterpenes, with TPS-f also playing a role in gibberellin and diterpene metabolism. Evolutionary divergence has driven functional specialization, wherein TPS-c, TPS-e, and TPS-f are predominantly involved in the biosynthesis of primary metabolites such as gibberellins and abscisic acid, whereas TPS-a, TPS-b, and TPS-g are primarily associated with secondary metabolite biosynthesis [55]. The TPS-h subfamily is mainly found in lycophytes, bryophytes, liverworts, and ferns. The structural diversity of plant terpenoids is attributed to the regulatory roles of TPSs, whose gene expression determines the initiation of metabolic pathways and biosynthesis of associated volatile compounds. For instance, in _Lilium 'Siberia', LoTPS2_ primarily catalyzes the synthesis of (E,E)-\(\alpha\)-farnesene [28]. In _Dendrobium officinal_, _DoTPS10_ was identified as a key regulatory enzyme controlling linalool biosynthesis [56]. In peach (_Prunus persica_), the transcription factor _PpERF61_ activates the expression of _PpTPS1_ and _PpTPS3_, thereby promoting the accumulation of the monoterpene geraniol during fruit ripening [57]. Genome-wide analysis of _Perilla fufescens_ revealed 109 _PfTPS_ genes, with co-expression network analysis across four chemotypes pinpointing critical biosynthetic regulators: _PfTPS46-PL_, _PfTPS46-PK_, _PfTPS18-PA_, and _PfTPS49-PA_ encode linalool synthases, whereas _PfTPS47-PA_ predominantly produces citronellol, with geraniol as a secondary product [58]. Furthermore, in Newhall sweet orange (_C. sinensis Osbeck_), _CitTPS16_ expression modulates E-geraniol synthesis, directly contributing to the formation of fruit flavor-associated aroma compounds [59].
## 4 Starlink as Case Study
### Stage 5: Midpoint Indicators Calculation  
In order to calculate the midpoint indicators, its necessary to have the final mass budget table as input. The midpoint indicators can be calculated either on the subsystem level or component level for the space segment [10]. Table 6 below represents the final midpoint indicator results for 1665 Starlink version 1 satellite. We can see that resource use of minerals and metals contributes the most among other indicators considering the system boundary of the analysis. Since we focus on the production phase of the satellite, usage of minerals and metals contributes to 58 kilopoints out of the total 76 kilopoints. An adapted EF method (version 1) is used for this analysis, and the description of the impact category in Table 6 is taken from the Simapo manual [67]. To demonstrate how the method works, we apply the environmental footprint (EF) method (version 1) as an example. Users of this method should always use the latest adapted version of the EF method [68]. The unit of measurement \"point\" (Pt) is used here for the analysis. It signifies the annual environmental load. This load encompasses the entirety of production and consumption activities within the U.S. economy. The division of this load is allocated proportionally to represent the share attributable to a single American individual [18].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Midpoint Indicators** & **Description of Impact Category** & **kPt** \\ \hline Climate change & Global warming potential over a 100-year period. & 1.71 \\ \hline Ozone depletion & Ozone depletion potential (ODP) calculated to assess the destructive effects on the stratospheric ozone layer over a time horizon of 100 years. & 0.091 \\ \hline \multirow{3}{*}{Ionizing radiation, HH} & Ionizing radiation potentials: Quantifying the impact of ionizing radiation on the population relative to uranium-235. & \\ \cline{1-1} \cline{2-3}
**Sustainability** & **2024**, 16, 9431 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Midpoint indicators results for Starlink version 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ESA LCI Database** & **Dry Mass Partitioning and** & **Final Starlink Mass Budget** \\
**Subsystem/Components** & **Propellant Mass** & **Distribution (kg)** \\ \hline Payload & 31\%Mdry & 79.2 \\ Structure & 27\%Mdry & 69 \\ Thermal control & 2\%Mdry & 5.11 \\ Power & 21\%Mdry (-) 0.02 \(\times\) Mdry & 48.6 \\ Harness & 0.02 \(\times\) Mdry & 5.11 \\ Tracking, telemetry, and & 2\%Mdry & 5.11 \\ control (TT\&C) & 2\%Mdry & 5.11 \\ Data handling & 5\%Mdry & 12.79 \\ Attitude determination and & 6\%Mdry & 15.34 \\ control & 3\%Dry mass (-) 10\% of & 7.2 \\ Thruster & Propellant calculation & 0.4 \\ Tank & 3\%Mdry & 7.67 \\ Propellant & Propellant calculation & 4.2 \\ Total & & 260 kg \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mass budget table for Starlink version 1.
# A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN
## 3 Multipath/NLoS Bias Estimation Using a Clustering Algorithm
### Implementation of Multipath/NLoS Estimation Based on DBSCAN  
In total, four steps are involved in estimating the \(F^{s}(t)\) term. The fundamental concept is to isolate \(c\cdot dt_{\text{rcv}}\) from \(L^{s}(t)\) for the pseudorange containing multipath/NLoS using the DBSCAN algorithm.  
In the initial step, all \(M\) leftover terms according to (2) for a certain epoch are computed:  
\[L^{s}(t),s=1,\ldots,M \tag{5}\]  
The second step is detecting which leftover terms are affected by multipath/NLoS. By setting the parameters \(\varepsilon\), \(minPts\), and \(dist\), DBSCAN will analyze and identify clusters that meet the specified parameter configuration. Let \(\bar{L}^{s}(t)\) be the leftover terms belonging to the largest cluster assumed to represent the one free from multipath/NLoS effects. The leftover terms not belonging to the largest cluster are determined to be affected by multipath/NLoS effects.  
Although such a condition is very rare, sometimes \(L^{s}(t)\) values with multipath/NLoS effects from different satellites could be similar and, therefore, form a cluster. Considering the low probability of different multipath/NLoS biases being similar to each other, selecting the largest cluster further decreases the risk of incorrectly judging the multipath/NLoS-free cluster.  
The third step is to estimate \(dt_{\text{rcv}}\) by computing the mean value of \(\bar{L}^{s}(t)\):  
\[\hat{d}_{\text{rcv}}=mean\big{(}\bar{L}^{s}(t)\big{)} \tag{6}\]  
The final step is to estimate the biases on pesudorange measurements due to multipath/NLoS effects. According to (3), this estimation is completed by separating \(c\cdot\hat{d}_{\text{rcv}}\) from \(L^{s}(t)\) using the estimated \(\hat{d}_{\text{rcv}}\) obtained from the previous step.  
\[\hat{F}^{s}(t)=L^{s}(t)-c\cdot\hat{d}_{\text{rcv}},\forall\text{s out of the largest cluster} \tag{7}\]
# A Blockchain-Based Decentralized Public Key Infrastructure Using the Web of Trust
## 4 Background Technologies
### X.509 Certificate  
X.509 is the standard for certificates used in SSL/TLS protocols [2] to establish secure connections by binding entities--such as users, organizations, or devices--to a public key, thereby ensuring privacy and authenticity. An X.509 certificate includes the subject's details, issuer information, and key details such as the version number, serial number, signature algorithm, validity period, and the subject's public key [4, 37]. Version 3 of X.509 introduced an extension field that allows for the inclusion of additional standardized information. Although there are standard extensions, the system is flexible, allowing for new extensions to be registered with regulatory bodies like the ISO. These extensions are categorized into groups related to key information, policy information, user and CA attributes, and certification path constraints, enhancing the certificate's functionality and security.  
The certification path constraint extensions in X.509v3 certificates allow a CA to manage and restrict the level of trust extended to third parties in a cross-certified setup. These extensions are divided into three fields: basic constraints, name constraints, and policy constraints. Each X.509v3 certificate contains three attributes to define these extensions:  
* **extnID:** Denoting Extension ID, which is an Object Identifier (OID) defining the extension's definition and format.
* **critical:** A boolean indicating the importance of the extension.
* **extnValue:** The actual value of the extension.  
Below is the ASN format for the extension field [2, 4]:  
Extensions ::= SEQUENCE SIZE (1..MAX) OF Extension ::= SEQUENCE {  extnID OBJECT IDENTIFIER,  critical BOOLEAN DEFAULT FALSE,  extnValue OCTET STRING  - contains the DER encoding of an ASN.1 value  - corresponding to the extension type identified  - by extnID  }  
The _critical_ flag signals whether an extension is essential; if set to true, the extension must be recognized by the verifying application for the certificate to be valid, while non-critical extensions can be disregarded. Our presented architecture incorporates the use of an extension field to better support the inclusion of multiple signatures from ring participants. It is crucial that this proposed extension is marked as mandatory, ensuring that web browsers recognize our custom extension field. While the incorporation of a unique extension field is possible, its implementation should provide significant benefits to be deemed important.
## 3 Control Design and Stability Analysis
### Controller Design
Substituting the update laws (65) to (71) into Equation (64) and arranging yields  
\[\dot{V}_{z}\leq-k_{1}f_{21}^{2}-k_{2}f_{22}^{2}-\varphi^{\top} \tilde{\theta}_{1}f_{z1}\]0  
**Remark 5**. : _The formulated controller comprises (i) the control law (58); (ii) the update laws (65) to (71); and (iii) the auxiliary system (47a), (47b). The signals involved therein are (i) \(z_{1}\) (6), \(f_{z1}\) (11), \(sat_{z1}\) (14), \(z_{2}\) (22), \(f_{z2}\) (32), \(sat_{z2}\) (44), \(\varphi_{1}\) (19), \(B_{1d}\) (27), \(B_{1e}\) (28); (ii) the desired output \(y_{d}\), provided by model (5), according to subsection 2.2; (iii) the input error \(\Delta u\) (37), which involves \(u\) (58) and \(v\) (4), (iv) the constants \(k_{1}\), \(k_{2}\), \(K_{\psi 1}>1/2\), \(K_{\psi 2}>1/2\), which are user-defined and positive; (v) the user-defined positive constant \(C_{b}\), which is the width of the residual set \(\Omega_{co}\) defined in Section 2.3; and (vi) the constant \(k_{1b}=C_{b}\)._  
**Remark 6**. : _In the controller development, a new treatment of the \(b\Delta u\) term is proposed, and the main tasks of this treatment are (i) the term \(b\Delta uf_{z2}\) is expressed in terms of its upper bound (40); (ii) as such upper bound is unknown, it is expressed in terms of parameter updating error and update parameter (41); and (iii) the update law (69) is defined so as to obtain adequate time derivative of the overall Lyapunov-like function._  
**Remark 7**. : _The resulting auxiliary system (47a), (47b) is quite different with respect to the current ones (see [11, 12, 13]): it involves a saturation-like function of the \(z_{2}\) signal; it involves the updated parameter \(\dot{\theta}_{ub}\), which is function of \(f_{z2}\) and \(\Delta u\); the \(\Delta u\) signal is in absolute value._  
**Remark 8**. : _The modified tracking error \(z_{1}\) asymptotically converges to the compact set \(\Omega_{z1}\), whose width \(C_{b}\) is user-defined, so that it does not depend on the bonds of external disturbances,model coefficients, or model terms. Consequently, the convergence of \(z_{1}\) is achieved without requiring knowledge on these bounds. This is in contrast to common robust adaptive backstepping designs (see [11; 12; 14]), where the convergence region depends on such kind of bounds, so that the convergence of the modified error to a compact set of user-defined size requires the knowledge on such bounds._  
**Remark 9**. : _Some remarkable features of the formulated controller are (i) the control law, the update laws and the auxiliary system are function of modified error \(z_{1}\) instead of the regular tracking error \(e=x_{1}-y_{d}\), and (ii) saturation functions of the tracking error are used instead of discontinuous functions, in order to avoid undesired chattering._
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 4 Design of Purification Algorithm and Simulation Results
### Simulation Results
Here, we choose bit-flip noise and depolarizing noise. Since successful purification is a probabilistic event, not all the simulation results are of reference value. Thus, the consumption is huge. With limited computing power, we set the simulation time for the 3-node network to 5000 and the simulation time for the 5-node network to 1000. The circuit of the 5-node network on Cirq is designed based on the purification algorithm and shown in Figure 12. Simulation results of the 3-node and 5-node network on Cirq are shown in Figure 13. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node Number** & **Bell States Scheme** & **Fusion Scheme** & **GHZ States Scheme** \\ \hline
3 & 16/14 & 20/17 & 36/33 \\
5 & 64/62 & 68/65 & 216/213 \\
9 & 256/254 & 260/257 & 1296/1293 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Basic states consumed in different schemes, writing as required/measured qubits. Figure 11: The fidelity in simulation of networks with different numbers of nodes in the presence of bit-flip noise, where \(q=0.15\). purification stays around 0.25 on Cirq no matter how many nodes there are. Entanglement swapping can bring fidelity loss not only because of the noise in the channel. The loss value in entanglement swapping we use comes from the work in [18], which is hard to realize on Cirq. However, we can still observe that the fidelity gain comes from purification. The network based on fusion state shows stronger robustness under depolarizing noise. Since entanglement swapping brings serious loss of fidelity, the influence of bit-flip noise is hard to observe. After purification, the network based on fusion state has higher fidelity than that of the Bell state network under both bit-flip and depolarizing noise.
## 2 Materials and Methods
The LMVM pansharpening algorithm implemented in OTB software uses an LMVM filter that applies a normalization function at a local scale within the images to equate the local mean and variance values of the high spatial resolution panchromatic band with those of the lower resolution multispectral image [38; 40]. The resulting small residual differences are then considered to arise from the high-resolution panchromatic band [40]. [PERSON] [40] further notes that this form of filtering improves the correlation between the pansharpened image and the original multispectral image. The LMVM algorithm is highlighted below. \[\mathrm{F_{ij}=\frac{\left(H_{ij}-\overline{H}_{ij}\right)\text{-}s(L)_{ij(w, h)}}{s(H)_{ij(w,h)}}\;E} \tag{3}\]  
where \(\mathrm{F_{ij}}\) refers to the fused image, \(\mathrm{H_{ij}}\) and \(\mathrm{L_{ij}}\) denote high and low spatial resolution images respectively at pixel coordinates i.j. \((H)_{ij(w,h)}\) and \((L)_{ij(w,h)}\) are local means calculated inside the window of size \((w,h)\). s denotes the local standard deviation.
## 3 Main Framework, Dataset, and Feature Extraction Overview  
### Main Framework  
This paper first converts EEG signals into corresponding associated networks using five different methods: VG, HVG, LPVG, MFDM, and QG. For each algorithm, the related network measures of the associated networks are all firstly calculated. Then, features with a significance level greater than 0.05 are filtered out using ANOVA. The remaining network features are concatenated into a vector and input into the XGBoost classifier for classification. The main framework process of this paper is shown in Figure 7.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 6 Performance Evaluation
### Discussion
#### 6.5.3 Computational Overhead  
SL_GAS exhibits remarkable efficiency in terms of computational overhead. The cryptographic operations in SL_GAS, especially using the LiCi block cipher (LiCi-128), are optimized for the limitations of devices based on 6 LoWPAN. Although SEIP has a slight advantage in computational cost for smaller groups of nodes, SL_GAS scales more efficiently as the number of devices increases, making it preferable in larger networks. SL_GAS significantly reduces the execution time for cryptographic operations in comparison with SGMS, SEIP, and CGM6 while maintaining a balance between security and speed. A resource-constrained network in which computation and energy resources are limited is particularly in need of this efficiency. As seen in Tables 6 and 7, as a result of the decreased computing overhead, the devices remain responsive, and the energy consumption remains within acceptable limits, resulting in a longer service life for network equipment. As \(n\) increases, SL_GAS scales better than [11; 20], which exhibit a steeper rise in computational cost.
## 4 Discussion
### Deflection Characteristics of Baffle Under Intrush Current  
Figure 11 shows the change of the deflection angle of the gas relay baffle with time under the corresponding working conditions. Figure 12 is the result of the deflection of the gas relay baffle at different times under the corresponding working conditions. The legend shows the displacement of the baffle in the horizontal direction. It can be seen from Figure 12 that under the action of intrush current, the insulating oil flowing into the gas relay will impact the baffle. At 4 ms, the oil flow through the gas relay reaches about 0.01 m/s, and the baffle of the gas relay begins to deflect. Before the flow rate of the gas relay reaches the peak, that is, from 4 ms to 6 ms, the speed of the baffle deflection increases with the speed of the input insulating oil. After 6 ms, the flow rate of the insulating oil into the gas relay decays, and then the shock of the insulating oil causes the baffle to continue to deflect, but the speed of deflection of the baffle decreases slowly. At 9 ms, the baffle deflection angle of the gas relay reaches the maximum baffle rotation value of 0.436 rad, i.e., the baffle is deflected by 25\({}^{\circ}\), at which time the baffle is connected to the reed switch, which causes the gas relay to trip [13].  
Figure 11: Variation of deflection angle of gas relay baffle with time.  
Figure 10: Oil flow velocity through gas relay.  
Figure 12: Displacement cloud picture of gas relay flap.  
From this, it can be concluded that there are mainly the following processes in the insulating oil impacting the baffle under the influence of inrush current:  
(1) Stationary state: Due to the small flow rate of oil through the gas relay, the insulating oil is not enough to deflect the baffle, and the baffle remains stationary at this time.  
(2) Accelerated state: When the impact of insulating oil on the baffle makes the baffle move to the peak flow rate of the gas relay, the deflection speed of the gas relay baffle will rise.  
(3) Decay state: After the velocity of transformer oil decays, the influence of oil on the baffle is weakened. At this time, the baffle still continues to deflect, but the deflection speed decreases.  
(4) Tripping state: When the deflection angle of the gas relay baffle reaches the point where it is connected to the reed switch of the gas relay, i.e., when the deflection angle reaches \(25^{\circ}\), the baffle stops deflecting and the gas relay trips.