esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Limitations and Challenges
### Security, Privacy, and Scalability Challenges  
Like any technological solution, blockchain can introduce new challenges even as it improves certain aspects. While a separate study is required to determine which of these challenges are most relevant to PARFSET, some challenges related to security, privacy, and scalability in PARFSET applications include the following.  
Consensus mechanisms can be vulnerable to attacks if a majority of nodes, depending on the type of mechanism used, act maliciously or unethically [32]. In PARFSET, this risk could be particularly relevant in competitive sports scenarios, where manipulating records may provide unfair advantages to a group or individual.  
Although PARFSET data can be encrypted and stored publicly on a blockchain, there is still a risk of private keys being lost or stolen. This contrasts with traditional systems, where data may not be encrypted but is instead protected by network security and access controls. The risk becomes even greater if a flaw in a smart contract exposes sensitive information. Additionally, a blockchain's immutability makes it difficult to correct such errors once they occur, potentially leading to wide-spread security breaches [32; 33].  
Another challenge is that even when the main content of records is encrypted, the mere existence of a record--along with its timestamp and ownership details--can reveal information about an individual's PARFSET activities [34]. This unintentional data exposure is undesirable. One possible mitigation strategy is introducing random void records to obscure time patterns and reduce traceability.  
Scalability can also be a concern if consensus mechanisms are not carefully selected based on the nature of PARFSET data and its applications. For example, proof of work (PoW) consumes significant computing power and energy, making it unsuitable for PARFSET applications with an extensive number of data records. Additionally, the level of granularity in recorded data can impact scalability. If every individual activity is logged on the blockchain, storage requirements would become excessive. One possible solution is to leverage off-chain databases for storing detailed records while maintaining only summarized session data on the blockchain, supplemented with reference hashes to ensure data integrity and validation.
## 4 Sensor Integration Platform
### Base Station
The drones provided high-resolution images of damaged structures and buildings that were very useful in making important decisions for the local government [85]. In China, emergency rescuers used the mobile public network base station carried by the aerial emergency communication platform of the drone to provide continuous mobile communication signals. The multi-rotor drones provided instant calls, internet access, and other services 24 h a day during the Juzhaigou valley earthquake, China, on 8 August 2017. In addition, mobile base stations are very effective in situations where all kinds of communication channels are destroyed by an earthquake. For example, on 21 July 2021, an extraordinarily heavy rainstorm hit Zhengzhou city, China, destroying the electric communication equipment in many places and resulting in the interruption of all cell phone and network signals for the affected people. The emergency rescuers could not get information from the affected area. Therefore, the Ministry of Emergency Management urgently dispatched a winged dragon drone to fly long distances across the region.
## 7 Future Research Directions
### Data Transmission  
Internet of Things (IoT) is identified as one of the enablers of Industry 4.0 [267]. IoT is defined as the \"interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications\" [25]. As more smart devices, such as robots, are deployed in the built environment, their interconnection may be used to improve their performance. Since Wi-Fi may not be always available in live construction projects, a direct device-to-device protocol can be developed for robots and other smart devices to communicate with each other. Live construction sites are dynamic and cluttered environments. Many changes in the environment may render the robot lost and cause it to completely fail its mission [125]. However, with the ability to communicate with other devices at the site, such as fixed cameras, construction equipment, or other robots, the robot may become more robust in path finding and other operations. Future research can identify what devices already exist in the buildings and infrastructure and how they can communicate with robots for a collaborative inspection activity.
## 2 Numerical Method
It is widely used in simulations of pattern formation and long-time morphological evolution. \[\frac{\phi^{k+1}_{mn}-\phi^{k}_{mn}}{\Delta t}=\Delta\Big{(}2\phi^{k+1}- \epsilon^{2}\Delta\phi^{k+1}+f(\phi^{k})\Big{)}_{mn}-\sigma\Big{(}\phi^{k+1}_ {mn}-\overline{\phi}_{mn}\Big{)}, \tag{11}\]  
where \(\Delta t\) is the temporal step size. We substitute Equations (8), (9), and (10) into Equation (11) to obtain  
\[\frac{1}{\Delta\Lambda N_{x}N_{y}}\sum_{p=-\frac{N_{x}}{2}+1}^{ \frac{N_{x}}{2}}\sum_{q=-\frac{N_{y}}{2}+1}^{\frac{N_{y}}{2}}(\hat{\phi}^{k+1} _{pq}-\hat{\phi}^{k}_{pq})e^{i(\xi_{p}x_{n}+\eta_{q}y_{n})} \tag{12}\] \[= \frac{1}{N_{x}N_{y}}\sum_{p=-\frac{N_{x}}{2}+1}^{\frac{N_{x}}{2}} \sum_{q=-\frac{N_{y}}{2}+1}^{\frac{N_{y}}{2}}-(\xi_{p}^{2}+\eta_{q}^{2})e^{i( \xi_{p}x_{n}+\eta_{q}y_{n})}\Big{(}2\hat{\phi}^{k+1}_{pq}+\epsilon^{2}(\xi_{p }^{2}+\eta_{q}^{2})\hat{\phi}^{k+1}_{pq}+\hat{J}^{k}_{pq}\Big{)}\] \[-\frac{\sigma}{N_{x}N_{y}}\sum_{p=-\frac{N_{x}}{2}+1}^{\frac{N_{y }}{2}}\sum_{q=-\frac{N_{y}}{2}+1}^{\frac{N_{y}}{2}}e^{i(\xi_{p}x_{n}+\eta_{q}y _{n})}\Big{(}\hat{\phi}^{k+1}_{pq}-\hat{\phi}_{pq}\Big{)},\]  
where \(\hat{\phi}_{pq}\) denotes the Fourier coefficient of the piecewise-defined field \(\overline{\phi}(x,y)\), which characterizes the spatial heterogeneity across different subdomains. Then, Equation (13) can be rearranged into the following form:  
\[\frac{\hat{\phi}^{k+1}_{pq}-\hat{\phi}^{k}_{pq}}{\Delta t}=-\Big{(}\xi_{p}^{2}+ \eta_{q}^{2}\Big{)}\Big{(}2\hat{\phi}^{k+1}_{pq}+\epsilon^{2}(\xi_{p}^{2}+ \eta_{q}^{2})\hat{\phi}^{k+1}_{pq}+\hat{J}^{k}_{pq}\Big{)}-\sigma\Big{(}\hat{ \phi}^{k+1}_{pq}-\hat{\phi}_{pq}\Big{)}. \tag{13}\]Consequently, the discrete Fourier transform takes the following form:  
\[\phi_{mn}^{k}=\frac{1}{N_{x}N_{y}}\sum_{p=-\frac{N_{x}}{2}+1}^{\frac{N_{y}}{ 2}}\sum_{q=-\frac{N_{y}}{2}+1}^{\frac{N_{y}}{2}}\phi_{pq}^{k}e^{i(\xi_{p}x_{m} +\eta_{q}y_{n})}. \tag{7}\]0  
Finally, \(\phi_{mn}^{k+1}\) can be obtained using Equation (7):  
\[\phi_{mn}^{k}=\frac{1}{N_{x}N_{y}}\sum_{p=-\frac{N_{x}}{2}+1}^{\frac{N_{y}}{ 2}}\sum_{q=-\frac{N_{y}}{2}+1}^{\frac{N_{y}}{2}}\phi_{pq}^{k}e^{i(\xi_{p}x_{m} +\eta_{q}y_{n})}. \tag{7}\]1  
For the implementation of homogeneous Neumann boundary conditions, one effective strategy is to employ the discrete cosine transform (DCT), which naturally accommodates zero-flux boundary conditions by enforcing symmetry at the domain boundaries. The DCT allows for efficient diagonalization of the discrete Laplacian operator under Neumann conditions, thereby facilitating fast and accurate computation in numerical schemes. This approach has been widely adopted in the recent literature due to its computational efficiency and compatibility with finite difference discretizations; see, for example, [33].
## 4 Methods
### Q Estimation
#### 4.1.2 Travel Time Dispersion  
An alternative algorithm to the spectral ratio method for Q estimation is the so-called dispersion method, used in [30,34]. Well-geophysical measurements (VSP and DT) were used to analyze wave dispersion, that is, the dependence of wave propagation velocity on frequency. The method was applied to well-log and check-shot data recorded in DS-2 borehole to compare with results obtained by the spectral ratio method.  
The velocity of seismic/acoustic waves shows a clear dependence on frequency due to the occurrence of dispersion. This results in the observed effect of the occurrence of seismic reflections at a time longer than that of the same reflection in a synthetic seismogram. The difference between the recording time of the reflection of a wave of the dominant frequency typical of seismic (usually about 30-50 Hz) and the time of the acoustic frequency waveform, in the case of borehole geophysics data (about 20 kHz for acoustic velocity profiling), can be referred to as drift time [35]. The relationship that we used to calculate the value of Q\({}_{\text{ave}}\) was  
\[\text{Q}_{\text{ave}}(\text{z}_{\text{n}})=\frac{\text{t}_{\text{n}}}{\pi\, \text{drift}(\text{z}_{\text{n}})}\ln\frac{\text{f}_{\text{w}}}{\text{f}_{ \text{s}}} \tag{3}\]  
where  
Q\({}_{\text{ave}}(\text{z}_{\text{n}})\)--averaged Q from surface to nth layer.  
t\({}_{\text{n}}\)--two-way-time to nth layer.  
f\({}_{\text{s}}\) and f\({}_{\text{w}}\) are dominant frequencies for both seismic and well-log data.  
drift(z\({}_{\text{n}}\))--time delay drift.
## 4 Metabolic Pathway and Host Engineering for Advanced Biofuels Production
### Non-Fermentative Pathways for Short Chain Alcohols: 2-Keto Acid Pathway  
Short-chain alcohols can be produced via non-fermentative pathways such as the 2-keto acid pathways, and the latest advances in production titer and engineering approaches can be found in recent review papers [38,39]. In these pathways, 2-keto-acid intermediates are transformed to corresponding aldehydes and subsequently to alcohols by decarboxylases and alcohol dehydrogenases, respectively [40] (Figure 4). 1-butanol and branched-chain alcohols such as isobutanol (C\({}_{4}\)) and isopentanols (C\({}_{5}\)) are produced from keto acids intermediates of valine and leucine biosynthesis pathways by increasing availability of specific keto acids and by expression of promiscuous keto acid decarboxylase (KivD) from _Lactococcus lactis_ with alcohol dehydrogenase 2 (Adh2) from _S. cerevisiae_[38]. Subsequently, the highest isobutanol production titer from keto acid pathways has been achieved up to 50 g/L in _E. coli_ with gas stripping [21]. Linear alcohols ranging from 1-pentanol (C\({}_{5}\)) to 1-octanol (C\({}_{8}\)) were also produced from threonine over-producing _E. coli_[41]. In this paper, the carbon chain of 2-keto acids was recursively elongated by an engineered leucine synthesis pathway from _E. coli_ (_Ec_LeuABCD). The production titer was about 1.4 g/L [41].  
Alternative hosts such as amino acid overproducing _Corynebacterium glutamicum_ and more isobutanol-tolerant _B. subtilis_ have been also used, and mitochondrial targeting and expression of cytoplasmic Ehrlich pathway enzymes improved isobutanol production by 2.6-fold in yeast [42]. Recently, [PERSON] and colleagues demonstrated an alternative pathway to produce odd-carbon chemicals such as pentanol more efficiently with higher theoretical yield in _E. coli_ by assembling different pathways modularly [43].  
Short-chain alcohols have been produced via various metabolic pathways and to relatively higher titers than other advanced biofuel molecules. Even though they are considered to have better fuel  
Figure 4: Non-fermentative pathways for short chain alcohols: 2-keto acid pathway. (**A**) General reaction from 2-keto acids to alcohols. 2-keto acid decarboxylase (KDC) catalyzes the decarboxylation of 2-keto acid to form the aldehyde. Then, alcohol dehydrogenase (ADH) reduces the aldehyde to the alcohol. (**B**) Examples of short chain alcohols synthesized from 2-keto acid pathway.  
properties than ethanol, there has still been an increasing demand for biofuel molecules with better properties such as higher energy content and lower freezing points. To address this issue, microbial hosts have been engineered to produce biofuel molecules with longer hydrocarbon chains and more branching methyl groups via various metabolic pathways such as fatty acid metabolic pathways (Section 4.3.) and isoprenoid pathways (Section 4.4.).
## Appendix F Description of Mechanisms for High Performance
### Scalable BFT Consensus Mechanism through Aggregated Signature Gossip [447]  
The proposed multi-level BFT consensus mechanism aims to achieve high performance (as measured by transaction finalisation time and transaction throughput) while delivering a high degree of decentralisation. This is approached through validator committees, sets of dedicated nodes that are responsible for block production. The proposed protocol can operate in permissioned and permissionless configurations: Sybil attacks are prevented by requiring nodes to lock up a certain amount of stake, thereby, providing PoS-like Sybil attack resistance.
# Foams in Wastewater Treatment Plants: From Causes to Control Methods
## 4 Evaluation Method
### Direct Methods
#### 4.1.5 Scum Index (SI)  
The scum index (SI) is used to control biological foams in CAS and its quantification is based on the flotation induced by standardized and constant aeration, as proposed by [PERSON] and [PERSON] [77]. In these tests, an aliquot of mixed liquor equal to 2 L was placed in the flotation cell (80 mm internal diameter, 500 mm height) and aerated with an intensity of 10 L\({}_{\text{air}}\) L\({}^{-1}\) h\({}^{-1}\) for 15 min. During the aeration a stable foams layer could be formed. The aeration was then suspended, and the foam layer was separated from the mixed liquor by draining the latter. The remaining foam layer was removed from the flotation cell and collected. The removed non-floating mixed liquor was placed again in the flotation cell and the separation-flotation process was repeated until no more foams were formed (aeration times of 15 min in the first hour and subsequently at 60-min intervals). Since some non-foaming microorganisms were separated with the foaming microorganisms, the foaming microorganisms (which were collected) were further purified as follows: these foams were resuspended in tap water and the foam separation process was repeated until the concentration of suspended solids (SS) in the non-floating part was less than 3 mg L\({}^{-1}\). The dry mass of the recovered/purified foams was determined, and the scum index was calculated as follows in Equation (5) [21,77]:  
\[SI\left[-\right]=\frac{mass\;of\;recovered\;foam}{mass\;of\;suspended\; solids\;(SS)\;initially\;presents}*100 \tag{5}\]  
The original method proposed by [PERSON] and [PERSON] [77] has been slightly modified to define the specific role of EPS concentration in MBR foam formation: the results of the flotation operation alone, without the subsequent dilution and purification, were also considered as a modified scum index test since these last steps can negatively influence the effects of the EPS [21,23].
# Improvement of Network Flow Using Multi-Commodity Flow Problem
## 5 Experiment
### Routing Using Multi-Commodity Flow Problem
#### Number of sources: 3, number of destinations: 3  
Figure 10 shows the result when there are three commodities in total, with commodity 1 flowing from node 1 to node 11, commodity 2 flowing from node 2 to node 10, and commodity 3 flowing from node 8 to node 7.  
The proposed method maintained a higher packet arrival rate than OSPF at higher transmit bit rates, and the degree of decline in arrival rate from 100% was slower (Figure 10).  
Table 5 compares the average end-to-end delay between OSPF and the proposed method at 300 kbps where the OSPF's packet arrival rate for all commodities was 100%. Only commodity 3 was experimented with at an additional 400 kbps and 500 kbps.  
Basically, the average end-to-end delay was shorter for OSPF than for the proposed method (Table 5). However, at 400 kbps and 500 kbps for commodity 3, the OSPF packet arrival rate was 100%, but the value increased. Therefore, the delay in OSPF was longer than that of the proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline  & **OSPF** & **Proposed Method** \\ \hline Commodity 1 & 48 ms & 54 ms \\ \hline Commodity 2 & 31 ms & 56 ms \\ \hline Commodity 3 & 15 ms & 22 ms \\ \hline \end{tabular}
\end{table}
Table 4: Average end-to-end delay for 3 sources and 1 destination.  
Figure 9: Packet arrival rate for 3 sources and 1 destination.
## 5 The Computational Results and Analysis
### Performance Analysis of IQABC Algorithm without Learning and Deteriorating Effects
Furthermore, the QL mechanism in the IQABC algorithm needs more time to learn optimal policies. Therefore, it is not meaningful to compare the running times of the IQABC and ABC algorithms with those of the VNS and INEH heuristics. In Figures 3-6, we also compare the convergence curves of the IQABC and ABC algorithms. We note that the ABC algorithm is more likely to become trapped in premature convergence, while the IQABC algorithm still finds better optimal solutions at a relatively more efficient rate, and the better results can be obtained at the final stage. We can conclude that the QL algorithm in the employed bees phase helps the IQABC algorithm to converge in a robust and efficient way. This conclusion is supported by the convergence curves of 10 trials on the instance 50x20_M1 as well as the average convergence curve in bold in Figure 7. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **M1** & \multicolumn{2}{c}{**M2**} \\ \hline
**Total N** & 120 & **Total N** & 120 \\ \hline
**Test statistic** & 36,300 & **Test statistic** & 28,033 \\ \hline
**Degree of freedom** & 1 & **Degree of freedom** & 1 \\ \hline
**Asymptotic sig** & \textless{}0.001 & **Asymptotic sig** & \textless{}0.001 \\ \hline
**Decision** & \
\begin{tabular}{l} Reject the \\ null hypothesis \\ \end{tabular} & **Decision** &
\begin{tabular}{l} Reject the \\ null hypothesis \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: IQABC-ABC Friedman’s two-way analysis of variance summary for M1 (left) and M2 (right). Figure 2: Mean plots of ABC and IQABC for the two maintenance modes. Figure 4: Convergence curves of ABC and IQABC for 100x10_M1 and 100x10_M2 instances. Figure 5: Convergence curves of ABC and IQABC for 100x20_M1 and 100x20_M2 instances. Figure 3: Convergence curves of ABC and IQABC for 50x20_M1 and 50x20_M2 instances. Figure 6: Convergence curves of ABC and IQABC for 200x20_M1 and 200x20_M2 instances. It is important to note that high ARPD values do not reflect poor solution quality since they are calculated relative to the best-known Taillard solutions, without taking into account maintenance tasks and learning/deterioration effects. Therefore, the presented ARPD deviations (for all experiments) are much higher than they would be if they were calculated with respect to a more accurate lower bound that takes into account maintenance tasks and learning/deterioration effects.
## 3 Numerical Calculation of the Three-Phase Model  
### Physical Geometry and Meshing  
The mixed model selected is a semi-circular bottom container with a baffle plate. The model mainly includes a mixing impeller, mixing shaft, mixing tank wall, inlet and outlet, and four uniform baffles. The structure is described in Figure 2, and the following physical characteristics can be obtained: diameter \(W=200\) mm, installation height \(D=93\) mm, paddle length \(L=45\) mm, inclination angle \(45^{\circ}\), liquid level height \(h_{1}=W\), mixing shaft diameter \(d_{1}=14\) mm, height \(H=3T/2\), width \(W=T/10\), air inlet diameter \(d_{2}=14\) mm, impeller diameter \(Y=W/2\), height \(h_{\text{a}}=4\) mm, baffle height \(h_{\text{b}}=11W/10\), and width \(W/10\)  
Figure 1: Fluid–solid coupling flow chart.  
During mesh partitioning, proper resolution, smoothness, low skew, and a proper number of meshes should be considered [41,42]. The mesh must be fine in places with enormous pressure, velocity, and temperature gradient. To calculate efficiently in the area of small velocity and temperature gradient, there is no significant change in the flow behavior to be captured, and such an area does not need to be divided into a better mesh. Since the calculation cost is proportional to the element numbers, it is best to fully consider the flow-field properties, select appropriate grid sizes, and reduce unnecessary grid numbers. Here, the tetrahedral meshes are utilized to divide the fluid field domain and set up the three-phase dynamic model (Figure 3).  
The fluid region includes the vessel zone and rotating zone. The gradient variation in the strong shear zone is the biggest due to the huge variable gradient of the rotating region, particularly the mixed impeller. The partial grid division course should be refined so the grid scales of the vessel and rotating zones are 7 mm and 5 mm, respectively, and the total meshes are 527,890.  
Figure 3: Mesh division of the numerical model. (**a**) Vessel zone mesh. (**b**) Rotating zone mesh. (**c**) Paddle mesh.  
Figure 2: Hybrid space structure diagram.
## 2 Materials and Methods
### Hybrid Modeling Using DWT and VMD Approaches  
DWT-based soft computing models (DWT-MLP and DWT-CCNN) are the hybrid models combined with the standalone models (MLP and CCNN) and DWT, respectively. In the same manner, VMD-based soft computing models (VMD-MLP and VMD-CCNN) conjugate the standalone models (MLP and CCNN) and VMD, respectively. Therefore, DWT- and VMD-based soft computing models consist of three steps (1). The training and testing dataset are decomposed into an approximation and multiple details using the DWT approach, and multiple IMFs using VMD, respectively (2). The standalone models (MLP and CCNN) are developed for each decomposed training dataset (3). The final predictions of DO concentration values are obtained by aggregating the sub-time series predicted from the standalone models (MLP and CCNN), respectively. Figure 4 represents the flowchart for DWT- and VMD-based soft computing modeling.  
Figure 3: Flowchart for the variational mode decomposition (VMD) algorithm [73].