esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Assessment of Intracellular Metabolites in Single Cells through RNA-Based Sensors
## 1 Introduction  
Cell metabolism, the set of enzyme-catalyzed chemical reactions occurring in the cell leading to energy production and biomass formation, works as a coordinated system that results from the interaction of multiple metabolic pathways, where metabolites act as interconnecting nodes among these pathways. Cells regulate metabolic fluxes to adapt their metabolic phenotype to environmental conditions [1; 2; 3; 4]. Thus, the assessment of metabolic fluxes directly reports on the metabolic phenotype. Isotope tracing is an end-point approach performed at the cell population level, where an average response is measured; its implementation requires trained personnel and expensive equipment [5]. Therefore, isotope tracing is not a suitable approach to measure metabolic fluxes in real-time and/or at the single-cell level and is unaffordable to implement in most cell biology laboratories.  
In recent years, synthetic biologists have developed novel cellular devices that make it possible to assess the intracellular concentration of a small molecule by quantifying the fluorescence emitted by individual cells in a population. These sensors consist of a macromolecule (protein or RNA) expressed by the cell that has a domain that interacts specifically with the small molecule (e.g., a metabolite) (the _sensor domain_) and a different part that allows converting this interaction into a quantifiable response (e.g., fluorescence) (the _actuator_) [6; 7]. In this work, we describe molecular mechanisms employed by cells across all kingdoms to sense intracellular metabolites and regulate cell responses, focusing on those mediated by cellular RNA molecules. We review the development of synthetic RNA devices designed to serve as biosensors of intracellular metabolites and analyze themajor technical issues and solutions in this approach. We finish by pointing at current and future applications of synthetic RNA-based sensors of metabolites.
## 2 Related Work
### Authenticity  
Aside from securing physical assets' ownership, data authenticity is a common issue in both the physical and virtual worlds. Blockchain has proven to be a robust platform for addressing challenges related to verifying and tracing changes in systems' configurations over time [20; 21].  
The rise of IoT devices has introduced new challenges concerning the authenticity of connected devices, particularly at larger scales such as smart cities. Blockchain-based architectures have shown great potential in this area as well [22]. Similarly, documents like educational certificates are vulnerable to forgery, much like physical assets. As with the previous examples, blockchain can also be effectively leveraged to provide secure verification solutions in this domain [23].
## 5 Modelling Rationale
### Fault Model of the Modelled System  
In Section 4 we specified the fault model with which PTRF deals, i.e., temporary non-malicious hardware faults that affect the links. However, we cannot model links as isolated devices, instead, we need to model all the components of the communications subsystem, i.e., links, bridges and the communication controllers of end-systems. Thus, we need to extend the fault model to consider faults affecting the bridges and the communication controllers of end-systems too.  
On top of that, we need to take into account that we developed our model taking into consideration TSN's space redundancy and error contention mechanisms to be able to extend our analyses in the future. Therefore, we need to also include permanent non-malicious hardware faults that affect any device of the network. Therefore, the fault model of the modelled system is permanent and temporary non-malicious operational hardware faults that affect the links, the bridges and the communication controllers of end-systems. This extension of the fault model has resulted in an extension of the failure modes of the system too, which is thoroughly explained in Section 5.3.2.  
Particularly, in the analyses presented in this article we have tuned the models to only consider temporary faults that affect links, bridges and the communication controllers of end-systems. As said before, we do not include permanent faults nor space redundancy in the analyses presented in this article as we do not want to mask the benefits that PTRF can yield to the network. Nonetheless, we must note that all the networks that we analyse here include the error-containment mechanisms of TSN. Specifically, our networks include Ethernet's CRC and TSN's filtering and policing, which allow the network devices to drop frames that are incorrect in the time and the value domains, as long as these errors are produced at the network level, e.g., a bit flip in the output queue of an end-system or bridge.  
Finally, it is important to note that our models do not include faults happening in parts of the system other than the communication subsystem, e.g., software faults and hardware faults affecting the end-system's main memory. These faults affecting other parts should be considered in the case of modelling a complete system that may include mechanisms to tolerate them; which is not the case of this article.
## 3 Results
### Force Estimation from FEM Models  
Based on the methodology and hardening parameters introduced in Section 2, the force curves predicted by the models were compared to the experimental data obtained for the three AA1050-H24 pyramids in Figure 5. For the AISI304, Figure 6 compares the numerical and experimental force evolution of Cases 4 and 5. The maximum force estimations from the numerical models, along with their errors, compared to the experimental measurements, are presented in Table 4.  
Figure 4: The semi-automatic workflow employed to generate G-code tool paths, simulation velocity-based trajectories, and filtered force curves.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case ID**} & \multirow{2}{*}{**Material**} & \multirow{2}{*}{\begin{tabular}{c} **Vertical** \\ **Step** \\ **\(\Delta z\) (mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Tool** \\ **Diameter** \\ **\(d_{t}\) (mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Wall Angle** \\ **\(\alpha\) (deg)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Thickness** \\ **\(t_{0}\) (mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Experimental** \\ **\(F_{z}\) (kN)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Aerens\({}^{\prime}\)** \\ **\(F_{z}\) (kN)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Iseki’s** \\ **\(F_{z}\) (kN)** \\ \end{tabular} } \\ \hline
1 & AA1050-H24 & 0.5 & 10 & 45 & 0.8 & 0.309 & 0.355 & 0.700 \\
2 & AA1050-H24 & 0.5 & 10 & 60 & 0.8 & 0.282 & 0.322 & 0.624 \\
3 & AA1050-H24 & 0.5 & 10 & 70 & 0.8 & 0.277 & 0.253 & 0.471 \\
4 & AISI304 & 0.5 & 10 & 45 & 0.5 & 0.801 & 0.891 & 2.679 \\
5 & AISI304 & 0.5 & 10 & 60 & 0.5 & 0.857 & 0.810 & 3.051 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental cases used to validate the simulation results.  
According to Table 4, the maximum absolute error for the aluminum cases is 11.3%, with one instance below the experimental peak result. Beyond the maximum values, Figure 5 reveals that for Cases 2 and 3, the numerical force monotonically increases throughout the process, whereas the experimental curves exhibit a peak between 7 and 9 mm depth. For the stainless-steel cases, the results are both qualitatively and quantitatively poor.  
Based on the previous findings, enhancements focused on the hardening behavior of the models were implemented. The procedure and results are reported in the next sub-section.
# Performance and Scalability Analysis of SDN-Based Large-Scale Wi-Fi Networks
## 4 SDN Based Architecture
### Performance Parameters
#### 4.3.2 Flow Setup Latency  
In a typical switch-controller cycle, creating a network event notification on the controller side and installing flow rules at switches are not instantaneous activities. Rather, both of these activities take place over the course of the cycle. The performance of large-scale networks is negatively impacted in significant ways by the flow setup latencies. Therefore, in order to maximize the performance of large-scale SDN networks, low flow setup latencies are desired.  
The Round-Trip Transmission Time, abbreviated as RTT, was factored into the calculation of the flow setup latency. The first pings in SDN always take longer than the ones that come after them because they have to make route inquiries to the controller in the beginning. As a consequence of this, the RTT of the first packet has been investigated in order to acquire a sufficient comprehension of flow setup latency. The RTT of each execution of the following command is added together and used in the calculation of these [43].  
_Mininet-Wi-Fi > sta1 ping -c1 sta2_  
The process of setting up new user-initiated flows is made much easier by our solution, which dramatically minimizes the number of switches that must be altered to accommodate the new flow. On our testbed, we implemented our concept and monitored its performance using various metrics. According to the findings, it makes a significant contribution to performance enhancement, especially in the tail, by reducing the median and 99-percentile latencies to 5.9 and 7 milliseconds, respectively, under conditions that are identical to those previously tested.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 2 Fundamental Processes
### Reaction Pathways
#### 2.1.1 Hydrocarbon  
[PERSON] et al. [30] regarded acetic acid as a refractory intermediate for hydrocarbons degradation, which occurred in the oxidation of all the alcohols except methanol [31]. In the SCWO of aromatic hydrocarbons, understanding the oxidation chemistry of phenol is pivotal since phenol is a stable species [32; 33; 34], which would degrade into various carboxylic acids in the subsequent degradation process. The degradation of carboxylic acids in SCWO proceeded with the sequential oxidation from higher molecular weight to lower molecular weight [35]. The simplified reaction network for hydrocarbon oxidation is shown in Figure 1, where C\({}_{\text{m}}\)H\({}_{\text{n}}\)O\({}_{\text{r}}\) can be considered either as an initial reactant or as an unstable intermediate.  
Figure 1: The simplified reaction network for hydrocarbon oxidation in SCW (Adapted from [PERSON] et al.’s model [30]).
## 3 Formation Controller Design
### Basic Trajectory Planning  
In this subsection, the basic desired trajectories \(\left[\begin{array}{cc}x_{di}&y_{di}\end{array}\right]^{T}\) and velocities \(\left[\begin{array}{cc}\
u_{di}&\omega_{di}\end{array}\right]^{T}\) are planned.  
A group of \(N\) robots needs \(N\) desired trajectories to stay in formation. The virtual-structure approach is applied here to generate the desired trajectories.  
Consider a virtual structure whose center moves along the desired trajectories \(R_{d0}(s)=\left[\begin{array}{cc}x_{d0}\left(s\right)&y_{d0}\left(s\right) \end{array}\right]^{T}\) with \(s\) being the path parameter. Then, trajectories \(R_{di}(s)=\left[\begin{array}{cc}x_{di}\left(s\right)&y_{di}\left(s\right) \end{array}\right]^{T}\) can be specified by the displacement from the center of the structure to each place-holder (shown in Figure 3), which can be calculated as follows:  
\[R_{di}(s)=R_{d0}(s)+M(\theta_{d0}(s))L_{di}, \tag{3}\]  
where  
\[L_{di} =\left[\begin{array}{cc}l_{xi}&l_{yi}\end{array}\right]^{T},\] \[M(\theta_{d0}(s)) =\left[\begin{array}{cc}cos\theta_{d0}(s)&-sin\theta_{d0}(s) \\ sin\theta_{d0}(s)&cos\theta_{d0}(s)\end{array}\right].\]  
\(\theta_{d0}\) is the heading angle of the center of structure and can be defined as  
\[\theta_{d0}(s)=\arctan((\partial y_{d0}(s)/\partial s)/(\partial x_{d0}(s)/ \partial s)).\]  
Each trajectory is parameterized by the path parameter \(s\) so that the desired trajectories can always be in formation.  
Then, the desired liner velocity and angular velocity for each robot can be calculated as  
\[\begin{array}{c}\
u_{di}=\sqrt{\frac{\partial x_{di}\left(s\right)^{2}}{ \partial s}+\frac{\partial y_{di}\left(s\right)^{2}}{\partial s^{2}}}\dot{s} \\ \omega_{di}=\frac{\frac{\partial x_{di}\left(s\right)^{2}y_{di}\left(s\right)}{ \partial s}-\frac{\partial^{2}x_{di}\left(s\right)}{\partial s^{2}}\frac{ \partial y_{di}\left(s\right)}{\partial s}}{\frac{\partial x_{di}\left(s \right)^{2}}{\partial s}+\frac{\partial y_{di}\left(s\right)^{2}}{\partial s}} \dot{s},\end{array} \tag{4}\]  
where the time derivative of path parameter \(s\) is included in the desired linear and angular velocity, which indicates that \(\dot{s}\) determines the desired formation speed; the selection of \(s\) is discussed in Section 3.4.
## 3 Results and Discussions  
### Distribution of the Electric Field  
Using the COMSOL Multiphysics software (version 4.3, COMSOL Inc., Stockholm, Sweden), a simulation of the electric field distribution inside the microfluidic devices was performed. For the parallel electrode, the potential was considered ground potential, while the perpendicular electrodes were set to values varying between 10 and 60 V. The remaining components of the microfluidic device were set to insulating boundary, whereas the fluid flowing inside the microfluidic channel was considered to have the same electric properties as water. Figure 5 shows the electric field distribution (V/m) inside the microfluidic devices, for each electric potential difference, with a focus on the electrodes and the central channel. As can be observed, the configuration of the perpendicular electrodes allows for the concentration of the electric field in points, enabling higher gradient regions to form along the main channel. Another noticeable aspect is the high gradient region that forms around the first and last electrode disposed perpendicularly to the main channel. As the electric potential difference increases, the distance between these high-gradient regions decreases, leading to an almost uniform electric field at around a potential of 60 V (Figure 5g). Considering the positive dielectrophoretic behaviour displayed by the _Staphylococcus aureus_ ATCC 25923 cells [18] and the decrease in the distance between the high electric gradient regions could lead to the deposition of a more uniform bacterial biofilm along the side B (inlet side for the bacterial suspensions).  
Figure 4: (a) Cut side of the microfluidic systems after the crystal violet staining; (b) Diluted solutions for UV-Vis investigation.  
Figure 5: Electric field distribution (V/m) inside the microfluidic channels at **(a)** 0 V\({}_{i}\) (**b)** 10 V; **(c)** 20 V\({}_{i}\) (**d)** 30 V; **(e)** 40 V; **(f)** 50 V; and **(g)** 60 V.
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 4 PoENF: A Proof-of-ENF Consensus Protocol
### PoENF Committee Consensus Protocol: Overview  
Figure 3 is an overview of the PoENF consensus protocol, which includes the distributed ledger structure and PoENF committee consensus workflows. The distributed ledger in EconLedger follows a tree structure originated from the genesis block. Each new block extends its chain path through \(pre\_hash\) to point to a parent block. All nodes in such a ledger tree can be represented as confirmed blocks (blue) or finalized blocks (red), as the upper part of Figure 3 shows. The chain height follows a strictly increasing sequence of finalized blocks; therefore, a valid path can only proceed through those red nodes. The head of a blockchain is anchored on a recently confirmed block that has linked to a finalized chain with the largest height value.  
At the configuration stage, the system administrator specifies a group of validators as the initial PoENF committee to initialize an EconLedger network. The lower part of Figure 3 demonstrates workflows of the PoENF committee consensus protocol, including the dynasty cycle and the epoch cycle. A dynasty cycle starts from committee selection and ends when the global randomness string of current dynasty has been updated by the randomness change process. At the beginning of a dynasty's lifetime, the committee selection process uses the current global randomness string as a seed for committee electionprotocol, which exploits a Verifiable Random Function (VRF) based cryptographic sorting scheme [37]. Given the credit weights of all nodes, \(K\) validators are randomly chosen to construct a new PoENF committee \(D\), which will be added to the current block. Finally, validators of new \(D\) establish a fully connected P2P consensus network and start a new dynasty cycle.  
For each epoch cycle, PoENF block generation and chain finality are core functions that ensure liveness and termination in continuous consensus rounds. By calculating a squared distance of ENF proofs from validators, the PoENF mechanism determines that a validator with the minimal squared-distance-based score can generate a valid block in the current block proposal round. After \(n\) rounds of block proposal, chain finality relies on a voting-based chain finality mechanism to resolve fork issues caused by conflicting confirmed blocks and finalizes the history of ledger data by using a unique chain path.  
At the end of current dynasty, PoENF committee members utilize the RandShare mechanism to cooperatively reach agreement on proposing a new global randomness string. As a distributed randomness protocol, RandShare adopts Publicly Verifiable Secret Sharing (PVSS) [38] to ensure unbiasability, unpredictability, and availability in public randomness sharing. The proposed unbiasable and unpredictable global randomness string will be updated as the new seed for the committee selection process of the subsequential dynasty.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 5 A Case Study of UAV NTN Airborne Network in Mountainous Area
### Design of Field Trial of UAV NTN Network System
Multi-spectral images collected by cameras of relatively high-resolution require high data rates to be transmitted to cloud servers or disaster headquarters in order to post early warnings and identify victims. In mountainous disaster rescue scenarios, it is impossible for wheeled or tracked vehicles to move freely, and quadrupedal locomotion robots are utilized for rescue operations. Customer Premise Equipment (CPE) is mounted on the robot to provide access capabilities to the UAV 5G network. Cell phones are connected to a UAV base station with a frequency of 2.6 GHz, which is supported by most commercial operators.
Remote Sens._**2014**, \(6\), 4090-4108; doi:10.3390/rs6054090  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
Monitoring Changes in Rice Cultivated Area from SAR and Optical Satellite Images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Finnish Geodetic Institute, Geodeetinrinne 2, FI-02431 Masala, Finland;  
E-Mails: [EMAIL_ADDRESS] (O.N.); [EMAIL_ADDRESS] (A.K.);  
[EMAIL_ADDRESS] (M.K.); [EMAIL_ADDRESS] (S.K.)  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]SS];  
Tel.: +358-295-308-034; Fax: +358-929-555-211.  
_Received: 13 January 2014; in revised form: 8 April 2014 / Accepted: 24 April 2014 / Published: 2 May 2014_  
**Abstract:** The objective of this study was to obtain up-to-date information on land use and to identify long term changes in land use, especially rice, aquaculture and other crops in Ben Tre and Tra Vinh provinces in Vietnam's Mekong Delta. Long-term changes in land-use of the study area have not been studied using long time series of SAR and optical Earth observation (EO) data before. EO data from 1979-2012 was used: ENVISAT ASAR Wide Swath Mode, SPOT and Landsat imagery. An unsupervised ISODATA classification was performed on multitemporal SAR images. The results were validated using ground truth data. Using the Synthetic Aperture Radar (SAR) imagery maps for 2005, 2009 and 2011 were obtained. Different rice crops, aquaculture and fruit trees could be distinguished with an overall accuracy of 80%. Using available optical imagery the time series was extended from 2005 to 1979. Long-term decrease in the rice acreage and increase in the aquaculture acreage could be detected.  
**Keywords:** land-use change; Mekong Delta; Envisat ASAR WSM; Landsat; SPOT; multitemporal SAR; ground truth data; rice; aquaculture
## 5 Discussion
### Comparison with State-of-the-Art Methods  
To verify the obtained results, we compare both the machine learning and deep learning models with other detection attack methods. The NSL-KDD dataset was used by [45; 46; 47] since it is the most commonly used benchmark dataset, while this work considered both the NSL-KDD and UNSW-NB15 datasets. The comparison results are summarized in Table 7. Ref. [45] achieved the highest accuracy of 99.95%, while the deep learning model used in this work achieved an accuracy of 99.89% on the NSL-KDD dataset and 98.95% on the UNSW-NB15 dataset.
###### Abstract  
Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has the potential to cause extensive damage to buildings and infrastructure through ground failure. During the 2011 Great East Japan Earthquake, Urayasu City in the Chiba Prefecture experienced severe soil liquefaction, leading to evacuation losses due to the effect of the liquefaction on roads. Therefore, developing quantitative predictions of ground subsidence caused by liquefaction and understanding its contributing factors are imperative in preparing for potential future mega-earthquakes. This research is novel because previous research primarily focused on developing predictive models for determining the presence or absence of liquefaction, and there are few examples available of quantitative liquefaction magnitude after liquefaction has occurred. This research study extracts features from existing datasets and builds a predictive model, supplemented by factor analysis. Using the Cabinet Office of Japan's Nankai Trough Megathrust Earthquake model, liquefaction-induced ground subsidence was designated as the dependent variable. A gradient-boosted decision-tree (GDBT) prediction model was then developed. Additionally, the Shapley additive explanations (SHAP) method was employed to analyze the contribution of each feature to the prediction results. The study found that the XGBoost model outperformed the LightGBM model in terms of predictive accuracy, with the predicted values closely aligned with the actual measurements, thereby proving its effectiveness in predicting ground subsidence due to liquefaction. Furthermore, it was demonstrated that liquefaction assessments, which were previously challenging, can now be interpreted using SHAP factors. This enables accountable wide-area prediction of liquefaction-induced ground subsidence.  
machine learning; factor analysis; liquefaction; prediction model; SHAP method