esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### qPCR Gene Copy Number Analysis  
The integrated _T.r.eg2_ and _T.e.cb11_ genes were quantified using real-time quantitative PCR (qPCR) by Inqaba Biotechnical Industries (Pty) Ltd. (Pretoria, South Africa). Primers specific for amplification of the target genes were used to determine the copy number of each cellulase expression cassette. The gene encoding \(\alpha\)-1,2-mannosyltransferase (_ALG9_) was selected to normalize the copy number of our genes of interest since it is present as a single copy in the haploid and as two copies in the diploid complement of the _S. cerevisiae_ genome [30]. All DNA concentration measurements were carried out using the ND-1000 Spectrophotometer NanoDrop (Thermo-Fischer Scientific, Waltham, MA, USA). A standard curve was generated using a serial dilution of the pRDH180 (for _T.r.eg2_ gene) and pMI529 for (_T.e.cb11_ gene) from 1 ng to 0.1 fg and with the parental strain for _ALG9_ from 10 ng to 0.1 pg. qPCR was then performed in 96 well plates with Luna Universal qPCR Master Mix (New England Biolabs, Ipswich, MA, USA) using a dye-based qPCR assay. Each reaction contained 1 \(\upmu\)L of DNA template, 0.25 \(\upmu\)m forward and reverse primers and 1X Luna Universal qPCR Master mix. The reactions were run on a CFX96 Real-Time PCR System (Bio-Rad) following a standard two-step PCR program as suggested by the Luna Universal qPCR Master Mix manual. Three technical replicates were run for each DNA sample. The amplification of different input templates was evaluated based on the quantification cycle (Cq) value. Following that, the absolute copy number was calculated using a formula (Absolute copy number = DNA (g)/(g to bp const. \(\times\) genome size). The average Cq values were plotted against the absolute copy number of standards, and standard curves were generated by linear regression of the plotted points. The absolute copy number for the strains was calculated based on the standard curves. The efficiency of the PCR was determined using the formula E = \(-1+10^{(-1/\text{slope})}\), and the efficiency of all primer pairs used was over 95%. Standard melting curve analysis was performed to check the specificity of the qPCR products.
## 4 Genetic Algorithm Configuration
### Fitness Function and Weighting  
The fitness function quantitatively evaluates satellite deployment performance based on four key indicators:  
* **Total number of satellites (\(S_{\text{total}}\))**: Lower values are preferred to reduce system cost;
* **Average revisit time (\(R_{\text{avg}}\))**: The target is to have data collected at least once every 60 min;
* **Maximum revisit time (\(R_{\text{max}}\))**: Should not exceed 90 min to avoid excessive delays;
* **Average data transmission volume (\(D_{\text{avg}}\))**: Higher values indicate better data throughput--critical for our AIoT scenario.  
The fitness function is defined as follows:  
\[\text{Fitness} = w_{1}\times S_{\text{total}}+w_{2}\times R_{\text{avg}} \tag{7}\] \[+w_{3}\times R_{\text{max}}+\frac{w_{4}}{D_{\text{avg}}}\] \[+\alpha\times\text{max}(0,R_{\text{avg}}-R_{\text{th}})\] \[+\gamma\times\text{max}(0,R_{\text{max}}-R_{\text{max,th}})\] \[+\beta\times\text{max}(0,D_{\text{th}}-D_{\text{avg}}),\]with the following thresholds:  
* \(R_{\text{th}}=60\) min;
* \(R_{\text{max,th}}=90\) min;
* \(D_{\text{th}}=2\) MB.
## 4 Discussion
### Percentage Connectivity Trend  
Figure 7 represents the percent bond connectivity trends of three major bond types (Si-O-Si, B-O-B, and Si-O-B) within the glass composition tetrahedra in response to the increase in mol% boron substitution. It is observed that the connectivity of Si-O-Si bonds decreases sharply as the boron substitution increases. At 6P55-0B, the Si-O-Si connectivity is at 100%, while the Si-O-B and B-O-B bonds are at 0% connectivity; however, the Si-O-Si bond connectivity consistently declines as we increase the boron concentration. This indicates the transition in the replacement of the primary bonds that play a big role in the geometric structural tetrahedra by both Si-O-B and B-O-B bonds. Additionally, it confirms that boron contributes as a glass former. The B-O-B bond connectivity increases slightly and gradually with increased boron substitution to as high as approximately 20% connectivity within the glass tetrahedra. The connectivity percentage of Si-O-B bonds also increases with increasing boron substitution and is shown to maintain a greater percentage than B-O-B bonds throughout the glass composition series. At \(\sim\)6P55-13B, the Si-O-B bond types surpass Si-O-Si bond connectivity presence.  
Figure 7 ultimately illustrates a decreasing trend in the Si-O-Si bonds as such bonds are replaced with a combination consisting of an increased presence of Si-O-B and B-O-B bonds. This trend confirms that boron participates as a glass former, integrating into the glass structure by forming B-O-B and Si-O-B bonds, which alters the connectivity and geometry of the glass network as the compositions change.
## 5 Discussions
### Critical Open and Deployment Issues in IoE Security  
While the proposed security architectures offer strong theoretical guarantees, their real-world deployment involves challenges such as scalability, energy constraints, protocol interoperability, and integration with legacy IoE infrastructures. The heterogeneous nature of IoE devices, ranging from high-performance edge servers to low-power sensors, requires adaptive frameworks that balance computational cost with security requirements. Insights from practical case studies assessing how distributed learning approaches perform in real-world 6G-based IoT scenarios [182] suggest that deployment success depends on modular, configurable architectures, cross-layer optimization, and the careful selection of cryptographic primitives according to hardware capabilities. In light of these findings, several fundamental security challenges remain unresolved and must be addressed in the coming years:  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technology** & **Security Contribution** & **Challenges** & **References** \\ \hline Blockchain & Decentralized security, data immutability & High transaction cost, latency issues & [73; 90; 178] \\ \hline AI for Security & Autonomous threat detection, fast anomaly recognition & Vulnerable to adversarial AI attacks & [29; 30; 64; 29] \\ \hline Quantum Security & Long-term protection against quantum attacks & High computational requirements, hardware compatibility in IoE & [180; 181; 120; 181] \\ \hline \end{tabular}
\end{table}
Table 20: Comparison of blockchain, AI, and quantum security integration in IoE systems.  
How can Blockchain scalability be achieved in IoE environments?  
How can Edge AI models be made more resilient to attacks?  
How can the impact of quantum computing on IoE security be minimized?  
How can autonomous self-protecting security systems be developed for IoE devices?
# A Data-Driven Fault Tree for a Time Causality Analysis in an Aging System
## 1 Introduction
We aim to build a time-dependent multilevel causality model based on the selection of feasible solutions that characterize the fault occurrence at a certain period from a set of representative time series historical datasets to address the causality analysis over time in a meaningful way. The ITCA model is a combination of different common one-level fault trees that depict the changes of the fault causality structure at periodic intervals. At each defined period, the ITCA methodology identifies, isolates, and represents the possible causes of the fault event in the form of the interpretable one-level fault tree. These constructed trees over defined periods are merged into a common one-level fault tree that graphically summarizes the changes in the fault causality structure over time. This procedure is iteratively repeated for each unexplained cause from the previous level until the final multilevel ITCA model is constructed. The proposed construction procedure ensures that redundant knowledge is eliminated within the ITCA model, while maximizing its interpretability over the time. Finally, a set of causality rules are deduced from the ITCA fault tree that characterize the dynamic change effect of the causality structure in the causes of a fault occurrence. The rest of the paper is organized into four sections. Section 2 reviews the available methods for achieving the fault prognosis based on time causality analysis and discusses the main challenges. Section 3 develops the ITCA methodology. It explains the data preparation, the construction of the fault tree models over time, and the deduction of the time causality rules for fault prognosis. Section 4 illustrates the ITCA methodology using the NASA turbofan engine degradation dataset. The performance of the ITCA model to predict the fault is demonstrated by the fault trend over time. Section 5 concludes the paper and discusses the contribution of the ITCA methodology in achieving the fault prognosis task.
# M-Healthcare Model: An Architecture for a Type 2 Diabetes Mellitus Mobile Application
## 2 Analysis
### Application in Healthcare
\\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{[PERSON]} & \multirow{4}{*}{Exercise health applications, also called health and fitness applications, are applications that can provide information to users related to health and fitness without limitations of place and time, and calm help users to achieve their health and fitness targets. Some exercise applications provide features to be able to connect applications with other supporting devices to collect health information more comprehensively; for example, connecting applications with treadmills, cycles, smartwatches, or other supporting devices used. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multicolumn{5}{c}{**Comparison of our Previous Work**} \\ \cline{2-5}  & **Researcher** & \multicolumn{2}{c}{**Features**} & **IoT Devices** & **Participant** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{[PERSON]} & 1. & Blood glucose levels & 1. & Glucometer \\  & & 2. & Physical activities & 2. & Wearable band and aerobic exercise equipment \\  & & 3. & Dosage or injection & 3. & Smart medication monitor \\  & & 4. & Wake up and sleeping time & 4. & Wearable band \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{[PERSON]} & 1. & Blood glucose levels & 1. & Glucometer \\  & & 2. & Physical activities & 2. & Wearable band and aerobic exercise equipment \\  & & 3. & Food intake & 3. & Smart food try \\  & & 4. & Dosage or injection & 4. & Smart medication monitor \\  & & 5. & Wake up and sleeping time & 5. & Wearable band \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Application classifications in healthcare. The application can be categorized into three groups in its development, namely [21]:  
1. Desktop applications, namely applications that can only be run on a PC or laptop. 2. Web applications, namely applications that are run using a computer or laptop and an internet connection. 3. Mobile applications, namely applications that run on mobile devices.
## 4 Proposed QLHH-II Algorithm
### QLHH-II Detailed Steps
#### 4.2.2 Reward  
In RL, the reward is given for an action chosen in a certain state, i.e., for a vertex chosen in a certain solution in the CCUS3O context. Let \(S_{t}\) be the current solution at the time \(t\), \(v_{t}\) the vertex is chosen by a UAV at that time and \(S_{t+1}\) the resulting solution, the reward for such an action is given by  
\[r(S_{t},v_{t})=O(S_{t})-O(S_{t+1})\in\mathbb{R}^{3}\]  
Since the CCUS3O objectives must be minimised, the reward must be positive if the objective value after the action is smaller than before and vice versa. The reward is, therefore, \(O(S_{t})-O(S_{t+1})\) instead of \(O(S_{t+1})-O(S_{t})\). Therefore, minimising objectives is equivalent to maximising the reward.  
In QLHH-II, an update of the policy is made for a frame of \(\tau\) movements. Therefore, it considers a cumulative reward, i.e., the sum of consecutive rewards. The cumulative reward of \(\tau\) iterations from time \(t\) is given by \(R_{t,\tau}\). Due to the above definition of the reward calculated for an action, \(R_{t,\tau}\) is equivalent to the difference in the objective value of the solutions before and after the \(\tau\) movements, i.e., \(S_{t}\) and \(S_{t+\tau}\), respectively.  
\[R_{t,\tau}=\sum_{t=0}^{\tau-1}r(S_{t+i},v_{t+i})=O(S_{t})-O(S_{t+\tau})\in \mathbb{R}^{3}\]Scalarisation  
The vector of cumulative rewards \(R_{t,t+\tau}\), i.e., for a frame of \(\tau\) movements from time \(t\), is reduced to a scalar reward \(r_{t}\) using a linear scalarisation consisting of a convex combination of every reward.  
\[r_{t}=\sum_{o\in\mathcal{O}}w^{(o)}\cdot R_{t,\tau}^{(o)}\in\mathbb{R}\]  
with \(\sum_{o\in\mathcal{O}}w^{(o)}=1\). For experiments described later, all objectives are given the same importance, i.e., \(w^{(\textit{rate})}=w^{(\textit{time})}=w^{(\textit{com})}=\frac{1}{3}\).
## 3 Proposed Scheme
### Error-Detecting and -Correcting Scheme
#### 3.2.2 Error Correcting
Step b2-1: Pair the bit bits in the row repeat region; select one bit in the non-repeat region; select one bit in the row non-repeat region and the non-repeat region; calculate the channel reliability of all combinations (multiplication of the bit channel reliability); select the combination with the lowest channel reliability, bit flip the two positions, and recheck the bank. If the test result is 0, the error correction is completed; otherwise, continue error correction. Step b2-2: Select the bit combination with the second-lowest reliability, flip the two positions, and check the line for errors again. If the test result is 0, the error correction is complete; otherwise, the error correction is ended. (In the application process, the second-lowest-reliability bit combination may not be selected according to the device performance and requirements, and the lowest-reliability bit combination can be selected for flip verification.) The error correction rule here is to select the two cases with the highest error probability to try the error correction. Before the error probability calculation, we choose the setting threshold, that is, in each possible error combination, we select the error probability greater than a certain threshold to try error correction.
###### Abstract  
This paper analyzes the convergence time and the root mean square (RMS) error of single frequency (SF) precise point positioning (PPP) in the ionospheric-constrained (TIC1) and troposphere- and ionospheric-constrained (TIC2) conditions, when the stations are at a low latitude, mid-latitude, and high latitude area during the period of a magnetic storm (MS) and a non-magnetic storm (NMS). In this paper, 375 IGS (international global navigation satellite system (GNSS) service) stations were selected from all over the world for 30 days in September 2017. The 24 hour daily observations were split for each station into 8 data sets of 3 hours each, so that a total of 90,000 tests were carried out. After statistical analysis, it was concluded that: during the MS period, the percentage of TIC2 shortened compared to the TIC1 convergence time, and it was by at least 3.9%, 3.0%, and 9.3% when the station was at global, low latitude, and high latitude areas, respectively. According to the statistical analysis, during the NMS period the convergence time was shortened about at least 12.8%, 11.0%, and 30.0% with respect to the stations in the MS period at global, low, and high latitude areas, respectively. If the station was located in the mid-latitude region, the convergence time was not shortened in some modes. The ionospheric activity in the mid-latitude region was less than that in the low-latitude region, while there were more stations in the mid-latitude region, and the precision of the global ionospheric maps (GIMs) and zenith tropospheric delay (ZTD) products were also slightly higher. Overall, TIC1 and TIC2 have a greater impact on convergence time, but have less impact on positioning accuracy, and only have a greater impact in different environments during the MS and NMS periods.  
**Keywords:** single frequency (SF); precise point positioning (PPP); ionospheric-constrained (TIC1); troposphere- and ionospheric-constrained (TIC2); magnetic storm (MS); non-magnetic storm (NMS)
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 4 Results and Discussions
### Input Data Confusion Matrix  
As explained in Section 3.4, each node provided to the algorithm contains a particular system state. Therefore, since nine classes are supplied to the input, the confusion matrix is 9 \(\times\) 9 (Figure 13).  
Figure 12: Macro-areas in the output map.  
Table 5 collects all the parameters needed for analysis. The lowest precision value (0.72) is assigned to the class representing the steady state. Filtering the projected data on the output map shows that the steady-state area contains all the first anomalous test readings. The reason why this result appears is that the anomaly test data are not acquired automatically. The shut-off valves are mechanical. In addition, proper coordination is needed between the operator who turns on the acquisition system and the operator who rotates the shut-off valve obstructing the flow.  
Figure 14 displays all the airflow sensor output values from every experiment. The data are standardized, and the different tests are divided according to the colours shown in Table 4. The black dots indicate the samples in the node [6,10]. This result is confirmed further by the fact that when the shut-off valve is triggered, the system needs some time to migrate to a new operating point. Consequently, the first measurements obtained during anomaly tests often reflect the transition of the system from a steady state to anomaly state.  
The overall accuracy of the algorithm is 90%, so the network optimally classifies the readings in the output map.  
Figure 13: Input data confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Transient of Steady State & 0.79 & 0.75 & 0.77 & 848 \\ Steady State & 0.72 & 1.00 & 0.84 & 717 \\ Anomaly 3 & 0.98 & 0.81 & 0.89 & 301 \\ Anomaly 5 & 1.00 & 0.98 & 0.99 & 168 \\ Anomaly 6 & 0.98 & 0.75 & 0.85 & 328 \\ Anomaly 7 & 1.00 & 0.95 & 0.97 & 238 \\ Anomaly 8 & 1.00 & 0.98 & 0.99 & 918 \\ Anomaly 9 & 0.97 & 0.97 & 0.97 & 755 \\ Anomaly 10 & 0.98 & 0.91 & 0.94 & 1048 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification report of the level 3 anomalies.
# Impact of Mega Constellations on Geospace Safety
###### Abstract
The other one, which considers Starlink's first-batch distribution plan, is combined with MASTER calculations in consideration of the Alt-V and Alt-Mass orbital flux changes of the three LEO satellites before and after Starlink. It seeks to obtain the impact of Starlink on the flux changes of the Earth's space environment, calculate its impact on the ACPL values of LEO satellites and the orbital maneuvers required for LEO satellites \(\Delta V\) and the propellant mass fraction (PMF), and calculate the change in mission lifetime of the target satellite to quantitatively assess the impact of the mega constellation on the LEO satellite geospace environment in terms of satellite mission lifetime and safety.
# FEM Based Preliminary Design Optimization in Case of Large Power Transformers
## 2 Proposed Methodology  
### Transformer Model for the Optimization  
The transformer is modelled by its active part (the core and the windings). This approximation is widely used in the industry, because its determines well the final dimensions of the transformer [3; 10; 16]. However, this approximation omits the mass and the cost of the external cooling system and many assemblies, which are generally modeled and designed only in the final design stage. Many transformer models has been published in the literature for preliminary design optimization of power transformers [10; 16]. The proposed methodology is based on a widely used model, which was published in the following papers [25; 26; 27] and extends this FEM method based calculation to determine the load losses and the short circuit impedance of the transformer. The FEM methodology is used to calculate the magnetic field distribution in the working window of the transformer, which takes the radial part of the magnetic field into account. Moreover, the final, geometric programming based optimization models can calculate the detailed conductor layout for the windings, not only use a copper filling factor based winding model as the previous methods. The proposed geometric programming based equation system can be applied for disc type windings. The other winding types (helical and other special winding types) can be modeled by this method, but their equation system should be derived similarly. Figure 3: Typical winding arrangements: (**a**) a helical winding with 6 parallel strands in one turn (**b**) a disc winding from 1 strand. Typical winding materials (**c**): single conductor, axial twin, continuously transposed cable [1]. The proposed algorithm can handle one and three phase transformers. The analytical formulation of the proposed algorithm can handle three and five legged transformer cores, as well. The transformer core is modelled by its diameter \(D_{c}\) and its planned filling factor, which takes into consideration the applied manufacturing technology(lamination, number of cooling ducts in the core and the stacking factor). In the paper, the equation system and every calculation is shown on a three phase, three legged transformer with reversing tap-changing method (Figure 2). The realized winding model contains three windings: low voltage (LV), high voltage (HV) and a regulating winding (Reg) (Figure 2). All of the optimized variables are listed in Table 1.  
In the applied methodology, every possible transformer design is represented as an individual. These individuals contains independent and dependent parameters (Table 1), these parameters represents the genes of the individual. Every dependent parameter can be determined by the knowledge of the independent values and the specification. The independent parameters are generated