esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Obtaining a Configuration Based on the Greedy Strategy
Figure 9 illustrates the greedy strategy to find a configuration for the configurable process tree \(Q_{1}^{n}\). The derivation process is shown on the right part of the figure for two logs, \(L_{1}\) and \(L_{2}\), where, for every configurable node, the best configurator is selected among all feasible configurations for each configurable node. First, the order in which the configurable nodes will be processed is decided. \(n_{1}\) and \(n_{2}\) are dependent nodes because they have a common ancestor that is a \(\circlearrowleft\) operator. Meanwhile, \(n_{3}\) and \(n_{4}\) are independent nodes. Hence, \(n_{1}\) and \(n_{2}\) have a higher priority than \(n_{3}\) and \(n_{4}\). Since \(n_{2}\) is an operator node, it has a higher priority than \(n_{1}\). \(n_{3}\) and \(n_{4}\) are both activity nodes, so they are prioritized from left (\(n_{3}\)) to right (\(n_{4}\)). Therefore, the order is \(n_{2}\), \(n_{1}\), \(n_{3}\), \(n_{4}\), regardless of the event log that will be considered. For the event log \(L_{1}\), the algorithm starts from the deeper node \(n_{2}\). \(n_{1}\) is set to \(\tau\), and all possible configurators (\(B\) and \(E\)) for \(n_{2}\) are then evaluated, considering the subtree that has as a root the loop operator that is an ancestor of \(n_{2}\), and the sub-log obtained projecting the original log \(L_{1}\) on the activities contained in the subtree: \(c,d,e\). The best configuration for \(n_{2}\) is \(E\). Later, having  
Figure 8: Different configurable node dependencies. the configuration of \(n_{2}\), in a similar way, \(n_{1}\) is analyzed and configured to \(E\). Afterwards, \(n_{4}\) is set to \(\tau\), while \(n_{3}\) is configured to \(E\). Finally, once \(n_{3}\) is already configured, the last node \(n_{4}\) is configured to \(H\). The final configuration for \(L_{1}\) is then obtained and represented as the best configuration. For the event \(\log L_{2}\), the algorithm proceeds in a similar way. Notice that since the \(\log L_{2}\) contains fewer activities than the configurable process tree \(Q_{1}^{\alpha}\), the projection of the activities contained in the subtrees creates very simple sub-logs, even an empty event \(\log\), such as obtained when processing the configurable node \(n_{2}\). The best configuration in this case considers blocking the configurable node \(n_{2}\) and hiding the configurable node \(n_{4}\), illustrating how the algorithm adapts to different scenarios. As a result, \(Q_{1}\) is the derived process tree from \(Q_{1}^{\alpha}\) and \(L_{1}\), and \(Q_{2}\) is the derived process tree from \(Q_{1}^{\alpha}\) and \(L_{2}\).
## 3 Results
### Mechanism of Antibacterial Action  
SYTOX green uptake assay was performed to investigate the cell penetration property of LEAPs, using a previously described procedure [30]. The SYTOX green dye cannot cross intact bacterial membranes, and it shows a significant increase in fluorescence intensity when bound to nucleic acids [31]. If the peptide causes membrane damage, SYTOX green can cross the bacterial membrane along with the peptide and bind to nucleic acids, resulting in a rise in fluorescence intensity. The bacterial membrane permeability of LEAPs was evaluated at 2 \(\times\) MIC by monitoring the intracellular influx of SYTOX green dye. Melittin, the positive control, upon addition with bacterial solution, showed a significant increase in the fluorescence intensity of SYTOX green dye. LEAPs, upon addition with bacterial solution, showed a significant decrease in fluorescence intensity of SYTOX green dye, to a value well below the fluorescence intensity of the control without LEAPs (Figure 3). nLEAP-1, nLEAP-1, eLEAP-1, and eLEAP-2 showed a greater decrease in fluorescence compared with nLEAP-2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**MIC * Values (μM)**} \\ \hline
**Petides** & **5 \({}^{\circ}\)C** & **15 \({}^{\circ}\)C** & **25 \({}^{\circ}\)C** & **37 \({}^{\circ}\)C** \\ \hline nLEAP-1 & 4 & 9 & \(>\)9 & \(>\)9 \\ nLEAP-1 & 9 & \(>\)9 & \(>\)9 & \(>\)9 \\ nLEAP-2 & 0.25 & 1 & \(>\)9 & \(>\)9 \\ eLEAP-1 & 4 & \(>\)9 & \(>\)9 & \(>\)9 \\ eLEAP-2 & 0.25 & 1 & \(>\)9 & \(>\)9 \\ \hline \hline \end{tabular}  
* MIC is the lowest concentration of an antimicrobial agent needed to kill bacteria.  
\end{table}
Table 3: Temperature-dependent antimicrobial activity of peptides: hLEAP-1 (human), nLEAP-1, nLEAP-2 (_D. mansoni_), and eLEAP-1, eLEAP-2 (_Lys. dewarborni_) against _Listeria monocytogenes_ at 5, 15, 25, and 35 \({}^{\circ}\)C. The peptides effectively inhibited the growth of _L. monocytogenes_ at low temperatures. The nLEAP-2 and eLEAP-2 showed the highest antibacterial activity at low temperatures.
## 1 Introduction
This results in low accuracy and poor timeliness in traceability efforts. In summary, previous research has proposed theoretical models and implementation strategies for information traceability and management, which have provided valuable insights into the traceability and management of information in the process of coil manufacturing to some extent. However, these methods still have some shortcomings. They have not formed a unified traceability information identification and sharing system, and they lack comprehensive data integration of traceability process elements and modeling of traceability paths. These deficiencies challenge the efficiency and accuracy of information traceability in the transformer coil manufacturing process. Integrating industrial Internet identification resolution and extension Petri nets can effectively address these issues, thereby achieving unified identification, modeling, and traceability of process information. Therefore, this study proposes a method for traceability and management of transformer coil production processes by integrating identification resolution and extension Petri nets. Unique identifiers are assigned to various resources and stages within the manufacturing of transformer coil products. Based on the Extenics information primitive model of coil production, a process traceability model based on manufacturing information flow is constructed, which realizes real-time monitoring and data integration of the entire manufacturing process, focuses on key influencing factors and bottleneck links in the manufacturing process, and achieves efficient traceability and management of production process and quality. The main contributions of this paper include the following:  
Proposed a modeling method for a 4M1E identification resolution system and a shared space model of the entire process data for coil manufacturing. Proposed a method for modeling extensible identification fundamentals for information related to the coil manufacturing process. Developed a Petri net model for the collection and integration of comprehensive data elements in the coil manufacturing process, as well as for information traceability, enabling the formal modeling of information traceability paths.
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
## 3 Utility-Based Wireless Backhaul Routing
### Problem Formulation  
Given the above notations, the UWBR problem involves the following: given  
\[\text{BS},\,\{N_{i},\,U_{i}(\cdot),\,l_{i}(\cdot,\cdot)\}\text{ for all }i, \tag{4}\]  
find  
\[\hat{i},\hat{i},A_{i}=\{t_{i},s_{j}\text{ for all }j\in\hat{i}\},\text{ for all }i, \tag{5}\]to maximize  
\[\sum\
olimits_{i\in\text{BS}}U_{i}(r_{i}), \tag{6}\]  
subject to for all \(i\),  
\[r_{i}^{\Sigma}=l_{i}(s_{i},t_{i}), \tag{7}\]  
\[t_{i}+\sum\
olimits_{j\in i}s_{i}\leq N_{i}, \tag{8}\]  
\[r_{i}=r_{i}^{\Sigma}-\sum\
olimits_{j\in i}r_{j}^{\Sigma}\geq 0, \tag{9}\]  
\[\text{for all }j\in\tilde{\mathbf{i}},\qquad j>i \tag{10}\]  
Given the geographical distribution of BS, each \(BS_{i}\)'s available antennas \(N_{i}\), best-effort demand \(U_{i}(\cdot)\), as well as its channel condition \(l_{i}(\cdot,\cdot)\) is considered. UWBR includes finding the parent node of each BS and assigning antennas on both sides of each link to maximize the traffic of wireless network utility \(\sum\
olimits_{i\in\text{BS}}U_{i}(r_{i})\). The problem has the following constraints: (7) the maximum link data rate of each BS depends on the transceiver antennas on both sides of the link; (8) the sum of the uplink and downlink antennas of each BS cannot exceed its total antennas; (9) the uplink speed of the BS serving its own UE is the total link data rate minus the total speed of the child nodes and it is non-negative, that is, (9); (10) the child node of each BS must be more distant to the MBS.  
UWBR has an NP-complete complexity, as shown in Appendix A.  
The entire system's utility of UWBR is bounded by \((I-1)\cdot U\Big{(}\frac{l_{2}(N_{i}N_{i})}{I-1}\Big{)}\), as shown in the proof of Appendix B.
## 5 Discussion
In other words, the multispectral HLS data preserve a considerable amount of information about vegetation properties. This finding is in agreement with [PERSON] et al. [70] who reported similarity between the \(C_{\text{ab}}\) values over the range of tree species belonging to different biome types, retrieved from hyperspectral measurements and Landsat observations. Further, our finding is in line with [PERSON] et al. [71], who exploited the potential of Sentinel-2 data for the mapping of \(C_{\text{ab}}\) of Norway spruce. Our study highlighted thepotential of the newly produced HLS data set to monitor the vegetation properties by taking advantage of its denser time series. We further examined if the time series of vegetation properties, retrieved from the HLS data, could improve the simulation of GPPSIM by the SCOPE model. We observed that both constant (Fixed scenario) and time-varying vegetation properties (HLS info scenario) led to almost identical GPPSIM during the vegetation growing season (Section 4.4). These results confirmed that the GPPSIM in Norway spruce is mainly driven by meteorological conditions (Figure 6). Therefore, the time-varying vegetation properties' information could be significant for GPPSIM only when the tree is either under stress condition (e.g., due to drought) or exhibits strong temporal phenology (such as deciduous tree species). In a previous study, [PERSON] et al. [20] reported an improvement in temporal GPPSIM of the plant under stress using the time-varying vegetation properties input to the SCOPE model. In our study, the SCOPE model was mainly driven by the meteorological conditions because the Norway spruce did not show pronounced temporal variations in vegetation properties (Section 4.3) and were under no significant stress condition during the selected episode [32]. Therefore, constant vegetation properties were sufficient for simulating temporal GPPSIM with reasonable accuracy. We further observed the influence of time-varying \(V_{\text{cmax}}\) (HLS info & var Vcmax scenario) on GPPSIM. We expected that some fluctuations in GPPSIM (Figure 5a,c) would be further improved with time-varying \(V_{\text{cmax}}\) instead of using a constant value throughout the vegetation growing season. The time-varying \(V_{\text{cmax}}\), however, reduced the accuracy of GPPSIM (Figure 5f), which could be attributed to the empirical relationship of \(V_{\text{cmax}}\) and \(C_{\text{ab}}\) defined for C3 plants (Equation (1)). A more robust empirical relationship between the \(C_{\text{ab}}\) and \(V_{\text{cmax}}\) must thus be defined for this site. A biome specific definition [72] could improve the estimation of \(V_{\text{cmax}}\), but this should be tested at the local site in a future study.
# Study on Disturbance Mechanism of Squeezed and Non-Squeezed Soil Piles on Soft Soil Foundation
## 1 Introduction
However, to make a rational study of the disturbance impact of squeezed and non-squeezed soil piles using numerical simulation, a numerical model applicable to various pile-forming processes must be established, where both boundary conditions and mesh division have an impact on the outcomes of numerical calculations. Therefore, the density and topology of grid division, as well as the boundary conditions of numerical modeling, have to be rather close in order to realize a round comparison and selection. Nonetheless, this uniform modeling approach has not yet been observed in current research. In this study, squeezed piles, represented by hydrostatic piles and bag grouted piles, and non-squeezed piles, represented by bored piles, were selected to make a round comparison. \(\mathrm{FLAC}^{\mathrm{3D}}\) was employed to simulate the disturbance of a single installed pile on soil respectively for the three pile types. Specifically, a novel construction method for numerical models, which simulates the mechanical processes of different pile types under standard grids, is proposed. Based on the simulated results, the evolution characteristics of displacement and stress fields, as well as disturbance ranges of the soil under the disturbance of piles, are analyzed. Finally, a synthetic comparison is made among the pile types by usingproposed disturbance indicators. This study provides important references for pile driving in soft soil foundations.
## 6 Simulation Results and Discussion  
This section compares the performance of DRiPLOF with: (1) single-interface solutions (MRHOF); and (2) a state-of-the-art multi-interface solution (POOF). To this end, the overhead of DRiPLOF is evaluated both in conditions free of cross-technology interference (see Section 6.1) and in the presence of a jammer (see Section 6.2).  
**The most important takeaway is that DRiPLOF performs similar to the best alternative (single- or multi-interface) under normal circumstances, while it is superior when external stressors such as jamming are involved.** This makes DRiPLOF especially suitable for RPL deployments that suffer interference from competing technologies such as WiFi(tm) [39] (in the 2.4 GHz band) and LoRaWAN(tm) [40] (in sub-GHz bands).
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 5 Conclusions

Table 7: RMS of position and velocity differences over two days between real-time orbit determination solutions with different pseudorange combination weights and the reference of the precision orbit determination solutions. **Author Contributions:** Conceptualization, M.W. ; methodology, M.W. and T.S. ; software, M.W. and W.Z. ; validation, M.W. and T.S. ; formal analysis, M.W. and H.H. ; investigation, M.W. and W.Z. ; resources, M.W. ; data curation, M.W. ; writing--original draft preparation, M.W. and T.S. ; writing--review and editing, M.W. ; visualization, M.W. ; supervision, H.H. ; project administration, H.H. ; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 62073044. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Acknowledgments:** The authors would like to acknowledge the IGS for providing the broadcast ephemeris and GNSS Research Center of Wuhan University providing precision orbit determination solutions. **Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
The year 2020 was a very difficult time for human civilization, with the new challenge of COVID-19 staring the world in its face [1, 2]. The challenge of COVID-19 management was felt by the whole world, especially by developing countries across the globe.  
Healthcare facilities handled the humongous rush of patients all across the world by delivering remote health services through telemedicine wherever appropriate [3]. Telemedicine primarily concerns extending healthcare to people who are in locations where expert medical care is difficult to provide. Developing countries require an efficient telemedicine system to fill the gap between the nonavailability of proper healthcare in rural or suburban areas and multispeciality hospitals in urban areas [4, 5]. In most of these instances, an ambulance is employed. The ambulance used in these scenarios must be equipped with multiple pieces healthcare equipment that are normally available in a hospital's emergency ward. Most of the present telemedicine ambulances are only equipped with devices to monitor vital signs. However, in addition, the monitoring of the real-time health status of critical patients and reliable transmission of the same data to doctors when en route to reference hospitals might prevent the occurrence of an increase in the risk levels and even loss of life. Ambulances equipped for telemedicine need to be capable of transmitting a variety of time-critical and temporally varying biosignal elements with varying QoS requirements, apart from the possible transmission of multimedia data. This makes seamless network connectivity indispensable. Thus, the transition to telehealth has been unprecedented but can help shape and configure post-pandemic well-being.  
In this work, we consider a scenario where a mobile ambulance carrying a critically ill patient needs to reach a hospital, and the vital signs of the patient need to be transmitted to the hospital in the meantime from the ambulance. We have studied the transmission of biosignals such as pulse rate (PR) and electrocardiogram (ECG) along with voice signals in a vehicular ad hoc network (VANET) environment. A spatio-temporal study was conducted in Mysuru City, India, by dividing the day into seven temporal parts, and a 6 km stretch of a four-way highway road network was considered. However, the architectures, frameworks, and algorithms proposed in this manuscript are applicable across most geographical regions. In the literature, a comparison of various wireless protocols which are used in healthcare scenarios has been provided [6; 7]. [PERSON] et al. explore the usefulness of WiMAX in an e-health context [8].
## 2 Discriminative Bayesian--Active Shape Model (DB-ASM)
### Shape Update  
\(y_{i}^{WP}\) and \(\sum_{yi}^{WP}\) obtained in above section are used to update the current shape parameter and to perform a shape fitting. The equation to obtain the prior of shape parameter in Bayesian distribution is shown in Equation (6) as below.  
\[p_{i}\left(p_{k}|p_{k-1}\right)\varpropto N\left(b_{k}\bigg{|}\mu_{b}\,\sum_{j}\right) \tag{6}\]  
The calculated prior shape parameter \(\sum_{yi}^{WP}\), as well as \(y_{i}^{WP}\), are used in updating process. The following Equations (7)-(9) present the updating process.  
\[K=\ P_{k-1}\Phi^{T}\left(\Phi P_{k-1}\Phi^{T}+\Sigma_{y}\right) \tag{7}\]  
\[\mu_{k}^{\text{F}}=\text{A}\mu_{k-1}^{\text{F}}+\text{K}\left(\text{y}-\Phi \text{A}\mu_{k-1}^{\text{F}}\right) \tag{8}\]  
\[\Sigma_{k}^{\text{F}}=\left(\text{I}_{n}-\text{K}\Phi\right)\text{P}_{k-1} \tag{9}\]  
A = \(n\) dimensional identity matrix, \(P_{k}\) = covariance of shape parameter.  
Figure 1: Appearance of MOSSE (Minimum Output Sum of Squared Error)filter and response map on a PDM (Point Distribution Model)shape: the PDM shape on a given face having 56 landmarks (**left**); the MOSSE filters at five sampled locations (**middle**); and their corresponding response maps (**right**).  
Using these equations, the shape parameter and pose parameter can be updated concurrently. The updates are repeated 10 times. When the difference between two shape parameters reaches to a threshold, it ceases the updating process.
# Cost Analysis and Optimization of Modern Power System Operations
## 3 Implementation and Case Studies
Case 3--Integration of Renewables and Storage:  
To evaluate the benefits of a more flexible power system, this case incorporates **renewable energy sources** and **energy storage systems** alongside the existing thermal generation units. The objective is to assess how modern generation technologies impact operational cost and system performance. 4. Case 4--Demand Flexibility:  
This scenario explores the impact of **demand-side flexibility** on the system's operating cost. Different levels of demand flexibility are considered to quantify potential cost savings and improvements in system responsiveness.
## 3 Class Imbalance Problem
### Genetic Algorithm Architecture for Class Imbalance Problem  
In order to resolve the class imbalance problem through GA, the approach we followed is to extend the feature-base of the skewed class with more positive examples. For this, GA considers feature vectors in the skewed class to generate feature representations of the virtual images (Figure 1) by applying genetic operators. The details of the process will appear in subsequent sections. As feature vectors contain global features, therefore the virtual image representations (chromosome offspring) are even more powerful as they contain global feature components of multiple images. To further ensure that GA receives good features in every generation, we evaluate the chromosome population through the objective function that defines multiple chromosome populations and we select the most optimal one--to achieve this goal, we have modified the objective function that we defined in Equation (7). The GA in the present work is designed by keeping the single objective in mind that classifier ensembles generate precise semantic output by not suffering from imbalanced training samples.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Services and Platforms Prone to Malware Propagation
#### 3.2.4 Mobile Devices  
Mobile devices, especially those using the Android operating system, are frequent targets due to their large user base and the variety of available applications.  
* Risk Factors:
* Attackers use obfuscation techniques to evade detection.
* Vulnerability to various forms of malware, including ransomware, trojans, and spyware.
* Consequences: Theft of personal data, unauthorized access to confidential information, and distribution of malware through mobile networks [34; 35; 39].