esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 4 Results
### Comparisons with Different Models
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{0050} & \multicolumn{3}{c}{2330} & \multicolumn{3}{c}{1216} \\ \hline  & RMSE & MSE & MAE & RMSE & MSE & MAE & RMSE & MSE & MAE \\ \hline RNN & 12.456 & 155.162 & 8.307 & 110.801 & 12,276.819 & 78.284 & 1.702 & 2.897 & 1.439 \\ \hline RNN+corr & 6.272 & 39.317 & 3.877 & 92.393 & 8536.506 & 61.465 & 1.125 & 1.265 & 0.893 \\ \hline GRU & 11.333 & 128.432 & 8.084 & 99.277 & 9855.909 & 68.38 & 1.49 & 2.22 & 1.206 \\ \hline GRU+corr & 7.513 & 56.446 & 4.535 & 93.475 & 8737.567 & 59.556 & 1.144 & 1.309 & 0.906 \\ \hline LSTM & 8.479 & 71.889 & 5.666 & 91.747 & 8417.462 & 62.84 & 1.44 & 2.074 & 1.153 \\ \hline LSTM+corr & 6.374 & 40.627 & 4.269 & 78.969 & 6326.088 & 51.177 & 0.962 & 0.926 & 0.736 \\ \hline VMD-LSTM & 1.331 & 1.771 & 0.997 & 9.731 & 94.665 & 7.611 & **0.581** & **0.337** & **0.441** \\ \hline CSLSTM & **0.986** & **0.972** & **0.733** & **6.382** & **40.724** & **4.755** & 0.664 & 0.441 & 0.51 \\ \hline  & \multicolumn{3}{c}{2454} & \multicolumn{3}{c}{2882} & \multicolumn{3}{c}{Average} \\ \hline  & RMSE & MSE & MAE & RMSE & MSE & MAE & RMSE & MSE & MAE \\ \hline RNN & 89.528 & 8069.091 & 55.831 & 0.921 & 0.849 & 0.689 & 43.082 & 4100.964 & 28.910 \\ \hline RNN+corr & 61.367 & 3765.934 & 34.729 & 0.631 & 0.396 & 0.497 & 32.357 & 2468.684 & 20.292 \\ \hline GRU & 70.522 & 4973.111 & 40.329 & 0.935 & 0.873 & 0.799 & 36.711 & 2992.109 & 23.760 \\ \hline GRU+corr & 46.647 & 2175.947 & 33.486 & 0.598 & 0.358 & 0.444 & 29.875 & 2194.325 & 19.785 \\ \hline LSTM & 67.746 & 4589.464 & 39.655 & 0.654 & 0.427 & 0.479 & 34.013 & 2616.262 & 21.959 \\ \hline LSTM+corr & 30.383 & 923.142 & 18.019 & 0.578 & 0.334 & 0.458 & 23.453 & 1458.223 & 14.932 \\ \hline VMD-LSTM & 13.341 & 177.994 & 9.468 & **0.271** & **0.073** & **0.208** & 5.050 & 54.968 & 3.745 \\ \hline CSLSTM & **11.095** & **122.297** & **7.989** & 0.362 & 0.131 & 0.272 & **3.898** & **32.913** & **2.852** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison results, excluding COVID-19 period. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{0050} & \multicolumn{3}{c}{2330} & \multicolumn{3}{c}{1216} \\ \hline  & RMSE & MSE & MAE & RMSE & MSE & MAE & RMSE & MSE & MAE \\ \hline RNN & 3.425 & 11.732 & 2.469 & 28.821 & 830.622 & 20.455 & 1.848 & 3.415 & 1.545 \\ \hline RNN+corr & 1.511 & 2.279 & 1.023 & 22.328 & 498.542 & 13.795 & 1.132 & 1.281 & 0.831 \\ \hline GRU & 2.362 & 5.577 & 1.573 & 19.703 & 388.218 & 11.555 & 1.607 & 2.583 & 1.298
## 3 Network Models and Related Definitions
### Network Models
#### 3.1.4 Node Energy Consumption Model  
With the energy consumption of node sensing and data receiving significantly smaller than that of data transmission and node movement [29], so we only consider the latter as a primary component of energy consumption. In this study, the energy consumption model of the node based on the method is mentioned in [30; 31], and it can be calculated through the following formula:  
\[E_{c}(d,f)=P_{0}T_{p}d^{k}10^{\alpha(f)d/10} \tag{3}\]  
where \(P_{0}\) denotes the power threshold for a node to receive the information package. \(T_{p}\) denotes transmission delay of the information package, \(d\) denotes the transmitting distance of the information package, and \(k\) is the energy spreading factor. The absorption coefficient \(\alpha(f)\) can be calculated through the following formula:  
\[\alpha(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75\times 10^{ -4}f^{2}+0.003 \tag{4}\]  
where \(f\) is the frequency of the carrier acoustic signal in \(HZ\), and \(\alpha(f)\) is in \(dB/m\). In addition, the movement energy consumption \(M_{e}\) of node can be expressed as the product of the movement distance \(m_{d}\) and the energy consumption of per movement distance is \(m_{u}\). The relationship is described as follows:  
\[M_{e}=m_{d}*m_{u} \tag{5}\]
# TACRP: Traffic-Aware Clustering-Based Routing Protocol for Vehicular Ad-Hoc Networks
## 5 Performance Evaluation
### Network Performance Based on Dynamic Speed of Vehicles
To overcome this in the proposed TACRP protocol the traffic was controlled and monitored frequently so that we could achieve more efficiency. The network efficiency was evaluated by calculating the average CH and CM efficiency. The CH and CM efficiencies were high when compared with the earlier ECHS and NRHCS protocols. CH efficiency was calculated in the speed range of 0 to 90 Km/h, where the efficiency varied from 243 to 35 s for the TACRP protocol. For the earlier ECHS and NRHCS protocols the CH efficiency ranged from 137 s to 15 s and 152 s to 19 s, respectively. CM efficiency of the proposed TACRP protocol varied from 196 s to 39 s, where the earlier ECHS and NRHCS protocols ranged from 106 s to 9 s and 127 s to 15 s, respectively. The values are given in Table 4. The average cluster number counts, according to the variable speed, were calculated. For the TACRP protocol for a given speed, its ranged from 1 to 9 and for the earlier ECHS and NRHCS protocols it ranged from 5 to 21 and 3 to 15, respectively. In Figure 13, the CH, allocated according to the number of vehicles, was measured. From the figure, it is understood that the number of CH count reduced for the vehicles in the proposed method when compared with the earlier approaches. Figure 12: Average Cluster Number Calculation.
## Abstract  
The stringent emission regulations have motivated the development of cleaner fuels as diesel surrogates. However, their different physical-chemical properties make the study of their behavior in compression ignition engines essential. In this sense, optical techniques are a very effective tool for determining the spray evolution and combustion characteristics occurring in the combustion chamber. In this work, quantitative parameters describing the evolution of diesel-like sprays such as liquid length, spray penetration, ignition delay, lift-off length and flame penetration as well as the soot formation were tested in a constant high pressure and high temperature installation using schlieren, OH+ chemiluminescence and diffused back-illumination extinction imaging techniques. Boundary conditions such as rail pressure, chamber density and temperature were defined using guidelines from the Engine Combustion Network (ECN). Two paraffinic fuels (dodecane and a renewable hydrotreated vegetable oil (HVO)) and two oxygenated fuels (methylal identified as OME\({}_{1}\) and a blend of oxymethylene ethers, identified as OME\({}_{x}\)) were tested and compared to a conventional diesel fuel used as reference. Results showed that paraffinic fuels and OME\({}_{x}\) sprays have similar behavior in terms of global combustion metrics. In the case of OME\({}_{1}\), a shorter liquid length, but longer ignition delay time and flame lift-off length were observed. However, in terms of soot formation, a big difference between paraffinic and oxygenated fuels could be appreciated. While paraffinic fuels did not show any significant decrease of soot formation when compared to diesel fuel, soot formed by OME\({}_{1}\) and OME\({}_{x}\) was below the detection threshold in all tested conditions.  
**Keywords:** hydrotreated vegetal oil; oxymethylene ethers; ignition delay; liquid length; lift-off length; soot
## 2 Materials and Methods
The microCT scanning parameters were 100 kV, 100 \(\mu\)A, 16 \(\mu\)m resolution, Au filter and 360\({}^{\circ}\) rotation, for a total of 2400 projections. The images were reconstructed with NRecon (Bruker-microCT) using standard parameters for beam hardening (60%) and ring artefact correction (7%). Each root canal path was analyzed with high-resolution three-dimensional (3D) rendering and orthogonal bidimensional (2D) cross sections. Reconstructed volumes were analyzed with CTAn (Bruker-microCT), and the measurements were performed. The increase in canal volume and surface area were calculated for each group through the superimposition of the pre- and post-operative scanned volumes. The centroid shift and the amount of removed dentine up to the furcation in the middle and coronal third of the canals were measured using ImageJ 1.43u 64-bit software (National Institute of Health, Bethesda). A minimum threshold algorithm was set to avoid manual errors [22]. Root sections orthogonal to the canal axis were set at 4 different levels: at the furcation (F), 1.5 mm apically from the furcation (1.5F), 3 mm apically from the furcation (3F), and in correspondence to the point of maximum curvature (M). Results were analyzed by one-way ANOVA and post-hoc Student-Newmann-Keuls test (\(p<0.05\)) using SPSS (version 12.0).
## 2 Overview of IEEE 802.15.4 Standard  
The standard is designed for low-rate wireless personal area networks (LR-WPAN), that supports both star and peer-to-peer topology. The standard operates in both beacon and non-beacon enabled modes.  
In non-beacon-enabled mode, there is no duty cycle, as there are no active and inactive periods and allows nodes to communicate in an ad-hoc manner by using an un-slotted CSMA/CA algorithm. However, in beacon-enabled mode, the standard offers superframe structure, that comprises of an active and optional inactive period with an adaptive duty cycle, that ranges from less than 0.1% up to 100%. For 100% duty cycle, there is no inactive period. An active period is known as Superframe Duration (\(SD\)) and it comprises of the beacon, CAP and CFP. PAN coordinator broadcasts a beacon frame and it is mandatory for all member nodes to listen this beacon message not only for time synchronization but also to get the information about the CAP, CFP, inactive period and interval between two consecutive beacons (\(BI\)). \(SD\) comprises of 16 slots, minimum 9 slots are shared between Beacon frame and CAP, whereas maximum limit of CFP is 7 in a \(SD\). A complete superframe structure of IEEE 802.15.4 standard is shown in Figure 1.  
The parameter values of Superframe Order (\(SO\)) and Beacon Order (\(BO\)) determines \(SD\) and BI as mentioned in Equations (1) and (2).  
\[SD=aBasesuperfranduration\times 2^{SO} \tag{1}\]  
\[BI=aBasesuperfranduration\times 2^{BO} \tag{2}\]  
where, \(0\leq SO\leq BO\leq 14\).  
Duty cycle (\(DC\)) in a beacon-enabled mode is controlled by varying the values of \(SO\) and \(BO\). \(DC\) can be calculated as:  
\[DC=2^{BO-SO} \tag{3}\]  
During CAP, nodes transmit their requests to their PAN coordinator and PAN coordinator assigns Guaranteed Time Slots (GTS) to nodes for data transmission during CFP.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 3 System Model and Problem Statement
### Problem Statement
Additionally, it records the previous and next edge servers that are traversed by the sub-flow and deploys \(VNF^{l-1}\) and \(VNF^{l+1}\) instances, respectively. Finally, it selects the edge server with the lowest communication distance from the previous server to the server and from the server to the next edge server. If there are no more VNF instances that can be deployed in any edge servers without violating their resource capacities, it terminates the loop, drops the user request \(r_{i}\), and releases the resource it preempts. Otherwise, this procedure continues until the reliability requirement is satisfied.
## 4 Electrodes and Electrolytes for SSTFBs
### Thin-film Electrolytes
The ion-transport properties of LLZO thin films have been intensively studied with different deposition techniques such as PLD [324; 365; 429; 430; 431], RF sputtering [59; 432], sol-gel [433; 434; 435] and CVD [57; 436]. One of the features of LLZO is that as-deposited LLZO thin films tend to be amorphous [324; 365; 430; 431; 432; 433; 434; 435; 436]. Depending on the post-annealing conditions, different phases could form during the annealing process, resulting in a variation in conductivity, ranging from \(10^{-2}\) to \(10^{-6}\) S/cm at room temperature, as shown in Figure 15. Furthermore, it has been reported that deposition temperatures also play a role in the phase evolution [324]. Garbayo et al. [324] reported that LLZO films were found to be a mixture of an amorphous lithiated network and a cubic-type LLZO in the case of the deposition temperature between 50-500 \({}^{\circ}\)C. In contrast, the crystallized La\({}_{2}\)Zr\({}_{2}\)O\({}_{7}\) phase was found to become the major phase with a noticeable amount of cubic type LLZO at a deposition  
Figure 14: Conductivity summary of bulk [426] and thin-film Li\({}_{x}\)La\({}_{2/3-1/3x}\)TiO\({}_{3}\) (LLTO) deposited by PLD [414; 416], RF sputtering [422] and sol–gel method [421]. temperature of 750 \({}^{\circ}\)C. The authors also demonstrated that LLZO thin films deposited at 300 \({}^{\circ}\)C showed the highest ionic conductivity and lowest activation energy, while the film deposited at 750 \({}^{\circ}\)C showed the lowest ionic conductivity and highest activation energy. These results imply that the amorphous structure could be better for Li\({}^{+}\) conduction compared to the crystallized structure.
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
###### Abstract  
In this paper, we propose, design, and evaluate a new hybrid terrestrial-underwater optical communication link for providing high-speed connectivity between land and underwater systems. A device based on an amplify-and-forward strategy is considered and used for the hybrid optical link. A performance analysis of the proposed hybrid system is then carried out, taking into account both the atmospheric and underwater channels and their respective degradation sources. Different networking scenarios and conditions are evaluated. To this end, the channel model of the terrestrial free-space optical (FSO) link is modeled using the Gamma-Gamma distribution, while the underwater optical link is modeled using the Weibull distribution. The former takes into account atmospheric and turbulence attenuation, geometric spread and pointing errors, while the latter takes into account underwater and turbulence attenuation and geometric spread. Accordingly, a new analytical closed-form expression for the bit error rate (BER), which depends on the cumulative distribution function of the holistic hybrid system, is derived. Analytical results show that pointing errors as well as atmospheric and oceanic turbulence seriously degrade the performance of the hybrid system. In addition, ocean turbulence leads to the occurrence of a BER floor in some scenarios. This is the first time that such a network is proposed and modeled under the assumption of critical channel impairments.  
free space optical communication; underwater optical communications; atmospheric turbulence; ocean turbulence; amplify-and-forward; Gamma-Gamma; Weibull +
Footnote †: journal: _Photonics_
## 5 Simulation and Analysis
### Performance of the Routing Protocols with the Mobility Models
#### 5.2.4 Olsr  
The observations made for RGR also apply to OLSR to a certain extent. We see the highest PDR (Figure 15) and the least end-to-end delay (Figure 16) under RWP. RD once again causes the worst PDR. The explanation for this ranking is the same as for AODV, GRP and RGR. The PDR here ranges from about 61% to about 76%, thus a range length of about 15 percentage points. This range length is the same as that of GRP, and it is higher than that of both RGR and AODV.  
Figure 14: End-to-end delay of RGR under various mobility models.  
Figure 15: PDR of optimized link state routing (OLSR) under various mobility models.  
Based on these observations about the levels and ranges of the PDR, we can conclude that OLSR, a table-driven proactive routing protocol, and GRP, a position-based geographic routing protocol, are both three-times more sensitive than AODV, an on-demand reactive routing protocol, to the changes induced by the mobility models. Moreover, both OLSR and GRP are also about 67% more sensitive than RGR, a hybrid reactive-geographic routing protocol, to the changes induced by the mobility models. It is now clear that AODV is the less sensitive protocol to the mobility models, followed by RGR. OLSR and GRP are the most sensitive. The fact that RGR ranks right between AODV and GRP comes as no surprise given than RGR is in essence a combination of reactive and geographic protocols. Furthermore, GRP, closely followed by OLSR, presents the worst routing performance over all mobility models considered. This feeble performance points out the non-suitability of both position-based geographic and table-driven proactive protocols for UAANETs. The reasons for this are beyond the scope of this paper. In the meantime, RGR presents the best performance of all; and AODV lies in between.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 6 Simulation Results
### Comparison with a Classical Control System
Despite the PI structure was designed to ensure the desired voltage deviation, the change in the duty cycle modifies the operating point of the system, which prevents that the PI structure from being able to ensure the desired performance. Figure 16: Comparison between the proposed SMC and a classical PI structure. Moreover, the simulation of Figure 16 shows the dynamic advantage of the SMC over the PI structure, since the magnetizing current reaches the steady-state condition much faster, thus a lower bus voltage deviation occurs. This is also observed in the duty cycle imposed by the controllers, where the SMC imposes a faster control action in comparison with the PI structure, thus ensuring a fast compensation of the bus voltage. It must be noted that the PI structure defined in (52) and (54) was designed near the speed limit imposed by the switching frequency defined in Table 1: the maximum bandwidth of the inner controller is usually between \(1/10=0.1\) and \(1/5=0.2\) of the switching frequency since that is the range of validity for the linearized model [46], in this example, it was possible to increase that ratio to 0.266, but further increments could cause an unstable operation. Therefore, classical linear controllers are not able to ensure the desired behavior of the flyback charger/discharger for all the operating conditions; instead, following the design procedure proposed in this paper ensures that the proposed SMC imposes the desired performance under any condition.
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 7 Conclusions  
This study sheds light on the critical role of environmental assumptions in the emergence of RTD. By analyzing a real-world case study from the DroneResponse project, the analysis demonstrates how unvalidated, incorrect, evolving, or rigid environmental assumptions can lead to various forms of RTD, ultimately impacting system reliability, safety, and maintainability. We propose a novel classification framework that identifies five distinct types of RTD rooted in environmental assumptions. This classification offers a structured approach to understanding how assumptions, when left unchecked, contribute to technical debt early in the software lifecycle. Moreover, this study contributes to the sustainability of software-intensive systems by revealing how flawed or evolving assumptions introduce latent forms of RTD that impact long-term maintainability and system adaptability.  
Future work will focus on validating this classification across diverse domains such as automotive systems and industrial IoT, where environmental assumptions are similarly complex and volatile. In particular, we plan to empirically validate the classification of assumption-driven RTDs through controlled experiments and real-world case studies. We will conduct simulation-based experiments using cyber-physical testbeds (e.g., drone simulators like Gazebo or AirSim) to systematically vary environmental assumptions, measure their effects on system behavior, and categorize RTDs. We also plan to perform quantitative analyses (e.g., in autonomous vehicles, medical devices, and industrial automation) to test the generalizability of the framework. These studies will demonstrate the practical relevance and robustness of the classification for use in safety-critical engineering contexts.  
This research received no external funding.  
Not applicable  
The case study analyzed in this work is based on publicly available documentation of the DroneResponse system, which is publicly documented in [34]. All data used for analysis, including system requirements and environmental assumptions, are derived from open-source materials or previously published literature. In this study, no proprietary or confidential data were used.  
The author declares no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RTD & Requirements Technical Debt \\ sUAS & small Uncrewed Aerial System \\ RTDQM & Requirements Technical Debt Quantification Model \\ DR & Derived Requirement \\ ER & Requirements Engineering \\ CPS & Cyber-Physical Systems \\ EKF & Extended Kalman Filter \\ GPS & Global Positioning System \\ \end{tabular}
## 2 Materials and Methods
### Mann-Kendall Trend Analysis and Significant Test  
The Mann-Kendal (MK) significance test is a rank-based technique that can deal with the issues of seasonality, non-normality, missing values, and serial dependence [54]. This method is the same as a rank non-parametric test, which has been applied efficiently to analyze long term trends in hydro-meteorological variables [55; 56; 57; 58; 59]. Recently, the great availability of time-series remotely sensed data have enabled research communities to examine the spatiotemporal features of environmental changes [60; 61]. The selection of this method is based on the fact that it has the advantage of being less sensitive to outliers over the parametric method [62]. Hence, the application of the MK significance test in the research quantifies the spatiotemporal variations of the mangrove cover in the study area. The MK test statistic (\(S\)) is calculated in the following equations:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}(\text{NDVI}_{j}-\text{NDVI}_{i}) \tag{4}\]  
\[\text{sgn}(\text{NDVI}_{j}-\text{NDVI}_{i})=\left\{\begin{array}{l}+1\text{ NDVI}>0\\ 0\text{ NDVI}=0\\ -1\text{ NDVI}<0\end{array}\right. \tag{5}\]  
In the Equations (4) and (5), where \(\text{NDVI}_{i}\) and \(\text{NDVI}_{j}\)are the data values at times \(i\) and \(j\) (\(j>i\)), respectively and \(n\) indicates the length of the dataset (\(n=5\)). The following expression, as an assumption, is approximately normally distributed {variance [\(var(S)=1\)] and mean (\(l=0\)) value}.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Value** & **Description** \\ \hline Less than 0 & Less than chance agreement \\ \hline
0.01–0.2 & Sigh agreement \\ \hline
0.21–0.40 & Fair agreement \\ \hline
0.41–0.60 & Moderate agreement \\ \hline
0.61–0.80 & Substantial agreement \\ \hline
0.81–1.00 & Almost perfect agreement \\ \hline \hline \end{tabular}
\end{table}
Table 2: Interpretation of Kappa values.  
\[var(S)=\frac{n(n-1)(2n+5)}{18} \tag{6}\]  
\[Z=\left\{\begin{array}{ll}\frac{s-1}{\sqrt{\text{var}(s)}},&if\ S>0\\ 0,&ifS=0\\ \frac{s+1}{\sqrt{\text{var}(s)}},&if\ S>0\end{array}\right. \tag{7}\]  
The calculated standard \(Z\) value in the Equations (6) and (7) is compared with the standard normal distribution table with two-tailed confidence levels (\(\alpha=0.05\)). If the calculated \(Z\) is greater \(|Z|>|Z_{1-\alpha}|\), the null hypothesis (\(H_{0}\)) is invalid and therefore, the trend is statistically significant. Otherwise, the \(H_{0}\) hypothesis is accepted, that the trend is not statistically significant, and there is no trend in the time series (trendless time series) [63]. A positive (negative) value of \(Z\) signifies an upward (downward) trend.