esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, this study systematically evaluated the performance of AODV and GPSR routing protocols across varying environmental conditions and key performance metrics, including throughput, Packet Delivery Ratio (PDR), and delay. The findings emphasize the importance of tailoring routing strategies to specific network scenarios, as both protocols exhibit distinct behaviors in urban and highway environments. Simulation results reveal that AODV's throughput improves by 12% in cities with higher MaxJitter but decreases by 8% on highways, where the lower collision probability reduces the need for frequent updates. In urban areas, longer Hello intervals enhance AODV performance by 10%, whereas they lead to a 6% decrease in highway settings. On the other hand, GPSR's throughput declines by 15% in cities and 10% on highways as MaxJitter and Hello intervals increase, highlighting the protocol's sensitivity to these parameters, especially in high-density environments. Furthermore, extending route validity time boosts GPSR's PDR by 10% in urban scenarios, but the protocol's performance on highways could benefit from shorter validity times to reduce protocol overhead and improve data transmission efficiency. These results underscore the need to optimize routing parameters for each protocol to achieve the best possible performance in different vehicular network contexts. This research lays the foundation for further studies on adaptive routing strategies and the integration of dynamic adjustments or machine learning algorithms to enhance routing performance in autonomous vehicle communication systems. Future work should investigate the real-time adaptation of these parameters to improve reliability and efficiency in evolving traffic and network conditions.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/computers14010013/s1](https://www.mdpi.com/article/10.3390/computers14010013/s1)]([https://www.mdpi.com/article/10.3390/computers14010013/s1](https://www.mdpi.com/article/10.3390/computers14010013/s1)).  
Conceptualization, A.K.Y.D., M.E.E., A.E.T.A., A.F., N.M.O.S., T.A.A., J.S.A. and T.A.; Methodology, B.A.I.M. and J.S.A.; Validation, A.K.Y.D.; Formal analysis, A.K.Y.D., M.E.E., A.E.T.A., A.F., N.M.O.S. and B.A.I.M.; Writing--original draft, M.E.E.; Writing--review & editing, M.E.E., A.E.T.A., T.A.A., J.S.A. and T.A.; Supervision, T.A.; Funding acquisition, M.E., A.E.T.A., A.F. and T.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Higher Education through the Fundamental Research Grant Scheme (FRGS) under grant number FRGS/1/2021/ICT11/UNIMAP/03/1, and the authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/212/45.  
The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
In particular, we establish the following:  
1. the benchmark QSGW and QSGW\({}^{++}\) with a detailed comparison to the laser ARPES measurement [32]
2. we show how strain modifies both one- and two-particle properties in a markedly orbital-dependent manner: strain enhances the role of the \(d_{xy}\) orbital relative to the \(d_{xz+yz}\) orbitals
3. we show how the system becomes a better Fermi liquid with decreasing temperature; at low temperature, \(J\) becomes the dominant factor, and the increase in coherence is orbital specific, on account of the van Hove singularity
4. we show how the system becomes a better Fermi liquid with increasing strain, while at the same time, \(d_{xy}\) becomes heavier and \(d_{xz}\) and \(d_{yz}\) lighter; strain enhances the role of the \(d_{xy}\) orbital relative to the \(d_{xz+yz}\) orbitals and enhances spin singlet superconductivity
5. we use instability analysis to clarify how the relative strength of competing phases evolves with strain and compare against a spin density wave (SDW) (the latter eventually overtakes the instability towards superconductivity at a strain larger than the critical one)
6. we show how spin-orbit coupling affects superconductivity. In our original work, we took \(U\) and \(J\) from constrained RPA [33] calculations obtained from DFT [34], which yielded \(U=4.5\) eV and \(J=1.0\) eV, and \(J/U=0.22\). We recently discovered from a recent implementation of C-RPA [30] that \(U\) and \(J\) computed from DFT are too large to be used in a QSGW framework: in Hund's metals, \(U\) and \(J\) decrease in proportion to the bandwidth renormalisation, while \(J/U\) remains fixed. For Sr\({}_{2}\)RuO\({}_{4}\) in particular, QSGW renormalises the DFT bandwidth by about 0.6. Thus, for the present study, we use \(U=3.0\) eV and \(J=0.67\) eV; \(J/U=0.22\). This reduction does not change anything qualitatively, but important details change, the most important being that with the DFT-C-RPA estimates \(U\) and \(J\), the leading triplet eigenvalue was found to be slightly larger than the singlet eigenvalue in unstrained Sr\({}_{2}\)RuO\({}_{4}\), inconsistent with recent experimental findings [5; 7; 8]. In the present study, we use the newer parameterisation of \(U\) and \(J\). Before turning to the results, we note that our original work emphasised the interplay between charge and spin susceptibility. Those conclusions remain unchanged in the present work. As we have nothing new to report on this aspect, we focus on analysis spin susceptibility, which we denote as \(\chi(\mathbf{q},\omega)\), and label spin and charge susceptibilities as \(\chi^{m}\) and \(\chi^{d}\) only where a description of both is needed. The superconducting instabilities we present here include both spin and charge susceptibilities.
## 2 Experimental Investigation
### Microstructural Characterization  
The study adopted mercury intrusion porosimetry to evaluate the porosity and pore size distribution characteristics of concrete [39; 40]. For each mix, three sample tests were conducted, and the average results were used for analysis. The concrete samples were crushed into small sizes and dried by a solvent replacement method, changing the solvent every 6 hours for the first few days and then every day for one week. After that, the samples were vacuum-dried and subjected to mercury intrusion up to 210 MPa. The contact angle between mercury and concrete was set to 130\({}^{\circ}\). Assuming that the shape of the porous material was cylinder, the applied pressure \(p\) (in mN/m\({}^{2}\)) was converted to the pore diameter D (in m) by the Washburn equation [41]:  
\[D=(-4\sigma\cos\theta)/p, \tag{1}\]  
where \(\sigma\) is the mercury surface tension force in mN/m and \(\theta\) is the contact angle between mercury and the capillary surface.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**NO** & **OPC** & **Sand** & **CA** & **FA** & **W** & **W/C** \\ \hline PCFA0 & 409.0 & 720.0 & 1079.0 & 0.0 & 192 & 0.47 \\ PCFA15 & 347.7 & 697.3 & 1054.0 & 61.3 & 192 & 0.47 \\ PCFA30 & 286.3 & 689.0 & 1041.0 & 122.7 & 192 & 0.47 \\ \hline \hline \end{tabular} OPC = ordinary Portland cement; CA = coarse aggregate; FA = fly ash; and W/C = water to cement ratio; PCFA0 represents plain concrete with 0% fly ash.  
\end{table}
Table 1: Mix proportions of concrete. kg/m\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **CaO** & **SiO2** & **Al2O3** & **Fe2O3** & **MgO** & **SO3** & **K2O** \\ \hline Cement & 64.67 & 18.59 & 4.62 & 4.17 & 2.35 & 3.32 & 0.92 \\ Fly ash & 4.74 & 62.32 & 23.95 & 1.33 & 2.04 & 1.25 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical compositions (mass percentages of oxides, %).  
Figure 1: Measurement of chloride content in concrete.  
Microstructural studies of the concrete sample after exposure to seawater immersion were performed using SEM. The concrete samples were taken from the cylinder surface with a diameter of \(<\)25 mm and a thinness of \(<\)20 mm. The specimens were washed and dried to a constant weight in an oven under 105 \({}^{\circ}\)C. The specimens were then coated with a gold-plated film due to the poor electric conductive performance of concrete. Ultra-high resolution SEM was adopted to identify the hydration products, observe the fly ash, and evaluate the pore structures.
## 3 Experiments and Analysis
### Comparison with Other Methods
#### 3.2.1 Accuracy Comparison Experiment  
The specific experimental results are shown in Tables 1 and 2, which, compared to the DeepLabv3+ baseline model, CDFFNet achieves an improvement of 3.75% in mIoU, 3.09% in mPA, 5.85% in Recall, and 0.65% in Accuracy on the CHN6_CUG dataset. On the Massachusetts dataset, CDFFNet achieves an improvement of 3.31% in mIoU, 2.86% in mPA, 5.5% in Recall, and 0.6% in Accuracy, further demonstrating its effectiveness in different remote sensing data contexts. On CHN6_CUG, CDFFNet achieved an mIoU improvement of 3.86%, 0.42%, 0.25%, 5.2%, and 1.69% compared to the PSPNet, U-Net, HRNet, SegFormer, and RoadCNN models, respectively. The mPA metric increased by 4.1%, 0.8%, 0.98%, 6.44%, and 1.08%, while the Recall metric showed an increase of 8.47%, 1.69%, 2.41%, 13.02%, and 2.95%, respectively. The accuracy values are quite close, but CDFFNet achieves the highest value. Additionally, CDFFNet demonstrated superior accuracy on the Massachusetts dataset, reflecting its generalization capability. Among them, HRNet performs well, showing little difference in accuracy metrics compared to CDFFNet. However, the CDFFNet model processes remote sensing images of 512 \(\times\) 512 resolution at approximately three times the speed of HRNet. Specific details are provided in Section 3.2.2, Inference Efficiency Comparison Experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **mIoU/\%** & **mPA/\%** & **Recall/\%** & **Accuracy/\%** \\ \hline PSPNet (mobileNetV2) & 70.97 & 77.60 & 56.63 & 96.18 \\ U-Net (VGG16) & 74.41 & 81.06 & 63.41 & 96.68 \\ HRNet (hmetv2\_w18) & 74.58 & 80.88 & 62.96 & **96.74** \\ SegFormer (MixViTb1) & 69.63 & 75.42 & 52.08 & 96.09 \\ RoadCNN & 73.14 & 80.78 & 62.15 & 96.39 \\ DeepLabv3+ (Xception) & 71.08 & 78.77 & 59.25 & 96.05 \\ CDFFNet (MobileNetV2) & **74.83** & **81.86** & **65.10** & 96.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Accuracy Metrics on CHN6_CUG. (The bold font indicates the optimal value).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **mIoU/\%** & **mPA/\%** & **Recall/\%** & **Accuracy/\%** \\ \hline PSPNet (mobileNetV2) & 52.78 & 55.02 & 10.43 & 96.03 \\ U-Net (VGG16) & 68.77 & 73.13 & 46.95 & 97.21 \\ HRNet (hmetv2\_w18) & 68.70 & **74.56** & 45.98 & 97.13 \\ SegFormer (MixViTb1) & 62.57 & 66.35 & 40.32 & 96.88 \\ Road CNN & 68.88 & 74.17 & 47.22 & 97.12 \\ DeepLabv3+ (Xception) & 65.94 & 70.94 & 42.8 & 96.82 \\ CDFFNet (MobileNetV2) & **69.25** & 73.8 & **48.3** & **97.24** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Metrics on Massachusetts. (The bold font indicates the optimal value).
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 4 Analysis and Validation of Results
### Theoretical Maximum Throughput
#### 4.3.1 Simulation  
For the duration of the simulation run, Riverbed Modeler recorded the bytes received by the FTP client. For the scenario \(p_{drop}\) = 0.00%, the first value was recorded at \(t_{0}=10\) s and the last value at \(t_{1}=60\) s. The throughput \(T_{TCP}\) is the transferred file size \(s\) divided by the time that was needed to transfer the file:  
\[T_{TCP}=\frac{s}{t_{1}-t_{0}} \tag{6}\]  
\[T_{TCP}=\frac{10,000,000}{60-10}=200,000\text{ bytes} \tag{7}\]  
This calculation contains a small error as the simulation just records the values every second. Therefore, \(t_{0}\) is not exactly 10 s, but between 9 s and 10 s, and \(t_{1}\) is between 59 s and 60 s. For the order of the throughput, however, it does not have a significant impact.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{2}{c}{**Packet Retransmission**} & \multirow{2}{*}{**Difference between Calculated**} \\ \cline{2-3} \cline{4-4}  & **Calculated** & & **Simulated** \\ \hline \(p_{drop}\) = 0.00\% & 0 & 0 & No difference \\ \hline \(p_{drop}\) = 0.01\% & 0.68 & 1 & 47.06\% \\ \hline \(p_{drop}\) = 0.10\% & 6.85 & 7 & 2.19\% \\ \hline \(p_{drop}\) = 1.00\% & 68.49 & 62 & \(-\)9.48\% \\ \hline \(p_{drop}\) = 10.00\% & 684.93 & 1092 & 59.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Packet Retransmission (Calculated and Simulated).
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 2 Literature Review
### Summary  
As a result of reviewing the research related to the subjects above, it was found that there is a lack of research regarding the advantages of micro-EVs compared to traditional modes of transportation, as well as the types of use that are most suitable for micro-EVs. On the other hand, there have been plenty of studies dealing with the use of GPS data to extract and analyze the trip behavior of vehicles. Moreover, there have been studies on assessing the travel behaviors that group trips into trip chains for analysis. This study aims to identify the strengths of micro-EVs, which have not been addressed in previous research, and to analyze the service types most suitable for their use. For the analysis, GPS data from micro-EVs were utilized to examine their travel characteristics. Based on a review of studies on travel behavior analysis, individual trips and also trip chains were analyzed to better understand overall travel characteristics. Additionally, this study investigated the advantages of micro-EVs over conventional vehicles on narrow roads, an aspect not covered in previous research, and analyzed the characteristics of the roads where micro-EVsare commonly driven. Therefore, this study provides valuable insights into the strengths of micro-EVs and the most appropriate service types for their utilization.
# The Perspective of Large-Scale Production of Algae Biodiesel
## 3 Production of Algal Biodiesel
### The Environmental Impact of Algal Biodesel Production
#### 3.4.3 Use of Nutrients and Fertilizers  
Nutrients requirement for the cultivation of algae not only depends on the species but also on the stress that is induced to stimulate the accumulation of lipids or carbohydrates. Basic nutrients are nitrogen, phosphorus, and potassium. Since the proportion of dry algae consists of approximately 7% nitrogen and 1% phosphorus, large quantities of fertilizers are required to produce biodiesel from algae. These quantities of fertilizers generally can be a problem. To date, organic and inorganic chemicals are widely used as nutrients for large-scale microalgae cultivation [88]. However, their application is expensive, and there are environmental risks that do not favor the mass production of microalgae biomass. LCA studies show that 50% of total energy consumption and GHG emissions relate to the application of inorganic fertilizers [89]. The production of inorganic fertilizers is energy very intensive because the production of 1 ton of ammonia (N-fertilizer) requires 37 to 40 KJ of natural gas. The least fertilization requires oil palm plantations, i.e., 83-87% less than the farming of microalgae. For the cultivation of the jatropha, rapeseed, and sunflower, it needs 17-35%, 52-62%, and 59-68% less fertilizer, respectively [71]. Ammonium nitrate as fertilizer has the most significant impact on climate change.  
Taking into account that the wastewater contains a significant amount of nitrate and orthophosphate, the use of this media in the cultivation of algae appears as an acceptable economic alternative. However, respecting the fact that the mineral content in wastewater varies significantly on the location and season, it is difficult to rely entirely on this source of fertilizer.  
The recycling of nutrients from wastewater may provide some of the nutrients needed, so the fundamental design of processes includes nutrient recycling as a fundamental aspect of system operation.
## 3 Results and Discussion
### Melt Pool Formation and Thermal Behavior  
HEEDS-based simulations have thoroughly revealed the effects of laser power, laser spot diameter, layer thickness, and scan speed on the geometric and thermal characteristics of the melt pool. The simulation results show that increasing laser power enhances the  
Figure 4: Simulation results illustrating the melt track geometry obtained under specific processing parameters and the corresponding temperature distribution.  
Figure 3: LPBF simulation process: step-by-step illustration from powder spreading to melt pool formation.  
depth of the melt pool, contributing to complete material melting; however, beyond a certain threshold, irregularities are observed. One such irregularity is keyhole formation, which arises due to an imbalance in the vapor pressure within the melt pool, leading to defects such as micro-porosity in the final structure.  
On the other hand, excessively high scan speeds result in insufficient laser energy transfer to the material, causing irregularities within the melt pool. Incorrect selection of laser parameters can lead to melt pool instability and weaken inter-layer bonding.
# An Efficient Rice Virus-Induced Gene Silencing System Mediated by Wheat Dwarf Virus
## 2 Materials and Methods
### Construction of the Vector  
The WDV sequence was amplified using PCR, and the movement protein regions that did not affect virus infection were removed, according to the built WDV-Gate [21]. Restriction enzyme sites were inserted into the SIR and V1 regions to provide restriction enzyme sites for the insertion of the target gene fragment. The WDV genome is inserted into a plasmid containing the Ubiquitin promoter using the primers WF/WR, resulting in what is termed the WDV-VIGS system [22]. Subsequently, the target gene sequence is cloned into the _Spe_I and _StuI_ restriction sites using the primers WDV-F/R. Since the _StuI_ cutting site is a blunt end, only T4 ligase is required for the ligation of the reverse gene silencing sequence. _OsPi21_i (60 bp) and _OsPDS_i (60 bp) fragments were PCR-amplified from rice cDNA and subsequently integrated into the WDV-VIGS vector via homologous recombination. The vector designed with _OsPDS_si as the target genes are shown in (Figure 1a); other vector models are shown in (Figure S1). After it was successfully constructed and confirmed by electrophoresis (Figure 1b) and sequencing verification, the recombinant plasmid was transferred into _Agrobacteriumium_ GV3101. WDV-Rep-F/R was used to detect the expression of _OsPDS_ and _OsPi21_ after WDV infection (Figure 1c). All of the designed primer sequences used in this study are listed in Table 1.  
Figure 1: WDV-VIGS vector model, and tracking the presence of WDV in VIGS-infected ZH11. **(a)** VIGS vector model with _OsPDS_si as an example. **(b)** The target fragment was successfully inserted into the VIGS vector, as confirmed by gel electrophoresis analysis. Line1: pCambia1300-WDV-_OsPDS_si (unidirectional), 2: pCambia1300-WDV-_OsP21_i (unidirectional), 3: pCambia1300-WDV-_OsPDS_si (bidirectional), 4: pCambia1300-WDV-_OsPi21_i (bidirectional), 5: pCambia1300-WDV, 6: negative control.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 1 Introduction  
Due to the impact of the COVID-19 pandemic, remote work and virtual meetings have become a trend. In a zero-trust network environment, the security of voice conferencing systems has become important. However, given the properties of network packets, they can easily be intercepted and recorded, necessitating packet encryption. End-to-end encryption is a mature and reliable method, where encryption and decryption are performed on the user's device, and the server does not participate in any encryption or decryption. This ensures that only the communicating parties can decrypt and access the content, reducing the risk of communication content being leaked at any stage of the network (including intermediate servers), thereby securing the data during transmission. In this study, we implemented a voice conferencing solution that supports end-to-end encryption and is applicable in low-bandwidth network environments.  
The remainder of this article is organized as follows. The Section 2 describes homomorphic encryption for voice conferences and presents the findings from that process. The Section 3 presents the end-to-end encrypted voice conferencing system we designed and developed, based on terminal-side voice mixing, which ensures security and is applicablein low-bandwidth networks. The Section 4 provides the actual bandwidth measurements required for terminal-side voice mixing. Finally, the fifth chapter concludes the study.
## 7 Conclusions  
This paper proposes and implements a low-complexity MSD algorithm. By leveraging the different importances of the middle and edge bits and the phase symmetry property of the PCM/FM signals, the proposed algorithm groups and averages the local reference signal waveforms. As a result, it reduces the storage requirements of the local reference signals and the number of matching branches for correlation operations by 87.5% under different observation lengths. The software simulation results indicate that the proposed algorithm significantly reduces the computational complexity of correlation operations in traditional MSD algorithms, and at the same time, it ensures a negligible loss in the demodulation performance. Compared to the decision-feedback MSD algorithm, the proposed algorithm achieves lower computational complexity and higher demodulation gain.  
For the algorithm implementation on the FPGA platform, we use the structure of multiplexed FIR filters and optimize single-channel and single-coefficient FIR filters into four-channel and double-coefficient FIR filters. This optimization enhances the parallelism of computations in a single matching branch without affecting the maximum achievable clock frequency, thereby further reducing hardware resource consumption. The experimental results on the FPGA platform show that the hardware resource consumption of the proposed algorithm is only approximately 10% of that of the traditional MSD algorithm, and its hardware demodulation performance loss is less than 1 dB.  
Conceptualization, K.H., R.D., L.Z. and Y.Z.; methodology, K.H.; software, K.H.; formal analysis, K.H.; data curation, L.Z.; writing--original draft preparation, K.H.; writing--review and editing, K.H.; supervision, R.D.; project administration, Y.Z.; funding acquisition, R.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation (No. L202003) and China Scholarship Council (No. 202306510082).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Hubei SiEngine Technology Co., Ltd., Beijing Branch. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare no conflicts of interest regarding the publication of this paper.