esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Performance of LDPC-Coded Large Intelligent Antenna System
## 4 Simulation Results and Analysis
Nevertheless, it is observed that the degradation of performance is moderate for the MMSE and ZF receivers, being more visible for the MRC and EGC receiver types. Figure 8 displays the operational outcomes for 4X25 LIS System, with five users, with and without LDPC codes. With regard to these outcomes, we observe that the use of LDPC codes results in a performance improvement of the order of 3 dB, for all receiver types. Figure 9 displays the results of the effort for 4X225 LIS System, with five users, with and without LDPC codes. Similar to the results of Figure 8, we find that applying LDPC  
Figure 10: Results for 4X225 LIS System, with 5 users, without Selective LIS versus with Selective LIS (20 versus 100 antennas). codes resulted in an optimisation of performance of the order of 2 dB over a comparable uncoded scheme. Figure 10 displays the results of the effort for 4X225 LIS System, with five users, without Selective LIS versus with Selective LIS (20 and 100 antennas). Selective LIS corresponds to using a subset of the total number of antennas. The algorithm employed in the selective LIS corresponds to those antennas that are placed at shorter distance from the mobile terminal. As expected, the use of selective LIS results in a performance degradation relating to the system without selective LIS. Moreover, decreasing the number of antennas considered by the selective LIS also translates in a degradation of performance.
## 4 Results and Discussion
### Measuring Changes in Camera's Position and Orientation
This process was repeated, and the results were recorded. Using the results, a graph with a regression line was created (Figure 17). This graph was used to convert the image ratios related to the experimental data to meaningful rotation degrees. To find the change in orientation of the camera around the Z-axis, the average rotation values of each group of pictures were compared with the reference rotation (i.e., accuracy) and also with each other (i.e., precision). **Change in the orientation of camera around the X-axis (pitch)** To measure the orientation of the camera around the X-axis, the same principle and graph used for the Y-axis were applied for this axis. However, this time, turning the camera around the X-axis could impact the length of the upper and lower sides of the images. Therefore, the ratio for each image was measured (i.e., ratio = smaller horizontal side/larger horizontal side) (Figure 18). To measure the degree of rotation around the X-axis, the graph illustrated in Figure 17 was used. To find the change in orientation of the camera around the X-axis, the average rotation values for each group of pictures were compared with the reference rotation (i.e., accuracy), and also with each other (i.e., precision). Figure 16: How to measure the length of vertical sides. **(a)** If the vertical sides are parallel; **(b)** If the vertical sides are not parallel. Figure 17: Relationship between the image sides’ ratios and the camera’s degree of rotation.
### Devices and Mobile Systems Applications
This crowdsourcing approach increases the volume of data available for analysis and promotes a sense of community resilience as users share their experiences and observations [47]. Applications like the EDY App in Indonesia use Geographic Information Systems (GIS) to deliver real-time warnings, evacuation routes, and educational materials, improving disaster preparedness [48]. These comprehensive features ensure that users are informed about impending threats. The ML-EHSAPP employs ML algorithms to assess structural safety in real-time, providing users with information regarding building vulnerabilities during seismic events [49; 50]. Such technological innovations enhance mobile applications' ability to offer personalized risk assessments based on location and building characteristics.
## 1 Introduction
The deletion graph technique searches for redundant paths on the new pruned graph by gradually deleting all links on the original paths, ensuring that these paths are physically independent from the original paths. 4. Experimental validation and evaluation: We further validate the effectiveness of the proposed method through comparative analysis and experimental demonstration. The adaptability and robustness of the algorithm in complex and changing network environments are highlighted. The flexibility of the proposed algorithm in terms of optimization strategy is demonstrated by adopting a soft update strategy to dynamically adjust network weight parameters. This strategy allows for the collaborative optimization of the multi-objective problem under different priorities. Experimental results show that the proposed algorithm exhibits significant advantages in the face of malicious attacks and network failures, maintaining a high transmission success rate and low latency. This study highlights the potential of multi-objective optimized multipath transmission algorithms in addressing the challenges of secure and reliable communication in heterogeneous networks. By leveraging advanced techniques like NSGA-II and the deletion graph method, the proposed solution offers a robust and adaptable framework capable of maintaining high levels of data integrity and security even in the face of network failures and attacks. The rest of this paper is organized as follows. Section 2 details the specific methods for achieving multi-objective optimization in multipath transmission for heterogeneous networks. In Section 3, we demonstrate the effectiveness of our proposed method through experimental validation. In Section 4, we review the state of the art in research on multi-objective optimization and multi-objective transmission. Finally, Section 5 provides a summary and discusses future research directions.
## 1 Introduction
Factors such as terrain slope and elevation were found to significantly impact vegetation, as vegetation degradation mainly occurred in areas with a slope of less than 10 degrees and an elevation of less than 1000 m between 2000 and 2015 [23]. Furthermore, influenced by global climate change, the Qinling region has seen a marked increase in the number of days with a daily average temperature \(\geq\) 10 \({}^{\circ}\)C from 1960 to 2019. [PERSON] et al. [24] have crafted an ecosystem management planning map, taking into account the altered ecosystem services in the Qinling region amidst climatic shifts. In addition, [PERSON] [25] conducted an analysis of the landscape ecological index, revealing that zones with extremely high ecological risk within the Qinling predominantly lie in the central and southeastern areas. However, these studies often focus on single ecological elements and lack comprehensive monitoring and evaluation of the entire ecological environment, and place little emphasis on the drivers of these changes. Therefore, this study selected the Shaanxi section of the Qinling Mountains as the study area, employed RSEI to investigate the spatiotemporal patterns of the ecological environment over the past 30 years in the area, and analyzed the driving factors behind changes in its ecological environment quality. The aim of this article is (i) to reveal the variability in the ecological environment under human influence, (ii) to grasp the intrinsic factors affecting environmental quality, and (iii) to provide data support and a scientific basis for the ecological protection and sustainable development of the Qinling Mountains.
## 5 Price-and-Branch FMP Solution Algorithm
### Algorithm Complexity  
Recall that the algorithm we proposed consists of two phases:  
**Phase 1**: solving the linear relaxation of FMP.  
**Phase 2**: solving the MIP version of FMP with the c-sets generated in the first phase.  
In the first phase, we consider the linear relaxation of FMP and, since its formulation is non-compact, we have to solve it by means of the column and constraint generation technique, i.e., we generate the c-sets that are essential to reach optimality of the linear relaxation of FMP. Because the number of all c-sets grows exponentially with the network size, we cannot guarantee that the number of c-sets generated during the process grows polynomially, and not exponentially. Thus, in practice, the number of steps in this phase can grow exponentially with the network size. In each such step, we solve the dual master problem DMP and the pricing problem PP. We solve DMP by means of the simplex algorithm, which is very effective. PP is an \(\mathcal{NP}\)-hard MIP problem [14] that is solved with branch-and-bound (B&B) algorithm whose complexity is exponential [37].  
In the second phase of the algorithm, we use the B&B algorithm to solve the MIP version of FMP for the c-sets that are generated in the first phase (recall that, similarly to PP, this problem is also \(\mathcal{NP}\)-hard). Thus, the complexity of this phase is also exponential.  
However, although the complexity of the proposed algorithm is exponential, the algorithm is capable of solving, as illustrated in Section 7, medium sized problem instances in a reasonable time.
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 4 Experimental Results Analysis
### PConv Module Efficacy Analysis Experiment  
In an effort to reduce computational demands and enhance inference speed while maintaining accuracy, this study integrates the lightweight PConv module alongside the P2 detection head and SPD-Conv module. The PConv module selectively extracts spatial features from a specific subset of input channels. As a result, only a portion of the Model channels undergo convolution, meaning that the associated weight parameters do not require training or storage. This approach substantially decreases the floating-point operations (FLOPs) and the overall parameter count of the model. To evaluate the impact of adding the PConv module at different positions within the model's feature fusion network, an efficacy experiment was designed, as shown in Table 4. In this experiment, \"YOLO\" denotes the YOLOv8n model with the P2 detection head and SPD-Conv module added. \"PConv-TestOne\" represents the scenario where the C2f module in the feature fusion network of the YOLO* model is replaced with the PConv module, while the other structures remain unchanged. \"PConv-TestTwo\" refers to replacing the C2f module with the PConv module at positions 21, 24, and 27 within the feature fusion network of the YOLO* model, excluding the upsampling part, with other structures unchanged. \"PConv-TestThree\" indicates that the C2f module is replaced by the PConv module at positions 12, 15, 24, and 27 within the feature fusion network of the YOLO* model, with other structures unchanged.  
The experimental data reveal that incorporating the PConv module optimizes both the model's parameter count and computational load to varying degrees. Among them, PConv-TestThree achieves the highest detection speed, demonstrating the PConv module's excellent performance in improving the model.  
To validate the performance enhancement brought by the added PConv module, a comparison experiment was conducted by adding G_Ghost, GhostNetV2, and RepGhost modules based on YOLO*. The comparison results are shown in Table 5.
# User Experience of 5G Video Services in Indonesia: Predictions Based on a Structural Equation Model
## 4 Analysis
### Analysis of Correlation Results and Implementation Priorities  
The correlation values generated from processing the SEM construction model show the level of significance of the influence between variables and also between variables and factors that can be used to assign priority levels for the implementation of 5G. From the results of the SEM processing, it can be seen that although the level of curiosity and desire for video streaming with smartphones on a 5G network is quite high, experience with 4G networks still provides an influence value for later using 5G networks. The influence value of \(\gamma=0.42\) on a scale of 0-1, even though it is lower than 50%, shows that there are still quite a lot of users who will stick to 4G for video streaming and still need real evidence for 5G as they do not have personal experience with the new technology.  
In the relationship between variables and their factors, for 4G UX, the highest influence value obtained, \(\alpha_{7}=0.72\), was for satisfaction in using video streaming on the 4G network. The influence of the social aspects of estrangement on 4G gives the smallest correlation, namely \(\alpha_{9}=0.16\). These two examples of correlation values should be considered, especially by 4G network providers. As the satisfaction of enjoying streaming videos seems to be sufficient for user satisfaction at more than 50% on a scale of 0-1, there is a question of whether it needs to be improved or only sufficiently maintained, while the effect of social estrangement on the 4G network probably does need further attention or can be ignored, as it is not a significantly negative influence.  
For the 5G network, the effect with the largest correlation is the desire to test ease and convenience on new devices and networks, which is measured at \(\beta_{2}\) = 0.81. This shows user enthusiasm for the existence of a new material to compare to previous technologies and can be considered a priority for 5G network providers.
# Nucleotides as Bacterial Second Messengers
## 6 The pppGpp or ppGpp Nucleotide
Under stress conditions, amino acid degradation to produce by products of metabolic interest are regulated by alarmones. In _E. coli_, ppGpp inhibits lysine decarboxylase when amino acids are limited; ppGpp interacts with Ldcl (lysine decarboxylase inducible) protein to inhibit its activity, thus cytoplasmic lysine is preserved for protein synthesis. Similarly, protein synthesis and DNA replication are down-regulated by ppGpp through direct inhibition of translation elongation factor activity and DNA primase, respectively. Alarmones combine the nutritional stress response with the virulence of pathogenic bacteria [100]. Within the phagocyte, _Legionella pneumophila_ produces ppGpp to induce activation of two (noncoding) regulatory RNAs, RsmZ and RsmY. In the stationary growth phase, RsmZ and RsmY activate the sigma S factor and the two-component LetA-LetS system [101]. After intracellular infection, _Salmonella_ spp. Typhimurium induces the PhoPQ two-component system. The PhoPQ system expresses SlyA protein, and SlyA regulates the transcription of genes essential for virulence in _S. typhimurium_[102]. The ppGpp messenger activates the function of the SlyA protein, facilitating the binding of SlyA to target promoters involved in the virulence of this bacterium [103]. _Interactions between ppGpp, c-di-GMP, and c-di-AMP_  
In contrast to the inhibition of degradation, signaling interactions between ppGpp and c-di-GMP promotes biofilm formation. Low doses of antibiotics which act to inhibit of translation, such as erythromycinomycin, tetracycline, or chloramphenicol, induce degradation of ppGpp by SpoT activity and lead to an increase in the expression of _pgaA_ gene, which encodes the protein necessary to synthesize poly-acetylglucoseamine (poly-GlcNAc) [31]. Poly-GlcNAc mediates biofilm formation [reference]. Conversely, maximal poly-GlcNAc production also requires the formation of c-di-GMP via the enzyme DGC YdeH [31], suggesting that bacteria in antibiotic stress inactive alarmone signaling to induce a barrier of protection (biofilm) against antimicrobial agents. C-di-AMP likely influences stringent signaling; in vitro, ppGpp inhibits phospho-diesterase activity to which degrades c-di-AMP [100]. Consequently, ppGpp maintains the cellular pool of c-di-AMP. The interaction between ppGpp-mediated signaling and c-di-nucleotides shows the influence of alarmones on bacterial physiology.
## 3 Results and Discussion
Then, the deposited metal is directly layered on an active Al surface. The active Al surface then reacts with the electrolyte, and the reaction that might occur between the Al and sodium chloride electrolyte is described below. For EDTA, the addition variable has more superior performance compared to the citrate-based-only example because of stronger binding and a denser coating. The larger grain of the coating in the citrate-based variable will be easier to abrade [42]. The reaction mechanism is shown in Figure 11.  
Figure 9: Polarization curves at scan rate of 10 mV.s\({}^{-1}\) of AA 6061, Zn-AA 6061, and Zn(EDTA)-AA 6061 in 3.5% NaCl solution. Figure 10: Discharge behavior of Al–air battery using zinc-coated (**a**) pure aluminum and (**b**) aluminum alloy 6061 without and with EDTA addition in the aqueous zinc solution at current densities of 2 mA cm\({}^{-2}\). Another study by [PERSON] et al. reported that Zn can be used as a corrosion inhibitor to improve the electrochemical performance of the anode [43]. The crack formed in the thin layer via electrodeposition onto the Al surface, confirmed by the SEM images above, especially with the EDTA addition variable, also leads the electrolyte to react with the active surface of the anode. Then, the reaction of the anode based on coated aluminum with sodium chloride electrolyte will generate a side product called simonkolleite (M\({}_{5}\)(OH)\({}_{8}\)Cl\({}_{2}\cdot\)H\({}_{2}\)O), a white gel with low water solubility. The reaction of the anode in the sodium chloride electrolyte is expressed below:  
\[5\,\mathrm{M}^{2+}+8\,\mathrm{OH}^{-}+2\,\mathrm{Cl}^{-}+\mathrm{H}_{2}\mathrm{O }\longrightarrow\mathrm{M}_{5}(\mathrm{OH})_{8}\mathrm{Cl}_{2}\cdot\mathrm{H}_ {2}\mathrm{O}, \tag{1}\]  
where M is the metal that reacts with the electrolyte [44]. \[\mathrm{Al}+2\,\mathrm{H}_{2}\mathrm{O}\longrightarrow\mathrm{Al}_{2} \mathrm{OH}_{3}\cdot\mathrm{H}_{2}\mathrm{O}+3\,\mathrm{H}^{+}+3\,\mathrm{e}^{-} \tag{2}\]  
\[\mathrm{Al}_{3}^{+}\mathrm{in}\,\mathrm{Al}_{2}\mathrm{OH}_{3}\cdot\mathrm{H}_ {2}\mathrm{O}+\mathrm{Cl}^{-}\longrightarrow\mathrm{Al}(\mathrm{OH})_{2} \mathrm{Cl}(simonkelleite) \tag{3}\]  
Table 4 details the discharge capacities and energies for selected anode samples, highlighting that the aluminum alloy 6061 coated with zinc via EDTA-assisted electrodeposition offers the highest capacity and energy for the Al-air battery. This study indicates that electrodeposition has a significant impact on the anode stability. This is proven with a longer discharge process and more anodic characteristics. Further studies are needed to investigate the possibility of cyclicability, which is the main challenge in battery design employing metal-based materials. Figure 11: Reaction mechanism of aluminum and coated aluminum in 3.5% sodium chloride.
## 3 Materials and Methods
### Data Preprocessing  
To ensure comparability with our previous study [10], the same preprocessing techniques and hyperparameter settings were employed. We utilized the discrete wavelet transform to eliminate the baseline drift and high-frequency noise from the 1D ECG signals. The decomposition scale was adjusted to 9 in Daubechies-4 (db4) to effectively remove baseline drift. To address high-frequency noise, the decomposition scale was set to 6, and frequencies ranging between 50 Hz and 100 kHz were filtered. Subsequently, the denoised ECG signals undergo segmentation using a window size of 2.4 s. This size was determined to ensure that it is large enough to include one beat cycle and its adjacent beat cycles. The 2.4 s ECG segments are then transformed into time-frequency images (scalograms) using continuous wavelet transform (CWT).
## 5 Conclusions  
In this proof-of-concept study, machine learning algorithms, first of all RFs and GBMs, demonstrated high discriminatory accuracies in detecting even subtle changes in synthetically generated ECG beats despite variable degrees of artifacts present. The Boruta algorithm indicated that the features with the highest influence on adequate difference detection were the components of the ECG trace where the amplitude was artificially increased. No other parts of the ECG signal played a relevant role. Our results prove that changes in ECG traces can be detected even in the presence of a disadvantageous signal-to-noise ratio. Whether other inaccuracies can be controlled by our approach cannot be sufficiently demonstrated based on the data available. Our findings may have implications for enhancing clinical diagnosis and monitoring. Further studies are needed to validate our findings.  
**Author Contributions:** Conceptualization, J.M., M.N., C.B., C.M. ([PERSON]), C.M. ([PERSON]), M.W.D. and U.B.; Methodology, J.M., C.B., C.M. ([PERSON]) and U.B.; Software, J.M. and C.M. ([PERSON]); Validation, Formal Analysis, M.N., J.M. and C.B.; Writing--Original Draft Preparation, M.N.; Writing--Review and Editing, J.M., M.W.D., C.M. ([PERSON]) and U.B.; Visualization, M.N. and J.M. Supervision, J.M. and U.B.; project administration, J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partly funded by the Upper Austrian Medical Cognitive Computing Center (MC3).  
**Data Availability Statement:** There are no data available online that we can share with other researchers.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.