esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Antenna Broadcast Signal Propagation Model
### Northward Transmission  
When the eLoran signal propagates northward, the field strength of the signal will be affected by the terrain and change accordingly. Simulation Figure 17 shows the change of geographical environment during propagation, while Figure 18 shows that the signal field strength changes with the increase in propagation distance under the influence of geographical environment.  
The simulation results show that the topographic fluctuation of the propagation path has a slight influence on the signal intensity, with the propagation curves showing localized  
Figure 16: Field intensity propagation curve of the transmitted signal from the southward-oriented antenna.  
Figure 17: Landform profile of the Naqu region due north.  
Figure 18: Field intensity propagation curve of the transmitted signal from the northward-oriented antenna.  
-fading characteristics that correspond to the topographic profile. When the topographic fluctuations are less intense, the characteristics of the ground wave propagation decline are not as significant; only a few peaks demonstrate declines measuring 2-3 dB. The results indicate that the radiation signal field intensity of the Naqu transmitter can satisfy the required coverage distance of 1000 km. Furthermore, these results can be used to guide antenna construction in Western China. The results here demonstrate clearly that single-tower antennas with an increased effective height provide the optimal transmission range and efficiency [16; 17; 18].
## 5 Experiments
### Comparison Between FastSLAM and FastSLAM-PSO
#### 5.2.3 Large-Scale Scenario  
Figure 6 illustrates the outcomes of the algorithms in addressing the SLAM problem within a large-scale scenario. In the large-scale map experiment, the performance trends of all algorithms on both the x and y axes were consistent, indicating that the map design posed equivalent challenges to each algorithm. This uniformity in trend across algorithms suggests that the map effectively captured the complexities and nuances that are typically encountered in real-world environments, thereby validating the efficacy of the map design as a comprehensive testbed for SLAM algorithms.  
The overall performance exhibited a bifurcated pattern. Algorithms that incorporated DE, such as FastSLAM-DE, tended to perform worse than the base FastSLAM algorithm. This could be attributed to the DE's search mechanism, which may not have been adept at handling the intricate dynamics of large-scale maps, leading to suboptimal adjustments in the trajectory and map estimation. Conversely, algorithms that integrated PSO, particularly FastSLAM-PSO and FastSLAM-MO-PSO, demonstrated superior performance over the base FastSLAM. This enhancement underscores the effectiveness of PSO in optimizing particle distribution and enhancing the responsiveness of the SLAM process to environmental changes.  
FastSLAM-MO-PSO maintained a relatively low deviation and led in performance for the majority of the experiment. However, a noticeable increase in error was observed towards the end. This late-stage deviation could be due to the increased complexity andvariability in the map's latter sections, which may have overwhelmed the algorithm's capacity to maintain the same level of precision as in the earlier stages. The accumulation of small errors over a large and intricate map could also contribute to the final deviation, highlighting the need for further refinement in handling large-scale spatial data.  
This experiment not only substantiated the effectiveness of the FastSLAM-MO-PSO algorithm, but also showcased the merits of the benchmark design. The large-scale map, with its inherent challenges, provided a robust platform for evaluating the algorithms' capabilities in a manner that reflects real-world conditions. The ability of the benchmark to challenge the algorithms and reveal their strengths and weaknesses is a testament to its design, offering valuable insights into the performance characteristics of each SLAM variant. This comprehensive analysis serves as a foundation for further algorithmic improvements and provides a clear direction for future research in the field of SLAM.
## 8 Performance Evaluation
### Improved 3 FFs of 6G-Enabled UAVs  
As depicted in Figure 4, 6G offers a bandwidth of 1 Tbps, with a peak rate of back-haul and front-haul connections close to 10 Tbps [54]. Thus, 6G-assisted UAVs can be deployed in critically infected zones to assist 3 FFs over defined spatial coverage area, as depicted in Figure 7, to tackle the COVID-19 pandemic. Traffic capacity of 6G-FeMBB is \(\approx\)0.7 Gb/s/m\({}^{2}\) and 6G-umMTC is \(\approx\)1 Gb/s/m\({}^{2}\), compared with 5G-eMBB and 5G-mMTC, with traffic capacities of 0.05 Gb/s/m\({}^{2}\) and 0.1 Gb/s/m\({}^{2}\), respectively. The following is shown in Figure 7a. Thus, 6G-based UAVs can handle massive data requests generated per m\({}^{2}\), which improves the monitoring performance of inspection drones. Figure 7b shows the improved latency of 6G-umMTC over conventional 5G-mMTC and 4G-LTE-A services. Thus, real-time responsive actions can be performed by UAVs with GS in their spatial range. Figure 7c demonstrates the spectral efficiency coefficient \(\eta\) as \(\eta=P_{D}/\rho\times A\), where \(P_{D}\) is total data traffic by communicating antenna, \(\rho\) is the channel bandwidth, and \(A\) denotes the coverage area. As the \(\eta\) of 6G services is \(\approx\)5-10 times of 5G-URLLC, the spectral efficiency of fading antennas is high. Thus, 6G-enabled UAVs have a higher lifetime and can disseminate information at far-located GS over 5G counterparts.
## 4 Case Study
### Deployment of the CVROSS
#### 4.2.3 Calculation of Parking Gaps  
Using the matching results, the calculation of parking gaps was implemented. The roadside situations (at most six gaps, including the end gap) could be determined by one HD video camera. The total number of gaps was set to a large number \(M\) for the implementation. By applying the CVROSS, the parking gaps (considering various types of vehicles) could be identified and computed, for cases where \(L_{p}\), \(H_{c}\) and \(\theta_{c}\) are well-defined. Figure 14 shows the fuzzy logic toolbox running in the MATLAB runtime compiler on the server side of the CVROSS. The input and output--with their corresponding membership functions as given in Table 1--were created, and the fuzzification process, [PERSON]'s inference and the defuzzification process were defined. Thus, the fuzzy capability of parking reservation can be included in the computation of parking gaps.  
Figure 13: An illustration of geometric matching by the CVROSS.  
Figure 15 illustrates the lengths of the parking gaps in three lanes with different numbers of vehicles. When there was no vehicle in lane 1, the end gap was displayed as 640 pixels, which indicated that all the pixels were available, while the other gaps had zero value. From the computations, it was found that the algorithm was able to calculate the parking gaps programmatically, based on the previous matching results. For example, if no vehicles are in the traffic lane, only the end gap is shown. As a result, information on the length of all parking gaps can be produced and used for further processing. However, the number of gaps needed to be set before running the program. Thus, further calculation was required regarding the capacity of a particular road and traffic lane. Then, the possible number of parking gaps that may need to be computed was set.
# A Theoretical Study on the Resilience Evaluation Method of Operational Road Tunnel Systems
## 3 Basic Concept of Road Tunnel Resilience
### Evaluation Range of Road Tunnel Resilience  
The concept of resilience is relative to external disturbances. The resilience level of the road tunnel is an intrinsic attribute. This intrinsic attribute can only be motivated when the system is subjected to relatively severe external disturbances. So, the resilience evaluation will lose practical significance when the road tunnel is in the area of low external disturbance risk. Of course, this also needs to take into account the importance of the road tunnel itself. Therefore, the resilience evaluation of road tunnels should be targeted at major and important road tunnels, submarine tunnels, tunnel groups, and tunnels in areas with a high risk of external disturbances.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 4 Decision-Making Algorithm Based on Anti-Jamming Knowledge Graph
### Anti-Jamming Knowledge Graph Search Statement Design  
The process of extracting anti-jamming strategies from the anti-jamming knowledge graph is as follows: first, input different jamming patterns into the anti-jamming knowledge graph to determine/select/calculate the corresponding anti-jamming strategies and parameters. This input and output mode are similar to the knowledge graph question answering system. The difference is that the transmission and execution of anti-jamming decision-making are mostly carried out by machine language, which can save the semantic analysis step of the question answering system. The effective search can be realized only by matching the common input and output types in practical application to the search statement template, namely the \"question answering template\" used in the graph database.
# Assessment of AC Corrosion Probability in Buried Pipelines with a FEM-Assisted Stochastic Approach
## 3 Probabilistic Evaluation of the Leakage Current Density through a Holiday in the Coating  
Almost always, power frequency electromagnetic interference problems among power/railway lines and pipelines/telecommunication cables are based on a purely deterministic approach that assumes that all the parameters characterizing the calculation model are known and expressed by specific values. Nevertheless, this is not always the case; in certain situations, some of the model parameters have a random behavior that suggests a different approach to the problem that necessarily involves concepts of probabilistic-statistical nature.  
Just the issue which is the object of this paper has this kind of characteristic because the area size of the holidays present in the insulating coating of the pipeline can be considered, without any doubt, a random parameter.  
Therefore, under the assumption that area A of the holiday is represented by a random variable, this section proposes a probabilistic method to assess the value of the current density leaking from a holiday, present in the pipe insulating coating, to the soil. More explicitly, one may pose the problem as follows by starting from these two assumptions:  
Figure 5: Vertical cross-section of the finite element model for evaluating the spread resistance of a coating fault in the presence of the soil (the holiday is in dark brown and the soil in light brown). The mesh is shown in blue.  
Figure 6: Comparison spread resistance versus angle between coating and coating fault gutter obtained with FEM and analytical Formula (1).  
1. If V is the value of the induced voltage in a specific point along the pipeline due to the electromagnetic influence produced by nearby High Voltage power lines or electrified railway lines.
2. If, at the same point, a holiday in the pipeline insulating coating is present.  
Then, evaluate the probability that the induced AC current density, due to the electromagnetic interference from nearby power/railway lines, exceeds the limit value J\({}_{\text{lim}}\) established by the standards. See, for example, [10] or [35].  
One can summarize the basic steps of the whole calculation procedure in the flow chart in Figure 7.  
Before presenting the formulas to assess the above-mentioned probability, it is necessary to summarize some basic relationships, given in [39], that are here reported for convenience.
## 2 Materials and Methods
### Analyses of Volatile Organic Compounds  
Extraction of the volatile organic compounds from the infusions was performed by HS-SPME, using a 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane fiber (DVB/CAR/PDMS, Supelco(r)), and analyzed by a gas chromatographer (Agilent, 6890 Little Falls, DE, USA) coupled to a mass spectrometer (Agilent 5975) (GC-MS), according to the methodology described by [PERSON] et al. [23]. Before use, the fiber was conditioned according to the manufacturer's recommendations. 2 mL of each sample were placed in a 20 mL SPME vial, which was immediately sealed with silicone septa and conditioned for 5 min at 50 \({}^{\circ}\)C under continuous agitation. Then, the fiber was exposed to the vial headspace for 30 min, in agitation, and heated at 50 \({}^{\circ}\)C. After this period, the fiber was retracted, and inserted into the chromatographic injector, in splitless mode, for 2 min, for desorption of volatile compounds, with the aid of a carrier gas (helium) and transferred directly into the chromatographic column (SPB-5, 60 m \(\times\) 0.32 mm, film thickness df = 1 \(\upmu\)m 5%diphenyl--95%dimethylpolysiloxane, Supelco, Bellefonte, PA, USA) at 1 mL min/for 10 min, 250 \({}^{\circ}\)C. The chromatographic separation conditions were: 40 \({}^{\circ}\)C for 3 min, ramped to 200 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, subsequently ramped to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and held at final temperature for 3 min. The transfer line, ion source, and MS quadrupole temperature were 250, 230, and 150 \({}^{\circ}\)C, respectively. Electron impact mass spectra were measured at the acceleration energy of 70 eV. Data acquisition was performed in full-scan mode from \(m/z\) 50 to 550. Analytes were tentatively identified by the linear retention indices (LRI) and confirmed by the National Institute of Standards and Technology (ChemdataNIST V2.2, Gaithersburg, MD, USA) library database [24]. Agilent Chem Station (Agilent Technologies, Santa Clara, CA, USA) was used for data collection and processing. In addition, high purity external standards, when available, were injected. The LRI of each compound was calculated using the respective retention time (RT) compared against the RTs of a series of standard n-alkanes. The compounds were identified based on their LRI, the mass spectra of the NIST library [24], or authentic standards measured under the same conditions. To identify the compounds, substances with a probability greater than 50% were selected. To improve the accuracy of compounds' identification, only those substances that provided a match factor higher than 600 and a match factor versus reversed match factor ratio greater than 0.8 were selected for data processing [25]. LRI available from previous publications were also used for comparison.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 4 Technique Development and Implementation
### Minimal Error Synchronization
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Length of Observation Window \(d\)** & **PCMA** & **iLBC** \\ \hline
31 & 7 & 6 \\
63 & 7 & 8 \\
127 & 7 & 10 \\
255 & 10 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Maximum number of errors in the simulated VoIP channel. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(n\) & \(k\) & \(t\) & \(R\) [bit/s] & \(T\) [s] \\ \hline
31 & 6 & 7 & 4.03 & 1.488 \\
63 & 18 & 10 &  
For the purposes of transmission, the code vector was interleaved. In the receiving part, the synchronization procedure consists of continuously checking whether the BCH decoder can recognize the data frame in the extracted bitstream. If the BCH decoder determines that there are no errors or detects and corrects the errors, then it is assumed that synchronization is achieved. Otherwise, if the BCH decoder results in a negative syndrome, the speech signal is shifted by a certain number of samples and the steganographic data extraction and BCH decoding procedures are repeated. It should be emphasized that the main disadvantage of the presented method is the high computational complexity related to the continuous operation of the steganographic decoder and the BCH decoder. On the other hand, it should also be noted that this is a method that does not interfere with the steganographic signal in any way. Therefore, there will be no deterioration in signal quality.
# Increasing Photovoltaic Systems Efficiency Through the Implementation of Statistical Methods
## 2 Literature Review
### Prediction of PV System Performance Using PVGIS Platform
The output data include the following:  
* Global horizontal irradiation (GHI)--This value is the monthly sum of the energy of solar radiation hitting one square meter from a horizontal plane, measured in kWh/m\({}^{2}\);
* Direct average irradiation (DNI)--This value is the monthly sum of the solar radiation energy hitting one square meter always oriented in the direction of the sun, measured in kWh/m\({}^{2}\), including only the radiation coming directly from the sun;
* Global irradiation, optimal angle--This value is the monthly sum of the solar radiation energy hitting one square meter oriented toward the equator at the angle of inclination that gives the highest annual irradiation, measured in kWh/m\({}^{2}\);
* Global irradiation, selected angle (GTI)--This value is the monthly sum of the solar radiation energy hitting one square meter from a plane oriented toward the equator at the angle of inclination chosen by the user, measured in kWh/m\({}^{2}\);
* Ratio of diffuse radiation to global radiation--Much of the radiation that reaches the ground does not come directly from the sun but from scattering clouds and water vapour from the air (blue sky). This is known as diffuse radiation. This number represents the fraction of the total radiation that reaches the ground due to diffuse radiation. * Electricity production per month (January-December). According to input data for photovoltaic systems, PVGIS can also calculate the cost of electricity generated by the photovoltaic system. The calculation is based on a \"Levelized Cost of Energy\" (LCOE) method, similar to how a fixed-rate mortgage is calculated. * The total cost of purchasing and installing the photovoltaic system in the currency of the respective country;
* The interest rate, in % per annum, is assumed to be constant throughout the lifetime of the photovoltaic system;
* Estimated lifespan of the photovoltaic system in years. The data provided by PVGIS are used as a precursor to investigate the possibility of increasing efficiency through statistical methods, either by optimising the structure of photovoltaic systems (ANOVA method) or by optimising specific economic parameters (STEM method). Higher-efficiency photovoltaic plants will help reduce the cost of an installed photovoltaic system, decrease the occupied area, and implicitly substantially improve the technical and economic parameters related to circular economy principles.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 4 Experimental Design
### Experimental Settings  
#### 4.5.1 Validation Method  
The k-fold cross-validation (CV) [93] is the most commonly used method for validating the prediction performance of SDP models in previous studies [60; 94; 95]. In this paper, 5-fold CV with 30 runs is used. Five-fold CV is used because of two main reasons: (1) software defect datasets are usually very imbalanced and too many folds are likely to result in the case that testing data has few defective samples. (2) it has been used in previous studies [60; 95]. To alleviate possible sampling bias in random splits of CV, we repeat the five-fold CV process 30 times. 30 is used for the reason that it is the minimum needed samples to meet the central limit theory as done in [63].  
Specifically, for a defect dataset, we randomly split it into five folds, which are approximately the same size and the same defective ratio. Additionally then, four folds are used as the training data and the reminding one fold is used as the test data. In this way, each fold has a chance to be taken as the test data. Then we take the average performance on the 5 different test data as the performance of a five-fold CV. Finally, we repeat above process 30 times and report the average performance on each dataset.
# Potentiality of Rod-Type Chitosan Adsorbent Derived from Sewage Sludge
## 3 Results and Discussion
### Optimization of Rod-Type Biosorbent Modification Conditions
#### 3.2.2 Effect of Material Content in Biosorbent
The effect of chitosan content in RCS on the biosorption of As(V) is shown in Figure 1c. The values of the variables used in Equations (2)-(4) are shown in Table 2. Chitosan content of 6.0% in RCS resulted in higher As(V) removal compared to other RCS. The higher removal may be due to the effect of As(V) on the amine group of chitosan in RCS. When comparing correlation coefficients (\(R^{2}\)), the pseudo-second-order kinetic model was better suited to describing the biosorption kinetics of the RCS. The As(V) adsorption process was presumed to be chemisorption accompanied by valence forces through electron sharing or exchange between RCS and As(V) as covalent forces [24]. This promotes the electrostatic attraction of As(V) ions towards the RCS, increasing biosorption. However, as a result of examining the equilibrium uptake, rate constant, and initial sorption rate of As(V), the effect of chitosan content in RCS was not significant. Considering the price of chitosan, the chitosan content for producing biosorbent economically was determined to be 4.0.
# An Overview of the Process Mechanisms in the Laser Powder Directed Energy Deposition
## 3. Melt Pool Generation Mechanisms
### Thermal Behaviour
#### 3.1.1 Effects of the Laser Power  
It is generally accepted that increasing the laser power results in a higher temperature of the melt pool. Figure 19 shows the temperature distribution in the direction perpendicular to the travel speed for different values of laser power. It can be observed that, during  
Figure 18: (a) Digital image of the working area during the deposition of 316L thin wall and (b) temperature distribution measured across the gradient line (reproduced with permission from [95]; Copyright © 1999 Elsevier).  
the deposition of Ti6 Al4V alloy, increasing the laser power from 2 kW to 5 kW increased the maximum temperature from 1400 \({}^{\circ}\)C to 1850 \({}^{\circ}\)C, and, therefore, an enlarged melt pool width was obtained [85; 97; 98; 99; 100; 101; 102].  
However, contradictory results have been obtained in the literature by analyzing the effect of laser power on the height of the melt pool. [PERSON] and [PERSON] [45] and [PERSON], et al. [85] showed that the layer height remains constant by varying laser power values. [PERSON], et al. [99] observed that increasing the value of laser power results in a reduction of layer height. In contrast, [PERSON], et al. [100] and [PERSON] and [PERSON] [102] showed that layer height increases with laser power. These results indicate that different laser power levels influence the behavior of track height formation differently. In fact, the laser power strongly influences the temperature reached in the melt pool, and different laser power levels cause different convective flows in the molten material (Marangoni flows) [103]. To better understand this aspect, a more thorough investigation of the effect of laser power on melt pool dimensions should be conducted.