esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Comparative Experimental Analysis of Path Planning
#### 5.4.2 Experimental Analysis in Urban Environments  
Figures 9 and 10 illustrate sample results from two urban scenarios, while Figure 11 presents the fitness evolution curve under the urban environment. In the city 1 scenario, the paths generated by SSA, ABC, and WOA all show large turns and are not suitable for UAV flight; GA path smoothness is not high, but the higher flight path reduces economy; although AVOA outperforms the first four algorithms in overall performance, it still fails to find the optimal path. In terms of fitness change, SSA has a rapid decrease in fitness in the 25 th generation, reaching the first convergence state, followed by a second convergence in the 149 th generation, with a final fitness of 588.687; GA falls into local optimality three times, in generations 2, 153, and 190, with a fitness of 545.625; ABC fell into local optimality several times, the last time in generation 124, with a fitness of 516.886; WOA converged several times, the last time in generation 158, with a final fitness of 501.689; although AVOA converged relatively early at the 39 th generation, its fitness did not reach the optimum, ending at 482.18. SAVOA reaches convergence at generation 43 and is gradually fine-tuned later, with a final fitness of 461.04.  
In the city 2 scenario, the paths generated by AVOA, SSA, ABC, and WOA all have sharp turns with high fluctuation ups and downs; although the GA path is relatively smooth, its higher flight altitude causes the drone to take a longer detour. According to the fitness variation graph, WOA has the highest fitness value, converging at the 56 th generation with a final value of 609.014. SSA's fitness decreases sharply in the 27 th generation andthen converges in the 85 th generation, but its fitness is as high as 543.75; AVOA converged at generation 46, but experienced significant fluctuations starting at generation 94 and continued to vary thereafter, with a fitness value of 501.002. SAVOA began converging at generation 64; although slight variations occurred afterward, they were minimal, resulting in a final fitness value of 457.691.
# The Computability of the Channel Reliability Function and Related Bounds
###### Abstract  
The channel reliability function is a crucial tool for characterizing the dependable transmission of messages across communication channels. In many cases, the only upper and lower bounds of this function are known. We investigate the computability of the reliability function and its associated functions, demonstrating that the reliability function is not Turing computable. This also holds true for functions related to the sphere packing bound and the expurgation bound. Additionally, we examine the \(R_{\infty}\) function and zero-error feedback capacity, as they are vital in the context of the reliability function. Both the \(R_{\infty}\) function and the zero-error feedback capacity are not Banach-Mazur computable.  
Turing computability; channel reliability function; zero-error feedback capacity 1
## 3 Methodology  
### [PERSON]'s Approach to Yield Estimation  
Agriculture monitoring based on RS and GIS is a vital component of the food safety information system, which provides accurate and timely crop area estimates and crop output projections on a national, regional, and global scale.  
Multiple methodological approaches for measuring crop efficiency with RS data have been improved so far. Some of these procedures are straightforward, while others are more complicated.  
There are numerous methods for estimating crop yields using satellite data and GIS. These techniques fall into three classes:  
* RS techniques which are founded on empirical-statistical models.
* RS approaches which are based on an equilibrium model of water use.
* RS approaches that utilise biomass estimating models.  
[PERSON]'s approach is suitable for determining the yield's main elements as indices, and unlike other methods, it does not need previous years' yield results and data, which makes it relatively more time efficient [19].  
In the [PERSON] technique [19], which is based on [PERSON] and the biomass estimating model, the Yield Gradient Index (YGI) of the product is computed according to the level of the soil site as follows:  
\[\text{YGI}=\text{A}\times\text{B}\times\text{C},\text{ center} \tag{1}\]  
Here, A represents the vegetated area of the region, computed using the Normalized Difference Vegetation Index (NDVI), The range of B's SQI is 0 to 100, and C is the crop coefficient for calculating efficiency which is estimated using evapotranspiration.  
This study used geospatial analysis of multitemporal satellite data, groundwater conditions, soil salinity, and ground truth data to monitor crops in real-time and forecasted yields in low- and high-productive cottonfield areas of Jarkurgan district of Surkhandarya region, Uzbekistan.  
False Color, NDVI, and SI analyses of areas were performed utilising multi-temporal Sentinel 2 data from April and August 2019 to track the phenology of cotton vegetation and predict its crop yield.  
Figure 3: AOI from SRTM 30m resolution DEM.  
In our approach, the formula is therefore modified as follows:  
\[\text{YGI}=\text{NDVI}\times\text{SQI}\times\text{Kc},\text{ tonnes} \tag{2}\]  
where Kc is the special Kappa crop coefficient we are using as a crop coefficient based on the crop growth model.  
In [PERSON]'s approach, results are calculated by centners, and in 0 to 100 scale to determine YGI. However, in our study, we used tonnes. Therefore, the formula was also modified according to the desired measurement unit.  
Indicators derived from spatial-temporal Sentinel 2 imagery, together with other auxiliary environmental data such as groundwater status, soil quality (SQ), and expert knowledge, can provide more accurate crop growth and production predictions.
## 5 System Performance
### Comparison with Existing IEQ Monitoring Systems  
With the functionality of the PROMET&O system assessed, a comparative analysis was performed to evaluate the performance of the proposed system against those of existing systems proposed in the literature, such as SAMBA [23], ENVIRA [27], and commercial products such as [33; 64]. The results of such an analysis, which considers parameters and  
Figure 17: Loading time comparison between the proposed implementation and the original one, provided as a default by Grafana, for updating a chart embedded in an _iframe_. On each box, the central mark indicates the median, the bottom and top edges of the box indicate the 25 th and 75 th percentiles, respectively, and the whiskers extend to the most extreme data points, the minimum and the maximum values. indicators both at the monitoring device and at the cloud platform levels, are reported in Table 4.  
At the monitoring device level, factors such as the type of sensors employed, overall volume, cost, and data processing capabilities were considered. While most references evaluated all four IEQ domains with 5 to 10 different types of sensors, limited data processing was performed, typically restricted to average calculations on raw sensor data. The cost of such units ranged from hundreds to almost a thousand dollars, significantly lower than reference instruments, with a compact volume in the order of a few cubic decimeters. These research works provided valuable insights into the development of multi-sensor devices. Regarding the cloud platform, fewer details are typically available in the literature. Ready-made software platforms are more common in case of research work, probably due to their fastest set-up time, while architectural details for commercial IEQ monitoring systems are not available. The typical exploited dashboard is either a smartphone application or a web application. In all cases, little information regarding the development and optimization of the front-end and of the back-end are reported. In comparison, the proposed system aligns well with monitoring device indicators, featuring more sensors for indoor air quality than existing systems. Its volume and cost are also within the range of existing solutions, indicating a low-cost design. As reported in Section 3, the most expensive component is the 3D printing of the case, which could be significantly reduced through large-volume production. The intermediate architecture of the cloud platform is a novel aspect, which offers higher flexibility and customization, achievable even by non-IT professionals, compared to existing solutions.
## 3 Datasets and Methods
### ML Models and Configuration Parameters
F-score is calculated as follows:  
\[\text{F score}=\frac{2(\text{Recall Precision})}{\text{Recall}+\text{Precision}} \tag{3}\]  
The MCC is not a biased metric (as precision or recall) and is flexible enough to work properly even with highly unbalanced data. The main properties of MCC are [91]:  
* The MCC can be calculated using the CM. * The calculation of the MCC metric uses the four quantities (TP, TN, FP, and FN), which gives a better summary of the performance of classification algorithms. * The MCC is not defined if any of the quantities TP + FN, TP + FP, TN + FP, or TN + FN is zero. * MCC takes values in the interval [-1, 1], with 1 showing a complete agreement, \(-1\) a complete disagreement, and 0 showing that the prediction was uncorrelated with the ground truth. The MCC value is calculated as follows:  
\[\text{MCC}=\frac{\text{TP TN}-\text{FP FN}}{\sqrt{(\text{TP}+\text{FP})( \text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+\text{FN})}} \tag{4}\]  
The False Positive Rate (FPR) (also called False Alarm Rate) is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events [92]. This parameter in intrusion detection is important because it shows how wrongly a model has classified an action as non-intrusion or valid. The aim is to have as small a FPR as possible to prevent the security personnel from false alarms. FPR is calculated as follows:  
Figure 2:0  
Figure 2:1 Confusion matrix (CM) of the intrusion classification. TN = true negative; FN = false negative; FP = false positive; TP = true positive. In the same way, the False Negative Rate (FNR) shows the ratio of not detected intrusion attempts and should be also minimized [93]. FNR is calculated as follows:  
Figure 2:2  
The Classification Error (or Error Rate) tells how often the model makes a wrong prediction [90]. The formula for classification error is the following:  
Figure 2:3  
The training and testing times demonstrate the time needed to train a model and identify the intrusion/malware, respectively. For intrusion detection, the one-sample test time (decision does it as an intrusion/malware or valid application) is more significant as the ML model is not renewed/trained very often.
## 3 Results and Discussion
### FE-TEM and FE-SEM Analysis  
FE-TEM was used to examine the structure of the KKD-ZnO-NPs. As shown in Figure 4A-C, KKD-ZnO-NPs exhibited flower shapes with sizes ranging from 500 to 1000 nm. The formation of these nanoflowers was believed to be caused by the aggregation of smaller polygonal ZnO-NPs, resulting in ZnO nanoflowers with 4-6 broad arrow petal-like structures. The element mapping results revealed the distribution of zinc and oxygen in the nanoflowers, which were represented as red and green dots, respectively (Figure 4D). The hexagonal rings of electron spots in KKD-ZnO-NPs SAED patterns correspond to each facet of the sample's lattice planes, indicating the KKD-ZnO-NPs highly crystalline structure (Figure 4E) [40,41]. The EDX spectra revealed the highest optical peaks measured  
Figure 3: Dynamic light scattering (DLS) indicating size distribution of KKD-ZnO-NPs.  
Figure 2: FTIR spectrum of KKD-ZnO-NPs (red) and Kakadu plum extract (black).  
with 83.82% weight of zinc and 16.15% weight of oxygen, making it 100%, confirming the purity of KKD-ZnO-NPs (Figure 4F).  
The FE-SEM analysis confirmed that the KKD-ZnO-NPs exhibited a flower-shaped morphology, which is consistent with the FE-TEM data (Figure 5A,B). These findings showed similarly shaped ZnO-NPs are prepared using plant extract and bacteria, as reported earlier [42, 43]. A sharp peak signal of both metals in the EDX spectra indicated the presence of Zn and O, reveals the presence of metal oxide nanoparticles, and verifying the absence of aggregation, agreed with other references [44, 45].  
Figure 4: FE-TEM images of KKD-ZnO-NPs showing the structure of particles (**A**–**C**), elemental distribution (**D**), zinc (red) and oxygen (green), selected area electron diffraction (SAED) (**E**), energy-dispersive X-ray spectroscopy (EDX) spectrum (**F**).  
Figure 5: FE-SEM images of KKD-ZnO-NPs exhibited a flower shape in (**A**,**B**), elemental distribution (**C**,**D**), zinc (yellow) and oxygen (blue), and EDX quantitative analysis (**E**).
## 3 Preliminaries  
### Dec-POMDP  
We assumed the _decentralized partially observable Markov decision process_ (dec-POMDP) [70] of \(N\) agents. Dec-POMDP is formulated by a tuple:  
\[\langle\mathcal{I},\mathcal{S},\{\mathcal{A}_{i}\},p_{T},\{r_{i}\},\{\Omega_{ i}\},\mathcal{O},H\rangle \tag{1}\]  
where \(\mathcal{I}=\{1,\ldots,N\}\) indicates a set of agents in the system; \(\mathcal{S}\) is a finite set of possible states; \(\mathcal{A}_{i}\) is a finite set of action space of each agent \(i\in\mathcal{I}\). For \(a\in\mathcal{A}\) (\(=\mathcal{A}_{1}\times\cdots\times\mathcal{A}_{N}\)) and \(s_{s},s^{\prime}\in\mathcal{S}\), \(p_{T}(s^{\prime}|s,a)\) is a transition probability; \(r_{i}(s,a)\in\mathbb{R}\) is the reward obtained by \(i\in\mathcal{I}\); \(\Omega_{i}\) is a finite set of observations by \(i\in\mathcal{I}\); \(\mathcal{O}(o|s,a)\) is an observation probability that \(i\) sees \(o\in\Omega\) when \(i\) takes action \(a\) in state \(s\); \(H\) is the time horizon of the process. In dec-POMDP, the objective of agents is to maximize the discounted cumulative reward \(R_{i}=\sum_{t=0}^{H}\gamma^{t}r_{i}(s,a)\) by optimizing their policies \(\pi_{i}\), where \(\gamma\) is a discount factor (\(0\leq\gamma<1\)).
# Software Engineering Techniques for Building Sustainable Cities with Electric Vehicles
## 3 Leveraging Software Engineering for Sustainable Smart Cities with Electric Vehicles
### Envisioning the Future of Software Engineering for EV Smart Cities  
The rising popularity of electric vehicles (EVs) necessitates software-engineered smart cities equipped to support their adoption. By harnessing Internet of Things (IoT), artificial intelligence (AI), and blockchain technologies, these cities can efficiently manage traffic and infrastructure for EVs.  
IoT sensors and connected vehicles can optimize traffic and reduce congestion, a key urban challenge. For instance, sensors can detect available parking spots, mitigating traffic congestion [67]. AI-powered algorithms, meanwhile, can predict and prevent accidents, enhancing road safety [68].  
Data analytics generated by EVs and charging infrastructures offer opportunities to refine smart cities. Analyzing this data allows for the optimization of charging infrastructure and energy consumption [69]. Efficient battery management systems can extend the EV range and cut charging time. Cybersecurity for EV charging networks is also critical, ensuring infrastructure security against cyber threats [70].  
In EVs, software manages energy flow and optimizes powertrain performance while also providing driver assistance features [71]. In smart cities, software systems manage traffic, public services, and utilities, enabling smart applications like lighting and parking [72].  
Scalability, interoperability, and security are vital in software development for EVs and smart cities. Software must manage vast data volumes, integrate with diverse technologies, and resist cyberattacks. By leveraging advancements in software engineering and emerging technologies, it is feasible to build smart cities that are sustainable, efficient, safe, and convenient. The potential of software engineering in shaping EV-friendly smart cities issignificant, warranting multidisciplinary collaboration to address challenges and make this vision a reality.
# Advances of UAVs toward Future Transportation: The State-of-the-Art, Challenges, and Opportunities
## 4 Potential Research Directions
### Design UAVs for ITS  
To establish a robust UAV-based ITS, the design of UAVs should follow all the ITS requirements. Although there has been immense progress in UAV technology, there are still some limitations in both UAVs and the infrastructure design related to ITS applications. One of the main concerns of conventional UAVs is their capacity to fly as long as the monitoring requires. UAVs are primarily still battery-operated and have operating time constraints. However, the ITS also requires continuous monitoring of the traffic. The use of alternative energy resources and charging time optimization can be a potential research direction. This can enable a longer flying time for UAVs to ensure uninterrupted traffic monitoring.  
Additionally, more challenges lie in collecting valuable data through UAVs. This requires the addition of a variety of sensors in UAVs. Currently, GPS and visual data can be collected through UAVs. These data are processed to retrieve useful information. In the future, the research should focus on developing an in-built image processing unit in UAVs so that the data processing time can be reduced. Also, multi-sensors data processing capability can be included in the UAV design.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 5 Evaluation
### Computation of Metrics  
The query ground truth function was introduced in (7). According to this equation, given two representations, \(D^{xml}\) and \(D^{owl}\), of a set of devices \(D\) and two query expressions, \(Q_{i}^{xml}\) and \(Q_{i}^{owl}\), of a request for matching device capabilities \(Q_{i}\in Q\), the ground truth is assessed by taking the most complete ontology \(B_{0}\) as a reference and using it for evaluating both approaches.  
While this approach is not perfect, i.e., it would be better to have an independent source of ground truth, the simple fact is that such a source does not exist. While it would be possible to develop the ground truth by hand, such an approach is only applicable to cases where there is a relatively small set of device capabilities and a small set of queries. Since our intent was to assess the quality of matching on really large sets of device descriptions and queries, we had to come up with a solution that could be executed automatically without much participation by the human expert in the loop. While \(B_{0}\) is advantageous for the OWL-based approach, the idea of using a descending chain of less and less complete ontologies makes this method relatively fair for both OWL- and XML-based approaches. Further discussions on fairness of the method are presented in Section 6.  
The implementation of the query result completeness ((8) and (9)), soundness ((10) and (11)) and the F-measure ((12) and (13)) metrics are shown in Algorithms 1-3, respectively. It is worth noting that although XML-based and OWL-based approaches utilize their own notations to define the metrics, the definitions of the metrics for the two approaches are the same. Consequently, we used the same implementations for computing these metrics.
## 4 Estimation Equation for Maximum Load Bearing Capacity
### Applicability of Estimation Equation  
#### 4.2.1 Results for Varying Slenderness Ratios  
Figure 11 shows the maximum load bearing capacities for the numerical and estimated values for models with slenderness ratios \(\lambda\) of 0.2 and 0.4, respectively. The vertical axis shows the non-dimensionalized horizontal load bearing capacity \(P/P_{\text{u}}\), and the horizontal axis shows the axial force ratio \(n\). Open and closed dots in the figure show the results of the numerical analysis. Open dots indicate buckling just above the concrete, and closed dots indicate buckling at the base of the steel tube and in both locations, at the base of the steel tube and just above the concrete. The estimated values are indicated by dashed lines in the figure.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  &  
Firstly, focusing on Figure 11a. When the concrete filling ratio was 0.2, the buckling position changed as the axial force ratio increased. On the other hand, when the concrete filling ratio was 0.4 or 0.6, buckling occurred at the base of the steel tube. With \(L_{\mathrm{c}}/L=0.2\), the estimation equation gives a good approximation of the maximum bearing capacity in the numerical analysis. On the other hand, there was a difference between the numerical and estimated values for the model with \(L_{\mathrm{c}}/L=0.4\) at low axial force ratios and for the model with \(L_{\mathrm{c}}/L=0.6\) in general. These results were similar in Figure 11b-f, where the slenderness ratio varied, and the difference between the numerical and estimated values tended to increase as the concrete filling ratio increased.  
Figure 11: Maximum load bearing capacity and estimation equation for each model: (**a**) Model C-006-020; (**b**) Model C-006-040; (**c**) Model C-009-020; (**d**) Model C-009-040; (**e**) Model C-012-020; (**f**) Model C-012-040.
# Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT
###### Abstract  
Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0-20 Hz and 15-65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4% in the frequency ranges of 0-20 Hz, 15-65 Hz, and 60-105 Hz.  
Keywords:compression algorithms; principal component analysis; discrete wavelet transform +
Footnote †: journal: _Algorithms_