esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison of Experimental Results and Theoretical Model  
The comparison between the experimental results and the theoretical model predictions demonstrates general agreement in most cases. Figure 13 illustrates this comparison under various parameter conditions. For durations of less than 30 min, the theoretical results closely align with the experimental data, showing an average error of less than 24.4% and a maximum error of 36.3%. However, as the duration exceeds 30 min, the theoretical predictions begin to diverge from the experimental measurements, with the theoretical temperatures slightly higher than observed. This leads to an average error range of 28.7% to 63.7% and a maximum error of 90.2%. This discrepancy can be attributed to the simplified assumptions in the theoretical model and certain uncontrollable factors during the experiment.  
The sources of the error can be analyzed from two perspectives. First, differences between the assumptions and parameters used in the theoretical model and the actual conditions may lead to discrepancies. The thermal conductivity of the materials used in the experiment, such as the soil base and cement concrete base layer, could differ from the values assumed in the model, resulting in variations in the heat transfer rates. Second, during the verification process, the boundary conditions of the theoretical model were adjusted based on real-time environmental and temperature data during the first 15 min of cooling, which resulted in a minimal error. However, as the air temperature stabilized at the set value and the external conditions ceased to change, the model's ability to self-correct diminished, leading to an accumulation of errors over time, which further increased during the extended calculations.  
This phenomenon aligns with the findings of [PERSON] et al. [25], who identified similar issues in their study on the factors influencing asphalt pavement icing. They noted that while an increased wind speed and air temperature accelerated pavement cooling, these factors also introduced greater uncertainty, particularly during prolonged cooling periods, where the accuracy of the theoretical model predictions diminished. Additionally, [PERSON] et al. [26] employed a numerical model based on heat and mass transfer to validate the snow melting process on heated pavements. They similarly found that while the theoretical model matched the experimental results well in the short term, the discrepancies became more pronounced over time. In conclusion, although the theoretical model effectively captures the overall trends in the pavement temperature changes, its predictions may diverge from the actual conditions during extended periods or under complex environmental scenarios. Therefore, when utilizing these models for engineering design and practical applications, it is essential to consider these potential errors and incorporate experimental data for necessary adjustments.
# Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications
###### Abstract  
Land monitoring using temporal series of Synthetic Aperture Radar (SAR) images requires radiometrically well calibrated sensors. In this paper, the radiometric stability of the new SAR Sentinel-1A \"S-1A\" sensor was first assessed by analyzing temporal variations of the backscattering coefficient (\(\sigma^{\circ}\)) returned from invariant targets. Second, the radiometric level of invariant targets was compared from S-1A and Radarsat-2 \"RS-2\" data. The results show three stable sub-time series of S-1A data. The first (between 1 October 2014 and 19 March 2015) and third (between 25 November 2015 and 1 February 2016) sub-time series have almost the same mean \(\sigma^{\circ}\)-values (a difference lower than 0.3 dB). The mean \(\sigma^{\circ}\)-value of the second sub-time series (between 19 March 2015 and 25 November 2015) is higher than that of the first and the third sub-time series by roughly 0.9 dB. Moreover, our results show that the stability of each sub-time series is better than 0.48 dB. In addition, the results show that S-1A images of the first and third sub-time series appear to be well calibrated in comparison to RS-2 data, with a difference between S-1A and RS-2 lower than 0.3 dB. However, the S-1A images of the second sub-time series have \(\sigma^{\circ}\)-values that are higher than those from RS-2 by roughly 1 dB.  
Synthetic Aperture Radar; Sentinel-1A; radiometric stability and quality; Radarsat-2
# Academic Teaching Quality Framework and Performance Evaluation Using Machine Learning
## 4 Machine Learning and Performance Evaluation
### Machine Learning Algorithm  
Machine learning algorithms are classified into ontologies based on the intended result of the algorithm. ML types can be categorized into supervised, unsupervised, semi-supervised, and reinforcement learning [38]. Although, there are many different types of machine learning algorithms, the choice of which one to use will depend on the specific goals of the study and the nature of the data.  
Some commonly used algorithms for prediction tasks include linear regression, decision trees, support vector machines, and random forests. One of the supervised learning-type algorithms is Linear Regression, which executes a regression operation and is commonly used for predictive analysis. Two theories are approached via regression. First, regression analyses are frequently used for forecasting and prediction, areas in which machine learning and their application have a lot in common. Second, in some circumstances, causal relationships between the independent and dependent variables can be ascertained using regression analysis.  
Regression uses independent variables to model a goal prediction value. It is mainly used to determine how variables and predictions relate to one another. It is significant to highlight that regressions alone can only show correlations between a dependent variable and a fixed dataset collection of other factors [39]. Regression can be classified into two broad categories based on the number of independent variables.  
* Simple Linear Regression (LR): A linear regression procedure is referred to as simple linear regression if only one independent variable is utilized to predict the outcome of a numerical dependent variable.
* Multiple Linear Regression (MLR): A linear regression process is referred to as multiple linear regression if it uses more than one independent variable to anticipate the value of a numerical dependent variable.
## 3 Navigation
### Sensors
#### 3.1.3 Gps  
Outdoor navigation systems are generally based on GPSs, which is based on satellite navigation system that provides access to accurate positioning information anywhere on the globe. At present, the accuracy of differential GPS technology has been significantly improved, but GPS accuracy is still not high enough because of the weather, buildings, and the current visible number of GPS satellites [34; 35]. Moreover, GPS signals are not always available because they can be blocked by high buildings, canyons and forests, among others obstacles.  
Today, the positioning error of the low-cost GPS systems in mobile terminals, such as the mobile phones and the car-mounted mobile terminals, is still a few meters or more. This can pose a great problem in certain situations, such as military maneuvers or even for emergency responders. Therefore, the data fusion method for GPS and IMU has become a hot research topic in navigation systems research, which is explored further in Section 3.2.  
Figure 3: Inertial Navigation Systems (INS) process.
## 6 Conclusions  
In this research, we introduced an innovative enhancement to the FastSLAM algorithm by integrating MO-PSO, creating the FastSLAM-MO-PSO framework. This integration addresses the critical challenges of particle degradation and the need for robustness in complex and dynamic SLAM scenarios. Our approach harnesses the multi-objective optimization capabilities of MO-PSO to balance the competing demands of mapping accuracy and computational efficiency, leading to a more holistic optimization of the SLAM process. The FastSLAM-MO-PSO algorithm employs a specialized fitness function that guides particles towards areas of higher posterior probability, thereby mitigating particle degradation and enhancing the overall robustness and accuracy of the SLAM solution. Through a series of comprehensive experiments, we demonstrated the effectiveness of our proposed method. The results consistently showed that FastSLAM-MO-PSO outperformed traditional particle filtering approaches, especially in terms of localization accuracy and resistance to particle degeneration.  
The strength of FastSLAM-MO-PSO is its resilience in environments with high noise levels and large-scale spaces, where traditional SLAM algorithms often falter. Our algorithm effectively utilizes the global search capabilities of MO-PSO to optimize particle distribution, resulting in improved estimation accuracy and robustness. This advancement places FastSLAM-MO-PSO at the cutting edge of SLAM technology, offering a viable solution for real-world applications that demand high precision and reliability. The insights from this study underscore the potential of swarm intelligence techniques in enhancing SLAM algorithms. Our successful approach indicates that by refining particle distribution through multi-objective optimization, we can achieve superior SLAM performance even in challenging conditions characterized by high noise levels and expansive spaces. Future work will explore adaptive mechanisms that dynamically adjust particle counts and other MO-PSO parameters in real-time, based on environmental conditions and algorithmic states, further enhancing the adaptability and efficiency of SLAM in diverse operational scenarios.  
X.B.: investigation, methodology, writing--review and editing; W.Z.: formal analysis, software; L.T.: conceptualization, data curation; H.Z.: resources, supervision; X.M.: resources, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2022 YB8305000.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[[https://github.com/yingkunwu/FastSLAM](https://github.com/yingkunwu/FastSLAM)]([https://github.com/yingkunwu/FastSLAM](https://github.com/yingkunwu/FastSLAM))].  
The authors declare no conflict of interest.
## 1 Introduction
More than 20% removal in suspended solids (SS) and turbidity was also observed at a dose of 1.0 kGy [21]. Application of EB irradiation for wastewater treatment brings benefits for all types of streams treated. De-coloration, turbidity and COD (chemical oxygen demand) decrease in wastewater from the dye manufacturing industry [21], degradation of pesticides and herbicides present in agriculture wastewaters [22; 23], BOD5 (biochemical oxygen demand), COD and TOC (total organic carbon) and microbial contamination reduction to acceptable limits [24] for municipal wastewaters, improvement of biodegradability and toxicity decrease in drugs present in the wastewater from pharmaceutical industry [25; 26] and decomposition of polycyclic aromatic hydrocarbons (PAH) in wastewaters from the petrochemical industry [27] have been reported in the literature. The system for wastewater EB treatment must always contain a pre-treatment unit (screens, filters, etc.) and may also be equipped with the traditional biological treatment system [28]. Present electron accelerators capabilities are well suited for continuous treatment of the wastewater with suitable thickness at a rate defined by electron beam power [29]. In water or wastewater electron beam treatment facilities, as in each irradiation installation, the issue of radiation protection arises. The lower energy of electrons used in the process, the lower energy, and the yield of generated electromagnetic radiation. Reducing energy of electrons results in the decrease in energy of X-rays, which reduces the thickness of necessary shillings and investment costs. The accelerators working at or below an energy of 300 keV are constructed as self-shielded units and can easily be incorporated in existing processing lines [30]. However, penetration of such beams is insufficient to treat water in order to decompose chemical compounds or inactivate biological hazards. Thus, a generated X-ray field needs to be considered in case of the application of electrons at energy levels of 800 keV.
# Heterologous Production of Flavour and Aroma Compounds in _Saccharomyces cerevisiae_
## 5 Conclusions  
The estimated turnover of the flavour and fragrance industries has been estimated to reach over US $30 billion in 2017, with a compounding annual growth rate of 5.6% between 2012 and 2017 [97]. To fulfil the demands of this constantly growing market, chemical synthesis of flavour and aroma compounds has routinely been used. However, the processes involved are generally not sustainable and the final products are considered of synthetic origin, which dramatically decreases their market value. While extraction of flavours and fragrances from natural sources provides a more valuable, natural product, there are number of limiting factors that make these processes inefficient and relatively expensive, including the availability of the source plant materials, and the low levels of the desired compounds in planta. Thus, there is a constantly growing demand for new, improved biotechnologies which allow more efficient, cheaper, and environmentally friendly ways to produce aroma and flavour compounds.  
Over the last two decades, the rapidly growing field of synthetic biology has provided the possibility for the heterologous production of specific flavours and aromas in microorganisms, a unique resource that could be used to help fulfil these global demands. The biotechnological company Evolva [98] has taken advantage of synthetic biology and yeast for the industrial scale heterologous production of natural flavour and aroma compounds, such as nootkatone, valencene, vanillin, and next-generation sweeteners. However, from the thousands of flavour and aroma compounds that have been identified to date, only a limited number have been successfully produced by heterologous means. Along with new advances in metabolic engineering of _S. cerevisiae_[99], this presents an opportunity for development and potential industrial applications of new heterologous pathways to produce novel valuable compounds in yeast.  
The AWRI, a member of the Wine Innovation Cluster in Adelaide, is supported by Australia's grape growers and winemakers through their investment body Wine Australia with matching funds from the Australian Government.  
The authors declare no conflict of interest.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 2: Develop and Down-Select Nanogrid Architectures
#### 3.2.3 Step 2.3: Develop Nanogrid Operational Concept View  
In order to understand how various threats may impact the operation of a microgrid and associated nanogrids, we advocate developing an operational concept view that is commonly used throughout many military organizations [41]. The operational concept view combines information such as geographic location of energy infrastructure and critical loads, the one-line diagram of the microgrid and associated nanogrids, locations of perimeter fences and entry control points, and other site-specific information into one figure that allows for better communication with stakeholders and permits more rapid human analysis of potential threats. An example operational concept view is provided in the Case Study (Section 4).
# Characterization of Asphalt Binders Modified with Bio-Binder from Swine Manure
## 2 Materials and Methods
### Methodology  
Several characterization tests were performed to study the effect of the addition of bio-binder on the resulting bio-modified asphalt binder: the penetration test, ring-and-ball test, Fraass test, viscosity test, Cleveland open cup test, and the UCL method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic** & **Standard (EN)** & **Unit** & **15/25** & **50/70** \\ \hline \multicolumn{4}{c}{_Original Bittumen_} \\ \hline Penetration at 25 \({}^{\circ}\)C & 1426 [41] & 0.1 mm & 15–25 & 50–70 \\ Softening point R\&B & 1427 [42] & \({}^{\circ}\)C & 60–76 & 46–54 \\ Fraass breaking point & 12593 [43] & \({}^{\circ}\)C & TRB & \(\leq-8\) \\ Flash point & 2592 [44] & \({}^{\circ}\)C & \(\geq\)245 & \(\geq\)230 \\ Solubility & 12592 [45] & \% & \(\geq\)99.0 & \(\geq\)99.0 \\ \hline \multicolumn{4}{c}{_Residue after ageing_} \\ \hline Mass variation & 12607-1 [46] & \% & \(\leq\)0.5 & \(\leq\)0.5 \\ Retained penetration & 1426 & \% & \(\geq\)55 & \(\geq\)50 \\ \(\Delta\) Softening point & 1427 & \({}^{\circ}\)C & \(\leq\)10 & \(\leq\)11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of reference asphalt binders according to manufacturer [25].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Elemental Analysis** & **Bio-Binder (\%, g/100 g)** \\ \hline C & 69.18 \\ H & 7.85 \\ N & 3.26 \\ O & 19.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Organic elemental analysis of the bio-binder.
# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
## 6 Conclusions  
This paper proposes an improved ant colony algorithm to overcome the difficulties associated with slow convergence, excessive turning points, and susceptibility to local optima in the path planning process for patrol robots in a stationary 2D grid environment. Considering the simulation outcomes of the overall path and various performance indicators, the following insights are obtained:  
1. By enhancing the heuristic function and pheromone update rule of the traditional ant colony algorithm, and by addressing redundant nodes in the path, the enhanced ant colony algorithm presented in this research demonstrates notable advantages. In two grid environments with varying complexities, it outperforms the other two algorithms in terms of convergence efficiency, optimization ability, number of turning points, and environmental adaptability.  
2. While the improved ant colony algorithm in this paper shows significant improvements in various performance metrics, it has not achieved a decrease in execution time compared to the traditional ant colony algorithm, and it is tested within a static 2D grid framework. The next course of action will focus on reducing the algorithm's running time and addressing the challenge of dynamic obstacle avoidance in local contexts.  
Conceptualization, S.W.; methodology, S.W.; software, S.W.; validation, S.W.; formal analysis, S.W.; investigation, S.W. and T.Y.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 3 System Model
### Jamming Attacks  
Every communication system that works with data is exposed to security threats. Non-authorized users of the communication system may want to access, manipulate or  
Figure 1: A network composed of \(N\) sensor nodes distributed in a square area with \(H\) edge-length and equal space \(D\) between nodes. To improve the readability of the nodes and positions on the network, we use a chessboard approach to simplify when we speak about a particular position along the work.  
destroy the data. In consequence, the communication system needs the timely identification of each attack class to neutralize them.  
The WSNs may be exposed to two possible main attack classes: eavesdropping and jamming. In eavesdropping attacks, the spy user aims to passively listen to the information. This type of attack is normally combated by encrypting the information. On the other hand, jammers generate radio frequency signals in the same operation band and the main objective is to interrupt the system operation. The jammer can assume different operating strategies:  
* Constant jammer: The radio signal from the jammer is emitted constantly in the communications channel. Consequently, the jamming signal and the legitimate signal collide almost all the time, which provokes the discard of the packets on the receiver side. However, this strategy demands excessive energy to perform the emission of the radio signal and it is easy to detect.
* Random jammer: The radio signal is randomly generated, decreasing energy consumption and making it difficult to predict the attack. Therefore, the probability of a collision is less than in the constant strategy but with higher stealth.
* Reactive jammer: This strategy takes advantage of the ability to hear the communication channel. The jammer device records the different sniffed data transmitted in the channel. Next, the attacker decides to emit the radio signal to target specific data packets. This capability improves energy consumption and stealthiness of the jammer.  
A WSN may apply different strategies to defend against attackers. These strategies depend on the successful detection of attacks and the timely identification of the attack class to execute the defense mechanisms. Therefore, it is crucial to successfully identify whether an anomalous situation is due to an attack or other system factors. Several studies have shown that the use of metrics of received power, energy consumption, packet data rate (PDR), and bit error rate (BER) permit the detection of attackers in the majority of cases [46; 47; 48].