esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Environmental and temporal conditions, particularly dust accumulation, can significantly impact the performance of photovoltaic solar panels, potentially reducing their efficiency by up to 20%, and thereby affecting profitability. Accurately estimating these losses is crucial for optimising maintenance and avoiding unforeseen losses. Various models have been proposed in the literature for this purpose. In this context, four machine learning models were developed using meteorological and air quality data from the Solar Energy Research Center (CIESOL). A Gradient-Boosting model (LightGBM) and a neural network achieved RMSE values of 0.68% and 0.88% of soiling loss, and \(R^{2}\) values of 0.86 and 0.76 between measured and estimated values, respectively, on their test sets. The generalisation capability of these models was tested by extrapolating them to other regions in Spain. To enhance robustness across locations, a global artificial neural network (ANN) model was trained using combined data from two sites, achieving an RMSE of 1.02% when estimating soiling losses. This result highlights a significant improvement over models trained on a single location and tested elsewhere, demonstrating the global model's stronger ability to generalise across different geographic settings.  
PV soiling; machine learning; kNN; gradient boosting; LightGBM; CatBoost; artificial neural networks 2020  
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## 1 Introduction  
Carbonyl compounds are volatile organic compounds (VOCs) emitted into the troposphere by several natural sources and by on-road traffic [1; 2; 3]. In addition, they can be generated through the oxidation of other VOCs present in the troposphere [4]. For example, unsaturated carbonyls, such as _trans_-2-methyl-2-butenal (_E_-CH\({}_{3}\)CH=C(CH\({}_{3}\))C(O)H), are emitted into the lower atmosphere from vegetation and human activities such as road vehicles and industries, and methylbutenals have been proposed as oxidation products of isoprene initiated by hydroxyl (OH) radicals in the presence of NO [5; 6]. _Trans_-2-methyl-2-butenal can also be found in food items such as herbs and spices, tomatoes, animal foods, and spearmint, and it is a building block in organic synthesis [7].  
Recently, there have been multiple studies on the gas-phase reactivity of unsaturated oxygenated compounds with tropospheric oxidants such as OH radicals, nitrate (NO\({}_{3}\)) radicals, or ozone (O\({}_{3}\)), but kinetic data on the reaction initiated by chlorine (Cl) atoms are scarce [8]. Some examples are the results previously published on the Cl reactivity of linear unsaturated aldehydes [9; 10], methacrolein [11], or 3-methyl-2-butenal [12]. The gas-phase kinetics of _trans_-2-methyl-2-butenal was experimentally studied with OH radicals and O\({}_{3}\)[13; 14] and an estimation was reported for the NOand OH reactions by using Structure-Reactivity and Linear Free-Energy Relationships [15]. In contrast, there is no investigation or estimation of the rate coefficient for the reaction of _trans_-2-methyl-2-butenal with Cl or the products that are generated in reaction (R1).  
\[E\text{-CH}_{3}\text{CH}=\text{C}(\text{CH}_{3})\text{C}(\text{O})\text{H}+ \text{Cl}\rightarrow\text{Products}.\] (R1)  
As aldehydes are known to be important contributors to the formation of secondary organic aerosols (SOA) [16; 17], it is expected that SOA could be produced during the tropospheric oxidation of _trans_-2-methyl-2-butenal by Cl atoms. For the OH reaction, a SOA yield of 39.1% was determined by [PERSON] et al. [18].  
The aim of this research is, firstly, to determine the rate coefficient, \(k\), of the gas-phase reaction (R1) at \(298\pm 2\) K and \(760\pm 5\) Torr under free-NO\({}_{\text{x}}\) conditions by using two atmospheric simulation chambers. In the kinetic study, a relative method has been employed, using Fourier transform infrared spectroscopy (FTIR) as quantification technique. Secondly, in a separate set of experiments, the gaseous products of reaction (R1) have been detected by three different techniques: FTIR, gas chromatography coupled to mass spectrometry (GC-MS), and proton transfer time-of-flight mass spectrometry (PTR-ToF-MS). The formation of SOA has been monitored as a function of the reaction time by a Fast Mobility Particle Sizer (FMPS) spectrometer. A reaction mechanism is proposed based on all the identified products. Finally, a discussion on the impact of reaction (R1) in coastal clean environments, where there are high Cl and low NO\({}_{\text{x}}\) concentrations is presented.
## 3 Experimental Results and Analysis
### Convergence Accuracy Analysis  
Table 2 presents the average (AVG) and standard deviation (STD) of the average localization errors for the six meta-heuristics. Among them, R in Table 2 is explained in detail in Section 3.2.
# A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer
## 4 The System Hardware and Software Implementation Options  
In this study, the implementation of our new algorithms was performed by simulation in the MATLAB/Simulink and/or Maple environment. However, the obtained simulation results may have depended on MATLAB and/or Maple computation engine features. To test and validate the system, it is advisable to perform simulations on various software and/or hardware platforms.
## 3 Test Results and Analysis
### 3.1. Triaxial Shear Test Results and Analysis
#### 3.1.2 Strength Characterization  
[PERSON]'s stress circle was drawn on the right-angle coordinate system based on the relationship between the axial strain and bias stress. The effective stress-shear strength expression and the effective stress strength indices (angle of internal friction and cohesion) of the specimens with different fly ash admixtures were calculated to obtain the effective shear strength indices of the granite residual soil with different fly ash admixtures. The results are displayed in Figure 13.  
Fly ash was added, and as shown in Figure 13, this greatly increased the shear strength index of the granite residual soil. Their cohesiveness and angle of internal friction of the fly ash-modified granite residual soil first increased and then decreased. The effective cohesion of the vegetative soil was measured as 12.28 kPa, and its internal friction angle was 19.41\({}^{\circ}\). The highest shear strength index was found in granite residual soil treated with 15% fly ash, with an internal friction angle of 22.15\({}^{\circ}\) and effective cohesion of 38.51 kPa. In comparison to vegetative soils, all four types of fly ash-amended soils showed enhanced cohesion and a greater angle of internal friction. This is because the fly ash and water combined to form a gelling substance. As the fly ash content increased, the lubricating effect gradually diminished, and the ability of the water film to bind the particles together weakened, increasing the friction between the soil particles and the granite residual soil angle of internal friction. When combined with fly ash, this gelling material closes the soil's internal pores, decreases the soil porosity, increases the soil compactness and strengthens the bonds between the soil particles. However, after the dosage of fly ash reaches 20%, excess fly ash appears, which reduces the internal skeleton of the soil body, resulting in  
Figure 12: Variation in the peak strength as a function of the peripheral pressure.  
Figure 13: Variation of the effective cohesion and effective internal friction angle of granite residual soil specimens with different fly ash contents.  
reduced friction between the soil particles and weaker adhesion between the soil particles, thus reducing the angle of internal friction and cohesion.
## 2 The Proposed Timing Mismatch Calibration Technique
### Timing Mismatch Compensation
It is noted that when M is odd, there are \((M-1)/2\) conjugate error pairs in total, whereas when \(M\) is even, an extra spurious signal appears in \(\pi\) and the corresponding frequency shift in input signal will not generate imaginary components, expressed as follows:  
Figure 3:5 Figure 3:6  
with  
Figure 3:7  
However, the ideal signal \(x[n]\) and its derivative \(x^{{}^{\prime}}[n]\) are not available in the blind calibration technique, so we use the outputs of TIADC y[n] and their derivative \(y^{{}^{\prime}}[n]\) to approximate them. Similarly, the second-order error terms possess the same conjugate symmetry, and thus we can also use this to reduce the complexity of the operation. Expanding our previous research [22], a coarse-fine correction method is employed, based on the second-order Taylor series, to eliminate higher-order timing-skew errors. During the initial coarse correction stage, the first-order Taylor series approximation is utilized to correct \(y[n]\), expressed as follows:  
Figure 3:8  
with  
Figure 3:9  
where \(<\cdot>\) denotes the rounding down operation. In order to mitigate the impact of higher-order time-skew errors, the fine-calibration process employs a second-order Taylor series approximation, expressed as follows:  
Figure 2:0  
with  
Figure 2:1Figure 2:2  
Figure 2:3  
Figure 2:4  
where \(w_{i,2}[n]\) and \(w_{M/2,2}[n]\) denote the coefficient of the second-order error term. To improve the accuracy of the approximation and mitigate the impact of higher-order error terms, the output of the coarse correction process, represented as \(\widehat{y_{c}}[n]\), is substituted for the first-order derivative of the ideal output \(x^{{}^{\prime}}[n]\). Additionally, to reduce latency in the calibration process, the uncorrected second derivative of the output \(y^{{}^{\prime\prime}}_{i}[n]\) is utilized to replace \(x^{{}^{\prime\prime}}_{i}[n]\), expressed as follows:  
Figure 2:5  
with  
Figure 2:6  
Figure 2:7  
Based on the above analysis, the proposed compensation architecture is shown in Figure 2:8 The outputs of the coarse correction are sent to the fine-correction to eliminate the impact of higher-order error terms. Note that, in the case of a determined number of channels, the part of the error coefficient \(w_{i}\) and \(w_{i,2}\) other than the time-skew errors can be calculated in advance and stored in the circuit, without consuming additional hardware resources. Figure 2:9 _Cont._
## 4 Performance Metrics for Path Loss Models
### R2 Score  
The R2 score, alternatively referred to as the coefficient of determination, serves as a statistical measure for evaluating the quality of fit in regression models, including those used in path loss prediction. This metric quantifies the model's ability to explain the variances observed in path loss data. It operates on a scale from 0 to 1, with 1 signifying a model that precisely predicts the dependent variable and 0 indicating a lack of predictive capability. The proximity to 1 indicates a stronger fit in the regression [57]. The mathematical expression to calculate the R2 score is presented in (26) and (27) as follows [58; 59]:  
\[R2=1-\frac{\sum_{i=1}^{N_{test}}\Big{(}PL_{i}^{msd}-PL_{i}^{pred}\Big{)}^{2}}{ \sum_{i=1}^{N_{test}}\Big{(}PL_{i}^{msd}-\overline{PL}\Big{)}^{2}}, \tag{26}\]  
\[\overline{PL}=\frac{1}{N_{test}}\sum_{i=1}^{N_{test}}PL_{i}^{msd}, \tag{27}\]  
where \(PL_{i}^{msd}\), \(PL_{i}^{pred}\), \(N_{test}\), \(\overline{PL}\), and \(i\) are the measured path loss value, predicted path loss values, total number of samples to be tested, mean values, and the index of the measured sample, respectively.  
For a more comprehensive assessment of the overall prediction accuracy, metrics such as mean absolute error (MAE) and root mean square error (RMSE) are often used in combination with ME to evaluate path loss models.
## 2 Materials and Methods
### Removal of the Noisy Spectral Data  
Images captured by the hyperspectral camera were at the range of 400-1100 nm. Due to various reasons such as ambient noise, light effect and others, the beginning and the end of the spectrum range were highly noisy. Therefore, those noisy frequency bands data were discarded since they did not provide useful information (see a number of hyperspectral image examples in Figure 3. At the end, a total of 174 spectral images where picked from each individual input fruit sample, at a range of 550-900 nm and selected to be further analyzed.
## 4 Discussion
### Bacterial Functional Diversity and Fungal Trophic Guilds  
In addition to substantial changes in bacterial community composition and diversity, metagenomic predictions using PIRUSt2 and FUNGuildR revealed distinct microbiome functional profiles and a significant increase in microbial functional diversity after vermicomposting of sewage sludge. Thus, significant changes were found in important bacterial functional pathways and fungal guilds. The increase in biosynthesis pathways indicatesheightened metabolic activity in microbial communities of cast and vermicompost, with various different bacterial taxa fulfilling different roles [43; 44; 51]. This is consistent with the increase in the bacterial \(\alpha\)-diversity in these pathways. Our findings on antibiotic biosynthesis could be attributed to recent findings, indicating that vermicomposting may reduce antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs) in sewage and dewatered sludge [57; 58; 59; 60; 61]. This could be due to the activity of earthworms and their endosymbiotic microbes during earthworm digestion, as pathogens could be exposed to physical disruption by antimicrobial substances, microbial antagonists or could even be enzymatically digested and assimilated directly [2; 6]. Fungal guilds associated with pathogens and parasites are almost absent from earthworm casts, disappearing during GAPs. The saprotroph-related guilds did not undergo any significant changes. As far as we know, the fungal communities in vermicompost have not been studied. However, similar results were observed for compost made from sewage sludge, with a decrease in animal pathogens and the saprotroph guild predominating at the end of the process [62]. Overall, vermicomposting improves the safety of sewage sludge for application on agricultural and forest land by reducing the amounts of potentially harmful organisms the waste material contains.
## 3 Data Processing and Kinematic Analysis
### Extraction of Ground Point Cloud  
The raw point cloud data represents many points containing ground and non-ground points (Figure 3a). Since our focus is on the detection of moving objects around the excavator, the point clouds representing a ground place (earth surface) firstly need to be eliminated from the point cloud. This is because the moving objects belong to the non-ground points and the ground plane can act as unwanted points during the object detection. Therefore, the extraction of these unwanted points is an essential step, and it can also help reduce the computation load by removing them from the large quantity of unprocessed raw data of LiDAR measurements.  
In this research, the point cloud data representing the ground plane were extracted by adopting the MSAC (M-estimator sample and consensus) algorithm as a variant of RANSAC (random sample consensus) [19]. This is a repetitive method to estimate a model from a given dataset containing outliers. To extract the ground points, a plane model was fitted to the raw points cloud by the MSAC algorithm. The RANSAC algorithm can determine the number of outliers by selecting a subset of data and fitting a model to it.  
The point clouds representing non-ground points are shown in Figure 3b. After detecting the non-ground points, the next step was to cluster the non-ground points and detect the obstacles (Figure 3c), which is discussed in the next section.
# When Maritime Meets Aviation: The Safety of Seaplanes on the Water
## 3 Results
### Analysis of Previous Accidents and Incidents
#### 3.1.1 Narrative Analysis  
The occurrence narratives were used to identify their root causes from the following categories: airborne, ground operations, take-off and landing, aircraft-related, non-aircraft-related, weather conditions, and miscellaneous. Within these categories, factors such as visibility, wind, loss of control, mechanical errors, human errors in making decisions or in operating the plane, the reasons for pilots making certain decisions (such as abrupt maneuvers), missing emergencies, etc., were considered. This particularly timely task revealed that loss of control and abnormal runway contact led to the majority (59.23%) of seaplane occurrences on the water (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category Name** & **Category ID** & **Count** & **Percentage** \\ \hline Loss of Control—Ground and Inflight & LOC-G and LOC-I & 152 & 32.62\% \\ \hline Abnormal Runway Contact & ARC & 124 & 26.61\% \\ \hline System/Component Failure or Malfunction (Non-Power-Plant) & SCF-NP & 42 & 9.01\% \\ \hline Collision with Obstacle(s) During Take-Off and Landing & CTOL & 29 & 6.22\% \\ \hline Unintended Flight in IMC & UIMC & 20 & 4.29\% \\ \hline System/Component Failure or Malfunction (Power Plant) & SCF-PP & 18 & 3.86\% \\ \hline Abrupt Maneuver & AMAN & 15 & 3.22\% \\ \hline Unknown & UNK & 15 & 3.22\% \\ \hline Ground Handling & RAMP & 10 & 2.15\% \\ \hline Other & OTHR & 9 & 1.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Five most recurrent types of seaplane accidents and incidents on the water.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Description** & **Method of Analysis** & **Reason** \\ \hline \multirow{6}{*}{-} & \multirow{6}{*}{Narrative analysis} & To extract further information and improve the quality of the analysis. It includes the occurrence categorization. \\ \cline{3-3}  & & Trend analysis & Calculate trends and determine patterns. \\ \cline{3-3}  & & Contingency analysis & To detect potential associations and relationships between two and three variables. \\ \cline{3-3}  & & Three-way associations & \\ \cline{3-3}  & & & Engagement with industry professionals to discuss the data analysis results and assess mitigation methods. \\ \cline{3-3}  & & Focus groups & Engagement with seaplane pilots and industry professionals to determine potential factors not included in the database and bring awareness of current areas of concern. \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & & \\ \cline{3-3}  & & Questionnaire & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the accident/incident dataset and the reasons of employing different methods of analysis.
## 3 Simulation Results
### Multifrequency Noise Simulations  
The adaptive feedback FxLMS was performed on the noise source that emits a sound in which periodic signals of the same magnitude (from 20 to 200 Hz) were added at 15-Hz intervals. The location and the number of noise sources were the same as before, and Figure 8 shows the results after adaptive filter convergence for each of the three cases: case 1 = [\(R_{mic}\) = 0.6 m \(R_{s}\) = 2.0 m], case 2 = [\(R_{mic}\) = 0.8 m \(R_{s}\) = 2.0 m], and case 3 = [\(R_{mic}\) = 0.8 m, \(R_{s}\) = 1.6 m]. The wave field after convergence is shown in Figure 8a-c, and the average sound pressure level (SPL) of the ROI in the frequency domain is shown in Figure 8d. In cases 1-3, average reductions of 27, 18, and 16.7 dB, respectively, were noted. At 155 and 170 Hz, there were no significant differences in noise reduction between cases 2 and 3. For other frequencies, the ANC performance adhered to the following order: case 1 > case 2 > case 3. This proves that the multifrequency test shows the same noise reduction trend as the single-frequency test.  
Figure 7: Average noise reduction and variance with respect to frequency at different radii of microphones and loudspeaker arrays. (**a,c**) Average noise reduction. (**b,d**) Variance.
## 4 NFV and SFC Frameworks Taxonomy
### Resilience and Fault Recovery Frameworks
#### 4.3.3 [PERSON] et al.  
[PERSON] et al. [91] presented a solution that uses integer linear programming to solve the problem of link and node failures in service chains. They modelled the physical infrastructure using a directed network graph, with representations for physical devices with the ability to host virtual network functions and links. The latencies of physical links and packet processing latency by vNFs are also represented in the model of the physical network topology. The other components that were modelled are the service chains and the vNFs, which are considered as abstract components that can process ingress packets before forwarding to the next vNF or host device.  
The constraints considered for the proposed model are (i) node capacity and link latency, which capture the current state of a node, the capacity of the link in use, the latency and the highest number of virtual machines that can be hosted by a particular node; (ii) constraints related to routing, such as the location of virtual links in the physical network, to ensure that routing paths are made available on the right physical node; and (iii) constraints related to the placement of vNFs on physical devices, by ensuring that active and standby vNFs are not collocated on the same node.  
The proposed models were evaluated using two example service chains, that is, a chain consisting of a web service and another service chain that models online gaming, where the impacts on node capacity and latency were considered. To solve the formulated ILP model, a bandwidth of 100 kbit/s was set for the online gaming service chain, while 50 kbit/s was set for the web service scenario. Latencies of 500 ms and 60 ms were set for the service chains. The proposed and solved models showed that achieving SFC resilience requires additional (redundant) nodes of approximately 107%. Although the proposed solution serves as a good mechanism that can handle link and node failures in service chains, it lacks the ability to provide shared protection against failures.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Fatigue  
A study that assessed fatigue indicated that UAV teams consisting of a pilot, sensor operator, and maintenance personnel show greater levels of fatigue than the crew of a manned aircraft [16]. However, another study found that operators consider remote monitoring and the shore control of unmanned vessels as a process full of \"_compliance and relaxation_\". This study also highlighted that information overload is a problem [22].  
Research on automated cars and automated driving shows that automation leads to a reduction in drivers' workload, but this leads to passive fatigue among drivers, leading to a reduction in overall performance [56]. Remote ship operators are also at risk of active and passive fatigue. [PERSON] et al. [57] describe active fatigue as a situation which \"_is derived from continuous and prolonged, task-related perceptual-motor adjustment_\". Active fatigue was highlighted by [PERSON] et al. [58], as a state which is associated with high cognitive workload. This situation can occur within an SCC during situations involving monitoring more than one vessel, accidental situations and high levels of workload. On the other hand, [PERSON] et al. [57] argue that \"_passive fatigue appears over several hours of doing what appears to be nothing at all_\", especially during system monitoring. This situation implies that SCC operators can be exposed to passive fatigue when SCC is only under the monitoring mode or they just monitor one vessel without any specific activity. The human-machine interface is another key factor in this field; for example, using multiple screens and insufficient furniture could also lead to a higher level of fatigue. In addition, [PERSON] et al. [30] argue that excessive fatigue is an important factor that leads to failure in emergency response processes.