esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cultural heritage is the most effective medium for the transfer of historical information between generations and civilizations. Cultural heritage assets are distinguished by their variety and importance. These items are generally priceless as they represent great moral values. Unfortunately, cultural heritage assets face various risks related to physical degradation and information loss. Therefore, many researchers all over the world are placing much effort into finding reliable approaches to increase the value of these assets through preservation and curation using digital tools [1]. Today, new technologies provide cost-effective yet reliable techniques for the documentation and management of cultural heritage. However, several challenges have arisen regarding the curation and completion of missing information for these assets. The classification and annotation of cultural assets is a tedious and labor-intensive task that requires the involvement of highly qualified and experienced art specialists. This is mainly due to the nature and the specificities of cultural assets, as they usually come from various locations, old civilizations, or else their level of degradation simply prevents accurate annotation. To increase the value of their collections, heritage institutions are currently funding numerous research efforts to develop innovative methods for the completion and annotation of cultural data.  
Recent advances in data science and machine intelligence are being applied in the cultural context. Many researchers are developing tools that can classify and annotate cultural assets based only on their visual features. Cultural assets are generally partially annotated with textual labels that may be used as additional information for their classification. To our knowledge, leveraging this additional information has not yet been used for the classification of cultural data.  
In this paper, we propose a novel approach for the classification of cultural content. Our approach is multimodal, as, in addition to asset visual features, we leverage the available asset metadata. The experimental results for a painting dataset show that leveraging additional data in the classification improves the classification performance.  
In the following sections, we present the impact of digital technologies on cultural heritage, and also give a literature review of the work related to cultural data classification and annotation using computer vision and machine learning.
## 3 Data Collection  
The present study uses a comprehensive dataset derived from the Kirkuk gas power plant, which is currently being upgraded to a Combined Cycle Power Plant (CCPP). Data collection spanned the entire month of December 2021, starting at 12:00 a.m. on the 1 st and ending at 11:59 p.m. on the 31 st. The dataset includes the output of four CA 202 accelerometers installed on four bearings (Figure 2a) [34]. These readings show plant operations at any given time, including machine activity, load levels, and signs of wear.  
The CA 202 accelerometer has internal body insulation with a symmetrical polycrystalline shear-mode measuring element. This piezoelectric accelerometer has a sensitivity rate of 100 pC/g at 120 Hz. This device is resilient and can withstand transient overloads with a maximum weight of 250 g. With its linear fine-tuning, it maintains 1% accuracy from 0.0001 g to 20 g with an average deviation of up to 2% between the peak range of 20 g and 200 g. Its dynamic measurement range is from a tiny 0.0001 g to a remarkable peak of 200 g. When discussing its features, this accelerometer exhibits a transverse sensitivity of 5% and resonates at a nominal frequency of 11 kHz. The frequency response is consistent within 5% from 0.5 Hz to 3000 Hz, with a low threshold determined by the associated conditioner, and exhibits a 10% response rate between 3 kHz and 4.5 kHz. Amplifying the accelerometer capabilities is the IPC 704 signal regulator. This device effectively converts the piezoelectric transducer signal into current or voltage. In this regard, standard 2- or 3-wire cables can carry this signal without the need for expensive connectors. In addition, it exhibits admirable temperature stability, typically recorded at 100 ppm/\({}^{\circ}\)C [35]. Figure 3 presents an example of the collected data.  
As mentioned earlier, the data were collected using accelerometers installed on key bearings of the gas turbine in the Kirkuk gas power plant (Figure 2). For this purpose, each sensor was calibrated with a vibration sampling rate of 1 kHz. The reason for using data capturing with such a resolution (high level) was to ensure the accuracy necessary to detect even subtle anomalies in turbine operation. The turbines were monitored in different operational modes, including start-up, normal operation, and shutdown phases. This diversity ensures that our model is robust to different states of turbine operating. Temperature, pressure, and humidity levels were monitored continuously, as these can significantly affect the sensor reading. A total of about 1,000,000 individual data points were collected over a one-month period, which is generally remarkable data for training and testing a robust model. Data were sampled every second, and each sample included multi-dimensional readings from each accelerometer.  
Figure 2: Equipment used to monitor turbine vibrations in Kirkuk gas power plant, Iraq, including (**a**) CA 202 accelerometer and (**b**) accelerometer installation [34].
# Communication Manager for Hyper-Connected RPAS Environments
## 3 Communication Infrastructure Manager: Design and Deployment
### Software-Defined Networks  
#### 3.4.1 SDN Switches  
At this point, to enable inter-domain communications (from RPS and RPA apps in Figure 3), it is necessary to deploy a programmable switch to forward traffic from the domains (RPS/RPA apps in Figure 3) over the overlay networks (VXLANs in Figure 3). This programmable switch has different ports to which the applications and VXLANs are connected. Therefore, specific rules should be installed on this switch to forward incoming traffic toward the desired output port.  
Open virtual Switch (OvS) (Open vSwitch. Available: [[https://www.openvswitch.org/](https://www.openvswitch.org/)]([https://www.openvswitch.org/](https://www.openvswitch.org/)) (accessed on 9 February 2023)) has been selected for this purpose. OvS is one of the most popular implementations of virtual programmable switches agreeing with OpenFlow. The basic functionality can also be realized with Linux Bridges, as seen in the previous version of this work [10]. However, the operation of OvS, which can be through an SDN controller (explained in the following Section 3.4.2) or standalone, provides definite advantages. Firstly, by using OvS and an SDN controller, it is feasible to react dynamically to the high rate of changes in the network (very common in multi-RPA environments) by modifying the network configuration. On the other hand, OvS offers great granularity by adding different rules that operate simultaneously and are the ones that allow the instantiation of the selected metric to be honored by the CIM in a particular scenario.
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## 5 Conclusions  
The purpose of this study was to assess the usefulness of several satellite-based indices to monitor post-wildfire vegetation recovery following three significant grassland wildfires in the southern Great Plains. The results show that satellite-based information can indeed inform post-wildfire response and recovery efforts. Several vegetative indices--NDVI, EVI, and LSWI--and a data-driven model of GPP all provided useful information about the vegetation response to the Anderson Creek, Starbuck, and Perryton wildfires. However, the LSWI and GPP indices provided a distinct response to the wildfire events, and differentiated between burned and unburned plots throughout the subsequent growing seasons better than the NDVI and EVI indices. In particular, the LSWI responded distinctly differently in burned and unburned plots at the time of the fire event and may provide a useful tool for delineating the footprint of future wildfires. Such information could be used by federal agency, extension, and conservation programs to target post-wildfire program assistance and recovery resources. In addition, the GPP differentiated between burned and unburned areas throughout the post-fire seasons, and may provide information to guide post-wildfire grazing management. It is beneficial to the ranchers to return cattle to graze in the pastures as soon as biomass stock and productivity is adequate to support livestock requirements while also sustaining grassland ecosystem recovery. Further research is needed to develop these promising approaches to support operational needs, and to identify other data requirements necessary for fire preparedness planning from ranch to community scales.  
Conceptualization, J.L.S., D.B., X.X.; methodology, J.L.S., J.W., S.R., S.T.; analysis, J.L.S., S.R., J.W.; data curation, J.W., S.R., X.X.; writing--original draft preparation, J.L.S, J.W., S.R.; writing--review and editing, S.T., J.W., X.W., Y.Z., D.B., X.X.; visualization, J.W., S.R., J.L.S.; supervision, J.L.S., S.T., X.X., D.B. All authors have read and agreed to the published version of the manuscript.  
This work was funded in part through USDA-ARS and University of Oklahoma Agreement No. 58-3070-8-012.  
This analysis was undertaken as part of a broad-based, partner-focused response to the recent Southern Plains wildfires, coordinated through the USDA Southern Plains Climate Hub.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Plot ID** & **Sum Date(s/23)** & **Soil Type (\%)** & **Land Cover Type (\%)** & **Elevation Mean (m)** & **Elevation Range (m)** & **Gradient Mean (\%)** & **Gradient Range (\%)** \\ \hline \multirow{2}{*}{S3U} &
## 3 Results and Discussion
### Preparation and Characterization of Rubber Composites
#### 3.2.7 Structure of the Composite; TEM Analysis  
Experimental findings from dynamic-mechanical, tensile, and fracture tests suggest that, thanks to functionalization with SP, a good dispersion of the graphitic filler was achieved. To confirm such hypothesis, the structure of cured composites based on HSAG or HSAG-SP was investigated by TEM analysis at different magnifications. Representative micrographs of the composites containing 24 phr of nanofiller are shown in Figure 10.  
A relatively homogenous distribution of sub-micrometric aggregates was observed at low magnifications (Figure 10a) for both the samples, suggesting the presence of a well-structured nanofiller network. The morphological analysis carried out at different magnifications indicates a similar nanofiller dispersion in the two samples; however, in the sample containing HSAG-SP (Figure 10b,d,f) smaller aggregates and higher amounts of few layers stacks and isolated layers can be observed within the elastomer matrix.  
Figure 10: TEM micrograph taken at different magnifications of the samples containing 24 phr of HSAG (**a**,**c**,**e**) and HSAG-SP (**b**,**d**,**f**).
### Focus and Outline of the Paper  
The main goal of this study was to find and investigate in detail interference-induced anomalies for different jamming devices in simultaneous open-field experiments of jamming smartphones and geodetic GNSS receivers. Similar studies were conducted previously at the University of Ljubljana, Slovenia, to compare the response of different surveying instruments to the L1/E1 jammer in static [4; 5] and kinematic [6] positioning. This follow-up study uses multi-frequency jammers and includes dual-frequency smartphones from different manufacturers. Again, all the tests were performed in the field, thus ensuring a realistic environment.  
The basic research questions addressed here are: (a) Are there significant differences in the robustness and the response of various smartphones and geodetic GNSS receivers to intentional jamming?; (b) Could several smartphones' responses be used for localization of the interference source?; and (c) At what distance do different GNSS receivers detect interference and what is the deviation in the positional determination during jamming? The main point is to show, especially to geodetic users, that the use of low-cost equipment, even if it promises high-accuracy positioning, can be risky. Now, the use of low-cost GNSS sensors in processes where reliable georeferenced data is important is questionable, primarily because of interference and spoofing.  
The structure of the paper is as follows. First, an overview of the current state of the art and earlier important research results is given (Section 1). Section 2 presents two-day experiments with dual-frequency smartphones and geodetic receivers, with a focus on investigating the performance of these devices in various jamming scenarios. In addition, Section 2 describes the study area, the equipment used, and the methods. Then, the processing strategy and results are presented and discussed (Sections 3 and 4). Finally, the conclusions from the study are drawn (Section 5).
## 5 Propeller Design for HAPS Operation
### Recent Development on Propeller Design Methodology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Max Efficiency [\%]** &
\begin{tabular}{c} **Optimization Technique** \\ **and Design Method** \\ \end{tabular} & **Model Used in Optimization** & **Operating Condition** & **Diameter and N\({}_{\text{b}}\)** & **Airfoil** \\ \hline \multirow{3}{*}{[PERSON] et al. [141], 2024} & \multirow{3}{*}{86.11} & Multi-fidelity & \multirow{3}{*}{3D RANS/Vertex Theory} & \(V=55\) m/s, \(h=16\) km, & \(D=3\) m, \(N_{b}=3\) & Optimized airfoil \\  & & Bayesian optimization & \multirow{3}{*}{3D RANS/Vertex Theory} & \(V=55\) m/s, \(h=16\) km, & \(D=27\) m, \(N_{b}=3\) & Optimized airfoil \\  & & Multi-fidelity Bayesian & \multirow{3}{*}{3D RANS/Vertex Theory} & \(V=55\) m/s, \(h=16\) km, & \(D=27\) m, \(N_{b}=3\) & Optimized airfoil \\  & & optimization & & & & \\  & & Based on Wald design & \multirow{3}{*}{BEMT} & \(V=10\) m/s, \(T=100\) N, & \(D=8\) m, \(N_{b}=4\) & NACA4412 \\  & & method [150] & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [141], 2022} & \multirow{3}{*}{84.2} & Bayesian optimization & \multirow{3}{*}{3D RANS} & \(V=55\) m/s, \(h=16\) km, & \(D=34\) m, \(N_{b}=4\) & Optimized airfoil \\  & & & & & & \\  & & Genetic algorithm (PSO) & Vortex Theory & \(V=77\) m/s, \(T=318\) & \(D=19\) m, \(N_{b}=3\) & Clark-Y \\  & & & Multi-level: Level 1 Beta & \multirow{3}{*}{3D RANS/Vertex Theory} & \(V=32\) m/s, \(T=20\) N, & \(D=2\) m, \(N_{b}=2\) & Optimized airfoil \\  & & & Level 3 AGA & & \(V=22\) m/s, \(T=20\) N, & \(D=32\) m, \(N_{b}=2\) & Optimized airfoil \\  & & & & & & \\  & & & & & \(V=25\) m/s, \(T=25\) m/s, & \(D=2\) m, \(N_{b}=2\) & E387 \\  & & & & & & \\  & & Pitch propeller & & & & \(h=20\) km & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [94], 2023} & \multirow{3}{*}{79.29} & Multi-level: adjoint, & \multirow{3}{*}{2D RANS/3D RANS/BEMT} & \(V=17\) m/s, \(T=13\) N, & \(D=14\) m, \(N_{b}=2\) & Optimized airfoil \\  & & flow reconstruction & & & & \\  & & Kevin method with & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of the techniques, methodologies, and design models developed over the years for high-altitude platforms propellers and the maximum efficiency values achieved. Adapted from [141]
## 5 Experimental Evaluation
### Communication Efficiency  
Figure 14 presents a comparative study of the normalized communication costs associated with different FL methods, Fed_Share, Fed_SGD, Fed_AVG, Fed_Prox, and UA-FedRL, utilizing two datasets, MNIST and CIFAR10. Each method is represented on the x-axis, while the y-axis quantifies the associated normalized communication cost. Two distinct bars represent each FL method, corresponding to the MNIST (light blue) and CIFAR-10 (light yellow) datasets. The experiment was designed to achieve a target accuracy of 90% for the MNIST dataset and 45% for the CIFAR-10 dataset. Different FL methods with 100 clients were implemented to calculate the normalized communication cost for each dataset. The detailed calculation description of normalized communication cost for a single FL model is given below. The communication cost for each device during a communication round was calculated using the following equation:  
\[C_{i}(t)=\text{bits}\Big{(}w_{(t+1,j)}\Big{)}+\text{bits}(ID_{i}). \tag{26}\]  
The total communication cost for all devices at round t was then calculated as follows:  
\[C_{T}(t)=\sum_{i=1}^{N}C_{i}(t). \tag{27}\]  
The cumulative communication cost across all rounds to achieve the target accuracy was defined as:  
\[C_{\text{cumulative}}=\sum_{t=1}^{T}C_{T}(t). \tag{28}\]  
Lastly, the normalized communication cost for MNIST and CIFAR-10, given the target accuracies of 90% and 45%, respectively, was calculated as:  
\[C_{normalized,MNIST}=\frac{C_{\text{cumulative}}-C_{\text{min},MNIST}}{C_{\text {max},MNIST}-C_{\text{min},MNIST}}, \tag{29}\]  
where \(C_{\text{min},\,MNIST}\) is the minimum communication cost achieved to reach the target accuracy. \(C_{\text{max},\,MNIST}\) is the maximum possible communication cost given a 90% accuracy target for MNIST.  
\[C_{normalized,CIFAR-10}=\frac{C_{\text{cumulative}}-C_{\text{min},\,CIFAR-10} }{C_{\text{max},\,CIFAR-10}-C_{\text{min},\,CIFAR-10}}, \tag{30}\]  
where \(C_{\text{min},CIFAR-10}\) is the minimum communication cost achieved to reach the target accuracy. \(C_{\text{max},CIFAR-10}\) is the maximum possible communication cost given a 45% accuracy target for the CIFAR-10 dataset.  
The results represent the variation in efficiency among FL methods to reduce communication costs. UA-FedRL emerged as the most communication-efficient method for both datasets, demonstrating costs of 0.19 and 0.24 for MNIST and CIFAR-10, respectively. On the other hand, Fed_Share exhibited the highest communication costs for both datasets, peaking particularly for CIFAR-10 with a cost of 0.9. Therefore, the chart highlights the critical role of selecting the appropriate FL method for optimizing communication costs in heterogeneous industrial IoT scenarios.
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
###### Abstract  
This study focuses on the quantification of heavy metals present in 18 sand beaches on the islands of Malta and Gozo. A total of 134 samples were collected, digested using Aqua Regia, and analysed using flame AAS to find the concentration of six heavy metals. Concentrations obtained in descending order are: Sr > Fe > Mn > Pb > Zn > Cu. Using PCA, Fe and Mn resulted as homogenous distributions with a probable prevalent lithogenic origin. Pb is possibly dominantly anthropogenic, while Cu and Zn are of a mixed nature. Cluster analysis was used to prove the interaction between concentrations and different bays from where the samples were gathered. This showed that Ballut Reserve Bay and Rinella Bay in Malta and Marsalform Bay in Gozo are amongst the bays most affected by heavy metal content. It has been observed that bays with higher heavy metal content lie in the same zones with the highest geological wear rate induced by sea waves. Health risk assessment undertaken for adults and children shows negligible effects of non-carcinogenic risk and cancer risk indices. Potential ecological risk computed for the concentrations obtained showed considerable Cu risk and a moderate Pb risk at the bays analysed, none of which are contaminated with these elements.  
pollution; toxicity; environment +
Footnote †: journal: toxicity; environment
## 5 Enterprise Applications of the PH-RPM  
Currently, the PH-RPM proposed in this paper is mainly applied to the testing and production guidance of the PMCU pushing process. Usually, before an enterprise conducts PMCU pushing, the production department will first carry out a process simulation using an actual model frame of the same size as the actual cabin to test whether the cabin can pass near certain key obstacles. However, this test session cannot test all PMCUs and takes up a lot of time, manpower, equipment, and other costs for the enterprise. Through the PH-RPM method proposed in this paper, the PMCU pushing route can be effectively planned toensure that the whole process path is collision-free, which effectively reduces the cost of the enterprise and improves the overall efficiency of the project.  
In addition, the paths calculated by the PH-RPM are used to guide the PMCU PUSHING process by being sent down to the field, providing efficient pushing paths for the production environment. According to the enterprise single-day data estimation, the average effective transport time of a PMCU is about 4-8 min (excluding preparation work as well as process adjustment). Through the application of the method in this paper, it is possible to plan the pushing paths in advance, effectively guide the motion attitude of the push cabin process, reduce the narrow aisle passing time, and reduce the effective motion time of the actual PMCU by an average of 38-45%.
## 2 Mathematical Model
### [PERSON]'s Algorithm  
As the target function approaches the minimum value with each iteration, deviations could occur with respect to the step value \(\beta_{(\mathrm{n})}\) that was obtained from Equation (16). In order to obtain the optimal step values in the iteration direction, [PERSON]'s algorithm [21] was used to derive the approximate optimal value \(\beta_{(\mathrm{n})}\). With regard to the numerical analysis, [PERSON]'s method is a complex but widely used root-finding algorithm that combines dichotomy, the secant method, and inversequadratic interpolation. In [PERSON]'s algorithm, the new value \(\beta_{(n)}\) can be obtained through the following iteration process:  
\[\beta_{(n),i+1}=b-\frac{1}{2}\frac{(b-a)^{2}[J_{B}-J_{c}]+(b-c)^{2}[J_{B}-J_{A}]}{ (b-a)[J_{B}-J_{C}]+(b-c)[J_{B}-J_{A}]} \tag{17a}\]  
where  
\[a=0.001\beta_{(n),i},\ b=\frac{1}{2}\beta_{(n),i},\ c=\beta_{(n),i} \tag{17b}\]  
\[J_{A}=J(\mathbf{w}_{(n)}-a\mathbf{P}_{(n)}),\ J_{B}=J(\mathbf{w}_{(n)}-b \mathbf{P}_{(n)}),\ \text{and}\ J_{C}=J(\mathbf{w}_{(n)}-c\mathbf{P}_{(n)}) \tag{18}\]  
where \(i\) is the iteration index; \(a\), \(b\), and \(c\) form a parabolic target function; \(b\) is the initial center point obtained by halving \(\beta_{(n)}\) in Equation (16); and, \([a,c]\) is the established time interval. With each iteration the interpolation of the parabolic curve can reach optimal \(\beta_{(n),i}\) under two conditions:  
1. The selected \(\beta_{(n),i}\) value must be within the \([a,c]\) interval.
2. The variation approaching the optimum value cannot exceed the previous variation by more than half, which means that the following inequality must be met: \(\left|\beta_{(n),i}-\beta_{(n),i-1}\right|<1/2\left|\beta_{(n),i-1}-\beta_{(n),i-2}\right|\).  
If the two conditions cannot be satisfied, then the golden section search method must be applied in iteration. The iteration method always requires a stop criterion, which is given, as follows:  
\[\frac{c-a}{2}<2\left|\varepsilon_{s}\beta_{(n),i}\right|-\left|\beta_{(n),i}- \frac{c-a}{2}\right| \tag{19}\]  
When Equation (19) is established, iteration steps are no longer conducted.