esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### BICOS+ Algorithm  
The difference between the original BICOS and the improved BICOS+ algorithm is the type of binary features which we use. Both algorithms use the binary features which are calculated by comparison of single brightness values with the mean brightness value (Figure 2c). However, the other binary features in BICOS+ are calculated with the following rule which we show exemplary in Figure 3.  
1. form pairs of brightness values (from the brightness value sequence of a pixel)
2. sum up the two brightness values of each pair
3. compare two such sums with each other and save the result of the comparison as a binary feature (only sums which do not share a common brightness value are compared)  
The main advantage of this method over the original BICOS is the higher number of features which can be created this way (Figure 4). Only for sequence lengths of 6 and lower this method yields less than 64 binary features. For these short sequence lengths, the missing binary features are filled up with other binary features from direct comparisons of 2 brightness values (as in the original BICOS algorithm, Figure 2a,b).  
Figure 4: Number of binary features (BF) by type. Blue: BF created by comparing single brightness values with the mean brightness of the pixel. Orange: BF created by comparing 2 brightness values of the sequence with each other. Green: BF created by comparing two sums with each other, which have each been calculated from two brightness values (only for BICOS+).  
Figure 3: Creation of binary features from two sums of brightness values (BICOS+).
# An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments
## 2 Related Works
The traditional solutions for resources allocation in Cloud Computing and WSN context are not suitable, since they do not consider the specificities of sensor clouds. First works designed specifically for sensor clouds considered the provisioning of all sensors node, which is energy inefficient since redundant nodes can be selected. Recent works tried to exploit clustering but considering only homogeneous sensor nodes. Therefore, they are not appropriate for sensor cloud environments, which generally comprises nodes with different set of sensors devices. In ACxSIMv2, we consider the exploiting of temporal and spatial correlations between sensor nodes makes to create clusters based on the similarity of its measurements, but taking into account the sensor cloud heterogeneity. Consequently, there is a reduction in the selection of redundant nodes since one node can represent different areas from which it is deployed (Spatial correlation). Because of the temporal correlation between the measurements, the transmission of the selected nodes can be reduced since the middleware of the sensor cloud can predict (with high accuracy) consecutive sensing measurements. In this way, the overall energy consumption of the sensor nodes is reduced, prolonging the lifetime of the sensor cloud.
# Optimizing Routing Protocol Design for Long-Range Distributed Multi-Hop Networks
## Network Model and Problem Analysis  
### Network Model  
The LoRa distributed multi-hop network model, as illustrated in Figure 1, primarily comprises three modules: the control center module, the LoRa distributed network module, and the sensor module [15; 16]. The sensor module is responsible for sensing and measuring environmental parameters. Within the distributed network module, LoRa nodes within the gateway's coverage area directly send the data collected by the sensors to the gateway. If there are obstacles between the LoRa nodes and the gateway, or if the nodes are subject to interference from other wireless devices or signals, preventing direct communication with the gateway, the LoRa nodes will use a routing algorithm to select the optimal relay nodes to transmit data until it reaches the target node or gateway [17]. The LoRa network server receives the data packets, removes duplicates, and forwards the data to the application server.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 6 Simulation Studies
### Fault Detection Performance
This occurs with positive slopes as well as different slopes in two satellites. In order to check if the full-state EKF can detect any other ramp faults, Figure 13 plots \(\alpha_{EKF}\) as the magnitude of the fault slope in N2 and N6 is increased. Detection occurs when the slope is \(-0.5\) m/s. Next, to see if detection occurs before the VPE crosses its alert limit or not, faults are injected into N2 and N6 from 895 s in a simulation run. It should be noted that the final approach of the aircraft starts at 911 s and ends around 975 s when the minimum decision altitude of 350 ft is reached. During this period, VPE can be compared against the alert limit of 50 m for EKF. Table 3 shows the performance of EKF and SKF. With \(-0.5\) m/s, \(\alpha_{EKF}\) detects faults after the VPE crosses the alert limit, but with the other two slopes, detection occurs before exceeding the alert limit, as desired. Thus, full-state EKF can detect ramp faults successfully when the slope magnitude is greater than a minimum value, which in this case is more than 0.5 m/s. It is also interesting to note that the VPE of \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fault Type**} & \multicolumn{2}{c}{**WLS**} \\  & **Fault Detection Time** & **If PE \(>\) PL** \\  & **1 sat (N2)** & **2 sat (G28 and N2)** & \\ \hline \(-5\) m step & No & No & No; constant offset; \\  & detection & detection & 1 sat: PE \(<5\) m \\  & & & 2 sat: PE \(<10\) m \\ Ramp & & & Yes \\ with slope & 495 s & 534 s & 1 sat: from 943 s \\ \(-0.05\) m/s & & & 2 sat: from 584 s \\ Sinusoidal & No & No & No; sinusoidal PE \\ \(5\)in \((2\pi t/3600)\) m & detection & detection & 1 sat: PE \(<5\) m \\  & & & 2 sat: PE \(<10\) m \\ Sinusoidal & No & No & No; sinusoidal PE \\ \(5\)cos \((2\pi t/3600)\) m & detection & detection & 1 sat: PE \(<5\) m \\  & & & 2 sat: PE \(<8\) m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fault detection performance of WLS. Fault detection time is calculated from the beginning of simulation. EKF grows faster than that of SKF in the presence of ramp faults. As a result, it crosses its alert limit earlier than its counterpart in SKF. Figure 12: Fault detection with full-state EKF and SKF. Circle indicates when the PE or test statistic of SKF crosses the respective threshold. Vertical alert limit is shown for EKF during the final approach. Figure 13: EKF test statistics with different ramp faults. Test results of KF RAIM for a 54.48 min aircraft trajectory are also presented in the Supplementary Materials. The results demonstrate the following: (1) adaptive calculations of all KF RAIM parameters of Figure 9 for long duration data, and (2) the ability of the algorithm to respond to an anomalous behavior and recover from it when the source of the anomaly is gone. The low-end laptop needs 16.3 min to run the KF RAIM algorithm in Matlab. WLS and full-state EKF results are also included. Details of all figures are provided in the description of the Supplementary Materials.
###### Abstract  
The main objective of proton beam therapy is to precisely irradiate diseased tissue while minimizing damage to healthy cells. For effective treatment, the linear energy transfer (LET) is a key parameter in ensuring the destruction of diseased cells, and both the dose and LET are typically represented as functions of depth. The distribution of dose and LET in the target depends on the beam properties, including beam energy, energy spread, beam size, and beam emittance. The aim of this work is to present the method used to characterize the proton beam properties obtained from the machine employed in the simulation and to determine the dose and dose-averaged LET (\(\mathrm{LET_{d}}\)) values, including their peak positions in depth. These results are used to predict the dose and \(\mathrm{LET_{d}}\) at different depth positions under experimental conditions. We utilized GEANT4, a Monte Carlo (MC) simulation-based software, to examine the integral depth-dose position and the peak position of the \(\mathrm{LET_{d}}\). The proton source was obtained from a cyclotron accelerator, specifically the Varian ProBeam Compact spot scanning system at King Chulalongkorn Memorial Hospital in Bangkok, Thailand. The system provides proton energies ranging from 70 MeV to 220 MeV. In this study, four proton energies--70 MeV, 100 MeV, 150 MeV, and 220 MeV--were chosen to characterize the beam properties. The 80%-20% distal fall-off obtained from the simulation was used to determine the energy spread for each selected energy by matching the depth-dose peak with the measurement data. The optimal energy spreads were found to be 1.5%, 1.25%, 1%, and 0.5% for proton energies of 70 MeV, 100 MeV, 150 MeV, and 220 MeV, respectively. These energy spreads ensure that the difference in the depth-dose profile is below 1% when comparing the simulated and measured depth-dose profiles. Furthermore, the peak \(\mathrm{LET_{d}}\) was found to be approximately 1 mm away from the \(\mathrm{R_{80}}\) position, a depth that corresponds to 80% of maximum dose, for each energy. This information can be used to guide the desired \(\mathrm{LET_{d}}\) position by utilizing the \(\mathrm{R_{80}}\) depth position.  
Monte Carlo simulations; cyclotron accelerator; proton therapy; linear energy transfer +
Footnote †: journal: applied sciences
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 2 Actions for Collision Avoidance  
### International Regulations Preventing Collisions at Sea  
The International Regulations for Preventing Collisions at Sea 1972 (COLREGs) were announced as a convention of the International Maritime Organization on 20 October, 1972. This regulation applies to all vessels on the high seas and in all waters connected therewith that are navigable by seagoing vessels. The five main rules of this regulation [11], Rule 13--overtaking, Rule 14--head-on situation, Rule 15--crossing situation, Rule 16--action by give-way vessel, and Rule 17--action by stand-on vessel, which are related to collision avoidance will be used for verifying the collision avoidance control design in this investigation.  
Based on Rules 13, 14, and 15, when a vessel in sight of another, there are three meeting situations: (a) head-on, (b) crossing, and (c) overtaking, as illustrated in Figure 1.  
Figure 1: (**a**) Head-on; (**b**) crossing; and (**c**) overtaking.  
Figure 2: Relative bearings of three meeting situations.  
As shown in Figure 2, the relative bearings (RBs) of these three meeting situations [17] can be defined as:  
Region A (head-on): RB \(\in[0^{\circ},5^{\circ}]\cup[355^{\circ}360^{\circ}]\)  
Region B and D (crossing): RB \(\in[5^{\circ},112.5^{\circ}]\cup[247.5^{\circ},355^{\circ}]\)  
Region C (overtaking): RB \(\in[112.5^{\circ},247.5^{\circ}]\)
## 2 Nanomaterials in the Construction Sector  
### Overview  
Several applications of nanomaterials can be identified in the construction sector, e.g., concrete, mortars and renders, thermal insulation materials, glass windows, solar panels, paintings, and coatings, among others [14]. Nanotechnology can either improve the properties of the final materials or extend their service life and life cycle [16; 18]. Utilizing nanomaterials as construction materials has the potential to improve their inherent properties or introduce additional functionalities. For instance, the incorporation of SiO\({}_{2}\) nanoparticles has been reported as an effective method for reinforced concrete [16; 18]. Similarly, the inclusion of metal oxides, such as iron, titanium, aluminum, and zinc, in paints and surface coatings can serve to prevent corrosion and resist dirt accumulation [11]. The addition of small percentages of TiO\({}_{2}\) and ZnO has also been used as UV filters in glass [2] or as photocatalytic additives [15]. Nanoclays have been introduced into cementitious matrixes for pervious concrete pavement [28]. Nanostructured (calcium, barium, or magnesium) hydroxides and alkoxides have been used for the conservation of porous calcareous materials, such as mural paintings, limestone, and historical mortars [29]. Carbon-based NMs (CNs) and graphene-based materials (GO) improve serviceability, including high thermal and electrical conductivity, proper elasticity and flexibility, low thermal expansion coefficient, and electron field emitter capabilities [30; 31; 32].  
Table 1 summarizes several types of nanomaterials, as well as their compositions, applications, and functionalities, based on the literature review.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nanomaterial** & \begin{tabular}{c} **Synthesis or** \\ **Production Method** \\ \end{tabular} & **Applications** & **Functionality/Improvement** & **References** \\ \hline Nanosilica & Sol-gel & & & \
\begin{tabular}{c} Abrasion resistance; Acceleration \\ on cement hydration; \\ Concrete-to-steel bonding; \\ Improved freeze–thaw resistance; \\ Mechanical improvement; \\ Pozzolanic activity; \\ Paste–aggregate bonding; \\ Permeability reduction \\ \end{tabular} &
\begin{tabular}{c} \\ **S**(33-42) \\ **S**(43-42)  
Although NMs can improve the properties and performance of construction materials, several NMs are not easily available due to restrictions on their use and commercialization, as well as to the availability of raw materials and, as previously stated, to high production costs [11]. Furthermore, nanoparticles (NPs) can have a low compatibility with some construction materials and are prone to aggregation phenomena, which hamper homogeneous dispersion [94; 95; 96; 97]. Another relevant challenge is related to their potential toxicity and thus the risk to human health and the environment [98; 99; 100; 101; 102; 103; 104].
## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Top-Performing Model Outcome Interpretation  
In modern AI research, model interpretation plays an increasingly important role. Often, understanding why a model has made a certain decision is just as important as the decision itself. While feature importance is often simply used to reduce feature lists or debug complex models, feature importance interpretation can provide valuable insights into the problem being addressed itself.  
Several approaches exist in the modern literature for tackling interpretation, and a popular choice among researchers is the use of SHAP [66] to determine feature impacts, using techniques from game theory. Additionally, the XGBoost approach comes with a native method for determining feature importance based on gini impurity [80]. Understanding and comparing the importance can help improve vessel identification, as well as improve future research. The outcomes of both analyses are showcased in Figure 9.  
The feature importance analysis suggested that vessel length played an important role in accurate classification for all classes of vessel, followed by vessel width and draft, and mmsi. Dynamic parameters such as speed over ground, heading, and course over ground played a less significant role. This is to be expected, regardless of vessel class, as these parameters can coincide. Similar outcomes were established with an XGBoost importance analysis. However, speed over ground played a larger role. This was likely due to the speed limitations of larger vessels.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Problem/\(p\)-Values** & **PSO** & **GA** & **ABC** & **BA** & **WOA** & **HHO** & **ChOA** \\ \hline Vessel error & 0.042 & 0.003 & 0.018 & 0.037 & 0.04 & 0.027 & 0.031 \\ Vessel kappa & 0.043 & 0.027 & 0.023 & 0.045 & 0.044 & 0.047 & 0.036 \\ Trajectory & 0.045 & 0.007 & 0.041 & **0.054** & 0.024 & 0.044 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Wilcoxon signed-rank test values, exhibiting \(p\)-values for all three experiments (BPSO vs. others).  
Figure 9: Feature importances determined using XGBoost and SHAP.  
Another important part of feature analysis is that features influence different decisions in distinct ways. This is demonstrated in Figures 10 and 11, where the feature dependencies for the top-six features were determined for the cargo and tug vessel classes.  
Several interesting observations can be made from Figures 10 and 11. Vessel length played a more significant role in cargo vessel identification than tug boats. Vessel width played a similar role. On the other hand, draft was significantly more important for tug vessels than cargo vessels.  
Figure 11: Feature dependence plots for tug vessel types.  
Figure 10: Feature dependence plots for cargo vessel types.
## 5 Conclusions  
RadWet-L can map inundation within forested wetlands using time series L-band radar imagery. In this instance, RadWet-L was applied and evaluated using 50 m ALOS-2 PALSAR-2 ScanSAR imagery, at approximately 42-day time steps, providing new insights into the temporal and spatial dynamics in inundation within the Amazon Basin at both inter- and intra-annual scales. This unprecedented granularity is potentially transformational for the way that we monitor forested wetlands for applications in food and water security, the protection of ecosystems, the sustainable use of wetlands, and carbon budgeting.  
In this paper, RadWet-L was shown to perform consistently well over the 5.95 million km\({}^{2}\) area of the Amazon Basin, a region that includes a range of land covers and forest types. As such, RadWet-L may be transferable to other forested wetlands such as the Congo River Basin in Central Africa. In working towards a comprehensive wetland mapping system, future studies should also consider combining L- and C-band radar data, allowing both the forested and herbaceous inundated vegetation dynamics to be mapped.  
Conceptualization, G.O., A.H., P.B. and A.R.; methodology, G.O., A.H., P.B. and A.R.; software, G.O.; validation, G.O.; formal analysis, G.O. and A.H.; investigation, G.O.; resources, G.O. and P.B.; data curation, G.O. and A.R.; writing--original draft preparation, G.O. and A.H.; writing--review and editing, G.O., A.H., P.B. and A.R.; visualization, G.O.; supervision, A.H. and P.B.; project administration, A.H.; funding acquisition, A.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Aberystwyth University's AberDoc Programme and Aberystwyth University, Faculty of Earth and Life Sciences Research Boost award.  
Data Availability Statement: Data are available from Zenodo (10.5281/zenodo.10566417) with scripts used for image classification available at GitHub ([[https://github.com/gro5-AberUni/ALOS-LBand](https://github.com/gro5-AberUni/ALOS-LBand)]([https://github.com/gro5-AberUni/ALOS-LBand](https://github.com/gro5-AberUni/ALOS-LBand)) accessed: 30 May 2024).  
**Acknowledgments:** This work has been undertaken in part within the framework of the JAXA Kyoto & Carbon (K&C) Initiative. ALOS-2 PALSAR-2 data have been provided by JAXA EORC.  
**Conflicts of Interest:** Author [PERSON] is the founder of the company solo Earth Observation (soloEO). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Head Rotation Estimation
#### 2.3.2 Machine Learning (ML) Estimation  
We explored the ML approach, which could be useful in the prediction of left-right neck rotation classification as an alternative to the equation model. This was obtained from the previous experiment and by the equation model. The estimated angle is found to be erratic and requires computationally expensive filters to obtain smooth signals in real-time. Given the nature of the required performance, we targeted an ML model that was pre-trained with one or two subject data and generalized to all test subjects. In this way, setup time is reduced simply by connecting the EMG electrodes in a plug-and-play model.  
For training, data on 2 subjects were recorded performing left and right neck movements for 2.5 min per subject. The data were further subdivided into processing window sizes (50 ms) and corresponding classes defined from visual observations. Three classes were used: left turn, right turn, and center. The total data generated for training by two subjects in 5 min at a sampling rate of 2000 Hz is (2000 \(\times\) 5 \(\times\) 60) = 600,000 samples per channel. After window subdivision, we have 6000 labeled train sequence observations per channel.  
In this step, we derive the features of the EMG sequence. Five features to choose from were mean absolute value, zero crossings, integrated absolute value, waveform length, and band power of the sampled data in accordance with other classification researches [46]. In total, 10 predictor features (5 features of each SCM signal) were used for classification. We employed a leaner classification in MATLAB to conduct training with the prepared data set. Three types of ML algorithms, k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Ensemble, were used [47]. The hyper-parameters for each classifier were initialized with default settings with fivefold cross-validation. In KNN, the cosine distance metric and weighted distance method were evaluated. In SVM, linear and Gaussian kernel functions were evaluated. In Ensemble, bagged trees and boosted trees methods were evaluated.  
The performance of the three types is proportional to the demand for different computing resources. The adopted method is based on classification accuracy and computation time. The performance of the ML model is validated with the classification accuracy indicators, as shown in Table 1. From this performance, the Ensemble is selected, which has the best repeat accuracy and response time.