esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 3 Atmospheric Light Estimation
### Polarization State Estimation Model  
From the Fresnel reflection law, it is known that objects can produce characteristic polarization determined by their own properties, such as surface morphology, texture, water content, dielectric constant, and incident angle. By exploiting the sensitivity of polarization to physical properties, it is possible to enhance target features and facilitate target identification. A mathematical tool called the Stokes vector [21]\(S\) is used to practically and efficiently describe the modification of the polarization states when the light travels and interacts with different materials [22]:  
Figure 2: (**a**) Original hazy image. (**b**) The dehazed image using 0.6 times the estimated atmospheric light value. (**c**) The dehazed image using 0.8 times the estimated atmospheric light value. (**d**) The dehazed image using 1 times the estimated atmospheric light value. (**e**) The dehazed image using 1.2 times the estimated atmospheric light value. (**f**) The dehazed image using 1.6 times the estimated atmospheric light value.  
\[S=\left[\begin{array}{c}s_{0}\\ s_{1}\\ s_{2}\\ s_{3}\end{array}\right]=\left[\begin{array}{c}\frac{1}{2}(i_{0}+i_{45}+i_{90}+ i_{135})\\ i_{0}-i_{90}\\ i_{45}-i_{135}\\ i_{r}-i_{l}\end{array}\right] \tag{9}\]  
In the Stokes vector representation, \(S_{0}\) represents the intensity element, \(S_{1}\) is the difference between \(0^{\circ}\) polarization and \(90^{\circ}\) polarization, \(S_{2}\) is the difference between \(45^{\circ}\) polarization and \(135^{\circ}\) polarization, \(S_{3}\) is the difference between right circular polarization and left circular polarization, and \(i\) denotes the polarization detection channel. Using the Stokes vector, we can further obtain the polarimetric degree parameter, which characterizes the proportion of the polarization component in the total light intensity:  
\[P=\frac{\sqrt{s_{1}^{2}+s_{2}^{2}+s_{3}^{2}}}{s_{0}} \tag{10}\]  
In our method, we only consider the degree of linear polarization, and therefore, we ignore the \(S_{3}^{2}\) terms in Equation (10). The degree of linear polarization can be represented by the following formula:  
\[P=\frac{\sqrt{s_{1}^{2}+s_{2}^{2}}}{s_{0}} \tag{11}\]
# Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
## 2 System Model
### Jamming Scenario  
Assume that both the jammed constellation \(\Psi_{A}\) and non-cooperative jamming constellation \(\Psi_{B}\) are Walker constellations. Since the jamming effect is closely related to the relative positions of the satellites, we first define the satellite coordinates of \(\Psi_{A}\) and \(\Psi_{B}\) in the Cartesian coordinate system.  
Let the radius of Earth be \(R_{e}\). The Walker constellation determines the positions of satellites by seven parameters, i.e., \(Z/N/F/h/\mu/\Omega_{0}/\varphi_{0}\), where \(Z\) denotes the satellite number in the constellation, \(N\) denotes the satellite orbit number, \(F\) is the phase factor, \(h\) is the orbital altitude, \(\mu\) denotes the orbital inclination, \(\Omega_{0}\) is the initial right ascension of the ascending node (RAAN) and \(\varphi_{0}\) is the initial true anomaly. On this basis, we define the spatial dynamic coordinates of all satellites as well as Earth stations.  
Assume that the parameters of the jammed constellation \(\Psi_{A}\) are \(Z_{A}/N_{A}/F_{A}/h_{A}/\mu_{A}/\)\(\Omega_{A0}/\varphi_{A0}\). The coordinates of the \(j\)th satellite in the \(i\)th orbit of \(\Psi_{A}\) can be expressed as  
\[A(i,j,t)=(x_{A}(i,j,t),y_{A}(i,j,t),z_{A}(i,j,t)) \tag{1}\]  
\[\left\{\begin{array}{l}x_{A}(i,j,t)=(R_{e}+h_{A})(\cos\Omega_{A}\cos(\varphi _{A}+\omega_{A}t)\\ -\sin\Omega_  
Assume that the parameters of \(\Psi_{B}\) are \(Z_{B}/N_{B}/F_{B}/h_{B}/\mu_{B}/\Omega_{B0}/\varphi_{B0}\); the satellite coordinates in \(\Psi_{B}\) are similar to those of \(\Psi_{A}\).
# Explainable AI-Enhanced Human Activity Recognition for Human-Robot Collaboration in Agriculture
## 4 Discussion
### Future Research Directions  
The model's current performance could be further improved by incorporating a more diverse dataset that encompasses a wider range of agricultural material handling tasks in future research endeavors. For instance, the inclusion of older participants or individuals with different physical capabilities might reveal different movement patterns or physical limitations [71, 72]. Investigating alternative sensor placements, mounted on lower extremities, could further optimize the combination of upper- and lower-body data, potentially identifying the most effective sensor configurations for different use cases. Exploring other sensor types, such as electromyography, could provide valuable complementary data, improving the model's accuracy for specific tasks [73, 74]. Future research should also focus on addressing the challenges posed by possible sensor misplacement and variability in agricultural environments. Moreover, real-world experiments in agricultural collaborative robotics, combined with direct feedback from workers for also assessing the mental workload [75, 76], are essential for addressing practical needs and usability challenges. For practical deployment, the Thorvald [77] or Husky robotic platforms [78] can be used for related agricultural tasks [26], as both are designed to support various sensors and autonomous operations in outdoor environments. Indicatively, multiple sensors such as LIDAR, RTK GPS, and IMUs for precise localization and navigation could be used. As presented in [18], the data collected by these sensors can be processed, for instance, by Jetson TX2 combined with CUDA, running a modified ROS Navigation Stack [79] with planners like TEB for obstacle avoidance [80]. Finally, integrating human intelligence and expertise into the robot's decision-making process through \"human-in-the-loop optimization\" can address the challenges of dynamic agricultural environments, foster user trust, and ensure safe, ethical robot behavior within adaptable, human-centered HRI systems [81, 82, 83].
# Current Challenges Supporting School-Aged Children with Vision Problems: A Rapid Review
## 3 Overall Context of Vision Screening
### Conventional (Traditional, Manual) Screening  
The traditional way of diagnosing vision problems among children and adults is by \"manual\" vision screening during which vision specialists examine vision problems through several tests. Specialists define the procedure (the included tests and how the chosen tests are combined in a test battery). Already, these procedures use several aids, e.g., the Snellen chart (see Figure 2a), the plus lens test, and stereo test [48]. These are aids helping to investigate problems such as reduced visual acuity, hypermetropia, and binocular vision. Traditional methods of vision screening are considered gold standard procedures. As such, they have the advantage of having been tested and approved extensively by now. However, administering them requires expert knowledge from vision specialists, or specially trained school nurses, or educated volunteers.  
In most countries, systematic screening is implemented around the age of four or five [13] as a last recommended and state-financed vision screening. This screening seldom includes functional vision screening, e.g., screening for identifying ocular motility disorders or other problems with the vision system in one's life [6; 49]. Common vision problems occur in preschool- and primary school-aged children, and the main focus of most screening programmes is to detect amblyopia, refractive errors, binocular vision, and ocular motility disorders (e.g., strabismus) [47].  
Meanwhile, the most common vision problems in primary school- and secondary school-aged children are uncorrected refractive error [50], oculomotor dysfunction (an anomaly in fixation, saccades, or pursuit eye movements) [51], and accommodative and non-strabismic binocular disorders (e.g., accommodative insufficiency, convergence insufficiency) [52; 53], especially in learning disabilities and children with ADHD [54]. However, visual screenings to assess this visual functionality do not exist.
# CubeSat Mission Scheduling Method Considering Operational Reliability
## 4 CubeSat Scheduling Model
### Selection of Weight Coefficients by Analytical Hierarchy Process (AHP)  
The existing methods for determining weight mainly include principal component analysis, AHP, fuzzy AHP, the risk preference coefficient method, etc. [31]. Considering that the hierarchical structure relationship in the AHP can make a rational judgment on the correlation between each sub-objective and there are few evaluation indicators in this example, the AHP is used to select the weight coefficient. The specific process is as follows:  
1. Determine the index system: according to the components of the objective function, the criterion layer consists of three parts: the sum of task priorities, the average state of charge, and the total number of task start-ups.
2. Build a judgment matrix: The judgment matrix is composed of elements \(a_{ij}\), and the weight of the criterion layer to the target layer is determined through the comparison between each element. The element \(a_{ij}\) is given by the 1-9 scale method, as shown in Table 3. On the Saaty Rating Scale, the decision maker needs to assign a weight value to each attribute, indicating the importance of that attribute relative to other attributes.  
Considering that both \(f_{2}\) and \(f_{3}\) are indicators to describe the operational reliability of the CubeSat, setting both of them is of equal importance [33]. After consulting with experts in standardization management, we wrote the results of the experts' comparative judgments as the judgment matrix \(A=\begin{bmatrix}1&a_{12}&a_{13}\\ a_{21}&1&1\\ a_{31}&1&1\end{bmatrix}\) by introducing a suitable scale expressed numerically through the Saaty Rating Scale.
3. Calculate the weight vector: calculate the element product \(M_{i}=\prod_{j=1}^{n}a_{ij}(i=1,2,\ldots,n)\) of each row of the judgment matrix A, calculate the \(n\)-th root \(\overline{W_{i}}=\sqrt[n]{M_{i}}\) of \(M_{i}\), and finally, normalize \(W_{i}=\overline{W_{i}}/\sum_{i=1}^{n}\overline{W_{i}}\).
4. Consistency check: Consistency index \(CI=\frac{\lambda_{max}-n}{n-1}\), stochastic consistency indicator \(RI=\frac{\sum_{i=1}^{n}C_{i}}{n}\), consistency ratio: \(CR=\frac{CI}{RI}\). When \(CR<0.1\), it means that \(A\) has a satisfactory consistency and can be normalized. Selection of the final weight coefficient \(\lambda_{1}=0.5\), \(\lambda_{2}=\lambda_{3}=0.25\), and the calculation results are shown in Table 4.
_Applied_  
_sciences_  
_Article_  
**High-Definition Dynamic Voltage Restorer Systems Using Equivalent Time Sampling Techniques and Circular Structural Memory Filters**  
**[PERSON]**  
Department of Electrical Engineering, Honnam University, 417 Eodeung-daero, Gwangsam-gu,  
Gwangju 62399, Republic of Korea; [EMAIL_ADDRESS]; Tel.: +82-062-940-3630  
**Abstract: Due to advances in power electronics technology and the evolution of automation devices, the number of electrical devices that are sensitive to power quality is rapidly increasing, and for this reason, users are increasing their demand for high quality. To meet power quality demands, many power conversion devices are used, including dynamic voltage restorers (DVRs). DVRs are recognized as devices that can effectively manage problems such as voltage segments, swells, and harmonics. DVR control requires many samples for harmonic compensation, which has the disadvantage of being complicated to implement due to fast digital signal processing computation and the application of the cyclic discrete Fourier transform. In this paper, a high-precision DVR system configuration is proposed that compensates for harmonics using a periodically equivalent time-interval sampling technique and a novel circular-structured memory filter. The proposed circular-structured multi-pointer memory filter is an effective filter algorithm for high-precision input voltage measurement because it can remove noise and compensate for the delay of the phase angle of the filter in voltage measurement. A simulation and DVR prototype system were built, and the feasibility and effectiveness of the phase angle multi-filter voltage detection method and the compensation method were verified by experiments.  
dynamic voltage restorer; voltage measuring filter; phase angle multiple pointer; circular structural memory filter; equivalent sampling
## 4 Network Creation and Energy-Efficient Clustering  
The developed work concerns three sets of medical data that include breast cancer data, heart data and lung cancer data. The breast cancer data comprise 289 records and 9 attributes (289 \(\times\) 9), heart data comprise 270 records and 14 attributes (270 \(\times\) 14) and lung cancer data comprise 22 records and 14 attributes (22 \(\times\) 57). Accordingly, the total number of records is considered as the total number of nodes, i.e., (289 + 270 + 22 = 578 nodes). Further, the energy of nodes is assigned between 0 and 1 for each node (in IoT with BS). In the developed model, the energy-efficient clustering of nodes takes place by considering distance and energy.  
**Distance [26]**: It is computed as per Equation (1), where \(f^{di}(q)\) represents the distance amongst nodes and centroid (CH) as shown in Equation (3) and distance amongst centroid (CH) and Base Station (BS) as specified in Equation (2), here \(f^{di}(p)\) refers to distance amongst two nodes as specified in Equation (4). Here, \(f^{di}(q)\) lies amongst [0, 1].  
\[f^{di}=\frac{f^{di}(q)}{f^{di}(p)} \tag{1}\]  
\[f^{di}(q)_{1}=\sum_{i=1}^{M}\sum_{j=1}^{L}\|B_{i}-CH_{j}\| \tag{2}\]  
\[f^{di}(q)_{2}=\sum_{i=1}^{M}\sum_{j=1}^{L}\|CH_{j}-BS_{s}\| \tag{3}\]  
Figure 1: Schematized representation of the proposed framework.  
\[f^{di}(p)=\sum_{i=1}^{M}\sum_{j=1}^{L}\|B_{i}-B_{j}\| \tag{4}\]  
**Energy Model [26]**: Energy utilization is another major concern. In fact, the battery cannot be re-energized as there is no power supply. However, additional energy is required for transmitting information to BS from all nodes. Therefore, the energy model for transferring whole data is revealed in Equation (5), where \(E_{ete}\) signifies the electronic energy relied and \(E_{TX}(M:e)\) signifies the energy necessary for transferring \(M\) bytes of packets at \(e^{th}\) distance. The energy model is shown in Equation (6), wherein \(E_{ggg}\) refers to the energy utilization during data collection. The whole energy needed for \(M\) packets at distance \(Di\) is shown in Equation (7). Equation (8) indicates the amplification energy and \(E_{pr}\) denotes the \"power amplifier energy\" and \(E_{fr}\) denotes the energy required for employing the free-space technique.  
\[E_{TX}(M:e)=\left\{\begin{array}{l}E_{ete}*M+E_{fr}*M*e^{2},if\ e<e_{0}\\ E_{ete}*M+E_{pr}*M*e^{2},if\ e\geq e_{0}\end{array}\right. \tag{5}\]  
\[E_{ete}=E_{TX}+E_{ggg} \tag{6}\]  
\[E_{RX}(M:e)=E_{ete}M \tag{7}\]  
\[E_{agg}=E_{fr}e^{2} \tag{8}\]  
\[e_{0}=\sqrt{\frac{E_{fr}}{E_{pr}}} \tag{9}\]  
Usually, the entire network energy is determined as shown in Equation (10), wherein \(E_{1}\) denotes the energy at idle state and \(E_{ST}\) denotes energy cost.  
\[f^{energy}=E_{TX}+E_{RX}+E_{1}+E_{ST} \tag{10}\]  
**5. Disease Diagnosis in Internet of Things: A Deep Learning-Based Model**  
The disease diagnosis is carried out in IoT. At first, the statistical features and higher-order statistical features are extracted from the data that are described in the below section.
# Intelligent Prediction of Nitro Oxide Capture in Designable Ionic Liquids
###### Abstract  
As a greenhouse gas, nitrous oxide (N\({}_{2}\)O) is increasingly damaging the atmosphere and environment, and the capture of N\({}_{2}\)O using ionic liquids (ILs) has recently attracted wide attention. Machine learning can be utilized to rapidly screen ILs suitable for N\({}_{2}\)O removal. In this study, intelligent predictions of nitrous oxide capture in designable ionic liquids are proposed based on a series of machine learning methods, including linear regression, voting, and a two-layer feed-forward neural network (TLFFNN). The voting model can utilize various algorithms and is highly generalizable to various systems. The TLFFNN model produced the most accurate prediction, with an MSE of 0.00002 and R\({}^{2}\) of 0.9981 on test sets. The acceptable performance of the TLFFNN model demonstrates its utility as an accurate and promising candidate model for the prediction of N\({}_{2}\)O solubility in ILs over other intelligent models. Based on the analysis of the thermodynamic and molecular properties of ionic liquids, in the low-pressure zone, components of [(OH)\({}_{2}\)IM] and [AC] perform best in capturing N\({}_{2}\)O, while in the high-pressure zone, components of [(ETO)\({}_{2}\)IM] and [SCN] are best. This finding will provide new chemical insights for the industrial synthesis of ionic liquids in capturing N\({}_{2}\)O.  
ionic liquids; machine learning; nitrous oxide; solubility; greenhouse gases Article
# Streaming-Based Anomaly Detection in ITS Messages
## 4 Discussion  
Advances in battery technology and the availability of low-cost storage devices permitted the collection of densely sampled trajectory data over a long period of time. With more data, it is now feasible to identify more intriguing patterns. A great deal of progress has been made in the field of anomaly detection systems, with several strategies developed to handle the problem of anomaly identification. In the field of connected autonomous vehicles, [PERSON] et al. [55] developed a data collection and analytics framework for vehicle trajectory extraction, reconstruction, and assessment. To minimise noise and remove outliers in the trajectories, a Kalman filter and the Chi-square test were used. They also introduced a trajectory discontinuity detection approach that can detect and reconstruct discontinuous trajectories using a forward-backward prediction smoothing method.  
Given that there is a lot of data without labels, unsupervised learning is widely favoured for real-life applications, particularly anomaly detection. In this work, we performed unsupervised anomaly detection in CAM data streams acquired from the C-ITS environment. We evaluated anomalies that might have implications, such as an accident or incident that requires motors on that segment of the road to significantly lower their speed as they approach the event location. We propose an ensemble anomaly detector, enhanced locally selective combination in parallel outlier ensembles (ELSCP). ELSCP is tailored for streaming scenarios by leveraging a pipeline framework that transforms data into a stream using a reference window model that implements a sliding window approach. The approaches in the ensemble are combined in such a way that the total complexity does not surpass that of the individual model with the highest complexity. Based on our findings, ELSCP can detect anomalies in CAMs.
# Influence of Hot Carrier and Thermal Components on Photovoltage Formation across the p-n Junction
## 3 Results
Fourth, the input of the slow lattice heating-caused component \(U_{lh}\) seemed to increase since it went into the superlinear shape. Actually, it was not the real case. Part of the energy remaining from the electron-hole generation due to two-photon absorption (\(2\times h\
u-E_{g}=0.92\) eV) was most probably used to generate a hot carrier, which supported formation of the hot carrier photovoltage (Figure 1a, process 3). However, this process followed the slow temporal behavior of the \(U_{gon}\) as both of them did happen at the same time and therefore could not be separated. Therefore, the decrease of the ratio \(U_{h\
u}/U_{lh}\) (black diamonds in Figure 4) should be treated as the relative decrease of that HC component, which resulted only from direct intraband free carrier absorption. Nevertheless, one should note a ten times stronger impact of the hot carriers as compared to that of the heated lattice at low light intensity. It is well known that bias voltage alters the potential barrier height and the depleted region of a p-n junction. Thus, forward bias should set favorable conditions for the hot carrier flow across the lowered potential barrier (see Figure 1a). Indeed, the experiment shows that the negative fast photovoltage component increased with the forward bias voltage and decreased with the reverse one (Figure 5). As process 1 in Figure 1a illustrates, a p-n junction solar cell got forwardly biased by the electron-hole pairs generated and separated by the internal electric field what should work for the good of the damaging impact of the hot carriers on the cell operation. However, in the case of the reverse-biased junction, sharp drop of the magnitude of the fast component is seen in the region of light intensity when the carrier generation-induced component met favorable conditions and started manifesting itself (Figure 5, at above 5% of maximum intensity value); the rise of the latter was well-seen in the inset at U \(<0\). This way, correlation between the hot carrier-induced photovoltage and the generation-caused one was ambiguous, but the opposition between these two photovoltages was obvious.
## 3 Results and Discussion
### Influence of foliar Ca, Si and Ca + Si Treatments on Grape Phenolic Compounds
Grapes from grapevines sprayed with Si had a lower concentration of epicatechin and procyanidin B1 than control grapes, whereas the Ca + Si treatment induced a decrease in epicatechin, procyanidin B1 and procyanidin B2 values compared to control grapes (Table 3). These results do not agree with those reported by [PERSON] et al. [37]. These authors observed an increase in epicatechin and catechin content because of foliar Si application. On the other hand, they also showed higher concentrations of epicatechin in relation to Ca spraying. Differences in gallic acid concentration between control and foliar treatments were not observed (Table 3). A similar lack of effect on this acid was observed after foliar Ca treatment by [PERSON] et al. [36]. Hydroxcircinamic acids contribute to the astringent properties of grapes and wines [52]. The grapes from grapevines treated with Ca sprays presented had the highest concentrations of _trans_-catfaric acid, _trans_+_cis_-coutaric acids, caffe acid and total hydroxcinamic acids (Table 3). However, these increases were not significant in comparison with control grapes content. Silicon foliar treatment did not induce changes in any hydroxcinamic acids when compared to control grapes, whereas Ca + Si sprays decreased _trans_-catfaric acid concentrations (Table 3). Previously, [PERSON] et al. [37] described a higher concentration of _trans_-catfaric acid in the musts of vines sprayed with Ca and Si salts. [PERSON] et al. [36] also showed an increase in grape phenolic acids after Ca treatment. However, the results from the present work did not support the hypothesis that Ca and Si foliar treatments influence the phenolic composition of musts [37]. Finally, the concentration of stilbenes was not affected by any of the foliar treatments evaluated (Table 3). These compounds are of interest because of their healthy properties for humans. However, previous studies showed an increase in the content of some stillenes after Ca and Si treatments. For example, [PERSON] et al. [36] observed an increase in cis-piceid content after Ca treatment, while the remaining stilbenes were not affected. [PERSON] et al. [37] reported an increase in trans-resveratrol content with Ca and Si treatments compared to the content of control grapes.