esa-sceva/satcom-chunk-collection huggingface dataset:
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 2 Formation Transformation in UAVs-LS
### A Dynamic Formation Transformation Algorithm  
#### 2.3.1 Graph Theory in Formation Transformation  
The communication topology between UAVs during formation transformation can be represented by an undirected graph \(G=\{V,E\}\), where \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) is the set of UAVs and \(E\subseteq V\times V\) is the set of edges representing the adjacent relationship among UAVs, i.e., edges \(e_{ij}=(i,j)\) and \(e_{ji}=(j,i)\) exist if the distance between the \(i\)-th UAV and the \(j\)-th UAV is less than \(\rho_{0}\). The adjacent matrix \(A\triangleq[a_{ij}]_{N\times N}\) contains elements \(a_{ij}=1\), if there is an edge \(e_{ij}\). A path between the \(i\)-th UAV and the \(j\)-th UAV is a sequence of edges \((i,j_{1})\), \((j_{2},j_{3}),\ldots,(j_{l},j)\). The graph \(G\) is said to be connected if there is a path for each pair of UAVs. A connected component is a subset of the graph which is associated with the smallest partition of the set of UAVs, and each partition is connected. Note that a disconnected graph has more than one component.
## 2 The Proposed Method
### Time-Frequency Compression Fusion
#### 2.1.3 Comparison of the Two Methods
The more iterations are, the closer the group delay estimation and instantaneous frequency estimation are to the time-frequency ridgeline. However, in the time-frequency image, the time-frequency ridge line forms of different signals are different. The time-frequency ridge of the SCSVF is more inclined to the horizontal state, while that of SCRVF is vice versa. To further investigate the applicable scenarios of TMSST and MSST, We define a simulation signal including fast and slow variable signals. The simulation signal \(x_{e}(t)\) is defined as follows:  
\[\frac{1}{G^{*}(0)}\mathcal{F}^{-1}\bigg{\{}\int_{-\infty}^{+\infty }TMSST^{[N]}(\tau,v)d\tau\bigg{\}}\]2  
Figure 2 shows the time-domain waveform and related time-frequency images of the simulation signal. Figure 2a,b are the time-domain waveform and short-time Fourier transform time-frequency image of the simulation signal. It can be seen that, compared with the time-domain waveform, the time-frequency image can clearly describe the change rule of signal frequency over time to better express the characteristics of the signal. Figure 2c,f are TMSST and MSST time-frequency images of simulation signals, respectively. 1 and 2 represent the two red square box regions marked in the figure. It can be observed that the frequency changes slowly in region 1, while the frequency changes quickly in region 2, where the small red arrow represents the compression direction. Time redistribution is to move the time-frequency coefficient in Figure 2b to a new position along the time axis according to the group delay estimation calculated at the time-frequency point to  
realize the conversion from Figure 2b to c. Frequency redistribution refers to moving the time-frequency coefficient along the frequency axis according to instantaneous frequency estimation, as shown in Figure 2f. By comparing Figure 2c,f, it can be seen that time redistribution has a good compression effect in region 2 but leads to time-frequency energy dispersion in region 1. Frequency redistribution is the opposite. Although the two methods have the defect of energy dispersion, their advantages and disadvantages are complementary, so we can consider combining the two methods for signal analysis.
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 2 Overview of Classifiers
### Support-Vector Machine  
The SVM classifier is frequently used in BCI research to achieve accurate classification of brain signals' features. This classifier uses kernel functions to transform the data from one dimension to another and then constructs an optimal hyperplane to separate the data classes. The transformation is called kernel trick, and it usually requires such functions as linear kernel, polynomial kernel, Gaussian radial basis function (RBF), sigmoid, hyperbolic tangent, etc. Kernel function is the most important hyperparameter of the SVM classifier,as it significantly affects the computational complexity, further data representation, and classification accuracy of the model.  
When the kernel trick is done, the optimal hyperplane can be found by solving the quadratic optimization problem. Derivative tests, such as Karush-Kuhn-Tucker (KKT) conditions, are usually applied to solve this problem [23]. SVM is frequently used for time-series EEG classification for the oddball paradigm [24]. Ensembled with convolutional neural network (CNN) and F-ratio features selection, SVM achieved 99% accuracy for 15 epochs for two different subjects [25]. Ensembled SVM with Platt scaling [26] reached 98.5% accuracy for 15 trial blocks [27].  
By comparing different kernel functions using grid search (GS) when using EEG data of healthy subjects, the most efficient kernel function turned out to be hyperbolic tangent (tanh), which outperformed the Gaussian radial-basis function (RBF), sigmoid, and polynomial functions by different degrees varying from 2 to 5. The tanh is calculated as  
\[\tanh(X_{i})=\frac{e^{X_{i}}-e^{-X_{i}}}{e^{X_{i}}+e^{-X_{i}}}, \tag{1}\]  
where \(X_{i}\) is the EEG feature vector.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 3 Multi-Parameter Optical Analysis and Design of PM Scanning Heads
### Rules-of-Thumb for the System Design  
To conclude the discussion, several rules-of-thumb can be extracted from the multi-parameter analysis to allow for choosing the constructive parameters of the PM. Thus, from Figure 2b and considering the above discussion, one should (ideally) have the entire scanning domain available to position the lens, therefore \(\theta_{1}>0\) and \(\theta_{2}<\pi/2\), as well as the scanned space situated on both sides of the O.A., therefore \(\theta_{min}<\pi/4<\theta_{max}\). Using all these conditions, one obtains for the \(\eta<1\) case, therefore with Equation (22),  
\[0<\theta_{min}<\theta_{1}<\pi/4<\theta_{2}<\theta_{max}<\pi/2\]  
which gives, using Equation (12), Table 1,  
\[\Big{|}1-x\sqrt{2}\Big{|}<tan\alpha<x<1. \tag{31}\]  
From these conditions, one obtains, for the design of PM scanning heads, the following:The transversal migration function \(y(\theta)\), Equation (7), Table 1, is characterized by the graph obtained for \(\sqrt{2}-1<x<1\) in Figure 4(a2). The other two cases of the \(y(\theta)\) function in Figure 4 do not correspond to this scanner configuration, but they can be used for the other two cases of the duty cycle in Section 3.2.  
1. The longitudinal/axial migration function \(z(\theta)\), Equation (8), Table 1, is characterized by the graph in Figure 4(b) considered for the same interval of \(x\) (i.e., for the \(x_{2}\) case).
2. The \(x<1\) interval in Figure 6(b1) is the one that applies to the angles \(\theta_{\min}\) and \(\theta_{\max}\) studied as functions of the angle \(\alpha\).
3. The conditions \(\theta_{\min}>0\) and \(\theta_{\max}<\pi/2\), which are necessary to increase the duty cycle, Equation (12), Table 1, provide \(n\geq 5\), therefore at least a pentagon PM must be used for this scanner configuration.
## 3 Test and Results  
The proposed SDKU-Net method is evaluated using the AISD dataset, which is a widely recognized benchmark in remote sensing and has been extensively used to validate numerous shadow detection models. This study compares the proposed network against three recent and validated methods: CADDN, MSASDNet, and DDSNet, which are published works focused on shadow detection in remote sensing images. Additionally, two established semantic segmentation models, U-Net and U-Net++, are included to provide a broader comparative analysis.  
It is important to note that the number of training epochs for SDKU-Net is set to 75, consistent with U-Net and U-Net++ to ensure a fair comparison. For the remaining methods (CADDN, MSASDNet, and DDSNet), the number of training epochs follows the original proposals of their respective authors, which is 100. Furthermore, MSASDNet and DDSNet include the use of data augmentation techniques as outlined in their original implementations, while SDKU-Net does not rely on data augmentation in this study.
###### Abstract  
The purpose of this study was to investigate the ooseointegration aspects of alkali-treated implants by histological and bone morphometric evaluations. Titanium implants (control) and alkali hydrothermally-treated titanium implants were used. Samples were evaluated by surface structure observation and wettability tests. Both implants were placed into the femurs of five rabbits, and ooseointegration was assessed by measurement of removal torque (RT), bone-implant contact ratio (BIC), and bone tissue area ratio (BTA). Measurements were performed at the whole portion around the implant, the cortical bone portion, and the bone marrow portion. The surface structure of alkali-group showed nanoscale pores and super hydrophilicity. RT, BIC, and BTA values of alkali-group were significantly higher than those of control-group at the whole portion. In the cortical bone portion, the BIC value was higher in the alkali-group than in the control-group, and BTA showed no significant difference between groups. In the bone marrow portion, even though no significant difference between control and alkali groups, the latter had higher ratio of BIC than the former. These results indicate that alkali treated implants enhance bone integration in areas where the implant is in contact with bone, and may promote osteoinduction in the non-bone marrow portion.  
al 2018; 8, 635; doi:10.3390/app8040635
## 3 Results
FOF texture feature maps performed well in detection of sclerotic lesions, with recognition of radiodensity, border definition, and tissue contrast in 73%, 70%, and 57% of cases, respectively, with high specificity for the chosen features of 60%, 83%, and 73%. No important refinement of recognition of sclerotic lesions was observed for CLU texture feature maps, which had low sensitivity of values of 60%, 60%, and 3% for the three radiological features. The highest sensitivity for detection of sclerotic lesions was shown for the (HEQ) RLM texture feature maps in terms of radiodensity differentiation, border definition, and tissue contrast recognition, but its specificity was not higher than that of the CLU and FOF texture feature maps. FOF texture feature maps showed good sensitivity for detection of sclerotic lesions and had better specificity than the CLU and RLM texture feature maps. Figure 4: _Cont_. The best performance in terms of recognition of the features analyzed in both sclerotic and lytic lesions was achieved for the (HEQ) RLM texture feature map, although the FOF texture feature map showed acceptable specificity for recognition of these parameters. CLU was also a well-performing  
Figure 4: Example of assessment of a periapical lesion. texture feature map for detection of lytic changes, with high sensitivity but lower specificity in comparison with the FOF texture feature map (see the comparison of parameters in Figure 6). Figure 5: Sensitivity and specificity of different texture feature maps for detection of lytic (**a**,**b**) and sclerotic (**c**,**d**) lesions. CLU, clustering with k-means approach; FOF, first-order features; GTDM, gray-tone difference matrices; HEQ, histogram equalization; LBP, local binary patterns; RLM, run-length matrices. Figure 6: _Cont._
# In Situ Transformation of Electrospun Nanofibers into Nanofiber-Reinforced Hydrogels
## 3 Results
### Interaction of Nanofiber Scaffolds with Water  
Uniaxially and coaxially electrospun scaffolds demonstrated different behavior upon hydration. While both types experienced morphological transformation from nanofibers into hydrogels with large pores, zeinPEO-GPP and zeinPEOT-GPP still contained visible nanofibers throughout the hydrogel structure that had formed during hydration (Figure 2).  
The wettability (water contact angle) experiments over 50 s on dry samples showed that the uniaxially electrospun samples G and GPP immediately absorbed the water droplet, making it impossible to measure contact angles. In comparison, contact angles for the coaxial samples zeinPEO-GPP and zeinPEOT-GPP were \(\sim\)120\({}^{\circ}\) initially. While the water droplet was slowly absorbed into the zeinPEO-GPP scaffolds, it was more quickly absorbed in the case of their drug-loaded counterparts (zeinPEOT-GPP) (Figure 3a).  
Figure 2: SEM analysis of the fiber morphology and diameter for different fiber scaffolds freshly prepared and after 24 h exposure to Milli-Q water and freeze-drying, respectively. Key: G, sample containing gelatin; GPP, sample containing gelatin crosslinked by polydopamine and polyethyleneimine; zeinPEO-GPP, sample containing zein and polyethylene oxide in the core and gelatin crosslinked by polydopamine and polyethyleneimine in the shell; zeinPEOT-GPP, sample containing zein, polyethylene oxide and tetracycline hydrochloride in the core and gelatin crosslinked by polydopamine and polyethyleneimine in the shell.  
Incubation of the samples in water for 24 h led to substantial physical swelling of all tested samples (Figure 11), as confirmed by their water uptake between \(\sim\)2500% (zeinPEOT-GPP) and 8000% (GPP) as well as volume change between \(\sim\)800% (zeinPEO-GPP and zeinPEOT-GPP) and 2000% (GPP) (Figure 2(b)). Mass losses between 40% (zeinPEOT-GPP) and 80% (GPP) were found, with the highest mass loss being detected for GPP, which also showed the highest swelling (Figure 2(b)). Moreover, a water vapor sorption of approximately 45% and 33% was found for the uniaxially and coaxially electrospun scaffolds, respectively (Table 2).  
The pore analysis revealed pore sizes of 3.0 \(\upmu\)m \(\pm\) 2.3 \(\upmu\)m (G), 9.2 \(\upmu\)m \(\pm\) 4.9 \(\upmu\)m (GPP), 36.2 \(\upmu\)m \(\pm\) 6.8 \(\upmu\)m (zeinPEO-GPP) and 26.3 \(\upmu\)m \(\pm\) 4.7 \(\upmu\)m (zeinPEOT-GPP) for the different samples after 24 h of hydration.
## 2 Materials and Methods
### Spatial Prediction and Verification Method  
Geostatistical methods are widely used to predict the spatial distribution of soil properties [25; 26; 27; 28; 29]. In this paper, we chose the Ordinary Kriging (OK) method to predict the spatial distribution of soil pH [30]. The OK method satisfies the intrinsic hypothesis, and the average value of the regionalized variables is an unknown constant [31]. OK is a linear estimation of regionalized variables, which is similar to weighted moving average in the process of interpolation research. However, the weights of weighted moving average are determined from different sources. The weighted sliding average weight values are derived from known spatial functions, while the weights of ordinary kriging are derived from spatial data analysis [31]. It is necessary to verify the prediction results after spatial distribution prediction. In this paper, an independent verification method was adopted.  
Figure 1: Overview of the study area.  
The OK interpolation results were verified. The verification method of independent data set extracts some samples from all samples as independent data sets, takes the remaining samples as simulation data sets without repetition, and takes each sample in independent data sets as an inspection point [32]. In this paper, after 797 samples were extracted from 908 samples, the remaining 111 samples were taken as independent verification sets. The spatial prediction results of 12 sample sets were verified. In the verification method of the independent data set, the most representative evaluation indexes are root mean square error (RMSE), mean error (ME) and average standard error (ASE), which were chosen to evaluate the accuracy of prediction.  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{n}\bigl{[}\;Z(\text{X}_{i})-Z^{\prime }(\text{X}_{i})\bigr{]}^{2}} \tag{1}\]  
\[\text{ME}=\frac{1}{N}\sum_{i=1}^{n}\bigl{[}\;Z(\text{X}_{i})-Z^{\prime}(\text{ X}_{i})\bigr{]} \tag{2}\]  
\[\text{ASE}=\sqrt{\frac{1}{N}\sum_{i=1}^{n}\bigl{[}\;Z^{\prime}(\text{X}_{i})- \;\sum_{i=1}^{n}\bigl{(}Z^{\prime}(\text{X}_{i})\bigr{)}/N\bigr{]}^{2}} \tag{3}\]  
where \(N\) is the number of known samples, the actual value is \(Z(\text{X}_{i})\), and the estimated value is \(Z^{\prime}(\text{X}_{i})\).  
Figure 2: Distribution map of soil pH at different point sets. The letters (**a**–**l**) represent the spatial variation expression of soil pH at 908, 797, 700, 594, 499, 398, 299, 200, 149, 100, 75 and 50 sampling points.  
The smaller the RMSE, the closer the ME to zero, indicating that the accuracy of spatial prediction is higher. The average standard error was used to measure the uncertainty of the Kriging prediction value.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### HetNet Deployment with Small Cells near Macro Cell Edge
#### 3.4.1 Transmit and Receive Power  
The expressions for the transmit and receive power of the macro and small cells were taken from Equations (2) and (3).
# Experimental Characterization of the Capacitively Coupled RF-Plasma Thruster
## 1 Introduction  
In-space electric propulsion is becoming a more and more popular instrument for spacecraft orbit correction, orbit sustaining, and even larger \(\Delta\)V maneuvers. While standard types of thrusters, such as Hall Effect thrusters, gridded ion thrusters, and arcjets are constantly improving and are already being used in various space missions, space agencies are interested in disruptive technologies for electric propulsion as well. Recent successes in the development of neutralizer-free electric propulsion technologies, for example the electron cyclotron resonance thrusters (MINOTOR [1, 2], AQUAJET [3]), show the potential of ion acceleration in expanding plasmas. The acceleration is supposed to take place in the electric field formed in the plasma plume. The formation of this field is attributed to an ambipolar field created by plasma expansion [4, 5].  
Two possible mechanisms of RF power coupling to a plasma exist. The inductive (ICP) and capacitive (CCP) coupling result in different plasma properties. While the ICP delivers higher plasma densities, the CCP has the benefit of easier plasma ignition. In certain configuration the so called self-biasing effect in CCP plasmas takes place when one of the electrodes gets charged to several hundred volts relative to the plasma. This creates an ion flux of the same energy to this electrode and is usually employed for ion bombardment purposes in plasma etching devices. It is also possible to use this effect in electric propulsion, for example as described in [6] where a combination of ICP and CCP plasmas is used. In CCP plasmas two types of heating mechanisms for electrons exist--collisionless and ohmic heating [7, 8, 9] the former dominating at lower and latter at higher gas pressures. The transition between these two modes of energy transfer is also accompanied by an increase in the role of secondary electrons in the sustainment of the discharge [7, 8, 9]. These electrons which are created by the ion bombardment of the electrode surface are called \(\gamma\)-electrons. The corresponding transition is sometimes called the transition between \(\alpha\) and \(\gamma\) modes of a CCP discharge with the \(\gamma\) mode beingcharacterized by an increased value of electron density [9]. It appears that the investigated design is also affected by this mode change.  
The presented paper investigates the new thruster C-STAR based on the CCP mode of an RF discharge operating at small powers of \(\sim\)20 W and moderate mass flows. According to the results of the first measurements this thruster is able to create ion beams accelerated to energies of 10 eV.
## 2 Routing Goals
### Fast Delivery  
Timely delivery of data is required in many sensor network applications such as real-time target tracking in battle environments and emergent event triggering in monitoring applications. In such applications, latency cannot be tolerated and data should be delivered within a certain period of time from the moment they are sensed; otherwise, the data will be useless. Also, bounded latency for data delivery is another condition for time-constrained applications [45].  
In some real-time applications, data delivery is done based on a deadline by which data should be received at the BS. In this case, any message which received at the BS after the deadline is ignored. However, in some other real-time applications, there is no specific deadline for data delivery and a portion of data can be received at the BS after the deadline. In this situation, the part of data which is received after the deadline is considered the _miss ratio_, and some data routing approaches only focus on reducing the _miss ratio_ by prioritizing the time-critical messages. Additionally, it should be noted that the principal goal of data routing approaches for these application is to control the network delay, but simultaneously they try to reduce energy consumption in transmission, in order to extend the network lifetime as much as possible under the delay constraints [11; 46; 47; 48].
## 1 Introduction
Its most developed application has focused on the disinfection and elimination of bacteria and viruses, although the effect is strongly dependent on the nature of the microorganism. Different trials evidenced that in the presence of EW, _Escherichia coli_ population underwent a significant reduction (4.83 log) in colony-forming units (CFUs) at FAC of 43 ppm in only 1 min [18], and even the _E. coli O157:H7_ strain had a notable reduction in 3 min with FAC of 1.5 ppm [1]. _Salmonella_ spp. also showed high susceptibility to EW, but _Listeria innocua_ needed FAC of 89 ppm to achieve the same reduction (5 log) [12; 19]. Additionally, research on efficacy against viruses, including the avian flu virus, has yielded highly promising results. In fact, a reduction of up to 99% was achieved using a sample of EW with FAC of 10 ppm [20]. Although it is a less-explored area, EW has demonstrated its promising application in aquaculture. Hypochlorous acid at 25 ppm reduced the concentration of the bacterial pathogen _Vibrio vulnificus_, which causes warm water vibriosis in fish, by 5 log units [21]. These results suggest that EW could find versatile applications in various industrial processes, clinical environments, surface cleaning, among others. _V. harveyi_, a Gram-negative marine bacterium, stands out as a significant pathogen for different marine organisms of economic relevance, such as fish, mollusks, and crustaceans [22, 23]. Due to climate change, it has become an aquaculture-emerging pathogen causing economic and biological losses. Moreover, in the last three decades, coinciding with the rapid development of shrimp aquaculture, _V. harveyi_ has risen as a significant factor in the occurrence of diseases, causing luminous vibrioisis and other syndromes with massive mortality in larvae and adult shrimps, especially in regions of South America and Asia [22, 24]. The abundance of _V. harveyi_ in water and aquatic biofilms is another factor that can exacerbate the development of the disease in different cultured fish species such as seabass [25, 26]. Building on previous studies, we hypothesize that electrolyzed water (EW) could serve as an effective disinfection tool in aquaculture systems to control significant diseases like vibrioisis caused by _V. harveyi_, a major threat to both invertebrate and vertebrate marine animals. Our aim is to optimize chlorine concentrations, reducing them to levels suitable for future in vivo applications, ensuring the effective elimination of bacteria without negatively impacting marine species.