esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Atmospheric Effect of the Proposed Method  
The abovementioned impervious surface distributions were extracted on the basis of the DN values in the images.  
When electromagnetic waves are transmitted across the atmosphere, they are scattered because of molecules and minute particles; the shorter the wavelength is, the stronger the scattering is, which causes radiation distortion in the coastal and blue bands of Landsat 8 images. Atmospheric correction of images is usually required in order to obtain more realistic surface reflectivity, but this is often a cumbersome process.  
Figure 10: NDBI values for impervious surfaces and bare soil.  
As a comparison, the case images were corrected according to the FLAASH model in ENVI, and reflectance values that were less than 0 or greater than 100 were modified according to Equation (16):  
\[g(x,y)=\left\{\begin{array}{rl}0&f(x,y)<0\\ f(x,y)&0\leq f(x,y)\leq 100\\ 100&f(x,y)>100\end{array}\right. \tag{16}\]  
where \(f(x,y)\) and \(g(x,y)\) represent the images before and after modification, respectively.  
The impervious surface distributions extracted after atmospheric correction are shown in Figure 11. From a comparison with Table 1, impervious surfaces were less widely distributed, and the average recall, precision, and F1 score were 89%, 77%, and 83%, respectively, which represented decreases by 6%, 14%, and 10%, respectively.  
Atmospheric correction did not provide additional information that could supplement the results based on the DN, but instead resulted in more mis-extraction and losing results.  
Taking Case 3 as an example, Figure 12 shows the difference between the impervious surface distribution extracted before and after atmospheric correction, where (a) is a false colour composite of the (5, 4, 3) band and (b) is an RGB composite of the impervious surface distribution extracted before atmospheric correction, ground truth for the impervious surface distribution and the impervious surface distribution extracted after atmospheric correction. Here, the white areas were extracted in both instances, the green areas were lost in both instances, the purple areas were mis-extracted in both instances, the yellow areas were lost after atmospheric correction, and the blue areas were only mis-extracted after atmospheric correction.  
Actually, atmospheric correction using an atmospheric correction model is not always necessary and may decrease the accuracy of the extraction of remote sensing information [41]. With respect to our three case images, atmospheric correction did not help to improve the accuracy of the extraction of impervious surface distribution.  
Figure 11: The impervious surface distribution extracted by the proposed method after atmospheric correction: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3.
###### Abstract  
During impressions, bacteria, viruses, and fungi remain on the impression material, representing a significant risk for the medical team (dentists, dental assistants, and laboratory technicians). Impression disinfectants have been introduced into dentistry to reduce the risk of cross-infection. This study was performed by examining the surface disinfection of five commonly used impression materials in prosthodnics: alginate-Tropicalgin ([PERSON](r)), condensation silicone-Zetapilus ([PERSON](r)), Oranwash L ([PERSON](r)); and addition silicone-Elite HD + Putty Soft ([PERSON](r)), Elite + Light Body ([PERSON](r)) after the disinfection with three disinfectants: Zeta 3 Soft ([PERSON](r)), Zeta 7 Spray ([PERSON](r)) and Zeta 7 Solution ([PERSON](r)). Before disinfection, the impression materials were contaminated with _Staphylococcus aureus_ ATCC 25923, _Pseudomonas aeruginosa_ ATCC 27853, and _Candida albicans_ ATCC 10213. Our results demonstrate the efficacy of the two examined specific disinfectants in reducing cross-infection risk. The surface disinfectant-spray is not adequate for impression disinfection. A high number of bacterial colonies were found on the surface of all impressions disinfected with this spray. The immersion-disinfection method effectively eliminates the risk of cross-infections (Kruskal-Wallis test showed a \(p<0.001\)).  
infection prevention; cross-infection; disinfectants; dental impression 202313  
to reduce the risk of cross-infection. Disinfection of impressions, before transport to the dental laboratory, is essential [6]. Sometimes the disinfection of the impressions is carried out with surface disinfectants, which can cause surface modifications and dimensional changes, resulting in inadequate restorations [7,8]. The British Dental Association recommends rinsing the impressions with water after the removal from the oral cavity. A large percentage of microorganisms can persist on the surfaces of the materials. This method is not adequate for preventing cross-infections [9,10]. An adequate disinfection method must consider the solution type, concentration, and action time [11].  
The aim of the study is to compare three commonly used disinfectants and their antimicrobial effect on impression-material surfaces.
# RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction
## 2 Related Work
### Modern point cloud upsampling  
Modern point cloud upsampling methods have emerged in recent years that are unsupervised or arbitrary magnification factor point cloud upsampling methods. These methods do not require paired datasets as training data or can upsample point clouds at arbitrary rates [28; 29].  
In the context of unsupervised point cloud upsampling, some methods have been proposed to generate point clouds without the need for paired datasets. [PERSON] et al. introduced a self-supervised point cloud upsampling method SSPU-Net based on differentiable rendering [30]. By leveraging image consistency as a supervisory signal, the network parameters are updated through differentiable rendering. SSAS [31] views point cloud upsampling as the task of finding projection points on the implicit surface. The approach selects seed points, projects them onto the original surface, fits the projection direction and distance using two preceding subtasks, and achieves arbitrary-rate upsampling through furthest point sampling. SPU-Net [32] presents an upsampling framework from coarse to refined, involving downsampling the original point cloud, recovering the original point cloud through feature extraction and expansion, and introducing a self-projection loss to reduce noise in the generated point cloud.  
In the context of arbitrary magnification factor point cloud upsampling, some methods have been proposed to upsample point clouds at arbitrary rates using a single model. Inspired by Meta-SR [33] in image super-resolution, [PERSON] et al. proposed Meta-PU [34], a method that dynamically adjusts the upsampling network's weights based on different magnification factors using a sub-network. [PERSON] et al. introduced a novel point cloud representation called Neural Points [35], where each point represents local continuous geometry through a neural field, offering enhanced representational capacity. [PERSON] et al. integrated normalizing flows and point cloud upsampling in PU-Flow [36]. Leveraging the reversible nature of normalizing flows, PU-Flow achieves transformations between 3D Euclidean space and feature space. [PERSON] et al. propose PU-INN [37], a novel reversible residual neural network that allows for an unconstrained architecture design to learn more expressive feature transformations. Grad-PU [38] interpolates low-resolution point clouds based on the given upsampling rate and refines the interpolated point positions through iterative optimization.  
In this paper, we propose a novel point cloud upsampling method, named RE-PU, which is based on point cloud reconstruction, achieving self-supervised upsampling at arbitrary rates.
## 4 Result Analysis
### Analysis of the Document Collection
#### 4.1.3 Document Publication Frequency Analysis  
Figure 6 depicts the annual scientific production. Based on the annual growth rate (25.35%), an increased interest in the specific topic is observed. The year 2023 was the one with the most published documents, followed by 2024. Even though there is a slight decrease in the documents published in 2024, due to the emergence of these technologies and methods and their being more accessible and more widely used, it is expected that the research around them will increase in the near future as the topic is still in its infancy.
## 4 Experimental Results  
The operation of the proposed reversible data-hiding scheme was analyzed by testing its results on the \(USC-SIPI\) dataset [57]. The \(USC-SIPI\) dataset is a digitized image data set provided by the University of Southern California which contains 210 images. Of these 210 images, 38 are aerials, 69 are sequences, 64 are textures and 39 are miscellaneous images. Here, during the testing, we considered the block size for all the images as 8, i.e., \(A=8\), and all the results are for the same block size. The experimental study was performed on a computer with processor Intel(R) Core(TM) i5-9300H CPU @ 2.40 GHz, and 8.00 GB installed RAM. The operating system was a 64-bit operating system. The simulations were carried out using the MATLAB software tool, version R2019a.
## 3 Results and Discussion
### Effects of SGS Severity on Glottal Flow Dynamics, Vocal Fold Vibration, and Acoustics
#### 3.2.3 Acoustics  
The signal-to-noise ratio, sound intensity, and vocal efficiency for each SGS severity case were computed based on the sound pressure and spectrum. These quantities are plotted against the severity level in Figure 11. These figures show, again, negligible differences of these quantities up to a 75% SGS severity. Beyond this threshold, SNR decreases by 0.6, 3.5, and 7.1 dB, and sound intensity decreases by 0.93, 3.34, and 15.1 dB at 90%, 96%, and 99% SGS severity, respectively. Most significantly, the vocal efficiency decreases by 38%, 82%, and 99.9% at the SGS severity of 90%, 96%, and 99%, respectively, suggesting a significant increase of vocal effort when the area reduction is extremely large.  
Figure 11: The signal-to-noise ratio, sound intensity, and vocal efficiency for different SGS severities.  
Figure 10: (**a**) The energy percentage of the two dominant proper orthogonal decomposition (POD) modes of the vocal fold vibration versus SGS severity. (**b**) The similarity of the corresponding POD modes of the vocal fold vibration versus SGS severity. (**c**) The mid-coronal profile of the vocal fold in the two dominant POD modes in the baseline case (solid lines) and the 99% SGS severity case (dashed line). The two subfigures of each mode show the two extreme phases of the mode. (**d**) The fundamental frequency of vocal fold vibration versus SGS severity.
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 5 Phase Estimation of Noisy Coherent State
### Distributed Outlier Quantum States  
We will now consider a more general setting where outlier quantum states are distributed around the vacuum state of the form Equation (30). Outlier quantum states are generated according to the normal distribution as \(\alpha_{R}\sim N(0.1,0.1)\), \(\alpha_{I}\sim N(0.1,0.1)\) with a fixed value of dispersion \(\kappa_{0}=0.1\). We set the true coherent state as \(\alpha=10-4i\) in this example. The true phase value is \(\theta=\arctan(-0.4)\simeq-0.3805\). In Figure 6, we show the performances of the M-estimators together with the sample mean and the median for the contamination parameter \(\varepsilon=0.01\). The same convention as in Figure 2 is used. From Figure 6, we observe that the bisquare and gamma M-estimators performed well compared to other estimators. In this class of outlier quantum states, the median seems to be a bad choice to use. In fact, its performance was worse than that of the sample mean.  
Finally, we analyzed the robustness of M-estimators by the \(\varepsilon\)-curve. In Figure 7, we plot the \(\varepsilon\)-curve for phase estimation of the sample size \(n=5000\). From this result, we found that the gamma M-estimator was most robust and the bisquare M-estimator was second. The sample mean was also found to be relatively robust with small fluctuation. However, this behavior in fact depends on the nature of outlier quantum states as discussed in the next subsection. Lastly, the result of the median showed that it is not trustable for the distributed outlier quantum states of this kind.
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 3 Results
### Evaluation of the Proposed Fuzzy Based KAQI Prediction Model  
In this section, the proposed fuzzy inference rules that were used in the output evaluation of the proposed fuzzy based KAQI prediction model are captured as shown in Figure 8. The \"AND\" connector is applied in designing the inference rules and it is important in determining the minimum concentration levels of the estimated Air Quality Index, given a set of fuzzy input parameters as already discussed.  
In the model evaluation, all the fuzzy inference rules are considered to have an equal weighted priority function (W = I). This means that all rules have equal priority during model evaluation and therefore, the order does not matter.  
Figure 4: NO\({}_{2}\) input variable.  
Figure 5: SO\({}_{2}\) input variable.  
Figure 6: PM\({}_{2.5}\) input variable.  
Figure 7: KAQI output variable.
# Influence of the Polarity of the Electric Field on Electrorheometry
## Materials and Methods
### Rheometry
#### 2.2.2 Capillary Breakup Extensional Electrorheometry
All the components of the experimental setup are indicated in Figure 3.  
In the experimental procedure, the fluid was first loaded between the plates, which were separated by the initial height \(h_{0}\). Then, the voltage was applied (corresponding to an electric field of \(E_{0}=\Delta V/h_{0}\)), and the CaBER was ordered, without delay, to trigger the step strain deformation. [PERSON] et al. [23] showed that the delay between applying the electric field and triggering the experiment plays an important role in CaBEER results. The difference in density between the particles (\(\rho=1600\) kg/m\({}^{3}\)) and the carrier fluid (\(\rho=917\) kg/m\({}^{3}\)) can be a problem in the CaBER experiment. From the Stokes equation, the sedimentation time taken by a particle to move from the midpoint of the filament (\(h_{0}/2\)) to the bottom plate was 275 s. Once the sample was loaded, the field was immediately imposed,  
Figure 3: Experimental setup: (1) high speed camera, (2) CaBER device, (3) Telecentric Backlight Illuminator, (4) light source, (5) voltage generator, and (6) ExERF ([13]). the stretch profile was imposed (maximum delay smaller than 5 s), and the thinning process of the filament was recorded. The whole process, from loading the sample until the end of the thinning, lasted less than 10 s. Therefore, the sedimentation time was more than 20 times the duration of the experiment, and the dispersion could be considered stable enough for the characteristic time of the CaBEER experiments, with the possibility of the appearance of a particle gradient in the sample being ruled out before starting the experiment. Another relevant aspect to consider is the relationship between field strength and the thermal fluctuations of the particles. This ratio was studied by [PERSON] and [PERSON] [6], who defined a dimensionless parameter \(\lambda=\frac{\pi\epsilon_{0}\epsilon c^{3}\beta^{2}E_{0}^{2}}{2 kT}\), where \(\epsilon_{0}\) is the permittivity of free space, \(a\) the particle diameter, \(\epsilon_{c}\) the relative dielectric constant of the continuous phase [24], \(\beta=\frac{\sigma_{p}-\sigma_{c}}{\sigma_{p}+2\sigma_{c}}\)(dc field), \(\sigma_{p}\) and \(\sigma_{c}\) are the electrical conductivities of the particulate and the continuous phases [25], \(E_{0}\) the applied electric field strength, \(k\) the Boltzmann constant, and \(T\) the absolute temperature. Hence, if \(\lambda\gg 1\), strong electric interactions are identified, while if \(\lambda\ll 1\), the Brownian motion dominates. For the experimental conditions and the working fluids considered in this study, it could be confirmed that \(\lambda\gg 1\), which was representative of a structure possessing sizeable yield stress and typical shear viscosity orders of magnitude larger than that of the solvent at low shear rates.
# A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
## 2 Materials and Methods
### Coordinate Attention
The attention map \(M\) is derived from the channel information and height information, and the attention map \(N\) is derived from the channel information and width information. With the two attention maps, the detection method can obtain guidance for feature extraction in both the channel dimension and the spatial dimension. \[f_{t}=\delta(\theta_{t}([m,n])) \tag{10}\]  
\[M=\sigma\Big{(}\theta_{h}\Big{(}f_{t}^{h}\Big{)}\Big{)} \tag{11}\]  
\[N=\sigma(\theta_{w}(f_{t}^{w})) \tag{12}\]  
Finally, the input \(f_{in}\) is reweighted using the attention maps \(M\) and \(N\). The detailed reweighting process is shown in Equation (13), where \(f_{out}(\cdot,\cdot,\cdot)\) denotes the output value of the specified spatial location in each channel, \(M(\cdot,\cdot)\) denotes the weight for the input value at the specified channel and height, and \(N(\cdot,\cdot)\) denotes the weight for the input value at the specified channel and width. \[f_{out}(c,i,j)=f_{in}(c,i,j)\times M(c,i)\times N(c,j) \tag{13}\]  
The horizontal and vertical pooling operations described above are implemented by the AdaptiveAvgPool2d function in the pytorch library. The rest of the operations of the coordinate attention are implemented using common operators in the pytorch framework. We embed the coordinate attention process into all classical residual blocks and lightweight sandglass blocks in our crop pest detection method to guide the feature extraction process and better learn the feature representation. To ensure that the coordinate attention process obtains sufficiently rich pest features to play a more accurate guiding role, the coordinate attention block is embedded after the convolution used for dimension expansion in the classic residual block. Meanwhile, following the description of coordinate attention, the coordinate attention is embedded after the first depthwise convolution in the lightweight sandglass block. The classic residual block and the lightweight sandglass block with the embedded coordinate attention block are named Bottleneck-CA and LightSandGlass-CA, respectively, and their structures are shown in Figure 7b,c.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 2 Related Work
### Low-Power Wide-Area Network (LPWANs)  
LoRaWAN stands out for its ability to support long-range communications and low power usage, crucial for developing efficient wide-area networks as demonstrated by [12], in their irrigation control system. However, the authors in [13] argue that because of LoRaWAN's slow update rate, it is less suitable for real-time control.  
Similarly, Sigfox is also known for its long-range communications and low-power consumption, as showcased by [14] in their smart trash bin system. Despite its benefits, Sigfox's limited data transmission and vulnerability to interference may hinder its suitability for dynamic, real-time applications [15].
## 5 Case Study: Smart Energy Use Case
### VICINITY Bridge  
To also demonstrate the functionality of the hardware-in-the-loop interface to the real VICINITY network, the use case was extended with a VICINITY bridge on the lowest abstraction level. As discussed in Section 1.1, VICINITY offers an infrastructure for connecting otherwise isolated islands in the IoT landscape. Naturally, our simulated network can be seen as such an island. As outlined in Section 4.4.2, we can integrate VICINITY and our simulation seamlessly by introducing a VICINITY adapter to the Omnet++ simulation. To VICINITY and all its connected devices and value-added services, all simulated devices that exist only virtually within our simulated network will appear as any other ordinary device. To our value-added service, they look and behave the same way as any physical device would, which enables us to:  
* rapidly prototype any given scenario or use case and test it without first deploying devices in the field; and
* scale any existing use case up and test how it will behave with more attached devices  
As installing and deploying new sensors on our parking deck outline in Figure 6 is costly and time consuming, this gives us a much quicker way to start and continue implementing our front-end application, while this deployment takes place in the real world. Furthermore, we can utilize this virtual setup to evaluate different approaches for our enhanced privacy modules based on homomorphic encryption. In the following, we utilize this setup to measure the impact of different homomorphic encryption schemes on the overall runtime behavior in a real, online test scenario.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems
### Algorithm Development
#### 4.3.3 Mutation  
Mutation is an operator that inserts random changes into the new individuals generated by crossover. The mutation generates a random array of variables and inserts them into the passing times of the trains for an individual. In this paper, we introduce the extended-line mutation, which can generate the individual with the best diversity. Equation (14) states the extended-line mutation:  
\[\left\{\begin{array}{l}\mathbf{X}_{mut}=\mathbf{X}_{ori}+F*(\mathbf{X}_{best}-\mathbf{X}_{ ori}),\textbf{ IF}\ 0<P_{xline}<\frac{1}{3}\\ \mathbf{X}_{mut}=\mathbf{X}_{ori}+F*(\mathbf{X}_{ori}-\mathbf{X}_{best}),\textbf{ IF}\ \frac{1}{3}<P_{xline}<\frac{2}{3}\\ \mathbf{X}_{mut}=\mathbf{X}_{best}+F*(\mathbf{X}_{best}-\mathbf{X}_{ori}),\textbf{ IF}\ \frac{3}{3}<P_{xline}<1\end{array}\right. \tag{14}\]  
where \(\mathbf{X}_{ori}\) is the original individual made by crossover, and \(\mathbf{X}_{mut}\) is the individual generated by the mutation operator. \(\mathbf{X}_{best}\) is the best individual from the last generation, and \(\mathbf{F}\) is a scaling array with the same size as individual \(\mathbf{X}_{ori}\), and the elements in \(\mathbf{F}\) are chosen from \((0,\,1]\). \(P_{xline}\) is a probability variable, which is chosen from \((0,\,1]\).