esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Approach and Associated Methods
### Addressing Offsets in Empirical Equation Results
The program, then, processes one row at a time (i.e., Row 1 to Row \(n\) in Figure 3), where each row has two main parts; the first is the original value (e.g., Value 1 in Figure 3) of the recorded visibility, and the second part is a corresponding row of  
Figure 3: Breakdown of tabulated data for coding interpretability. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **E1** & **E2** & **E5** & **E3** & **E4** \\ \hline KSA [17; 18] & 35.93 & 22.55 & 2.10 & 4.42 & 4.18 \\ USA [3; 19] & 167.51 & 134.92 & 20.07 & 15.04 & 12.69 \\ North-East & 2.87 & 0.38 & 1.87 & 2.37 & 2.35 \\ North-East & 101.54 & 82.02 & 18.81 & 3.58 & 11.14 \\ Asia [14; 20] & 58.26 & 40.66 & 7.40 & 9.81 & 5.71 \\ Australia [15] & 107.41 & 93.61 & 19.94 & 7.72 & 6.35 \\ \hline Average & 78.92 & 62.36 & 11.70 & 7.16 & 7.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance (RMSE) of the equations in Table 1 compared against recorded visibilities using the TSP values from sources in Table 2.  
calculated values (Value 1-1 to Value 1-5) according to each equation (E1 to E5). The steps are repeated for the subsequent rows, and stored values are retrieved from the arrays via the included counters (_colcounter_ and _resultsCol_). Each row is processed to locate the two most suitable (i.e., lowest percentage error with reference to an original value) averages of the summed empirical equations. Every empirical equation value is initially set as a constant and then summed and averaged with every other equation value in the same row. For example, the program starts with _Value 1-1_ as a constant, then it is summed and averaged with each remaining value (_Value 1-2_ to _Value 1-5_). Finally, the results are calculated for the percentage of error against the original value (_Value 1_) as follows:  
\[\textit{Percentage of Error}=\left|\frac{\frac{(value_{n-c})+(value_{n-c})}{2}}{ Value\ n}-1\right|*100, \tag{18}\]  
where \(n\) indicates the row number to point to the values, \(c\) is the number (1 to 5) to set the program to the corresponding value as a constant until it has been processed for percentage of error with the other values, and \(v\) is a varied number (1 to 5) to point to values to be summed with the currently set constant. Figure 4: Flow chart of the designed computer program.
## 3 Results and Discussion  
### Surface Chemistry  
The solid-state \({}^{13}\)C CP/MAS NMR spectra for the MIP and NIP are shown in Figure 3. The baseline was adjusted, and the relevant peaks were identified between 0 and 200 ppm spectral regions. There seemed to be no dissimilarities between the chemical shifts and signal intensities of the polymers. This suggested that the polymer materials are chemically the same. Resonances were observed corresponding to the various methyl groups represented by the wide peak at 23 ppm. Other methylene groups were found to correspond to the cross-linker agent used at 47 and 65 ppm, as well as the carbonyl CO\({}_{2}\)R group observed at 175 ppm. Given the kind of polymer synthesis and mechanism of imprinting, which involves a considerable quantity of EGDMA and 2-VP, these results were anticipated. All signals could be assigned in accordance with the estimated polymer design, and the appropriate peaks matched the results from previous studies [30; 31; 32]. [PERSON] et al. [13] reported the same chemical shifts as those used in the process for the analysis of MIP and NIP of NSAIDs and used the same functional monomer. At the same time, carbonyl clustering in CO\({}_{2}\)R is clearly visible at the far end, which is consistent with what is found in the literature [30; 32].  
Figure 4 shows FTIR spectra of the synthesized and washed MIP and NIP. It can be observed that the IR spectra of the polymers are very similar in terms of bands, positions, and shape, which is consistent with the fact that both polymers were created using the same monomer, cross-linker, and initiator. The interface between templates and monomer provided variable peaks in the spectra, with MIPs exhibiting a wide OH stretching vibration peak at 3500 cm\({}^{-1}\), the OH formed a bond that overlaps with that from the NH from the MIP, and this peak slightly smooths out in the NIP. These peaks are associated with the carboxylic group (COOH) of methacrylic acid. Because of the methylene group in 2-VP and  
Figure 3: Solid-state 13C CP/MAS NMR spectra for (**a**) the MIP and (**b**) the NIP. * Denotes solvent peaks.  
EGDMA, the -CH\({}_{2}\) stretching peak was also seen at 2900 cm\({}^{-1}\). At 1700 cm\({}^{-1}\), the carbonyl group C=O stretching peak was seen in both MIP and NIP, which might have arisen from the template and cross-linking molecules. Weak bands from 1600 cm\({}^{-1}\) to 1200 cm\({}^{-1}\), as well as the sharp band at 1100 cm\({}^{-1}\) in the MIP spectrum, indicate the existence of an aromatic ring from the 2-VP. All the notable peaks matched the data from previous studies [30], where they reported the same stretchings and the presence of these functional groups around the same wavelength.
# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 3 Materials and Methods
### Experiment and Analysis
#### 4.4.2 Experiment Comparing Attentional Mechanisms  
From Table 3, it can be observed that adopting MobileNetV2 as the backbone network undoubtedly reduces the model parameters and GFLOPs while improving the recognition speed. However, both the MIoU and MPA show a decreasing trend compared to the commonly used original backbone network in DeepLabv3+, ResNet50. In order to maintain the lightweight effect at the network level by retaining MobileNetV2 as the backbone network while improving the accuracy of model recognition, we conducted experiments by introducing various attention mechanisms at the same position in the network.  
We selected representative attention mechanisms for integration, including Efficient Channel Attention (ECA), a Squeeze-and-Excitation (SE) block [38], a Convolutional Block Attention Module (CBAM) [39], and Coordinate Attention (CA) [40]. These attention mechanisms were embedded at the same location within the backbone network to explore their impact on model performance.  
The SE mechanism, through the introduction of a \"Squeeze-and-Excitation\" block structure, explicitly learned relationships between channels, resulting in more effective attention allocation. ECA focused primarily on channel dimensions, allowing the network to concentrate more on crucial features in the image, enhancing its perception of global information. The CBAM comprehensively considered both spatial and channel attention, capturing key information in the feature map. The CA attention mechanism encoded precise positional information in the neural network, aiding in modeling channel relationships and long-term dependencies.  
The comparative experiments from Table 4 reveal that ECA demonstrates the most significant improvement in the MIoU when employed as the backbone network in the MobileNetV2-based model, showcasing its remarkable performance. Therefore, we opted for ECA as the attention mechanism for our model in this paper, aiming to further enhance the model's performance. Regarding the MIoU parameter rather than the MPA, the introduction of the ECA attention mechanism leads to a 0.52% increase in the MIoU for models switched to lightweight backbone networks. The MIoU serves as a metric for measuring the degree of overlap between predicted results and ground truth labels in semantic segmentation models, whereas the MPA predominantly focuses on the accuracy of predicted results. In the comparison provided in Table 4, we place greater emphasis on the impact on the MIoU because it not only considers the accuracy of predicted results but also consistency with ground truth labels. Therefore, in the comparison of these attention mechanisms, the MIoU holds more reference value.
## 4 Case Study of Interworking of QKD Networks and the Role of SDN Orchestrator
### Implementation Results of SDN Controller and SDN Orchestrator for KOREN
#### 4.2.1 General Configuration and Overview of the Use Case
(**a**) Link between Seoul and Pangyo. (**b**) Link between Seoul and Suwon. (**c**) Link between Suwon and Pangyo. Figure 5: A use case for the interoperable key management with a border node, i.e., Site 2, between two different QKD networks with each different vendor. Examples of Site 2 are border nodes in Pangyo and Daejeon in Figure 1.
# Magnetic Properties of Gd-Doped Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
## 3 Results and Discussion
### Size Dependence of the Magnetization and Curie Temperature  
At first, we will present numerical calculations of the dependence of the spontaneous magnetization \(M_{s}\) and the magnetic phase transition temperature \(T_{\rm C}\) on the size of Fe\({}_{3}\)O\({}_{4}\) NPs. Let us emphasize that the lattice parameters of the NPs increases slightly compared to the bulk value [26].  
The results are presented in Figures 1 and 2. With a decrease of the size of the NP, \(M_{s}\) and \(T_{\rm C}\) decrease. A similar decrease of \(M_{s}\) in Fe\({}_{3}\)O\({}_{4}\) NPs is observed experimentally by [PERSON] et al. [27]. Let us emphasize that \(M_{s}\) decreases with increasing temperature and vanishes at \(T_{\rm C}\). Moreover, \(M_{s}\) and \(T_{\rm C}\) depend on the spin value \(S\) and the exchange interaction constants \(J_{s}\) and \(J_{b}\). A similar effect, a decrease with decreasing \(d\), is observed also for the Curie temperature \(T_{\rm C}\) (see Figure 2). We obtain \(T_{\rm C}\approx\) 690 K for \(d\) = 20 nm and \(T_{\rm C}\approx\) 600 K for \(d\) = 10 nm. The decrease is due to size and surface effects becoming predominant as the ratio surface to core volume increases. Below \(d\) = 10 nm, we have a superparamagnetism. It must be mentioned that [PERSON] et al. [27] obtained a \(T_{\rm C}\) value of 665 K for \(d\) = 10 nm for Fe\({}_{3}\)O\({}_{4}\) NPs, whereas [PERSON] et al. [28] reported \(T_{\rm C}\) = 713 K for \(d\) = 12 nm. Using Monte Carlo simulation, [PERSON] et al. [20] calculated for thin magnetite films a \(T_{\rm C}\) increase with increasing film thickness.  
Figure 1: Size dependence of the spontaneous magnetization \(M_{s}\) of a pure Fe\({}_{3}\)O\({}_{4}\) NP, \(J_{s}=0.9J_{b}\).  
Qualitatively, these results can be explained as follows: with decreasing the NP size, the surface area/volume ratio increases, i.e., the influence of the surface on the structural and magnetic characteristics increases. The oxidation, broken bonds at the surface, unpaired electron orbitals, and increased surface anharmonism lead to reduction of the net magnetic moment. Therefore, as the influence of the surface grows (i.e., the diameter of the magnetic NP decreases), the total magnetization become smaller. The decrease of the phase transition temperature with size reduction of the NP can be qualitatively explained by the fact that, with decreasing the size, the volume of the elementary cell increases, which leads to an increase in the distance between the spins. To the extent that the exchange interaction is highly sensitive to the distance between the interacting magnetic ions, this will lead to a decrease of the exchange interaction in the shell and at the surface of the magnetic NP and to a reduction of \(T_{C}\). The observed qualitative coincidence with the experimental results is evidence that the presented microscopic model of magnetic NPs adequately represents and explains the main magnetic characteristics of Fe\({}_{3}\)O\({}_{4}\) NP and their dependence on surface and size effects.
## 2 Numerical Models
### Comparison of the Numerical and Analytical Model for the Circular Tunnel Case Study
#### 2.3.1 Effect of the Peak Horizontal Seismic Acceleration (a\({}_{\rm H}\))  
A parametric study was conducted to investigate the seismic loading magnitude effects represented here by the maximum horizontal acceleration, \(a_{H}\). The maximum horizontal acceleration is varied in the range between 0.05 and 0.75 g, corresponding to the respectively maximum shear strains, \(\gamma_{max}\), of 0.038 and 0.58%. The other parameters of the reference case in Table 1 are used. The following conclusions can be deduced from Figure 5:  
For both the no-slip and full-slip conditions, numerical results show good agreement with the analytical solution. A discrepancy of approximately 1% for both the extreme incremental bending moments and normal forces is obtained.  
The absolute values of the extreme incremental bending moments and normal forces increased gradually with the \(a_{H}\) increase from 0.05 to 0.75 g. Incremental bending moments  
Figure 4: Distribution of the incremental internal forces in the circular tunnel by FLAC\({}^{\rm 3D}\) and Wang solution. (**a**) Incremental bending moments; (**b**) incremental normal forces; (**c**) incremental bending moments; (**d**) incremental normal forces.  
Figure 5: Effect of \(a_{H}\) on the extreme incremental internal forces of the circular tunnel lining. (**a**) Extreme incremental bending moments; (**b**) Extreme incremental normal forces.  
for both no-slip and full-slip conditions are strongly dependent on the \(a_{H}\) value (Figure 5a). However, while incremental normal forces in the tunnel lining for the no-slip condition are strongly affected by the \(a_{H}\) value, insignificant incremental normal forces variations due to \(a_{H}\) for the full-slip condition are observed (Figure 5b).
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 2 WSN Coverage Optimization Based on PSO  
### Network Node Set Coverage  
Assume that the target area (Region of Interest) A is a two-dimensional plane, which is digitized into \(l\times w\) grids where the size of each grid is equal to 1. The WSN with \(n\) total number of sensors is deployed on the target area with two specifications:  
1. All sensors have the same sensing range \(r\) and communication range \(R_{c}=2\ r\)
2. The sensor nodes with the same parameters are placed, and the coordinates of each node are \((x_{i},y_{i})\), where \(i=1,2,\ \ldots,n\).  
The monitoring field of the \(i\) sensor node is centered on sensor position coordinate \((x_{i},y_{i})\) and \(r\) is the circle of the monitoring radius. The node of each sensor node set is denoted by \(C=\{c_{1},c_{2},\ldots,c_{n}\}\), where \(c_{i}=\{x_{i},y_{i},r\}\). Assuming that the monitoring area A is digitally discredited into \(l\times w\) pixels where the coordinates of each pixel are denoted by \((x_{j},y_{j})\) and \(j=1,2,\ \ldots,\ l\times w\). The Euclidean distance [23] between the \(j\) pixel and the \(i\) sensor node is:  
\[d(c_{i},p_{i})=\sqrt{\big{(}x_{j}-x_{i}\big{)}^{2}+\big{(}y_{j}-y_{i}\big{)}^ {2}} \tag{1}\]  
The probability of the event which the pixel \((x_{j},y_{j})\) is covered by the sensor node \(c_{i}=\{x_{i},y_{i},r\}\) is:  
\[P_{cov}\big{(}x_{j},y_{j},\ c_{i}\big{)}=\left\{\begin{array}{ll}1,&\quad \text{if }d(c_{i},p_{i})<r\\ 0,&\quad\text{otherwise}\end{array}\right. \tag{2}\]  
Due to the interference of environment, noise, and other factors in sensing model, the sensor nodes should distribute in a certain probability [24],  
\[P_{cov}\big{(}x_{j},y_{j},\ c_{i}\big{)}=\left\{\begin{array}{ll}1,&\quad \text{if }d(c_{i},p_{i})\leq r-re\\ \frac{-a_{1}\lambda_{1}\beta_{1}}{e^{\frac{-a_{1}\lambda_{1}\beta_{1}}{2 \beta_{2}+\alpha_{2}}}},&\quad\text{if }r-r<d(c_{i},p_{i})<r+re\\ 0,&\quad\text{otherwise}\end{array}\right. \tag{3}\]where \(re\) is the reliability parameter of the measuring of sensor nodes with \(re(0<re<r)\), \(\alpha_{1},\beta_{1},\alpha_{2},\beta_{2}>0\) are the measurement parameters about the characteristics of the sensor node, where  
\[\lambda_{1}=re-r+\ d(c_{i},p_{i})\ \text{and}\ \lambda_{2}=re+r-d(c_{i},p_{i}) \tag{4}\]  
Considering that the target pixel is simultaneously covered by multiple sensor nodes [24], the joint monitoring probability is  
\[P_{cov}\big{(}Cov_{j}\big{)}=1-\prod_{c_{i}\in Cov_{j}}(1-P_{cov}\big{(}x_{j}, y_{j},\ c_{i}))\quad i=1,2,\ldots,n;\ j=1,2,\ \ldots,\ l\times w \tag{5}\]  
where \(Cov_{j}\) is the set of sensor nodes that measure the target pixel \(j\).
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### Optimal Network Selection Algorithm  
In case the predicted cell is unable to satisfy the requirement, then the best access network is selected by applying Access Network Selection Function (ANSF) as shown in Figure 3.  
ANSF is a neutral function used to compute the efficiency of using a radio resource. It enhances QoS of mobile users acquired by performing handover to a particular network. It represented all elective objective connectivity networks that could serve a mobile client's coverage area. The network with the highest ANSF value would be deemed the best choice for transitioning from the current access network.  
The following events occur during the initiation of ANSF:  
* Initiation of new service request.
* The MT discover the availability of new network
* A user alters his preferences
* Critical signal degradation or complete signal loss of current radio link  
The network selection calculation requires input information from client and framework both. An ideal wireless access network is chosen for excellent service that can fulfill the accompanying targets:  
* Strong signal quality implies the accessibility of a network. Accessible network is identified if its signal quality is strong.
* If a network with high optimal data rate is selected, it will reduce time of service delivery of non-real time system and improves the QoS of real-time system.
* A network with high reliability does not have an error and offers a high level of performance.
* Handover should be performed to a network where the battery power requirement is low.  
Mobile clients are associated with the upper layers and get favorable position of more prominent coverage region.
## 2 Materials and Methods  
The investigation and laboratory analyses were conducted at WWTP Como Acqua, an Italian wastewater treatment facility in Como, Italy, recognized for its advanced wastewater management capabilities. The sampling points are represented in Figure 1. The plant operates continuously 24 h a day to treat wastewater and ensure compliance with regulatory discharge standards. The treatment process follows a multi-stage approach, including preliminary, primary, secondary, and tertiary treatments, along with sludge management. Each stage is designed to effectively remove different pollutants, with retention times varying depending on the treatment type. The treatment types are as follows:  
1. Preliminary treatment: the influent passes through a bar screen to remove large debris, followed by an equalization tank that regulates flow and balances fluctuations. The retention time for this stage is typically 1-2 h;
2. Primary treatment: wastewater is sent to a primary sedimentation tank where settleable solids are removed through gravity. This stage reduces organic load and suspended solids, with a retention time of 2-4 h;
3. Secondary treatment: the wastewater undergoes biological treatment in anaerobic, anoxic, and aerobic zones, where microorganisms break down organic pollutants and reduce nitrogen. Afterward, water passes through the secondary clarifier for biomass settlement. The aerobic zone requires a retention time of 4-6 h, while the clarifier generally has a retention time of 2-3 h;
4. Tertiary treatment: this stage includes coagulation, sand filtration, and UV disinfection, which remove the remaining particulates and disinfect the effluent. The retention time for tertiary treatment is typically 1-2 h;
5. Sludge treatment: excess sludge from the secondary clarifier undergoes thickening, digestion, and dewatering processes. The sludge digestion process, which breaks down organic matter, typically requires 15-30 days of retention, depending on the digestion method.
## 5 Conclusions  
A remote sensing technique using a real-aperture radar to perform displacement-based dynamic monitoring under environmental excitations was presented. The capability of this technique to estimate the fundamental frequency of a damaged mid-rise building was studied. For this, the resonant frequencies of the structure were estimated with a traditional method, using accelerometer array, and later compared with the contactless method.  
In order to estimate the resonant frequencies of low and rigid structures with these types of remote displacement-based measurements, it is necessary to optimize the capacity and resolution of frequency domain methods, taking advantage of the multiple synchronized measurements that the RAR is able to perform. The results showed that the use of different techniques, such as the MSC and the CPSD, improve identification of the fundamental frequencies. Despite the shortcomings using RAR in mid-rise buildings, such as sensitivity to ambient noise and low displacement amplitude, for this structural typology, it is possible to find, with precision, at least the fundamental frequency of the structure by remote sensing with RAR. The fundamental frequency of the studied damaged structure is also consistent with estimations by empirical formulas.  
The RAR technique presents a clear advantage over traditional methods by not requiring contact with the structure. It is possible to relate the structural damage in conventional buildings to the fundamental frequency shift, which can be monitored accurately using remote sensing with RAR. This suggests the possibility of remotely assessing damage in buildings, which could be especially useful after a strong earthquake.  
R.E.A., data curation, formal analysis, and software and writing; L.G.P. and R.G.-D., supervision and review; G.L. and O.C., data acquisition; L.A.P., validation, review and editing. All authors have read and agreed to the published version of the manuscript.  
This research has been partially funded by the Ministry of Economy and Competitiveness (MINECO) of the Spanish Government and by the European Regional Development Fund (ERDF) of the European Union (EU) through projects with references CGL2015-65913-P (MINECO/ERDF, EU) and EFA158/16/POCRISC (INTERERG/POCTEFA. EU). [PERSON] holds a PhD fellowship from the _Consejo Nacional de Ciencia y Tecnologia_ (CONACyT) in Mexico. [PERSON] is supported by a Ph.D. scholarship grant from the Institute for the Training and Development of Human Resources (IFARHU) and the Government of Panama's National Secretariat of Science, Technology, and Innovation (SENACYT).  
The authors declare no conflict of interest.
## 5 Model Architecture
Doctors can operate in a more secure environment and make better decisions for their patients' cases. The doctor can see the corresponding clinical data of each patient in higher resolution and quality, such as Ultra High Definition (UHD), thanks to 3D visualization and the use of haptic interfaces. Using a communal virtual 3D display system that provides concurrent real-time visual feedback monitoring collaboration, the doctor can consult with other doctors from different medical centers and hospitals to diagnose and treat various ailments and diseases. In addition, this scheme allows more close-up views of hidden areas that are not easy to reveal by using the current conventional medical methods. Such a procedure ensures greater accuracy in the identification of potential complications, more effective patient care, quicker return to daily activities, precise clinical outcomes, faster recovery, and fewer complications, allowing patients to safely return home and visit their family sooner. The suggested approach may be employed in both hospitals and medical facilities. Research facilities can adapt it as well, for use in testing environments for potential improvements.