esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Methodology  
### Test 1: Spectral Radiance  
For the analysis of spectral radiances, one measurement per facade/orientation was made, and the following graph was obtained for the measurement of both the ideal target and the sample (in the same position and orientation of the instrument), as shown in Figure 13.  
After measuring each facade of its four orientations, it is obtained that the initial spectral radiances of both the target and the sample (in the same position and orientation of the instrument) are.  
Below is a detailed analysis of each of the 4 sides of the building:  
* North Face: according to the graph in Figure 14, the material's fluorescent behaviour can be seen. This is mainly due to the fact that in some spectral bands, specifically from 480 to 680 nm, the radiance emitted by the material is higher than that of the ideal target in the same position and orientation. It can be seen then that the differences between both spectra are smaller so that the reflectance factor of the material on the east side will be above 1 in some spectral bands.
* West side: according to the graph shown in Figure 15, the fluorescent behaviour of the material on this side can be seen. As in the east side in some spectral bands, from 480 to 680 nm, the radiance emitted by the material is higher than that of the ideal target in the same position and orientation. The differences between the two spectra are therefore smaller, which will give a factor of reflectance of the material on the north face above 1 in some spectral bands. In other words, both the north and west faces appear optically and colourimetrically very similar.  
Figure 13: Daylight spectrum, with its gentle ripples of gas absorption in the atmosphere.  
* East side: according to the graph in Figure 16, the spectral and optical behaviour is different from the two previous sides analysed. In this case the material (facade) always radiates less than the white in the same position and orientation. Therefore, in principle, there would be no fluorescent behaviour. On the other hand, the differences in height of the white vs. facade spectra are notable, implying that the material of the east facade will no longer be optically and colourimetrically the same or similar to the north and west facades, which implies a darkening of the material, which will be analysed later.  
Figure 14: North face radiometric data.  
Figure 15: West side radiometric data.  
In addition to the east side of the 17-storey tower, measurements were taken of the facade of the 6-storey block B located in the same area with the same orientation, situated slightly to the left, set back on a slight slope, and also with views of the sea.  
South Face: this facade has a spectral and optical behaviour similar to that of the east facade, but not as pronounced in the darkening of the material Figure 17.
# Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management
## 2 Literature Review
### AI for Disaster Management  
AI is used in various applications, such as customer service, transactions, and healthcare. Recently, researchers have found that AI can be employed to predict disasters. With numerous high-quality datasets, AI can predict the occurrence of natural disasters, which can assist in saving the lives of thousands of people and in preventing financial losses. Natural disasters that can be predicted by AI include earthquakes, floods, storms, forest fires, and volcanic eruptions. In the field of earthquake prediction, researchers are collecting vast amounts of seismic data for analysis using deep-learning systems. Researchers use the data collected from past earthquakes to make improved predictions for future earthquakes. [PERSON] et al. [14] proposed an AI prediction system based on artificial neural networks, which could be used to predict the magnitude of an earthquake. AI can also employ seismic data to analyze the magnitude and pattern of an earthquake, as well as to aid in predicting the occurrence of an earthquake. [PERSON] et al. [15] presented data for the establishment, training, and evaluation of a neural network to detect flood and water levels. AI-based systems can learn rainfall records, climate records, and flood simulation tests and can predict floods better than traditional systems.
# Improvement of Buildings' Air Quality and Energy Consumption Using Air Purifying Paints
###### Abstract  
Among the existing techniques to mitigate the problem of contamination in the indoor environment, photocatalytic technology is considered to be the most promising solution in terms of effectiveness and cost. To that end, in the frame of the LIFEVISIONS project, a novel photocatalytic powder (photo-powder) was mixed in paints' matrix, producing a photocatalytic building material (photo-paint) able to improve indoor air quality (IAQ), upon its application, without downgrading paint physical properties. As a result, of IAQ improvement, less energy will be needed from ventilation systems, addressing not only health issues related to air quality but also energy reduction targets. Many powder formulae were synthesized using different synthetic pathways, concentration of dopants, and TiO\({}_{2}\) particles' size. They were tested in a photocatalytic reactor (lab-scale tests), according to EN 16980-1:2021, under visible light and the results showed that the most promising photocatalytic performance degrades 85.4% and 32.4% of nitrogen oxide (NO) and toluene, respectively. This one was used for the production of two different kinds of paints, organic (with organic binder) and inorganic (with potassium silicate binder), in an industrial scale. Both were tested in the Demo Houses' prototype demonstrator (real-scale tests) with an ultimate scope to estimate their effectiveness to degrade air pollutants under real-world conditions. In addition, the reduced energy consumption as a result of less ventilation needs was calculated in Demo Houses. More specifically, the energy reduction based on simulation results on Demo Houses was more than 7%. Although lab-scale tests showed better photocatalytic performance than the real scale, the efficiency of the paints under a more complicated environment was very promising.  
IAQ improvement; photo-paint; NO; toluene degradation; energy consumption +
Footnote †: journal: _Sci._  
_Applied_  
_sciences_
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### ICT for in-Home and Building Energy Management
#### 4.3.10 Encourage  
Energy management in buildings is an issue also addressed by the \"Encourage\" project, which included participants from five countries. Its scope has been to develop techniques that would contribute to optimum energy consumption in buildings and also to create mechanisms for communicating with external buildings or utilities. The communications technologies recommended within the building were the ZigBee protocol, the 802.11n protocol and the PLC Home Plug Green Phy technology. The Home energy Controller (HEM) could function with all these technologies, whereas the Home Energy Gateway (HEG) which served to transfer data from AMI meter to utility could operate with the wireless technology solutions, like GPRS, ZigBee, WLAN [90].
# Federated Learning-Based Service Caching in Multi-Access Edge Computing System
## 4 Proposed Service Caching Framework
### Service Caching Policies
```
1:Input:
2:\(\mathcal{S}_{m}(t_{m})\): List of current cached services in MEC server \(m\) at \(t_{m}\)
3:\(D_{m}(t_{m})\): List of service popularity scores in MEC server \(m\) at \(t_{m}\)
4:\(c_{i}^{m}\): Required resource for service \(i\) in MEC server \(m\)
5:\(C_{m}\): Resource capacity of MEC server \(m\)
6:Output:\(\mathcal{S}_{m}(t_{m}+1)\): List of optimal cached services in MEC server \(m\) at \(t_{m}+1\)
7: Initialize available resource \(C_{a}=C_{m}\)
8: Initialize score list index \(i=0\)
9: Initialize need-to-download services \(\mathcal{S}_{d}(t_{m})=\varnothing\)
10: Initialize optimal cached services \(\mathcal{S}_{m}(t_{m}+1)=\varnothing\)
11: Calculate list of service cache scores \(\mathcal{C}S_{m}(t_{m})\) by Equation (14)
12: Sort \(CS_{m}(t_{m})\) in descending order
13:while\(C_{a}\geq 0\)do
14: Set \(s\) is the \(i\)th service in \(CS_{m}(t_{m})\)
15:if\(c_{s}^{m}\leq C_{m}\)then
16: Push \(s\) to \(\mathcal{S}_{m}(t_{m}+1)\)
17:if\(s\
otin S_{m}(t_{m})\)then
18: Push \(s\) to \(\mathcal{S}_{d}(t_{m})\)
19:endif
20: Set \(C_{a}=C_{a}-c_{s}^{m}\)
21: Set \(i=i+1\)
22:endif
23:endwhile
24: Download services \(s\in\mathcal{S}_{d}(t_{m})\) from Cloud server. 25: Delete services \(s\in\mathcal{S}_{m}(t_{m})-\mathcal{S}_{m}(t_{m}+1)\). 26:Return \(\mathcal{S}_{m}(t_{m}+1)\). ```  
**Algorithm 1** Adaptive Service Cache Replacement  
The summary of notations used in this work is shown in Abbreviations section.
## 6 Existing Multi-UAV Communication Architectures
### Single-Group Communication Architecture  
A single-group architecture is commonly used for collaborative missions in small coverage areas. It is a decentralized architecture that promotes intra-communication between UAVs (air-to-air), thus eliminating the dependency on the GCS, as shown in Figure 10. The communication between the GCS and the UAVs (air-to-ground) is achieved through a particular UAV called a \"Gateway UAV\" [1]. It oversees the analysis of the information from the GCS before sharing it with all the UAVs according to the ad hoc network topology. In [1], the authors present three types of intra-UAV communication topologies, ring, star, and meshed, as shown in Figure 11.  
1. **Ring**: in an intra-UAV communication-based ring topology, the UAVs communicate in a bidirectional loop in a closed network. To guarantee the high availability of the information, any UAV can be used as a gateway to relay the information between the GCS and the rest of the UAVs. On the other hand, it is difficult to maintain the scalability of the UAVs because of the network topology.
2. **Star**: in a star topology, a single UAV relays information from the GCS infrastructure and shares it with the others. Any communication between two UAVs must pass through the gateway UAV. It is easy to maintain scalability. However, it does not guarantee fault tolerance because, if the gateway UAV fails, the whole system stops.
3. **Meshed**: it takes advantage of the ring and star topologies. It allows us to guarantee both scalability and fault tolerance.  
The diversified applications of UAVs have led to the increasing use of a variety of UAVs of different sizes and functions. Unfortunately, this architecture does not meet this type of demand because it does not allow the incorporation of different types of UAVs [1]. Moreover, it is adapted for short-range intra-communication, but it may be the case that some UAVs move away from the others during certain missions in large environments. To overcome this problem, an alternative is to create several groups of UAVs. In the following, we will discuss the two types of multi-group architectures.  
Figure 10: Multi-UAV communication architecture based on single group.
# ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network
## 3 Method
### Activation Clipping Optimization
\[\sigma=norm(ReLu(XV^{1})) \tag{12}\]  
Figure 6 shows the change in spatial attention from shallow to deep layers in MobileNetV2. In the shallow layers, the attention is concentrated at the boundaries of the image, which indicates that the shallow network layers focus on extracting local details of the input. As the network goes deeper, the receptive field gradually becomes larger, and the spatial attention focuses on core parts of foreground objects. The activation attention areas of different network layers have their own emphasis. Areas that are not of interest to some network layers may be the concern of other network layers. Since the quantization deviation of the shallow activations will be transmitted to the deep layers, it is necessary to consider the spatial attention of the target layer and other network layers simultaneously in activation quantization. As a result, we sum and normalize the spatial attention of activations at each layer to construct the global spatial attention \(\sigma_{S}\in R^{l/n^{\prime}}\). \[M(l,m,n)=I_{l}+\alpha_{1}\cdot\sigma(m,n)+\alpha_{2}\cdot\sigma_{S}(m,n) \tag{13}\]  
Based on the above single-layer spatial attention, global spatial attention and channel attention, we can obtain the attention of the activation. For example, with the activation data \(A(l,m,n)\), their attention is defined as (13), where \(\alpha_{1}\) and \(\alpha_{2}\) are balancing factors for single-layer spatial attention and global attention. In summary, the optimization objective of activation clipping is as follows:  
\[ \begin{split}\underset{A_{min},A_{max}}{min}&\sum _{l}^{c}\sum_{m}^{l^{\prime}}\sum_{m}^{w^{\prime}}\frac{M(l,m,n)}{s_{l}}\\ &\times\|A_{q}(l,m,n)-A(l,m,n)\|_{2}\end{split}  \tag{14}\]  
where \(s_{l}\) represents the standard deviation of the \(l\)th channel. It suppresses the detrimental effect of numerical fluctuations of different channels. In particular, to exclude the interference of outliers, we screened the activation data based on the percentage criterion. In theory, as with weight clipping optimization, we can utilize particle swarm optimization to minimize the objective function. However, since the amount of activations is far more than the weights, the time consumption of executing the particle swarm algorithm is unacceptable. What is more, the activation data are only from a few samples. Even if we obtain the optimal clip threshold that matches these samples, it may not fit the entire test dataset,  
Figure 6: Spatial attention of shallow activations to deep activations in MobileNetV2. which can even lead to overfitting problems. Therefore, we employ a grid search to solve the activation clipping thresholds \((A_{min},A_{max})\).
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 3 System Expected Capabilities
### Elevation for Initial and Final Target Acquisition  
The location of the radar and its surroundings need to be taken into account for object tracking. ATLAS is located at (40.18\({}^{\circ}\)N, 7.87\({}^{\circ}\)W) and is surrounded by mountains, which makes the horizon altitude profile vary in some directions. In these directions, this decreases the arc length of a debris passage, thereby reducing its potential tracking time. We thus defined that the minimum elevation for initial and final target acquisition is 30\({}^{\circ}\).
## 3 Results  
The proposed method has been tested on a wide dataset, consisting of 194 images acquired from 30 different patients with macular edema, including both images with and without motion artifacts (about 27%).  
The values of sensitivity, specificity, accuracy and precision are reported in Table 1.  
The values shown in Table 1 clearly highlight the effectiveness of the proposed method, which is always capable of correctly detecting the cysts' margins excluding the motion artifacts.  
The effectiveness of our algorithm is also shown by the Receiver Operating Characteristic (ROC) curve reported in Figure 5.  
Figure 6 shows some images where the cysts' edges delineated by the proposed algorithm are highlighted in red, and in this way shows how the proposed method is capable of correctly delineating the cysts' borders. In the third image of the second row, a motion artifact is present. As highlighted by the red borders, the motion artifact is excluded from the cyst detection.  
The computational complexity of the proposed algorithm has been evaluated by measuring the processing times taken by the Matlab script. The test has been conducted on a PC with an Intel i7-6700 CPU, working at 3.40 GHz and equipped with 32 GB of RAM. Considering the worst case, i.e., when the motion artifacts compensation is performed, the processing time is always lower than 100 ms. This time is negligible if compared to the duration of a typical OCT-A acquisition, which lasts about 15 s. Moreover, if the patient is affected by low visual acuity, tremors or dementia, the exam duration is longer, and there is a higher probability of motion artifacts in the acquired image. However, this does not change the maximum time needed by the proposed algorithm. Therefore, the proposed algorithm is real-time compliant.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Value [\%]** \\ \hline \(J(A,B)\) & 98.97 \\ \hline \(Sensitivity\) & 99.49 \\ \hline \(Specificity\) & 90.64 \\ \hline \(Accuracy\) & 99.62 \\ \hline \(Precision\) & 99.47 \\ \hline \(F_{1}score\) & 99.48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the proposed method.  
Figure 5: ROC curve for the proposed method.
## 5 Case Studies
### Influence of Initial Value on Convergence
As for Model IV, after the second iteration, the flow value in compressor 3 becomes \(-\)0.63 MSCMD, while the flow value in pipeline 8 remains negative at \(-\)0.62 MSCMD, leading to the ill condition occurring at node 9, as illustrated in Figure 5. This also causes the Jacobian matrix of the DGN to become singular, preventing further EFC solution in Model IV. In contrast, Model III successfully avoids the convergence difficulties caused by the above-mentioned model defects and achieves the preset computational accuracy after 15 iterations. These results all demonstrate the effectiveness of the proposed modification strategy. Figure 11: Iteration process of Models II–IV with \(k_{1}\) = 0.04 and \(k_{2}\) = 0.8. Figure 10: The convergence domains of Models II–IV under different initial values. (**a**) Model II; (**b**) Model IV; (**c**) Model III. Furthermore, when considering the variable compressibility factor \(\mathbf{Z}\), the convergence domains of Models II-IV are obtained in the same manner and are presented in Figure 12. By comparing Figure 12a-c horizontally, similar conclusions to those drawn from Figure 10 can be obtained, indicating that the proposed modification strategy remains effective even under non-ideal gas conditions. Additionally, by comparing Figures 10 and 12 vertically, it can be observed that the convergence domains of the models do not exhibit significant changes before and after tracking and updating \(\mathbf{Z}\). This demonstrates that the model incorporating the variable compressibility factor, as proposed in this paper, also exhibits robust convergence performance. It should be noted that the model convergence domain detected in Figures 10 and 12 is the convergence domain of the proposed MES model under the classic NR iteration format, from which it can be seen that there is still a large non-convergence space in the model at this time. Therefore, exploring advanced NR iterative formats becomes one of the feasible methods to further improve the convergence of the MES model, and it is also a potential direction for our future research.
## 4 Materials and Methods
### HS-SPME and GC-HRMS Analysis  
Five authentic standards for metabolite identification, including allyl isothicyanates, 3-butenyl isothianate, phenethyl isothianate (PEITC), 3-(methylthio) propyl isothianate, and dimethyl disulphide, and the n-alkanes (C8-C20) standard solution for retention index determination, were obtained from Sigma Aldrich (Singapore).  
The procedure of HS-SPME was modified according to the method developed earlier in our lab [47]. The SPME fibre (50/30 DVB/CAR/PDMS, Agilent, USA) was conditioned before use according to the manufacturer's instruction (270 \({}^{\circ}\)C for 1 h). The SPME fibre was exposed into the headspace of the vessel containing the vegetable sample for 30 min. To eliminate potential contamination and capture freshly emitted VOCs, the procedures of SPME fibre pre-condition and sample preparation were optimized to assure that SPME fibre pre-condition was done just before the blending of vegetable sample. After extraction, the SPME fibre was introduced into the GC injector for 30 s at 230 \({}^{\circ}\)C in splitless mode for sample injection.  
Agilent 7890A gas chromatography with 7200 quadrupole time-of-flight mass spectrometer (GC-Q-TOF/MS) was used throughout the present study. The separation was performed on DB-5 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent, USA). Helium was used as the carrier gas. The oven temperature was held at 40 \({}^{\circ}\)C for 2 min, and then increased to 185 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and to 30 \({}^{\circ}\)C at 30 \({}^{\circ}\)C/min, and held for 2 min. The GC total run time was 36.8 min. The starting time of data acquisition was 0.5 min. The ion source was operated in electron ionization (EI) mode at a temperature of 230 \({}^{\circ}\)C. The TOF MS was operated in full scan mode and the mass range was from m/z 35 to 500. Mass calibration was conducted after every 5 five samples to maintain the high mass accuracy.  
It is known that the efficiency of SPME fibre and performance of GC-HRMS may change according to prolong usage. To evaluate the stability of the analytical platform, reference vegetable sample was prepared as QC samples and analysed along with the vegetable samples. To obtain a reference vegetable sample, broccoli, rocket, radish, Chinese cabbage and choy sum (50 g each) were cut into small pieces and blended with 250 mL MilliQ water for 1 min. After centrifugation of the homogenized sample at 3000\(\times\)\(g\) for 5 min, 5 mL of the combined supernatant was aliquoted into multiple 20-mL glass vials. The QC samples in the glass vials with magnetic screw caps were stored at \(-\)20 \({}^{\circ}\)C and routinely analysed through HS-SPME GC-HRMS. The results of the QC samples were evaluated through PCA analysis (Figure S4), which showed that the analytical platform was stable.
## 2 Data and Methods
### NOAA19/POES Satellite  
NOAA19 is the youngest element in the constellation of the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POES), moving along a Sun-synchronous low-Earth orbit (revisit time: 102 min; inclination: 98.7\({}^{\circ}\)) at an altitude of \(\sim\)850 km, and currently serving as the Prime Service Mission.  
The onboard SEM-2 package mounts the Medium Energy Proton and Electron Detector (MEPED) [33], also including two couples of 30\({}^{\circ}\)-wide telescopes, of which two are approximately zenith-pointing (MEPED-0\({}^{\circ}\)) and two have an azimuthal orientation (MEPED-90\({}^{\circ}\)). The electron telescope pair operates in the range from 40 to 2500 keV, over four integral energy channels (E1: \(>\) 40 keV; E2: \(>\) 130 keV; E3: \(>\) 287 keV; E4: \(>\) 612 keV).  
The Sub-MeV fluxes used in this study were from the MEPED-90\({}^{\circ}\) electron telescope in the E2 channel, which represents the best compromise in terms of detection efficiency over the available energy range [34] vs. proton contamination [35].  
Figure 2: The HEPD geometrical factor for electrons, as estimated from Monte Carlo simulations, as a function of kinetic energy and trigger configuration.