esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of the Selected Coating Sorbent  
The FT-IR spectrum of DES-3(1:3) contains characteristic bands: intermolecular hydrogen bonds (3600-3200 cm\({}^{-1}\)), stretching bands of C-H bonds of hydrocarbon chains (2920 cm\({}^{-1}\) and 2851 cm\({}^{-1}\)), and stretching bands of C-H bonds of CH\({}_{3}\) groups at 2952 cm\({}^{-1}\). Moreover, there are bands at 1409 and 1394 cm\({}^{-1}\) of the O-H groups of carboxylic acids and a band at 1728 cm\({}^{-1}\) characteristic for the C=O bond in carboxylic acids. The band characteristic for the C-N bond occurs at 1083 cm\({}^{-1}\), and the band characteristic for C-O occurs at 1161 cm\({}^{-1}\). The FT-IR spectrum of DES-3(1:3) is presented in Figure 4.  
SEM was used to examine the morphology as well as observe the film thicknesses of the DES-3(1:3) sorbent coated on the support mesh (Figure 5). A fine structure of DES-3(1:3) was found on the stainless steel mesh.  
Figure 4: The FT-IR spectrum of DES-3(1:3).  
Figure 3: Influence of coating DES-based sorbent prepared with DES-1 (orange), DES-2 (green), DES-3 (burgundy) and DES-4 (violet) each in molar ratios 1:1, 1:2, 1:3 and desorption solvent (ACN/H2O in different volume ratios) on analytical signals of formaldehyde derivative obtained by the DES-TF-SPME/HPLC-UV-Vis method. The measurements were performed using 200 mL samples containing 125 ng mL\({}^{-1}\) of formaldehyde derivative. Conditions: extraction time 30 min with the addition of 0.05 mg L\({}^{-1}\) NaCl and desorption for 12 min (\(n=4\)).  
Qualitative and quantitative analysis of elements found in DES-coated mesh was performed on the surface of the tested materials (without the use of graphite sputtering). The EDS analysis of DES-3(1:3) enabled to determine the elemental composition of elements, but a precise percentage of particular elements could not be calculated. Even though DES was liquid during its synthesis different percentage of elements was measured on various surface parts after its solidification (Figure 6).
# UAV Support for Mission Critical Services
## 4 Potential Roles of UAV in MCS
### Scenario 1
The UAV also can play a role of a'mobile sensor' flying autonomously between pre-programmed waypoints and collecting environmental data or conducting audio-video surveillance.
# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 4 Experiments
### Quantitative Comparisons  
Table 1 shows the results on the EUVP and UGAN datasets. Our method obtains the best PSNR and SSIM results on three subsets of the EUVP dataset and the UGAN dataset. For UISM and MUSIQ, our approach also achieved the best or second-best results on almost all subsets of the EUVP dataset. This shows that our method achieves the closest enhancement results to reference images, while also meeting the requirements of human visual perception. On the UGAN dataset, our method obtains the second-best UISM and MUSIQ values, while the MLLE method obtains the best UISM and MUSIQ values. Compared with our results, the underwater images enhanced by MLLE showed  
Figure 4: The training loss curves of the proposed model. higher contrast. And images with higher contrast tend to obtain higher non-reference evaluation values. Therefore, MLLE obtains the best UISM and MUSIQ values on the UGAN dataset. For PSNR, SSIM, and MUSIQ, our method is best on Underwater Dark and Underwater Scenes. Most of the images in Underwater Dark have weak light and serious degradation. Our model also achieves a 0.5 percentage point increase in the PSNR metric. This shows that, when the degradation is serious or the scene is complex, our method is still able to obtain satisfactory results. This also indicates that the PAM can provide beneficial information, enrich the feature representation, and accurately enhance the underwater images. The images in the UGAN dataset are the closest to real underwater environments. Regardless of the SSIM or PSNR metric, our model surpasses the second-best algorithm by 2 points. Table 2 shows the quantitative results of different methods on the real-world underwater image datasets, including UIEB, C60, and U45. The PSNR and SSIM values of our method are best on the real underwater scene dataset, UIEB. Our method improves the PSNR and SSIM values by 3.84% and 1.91% on average compared with the second-best methods Water-Net and Ucolor, respectively. Combined with Figure 5, it can be seen that some methods, such as IBLA, RGHS, and ULAP, obviously do not remove color distortion well, but still obtain high UISM and MUSIQ values. Although our method did not achieve the best no-reference metrics, it achieved the highest PSNR and SSIM, and the visual results on these datasets were outstanding. The C60 dataset is composed of 60 challenging images from UIEBD without satisfactory reference images. PGN obtains the highest UISM and MUSIQ on the C60 dataset, which shows that PGN has great enhancement performance. PGN also obtained the best UISM value and second-best MUSIQ value on the U45 dataset. The results prove that our supervision strategy helps the network progressively learn accurate features and finally obtain great results. Experiments demonstrate that our algorithm not only shows significant restoration effects on severely degraded images but also performs well on real-world images.
# Experimental Study on the Condensation Heat Transfer on a Wettability-Interval Grooved Surface
## 2 Description of Experiment
### Data Reduction  
The wall temperature was calculated using the average of the four thermocouples arranged on the back of the sample. The condensation heat flux can quantitatively describe the condensation heat transfer performance. In the experiment, the condensation heat flux was calculated by the water temperature difference between the inlet and outlet of the heat exchanger plate, that is:  
\[q=\frac{\text{C}\dot{m}(T_{\text{out}}-T_{\text{in}})}{A} \tag{1}\]  
where C was the specific heat capacity of water, \(m\) is the mass flow rate of cooling water, \(T_{\text{in}}\), \(T_{\text{out}}\) was the temperature of inlet and outlet water of the heat exchange plate, and \(A\) was the condensation surface area.  
Figure 3: Schematic diagram of the experimental setup.  
The uncertainty of heat flux was analyzed using the error propagation theory, which can be expressed as follows:  
\[\sigma_{q}=\sqrt{\left(\frac{\partial q}{\partial C}\sigma_{\text{C}}\right)^{2}+ \left(\frac{\partial q}{\partial m}\sigma_{\text{\tiny{II}}}\right)^{2}+\left( \frac{\partial q}{\partial T_{\text{in}}}\sigma_{T_{\text{in}}}\right)^{2}+ \left(\frac{\partial q}{\partial T_{\text{out}}}\sigma_{T_{\text{out}}} \right)^{2}+\left(\frac{\partial q}{\partial A}\sigma_{A}\right)^{2}} \tag{2}\]  
where C was constant, the uncertainty of \(m\) was 1.04 \(\times\) 10\({}^{-4}\) kg/s according to the specification of the flow meter, the uncertainties of \(T_{\text{in}}\) and \(T_{\text{out}}\) were 0.2 K according to the resolution of T-type thermocouples, and the uncertainty of \(A\) was 2.5 \(\times\) 10\({}^{-9}\) m\({}^{2}\). The maximum uncertainty of heat flux was 4.7%.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 2 Problem Formulation
### Equivalent Spherical Harmonic Decomposition of a Sound Field in Time-Space Domain
\begin{split} b_{nm}(\
u,R_{Q})&=a_{nm}(\
u)*p_{n} (\
u,R_{Q})\\ &=\sum_{\mu=-L_{p}}^{L_{p}}p_{n}(\mu,R_{Q})a_{nm}(\
u-\mu),\end{split}  \tag{14}\]  
where  
\[p_{n}(\
u,R_{Q})=\left\{ \begin{array}{cc}\frac{c}{2R_{Q}}P_{n}(\frac{\
u c }{R_{Q}^{2}F_{s}})&-\frac{R_{Q}F_{s}}{c}\leq\
u\leq\frac{R_{Q}F_{s}}{c}\\ 0&|\
u|>\frac{R_{Q}F_{s}}{c}\end{array} \right., \tag{15}\]  
is a time limited function with \(p_{n}(\
u,r)\
eq 0\) when \(-L_{p}\leq\
u\leq L_{p}\), \(L_{p}=\lceil R_{Q}F_{s}/c\rceil\) such that the length of \(p_{n}(\
u,R_{Q})\) is \(2L_{p}+1\). With (14) in hand, our problem reduces to obtaining \(a_{nm}(\
u)\) from the measured \(b_{nm}(\
u,R_{Q})\). This is not achievable since it is an under-determined problem. We always have \(2L_{p}+1\) more unknowns (\(a_{nm}(\
u)\)) than knowns (\(b_{nm}(\
u,R_{Q})\)). Moreover, this is not practically feasible because the z-transform of \(p_{n}(\
u,R_{Q})\) has zeros on the unit circle, resulting in poles on the unit circle in its direct inverse, making the system unstable. Alternatively, \(a_{nm}(\
u)\) can be extracted from \(b_{nm}(\
u,R_{Q})\) using an appropriately designed filter. In this paper, we attempt to design a filtering solution while overcoming the above challenges. It is important to note that the Fourier transform relationship discussed in this section were solely used to formulate the definition of the time-domain spherical harmonic decomposition of a sound field. From this point onward, we will focus on signal processing of the captured sound field only in the time domain.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 3 Adaptive Energy Managements: Requirements, Components, and Mechanisms
### Requirements for Adaptive Energy Management
* _ICT infrastructure for performing management strategies_: the ecosystem for adaptive energy management requires an entity that coordinates all actions between the ES and the ECs. In the scheme presented in Figure 1 these functionalities are performed by the EM; this component is normally part of the ES and is in charge of performing all calculations and executing all strategies (algorithms) that are associated with energy management from ES to ECs. In this regard, Ref. [5] demonstrates that NFV technology deployed on cloud computing infrastructures is an ideal solution to meet the scalability, flexibility, and reconfigurability requirements of adaptive energy consumption. An NFV-enabled EM can grow proportionally (increase in computing, storage, and networking resources) according to the varied requirements of the ECs, and it can dispose of the Management and Orchestration (MANO) entities [6], so that all the components of the generation and consumption ecosystem work in a coordinated manner. Thanks to NFV technology, the manageability of the ECs through the underlying network (e.g., SDN) is separated (abstracted) from the functionality (i.e., management strategies), a feature that enables energy management in different scenarios (e.g., HEMS, companies, or smart cities). * _Primary use of renewable energy sources and control of their contribution_: although this requirement is not indispensable in optimizing the consumption of the available supply, our proposal encourages the primary use of green energy and the transition to 100% renewable energy systems, as an emissions-aware and sustainable solution. In this regard, the proposed energy management mechanisms, such as workload scheduling through time-shifting capabilities, prioritization in energy supply, or rejection of energy demands if necessary, enable the optimal use of the sporadic energy capacity from green sources such as solar or wind. Furthermore, given that the change towards zero-emission energy systems is gradual and it requires the co-existence of both renewable and non-renewable sources, controlling the contribution of each source according to its availability and need is an important parameter in the operation of the ES.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 5 Simulation Setup and Results
### Simulation Environment
Therefore, a jitter profile added to simulation is a must to get realistic results. A white Gaussian timing jitter profile, with a standard deviation of 0.8 pico-seconds, is assumed combined at the DAC and ADC.
## 3 Results and Discussion
### Validation--Recent climate
#### 3.1.1 Temperature  
Figure 3 shows the monthly averages (1986-2005) of Tmax for EOBS, HIST for (a) NC and (b) BC data for EOBS and WRF and CORDEX models, whereas Figure 4 shows Q-Q graphs for NC data.  
Figure 3: _Cont._All models, namely WRF and CORDEX, present a generalized cold bias before bias correction with an all-model average annual bias of \(-2.5\)\({}^{\circ}\)C (minimum and maximum biases are \(-4.2\)\({}^{\circ}\)C and \(-1.2\)\({}^{\circ}\)C). Biases for Tmin are smaller than those for Tmax (not shown). For monthly biases, one model  
Figure 4: Quantile–quantile graph of Tmax for EOBS, WRF NC, and CORDEX NC ensemble average (CORDEX ave), and maximum and minimum (gray shading). EOBS is represented in the x-axis and the models in the y-axis.  
Figure 3: (**a**) Monthly averages of maximum temperature (Tmax) for the E-OBS Version 17.0 dataset (EOBS) and HIST for (**a**) raw uncorrected data (NC) and (**b**) bias correction (BC) data. The shaded ‘envelope’ represents maximum and minimum values across the CORDEX model ensemble. HIST: Simulation for the 1986–2005 period where the WRF was forced by the MPI-ESM-LR (Max Planck Institute Earth System Model—low resolution) (WRF-MPI) global climate model.  
has a bias of \(-\)5.9 \({}^{\circ}\)C for June/July. These biases are reported in a number of other studies [65] and are, at least, partially related to cold biases present in the forcing global models caused by large-scale dynamics' deficiencies in these models [32; 66] ([PERSON] et al. 2017; [PERSON] et al. 2019). For both Tmax and Tmin, WRF is located within the CORDEX envelope of all CORDEX models.  
After BC, all models approach EOBS, as expected, since BC projects uncorrected temperature onto the EOBS distribution. WRF presents a small differences relative to EOBS.  
The cold bias mentioned before is now more evident with smaller WRF and CORDEX ensemble average biases present for extreme Tmax. Larger inter-model variability is observed for CORDEX for extreme high temperature. Similar results are observed for Tmin (not shown). After correction, by definition, the distributions of model temperature are the same as for the EOBS for HIST and therefore are not shown.  
Cross-validation was performed as described at the end of Section 2.4. The 50 10-year bootstrapping runs have an average bias relative to EOBS of \(-\)3.3 \({}^{\circ}\)C for NC and 0.0 \({}^{\circ}\)C for BC. The bias for HIST NC is also \(-\)3.3 \({}^{\circ}\)C. The 25 th and 75 th percentiles across the 50 runs biases are \(-\)0.18 \({}^{\circ}\)C and \(+\)0.17 \({}^{\circ}\)C, respectively. Therefore, one can conclude that the period chosen to estimate the transfer functions is independent of the calibration period.
# Nano-/Microcapsules, Liposomes, and Micelles in
## 2 Spherical Nanoparticles
### Nanocapsules
#### 2.1.2 Methods of Obtaining, Factors Affecting Stability, and Properties
The benefits of electrospinning and electrospraying encompass several crucial features, including low energy costs, control over encapsulation morphology and physical properties, high encapsulation efficiency (increased surface area-to-area ratio),efficacy for components that are poorly soluble in water, encapsulation of hydrophilic and lipophilic bioactive compounds, compatibility with animal and plant polymers, ability to use both water and organic solvents, preservation of the functional and structural stability of encapsulated biomolecules, uniform size distribution, and reduction in aggregation [65]. In terms of the encapsulation process, chemical (polymerisation, interaction, in-situ emulsion) and physical (physical-chemical, e.g., layer-by-layer adsorption, sol-gel encapsulation; physical-mechanical, e.g., spray-drying, electro-spraying-coaxial electrohydrodynamic atomisation (CEHDA)) methods can be distinguished. The layer-by-layer technique typically leads to the formation of core-shell capsules and operates on the principle of bottom-up assembly. This technique provides control over the physical properties of the coating and enables the customisation of the size and properties of the resulting capsules [56]. In their research, [PERSON] et al. [66] implemented a layer-by-layer approach to enclose anthocyanins in yeast capsules, which were progressively layered with oppositely charged polysaccharides. Chitosan, a positively charged polysaccharide, was complexed with chondro sulphate, a negatively charged polysaccharide. This complex was used to coat the yeast surface, which subsequently promoted the formation of anthocyanin-containing capsules. Obtaining core-shell structures via convective techniques presents various challenges, such as limited control of morphology, polydispersity, and low reproducibility. State-of-the-art techniques, like electrospraying, electrospinning, or microfluidics, offer a promising solution to these difficulties. These techniques provide more precise microscale flow control, resulting in uniform capsules that are biologically and chemically compatible. These methods are applicable to an extensive variety of coating or core materials [56]. The process of encapsulation typically entails active components that are sensitive to various external factors (such as light, temperature, oxygen, pH, and enzymes), as noted by [PERSON] et al. [67] and [PERSON] et al. [68]. Among the most frequently encapsulated active compounds are bioactive compounds from plants (polyphenols, anthocyanins, carotenoids), which are recognised for their therapeutic and/or preventative effects on cardiovascular diseases, metabolic diseases, degenerative diseases, urinary tract infections, gastric ulcers, and some types of cancer [53; 69]. Consequently, to improve the quality of bioactive compounds whilst enhancing their quantity, it is necessary to take proper measures.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 5 Experimental Results and Discussions  
### Environment  
We implemented and tested our proposed idea configuration using two computers. One computer is Raspberry Pi 4 model B, and another computer is a personal computer that uses Windows OS, as seen in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware** & **Detailed Specifications** \\ \hline Processor & Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5 GHz \\ \hline Memory & 1 GB, 2 GB, 4 GB or 8 GB LPDDR4 \\ \hline Connectivity & 2.4 GHz and 5.0 GHz IEEE 802.11 ac wireless, Bluetooth 5.0, BLE Gigabit Ethernet \\  & 2 USB 3.0 ports; 2 USB 2.0 ports. \\ \hline GPIO & 40 pin GPIO header \\ \hline Camera & Webcam logitech c920 \\ \hline Storage & SSD 120 GB \\ \hline Operating System & Raspberry Pi OS \\ \hline Power & 5V 3A 1.5M \\ \hline Processor & Intel i7 8700K 3.70 GHz \\ \hline Memory & 32 GB DDR4 \\ \hline Local Area Network & Internal port—10/100 Mbps \\  & External port—10/100 Mbps \\ \hline GPU & 1080 ASUS \\ \hline Motherboard & TUF Z390-PLUS GAMING \\ \hline Storage & 500 GB M.2, 1 TB SSD, 4 TB Hard Drive \\ \hline Operating System & Windows 10 Pro \\ \hline Power & SuperFlower SF-1000 Watt \\ \hline \end{tabular}
\end{table}
Table 2: Raspberry Pi 4 Model B specifications and Windows Desktop specifications.  
Figure 3: YOLOv5 SOTA Real-Time Instance Segmentation.  
We used the Raspberry Pi 4 Model B to detect the smell and sight using the MQ-2 flammable gas and smoke sensor and a Logitech C920 webcam. Due to its portability, we were able to put this device anywhere to detect smoke and fire. Therefore, it could also be installed in the forest. After detecting the fire using the MQ-2 flammable gas and smoke sensor, it records it with the Logitech C920 webcam and sends it to the personal computer in Table 2. Then, with its high performance, it uses the Anaconda console to execute the video and automatically detects if there is a fire through YOLOv5. Once the fire is confirmed, the video is sent to the fire department for further action.
## 1 Introduction and State-of-the-Art
### State-of-the-Art
Their proposal demonstrated favourable outcomes in terms of latency, goodput, and fairness metrics within multi-hop wireless network scenarios, outperforming varied alternatives including NRT, OQS, and Westwood. A concept structurally close to the system described in Section 1.1 was presented in [31; 32]. Those papers introduce a system for device indoor localization that uses variations in the strength of the wireless signal. Their proposed system addresses logistics use cases in which it is imperative to achieve reliable end-to-end delivery, such as pharmaceu delivery, delivery of confidential documents and court exhibits, and even food delivery, since vulnerabilities in these deliveries present a potential risk of terrorism or other attacks. This work proposes a concept based on a low-power and low-cost LoRaWAN system that utilizes a Machine Learning technique based on Neural Networks to achieve high accuracy in device indoor localization by measuring the signal strength of a beacon device. However, in these studies, LoRaWAN is used as a communication technology. This platform does not provide the speed characteristics necessary for our case. We also note that the use of neural networks as a model of the studied process requires significant preliminary work on collecting and summarizing training data. In general, it should be noted that the authors of the aforementioned studies strive to pay attention to the description of the competition for resources for their information and communication systems between a large number of non-resource-intensive connections. This does not fit into the concept of using the SNEIoTD described in Section 2.1, which determines the relevance of this research.
# A Survey of NFC Sensors Based on Energy Harvesting for IoT Applications
## 2 NFC Energy Harvesting
### Link Budget
#### 2.1.2 Reverse Link  
The green line in Figure 2 shows the magnitude of the frequency response for a low \(Q\) reader, while the blue line shows the magnitude for a high \(Q\) reader. We can see that, as the \(Q\) factor increases, bandwidth decreases and attenuation increases at the subcarrier frequencies. The return signal, therefore, becomes smaller due to increased attenuation. In this case, the return signal power (\(P_{b}\)) is smaller than the reader sensitivity (\(S_{min,reader}\)) and the reader cannot decode the load modulation. This condition in the reverse link can be mathematically expressed as  
\[P_{bs}=P_{m}G_{T}(f_{sub})>S_{min,reader}, \tag{4}\]  
where \(G_{T}(f_{sub})\) is the system transducer gain at the subcarrier frequency, and \(P_{m}\) is the modulating power (\(P_{m}=m^{2}/4P_{d}\)), which depends on the modulating factor, \(m\). Typically, reader sensitivity is 110 dB below the level of the transmitter carrier signal (\(S_{min,reader}=-110\) dB) [1].  
The read range can be limited by the forward link (Equation (1)) or by the reverse link (Equation (4)). In both cases, transducer gain is a function of the coupling coefficient between the two antennas, \(k\). The coupling depends on the design, shape, area, materials, and distance of the antenna. The reader design may be different for different mobile devices. Differences in the read range are, therefore, expected depending on the coupling of each reader. The loaded quality factor of the tag  
Figure 4: Typical time-domain modulated waveform (**top**) and idealized response of a second-order system with quality factor \(Q\) (**bottom**). The transitory response duration is of the order \(f_{c}/Q<T_{p}\).  
is also not constant because the input impedance of the IC is nonlinear. For short distances, the IC receives high power and decreases load resistance and quality factor. The quality factor of the tag antenna is, therefore, not adjusted with external resistors, because it depends on the distance, which also simplifies the tag.