esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A flat-plate unglazed solar water heater (SWH) with a polymer thermal absorber was developed and experimented with. Polymer thermal absorbers could be a viable alternative to metal thermal absorbers for SWH systems. The performance of this polymer SWH system was measured based on inlet and outlet water temperature, water flow rate, ambient air temperature and solar irradiance. The polymer thermal absorbers were hollow Polyvinyl Chloride (PVC) tubes with a 20 mm external diameter and 3 mm thickness and were painted black to enhance radiation absorption. The pipes are arranged in a rectangular spiral inward-outward alternating-flow (RSioaf) pattern. The collector pipes were placed in a 1 m \(\times\) 1 m enclosure with bottom insulation and a reflective surface for maximized radiation absorption. Water circulated through a closed loop with an uninsulated 16 L storage tank, driven by a pump and controlled by two valves to maintain a mass flow rate of 0.0031 to 0.0034 kg\(\cdot\)s\({}^{-1}\). The test was conducted under a partially clouded sky from 9 a.m. to 5 p.m., with solar irradiance between 105 and 1003 W\(\cdot\)m\({}^{-2}\) and an ambient air temperature of 27-36 \({}^{\circ}\)C. This SWH system produced outlet hot water at 65 \({}^{\circ}\)C by midday and maintained the storage temperature at 63 \({}^{\circ}\)C until the end of the test period. Photothermal energy conversion was recorded, showing a maximum value of 23%. Results indicate that a flat-plate solar water heater with a polymer thermal absorber in an Rsiad design can be an effective alternative to an SWH with a metal thermal absorber. Its performance can be improved with glazing and optimized tube sizing.  
solar water heater; polymer thermal absorber; photothermal conversion; residence time; active system Article
## 6 Performance Evaluation
### Parameter Analysis  
We carry out extra simulations to see the impacts of the storage cycle, the size of assistant storage, the communication radiuses of vehicles and roadside units, and the request model on the overall performances. Here, we use the configurations in Table 4, and each roadside unit has 10 storage cells.
## 3 Experimental Results
In Figure 7, the dependence of the optical spectra of the MOPA on the pulse current \(I_{\text{TS}}\) injected into the tapered section (\(I_{\text{TS}}\) = 4, 8,...,18.4 A) is shown. It can be clearly seen that the intensity of the ASE increases with rising \(I_{\text{TS}}\) but the spectral shape of the mode remains almost constant. Although we did not measure the spatial beam properties of the MOPA system, we know from CW operated tapered amplifiers that they emit a near diffraction limited beam if the input beam is diffraction limited. For example, in Ref. [30] a lateral beam quality factor M\({}^{2}\) = 1.7 was measured for a 3 mm long amplifier at a CW output power of 4.5 W.  
In this study, we use a repetition frequency of 25 kHz corresponding to a duty cycle of only 0.02% for a pulse width of 8 ns. An increasing repetition frequency results in a larger duty cycle and hence, in a rise in the average thermal load, which could have an impact on both the performance of the driver circuit and the amplifier. However, we believe that repetition frequencies of several 100 kHz are still feasible because the time-averaged thermal load of the PA is of the order of only 100 mW, assuming a conversion efficiency of 30%. These properties prove that the concept of using a wavelength-stabilized diode laser emitting two wavelengths alternating in CW mode and a pulsed-driven multi-section tapered amplifier is well suited to be utilized for DIAL measurements of water vapor in the atmosphere. A correspondingly tailored laser source would be capable of providing high-peak power, small spectral line widths and rapid wavelength switching by applying the current of a driver circuit capable to provide ns-pulses to our monolithic MO. Figure 7: Time-averaged optical spectra of the MO (black curves) and the MOPA (colored curves) for different pulse currents as indicated in the legends emitted by the left (**a**) and right (**b**) branches of the Y-DFB laser.
## 4 Conclusions  
sPEEK-PSLM nanocomposite membranes have been synthetized and investigated in terms of their mechanical properties and proton transport performance. Sulfonation of polyether ether ketone (sPEEK) was achieved by using a specific reaction time (i.e., 5 h), whereas the synthesis of phosphonated silica layered materials (denoted as PSLM) was synthesized by the simple SOL-GEL approach. PEMs at three different filler loadings (1, 3, and 5 wt% with respect to the polymer) were prepared by a simple solution intercalation procedure. The introduction of PSLM materials produced significant strengthening of the mechanical resistance, improvement of the water retention capacity, and a favorable effect on the transport properties on the resulting nanocomposite membranes, as demonstrated by NMR and EIS investigations. The sample sPEEK-PSLM\({}_{3}\) exhibits a proton conductivity of 4.8 mS cm\({}^{-1}\) under very harsh operative conditions (i.e., @90 \({}^{\circ}\)C/30% RH), which is very close to the Nafion 212 used here as a reference (7.4 mS cm\({}^{-1}\)). The data reveal the feasibility of producing cost-effective, high-performance membranes based on sPEEK that can work efficiently above 110 \({}^{\circ}\)C and with low humidification.  
Conceptualization, I.N. and A.E.; methodology, C.S.; investigation, E.L., A.E., and C.S.; resources, I.N.; data curation, C.S. and E.L.; writing--original draft preparation, E.L.; writing--review and editing, I.N.; supervision, I.N.; funding acquisition, I.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 7: (**a**) Proton conductivity of sPEEK membranes and Nafion 212 at 90 \({}^{\circ}\)C as a function of RH. (**b**) Hydrolytic stability for sPEEK and sPEEK_PSLM\({}_{3}\) membranes at 90 \({}^{\circ}\)C and 95% RH during 160 h.
## 4 Applications of Microextraction Techniques to the Determination of Organic Micropolultants
### Pesticides
Triclosan was found at levels \(<\)LOQ in river waters and was commonly below 25 \(\mathrm{ng\cdot L^{-1}}\) in the sewage effluent. Another technique for determining pesticides is dispersive liquid-liquid microextraction (DLLME). [PERSON] _et al._[65] applied this microextraction technique using acetonitrile as the dispersive solvent and carbon tetrachloride as the extraction solvent to extract and pre-concentrate different classes of pesticides (carbofuran, clomazone and tebuconazole) in aqueous samples. Under the optimal conditions, the recoveries of the pesticides in the spiked water ranged from 62.7% to 120.0%; the LOQs of the method were 0.02 \(\mathrm{\SIUnitSymbolMicro g\cdot L^{-1}}\) after accounting for the 50-fold pre-concentration. [PERSON] _et al._[66] developed a novel dispersive liquid-liquid microextraction based on the solidification of a floating organic droplet (DLLME-SFO) to analyse triclosan and its degradation product in real water samples using acetonitrile as the dispersive solvent and 1-dodecanol as the extractant. The major difference between DLLME-SFO and DLLME is that the extractant used in the former has a low melting point and hypotoxicity. The extraction solvent used in this work has a low density, low volatility, low toxicity and proper melting point near room temperature. The extractant droplets can be collected by solidification at a lower temperature. The LODs of this microextraction technique in combination with LC-MS/MS ranged from 0.002 to 0.02 \(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\). Wide linearities, good precisions and satisfactory relative recoveries (83%-119%) were obtained. Dispersive liquid-phase microextraction (DLPME) has also been used to extract these compounds. A recent study by [PERSON] _et al._[67] described a temperature-controlled ionic liquid dispersive liquid-phase microextraction (IL-DLPME) developed to enrich and to determine triazine herbicides in water samples using 1-octyl-3-methylimidazolium hexafluorophosphate ([C\({}_{8}\)MIM][PF\({}_{6}\)]) as the extractant. Under the optimal conditions, the LODs ranged from 0.05 to 0.06 \(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\). Different real water samples were analysed, and the experimental results showed that the spiked recoveries were satisfactory. This same technique was used previously by [PERSON] _et al._[68] to analyse triclosan and triclocarbon in environmental water samples. Methanol was used as the dispersant, and [C\({}_{6}\)MIM][PF\({}_{6}\)] was the extractant. This method was used to analyse real environmental water samples with satisfactory results. The average recoveries of the spiked compounds ranged from 70.0% to 103.5% with LODs from 0.040 to 0.58 \(\mathrm{\SIUnitSymbolMicro}\mathrm{L}^{-1}\).
## 3 Multi-Exposure Fusion via Sparse Representation of Gradient Features
### Local Gradient Sparse Descriptor Extraction  
The local gradient sparse descriptor is obtained by the sparse representation model. Since the digital signal can be represented with a linear combination of a \"few\" atoms from an over-complete dictionary [26; 27], the inherent feature of the signal can be extracted through the sparse coefficient combination of the corresponding atoms. Mathematically, the digital signal y can be presented by the following model:  
\[y=\sum_{i=1}^{k}d_{i}\alpha_{i}=DA, \tag{1}\]  
where \(D=(d_{1},d_{2},\cdots\cdots d_{k})\in R^{n\times k}(n<k)\) denotes an over-complete dictionary, \(d_{i}\in R^{n}(i=1,2,\cdots\cdots k)\) is an atom of the over-complete dictionary, and \(A=(\alpha_{1},\alpha_{2},\cdots\cdots\alpha_{k})\in R^{k}\) indicates the sparse coefficient vector, which has a value that can be obtained by solving the following optimization problem:  
\[\min_{a}\lVert a\rVert_{0}\text{ subject to }y=DA, \tag{2}\]  
where \(\lVert\cdot\rVert_{0}\) denotes the \(l_{0}\)-norm: The number of nonzero components.  
Generally, there is noise in the original digital signal. Thus, the sparse representation model, including noise, can be expressed as  
\[\min_{a}\lVert a\rVert_{0}\text{ subject to }\lVert y-DA\rVert_{2}^{2}<\epsilon, \tag{3}\]  
where \(\epsilon>0\) is the error tolerance.  
In the proposed method, the process of sparse representation is realized in two steps, namely dictionary learning and feature extraction.
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### 5G-NR Delay
#### 4.2.1 Control Plane
In [28], 3 GPP evaluates the control plane latency, compared to LTE-A, for the transition of the UE from the RRC INACTIVE to RRC CONNECTED state (Table 8)--the impacted values are denoted in bold. The evaluation follows the same sequence as the transition (RRC INACTIVE-RRC CONNECTED), analyzed for the 5G-NR above. The only difference is that for the 5G-NR case, there are a variety of numerologies. In comparison to Table 3, the processing values for eNB and UE have been reduced [28]. As for the RACH scheduling period and the transmission of the RRC Connection Resume Complete message, the assumption is the same as in the 5G-NR case (0 ms). In relation to the paging cycle, it is significantly more flexible in 5G systems than in LTE, varying from 10 ms to 10.24 s [38]; additional transmission and processing time should be considered, which would be in the range of 1-2 ms. Upon the reception of a paging message, a UE has to follow the RRC connection. Finally, 5G supports dual connectivity, as was the case in LTE. This feature can be used to minimize the HO delay at the penalty of using more radio resources.
## 3 Experimental Results  
We used data from 55 CT scans to verify the segmentation performance of the proposed method. This dataset was provided by the organizers of LOLA11 (Lobe and Lung Analysis 2011 Competition). It was obtained from a number of clinical institutions using different CT scanning equipment and covered different degrees of pathological data. At the same time, the competition organizers also provided the manual segmentation results for the performance evaluation of the segmentation methods. However, the gold standard data will not be released to the public and the segmentation results can only be uploaded to the competition website [18] for online evaluation.  
In this section, the proposed method's performance was verified from two aspects, including visual and quantitative evaluation. Mevislab [19] was used for 3D visualization. The evaluation score was obtained by observing and quantifying the size of the coincidence area between the segmentation results and the real lung lobes. The method for evaluating the size is  
\[OV=\frac{L_{seg}\cap L_{gt}}{L_{seg}\cup L_{gt}} \tag{4}\]  
Figure 4: Results of lung fissure surface fitting: (**a**) original right lung oblique fissure, (**b**) fitted right lung oblique fissure, (**c**) original right lung horizontal fissure, and (**d**) fitted right lung horizontal fissure.  
Figure 5: Results of lung lobe segmentation: (**a**) original right lung, (**b**) segmentation using oblique fissure, (**c**) segmentation using horizontal fissure, and (**d**) segmentation result.  
where \(L_{seg}\) is the lung segmentation result and \(L_{gt}\) is the lung lobe gold standard.
## 2 Related Work
This study stressed the necessity for hybrid models with optimization strategies to improve detection. The summarized review in Table 1 emphasizes the importance of FL and advanced deep learning in IoT security. This research provides the groundwork for scalable, privacy-preserving, and efficient intrusion detection systems for IoT-based DDoS attacks. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Methodology** & **Key Contributions** & **Challenges Identified** \\ \hline \multirow{3}{*}{[12]} & \multirow{3}{*}{FL-LSTM} & Achieved 97.16\% accuracy in detecting cyberattacks in the IoHT with embedded feature selection minimizing computational complexity. & Focused solely on LSTM, limiting applicability to diverse IoT environments. \\ \cline{3-4}  & & Enhanced real-time detection and privacy preservation in IoHT networks. & Lacked evaluation of hybrid architectures for diverse attack scenarios. \\ \hline \multirow{3}{*}{[13]} & \multirow{3}{*}{FL-based IDS with differential privacy} & Preserved data privacy during gradient exchanges; effective for Industrial IoT. & Relied on shallow learning techniques, limiting detection of complex attack patterns. \\ \cline{2-4}  & & Demonstrated resilience to label noise and effective intrusion detection. & Lacked scalability for large-scale deployments. \\ \hline \multirow{3}{*}{[16]} & \multirow{3}{*}{ML-based solution for UAV security spoofing attacks.} & Achieved high detection accuracy for eavesdropping and GPS spoofing attacks. & Depended on single-network models, limiting adaptability to evolving attack patterns. \\ \hline \multirow{3}{*}{[17]} & \multirow{3}{*}{Hybrid LSTM-GRU models} & Enhanced anomaly detection in UAV networks. & More scalable solutions for dynamic IoT traffic are required. \\ \cline{2-4}  & & Achieved high accuracy and minimized communication overhead for zero-day botnet detection. & Did not integrate hybrid optimization methods, limiting robustness. \\ \hline \multirow{3}{*}{[19]} & \multirow{3}{*}{FLAD (Adaptive FL for DDoS detection)} & Addressed imbalanced datasets; improved dynamic attack profile detection. & Required further validation in resource-constrained environments. \\ \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review summary.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 2 Experiment Design
### Methodology of Data Exploration
#### 2.2.3 Supervised Relevancy Estimation  
The Kraskov algorithm is one of many approaches to estimate MI among two random variables. The Kraskov algorithm is widely used as it is considered as one of the most effective and accurate. It is able to evaluate the dependency among two multivariate variables in the same way as in case of univariate variables. Kraskov estimation is based on Kozachenko-Leonenko entropy estimation [24]:  
\[\hat{H}(X)=-\psi(K)+\psi(N)+log(c_{d})+\frac{d}{N}\sum_{n=1}^{N}log(e_{X}(N,K)) \tag{4}\]  
where \(N\) means the number of samples in \(X\), \(d\) is the dimensionality of samples \(x\), \(c_{d}\) means the volume of a \(d\)-dimensional unitary ball, and \(e_{X}(N,K)\) is twice the distance (usually chosen as the Euclidean distance) from \(x_{i}\) to its \(k\)-th neighbor. The most known derivate of MI estimation has the following form (Equation (2)) [24]:  
\[\hat{I}(X;Y)=\psi(K)+\psi(N)-\frac{1}{K}-\frac{1}{N}\sum_{i=1}^{N}(\psi(\tau_{ x_{i}})-\psi(\tau_{y_{i}})) \tag{5}\]  
where \(\tau_{x_{i}}\) is the number of points whose distance from \(x_{i}\) is not greater than \(0.5\times e_{X}(i)=0.5\times max(e_{X}(i),e_{Y}(i))\). As we can see, the [PERSON]'s MI does not require the computation of underlying probability distributions of the given variables, but simply estimates the dependency by its neighbor based clustering, which simplifies the entire approach. A comparative review by [PERSON] and [PERSON] confirmed its performance against other widely known estimation approaches [25].  
K nearest neighbor (kNN) is a classification algorithm proposed by [PERSON] and [PERSON] [26]. It is a frequently used approach to classify future data due to its simplicity, ease of implementation and effectiveness. The model does not execute any training phase, only training tuples with their class labels are stored and the model performs the classification for each test instance. The class prediction of a given sample is simply estimated as a mean of its \(n\) neighbors, which implies that the process does not need any information about underlying data distribution. Several similarity functions can be employed in the process of neighbor searching, where the euclidean distance is one of the most used.
## 3 Design of CRLH Transmission Line Antenna
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline \(\mathrm{C_{R}}\) & 7.382 pF \\ \(\mathrm{L_{L}}\) & 19.955 nH \\ \(\mathrm{C_{L}}\) & 4.177 pF \\ \(\mathrm{L_{R}}\) & 1.013 nH \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters of the CRLH TL unit cell. Figure 11: Designed CRLH transmission line antenna. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline \(\mathrm{w_{c}}\) & 15.7 \\ \(\mathrm{w_{s}}\) & 2 \\ \(\mathrm{l_{IDC}}\) & 7.5 \\ \(\mathrm{idc}\) & 0.5 \\ gap & 0.3 \\ \(\mathrm{w_{sp}}\) & 0.7 \\ \(\mathrm{R}\) & 0.5 \\ \(\mathrm{gap_{sp}}\) & 1 \\ \(\mathrm{w_{c}}\) & 15.7 \\ \(\mathrm{w_{s}}\) & 2 \\ \hline \end{tabular}
\end{table} 
Table 3: Parameters of the CRLH TL unit cell. Figure 10: 3D view of the CRLH transmission line unit cell. the negative input reactance of the antenna by generating negative capacitance. Since the non-Foster had a constant negative capacitance across the target operating bandwidth, the input resistance of the antenna had to be maintained at about 30 to 70 \(\Omega\) over the target frequency band, and the slope of the antenna input reactance value, i.e., the capacitance, had to be kept constant. The capacitance of the antenna is approximated by the following equation. \[\text{Imag}\;(Z_{in})=\frac{1}{j\omega C}\;\;(\text{C}\;=\;\text{ capacitance}) \tag{2}\]  
Equation (2) is converted to Equation (3). \[C=\frac{1}{j\omega\text{Imag}\;(Z_{in})} \tag{3}\]  
Figure 12 shows the input impedance of the CRLH TL antenna. Figure 13 shows the simulated radiation patterns on the Y-Z (E-Plane) and X-Z (H-Plane) planes at 155 MHz. Table 4 shows the antenna capacitance versus frequency. It was confirmed that a constant capacitance of 9 to 12 pF was obtained at 126-186 MHz. Figure 12: Input impedance of CRLH transmission line antenna. Figure 13: Simulated radiation patterns of the antenna.
## 5 Conclusions  
In this study, we constructed a vegetation ecospostial network in the YRB and calculated multiple topological indicators of the entire network and vegetation nodes. At the same time, we used the Biome-BGC model to calculate the carbon sequestration capacity of vegetation nodes. Then, we analyzed the relationship between the topological indicators and carbon sequestration capacity of vegetation nodes in the YRB, and we made suggestions for the optimization of the network in the background of carbon neutrality. The key conclusions of this study are as follows:  
1. The vegetation ecospostial network of the YRB in 2018 had a large clustering coefficient, a small-world characteristic, and a relatively stable overall structure. Moreover, the vegetation ecosspatial network of the YRB had more carbon sink nodes than carbon source nodes in 2018.
2. The net carbon sequestration capacity of forest nodes in the YRB had a negative linear correlation with its betweenness centrality; the carbon sequestration capacity of grassland nodes had a positive linear correlation with its clustering coefficient.
3. For the purpose of increasing the carbon sequestration capacity of vegetation, we put forward optimization suggestions for the vegetation ecos spatial network in the YRB. In addition, the eastern region of the YRB is a key optimization area for forest nodes, and the western region is a key optimization area for grass nodes.  
The following are available online at [[https://www.mdpi.com/article/10.3390/rs13234926/s1](https://www.mdpi.com/article/10.3390/rs13234926/s1)]([https://www.mdpi.com/article/10.3390/rs13234926/s1](https://www.mdpi.com/article/10.3390/rs13234926/s1)): Text S1. Calculation formulas of topological indicators; Table S1. Details of 27 ecological nodes in the Yellow River Basin; Table S2. NBP simulation results of ecological nodes from 2009 to 2018 (NBP unit: g\(\cdot\)cm\({}^{-2}\cdot\)year\({}^{-1}\)); Table S3. Ranking of topological indicators of nodes from largest to smallest values; Table S4. The ranking of betweenness centrality of forest nodes in the Yellow River Basin; Table S5. The ranking of the clustering coefficient of the grassland nodes in the Yellow River Basin.  
[PERSON] and [PERSON] conceptualized and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] wrote the paper; [PERSON] revised the paper and all authors edited the paper. All authors read and agreed to the published version of the manuscript.  
This work was supported by the Youth Science Foundation of National Natural Science Foundation of China (No. 42001211).  
Not applicable.  
Publicly available datasets were analyzed in this study. The data sources and access links are indicated in the text.  
We thank [PERSON] from Beijing Forestry University and Pratt institute for her support to the author. We also thank [PERSON], [PERSON], and [PERSON] from the Institute of Forestry and Climate Change Research for their discussion on technical details. In addition, we thank the anonymous reviewers.  
The authors declare no conflict of interest.
# A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain
## 2 Preliminaries
### Blockchain
Through the combination of the alliance chain and the public chain, the protection, verification, and recovery of archive data were realized.