esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of a Mobile Node into a Hybrid Wireless Sensor Network for Urban Environments +
## 3 Mobile Node
### Architecture and Implementation
#### 3.2.2 Networked Mode  
In the networked mode, when data from any of the Transmitter nodes are available, a frame is constructed and sent to the embarked Receiver node. A frame may contain information from one or several sensors from the same Transmitter node. The Receiver node updates its internal database, and then synchronization with the external database takes place via 3G, or Wi-Fi if it is available. A simplified protocol message flow diagram for this working mode is shown in Figure 7. Figure 8 shows examples of frames with data from a Gas node. In Figure 8a, the frame includes CO\({}_{2}\) and O\({}_{2}\) measurements. In Figure 8b, the frame contains measurements from three sensors: NH\({}_{3}\), temperature and humidity. Location is obtained by means of a GPS node, acting as an additional Transmitter node. This way, more positioning data are available.  
Figure 5: Simplified protocol message flow diagram for the local mode.  
Figure 6: Part of an enhanced frame.  
Figure 7: Simplified protocol message flow diagram for the networked mode.  
This working mode makes available a larger amount of data, through a higher number of updates with the external database. Data are also available to the user faster, since the synchronization of the databases takes place as soon as new data are collected.
## 4 Results and Discussion
### WST Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**LES**} & \multicolumn{3}{c}{**LAE**} & \multicolumn{3}{c}{**LSEP**} \\ \hline
**Data** & **BGW** & **AHD** & **AHD** & **AHD** & **BGW** & **AHD** & **AHD** & **AHD** & **ABW** & **AHD** & **AHD** & **AHD** \\
**Sea Area** & **Mean** & **Wind** & **Swell** & **BGW** & **Mean** & **Wind** & **Swell** & **BGW** & **Mean** & **Wind** & **SWELL** \\ \hline Sea area S & 2.9 & 2.7 & 2.5 & 1.6 & 2.8 & 2.6 & 2.5 & 1.6 & 2.8 & 2.7 & 2.5 & 1.6 \\ Sea area 8 & 3 & 2.8 & 2.6 & 1.5 & 2.9 & 2.7 & 2.6 & 1.6 & 2.9 & 2.7 & 2.6 & 1.5 \\ Sea area 9 & 2.9 & 3 & 2.8 & 1.6 & 2.8 & 2.9 & 2.8 & 1.7 & 2.8 & 2.9 & 2.8 & 1.6 \\ Sea area 15 & 2.6 & 2.4 & 2.1 & 1.3 & 2.5 & 2.3 & 2.1 & 1.3 & 2.5 & 2.3 & 2.1 & 1.3 \\ Sea area 16 & 2.8 & 2.8 & 2.6 & 1.7 & 2.7 & 2.7 & 2.5 & 1.7 & 2.7 & 2.7 & 2.5 & 1.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Rayleigh shape parameter for full year. Figure 8: Plots of WST mean sea pdf (sea area S—full year). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**LES**} & \multicolumn{4}{c}{**LAE**} & \multicolumn{4}{c}{**LSEP**} \\ \hline
**Data** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** & **Season** \\
**Sea Area** & **1** & **2** & **3** & **4** & **1** & **2** & **3** & **4** & **1** & **2** & **3** & **4** \\ \hline Sea area S & 2.8 & 1.9 & 2.8 & 3.8 & 2.7 & 1.9 & 2.7 & 3.7 & 2.7 & 1.9 & 2.7 & 3.7 \\ Sea area 8 & 2.9 & 2 & 3 & 3.8 & 2.8 & 2 & 2.9 & 3.7 & 2.9 & 1.9 & 2.8 & 3.8 \\ Sea area 9 & 3 & 2.1 & 3.1 & 4.3 & 2.9 & 2.1 & 3 & 4.2 & 2.9 & 2 & 3 & 4.2 \\ Sea area 15 & 2.5 & 1.8 & 2.4 & 3.1 & 2.4 & 1.8 & 2.3 & 3.1 & 2.5 & 1.8 & 2.4 & 3.1 \\ Sea area 16 & 2.9 & 2 & 2.8 & 3.9 & 2.8 & 2 & 2.7 & 3.8 & 2.7 & 1.8 & 2.6 & 3.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Rayleigh shape parameter for AHD data (mean sea). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Season 1**} & \multicolumn{2}{c}{**Season 2**} & \multicolumn{2}{c}{**Season 3**} & \multicolumn{2}{c}{**Season 4**} \\ \hline Mean & 2.76 & 1.9133 & 2.746 & 3.7267 \\ Standard & 0.1764 & 0.091548 & 0.2416 & 0.3788 \\ deviation & 2.6\% & 32.46\% & 3\% & 31.55\% \\ \hline \hline \end{tabular} \(\mathbb{S}\%\): percentage change in shape parameter with respect to IMO recommended standard WST.  
\end{table} 
Table 7: Statistical comparison of Rayleigh shape parameter for seasons 1, 2, 3 and 4. Figure 9: Plots of WST swell sea pdf (sea area S—full year).
## Appendix C Characterization by Optical Methods of BMFPO Films Deposited on LAO Substrate
### Optical Evaluation of Surface Topography  
The topography of the BPFMO films deposited on LAO substrate was characterized by optical profilometry (SNeox, Sensofar, Barcelona) on a 300 \(\times\) 300 \(\upmu\)m\({}^{2}\) region (Figure 16).  
Over a 300 \(\times\) 300 \(\upmu\)m\({}^{2}\) range, all the samples have smooth surfaces with root-mean-square (RMS) surface roughness of less than 14 nm. For all the samples except 5% and 15% Pr substitution, some terraces can be seen. These originate from the terraces at the surface of the LAO substrate [47; 48; 49]. The measured roughness reflects the presence of the terraces. Note that the terraces on the surfaces of the films with 5% substitution of Pr, which are invisible in Figure 15, are visible in Figure 15. Similarly, the terraces on the films with 15% Pr substitution, which are invisible with optical profilometry, are visible with optical microscopy with DIC (Figure 15). This demonstrates the complementarity of the optical techniques applied by us for the characterization of BPFMO films.  
Appendix D Characterization of BPFMO Films on LAO Substrate by Atomic Force Microscope (AFM) Topography  
The morphology of the films at the nanoscale was investigated by tapping-mode AFM in air (Agilent 5500 AFM, Agilent Technologies, CA, USA). As shown in Figure 6, the morphology is granular for all the compositions. It can be seen that for Pr substitution concentrations higher than 15%, some polydispersity can be seen in the grain size distribution.  
Appendix E Thickness Measurement and Surface Imaging of BPFMO Films Deposited on LAO Substrate by Scanning Electron Microscopy  
The samples were studied by scanning electron microscope (SEM) using a JEOL JSM-6510 LV instrument (JEOL Ltd., Tokyo, Japan).  
The BPFMO films deposited on LAO substrate had been cleaved along the direction normal to the surface to be imaged with SEM. Examples of side images obtained, up to X50,000 magnification, are presented in Figure A7.  
These measurements demonstrate that the surfaces of some of the samples are not perfectly flat. In addition to possible damages and contamination caused by the cleavage process, the surfaces also unambiguously have an intrinsic roughness, as also seen from direct SEM images (Figure A8). This is at least partly due to the existence of large tilted and intricated domains, as optically observed (see Appendix C) and as already reported [47; 49]. The apparent degradation of the quality of the layers observed by SEM for increasing Pr doping concentration may be due to such an effect and/or the evolution of the mismatch with the lattice parameters of the LaAlO\({}_{3}\) substrate. However,a homogeneous layer of approximately equal thickness can be distinguished in all the images: the SEM images quantitatively show that the thickness of all the deposited films is 500 nm with a typical error of \(\pm\) 20 nm independently of Pr doping concentration.
# Optical Vortex-Pumped KTiOAsO\({}_{4}\) Narrow-Linewidth Picosecond-Pulsed Parametric Oscillator
## 3 Results and Discussion
### Output Performance of Laser Vortices  
The beam quality factor \(M^{2}\) of the vortex idler field generated in this research was precisely measured by employing the knife-edge method, considering both the calculated beam diameter at the resonator focus and the measured beam diameter in the far field to determine the divergence of the idler beam. Figure 4a depicts the variation in the beam size as a function of position through the focus of a f = 150 mm lens for the idler field. The beam quality factors in the two orthogonal directions were \(M_{\mathrm{x}}\)2 = 2.46 and \(M_{\mathrm{y}}\)2 = 2.25, respectively, indicating that the beam shape of the output picosecond-pulsed idler optical vortex approaches the ideal beam quality of an optical vortex with \(\ell_{i}=1\).  
Figure 3: Plot showing the spatial overlap efficiency between the pump field and the signal or idler fields as a function of the pump beam radius (\(\omega_{p}\)).  
The average output powers of the signal and idler fields in relation to the average pump power are presented in Figure 4b. Here, the maximum vortex output powers of the signal and idler fields were 2.14 W and 0.95 W, respectively, when the pump power was 17 W. Optical slope efficiencies of 20.8% and 9.2% were achieved for the signal and idler fields, respectively. In ultrafast OPOs, the transmittance of the output mirror has a significant impact on the overall system output power [43]. It was discovered throughout our work that an output mirror with 20% transmittance for the resonant signal field provided the optimal balance of a high signal and idler field output from the OPO while maintaining a relatively low oscillation threshold.  
The spectral characteristics of the signal and idler fields are shown in Figures 4c and 4d, respectively. The spectra were recorded using a scanning monochromator (SpectraPro HRS-500, PI, Trenton, NJ, USA) with 300 lines/mm, an aperture size of 30 \(\upmu\)m, and a spectral resolution of 0.1-0.2 nm. The KTA crystal that complies with the NCPM can only output signal and idler fields of fixed wavelengths. The central output wavelength of the signal vortex field was located at 1535 nm in the near-infrared band, and the central output wavelength of the idler vortex field was placed at 3468 nm in the mid-infrared band. The full width at half maxima (FWHM) of the signal vortex and idler vortex outputs were measured to be \(\Delta\lambda_{s}\) = 0.502 nm and \(\Delta\lambda_{i}\) = 1.216 nm, respectively. Compared with a MgO:PPLN-OPO, the KTA-OPO in this work has lower temperature sensitivity and a  
Figure 4: (**a**) The beam quality of the output idler vortex; (**b**) the output power of the signal and idler vortices; (**c**,**d**) the spectral of the output signal and idler vortices. Insets show the spectral bandwidths of the output signal and idler vortices, respectively.  
narrower spectral acceptance angle, which results in narrower spectral linewidths and better monochromaticity of the output signal and idler fields [31].
# Farm Monitoring System with Drones and Optical Camera Communication
## 5 Experimental Results
### Results
#### 5.2.3 Signal Reception  
First, we confirmed that the drone had successfully filmed all the sensor nodes for a sufficient duration. The drone autonomously moved along the calculated trajectory to film the optical signals from the LED panels. The sensor nodes detected the drone and transmitted sensor data. Second, we measured the bit error rate (BER) to evaluate the accuracy of data transmission in the real environment. To evaluate the performance under different outdoor lighting conditions, the BER was measured per filmed hour. The calculated BER is shown in Figure 13. The average BER measured in this experiment was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Batch** & **Img-Size** & **Weight** & **Epochs** & **Loss** & **mAP\_0.5:0.95** \\ \hline
16 & 640 & YOLOv5l & 300 & 0.0030712 & 0.71541 \\
16 & 320 & YOLOv5l & 300 & 0.0034124 & 0.70959 \\
8 & 640 & YOLOv5l & 300 & 0.0033053 & 0.68308 \\
8 & 320 & YOLOv5l & 300 & 0.0036273 & 0.68849 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection accuracy per parameter.  
Figure 12: Recognition accuracy of the LED panels.  
0.00043338. Assuming the use of 7% hard decision forward error correction (HD-FEC), the BER threshold was set to \(1\times 10^{-3}\). A receiver drone equipped with a camera received the transmitted data with sufficiently few errors. A receiver drone equipped with a camera received the transmitted data without error. As a result, it was confirmed that the BER was low enough to receive the sensor data regardless of the observation time.
###### Abstract  
Although atmospheric CO2 concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25\({}^{\circ}\) grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: \(R^{2}=0.936\), \(RMSE=1.3\) ppm, \(MAE=0.946\) ppm; compared with TCCON: \(R^{2}=0.954\), \(RMSE=0.898\) ppm and \(MAE=0.741\) ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO2 concentrations, thereby supporting policy formulation and actions to address climate change.  
XCO2; CNN model; spatiotemporal Kriging; TCCON; spatial inhomogeneity; greenhouse gases Article
# A Stable Backup Routing Protocol for Wireless Ad Hoc Networks
## 2 Stable Backup Routing Scheme (SBR)
### Backup Route Construction
#### 2.2.3 Dbr  
Different from IBE and EBR, DBR is constructed by the downstream node on the main route. In Figure 5c, node E has the main path to the destination itself. Through querying the MAC cache, if E overheard RTS/CTS exchange from upstream link B-C with \(p^{\text{(B,\,C)}}.p^{\text{(C,\,E)}}<p^{\text{(B,\,E)}}\), the \"DBR\" route entry will be appended into the backup route table of node E with \"inefficient node\" set as the address of C and \"next hop\" as F. The item \"successful rate\" is then assigned a value equivalent to \(p^{\text{(B,\,E)}}\).  
Immediately after the entries of MAC cache for a certain destination change, the backup route table will be updated accordingly. For further ensuring timeliness, each node accesses its MAC cache periodically with a period P\({}_{\text{a}}\) to update backup route table or delete unavailable alternate routes. It needs to be emphasized that a node at most maintains one backup route with highest priority for each user session according to the algorithm above.
Chromotography_**2015**, \(2\), 293-381; doi:10.3390/chromotography2030293  
_chromotography_  
ISSN 2227-9075  
www.mdpi.com/journal/chromotography  
_Review_  
Recent Developments and Applications of Solid Phase Microextraction (SPME) in Food and Environmental Analysis--A Review  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 1,\({}^{\star}\)**  
1. Faculty Life Sciences, Department Nutrition & Home Economics/Food Science, Hamburg University of Applied Sciences, Ulmenliet 20, 21033, Hamburg, Germany; E-Mail: [EMAIL_ADDRESS]
2. ODOCON Dr. [PERSON] & Dr. [PERSON], Amtsstrasse 7, 22143, Hamburg, Germany; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-40-42875-6163; Fax: +49-40-42875-6205.  
Academic Editor: [PERSON]  
_Received: 1 April 2015 / Accepted: 11 June 2015 / Published: 30 June 2015_  
**Abstract:** Solid-phase microextraction (SPME) is a simple, sensitive, rapid and solvent-free technique for the extraction of analytes from gaseous, liquid and solid samples and takes a leading position among microextraction methods. Application of SPME in sample preparation has been increasing continuously over the last decade. It is most often used as an automatized fiber injection system coupled to chromatographic separation modules for the extraction of volatile and semivolatile organic compounds and also allows for the trace analysis of compounds in complex matrices. Since SPME was first introduced in the early 1990s, several modifications have been made to adapt the procedure to specific application requirements. More robust fiber assemblies and coatings with higher extraction efficiencies, selectivity and stability have been commercialized. Automation and on-line coupling to analytical instruments have been achieved in many applications and new derivatization strategies as well as improved calibration procedures have been developed to overcome existing limitations regarding quantitation. Furthermore, devices using tubes, needles or tips for extraction instead of a fiber have been designed. In the field of food analysis, SPME has been most often applied to fruit/vegetables, fats/oils, wine, meat products, dairy and beverages whereas environmental applications focus on the analysis of air, water, soil and sediment samples.  
Keywords:SPME; food analysis; environmental analysis; volatile compounds; aroma; off-flavor; GC; LC; SBSE
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Data Preprocessing
#### 2.3.3 Supervoxels with Voxel Cloud Connectivity Segmentation (VCCS)  
In our study, the Voxel Cloud Connectivity Segmentation (VCCS) [22] algorithm was employed as a crucial preprocessing step for creating supervoxels from complex volumetric data. Seed points \(S=\{s_{1},s_{2},\dots,s_{K}\}\) were selected from the set of voxels \(P=\{p_{1},p_{2},\dots,p_{N}\}\), with each voxel \(p_{i}\) characterized by spatial coordinates \(x_{i}\) and a feature vector \(f_{i}\). These seeds were either uniformly selected or strategically chosen based on a specific heuristic, and each seed \(s_{k}\) had a unique spatial position \(x_{s_{k}}\) and an associated feature vector \(f_{s_{k}}\).  
A specialized distance metric \(d\) was formulated to integrate spatial and feature space distances, providing a holistic measure of dissimilarity between each voxel \(p_{i}\) and seed \(s_{k}\):  
\[d(p_{i},s_{k})=\sqrt{\alpha\cdot\|x_{i}-x_{s_{k}}\|^{2}+(1-\alpha)\cdot\|f_{i} -f_{s_{k}}\|^{2}}\]  
The parameter \(\alpha\) served as a balance between spatial and feature space contributions. Each voxel \(p_{i}\) was assigned to the supervoxel of the nearest seed \(s_{k}\) by minimizing the distance \(d(p_{i},s_{k})\). The positions and feature vectors of the seeds were then updated to reflect the centroidal position and average feature vector of the voxels within each supervoxel, following the formulas\[x_{\text{g}_{k}}^{new} =\frac{1}{|S_{k}|}\sum_{p_{i}\in S_{k}}x_{i}\] \[f_{\text{g}_{k}}^{new} =\frac{1}{|S_{k}|}\sum_{p_{i}\in S_{k}}f_{i}\]  
This iterative process was repeated until convergence was achieved or a maximum number of iterations was reached. The parameter \(\alpha\) was crucial in regulating the compactness of the supervoxels, with higher values favoring spatial compactness and lower values emphasizing feature-based homogeneity. In our study, we evaluated the performance of different \(K\) values--3000, 10,000, 30,000, and 50,000--on supervoxel segmentation. The dice coefficients for the whole tumor region were determined as 0.64, 0.70, 0.85, and 0.88, respectively. Given these results, we selected \(K\) = 30,000 as our preferred setting. This decision was based on the observation that the improvement in dice coefficient from \(K\) = 30,000 to \(K\) = 50,000 was marginal, whereas the computational cost increased significantly, making \(K\) = 30,000 the most efficient choice in terms of both accuracy and computational expense.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 5 Prototype Development and Validation
### Captive Portal Integration with OpenNDS
#### 5.2.3 Cryptographic Notes on FAS in OpenNDS  
For the implementation of the integration, there are two cryptographic particular notes that should be considered: (1) the initial encrypted details, and (2) the authorisation token of the OpenNDS captive portal.  
Steps 3 to 6 of Figure 5 show how the client traffic is redirected to the captive portal at the FAS Server. OpenNDS adds some parameters necessary for the later client authorisation, encoded in base64 and encrypted with AES-256-CBC. Therefore, the WebAuthn authentication server installed at the FAS Server should decode and decrypt these parameters. Specifically, the AES block cipher in the CBC mode used by OpenNDS has a 256-bit block length. In the WebAuthn authentication server in NodeJS, the required key length is 32 bytes, which is shared with OpenNDS.  
On the other hand, step 5 of Figure 5 shows the calculation of the authenticated hash performed by OpenNDS. This authenticated hash has to be used by the FAS server to send an authorisation token that allows a client to be authorised by OpenNDS. The hash used by OpenNDS is SHA256. However, in order to authenticate the hash, OpenNDS developers have chosen to include the symmetric key at the end of the payload to hash. In this case, the FAS server should return a re-hashed version of the hid parameter when the client is authenticated. Letting \(k\) be the 32 byte shared key, the operation is shown in Equation (1):  
\[\mathit{rhid}=\mathit{sha256}\left(\mathit{hid}\,||\,k\,\right) \tag{1}\]
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 3 Experimental Evaluation
### Training Results  
We compare the mean IoU of the proposed method and the original method to evaluate. The original method uses the raw depth image data (one channel) for training without extending it to three channels though the proposed method. At the end of data training for one epoch, IoU is calculated from the validation data. We use the average value of the IoUs of this verification data as the accuracy index of the deep learning model.  
For comparison, (1) original 1-channel depth image, and (2) 3-channel image with additional information were used as training data, based on the same model and hyperparameters. As a result of training, the proposed method showed a mean IoU improvement of about 7%, compared to the original method. Figure 13 compares the validation of the mean IoU of the original method and the proposed method. Defect areas were inferred from the test image based on the weights in the last epoch. Figure 14 shows the inference results.  
We measured the average IoU data for the training set and the separate validation set with 1000 iterations of training. In Figure 13, it can be seen that the proposed method converges more stable than the original, and training proceeds. In the original method, the time point at which the mean IoU was stably maintained above 0.61 was 516 epochs, but in the case of the proposed method, the time was shortened by about 80% to 100 epochs.  
In addition, various performance indicators were derived through various CNN networks (Mobilenet, Resnet-50, and Resnet-101) that can be applied inside the DeepLabV3+ model. As a result of the test, the precision/recall values in units of image pixels were derived, and the harmonic average of these values, the F1-score, was calculated.  
When the model using Resnet-101 with the best performance was compared, the precision result of the proposed method was found to be lower than that of the existing method. However, it was deduced that the F1-score value, which is the harmonic average  
Figure 12: Example of calculating intersection of union; the traditional object detection methods calculate IoU through the bounding box area of the detection object (**left**), but the segmentation methods perform pixel-wise calculations (**right**).  
with recall, increased, resulting in reliable results, compared to the existing method. Table 2 shows the results of these indicators.