esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 5 Performance Evaluation  
In this section, results of the performance analysis for the fingerprinting system proposed in Section 3 are presented. The proposed system is compared against the fingerprinting approach proposed in [7], referred to in the following as the _Single Cell_ strategy. Note that while in the data used in [7], there was no ambiguity on which cell to consider since only one cell was recorded per location, a criterion must be adopted to select a single cell in the data used in this work. The approach adopted in the following is to associate to each location the NPCI characterized by the best value for the considered RF parameter. This choice is justified by the reasonable assumption that, whenever multiple cells are available, a device will select and connect to the one characterized by the most favorable propagation conditions.  
The rest of this section is organized as follows. Section 5.1 describes the setup adopted in the experiments and introduces the performance indicators. Next, Section 5.2 presents the results obtained using the Oslo dataset, while Section 5.3 presents the results obtained using the Rome dataset, focusing on the impact of the linear interpolation technique selected on the basis of the analysis presented in Section 4.4. Finally, Section 5.4 proposes two approaches to further improve positioning accuracy.
## 1 Introduction
Furthermore, since the consumption rate of acids for PHA production is dependent of the type of carboxylate, less preferred carboxylates could accumulate in the fermentation medium, resulting in a toxic effect on the microbial catalyst [4]. Data on process performance in terms of biomass and PHA concentration, productivity, etc., is also lacking. Moreover, most of the studies dealing with PHBV production using a pure strain deal with double substrate strategy using glucose or fructose and propionic acid or valeric acid as co-substrate. The targeted production of PHBV from acetic acid as the main carbon source with a co-substrate such as valeric acid by pure cultures has, however, not yet been reported [15]. Although calculations indicate that the indirect conversion of acetic acid from CO\({}_{2}\) could be an interesting alternative for the direct production of PHB from CO\({}_{2}\), it is clear that a proof-of-concept is currently lacking to substantiate these calculations. This study is the first to compare the key performance parameters of different PHA production processes using CO\({}_{2}\) as a feedstock by _C. necator_. An alternative cultivation method using acetic acid as an indirect sink of CO\({}_{2}\) was first developed for the synthesis of PHB homopolymer and PHBV copolymer using a pure culture. Subsequently, this approach was benchmarked with the existing cultivation methods for direct conversion of CO\({}_{2}\) to PHA.
## 3 Design and Optimization of the Proposed Extended DD Coil
### Optimizing the Turns and Inner Diameter of the Extended DD Coil  
To increase the second magnetic coupling coefficient between the compensation inductor and receiver coil, this study also optimizes the number of turns and inner diameter of the compensation inductor coil. The optimization process is shown in Figure 7, and the influence of different inner diameters and turns on the inductance value of the compensation inductor is analyzed in detail. Interestingly, the simulation results show that, for different inner diameters with the same number of turns, the larger the inner diameter, the higher the inductance value and the better the \(K_{in-s}\) value in the system in a misaligned state. Therefore, when the compensation inductor is a DD coil, the number of coil turns is meant to be eight. Then, \(D_{1}\) and \(D_{2}\) are moved by 80 mm along the \(\pm\)\(X\)-axis direction to construct the proposed extended DD coil. The simulation results show that the inductance value gradually decreases with the increase in the distance between \(D_{1}\) and \(D_{2}\), as shown in Figure 8. To fairly calculate the system output voltage when the compensation inductor is the DD coil or extended DD coil, it is necessary to ensure that the inductance values of the two types of coils are the same and that all parameters are the same. As a result, after several simulation optimizations, the extended DD coil is increased by 0.2 turns so that the simulated inductance value of the two compensation inductors is 19.2 \(\upmu\)H, and the inner diameter of the coil is set to 20 mm.  
Figure 7: Design procedure of the proposed extended DD coil.
## 4 Conclusions  
This study investigated the determination and quantification of heavy metals in river sediments using a LIBS system and multivariate chemometric analysis. Based on the characteristics of LIBS data and a PCA conducted on the full spectra, 11 characteristic lines of the main elements were identified. To simplify the discriminant model, the 11 emission lines of Al, Ca, Cd, Cr, Fe, K, Mg, Na, Ni, Pb, and Si were selected for further analysis. The PCA, which was based on the LIBS spectra at the emission lines, showed apparent clustering of four types of sediment samples, suggesting the possibility of applying this technique to discriminate different types of sediments.  
PLSR models were established, and their performance was evaluated in terms of the \(R^{2}\), RMSEC, and RMSECV. The results obtained using the PLSR with an SG derivative were improved with respect to the \(R^{2}\) and RMSECV, except for Cr. In particular, the results for Cd obtained with the SG derivative showed a decrease of 25% in the RMSECV value. This demonstrates that the PLSR model with the SG derivative is suitable for the quantitative analysis of metal components in sediment samples. This combination can play a significant role in the quality control and water quality management of rivers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metal** & **Data Type** & **RMSEC Average (\%)** & **RMSECV Average (\%)** \\ \hline \multirow{2}{*}{Cd} & Original & 2.7227 & 11.1519 \\  & SG derivative & 1.49018 & 8.35241 \\ \hline \multirow{2}{*}{Cr} & Original & 19.5385 & 46.213 \\  & SG derivative & 18.0409 & 50.6646 \\ \hline \multirow{2}{*}{Pb} & Original & 8.03888 & 27.7485 \\  & SG derivative & 6.12366 & 26.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The averages (%) in the middle of the values (RMSEC and RMSECV).  
To improve the applicability of our approach, more samples and a diversified analytical dataset should be considered to obtain sufficient spectral data in further investigations. This could provide theoretical guidance for the management of freshwater sediments and water quality.  
Conceptualization, J.H.C.; methodology, J.C. and S.Y.; formal analysis, S.Y. and J.C.; investigation, S.Y.; writing--original draft preparation, S.Y. and J.C.; writing--review and editing, J.H.C. and S.-J.M.; visualization, S.Y.; supervision, J.H.C. and S.-J.M.; project administration, J.H.C.; funding acquisition, J.H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation (NRF) of Korea entitled \"NRF-2016R1D1A1B04934910\".  
The authors declare no conflict of interest.
# Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China
## 3 Results
### Comparison with SMAP  
In order to compare CYGNSS and SMAP, as shown in Figures 14-16, we respectively made temporal changes of CYGNSS reflectivity and brightness temperature and soil moisture from SMAP during and before the flood (on 18 July 2021).  
It can be seen from Figure 14 that the changes of CYGNSS reflectivity in the black box and the blue box are obvious, and the maximum change is 15.7 dB. Correspondingly, in Figures 15 and 16, the maximum change in the brightness temperature is 40.2 K, and the maximum change in the soil moisture is 0.33 cm\({}^{3}\)/cm\({}^{3}\). As shown in Figure 14, the area of the black box (on 20 July 2021) represents Zhengzhou, which is consistent with the area represented by the black boxes in Figures 15 and 16. From 22 July to 24 July 2021, the area represented by the blue box is Xinxiang, which is consistent with the area represented by the red box in Figure 15 and the blue box in Figure 16. This shows that the monitoring results of CYGNSS are in good agreement with SMAP.  
Figure 12: (**a–d**) are using the threshold (248 K) of the brightness temperature to monitor changes in the inundated area.  
Figure 13: (**a–d**) are using the threshold (0.38 cm\({}^{3}\)/cm\({}^{3}\)) of soil moisture to monitor changes in the inundated area.
## 3 Research Methodology
### Practical Deployment Challenges of OTP\({}^{3}\)  
The major deployment challenge of the OTP\({}^{3}\) mechanism lies in Step 0 in Figure 4, where devices A and B are preset with an identical Serial Number (SN) for communication pairing. During the production or installation of IoT devices, engineers preset a series of non-repeating OTP Serial Numbers in the hardware of both paired devices A and B at both ends (e.g., in ROM). While this method requires additional engineering effort, establishing the pairing during device production is a crucial step toward achieving un-breakability based on [PERSON]'s theory [16]. Although the Diffie-Hellman [48] method used in Step 3 is presently the most commonly employed symmetric-key exchange technique, DH, however, is susceptible to cracking. With the ongoing advancements in computer processing power, such as quantum computing, even methods like eSTREAM [38] and Diffie-Hellman [48] may become vulnerable within a finite computation time. In essence, achieving cryptography security standards means that, with the current level of human technological prowess, future scientific developments are not limited in this aspect. Hence, to achieve the un-breakability based on [PERSON]'s theory [16], considering Step 3, using the OTP password exchanged on the public network, even if it is cracked and leaked, the OTP Serial Number preset in Step 0 on the device remains unknown to third parties (as it is not transmitted via the public network). Consequently, the OTP keys used in the encryption operation remain secure.
## 2 Experimental
### Preparation, Quality Tests and Physical Properties
#### 2.1.2 Structure and Composition  
The x-ray diffraction test, known as XRD, was carried out for a sample of CNP using GNR APD 2000 (Italy) at ambient temperature. The data were collected in continuous mode and with the step size of 0.010 and at the range of 3-80\({}^{\circ}\) similar to our previous works [36, 37, 38, 39]. For the particle size determination, solid samples were dispersed in Milli-Q water in an ultrasound bath (Sonorex Super, Bandelin, Germany) for 30 min. High resolution particle detecting and sizing experiment on the CNP sample was performed using a NanoSight LM10 instrument (Malvern Instruments, Malvern, UK). Results of the XRD test have been represented in Figure 5. The peak observed at 26 degree is the main characteristic peak of carbon due to structure of the particle, which is (002) lattice plane. In addition, the small peak seen in 45 degree is due to the presence of some amorphous carbon in the system, which can be ignored.
# An Intelligent Real-Time Driver Activity Recognition System Using Spatio-Temporal Features
## 4 Results and Evaluation
### Comparison with Existing Approaches  
In this section, we benchmark our proposed model against selected existing state-of-the-art studies. Although several features have been widely used to study the effect of driver activity on road safety, for a fair comparison, we selected studies that have similar feature inputs (images) to our study to determine the driver activity state. As shown in Table 6, the common limitation of most existing works [17; 30; 31; 33; 45; 46; 47; 48; 49; 50] is the absence of a driver activity recognition system for nighttime scenarios. These studies adopted several techniques that can enhance the accuracy of driver activity recognition, and some meet the real-time requirement. However, due to the lack of a full-time detection system throughout the day- and nighttime conditions, we believe that this may not be suitable for practical use to integrate with the modern ADAS. Meanwhile, great effort was made in [51] by collecting a large-scale dataset (Drive & Act datasets) including RGB, infrared, and depth images to facilitate future research on activity recognition during both day- and nighttime situations.  
Figure 10: Real-time assessment results for binary classification driver 1: (**a**) RGB images in the daytime (accuracy of 99.6), (**b**) IR images in the nighttime (accuracy of 97.9).  
A previous study evaluated numerous drivers by recruiting around 50 participants and achieved a reasonable result [30]. However, the whole process was conducted in a mock-up car environment. Thus, the results from the simulator may appear very different for a real vehicle environment. Another study [31] introduced various regularization techniques to their network construction process and achieved higher detection accuracy. However, the evaluation setting was based on a simple train-test split method. This evaluation system does not accurately reflect the true performance of the model, as the result highly depends on which data points end up on the training set and the testing set. In the proposed model, we integrated a seven-fold cross-validation evaluation system. This evaluation strategy has many advantages over train-test evaluation techniques since it provides observation of all of the data by making use of both training and testing techniques in the main time. Moreover, each testing set is used exactly once and, in this sense, the classifier model is not trained for the same training and testing sets.
# Comparison of Image Normalization Methods for Multi-Site Deep Learning
## 2 Methods  
### Image Data  
In this retrospective study, we used image data from 144 patients enrolled in six different centers within the CAO/RAO/AIO-12 study [18]. In addition, we retrospectively selected 57 patients from our institute as internal data. Our center participated in the study, so we pooled the study data with the internal data.  
According to the study protocol, for each patient, a transverse 2D T2w sequence and a DWI sequence were acquired before therapy and between neoadjuvant therapy and operation. Although a study protocol was given, the obtained image data showed varyingacquisition parameters for the individual sequences. Figure 1 depicts, for example, two of these parameters for the T2w sequence. Similar differences in the acquisition parameters were observed for the DWI sequence. Example images can be seen in Figure 2.  
Figure 1: Distribution of acquisition parameters for the T2w axial images: The image acquisition parameters vary widely. The left column shows the in-plane resolution and the right the echo time. Each row shows the data from one center. The data from center 3 were split into the data from the study (3a) and from clinical routine (3b). The in-plane resolution is supposed to be 0.8 mm but varies between 0.26 mm and 1.64 mm. The echo time is supposed to be 110 ms but varies between 69 ms and 219 ms.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods
### Communication Protocols
#### 3.1.1 NMEA 0183  
This protocol is a standard in the industry, and it is used for multiple applications, such as marine electronics, anemometers, IMUs, autopilots, GNSS receivers, etc. The information is sent in ASCII characters, so the messages can be read without translators. However, its size is not optimal.  
Messages are protected by a checksum that is computed as an exclusive \"or\" of all characters (8 bits) between the initial character and the \"**\" that precedes the checksum. This checksum is the easiest, but it has a high percentage of undetected errors [28].  
This protocol has a series of standard messages, but it also allows each developer to create their own messages simply by following the specifications.  
In this work, the NMEA (National Marine Electronics Association) protocol is used to generate a unique message with the attitude and position of the solid.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 3 Results
### Results of Study Area 2  
In study area 2, a total of 377,579 superpixels were acquired, and all of them were manually labeled through pre-classification. The ground truth can be seen in Figure 12g. A total of 575 water superpixels and 2717 land superpixels were used for training, and other superpixels were used for validation. Figure 12 shows the results of various methods. Overall, the GNN achieved the best performance in detecting small targets and preserving boundaries. The red rectangles in Figure 12a indicate where ships were accurately removed. Similar to study area 1, pixel-based methods (see Figure 12d-f) generated more noise than superpixel-based methods.  
Quantitative evaluations are given in Table 2. For superpixel-based methods, our method acquired an increase of 5 to 6 percent in precision, 12 to 13 percent in recall, and 9 to 10 percent in F1 scores, compared to RF and XGBoost. GAT network acquired the second performance, and about 6 percent in F1 scores lower than ours. The positive effect of GNN network was validated in this experiment. Meanwhile, there were much bigger increases for pixel-based methods--a maximum of 12 percent in precision, 22 percent in recall, and 17 percent in F1 scores were acquired. This shows the power of our proposed method in processing complex scenarios.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dimension** & **Method** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{Superpixel-based} & Random forest & 0.9949 & 0.9155 & 0.9536 \\  & XGBoost & 0.9939 & 0.9299 & 0.9608 \\  & GAT & 0.9543 & 0.9492 & 0.9518 \\  & Ours & 0.9955 & 0.9601 & 0.9775 \\ \hline \multirow{4}{*}{ Pixel-based} & Random forest & 0.6295 & 0.7716 & 0.6934 \\  & XGBoost & 0.6696 & 0.8120 & 0.7339 \\ \cline{1-1}  & Ours (transformed from & & & \\ \cline{1-1}  & superpixel-level to pixel-level) & 0.9972 & 0.9623 & 0.9794 \\ \hline \end{tabular}
\end{table}
Table 1: Performance evaluation in study area 1.  
Figure 10: Study area 1 classification results of (**a**) superpixel-based GNN, (**b**) superpixel-based RF, (**c**) superpixel-based XGBoost, (**d**) pixel-based Ostu, (**e**) pixel-based RF, and (**f**) pixel-based XGBoost. (**g**) shows the ground truth.  
Figure 11: Comparison of XGBoost and GNN results (TP: true-positive; FN: false-negative; FP: false-positive; A: Rectangle A corresponding to details of XGBoost; B: Rectangle B corresponding to details of GNN).  
Figure 12: Study Area 2 classification results of (**a**) superpixel-based GNN, (**b**) superpixel-based RF, (**c**) superpixel-based XGBoost, (**d**) pixel-based Ostu, (**e**) pixel-based RF, and (**f**) pixel-based XGBoost. (**g**) shows the ground truth. The red box area shows GNN is better to cope with noise.
### Comparison of Technologies Based on Information They can Provide
Although RFID tags and radio transmitters are not designed to measure location, location information can be calculated from specifications of the signal transmitted between the device on the animal and readers (or receiver). A single device which is equipped with GPS and inertial sensors estimates location and there is no need to calculate the location by taking the measurement of other devices into account. The major difference between technologies used by the Langrangian and Eulerain approaches is the applicability of the Eulerian based technologies for extracting spatio-temporal features from any animal in their field of view, while technologies used by the Lagrangian approaches are only useful for extracting these features from the tagged animals. Track is the only spatio-temporal feature which is not mentioned in Table 1, because it has to be inferred from combination of other features. For instance, a network, which can continuously detect presence of an animal, or a sparse network with identification capability, can both provide tracks. Choosing which spatio-temporal features to use for tracking depends on the data needed for the purpose of later analysis. For instance, for monitoring migration of specific species, species recognition with coarse grained localization is enough to provide the necessary tracks since individuals stay in the flock. Therefore, sometimes the group track might be more important than the individual track.
# Integrated Carbonate-Based CO\({}_{2}\) Capture--Biofixation through Cyanobacteria
## Materials and Methods
### Growth Experiments, Biomass Quantification, and Characterization  
Each biomass growth test was conducted in duplicate, with each test using a single cultivation tube of the PBR (Figure 2) and was followed for 72 h. Initially, each cultivation had a working volume of 65 mL and an initial OD680 of approximately 0.25. Every 24 h, a small sample of suspension (1.5 mL) was taken from the culture to monitor microalgal growth via OD680. At the end of the 72 h experiment, Spr01 was harvested through two centrifugation cycles at 8000 rpm, with an intermediate washing step using distilled water (35 mL). In the case of Spr02, its morphology (Figure S1b) allowed for a simple harvesting method: filtration on a polyamide membrane (Sartolon(r), 0.45 \(\mu\)m pore size) and washing of the residue with distilled water (35 mL).  
The biomass obtained from strains was dried at 65 \({}^{\circ}\)C for 24 h and weighed to determine its dry mass. The dry biomass was characterized through elemental analysis to determine its carbon, nitrogen, and hydrogen content (see Section 2.2).  
Biomass Productivity (P) was calculated from the final mass (\(m_{t_{1}}\)) with respect to the starting mass (\(m_{t_{0}}\)), which was obtained by the OD680 ratio (initial value divided by final value), according to Equation (5). t\({}_{0}\) and t\({}_{1}\) are the initial (0 h) and final (72 h) times, respectively. V is the working volume, expressed in liters.  
\[\mathrm{P}\left(\frac{mgbiomass}{L\cdot d}\right)=\frac{m_{t_{1}}-m_{t_{0}}}{V \cdot(t_{1}-t_{0})}=\frac{m_{t_{1}}-m_{t_{1}}\cdot\frac{\mathrm{OD680}_{t_{0}}} {\mathrm{OD680}_{t_{1}}}}{V\cdot(t_{1}-t_{0})} \tag{5}\]  
The CO\({}_{2}\) absorption rate (R) is proportional to P, and expressed as depicted in Equation (6), where C\({}_{\mathrm{C}}\) is carbon wt% of the biomass dry weight. The equation accounts for the molecular weight ratio of CO\({}_{2}\) and the atomic weight of the carbon element (= 44/12 = 3.67).  
\[\mathrm{R}\left(\frac{mgCO_{2}}{L\cdot d}\right)=\mathrm{P}\cdot\mathrm{C}_{ \mathrm{C}}\cdot\frac{MW(CO_{2})}{MW(C)} \tag{6}\]  
Finally, the CO\({}_{2}\) utilization efficiency (U, Equation (7)) was obtained from the moles of biofixed carbon (\(n_{OC}\)) and the moles of maximum theoretical biofixable carbon (\(n_{OC}\)(100)), that is, the moles of CO\({}_{2}\) carried by the carbonate and equal to half of the moles of the bicarbonate (see Figure 1).  
\[\mathrm{U}=\frac{n_{OC}}{n_{OC}(100)}\cdot 100=\frac{n_{OC}}{n_{HCO_{3}^{-}}/2} \cdot 100 \tag{7}\]