esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, the above methods all require complicated instruments and equipment, so that experiments cannot be carried out on real roads. Moreover, they ignore the complex traffic scenes and have certain limitations. In recent years, there have been few studies on the driver attention area based on real traffic scenes. [PERSON] et al. [13] focus on a driver's head, detecting facial landmarks to predict driver attention area. [PERSON] et al. [14] put forward a novel computational framework, which uses a multiresolution convolutional neural network (Mr-CNN) to predict eye gaze. [PERSON], [PERSON] et al. [15] proposed a driver visual attention network (DVAN), which can extract the key information affecting the driver's operation by predicting the driver's attention points. The above method provides a new idea for the driver's attention area extraction but there is no certainty about the adherence of predictions to the true gaze during the driving task. [PERSON] et al. [16] published the data set of DR(eye)VE, which is a traffic scene video database for predicting the attention position of drivers. The DR (eye)VE data set contains 74 traffic driving videos, each of which lasts 5 min, and records the eye movement data of eight drivers during real driving. The data set is not only composed of more than 500,000 images, but also records the driver's gaze information and its geographic location information, driving speed and driving route information; this information is not recorded in other data sets. In the follow-up work, they used the ready-made Convolutional Neural Network (CNN) algorithm to train on their database to predict the location of the driver's attention area in the driving scene [17; 18]. [PERSON] and [PERSON] [19] further improved the prediction results of driver's attention area on DR(eye)VE data set based on Bayesian theory. However, for the study of predicting driver's attention area in the driving scenes, each video only includes the eye movement data of a single driver, which not only makes the eye movement experimental data too limited, but also is easily affected by individual differences, resulting in some important traffic scene information being ignored. In our paper, we propose a driver attention area extraction method based on deep network feature visualization. This method mainly includes the Driving Behavior Information Network (DBIN) and the Deep Network Feature Visualization Method (DNFV). Firstly, we use the DBIN and DBNet data set [20] to construct the relationship between driver's horizon information and driving behavior. Then, we use the DNFV to obtain the driver's attention area. Finally, we analyzed the predicted results based on the real traffic scene and driving behavior.
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
## 4 \({}^{223}\)Radium-dichloride (Xofigo)
### Imaging for Patients' Selection and Follow-Up after \({}^{223}\)RaCl2  
#### 4.1.1 Bone scintigraphy and \({}^{18}\)F-Fluoride PET  
\({}^{99m}\)Tc-bisphosphonates bind bone hydroxyapatite such as \({}^{223}\)Ra-Cl2: therefore, bone scinitigraphy \({}^{99m}\)Tc-bisphosphonates is used in the patient selection for \({}^{223}\)Ra-Cl2 treatment [48], identifying sites of pathological and increased osteoblastic activity [49]. Moreover, bone scintigraphy has been used for the evaluation of the response to treatment with \({}^{223}\)Ra-Cl2 [50]. In these contexts, an alternative to bone scintigraphy may be represented by \({}^{18}\)F-Fluoride PET/CT, since \({}^{18}\)F-Fluoride binds bone hydroxyapatite with superior pharmacokinetic characteristics in comparison to \({}^{99m}\)Tc-bisphosphonates [51]; moreover, \({}^{18}\)F-Fluoride PET/CT evaluates bone metabolism with high spatial resolution [52]. Nevertheless, the expensive costs of this imaging technique [53] limit its use in clinical practice. It is important to highlight that the skeletal burden on \({}^{18}\)F-Fluoride PET/CT may predict the risk of bone marrow failure after \({}^{223}\)Ra-Cl2 treatment, providing important prognostic information [54].
###### Abstract  
The recent advancements in the field of superconductivity have been significantly driven by the development of nitride superconductors, particularly niobium nitride (NbN). Multicomponent nitrides offer a promising platform for achieving high-temperature superconductivity. Beyond their high superconducting transition temperature (Tc), niobium-based compounds are notable for their superior superconducting and mechanical properties, making them suitable for a wide range of device applications. In this work, machine learning is used to identify ternary and quaternary nitrides, which can surpass the properties of binary NbN. Specifically, Nb\({}_{0.35}\)Ta\({}_{0.23}\)Ti\({}_{0.42}\)N shows an 84.95% improvement in Tc compared to base NbN, while the ternary composition Nb\({}_{0.55}\)Ti\({}_{0.45}\)N exhibits a 17.29% improvement. This research provides a valuable reference for the further exploration of high-temperature superconductors in diversified ternary and quaternary compositions.  
superconductor; materials design; ternary nitrides; quaternary nitrides; elemental substitution; machine learning; high-temperature superconductors +
Footnote †: journal: Applied
## 6 Results and Discussion
### Comparison with the State of the Art Approaches
These algorithms are as follows: bAAA, bBAT, bFA, bGWO, bMFO, bMVO, bPSO, and bWOA. Table 8 presents the performance of the bWSO-log and the other algorithms in terms of classification accuracy. According to the results obtained, the bWSO-log algorithm demonstrated superior performance in eleven out of nineteen datasets. In other words, the bWSO-log method achieved approximately 58% superior performance across the datasets.
## 2 Materials and Methods
### Production of Electrospun PCL Membranes
#### 2.2.3 Preparation of the GNPs-PCL Membranes  
In order to incorporate the GNPs onto the PCL electrospun membranes, several solutions with different GNPs concentrations (0.05, 0.1, 0.2, 0.5, 1 and 2% (_w/v_)) were prepared. GNPs powder was dispersed in ethanol and kept under magnetic stirring for 4 h using 350 rpm at room temperature. Then, the solution was placed in an ultrasound bath (Sonica Ultrasonic Cleaner, Soltec, Italy) for another hour at room temperature. Then, the PCL electrospun membranes were cut into 160 mm \(\times\) 200 cm rectangles, immersed in the solution of GNPs, and stirred by an orbital shaker (Ivymen System, Comecta, Spain) at 120 rpm for 5 min, 15 min, 30 min, and 60 min in order to evaluate the influence of the immersion time in the GNPs adsorption. Subsequently, GNPs-PCL membranes were taken out of the previous solutions and dried at room temperature. The membranes were then subjected to a treatment of 5 consecutive cycles of washing (with distilled water for 1 h each) and drying at room temperature to remove the superficial excess (water treatment cycles), using an adaptation of the ISO 6330:2012 standard \"Textiles-Domestic washing and drying procedures for textile testing. Based on the GNPs content in the impregnation solutions, the membranes were named as 0.05%GNPs-PCL, 0.1%GNPs-PCL, 0.2%GNPs-PCL, 0.5%GNPs-PCL, 1%GNPs-PCL, and 2%GNPs-PCL. PCL membrane dipped in an ethanolic solution was used as control.  
The GNPs loading mass of GNPs-PCL membranes was determined by the Formula (1):  
\[\text{m}_{\text{GNPs}}=\text{m}_{\text{GNPs}}/\text{PCL membrane}-\text{m}_{\text{PCL membrane}} \tag{1}\]  
where mGNPs, mGNPs/PCL membrane, and mPCL membrane refer to the GNPs loading mass, the quantity of the PCL membrane with GNPs, and the quantity of the PCL membrane, respectively.
## 4 Discussion  
Our results showed that the outdoor concentrations of O\({}_{3}\) and PM\({}_{2.5}\) were significantly higher than indoor concentrations. The Kaohsiung City is a city with intense traffic and heavy industries, and previous studies believed SO\({}_{2}\), NO\({}_{x}\), PM\({}_{2.5}\), and CO were the major conventional air pollutant in steel plants, oil refineries, and vehicular exhaust emissions [12; 13; 14; 24]. In addition, outdoor O\({}_{3}\) might be formed by the photochemical reaction of nitrogen oxides absorbing sunlight, and VOCs [25; 26]. According to the PSI database from 2010 to 2012 of Taiwan EPA, only O\({}_{3}\) and total suspended particulate (TSP) would exceed the standard [27]. This may be the reason why outdoor PM\({}_{2.5}\) and O\({}_{3}\) concentrations were higher than indoor concentrations. In our study, outdoor median PM\({}_{2.5}\) levels (90 \(\mu\)g/m\({}^{3}\)) were higher than both the National Ambient Air Quality Standards of UAS and Taiwan EPA with the 24-hour standard for PM\({}_{2.5}\) of 35 \(\mu\)g/m\({}^{3}\). In addition, the median value of indoor PM\({}_{2.5}\) concentrations (40 \(\mu\)g/m\({}^{3}\)) was also higher than the criteria of indoor air quality (IAQ) standards of Taiwan EPA (35 \(\mu\)g/m\({}^{3}\)/24 h). In our study, indoor CO, CO\({}_{2}\), and NO\({}_{2}\) levels were significantly higher than outdoor levels. The number of occupants and human activities such as cooking, smoking, etc. might be the factors affecting indoor pollutants whereas liquefied petroleum gas (LPG), not electric stoves, was the main cooking way in Kaohsiung City [28; 29]. In addition, most of the houses were just by the roads and very close to the mobile sources in Kaohsiung City, which was thought of as a traffic-intensive city with the number of cars and motorcycles of approximately 430,000 and 1,230,000, respectively, in 2010 [30]. Thus, the main combustion products of vehicular engines such as CO, NO\({}_{X}\), etc. entering the houses through cracks and windows might be the reason why the indoor concentrations of CO, CO\({}_{2}\), and NO\({}_{2}\) were higher than outdoor concentrations [15; 16]. In comparison with traffic, industrial, and general areas, the highest household CO concentration was found in the traffic area among the three areas. According to the previous study, the greatest source of CO (more than 90%) in cities was motor vehicles [24]. The high traffic flow in the traffic area might be the reason for the observation. For CO\({}_{2}\), our study indicated that the lowest household CO\({}_{2}\) level was in the general area among the three areas. The main source of CO\({}_{2}\) was from human respiration [24; 31]. The number of residents might be one possible reason since the number of residents \(>\) 4 people in the traffic area, the industrial area, and general area were 40.40%, 57.01%, and 34.41%, respectively. We also found both household NO\({}_{2}\) and O\({}_{3}\) concentrations of the industrial area were
## 3 Results and Discussion  
### Mobile Phase Selection  
The objective of the study was to develop a simple and suitable HPTLC method for quantifying four fat-soluble vitamins in complex matrices like pharmaceutical preparations in a single development step. During the method development stage of this study, various mobile phases were trialled to accomplish this goal (Table 3). Mobile phase (MP) 5 was found to be the optimum mobile phase, which ensured adequate separation of the fat-soluble vitamins D, K, and E based on their respective \(\text{R}_{\text{F}}\) values (Figure 2). However, given their closely related structures (Figure 1), the optimised mobile phase could not separate the two forms of vitamin D, D\({}_{1}\) and D\({}_{2}\), as they produced the same \(\text{R}_{\text{F}}\) value (0.20). However, since vitamin D\({}_{2}\) is only sourced from plants and fungi and vitamin D\({}_{3}\) is derived from animals, these two vitamins are rarely combined in a single nutraceutical preparation as the former is mainly found in vegan friendly preparations [3; 22]. The developed method is thus suitable for the quantitative analysis of fat-soluble vitamins as long as only one form of vitamin D is present. The optimised mobile phase was used previously to quantify retinol (vitamin A) and \(a\)-tocopherol (vitamin E) in human serum [18], however, in this study, satisfactory analysis results for vitamin A could not be achieved.
## 3 Implementation and Design
### Mathematical Model  
The performance of the kinematic design has been evaluated by mathematical modeling via a Matplotlib [29] module, using the Cartesian and Spherical coordinate conversion equations to represent the current coordinates. Figure 10 illustrates the mathematical modeling results when varying either the azimuthal angle (\(\mathbf{\theta}\)) or the polar angle (\(\mathbf{\varphi}\)), or both. Figure 10a-c show how **Yaw** motion can be achieved in this system. Although it cannot be achieved straightforwardly, but necessitates fixing \(\mathbf{\theta}\) to 90\({}^{\circ}\) and varying \(\mathbf{\varphi}\) from 0\(\sim\)90\({}^{\circ}\), the first half of the movement in the **Yaw** panel can be obtained (Figure 10a). Then, moving \(\mathbf{\varphi}\) back to 0\({}^{\circ}\) and then fixing \(\mathbf{\theta}\) to \(-\)90\({}^{\circ}\) and varying \(\mathbf{\varphi}\) from 0\(\sim\)90\({}^{\circ}\), the second half of the panel can be fulfilled (Figure 10b). Furthermore, to illustrate the coordinate trajectories in the **Roll** panel, by varying the \(\mathbf{\theta}\) in the **Roll** panel between \(\pm\)150\({}^{\circ}\), Figure 10d shows how the \(\mathbf{\varphi}\) is fixed to 30\({}^{\circ}\) and 60\({}^{\circ}\). Additionally, Figure 10e shows the coordinate trajectories in the **Pitch** panel (varying the \(\mathbf{\varphi}\) in the **Pitch** panel from 0\(\sim\)90\({}^{\circ}\)) when \(\mathbf{\theta}\) is fixed to five positions, including 0\({}^{\circ}\), \(\pm\)45\({}^{\circ}\), and \(\pm\)135\({}^{\circ}\). Lastly, Figure 10f depicts the whole coverage (nearly the entire sphere) which is caused by varying \(\mathbf{\theta}\) between \(\pm\)150\({}^{\circ}\) (hence not a full sphere) and \(\mathbf{\varphi}\) varied from 0\(\sim\)90\({}^{\circ}\).
## 5 Experiment Results
### Comparisons with Several Efficient Algorithms  
To further evaluate the performance of the IIG in solving the collaborative helicopter rescue routing problem, four efficient algorithms were selected for comparison, i.e., the improved artificial bee colony (IABC) algorithm [17], the ALNS algorithm [38], the IG algorithm based on a learning-based variable neighborhood search (VNIG) [39], and the self-adaptive iterated greedy (SAIG) algorithm [40]. All four algorithms were implemented and run on the same PC under the same stopping condition. For each instance, the algorithms were run independently 30 times for comparison and the average fitness values were collected. Table 8 reports the comparison results of the average fitness values and RPI values obtained by five algorithms. From this table, that the following is evident: (1) the proposed IIG achieved 42 best average fitness values out of 56 instances, whereas the four compared algorithms obtained only 14 optimal values. (2) As shown in the last row of Table 8, the average RPI value of the IIG is 1.06, showing a significant improvement compared to the other algorithms. These findings demonstrate that the proposed IIG exhibits competitive performance in solving the considered problems compared to the four algorithms. Figure 7 presents the ANOVA for the obtained results in Table 8. The \(p\)-value is 1.5273 \(\times\) 10\({}^{-35}\), far less than 0.05, indicating that the proposed IIG demonstrates a significant improvement in performance over the compared algorithms. Furthermore, the IIG shows greater stability in achieving an optimal solution. Figure 6: ANOVA comparisons with the IIG, DI, and TSH. Figure 7: ANOVA comparisons with the IIG, IABC, ALNS, VNIG, and SAIG.
## 5 Results and Discussion
### Empirical Results
#### 5.2.1 Evaluation of BERT and LSTM with Respect to the Three Datasets Using Rule R1  
To show the applicability of the proposed scheme, Rule 1 was assessed as an example. A value of \(\
u\) = 0.5 was used. Tables 4 and 5 provide the results of the two models with respect to each dataset using Rule 1. In Table 4, it is observed that the BERT algorithm gives the best results for the LingSpam dataset, where the best value of \(\mu_{O}\) was 0.96 (for 10 and 20 epochs). Furthermore, for the Enron and PU datasets, the best \(\mu_{O}\) were 0.89 (for 30 epochs), and 0.82 (for 10 epochs), respectively. The interpretation is that for the LingSpam dataset, the performance of BERT (with regard to Rule 1) is near ideal (for 10 and 20 epochs), since \(\mu_{O}\) = 0.96 is very close to the ideal membership of \(\mu_{O}\) = 1. In the same sense, for the Enron and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Total Emails** & **Spam** & **Ham** & **\% of Spam** & **Year** \\ \hline Spam archive & 15,090 & 15,090 & 0 & 100\% & 1998 \\ \hline Spambase & 4601 & 1813 & 2788 & 39\% & 1999 \\ \hline Lingspam & 2893 & 481 & 2412 & 17\% & 2000 \\ \hline PU1 & 1099 & 481 & 618 & 44\% & 2000 \\ \hline Spamassassin & 6047 & 1897 & 4150 & 31\% & 2002 \\ \hline PU2 & 721 & 142 & 579 & 20\% & 2003 \\ \hline PU3 & 4139 & 1826 & 2313 & 44\% & 2003 \\ \hline PUA & 1142 & 571 & 571 & 50\% & 2003 \\ \hline Zh1 & 1633 & 1205 & 428 & 74\% & 2004 \\ \hline Gen spam & 40,408 & 31,196 & 9212 & 78\% & 2005 \\ \hline Tree 2005 & 92,189 & 52,790 & 39,399 & 57\% & 2005 \\ \hline Biggio & 8549 & 8549 & 0 & 100\% & 2005 \\ \hline Phishing corpus & 415 & 415 & 0 & 100\% & 2005 \\ \hline Enron & 36,715 & 20,170 & 16,545 & 55\% & 2006 \\ \hline Tree 2006 & 37,822 & 24,912 & 12,910 & 66\% & 2006 \\ \hline Tree 2007 & 75,419 & 50,199 & 25,220 & 67\% & 2007 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A list of existing email datasets [2; 58].  
PU datasets, the performance of BERT is somewhat distant from the ideal value of \(\mu_{O}=1\), since values of \(\mu_{O}=0.89\) and \(\mu_{O}=0.82\) were observed.  
With regard to the LSTM model, the results in Table 5 are different from that of BERT. The results indicate that the best results in terms of \(\mu_{O}\) were obtained for the Enron dataset, with the best \(\mu_{O}\) of 0.96. The results of \(\mu_{O}=0.94\) were obtained for PU with 30 epochs, while for PU, the best \(\mu_{O}\) was 0.92 for 20 and 30 epochs. Again, the results can be interpreted as explained in the paragraph above.
## 3 Results and Discussions
### Analysis of Experimental and Numerical Data Comparisons
#### 3.1.2 Utilization of Nanofluid as a Cooling Fluid  
Nanofluid was used to enhance the heat transfer within the TPMS block [18]. In order to fully remove metallic particles from the nanofluid in the piping system, the system was flushed with clean water between tests. Moreover, the TPMS porous structure was cleaned at the end of the experiment to prevent the solidification of nanoparticles within the structure. It is crucial to note that the inlet temperature varied in each experiment; therefore, examining the \(\theta\) variable is the correct approach. The observed stability in the temperature distribution when utilizing nanofluid can be attributed to the gyroid structure design. This unique geometric configuration plays a crucial role in maintaining a consistent thermal profile throughout the system.  
Figure 11 illustrates a comparative analysis of numerical and experimental temperature profiles using nanofluid in the Al TPMS structure across varying flow conditions. The study examined three distinct scenarios: inlet temperature of 12.26 \({}^{\circ}\)C with flow rate of 23.56 cm\({}^{3}\)/s, a inlet temperature of 12.21 \({}^{\circ}\)C with flow rate of 27.49 cm\({}^{3}\)/s, and a inlet temperature of 12.11 \({}^{\circ}\)C with flow rate of 35.34 cm\({}^{3}\)/s. The temperature variations measured along different thermocouple positions demonstrate an excellent correlation between numerical simulations and experimental measurements. The results reveal that the highest temperature increase occurred at the lowest flow rate of 23.56 cm\({}^{3}\)/s, while temperature rises became progressively smaller as flow rates increased. This trend can be explained by the reduced residence time of fluid within the heat exchanger at higher flow velocities. The numerical predictions show good agreement with experimental data. The average deviation remains below 5% across all test conditions. This validates the accuracy of our numerical model. Notable temperature fluctuations were observed in measurements taken beyond the 20 mm thermocouple position, which can be attributed to the complex flow dynamics near the outlet region. The consistency between experimental data points and numerical prediction curves throughout the measurement range demonstrates the robustness of both the experimental setup and the numerical methodology employed in this study.
## 3 Methodology
into four steps: (a) search for the selected analysis site by entering latitude and longitude coordinates; (b) specify the period for analysis; (c) utilize LANDSAT COLLECTION 1 LEVEL-1, with additional criteria of Operational Land Imager (OLI)/TIRS C1 LEVEL 1; and (d) download the group of files to obtain a compressed folder containing all the necessary files for processing in ArcGIS. The data acquired from the Landsat 8 satellite include digital numbers from the Operational Land Imager (OLI) and the Mono-Window Algorithm (MWA), which translates surface temperature using bands 10 and 11. This process incorporates parameters established in the downloaded data, including the average atmospheric temperature, brightness temperature, atmospheric transmittance, and emissivity of the land surface [50; 51; 52]. These thermal infrared images are subsequently processed using geographic information system (GIS) software (ArcGis Desktop 10.8 Version 10.7.0.10450) to derive values for radiance, the NDVI (normalized difference vegetation index), reflectance, and ultimately, the LST.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 1 Introduction
In that perspective, in a recent contribution, we provided the derivation of a compact joint time-delay and phase estimation Cramer-Rao bound (CRB) and the time-delay ML estimation (MLE) performance limits of different GPS and Galileo signals, including two Galileo-based meta-signals [10]. However, no further discussion was provided on the impact on the position estimation, considering both code and phase observables. With respect to [10], the main contributions of this article are:  
* An overview of the GNSS meta-signals, including combinations of GPS, BeiDou, and Galileo signals. * A comparison of GNSS meta-signals with a set of representative HO-BOC signals. * A short discussion of the use of very large bandwidth meta-signals. * The impact of the time-delay MLE behavior (related to the secondary peaks of the autocorrelation function (ACF)) on the phase MLE and the convergence to the phase CRB. * The performance limits of code-based SPP considering the use of GNSS meta-signals, for both good and bad satellite geometries; results for HO-BOC-based SPP are also provided. * The application of GNSS meta-signals in the context of precise carrier phase-based RTK positioning, to assess whether exploiting the signal phase information is worthwhile for large bandwidth signals; again, both good and bad satellite geometries are considered; for completeness, the results for HO-BOC-based RTK are also provided. * Recommendations on the best candidates that may lead to precise PNT under harsh conditions. Notice that the goal of this article is to obtain the asymptotic positioning performance limits, that is the estimation errors only related to the GNSS signal and not to external errors. Therefore, we do not consider ionospheric/tropospheric delays, orbital or satellite clock errors, or environment-specific effects such as multipath [11]. Moreover, these external errors are the same whatever the processing. Then, we are interested in the asymptotic region of the MLE, which is characterized with respect to (w.r.t.) the signal-to-noise (SNR) ratio and considering only the thermal noise. Refer for instance to [12] for the analysis of the specific impact of external errors and different types of corrections.