esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
To explore the FO-LIBS approach in the analysis of nuclear material in a radiation field, Gd detection in surrogate nuclear fuel debris was examined under gamma irradiation (0-10 kGy/h) using a portable FO-LIBS apparatus developed by our group. The LIBS spectra of the surrogate debris revealed that the emission lines of Gd at 501.5 nm and 510.3 nm are potential candidates for the identification of Gd in real fuel debris. The observed spectral intensities were diminished upon gamma irradiation as a result of radiation-induced damage to the optical fiber, while the plasma characteristics were hardly affected by the gamma radiation field. Calibration curves were established from the intensity ratio of Gd (501.5 nm) and Ce (474.5 nm) emission lines, exhibiting good linearity with the Gd concentrations. The LOD of Gd was calculated to be less than 0.1 wt%, regardless of the radiation dose rate.  
This study highlights the capability of FO-LIBS to perform in situ and in the remote analysis of nuclear material in a high-radiation field. Furthermore, the combination of our probing system with a robotic arm could improve accessibility to remote samples. Inspection of the damaged reactor vessels requires a longer fiber-optic cable, which significantly attenuates the delivered laser energy by absorption and scattering in the fiber [9; 13]. As the use of intense laser pulses in compensation may damage the fiber, the laser fluence at the focal spot will be eventually limited, thereby reducing the LIBS signal intensity. This limitation will be overcome by combining FO-LIBS with a giant-pulse microchip laser [30]; a lasing medium is installed in a probe head to directly irradiate a sample with the high-power laser [31; 32]. Experiments in this mode are presently underway in our group.  
Investigation, R.N., M.S. and H.O.; writing--original draft preparation, R.N.; writing--review and editing, M.S. and H.O.; supervision, H.O. and I.W. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dose Rate (kGy/h)** & \(R^{2}\) & \(b\) & \(a\) & **LOD (wt\%)** \\ \hline
0 & 0.993 & 1.573 & 0.020 & 0.027 \\
0.8 & 0.999 & 1.481 & \(-\)0.031 & 0.075 \\
10 & 0.980 & 1.595 & \(-\)0.045 & 0.039 \\ \hline \end{tabular}
\end{table}
Table 2: \(R^{2}\) values and fitting parameters for Gd calibration curves. \(b\): slopes of the curves, \(a\): intercepts.  
Figure 5: Calibration curves derived from the intensity ratio of Gd 501.5 nm/Ce 474.5 nm lines. The \(R^{2}\) values and fitting parameters are listed in Table 2.  
**Funding:** This research was supported by \"Advanced study on remote and in-situ elemental analysis of molten fuel debris in damaged core by innovative optical spectroscopy,\" the Center of World Intelligence Project for Nuclear S&T and Human Resource Development, Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.  
**Acknowledgments:** The authors wish to thank [PERSON] and [PERSON] for his technical support in the experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Dissolved Oxygen  
At the PZ2 injection well, the mean concentration of dissolved oxygen during baseline-ing equaled 6.30 mg\(\cdot\)L\({}^{-1}\)[6]. The day after the injections, the first measurement taken at 6.27 mg\(\cdot\)L\({}^{-1}\) has the same order (Figure 17a) of magnitude. This shows that the impact of the injection on this parameter is already no longer visible at this point.  
In contrast, at PZ2 BIS, the mean concentration dropped from 6.24 mg\(\cdot\)L\({}^{-1}\) prior to the injections to a minimum of 4.17 mg\(\cdot\)L\({}^{-1}\) at t = 0.82 days (Figure 17b). As with the oxidation-reduction potential, there are also significant fluctuations in the dissolved oxygen concentration during the first two days following the injections, probably also linked to the increase in the frequency of measurements and the disturbances induced by operations at the piezometers.  
A detailed inspection of the first day of the experiment clearly shows the existence of three successive minima on 19 November 2019 at 14:45 with 5.13 mg\(\cdot\)L\({}^{-1}\), on 19 November 2019 at 15:45 with 5.77 mg\(\cdot\)L\({}^{-1}\), and on 20 November 2019 at 10:35 with 4.17 mg\(\cdot\)L\({}^{-1}\) (Figure 18):  
1. The first one, synchronous with the first tracer peak, corresponds to the passage of water from the first tracer tank (see circled 1 in Figure 18). This water is deoxygenated due to the bubbling of helium.
2. The second one, synchronous with the second tracer peak, corresponds to the rapid passage of water from the second tank. This water is deoxygenated due to the bubbling of hydrogen (see circled 2 in Figure 18).
3. The third one, synchronous with the third tracer peak, corresponds to the slow passage of water from the second tank (see circled 3 in Figure 18). It signals the arrival of the main plume of hydrogenated water, which circulated more slowly within the aquifer and still contained under-oxygenated water for about a day after the injections.  
Regarding the other piezometers located further downstream, fluctuations are systematically noted during the first two days of monitoring with dissolved oxygen concentrations below 6 mg\(\cdot\)L\({}^{-1}\): this is also considered as an artifact associated with the higher frequency of measurements (Figure 19). Apart from these fluctuations, as the distance downstream of the injection well approaches 10 m, there is no evidence of an impact due to the injections on the dissolved oxygen concentration in the water.  
Figure 17: Comparative evolution of the dissolved oxygen and the tracer concentrations at the main piezometers: (**a**) PZ1 (20 m upstream) and PZ2 (injection well); and (**b**) PZ1 and PZ2 BIS (5 m downstream).
# Meander Line Super-Wideband Radiator for Fifth-Generation (5G) Vehicles
## 4 Results and Discussion
### Measured Results  
The prototype of the 5G FR-II band antenna is developed and depicted in Figure 8. The antenna is developed on an FR4 substrate and 0.07 mm thin adhesive copper tape. As per availability, a 40 GHz connector is used to connect the 43.5 GHz vector network analyzer (VNA) for the measurement. Also the full range of the VNA is used for the scattering parameter measurement, leading to a higher noise level in the measured results. Measuring the scattering characteristics of patch antennas poses challenges related to accurate near-field assessments, precise frequency coverage, and polarization considerations. Achieving a true far-field condition for measurements can be difficult in limited-space environments, which impacts the reliability of the results. Precise calibration of the measurement system is essential to mitigate errors, and the influence of surrounding materials on antenna performance must be carefully addressed during testing. However, simulated results show that the antenna resonates beyond 60 GHz.  
The measured scattering parameter and radiation patterns at 28 GHz are shown in Figure 9. The agreement between the measured and simulated scattering parameters are satisfactory. This validates the proposed design approach and the theoretical calculations of the meander-line-radiator-based wideband antenna. The antenna's complex radiation pattern may require sophisticated measurement setups to capture its directional properties comprehensively. Ensuring precise antenna positioning and calibration becomes crucial to mitigate errors and achieve reliable radiation pattern data. Addressing these challenges demands a careful balance of advanced measurement techniques and meticulous experimental procedures. However, the good agreement seen between simulated electric and magnetic field radiation patterns also confirms successful radiation from the proposed structure. The dimensions calculated for the desired operating bands are identical between simulation and measurement and have satisfactory radiation characteristics. The radiation efficiency of the antenna as measured at 28 GHz was found to be 87.9%.  
Figure 8: Prototype of the super-ultra-wideband antenna.  
Figure 9: Measured results of the proposed antenna: (**a**) S11, (**b**) E-plane pattern at 28 GHz, and (**c**) H-plane pattern at 28 GHz.
## 3 Results and Discussion
### Bending Strength  
The bending strength indicates if an anode can withstand manipulations during its placement and removal in and out of the baking furnace as well as the electrolysis cell. The results are given in Figure 6. As can be seen from this figure, the modification of biocoke with A(1) and A(3) (i.e., B3-A(1)3 and B3-A(3)3) increased the bending strength of the anodes compared to that of the standard anode (i.e., B0-A(0)0), whereas the modification of biocoke with A(3) reduced it (i.e., B3-A(3)3). This is in agreement with the wettability and the XPS results explained previously.  
Figure 5: CO\({}_{2}\) reactivity of anodes.  
Figure 6: Bending strength of the anodes.
# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 3 Data and Methodology
### Experimental Setup  
We compared the performance of the TCN model (described in Section 3.3) with the ARIMA model (described in Section 3.2) across different evaluation metrics and time horizons. We split our data into training (70%), validation (15%), and test (15%) sets. The split was performed chronologically, to maintain the temporal structure of the data, with the most recent observations reserved for testing. This approach ensured that we evaluated the model's performance on truly unseen data, simulating a real-world forecasting scenario.  
The TCN model was trained to predict carbon tax prices for the next year based on historical data from the previous five years. We used the mean absolute error (MAE) and root mean squared error (RMSE) as evaluation metrics. These metrics are defined as  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}| \tag{3}\]\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}} \tag{4}\]  
where \(y_{i}\) is the actual carbon tax price and \(\hat{y}_{i}\) is the predicted price.  
For comparison, we implemented the ARIMA model [20] as a baseline, representing traditional time series forecasting approaches. The ARIMA model's parameters (p, d, q) were selected using the Akaike information criterion (AIC) for each country in our dataset. We used a grid search approach to find the optimal parameters, considering values of p and q up to 5, and d up to 2.  
To ensure the robustness of our results, we performed 5-fold cross-validation, reporting the average performance metrics across all folds. This approach helped to mitigate the impact of data partitioning on the model performance and provided a more reliable estimate of the model's generalization ability.  
Additionally, we conducted a sensitivity analysis to assess the impact of different hyperparameters on the TCN model's performance. We explored the following hyperparameter ranges:  
* Number of convolutional layers: 2 to 6
* Number of filters per layer: 32 to 128
* Kernel size: 2 to 5
* Dilation rates: various exponential schemes (e.g., [1; 2; 4; 8], [1; 2; 4; 8; 16])
* Dropout rate: 0.1 to 0.5  
The sensitivity analysis was performed using random search, with 100 iterations. This allowed us to efficiently explore the hyperparameter space and identify the most influential parameters for model performance.
## 2 Related Work
### Facility Location Problems
In their study, the authors of [52] employed a multinomial logistic model to assess the inclination of customers to utilize APLs, taking into consideration factors such as APL availability at home and travel distance. Their analysis of various regional clusters revealed that strategically locating APLs could yield cost savings of up to 11.0%. While APLs demonstrated a positive impact on overall CO2 emission reduction in urban areas (up to 2.5%), they concurrently led to additional emissions (4.6%) in less populated regions due to extended travel distances during the pickup process. Building upon this, ref. [53] presented a location and routing problem formulation for last-mile delivery services employing APLs. They introduced a mathematical model based on mixed integer programming to optimize the environmental impact by minimizing distances traveled by both the delivery company and consumers. The model incorporated consumer behavior through two parameters: the maximum acceptable travel distance to reach an APL using any means of transport and the maximum eco-friendly distance a consumer is willing to travel by foot or bicycle, thus producing no CO\({}_{2}\) emissions. Furthermore, ref. [54] conducted an analysis of the CO\({}_{2}\) impact associated with APLs in last mile parcel delivery. Employing continuous approximation techniques, the paper assessed the potential enhancement in delivery route efficiency. Multinomial logistic regression was utilized to estimate travel distance and mode choice for customers collecting their parcels. The study underscored the importance of weighing the efficiency gains for the delivery vehicle against the environmental impact of customers traveling to the APL, particularly when using private cars. In summary, the research in the field has laid a solid foundation for the current study. The extensive investigation of various aspects of alternative APL locations, including roaming delivery, pickup points, lockers with capacity, and home and shared delivery locations, has delivered valuable insights. Building upon these findings, the primary objective is to tackle the challenges related to CO\({}_{2}\) emissions while simultaneously optimizing the routing process. The comprehensive analysis of alternative APL locations has provided a basis for further exploration and refinement. With a focus on sustainability, the aim is to derive innovative strategies to reduce CO\({}_{2}\) during the delivery process. By implementing environmentally friendly practices and leveraging state-of-the-art routing technologies, the overall efficiency of APL systems should be enhanced. Therefore, this research is contributing significantly to the field of LML and sustainable transport. The final research goal is to develop a practical and environmentally conscious solution that not only benefits businesses and consumers but also contributes positively to the environment. By addressing the critical issues associated with CO\({}_{2}\) emissions in the delivery sector, the way could be paved for a greener and more sustainable future.
## 4 Discussions  
### Prediction of Corrosion Rates  
The mass loss of Q235 steel after long-term exposure to the urban industrial atmosphere demonstrated that the steel undergoes a continuous process of corrosion (Figure 2).  
Figure 11: Full spectrum fitting analysis.  
Figure 12: Fitting results using the peak splitting.  
In the first two years of exposure corrosion, the rust layers were thin (Figure 8a) and loose (Figures 3a, 4, 5 and 8a). During this period, it was not easy to accumulate pollutants under rainfall erosion, and liquid films evaporated easily under long sunshine hours (Table 3). Therefore, corrosion was relatively difficult to occur. From the third year to about the first half of the fourth year, the rust layer grew to a certain thickness (Figure 8b) but remained loose (Figures 3b and 6), and the SO2 concentration was high. Many pores (voids) and microcracks in the rust layer (Figure 8b) provided access to the corrosive media, making the rust layers highly permeable, whereas the rust layer shielded the sunlight and impeded the rust layers from drying. Consequently, the corrosion rate increased, and this agrees well with the other available results [25]. When the corrosion time is less than 3 years, the \(n\) value is 1.317, and this is consistent with the other available result [64], that is, corrosion is more serious in industrial regions than on the coast. However, the corrosion rate during the fourth year showed a decrease compared to the third year. After four years of corrosion, the corrosion rate decreased significantly, which resembles the rule of corrosion in an industrial atmosphere described in one study [4]. After seven years of corrosion, the \(n\) value for the second stage of corrosion was found to be 0.614, indicating that corrosion after four years is a decelerated and convergent process. The deceleration in corrosion rate can be explained by recrystallization, agglomeration, and compaction of rust layers as time increases, making the rust layers denser (Figure 3c). Parts of the rust layers became dense and smooth (Figure 7b,d). The dense rust layer impedes the diffusion of reactants, thus slowing down the corrosion rate [65; 66; 67; 68]. Furthermore, as shown in Table 3, the air quality of Taiyuan significantly improved in the preceding years, and the concentration of SO2 in 2018 decreased by 63.73% compared with that in 2013. Therefore, corrosion caused by SO2 decreased significantly after the fifth year.  
Based on the results mentioned above, it can be concluded that the corrosion power function parameters of Q235 steel, obtained after three years of exposure to corrosive environments, do not entirely reflect the corrosion performance in highly corrosive environments. This conclusion is consistent with the conclusions of some previous studies [25; 69]. Reliable atmospheric corrosion parameters can only be derived from long-term exposure. It is inappropriate to apply the results of one year of atmospheric corrosion to determine the corrosivity of the environment.
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 3 Materials and Testing
### Mix Design  
The Marshall mix design method was utilized to achieve the objectives of this study, especially to determine the Optimum Asphalt Content (OAC) for the various asphalt mixtures. The OAC was calculated as the average asphalt content corresponding to three critical parameters: 4% air voids (AV%), maximum stability, and maximum unit weight. Additionally, the remaining properties were checked against their specified ranges at the OAC, as outlined by the Marshall method guidelines.  
For the surface course mixes with different gradations, the Marshall plots, shown in Figure 2, depict the relationships between asphalt content and Marshall properties, including stability, flow, unit weight, air voids (AV%), voids in mineral aggregate (VMA%), and voids filled with asphalt (VFA%). The OAC obtained for WCG 1 and WCG 2 was 4.6% by weight of the total mix. For WCG 3, the calculated OAC was slightly lower at 4.53%; however, it was rounded to 4.6% for practical purposes. Similarly, the Marshall plots for the binder course (BiCG) and base course (BaCG) mixes are presented in Figure 3. The OAC values determined were 4.2% for BiCG and 3.9% for BaCG by weight of the total blend. These OAC values ensure optimal performance of the asphalt mixtures while meeting the specified Marshall property requirements.
# A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets
## 4 The Proposed Algorithm  
This section presents the proposed array-embedded tree algorithm for _TFUIM_, called the _ATTFUIM_ algorithm. In the past, a two-phase algorithm [18] was introduced to solve the temporal fuzzy utility mining problem. This study uses a one-phase method to find _HTFUIs_ according to the upper bound model [16] and FP-tree [19]. First, the data-processing step needs to be processed. The item quantities in all the transactions are transformed into fuzzy sets based on the membership functions. If the _ftuibr_ value of a linguistic term is not less than the specified threshold, it is treated as an _HTFUIUBI_. Then, the linguistic terms which are not _HTFUIUBIs_ are deleted from the transformed database. The remaining terms are then sorted in descending counts. The sorted terms in each transaction are used to build the tree. Finally, a mining procedure similar to the FP-Growth in FIM is designed to find out potential candidates and determine whether they are _HTFUIs_. The details for the steps of the proposed method with examples are described in the following four subsections.
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
## 3 Quantitative Evaluation Method for Aircraft Collision Risk
### Establishment of the Apron Operations Petri Net  
After obtaining information on the position distribution and operational status of the airport apron taxiways, a Petri net for apron operations is established, tailored to the taxiway distribution network. This involves constructing a model of apron taxing activities:  
\[N=\{P,T,Pre,Post,m,K\} \tag{1}\]  
The set of places \(P\) represents subregions where aircraft are permitted to operate, with the subdivision principle being that each subregion is defined such that only one aircraft is allowed to operate within it at any given time. The set of transitions \(T\) represents the collection of boundaries between two operational subregions for aircraft. \(Pre\) and \(Post\) represent the forward and backward incidence matrices, respectively, between the places \(P\) and the transitions \(T\). The marking \(m\) represents the operational state, indicating the number of aircraft currently taxiing. The mapping \(K:P\to A\) represents the aircraft that are prohibited from entering specific subregions.  
In the established apron operations Petri net, tokens are used to represent the aircraft currently taxiing on the apron. The state of the operational model is represented by the movement of tokens between places within the Petri net. If a place contains a token, it indicates that an aircraft is currently occupying that subregion. Conversely, if a place does not contain a token, it signifies that the subregion is currently unoccupied and in a free state. The Figure 2 illustrates an example of an apron operations Petri net. When an aircraft taxis from the first subregion to the second, the token in the Petri net moves from place \(p_{1}\) to place \(p_{2}\). The transition \(t_{1}\) represents the movement of the aircraft as it crosses the boundary between the two subregions.  
Figure 1: Apron collision risk assessment flow chart.
## 4 Conclusions  
In this paper, the capability of polarimetric SAR to detect and classify marine oil spills was investigated. Potential features were extracted from a covariance matrix, a coherence matrix and a Stokes vector of the original SLC quad-pol SAR data. Deep learning algorithms together with classic classifiers were compared and analyzed. A key discovery of this paper is that given insufficient number of data samples, deep learning algorithms such as SAE and DBN can achieve better performance than traditional algorithms by initializing their parameters from a position closer to the optimum solution. Polarimetric SAR data confirmed its strong capacity in distinguishing mineral oil and its biogenic lookalikes. This can be achieved by a one-step operation, with no need to firstly segment and then classify data samples based on auxiliary information. The advantages demonstrated by polarimetric SAR can greatly boost the efficiency and accuracy of marine oil spill detection. Further studies will be conducted on features extracted from compact polarimetric SAR modes, with wider swath width, to achieve larger monitoring areas and shorter revisit times: two of the prime requirements for marine surveillance through large areas.  
The RADARSAT-2 data provided by CSA and MDA is highly appreciated. This research is jointly supported by the National Key Research and Development Program of China (2016 YFB0501501) and the Natural Scientific Foundation of China (41471353 and 41706201). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that helped improve the quality of this manuscript.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON] performed the experiments and analyzed the data. [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflicts of interest.