esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Proposed Catalytic Pyrolysis Mechanism for Tar Formation
The amount of methoxyphenols produced from catalytic pyrolysis at 500 \({}^{\circ}\)C was lower than that from non-catalytic pyrolysis; however, at 700 \({}^{\circ}\)C, the catalytic phenols via co-pyrolysis increased from 63.03% to 71.57%, higher than the non-catalytically produced phenols. Some glycosidic bonds were trapped in the molten clusters with AD, as discussed in previous sections. The pyrolysis intermediates of anhydro-oligosaccharides could have produced phenols via the catalytic pyrolysis, as shown in scheme 4 in Figure 12[43], leading to an increment of phenols at 700 \({}^{\circ}\)C. Meanwhile, the etherified guiaacyl assigning at 146 ppm was more active to be ruptured and formed, contributing to phenol formation at 700 \({}^{\circ}\)C. Both factors resulted in higher phenol production via co-pyrolysis at elevated temperatures. The content of components other than furans, ketones, and phenols in tar was much lower from catalytic pyrolysis. AD improved selectivity for furans, ketones, and phenols, which were higher value-added products [44]. Further, the catalytically produced tar could be purified at facile conditions via cracking and reforming in the successive processes [44]. The energy demand was decreased and the overall economic benefit could be promoted.
# Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities
## 3 Background
### Unsupervised Learning  
Clustering methods like k-means, hierarchical clustering, GMM, and SOM are well suited for analyzing smart city indices as they reveal patterns and group cities with similar characteristics.  
k-means is ideal when the number of clusters is known, efficiently minimizing variance within clusters, and works well for grouping cities based on similar features such as development and governance. Hierarchical clustering, which builds a hierarchy of clusters, is useful when the number of clusters is unknown. It allows visualization through dendrograms, showing how clusters merge and providing insights at different levels of similarity.  
GMM offers a probabilistic approach, assigning cities to multiple clusters with varying probabilities. It is effective when clusters overlap or when they are not well separated, providing flexible clustering. SOM, a neural network, visualizes high-dimensional data by preserving topological properties, making it ideal for interpreting complex relationships in smart city data.  
Each method offers unique strengths--k-means and GMM focus on cluster assignment, while hierarchical clustering and SOM provide deeper structural insights into the data--making them suitable for understanding different aspects of smart city development.
## 5 Conclusions  
In this work, we presented an optimized cylindrical metasurface design enabling precise beamforming and null-steering through a genetic algorithm-based approach. The proposed method provides a flexible and computationally efficient solution for tailoring radiation patterns in cylindrical geometries. Future research will focus on extending the design for higher-frequency bands, including 5G and 6G, as well as addressing fabrication challenges to facilitate experimental validation. A key contribution of this work is the use of a GA optimization tool combined with a rigorous model of individual HMS unit cells acting as radiating elements in an equivalent circular array. This approach allows for the precise analytical derivation of the phase-insertion values of the HMS cells, enabling accurate steering of both the main beam and nulls to the desired directions.  
The proposed model significantly enhances the potential applications of HMS coatings in controlling the radiation characteristics of wire antennas. It enables a comprehensive synthesis of the radiation pattern, paving the way for advanced manipulation of antenna performance. Moreover, by integrating the GA-based approach, this method introduces a novel pathway for the design of reconfigurable antennas, particularly suited for next-generation communication systems where precise beam and null control is critical.  
Writing--original draft, M.L., S.V., M.B., A.M., F.B. and A.T. All authors have read and agreed to the published version of the manuscript.  
This research has been developed in the frame of the activities of the research contract: \"MEETAPP\" PRIN 2022 project (protocol number 2022 ZZ8 APA), funded by the Italian Ministry of University and Research.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Broad Frequency Shift of Parametric Processes in Epsilon-Near-Zero Time-Varying Media
## 1 Introduction  
Controlling the phase of an optical signal with a temporal resolution below one picosecond could prove essential for ultrafast signal processing. Such short time-scales can be achieved by exploiting nonlinear light-matter interaction in second-order nonlinear crystals (electro-optical effect) or Kerr effects in centrosymmetric media. In these processes, the instantaneous frequency is temporally modulated, and if the temporal phase change is uniform over the pulse duration, the optical spectrum can be rigidly shifted. This effect can be interpreted as time refraction [1; 2], a phenomenon that has attracted the attention of the research community owing to its link to the dynamical Casimir effect and to Hawking radiation, as well as its implication in the formation of temporal band-gap structures and non-reciprocal devices [3; 4; 5; 6; 7; 8]. Strong enhancement in light-matter interactions has been observed in the spectral region, where the real part of the dielectric permittivity (\(\Re[\varepsilon_{r}]\)) of a medium approaches zero (ENZ) [9; 10; 11]. In the case of sub-wavelength thin films of transparent conductiveoxide (TCO), such enhancement combined with an ultrafast response of the medium [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] results in the generation of phase conjugation (PC) and negative refraction (NR) with near-unity conversion efficiency [24]. One of the intriguing effects associated with the enhanced ultrafast material response is the observation of spectral shifts of the order of the pulse bandwidth (10-15 nm) for the transmitted and the reflected radiation in pump-and-probe experiments [15; 16]. This phenomenon has been interpreted as originating from the adiabatic shift of the material refractive index enhanced, at the ENZ wavelength, by the associated slow-light condition [25].  
In this work, we investigate the spectral shift of PC and NR in a time-varying TCO film (aluminium-doped zinc-oxide (AZO) [13]) at the ENZ wavelength. We report the efficient generation of a \(>\)60 nm wavelength red-shifted PC and of both a red- and blue-shifted NR (covering nearly the same bandwidth, \(\simeq\)60 nm) from a 500 nm thick AZO film pumped at the ENZ wavelength.
## 2 Material and Methods  
### Study Area  
The Chiemsee is the largest lake in Bavaria (80 km\({}^{2}\)) with a maximum depth of 73 m and a volume of 2048 hm\({}^{3}\)[34]. It is located approximately 80 km southeast of Munich at an approximate altitude of 518 m above sea level (m.a.s.l.). During the last five decades, the spatial extent of the reed population has declined by about 50% at the Chiemsee [2]. Substantial reed beds at the Chiemsee are still existent and their decrease has been well documented which was why this lake was selected for our study. On the northwest side of the Chiemsee and at the Herreninsel, the most representative stocks of aquatic reed are still present [21]. Through an analysis of previous surveyed data, field observations, distribution stocks, and of water depth, the areas of interest (AOI) were selected for gathering validation data. The actual density of reed stems obtained from field observation was also considered. A total number of 14 AOIs with the occurrence of _Phragmites australis_ at a mean area of 1694 m\({}^{2}\) (100 m in length at varying width) were selected for this study. AOIs were delimited at Aiterbacher Winkel (AOI-1), at the shores of Holzen (AOI-2) at Keilbacher Eck (AOI-3), in Kailbacher Winkel (AOI-4 and -5), at Scheren (AOI-6), at the shores of town Muhln (AOI-7 and -8), and at Herreninsel (AOI-9, -10, -11, -12, -13, -14). In order to consider the variability of vegetation status, AOIs were selected in areas of different levels of protection: (a) protected all year long, (b) protected only during the avian breeding season, and (c) in areas without any form of environmental protection (Figure 1).  
Figure 1: Location of the study area at country (**a**) and local level (**b**). Distribution of Areas of Interest (AOIs) at the Chiemsee. Coordinate System is Deutsches Hauptdreiecksnetz (DHDN) Gauss Krüger Zone 4 (European Petroleum Survey Group (EPSG) 31468).
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
###### Abstract  
Unmanned Aerial Vehicles (UAVs) demand technologies so they can not only fly autonomously, but also communicate with base stations, flight controllers, computers, devices, or even other UAVs. Still, UAVs usually operate within unlicensed spectrum bands, competing against the increasing number of mobile devices and other wireless networks. Combining UAVs with Cognitive Radio (CR) may increase their general communication performance, thus allowing them to execute missions where the conventional UAVs face limitations. CR provides a smart wireless communication which, instead of using a transmission frequency defined in the hardware, uses software transmission. CR smartly uses free transmission channels and/or chooses them according to application's requirements. Moreover, CR is considered a key enabler for deploying technologies that require high connectivity, such as Smart Cities, 5G, Internet of Things (IoT), and the Internet of Flying Things (IoT). This paper presents an overview on the field of CR for UAV communications and its state-of-the-art, testbed alternatives for real data experiments, as well as specifications to build a simple and low-cost testbed, and indicates key opportunities and future challenges in the field.  
unmanned aerial vehicles; cognitive radio networks; software defined radio; network sensing; security; internet of flying things; machine learning; energy management +
Footnote †: journal: Sensors
## 3 The Impact Patterns of SRRV Deviation on Wheel-Rail Dynamic Interaction
### The Impact of SRRV Deviation on the Wheel-Rail Dynamic Interaction Force  
When the high-speed vehicle passes through a turnout that has varying degrees of positive SRRV deviation, the wheel-rail vertical and lateral force on the side of the switch rail are shown in Figure 6. As indicated in the figure, the positive SRRV deviation has a minimal impact on wheel-rail vertical force, with the peak vertical force mainly influenced by the geometric irregularities in the turnout area. The wheel-rail lateral force is much smaller than the vertical force, and the positive SRRV deviation has a more noticeable effect on the peak wheel-rail lateral force.  
When the high-speed vehicle passes through turnout that has varying degrees of negative SRRV deviation, the wheel-rail vertical and lateral force on the switch rail side is depicted in Figure 7. As shown in the figure, similar to the case of positive SRRV deviation, the negative SRRV deviation has minimal impact on the wheel-rail vertical force on the  
Figure 5: The rail profiles with different SRRV deviations: (**a**) positive SRRV deviation; (**b**) negative SRRV deviation.  
Figure 6: The wheel–rail force when the vehicle passes through turnout with different positive SRRV deviation: (**a**) wheel–rail vertical force; (**b**) wheel–rail lateral force.  
switch rail side. The negative SRRV deviation, in contrast, has a more noticeable effect on the peak wheel-rail lateral force.  
By comparing the above results with those of existing studies on wheel-rail force variations caused by SRRV changes, it is evident that prior research predominantly employs rigid turnout dynamics models based on a moving mass beneath the rail. These models tend to overestimate wheel-rail impact forces and have a limited effective frequency range [3,6], making them insufficient for filtering and the feature extraction of wheel-rail force data. In contrast, the flexible turnout dynamics model used in this study not only provides more accurate and realistic calculations of wheel-rail dynamic interaction forces but also includes richer medium- and high-frequency information [41].
# Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree
## 3 Analysis of Experiment Results and Discussion
### Extreme Random Tree Verification Analysis  
After many algorithms are compared, among the current integrated learning classification algorithms, the random forest algorithm is the most common most effective. Therefore, a comparative experiment is carried out with the extreme random forest algorithm. The accuracy and time efficiency of the XGBoost and GBDT algorithms are also compared. Figure 10 and Table 3 show that the extreme random tree algorithm is 4.42% more accurate than the random forest algorithm, 5.00% more accurate than the XGBoost algorithm, and 7.99% more accurate than the GBDT algorithm.  
In the above model construction, the number of decision trees in the extreme random tree was 120. However, the number of decisions trees affected the accuracy of the model. The experimental results for the number of decision trees are shown in Figure 11. There are several important data points, which are peaks in the image when the number of decision trees is 120, 300, 500, 800, and 1100. Because of a small number of decision trees, it is easy to cause the underfitting state. If the number of decision trees is too large, the improvement of algorithm accuracy is not of great significance. When the number of decision trees is 300, the classification accuracy reaches the highest 99.28%.  
The results from the experiments for the runtime of the algorithms are shown in Figure 12. The extreme random tree algorithm has the shortest running time of only 103.2568 s, which is 66.85% lower than that of the random forest algorithm. The XGBoost algorithm has the longest running time because it is the most complex model.  
Therefore, the proposed extreme random tree algorithm achieves a significant improvement in accuracy and time efficiency.  
Figure 9: 10-fold cross-validation accuracy after feature construction.  
Figure 11: Decision tree number and classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Algorithm** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Accuracy** \\ \hline ET & 99.29\% & 98.66\% & 99.51\% & 99.83\% & 98.46\% & 99.88\% & 99.41\% & 98.03\% & 98.83\% & 99.79\% & 99.17\% \\ RF & 95.09\% & 96.08\% & 98.65\% & 92.87\% & 96.12\% & 95.20\% & 92.75\% & 96.48\% & 94.68\% & 99.56\% & 94.75\% \\ XGBoost & 84.73\% & 97.83\% & 96.79\% & 96.25\% & 96.03\% & 94.34\% & 93.69\% & 97.65\% & 99.68\% & 84.73\% & 94.17\% \\ GBDT & 92.75\% & 92.53\% & 96.48\% & 95.41\% & 78.67\% & 92.47\% & 92.30\% & 96.37\% & 96.11\% & 78.73\% & 91.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Algorithm classification accuracy ratio comparison data.  
Figure 10: Algorithm classification accuracy comparison. GBDT: Gradient boosting decision tree; XGboost: extreme gradient boosting; RF: random forests; ET extreme random tree.
## 3 Implementation and Results  
This study focused on two satellite missions: TOPEX (SSN# 22076) (see Supplement Section 2 for the results of TOPEX) and Envisat (SSN# 27386). Both these satellites have TLE sets of high quality, and their maneuver histories are obtainable. The TLEs for both were procured from the CelesTrak website. The maneuver histories for both satellites were obtained from the International Laser Ranging Service website [46; 47]. The TLE datasets and the maneuver history of both are unclassified resources and are accessible for everyone. Although the TLE data are available for almost 45,920 satellites, including decayed and in-orbit satellites, such as SES 1, NOAA 19, and GOES 13, TOPEX and Envisat were specifically chosen to represent an example of simplest fine control, in-plane maneuvers, most complex orbit control, and out-of-plane maneuvers.  
Figure 2: Difference between the propagated and the observed semimajor axis of TOPEX. Horizontal green line is the detection limit.
# Spatial Shape-Aware Network for Elongated Target Detection
## 7 Results and Discussion
### Ablation Study Analysis
Experiments were carried out on the HRSC2016 and UCAS-AOD dataset to further validate the effectiveness of SSM. The outcomes are comprehensively presented in Tables 4 and 5. We introduced a weighted factor function based on the elongation ratio to adjust the threshold parameter for sample selection. As shown in Table 4, the HRSC2016 dataset contains a high proportion of elongated targets. When the parameter \(w\) approached 0, such as \(w\) = 0.3, the decision threshold was close to 0, causing nearly all anchor boxes to be classified as positive samples. This resulted in many low-potential negative samples being incorrectly treated as positives, significantly impairing the model's training, and the mAP dropped to 89.5%. As \(w\) increased, the decision threshold also increased, leading to improved detection performance. When \(w\) was set to around 4, the model achieved its best performance. At this value, the elongated objects in the dataset were assigned an appropriate decision threshold, avoiding premature exclusion while balancing sample selection. The mAP at this setting was 91.3%. However, when \(w\) continued to increase and approached 7, the threshold became overly dependent on statistical information and neglected the shape characteristics of the targets. Consequently, the mAP dropped to 90.1%, indicating a loss of effectiveness in distinguishing elongated targets.
# A Review on Two-Phase Volumetric Expanders and Their Applications
## 6 Two-Phase Vane Expander  
### Working Principle of Two-Phase Vane Expander  
The vane expander consists of a cylindrical rotor and a cylindrical stator (Figure 14a). The rotor is offset from the center of the stator. Several slots exist over the rotor where the vanes are inserted and pushed out to the stator wall by means of spring or similar mechanism. The area between the stator wall, rotor, and two consecutive vanes realizes one rotating chamber for the working fluid (Figure 14a). The suction process takes place when the working chamber is in communication with the admission port. The expansion process begins when the contact between the working chamber and the admission port finishes, and it ends when the chamber faces the exhaust port. In this time, the discharge process starts. The pressure exerted by the working fluid on the vanes results in the motion of the rotor. During the motion of the rotor, the centrifugal force pushes each vane on the stator, thus, ensuring a contact that seals the working chamber. Nonetheless, when the pressure difference on the vane surfaces exceeds the centrifugal force, the vane moves away from the stator, thus, resulting in a loss of contact that leads to a fluid leak. These considerations indicate the occurrence of a vane chatter, which is a discontinuous contact of the vane with the stator. As a result, the vane chatter is one of the several leak pathways that affect the performance of a vane expander (Figure 14b) [59].
# An Experimental Study on Alkali Silica Reaction of Concrete Specim Using Steel Slag as Aggregate
## 4 Conclusions  
The existing test methods for the evaluation of ASR expansion of concrete using steel slag as aggregate were examined. The important conclusions are as follows:  
1. To satisfy the JIS A 5015 standard of immersion expansibility of 1.5%, the steel slag should be aged in water for 1 month.
2. When steel slag used as fine aggregate in mortar bars, the ASR expansion increased with the increase in the duration of the test period. However, the ASR expansion was within the permitted limit of 0.1% after 14 days, as specified by the ASTM C 1260 standard.
3. When steel slag was used as coarse aggregate in concrete specimens, ASR expansion could not be maintained below the permitted limit of 0.04% specified by the ASTM C 1293 standard, regardless of the presence of mineral admixtures. In addition, although the expansion rate did not exceed the permitted limit after 7 days, cracks occurred in all specimens.
4. The evaluation of ASR expansion is affected by the mineral admixture type and the size of the steel slag. Furthermore, when mineral admixtures were used as the binder in the concrete specimens using steel slag aggregate, there were discrepancies in the results between the expansion rate and the crack properties, such as maximum length and total crack length. Thus, to accurately predict the deterioration due to ASR in concrete using steel slag as coarse aggregate, it is recommended to evaluate the expansion rate as well as to check crack patterns.  
S.Y.C. performed ASR test and verification of test results; E.I.Y. supported for making a research plan and performed the verification of test results; all authors contributed to the writing of this paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Government of Korea (MOTIE) (No. 20171520101680).  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Experiments and Analysis
### Ablation Studies
#### 4.3.2 Validity of the Proposed Module  
We investigated the effect of our proposed structures. We present the results of the ablation study in Table 3. Ours w/o trans indicates the model was trained without the transformer encoder layer and convolutional block attention module. Ours w/trans indicates the model was trained with the transformer encoder layer only. Ours w/trans+attention indicates the model was trained with the cross-attention strategy transformer and the convolutional block attention module.  
The Ours w/trans method achieved a result of 40.9% mAP, surpassing the Ours w/o trans method and gaining a +4.8% improvement. This finding proves that the cross-attention strategy transformer was effective for foggy weather adaptive detection. Ours w/trans+attention achieved the best performance, mAP, and \(AP_{0.5:0.95}\), outperforming Ours w/trans, gaining +2.4% and +2.1%, respectively. The effectiveness of the convolutional block attention module was demonstrated.