esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A period-reconfigurable leaky-wave antenna (LWA) with circular polarization (CP) and fixed-frequency beam scanning (FFBS) is developed in this article. Operating in the Ka-band, this antenna consists of a low-loss groove gap waveguide (GGW) as the slow-wave transmission structure, a circular split-ring patch array on the top layer for radiation, and a slotted ground between them for energy coupling. Each slot is independently and electrically controlled by a pair of PIN diodes under the coupling slot. Thus, the period length of the patches can be manipulated and an LWA with CP and FFBS is achieved with \(-1\)th spatial harmonics radiated. The simulation results show that the bean-scanning range from \(61^{\circ}\) to \(63^{\circ}\) can be realized during the observation frequency band, with good circular polarization and a peak gain of 17.1 dBi, which is verified by the measurement.  
c 2025 12 274 2025 1212 2025 12 2025 12 2025 12 2025  
is proposed for FFBS. In [12], a microstrip line with surface impedance modulated by the loaded varactor diodes is presented for beam scanning. In [13, 14], two SIW-based period-reconfigurable LWAs are investigated for beam scanning, with the period length manipulated by biasing of the PIN diodes. For a higher gain, a low-loss GGW transmission structure has commonly been used recently, especially in mmWave applications [15, 16, 17].  
Due to the unique properties when facing multipath interference and polarization mismatch [18], circular polarization is also demanded in many scenarios, for higher efficiency and lower signal loss in communication. Many CP LWAs have been presented during the past years, based on different theories and technologies, like feeding networks [19], phase compensation [20], and the holographic method [21], among others [22, 23, 24].  
In this article, a CP LWA based on GGW is proposed for fixed-frequency beam scanning. The period length of radiating patches on the top layer is reconfigured by manipulating the operating state of the coupling slot, which is electrically controlled by a pair of PIN diodes. A CP radiation is generated by a circular split-ring-shaped patch unit with properly designed structural parameters. Thus, a CP fixed-frequency beam scanning LWA is realized, which could be a good candidate in wireless communications, radar, and electronic and optical sensing.
# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 5 Conclusions  
The paper proposes a method called YOLOv7-3D for single-camera 3D traffic object detection in roadside monitoring scenarios. Through experiments and evaluations on the Rope3D dataset, our YOLOv7-3D algorithm achieves significant performance results in single-camera 3D traffic object detection. Compared to traditional methods based on 2D image processing, our algorithm accurately captures the position, size, and orientation information of traffic objects, as well as estimates the distance between vehicles and the camera. By overcoming the limitations and challenges of a single-camera perspective, our algorithm enables more accurate and reliable traffic object detection, providing a more reliable guarantee for urban traffic safety and efficiency. Future research directions  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Range (m)**} & \multicolumn{4}{c}{**Score**} \\ \cline{3-6}  & & **Car** & **Big Vehicle** & _Cyclist_ & _Pedestrian_ \\ \hline \multirow{4}{*}{MonoDLE} & all & _92.8_ & 86.3 & _88.5_ & _92.2_ \\ \cline{2-6}  & 0–30 & _89.8_ & _84.0_ & _87.1_ & _92.0_ \\ \cline{2-6}  & 30–60 & _91.7_ & _85.3_ & _88.7_ & _92.7_ \\ \cline{2-6}  & 60–90 & _92.7_ & _90.9_ & _88.9_ & _91.9_ \\ \cline{2-6}  & 90–120 & _92.89_ & _89.3_ & _86.2_ & _91.3_ \\ \hline \multirow{4}{*}{Ours} & all & _94.4_ & _89.7_ & _89.1_ & _91.9_ \\ \cline{2-6}  & 0–30 & 92.8 & 86.9 & 88.5 & 91.8 \\ \cline{1-1} \cline{2-6}  & 30–60 & 94.7 & 92.0 & 89.3 & 92.5 \\ \cline{1-1} \cline{2-6}  & 60–90 & 94.5 & 91.9 & 88.6 & 92.4 \\ \cline{1-1} \cline{2-6}  & 90–120 & 93.7 & 90.0 & 88.8 & 90.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison results of detection performance in different distance ranges.  
include further optimizing algorithm performance and exploring integration with other traffic monitoring systems to achieve more intelligent and efficient traffic management and control.  
Conceptualization, software, formal analysis, investigation, resources, data curation, writing--original draft preparation, Z.Y.; validation, Z.Y., H.Z., J.G., and X.L.; writing--review and editing, supervision, project administration, funding acquisition, H.Z. and J.G.; visualization, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Innovation and Development Fund Project of the China Academy of Engineering Physics(CX2020033).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
###### Abstract  
Synthetic aperture radar (SAR) interferometry has rapidly evolved in the last decade and can be considered today as a mature technology, which incorporates computationally intensive and data-intensive tasks. In this paper, a perspective on the state-of-the-art of high performance computing (HPC) methodologies applied to spaceborne SAR interferometry (InSAR) is presented, and the different parallel algorithms for interferometric processing of SAR data are critically discussed at different levels. Emphasis is placed on the key processing steps, which typically occur in the interferometric techniques, categorized according to their computational relevance. Existing implementations of the different InSAR stages using diverse parallel strategies and architectures are examined and their performance discussed. Furthermore, some InSAR computational schemes selected in the literature are analyzed at the level of the entire processing chain, thus emphasizing their potentialities and limitations. Therefore, the survey focuses on the inherent computational approaches enabling large-scale interferometric SAR processing, thus offering insight into some open issues, and outlining future trends in the field.  
high performance computing (HPC); parallel computing; SAR interferometry (In-SAR); synthetic aperture radar (SAR); scientific computing; cloud computing +
Footnote †: journal: Remote Sensing  
1
Footnote 1: [[https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))
# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 3 Hyperparameter Optimization Problem
### HyperBand Optimization Algorithm  
The HyperBand algorithm, based on the principle of adaptively allocating resources to a hyperparameter configuration [11], is designed to expedite the optimization process. Its primary strength lies in the use of early stopping to concentrate computational resources on promising configurations. This approach, coupled with the division of the optimization process into rounds, streamlines the search for optimal hyperparameters.  
Despite its simplicity and ease of implementation, which make it an attractive choice for certain applications, HyperBand's reliance on random sampling of hyperparameterconfigurations can lead to suboptimal solutions. Therefore, fine-tuning the parameters that govern the early stopping mechanism is crucial for achieving optimal performance. The formalization of the algorithm is presented in Algorithm 1.  
```
1:Input: \(R\) (maximum resources per configuration), \(\eta\) (resource reduction factor)
2:Output: the best hyperparameter configuration
3:procedureHyperBand(\(R,\eta\))
4:\(s_{\max}\leftarrow\lfloor\log_{\eta}R\rfloor\)
5:\(B\leftarrow(s_{\max}+1)\cdot R\)
6:for\((s=s_{\max})\)down to \(0\)do
7:\(n\leftarrow\lceil\frac{B}{R}\cdot\eta^{s}/(s+1)\rceil\)
8:\(r\gets R\cdot\eta^{-s}\)
9:\(T\leftarrow\text{Sample}(n)\)
10:for\(i=0,\ldots,s\)do
11:\(n_{i}\leftarrow\lfloor n\cdot\eta^{-i}\rfloor\)
12:\(r_{i}\gets r\cdot\eta^{i}\)
13:\(L\leftarrow\text{Run}(T,r_{i})\)
14:\(T\leftarrow\text{Top}(\frac{\eta}{\eta},L)\)
15:endfor
16:endfor
17:return best configuration of \(T\)
18:endprocedure
```  
**Algorithm 1** HyperBand.  
In this context, \(R\) represents the maximum number of resources that can be allocated to a single configuration, while \(\eta\) is a resource reduction factor. The maximum number of iterations is denoted by \(s_{\max}\), and \(B\) signifies the total resource budget. The number of configurations to test is represented by \(n\), and \(r\) is the number of resources allocated to each configuration. The set of hyperparameter configurations is represented by \(T\). The function \(\text{Sample}(n)\) randomly selects \(n\) hyperparameter configurations, and \(\text{Run}(T,r_{i})\) runs the model with the configurations in \(T\) and \(r_{i}\) resources. Lastly, \(\text{Top}(k,L)\) is a function that selects the top \(k\) configurations from \(L\) based on a specific performance metric.  
The choice of \(R\) and \(\eta\) can greatly impact the results of the algorithm.
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion
### Hydrocarbons and Chlorinated Hydrocarbons
#### 4.1.2 Cyclic Hydrocarbons  
Cyclic hydrocarbons, encompassing both aliphatic and aromatic types, significantly outperform linear aliphatic hydrocarbons in oil recovery efficiency, as shown in Figure 2c. These cyclic molecules likely experience fewer steric hindrances compared with their linear counterparts, enhancing their ability to destabilize the oil-water interface. Consider limonene, which possesses a branched substituent within its cycle. Though not excessively large, this structure might introduce some steric hindrance, potentially limiting its effectiveness in destabilizing emulsions. Xylene, with its two methyl substituents positioned ortho, meta, or para, is small enough to facilitate droplet coalescence, seemingly acting as \"destabilizing tweezers\". Both xylene and cyclohexane demonstrate high efficacy even in low volumes. Additionally, these substances are not only readily available and cost-effective but also recyclable and reusable in water purification processes, provided the right recovery system is in place. In terms of toxicity and handling, within the context of the oil extraction industry, they are not deemed hazardous, offering a practical advantage in  
Figure 2: Percentage of residual oil removed from produced water: (**a**) aliphatic hydrocarbons, (**b**) influence of the number of carbons in aliphatic hydrocarbons, (**c**) cyclic hydrocarbons, and (**d**) chlorinated hydrocarbons.  
this sector. Supplementary Document S1 presents data on the toxicological properties of cyclohexane and xylene as well as crude oil for reference.
# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
## 5 Conclusions  
A grape leaf disease recognition model with strong generalization ability is proposed based on Fourier domain adaptation. By exchanging the low-frequency amplitude of the source image spectrum with the low-frequency amplitude of the target image spectrum, the Fourier transform designed in this study is applied to the adaptive method of grape leaf disease recognition in different domains, which does not require any large amount of calculation and complex model and improves the generalization ability of the recognition model. The results showed that the accuracy of the model trained by the selected three \(\beta\) values to identify grape leaf diseases in the target domain can reach 96.2%, 96.2%, and 96.7%.  
In the research of agricultural disease image classification, how to improve the generalization ability of image classification conveniently is still a difficult problem. Fourier domain adaptation can solve this problem in some cases. The low-level statistical data of the image can change greatly without affecting the underlying scene. This preprocessing can replace complex architecture or laborious data enhancement, so as to solve some problems in image recognition of grape leaf diseases and improve the recognition generalization ability. In the future, it is hoped that this method can be applied to actual production.  
Conceptualization, J.W. and Q.W.; Formal analysis, Y.W.; Investigation, Z.J.; Methodology, J.W., T.L. and Z.J.; Project administration, Q.W.; Software, J.W., Y.W., P.L. and T.Y.; Supervision, Q.W.; Validation, T.L., Y.W. and P.L.; Visualization, T.Y.; Writing--original draft, J.W.; Writing--review and editing, Q.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Social Science Foundation of China, grant number No. 19 BY036 and The Excellent Young Teachers Basic Research Support Program for Provincial Undergraduate Universities in Heilongjiang Province, grant number No. YQJH2023200.  
The data that support the findings of this study are openly available in the 2018 AI Challenge pest classification dataset. The URL is currently disabled. To download the data, please visit the link [[https://github.com/baliwaka/grape-leaf-disease](https://github.com/baliwaka/grape-leaf-disease)]([https://github.com/baliwaka/grape-leaf-disease](https://github.com/baliwaka/grape-leaf-disease)) (accessed on 25 April 2024).  
This work was supported by the National Social Science Foundation of China No. 19 BY036.  
The authors declare no conflicts of interest.
# Monitoring Small Water Bodies Using High Spatial and Temporal Resolution Analysis Ready Datasets
## 2 Methods  
### Study Region  
Eastern Arkansas is one of the largest irrigated regions in the USA that has seen a rapid increase in the number of OFRs during the last 40 years [35; 36; 37]. The region has a humid subtropical climate with an average annual precipitation of 1300 mm, mostly distributed between March and May and November and January [23]. Recent studies [35; 36] mapped the spatial distribution of 340 OFRs with surface area <30 ha and distributed across three sub-watersheds in the study region (Figure 1). The OFR dataset was manually mapped using the high-resolution (1 m) National Agriculture Imagery Program archive in combination with 2015 Google Earth satellite imagery. The authors of the OFR dataset used Google Earth Explorer to sharpen the image details when zooming in and to provide a validation for features appearing indistinct or pixelated in the 1-m mosaic imagery [35]. We assigned the OFRs to three size classes (0.1-5 ha, 5-10 ha, and 10-30 ha) based on the surface area mapped in the OFR dataset. These classes were used to support the surface area monitoring analyses when accounting for different OFR sizes (Figure 1).  
Figure 1: Study region in eastern Arkansas, USA, and the OFRs size distribution. The inset map represents the OFRs shapefile overlaid on SkySat satellite imagery.  
We downloaded PlanetScope images and processed daily Basemap and Planet Fusion images between July 2020 and July 2021. This time frame was chosen based on the imagery availability to generate both analysis ready datasets. The images spatial resolution and band-wavelength ranges are presented in Table 1. In addition, the general workflow used to assess the OFRs' surface area time series is provided in Figure 2.
# Development of Fiber Reinforced Sustainable Dredge Bricks
## 3 Results and Discussion
### Ersion Tests  
To evaluate the effect of erosion on the bricks, two tests were performed. A control test consisting of a 24 h percent absorption following ASTM C140 and an erosion from rainfall simulation test. The absorption test was performed on bricks without coating and with two coatings used in the industry, i.e., Enviro-Crete and lime-sand mortar. Figure 3 presents the results of the absorption test for these three samples. The percentage absorption is defined as 100 \(\times\) (saturated weight-oven dry weight)/oven dry weight. As shown in the figure, uncoated bricks have significant absorption of about 13.6% while the lime stabilized sand coated was somewhat better with an absorption of 9.4%. The Enviro-Crete performed significantly better with an absorption of only 0.8%. Although the brick coated with lime stabilized sand shows better performance with 31% improvement than the brick without coating, the high absorption rate of 9.4% is not expected and it is still much higher than the maximum of 2.5% requirement. The high absorption rate of the brick coated with lime stabilized sand may be due to lacking good bonding between the coating and the brick  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fiber** & **Application Rate (\%)** & **7-Day UCS** & **28-Day UCS** & **Flexural Strength** \\ \hline None & - & 1070 & 1394 & 381 \\ \hline  & 0.25 & 724 & 907 & 201 \\ PVA & 0.50 & 354 & 666 & 108 \\  & 0.75 & 240 & 497 & 108 \\ \hline Polyester & 0.05 & 740 & 1123 & 195 \\ Kenaf & 0.10 & 872 & 1151 & 260 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the strength tests for CSEB containing different fiber inclusions.  
surface and therefore, forming uneven shrinkage and tiny hairline cracks in the surface during the curing process. With these results in mind the erosion from rainfall simulation test was performed on bricks with different types of fibers.
## 4 Experiment and Result Analysis
### Results of SEI Experiment on the ESP20 Dataset
Figure 7: Confusion matrices of the proposed method and the comparison methods at SNR of 6 dB. The confusion matrix of our model has been highlighted with a red square. The stronger the blue color, the higher the classification accuracy. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR** & **Index** & **ENN** & **CNN** & **GCN** & **KNN** & **TREE** \\ \hline \multirow{4}{*}{6 dB} & Accuracy & **76.41** & 72.82 & 72.27 & 19.15 & 30.21 \\  & Precision & **77.61** & 72.55 & 72.09 & 25.09 & 30.84 \\  & Recall & **77.14** & 72.37 & 71.86 & 19.3 & 29.95 \\  & F1-Score & **77.24** & 72.41 & 71.87 & 16.28 & 30.31 \\ \hline \multirow{4}{*}{4 dB} & Accuracy & **76.05** & 74.18 & 70.27 & 17.15 & 27.39 \\  & Precision & **76.63** & 73.81 & 69.98 & 26 & 27.96 \\  & Recall & **75.95** & 73.67 & 69.92 & 17.64 & 27.31 \\  & F1-Score & **76.07** & 73.71 & 69.86 & 15.06 & 27.54 \\ \hline \multirow{4}{*}{2 dB} & Accuracy & **73.55** & 70.68 & 69.5 & 16.82 & 22.48 \\  & Precision & **73.63** & 71.48 & 70.28 & 25.76 & 22.96 \\  & Recall & **73.49** & 71.32 & 70.04 & 16.98 & 22.46 \\  & F1-Score & **73.49** & 71.29 & 70.1 & 14.46 & 22.59 \\ \hline \multirow{4}{*}{0 dB} & Accuracy & **69.73** & 67.64 & 63.95 & 17.03 & 17.82 \\  & Precision & **70.43** & 67.77 & 63.98 & 23.51 & 18.26 \\ \cline{1-1}  & Recall & **70.54** & 67.93 & 64.3 & 17.51 & 17.94 \\ \cline{1-1}  & F1-Score & **70.38** & 67.79 & 64.07 & 12.29 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of the indicators at various SNRs. Experimental results of our model are bolded.
## 5 Performance Evaluation
### Simulation Results
Every QoS provisioning scheme has to exchange additional control packets (in addition to those for the basic routing mechanism) in order to update the nodes with the current neighborhood information necessary to provide better QoS services, incurring extra overhead. As expected theoretically, in all approaches, the amount of overhead quickly rises to higher values with increasing numbers of traffic sources. Because each source node needs to exchange routing control packets, the number of such control packets increases with the traffic load of the network as the routing parameters quickly vary. Figure 6(a) shows that MMSPEED incurs the highest overhead since it uses reliability and delay backpressure packets in addition to the neighborhood route information update messages; however, the overheads in the other three approaches are very close to each other as they all use only HELLO (or BEACON) packets to update single-hop neighborhood information. For the same reason, in Figure 6(b), we observe that the overheads in all approaches increase almost linearly with increasing bit error rates.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 6 Data Preprocessing and Feature Selection
### The Impact of Feature Scaling in the Evaluated Machine Learning Models  
Feature scaling is a model dependent parameter in ML. It is a technique to normalize or standardize the range of independent variables or features of input measured data in a preprocessing step. This typically allows a faster training time and a better performance in many ML models. This section briefly reveals the effects of feature scaling in the three evaluated ML models. Besides, there exist ML models where their performance is not affected by the feature scaling in the preprocessing of the input data. A good representative of such a model in our evaluation was the RF classifier (Figure 6). In this paper, the feature scaling was performed using the standardization technique. This typically means rescaling the data in preprocessing to have a mean of zero and a standard deviation of one (unit variance).  
Figure 6a depicts the impact of feature scaling for the three ML models in terms of the overall accuracy. Scaling the input data in the preprocessing phase had a notable impact on the SVM and MLP classifiers in terms of accuracy. In SVM, the overall accuracy was improved from 67.95% to 82.95% by scaling the features of the input data. Similarly, the accuracy of the MLP was increased from 85.70% to 91.36%. However, the RF gave equivalent outcomes in both scaled and unscaled features.  
In terms of training time using three features in each model, SVM learned significantly faster when feature scaling was used (Figure 6b). To be precise, the training time of SVM reduced from 4195.14 \(\pm\) 210.38 us to 1977.12 \(\pm\) 185.45 us by scaling the features. However, RF and MLP did not show obvious improvement for training time in our small-scale three feature evaluation. Specifically, the training time of RF for the scaled and unscaled features was 44.97 \(\pm\) 2.38 us and 47.98 \(\pm\) 4.74 us, respectively. Likewise, the training time of MLP for the scaled and unscaled features was 5562.66 \(\pm\) 1313.85 us and 5737.97 \(\pm\) 1949.05 us, respectively.  
Figure 5: Performance comparison of three ML models (SVM, MLP, and RF) using different extracted features at training and test data collected for the same scenarios.  
In terms of test time, feature scaling hurt the performance of MLP (Figure 6b). Specifically, the value of the test time (the smaller, the better) in MLP degraded from 10.41 \(\pm\) 0.54 us to 17.58 \(\pm\) 1.27 us by feature scaling. On the contrary, the performance of the test time in SVM improved when feature scaling was applied, i.e., 1839.53 \(\pm\) 53.70 us for unscaled features and 956.28 \(\pm\) 13.94 us for scaled features. Again, RF did not show any significant improvements except a small variation in its standard deviation, which implied 11.48 \(\pm\) 0.99 us for unscaled features and 11.48 \(\pm\) 1.42 us for scaled features, respectively.
## 1 Introduction
However, the laboratory experiments carried out to date have typically involved punches with a diameter of less than 50 mm. In the real-world industrial environment, the diameters of punches are usually higher than 100 mm. Taking this fact as a starting point, the author here presents a study on the performance scalability of polymer punches produced with FFF technology. Two different punch geometries were designed: the first with a diameter similar to that of punches already tested and reported in the literature; the second with a diameter three times higher. Experimental and numerical analyses were then carried out to determine process accuracy and stresses undergone. The results demonstrate that scalability leads to improved process performance.