esa-sceva/satcom-chunk-collection huggingface dataset:
# Icon Generation Based on Generative Adversarial Networks
## 1 Introduction
Since then, inspired by research combining logo design with GANs, researchers have started to focus their attention on the icon generation task. In graphic design, both logos and icons are visual symbols, but a logo is more to carry a company's values, and its purpose is to spread the value connotation of the brand; while an icon has stronger functionality and unity, it carries more single information, and its existence value makes it easier for users to identify and understand when using or browsing a product. Thus, unlike the logo generation task, the generated icon needs to have more functional characteristics and not just be an abstract visual symbol. [PERSON] et al. constructed a bi-conditional GAN with color and shape for the first time in 2019, which could automatically color the icons desired by designers [21]. [PERSON] et al. used the CycleGAN [22] for the domain transformation task from natural images to icons [23]. However, there are two key challenges in applying GANs directly to the research of icon design: (1) Icon styles differ (linear, faceted, etc. ), and the icons applicable to different scenarios have little in common (medical treatment, emotion, traffic, etc.). Therefore, for deep learning models such as GANs, it is challenging to capture the local correlation in the icon. (2) GANs require a large quantity of high-quality data; no such relevant icon datasets currently exist, and this could hinder research progress. In view of the two aforementioned challenges, in this study, we constructed a large-scale icon dataset comprising 8 different categories. This study is the first icon generation study based on a large dataset (about 20,000 samples), but in addition, in contrast to previous studies, which used color and shape as the classification conditions, we classified the  
Figure 1: When searching for weather apps in the Apple Store, you can see a strong similarity in the icon design of these apps. icons according to application scenarios. Inspired by the study of [PERSON] et al. [20], in this study, we introduced conditional information on the StyleGAN to enable it to generate icons by category. Besides this, we added a self-attention mechanism and a spectral normalization operation to the original model to improve the quality and diversity of the model. We also adopted the idea of \"relative accuracy\" in the RGAN [24] and reconstructed the loss function of the model, which gives RGAN better training stability and generation effects with a limited number of samples. The main contributions of this study are as follows. 1. It is an effective attempt to introduce deep learning techniques into the field of icon design, and for the first time the task of icon generation is implemented based on GAN, expanding the field of this research. 2. The first construction of an icon dataset consisting of 8 types of icons with different styles. 3. Applying conditional constraints in the StyleGAN and introducing new methods such as the self-attention mechanism to make it have better icon generation results. 4. Making some suggestions for future related research.
## 4 Conclusions  
Currently, there are few studies on the mechanical performance monitoring of piles used in high-piled wharfs in coastal ports during pile driving with the FBG sensor technique. Based on the properties of precast, prestressed concrete piles used in high-piled wharfs in coastal ports and severe marine environments, a state-of-the-art FBG sensor technique is used for mechanical performance monitoring of a driven pile used in the South 27# Wharf in the Tianjin port of China. The sensors used for performance testing of the prestressed concrete piles are FBG strain sensors and FBG angle sensors, which were embedded in the body of concrete piles to measure the strain of different locations of the pile and the dip angle of the pile. In one concrete pile, three FBG sensors were setup near the head, middle and tip of the pile. The FBG strain sensor used in the testing is a new type of sensor, which is less thermosensitive and does not require temperature compensation. The FBG angle sensor was set near the head of the pile to measure the dip angle of the pile. Based on the performance testing of the pile during driving, the time history of strain at the three locations was captured, as were the dip angle data. The testing data acquired recorded all the details of the pile driving, including the process of moving the pile from flat to upright, pile driving, and driving ending.  
According to data processing and analysis, it is clear that the compressive strain at the middle of the pile during driving is larger than that near the head and tip of the pile, and the maximum reached 177 \(\mu\varepsilon\), which does not exceed the compressive ultimate strain. Therefore, the middle of a prestressed concrete pile is the key location that should be preferentially monitored during pile driving. Meanwhile, during continuous hammer impacting of the pile, the obvious tension strain at the tip of the pile increased, and the maximum of the dynamic tension strain reached 56 \(\mu\varepsilon\), which is close to the tension ultimate strain. However, the tension strain at the middle and head of the pile is very small. Furthermore, the tension strains at all the locations of the pile are small at the completion of pile driving. This occurs because the frictional resistance of the soil is too small to load the middle of the pile when the tip meets the significant supporting soil layer, and the frictional resistance of the soil is too low to affect the tip of the pile when it passes through the supporting soil layer.  
This research work was jointly supported by the Natural Science Foundation of Tianjin (16 CYBJC21900) and the Fundamental Research Funds for Central Public Research Institutes (TKS150208, TKS140101). The authors would like to thank the Tianjin Research Institute for Water Transport Engineering, MOT for its financial support. [PERSON] conceived and wrote the paper; [PERSON] performed the testing; and [PERSON] offered useful suggestions for the preparation and writing of the paper. The authors declare no conflict of interest.
# Evaluation of Turkey's Road-Based Greenhouse Gas Inventory and Future Projections
## 2 Materials and Methods  
### Study Area  
Turkey is located between 36\({}^{\circ}\) and 42\({}^{\circ}\) north latitude and 26\({}^{\circ}\) and 45\({}^{\circ}\) east longitude and acts as a bridge between Asia and Europe through the Bosporus and Dardanelles Straits. Due to its geographical position, the country is situated on key trade routes, migration corridors, and major east-west and north-south routes, making it strategically important [25]. The country's population increased from 56.47 million in 1990 to 84.68 million in 2021. The growth in population substantially influences the escalation in demand for housing, energy, and transportation, particularly in Turkey's urban regions [26]. According to the 2020 data, Turkey has a road network with a total length of 68,266 km. This network comprises 5% (3095 km) motorways, 45.4% (31,006 km) state roads, and 50.1% (34,165 km) provincial roads [27]. Figure 1 displays a map of state roads in Turkey.  
Figure 1: Turkey roads map [28].  
The number of motor vehicle registrations experienced significant growth, rising from 15,095,603 in 2010 to 26,482,847 by 2022. Furthermore, when analyzed by fuel type, the number of petrol cars increased from 3,191,964 to 3,817,104, diesel cars rose from 1,381,631 to 5,261,876, and LPG cars increased from 2,900,034 to 5,005,563 [29]. Despite an annual increase in the overall number of vehicles, car ownership in Turkey remains significantly lower than the European average, primarily because of exorbitant car prices and taxes. Based on the 2019 data, Luxembourg has a car ownership rate of 681 cars per thousand people, Italy has a rate of 663 cars per thousand people, and Turkey has a rate of 150 cars per thousand people. The average for the EU-27 in 2019 was 553 [30].  
The Annual Average Daily Traffic (AADT) statistics for every section of Turkey's state roadways were collected in order to compare the number of cars using these routes between 2010 and 2020. Information regarding the number of vehicles traversing state roads, their classifications, and the distances of the roads was acquired from the reports of the General Directorate of Highways (GDH) spanning 11 years from 2010 to 2020. It was found that the total increased by 60% from 11,193,242 in 2010 to 17,917,138 in 2020 [31]. Although these total values (total AADT numbers) do not clearly show the number of vehicles using the roads, it is significant to notice the rise throughout the years because these increases also increase traffic-related emissions.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 3 Bridge Experiment
### Improved Accuracy of GNSS Positioning Solution
#### 3.3.2 Horizontal Direction Positioning Solution  
Following the increase in accuracy for vertical deflection, the application of the developed algorithm for quality control was considered in terms of horizontal positioning. This position is regarded as the lateral and longitudinal directions, which are also important in the construction of a BHMS because deflection in the lateral direction is normally induced by wind loading; moreover, deflection in the longitudinal direction is used along with pier inclination monitoring. Figures 15 and 16 show the comparison results of the horizontal solutions before and after applying the developed algorithm for quality control.  
Figure 12: Analysis of external reliability.  
Figure 14: QC applied to height coordinate.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Without QC** & **Applied QC** & **Improvement** \\ \hline
**Max** & 1.055 & 0.051 & 483\% \\
**Min** & \(-\)0.062 & \(-\)0.076 & 122\% \\
**RMS** & 0.088 & 0.018 & 489\% \\ \hline \end{tabular}
\end{table}
Table 1: Vertical Deflection (unit: m).  
Figure 13: QC not applied to height coordinate.  
The figures in Table 2 show that the elimination of outliers was achieved through the developed algorithm for quality control. However, despite applying quality control, seven outliers remained, which accounted for 6.3% of the total 110 outliers. However, this percentage was because the significance level was set to be 5% in the statistical process.  
In general, analysis on the error ellipse is additionally used as a statistical indicator. In this case, the major axis of the error ellipse indicates the direction of the larger errors (_i.e._, the largest standard deviation); the minor axis indicates the direction of lower errors. The accuracy of the horizontal position in the error ellipse is then represented by considering the correlation between the north-south and east-west directions.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Without QC**} & \multicolumn{2}{c}{**With QC**} \\ \hline \multirow{2}{*}{**Easting**} & **Max** & \(-\)3,059,909.6 & \(-\)3,059,886.35 \\  & **Min** & \(-\)3,059,909.97 & \(-\)3,059,886.43 \\ \hline \multirow{2}{*}{**Northing**} & **Max** & 4,084,206.02 & 4,084,201.51 \\  & **Min** & 4,084,205.42 & 4,084,201.4 \\ \hline \multirow{2}{*}{**RMS**} & **0.028** & 0.01 (280\%) \\ \hline \end{tabular}
\end{table}
Table 2: Horizontal positioning solution.  
Figure 16: Horizontal positioning solutions with QC.  
Figure 15: Horizontal positioning solutions without QC.
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 1 Introduction
They are equipped with mechanisms inspired by biological evolution, such as mutation, crossover, and selection, to adapt the search to the objective function's landscape and, at the same time, to prevent convergence to a local optimum. EAs have a stochastic direct-search nature; thus they cannot ensure globally optimal solutions. However, there are many different algorithms available and each of them is suitable for different types of optimization problems. One of the most widely known evolutionary algorithms is Differential Evolution (DE) [18]. The legacy scheme of DE algorithm is controlled by several parameters. The optimal values of these parameters differ from problem to problem and even from one stage of the search process to another [19]. Tuning DE's control parameters before its application to a specific problem is an important and time-consuming task. As a result, the overall performance of this algorithm is highly engaged to the values of the control parameters. Researchers have put a lot of effort to develop algorithms that automatically adapt these control parameters during the search process. Two of the algorithms that have been proposed towards this direction are Composite Differential Evolution (CoDE) [20] and Success History based Adaptive DE with Linear population size reduction (L-SHADE) [21]. On the other hand, Jaya [22] is a well-promising algorithm for different types of optimization problems, since it does not have any algorithm-specific control parameters to be tuned. Considering all the above, the optimization of the transmitted waveforms, based on a simple rectenna model and the channel state information is a promising technique to improve the overall power transfer efficiency in any WPT system. The objective of this paper is to apply various DE algorithms (DE, CoDE, L-SHADE) and the Jaya algorithm to optimize the waveform design of a WPT system and to assess the performance of the selected algorithms compared to the SCP-QCLP (Quadratically Constrained Linear Programming) method used in [10]. The novelty of this work lies in the examination of feasibility to apply various DE and Jaya algorithms in this optimization problem. To this end, there is a detailed description of the mechanisms that need to be incorporated into these algorithms for their successful implementation. These mechanisms concern the enforcement of boundary constraints. Also, a rigorous comparison between the examined algorithms is presented, taking into account not only their ability to track the optimal value of the objective function, but also their convergence speed for the tested cases. The paper is organized as follows. In Section 2, we describe the WPT system model, including the structure of the multi-tone waveforms, the propagation channels, the rectenna circuit model, and the selected objective function. Section 3 depicts the tested EAs and the modifications needed for their application in this optimization problem. Finally, In Sections 4 and 5, we outline the derived results and the conclusions of the paper, accordingly.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 6 Conclusions  
In this paper, we proposed an analog precoder combined with an efficient hybrid analog-digital multi-user equalizer for sub-connected mmW massive MIMO SC-FDMA systems. At the UT, we proposed a low complexity pure analog precoder that requires the knowledge of a quantized version of the average AoD of each cluster. At the BS, a hybrid analog-digital multi-user equalizer was developed for a sub-connected architecture. It was assumed that the analog part was constant over all subcarriers, while the digital part was computed on a per subcarrier basis. We considered a minimum MSE-based equalizer for the digital part, and the analog part was optimized using the average bit-error-rate of all subcarriers as metric. In order to simplify the optimization problem at hand, the merit function was first upper bounded and then, due to the specific properties of the resulting problem, we showed that the analog part of the hybrid equalizer may be computed iteratively over the RF chains by assigning the users in an interleaved fashion to the RF chains.  
The numerical results show that the proposed wideband hybrid multi-user linear equalizer is quite efficient at removing the multi-user interference, and the performance tends to the one achieved by the fully connected counterpart as the number of RF chains increases. Furthermore, only a few bits are required for the quantization of the average AoD of each cluster to obtain a performance close to the perfect case. The small performance gap between the proposed sub-connected approach and the fully connected one, together with the lower complexity, make it a very interesting choice for practical systems.  
Conceptualization, D.C. and A.S.; software, S.T. and R.S.; validation, S.T. and R.S.; formal analysis, D.C.; investigation, S.T. and R.S.; writing--original draft preparation, D.C., A.S., and A.G.; writing--review and editing, D.C., A.S., and A.G.; funding acquisition, A.S.  
This work is supported by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Program (COMPETE 2020) of the Portugal 2020 framework, Regional OP Centro (CENTRO 2020), Regional OP Lisboa (LISBOA 14-20), and by FCT/MEC through national funds, under Project MASSIVE5G (AAC n\({}^{\circ}\)02/SAICT/2017), and in part by the European Structural and Investment Funds (FEEI) through the Competitiveness and Internationalization Operational Program--COMPETE 2020 and by the National Funds through the Foundation for Science and Technology under the Project RETOT under Grant POCI-01-0145-FEDER-016432.  
Figure 6: Performance of the proposed hybrid sub-connected scheme for different numbers of quantization bits of the average AoD, \(U=8\).
# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
## 3 Experimental Results
### Results and Analysis
#### 3.4.3 Comparison Experiment on Exdark+  
To further verify the generalization ability of the 3L-YOLO, experiments were conducted on the synthesized low-light ExDark+ dataset. The ExDark+ dataset presents greater challenges due to its complex lighting conditions, significant brightness fluctuations, and noise. The 3L-YOLO method is compared with YOLOv5n [52], YOLOv5s [52], YOLOv7-tiny [11], and YOLOv8n [35].  
As shown in Table 4, the 3L-YOLO model performs well on the ExDark+ dataset, a synthetic low-light dataset. Specifically, 3L-YOLO achieves a [EMAIL_ADDRESS] score of 0.673 and a precision of 0.701 on the ExDark+ dataset, representing improvements of 4.3% and 3.9%, respectively, compared to YOLOv8n.  
To fully highlight the advantages of the 3L-YOLO network in low-light image target detection, Figure 9 presents a comparison of detection performance in representative complex scenes. In the second column, the bounding box predicted by YOLOv8n is significantly larger than the actual bounding box, whereas the bounding box predicted  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS] (\%)** & **P (\%)** & **R (\%)** \\ \hline YOLOv5n & 58.0 & 63.3 & 53.9 \\ YOLOv8n & 63.0 & 58.2 & **66.6** \\ YOLOv7-tiny & 63.4 & 66.2 & 59.8 \\ YOLOv5s & 64.5 & 59.5 & 64.5 \\
**3L-YOLO (Ours)** & **67.3** & **70.1** & 61.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of different models on the ExDark+ dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **P (\%)** & **R (\%)** & **Params (M)** & **GFLOPs** \\ \hline YOLOv5n & 65.1 & 38.2 & 68.5 & 58.0 & 2.5 & 7.1 \\ YOLOv7-tiny & 63.5 & 35.5 & 68.8 & 56.6 & 6.04 & 13.3 \\ YOLOv8n & 66.1 & 39.6 & 71.3 & 58.8 & 3.01 & 8.1 \\ Zero\_DCE + & & & & & & \\ YOLOv8n & 63.9 & 38.5 & 72.3 & 55.6 & **3.08** & 73.7 \\ LOL-YOLO [30] & 68.1 & **42.3** & 70.9 & **62.5** & 5.66 & 20.6 \\
**3L-YOLO (Ours)** & **68.8** & 42.0 & **76.0** & 59.3 & 5.89 & **16.6** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of different models on the ExDark dataset. Bold values indicate the best results for each metric among low-light object detection algorithms.  
by our method closely matches the ground truth. In the last column, YOLOv8n not only mispositions the bounding box but also misclassifies the target, while our method achieves correct predictions and classifications. Under low-light conditions and across targets of varying scales, the proposed 3L-YOLO network outperforms YOLOv8n in classification accuracy, localization precision, and confidence.
# Advanced Thermal Management for High-Power ICs: Optimizing Heatsink and Airflow Design
###### Abstract
[9] adopted a direct cooling approach, designing microchannel heatsinks on silicon substrates to cool GaN-on-Si ICs, achieving lower thermal resistance and reduced energy consumption. While effective, this method requires careful consideration of microchannel dimensions to avoid manufacturing inefficiencies [10; 11]. Another technique involves the use of near-junction heat spreaders with micron-thick coatings of copper and diamond. Though effective at dispersing heat, this method introduces challenges in manufacturing and can impair high-frequency performance [12]. Compared to experimental testing, CFD tools are both cheaper and less time-consuming, making them invaluable for anticipating experimental results. Previous studies have used CFD to analyze the temperature distribution in IC chips [6] and to evaluate the thermal performance of battery pack thermal management systems in electric vehicles (EVs) [13]. In a study by [PERSON] et al., a microjet impingement cooling system for GaN transistors was designed using CFD tools and validated with a 3D-printed prototype. The results were confirmed through Raman thermography temperature measurements [14]. Using CFD to predict device performance before prototyping is therefore well justified. Through this integrated approach, our research not only pioneers new theoretical insights into the thermal dynamics of high-power ICs but also ushers in practical applications. By merging detailed CFD simulations with empirical testing, we developed optimized heatsink and fan configurations that significantly improve the efficiency and effectiveness of IC cooling systems. Our contributions extend beyond technical advancements. We redefine industry standards for thermal management by providing robust, scalable solutions that enhance the reliability and performance of electronic devices as power densities increase. The innovative designs from our study promise significant reductions in energy consumption and operational costs, marking a shift toward more sustainable electronics manufacturing. Moreover, the methodologies and findings from our research serve as a valuable resource for IC manufacturers, helping them overcome the thermal challenges of next-generation electronic systems. By setting new benchmarks for cooling efficiency, our work contributes to the broader field of electronics design, ensuring that thermal management evolves alongside rapid advancements in IC technology. This research not only underscores our commitment to technological innovation but also reflects our dedication to solving complex engineering problems with wide-reaching implications for the electronics industry and beyond.
# A Multipath Data-Scheduling Strategy Based on Path Correlation for Information-Centric Networking
## 3 System Design
### Multipath Transmission Mechanism
After receiving the data packet sent by the data source, the relay nodes can initiate the query operation to the NRS, according to the receiver device ID, to obtain the destination IP address. Finally, the relay nodes perform network address translation and forward the data packets to the real destination. For security reasons, the IP addresses of the ICN routers inside the network should be hidden from users outside the network. Therefore, the ICN edge router which the data source accesses performs the name resolution operation. Figure 1 shows the principle of the multipath transmission method. In the IP plane, the source node and destination node need to follow the shortest path rules to transmit data through the default IP path (marked by the blue arrow). From the perspective of the ID plane, the ID-based addressing can integrate multiple paths of underlay networks, creating multiple parallel ID paths (marked by the green arrows) between the data source and the receiver to overcome the rigid and cumbersome drawbacks of IP routing protocol.