esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Noise Reduction Strategy in a Large City and an Analysis of Its Effect
## 3 Results and Discussion
### Noise Compliance Map with Noise Barriers  
In addition to the normal traffic noise maps presented in Figures 6-8, daytime and nighttime noise compliance maps of Panyu District were also developed, and they are shown in Figures 10 and 11, respectively. By comparing them with the compliance maps without noise barriers from [PERSON] et al. [36], it can be noted that by building noise barriers, the number of qualified areas in both the daytime and nighttime along the traffic arteries increases. Among them, the residential areas along the traffic arteries increase the most. Thus, it can be concluded that the implementation of noise barriers is an efficient approach to the reduction of the impact of traffic noise on the residents living along the traffic arteries. From Figures 10 and 11, it can be calculated that, with noise barriers, the total compliance rates of Panyu District in daytime and nighttime are 80.00% and 29.08%, respectively.  
Figure 10: Daytime noise level compliance map in Panyu District of Guangzhou City.  
Figure 9: Comparison of the areas covered by different noise quality levels between the cases with and without noise barriers: (**a**) daytime; (**b**) nighttime.  
Figure 12 shows the comparison of the compliance rates of various acoustic environment functional areas between the cases with and without noise barriers. It could be found that, with noise barriers, the compliance rates of all acoustic environmental functional areas obviously increased in both daytime and nighttime. The compliance rates of class 4 acoustic environment functional areas were increased most significantly by 33.21% and 16.54%, respectively, in daytime and nighttime. However, the compliance rates of class 1 acoustic environment functional areas increased the least, with 7.63% and 6.19% in daytime and nighttime, respectively. Overall, it can be calculated that, with the application of noise barriers, the total compliance rates of Panyu District in daytime and nighttime could be increased by 18.38% and 12.62%, respectively.  
Figure 11: Nighttime noise level compliance map in Panyu District of Guangzhou City.  
Figure 12: Comparison of the compliance rates of various acoustic environment functional areas between the cases with and without noise barriers. (**a**) Daytime; (**b**) nighttime.
# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 5 Discussion
### Datasets
#### 5.2.2 Trends and Future Research  
The following is the potential future trend in related research:  
* Sensor Placement Testing A quick and precise evaluation of roadside sensor placement before installation is crucial for effective data collection or monitoring. Sensor placement without proper evaluation can be time-consuming and may make it difficult to identify the optimal viewpoint. This is especially critical for VRU detection, where it is essential to select locations that cover sidewalks and crossings while minimizing occlusion. Therefore, evaluating the influence of sensor placement on the view and performance of baseline detection models is necessary to ensure optimal functionality.  
For instance, [PERSON] et al. (2023) [154] conducted a simulation study comparing various LiDAR placements across multiple intersection scenarios. This approach enabled the evaluation of different placements alongside various detection algorithms, using real-world mirror scenarios for comparison. This study offers valuable insights into sensor placement testing, highlighting how small changes in sensor positioning can significantly impact VRU detection accuracy. Proper sensor placement is thus a key factor in optimizing detection performance and minimizing false negatives, making it an essential aspect of system design.
# An Integrated SWOT-PESTLE-AHP Model Assessing Sustainability in Adaptive Reuse Projects
###### Abstract  
In the recent past, sustainable development has been considered a major issue for urban and regional studies. Adaptive reuse appears to be a practical solution for sustainable urban development. Beyond and in addition to a conceptual base consistent with circular economy and sustainability principles, how do we know if adaptive reuse is actually sustainable, provided that it constitutes a multidisciplinary and multilevel process? The present study aims at evaluating, in as much as feasible quantitative terms, adaptive reuse practices sustainability. This was attained using a set of indicators, developed combining PESTLE (the Political, Economic, Technical, Social, Legal, and Environmental aspects) and SWOT (the Strengths, Weaknesses, Opportunities, and Threats) approaches, of which the results were subjected to evaluation by experts (pairwise comparisons), following the Analytic Hierarchy Process (AHP). The indicators representing strengths and opportunities of the process were calculated to be of higher value (overall level of final cumulative indicators values; 70.4%) compared with indicators representing weaknesses and threats. Enhancing strengths and opportunities and counteracting weaknesses and threats contribute making the potential of adaptive reuse practices in urban sustainability more evident. Among analysis dimensions, political and economic aspects rank first, followed by environmental, socio-cultural, technological-technical, and legal aspect. The empirical results of this paper serve as a useful reference point for decision-making and policy formulation addressing adaptive reuse practices in sustainable development strategies.  
adaptive reuse; urban sustainability; SWOT analysis; PESTLE analysis; AHP technique; heritage preservation
## 3 Materials and Methods
### Initial AHP-QFD Matrices  
The selection of the three case studies by [PERSON] et al. [23], [PERSON] et al. [30], and [PERSON] et al. [24], for the initial AHP-QFD matrices to integrate with PCA as PCA-AHP-QFD approach is based on several key reasons. These reasons align with the key challenges faced in product design decision-making, such as the need to reduce development time, achieve sustainable product design, and ensure ease of understanding for all stakeholders involved, and still align with customer or user requirements. The chosen case studies span different application domains as follows: airport ground handling services, medical equipment design, and consumer electronics. This diversity ensures that the PCA-AHP-QFD  
Figure 2: Research framework model.  
approach is tested across varied contexts, enhancing its generalizability and applicability to different industries.  
Each case study addresses multifaceted decision-making scenarios. [PERSON] et al. [23], dealt with the selection of optimal locations for cargo logistics centers, requiring consideration of multiple criteria and budget constraints. [PERSON] et al. [30], focused on designing a product that met the needs of multiple stakeholders in the healthcare sector. [PERSON] et al. [24], highlighted the importance of prioritizing customer requirements in new product development. The complexity and depth of these cases provide a robust foundation for testing the effectiveness of integrating PCA with AHP-QFD.  
The selected studies employed rigorous methodological approaches that combine AHP with QFD effectively. [PERSON] et al. [23], incorporated zero-one goal programming to manage budget constraints, while [PERSON] et al. [30] and [PERSON] et al. [24], utilized comprehensive data collection methods (questionnaires and interviews) to capture customer needs and preferences. This methodological rigor ensures that the initial AHP-QFD matrices are well-founded, providing a solid basis for the integration of PCA.  
All three case studies emphasize the importance of capturing and prioritizing stakeholder preferences. By considering the professional experiences of QFD teams, gathering inputs from pharmacists, patients, and their relatives, and focusing on customer requirements for new product development, this focus on stakeholder preferences aligns well with the objectives of integrating PCA to streamline and simplify the decision-making process.  
The selected case studies are categorized by industry as follows.
## 2 Results
### DAMs Identification and Enrichment Analyses  
The 377 DAMs could be divided into 12 clusters through a K-means analysis (Figure 2A). Among them, 59 (subclass 2), 36 (subclass 3), and 17 (subclass 6) metabolites were significantly increased in DF7, DF, and DF14, respectively. Subclass 2 mainly included flavonoids, phenolic acids, lipids, alkaloids, amino acids and their derivatives, organic acids, and quinones. Subclass 3 primarily consisted of flavonoids, phenolic acids, amino acids and their derivatives, nucleotides and their derivatives, alkaloids, and organic acids. In order to identify the functional classification of the DAMs obtained in each comparative group, the metabolites were subjected to KEGG enrichment analysis. The KEGG results indicated that each comparative group of DAMs was enriched in semblable pathways, including the biosynthetic pathways of flavones, flavonoids and flavonoids, the secondary metabolites biosynthesis pathways, and the amino acid biosynthesis pathways. Compared with the CK group, DF14 exhibited a significant accumulation of 52 metabolites, which included sixteen flavonoids, eight phenolic acids, six organic acids, four alkaloids, and four lipids. The KEGG enrichment analysis results indicated that the significant DAMs between CK and DF14 were mainly concentrated in the \"metabolic pathway\" and \"secondary metabolite biosynthesis\" pathways, which accounted for 64.71% and 31.37% of the total, respectively (Figure 2B).
## 2 Experiments and Results  
### Picosecond Amplifier System at 1 kHz  
The picosecond laser system consisted of a picosecond oscillator, a regenerative amplifier, four stages of end-pumped single-pass amplifiers, and a side-pumped amplifier. The whole system was water-cooled and the timing was controlled by a homemade delay generator for synchronization. The generator had seven output channels and each channel provided a control signal at a repetition rate of 1 kHz with delay adjustable from 0.25 ns to 1 ms for the pump lasers and Pockels cells.
## 3 Experiments and Analysis
### Comparison of Dehazing Detection Algorithms
#### 3.2.2 Objective Data Comparison  
Based on the detection results, a comparative statistical analysis of the mean Intersection over Union (mIoU) and runtime is performed. The experimental findings are presented in Table 4 below:  
The haze dataset processed by the algorithm in this paper achieves the second-highest average Intersection over Union (mIoU) in the detection algorithm employed herein, second only to the haze-free original images. Furthermore, leveraging CUDA parallel computing can substantially enhance computational speed, thereby fulfilling the accuracy and real-time requirements of this study.
## 6 Worst-Case Latency Analysis of the FWOS Model
### Length of the Contention-Free Interval
#### 6.1.4 Overall Effects  
Based on the overlapping considerations discussed above, the starting and ending times of the contention-free \(Q_{k}^{h}\) window are influenced by several parameters accordingly. By accumulating all these aspects together in worst-case computations, the starting and ending times are constrained by  
\[t_{k}^{h,B,i}=max\left\{t_{L}^{h,B,i},\ t_{H}^{h,B,i}\right\} \tag{20}\]  
\[t_{k}^{h,E,i}=min\left\{t_{k}^{h,gb,i},\ t_{H}^{h,E,i}\right\} \tag{21}\]  
Then, the length of the \(Q_{k}^{h}\) contention-free window in the \(i\)-th cycle is  
\[\overline{W}_{k}^{h,i}=\left\{\begin{array}{ll}max\left\{t_{k}^{h,E,i}-t_{k }^{h,B,i},\frac{f_{k}^{h,max}}{C_{h}}\right\}&,t_{k}^{h,B,i}<t_{k}^{h,E,i}\\ 0&,t_{k}^{h,B,i}\geq t_{k}^{h,E,i}\end{array}\right.. \tag{22}\]  
By considering the \(i\)-th open window as a benchmark, as shown in Figure 9, the relative offset of the \(i\)-th contention-free window from the opening edge can be given by  
\[R_{k}^{h,j}=\left\{\begin{array}{ll}t_{k}^{h,B,i}-t_{k}^{h,o,i}&,\overline{ W}_{k}^{h,i}\
eq 0\\ 0&,\overline{W}_{k}^{h,i}=0\end{array}\right. \tag{23}\]  
In addition, the relative offset of the \(j\)-th contention-free window, considering the \(i\)-th window as a benchmark, is  
\[R_{k}^{h,j,i}=(j-i)\.T_{k}^{h}-R_{k}^{h,j}+R_{k}^{h,j} \tag{24}\]  
Figure 9: Relative Offsets of contention-free windows with the benchmark in the hyper-period.
## 1 Introduction and Background
### Information Overload  
Human attention is a complex mechanism with known (but _not_ too well understood) limitations, and when coupled with memory limitations, we observe a phenomenon called \"information overload\" in information science. The concept basically refers to the maximum amount of information we can handle, after which, our decision making performance declines (Figure 1) [17; 29; 30]. Human working  
\begin{table}
\begin{tabular}{|l|r|r|r|r|r|} \hline
**Bit rate \(\bigtriangledown\)** & **56 kbit/s** & **1.5 Mbit/s** & **622 Mbit/s** & **1 Gbit/s** & **10 Gbit/s** \\ \hline \multicolumn{5}{|c|}{Download times in hh:mm:ss} \\ \hline
1 MB Single dataset small project & 00:02:22 & 00:00:05 & 00:00:00 & 00:00:00 & 00:00:00 \\ \hline
1 GB Street network large city & 39:40:57 & 01:26:21 & 00:00:12 & 00:00:08 & 00:00:00 \\ \hline
1 TB Elevation entire Earth & 39682:32:22 & 1439:15:47 & 03:34:21 & 02:13:20 & 00:13:20 \\  & (>1653 days) & (>29 days) & & & \\ \hline
1 PB Sat. image entire Earth & 39682539:40:57 & 1439263:05:50 & 3572:42:16 & 2222:13:20 & 222:13:20 \\  & (> 4526 years) & (>164 years) & (>148 days) & (>92 days) & (>9 days) \\ \hline
1 EB Future 3D Earth & 39682539682:32:22 & 1439263097:17:39 & 3572704:32:14 & 222222:13:20 & 222222:13:20 \\  & (>452696 decades) & (> 16419 decades) & (>407 years) & (>250 years) & (>25 years) \\ \hline \end{tabular}
\end{table}
Table 1: Values are calculated without overhead [24]. Overhead depends on the application and can be as much as 50% (e.g., adds approximately 20 hours more waiting time for a 1 G street network on a 56 kbit/s connection or another 2h with a 622 Mbit/s connection). The data sizes are in measured in mega (MB), giga (GB), tera (TB), peta (PB) and exabytes (EB). We include a conversion of the hh:mm:ss format to a more “readable” format to help the reader to interpret the values more easily.  
memory, for instance, is known to be able to handle maximum seven, in some more conservative measures, only three pieces of information for a very short period of time (_i.e._, less than 30 s) [31, 32, 33].  
The linkage between cognitive limitations (perception, working memory) and the bandwidth usage/allocation is obvious, _i.e._, bandwidth should not be \"wasted\" for information that cannot be processed by humans adequately.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 4 Case Study
### Platform Independent Computational Model  
Figure 5 shows a partial view of the CPN built systematically using the structural design patterns of the editor services.  
In Figure 6, the ellipse representing the notebook appears in the center in pink, with seven pages, one for each room. In Figure 6, the CPNs representing the instances of structural patterns that serve the publisher update services appear in the upper part in green. At the bottom, in red, the CPNs that represent the instances of the subscription-notification service patterns appear. It should be noted that the fragment corresponding to the subscription has been omitted to reduce the excess of visual elements that impede its understanding. Likewise, in the lower part, in blue, appear the CPNs that represent the pattern of the collection service.  
Figure 6 shows the representation of the update service in greater detail. Compared to the update pattern shown earlier in Figure 2, only the branch with the transition with condition \"\(p<p^{\prime}\)\" has been included here.  
Figure 5: CPN systematically constructed using the SCM-IoT patterns.  
Figure 6: CPN of the update service for the case study.  
Figure 7 shows the detail of the subscription notification and collection services. The notification conditions for the activation or deactivation of the heater are shown. The activation condition \"[\(p\) = SI andalso T = LOW andalso c = NO]\" in the transition, determines the updating of the field \"c\" that refers to the state of the heater, changing the entry arc annotation from \"(u, \(p\), T, h, g, c, v, A)\" to \"(u, \(p\), T, h, g, SI, v, A)\" in the starting arc annotation. Similarly, the inactivation condition \"[(\(p\) = SI andalso T = NOR andalso c = YES) or else (\(p\) = NO andalso c = YES) or else (A = SI andalso c = YES)]\" specifies the cases in which that the heater should be turned off corresponding to the cases: (i) when the room is occupied with normal temperature, and the heater is on, (ii) when the room is not occupied, but the heater is on, and (iii) when the fire alarm has been activated, and the heater is on. The activated alarm condition is assumed to be equivalent to the house on fire.  
Finally, in Figure 7, the CPN shows the collection and notification service. The three criteria give the condition to start the collection to determine a fire situation in at least one house room: very high temperature, smoke, and toxic gas present. The transition \"NOITFES FIRE\" activates the search for residents in each room of the house under the condition \"[\(p\) = YES]\". The information with all the resident locations is sent to the emergency services to facilitate their rescue.
## 3 Experiment
### The Effect of Different Loss Functions on the Urban Green Space Segmentation Task  
Traditional segmentation models often utilize cross entropy or binary cross entropy as the loss function. In contrast to nature image segmentation tasks [16; 17; 18], the UGS segmentation task exhibits a relatively balanced proportion between the foreground (UGS) and background (non-UGS). In some specific areas (such as forest parks), the foreground may even outnumber the background. Simultaneously, the spatial distribution of a UGS is fragmented and scattered, with isolated green spaces (such as individual trees) appearing frequently. In such cases, cross entropy performs poorly, especially in the boundary regions of a UGS.  
In the previous section's experiments, we only discussed the use of hybrid loss (the combination of Focal Loss and the Dice coefficient). In this section, we explore the impact of different loss functions on UGS segmentation task accuracy. Keeping the other conditions consistent, we train the model using cross entropy, binary cross entropy, and hybrid loss, respectively.  
Figure 10 illustrates the variation trend in the IoU as the epoch increases when using different loss functions. In comparison with models using cross entropy or binary cross entropy, the model using hybrid loss achieves the highest IoU metric at the beginning of the training and exhibits the highest accuracy improvement rate over the next 20 epochs. On the other hand, models using cross entropy or binary cross entropy loss experience  
Figure 9: Ablation study for different loss weight settings on the GS2018 validation dataset. (**a**) The change trend in the training IoU with the increase in the Focal Loss weight. (**b**) The change trend in the training IoU with the increase in the Dice coefficient weight.  
significant fluctuations in the IoU metric during the first 60 epochs, whereas the model using hybrid loss demonstrates a smoother IoU improvement. Furthermore, over the entire training period of 200 epochs, the model using hybrid loss maintains a performance advantage in the IoU compared with models using the other two loss functions.  
As shown in Table 2, the model using hybrid loss achieved the highest values of 93.665%, 86.702%, and 94.393% for precision (Pre), intersection over union (IoU), and overall accuracy (OA), respectively. Notably, the model using hybrid loss reached its peak accuracy at the 136 th epoch, while the other models attained their best accuracy after the 150 th epoch. This indicates that the model using hybrid loss exhibits a faster convergence speed. The results above suggest that the use of hybrid loss can more effectively guide model training, enabling a model to store more features of a UGS and enhancing the performance of the model.
## 2 Literature Review
### Related Work
#### 2.1.2 Research on Deep Prediction Models  
In recent years, Deep learning has rapidly transformed both weather prediction and traffic flow prediction. In 2020, [PERSON] et al. [11] introduced a data-driven global weather prediction framework using a deep CNN to fuse multichannel weather data, significantly boosting prediction accuracy. Building on this, [PERSON] et al. [12] combined ConvLSTM with 3D CNN for precipitation nowcasting, demonstrating deep models' strength in capturing spatiotemporal dynamics, while [PERSON] et al. [13] proposed Broad UNet to reduce parameter counts without sacrificing performance in nowcasting. In 2022, [PERSON] et al. [14] advanced a real-time collaborative machine learning framework that integrates distributed data sources to improve situational awareness for convective weather. More recently, [PERSON] et al. [15] developed a CNN BiLSTM attention model that enhances severe convective weather forecasts, and [PERSON] et al. [16] created a CNN-based system for pre-tactical thunderstorm prediction to support flight scheduling. For very short-term predictions, [PERSON] et al. [17] compared threshold methods, neural networks, and belief functions for lightning strike risk, and [PERSON] et al. (2023) [18] showed that a hybrid multichannel CNN-GRU model yields high precision in ultra-short-term wind power forecasting. Additionally, GraphCast [19] delivers global daily forecasts up to 10 days with higher accuracy and much lower cost than NWP; MetNet-2 provides 1 km, 12 h end-to-end forecasts, showcasing deep learning's extended lead-time edge [20].  
In the traffic flow domain, traditional CNN/LSTM pipelines evolved toward graph architectures. [PERSON] et al. [21] introduce the Bo-GRA-CNN-BiLSTM model and employ Bayesian optimization for automatic hyperparameter tuning to enhance adaptability and accuracy under varying weather conditions. [PERSON] et al. proposed a Geographical and Operational GCN, or multi-airport flight delay prediction, effectively capturing spatial-operational dependencies [22]. [PERSON] et al. [23] proposed STFGCN, which fuses weather and traffic via spatial-temporal relationship modeling to achieve top performance across all weather conditions. Beyond Bayesian and Adam optimization methods, genetic algorithms and other evolutionary strategies are increasingly adopted for hyperparameter tuning in intelligent transportation systems. For instance, [PERSON] and [PERSON] [24] proposed a hybrid clustering-assisted genetic algorithm for the evolutionary synthesis of high-capacity multilayer road networks. Similarly, [PERSON] and [PERSON] [25] integrated a multi-objective genetic algorithm with RNNs to enhance traffic speed prediction accuracy in complex urban environments. Collectively, these advances illustrate deep learning's power to improve predictive accuracy and model design, while highlighting ongoing challenges in model complexity, data heterogeneity, and generalization.