esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
This section compares the accuracy of the proposed designs with previous methods, when applicable. Furthermore, the computing time required to calculate the optimized EQ filter gains and to update the band filter coefficients is evaluated for all proposed designs. The error, with which the accuracy was evaluated in this work, was defined in the same way as in [22]. That is, the maximum of the absolute difference between the user-set command gains and the magnitude response of the GEQ at the command frequencies was evaluated. Additionally, the maximum error was evaluated in the frequency range between any two adjacent command gains when their command gain values were the same. The error was evaluated this way because the target magnitude response was not well defined between command frequencies, and thus, evaluating the error in those regions became cumbersome and controversial. Furthermore, the magnitude response, being composed of the responses of cascaded second-order filters, could be assumed to change monotonically between command points. Furthermore, when two adjacent command gains had the same value, the target gain could be naturally assumed to be constant between the points, with minimal undulation [3].
## 3 Results and Discussion  
### Reflectance Values of the Dyed Samples  
Figures 3-5 depict reflectance curves of red, yellow and blue color, respectively, of the batch-dyed samples, while Figure 6 presents reflectance curves of the color mixture of dyed standard samples. With regards to Figures 3-5, the results show that reflectance curves of red, yellow and blue color of the samples batch dyed by using the conventional water-based method and the reverse micellar D5-assisted method do not overlap at dye concentrations from 0.5% to 6.0%. Reflectance values of the sample batch dyed in water from 0.5% to 6.0% are higher than those of the sample dyed in D5. This indicates that color yield of the sample batch dyed in water was lower with lower dye absorption in the fiber, and the shade thus appeared to be lighter when compared with the batch samples dyed in D5.  
As presented in Figures 3-6, although the reflectance values of batch and standard dyed samples are not similar, the shapes of the reflectance curves of the batch and standard samples dyed by conventional and reverse micellar D5-assisted methods are similar to each other, without any peak shifting of the curves found. This indicates that the use of D5 for the preparation of PEG-based reverse micelles and as the medium for solvent-assisted dyeing can be one of the alternative methods to achieve electrolyte-free and water-saving dyeing of cotton fiber and for computer color matching (CCM) without chromatic shift. The possible reason was the use of the PEG-based reverse micellar system, which could strongly reduce the ionization effect between reactive dye molecules and the cotton fiber and improve the swelling of cotton fiber [15; 18].  
Figure 4: Reflectance curves of the yellow color of batch samples dyed in water and D5 medium.  
Figure 3: Reflectance curves of the red color of batch samples dyed in water and D5 medium.
# Flood Monitoring Based on the Study of Sentinel-1 SAR Images: The Ebro River Case Study
###### Abstract  
Flooding is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydomorphological alterations on ecosystem services, and human health. The mitigation of the risks associated with flooding requires a certain management of flood zones, sustained by data and information about the events with the help of flood maps with sufficient temporal and spatial resolution. This paper presents the potential use of the Sentinel-1 SAR (Synthetic Aperture Radar) images as a powerful tool for flood mapping applied in the event of extraordinary floods that occurred during the month of April 2018 in the Ebro (Spain). More specifically, in this study, we describe accurate and robust processing that allows real-time flood extension maps to be obtained, which is essential for risk mitigation. Evaluating the different Sentinel-1 parameters, our analysis shows that the best results on the final flood mapping for this study area were obtained using VH (Vertical-Horizontal) polarization configuration and Lee filtering \(7\times 7\) window sizes. Two methods were applied to flood maps from Sentinel-1 SAR images: (1) RGB (Red, Green, Blue color model) composition based on the differences between the pre- and post-event images; and (2) the calibration threshold technique or binarization based on histogram backscatter values. When comparing our flood maps with the flood areas digitalized from vertical aerial photographs, done by the Hydrological Planning Office of the Ebro Hydrographic Confederation, the results were coincident. The result of the flooding map obtained with the RADAR (Radio Detection and Ranging) image were compared with the layers with different return periods (10, 50, 100, and 500 years) for a selected zone of the study area of SNCZI (National Flood Zone Mapping System in Spain). It was found that the images are consistent and correspond to a flood between 10 and 50 years of return. In view of the results obtained, the usefulness of Sentinel-1 images as baseline data for the improvement of the methodological guide is appreciated, and should be used as a new source of input, calibration, and validation for hydrological models to improve the accuracy of flood risk maps.  
Ebro River; flood mapping; flood risk areas; RADAR SAR; Sentinel-1; RGB composition; calibrated thresholding +
Footnote †: journal:
###### Abstract  
The use of very long spatial datasets from satellites has opened up numerous opportunities, including the monitoring of vegetation phenology over the course of time. Considering the importance of grassland systems and the influence of climate change on their phenology, the specific objectives of this study are: (a) to identify a methodology for a reliable estimation of grassland phenological dates from a satellite vegetation index (i.e., kernel normalized difference vegetation index, kNDVI) and (b) to quantify the changes that have occurred over the period 2001-2021 in a representative dataset of European grasslands and assess the extent of climate change impacts. In order to identify the best methodological approach for estimating the start (SOS), peak (POS) and end (EOS) of the growing season from the satellite, we compared dates extracted from the MODIS-kNDVI annual trajectories with different combinations of fitting models (FMs) and extraction methods (EM), with those extracted from the gross primary productivity (GPP) measured from eddy covariance flux towers in specific grasslands. SOS and POS were effectively identified with various FM\(\times\)EM approaches, whereas satellite-EOS did not obtain sufficiently reliable estimates and was excluded from the trend analysis. The methodological indications (i.e., FM\(\times\)EM selection) were then used to calculate the SOS and POS for 31 grassland sites in Europe from MODIS-kNDVI during the period 2001-2021. SOS tended towards an anticipation at the majority of sites (83.9%), with an average advance at significant sites of 0.76 days year\({}^{-1}\). For POS, the trend was also towards advancement, although the results are less homogeneous (67.7% of sites with advancement), and with a less marked advance at significant sites (0.56 days year\({}^{-1}\)). From the analyses carried out, the SOS and POS of several sites were influenced by the winter and spring temperatures, which recorded rises during the period 2001-2021. Contrasting results were recorded for the SOS-POS duration, which did not show a clear trend towards lengthening or shortening. Considering latitude and altitude, the results highlighted that the greatest changes in terms of SOS and POS anticipation were recorded for sites at higher latitudes and lower altitudes.  
Keywords:start of season (SOS); peak of season (POS); end of season (EOS); vegetation index; GPP; kNDVI +
Footnote †: journal:
## 4 Genetic Algorithm Configuration
### Range of Genetic Parameters  
The ranges for the genetic variables were set based on practical operational limits:  
* **Inclination**: 35\({}^{\circ}\) to 70\({}^{\circ}\);
* **Number of planes**: 1 to 20;
* **Satellites per plane**: 1 to 20
* **Phase difference**: 0\({}^{\circ}\) to 19\({}^{\circ}\).  
These ranges ensure that our modified Delta-Walker constellation can flexibly accommodate various orbital configurations while maintaining efficient and economic optimization.
###### Abstract  
Current research on scene text recognition primarily focuses on languages with abundant linguistic resources, such as English and Chinese. In contrast, there is relatively limited research dedicated to low-resource languages. Advanced methods for scene text recognition often employ Transformer-based architectures. However, the performance of Transformer architectures is suboptimal when dealing with low-resource datasets. This paper proposes a Collaborative Encoding Method for Scene Text Recognition in the low-resource Uyghur language. The encoding framework comprises three main modules: the Filter module, the Dual-Branch Feature Extraction module, and the Dynamic Fusion module. The Filter module, consisting of a series of upsampling and downsampling operations, performs coarse-grained filtering on input images to reduce the impact of scene noise on the model, thereby obtaining more accurate feature information. The Dual-Branch Feature Extraction module adopts a parallel structure combining Transformer encoding and Convolutional Neural Network (CNN) encoding to capture local and global information. The Dynamic Fusion module employs an attention mechanism to dynamically merge the feature information obtained from the Transformer and CNN branches. To address the scarcity of real data for natural scene Uyghur text recognition, this paper conducted two rounds of data augmentation on a dataset of 7267 real images, resulting in 254,345 and 3,052,140 scene images, respectively. This process partially mitigated the issue of insufficient Uyghur language data, making low-resource scene text recognition research feasible. Experimental results demonstrate that the proposed collaborative encoding approach achieves outstanding performance. Compared to baseline methods, our collaborative encoding approach improves accuracy by 14.1%.  
Keywords:scene text recognition; low-resource languages; collaborative encoding; data augmentation
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 4 Performance Evaluation
### Experimental Topology  
For our end-to-end experiment, we evaluated the accelerator on a small test bench consisting of two end nodes and an intermediate forwarding node. The end node is responsible for sending and receiving data packets, and the intermediate node forwards the data packets. All nodes are equipped with our accelerator prototype. The end nodes and two ports of the intermediate node are connected in pairs using 100 G optical fiber. We use the accelerator model implemented by Verilog to program on the FPGA board. The multi-node network topology based on the FPGA boards is shown in Figure 6. After completing the DPDK initialization, sending and receiving operations can be performed according to the requirements. Take \"pktgen\" as a sample program in the examples directory of the zero-copy driver. After running, it will start a corresponding send/receive packet thread on each corresponding core according to the number of enabled queues and then distribute the received packets to its designated output port directly after a bit of processing according to the forwarding rules.  
Figure 5: FPGA resource requirement for various queues (In X2000).  
```
1:\(incoming\ packet\ \ Ptkt,flow\_table\ \ Table,hash\_key\ \ K\)
2:\(A\ action\ to\ delivery\ packet\)
3:functionFlow_Classifier(\(Pkt\))
4:\(Extract\ \ Ptkt.tuple\ \ Ptkt.type\ \ from\ \ Pkt.header\)
5:if\(Pkt.type\!=\ QUIC\ \ then\ flag\gets 2\)
6:else\(flag\gets 1\)
7:endif
8:endfunction
9:/* Get the packet type and flag from the packet header */
10:functionMatch(\(Table,flag,Pkt.tuple\))
11:if\(Pkt.tuple\ match\ in\ Table\ is\ drop\ \ then\ ret\leftarrow-1\)
12:elseif\(Pkt.tuple\ match\ in\ Table\ is\ forward\ \then\ ret\gets 0\)
13:else\(Pkt.tuple\ match\ in\ Table\ is\ process\ ret\gets 1\)
14:endif
15:\(signal\gets flag\ *\ ret\)
16:return\(signal\)
17:endfunction
18:/* Get signal using the flag matching flow table */
19:functionRepeater(\(Pkt,signal,K\))
20:if\(signal=1\)then
21:\(Extract\ \ stream\ \ for\ quic\ payload\)
22:\(num\gets hash(K,streamid)\)
23:\(delivery\ to\ \ \mathbf{num}\ glue\ for\ processing\)
24:elseif\(signal=0\)then
25:\(num\gets hash(K,streamid)\)
26:\(delivery\ to\ \mathbf{num}\ glue\ for\ transition\)
27:elseif\(signal<0\)then\(drop\ it\)
28:else\(num\gets hash(K,Pkt.tuple)\)
29:\(delivery\ to\ \mathbf{num}\ glue\ for\ processing\)
30:endif
31:endfunction
32:/* The forwarding behavior of different type of packets */
```  
**Algorithm 1** Rule-based flow classification algorithm
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 5 Parameters Analysis on Deformation
### Pile Size and Strength  
In Figure 12(a), the horizontal deformation curve of the pile at \(x\) = 70 m is depicted for various pile sizes, while Figure 12(b) displays the same curve for piles with different material strengths. It can be observed that the horizontal deformation of the pile gradually decreases as the pile size and strength increase. This phenomenon is attributed to the fact that larger and stronger piles possess higher bending stiffness, resulting in reduced horizontal deformation. Consequently, modifying the pile size and material strength has the potential to significantly enhance the overall stiffness of the retaining structure, leading to a reduction in the horizontal deformation of the pile.  
Figure 12: Waist beam size and material strength.
## 3 Results and Discussion
### Force-Displacement Response  
Figure 7 shows the hysteretic behavior of the tested walls. The skeleton curves of the specimens are shown in Figure 8. PCMW, PCMWT, and CMWT displayed symmetric behavior during the test. However, CMW showed asymmetric curves at the post-peak stage. This was because the first major diagonal crack in the positive direction was earlier than that in the negative direction. The pinching effect in prefabricated walls was more  
obvious than it was in cast-in-place walls. All the walls displayed elastic behavior before the first major diagonal crack occurred. The hysteretic loop was narrow, which illustrates low energy dissipation capacity. As the imposed displacement increased, the loop enlarged rapidly, which also indicates a high level of energy dissipation at the post-peak phase.  
The bearing capacity of the prefabricated walls PCMW and PCMWT decreased slowly, which indicates a ductile failure process. However, the cast-in-place ones exhibited a rapid capacity degradation, which indicates a brittle failure mode.  
All the tested walls had the same level of axial compressive stress and horizontal reinforcement ratio. The differences between the walls were mainly in the types of cross  
Figure 8: Skeleton curves of the test walls.  
Figure 7: Hysteresis curves of the specimens: (**a**) PCMW, (**b**) CMW, (**c**) PCMWT, and (**d**) CMWT.  
sections and construction methods. The lateral capacity of PCMWT and CMWT with a T-shaped section was 21% and 7% higher than that of the corresponding rectangular sections of PCMW and CMW, respectively. This illustrated that the flanges of the wall enhanced the bearing capacity in compression. However, the lateral resistance of the T-shaped walls dropped more rapidly than that of the rectangular section walls. This illustrated that increasing the compression area of the wall did not improve the post-peak strength or ductility of the wall. Regarding the construction method, the lateral capacity of PCMW and PCMWT was 12% and 27% higher than that of the corresponding cast-in-place walls, CMW and CMWT, respectively. This indicates that the vertical joint enhanced the shear capacity of the wall. The residual dropping mortars during the construction weakened the bond strength and friction at the interface of the walls and the bottom of the concrete beam. The continuous vertical reinforcement in PCMW and PCMWT performed better than that in CMWT and CMW where the vertical reinforcement was overlapped. The experiment results are summarized in Table 2 for a quantitative comparison of the specimen deformation characteristics at the stages of the first diagonal crack, peak load, and 15% strength degradation.
## 5 Case Study
### Protection Strategy Analysis  
As illustrated in the Methodology section, our analyses of protective strategies were conducted using a network-assisted game-theory approach, and considered protection methodologies aimed at improving both the redundancy and robustness of the substation under varied terrorist attack threats from TNT explosives of 10 to 20 kg. The losses presented here are based on an assumption that both the critical infrastructure defenders and the attackers are using optimized strategies; as such, the losses from real attacks may be lower than these projections.  
\begin{table}
\begin{tabular}{c c} \hline
**Loss Causing Factor** & **Quantity** \\ \hline Total indirect loss of the substation & 25,000 N.I.S./h (6,304 USD/h) \\ Interruption to the power-supply network & 75,000 N.I.S./h (18,911 USD/h) \\ Estimated time to recovery & 10 d \\ Total indirect loss & 24,000,000 N.I.S. (6,059,688 USD) \\ \hline \end{tabular}
\end{table}
Table 4: Total Indirect Loss to Substation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Level of Protection** & **Protective Solution** & **Effectiveness (\(\Delta Ro_{I}\))** \\ \hline I & Steel construction & 99\% \\ II & Reinforced concrete construction & 90\% \\ III & Burial of critical components & 65\% \\ IV & Partitioning reinforced concrete walls & 55\% \\ V & Secured space & 35\% \\ \hline \end{tabular}
\end{table}
Table 5: Protection Methods for Improving Robustness.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 1 Introduction  
### Research Overview  
Web platforms are commonly used to distribute software to users. As the user base grew, applications evolved to improve the user experience. This progress has led to the development of new requirements and standards for high performance, scalability, and rapid response to user inputs [1, 2]. Over time, developers have used a variety of strategies and technologies to meet these goals, focusing on different areas of the software development process.  
Initially, early solutions aimed to improve current hardware and optimize code for better efficiency. Later on, there was a significant transition to a paradigm that included the use of multi-threading asynchronous programming approaches, which allow the application to handle multiple processes simultaneously to improve performance by parallelizing the program flow.  
Within the Java ecosystem [3, 4], multiple programming interfaces have been created to support the asynchronous multi-threading paradigm. Some of these include _wait/notifymechanisms and the _java.util.concurrent_ library, and abstractions such as _CompletableFuture_ and _Executors_[5]. These advancements have enabled the development of newer, more efficient, and faster applications. However, they have not eliminated the complexity of thread management and concurrency. This signifies the need for further enhancements in the simplification and optimization of modern application development.  
Reactive programming [6] aims to address these challenges by reducing the complexity of concurrency management while enhancing application performance and scalability. This approach has its roots in a paradigm that leverages non-blocking I/O [7] and an event-driven architecture [8].  
Major companies like Netflix and PayPal have adopted reactive programming to improve system scalability, reduce latency, and enable real-time data processing. For example, Netflix leverages RxJava to streamline server-side concurrency, significantly improving responsiveness in its streaming services by managing asynchronous processes efficiently. Similarly, PayPal relies on Akka to address scalability and latency challenges, enabling seamless transaction handling and real-time updates to payment statuses, even under high-demand conditions. These implementations demonstrate how reactive programming effectively addresses scalability and performance challenges in large-scale, distributed systems.  
The integration of reactive programming interfaces into Java streamlines the development of applications that meet contemporary performance and scalability requirements while minimizing system resource utilization.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### VCO Phase Noise and Timing Jitter  
VCO implementation introduces two primary non-idealities: phase noise and timing jitter. Simulation results show phase noise RMS of 1.1903 radians and timing jitter standard deviation of 1.0 ms. These impairments degrade the VCO output SNR to \(-2.87\) dB, representing significant performance degradation from ideal conditions. Phase noise manifests as random variations in the VCO output phase, while timing jitter affects the precision of pulse positioning in the PPM signal. The combined effect reduces overall system correlation from the ideal case, with measured performance showing substantial deviation from theoretical predictions.
## 4 Results
### Study 2
#### 4.2.2 Quantitative Results
The lower quadrant **(e)** contains the SCM mean values for each Keyword: _No Keyword_ (purple), _Usability_ (orange), _Social Acceptability_ (green), and _Sporty Design_ (brown). The rectangles represent the 95% confidence interval. \(F(3,371)=6.270\), \(p=0.0003\), \(\eta_{p}^{2}=0.123\) (medium effect size). However, no interaction effect was found for GPT \(\times\) keyword, \(F(3,371)=1.948\), \(p=0.121\), and \(\eta_{p}^{2}=0.042\) (small effect size). Bonferroni-corrected pairwise comparisons were conducted using Wilcoxon signed-rank tests, which revealed statistically significant differences between _No Keyword_\(\times\)_Sporty Design_ (\(p_{\text{adj}}<0.0001\)), _Usability_\(\times\)_Sporty Design_ (\(p_{\text{adj}}=0.0002\)), and _Social Acceptability_\(\times\)_Sporty Design_ (\(p_{\text{adj}}<0.0001\)). The pairwise comparison between _ChatGPT_ and _OrthoticFootGPT_ was statistically significant, with \(p<0.0001\). Pairwise comparisons between the GPT and Keyword demonstrated statistically significant differences across all keywords: _No Keywords_ (\(p_{\text{adj}}=0.0004\)), _Usability_ (\(p_{\text{adj}}=0.0002\)), _Social Acceptability_ (\(p_{\text{adj}}<0.0001\)), and _Sporty Design_ (\(p_{\text{adj}}=0.009\)). The results of the WEAR scale are summarized in Figure 8.