esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 3 Results
### Comparison with Other Flood-Mapping Products  
Figure 6 overlays the results obtained with the processing chain proposed here on the map supplied by Copernicus emrs564. This map shows the southeast region of Madagascar, which was hardest hit by Cyclone Batsirai. The images used for this study were acquired on 9 February 2022, i.e., three days after the floods peaked, potentially explaining why the flood areas identified using the processing chain appear smaller compared to the Copernicus emrs564 product. The Copernicus emrs564 product was generated from a TerraSAR-X image acquired on 6 February 2022. Specifically, the flood area detected by the Copernicus emrs564 product covers approximately 3776 hectares, while the processing chain identifies around 582 hectares of flooded area. There is an overlap of approximately  
Figure 5: Flood map of Madagascar during the 12-day period following Cyclone Batsirai generated from the processing chain in GEE. Post-event images were acquired from 5 to 17 February 2022. The enlarged area shows the district of Vanguindrano. The red line is the track of the cyclone.  
424 hectares detected by both processes. These numbers demonstrate that a large portion of the water has receded by the time our processing chain was applied, resulting in a reduction in the identified flooded area when compared to the Copernicus product.  
The results again identified persistent flooding that represents a long-term vulnerability, especially where crop fields are affected. Such information can be of value during the post-flood recovery phase. Availability of Sentinel-1 imagery is an issue that may preclude the timely application of this methodology, even more so now that Sentinel-1B is no longer in service. As Sentinel-1A is not retaskable, images may take several days to arrive (up to 16 days). The results are also compared with those obtained by the S1 Chain (Figure 7). They are in close agreement, with a kappa value of 0.78. However, differences are observed in several small flooded areas marked by scattered red areas in our results. These anomalies are more likely noise and are likely due to filtering effects, as the two chains apply speckle filtering at different times. In the S1 Chain, this step is performed before terrain correction, whereas in GEE, images have already been corrected for terrain effects before being posted online. These anomalies further underscore the importance of filtering methods in the flood detection process and their impact on the interpretation of smaller flooded regions. Indeed, these anomalies occupy 7993 Ha if the flood stably detected by the two chains is about 50,700 ha.  
Figure 6: Flood map obtained with the processing chain overlaid on a flood map supplied by Copernicus emsr564 in the district of Mananjary. In blue are flooded areas detected by the processing chain generated by using Sentinel-1 image acquired on 9 February 2022. EMSR product is derived from TerraSAR-X image acquired on 6 February 2022.
# Anti-Jamming Low-Latency Channel Hopping Protocol for Cognitive Radio Networks
## 6 Conclusions  
In this paper, we propose the anti-jamming channel hopping protocol ALL, which provides a rendezvous guarantee in CRNs. The ALL protocol, which enables each SU to adjust the location of \(T\) and \(R\) frames in its channel hopping matrix based on the channel hopping structures of its receivers, is constructed using the existing OLAA_T protocol as a foundation. We have proven that the ALL protocol can ensure each SU has a rendezvous with its intended receiver on all commonly available channels. The simulation results verify that ALL can improve network performance by 25% and 20%, on average, in terms of throughput and ATTR, respectively, when compared to OLAA_T and OLAA_R.  
Shortening the ID of SUs is advantageous for the ALL protocol, but it also results in multiple nodes having the same or similar shortened IDs. This could cause issues as nodes with identical shortened IDs may not be able to meet, and thus, a user cannot transmit data to another user with the same ID. This constraint may pose a challenge in a network that is not densely populated. To resolve this issue, it may be beneficial to not significantly reduce the ID length. However, determining the appropriate ID length requires further exploration. Further, we plan to further investigate the impact of the \(T\) frame symbol assignment strategy in the future. A possible strategy is that a sender, SU \(i\), assigns \(T\) frame symbols such that SU \(i\) can have a rendezvous with most of its receivers when they are \(TR\) overlapped.  
Conceptualization, Y.-H.H., C.-M.C., C.-Y.L. and C.-C.-Y.; methodology, Y.-H.H. and C.-M.C.; software, Y.-H.H.; validation, C.-M.C., C.-Y.L. and C.-C.Y.; formal analysis, Y.-H.H. and C.-M.C.; investigation, Y.-H.H. and C.-M.C.; resources, C.-M.C.; data curation, Y.-H.H.; writing--original draft preparation, Y.-H.H.; writing--review and editing, C.-M.C., C.-Y.L. and C.-C.Y.; visualization, Y.-H.H.; supervision, C.-M.C.; project administration, C.-M.C.; funding acquisition, C.-M.C. All authors have read and agreed to the published version of the manuscript.  
This research was sponsored by National Science and Technology Council, R. O. C., under grants MOST 110-2622-E-019-003 and MOST 111-2221-E-019-050.  
The authors declare no conflict of interest.  
Figure 13: This figure displays the impact of shortening ID and allocating unfixed \(R\) frames on the performance of mesh topology in a synchronous network, as measured by two metrics: (**a**) throughput and (**b**) ATTR.
## 3 Methodology
### Fusion-Based BIM Structured Query Strings Generation
#### 3.2.2 LLM-Based Wrapper  
In real-world situations, applying the trained SQLCoder model directly to informal or vaguely worded questions often yields unsatisfactory results. Therefore, a pre-trained large language model is utilized to transform imprecise user queries into clearly formulated questions.  
The ERNIE model was employed to convert raw queries into well-structured and explicitly directed queries. A specifically designed prompt was attached to guide the large language model in processing every raw query. Although this prompt is used with the ERNIE model, it does not mean ERNIE is the only choice. Any large language model that supports prompts can be used as a Wrapper model. Table 3 presents the prompt for the LLM-based Wrapper model.  
\begin{table}
\begin{tabular}{l} \hline This is a set of 12 precisely defined example queries. Please modify the given query to align with one of \\ these types, ensuring structural consistency. If no appropriate match is found, retain the original query. \\ \hline
1. Provide a 3D view of the entire building. \\
2. Show only the “wall” components of the building. \\
3. Show both the “wall” and “door” components of the building. \\
4. Retrieve a specific component in the building using GUID: 16M94 jbxL3f8 bk1S7S. \\
5. Query components located within a spatial volume of \(123\times 123\times 123\). \\
6. Retrieve properties of a specific component using GUID: 06 gL_hxB14 lL28 gqCscQ. \\
7. Query the properties of a given building element. \\
8. List all component types and subtypes, including attributes such as OwnerHistory, Representation, and \\ ObjectPlacement. \\
9. Show all walls with openings, including their subtypes, fills, and associated stairs. \\
10. Use GUID: 20 adBPbKiC28 brfp_lintDTM to locate a specific floor and list all elements on that floor. \\
11. Show only the external walls of the building. \\
12. List all IfcProduct components of type 57.2 along with their subtypes. \\ \hline \end{tabular}
\end{table}
Table 3: Prompt for LLM-based Wrapper Model.  
Figure 1: The framework of the BIMCoder Model.  
As shown in Table 4, once questions are transformed in this manner--more structured and directed--they can be passed on to the specialized BIMcoder model to generate more accurate query statements.
## 3 Problem Description
### Sequence-Dependent Setup Transitions  
A crucial aspect to address in the model is the sequence-dependent setup times. In the literature, this is typically classified into families of setups, including both major and minor operations. Major operations involve tasks such as bottle changes, while minor operations include cleaning the line or labelling [24; 30].  
In particular, we define one aspect of the sequence-dependent setup times for the desired SKU format within a specified batch, denoted as \(W_{2}\). This refers to adjustments made to the bottling line to meet the specific requirements of the SKU. Factors to consider include the type of bottle (size, shape, weight), labeling, the type of closing (crown or working with respective capsule), and packaging. It must be acknowledged that the operational constraints of each line determine the admissibility of specific formats.  
Subsequently, a transition matrix will be defined for each setup task, as changes from one SKU to another may require one or more adjustments, with execution times varying accordingly. In general, large wineries handle between 100 to 200 SKUs. The more complex setup processes typically require approximately 60 to 90 min, reflecting enhanced operational efficiency enabled by novel machinery. Additionally, the setup transition matrix can be clustered in order to reduce its dimensionality. For instance, labeling can be defined as fixed with a lower-bound setup time, as it is necessary to change the label for every SKU transition.  
Moreover, accounting for available workforce is essential for effective planning [30]. We will consider the maximum setup time across all tasks, allowing for parallel execution if there are sufficient workers. Otherwise, if the workforce is limited, we will sum the individual task-execution times. This approach allows for the adaptation of setup times based on the operational conditions of the winery.
## 2 General ESS-Based VSM with Energy Recovery Control
### General ESS-Based VSM with Energy Recovery Control  
The power electronic converter interface for VSM implementation may vary with the installation and application of ESS. The energy storage for VSM implementation can be batteries located on the generator or load side [30,31]. The installation of storage can be either centralized, including a large-scale battery storage station in the generation, transmission, and distribution system levels, or distributed, including a small-scale electric vehicle battery, a factory or family back-up storage [32]. In the latter case, proper ESS aggregation and dissemination of the total charging and discharging demand of the electric vehicle batteries are needed to form as a qualified player in the frequency system. As illustrated in Figure 1, this can be electric vehicle batteries interfaced by a DC-AC converter, or hydropump storage, a flywheel and a diesel generator interfaced by AC-DC-AC converter [33]. Due to isolation of the rotational speed from grid frequency in the latter case, more speed variation and thus more kinetic energy can be excavated from ESS for VSM implementation compared with directly connected synchronous generator.  
In general, VSM can be implemented on a single ESS with a high response speed and proper energy capacity, for example a supercapacitor or lithium-titanite battery, especially  
Figure 3: Comparison of energy and power density of energy storage (adapted from [29]).  
Figure 2: Power rating and discharge time of energy storage (adapted from [28]).  
in small-scale power network, as Figure 4a shows. However, since a single ESS with both large energy capacity and high response speed is still technically immature and economically less profitable, hybrid storage combining both FAESS and SAESS may be a more feasible solution. Based on the connection topology, hybrid ESS can be DC or AC coupled, as shown in Figure 4b,c, respectively.  
VSM control for \"general\" forms of energy storage installation in the power system is of fundamental importance. Given such complexity of energy storage installation, it is meaningful to develop a \"general\" VSM control, regardless of the ESS installation or the interfacing topology, as illustrated in Figure 5. For one set of VSM control parameters, the dynamic performance of power and frequency will be equivalent, no matter the VSM is based on a single ESS, hybrid DC- or AC-couple ESS. To compensate for the energy losses and reduce the energy capacity needed, an energy recovery loop is also added to VSM control, except for conventional virtual inertial, damping and governor control.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 4 Prediction of Linear Guide Preload--ANN Modeling  
The previous experiments roughly showed the correlation between the vibration magnitude and structural rigidity of the positioning stage. The stage was found to vibrate at different levels of motion owing to the change in the preload status of the linear guides. A statistical analysis further shows that the RMS vibration level from the moving stage is sensitive to changes in stage rigidity. This implies that the preload status of the linear guides can be predicted based on the vibration of the stage. In practice, a preload is normally generated by the interference between the rolling elements and raceways. The surface worn out of these components will lessen the interference and hence reduce the preload amount. It further enables the stage to exhibit different static and dynamic characteristics. This concept can initialize the development of a monitoring system to predict the change in the linear guide preload during operation.
# Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
## 4 Experiments
### Comparison with State-of-the-Art Methods
Table 2 shows the PSNR, SSIM, and LPIPS results of our LACCNet and SOTA methods on the LOL-V2-real, LOL-V2-syn, and LSRW test sets. From the results, it can be seen that LACCNet has better performance on the LOL-V2-real and LOL-V2-real datasets. All indicators on LSRW have reached the best level. Among them, the PSNR on LSRW is 20.087, which is significantly improved compared to the previous method, increasing by 2.006. Since no reference images exist in DICE and MEF, the results are not listed in Tables 1 and 2. Table 3 shows the results of our LACCNet and other comparison methods on the non-reference evaluation index NIQE. The NIQE index is more consistent with the subjective quality evaluation of the human eye, closer to the human visual system, and can effectively evaluate image quality. The smaller the NIQE value is, the better the visual perception quality of the image is. As can be seen from Table 3, the method proposed in this paper achieves the best NIQE index on the LOL-Blur, LOL-V1, LOL-V2-real, LOL-V2-syn, LSRW, and DICM datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**LOL-Blur**} & \multicolumn{3}{c}{**LOL-V1**} \\ \cline{2-7}  & **PSNR** & **SSIM** & **LPIPS** & **PSNR** & **SSIM** & **LPIPS** \\ \hline Retinexformer & 17.525 & 0.686 & 0.455 & 25.160 & 0.845 & 0.252 \\ SMG & 19.669 & 0.746 & 0.429 & 24.306 & 0.893 & 0.308 \\ LEDNet & 25.740 & 0.850 & 0.224 & 14.857 & 0.746 & 0.374 \\ SNR & 16.191 & 0.687 & 0.431 & 24.610 & 0.842 & 0.257 \\ Zero-DCE & 18.448 & 0.643 & 0.481 & 14.861 & 0.681 & 0.372 \\ EnlightenGAN & 16.677 & 0.633 & 0.478 & 17.555 & 0.733 & 0.381 \\ LACCNet (Ours) & 26.728 & 0.841 & 0.199 & 24.388 & 0.870 & 0.227 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results of PSNR, SSIM, and LPIPS indicators with other SOTA methods on LOL-Blur and LOL-V1 datasets. Figure 6: Comparison of visualization effects of different low-light image enhancement methods on the LOL-V1 test set.
# Implementation and Evaluation of HTTP/3 Connectivity Check Using Happy Eyeballs Algorithm
## 2 Related Works
### Happy Eyeballs  
Happy Eyeballs [10] is an algorithm that was developed to deal with the fallback problem (Latency and connectivity issues when switching to IPv4 after the IPv6 connection fails) that is caused by the mixing of IPv4 and IPv6 environments. As it assumes that TCP is selected as the transport layer protocol, it cannot be directly applied to HTTP/3, which uses UDP-based QUIC. However, there exist similarities with a previous study regarding the objective of addressing connectivity issues in environments where incompatible protocols coexist. This algorithm is also actively used by major browsers and is a common method for IPv4/IPv6 fallback. Without Happy Eyeballs, it is necessary to attempt a TCP connection on one side (e.g., IPv6) first, and then on the other if it fails completely. However, this method tends to result in a waiting time of tens of seconds when the first attempt fails. The greatest advantage of adopting Happy Eyeballs is that it prevents the foregoing and improves the end-user experience. Conversely, during the several steps to be followed before the TCP handshake is established, IPv4 and IPv6 communicate twice, which certainly increases the overall network traffic volume. This is burdensome for service providers and backbone communication networks, and is thus cited as a disadvantage. The complexity of the software to be implemented is another qualitative disadvantage owing to the occurrence of parallel processing. An overview of the process is illustrated in Figure 2.  
First, the DNS is queried for A and AAAA records. As these are in IPv4 and IPv6 record format, if both addresses are obtained, it is known that this is a dual-stack environment where both addresses are available. In this case, asynchronous TCP connection attempts are made over IPv4 and IPv6, and the one that completes the TCP handshake earlier is adopted. The connection attempts should not start at exactly the same time, and a delay of approximately 150-250 ms is recommended. if the delay is too large, it will not improve the user experience that Happy Eyeballs attempts to provide. If too small (on the order of tens of milliseconds), it will achieve this goal, but at the cost of network traffic. Therefore, the RFC6555 document states, \"It is RECOMMENDED that connection attempts be paced 150-250 ms apart to balance human factors against network load.\" The name Happy Eyeballs is derived from the improved end-user experience.
## 3 Results and Discussion
### Tools for Estimating the Volume of Multicellular Aggregates  
In order to compare the accuracy of _ReViMS_ with other tools for segmenting cell _aggregates_, we segmented MA1, MA2, and MA3 also with _MITK_ and _OpenSegSPIM_. First of all, we asked the same operator that previously obtained the Ground Truth, to use _MITK_ for manually re-segmenting five distributed slices for each orthogonal plane (e.g., axial, sagittal, coronal plane), for a total of 15 slices segmented for each considered multicellular _aggregate_. Then, the _MITK_ _3D Interpolation_ plugin was used to compute the 3D surface and volume of MA1, MA2 and MA3. On the other hand, no prior data were required to estimate the volumes with _OpenSegSPIM_, except that the original image datasets of the multicellular _aggregates_ (approximately, 1 GB each dataset) resulted in being too big to be loaded in _OpenSegSPIM_ and a 0.1 _x-y_ resize factor was needed. In this case, the final volume for each multicellular _aggregate_ was obtained by summing the volume of the single cells automatically segmented by _OpenSegSPIM_ and multiplying it for a resize factor of 10.  
Figure 3h,i show the final MA3 surface obtained by _MITK_ and _OpenSegSPIM_, respectively. Table 2 reports the volume measurements obtained using _ReViMS_, _MITK_ and _OpenSegSPIM_, together with AD%. The average AD% for _MITK_ and _OpenSegSPIM_ resulted in being 1,1 and 9,6, respectively. Thus, while MITK resulted in being perfectly comparable with _ReViMS_ (average AD% = 0.8), the _OpenSegSPIM_ accordance was slight smaller. It is worth noting that _MITK_ is the only tool working also in case of images containing multiple objects, as images containing more spheroids, but to use _MITK_ is time-consuming because it always requires initial manual segmentations of the object of interest.
## Numerical Simulation and Parametric Analysis
### Comparison of The SCS Wall and SDC Wall  
Figure 11 shows the compression force versus in-plane displacement curves (P-\(\Delta\) curves) of the SCS wall and SDC wall (the thickness of its rubber layer is 2 mm) with the same cross-section area in FEM. Three working stages are observed in both curves: Elastic stage, nonlinear stage, and recession stage. During the elastic stage (curve OA), the reaction forces of the composite walls linearly increase with the rising displacement and the two types of walls show similar initial stiffness. In the nonlinear stage (curve AB), nonlinear behaviors and similar ultimate compressive resistances can be seen in the two curves. In the recession stage (curve BD and BD'), the two types of walls show different reducing rates of the reaction forces. Compared with curve BC', curve BC shows the reaction force of the SDC wall decreasing to its 85% peak load with more displacement which means the SDC wall is characterized with more ductility than the traditional SCS wall. This can be attributed to the rubber layer and reinforcement enhancements in the SDC wall.  
Figure 11: P-\(\Delta\) curves of the SCS wall and SDC wall.  
Figure 10: Two failure modes of the SDC walls (deformation scale factor = 5.0).
## 2 Key Technology Discussion
### Adaptive Bias Control:  
Implementing adaptive bias control strategies allows for real-time optimization of the amplifier's performance based on the input signal characteristics [7]. By adjusting the bias points of the amplifier stages dynamically, the system can maintain an optimal balance between efficiency and linearity across varying operational conditions [8; 9]. This is particularly critical in 5G applications, where the signal characteristics can change rapidly due to varying modulation schemes and channel conditions. Through the use of feedback mechanisms and digital signal processing, the amplifier can continuously monitor its performance and make necessary adjustments to the biasing, ensuring that it operates efficiently across its entire range, particularly in the back-off regions.  
Three-Way Doherty Configuration:  
The three-way Doherty configuration has emerged as a prominent technique for enhancing power amplifier efficiency at high output back-off levels, while introducing design trade-offs in linearity and bandwidth performance [17; 18; 19]. The three-way DPA configuration represents a significant advancement in efficiency performance for modern wireless systems, particularly in high PAPR applications like 4G/5G. By incorporating an additional peak amplifier branch compared to conventional two-way designs, this architecture creates three distinct efficiency peaks--typically at 0 dB, 6 dB, and 12 dB back-off points. This multi-stage load modulation enables the amplifier to maintain significantly higher efficiency across a wider power back-off range when processing complex modulated signals. However, these efficiency gains come with important trade-offs. The interaction between three amplifier paths introduces more pronounced nonlinearities, resulting in 4-6 dB worse inter-modulation distortion, requiring substantially more complex digital pre-distortion. Bandwidth performance also suffers due to phase matching challenges in the complex impedance inverter network. These characteristics make the three-way Doherty not suitable for 5G NR handset application.