esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study demonstrated that the WGAN-GP-based color correction technique enables more precise texture and structure reproduction in high-resolution remote sensing images compared to existing methods and can contribute to enhancing pilot immersion. In particular, the results suggest that the proposed method can significantly improve operational efficiency by saving time and cost compared to the ROK Air Force's existing manual processing methods, and this can be useful for generating training content based on various terrain images. Furthermore, this research is expected to expand the applicability of GAN-based technologies and advance the field of remote sensing image color correction. The proposed WGAN-GP model can contribute to solving practical problems in this field by delivering better performance than existing traditional methods.  
This study shows that the proposed WGAN-GP model outperforms existing traditional methods and can more consistently correct irregular color distributions. Thus, the proposed method successfully improves the accuracy of color correction while preserving the structural characteristics of the image.  
However, this study has some limitations. First, it may be difficult to perfectly transfer the style of the reference image to the target image by simply combining generators. Second, the structure of the Critic needs to be improved to more clearly distinguish between the reference image and the generated image. To overcome these limitations, future research should explore ways to further improve performance by combining additional models such as StyleGAN or extending the Critic's comparison structure.  
This study makes an important contribution to the field of color correction for remote sensing images, and further validation in various environments and datasets will expand its applicability across different fields. Therefore, future research is needed to enhance the performance and expand the applicability of this model through additional experiments and validation in various environments. Based on these results, it is hoped that the proposed method will become a new standard for remote sensing image processing in the future.  
Conceptualization, C.L. and H.K.; Funding acquisition, D.S.; Methodology, C.L. and H.K.; Design of Scenario, H.C., J.C. and I.L.; Supervision, D.S.; Validation, B.K. and J.J.; Writing--original draft, C.L. and H.K.; Writing--review and editing, D.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Challengeable Future Defense Technology Research and Development Program through the Agency For Defense Development(ADD) funded by the Defense Acquisition Program Administration(DAPA) in 2024 (No. 915073201).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Experimental Setup and Results
### Measurement System
#### 3.1.3 Microcontroller Unit  
From the signal conditioning unit, two parameters are received by the microcontroller unit which includes:  
* The step size of the integrator
* Received PD pulses count  
The function of the microcontroller within this entire system is to provide the interface between the sensor and the wirelessHART unit. The microcontroller used is PIC24 EP512 GP810 (from Microchip, Chandler, AZ, USA). The microcontroller has the random access memory (RAM) of 52 KB, the program memory of 512 KB and speed of 70 million instructions per second (MIPS). The microcontroller unit offers analogue to digital conversion (ADC) that is configurable as 10 bits with 1.1 mega samples per second (MSa/s). There are four simultaneous channels are provided that ensure that sampling of the PD pulses is performed adequately.  
The second important function of the microcontroller unit is to establish and maintain the wireless connection via the wirelessHART unit. Microcontroller is brought to the sleep mode if there is no data collection to save energy. When the signal is received via a supervisory application, the microcontroller unit wakes up from the sleep mode. The data is collected and it is transferred to the system. PD occurrences are monitored by the microcontroller for one second, i.e., 50 cycle of the power supply. During this one second, three main tasks are performed which include, the counting of PD pulses, sampling of integrator step size and recording of the relative time stamp of PD pulses. The data is then transferred to the supervisory application via the wirelessHART unit after calculating the average step size. The whole process is repeated on an hourly basis.
## 3 Results and Discussion
### Description of the Pilot Case Study
Taking into consideration that the indicator to be calculated is the Percentage of Green Areas, the _shared parameter_ is named \"_IsGreenArea_\". Using the _shared parameter_ created, all the green areas in the project must be characterized. Figure 4 illustrates the process in Revit, where the area marked in blue is the selected area to be characterized with the _shared parameter \"IsGreenArea\"_.  
Figure 4: Selected areas in Revit and characterized according to the parameter _”IsGreenArea”_. Figure 3: Aerial view of the chosen urban area—Boavista Neighborhood—Porto, Portugal. The calculation process is then carried out by creating a Dynamo routine. Generally, programming will check all the areas in the model that are characterized as green areas using the shared parameter \"_IsGreenArea_\". The routine identifies the areas in the model and creates a list that will be the input of the calculation, Figure 5 illustrates this first phase of the routine. Then, using the inputs of the list (total area, total of green area, total of non-green area) the routine will calculate the ratio of the green areas and the total project area, and the result is the Percentage of Green Spaces, shown in Figure 6.  
The result of the Percentage of Green Spaces obtained by the routine, shown in Figure 7 is 0.131449512397989, or simply 0.13, in accordance with the result presented before. According to \(\mathrm{SBTool^{PT}}\) Urban, once the Percentage of Green Spaces has been calculated, the next steps are to apply benchmarks and normalize the data in order to obtain the classification. The value is converted into a qualitative scale where A+ represents the most sustainable, while E represents the least sustainable. As illustrated in Figure 7, Dynamo is capable of performing both processes, and the result is a \"C\" classification. In this example, the application of CIM as a method of calculation has been demonstrated, along with the potential for its applicability. There are many advantages to this approach, but there are also some limitations. The ease and speed with which the calculations can be made are among the advantages. As well, this method may be useful for designers and urban planners when evaluating the sustainability of urban projects at an early stage. On the other hand, the first stage is a laborious process because it requires adding a significant amount of information to the model. It must be noted that the results of the Dynamo programming routine depend heavily on this stage. In addition, the size of the  
Figure 5: Information characterized into the Dynamo routine. Figure 6: Areas characterized according to the parameter “_IsGreenArea_”. urban area is another limitation, large urban areas will require a high level of information to be incorporated into the model.
# Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
## 2 Sensor Characteristics and Information Fusion
### On-board Ranging Sensors
As shown in Figure 1, vision sensor and radar sensor have similar detection error pattern within the 10 m range. On the other hand, range error of the vision sensor is bigger than the radar sensors over the 10 m range situation. In terms of range and speed measurement for application to vehicle longitudinal safety, radar data demonstrate more accurate results compared to camera.
## 5 Discussion
### Integration and Optimization of Multi-Source LST Data for Geothermal Anomaly Detection
#### 5.1.3 Geothermal Anomaly Stability from Joint Daytime and Nighttime LST Analysis  
This study integrated daytime and nighttime LST data to improve the accuracy of geothermal anomaly identification. This approach effectively reduced the influence of daytime solar radiation and environmental factors on temperature distribution, minimizing the effects of natural surface features that cause cold or hot anomalies at different times of day. Incorporating the thermal diffusion effect of fault structures further enhanced the stability of geothermal anomaly detection.  
Comparison between daytime and nighttime LST (Figures 6c and 7f) reveals clear spatial differences in maximum temperature values. Different surface types, such as water bodies and bare land, exhibit contrasting temperature patterns between day and night. Due to their high specific heat capacity, water bodies warm slowly during the day, producing cold anomalies, and cool gradually at night, appearing relatively warm [59]. In contrast, bare land responds more quickly to solar radiation, heating rapidly during the day and cooling quickly at night, resulting in greater diurnal temperature variation [60].  
These temperature fluctuations, driven by surface thermal property differences, indicate that single-period LST data may not accurately reflect the true distribution of geothermal anomalies. Specifically, high daytime temperatures may cause false identification of bare land as geothermal anomalies, while high nighttime temperatures may lead to overestimation of anomalies in water bodies [61].  
Therefore, this study accounted for surface thermal property differences by retaining only regions that exhibited anomalies in both daytime and nighttime LST data. This approach effectively eliminated false anomaly signals, significantly reduced misidentifications, and improved the accuracy of geothermal anomaly mapping [62].
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 2 Theory Background of Drive-by Bridge SHM  
Without losing the generality of VBI, a simple model of the VBI system is introduced and illustrated in Figure 1, where the bridge is represented as an Euler-Bernoulli beam and the vehicle is represented as a sprung mass. The mass of the vehicle and the supported stiffness are represented as \(m_{v}\) and \(k_{v}\), respectively. The Euler-Bernoulli beam is simply supported, with a length of \(L\). The bending rigidity of this beam is \(EI\), and the density of this beam is \(m\) per unit length. The vehicle travels across this bridge with a constant velocity, \(v\). For the VBI system, the equations of motion are  
\[m_{v}\ddot{q}_{v}+k_{v}(q_{v}-\left.u\right|_{x=vt})=0 \tag{1}\]  
\[\overline{m}\ddot{u}+EIu^{m}=f_{c}(t)\delta(x-vt) \tag{2}\]  
where \(q_{v}(t)\) and \(u(x,t)\) represent the vertical displacements of the vehicle and the modeled beam, respectively, and a dot and a prime denote the derivatives with respect to time \(t\) and with respect to the longitudinal distance coordinate \(x\), respectively. \(\delta(x-vt)\) represents the Dirac delta function; \(f_{c}(t)\) represents the contact force between beam and vehicle, expressed as  
\[f_{c}(t)=-m_{v}g+k_{v}(q_{v}-\left.u\right|_{x=vt}) \tag{3}\]  
where \(g\) represents the acceleration due to gravity. The modal superposition method is used to solve Equation (2) and the result is shown in Equation (4), where \(\varphi_{n}(x)\) and \(q_{b,n}(t)\) are the mode shapes and modal coordinates of the bridge. \[u(x,t)=\sum_{n}\varphi_{n}(x)q_{b,n}(t)=\sum_{n}sin\frac{n\pi x}{L}q_{b,n}(t) \tag{4}\]  
Assuming that the mass of the bridge is much greater than that of the vehicle, i.e., \(m_{v}/\overline{m}L\ll 1\), the displacement responses of the bridge to the traveling vehicle can be approximately calculated as [10]  
\[u(x,t)=\sum_{n}\frac{\Delta_{st,n}}{1-S_{n}^{2}}\left\{sin\frac{n\pi vt}{L} \times\left[\sin\left(\frac{n\pi vt}{L}\right)-S_{n}\sin(\omega_{b,n}t)\right]\right\} \tag{5}\]  
where  
\[\omega_{b,n}=(\frac{n\pi}{L})^{2}\sqrt{\frac{EI}{\overline{m}}} \tag{6}\]  
\[S_{n}=\frac{n\pi v}{L\omega_{b,n}} \tag{7}\]  
Figure 1: A simple VBI model.
## 5 Results and Discussion
### Effect of Hartmann Number and Rayleigh Number on Velocity and Temperature along the Diagonal Line  
The simulations were carried out under the following data, along the diagonal line between the two corners:  
* The corner radius was \(R=0.25\)
* The solid volume fraction was \(\phi=0.05\)
* The Prandtl number was \(Pr=0.7\)
* The Eckert number was \(Ec=10^{-5}\)  
Figure 11 shows the effect of Hartmann number: \(Ha=0\); \(30\); \(60\) and the effect of Rayleigh number \(Ra=10^{3}\); \(10^{4}\); \(10^{5}\) on the velocity magnitude and the temperature of the nanofluid along the diagonal line that connects the two round corners.  
Figure 10: Effect of Hartmann number and round corner radius on the surface velocity magnitude distribution.  
Results show that, when the magnetic field was null (\(Ha=0\)), the evolution profiles of the nanofluid velocity magnitude and temperature were symmetrical, with respect to the central point of the diagonal line, for all Rayleigh number. As the Hartmann number increased until \(Ha=60\), the evolution profile of the velocity magnitude moves towards the cold wall and its amplitude was reduced, while the evolution profile of the temperature becomes more linearized, for all Rayleigh number. As the Rayleigh number increased from \(Ra=10^{3}\) to \(Ra=10^{5}\), the velocity magnitude was considerably reduced, while the temperature was more linearized for lower Rayleigh number.
## 2 Background and Theory
### Cross-Validation  
Cross-validation accuracy provides a standard approach for evaluating a machine learning model's effectiveness [61]. This technique partitions the dataset into \(k\)-folds, ensuring each fold is used for both training and testing. For instance, let the dataset \(D\) be split into \(k\) portions, with \(D_{i}\) for \(i=1\dots k\). The model is trained on all subsets except \(D_{i}\) and then evaluated on \(D_{i}\) to yield a performance measure \(S_{i}\). The overall model performance is then calculated as the average of these individual scores, illustrated mathematically in Equation (31):  
\[CV=\frac{1}{k}\sum_{i=1}^{k}S_{i} \tag{31}\]  
In this context, \(CV\) represents the cross-validation score, which evaluates the model's performance. The parameter \(k\) denotes the number of folds in the cross-validation process. The variable \(S_{i}\) represents the performance score obtained when evaluating the \(i\)-th subset \(D_{i}\). Depending on the evaluation objectives, this performance score can be quantified using various metrics, including accuracy (as utilized in this research), mean squared error, F1-score, recall, and precision.
# Centralized Task Allocation and Alignment Based on Constraint Table and Alignment Rules
## 2 Preliminaries
### Coupled-Constraint Consensus-Based Bundle Algorithm (CCBBA)  
CCBBA is an extension version of CBBA. CCBBA is designed to supplement the weakness of CBBA which is incapacity to resolve task allocation and alignment problems with coupled constraints. The coupled constraints which are considered in CCBBA are described in Tables 1 and 2[13; 14; 15].  
For applying these coupled constraints to CCBBA, tasks are partitioned into subgroups called activity that share coupled constraints. The activity does not share constraints with other activities. For each activity, two matrices called dependency matrix and temporal constraint matrix, which are defined as shown in Tables 3 and 4, are used [13; 14; 15]. Using these matrices, each agent decides bidding strategies, optimistic and pessimistic, for each task before bundle construction. The agent decides to bid or not according to these strategies in bundle construction and consensus phase. In this process, the concept of 'number of iterations in constraint violation' is applied. These are additional processes compared to CBBA. Although CCBBA can resolve most coupled constraint task allocation and alignment cases, there are some problems in CCBBA. First of all, it faces convergence problems in some cases due to latency. Additionally, the user must set maximum amount of time to the temporal constraint matrix. It means that the user should calculate the time for task  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline Unilateral dependency & Task A can be allocated if task B is allocated \\ Mutual dependency & Task A and B must either be all allocated or not at all \\ Mutual Exclusive & Only either task A or B can be allocated at each time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spatial coupled constraints in CCBBA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline Simultaneous & Task A and B must start at the same time \\ Before & Task A must end before task B starts \\ After & Task A must start after task B ends \\ During & Task A must start between task B starts and ends \\ Not during & Task A must either end before task B starts or start after task B ends \\ Between & Task A must start after task B ends and end before task C starts \\ \hline \hline \end{tabular}
\end{table}
Table 2: Temporal coupled constraints in CCBBA.  
alignment in a defined order. However, expected arrival time, start-time, and end-time of tasks are changed frequently based on multi-agent operation environment, so the user finds it hard to expect the maximum amount of time.
## 4 Results and Analyses
### Spatial Structure of Linkages and Node Identification
Based on these findings, we can begin to outline a typology of development nodes and corridors for the Ning-Ju-Zhen region:  
* Core interchange hubs: The main urban area of Nanjing, the central district of Jurong, and the urban area of Zhenjiang constitute the primary functional anchors. Among them, the central district of Jurong functions as the pivotal intermediary hub connecting the other two cities. * Secondary gateways: Baohua Town operates as a gateway through which the functional influence of Nanjing extends into Jurong, effectively serving as an extension of the Nanjing metropolitan zone while also directing urban growth into Jurong. In the southwestern part of Jurong, Guozhuang Town plays a similar, albeit smaller, role as a connector to the southern peripheral areas of Nanjing. * Supporting nodes: Towns such as Huangmei (north Jurong, on S122), Biancheng (east Jurong, on the highway to Danyang), and perhaps others, such as Xiashu, help connect local areas into the main corridors but have more limited roles. * Potential development corridors: _Tangshan-Huangmei-Jurong City Corridor:_ This north-south corridor (following S122) is already taking shape as both Nanjing and Jurong develop toward each other along  
Figure 11: Identification of the Nanjing–Jurong–Zhenjiang Development Corridor. this route. Nanjing-Huangmei-Biancheng-Danyang CorridorAn east-west corridor along the expressway linking Nanjing to Danyang (via Jurong) has high accessibility and has the potential to form a continuous development belt connecting the three cities. Southern extensionsThere is potential to extend connectivity further south within Jurong, for instance, improving linkages from the center of Jurong down to Guozhuang (via provincial road S243) and onward, or extending the S122 corridor southward to better integrate the Maoshan area. These actions could incorporate currently peripheral areas into the regional network. Identifying these spatial patterns and corridors helps inform where future growth and infrastructure investment could be directed to maximize regional integration. The data-driven connectivity map of Jurong points to the Nanjing-facing side of Jurong (Baohua, Huangmei, etc.) as the key avenue of interaction, which suggests that planning efforts should prioritize those zones for transit improvements, industrial parks, or housing that caters to cross-boundary commuters. Meanwhile, the eastern Jurong connection toward Zhenjiang, while less pronounced, is still significant and could be enhanced, ensuring that Jurong does not exclusively become a suburb of Nanjing but also continues to interface with Zhenjiang in a balanced manner (Figure 11).
# mGEODAR--A Mobile Radar System for Detection and Monitoring of Gravitational Mass-Movements
## 2 Materials and Methods
### Reconfigurable Signal Generation
For this mode, the DDS is programmed to generate a sawtooth modulation. The chirp pulses are not transmitted continuously but are triggered with an external logic pulse. Currently, the detection mode consists of a burst of 25 pulses with each a duration of \(\tau=7.5\times 10^{-4}\,\mathrm{s}\) and a \(10\,\mathrm{Hz}\) repetition of these bursts. These 25 short pulses are averaged during offline processing to increase the signal-to-noise ratio. The frequency flexibility that the filters in mGEODAR permit can also be a disadvantage as any external signal is received as interference rather than rejected. For example, if a second radar is running simultaneously for comparison and validation purposes, it is usually insufficient to tune both radar outputs to separate frequency bands because they still may interfere. A second radar must therefore operate at frequencies outside the above stated valid frequencies for \(f_{tx}\) (panel C in Figure 2). However, external narrow bandpass filters may connect between the receiving antenna and the receiver unit. Such external filter is, for example, used in the GEODAR receiver chain to prevent saturation and signal leakage from a nearby pulse Doppler radar that operates at a similar frequency range [9].