esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Real-World Applications and Challenges
### Adaptation in Dynamic Market Conditions  
While ElliottAgents demonstrates reliability under standard market conditions, significant limitations emerge during periods of market dislocation or unprecedented external shocks [52].  
This limitation became particularly evident during the COVID-19 pandemic-induced market turbulence of March 2020, where traditional price formation mechanisms experienced substantial disruption. During such periods of extreme market stress, several critical constraints emerge in the system's analytical framework.  
First, the pattern recognition algorithms, while effective in standard market conditions, demonstrate reduced efficacy when confronted with unprecedented volatility regimes. The system's Elliott wave identification mechanisms, calibrated on historical market behavior, may fail to adapt sufficiently rapidly to fundamental shifts in market structure. This temporal lag in pattern adaptation can result in decreased prediction accuracy during the initial phases of market dislocations.  
Second, the backtesting process requires substantial data accumulation within new market regimes before achieving optimal recalibration. This inherent latency in the learning process potentially compromises the system's predictive capabilities during the acute phase of market disruptions. While the continuous learning framework eventually adapts to novel market conditions, the intermediate period of adjustment may result in suboptimal pattern recognition and wave identification.  
Third, the system's current implementation lacks integration of mechanisms for incorporating sudden macroeconomic shocks or global event-driven market dislocations. While the RAG component provides some capability for processing real-time market information, the translation of unprecedented external events into actionable pattern recognition parameters remains a significant challenge. In our future research, we want to resolve this limitation with integration of an agent that could analyze live macroeconomic factors.  
The identified constraints underscore the inherent challenges in developing fully adaptive market analysis systems capable of maintaining performance across all market conditions. While ElliottAgents represents a significant advance in automated technical analysis, its current implementation remains bounded by these fundamental limitations in extreme market scenarios.
## 4 Design and Control of SCIM-FESS
### Control Structure of SCIM-FESS  
Important quantities to be controlled in a FESS are the DC-link voltage (\(V_{dc}\)), electromagnetic torque (\(T_{e}\)), speed (\(\omega_{m}\)) and position of the rotor magnetic flux vector (\(\theta_{e}\)) during acceleration and deceleration of the flywheel when operating within a specified range of speed. Field oriented control  
Figure 3: Schematic diagram of FESS.  
(FOC) and space vector pulse width modulation (SVPWM) have been utilised to control the gate pulses of MSC and GSC. Figure 4 shows a detailed control structure of both converters.
# Blur Kernel Estimation by Structure Sparse Prior
###### Abstract  
Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel. However, the sparse representation has not considered the structure relationships among original pixels. In this paper, a blur kernel estimation method is proposed by introducing the locality constraint into sparse representation framework. Both the sparsity regularization and the locality constraint are incorporated to exploit the structure relationships among pixels. The proposed method was evaluated on a real-world benchmark dataset. Experimental results demonstrate that the proposed method achieve comparable performance to the state-of-the-art methods.  
blind image deblurring; blur kernel estimation, structure information; sparsity regularization; locality constraint
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 3 Proposed Algorithm
### Atmospheric Light Estimation  
Methods for estimating the atmospheric light in recent haze removal algorithms usually suffer from one of two major issues: high cost in terms of computational complexity, and incorrectly taking pixels belonging to white objects into consideration. DCP's or CAP's atmospheric light estimation suffers from the former, and even the latter in specific cases, for example. This is because finding the pixel in the hazy image that corresponds to the brightest pixel amongst a percentage of pixels within a transmission map or depth map requires sorting all pixels and then searching over the selected ones. Additionally, if the percentage is not chosen carefully, image pixels within white objects may be included mistakenly in the estimation process. Using FFD's atmospheric light estimation approach, setting \(A\). to (1, 1, 1) after white balance absolutely does not work for images containing white objects therein. Thus, we have decided to use the quad-decomposition algorithm [19], which has been proven to be an efficient and fast estimation method.  
This algorithm operates on the image's luminance channel which, to prevent the unwanted influence of white objects, must be processed by a minimum filter in advance. Then, it iteratively divides the image's luminance into quarters and selects a quarter with the largest average luminance, until the quarter's size is less than a predetermined value. Using the finally selected quarter, a pixel with minimum Euclidean distance to (1, 1, 1) coordinate in the RGB space is chosen as the atmospheric light. Figure 14a shows an image in the IVC dataset where CAP fails to estimate the atmospheric light correctly. The red pixels are those corresponding to 0.1 % of the farthest pixels in the depth map, and the chosen one is the pixel in the train's front light. Furthermore, CAP needs to search through the entire spatial area of an image to find these pixels. To contrast, since the train's front light cannot dominate the average brightness in the image quarters, quad-decomposition does not take it into consideration. Simultaneously, the search area also is reduced iteratively by a factor of four for accurate estimation. The red dot in Figure 14b refers to a pixel being chosen as the atmospheric light, but it is enlarged for visual convenience.  
Figure 13: Dehazed images: (**a**) before applying \(w_{t}\) and (**b**) after applying \(w_{t}\).
# Benefit Optimization of Short Food Supply Chains for Organic Products: A Simulation-Based Approach
## 4 Simulation-Based Multi-Objective Optimization of SFSC
### Analysis of the Results  
The optimizer, together with the simulation software, searches for the optimal solution in the simulation model, i.e., for a set of decision variables (proportion of delivery for each type of market) that maximize the profit and minimize the number of unsold products in each category. The algorithm was set to 500 iterations.  
Figure 5 depicts the progress of the algorithm search. The graph shows the value of the criterion function: the profit (in PLNs) for the current solution (dashed line) and the best ones so far (solid line).  
The algorithm started with the scenario with an even distribution of products among all markets. The results showed that it is worth looking for the best way to sell products in an SFSC because different proportions of deliveries produce different results in terms of profit. The best solution was found in iteration 104: 78% of production should be sold in face-to-face markets, 17% in spatially proximal markets, and only 5% in spatially extended markets. The detailed results of the 10 best solutions are shown in Table 7. The results of the computer simulation showed that despite lower risk related to the uncertainty of demand, directing the largest amount of agricultural products to spatially extended markets produces the weakest profits. This occurs because the prices in these markets are lower than in face-to-face markets, where directing the largest amount of products produced the best results (despite the greater risk due to the greater uncertainty of demand). The near-optimal results outline cases where a little less is delivered to the face-to-face markets and more to the spatially proximal markets (iterations 110 and 93). The best obtained solution (97,456.53 PLN) yields a 18% higher profit than the initial iteration of an even distribution of products among all markets (82,850.61 PLN) and 26% higher than the worst found solution (profit 77,397.88 PLN), where deliveries to each market were: face-to-face markets 17%, spatially proximal markets 78%, and spatially extended markets 5%.  
Figure 5: Progress of the optimization algorithm.
## 3 Results and Discussion
### Characterizations of the Samples
As shown in Figure 5a, the XPS spectra of GO, rGO, silica, SG 0.30, and SG 1.00 exhibited peaks at 531.9 (O 1s), 400.0 (N 1s), 284.4 (C 1s), and 102.8 eV (Si 2p), indicating the existence of silica on the surface. In Figure 5b, the XPS C 1s spectra of GO, rGO, SG 0.30, and SG 1.00 showed the presence of carbon functional groups, such as O-C=O, C-O-C, C-N, C-C, and C=C, in multiple chemical states and at various relative contents. The water adsorption isotherms of SiO\({}_{2}\), SG 0.30, and SG 1.00 at 298 K are shown in Figure 6. As shown in Figure 5a, the water adsorption of SiO\({}_{2}\) was nearly complete up to P/P\({}_{0}\)\(-\)0.5. Both SG 0.30 and SG 1.00 exhibited water adsorption up to P/P\({}_{0}\)\(\sim\)1.0, which was caused by some exposed surfaces of SiO\({}_{2}\) and the graphene-formed micropores, as shown in Figure 6b,c. On the surface of SG 0.30 and SG 1.00, there were C-C, C-O-C, C=C, C-N, and O-C=O groups, providing adsorption sites for water. However, in SG 1.00, excess rGO coated the SiO\({}_{2}\) and decreased the number of adsorption sites, which might be the reason why the adsorption isotherm differed from that of SG 0.30. The water adsorption amounts were possibly related to the concentration of oxygen and nitrogen functional groups, such as C-O-C, C-N, and O-C=O, because these groups have the highest affinity toward water molecules [33]. SG 0.30 had a higher concentration of oxygen and nitrogen functional groups than SG 1.00; therefore, the amount of adsorbed water on SG 0.30 was higher than that on SG 1.00. To investigate the effect of defects on the adsorption properties of the materials, Raman spectroscopy was used. The D band and G band were observed in all samples at shifts of 1350 and 1590 cm\({}^{-1}\), respectively, representing disordered and ordered graphitic carbons. The ratio of the D band to G band intensity is shown in Figure 7. The ratio of I\({}_{d}\)/I\({}_{g}\) is a sensitive characterization of the disorder of a tested sample, with higher values indicating a higher disorder and more obvious defects and voids. The number of defects of rGO and the rGO/SiO\({}_{2}\) nanocomposites increased as GO was reduced. Compared with that of rGO and SG1.00, the highest I\({}_{d}\)/I\({}_{g}\) was obtained with SG 0.30. It is possible that the abundant defects of SG 0.30 provided more adsorption sites for cationic NR adsorption. Figure 5: (a) XPS survey spectra of GO, rGO, silica, SG 0.30, and SG 1.00 and (b) XPS C 1s spectra of GO, rGO, SG 0.30, and SG 1.00.
# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 2 Results of Research
### Data Analysis Methods  
Data analysis was mainly conducted using TensorFlow's built-in mechanisms for tracking training history, which was then visualized using the Matplotlib 0.13.1 library. Callbacks were also used to monitor and respond to changes in metrics as models were trained. In addition, tools such as TensorFlow and TensorFlow Profiler allowed for more advanced visualization and monitoring of training metrics, such as accuracy and loss, and resource consumption (GPU, RAM, CPU). This made it possible to optimize models and compare the results of different configurations, allowing a thorough understanding of the impact of different hyperparameter settings on model performance. The time module was used to accurately measure training time, while the pynvml library allowed monitoring GPU resource consumption, which was crucial for optimizing computational processes when training deep neural networks.
## 2 Materials and Methods
### Model of Estimating the Time of MBES Bathymetric Survey  
The distance between the main lines or check lines (crosslines) and their general distribution will largely depend on the size and location of the test water reservoir and the requirements included in the specification. The initial layout of the survey lines and orientation should be prepared in the initial planning stage. That system may be subject to modifications resulting from a better adaptation to the site bottom shape, the layout of the contours, the weather conditions, the intensity of vessel traffic, the occurrence of panicles, and other environmental factors. In measurements with a single-beam echosounder, the sounding lines are designed perpendicular to the general course of the contours. In the case of multi-beam sonar, the main survey lines should follow the direction of the isobaths. The parallel arrangement of the survey lines in relation to the local isobaths ensures, as far as possible, uniform bottom coverage and the maximum use of the optimal swath angle of the multi-beam echosounder.  
The calculation of the time necessary to perform the survey should include many variables. The most important are the time needed to perform depth measurements on the main survey lines, the calibration of MBES (patch test), the reach of the survey site, the return to the port, and the time that should be allocated to verify discovered underwater objects and potential delays or interruptions in the survey process resulting from various external factors. The total time of survey measurements \(T_{son}\) is calculated from:  
\[T_{son}=T_{pp}+T_{PK}+T_{kul}+T_{cc}+T_{rej}+T_{wp}+T_{us}+T_{we\sigma}, \tag{1}\]  
where the following definitions apply:  
\(T_{PP}\)-- total time of the depth measurements on main survey lines [h];  
\(T_{PK}\)-- total time of the depth measurements on cross check lines [h];  
\(T_{kul}\)-- time to perform MBES calibration (patch test) [h];  
\(T_{cc}\)-- total time of cross-check maneuvers [h];  
\(T_{rej}\)-- time of transit to survey site and return to the port [h];  
\(T_{wp}\)-- delay time due to weather conditions [h];  
\(T_{us}\)-- delay in the survey due to equipment failure [h];  
\(T_{we\sigma}\)-- time spent on examination of detected underwater objects [h].
## 4 Results and Discussion
### Effect of Target Location on Discrimination Performance  
Finally, the discrimination performance of the DVB method is simulated and analyzed when the target performing self-defense jamming is located at different locations. The target position varies from \(-100\) km to \(100\) km on the X-axis and from \(20\) km to \(100\) km on the Y-axis. The deception distance of the active false target generated by the jammer carried by the target is \(1000\) m, and the deception speed is \(300\) m/s. The measurement error information of the T/R-R2 multistatic radar system is shown in Table 1. The X and Y coordinates of the target are in intervals with a step of \(5\) km, and \(5000\) Monte Carlo experiments are performed for each location of the target. The simulation results are shown in Figure 9. In the figure, \(\bigstar\) represents the T/R station, and \(*\) represents the R station. The desired misjudgment probability of the physical target is set as \(P_{PT}^{\prime}=0.01\).  
As shown in Figure 9a, within the illustrated airspace range, the discrimination probability of the proposed method is maintained at \(99\%\) for the physical target, which is consistent with the expected constant misjudgment of the physical target. In Figure 9b, the high discrimination probability of the false target is mainly distributed in the area between two receivers, and radiates out from the baseline of the multistatic radar system, and the discrimination probability decreases with far distance. When the X coordinate of the target is less than the receiver R\({}_{2}\) (i.e., \(-50\) km) or greater than receiver R\({}_{1}\) (i.e., \(50\) km), the discrimination performance decreases sharply. This is mainly because the difference in the angle of the target relative to each receiver in the above region becomes smaller, which increases the deviation of the target velocity and the deception velocity solution.  
Figure 8: Discrimination performance of proposed method versus different pitch angle measurement accuracy.
# Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity
## 5 Discussion  
The present study's contributions are two-fold; one, the introduction of Physarum foraging behavior to BLN design that considerers the constraints of a megacity, and two, the implications of the proposed model for Dhaka city, a real-life congested megacity with poor mobility. In this section, we discuss the computational efficiency of the proposed Physarum-inspired method over other bioinspired methods, its ability to obtain the optimal BLN, and improvement in green mobility with BLN by comparing it with existing transport.  
Physarum directly represents a single naturally growing network, and in the proposed Physarum-inspired method, a single BLN solution is computed gradually through a simple iterative process. In contrast, the widely used bioinspired population-based methods (e.g., particle swarm optimization, genetic algorithm) require evaluating all the individuals (may be hundreds) in the population, interactions among them, all while updating throughout the iterative process. Therefore, Physarum's method is computationally much more efficient than these other bioinspired methods [48]. This is the motivation behind the Physarum-inspired model development for BLN design--the pursuit of computational simplicity and efficiency.  
The developed Physarum-inspired BLN design method was applied to the megacity Dhaka and evaluated the designed BLN. We have compared the optimality under given constraints of its individual routes by taking the samples as described in Section 4.3.1 (BLN for the central Dhaka area) and Section 4.4.1 (BLN for the entire Dhaka area). Specifically, we verified whether there is a better and/or less costly route for any sample route connecting two arbitrary nodes given in the designed BLNs. In this way, we have confirmed that other (unselected) routes are non-optimal (in terms of travel distance, avoiding main roads). Any other optimization method (if developed) is expected to choose the same optimal routes that our method has selected.  
Finally, we have compared the designed BLNs with other modes of transport to verify green mobility by application to Dhaka city. Dhaka is the most congested megacity, with poor mobility from existing transport. The designed BLN was evaluated by comparing its available routes between different locations with the existing vehicle routes of the city in terms of required travel times during different time periods. The BLN routes were found to be suitable alternatives to congested main roads. The expected travel time using BLN is shorter than with other transport (e.g., car and public bus); at a glance, the average travel speed on BLN is almost double that of a public bus at peak hours. Such empirical outcomes justify the optimality of the designed BLN and hence ensures the proposed method's rationality, benefit and efficiency.
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 2 Related Work  
While few-shot object detection has seen breakthroughs on large-scale datasets, its application has been urgently needed in specialized fields to tackle real-world manufacturing issues, such as steel defect detection. The inspections of steel strips within factories, however, have largely been conducted manually by inspectors. In an effort to improve the efficiency of steel strip defect detection, the adoption of object detection technologies has emerged as an essential replacement for manual inspections.
###### Abstract  
The effective management of mosquito vectors is a timely challenge for medical and veterinary entomology. In this study, we evaluated the acoustic Larvasonic device to control young instars of the mosquito _Aedes aegypti_ in diverse freshwater environments. Under laboratory conditions, we investigated the effect of exposure time and distance from the transducer on the mortality of larvae and pupae of _Ae. aegypti_. Furthermore, we evaluated the effectiveness of the ultrasound window of the electromagnetic spectrum under different field conditions. Results showed that first and second instar larvae were more sensitive to the frequency range of 18-30 kHz of the Larvasonic device. Ultrasonic waves applied for 180 s at a frequency from 18 to 30 kHz caused 100% larval mortality at a distance of 60 cm from the transducer. No mortality was observed in the non-target copep _Megacyclops formosanus_. The exposure to the soundwaves produced by the acoustic larvicidal device over different distances effectively damaged _Ae. aegypti_ through destruction of the larval dorsal tracheal trunk, thorax and abdomen. Overall, results indicated that the Larvasonic device tested can provide an alternative tool to reduce young instar populations of _Ae. aegypti_, without any effects on non-target aquatic invertebrates like copepods. It turned out to be a useful device for mosquito biocontrol. This technology has a relevant potential to fight the spread of mosquito-borne diseases.  
mosquito control; ultrasound; electromagnetic control; biological control; _Aedes aegypti: Megacyclops formosanus_; biosafety
## 5 Evaluation  
### Framework Assessment Approaches  
A structured expert evaluation approach utilizing the Delphi Method [73] was adopted to assess the validity, feasibility, and applicability of the proposed framework. This systematic, iterative method collects structured feedback from expert panels, ensuring the integration of Lean Construction, BIM, and Emerging Technologies aligns with the industry's best practices. The evaluation process includes the following:  
* Expert selection: Diverse experts in Lean Construction, BIM, and Emerging Technologies, with a minimum of 10 years' experience.
* Questionnaire development: A structured questionnaire based on the SEQUAL framework [74,75].
* First round assessment: Experts review the framework and provide quantitative (Likert-scale) and qualitative feedback.
* Second round refinement: Feedback is analyzed, and the framework is adjusted accordingly.
* Final validation round: Experts reassess the revised framework for validity and practicality.  
Using this Delphi approach ensures the framework's robustness, iterative refinement, and practical relevance to construction management.