esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 5 Conclusions  
Addressing the prevalent issues of excessive noise and color distortion in current low-light image enhancement algorithms, this paper proposes a low-light image enhancement algorithm based on deep learning and the Retinex theory. By decomposing, enhancing, and reconstructing input images, the quality of low-light images is effectively improved. Within the image decomposition module, a network model with an encoding-decoding structure is designed, incorporating dilated convolutions and residual connections to gather contextual information and minimize detail loss during the decomposition process. This allows the input low-light image to be decomposed into smoother illumination and reflectance maps. In the illumination enhancement module, suitable mapping curves are employed for iterative enhancement of the illumination map. To eliminate noisy portions and rectify color distortion, a 3D LUT-based color restoration module is introduced. The experimental results demonstrate that our proposed algorithm effectively enhances the brightness and contrast of input images, effectively addresses noise and color distortion issues, and significantly enhances subjective visual experience compared to other algorithms. It achieves superior results in terms of PSNR, SSIM, and NIQE image quality assessment metrics. Future research directions include expanding the dataset, optimizing network loss functions,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **PSNR** & **SSIM** & **NIQE** \\ \hline Input & 7.150 & 0.144 & 14.291 \\ CLAHE & 8.265 & 0.334 & 10.735 \\ LR3M & 8.167 & 0.352 & 7.522 \\ DeepUPE & 8.872 & 0.349 & 7.364 \\ Zero-DCE & 13.231 & 0.418 & 7.762 \\ LIME & 16.293 & 0.541 & 8.378 \\ RetinexNet & 16.588 & 0.601 & 8.859 \\ Zhang et al. & 13.253 & 0.385 & 7.935 \\ MSRCR & 15.361 & 0.494 & 8.114 \\ EnlightenGAN & 15.319 & 0.593 & 4.684 \\ Ours & 19.945 & 0.653 & 4.392 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Objective experiment results.  
enhancing model speed and efficacy, and increasing the algorithm's applicability across different domains.  
Conceptualization, C.L. and Q.T.; methodology, C.L.; software, C.L.; validation, C.L.; formal analysis, C.L. and Q.T.; investigation, C.L.; resources, C.L.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, C.L. and Q.T.; visualization, C.L.; supervision, Q.T.; project administration, Q.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The LOw Light paired dataset (LOL) used in this paper is available through the following link: [[https://daooshee.github.io/BMVC2018](https://daooshee.github.io/BMVC2018) website]([https://daooshee.github.io/BMVC2018](https://daooshee.github.io/BMVC2018) website) (accessed on 24 June 2023).  
The authors declare no conflict of interest.
## 3 Experiment Results
### IRBM (Internally Reinforced Brick Member)  
In this specimen, the SI rods were fixed by nuts after sandwiching both ends of the specimen with steel plates to enhance integrity. Figures 9c and 10c show the final crack pattern and the vertical load-deflection relationship, respectively. The yielding point of the bottom side rod is plotted in Figure 10c. In this specimen, stiffness degradation was observed after flexural crack development, and a significant stiffness decrease was found near the yielding point of the rod, which was a behavior quite similar to the reinforced concrete (RC) member. After the reinforcing rod yielded, the vertical strength gradually increased, and its maximum strength was recorded when the vertical deflection reached about 37 mm. At this time, the neutral axis depth was observed to be almost located at the extreme compression fiber of the damaged section, and the compression failure of the joint mortar and bricks was very severe. As shown in Figure 10c, a maximum strength of 126.2 kN was recorded, demonstrating more than four times the maximum  
Figure 11: Vertical load–strain relationship: (**a**) IRBM; (**b**) IRBM—F; (**c**) IRBM—FNM.  
strength of the IRBM specimen. It was also noted that the \(R\) at its maximum strength was about 1/10, which was an extremely large deformation. There was no abrupt strength decrease after the maximum strength was obtained, but the fracture of the bottom rod occurred at the moment when the vertical deflection reached 40 mm, which resulted in sudden failure. Therefore, even though the strain of the rod at the maximum strength could not be measured, the stress at the bottom rod was considered to reach its tensile strength. Such a failure pattern, shown in this specimen, is very dangerous, but it should be emphasized that it occurred at an extremely large deformation, larger than \(R=1/10\). From the load-deflection curve of IFBM--F, superior structural behaviors were achieved by the proposed material and method.  
The strain distribution of the bottom and top reinforcing rods is shown in Figure 11b. As can be found in the Figure, compared to IRBM, the strain values of the rods in IRBM--F show much larger values. All the strain values of the bottom rods exceeded the yielding strain, and the strain of T2 also yielded at the maximum strength. From the strain data, fixing the SI rods with steel plates and nuts enabled the efficient use of the reinforcement, and great performance enhancements were obtained in both the strength and the deformation capacities. It should be also noted that the compressive force was acting on the brick members as a reaction force to the tensile force of the rods as the deformation grew; therefore, it not only improved the integrity of the member but also led to an increase in shear and bending resistances.
## 4 Performance Analysis
### Comparison of the Power Patterns for All Beamformers
#### 4.3.3. Comparison of the Power Pattern for the VDL-Based Beamformer  
In Figure 4c, the output power of the LCAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)0.01 dB, \(-\)0.05 dB, and \(-\)0.11 dB, respectively. For 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity, the output power of LCPAA is \(-\)0.0227 dB, \(-\)0.084 dB, and \(-\)0.185 dB, respectively. Then, the output power of the LRNAA beamformer with 1\({}^{\circ}\), 2\({}^{\circ}\), and 3\({}^{\circ}\) disparity is \(-\)0.02 dB, \(-\)0.  
3\({}^{\circ}\) disparity is \(-0.022\) dB, \(-0.082\) dB, and \(-0.18\) dB, respectively. The output power of the LCPAA is higher than that of the other beamformers, as seen in the figure. The LCPAA beamformers performed very well.  
Figure 4: Cont.  
power of the LRAA beamformer with \(1^{\circ},2^{\circ}\), and \(3^{\circ}\) disparity is \(-0.018\) dB, \(-0.075\) dB, and \(-0.171\) dB, respectively. The output power of the LCCAA beamformer with \(1^{\circ}\), \(2^{\circ}\), and \(3^{\circ}\) disparity is \(-0.019\) dB, \(-0.076\) dB, and \(-0.171\) dB, accordingly.  
The output power of the LCPAA is higher than that of the other beamformers, as seen in the figure. In the NVL robust technique, the performance of the LCPAA beamformer is better. In Table 1, the SINR of the robust techniques (FDL, ODL, VDL, and NVL) is shown with respect to the disparity angle.  
The comparison of the power pattern for all beamformers using robust techniques is shown in Table 2 with respect to the corresponding beamformers for the different methods. The NVL technique outperforms the others. The comparison of the power pattern with the disparity angle for different robust techniques is shown in Table 2. The LCPAA beamformer performs better when using the NVL robust technique.
# Climate-Smart Agro-Hydroological Model for a Large Scale Rice Irrigation Scheme in Malaysia
## Materials and Methods
### Design and Development of Climate-Smart Agro-Hydrological Tool  
The generated daily big data of about 700 GB was integrated using Excel-based Visual Basic for Applications (VBA), to visualize and analyze different irrigation and water parameters in the scheme under various management options for the historical (1976-2005) and future (2010-2099) periods. These parameters include various flows at SKC, BRH, TRH and at the key points along the main canal, water demand at different ISAs in the scheme, and reservoir releases/inflows and storage. Several programs based on ten GCMs under three RCP scenarios (RCP4.5, RCP6.0 and RCP8.5) were developed to design the interface. The graphical user interface is based on a mouse-driven pattern with pop-up windows, pull-down menus, and control buttons. The main interface of the model is linked with reference evapotranspiration and rainfall sub-modules. The former was designed using the FAO-56 Penman-Monteith model, to estimate daily ETo (mm/day), while the latter was used to compute daily effective rainfall. The generated outputs from the two sub-modules can be imported directly to the main module. The tool, under different GCM and RCP realizations, is capable of visualizing and analyzing the integrated daily big data of all water resources, from the basin outlet upstream to the rice field downstream, through the flow within the rivers and the reservoir release/inflow/storage patterns.  
To visualize and analyze any variable during the historical period, the name of the variable(s) and date are selected. For future periods, a realization of GCM and RCP scenarios must be selected from the list of the GCMs and RCPs 4.5, 6.0, and 8.5 before the selection of the date and variable(s) to be analyzed. The analysis could be for a single time segment (2010-2039, 2040-2069, or 2070-2099) or multiple periods, using a single GCM or multiple models. A maximum of four parameters can be visualized at one time. The visualization could be chosen in daily, weekly, monthly, seasonal, or longer time series and the results viewed in tabular and graphical forms. The developed climate-smart agro-hydrological tool was applied to Tanjung Karang Rice Irrigation Scheme Malaysia to assess climate change impacts on key agro-hydrological information. Numerous outputs from the model were obtained using both single GCM model(s) and multi-model projections. Nonetheless, due to space constraints, this paper only discusses a few selected outputs.
# Research on Sparse Representation Method of Acoustic Microimaging Signals
## 3 Simulations  
### Simulation Model  
As shown in Figure 4a, a simple model of scanning acoustic microscopy of electronic packages was established using PZFlex software (Weidlinger Associates, Los Itos, CA, USA), where the electronic package was simulated by Epoxy Molding Compound (EMC) and silicon die. The transducer frequency was set to 20 MHz, and the material properties are listed in Table 1. As shown in Figure 4b, the reference interface is the top layer of the EMC, corresponding to the reference echo \(x_{0}(t)\), and the target interfaces are the top and bottom layers of the die, corresponding to the target echoes \(x_{1}(t)\) and \(x_{2}(t)\), respectively. \(x_{3}(t)\) to \(x_{m}(t)\) are the multiple reflected echoes within the die. When the thickness of the die becomes smaller, \(x_{2}(t)\) to \(x_{m}(t)\) will all move toward \(x_{1}(t)\), resulting in the overlap of multiple echoes. The degree of overlap \(\delta\) is defined as the relative size of the overlap between \(x_{1}(t)\) and \(x_{2}(t)\).  
\[\delta=\left(T-\frac{2d}{c_{L}}\right)/T. \tag{14}\]  
where \(d\) is the thickness of the die, \(c_{L}=7526\) m/s is the longitudinal wave velocity in the die, and \(T=110.0\) ns is the FWTM duration of \(x_{1}(t)\). When the thickness of the die is half-wavelength, that is, \(d=Tc_{L}/2=413.9\) m, the degree of overlap is zero. By changing the  
Figure 3: Sparse decomposition and echoes reconstruction: (**a**) AMI signal; (**b**) time-frequency diagram of atoms obtained by sparse decomposition; (**c**) reconstructed \(x_{1}(t)\) and \(x_{2}(t)\).  
thickness of the die, different degrees of overlap between \(x_{1}(t)\) and \(x_{2}(t)\) can be simulated. Figure 5a-c are the cases where the overlap degree is \(-45\%\), \(30\%\) and \(60\%\), respectively.
###### Abstract
The following are the main contributions of this article:  
* To the authors' knowledge, this is the first article that demonstrates the use of Bluetooth 5 in combination with libp2p for implementing practical opportunistic networks. Libp2p is a modular networking library initially developed as part of the IPFS (Inter-Planetary File System) project [17]. It provides a flexible architecture for developing P2P applications that manage tasks such as peer discovery, message routing, and connection management via various transport channels. Libp2p is a relevant option for P2P communication as it supports multiple network topologies and protocols, making it adaptable to different environments and resilient to network changes. * It details a novel integration of libp2p with Bluetooth 5 in order to enable peer-to-peer (P2P) features like peer discovery and decentralized management, which enhance network flexibility and reliability. * The proposed opportunistic developments are evaluated in real home and industrial scenarios, including inside a military shipyard workshop, evaluating latency and the impact of physical obstacles on network performance and providing key data on the effectiveness of the architecture. The rest of this article is structured as follows. Section 2.3 analyzes the current state of the art about opportunistic communication networks. Section 3 describes the proposed communications architecture. Section 4 details the implementation of the architecture. Section 5 presents the performed experiments. Finally, Section 7 is devoted to conclusions.
# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
###### Abstract  
The cellular industry faces challenges in controlling the quality of signals for all users, given its meteoric growth in the last few years. The service providers are required to place cellular towers at the optimal location for providing a strong cellular network in a particular region. However, due to buildings, roads, open spaces, etc., of varying topography in 3D (obstructing the signals) and varying densities of settlements, finding the optimal location for the tower becomes challenging. Further, in a bigger area, it is required to determine the optimum number and locations for setting up cellular towers to ensure improved quality. The determination of optimum solutions requires a signal strength prediction model that needs to integrate terrain data, information of cellular tower with users' locations, along with tower signal strengths for predictions. Existing modeling practices face limitations in terms of the usage of 2D data, rough terrain inputs, and the inability to provide detailed shapefies to GIS. The estimation of optimum distribution of cellular towers necessitates the determination of a model for the prediction of signal strength at users' locations accurately. Better modeling is only possible with detailed and precise data in 3D. Considering the above needs, a LIDAR data-based cellular tower distribution modeling is attempted in this article. The locations chosen for this research are RGIPT, UP (45 Ace), and Shahgaraj, Agra, UP, India (6 km2). LiDAR data and google images for the project sites were classified as buildings and features. The edges of overground objects were extracted and used to determine the routes for transmission of a signal from the tower to user locations. The terrain parameters and transmission losses for every route are determined to model the signal strength for a user's location. The ground strength of signals is measured over 1000 points in 3D at project sites to compare with modeled signal strengths (an RMSE error 3.45). The accurate model is then used to determine the optimum number and locations of cellular towers for each site. Modeled optimum solutions are compared with existing tower locations to estimate % over design or under design and the scope of improvement (80% users below \(-\)80 dB m improves to 70% users above \(-\)75 dB m).  
cell phones; transmission tower; LiDAR; GIS; signal strength; optimum location +
Footnote †: journal:
## 3 Results and Analysis of Influencing Factors
### The Impact of Drill String Properties on Signal Attenuation  
The drill string is composed of drill pipes, each about 9 m in length, strung together. As the drilling depth increases, the number of drill pipes connected in series needs to be continuously added. There is contact resistance at the connection between drill pipes, and the drill string itself is not an ideal conductor; its electrical conductivity and relative magnetic permeability both vary. Moreover, the dimensions of the drill string may differ in different wells. Therefore, it is necessary to delve into the impact of factors such as electrical conductivity, magnetic permeability, dimensions of the drill pipes, and contact resistance on signal transmission.
# Landsat Surface Product Validation Instrumentation:
## 1 Big MAC Overview
### Purpose
#### 1.1.1 Targets  
A variety of targets were developed for the campaign. Since the campaign focused on surface reflectance and temperature measurements, most targets could be developed at a small scale for fabrication and deployment. Figure 2 illustrates an array of human-made reflective targets. These included felt radiometry panels and spectral panels developed by RIT, two Permaflect panels developed by Labsphere, and two'mystery panels'. Unlike the rest of the human-made reflective panels, the'mystery panels' were obtained from an organization not participating in the BigMAC--University of Arizona Remote Sensing Group. Thus, they served as blind targets to the participants to eliminate bias as much as possible for at least a small subset of the targets that were used. A large, 100 m by 100 m, portion of the alfalfa field served as a large natural target, as indicated in Figure 2. The small human-made reflective targets provided an opportunity for measuring precision and repeatability in a more controlled environment, while the large natural alfalfa target represented what would be a more 'normal' measurement in the operational assessment of Landsat surface product validation. Alfalfa is advantageous in that it represents agricultural targets and is short enough so that reflectance can be measured using handheld or backpack-based spectrometers. This particular field had just been moved and baled for hay the previous week. So, the field was optimally short and greening up well after the moving. A disadvantage of using alfalfa as a convenient natural target is that the plant canopy is substantially different than forests and other taller vegetation. Additional targets included a portion of an adjacent asphalt roadway which served as a very stable larger target to assess repeatability, and a set of thermal targets-warm and cool pools of water, sand, and rubber mats. The water pools were used because the emissivity of water is well understood, and water tends to be fairly stable with temperature over time. The sand and rubber mats represent a more natural target and a reasonably stable non-water target. Figures 3 and 4 show the thermal targets in both the reflective and temperature space, respectively. Thermal measurements were taken for the large natural alfalfa target as well.  
Figure 2: Location of the various targets used in the BigMAC activity.
## 4 Results
The most distinguishable was the traffic generated by Netflix, due to its unique statistical properties. As presented in Figure 6, this traffic has a pulsating character, with fairly even intervals between individual beats. In the Amazon-generated flow, we can also notice bursts, but their interval is greater than that of Netflix. Furthermore, the service still transmitted low variability traffic between these bursts. Lastly, YouTube traffic has a typical ON/OFF architecture, where ON and OFF periods have similar lengths. These unique characteristics make it easier to distinguish the video from non-video traffic. It is also easier to distinguish individual flows from each other within a video traffic group.
# Blocking Cyclic Job-Shop Scheduling Problems
###### Abstract  
Cyclic scheduling is of vital importance in a repetitive discrete manufacturing environment. We investigate scheduling in the context of general cyclic job shops with blocking where there are no intermediate buffers between the machines. We also consider sequence-dependent setups (anticipatory and nonanticipatory), which commonly appear in different manufacturing environments. The choice of blocking condition, that is whether the sequence-dependent setups are anticipatory or not, significantly impacts the optimal schedules. We provide a novel mixed-integer programming (MIP) model for the above problem, namely blocking cyclic job-shop scheduling. Furthermore, we study the impact of sequence-dependent setups in this research. The problem is analysed in detail with respect to anticipatory and nonanticipatory setups and the efficiency of the proposed model is investigated via a computational study that is conducted on a set of randomly generated problem instances. The proposed MIP models are capable of solving small-to-medium-sized problems. Moreover, the analysis presented demonstrates that anticipatory setups directly affect blocking conditions, since intermediate buffers between the machines are not present. Hence, in systems with anticipatory setups, cycle times increase to a greater extent compared to systems with nonanticipatory setups.  
c pacs: 1
###### Abstract  
Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in real time remains a significant challenge in ensuring operator compliance and site safety. Traditional object detection models often fall short in crane operator cabins due to complex lighting conditions, cluttered backgrounds, and the small physical scale of target objects. To address these challenges, we introduce YOLO-TC, a refined object detection model tailored specifically for tower crane monitoring applications. Built upon the robust YOLOv7 architecture, our model integrates a novel channel-spatial attention mechanism, ECA-CBAM, into the backbone network, enhancing feature extraction without an increase in parameter count. Additionally, we propose the HA-PANet architecture to achieve progressive feature fusion, addressing scale disparities and prioritizing small object detection while reducing noise from unrelated objects. To improve bounding box regression, the MPDIoU Loss function is employed, resulting in superior accuracy for small, critical objects in dense environments. The experimental results on both the PASCAL VOC benchmark and a custom dataset demonstrate that YOLO-TC outperforms baseline models, showcasing its robustness in identifying high-risk objects under challenging conditions. This model holds significant promise for enhancing automated safety monitoring, potentially reducing occupational hazards by providing a proactive, resilient solution for real-time risk detection in tower crane operations.  
c 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE), Vol., pp. 1-10. External Links: Document Cited by: SS1.