esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Solution for Reconstructing More Details High Dynamic Range Image
## 2 Related Work
### Datasets
Since the shutter time of Figure 5e is longer than that of Figure 5a, Figure 5e can hardly preserve the image details in the image. We call this phenomenon a highlight saturation. The histogram of Figure 5e shows that all pixels are close to the maximum brightness. Figure 4: Images and histograms of the low light area at different shutter speeds. Figure 5: Images and histograms of the high light area at different shutter speeds. High-brightness LDR images cannot retain high-brightness image details. In other words, the high-brightness areas in LDR images are prone to detail distortion. Comparing the histograms of Figure 4a,f and comparing the histograms of Figure 5a,f, the HDR image still retains more image details in the high-brightness area and the low-brightness area than an LDR image. Based on the above, scholars intuitively use the fusion technology of multi-exposure LDR images to reconstruct HDR images. The advantage of this is that the reconstructed HDR image will have image detail information of different brightness areas. Establishing an HDR image database is very important. Public datasets can provide scholars with an objective benchmark to evaluate the performance of reconstructing HDR images. In order to provide a public benchmark for reconstructing HDR images, scholars have successively established HDR datasets. Until 2024, the well-known datasets are HDReye dataset [43], [PERSON] dataset [38], [PERSON] dataset [10], [PERSON] dataset [44], [PERSON] dataset [36], and NITIRE2021 dataset [45]. Some datasets are single-exposure images, and some datasets are multi-exposure images. Some datasets have ground truth; some datasets do not have ground truth. Some datasets are real images, and some datasets are synthetic images. The differences between these famous datasets are summarized in Table 1.  
The fusion of multi-exposure LDR images to generate HDR images is prone to artifacts. Artifacts are caused by the misalignment of the position of the moving object in the LDR images at different exposures. To this end, scholars face challenges in reconstructing HDR images. As time goes by, the alignment-based method, rejection-based method, patch-based method, and CNN-based method are proposed in sequence to improve the technology of reconstructing HDR images.
#### Aims and Objectives  
In this study, we consider an urban and dynamic vehicular network with multi-VUs, multi-RSU scenarios, and we propose an optimization-based approach to schedule VU-RSU association, channel assignment and transmit power allocation. The proposed approach utilizes NOMA to accommodate a large number of vehicles with a limited number of orthogonal channels. For a better utilization of NOMA, we also propose an optimal user pairing method employing a two-user clustering approach. Additionally, by allowing NOMA-OMA switching, the proposed solution can further improve the energy efficiency when the number of VUs on the network is low. Finally, to make the proposed approach applicable to real-time scheduling, we introduce low-complex algorithms that can yield sub-optimal solutions with minimal time overhead. The summary of the contributions we make in this paper is as follows;  
* We mathematically formulate the joint RSU-user association, channel assignment, NOMA-OMA switching, user pairing, and power control problem for NOMA-enabled V2X networks, aiming at minimizing the total transmit power consumption for downlink transmission. These operations are closely interconnected with each other to maximize resource utilization, making joint optimization essential.
* We propose a novel approach to derive a low-complex, sub-optimal solution by transforming the proposed joint optimization formulation to reduce time complexity, thus enabling real-time scheduling of network resources. The methods for complexity reduction include binary variable relaxation and problem partitioning.
* To extensively evaluate and validate the proposed approach, we have implemented a customized, discrete-time simulator. We also compared the performance of the proposed approach to the conventional solutions to validate its effectiveness with respect to total power consumption, association fairness and CPU time. In addition, to investigate the effect of VUs' mobility on performance, we carry out a long-term simulation that incorporates realistic movement in a complex intersection scenario.  
The remainder of this paper is structured as follows. Section 2 summarizes the related studies. The following Section 3 presents the proposed user pairing and power control method for NOMA on dynamic urban vehicular networks. Evaluation, validation, and analysis of the proposed solution are carried out in Section 4, and Section 5 concludes this paper. The frequently used abbreviations and terminologies are summarized in Table 1.
# Parametric Analysis of the Shear Lag Effect in Tube Structural Systems of Tall Buildings
###### Abstract  
Horizontal loads such as earthquake and wind are considered dominant loads for the design of tall buildings. One of the most efficient structural systems in this regard is the tube structural system. Even though such systems have a high resistance when it comes to horizontal loads, the shear lag effect that is characterized by an incomplete and uneven activation of vertical elements may cause a series of problems such as the deformation of internal panels and secondary structural elements, which cumulatively grow with the height of the building. In this paper, the shear lag effect in a typical tube structure will be observed and analyzed on a series of different numerical models. A parametric analysis will be conducted with a great number of variations in the structural elements and building layout, for the purpose of giving recommendations for an optimal design of a tube structural system.  
shear lag effect; tall buildings; tube structural system; horizontal loads; reinforced concrete columns +
Footnote †: journal: Applied Sciences
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 3 Methods
### QCNN-LSTM Model Architecture
Finally, the fully connected layer uses a deep neural network with a single hidden layer as the output layer of the QCNN-LSTM network model to fit and predict the data, and the output is the predicted value at time t. This retains the high computational performance of the QCNN model while incorporating the LSTM model, giving the framework higher sequential prediction capability.
# Autonomous Wireless Sensor Networks in
## Materials and Methods
### Controller Board  
The PentaSense board connects up to five external sensor or communication devices with the main controller (Figure 2). It also provides the flexibility to be AC or DC powered and can be configured using a USB-HDI connection. The board has an integrated RF-communication module which operates on the 866 MHz frequency. Optimized time-division multiplexing based meshed routing protocol is implemented on the board to overcome communication distance limitations. The controller board is built around a PIC18F46J50 microcontroller which supports a range of features that can significantly reduce power consumption during operation such as alternate run modes, multiple idle modes, and on-the-fly mode switching.  
This can be done by using several techniques such as clocking the controller from the timer source or the internal RC oscillator (up to 90% reduction), the control can remain operational with only the peripherals active on the CPU core disabled.  
A fully-featured USB communications module with a built-in transceiver that is compliant with the USB Specification Revision 2.0 is incorporated with low-speed and full-speed communication. The controller board provides ample room for application code, from 16 Kbytes to 64 Kbytes of code space.  
The Flash cells for program memory can sustain up to 10,000 erase/write cycles, while data retention is over 20 years. It is possible to read/write the flash program memory during normal operation. The PIC18F46J50 family also provides plenty of room for dynamic application data with up to 3.8 Kbytes of data RAM. The board incorporates a range of serial and parallel communication peripherals. This device also includes two independent Enhanced USARTs and two Master Synchronous Serial Port (MSSP) modules, capable of both Serial Peripheral Interface (SPI) and I2C(tm) (Master and Slave) modes of operation. The board provides five antilog A/D converters with a  
Figure 1: Overview of system architecture.  
sampling frequency up to 100 KS/s. The 5 A/D conversion channels are time multiplexed. The board can operate between 2.15 V-3.6 V.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs
### Planning experiments in right-angle turn scenarios
#### 4.1.2 Planning Experiments in Dynamic Scenarios  
The planning experiments in scenarios combining dynamic and static obstacles are shown in Figure 27. Obstacle 1 is about 3.4 m in front of the robot, and the \(X\)-axis and \(Y\)-axis distances between obstacle 1 and obstacle 2 are about 1.6 m and 1.1 m. The \(Y\)-axis distance between the pedestrian and obstacle 2 is about 2.8 m. The target point is set 14 m in front of the robot for the planning experiment. When the robot is about to cross the blue barrel barrier, the pedestrian walks to the middle of the laneway. At this time, the robot should replan to avoid the pedestrian and reach the end point.  
The experimental results of the MINCO trajectory method are shown in Figure 28. Figure 28a shows the better planning results given by the algorithm after being adjusted for the given target point. Figure 28b shows the pedestrian obstacle moving to the middle of the alley. Figure 28c shows the replanning result when a new obstacle is encountered. Figure 28d shows the robot avoiding the pedestrian obstacle.  
The experimental results of the Fast-Planner planning algorithm are shown in Figure 29.  
Figure 26: Trajectory of the proposed algorithm in right-angle bend scenario.  
Figure 25: Fast-Planner algorithm test process in right-angle bend scenarios.  
A comparison of the final trajectories of the two algorithms is shown in Figure 30. The MINCO trajectory method performed a total of seven planning passes during the robot movement, with a total planning time of 0.692 s and a robot trajectory length of 14.8 m. The Fast-Planner algorithm performed a total of 438 planning passes throughout the entire process, with a total time of 2.61 s and a robot trajectory length of 15.38 m.  
Figure 28: Experimental results of dynamic scene by MINCO trajectory method: (**a**) start planning; (**b**) pedestrian presence; (**c**) replan; (**d**) avoiding pedestrians.  
Figure 27: Dynamic and static obstacle combination scenario.  
Figure 29: Experimental results of dynamic scene by Fast-Planner trajectory method: (**a**) start planning; (**b**) pedestrian presence; (**c**) replan; (**d**) avoiding pedestrians.  
As can be seen from the above data and trajectory comparison, in a complex scenario combining dynamic and static obstacles, the Fast-Planner algorithm will keep planning to find a safe path, so the number of planning times is very large. On the other hand, the MINCO trajectory method can still obtain a smooth trajectory with better performance in many indexes with only seven times of planning.  
Combining the multiple experiments above, it can be found that in different scenarios, the planning algorithms based on MINCO trajectories and safe corridors proposed in this paper can complete the planning task within 10 times of planning. And the smoothness of the trajectory, the length of the trajectory, and the planning time are better than the Fast-Planner algorithm.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 6 Performance Analysis
### Science Data Flow (DTNbox Approach, Multilateral Synchronization)  
These encouraging results have led us to introduce a variant in the experiment, to examine performance in the case of two DTNbox synchronization hops. In this variant, instead of bilateral synchronization between Lander and PDGS, we have multilateral synchronization similar to that illustrated in Figure 2, with a first synchronization hop from Lander to ESA-MCC and a second hop from ESA-MCC to PDGS. The results are shown in Figure 11. Comparing delivery results with those in Figure 10 (one synchronization hop between ESA-MCC and PDGS), we can observe that the qualitative behavior is still the same and that the average penalization due to the second synchronization hop is marginal (only about 5 s more, i.e., 15 s more than in the direct transfer).  
Figure 11: Science data flow (bulk priority), from Lander to PDGS: time sequences of bundle generation and delivery. Alternative delivery by means of DTNbox, but with a multilateral synchronization (Lander to ESA-MCC and ESA-MCC to PDGS hops).  
Figure 10: Science data flow (bulk priority) from Lander to PDGS: time sequences of bundle generation and delivery. Alternative delivery by means of DTNbox.  
The advantage of this scheme, with respect to direct delivery to both ESA-MCC and PDGS nodes, is that original bundle payloads are sent on space links only once (from Lander to ESA-MCC), instead of twice (to ESA-MCC and to PDGS). Thus, the DTNbox approach seems particularly appealing where there is one sender in space and multiple destinations on Earth.
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 3 Development of the Smart Bolt
### Smart Bol Tensile Test and Review of Applicability  
The robustness of the smart bolt was confirmed by determining the maximum load through a tensile test of the smart bolt that showed relatively high equivalent stress. Tensile tests were performed on four specimens with a strain gauge inserted. The tensile tests were conducted on two specimens each, at tensile speeds of 1 mm/min and 2 mm/min, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Specification** \\ \hline \multirow{3}{*}{Accelerometer (3055D4)} & Frequency Response, \(\pm\)10\%: \\  & 1 to 10,000 Hz \\ \cline{1-1}  & Sensitivity, \(\pm\)5\% [1]: 5 mV/m/s\({}^{2}\) \\ \cline{1-1}  & Broadband resolution: 0.010 m/s\({}^{2}\) RMS \\ \hline \multirow{3}{*}{Strain gauge (BTM-6C)} & Gauge length: 6 mm \\ \cline{1-1}  & Gauge resistance: 120 \(\pm\) 0.5 \(\Omega\) \\ \cline{1-1}  & Gauge factor: 2.14 \(\pm\) \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of sensors in the smart bolt.  
Figure 3: Finite element analysis of the smart bolt.  
The fracture location of the smart bolt occurred close to where the equivalent stress occurred, as confirmed through FEA. The maximum load of each specimen was 170.56 kN on average, as shown in Table 2, and satisfied the 163 kN standard of the 10.9 thread strength class of M16 bolts in ISO 898-1 (minimum ultimate tensile load). This confirmed that there was no loss of robustness due to insertion of the strain gauge into the bolt.  
Although the bolt cross-section was reduced by drilling the inside of the bolt and inserting the strain gauge, it still met the bolt tightening torque required by the standard specification for connections by water supply pipes in Korea (KCS 57 30 20: 2017).
## 4 Applications of Integrated Photonics
### WG Lasers
This investigation underscored the distinct lasing properties between PCMs such as VO\({}_{2}\) and 2D materials, emphasizing VO\({}_{2}\)'s potential as a cost-effective saturable absorber for subnanosecond laser applications [104]. Figure 31 illustrates the process of generating room-temperature Q-switched WG lasers, accompanied by an optical microscope cross-sectional image of the WG inset. The WG structure comprised an unmodified core encircled by a major arc geometry of parallel low-index tracks, which formed a superficial cladding shape at the sample-air interface. The WG, with a diameter of 50 \(\upmu\)m and a lateral separation of 3 \(\upmu\)m between adjacent damage tracks, was designed to minimize intracavity losses and support both TM and TE polarizations. This configuration was advantageous for constructing fiber-WG-fiber integrated photonic chips with high coupling efficiency. Figure 31 compares the \(\upmu\)-PL emission spectra of Nd\({}^{3+}\) ions related to the \({}^{4}\)F\({}_{3/2}\) \(\rightarrow\) \({}^{4}\)I\({}_{9/2}\) and \({}^{4}\)F\({}_{3/2}\) \(\rightarrow\) \({}^{4}\)I\({}_{11/2}\) transitions from the bulk material and the WG core. The spectra demonstrated similar fluorescence properties, indicating well-preserved Nd\({}^{3+}\) ion characteristics suitable for integrated laser sources. The spatial distribution map of emission intensity in the inset of Figure 31 shows localized modifications induced by fs laser pulses, forming microstructural amorphized regions and reducing the refractive index to create a low-index cladding that confines light within the WG structure. This map confirmed the efficient preservation of fluorescence in the WG volume [104].
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 6 Simulation Experiments and Performance Evaluation
### Simulation Setup and Testing Scenarios  
All simulation experiments were conducted on synthetic networks with different topologies. We assumed that servers and switches could operate in two energy states: active and sleeping, which differed in power requirements. The energy cost of waking up each device was assumed to be approximately 0.07 uJ/ns, and energy used for the flow transmission by a single device was 0.00043 uJ/b [43]. For simplicity, we assumed zero energy cost in the sleeping state of the device. The parameters of the links were as follows: the throughput of each network link was equal to 10 GB/s and the delay was 20 ns. The buffer capacity of switch ports was equal to 150,000 bytes. We simulated 0.1 s network operation. Conducting experiments for a longer time horizon is easy; it requires only rescaling the values of the relevant parameters. Such an approach shortens the time needed to complete the simulation.  
Two series of experiments for two network topologies were conducted:  
* Fat-tree topology. A network composed of four servers acting as the root of the tree structure and switches divided into four groups at the lowest level. Depending on the test scenario, one or two servers were connected to each of the lowest-level switches.
* Butterfly topology. A network composed four servers and four layers of switches. Each of the switches was connected to two switches from the adjacent layer. Depending on the test scenario, one or two servers were connected at the lowest topology level depending on the given test case.  
More servers do not carry any additional research value because of their equivalence. At the same time, two of them allow simulation transmission of flows starting and ending in servers connected to the same switch. It should be noted that increasing the number of servers significantly increases the calculation time needed to solve the optimization problem (1)-(6), which is used to ensure the energy efficiency of various network management methods' comparison.  
All experiments aimed to check and compare the heuristic routing algorithm's performance, efficiency, and scalability with the results of the SEUlm strategy.  
In particular, we investigated the impact of node activation and deactivation on maximum network throughput and the influence of different node activation and deactivation policies on the energy cost of the network, its reliability, and quality of service. We also performed a series of experiments to show the impact of the selection method of pivot nodes on the cost and quality of network performance.
## 3 Multidisciplinary Data and Methods
### Ionospheric Data Analysis
For the magnetic and electron density satellite data analyses, we carried out an automatic search (e.g., [12; 62]) using the two algorithms developed to extract anomalous signals possibly related to the impending earthquake: Magnetic Anomaly Search by Spline (MASS) and Ne Anomaly Detection (NeAD), for magnetic and Ne density data, respectively. In principle, the rationale behind the two algorithms is the same: for each single satellite semi-orbit (or track), confined in the interval 50\({}^{\circ}\) of magnetic latitude, the first difference (first derivative) between successive values of the measurement is computed to remove the lower frequencies; then, the fit to a proper function (a cubic spline and a polynomial for MASS and NeAD, respectively) is first computed and then removed from the derivative to enhance the emergence of the small and otherwise hidden features. Once the whole track root mean square (RMS) of the \"residual\" signal is computed, a small window slides along the track returning the \"local\" rms, which, in turn, is compared to the RMS: when \(\rm rms>k_{t}\cdot RMS\), then that window is marked as \"anomalous\". Not all tracks are considered. Indeed, we restrict our inspection only to those which (1) crossed the theoretical preparation circular area centred in the epicentre [63], whose radius (in km) scales with the magnitude M of the mainshock, according to the formula: \(R=10^{0.43M}\); and (2) when the geomagnetic indices Dst and ap respect the following conditions: Dst < 20 nT and ap < 10 nT, meaning that the geomagnetic activity, which can heavily perturb the magnetic field and the ionospheric plasma and, therefore, mislead the anomaly search results, is low. Figure 3 shows the trend of the geomagnetic indices Dst and ap in the period from 25 February to 8 February 2023: when the ap (Dst) index values are lower than (within the) red line(s), geomagnetic conditions are quiet and the diverse analyses are applied to data (ionosonde, particle burst, etc.) since no external solar influence is expected; dashed magenta vertical line is the origin time of the first mainshock. Figure 3: Geomagnetic indices (ap and Dst) from 25 January to 8 February 2023. Red horizontal lines highlight the veto thresholds applied for the ionospheric data analyses. Magenta dashed vertical line corresponds to the 6 February mainshock origin time.
## 3 Experiment and Results  
### SNR Distribution Experiment  
Several experiments were conducted with an LED at the center of the room, as shown in Figure 4. The parameters of the VLC experiments considered in this research are listed in Table 1.  
In the experiment, we used Newport 1918 Power Meter (Newport Corporation, Irvine, CA, USA) to measure the average power of light (unit) every 50 s, repeating 10 times. We measured under three  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Experimental Value** \\ \hline Room size & 5 m \(\times\) 5 m \(\times\) 3 m \\ Numbers of fluorescent lamps & 4 \\ Average ambient power & 43 \(\mu\)W/cm\({}^{2}\) \\ Number of LEDs & 1 \\ Height of LED & 2 m \\ Power of LED & 2 W \\ LED’s field of view & 0–20\({}^{\circ}\) \\ Height of Receiver & 0 m–1.5 m \\ Modulation method & OOK \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Figure 4: Experimental setup.  
conditions, only LED transmitter, ambient light without the emitted light from the LED, and LED under interference. The SNR can be calculated using Equation (6).  
As Figure 5 shows, the distribution of the SNR expresses the performance of the directed light including the ambient light. From these figures, when the distance between the transmitter and the receiver increases or the semi angle increases, the SNR decreases exponentially. At the same height of 1.25 m, we have the same noise power and the maximum SNR is \(-0.8\) dB, while the minimum SNR is \(-7\) dB when the receiver is located at the corner. The 6.2 dB peak-to-trough SNR difference is smaller compared to the scenario when the intersymbol interference is considered, which shows a 22.7 dB peak-to-trough SNR difference [9]. Thus, the VLC systems with an LED under the interference of fluorescent lamps have a small variation of the SNR.