esa-sceva/satcom-chunk-collection huggingface dataset:
# Sustainable Energy Solutions: Utilising UGS for Hydrogen Production by Electrolysis
## 2 Methodology and Concept Description
### UGS Facility Description  
The underground gas storage facility considered for the case study was located in the central part of Croatia about 55 km southeast of Zagreb. The facility was developed in 1986, with 26 working wells having a maximum gas withdrawal capacity of 5,760,000 m\({}^{3}\)/d and a working gas storage volume of 450 \(\times\) 10\({}^{6}\) m\({}^{3}\)[43]. The gas was stored in three separate sand layers labelled a1-a3, bounded from above by impermeable rock and from below by an aquifer. The average depth of the reservoir was approximately 1850 m, with a maximum reservoir pressure of 19.6 MPa and an average reservoir temperature of 185 \({}^{\circ}\)C. The gas storage facility was physically connected to the main gas transportation system of the Republic of Croatia. The technical process involved two cycles: the gas withdrawal cycle and the gas injection cycle. Figure 1 shows a simplified technical scheme of the UGS \"Okoli\" process.  
The gas flowed from the reservoir through withdrawal wells, all of which were equipped with gas filters and an adjustable nozzle for flow regulation. After flow regulation, the well stream gas passed through a flow measuring point and a two-phase separator, where the liquid phase was separated from a gas stream and then sent to the facility's gas reduction station. The gas from the wells flowed to the gas heater and then to the pressure reduction station; after pressure reduction the gas was dried in the dehydration columns, and the dry gas was filtered, measured, and sent to the national gas transportation system. During the withdrawal cycle, all formation water was collected in the main process plant separator and disposed of in an environmentally sustainable manner. The annual reservoir  
Figure 1: UGS Okoli simplified process scheme.  
water production ranged from 380 m\({}^{3}\) to 800 m\({}^{3}\). The water analysis of a representative sample and the formation water production data are shown in Tables 1 and 2.
## 4 Experiment Results
### Through-Wall Experiment Results  
In the third experiment, to assess the through-wall performance of the designed MIMO radar system and the proposed improved BP algorithms, the scene consisted of two humans hidden behind a solid wall with thickness of 22.5 cm. The walls are made of brick and concrete. After preliminary measurement (a former reflection test on the wall was conducted, which estimated that the speed of electromagnetic waves is 0.137 m/ns, gives a dielectric constant of 4.795 for the wall), the relative dielectric constant of the wall is about 4.8. The experimental setup is shown in Figure 18. The MIMO array was placed parallel along the azimuth direction on the side of the wall. The equivalent phase center line of the array was measured about 15 cm away from the surface of the wall. The original point of the coordinate system of the imaging region was the midpoint of the equivalent phase center line.  
Figure 16: Experiment setup for human imaging.  
Figure 17: Imaging results obtained by the CC-TDBP algorithm with: (**a**) direct coupling removal; (**b**) background removal.  
First of all, we directly utilized the CC-TDBP algorithm proposed in Section 2.1 to focus the echo data regardless of the wall effect. Background subtraction was applied to filter the reflections from the wall and the direct coupling among the antennas. It can be observed that the two targets in 2D imaging (Figure 19a) can be easily reconstructed and distinguished. The locations of the targets are marked by the red circle in Figure 19a, which verifies the imaging and detection performance of the designed MIMO radar system after the EM wave is attenuated due to the wall. However, it is worth noting that the wall effects, such as refraction dispersion and change of the EM wave speed, will impact the aforementioned data processing, generating an extra time delay and shifting of the targets. The intersection of white lines in Figure 19a is the real positions of the targets. It is obvious that the targets appear more distant compared to the intersection points.  
Then, the improved BP imaging algorithm mentioned in Section 3 was applied, which considers the existence of the wall. The experimental result was improved and the corrected 2D imaging result is shown in Figure 19b. It can be observed that the positions of the targets in the image basically coincides with the intersection positions of the white lines. The reconstructed locations of the two human targets were much closer to the actual positions, which demonstrates the effectiveness of the improved BP algorithm for more accurate location of human targets behind the wall.  
Figure 19: 2D through-wall imaging results for two humans behind a wall: **(a)** Imaging directly by the CC-TDBP algorithm with background removal; **(b)** Imaging by improved the CC-TDBP algorithm considering the presence of the wall.  
Figure 18: Experimental setup for the through-wall scene.
## 2 Modeling of Grid Connected Inverter with LCL Filter  
Figure 1 shows the single line diagram of a three-phase grid-connected VSC with an LCL filter [30; 31]. In this figure, the grid is modelled by its Thevenin equivalent. The dynamic equations of the system including AC part and DC link is presented:  
\[L_{1}\frac{di_{I(abc)}}{dt}=v_{I(abc)}-v_{c(abc)} \tag{1}\]  
\[C_{f}\frac{dv_{c(abc)}}{dt}=i_{I(abc)}-i_{g(abc)} \tag{2}\]  
\[L_{2}\frac{di_{g(abc)}}{dt}=v_{c(abc)}-v_{g(abc)} \tag{3}\]  
\[C_{dc}v_{dc}\frac{dv_{dc}}{dt}=P_{dc}-v_{I(abc)}i_{I(abc)} \tag{4}\]  
All parameters are depicted in Figure 1.  
To remove the nonlinearity on the left-hand side of the DC-link voltage dynamic Equation (4), the energy stored in the DC link capacitor, \(W_{c}=\frac{1}{2}C_{dc}v_{c}^{2}\), is used as an alternative variable [32]. Then, (4) can be rewritten as:  
\[\frac{dW_{c}}{dt}=P_{dc}-v_{I(abc)}i_{I(abc)} \tag{5}\]  
Using a phase-locked loop (PLL) for extraction of angular frequency of \(v_{g(abc)}\), and applying Park transformation to the state equations of (1) and (5) yields:  
\[\frac{di_{Id}}{dt}=-\omega\cdot i_{1q}+\frac{(v_{1d}-v_{cd})}{L_{1}} \tag{6}\]  
\[\frac{di_{1q}}{dt}=\omega\cdot i_{1d}+\frac{(v_{1q}-v_{cq})}{L_{1}} \tag{7}\]  
\[\frac{dv_{cd}}{dt}=-\omega\cdot v_{cq}+\frac{\left(i_{1d}-i_{gd}\right)}{C_{f}} \tag{8}\]  
\[\frac{dv_{cq}}{dt}=\omega\cdot v_{cd}+\frac{\left(i_{1q}-i_{gq}\right)}{C_{f}} \tag{9}\]  
\[\frac{di_{gd}}{dt}=-\omega\cdot i_{gq}+\frac{(V_{cd}-v_{gd})}{L_{2}} \tag{10}\]  
\[\frac{di_{gq}}{dt}=\omega\cdot i_{gd}+\frac{(v_{cq}-v_{gq})}{L_{2}} \tag{11}\]  
\[\frac{d}{dt}(W_{c})=P_{dc}-\frac{3}{2}\Big{(}v_{gd}i_{gd}+v_{gq}i_{gq}\Big{)} -P_{L-\text{ins}} \tag{12}\]  
Figure 1: Single line diagram of a three-phase grid-connected VSC with LCL filter.  
In the equations, (\(i_{ld}\), \(i_{lg}\)) and (\(i_{gd}\), \(i_{gq}\)) are currents flowing through inductors \(L_{1}\) and \(L_{2}\), respectively; (\(v_{cd}\), \(v_{cq}\)) are voltages across the output filter capacitor, (\(v_{ld}\), \(v_{lg}\)) are inverter terminal voltages, and (\(v_{gd}\), \(v_{gq}\)) are PCC voltages, all in dq frame. Also, \(P_{L-ins}\) is a nonlinear term which shows the instantaneous power absorbed by the elements of the LCL filter. For practical applications, the following assumptions can be considered [4]:  
* \(v_{gq}\) is zero because the PCC voltage is used as PLL input;
* the resistances of the filters are negligible, which represents the worst possible case of stability,
* the \(P_{L-ins}\) is negligible knowing its small amplitude and zero average value. As a result, the DC-link voltage dynamic equation can be rewritten as: \[\frac{d}{dt}W_{c}\approx P_{dc}-\frac{3}{2}v_{gd}i_{gd}\] (13)  
Also, the injected active and reactive power into the grid at the PCC are shown in (14) and (15), respectively.  
\[P_{s}=\frac{3}{2}v_{gd}i_{gd} \tag{14}\]  
\[Q_{s}=\frac{3}{2}v_{gd}i_{gq} \tag{15}\]
# Design of a Laparoscopic Robot System Based on Spherical Magnetic Field
###### Abstract  
In single incision laparoscopic surgery (SILS), because the laparoscope and other surgical instruments share the same incision, the interferences between them constrain the dexterity of surgical instruments and affect the field of views of the laparoscope. Inspired by the structure of the spherical motor and the driving method of an intraocular micro robot, a fully inserted laparoscopic robot system is proposed, which consists of an inner laparoscopic robot and external driving device. The position and orientation control of the inner laparoscopic robot are controlled by a magnetic field generated by the driving device outside the abdominal wall. The instrumental interferences can be alleviated and better visual feedback can be obtained by keeping the laparoscopic robot away from the surgical incision. To verify the feasibility of the proposed structure and explore its control method, a prototype system is designed and fabricated. The electromagnetism model and the mechanical model of the laparoscopic robot system are established. Finally, the translational, rotational, and deflection motion of the laparoscopic robot are demonstrated in practical experiment, and the accuracy of deflection motion of the laparoscopic robot is verified in open-loop condition.  
Keywords:single incision laparoscopic surgery; magnetic field; laparoscopic robot; mathematical modeling; electromagnetic simulation +
Footnote †: journal: Applied Sciences
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 4 Approach, LPWAN Communication Technology, and Energy Consumption Analysis  
In this section, we discuss the approach and assumptions used for calculating the energy consumption of the LPWAN technologies. We start with the approach, experimental setup, and then introduce the overview of different LPWAN technologies (LoRaWAN, DASH7, Sigfox and NB-IoT) in the context of both generic architecture and power consumption. Subsequently, the respective real-time energy consumption measurements are presented.
# Satellite Image Super-Resolution by 2D RRDB and Edge-Enhanced Generative Adversarial Network +
## 4 Results and Discussions
### Comparisons with State-of-the-Art Methods in the AID Dataset  
We also compare our proposed approach with other well-performed models in the AID dataset. As shown in Table 6, the proposed model still has very good scores among three performance indices, including PSNR and SSIM. Figure 9 shows the visual SR results for all compared models. Figure 10 is an enlarged partial view of the SR result of each compared method in the AID dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SRGAN**[3] & **ESRGAN**[4] & **EEGAN**[5] & **ESRGAN**[6] & **Ours** \\ \hline PSNR & 32.164 & 32.250 & 32.140 & 32.335 & **32.399** \\ SSIM & 0.876 & 0.883 & 0.903 & 0.896 & **0.908** \\ FSIM & 0.980 & 0.989 & 0.990 & **0.992** & 0.990 \\ \hline \hline \end{tabular}
\end{table}
Table 6: SR result performance comparison in the AID dataset, where the best results are shown in boldface.  
Figure 9: SR visual result comparison for all models in the AID dataset, where the best results are shown in red font.
## 2 Materials and Methods
### Functional Properties  
The water absorption (WAI) and water solubility (WSI) indices were determined according to [PERSON] et al. (2015); 1 g of SPI was added to 15 mL of distilled water and homogenized, then it was left to rest for 30 min, and after that, it was centrifuged, decanted, and finally dried for WAI and WSI determination ([39]). Urease activity (UA) was calculated as a change in pH according to method Ba 9-58, adding 0.2 g of SPI with 10 mL of urea solution, it was incubated at 30\({}^{\circ}\)C for 30 min, and pH of the test and blank sample was measured to determine UA ([40]). Foam activity (FA), foam stability (FS), foam density (FD), and emulsion stability were evaluated according to [PERSON] and [PERSON] (1983); a solution with 3% content of protein was prepared, then 75 mL of this solution was added to 75 mL of distilled water, this solution was stirred for 30 min, after that, it was whisked for 13 min for the determination of FA and FD, and finally, after 15 min, the FS was determined ([41]). Emulsifying capacity (EC) and emulsion stability (ES) were determined according to [PERSON] et al. (2021) with slight modifications and [PERSON] et al. (2020), respectively; a solution with 7 g of SPI in 100 mL of distilled water was prepared, and 10 mL of this solution was homogenized with 10 mL of soybean oil for 2 min to determine EC and ES after 24 and 48 h ([42; 43]). Functional properties tests were done at pH 4.5 and 7. After the acid precipitation, the pH of the SPI was 4.5, and at this moment, the functional properties were tested, thus testing the functionality of the SPI in acidic conditions. To compare the functional characteristics at neutral pH, NaOH was added to obtain a pH of 7.
## 3 Problem Statement  
There is always a possibility for a data packet to be dropped during the packet-forwarding phase if the relay node is unable to discover any qualifying node with upward progress toward the sink node which thus leads to void issues. In such cases, the greedy forwarding approach deteriorates the overall performance of the network. The channel characteristics of UASN can further intensify the difficulty of this issue.  
Furthermore, in densely deployed environments, the possibility of void issue occurrence is rare and may occur on a short-term basis; however, in sparsely deployed environments, the possibility of void area occurrence increases, which can stay for longer durations. Another significant challenge for UASN is energy consumption. Unlike terrestrial environments, the underwater environment does not facilitate the replacement or replenishment of batteries. Due to excessive energy utilization, the nodes can battery drain shortly, and network performance can be degraded. Thus, it is necessary to consider energy parameters for designing routing protocols that support network longevity.  
Both problems can simply be resolved by raising the number of sensors in the monitoring region; however, a single underwater sensor node can be as expensive as USD 8000 [39]. Due to cost constraints, it is most likely not feasible to expand the number of sensor nodes. Additionally, randomly deployed sensor nodes do not guarantee covering every part of the monitoring region equally.  
An UASN faces critical challenges, such as the void hole problem, where data packets fail to find forwarding nodes, and high energy consumption, which shortens network lifespan. These issues are exacerbated by random node deployment. To address this, our research proposes a strategic deployment approach, placing nodes at various depths to mitigate randomization and reduce voids. Additionally, optimal clustering and routing path selection based on energy, node availability, and link quality enhance battery preservation, network longevity, and reliable communication. These solutions collectively improve the packet delivery ratio and extend network life expectancy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
# Staircase Detection, Characterization and Approach Pipeline for Search and Rescue Robots
###### Abstract
Other algorithms that use depth cameras or LIDARs detect the horizontal and vertical planes of the staircase by either clustering points in a point cloud [14] or by finding planes using algorithms such as RANSAC [15; 16; 17]. In [14] they first start by computing surface normals using a two stage approach. In the first stage, the normal of a point is calculated using tangential vectors computed on a sparse multi-scale neighbourhood. In the second stage, these normals are analyzed in a filtering kernel and normals in a neighborhood are combined into a final result. The direction of the normal vectors obtained are modified based on the orientation data provided by an IMU. They then extract normal surfaces using an edge detector that has as inputs both the the depth and normal map and use patch filtering to eliminate non-candidate regions. The remaining regions are used to decide whether a region contains a staircase or not. In comparison, in [15] an improvised plane fitting algorithm, based on RANSAC, is implemented to calculate the staircase plane and its slope. Once this is done, the staircase bottom mid-point is calculated. Finally in [18], they present a complete pipeline to detect, localize and estimate the characteristics of the staircase using point cloud data obtained from a LIDAR. For detection, they use a plane-based approach and later estimate the staircase parameters with an error of only 2.5 mm. While most of the works have focused mainly on the staircase detection and characterization, some of them offer solutions that are not robust enough to use in noisy sensors and only a few offer information on the implementation of the algorithm in physical robots. This is a big concern due to the fact that some rescue robots have constraints on energy consumption, computational resources, or communication to the teleoperator's computer. For this reason, this work focuses on providing a robust pipeline using the OAK-D camera that would allow for a quick implementation on robots running ROS and also would allow the robot to keep its autonomy by running all algorithms on board.
# A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem
## 2 Problem Analysis and Formulation  
### Existing Work of the S-WTA Problem  
In this paper, the engagement scenario is described as follows. In one engagement of a military conflict, \(N\) targets belonging to the opponent are coming under attack from the defender. The target can be any object that poses a threat to the security of the defender, such as missiles, unmanned aerial vehicles (UAVs), and so on. As each target is coming from a different direction, each one has a certain value of threat. The defender has \(M\) sensors (e.g., Doppler radar and millimeter-wave radar) to detect and track the targets. It also has \(W\) weapons (e.g., missile and antiaircraft gun) to intercept the targets.  
Based on this scenario, to the best of our knowledge, there are five different formulations of the S-WTA problem. In order to present these formulations clearly, we describe these formulations with the uniform variable name. The variables used in these formulations are defined in Table 1.  
According to existing work of the S-WTA problem, the problem can be divided into two typical categories: independent and dependent. As the dependent model is more in line with actual engagement, most of the current models are based on the dependent model. However, the independent model has its own unique advantages. Therefore, in order to design an appropriate framework to fully describe the characteristics of the S-WTA problem, all of these models should be deeply analyzed to explore the relationship between independent and dependent S-WTA problems.  
As the S-WTA problem can be categorized into the independent part and dependent part, the framework of this problem also consists of these two parts. Based on these existing formulations, we summarize these models and divide them into three frameworks: one independent framework and two dependent frameworks. The details of these frameworks are summarized as follows.
## 4 Implementation
### The Experimental SNR Measurement  
The SNR measurements illustrate the link between communication distance, SNR, and exposure times. Additionally, the appropriate exposure period could be changed to obtain the desired BER value, as well as the OCC system's communication distance. The SNR was measured during the trials using an LED (9 V DC-3W). The devices used in this experiment are shown in Figure 5. A rolling-shutter camera was set to exposure durations of 50-500 \(\upmu\)s and communication distances of 5-20 m. In the model of the system, an LED (9 V DC-3W) was employed.  
Figure 4: The theoretical relationship between pixel energy per \(E_{b}/N_{0}\) ratio and image amplitude.  
The Signal to Noise (SNR) values were measured at distances of 5, 10, 15, and 20 m for both the ON and OFF states of the LEDs. When LEDs are turned off, it is considered as background noise; the pixel amplitude SNR is shown by the green line in Figures 6-9. The background noise depends on the noise of the optical environment, but it does not depend on exposure time or distance. When the LEDs were turned on, their signal power was considered; the SNR is presented as a red line with varying distances. The SNR in decibel (\(dB\)) was determined according to the measurement below.  
\[SNR_{dB}=20\log\left(\frac{\sqrt{\frac{1}{n}\sum\limits_{i=0}^{n-1}\left|A_{i} \right|^{2}}}{\sqrt{\frac{1}{n}\sum\limits_{i=0}^{n-1}\left|B_{i}\right|^{2}}}\right) \tag{3}\]  
Figure 5: The device for the SNR measurement.  
Figure 6: The SNR measurement using a rolling-shutter camera at 5 m and a 50 μs exposure-time setting.  
Figure 8: The SNR measurement using a rolling-shutter camera at 15 m and a 50 \(\upmu\)s exposure-time setting.  
Figure 7: The SNR measurement using a rolling-shutter camera at 10 m and a 50 \(\upmu\)s exposure-time setting.  
Figure 9: The SNR measurement using a rolling-shutter camera at 20 m and a 50 \(\upmu\)s exposure-time setting.  
In Equation (3), \(B\) is the background noise, and \(A\) is the LED signal power. The number of samples measured is denoted by \(n\). The camera's received pixel intensity is larger when there is a shorter communication distance and lower with an extended communication distance. Furthermore, the SNR calculation was also affected by the exposure time as shown in Figure 10. In this case, the image sensor acted as a low-pass filter, which smoothed out the high-tone signal with longer exposure times. The communication bandwidth decreases as the exposure period increases, resulting in a reduction in the overall noise power dispersed over the bandwidth.  
At a short distance, the received pixel intensity values are higher, while at a long distance, they are modest. As revealed in Figure 10, the shutter speed, distance, and camera exposure time also affect the SNR values. Figure 10 depicts the relationship between SNR and pixel intensity at 50, 100, 300, and 400 us of exposure time.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Early Warning Trends
Some approaches monitor an online search engine in order to respond quickly to a large number of users making relevant queries, exploiting search queries as effective \"crowd sensors\" for event monitoring [68]. Other approaches have constructed a KD tree organized by nearest neighbors (NNs) which reduces the search time in a large data set during ongoing earthquake records [70]. Deep learning techniques (ConvLSTM) have also been used to indirectly predict the behavior of seismic events with time-frequency analysis of the acceleration response and filtering of time series data with discrete wavelet transform [66]. All of these efforts can reduce the warning delivery time during a seismic event. On the other hand, regarding efficient computational resources, studies have proposed minimizing the time taken to make accurate earthquake reports [71]. Some approaches reduce the simulation workflow in seismic events using (HPC) super-computational resources to enable the execution of automatic warning service, intelligent access, control centers, and post-processing services by automatically collecting information on possible earthquakes and also validating 3D simulations or processing new information for simulations. Other authors have developed algorithms and software tools to homogenize instrumental seismic networks by adapting modules for acquisition, preprocessing, and data storage, allowing information systems to better integrate information for research and monitoring of natural hazards in a specific geographic area [63]. With the efficient use of computational resources, the workflow to deliver accurate reports of seismic events is accelerated and the delivery time to the end user from the occurrence of the earthquake is shortened.