esa-sceva/satcom-chunk-collection huggingface dataset:
# RGB-D Camera for 3D Laser Point Cloud Hole Repair in Mine Access Shaft Roadway
## 4 Data Processing
### Comparative Analysis of Two Kinds of Data  
The accuracy of different point cloud data usually refers to the accuracy of the scanning instrument of point cloud, the accuracy of point cloud coordinates, the relative error of different collected data, etc. The data acquisition ranging accuracy of the 3D laser scanner is \(\pm\)6 mm. When the ranging depth of the Realsense D455 depth camera is less than 4 m and the ranging accuracy is less than 2%, the relative accuracy of the point cloud can meet the needs of 3D modeling. Based on the 3D laser scanning point cloud data, the 3D point cloud data of the shaft roadway were collected and processed by two different devices. The characteristic points of the shaft roadway and the vertical section of the roadway were compared at the same position with the point cloud data of the depth camera.  
We collected the same coordinate values of feature points for the two types of processed point cloud data respectively, calculated the three-dimensional space distance through the coordinate values of the two feature points, and measured the relative distance of the roadway vertical section from different point cloud data at the same position of the shaft. The smaller the three-dimensional distance difference, the higher the accuracy, and vice versa. The coordinates of three-dimensional feature points are shown in Figure 12. By measuring the distance between feature points, the maximum error was 2.617 cm and the minimum error was 0.031 cm, which can meet the visual repair of the missing part of 3D laser scanner data acquisition.  
Figure 11: (**a**) Python point cloud data processing interface. (**b**) Process diagram of point cloud data registration of depth camera. (**b1**) Before registration; (**b2**) Registration in progress; (**b3**) After registration.
###### Abstract  
At the present time, power-system planning and management is facing the major challenge of integrating renewable energy resources (RESs) due to their intermittent nature. To address this problem, a highly accurate renewable energy generation forecasting system is needed for day-ahead power generation scheduling. Day-ahead solar irradiance (SI) forecasting has various applications for system operators and market agents such as unit commitment, reserve management, and biding in the day-ahead market. To this end, a hybrid recurrent neural network is presented herein that uses the long short-term memory recurrent neural network (LSTM-RNN) approach to forecast day-ahead SI. In this approach, k-means clustering is first used to classify each day as either sunny or cloudy. Then, LSTM-RNN is used to learn the uncertainty and variability for each type of cluster separately to predict the SI with better accuracy. The exogenous features such as the dry-bulb temperature, dew point temperature, and relative humidity are used to train the models. Results show that the proposed hybrid model has performed better than a feed-forward neural network (FFNN), a support vector machine (SVM), a conventional LSTM-RNN, and a persistence model.  
L +
Footnote †: journal: Applied Sciences  
17 July 2021  
**Keywords:** LSTM-RNN; solar irradiance; k-means; FFNN; SVM; RESs
## 2 Results
### Drought-Induced Changes in the Photosynthesis-Fluorescence Relationship  
We compared changes in \(\mathrm{ChIF_{P\_F}}\) during the light-response curves against the \(A_{\mathrm{net}}\) of individual samples in the WS treatment under different water stress conditions: zero (\(\theta_{\mathrm{SWC}}\) = 19.1%), moderate (\(\theta_{\mathrm{SWC}}\) = 12.0%), and high (\(\theta_{\mathrm{SWC}}\) = 9.7%), which occurred at WS0, WS5, and WS8, respectively (Figure 3). Water stress regulated both the saturation levels of \(A_{\mathrm{net}}\) and how the saturation level was approached as \(\mathrm{ChIF_{P\_F}}\) increased. Under no drought stress, \(A_{\mathrm{net}}\) showed an initial linear increase with increasing \(\mathrm{ChIF_{P\_F}}\) and reached around 25 umol m\({}^{-2}\) s\({}^{-1}\) when \(\mathrm{ChIF_{P\_F}}\) = 10 umol m\({}^{-2}\) s\({}^{-1}\), after which it largely leveled off with a further increase in \(\mathrm{ChIF_{P\_F}}\) (Figure 3). As water stress increased, \(A_{\mathrm{net}}\) tended to increase less steeply and reached a plateau at a lower \(\mathrm{ChIF_{P\_F}}\). For the plants exposed to moderate drought, for example, \(A_{\mathrm{net}}\) started to remain stable at nearly 17 umol m\({}^{-2}\) s\({}^{-1}\) when \(\mathrm{ChIF_{P\_F}}\) > 6 umol m\({}^{-2}\) s\({}^{-1}\) (Figure 3). At severe water stress levels, the dynamic of \(\mathrm{ChIF_{P\_F}}\) was relatively less affected; \(\mathrm{ChIF_{P\_F}}\) still increased from 0 to 20 umol m\({}^{-2}\) s\({}^{-1}\), with PAR ranging from 0 to 2100 umol m\({}^{-2}\) s\({}^{-1}\). However, \(A_{\mathrm{net}}\) started to saturate even when \(\mathrm{ChIF_{P\_F}}\) > 4 umol m\({}^{-2}\) s\({}^{-1}\) and remained less than 8 umol m\({}^{-2}\) s\({}^{-1}\) for the complete light-response curve (Figure 3).  
Figure 2: The impact of progressive drought stress on the responses of net photosynthetic carbon assimilation (\(A_{\mathrm{net}}\), umol m\({}^{-2}\) s\({}^{-1}\)), stomatal conductance (\(\mathrm{G_{S}}\), mol m\({}^{-2}\) s\({}^{-1}\)), actual rate of electron transport (\(J_{\mathrm{a\_PAM}}\), umol m\({}^{-2}\) s\({}^{-1}\)), and full-band chlorophyll fluorescence emission at the photosystem level (\(\mathrm{ChIF_{P\_F}}\), umol m\({}^{-2}\) s\({}^{-1}\)) to changing light intensity. The number in the upper right corner of each plot is the number of days of progressive drought stress. The soil water content (\(\theta_{\mathrm{SWC}}\), %) is also indicated. The solid lines represent the mean, and the shaded areas are \(\pm\)1 standard deviation, of four replicates. \(A_{\mathrm{net}}\) and \(\mathrm{G_{S}}\) are measured by the gas-exchange system.
## 2 Methodology
### Driving Workload  
When driving a vehicle into the affected area of a work zone, drivers need to observe their surroundings to obtain information on the roads, traffic, and environment, and make judgments based on this information. Furthermore, when operating their vehicles, they must accelerate, decelerate, and follow and change lanes in order to cross through the work zone quickly and safely. The physiological and psychological effects of taking in this information and performing operations during this period are known as the driving workload [32; 33]. In the work zone, driver factors, vehicle characteristics, road and traffic conditions, and environmental conditions will lead to varying degrees of driver stimulation, which is reflected in real time as the driving workload. When this stimulation is too high or too low, it can cause tension or fatigue. Therefore, the work zone should provide drivers with safe and comfortable road traffic conditions to ensure that their driving workload is within a reasonable range.  
In recent years, research on drivers' psychophysiological and driving safety has indicated that measuring heart rate variability (HRV) has the advantages of stability and ease of measurement and processing, compared to indicators such as heart rate, blood pressure, brain waves, and skin electricity, which can enable better characterization of the driving workload [34]. The low frequency (_LF_) and high frequency (_HF_) of HRV reflect the activity of the driver's nervous system. If drivers experience more stimulation in the work zone, their _LF_ will increase, _HF_ will decrease, and driving workload will increase;  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Closed Lane Width (m)**} & \multicolumn{2}{c}{3.25} & \multicolumn{2}{c}{3.5} & \multicolumn{2}{c}{3.75} \\ \hline
**Traffic Volume (pcu/h)** & 500 & 1000 & 1500 & 500 & 1000 & 1500 & 500 & 1000 & 1500 \\ \hline
**40** & 55 & 75 & 100 & 70 & 85 & 110 & 80 & 100 & 120 \\
**Speed Limit** & 50 & 70 & 95 & 125 & 85 & 110 & 135 & 100 & 120 & 150 \\
**(km/h)** & 60 & 85 & 115 & 150 & 100 & 130 & 165 & 120 & 145 & 180 \\
**70** & 100 & 130 & 175 & 120 & 150 & 190 & 140 & 170 & 210 \\
**80** & 115 & 150 & 200 &conversely, if drivers experience less stimulation, their driving workload will decrease. The driving workload can be calculated using Equation (7):  
\[K_{i}=[\left(\frac{LF}{HF}\right)_{i}-A]/V_{i} \tag{7}\]  
where \(K_{i}\) is the driving workload at site \(i\), \((LF/HF)_{i}\) is the HRV at site \(i\), \(A\) is the HRV when the driver is calm, and \(V\) is the vehicle running speed at site \(i\), km/h.  
The different levels of driving workload risks and thresholds are shown in Table 3[32].
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 3 Results
### Flight Experiments and Results  
In order to verify the method mentioned above, a UAV equipped with a POS system and the airborne optoelectronic platform was used for flight experiments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Conditions** & **Items** & \begin{tabular}{c} **Longitude** \\ **(deg)** \\ \end{tabular} & \
\begin{tabular}{c} **Latitude** \\ **(deg)** \\ \end{tabular} &
\begin{tabular}{c} **Altitude** \\ **(deg)** \\ \end{tabular} & **Altitude (m)** & **Error (m)** \\ \hline before & sigma & 0.0002730 & 0.0001810 & 25.68 & 53.28 \\ correction & mean & 0.0005126 & 0.0001104 & 4.67 & 22.13 \\ \hline after & sigma & 0.0001165 & 0.0000836 & 7.44 & 13.61 \\ correction & mean & 0.0000076 & 0.0000011 & \(-\)0.33 & 6.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of geo-location between before and after correction.  
Figure 8: Simulation result of geo-location before and after correction: (**a**) Latitude error of geo-location before correction; (**b**) Latitude error of geo-location after correction; (**c**) Longitude error of geo-location before correction; (**d**) Longitude error of geo-location after correction; (**e**) Altitude error of geo-location before correction; (**f**) Altitude error of geo-location after correction; (**g**) Box plot of latitude error of geo-location before and after correction; (**h**) Box plot of longitude error of geo-location before and after correction; (**i**) Box plot of altitude error of geo-location before and after correction; (**j**) Error of geo-location before and after correction.
## 4 Matrixes for Enzyme Immobilization
### Semi-Synthetic Polymers  
Semi-synthetic polymer hybrids of natural and synthetic materials are excellent for enzyme immobilization due to their mechanical strength, biocompatibility, and tunable properties. Natural components enhance biocompatibility and biodegradability, while synthetic components improve chemical stability and strength. Chemical modifications like cross-linking (glutaraldehyde, carbodiimide) or adding reactive groups (-NH\({}_{2}\), -COOH, OH) boost enzyme binding. Common examples include chitosan-based hybrids (e.g., chitosan-alginate, chitosan glutaraldehyde, carboxymethyl chitosan), cellulose derivatives (e.g., carboxymethyl cellulose, cellulose with vinylpyrrolidone, cellulose acetate), starch derivatives (starch-polyacrylamideonitrile (PAN)), alginate-based hybrids (e.g., alginate-polylysine composites, alginate graft polyacrylamide, and other starch-, dextran-, protein-, and lignin-derived polymers.  
Carboxymethyl cellulose (CMC) is the most common water-soluble cellulose derivative that enhances gel products by improving water retention through gel formation [95]. The addition of CMC to alginate gel boosts lactase immobilization efficiency. At a CMC-to-sodium alginate ratio of 1.0:1.5, the immobilization yield reached 58.2%, which is 14.2% higher than that for alginate alone [96]. The CMS-alginate gel also provided better thermal and pH stability for the enzyme. Similarly, carboxymethyl-chitosan (CMcht),derived from chitosan, is a biocompatible, water-soluble biopolymer. Its structure resembles that of glycosaminoglycans (GAGs), key components of articular cartilage. CMCht enhances water solubility and provides COOH groups for enzyme coupling. For example, [PERSON] et al. developed biocatalysts using cysteine proteases (ficin, papain, and bromelin) bound to chitosan and carboxymethyl chitosan micro- and nanoparticles [97]. Molecular docking showed that these enzymes interact with chitosan derivatives via hydrogen bonding and hydrophobic interactions, particularly through active site residues. This complex formation enhances proteases activity and stability. Starch-polyacrylamideonitrile (PAN) copolymers combine natural starch with synthetic PAN, offering biocompatibility, mechanical stability, and easy modification for enzyme immobilization [98]. Graft copolymers (PAN chains on starch) and cross-linked networks (using glutaraldehyde or epichlorohydrin) provide a high surface area and functional groups (-COOH, -NH\({}_{2}\)) for covalent enzyme binding. For example, electrospun PAN/montmorillonite (O-MMT) membranes, after alkaline hydrolysis, immobilized increase effectively, achieving high crystal violet removal (optimal pH 5, -40 \({}^{\circ}\)C, 100 mg/L) [99]. The immobilized increase outperformed both the free enzyme and the support alone, demonstrating synergetic enhancement.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## 6 Results
### Computational Intensity Analysis  
In order to better understand the amount of processing required in each of the methods, three scenarios are used:  
* **Scenario 1:**\(L=40\), \(\tau_{p}=16\), \(K=100\), \(N=8\), \(p_{k}=100\) mW for each UE, \(v=100\) k/h, delay = [0, 001] ms and Dopper \(f_{d}T_{s}=0.002\).
* **Scenario 2:**\(L=40\), \(\tau_{p}=16\), \(K=100\), \(N=8\), \(p_{k}=100\) mW for each UE, \(v=100\) k/h, delay = 0 ms and Dopper \(f_{d}T_{s}=0\).
* **Scenario 3:**\(L=50\), \(\tau_{p}=16\), \(K=500\), \(N=8\), \(p_{k}=100\) mW for each UE, \(v=150\) k/h, delay = [0, 001] ms and Dopper \(f_{d}T_{s}=0.002\).  
The results are obtained through a hundred simulations of a hundred scenarios [7].  
The summary of this section's simulations and analysis is shown in Tables 1-3. The time presented is the time for each scenario.  
Tables 2 and 3 show the number of operations for matrices of the same order. However, the processing time is quite different. The reason is that the order of the operations needed for computation hides many minor operations, which are time-consuming.
# Overflow Energy Loss Recovery System Based on Hydraulic Motor-Electric Generator
## 6 Experimental Research
### Control Characteristics  
In the differential pressure control test of energy regeneration system, the change of outlet pressure is observed by changing the pressure at the inlet of the proportional relief valve. The test results are shown in Figure 22. It can be seen that the outlet pressure changes with the inlet pressure, so that the valve port pressure difference is maintained at the target value. The experimental results are consistent with the simulation.  
The response curve of the control outlet pressure step change is given in Figure 23. When the target pressure step changes, the actual pressure can quickly follow the response. The response of outlet pressure under different pressure differences when the step change of the inlet pressure is controlled, which is given in Figure 24. Under different pressure difference, outlet pressure can respond quickly.  
Figure 21: Overflow loss energy regeneration test platform.  
Figure 22: Differential pressure control curve.  
The pressure curve when the system flow changes is given in Figure 25. The inlet target pressure of the relief valve is 18.5 MPa, and the pressure difference of valve port is 4 MPa. When the flow rate changes, due to the influence of the pressure regulating characteristics of the relief valve, the inlet pressure of the relief valve slightly decreases, but it is basically not affected by the flow rate change.  
The pressure control of relief valve is tested under different outlet pressures, and the results are shown in Figure 26. When the outlet pressure of the relief valve is 0 MPa, the pressure of the relief valve is 0 MPa, and the pressure of the relief valve is 0 MPa. When the outlet pressure of the relief valve is 0 MPa, the pressure of the relief valve is 0 MPa.  
the pressure regulating deviation of the relief valve is 10%. When the outlet pressure is 15 and 18 MPa, the pressure regulating deviation is 5% and 2.8%, respectively. The larger the outlet pressure is, the smaller the pressure regulation deviation of the relief valve is. Therefore, the energy recovery of the relief valve can improve the pressure control characteristics of the relief valve.
## 2 Materials and Methods
### Procedure  
For the player and ball tracking data acquisition, a commercially available LPS (Kinexon Sports & Media GmbH, Munich, Germany) was used. Firmware versions and application software versions corresponded to the latest releases on the testing date (APP version: 10.1.15, stream processor version: 10.1.11). The installation and calibration of the system was carried out by technicians of the manufacturer. A total of 18 antennas and 1 base station were evenly distributed around the playing field at the same height level. The calibration required the exact assessment of all antennas' 3D positions with respect to the local measurement area, using a tachymeter with millimeter accuracy. With the help of these reference positions, the LPS determines the location of the player and ball tags. This calibration procedure is necessary only one time, when the system is installed at a new facility.  
For each match, the participants wore a 15 g portable sensor attached between the scapulae by means of a chest vest. The players and ball sensors transmitted time information via ultra-wideband technology (UWB) to the antennas, which then forwarded the signal via a wide local area network to the local static base stations. Afterwards, the time information of all antennas was aggregated by a central computer and combined into positional data. The momentary position of a player was determined with a frequency of 20 Hz and the ball at 50 Hz. The match balls used also incorporated a 14 g sensor that combines an inertial measurement unit (IMU-560 Hz) and an ultra-wideband sensor (UWB). The balls used in this study (Derbystar model) are approved by the International Football Federation (FIFA).  
Through Kinexon's sports analytics software, real-time position and external load data were recorded. Subsequently, they were exported for treatment and analysis with the statistical software R (v. 4.3.1). To construct the minute-by-minute analysis of both matches, the mean and maximum values of Ac/Dec were included in this study, as well as the total number of Ac/Dec (Ac threshold: 2 m/s\({}^{2}\), minimum duration 0.5 s, minimum distance 1 m; Dec threshold: \(-\)1.5 m/s\({}^{2}\), minimum duration 0.5 s). The acceleration values of the players below \(-\)6 m/s\({}^{-2}\) and above 6 m/s\({}^{-2}\) were not taken into account as they could be due to a technical error [28]. The data obtained from the ball made it possible to establish which player and team had possession at all times, and therefore whether they were in the attack or defense phase.  
The depth and width of the playing area were determined from the length (long side) and width (short side) of the rectangle on the field covering the average position of each player most distant from each other, excluding the goalkeeper [36].
## 6 Conclusions  
This paper improves the FACMAC algorithm by converting the VSL and LCC integrated control strategies into a distributed partially observable Markov decision process. Heterogeneous VSL and LCC agents were designed, and a multi-ramp merging integrated control framework based on FACMAC was proposed. The study considers the introduction of the dynamic control area concept under the premise of a vehicular network environment. By granting VSL and LCC agents the ability to configure spatially, control issues such as speed limit failures are avoided, enhancing the adaptability of control strategies to different traffic environments. Testing the proposed integrated control strategy in a multi-ramp scenario demonstrated its effectiveness in solving merging bottleneck problems and verified the significant effect of dynamic control areas in alleviating traffic pressure, proving the potential and effectiveness of the improved integrated control scheme. To further demonstrate the practicality of the proposed control framework, Figure A1 in Appendix A presents a schematic diagram detailing the framework's process and implementation plan. This diagram encompasses the entire workflow, ranging from real-world data acquisition and simulation environment construction to agent training and testing, as well as the validation of control strategies.  
In conclusion, this paper not only provides an effective intelligent traffic integrated control solution for the highway ramp merging bottleneck problem but also introduces the innovative concepts of MARL algorithms and dynamic traffic control areas into the control system. This offers valuable theoretical and practical guidance for the design of future intelligent traffic systems and traffic flow management.  
**Author Contributions:** Conceptualization, J.D.; methodology, J.D. and Q.J.; validation, Q.J. and H.Z.; formal analysis, J.D. and Q.J.; investigation, A.Y.; resources, J.D.; data curation, Q.J.; writing--original draft preparation, A.Y., H.Z. and X.B.; writing--review and editing, A.Y., H.Z. and X.B.; visualization, A.Y.; supervision, J.D.; project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is funded by the National Natural Science Foundation of China (grant number 72471133).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 4 Performance Evaluation
### Coverage Rate Comparison According to the Size of Simulation Area  
In this subsection, we evaluate the effect of the zone size according to the scanning area. We simulated the proposed model for 20 km \(\times\) 20 km, 30 km \(\times\) 30 km, and 40 km \(\times\) 40 km areas, respectively. Figure 11 shows the coverage rate results of three different area. In each area, we also varied the number of zones from 5 \(\times\) 5 to 40 \(\times\) 40. As shown in Figure 11, the number of zones in each area affects the coverage rate. The best numbers of zones in 20 km \(\times\) 20 km, 30 km \(\times\) 30 km, and 40 km \(\times\) 40 km areas are 10 \(\times\) 10, 15 \(\times\) 15, and 20 \(\times\) 20, respectively. That is, the appropriate size of a single zone is 2 km \(\times\) 2 km assuming that the parameters of UAVs are given in Table 1.  
Figure 8: The average inter-visiting times: (**a**) 7200 s; and (**b**) 10,800 s.  
Figure 10: The comparisons of skewness: (**a**) Random Waypoint model; (**b**) Pheromone Repel model; and (**c**) RDPZ 15 \(\times\) 15 model.  
Figure 9: The results of RDPZ: (**a**) the coverage distribution; and (**b**) the number of the visits in each unit region.
## 2 Underwater Wireless Sensor Network  
In general, Underwater Wireless Sensor Networks (UWSNs) can be defined as a group of wireless sensor networks that are used to monitor underwater environments for various purposes including military, natural disaster prediction, and weather forecasting. Since UWSNs use acoustic links as a communication medium, they are also known as Underwater Acoustic Sensor Networks (UASNs). Some of the unique features of UWSNs, which are quite different from traditional Wireless Sensor Networks (WSNs), are discussed below.