esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision Systems for a UR5 Cobot on a Quality Control Robotic Station
## 3 Vision-Based Measurement Algorithm
### Camera Calibration and Camera-to-Robot Calibration
#### 3.2.2 Camera-to-Robot Calibration  
As it has been mentioned in Section 1, in a robot vision system, a camera-robot calibration process is necessary. To express vision data in a robot coordinate system, a camera-to-robot calibration module has been included in the vision system.  
As a result of the calibration procedure, a homographic transformation is obtained, mapping the system from the camera's image plane to the coordinate system associated with the robot. Below is a schematic diagram of the calibration process (Figure 13).  
To estimate the homography matrix, four pairs of corresponding points were extracted simultaneously from an image and the cobot's working spaces. Four pairs of image point coordinates were calculated automatically by the dedicated algorithm, while the latter was  
Figure 12: Graph of calculated reprojection errors for (a) USB camera and (b) Mako camera.  
Figure 13: Sequence diagram for determining the homography matrix.  
performed manually by moving the cobot's wrist equipped with a special pointer to the predefined locations.  
To obtain the position of the analyzed objects in the cobot's coordinate system, the resulting data from the vision system are transformed into the cobot system using a designated homography matrix.  
The camera-to-robot calibration procedure is performed only once for a given arrangement between cameras and a cobot.
# Self-Service Data Science in Healthcare with Automated Machine Learning
## 7 Future Research
### AutoML Model Uncertainty  
One of the benefits that are proposed by AutoML is the reproducibility of created ML pipelines [8; 9; 12]. However, these authors state that the outcome of the creation of an ML pipeline with an AutoML method is dependent on the time budget allocated to the AutoML method. Besides that, EA based AutoML methods start with a random population. Thus, it is harder to reproduce the result of a single run without explicitly setting the seed. [53] investigated model uncertainty in a medical context. They have found that as much as changing the seed can influence the prediction outcome for an individual patient. Hence, we argue that there should be more research on the stability of AutoML pipelines in the medical domain.
# A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
## 2 Data Preprocessing
### Basic Data Construction  
Based on the spatiotemporal discrete travel data, the spatial grid attribute data are constructed for the TAZ division model.  
(1) Adjacency matrix \(M\) of cells: This describes the adjacency relationship between discrete-space cells, which is a \(K\times K\) sparse matrix, as shown in Equation (7). \(K\) is the total number of cells in the study area. When cells \(i\) and \(j\) share an edge, the two cells are considered to be adjacent, in which case, \(M_{ij}\) is 1; otherwise, \(M_{ij}\) is 0.  
\[M=\begin{bmatrix}M_{11}&M_{12}&\cdots&M_{1K}\\ M_{21}&M_{22}&\cdots&M_{2K}\\ \vdots&\vdots&\ddots&\vdots\\ M_{K1}&M_{K2}&\cdots&M_{KK}\\ \end{bmatrix} \tag{7}\]  
(2) Travel time similarity of cells \(\rho\): This is a \(K\times K\) numerical matrix, as shown in Equation (6). The travel time correlations between this cell and other cells are described by \(F_{i}\) and the Pearson correlation coefficient \(\rho_{ij}\), as shown in Equation (9). Here, \(\mu_{i}\) and \(\mu_{j}\) are the average trips of cells \(i\) and \(j\), respectively; \(\sigma_{i}\) and \(\sigma_{j}\) are the variance of the trips of cells \(i\) and \(j\).  
\[\rho=\begin{bmatrix}\rho_{11}&\rho_{12}&\cdots&\rho_{1K}\\ \rho_{21}&\rho_{22}&\cdots&\rho_{2K}\\ \vdots&\vdots&\ddots&\vdots\\ \rho_{K1}&\rho_{K2}&\cdots&\rho_{KK}\\ \end{bmatrix} \tag{8}\]  
\[\rho=\frac{E[(F_{i}-\mu_{i})(F_{j}-\mu_{j})]}{\sigma_{i}\sigma_{j}} \tag{9}\]  
(3) Cell area \(A\): This describes the space area of each cell, where \(K\) is the number of cells, as shown in Equation (8). Since square isometric grids are adopted in this study, the area of all cells is equal.  
\[A=(a_{1},a_{2},\ldots,a_{K}) \tag{10}\]  
(4) The distance between cell centroids \(D\): This describes the straight-line distance between cells in space, which is a \(K\times K\) numerical matrix, as in Equation (11). \(d_{ij}\) is the straight-line distance calculated based on the coordinates of cells \(i\) and \(j\).  
\[D=\begin{bmatrix}d_{11}&d_{12}&\cdots&d_{1K}\\ d_{21}&d_{22}&\cdots&d_{2K}\\ \vdots&\vdots&\ddots&\vdots\\ d_{K1}&d_{K2}&\cdots&d_{KK}\\ \end{bmatrix} \tag{11}\]  
Based on the spatiotemporal discretized travel data, the travel feature information of a cell is obtained in the form \((Q,M,\rho,A,D)\), which is expressed as \((q_{k},M_{k},\rho_{k},a_{k},d_{k})\) for the grid \(k\).
## 4 Conclusions  
This article proposed a beamwidth-reconfigurable HMSIW CP slot antenna for the K-band with an RF switch and HMSIW CP slot antenna. The K-band RF switch, operating as an SPDT/PD and consisting of a T-junction PD, \(\lambda\)/4 lines, and beam lead PIN diodes, demonstrated a good performance in terms of insertion loss, isolation, and DC power consumption, owing to the excellent on/off characteristics of the beam lead PIN networks and the current controls of the DC bias networks. The HMSIW CP slot array antenna included a circular polarizer and a pair of \(-45^{\circ}\) and \(+45^{\circ}\) LP HMSIW slot arrays. The LP HMSIW slot array had the advantage of easier integration with other components, owing to the inclusion of 16 slots and short-circuited termination. The \(-45^{\circ}\)/\(+45^{\circ}\) LP HMSIW slot arrays provided LP and CP, respectively, and the CP antenna was applied to the RF switch. The beamwidth reconfiguration of the antenna was confirmed through measurements. It is expected that the proposed antenna structure will be used for the ISL of the LEO formation flying and constellation, in order to prevent unauthorized satellites from eavesdropping.  
Writing--original draft, J.-H.P.; investigation and validation, J.-H.P.; editing, J.-H.P. and M.-Q.L.; supervision, M.-Q.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-01658, Key Technologies Development for Next-Generation Satellites).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 15: (**a**) Gain, AR, (**b**) efficiency, and (**c**) reflection coefficient of the designed antenna for each mode.
# Simulation of an Indoor Visible Light Communication System Using Optisystem
###### Abstract  
Visible light communication (VLC ) is an emerging research area in wireless communication. The system works the same way as optical fiber-based communication systems. However, the VLC system uses free space as its transmission medium. The invention of the light-emitting diode (LED) significantly updated the technologies used in modern communication systems. In VLC, the LED acts as a transmitter and sends data in the form of light when the receiver is in the line of sight (LOS) condition. The VLC system sends data by blinking the light at high speed, which is challenging to identify by human eyes. The detector receives the flashlight at high speed and decodes the transmitted data. One significant advantage of the VLC system over other communication systems is that it is easy to implement using an LED and a photodiode or phototransistor. The system is economical, compact, inexpensive, small, low power, prevents radio interference, and eliminates the need for broadcast rights and buried cables. In this paper, we investigate the performance of an indoor VLC system using Optisystem simulation software. We simulated an indoor VLC system using LOS and non-line-of-sight (NLOS) propagation models. Our simulation analyzes the LOS propagation model by considering the direct path with a single LED as a transmitter. The NLOS propagation model-based VLC system analyses two scenarios by considering single and dual LEDs as its transmitter. The effect of incident and irradiance angles in an LOS propagation model and an eye diagram of LOS/NLOS models are investigated to identify the signal distortion. We also analyzed the impact of the field of view (FOV) of an NLOS propagation model using a single LED as a transmitter and estimated the bitrate \((R_{b})\). Our theoretical results show that the system simulated in this paper achieved bitrates in the range of \(2.1208\times 10^{7}\) to \(4.2147\times 10^{7}\) bits/s when the FOV changes from \(30^{\circ}\) to \(90^{\circ}\). A VLC hardware design is further considered for real-time implementations. Our VLC hardware system achieved an average of 70% data recovery rate in the LOS propagation model and a 40% data recovery rate in the NLOS propagation model. This paper's analysis shows that our simulated VLC results are technically beneficial in real-world VLC systems.  
visible light communication (VLC); light-emitting diode (LED); optical communications; optisystem 2022  
3
## 2 Literature Review
Various tools have been used to evaluate retrofit strategies, including field measurements [34; 35], numerical modeling [36; 37], post-occupancy surveys [38; 39], and life cycle assessments [40; 41], where performance before and after retrofitting is compared. While many studies focus on cold climates [42; 43; 44], others address hot climates [45; 46; 47]. However, limited research has explored the unique urban and climatic conditions of Saudi Arabia. One example is a study by [PERSON] et al. [4], which assessed the post-retrofit energy performance of an office building in Saudi Arabia through an energy audit, field measurements, and a verification protocol. The retrofit measures included installing a chiller plant manager with a smart control system, LED lighting, and rooftop photovoltaic (PV) panels. The results showed that these measures reduced EUI from 458.9 kWh/m\({}^{2}\)/year to 361.4 kWh/m\({}^{2}\)/year, with an economic analysis indicating a payback period of six years. [PERSON] et al. [48] investigated the energy savings potential of retrofitting a residential building in Jeddah, Saudi Arabia. The retrofit measures included window replacement, upgrading external wall and roof insulation, and replacing air conditioning units. The study utilized DesignBuilder to perform a numerical simulation to assess these measures, with average annual energy consumption as the primary performance indicator. The results demonstrated a significant reduction in energy consumption, ranging from 25% to 66%. Similarly, [PERSON] et al. [49] explored various energy retrofitting strategies in Saudi public schools. They conducted field measurements to gather data on the indoor thermal environment of three school buildings and employed thermal simulations using DesignBuilder to evaluate the effects of several retrofit strategies applied to the building envelope, including thermal insulation, glass type changes, and the installation of shading devices. Their findings indicated a substantial 30% reduction in cooling energy consumption. In light of these studies, this research aims to address the identified gap in the literature by considering the climatic and construction market conditions in Saudi Arabia. The study evaluates multiple individual and combined facade retrofit strategies for residential buildings, proposing a framework that uses optimization to assess these strategies, with a focus on reducing cooling energy and enhancing cost efficiency as key performance indicators.
## 3 Results
### Gpp  
Figure 8 presents the distributions of global annual GPP in 2014. Similar to LUE, there were three equatorial regions, including the northern regions of South America, middle Africa and Southeast Asia, in which vegetation absorbed the most carbon, with GPP greater than 2500 gC/m\({}^{2}\)/yr. Next to these high carbon sequestration areas, South America, South Africa, South China, India, the coastline of Australia, and some mid-high latitudes in the Northern Hemisphere, such as the eastern USA and Europe, cultivated vegetation with approximately 1500 gC/m\({}^{2}\)/yr GPP. High-latitude areas, such as Canada and northern Europe, and high-altitude regions, including the USA, the western coastline of South America, the south of Africa, and the Tibetan Plateau of China, saw low carbon sequestration because of the low-temperature stress. Drought-hit areas, such as northern Africa, northern Australia, and Northwest China, also experienced low GPP because of the lack of water.  
We compared our 3 GPP results with MODIS GPP (MOD17). The four approaches produced roughly consistent global annual GPP values ranging from 109.23 to 120.65 Pg/yr. The highest value (120.65 Pg/yr) was obtained by the parameterization approach with the CI because this approach considered the different effects between direct and diffuse radiation. The lowest value (109.23 Pg/yr) was estimated by the parameterization approach without the CI. The lands covered by dense vegetation are usually moistened by abundant rain, consequently having a low CI. Therefore, the addition of the CI increased the GPP. We regard this approach as the best method for calculating LUE and GPP. In comparison with the parameterization approach with the CI, the Cubist regression tree approach produced a GPP of 116.41 Pg/yr. This approach divided all pixels into 10 classes and then put them into a corresponding linear rule or rules. The Cubist regression tree approach had little mechanism consideration behind the linear formulas. In addition, 10 rules might not sufficiently satisfy all pixels. Therefore, linear rules are most likely to output negative values. Although we set values to zero in these cases, most were still underestimated. We also plotted the difference between V2 GPP and MODIS GPP (MOD17). It shows that the parameterization approach with the CI gets a higher GPP than MODIS algorithm, especially in the equatorial regions (the northern regions of South America, middle Africa and Southeast Asia) where even more than 600 gC/m\({}^{2}\)/yr of difference was produced. There were minor differences which means V2 GPP were lower occurred in the south of China, Madagascar islands.  
Figure 7: The distributions of LUE by the Cubist regression tree approach (V3).
# Effect of C-S-H Nucleating Agent on Cement Hydration
## 3 Results and Discussion
### Electrical Resistivity  
Figure 6a shows the electrical resistivity curve of cement slurry with a different type of early strength agent (\(\rho\) represents electrical resistivity). All the electrical resistivity curve exhibits the same trend. Firstly, during the dissolution and precipitation processes of cement particles, more ions will be released into the system, causing a decreasing trend in the electrical resistivity curve. Then the electrical resistivity curve reaches a dynamic balance due to the competitive balance between dissolution and precipitation processes [33]. When the calcium hydroxide (CH) is supersaturated in the solution, hydration products will precipitate on the surface of the cement particles [34], which results in a rapid increase in electrical resistivity.  
Figure 5: Hydration reaction rate curve of cement mixture with nothing (**a**) and CNA (**b**).  
Figure 6: Resistivity development curve of cement slurry with different types of early strength agent (**a**) and the differential resistivity curve of the blank sample (**b**).  
From Figure 6a, it can be found that the electrical resistivity curve with CNA increases rapidly, which is always higher than that with TEA. The electrical resistivity curve with TEA is also higher than the blank sample. This indicates that the beginning of precipitation processes of mixture with CNA is earlier than that with TEA and blank sample. Both CNA and TEA may promote the early hydration process and early strength development of cement, but CNA is better.  
Peak1 appears at the beginning of the induction period, and the hydration age corresponding to Peak1 time may be understood as the beginning time of the induction period [35]. Peak2 mutation occurs during the acceleration period. At this time, the AFt content increases and the gypsum content is insufficient, and AFt starts to transform to AFm [36]. Peak2 time can be considered as the boundary point between cement setting and hardening periods. Primary Peak time corresponds to the time point where the electrical resistivity curve increases rapidly. Table 3 summarizes the characteristic points of mixtures with a different type of early strength agent.  
Compared with the blank sample, the Peak1 time, Peak2 time and Primary Peak time of mixtures with CNA are earlier 49 min, 82.1 min and 105 min, respectively. This indicates that CNA may effectively shorten the beginning time of the induction period, shorten the acceleration period of cement hydration and decreases the final setting time of cement. TEA may also reduce the value of Peak1 time, Peak2 time and Primary Peak time of cement mixtures, but its effect is not as well as CNA.
## 4 Description of Setup, Control Mode, and ROV Trajectories for Real Experiments
### Setup  
The cable feeder was fixed on the border of a 16 \(\times\) 8 m water tank and connected to the ROV through a 25 m long 4 mm diameter tether, namely the Fathom Slim, equipped with the buoy-ballast system, of which a technological description can be found in [27]. The ROV used was a BlueROV2-Heavy, as in the simulation.  
A flex sensor was mounted on the cable at the ballast location using a fixed bracket and guides along the cable (Figure 11). The sensor has negligible bending stiffness. It was wrapped in a thin plastic envelope for waterproofing. The sensor resistance varies when the cable is bent. This varying resistance is transformed into a voltage signal through a voltage divider, then filtered, centered, and trimmed. The variation in the resulting signal is used to fit the corresponding distance between the buoys through a third-order polynomial (Figure 12).  
Table 2 summarizes the characteristics of the buoy-ballast system to which the Fathom Slim tether is equipped, taking into account the previously described modeling analysis.  
The controller board inside the robot was run on a Linux/ROS-1 system with the _mavros_ library to interface the high level control with the autopilot _ArduSub_. The BlueROV2 was controlled remotely via a gamepad connected to the control computer. The remote control computer contains the embedded ROS _master_. It was connected to the feeder for the  
Figure 11: Mounting of the flex sensor near the eballast.  
transmitting of control commands while taking into consideration the data received from the flex sensor through the ROS system embedded in the robot. The diagram in Figure 13 shows the architecture of the ROS _nodes_ for controlling the global system during the experiments.  
The robot and the cable were tracked by a camera motion-tracking system, namely _Qualisys_, with five underwater optical cameras _Miqus M5u_ at 180 frames per second. The cameras use blue LEDs, whose light is reflected by markers placed on the ROV and the cable. Data are recorded from the _QTM_ software, version 2020.3 build 6020, which is  
Figure 12: Measured distance between the buoys as a function of the centered resistance of the flex sensor with experimental data points.  
Figure 13: Graph of the global ROS nodes architecture for the control. The oval boxes correspond to the _nodes_ and the rectangular boxes to the topics exchanged by these _nodes_.  
controlled from a dedicated computer during the experiments. This software computes the current 3D position of each marker provided they are detected by several cameras. Across the experiments, the position residuals of the markers were about 1.3 mm for the ROV, with a minimum of 0.9 mm and a maximum of 10 mm.
## 4 Results and Discussion
### Dynamic Test  
In dynamic experiments, the determination of the direction between the smartphones and the receiver is not possible without the use of additional sensors. However, a preset value for the horizontal deviation distance between them is in place. Therefore, in dynamic experiments, the deviations in the horizontal plane and U-direction RMSE were calculated.  
Figure 9 shows the positioning errors of HP40 and Mi11 in the dynamic scenes. The left side of each picture is a typical urban scenario, and the right side is a complex urban scenario. From Figure 9, it can be observed that the positioning results were discontinuous due to the low-cost receiver. In some places, it observed too few satellite data to provide a reference position. For this, the CHC i70 and smartphones' positioning rate was calculated, which is the ratio of the number of solution position epochs to the number of observation epochs. The results show that for HP40, the positioning rate in all dynamic scenarios was 100%, while for Mi11, the positioning rate in the dynamic typical urban environment was 98.94%, and it was approximately 98.8% in the dynamic complex urban environment. For CHC i70, the position rate in the dynamic typical urban environment was 86.63%, while it dropped to as low as 46.02% in the dynamic complex urban environment.  
Figure 9: Position errors in the dynamic scenario. (**a**) HP40 positioning errors in the IF-PII and IFUC-PII models; (**b**) Mi11 positioning errors in the IF-PII and IFUC-PII models; (**c**) Mi11 positioning errors in the IF-P1C and IFUC-P1C models.  
From Tables 8 and 9 and Figure 10, it can be observed that the positioning performance of the PCP models was not significantly improved. Compared to SPP, some PCP models showed only a modest improvement, with an increase of around 10% in positioning accuracy in the horizontal plane or U direction. Otherwise, the TF-P model, compared to other PCP models for Mi11, also had the worst positioning performance in both the typical and complex dynamic urban scenarios. This is the same as static test. It could be inferred that the TF-P model is not fully suitable for Mi11 under the same quality control method.  
Combined with Section 2, it could be observed that during the dynamic test, the overall satellite tracking ability of the smartphone is affected by the environment and frequent lock loss occurs. In addition, since low-cost receivers cannot continuously provide reference positions, while the positioning rate of smartphones reaches more than 98%, this will cause the smartphone to re-converge when the receiver provides a new reference position. All of this will affect the obtained position accuracy.
## 4 Experiments and Analysis
### Visualized Comparative Analysis of Experimental Results
#### 4.4.2 Visual Analysis of Ship-2 Trajectory Prediction  
The AIS data of ship-2 are also used to predict the LON and LAT of the trajectory by the GA-LSTM model. The experimental results are shown in Figure 9. It can be seen from Figure 9 that the LON and LAT of ship-2 predicted by GA-LSTM are basically the same as the LON and LAT of the actual position in the position prediction of ship-2. Similarly, the trajectory was predicted by the SVM and LSTM models, and the experimental results are shown in Figure 10. It can be seen that the method proposed in this paper has higher prediction performance and better effect, and it can effectively predict the navigation position of ship-2, which further proves the effectiveness and feasibility of the method proposed in this paper.  
Figure 8: Comparison of experimental results of ship-1 trajectory prediction.