esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameterization and Design of Telepresence Robot to Avoid Obstacles
## 4 Results and Discussion
### Self-Localization  
This section describes a simple but impressive test. Using the magnetometer [56], the robot was driven in manual mode twice in a circle across the corridor of the EE building of the University of King Abdulaziz.  
One tour was driven clockwise, and one counter-clockwise. The position was recorded, and the resulting trajectories were finally superimposed on a Google Maps aerial photograph. The size of the trajectories was subsequently adjusted so that they fit reasonably into the building. However, the aspect ratio and orientation have been retained. The result can be seen in Figure 12.  
The red trajectory, which runs clockwise, was recorded first. It can be seen that the calibration of the magnetometer was very good. The start and end points of the trajectory are the same. In the image, however, these points are slightly apart. Each control cycle adds a correction value to the old position, so the position is an incremental value. This, of course, leads to error accumulation so that the estimated position deviates more and more from the actual position as the distance increases. On the first three straight lines of the red track, one can see that the tracks run fairly parallel to the outline of the building. This angle is no longer so accurate in the fourth straight line, so the error has probably increased the most here. On the other hand, one looks at the blue track, which was travelled counter-clockwise, the second straight section is parallel to the outline of the building. However, the other straight sections then deviate.  
The strong angle errors in the trajectories can have many causes. On the one hand, the magnetometer calibration can be insufficient since it may have been held at an angle during calibration, rotated too quickly, or had interference fields. On the other hand, many sources of interference in the building generate magnetic fields or deform the earth's magnetic field. Power lines, heating systems and stair railings are examples of this. Drifting of the magnetometer cannot be ruled out either. Online calibration would help here.  
Figure 12: Self-estimates of robot position. Blue track was travelled counter-clockwise and red track was travelled clockwise.
## 4 Results and Discussion
### Data Generation Configuration
#### 4.2.1 Ablation Study
However, the visual enhancement resulting from color differentiation improves the appearance of the synthetic images. _(V) Two classes max:_ The next improvement to add to the augmentation method is to use at most two microbial species per agar dish. This reflects the real AGAR dataset [4], as at most two species of colonies were inoculated on each agar dish. The pre-trained and fine-tuned model (see Table 2) showed an AP\({}^{50}\) of 92.1 and an mAP of 60.6. _(VI) Big clusters of colonies:_ To further diversify the generated images, clusters of microbial colonies with more than two colonies are used for data generation. This experiment serves the purpose to see if bigger clusters of colonies are detected better if there are more training examples of bigger clusters. The pre-trained and fine-tuned model (see Table 2) had an AP\({}^{50}\) of 92.1 and an mAP of 60.6, showing no improvement compared to the former fine-tuned model. _(VII) Dish color differentiation:_ To further improve the visual realism of the generated agar dishes, we also differentiated the colors of the agar dishes into specific color categories. While this adjustment was not expected to significantly improve the performance of the model, especially as the more informative experiment differentiating the colors of the microbial colonies only provided marginal benefits, it does help to make the synthetic images appear more realistic. _(VIII) Opacity 90%:_ To make the synthetic images look more realistic, the microbial colonies were given a certain amount of transparency in order to let the lettering and other artifacts of the agar dish shine through the colonies. An experiment was conducted to see which transparency value saw the most improvement while training the neural network. For this, images were generated with a set transparency value for all colonies. Ten different values were tested at an interval of 10%, ranging from 10% to 10%. Figure 4 presents the mAP values for all the classes in the dataset following the fine-tuning of these pre-trained models with real images. The figure shows that a transparency value of 90% gives the best results. Based on the findings from steps (I) to (VIII), the configuration of the best performing model was used in all further experiments.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 4 Experimental Setup
### Frame Configuration  
Each EN packs the sensor's information in a binary frame. To enlarge the corresponding scales, we multiply the measurements by 10 before sending them, and then, the NS divides by 10, preserving one decimal point as shown in Table 4, so the corresponding frame length is 74 bits. Moreover, we added some dummy bits to enlarge the frame length (six different sizes), so the dataset includes the effects of this parameter on energy consumption.  
Furthermore, we used a BW of 125 kHz and iterated various subbands in the ISM US915 band (903.9, 904.1, 904.3, 904.5, 904.7, 904.9, 905.1, and 905 MHz).  
We also varied the SF with values of 7, 8, 9, and 10, according to the spectrum usage regulations. In summary, there are six frame configurations, eight subbands, and four SFs, so we transmit in 192 different radio configurations. Once the GW resends the information to TTN, the latter runs a payload formatter where the sensors' readings are decoded, and the RSSI, ToA, and SNR are added to the final payload. Finally, TTN exposes an MQTT broker where the payloads are published, so we used a cloud-based server as an MQTT subscriber to get the messages and store them in a MySQL database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Max. Value** & **Max Value \(\times\) 10** &
\begin{tabular}{c} **Binary** \\ **Representation** \\ \end{tabular} & **\# bits** \\ \hline Temperature & 80 \({}^{\circ}\)C & 800 & 1100100000 & 10 \\ RH & 100\% & 1000 & 111101000 & 10 \\ Barometric & 1000 hPa & 10000 & 10011100010000 & 14 \\ pressure & 400 \(\mathrm{\SIUnitSymbolMicro m}^{3}\) & 4000 & 111110100000 & 12 \\ PM10 & 400 \(\mathrm{\SIUnitSymbolMicro m}^{3}\) & 4000 & 111110100000 & 12 \\ Energy & 65535 Joule & 65535 & 1111111111111111 & 16 \\ \hline
**Total** & & & & 74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Frame configuration sent from the ENs to the GW.  
Figure 7: End Node testbed. The elements are (1) microcontroller, (2) pressure sensor, (3) energy sensor, (4) relative humidity and temperature sensor, (5) PM2.5 sensor inlet, and (6) antenna connection outlet.
# Study on the Seismic Response of Shield Tunnel Structures with the Preload Loss of Bolts
## 4 Result Analysis
### Ellipticity Analysis  
Ellipticity is often used to describe the overall deformation of shield tunnels, and it can be calculated by Equation (5). However, it should be noted that the major and minor axes of the elliptical deformation of the shield tunnel are not fixed under the influence of seismic loads. Figure 9a shows the time-history curves of radial displacements extracted from four reference points, including the left and right arch waist, vault, and bottom of the shield tunnel. It can be observed that the horizontal deformation of the shield tunnel cross-section is significantly greater than the vertical deformation. Therefore, it is assumed that the horizontal direction is the major axis, and the vertical direction is the minor axis.  
\[T=\frac{D_{H}-D_{V}}{D_{0}} \tag{5}\]  
where, \(D_{H}\) and \(D_{V}\) are the deformations of major and minor axis; \(D_{0}\) is the design outer diameter of the shield tunnel.  
Figure 9: Ellipticity of the tunnel. (**a**) Radial deformation of segments. (**b**) Time-history of changes in ellipticity. (**c**) Non-recoverable ellipticity deformation.  
The elliptical deformation of the shield tunnel is shown in Figure 9b. It can be observed that the ellipticity varies with the fluctuation of seismic wave, but for the majority of the time, it remains positive. This indicates that the shield tunnel exhibits lateral elliptical deformation. The maximum ellipticity occurs at 7.55 s. Additionally, it is noticeable that the influence of seismic intensity is much greater than the effect of preload, and as the PGA increases, the overall ellipticity deformation also increases. When PGA is 0.1 g, the maximum ellipticity is 0.707%, while the PGA is 0.5 g, the ellipticity is 7%, which represents an increase of 943% compared to PGA = 0.1 g. To more intuitively depict the influence of preload loss on ellipticity changes, the initial deformation difference between the end of the fourth stage and the beginning of seismic loading in the third stage is extracted as shown in Figure 9. This represents the non-recoverable deformation induced by seismic loading. It is observed that as the level of preload loss increases, the non-recoverable ellipticity deformation after the seismic event gradually increases. This indicates that preload can effectively control the overall post-seismic deformation of the structure and reduce the overall deformation during the entire process.
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 3 Establishment of Safety Potential Field Model in Bridge Area
### Behavioural Fields Based on Crew Behaviour  
Bridge-area waters are usually characterised by narrow channels, complex currents and restricted navigational conditions, requiring ships to perform delicate manoeuvres to ensure safe passage. The influence of the driver on the safety of ship navigation is transmitted outward through the ship driven. Therefore, when constructing the behavioural field model, the product of the anthropogenic risk factors and the kinetic energy field formed by the navigating ship can be used to represent the driver's behavioural field. Through the analysis, the strength of the behavioural field formed by the driver of the target ship \(O(x_{o},y_{o})\) at its surrounding \(i(x_{i},y_{i})\) can be expressed as follows:  
\[E_{Hi}(x,y)=E_{Di}(x,y)H_{r} \tag{12}\]  
i.e.:  
\[E_{Hi}(x,y)=\begin{cases}H_{r}\cdot\tau\cdot\exp(\frac{(\gamma_{balman}-\mu)^ {2}}{2\sigma^{2}})\cdot g(\theta,d_{1},d_{2})\cdot v_{kalman}&v_{kalman}\in \mu\\ H_{r}\cdot K&v_{kalman}\
otin\mu\end{cases} \tag{13}\]  
where \(E_{Hi}(x,y)\) is the behavioural field vector of the driver at \(i(x,y)\), which reacts to the degree of potential danger generated by the driver in the ship's surroundings under a certain channel condition. The larger the field strength, the greater the potential danger generated by the driver, and the smaller the field strength, the smaller the potential danger generated by the driver; the direction of this behavioural field strength vector is the same as the direction of the ship's kinetic energy field strength. \(H_{r}\) is the hazard factor affecting the safety of the ship's officer and includes the crew training, situational awareness, emergency response, skill level and violation of regulations [31]. \(v_{kalman}\) is the velocity vector predicted by the Kalman filter, taking into account the influence of wind flow and other factors. The schematic diagram of the behavioural field strength is shown in Figure 5.  
Figure 4: Schematic diagram of the channel boundary model. (**a**) Schematic diagram of the channel environment boundary model. (**b**) Example of uniform channel boundary field intensity.
## 3 Problem Formulation
### Inner Model: Stochastic Optimal Dispatching Model of IPGES  
#### 3.1.1 Objective Function  
Under the centralized dispatching strategy, considering the stochastic characteristics of wind power output, the stochastic optimization model of IPGES in the dispatching cycle aims at minimizing the comprehensive operation cost (\(F^{\text{sod}}\)) of the system, as shown in (5). The clear identification and explanation of several terms relevant to the objective function is provided in (6).  
\[\text{min}F^{\text{sod}}=\text{min}\left\{\sum\limits_{t=1}^{T}\left\{\begin{array} []{l}\sum\limits_{i=1}^{N_{c}}\left(a_{i}(P_{i,t}^{c})^{2}+b_{i}P_{i,t}^{c}+ c_{i}\right)+\sum\limits_{i=1}^{N_{\text{spf}}}\left(c_{i}^{\text{gas}}F_{i,t}^{N }\right)\\ +\sum\limits_{i=1}^{N_{c}\beta_{2}}\left(c_{i}^{p2g}P_{i,t}^{p2g}\right)+\sum \limits_{i=1}^{N_{\text{sp}}}\left(\Delta P_{i,t}^{\text{sp},\text{sp}}\right) \\ +\left(\sum\limits_{i=1}^{N_{c}+N_{g}}\left.p_{rc}^{i}(\mu_{i}P_{i,t}-\epsilon P _{i,t})-\sum\limits_{j=1}^{N_{\text{sp}}\left.p_{rc}^{i}(\mu_{j}P_{j,t}^{p2g} \right)\right)\end{array}\right\}\right\}\right.\right\} \tag{5}\]  
\[\text{min}\left[\begin{array}{l}(\text{power generation costs of CPFF})+(\text{gas supply costs of natural gas sources})\\ +(\text{operating costs of P2G})+(\text{wind abandonmed penalty cost})\\ +(\text{carbon trading costs of IPGES include CFPP, GFPP and P2G})\end{array}\right] \tag{6}\]
###### Abstract  
Iliotibbial band syndrome (ITBS) is a common running related injury. While previous studies have evaluated the relationship between biomechanical variables and ITBS, most have found limited evidence, particularly with measures related to ground reaction force (GRF). The purpose of this study was to use a classification and regression tree (CART) analysis to determine whether the combination of GRF measures would be strongly associated in runners with ITBS. A cross-sectional study was performed at an outpatient center focused on running injuries. A convenience sample of 52 runners with ITBS, assessed between September 2012 and July 2022, were evaluated for eligibility, from which, 30 rearfoot strike runners with ITBS and no secondary running-related injuries were selected. Injured runners were matched to 30 healthy controls from a normative database. Each ran on an instrumented treadmill at a self-selected speed. GRF variables were calculated, including peak GRFs, loading rates, and impulses. CART analysis was performed to identify interactions between GRF data and runners with ITBS. An ROC curve was executed, to determine the accuracy of the model. Posterior GRF impulse (PGFRP), anterior GRF (AGRFI), peak anterior GRF (PACGRF), and vertical stiffness at initial loading (VSIL) all emerged as variables associated with ITBS in the CART analysis. The model was able to correctly identify 25 (83.3%) runners with ITBS and 25 (83.3%) controls. The area under the ROC curve (accuracy) was 0.87 (95% CI, 0.77-0.96; SE, 0.04; \(p<0.001\)). In conclusion, interactions between GRF variables were associated with ITBS in runners. The best classification included interactions between PGFRI, AGRFI, AGRFP, and VSIL, using specific cut-off values. Loading rates were not independently associated with ITBS.  
running; athletic injuries; liliotibial band syndrome; biomechanics; cross-sectional studies 2023
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 4 Discussion
### Errors in GRACE-Derived TWS Changes
Figure 12f further shows TWS rates from JPL mason with scaling factors applied. Significant spatial differences in TWS rates in Figure 12 indicate the potential uncertainty caused by selecting difference GRACE solutions and methods. Figure 12: (**a**) Topography of the TP and its surroundings. TWS rates from GRACE during 2002–2014 based on CSR SH products with (**b**) the DDK4 filtering, (**c**) 100-km Gaussian smoothing applied, and based on (**d**) CSR mason products, (**e**) JPL mason products, and (**f**) JPL mason products with gridded scaling factors applied. The white line shows the boundary of TP. The red boundary outlines the Inner TP.
# Influence of Acoustic Streams on the Efficiency of Ultrasonic Particle Agglomeration
## 3 Results
### Effect of Mass Concentration on Particle Capture Efficiency  
To determine the influence of particle mass concentration on the capture efficiency and particle size at the outlet of the agglomeration chamber, experimental studies were  
Figure 9: Dependence of the capture efficiency on the sound pressure level (**A**) and the dependence of the particle size at the outlet of the agglomeration chamber on the sound pressure level (**B**): 1—flat radiator; 2—step radiator.  
carried out at a sound pressure level of 165 dB. The curves below (Figure 10) were obtained as a result of these studies.  
Figure 10: Dependence of capture efficiency on particle mass concentration (**A**) and the dependence of the particle size at the outlet of the agglomeration chamber on the mass concentration of particles (**B**): 1—flat radiator; 2—step radiator.  
As follow from the presented curves, at concentrations up to \(N_{inlet}\) = 2 \(\times\) 10\({}^{-2}\) g/m\({}^{3}\) the particle capture efficiency increases significantly. At the same time, the capture efficiency is higher when using a flat radiator, due to increased efficiency of particle agglomeration. At concentrations of \(N_{inlet}\) = 3 \(\times\) 10\({}^{-2}\) g/m\({}^{3}\) and above, the increase in efficiency slows down, and at concentrations above \(N_{inlet}\) = 4 \(\times\) 10\({}^{-2}\) g/m\({}^{3}\) the capture efficiency becomes almost the same with both types of radiators. This happens because at the specified concentrations the distances between particles appear sufficient for realization of orthokinetic and hydrodynamic mechanisms of particle agglomeration, and the influence of vortex flows has little effect on increasing the particle collision probability.  
Analysis of the dependencies presented in Figure 10B shows that the impact of a flat radiator on particles leads to a significant increase in particles already at a concentration of \(N_{inlet}\) = 0.2 \(\times\) 10\({}^{-2}\) g/m\({}^{3}\), while particles with a size of 2.5 \(\upmu\)m increase to a size of 7.4 \(\upmu\)m. At the same time, due to the impact of a stepped radiator on the particles, the agglomerates at the exit from the agglomeration chamber increase to only 4.2 microns. Such a significant difference in the sizes of agglomerates is due to an additional intensifying factor: acoustic flows arising in a non-uniform ultrasonic field generated by a flat radiator. This allows particles to interact not only within the nodal regions, as happens when exposed to a uniform field generated by a stepped radiator, but also to interact with particles moving from one nodal region to another.  
As the concentration increases, the difference between the sizes of the formed agglomerates decreases and, at a concentration of \(N_{inlet}\) = 5 \(\times\) 10\({}^{-2}\) g/m\({}^{3}\), it is less than 10%.
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
###### Abstract  
Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.  
traffic simulation models; connected car; calibration; V2I; machine learning; intelligent analysis of the video stream Article
## 4 Analysis of Bilateral Signals
### Visualization  
The signals measured in Session 2 are visualized. Figure 4 shows the averaged signals and their frequency responses for all thirty trials of the three subjects. The frequency response is plotted to show the variability. Only the averaged signal is shown because the time-series signal variability was difficult to visualize. Figure 4a-c show the preprocessed ECIR data, and Figure 4d-f show its frequency response. Comparing the time series data of the left and right ears, it can be observed that the two types of waveforms are different without complete overlapping.  
The frequency response shows that all subjects have peaks in the same frequency band. It can also be observed that the characteristics of the data for the left and right ears are similar. Therefore, we can conclude that there was no significant discrepancy in the measurement.  
As shown in Figure 4, although a difference exists between the left and right ear data, it is unclear whether it is sufficiently large or small to have a positive effect on authentication. These differences are presumed to be caused by the difference in the shape of the actual ear and the differences in the characteristics of the left and right earphones and microphones. Different data for the left and right ears may increase the amount of information available for authentication, which may probably improve the accuracy of the authentication process. If the data from the left and right ears do not have a positive effect on authentication, the accuracy will not improve and will be similar to that of a single ear authentication.  
Figure 3: Photos of a special earphone and measurements. (**a**) a special earphone. (**b**) the subject wearing the earphone. (**c**) measurements. The earpieces in (**b**,**c**) are black, however the white earpieces shown in (**a**) were used in the experiment.  
Figure 2: The measurement system for measuring bilateral ear data.
## 3 Results
### Simulation Results
#### 3.1.2 Active Power Regulation  
As defined above, \(P_{ref}\) must take the value of the maximum power or that obtained by the MPPT. In this case, the maximum power determined was 300 W in order to supply the maximum active power and avoid affecting the dynamics of the reactive power control. However, the active power injected by the microinverter could be regulated by changing the reference voltage of the DC bus \(V_{ref}\) within the range of 220 to 380 V (hardware limitations). Figure 36 shows the variation in \(V_{ref}\) from 250 to 330 V and the decrease in the active power injected by the microinverter.  
Figure 34: Possible working range of the microinverter based on reactive power.  
Figure 35: Interaction of the microinverter with the grid when \(Q_{ref}=Q_{load}\). Reference value: \(V_{ref}=250\) V.