esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ampicillin-Resistant Enterobacteriaceae  
The average counts of ampicillin-resistant _Enterobacteriaceae_ isolates revealed a high variability throughout the irrigation campaigns, namely at some inlets and at the LVID outlet (Lis River at the Bajanca section). The samples with the highest average counts were collected at the LVID inlets (seven orders of magnitude difference among the river section). Despite this high variability, there was an average reduction of two orders of magnitude between the Amp\({}^{r}\) counts in the inlets and the outlet of the LVID (Figure 3a). Conversely, in Block II, there was an increase of five orders of magnitude (85%) between the inlet and outlet (Figure 3b), which can be explained by the effect of drainage water discharges from other sources, which occurs downstream of the rice fields, with high concentrations of _Enterobacteriaceae_ Amp\({}^{r}\).  
In fact, the other cultures practiced in Block II (maize, pasture) are manured. Thus, the microbiological load from livestock, delivered directly into the soil of some areas of the LVID, makes the microbiological risk emergent, possibly due to the presence of AB resistance genes [29]. These results demonstrate that Good Agricultural Practices must be observed in terms of manure and slurry management [26]. Intensive livestock activity should fully compost these residues before being incorporated into the soil, mainly for corn and pasture production.  
Significantly higher \(\text{Amp}^{\text{r}}\) counts were recorded in rice paddy soils than in the irrigation water at the inlet of the paddies (five orders of magnitude difference), but in groundwater and in drainage from the paddies these counts decrease significantly (Figure 3c), which may be explained by the effect of the soil in reducing the number of _Enterobacteriaceae_[23].  
An additional critical aspect associated with fecal contaminants is the frequent resistance to ABs found in the aquatic systems, namely to the \(\beta\)-lactams (e.g., ampicillin), placing the water quality and public health in a critical situation. Once contaminated, the soils from agricultural systems can become a source of environmental contamination through the runoff into water bodies, such as aquifers and rivers, and the leaching of these AB-resistance determinants deposited on the soil surface to groundwater [19]. The identification in some cases of microbiological contamination risk in irrigation water of LVID, whose main responsibility is external to the Lis valley, requires special precautionary measures, in particular regarding the safety of farmers and consumers. These risks need to be evaluated for the real knowledge of the influence of agricultural activity within the irrigation district, particularly at the drainage network level.  
Figure 3: Mean values of _Enterobacteriaceae_ Amp\({}^{\text{r}}\) counts, expressed in CFU/mL (and standard deviation) at the inlet and outlet of: (**a**) LVID; (**b**) Block II (HB II); (**c**) rice paddies of Block II, including at the soil, soil water and groundwater.
# GNSS Based Low-Cost Magnetometer Calibration
## 5 Conclusions  
The calibration of the magnetometer is very important as the magnetic field is strongly influenced by the environment. The calibration method proposed by us is especially advantageous for using the magnetometer in inertial navigation in combination with GNSS. The magnetometer or other sensors can be calibrated based on the measurement of the direction vector from GNSS if the vehicle is moving. In case of signal failure, we can predict the position based on the INS, whose sensors can be cheap. Unlike a gyroscope, a magnetometer is not subject to drift. In our tests, we achieved an average deviation in magnetometer accuracy of 10\({}^{\circ}\) with a calibration time of at least 5 min, using linear regression and matrix calibration. This error may seem large, but you have to remember that in a passenger car, the magnetic field is strongly influenced by the surroundings. The magnetometer had deviations when passing traction lines and iron bridges. It also had minor deviations when driving around trucks. The magnetic field in the car cabin was many times greater than the real value of the Earth's magnetic field at our latitude and altitude. Such calibration is strongly dependent on the traveled path; in case the data was calibrated only during a section on a straight path, the calibration results were imprecise but still better than in the case of data without calibration. Such a calibration approach can be used in all mobile objects with GNSS or other localization. However, it is necessary to know the current rotation of the sensor relative to the track. In the further direction of the project, we want to look at the possibilities of gyroscope and magnetometer calibration using the direction vector from GNSS. By fusing the data from the gyroscope and the magnetometer, we would be able to determine the direction of the vehicle more accurately. Using the estimated direction vector and speed of the vehicle, we can predict the position. The speed data can be obtained from the vehicle's control unit, as cheap accelerometers are too inaccurate for integrating the speed of an object, and there is a thermal drift that we cannot address.  
Conceptualization, J.A.; methodology, J.A.; software, J.A.; validation, J.A., V.S., A.K. and R.P.; formal analysis, J.A., V.S., A.K. and R.P.; investigation, resources, J.A. and V.S.; data curation, J.A.; writing--original draft preparation, J.A.; writing--review and editing, J.A., V.S., A.K. and R.P.; visualization, J.A. and A.K.; supervision, V.S.; project administration, R.P. and A.K.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Mobile robotic systems for support during crisis situations (Robot Search & Rescue robotics crisis situations),\"project of the Slovak Research Agency VEGA 1/0241/2022 and Smart tunnel: \"Telematic support in case of emergencies in the road tunnel\" the project APVV-17-0014.  
Not applicable.  
Data Availability Statement: Data is available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Proposed Control Methodology
### Learning Control Algorithm (LCA)
\begin{table}
\begin{tabular}{l l} \hline \hline
**Control Term** & **Mathematical Expression** \\ \hline Dissipative term & \(-\delta\,u(t)\) \\ Model-reference tracking term & \(\gamma\big{(}u(t)-u_{lqi}(t)\big{)}\) \\ Anti-dissipative term & \(\delta\big{(}e_{\theta}(t)+\rho e_{\theta}^{3}(t)\big{)}\text{ sign}\big{(}\dot{e}_{\theta}(t)\big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of constituent terms in the control law. Figure 3: Error profile of a system under disturbance [37]. \[\text{sign}\big{(}\dot{e}_{\theta}(t)\big{)}=\left\{ \begin{array}{l}1,if\ \dot{e}_{ \theta}(t)>0\\ 0,if\ \dot{e}_{\theta}(t)=0\\ -1,if\ \dot{e}_{\theta}(t)<0\end{array} \right. \tag{26}\]  
The error cube polynomial \(\big{(}e_{\theta}(t)+\rho e_{\theta}^{3}(t)\big{)}\) in the anti-dissipative term introduces distinct amplified and attenuated error regions, as shown in Figure 4, flexibly adjusting the control response based on error magnitude [38]. It enhances the controller's responsiveness by intensifying control efforts for large errors, ensuring rapid correction while attenuating control actions for small errors, preventing excessive actuator activity, and minimizing steady-state fluctuations. Consequently, the applied control input exhibits a sharp increase in magnitude under significant error conditions, while maintaining a more conservative response in near-equilibrium states.
## 3 Analysis of Edge Method Accuracy
### Oversampling Ratio
Figure 10a-f shows that when the ideal optimal or suboptimal OSR calculated based on \(1/[k\cdot\tan(\theta)]\) (\(k\)\(\in\)Z\({}_{+}\)) is determined for arbitrary angle edge of [0, 45] deg, the edge method accuracy is higher than that of other higher or lower OSR. The consistency of MTF curves with different OSRs in Figure 10a-c is higher than in Figure 10d-f. On the one hand, it indicates that OSR has little influence on STA edges (e.g., \(\leq\)10 deg), which is related to the tight projection path with more data involved in binning and less possibility of an empty bin. On the other hand, it indicates that OSR greatly influences the large angle edges (e.g., \(\geq\)14 deg) when the higher or lower OSR is used, which is related to the sparse projection path with fewer data involved in binning and the greater possibility of the empty bin. Figure 10: MTF for some tilt angle BDE images when adopting different OSR (ideal optimal, ideal suboptimal, advised, proper, high, and low OSR) using ISO-cos edge method: (**a**) when edge angle is 3 deg, (**b**) when edge angle is 5 deg, (**c**) when edge angle is 10 deg, (**d**) when edge angle is 14 deg, (**e**) when edge angle is 25 deg, (**f**) when edge angle is 40 deg. Figure 9: Theoretical relationship between OSR and edge angle: (**a**) OSR when angle belong to [0, 45] deg and some OSR labels at integral angle, (**b**) angle when OSR belong to [1, 30] and some angle labels at integral OSR.
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 2 Materials and Methods
### Batch Adsorption Experiments  
The adsorption of fluoride was carried out by adding 50 mg of Al-S957 in 50 mL of fluoride solution (4 mg/L), which was shaken thoroughly at 298 K to reach the adsorption equilibrium. The effect of pH on fluoride adsorption was evaluated by adjusting the initial pH of the feed solution from 3 to 11. The selective fluoride removal was examined in the presence of coexisting anions, including Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), HCO\({}_{3}\)\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), and PO\({}_{4}\)\({}^{3-}\). Humic acid (HA), tannic acid (TA), sodium dodecyl benzene sulfonate (SDBS), L-tryptophan (Trp), and sodium alginate (SA) were introduced to assess their influence on the fluoride removal capacity. Experiments on the effects of coexisting anions and organic matter were also conducted for Al-D412 and Al-D001. Adsorption isotherms of fluoride were explored at different initial concentrations (2 to 30 mg/L) in the temperature range of 283, 298, and 313 K. To determine the adsorption kinetics, 0.5 g of Al-S957 was contacted with 500 mL of fluoride solution, and 1 mL of solution was extracted and analyzed at various time intervals for 10 h. Adsorption experiments were performed three times, with the average value taken as the adsorption capacity. The error bars were calculated using the standard error formula.
## 6 Future Research Directions
### Computation Efficiency and Accuracy  
The collection rates of large numbers of data and intricate network topologies of the 6G networks hinder AI-enabled learning and training procedures. Additionally, it is possible that the restricted computer capacity will not be able to analyze the enormous numbers of high-dimensional data required to accomplish the training accuracy rate. Deep learning is very costly, since it typically necessitates sophisticated computing systems. Therefore, one of the most crucial research fields is how to create effective AI learning methods that will increase computing efficiency and accuracy. Recently, it has been apparent that high-performance computing abilities may be used to hasten convergence, lessen difficult computations, and boost training precision. These methods include feature matching, graphics processing, residual networks, and offline training [35; 36].
## 2 Related Work
### The Relay Selection of Ad Hoc Network Enabled by DRL  
The deep learning-based relay selection method can make a better choice for next-hop relay where transmission rate and node congestion change in real time, and it can also synthesize multiple performance factors to make comprehensive decisions. Therefore, the routing method based on learning-based algorithms performs better in mobile communication network.  
Learning-based routing methods are widely used in MANETs and VANETs [24]. As the mobility of nodes may lead to constant variations in the network topology [25], the learning-based approach can make decisions according to real-time changes and is, thus, more suitable for both scenarios. In [26], [PERSON] et al. proposed a multiagent method, wherein all agents share the same training experience based on DQN, but make the next-hop decision individually. The aim of this method is to select the optimal route in MANET, which minimizes transmission delay and queuing delay. In order to minimize delay and make a reasonable power allocation in a vehicular-to-vehicular (V2V) communication network, [PERSON] et al. [27] proposed a deep reinforcement learning method to choose the optimal relay according to the velocity, location, and packet number of each vehicle. In [16], [PERSON] et al. proposed a Q-learning algorithm to find optimal UAV relays to assist V2V communication. Their proposed method improved the delivery ratio and delivery latency with comprehensive consideration of the state transition probability of communication interruption, delay consumption, and energy consumption.  
However, these methods only consider the delay of a single hop and ignore the total delay of the whole link. In some cases, if we only consider the shortest delay of a single hop, it is likely to choose the closest next-hop relay. This causes the hop number of the whole link to increase, along with the total link latency. Meanwhile, most methods use an infinitely long cache during the computation of system throughput. However, in real life, the buffer size is limited. Therefore, the impact of packet loss on throughput must be considered.
## 5 Experimental Verification and Result Analysis
### Reward Function Evaluation  
The data for different solutions are shown in Table 2. Figure 7 shows the performance of planning operation steps under different reward function solutions. The same curve trend can be observed between the unimproved and improved solutions, indicating that these two solutions play a role in solving obstacle avoidance and goal orientation. However, the convergence speed of the improved solution is faster. Meanwhile, the search path-planning results under different schemes are shown in Figure 8. During the learning process, the learning speed of the unimproved solution is slower, resulting in inaccurate planning multiple times. The improved solution provides more accurate results than the unimproved one during the learning process. It reduces the repetitive path rate of the agent and improves the efficiency of online planning. This proves that the reward function is goal-oriented for the intelligent agent, enabling it to learn gradually.  
Figure 5: A comparison of the step size values of UGV agents successfully searching for targets under different scheme settings. The black line in the figure represents the edge value, the blue line represents the quartile value, and the red line represents the median value.  
Figure 6: The results of path planning for different schemes. The orange arrow in the figure represents the search path. The green arrow represents the movement of the target. The yellow star represents the final capture position.
## 3 Results and Discussion
### Results and Analysis
#### 3.2.1 Effect of the Radial Load on the Slip Rate and Temperature  
The test conditions are as follows: the inner ring speed is 1050 r/min; the lubricating oil quantity is 5 mL/min; the radial loads are 70 N, 100 N, 130 N 160 N, and 190 N. The error between the measured value and the benchmark value is shown in Table 1.  
Figure 5: Components of the test rig: (**1**) Dovetail platform; (**2**) Support platform; (**3**) Linear guides; (**4**) Loading board; (**5**) Spindle fixture; (**6**) Motorized spindle 1; (**7**) Protective cover; (**8**) Spindle fixture; (**9**) Motorized spindle 2; (**10**) Spindle support frame; (**11**) Bolt; (**12**) Vibration reduction table; (**13**) Computer; (**14**) Water outlet; (**15**) Pressure sensor; (**16**) Loading screw; (**17**) Power connector; (**18**) Water inlet; (**19**) Pressure testing system; (**20**) Vibration testing system; (**21**) Revolving speed testing system (displacement sensor); (**22**) Infrared thermograph temperature measuring system; (**23**) Revolving speed testing system (displacement sensor); (**24**) Lubricating device.  
Figure 6a analyzes the variation tendency of the temperature and slip in the contact area under different radial loads. The results show that the temperature in the contact area continues to increase with the increase of the radial load, but the trend gradually slows down. The reason is that the slip rate decreases rapidly due to the radial load increase, which reduces the temperature in the contact area to some extent. Figure 6b is the scatter diagram of load for the five cases.
###### Abstract  
This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle's DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.  
vehicle detection; vehicle type classification; vehicle color classification; deep convolutional neural network (DCNN); YOLO
## 3 Results  
Scatter diagrams of modeled T ((a,d) T\({}_{\text{SLR}}\) estimated by the SLR method, (b,e) T\({}_{\text{WUE}}\) estimated by the WUE method, and (c, f) T\({}_{\text{S}}\) estimated by the conductance method) versus T\({}_{\text{Zhou}}\) estimated by the uWUE method are shown in Figure 2. Both the WUE method and conductance method showed good performance at hourly (upper row) and daily (lower row) scales. In general, the conductance method outperformed the other two methods with higher coefficients of determination (R\({}^{2}\)) (0.67 for the hourly scale and 0.78 for the daily scale) and lower root-mean-square error (RMSE) (58.13 W/m\({}^{2}\) for the hourly scale and 39.56 W/m\({}^{2}\) for the daily scale). The SLR method and WUE method tended to overestimate T at the high-value area, while most points of the conductance method fell near the 1:1 line. All three methods showed better performance at the daily scale than the hourly scale, which could be attributed to the uncertainty of input data decreased with time aggregation. The difference in the performance of the SLR and WUE methods narrowed at the daily scale.  
As shown in Figure 3, we also compared the latent heat estimated by three different methods ((a,d) LE\({}_{\text{SLR}}\) estimated by the SLR method, (b,e) LE\({}_{\text{WUE}}\) estimated by the WUE method, and (c,f) LE\({}_{\text{S}}\) estimated by the conductance method) with latent heat observed from eddy covariance. Three methods had R\({}^{2}\) = 0.56, 0.61, and 0.65 (RMSE = 91.36, 88.06, and 80.87 W/m\({}^{2}\)) at the hourly scale (upper row), and R\({}^{2}\) = 0.52, 0.66, and 0.67 (RMSE = 75.17, 65.52, and 61.07 W/m\({}^{2}\)) at the daily scale (lower row), respectively. Likewise, two developed methods showed better performance than the SLR method at the hourly scale, and the leading advantage widened at the daily scale. There was also an overestimation at the high-value area for the SLR and WUE methods. Details of the performance at four sites are shown in supplementary Table S3.  
Figure 2: Scatterplot of hourly (first row) and daily (second row) T estimates from the three approaches (linear method, water-use efficiency (WUE) method, and conductance method) versus T\({}_{\text{zhou}}\) (estimated by underlying WUE method) with the 1:1 line (black line), along with coefficients of determination (R\({}^{2}\)) and root-mean-square error (RMSE, W/m\({}^{2}\)). Subfigures (**a**,**d**) refer to the linear method, (**b**,**e**) to the WUE method, and (**c**,**f**) to the conductance method.
## 4 Illustrative Examples
### Beam Analysis
#### 4.2.2 Concerning the Reproduction of 3D Deformation  
The vibration modes of the illustrative thin-walled structure were presented in order to exhibit the applicability of the proposed model in performing the 3D analysis of thin-walled structures. The results obtained were compared with finite element models implemented in ANSYS 17.0. Toward the plotting of the deformed configuration, the displacement field obtained from the proposed model was computed in a \"mesh\" of sampling points. The mesh was defined over the beam cross-section with 44 points and lengthwise along the beam axis with 64 points. Thus, 44 \(\times\) 64 squares were employed to describe the thin-walled structure configuration. To this end, the displacements of these sampling points were obtained from the finite element solution of the proposed beam model and then used to compute the new coordinates of these sampling points. Then, in a global coordinate system describing the deformed configuration, these sampling points were interconnected over the cross-section and along the beam axis, respectively. It should be noted that the mesh was just used to represent the deformed configuration and not the discretization adopted for the cross-section analysis or the finite element solution.  
Figure 13 shows the comparison regarding the third to tenth vibration modes of the illustrative thin-walled structure. The first and second vibration modes were not listed because the cross-section deformation was not obvious enough and thus may not be typical in exhibiting the capability of reproducing three-dimensional deformation. The comparison was divided into eight pairs according to the order of the vibration modes. Within each pair, the left was derived with the proposed beam 4, while the right represents the ANSYS shell results. In general, there was no visible difference between all the eight pairs. This proved the capability of the proposed model in predicting the vibration modes of thin-walled structures. Simultaneously, the comparison reconfirmed the good agreements between the proposed model and ANSYS shell model. In this sense, it is rational to believe that the proposed model can accurately reproduce 3D deformation and forecast the dynamic behaviours of thin-walled structures.  
Figure 13: The comparison between the proposed model and ANSYS shell model concerning the third to tenth vibration modes of the illustrative thin-walled structure.