esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Analysis  
Figure 1 illustrates a parameter-lumped simple wheel-track model that considers wheel OOR to analyze the axle box vibration caused by wheel polygons. In this model, \(v\) is the vehicle velocity, \(m_{1}\) is the unsprung mass, \(z_{1}\) is the vertical displacement of the wheelset, \(k_{1}\) is related to the vertical stiffness of the primary spring, \(m_{2}\) is the mass of the track, \(k_{2}\) is the vertical stiffness of the track, \(c_{2}\) is the track damping, and \(z_{2}\) is the vertical displacement of the track. The downward direction is positive. Based on conservation of energy, a track may be conceptually modeled as an infinite beam with dampers on an elastic foundation. The track model allows for deflections and forces in the \(z\) direction. The parameters of the elastic foundation beam are converted into equivalent mass, equivalent damping, and an equivalent spring attached to each wheel. For a train with a polygonal wheel of a given OOR amplitude \(a\) rolling along the track at speed \(v\), the vertical dynamic equations for the wheel-rail system are  
\[\left\{ \begin{array}{c}m_{1}\ddot{z}_{1}+k_{1}(z_{1}-a-z_{2})=0\\ m_{2}\ddot{z}_{2}+c_{2}\ddot{z}_{2}+k_{2}z_{2}-k_{1}(z_{1}-a-z_{2})=0\end{array} \right. \tag{1}\]  
The solution to the numerical ordinary differential Equation (1) is  
\[\left\{ \begin{array}{c}\frac{\ddot{z}_{1}}{\omega_{1}^{2}}+z_{1}-z_{2}=a\\ \frac{\ddot{z}_{2}}{\omega_{2}^{2}}+\frac{c_{2}}{k_{1}}\dot{z}_{2}+\left(\frac {k_{1}+k_{2}}{k_{1}}\right)z_{2}-z_{1}=-a\end{array} \right. \tag{2}\]  
where \(\omega_{1}=\sqrt{k_{1}/m_{1}}\), \(\omega_{2}=\sqrt{k_{1}/m_{2}}\), and \(\ddot{z}_{1}\) is the axle-box acceleration. The initial irregularity of the wheel polygon is defined by a harmonic displacement function  
\[a(t)=a\sin(\omega t+\varphi) \tag{3}\]  
where \(a(t)\) is the time-dependent OOR amplitude, \(\omega\) is the frequency corresponding to the harmonic orders of the OOR, and \(\varphi\) is the phase angle between the two wheels on the same axle. The Laplace transform of Equation (3) is  
\[a(s)=\frac{a(s\sin\theta+\omega\cos\theta)}{s^{2}+\omega^{2}} \tag{4}\]  
To obtain transfer functions of \(z_{1}\) and \(\ddot{z}_{1}\) induced by \(a(t)\), we use  
\[\left\{ \begin{array}{c}z_{1}(s)=H_{z1}(s)a(s)\\ z_{2}(s)=H_{z2}(s)a(s)\end{array} \right. \tag{5}\]  
where \(z_{1}(s)\) and \(z_{2}(s)\) are the Laplace transforms for \(z_{1}\) and \(z_{2}\), respectively. \(H_{z1}(s)\) and \(H_{z2}(s)\) are the transfer functions corresponding to \(z_{1}\) and \(z_{2}\) under excitation \(a(t)\). When the initial state is zero, we get the Laplace transform of Equation (2):  
\[\left\{ \begin{array}{c}\left(\frac{s^{2}}{\omega_{2}^{2}}+1\right)\dot{z}_{ 1}(s)-z_{2}(s)=a(s)\\ z_{1}(s)-\left(\frac{s^{2}}{\omega_{2}^{2}}+\frac{c_{2}s}{k_{1}}+\frac{k_{2}}{k _{1}}+1\right)\dot{z}_{2}(s)=a(s)\end{array} \right. \tag{6}\]  
Figure 1: Parameter-lumped simple wheel-track model after introducing wheel out-of-roundness.
###### Abstract  
A novel method was developed based on the amplitude data of the EM waves measured by Digisondes to calculate and investigate the relative ionospheric absorption changes. The effect of 13 solar flares (>C8) that occurred from 4 to 10 September 2017 were studied at three European Digisonde stations (Juliusruh (54.63\({}^{\circ}\)N, 13.37\({}^{\circ}\)E), Prahonice (49.98\({}^{\circ}\)N, 14.55\({}^{\circ}\)E) and San Vito (40.6\({}^{\circ}\)N, 17.8\({}^{\circ}\)E)). The present study compares the results of the amplitude method with the absorption changes measured by the Finnish Rionometer Network and determined by the NOAA D-RAP model during the same events. The X-class flares caused 1.5-2.5 dB of attenuation at 30-32.5 MHz based on the rionmeter data, while the absorption changes were between 10 and 15 dB in the 2.5-4.5 MHz frequency range according to the amplitude data. The impact caused by energetic particles after the solar flares are clearly seen in the rionmeter data, while among the Digisonde stations it can be observed only at Juliusruh in some certain cases. Comparing the results of the amplitude method with the D-RAP model it seems evident that the observed absorption values almost always exceed the values given by the model both at 2.5 MHz and at 4 MHz during the investigated period. According to the comparison between the rioneter data with the D-RAP, generally, the model underestimates the absorption values obtained from the rioneters during solar flares except at the highest latitude stations, while D-RAP overestimates the impact during the particle events.  
s 2024163975202416397520241639752024163975202416397520241639752024163975202416397520241639752024163975202416397520241639752024163975  
1  
Ionospheric Absorption Variation Based on Ionosonde and Rionometer Data and the NOAA D-RAP Model over Europe During Intense Solar Flares in September 2017
## 3 Materials and Methods
### General Procedure for the Asymmetric DA Reaction  
To a Schlenk tube equipped with a magnetic stirring bar, ionic, core-corona polymer microsphere-immobilized catalyst (0.050 mmol of catalyst), (_E_)-cinnamaldehyde (66 mg, 0.50 mmol), and CH\({}_{3}\)OH/H\({}_{2}\)O (95/5, v/v) mixed solvent (0.25 mL) were added. Afterwards, 1,3-cyclopentadiene (99 mg, 1.5 mmol) was added at room temperature, and the reaction mixture was stirred at 25 \({}^{\circ}\)C for 36 h. The catalyst was then isolated by centrifugation and washed with Et\({}_{2}\)O three times. The solution of products was concentrated by rotary evaporator until the solution was 1 mL. For the deprotection of acetal, 2/2/1 CH\({}_{2}\)Cl\({}_{2}\)/H\({}_{2}\)O/trifluoroacetic acid was then added to the solution and stirred at room temperature for 2 h. That was followed by adding saturated NaHCO\({}_{3}\) aqueous solution. After the extraction with Et\({}_{2}\)O, the collected organic layer was washed with saturated NaCl and dried with anhydrous MgSO\({}_{4}\). The removal of MgSO\({}_{4}\) by filtration and the concentration by rotary evaporator and vacuum gave the crude. The leaching of MacMillan catalyst was checked by TLC and the elemental analysis for nitrogen content. The crude was purified by silica gel column chromatography (with 1/19 ethyl acetate/hexane) to give the Diels-Alder adducts as colorless liquids. The yield and _exo/endo_ ratio were determined using 1H NMR spectrum through comparison of the 1H signals of aldehydes. The enantiomeric excess (ee) for _exo_ and _endo_ isomers was determined using GC through a comparison of the peak area ratio of isomers (Astec CHIRALDEX \(\beta\)-PH; injection temperature of 180 \({}^{\circ}\)C; detection temperature of 180 \({}^{\circ}\)C; the column temperature was increased from 150 \({}^{\circ}\)C to 180 \({}^{\circ}\)C with 1 \({}^{\circ}\)C/min; retention times: 35.1 min (_exo_(2_R_)), 35.7 min (_exo_(2_S_)), 36.6 min (_endo_(2_R_)), and 37.1 min (_endo_(2_S_))).  
**9**: \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\), \(\delta\) = 7.26 (CHCl\({}_{3}\))): \(\delta\) = 9.92 (d, \(J\) = 2.1 Hz, 1H), 9.60 (d, \(J\) = 2.1 Hz, 1H), 7.31-7.16 (m, 10H), 6.42 (dd, \(J\) = 5.8, 2.4 Hz, 1H), 6.34 (dd, \(J\) = 5.5, 1.8 Hz, 1H), 6.17 (dd, \(J\) = 5.5, 2.8 Hz, 1H), 6.07 (dd, \(J\) = 5.5, 2.4 Hz, 1H), 3.73 (dd, \(J\) = 4.9, 1.5 Hz, 1H), 3.34-3.33 (m, 1H), 3.24-3.22 (m, 2H), 3.14-3.13 (m, 1H), 3.10 (d, \(J\) = 1.22 Hz, 1H), 3.08 (d, \(J\) = 1.22, 1H), 3.00-2.97 (m, 1H), 2.61-2.59 (m, 1H), 1.82-1.80 (m, 1H), 1.64-1.60 (m, 2H), 1.57-1.54 (m, 2H). \({}^{13}\)C NMR (100 MHz, CDCl\({}_{3}\), \(\delta\) = 77.1 (CDCl\({}_{3}\))): \(\delta\) = 203.5, 202.8, 143.5, 142.6, 139.2, 136.5, 136.3, 133.8, 128.6, 128.1, 127.9, 127.3, 126.3, 126.2, 60.8, 59.4, 48.4, 48.4, 47.6, 47.1, 45.6, 45.5, 45.4, 45.2.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 3 CLAMBDA-Search Model
### Error Analysis
#### 3.3.3 Attitude Angle Check  
If any given \(\gamma_{k}\) is close to its true value \(\gamma\), the calculated value \(\gamma^{\prime}{}_{k}\) with the fixed ambiguity should also be close to \(\gamma_{k}\). Then, we can check by using the following:  
\[\left|\gamma^{\prime}{}_{k}-\gamma_{k}\right|<\tau_{att} \tag{32}\]
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 2 Solar Collector Systems Employed for Wastewater Treatment
### Concentrating Systems
#### 2.1.4 Fresnel Solar Concentrators  
Fresnel solar collectors have been developed and applied for solar energy production, and can be found in both a line or point-focus design. They can work in two basic configurations exploiting either mirrors or lenses to produce the concentration effect. Usually they are ground-mounted mirrors or lenses with flat or nearly flat surfaces tracking the sun and concentrating onto a focal receiver which, for wastewater treatment, is usually of tubular design and is equipped with secondary reflectors [55].  
Compared to PTCs, Fresnel configurations offer two distinct advantages: (1) the receiver is fixed and usually with a larger collection area, which can lead to a simpler photoreceptor design; (2) mirrors can be smaller and flat or nearly flat, and are thus of simpler and cheaper fabrication. However, due to a greater distance between the receiver and the mirror array, they suffer from lower optical efficiency and are particularly prone to tracking errors [56].  
Figure 4 presents the schematic drawing and an actual installation of Fresnel reflectors. This example shows the most common configuration, with evenly-placed, identical mirrors, a convenient design in terms of simplicity, but not necessarily the most efficient [57].  
A few applications to wastewater purification can be found in the literature. For example, Fresnel transmitting concentrators, which employ lenses instead of mirrors, were recently investigated. The lenses were made of an acrylic material with a transmittance of 0.92 in the range 400-1100 nm [58; 59]. Despite the drawback of difficulties of concentrating UV light (<400 nm), results evidenced a possible reasonable option if used with low costs catalysts to accelerate the degradation of orange II and blue  
Figure 4: Schematic drawing of a Fresnel lens receiver [56].  
Figure 3: Schematic drawing and picture of a parabolic dish [52; 53].  
4 in water models, as a comparison of solely using direct solar radiation. A scheme of Fresnel lens transmitter concentrator is presented in Figure 5.
# Approximate Sensory Data Collection: A Survey
## 6 Further Works
However, the metric in [56] is suitable for the scalar data; moreover, it also requires that the sensors in the network should be synchronous and sample sensory data with the same frequency. Such constraints may be common for WSNs, but seem a little strong for IoT systems, especially when multi-modal sensory data are involved. Thus, the complicated correlations among sensory data should be taken into account, and the new metrics to evaluate the global information loss rate need to be reconsidered. Since the sensory data can be regarded as the information carriers, the information entropy based metric may be a good choice to define the _global information loss rate_, and the entropy based techniques may be valuable for designing the kernel dataset drawing algorithms. Fourth, the sensor nodes in IoT systems are usually very cheap and their abilities on sensing, computation, storage and communication are quite limited, so that the quality of sensory data is extremely low. Furthermore, some of the battery-free nodes may also be involved in some IoT systems, which makes the quality of sensory data to be even harder for controlling. Taken the currency as an example, the battery-free sensor nodes in a given IoT system cannot sensory data if the energy gained from environment does not exceed a certain threshold, and thus it cannot guarantee that the object is monitored in real time. Similarly, the precision, completeness and consistency of a sensory dataset also cannot be guaranteed in some IoT systems. Thus, the sensory data should be repaired during collection in order to provide the high quality dataset to users. Unfortunately, few of the existing methods have considered such a problem. Therefore, the cooperative data repairing techniques during collection are also worth being studied in the future. Similar to constructing the kernel data drawing methods, the correlations among the sensory data are very useful for designing cooperative data repairing algorithms, where the logistics based techniques, stochastic process based models (e.g., continuous Markov model) _etc._, can be utilized for sensory data repairing. Finally, the data collections to support the advance applications deserve to be studied intensively in the future as well. For example, visualization has become a popular application for networks and provides a friendly interface for users to observe and comprehend the physical worlds. However, almost none of the existing data collection methods consider how to support such an application. Furthermore, in future IoT systems, the data collectors may no longer be computers, and they could be smart phones, Pads or other small hand-held devices, so that the data collectors may have limited abilities on processing and exhibition. These limitations should be sufficiently taken into consideration in order to transmit the processable sensory data only and save energy during data collection.
# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
## 4 Experiments
### Performance Measurement  
To analyze the results of the performance of YOLOv5, precision, recall, [EMAIL_ADDRESS], [EMAIL_ADDRESS], F1 score, and model size were used as evaluation indicators in this study. Precision is the ratio of correctly predicted positives to the total number of predicted positives, as shown in Equation (1). Precision refers to how accurately the model when compared with positive sample features. In other words, it is an indicator of how accurate the prediction value of the model is, and it is the ratio that correctly fits the positive among  
Figure 3: The architecture of the YOLOv5.  
the predictions. The higher the accuracy, the more accurate the prediction of positive samples [8].  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
Recall is the percentage of correctly predicted positive numbers to the total number of true positives as shown in Equation (2). The higher the recall, the more accurately the target sample is predicted and the less likely it is to miss a bad sample [25].  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
where TP (True Positive) denotes the number of predicted positive cases that are correctly classified, and TN (True Negative) is the number of predicted negative cases classified as negative. FP (False Positive) is the number of predicted positive cases that are incorrectly classified as negative cases, and FN (False Negative) is the number of predicted negative cases that are incorrectly classified as positive cases [26]. The criteria for evaluating performance are defined using the above four criteria.  
F1 score is the harmonic mean of precision and recall, shown in Equation (3) [27]. The F1 score accurately evaluates the performance of the model when the data label is an unbalanced structure and expresses the performance in a number. It is also called the test accuracy of the model. The value of F1 is from 0 to 1, and the more perfect precision and recall values are, the closer it is to 1, and the lower precision and recall values, the closer it is to 0 [28].  
\[F1\ score=2\times\frac{(precision\times recall)}{precision+recall} \tag{3}\]  
In addition, mAP is the performance of the multiple object detection algorithm in one scalar value. It is calculated by taking the mean of the AP (Average Precision) of the classes [29].
## 3 Results
### Discussion  
We conducted an in-depth analysis of our results, and the most important aspects to highlight are as follows:  
* While the classification of alphabet signs has been explored in the existing literature, user flexibility is often neglected. Our approach stands out by recognizing signs without imposing constraints on the camera's position. This flexibility was made possible by the preprocessing method proposed in this work.
* Although MSL includes dynamic signs, our analysis concludes that most of them can be expressed using a single handshape (with the exceptions of the letter \"K\" and double letters), allowing these signs to be treated as static to simplify the classification problem.  
* The inclusion of double letters, such as \"rr\" and \"ll\", is often overlooked since they are not part of the basic Spanish alphabet. However, considering their significance in MSL, it is crucial to incorporate them into sign recognition systems.
* The experiment with CNNs demonstrated that, in this case, directly using images of the signs did not yield the best classification results. However, it presents an area for further exploration, particularly by experimenting with other types of neural networks and transitioning from static images to video data.
* The MediaPipe API for extracting hand key points provided sufficient information to achieve optimal classification performance for static signs, as evidenced by the FC-NN results in Table 7. Additionally, this approach resulted in a model with fewer parameters, thereby reducing computational costs. However, it is worth noting that reducing the dataset by approximately 50% is an interesting phenomenon to investigate further. It may be the case that specific lighting conditions negatively impact the API's performance.
* For double letters, although only \"rr\" and \"ll\" were presented in this work, Algorithm 3 can be adapted to detect other double letters. The process merely requires recording the corresponding video to include a new letter represented by a dynamic sign.
* Regarding misclassifications, such as those involving \"m\" and \"n\", incorporating a binary classifier to distinguish between these two letters could improve classification accuracy.
* Despite the occurrence of some classification errors, the overall metrics for accuracy, recall, precision, and F1 score all exceed 90%, indicating strong model performance.
* The significance of this research lies in its computational achievements and its potential to promote the inclusion of disadvantaged communities. This work aligns with the United Nations Sustainable Development Goals by applying AI to address communication challenges.
## 3 Results and Discussion
### Flexural Load-Deflection Behavior  
Figure 6 shows the flexural load-deflection curves of the base concrete containing steel and polyolefin fibers at fiber contents of 0.54% and 1.08%, respectively. At a fiber content of 0.54%, a large drop in deflection was observed after maximum peak load. However, in the case of 1.08% fiber content, even when 100% polyolefin fiber was used, there was a recovery of the load after the first decrease in the load, which indicated considerable toughness could be secured by addition of fiber.  
The load-deflection curve of the base concrete with 100% steel fiber showed gradual decrease after the peak load. In case of base concrete with 100% polyolefin fiber, a rapid drop was observed after the peak, but an intensive load recovery was also observed after the rapid drop. As the volume proportion of polyolefin fiber increased to 25%, 50%, and 75%, the base concrete showed a considerable increase in flexural toughness. This phenomenon was observed for both fiber contents (0.54 and 1.08 vol%), but the trend was more evident in the case of 1.08 vol%.  
When steel and polyolefin fiber were hybridized, the positive effects seemed to be combined. As a result, the base concrete showed higher load carrying capability. For example, in the case of 50% replacement with polyolefin fiber at 1.08 vol%, the load continued to increase after the first crack and sustained a similar level of the load until the termination of the test. The advantages that can be obtained from each of the fibers, (1) higher peak load from steel fiber and (2) load recovery from polyolefin fiber, seemed to combine and show a synergetic effect. Such an effect was also observed at a fiber content of 0.54 vol%, but it was not as synergetic as at the fiber content of 1.08 vol%. In general, hybridization of steel and polyolefin fibers provided higher peak load as well as higher capability to sustain the load after the breakage of the concrete. However, the fact that the flexural toughness of the test specimen with 100% steel fiber ratio was observed as being rather low is somewhat contrary to the predicted result. It is thought that some problems occurred with the agglomeration or dispersion between the fibers in the specimen.  
Figure 6: Flexural load-deflection curve of 28-day old base concrete at fiber content of (**a**) 0.54 vol% and (**b**) 1.08 vol%.
## 5 Simulation Results and Discussion
### Results and Discussion under Different Scenarios
#### 5.2.2 Case Study under Different Price Signals  
This scenario considers analysis under different price signals. Three price signals corresponding to three different seasons (winter, summer, and spring on weekdays) have been considered for the analysis. An EV penetration of 100% is assumed for this scenario as it represents the worst-case scenario based on the results obtained in the previous section.  
Figure 13(a),b shows the active and reactive power schedules obtained using different methods. The plot given in Figure 14(a), shows that the bus voltage lies well below the prescribed limit with uncoordinated charging. However, the voltage profile improves with Method 1 and is nearest to the prescribed limit with the proposed method. The plot of VUF as shown by Figure 14(b) shows that the VUF is worse with Method 2 but lies within the limit for the other methods. A similar inference can also be made from Figures 16 and 17 regarding voltage and VUF for other seasonal price variations.  
Figure 15(a)-c shows that the total costs for the winter, summer, and spring seasons are lowest with the proposed method, but the battery degradation costs are a little bit expensive with the proposed method. However, the revenue savings from the charging costs and the reward from V2G far outweigh the battery degradation costs with the proposed method.  
Figure 12: Bus 846 (**a**) voltage and (**b**) VUF under 100% EV penetration.  
Figure 13: State of charge for the proposed method.  
Figure 16: Bus 846 (**a**) voltage and (**b**) VUF for price signals in the summer season.  
Figure 14: Schedule of (**a**) active power for EVs under different methods and (**b**) reactive power for EVs under different methods for price signals in the spring season.  
Figure 15: Bus 846 (**a**) voltage and (**b**) VUF for price signals in the spring season.  
We can observe from Table 5 that the total cost is lowest with the proposed method compared to the other methods and the VUF and voltage also lie within the prescribed limits for this scenario.  
Figure 17: Bus 846 (**a**) voltage and (**b**) VUF for price signals in the winter season.  
Figure 18: Total cost for (**a**) winter, (**b**) summer, and (**c**) spring (1 = uncoordinated charging; 2 = Method 1; 3 = Method 2; 4 = Method 3; 5 = proposed method).
# Removal of Heavy Metal Cd Element from Paddy Soil by Geo-Electrochemical Technology
## 4 Results and Discussion
### Variation in Cd Content in Various Parts of Rice
[39] found that over 90% of the Cd in rice is directly absorbed from the soil by the rice root system during the grain-filling period and transported to the grains. The higher the soil moisture content during the rice grain-filling period, the lower the activity of Cd in the soil and the lower the Cd content in the rice. Conversely, in the mid-to-late stage of rice grain filling, the lower the soil moisture content due to the dry climate or early drainage of the paddy field, the higher the activity of Cd in the soil and the higher the Cd content in the rice.