esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The satellite-induced code bias variation of geostationary satellite orbit satellites and medium earth orbit satellites of the second-generation BeiDou Navigation Satellite System (BDS-2) exceeds 1 m, which severely affects the accuracy and stability of the ambiguity resolution and high-precision positioning. With the development of the third-generation BDS (BDS-3) with a new system design and new technology, analysis of the satellite-induced code variation characteristics of BDS-3 has become increasingly important. At present, many scholars have explored the satellite-induced code bias of BDS-3, but most of them focus on BDS-3 experimental satellites via normal geodetic antenna. Compared to normal geodetic antenna, the 40-m dish antenna from the National Time Service Center can accurately detect satellite-induced code variations with low noise and high gain. Thus, observational data from fifteen BDS-3 medium earth orbit satellites are collected with the B1I/B2b/B3I/B1C/B2a frequency bands on the day of year (DOY) 199-206 in 2019, the PRN numbers of which are C19/C20/C21/C22/C23/C24/C25/C26/C27/C28/C30/C32/C33/C35/C37, via the 40 m dish antenna to analyze the code bias variation characteristics. The results show that the obvious satellite-induced elevation-dependent code bias variations exist in the B1I/B2b/B3I/B1C/B2a frequency bands of C28, compared with other satellites. Similarly, the multipath (MP) combination of B3I has an obvious elevation-dependent variation within a range of 0.1 m for C21/C24/C27/C28/C37 and elevation-dependent variation of the B2a and B2b frequency bands also exists in most satellites with a range of 0.1 m. However, the MP combination values of some satellites are asymmetric with respect to elevation, which is different from BDS-2 satellites and especially obvious for BDS-3 satellites B1I and BIC frequency bands with elevation-dependent variations of 0.2 m, indicating that the code bias variation is not uniquely related to elevation, especially for the B1I/BIC frequency bands. What's more, the satellite-induced code bias variation of the BDS-3 satellites is greatly reduced compared with that of the BDS-2 satellites. In addition, the similar code bias variation appears at the Xia1 station with a normal geodetic antenna of B1I/B1C/B3I/B2a/B2b of C21, B3I/B2a/B2b of C24 and B2b of C28 among B1I/B1C/B3I/B2a/B2b of C21/C24/C27/C28/C37. The influence of the BDS-3 satellite-induced elevation-dependent code bias on precision positioning and ambiguity fixing is worth further study using different antennas or receivers.  
third-generation BDS; 40 m dish antenna; satellite-induced code bias variation; characteristics analysis 2020, 20, 1339; doi:10.3390/s20051339
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 4 Results and Discussion
### Shear-Wave Velocity Models  
The Vs velocity profiles shown in Figure 6 have been obtained by inverting the dispersion curves obtained with the f-k analysis. The inversion of the dispersion curves is a delicate process. For example, at some sites, the procedure has to be repeated several times until reaching an acceptable misfit with a reliable and logical shear-wave velocity model. The obtained Vs models are in agreement with the ones obtained in previous works on the Lower- and Middle-Chelif Basins [23; 24; 25; 27].  
Figure 6: Rayleigh wave dispersion curves (**left panels**) and Vs models (**right panels**) estimated for all the analyzed sites from P1 to P15. In the left panels, the experimental dispersion curve is represented by the mean value (black thick line) and the corresponding standard deviation (vertical black bars). On the other hand, the theoretical dispersion curves associated with the different estimated Vs models are represented by dark gray curves (related with the models with a minimum misfit plus 10%) and light gray curves (related with all the tested models). In the right panels, the Vs model with the minimum misfit (black thick line), the Vs models with the minimum misfit plus 10% (dark gray lines), and all the tested models (light gray lines) are shown.  
From the lithological boreholes, three sedimentary layers have been identified, belonging to the Quaternary, Pliocene, and Miocene deposits. The obtained Vs models have allowed for inferring the thickness and Vs values for these layers at the topmost 30 m of the soil column. The topmost layer is formed of alluviums and limestones of the Quaternary age. The thickness of this layer ranges from 2 to 20 m, and its velocity changes between 200 and 380 m/s. The sedimentary deposits forming the second layer belong to the Pliocene. They are mainly sandy and vary in thickness from 8 to 36 m, with a velocity ranging between 400 and 800 m/s. This variation is most probably related to the change in facies between sands and sandstones.  
The final layer represents the Miocene sediments. It is mainly composed of sandstones, limestones, and marls. Its velocity varies between 850 and 1380 m/s. This variation could be related to the lateral variation from hard limestones to soft marls.  
For better illustrating the stratigraphy of the first layers of the velocity model, seven cross-sections (Figure 7) have been established.  
Figure 7: Cont.
## 3 Path Loss Models
### UMi Path Loss Models  
The UMi path-loss model, PL expression, and parameters range, like shadow fading, carrier frequency, distance, and antenna heights, are listed in Table 2. In the UMi environment, the propagation path is divided into two types: street canyon (SC) and open square (OS).
## 2 Design of Low-Pass Filter
### Calibration and Bandwidth Tuning
#### 2.2.1 PVT Compensation  
Resistor tuning banks in the 2 nd and 4 th order filters of both LP and HP paths use discrete-step tuning controls. They are composed of binary-controlled n number of MOSFET resistors connected in parallel with a resistor of fixed value. The implementation of the resistor tuning bank with discrete control is shown in Figure 5a. The capacitor tuning bank utilizes the same structure, with a small modification for the \(\text{C}_{\text{MFB1}}\) bank (refer to Figure 3) of the 2 nd order MFB filter, as shown in Figure 5b, where instead of one, two symmetrically placed MOS switches are used to equalize parasitic load on both summing nodes.  
Careful selection of the quantity of the control bits for digital control of the resistor and capacitor tuning banks is important to minimize the total parasitic value from MOS switches. The value of the resistor \(\text{R}_{\text{fixed}}\) in the resistor tuning bank, as shown in (4), can be determined from the nominal value of the tuning bank R and the tuning range \(\Delta\omega\), which is defined by the specific IC process and the desired margin. In this case, the minimum value of the resistance can be calculated from (5).  
\[\text{R}_{\text{fixed}}=\ R\ \times\ (1\ +\ \Delta\omega/100\%), \tag{4}\]  
Figure 5: Schematic of: (a) resistor tuning bank with discrete control; (b) capacitor tuning bank with discrete control.  
\[\text{R}_{\text{min}}=\text{ R }\times(1\ -\ \Delta\omega/100\%), \tag{5}\]  
where \(\Delta\omega\) is the bandwidth tuning range expressed in percentage. The resistance, which is needed to change the value of the resistor tuning bank by a maximum tuning step size \(\varphi_{\text{R}}\) can then be found:  
\[\text{R}_{\text{p}}=\ 100\%\ \times\ (\text{R}_{\text{fixed}})^{2}/(\text{ R }\times\ \varphi_{\text{R}})\ -\ \text{R}_{\text{fixed}}, \tag{6}\]  
where \(\varphi_{\text{R}}\) is the maximum allowed tuning step size from the wanted frequency, expressed in percentage. The minimum number of MOS-switched resistors \(n\) needed to satisfy requirements of the tuning range \(\Delta\omega\) and tuning step size \(\varphi_{\text{R}}\) can be found using (7)-(9).  
\[n=\left\{\begin{array}{ll}\alpha,&\beta>1\\ \alpha+1,&\beta\leq 1\end{array},\right. \tag{7}\]  
\[\alpha\ =\ \lceil log_{2}(2\ \times\ \text{R}_{\text{fixed}}\times\ \Delta\omega/(\text{ R }\times\ \varphi_{\text{R}}))\rceil, \tag{8}\]  
\[\beta\ =\ \text{R}_{\text{min}}\times\ \big{(}\text{R}_{\text{fixed}}\times \ 2^{n}+\ \text{R}_{\text{p}}\big{)}/\big{(}\text{R}_{\text{fixed}}\times\ \text{R}_{\text{p}}\big{)}, \tag{9}\]  
The values of the individual resistors in the tuning bank can then be found using (10).  
\[\text{R}_{n}=\ \text{R}_{\text{p}}/2^{n}. \tag{10}\]  
A resistor bank with step size of 7% and 5-bit control is used in the proposed filter, which has a tuning range of 42%. The tuning range is derived from the corner models of the 65 nm node process, where value spread of both resistors and capacitors are taken into account.
## 3 Results and Discussion
### Effect of Organic Grinding Aids on the Grinding Property of Converter Steel Slag  
From the above results of studies, it can be seen that the agglomeration phenomenon of fine particles will occur during deep grinding periods, which seriously affect or reduce the grinding efficiency of steel slag. In recent years, organic grinding aids (GA) have been used to improve the grinding efficiency of cement. These researches show that organic GA molecules can be adsorbed on the particles and effectively weaken the agglomeration of fine particle in the grinding process, thus improving the dispersion of powder and grinding efficiency [28; 29; 30; 31]. Therefore, it is a good choice to use GA in the grinding process of steel slag for many manufacturers to improve preparation efficiency of steel slag powder. In additional, on the basis of pretreatment, the grinding property of steel slag can be further improved by GA. In this study, glycerol, which is a common cement GA, was selected as the GA of steel slag. The effects of GA on the grinding property of steel slag were studied from sieving residue, particle size distribution, angle of repose, and particle morphology.  
Figure 8: Particle morphology of ground steel slag powder after 50 min grinding time: (a) untreated steel slag; (b) pretreated steel slag.
## 4 Discussion
For example, analyzing data, such as attendance, grades, and course completion rates, can identify students at risk of falling behind or dropping out, enabling early intervention and targeted support. However, ethical implications surrounding data that may not be relevant or applicable to international students must be carefully considered to avoid inaccurate or misleading predictions. Chatbots powered by AI have the potential to provide timely support to international students struggling with coursework. They can offer personalized feedback on writing tasks, assist with navigational and technological issues, and provide language translation services. While chatbots can be valuable resources, addressing language barriers and cultural differences that may impact the quality and accuracy of the information provided is essential. It is important to recognize that chatbots cannot replace the expertise and guidance of human educators but should be seen as complementary resources. Discussing these AI applications in education highlights the opportunities and limitations associated with their implementation for international students. While AI holds great potential for enhancing the educational experience, it is crucial to balance leveraging AI's benefits and preserving human educators' critical role in supporting students' academic and personal growth. Therefore, AI should be viewed as a tool that complements and enhances the educational process rather than as a substitute for human interaction. When comparing the results of this study with the previous literature, it is evident that the integration of AI in education is a rapidly evolving field. The current applications of AI discussed in this study represent significant advancements that have the potential to enhance learning efficiency and provide customized education support. However, it is essential to acknowledge the limitations and risks associated with AI technologies, such as privacy concerns, cultural differences, language proficiency, and ethical implications. The findings of this study underscore the need for higher education institutions to take an active role in defining the usage and scope of AI in education for international students. By carefully evaluating the benefits and risks, institutions can ensure that AI applications are implemented to maximize their benefits and address the specific needs and challenges of international students. Further research is required to delve deeper into the results, limitations, and future study directions regarding the use of AI in education for international students. This ongoing research collaboration between universities and researchers will contribute to the continued development and implementation of innovative approaches that cater to the unique needs of international students and enhance their learning and success.
# Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation
## 2 Methodological Issues
### Levels of Interpretation  
Traditional 2D eye-tracking has two distinct levels of interpretation, one through eye-tracking coordinates (_point of regard_) and the other through defined areas of interest (AoI). As with eye-tracking in 2D, eye-tracking in 3D VR can observe coordinates in virtual 3D space (_point of fixation (PoF)_--a positional vector coordinate) and virtual 3D objects (as visually defined by 3D meshes). The informational value obtained from eye-tracking in 3D is similar to its 2D counterparts; although, differences exist because of the three-dimensionality of the virtual coordinates and the predefined surface area of the in-engine 3D meshes. Since the virtual 3D space is defined in exact mathematical coordinates, this allows for executing spatial algorithms on top of it.  
A virtual 3D world is composed of virtual objects (3D objects and 3D meshes, depending on the terminology and technology of the 3D engine). The eye-trackable 3D objects have physics boundaries (collision geometry) attached to them. At a detailed interpretation level, the 3D gaze coordinate can be obtained when the user's eye-tracking raycast hits the object collision geometry at a particular positional vector. At the simplified interpretation level, the information that a user is looking at the object, is aggregated.  
The problem with 3D coordinate tracking is that the subsequent data filtering and analysis is demanding on the required implementation, algorithmization, and processing time. Similarly, the problem with 3D object tracking is loss in measurement accuracy and the inherent issues of object-based geometry, for example, the non-uniform sizes and scales of objects, their frequently illogical segmentation/separation, or their partial visibility in a scene.  
Nevertheless, processing eye-tracking data from 3D VR at either the coordinate level or object level does not need to be antagonistic. One approach can complement the other, and hybrids can be implemented. This is discussed in the implementation section (e.g., fixation magnetism, distance-based object segmentation, and fixation algorithms).
## 3 Results
### Verification of Maneuverability Model  
To evaluate the regression prediction ability of the RNN-LSTM model, the RNN-LSTM and BP models were trained synchronously using the above dataset. In the proposed RNN-LSTM model, the five input nodes contain the actual acceleration (\(Acc_{x}\) and \(Acc_{y}\)), velocity (\(v_{x}\) and \(v_{y}\)), and power information of the UAV. Each hidden layer (five layers in total) uses 256 memory units and uses ReLU as the activation function. The output layer consists of four neurons that are activated using the ReLU function to output the four control variables of the UAV. In addition, the timestep parameter in the RNN-LSTM network was set to 6. During model training, the Root Mean Square Error (RMSE) loss function was optimized for the regression task. The Adaptive Moment Estimation (Adam) implementation of gradient descent was used to optimize the weights, and the initial learning rate of Adam was \(7\times 10^{-6}\). Out of a total of 12,800 flight datasets, 10,240 flight data sets were used for model training, with a sample batch size of 320 and a total iteration count of 500, while 20% of the data collected during the last flight in each direction were used as the actual test dataset. Figure 7 shows the comparison of the actual instruction values of the UAV and the predicted instruction values of the RNN-LSTM model in eight different directions with the next 32 consecutive time points. For the UAV flight, the flight mode was set to a fixed altitude, the variables THR and YAW were kept at the median value of 1500, and the variables PIT and ROLL were mainly changed. To further compare the accuracy of different models, Table 1 shows the RMS errors of the BP and RNN-LSTM models. Since the THR and YAW variables are almost unchanged in the fixed-height mode, the corresponding RMS errors are relatively close. The ROLL and PIT variables change a lot, but it can be seen that the RMS errors of the RNN-LSTM model are smaller than those of the BP model. Since the ROLL and PIT variables are directly related to the acceleration values, it can be speculated that the UAV can be controlled more accurately by the RNN-LSTM model.  
Figure 6: A set of the real-time flight data varies with the time at the 90\({}^{\circ}\) direction: (**a**) acceleration; (**b**) voltage; (**c**) velocity; and (**d**) control value.
## 3 Analysis of Results
### Tensile Strength  
To determine the tensile strength, an indirect method was followed. We took the stress-strain curves obtained in the flexural test as the starting point, and we followed the methodology proposed by [PERSON] in his doctoral thesis [27]. First of all, three key points have to be determined, according to Figure 10.  
Figure 9: Flexural strength of UHPFRC for both wastes (GCW-D [14] and GCW-GS).  
Point 1 is the intersection of the curve test with a line of a slope equal to 0.75 of the slope of the elastic zone of the curve. - Point 2 is the intersection of the curve test with a line of a slope equal to 0.40 of the slope of the elastic zone of the curve. - Point 3 is the point of the ascending zone of the curve, with 97% of the highest stress.  
Once we obtained these points, the tensile strength of the UHPFRC can be determined using the following equations [27].  
\[E=2.40\ h\ m\]  
\[f_{t}=\frac{\sigma_{75}}{1.63}\bigg{(}\frac{\sigma_{75}}{\sigma_{40}}\bigg{)}^{0.19}\]  
\[\varepsilon_{t,u}=\frac{f_{t}}{E}\bigg{(}7.65\ \frac{\delta_{loc}}{\delta_{75}}-10. 53\bigg{)}\]  
\[\varepsilon_{t,\ el}=f_{t}/E\]  
\[\alpha=\varepsilon_{t,u}/\varepsilon_{t,el}\]  
\[f_{t,u}=\alpha^{-0.18}\bigg{(}2.46\ \frac{\sigma_{loc}}{\sigma_{75}}-1.76\bigg{)} f_{t}\]  
In these equations, \(E\) represents the elasticity modulus, \(m\) represents the slope of the elastic region of the stress-strain curve, \(h\) is the thickness of the specimen in mm, \(f_{t}\) is the crack resistance of the matrix reinforced with fibers, \(f_{t,u}\) is the ultimate tensile strength, and \(\varepsilon_{t,u}\) is the peak deformation.  
Figure 11 shows the mean values obtained for the tensile strength of UHPFRC. The results obtained replicate those obtained in the preceding test. For a ratio of 35%, an increase in tensile strength was observed, reaching 10.8 MPa for GCW-D and 11.8 MPa for GCW-GS. These are increases of 24% for GCW-D and 35% for GCW-GS. However, for substitutions of 70% or more, the values obtained are similar to those of the control UHPFRC. Except for the 70% GCW-GS where a loss of 22% was observed, the variations are less than 5% and are within the error bars of the test.  
Figure 10: Stress–strain curves and key points.  
The presence of iron oxide explains both the improvement in results with 35% replacement and the deterioration for higher percentages of replacement, as mentioned in the section corresponding to the compression strength test.  
These tensile strength results are similar to those obtained in other studies. The results obtained by [PERSON] [39] showed an improvement in the tensile and flexural strength of UHPFRC when 50% of the cement was replaced by palm oil fuel ash. [PERSON] attributed this improvement to the higher content of amorphous SiO\({}_{2}\) present in this residue, which favors the pozzolanic reaction. [PERSON] et al. [14] observed the feasibility of replacing 100% of the silica fume with granite cutting waste without significant impact on the tensile strength of UHPC. These authors also attributed these good results to the irregular shape of the granite particles.
## 6 User Testing Results and Impact Analysis  
The testing results provided insights into the strengths and areas for refinement of the VA. Correct responses were generated by the VA for 85 out of 1210 questions, resulting in a percentage of 77.27%. The level of user satisfaction was notably high regarding grammatical and syntactic quality, with an average score of 4.425 recorded. This metric indicated that the clarity and professionalism of the VA's responses were found to enhance trust in its functionality among users. The average score for query comprehension was recorded at 4.225, indicating that it was generally perceived that the VA understood the questions posed by users and appropriately addressed their queries, as shown in Figure 8:  
Despite these strengths, the VA faced challenges regarding relevance and context accuracy. Hallucinations were observed in 30% of the responses, with plausible yet inaccurate information generated by the model. This finding underscores the need for refined context integration, as hallucinations impacted the VA's perceived reliability, which could potentially mislead users if left unchecked, as shown in Figure 9:  
The hypothesis that contextual information significantly enhances response accuracy was supported by statistical validation through z-score and \(p\)-value calculations. For instance, the probability of hallucination-free responses was statistically higher with context (\(p<0.001\)), confirming the benefits of the RAG method for mitigating hallucinations. Furthermore, when accuracy was examined, it was found that the VA maintained a 75% accuracy rate in samples with relevant literature, indicating that improved performance was demonstrated by the model when grounded in specific, applicable data.  
The potential adverse effects of incorrect responses on user decisions were further examined in the study. Of the 199 incorrect responses identified, it was determined that 21 could significantly impact users' actions, including the misinformation of students regarding critical academic procedures, as can be seen in Figure 10:  
The importance of reliable information in educational contexts is highlighted by this finding, where it is noted that inaccurate responses could affect a user's academic standing, including meeting attendance requirements or missing exam deadlines due to incorrect guidance.  
Figure 8: Overall User Satisfaction Ratings Across Metrics.  
Figure 9: Proportion of Accurate vs. Hallucinated Responses.