esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### EEG and ECG Patterns
According to the authors, positive valence and negative arousal were identified as calmness, positive valence and positive arousal were identified as happiness, negative valence and negative arousal were identified as sadness, and negative valence and positive arousal were identified as fear. Because only data from one subject were analyzed, the results of this study are not discussed to avoid bias and perhaps the presentation of inaccurate information. Even though the importance of a learner's emotional state to effective learning is highlighted in recent research, the authors did not elaborate on or explain the reasons for choosing the above four emotions and not other emotions, e.g., hopefulness, confusion, or frustration, which are well known to affect the learning process and are referred to as learner's emotions in several studies, e.g., [33; 81]. The discussed studies in this section showed the feasibility of using EEG to detect interest. The number of EEG channels, in this case, ranged between 1 and 33 channels. The majority of these channels were placed in the frontal brain regions which are claimed to play a significant role in detecting interest. Several methods and classifiers were employed for analyzing and classifying EEG data and are summarized in Table 1 with their respective accuracy. ECG modality was also employed successfully to detect interest, but it had lower performance than EEG. Differences in experimental designs should be considered in interpreting the result because certain stimulations might be more effective than others, e.g., using video clips from movies compared to using tests and educational content.
## 3 Results and Discussions
### Moisture Susceptibility Analysis  
Rolling bottle test was used to check the moisture susceptibility of asphalt binder with aggregate. In Figure 16, the percentage loss of aggregate at different durations with unmodified and CNT-modified bitumen is represented. In Figure 17, it can be observed that, with the addition of CNTs in bitumen, the percentage loss of coating was decreased, which means the adhesion between aggregate and bitumen under moist conditions tended to increase in the presence of CNTs. This addition of 1% and 3% CNTs in bitumen improved the coating of binder by 20% and 40%, respectively, at 72 h. According to [PERSON] et al. [49], stiff binder shows better resistance against moisture. This improvement in coating may be attributed to the presence of CNTs in asphalt binder, which made bitumen stiff, as well as to the hydrophobic nature of CNTs [37].
## 3 Results  
### SA Application Decreased Fruit Cracking Incidence and Severity  
In year 1, controls (0 mM) consisted of 15% sound fruit (C0), 21% slightly cracked (C1), and 65% highly cracked (C2) (Figure 1a). SA application increased the percentage of fruit in categories C0 and C1 (Figure 1a). This effect was more prominent at higher concentration (2 mM). In year 2, controls (0 mM) comprised of 65% sound fruit (C0) and 35% slightly or highly cracked (C3; Figure 1b). Similar to year 1, SA application (2 mM) increased the percentage of fruit in category C0 in year 2.  
In each experiment, the \(\chi^{2}\) tests showed highly significant effects (\(p<0.001\)). Consequently, to further examine the effect of concentration in each category, data were separately analyzed for each experiment by one-way ANOVA. The analyses showed significant effect of SA concentration in all categories in both experiments. The concentration effect in C1 was significant at \(p_{sa}<0.01\), while in all the remaining categories was at \(p_{sa}<0.001\) (Table 2).  
In years 1 and 2, the higher concentration (2 mM) increased the frequency of sound fruit (C0) by 2.6- and 1.4-fold, respectively, in comparison to the respective control. In year 1, treatment with 2 mM SA decreased the highly-cracked fruit by 2.3-fold and increased the percentage of marketable fruit (C0 + C1) by 2-fold, as compared to controls. Although both SA concentrations (year 1) prevented cracking, the higher one was more effective, resulting in higher C0 and lower C2 percentages.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene** & **Encoded Enzyme** & **Sequence** & **Sequence 5\text
## 1 Introduction
To date, in field pilot scale, the effectiveness of urban stormwater treatment was tested using the following reactive materials and their specialized mixtures: peat [14], lignite coke [15], vermiculite [17; 20], granulated Al\({}_{2}\)O\({}_{3}\) and Fe\({}_{2}\)O\({}_{3}\) and aerated concrete [18], activated concrete [23], activated carbon [17; 18], zeolite [17; 19; 21], sandy loam, perlite, compost and hardwood mulch [20], tire crumb [7], zero valent iron [6; 21], silica spongolite [21], limestone sand [21; 24] and gravel [22]. These investigations explicitly point out that the application of reactive materials in infiltration systems of stormwater runoff is effective and should be introduced in practice in urbanized areas. Systems of engineered natural attenuation (permeable treatment zones and sealing systems) located in the soil subsurface layer are vulnerable to the impact of external factors such as temperature. As indicated by [PERSON] and [PERSON] [25], soil temperature determines both the type and efficiency of physical and chemical processes in soil. It is assumed that the zone of daily temperature oscillations in Polish soils varies from 0.8 to 1.0 m below surface level, with the maximum throughout the year being between 5 and 8 m below surface level [26]; however, databases with soil temperatures at various depths are not commonly accessible for many regions and the measurement of this parameter is time-consuming and expensive [27]. The temperature of the medium below surface level depends on numerous factors, such as incident solar radiation, rainfall, and ambient temperature fluctuations, but also local vegetation cover, type of soil, water presence and its movement [28; 29]. Due to this, the flow of contaminated stormwater through treatment zones may take place in a wide range of temperatures, from around 0 \({}^{\circ}\)C to summer temperatures. Therefore, temperature may significantly influence the daily, monthly and annual variability of the removal efficiency of transported contaminants. Most reports are focused on the analysis of the intensity of contaminant removal at ambient temperatures [30; 31; 32; 33] or on assessing the impact of temperature increases above room temperature on the intensification of the process [34; 35; 36; 37]; fewer studies indicate how temperature decrease, even to 0 \({}^{\circ}\)C, influences the ability of reactive materials to remove contaminants [38; 39]. The main aim of the studies here was to determine the qualitative and quantitative impact of temperature on the sorption of selected heavy metals (Cu and Zn) and dissolved forms of biogenic substances (NH\({}_{4}\)-N and PO\({}_{4}\)-P) on five different reactive materials. Contaminant removal was determined both at low concentrations and at concentrations sufficient to attain a maximum loading capacity, significantly exceeding those that are characteristic of stormwater.
## 2 Material and Methods
### General Thermodynamic Indexes  
The governing equations of the components are described in the above sections. Both the energy and exergy balance expressions of each component are revised in Table 2.  
Taking into account all the aforementioned equations, the system is evaluated thermodynamically. This evaluation is made through the main energy and exergy indexes, which are presented in this section. First, the overall net electricity load (\(P_{el,net}\)), which is absorbed by the grid, can be described as below:  
\[P_{el,net}=P_{el,ORC}-P_{el,PEM}-P_{el,comp} \tag{24}\]  
The system's energy efficiency (\(\eta_{en}\)) is calculated as:  
\[\eta_{en}=\frac{P_{el,net}+LHV_{hydrogen}\cdot\dot{m}_{hydrogen}}{Q_{solar}} \tag{25}\]  
The system's exergy efficiency (\(\eta_{ex}\)) is calculated as:  
\[\eta_{ex}=\frac{Ex_{el,net}+ex_{hydrogen}\cdot\dot{N}_{hydrogen}}{Ex_{solar}} \tag{26}\]  
More specifically, (\(LHV_{hydrogen}\)) is the hydrogen low heating value, and the hydrogen exergy (\(ex_{hydrogen}\)) is defined by the following expression [36]:  
\[ex_{hydrogen}=ex_{hydrogen,chem}+ex_{hydrogen,phy} \tag{27}\]  
(\(ex_{hydrogen,chem}\)) is the chemical exergy of hydrogen which is equal to 236.1 kJ/mol [39], and (\(ex_{hydrogen,phy}\)) is the physical exergy which is calculated as below [36]:  
\[ex_{hydrogen,phy}=(h-h_{0})-T_{0}\cdot(s-s_{0}) \tag{28}\]  
Enthalpy (\(h\)) and entropy (\(s\)) are defined taking into account the electrolyzer conditions, while subscript \"0\" refers to the reference ambient conditions.
## 4 Results and Discussion
### TS-InSAR Deformation Monitoring Results
#### 4.3.3 Comparative Analysis of TS-InSAR Results: \"Fucheng-1\", \"Shenqi\", and Sentinel-1A
The analysis of annual deformation rates and cumulative deformation values reveals consistent subsidence trends from October 2024 to April 2025 across all monitoring points G1-G5, with G1 and G2 demonstrating linear deformation patterns and G2 accumulating the maximum deformation of \(-\)65.32 mm, whereas points  
Figure 14: Time-series PS-InSAR deformation monitoring results for the Jinchuan mining area based on “Fucheng-1” and “Shenqi”. CR01-CR11 represent the locations of deployed CRs, G1–G5 are selected sites within the mining area, and L1–L3 are profile lines for annual deformation rates. G3-G5 exhibit stabilization tendencies after March 2025. We recommend that subsequent monitoring of the mining areas be carried out, with frequent imaging tasks. A profile analysis of annual deformation rates was performed for areas P1 and P2 using 50-m buffer zones along the line segments L1-L3, with the buffer zones containing 21,100, 7900, and 33,700 PS monitoring points, respectively, as shown in Figure 16. The deformation rate profiles reveal a funnel-shaped subsidence pattern in both P1 and P2 areas, while demonstrating relative stability at the 2200-m position along the profile line L3 between these areas, indicating that mining activities in P1 and P2 have not induced large-scale deformation in the intervening region. \begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Deformation (mm)** & **Rate (mm/yr)** \\ \hline G1 & \(-\)52.45 & \(-\)83.00 \\ G2 & \(-\)65.32 & \(-\)115.28 \\ G3 & \(-\)30.81 & \(-\)62.84 \\ G4 & \(-\)18.31 & \(-\)37.70 \\ G5 & \(-\)13.29 & \(-\)24.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Time-series PS-InSAR deformation rate and cumulative deformation results at points G1-G5 based on the “Fucheng-1” and “Shenqi“ dual-star constellation. Figure 16: Spatial distribution of line-of-sight deformation rates and profile line deformation rate results for buffer zones L1–L3 (**a–f**). Figure 15: Time-series PS-InSAR line-of-sight cumulative deformation results for the dual-star constellation at points G1–G5 in the Jinchuan mining area.
# Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
## 4 Discussion
### Availability Evaluation of Smartphone Positioning in a Forest Environment  
Some scholars believe that survey-GNSS devices must be selected when the positioning accuracy is required to be better than 10 m [59]. However, the results of this study show that, in SPP mode, the average accuracy of single-frequency smartphones at 57 test points is about 10 m and that of dual-frequency smartphones is about 9 m. After correcting by PPP mode, the average accuracy of single-frequency smartphones in the forest environment (DRMS) reaches 3.7 m and that of dual-frequency smartphones is 2.9 m. The horizontal accuracy of the recreational-grade receiver is 4 m in a high-canopy-openness environment and 5 m in a medium- and low-canopy-openness environment. The horizontal accuracy of smartphones in PPP mode has been comparable to that of some recreational-grade receivers in an environment of high-canopy openness. The positioning accuracy of smartphones even shows certain advantages, which can be used as an alternative to projects with low-positioning-accuracy requirements in forestry tasks, such as under forest navigation, preliminary positioning of points of interest, and forest route planning [43]. In addition, to ensure that the positioning data of this experiment will not be interfered with by the base station's signal, all smartphones adopt flight mode for positioning observation. If the mobile communication function is enabled, AGNSS technology will play an auxiliary role in improving positioning accuracy [60; 61] and the offline availability of smartphones will be further improved.  
In this study, the raw data of smartphone location are modified by post-processing PPP mode. The test results show that the smartphone forest environment combined with PPP post-processing technology can achieve good location accuracy. Compared with relatively high-precision positioning methods, such as RTK, PPP technology does not require the cooperation of reference stations, so the measurement distance is not limited by base stations [62; 63]. Suppose we combine the real-time PPP applications that have appeared on the Android platform, such as PPPAnd (a real-time precision single-point positioning (PPP) software on the Android platform developed by [PERSON] et al. [64]) or the real-time precision single-point positioning software based on Android mobile terminals developed by [PERSON] et al. [65] and [PERSON] et al. [66]. In that case, we can obtain a high level of real-time accuracy.  
This study proves that smartphones, combined with modern positioning technology, have achieved a very similar accuracy level with recreational-grade receivers without differential correction in a forest and reached a positioning accuracy equivalent to that of recreational-grade receivers and older map-grade receivers. It proves that low-end receivers can be replaced by mobile intelligent devices [67]. It should be pointed out that advanced survey-grade receivers are still needed under a very dense canopy to carry out high-precision positioning [68].
## 3 Results
### Measurements and Model Validation
#### 3.2.2 Computer Model Validation
In Appendix B, simulations showing the effect of using \"open add space\" boundary conditions vs. using an unlimited phantom in x and y axes (\"periodic\" boundary conditions) are shown to reinforce this idea. The results certify that the methodology and the simulation tool were working properly with the aforementioned assumptions. Consequently, the computer model was representative of a whole body once the theoretical dielectric values [25; 26; 27] were employed in the analysis (as in Section 3.1) and it allowed to extract conclusions about the significant size of the phantom depending on the operational frequency.
###### Abstract  
An equalizer based on a recurrent neural network (RNN), especially with a bidirectional gated recurrent unit (biGRU) structure, is a good choice to deal with nonlinear damage and inter-symbol interference (ISI) in optical communication systems because of its excellent performance in processing time series information. However, its recursive structure prevents the parallelization of the computation, resulting in a low equalization rate. In order to improve the speed without compromising the equalization performance, we propose a minimalist 1D convolutional neural network (CNN) equalizer, which is reconverted from a biGRU with knowledge distillation (KD). In this work, we applied KD to regression problems and explain how KD helps students learn from teachers in solving regression problems. In addition, we compared the biGRU, 1D-CNN after KD and 1D-CNN without KD in terms of Q-factor and equalization velocity. The experimental data showed that the Q-factor of the 1D-CNN increased by 1 dB after KD learning from the biGRU, and KD increased the RoP sensitivity of the 1D-CNN by 0.89 dB with the HD-FEC threshold of \(1\times 10^{-3}\). At the same time, compared with the biGRU, the proposed 1D-CNN equalizer reduced the computational time consumption by 97% and the number of trainable parameters by 99.3%, with only a 0.5 dB Q-factor penalty. The results demonstrate that the proposed minimalist 1D-CNN equalizer holds significant promise for future practical deployments in optical wireless communication systems.  
artificial intelligence; machine learning; neural network; knowledge distillation; nonlinear equalizer; biGRU; 1D-CNN +
Footnote †: journal: _Sensors_**2024**, 24, 1612. [[https://doi.org/10.3390/s24051612](https://doi.org/10.3390/s24051612)]([https://doi.org/10.3390/s24051612](https://doi.org/10.3390/s24051612))
###### Abstract  
With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs.  
Keywords:network latency; teleoperation; connected and autonomous vehicle (CAV); latency mitigation strategies--perception and control +
Footnote †: journal: Sensors
# Research on a Dynamic Calibration Method for Photogrammetry Based on Rotary Motion
## 4 Experiment and Analysis
### Test Procedure
The deviation in the \(Z\) direction can be understood as the amount of change in the direction of the motor axis of the target at the end of the swing arm during the rotation, and the change in this direction is perpendicular to the linear velocity direction at the end, so there is almost no effect. Figure 17: The relationship between the distance between two points and the rotational speed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Speed (r/s)** & \multicolumn{2}{c}{**Distance**} \\  & **Mean Value (mm)** & **Standard Deviation (mm)** \\ \hline
0 & 242.039 & 0.004 \\
0.5 & 241.712 & 0.037 \\
1.0 & 241.720 & 0.038 \\
2.0 & 241.723 & 0.036 \\
3.0 & 241.727 & 0.035 \\
4.0 & 241.725 & 0.035 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurement results of the distance between two targets at different speeds. From the above data and analytical results, the following conclusions can be drawn:  
* The radius of the circular trajectory of the target movement has no obvious relationship with the movement speed, and the difference does not exceed 10 \(\upmu\)m. The experimental data are consistent with the relative position of the target point in the design of the device, and the error is mainly caused by the change in the verticality introduced by the rotating shaft system. * The experimental data show that with an increase in the rotational speed, the RMS value of the circular trajectory fitting error increases to a certain extent, which indicates that the rotating motor selected for the calibration system has a certain rotary centrifugal error under high-speed motion. At the same time, the structure of the circular trajectory-generating device needs to be further strengthened to support its rigidity. * When the two targets are in motion, the distance between the two points is calculated by the photogrammetry system, that is, the length decreases, and the decrease is about 0.2 mm. The decrease has no obvious relationship with the rotational speed, which is a system error. It is found that the reason for this phenomenon may be the shooting synchronization error between the two cameras of the binocular photogrammetry system, that is, there is a certain time difference in the shooting time of the two cameras for a dynamic target. In addition, when the target is in motion, the standard deviation of the distance between two points calculated by the photogrammetry system increases relative to the static state. * When the rotational speed is less than 4.0 r/s, the speed value calculated by the photogrammetry system is consistent with the standard value, and the speed fluctuation
# Integrating Interactive Detection of Code Smells into Scrum: Feasibility, Benefits, and Challenges
## 6 Online Validation: Insights from a Controlled Experiment
### Validity Threats  
Subsequently, we address the identified threats in connection with the study and outline the undertaken measures for their mitigation, in accordance with the classification scheme proposed by [PERSON] et al. [34].  
_Construct Validity_: Participants' prior experience and technical knowledge may vary, potentially influencing the results and data interpretation. To mitigate this, teams were balanced in terms of experience and background to avoid biases. Clear guidelines were provided to ensure consistency in development practices, even within the Scrum framework.  
_Internal Validity_: Participants' prior knowledge of code smell detection could influence their decisions during the experiment. To minimize this, detailed training was provided on the detection techniques and the software development approach that integrates the ID into Scrum, aiming to level participants' knowledge. Additionally, independent experts validated code smell instances using transparent and objective criteria.  
_External Validity_: The simulated bookstore domain system may not fully represent a real-world development environment, potentially limiting generalizability. However, its selection was based on simplicity and suitability for the experiment's timeframe. The presence of researchers during tasks could have influenced participants' behavior, but it was emphasized that decisions and actions should reflect real software development practices.  
_Conclusion Validity_: The study's sample size of 24 participants may limit generalization to a larger population. However, appropriate methodological procedures were implemented to ensure internal validity within the experiment's context. The use of student participants facilitated recruitment and provided valuable insights. Limitations also exist concerning the specific language (Java) and technology (Spring framework) used, potentially affecting generalizability to different software development contexts. The simulated system's characteristics might not fully align with real-world scenarios, but efforts were made to minimize this effect by selecting a simple and representative simulation