esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The Trail Making Test in Virtual Reality
#### 1.5.1 Interaction Modes  
The ability for users to frequently shift their body position is a defining feature of 3D virtual environments, distinguishing them from traditional 2D scenes. Various interaction methods are available for engaging with VR environments, ranging from sensors, keyboards, and voice input in less immersive scenarios to controllers, hand motions, eyetracking, and head movement in fully immersive VR (IVR) [35; 42]. Given the specific requirements of the Trail Making Test in Virtual Reality (TMT-VR), which necessitates selecting targets from a distance within an immersive environment, the choice of appropriate interaction methods was critical. The TMT-VR, initially developed for assessing cognitive functions in older adults, necessitates interaction modes that minimize the need for extensive body movement or prior technological experience. Hands-free interaction methods, such as head movement and eye-tracking, are particularly promising for this demographic, as they require minimal muscle effort and offer accessible input options [41; 43; 44].  
Eye-tracking, in particular, can be invaluable in scenarios where other modalities are unavailable or impractical, such as when users' hands are occupied or when severe motor disabilities are present [45]. Recent studies have highlighted that physiological signals like eye movements are both intuitive and natural, with a strong association between eye tracking and immersion in VR [46]. Furthermore, research suggests that eyes naturally rotate faster than the head, making eye-gaze interaction quicker when it comes to task completion time compared with head-gaze interaction, especially in tasks with a larger field of view [47]. Lastly, eye tracking has also demonstrated higher accuracy in tasks involving depth perception [48].  
Conversely, head-gaze interaction is noted for its superior precision and stability, making it more effective for tasks requiring exact manipulation. Studies have shown that head-only interaction results in the fastest selection times and accuracy compared to eye-only and hybrid modalities, with participants expressing a preference for its comfort and precise response [49]. Despite their distinct strengths, eye tracking and head-gaze interaction modes are considered complementary or interchangeable depending on the context. However, it has been suggested that significant technological advancements are needed for eye tracking to fully replace head selection due to inherent physiological characteristics and current limitations in eye-tracking data quality [50; 51; 52]. While eye tracking and head-gaze interaction offer minimal effort and intuitive use, the controller input is a widely used method in VR environments due to its familiarity and tactile feedback, making it essential to compare its effectiveness against hands-free methods. Understanding the impact of these different interaction modes can help optimize TMT-VR for diverse user populations, including those accustomed to traditional VR controls.
# Influence of Random Modulated Power Converter on G3 Power Line Communication
## Appendix A  
RPPM can be generated using a C2000 TI microcontroller and MATLAB SIMUINK. For conventional PWM, the ePWM module in SIMULINK can be programmed as follows, and the details can be inferred from [26; 27]:  
\[\text{TBPRD}=\text{TPWM}/\text{2 TBCLK} \tag{1}\]  
and  
\[\text{CMPA}=(1-\text{D})\times\text{TBPRD} \tag{2}\]  
where TBCLK is the Time-Base Clock, TBPRD is the Time-Base Period; TPWM is the period of the PWM, and CMPA and CMPB are the references-comparing A and B values.  
However, the implementation of RPPM is somewhat more complex. In this case, controlling the CMPA and CMPB reference values according to (A3) and (A4) is required.  
\[\text{CMPB'}=\text{CMPB}+\Delta\text{CMPB}\times\text{RAND} \tag{3}\]  
and  
\[\text{CMPA}=2\times\text{TBPRD}-\text{CMPB'}-(\text{D}\times 2\times\text{TBPRD}) \tag{4}\]  
where RAND is a pseudo-random number between \(1\) and \(-1\), CMPB' is the random CMPB value, D is the duty cycle, and \(\Delta\text{CMPB}\) is the change in CMPB reference. The randomization factor, \(\alpha\), or modulation index can be expressed as:  
\[\alpha=\
icefrac{{\Delta\text{CMPB}}}{{\text{TBPRD}}} \tag{5}\]  
Note that the UP/Down counter should be selected where CMPB UP is 'Set' and CMPA DOWN is 'Clear' for this specific RPPM set up. The random PWM starts when the counter counts up and reaches the CMPB reference and stops when the counter counts down and reaches the CMPA value. The SIMULINK model implemented for the specific RPPM considered in this paper is shown in Figure 11.  
The Uniform Random Number block generates uniformly distributed random numbers over an interval between \(1\) and \(-1\). The time interval between samples is the sampling time (Ts).  
Random Number Repetition (RNR) is the number of periods of the RPWM within the same random number. RNP can be expressed as:  
\[\text{RNR}=\frac{\text{Ts}}{\text{TRPWM}} \tag{10}\]  
where Ts is the sampling time of the random number generator SIMULINK block (see Figure 11) and TRPWM is the period of the RPWM signal. For example, in Figure 12, the pulse position changes after two consecutive periods of the RPWM signal (RNR = 2). In general, the higher the RNR, the lower the pulse position variation.  
The Random Number Update Rate (RNUR) is the rate of the switching period over one sampling time, Ts, and can be expressed as:  
\[\text{RNUR}=\frac{\text{TRPWM}}{\text{Ts}} \tag{11}\]  
To obtain the pulse position variation of the RPWM signal, the sampling time, Ts, should be greater than or equal to the period of the RPWM. Therefore, the value of RNUR is \(\leq\)1. In general, the higher the RNUR, the more pulse position variation repetitions of the RPWM signal. RNUR can also be expressed in percentage as:  
\[\text{RNUR}=\frac{\text{TRPWM}}{\text{Ts}}\times 100 \tag{12}\]
## 3 Results and Discussion  
### Non-Isothermal Crystallization Behavior of P3 HBHrk--Effect of Nucleating Agents  
#### 3.1.1 Thermal Behavior of Neat P3 HBHrk  
Figure 1 shows the DSC thermograms of neat P3 HBHrk during non-isothermal crystallization, revealing two distinct crystallization events. During the cooling run at 10 \({}^{\circ}\)C/min (see inset of Figure 1), a weak and broad crystallization peak appeared at 36.4 \({}^{\circ}\)C, with a relatively low enthalpy of 8.9 J/g. This indicates that only a small fraction of the polymer crystallized during cooling. The broad shape and low intensity of the  
peak suggest non-uniform and kinetically limited crystallization, likely due to the random incorporation of 3 HHx comonomer units, which disrupts chain regularity and hinders nucleation and crystal growth.  
Upon reheating, a second crystallization peak appears at 44.7 \({}^{\circ}\)C, with a significantly higher enthalpy of 44.6 J/g. This cold crystallization event accounts for the majority of the crystalline fraction and suggests that more stable and ordered crystalline domains preferentially form from the amorphous solid state rather than directly from the melt. It is noticeable that the cold crystallization peak exhibits a long tail extending beyond 100 \({}^{\circ}\)C, highlighting the difficulty in achieving complete crystallization due to the presence of 3 HHx units.  
A double melting peak was observed in the reheating curve. This behavior is typical of semi-crystalline polymers and is commonly attributed to the coexistence of multiple crystalline phases. The lower melting peak at 130 \({}^{\circ}\)C corresponds to less ordered, imperfect crystals formed during the initial crystallization, while the higher melting peak at 150 \({}^{\circ}\)C is associated with more stable lamellae formed during the heating process.  
These results highlight a significant challenge in processing neat P3 HBHHx for industrial applications, especially its slow and incomplete crystallization behavior. In manufacturing processes such as injection molding and extrusion, where rapid cooling is essential, a limited crystallization rate can lead to incomplete solidification, uneven mechanical properties, and defects such as warping. DSC analysis clearly demonstrates the need for nucleating agents to overcome these limitations. By increasing the crystallization rate, these additives are expected to promote faster crystal growth, improve the uniformity of crystalline regions, and reduce processing times. As a result, the overall suitability of P3 HBHHx for industrial use can be significantly improved.
# Pore Solution Chemistry of Calcium Sulfoaluminate Cement and Its Effects on Steel Passivation
## 2 Materials and Methods
### Corrosion Potential in Simulated Pore Solutions  
Based on the solution analysis, a series of SPSs was designed in consideration of the coupling effects of three variables: alkalinity, sulfate and aluminate concentration. The constitution of each solution is shown in Table 2. All the solutions were prepared using deionized water and analytical grade reagents. The effects of the pore solution on steel passivation were studied by monitoring the corrosion potential of steel in different SPSs. 10 \(\times\) 10 \(\times\) 2 mm steel plates working as electrodes were cut from HRB 400 steel rods. A copper wire, protected by a Teflon tube, was soldered on the surface of each plate. Then, every plate was mounted into epoxy resin such that one end of the plate was open to the solution. The exposed area was 1 cm\({}^{2}\), and the surface was polished by emery papers of progressively increasing grit sizes, followed by a burnish with a 0.5 \(\upmu\)m diamond polishing agent. This gave the sample a mirror finish with no noticeable scratches. The plates were finally rinsed with alcohol and deionized water, then stored in a desiccator after drying.  
Corrosion potential monitoring was performed in a sealed glass container with a calomel reference electrode. A volt meter was applied to measure the corrosion potential of the steel electrodes. After finishing the tests, the steel electrodes were taken out and investigated by a magnifying glass, avoiding mistakes caused by crevice corrosion.
## 2 Methods
### Machine Learning Algorithms
#### 2.5.2 Classification Models  
For the classification task of predicting diabetes status based on combined pressure and temperature features, the following models were employed:* **Extra Trees Classifier**: An ensemble learning method that aggregates the results of multiple randomized Decision Trees to make predictions.
* **Random Forest Classifier**: A tree-based ensemble method that creates multiple Decision Trees for classification and averages their outputs.
* **Extreme Gradient Boosting (XGBoost)**: A highly efficient boosting algorithm used for classification tasks, known for its high performance in structured data.
* **AdaBoost Classifier**: A boosting algorithm that improves classification by combining weak classifiers to form a stronger overall classifier.
* **Gradient Boosting Classifier**: An iterative boosting method that combines weak classifiers to produce a strong predictive model by sequentially reducing the classification error.
* **Naive Bayes**: A probabilistic classifier based on Bayes' theorem, assuming independence between the features.
* **Logistic Regression**: A simple linear classifier used to predict the probability of a binary outcome (diabetes or non-diabetes).
* **Decision Tree Classifier**: A non-linear model that classifies instances by recursively partitioning the feature space based on feature values.
* **Linear Discriminant Analysis (LDA)**: A classification algorithm that models the differences between multiple classes by assuming normally distributed features.
* **Ridge Classifier**: A variant of Logistic Regression that uses regularization to handle collinearity and improve classification.
* **Quadratic Discriminant Analysis (QDA)**: A classifier that assumes each class is normally distributed but with different covariance matrices.
* **K Neighbors Classifier**: A non-parametric method that classifies instances based on the majority class of the k-nearest neighbors in the feature space.
* **Support Vector Machine (SVM) with a linear kernel**: A classification algorithm that creates a linear boundary between classes to maximize the margin between them.
* **Light Gradient Boosting Machine (LightGBM)**: A highly efficient gradient boosting method optimized for classification tasks on large datasets.
* **Dummy Classifier**: A simple baseline model that makes predictions using basic strategies such as stratified or most frequent class predictions.
# City Ditty: An Inmersive Soundscape Sketchpad for Professionals of the Built Environment
## 3 Evaluating City Ditty with a Usability Study
### Results
#### 3.2.4 Themes  
The exit interview data were transcribed and coded in the same manner as the 2019 workshop. However, rather than seeking new themes, supporting evidence for these existing themes were gathered through the study data. Additionally, the user engagement and presence frameworks were also utilized as themes to further understand the participants' experience. These themes are presented in ways which may identify barriers, improve usability, identify new functionalities, and support user engagement.  
Raising Sound Awareness  
Participants reported a positive learning experience which changed the way they think about sound as it applies to their own work. E.g., [P03]: _\"It was interesting. I guess I haven't thought too much about an ideal soundscape, and as I was making it, I realized it's kind of subjective and impossible sometimes [to please everyone], you know. But yeah, I enjoyed that part\"; [P04]: \"So I feel like we are very careful to consider everything else when we're designing a space, but you're right. Noise has been something that's like 'oh it's loud' or 'it's not loud' and that's about the full extent of the thought that goes into it, so it was neat to actually experience the differences and be able to kind of compare that easily.\"_  
Participants also commented on how such as tool could be used to raise awareness or support planning interventions. E.g., [P02]: _\"That's what it would sound like with no cars and it's all like, nice and peaceful. I feel like that would be helpful to get people on board with pedestrianizing more streets. Like, here is traffic noise with people streaming versus nice quiet streets without cars.\"_  
Figure 4: Mean rating and standard error of user engagement and presence scales, collapsing over six participants. FA, PU, AE, and RW represent factors from user engagement’s UES [43], followed by a measure of presence [37]. These self-reported ratings identified factors from the user’s overall experience with the software and served as discussion points for an exit interview. Responses trended toward the positive, with neutral-to-negative ratings investigated further during the exit interview.  
Locating and Visualizing Sound Sources  
Several functions allowed participants to visualize and locate sound sources. Participants appreciated the color-coded highlighting of different sound sources (Figure 1) to help visualize _which_ objects produced sounds, along with the different colors to help differentiate between different categories of sounds (i.e., machinery, nature, and people). They also commented on the ease of locating sound sources through the Find button (which produced a line connecting the user to the sound) but suggested using a more visually appealing line.
## 3 Results  
### Spatial Distribution of Meteorological Drought Characteristics in the Shendong Mining Area  
Meteorological drought has a great impact on the change in surface vegetation [56]. This study focuses on the characteristics of meteorological drought in the land covered by vegetation after removing water bodies and constructions. As shown in Figure 6, on the whole the frequency of meteorological droughts in the Shendong mining area was about 0.78 times/year, the duration of meteorological drought was 6.1 months, and the average drought intensity was 0.92. The blank area mainly presents the characteristics of linear distribution in the middle of the study area, indicating that the vegetation covered in this area is sparse. The incidence of meteorological drought in the study area exhibited a pattern of \"high in the west, low in the east, high in the north, and low in the central region\", with an area in the eastern central region marked in light green, signifying a near absence of meteorological drought in this region. The north and the south sides of the study area showed a red color, indicating that the frequency of meteorological drought in these areas was relatively high, possibly reaching the degree of annual occurrence. The duration of meteorological drought in the study area showed an increasing pattern from southeast to northwest. In the regions with a low frequency of meteorological drought, the duration of drought was shorter. In regions with a higher drought frequency, the drought duration was not necessarily longer. The drought intensity in the study area was high in the middle and low on both sides. However, from the numerical point of view the meteorological drought intensity of the study area is relatively high, both above 0.85, which has a great impact on vegetation growth. Numerous substantial mines in the Shendong mining region are situated in the upper middle section of the study area. The prevalence of meteorological drought in the mining region is notably high, with an extended duration of drought conditions. The meteorological drought intensity near Jitu well and Daliuta well is greater than that of other mining locations. This suggests that the extensive degradation of surface vegetation due to mining activities exacerbates the incidence of meteorological drought and hinders the recovery of surface vegetation.  
Figure 5: Schematic diagram of the transition of a meteorological drought to an ecological drought.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## Abbreviations  
\begin{tabular}{l l} \(q\) & A k-bit prime \\ \(F_{q}\) & A prime finite field \\ \(E/F_{q}\) & An elliptic curve \(E\) over \(F_{q}\) \\ \(G\) & A cyclic additive group of composite order \(q\) \\ \(P\) & A generator for the group \(G\) \\ \(s\) & A secret key of the system \\ \(PK\) & A public key of the system, \(PK=sP\) \\ \(PK_{x},SK_{x}\) & \(x\)'s public key and private key, issued by the blockchain center \\ \(H_{i}(\ )\) & \(i^{th}\) one-way hash function \\ \(ID_{x}\) & \(x\)'s identity, like a universally unique ID code \\ \(r_{x},a,b,c,d,e,f\) & Random numbers of the elliptic curve group \\ \(S_{x}\) & \(x\)'s elliptic curve group signature \\ \(SEK_{xy}\) & A session key established by \(x\) and \(y\) \\ \(E_{x}(m)\) & Use a session key \(x\) to encrypt the message \(m\) \\ \(D_{x}(m)\) & Use a session key \(x\) to decrypt the message \(m\) \\ \(S_{SK_{x}}(m)\) & Use \(x\)'s private key \(SK_{x}\) to sign the message \(m\) \\ \(V_{PK_{x}}(m)\) & Use \(x\)'s public key \(PK_{x}\) to verify the message \(m\) \\ \(c_{i}\) & The _ith_ cyphertext \\ \(Sig_{xy}\) & The signed message for parties \(x\) and \(y\) \\ \(CHK_{x}\) & \(x\)'s verified message \\ \(A\overset{\text{\tiny{$\pm$}}}{=}B\) & Determines if \(A\) is equal to \(B\) \\ \(message\) & The information between the patient and the hospital \\ \(EMR\) & The medical record established by a doctor \\ \end{tabular}
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results
### Antenna Gain  
Most often, the antenna gain is given in relation to the isotropic antenna and is expressed in dBi units. Sometimes it is also given in relation to a dipole antenna and is expressed in units of dBd. The antenna gain depends on its directivity and antenna energy losses are dependent on the material from which it is made. The value of the antenna gain as a function of frequency is shown in Figure 10. The continuous line shows the results of the simulations obtained in CST Microwave Studio, while the dashed line shows the results  
Figure 8: The voltage stand wave ratio as a function of frequency for the proposed antenna working in 5G and LTE-U systems.  
Figure 9: The input impedance as a function of frequency for the proposed antenna working in 5G and LTE-U systems (real part—red line, imaginary part—green line).  
of the measurements made for the physical model of the antenna. The proposed antenna has a maximum energy gain of 4.51 dBi at the resonance frequency of 3.70 GHz for the simulation results and 4.35 dBi at the resonance frequency of 3.70 GHz for the measurement results.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 4 Discussion and Future Work
### Current Research Challenges
Furthermore, creating 5G exclusion zones near airports presents logistical challenges. Using predictive tools to define interference zones dynamically and collaborating with telecom providers to optimize base station configurations could provide a viable solution. The limited testing of GNSSs under various conditions also poses risks to aviation reliability. Conducting controlled experiments in high-interference environments, such as those caused by multipath effects, would allow for better GNSS validation. Similarly, UAV-based RFI monitoring systems lack certification for airworthiness in aviation. Developing lightweight UAVs with certified airframes for regulatory compliance is essential before they can be fully deployed in aviation applications. Further research is required in understanding UHF path loss in long-range air-to-ground communication systems, as current models lack sufficient empirical data. Field experiments that assess signal behavior over various environmental conditions can improve aviation communication reliability. Moreover, cognitive radio systems, which dynamically adjust frequency usage, require operational testing to ensure their effectiveness in managing real-time interference. Another challenge is insufficient training for ILS maintenance personnel in handling advanced signal analysis techniques. Implementing standardized training programs focused on spectrum analyzer usage and interference mitigation strategies could improve maintenance efficiency. Additionally, higher-order intermodulation effects on navigation systems need more in-depth analysis, as interference from intermodulation products could disrupt critical signals like ILS and VOR. Employing frequency domain analyzers for in-depth assessment would help in mitigating such risks. Testing UAV RF immunity in high-interference environments is another underdeveloped area, as UAVs must be designed to withstand radio frequency noise while performing sensitive aviation-related tasks. Introducing multi-channel redundancy can improve UAV resilience in these environments. Additionally, real-time 5G interference mitigation during flight requires further exploration. Onboard interference filtering mechanisms could help dynamically adjust altimeter readings in response to signal disruptions. The spectrum allocation strategies in aviation often lack data-driven insights. Establishing a centralized repository for RFI incidents and applying predictive analytics to spectrum allocation policies can ensure more efficient frequency management. By addressing these gaps with advanced research, improved regulations, and technological innovations, aviation systems can become more resilient to RFI threats, ensuring safe and reliable global air travel.