esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
This may be attributed to the difference of the detection methods. For XRD, the scan beam is of high energy and its detection depth is over dozens of microns, which means the residual stress measured from XRD is the average residual stress of the whole film [44]. In contrast, for Raman spectra, the scan beam is low energy and its detection depth is only around the surface; hence, the residual stress extracted from the Raman spectra presents the residual stress of the film surface [45]. It is well known that the residual stress of the film surface is naturally smaller than the average residual stress of the whole film thanks to the absence of covered materials [45; 46]. Possibly due to the release of the residual stress generated from annealing process as well as the different detection location [47], the residual stress (0.909 GPa) of the ZnO film surface on the quartz glass substrate was significantly smaller than that calculated from the XRD detection (3.312 GPa). Furthermore, the A\({}_{1}\)(LO) mode is normally shown in the ZnO thin film composed of nano-particles [48]. It has been demonstrated that the FWHM value of the A\({}_{1}\)(LO) mode is closely associated with the degree of damage or disorder in the crystal lattice [49]. Thus, the FWHM value of this mode is considered as an index of the degree of damage or crystal disorder in the as-received ZnO thin films. As can be seen in Figure 7, the FWHM value of the A\({}_{1}\)(LO) mode was about 68 cm\({}^{-1}\) for the ZnO thin film on the quartz glass substrate, which was much larger than that for the ZnO thin film on the sapphire (001) substrate(\(\sim\)15 cm\({}^{-1}\)) but smaller than the value of a previous report (\(\sim\)95 cm\({}^{-1}\)) [48]. Figure 7: Raman spectra of the as-received ZnO thin films grown on the quartz glass and sapphire substrates, respectively. The result evidently indicates crystal disorder in the as-received ZnO thin film on the quartz glass substrate was much poorer than that on the sapphire (001) substrate, which is in good accordance with the observations from the SEM images (Figure 2a,b).
## 2 Materials and Methods
### Statistical Analysis and Correlation Assessment
#### 2.6.4 Statistical Evaluation of Error  
In line with the recommendations from the American Statistician [40], this study refrained from using \(p<0.05\) as a threshold for defining statistical significance and avoided the term \"significant\". Instead, all the testing of hypothesis was conducted using \(p\)-value to Bayes Factor Bound (BFB) calibrations, following the guidelines of [PERSON] and [PERSON] [41]. False-Positive Risk (FPR) values, as advocated by [PERSON] [42], were also calculated to assess the ratios between the Null Hypothesis (H0) and Alternative Hypothesis (Ha).  
\(p\)-values were evaluated using a robust threshold of 0.003 (3\(\sigma\)) for more conclusive results. This threshold corresponds to an FPR of 4.5 \(\pm\) [1.2, 15.9]%, with priors set at 0.5 \(\pm\) [0.2, 0.8] [43], unless otherwise stated.  
In addition to using \(p\)-values to evaluate statistical hypotheses, effect size was calculated using the Kruskal-Wallis \(\varepsilon^{2}\) statistic in [44], and 95% confidence intervals for correlation analysis [45].  
For model evaluation, Root Mean Square Error (_RMSE_), Mean Absolute Error (_MAE_), and Mean Absolute Percentage Error (_MAPE_), as in Equation (4), were employed as the metrics to measure the discrepancy between predicted and actual Land Surface Temperature (LST) values, provided their adequateness to evaluate mathematical models [46]:  
\[\begin{array}{c} RMSE=\sqrt{\frac{\Sigma\left(\frac{\dot{P}_{i}-P_{i}}{n} \right)^{2}}{n}},\quad\quad MAE=\frac{1}{n}\Sigma_{i=1}^{n}\left|\dot{P}_{i}-P_ {i}\right|,\\ MAPE=\frac{1}{n}\Sigma_{i=1}^{n}\left|\frac{\dot{P}_{i}-P_{i}}{P_{i}}\right| \times 100\end{array} \tag{4}\]  
where \(\dot{P}_{i}\) and \(P_{i}\) represent the measured and predicted values of the LST for the 50 m road/street segment \(i\), respectively, and \(n\) is the total number of roads/streets segments considered in the validation dataset.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 4 Hybrid Engineered Water-Polymer Flooding (EWFF)
### Recovery Mechanisms
#### 4.2.7 Impact of Polymer on EW Performance  
While EW improves the polymer stability, the polymer can also have positive or negative effects on the EW performance, which can be qualitatively assessed using electrokinetic surface potential (zeta potential) and contact angle measurements. The zeta potential is a measure of the overall surface charge in a system and is an indicator of wettability alterations in the system. A higher negative zeta potential for carbonates shows a higher net repulsion force between the carboxylic oil material and carbonate surface, resulting in oil detachment and a wettability shift towards being water-wet. In cases where it is not possible to measure the zeta potential, the electrophoretic mobility can be used as an indication of wettability alteration as it is directly proportional to the zeta potential.  
Electrophoretic mobility and contact angle experiments were performed by [PERSON] et al. [180] to show the positive impact polymer has on the LSW performance. The HPAM polymer solution prepared using smart water (10-fold dilution of injection water) resulted in the highest decrease in electrophoretic mobility, and hence in the zeta potential, as well as the greatest wettability shift towards the water-wet state (contact angle of 88.9\({}^{\circ}\) for smart water-polymer compared to 102.4\({}^{\circ}\) for smart water only), for an aged carbonate reservoir material (Figure 15). The reasons for the reduced electrophoretic mobility  
Figure 14: Studies showing reductions in the required polymer adsorption using LSW.  
Figure 13: Polymer retention in the presence of (**a**) injection water and (**b**) smart water [180].  
and contact angle in the presence of polymer could be the negative charges on the polymer backbone or adsorption of anionic polymer molecules onto the positive carbonate rock particles suspended in the solution. These experiments confirmed that polymer has no adverse effects on the LSW the wettability alteration capability; in fact, the polymer can slightly enhance the LSW effects.
# Thermomechanical Behavior of Textile Reinforced Cementitious Composites Subjected to Fire
## 3 Results and Discussion
### Results from Coating Burn-off Tests  
The most critical parameter seems to be the failure of the bond between the matrix and the reinforcement, which is caused by the coating burn-off. Thus, some additional tests were performed, where samples of textiles were exposed to several temperatures and their weight was measured before and after exposure. Thus, the mass loss of the coating could be calculated.  
It is important to note that:  
* The equipment that was used was a small electrical furnace with the capacity to reach 1000 \({}^{\circ}\)C.
* Apart from the temperature, the time of exposure also plays a significant role. The heating rate in the middle of the specimens (closest measurement to the level of the effective depth, thus, the fibers that are of interest) was almost the same in both the 15-min and the 30-min fire tests,  
Figure 6: (**a**) Temperature measurements from fire test 1; (**b**) temperature measurements from fire test 2; (**c**) temperature measurements from fire test 3.  
equal to 18-19 \({}^{\circ}\)C/min. Therefore, the heating time was decided each time according to the target temperature and a standard heating rate of 18 \({}^{\circ}\)C/min.
* The cooling down of the specimens, after reaching the maximum temperature, was performed with a rate of 1.5 \({}^{\circ}\)C/min until a temperature of 200 \({}^{\circ}\)C, which is also a good approach of the cooling down rate that was measured at the 15-min fire test.
* The initial mass of the coating was calculated based on the weight of the textiles before and after coating, as provided by the technical datasheets.  
From the results which are presented in Figure 7, it was observed that the critical temperature after which the mass loss is becoming significant is close to 300 \({}^{\circ}\)C. It was also observed that after 500 \({}^{\circ}\)C, the coating was almost completely burnt-off.
## 4 Numerical Simulation Applied to the FFF Process
### Thermal-Mechanical
Their study showed that smaller time steps improve temperature accuracy but have minimal effect on residual stress, while finer meshing improves stress gradient predictions but significantly increases computational cost. Their model was validated against experimental data for a planar spring and a bridge model, showing good agreement, with vertical displacement errors of 23% and 12%, respectively. However, the study highlighted the need for better adhesion models to capture first-layer interactions and improved methods for predicting part deta  
Figure 14: Residual stress distribution in FFF-manufactured PLA/wood biocomposite cubes with varying layer thicknesses and extrusion temperatures. Adapted with permission from [56]. The impact of temperature variations on residual stress and part deformation was also investigated by [PERSON] et al. (2019) [58], who developed a 3D thermo-mechanical model to predict temperature gradients, residual stresses, and delamination in FFF-printed polymer composites. Their results showed that rapid temperature changes between layers increase stress concentrations, raising the risk of delamination. Their analysis also found that filament deposition produces higher temperature gradients than layer-by-layer printing, leading to more distortion and stress accumulation. Experimental validation showed temperature deviations below 5%, confirming the model's accuracy. However, they emphasized the need for better modeling of anisotropic mechanical behavior and complex thermal interactions to improve stress predictions. These studies jointly confirm that residual stresses and thermal deformations remain significant challenges in FFF, requiring a combination of multi-scale modeling and optimized print parameters to mitigate these limitations. The next section will explore the role of material properties and their characterization, detailing how processing conditions, fiber integration, and post-processing techniques influence final part performance.
# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
## 3 Results and Discussion
Figure 9c-e show how the algorithm retrieves the mineral concentration, aerosol volume fraction, and volume fraction of fine-mode aerosol particles, respectively, for varying values of the SNR and four different SZA values. For all values of the SZA and for \(\mathrm{SNR}\geq 100\) the algorithm is seen to predict the three parameters with correlation coefficients greater than 0.88, 0.77, and 0.57 for the mineral concentration, volume fraction, and volume fraction of fine-mode aerosol particles, respectively. It follows from Figure 9 that the algorithm performance increases when the SZA or the level of white noise is reduced. Figure 8: Effects of SZA on the TOA radiance contribution from the ocean. \(\overline{R}_{\mathrm{TOA}}\) and \(\overline{R}_{\mathrm{OCEAN}}\) are the average TOA radiance and average radiance from the ocean, respectively. Figure 7: Comparison between input values and retrieved values for the aerosol volume fraction for different solar zenith angles (SZAs).
# Collaborative Mobile Robotics for Semantic Mapping: A Survey
## 2 Single-Robot Semantic Mapping
### Data Acquisition and Processing
#### 2.1.1 Perception-Based Data Acquisition
The depth map provides precise information on the distance of the detected elements. The visual sensors are frequently chosen for the acquisition of semantic information because they allow a perception of the environment close to that perceived by the human eye. They also allow the collection of low-level data, such as points, and high-level data, such as object categories [9].
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 3 Node Weight Allocation Strategy Based on Data Fusion
### Node Weight Allocation Strategy
Similarly, the complexity of the channel perception result calculation process is mainly influenced by the number of nodes within the one-hop range. In this context, since the number of characteristic parameters for each node increases to 5 in the data fusion process, in contrast to the single characteristic parameter used in the existing method, this method indeed increases the amount of calculation. Nonetheless, the overall computational complexity of this method is still constrained by the number of nodes within the one-hop range, rather than the total number of nodes in the network. Consequently, when this method is applied to channel perception in large-scale networks, despite the fact that the computational complexity is higher than that of the existing methods, the limited number of nodes within the one-hop range ensures that it does not significantly elevate the requirements for hardware processing capabilities.
## 5 Results and Analysis
### Cold-Start Problem Analysis  
In the task of next POI recommendation, the cold-start problem presents a significant challenge, particularly in providing accurate recommendations for inactive users with sparse historical data. To evaluate the effectiveness of our model in handling cold-start scenarios, we calculated the number of user trajectories and categorized users based on their trajectory counts. Users in the top 30% are defined as active, those in the bottom 30% as inactive, and the remainder as normal.  
We then compared the performance of our model with the STHGCN model, which has demonstrated the best performance in addressing the cold-start problem in prior studies. The results of this comparison are presented in Figure 4.  
For both the NYC and TKY datasets, our model demonstrates notable improvements across all user groups, as shown in Figure 4, particularly in the Recall@5 and Recall@10 metrics. This suggests that while our model enhances the accuracy of the top recommendation, it excels at identifying a broader set of potential POIs, effectively capturing users' diverse interests. Even in the challenging inactive user group, our approach significantly  
Figure 4: Performance comparison between STHGCN and our HGL model for different user groups on NYC and TKY datasets. Users are categorized as active (**top** 30%), normal (**middle** 40%), or inactive (**bottom** 30%) based on their trajectory counts.  
outperforms the STHGCN model, showcasing its capability to handle sparse data and effectively utilize limited interactions.  
To highlight the interpretability of our hierarchical structure, we present a real-world example demonstrating _preference transfer_ via shared _global-level semantics_. User #124 from the _TKY_ dataset is a cold-start user with only a limited number of check-ins, such as an _art museum_, a _government building_, and several _road_ or _park_ locations. Importantly, this user has never visited _POI_ #114, a train station located in the southern part of Tokyo. However, _POI_ #114 belongs to _Cluster_ #37, a densely populated, transport-oriented _global-level_ cluster containing multiple train stations (e.g., _POI_ #4902, _POI_ #6451, _POI_ #212, and _POI_ #114 itself). This cluster corresponds to real-world transit hubs around Oimachi, Omori, and Kamata--areas frequently traversed by commuters. During training, the global-level embedding \(\mathbf{h}_{\text{C}_{37}}^{(2)}\) of _Cluster_ #37 aggregates spatio-temporal patterns from all contributing users, forming a general representation of transport-related mobility behavior. When generating recommendations for user #124, the model leverages \(\mathbf{h}_{\text{C}_{37}}^{(2)}\) to infer _POI_ #114 as a likely destination, despite it being unseen in the user's own history.  
This robust performance across varying metrics and user activity levels highlights our model's adaptability and strength in providing relevant and comprehensive POI recommendations.
## 4 Experiments
### The Impact of Resolution Layers on Pose Estimation
#### 4.2.2 Results of Different Levels of Hash Encoding  
As shown in Figure 5(a),b, as the number of layers increases, the estimation error shows a decreasing trend. On the surface, this seems to suggest the use of more layers. However, a deeper analysis reveals a more complex picture. When further analyzing the PSNR values in Figure 5(c), we find that the improvement in rendering accuracy is closely related to the accuracy of pose estimation. Specifically, the increased layers enhance the model's ability to capture hash-encoded features in the scene, resulting in a finer presentation of image details. As the image details continue to refine, the accuracy of the pose estimation increases, forming a mutually promoting cycle. However, what we really care about during the pose estimation phase is the convergence properties of the model, not the absolute accuracy. As illustrated in Figure 5(a),b, starting from the second layer, the pose estimation trends of different layers are roughly the same, with no significant differences. This means that, with only two or three layers, we can achieve a stable estimation of the pose. Further increasing the number of layers will lead to an increase in model parameters, thus increasing the computational cost of training and inference.  
Figure 6: The impact of resolution layers on pose and rendering quality.
###### Abstract
Similarly, researchers in Sri Lanka have developed a voice-operated online examination system that enables students to take exams remotely. This system utilizes speech synthesis to present questions verbally, while speech recognition converts students' spoken responses into text. Responses are stored in a database for future reference, and scores are calculated immediately upon submission. Proposed future enhancements include incorporating noise-filtering techniques to improve command performance and integrating fingerprint sensors, facial recognition methods, and other advanced technologies to prevent misconduct and enhance system security [14]. Together, these innovations reflect a global commitment to creating more inclusive educational environments for visually impaired students, ensuring they have equal opportunities to demonstrate their knowledge and skills. This paper presents an innovative solution to assist BVI students in taking exams independently by designing and developing the ExamVoice system with the following features:  
1. Cost-effective and automated. 2. Allows blind and sighted students to take exams together in an environment with minimal distractions, promoting inclusivity. 3. Ensures the privacy and confidentiality of examinations, allowing the instructor to retain complete control over the entire examination process. Promotes confidence-building among BVI individuals, encouraging active and inclusive participation in various exam scenarios. The paper is structured as follows: It starts with an introduction and literature review, followed by the methodology, which includes requirements and analysis. Next, it covers the application design, comprising UML diagrams, and then the implementation details. Subsequently, a performance evaluation is conducted, and the paper concludes with findings and suggestions for future work.
## 2 Related Work
### Localization Schemes  
Another important research topic in MANAL algorithms is to design a localization scheme where UNNs locations are calculated using MAs. Localization schemes are an essential criterion in evaluating the performance of mobile path models. If a mobile path planning model works well in scenarios using different localization schemes, it can be said that this model is compatible with working in different locations. In the literature, in general, trilateration and triangulation-based localization schemes were used to evaluate mobile path models. RSSI and TOA approaches are generally preferred for these schemes.  
In this study, since Weighted Centroid Localization (WCL) and Accuracy Priority Trilateration (APT) schemes are used, these schemes are examined separately in the following subtitles.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 6 Application: Comparing DP Mechanisms  
In differential privacy, it is common to compare the privacy guarantees provided by different mechanisms by 'comparing the epsilons'. However, it is interesting to ask to what extent \(\varepsilon\)-equivalent mechanisms are comparable wrt the other leakage measures defined here--or we might want to know whether reducing \(\varepsilon\) in a mechanism also corresponds to a _refinement_ of it. This could be useful if, for example, it is important to understand the privacy properties of a mechanism with respect to _any_ max-case leakage measure, and not just the DP measure given by \(\varepsilon\).  
Since the \(\varepsilon\)-based order given by \(\sqsubseteq^{\text{prv}}_{d}\) is (strictly) the weakest of the orders considered here, it cannot be the case that we _always_ get a refinement (wrt other orders). However, it may be true that, for particular _families_ of mechanisms, some (or all) of the refinement orders hold.  
We investigate three families of mechanisms commonly used in DP or LDP: _geometric, exponential_ and _randomized response_ mechanisms.
# Empirical Study on 5G NR Cochannel Coexistence
## 1 Introduction
In such scenario, all transmission slots will be aligned, even if in opposite directions, so any interference from a given slot is confined to a single slot. Earlier research has shown that coexistence of different TDD patterns for cochannel and adjacent channel networks have a significant role in the interference characteristics [7; 8]. We also investigate coexistence interference mitigation aspects in our measurement-based study. The rest of the article is organized as follows: In Section 2, we give an overview on related works on 5G NR coexistence analysis. Section 3, describes the cochannel coexistence problem and illustrates the different types of interference situations encountered in a practical cochannel deployment setup. In Section 4, we describe our systematic measurement methodology and deployment setup. We also give an overview of the networks used in our empirical evaluation in this section. Section 5 presents the detailed performance results and their analysis. Finally, Section 6 concludes the article and outlines our current and future work directions.