esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Performance of RFF Estimation Methods
Future work therefore should aim to validate the aRFF algorithm (aRFF-AP, aRFF-APH) across a balanced set of HVD data. Although the aRFF-APH algorithm demonstrated greater accuracy in detecting voiced/unvoiced boundaries, the aRFF-AP algorithm remains the gold-standard method for semi-automatically estimating RFF. This is because the aRFF-AP algorithm was developed and validated using independent training and test sets to improve the clinical applicability of RFF. The aRFF-APH algorithm, on the other hand, was developed with the goal of improving the acoustic voiced/unvoiced detection rather than clinical applicability and was specifically tuned to the limited database examined here. As part of this investigation, all speakers were recorded in a sound-attenuated booth in the presence of constant noise from the endoscopic light source. In addition to this single recording location, the voice sample characteristics that were captured in the speaker dataset were more limited that those used in the development of the aRFF-AP algorithm: [PERSON] et al. (2014) included over 20 different primary voice complaints with an overall severity of dysphonia ranging from 0 to 100, whereas the current study included a smaller range of diagnoses (57% typical, 16% MTD, 3% nodules, 2% polyp, 1% scarring, 1% lesion, 20% Parkinson's disease) and resulting dysphonia severity (0-51.3). Because of the limited spectrum of vocal function captured here, pitch strength-tuned parameters and independent training/test sets were not implemented in the development of the aRFF-APH algorithm in the current study.
## 5 Conclusions  
In conclusion, a Multiscale Soft Attention Convolutional Neural Network structure is proposed to alleviate the mixture-pixel problem of cloud extraction in optical satellite images. MSACN is an end-to-end structure that consists of a concurrent dilated convolution module and a multi-head soft attention module. Experiments prove that for high-resolution images, MSACN achieves remarkable results. There is a certain relationship between the performance of the CNN structure and the attention mechanism for the complex scenes. For ZY-3 satellite images, the combined dilated convolution module and multi-head soft attention model significantly enhance the extraction accuracy of complex scenarios in satellite imagery with cloud and snow. To further assess the effectiveness of MSACN, we compared TransUnet, SwinUnet, UNet, and DeepLabV3+. The experimental outputs demonstrate the outstanding performance of MSACN. On the ZY-3 dataset, the accuracy of MSACN achieves 98.79%. Although MSACN has good performance on cloud extracting tasks, there are still several directions for further improvement, including optimization of hyper-parameters and network structure. Furthermore, we will make more improvements in proposing the novel cloud extraction method integrating prior knowledge as a post-processing step.  
Conceptualization, L.G. and C.G.; methodology, C.G.; investigation, S.L.; resources, S.L.; writing--original draft preparation, C.G.; writing--review and editing, L.G.; supervision, J.Z.; project administration, J.Z. All authors have read and agreed to the published version of the manuscript.  
This study was supported by Liaoning Provincial Department of Education Youth Project (No. 1030040000560), National Natural Science Foundation of China (No. 42071428), China Scholarship Council (No. 202208210120), Liaoning Province Applied Basic Research Program (Youth Special Project) (2023)H2/101600038), Shenyang Youth Science and Technology Innovation Talent Support Program (RC220458).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
Author [PERSON] was employed by Shen Kan Engineering & Technology Corporation, MCC. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 The PSOTSC Algorithm
### Results of Segmentation Accuracy
#### 4.2.1 Real Trajectory Data
The average segmentation accuracy comparison of the two methods under different number of segment feature points is obtained, as shown in Figure 9. When the numbers of particles, iterations and reserved trajectory feature points are the same, the PSOTSC algorithm using NARJ strategy can obtain higher trajectory segmentation accuracy than the standard PSO algorithm. In other words, the neighborhood adjustment and random jump make the particle swarm to search the trajectory segmentation solution in a more reasonable mode. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Particles** & **Iterations** & **Compression Ratio** & **m_AY** & **sd_AY** \\ \hline
5 & 5 & 20\% & 82.7117\% & 0.0431\% \\
10 & 5 & 20\% & 83.1876\% & 0.0368\% \\
20 & 10 & 20\% & 84.1799\% & 0.0249\% \\
5 & 5 & 30\% & 86.6399\% & 0.0347\% \\
10 & 5 & 30\% & 87.4521\% & 0.0254\% \\
20 & 10 & 30\% & 88.6618\% & 0.0189\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance with different parameter values in ungulate data set experiments. Figure 9: Performance of NARJ particle update strategy: (**a**) Hurricane trajectory data set; (**b**) Unqu-late data set.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 7 Simulations and Discussions
### Discussion about Uncertain Environment  
The simulations above are based on the assumption of a deterministic environment in which wind velocities and atmospheric density are steady. However, uncertain and unknown factors exist in the IPCB running environment.  
[PERSON] has investigated horizontal velocity in the midlatitude stratosphere using the observation data of Project Loon and has found the existence of horizontal wind velocity perturbations [61]. [PERSON] has proposed a model for modeling uncertain winds, which employed a Von Mises distribution to simulate the direction of uncertain wind and a Gaussian distribution to simulate the magnitude of uncertain wind [39]. However, in general, direct observations of winds in the stratosphere are sparse, so a precise model of stratospheric wind is very challenging.  
Since this paper focuses on the problem of IPCB geometry configuration, we will not discuss the environment model in detail.  
An uncertain environment may degrade the effect of the proposed algorithm because the uncertainty may make IPBs' trajectories deviate from expectation, and as a result, the geometry configuration of IPCB cannot reach the ideal state.  
To improve robustness, data filters or environment prediction models can be developed. When perceiving uncertain factors, data filters and prediction models can help to filter out data errors and keep real environment changes. The IPCB can update the environment model and implement the planning algorithm with the new environment model.
# Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis
## 3 Methods
### Image Generated According to Viewpoints
```
Input: \(\vartheta_{L},\vartheta_{U}\), //rang \(\vartheta_{L}\leq\vartheta\leq\vartheta_{U}\), \(\varphi_{L}\)\(\varphi_{U}\), //rang \(\varphi_{L}\leq\varphi\leq\vartheta_{U}\), \(d_{vp}\), //The minimum distance between neighboring viewpoints \(I_{r},I_{c}\).//The size of the output image Output: \(\Omega=\left\{l_{i},\vartheta_{i},\varphi_{i}\right\}\) //images with labeled viewpoints
1: Generate viewpoints using Algorithm 1;
2: Remove viewpoints that are not within the specified ranges;
3: Remove neighboring viewpoints with distances less than \(d_{vp}\);
4: Generate a projection image for each viewpoint using Equation (7);
5: Construct the AABB (Axially Aligned Bounding Box) of the object projection image, and cut off the external redundant margins which are out of the AABB;
6: Enlarge or reduce the image to the specified size (e.g., \(32\times 32\));
7: Output the images \(\Omega=\left\{l_{i},\vartheta_{i},\varphi_{i}\right\}\) to a folder. ```  
**Algorithm 2** Generating images according to specified viewpoints  
In the labeled image dataset, the viewpoints we have chosen are in front of the upper hemisphere, considering that the viewpoint is often higher than the horizontal line of sight in most applications, such as autonomous driving or furniture exhibitions.
## 2 State-of-the-Art
### International Mobile Telecommunications (IMT) for 5G beyond 2020  
By 2030 it is estimated that 5G technology will increase the global manufacturing gross domestic product (GDP) by 4%, which is equivalent to approximately $740 billion. This forecast is based on the new use cases and improvements in existing applications that 5G brings versus other technologies, as well as how these improvements will affect productivity (Figure 3) [12; 30; 31]. The evolution of mobile communications has significantly affected socio-technical evolution in recent decades, by contributing to the economic and social development of both developed and developing countries. Mobile communications have become deeply ingrained in all aspects of daily life. The evolution of mobile communications systems and socio-technical trends are expected to remain tightly coupled, forming a foundation for the next generation of society in 2020 and beyond. Therefore, new demands, such as increased traffic volume, a greater number of devices with varying service requirements, improved quality of user experience (QoE), and improved affordability through cost reduction, will necessitate a greater number of innovative solutions soon. In a research published by the International Mobile Telecommunications (IMT) [12],the potential user and application and technological trends, spectrum applications can be summarized to the following:  
* Support very low latency and high reliability human-centric communication
* Support very low latency and high reliability machine-centric communication
* Support high user density
* Maintain high quality at high mobility
* Enhanced multimedia services
* Internet of Things (IoT)
* Convergence of applications
* Ultra-accurate positioning applications
## 2 Description of the IMM-UKF Filtering Problem
### Standard IMM  
From Figure 1, the standard IMM method can be described in the three following steps [11]:  
Step 1: Model probability updating. The mixing probability is defined and calculated as  
\[u_{ijk-1}=\frac{1}{c_{j}}p_{ij}u_{i,k-1},\ i,j=1,\ 2,\ \ldots,\ m \tag{3}\]  
where \(p_{ij}\) is the transition probability; \(u_{i,k-1}\) is the correct probability of model \(i,\ i=1,\ 2,\ \ldots,\ m\) at time step \(k-1\); and \(c_{j}\) is the prediction probability of model \(j,\ j=1,\ 2,\ \ldots,\ m\) from time step \(k-1\) to time step \(k\)[11] which provides  
\[c_{j}=\sum\limits_{i=1}^{m}p_{ij}u_{i,k-1},\ i,j=1,\ 2,\ \ldots,\ m. \tag{4}\]  
Therefore, we can obtain the updated model probability:  
\[u_{j,k}=\frac{1}{a}\Lambda_{j,k}c_{j} \tag{5}\]  
where \(a=\sum\limits_{j=1}^{m}\Lambda_{j,k}c_{j}\); \(\Lambda_{j,k}=N(\widetilde{\mathbf{Z}},\mathbf{P}_{\widetilde{\mathbf{Z}} \widetilde{\mathbf{Z}}})\); \(\widetilde{\mathbf{Z}}\) represents the innovation residual vector of UKF filter \(j\); and \(\mathbf{P}_{\widetilde{\mathbf{Z}}\widetilde{\mathbf{Z}}}\) represents the covariance matrix of \(\widetilde{\mathbf{Z}}\).  
Step 2: Input interaction.  
\[\mathbf{\hat{X}}_{j,k-1}^{o}=\sum\limits_{i=1}^{m}u_{ij,k-1}\mathbf{\hat{X}}_ {i,k-1} \tag{6}\]  
\[\mathbf{P}_{j,k-1}^{o}=\sum\limits_{i=1}^{m}u_{ij,k-1}[\mathbf{P}_{i,k-1}+ \widetilde{\mathbf{X}}_{ij,k-1}\cdot\widetilde{\mathbf{X}}_{ij,k-1}{}^{T}] \tag{7}\]  
where \(\widetilde{\mathbf{X}}_{ij,k-1}=\mathbf{\hat{X}}_{i,k-1}-\mathbf{\hat{X}}_{j,k-1}^{o}\).  
Step 3: Resultant mean and covariance matrix computation.  
\[\mathbf{\hat{X}}_{fusion,k}=\sum\limits_{j=1}^{m}\mathbf{\hat{X}}_{j,k}u_{j,k} \tag{8}\]  
\[\mathbf{P}_{fusion,k}=\sum\limits_{j=1}^{m}u_{ijk}\bigg{\{}\mathbf{P}_{j,k}+ \left[\mathbf{\hat{X}}_{j,k}-\mathbf{\hat{X}}_{k}\right]\left[\mathbf{\hat{X} }_{j,k}-\mathbf{\hat{X}}_{k}\right]^{T}\bigg{\}}. \tag{9}\]
## 3 Security Vulnerabilities
### Surface-Associated Vulnerabilities
#### 3.2.3 Service Vulnerability  
**Vulnerable Edge Service:** IoT devices provide various types of services in the edge networks. These devices expose Web and Application Programming Interfaces (APIs) that enable users to interact with them. For instance, a smart thermostat allows a user to adjust the room temperature using their smartphone. A Coordinator device enables a networkadministrator to manage the IoT nodes through a device management service. These interfaces can be vulnerable to various web attacks, such as SQL injection [83], password enumeration [84], and cross-site scripting [85], if they are not designed with proper security guidelines. The services may not implement a security policy that locks a user's account after a limited number of password guesses or may support vulnerable words as account credentials, such a a dictionary word, as a password [76; 77].  
**Vulnerable Cloud Service:** Cloud IoT services enable users to interact with smart devices remotely. These services also store sensor information in the cloud servers for further analysis. Although the cloud services ensure a user's seamless access to IoT nodes, they introduce various security risks at the same time. For instance, there may have security threats to data privacy and confidentiality, service availability, and continuous access to IoT nodes if cloud servers and services are not patched with the latest security updates [86].  
**Vulnerable Partner Cloud:** IoT applications and cloud services may interact with third party clouds to provide analytics to users. A smart healthcare system can share sensor information from a patient's medical devices with third-party cloud services to help physicians to understand the patient's health condition(s). Sharing the data with third-party clouds can lead to critical security risks, such as privacy and confidentiality threats, if the third-party clouds do not adopt proper security schemes to receive and share the shared information. A third-party cloud provider may not use a reliable security method for user authentication, message encryption, and message integrity verification. The third-party cloud service can use SHA-1, which is vulnerable to collision attacks [87], as the hash algorithm to verify message integrity.
## 3 Comparative Analysis of FEM-Calculated and ART-Measured Eigenfrequencies
### Status Quo: Differences in Eigenfrequencies When Applying a Low Model Accuracy for Simulations  
The measured eigenfrequencies of the reference parts were taken as a basis. They were compared to the simulated eigenfrequencies of the corresponding virtual representations assuming a low level of model accuracy. This means that simulation data were used, for whose generation both the nominal geometry with 8 parameters and literature material properties were used. Figure 4 shows the relative deviations from simulation to measurement separately for each part. The deviations are approximately in the range from \(-2.65\%\) to \(-0.65\%\) depending on the vibration mode, that is, simulated eigenfrequencies are up to almost \(2.7\%\) smaller compared to measured frequencies. This level of error is significant and not negligible in the context of suitable ART training data, since the effect of structural defects or other features to detect with ART can be much smaller by amount. Therefore, further investigations are presented in the following subsections aiming at synthetic training data that are more precise compared to experimentally acquired training data. Since the results are similar for all reference parts, averaged deviation data will be shown in the following figures.  
Figure 4: Relative deviations between eigenfrequencies of virtual representations simulated by FEM using a low model accuracy and eigenfrequencies of the machine-made reference parts measured by ART depending on vibration mode (individual values for each reference part).
## 3 Power Allocation Optimization and Problem Formulations  
As presented in Equation (9), the performance of the DASR depends on the statistical information of the channel and the CSI estimation. The following subsection discusseschannel estimation using the MMSE estimation method. The CSI estimation presented here differs from the channel estimation procedure performed in the uplink transmission such as in the TDD operation mode. In this case, channel reciprocity can be exploited to provide the precoder with the estimated channel.
## 4 Results and Discussion
### Geological Description of the Study Area  
From the geological mapping, it was established that the study area is dominated with basalts. In contrast, arenite and norite are disturbed along the boundaries of basalt (see Figure 4). Following this, the majority of active unstable slopes were located along with basalts while at the contact zone of basalt and arenite, slopes are more stable. The geological mapping also revealed that post and active slope instability is mostly experienced in red clay soil where basalts are the country-rock. The other interesting observation was that slope failures were mostly composed of soil rather than rock. This reassured the sampling and testing of soil since the collection of core samples was stopped after an equipment failure. From the survey of the target section in Figure 5, a cross-section of the study area was produced. What resulted from this is that layers sitting on top of the country rocks (see Figure 5) were likely to slide when their phase changes from solid to liquid due to rainfalls. One could argue that clay soil on the upper horizon may experience slipping movement along the disturbed boundaries of the study area.  
Figure 4: Constructed geological through geological mapping.  
A last note on the geological mapping is that road construction in the Thulamela Municipality took place in the mountainous area. Explosives were used for the initial excavation leaving behind fractures and compromised the stability of the rock mass. The fractures generated within the rock mass can readily disintegrate the material when exposed to extreme rainfalls. Large faults and multiple streams cutting across the area were also observed. These features further strain the stability and internal movement of material in the study area. Created tension cracks propagate which in turn weakens the material and leads to gradual movement. Clayey layers capping the country-rock swell when exposed to rainwater and groundwater. The increased internal pressure finally forces rock slopes to collapse. In some cases, the disappearance of vegetation around the slopes is a precursor of a looming landslide.
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation
### Concentration of Pollutants and Photocatalysts  
The initial concentration of the target pollutant can influence photocatalytic oxidation efficiency in various ways. Generally, lower initial concentrations lead to higher degradation rates. High pollutant concentrations can quickly saturate the catalyst's active sites with by-products, limiting efficiency. Additionally, elevated pollutant concentrations can reduce quantum yield by adsorbing or scattering light, thus decreasing the light available to the photocatalyst. For instance, the removal of toluene, ethylene, and propylene using TiO\({}_{2}\) nanoparticles decreased as initial pollutant concentrations rose from 100 to 500 ppm (Figure 6) [96].  
Conversely, another study on formaldehyde degradation at initial concentrations of 0.6, 1.2, and 2.4 ppm found that the highest conversion rate occurred at 1.2 ppm. This result suggests that low pollutant concentrations may limit contact with active sites, restricting adsorption [95].  
Pollutant adsorption directly impacts photocatalytic oxidation efficiency. When more pollutant molecules are adsorbed on the catalyst surface, the likelihood of reactions between positively charged holes and adsorbed pollutants increases. The non-uniform surface of the photocatalyst features stronger adsorption sites that are filled first at lower concentrations, whereas higher concentrations lead to interactions among adsorbed molecules and gas-phase pollutants. Thus, adsorption sensitivity varies with concentration [89].  
At low pollutant concentrations, the adsorption rate on the photocatalyst surface is slower than the reaction rate, making adsorption the limiting factor for photocatalytic oxidation. As initial pollutant concentrations exceed a certain threshold, the adsorption rate can surpass the photocatalysis rate, shifting the limitation to photocatalytic activity. Since indoor pollutant concentrations typically range from parts per billion (ppb) to sub-ppb, while laboratory conditions often test in parts per million (ppm), the efficiency of photocatalysts used in real indoor conditions tends to be lower [89].  
Additionally, the concentration of the photocatalyst itself influences photocatalytic activity. For instance, in formaldehyde removal, efficiency increased from 33% to 48% with the amount of g-C\({}_{3}\)N\({}_{4}\)-TiO\({}_{2}\) rising from 0.05 to 0.25 g (Figure 6). However, this relationship is not linear due to limited contact between pollutant molecules and the catalyst's active sites [95].  
Figure 5: (**a**) Degradation of p-xylene using USPNT under different power daylight (8 and 24 W) [85] and (**b**) degradation rate of toluene using Pt-rGO-TiO\({}_{2}\) with different light intensities (95, 106 and 116 mW/cm\({}^{2}\)) [97].