esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
The cost of purchasing and deploying one or several laser scanners is not trivial. If one compares the traditional quality control method and the method of applying laser scanning and BIM in modular construction quality control, it may be cheaper to use the traditional method. However, laser scanning technology brings many benefits to the project, with quality control being only a minor one. Laser scanning can be used for multiple purposes, including measuring distances and clearances, communicating between stakeholders, referring to recorded data for operation, maintenance, and renovation/rehabilitation, and automating construction progress control [44]. Quality control with laser scanning and BIM can be economically feasible when used alongside other applications such as generating a detailed record of the construction or measuring clearances. One of the limitations of this study is the training. Many workers on the jobsite are not familiar with laser scanning and BIM. It takes time to train those workers to operate the laser scanner and have the data processed. This can be improved in the future by using a laser scanning specialist and automating the data process. In addition, besides the terrestrial laser scanner, there is a variety of tools that can be used to collect point cloud data. For a linear project, such as a removable floodwall, a mobile laser scanning system could be a good selection, which can reduce the time of capturing point cloud data. This research established a solid framework for using laser scanning and BIM for modular construction quality control. However, the full potential of digital modeling and automation processes can be expanded in the future.
## 4 Joint Decoding-Complexity and Rate Control  
As with any rate control algorithm, the joint control of both decoding-complexity and rate also requires target decoding-complexities and bit rates to be defined. In this context, this section first describes how these can be allocated at the CTU-level. This is followed by the derivation of an appropriate QP and content-adaptive decoding-complexity and rate trade-off factors in (6), and finally by an update algorithm to dynamically adapt the model parameters in (6).
# Compressive Sensing-Based IoT Applications: A Review
## 3 Compressive Sensing Overview
### Reconstruction Algorithms  
Fast and efficient reconstruction algorithms are the keys to incorporate CS in real-world applications, thus developing such algorithms have been the main concern in the CS community. Several reconstruction algorithm classes have been proposed in the literature. Nevertheless, the well-recognized algorithms fall under the umbrella of two major algorithmic approaches, namely, convex optimization and greedy algorithms.
# An Improved Chinese Pause Fillers Prediction Module Based on RoBERTa
###### Abstract  
The prediction of pause fillers plays a crucial role in enhancing the naturalness of synthesized speech. In recent years, neural networks, including LSTM, BERT, and XLNet, have been employed for pause fillers prediction modules. However, these methods have exhibited relatively lower accuracy in predicting pause fillers. This paper introduces the utilization of the RoBERTa model for predicting Chinese pause fillers and presents a novel approach to training the RoBERTa model, effectively enhancing the accuracy of Chinese pause fillers prediction. Our proposed approach involves categorizing text from different speakers into four distinct style groups based on the frequency and position of Chinese pause fillers. The RoBERTa model is trained on these four groups of data, which incorporate different styles of fillers, thereby ensuring a more natural synthesis of speech. The Chinese pause fillers prediction module is evaluated on systems such as Parallel Tacotron2, FastPitch, and Deep Voice3, achieving a notable 26.7% improvement in word-level prediction accuracy compared to the BERT model, along with a 14% enhancement in position-level prediction accuracy. This substantial improvement results in a significant enhancement of the naturalness of the generated speech.  
RoBERTa; naturalness of speech; speech synthesis; Chinese pause fillers; prediction module +
Footnote †: journal: _Sciences_  
0000-0001-9999-4000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00000-000000-00000-00000  
ployed in pause filler prediction tasks, achieving favorable results. However, the accuracy of existing pause filler prediction modules still has room for improvement, necessitating the adoption of more effective strategies and more efficient natural language processing models for further advancements.  
The main contribution of this study lies in the significant practical importance of the proposed method for enhancing the robustness of speech synthesis systems, especially when facing the challenge of increased text length. Traditionally, researchers tend to train personalized speech synthesis models applicable to everyone. However, our paper introduces a novel approach by grouping speakers based on the positions and usage frequencies of pause fillers, allowing the training of personalized pause filler prediction modules for each individual. This method effectively reduces the data and time required to train models for each speaker, thereby improving model practicality. Through this approach, we have successfully addressed multiple challenges in pause filler prediction, laying a solid foundation for achieving more natural and high-quality speech synthesis.
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 3 Optical Camera Communication Link Model
### Link Budget  
According to the typical formulation of an OWC link budget, the optical power impinging the main lens of a camera can be expressed as shown in Equation (1).  
\[P_{rx}=P_{tx}R(\theta,\phi)\frac{A_{lens}}{d^{2}}\cos(\
abla) \tag{1}\]  
where \(P_{rx}\) is the received power, \(P_{tx}\) is the optical source's radiant power, \(R(\theta,\phi)\) is the source's radiation pattern at elevation \(\theta\) and azimuth \(\phi\), \(A_{lens}\) is the main lens' cross section, \(d\) is the link range, and \(\Psi\) is the impact angle. Furthermore, thanks to the use of image-forming optics, in a perfectly focused situation the received optical power would be divided among the projected size of the light source (Equation (2) [8]).  
\[A_{proj}=\frac{N_{x}N_{y}}{FOV_{x}FOV_{y}}\frac{A_{tx}}{d^{2}} \tag{2}\]  
\(A_{proj}\) is the projected size of the light source on the sensor (expressed in pixels); \(N_{x}\) and \(N_{y}\) define the sensor's pixel resolution; \(FOV_{x}\) and \(FOV_{y}\) are the camera's horizontal and vertical fields of view, respectively; and \(A_{tx}\) is the transmitter's effective area (from the  
Figure 2: Filter patterns for the spectral information acquisition. (**a**) Foveon-based sensor. (**b**) Bayer filter sensor.  
receiver's viewpoint). Joining Equations (1) and (2) it yields the received optical power at each pixel (Equation (3)).  
\[P_{px}=P_{tx}R(\theta,\phi)\frac{A_{lens}}{A_{tx}}\frac{FOV_{x}FOV_{y}}{N_{x}N_{y }}A_{px}\cos(\Psi) \tag{3}\]  
\(P_{px}\) is the pixel power (in watts) and \(A_{px}\) is the pixel area. This last equation is valid for scenarios in which \(A_{proj}\) is strictly greater than one pixel. Finally, the electron yield \(S_{px}^{(i,j)}\) of the i-th emitter on the j-th sub-pixel (color) after the optoelectronic conversion takes into account the filter response \(F_{j}(\lambda)\) (from the Bayer filter or the Foveon architecture); the emission spectrum \(\hat{S}^{(i)}(\lambda,T)\), which depends on wavelength (\(\lambda\)) and temperature (\(T\)); and silicon quantum efficiency, as Equation (4) describes.  
\[S_{px}^{(i,j)}=P_{px}\int_{\lambda}S^{(i)}(\lambda,T)F_{j}(\lambda)\mathfrak{R }(\lambda)\frac{E_{ph}(\lambda)}{q}\mathrm{d}\lambda \tag{4}\]  
Silicon quantum efficiency is described by the responsivity curve of the material \(\mathfrak{R}(\lambda)\), the photon energy \(E_{ph}(\lambda)\), and the electron charge \(q\). As it can be observed, the link budget depends on two separate phenomena. The link's geometry defines the amount of energy that arrives at the main lens of the receiver, which is wavelength-independent at least in Line-Of-Sight (LOS) scenarios, which is the case under study. On the other hand, the emission spectrum, which is affected by the device's p-n junction temperature, and the wavelength-dependent behavior of the receiver define the final electron yield.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 7 Conclusions  
This work presented a comparative study between some slice selection strategies. We used a test scenario including 3 vertical slices in a multi-domain scenario, aiming at three common services in the context of 5G networks. In addition, we used a strategy from the K-means method to group the UE data flows, so as to characterize the available slices and, subsequently, define the weights that would be used in the MCDM methods, whose purpose consisted of defining and selecting the best slice available to receive the network flow during operating time.  
Figure 17: Method percentage results for Slice 2.  
Figure 18: Method percentage results for Slice 3.  
Initially, it was necessary to study the slice selection models available in the literature, verifying the techniques that aim to solve the problem, so as to support the choice and option for the methods covered herein that constitute the core of the NSSF DAF framework.  
The MCDM methods proved to be favorable, due to their simplicity and efficiency when compared to other strategies; they did not require a history of virtual network operations and slices, which can be very useful for newly-created slices. To define the weights and priorities between the criteria, the K-means clustering algorithm was considered, providing the opportunity for further studies and to adopt hybrid methods, considering the possibility of exploring reinforcement learning/genetics algorithms, fuzzy systems, or even networks recurrent along with the MCDM methods in future works.  
Considering the restrictions of the scenario and from the analysis of the results, it was verified that the proposed solution proved to be promising, making it necessary to carry out new tests considering other scenarios, to propose improvements to the algorithms developed, as well as to evaluate other techniques, strategies, and methods for the core of the proposed framework.  
We also observed that there is no significant difference between the evaluated methods. It is thus possible to evaluate the adoption of data grouping models, aiming to select the most suitable slice for the user considering the traffic generated. Hence, we proposed an optimized E2E approach for the traffic management of access networks to the 5G Core.  
Finally, we concluded that selecting slices is still an open problem, which invites the study and application of new techniques, approaches, and solutions. Our proposed framework provides compatibility with current specification standards, thus constituting a promising solution for 5G networks in the context of NS.  
Our proposal can also be applied in several application niches, such as when integrating large amounts of data from devices linked to the IoT context to cloud services, as well as to integrate applications that have multimedia requirements, a high density of video, and audio traffic.
# Cell Therapy in the Treatment of Coronary Heart Disease
## 2 Non-Cardiac Origin Cells for Cardiac Cell Therapy
### Endothelial Progenitor Cells  
In 1997, researchers described a group of stem cells from bone marrow known as EPCs [39]. These cells represent a provasculogenic subpopulation of HSCs and display surface markers CD34 and CD133 [23]. EPCs hold promise for the treatment of CAD due to their potential to directly contribute to blood vessel formation in recipient tissues and to stimulate angiogenesis through paracrine signaling. Systemic administration of EPCs has been found to increase neovascularization in ischemic tissues [40; 41].  
The clinical trial RENEW was dedicated to intramyocardial transplantation of autologous EPCs to patients with refractory angina. The authors confirmed the safety of using these cells and observed improvements in some functional parameters of the left ventricle [17]. The PERFECT clinical trial reported a reduction in scar size following MI and an improvement in segmental myocardial perfusion due to intramyocardial transplantation of autologous EPCs during coronary artery bypass grafting in patients with acute MI. However, the study did not uncover any variation in left ventricular ejection fraction after cell transplantation [42].
## 3 Test Methods and Result for Analysis of Swelling Property of Water-Expandable Polyurethane
### Swelling and Leakage Test Methods and Results
#### 3.1.2 Leakage Test Methods and Result  
(1) Leakage Test Methods  
1 Specim Preparation  
In accordance with the method shown in Section 3.1.1, Specim Preparation, the water-expandable polyurethane is applied for each superabsorbent polymer mixing ratio, and the cured water-expandable polyurethane specimens are prepared by dividing them into two 200 \(\times\) 70 \(\times\) 2 mm sheets according to the superabsorbent polymer mixing ratio.  
2 Leakage Test Method  
The prepared specimen is placed between two glass plates of the same size as the specimen and fixed to make a total of two specimens. The two specimens are placed 1 mm apart (to simulate a crack width of 1 mm), butted together, and fixed to create a crack environment. A plastic cylinder of \(\mathcal{O}\)100 mm and 100 mm in height is installed on the top, creating one specimen. The specimen fabrication process is shown in Figure 8.  
The prepared specimen is immersed in distilled water for 24 h, only the water from the surface of the specimen is then removed, and the cylinder of \(\mathcal{O}\)100 mm \(\times\) 100 mm is filled with distilled water to a height of 80 mm. After that, the presence of leakage is checked through cracks in the lower part of the specimen. It is determined that if leakage occurs within 60 min from the time of filling with distilled water, there is no waterproofing performance, and if leakage dose not occur after 60 min, waterproofing performance exists. The test is terminated when the waterproofing performance exerts as the superabsorbent polymer content increases, and the test status is shown Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Division**  
(2) Leakage Test Result  
Figure 10 shows the waterproofing performance evaluation results of water-expandable polyurethane according to the superabsorbent polymer mixing ratio.  
The evaluation results confirmed that the waterproofing performance was not exerted in the samples incorporating 1-9% superabsorbent polymer, but the waterproofing performance was exerted in the samples with a superabsorbent polymer mixing ratio of 10% or higher. This suggests that the expansion ratio of the material increases to the simulated crack area in the samples with a superabsorbent polymer mixing ratio of more than 10%, and the simulated crack area is thus water-tightened/waterproofed with sufficient expansion pressure. On the other hand, as the expansion ratio is low in the samples with a superabsorbent polymer mixing ratio of less than 10%, the water-expandable urethane does not sufficiently expand to the simulated crack area, and thus leakage occurs through the crack.
## 3 Experimental Methods
### System Configuration
#### 3.1.2 RF Configuration for 60 GHz Band Communication  
Figure 5 presents the configuration of the 60 GHz band communication segment. Within this system, we employed a communication device compliant with IEEE 802.15.3e standards (Transfer Jet X, Sony Semiconductor Solutions Corp., Kanagawa, Japan), capable of completing an association phase in under 1 millisecond. This feature permits the utilization of ultra-narrow communication zones with a single antenna, obviating the need for beamforming, even within short-duration zones of less than 1 s.  
The transceiver output (linearly polarized waves) is transformed into left-handed circular polarization (LHCP) through a circular waveguide mode transition (SWT-15141-SB, Eravant, Torrance, CA, USA) and an mmWave polarizer (SAS-603-14115-F1, Eravant). A conical horn antenna (SAC-2309-141-S2, Eravant) was used to emit the generated LHCP mmWaves into space. It is worth noting that during flight UAVs typically tilt forward, leading to a rotation of the polarization plane (Figure 5c). In the case of linear polarization, this polarization plane rotation results in losses attributed to cross-polarization isolation. To circumvent polarization mismatch stemming from fluctuations in UAV attitude, our experiments used circular polarized waves.  
The MAC addresses of the communication devices used in our experiments were treated as known to each other and shared in advance. The specifications regarding with the 60 GHz band communication are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline Standard & IEEE802.15.3e \\ Frequency band & Unlicensed 60 GHz band \\ Center frequency & 58.320 GHz \\ Band width & 2.16 GHz \\ Transmission power & \(-5\) dBm (approx.) \\ Polarization & Left-handed circular polarized 1  \\ Antenna & Conical horn (23 dBi, HPBW = 11\({}^{\circ}\)) \\ \hline \end{tabular}
\end{table}
Table 1: Specification of the 60 GHz wireless device integrated into the UAVs.  
Figure 5: Configuration of the mmWave equipment. (**a**) Block diagram outlining the principal components related to mmWave emission. The transceiver’s linear polarized output is converted to left-handed circular polarization (LHCP) using the mmWave polarizer. (**b**) Photograph of the part of mmWave communication components. (**c**) An illustration of polarization mismatch arising from the UAV’s forward-leaning attitude during flight.  
The data throughput of the used transceiver is dependent on the received signal strength, as the appropriate modulation scheme is autonomously chosen to balance noise tolerance and data rate considerations. Table 2 shows the designated modulation options in accordance with the IEEE 802.15.3e standard [28]. The throughput and receiver sensitivity are approximate values for single channel operation. Our transceiver chip also supports BPSK modulation for lower-rate transfers, expanding upon the modulation options enumerated in the table.
## 2 Related Work
However, DRL-CaCC's reliance on DRL models introduces significant computational complexity, which may limit its feasibility for deployment in real time, resource-constrained WSN environments. Mazloomi, Gholipour [25] proposed a hybrid methodology termed Fuzzy Structure and Genetic-Fuzzy (FSFG), specifically aimed at managing congestion control in WSNs. The FSFG method combines Fuzzy C-Means (FCM) clustering, Support Vector Regression (SVR), Fuzzy Inference Systems (FIS), and Genetic Algorithm (GA) techniques to address congestion. This methodology operates in two phases: Structure Identification, where fuzzy clustering is used to categorize network nodes based on congestion levels, followed by SVR to ensure minimal error in membership functions, and Parameter Identification, which utilizes GA to fine-tune system parameters. Simulation results indicate that the FSFG algorithm surpasses existing congestion control approaches, such as GASVM and CODA, achieving a 32% improvement in packet delivery rate and a 28% reduction in end-to-end delay compared to baseline methods. However, while effective, the FSFG method's complexity may pose limitations in highly resource-constrained networks, suggesting the need for further optimization for practical applications.
## 4 Results and Discussion
### TS-InSAR Deformation Monitoring Results
#### 4.3.2 \"Fucheng-1\" and \"Shenqi\" PS-InSAR Deformation Inversion Results  
Time-series PS-InSAR processing was conducted using 15 \"Fucheng-1\" images and 5 \"Shenqi\" images with 22 January 2025 as the reference master image, revealing significantly denser monitoring points due to the higher-resolution imagery, as demonstrated in Figure 13. The analysis yielded tens of millions of monitoring points with a remarkable density of 60,712.75 point/km\({}^{2}\), showing deformation rates ranging from \(-\)115.28 to 55.40 mm/yr. Notably, 86.99% of measured deformation rates fell within the stable range of \(-\)5 to 5 mm/yr, confirming the overall stability of non-mining areas while effectively capturing the substantial deformation characteristics in active mining zones through the enhanced point density and measurement precision enabled by the high-resolution satellite data.  
Figure 11: (**a**) Time-series PS-InSAR line-of-sight deformation monitoring results for the Jinchuan mining area based on Sentinel-1A. (**b**) DS-InSAR line-of-sight deformation monitoring results for the Jinchuan mining area. (**c**,**d**) Cumulative distribution histograms for the deformation monitoring results corresponding to (**a**,**b**).
## 3 Results
#### Interictal Photophobia and Phonophobia
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Control (n = 30)** & \begin{tabular}{c} **Migraine without** \\ **Aura (n = 30)** \\ \end{tabular} & \
\begin{tabular}{c} **Migraine with** \\ **Aura (n = 30)** \\ \end{tabular} &
\begin{tabular}{c} **Chronic Migraine** \\ **(n = 30)** \\ \end{tabular} & \(p\)**-Value** \\ \hline Photophobia & & & & \\ Driving \({}^{*,a}\) & 57.7 (15) & 65.4 (17) & 76.9 (20) & 92.3 (24) & 0.031 \\ Looking at a bright screen \({}^{a}\) & 36.7 (11) & 76.7 (23) & 76.7 (23) & 53.3 (16) & 0.002 \\ Social activities \({}^{a}\) & 16.7 (5) & 60.0 (18) & 63.3 (19) & 40.0 (12) & 0.001 \\ Walking during a sunny day & 50.0 (15) & 73.3 (22) & 76.7 (23) & 60.0 (18) & 0.136 \\ \hline Phonophobia & & & & \\ Social situations \({}^{a}\) & 33.3 (10) & 83.3 (25) & 86.7 (26) & 63.3 (19) & \textless{}0.001 \\ Hearing sounds in everyday situations \({}^{a}\) & 43.3 (13) & 70.0 (21) & 80.0 (24) & 60.0 (18) & 0.027 \\ Listening to conversations & & & & \\ in noisy places & 83.3 (25) & 90.0 (27) & 93.3 (28) & 90.0 (27) & 0.735 \\ Concentrating in noisy surroundings & 86.7 (26) & 93.3 (28) & 96.7 (29) & 86.7 (26) & 0.542 \\ \hline \hline \end{tabular} \({}^{*}\) N = 26 in each group; \({}^{a}\)\(p\) < 0.05.  
\end{table} 
Table 2: Prevalence (%, n) of interictal photophobia and phonophobia during daily activities. group than the migraine without aura group for performing social activities (\(p\) = 0.010, ES = 0.73) and listening to conversations in noisy places (\(p\) = 0.010, ES = 0.71), and in the migraine with aura group in comparison to the migraine without aura group for hearing sounds in everyday situations (\(p\) = 0.010, ES = 0.82; Table 3). Differences in the remaining between-group comparison were not verified (\(p\) < 0.05, Table 3). Correlation analysis revealed weak positive associations between photophobia and phonophobia intensities with migraine features (migraine intensity, frequency of attacks, and frequency of aura) (\(p\) < 0.05, Table 4).