esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Soundscape Evaluations and Influencing Factors
### Subjective Evaluations  
The results of the subjective evaluations in terms of soundscape descriptors of annoyance and acoustic comfort from the in situ surveys were analyzed. The average values of the evaluations of each block are shown in Table 4, indicating that the square had a good soundscape, as all blocks received evaluation values greater than three (neither agree nor disagree). Considering the participants' social and demographic backgrounds (white-collar working people with higher education, as shown in Table 1), the soundscape evaluations could be even better as previous studies have suggested these individuals are more sensitive to noisy environments [58].  
Table 4 shows that the soundscape evaluations of the four blocks did not vary much, with the values ranging from 3.42 to 3.80 for the annoyance evaluations and from 3.54 to 3.92 for the acoustic comfort evaluations. The ANOVA analyses of the subjective evaluations of the four blocks are shown in Table 4. They indicate that the differences in the annoyance and acoustic comfort evaluations among the four blocks were not significant. However, the differences in the sound levels and psychoacoustic parameters among the four blocks were significant, as illustrated in Table 3. Such results imply that factors other than the acoustic and psychoacoustic parameters may have influenced the subjective evaluations of the soundscape.  
Nevertheless, BLK-IV, identified as having a natural soundscape, was the block with the lowest annoyance and highest acoustic comfort, with values of 3.80 and 3.92, respectively. BLK-III had the lowest annoyance evaluation value and BLK-I had a slightly higher value, indicating that both blocks had poor annoyance evaluations. In addition, BLK-III also had the lowest acoustic comfort evaluation value but BLK-I had a higher value that was only lower than that of BLK-IV.  
The results of the acoustic comfort evaluation analyses reveal that BLK-IV had the best acoustic comfort evaluation, followed by BLK-I. It is worth noting that BLK-II and BLK-III had worse evaluations than BLK-I. As shown in Table 4, it is noteworthy that BLK-III had a worse annoyance evaluation than BLK-I, although the former was identified as having a neutral-to-artificial soundscape, whereas the latter was identified as having an artificial soundscape. These results indicate that soundscape identification based solely on sounds and landscape features may be a reliable but imprecise method for determining a soundscape. In addition, the difference between BLK-I and BLK-III is noteworthy, as they had different spatial functions. The former was used for physical exercise while the latter was used for communication and needed to be quieter. This implies that the spatial functions may have influenced the subjective evaluations.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 2 Materials and Methods
### Control Model of the System
#### 2.3.4 Path Planning  
In view of the terrain characteristics and planting pattern of the orchard, the navigation path was designed as shown in Figure 9. The line with arrows represents the pre-set path, point 1 is the starting point, points 2 and 3 are the turning points, and point 4 is the stopping point. The point P is any point on the driving trajectory of the spraying robot operation, the vertical distance from the point P to the pre-navigation path is D, and D is used to evaluate the path tracking error. \(D_{1}\) is the minimum safe distance for the spraying robot operation without touch the fruit trees. D is less than \(D_{1}\) when the spraying robot operates, and \(D_{1}>d_{lr}/2\).
# Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit
## 3 Simulation Analysis of T-Joint Welding Based on TEPM
### Welding Process Impact Analysis and Optimization  
The typical T-joint weldments obtained from welding experiments exhibit good surface quality. However, after inspection of the weld pool morphology, quality inspectors noted slight deficiencies in penetration depth for the first and second weld passes, which only meet the minimum quality requirements, and subsequent weld passes merge well with the preceding ones. During the formulation of the test scheme, welding current and voltage were in accordance with the welding process specifications set by the company. Additionally, frequent adjustments to welding current and voltage during the welding process, which change the welding heat input, will affect the welding efficiency. Therefore, only two welding processes, welding speed and interlayer cooling time, were studied in this paper to analyze their effects on welding residual stress and deformation.
# Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins
## 3 Results
### Secondary Structure Estimation for Standard Proteins
The regions between 1649 and 1660 cm\({}^{-1}\), and 1660 and 1665 cm\({}^{-1}\) are attributed to \(\alpha\)-helix and random coil structures, respectively. The region between 1620 and 1647 cm\({}^{-1}\), and 1665 and 1680 cm\({}^{-1}\) are signature regions for \(\beta\)-sheet structures [43; 44], while bands centered between 1680 and 1699 cm\({}^{-1}\) are assigned to \(\beta\)-turn structures. The quantitative analysis of the area under each FTIR band revealed very similar and consistent results to the quantitative analysis of the FT-Raman bands (Table 2). Lysozyme exhibited a dominant \(\alpha\)-helical component around 1654 cm\({}^{-1}\) (Figure 5) corresponding to 42% of its total secondary structural content. Both concanavalin A and immunoglobulin G showed a major band at about 1669 cm\({}^{-1}\) (30% of the total amide I band), which was assigned to \(\beta\)-sheet secondary structures. Peak fitting of the amide I band of trypsin also produced distinct components at 12 wavenumber values (Figure 5). \(\beta\)-sheet was the dominant secondary structure of trypsin as was also observed in its FTIR spectrum. Figure 4: FT-Raman spectra of standard proteins in aqueous solution (20% _w/v_). Goodness-of-fit tests were used to assess all peak fittings. By performing the nonlinear curve fitting, the reduced chi-square is minimized in the iteration process to obtain the optimal parameter values. The reduced chi-square is obtained by dividing the residual sum of square (RSS) by the degrees of freedom (DOF). It measures the deviation between the model (fitted curve) and observed data (original spectrum). Thus, the smaller the reduced chi-square is, the better the fit is. However, this assessment is not a good enough measure to determine the goodness-of-fit as adding more parameters in the model will inherently reduce the chi-square, while, the added parameters might not be justified for the given dataset. R\({}^{2}\) value (also known as coefficient of determination) is another measure of the goodness-of-fit. The closer the fitted value is to the actual data point, the closer R\({}^{2}\) will be to the value of 1. This will be achieved by introducing more parameters and does, hence, also not imply a better fit. The adjusted R\({}^{2}\) can be a better measure of goodness-of fit as it also penalizes for the number of model parameters. The reduced chi-square, R\({}^{2}\), and adjusted R\({}^{2}\) are reported for each peak fitting in the Tables S1-1-S1-16) and Tables S2-1-S2-10.
# Immobilization of Detonation Nanodiamonds on Macroscopic Surfaces
###### Abstract  
Detonation nanodiamonds (NDs) are a novel class of carbon-based nanomaterials, and have received a great deal of attention in biomedical applications, due to their high biocompatibility, facile surface functionalization, and commercialized synthetic fabrication. We were able to transfer the NDs from large-size agglomerate suspensions to homogenous coatings. ND suspensions have been used in various techniques to coat on commercially available substrates of pure Ti and Si. Scanning electron microscopy (SEM) imaging and nanoindentation show that the densest and strongest coating of NDs was generated when using 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and N-hydroxysuccinimide (EDC/NHS)-mediated coupling to macroscopic silanized surfaces. In the next step, the feasibility of DNA-mediated coupling of NDs on macroscopic surfaces is discussed using fluorescent microscopy and additional particle size distribution, as well as zeta potential measurements. This work compares different ND coating strategies and describes the straightforward technique of grafting single-stranded DNA onto carboxylated NDs via thioester bridges.  
Keywords:detonation nanodiamonds; bio-conjugation; de-agglomeration; fluorescent microscopy; nanoindentation +
Footnote †: journal: Applied Physics
## 2 Related Works
### Real-Time Ego-Motion Estimation  
In the domain of estimation of ego-motion, GAs offer a compelling solution to explore potential vehicle movements within a limited set of degrees of freedom. Each candidate solution represents a possible motion trajectory, and the fitness of each candidate is assessed on the basis of its ability to minimize the difference between image frames. For example, ref. [27] illustrated how GAs can optimize path planning in robotics, demonstrating their adaptability to dynamic environments.  
In [28], the authors proposed a new stereo visual odometry system for automotive applications that integrates advanced monocular techniques. The results indicate that adapting these techniques to a stereo context can significantly improve the accuracy and robustness of the system, validated through the results obtained on the KITTI dataset. The authors in [29] proposed an introspective vision for SLAM (IV-SLAM) that addresses the issue of assuming a uniform distribution of errors in feature extraction and matching. Experimental results demonstrate that IV-SLAM can accurately predict error sources in input images and significantly reduce tracking errors compared with previous algorithms.  
In [30], a network architecture that processes 3D point clouds in an end-to-end manner was presented without the need for predefined corresponding pairs of points. Experiments on the KITTI Vision Benchmark Suite have shown that the performance is comparable to other works, even by employing significantly fewer parameters. Finally, in [10], an estimation of ego-motion was formulated based on a lookup table approach and a new feature matching algorithm. The results indicate that this methodology not only reduces the complexity of the algorithm but also provides higher estimation accuracy, achieving significant processing speed improvements compared with previous approaches.
## 3 Construction of the Model
### Level 4 Evaluation: Antenna Isolation  
At Level 4, the EMC evaluation focuses on vehicle-mounted antenna isolation. Antenna isolation reflects the degree of influence of electromagnetic signals between antennas [34; 35]. The greater the antenna isolation, the smaller the interference. Due to the limited space on the roof of an armored vehicle, the antennas mounted on it are relatively close together. When vehicles communicate, multiple antennas operate simultaneously, and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Receiving** & **Transmitting** & **Horizontal Polarization** & \multicolumn{3}{c}{**Vertical Polarization**} & \multicolumn{3}{c}{**Circular Polarization**} \\
**Antenna** & **\(G<10\) dB** & \(G\)\(\geq\)**10 dB** & \(G<10\) dB** & \(G\)\(\geq\)**10 dB** & \(G\)\(\geq\)**10 dB** & **Polarization** \\ \hline Horizontal & \(G<10\) dB & \(0\) & \(0\) & \(-16\) & \(-16\) & \(-3\) \\ Polarization & \(G\geq 10\) dB & \(0\) & \(0\) & \(-16\) & \(-20\) & \(-3\) \\ Vertical & \(G<10\) dB & \(-16\) & \(-16\) & \(0\) & \(0\) & \(-3\) \\ Polarization & \(G\geq 10\) dB & \(-16\) & \(-20\) & \(0\) & \(0\) & \(-3\) \\ Circular Polarization & \(-3\) & \(3\) & \(-3\) & \(-3\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antenna polarization mismatching loss.  
their isolation is reduced. Combined with Equation (12), the antenna isolation is calculated using the following formula:  
\[P_{aT}=P_{t}-L_{T}+G_{t} \tag{23}\]  
\[P_{aR}=P_{r}-L_{\mathrm{R}}+G_{r} \tag{24}\]  
\[I=10\lg\frac{P_{aT}}{P_{aR}} \tag{25}\]  
Here, \(I\) is the antenna isolation, \(P_{aT}\) is the actual power of the transmitting antenna, and \(P_{aR}\) is the actual power of the receiving antenna.  
The above equations can be used to calculate antenna isolation. If the result is less than the threshold of the system EMC, the fourth-level EMC evaluation result will be \(\mathrm{IM}>0\), which indicates that a potential EMI is present and a fifth-level evaluation is required.
# Super Twisted Sliding Mode Observer for Enhancing Ventilation Drive Performance
## 1 Introduction
This enhanced observer employs a sigmoid function as an alternative to the traditional sign function, effectively reducing chattering. Another study also used the sigmoid function instead of the conventional sign function to estimate rotor position in IMs, reducing chattering [18]. Previous research has explored various approaches to the SM control law using fuzzy-logic inference systems. A fuzzy-logic-based SMO was established to mitigate chattering and enhance the observer's resilience to load distortion [19]. Improved SMOs utilize fuzzy-logic techniques instead of switching functions to estimate rotor flux and speed, effectively eliminating the chattering associated with conventional SMOs [20]. To address the need for sensorless control in IMs, one study introduced a fuzzy SM speed estimator in which the conventional SMO's sign function was replaced with a fuzzy-logic system [20; 21]. In [22], the author suggested the implementation of sliding mode control (SMC), which relies on the super-twisting observer (STO); hence, achieving second-order sliding mode (SOSM) by continuous control on the designated sliding surface is unfeasible. For the FOC-PMSM drive system, this paper suggests a new speed controller called disturbance-observer-based complementary sliding-mode (DO-CSM) [23]. One piece of research [24] presents a unique control framework for precisely managing the output voltage of floating interleaved boost converters (FIBCs), which have been extensively utilized in fuel cell applications in recent years. The summary of the research studies is shown in the Table 1.  
Meanwhile, high-order SMOs effectively reduce chattering while maintaining system robustness and stability. In a previous study, we adopted a super-twisting slide mode observer (STSMO) to evaluate the speed of IMs. The observer parameters were optimized utilizing the response surface method [25] to reduce chattering. In other studies, the STSMO was utilized for speed estimation to achieve the sensorless control of IMs [15; 26]. However, these studies did not evaluate stability, the trade-off between chattering and efficiency under transient conditions, or the impact of observer control law gains on chattering suppression. This article introduces a novel speed estimate methodology utilizing an STSMO to precisely determine the speed of induction motors (ventilation system) at extremely low and zero speeds while effectively mitigating the chattering issue. Observer gains are adaptively determined based on stability evaluations derived from [PERSON]'s theory. The proposed observer gains are adaptively formulated based on the stability analysis derived from [PERSON]'s theory. The indirect field-oriented control for a sensorless ventilation drive system, utilizing the proposed estimate technique, is developed using MATLAB/Simulink and a LabVIEW-based experimental platform. The modeling and experimental outcomes are shown under several operating scenarios to demonstrate the proposed approach.
## 2 Materials and Methods
### Methods
#### 2.2.2 Factorial Design of Experiments  
A 24-full factorial experimental design was executed to assess the impacts of flocculant dosage, settling time, RM time, and SM time on harvesting efficiency. This type of design allows the identification of important factors to focus on with further experimentation. Each factor underwent testing at low (\(-1\)) and high (\(+1\)) levels in duplicate. Table 1 displays the coded values for each run, while Table 2 presents the actual values for each flocculant. A four-way ANOVA (\(p\)-value \(<0.05\)) was conducted, leading to the establishment of empirical models characterizing flocculation for each flocculant. These models were subsequently employed to analyze the variables' effects on harvesting efficiency and predict optimal flocculation conditions. Statistical analysis and model construction utilized Minitab Statistical Software 21.1.0.0. The optimal conditions determined by the models were then validated through experimental testing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Run**} & **Dosage** & **Settling Time** & **RM Time (min)** & **SM Time (min)** \\ \hline
1 & \(-1\) & \(-1\) & \(-1\) & \(-1\) \\
2 & \(+1\) & \(-1\) & \(-1\) & \(-1\) \\
3 & \(-1\) & \(+1\) & \(-1\) & \(-1\) \\
4 & \(+1\) & \(+1\) & \(-1\) & \(-1\) \\
5 & \(-1\) & \(-1\) & \(+1\) & \(-1\) \\
6 & \(+1\) & \(-1\) & \(+1\) & \(-1\) \\
7 & \(-1\) & \(+1\) & \(+1\) & \(-1\) \\
8 & \(+1\) & \(+1\) & \(+1\) & \(-1\) \\
9 & \(-1\) & \(-1\) & \(-1\) & \(+1\) \\
10 & \(+1\) & \(-1\) & \(-1\) & \(+1\) \\
11 & \(-1\) & \(+1\) & \(-1\) & \(+1\) \\
12 & \(+1\) & \(+1\) & \(-1\) & \(+1\) \\
13 & \(-1\) & \(-1\) & \(+1\) & \(+1\) \\
14 & \(+1\) & \(-1\) & \(+1\) & \(+1\) \\
15 & \(-1\) & \(+1\) & \(+1\) & \(+1\) \\
16 & \(+1\) & \(+1\) & \(+1\) & \(+1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Organization of the 24-full factorial experiments, with the coded values of each factor (dosage, settling time, rapid mixing (RM) time, rapid mixing (RM) time, rapid mixing (RM) time, rapid mixing (RM) time, rapid mixing (RM) time, and slow mixing (SM) time).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Flocculant**} & **Coded** & **Dosage** & **Settling** & **RM Time** & **SM Time** \\  & **Value** & **(mg L-1)** & **Time** & **(min)** & **(min)** \\ \hline \multirow{2}{*}{Aluminum sulfate} & \(-1\) & \(50\) & \(5\) & \(0\) & \(0\) \\  & \(+1\) & \(200\) & \(15\) & \(3\) & \(15\) \\ \hline \multirow{2}{*}{Ferric sulfate} & \(-1\) & \(150\) & \(5\) & \(0\) & \(0\) \\  & \(+1\) & \(200\) & \(15\) & \(3\) & \(15\) \\ \hline \multirow{2}{*}{Chitosan} & \(-1\) & \(10\) & \(5\) & \(0\) & \(0\) \\  & \(+1\) & \(50\) & \(15\) & \(3\) & \(15\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Real values of the experimental factors (dosage, settling time, rapid mixing (RM) time, and slow mixing (SM) time) for each flocculant studied to harvest _C. vulgaris_.
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 7 Open Challenges
### Latency and Response Time  
Edge, fog, and cloud computing are integral components of ambient intelligence (AMI) environments. Edge servers positioned near data acquisition points face constraints such as limited computational resources, storage capacity, and energy efficiency. These limitations frequently result in latency issues, especially for real-time healthcare applications demanding immediate processing and response. Consequently, edge servers must implement efficient mechanisms to distinguish regular data from emergency data to deliver prompt responses during critical situations.
## 5 Deployment and Integrability
### Mobile Integration  
Apart from the two previous options (which would also allow access through mobile devices), deployment in mobile devices could be an alternative for constrained resources (remote or rural locations), as it does not need specific hardware or a connection to the clinic network. The model would be directly deployed to the phone after being adapted to mobile deployment (using frameworks like Tensorflow-Lite [88], and using 8-bit integer quantization or other techniques to limit the computer processing needed).  
The main drawbacks for this method are the lack of integration with the rest of the network, as well as the drop in speed and accuracy versus the full-precision pipeline.  
All described methods would need to comply with local regulations in terms of PHI/PII (EU GDPR/US HIPAA alignment), especially when the data leave the hospital network, and also enforce security measures such as HTTPS/TLS in transit, AES-256 encryption at rest, and role-based access control.
# Structure-Guided Image Inpainting Based on Multi-Scale Attention Pyramid Network
## 3 Methodology  
The methodology proposed in this document is illustrated in Figure 1. Overall, the structure-guided multi-scale attention pyramid Network for image inpainting can be divided into two main parts. The first part consists of a structure repair network with strong constraints, employing a U-Net-based encoder-decoder network as its backbone, a structure that has been extensively validated for its effectiveness. Additionally, this part includes an adversarial network with the same architecture, which uses real structures for feature extraction to constrain the structure prediction network. After the edge structure repair network generates the structural view, the second part, the cross-scale attention pyramid repair network, commences operation. The core strength of this network lies in its ability to fully utilize multi-scale information of the image and enhance the extraction and repair of key features through the attention mechanism. Initially, the cross-scale attention pyramid repair network performs multi-scale feature extraction on the input structural view. Through a pyramidal hierarchical structure, the network captures feature information from global to local scales, facilitating a comprehensive understanding of the image's structure and details.