esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Soundscape Evaluations and Influencing Factors
### A Comparison among the Four Blocks  
BLK-IV had a significant negative correlation between the sound levels and the annoyance evaluations, indicating that when this place had a higher sound level it was perceived to be noisier. In contrast, BLK-III had a significant positive correlation between most sound levels and the annoyance evaluations, indicating that when this place had a higher sound level, more annoyance was perceived. BLK-I and BLK-II had weak negative correlations in this regard. BLK-I and BLK-III had positive correlations between their sound levels and acoustic comfort, whereas BLK-II and BLK-IV had negative correlations. These results indicate that in places with more artificial soundscapes, acoustic comfort increases when the sound level increases, whereas in places with more natural soundscapes, acoustic comfort decreases when the sound level increases. Furthermore, similar results were obtained for correlations between loudness and the acoustic comfort evaluations. One possible explanation for these results could be tied to the spatial function of the space. A place with a more natural soundscape, like BLK-IV in the OCT ecological square, is usually used for restoration. Quietness is absolutely required and a higher sound level is rejected. In contrast, a place with a more artificial soundscape, like BLK-I, is usually used for exercise. Quietness is not emphasized, as recreation through events is paid more attention. This is in agreement with previous studies in spaces with high amounts of human activity and the evaluations confirmed increased acoustic comfort when sound energy increased [30; 59; 60]. This result implies that the spatial function of a place may play a role in determining soundscape evaluations. According to the affective attributes stated in ISO 12913-2 [33], eventful perception is an attribute used to evaluate soundscapes. For factors influencing eventful perception, the spatial function is important [11]. Even in the absence of enough formal research, some architectural designs have used soundscape approaches to meet different functional needs [61]. It is interesting to note that all blocks except BLK-IV (identified as a natural soundscape) showed positive correlations between their percentages of natural sounds and annoyance evaluations, which means that blocks with more natural sounds were perceived
## 1 Introduction
It produces high-quality results with an expensive computational burden [29]. Sub-Pixel Convolutional Upsampling rearranges the feature maps via a periodic shuffling operator to increase the spatial resolution [26]. It is fast and computationally efficient; however, it may lead to some artifacts. Many existing downsampling techniques are often combined with the above upsampling methods as it is difficult to implement an inverse transform for generating low-dimensional spatial features [11]. However, our proposed feature-slicing method can reconstruct high-resolution feature maps by reorganizing a feature map to enable the upsampling operation. In this paper, we present a comprehensive review of state-of-the-art downsampling methods in neural networks, focusing on their advantages and disadvantages, theoretical properties, and practical applications. To overcome the limitations faced by existing downsampling methods, we intend to develop a simple downsampling method that achieves efficient feature extraction and captures channel importance with low computational complexity without discarding the semantic and detailed information of the feature maps. The main contributions of this paper are described briefly below. 1. A novel downsampling method based on feature slicing and depthwise separable convolution, SliceSamp, is proposed which is designed to retain all feature information while optimizing computational efficiency. 2. An upsampling component, SliceUpsamp, is constructed by reconstructing the feature maps from the channel dimension to the spatial dimension, enabling the inverse operation of downsampling. 3. An ablation study is introduced to evaluate the effectiveness of the key design elements. The proposed method can achieve a better balance between algorithmic complexity and model performance. 4. Extensive experiments are conducted via various computer vision tasks, including image classification, object detection, and semantic segmentation, based on several benchmark datasets. Our method exhibits significant advantages over classical downsampling methods by replacing the original downsampling layers in different network architectures. The rest of this paper is organized as follows. Existing work relating to downsampling methods and depthwise separable convolution is reviewed in Section 2. The algorithmic concept and network architecture of the proposed method are described in Section 3. The dataset, implementation details, the results of experiments on a variety of computer vision tasks, and ablation studies are reported in Section 4. Study limitations and future work are discussed in Section 5. The conclusions of this work are summarized in Section 6.
## 4 Results and Evaluation
### Trajectories and Average Speed of 3 Scenarios  
In this section, we present graphs demonstrating the position and average speed over time for the 3 scenarios, DRL, ACTS, and MP, for the case of a platoon consisting of 3 vehicles.  
The distance-time relationship between vehicles from west to east for one lane in 200 s is plotted in Figure 11. Colors in the graphs show the speed of the vehicles when moving, in which green corresponds to the highest speed (20 m/s) and black corresponds to the lowest speed (0 m/s). Alternatively, one can perceive the vehicle's speed adjustment based on the slope of these lines. Platoons started at 5 m/s (starting speed of vehicles when entering the model) and then increased to the desired speed. When approaching the intersection, if the light was green, vehicles kept moving and passed the intersection. If the light was red, vehicles had to stop and wait until the light was green to pass. After passing the intersection, platoons moved at the desired speed. The green time duration depends on the algorithm applied in each scenario.  
Figure 11: Position–time graph of DRL-, ATSC-, and MP-based scenarios.  
It can be seen that the green time in the ATSC-based scenario tends to be longer than in the DNN- and MP-based scenarios. In this scenario, the green light phase depends on the sensor's vehicle detection. If vehicles are still detected, the green light will be extended until the maximum limit or no more vehicles are detected. In contrast to ATSC, the MP controller is stable in terms of queue length, so it quickly changes phase when detecting a larger pressure in the other direction. Therefore, the length of the green phase in this scenario is shorter. The DRL-based scenario has the most reasonable green time compared to the two aforementioned scenarios. After learning, the traffic agent optimized the intersection's green time and cycle length. Although the traffic light duration is variable, the trained model can handle this uncertainty.  
The average speed graph of the three scenarios is also expressed in Figure 12. The average speed of scenarios decreases when many cars must wait in line at the intersection. With a shorter red and green duration, the model efficiency is improved, and the vehicle speed is also greater. The speed perturbation is amplified by the RVs between platoons and reduced by the platooned vehicles. The DRL-based scenario optimizes the waiting time, so all vehicles (CVs and RVs) move at the highest speed. We can see that this average velocity is cyclic with traffic signals and peaks at the middle of the green phases. With the current traffic demand, the average speed of the DRL-based scenario is larger than in the other two scenarios, but this difference is not significant (approximately 10%).
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 4 The Cross-Layer Approach
### The Power Metric and the Continuous Reading Constraint
The user-perceived capacity, on the other hand, is the capacity of the wireless communication system under a given power consumption. Therefore, we propose Algorithm 1 to address this issue, that is, to find the user-perceived capacity under a given power consumption. Meanwhile, the complexity of Algorithm 1 is \(\mathcal{O}\left(\log\left(\frac{P_{ave}T}{\Delta P_{\min}}\right)\right)\), where \(\Delta P_{\min}=\min_{s,h_{l}}P(s+1,h_{l})-P(s,h_{l})\). ```
1:Input:\(T\), \(P_{ave}\). 2:\(B_{1}\gets 0\), \(B_{2}\leftarrow\frac{P_{ave}T}{\Delta P_{\min}}\)
3:\(B\leftarrow\left\lfloor\frac{(B_{1}+B_{2})}{2}\right\rfloor\)
4:\(P(B)\leftarrow\) the optimal objective function of problem (25) with the frame size as \(B\)
5:\(P(B+1)\leftarrow\) the optimal objective function of problem (25) with the frame size as \(B+1\)
6:while\(P(B)>P_{ave}\) or \(P(B+1)<P_{ave}\)do
7:if\(P(B)>P_{ave}\)then
8:\(B_{2}\gets B\)
9:else
10:\(B_{1}\gets B\)
11:endif
12:\(B\leftarrow\left\lfloor\frac{(B_{1}+B_{2})}{2}\right\rfloor\)
13:\(P(B)\leftarrow\) the optimal objective function of problem (25) with the frame size as \(B\)
14:\(P(B+1)\leftarrow\) the optimal objective function of problem (25) with the frame size as \(B+1\)
15:endwhile
16:\(\frac{B}{T}\) is the maximum user-perceived capacity. ```  
**Algorithm 1** User-Perceived Capacity Search Algorithm
## 3 Material and Methods
### Quantitative Real-Time PCR  
Quantitative real-time PCR (qRT-PCR) was performed to explore the transcription of genes involved in astaxanthin and DHA biosynthesis and also to validate the RNA-Seq results. Total RNA was extracted from eight different time points of cell cultivation (24, 36, 48, 60, 72, 84, 96 and 108 hours). RNA samples were treated with RNase-free DNase I (TaKaRa Biotech Co., Ltd., Dalian, China) to remove any contaminating DNA. Agarose gel electrophoresis (1.5%), Nanodrop 2000 (Thermo Scientific, Wilmington, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, USA)were used to check RNA integrity, concentration, and quality. A total of 2.5 \(\upmu\)g RNA was used for the first-strand cDNA synthesis in a volume of 20 \(\upmu\)L reaction, using Prime Script II 1 st Strand cDNA Synthesis kit (TaKaRa Biotech Co., Ltd., Dalian, China). The cDNA synthesized was diluted 15-folds before being used. qRT-PCRs were performed on a CFX Connect Real-Time System (Bio-Rad, Hercules, CA, USA) with a 20 \(\upmu\)L reaction volume consisting of 0.25 mM of each primer (Table S4), 10 \(\upmu\)L of iTaq SYBR Green Supermix (Bio-Rad) and 2 \(\upmu\)L of template cDNA. The PCR profile was: 30 seconds at 95 \({}^{\circ}\)C followed by 40 cycles of 5 seconds at 95 \({}^{\circ}\)C, 15 seconds at 60 \({}^{\circ}\)C and followed by 0.5 \({}^{\circ}\)C increment at 5 seconds/step from 65 to 95 \({}^{\circ}\)C for the Melt Curve analysis. Actin gene was set as reference gene and the 2\({}^{-\Delta\)ACT method was used to calculate the relative changes in gene expression. The data obtained was analyzed using CFX Manager(tm) Software v3.1 (Bio-Rad).
# HBIM: Background, Current Trends, and Future Prospects
## 2 HBIM's Origins and Development
In any case, the need to differentiate between traditional BIM and HBIM stems from their differing purposes and the specific characteristics of the buildings they are designed for. While BIM focuses on new building design and construction, HBIM is centered on existing constructions that usually require interventions such as conservation, restoration, repurposing, and rehabilitation [8]. In addition, historical buildings often present complex geometries and structural systems, and the information available is limited or incomplete, having to deal with several uncertainties. This complexity requires a multidisciplinary collaboration among various experts to ensure the accurate acquisition and processing of data, as well as to value the historical significance of the building, which is critical for decision-making regarding its maintenance and potential interventions [26; 27]. In fact, the HBIM-related scientific publications analyzed in this review span a wide range of research areas, including Computer Science, Engineering or Construction Building Technologies, Art and Humanities, Geography, and Environmental Sciences, among others. The main research areas to which these publications belong are shown in Figure 3 (only categories with values over 5% are represented; the total exceeds 100% since several publications are indexed under multiple categories). Regarding the geographical distribution of the studies, HBIM has generated interest in many countries worldwide (see Figure 4), especially those who are eager to explore its potential for preserving their architectural heritage. Notably, a significant number of scientific publications have been developed in Italy, accounting for up to 44% of the total, followed by Spain (14%), China (11%), and the UK (8%). These numbers can be attributed to the vast architectural heritage found in these countries, along with a strong awareness and tradition of its preservation and maintenance. In fact, countries like Italy and Spain have been global leaders in the development and implementation of BIM policies and are at the forefront of advancements and scientific research in the HBIM field [28]. Figure 2: Publications and citations in the Web of Science regarding HBIM between 2010 and 2023.
# Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method
## 5 Experiment and Discussion
### Comparative Experiment between GVMS and P26
#### 5.3.1 Comparison of Integrated Measurement Results  
The results of the pitch deviation, base pitch deviation, profile deviation, and tangential composite deviation measured by GVMS and P26 are summarized in Table 2. The base pitch deviation and tangential composite deviation measured by P26 are decomposed from the gear integrated error curve.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Items** & \multicolumn{3}{c}{**Right-Hand Tooth Flank**} & \multicolumn{3}{c}{**Left-Hand Tooth Flank**} \\ \hline  & GVMS & P26 & Error & GVMS & P26 & Error \\ \hline \(F_{i}^{\prime}\) & 20.8 & 15.5 & 5.3 & 22.7 & 18.8 & 3.9 \\ \(f_{i}^{\prime}\) & 4.5 & 3.3 & 1.2 & 6.3 & 3.4 & 2.9 \\ \(F_{\alpha}\) & 5.5 & 7.3 & \(-1.8\) & 6.2 & 5.7 & 0.5 \\ \(f_{f\alpha}\) & 2.5 & 1.5 & 1.0 & 2.9 & 1.2 & 1.7 \\ \(f_{H\alpha}\) & 3.5 & \(-7.3\) & \(-10.8\) & 3.7 & \(-5.8\) & 9.5 \\ \(F_{p}\) & 15.1 & 12.9 & 2.2 & 15.2 & 14.9 & 0.3 \\ \(f_{p\mathrm{tmax}}\) & 7.0 & 7.4 & \(-0.4\) & 9.5 & 11.1 & \(-1.6\) \\ \(f_{p\mathrm{tmin}}\) & 0.2 & 0.1 & 0.1 & 0.3 & 0.1 & 0.2 \\ \(f_{p\mathrm{tmax}}\) & 10.1 & 10.3 & \(-0.2\) & 11.5 & 10.6 & 0.9 \\ \(f_{p\mathrm{tmin}}\) & 0.3 & 0.1 & 0.2 & 0.2 & 0.1 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of error results decomposed from the gear integrated error curve (unit: \(\upmu\)m).  
Figure 17: Polar coordinate representation of the gear integrated error curve. (left profile). (**a**) Gear integrated error curve. (**b**) Gear integrated error curve and its outer envelope.  
Figure 18: P26 measuring equipment.
# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
## 1 Introduction
Additionally, [PERSON] (2018) studied an iterative inversion spectral decomposition method based on shape regularization and applied it to the spectral analysis of gas-containing reservoirs [29]. Furthermore, shape regularization methods have shown promising results in one-dimensional data interpolation, velocity estimation, and local seismic attribute estimation. Although the constrained least squares spectral analysis (CLSSA) method based on inversion concepts offers the advantage of high resolution, it has the drawback of being computationally expensive when dealing with large datasets. In numerical analysis and inversion, the L1 norm encourages sparsity, meaning it tends to produce solutions with many zero-valued coefficients, which is beneficial in feature selection and sparse modeling, particularly with high-dimensional data. Additionally, it is robust to outliers, as it minimizes the sum of absolute differences, making it less sensitive to extreme values. However, the L2 norm is sensitive to outliers, as squaring large errors disproportionately amplifies their influence, potentially skewing the model. Additionally, it does not encourage sparsity, yielding dense solutions that can increase model complexity, particularly in high-dimensional data. Therefore, this paper replaces the Tikhonov regularization objective function with the L1-norm regularization objective function in the CLSSA method, and employs a fast iterative algorithm to significantly enhance its computational efficiency. The structure of this paper is as follows: First, a brief introduction to the CLSSA method and its theoretical foundation is presented. Next, an improved iterative updating algorithm is introduced. Finally, the method is applied to a research area in southern China, demon strating its effectiveness in practical applications and yielding high-resolution, reliable time-frequency spectrum profiles.
## 3 Performance Analysis
### Average Capacity  
According to the definition of the average channel capacity [32; 33], \(\overline{C}=E\big{[}\log_{2}(1+\gamma)\big{]}\), \(E[\bullet]\) denotes the expectation operator, the expression for the average channel capacity of the hybrid system is given by:\[\begin{array}{l}C_{ave}=\int\limits_{0}^{\infty}{\log_{2}(1+\gamma)f_{y}(\gamma)d \gamma}\\ =\frac{1}{\ln 2}\int\limits_{0}^{\infty}{\frac{1}{1+\gamma}F_{y}^{c}(\gamma)d \gamma},\end{array} \tag{8}\]  
where \(F_{y}^{c}(\gamma)=1-F_{y}(\gamma)\), \(\frac{1}{1+\gamma}=G\begin{array}{l}1,1\\ 0,0\end{array}\left(\begin{array}{l}0\\ x\end{array}\right)\), substituting Equation (4) into Equation (8) and using the expression of the extended generalized bivariate Meijer's G function (EGBMGF) in reference [34]. The average channel capacity is obtained as  
\[C_{ave}=\frac{2^{(\alpha+\beta)}A\sqrt{C}}{16\pi\ln(2)}\frac{\gamma_{1}G}{0,0 :0,0:1,6}\left(\begin{array}{l}-\\ -\end{array}\right|\begin{array}{l}0\\ 0\end{array}\left|\begin{array}{l}\frac{\xi^{2}+1}{2}\\ \kappa_{1}\end{array}\right|\frac{(\alpha\beta)^{2}C}{16\gamma_{2}}\end{array}\right). \tag{9}\]
# Validation of a Manual Methodology for Measuring Roundness and Cylindicity Tolerances
## 2 Materials and Methods
### Experimental Procedure
#### 2.2.2 Verification with the Cylindricity Measuring Equipment (CME Methodology)  
In the work plan proposed in this paper, an automated roundness and cylindricity measuring equipment Talyrond 131C from Taylor Hobson is employed, see Figure 6. This equipment is a coordinate measuring machine that allows the roundness and cylindricity tolerances to be evaluated, according to the ISO 1101 standard [19], with a measurement range of 2 mm. It consists of a rotating table with a stylus that makes contact with the workpiece. A total of 3600 points are recorded in each section. The axial positioning of the stylus is defined as the number of sections to be evaluated and the distance between them.  
The CME methodology requires application to some previous operations: to level the table, to calibrate the axis of the part with respect to the center of the table, to select the correct position of the stylus, and to choose the plane to be worked. The use of the software that controls the equipment is also an important feature to take into consideration, at least to select the main parameters from a quantitative viewpoint: exterior or inner roundness or cylindricity and the planning of the vertical stylus position, especially for cylindricity measuring. Nevertheless, the most important fact is the visualization of the results, as users can consider the four reference circles defined by the ISO 12181-1 standard [21]. The results menu offers the total description of the roundness tolerance of a selected cross section, as well as the cylindricity tolerance for the whole part. Moreover, one of the aspects that can be highlighted in this context is the possibility of evaluating the roundness and cylindricity in a qualitative way by observing the 3D sketch of the part, where the deviations of the shape are magnified and supported with colors and a bar scale.  
Particularly, the CME methodology consists of testing the shape of the part according to the least squares circle and least squares cylinder criteria, for the same sections that have been measured by the manual method. This allows the measurements performed manually to be compared and contrasted with the results obtained using high-precision equipment.  
Figure 6: Talyrond 131C cylindricity measuring equipment by TaylorHobson. Red arrows indicate the possible movement directions for the location of the stylus and the rotation of the table.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion
### Discussion of xLSTM Results
#### 4.2.3 Seasonal Probabilistic Distribution Prediction Performances of Power Generation  
Table 7 presented above provides a comprehensive quantitative analysis of power output probability distributions across four seasons, comparing actual SCADA measurements with xLSTM model predictions. The probability density distributions exhibit a consistent bimodal structure across all seasons, with primary concentration at 0 kW and secondary concentration at rated capacity. This bimodality is quantified through coefficients ranging from 0.523 to 0.621 in SCADA measurements, with the xLSTM model accurately capturing this structural characteristic (coefficients 0.517 to 0.612). Seasonal variations in power generation are evident through central tendency metrics. Winter demonstrates the highest mean power output (1485 kW for SCADA measurements, 1523 kW for xLSTM results), followed by spring (1326 kW for SCADA measurements, 1375 kW for xLSTM results), summer (1297 kW for SCADA measurements, 1342 kW for xLSTM results), and autumn, which exhibits the lowest output (1186 kW for SCADA measurements, 1232 kW for xLSTM results). The xLSTM model exhibits a consistent slight overestimation of mean power across all seasons, with deviations ranging from 2.6% to 3.9%.  
Distribution shape parameters reveal important seasonal characteristics, with autumn displaying the highest skewness (0.824 for SCADA measurements, 0.793 for xLSTM results) and coefficient of variation (0.982 for SCADA measurements, 0.965 for xLSTM results), indicating greater variability and asymmetry. Summer exhibits the most symmetric distribution with the lowest skewness value (0.587 for SCADA measurements, 0.576 for xLSTM results), and winter demonstrates the most consistent production pattern with the lowest coefficient of variation (0.913 for SCADA measurements, 0.911 for xLSTM results). The probability of low power production (<500 kW) shows significant seasonal variation, with autumn exhibiting the highest probability (0.344 for SCADA measurements, 0.316 for xLSTM results) and winter the lowest (0.264 for SCADA measurements, 0.243 for xLSTM results). Conversely, the probability of high power production (>3000 kW) is greatest in winter (0.187 for SCADA measurements, 0.196 for xLSTM results) and lowest in autumn (0.124 for SCADA measurements, 0.137 for xLSTM results), reflecting seasonal wind resource availability.  
Model performance metrics indicate strong predictive capability across all seasons, with \(R^{2}\) values ranging from 0.967 in autumn to 0.982 in winter. The MAPE in probability density estimation exhibits seasonal variation, with winter showing the lowest error (5.82%)and autumn the highest (8.76%). This pattern suggests that the xLSTM model performs optimally during steady meteorological conditions typically found in winter, with performance decreasing slightly during transitional seasons characterized by more variable weather patterns.