esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theoretical Considerations  
The SPD (uW/cm\({}^{2}\)) from 300 nm to 1000 nm, the CCT, and the colour coordinates x and y of the selected day were recorded for further processing. The photopic irradiance \(E_{e,photopic}\)\(\left(\text{W}/\text{m}^{2}\right)=\int_{\lambda=380}^{780}SPD(\lambda)V( \lambda)d\lambda\) and illuminance \(E_{photopic}(lux)=K_{m}\int_{\lambda=380}^{780}SPD(\lambda)V(\lambda)d\lambda\), were determined (\(K_{m}=\frac{683\text{m}}{\text{W}}\) for photopic vision, \(SPD(\lambda)\) is the spectral power distribution of the lighting source and \(V(\lambda)\) is the CIE photopic spectral luminous efficiency function) [10; 19]. The melanopic irradiance \(E_{e,melanopic}\left(\text{W}/\text{m}^{2}\right)=\int_{\lambda=380}^{780}SPD( \lambda)S_{mel}(\lambda)d\lambda\) was analogously calculated (\(S_{mel}(\lambda)\) is the CIE melanopic spectral weighting function) [10]. The ratio \(\frac{\tilde{E}_{e,melanopic}}{E_{e,photopic}}\) is named the Melanopic Action Factor (MAF) [20], following the Circadian Action Factor (CAF) proposed by [PERSON] [21], based on spectral irradiance behaviour and was used to propose a method to evaluate the nonvisual potential of daylight. In a previous publication by this research group, the conversion factors from photopic values of illuminance to the CIE melanopic standard and to other recommendations such as WELL were calculated in detail [20]. To understand these conversions, a summary of the equations is added.  
The normalization imposed by the CIE standard with melanopic illuminance was provided by the standard illuminant D\({}_{65}\) (daylight CCT = 6500 K) in accordance with the EDI. A light-source type D\({}_{65}\) furnishing photopic illuminance E\({}_{\text{e,photopic,D65}}\) to provide the same melanopic illuminance E\({}_{\text{ melanopic,D65}}\) can be calculated.  
If the photometric quantity is defined by E\({}_{\text{photopic,D65}}\) = Km E\({}_{\text{e,photopic,D65}}\)[19], the same melanopic E\({}_{\text{ melanopic,D65}}\) = Km\({}_{\text{ melanopic,D65}}\) E\({}_{\text{e, melanopic,D65}}\) for light source D\({}_{65}\) can be described in terms of an equality as follows: E\({}_{\text{photopic,D65}}\) = E\({}_{\text{ melanopic,D65}}\). For any source:  
\[\text{EDI}=753.8\int_{\lambda=380}^{780}SPD(\lambda)Smel(\lambda)d\lambda\]  
In addition, the relation of the EDI with the photometric illuminance can be deduced as follows [20]:  
\[\text{EDI}=1.104\text{ MAF E}_{\text{photopic}}\]  
The Equivalent Melanopic Lux (EML) is defined by the WELL recommendations [13] with reference to the equi-energy illuminance and can be calculated for any source, in terms of melanopic parameters; the EML is defined as follows [20]:  
\[\text{EML}=831.8\int_{\lambda=380}^{780}SPD(\lambda)Smel(\lambda)d\lambda\]  
In addition, the relation of the EML to photometric illuminance is described as follows:  
\[\text{EML}=1.218\text{ MAF E}_{\text{photopic}}\]  
Using these conversion factors and photopic values of the illuminance, transformations from one melanopic metric to another are easily calculated by EML = 1.104 EDI.
## 6 Analysis of the Findings of This Review
### Summary of Findings and Responses to Research Questions  
Section 3 of this paper describes the common programming and control methods for cobots, existing and proposed in the literature. Solutions from seven leading cobot vendors were evaluated, and systems proposed in the literature were critiqued. The impact of a cobot's task complexity on the skills required to program it was addressed in Section 5, with a graphical representation and summary of the analysis provided at the end of that section. The effectiveness of existing cobot programming and control methods and the practicality of methods proposed in the literature were assessed in Sections 6.1 and 6.2, which focused on existing and proposed programming and control methods, respectively. The final considerations were to determine the most appropriate candidate for the role of cobot programmer and assess whether the current or proposed solutions can be matched to that person's skillset. In Section 2, it was argued that rather than focusing on the skills required to _program_ a cobot, the knowledge of the _task_ should be the focal point. The view was that it is often the case that the person who knows the task well, is not competent with programming and the person who is competent with programming does not know the task well. Operators with knowledge of and experience with a task that a cobot is intended to assist with possess valuable insights into precision and efficiency, details of which may be difficult to relay to a contracted programmer. Moreover, adaptive task-sharing principles aim to embed cobot programming into workers' duties. Based on these findings, a task-experienced worker would be the most appropriate programmer, but the programming methods analysed have not been specifically developed for a novice, as discussed in Section 5. Compounding this problem, the scale in Figure 7 indicates that existing cobot programming and control methods require more skill as cobot task complexity increases. In addition, the investigation in Section 6.2 revealed that the methods proposed in the literature were impractical or not sufficiently developed for reliable deployment. In general,these current cobot programming and control methods and those proposed in the literature require more advanced skills or are underdeveloped to be used by a worker with no robotic or programming experience. In the absence of a designated cobot programmer, therefore, either the upskilling of cobot novices, who are task-savvy operators or the development of a new generation of simplified cobot programming and control methods are the only viable options.
# Space Efficiency in European High-Rise Timber Buildings
## 3 Results
### Space Efficiency  
In this research, following an analysis of 56 cases, we observed that the average spatial efficiency and core-to-GFA ratio were 82% and 11%, correspondingly. The spectrum of these metrics ranged from a smallest of 70% and 4% to a highest of 90% and 21%, respectively.  
Table 1 presents a comparative analysis of the findings above on average space efficiency and the ratio of core to GFA with those documented for Middle Eastern non-timber skyscrapers as referenced in Asia [41], and Middle Eastern skyscrapers as detailed in [42].  
Treat, Timber Pioneer, and Docenten buildings demonstrate outstanding progress in space usage and architectural design, achieving impressive spatial efficiencies of up to 90%. These structures have set a new benchmark within the study group by boasting the smallest GFA ratios. This exceptional performance is attributable to meticulously designed cores that concentrate on compactness through the optimization of service areas and shaft arrangements, thereby maximizing usable floor space. Furthermore, these towers  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **The Findings** & **Asian** & **Middle Eastern** \\  & & **Skyscrapers (75 Cases)** & **Skyscrapers (27 Cases)** \\ \hline Average space & 82\% & 67.5\% & 75.5\% \\ efficiency & (max. 90\%, min. 70\%) & (max. 82\%, min. 56\%) & (max. 84\%, min. 63\%) \\ \hline Average ratio of & 11\% & 29.5\% & 21.3\% \\ core to GFA & (max. 21\%, min. 4\%) & (max. 38\%, min. 14\%) & (max. 36\%, min. 11\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of average space efficiency and ratio of core to GFA.  
employ innovative structural systems--Treat with a tube system, Timber Pioneer with a shear-walled frame, and Docenten with a shear wall system--each enhancing their resilience against both vertical and horizontal forces. By utilizing compact cross-sections of structural elements, these buildings competently distribute and resist loads, ensuring both stability and durability. The integration of these cutting-edge design norms not only guarantees superior space efficiency but also significantly bolsters the structural robustness of these towers, establishing a new model for sustainable urban design. This holistic approach to architectural and structural innovation underscores the potential for combining advanced engineering techniques with optimized space utilization strategies to create resilient, efficient, and sustainable urban environments.
## 5 Safety
### Safety Zones  
The safety requirements of Phollower are considered to include both passive and active machinery safety. Protective metal covers ensure passive safety. Active safety consists of electrical drives with their safety codecs, a safety PLC that evaluates the direction and speed of the robot according to signals from safety encoders, and safety scanners that are used for monitoring the area in front of and behind the machinery (i.e., the safety zone). Note that it is essential to use a safety chain consisting of safety encoders, safety PLC, and safety laser scanners.  
It must be stressed that the size and the shape of the safety zone vary with the direction of movement and the actual speed of the robot. Therefore, its size and shape need to be thoroughly calculated, implemented, and experimentally verified. The setup and the braking distance protocol was created for the safety scanners. It characterizes the relationship of braking distance depending on  
Figure 5: The measuring slot and placement of safety zone scanners.  
the direction of movement, actual speed, and the shape and size of the safety zone. These parameters were calculated for a fully loaded vehicle with a safety factor of 1.2 (120% weight overload).  
Safety LiDAR operation can be disrupted by a random reflection of shiny objects, such as a glass or mirror. This is detected by Phosphlower as a \"false\" safety zone disruption, and it is followed by an immediate stop. These glints are unpredictable, so this routine has been adopted and safety stop is executed every time the glint is detected. There is one more additional 3D camera-based algorithm that detects obstacles above or below the LiDAR scan height range. Used 3D cameras do not have safety certification, but a camera certification is not required by the safety standard. This additional algorithm records a point cloud in front of the robot in the longitudinal direction, and if an obstacle is detected in the point cloud, the robot will decelerate and stop to avoid collision. This protection is applied across the entire width of the robot's body and detects obstacles 5 cm above the floor.
## 3 Fault Feature Extraction Technique
### FIR Bandpass Filter Design  
Filter is widely used in signal processing in order to filter out the components which include the transient impulse feature for fault diagnosis [25]. For vibration signals, it is necessary and essential to design a desired filter to extract the transient impulse from strong background noise and other interference signals. Thus, a finite impulse response (FIR) bandpass filter is introduced to the proposed method in the paper. Compared with infinite impulse response (IIR) filter, FIR filters are inherently stable because they don't need to feedback and recursive [26; 27]. The impulse response of the FIR filter is of finite duration. The output sample of that is a weighted sum of the last \(N\) input samples, where \(N\) represents the order of the filter.  
The FIR filter has many greater abilities compared with IIR filter: (1) FIR filters are non-iterative and don't require feedback, which make the implementation simpler. (2) FIR filters can utilize the FFT algorithm due to the finite impulse response, which will make the calculation process faster. (3) FIR filters do well in linear phase which is necessary on essential to phase-sensitive applications. (4) FIR filters are intrinsically stable without the application of the largest value in the input.  
As stated above, FIR filters have a good character of filtering out the fault feature and information from the original signal occupied by a large amount of background noise. It is stated that the transient impulse involves a quasi-periodic oscillation damping process which is filled with abundant fault features. The oscillation frequency is quite near to the resonance frequency of the rotating bearing. Therefore, the paper develops the proposed method with a FIR bandpass filter in order to separate the transient impulse from low frequency disturbed signal.  
In order to further extract the fault signature, the frequency-domain calculation of the reconstructed short segment \(y_{j}\) is applied as follows:  
\[H(i)=\ \ \sum\limits_{n=0}^{Nw-1}y_{j}(n)e^{-j\frac{2\pi}{Nw}ni}\ \ \ (0\leq i\leq Nw-1) \tag{25}\]  
where \(H(i)\) is the frequency domain series of \(y_{z}\).  
The main difficult in the designing of FIR filter is how to choose the optimal filter parameters: the central frequency \(fc\) and the bandwidth \(\Delta f\). Based on the short sliding segment with the minimum  
Figure 6: Comparison results of the noisy simulated signal waveform by different methods: (**a**) the traditional vector entropy value sequence; (**b**) the proposed MEVS of 1-scale; (**c**) The proposed _MFEVS_.  
Figure 7: (**a**) The waveform of the reconstructed short sliding signal; (**b**) the corresponding frequency spectrum.
## 4 Actual Project Study
### Comparison of Calculation Results  
Figure 11 displays the settlement deformation curve of the bottom slab, while Figure 12 illustrates the additional bending moment in the slab due to tunnel excavation. Notably, both analytical and finite element results, as well as measured data, exhibit similar deformation and bending moment patterns. In terms of quantities, the analytical solution predicts a maximum settlement of 3.58 mm, with a measured value of 3.1 mm, differing by 0.48 mm. Numerical simulation and analytical solutions yield additional bending moments of 84.13 kN\(\cdot\)m and 98.34 kN\(\cdot\)m, respectively, with a 14.27% error. These comparisons validate the feasibility of using the proposed elastic foundation beam method.
## Abbreviations  
The following abbreviations are used in this manuscript:  
i, j subscripts indicating generic bands  
\(pp,pq\) subscripts indicating like and cross polarizations  
ASAR Advanced Synthetic Aperture Radar  
AirSAR Airborne Synthetic Aperture RadarB, V  
bare or vegetated soils  
CSK  
COSMO-SkyMed-Constellation of Small Satellites for Mediterranean basin Observation  
DEMMIN  
Durable Environmental Multidisciplinary Monitoring Information Network  
DLR  
_Deutsches Zentrum fur Luft- und Raumfahrt_  
EA, SEA  
empirical and semi-empirical approaches  
EO  
Earth Observation  
E-SAR  
Experimental SAR  
ERS-2  
European Remote Sensing 2  
GO  
Geometrical Optics model  
HH  
horizontal like polarization  
HV  
horizontal vertical cross polarization  
IEM  
Integral Equation Model  
MIMICS  
Michigan Microwave Canopy Scattering  
PALSAR  
Phased Array type L-band Synthetic Aperture Radar  
RTM  
Radiative Transfer Model  
SAR  
Synthetic Aperture Radar  
SIR-C  
Spaceborne Imaging Radar-C  
SVV  
Soil Vegetation Variables  
VV  
vertical-vertical like polarization  
VH  
vertical-horizontal cross polarization  
WC  
Water Cloud model  
X-SAR  
X-Band Synthetic Aperture Radar  
ZALF  
_Leibnitz-Zentrum fur Agarlandschaftsforschung_  
X  
SAR band at 2 cm wavelength  
XUV, XHH  
band X in VV and HH like polarizations  
C  
SAR band at 5 cm wavelength  
CHH, CUV  
C band in HH and VV like polarizations  
CHV, CVH  
C band in HV and VH cross polarizations  
L  
SAR band at 21 cm wavelength  
LHH, LUV  
L band in HH and VV like polarizations  
LHV, LVH  
L band in HV and VH cross polarizations
## 2 Methods
### Evaluation Criteria  
To evaluate the accuracy of the eight algorithms, we adopted the explained variance score (EVS) and root mean squared error (RMSE) to evaluate the performance of the models. For the raw value \(Y=\{y_{1},y_{2},\ldots,y_{n}\}\) and the predicted value \(\overset{\wedge}{Y}=\left\{\overset{\wedge}{y}_{1},\overset{\wedge}{y}_{2}, \ldots,\overset{\wedge}{y}_{n}\right\}\) of the SMA parameters, these statistical metrics were calculated as follows [51]:  
\[\text{EVS}=1-\frac{\textit{Var}(y_{i}-\overset{\wedge}{y}_{i})}{\textit{Var} (y_{i})}, \tag{10}\]\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}} \tag{11}\]  
where _Var_ is the sample variance; \(i\) is the sample number, ranging from 1 to \(n\). EVS is the variance score explaining the regression model, and the best possible score is 1.0. The closer EVS is to 1, the better predicted the values are to explain the variance of the measured values. RMSE represents the standard error between the predicted values and the measured values. Instead, the smaller the RMSE, the better predictability of the model. Therefore, we expect the prediction algorithms with higher EVS and lower RMSE.
## 3 Analysis of Factors Affecting Shear Behavior of Reinforced Concrete Beams
### Effect of the Amount of FRP Grid Reinforcement
#### 3.1.3 Summary of the Effect of Strengthening Amount  
According to the FRP grid reinforcement amount calculation, Formula (16), \(\rho_{\rm g}=\frac{nA_{\rm g}}{\rm BS_{\rm g}}\), given in Section 2, when the number of layers is 1, thus n = 1. The reinforcement amounts of beam B-T5 are a grid thickness of 2 mm, a width of 10 mm, and a spacing of 50 mm. The reinforcement amounts of beam B-I3 are a grid thickness of 5 mm, a width of 10 mm, and a spacing of 125 mm. Their reinforcement amounts are all 0.53%. As described in Sections 3.1.1 and 3.1.2, the shear behavior capacity of beam B-I3 is 5.23% lower than that of beam B-T5. Thus, under the condition of the same reinforcement amount, the beam B-T3 with a smaller thickness and a smaller spacing has a higher shear behavior capacity. It can be inferred that the combination of a small thickness and a small spacing contributes more to the shear behavior capacity of the strengthened beam. The author believes that the possible reason is that when the grid spacing is smaller, the probability of the cracks at the bottom of the beam encountering the grid during the expansion process increases significantly, increasing its utilization rate, which can more effectively delay the crack expansion and, to a certain extent, enhance the stiffness and bearing capacity of the beam. It is also found from the charts in the previous two sections that as the reinforcement amount increases, the shear behavior capacity of the strengthened beam gradually increases. However, this enhancement effect is not completely linear. When the reinforcement amount has reached a certain value, that is, about 1.33%, further increasing the thickness will result in an increase rate of the beam's shear behavior capacity within 1%.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 3 Experiment and Analysis
### Comparison of Other Anomalous Sound Detection System
For instance, SW-WaveNet [14] has 21.56 G FLOPs and 34.47 M parameters. Although it delivers strong performance (AUC of 93.25%), its high computational complexity renders it less feasible for practical applications, particularly in resource-constrained environments. Among multi-feature methods, ASD-AFPA [23] and CLP-SCF [39] strike a more favorable balance between performance and complexity, with FLOPs of 1.71 G and 1.62 G, and parameter counts ranging from 1.19 M to 1.55 M. However, their complexity still exceeds that of single-feature methods by a significant margin. In contrast, LMS-MobileNetV3 [24], although having relatively low FLOPs (417.36 M), exhibits higher computational complexity compared to ASDNet's 85.4 M and SpecMFN's 184.2 M. Moreover, LMS-MobileNetV3 has a substantially larger number of parameters than both ASDNet and SpecMFN. \begin{table}
\begin{tabular}{c c c} \hline
**Network** & **Flops** & **\# Params.** \\ \hline
**Multi-feature input methods** & & \\ STgram-MFN [13] & 1.62 G & 1.16 M \\ SW-WaveNet [14] & 21.56 G & 34.47 M \\ ASD-AFPA [23] & 1.71 G & 1.55 M \\ CLP-SCF [39] & 1.62 G & 1.19 M \\ LMS-MobileNetV3 [24] & 417.36 M & 1.80 M \\ \hline
**Single-feature input methods** & & \\ LogMel-MFN [13] & 1.38 G & 0.87 M \\ Spectrogram-WaveNet [14] & 10.78 G & 17.17 M \\ LogMel-MobileNetV3 [24] & 411.59 M & 1.79 M \\ SpecMFN & 184.2 M & 1.05 M \\ ASDNet & 85.4 M & 0.51 M \\ \hline \end{tabular}
\end{table} 
Table 6: Complexity comparison of different ASD systems in terms of FLOPs and the number of parameters. In summary, the results further emphasize the ideal balance between performance and complexity achieved by our single-feature methods (ASDNet and SpecMFN). These methods not only lead in AUC and pAUC but also exhibit extremely low FLOPs and parameter counts, making them particularly suitable for resource-constrained scenarios. Moreover, the findings confirm that spectral features, combined with efficient model designs, allow these methods to achieve optimal performance while avoiding the redundancy and high complexity typically associated with multi-feature methods. This makes them an efficient and robust solution for anomaly detection tasks.
## 2 Structures and Calculation Methods  
In the calculations, the Ti\({}_{2}\)CoAl bulks with an Hg\({}_{2}\)CuTi structure were geometrically optimized to find the minimal energy structures. Afterward, the optimized bulk structure was cleaved along the Miller indices (100) crystal direction to create all cases of \"ideal\" epitaxial terminated surfaces, namely, TiCo and TiAl terminations. The modified TiTi, CoCo, and AlAl terminations were created by the surface atoms that act as substitutes for the Ti, Co, and Al atoms in the \"ideal\" terminations. In the interface calculations, nine and seven atomic layers were taken for Heusler alloys and MgO, respectively, and they were connected with each other to form a supercell. When all possible atomic interfaces came in contact with one another, 10 potential Ti\({}_{2}\)CoAl/MgO(100) junctions were created, as shown in Figure 1. The thicknesses of these junctions were large enough for the central regions. The tested calculations revealed that the atomic moments in the middle layer were extremely close to the bulk values. In the interface calculations, we only focused on the region of three interface layers in the heterojunction given that they produced the greatest influence on the electronic and magnetic properties of Ti\({}_{2}\)CoAl/MgO(100) junctions.  
All calculations were performed using the CASTEP Package and by adopting the density functional theory (DFT). The exchange-correlation interaction was described by performing Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) [35]. To deal with the electron-ion interaction, we adopted Vanderbilt-type ultrasoft pseudopotentials [36] and the valence electron configurations of Ti (3d\({}^{2}\)4s\({}^{2}\)), Co (3d\({}^{7}\)s\({}^{2}\)), Al (3s\({}^{2}\)3p\({}^{1}\)), Mg (3s\({}^{2}\)), and O (2s\({}^{2}\)2p\({}^{4}\)). For the optimizations of the bulks, we initially assumed that all alloys were ferromagnetic and then applied spin polarization and the \(7\times 7\times 7\) mesh of special k-points in the Brillouin zone. In the self-consistent calculation, we selected the refined \(1\times 10^{-6}\) eV/atom and 360 eV as the self-consistent field (SCF)convergence criterion and energy cutoff, respectively. When the positions of atoms were relaxed, we set a convergence criterion of 0.02 eV/A. To investigate the electronic density of states, we inserted a refined 0.3 A grid space between k-points in the Brillouin zone. For the interface calculations, we geometrically optimized all supercells using the same parameters employed in the bulk calculation. All technical parameters were tested carefully to ensure the accuracy of the results.