esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Reconfigurable SOA-Based Wavelength Conversion of Advanced Modulation Format Data +
The switching speed of this SOA-based wavelength converter mainly depends on the tunable pump lasers and the tunable optical filter [32], but in this work we focus on the effect of the tunable laser. The laser wavelength tuning speed can be as low as several ns for various tunable laser designs such as the sampled grating distributed Bragg reflector (SG-DBR) laser [33], the digital super-mode distributed Bragg grating (DS-DBR) laser [34], and the modulated grating Y-branch (MGY) laser [35]. These lasers can also achieve a wide tuning range (>45 nm), a high side mode suppression ratio (SMSR > 35 dB), and large output power (>10 dBm). All the components in the wavelength converter (lasers and SOA) can be integrated and these results motivate the construction of a compact, optically-integrated, and rapidly reconfigurable all-optical wavelength converter. Most of the previous research on wavelength conversion is undertaken by using static, fixed-wavelength lasers. However, wavelength converters with rapid, dynamic wavelength re-configurability would be required to bypass the power-hungry electronic switches with high latency in the switching nodes of future transparent optical networks. The main focus of this paper is thus to develop a wavelength converter comprised of an SOA as the nonlinear element and a fast-switching SG-DBR tunable laser as one of the pump sources. In Section 2, we initially discuss the theory of the phase noise transfer issue in FWM and the dual co-polarized pumping scheme. In Section 3, using the detailed characterization of the SGDBR laser, we discuss the phase noise of the SGDBR. In Section 4, we provide a complete set of experimental results for single polarization QPSK [36] and experimentally demonstrate rapid wavelength conversion of a PM-QPSK signal with the switching time of tens of nanoseconds using a fast-switching, tunable laser as one of the pumps in a dual wavelength pumping scheme. After the reconfiguration time, the performance under a switching scenario is the same as the case when the wavelengths are held static. The experimental results indicate that the incoming signal can be precisely and quickly converted to the required wavelength channel on a nanosecond timescale.
## 4 Results
### Self-Consumption  
Simulation results, without taking into account the battery storage, suggest that through optimized heat pump operation for PV self-consumption, up to 50% of cost savings can be achieved during the course of one day under ideal conditions. Ideal conditions mean a low prediction inaccuracy, a good coincidence of demand and production by time and extent (amount of energy and peak power), and the available amount of thermal storage. The following figures show this case. Thereby, Figure 24A shows the normal PV yield and the corresponding heat pump operation as it would have occurred, and Figure 24B shows the optimized operation, shifting a significant part of the of the heat pump's workload to the time of the maximum PV production. Figure 25 shows the corresponding development of the aggregated electricity costs without and with optimization. It can be seen that at first, during midday, the costs without optimization are cheaper since electricity is fed into the grid, and thus earnings are made by the assumed fixed price feed in the tariff. In contrast, during the later hours, this changes and the operation with optimization becomes significantly more favorable due to the discrepancy of feed in tariff costs (0.123 \(\upmu\)kWh in that case) and heat pump electricity costs (0.28 \(\upmu\)kWh in that case).  
Figure 24: Standard (**A**) and optimized (**B**) heat pump operation for PV self-consumption.  
The hurdles for the optimization of self-consumption are that in summer (obviously) only DHW is prepared, and thus less demand exists, and also DHW storages tend to be too small dimensioned. In the transition period and in winter, there is a high heating demand, but the PV yield is significantly lower. In that time also, the PV system's power output is not matching the heat pumps demand, resulting in additional electricity taken from the grid even at daily peak PV production. Installing larger buffer storages, matching the installed PV winter power output to the heat pump's demand, and general conditions such as low or no PV feed in the tariff and high electricity tariff costs would results in higher monetary optimization benefits.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 4 Discussion
### Collaborative Classification of Multimodal and Subjective Data  
As a crucial method for assessing visual quality in human-environment interactions, emotion calculation is primarily conducted through single modalities, such as GIS, questionnaires, and physiological sensors. Although multimodal sensors, such as eye movement + EEG and skin electrocardiogram + ECG, have been employed, subjective questionnaires are rarely used as independent variables. Consequently, this study integrates subjective questionnaires (beauty assessment) with objective physiological signals (eye movement, EEG) to create a multi-source dataset encompassing eight semantic variables (e.g., naturalness, diversity, coordination). The symmetric distribution test (with absolute values of skewness and kurtosis both \(<\) 1) and unidimensional test (KMO = 0.916, factor load 60.14%), as described in [PERSON]'s study [66], confirm that the [PERSON] scale satisfies the conditions for continuous variable analysis in emotion questionnaires, thus mitigating the potential confounding effects of multi-dimensionality on model fusion. In model validation, robust fusion of multi-source features is achieved through ensemble learning. For instance, in the triadic classification task, the model combining subjective questionnaire and eye movement data achieved accuracy rates of 77.2% and 74.3% for valence and arousal emotion classification, respectively. This performance exceeds the accuracy of valence (76.1%) and arousal (67.7%) in triadic classification using the fusion of eye movement and EEG by [PERSON] et al. Additionally, the accuracy surpasses that of [PERSON] et al. [30] and [PERSON] [79], who utilized eye movement and EEG as standalone modalities for emotion calculation, thereby confirming the efficacy of multimodal and subjective data collaboration in emotion classification.  
Figure 15: Model experiment process diagram. The classifiers used are the following: (**a**) for the participants; (**b**) for the main evaluation process; (**c**) for the output of the evaluation decision.
# Investigation of the Influence of Sublayer Thickness on Pairing of Metallic MEMS Shutter Blades
## 4 Experimental Results and Discussion
### Freestanding Unpaired Shutter Blades  
We expected to observe a circular geometry of the unpaired shutters due to the intrinsic stress within the metal stack layers. Hence, a circle was fitted to the curling geometries of various samples for analysing the shutter blades and measuring the radius of curvature (RoC). Figure 5a,b shows an example of the analysis that we conducted.  
From the plotted graph in Figure 6, a robust nonlinear trend of an increase in RoC of the shutters is observed with an increasing thickness of \(d_{\text{A12}}\), i.e., with a higher \(d_{\text{A12}}\), less curling of the shutters can be seen. The RoC starts at a value of around 17 \(\upmu\)m for \(d_{\text{A12}}\) = 55 nm, and it continuously increases to 79 \(\upmu\)m for \(d_{\text{A12}}\) = 92 nm. For each thickness \(d_{\text{A12}}\), the evaluation of a set of side-view SEM and CLSM micrographs of at least 10 shutter blades provided us with data of sufficient statistical variance, finally leading to different error bar sizes. Note that for an increasing RoC, the uncertainty of the evaluation increases due to the fixed length of the micro shutters, hence, representing only the smaller sections of a full circle that must be fitted. As mentioned above, the hybrid metal stack of the shutter blade comprises three metal layers: Al, Cr, and Al. The deposited thin films of heavy metals (Cr) tend to reveal compressive stress, and light metals (Al) tend to be under tensile stress [30], resulting in the net intrinsic stress within the metal stack, and enabling the shutter blade to curl. With systematically changing \(d_{\text{A12}}\) values, the shutter blades reveal a steady, but nonlinear, dependence of the parameters. In extreme cases, for \(d_{\text{A12}}\) = 55 nm, the shutter blades close and form absolute circular tubes (see Figure 7). In opposite extreme cases, the blades flatten [12].  
Figure 5: Cross-section SEM micrographs of unpaired and paired micro-shutters with illustrations depicting the conducted analysis: (**a**) unpaired shutter blades with a fitted green circle to identify the radius of curvature (RoC), (**b**) the pairing and the overlapping area between the shutter blades, A and B, and the fitted orange and blue ellipses to idenfity both of the shutter blades. The extracted fit parameters are the major and minor axes.  
An intuitive representation of the observed trend of decrease in the RoC is shown in Figure 7. Each of the displayed profiles has been constructed using the mean value of the statistically evaluated RoC of the shutter blades, revealing different curling states that correspond to the studied different thicknesses of \(d_{\text{A2}}\).
## 4 Experimental Results
### Evaluation Indicators
#### 4.3.2 Model Parameter Quantity (Params/M)  
The model parameters are the sum of all the parameters in the model. Convolutional neural networks are mainly composed of the weights of the convolution layer and the fully connected layer. Therefore, the parameters are mainly related to the network structure of the model. The more complex the model is, the deeper the number of network layers is and the greater the parameters of the model are. Generally, for a convolution kernel with \(N\) input channels and \(M\) output channels and the size of \(H\times W\), its parameter calculation is shown in Equation (9):  
\[\text{params}=(N\times H\times W+1)\times M, \tag{9}\]  
The unit of parameter quantity is generally expressed in millions (M). Therefore, the number of parameters does not directly affect the reasoning performance of the model. However, the size of the parameters will affect both memory occupation and program initialization time.
###### Abstract  
The global navigation satellite system (GNSS) radio occultation (RO) is becoming an essential component of National Oceanic and Atmospheric Administration (NOAA) observation systems. The constellation observing system for meteorology, ionosphere, and climate (COSMIC) 2 mission and the Formosa satellite mission 7, a COSMIC follow-on mission, is now the NOAA's backbone RO mission. The NOAA's dedicated GNSS RO Satellite processing and science Application Center (RO-SAAC) was established at the Center for Satellite Applications and Research (STAR). To better quantify how the observation uncertainty from clock error and geometry determination may propagate to bending angle and refractivity profiles, STAR has developed the GNSS RO data processing and validation system. This study describes the COSMIC-2 neutral atmospheric temperature and moisture profile inversion algorithms at STAR. We used RS41 and ERA5, and UCAR 1D-Var products (wetPrf2) to validate the accuracy and uncertainty of the STAR 1D-Var thermal profiles. The STAR-RS41 temperature differences are less than a few tenths of 1 K from 8 km to 30 km altitude with a standard deviation (std) of 1.5-2 K. The mean STAR-RS41 water vapor specific humidity difference and the standard deviation are \(-0.35\) g/kg and 1.2 g/kg, respectively. We also used the 1D-Var-derived temperature and water vapor profiles to compute the simulated brightness temperature (BTs) for advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) channels and compared them to the collocated ATMS and CrIS measurements. The BT differences of STAR COSMIC-2-simulated BTs relative to SNPP ATMS are less than 0.1 K over all ATMS channels.  
radio occultation; COSMIC-2; water vapor profiles; climate; numerical weather prediction Article  
Processing and Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles in the Neutral Atmosphere  
1
## Materials and Methods
### Supply Assessment
#### 2.3.2 General Model Restrictions  
The extremely large number of spatial units in the study area and the high number of combinations of all possible solutions for these units, make the number of variables involved in the linear programming problem (supply-demand component) very high. To reduce the complexity of the problem, we applied the following restrictions in the supply component of the model:  
1. Forest spatial units are even-aged maritime pine stands;
2. Species composition in a particular U\({}_{\text{ij}}\) is constant in time;
3. Site Index (SI) for a particular U\({}_{\text{ij}}\) is constant in time;
4. The Density Factor (FN) for a particular U\({}_{\text{ij}}\) is constant in time;
5. When a unit is harvested, Density is reset to 2000 trees/ha the following year.
6. When thinning is applied, no other operation (either thinning or felling) can be applied again in less than 6 years.
7. No spatial restrictions apply to thinning or felling.
# Reinforcement-Learning-Based Trajectory Learning in Frenet Frame for Autonomous Driving
## 4 Methods
### Trajectory Learning  
#### 4.1.1 State Space  
The state space \(s\) consists of the states of the agent vehicle and obstacle vehicles as well as the curvature of the road at the current location and the curvature of the road 15 m ahead. The default values for each agent and obstacles are indicated in the Table 1. The total state dimension is 21, with the agent's information represented in 4 dimensions and each obstacle vehicle in 5 dimensions. Considering three neighbor vehicles, the obstacle information totals 15 dimensions \((5\times 3)\). Including the 2 dimensions for curvature, the total state space is thus 21 dimensions.  
The agent state includes a heading error in the Frenet frame \(\psi_{agent}\), the lateral distance in the Frenet frame \(d_{agent}\), the distance with the closest front vehicle \(b_{fp}\) in the Frenet frame, and the current velocity \(v_{agent}\). The obstacle state includes two vehicles within a 30-m range in front of the agent vehicle and one vehicle within a 10-m range behind the agent vehicle. The state of each obstacle vehicle consists of a heading error in the Frenet frame \(\psi_{obs_{z}}\), the differences between the agent vehicle and other vehicles after coordinate transform to the agent vehicle \(\Delta x_{obs_{z}}\), \(\Delta y_{obs_{z}}\), the lateral distance in the Frenet frame \(d_{obs_{z}}\), and the length of obstacle vehicle in the Frenet frame \(len_{obs_{z}}\), where \(z\) is the \(z\)-th obstacle vehicle.  
Figure 3: The illustration of the proposed reinforcement-learning-based trajectory learning in the Frenet frame, where \(\tau_{grp}\) is the trajectory generated by GRPs and \(\tau_{\pi}\) is the trajectory generated from the policy \(\pi\). The action of the policy is optimized to minimize the error with the trajectory in the replay buffer.  
Figure 2: Comparison of Cartesian frame and Frenet frame coordinate representations. The blue line represents the road sideline, and the blue dotted line represents the center of the road. (**a**) Cartesian frame, (**b**) Frenet frame.
###### Abstract  
The article presents a three-stage methodology for calibrating 3D printing using mSLA technology, aimed at improving dimensional accuracy and print repeatability. The proposed approach is based on procedures that enable the collection and analysis of numerical data, thereby minimizing the influence of the operator's subjective judgment, which is commonly relied upon in traditional calibration methods. In the first stage, compensation for the uneven illumination of the LCD matrix was performed by establishing a regression model that describes the relationship between UV radiation intensity and pixel brightness. Based on this model, a grayscale correction mask was developed. The second stage focused on determining the optimal exposure time, based on its effect on dimensional accuracy, detail reproduction, and model strength. The optimal exposure time is defined as the duration that provides the highest possible mechanical strength without significant loss of detail due to the light bleed phenomenon (i.e., diffusion of UV radiation beyond the mask edge). In the third stage, scale correction was applied to compensate for shrinkage and geometric distortions, further reducing the impact of light bleed on the dimensional fidelity of printed components. The proposed methodology was validated using an Anycubic Photon M3 Premium printer with Anycubic ABS-Like Resin Pro 2.0. Compensating for light intensity variation reduced the original standard deviation from 0.26 to 0.17 mW/cm\({}^{2}\), corresponding to a decrease of more than one third. The methodology reduced surface displacement due to shrinkage from 0.044% to 0.003%, and the residual internal dimensional error from 0.159 mm to 0.017 mm (a 72% reduction).  
active manufacturing; calibration; accuracy; masked stereolithography
## 4 Experimental Results
### Experimental Results Using the ABC Algorithm
For the G110 inverter data set, 13 attributes of the DMI were selected (46, 62, 74, 88, 89, 109, 110, 111, 117, 118, 121, 125, 145). Table 4 illustrates the classification results for the Motor 2 tests. Table 4 shows that the best results are obtained with the MLP meta-heuristic classifier regardless of the inverter. When the MM440 inverter supplied Motor 2, 98.5% of the samples were correctly identified. Using the G110 as a supply source of Motor 2, the proposed method achieved a classification accuracy of 98.7% of the global samples. Kappa indexes over 0.95 confirm the full results in both situations. Moreover, when the C4.5 meta-heuristic classifiers were used in the MM440 and G110 data set, slightly lower classification rates were obtained. Kappa coefficients over 0.81 confirm the satisfactory performance of the proposed system using the C4.5 meta-heuristic classifier. From Tables 1-4, it was observed that the use of the ABC algorithm resulted in a reduction of a maximum of 2% in the classification rates of the global samples. Specifically using the MLP meta-heuristic classifier, it was achieved a global accuracy of over 95.7%. Analyzing the healthy samples, it is possible to verify that there was greater difficulty identifying these samples, mainly employing the C4.5 meta-heuristic classifier. In some cases, there is a reduction of approximately 8% in the correct classification rates. Again, the MLP meta-heuristic classifier shows superior results. Moreover, there is a small difference in the classification accuracy of the samples with bearing faults. Especially using the MLP meta-heuristic classifier, more than 97.3% of these samples were correctly classified. In general, it is noteworthy that using the ABC algorithm in selecting attributes, a similar accuracy was obtained with less computational time to build the model compared to previous tests. Thus, considering the computational scope, these tools are more suitable for the bearing failure diagnosis in TIMs, since satisfactory results are reached with a lower computational cost. Figure 7 illustrates the performance comparison of the proposed method using the meta-heuristic classifiers for bearing fault diagnosis in TIMs fed by frequency inverters. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Data Set** & **MM440 Inverter** & \multicolumn{2}{c}{**G110 Inverter**} \\ \hline Classifier & C4.5 & MLP & C4.5 & MLP \\ Global Accuracy (\%) & 93.9 & 98.5 & 94.7 & 98.7 \\ Healthy Accuracy (\%) & 82.3 & 96.2 & 82.9 & 94.7 \\ Faulty Accuracy (\%) & 96.8 & 99.0 & 97.7 & 99.7 \\ Kappa Index & 0.81 & 0.95 & 0.83 & 0.96 \\ Time to build the model (s) & 0.04 & 1.22 & 0.05 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results using the ABC algorithm—1.48 kW motor. From the results presented in Sections 4.1 and 4.2, it was demonstrated that the proposed methodology presents a promising performance in the diagnosis of bearing failures in inverter-fed TIMs, regardless of the operating frequency and load.
## 2 Materials and Methods
### Statistical Analysis  
All statistical analyses were performed in triplicate, with the experimental results expressed as means \(\pm\) SD. The data were analyzed using the Statistical Program for Social Sciences (SPSS 20.0, Chicago, IL, USA) software for analysis of variance and Duncan's test. The significance was established at \(p<0.05\). The aroma recombination experiments data were collected and analyzed by Excel (Microsoft Office 2018, Redmond, WA, USA).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Odor** & **Reference** & \multicolumn{6}{c}{**Concentrations in Water (\(\upmu\)g/mL)**} \\ \cline{3-8}
**Descriptors** & **Odorants** & **0 a** & **1 a** & **2 a** & **3 a** & **4 a** & **5 a** \\ \hline mushroom & 1-Octen-3-ol & 0 & 1.50 \(\times\) 10\({}^{-2}\) & 1.50 \(\times\) 10\({}^{-1}\) & 1.50 & 1.50 \(\times\) 10 & 1.50 \(\times\) 10\({}^{2}\) \\ grass & (E)-2-Octen-1-ol & 0 & 33.60 \(\times\) 2\({}^{-2}\) & 33.60 \(\times\) 2\({}^{-1}\) & 33.60 & 33.60 \(\times\) 2 & 33.60 \(\times\) 2\({}^{2}\) \\ metallic & (E)-2-decenal & 0 & 0.13 \(\times\) 10\({}^{-2}\) & 0.13 \(\times\) 10\({}^{-1}\) & 0.13 & 0.13 \(\times\) 10 & 0.13 \(\times\) 10\({}^{2}\) \\ sulfury & Lenthionine & 0 & 0.27 \(\times\) 10\({}^{-2}\) & 0.27 \(\times\) 10\({}^{-1}\) & 0.27 & 0.27 \(\times\) 10 & 0.27 \(\times\) 10\({}^{2}\) \\  & 4-Hydroxy-2,5- & & & & & & \\ caramel & dimethyl-3(2H)- & 0 & 10.00 \(\times\) 5\({}^{-2}\) & 10.00 \(\times\) 5\({}^{-1}\) & 10.00 & 10.00 \(\times\) 5 & 10.00 \(\times\) 5\({}^{2}\) \\ fatty & (E)-2-Octenal & 0 & 3.00 \(\times\) 10\({}^{-2}\) & 3.00 \(\times\) 10\({}^{-1}\) & 3.00 & 3.00 \(\times\) 10 & 3.00 \(\times\) 10\({}^{2}\) \\ cabbage & 3-(Methyl-thio)-1- & 0 & 9.00 \(\times\) 5\({}^{-2}\) & 9.00 \(\times\) 5\({}^{-1}\) & 9.00 & 9.00 \(\times\) 5 & 9.00 \(\times\) 5\({}^{2}\) \\ chocolate & 3-Methyl-butanal & 0 & 1.10 \(\times\) 10\({}^{-2}\) & 1.10 \(\times\) 10\({}^{-1}\) & 1.10 & 1.10 \(\times\) 10 & 1.10 \(\times\) 10\({}^{2}\) \\ sweaty & Isovaleric acid & 0 & 44.10 \(\times\) 3\({}^{-2}\) & 44.10 \(\times\) 3\({}^{-1}\) & 44.10 \(\times\) 3 & 44.10 \(\times\) 3 & 44.10 \(\times\) 3\({}^{2}\) \\  & 3-Hydroxy-4,5- & & & & & & \\ seasoning-like & dimethyl-2(5H)- & 0 & 0.49 \(\times\) 10\({}^{-2}\) & 0.49 \(\times\) 10\({}^{-1}\) & 0.49 & 0.49 \(\times\) 10 & 0.49 \(\times\) 10\({}^{2}\) \\ cooked & 3-(Methyl-thio)-1- & & & & & & \\ potato-like & propanal & 0 & 0.43 \(\times\) 10\({}^{-2}\) & 0.43 \(\times\) 10\({}^{-1}\) & 0.43 & 0.43 \(\times\) 10 & 0.43 \(\times\) 10\({}^{2}\) \\ \hline \hline \end{tabular}  
* The odor intensities from 0 to 5.  
\end{table}
Table 1: The concentrations of the reference odorants in different odor intensities.