esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
We selected boron-doped single-crystal Si wafers with a native oxide layer as the substrates for further Mo\({}_{\rm x}\)W\({}_{1-x}\)S\({}_{2}\) growth. At first, the pressure of the sputter was pumped down to 5 \(\times\) 10\({}^{-6}\) torr. Before sputtering, Ar was injected as a carrier gas and the pressure of the sputter was kept at 5 \(\times\) 10\({}^{-3}\) torr. Consequently, the Mo and W thin films were simultaneously deposited by using radio frequency (50 W) and direct current (25 W) sputtering methods at room temperature, respectively. The thickness of the MoW alloy film was about 100 nm.  
Then, the MoW films were transferred to a two-zone vacuum furnace for further sulfurization processes to grow the Mo\({}_{\rm x}\)W\({}_{1-x}\)S\({}_{2}\). We placed 8 g of sulfur powders at the upstream source zone while the MoW films were placed at the downstream reaction zone. We set various sulfurized temperatures of 800, 850, 900, and 950 \({}^{\circ}\)C for 60 min in the reaction zone for progressive sulfurization and to investigate the effect of different sulfurized temperatures on the Mo\({}_{\rm x}\)W\({}_{1-x}\)S\({}_{2}\) films. The temperature in the source zone was set to about 400 \({}^{\circ}\)C. The temperature ramping rate was set to 5 \({}^{\circ}\)C/min for the heating and cooling procedures. Argon was injected at a fixed rate of 200 sccm as the carrier gas at a pressure of around 0.1 torr during the whole process. The sulfur vapor was transported from the source zone via the carrier gas and reacted with MoW alloy to transform into the Mo\({}_{\rm x}\)W\({}_{1-x}\)S\({}_{2}\) thin film on the substrates in the reaction zone.  
SEM (FEI Helios 1200+, Hillsboro, OR, USA) was used to observe the morphology and thickness of the as-grown samples. The Raman spectra were recorded using a confocal Raman microscopy system (Tokyo Instruments, Inc., Nanofinder 30, Tokyo, Japan) with a solid-state laser excitation (laser power: 0.05 mW). The laser beam was focused to 1 \(\upmu\)m in diameter via a microscope objective with a magnification of 100\(\times\) and an NA of 0.9. The grating in the spectrometer was 300 lines/mm. Before the Raman measurements of the samples, the spectra were calibrated using the Si peak position (520 cm\({}^{-1}\)) from a bulk Si substrate. A charge-coupled device mounted within the spectrometer was cooled to \(-\)50 \({}^{\circ}\)C by using a thermoelectric cooling chip to reduce noise during measurement. The relevant parameters related to sulfurization and Raman measurement can be found in our previous work [14]. The crystal structure was analyzed using XRD with CuK\(\alpha\) radiation. Furthermore, recorded the temperature-dependent current density-voltage (I-V) characteristics of the samples from 290 to 570 K. Meanwhile, we also analyzed the electrical properties of the samples, including their resistance and temperature coefficient of resistance (TCR) according to the temperature-dependent I-V results.
## 4 Integration of Biosensors
### Node Configuration
#### 4.2.1 Biosensors Node Configuration  
In the integration of biosensors with microcontrollers, establishing a reliable connection is fundamental to ensuring accurate data acquisition and transmission. The attachment process involves several considerations for different types of biosensor. For ECG biosensors like the AD8232, establishing a secure connection involves connecting the electrodes to the microcontroller's analog input pins. This configuration ensures precise reception of ECG signals, which is crucial for accurate patient monitoring. The ESP32 microcontroller, equipped with analog input capabilities, plays a pivotal role in capturing and processing real-time ECG data. The circuit design is shown in Figure 9. Its processing capabilities enable the implementation of real-time ECG signal processing algorithms, contributing to accurate heartbeat detection. The MAX30102 biosensor, designed for heart rate and pulse oximetry monitoring, employs a PPG method using a photodetector plus red and IR LEDs. Configuring this biosensor with the ESP32 involves several key steps. First, the ESP32's GPIO pins are utilized to establish connections with the necessary MAX30102 interfaces, including I2C for communication. The I2C protocol allows efficient data exchange between the ESP32 and MAX30102. Next, the red and IR LEDs of the MAX30102 are connected to the appropriate GPIO pins of the ESP32. These LEDs cast light onto the skin, and the photodetector captures the reflected light, generating the PPG waveform. The ESP32 is programmed to read and process PPG data from the MAX30102, which the circuit design in Figure 10 shows. The real-time processing capabilities of the ESP32 enable the extraction of vital information, such as heart rate and oxygen saturation levels, from the PPG waveform. Figure 9: Circuit diagram of ESP32 and AD8232 integration for reading a real-time ECG signal. In establishing a multi-hop IoT system for health monitoring, the integration of biosensors with microcontrollers like the ESP32 becomes pivotal. Several kinds of biosensor that can detect vital signs such as blood oxygen level, heart rate, and ECG play a crucial role in the multi-hop communications network, facilitating seamless transmission of health data across nodes. This collaborative approach enhances the efficiency and reliability of health monitoring systems by leveraging biosensors attached to microcontrollers. The interconnected nature of biosensors within a multi-hop IoT framework ensures robust and continuous health data monitoring, offering a promising avenue for advancements in remote health monitoring systems. Some of the recent studies into patient monitoring through biosensors in the IoT healthcare realm are shown in Table 1.  
Figure 10: The ESP32 and MAX30102 integrate into a circuit design for monitoring pulse oximetry and heart rate in real time.
## 2 Materials and Methods  
Figure 1 illustrates the proposed methodology. The first step involves gathering hourly historical electrical load data from the region of interest. The larger the dataset, the more accurate the predictions will be. To capture the seasonality of the electrical load, it is preferable to work with datasets that cover more than three years. Next, a cluster analysis is performed on the dataset to identify its seasonality. After establishing the seasonality, the load demand dataset is divided into the identified seasons. Additionally, meteorological time-series datasets representative of the region are created and similarly grouped into the identified seasons. Then, for each season, an individual LSTM neural network is trained using data from similar days. By training the LSTM with data from each season that share similar characteristics, such as weekdays or weekends, the model learns more effectively, enhancing its ability to forecast day-ahead energy demand for the specific region under study. The following subsections outline this process in detail.
## Appendix B  
_Appendix B.1._\(A_{CS}\), \(B_{CS}\), \(A_{\delta S}\), \(B_{\delta S}\) and First- and Second-Order Forcing Functions \(F_{NS}^{(1)}\) and \(F_{NS}^{(2)}\)  
\[\mathbf{A_{CS}}=\left[ \begin{array}{cccc}-v^{-1}Q_{A}^{-1}&-(1+\psi_{CS})&0& \rho k_{r}v^{-2}\psi_{CS}\\ 1&0&0&0\\ 0&-\psi_{CS}&-Q_{T}^{-1}&\rho\psi_{CS}-1\\ 0&0&1&0\end{array} \right] \tag{19}\]  
\[\mathbf{B_{CS}}=\left[ \begin{array}{cccc}0&\psi_{CS}k_{r}v^{-2}&0&-\rho\psi_{ CS}k_{r}v^{-2}\\ 0&0&0&0\\ 0&\psi_{CS}&0&-\psi_{CS}\rho\\ 0&0&0&0\end{array} \right] \tag{20}\]  
\[\mathbf{A_{\delta s}}=\left[ \begin{array}{cccc}0&-\psi_{\delta s}k_{r}v^{-2}&0& \rho\psi_{\delta s}k_{r}v^{-2}\\ 0&0&0&0\\ 0&-\psi_{\delta s}&0&\rho\psi_{\delta s}\\ 0&0&0&0\end{array} \right],\mathbf{B_{\delta s}}=\left[ \begin{array}{cccc}0&\psi _{\delta s}k_{r}v^{-2}&0&-\rho\psi_{\delta s}k_{r}v^{-2}\\ 0&0&0&0\\ 0&\psi_{\delta s}&0&-\rho\psi_{\delta s}\\ 0&0&0&0\end{array} \right] \tag{21}\]  
\[\mathbf{F_{NS}}=\varepsilon\mathbf{F_{NS}^{(1)}}+\varepsilon^{2}\mathbf{F_{NS}^{(2)}} \tag{22}\]\[\mathbf{F}_{NS}^{(1)}=\left[ \begin{array}{c}\psi_{CS}k_{r}v^{-2}\tau_{n0}\alpha \chi_{1}\\ 0\\ \psi_{CS}\tau_{n0}\alpha\chi_{1}\\ 0\end{array} \right],\mathbf{F}_{NS}^{(2)}=\left[ \begin{array}{c}\psi_{CS}k_{r}v^ {-2}\tau_{n0}\alpha\chi_{2}+\frac{1}{2}\psi_{CS}k_{r}v^{-2}\tau_{n0}^{2}\alpha^ {2}\chi_{3}\\ 0\\ \psi_{CS}\tau_{n0}\alpha\chi_{2}+\frac{1}{2}\psi_{CS}\tau_{n0}^{2}\alpha^{2} \chi_{3}\\ 0\end{array} \right] \tag{13}\]  
\[\chi_{1}=\left(x_{4}^{(0)}-x_{44}^{(0)}\right)\left(x_{d1}^{(0)}-\rho x_{d3}^{ (0)}\right) \tag{14}\]  
\[\chi_{2}=x_{t4}^{(1)}\left(x_{d1}^{(0)}-\rho x_{d3}^{(0)}\right)+x_{t4}^{(0)} \left(x_{d1}^{(1)}-\rho x_{d3}^{(1)}\right)-\tau_{n0}D_{1}\left(x_{4}^{(0)}-2x_ {d4}^{(0)}\right)\left(x_{d1}^{(0)}-\rho x_{d3}^{(0)}\right) \tag{15}\]  
\[\chi_{3}=x_{t4}^{(0)}\left(x_{d1}^{(0)}-\rho x_{d3}^{(0)}\right)\left(D_{0}x_{ t4}^{(0)}-2x_{d3}^{(0)}\right) \tag{16}\]  
\[x_{tk}^{(p)}=x_{k}^{(p)}-x_{dk}^{(p)},x_{dk}^{(p)}=x_{k}^{(p)}(T_{0}-\tau_{n0},T_{1},T_{2}),p=0,1;k=1,2,3,4. \tag{17}\]  
**Appendix B.2**: \(\mathbf{P}_{2}^{(1)},\mathbf{P}_{0}^{(1)},\overline{\mathbf{P}_{2}^{(1)}}\)__  
Substitution of Equations (50) and (51) into Equation (48) yields:  
\[D_{0}\mathbf{x}^{(1)}-\mathbf{A}_{\mathbf{CS}}\mathbf{x}^{(1)}-\mathbf{B}_{\mathbf{CS}}\mathbf{x}_{d}^{(1 )}=\mathbf{Q}_{2}^{(F_{1})}A_{1}^{2}e^{2i\omega_{c}T_{0}}+\mathbf{Q}_{0}^{(F_{1})}A_{1 }A_{2}+\overline{\mathbf{Q}_{2}^{(F_{1})}}A_{2}^{2}e^{-2i\omega_{c}T_{0}}, \tag{18}\]  
where  
\[Q_{2}^{(F_{1})}=\left[ \begin{array}{c}\psi_{CS}k_{r}v^{-2}\tau_{n0}\alpha Q _{2}\\ 0\\ \psi_{CS}\tau_{n0}\alpha Q_{2}\\ 0\end{array} \right],Q_{0}^{(F_{1})}=\left[ \begin{array}{c}\psi_{CS}k_{r}v^ {-2}\tau_{n0}\alpha Q_{0}\\ 0\\ \psi_{CS}\tau_{n0}\alpha Q_{0}\\ 0\end{array} \right],\overline{Q_{2}^{(F_{1})}}=\left[ \begin{array}{c}\psi_ {CS}k_{r}v^{-2}\tau_{n0}\alpha\overline{Q_{2}}\\ 0\\ \psi_{CS}\tau_{n0}\alpha\overline{Q_{2}}\\ 0\end{array}
## 2 Materials and Methods
### Data Arrangement for PCA  
The data for 'Clery', 'Frida', 'Gariguette' and 'Sonata' grown at the locations in France, Italy, Germany, Poland and Norway were arranged in a matrix of dimension 60 \(\times\) 22; 60 represents the number of samples (five locations, two cropping seasons, two picking dates and three biological replicates) and 22, the number of variables (cyano-3-glc, pel-3-glc, pel-3-rut, cya-3-mal-glc, pel-3-mal-glc, total anthocyanin as well as global radiation, mean, maximum and minimum temperatures summarized 3, 5, 10 and 15 days prior to harvest). The data for 'Candonga' and 'Florence' were arranged in matrices of dimension 48 \(\times\) 22, where 48 is the number of samples (four location \(\times\) two seasons \(\times\) two picking dates \(\times\) three biological replicates) and 22 is the same variables as specified above. The PCA analysis carried out on all the cultivars together clearly indicated (given in the Supplementary Figure S1) that the anthocyanin profiles of the strawberries are mainly influenced by the location. Thus it was important that each of the six cultivars were analyzed separately.  
To ensure that each component had equal variance and comparable impact on the PCA modeling, the specific data for each cultivar were auto-scaled prior to PCA. Autosscaling [29] is a common pre-processing method that uses mean-centering followed by dividing each variable by the corresponding standard deviation. Each variable upon auto-scaling has a mean of zero and unit variance.
###### Abstract
Therefore, when attempting to integrate other information sources, such as user reviews or item descriptions, it can cause interference and potentially reduce the accuracy of the system. Other multimodal methods that combine multiple sources often only utilize textual sources, while disregarding the utility matrix information, such as Refs. [25; 31; 32]. We believe that combining data from multiple sources, such as utility matrix, user profiles, and item descriptions, can lead to more personalized recommendations that reflect a user's unique interests and preferences. However, while the benefits of using a multimodal approach for recommender systems are clear, there are significant challenges that must be addressed. One major challenge is how to integrate the diverse sources of information effectively, without being affected by noise or conflicting signals that could lower the accuracy of the recommendations. This is particularly important because the information from different sources may complement each other or contain redundant information, and integrating them in a way that preserves their usefulness requires a careful balancing act. Multimodal and multiview approaches provide numerous benefits that make them attractive for recommender systems. One significant advantage is their capability to capture multiple facets of user preferences and item characteristics, resulting in more precise and diverse recommendations. This is achievable because different information sources can provide complementary insights that may not be captured by a single modality or view [33; 34; 35]. Overall, our study makes several important contributions compared to other related studies, specifically as follows:  
* Developing a novel model that combines views from both the utility matrix and textual sources, which utilizes feature extraction techniques from various information sources and a conversion algorithm to segment the feature vectors of each pair (user, item) into a sequence of token vectors, which serve as input for a classification model;
* Incorporating Transformer models into our approach for multimodal recommender systems, that serves as a strong tool for handling the challenge of integrating diverse sources of information in RSs, that can self-select features that avoid or minimize noise or conflicting signals, and it has the potential to significantly enhance the performance of RSs in practical settings;* Conducting experiments on the MovieLens and Amazon datasets to verify the effectiveness of our proposed model. The rest of the paper is organized as follows: Section 2 provides a summary of recent studies related to our work. Section 3 presents the conceptual and technical background for user-based and item-based recommender systems, the Transformer encoder architecture, matrix factorization, and feature extraction. In Section 4, we introduce our proposed model, which uses the transformer model on multiple views from diverse information sources. Section 5 presents the experimental results, comparisons, and discussions. Finally, in Section 6, we conclude our contribution.
# Performance Studies of High-Power Optical Parametric Oscillators Pumped by a Pulsed Fiber Laser
## 3 Experimental Results and Discussion
### Parameters Affecting Idler Power
To investigate this aspect, three output couplers with different curvature radii, including \(-100\) mm, \(-200\) mm and \(-300\) mm (corresponding to the theoretical waist sizes of \(145.0\) um, \(183.6\) um and \(205.7\) um), were employed individually. To protect MgO:PPLN crystal against breakage, the pump beam waist in the center of the crystal was adjusted to \(201\)\(\pm\)\(4.0\) um. The crystal length was \(38\) mm. As shown in Figure 5a, the output power of the OPO decreases as the curvature radius increases in the low pump power range, while the trend is reversed when pump power exceeds \(50\)\(\pm\)\(3\) W. The phenomenon might be attributed to the mismatch between the actual pump beam waist and required values in  
Figure 4: (**a**) Idler power and (**b**) corresponding optical–optical efficiency versus pump power for different waist locations. Figure 3: (**a**) Idler power and (**b**) corresponding optical–optical efficiency versus pump power for different pump beam sizes. the low-power state. The thermal guiding arising from high optical power expanded the signal beam size, which was more suitable for the OPO with a small curvature radius. The length of the crystal was a crucial factor for the OPO output power. The pump beam with a waist of 172 \(\pm\) 3.4 \(\upmu\)m lay at the center of the MgO:PPLN crystal. M2's curvature radius was stabilized at \(-\)200 mm, while the crystal length varied by 38 mm, 30 mm and 20 mm. The estimated waist sizes were \(\sim\)150 \(\upmu\)m and the difference of beam waists caused by different crystal lengths could be ignored. The dependence of the idler power and optical-optical conversion efficiency on the pump power is depicted in Figure 6. Optical-optical conversion efficiency increases while the pump threshold decreases with the rise of crystal length, which is identical to the prediction in reference [8]. As the dash-dot line shows, there is no roll-off in idler power, which means higher output power and conversion efficiency may be obtained with pump power increase. Similar to Figures 3-5, an inconspicuous step increase in the idler power is observed when pump power is raised from 66 \(\pm\) 3 to 69 \(\pm\) 4 W.
###### Abstract
First, there are few datasets in MER involving listeners' physiological responses to music pieces and the original audio of the music, as well as emotion labels reported by the listeners. By constructing and sharing a new dataset of 956 sets of aligned physiological signals, (copyright-free) music audio and user-reported emotion, this study provides an open testbed for combining the two modalities for MER. Second, as physiological signals have not yet been thoroughly exploited in the domain of MIR, this study explores methods of physiological signal processing and extracts a fairly comprehensive set of physiological features, including those specific to electrodermal activity (EDA) and heart rate variability (HRV), further bridging the literature of MIR and physiological computing. Third, through comparing MER performances of features in combined and individual modalities, this study helps verify the possibility of designing MIR systems that can make use of physiological data and model listeners' emotional responses to music. Last but not least, through connecting music and human emotion at the physiological level, deliverables of this research can be applicable to related fields such as music perception, music therapy [8], human-computer interaction, and well-being studies [9].
## 4 Results and Discussion
### Sizing Thermal Storage Tank
#### 4.2.2 Thermal Efficiency  
Calculating thermal efficiency is crucial for demonstrating the system's effectiveness, optimizing its design, establishing its economic and environmental benefits, and validating the overall viability of the proposed thermal energy storage system. Equation (18) expresses the thermal efficiency:  
\[\eta=\frac{\int_{0}^{t_{disc}}\left[T_{disc}-T_{L}\right]dt}{\left(T_{H}-T_{L }\right)\cdot t_{disc}}, \tag{18}\]  
where \(T_{H}\) is the temperature of the hot fluid in [K], \(T_{L}\) is the temperature of the cold fluid in [K], \(T_{disc}\) is the temperature of the fluid during the discharge process in [K], and \(t_{disc}\) is the discharge time operational in [h].  
Regarding the thermal efficiency analysis, also called energy dispatch efficiency, a magnitude of 92.2% is obtained for the selected geometric parameters that result in an H/D of 2.1, as shown in Figure 11. The algorithm indicates that increasing the H/D by 0.05 improves efficiency. However, it implies increasing the height and modifying a series of parameters that interfere with the compactness of the design. The increase in efficiency is not negligible, but neither does it compensate for the alterations involved. Therefore, the efficiency obtained has an acceptable magnitude for the established requirement.  
Figure 11: Thermal efficiency (\(\eta\)) versus \(H/D\) for the proposed design.
## 2 Data and Methods
### Short-Term Analysis  
The first question we asked is which MODIS product(s) best explains the short-term (_i.e._, intra-annual) variations in flux tower GPP. We averaged daily flux tower GPP estimates over each  
Figure 2: Scatterplots of flux tower sites over the global climatology contour plot in the temperature-precipitation domain. The temperature and precipitation of flux towers are averaged measured values in the years when gross primary production (GPP) is available for analysis. The temperature and precipitation of the global climatology were calculated using Climate Research Unit (CRU) datasets from 1982 to 2009 base years. The contour are area-weighted histogram and the units of the color scale is km\({}^{2}\)/(1 \({}^{\circ}\)C)/(1 mm) bins. Only forested areas were plotted (as identified by MCD12C1 IGBP classification land cover data).  
composite periods (8-day, 16-day, and 32-day), and applied MVC to each MODIS product to the corresponding composite periods. We then calculated the \(R^{2}\) between the flux tower average GPP and corresponding satellite-derived composite for each available year from 2001 to 2008, and then averaged the \(R^{2}\) of all years. \(R^{2}\) between flux tower GPP and satellite composites were also calculated after stratifying by forest type class (_i.e._, deciduous, evergreen, non-tropical, and tropical). In the same way as done with average \(R^{2}\), we also calculated average root mean square error (RMSE) using the reduced major axis regression.
## 4 Applications
### Tomography
Another crucial aspect of tomography is the capability to quantify a perturbation. In the paper by [PERSON] et al. [146] the effect of the time-gating in the retrieval of the absorption coefficient of the absorbing inclusion buried at a depth of 1 to 2 cm within a homogeneous medium was studied. The quantification was demonstrated to be affected by the depth of the inclusion and the amount of the optical perturbation. The use of a-priori information for both volume and position improves the reconstruction, thus pushing to the multimodal imaging where the anatomical information is provided by other techniques (such as ultrasound). In this case, as clearly stated by the authors, the TG approach seems not to improve the quantification and this conclusion is supported both by experimental measurements and simulations. At any rate, even if the quantification is not precise, the tomography based on fast-gated acquisition can be used for clinical applications. To the best of our knowledge, the only one presented in literature is a preclinical study on the assessment of flap viability on rats [147]. In this study, the authors used four source-detector couples to get a tomographic reconstruction of the vessel below the flap where a venous occlusion was induced by clamping the vein. Using different wavelengths, they reconstructed the maps of O\({}_{2}\)Hb and HHb in space and over time as depicted in Figure 15 and they were able to properly identify the venous occlusion. The work shows the suitability of the TG tomography to reconstruct variations of O\({}_{2}\)Hb and HHb, thus paving the way to a wide range of applications where the deep monitoring of the vessel permeability is needed. Figure 14: Reconstructions of inclusion II and I2 (set at the same depth, 15 or 25 mm), obtained for 10 and 15 mm \(\rho\). Results obtained in the same conditions (inclusion depth and \(\rho\)) are computed both on simulated and experimental curves [144]. In the simulation, spherical perturbations were used while in the measurements cylindrical (almost rectangular) ones were adopted. However, it has been demonstrated for totally absorption inclusion that different shapes (e.g., cylinder or sphere as in this case) yield to comparable optical perturbation provided that the volume is the same [145].