esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Large Arrays Fabrication  
A TES is in essence a superconducting metal slice deposited on a substrate. The earliest TES, fabricated in 1941, was just a thin film of lead evaporated onto a 1.5 cm \(\times\) 1 mm glass ribbon [83]. A TES-based X-ray microcalorimeter fabricated nowadays is more complex and consists of three key components: a temperature sensing element (the TES), a thermal isolation structure, and an X-ray absorbing layer. In this section, we will briefly review these components from the aspect of fabrication including materials and microstructures with the emphasis on the technical scalability. An array of thousands of TESs, realized on a single 4-inch silicon wafer, are under fabrication for the X-IFU onboard of Athena [2] (Section 6.1). Moreover, as it will be reported in Section 6.2, beyond the 2040s, arrays of hundred of thousands of TESs will be required for very large X-ray observatories like, for example, the proposed Lynx space mission [84] or the Cosmic Web Explorer [85]. In parallel to these space missions, increasing the size of the array is also mandatory for many other ground-based applications (Section 6.3). We will discuss here the technical challenges and solutions required to make these large scale arrays a reality.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
###### Abstract  
Multimodal interaction technology has become a key aspect of remote education by enriching student engagement and learning results as it utilizes the speech, gesture, and visual feedback as various sensory channels. This publication reflects on the latest breakthroughs in multimodal interaction and its usage in remote learning environments, including a multi-layered discussion that addresses various levels of learning and understanding. It showcases the main technologies, such as speech recognition, computer vision, and haptic feedback, that enable the visitors and learning portals to exchange data fluidly. In addition, we investigate the function of multimodal learning analytics in order to measure the cognitive and emotional states of students, targeting personalized feedback and refining instructional strategies. Though multimodal communication may bring a historical improvement to the mode of online education, the platform still faces many issues, such as media synchronization, higher computational demand, physical adaptability, and privacy concerns. These problems demand further research in the fields of algorithm optimization, access to technology guidance, and the ethical use of big data. This paper presents a systematic review of the application of multimodal interaction in remote education. Through the analysis of 25 selected research papers, this review explores key technologies, applications, and challenges in the field. By synthesizing existing findings, this study highlights the role of multimodal learning analytics, speech recognition, gesture-based interaction, and haptic feedback in enhancing remote learning.  
m _Applied sciences_  
1  
A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges. _Appl. Sci._**2025**, _15_, 3937.  
[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
# 185-215 GHz CMOS Frequency Doubler with a Single Row Staggered Distribution Layout Design
## 4 Conclusions  
In this paper, an amplifier-doubler chain is presented. The presented amplifier-doubler chain consists of a rat-race balun, a sixth-stage DA, and a frequency doubler. The frequency doubler was designed with a single-row staggered distribution structure, which effectively reduced the parasitic component. According to the measurements, the presented amplifier-doubler chain provided a peak output power of 7.9 dBm at 200 GHz and a 3-dB bandwidth of 30 GHz. The DC-RF efficiency was 1.87%.  
In summary, the presented amplifier-doubler chain provided the highest output power in CMOS technology at 200 GHz. The presented amplifier-doubler chain could be applied to such fields as THz imaging, radar, sensing, biomedicine, and ultra-fast communications.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & **Tech.** & **f\({}_{\text{max}}\)** & **Freq** & **Last Multiplier** & **Multi.** & **DC-RF** & **Peak Pout** & **Size** & **DC** \\  & & **[GHz]** & **Topology** & **Factor** & **[\%]** & **[dBm]** & **[mm\({}^{\text{2}}\)]** & **[mW]** \\ \hline
[35] & 90 nm & 350 & 200–230 & DA + Mult. & 2 & 0.23 & 8 & 3.63 & 2700 \\
[36] & 130 nm & 500 & 200–255 & DA + Mult. + PA & 8 & 1.60 & 12 * & 2.15 & 990 \\
[37] & 130 nm & 250 & 165–230 & DA + Mult. & 2 & 0.87 & 5.2 & NA & 380 \\
[29] & 65 nm & NA & 160–310 & DA + Mult. & 2 & 2.85 & 3 & 0.71 & 70 \\
[30] & 40 nm & 290 & 213–233 & DA + Mult. + PA & 9 & 1.39 & 4.1 & 1.7 & 185 \\
[38] & 65 nm & NA & 237–263 & Mult. & 2 & 2.87 & 0.9 & 0.071 & 37 \\ This & 40 nm & CMOS & 290 & 185–215 & DA + Mult. & 2 & 2.0 & 7.9 & 0.78 & 307 \\ \hline \hline \end{tabular}
* 12 dBm w/o de-embedding the output balun and pad.  
\end{table}
Table 1: Comparison with other reported designs in the literature.  
Figure 11: Measurement results of presented amplifier–doubler chain.  
**Author Contributions:** Conceptualization, R.D.; methodology, R.D.; software, R.D. and C.Y.; validation, R.D. and C.Y.; formal analysis, R.D. and C.Y.; investigation, R.D. and C.Y.; resources, R.D.; data curation, R.D. and C.Y.; writing--original draft preparation, C.Y.; writing--review and editing, R.D. and C.Y.; visualization, R.D. and C.Y.; supervision, R.D.; project administration, R.D.; funding acquisition, R.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The National Natural Science Foundation of China, 61988102. The Key Research and Development Program of Guangdong Province, China, 2019B090917007. The Science and Technology Planning Project of Guangdong Province, China, 2019B090909011.  
**Data Availability Statement:** Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
**Acknowledgments:** The authors thanks [PERSON], [PERSON] and [PERSON] for their helpful discussion.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Analytical Method
#### 2.3.7 Betalain Content  
Betalain content was measured by two methods. For the calculation of the amount, the spectrophotometric method was used, and for the identification of betalain, the HPLC method was used.  
(A) HPLC method  
The HPLC method is based on research of [PERSON] et al. [3]. For freeze-dried samples, approximately 1 g was extracted with 20 mL of a mixture of 0.2% formic acid and acetonitrile (4:1, \(v\)/\(v\)). The samples were stirred for 5 min, centrifuged at 5000\(\times\)\(g\) for 5 min. The supernatants were filtered through 0.45 \(\mu\)m syringe PTFE filters (Macherey-Nagel, Duren, Germany) to chromatographic vials.  
The analyses were conducted using an equipment from Waters (Milford, MA, USA): the 2695 Separations Module connected with 2995 PDA detector, and 2475 Multi-Wavelength Fluorescence Detector (Waters, Milford, MA, USA). The samples of 10 \(\mu\)L were injected on a Sunfire C8 column (5 \(\mu\)m, 4.6 \(\times\) 250 mm, Waters) with a precolumn with identical chemistry, which were kept at the temperature of 30 \({}^{\circ}\)C and rinsed at 1.0 mL/min with a gradient of 0.2% formic acid and acetonitrile as described by [PERSON] et al. [3]. Betacyanins were quantified at a wavelength of 538 nm, while betaxanthins at 480 nm. Two independent analyses were performed for each sample.  
The analytes were identified using their retention times, UV/VIS spectra, and previous results from our team [16; 28; 29; 30; 31].  
(B) Spectrophotometric method  
The spectrophotometric method was based on the methodology presented by [PERSON] et al. [32]. The measurement was conducted on the Evolution 220 (Evolution 220, Thermo Fisher Scientific Inc., Waltham, MA, USA). The 0.5 g of sample (powder or juice) was mixed with phosphate buffer (5000 mg). Betanin (mg betanin/100 g of dm) and vulgaranthin I (mg vulgaranthin I/100 g of dm) determination was based on the red and yellow pigments, respectively. The samples were analyzed in triplicates.
## 3 Theoretical Analysis  
Before the analysis, the definition of the key parameters of SP-PMVM should be given, which are shown in Figure 4. It should be noted that the predefined positive rotating direction of stator is counter-clockwise.  
To calculate the flux density and the permeance, the rotor can be divided into 44 pieces, angularly, and the magnetic circuit of each piece is shown in Figure 5. The potential along the edge facing the  
Figure 4: Definition of parameters of SP-PMVM.  
Figure 3: Comparison of the two machines: (**a**) conventional PMVM; and (**b**) SP-PMVM.  
outer air gap is magnetically floating and the magnetic potential of each piece could be calculated. In the figure, \(V_{k}\) is the potential of \(k^{th}\) rotor piece, \(F_{mag}\) is the MMF of permanent magnet, \(R_{m}\) is the lump reluctance of a permanent magnet and \(\overline{P_{g,k}}\) is the lumped air gap permeance facing the \(k^{th}\) rotor piece.  
Since the inner stator is shifted by half of the pitch relative to the outer stator, the outer air gap and inner air gap specific permeance [9; 10], \(P_{g}^{\pm}(\theta)\) is given in Equation (2):  
\[P_{g}^{\pm}(\theta)=P_{0}^{\pm}\mp P_{1}^{\pm}\cos(N_{S}\theta) \tag{2}\]  
where the quantities \(P_{0}^{\pm}\) is the average permeance, \(P_{1}^{\pm}\) is the fundamental permeance, and \(\theta\) is the angle of air gap of angular position.  
In order to utilize the vernier effect and to avoid MMF oscillation and loss of permeability [11], the rotating MMF and the air-gap flux density are given by Equations (3) and (4):  
\[F_{g.PM}^{\pm}(\theta)=\frac{4}{\pi}\frac{F_{mag}}{2+R_{m}\overline{P_{0}}}\cos (N_{R}(\theta-\theta_{m})) \tag{3}\]  
where \(F_{g,PM}^{\pm}\) is the MMF developed by PM, \(\overline{P_{0}}\) is the permeance of a core piece, \(\theta\) is the angle of air gap of angular position, and \(\theta_{m}\) is the angle between the rotor permanent magnet. The air-gap flux density are given by Equation (4):  
\[B^{\pm}(\theta)=\frac{4B_{r}l_{m}}{\pi\mu_{m}\big{(}2+R_{m}\overline{P_{0}} \big{)}}\cos(N_{R}(\theta-\theta_{m})\big{(}P_{0}^{\pm}\mp P_{1}^{\pm}\cos(N_{ S}\theta)\big{)} \tag{4}\]  
where \(B_{r}\) is the residual flux density of PM, \(h_{m}\) is the rotor permanent magnet thickness, and \(\mu_{m}\) is the permeability of permanent magnet.  
By using Equation (4), the back-EMF (rms) of a phase winding induced in outer stator with \(q\) slots per pole per phase and \(N_{ph}\) turns can be simplified, as in Equation (5):  
\[E_{cp-pmvm}=\frac{4l_{sk}\omega_{m}}{\sqrt{2}\pi}B_{r}D_{g}^{+}\Big{(}k_{w}N_{ ph}\Big{)}\epsilon_{E.cp-pmvm} \tag{5}\]  
where \(l_{sk}\) is the stack length of core, \(\omega_{m}\) is a mechanical rotor speed, \(D_{g}^{+}\) is the air gap diameter, \(k_{w}\) is a slot space factor, \(N_{ph}\) is a slot space factor, and \(\epsilon_{E.cp-pmvm}\) is the back EMF coefficient of the SP-PMVM motor.
## 4 Discussion
### Limitations and Potential Solutions  
The use of rolling shutter cameras, such as the Zenmuse X5S employed in this study, introduces unique challenges in UAV photogrammetry due to the sequential image capture mechanism [35]. Unlike mechanical or global electronic shutters, rolling shutters expose each image line incrementally rather than simultaneously, which can lead to geometric distortions when the camera or subject is in motion [33]. These distortions, referred previously to as the rolling shutter effect, are not inherently accounted for by traditional photogrammetric models, potentially affecting the accuracy of derived products. This is especially problematic in dynamic environments or at higher UAV speeds, as the standard photogrammetric model does not inherently account for the temporal offsets between image lines. The authors of [35] recommend a flight speed below 4 m/s to reach results that are compatible with the well-accepted practical accuracy bound. As a result, some of the larger residuals observed in the CPs are attributed to uncorrected rolling shutter effects. In fact, Pix4 Dmapper estimated a median camera speed of 14.5 m/s. Despite this, the algorithm of rolling shutter mitigation integrated into Pix4 Dmapper improved the results across all four scenarios analysed (Table 4). The values obtained in this study after the algorithm application are comparable to those reported in studies using mechanical shutters [39; 43]. For the production of more accurate photogrammetric outputs, the use of unmanned aerial vehicles (UAVs) equipped with mechanical shutters is recommended, as this type of shutter exposes the entire image sensor frame to light simultaneously.  
This study demonstrates that strategic flight planning, incorporating oblique imagery, and optimising the GCP layout can significantly improve accuracy up to a limit level of 3x the GSD (Scenario B, Configuration 3), even when using the 8-parameter model. These findings highlight the importance of leveraging advanced processing algorithms and thoughtful survey design to overcome hardware limitations in order to achieve acceptable photogrammetric results.
###### Abstract  
In recent years, nanomaterials have gained special attention for removing contaminants from wastewater. Nanoparticles (NPs), such as carbon-based materials and metal oxides, exhibit exceptional adsorption capacity and antimicrobial properties for wastewater treatment. Their unique properties, including reactivity, high surface area, and tunable surface functionalities, make them highly effective adsorbents. They can remove contaminants such as organics, inorganics, pharmaceuticals, medicine, and dyes by adsorption mechanisms. In this review, the effectiveness of different types of carbon-based NPs, including carbon nanotubes (CNTs), graphene-based nanoparticles (GNPs), carbon quantum dots (CODs), carbon nanofibers (CNFs), and carbon nanospheres (CNSs), and metal oxides, including copper oxide (CuO), zinc oxide (ZnO), iron oxide (Fe\({}_{2}\)O\({}_{3}\)), titanium oxide (TiO\({}_{2}\)), and silver oxide (Ag\({}_{2}\)O), in the removal of different contaminants from wastewater has been comprehensively evaluated. In addition, their synthesis methods, such as physical, chemical, and biological, have been described. Based on the findings, CNPs can remove 75 to 90% of pollutants within two hours, while MONPs can remove 60% to 99% of dye in 150 min, except iron oxide NPs. For future studies, the integration of NPs into existing treatment systems and the development of novel nanomaterials are recommended. Hence, the potential of NPs is promising, but challenges related to their environmental impact and their toxicity must be considered.  
capon-based nanoparticles; metal-oxide-based nanoparticles; adsorption; pollutants; wastewater treatment +
Footnote †: journal: Applied sciences  
They are classified into different categories, such as toxic metals, metal compounds, and inorganic salts [5]. Organic pollutants comprise pesticides, dyes, and phenols, which are mainly from the pharmaceutical and agricultural industries, whereas biological pollutants are classified as viruses, bacteria, and protozoa [6].
## Appendix B Estimated Results of the Scale Factor Error and Misalignment Error (ACCs)  
Figure 12 showed that the estimation of the ACC scale factor error and the misalignment error derived from the two EKFs all converge to the stable value. The scale factor errors Kxx, Kyy, and Kzz of the three-axis ACC estimated by the nonlinear EKF are 14.77 ppm, 99.64 ppm and 358.00 ppm, respectively. The misalignment errors Kxy, Kxz,Kyx, Kyz, Kzx and Kzy of the ACC as estimated by the nonlinear EKF are \(8280.74\)\(\upmu\)rad, \(690.50\)\(\upmu\)rad, \(-8397.52\)\(\upmu\)rad, \(-604.28\)\(\upmu\)rad, \(-996.20\)\(\upmu\)rad, and \(472.14\)\(\upmu\)rad. The scale factor errors Kxx, Kyy, and Kzz estimated by the linear EKF are \(-25.71\) ppm, \(121.58\) ppm and \(532.47\) ppm, respectively. The misalignment errors Kxy, Kzx, Kyx, Kyz, Kzx and Kzy of the ACC as estimated by the linear EKF are \(8041.73\)\(\upmu\)rad, \(849.80\)\(\upmu\)rad, \(-8284.30\)\(\upmu\)rad, \(-188.45\)\(\upmu\)rad, \(-1089.58\)\(\upmu\)rad and \(420.35\)\(\upmu\)rad.
## 5 Experimental Results and Discussion
### Training Data  
Sample pixels require to be extracted from the raw data and then reordered to feed the devised architecture. Indeed, the first RNN stage requires data points to be collected in slices of time series.  
So, we separated labeled pixels from raw data, and we divided them in chunks of data, forming a tri-dimensional tensor \(\mathbf{X}\in\mathbb{R}^{i\times t\times b}\) for the successive pre-processing pipeline. In Figure 8, a visual representation of the data set tensor \(\mathbf{X}\) generation, where fixing the first dimension \(\mathbf{X}_{i,:,}\) there are the individual pixel samples \(X^{(i)}\) with \(t=9\) time steps and \(b=5\) spectral bands. It is worth to notice that the number of time steps and bands are completely an arbitrary choice dictated by the raw data availability.  
Subsequently, we adopted a simple pipeline of two steps to pre-process the data. Stratified sampling has been applied in order to divide the data set tensor \(\mathbf{X}\), with shape (92116, 9, 5), in a training and test set. Due to the natural unbalanced number of instances per class present in the data set Table 3, this is an important step to preserve the same percentage in the two sets. After selecting a split percentage for the training of 60%, we obtained two tensors \(\mathbf{X}_{train}\) and \(\mathbf{X}_{test}\) with shape (55270, 9,  
Figure 8: Overview of the tensor \(\mathbf{X}\in\mathbb{R}^{i\times t\times b}\) generation. The first dimension \(i\) represents the collected instances \(X^{(i)}\), the second \(t\) the different time steps, and finally the last one \(b\) the five spectral bands, red, green, blue, near-infrared and NDVI. On the top, labeled pixels are extracted simultaneously, from all-time steps and bands starting from the raw satellite images. Then, \(X_{i,j,k}\) are reshaped in order to set up the \(X^{(i)}=\mathbf{X}_{i,:,}\) of the data set tensor \(\mathbf{X}\).  
5) and (36846, 9, 5), respectively. Secondly, as common practice, to facilitate the training, we adopted standard scaling, \((x-\mu)/\sigma\), to normalize the two sets of data points.
## 1 Introduction
Several researchers have also studied the movement characteristics of solid particles in centrifugal pumps from different points of view through experiments while analyzing the influence of the slurry concentration and particle size on the slurry pump head, efficiency, and wear of the flow passage components [21; 22; 23]. The flow process in an electric pump is very irregular considering the actual operating conditions. Therefore, it is of practical significance to analyze the unsteady flow characteristics in electric pumps. [PERSON] et al. [24] simulated the transient cavitation flow in a jet pump with a non-uniform inflow in an attempt to reveal correlations between the jet pump cavitation, vortex diffusion, and outlet pressure fluctuations. Their results showed that acceleration in the cavity is the main source of internal pressure fluctuations in mixed-flow water jet pumps. In the study of [PERSON] et al. [25], the time-dependent flow rate was captured and analyzed in computational fluid domains by Lagrangian and Eulerian viewpoints. They showed that starting from the 1-8 section of the volute, the time-averaged velocity decreased overall, and the accuracy of the simulation results was verified using experiments. [PERSON] et al. [26; 27; 28] studied the unsteady flow, pressure pulsation, eddy current, and other phenomena in a pump based on the delayed detached eddy simulation (DDES) model, which provides an important reference for analyzing the unsteady flow characteristics in pumps. In specific engineering applications, cases have occurred in which deep-sea mining tests have been terminated due to the wear of coarse particles on the deep-sea pumps, thereby indicating the importance of analyzing the wear of electric pumps. In particular, the non-steady-state wear characteristics of the over-current components in electric pumps under different operating conditions should be studied [29; 30; 31], as these components are extremely important to the design, optimization, testing, and application of these pumps. In order to realize the analysis of the non-steady-state wear characteristics of the mining pump, this paper uses the DPM wear model to analyze the evolution characteristics of the surface wear of the flow passage components under three flow rates. In addition, a wear area test was performed on the test pump, and the test results were compared with the numerical analysis results to verify the rationality and applicability of the numerical calculation method and the wear model. The research results of this paper have important practical significance for predicting the wear characteristics of deep-sea mining pumps.
## 4 Discussion
### Acetic Acid Addition  
Acetic acid is a commonly used food additive with well-known antimicrobial activity against bacteria and yeasts [27]. As expected, the microbial count (Figure 1) and the number of yeasts and molds (Figure 2) were significantly reduced by acetic acid addition. It also suppressed fermentation and almost completely prevented propionate and butyrate formation (Figure 5). The addition of organic or inorganic acids was previously shown to reduce butyrate levels during the anaerobic incubation of _Scenedesmus obliquus_[47]. This was explained by growth inhibition of butyrate producing species such as _Clostridium_ species due to the low pH. In the current study, no off odors were perceived by the authors for the acetic acid supplemented algae. A standardized evaluation of the odor by a group of panelists is now required to ascertain this observation. In any case, there is no doubt that acetic acid addition had a positive impact on the microbial count and that it suppressed the formation of odorous fermentation products.  
Unfortunately, acetic acid addition was found to induce lipid hydrolysis when concentrates were stored for more than one week. Acetic acid supplementation also had a negative effect on chlorophyll levels when algae concentrates were stored for longer than 7 days. In a study of [PERSON] et al., a 10 min exposure to acetic acid (34 mM, pH 5) resulted in photosynthetic pigment degradation in Chlamydomonas reinhardtii and even caused programmed cell death [48]. This was explained by acetic acid diffusion across the cell membrane resulting in a decreased intracellular pH and cell damage [48]. In the current study, chlorophyll levels were not decreased after acetic acid addition (50 mM) and one week storage, while FFA levels remained low the first week. This suggests that _N. gaditana_ is less susceptible to acetic acid induced stress. The fact that algae were concentrated by membrane filtration in our study and were not centrifuged such as in the study of [PERSON] et al. may have protected algal cells from shear forces and possibly contributed to cell integrity. Moreover, the recalcitrant cell wall of _Nannochloropsis_[49] can also be expected to act as a protective barrier.  
Given its positive effects on microbial numbers and fermentation, acetic acid addition remains an interesting additive for short term _Nannochloropsis_ storage. Yet, one must bear in mind that cell integrity will be impaired when acid exposure is prolonged.