esa-sceva/satcom-chunk-collection huggingface dataset:
# DR-Transformer: A Multi-Features Fusion Framework for Tropical Cyclones Intensity Estimation
## 4 Experiment  
### Experimental Settings  
Datasets: We conducted our experiments on a benchmark dataset: TCIR [3]. The TCIR dataset consists of 70501 TC images from 2003 to 2017. Following the standard data split protocol, we used TC images from 2003-2016 for training and TC images from 2017 for testing. Images from the TCIR dataset have four channels (IR1, WV, VIS, PMW); we only used IR1 and PMW channels to train our model.  
Implementation details:: Our method was implemented using Pytorch on a Nvidia RTX2080. We used Resnet-18 [24] as the backbone, which was pre-trained on the ImageNet ILSVRC 2012 dataset [25]. The feature embedding size was fixed as 512 throughout the experiments. Our transformer contains two encoders and two decoders. The hidden dim and head number of transformers were set to 512 and 4, respectively. All images were cropped to 224 \(\times\) 224 before feeding into the network. Following [4], the images from the Southern Hemisphere were flipped horizontally to be simultaneously trained with images from the Northern Hemisphere. Random crop and random rotation were used for data augmentation during training and single-center crop was used for testing. We first trained the backbone for 60 epochs with the Adam optimizer [26] and then we trained the transformer for 20 epochs with the RAdam optimizer [27]. The initial rates for parameters in the backbone and transformer were \(1\times 10^{-3}\) and \(1\times 10^{-4}\), respectively. We adopted a random sampling strategy to construct a mini-batch of size 8 from backbone training and we randomly sampled a temporal sequence of length 5 for transformer training. We set hyperparameters \(\alpha=0.1\).  
Evaluation metrics: We obtained the final results by searching for the nearest neighbors in the class vectors set. We adopted the Root mean squared error (RMSE) and mean absolute error as the evaluation metrics.  
\[RMSE=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\widehat{y_{i}})^{2} \tag{15}\]  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}|y_{i}-\widehat{y_{i}}|^{2} \tag{16}\]
# Synthesis of Nitrogen-Doped Graphene Quantum Dots from Sucrose Carbonization
## 3 Results and Discussion
### Photoluminescence (PL)
The photos in Figure 12d show the PL of N-GQDs under excitation with cw lasers of different wavelengths. Although the chromaticity diagrams showed a possibility of tuning the luminescent emission in the GQDs without doping, it is worth mentioning that the PL in the non-blue regions was much less intense. Figure 13 shows the normalized maximum PL intensities presented by the GQDs as a function of the excitation wavelength. The PL intensity in the orange region (under excitation at ~550 nm) corresponded to less than 4% of the emission intensity in the blue region (under excitation at ~400 nm) for the same sample. In the case of N-GQDs, the intensity of the emission in the red region (under excitation at 550 nm) corresponded to approximately 45% of the intensity of the luminescent emission in the blue region (under excitation at ~400 nm). Figure 12 also shows that the luminescent emission between the white and green regions (under excitations between 375 and 500 nm) showed practically no differences in the PL intensity, quite different from what occurred for undoped GQDs. It is notable, therefore, that the emission tuning capability with N-GQDs was much higher than that with non-doped GQDs. Figure 12: CIE 1931 chromaticity diagram (**a**) of the GQDs and (**b**) of the N-GQDs under different excitations. White dashed lines are guides to the eye; photos of (**c**) undoped GQDs and (**d**) N-GQDs dispersed in DMSO and excited with cw lasers at 405, 470, and 532 nm, respectively.
# Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
## 2 Methods  
TOF-UWB tag communicates with the base stations for ranging and exchanging timestamp information. The distance from the tag to each base station was \(d_{i}\) (\(i=1,2,\ldots,\)\(N\), \(N\) is the number of base stations), which can be expressed as [27]  
\[\left\{\begin{array}{l}d_{1}^{2}=\left(x-x_{1}\right)^{2}+\left(y-y_{1} \right)^{2}+\left(\mathrm{z}-z_{1}\right)^{2}\\ d_{2}^{2}=\left(x-x_{2}\right)^{2}+\left(y-y_{2}\right)^{2}+\left(\mathrm{z}- z_{2}\right)^{2}\\ \vdots\\ d_{N}^{2}=\left(x-x_{N}\right)^{2}+\left(y-y_{N}\right)^{2}+\left(\mathrm{z}- z_{N}\right)^{2}\end{array}\right. \tag{1}\]  
where \(\left(x,y,z\right)\) and \(\left(x_{i},y_{i},z_{i}\right)\) are the coordinates of the tag and base station.  
As shown in Equation (1), the relationship between UWB ranging distances and the tag's coordinates is nuclearization. Meanwhile, such a strict relationship would be destroyed while suffering NLOS errors or while using the linearized adjustment methods. Therefore, the traditional linearized adjustment methods named as least square and extended Kalman filter were first provided. Then, the robust particle filter was introduced in detail.
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
###### Abstract  
Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing the PSALASAR framework, incorporating the PICOC method and PRISMA statement for comprehensive research, 22 publications were scrutinized from the Scopus, Web of Science, and PubMed databases. A diverse range of sensor fusion methods, data signal analysis techniques, and classifier performances were investigated. Notably, [PERSON]'s algorithm and the Principal Component Analysis showed superior performance in tracking respiratory movements. Classifiers like Long Short-Term Memory Recurrent Neural Networks exhibited high accuracy in detecting breathing events. Motion artifacts, limited sample sizes, and physiological variability posed challenges, highlighting the need for further research. Optimal sensor configurations were explored, suggesting improvements with multiple sensors, especially in different body postures. In conclusion, this systematic literature review elucidates methods, challenges, and potential future developments in using inertial sensors for assessing adult human breathing patterns. Overcoming the challenges related to sensor placement, motion artifacts, and algorithm development is essential for progress. Future research should focus on extending sensor applications to clinical settings and diverse populations, enhancing respiratory health management.  
breathing mobility dysfunction; breathing pattern assessment; inertial sensors; non-intrusive measurement; machine learning classifiers; algorithm; systematic literature review +
Footnote †: journal: _Algorithms_
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Field-Effect Transistors (FET)
#### 3.2.1 Selective Applications  
Selective FET sensors are commonly developed using nanomaterials for the formation of the conductive channel and used mainly for the detection of simple gases. CNTs (specifically SWCNTs acting as p-semiconductors) have been extensively investigated for CNTFET gas sensors, possessing p-transistor characteristics [7; 50]. Pristine CNTs have been used for the detection of the oxidizing gases NO [145] and NO\({}_{2}\)[145; 146; 147] and the reducing gas NH\({}_{3}\)[145; 147], as well as for ethanol and benzene detection [147]. Remarkably, the CNTFET sensor developed by [PERSON] et al. exhibited the ability to discriminate the gaseous analytes due to distinguishable temporal response of conductance to gate voltage pulse [147].  
2D material-based FETs have been also developed using TMDs or graphene derivatives. WS\({}_{2}\) n-type semiconducting multilayer nano-flakes have been used for ethanol and NH\({}_{3}\) detection under illumination [148], while MoS\({}_{2}\) multilayer-based FETs permit NO [149], NH\({}_{3}\), and NO\({}_{2}\) detection [150]. rGO-based FETs have also permitted ethanol [151], NH\({}_{3}\) and NO\({}_{2}\) detection achieving low ppb LOD [152], while NH\({}_{3}\) detection was also reported using NO\({}_{2}\)-dopped graphene [153].  
Hybrid materials have also been used in selective FET sensors. 2D graphene/MoS\({}_{2}\) heterostructures used in flexible and potentially wearable p-type devices, in which graphenereplaces the metallic electrodes, have been reported for NO\({}_{2}\) and NH\({}_{3}\) detection albeit with lower sensitivity [154]. Combinations of CNTs with other materials for sensing performance improvement are also reported in the literature. Polypyrrole-SWCNTs have been used for NH\({}_{3}\) detection, achieving increased sensitivity and reduced response/recovery times by controlling film thickness, SWCNTs concentration and annealing temperature [155]. Selective H\({}_{2}\)S detection has been also attempted using Au-decorated SWCNTs, leading to high Au NPs size-dependent sensitivity and lower LOD (\(\leq\)100 ppb) in comparison to carboxylated SWCNTs (Figure 8) [156]. A lower LOD of 10 ppb and a theoretical LOD of 500 ppt for selective H\({}_{2}\)S detection have been recently achieved using a FET based on Au NPs-functionalized ZnO NWs [157]. Au NPs of 1, 3, 5, 7, and 10 nm were tested, with those of 7 nm leading to the maximum interaction between the analyte and the sensing film.
## 2 Numerical Modeling
### Plasma Modeling
When employing plasma actuators on the compressor rotor, the transmission of the signal from the fixed to the rotating frame will be realized by means of devices like those produced by Jordil Technic Sarl, which have already been used in [44] for a piezo-actuated aero engine blisk. An alternative, which would avoid the transmission of electrical signals from a fixed to a rotating system, was proposed by [PERSON] in [45]. [PERSON] suggested exploiting the rotation to generate the required voltage, by using electromagnetic effects. The concept of [45] is targeted to plasma actuators employed to control the tip clearance flow on gas turbine engines. Therefore, it is suitable also for the application of the present work. [PERSON] proposed locating one or more magnets on the casing of the engine and a magnet with a solenoid on each of the plasma actuated blades. The solenoid is connected to the electrodes of the plasma actuator. Due to the relative rotation of the blade and of the casing magnets, a voltage is generated on the solenoid. This voltage can indeed be used to feed the actuators, if an appropriate signal modulation is applied. A further path, which is currently pursued by the authors in cooperation with the University of Salento, consists of developing micro controllers and micro generators, small enough to be located in targeted hollows, realized internally to the blades. In order to avoid a possible interference of the electrodes on the pressure side with those on the suction side, proper electromagnetic shielding will be employed between the two actuators. This shield can be realised by means of e.g., metal screens, metal foams or metal sheets.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 3 Sensor Operating Modes and Energy Consumption Measurements
### Consumption Profiles in Active and Idle Mode
#### 3.3.4 System Tick Event  
The MCU uses a system tick timer for real-time event execution. A tick timer is set for continuous operation with a predefined time interval based on a clock from the 32.768 kHz RTC quartz oscillator. Each time the tick interrupt is generated, the MCU increases the tick counter and decreases the remaining time of tasks waiting for execution. The measured mean energy consumption \(e_{tick}\) of the MCU system tick event with a period of \(t_{tick}=0.133\,\mathrm{ms}\) is \(1.85\,\upmu\)J with 0.0 \(\upmu\)J standard deviation. An example of a current consumption profile associated with an MCU system tick event is presented in Figure 7. The current profile is actually a short burst of activity where the MCU wakes up after being interrupted by the system tick timer, increases the value of the system tick counter, and returns to sleep.  
Figure 6: Current consumption profile of an MCU ECG sampling event.  
Figure 7: Current consumption profile of an MCU tick event.
## 1 Introduction
Since both the UCS and TCP are typically unknown in real-world applications, simultaneous estimation of both variables has been proposed and addressed in several studies [10; 11; 12]. Figure 1 illustrates the relationships among the relevant coordinate systems and their transformations. This work introduces a novel, measurement-driven calibration method that simultaneously estimates the UCS and TCP without requiring access to internal robot controllers. By leveraging high-precision external measurements, the proposed approach achieves sub-millimeter accuracy and demonstrates robust performance across varying configurations, making it especially well-suited for industrial applications involving digital twins and offline programming. The increasing reliance on offline programming (OLP) and digital twin technologies in modern robotic production lines has made accurate alignment between simulated and physical environments a critical requirement. The motivation for addressing this research includes the following:  
* Reduction of implementation steps--simplifying the deployment of simulation-based robot programs by minimizing the number of required measurement procedures. * Shortened commissioning time--enabling more efficient integration of executable programs onto real robotic systems. * Robot-specific accuracy--allowing for the best-fitting calibration solution to be directly tailored to each individually measured robot, improving positional precision. * Scalability in multi-robot setups--increasing the efficiency of calibration processes in production cells involving multiple robots operating on a shared tooling station. This article is organized as follows: Section 2 presents a comparative evaluation of four methods for estimating the User Coordinate System (UCS) and Tool Center Point (TCP), including a newly proposed algorithm. Section 2.1 outlines each method, while Section 2.2 describes the experimental setup using two ABB IRB-6640 robots and a Leica laser tracker. Section 2.3 details the data recording process combining internal robot calibration and external coordinate alignment. Section 3 introduces the iterative estimation algorithm based on an AX = YB formulation, and Section 4 presents the comparative validation results. The paper concludes with a summary of the findings and the demonstrated advantages of the proposed approach in bridging simulation and real-world robotic applications.
# Modeling the Directional Clumping Index of Crop and Forest
## 2 Modeling and Evaluation
### Simulation Method of Directional Clumping Index
#### 2.2.1 Crop Directional Clumping Index Simulation  
The MC method simulated the radiative transfer process of photons in the canopy by generating the incident photons, determining the scenarios regarding photons colliding with leaves or soil, randomly assigning the scattering direction, calculating the free paths of the photons, and counting the photons satisfying specific conditions. The detailed procedures and parameter setting was introduced in [38].  
A set of parameters from maize canopy was set as the hypothesis example (\(a_{1}=0.2\), \(a_{2}=0.3\), \(H=1\), \(r_{g}^{h}=0.22\), \(\rho=0.0999\), \(T=0.1303\), Table 1).  
We calculated \(\Omega(\theta,\varphi)\) while using the MC method for the same scenario. In the MC method, the radiative photon transfer process in the canopy is simulated for predefined canopy structural parameters. Each photon was traced and collisions were recorded. The statistics from many photon behaviors approximately reflect actual canopy characteristics and provide validation. Therefore, the gap fraction is the fraction of photons penetrating the canopy, and \(\Omega(\theta,\varphi)\) can be calculated from Equation (3).
# Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation
## 4 Experiments
### Experimental Results and Analysis
#### 4.2.2 Result 2: The Impact of the Latent Vector Dimension on the Performance  
To explore the impact of the latent vector dimension on the performance of the model, we compared the performance of the four models: GMF+MLP, concate, sum pooling, and IFDNAMF under the different latent vector dimensions of 10, 20, 40, and 80 on MovieLens and Book-crossing. The results are shown in Figures 2 and 3.  
Figures 2 and 3 show that with the continuing increase in the potential dimensions, the HR@10 and NDCG@10 of the four models increase continuously, and the performance of recommendation systems of the models gets improve continuously. This is because before reaching the optimal latent vector dimension, as the dimension increases, the latent vector could contain more information, which assists the model in describing the user features and item features more comprehensively and specifically to model the latent features of users and items better, so the performance of recommendation systems is better. However, the dimension should not be too large. Because when the latent vector dimension is too large, it may lead to over-fitting. This is why when the latent vector dimension reaches 40 on the MovieLens, the HR@10 and NDCG@10 of models begin to decline, and when the latent vector dimension reaches 20 on the Book-crossing, HR@10 and NDCG@10 also begin to decline. Meanwhile, we can conclude from Figures 2 and 3 that the optimal latent vector dimension of the IFDNAMF model is 40 on MovieLens and 20 on Book-crossing. Furthermore, in different dimensions (20, 40, and 80), compared to the GMF+MLP model, the performance of the sum pooling model and IFDNAMF model which cross different features by element-wise product achieve great improvement, while the performance of  
Figure 2: The HR@10 and NDCG@10 of models with different potential vector dimension on MovieLens. (**a**) HR@10. (**b**) NDCG@10.  
the concat model which concatenates the features by the fully-connected network gets limited improvement. This demonstrates that, when fusing attribute information, it is an effective way to simulate the interaction of features by element-wise product, while combining the segmented feature vectors through the fully-connected network is not effective to represent the features, so the improvement of model performance is small. In addition, in different dimensions (20, 40, and 80), compared with the sum pooling model, the HR@10 and NDCG@10 of the IFDNAMF model introducing the attention mechanism increase, and the model performance has been further improved. This indicates that not all cross-features have the same degree of association with the result, and by assigning the cross-features with different weights, the model can obtain the ability to adjust the feature weights according to the characteristics of the sample, which is significative to improve the prediction ability of the model.
## 4 Discussion
### Direct Root Formation and Development Are not Related to Essential Oil Production  
Root formation happens in all 2 iP, tZ, and KIN concentrations during in vitro shoot propagation ( Figures S7 and S8). This rooting was unexpected because CKs are considered negative regulators of root growth and lateral root formation [41] and have not been previously reported for in vitro propagation of _C. citratus_. However, since 1998, this phenomenon had already been registered by [PERSON] and colleagues [42]. They found that the CKs BAP and KIN riboside (KR) produced roots from in vitro shoots of _Rhododendon forestii_. However, in this work, BAP did not induce a root system, except in vitro shoots propagated on MS-N and MS-S at the lowest quantity of 0.5 mg L\({}^{-1}\) (Supplementary Figures S7 and S8). This different effect of BAP in two plants implies a differential response of plants to the same CKs. The roots developed in the presence of 2 iP, tZ, and KIN were generally thick, with intense purple pigmentation, and longer compared to the white and thinner roots grown on a cytokinin-free basal medium (Supplementary Figure S11). Considering the EO profile production, we cannot correlate root development to essential oil production. For instance, in vitro shoots grown on 0.5, 1.0, or 2.0 mg L\({}^{-1}\) KIN produced roots, and at the same time, 1.0 or 2.0 mg L\({}^{-1}\) BAP did not, and there were no significant differences in the production of \(\beta\)- and \(\alpha\)-Citral between tZ and BAP at 0.5 mg L\({}^{-1}\).
## 3 Results
Specifically, the accuracies were increased from 91.36% to 96.97% (3 s TW, \(p=0.056\), close to significant degree), from 93.40% to 98.18% (4 s TW, \(p<0.05\)), from 92.10% to 97.58% (5 s TW, \(p<0.05\)), and from 94.63% to 98.18% (6 s TW, \(p<0.05\)), respectively. As shown in Figure 5, the classification accuracies of the L1-MCCA related methods were higher than those of the CCA-related and PSD-related methods. More importantly, the PSO-SVM optimization can further improve the classification accuracy of PSD, CCA and L1-MCCA technologies, indicating that the combination method of them with classifiers is very effective. The PSO-SVM optimization provides new ideas for the classification of BCI systems based on SSVEP. Figure 5: The group-averaged accuracies with different time window (TW) from 1 to 6 s for different methods. Error bar represents the standard error of accuracy. The solid and the dotted lines represent the method before and after optimization.
# Innovative Approaches to Semi-Transparent Perovskite Solar Cells
###### Abstract  
Perovskite solar cells (PSCs) are advancing rapidly and have reached a performance comparable to that of silicon solar cells. Recently, they have been expanding into a variety of applications based on the excellent photoelectric properties of perovskite. Semi-transparent PSCs (ST-PSCs) are one promising application that utilizes the tunable transmittance of perovskite photoactive layers, which can be used in tandem solar cells (TSC) and building-integrated photovoltaics (BIPV). However, the inverse relationship between light transmittance and efficiency is a challenge in the development of ST-PSCs. To overcome these challenges, numerous studies are underway, including those on band-gap tuning, high-performance charge transport layers and electrodes, and creating island-shaped microstructures. This review provides a general and concise summary of the innovative approaches in ST-PSCs, including advances in the perovskite photoactive layer, transparent electrodes, device structures and their applications in TSC and BIPV. Furthermore, the essential requirements and challenges to be addressed to realize ST-PSCs are discussed, and the prospects of ST-PSCs are presented.  
perovskite solar cells; semi-transparent; tandem solar cells; building integrated photovoltaics; efficiency +
Footnote †: journal: _Nanomaterials_
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Atomic Emission Spectroscopy  
Single element measurement, poor sensitivity, and slow analysis rates are some of the limitations of atomic absorption spectroscopy that have been overcome with atomic emission spectroscopy techniques. Although it is possible to attain the emission spectrum of an element using atomic spectrometers, such as FAAS, the flame may not be the best excitation source of the elements. Inductively coupled argon plasma (ICP) has proven to provide higher temperatures than flames to give broader elemental coverage and higher sensitivity than AAS techniques. ICP can reach temperatures close to 10,000 K, which results in complete atomization and significant ionization of the analyte. Therefore, molecular interferences are reduced, and the detection of atom and ion emissions is better achieved [69].