esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis  
The experimental system of the proposed method as shown in Figure 9 is set up by a HDLP (DLP light crafter 4500 with resolution of 912 pixel \(\times\) 1140 pixel) and a high frame rate CMOS monochrome camera (HXC20 with frame rate of 234 fps for 800 pixel \(\times\) 600 pixel image). The camera uses an 8 mm focal length lens (SE0813-2). Due to the DLP light crafter 4500 (DLP 4500) can control its R, G, and B LEDs on/off individually in each channel, which is independent of the image refresh timing sequence. Before projecting fringe, the R, G, and B LEDs of the DLP 4500 are set to lighting on mode in each channel and remain independent of the tricolor binary fringe projecting timing sequence. That is the projecting light source is set to be monochrome projection mode. So that the every frame binary fringe in R, G, and B channels of the tricolor binary fringe can be projected separately in sequence as a grayscale fringe combined TDM principle. Its projection frame rate can be 4225 fps for 1-bit binary fringe. In the experiment, the period of the proposed tricolor binary fringe is encoded 24 pixels, its R, G, and B components share the same duty cycle of 1/3 but shifted 1/3 periods (8 pixels). The obtained 3D shape information data is implemented on a computer with configuration of Intel(R) core (TM) i5-7500 CPU @3.40 GHz and 8 GB extendible physical memory.  
Figure 8: The special TDM timing sequences of the whole acquisition process.  
Figure 9: The experimental system of the proposed method.
# Two Design Options for Compact Linear Accelerators for High Flux Neutron Source
## 3 Physical Design of DTL Accelerator
### Transverse Beam Dynamics Considerations  
The transverse focusing scheme in the DTL transverse beam dynamics is selected to be a FODO lattice, which has merits in requiring a smaller number of PMQs and imposing a less stringent requirement on magnetic gradients.  
However, for very high intense beams, the strong space charge effect could yield an equipartitioning process, which will lead to emittance growth and increase the risk of beam losses in the downstream parts of the DTL tanks; therefore, the equipartitioning design  
Figure 8: Accelerating gradients (**a**) and synchronous phases (**b**) of both DTL tanks.  
Figure 9: Simulated longitudinal beam envelopes inside the DTL cavities: (**a**) 162.5 MHz design, (**b**) 325 MHz design.  
method is introduced. The quadrupole gradients of FODO lattices are selected to comply with the following design guidelines [38; 47]:  
\[\sigma_{\text{0t}}<90^{\circ}/\text{period};\]  
\[\sigma_{\text{0t}}\
eq\text{n}\sigma_{\text{0t}}/2\text{ for n}=1,3,\ldots;\]  
\[\text{Equipartitioning ratio}\approx 1.0\text{ at full current};\]  
\[\text{(4)}\]  
Avoid known parametric resonances;  
The PMQ lengths within one DTL tank are fixed, which are 80 mm (162.5 MHz design) and 40 mm (325 MHz design), respectively. Figure 10 shows the required quadrupole gradients to satisfy these constrains for the FODO lattices in both DTLs. The highest quadrupole gradient in the 162.5 MHz design is about 1/3 to that of the 325 MHz DTL design.  
The beam envelopes along the DTL tanks with FODO lattices are shown in Figure 11. Compared to the 325 MHz design, the transverse beam envelopes in the 162.5 MHz DTL tank have a larger beam size. It should be noted that the beam sizes are all within the aperture radii of drift tubes. As shown in Figure 12, the transverse and longitudinal emittance growths for both designs are within 10%, which presents a low risk of beam losses.  
Figure 11: Transverse beam 3 * RMS envelopes (X in blue and Y in red) along both DTL linacs: (a) 162.5 MHz design, (b) 325 MHz design.  
Figure 10: Quadrupole gradients of PMQs inside DTL tanks: 162.5 MHz design in yellow and 325 MHz design in blue.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 6 Effects of Carbon-Based NPs on Medicinal and Aromatic Plants
### Graphene Quantum Dots and Graphene Nanosheets  
Treatment of coriander and garlic seeds with 0.2 mg/mL of GR QDs for 3 h before planting resulted in improved growth rate of plant organs of both plants, including fruits, suggesting that GR QDs could be used as plant growth regulators [304]. Spraying of graphene nanosheets (GNS) on _Capsicum annuum_ L. and _Solanum melongena_ L. plants during two seasons at doses 0.1, 0.2 and 0.3 GNS/g induced metabolic regulation of the leaves physiological status and resulted in an increased number of branches and number of fruits per plant, as well as fruit yield. GNS were localized on plastids, cell walls, and intercellular spaces of both plants, and GNS situated inside chloroplasts were found to activate assimilation pigments resulting in promotion of fructose, sucrose, and starch. Application of GNS triggered activity of antioxidant enzymes CAT, APX, GPX, and glutathione-S-transferase, and induced SOD and antioxidant molecules, reducing the levels of OH and O\({}_{2}\)\({}^{-}\) radicals and, thus, prevented the lipid peroxidation and electrolyte leaching. Moreover, increased PAL activity due to GNS treatment stimulated the formation of valuable secondary metabolites [305].
## 5 Discussion
### System Model and Complexity Burden  
The Max-value tactic is applied for detection. The verification part of DC-XFAST is less complex than an architecture that combines over-threshold and max-value detection. The proposed verification tactic does not have any magnitude or power detection, and only the relative position of each peak is of interest. Thus, eliminating the signal power and/or noise power estimations is indispensable in fixed or unfixed threshold approaches.  
In addition, with proper timing control, two channels can be processed sequentially instead of in parallel. As reflected by the system model in Figure 1, two channels share the same correlator and do not put any extra burden on the computation complexity and the resulting brief delay in the inter-satellite links is acceptable.  
Three FFT modules are instantiated in total. The incoming signal blocks in two channels and local block would perform a 2N points FFT. Their multiplication would perform a 2N points IFFT. The procedure includes five FFTs and the FFT operations are implemented with 5 Nlog2N complex multiplications and 10 Nlog2N complex additions in total. The XFAST procedure includes three FFTs and the FFT operations are implemented with 3 Nlog2N complex multiplications and 6 Nlog2N complex additions in total. However, the search range of DC-XFAST is twice the size of that of XFAST, thus DC-XFAST does not increase the computational complexity.
## 6 Conclusions  
In this paper, we experimentally investigated a 4-ASK-FOFDM transmission over a turbulent air-water channel, which was comprised of a weak turbulence channel generated by an atmospheric turbulence simulator, together with a water tank. The BER performance was compared when the 4-ASK-FOFDM signals were transmitted over a turbulent air-water channel and a free-space air-water channel, respectively. The results showed that the BER performance difference under these two kinds of channels was tiny; hence, the 4-ASK-FOFDM signals were not sensitive to weak turbulence. Moreover, the performance of the 4-ASK-FOFDM scheme was compared with a conventional 16-QAM-OFDM scheme under the condition of the same spectra efficiency. The results showed that there was a slight BER performance penalty for 4-ASK-FOFDM compared with the 16-QAM-OFDM scheme. Accordingly, the feasibility of the 4-ASK-FOFDM-based OWC system was vali  
Figure 9: (**a**) Constellation of the 4-ASK-FOFDM signal and (**b**) constellation of 16-QAM-OFDM signal when the link distance through water tank was 1.6 m.  
dated. It is a promising alternative to conventional OFDM techniques in optical wireless communications. To further improve the performance of 4-ASK-FOFDM, more effective channel estimation and compensation algorithms will be researched in the future. With these more effective algorithms, it is believed that FOFDM will be a promising alternative to conventional OFDM techniques in optical wireless communications.  
Conceptualization, L.Z. and Y.C.; methodology, L.Z.; software, Y.C.; validation, L.Z. and Y.C.; formal analysis, L.Z.; investigation, L.Z.; resources, Y.C.; data curation, Y.C.; writing--original draft preparation, L.Z.; writing--review and editing, L.Z.; visualization, L.Z.; supervision, L.Z.; project administration, Y.C.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61605149 and the Natural Science Foundation of Shaanxi Province under Grant No. 2018 JM6074.  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
###### Abstract  
The rising popularity of wearable devices allows for extensive and unobtrusive collection of personal health data for extended periods of time. Recent studies have used machine learning to create predictive algorithms to assess symptoms of major depressive disorder (MDD) based on these data. This review evaluates the clinical relevance of these models. Studies were selected to represent the range of methodologies and applications of wearables for MDD algorithms, with a focus on wrist-worn devices. The reviewed studies demonstrated that wearable-based algorithms were able to predict symptoms of MDD with considerable accuracy. These models may be used in the clinic to complement the monitoring of treatments or to facilitate early intervention in high-risk populations. In a preventative context, they could prompt users to seek help for earlier intervention and better clinical outcomes. However, the lack of standardized methodologies and variation in which performance metrics are reported complicates direct comparisons between studies. Issues with reproducibility, overfitting, small sample sizes, and limited population demographics also limit the generalizability of findings. As such, wearable-based algorithms show considerable promise for predicting and monitoring MDD, but there is significant room for improvement before this promise can be fulfilled.  
depression; wearables; prediction; mental healthcare +
Footnote †: journal: _Algorithms_  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-00  
in mental health care has been a topic of interest for some time. While existing reviews focus on exploring the various wearable sensors and features that can inform predictive models [8; 9; 10; 11], this review focuses more on the practical applications and their feasibility. Specifically, this review aims to evaluate the role of wearables in a clinical setting, in particular, how they may be used to identify and monitor symptoms of major depressive disorder (MDD) in a way that is accessible to both researchers and clinical professionals.
# Fabrication and Photo-Detecting Performance of 2D ZnO Inverse Opal Films
## 3 Results and Discussion
### Photo-Detecting Properties of 2D ZnO IO Film Devices  
IO film-based photodetectors were further constructed by depositing Cr/Au (80 nm/30 nm) electrodes on the as-obtained 2D ZnO IO films. Figure 7 shows the current-voltage (I-V) curves and time-dependent response of the devices. The I-V curves were measured at bias from \(-1\) to 1 V under dark conditions, 600 nm (1.16 mW\(\cdot\)cm\({}^{-2}\)), 500 nm (2.30 mW\(\cdot\)cm\({}^{-2}\)), 400 nm (2.37 mW\(\cdot\)cm\({}^{-2}\)), and 370 nm (2.12 mW\(\cdot\)cm\({}^{-2}\)) illumination. The linear I-V characteristics indicate good Ohmic contacts between films and electrodes. All samples showed no obvious change of current under dark condition and visible light (600, 500, and 400 nm), while the photocurrent increased dramatically under 370 nm illumination. This presents good spectral selectivity of these 2D ZnO IO film devices. As reported in previous study, the enhancement in photocurrent can be explained by an increase in the excited electron-hole pairs and a decrease in the width of the oxygen depletion layer when the device is illuminated by light of an energy above the threshold excitation energy of ZnO [27]. Time-dependent on/off characteristic measurements were carried out under 370 nm illumination at a bias of 1 V with an on/off internal of 1 min. It was shown that the devices can be switched reversibly between on and off states.  
Figure 7: The current–voltage (I–V) curves and time-dependent response (at 370 nm, 1 V bias) of film devices with different CA/ZA molar ratios: (**a,b**) 0:1; (**c,d**) 0.2:1; (**e,f**) 1:1.  
The detailed performances of the IO film-based photodetectors are listed in Table 1. The photocurrent becomes higher with the increase of the CA/ZA molar ratio. When the CA/ZA molar ratio is 1:1, the as-prepared device has the largest photocurrent and UV/dark current (\(I_{\text{P}}/I_{\text{d}}\)) ratio up to 4.6 \(\mu\)A and 1917, respectively. Many factors, such as crystallite orientations, crystallite sizes, porous microstructures, and film thickness may influence the photoresponse characteristics [28]. There are probably two reasons for the enhancement in photocurrent when increasing CA/ZA ratio in this study. On one hand, the crack-free 2D ZnO IO nanofilms fabricated with the addition of CA have better connectivity and larger coverage on the substrate, thus causing stronger UV absorption than the broken one fabricated without CA. On the other hand, photocurrent may also increase with the grain size [29]. Since the nanofilms fabricated with the CA/ZA ratios of 0.2:1 and 1:1 have similar coverage and UV absorption, the increase in photocurrent probably comes from their rougher bottom with larger grain size. Table 2 summaries a comparison of the photoconduction properties based on the present ZnO IO film with other ZnO nanostructures, including nanoparticles [30] and hollow-sphere nanofilm [27]. These key parameters are comparable to or better than those of other ZnO nanostructures with different shapes.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 3 Thermomechanical Characterization of Amorphous Metals  
### Introduction to Conventional Measurements  
The thermal analysis of all solid materials has always been closely followed by mechanical and thermomechanical analysis (TMA), which is a method where deformation (elongation) is measured as a function of temperature [37]. However, in modern times, the definition and application have expanded somewhat to include a range of techniques in order to determine, to the full extent, the temperature dependency of mechanical properties,such as the Young and shear modulus, the degree of plastic deformation, and hardness [71]. Moreover, the wide range of techniques that can fit into this extended category may also be utilized to apply mechanical load or stress in order to determine and analyze various thermodynamic parameters, such as glass transition temperature, thermal expansion coefficients, softening, and structural behavior [72], including relaxation processes due to internal stress [37]. The transition to viscous flow has been experimentally determined and modeled by measuring elongation with increasing temperature. TMA has also been utilized to determine the viscosity and fragility indexes in the glass transition region, where [PERSON] et al. observed an increase of almost an order of magnitude within the \(T_{g}/T\) range of 0.9-1.02 [73].
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 10 Advancing Medical Diagnostic Videos: Limitations and Future Work
### Incorporating Medical History  
Deep learning models should not only rely on one type of data, such as text or images. The models can be fed a combination of text, videos, and images, for example, the current state of the particular organ in the form of a video or image, the previous medical history of the patient, and other vital data such as age, gender, and weight. This will improve performance and help the models make informed decisions about the patient.
## 3 Results and Discussion
### HCl Elution Experiment
#### 3.3.2 Effect of Elution Time on Lithium Elution Efficiency  
After the two steps of Section 3.3.1 (1) and (2), the separated solid sample was combined with HCl with a concentration of 0.01 mol/L in conical flasks. The mixtures were placed on a rotary stirrer and stirred for 10 min, 20 min, 30 min, 40 min, 50 min and 60 min, respectively. During stirring, the temperature was maintained at 45-50 \({}^{\circ}\)C, and the stirring speed was set to 500 r/min. Centrifugation was then performed for 2 min, followed by  
Figure 3: Effect of HCl concentration on the elution efficiency.  
the extraction of samples from the separated liquid. Sequential AAS measurements were performed on each sample, and the analysis of the elution results is shown in Figure 4.  
As shown in Figure 4, the elution efficiency gradually increased as the elution time increased, while other conditions remained unchanged. The elution efficiency reached 100% at an elution time of 40 min. A further increase in the elution time resulted in a subsequent decrease in elution efficiency. When the elution time is short, H+ cannot completely replace Li+ in the solution. When the time is too long, H+ completely replaces Li+ in the solution, and at this time the reaction products of HCl and other substances affect the lithium elution efficiency [44]. Therefore, based on a comprehensive data analysis, an elution time of 40 min was optimum.  
Through three parallel experiments, data were recorded, and the relative standard deviation of lithium elution efficiency was calculated. When the elution time was 30 min, the relative standard deviation was minimal at 2.17%. At an elution time of 20 min, the relative standard deviation was maximal at 3.80%. Overall, the relative standard deviation range for the experiment results was 2.17% to 3.80%, indicating that the lithium elution rate data had relatively low dispersion and high experimental precision.
## 5 Discussions
### Limitations and Future Improvements  
Although we were able to adequately estimate crop yields over large geographical regions, some limitations necessitate further improvements. Our algorithm is based on the assumption that the LAI of other vegetation is relatively constant after mid-May. For much of the study area where corn and soybeans are planted, this assumption is reasonable, and our model improved the estimated crop yields as demonstrated. However, such an assumption is not entirely correct and does not hold for crops with different phenological cycles such as winter wheat. It would be of interest to identify appropriate solutions to modeling GPP by integrating MODIS data and fine-resolution land use maps.  
There are also uncertainties related to irrigation practice and cloud cover. Although the MODIS GPP/NPP algorithm considers the daily atmospheric water deficit [48], our study implies that it still requires better parameterization for irrigated areas. Given the availability of time-series measurements from the flux towers located in irrigated areas [60], we should be able to calibrate the production efficiency models to improve yield estimation. Cloud cover is another factor that may influence the yield estimation from remotely sensed data. Even though the fitting process can reduce the influence of intermittent cloud-cover, the proposed method may not perform well in areas with persistent cloud covers, which could result in large data gaps in the MODIS time series.
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 5 Simulation Results  
In this section, we first demonstrate the rationality of adopting the optimal power ratio \(\beta_{\text{opt}}\). Utilizing \(\beta_{\text{opt}}\), we evaluate the performance of the proposed SPBA-OTFS channel estimation method in terms of SE, channel estimation and PAPR performance. The OTFS frame size in the DD domain is set to \(N=128\) and \(M=512\). The symbols are drawn from the QPSK constellation. The carrier frequency is 4.9 GHz, and the subcarrier spacing is 30 kHz. The channel model is the Extended Typical Urban (ETU) model [22], where the number of propagation paths is \(P=9\). For each path, its Doppler shift is generated in accordance with the Jakes formula [13]. The maximum delay index is \(l_{m}=77\), and the maximum Doppler index is \(k_{m}=10\), corresponding to a UE speed of 500 km/h. For the proposed SPBA method, we set the power of each superimposed symbol to \(\rho=1\) and the threshold to \(\gamma^{2}=12.25\)MSE.
# Calibrating Mini-Mental State Examination Scores to Predict Misdiagnosed Dementia Patients
## 3 Problem & Approach
### Approach and Metrics
#### 3.2.2 Predictive Models  
Our goal is to improve the baseline accuracy of'misdiagnosis', occurring due to the imprecise nature of the MMSE measurements, by employing machine learning-based predictive models. In supervised machine learning, a decision tree algorithm is widely used as a predictive modelling approach due to its explainability. The decision tree algorithm (such as ID3 [33], C4.5 [34] and CART [35]) is a white box method which learns the decision rules from data features and predicts the value of the target variable [33]. However, very complex trees (due to complex decision rules) do not generalize the data well and tend to overfit. In addition, individual decision trees suffer from high variance [36].  
We also perform experiments with other supervised machine learning models such as SVM, MLP and logistic regression. Nevertheless, we keep our focus on the random forest model (advantages and applications are mentioned in the literature review Section 2) for experiments due to unsatisfactory results from other models. Our all predictive models implement an ensemble learning method implemented by the random forest model, which utilizes different decision trees over sub-samples of the dataset [37]. The decision of the algorithm uses the average of results provided by different decision trees. Besides, using the decision tree algorithm over a sub-sample of a dataset helps random forest to control over-fitting [36]. In our experiments, we make use of two predictive modelling approaches to deal with the high imbalance of the classes. We discuss in detail how the first model utilizes the majority class and the problems associated with the second predictive model due to class imbalance. The predictive models are described as follows:  
* Regression and Classification Model: The regression model attempts to predict the MMSE value of a patient's episode, based on various input features, provided in Listing A1. The regression model is trained using an ensemble learning of random forest Regressor. Then, the difference between predicted MMSE value and previous episode MMSE value in the given episode-interval is used to decide the class of each patient (misdiagnosed = YES'/'NO'), as mentioned in the hand-crafted rules of Section 3.2.1.
* Classification Model: The random forest classification model simply attempts to predict the class (misdiagnosed = YES'/'NO') of patients, based on the various input features, provided in Listing A1. We also deal with the imbalance of the two classes in the dataset to avoid classification error towards minority class.
# Data-Driven Redundant Transform Based on Parseval Frames
## 3 Data-Driven Redundant Transform Model Based on Tight Frame  
In this section, we present our data-driven redundant transform based on Parseval frames (DRTPF, Section 3.1) model along with an efficient redundant transform learning algorithm (Section 3.2) which contains the sparse coding algorithm (Section 3.2.1) and the transform pair update algorithm (Section 3.2.2).