esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study reports on a global analysis of solar-induced chlorophyll fluorescence (SIF). We applied a rotated EOF analysis on a 13-year record of GOME-2 satellite-derived SIF data retrieval. Both long-term trends and seasonal cycles were removed prior to the EOF analysis to focus on variations on different temporal scales. Our analysis reveals that the three leading modes in global SIF variability closely follow the ENSO, NAO, and PDO climate indices. Relative to the 2007-2019 global mean SIF (0.16 mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\)), the 2011 La Nina (2015 El Nino) elevated (decreased) global SIF. Combined, the net ENSO-induced global mean SIF difference between 2011 and 2015 is \(-0.02\) mW m\({}^{-2}\) sr\({}^{-1}\) nm\({}^{-1}\), equivalent to 12.5% of the 2007-2019 global mean SIF.  
Understanding major sources of SIF and its corollary gross primary productivity (GPP) variability is necessary for effectively designing global observing systems related to water, carbon, and nitrogen cycles and for developing and testing global ecosystem models. Our results contribute to the scientific efforts of placing satellite-derived SIF variability within a global perspective and contribute to improving understanding of interannual and decadal sources of variability in global SIF and primary production. As the scientific community continues to collect and develop remotely sensed SIF time series (e.g., [45; 46; 47]), the robustness of the relationships between the variability of SIF (proxy of GPP) and climate indices identified in this research should be re-examined in future studies using longer-term time series.  
Conceptualization, K.H., W.L. and R.H.; methodology, K.H.; software, R.H.; validation, K.H., W.L. and R.H.; formal analysis, K.H., W.L. and R.H.; writing--original draft preparation, K.H. and R.H.; writing--review and editing, W.L.; visualization, K.H. and R.H.; supervision, W.L. and R.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Kenan Institute of Engineering, Technology and Science of North Carolina State University.  
The GOME-F V28 terrestrial chlorophyll florescence data are available at: [[https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/](https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/)]([https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/](https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/)) (accessed on 15 June 2021). Climate indices monthly time series data are available at: [[https://psl.noaa.gov/gcos_wgsp/Timeseries/](https://psl.noaa.gov/gcos_wgsp/Timeseries/)]([https://psl.noaa.gov/gcos_wgsp/Timeseries/](https://psl.noaa.gov/gcos_wgsp/Timeseries/)) (accessed on 15 June 2022).  
We thank [PERSON] for her editorial assistance.  
The authors declare no conflict of interest.
## 3 Methodology
### The Gaussian Similarity Function  
The Gaussian similarity function is often used to measure the similarity between two vectors, and its expression is shown in Equation (16) [47]. where \(\omega(i,j)\) represents the weight of the edge connecting nodes \(v_{i}\) and \(v_{j}\), indicating the spatial correlation between two monitoring points. The value \(\omega(i,j)\) is larger, indicating stronger spatial correlation between the monitoring points.  
\[\omega(i,j)=exp\Big{(}-\big{\|}v_{i}-v_{j}\big{\|}^{2}/2\sigma^{2}\Big{)}, \tag{16}\]
## 3 Results and Discussion
### Changes in the Maximum Possible Tidal Range in the Southwestern Part of the Gulf  
The tidal waves of Laizhou Bay enter from the bay mouth and propagate toward the top of the bay. As the bay gradually becomes narrower and shallower, the tidal wave energy concentrates, causing the tidal amplitude to increase, making the tidal range gradually larger. The maximum possible tidal range in Laizhou Bay gradually increases from the bay mouth to the bay interior, and the maximum value is located at the top of the bay in the southwest part of the bay. In 2000, the maximum possible tidal range in the southwestern part of the bay could reach a maximum value of 2.5 m, and the minimum value was less than 2.1 m; in 2020, the maximum possible tidal range in the southwestern part of the bay could reach a maximum value of 2.7 m, and the minimum value was less than 2.2 m (Figure 4).  
According to tidal dynamics theory, from the perspective of tidal energy distribution, mudflats mainly affect tidal energy distribution through horizontal diffusion and bottom friction. During high tide, mudflats store tidal energy, while during low tide, they release tidal energy. Moreover, the tidal energy stored in mudflats is much higher than the tidal energy lost due to bottom friction [41]. Long-term human activities have led to a continuous reduction in the area of tidal flats in the bay. Since the inflow and outflow of tides at the open boundary are not affected by coastline changes, as the tidal flats disappear, the tidal energy originally stored in the area will be redistributed in the outer sea area, causing the tidal energy (kinetic energy, potential energy) of the entire affected sea area to increase, including tidal wave potential energy, namely tidal range, which has caused an increase in the tidal range in the southwestern part of Laizhou Bay [42]. The maximum possible  
Figure 3: Change in coastline and centroid in southwest Laizhou Bay.  
tidal range in the southwestern part of Laizhou Bay has shown an increasing trend in the past 20 years, with a maximum increase of 22 cm. It is located at the top of the bay between Weifang Port and Guangli Port. Weifang Port and Guangli Port adopt a double embankment layout, making this area form a semi-enclosed sea area. The sharp narrowing of tidal waves entering this area increases the propagation resistance of tidal waves, which increases the potential energy of tidal waves entering this area, thus causing a significant increase in the maximum possible tidal range (Figure 5). The increased tidal range caused by tidal flat reclamation puts coastal lowlands at risk of being flooded, and will also place higher demands on coastal engineering protection and design standards.
## 1 Introduction  
The outbreak of the COVID-19 pandemic in 2020 has been a challenge for healthcare systems all over the world [1; 2], showing how crucial it is to develop effective methods of fighting infectious diseases. The determinants of achieving the expected results of these methods involve the demographic situation of a given country, medical resources, expenditures, and model of healthcare system. Cooperation between the various stakeholders of the healthcare system, such as medical communities, government representatives, and the media, is also of great significance. This issue is particularly important in the light of the population ageing observed in many countries [3; 4], and also shortages of medical staff [5; 6].  
Meeting the challenges posed on healthcare systems, largely associated nowadays with the necessity of effective fighting infectious diseases, requires the engagement of many groups of medical staff. Here, the role of pharmacists should be emphasized. Despite being an essential element of the public healthcare system, their participation in efforts to prevent diseases, lengthen life, and promote health is relatively low. Apart from providing basic and advanced pharmaceutical services consisting in ensuring and optimizing pharmacotherapy, in the current situation, extended services focused on prevention, e.g., preventive vaccinations, such as flu vaccinations, are also essential [7].  
In some European countries, preventive vaccinations are administered by pharmacists or another medical staff in community pharmacies [8], with the positive outcomes of these actions encouraging other countries to enable pharmacists to conduct infectious disease vaccinations [9; 10]. This is particularly significant not only during the COVID-19 pandemic, but also due to large shortage in medical staff, the rapid population ageing, and the relatively low vaccination coverage percentage in high-risk patients [11; 12].  
The objective of this article is to aggregate existing knowledge in the effectiveness of engaging pharmacists to conduct preventive vaccinations, and to prove the role of pharmacists and pharmaceutical care in infectious disease prevention. Scientific literature review was carried out to summarize key aspects related to the vaccination provided by pharmacies and the role of the pharmacists in that process. Following searching strategy was applied: articles were extracted from PubMed using the terms \"vaccine\", \"vaccination\" \"vaccination coverage\" \"infectious diseases, \" and \"vaccination implementation\" in combination with \"community pharmacy\" and \"community pharmacist\".  
This article depicts the guidelines of international organizations and experiences connected with implementing models of flu vaccinations in pharmacies all over the world. Presentation of the current practices and outcomes of extending pharmaceutical services in these countries can foster discussion on the model of pharmacist involvement in conducting preventive vaccinations, especially against flu.
## 4 Discussion
### Privacy and Security  
JAVIS adheres to strict privacy standards by operating entirely within a secure hospital network, ensuring compliance with laws such as Korea's Data 3 Laws and similar regulations globally. Its reliance on existing hospital security measures, including encryption and network isolation, guarantees the safeguarding of sensitive data. This approach has proven effective in addressing privacy concerns without introducing vulnerabilities, making JAVIS a reliable solution for data-sensitive environments.
# A Supervised Neural Network Control for Magnetorheological Damper in an Aircraft Landing Gear
## 4 Hybrid Controller with Load Cell Data and Known Aircraft Mass  
### Control Principle  
Figure 6 shows the principal concept of the hybrid controller with load cell data for measuring \(F_{T}\) and exact mass information. It can be seen that the hybrid controller is the combination of the skyhook controller and force controller [11]. The skyhook controller [20] is designed to achieve the maximum damping force at the point where the stroke velocity reaches the maximum value (the first peak at \(s=s^{\ast}\) in Figure 6a). The force controller [10] is used to maintain the maximum damping force as far as possible during the first compression phase to fill the dent between the two peaks. Thus, the hybrid controller input (\(u_{h}\)) is given by:  
\[u_{h}=u_{s}+u_{f} \tag{3}\]  
where \(u_{s}\) is the skyhook control input, and \(u_{f}\) is the force control input.  
The main idea of the skyhook control is to generate a damping force linear to the unsprung mass velocity. The control input of the skyhook controller is given by [21; 22]:  
\[u_{s}=\left\{\begin{aligned} C_{\dot{s}}\dot{z}_{1},\dot{z}_{1}\dot{ s}>0\cup\dot{s}>0\\ 0,\,otherwise\end{aligned}\right. \tag{4}\]  
Figure 6: (**a**) Principles of hybrid controller, (**b**) hybrid control block. Hybrid controller with a load cell sensor and known aircraft mass method.  
where \(\dot{z}_{1}\) is the vertical velocity of the sprung mass; \(\dot{s}\) is the stroke velocity; \(C_{s}\) is called the skyhook gain. The skyhook gain is selected so that the resultant efficiency curve has the same level of peaks. The optimal gain is searched by testing the controller with the numerical simulation model of the damper for each landing condition composed of different aircraft masses and sink speeds (refer to Figure 7).  
The force controller is used to maintain the force level that is attained at the first peak. The controllable MR damping force is generated to compensate for the difference between the measured ground reaction force from the load cell and desired force \(F_{T}^{max}\). The damping force applies to only the opposite direction of the damper's motion, and thus the force control input can be given by:  
\[u_{f}=\begin{cases}0,\,F_{T}^{max}<F_{T}\\ F_{T}^{max}-F_{T},\,F_{T}^{max}\geq F_{T}\end{cases} \tag{5}\]
## 4 Experiments and Results
### Loss Function  
In the training process, the binary cross-entropy loss (BCE Loss) is used as the loss function of the network. The training Epoch is 800. The initial learning rate (LR) is 0.01. The weight decay is 10-4. The optimizer uses the SGD optimizer with momentum. The momentum value is 0.7. The loss function is used to estimate the difference between the training segmentation label and the actual segmentation result. When the value of the loss function decreases, the difference between the labels and actual segmentation results becomes smaller. The loss can be expressed as:  
\[loss=\frac{1}{N}\sum_{n=1}^{N}l_{n} \tag{1}\]  
Here, represents the loss of a single sample, which is expressed as:  
\[l_{n}=-w[y_{n}\cdot\log x_{n}+(1-y_{n})\cdot\log(1-x_{n})] \tag{2}\]  
where \(x_{n}\) represents the actual segmentation result of the nth sample, and \(y_{n}\) represents the training segmentation label. w is set to 1 for single-label binary classification. The loss curve is used to explain the optimization process during the training process. The loss curve in the training process of CSTA is shown in Figure 9. As Figure 9 shows, with the increase in the epoch, the value of the loss function decreases. Moreover, when the epoch is more than 300, the loss function curve tends to be stable. The whole training process is relatively stable, which meets the requirements of training.  
The network model proposed in this paper is implemented using the PyTorch.8.1 framework, and the mobile version of NVIDIA GeForce RTX2070 is used for accelerated operations.
## 3 Circuit Design
### Output Amplifier  
A schematic of the output amplifier is shown in Figure 11. The bandwidth of the output stage is limited by unavoidable large parasitic capacitance. An inductor is used in the output stage to compensate the effect of this parasitic capacitance. The inductance in series with a resistor creates zero peaking for bandwidth extension. The adjusting current source is programmable for variable output swing. In order to get a much cleaner eye diagram with high eye height, the current source was set with large output current in the high-power mode. The simulated 25 Gb/s and 28.9 Gb/s eye diagrams of the optical receiver in high-power mode are shown in Figure 12a,b, and the simulated 25 Gb/s and 28.9 Gb/s eye diagrams of the optical receiver in high-power mode are shown in Figure 13a,b. The simulated eye heights in high-power mode and low-power mode are 255 mV and 90 mV, respectively. The eye height also decreases with higher data rates.  
Figure 11: A schematic of the output stage.  
Figure 10: The simulated frequency response of the variable-gain amplifier (VGA).
# Improved Interpolation and Anomaly Detection for Personal PM\({}_{\mathbf{2.5}}\) Measurement
###### Abstract  
With the development of technology, especially technologies related to artificial intelligence (AI), the fine-dust data acquired by various personal monitoring devices is of great value as training data for predicting future fine-dust concentrations and innovatively alerting people of potential danger. However, most of the fine-dust data obtained from those devices include either missing or abnormal data caused by various factors such as sensor malfunction, transmission errors, or storage errors. This paper presents methods to interpolate the missing data and detect anomalies in PM\({}_{2.5}\) time-series data. We validated the performance of our method by comparing ours to well-known existing methods using our personal PM\({}_{2.5}\) monitoring data. Our results showed that the proposed interpolation method achieves more than 25% improved results in root mean square error (RMSE) than do most existing methods, and the proposed anomaly detection method achieves fairly accurate results even for the case of the highly capricious fine-dust data. These proposed methods are expected to contribute greatly to improving the reliability of data.  
data interpolation; anomaly detection; bootstrap; fine dust; PM\({}_{2.5}\)
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology
### Tracking Module  
The initialization performance of vSLAM has a great impact on the subsequent pose estimation and mapping [18]. Conventional RGB-D SLAM usually adopts the first frame \(k_{1}\) as the key frame after the initialization condition is satisfied. The local map is initialized by projecting \(k\_1\) into the 3D space based on the depth information acquired by the camera. Then, Perspective-n-Point (PnP) is used to calculate the matching relationship between the feature points of the current frame \(k_{2}\) and the local map points of \(k_{1}\) to complete the  
Figure 1: System Framework.  
initial pose. However, when vSLAM initializes in a dynamic environment using traditional methods, objects that are moving in the camera's field of view are not only appended to the local map, but feature points located on dynamic objects in two adjacent frames can also significantly affect inter-frame matching. Therefore, we propose a strategy for vSLAM initialization in dynamic environments that can robustly and efficiently initialize local map and pose. First, the ORB feature points of \(k_{1}\) are extracted. At the same time, the semantic segmentation function in the semantic segmentation module (Section 3.4) is used to obtain the semantic information of dynamic objects (e.g., pedestrians) in \(k_{1}\). The semantic information will be used to eliminate the key points located on dynamic objects in \(k_{1}\). Next, the matching relationship between the key points of \(k_{1}\) and \(k_{2}\) is determined whether the initialization condition of ORB-SLAM2 is satisfied. If not, \(k_{1}\) is released. \(k_{2}\) is reinitialized as \(k_{1}\). On the contrary, the dynamic objects in \(k_{1}\) that are located on the 3D local map will be eliminated. The matching relationship of key points in \(k_{2}\) and \(k_{1}\) is calculated according to the dynamic detection module (Section 3.3) to obtain the initial pose and eliminate the dynamic feature points in \(k_{2}\). After initialization, ORB feature extraction, dynamic feature rejection, feature matching set building and pose estimation are performed on the subsequent images.
## 3 Results and Discussion
### Spectral Observation
The nucleic acid region at 1236 cm\({}^{-1}\), designated as the PO\({}_{2}\)\({}^{-}\) of DNA, was increased in the contaminated group. There was no change of the peak at 1085 cm\({}^{-1}\), which is assigned to \(\upnu_{\text{s}}\)PO\({}_{2}\)\({}^{-}\) of RNA. The peak at 968 cm\({}^{-1}\), designated as the DNA/RNA ribose phosphate main chain, was also decreased in the contaminated cells (Figure 3C). Moreover, the major alteration in the nucleic acid region was observed at 1236 and 1224 cm\({}^{-1}\) (Figure 3C), which are conformational specific bands of A-DNA (predominant) and B-DNA, respectively [25]. In contaminated cells, the peak intensity at 1236 cm\({}^{-1}\) was high and later found to be decreased along with the increasing intensity of the peak at 1241 cm\({}^{-1}\), after the cells were treated. The result indicates the conformational changes from A-DNA to B-DNA after the mycoplasma was removed. Accordingly, the FTIR spectra indicate the alterations in all biomolecular regions. The different wavenumbers between decontaminated and contaminated cells are summarized in Table 2 with the band assignment [7,26]. Mycoplasma affects eukaryotic cells at a molecular level of lipid, protein, and nucleic acid, including, e.g., interruption of nucleic acid synthesis, chromosomal aberration, alteration of protein type and level, disruption of cellular membrane component and structure, and several cellular biochemical processes, which consequently promote cellular transformation [2]. Our result defines most, if not all, of these effects on the cells, according to the alteration of IR absorption bands. The major changes detected by FTIR were observed on lipid, protein, and nucleic acid bands, as seen in Table 2, that can be used as a specific marker for mycoplasma contamination indicators. Figure 2: (**A**) Average Fourier transform infrared (FTIR) primary and (**B**) second derivative spectra of mycoplasma decontaminated (67 spectra from triplicate samples) and contaminated (58 spectra from triplicate samples) HepG2 cells. Standard deviation was indicated in the primary spectrum and displayed as the parallel band with relevant color.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### 2.1.7 Deep Learning
In the inception network structure, there are also some auxiliary classifiers. These are structures used to compute an auxiliary loss on the inception layers and support the training phase [55]. A comparison of 3 methods was mentioned earlier to achieve short-term traffic predictions in large urban traffic networks [18]. The authors compared the performance of BN-SARIMA, an FF-NN, and a Nonlinear Autoregressive Exogenous Model (NARX). The evaluation was performed with the MAE, MAPE, MRSE indexes and considering the NARX method they revealed that it gave reliable 5 min predictions, similar to the BN-SARIMA. However, NARX performance was poorer in the other time ranges predictions. NARX is a recurrent Neural Network that finds a correlation between the current values of a time series, with older values of the same series.
## 2 Materials and Methods
### Quantitative Analysis  
Quantitative analysis was conducted using UHPLC with analytical-grade solvents. Acetonitrile and water were sourced from J. C. Baker (Mallinckrodt Baker B.V., Deventer, The Netherlands). The analysis utilized a hydromethanolic extract from 100 mg of lyophilized and micronized _S. austriaca_ hairy roots. To remove non-polar contaminants, an initial extraction with \(n\)-hexane (3 \(\times\) 20 mL) was performed, followed by extraction with methanol-water (8:2, \(v/v\)) using an ultrasonic bath for 45 min at room temperature. After the first extraction with \(n\)-hexane, the plant material was washed three times with \(n\)-hexane, filtered, and dried in a vacuum. The pre-cleaned material was extracted with 10 mL of a methanol-water mixture (8:2, \(v/v\)) via ultrasonication for 45 min at room temperature. The extracts were then centrifuged (12,000 rpm for 5 min) and filtered (0.2 \(\upmu\)m pore size) into the HPLC vials before analysis.  
The UHPLC analysis was carried out using a 1290 Infinity UHPLC system (Agilent Technologies, Waldbronn, Germany) equipped with a diode array detector (DAD) and gradient elution on a Zorbax Eclipse Plus C18 column (100 \(\times\) 3.0 mm i.d., 1.8 \(\upmu\)m). The mobile phases consisted of 0.1% formic acid in acetonitrile (A) and 0.1% formic acid in water (B). The gradient elution profile was as follows: 0-15 min, 10-20% A; 15-17 min, 20-25% A; 17-19 min, 25-29% A; 19-21 min, 29% A and 21-21.1 min, 29-95% A. The flow rate was 0.5 mL/min, and the injection volume was 10 \(\upmu\)L. Detection was performed at 328 \(\pm\) 2 nm.  
Phenolic acid concentrations were determined from peak areas and calibration curves, previously prepared using solutions of standard substances of phenolic acids (Sigma, St. Louis, MO, USA) dissolved in methanol (HPLC purity) in the concentration range of 5-100 \(\upmu\)g/mL. Calibration curves were drawn using 6 concentration levels, and each of them was repeated 3 times. Calibration UHPLC analyses were performed under the same conditions as described above for UHPLC analyses of extracts. Results were expressed as mg per liter of culture medium, calculated as the average dry weight (g/L) multiplied by the average compound content (mg/g dry weight).