esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of SPE Procedures
#### 3.2.2 Effect of Elution Volume  
To effectively elute an analyte that is being retained on a mixed-mode surface via two retention mechanisms, it is necessary to use an elution solvent that simultaneously disrupts both of the retention mechanisms. Therefore, methanol with 2% NH\({}_{3}\):H\({}_{2}\)O was selected as the elution solvent to deprotonate the stationary phase. Theoretically, the amount of elution solvent is positively related to the elution ability; however, to minimize the volume of the elution solution and reduce the nitrogen purge time for subsequent experiment, the optimization experiments of elution volume, including \(1\times 2\), \(1.5\times 2\), \(2\times 2\), and \(2.5\times 2\) mL, were carried out. The experimental results are shown in Figure 6. As shown in Figure 6, we found that, even with the smallest \(1\times 2\) mL elution volume, the recoveries of all pharmaceuticals were still satisfying. Therefore, in the subsequent experiments, we chose \(1\times 2\) mL as the elution volume, which helped reduce the eluent volume and labor cost.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### PPP Performance
#### 3.4.1 Static PPP Solutions  
Table 12 summarizes the average RMS values of positioning errors for static PPP solutions with different products. Compared to the GPS-only PPP, a similar positioning accuracy of 3-5 mm in the horizontal directions was achieved in the case of Galileo-only PPP for most products used. The positioning errors in Galileo-only mode were about 11 mm in the up direction, which is larger than in GPS-only mode. As for BDS-only PPP, the positioning accuracy in the up direction depended on the number of BDS satellites included in the products. WUM provided products for all available BDS satellites, including BDS-3. Thus, the BDS-only PPP achieved the best positioning accuracy of 9 mm in the up direction with WUM products. A worse positioning accuracy of 14-18 mm resulted with ESM and GFZ products applied. This is because ESM included BDS-3, but excluded BDS-2 GEOs, while GFZ included BDS-2 GEOs, but excluded BDS-3. Without BDS-2 GEOs and BDS-3, BDS-only PPP with COD products presented the largest positioning error of 56 mm in the up direction. The PPP performance in GPS+QZSS mode was close to that in GPS-only PPP. This is reasonable, since QZSS is designed to complement GPS in urban and mountainous areas where the number of available GPS satellites is small.
## 1 Introduction
Wind fields have been estimated from calibrated SAR data by using the empirical geophysical model function CMOD developed at Institut Francais de Recherche pour l'Exploitation de la Mer (IFREMER), which relates the SAR backscatter to the actual wind vector. SAR wind inversion used the hindcast information provided by the WRF-ARW atmospheric model as reference wind vector. Moreover, the SAR estimated velocity fields were compared with the Doppler model developed by [28]. The paper is organized as described in the following section. The detailed description of the technique for measuring sea radial velocity based on the DCA estimation is presented in Section 2. In Section 3, an overview of the Bayesian approach used to extract the wind information from the SAR data is provided. In Section 4, we describe the Doppler modeling of the wind component. Section 5 is devoted to the description of the overall data analysis method. Section 6 provides details on the analyzed area and motivates the dataset choice. Section 7 is devoted to the description of the experimental results over all the different considered dates. Conclusions are finally addressed in Section 8.
###### Abstract  
This study used Theil-Sen median trend analysis and Mann-Kendall test, which are more suitable for long-term series research, to analyze high-precision NPP data and investigate the spatiotemporal characteristics of NPP (net primary productivity) in northwest Liaoning from 2001 to 2021. We further analyzed the impact of the natural environment and land use types on NPP in the region using correlation and a one-way linear regression analysis. The results show that (1) the interannual NPP values exhibited a generally increasing yet fluctuating trend, with the gradient of NPP decrease moving from northwest to southeast. (2) NPP in northwest Liaoning was predominantly influenced by climatic variations, with significant impacts from the combined precipitation-temperature effects seen in 74% of the region, 65% influenced by precipitation alone, and 26% by non-climatic factors. (3) The interaction of soil and vegetation also impacted NPP, which displayed variable rising trends across different soil types in northwest Liaoning. (4) Alterations in land use led to a hierarchical arrangement of the multi-year average NPP values, listed from highest to lowest as forest land, grassland, cultivated land, built-up areas, unused land, and watersheds.  
Keywords:Northwest Liaoning; NPP; spatial and temporal changes; driving factors +
Footnote †: journal: Applied sciences
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
###### Abstract  
Ordinary kriging (OK) is a popular interpolation method for its ability to simultaneously minimize error variance and deliver statistically optimal and unbiased predictions. In this work, the adaptive moving window kriging with K-means clustering (AMWKK) technique is developed to improve the estimation obtained from the moving window kriging based on the K-means clustering proposed by [PERSON] et al. This technique specifically addresses the challenge of selecting appropriate windows for target points located near the borders, which can potentially be the source of errors. The AMWKK algorithm introduces a dynamic clustering approach within the moving window kriging, where each target site sequentially serves as a cluster centroid. The OK is then applied within the cluster encompassing the target point, ensuring localized and adaptive interpolation. The proposed method is compared with ordinary kriging and other moving window kriging variant approaches to estimate Thailand's mean annual pressure and humidity in 2018. The results indicate superior estimation capabilities of the AMWKK approach in terms of distinct quantitative performance statistics. The advantage of using the AMWKK method for spatial interpolation can be attributed to the fact that it facilitates the automatic tuning of the window size at any estimation point. The algorithm is particularly effective when observations in the same cluster as target points are sparse.  
window selection; ordinary kriging; moving window kriging; K-means clustering; spatial interpolation; geostatistics 2023  
1757[[https://doi.org/10.3390/a17020057](https://doi.org/10.3390/a17020057)]([https://doi.org/10.3390/a17020057](https://doi.org/10.3390/a17020057))
# Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting
## 2 Model's Background
### XGboost Model  
XGBoost was developed by [PERSON] and [PERSON] [73] as a scalable machine-learning method for tree boosting. This algorithm has shown outstanding performance in different engineering problems compared to other machine learning algorithms due to its effective tree pruning, regularization, and parallel processing [74; 75]. The general idea behind the development of boosting algorithms is that weak learners, such as DT, use a random guessing procedure and cannot use errors to improve the final model. However, XGBoost combines weak-based learners with stronger learners, and residuals in each iteration are used to correct the previous predictors, as shown in Figure 3.  
Figure 3: Flowchart of the XGBoost algorithm (X represents a variable, and \(\theta\) represents the DT features, e.g., TD, splitting number, etc., which can be used to develop a tree for prediction of X, and \(f\) is the learned tree).  
The main advantages of XGBoost are [76]:  
XGBoost is ten times faster than other popular algorithms, and this speed enables it to solve the issues of time consumption in big data and problems where less run time is demanded.  
* Parallel processing of XGBoost causes the high scalability and generation of billions of examples using the lowest resource consumption, which allows it to be effective in classification and high-level pre-processing data problems.
* XGBoost can be programmed using a broad scope of languages, e.g., Java, Python, R, and C++.
* XGBoost is less likely to overtrain because it makes strong learners by combining weak learners. This makes XGBoost more accurate at making predictions.
* XGBoost can effectively handle the missing data.
* Cross-validation can be done using the training data, and there is no need for extra cross-validation packages.
* To reach the highest performance of XGBoost, several model choices are needed. To avoid overfitting or to develop too complex models, XGBoost must tune the parameters. XGBoost is susceptible to learning noises or random fluctuations, and overfitting happens when these types of data are considered meaningful to XGBoost.
* Some internal parameters of XGBoost that should be regulated to avoid overfitting include iteration number, which is the number of trees that were fitted in the model; the maximum depth, which shows the maximum number of splits; an increase in this parameter increases the overfitting probability; subsample that shows the percentage of the dataset which are selected for training; learning rate which modifies the weights and impact of each tree to improve the performance of model; colsample_bytree is the ratio of subsample columns in tree construction; lambda and alpha that make regularization on weights and increase in these parameters make the model more conservative [73].
## 3 Experimental Verification
### Validation of the NDT  
In order to verify the accuracy of the NDT, a strain gauge was attached near the pressure sensor installed at the stage of pipe fabrication. The reconstructed pressure based on the NDT (Section 3.2) and the pressure measured by a pressure sensor (PSP) were compared during the entire test run (Figure 6). The pressure change based on the NDT was in good agreement with the PSP, particularly during the steady load process, with the exception of the initial upload time. The deviation during the initial upload process can be explained by the strain gauge's inability to distinguish the stress caused by the minute but sudden changes in the internal pressure. However, as the working pressure was increasing gradually and the stress fluctuation could be negligible, the deviation had very limited effects.  
Correspondingly, the results indicate that the NDT was valid and the pressure reconstructed by the strain on the outside of the pipe could be used to analyze the pressure pulsation of the hyper-compressor system.
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 5 Failure Detection and Localization Based on Big Data
### Failure Detection
The red row shows the moment where _Threshold_d_ is violated; thus, in this moment failure is detected because the _Failure_ value dropped below 0.85. If the failure lasted more than \(K\) minutes, then at one point _Threshold_d_ would not be triggered according to (2) because \(N_{out\_avg}\) would be averaged to a lower number of active users. However,  
this is not an issue because threshold triggering does not have to be continuous since the first capture of threshold violation will cause failure detection. In the example provided in Table 1, we also demonstrate the possibility of detecting resolution. The green row shows the moment when resolution of the problem is detected because _Resolution_ value is greater than 1.15. This example shows that failure resolution can be detected even when failures only last several minutes.
## 4 Formulation of an Operation and Maintenance Strategy for Equipment with a High Failure Rate
### Data Processing and Fault Prediction  
There are 5196 sets of mechanical and electrical equipment in three sections of the Xinjiang expressway: Urumqi Round City Expressway (East), Xiaocahu Urumqi Expressway (G30), and the Jingxin Expressway Dahuangshan-Urumqi Section Reconstruction and Expansion Project (G7). Combining the operation and maintenance data of the above sections in October 2023, 5196 effective fault operation and maintenance records are obtained. In this record, the equipment failure of the expressway electromechanical system is mainly shown in the related equipment of the toll collection system and is summarized in Table 3.  
The operation and maintenance records show that video surveillance equipment, weighing equipment, automatic railing machines, and license plate recognition instruments have a higher failure rate in the two sections. Based on this, this section takes video surveillance as an example to explore its optimal external operation and maintenance strategy.
# Virtual Career Fairs: Perspectives from Norwegian Recruiters and Exhibitors
## 4 Results
### Benefits of Virtual Fairs (RQ1)
#### 4.1.3 Analytics-Based Customization  
Virtual fairs also support customer-oriented analytics. The computer-mediated nature of virtual fairs provides organizers, but also recruiters and exhibitors to collect data about the number and characteristics of individuals who showed an interest in their company by attending and reviewing information about the exhibitors and recruiters and selected content in their virtual booths. Such recorded data can, in combination with verbal feedback, provide a rich source of engagement for the participants, recruiters and exhibitors alike.  
The virtual booths can be designed to the specifications of the business and business needs, allowing for customization--for the benefit of exhibitors and participants. The employers may interact with and even interview potential candidates. Participants may interact on public message boards, or privately with employers via virtual booths. The employers may also use different design functions for virtual booths, allowing them to design them in the corporate image and brand. Depending on the needs of clients, virtual fairs such as those run by Global Forums can include panel discussions and company presentations in a virtual lecture hall, which are opened to invited guests only. Membership and student bodies, partners and exhibitors can selectively invite people in pre-specified target groups to attend such events. Profile matching to events is another option available to candidates, making them beneficiaries of this customization of virtual events during career fairs. And as we would argue, both these two drivers are also related to one another and mutually reinforcing. For those visitors who are in employment at the fair, the online option allows them to engage with recruiters without having to share their current work details.  
At the bottom of Figures 1-3, a number of options are presented. All attendees--job seekers, recruiters and exhibitors--can use these options to (a) navigate the online fair and (b) chat with others (all attendees at the virtual fair can chat with several others simultaneously). In addition, all attendees are presented with an additional (c) contact option to start conversations with others at the fair. The last option (labelled 'Briefcase') allows all attendees to save all chats of interest for future reference and later access. These options support decision making, but also monitoring and optimization, both of which are referenced in the next subsection.
## 3 Results and Discussion  
### Implementation Results  
The implemented blood-flow-monitoring circuit system is presented in Figure 13. It consists of a single-element transducer (P703-SU, Hagisonic, Daejeon, South Korea), a fully custom-designed printed circuit board (PCB) for the analog interface circuit, an FPGA (Spartan-6 LX45, Xilinx Inc., San Jose, CA, USA), and a bridge board for test purposes. The center frequency of the utilized transducer is 2 MHz. A system clock with a frequency of 20 MHz is utilized in the FPGA and ADC. The analog interface circuit consumes 196 mW for supply voltages of \(\pm\)10V and \(\pm\)3.3V.  
Figure 12: Behavioral simulation results of spectrograms from (**a**) Field II program and (**b**) proposed scheme.  
Figure 13: Implemented image-free ultrasound blood-flow-monitoring circuit system.  
To maintain a relatively uniform beam width along the scanline, an unfocused-type transducer is adopted in this work. Considering the physical parameters of the transducer, such as the center frequency of 2 MHz and the diameter of 0.5 inch, the length of the near field of the transducer is estimated to be around 9.4 cm [26]. The beam width of the unfocused transducer within the near field is approximately equal to the diameter of the transducer [26]. In this work, the target maximum scanning depth is set to 5 cm, and the range of time-gain compensation (TGC) is found to be 10 dB by considering an attenuation coefficient of 0.5 dB/MHz/cm in human tissue. Since the TGC range is relatively small, the performance of the system is not significantly affected even if a fixed voltage-gain value is applied for the analog front-end circuit.  
The proposed automatic range-gate tracking scheme is implemented in the FPGA for real-time operation. A logic synthesis along with place and route (PnR) is performed with the ISE program (Xilinx Inc., San Jose, CA, USA). The device utilization summary after PnR is presented in Table 1. The utilization rate of slice look-up-tables (LUTs) is 5.6%. This metric is one of the measures used to represent the efficiency of hardware utilization in the FPGA. This work shows a relatively low value of slice LUTs. This implies that the proposed scheme can be compactly implemented in FPGA. The PnR result is presented in Figure 14, which illustrates the occupancy of hardware resources in the FPGA.
## 4 Conclusions  
In this research, the mass transfer, energy parameters, and bioactive compounds were investigated during the drying of apple slices with four models of different drying strategies in a combined hot air-infrared dryer and the results compared with convective drying. In general, with increasing the temperature and radiation intensity, the drying time in most experiments becomes shorter. However, in some experiments, higher temperature and radiation intensity in the first stage increased the drying time due to the formation of a hard layer on the product. Among all the methods, HA70-IR750 had the fastest drying time; it was about 27.2% less than the shortest time in the hot air method (HA70). Mass transfer (moisture) in the product also increased with increasing temperature and intensity of infrared radiation. The highest effective diffusion coefficient in the two Crank and Dinner models was for the HA70-IR750 method, which was about 52% higher than the highest moisture diffusion coefficient in the hot air methods (HA70). The highest convective mass transfer coefficient in the two Crank and Dinner models was for HA70-IR750. In general, the activation energy increased with the involvement of infrared waves. The highest amount of activation energy was in the HA-IR500 method. The specific energy consumption of the device was reduced in most cases when using infrared waves. Although the drying time was shorter in the simultaneously combined strategies (second strategy), the specific energy consumption was higher in this strategy. Therefore, the lowest value of specific energy consumption was obtained in HA40-IR500, which was about 42.1% lower than the lowest SEC value in the hot air method (HA70). On average, the lowest amount of specific energy consumption was obtained in the third strategy (HA+IR). Energy efficiency was higher in cases where infrared waves with high radiation intensity were used. The highest value of energy efficiency in the combined method (HA40+IR500) was about 72.8% higher than the highest value of energy efficiency in the hot air method (HA70). The drying efficiency on average in the third strategy (HA+IR) was higher than that of the other strategies. Therefore, the highest drying efficiency was obtained in HA70+IR500, which was about 55.2% higher than the highest value of drying efficiency in the hot air method (HA70). The SMER value was higher in the methods in which infrared energy was used with high radiation intensity. In this way, compared to other drying strategies, on average, the second strategy (HA-IR) performed better at drying time and mass transfer rate, and the third strategy (HA+IR) was better at energy efficiency. The first strategy, especially the infrared method, was better than the other strategies due to the preservation of antioxidant activity, total phenol content, and total flavonoid content in terms of preserving bioactive compounds.
# Assessment of DPPC Liposome Disruption by Embedded
## 3 Results  
### The ANS Emission in DPPC Liposomes  
The spectroscopic properties of ANS change with the polarity and viscosity according to its location [41]. The ANS fluorescence emission spectrum in DPPC liposomes show an emission maximum at about 477 nm. That position of the fluorescence maximum is characteristic for ANS when this fluorescence probe is located in hydrophobic surroundings. However, the fluorescence intensity (FI) and the position of the emission maximum (\(\lambda_{\text{max}}\)) changes with the temperature and additionally depends on TM concentration. Thus, FI and \(\lambda_{\text{max}}\), which are related to the ANS microenvironment, may serve as sensors to monitor DPPC structural changes caused by its interactions with a TM.
## 2 Problem Description
### Threat Model  
Any threat can be described by a five-element combination \(<q_{o,i}\), \(R_{o,i}\), \(R_{a,i}\), \(p_{o,i}\), \(F_{o,i}>\), where \(q_{o,i}\) is the position vector of the threat \(O_{i}\) center, \(R_{o,i}\) is the non-fly zone radius of threat \(O_{i}\), and \(R_{a,i}\) is the impact radius of threat \(O_{i}\). When a UAV flies into the no-fly zone, it will be destroyed; when the UAV enters the impact area, it will be affected by the threat. \(p_{o,i}\in[0,1]\) expresses the estimation of threat impact. \(F_{o,i}\in\{0,1\}\) indicates the detected status flag of the target, where the value 0 means the target is undetected; otherwise, the value is 1.  
The estimation \(p_{o,i}(\mathbf{q})\) of the threat \(O_{i}\) impact on any point in space is denoted as  
\[p_{o,i}(\mathbf{q})=\left\{\begin{array}{l}1\,,\ 0\ <\|\mathbf{q}-\mathbf{q}_{o,i}\|<R_{o,i} \\ A_{i}(\mathbf{q},\mathbf{q}_{o,i}),\ R_{o,i}\leq\|\mathbf{q}-\mathbf{q}_{o,i}\|\leq R_{a,i}\\ 0\,,\ \|\mathbf{q}-\mathbf{q}_{o,i}\|>R_{a,i}\end{array}\right. \tag{3}\]  
where \(A_{i}(\mathbf{q},\mathbf{q}_{o,i})\in(0,1)\), which is a function of the distance between the point and the threat center, represents the effect evaluation within the threat range; for the convenience of the study, \(A_{i}(\mathbf{q},\mathbf{q}_{o,i})\) can be taken as a constant value. When the UAV approaches threat \(O_{i}\) and enters the impact range, its survivability will decrease. We assume the survivability of UAV \(V_{j}\) is \(h_{j}\) and the survivability becomes \(h^{\prime}_{j}\) under the impact of threat \(O_{i}\), of which the process can be expressed as  
\[h^{\prime}_{j}=h_{j}\cdot(1-A_{i}) \tag{4}\]