esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Catalytic Conditions
#### 3.3.2 Effect of Catalyst Dosage  
The amount of PEK-C-ILs catalyst was varied in a range of 1.0-5.0 wt % to carry out the hydrolysis reaction of inulin at 65 \({}^{\circ}\)C. The corresponding results were shown in Figure 5, we can see that the catalyst dosage has a profound effect on TRS of inulin. When the catalyst dosage increased from 1.0 wt % to 4.0 wt % (1.02-5.11 mol %), the TRS increased obviously from 52.1% to 98.7%. With further increase of the dosage, the TRS of inulin change slightly. This result may be due to the fact that the greater the catalyst dosage, the more available active sites for the hydrolysis reaction. However, when catalyst dosage was above 4.0 wt %, the active sites were excess and could not been fully utilized for inulin hydrolysis, so that the hydrolysis rate of inulin had no significant increase. In addition, the comparison result between PEK-C-ILs powder catalyst of 4.0 wt % (0.628 mmol ILs) and homogeneous ILs clearly showed that the catalytic activity of ILs has been well improved after immobilization.  
More importantly, we can evaluate the catalytic performance of catalyst by the TOF value in that the higher TOF value reflects the better catalytic property. The experimental results showed that the TOF of PEK-C-ILs is 6.767 \(\times\) 10\({}^{-3}\) s\({}^{-1}\), which is higher than homogeneous ILs of 4.873 \(\times\) 10\({}^{-3}\) s\({}^{-1}\). Therefore, the hydrolysis property of PEK-C-ILs powders was better than that of homogeneous ILs in the equimolar condition. Considering the catalytic activity and economical evaluation of the overall reaction process, the appropriate catalyst dosage is controlled in 4.0 wt %.  
Figure 4: Effect of the DCM on the TRS of inulin. Hydrolysis conditions: inulin concentration 20%, concentration of PEK-C-ILs 4 wt %, temperature 65 \({}^{\circ}\)C.  
In addition, the reasons why and how the macroporous PEK-C powder modified with an acidic ILs revealed high catalytic performance are mainly due to the reduced mass transfer resistance in pores and high-density spatial distribution of active sites on the powder. One hand, for the macroporous PEK-C powder, both the transfer resistance of inulin from outside into pore and the release resistance of the hydrolyzed fructose (reducing sugar) from pore into solution are small. On the other hand, the hydrolysis reaction was dominated by synergistic catalytic effect of anion and cation due to its low activation energy [27]. Because the PEK-C-ILs powder has a high-density spatial distribution of active sites on the surface and inner pore wall after the ILs was immobilized. Thus, the probability of synergistic action of ILs increased in the hydrolysis reaction, also resulting in the excellent catalytic performance of PEK-C-ILs powder catalyst.
# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 4 Discussion  
The quality of the pansharpened image was inconsistent among the different regions and different land covers; thus, most of the quantitative quality assessment strategies that involve assigning a single value to the whole image could not efficiently evaluate the effectiveness of the pansharpening methods [44]. An object-level or land-cover-based pansharpening quality assessment strategy could overcome the limitations of the traditional quantitative quality assessment strategies, evaluate the performance of the pansharpening process for different land covers, and quantify the quality of different land covers, thus improving our understanding of the influence of pansharpening methods across different land covers [15; 44]. However, relevant research has only focused on a few types of land covers [15; 44], and it is necessary to evaluate the performance of the pansharpening process for more land covers. The objective of the present study was to elucidate the effects of commonly used pansharpening methods when they are applied to GF-2 imagery by performing quality assessments based on the region and land cover. The GS, CN, PCA, and mIHS pansharpening techniques were applied and evaluated using a series of quality metrics.  
Visual inspections were performed only for the regions composed of different proportions of the different land covers. Generally, all four approaches satisfied the requirements for improving the spatial resolution of the original MS image. The mIHS pansharpening method produced better results than the GS, CN, and PCA pansharpening methods. The previous studies also indicated that the colors of PCA pansharpened images were lighter than those of the original MS images [27; 45]. It was difficult to compare the color fidelity of the GS and CN methods through visual analysis. Our results are consistent with those of [PERSON] and [PERSON] (2015) and [PERSON] et al. (2012) and partly consistent with those of [PERSON] et al. (2013) and [PERSON] et al. (2015) [12; 32; 37; 46], who reported that the behavior of the pansharpening method was related to the context of the images. However, our results are inconsistent with those of [PERSON] et al., who reported that the false-color composite of the GS pansharpened WorldView-2 bands 3 (green band), 5 (red band), and 7 (blue) was closer to the colors in the original low-resolution MS image than that of an mIHS pansharpened image [47]. Our results are consistent with those of [PERSON] et al., who reported that a mIHS pansharpened image had little change in the colors of water and rock, whereas the color distortion for a PCA pansharpened image was significant [48]. The previous studies indicated that the mIHS, GS, CN, and PCA pansharpening methods performed well for some images but not for others [31; 49]. In fact, visual analysis was not sufficient to evaluate the quality of the pansharpening methods; the results should always be confirmed via quantitative quality assessment [12; 37].
## 3 Proposed Reduction Criteria for RESs Uncertainty  
Considering a large number of uncertainty cases may cause a very long computation time and sometimes will result in no solution for the problems. Thus, considering hourly historical data for one year results in 8784 uncertainty cases that are responsible for complexing the problem, especially when there is a need for doing power flow calculations for each uncertainty case. Therefore, there is a need for reducing the number of uncertainty cases without losing accuracy. In this paper, four stages are proposed to reduce the uncertainty cases as follows:  
- First Stage  
1. Establishing the base matrix that consists of 8784 rows and 4 columns; the rows represent the year hours, and the columns represent the uncertainty parameters \((G,T,V_{W},LR)\).
2. All the matrix row values are rearranged according to the G values when it is arranged in ascending order.
3. Forming the G sub-groups as follows: G sub-groups are the groups which are formed by dividing the practical irradiance range into three equal sub-groups limited by four boundaries \((G_{0},G_{1},G_{2},G_{M})\) as shown in Figure 3, where G0 is the lowest practical solar irradiance, \(G_{M}\) is the highest practical solar irradiance, \(G_{1}\) is the boundary between the first and second sub-groups, and \(G_{2}\) is the boundary between the second and third sub-groups. The values of \(G_{1}\) and \(G_{2}\) are calculated as shown in Figure 3.
4. Using the formed sub-groups to divide the base matrix into three sub-matrices (M1, M2, M3), where each sub-group forms a sub-matrix.  
Figure 3: Sub-group formation according to G values.  
Figure 2: PV output power for one day by using the approximate PV modeling and the accurate PV modeling.  
- Second Stage
* Rearrange the rows of the sub-matrices (\(M_{1},M_{2},M_{3}\)) according to the values of T in ascending order.
* Follow the sub-grouping technique used in Stage 1 to form sub-matrices (\(M_{11,12,13}\), \(M_{21,22,23}\), \(M_{31,32,33}\)) from (\(M_{1},M_{2},M_{3}\)) according to T values as shown in Figure 4.  
After completing the four stages, there will be 81 sub-matrices produced. In each sub-matrix, the elements of each column have close values (small numerical range). Therefore, each sub-matrix produces one uncertainty case by determining the average value of each column value. The whole process of the proposed uncertainty case reduction is summarized in Figure 7. Also, a simplified numerical example (only two uncertainty parameters and the number of sub-groups of the uncertainty parameter values range equal to 2) is shown in Figure 8. The figure shows that data of any hour does not overlap or separate during the different stages of arrangement  
Figure 4: Sub-group formation according to T values.  
Figure 5: Sub-group formation according to \(V_{W}\) values.  
Figure 6: Sub-group formation according to \(LR\) values.
###### Abstract  
Over the past few decades, most cities worldwide have experienced a rapid expansion with unprecedented population growth and industrialization. Currently, half of the world's population is living in urban areas, which only account for less than 1% of the Earth. A rapid and unplanned urban expansion, however, has also resulted in serious challenges to sustainable development of the cities, such as traffic congestion and loss of natural environment and open spaces. This study aims at modeling and predicting the expansion of urban areas in South Korea by utilizing an explainable artificial intelligence (XAI) model. To this end, the study utilized the land-cover maps in 2007 and 2019, as well as several socioeconomic, physical, and environmental attributes. The findings of this study suggest that the urban expansion tends to be promoted when a certain area is close to economically developed area with gentle topography. In addition, the existence of mountainous area and legislative regulations on land use were found to significantly reduce the possibility of urban expansion. Compared to previous studies, this study is novel in that it captures the relative importance of various influencing factors in predicting the urban expansion by integrating the XGBoost model and SHAP values.  
urban expansion; explainable artificial intelligence (XAI); land cover; remote sensing Article
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 6 Conclusions  
In this work, we proposed a dual-band RF to DC rectifier at sub-6 GHz 5G bands. The circuit was designed on an RT/Duroid 5880 substrate with a dielectric constant of 2.2. This circuit is suitable to work within Frequency Zone 1 (FR1) in the n48, n77, and n78 bands with a central frequency of 3.5 GHz and the n79 with a central frequency of 5 GHz. The Avago HSMS-286C diodes were utilized, while the Greinacher topology was selected. The impedance-matching network has distributed elements that consist of two different branches, one for 3.5 GHz and one for 5 GHz. The system presents a PCE of 53% when the output load is 1.74 k\(\Omega\) and the input power is 12 dBm. The output DC voltage is 3.8 V, and it can supply low-power electronic devices, such as sensors and microcontrollers.  
Conceptualization, I.D.B. and M.S.P.; methodology, I.D.B., M.S.P. and A.D.B.; software, I.D.B., M.S.P. and A.D.B.; validation, I.D.B., M.S.P. and A.D.B.; formal analysis, I.D.B., M.S.P. and A.D.B.; investigation, S.N.; resources, S.N.; data curation, S.K.G.; writing--original draft preparation, I.D.B.; writing--review and editing, S.K.G.; visualization, I.D.B., M.S.P. and A.D.B.; supervision, S.K.G. and S.N.; project administration, S.K.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Ultrasound-Guided Procedures in Common Tendinopathies at the Elbow: From Image to Needle
## 3 Common Elbow Pathologies
### Tennis Elbow
#### 3.1.1 Essential Anatomy  
A conjoint tendon, called the CET, represents the forearm's extensor muscles' proximal insertion, which is located in the ventral and lateral aspects of the lateral humeral epicondyle (LE) (Figures 1 and 2). Its course is more distally and deeper to the brachoradialis and extensor carpi radialis longus (ECRL) muscles, which originate more proximally. The CET consists of fibers derived from the extensor carpi radialis brevis (ECRB), extensor digitorum communis (EDC), extensor digiti minimi (EDM), extensor carpi ulnaris (ECU) tendons, and receives fiber from the ECRL. The ECRB represents the anterior and deep portion, and the EDC makes up the anterior and superficial parts. The ECU forms the posterior part. The ECRB, the prime dorsiflexor of the wrist and the key tendon in tennis elbow development, originates from the LE of the humerus, from the lateral collateral ligament (LCL), and the adjacent intermuscular septum [30]. [PERSON] with coauthors [31] in a cadaveric study described no clear margins between the ECRB and EDC; thus, the authors considered those two tendons mentioned above as one common tendon. Furthermore, interconnecting tendons between the ECRB and ECRL were found in 35% of limbs [32]. Distally, the ECRB inserts onto the base of the third metacarpal. These muscles' action extends the wrist and fingers while also supinating the forearm and abducting the wrist. The radial nerve innervates all of the muscles mentioned above. The brachioradialis, ECRL, and ECRB are supplied by direct branches from the radial nerve. Other extensors are innervated via the deep branch of the radial nerve (posterior interosseus nerve) [33]. Deep to the CET, the LCL bridges from the LE down to the radial head.
## 3 Results and Discussion
### Hole Diameters  
Hole diameters for the entry and exit holes can be seen in Figure 6. The target diameter for the holes were 0.6 mm, so it can be seen that the entry holes are a bit larger and the exit holes are much smaller than the target diameter. Too large entry holes can usually be fixed by using a large enough beam offset with the control program. However, even with corrections, it is difficult to prevent entry hole erosion due to the upwards melt flow, as discussed by [PERSON] et al. [32]. In practice, the entry hole will always be slightly rounded in laser drilling, unless the material is directly vaporized by a high enough power density. The hole diameter clearly increases with increasing pulse length. This can be attributed to the higher pulse energy and longer interaction with the material, which leads to more molten material being ejected by vapor pressure. When the pulse length is longer than 50 ns pulses, the hole diameters seem to increase nearly linearly with the increase in pulse length. When the pulse length is shortened to less than 50 ns, the hole diameters start to decrease rapidly. It is commonly known that when the pulse length is reduced to the picosecond range, the laser-induced material removal starts to resemble so-called cold ablation, where material is removed with only minimal heat effect [33]. The decrease in hole diameter seen here is likely the beginning of a transition from a highly thermal process, where the primary mechanism of material removal is the ejection of molten material by vapor pressure, to material removal by direct evaporation.  
According to the results, there is at most a very slight difference between laser drilling cold rolled steel and PBF steel. On both sides, the differences are almost negligible. In four instances, the holes in cold rolled sheet are slightly larger, but overall the holes in PBF sheets are slightly larger.
# Second-Order Text Matching Algorithm for Agricultural Text
###### Abstract  
Text matching promotes the research and application of deep understanding of text information, and it provides the basis for information retrieval, recommendation systems and natural language processing by exploring the similar structures in text data. Owning to the outstanding performance and automatically extract text features for the target, the methods based-pre-training models gradually become the mainstream. However, such models usually suffer from the disadvantages of slow retrieval speed and low running efficiency. On the other hand, previous text matching algorithms have mainly focused on horizontal domain research, and there are relatively few vertical domain algorithms for agricultural text, which need to be further investigated. To address this issue, a second-order text matching algorithm has been developed. This paper first obtains a large amount of text about typical agricultural crops and constructs a database by using web crawlers and querying relevant textbooks, etc. Then BM25 algorithm is used to generate a candidate set and BERT model is used to filter the optimal match based on the candidate set. Experiments have shown that the Precision@1 of this second-order algorithm can reach 88.34% on the dataset constructed in this paper, and the average time to match a piece of text is only 2.02 s. Compared with BERT model and BM25 algorithm, there is an increase of 8.81% and 13.73% in Precision@1 respectively. In terms of the average time required for matching a text, it is 55.2 s faster than BERT model and only 2 s slower than BM25 algorithm. It can improve the efficiency and accuracy of agricultural information retrieval, agricultural decision support, agricultural market analysis, etc., and promote the sustainable development of agriculture.  
natural language processing; deep learning; text matching; agriculture text
## 2 Materials and Methods
### Instruments and Measurements  
The shaker Intelli-Mixer (ELMI, Riga, Latvia) was used for conjugate synthesis. Absorption spectra of GNPs and their conjugates with PAO were measured by Biochrom Libra S80 spectrophotometer (Biochrom, Cambridge, UK). The morphology of GNPs was characterized by transmission electron microscopy (TEM) using microscope model JEM CX-100 (Jeol, Japan). Dynamic light scattering (DLS) measurements were implemented using a Malvern Zetasizer Nano (Malvern, UK).
## 3 Simulation Setup and Performance Evaluation
### Average Delivery Rate  
One of the significant metrics for evaluating the performance of the routing protocols is an average delivery rate. We compare the performance using the node failure probability and an average delivery ratio as shown in Figure 6. We observe that the performance of the routing protocols is comparatively similar, but PT shows slightly higher performance than other routing protocols because PT has the support of a least distance smart search model that helps find the shortest path. We notice that these routing protocols experience problems due to node failure. As a result, the protocols show reduced performance. The reason of the reduction in the performance of the protocols is the lack of load-balancing algorithms.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Size of network & 1600 \(\times\) 1600 m\({}^{2}\) \\ Number of nodes & 450 \\ Queue-Capacity & 40 Packets \\ Mobility Model & Random way mobility model \\ Maximum number of retransmissions allowed & 03 \\ Initial energy of node & 5 J \\ Size of Packets & 128 bytes \\ Data Rate & 300 Kb/s \\ Sensing Range of node & 35 m \\ Simulation time & 5 min \\ Average Simulation Run & 06 \\ QoS Routing Protocol & QoSR, QoSAM, MQoSR, PRTR, QEMPAR, PT and MIMO \\ Base station location & (0,500) \\ Transmitter Power & 12.3 mW \\ Receiver Power & 13.4 mW \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters and its corresponding values.
# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 4 Experimental Results and Analysis  
### Classification Results of Gaze Points  
Figure 10 shows the gaze point classification process. The experimental results are analyzed from two aspects: subjective result analysis where the result of gaze point classification is analyzed from gaze point visualization, and objective analysis of the results, where the proposed method is compared with the relevant data classification method.  
Compared with related classification method, the clustering method [10], the SVM method [11], the RF method [12], the GHMM method [13], the HMM method [14] and the CRF method [16] to further analyze proposed method results.
# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
## 3 Highway Merging Area Accident Impact Range Model  
### Gaussian Plume Model  
To investigate the mechanism of accident risk propagation in the merging area, it is necessary to determine the impact range first. In this study, the Gaussian plume model was applied to describe the spread of accident risk. The accident position was regarded as the diffusion source in the Gaussian plume model, and a risk propagation model based on the Gaussian plume model was established.  
The Gaussian plume model [30] is a mathematical model used to describe the transport and diffusion of air pollutants in the atmosphere. Various factors must be considered in constructing the Gaussian plume model, such as diffusion properties, light intensity, temperature, and external wind force. The Gaussian plume model is described in Equation (3) [30]:  
\[C^{\prime}(x^{\prime},y^{\prime},z^{\prime},H^{\prime})=\frac{q^{\prime}}{2\pi u ^{\prime}\sigma_{y}^{\prime}\sigma_{z}^{2}}\exp(-\frac{y^{\prime 2}}{2 \sigma_{y}^{\prime 2}})\cdot\{\exp[-\frac{1}{2}\frac{\left(z^{\prime}-H^{ \prime}\right)^{2}}{\sigma_{z}^{\prime 2}}]+\exp[-\frac{1}{2}\frac{\left(z^{ \prime}+H^{\prime}\right)^{2}}{\sigma_{z}^{\prime 2}}]\} \tag{3}\]  
where \(C^{\prime}(x^{\prime},y^{\prime},z^{\prime},H^{\prime})\) represents the concentration of pollutant gas at point \((x^{\prime},y^{\prime},z^{\prime})\); \(H^{\prime}\) represents the height of the diffusion source; \(q^{\prime}\) represents the release rate of the diffusion source; \(u^{\prime}\) represents the average wind speed outside; \(\sigma_{y}^{\prime}\) and \(\sigma_{z}^{\prime}\) are the lateral and vertical diffusion parameters, respectively; \(x^{\prime}\) is the distance from the spatial point on the wind direction axis to the source; \(y^{\prime}\) is the distance from the spatial point to the source in the vertical direction of the wind direction axis; and \(z^{\prime}\) is the height of the spatial point.  
In the field of pollutant diffusion research, the Gaussian plume model has high accuracy, and has achieved many valuable research results. Some researchers have successfully applied this model to predict the impact of traffic in both urban areas and mountainous highways. [PERSON] et al. [31] proposed a plume diffusion model to account for the non-linear relationship between conflict points and the spread of traffic risks, enabling the creation of a detailed urban road intersection risk map for real-time analysis. [PERSON] et al. [32] proposed an ellipse-like radiation range model for mountainous highways of road and intersection traffic accidents, based on the Gaussian plume model and considering cascading failures ofthe road network. They verified the accuracy and practicality of the 3D Gaussian plume model in low-dimensional situations.