esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Huanglongbing Effect on Fruit Size, Color, SSC, and TA  
Healthy grapefruit trees (HLB\(-\)) cultivated under the protective screen had a healthy canopy, fruits, and leaves, and there was no ACP under the screen [22]. The qPCR results showed that all leaf samples were CLas negative with Ct values > 40. On the other hand, all HLB+ trees grown in the open air were exposed to a high ACP population, and therefore a high CLas pressure, and had a compromised canopy, very few leaves, and greener, smaller, misshapen fruits, all typical symptoms of the HLB disease. The qPCR results showed that Ct values of CLas were 27.3 \(\pm\) 0.64 in the leaf samples, confirming those trees were all infected by the bacterium [22]. HLB+ fruit samples exhibited a lower chromameter a\({}^{\star}\), a\({}^{\star}\)/b\({}^{\star}\) ratio and higher hue angle than healthy ones, indicating a greener and less orange fruit peel color in both the 2020 and 2021 seasons. Chromameter b\({}^{\star}\) values did not statistically differ between the treatments but were slightly higher for HLB+ samples, confirming a less yellow and more blue peel color in HLB+ fruit samples (Table 1 and Figure 2). In 2021, the HLB+ fruits were less green than the ones of the 2020 season, as revealed by the higher peel a\({}^{\star}\)/b\({}^{\star}\) ratio and lower hue angle (Table 1). Regarding fruit size, HLB+ fruits were smaller in weight, length, and diameter in 2020 and 2021 compared to HLB\(-\) ones (Table 1 and Figure 2). The lower fruit size associated with the higher preharvest drop caused by HLB results in low fruit yield [7]. HLB+ grapefruit juice had a statistically reduced SSC and SSC/TA ratio (Table 1). However, there was no significant difference in TA (Table 1). At the point of evaluation, the samples, regardless of HLB+/ \(-\) and harvest season, met the minimal maturity standards for grapefruit in Florida, USA [28], with the SSC of the juice above 7.5%, the SSC/TA ratio higher than 7.0, and more than 25% of the fruit surface with yellow or red color (Table 1). Overall, HLB+ fruits were smaller and greener with a lower SSC and SSC/TA ratio (Table 1 and Figure 2). These aspects are characteristics of HLB+ fruit and have been reported by several authors, particularly for sweet oranges, such as 'Valencia' and 'Hamlin', which were the most studied cultivars for the effect of HLB on fruit quality [4; 6; 8; 10; 29]. Usually, as the maturity progresses, the peel color of a healthy orange becomes less green and more orange, and TA in the juice decreases [12]. In citrus peel, chlorophylls are more abundant in dark green fruits. Carotenoids, however, follow the opposite trend [25]. Previous research showed that grapefruits grown inside the tree canopy or bagged accumulated more lycopene and carotenoids and less chlorophyll, which were evidenced by an acceleration of chloroplasts to chromoplast transition in the peel and the relative expression of genes related to chromoplast development was enhanced by light avoidance [30]. HLB+
## 4 Conclusions  
In this study, a three-dimensional model of a station hall with an outside computational domain was established by considering an actual subway station as an example, and the accuracy of the numerical model was verified through field hot smoke testing. CFD was used to simulate the influence of the TSE system and smoke flow characteristics on the fire in the station hall under different external wind directions and speeds in Tianjin. In addition, the OSC system with smoke barriers was proposed, and the smoke exhaust efficiency of the OSC system was analyzed. The key findings are summarized as follows.  
1. When all entrances and exits were on the windward side, the longitudinal diffusion problem of the station hall was severe, and smoke tended to spread to the side with fewer entrances and exits. With an increase in external wind speed, the longitudinal diffusion distance of the station hall increased, reaching a maximum diffusion distance of 61.32 m at 5 m/s, which was 67.9% greater than that under no wind.
2. When all entrances and exits were on the leeward side, the overall diffusion of smoke in the station hall was similar to that with no wind. The smoke in the station hall spread symmetrically with the fire source at the center and was not affected by the variation in the external wind speed. The longest smoke diffusion distance in the station hall at 5 m/s external wind speed was 38.27 m, which was only 4.76% longer than it was under no wind.
3. When two entrances and exits were on the windward side and the other on the leeward side, the smoke tended to spread to the entrances and exits located on the leeward side. As the external wind speed increased, the smoke diffusion distance in the station hall also increased. The longitudinal diffusion distance of the station hall at 5 m/s external wind speed was 36.97 m, which was similar to that with no wind; the smoke entered the passageway on the leeward side, and the longest diffusion distance was 7.28 m.
4. The OSC system can effectively shorten the longitudinal diffusion of smoke in the station hall and prevent smoke from spreading to passageways. Compared with the TSE system, the diffusion distances of the station hall were shortened by 35.45%, 13.64%, and 2.35%, respectively, and smoke diffusion did not occur in all passageways.  
Conceptualization, J.L., X.F., T.Y. and B.W.; methodology, J.L. and X.F.; software, J.L. and X.F.; validation, B.W.; formal analysis, J.L. and X.F.; investigation, T.Y. and E.X.; resources, B.W.; data curation, B.W.; writing--original draft preparation, J.L. and X.F.; writing--review and editing, T.Y. and Z.W.; visualization, J.L.; supervision, T.Y.; project administration, T.Y.; funding acquisition, T.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China (No. 2021 YFC2600500).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Numerical Results and Discussion
### Free Vibration of a Simply Supported Euler Beam  
In order to further explore the accuracy and efficiency of DQTFEM, the free vibration of a simply supported Euler beam of length \(L\) = 3 m is studied in this section; flexural rigidity \(EI\) = 1 \(\times\)\(10^{6}\) N/m and mass density per unit length \(\rho A\) = 420 kg/m. The beam is meshed into 16 uniform cubic elements. All initial velocities and displacements are equal to zero except the initial velocity at mid-span is 1 m/s; apparently the problem is analogous to the impact problem.  
Consider time domain [0, 3.6 \(\times\)\(10^{-3}\)] s, which is meshed into 10 uniform time elements, that is \(h\) = 3.6 \(\times\)\(10^{-4}\) s, and 20 Gauss-Lobatto-Legendre sampling points are involved in each element. Note that, if the highest frequency is \(\omega_{33}\) = 6.967 \(\times\)\(10^{4}\) rad/s, then \(\omega_{33}h\) = 25.08 falls into the first stable interval 0 \(<\omega h\leq\) 25.11 for \(N\) = 20; see Table 1.  
Figure 6 shows an excellent agreement of DQTFEM with the exact method. For reaching the same order of accuracy, NMA needs 2.110 \(\times\)\(10^{5}\) time steps and much more CPU time than DQTFEM; see Table 4. Figure 7 presents the RelErr comparisons between DQTFEM and NMA; it follows that the error of NMA accumulates while the error of DQTFEM does not.  
Figure 5: RelErr for damping case (\(N\) = 33, \(h\) = 0.59375).  
Figure 6: Displacement comparison of beam.  
For validating the effectiveness of DQTFEM for long-term simulation, the computational time changes from 3.6 \(\times\) 10\({}^{-3}\) s to three times the maximum period \(T_{\max}\) = 2\(\pi\)/\(\omega_{1}\) = 0.1174 s, where \(\omega_{1}\) = 53.51 rad/s, the number of elements is \(3T_{\max}\)/\(h\)\(\approx\) 978. Figure 8 gives a comparison of displacements by DQTFEM with the exact solution, which shows that DQTFEM is also highly accurate for this case and the needed CPU time is only 75.53 s. Nevertheless, the NMA results are not presented since NMA requires a prohibitively long CPU time.
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 2 Materials and Methods
### Materials
#### 2.1.2 Ocean Model Data  
The hybrid coordinate ocean model (HYCOM, [[https://www.hycom.org/](https://www.hycom.org/)]([https://www.hycom.org/](https://www.hycom.org/)) (accessed on 8 November 2021)) was used as the ground truth for satellite sampling [32]. It is a hybrid coordinate ocean model with vertical coordinates that remain at equal density in the open stratified ocean. In this paper, we used HYCOM/NCODA global \(1/12^{\circ}\) reanalysis data (GLBy0.08 expt_93.0), the dataset with a horizontal resolution of \(0.04^{\circ}\times 0.08^{\circ}\) (latitude \(\times\) longitude). In order to unify the resolution, we resampled it to \(0.08^{\circ}\times 0.08^{\circ}\). Since we were studying the orbital characteristics of the satellite constellation rather than their detection precision, we used perfect observation [1] here during the satellites simulation sampling process. The perfect observation means that no observation error is added to the pseudo-observation.
## 1 Introduction  
The patellofemoral joint (PFI) consists of two bony components, the patella and femoral trochlea, sharing the same joint surface [1]. It may be prone to instability, causing patellar dislocation usually on the lateral side of the knee. The incidence of primary patellar dislocation has been reported to be 5.8 cases per 100,000 in the general population, with the highest incidence occurring in the 2 nd decade of life (29 per 100,000) [2]. Patellar instability can be classified as traumatic, when the dislocation is caused by an external force, or atraumatic, when the native knee anatomy predisposes to instability [1]. Non-operative treatment is usually the gold standard for the first episode of lateral patellar dislocation, except for cases with large bony avulsions or chondral fragments, and it consists of knee immobilization, physical therapy and non-steroidal anti-inflammatory drugs (NSAIDs) administration [3, 4]. The recurrence rate after conservative management can be up to 15-44%, and patients with a clinical history of two or more dislocations have a 50% chance of recurrent dislocation episodes [5]. Chronic instability is a multifactorial problem that relies on limb alignment (valgus knee), the anatomy of the patella and trochlear groove and soft-tissue constraints, especially the medial patella-femoral ligament (MPFL). The management of recurrent patellar instability is still debated due to a heterogeneous patient population, skeletalmaturity, a challenging surgical approach and a lack of long-term and robust clinical outcome studies [6,7]. Radiological evaluation should investigate three main characteristics of instability: trochlear dysplasia, abnormal patellar height and a pathological tibial tubercle-trochlear groove (TT-TG) distance [8]. The patellar height can be easily evaluated through standard lateral-view knee radiographs. Several indexes have been described in the literature to calculate patellar height [9, 10, 11]. The Caton-Deschamps index (CDI) is routinely employed to measure the patellar height in patellar instability. The CDI relies upon the length of the articular surface of the patella and its distance from the anterior border of the tibial plateau [12].  
The purpose of this study was to evaluate the reliability and reproducibility of the CDI and its employment in the preoperative planning of patellar stabilization surgery.
## 3 Building Blocks of the Overall DFE Architecture  
### Fri (CIC Filter)  
The CIC filter [18] (FIR1) illustrated in Figure 4 dispenses with the multiplier and appears to have a regular structure. It leads to a reduced amount of computation and hence is amenable to high-speed and low-power implementation of decimation and interpolation filters. It is used as the first block of our DFE, namely, immediately after the ADC, which operates at a higher frequency than the latter blocks. As shown in Figure 4, the CIC filter comprises the integrator, the decimator (or downsampler), and the comb filter, where the integrator actually consists of a cascade of \(N\) unit integrators.  
The transfer function of the integrator and decimator is expressed as Equation (1), where \(R\) is the decimation factor and \(D\) is the differential delay of the comb filter.  
\[H_{Integrator}(z)=\left(\frac{D}{R}\frac{1}{1-z^{-1}}\right)^{N} \tag{1}\]  
The transfer function of the comb filter is affected by the downsampler and equated to Equation (2).  
\[H_{Comb}(z)=\left(1-z^{-\frac{D}{R}}\right)^{N} \tag{2}\]  
The transfer function of the overall CIC filter is expressed as Equation (3).  
\[\begin{split} H_{CIC}(z)&=H_{Integrator}(z)\cdot H _{Comb}(z)\\ &=\left(\frac{D}{R}\frac{1-z^{-\frac{D}{R}}}{1-z^{-1}}\right)^{N }\end{split} \tag{3}\]  
From these equations, the CIC filter is identified to have the structure in Figure 5, where each small square represents the D-type flip-flop (FF). Figure 6 shows the output of the ADC (left) and the output of the FIR1 (CIC filter).  
Figure 4: Cascaded integrator comb (CIC) filter.  
Figure 5: CIC filter structure.  
FIR1 (CIC filter) is designed as follows. Ten cases or frequency modes for the LTE DFE have different parameter values for FIR1. The CIC filter order, N, is set to 4 for all the cases while the differential delay of the comb filter, \(D\), can have different values of 3, 2, 4, or 8 for different cases. The downsample rate \(R\) is set to be identical to \(D\) in each case and hence noble identity [19] implementation is possible, yielding high-speed operation and low-power drain in the comb filter, as shown in Figure 7.
# Relationship between Unilateral Leg Extension Strength and Dynamic Balance in Healthy Young Men
## 3 Results  
Participant characteristics and demographics were reported in Table 1 as mean \(\pm\) SD with 95% CI. Participants' results on unilateral leg extension strength (absolute and relative) and Y Balance Test (3 directions and composite score) are shown in Table 2.  
Figure 1 shows a large and very large Pearson product-moment correlation between absolute strength and anterior reach (\(r\) = 0.757; 95% CI = 0.456 to 0.920), posterolateral (\(r\) = 0.585; 95% CI = 0.338 to 0.772), posteromedial (\(r\) = 0.605; 95% CI = 0.334 to 0.799), and composite score (\(r\) = 0.673; 95% CI = 0.442 to 0.823). Moderate and large correlations were found between relative strength and anterior reach (\(r\) = 0.658; 95% CI = 0.388 to 0.818), posterolateral (\(r\) = 0.466; 95% CI = 0.139 to 0.703), posteromedial (\(r\) = 0.470; 95% CI = 0.114 to 0.703), and composite score (\(r\) = 0.537; 95% CI = 0.209 to 0.741).  
Linear regression analyses showed that leg extension strength was a significant predictor of performance in the Y Balance Test. Leg extension strength proved to be significantly predictive of three directional scores as well as the composite score (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Mean \(\pm\) SD** & **95\% CI** \\ \hline Age (years) & 21.21 \(\pm\) 1.24 & 20.81–21.63 \\ \hline Height (m) & 1.74 \(\pm\) 0.07 & 1.72–1.77 \\ \hline Weight (kg) & 67.30 \(\pm\) 9.12 & 64.27–70.48 \\ \hline BMI (kg/m\({}^{2}\)) & 22.04 \(\pm\) 3.10 & 21.04–23.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics and demographic data of participants (\(n\) = 33).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Mean \(\pm\) SD** & **95\% CI** \\ \hline Y Balance Test (\% leg length) & & \\ Anterior (A) & 68.50 \(\pm\) 6.88 & 66.26–70.86 \\ Postrolateral (PL) & 110.74 \(\pm\) 14.97 & 105.80–115.46 \\ Posteromedial (PM) & 104.60 \(\pm\) 13.08 & 100.15–109.23 \\ Composite & 96.66 \(\pm\) 11.55 & 92.68–100.41 \\ Leg Extension Test & & \\ Absolute Strength & 45.83 \(\pm\) 7.25 & 43.41–48.18 \\ Relative Strength & 0.70 \(\pm\) 0.13 & 0.65–0.74 \\ \hline \hline \end{tabular} (% leg length = (reach distance/limb length) \(\times\) 100; Composite = [(A + PL + PM)/(leg length \(\times\) 3]] \(\times\) 100; Absolute Strength = maximum strength; Relative Strength = maximum strength/body weight; SD = standard deviation; CI = confidence interval).  
\end{table}
Table 2: Results of the unilateral leg extension test and the Y Balance Test (\(n\) = 33).  
Figure 1: Scatter plots for unilateral leg extension strength (absolute and relative) and Y Balance Test 3 directions, and as well as composite scores and linear regression line with 95% CI.
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 3 Resilient Moving Object Index  
In this section, we present our adaptive approach for trajectory indexing. Based on the work in [34], most of the MapReduce spatial indexes generally follow three steps: partitioning, building the local index, and building the global index. In the partitioning phase, data is partitioned into smaller pieces based on specific measures such as space, time, object, etc. Then, the resulted partitions are distributed to the worker nodes and each worker node builds a local index for each partition. Finally, the driver node collects the needed information from worker nodes to build the global index.  
The main goal of RMOI is to have a flexible index that considers both space-based and object-based partitioning techniques. It is capable of balancing both spatial and object localities by providing a locality preservation mechanism, which gives the flexibility to satisfy different applications' demands. RMOI uniquely provides a Locality Pivot (\(LP\)) parameter that is predicted based on several different features. Consider the trajectory set in Figure 1 with \(\alpha=3\) and \(\beta=2\), where \(\alpha\) is the number of the required spaced-based partitions, and \(\beta\) is the required object-based partitions per \(\alpha\). RMOI starts by partitioning the global space into \(\alpha\) spatial groups. Then, each spatial group is hashed into \(\beta\) partitions. The total partitions number (\(tpn\)) is 6. The result is a combination of spatial and object partitioning, which provides a balance of both localities simultaneously.
# Searching Online for Art and Culture: User Behavior Analysis
## 4 Discussion
### Findings Summary
#### 4.2.8 Device Preference  
Three out of ten sessions that took place during the data collection period of the Art Boulevard platform were performed using a mobile device, while the remaining 70% were performed using a desktop or laptop device. This observation is in contrast with what appeared to be the case in general purpose search engines. For example, in Google search, 64% of searches were performed on the mobile phone [63], while 35% were performed on desktop devices. This was also the case during the beta testing period of the platform, with only 13.12% of sessions being on mobile [37]. [PERSON] et al. [64], in their analysis of mobile search queries, also discovered that in the thematic category of arts and literature, desktop searches were much more prevalent compared to mobile searches than in any other thematic category. From all of the above, a pattern begins to emerge concerning the use of mobile devices in this field. It is also worth noting that in previous research leading up to the development of the Art Boulevard platform, interviewees from the fields of arts and culture showed a clear preference for the use of desktop or laptop computers for their art search purposes [65]. Art searchers both explicitly stated [65] and were observed through this research and others [64] to have a clear preference to desktop and laptop devices over smartphones and tablets. Moreover, as observed by [PERSON] and [PERSON] [21], mobile search leads to fragmented searcher attention, which creates a perception of the search process being more difficult. The nature of art search as a deliberate and focused activity makes the office or home office environment, which involves desktop and laptop devices, more suitable for its purposes, and comes in contrast with the on-the-go or casual nature of smartphone and tablet usage which is often accompanied by various distractions.  
With regard to the query-related measurements, [PERSON] et al. (2018) in their general purpose search engine log analysis detected, on average, fewer search terms in desktop than mobile (2.73 vs. 3.05). The high usage of desktop devices, when searching for art and culture online, might also contribute to the lower average terms per query that was discovered in the field by this study and others ([PERSON] et al., 2018), alongside other factors mentioned in the first paragraph of this section.  
RQ2: How Are These Key Values That Describe the Behavior of Users Searching Online for Art and Culture Affected by the Device Used to Perform the Search Session, the Users' Choice to Login and the Session's Total Duration?
## 3 Research Design
### Data Sources and Descriptive Statistics  
There are 338 prefecture-level regions in Chinese mainland. This paper chooses the data of 286 prefecture-level cities in the Chinese mainland from 2005 to 2018 as samples. The samples are highly representative. Data sources of relevant indicators cover the _China Urban Statistical Yearbook, China Urban Construction Statistical Yearbook, China Environmental Statistical Yearbook, China Water Conservancy Statistical Yearbook, China Regional Economy Statistical Yearbook_, statistical yearbooks of some provinces, statistical yearbooks of water conservancy in some provinces, _Zhejiang Province Natural Resources Yearbook_, _Zhejiang Province Natural Resources and Environment Statistical Yearbook_, China Meteorological Data Service Center, and National Intellectual Property Administration. For some missing data, to prevent the mutation of data in the time sequence, this paper mainly uses the autocorrelation fitting method to estimate. Noticeably, in terms of days of air quality with Grade II or above, the data are available in only 80 prefecture-level cities, which are missing in some years. The missing data in some years are filled with the provincial average value. To eliminate the influence of data dimension and improve the goodness of fit of the model, this paper standardizes all indicators. The descriptive statistics of variables are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Sample Size** & **The Mean Value** & **Standard Deviation** & **The Minimum Value** & **The Maximum Value** \\ \hline lnAEP & 3990 & \(-\)21.087 & 2.286 & \(-\)27,250 & 0.000 \\ lnPM & 1120 & 5.706 & 0.209 & 3.892 & 5.903 \\ lnAPI & 3990 & \(-\)0.211 & 0.266 & \(-\)1.297 & 0.295 \\ wnAPI & 3990 & \(-\)0.210 & 0.262 & \(-\)0.879 & 0.215 \\ lner & 3990 & 10.304 & 2.195 & \(-\)13.816 & 16.728 \\ lnden & 3990 & 7.942 & 0.806 & 3.296 & 9.920 \\ lnbua & 3990 & 4.366 & 0.876 & 1.879 & _7.979_ \\ lner & 3990 & 10.313 & 0.776 & 4.595 & 15.675 \\ linin & 3990 & 3.847 & 0.250 & 2.197 & 4.511 \\ \hline \hline \end{tabular} Note: In line with the comparison between standard deviation and mean value, this paper winsorizes lnAPI at the 99% level only when the standard deviation of the lnAPI proves higher than the mean value.  
\end{table}
Table 1: The Descriptive Statistics of Variables.