esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### 3.5 Bunch Architecture and Botrytis cinerea Susceptibility
#### 3.5.3 Botrytis cinerea  
Incidence and Severity at Harvest  
As harvest in SMPH training system occurred later than in VSP, _B. cinerea_ infections were assessed 13 days later in 2017 and 21 days later in 2019. Generally, high _B. cinerea_ infections occurred in 2017. At the postponed harvest date, incidence was higher in SMPH training system than in VSP. In SMPH BT incidence was significantly reduced, but did not show the differences with VSP at a postponed harvest date of three weeks in 2019 (Figure 4A). On average, no significant differences in incidence were detected between all SMPH treatments and VSP at harvest except for SMPH ST (_p = 0.005_). Shoot thinning had a 21.5% higher incidence compared to VSP and 3.6% compared to SMPH. _B. cinerea_ incidence decreased about 2.5% in SMPH GA and 12.5% using SMPH BT.  
Figure 3: Mean values of incidence (**A**) and severity (**B**) of _Botrytis cinerea_ infections [%] for cv. [PERSON] trained in Vertical Shoot Positioning (VSP ) and Semi-Minimal-Pruned Hedge (SMPH) with non-thinned control (SMPH ), shoot thinning (SMPH ST ), biotechnological thinning (SMPH GA ) and bunch thinning using a harvester (SMPH BT ) during ripening progress on 22 nd of September in 2017. Different letters indicate statistically significant differences between the treatments with \(p\) = 0.017 for incidence and \(p\) = 0.008 for severity. Error bars are representing \(\pm\)SD.  
At the later harvest date, _B. cinerea_ severity was higher in SMPH training system compared to VSP in 2017 and 2019. SMPH BT and SMPH GA decreased severity in both years. Whereas, SMPH ST increased severity (Figure 4B). Average severity was significantly higher in SMPH and SMPH ST compared to VSP at the postponed harvest dates across 2017 and 2019. SMPH GA and SMPH BT did not differ significantly from VSP. _B. cinerea_ severity decreased by 9.4% using SMPH GA and 15.6% in SMPH BT whereas SMPH ST increased severity by 8.4%, compared to SMPH. Significant differences to SMPH were determined for SMPH BT (_p_ < 0.001).
## 2 Materials and Methods
### System Evaluation by Therapists
#### 2.4.1 Results of System Evaluation by Therapists  
From the first part of the second therapists' questionnaire, therapists agreed that the VE content was appropriate for rehabilitation and would motivate their patients' therapies. Regarding the systems use in a clinical environment, two therapists were undecided about its use in a clinical area; it meant they did not agree or disagree with its use in a clinical environment. Some reasons were that the system seemed complicated and some functions were unpredictable; also, the keyboard did not work to interact with the VE except to enter text.  
In the usability test, the system got a score of 78/100, representing a system with good usability (see Section 4.1). These results showed that some therapists thought the system could be more straightforward and, consequently, the system functions (screens, buttons, instructions) needed to be integrated. On the other hand, therapists thought it was easy to use the system, and the assistance of a technician in all the sessions was not necessary, only at the beginning, when they learned how to use it. Regarding the user experience evaluation (AttrakDiff-Short), a diagram of the description of word pairs was obtained (see Section 4.2). In this questionnaire, ten pairs of words were presented. These pairs were grouped into three categories: pragmatic quality, hedonic quality, and attractiveness. Pragmatic qualities refer to the utility and usability of the system [48], hedonic qualities are related to emotional needs [49], and attractiveness refers the qualities that make a system aesthetically attractive. The average score in these areas was 0.6 for hedonic qualities, 1.25 for pragmatic qualities, and 1.3 for attractiveness. With this test, some characteristics were detected to improve the system. For example, therapists mentioned that the system was not very predictable, and that it looked a little cheap, which meant that the system did not look attractive enough to acquire. Furthermore, the system should be more straightforward and structured, with more user-friendly buttons, menus, and tools. Simultaneously, the system needed to be more creative to result in a captivating system, adding more colors or tools that make the system interesting for users.
## 2 Contributions of This Work  
To the best of our knowledge, this work is the first effort that uses real datasets to provide system modeling in an object-oriented environment such as colored Petri nets. The main contributions of this work are the following:  
1. It defines sets of rules that can be used to model common sequences of events and transform them to CPN transitions.
2. It defines a simple timing mechanism for determining the time that transitions fire.
3. It presents a scheme for transforming real datasets into CPN transitions and places.
4. Based on the actual data, it defines CPN nests, which provide high flexibility to the model design.
5. The approaches discussed can be used to model a variety of different systems.
6. The models become more flexible and re-configurable.
## 4 Discussion
### Data Partition Characteristics  
This section discusses FL based on the healthcare data partition characteristics. Since FL uses data kept in various medical institutions, it is frequently presented in a feature matrix. Let matrix \(\mathcal{D}_{k}\) denote medical data held by the medical institution \(k\). Notably, a row in the matrix represents a patient index denoted by \(\mathcal{I}\), a column represents a patient features diagnosis denoted by \(\mathcal{X}\), and some data may contain a label data \(\mathcal{Y}\). The complete training medical dataset \(\mathcal{D}_{k}\) in a medical institution \(k\) is denoted by \((\mathcal{I}_{k},\ \mathcal{X}_{k},\ \mathcal{Y}_{k})\). Thus, data partition in FL can be divided into horizontal federated learning (HFL), vertical federated learning (VFL), and federated transfer learning (FTL) [26].
## 2 Study Area and Data Processing
### Satellite Precipitation Data
#### 2.3.3 GSMaP Version 07 Products  
The GSMaP version 07 (V07) products were released by JAXA in January 2017 using the latest algorithm for the GPM mission and have provided retrospective precipitation data since March 2014. This study evaluated three GSMaP V07 products, namely, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE. GSMaP-NRT synergistically uses available MW imagers and sounders, including GPM, with a 4-h latency. Similar to GSMaP-NRT, GSMaP-MVK also uses IR to correct satellite estimates but adopts various PWM imagers and sounders; it has a latency of 3 d. In addition to PWM and IR, GSMaP-GAUGE uses the CPC Unified Gauge-based Analysis of Global Daily Precipitation data set to adjust precipitation biases with a latency of 3 d. All three products are on a 0.1\({}^{\circ}\) spatial resolution and at 1-h time intervals. In this study, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE hourly SPPs from 1 April 2014 to 31 December 2016 were obtained from the JAXA Global Rainfall Watch website [65]. The hourly GSMaP SPPs were converted into precipitation estimates at 3-h time intervals, and the time gap between local time and UTC was considered. The three GSMaP products were also transformed from a 0.1\({}^{\circ}\) spatial resolution to 0.25\({}^{\circ}\) using the same approach for the IMERG products [47].
# Using a GPU to Accelerate a Longwave Radiative Transfer Model with Efficient CUDA-Based Methods
## 3 Model Description and Experiment Platform
### RRTMG_LW Code Structure  
A profiling graph of the original RRTMG_LW Fortran code is shown in Figure 1. The subroutine \(rad\_rrtmg\_lw\) is the driver of long-wave radiation code. The subroutine \(mcica\_subcol\_lw\) is used to create McICA stochastic arrays for cloud physical or optical properties. The subroutine \(rrtmg\_lw\) is the driver subroutine for the RRTMG_LW that has been adapted from the RRTM_LW for improved efficiency. The subroutine \(rrtmg\_lw\) (a) calls the subroutine \(inatm\) to read in the atmospheric profile from the GCM for use in the RRTMG_LW and to define other input parameters; (b) calls the subroutine \(cldprmc\) to set cloud optical depth for the McICA based on the input cloud properties; (c) calls the subroutine \(setcoef\) to calculate information needed by the radiative transfer routine that is specific to this atmosphere, especially some of the coefficients and indices needed to compute the optical depths, by interpolating data from stored reference atmospheres; (d) calls the subroutine \(taunol\) to calculate the gaseous optical depths and Planck fractions for each of the 16 spectral bands; (e) callsthe subroutine \(rtrtmmc\) (for both clear and cloudy profiles) to perform the radiative transfer calculation using the McICA to represent sub-grid scale cloud variability; and (f) passes the necessary fluxes and heating rates back to the GCM.  
As depicted in Figure 1, \(rtmrg\_lw\) takes most of the computation time in \(rad\_rtmrg\_lw\), so the study target was to use the GPU to accelerate \(rttmg\_lw\). The computing procedure and code structure of \(rttmg\_lw\) are shown in Algorithm 1. Therefore, more specifically, the subroutines \(inatm\), \(cldprmc\), \(setcoef\), \(taumol\), and \(rtrtmmc\) are accelerated on the GPU.  
```
subroutine\(rttmg\_lw\)(parameters) //ncol is the number of horizontal columns
1. do iplon=1, ncol
2. call\(inatm\)(parameters)
3. call\(cldprmc\)(parameters)
4. call\(setcoef\)(parameters)
5. call\(taumol\)(parameters)
6. if aerosol is active then //Combine gaseous and aerosol optical depths
7. \(\text{taut}(k,\,ig)=\text{taug}(k,\,ig)+\text{taua}(k,\,\text{ngb}(ig))\)
8. else
9. \(\text{taut}(k,\,ig)=\text{taug}(k,\,ig)\)
10. end if
11. call\(rtrtmmc\)(parameters)
12. Transfer fluxes and heating rate to output arrays
13.end do end subroutine
```  
**Algorithm 1**Computing procedure of original \(rttmg\_lw\).
## 2 Results and Discussion
### The Nectars' Chemical Composition of the Headspace, Volatiles and Semi-Volatiles
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RI**} & \multicolumn{5}{c}{**Area (\%)**} \\ \cline{3-8}  & & & **HoS** & **NE\({}_{\rm A}\)** & **NE\({}_{\rm B}\)** & **NE\({}_{\rm C}\)** & **NE\({}_{\rm D}\)** \\ \hline
1 & 2-Phenylethanol & 1118 & - & 1.9 & - & - & - \\
2 & Phenylacetic acid & 1262 & 3.0 & - & - & - & - \\
3 & 1\(H\)-Indole & 1295 & 1.6 & 6.8 & 2.7 & 10.3 & 6.4 \\
4 & Methyl anthranilate & 1344 & 1.4 & 11.7 & 0.9 & 1.4 & 1.2 \\
5 & 1-Hydroxylinalool ** & 1366 & 5.4 & - & - & - & - \\
6 & 1,3-Dihydro-2\(H\)-indol-2-one & 1471 & 6.4 & 18.1 & 0.2 & 3.1 & 0.8 \\
7 & Caffeine & 1842 & 11.5 & 8.9 & 1.1 & 4.3 & 2.1 \\
8 & Hexadecan-1-ol & 1882 & 11.6 & 6.9 & 10.7 & 15.7 & 16.5 \\
9 & Methyl hexadecanoate & 1929 & - & 4.3 & - & 13.7 & 0.0 \\
10 & (Z)-Octadec-9-en-1-ol & 2059 & 45.3 & 25.4 & 69.2 & 14.3 & 52.3 \\
11 & Octadecan-1-ol & 2084 & 8.6 & 6.1 & 5.3 & 26.0 & 12.7 \\ \hline \multicolumn{8}{l}{HoS = honey-sac, NE\({}_{\rm A}\) = nectar _Kawano Wise_, NE\({}_{\rm B}\) = nectar _Chuhara_, NE\({}_{\rm C}\) = nectar _Okitsu_, NE\({}_{\rm D}\) = nectar _Zorica_, RI = retention indices on HP-SMS column, ** = correct isomer is not identified.} \\ \end{tabular}
\end{table} 
Table 2: The results of GC-FID and GC-MS analysis of the extracts from the honey-sac (HoS) and nectars (NE) obtained by USE. Figure 2: (**a**) Formation of _cis_-jasmone. ; and (**b**) formation of phenylacetonitrile. (0-22 nmol-mL\({}^{-1}\)), theophylline (0-55 nmol-mL\({}^{-1}\)) and paraxanthine (0-12 nmol-mL\({}^{-1}\)). It is difficult to compare the abundance of caffeine (Table 2) determined by USE/GC-MS/FID with the results of direct HPLC analysis, which is focused on the non-volatiles analysis. Generally, Satsuma mandarin peel essential oil was characterized by a high percentage of limonene (67.4%), followed by \(\beta\)-myrcene, car-3-ene, \(\alpha\)-pinene, \(p\)-cymene, \(\beta\)-pinene, sabinene, terpinolene, and \(\alpha\)-thujene [25]. The principal chemical constituents of _C. unshiu_ flower essential oil included \(\gamma\)-terpinene (24.7%), \(\beta\)-pinene (16.6%), \(\alpha\)-cymene (11.5%), limonene (5.7%), \(\beta\)-cimene (5.6%) and \(\alpha\)-pinene (4.7%) [25]. It is already known that the composition of the nectar and essential oil from the same plant is very different [26]. However, several common compounds were found in the flower essential oil and _C. unshiu_ nectar headspace investigated herein, such as \(\gamma\)-terpinene, limonene, \(\alpha\)-pinene, and \(\beta\)-pinene.
## Materials
### Data  
A total of 78 Sentinel-1 SAR images were obtained for A68A from 22 July 2017 to 29 November 2018, which were used to estimate area and drift. The two Sentinel-1 constellation satellites (Sentinel-1A and 1B) are equipped with C-band SAR with a center frequency of 5.405 GHz [24]. Each satellite has a temporal resolution of 12 days, while the satellite constellation follows the same ground track and can thus provide images for the same area every six days. All Sentinel-1 SAR images were obtained with HH-polarization in the Extra Wide swath mode, which captures an area of 400 \(\times\) 400 km with a spatial resolution of 20 \(\times\) 40 m (range \(\times\) azimuth).  
CryoSat-2 SIRAL (Ku-band RA with a center frequency of 13.575 GHz) surface elevation profiles were used to estimate the freeboard of iceberg A68A. The repeat cycle of CryoSat-2 is 369 days with a 30-day sub-cycle [19]. The along-track spacing of CryoSat-2 is approximately 350 m and the across-track spacing is less than 2.5 km, at 70 S, with footprint of roughly 300 m and 1.5 km, respectively [19; 25], which provides high-density data for estimating freeboard. Over the margins of ice sheets and mountain glaciers, CryoSat-2 is operating in SAR Interferometric (SARIn) mode, which provides more accurate topography information in steep areas than previously operated RA systems [19; 25; 26]. The CryoSat-2 level 2 Baseline C data in SARIn mode were used, which covered iceberg A68A during the Sentinel-1 SAR observation period. We used the EIGEN-6C4 Geoid [27], DTU12 MDT mean dynamic topography [28], and Circum-Antarctic Tidal Simulation Inverse tide model version 2008 (CATS2008a) [29] to correct the freeboard of the iceberg, converted from the ellipsoidal iceberg surface elevation provided by the CryoSat-2 Level 2 data.  
The 10-m air temperature, 10-m wind speed, and snow depth around A68A were predicted from Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis data [30], using a grid size resolution of 0.5\({}^{\circ}\times\) 0.625\({}^{\circ}\) (latitude \(\times\) longitude) to analyze the effects of environmental factors on iceberg changes. MERRA-2 has the latest atmospheric reanalysis data produced by NASA's Global Modeling and Assimilation Office (GMAO), which provides a long-term record of global atmosphere since 1980. Air temperature and wind speed can affect iceberg area and drift, respectively. Snow depth was considered in the analysis of iceberg freeboard change assuming CryoSat-2 Ku-band SIRAL might not penetrate snow cover on the iceberg A68A owing to high firm densification which is causative of low penetration depth of the radar signal [19].  
To investigate the relationship between iceberg evolution and submarine topography, seabed elevation data from Bedmap2 [31] were used with a 1-km grid size. Bedmap2 incorporates a number of Antarctic sub-ice shelf and deep ocean bathymetry datasets provided by British Antarctic Survey.
# Recent Spatiotemporal Changes of Subhourly Extreme Rainfall Events in Seoul
## 3 Methodology  
### Mann-Kendall Trend Detection Test  
The MK test is a statistical method used to detect and analyze trends in time series data [19; 20]. This test is particularly valuable for examining long-term (e.g., longer than 20 years) changes in natural phenomena, including but not limited to climate data and hydrological records [21; 22]. As a nonparametric method, the MK test is versatile and robust because it does not require any assumptions concerning data distribution. This procedure involves comparing the ranks between pairs of observed values to ascertain whether there is an overall increasing or decreasing trend. Specifically, the test evaluates whether the observed data exhibit a monotonic increase or decrease over time. The MK test can determine the statistical significance of the identified trend through \(p\)-values, which quantify the likelihood that the observed trend occurred by chance. This feature makes the MK test a powerful tool for hypothesis testing. Furthermore, the MK test is renowned for its effectiveness in handling datasets with substantial noise or non-normality from measuring noise, human error, and the nature of positive numbers, making it a preferred choice in environmental and climate research, where data irregularities are common [23; 24].  
Figure 2: Basic statistics of the annual maximum precipitation (AMP) series for selected stations, including (**a**) mean, (**b**) standard deviation, (**c**) coefficient of skewness, and (**d**) coefficient of kurtosis.  
The test statistic (S) used in the MK test is expressed as follows:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sign\big{(}y_{j}-y_{i}\big{)},\;sign(\alpha)= \left\{\begin{array}{ll}1\;if\;\alpha>0\\ 0\;if\;\alpha=0\\ -1\;if\;\alpha<0\end{array}\right. \tag{1}\]  
where \(y_{i}\) and \(y_{j}\) represent the sequential data values and \(n\) denotes the total number of observations in the time series. A positive \(S\)-statistic signifies an increasing trend, whereas a negative \(S\)-statistic indicates a decreasing trend. According to [PERSON] [25] and [PERSON] [26], when \(n\) exceeds 8, the S-statistic approximates a normal distribution with its variance, which is mathematically expressed as follows:  
\[\mathrm{V(S)}=\frac{n(n-1)(2n+5)-\sum_{l=1}^{n}t_{l}l(l-1)(2l+5)}{18} \tag{2}\]  
where \(t_{l}\) indicates the number of ties of extent \(l\). The standard MK statistic Z is computed using Equation (3).  
\[Z=\left\{\begin{array}{ll}\frac{S-1}{\sqrt{V(S)}}&for\;S>0\\ 0&for\;S=0\\ \frac{S+1}{\sqrt{V(S)}}&for\;S<0\end{array}\right. \tag{3}\]  
Under the null hypothesis of no trend, the \(Z\)-statistic follows the standard normal distribution.
## 2 Background and Preliminaries
### Centralized Federated Learning Architecture  
In centralized federated learning, there exists a centralized server that coordinates the whole training process.  
The central server is responsible for the following task:  
1. Determines a global model to be trained.
2. Selects participants (i.e., local nodes) for each training round.
3. Aggregates local training results sent by the participants.
4. Disseminates the updated model to the participants.
5. Terminates the training when the global model satisfies some requirements (e.g., accurate threshold is reached).  
Figure 2 shows the mechanics of the centralized architecture. From the network perspective, we can immediately deduce that this architecture generates high communication costs between servers and clients and is also a vulnerable point of failure for the overall learning process.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 3 UAV-BS Deployment Process (UDP)
### User Clustering Process (UCP)  
The UCP subprocess utilizes the k-medoids machine learning algorithm [19; 22]. The aim of the k-medoid method is to partition \(N\) UEs into \(K\) (\(1\leq K\leq N\)) disjoint subsets \(S=\{S_{1},S_{2},...,S_{K}\}\), such that the within-cluster squared error is minimized. The total within-cluster dissimilarity can be expressed as follows:  
\[A=\sum_{k=1}^{K}\sum_{u_{i}\in S_{i}\cap U}\|u_{i}-\mu_{k}\|^{2}, \tag{1}\]  
where \(\mu_{k}\) is the candidate medoid of the cluster \(S_{k}\). The objective function of the k-medoid algorithm is to minimize (1) across the \(K\) clusters and it can be expressed as follows:  
\[\min_{S}A=\sum_{k=1}^{K}\sum_{u_{i}\in S_{i}\cap U}\|u_{i}-\mu_{k}\|^{2}, \tag{2}\]  
The maximum number of the formed clusters \(K\) is given by the ceiling function of \(K=\left\lceil\frac{N}{C}\right\rceil\), where \(C\) represents the maximum number of UEs that a single UAV-BS can serve simultaneously.  
The operation of the UCP subprocess is summarized in Algorithm 1.  
```
1:input: Total number of UEs \(N\), Maximum number of UEs that a single UAV-BS can serve \(C\), and the set of coordinates of UEs \(U\)
2:Convergence tolerance \(\epsilon=10^{-6}\)
3:Calculate the total number of clusters \(K=\left\lceil\frac{N}{C}\right\rceil\)
4:Randomly choose initial medoids \(\mu_{1},\ldots,\mu_{K}\in U\) for the clusters \(S_{1},\ldots,S_{K}\in S\)
5:for\(i\gets 1\) to \(N\)do
6: Find the closest medoid \(\mu_{c}\) to user \(u_{i}\) via the expression: \(c=\underset{j=1...K}{\arg\min}\left\|u_{i}-\mu_{j}\right\|\)
7:Allocate \(u_{i}\) to the cluster \(S_{c}\) with medoid \(\mu_{c}\)
8:endfor
9:Calculate the total within-cluster dissimilarity using (1) and store it to \(A^{[1]}\)
10:repeat
11:\(A^{[0]}=A^{[1]}\)
12:for\(k\gets 1\) to \(K\)do
13:for\(i\gets 1\) to \(N\)do
14:Swap the role of \(\mu_{k}\) and \(u_{i}\)
15:Allocate each \(u_{j}\in U\) to the cluster with the closest medoid under this new configuration
16: Calculate the total within-cluster dissimilarity using (1) and store it to \(B\)
17:if\(B<A^{[1]}\)then
18: Keep the swap
19:\(A^{[1]}=B\)
20:endif
21:endfor
22:endfor
23:until\(A^{[0]}-A^{[1]}<\epsilon\)
24:output: The formed clusters \(S_{1},\ldots,S_{K}\in S\) along with the corresponding medoids \(\mu_{1},\ldots,\mu_{K}\in U\), where the UAV-BSs should be deployed
```  
**Algorithm 1** User Clustering Process (UCP).
## 3 Illustration of the Methodology  
The section describes the illustration of the methodology using the load data from the Saskatchewan province in Canada and the outage cost data from the survey [43] conducted by the University of Saskatchewan for generation adequacy assessment. Sections 3.1 and 3.2, respectively, illustrate the development of the SPD model and the integrated interruption cost model. Although the proposed methodology is aimed at low-carbon bulk power systems, it can be applied to all types of power systems. This is because the SPD model includes clusters of identical diurnal load profiles distributed across the annual evaluation period. The number of periods in the SPD model will differ based on the net load variability. Since conventional power systems have approximately similar load profiles in each season, the SPD model can be divided into four seasonal periods. The application of the proposed methodology is illustrated for an annual evaluation period. System planning typically considers a planning horizon of 10 or more years. The annual evaluation is therefore repeated over the planning horizon using forecast load data provided by the power utility in a practical application of the methodology.