esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The research equipment in this paper is a pharmaceutical conical fluidized bed with negative pressure. First, the relationship between particle size/material mass and \(U_{0\text{mf}}/\Delta P_{\text{mf}}\) is explored. Then, an Ergun equation modifying method and the dimensional analysis method are used to establish prediction models for \(U_{0\text{mf}}/\Delta P_{\text{mf}}\), and linear and nonlinear regression analysis methods are both used to determine the coefficients in the models. Finally, the advantages and disadvantages and applicable scope of the two modeling methods are compared and evaluated.  
The conclusions of this study can be summarized as follows:  
For \(U_{0\text{mf}}\), the main influencing factor is the particle size, and the larger the particle size, the larger the \(U_{0\text{mf}}\) is; the influence of the material mass is small. For \(\Delta P_{\text{mf}}\), the main influencing factor is the material mass, and \(\Delta P_{\text{mf}}\) is approximately proportional to the material mass; the influence of the particle size is relatively small. Experiments with small particles show a significant increase at large superficial gas velocity; we propose a conjecture that the particles' collision with the fluidization chamber's top surface causes this phenomenon.  
The Ergun equation modifying method takes into account the physical laws between physical quantities. The original Ergun equation has low prediction accuracy for \(U_{0\text{mf}}\) and \(\Delta P_{\text{mf}}\) for this study. Modifying the coefficients of the Ergun equation can significantly increase the prediction accuracy. By analyzing the data, we draw the inference of \(F_{\text{mf}}=0.8995G\), which may be due to the upward supporting effect of the conical cylinder wall on the material and needs to be further explored and verified.  
The dimensional analysis method considers only the mathematical relationships between independent variables and dependent variables. Compared with the Ergun equation modifying method, the dimensional analysis method has a higher fitting accuracy. There is little difference in the fitting accuracy between the linear regression method and the nonlinear regression method.  
Conceptualization, S.F.; Data curation, S.F.; Formal analysis, S.F.; Investigation, S.F.; Methodology, S.F., L.F., and G.T.; Project administration, Y.W. and H.Q.; Software, L.F. and G.T.; Supervision, Y.W. and H.Q.; Validation, L.F. and G.T.; Writing--original draft, S.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Standardization Project of TCM, grant number ZYBZH-C-TJ-55; National Science and Technology Major Project, grant number 2018 ZX09201011-002.  
The authors declare no conflict of interest.
## 3 Results
### In-Silico Assessment of the Proposed Variable Ranking Algorithm
#### 3.1.2 Results on All the Simulated Scenarios  
The results obtained on all the simulated scenarios of Section 2.2.3, in terms of ranking performance, are reported in Figure 2 for scenarios 1-3, Figure 3 for scenarios 4-6, Figure 4 for scenarios 7-9, and Figure 5 for scenarios 10-12. In particular, the RMSE distribution obtained for the standard RFE-Borda count method and the proposed algorithm are shown in green and red, respectively. We can observe that, for all the scenarios, the RMSE presents lower values for the proposed approach compared to the standard RFE-Borda count method. The advantage of using the proposed algorithm is very limited for the scenario with very little noise (scenario 1--CV = 10%) and the one with 12 noise variables (scenario 6--Nnoise = 12). Indeed, in these scenarios, the standard RFE-Borda count method already performs well with almost 75% of RMSE values below 1. However, for all the other scenarios, the proposed algorithm clearly improves the ranking performance of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Global Rank**} & \multicolumn{2}{c}{**Standard RFE-Borda Count Method**} & \multicolumn{2}{c}{**Proposed Algorithm**} \\  & \multicolumn{2}{c}{**(without Correlation)**} & \multicolumn{2}{c}{**(with Correlation)**} \\ \hline  & **Variable** & **Mean Rank** & **Variable** & **Mean Rank** \\ \hline
1 & x\({}_{1}\) & 1.29 & x\({}_{1}\)\(\sim\)x\({}_{11}\) & 1.22 \\
2 & x\({}_{3}\) & 2.37 & x\({}_{2}\) & 2.47 \\
3 & x\({}_{2}\) & 2.76 & x\({}_{3}\)\(\sim\)x\({}_{12}\) & 2.47 \\
4 & x\({}_{4}\) & 4.55 & x\({}_{4}\) & 4.69 \\
5 & x\({}_{6}\) & 6.10 & x\({}_{5}\)\(\sim\)x\({}_{13}\) & 5.68 \\
6 & x\({}_{5}\) & 6.16 & x\({}_{6}\) & 5.94 \\
7 & x\({}_{7}\) & 9.53 & x\({}_{7}\)\(\sim\)x\({}_{14}\) & 8.59 \\
8 & x\({}_{15}\) & 11.25 & x\({}_{9}\)\(\sim\)x\({}_{15}\) & 10.04 \\
9 & x\({}_{8}\) & 11.76 & x\({}_{8}\) & 10.15 \\
10 & x\({}_{19}\) & 12.73 & x\({}_{10}\) & 10.87 \\
11 & x\({}_{18}\) & 13.44 & x\({}_{18}\) & 11.21 \\
12 & x\({}_{14}\) & 13.48 & x\({}_{19}\) & 11.28 \\
13 & x\({}_{12}\) & 13.61 & x\({}_{20}\) & 11.34 \\
14 & x\({}_{9}\) & 13.88 & x\({}_{16}\) & 11.82 \\
15 & x\({}_{13}\) & 13.89 & x\({}_{17}\) & 12.23 \\
16 & x\({}_{11}\) & 14.08 & & \\
17 & x\({}_{20}\) & 14.38 & & \\
18 & x\({}_{10}\) & 14.47 & & \\
19 & x\({}_{16}\) & 15.01 & & \\
20 & x\({}_{17}\) & 15.26 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of variable ranking for the representative simulated dataset.
# Development of an On-Line Defect Detection System for EDM Process
## 2 Proposed Methodology  
The methodology of our proposed system will focus on EDM abnormal monitoring based on the collected raw data of the EDM machine by retrieving the pulse voltage and current as input data. The defect detection model of EDM was constructed by feature data, which were obtained and picked through the data collection and feature calculation. The output of our proposed system would indicate the status of EDM machine and process quality. The processing flow of the EDM abnormal monitoring system is shown in Figure 3. A high-voltage probe and a current check meter are installed on the processing machine. While starting the machining process, the characteristic calculation would synchronously be to perform based on the retrieving pulse voltage and current data. After obtaining the characteristics, it will be divided into storage and data exchange. The stored data are a log file, and the exchanged data will enter the model analysis. Models are established separately based on characteristics and anomaly monitoring methods, and then decisions are made by voting methods. An abnormality detection system is developed for electrical discharge machining, and transmitting its information to Node-RED via the MQTT (message queuing telemetry transport) communication protocol, which can provide real-time monitoring of machine abnormalities and display the current status of machining.
## 3 Data
### DTLB Data Layer  
We used a set of 74 DTLBs covered in TSX scenes with known age since drainage (Figure 1). For 14 of these DTLBs, age was determined using accelerated mass spectrometry (AMS) radiocarbon-dating of the basal portion of terrestrial peat covering lake sediments, ranging in the age from 225 to 9,410 years BP [10]. For another 60 DTLBs (3-47 years old), the age since their last drainage was determined from multi-sensor aerial and satellite image time series covering the 1950-2010 period using the change detection technique [43]. The ages of these DTLBs were used to compare SAR backscatter and surface spectral properties of each basin for determination of relationships of basin age with backscatter and with NDVI. All radiocarbon-dated basins were completely drained, while basins that drained in the past decades were either partially (approximately less than 10% water by basin area observed in 2009 images) or completely drained.
## 4 Numerical Case Study--Assessment of SSI Effects on a Steel Building  
The conclusions acquired within numerical and experimental research presented in earlier chapters were further assessed on a building designed according to Eurocode regulations under a nonlinear static analysis. Nonlinear static analysis was chosen because of its low computational costs and significant numerical accuracy. The building was designed without the consideration of SSI effects by [PERSON] and [PERSON] [43]. The steel frame building comprised of five stories with composite steel-concrete slabs. Seismic capacity of the building has already been extensively analyzed in the two doctoral theses [43; 44], and is used herein for calibration of the structural numerical model shown in Figure 8. The columns are regularly spaced with 9.0 m of spacing in both directions and are oriented to form a moment resisting frame in X-direction and a braced frame in Y-direction as shown in Figure 7. The columns and beams are made of HEB and IPE steel cross-sections (mild structural steel S275) to suit the provisions of Eurocodes 3, 4, and 8 (Figure 7). Structural slabs are assigned with 2.0 kN/m\({}^{2}\) of additional dead load and 3.0 kN/m\({}^{2}\) of live load. The building was designed by considering foundations on rock (Ground Type A) with a design peak ground acceleration (PGA) of 0.30 g.  
In order to study the SSI effects on frames, a moment resisting frame and a braced frame were studied as separate two-dimensional (2D) frames. The moment resisting frames are flexible and exhibit a frame behavior while the braced frames acquire higher stiffness potentially representing the behavior of shear walls. Since the observed building is regular in plane and in elevation, designed not to suffer the high impact of three-dimensional (3D) effects, it is possible to research such building frames in 2D as it was already done in the literature [45; 46; 47]. The frames are firstly observed as fixed at the base and later with foundations and the links representing the compliance of the soil. Numerical models were made in the SAP2000 v21.0.2 software [36].
# A Review of Variable-Beam Divergence Angle FSO Communication Systems
## 4 Challenges and Future Work
### Other Future Trends  
In addition to the above trends, VBDA technology can be combined with other technologies in FSO communication to achieve better performance. For example:.4.1 To Realize UAV Swarm Communication and Positioning under Electromagnetic Suppression Conditions  
During UAV swarm flight on the battlefield, the communication and navigation systems may be disrupted by electromagnetic suppression, which may result in failure. To maintain the safety and stability of the swarm, the VBDA system can be used for communication and navigation at different distances. For instance, when swarm members are closer together, the beam divergence angle can be increased to cover uncertain areas. However, when the leader directs the swarm members to depart from the swarm to perform independent tasks, the beam divergence angle can be reduced to maintain communication under the increasing communication distance. This flexibility enhances the ability of UAV swarm to perform a wide range of operations.
## 3 Methodology  
### Network Architecture  
The overall architecture of our method is depicted in Figure 2. On the left branch, Stage 1, Stage 2, Stage 3, and Stage 4 form the encoder, which is mainly responsible for extracting image features. In the right section, Decoder 1, Decoder 2, Decoder 3, and Decoder 4 gradually restore the detailed information of the image. The two convolutions, two SCER blocks and the downsampling block are included in each stage of encoder. The evolving normalization-activation layers (EVO Norm) ([PERSON] et al., 2020) are used after each convolution, with kernel 3 \(\times\) 3 \(\times\) 3 and stride of 1. The SCER blocks take advantage of the small kernelconvolution of effective receptive field to extract features from the fusion of local semantic information and features with a large receptive field. The layer normalization (LN) [40] and convolution with kernel \(2\times 2\times 2\) and stride of 2 are sequentially utilized as downsampling blocks of the encoder block.  
The decoder contains two convolutions with kernel \(3\times 3\times 3\) and stride of 1, a CSAM block, a trilinear interpolation operation, and a convolution with kernel \(1\times 1\times 1\) and stride of 1. In the decoder, the convolution with kernel \(1\times 1\times 1\) and stride of 1 is used to improve the non-linear expression ability of the network. The EVO Norm is applied after it. A trilinear interpolation operation is used for gradually restoring image size by twice. To focus on important features and ignore unimportant ones, channel and spatial attention mechanisms, called CSAM, are also applied after the trilinear interpolation operation. The last operation of each decoder uses two convolutions with kernel \(3\times 3\times 3\) and stride of 1 to obtain segmentation details.  
To further reduce the number of parameters, a \(1\times 1\times 1\) convolution and EVO Norm were added into the skip connections between the encoder and the corresponding decoder, which halved the number of channels. There are two CSAM modules and an Atrous Spatial Pyramid Pooling (ASPP) [41] in the bottle layer. ASPP with atrous rates 1, 2, 3, and 5 is applied to capture multi-scale contextual image information. At the end of the network, a convolution with kernel \(1\times 1\times 1\) and a stride of 1 serves as the classifier. The dimensions  
Figure 2: An illustration of the proposed SCENet for brain tumor image segmentation.  
of the input image are 4 \(\times\) 128 \(\times\) 128 \(\times\) 128, and the classifier layer maps deep dimensional features to 3 \(\times\) 128 \(\times\) 128 \(\times\) 128.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Data  
#### 2.2.1 GRACE-DA-DM GLV3.0 Data  
The GRACE-DA-DM GLV3.0 dataset (NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, MD, USA) [37] provided GWS estimates by integrating GRACE (2002-2017) and GRACE-FO (2018-2023) data, processed on a 0.25\({}^{\circ}\) (-28 km) spatial grid.  
The effective spatial resolution of GRACE/GRACE-FO measurements is approximately 300 km because of the satellites' sensitivity and filtering processes, with the 0.25\({}^{\circ}\) grid representing interpolated data for analysis.  
This product uses RL06 spherical harmonics from Center for Space Research (CSR), Jet Propulsion Laboratory (JPL), and German Research Centre for Geosciences (GFZ), provided by the University of Texas at Austin, to estimate the total water storage (TWS) changes. It includes drought indicators--groundwater percentile, root-zone soil percentile, and surface soil moisture percentile-calculated relative to a 2003-2023 climatology. Lower percentile values indicate drier-than-normal conditions, while higher values denote wetter-than-normal conditions. For this study, we developed GWS models for Peru using continuous GRACE-DA-DM GLV3.0 data from 2003 to 2023 obtained from the University of Texas at Austin's public repository. Annual data analyses explored correlations with climatological events, particularly El Nino and La Nina, to assess their impacts on regional groundwater conditions.
# Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences
###### Abstract  
The standard approach for constructing a phylogenetic tree from a set of sequences consists of two key stages. First, a multiple sequence alignment (MSA) of the sequences is computed. The aligned data are then used to reconstruct the phylogenetic tree. The accuracy of the resulting tree heavily relies on the quality of the MSA. The quality of the popularly used progressive sequence alignment depends on a guide tree, which determines the order of aligning sequences. Most MSA methods use pairwise comparisons to generate a distance matrix and reconstruct the guide tree. However, when dealing with highly diverged sequences, constructing a good guide tree is challenging. In this work, we propose an alternative approach using three-way dynamic programming alignment to generate the distance matrix and the guide tree. This three-way alignment incorporates information from additional sequences to compute evolutionary distances more accurately. Using simulated datasets on two symmetric and asymmetric trees, we compared MAFFT with its default guide tree with MAFFT with a guide tree produced using the three-way alignment. We found that (1) the three-way alignment can reconstruct better guide trees than those from the most accurate options of MAFFT, and (2) the better guide tree, on average, leads to more accurate phylogenetic reconstruction. However, the improvement over the L-INS-i option of MAFFT is small, attesting to the excellence of the alignment quality of MAFFT. Surprisingly, the two criteria for choosing the best MSA (phylogenetic accuracy and sum-of-pair score) conflict with each other.  
s +
Footnote †: journal: _Algorithms_  
sequence alignment; dynamic programming; three-way alignment; Carrillo-Lipman algorithm
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### Jiangsu Power Grid
#### 5.2.1 Analysis of Voltage Stability of the Sunan Grid  
Based on the optimal generator operation schedule of cases 0 and 1, static voltage stability of the Sunan grid with the Jinsu HVDC is analyzed. The results are shown in Table 4.  
During load-peak hours such as hours 12 and 13, load of the Sunan grid is supplied by the Jinsu HVDC and external sources in case 0. As a result, less generators in the internal gird are turned ON and the static voltage stability margin is 7.6% and 7.8%, which is lower than the operation requirement of 8%. After minimum voltage stability constraints are added into case 1, relevant Benders cuts are generated and operation status of units in the Sunan grid are re-dispatched. As shown in Table 5, the static voltage stability margin would increase to 9.1% and 9.4% in hours 12 and 13, satisfying the operation requirement.
## 3 Research Methods
### Measurement
#### 3.1.2 Pilot Test, Sample Selection, and Data Collection  
To avoid potential issues such as an incomplete questionnaire and respondents misunderstanding the questions, a pilot test was conducted with 50 students. The participants were given clear instructions on how to complete the questionnaire, and any doubts they had were addressed. After all participants completed the questionnaire, the responses were collected. After that, a reliability analysis was conducted on the variables from the pilot test, primarily using the widely adopted Cronbach's \(\alpha\) reliability coefficient. The Cronbach's \(\alpha\) coefficients for all variables were found to be above 0.8, indicating that the measurement indicators have a high level of internal consistency reliability, and the survey data are relatively reliable.  
Figure 1: Research framework.  
From a regional analysis perspective in China, cities such as Zhejiang, Guangdong, Beijing, and Shanghai show higher levels of engagement in discussions about ChatGPT. Notably, extensive discussions have already taken place in southern and coastal regions of China [48]. Consequently, the sample for this study was primarily drawn from selected universities in Zhejiang Province, China. Convenience sampling was conducted based on the total population of 15,000 students. Participants were informed about the study's purpose and confidentiality before they consented to participate. The survey was primarily conducted through the Wenjuanxing platform from late September to the end of November 2023. To ensure the completeness of the collected data, the time-tracking feature of the Wenjuanxing platform was activated during the survey distribution. Overall, 485 responses were collected from university students across various universities in Zhejiang Province. Strict measures were implemented to ensure the quality of the samples, consistent with the methodological recommendations outlined by [PERSON] et al. [49], as described below. First, based on insights from the pilot test, it typically took 2-5 min to complete the questionnaire. Therefore, participants who completed the survey in an unusually short amount of time (100 s) were considered to have approached the task irresponsibly, leading to their data being classified as invalid. Secondly, a reverse item was included in the questionnaire. Data from participants who failed to accurately answer this item were considered invalid.  
After rigorous screening, 98 invalid questionnaires were excluded. Among these, 32 questionnaires were discarded due to an insufficient response time, and 66 were excluded for incorrect responses to the reverse item. As a result, 387 valid questionnaires were retained for formal data analysis, representing a valid response rate of 79.8%. In terms of demographic composition, 44.5% of respondents were female, and 55.5% were male, indicating a relatively even gender distribution.
# A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization
## 3 Proposed Framework
### Interoperability Dimension  
This dimension harmonizes the big data ecosystem through EI. EI allows the removal of incompatibilities between the CNO's participants. Thus, each participant keeps its focus on and autonomy in processes, technologies, terminology and operational philosophy. This dimension has three essential aspects: (1) the measurement of interoperability potential to analyze participants' AS-IS situation, (2) a maturity model that establishes levels of interoperability through quantitative values and (3) a methodology that orders the steps and factors necessary to achieve EI.  
The CNO-BD framework uses the Interoperability IRIS framework [13] which covers the three essential aspects mentioned above, utilizing the six dimensions described in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **Question Respended** & **Description of Dimension** \\ \hline Methodology & How is the measurement performed? & It systematically specifies the activities, tasks, resources and roles necessary to achieve EI. \\ \hline Models and modeling & What models and modeling languages are used? & It is based on three approaches: (1) enterprise models (I*, UML 2.0 and BPMN); (2) model-driven interoperability (model-driven approaches composed of the CIM, PIM and PSM models); and (3) model morphisms. Additionally, it uses the MDKe-IRIS modeling language. \\ \hline Techniques & What mechanisms are used to collect and record project information? & It uses three techniques to support the methodology: open-ended interviews, collaborative work sessions and templates. \\ \hline Interoperability measurement system & How are problems and opportunities to improve EI identified? & It measures the potential interoperability through the MM-IRIS maturity model. It has these interoperability views: business, process management, knowledge, human resources, ICT and semantics. \\ \hline Semantic alignment & How are the syntactic and semantic drawbacks of the domain solved? & It uses specific ontologies for each participant. Ontologies are based on the general-purpose thesaurus of the domain where the framework will be applied. \\ \hline Technologies & What technologies are used? & It uses TAOM4E, agent-oriented software development, case tools for BPMN modeling, Olivanova, Eclipse, Java, Protege, SOA and Incremental Commitment Model to define the testing strategy. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dimensions of the interoperability IRIS framework.  
The Interoperability IRIS framework not only covers the technical aspects required to achieve EI, but also includes:  
* improvement strategies in the short and medium term (TO-BE situation);
* human resources' view;
* the flexibility of use in scenarios of collaboration other than business such as VT, disaster rescue networks and PVC.