esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Our approach successfully estimates the intrinsic and extrinsic parameters of multi-view cameras in McSL using non-feature-rich multi-view image sequences. The generated multi-layer scene enables disoccluding visual disturbances, such as a surgeon's head, for better surgical field visibility. However, we found several limitations to be addressed that indicate future research directions.  
**Shorter video extraction.** The current calibration procedure assumes that the user extracts a shorter video, in which McSL does not move, from a long video. Although such changes in McSL videos are obvious and easy to detect visually, automation of the process is a reasonable future extension. In the previous approach [1], experts need to align images from McSL by hand, although the resultant alignment is image-to-image registration in 2D. Once a frame chunk is specified, our approach can automate the process, and the resultant alignment is 3D registration and 3D view synthesis.  
**Blurry artifacts.** We observe blurry artifacts in MPI rendering results, especially when there are significant occlusions. The LLFF network, which we rely on for MPI generation, implicitly finds color matches between images. Therefore, ambiguous matches introduced by occlusion pixels can lead to blurred pixels. Denser camera arrangements can provide appropriate plenoptic sampling [29]. However, further quality improvements in MPI inference using non-feature-rich images exceeds our focus and remains for future work.  
**Scale ambiguity.** As pointed out by the medical doctors, scale ambiguity needs to be manually corrected in our system. Camera baselines or scene objects with a known scale can potentially resolve this issue. Whereas the former varies depending on McSL video frames, the latter can be estimated from known faces, hands, and tools in the field of view.
## 2 Materials and Methods  
### Study Location  
This study selected to examine three ORs in the Linkou Chang Gung Memorial Hospital in Northern Taiwan. These three different ORs included (1) a trauma OR using a conventional HVAC system with a make-up AHU (defined as overcooled mixed air below the dew point temperature which removes sufficient moisture by using low temperature chilled water (about 6 to 7 \({}^{\circ}\)C)) (ss-10, mine quality, Hsinchu, Taiwan) and a regular AHU (ss-37, mine quality, Hsinchu, Taiwan); (2) a trauma OR using an LDAC system with a make-up AHU (ss-10, mine quality, Hsinchu, Taiwan), a liquid desiccant dehumidifier (DT-S 6, Ducool, Haifu, Israel), and a regular AHU (ss-37, mine quality, Hsinchu, Taiwan); and (3) an otorhinolaryngology (ENT) OR using an RDAC system (CLCP 025,Trane, Taoyuan, Taiwan) (Figure 1). The three ORs at rest were categorized as ISO 14644-1 Class 7. The two trauma ORs measured 6.6 (L) \(\times\) 5.43 (W) \(\times\) 3.0 (H) m (volume 107.59 m\({}^{3}\)), while the ENT OR measured 6.42 (L) \(\times\) 5.43 \(\times\) 3.0 (H) m (volume 119.12 m\({}^{3}\)). Each OR had four return air vents capable of returning 85% of the total circulating airflow. The return air filter was cleaned weekly, and the high-efficiency particulate air filter (HEPA filter) was changed annually. The indoor thermal-hygrometric design conditions were set at 19-23 \({}^{\circ}\)C for temperature and 55%-65% for RH.  
Figure 1: Design diagram of conventional heating, ventilation, and air conditioning (HVAC) (**A**), liquid desiccant air conditioning (LDAC) (**B**), and rotary desiccant air conditioning (RDAC) (**C**) systems. OA: outdoor air, RA: return air, MAU: make-up air unit, AHU: air-handling unit, OR: operating room.
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
###### Abstract  
With the complexity and variability of data structures and dimensions, traditional clustering algorithms face various challenges. The integration of network science and clustering has become a popular field of exploration. One of the main challenges is how to handle large-scale and complex high-dimensional data effectively. Hypergraphs can accurately represent multidimensional heterogeneous data, making them important for improving clustering performance. In this paper, we propose a hypergraph-clustering method dubbed the \"high-dimensional data clustering method\" based on hypergraph partitioning using an improved Apriori algorithm (HDHPA). First, the method constructs a hypergraph based on the improved Apriori association rule algorithm, where frequent itemsets existing in high-dimensional data are treated as hyperedges. Then, different frequent itemsets are mined in parallel to obtain hyperedges with corresponding ranks, avoiding the generation of redundant rules and improving mining efficiency. Next, we use the dense subgraph partition (DSP) algorithm to divide the hypergraph into multiple subclusters. Finally, we merge the subclusters through dense sub-hypergraphs to obtain the clustering results. The advantage of this method lies in its use of the hypergraph model to discretize the association between data in space, which further enhances the effectiveness and accuracy of clustering. We comprehensively compare the proposed HDHPA method with several advanced hypergraph-clustering methods using seven different types of high-dimensional datasets and then compare their running times. The results show that the clustering evaluation index values of the HDHPA method are generally superior to all other methods. The maximum ARI value can reach 0.834, an increase of 42%, and the average running time is lower than other methods. All in all, HDHPA exhibits an excellent comparable performance on multiple real networks. The research results of this paper provide an effective solution for processing and analyzing large-scale network datasets and are also conducive to broadening the application range of clustering techniques.  
cluster; high-dimensional data; Apriori algorithm; hypergraph partitioning
## 2 Materials and Methods
### General Architecture of the Self-Adaptive Temporary Transtibial Prosthesis  
The prosthetic system was designed to utilize a smartphone as a key component to assist the entire process, from evaluating the limb length discrepancy (LLD) to compensating for this issue faced by prosthetic users.  
The fundamental block diagram of the system for detecting and correcting the simulated limb length discrepancy is presented in Figure 3. The architecture encompasses a microcontroller (1), which orchestrates the functional connections and control processes of the extensible prosthetic module (3) (EPM). The microcontroller is responsible for orchestrating the interaction with the integrated actuator in the EPM via a control driver. This ensures the operation of the EPM's DC motor.  
Figure 3: General block diagram of the prosthetic system.  
The block diagram also incorporates a user-prototype feedback loop facilitated by the functional link between the microcontroller and a notification module (4). This module provides real-time information to the user regarding the operations of the prosthetic system, thus enabling monitoring and adjustment of ongoing processes.  
The detection of LLD is facilitated by a smartphone equipped with a video camera (5). The smartphone can be placed on a tripod or held manually by the user. It captures the necessary data, including a series of reference markers strategically placed on the prosthetic user and on the wall behind the user, and transmits this data wirelessly to a signal processing unit (6), which can be an interface connected to a PC. Subsequent to this, an image processing algorithm is applied, relevant data is extracted, and based on this, commands are sent to the electric actuator within the EPM.  
As a result of these functional connections, the EPM is capable of adjusting the prosthesis length, thereby compensating for the existing lower limb length discrepancy. The entire prosthetic system is powered by an external power source (7) to avoid increasing the weight of the portable system. The displacement performed by the EPM is subsequently measured using instruments to evaluate the accuracy of the adjustments.
## 2 Methods  
### Preparation of Cells and Molecules  
Transfected fibroblast L-cell (EI cells) with a stable human E-selectin and ICAM-1 expression (kindly provided by Dr. [PERSON], University of California Davis, Davis, CA, USA) was chosen as an alternative model for the endothelial monolayer substrate. In previous studies, flow cytometry was used to periodically check the E-selectin and ICAM-1 levels on EI cells to verify the expression levels were comparable with IL-1\(\beta\) stimulated human umbilical vein endothelial cells (HUVECs) [28]. The EI culture medium was prepared by mixing 500 mL RPMI 1640 (GIBCO; Carlsbad, CA, USA) with 5 mg HT (Sigma; St Louis, MO, USA), 5 mL xanthine (Sigma; St Louis, MO, USA), 5 mL mycophenolic acid (Sigma; St Louis, MO, USA), and 5 mL glutamine (Invitrogen; Carlsbad, CA, USA), which was then filtered and stored at 4 \({}^{\circ}\)C. EI cells were put in EI medium with 10% FBS (BioSource; Carlsbad, CA, USA) supplement in a 37 \({}^{\circ}\)C tissue culture incubator with 5% CO\({}_{2}\) until confluent.  
Metastatic Lu1205 melanoma cells (generously provided by Dr. [PERSON], Penn State Hershey Medical Center, Hershey, PA, USA) were cultured in DMEM/F12 medium (Dulbecco's Modified Eagle Medium Nutrient Mixture F12, GIBCO; Carlsbad, CA, USA) with 10% FBS supplement. Prior to flow experiment, Lu1205 cells were cultured in a 37 \({}^{\circ}\)C tissue culture incubator with 5% CO\({}_{2}\) until confluent. Next, 0.05% trypsin/EDTA (Invitrogen; Carlsbad, CA, USA) was applied to the confluent monolayer for detaching the cells, which were then washed twice with fresh culture medium. The cells were suspended in fresh medium at 37 \({}^{\circ}\)C for 1 h and then remixed with DMEM/F12 containing 1% BSA (Sigma; St. Louis, MO, USA). The concentration was adjusted to 1 \(\times\) 10\({}^{6}\) cells ml\({}^{-1}\) for the experiment.  
The separation and the preparation of PMNs from fresh human blood by Ficoll-Hypaque density gradient Histopaque-1077 and Histopaque-1119 (Sigma; St. Louis, MO, USA) followed the protocols described in previous studies [22]. After separation, the PMNs were remixed in Dulbecco's phosphate-buffered saline (DPBS) containing 0.1% HSA (Calbiochem; La Jolla, CA, USA) at a concentration of 1 \(\times\) 10\({}^{6}\) cells ml\({}^{-1}\), which was put in a 4 \({}^{\circ}\)C freezer for up to 4 h to conduct the experiment. The study was specifically approved by The Pennsylvania State University Institutional Review Board (IRB, NO. 19311), and written informed consent was obtained from all subjects. The methods were carried out in accordance with the approved guidelines.  
Prior to the flow experiment, 1 mL soluble Fn was generated by mixing 120 \(\upmu\)L 25 mg/mL soluble Fg (Sigma; St Louis, MO, USA) with 84 \(\upmu\)L 24 mM Gly-Pro-Arg-Pro amide (GPRP, Sigma; St Louis, MO, USA) and 200 \(\upmu\)L 10 U/mL thrombin (Sigma; St Louis, MO, USA) at 37 \({}^{\circ}\)C for 5 min. The role of GPRP was to prevent soluble Fn molecules from becoming polymerized after thrombin cleavage [23].
# Multi-Modal Entity Alignment Method Based on Feature Enhancement
## 3 Experiment and Discussion
### Results and Analysis  
Tables 4 and 5 report the performance of MEAFE on bilingual datasets DBP15K (ZH/JA/FR-EN) and cross kg datasets FB15K-DB15K/YAGO15K. MEAFE performs the best across all the datasets against other baselines. Table 4 reports the performance of MEAFE against the supervised baselines on DBP15K. Compared to MCLEA, MEAFE achieved parity or improvement in all indicators on the DBP15K. Although the improvement is not significant due to the high completion of the model for this dataset, it still proves the adaptability of dynamic graph attention networks for entity alignment tasks, achieving improvements even when only modifying the single-layer network without adding external information. Table 5 reports the performance of MEAFE against the other baselines on the cross kg datasets FB15K-DB15K/YAGO15K. When the training set ratio was set to 20%, MEAFE compared to the optimal baseline (MCLEA), H@1 increased by 0.172, H@10 improved by 0.112, and MRR by 0.152 on FB15K-DB15K, while H@1 increased by 0.179, H@10 improved by 0.104, and MRR by 0.154 on FB15K-YAGO15K. When the training set ratio was set to 50% and 80%, all indicators also achieved a certain improvement. Additionally, as the proportion of the training set decreased, the extent of improvement in the entity alignment \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**DBP15K\({}_{\text{ZH-EN}}\)**} & \multicolumn{3}{c}{**DBP15K\({}_{\text{JA-EN}}\)**} & \multicolumn{3}{c}{**DBP15K\({}_{\text{FR-EN}}\)**} \\ \cline{2-10}  & **_H@1_** & **_H@10_** & **_MRR_** & **_H@1_** & **_H@10_** & **_MRR_** & **_H@1_** & **_H@10_** & **_MRR_ \\ \hline MultiKE [23] & 0.437 & 0.516 & 0.466 & 0.570 & 0.643 & 0.596 & 0.714 & 0.761 & 0.733 \\ HMAN [17] & 0.562 & 0.851 & - & 0.567 & 0.969 & - & 0.540 & 0.871 & - \\ RDGCN [24] & 0.708 & 0.846 & - & 0.767 & 0.895 & - & 0.886 & 0.957 & - \\ AttrGNN [25] & 0.777 & 0.920 & 0.829 & 0.763 & 0.909 & 0.816 & 0.942 & 0.987 & 0.959 \\ BERT-INT [26] & 0.968 & 0.990 & 0.977 & 0.964 & 0.991 & 0.975 & 0.992 & 0.998 & 0.995 \\ ERMC [27] & 0.903 & 0.946 & 0.899 & 0.942 & 0.944 & 0.925 & 0.962 & 0.982 & 0.973 \\ MCLEA [11] & 0.972 & 0.996 & 0.981 & 0.986 & **0.999** & 0.991 & **0.997** & **1.00** & **0.998** \\ MEAFE (Ours) & **0.973** & **0.997** & **0.982** & **0.987** & **0.999** & **0.992** & **0.997** & **1.00** & **0.998** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative results of MEAFE against other baseline methods on three bilingual datasets. The best results are marked in bold and the second best results are underlined.
# Biomass Dynamics in a Fragment of Brazilian Tropical Forest (Caatinga) over Consecutive Dry Years
## 2 Materials and Methods
### Shrub Layer  
To investigate the effect of consecutive dry years on the potential for biomass production in the DTF/CPD, the functional composition of the vegetation was analyzed via a ten-year series (2009-2019). The study period corresponds to a transition between wet (2008-2011) and dry (2012-2019) years; during this period, the area was surveyed six times (September 2009, September 2010, March 2015, March 2016, March 2018 and February 2019).  
Figure 1: Location of the experimental area in the district of Iguatu, Ceará, Brazil.  
Figure 2: Distribution of climate variables in the district of Iguatu, Ceará, Brazil.  
In 2009, eight permanent and contiguous experimental plots of \(10\times 10\) m were installed along the watercourse [21]. All living individuals of the woody component with Diameter at Ground Level (DGL) \(\geq\)3 cm and height (h) \(\geq\)100 cm were marked with identification plates, measured and quantified in each plot [12]. The measurements were made 10 cm above the ground level through markings on the shafts. To characterize the structure of the woody community, the number of individuals and the absolute and relative density were calculated according to the methodology of [PERSON] et al. [22]. The allometric equation developed by [PERSON] and [PERSON] [23] for the Caatinga species was used to estimate the biomass of the woody stratum (Biomass (Mg ha\({}^{-1}\)) = 0.0644 \(\times\) DGL\({}^{2.3948}\)), where DGL is the diameter at ground level.  
All the plants that met the inclusion criteria in 2009 were surveyed between 2010-2019. Plants that had not been registered in previous surveys due to not meeting the inclusion criteria were identified, marked, measured, and considered recruits. Plants measured in previous years, which were not found in later surveys, were counted as dead, together with the dead trees, whether standing or on the ground.
## 1 Introduction
In the case of sensor failure, a trained convolutional neural network was used to recover the missing strain response using functional sensors alone [15]. The application of sensor state self-confirmation technology to dry-type transformer temperature monitoring equipment has application requirements. However, most of the existing research on the fault diagnosis of sensors is only for a single sensor, and there is a lack of research and differential diagnosis for multi-sensor faults. Additionally, the research and application of fault sensor location are insufficient and the accuracy of the fault data recovery is not high, which limits the correctness of the data diagnosis. [PERSON] et al. highlighted the importance of the no free lunch theorem of mathematical optimization [16]. The selection of the model must be aimed at the specific learning problem. The model can only work best if the characteristics of the model match the characteristics of the problem. Sensor data recovery is a typical time series forecasting problem. Its characteristic is that it needs to make trend predictions based on sufficient historical data, fully considering the statistical characteristics and random characteristics. Among the various data prediction models, the RNN neural network can process time-series data. Its variant LSTM further overcomes the problem of gradient disappearance or explosion, which is prone to occur when the RNN processes long sequences. Experiments show that its performance is overall better than the traditional RNN [17]. In summary, this research constructed an algorithm for sensor fault diagnosis and recovery based on the PCA, LSTM neural network, and decision tree and then transplanted the algorithm to the developed platinum resistance dry-type transformer temperature monitor. Using the designed temperature monitor to collect the temperature data, a PCA-based fault diagnosis model and a LSTM-based data recovery model were constructed. A decision tree-based fault location model was constructed based on five typical sensor fault types. Finally, the three models were constructed to obtain the sensor fault diagnosis and recovery algorithm, and the algorithm was transplanted into the developed platinum resistance dry-type transformer temperature monitor. The results of the laboratory simulation and field experiments showed that the algorithm had good effects on the multi-sensor fault diagnosis and data recovery of dry-type transformers. Therefore, it provides a reliable method for ensuring the normal operation of the dry-type transformer temperature monitor.
## 3 Proposed Framework  
As mentioned in the introduction, this study aimed to propose a novel Pa-PCA-Ca ensemble structure for crop type classification in RS satellite data. The outline of the proposed methodology can be seen in Figure 3, which consists of four main steps: (1) dataset preprocessing and preparation, (2) Pa structure, (3) PCA-Ca structure, and (4) accuracy assessment. Each of the steps is going to be elaborated more fully in the following sections. It should be emphasized that the entire procedure was designed based on the capabilities of GEE and fully implemented within this cloud-based platform.
## 3 Discussion
These maps provide information on land use forms, water networks and relief [3; 4]. The disadvantage of this method involves the lack of up-to-date information in the case of standard cartographic materials. In contrast, RS data can be collected repeatedly over time, ensuring a much better accuracy. Geography and related hydrological, hydroclimatic and land-use conditions, along with the changes therein, determine the condition and dynamics of wetlands and their ecosystem services. As noted by [PERSON] et al. (2019) [4], the impact of these dependencies is not limited to the local scale of individual wetlands but extends to larger landscape areas that integrate multiple wetlands and their entire hydrological catchment area--the wetlandlandscape. As an essential element of all wetlands, peatlands are an essential element of the water cycle in the environment. Therefore, the use of RS methods in analysing changes in water resources is more and more prevalent [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72]. Radar data proved invaluable for mapping peatlands in areas where cloud cover is persistent throughout the year [2]. Imaging radars equipped with a C-band are generally not hindered by atmospheric effects and are capable of imaging through tropical clouds and rain showers. Sentinel-1 radar data use a nominal frequency range from a 3.75 to 7.5 cm wavelength within the electromagnetic spectrum's microwave (radar) portion. Their penetration capability with regard to vegetation canopies or soils is limited and restricted to the top layers [91]. Our analysis indicates a considerable interest in the use of radar data after the launch of the Sentinel-1B mission in 2016 (Figure 1; Supplementary Material, Table S2). The stated data were used, for example, in studies concerning the identification of peatlands, estimating carbon resources in peatlands, the monitoring of peatland state, monitoring soil moisture in peatlands, and changes in water conditions in peatlands [26; 37; 38; 58; 70; 77]. In their study, [PERSON] et al. (2019) [58] analysed the impact of the depth of the groundwater level in areas characterised by a high proportion of organic soils. In an analysis of the water table depth (WTD), the authors noted: deep WTD has a low radar wave backscatter, shallow WTD has a high radar wave backscatter, and inundation features have a very low radar wave backscatter. Their analyses of radar remote sensing images show a partial correlation with the results of field measurements [58]. The authors of that study observed the highest correlations at sites with a mean annual WTD of approximately \(-0.40\) m. A distinct decrease in correlation was found at sites with a mean annual WTD deeper than approximately \(-0.60\) m or shallower than approximately \(-0.20\) m. These results may indicate the efficacy of this method in the analysis of the biologically active surface of peatlands.