esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Determination of Propidium Iodide Uptake
In future studies, evaluation of this metabolic damage could be accomplished by a combination of nuclear magnetic resonance spectroscopy and gas chromatography-coupled mass spectrometry, using  
Figure 3: Permeability of _Listeria monocytogenes_ to PI after exposure to the control, pure GSE and pure GE. For GSE, samples are taken after 0 min, 5 min, 10 min, 15 min, 30 min, 1 h, and 2 h. For GE, samples are taken after 0, 12, 18, and 24 h. For the control, samples are taken at all aforementioned time points. Results are expressed as Fluorescence/OD \(\pm\) standard deviation (n = 6). For the same medium (control, GSE, or GE), bars bearing different uppercase letters are significantly different (\(p\leq\) 0.05). For the same exposure time, bars bearing different lowercase letters are significantly different (\(p\leq\) 0.05). principle component analysis to distinguish between the metabolite profiles of treated and untreated bacteria [47]. A different behavior was observed for pure GE. There was an initial presence of dead cells with damaged membranes in GE and the statistical analysis indicates that this membrane damage remained approximately constant over time. This observation suggests that GE does not induce membrane damage to _L. monocytogenes_. In this regard, there have also been no previous reported cases of membrane damage induced to microorganisms by GE. Allicin, which is the main antimicrobial compound in GE, has not been reported to cause membrane leakage, fusion, or aggregation [10]. The antimicrobial effect of allicin is caused by the chemical reaction of allicin with the thiol groups of several enzymes inside the bacterial cells. As a consequence, allicin delays and inhibits DNA, RNA and protein synthesis [10; 48]. Therefore, it can be concluded that the SI caused by pure GE, demonstrated in Figure 2, was of a metabolic nature.
## 3 Results
### Acidifying Properties of Bacterial Strains Isolated within Subsequent Seasons  
All samples showed a high percentage of bacteria from the families Acidobacteriaceae and Acetobacteraceae, families with known acidifying properties. Therefore, to establish if  
Figure 9: Bacterial (**a**) and fungal (**b**) genera dominating across samples. On two heat map-like figures, genera that represent at least 3% (in case of bacteria) or 1% (in case of fungi) relative abundance in each sample are shown. Overall, there are 34 bacterial genera (**a**) that seem to be the most represented. In case of fungal analysis (**b**), 10 different genera are shown. The abundance of each genus above the threshold was colored from green (the smallest percentage) to red (the highest percentage) while the empty cells were colored according to the season, i.e., white–summer (S prefix), grey–winter (W prefix).  
bacteria identified in samples from Pergola's surfaces can demonstrate a biodeteriorative potential, acidifying properties of isolated strains were analyzed.  
Tests were performed for 32 morphologically different, randomly selected bacterial strains isolated (on a medium with calcium carbonate) from bioenoses on the Pergola's surfaces in each season (Figure 10). Most bacterial strains isolated during summer demonstrated ability to acidify the environment (pH of culture medium raised above 7 only for 5 isolates), while strains isolated in winter showed alkalizing properties (only four isolates reduced pH below 6 during 72 h cultivation).
###### Abstract  
Continuous, long-term sequence, land surface albedo data have crucial significance for climate simulations and land surface process research. Sensors such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer (VIIRS) provide global albedo product data sets with a spatial resolution of 500 m over long time periods. There is demand for new high-resolution albedo data for regional applications. High-resolution observations are often unavailable due to cloud contamination, which makes it difficult to obtain time series albedo estimations. This paper proposes an \"amalgamation albedo\" approach to generate daily land surface shortwave albedo with 30 m spatial resolution using Landsat data and the MODIS Bidirectional Reflectance Distribution Functions (BRDF)/ Albedo product MCD43A3 (V006). Historical MODIS land surface albedo products were averaged to obtain an albedo estimation background, which was used to construct the albedo dynamic model. The Thematic Mapper (TM) albedo derived via direct estimation approach was then introduced to generate high spatial-temporal resolution albedo data based on the Ensemble Kalman Filter algorithm (EnKF). Estimation results were compared to field observations for cropland, deciduous broadleaf forest, evergreen needleleaf forest, grassland, and evergreen broadleaf forest domains. The results indicated that for all land cover types, the estimated albedos coincided with ground measurements at a root mean squared error (RMSE) of 0.0085-0.0152. The proposed algorithm was then applied to regional time series albedo estimation; the results indicated that it captured spatial and temporal variation patterns for each site. Taken together, our results suggest that the amalgamation albedo approach is a feasible solution to generate albedo data sets with high spatio-temporal resolution.  
Keywords:land surface albedo; time series; high spatio-temporal resolution; EnKF +
Footnote †: journal: Remote Sensing, 2019, 11, 753; doi:10.3390/rs11070753
## 1 Introduction
The connection of having common decay amplitudes in the computed NMEs of the two processes prompted the search for the potential correlations and trends between \(0\
u\beta\beta\)-decay NMEs and OMC rates/NMEs, as presented in References [10; 11; 12; 32]. Such connections can be used to determine the accuracy of the computed \(0\
u\beta\beta\)-decay NMEs. In Reference [32], average OMC NMEs and \(0\
u\beta\beta\)-decay NMEs for key \(0\
u\beta\beta\)-decay candidates including \({}^{136}\)Xe (the focus of this work), were compared in the framework of pnQRPA, and systematic correspondences were observed between the two. The NMEs were compared in order to minimize the kinematic and phase-space effects in the anticipated correlations. Given the trends observed in Reference [32], we anticipate seeing this correspondence map to correspondences between OMC rates and \(0\
u\beta\beta\)-decay NMEs. Evidence for such a connection is foreshadowed from trends between OMC rates and \(2\
u\beta\beta\)-decay NMEs, as presented in References [10; 11; 12]. Correlations between OMC rates and \(0\
u\beta\beta\)-decay NMEs are of high interest, as this can be a direct bridge between experimental OMC rates and theoretical \(0\
u\beta\beta\)-decay NMEs. The focus of this work is to further elucidate this bridge in the context of \(0\
u\beta\beta\) decaying \({}^{136}\)Xe, using, for the first time, the pnQRPA and nuclear shell model (NSM) together in OMC and non-closure \(0\
u\beta\beta\) formalisms. We compute the \(0\
u\beta\beta\)-decay amplitudes in the above frameworks and compare them with results obtained for the OMC rates of \({}^{136}\)Ba, as presented in Reference [33], using the same nuclear models. In using different nuclear models, one can see if the potential correlations are model-independent and follow the similarities and differences in the trends that emerge.
# Factors of the Technology Acceptance Model for Construction IT
## 3 Research Model and Hypotheses
### Analysis Results on TAM Variables in Construction IT  
#### 4.3.1 Analysis about External Variables  
We conducted a multiple regression analysis of perceived ease of use and usefulness based on the external variables and the TAM to verify the correlation between the external variables (hypotheses H1a to H6b) impacting construction IT acceptance.  
The external variables proportional to the perceived ease of use are acceptance type, frequency of use, educational satisfaction, usage knowledge, and usage enjoyment, whereas usage experience is inversely proportional to the perceived ease of use. By looking at the beta values that have a similar impact on the perceived ease of use, we found that there is a correlation among usage enjoyment, educational satisfaction, and usage knowledge, in that order. Here, the beta values denote a normalized coefficient of the linear regression analysis result.  
The external variables proportional to perceived usefulness are acceptance type, educational satisfaction, usage enjoyment, and usage experience, whereas usage frequency and usage knowledge are inversely proportional to perceived usefulness. By looking at beta values having an impact on perceived usefulness, we found that there is a correlation among the usage enjoyment, usage experience, acceptance type, and educational satisfaction, in that order. However, we found that usage frequency had nothing to do with either perceived usefulness or ease of use.
# A Channel Measurement-Based Listen-Before-Talk Algorithm for LTE-LAA and WLAN Coexistence
## 2 Related Work
### Load-Based LBT Schemes  
The LB-LBT scheme was proposed for its flexible spectrum utilization and adaptability to varying traffic loads [7; 8]. Figure 1 illustrates the operation of the LB-LBT scheme in the unlicensed band. The scheme employs backoff functions similar to those used in WLAN.  
The LTE-LAA eNB first listens to the target channel to detect the energy level over a time period equal to a distributed inter-frame space (DIFS). If the detected energy level is below the predefined CCA threshold, the LTE-LAA eNB proceeds with transmission. Otherwise, if the channel is sensed as busy, it continues monitoring until the channel is deemed idle for a DIFS. Once the channel is idle, the LTE-LAA eNB selects a random number between 0 and the predefined contention window size, counting down that number of idle slots before initiating transmission. Unlike WLAN nodes, which use a binary exponential backoff in the event of a collision, the LTE-LAA eNB maintains a fixed contention window size regardless of collisions.  
Ref. [7] simulated a coexistence scenario between LB-LBT-based LTE-LAA and WLAN, analyzing how the contention window size and CCA threshold of LTE-LAA eNB affect throughput performance. Ref. [8] proposed a mathematical model to analyze the throughput of LTE-LAA and WLAN systems with respect to the contention window size of LTE-LAA eNB employing the LB-LBT scheme.
## 4 Conclusions
The study highlights the temporal variation characteristics of DWL detection efficiency at Gonggar Airport and its relationship with meteorological factors, providing crucial data support for the application of the DWL at plateau airports. However, since the DWL system was deployed for the first time at Gonggar Airport and the study covered only 9 months of observations, the focus was primarily on analyzing monthly variation patterns. A seasonal variation analysis could not be thoroughly conducted. Future work will involve longer-term DWL observation data, combined with aerosol and sounding data, to further refine the analysis of detection efficiency variations under different seasons and meteorological conditions. This will offer a more comprehensive theoretical framework for applying DWL in complex plateau airport environments. Conceptualization, J.W. and H.D. ; methodology, J.W., H.D., C.X., and X.H. ; software, H.D. ; validation, J.W. and H.D. ; formal analysis, J.W. and H.D. ; writing--original draft preparation, J.W. ; writing--review and editing, J.W., H.D., and C.X. All authors read and agreed to the published version of this manuscript. This research was funded by the the National Key Research and Development Program of China (Grant No. 2021 YFB2601701), the Open Foundation of Key Laboratory of Poyang Lake Wetlandand Watershed Research, Ministry of Education (Grant No. PK2023011), and the Fundamental Research Funds for the Central Universities (Grant No. JC2022-24). Not applicable. **Data Availability Statement:** The data used in this paper can be provided by [PERSON] ([EMAIL_ADDRESS]) upon request. **Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Results Analysis
### Ablation Experiment  
Building on the strong performance of the PRI-SWT model, ablation experiments were conducted to further investigate which design choices most significantly contribute to its classification accuracy. These experiments are crucial for optimizing model architecture and understanding the impact of various components. The focus was on evaluating different fusion network architectures--comparing convolutional versus linear mapping layers to integrate real and imaginary information--as well as assessing the influence of moving window size, network depth, and embedding vector dimensionality within the PRI-SWT model.
## 2 OpenFOAM Tritium Transport Model Upgrades
### Residence Time Release Model  
The previously described porosity model is a prerequisite for the following release model. The release model simulates the release of tritium from the solid fraction (i.e., the pebble bed) to the fluid fraction (i.e., the purge gas) of the same cell of a porous fluid region.  
The tritium concentration in the solid fraction is given by a simple residence time equation:  
\[\frac{\partial c}{\partial t}=\text{Q}-\frac{c}{\tau} \tag{4}\]  
The tritium concentration in the solid fraction is \(c\), Q is a tritium source term due to neutronic radiation, and \(\tau\) is the residence time--a quantity that usually is determined from tritium release experiments (e.g., EXOTIC [12]). Equation (4) contains no transport terms and may be either solved easily element by element or in the frame of the OpenFOAM equation formalism. We selected the latter option, though in this case the matrix formalism would not be required.  
The release term \(c/\tau\) has to appear in the fluid equation as an additional source term in Equation (3). A factor of (1\(-\varepsilon\))/2\(\varepsilon\) takes into account the solid vs. fluid volume fractions and the factor two corrects for atoms vs. molecules in the case that release is done in the form of the pure species molecule T\({}_{2}\). The model may alternatively release to the mixed-species molecule. In this case, the user has to make sure that enough of the second species (H\({}_{2}\)) is present to avoid negative concentrations (which is usually no issue for a H\({}_{2}\)-doped purge gas).
## 5 Training Methodology
### Semantic Parsing Model  
We fine-tune a multilingual T5 model (mT5-Base) using a sequence-to-sequence formulation. Input tokens consist of serialized dialogue and patient features, while outputs are linearized EUREKA schema instances. We apply constrained decoding to enforce type and slot consistency.  
TriagE-NLU utilizes the multilingual T5 model (mT5-Base) fine-tuned to perform semantic parsing of emergency teleconsultation dialogues into structured EUREKA frames. The input to the model is a dialogue transcript, and the output is a serialized EUREKA slot structure representing the extracted clinical semantics. The fine-tuning was conducted on the synthetic and weakly labeled multilingual data described in Section 5.1 using a text-to-text paradigm.  
To improve parsing consistency and ensure adherence to the structured EUREKA schema, we implemented a slot-constrained decoding mechanism during inference. This mechanism restricts the model's output space to valid slot sequences and slot values based on pre-defined constraints. These constraints were defined through a set of deterministic rules that enforce schema compliance and semantic correctness.  
The development of decoding constraints followed a rule-based specification informed by the design of the EUREKA annotation schema. For instance, slots such as symptom, onset_time, urgency, and intervention were each associated with specific constraints. These included a closed or semi-closed list of acceptable values (such as \"chest pain\", \"slurred speech\", or \"30 min\"), type-specific formatting rules (for example, expressing onset time in minutes or hours, or using Boolean indicators for presence or absence), and logical dependency rules that enforce clinical consistency, such as requiring a high urgency level to be inferred when both slurred speech and unilateral weakness are present.  
The decoding rules were validated through simulation and backtesting on a subset of the synthetic dataset where the expected output structure was known. During decoding, if the model attempted to produce invalid or incomplete slot-value pairs (e.g., an unrecognized symptom or malformed time expression), the decoder would dynamically adjust the output beam to select a valid alternative, prioritizing candidates that matched allowable slot configurations.  
An example English input is as follows:  
Patient: \"She suddenly couldn't speak and couldn't move her right arm. It's been about 15 min.\"  
The model, aided by constrained decoding, generated the following valid frame: symptoms \"slurred speech\", \"right arm weakness\", onset time \"15 min\", urgency \"critical\" and intervention \"activate stroke protocol\".  
Without slot constraints, the model sometimes produced inconsistent frames, such as \"symptoms\": \"arm problem\" or omitted essential fields, which compromised usability. The reduced constraints have the role of reducing errors. In summary, slot-constrained decoding ensures structural fidelity, prevents schema violations, and allows reliable use of mT5 for multilingual clinical parsing across the diverse linguistic inputs targeted by TriagE-NLU.
# Performance Analysis of RIS-Assisted SatComs Based on a ZFBF and Co-Phasing Scheme
## 3 Active Beamforming and Phase Shift Design  
We provide an asymptotic analysis of \(\gamma_{k}\) in the following two scenarios: (i) full CSI is available at the ES and (ii) only partial CSI composed of the deterministic information, such as the locations of satellites and UEs, is available at the ES.
## 4 Results and Discussion
### 34 and 85 Node RDNs - \(Cs_{2}\)
This loss reduction only corresponds to 12% and 12.5% for active and reactive losses, respectively, for the April-June season, as shown in Table 8. Consequently, the annual cost reduction is USD 70,058/year for use case 1. Due to reasons discussed earlier, the \(S_{2}\) produced improved results and are discussed in Table 9.  
Results of \(N_{2}\) for \(S_{2}\) are shown in Table 9. For April-June season, sizes of optimal capacitors are updated at the same location. Moreover, for \(N_{2}\), some new capacitors are needed to be installed at new locations for this specific season to ensure better annual cost savings in both use cases. Unlike \(S_{1}\), after optimization, the reduction in active power loss is 30.8%, which is 12% in case of \(S_{1}\) (use case 1). Moreover, for (April-June) reactive power loss is reduced to 27.9%, which is 12.5% in case of \(S_{1}\). Comparing both scenarios, results of \(S_{2}\) are better than \(S_{1}\) in all aspects shown in Table 9. Unlike \(N_{1}\), installation of capacitors at new location in \(N_{2}\) may need better optimization. However, as per common practice, the capacitors are installed at various locations and brought into the operation as and when needed. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Months**} & \multicolumn{4}{c}{**Uncompensated**} \\ \cline{2-5}  & **January–March** & **April–June** & **July–September** & **October–December** \\ \hline Active loss (kW) & 221.7 & 782.1 & 674 & 523.5 \\ Reactive loss (kvar) & 65.1 & 229.3 & 198 & 153 \\ Total cost (USD (\$)/year) & 37,241 & 131,394 & 113,372 & 87,991 \\ \(V_{min}\) at node & 0.94 (27) & 0.89 (27) & 0.88 (27) & 0.90 (27) \\ \(V_{max}\) at node & 0.99 (2) & 0.99 (2) & 0.99 (2) & 0.99 (2) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Base data (uncompensated) of \(N_{1}\) for \(CS_{2}\). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Months**} & \multicolumn{4}{c}{**Uncompensated**} \\ \cline{2-5}  & **January–March** & **April–June** & **July–September** & **October–December** \\ \hline Total active loss (kW) & 319.2 & 1436.9 & 717.6 & 595.7 \\ Reactive loss (kvar) & 182.1 & 810.9 & 407.7 & 338.9 \\ Total cost (USD (\$)/year) & 53,619.6 & 241,406.4 & 120,556.7 & 100,082.8 \\ \(V_{min}\) at node & 0.85 (76) & 0.67 (76) & 0.77 (76) & 0.79 (76) \\ \(V_{max}\) at node & 0.99 (2) & 0.99 (2) & 0.99 (2) & 0.99 (2) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Base data (uncompensated) of \(N_{2}\) for \(CS_{2}\).
## 3 Results
### Experimental Considerations
#### 3.3.3 Angular Resolution  
The step size of the rotary stage used in these measurements was 0.2\({}^{\circ}\). However, as seen in Figure 2, the angular resolution \(\theta_{\text{res}}\) of the measurements was limited by the diameter of the detector aperture and the distance between the emitter and detector. The aperture of the Golay detector was 11 mm and its distance from the emitter was 60 mm. Using these values, the acceptance angle of the detector is calculated to be 10.4\({}^{\circ}\). To confirm this estimate, additional measurements were made at distances of 35 mm and 70 mm from the emitter, where the data at 35 mm should have an acceptance angle approximately double that at 70 mm. An extrapolation procedure was then applied to the 70 mm data to obtain an estimated profile for 35 mm, which allows to compare it with the experimental data measured at 35 mm distance. The results of this test at 600 GHz are shown in Figure 7a,b for both orientations. Both extrapolated profiles agree quite well with the measured data. Small differences in the profiles may be attributed to the aperture of the Golay being circular, whereas a square aperture was assumed in order to simplify the calculation.  
Simulations of the effect of angular resolution on the observed beam profile are shown in Figure 8a-f. A variety of model beam profiles were generated with a resolution of 0.2\({}^{\circ}\), including a single Gaussian, a skewed Gaussian, and Gaussians with additional lobes. These profiles were then extrapolated to show their appearance with a coarser angular resolution. The following can be concluded from these simulations:  
1. In the case of a single central peak (FWHM \(>\)\(\theta_{\text{res}}\)), whether a perfect (Figure 8a) or a skewed (Figure 8b) Gaussian, coarser resolution will cause negligible distortion or none, even when resolution \(\theta_{\text{res}}\) is reduced by a factor of 10.
2. When narrow features are added to the curve (Figure 8c-f), then angular resolutions greater than the width of the feature (FWHM \(<\)\(\theta_{\text{res}}\)) cause broadening and flattening of the feature.  
Figure 6: Beam profiles measured at varying distance between the emitter and the detector, at 600 GHz for: (**a**) E-plane orientation and (**b**) H-plane orientation.