esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Errors in Satellite-Based LE Products  
The satellite-based LE models, the simplified equations of the physical process, are widely developed and used to understand the process of evaporation and transpiration. However, different models' structure may lead to considerable uncertainties in LE estimations. In our study, six satellite-based LE products were generated with different strategies including empirical methods (e.g., machine learning method) and process-based methods. Validation results illustrated that both GLASS and FLUXCOM LE products based on the empirical methods outperformed other four process-based LE products (PML, GLEAM, BESS and MODIS) at site scale. This may be attributed to the fact that both GLASS and FLUXCOM LE products make full use of EC observations (more than 200 sites) distributed around the world [18; 32]. GLASS LE product integrates multiple single LE process-based models by BMA method and FLUXCOM LE product uses the machine learning methods to estimate LE [18; 32]. GLEAM LE product applies data assimilation method into Priestley-Taylor model to capture accurate soil moisture evaporation, contributing to more accurate LE [26]. PML LE product takes advantages of EC data from 95 widely-distributed sites to calibrate model, leading to better performance that BESS and MODIS LE products at site scale [30]. On the other hand, based on our water balance validation, BESS LE product has the best performance among all six products. This may be caused by the fact that BESS LE  
Figure 9: Spatial differences in the average annual terrestrial LE (2013–2015) between GLASS LE and other five LE products.  
product uses a carbon-water-coupled module, which includes better physical mechanism with higher accuracy at basin scale [28]. MODIS LE product improves estimates of LE based on Penman-Monteith equation at global or regional scale [20; 59].  
The model input data, such as satellite and meteorological data, also has a great influence on the accuracies of LE products. Previous substantial studies has reported that vegetation parameters derived from satellite data had approximately 15-30% relative error [5; 60]. And the classification accuracy of the land cover data from remote sensing data is general less than 78% [61]. More importantly, error in surface net radiation (Rn) data can introduce large biases into LE estimation. The Rn has a large influence on the partition of LE and H that is the energy source of LE [60]. Meanwhile, Rn is susceptible to the influence of clouds sky, leading to the overestimation of Rn [62]. Moreover, the meteorological data with low spatial resolution has large errors compared with ground measurement [63; 64]. [PERSON] et al. has found that there is no most accurate meteorological reanalysis data for estimating land surface energy budgets [65]. Therefore, the errors in the input data may cause large errors in LE products.
# Synchronization of Acoustic Signals for Steganographic Transmission
## 4 Technique Development and Implementation
### Direct Spread Spectrum
At the same time, an additional condition is introduced for the DSS signal related to the correction of the signal energy. The correction factor \(C_{i}\) (Equation (1)) is chosen such that for each signal frame the following condition is satisfied:  
\[10\log_{10}\frac{\sum\limits_{n=0}^{N-1}\left(x_{i}^{s}(n)\right)^{2}}{\sum \limits_{n=0}^{N-1}\left(DSS_{i}(n)-x_{i}^{s}(n)\right)^{2}}\leq M, \tag{7}\]  
where:  
\(x_{i}^{s}(n)\)--i-th frame of steganographic signal, noise signal for DSS signal,  
\(DSS_{i}(n)\)--i-th frame of DSS signal. Figure 11: The signal at the output of the low-pass filter in the time domain (upper picture) and frequency domain (lower picture). In the receiving part, the synchronization procedure is based on a system similar to that shown in Figure 3. The difference is in the different principle of the time synchronization block. The time synchronization procedure consists of determining the value of the cross-correlation function between the received signal (corrected by the determined correction of the phase angle) and the reference signal generated on the receiving side. The received signal is \"shifted\" relative to the reference signal. The signals are considered synchronized when the value of the cross-correlation function reaches a maximum for \(\tau\) = 0 s. Figure 12 shows an example of the course of the cross-correlation function value determined in the time synchronization block.
## 4 Results
### Performance in Hand-Object Interaction Detection--Experiment B  
To evaluate MiDaS in the hand-object interaction detection application, we compared the number of detected interactions based on the RealSense depth data and estimated depth by MiDaS (Table 4).  
We mentioned that we recorded 100 interactions, but as you can see in the Table 4, there are 86 interactions because we focused only on the interactions where the algorithm could detect both the hand and the object in the same frame.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **RealSense** & **MiDaS** \\ \hline
**TP** & 81 & 77 \\ \hline
**FP** & 28 & 34 \\ \hline
**FN** & 4 & 9 \\ \hline
**Total No. of Interactions** & 86 & 86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of Confusion matrices for two selected methods.  
Figure 13: Success of the method in estimating the depth corresponding to the distance ratios between objects for each lighting condition.  
For hand-object interactions, it is important for a reliable performance that the system does not detect activity that does not exist. Therefore, it is especially important that there are fewer FP cases. To determine the reason for the 34 cases in MiDaS, we reviewed the images that were incorrectly detected as interactions. As you can see in the Figure 14, there is an interaction between hand and apple, but no interaction between hand and cup. Since we have two criteria to report the interaction, we can see the difference in depth between the hand and the cup. Also, the difference between the pixels is very small, so the system incorrectly detected this as an interaction between the hand and the cup. Fortunately, this kind of FP cases can be mitigated by adding a third criterion, such as the speed of the hand and the object. So, in this case, if we compute the velocity of the apple, the hand and the cup, we will see that the system can easily detect the interaction between the hand and the apple and not with the cup in these images, since the velocity of the cup is about zero in the different images.  
More importantly, the system is able to detect the difference in depth between different objects and the hand, as confirmed by the results in the table. This is because in order to report the interaction (TP), the system must be consistent in measuring the difference of depth between the object and the hand.  
The Table 5 shows different performance metrics. We can see that the performances of MiDaS and RealSense are quite similar.  
From the above results, we can see that the MiDaS and RealSense results are quite similar if we consider the RealSense results as a kind of ground truth. Precision indicates the percentage of all positive detections that are truly positive: 74% for RealSense and about 70% for MiDaS. Recall, which indicates the percentage of all positive detections that are correct, is also about 90% for the methods. Moreover, the F1 score for RealSense and MiDaS are quite good and close to each other, namely 90% for MiDaS and 95% for RealSense.
## 2 Materials and Methods
### Endpoint Localization Method
#### 2.4.3 Localization  
After wavelet decomposition, \(D_{1}\) appears as short-length high-frequency noise segments near the endpoints, and the values of \(D_{1}\) are small in the non-endpoint range. The endpoints can be located by identifying the center of each noise segment in \(D_{1}\). A windowed moving average is performed on \(D_{1}\) as (31), which converts the irregular noise segments into peaks. Then, the endpoints can be easily located by identifying the midpoints of peaks in \(D_{1}\).  
\[D_{1}(k)=\sum_{k-width}^{\overline{k+width}}\lvert D_{1}(i)\rvert \tag{31}\]  
The endpoints positioning process needs to be conducted on the velocity signals of the X, Y, and Z axes, respectively. Considering the positioning results in the three directions, including the number of positioning points and the width of the wave crest, the final positions of the endpoints are obtained.  
With the endpoint localization method, vibration signals of the moving process can be cut into pieces for each action, which makes it possible to score parts of the robotic arm movements.
# Near-Optimal Active Learning for Multilingual Grapheme-to-Phoneme Conversion
## 6 Experiments
### G2P Experimental Results  
In this paper, 9620 dictionary corpa in the pronunciation dictionary were divided into two parts--90% training data and 10% testing data for the G2P experiment. The results are shown in Table 3.  
As shown in Table 3, the \"word error rate (WER)\" for Tibetan was 4.16%, indicating a satisfactory performance of the G2P model in the Tibetan script. However, some errors still existed. We focused on data for prediction errors, and the sources of error included ambiguity introduced by context, i.e., a lexeme with multiple valid phonemic interpretations. The WER for Chinese was 0.7%, which is an exceptionally low value that highlights the remarkable performance of the G2P model in the context of Chinese. This suggests a relatively straightforward mapping between Chinese characters and their phonemes. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Train Data** & **Test Data** & **Epoch** & **WER** \\ \hline Tibetan & 2331 & 259 & 20 & 4.16\% \\ \hline Chinese & 2592 & 288 & 20 & 0.7\% \\ \hline English & 1035 & 115 & 20 & 13.96\% \\ \hline Korean & 2700 & 300 & 20 & 5.53\% \\ \hline Multi-language & 8658 & 962 & 20 & 6.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results.  
WER for English is 13.96%, signifying more errors compared with other languages. Potential sources of errors encompass ambiguity in English spelling, where the same morpheme can have different pronunciations (e.g., \"read\" pronounced as \"reed\", while \"read\" is pronounced as \"red\"). Irregularities in English pronunciation, where certain words deviate from typical phonetic patterns, also contribute to errors. The multi-language WER for Korean was 5.53%, indicating a moderate level of errors. Some potential sources of errors stemmed from the intricate phonetic rules in Korean, such as consonant assimilation or vowel harmony. Pronunciation ambiguity arises as pronunciation can vary based on context. The multilingual case (average WER across the four languages) was 6.2%. The average WER across different languages provides a holistic assessment. It is worth noting that distinct languages possessed varying features and grammatical rules, leading to noticeable differences in error distribution. However, this relatively low average value suggests a reasonably good performance of the G2P model across different languages.
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 4 Effect of Process Parameters on Biomass Chemical Looping Gasification
### Steam-to-Biomass Ratio  
Steam-to-biomass ratio (SBR) refers to the amount of steam the mass of biomass fed into the gasifier. SBR is a key parameter strongly affecting hydrogen production and carbon conversion efficiency, as well as total gas yield. High SBRs result in a lower amount of unreacted char and greater both the yield and concentration of hydrogen in the product gas. Many researchers have reported that increasing SBR leads to a rise in hydrogen production and carbon conversion efficiency, as well as a low amount of tar produced. Figure 11 shows that hydrogen content is proportional to the ratio of steam-to-biomass.  
In the presence of high steam content in the gasifier, there is an increase in both the yield and content of hydrogen. Along with the increase in hydrogen production, process efficiencies have been improved corresponding to higher SBR. It should be noted that CO content decreases with higher SBRs. These tendencies are probably attributed to water gas shift reaction, char gasification reaction and steam reforming reaction. However, the undesired rise in CO\({}_{2}\) content has been reported which could be problematic in hydrogen-enriched gas production. This fact can be evidenced from study results [27; 34; 42; 114; 117; 118].  
Along with the enhancement of hydrogen production, the higher amount of steam used in the gasifier promotes tar decomposition, which is one of the most serious issues in biomass gasification. It is likely attributed to tar cracking reactions with the presence of steam. [PERSON] [119] found that steam could strengthen participation of tar in steam gasification which leads to the decrease in tar content at higher SBR. Consequently, hydrogen and total gas yields can enhance as products of tar cracking reaction (tar reforming reaction). The addition of steam mainly brings about favorable aspects. Yet, in a large-scale process steam addition is one of the main sources of energy consumption. Hence, one would always try to minimize steam addition to the lowest possible level.  
Figure 11: Hydrogen concentration as a function of steam-to-biomass ratio.
# Experimental and Numerical Evaluation of a Wildland-Urban Interface Fire Scenario
###### Abstract  
This paper presents the results obtained from a field fire test, aiming to reproduce a wildland-urban interface scenario to collect relevant information concerning the impact of wildfires on the built environment. The objective was to understand heat transfer mechanisms from forest fires to structures. During the fire test, the temperatures at the exposed face of one building component were monitored, as well as those in the vicinity of that component, using thermal imaging. The detailed characterization of the field test and building component and obtained experimental results of the fire test were then used to develop and validate a complex computational fluid dynamics model (full physics models) using the Fire Dynamics Simulator (FDS). Several numerical models were previously developed to reproduce the behaviour of individual shrubs and trees in fires considering available results in the literature. The developed Computational Fluid Dynamics (CFD) models can accurately reproduce the field test, including the fire spread and the temperature evolution on the surface of the exposed construction component. The obtained maximum temperature in the construction element was 1038 \({}^{\circ}\)C, whereas the maximum average temperature was approximately 638 \({}^{\circ}\)C. According to the results from the numerical model, the construction element was exposed to a very high heat flux (above 40 kW/m\({}^{2}\)), indicating direct contact of the flames with the construction element. The use of CFD enables the quantification of the characteristics of the fire and the exposure of structures to fire in the wildland-urban interface (WUI), allowing for the definition of a performance-based design approach for buildings in the WUI. This contributes to developing safe and resilient structures, as well as mitigating and reducing the impacts of wildfires in the built environment.  
wildfires; wildland-urban interface fire spread; modelling; CFD; performance-based design Article
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Distance Functions
#### 2.2.2 Dynamic Time Warping Distance
Moreover, as \(\beta\) increases above a certain (instance-dependent) threshold, \(\beta\)-DTW becomes a lockstep distance and essentially coincides with the Euclidean distance.
# Evaluating the Feasibility of Intelligent Blind Road Junction V2I Deployments
## 2 Intelligent Perception System (IPS) Overview
### Measurement Scenarios
#### 2.1.1 Configuration 1: Edge Computing  
The first configuration of the IPS that will be evaluated, illustrated in Figure 3, is where the computational resources are integrated within the FSN(s) or located nearby in the form of an edge server [39]. This configuration could be considered the default choice as advanced sensing systems are often deployed with their computational resources integrated into the same physical equipment. In this configuration, the sensor data are sent to the local computational resources, to be processed and converted into an object data format. This object data format represents, in a local or global coordinate system, the distinct objects within the region of interest that are of note to CIVs, e.g., other vehicles, pedestrians, cyclists, animals, debris, or other possible hazards. The processed object data are then broadcast to the CIVs subscribed to the IPS, informing them of the content in the region of interest.  
This configuration should have a significant advantage in terms of network performance, as the data can be sent in the form of ITS-specific packets such as Cooperative Awareness Messages (CAMs) [46] and Decentralised Event Notification Messages (DENMs) [47], which are small packets, on the order of hundreds of bytes. Thus, the latency of packets can be kept low, and the throughput requirements are also quite low. However, this configuration should also incur a significant cost, particularly regarding maintenance, as the computational resources would require substantial power, climate control, robustness, and security. Additionally, in this configuration, the system responsible for processing the sensor data is now also responsible for the quality of the decisions that can be made by the CIVs, which may not be desirable.
## 4 Antibofilm Coatings Based on Antimicrobial Enzymes
coli_ biofilm was decreased from 88% for the bare SIBS control to only 0.3% for the SIBS-_g_-PCA-DNase, while the _S. aureus_ biofilm was reduced from 68.1% of the pristine (unfunctionalized) SIBS support to 2.6% of SIBS-_g_-PCA-DNase. However, the inactivation of DNase coating resulted in a dramatic increase in the level of substratum coverage by _E. coli_ biofilm. RNase-functionalized SIBS substrates were proved to be more effective against _E. coli_ (proportion of the substratum surface contaminated with biofilm bacteria equals about 1.5%) than against _S. aureus_ (21.4% of the substratum area was covered by bacterial biofilm). Unlike DNase-functionalized SIBS, the antibacterial activity of SIBS-_g_-PCA-RNase was independent of nuclease activity, proving that the capacity of SIBS-_g_-PCA-RNase to inhibit bacterial adhesion was mainly due to its hydrophilicity. Furthermore, the authors tested the capacity of these nuclease coatings to reduce the interactions of host proteins with the surface of SIBS medical devices, thereby avoiding the formation of a conditioning film and further bacterial colonization. Investigating the adsorption of bovine serum fibrinogen (BFg), the authors found out that the amount of adsorbed Bfg was reduced from 6.5 ug/cm\({}^{2}\) for unfunctionalized SIBS to about 1.8 ug/cm\({}^{2}\) for SIBS-_g_-PCA-DNase and to less than 1.5 ug/cm\({}^{2}\) for SIBS-_g_-PCA-RNase. Using a synthetic strategy, [PERSON] et al. (2014) immobilized DNase I on polymethylmethacrylate (PMMA) substrate via a dopamine intermediate coupling layer. Initial bacterial adhesion to the DNase I-coated PMMA substrate was strongly inhibited compared to inactive control substrates, namely bare PMMA, dopamine-coated PMMA, and heat-inactivated DNase I-coated PMMA. The bacterial cells adhered to the substrate surfaces during the first 60 min were counted using fluorescence microscopy after staining the biofilm slime with live/dead stain (BacLight(tm) Bacterial Viability and Counting Kit). Coating of the PMMA substrate with active DNase I produced a reduction of the number of adhering bacteria by 99% for _P. aeruginosa_ PAO1 and 95% for _S. aureus_ ATCC 12600 compared to controls. Moreover, biofilm growth was monitored by confocal laser scanning microscopy (CLSM). CLSM images of biofilms developed on the substrata showed average biofilm thicknesses of 10 um and only of 0.2 um for _P. aeruginosa_ PAO1 biofilms grown on inactive control substrata and on DNase I-coated PMMA substrate, respectively. Similarly, in the case of _S. aureus_ ATCC 12600, biofilm thickness was reduced from 18 um for controls to 3 um for the active DNase I coating. Biocompatibility of DNase I coatings with mammalian cells was assessed in vitro using human osteosarcoma U2 OS cells. Neither adhesion nor proliferation of U2 OS cells onto DNase I coated PMMA substrate was affected.
###### Abstract  
People spend two-thirds of their time in buildings. Building materials are, therefore, natural shielding for us. Many studies describe the shielding effect of non-building materials. This study evaluates the shielding effectiveness (\(SE\)) of electromagnetic fields for various building materials over a frequency range of 1 GHz to 9 GHz. Measurements of SE, reflection (\(R\)), and calculated absorption (\(A\)) were conducted to determine the shielding properties of mineral wool (MW), hardened polystyrene (PT), extruded polystyrene (PE), polyurethane board (PUR), brick wall (BW), brick wall filled with mineral wool (BW-MW), and concrete wall. The results demonstrate that MW, PT, PE, and PUR exhibit low \(SE\) and \(R\), indicating minimal shielding capabilities, with absorption values that do not significantly deviate from the level of measurement uncertainty. Conversely, BW, BW-MW, and concrete wall materials exhibit high SE, with notably increased absorption at higher frequencies, highlighting their potential for effective EMI shielding. Particularly, the concrete wall presents the highest absorption values, making it a superior choice for shielding applications. Reflection trends revealed a plateau for BW in the 6 GHz to 9 GHz range, indicating a frequency-dependent behavior of shielding mechanisms. This study underscores the importance of balancing reflective and absorptive properties in shielding materials and suggests that composite materials may offer enhanced performance. The findings of this research provide guidance for the selection and design of shielding materials in environments with a frequency spectrum of electromagnetic frequencies from 1 GHz to 9 GHz.  
Keywords:construction materials; electromagnetic interference; shielding effectiveness; reflection; absorption; frequency-dependent behavior +
Footnote †: journal: Applied sciences
## 3 Experimental Results and Discussion  
As shown in Figure 4, the self-developed autofocus system for NMFP mainly consists of a non-mydriatic fundus camera (860B; MEDA, Tianjin, China), an industrial CCD sensor (MUX3S640C; CatchBest, Beijing, China), a 3D translational stage, a stepper motor module (7 TRSM424X with RS-485 communication; Shidaichaoqun, Beijing, China), a driving power (HY3005B; Huayi Electronics, Hangzhou, China), and a PC. In this system, the fundus camera previews the retina through near-infrared illumination (680-830 nm) and switches to white light illumination (580 nm) to obtain fundus images (1280 \(\times\) 1024 pixels). The stepper motor module (driven by the power) changes the object distance by shifting the camera's objective lens under the PC control, thereby realizing fundus autofocus within the range of [\(-\)16 D, \(+\)16 D]. The PC's specifications include an Intel Core i5-9500 CPU with a main frequency of 3.0 GHz and 4 GB of memory. The entire workflow, including image acquisition, processing, and motor control, was implemented in Visual Studio 2013 using C++.