esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Evaluation of Document Embeddings and Similarity Metrics for Scientific Articles
## 2 Basic Notions of Similarity Measures  
Mathematically, a similarity measure is a real-valued function that quantifies the notion of how two entities differ. The similarity measures can be defined in many ways. Two approaches are very common: formulation as a similarity value or as a distance. In this context, a distance is the inverse of a similarity value: the larger the distance between two entities, the more dissimilar they are. A distance can be a metric or not, depending on whether it fulfills certain axioms. Given two elements \(x\) and \(y\) of a set \(X\), a distance function \(d\) is a metric on the set \(X\) if the following conditions are fulfilled:  
1. **Non-negativity:**\(d(x,y)\geq 0\);
2. **Identity of indiscernibles:**\(d(x,y)=0\Leftrightarrow x=y\);
3. **Symmetry:**\(d(x,y)=d(y,x)\);
4. **Triangle inequality:**\(d(x,y)\leq d(x,z)+d(z,y)\).  
The accomplishment of these axioms is relevant in some cases, such as when may use the metric as a distance to position documents' representations in a 2D or 3D layout. However, in many contexts, it is not imperative to work with distances that are metrics. Some other factors need to be considered. For example, large objects that differ only in a small portion are intuitively more similar than smaller objects that differ in the same portion. Therefore, some authors also use the notion of _normalized admissible distance_[3], which takes into account this fact. Thus, the absolute difference between two objects does not govern similarity. However, the relative difference seems to be a more intuitive indicator of similarity. As a result, some authors have proposed measures based on this notion of normalized distance, as we will see later.
# Experimental Study of Methane Combustion Efficiency in a High-Enthalpy Oxygen-Containing Flow
## 1 Introduction
Some of the data presented were obtained by methane combustion in the presence of water vapor or other inhibitory additives, which should be taken into account during their analysis. It should also be pointed out that the data obtained in low-sized laboratory facilities are of limited use due to the difference in mixing and turbulence parameters from full-scale values. Some studies describing the air enthalpy effect on methane combustion under conditions as close as possible to natural conditions are presented in studies [42; 43; 44; 45; 46; 47; 48]. In particular, in the study [48] the issues of methane combustion in a short combustion chamber were investigated with the parameters of the air flow at the inlet, corresponding to the Mach number M = 2, the total pressure p0 = 3.6 \(\div\) 7.6 bar, and the air stagnation temperature T\({}^{\prime}\)= 690 \(\div\) 910 K. The configuration of the combustion chamber, the methane injection system, and combustion modes investigated in the study [48] have the specific features related to the organization of a nonuniform supersonic flow at the inlet, heating of the methane (up to 880 K temperatures), gas-dynamic throttling of the flow and the organization of combustion stabilization behind the ledges. The fragmentary data on the establishment of methane combustion deflagration and detonation modes at high oxidizer temperatures are pointed out in studies [49; 50; 51]. Despite a large number of well-known publications, there are no systematic data on the methane combustion in a high-enthalpy turbulent oxygen-containing flow (HOF) at flow velocities of -100 m/s. Besides studies mentioned above, the positive effect of temperature on the combustion stabilization is shown in study [52], but the data presented in it were obtained for temperatures over 1500 K.  
Considering the above, the main task of this study is to conduct parametric experimental studies and analyze the obtained regularities to reveal the unheated methane combustion efficiency in a high-enthalpy (up to 700 kJ/kg) oxygen-containing flow (HOF).
## 3 Data Acquisition and Preprocessing of EMG Signals
To overcome this issue, this study takes on the challenge of using a relatively low-cost consumer grade EMG sensor with lower sampling rate to decode large number of wrist and finger motions. The results of this work will help determine the validity of low cost EMG sensors as a modality in myoelectric prosthetic control. The acquired raw EMG (rEMG) signals comes with a DC offset of 2.5 V which needs to be removed before any analysis can be performed. The offset is removed by demeaning, i.e., subtracting the average amplitude of rEMG signal during the first 10 s from the whole signal. The preprocessed EMG (pEMG) signals will be later used in the feature extraction process. An example of raw EMG signals during the execution of 17 motions is shown in Figure 5.
## 4 Results and Discussion
### Optimization of Hot Water Flow Rate  
According to the analysis of Section 4.1, it is known that the improvement of hot water flow rate is favorable for the energy saving of SASCHCS. However, it should be noted that the energy saving rises scarcely in addition to the growth of consumption in hot water pumps for the excessive increase of hot water flow rate. As a result, the collector and generator hot water flow rates are optimized based on the trade-off of energy saving and pump power, similar to Iranmanesh [20]. The power of hot water pumps is calculated by the reference [33]. Moreover, the optimization is implemented by the combination of the SASCHCS model and the module of a genetic algorithm in the MATLAB environment. The maximum generation number and population number are set as 50 and 20, respectively. The range of hot water flow rate is from 1 m\({}^{3}\)/h to 10 m\({}^{3}\)/h. Furthermore, the maximum energy saving of hybrid systems is employed as the objective function. It is obtained that 3.6 m\({}^{3}\)/h (13.33 kg/(h m\({}^{2}\))) of a collector flow rate with 5.2 m\({}^{3}\)/h (19.26 kg/(h m\({}^{2}\))) of generator one is optimal for the annual operation of SASCHCS. The above-mentioned result implies that the generator hot water flow rate to the collector one approaches to 1.44 in the optimal case, which is slightly more than the ration gotten by [PERSON] [21]. This difference is mainly attributed to the higher heat loss of ETC and the effect of month with low solar irradiance. On the other hand, the peak annual energy saving of SASCHCS in the optimal case is 8836.5 kW h, which is equivalent to be 32.73 kW h/m\({}^{2}\) of specific ones.
## 2 Materials and Methods
### Methodology of ResRAD-OFFSITE  
RESRAD-OFFSITE evaluates the radiological dose and excess cancer risk to an individual who is exposed while living or working in or near a radionuclide-contaminated area [25]. It evolved from RESRAD-ONSITE during the BIOMOVS II model comparison first by adding an offsite soil accumulation submodel (BIOMOVS II 1995) and then including an adjective-dispersive groundwater transport submodel (BIOMOVS II 1996). In this study, the ability to accept a time series for release from contaminated soil was also added. During participation in the multimedia model comparison study, the adjective-dispersive groundwater transport submodel was improved to better predict the transport of progeny produced in transit. An atmospheric transport submodel and a surface water body accumulation submodel were added. The computational algorithm of the RESRAD-OFFSITE code differed from that of RESRAD-ONSITE. RESRAD-ONSITE numerically evaluates the analytical expressions for concentration, dose, and risk at any desired time since the site survey, whereas RESRAD-OFFSITE numerically computes the concentration, dose, and risk progressively over time. To simulate radiological dose using RESRAD-OFFSITE, we assumed an offsite resident scenario, where a person lives within the vicinity of a landfill. Another assumption was that the person would not consume any food or water from the contaminated site; the exposure pathways considered were inhalation and external irradiation. The code uses a Gaussian plume model, where the radionuclide concentration and plume remain constant over time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Specific Activity Concentration (Bq/kg)**} & \multicolumn{3}{c}{**Total Activity (Bq)**} \\ \hline
**Categories of CP** & **238U** & **232 Th** & **40K** & **238U** & **232 Th** & **40K** \\ \hline Combustible (below 1000 Bq/kg) & 164 & 226 & 125 & \(5.72\times 10^{6}\) & \(7.86\times 10^{6}\) & \(4.36\times 10^{6}\) \\ Combustible (above 1000 Bq/kg) & 1917 & 2320 & 1358 & \(6.68\times 10^{7}\) & \(8.088\times 10^{7}\) & \(4.73\times 10^{7}\) \\ Noncombustible (below 1000 Bq/kg) & 114 & 153 & 150 & \(3.97\times 10^{6}\) & \(5.33\times 10^{6}\) & \(5.23\times 10^{6}\) \\ Noncombustible (above 1000 Bq/kg) & 2607 & 7250 & 1310 & \(9.09\times 10^{7}\) & \(2.53\times 10^{8}\) & \(4.41\times 10^{7}\) \\ Total AC for landfills (below 1000 Bq/kg) & 278 & 379 & 275 & \(9.69\times 10^{6}\) & \(1.32\times 10^{7}\) & \(9.59\times 10^{6}\) \\ Total AC for landfills (above 1000 Bq/kg) & 4638 & 9570 & 2668 & \(1.62\times 10^{8}\) & \(3.33\times 10^{8}\) & \(9.15\times 10^{7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: 95% confidence interval for the total activity concentration (Bq).
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Effect of Text Transcription Errors on Performances
#### 4.6.1 Effect of Replaced Characters  
We replace \(p\%\) of the characters uniformly at random in the text of Chapter 10 and Playlist 50, leaving the space characters untouched, so the number of words remains the same. When \(p\) decreases towards \(-100\%\), it becomes likely that several characters will be removed on each word; thus, we make sure that each word retains at least one character. We perform the alignment of the altered text again with the audio posteriorgram.  
Figure 13(a) shows that the alignment error on speech remains remarkably stable around the baseline, and for up to 50% of the added/removed characters. The error increases steadily when adding extra characters, as expected. More surprisingly, the error decreases slightly, as compared to the baseline when removing 0-50% of the characters. One interpretation is that removing some characters could be beneficial for the CTC decoding if the model fails to recognize them properly. When more than 50% of the characters are removed, the error increases again. Interestingly, the MAAE always remains under 200 ms, even after each word contains only one single character. This indicates that the space character is a very powerful anchor, and plays a crucial role in aligning the text with the posteriorgram.  
Figure 13(b) shows that the singing aligner is much more sensitive to these kind of character alterations. Similarly to the character replacement in the previous experiment, we observe that the singing voice is much more sensitive than speech to the altered transcriptions. As in the previous, this can be explained by the fact that anchor characters are more ambiguous than for plain speech, which causes the optimal path to diverge more easily.  
Figure 13: Mean absolute alignment Error for different percentages of replaced characters. (**a**) Speech (Chapter 10); (**b**) Singing (Playlist 50).
# Utility-Based Wireless Routing Algorithm for Massive MIMO Heterogeneous Networks
## 6 Conclusions and Future Work  
In this paper, we studied a brand-new issue: the joint consideration of route establishment and antenna allocation in heterogeneous massive MIMO backhaul networks. Given the number of UE of each BS, the available antennas, and the distance between all BSs, we introduced the new problem, i.e., utility-based wireless backhaul routing (UWBR), which involves adjusting the antennas of each link and selecting routes to improve overall user utility with antennas, links, and data-rate constraints. To tackle this problem, we proposed a heuristic algorithm called PSR. In this algorithm, all BSs are connected to the MBS, and antennas of the MBS are preallocated to the BSs. Then, each BS selects its candidate parents that yield the largest utility difference if reconnected to its subtree. Using PSR, the overall utility is improved, and the outer BSs can obtain better data rate and utility performance. We proved that UWBR is an NPC problem and analyzed the maximum utility that the system can possibly achieve. The simulation results show that our algorithm is superior to other existing schemes in terms of the data rate and total utility of the outer BSs. As the channel is modeled in a general way, this algorithm can be applied to different types of massive MIMO wireless mesh networks.  
There are still some issues to be discussed in the future. First, links with different uplink and downlink requirements seem to be an interesting issue that is worth considering. In addition, this work can be extended by considering the detail signaling overhead and the mobility of user equipment and base stations.  
Conceptualization, W.-H.K. and W.Z.; methodology, W.-H.K. and W.Z.; software, W.Z.; validation, W.-H.K.; formal analysis, W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, W.-H.K. and W.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by Ministry of Science and Technology, grant number 107-2221-E-155-024-MY3. The authors declare no conflict of interest. The authors declare no conflict of interest.
## 2 Materials and Methods
### Design of Simulation
#### 2.1.2 Simulation Flowchart  
The methodology for evaluating indoor noise levels through ray tracing is outlined in Figure 2. The initial step involves selecting an appropriate platform that supports distinct front-end and back-end functionalities to ensure a streamlined process. An ideal platform should be cost-effective, widely used, and accessible, with an extensive user base. A representative test floor plan is created, detailing the placement of sound sources and receivers. Before coding in MATLAB, the test layout is designed in SolidWorks and exported as a Stereolithography (.stl) file for MATLAB compatibility. The floor plan dimensions mimic an average factory setting, with noise-emitting machines strategically placed, each having specific sound output levels. Receiver locations are mapped using a Cartesian coordinate system, providing input data for the simulation.  
Following the setup, the simulation model is configured. Key inputs include the floor plan, the Cartesian coordinates of both receivers and sound sources, and each source's Sound Pressure Level (SPL). Once inputs are defined, the ray tracing algorithm is executed, incorporating parameters such as the transmission and receiver sites, the propagation model, and the layout. The results are visually represented to display simulated sound propagation paths upon completion. The final step is analysing the simulation output, specifically the SPL at each receiver. Additional commands allow for extracting relevant data, including overall signal strength and path loss, facilitating comprehensive noise evaluation.  
Figure 2: Overall process of noise assessment and safety recommendations.
## 2 Materials and Methods
### ERA-5 Land Reanalysis Data
#### 2.2.1 Input Variables  
Two input datasets were used to train the models. The first dataset includes all 47 parameters from the ERA5-Land reanalysis. Some input variables have an extremely high linear correlation coefficient (r > 0.99), for example, air temperature and dew point temperature. Multiple regression models can be successively used when model input variables are linearly independent of each other [15]. Therefore, the second dataset with only 25 variables was formed. It includes the first 25 principal components from the decomposition of the initial 47 variables into orthogonal components [56]. Time series from the second dataset are not correlated according to the definition. The first 25 principal components cumulatively describe 99.4% of the variability of the initial dataset. The first dataset was marked as \"ERA data\" and the second as \"PCA data\" (principal component analysis data).  
The input variables from the ERA or PCA datasets (Table 2) were used for the modelling of the target variables (Table 1). The continuously modelled time series for each target variable was constructed individually for each model. The impact of the number of input variables on the model performance was also estimated.
# Comparison of DFN Modelled Microfracture Systems with Petrophysical Data in Excavation Damaged Zone
###### Abstract  
Physical and petrographic properties of drill core specimens were determined as a part of investigations into excavation damage in the dedicated study area in the ONKALO(r) research facility in Olkiluoto, Western Finland. Microfractures in 16 specimens from two drillholes were analysed and used as a basis for fractal geometry-based discrete fracture network (DFN) modelling. It was concluded that the difference in resistivity between pegmatoid granite (PGR) and veined geniss (VGN) specimens of similar porosity was likely due to differences in the types of microfractures. This hypothesis was confirmed from microfracture analysis and simulation: fractures in geniss were short and mostly in one preferred orientation, whereas the fractures in granite were longer and had two preferred orientations. This may be due to microstructure differences of the rock types or could suggests that geniss and granite may suffer different types of excavation damage. No dependencies on depth from the excavated surface were observed in the geometric parameters of the microfractures. This suggests that the excavation damaged zone cannot be identified based on the changes in the parameters of the microfracture networks, and that the disturbed layer observed by geophysical methods may be caused by macro-scale fractures.  
discrete fracture network model; petrophysics; physical properties of rocks; laboratory testing; simulation; excavation damage; percolation cluster +
Footnote †: journal: Applied Sciences
# Application of Date Palm Tree Branch-Based Activated Carbon for Aqueous Toxicity Reduction
## 3 Results
### Copper Adsorption  
Along with water pollution resulting from organic contaminants, several heavy metal species also negatively impact water quality and hence require appropriate treatment [29; 30; 31; 32; 33; 34; 35]. Therefore, the removal of aqueous phase copper species, using the date palm branch-based GAC, was also investigated. Similar to phenol and \(p\)-cresol studies, the Langmuir adsorption isotherm provided a better fit for copper adsorption, with results given in Figure 6a. The value of the respective Langmuir adsorption modeling constant 'a' was 0.009 and that of constant 'b' was 0.529 L/mg. Furthermore, the pH-dependent adsorption trend for copper is provided in Figure 6b. Different from the previously reported trends for phenol and \(p\)-cresol, we note increasing copper removal with an increase in pH. We suggest that the GAC acidic surface functional groups [25] that will be predominantly present in the anionic forms at higher pH will favor cationic copper attachment. Such an electrostatic attraction-initiated adsorption is also conducive for cationic organic pollutants; for example, [PERSON] et al. [11], who studied cationic methylene blue dye adsorption equilibrium using date pit-based GAC, also reported an increase in adsorption with an increase in solution pH. [PERSON] et al. [36], who produced activated carbon from coir path agricultural waste and used that for the removal of several heavy metals including Cu(II), also reported that the percent metal removal increased in a pH range of 2 to 6, after which a constant trend was noted until a pH of 10. On the other hand, [PERSON] et al. [16], who investigated aqueous phase chromium removal using GAC produced from date pits, reported that the adsorption efficiency was enhanced with a decrease in pH value, which could be explained based on the anionic speciation of hexavalent chromium. A similar trend was reported by [PERSON] et al. [37], who also studied hexavalent chromium adsorption on activated carbon from coconut tree sawdust. However, a high copper removal at high pH, as noted in the present study, can also partially result from precipitation.  
Figure 5: Breakthrough curve obtained from the continuous flow column study results for \(p\)-cresol adsorption (initial \(p\)-cresol: 100 mg/L; flow rate: 4 mL/min).
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 3 Methodology
### Storage Methods of the BuildingTree
#### 3.3.1 NoSQL Databases  
MongoDB  
To store the 3D city models, a database is required. Compared with relational databases such as Oracle, MySQL or PostgreSQL, it is better to store the dataset in the cloud environment, considering extensibility. MongoDB is a cross-platform, document-oriented database built for the cloud environment. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster. Here, we build a MongoDB framework for city models using four computers as a cluster. This structure can be easily extended by adding more data nodes to support larger and more cities.  
Figure 7: 3D city model management framework.  
Then, the web server side can employ the MapReduce programming method to communicate with the database. MapReduce is suitable for parallel computing and can take advantage of the server cluster. The MapReduce framework is widely applied for big data processes. It can divide the work loads of an operation, such as searching or analysis, onto multiple computers and run the process simultaneously. The MapReduce framework can increase the analysis speed by adding more computers to the system, which is usually achieved by replacing the existing system with better machines. MongoDB supports the MapReduce framework which will improve the process performance for big city models.
## 2 Materials and Methods
### Experimental Procedure  
A series of batch experiments were conducted at room temperature to evaluate the variability of carbon isotope fractionation during KMnO\({}_{4}\)-mediated TCE degradation. KMnO\({}_{4}\) stock solution (10 g/L) was prepared by dissolving 0.5 g KMnO\({}_{4}\) crystals in 50 mL deionized water in a volumetric flask. A near-saturated TCE stock solution (1000 mg/L) was prepared by adding 340 uL of pure-phase TCE to 500 mL of deionized water in a 500-mL anaerobic flask, followed by in the oscillator at 25 \({}^{\circ}\)C 180 rpm for 12 h. To initiate the reaction, 18 mL phosphate buffer solution (pH \(\sim\)8.0) was placed in a 65 mL anaerobic flask and sealed with a PTFE membrane rubber gasket. Then, 1.5 mL of TCE stock solution (1000 mg/L) was injected into each vial. Subsequently, 19.5 mL of the reaction mixture was added to 65 mL conical flasks, and kinetic reactions were initiated by adding 0.5 mL of KMnO\({}_{4}\) stock solution (10 g/L), resulting in a final TCE concentration of to the 75 mg/L. Kinetic experiments were conducted under ambient temperature with stirring at 500 rpm. Prior to KMnO\({}_{4}\) addition, the mixture was allowed to equilibrate for 1 h to establish gas-liquid equilibrium and the headspace gas was sampled to determine the initial TCE concentration. In the batch experiments, the effects of various parameters were investigated, including pH (2-12, adjusted using phosphate buffer), KMnO\({}_{4}\) dose (50-400 mg/L), TCE concentration (25-250 mg/L), inorganic ions (0.2 M, including Cl\({}^{-}\), HCO\({}_{3}\)\({}^{-}\), NO\({}_{3}\)\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\)), and HA (0.5-250 mM). HA stock solution (1 M) was prepared by mixing 11.35 g of HA (sieved through 200-mesh to ensure uniform particle size) with 50 mL of deionized water and in the oscillator at 25 \({}^{\circ}\)C 180 rpm for approximately 24 h.