esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths
## 5 Results
### Asia  
The first COVID-19 cases were reported in Wuhan, China in December 2019 [20]. Figure 4 shows the actual and predicted trends of COVID-19 deaths in this country. On 17 April 2020, the daily number of deaths (Figure 4a) presents an isolated peak of 1290 deaths due to a change in the tally criteria [21]. Correspondingly, a strong discontinuity can be observed in the total number of deaths (Figure 4b). The model does not fit exactly such an artificial discontinuity, but reproduces well the overall trend. In the last months, the official number of deaths shows only a very slight increase, thus indirectly demonstrating the effectiveness of the strict lockdown imposed by local Authorities.  
The two Asian countries with the largest number of actual and predicted COVID-19 deaths are India (Figure 5) and Iran (Figure 6). Quite different trends in time are observed in the two countries. In India, the daily deaths seem to have reached a peak value in September 2020 and a descending trend is expected afterwards (Figure 5a). The model is able to fit very well the available data, thus the extrapolation may be considered reliable (Figure 5b). In Iran, after a first wave of the epidemic with a peak of daily deaths in April 2020, a second wave has hit the country with a higher peak of daily deaths in August 2020. Recent data show the onset of a third wave with a tendency towards an even higher third peak (Figure 6a). The model assumes at most two peaks, so it is not able to fit very well all of the available data. Based on the above, the prediction of the total number of deaths at the end of 2020 may turn out to be underestimated for this country (Figure 6b).  
Figure 4: Actual and predicted trends in China: (**a**) daily deaths, (**b**) total deaths.  
Figure 5: Actual and predicted trends in India: (**a**) daily deaths, (**b**) total deaths.  
Figure 9 shows the time trends obtained for the Asiatic continent by summing the available data and model predictions for each country. Several peaks of daily deaths can be observed, as a result of the different local trends (Figure 9a). Overall, the current trend is descending and a weakening of the epidemic in the continent is expected by the end of year 2020. Figure 9b shows the total number of deaths in the continent with an extrapolation of the model until the end of year 2020. The predicted trend is strongly increasing. Error bars correspond to the error rate (\(-\)2.5%, +5%)/15 days estimated through the validation study. The forecast total number of COVID-19 deaths on 31 December 2020 in Asia is 310,819 + (-47,659, +95,318).
## 3 Materials and Methods
### Features Extraction and Selection
#### 3.3.2 Tweet to Lexicon Feature Vector  
It calculates features from a tweet using several lexicons:  
* Multi-Perspective Question Answering (MPQA) sums up the amount of positive and negative words in the MPQA subjectivity lexicon.
* [PERSON] (BL) keeps track of the positive and negative terms in the BL lexicon.
* [PERSON] (AFINN) derives positive and negative variables from the positive and negative word scores provided by AFINN's lexicon.
* Sentiment140 produces positive and negative factors by averaging the positive and negative word scores offered by this lexicon developed from emoticon-annotated tweets.
* National Research Council's (NRC) Hashtag Sentiment lexicon calculates positive and negative variables by aggregating positive and negative word values derived from tweets marked with emotional hashtags.
* NRC Lexicon of Word-Emotion Association counts the number of words corresponding to each emotion in this lexicon.
* NRC-10 Expanded includes the emotion associations for the terms that fit the NRC Word-Emotion Association Lexicon's Twitter-specific expansion.
* NRC Hashtag Emotion Association Lexicon augments this lexicon by including the emotion connections for the terms that match.
* SentiWordNet uses SentiWordNet to determine positive and negative scores. It computes a weighted average of the synsets' sentiment distributions for words that appear in several synsets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Sentiment Class** & **Instances** & **No.** & **Sentiment Class** & **Instances** \\ \hline
1 & Not\_Relevant & 214 & 8 & Angry & 57 \\
2 & Neutral & 1572 & 9 & Sad\_Angry & 2 \\
3 & Happy & 1137 & 10 & Disgust & 6 \\
4 & Surprise & 35 & 11 & Sad\_Disgust & 2 \\
5 & Happy\_Surprise & 11 & 12 & Angry\_Disgust & 7 \\
6 & Sad & 32 & 13 & Sad\_Angry\_Disgust & 1 \\
7 & Happy\_Sad & 9 & & Total Instances & 3085 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tweets distribution in sentimental classes.  
* Emoticons generate a positive and negative score based on the word associations associated with a collection of emoticons. The list was compiled as part of the AFINN project.
* Negation is a metric that indicates the quantity of negating terms in a tweet.
## 3 Results and Discussion
### Validation Experiment  
Twenty male university students from King Saud University (KSU) with a mean age of \(25.5\pm 2.5\) years were randomly selected for this study. This study aimed to validate the results obtained in the previous analysis, as well as study the effect of humans. All participants were right handed with normal vision (self-reported), and none of them had any health problems. Also, the participants were randomly selected to scan the car door part using the laser and white light scanners. All experiments were conducted using the obtained optimized parameter settings. Ten students scanned the car door part using the laser scanner, while remaining ten used the white light scanner.  
When the two sets of results (ten in each set) were compared, it was found that in each of the ten runs, laser scanner provided better accuracy than the white light scanner. The white light scanner required lesser scanning time than the laser scanner. Furthermore, the number of triangles generated by the white light scanner was lesser than the number of triangles generated by the laser scanner.
## 4 Results and Discussion
### Electrical Resistivity Tomography 3D Modelling
#### 4.3.2 3D Model
The upper part of the borehole (Figure 6f) corresponds to coarse materials (gravel and pebbles) linked to resistivity values above 5000 \(\Omega\).m. An intermediate layer (Figure 6d,e), 1 m thick, composed of gravel with abundant sand, reduces the range of resistivity at the middle stretch of BH2 (3000-5000 \(\Omega\).m). In general, the 3D model detected in the central channel part an upper subsurface layer of 1-1.2 m thick, with resistivities above 4500 \(\Omega\).m, which corresponded to the coarsest materials (gravel and pebbles) and gravels with a supported matrix. In addition, resistivity reduces according to depth (Table 5), owing to a progressive decrease in the median grain size and an increase in the sorting index with depth. The lateral parts, close to the banks, have undergone a different geomorphological evolution: the right side currently constitutes the active channel of low waters, with a uniform bed composed of medium gravels, which, during much of the filling period, received finer-sized materials (small gravels and sands). The resistivities in this case decreased from 4000 \(\Omega\).m at the top to 500 \(\Omega\).m at the limit with the loamy substrate. On the other hand, the left part shows a higher concentration of thicker sediments due to the presence of a lateral bar still under development today, which is in accordance with the measured ERT values in the 3D model (>4500 \(\Omega\).m).
###### Abstract  
Based on multiple linear regression (MLR) models, we estimated the PM\({}_{2.5}\) at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM\({}_{2.5}\) estimation through the MLR method: mostly correlation coefficients \(\sim\)-0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM\({}_{2.5}\) estimation. We also found that the quality of estimated PM\({}_{2.5}\) shows some seasonal variation; the estimated PM\({}_{2.5}\) values consistently have the highest correlation with in situ PM\({}_{2.5}\) in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM\({}_{2.5}\) estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM\({}_{2.5}\) estimation, our analyses of the difference in estimated PM\({}_{2.5}\) reveal the importance of utilizing the proper AOD for the best quality of PM\({}_{2.5}\) estimation.  
Keywords:PM\({}_{2.5}\) aerosol optical depth (AOD); multiple linear regression; boundary layer height +
Footnote †: journal: Applied Sciences
# Iteratively Refined Multi-Channel Speech Separation
## 4 Results
### Performance on the Joint Separation and Dereverberation Tasks  
In the previous section, we discussed the performance of the proposed method in the task of reverberation. In order to comprehensively evaluate the performance of the proposed method and explore its performance in different tasks, this section explores the performance of the joint separation and dereverberation tasks, where the goal was to separate reverberant mixtures and produce anechoic speech signals.  
The experimental results in Table 4 show the performance of our proposed method and the reference methods on the dereverberation task. We can see that our proposed method demonstrated a significant advantage over the reference methods. Specifically, the proposed method achieved an SDR of 20.2 dB, much better than Beam-TasNet, and exceeded the beam-guided TasNet by 2.1 dB. Additionally, it exceeded the oracle mask-based MVDR by 8.2 dB and narrowed the gap to just 0.9 dB compared with the oracle signal-based MVDR. These results highlight the effectiveness of our proposed method in joint separation and dereverberation tasks, demonstrating its potential for real-world applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Param** & **SDR** & **SI-SDR** & **PESQ** & **SIR** & **STOI** \\ \hline FaSNet & 2.8 M & 11.96 & 11.69 & 3.16 & 18.97 & 0.93 \\ NB-BLSTM & 1.2 M & 8.22 & 6.90 & 2.44 & 12.13 & 0.83 \\ Beam-TasNet & 5.4 M & 17.40 & - & - & - & - \\ Beam-guided TasNet & 5.5 M & 20.52 & 19.49 & 3.88 & 27.49 & 0.98 \\ Beam-TFDPRNN & 2.7 M & 17.20 & 16.80 & 3.68 & 26.77 & 0.96 \\ iBeam-TFDPRNN & 2.8 M & 24.17 & 23.48 & 4.23 & 33.21 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with reference methods on the spatialized version of the WSJ0-2 mix dataset.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 5 Experimental Results
### Power Conversion Efficiency of the Whole System  
The Line diagram in Figure 11 is to illustrate the input and output power available for each subsystem so that the power-conversion efficiency can be resolved. Every value is measured and calculated at the input and output of every subsystem. The data in blue color represents the value of the reference system [24], while the data in red is ours. The test starts from the input with a wind speed of 4.5 m/s where 128.2 mW of raw wind power is supplied to the wind turbine with an efficiency of 39% and 50 mW of mechanical power is available for harvesting. Due to the standard diode-based full-bridge rectifier, 65% of the raw electrical power (ac) is generated at the output of the wind turbine generator. Thus, approximately 20 mW is converted into raw electrical power (dc) of 13 mW. Then after the supercapacitor-charging period (85 min), the energy stored in our system is 19.88 J. In contrast, the reference system has a higher rectifier conversion efficiency because of its smart adaptive strategy. Whereas it adopts a traditional MPTT scheme and gains a lower bit output. However, our system gains significant enhancement in the next stages with a best-matching TPC based converter, where the wireless sensor node has survived for 1.5 h other than the referenced 1.2 h under the working cycle mentioned in a prior achievement [37]. Moreover, certainly in this \"ENO\" process, the prediction module takes effect as a foundation stone while consuming a neglectable amount of energy.  
Figure 10: Average transmission cost for different schemes.
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### Biochemistry of Forest VOCs
#### 3.1.1 Isoprenoids  
The volatile isoprenoids include isoprene (C5), monoterpenes (C10), homoterpenes (C11 or C16), and sesquiterpenes (C15), and they all derive from the precursor dimethylallyl diphosphate (DMAPP) and its isomer isopentenyl diphosphate (IPP). These precursors are synthesized by two separate pathways, one active in plastids (the deoxyxylulose-5-phosphate (DXP), also known as the methylerythritol phosphate (MEP) pathway), and the second in the cytosol of the secretory cells themselves (the mevalonate (MVA) pathway) [17]. In fact, IPP and DMAPP seem to originate in the plastids from the MEP pathway, while leucoplasts contain the enzymes for the first steps of monoter biosynthesis. In the MVA pathway, DMAPP accepts one IPP (by preyltransferase--PT), leading to the production of farnesyl diphosphate (FPP) and, consequently, to sesquiterpenoids (although, in some instances, there is a very small production of monoterpenes) and to the homoterpene DMTT. In the MEP pathway, the herimetpene isoprene is synthesized from DMAPP alone by terpene synthase or cyclase  
Figure 2: Main biosynthetic pathways for BVOCs. In green and yellow colors, the methylerythritol phosphate (MEP) and mevalonate (MVA) pathways to isoprenoids, respectively, in blue color, the lipoxygenase (LOX) pathway to GLVs, and in purple color, the shikimate pathway to aromatic compounds. Adapted from [PERSON] et al. (2011) [17].  
(TPS), and the fusion of DMAPP to one or more IPPs leads to the production of geranyl diphosphate (GPP) (precursor of the monoterpends) and geranyl geranyl diphosphate (GGPP), which leads to the synthesis of diterpenes from which the homoterpene TMTT derives. Although the MEP pathway is plastid-located, the enzymes necessary for sesquiterpene synthesis are cytosolic. Hence, MEP-derived IPP or DMAPP need to travel from plastids to the cytosol [17,18]. The biosynthesis of the homoterpenes DMNT and TMTT follows a less direct pathway. DMNT derives from the oxidative degradation of (E)-neralidol (a C15-alcohol), and TMTT from the oxidative degradation of (E,E)-geranyl linalool (a C20-alcohol) [28].
## 4 Dynamic Analysis of the PIM System
### Sensitivity Analysis of the Boundary Conditions of System Parameters
#### 4.2.1 Fluid Velocity  
In this study, the fluid velocity ranged from 0.2 to 1.0 m/s, with other parameters remaining constant. Figure 13, respectively, illustrates the variations in tension at the connection point between the cable and the floating structure over time at different flow velocities, as well as the curves of the coordinates of the center of gravity of the floating structure on the x- and y-axes over time. Figure 13a illustrates the axial force at the connection point between the cable and the floating structure at different flow velocities. The results show that increasing flow velocity does not change the trend of the axial force. Specifically, the tension rapidly increases within the first 10 s, then enters the \"resonance zone\", and finally gradually stabilizes. However, with the increasing flow velocity from 0.2 to 1.0 m/s, the axial force at the connection point exhibits a slow increasing trend and stabilizes at approximately 1.355 \(\times\) 108 N after around 300 s.  
Figure 13b depicts the horizontal displacement of the center of gravity of the structure. Overall, variations in flow velocity have minimal effect on the horizontal displacement of the structure's center of gravity. An increase in flow velocity reduces structural oscillation only in the 'oscillation zone', enhancing stability.  
Considering that this study addresses only the impact of ocean currents in the horizontal direction and presumes that the structure remains unaffected by vertical fluid flow, the displacement and velocity changes in the structure's center of gravity in the vertical direction, as depicted in Figure 13c,d, are minimal. As flow velocity increases, the structure's vertical displacement decreases by about 10 m, while vertical velocity remains nearly unchanged. Figure 13e depicts the changes in the horizontal velocity of the structure's center of gravity. Figure 13f shows that increasing flow velocity decreases the structure's maximum horizontal velocity. This occurs because the direction of the structure's swing is opposite to that of the water flow, and the greater the flow velocity, the more resistance the structure encounters. This phenomenon is further illustrated at 140 s in Figure 13e, where there is a noticeable decrease in the magnitude of the \"oscillation zone\" following the structure's swing to its lowest point. Therefore, this phenomenon greatly facilitates the swinging of the structure to the designated target area, thus aiding in the accurate positioning of the sinking point of underwater equipment.
# Clustering Approach for Reliable Wireless Communication
## 4 Results and Discussion
### Scenario 1: Two Antennas at BS/20 Users/MUSA Codes Length 4  
Figure 12 depicts the results when two detectors, MMSE and MMSE-SIC, are used at the base station. Spreading codewords from Table 1 are allocated to users in two manners: randomly (without any rules) and from different clusters (the codes are divided into 20 clusters using the \(k\)-means method and the sample correlation distance metric, as presented in Section 2). Each of the \(K\) users is using a single code from only one cluster. The MMSE-SIC detector is leading to better performance in both situations (random and cluster), so this detector will be implemented for the other scenarios. It can also be noted that for two antennas at the base station, using the novel clustering approach, the performance is improved since the BER values decrease faster as the SNR increases.
# A Typology Model of Temporary Facility Constraints for Automated Construction Site Layout Planning
## 4 A Constraint Typology Model for CSLP
### Inter-Facility Distance Constraint  
Inter-facility distance constraints limit the distance between temporary facilities and other objects for reasons such as safety, security, and accessibility. In this case, the distance may simply mean a physical distance but can also refer to travel distance in some cases. In addition, it was found to be a type of constraint that occupies the largest proportion among the constraints identified in this study as at least one was identified for each temporary facility (Table 5). Inter-facility distance constraint has four subtypes as follows:  
* Min distance constraint to determine the minimum linear separation distance between the temporary facility to be laid out and other objects
* Max(L) distance constraint to determine the maximum linear distance between the temporary facility to be laid out and other objects
* Min-max distance constraint to ensure that the temporary facility to be laid out should be placed within a specific radius of other objects
* Max(T) distance constraint to determine the maximum distance based on the actual travel path between the temporary facility to be laid out and other objects  
Most of the constraints exist as conditions to keep a separation distance between objects (i.e., min distance constraint). Incorrect reference point settings in these constraints can lead to completely different calculation results. Therefore, the reference point, which serves as a basis for calculation, should be set differently depending on the goal that each constraint pursues. For example, in IF11, the radius of the tower crane should be larger than the distance between the center of the crane and the farthest point of the structure. That is, reference points for the two objects should be set to measure the distance. In this context, different reference points, such as center-to-center (measuring the distance based on the center of each object), center-to-closest/farthest (measuring the distance between the center of one object and the closest/farthest point of the other object), and edge-to-edge (measuring the distance between the edges of each object) reference points can be used [33]. Figure 6: Non-overlap constraint: (a) non-overlapping and (b) non-overlapping with offset constraints. In addition, IF15 through IF20 have a direct impact on the travel distance of labor, equipment, and materials. Therefore, the distance should be calculated using the actual travel distance rather than the linear distance. Nevertheless, only the linear distance is used in most studies [36]. For example, in IF16, if the linear distance is used to calculate the distance between the laydown area outside the building and the closest hoist, then detours to avoid obstacles when moving from the laydown area to the hoist cannot be considered (Figure 7). This, in turn, leads to the incorrect layout of temporary facilities. In this connection, it is important to explore the actual path where real people or equipment are moving.
# Influence of Lubrication Systems on the Fatigue Strength of Bolted Joints
## 1 Introduction
### Objectives and Concept  
IGF project 20412 is currently investigating the influence of zinc-based corrosion protection systems on the fatigue resistance of bolted joints. With the aim of considering the lubricant influence for the bolt design, different lubricants are selected for a typical chosen basecoat and systematically compared in the fatigue test, as shown in Figure 1.  
The concept is based on the fatigue tests, which are carried out accompanied by supporting methods for crack detection and for evaluating the damage development. The measurement technology developed for this purpose includes, for example, frequency analysis, potential measurement or the heat-tinting method. The present paper deals with the first key point '1. Lubricant', in which the influence on the fatigue resistance due to the selection of the lubricant is investigated separately from the influence of the corrosion protection system. The objective of this investigation is to determine the lubricant's influence on the initiation of cracking and the crack propagation behavior up to the end of lifetime, which is reached by the limited number of cycles N\({}_{\text{G}}\) or by failure due to fracture. For this purpose, the following investigations are carried out systematically on a representative chosen bolt-nut connection:  
* Tightening test according to DIN EN ISO 16047, with the aim of determining the thread friction coefficient for selected lubricants
* Experimental investigation of lifetime in the fatigue test, with the determination of crack initiation time and crack propagation phase by means of suitable measurement technology
* Fracture surface analysis, using the methods of material analysis  
Based on the experimental test results, a crack initiation criterion is defined by means of frequency analysis and the heat-tinting method. The crack initiation time is compared for the lubricant systems investigated.