esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The integrative potential of LPBF-printed parts for various innovative applications depends upon the robustness and infallibility of the part quality. Eliminating or sufficiently reducing factors contributing to the formation of defects is an integral step to achieving satisfiable part quality. Many advanced sensing, modeling, and artificial intelligence techniques have proven effective in characterizing, detecting, and monitoring defects. Often, combinations of sensing techniques are applied to deepen the understanding of the investigated phenomena. The AM-LPBF method continues to innovate and provide solutions to part quality issues through these advanced sensing techniques.  
Conceptualization, DG, S.W. and A.A.; investigation, S.W. and A.A.; writing--original draft preparation, S.W. and A.A.; writing--review and editing, DG. and S.W.; project administration, DG.; funding acquisition, DG. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Center for Advanced Energy Studies (CAES) business development funds as part of Idaho National Laboratory under the Department of Energy (DOE) Idaho Operations Office (an agency of the US Government) Contract DE-AC07-05 ID145142.  
Not applicable.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Defect Type** & **Defect** & **ECT** & **AFM** & **GP** & **CEMS** & **ND** & **EIS** \\ \hline \multirow{2}{*}{Surface-quality defects} & Surface oxidation [(61; 97)] & & & & ✓ & & ✓ \\ \cline{2-8}  & Surface roughness [(146; 207)] & & ✓ & & & & ✓ \\ \hline \multirow{3}{*}{Microstructure defects} & LOF porosities [(68; 205)] & ✓ & & & & ✓ & \\ \cline{2-8}  & Keyhole porosities [(205)] & ✓ & & & & & \\ \hline \multirow{2}{*}{Mech. defects} & Sol. cracking [(205)] & ✓ & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Available miscellaneous sensing technologies to detect LPBF defects.  
\begin{table}
\begin{tabular}{c l c c c c c c c c} \hline \hline
**Defect Type** & **Defect** & **AES** & **FBG** & **NRS** & **IU** & **LU*** & **PU** & **RUS** & **PAUT** \\ \hline \multirow{3}{*}{Surface-quality defects} & Balling [(173; 181)] & ✓ & & ✓ & & & & & \\ \cline{2-10}  & LOF porosities [(179; 181; 182; 187; 191; 200; 202)] & & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \cline{2-10}  & Gas porosities [(183; 187)] & & & & ✓ & ✓ & & & \\ \cline{2-10}  & Keyhole porosities [(184)] & & & & ✓ & & & & \\ \hline \multirow{3}{*}{Mech. defects} & Liq. cracking [(168)] & ✓ & & & & & & & \\ \cline{2-10}  & Sol. cracking [(170; 184; 189)] & ✓ & & ✓ & ✓ & & & \\ \cline{1-1} \cline{2-10}  & Delamination [(184; 188)] & & & ✓ & ✓ & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Available ultrasonic sensing technologies to detect LPBF defects.
## 2 Study Area  
Iran is the study area (see Figure 1a), which is located in the western part of Asia between latitudes and longitudes of 24-40\({}^{\circ}\)N and 44-64\({}^{\circ}\)E, respectively. Iran is the seventeenth largest country in the world, with an area of around 1.7 million km\({}^{2}\). Iran is a mountainous country with two main mountain ranges, Alborz and Zagros, located in the country's northern and western (spans from northwest to the southwest) parts, respectively. The southern border of Iran is formed by the coastal frontiers of the Persian Gulf and the Oman Sea, and the Caspian Sea is situated in the north of Iran. Moreover, the central parts of the country are covered by barren areas [59], particularly formed by two deserts namedDasht-e-Lut and Dasht-e-Kavir. Due to the massive latitudinal range of Iran, its climate varies from hyper-arid to very-humid climate zones with the dominant types of arid and semi-arid climate zones [60; 61]. The geographical characteristics (i.e., topographic and latitudinal features) of Iran are the dominant factors of precipitation variations throughout the country (see Figure 1b; [62; 63]). For instance, the annual accumulated precipitation varied between nearly 45 mm and 1950 mm for 2020 (calculated based on in situ data), representing high precipitation dynamics throughout the country. It has been reported that the western Mediterranean oscillation is the most influential source of precipitation in Iran [64]. Most parts of the country, i.e., arid and semi-arid climate zones, are characterized by low precipitations and high potential evapotranspiration. Additionally, Iran has experienced many climate extreme events, such as droughts and floods [65], emphasizing the significance of identifying accurate and reliable precipitation products. In this regard, reliable GPPs can be employed for monitoring and modeling tasks to promote informative management.
# Extending Appearance Based Gait Recognition with Depth Data
## 5 Experimental Validation
### Discussion  
The classification based on height feature alone shows poor results if compared to other approaches. This is expected for a large population where many individuals have similar height. This is particularly true considering that the average height of a person over a gait cycle cannot be estimated accurately for a variety of reasons, related to the sensor and method used and environment conditions, as well as variations in the person's posture, shoes, etc., thus confusion between persons of similar height is to be expected. Comparing the classification results using human height alone for populations with different height distributions between individuals further confirms that height feature is not adequate for cases when multiple individuals have similar heights.  
The results from the experiments confirm the expected overall and relative performance of Height, GEI, and BGEI features taken alone. The classification based on gait features using only the appearance based GEI method shows results consistent with those reported in the literature [5], when a population of similar size is considered (155 [5] and 176 [1]). Differently from GEI feature, which is derived from depth information of the entire silhouette, BGEI is constructed using front facing depth information only. This is by design to make the construction of BGEI possible from front and lateral views. With less information available for classification than GEI, and considering that the dataset used in this work contains only side view experiments, view invariant BGEI feature shows slightly less accurate classification results throughout the experiments.  
The proposed methods (HGEI-i, HGEI-f, HBGEI-i, and HBGEI-f) that combine height with appearance based features improved recognition accuracy significantly. The average increase in the recognition accuracy of HGEI-i with respect to GEI was 19% for _small_, 23% for _medium_, and 24% for _large_ population size. In general, between the two proposed methods, Height-GEI integration (HGEI-i) outperforms the Height-GEI fusion (HGEI-f) and can be the preferred choice. In some cases, for example when the population size is small, the recognition accuracy based on the height feature may be sufficiently high to make the late fusion efficient to a point of outperforming the HGEI-i method.
## 3 Field Measurements and Model Verification
### Outline of Measurements
A direct integration method was used to calculate the ground vibration response with a calculation time of 11 s, covering the passing time of the entire train. When using a FE model for calculation, both the calculation accuracy and efficiency need to be considered. The shorter the integration step, the higher the theoretical analysis frequency, but the longer the calculation time. Based on the previous study [6; 10], stable calculation results can be obtained within the frequency range of environmental vibration concern (1-100 Hz) by taking the integration step as 0.002 s, which theoretically enables an effective analysis frequency to 250 Hz. Therefore, the integration step applied in this paper is 0.002 s.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges
### Methods to Attribute SUHI  
Attributing SUHI is also challenging in SUHI research. Spatial-statistical methods such as the ordinary least squares regression [171], comparative analysis [133], and geographically weighted regression [241] have been used to examine the potential drivers of the SUHI. The statistical relationships, on one hand, depend strongly on variables chosen, study scales, research time, and/or data availability. For example, [PERSON] et al. [52] attributed the SUHI variations across cities in the continental United States to the ecological context. [PERSON] et al. [4] emphasized the role of vegetation and surface albedo in driving global SUHI distributions during the daytime and nighttime, respectively. [PERSON] et al. [53] stressed the climate-vegetation control on the diurnal and seasonal cycles of SUHI in mainland China. Local-scale SUHI variations have been widely attributed to two-dimensional land surface properties such as ISA, NDVI, water body, surface material, population density, and landscape structure [30]. Contributions of the three-dimensional features of buildings [94], climatic conditions [126], and anthropogenic heat emissions [242], however, have been rarely investigated due to data limitations. In addition, there are strong interactions and serious multi-collinearity problems among different variables (Figure 5) [6]. For instance, a higher ISA was nearly always accompanied by a larger population density and lower vegetation activity at a local scale. The independent and combined effects of different drivers as well as their underlying mechanisms remain poorly understood. As a consequence, contributions of some factors such as the urban area size, population, and landscape structure remain highly disputed (see Section 5.3).
# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
## 5 Evaluation
### Evaluation of Data Augmentation  
In this section, we evaluate the performance of our proposed ECG diagnosis model in MIT-BIH dataset. Figure 13 shows the main process of ECG data identification. We compare the diagnostic accuracy of the model before and after the data enhancement. The confusion matrix of the test results before data enhancement is shown in Figure 14. It can be seen from the table that the four categories of normal beat (1), left bundle branch block (2), right bundle branch block (3) and ventricular premature beats (5) with sufficient data volume have high diagnostic accuracy. The four categories of atrial premature beats (8), pacing mixed heart rate (38), ventricular fusion heartbeat (6) and ventricular flutter (31) decrease with the amount of data, and the diagnostic accuracy also drops significantly, until the diagnosis goes down to zero. The confusion matrix of the test results after data enhancement is shown in Figure 14. It can be seen from the matrix that almost all ECG categories are most likely to be misclassified as ventricular premature beats, which indicates that there may be some common features of ventricular premature beats. Comparison with Figure 14 shows that data enhancement plays an important role in improving the overall accuracy.  
Figure 15 shows the change in diagnostic accuracy of eight ECG categories before and after data augmentation. It can be seen that, before the data augmentation, although the diagnostic accuracy of the four ECGs of normal beat, left bundle branch block, right bundle branch block and ventricular premature beats have a good performance of more than 90%, the diagnostic accuracy of the three types of pacing mixed heart rate, ventricular fusion heartbeat and ventricular flutter are very poor. The diagnostic accuracy of pacing mixed heart rate is as low as 23.14%, while ventricular  
Figure 14: Confusion matrix: (**a**) Confusion matrix of test results before data enhancement, (**b**) Confusion matrix of test results after data enhancement.  
Figure 13: The main process of ECG data identification.  
fusion heartbeat and ventricular flutter cannot even be classified at all. After the data are enhanced, the diagnostic accuracy of the unenhanced data categories is more than 93%, which have a slight fluctuations compared with the previous ones. However, the fluctuations are within the normal range. The diagnostic accuracy of the enhanced data categories have undergone qualitative changes. Among them, the pacing mixed heart rate has increased from 23.14% to 84.46%, and the increase rate has reached 265%. Ventricular fusion heartbeat and ventricular flutter have, respectively, 79.17% and 95.97% diagnostic accuracy. Experimental data show that data enhancement has a substantial meaning for the classification of ECG diagnosis. The expansion of diagnosis categories and the improvement of accuracy make ECG diagnosis more practical.
## 4 Results
### Atmospheric Data Analysis  
SKT and OLR were analyzed to conduct the climatological study. We examined 90 days of ECMWF data preceding the Fano EQ, comparing it with a historical time series spanning the previous 42 years, i.e., from 1980 to 2021. The analysis revealed two highly anomalous days for each atmospheric parameter where the 2022 time series reached the limit of the two standard deviation bands of the historical series. The first anomalous day for SKT occurred on 18 August, i.e., 82 days before the EQ, while the second occurred on 15 September, i.e., 54 days before the EQ (as shown in Figure 8). Furthermore, Figure 9 shows the spatial distribution of these anomalous values, confirming their proximity to the epicenter, especially for the main anomaly. Please note that SKT is defined only on land.  
Figure 8: Analysis of the SKT parameter for the Fano EQ with comparison between the 2022 time series (dashed red line) and the historical time series (1980–2021, blue line). Evidenced by red circles there are two quite anomalous values near the second standard deviations from the mean: the first one refers to 18 August, and the second one to 15 September.  
Figure 7: Outcome of the R-AMR algorithm applied to the extracted seismic dataset. The red points represent EQs that are closer to the fault (within 37 km) than those represented by the blue points. At the bottom of the main figure, the magnitudes of the involved events are represented: red is used for EQs falling within 37 km from the fault and green those outside that limit.  
Regarding the OLR parameter, Figure 10 shows two anomalies exceeding the historical mean by two standard deviations on 5 and 12 September 2022, i.e., respectively, 65 and 58 days before the Fano EQ. Interestingly, both anomalies occurred after the first SKT anomaly but before the second one. Figure 11 depicts the spatial distribution of these OLR anomalies, with the first map (a) revealing an extended structure located to the north of the EQ epicenter.  
Figure 11: Maps of OLR anomalous days maps in terms of difference with respect to the historical mean: (a) 5 September; (b) 12 September. The epicenter is indicated by the central star.  
Figure 9: Maps of the SKT anomalous days in terms of difference with respect to the historical mean: (a) 18 August; (b) 15 September. The EQ epicenter is indicated by the central star. SKT is defined only on land.
## 3 Part I: Localization of Real and Virtual Sources in the Absence of Visual Cues
### Experiment Design
#### 3.1.2 Localization of Real Sound Sources (TestReal)  
During TestReal, each subject sat with his interaural center in the listener position in the anechoic room. In order to eliminate any visual information about the source positions, subjects sat inside an acoustically transparent tent (see Figure 3a,b) and the room was darkened. The only source of light was a tablet monitor, installed in front of the subjects and used by them to conduct the listening test and to give their responses. The loudspeaker arc was rotated to one of the 15 azimuthal target positions shown in Figure 2a. The test signal was played back from the loudspeaker channel corresponding to the target elevation. Subjects were encouraged to rotate their heads when listening to the signals within an allowable range of \(\pm 90^{\circ}\) horizontally and \(\pm 15^{\circ}\) vertically and to not exceed this range even if they perceived the sound source as behind them. Each of the 15 target source positions was presented once, and the presentation order was randomized. Five target positions were chosen randomly to be presented at the beginning for familiarization. Responses given to these five target positions were discarded from the evaluation. No feedback was given to the subjects during the familiarization or during the listening test.
## 3 Methods
### Turn-Taking Failures  
This study evaluates the challenges of turn-taking by analyzing the outcomes of participants' speaking intentions. In addition to unsuccessful speaking intentions (i.e., turn-taking failures), we also examine instances of speech conflicts, as they reflect breakdowns in coordination during the turn-taking process.  
Figure 3: Example data of speaking behavior from the dataset, including participant-labeled speaking intention tags (light gray rectangles indicate successful speaking intention tags, dark gray rectangles indicate unsuccessful speaking intention tags; colored rectangles represent the utterances of individual participants).  
In an ideal multi-party conversation, smooth turn transitions rely on the effective transmission and reception of social cues among all participants, ensuring seamless alternation of speech. However, when participants fail to secure speaking turns through their cues, it can lead to \"interruptions\", where a participant initiates their turn before the current speaker finishes [62]. Such intrusive behaviors not only disrupt conversational flow [63] but may also negatively affect interpersonal attitudes among participants [64].  
Another form of speech conflict arises when multiple participants simultaneously initiate their speaking turns. This scenario indicates a failure to accurately perceive others' intentions, resulting in inappropriate timing of speech initiation and triggering a turn-taking conflict.  
To systematically analyze the outcomes following the expression of speaking intentions, we classified them into the following categories based on whether the intention successfully transitioned into a speaking turn and whether a speech conflict occurred during turn initiation:  
* **Obstructed**: The speaking intention did not result in speaking behavior, or the turn initiation involved overlapping speech.
* **Unobstructed**: The speaking intention successfully transitioned into speaking behavior, and the turn initiation was characterized by non-overlapping, smooth turn transitions.  
Notably, in evaluating overlapping speech, we considered not only overlaps at the exact moment of turn initiation but also overlaps occurring within 0.2 s after the turn began. This is because, even if no apparent overlap occurs at the onset, a very short delay between the turns of two speakers might indicate a failure to detect the first speaker's intention. Figure 4 illustrates the structural relationship between speaking intention tags and the categories of turn-taking outcomes.
## 2 Three-Dimensional Image and Brightness Loss
### Three-Dimensional Display and Three-Dimensional Glasses  
There are two kinds of stereoscopic displays: passive and active. Passive polarization glasses, which are also called passive 3D glasses, are an example of passive stereoscopic technology. The working principle of this technology is to paste a micro-retarder layer on the front of a general television or monitor, using the polarization direction of the light to separate the left- and right-eye images. Then, the passive polarization glasses can correctly cause each of the viewer's eyes to see the appropriate right and left images, producing a 3D effect. The advantage of this approach is its low cost, but the screen resolution is reduced to half the original 2D image resolution, and the overall brightness is also reduced. Color-coded anaglyph 3D glasses can be divided into those using red and cyan, green and red, or blue and yellow filters, as shown in Figure 3.  
Active 3D glasses, shown in Figure 4, are another type of 3D glasses that are also called shutter glasses. In this process, the 3D screen continually alternates the display between the left- and right-eye images at a frequency of 120-240 Hz, with the right and left eyes being quickly switched and shielded alternately by the shutter glasses. This causes the left and right eyes to see the correct respective images, causing the brain to perceive a 3D image.  
Figure 3: Classification by color-code: (**a**) red and cyan and (**b**) red and green anaglyph 3D glasses used to create 3D stereo images.  
The main advantage of this technology is that the picture resolution, color, and stereoscopic 3D effect are not sacrificed. However, some users of active 3D glasses suffer from dizziness and discomfort. The other advantages are that there is less blur, this technology is low cost, and it is applicable to television or computer screens as well as projectors, provided their update frequencies can meet the requirements of the glasses. Therefore, active shutter glasses are being used in the majority of the 3D display systems being introduced onto the market at present, including 3D televisions, 3D glasses, and 3D cinema screens.
# Impact of Selected Yeast Strains on Quality Parameters of Obtained Sauerkraut
###### Abstract  
The aim of this research was to determine the influence of yeast strains (previously isolated from the fermentation process) on selected quality parameters of sauerkraut. For this purpose, shredded and salted (2.5% \(w/w\)) cabbage of the Galaxy variety was fermented in the absence of oxygen with the addition of 2 \(\times\) 10\({}^{6}\) cells of a selected yeast culture. The control sample was spontaneously fermented sauerkraut without yeast addition. The obtained sauerkraut was analysed in terms of the content of selected organic acids, sugars and polyols (HPLC), selected volatile compounds (HS-SPME-GC-TOFMS), colour (CieLAB) and aroma (QDA). Yeast _P. fermentans, Rh. mucilaginousa_ and _W. anomalous_ reduced crucial sauerkraut components such as lactic acid, glycerol, and certain volatile compounds, leading to decreased aroma intensity and acceptability. Additionally, an increase in glucosinolate decomposition products was observed. Conversely, _D. hansenii_ positively influenced sauerkraut quality by enhancing lactic acid content and exhibiting similar volatile characteristics to those of the control. Two of the three samples fermented with _D. hansenii_ received high sensory analysis scores akin to those of the control. Sauerkraut fermented with _Cl. lustiniae_ yeast contained elevated levels of volatile compounds--alcohols, esters and lactones--resulting in an intense floral aroma, albeit receiving lower overall ratings due to deviation from the typical profile.  
sauerkraut; yeasts; volatile components; organic acids; _Deharyomyces hansenii_
## 3 Results and Discussion
### Statistical Features in Different Terrains
#### 3.2.1 TA EMG  
The peak, mean absolute value (MAV), standard deviation (STD), root mean square (RMS), and waveform length (WL) of the TA are significantly larger for uphill than for flat and downhill, indicating that the TA is more active during uphill. This is because more efforts are made in order to overcome gravity and lift the body up when walking uphill [74]. In addition, the TA peak increases from level to downhill. The reason for that may be that the TA contracts more to resist the downward trend when walking downhill, resulting in more contraction of the TA, which aligns with the profile presented in [75]. Meanwhile, the feature \"TA 80\" decreases when going downhill because the gravity helping the foot clearance at 80% stride reduces the activity of the TA.