esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Innovative Methodology  
As shown in Figure 1, the overall process of on-line point cloud data compression in this work consists of four steps. in Step 1, the initial point cloud flow is obtained by 3D scanning measuring devices using an isochronous [42] or equidistant sampling method and the layer-by-layer scanning path is adopted (Figure 2). in Step 2, the initial point cloud data flow is immediately compressed by the chordal method [36] or bi-Akima method [37], both of which compress the amount of point cloud data based on the data redundancy in the current single scanning layer. In Step 3, the data redundancy in the compressed point cloud which is obtained in the previous step is further identified. In Step 4, the identified redundant point data is eliminated, and then we can obtain the final compressed point cloud. At last, the final compressed data flow is transmitted to the storage space of the measurement system.  
Herein, the real-time performance of the proposed data compression algorithm needs to be further analyzed and described. The path planning is performed before the start of the scanning measurement in Step 1. As shown in Figure 2, a layer-by-layer scanning path is adopted. The distance between the adjacent scanning layers is determined by the preset measuring accuracy. The measured surface is cut by the scanning layers to form a number of corresponding scanning lines. As shown in Figure 2, there are two planning modes for scanning directions: (i) the progressive scanning mode, and (ii) the S-type scanning mode. Regardless of the scanning mode, the measuring device in Step 1 will continuously transmit the initial point cloud data flow to the data compressor in Step 2. The compressor performs data compression immediately after receiving all initial point data of a single scanning layer, rather than waiting for the entire surface to be scanned before performing data compression. That is, each time the point cloud data in the current scanning layer is completely transmitted to the compressor, the subsequent data compression algorithm is executed immediately.  
Figure 1: The overall process of on-line data compression for 3D free-form surface scanning measurement.  
Therefore, the proposed data compression algorithm is essentially a quasi-real-time method, which we call an on-line data compression method.  
The flow chart of this algorithm is illustrated in Figure 3, and its principle is described in detail as follows:  
Figure 3: The flow chart of point cloud data redundancy identification and elimination algorithm.  
Figure 2: Layer-by-layer scanning path for 3D free-form surface scanning measurement.
## 3 Results and Discussion
### Thermal Resistance, Conductivity, and Diffusivity  
From the point of view of a building material, experimental results show that finer-grained blocks (R1) exhibited better thermal performance than coarser-grained blocks (R2). R1 CEBs showed a 30% increase in thermal resistance compared to R2 CEBs due to the sole effect of different maximum particle sizes. A substantial reduction in thermal conductivity and diffusivity, by 23% and 17% respectively, is noticeable. Table 6 summarises the results obtained per mixture and age. The results suggest that the size of the maximum soil particles can significantly influence the thermophysical properties of CEBs. To study the problem and isolate the considered variable, other important factors were kept constant. These are:  
* Soil particle mineralogy--the sieving strategy adopted to modify the grain size curve of the natural soil made it possible not to alter its mineralogy. Alternatively, sand should have been added. Considering that quartz, the most common constituent of sand can have three times higher the thermal conductivity of clay minerals, i.e., 8.80 W/mK vs. 3.0 W/mK [41], it is reasonable to suspect that, when this element is added to alter the PSD of the soil, the thermal response will inevitably be altered as well. * Degree of compaction--the applied pressure can significantly affect particle packing and the overall heat and mass transfer mechanisms [9; 42; 43]. In this study, the degree of compaction was kept constant as the blocks underwent the exact same manufacturing process. * Moisture content--thermal results presented refer to a dry-reached state. It is generally acknowledged that moisture content has a great influence on the thermal attributes of a hygroscopic material such as soil. However, the extent of this influence has not been conclusively established [4]. Consequently, when presenting thermal parameters, it is common to refer to dry-state conditions to avoid excessive fluctuations in results and to establish a baseline. Indeed, depending on the degree of saturation, water menisci between soil particles act as bridges for heat transfer. A study from 2015 [38] showed that thermal conductivity was lower in fine sands than in coarse ones under dry conditions but higher in fine sands than in coarser ones at low moisture content. Considering the above, the increase in thermal resistance can be attributed to finer particles in R1 CEBs. This resistance is due to the more numerous contact zones between smaller grains, which, when filled with air, offer greater resistance to heat flow [38; 44]. In fact, finer particles make the surface area of a given portion of material larger than the same portion composed of coarser particles. In the former case, the contact areas increase, and the heat flow encounters more dispersion points. The mechanism is presented in Figure 7.
## 5 Discussion  
In this work, we present a research methodology for modeling bridge maintenance with the goal of evaluating the relationship between performance, maintenance, deterioration, and other influential variables of bridge decks with various surface wearing types. We analyze six different wearing surfaces of bridge deck: Monolithic Concrete, Gravel, Wood or Timber, Low Slump Concrete, Other, and Bituminous, from the perspective of bridge health metrics. Our approach offers a way to analyze and investigate structural, environmental, and regional variables that shape the health of bridge decks. Our results portray an interaction between such variables through different perspectives of bridge health on different types of bridge wearing surfaces.  
We identify relationships across various types of bridge deck surfaces. Statistical and machine learning models indicate that each bridge deck surface type exhibits a distinct pattern that can be characterized by influential variables and bridge health metrics introduced in this work. Although deterioration patterns between all types of bridges seem similar, there is a statistically significant difference between the deterioration rate of bridge decks among wearing surfaces. Moreover, the deterioration rate of a bridge deck across all wearing surfaces is one of the top influential factors. The frequency of maintenance is high for concrete and Wood type surfaces of bridges. Finally, overall performance patterns indicate individual baseline compared with the general baseline of the deck in the state of Nebraska, showing varied influence of deterioration and maintenance patterns. In the following sections, we will discuss findings from previous literature mainly focused on Monolithic Concrete, Gravel, Wood or Timber, Bituminous, and Low Slump Concrete wearing surfaces in light of new results. We do not include a discussion on Other wearing surface type, as previous researchers have not provided a comprehensive understanding of this type of wearing surface and its influential variable in modeling bridge deck maintenance.  
Figure 19: A two-way PDP shows the interaction between top influential factors for bridge deck with Other wearing surface. \(X\)-axis Deterioration Score and \(y\)-axis Average Daily Traffic. The left side illustrates the grid format, and the right side presents contours. The lighter shaded regions in the PDP plot are interaction between influential variables regarding intervention of bridge deck and darker shades are related to non-intervened bridge deck.
# Vocal Synchrony of Robots Boosts Positive Affective Empathy
## 3 Implementation of the Vocal Synchrony
### Details of the Estimation Methods for Prosodic Features  
As voice analysis and speech synthesis tools, our system uses Julius [48], which is an open-source large vocabulary continuous speech recognition engine and Speech Signal Processing Toolkit (SPTK) [49] as a voice analysis tool. In addition, VoiceText [50] was used as a speech synthesis tool. The process from the recognition of a human utterance to the generation of the robot voice is as follows. (1) The user's speech is recognized by Julius, (2) the speech rate is estimated by Julius, (3) the pitch and volume are estimated by SPTK and are used as the prosodic features, and (4) each estimated value is converted to a voice synthesis parameter specific to VoiceText, which performs the voice synthesis.  
The Japanese equivalent of \"beat\" is \"mora\", and each mora is temporally equivalent. In Julius, which estimates the speech rate, the average number of mora per second obtained by dividing the number of mora by the length of the utterance interval obtained in the preprocessing of speech recognition processing was used as a feature value representing the speech rate. Essentially, it has the structure of a consonant and vowel, although exceptions exist such as \"n\", which has no vowels, and \"tsu\", which is applied to the sound itself [51]. In SPTK, which estimates pitch and volume, the fundamental frequency (\(F0\)) of the wave file obtained from the human utterance is extracted, and the average pitch is estimated by calculating the average value in the logarithmic domain. Then, the analysis window is shifted every 10 us, and \(F0\) is output. The average value of \(F0^{\prime}\), obtained by removing the unvoiced sound section from the \(F0\) value obtained here, is estimated as the human pitch. To estimate the volume, the average value of the power term in the speech segment is used for simplicity, which is estimated as the volume of the human. In VoiceText, however, speech synthesis is performed using both male and female speech models. The voice outputted by speech synthesis for each gender is calibrated on the basis of the estimatedprosodic features. The calibration formula, determined from the estimated human prosodic features of the VoiceText speech synthesis parameters, is described below.  
VoiceText has limited parameters of 50-400% speech speed and 50-200% pitch and volume. Text data are required to generate synthesized speech, such as the following five specific Japanese sentences [52].
# Application of Extracellular Polymeric Substances during Cultivation of Microalgae Biomass
## 1 Introduction
In addition, the presented approach is connected to the necessity of purchasing a specific bacterial species, its cultivation, and the isolation of the biofocculant in a separate reactor, which may pose technological difficulties and significantly increase the costs of biomass production. There are many studies of the use of different substances to improve the sedimentation properties of microalgae. However, according to the authors' knowledge, EPS extracted from activated sludge from wastewater treatment plants have never been used for this purpose. However, as EPS are rich in organic carbon, they can also play a role as extracellular energy and carbon sinks. They are an abundant source of structurally and compositionally diverse biopolymers. Therefore, EPS are important carbon sources for different organisms in the food chain. The separation method for microorganisms' EPS can, in turn, influence the compositions, contents, structure, properties, and functions of EPS [9; 10]. Commonly applied methods, e.g., high-temperature or alkaline environments, could markedly change the properties and compositions of the extracted EPS. The cation-exchange resin method was selected as the most effective for the extraction of EPS, which prevents external forces from causing chemical contamination and changing the characteristics and properties of EPS [11; 12]. The presence of both proteins and polysaccharides in the extracted EPS could stimulate the aggregation of cells or production of EPS by microalgae, which can contribute to the better sedimentation properties of microalgae. Thus, the aim of this study was to assess the possibility of the utilization of extracellular polymeric substances (EPS) produced by the biomass of bacteria used in wastewater treatment (activated sludge) to densify the biomass of selected species of microalgae and cyanobacteria. The research analyzed the impact of isolated EPS on the cultivation of microalgae _Chlorella_ sp., _Tetraselims subcordiformis_, and cyanobacteria _Arthrosipra platensis_.
## 4 Vehicle Detection Using Ultrasonic Sensors
### Other Considerations  
The previous analysis results demonstrate the importance of determining the detection interval for power saving in WSN-based traffic information acquisition systems that use ultrasonic sensors. In this section, we discuss other considerations that influence the detection interval.  
Figure 13 illustrates a modified analysis model. When a vehicle passes the ultrasonic sensor, the maximum value of the detection distance \(l_{u}\) cannot exceed \(l_{l}+l_{g}-l_{w}\), where \(l_{w}\) is the width of the vehicle. Thus, we have that \(l_{u}<l_{l}+l_{g}-l_{w}\), _i.e._,  
\[0<l_{u}<l_{l}+l_{g}-l_{w} \tag{7}\]  
Equation (7) implies that reducing the maximum value of \(l_{u}\) decreases the longest possible minimum detection interval. Figure 14 shows the effect of Equation (7) on the available detection interval in Figure 12. The parameters used are the same as those used to produce Figure 12, and \(l_{w}=1.5\) m (the approximate width of a compact car). As shown in Figure 14, the constraints of Equation (6) mean that the maximum detection interval is fixed to 50 ms. In contrast, the longest minimum detection interval decreases from 20 ms to 12 ms. This reduction widens the range of the available detection interval, which indicates that data can be collected more frequently and increases the detectable vehicle speed.  
Figure 12: Available detection interval on a one-lane road.  
The analysis model of Figure 13 considered a one-lane road. The model can be extended to a two-lane road, as illustrated in Figure 15, in which one vehicle is placed in each lane. In this case, the maximum value of \(l_{u}\) for the vehicle in the inside (respectively outside) lane will be \(2l_{l}+l_{g}-l_{w}\) (respectively \(l_{l}+l_{g}-\frac{l_{w}}{2}\)). These two conditions can be summarized as follows:  
\[\begin{split} 0<l_{u}<l_{l}+l_{g}-\frac{l_{w}}{2}\\ l_{l}+l_{g}-\frac{l_{w}}{2}<l_{u}<2l_{l}+l_{g}-l_{w}\end{split} \tag{8}\]  
Equation (8) also influences the range of the available detection interval, and the impact on the longest minimum detection interval is the same as that resulting from Equation (7).  
Finally, we discuss the positioning of the ultrasonic sensors. As shown in Figure 2, we require \(\alpha<\frac{\theta}{2}\) to correctly detect vehicles, and this condition influences the positioning of the ultrasonic sensors. Figure 16 illustrates that, if an ultrasonic sensor is placed on a curved section of road, the value of \(\alpha\) will increase. Consequently, the reflected ultrasonic wave cannot be received by the ultrasonic sensor. Thus, ultrasonic sensors should be installed on straight sections of road to ensure reliable vehicle detection.  
Figure 14: Available detection interval on a one-lane road.  
Figure 13: Modified analysis model.
## 2 Acoustic Emission Technique (AET)  
Acoustic emission is the release and propagation of energy due to fracture in a stressed material. Based on this natural phenomenon, AET, which can be described as detecting, and analyzing elastic waves by appropriate sensors, has been used to examine different facilities such as structures, nuclear plants, high-pressure pipes or tanks and airplanes. Since attenuation and damage type classification analyses are made using information about an AE signal, some characteristics of an AE signal should be mentioned (Figure 1a). Among these parameters, maximum amplitude (or amplitude)--peak voltage reached in the signal--was used in this study to evaluate the attenuation properties of the specimens. This parameter is related to the magnitude of the source. On the other hand, clustering was conducted using some seconder parameters associated with peak and centroid frequencies (Figure 1b) obtained after fast Fourier transformation (FFT) [43] to classify damage types.  
As it was mentioned in Section 1, studies in the literature show that using weighted peak frequency (WPF) (Equation (1)) [42] for clustering AE activities is a good approach. Here, the center frequency (\(f_{center}\)) is the mass center of the frequency spectrum, and the peak frequency (\(f_{peak}\)) is the highest frequency value achieved. The parameter that WPF is evaluated together in clustering is partial power (PP). PP is the percentage contribution  
Figure 1: Characteristics of an AE signal: (**a**) temporal wave, (**b**) FFT spectrum.  
of specific frequency ranges in the frequency spectrum. Multiple PP can be calculated for different frequency ranges (Equation (2)) [39].  
\[Weighted\ peak\ frequency\ (WPF)=\sqrt{f_{center}\cdot f_{peak}} \tag{1}\]  
\[Partial\ power\ (PP,\ \%)=\frac{\int_{f_{i}}^{f_{i}}\widetilde{U}^{2}(f)df}{\int_{0}^{f _{max}}\widetilde{U}^{2}(f)df} \tag{2}\]  
where \(\widetilde{U}(f)\) is the FFT of the signal \(U(t)\).
# Principles of Different X-ray Phase-Contrast Imaging: A Review
## 2 Methods
### Coherent Diffraction Imaging
discussed applying this method on low-dimensional hard-condensed matter materials [PERSON] et al. (2018). In 2018, [PERSON] et al. characterized the stacking defects and strain in a single InGaAs (indium gallium arsenide) nanowire with coherent nanofocused X-ray using this method. A spatial resolution of 2.6 nm was achieved [PERSON] et al. (2018). In the same year, [PERSON] et al. used spectroptychography to image polymeric perfluorosulfonic acid (PFSA) ionomers in polymer electrolyte membrane fuel cell (PEMFC) cathodes in both and three dimensions. A two-dimensional spatial resolution of 15 nm was achieved [PERSON] et al. (2018). In 2019, [PERSON] used X-ray spectroptychography to visualize the three-dimensional cerium valence map in Ce\({}_{2}\)Zr\({}_{2}\)O\({}_{x}\) (x = 7-8) particles. This achievement may permit the chemical imaging of reaction tracking areas at the nanoscale in solid materials [PERSON] et al. (2018). In 2020, the fragmentation behavior of polyolefin catalysts was visualized in 3-D for the first time with this method by [PERSON] et al. (2018). In the same year, [PERSON] et al. imaged three-dimensional fragments of the petioles of wild type and C4H lignin mutant _Arabidopsis thaliana_ plants using synchrotron cryotypchography. The images revealed the heterogeneity of vessels, parenchyma, and fiber cell wall morphologies, highlighting the relation between disturbed lignin deposition and vessel implosion. A spatial resolution of 134 nm was achieved [PERSON] et al. (2018). Furthermore, [PERSON] et al. demonstrated X-ray linear dichroic ptychography and mapped the c-axis orientations of aragonite crystals [PERSON] et al. (2018). Additionally, in 2020, [PERSON] et al. applied multiple illuminations to the method and successfully received an extended FOV [PERSON] et al. (2018). With high coherency and extremely-focused X-ray sources, X-ray CDI, mainly the ptychography method, has been widely applied in material science, especially crystallography. It can also obtain good contrast with biological samples. However, the high dosage makes this method unsuitable for living samples.
# Effects of Nanoparticles on Algae: Adsorption, Distribution, Ecotoxicity and Fate
## 4 Ecotoxic Effects of NPs on Algae
### Effect of NPs on the Photosynthesis System  
Algae is a kind of phytoplankton, and photosynthesis is an important physiological process of algae. The adsorption of NPs on the surface of algal cells results in a shading effect that affects algal photosynthesis [26]. The shading effect caused by NPs affects the light, pigment and other conditions necessary for photosynthesis, weakening the algae absorption of light and thereby inhibiting the photosynthesis process [65; 66; 67]. However, the nanotoxicity caused by the shading effect is hard to be quantitatively determined separately because the attachment could also lead to other forms of toxicity (e.g., physical membrane damage) [47]. The chloroplast membranes that are rich in polyunsaturated fatty acids are potential targets of lipid peroxidation, which may cause damage to photosynthesis sites [57]. _Chloella_ sp. showed sensitivity to TiO\({}_{2}\) NPs and structural damage was observed in the nucleus and the cell membrane, as well as in the chloroplasts [68]. In a toxicity test of Al\({}_{2}\)O\({}_{3}\) NPs to _Chloella_ sp. and _Scenedesmus_ sp., it was found that Al\({}_{2}\)O\({}_{3}\) NPs could reduce the content of chlorophyll in algal cells [69]. It was also proven that SiO\({}_{2}\) NPs affect the content of the photosynthetic pigment of _Scenedesmus_ sp., in which chlorophyll a and b content decreased while carotenoid content was not affected [70]. Similarly, chlorophyll a content and photosynthetic efficiency in _Scenedesmus_ _bijugus_ decreased as a function of the TiO\({}_{2}\) NPs concentration [71]. Ag NPs significantly reduced chlorophyll content and inhibited the growth of the green algae _Chloella vulgaris_[72]. The change of photosynthetic pigment content directly influenced the photosynthesis of algal cells [51; 65]. One study has shown that NPs changed the photosynthetic rate and respiration rate of algae, resulting in metabolic disturbance [46]. CuO NPs were reported to reduce PSII capacity in converting light energy in photosynthetic electron transport [65]. Table 2 summarizes the different toxic effects of some NPs on algae.