esa-sceva/satcom-chunk-collection huggingface dataset:
# The Dependence of Ultrasonic Velocity in Ultra-Low Expansion Glass on Temperature
## 4 Tested Material and Experimental Setup
### Ultrasonic Measurement System  
The schematic diagram of the \(c_{\rm L}\). measurement experimental system is illustrated in Figure 4. A 75 MHz ultrasonic pulser/receiver (Olympus, Waltham, MA, USA, model 5073 PR) was used, which was connected to a 400 MHz data acquisition card (Spectrum Instrumentation GmbH, Grosshansdorf, Germany, model M4i.2220-x8) that transmits the ultrasonic signals to a computer to be processed. All signals were captured with 2,500,000 points at a sampling rate of 2.5 GHz. After the acquisition, the data were properly processed to determine the involved ultrasonic velocities. The temperature of the tested  
The self-generating and self-receiving transducer model was utilized to transmit and receive the ultrasonic waves. Considering the diameter and thickness of the tested sample, wideband focusing 19.05 mm diameter longitudinal wave immersion transducers (Olympus, Waltham MA, USA) with 3.5 and 5 MHz nominal center frequencies were employed in this investigation. Figure 5 illustrates the ultrasonic velocity measurement results for the samples 1#-6# at 20 \({}^{\circ}\)C using two different ultrasonic frequencies: 3.5 and 5 MHz. Each sample was subjected to five replicate measurements by each transducer. The ultrasonic frequency had little effect on the ultrasonic velocity of the sample, as the \(c_{\rm L}\) in the sample only depends on its inherent physical parameters, including its bulk modulus and density [25]. In addition, the repeatability of the \(c_{\rm L}\) measurement was essentially the same for both frequency transducers, both within 0.1 m/s. This suggests that any frequency could be used to characterize the CTE of ULE glass in engineering applications when using ultrasonic velocity measurement methods.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 3 Ground Theory
### Basic Terminology  
The literature regarding multidimensional projection, or even multidimensional data, often utilizes many different terms to name similar concepts, which can lead to some mistakes. For example, it presents terms such as \"multidimensional\", \"multivariate\", or even \"multivalued\" for defining datasets in which each data object is composed of a set of attributes. However, these terms are not consistently used [20], and we will adopt the term \"multidimensional\" to denote datasets composed of multiple attributes with or without dependence on each other.  
Since the beginning of this text, we have named each vector \(x_{i}\) as data objects, but there are terms such as \"instance\", \"observation\", \"data item\" or just \"item\", \"record\", \"array\" or even \"point\". We named the vectors \(x_{i}\) from a dataset \(X\) as \"data objects\" or \"instances\".  
Another terminology is related to \(x_{i}\) attributes, and one can find several terms denoting them, such as \"variable\", \"dimension\", \"property\", \"characteristic\", or \"feature\". We used throughout the text the terms \"attribute\" or \"feature\".  
In addition, we will use the term \"point\" exclusively to name instances transformed into the visual space. We finish this subsection by noting that matrix notation from mathematics is standard to denote the \(x_{i}\) data objects such as vectors. However, considering the context of data analysis, we argue that the terms adopted here are in line with precise data and mathematical terminology. This kind of explanation is due to the multidimensional projection domain being closely related to matrix theory on mathematical grounds.
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 3 CDR Products and Applications
### CDRs and Scientific Applications  
It is beyond the scope of this overview article to describe all application of CDRs. Therefore, we aim to provide a brief overview of the major international efforts to produce CDRs for scientific applications and show the breadth of climate variables that they cover. There are major initiatives across the globe for creating CDRs. We describe three such programs to create CDRs along with their major scientific applications.
## 5 Application of Our Proposed Approach  
As mentioned earlier, three case studies are adopted to demonstrate and validate the applicability of our proposed approach. These cases are based on (a) demand data for transformer tanks collected from a local manufacturer; (b) average sales data of a Slovenian company (i.e., Danfoss District Heating) as extracted from literature [36]; and (c) demand data for plastic bags of a manufacturer in south of Spain as extracted from the literature [41]. The whole process is discussed step by step in the following sections.
## 5 Conclusions  
The actual performance of feedback ANC headphones would be degraded if the primary and secondary paths present FR mismatches between the feedback microphone and the human ear. Within low frequency band where the ANC system is expected to be effective, the FR mismatch has a fatal influence on the NR performance and should be avoided at the acoustic design stage of the headphones. Meanwhile, large FR mismatches at high frequencies could also lead to significant noise enhancement for the human ear even if it is well constrained for the feedback microphone.  
In this paper, a new controller design procedure is proposed to deal with such problems. In order to minimize the delay, the feedback loop is constructed directly with the feedback microphone without any extra filters corresponding to the virtual sensing techniques. Instead, an optimization problem is established in which the comprehensive performance is evaluated at the human ear position. The feedback controller is constructed with cascade biquad filters, whose optimal parameters are found with DE algorithm. Experiments are carried out to validate the proposed method. The results have shown that the noise enhancement caused by the FR mismatch can be greatly reduced for the dummy  
Figure 6: Experiment results of the NR performances: (**a**) \(\delta\) = 5 dB and (**b**) \(\delta\) = 3 dB with Method 1, (**c**) \(\delta\) = 5 dB and (**d**) \(\delta\) = 3 dB with Method 2.  
head ear at the price of a slightly reduced NR performance within low frequency band. Generally, with the proposed method the noise enhancement at high frequencies can be limited below a prescribed value and good comprehensive performance can be obtained for the human ear.  
Conceptualization, F.A.; methodology, F.A.; software, F.A.; validation, F.A.; formal analysis, F.A.; investigation, F.A.; data curation, Q.W.; writing--original draft preparation, F.A.; writing--review and editing, Q.W. and B.L.; supervision, B.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by National Natural Science Foundation of China (grant number 11404367, 11874034 and 51905288) and Taishan Scholar Program of Shandong (No. ts201712054).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot
## 2 Images Acquisition and Preprocessing  
In the study of apple-harvesting robots' visioning systems, the most important thing is to accurately recognize the target fruit. However, in the process apple image capture, due to the diversity of apple growth postures in the natural environment and the different positions of the camera, it is easy for branches or leaves to block and overlap each other. So, the captured apple images show a variety of gestures, such as single fruit without shade, a fruit covered by branches or leaves, overlapped fruit, etc. Moreover, to some extent, the posture and degree of overlapped or shaded apples are a complicated variable. These unfavorable factors will cause interference to target the accurate recognition of the fruit.  
Compared with shaded fruit images, the recognition of the overlapped images is more difficult. Figure 1 shows some shaded and overlapped apple images. In this study, the focus is on the localization and recognition of overlapped images.  
In this study, a total of 150 apple images were captured from apple images captured from two different locations in Feng County and Penglai County. The specific information of image acquisition is as follows.  
Image acquisition location: The apple images came from two places--the apple demonstration base in Dashahe Town, Fengxian County, Jiangsu Province, and the apple production area in Penglai County, Yantai City, Shandong Province.  
Image acquisition information: All images were collected under natural light, including sunny and cloudy days, and smooth and backlighting. All the images in the collection were from between 8:00 am to 5:00 pm.  
Figure 1: Captured apple images. (**a**–**c**) overlapped images; (**d**) shaded image.  
Image acquisition device: The camera was connected to the computer, AFT-0814 MP (Vision Electronics Co., Ltd. Lanzhou, China), focal length 8 mm, field of view angle 54\({}^{\circ}\), closest object distance 0.15 m, to ensure that the image acquisition distance was between 50-200 cm.  
The variety of the apples was Fuji series, and 150 apple images were collected in the experiment. Compared with shaded fruit image, the recognition of overlapped images was more difficult. Figure 1 shows some shaded and overlapped apple images. In this study, the focus was on the localization and recognition of overlapped images.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 5 Microbial Remediation of Heavy Metal Pollution in Soil and Water
### Microbial Living Environments
#### 5.1.4 Substrate Concentration  
The concentration of heavy metal ions also affects the adsorption rate of microorganisms. Generally, a proper evaluation should be used to establish the quantification of the accumulative features of a bio-sorbent [53]. The two most frequently used equations to describe the features are the Langmuir model, whose parameters are interpretable and mainly describe the adsorption of a single-layer surface [76], and the Freundlich model, which is mainly applied to the adsorption equilibrium of the adsorption surface equation [61]. Even though the Freundlich model is much simpler, it grows unbounded [53]; therefore, until now the Langmuir model is more widely used than the Freundlich model. [PERSON] et al. and [PERSON] [41; 56] have used the Langmuir model to study the effect of heavy metal concentration. They showed that the concentrations of heavy metal ions with the highest adsorption rates vary according to the microorganisms and the heavy metal ions studied. However, the trend, which is similar in all cases, indicates that the adsorption increases to a certain value and then remains constant with the increase of the concentration of heavy metal ions (i.e., the equilibrium concentration). In addition, Fenton processes are helpful in the removal of heavy metals. [PERSON] et al. [79] showed that the lowest amount of total organic carbon removal is 50%, but removal can reach 100% under certain conditions. [PERSON] et al. [80] showed that in soil and water contaminated by Zn\({}^{2+}\) and Ni\({}^{3+}\), total organic carbon was completely removed for all runs tested in the present study after pH adjustments.
## 6 Image Compression  
The best lossless image compressors are enabled by arithmetic encoders based on the model driven method [32]. The _model driven method_ consists of two distinct and independent phases: _modeling_[33] and _coding_[34]. Arithmetic encoders are the best model driven compressors both for binary images (JBIG) [35] and for grey scale and color images (CALIC) [36], but they are often ruled out because they are too complex. As far as the model driven method for grey scale and color image compression is concerned, the modeling phase consists of three components: the determination of the context of the next pixel, the prediction of the next pixel and a probabilistic model for the _prediction residual_, which is the value difference between the actual pixel and the predicted one. In the coding phase, the prediction residuals are encoded. The use of prediction residuals for grey scale and color image compression relies on the fact that most of the times there are minimal variations of color in the neighborhood of one pixel. LOCO-I (JPEG-LS) [37] is the current lossless standard in low-complexity applications and involves Golomb-Rice codes [38, 39] rather than the arithmetic ones. A low-complexity application compressing 8x8 blocks of a grey-scale or color image by means of a header and a fixed-length code is PALIC [40] which can be implemented on an arbitrarily large scale system at no communication cost. As explained in [40], parallel implementations of LOCO-I would require more sophisticated architectures than a simple array of processors. PALIC achieves 80 percent of the compression obtained with LOCO-I and is an extremely local procedure which is able to achieve a satisfying degree of compression by working independently on different very small blocks. On the other hand, no perfectly scalable low-complexity binary image compressor has been designed so far on an array architecture with no interprocessor communication. BLOCK MATCHING [13, 14] is the best low-complexity compressor for binary images and extends data compression via textual substitution to two-dimensional data by compressing sub-images rather than substrings [2, 15], achieving 80 percent of the compression obtained with JBIG. However, it does not work locally since it applies a generalized LZ1-type method with an unrestricted window. [PERSON] extended the LZ1 method to binary image lossless compression by means of a square block matching technique [13]. A slower but more effective binary image compressor employs rectangular matches [14] and it is more suitable for scalable parallel and distributed implementations [8].
###### Abstract  
Visible/near-infrared (Vis/NIR) hyperspectral imaging (400-1000 nm) was applied to identify the growth process of _Aspergillus flavus_ and _Aspergillus punsiticus_. The hyperspectral images of the two fungi that were growing on rose bengal medium were recorded daily for 6 days. A band ratio using two bands at 446 nm and 460 nm separated _A. flavus_ and _A. parasiticus_ on day 1 from other days. Image at band of 520 nm classified _A. parasiticus_ on day 6. Principle component analysis (PCA) was performed on the cleaned hyperspectral images. The score plot of the second to sixth principal components (PC\({}_{2}\) to PC\({}_{6}\)) gave a rough clustering of fungi in the same incubation time. However, in the plot, _A. flavus_ on day 3 and day 4 and _A. parasiticus_ on day 2 and day 3 overlapped. The average spectra of each fungus in each growth day were extracted, then PCA and support vector machine (SVM) classifier were applied to the full spectral range. SVM models built by PC\({}_{2}\) to PC\({}_{6}\) could identify fungal growth days with accuracies of 92.59% and 100% for _A. flavus_ and _A. parasiticus_ individually. In order to simplify the prediction models, competitive adaptive reweighted sampling (CARS) was employed to choose optimal wavelengths. As a result, nine (402, 442, 487, 502, 524, 553, 646, 671, 760 nm) and seven (461, 538, 542, 742, 753, 756, 919 nm) wavelengths were selected for _A. flavus_ and _A. parasiticus_, respectively. New optimal wavelengths SVM models were built, and the identification accuracies were 83.33% and 98.15% for _A. flavus_ and _A. parasiticus_, respectively. Finally, the visualized prediction images for _A. flavus_ and _A. parasiticus_ in different growth days were made by applying the optimal wavelength's SVM models on every pixel of the hyperspectral image.  
Aspergillus flavus; Aspergillus parasiticus; growth identification; hyperspectral imaging +
Footnote †: journal: Applied Sciences
## 5 UAV-Assisted IoV and Blockchain Solutions for Smart Cities
### Coordinated UAV Services  
As UAVs' popularity rises, their application areas are also starting to expand. A group of UAVs can perform some tasks better compared to a single one. The range of the tasks varies, covering areas such as disaster recovery, surveillance, network relaying, energy-efficient device discovery, among others. The common element in these tasks is coordination among UAVs. However, such coordination needs improvements as it is based on communication between adjacent UAVs using some sort of broadcast/diffusion instead of a platform where can share common objectives and a certain level of knowledge to collaborate on this platform. Blockchain can be beneficial as it allows building a global channel for UAV communication.  
There are more basic requirements for coordination between UAVs, such as preventing mid-air collisions, decision-making based on data collected from other UAVs, and secure communication. The latter could be ensured by Blockchain both in unicast and broadcast models and via the use of the public key cryptographic mechanisms. Furthermore, in such networks, a voting system scheme could be implemented, empowering UAVs to make many types of decisions, contextual choices on other UAV's views.
## 2 Materials and Methods
### Polarization Feature Extraction  
The structural characteristics of the target can be obtained effectively by polarimetric decomposition. Among such techniques, Cloude-Pottier decomposition, Krogager decomposition, and Huynen decomposition have been widely used. Huynen decomposition is mainly used to distinguish between different scattering mechanisms such as surface scattering, body scattering, etc. Krogager decomposition is based on sphere, dihedral corner reflector, and helical components, and is suitable for analyzing symmetric structures. Cloude-Pottier decomposition quantifies the scattering randomness, anisotropy, and dominant scattering mechanism, and is suitable for asymmetric and complex scattering environments. Taking into account the complementary nature of these three polarization decomposition methods and the characteristics of the target data, this paper focuses on the three typical decomposition methods of Huynen decomposition, Cloude-Pottier decomposition, and Krogager decomposition.  
Assuming that the polarization state of the radar transmission antenna is \(\mathbf{P}_{E}\) and that the polarization state of the receiving antenna is \(\mathbf{P}_{R}\), the polarization state of the target scattering field is represented as follows:  
\[\mathbf{P}_{R}=\mathbf{S}\cdot\mathbf{P}_{E} \tag{1}\]  
where \(\mathbf{S}\) denotes the Sinclair scattering matrix:  
\[\mathbf{S}=\left[\begin{array}{cc}S_{hh}&S_{hv}\\ S_{vh}&S_{vv}\end{array}\right]. \tag{2}\]  
In Equation (2), \(h\) denotes the horizontal polarization for receiving and \(v\) signifies the vertical polarization for transmitting. Other parameters use the same definition.
## 3 Results
### Structural Analysis of Dentin-Pulp Interface  
We then conducted a structural analysis of the dentin-pulp interface to confirm photobiomodulation-induced neodentin formation. Premolar P4 was cut from both sides of the upper jaw and sectioned along the line 'A', as shown in Figure 2b. H&E staining of these sections demonstrated that Control and PBM-treated premolars were histologically similar in all sections. The pulp chamber was composed of dentin, predentin, odontoblasts, and pulp stem cells (Figure 3a,b).  
SEM analysis also revealed that both Control and PBM-treated teeth exhibited distinct dentin layers, odontoblasts (OB), cell-free zones (CFZs), and cell-rich zones (CRZs) (Figure 3c,h). Upon magnification, odontoblasts in PBM-treated samples appeared more clearly adjacent to predentin (Figure 3g,h) than those in Control samples (Figure 3d). This indicates the increased activity of odontoblasts in dentin formation after PBM treatment [20].  
Figure 2: Micro-CT images and analysis of the premolar teeth of the upper and lower jaws of the beagle model after PBM. (**a**) Micro-CT images clearly show the crown, pulp chamber, dentin, PDL, alveolar bone, and root apex areas of each tooth. The dotted lines marked from A to C in P4 teeth represent the sectioned planes for histological examinations. (**b**) A representative reconstructed image shows the pulp chamber in a P2 tooth. The volume ratios of the pulp chamber to the total tooth were calculated, and the differences of between the Control and PBM ((Control \(-\) PBM)/Control \(\times\) 100) were analyzed.  
Figure 3: Histology of A-sectioned P4 teeth, as marked in Figure 2. (**a–d**) present images of the Control teeth, and (**e–h**) present images of PBM-treated teeth. (**a**,**b**) present H&E staining images, and (**c–h**) present SEM images. (**b**,**d**,**f–h**) are enlargements of the inserted square areas in (**a**,**c**,**e**,**f**). (**f–h**) exhibit odontoblasts activated for enhanced tubular dentin formation in PBM-treated teeth.  
The vascular system plays an important role in supporting dentinogenesis by odontoblasts. H&E staining (Figure 4) was conducted on B-sections of P4 premolars to examine vascular development. In particular, a great number of vascular structures were observed in PBM-treated tissue (Figure 4f), whereas few were observed in Control tissue (Figure 4c). The vasculature appeared in the odontoblast layer of PBM-irradiated samples in contrast to the Control sample.