esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Processing  
### Close-Range Photogrammetry (CRP) Point Cloud Processing  
The next steps in the photogrammetric processing were devoted to yield as accurately as possible the 3D model of the hillside. 43.58 million points were obtained for the site area, reaching a mean density of 413 points/m\({}^{2}\). The mean distance among points in the 3D point cloud is 7.6 cm. All the steps presented up to this point were processed with Agisoft PhotoScan Professional v. 1.4.4 build 6848. However, for filtering the point cloud, 3 DReshaper was used. Noisy points were removed with the option delete points (over 20 cm). Afterward, from the segmented point cloud, tiny sets of points (below 11) were automatically deleted. Next, extensive manual filtering was undertaken to remove points identified as vegetation (trees and shrubs) or isolated points outside of the ground such as power poles or road signs. The result of this phase is visible in Figure 7, which presents the difference in a sector without and after filtering. The final number of points was 42.75 million points.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Scan Information**} \\ \hline Filename & /2019-11-27-10-58-50/scan\_info\_2019-11-27-10-58.yaml \\ Launch mode & Mapping with Camera \\ \hline \multicolumn{2}{c}{Time Stats} \\ \hline Start Time (y-m-d; h:m:s) & 2019-11-27; 10:59:02 \\ Stop Time (y-m-d; h:m:s) & 2019-11-27; 12:42:27 \\ Scan Time (h:m:s) & 01:43:25 \\ \hline \multicolumn{2}{c}{Point Cloud Stats} \\ \hline Average Confidence & 218,943.715342 \\ Number of Points & 482,799,466 \\ Trajectory Length & 2905.259698 m \\ Trajectory Points & 25,414 \\ File Size & 11,587,187,366 Byte \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scan info of the estimated trajectory executed with mobile mapping system (MMS) Kaarta Stencil 2.
## 4 Experts' Survey on Evidence-Based Systematic Review
### Data Collection and Analysis
#### 4.2.1 Information Management Features  
The responses collected for the questions belonging to the information management category are discussed below:  
* First, this section of the survey asked the experts about the reference management systems they use. In this case, the experts' responses enumerated the following: Mendeley, Zotero, JabRef, EndNote, and RefWorks.
* Second, they were asked about the search engines used. In this case, the experts from the Humanities mentioned ACM Digital Library, JSTOR, Web of Science, Google Scholar, and ResearchGate. On the other hand, the experts from the Applied Sciences mentioned PubMed, Scopus, EBSCO CINAHL, and MathSciNet; and experts from the Social Sciences mentioned--in addition to the search engines used in Humanities--Medline, Wolters Kluwer Ovid, and Sociological Abstracts. Researchers from the Natural Sciences mentioned AGRIS, Agricola, Academic Search Premier, CAB Direct, GreenFILE, Aquatic Sciences and Fisheries Abstracts, PsycINFO, DOAJ, EconLit, Sociological Abstracts, ProQuest Dissertations and Theses, DART eTheses Portal, and EThOS.
* Third, the experts were asked about the guides used to measure the quality of the methodology used in their selected studies. In this case, only the experts from the Applied Sciences, Natural Sciences and Social Sciences use PRISMA [37], ENTREQ [38] and Cochrane Collaboration [39].  
Then, the experts were asked about the significance score they assigned to the features included in the Information management category. Table 4 presents the results of the experts' opinions, classified by their research disciplines and expertise level. From these results, we can observe the following:  
* All features included in this category were considered relevant for the experts.
* The duplicate deletion feature has a lower than average score; this might indicate that integrating the remaining features into the SR tool to cover the experts' requirements should be a requisite. Besides, this can indicate that the features needed for information management in the SR process are applied in the same way in all disciplines.
* The experts suggested including the following features: stakeholder engagement, inter-rater reliability in the screening process, detect future lines, new questions and challenges, and the possibility of setting the sample size.
###### Abstract
Instead of using the whole image at once, it was broken into patches and those patches were used for training and testing. We report an increased accuracy of 14% from the default YOLOv3 accuracy, fewer false detections, and log-average miss rate. We also evaluated the proposed network framework on two publicly available datasets, namely GTSDB (German Traffic Sign Detection Benchmark) and STS (Swedish Traffic Sign) dataset [9], giving 16% and 5% rise in mAP, respectively. * Regressive anchor box selection: While analyzing the traffic sign size distribution in the German and Swedish traffic sign training set, we noticed that the majority of the traffic sign sizes are smaller and concentrated in the range from 20 pixels to 40 pixels. The base technique in YOLOv3 uses k-means clustering to select the anchors. We propose to make this selection adaptive using a regression model. We designed a cost function that adds more weight (by assigning higher numbers of clusters) to the bounding box size distribution where a majority of the traffic signs are concentrated. This helps us to select most of the anchors from the pixel value range that contains most of the traffic signs sizes and lesser anchors from the lesser concentration regions. The cost function helps the regression model to adapt the traffic sign sizes for any dataset. As a result, the detection accuracy of the traffic signs on GTSDB was further increased by 2% in addition to the increased accuracy achieved with the tweaked YOLOv3 detector. We noted that the increase in 2% accuracy was due to the perfect placement of anchors on test samples. The proposed model is adaptive and can be used with any object. * Focal loss: In this research, we also investigated the effect of incorporating focal loss [10] as Objectness score. We note that the hyper-parameters in focal loss are object shape as well as size-dependent. The optimal values for alpha and gamma for traffic signs have also been determined. Our proposed method achieved higher mean Average Precision (mAP) and equal Log Average Miss Rate (LAMR) as compared with the Focal loss implemented YOLOv3 detector. The rest of the paper is organized as follows: Section 2 discusses some recent related works. Sections 3 and 4 discuss the proposed methodology and experimental results, respectively. Finally, Section 5 concludes the paper.
###### Abstract  
Volatile organic compounds (VOCs) make up milk flavor and are essential attributes for consumers to evaluate milk quality. In order to investigate the influence of heat treatment on the VOCs of milk, electronic nose (E-nose), electronic tongue (E-tongue) and headspace solid-phase microextraction (HS-SPME)-gas chromatography-mass spectrometry (GC-MS) technology were used to evaluate the changes in VOCs in milk during 65 \({}^{\circ}\)C heat treatment and 135 \({}^{\circ}\)C heat treatment. The E-nose revealed differences in the overall flavor of milk, and the overall flavor performance of milk after heat treatment at 65 \({}^{\circ}\)C for 30 min is similar to that of raw milk, which can maximize the preservation of the original taste of milk. However, both were significantly different to the 135 \({}^{\circ}\)C-treated milk. The E-tongue results showed that the different processing techniques significantly affected taste presentation. In terms of taste performance, the sweetness of raw milk was more prominent, the saltiness of milk treated at 65 \({}^{\circ}\)C was more prominent, and the bittermess of milk treated at 135 \({}^{\circ}\)C was more prominent. The results of HS-SPME-GC-MS showed that a total of 43 VOCs were identified in the three types of milk--5 aldehydes, 8 alcohols, 4 ketones, 3 esters, 13 acids, 8 hydrocarbons, 1 nitrogenous compound, and 1 phenol. The amount of acid compounds was dramatically reduced as the heat treatment temperature rose, while ketones, esters, and hydrocarbons were encouraged to accumulate instead. Furfural, 2-heptanone, 2-undecanone, 2-furanmethanol, pentanoic acid ethyl ester, 5-octanolide, and 4,7-dimethyl-undecane can be used as the characteristic VOCs of milk treated at 135 \({}^{\circ}\)C. Our study provides new evidence for differences in VOCs produced during milk processing and insights into quality control during milk production.  
Keywords:milk; VOCs; E-nose; E-tongue; HS-SPME-GC-MS +
Footnote †: journal:
## 3 Experiments and Results
### Comparison Model
#### 3.1.2 DeepLab  
For DeepLab, we directly employed the DeepLab v3 model proposed by [PERSON] et al. [66]. DeepLab was also developed based on the VGG network. To ensure that the output size would not be not too small without excessive padding, DeepLab changes the stride of the pool4 and pool5 layers of the VGG network from the original 2 to 1, plus 1 padding. To compensate for the effect of the stride change on the receptive field, DeepLab uses a convolution method called \"atrous convolution\" to ensure that the receptive field after pooling remains unchanged and the output is more refined. Finally, DeepLab incorporates a fully connected conditional random field (CRF) model to refine the segmentation boundary.
## 5 Conclusions  
In this study, we constructed a remote sensing-based ecohydrological model to simulate the spatiotemporal changes in monthly ET and GPP between 1999 and 2018 over Mainland China. We quantitatively identified the contributions of climate factors (precipitation, temperature, wind speed, humidity, and solar radiation) and the LAI influenced by anthropogenic activities acting on ET and GPP variations. The model performance was satisfactory based on several evaluation indexes at the flux sites. Both interannual ET and GPP showed significant upward trends of 1.32 mm y\({}^{-1}\) and 8.01 g C m\({}^{-2}\) yr\({}^{-1}\). Four sub-regions experienced increasing trends for ET (GPP). TCon (TMon) had the strongest increasing trend of 2.46 mm yr\({}^{-1}\) (11.42 g C m\({}^{-2}\) yr\({}^{-1}\)), while SubT (TP) had the weakest increasing trend of 0.42 mm yr\({}^{-1}\) (2.41 g C m\({}^{-2}\) yr\({}^{-1}\)). Precipitation, temperature, wind speed, and humidity showed increasing trends over China, while solar radiation markedly decreased.  
Compared with climate factors, vegetation changes due to anthropogenic disturbance significantly increased over our observation period. Based on our ridge regression results, vegetation changes due to anthropogenic activities make a dominant contribution to GPP variations; the dominant factor influencing ET changes differs in each sub-region due to the joint influences of climate and vegetation on ET. We find that human activities have an important impact on ET and GPP variations, particularly in terms of vegetation greening. Careful attention should be paid to water conservation for the sustainable development of the Chinese economy and society in enacting ecological restoration policies nationally. Meanwhile, we can collect more observations to validate the reasonability of the model simulation and higher-precision inputs to decrease the model uncertainties of ET and GPP in China in the future.  
Conceptualization, Y.H. and H.Z.; methodology, Y.H.; software, S.Y.; validation, Y.H., S.Y. and H.Z.; formal analysis, S.Y.; investigation, S.Y.; resources, H.Z.; data curation, S.Y.; writing--original draft preparation, Y.H.; writing--review and editing, H.Z.; visualization, Y.H.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
The Central Public-Interest Scientific Institution Basal Research Fund (BSRF202205) and Ministry of Science and Technology (MOST) of China (2017 YFD0300400) supported this study. Data will be made available on request.  
The authors gratefully acknowledge the editors and reviewers for their constructive comments.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Soil and Plant Analysis
The detector was positioned outside the reaction chamber at a 45\({}^{\circ}\) angle to the beam path and the target sample. The finely ground, homogenized, and pressed samples of 1 cm diameter and 1 mm thickness were fixed on a target holder and introduced into the reaction chamber perpendicular on the beam direction. Specialized software at the 3 MV Tandetron accelerator of NIPNE was used to configure the operating parameters, such as irradiation time (-1200 s for soil and -2700 s for plants), proton beam position on the target, its dimension (-2 mm diameter), and current (-5 nA for plant and -22 nA for soil samples). Proton beam electrical charge on the target was in the range of 13-33 uC. Two measurements were taken for each soil/plant sample. The data were processed using SRIM-2013 software [27] for stopping power assessment of the proton beam in the sample and comparator standard required in applying matrix correction [28], as well as GammaW software version 2.70 [29], for PIGE spectra analysis (elemental characteristic peak areas determination as number of electrical counts). The results obtained through the PIGE method were quantified by establishing a ratio between the elemental concentration in sample and comparator standard/reference material [30]. To determine the elements Na, Cl, Mg, F, Fe, P Si, and Al, high-purity chemical compounds (NaCl, MgO, CaF\({}_{2}\), and Fe\({}_{2}\)P), as well as Si (monocrystalline) and Al foil, were used as comparator standards. Certified reference materials of a similar matrix to the analyzed material, namely INCT-TL-1 (tea leaves) and INCT-OBTL-5 (Oriental Basma tobacco leaves) for plants, as well as IAEA-356 and IAEA SD-M-2/TM (marine sediment) for soils, were considered for an analytical quality control.
# Spatiotemporal Dynamics and Obstacles of the Multi-Functionality of Land Use in Xiangxi, China
## 3 Results
### The Spatio-Temporal Dynamic Evaluation of the Total Function of Land Use
The southern regions (Jishou, Huayuan, Fenghuang, and Yongshun) demonstrated a high degree of land use coordination, with the highest coordination seen in Jishou (the standard deviation was as low as 0.0036 in 2014), and the central (Baojing and Guzhang) and northern regions (Yongshun and Longshan) had a low degree of land use coordination, of which Yongshun was the lowest (with a standard deviation as high as 0.1567 in 2017). This was mainly because of the strengthening of the approach to development within Jishou (the core of Xiangxi) and the policy guidance and technical support of the local government, which promotes coordinated development in economy, society, and environment. However, the northern areas are far away from Jishou, and the effect of such policy changes reaching this area is limited. In addition, the industrial structure is unbalanced, with traditional industries, agriculture, and animal husbandry accounting for a large proportion. However, the degree of coordination in land resource utilization is low. Figure 2: Changes of land use total function from 2013 to 2017 in Xiangxi. (**a**) The value of the function. (**b**) The dynamic degree. (**c**) The dynamic dominance. (**d**) The standard deviation.
# Non-Linear Dynamic Analysis of Drill String System with Fluid-Structure Interaction
###### Abstract
[26] established the generalized lumped parameter model of the drill string system, and the causes of three coexisting states of bit sticking, stick-slip vibration and constant rotation are emphatically analyzed. [PERSON] et al. [27] proposed a non-smooth borehole dynamic propagation model for planar directional drilling. Basic nonlinearities caused by bit-tilt saturation and non-ideal stabilizers are modeled using complementary conditions for comprehensive dynamic and parametric analysis. [PERSON] et al. [28] studied the dynamic characteristics of a horizontal drill string and analyzed its longitudinal vibration. The boundary conditions and normal state of the system were determined by means of the mode summation method. The dynamic simulation of the horizontal drill string was carried out. The influence of increasing the number of modes and the convergence of modes was discussed. [PERSON] et al. [29] established fully coupled finite element models of axial vibration, torsional vibration and the transverse vibration of the drill string. The stick-slip vibration and rotary vibration were numerically studied. [PERSON] et al. [30] modeled stick-slip vibration. The causes of torsional vibration with and without stick-slip were analyzed. [PERSON] et al. [31] established the dynamic model of the rotary steerable drilling system and proved the existence of low-dimensional chaos in the drilling process. [PERSON] et al. [32] developed an advanced control system using a lumped parameter model to describe downhole information to mitigate the torsional vibration of the drill string using communication tools for drilling measurements. [PERSON] et al. [33] designed a robust output feedback control method to eliminate torsional stick-slip vibration in the drilling system and performed closed-loop stability analysis on the nonlinear drill string model. From the above-mentioned references, although some of the literature on the drilling system has already been established and analyzed, an analysis of the dynamic response, considering the influence of fluid-structure coupling and motion constraint, is a less developed field of study. On this basis, the non-linear dynamic equation is discretized using the Galerkin method and solved using the Runge-Kutta integral method. The effects of forcing frequency, perturbation amplitude, mass ratio and flow velocity on the dynamic response of the system are investigated in detail through numerical simulation.
## 1 Introduction  
Coronary artery disease (CAD) clinical manifestations are one of the most common causes of death worldwide [1]. Cardiovascular risk factors and systemic lipid profiles are known to be associated with development of CAD. Reliable interpretation of coronary angiography using coronary computed tomography angiography (CCTA) requires sophisticated knowledge of normal coronary artery anatomy, pathophysiology of coronary artery atherosclerosis and congenital anomalies, characteristic features of coronary artery disease and its CCTA representation, the technology used and its limitations, the use of heart-specific three-dimensional interpretation software, and the ability to identify and overcome image artifacts in the available image dataset [2]. Computational modeling of invasive coronary imaging has been recently employed to investigate CAD pathophysiology and, in particular, the possible relationship between low endothelial shear stress (ESS) and plaque progression [3; 4]. CCTA derived blood flow modeling and imaging characteristics were also recently used for the development of non-invasive predictive models of disease progression [5; 6].  
Besides blood rheology, computational modeling was recently used to describe the lipid accumulation in the arterial wall [7; 8], which, in their majority, are in agreement with experimental findings [9]. The deployment of these models in a patient's database demonstrated that high accumulation of low-density lipoprotein (LDL) particles in the arterial wall is a predictor of disease progression [10].  
The possible interaction of clinical risk factors and lipid profile with CCTA imaging characteristics as well as the combination of computational modeling of blood rheology with LDL transport and accumulation have not yet been investigated, with the aim of developing predictive models of coronary plaque progression. We hypothesized that combining systemic cardiovascular risk factors with local imaging and modeling characteristics in a multi-level model could help identify which arterial site, within the whole coronary tree, is more likely to undergo plaque formation/growth in a single patient with a given generic atherosclerosis progression risk. To this purpose, we examined, for the first time, the combined effect of lipidomics derived biomarkers and computational modeling of coronary arteries in 187 patients, with low to medium CAD risk, recruited in the H2020 SMARTool clinical study. Data were collected at two time points with an interscan period of 6 \(\pm\) 2 years. ESS calculation, estimation of LDL transport, and non-invasive Fractional Flow Reserve (FFR) calculation was performed to 480 vessels.
# Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
## 2 Redundant Degrees of Freedom Optimization in Robotic Milling
### Flowchart for Solving the Redundant Degree of Freedom Optimization Model  
As illustrated in Figure 3, the functional redundant angle optimization model established in this article is based on a dynamic response index. This model is a multi-objective optimization framework that takes into account both kinematics and dynamics. The optimization process includes several steps:  
Figure 3: Optimization flowchart for the redundant degrees of freedom.  
Step 1. Discretization of the redundancy angle: The redundant angle is discretized, resulting in a set of robot joint angles (\(\theta\)) that adhere to the joint limit constraints. These joint angles are obtained through inverse kinematic calculations.  
Step 2. Posture-dependent frequency response function prediction: Using the modal tests and the inverse distance weighted method, the frequency response function for specific postures is predicted. The modal parameters are then identified with the modalfit function in MATLAB, scaling the mode shapes to unity modal A.  
Step 3. Stability analysis: With the posture-independent modal parameters, the stability of robotic milling system is assessed using the semi-discretization method.  
Step 4. Dynamic response index calculation: The evaluation index of the minimum amplitude is employed to calculate the dynamic response index for specified postures.  
Step 5. Optimization of redundant angles: The robot posture is optimized by minimizing the DRI.  
The model aims to enhance the efficiency and stability of a robotic milling system by optimizing the redundant angle. The process involves discrete angle determination, prediction of frequency response functions, stability analysis, and dynamic response index calculation to identify the optimal redundant angles for stable robotic milling operations.
# Review of Automatic Estimation of Emotions in Speech
## 7 Neural (Machine Learning) Techniques for Emotion Classification of Speech
### Common Architectures for ANNs
#### 7.2.1 Convolutional Neural Networks (CNNs)  
For many tasks of classification, pertinent information is localized in limited spans of data (e.g., objects in images; formants in speech). Thus, ANNs may contain \"convolutional\" layers of nodes that process data locally. If viewing input data from a representation in two dimensions (e.g., time-frequency, a wide-band spectral display for speech, where the frequency axis is linear or logarithmic), data are multiplied by a square weight matrix over a small range (e.g., 3 \(\times\) 3), and results are summed (pooled) spatially [98]. A sort of CNN that applies 1-dimensional convolution (multiplication) in time is called time-delay NN; such models have no recurrence (feedback).  
As an example for SER, one may combine different acoustic features in a one-dimensional array of mean values, fed into a 1-D CNN (Refs. [99; 100] show an efficient CNN SER). Applying CNNs to \"emotionally salient\" regions of speech [101; 102] has shown that information about emotion is not uniformly distributed in speech (see more on this idea in Section 7.2.3).