esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Risky Segment Identification and Impact on Human Safety  
Considering the case study of the 100-km long motorway (M-2) section for road safety analysis provides an opportunity to analyze the application of a new technique DEA for identification of risky road segments. During the application of DEA, data sets of two types were used. One was output data and other was input data. Output consisted of two major concerning variables: number of accidents and number of affected people (killing/injuries). Input consisted of three major inputs known as exposure variables related to traffic, i.e., VKT, VHT, and V/C. Usually with the increase of road traffic, accidents also increase, that is why exposure is necessary to be considered for analysis if we analyze the specific range of DEA-based calculations of risky segments and their ranking. Out of the top ten, seven are in the same range of Kallar Kahar (mountainous region). So, a focused technical analysis is required to identify the problems of that region. Another benefit of applying DEA is that one does not have to analyze all the 200 road segments (100 section each has two segments on both direction). Just consider only those segments whom have at least one accident during last years (the majority of the researchers have considered data within 3-5 years). Now after ranking, one can easily focus and identify the technical problem in that particular segment. Segments of sections at 229 km, 223 km, 224 km, 246 km, 286 km, 253 km, and 239 km were found to be the top-ten risky segments with respect to traffic exposure. Technical analysis also possibly confirmed that there may be problems with speeding and geometry because flow is already controlled by access. In Figure 8, the geometric problem can be observed. So, technical solutions will revolve around controlling speed and geometry design i.e.; re-analysis and re-designing.  
Figure 7: Comparative analysis of number of accidents and risk index.  
Furthermore, the whole story revolves around the human lives which are associated with road safety improvement. Targets like Zero Visions are also set to save human lives on roads. For instance, if we consider that we can only treat the first 20 segments due to budget limitations, an area with 51% of the accidents and 59% of killings and injuries can be addressed, which is a huge success to improve the protection of human lives on roads.
## 1 Introduction
### Evolution and Characterization of the HMGR Gene Family in Plants  
To explore the evolution of the _HMGR_ gene family in plants, we constructed a phylogenetic tree using all 124 HMGR protein sequences from 29 plants. The results showed that the taxonomic position of HMGRs in the seven lineages was consistent with the plant's evolutionary order, indicating that plant HMGRs developed into different branches after the lineages diverged (Figure 1A). LerHMGRs roughly divided into three portions in eudicots lineages, all of which closely related to the Lamids plant's HMGRs. LerHMGR5-LerHMGR7 was closely related to 10 members (SlyHMGR3/4/2/1, StuH-MGR1/5/2/3/6, CcaHMGR1) from _S. lycopersicum_, _S. tuberosum_, and _C. canephora_; the six LerHMGRs (LerHMGR1-LerHMGR4, LerHMGR8, LerHMGR9) were further classified into two broad categories that are more closely related to Lamiales SinHMGR2, OueHMGR5, and OueHMGR1; the closest member of LerHMGR10 and LerHMGR11 were seven members (OeuHMGR4/2/8/7, SinHMGR1, CcaHMGR2, StuHMGR4).  
Introns are closely related to gene expression, transcription shearing, and other processes, and they may play a key role in plant adaptability and evolution. Gene structure analysis using the GSDS tool predicted that the number of introns in all _HMGRs_ ranged from 0 to 6, with 69.35% having three introns (Figure 1B, Table S2). _LerHMGR1-LerHMGR4, LerHMGR8, LerHMGR9_, and _CbrHMGR_ are intronless genes (Figure 1B), implying that intronless _CbrHMGR_ has undergone a unique evolution process and that intronless _LerHMGRs_ emerged from multi-intron _HMGR_ in land plants, and these six _LerHMGR_ genes may be more involved in the quick response to stress than other members of the gene family [38]. In addition, we identified an intron in the 5' UTR region of _AspHMGR2_, _AspHMGR3_, _AspHMGR7_, and _ZmaHMGR4_. These 5' UTR introns may increase the promoter activity or translation efficiency of the corresponding genes [39; 40] (Figure 1B, Table S2).  
Physicochemical properties and the identification of 10 motifs in 124 plant HMGRs showed that most HMGRs were conserved throughout the evolution of lower plants into higher plants (Figure 1C and Figure S1, Table S2). In the catalytic domains, most of them have three conserved motifs: motif 3 represents the two HMG-CoA binding sites (EMPV-GYVQIP and TTEGCLVA) and motif 4 and motif 5 represent the two NADP(H) binding sites (DAMGMNM and GTVGGGT) (Figure 1D). Among them, the second NADP(H) binding site was detected in all 12 HMGRs with motif 5 deletion, which may be due to the low conserved flanking sequence of this binding site (Figure 1C). In addition, motif 8 or motif 9, which contain the transmembrane helix typically possessed by plant HMGRs, were not found in the N-terminal region of 15 HMGRs, including one branch of monocots (Figure 1C and Figure S1). Among them, the absence of motif 8 and motif 9 does not correspond to TMHMM Server v.2.0's prediction due to the presence of one transmembrane helix in MviHMGR2, PtaHMGR6, PtaHMGR13, and SbiHMGR2. This may be due to the diversity of the sequences.
# Predicting Activities of Daily Living with Spatio-Temporal Information
## 5 Experiments
### Experiment 2: Prediction Based on Second-Order Model  
This experiment aims to predict user activity based on time and location (\((time,location)\to activity\)). Using the example of the string '\(sen\)' (discussed in Section 5.1), the PPM model would be given the string '\(se\)' and see if it predicts '\(n\)'. In this experiment, the second-order model will be used for prediction.
## 4 Results Analysis and Discussion
### Single-Channel Deposition
#### 4.1.2 Effect of Laser Power on the Shape of the Single-Channel Deposition  
Simulation calculations for single-layer, single-channel deposition forming were performed using scanning speeds of 10 mm/s, powder feeding rates of 12.0 g/min, and laser powers of 400 W, 500 W, 600 W, and 700 W, respectively. Figure 7 depicts the simulation results when the deposited form achieves a stable state after 0.4 s. The deposited height and breadth decrease with increasing laser power when the scanning speed and powder feeding rate remain constant. Other process parameters were held constant, and when the laser power was increased from 400 W to 700 W, or by 75%, the deposition height and width increased by 30.67% and 25.31%, respectively, demonstrating that the effect of laser power change on the deposition height and width is similar.  
Figure 5: Morphology measurement of deposition layer at different powder feeding rates. (**a**) 16.8 g/min; (**b**) 14.4 g/min; (**c**) 12.0 g/min; (**d**) 9.6 g/min.  
Figure 6: Deposition channel size at different powder feeding rates. (**a**) Variation of sedimentary channel height with scanning speed; (**b**) Variation of sedimentary channel width with scanning speed.  
The specimens of the aforementioned single-channel forming experiments were measured using an ultra-deep field microscope, and Figure 8 depicts the shape and size of the deposited layers at various laser powers. Figure 9 depicts a comparison between simulation and experimental measurement outcomes. Since the laser power is a parameter in the Gaussian heat source energy source term, it is the Gaussian heat source energy source term and not the powder feeding equation that influences the shape and dimensions of the deposition. Consequently, the systematic error caused by the deviation of the powder feeding equation from the experimental powder flow concentration distribution no longer exists, and the calculated height and breadth of the deposition are no longer entirely greater than the experimental values. The relative error in the height of the deposited layer was as low as 1.60% at 400 W and as high as 7.61% at 700 W laser power, while the relative error in the width was as low as 1.03% at 600 W and as high as 5.58% at 500 W laser power.  
The model developed in this study is used to compute the form of the deposited layer by altering the powder feeding rate and the laser power parameters. The relative error between the simulation results and the experimental findings does not surpass 8%, proving the model's dependability in both cases.  
Figure 8: Measurement of the morphology of the deposition layer under different laser power. (**a**) 700 W; (**b**) 600 W; (**c**) 500 W; (**d**) 400 W.  
Figure 7: Simulation Results of Powder Deposition Layer Shape at Different Laser Powers.
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 6 Conclusions  
In this paper, research on hardware structure and design method was carried out, based on the processes of InSAR processing, namely coarse registration and interferogram generation. First, the proposed efficient system can ensure fast data processing from the perspective of data communication, processing flow, and storage. Second, given the dual constraints between resources and processing capacity, this paper proposes an FPGA design method based on the gradient descent theory. Mapping the two constraints to two-dimensional coordinates, this method helps identify the optimum trade-off through gradient values, providing guidance for engineering practice in general and, thus, shortening the system optimization process. In the empirical part, the experiment results verify the system's accuracy, the structure's efficiency, and the design method's effectiveness. The results show that the proposed method can be applied to the field of ocean observation. Finally, this paper discusses the generalizability of the proposed method. This paper argues that the proposed method is most suitable for ocean observation research and can be potentially applied to other fields as on-board processing technology develops and the algorithm's complexity increases.  
In the near future, we will verify the proposed FPGA design method in other algorithms and study the generalizability of the design method under multiple constraints.  
Conceptualization, J.L. and Y.X.; methodology, J.L. and M.X.; validation, M.X., J.L. and Y.X.; formal analysis, J.L.; investigation, M.X.; resources, H.C. and Y.X.; writing--original draft preparation, J.L.; writing--review and editing, M.X. and H.C.; supervision, H.C.; project administration, Y.X.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Science Fund for Distinguished Young Scholars under Grant 2018-JCJQ-ZQ-046 and partly by the Civil Aviation Program under Grant B0201.  
**Acknowledgments:** The authors would like to thank the reviewers for their kind comments and thoughtful suggestions which greatly improve this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### DH and NR  
DH can be used as an indicator of the break of peptide bonds, whereas NR reflects the ability to recover peptides from the hydrolysis process [20]. The DH and NR of fish bone protein, treated with Flavourzyme, increased with the prolonged hydrolysis time (Figure 1). The initial DH values of 8.65% and 7.43% at 0 h indicated that some protein molecules had been partially hydrolyzed to peptides or free amino acids, during the SE and HPE pretreatment. The DH and NR of SFBP and HFBP both increased sharply (\(p<0.05\)) during the first 0.5 h, and then showed a slow increase. With the increase in hydrolysis time, a large amount of soluble peptide was generated, resulting in higher NR values. After 3 h of hydrolysis, the NR values of SFBP and HFBP samples finally reached 80.12% and 79.57%, respectively. The hydrolysis curves were similar to those of protein hydrolysates from cod bone [20] and  
\begin{table}
\begin{tabular}{l l l} \hline
**Amino Acids** & **SFBP** & **HFBP** \\ \hline Aspartic acid/asparagine & 54.2 & 57.3 \\ Glutamic acid/glutamine & 85.1 & 90.2 \\ Serine & 27.1 & 30.9 \\ Histidine & 4.2 & 5.9 \\ Glycine & 333.5 & 328.5 \\ Threonine & 20.8 & 21.7 \\ Arginine & 53.3 & 52.5 \\ Alanine & 123.4 & 123.1 \\ Tyrosine & 2.7 & 3.9 \\ Valine & 23.3 & 25.3 \\ Methionine & 11.2 & 13.6 \\ Phenylalanine & 14.4 & 16.3 \\ Isoleucine & 12.5 & 14.7 \\ Leucine & 24.1 & 27.3 \\ Lysine & 27.0 & 28.3 \\ Proline & 101.1 & 90.9 \\ Hydroxyproline & 82.2 & _69.7_ \\ Total & 1000 & 1000 \\ Imino acids & 183.3 & 160.6 \\ \hline \end{tabular}
\begin{tabular}{l l l} \hline
**SFBP: fish bone protein prepared through steam explosion pretreatment; HFBP: fish bone protein prepared through hot-pressure extraction pretreatment.** \\ \hline \end{tabular}
\end{table}
Table 1: Amino acid composition of fish bone protein treated by steam explosion-assisted extraction and hot-pressure extraction (results are expressed as residues/1000 total residues).  
thornback ray [42]. Nevertheless, the values of DH and NR in this study were higher than that of the protein recovered from cod bone, which was treated at 121 \({}^{\circ}\)C for 90 min, followed by Flavourzyme and Trypsin hydrolysis [20]. The differences in enzymatic extraction between SFBP and HFBP, were found to be mainly due to the differences in the protein structure of the substrate. More significantly, how the pretreatment method affects the protein structure and enzymatic hydrolysis needs to be studied further.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 5 Architecture and Implementation
### Model Training Component
#### 5.2.2 Training the Gaussian Mixture HMM  
In contrast to [26] our implementation is not extracting features from a single touch event, but rather uses multiple sequences of available inputs, to train a HMM with an undetermined number of hidden states and Gaussian mixtures.  
A HMM can be built based on different topologies which describe the structure of the inter-connected graph. Simple models may not be able to make predictions of the generated dataset with a high likelihood, while complex models may overfit and thus do not generalize the data well enough [31], hence a balanced approach seems to be the most reasonable. For our use case we employ a left-to-right topology, which has a temporal structure, so a high number state can only occur after one with a lower number [30]. We chose this topology because we are also considering touch gesture data as time-varying data, just like speech input. To create this topology for training the model, we have to define a start probabilities matrix \(\pi\), where only state 0 has a 100% chance of occurring first. Furthermore, the used transition matrix has to be constructed as \(A\) listed in Equation (1). The example depicts a topology of a HMM using four hidden states.  
\[\pi=\begin{pmatrix}1&0&0&0\end{pmatrix} A=\begin{pmatrix}0.5&0.5&0&0\\ 0&0.5&0.5&0\\ 0&0&0.5&0.5\\ 0&0&0&1\end{pmatrix} \tag{1}\]  
The chosen model has different parameters characterizing the internal structure, like the number of hidden states or the start probabilities of starting in a specific state as explained previously, but also parameters that depend on the distribution describing the emissions of the model. The Gaussian Mixture HMM is also specified by its mixtures, which are the number of states in the Gaussian Mixture Model for each hidden state of the HMM. Since our touch input data can be approximated by a mixture of Gaussians, we use the _hmm.GMMHMM_ model from the _hmmlearn_ library. Furthermore, we use the Baum-Welch expectation-maximization algorithm to find the optimal parameters for a specific user's model. This method utilizes the provided data to find the parameters of a distribution that maximize the likelihood of assigning all data to the distribution [32].  
The number of the HMMs hidden internal states and the number of the Gaussian Mixture Model (GMM) mixtures are defined before applying the Baum-Welch algorithm. The expectation-maximization algorithm then finds all internal parameters of the model. However, it has to be considered that the algorithm is mostly converging into a local maximum and does not guarantee that a global maximum will be found [33]. Because of that, obtaining the perfect solution is hard, so we are aiming to look for one which satisfies our requirements.
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 2 Design, Materials, and Methods
### Zero Separation  
Since an ECG is composed of low-frequency (P and T waves and ST segments) and medium-frequency (QRS complexes) components, each ECG interval can be stored in different resolutions. Figure 4 shows that a nine-bit resolution can be used to present delta sequence data in MIT/BIH case 100. This step splits the delta value into two parts: high bits and low bits. Ideally, high bits contain only zero values with specific bit quantities (resolutions) in each interval. With a low-frequency component, a flat signal means a smaller dynamic range of delta volume and produces a zero value with a higher resolution in high bits, and a sign flag problem arises in this step when the delta value is negative. One additional bit, the Most Significant Bit (MBS), increases the data size to indicate either a positive or a negative delta value, and results in many non-zero values in high bits. Since neither maximal (2048) nor minimal (0) ECG signal values appear nearby, the additional bit can be ignored. The delta value must be rotated one bit to the left to encode the bit with the most negative value as zero. Figure 5 shows the MIT/BIH recording of 100 original volumes and rotated delta volumes.  
Figure 3: Comparison of probability of occurrence of each distinct symbol for an electrocardiogram (ECG) (original) volume and delta volume variance with MIT/BIH Arrhythmia Database 100.
## Appendix A Charrelation Matrix Derivation
### Diagonalization Verification  
Let \(\phi_{\text{b}}\left(\mathbf{u}\right)\) denote the generalized characteristic function of source signal \(\mathbf{b}\left(m\right).\) Due to the statistical independence of elements of \(\mathbf{b}\left(m\right)=\left[b_{\text{l}}\left(m\right),...,b_{\text{K}} \left(m\right)\right]^{T}\), we get:  
\[\phi_{\text{b}}\left(\mathbf{u}\right)=\phi_{\text{b}_{\text{l}}}\left(u_{ \text{l}}\right)\cdot\phi_{\text{b}_{\text{l}}}\left(u_{\text{3}}\right)... \phi_{\text{b}_{\text{K}}}\left(u_{\text{K}}\right) \tag{11}\]  
where \(\phi_{\text{b}_{\text{l}}}\left(u_{\text{i}}\right)=E\left[\exp\left(u_{ \text{i},b_{\text{i}}}\left(m\right)\right)\right],i=1,...K\). Defining \(\varphi_{\text{b}}\left(\mathbf{u}\right)=\log\phi_{\text{b}}\left(\mathbf{u} \right)\) is called second generalized characteristic function\" of source signal \(\mathbf{b}\left(m\right)\). Hence, we obtain:  
\[\varphi_{\text{b}}\left(\mathbf{u}\right)=\varphi_{\text{b}_{\text{l}}}\left( u_{\text{l}}\right)+\varphi_{\text{b}_{\text{l}}}\left(u_{\text{2}}\right)+...+ \varphi_{\text{b}_{\text{K}}}\left(u_{\text{K}}\right) \tag{12}\]  
Consequently, the charrelation matrix \(\Psi_{\text{b}}\left(\mathbf{u}\right)\) can be easily obtained:  
\[\Psi_{\text{b}}\left(\mathbf{u}\right) =\
abla_{\text{u}}^{T}\left[\
abla_{\text{u}}\varphi_{\text{b}} \left(\mathbf{u}\right)\right]=\frac{\partial}{\partial\mathbf{u}^{T}}\Bigg{[} \frac{\partial\varphi_{\text{b}}\left(\mathbf{u}\right)}{\partial\mathbf{u}} \Bigg{]} \tag{13}\] \[=diag\Bigg{[}\frac{\partial^{2}\varphi_{\text{b}_{\text{l}}}\left( u_{\text{l}}\right)}{\partial u_{\text{l}}^{2}},\frac{\partial^{2}\varphi_{\text{b}_{ \text{l}}}\left(u_{\text{2}}\right)}{\partial u_{\text{2}}^{2}},...,\frac{ \partial^{2}\varphi_{\text{b}_{\text{K}}}\left(u_{\text{K}}\right)}{\partial u _{\text{K}}^{2}}\Bigg{]}\]
# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 3 Results
### Predicting Neurodevelopment Outcomes  
The relationship between brain structure and cognitive function is complex. Research on brain activity and connectivity builds the network theory to capture the brain trajectories. It remains a challenge in the field of neuroscience to relate basic structural properties of brain to complex cognitive functions [138]. This study reviewed research on correlating brain structure and measurable neurodevelopment outcomes such as fluid intelligence, language function, and motor function (Table 5).  
The ABCD dataset provides neuroimaging data including sMRI, rsfMRI, and DTI as well as cognitive assessments such as fluid intelligence and oral reading scores. Large-scale studies based on the ABCD dataset involve thousands of data and a variety of modalities to predict neurodevelopment outcomes [138; 139; 140; 141; 142]. CNN models were also employed to predict motor function and cognitive deficits in very preterm infants [143; 144].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Year** & **Score** & **Population** & **Technique** & **Preprocessing** & **Method** & **Results** \\ \hline
[143] & 2021 & Cognitive Deficits & \begin{tabular}{c} 261 very preterm infants \\ (gestation age \(\leq\)32 weeks, scan at 39–44 weeks postmenstrual age) \\ \end{tabular} & \
\begin{tabular}{c} DTI, \\ rs-fMRI \\ \end{tabular} & FSL & CNN &
\begin{tabular}{c} Accuracy \\ 88.4\% \\ \end{tabular} \\ \hline
[145] & 2020 & Fluid Intelligence & ABCD Study 833 subjects (age 9–10 years) & sMRI & - & 3D CNN & MSE 0.75626 \\ \hline
[141] & 2021 & Fluid Intelligence & ABCD Dataset 7709 subjects (age 9–10 years) & sMRI & \
\begin{tabular}{c} FSL, \\ ANFI, \\ FreeSuer- \\ \end{tabular} & CNN &
\begin{tabular}{c} Pearson’s correlation \\ coefficient \\ r = 0.18 \\ \end{tabular} \\ \hline
[138] & 2022 & Fluid Intelligence & ABCD Dataset 8070 subjects (age 9–11 years) & sMRI & FreeSurfer & CNN &
\begin{tabular}{c} MSE 0.919 (ABCD \\ Dataset) \\ \end{tabular} \\ \hline
[140] & 2022 & Fluid Intelligence & ABCD Dataset 7693 subjects (age 9–11 years) & res-fMRI & FreeSurfer & CNN &
\begin{tabular}{c} MAE \\ \(5.582\pm 0.012\) \\ \end{tabular} \\ \hline
[142] & 2022 & Fluid Intelligence & ABCD Dataset Training: 3739 subjects, Validation 415 subjects, Testing 4515 subjects (age 9–11 years) & sMRI & \
\begin{tabular}{c} FSL, \\ ANFI, \\ FreeSuer- \\ \end{tabular} & CNN &
\begin{tabular}{c} MSE 82.56 \\ for testing \\ \end{tabular} \\ \hline
[146] & 2021 & Language Scores & \
\begin{tabular}{c} 31 subjects with persistent \\ language concerns (age \(4.25\pm 2.38\)years) \\ \end{tabular} & DTI &
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & CNN & MAE 0.28 \\ \hline
[147] & 2021 & Language Scores &
\begin{tabular}{c} 37 subjects with epilepsy \\ (age \(11.8\pm 3.1\)years) \\ \end{tabular} & DTI & FSL & CNN & MAE 7.77 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predicting neurodevelopment outcomes.
## 2 Experimental Details
### Apparatus  
Instruments used in this study were as follows: Inductively coupled plasma (ICP) spectroscopy was used for metal concentration analysis; an electric shaker (model Promax 2020, Heidolph Instruments GmbH & Co. KG, Schwabach, Germany) was used for adsorption tests with 20 mL solutions in 125 mL flasks. The solution pH was measured with a pH meter (model Orion Star A211).