esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 4 Implementation Details
### Anchor Setting  
In this article, we focus on vehicle tracking, which contains the class of car and van in the KITTI tracking dataset. It is complicated for us to select reasonable bounding box priors, which are a set of hyper-parameters because of the special appearance of these vehicles. The improper prior size may cause the predicted bounding box to be far from the corresponding ground truth and then affect the tracking performance. The number of anchor priors is set to 12 for each prediction head in our training phase. Hence, the K-means approach is applied to the tracking dataset, which only includes vehicles, since there are other classes in the entire dataset, such as pedestrians, cyclists, etc. The bounding box prior is generated in the following steps:  
1. K-means clustering. Input: all of ground truth bounding boxes of vehicles in the training dataset. Output: 12 width-height pairs of bounding boxes.
2. Initialization. Select 12 bounding boxes from the training dataset randomly as the center of the cluster.
3. Cluster. Calculate the IoU distance from each bounding box to the center of each cluster. Then, divide these bounding boxes into the nearest cluster.
4. Update. With the classified 12 clusters in step (3), the cluster center in each cluster is updated according to the median of all bounding boxes.
5. Repeat step (3) and (4) until the cluster centers are no longer changing to meet the given termination condition.  
In the design of anchor boxes, 12 appropriate clusters are: (23,18), (31,22), (37,30), (60,26), (53,40), (81,38), (67,56), (122,51), (107,76), (173,94), (230,150), and (333,183).
## 3 Results and Discussion
### Characterization of Lentzea sp. JNUCC 0626 as a Novel Strain via dDDH Analysis  
In this study, we applied digital DNA-DNA hybridization (dDDH), an in silico method for assessing genomic similarity between organisms, to investigate whether _Lentzea_ sp. JNUCC 0626 represents a novel species. dDDH provides a reliable and reproducible alternative to traditional DDH, enabling the precise evaluation of genome-wide similarity. This method allows for automated calculations based on genome sequence data, making it a key tool in modern microbial taxonomy.  
The dDDH values are computed based on three primary formulas, each providing a different perspective on genomic similarity. The first formula, D\({}_{0}\) (Genome-to-Genome Distance), evaluates the overall gene content between genomes, giving a comprehensive view of their genetic relatedness. The second formula, D\({}_{4}\) (High-Scoring Segment Pairs), specifically measures the similarity of conserved core gene sequences, focusing on essential genes shared by the organisms. Lastly, D\({}_{6}\) (Gene Family Content) examines the similarity in gene family structures, comparing the presence of shared gene families across the genomes. Together, these formulas offer a detailed assessment of genomic similarity, each contributing a unique aspect to the overall comparison. Typically, dDDH values of \(\geq\)70% are considered indicative of the same species, whereas dDDH values below 70% suggest different species. Values below 60% strongly indicate a novel species, as such low similarity is uncommon within the same species [39].  
To determine the taxonomic position of _Lentzea_ sp. JNUCC 0626, we performed dDDH analysis using the TYGS (Type Strain Genome Server), comparing the genome of JNUCC 0626 with the genomes of the 13 most closely related strains (Table S5). Among these, the closest strain identified was _L. pudingi_ CGMCC 4.7319. The dDDH values between _Lentzea_ sp. JNUCC 0626 and _L. pudingi_ CGMCC 4.7319 were as follows: dDDH (D\({}_{0}\)) = 46.7%, dDDH (D\({}_{4}\)) = 34.7%, and dDDH (D\({}_{6}\)) = 43.4%. Additionally, the confidence intervals (95% CI) were calculated as 43.4-50.2% for D\({}_{0}\), 32.3-37.2% for D\({}_{4}\), and 40.5-46.5% for D\({}_{6}\).  
These results demonstrate that the dDDH values between _Lentzea_ sp. JNUCC 0626 and _L. pudingi_ CGMCC 4.7319 fall significantly below the 70% threshold typically used to define species boundaries. As all three formulas yielded values well below this threshold, we conclude that _Lentzea_ sp. JNUCC 0626 represents a novel species. The substantial genomic divergence from _L. pudingi_ CGMCC 4.7319 further supports this conclusion.
## 2 Materials and Methods
### Assessment of Bone Micro-Architecture
#### 2.3.1 Micro-CT Analysis and 3D Estimation of Porosity  
Under those conditions, our gold-standard measurements were performed with a benchtop micro-CT built at the Physics Institute (IF) of the National Autonomous University of Mexico (UNAM), as well as with the use of a patented CT equipment (twinned orthogonal adjustable tomography -TORATOM-, EP 2835631):  
* The device at IF-UNAM [28] is an Oxford Instruments Apogee XTG5011 tungsten anode X-ray tube with a nominal focal spot size of 35 \(\upmu\)m, coupled to a Rad-icon Shad-o-Box 2048 flat panel detector (Teledyne DALSA Incorporated, Waterloo, ON, Canada). The collection of the projection image data were at 50 kVp, 1 mA with an integration time of 500 ms per frame and 360\({}^{\circ}\) orbit in 1\({}^{\circ}\) steps.
* TORATOM has a specific geometry movable to attain magnifications between 1.2 and 150 and comprises of two X-ray tube-detectors with accelerating voltage between 20 and 240 kV, maximum power of 260 W, and minimum spot size between 0.8 and 5 \(\upmu\)m. TORATOM included as well one Dexela 1512 CL X-ray detector (XWT-160-TCHR, X-ray WorX, Garbsen, Germany), with a sensitive area of 145.4 \(\times\) 114.9 mm, and resolution of 1944 \(\times\) 1536 mm pixels and pixel size of 74.8 \(\upmu\)m; and a flat panel detector XRD 1622 AP 14 (Perkin Elmer, Waltham, MA, USA) with an active area of 409.6 \(\times\) 409.6 mm, resolution of 2048 \(\times\) 2048 pixels and pixel size of 200 \(\upmu\)m.  
Figure 2: Representation of the machining process (**left**) and examples of the bone cubes (**right**). Note that only those bone cubes with 10 mm per side were used in this study, but smaller cubes were set aside for future studies.  
Four of our testing bone cubes were analyzed through micro-CT with the device at IF-UNAM. Additionally, we were able to use TORATOM to study samples of various materials: trabecular bone sample [29], alkaline activated composite, concrete glass, and sandstone. The purpose of using these materials, together with the bone cubes, was to have enough reference values to compare against to calibrate our proposed 2D imaging approximation of porosity. Next, we describe both the micro-CT 3D analysis and the 2D imaging approximation for the assessment of porosity.  
Micro-CT images allowed us to create virtual 3D models of our bone cubes with a high resolution. For proper filtering and thresholding of the images, we used the software packages Fiji [30] and ImageJ2 [31]. Furthermore, the software toolbox Pore3D [32] was used to filter and segment the images, as well as for appropriate selection and quantification of pores, attaining images of ten consecutive longitudinal and equally spaced slices of each sample from those models. Figure 3 shows examples of those images for one of our cubic bone sample and the sample of trabecular bone. For each of the samples, an isolation and quantification procedure of pores followed the corresponding micro-CT and their reconstructions.
# Cardiac Arrhythmia Classification Based on One-Dimensional Morphological Features
## 2 Materials and Methods  
### Dataset and Preprocessing  
In this study, we used a 12-lead ECG database collected by Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine) to verify the validation of the proposed morphological features [16]. The 12-lead ECG database covers 10,646 patients (5956 males) and was sampled at 500 Hz over a period of 10 s. The ECG database contains 11 heart rhythms that were labeled by professional physicians. Three steps were sequentially applied to process the raw ECG signals. As the first step, a Butterworth low-pass filter was applied to reject signals with frequencies above 50 Hz since the frequency range of a normal ECG is between 0.5 Hz and 50 Hz [17]. Then, the local polynomial regression smoother (LOESS) curve fitting was utilized to remove the baseline wandering effect that could be induced by respiration [18]. Finally, the non-local means (NLMs) were applied to reduce residual noises [19]. A total of 10,588 data samples were used in this experiment because some ECG records contained only zeros and some channel values were missing. In Table 1, details of the 11 ECG rhythms and the corresponding number of subjects are described. Some ECG rhythms contained less than 100 samples, which can cause data imbalance issues. Thus, as suggested by [PERSON] [16], the 11 rhythms were hierarchically merged into four groups (AFIB, GSVT, SB, and SR). As shown in Table 1, AFIB contained atrial fibrillation (AFIB) and atrial flutter (AF). GSVT consisted of atrial tachycardia (AT), atrioventricular node reentrant tachycardia (AVNRT), atrioventricular reentrant tachycardia (AVRT), sinus atrium to atrial wandering rhyth (SAAWR), sinus tachycardia (ST), and supraventricular tachycardia (SVT). SB only included sinus bradycardia (SB). SR included sinus rhythm (SR) and sinus irregularity (SI).  
For data manipulation, we rescaled the ECG signals to between 0 and 1 using a min-max algorithm to incorporate the amplitude scale and help reduce differences in gender and age effects. Then, we quantized the ECG signals to generate the GLCM and GLRLM. With the help of the Lloyd-max algorithm [20], the ECG signals were quantized into 16, 32, 64, and 128 levels. Finally, downsampling was done to reduce the computational burden. All ECG recordings were resampled to a 100 Hz sampling rate. Figure 1 shows a downsampled ECG signal and its quantized ECG signal. Preprocessing of the raw ECG signals, data manipulation, and feature extractions were implemented in Matlab 2020a ([[https://www.mathworks.com](https://www.mathworks.com)]([https://www.mathworks.com](https://www.mathworks.com)), accessed on 11 October 2021).
## 3 Results and Discussion
### Time-Averaged Results
#### 3.1.1 Velocity Distribution
This phenomenon was not favorable for heat transfer enhancement  
Figure 7: Time average of (**a**) streamwise velocity, (**b**) transverse velocity, (**c**) velocity magnitude, and (**d**) streamline for fully confined external region at Re= 2680, 6440, and 10,730 and z/D\({}_{\text{h}}\) = 0.  
because the film cooling was not complete, and some surfaces of the blades can melt due to the lack of appropriate film cooling. For the highest Re number, Re = 10,730, some oscillating patterns could be seen, and vortices that were created at the Re = 6440, by increasing the momentum of the jet, could be detached from the sidewall. As the figures show, the distance of the vortex's center from the sidewall for Re = 10,730 was higher than Re = 6440. However, the spreading angle and the influenced area were still small in comparison with the free external domain case. Figure 8 shows the instantaneous velocity field for Re = 6440, at which the time-averaged patterns for two geometries were significantly different. For the free external domain, the jet was moving between its maximum deflection (during T/2). In contrast, for the fully confined geometry, the vortices created in the recirculation zone were strong and did not let the jet oscillate periodically. Therefore, the maximum deflection of the jet was lower for the fully confined geometry. To give a more comprehensive understanding of the flow structures of the fully confined geometry, a video file of the velocity magnitude contours was given as the Supplementary Materials. It should be noted that all figures for fully confined case represent the jet, while it was attached to the left side wall, however, for confirming the non-ergodicity behavior of flow at the external domain, the jet was inclined to the right side wall in video files.
# Effect of Fuel Composition on Carbon Black Formation Pathways
## 3 Results and Discussion  
### Carbon Black Aggregate Morphology and Particle Structure  
TEM is used to visualize aggregate morphology and particle structure for the two carbon blacks, shown by the micrographs in Figure 1.  
Both image series show a range of aggregate sizes, consisting of a few to several dozen primary particles. Dendritic and compact morphologies are observed for aggregates from both feedstocks. While the degree of branching morphology appears to decrease with aggregate size, this is a consequence of the fewer primary particles within these aggregates but does not necessarily change the fractal descriptor. Gauging by mass thickness contrast, even large aggregates possess regions of compact primary clustering.  
The primary particles are distinctly different between the two carbon blacks. While FCC-CB shows well-defined, pseudo-spherical primary particles typical of many commercial carbon blacks, CT-CB does not show such defined \"particles\". Figure 2 shows a visual comparison of structural differences between primaries for FCC- and CT-CB.  
Figure 1: Aggregate morphology of (**A–C**) CT- CB and (**D–F**) FCC-CB.
# Indoor PV Modeling Based on the One-Diode Model
## 1 Introduction
In view of this, a one-diode model with two resistors is chosen to model PV behavior in indoor environments because such a model incorporates a parallel resistor, which is used to represent the leakage current [19]. Compared to other models reported in [20; 21; 22; 23; 24; 25], the one-diode model provides high accuracy while remaining simple. Furthermore, the proposed model takes a novel approach by applying the characteristic points translation technique to determine the three characteristic points at various illuminance levels. The obtained values are then integrated with the parameter extraction technique to extract the model's parameters. This approach is expected to improve the proposed model's accuracy at low light levels. Moreover, it only requires an indoor reference of the three characteristic points as input, and it can model the PV behavior under different operating conditions. To determine the reference value for the model's input, an experiment is carried out. Various light source technologies are used to illuminate multiple commercial PV panels, and the light intensity is extensively verified using light measurement devices. Several similar works utilizing different techniques are used for comparison purposed to evaluate the expected improvement in accuracy of the proposed model. Figure 1: Overview of PV modeling methods. The remainder of this paper is organized as follows. Section 2 describes the methodology in detail. In Section 3, the results of the proposed and compared models are presented, while Section 4 concludes the paper.
# Analyzing and Predicting Students' Performance by Means of Machine Learning: A Review
## 3 Techniques
### Impact of the Techniques  
The techniques described before had different efficiencies with regard to the students' behaviour. As shown in the bar graph of Figure 1, the different algorithms were not only applied to a greater or lesser extent (blue bars), but also had different performance (green bars) when compared to others. Thus, we check that ANN and SVM were more the most applied, followed by CF, DT, and NB.  
On the other hand, SVM was the best method in performance terms. This conclusion should be taken with caution, since it is necessary to consider which algorithms were involved in the comparison, as well as the particular case where they were applied. However, these results may show some guidance in making decisions about which techniques to use for particular scenarios.
## 2 Materials and Methods
### Elastic Constants for Parametric Study
#### 2.2.1 [PERSON]'s Moduli and Ratio \(E/E^{\prime}\)  
For the parametric study, the [PERSON]'s modulus \(E^{\prime}\) in direction normal to the isotropy planes was fixed with a value of 2000 MPa. This value can be considered to be arranged at the lower end of the values for the stiffness of various rock types, as could be encountered for fine-grained soft sedimentary rocks [31]. Also, this low value was chosen in agreement with the stiffness of artificial anisotropic samples that the authors are currently experimenting with. Keeping \(E^{\prime}\) constant facilitates the investigation of the influence of stiffness anisotropy (\(E/E^{\prime}\)) on the qualitative strain distributions at consistent strain magnitudes. The Young's modulus \(E\) was made dependent on \(E^{\prime}\) via the selected values for the anisotropy ratio \(E/E^{\prime}\).  
For natural anisotropic rocks, the Young's modulus \(E^{\prime}\) perpendicular to the planar fabrics is usually smaller than the Young's modulus \(E\) measured within the planes, with minor exceptions [5; 27; 32]. Thus, the lower bound for the selected \(E/E^{\prime}\) ratio was chosen with 1.0. [PERSON] et al. [5] found that the majority of values for this ratio lie in between 1.0 and 2.0 based on 98 investigated rocks, with only a single case reported with a value larger than 3.0. From the frequency distributions shown in Figure 3, based on data collected  
Figure 2: Local orientation of measurable normal strain components in the cylindrical coordinate system \(r\theta z\) for uniaxial compression tests with frictionless boundary conditions. Strain component \(\epsilon_{\theta}\) is oriented in tangential direction to the sample circumference, \(\epsilon_{r}\) in radial direction (based on [PERSON] et al. [25]).  
by [PERSON] [28] and [PERSON] [27], it can likewise be concluded that most of the rocks display low to moderate [PERSON]'s modulus anisotropy ratios \(E/E^{\prime}<2.0\). Nevertheless, [PERSON]'s dataset (Figure 3a) is less balanced toward intermediate ratios, with 80% of the samples having ratios less than 1.5. This can be explained by the high amount of quartzofeldspathic and basic/lithic rocks included in the assessment usually displaying the lowest degrees of modulus anisotropy. In the given context of the parametric study, anisotropy ratios \(E/E^{\prime}\) of 1.0, 1.5 and 2.0 were selected.
## 4 Results
### PII Detection and Masking Performance  
The performance of both IVM and OVM in handling Personal Identifiable Information (PII) was evaluated using a synthetic dataset comprising 70 sentences with fabricated PII (names, phone numbers, emails, addresses, and card numbers). The system's ability to correctly identify and mask these PII instances was measured using standard classification metrics. As shown in Table 6, the AVI PoC achieved high performance in PII detection, with macro-averaged scores of Precision: \(\sim\)0.96, Recall: \(\sim\)0.94, and F1-score: \(\sim\)0.95. Performance varied slightly across different PII categories, with structured data like phone numbers and card numbers being detected with slightly higher accuracy than less structured data like full names. These results confirm the effectiveness of the implemented PII detection mechanisms within the AVI framework for enhancing data privacy in LLM interactions.  
Figure 4: Distribution of Perspective API toxicity scores for LLM responses generated from RealToxicityPrompts (n = 150): (**a**) baseline responses generated directly by the LLM without AVI intervention, showing a concentration towards higher toxicity values; (**b**) responses after processing by the AVI PoC’s Output Validation Module, demonstrating a significant shift towards lower toxicity scores.  
\begin{table}
\begin{tabular}{c c c} \hline
**Metric** & **Baseline (Direct LLM)** & **AVI PoC Processed** \\ \hline Average Toxicity Score 1 & \(\sim\)0.72 & \(\sim\)0.18 \\ Toxicity Reduction (\%) & - & \(\sim\)75 \\ Output Modification/Block Rate (\%) 2 & - & \(\sim\)65 \\ \hline \end{tabular}
\end{table}
Table 5: Impact of AVI PoC Output Validation Module on response toxicity, evaluated using 150 prompts from RealToxicityPrompts.
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
## 5 Multi-Source Data and Data Assimilation
### Data Assimilation
Subsequently, the PF-MCMC method combining PF with Markov Chain Monte Carlo (MCMC) was proposed [275], which can effectively improve the accuracy of soil moisture estimation in the root zone. This method was later validated by [PERSON] and [PERSON] [277], and utilized in drought monitoring of spring and winter wheat over the entire continental United States (CONUS) [278]. The above data assimilation methods can estimate soil moisture more accurately and obtain the root-zone soil moisture vital to agriculture. Thus, better decisions and management become possible when we are faced with agricultural droughts.
## 3 Open-Circuit Fault Diagnosis Strategy
### Fault Detection  
Under normal operating conditions, the output current of the T\({}^{2}\)3L inverter closely matches the current estimated by the IASMO. However, the emergence of OC faults induces a substantial deviation between the inverter's actual output current and the observer-estimated value, leading to a mismatch between the two. Leveraging this distinctive current divergence phenomenon, this section establishes current characteristic values to quantitatively characterize the discrepancy mechanism. A fault detection variable and an adaptive threshold are then constructed accordingly. By comparing the detection variable with the threshold, the proposed method enables highly accurate detection of OC faults.  
According to the analysis of OC faults in T\({}^{2}\)3L inverters, power switch faults result in distortion of the output current. Exploiting these distortion characteristics, this paper proposes a fault detection variable \(R_{x}\) and an adaptive threshold \(T_{x}\), defined as:  
\[R_{x}=\frac{arsinh(\theta_{x})}{\theta_{x}} \tag{18}\]  
\[T_{x}=\frac{arsinh(|\hat{\theta}_{x}-\theta_{x}|)}{|\hat{\theta}_{x}-\hat{ \theta}_{x}|} \tag{19}\]  
where \(\hat{\theta}_{x}\) and \(\hat{\theta}_{x}\) denote the actual and estimated current characteristic values, as follows:  
\[\hat{\theta}_{x}=\left(\frac{1}{\tau}\int_{t-\tau}^{t}|i_{x}|^{2}dt\right)^{1 /2},\hat{\theta}_{x}=\left(\frac{1}{\tau}\int_{t-\tau}^{t}|\hat{i}_{x}|^{2}dt \right)^{1/2} \tag{20}\]  
Here, \(\tau\) denotes half of the fundamental current period. This definition of \(\hat{\theta}_{x}\) aims to reduce the influence of peak currents and harmonics, ensuring robust fault detection.  
The adaptive behavior of the threshold \(T_{x}\) is guaranteed by the mathematical properties of the inverse hyperbolic sine function (\(arsinh\)). For any \(x>0\), there exists a strict inequality:  
\[\frac{x}{1+x}<arsinh(x)<x \tag{21}\]  
Combining the above Equations (18), (19) and (21), when the inverter operates normally, the observed current value is highly consistent with the actual output value, meaning that \(\left|\hat{\theta}_{x}-\theta_{x}\right|\to 0^{+}\). In this case, the fault detection variable and adaptive threshold satisfy the following expressions respectively:  
\[R_{x}=\frac{arsinh(\theta_{x})}{\theta_{x}}\in(0,1) \tag{22}\]  
\[T_{x}=\lim_{\left|\hat{\theta}_{x}-\theta_{x}\right|\to 0^{+}}\frac{arsinh(\left|\hat{ \theta}_{x}-\theta_{x}\right|)}{\left|\hat{\theta}_{x}-\theta_{x}\right|}\approx 1 \tag{23}\]  
As demonstrated by the equations above, under this condition, the detection variable \(R_{x}\) remains consistently below the threshold \(T_{x}\). However, upon the occurrence of an OC fault in the inverter, \(\left|\hat{\theta}_{x}-\theta_{x}\right|\) will increase, resulting in a corresponding alteration of the threshold \(T_{x}\), and the detection variable \(R_{x}\) will rapidly exceed the existing threshold. Therefore, the proposed detection method achieves reliable OC fault identification through real-time monitoring of the detection variable-threshold relationship.