esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Analysis on the DAWN Dataset
In this case, surprisingly, the image appears neat and clean and thus results in a visually pleasing image due to the massive amount of snow which is present in the image. In this case, our developed method accurately detects and labels all the vehicles that appear therein. Particularly, the 3 rd image in this row also reveals a red light along with the snow. Yet in this case, the proposed method performs well and successfully locates all the vehicles. Moreover, the last image in this row shows a few vehicles that overlap and result in partial occlusion. However, our developed method performs well in this case as well. In Table 7, we compare our method with the works already described in Tables 5 and 6, respectively. A few of the observations from Table 7 are listed below. * As can be seen in Table 7, for the fog scenario the work developed in [40] ranks 1 st among 14 compared methods by yielding a 29.68% mAP value. Our developed method ranks 2 nd out of all compared methods in fog situation and yields a 29.66% mAP value. In the fog situation, the work developed in [38] yields the lowest mAP value (16.50%) followed by [29] whose method yields a mAP value of 24%. * For the rain scenario on the DAWN dataset, our proposed method and the work developed in [31] yield the highest mAP value of 41.21%. In this category, the work in [34] ranks 2 nd and yields an encouraging result of a 41.10% mAP value. For the  
Figure 9: Vehicle detection results on the DAWN dataset row-wise: (**a**) fog; (**b**) rain; (**c**) sand; and (**d**) snow. aforensaid category, results yielded by [36; 37] are also encouraging. For images that are affected by rain, the work in [38] delivers the lowest mAP value of 14.08%. * For the snow conditions on the DAWN dataset, the work developed in [37] ranks 1 st among all compared methods and slightly outperforms the proposed method by yielding a mAP value of 43.02%. For this category, our developed method yields a mAP value of 43.01%. It is important to state here that for this situation, the works in [31; 33; 36; 37] yield almost similar results. * For the sand condition, our method ranks 1 st and outperforms all compared methods by yielding a 24.13% mAP value. On this situation, the works in [28; 34; 35] yield similar mAP values. For the sand situation, the work in [38] yields the lowest mAP value (10.69%).
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 5 Threats to Validity  
Threats to internal validity. The construction of code base can pose a threat to internal validity. Although we extract projects from a well-maintained repository, i.e., Google BigQuery, the selected projects still cannot represent all the commonly-used search attempts. Additionally, we use the functional annotation of the code to approximate the natural language query of the code snippets. There is still a certain gap between the functional annotation and the real natural language query statement, but there are very few high-quality annotation \(\langle\) Codes, Natural Language Queries\(\rangle\) datasets. The cost and difficulty of labeling such datasets manually is unacceptable for our team. To reduce this treat, we plan to extend the experiments on more subject projects.  
The complex structure of neural architecture of JessCS model can also pose threats to internal validity. The multi-layer network may introduce cascading errors, which may result in over-fitting problems for our model. Additionally, we only investigate the impacts of two parameters, i.e., code features and embedding vector dimension, in the experiments. All the other parameters are set according to the existing work and our experience. These threats may impact the results of evaluation. We plan to design joint training models to check the effectiveness of joint embedding methods [39], and extend our experiments with various settings of parameters.  
Threats to external validity. We selected UNIF-based approach [12] to make the experimental comparison, because it is proved to be more effective than some code search approaches (e.g., DeepCS [4]). However, there are still some other code search approaches, especially in software industry (e.g., Apache Lucene). To reduce this treat, we plan to extend the experiments on more techniques which can be used in code search.  
The candidate code snippets are marked as independent functions, with the granularity of method level. Software developers may not always search for a method. So we plan to improve the embedding approach to meet the features from different levels of granularity for code snippets.  
Figure 11: The impacts of embedding vector dimension on the effectiveness of JessCS.
## 3 Numerical Results & Discussion  
### Absorbed Power and Temperature Distribution  
Figure 7 presents the graphs showing the distribution of temperature and power absorbed by the medium according to the volume fraction of the injected GNRs when the length of the tumor is 2.5 mm and the depth of the tumor is 0.4 mm. If the volume fraction of GNRs in the tumor is high (Figure 7a), the absorption coefficient of the tumor increases; accordingly, most of the heat of the laser is absorbed near the tumor surface. Consequently, it is assumed that the temperature increase inside the tumor is mainly caused by heat  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Numerical Parameter** & **Case** & **Number** & **Remarks** \\ \hline Depth of tumor (\(D_{t}\)) & 0.1 to 1 mm & 4 & Inv:0.3 mm \\ length of tumor (\(L_{t}\)) & 0.5 to 3.5 mm & 4 & Inv:1 mm \\ Laser power (\(P\)) & 0 to 0.2 W & 21 & Inv:0.01 W \\ Volume fraction of GNPs (\(f_{v}\)) & \(10^{-3}\) to \(10^{-6}\) & 7 & Inv:\(10^{-0.5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geometry of simulation conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Volume Fraction of GNRs (\(f_{v}\))** & \(10^{-3}\) & \(10^{-3.5}\) & \(10^{-4}\) & \(10^{-4.5}\) & \(10^{-5}\) & \(10^{-5.5}\) & \(10^{-6}\) \\ \hline Absorption coefficient & & & & & & & \\ \((\mu_{a})[1/\text{mm}]\) & & & & & & & \\ Scattering coefficient & & & & & & & \\ \((\mu_{s})[1/\text{conduction from the surface. On the other hand, if the volume fraction of GNRs in the tumor is low (Figure 7b), the absorption coefficient of the tumor decreases and the depth at which the laser can penetrate becomes longer. In addition, since a small amount of laser heat is absorbed over a wide range, it is shown that an increase in temperature occurs in the entire tumor tissue, and the peak of the temperature is low. For this reason, the temperature distribution of the tumor and surrounding normal tissues occurs differently depending on the volume fraction of GNRs injected into the tumor. Therefore, it is necessary to check the temperature distribution according to the volume fraction of various GNRs and to check the temperature band where apoptosis occurs.  
There are a total of four parameters to be changed in the results derived later: length of the tumor (\(L_{t}\)), depth of the tumor (\(D_{t}\)), variable related to laser power (\(P\)), and volume fraction of GNRs (\(f_{v}\)). All graphs illustrate results for various \(f_{v}\), and the \(x\)-axis is fixed as the intensity of the irradiated laser. To check various results, varying \(D_{t}\) and \(L_{t}\), and the results were checked for various \(f_{v}\).
## 3 Force Balance Conditions  
Since the sealing ring rotates with the outer rotor, its sealing performance mainly depends on the relative positions of the sealing ring and inner rotor. Figure 2a shows this relationship under a state of force balance. The force conditions of the sealing ring in such cases are shown in Figure 2b. In the axial direction, the sealing ring is subjected to the force generated by the inlet fluid pressure \(F_{1}\) and the force exerted on it by the outer rotor \(F_{4}\), which satisfies the following:  
\[\overset{\rightarrow}{F}_{1}+\overset{\rightarrow}{F}_{4}=0 \tag{1}\]  
In the radial direction, the sealing ring is subjected to the force exerted by inlet fluid pressure \(F_{3}\), the gravity of the sealing ring \(F_{G}\), bearing capacity of the gas film \(F_{g}\), side friction \(F_{f}\) and centrifugal force \(F_{2}\), and reaches balance as follows:  
\[\overset{\rightarrow}{F}_{G}+\overset{\rightarrow}{F}_{2}+\overset{ \rightarrow}{F}_{3}+\overset{\rightarrow}{F}_{f}+\overset{\rightarrow}{F}_ {g}=0 \tag{2}\]  
The magnitude of each force in Formula (2) is as follows:  
\[F_{G}=\rho_{2}\pi({R_{2}}^{2}-{R_{1}}^{2})Lg \tag{3}\]  
\[F_{2}=m\omega_{2}^{2}e=\rho_{2}\pi({R_{2}}^{2}-{R_{1}}^{2})L\omega_{2}^{2}e \tag{4}\]  
\[F_{3}=2\pi R_{2}LP_{in} \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Outer diameter of inner rotor \(r_{2}\) & 77.24 mm \\ Inner diameter of sealing ring \(R_{1}\) & 77.25 mm \\ Seal ring radial height \(H\) & 5 mm \\ Seal ring width \(L\) & 11.9 mm \\ Inlet pressure \(p_{in}\) & 0.14\(\sim\)0.22 MPa \\ Outlet pressure \(p_{out}\) & 0.1013 MPa \\ Inner rotor speed \(n_{1}\) & 5000–10,000 r/min \\ Outer rotor speed \(n_{2}\) & 8000–13,000 r/min \\ Air viscosity \(\eta\) & \(1.8\times 10^{-5}\) kg \(\cdot\) m \(\cdot\) s\({}^{-1}\) \\ Operation temperature \(T_{a}\) & 930 K \\ Gas constant \(\Re\) & 287 / Kg.K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters and operating conditions of the seal.  
Figure 2: Schematic diagram for the sealing ring, including: (**a**) circumferential movement: 1—Sealing ring, 2—Inner rotor, 3—Gas; (**b**) axial section force.  
\[F_{f}=\mu F_{4}\approx\mu P_{in}s \tag{6}\]  
where \(\rho_{2}\) is the density of the sealing ring material, \(m\) is the mass of the sealing ring, \(e\) is the eccentric distance of the sealing ring, \(\omega_{2}\) is the angular velocity of the sealing ring, \(\mu\) is the friction coefficient between the sealing ring and the outer rotor, \(p_{in}\) is the inlet pressure and \(s\) is the contact area between the right end face of the sealing ring and the outer rotor. The bearing capacity of the gas film \(F_{g}\) is obtained by flow field lubrication analysis (to be detailed in the next section).
## 1 Introduction
### Overview and Aim of This Work  
This paper presents the development of a computational model in GATE to generate virtual paths for tracer particles and produce simulated lines of response to perform PEPT measurements. A series of validation tests was conducted, where physical experiments were performed with a tracer particle moving along defined paths to produce experimental lines of response. After deriving location data from the LORs with the PEPT-ML algorithm [38], the location data from equivalent simulations were compared to the experimentally derived values to test if the accuracy and precision measured from the GATE simulation are comparable to those of the experimental data.  
Once the model was validated, two different types of tracer motion were simulated to investigate the tracking ability (in terms of accuracy and precision) of PEPT measurements performed on the HR++ PET scanner. The extended simulations were inspired by the target application of this work, improving the tracking of turbulent flows in mineral froth flotation with PEPT (following [8; 9; 10]). There do not yet exist sufficiently complex models of the interaction of the three phases in flotation that can produce Lagrangian trajectories of flow carriers of the solid, liquid, and gas phases to use with the HR++ PET scanner simulation (in comparison to recent studies with GATE and DEM simulations of granular flow [28]). Instead, features of flow paths expected to be found in multiphase flows were reproduced. The first motion was an oscillation in a single dimension to explore the ability of PEPT to track high rates of acceleration (following [24]) and the second was a helical path with constant tangential speed to explore the ability of PEPT to track turbulent vortices or eddies on different length scales. The tracking results were interpreted relative to the smallest eddies found in the water phase of flotation pulps with high energy dissipation rates to determine whether the PEPT measurement with the HR++ can reproduce Lagrangian paths of flow structures on the order of 10 \(\upmu\)m (in laminar flows) and 100 \(\upmu\)m (in turbulent flows) [39].
# Increasing Photovoltaic Systems Efficiency Through the Implementation of Statistical Methods
## 1 Introduction  
When considering applications such as PV power plants, substantial increases in technical and economic performance can be achieved by improving safety in operation, location, and mounting systems, as well as optimising specific management parameters. This involves the application of statistical methods.  
The first theme addressed in the article is selecting the tilt angle of the PV panels used on a PV plant to optimise the efficiency of photovoltaic systems using statistical analysis based on the ANOVA method. There are several nonparametric methods of statistical analysis, the most common being the description of the Kruskal-Wallis test in Excel [1], using the Taguchi and ANOVA Analysis for PV plants [2], a multi-objective design optimisation strategy for hybrid PV plants [3], one-way ANOVA analysis of variance [4], operating parameters and degradation of PV [5], and statistical methods and artificial neural network approaches for PV plant decisions [6].  
The second approach involves a managerial and statistical analysis based on a STEM-type method. Economic efficiency analysis should be conducted during the initial phase of developing a new photovoltaic (PV) plant. This analysis must consider technical and financial factors relevant to the specific location of the proposed facility. Quality management systems began to be developed in the early 20 th century as statistical sampling techniques were introduced into the quality control methodology. In the following years, there was an increasing demand for increased productivity. It became apparent that a more robust, structured, and logical approach to quality control was needed. There are numerous statistical methods for product quality control, including the impact of product quality on sales, as demonstrated by the Sales Training Evaluation Model (STEM) [7, 8]. Thus, the technical level method can be used to evaluate the quality of products based on the von Neumann-Morgenstern utility principle and the Cobb-Douglas production functions [9]. A relation of the form defines the absolute technical level (Hai) [10, 11].  
The STEM method currently used in the education system, where STEM stands for science, technology, engineering, and mathematics, although it shares the same acronym, is distinct from the sales method STEM discussed in this context.
## 3 Results
### Hydration Properties  
Table 3 illustrates the WAC and WSI of all granulometric classes and un-sieved samples of blackthorn berry powders.  
The amount of water that a food powder can absorb and retain to the maximum is indicated by the WAC [57]. A clear influence of particle size on WAC was certainly observed as the decreasing particle size resulted in higher absorption capacity. The WAC value for the un-sieved sample had an average value of 4.905 g/g. For the samples subjected to particle size distributions, the WAC value varied from 3.837% g for the particles with the largest size to 5.883% for the particles with the smallest size. Therefore, all of the components of the dried blackthorn berry hydrophilic groups were exposed once the cell walls were broken down by the grinding process. Also, the higher absorption capacity could even be due to the presence of the higher content of sugars in the powder with reduced particle sizes.  
One of the most crucial functional characteristics of food powders is their solubility, which demonstrates how the particles respond when they regenerate in water. The quantity of either dissolved or undissolved particles is indicated by the solubility index [58]. The WSI values increased for the powders with small particle sizes (cf. Table 3), indicating that the interaction with water was improved in the case of these samples. This can be understood by the fact that milling causes a reduction in particle size, which improves material functions like surface energy and particle surface specific area and improves the plant material's interactions with water [26; 34]. The solubility index values ranged from 32.672% for the un-sieved sample to 42.272% for the sample with particle size between 200 and 315 um.  
The current study's findings are in line with those of other authors, Miganeh Waiss et al. [26]; Bala et al. [34]; and Deli et al. [56], who noted that samples with smaller particle sizes showed improvements in WAC and WSI. Blackthorn berry powders with smaller particles should therefore be more soluble and interact with water more readily, which could help when ingesting them through food, facilitating their intestinal absorption [26].
## 3 Data Preparation and Pre-Processing  
This chapter provides a detailed description of the composition of input and output features and visualizes outliers using box plots. By cleaning missing values and outliers, a total of 1273 high-quality data points are obtained, laying the foundation for the construction of the three machines parameter prediction model for the fully mechanized mining face.
## 3 Results and Discussion
### Thermogravirentical Analysis (TGA)  
The comparative investigation between pure TiO\({}_{2}\) and NT samples was also supported by thermogravimetric analysis. Figure 5 displays the TGA thermograms for pure TiO\({}_{2}\) and all NT samples. It can be observed that for pure TiO\({}_{2}\) sample, the TGA curve declines in the range of 150-200 \({}^{\circ}\)C, indicating a weight loss mainly due to the evaporative loss of water or any organic matter involved during the synthesis. However, the NT samples, on the contrary, exhibits two weight loss regions; the first region in between 150-200 \({}^{\circ}\)C and the second region lies between 220-350 \({}^{\circ}\)C. The weight loss in the first region can be associated similarly as the evaporative loss of water from pure TiO\({}_{2}\) whereas, the second weight loss region can be attributed to the degradation of an organic matrix such as urea used as nitrogen dopant, as reported earlier [50].  
Figure 3: SEM images of (**a**) Pure TiO\({}_{2}\), (**b**) NT-1, (**c**) NT-2 and, (**d**) NT-3.  
Figure 4: FTIR spectra of pure TiO\({}_{2}\), NT-1, NT-2 and NT-3, synthesized by using 0.0 g, 0.983 g, 1.965 g and 2.940 g of urea respectively. (**a**) The complete spectrum; The enlarged view of regions between 750–200 cm\({}^{-1}\) (**b**), and 2000–4000 cm\({}^{-1}\) (**c**).  
Figure 5: TGA curves for pure TiO\({}_{2}\), NT-1, NT-2 and NT-3, synthesized by using 0.0, 0.983, 1.965 and 2.940 g of urea, respectively.
## 5 Discussion
#### Limitations and Future Work  
Analysing the scope of this work, several limitations are identified that allow future lines of work to be defined. One limitation of the present study is the small sample of participants, focused on a single designer, which restricts the generalisation of the results. No systematic comparison between different immersive devices and tools was conducted. Likewise, individual variables that can significantly influence the results, such as prior experience in 3D design, cognitive profile, or physical tolerance, were not included. Another limitation of the study is that some of the indicators, such as perceived creativity, are based on the self-assessment of the participant and were not validated by external evaluators, which introduces a possible subjective bias in the results. Finally, the study does not include control groups using traditional methodologies, which makes it difficult to establish direct comparisons with non-immersive approaches.  
It is proposed to replicate this study with methodological improvements in terms of (1) a broader sample of designers with different levels of experience; (2) the comparative study of different software-hardware solutions, expanding immersive design applications and immersive design peripherals in equivalent tasks; (3) inclusion of external concomitant variables that may influence usability indicators, such as previous experience in 3D design or CAD software, cognitive profile, physical tolerance to immersive environments, degree of familiarity with specific immersive tools, and participants' subjective expectations or motivation; (5) the introduction of control groups working with traditional methodologies to compare with immersive ones; (6) external evaluators and inter-rater reliability assessments. Other future work could also analyse immersive collaborative processes and, finally, evaluate the entire design cycle, including the evaluation of design outcomes, such as the level of creativity and innovation of the developed products.
### Contributions  
Our contributions to the field can be summarized as follows:  
* Streamlined segmentation. Our method operates in a detect-and-segment manner, where detected surface landmarks (rather than tooth centroids) offer practical advantages for clinicians, enabling quick corrections compared to laborious adjustments of segmented 3D regions. This demonstrates significant potential to reduce human efforts in orthodontic treatment planning.
* Information-rich projections. Utilizing predictions from the detector, our segmentator addresses typical projection issues related to unclear camera positioning and unknown shape orientation. It allows for localized segmentation and reduces the task to a binary segmentation problem. Given the general convex ovoid tooth shape and prior landmark detection, the multi-view configuration can be preset, so pose sensitivity is not an issue. Alongside the local tooth processing, we show that the rendered maps capture the 3D shape with minimal information loss.
* Highly competitive performance. We achieve an average weighted IoU score of \(0.97122\pm 0.038\) and a Hausdorff distance at the 95 th percentile of \(0.49012\pm 0.571\) mm. We provide a comparative analysis with other point-based [8; 9] and mesh-based [17] methods and with a Graph Cut-based solution. Our method outperforms each of the methods.
* Release of Poseidon3D dataset. We address a notable gap in the field by introducing a new dataset of real-world orthodontic cases of increased anatomical complexity. We name it Poseidon's Teeth 3D (Poseidon3D). This dataset contains 3D scans of patients who exhibit various dental anomalies like teeth crowding, damaged and missing teeth, and teeth with visible roots. To the best of our knowledge, we are the pioneering contributors of such a dataset, and we anticipate that it will establish a standardized basis for fair evaluation in future studies.